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Entry of Staphylococcus aureus into the bloodstream can lead to metastatic abscess formation and infective endocarditis . Crucial to the development of both these conditions is the interaction of S . aureus with endothelial cells . In vivo and in vitro studies have shown that the staphylococcal invasin FnBPA triggers bacterial invasion of endothelial cells via a process that involves fibronectin ( Fn ) bridging to α5β1 integrins . The Fn-binding region of FnBPA usually contains 11 non-identical repeats ( FnBRs ) with differing affinities for Fn , which facilitate the binding of multiple Fn molecules and may promote integrin clustering . We thus hypothesized that multiple repeats are necessary to trigger the invasion of endothelial cells by S . aureus . To test this we constructed variants of fnbA containing various combinations of FnBRs . In vitro assays revealed that endothelial cell invasion can be facilitated by a single high-affinity , but not low-affinity FnBR . Studies using a nisin-inducible system that controlled surface expression of FnBPA revealed that variants encoding fewer FnBRs required higher levels of surface expression to mediate invasion . High expression levels of FnBPA bearing a single low affinity FnBR bound Fn but did not invade , suggesting that FnBPA affinity for Fn is crucial for triggering internalization . In addition , multiple FnBRs increased the speed of internalization , as did higher expression levels of FnBPA , without altering the uptake mechanism . The relevance of these findings to pathogenesis was demonstrated using a murine sepsis model , which showed that multiple FnBRs were required for virulence . In conclusion , multiple FnBRs within FnBPA facilitate efficient Fn adhesion , trigger rapid bacterial uptake and are required for pathogenesis .
Staphylococcus aureus is a major human pathogen and the continuous emergence and spread of antibiotic resistant strains ( e . g . MRSA , VRSA ) mean treatment options are often severely limited [1] , [2] . Despite its normal role as a commensal organism living asymptomatically in the nasal cavities of a large proportion of the human population [3] , S . aureus is also responsible for a raft of different infections that range in both anatomical site and severity . These infections are facilitated by a vast array of different virulence factors such as adhesins , invasins , toxins and modulins , which not only enable evasion of host immune responses [4] , [5] , but also contribute to colonization , dissemination , tissue damage and transmission [1] . Whilst some infections are superficial and self-limiting , S . aureus is also responsible for serious invasive diseases . Indeed , S . aureus is a leading cause of sepsis and infective endocarditis [1] , [6]–[8] . Colonization of the heart and subsequent formation of vegetations involves a number of complex interactions [9]–[11] . Animal studies have shown that staphylococcal fibronectin-binding protein A ( FnBPA ) is able to support the colonization of heart valves by otherwise non-pathogenic Lactococcus lactis , as well as promote dissemination into the spleen [12] , [13] . In addition , FnBPs were significantly associated with systemic inflammation , severe weight loss and mortality in a murine sepsis model [14] . The role of FnBPA in virulence is supported by in vitro work showing that L . lactis expressing FnBPA is able to activate endothelial cells , inducing inflammatory and pro-coagulant responses [15] , [16] . In addition to binding fibronectin ( Fn ) and fibrinogen , FnBPA promotes attachment to the endothelium and triggers the uptake of S . aureus by endothelial cells , which is believed to facilitate bacterial persistence and the establishment of secondary ( metastatic ) infections [17]–[21] . In common with several other bacterial pathogens , S . aureus invades endothelial cells via cell surface integrins [22]–[24] . The bacterium binds Fn , which is attached to the endothelial cell by α5β1 integrins [18] , [19] . This triggers bacterial uptake via a host-cell driven process involving actin remodelling , focal adhesion kinase and Src family kinases [25] , [26] . In vitro studies suggest that Fn on the surface of endothelial cells is sufficient for S . aureus-integrin bridging and thus binding of exogenous Fn ( e . g . from plasma ) is not required [19] , [26] . In addition to FnBPA , many strains express a second , related , Fn-binding protein ( FnBPB ) . Indeed , the majority of clinical isolates screened encode both ( 77% ) , whilst the remainder encode just FnBPA ( 22% ) or , rarely , just FnBPB ( 1% ) [27] . Both proteins have N-terminal domains that bind fibrinogen and elastin [28] , [29] followed by a region containing 11 ( FnBPA ) or 10 ( FnBPB ) non-identical repeats that are responsible for binding F1 modules in the N-terminal domain of Fn [30] , [31] . Studies using synthesized peptides have demonstrated that some FnBRs ( 1 , 4 , 5 , 9 , 10 and 11 ) have a high affinity for Fn , whilst the others have a much lower affinity [30] , [31] . The repeat region is intrinsically disordered , allowing the relatively small FnBRs ( 27–39 aa ) to bind the much larger Fn molecule with high affinity [31] . It has been hypothesized that this arrangement enables a single FnBPA protein to bind multiple Fn molecules , leading to integrin clustering and bacterial uptake [30] , [32] , [33] . Previous work , using an old organizational scheme for FnBPA based solely on amino acid sequence similarity demonstrated that partial deletions in the repeat region did not detectably diminish the ability of bacteria to bind Fn or invade endothelial cells [13] , [19] . However , because it is difficult to relate this work to the new domain organization based on structural data [30] , [31] , it is not clear how many FnBRs are necessary for , or how the affinity of a given repeat for Fn affects invasion of host cells . Furthermore , it is unknown how the bacterium benefits from such apparent redundancy . This study addresses these questions by using bacteria expressing FnBPA variants containing various combinations of FnBRs to dissect their role in Fn-binding , the invasion of endothelial cells and virulence in a murine sepsis model .
FnBPA contains 11 non-identical repeats with either high or low affinity for fibronectin ( Fig . 1A ) . FnBPA variants were generated using a technique based on the circular PCR approach described by Massey et al . [13] ( Fig . 1A , B and Table 1 ) . Primers were modified to include 5′ phosphate , which facilitated simple blunt-ended ligation of PCR products to form a plasmid encoding fnbA variants lacking DNA encoding various repeats ( Fig . 1B ) . Certain constructs were themselves used as templates to produce further fnbA variants ( Table 1 ) . Previous work ( using the old domain organizational scheme ( Fig . 1A ) ) , suggested that only a few repeats were needed to confer binding to fibronectin and invasion of endothelial cells [13] , [19] and we therefore produced FnBPA variants containing various combinations of 1–3 repeats . As such , 3 different types of construct were produced; those containing a mixture of high- and low-affinity repeats , those containing just high-affinity repeats and those containing just low-affinity repeats ( Fig . 1C ) . Western immunoblots confirmed expression of each of the constructs , which appeared as bands of the expected size ( Fig . 1D ) . DNA sequencing ensured the integrity of each construct ( data not shown ) . To verify that each of the FnBPA variants were expressed at equivalent concentrations we employed an ELISA approach using antibodies raised to the N-terminal region of FnBPA that is conserved between our variants . Standard plots ( Fig . S1 ) indicated that this would allow us to accurately measure FnBPA expression levels and detect any differences between variants . Because S . aureus expresses protein A , we used a S . aureus ΔfnbA/B ( Δfnb ) strain ( DU5883 ) as the blank in our ELISA assay , meaning that values relate purely to FnBPA expression levels . As expected from our Western immunoblot data ( Fig . 1D ) , FnBPA expression levels in the 8325 . 4 wild-type ( WT ) strain were low ( Fig . 2A ) . These levels were higher amongst the bacteria expressing plasmid-encoded FnBPA , which were equivalent between variants ( Fig . 2A ) . We also measured FnBPA expression by quantifying adhesion of S . aureus expressing each FnBPA variant to immobilized fibrinogen ( Fig . 2B ) . Fibrinogen binding is conferred by the N-terminal region that is constant in all of the variants [28] . Attachment to fibrinogen was maintained in the 8325 . 4 Δfnb strain due to the presence of fibrinogen-binding ClfA and ClfB proteins . However , binding of S . aureus expressing plasmid-encoded FnBPA was significantly enhanced above that of the 8325 . 4 WT or Δfnb strains , reflecting the higher levels of plasmid-encoded FnBPA , which was equal between variants indicating equivalent expression levels ( Fig . 2B ) . Previous work using synthesized peptides [31] determined that some FnBRs have a high affinity for Fn , whilst others bind more weakly ( Fig . 1A ) . To examine how the composition of the Fn-binding region of FnBPA affects both adhesion to Fn and endothelial cell invasion , various combinations of FnBPA repeats were expressed on the surface of a S . aureus 8325 . 4 mutant strain lacking both FnBPA and FnBPB [34] . In keeping with previous work [19] , [34] , S . aureus lacking FnBPs exhibited significantly reduced binding to Fn ( Fig . 3A ) and invasion of endothelial cells ( Fig . 3B; Fig . S2 ) . S . aureus expressing FnBPA lacking the entire Fn-binding repeat region ( FnBPR0 ) did not adhere to Fn or invade endothelial cells above the levels of the FnBP-deficient mutant ( Fig . 3A , B ) . Expression of FnBPA from the plasmid was higher than that of WT bacteria ( Fig . 2A , B ) and accordingly S . aureus FnBPR1-11 bound Fn more strongly than WT 8325 . 4 ( Fig . 3A ) . Bacteria expressing FnBPA variants with increasing numbers of high-affinity repeats bound Fn incrementally , where three repeats were sufficient to confer the same level of binding to Fn as S . aureus expressing the full length protein ( or for consistency in nomenclature here FnBPR1-11 ) ( Fig . 3A ) . Despite binding Fn at a lower level , a single high-affinity repeat ( FnBPR1 or FnBPR11 ) was sufficient to confer equivalent invasion of endothelial cells when compared with the full length protein . A single low-affinity repeat ( FnBPR2 or FnBPR8 ) did not confer any binding to Fn above that observed for the knockout strain , but the addition of an extra low-affinity repeat ( FnBPR7 , 8 ) did increase this , albeit at a significantly lower level when compared with the high-affinity repeats ( Fig . 3A ) . Three low-affinity repeats ( FnBPR6–8 ) were needed before adhesion to Fn or invasion of endothelial cells was equivalent to that conferred by the full length protein ( Fig . 3A , B ) . Selected constructs were also expressed in the mouse-derived S . aureus strain LS-1 . Initial experiments were performed to assess the expression levels of each of the constructs by ELISA using anti-FnBPA antibodies ( Fig . 4A ) and fibrinogen binding ( Fig . 4B ) , as described above for 8325 . 4 . As for 8325 . 4 , LS-1 WT expressed FnBPA at lower levels than the plasmid-encoded gene . Expression levels were equivalent between variants ( Fig . 4A ) . LS-1 WT and FnBPA-defective strains both bound fibrinogen ( due to the presence of fibrinogen-binding proteins ClfA and ClfB [14] ) but LS-1 strains expressing each of the FnBPA variants bound in 2-fold greater numbers ( Fig . 4B ) , reflecting the higher levels of FnBPA expression that occurs when the gene is located on the plasmid . There were no differences in binding levels between plasmid-encoded FnBPA variants ( Fig . 4B ) . Binding to either human or murine Fn was dependant on the presence of at least 1 FnBR ( Fig . 4C , D ) . Similarly , invasion of EA . hy926 cells was dependent on the presence of at least a single high-affinity FnBR ( Fig . 4E ) . Although FnBPA variants expressing a single or very few high-affinity repeats were able to mediate adhesion and invasion at the same level as full length FnBPA , we considered the possibility that the relatively high levels of protein expression of the constructs ( Fig . 2A , B ) might mask important differences between full length FnBPA and variant forms . As such we chose the following constructs for further study; FnBPR1-11 ( full-length protein ) , FnBPR1 ( single high-affinity repeat ) , FnBPR2 ( single low-affinity repeat ) and FnBPR1 , 10 , 11 ( 3 high-affinity repeats and binds and invades equivalently to FnBPR1-11 ) . FnBPR0 was included as a control as it wouldn't be expected to bind or invade at any expression level . To assess how selected FnBPA variants might affect adhesion and internalization at lower expression levels an inducible expression system was employed [35] . This and similar systems have been used previously to vary expression levels of several Gram-positive proteins on the surface of L . lactis [35]–[37] . Surface expression of FnBPA variants was quantified by ELISA using antibodies to the A domain of FnBPA ( which is common to all variants ) . Analysis of L . lactis expressing FnBPR1-11 grown in a range of nisin concentrations ( 0–200 ng ml−1 ) revealed that , in keeping with previous reports [35] , [36] , low-level expression occurred in the absence of nisin ( Fig . 5A ) . A dose-dependent increase in FnBPR1-11 expression was seen with increasing concentrations of nisin up to 200 ng ml−1 ( Fig . 5A ) , beyond which bacterial growth was inhibited ( data not shown ) . Expression levels of each of the FnBPA variants were equivalent when identical nisin concentrations were used ( Fig . 5A ) . In addition to the ELISA we employed a fibrinogen-binding assay to assess FnBPA function as the A domain , which is constant in all our constructs , is known to bind fibrinogen ( Fig . 5B ) . In the absence of nisin L . lactis expressing each FnBPA variant bound fibrinogen at very low levels , which increased in a dose-dependent manner and were equal at nisin concentrations >10 ng ml−1 ( Fig . 5B ) . Adhesion to fibrinogen at lower surface expression levels ( <10 ng ml−1 ) was variable and , in keeping with recent work [13] , suggested that FnBR1 is required for optimal binding to this ligand . The abilities of five FnBPA variants to adhere to Fn and to trigger bacterial internalization were compared over a range of expression levels . In the absence of nisin , L . lactis expressing FnBPR1-11 adhered strongly to Fn ( Fig . 6A ) . Increasing FnBPR1-11 expression ( 0–10 ng ml−1 nisin ) promoted adhesion 2 . 5-fold before reaching saturation ( Fig . 6A ) . These results mirrored those seen for bacterial internalization , with high levels occurring at basal ( absence of nisin ) FnBPA expression levels , which increased steadily ( >10-fold ) before reaching a plateau , also at 10 ng ml−1 nisin ( Fig . 6B ) . By contrast , binding to Fn or invasion of endothelial cells by L . lactis expressing FnBPR0 ( no Fn-binding repeats ) was not enhanced at any level of expression ( Fig . 6 ) . At basal expression levels L . lactis expressing FnBPR1 ( containing a single high-affinity repeat ) bound to Fn at a low level and was not internalized ( relative to FnBPR0 , Fig . 6 ) . However , both adhesion and invasion levels increased dramatically ( 20-fold and 63–fold respectively ) with increasing levels of expression , eventually reaching similar levels to that of FnBPR1-11 at saturation ( Fig . 6 ) . Adhesion of L . lactis expressing FnBPR2 ( containing a single low-affinity repeat ) to Fn occurred only at levels of induction >1 ng ml−1 nisin and increased in a dose-dependent manner , eventually reaching levels similar to that supported by FnBPR1 at 100 ng ml−1 nisin ( Fig . 6A ) . However , FnBPR2 did not trigger bacterial internalization at any expression level ( Fig . 6B ) . The adhesion profile of L . lactis expressing FnBPR1 , 10 , 11 ( containing 3 high-affinity repeats ) was very similar to that of FnBPR1-11 ( Fig . 6A ) . At the basal-level of expression L . lactis FnBR1 , 10 , 11 triggered invasion at levels 3 . 5-fold lower than full length FnBPR1-11 but 3-fold higher than FnBPR1 . Invasion levels increased with increasing nisin concentration , reaching the same level as full length FnBPR1-11 at 10 ng ml−1 ( Fig . 6B ) . Together these findings suggest that although a single high affinity repeat can confer wild-type levels of adhesion and invasion , its expression needs to reach a high density before it can do so . The expression of multiple repeats within a single molecule allows invasion of endothelial cells at low FnBPA surface density . Fn-binding proteins of pathogenic bacteria are believed to cause clustering of cell-surface integrins , leading to cell signalling events that trigger uptake of the bacterium [24] . We hypothesized that multiple FnBRs might trigger uptake in a more efficient manner than a single repeat , leading to an increased rate of bacterial internalization . To assess this we determined the number of adherent and internalized bacteria at various time points from 15 to 60 minutes post-incubation with endothelial cells . Figures 7A and 7B show that adhesion of FnBPA-expressing bacteria to the endothelial cell surfaces was consistent between variants over time . Indeed , adhesion to this cell line does not appear to depend on fibronectin binding since S . aureus FnBPR0 binds as efficiently as FnBPR1-11 ( data not shown ) . S . aureus expressing full length FnBPR1-11 showed a continual increase in the number of internalized bacteria over this time period , reaching a plateau at 45–60 minutes ( Fig . 7C ) . S . aureus expressing only a single repeat ( FnBPR1 ) had approximately 20-fold fewer internalized bacteria after 15 minutes compared with the full length protein but this difference became less pronounced with increasing incubation time , and there was no significant difference in the number of internalized bacteria after 60 minutes ( Fig . 7C ) . S . aureus expressing three high affinity repeats invaded more quickly than those expressing a single repeat but less quickly that those expressing all 11 repeats , supporting the hypothesis that multiple repeats increase the rate at which S . aureus can invade cells . These results were verified by examining invasion mediated by full length FnBPA on the surface of L . lactis at varying densities ( Fig . 7D ) . After 60 minutes' incubation of bacteria and endothelial cells L . lactis expressing all densities of FnBPA had invaded at similar levels , but at 15 and 30 minutes incubation a clear association between surface density and bacterial uptake could be seen ( Fig . 7D ) . Although triggered by FnBPA , staphylococcal entry into endothelial cells involves host cell processes [18] , [20] . There appear to be two major mechanisms of invasion of human cells by pathogenic bacteria , the first involving integrin-triggered remodelling of actin and the second making use of host cell structures known as caveolae [24] . In keeping with previous reports [24]–[26] , the invasion of the cells used in this study by S . aureus appears to occur via an actin remodelling mechanism ( Fig . S2 ) . As the rate of bacterial internalization was affected by FnBPA composition and surface density , we hypothesized that this variation might reflect the triggering of different cellular uptake mechanisms . To examine this we investigated the mode of entry of S . aureus expressing FnBPR1-11 and FnBPR1 , as well as L . lactis expressing FnBPR1-11 at high or low surface densities , using inhibitors of cellular processes . Consistent with wild-type S . aureus 8325 . 4 ( Fig . S2D ) , internalization of S . aureus expressing either FnBPR1-11 or FnBPR1 was unaffected by genistein or methyl-β-cyclodextrin but was inhibited by Wortmannin and cytochalasin D ( Fig . 8A ) . Although uptake of both S . aureus expressing FnBPR1-11 and FnBPR1 was slightly reduced by the Src kinase inhibitor PP2 , it was only significant for FnBPR1 ( p = <0 . 05 ) ( Fig . 8A ) . L . lactis expressing FnBPR1-11 was similarly affected , regardless of expression level ( Fig . 8B ) . Internalization of bacteria weakly ( absence of nisin ) or strongly ( 200 ng ml−1 nisin ) expressing FnBPR1-11 was inhibited by wortmannin and cytochalasin D but not genistein or methyl-β-cyclodextrin ( Fig . 8B ) . Inhibition of L . lactis uptake by PP2 was much more pronounced than for S . aureus , particularly at low expression levels ( Fig . 8 ) . These findings verify that the density of Fn-binding repeats does not affect the uptake mechanism exploited by the bacteria . Blood contains high levels of soluble ( plasma ) Fn . Although FnBPA has been shown to bind fluid-phase Fn [20] , we hypothesized that to bind the endothelium in vivo S . aureus must be able to bind preferentially to immobilized ( cell bound ) Fn . To test this we measured the adhesion of bacteria expressing FnBPA variants to immobilized fibronectin in the presence of increasing concentrations of soluble fibronectin . Adhesion of S . aureus expressing FnBPR1-11 to immobilized Fn was unaffected by the presence of soluble Fn except at the highest concentrations used ( Fig . 9 ) . By contrast , Fn-binding by S . aureus expressing FnBPA variants containing one or three repeats was significantly inhibited at low concentrations of soluble Fn ( Fig . 9 ) . This suggests that full-length FnBPA is likely to be significantly more efficient at conferring adhesion to the endothelium under in vivo conditions . S . aureus has a number of mechanisms that aid evasion of the host immune system including surface adhesins such as Clf and Spa that inhibit phagocytosis [4] . Work with Streptococcus pyogenes suggested that the Fn-binding protein PrtF , which is structurally similar to FnBPA , inhibits phagocytosis in a whole blood model [38] . As such , we assessed whether the FnBR region of FnBPA might perform a similar function . Using a similar whole blood model ( see Materials and Methods section ) , we determined the survival of S . aureus strains 8325 . 4 and LS-1 expressing full length FnBPA ( FnBPR1-11 ) or the FnBR-deficient variant FnBPR0 . Over a period of up to 6 h there were no significant differences in bacterial CFU between strains expressing FnBPR1-11 or the FnBR-deficient FnBPR0 ( Fig . 10A ) , indicating that fibronectin binding does not affect S . aureus survival . In addition , we assessed whether Fn-binding might affect the inflammatory response of whole blood to S . aureus by determining the production of TNFα triggered by each strain . All bacteria examined elicited TNFα expression that was significantly greater than that of whole blood incubated without bacteria ( Fig . 10B ) . Although there was a significant difference between strains , the FnBR-region of FnBPA had no effect on TNFα production by whole blood ( Fig . 10B ) . We hypothesized from our in vitro data that bacteria expressing FnBPA variants with multiple repeats would be able to bind to and invade endothelium more efficiently in vivo than bacteria expressing FnBPA variants with few or no repeats , causing greater levels of mortality . To test this we employed a murine sepsis model to assess the virulence of S . aureus LS-1 ( a mouse infective strain ) expressing FnBPA variants . S . aureus LS-1 Δfnb expressing FnBPR0 , FnBPR1 , FnBPR1 , 10 , 11 or FnBPR1-11 were phenotypically similar to 8325 . 4 Δfnb expressing the same constructs with respect to Fn-binding and invasion of endothelial cells ( Fig . 3 ) . Mice were injected intravenously with one of these four S . aureus LS-1 strains and survival and weight loss monitored over 14 days . By day 9 , S . aureus expressing full length FnBPR1-11 had killed >60% of mice , which was significantly greater than any of the three other strains ( p = <0 . 007 , Fig . 11A ) . Those that survived continued to suffer significant weight loss of approximately 30% , suggesting severe infection ( Fig . 11B ) . By contrast , no significant difference in mortality was observed between S . aureus expressing FnBPR0 , FnBPR1 or FnBPR1 , 10 , 11 , which killed up to 20% of the mice ( Fig . 11A ) . The pattern of weight loss was also similar between these three strains , although mice infected with S . aureus expressing FnBPR1 and FnBPR1 , 10 , 11 showed significantly different weight changes between days 4 and 13 after inoculation ( p = 0 . 02 , Fig . 11B ) . In addition to measurements of mortality and weight loss we also assessed bacterial numbers ( CFU ) in the kidneys 3 days after inoculation ( Fig . 11C ) . This time point was chosen because previous experiments ( Fig . 11A , B ) indicated that it was close to the initiation of mortality ( indeed , one mouse in the FnBPR1-11 group died before sacrifice ) . As expected from the data in Figures 11A and 11B , the kidneys of mice inoculated with S . aureus FnBPR1-11 contained significantly more CFU than the kidneys of mice infected with S . aureus FnBPR0 ( 23-fold greater , p = 0 . 0022 ) . S . aureus FnBPR1-11 was also approximately 6-fold more abundant than S . aureus expressing either FnBPR1 or FnBPR1 , 10 , 11 ( p = 0 . 024 ) . There was no significant difference in the bacterial load between S . aureus FnBPR0 , FnBPR1 or FnBPR1 , 10 , 11 . We also used the bacterial colonies from the kidneys to determine loss of the FnBPA-encoding plasmids ( Fig . 11D ) . Plasmid loss was highly variable between animals but was not significantly different between FnBPA variants and did not correlate with bacterial load ( data not shown ) . The difference between S . aureus expressing FnBPR0 , FnBPR1 or FnBPR1 , 10 , 11 when compared with those expressing full length FnBPR1-11 demonstrates that full length FnBPA is required for full pathogenicity in this model , most likely during the early stages of infection .
Bacterial invasion of host cells is a process common to many pathogens . It likely aids evasion of extracellular immune factors and provides a protected niche from antibiotics , and can therefore allow bacteria to cause persistent and recurrent infections [39] , [40] . Additionally , bacterial invasion of the endothelium can lead to inflammation , endocarditis and may lead to traversal of blood vessels and subsequent formation of metastatic infections [22] . As such it is crucial that we gain a greater understanding of how this pathogen interacts with the host to identify means of blocking these infections [1] . There is clear and compelling evidence that FnBPA alone is sufficient to trigger S . aureus invasion of cells via Fn bridging to α5β1 integrins [18] , [20] . Previous work revealed that staphylococcal attachment to Fn and invasion of host cells is maintained even when substantial portions of the Fn-binding region have been deleted [13] , [19] . As such , it was unclear what benefits are conferred by multiple FnBRs . As there is evidence that a single FnBPA molecule is able to bind up to nine Fn molecules [33] , we hypothesized that a minimum number of repeats in close proximity would be required to trigger Fn clustering and subsequent integrin clustering , which leads to cell invasion [32] , [33] . Furthermore , we hypothesized that the composition of FnBPA variants with respect to high- and low-affinity repeats would affect adhesion and internalization . Here we show that high surface densities of repeats are required for adhesion and internalization in vitro . However , it is not important to have multiple repeats within a single FnBPA molecule because high level expression of individual FnBPA variants containing a single high-affinity binding repeat results in invasion at WT levels ( Fig . 6 ) . In addition to demonstrating that high surface density of FnBRs promotes cell invasion , we show that speed of uptake is modulated by composition and expression level of FnBPA ( Fig . 7 ) . Although the data presented here suggest that Fn binding is indicative of the potential to trigger invasion , our studies using low-affinity repeats suggest a more complicated situation . The inability of the FnBPA variant containing a single low-affinity repeat ( FnBPR2 ) to trigger internalization at high surface expression levels , despite supporting adhesion to Fn , suggests that invasin-receptor affinity is crucial ( Fig . 6 ) . Three tandem low-affinity repeats however support invasion ( Fig . 3 ) . It is therefore possible that certain FnBPA-Fn interactions induce a conformational change in the glycoprotein , which triggers integrin signalling and bacterial uptake . In vivo , S . aureus will not only encounter cell-bound Fn but also the soluble form . Although adhesion of S . aureus expressing FnBPR1 and FnBPR1 , 10 , 11 to immobilized Fn was significantly inhibited at even low concentrations of soluble Fn , S . aureus FnBPR1-11 was only affected at the highest concentration used ( Fig . 9 ) . This suggests that the three other high-affinity repeats ( 4 , 5 and 9 ) and perhaps also the five low-affinity repeats ( 2 , 3 , 6 , 7 and 8 ) are important in overcoming the presence of soluble ligand . Although our data suggest that , in tandem , multiple low-affinity repeats can perform almost as well as single high-affinity repeats , it is not clear what advantage the bacteria gain from maintaining these repeats . The answer may lie in studies of the immune response to FnBPA during staphylococcal infection . Analysis of a panel of antisera from different patients demonstrated that antibodies recognizing the Fn-binding region do not inhibit bacterial binding of Fn [41] . Furthermore , the binding of these antibodies to FnBPA was enhanced in the presence of Fn , presumably due to the creation of ligand-induced binding sites [41] . A more recent study revealed that despite strong antibody recognition of high-affinity repeats when complexed with Fn , there was significantly lower recognition of low-affinity repeats either in the presence or absence of the glycoprotein [31] . As such , the lack of antibody recognition of low-affinity repeats may allow the bacteria to adhere to host tissues even in the presence of anti-FnBPA antibodies . A role for FnBPA in sepsis has been previously established [14] , although it was not clear whether the fibrinogen- and elastin-binding region or the Fn-binding region was important . Using the same model of sepsis we show that the FnBRs are required for virulence ( Fig . 11 ) . We also show that increased efficiency of the full length protein ( FnBPR1-11 ) at mediating binding to Fn and cell invasion translates into a difference in virulence . Despite the ability to bind Fn and invade cells in vitro , neither S . aureus expressing FnBPR1 nor FnBPR1 , 10 , 11 were any more virulent than that expressing FnBPR0 . The enhanced pathogenesis conferred by multiple FnBRs does not appear to be due to elevated survival in blood or the stimulation of pro-inflammatory TNFα . However , the greater bacterial load in the kidneys of mice infected with S . aureus FnBPR1-11 , compared with FnBPR0 or FnBPR1 , suggests that multiple repeats significantly enhance systemic invasion . The demonstration of the role of FnBPA in septic death does not rule out the involvement of other staphylococcal factors in the progression of infection . Indeed , a role for clumping factor in sepsis using this model and strain has been shown previously [41] and it is likely that different factors will be required at different stages of the infective process [1] . Experiments examining the loss of FnBPA-encoding plasmids from infecting bacteria suggest that this protein acts early in the infectious process and its retention is not required for the latter stages of infection . Significant plasmid loss at day 3 is unsurprising since the bacterial load for S . aureus FnBPR1-11 is 10 times greater than the inoculum , indicating significant replication within this organ . These data indicate that the FnBRs within FnBPA significantly enhance bacterial exit from the blood and colonization of the kidneys soon after entry into the vascular system . This is supported by previous in vivo experiments that showed S . aureus attachment to the endothelium is dependent on FnBPA and occurs within 5 minutes of inoculation [21] . Once colonization is established , S . aureus is likely to employ other virulence factors ( e . g . other surface proteins , toxins and proteases ) , which together with host factors , promote progression to abscess formation [1] , leading to weight loss and mortality . However , early extravasation , mediated by FnBPA containing multiple FnBRs , appears to be essential for full virulence . In summary , the presence of multiple FnBRs within FnBPA results in a high surface density of Fn-binding sites . This triggers a rapid invasion of endothelial cells even at low surface expression levels and enables binding in the presence of soluble ligand . This appears to be particularly relevant to pathogenesis and the development of systemic infection .
Approval for experiments using human blood was granted by the Bath Research Ethics Committee ( NHS National Research Ethics Services , reference 08/H0101/18 ) . Donors gave informed consent in writing prior to the commencement of any procedures . Approval for animal experiments was granted by the Göteborg Animal Experimentation Ethics board and their ethical and husbandry guidelines followed for all experiments . NMRI mice were obtained from Charles River ( Sulzfeld , Germany ) and were maintained in the animal facility of the Department of Rheumatology , University of Göteborg , Sweden . Mice were housed up to 10 animals per cage with a 12 h light-dark cycle , and were fed standard laboratory chow and water ad libitum . The animals were 8 weeks old at the start of the experiments . The overall condition of each mouse was examined by assessing signs of systemic inflammation , i . e . weight decrease , reduced alertness , and ruffled coat . In keeping with approved husbandry standards , in cases of severe systemic infection , when a mouse was judged too ill to survive another 24 h , it was killed by cervical dislocation and considered dead due to sepsis . A detailed list of the strains used in this study is presented in Table 2 . S . aureus strains were cultured for 16 h in Brain-Heart Infusion ( BHI ) broth at 37°C in air with shaking . S . aureus CFU were quantified on Tryptic Soy Agar ( TSA ) plates incubated overnight at 37°C in air . L . lactis strains were cultured in M17 broth ( supplemented with 0 . 5% w/v glucose ) for 16 h at 30°C in air ( with the appropriate concentration of nisin where necessary ) . Escherichia coli was grown in Luria broth at 37°C with shaking . Where appropriate , bacteria were incubated in the presence of the following antibiotics: ampicillin 100 µg ml−1 , chloramphenicol 10 µg ml−1 and erythromycin 5 µg ml−1 ( L . lactis ) or 250 µg ml−1 ( E . coli ) . The cell line EA . hy926 , established by the fusion of human umbilical endothelial cells ( HUVEC ) and the permanent lung epithelial carcinoma cell line A549 [42] , was cultured in Dulbecco's Modified Eagle's medium ( DMEM; Invitrogen ) supplemented with foetal bovine serum ( FBS; 10% ) and L-glutamine ( 2 mM ) at 37°C and 5% CO2 . Pooled primary HUVECs were purchased from Lonza ( Basel , Switzerland ) and cultured in endothelial basal medium supplemented with 2% fetal bovine serum , bovine brain extract ( including heparin ) , human endothelial growth factor , hydrocortisone and GA-1000 ( Gentamicin & Amphotericin B ) at 37°C and 5% CO2 according to manufacturer's instructions ( Lonza ) . Endothelial cells were cultured in T75 flasks to approximately 95% confluency , liberated with trypsin-EDTA , resuspended in the relevant culture medium and added to 24-well plates containing thermanox glass coverslips [19] . Plates were incubated for 48 h as described above before the coverslips were removed , dip washed in PBS and added to new 24-well plates containing fresh medium and bacteria [19] . In experiments using metabolic inhibitors , these were incubated with the cells 1 h prior to the addition of bacteria and concentrations maintained during the assay; genistein ( 200 µM ) , wortmannin ( 20 nM ) , cytochalasin D ( 50 µM ) , PP2 ( 10 µM ) and methyl-β-cyclodextrin ( 2 mM ) . FnBPA constructs containing various combinations of Fn-binding repeats were produced using a circular PCR system based on that described by Massey et al . [19] . Primers were designed to various repeats or DNA just outside of the fnbA repeat region and were synthesized with 5′ phosphorylation in order to allow self-ligation of the PCR product . Primer sequences and the combinations used to produce the various products can be found in Table 1 . Plasmid pFnBA4 [34] was used as a template for the amplification of DNA lacking in various repeats . PCR was performed using Phusion high-fidelity DNA polymerase ( New England Biolabs ( NEB ) Ipswich , MA ) . Template DNA was digested with DpnI and products cleaned using a PCR purification kit or gel extracted ( Qiagen , Hilden , Germany ) . Linear DNA was self-ligated using T4 DNA ligase ( NEB ) to produce plasmids containing DNA encoding the fnbA gene lacking various combinations of FnBRs ( Table 2 ) . Ligated PCR products were transformed into CaCl2 competent E . coli [43] and plated onto LB agar containing 100 µg ml−1 ampicillin . Selected colonies were grown overnight in LB broth ( 5 ml , 100 µg ml−1 Ampicillin ) , bacteria pelleted by centrifugation and plasmids recovered using a miniprep kit ( Qiagen ) . Constructs were checked by sequencing ( Geneservice , London , UK ) and transformed into S . aureus RN4220 by electroporation . Plasmids were recovered using a combination of lysostaphin and the Qiagen miniprep kit [19] and re-transformed into S . aureus 8325 . 4 Δfnb . Controlled expression of FnBPA variants was achieved using a nisin-inducible system previously used to control the expression of the enterococcal surface protein PrgB [35] . The prgB gene was excised from plasmid pMSP7517 using NcoI and XhoI and the remaining plasmid recovered by gel excision . pFnBA4 and associated constructs were used as templates for PCR reactions using primers designed to amplify the entire fnbA gene ( and repeat-region variants ) and contained NcoI ( AAACCATGGAGGAGGTATTATAGTGAAAAACAATCTTAGG ) and XhoI ( AAACTCGAGCTAACTTTATCTCTCAGTTCGTTATC ) sites respectively ( underlined ) . Digested PCR products were ligated into digested pMSP7517 and transformed into CaCl2-treated E . coli K12 ER2925 , which was more suitable for antibiotic selection using erythomycin than E . coli DH5α ( data not shown ) . Plasmids were recovered , checked by sequencing and electro-transformed into L . lactis NZ9800 [36] . Expression was induced by culture ( 16 h , 30°C ) of L . lactis containing FnBPA variant constructs ( Table 2 ) in the presence of various concentrations of nisin ( range 0–200 ng ml−1 ) . Expression levels were determined by ELISA and binding to immobilized fibrinogen ( see below ) . S . aureus was cultured as described and the bacteria pelleted by centrifugation ( 5 , 000 x g , 8 min ) . Bacteria were washed 3 times in PBS , resuspended in spheroplasting buffer ( 30% raffinose , Tris-HCl pH 7 . 5 ) [27] containing 100 µg ml−1 lysostaphin and incubated for 1 h at 37°C . Spheroplasts were pelleted by centrifugation and the supernatant containing surface proteins recovered . Aliquots ( 40 µl ) were mixed with 5X concentrated sample buffer ( 10 µl; 50% glycerol containing 50 mM Tris-HCl pH 6 . 8 , 10% SDS and 10 mM 2-mercaptoethanol ) and heated at 100°C for 5 min before being subjected to SDS-PAGE ( 7 . 5% acrylamide ) . Separated proteins were electroblotted onto nitrocellulose membrane using a BioRad ( Hercules , Ca ) semi-dry blotter ( 25V , 1 h ) . Membranes were blocked by incubation in 3% BSA ( 1 h ) and probed with antibodies to the N-terminal domain of FnBPA [19] for 1 h with agitation . Membranes were washed 4 times with PBS and incubated with HRP-conjugated goat anti-rabbit antibodies ( 1 h with agitation ) . Membranes were subsequently washed 4 times with PBS , rinsed with ddH2O and reactive bands detected using the Opti-4CN detection kit ( BioRad ) . S . aureus strains were cultured as described above , washed with PBS , adjusted to OD600 = 0 . 1 and aliquots ( 50 µl ) immobilized onto triplicate plastic wells ( Nunc Maxisorp ) by incubation at 4°C for 16 h . Wells were blocked with BSA ( 1 h , 4°C ) , washed once with PBS and then incubated with antibodies ( 50 µl , 1∶500 in PBS for S . aureus 8325 . 4 or 1∶2000 for S . aureus LS-1 ) raised to the A region of FnBPA [19] for 1 h at 37°C . Wells were washed 4 times with PBS and incubated with HRP-conjugated protein A ( 50 µl , 1∶5000 , Sigma ) for 30 min at 37°C . Wells were washed a further 4 times with PBS and bound antibody detected and quantified with 100 µl TMB ( 3 , 3′ , 5 , 5′-tetramethylbenzidine ) ELISA detection substrate ( Sigma ) for 10 min at room temperature . The reaction was stopped with 100 µl 2M HCl and product measured at 450 nm using a microplate reader . L . lactis expressing FnBPA variants were washed in PBS as described above , diluted 1∶16 in PBS and 50 µl aliquots added to duplicate wells of a microtitre plate as described for S . aureus . Wells were blocked as described above and immobilized bacteria were incubated with anti-FnBPA antibodies ( 50 µl , 1∶1000 , 1 h , 37°C ) before washing three times with PBS . Bacteria were then incubated with HRP-conjugated anti-rabbit antibodies ( 50 µl , 1∶5000 , 1 h , 37°C ) and quantified using TMB as described above . The adhesion of bacteria to human Fn ( Sigma ) , murine Fn ( Biopur , Bubendorf , Switzerland ) or fibrinogen ( Fn-depleted , Enzyme Research Labs , Swansea , UK ) was assessed using a protocol based on those of Jakubovics et al . and Peacock et al . [27] , [44] . Fn or fibrinogen ( 1 µg protein in 100 µl PBS [pH 7 . 4] per well ) was immobilized onto plastic Nunc Maxisorp Immuno modules by incubation at 4°C for 16 h and remaining binding sites were blocked with 300 µl 3% BSA ( in PBS ) at 25°C for 1 h . Blocked wells were washed with PBS and incubated with 100 µl bacteria ( approx . 1×108 S . aureus or 5×108 L . lactis in PBS ) at 37°C for 1 h . Non-adherent bacteria were removed by 3 rounds of washing with PBS and adherent bacteria fixed with 0 . 25% paraformaldehyde ( 5 min , 25°C ) . Wells were washed with PBS a further 2 times . Fixed bacteria were stained with crystal violet ( 0 . 5% , 2 min ) before a further three rounds of washing , as described above . Adherent , fixed bacteria were enumerated by solubilization of crystal violet with 100 µl 7% acetic acid . The contents of each well was quantified by measurement at A595 using a microplate reader . For both Fn and fibrinogen binding , readings were blanked against bacteria binding to wells coated only with BSA . Absorbance measurements were converted to bacterial numbers by the use of standard plots of known bacterial numbers against A595 readings ( Figure S3 ) . These showed that the relationship of A595 to bacterial numbers is linear ( Figure S3 ) . Each experiment was performed at least three times . Since S . aureus was stained more strongly with crystal violet than L . lactis ( Figure S3 ) , different amounts of bacteria were used in assays in order to allow bacterial quantification over as wide a range of adhesion levels as possible . Cultured cells were dissociated from plastic flasks using trypsin-EDTA solution ( Invitrogen ) and approximately 5×105 ( in 0 . 5 ml medium ) were seeded into each well of 24-well plates ( Nunc ) containing 13 mm plastic Thermanox cover slips ( Fisher ) and allowed to attach for 48 h ( 37°C , 5% CO2 ) . Coverslips were dip-washed once in PBS and placed in the well of a new 24-well plate containing 450 µl of DMEM containing 10% FBS . To each well , 50 µl of washed bacteria were added ( approximately 1×107 CFU S . aureus or 5×107 L . lactis ) and incubated for 5–90 minutes at 37°C in 5% CO2 . To measure the total number of bacteria associated with the cells ( adherent and internalized ) , coverslips were dip-washed 3 times in PBS and added to wells containing 500 µl 0 . 5% Triton X-100 . Wells containing coverslips were agitated by pipetting to fully lyse the cells and CFU were enumerated by serial dilution and plating onto TSA agar plates and incubated overnight at 37°C in 5% CO2 . For invasion assays , the bacterial suspension was removed and replaced with 500 µl DMEM/10% FBS supplemented with 200 µg ml−1 gentamicin and incubated at 37°C in 5% CO2 for 60 min . Coverslips were washed 3 times in PBS , lysed and plated for CFU as described for the adhesion assay above . In assays where metabolic inhibitors were used , these were added to cell monolayers for 60 min prior to the experiment and concentrations maintained during incubation with bacteria . For adhesion and invasion assays , statistical analyses were performed with Student's t test by using the Bonferroni correction for multiple comparisons . Values that were statistically significantly different from control values are indicated by asterices in the figures . Error bars indicate the mean average ± standard deviation of multiple independent experiments ( indicated in the figure legend ) . The ability of S . aureus to survive in human blood and generate pro-inflammatory cytokine release was assessed using a whole human blood model . Blood from a healthy male was drawn into heparinised tubes and used immediately . Bacteria were washed and diluted in PBS and 10 µl ( containing approximately 20 , 000 CFU ) was added to 800 µl blood and incubated at 37°C for 1 , 2 or 6 h with mixing . Aliquots of the blood/bacteria mixture ( 5 µl ) were mixed with PBS ( 95 µl ) and plated onto tryptic soy agar to enumerate CFU . In addition to plating for CFU , blood at the final time point ( 6 h ) was centrifuged ( 5 , 000 x g , 5 min ) and 100 µl serum recovered for analysis of TNFα production using a BD OptEIA ELISA kit ( BD Biosciences , San Diego , USA ) . Groups of 15 mice were infected by intravenous injection with S . aureus strain LS-1 Δfnb [14] expressing FnBPR1-11 ( 1 . 1×107 CFU ) , FnBPR0 ( 1 . 1×107 CFU ) , FnBPR1 ( 1 . 0×107 CFU ) or FnBPR1 , 10 , 11 ( 1 . 2×107 CFU ) and mortality and weight change were followed until day 14 . The clinical evaluation was performed in a blinded manner . The overall condition of each mouse was examined by assessing signs of systemic inflammation , i . e . weight decrease , reduced alertness , and ruffled coat . In keeping with approved husbandry standards , in cases of severe systemic infection , when a mouse was judged too ill to survive another 24 h , it was killed by cervical dislocation and considered dead due to sepsis . The bacterial load in kidneys was assessed as described previously [14] . Two independent groups of 4 mice were inoculated with S . aureus ( as described above ) and sacrificed 3 days later . Kidney pairs were recovered and CFU enumerated on TSA plates . Retention of the FnBPA-encoding plasmid was determined by plating onto TSA with and without chloramphenicol . Statistical evaluation was done by using the Kruskal-Wallis test with a following post-hoc analysis , or the Logrank ( Mantel-Cox ) test at survival analysis . P = <0 . 05 was considered to be significant . No correction for multiple comparisons was employed . Data are reported as medians , interquartile ranges , and 80% central ranges , unless otherwise mentioned . FnBPA variants were derived from the fnbA gene of S . aureus 8325 . 4 , Swissprot accession SAOUHSC_02803 . | Staphylococcus aureus is a frequent cause of bacteremia and sepsis . Adhesion to and invasion of endothelial cells lining blood vessels by S . aureus can lead to colonization of the heart valves and/or dissemination into surrounding tissues and the establishment of secondary ( metastatic ) infections . Uptake by endothelial cells is triggered by the interaction of fibronectin-binding protein A ( FnBPA ) with host cell receptors called integrins via a fibronectin ( Fn ) bridge . FnBPA contains 11 non-identical repeats ( FnBRs ) that mediate binding to Fn . Previous work has shown that partial deletions in the FnBR region do not significantly affect binding to Fn or invasion of host cells in vitro . It was therefore unclear why FnBPA contains so many repeats . This work demonstrates that the number of repeats affects the amount of surface-exposed FnBPA required to trigger invasion , as well as affecting the speed of uptake . As such , the presence of multiple repeats within FnBPA facilitates adhesion and invasion even at low surface expression levels . Experiments using a murine model of sepsis demonstrate that these differences have a significant impact on virulence; S . aureus expressing FnBPA variants with no or few FnBRs are significantly less virulent compared to FnBPA with the full complement of repeats . | [
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] | 2010 | Staphylococcus aureus Host Cell Invasion and Virulence in Sepsis Is Facilitated by the Multiple Repeats within FnBPA |
Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram ( EEG ) and local field potential ( LFP ) in bulk brain matter , and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation . Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals , and independently from the spike train alone , but behavior or stimulus triggered firing-rate modulation , spiking sparseness , presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods , present challenges to searching for temporal structures present in the spike train . In order to study oscillatory modulation in real data collected under a variety of experimental conditions , we describe a flexible point-process framework we call the Latent Oscillatory Spike Train ( LOST ) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness , event-locked firing rate non-stationarity , and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation . We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment , and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm . Because LOST incorporates a latent stochastic auto-regressive term , LOST is able to detect oscillations when the firing rate is low , the modulation is weak , and when the modulating oscillation has a broad spectral peak .
Neural oscillations have generated considerable interest for their roles in cognition and as indicators for disease [1–14] . Electroencephalograms ( EEGs ) and local field potential ( LFPs ) recordings have revealed transient oscillations in many cortical and subcortical structures , to which neurons both near and far from the LFP recording site show phase preferences in spiking . Groups of neurons that are locked to a common oscillation , and are therefore active in tightly confined temporal windows , may define a cell assembly whose synchronous spiking could select and activate afferent structures [15–17] . Such transient cell assemblies may form and disband as objects are attended to in the visual scene [3 , 6–8 , 11] , as preparation for movements are made [2 , 13] , or during choice points for reward [9] . Prominent slow oscillations are observed as patients undergo anesthesia [10] , and exhibit changes in dynamics during transitions in unconscious brain state [14] . Furthermore , strong oscillatory signals are prominent in neurological disorders such as Parkinson’s disease , and can be used to characterize pathophysiology and its relation to behavior [12 , 18 , 19] . The oscillatory structure of individual neurons can thus provide insight into the dynamics of cognitive processing and motor planning , and their implementation in health and disease . However , identifying oscillations in neural spike trains , particularly within individual experimental trials , can be challenging because they occur in conjunction with both non-oscillatory fluctuations in neural firing rate and extraneous noise . To tease apart multiple factors that affect spiking activity , statisticians have suggested the use of point process models together with modern regression methods associated with generalized linear model ( GLM ) technology [20 , 21] . In this paper we develop a method that can find oscillations in spike trains even when the signal is comparatively weak , and can track changes in oscillatory behavior across trials . The starting point for our approach is the observation by Smith and Brown [22] that , for many purposes , evolving neural firing rates can be described using state-space models imbedded in point processes . We modify the models used by Smith and Brown , replacing their first-order autoregressive processes with higher-order processes [23] that can fit oscillations found in spiking neurons . This requires careful attention to the form of the higher-order autoregressive process . We take advantage of a Bayesian time-series decomposition introduced by Huerta and West [24] , using prior probability distributions to constrain the fit so that it has appropriate power spectral content . We also use a Gibbs sampling method developed recently in a different context [25] to compute posterior distributions efficiently . Our approach is superficially related to that of Allcroft et al . [26] , who used autoregressive moving average models to estimate spectral content from censored data , but their method does not accommodate easily the special situation we face with spiking neurons , including history effects that can account for hard and soft refractory periods . Point process models employing the GLM framework [21 , 27] have been employed to explain the spiking behavior by taking into account spiking refractoriness , behavioral and stimulus-induced non-stationarities , and spiking of neighboring neurons [20] . A series of investigations [12 , 18 , 19] have added long-term history effects to capture oscillation in the history dependence and have also used a state-space smoothing algorithm to track changes in the oscillatory dynamics over time . Eden et al used a long history to capture both , but as we shall see , LOST can better capture the irregularities present in realistic biological oscillations . Our approach is based on what we call the Latent Oscillatory Spike Train ( LOST ) model , which not only includes terms to describe the spiking refractoriness , event ( behavior or stimulus ) -locked effects , and trial-to-trial variability in firing rate , but also explicitly models the dynamics of the modulating oscillation itself . However , the latent state cannot capture the discontinuous change in the spiking probability that occurs after every spike , which the aforementioned GLM models do well in characterizing , so we utilize a short-term spiking history to capture the refractory period , while also introducing the latent state to model the oscillatory dynamics . Oscillatory structure is often considerably degraded when it is observed as a spike train . On the one hand , if the firing rate is low , or the oscillatory modulation is weak , noise associated with erratic spiking will dominate . On the other hand , even if the modulation is strong and the firing rate is high , the oscillatory signal may itself be unsteady , with substantial variation in period from cycle to cycle . LOST accommodates both spiking noise and oscillatory variation by imbedding into the point process intensity function a latent auto-regressive process , and then imposing a suitable soft constraint ( in the form of a prior probability distribution ) on the oscillatory dynamics . We provide details of the model , along with the posterior sampling scheme and a discussion on how to interpret the posterior samples , and study its effectiveness with leaky integrate-and-fire neural simulations . We then demonstrate the ability of the LOST framework to find interesting trial-to-trial variation in motor cortical neuron during a lever-pulling task .
Gibbs sampling [28] and data augmentation [29 , 30] are used to infer the model parameters Θ = [F1:p , σ2 , α , μ , v] and the oscillation x . The spike history and TAE are functions of time relative to the last spike of an event or behavior , respectively , and they are estimated by parameterizing a subset of continuous functions with splines . The estimation requires us to choose a set of basis splines , which we choose heuristically , using some prior belief about their general shape to choose the knot locations . In the current work , the number and locations of the knots are not found with the Gibbs sampling procedure , so we briefly describe how they are determined before we describe the Gibbs sampling procedure . We jointly sample model parameters and latent states from the joint posterior distribution , Eq ( 4 ) , using Gibbs sampling with data augmentation . The conditional posterior distribution can be read off from the full joint posterior distribution by considering all parameters fixed , except the parameter whose conditional posterior we are interested in . Our problem deviates from the standard Gibbs sampling by the missing data x in Eq ( 4 ) , and we utilize the data augmentation strategy [29 , 30] , a scheme of simplifying analysis by augmenting the observed data with missing values , to generate samples of X from the predictive distribution p ( X|y , Θ ) in between sampling parameters from the standard conditional posteriors . Even with samples of X in hand , the posterior distribution does not yield conditional posteriors that can be sampled easily . The recently developed Pólya-Gamma data augmentation scheme [25] allows sampling from simple Gaussian conditional posteriors at the cost of introducing the M × N matrix of Pólya-Gamma variables ω through the following identity for the spike probability: p ( y m n | Θ , H n , l , x m n ) = ( e x m n + μ m + λ l ( m , n ) R + f n ) y m n 1 + e x m n + μ m + λ l ( m , n ) R + f n = 1 2 e κ m n ( x m n + f n + μ m + λ l ( m , n ) R ) × ∫ 0 ∞ e - ω m n ( x m n + f n + μ m + λ l ( m , n ) R ) 2 2 P G ( ω m n | b = 1 , z = 0 ) d ω m n ∝ ∫ 0 ∞ N ( x m n + f n + μ m + λ l ( m , n ) R | κ m n ω m n , 1 ω m n ) P G ( ω m n | b = 1 , z = 0 ) d ω m n ( 8 ) where PG ( ωmn|b = 1 , z = 0 ) is the Pólya-Gamma distribution with parameters b = 1 and z = 0 , and κ ≡ y - 1 2 . With the introduction of the Pólya-Gamma variables , the joint posterior Eq ( 4 ) becomes p ( Θ | y ) ∝ p ( Θ ) ∫ X [ ∏ m = 1 M ∏ n ′ = 1 N p ( X m n ′ | X m , n ′ − 1 , Θ ) p ( X m 0 | Θ ) × ∏ n = 0 N ∫ 0 ∞ N ( x m n + f n + μ m + λ l ( m , n ) R | κ m n ω m n , 1 ω m n ) P G ( ω m n | b = 1 , z = 0 ) d ω m n ] d X , ( 9 ) from which we derive the conditional posteriors for the parameters and the predictive distributions for the augmented variables . We refer to the set of augmented variables as V+ = {X , ω} , and denote by Θ\x to be the set of all parameters except parameter x . Gibbs samplding was implemented in the Python programming language using the Numpy , SciPy , Matplotlib , StatsModels , and Patsy toolboxes . Numerical routines were written in C/C++ and Cython . Software for the Gibbs sampler and a Python wrapper for the Pólya-Gamma routine based on the original code by Jesse Windle , available at https://github . com/jwindle , is available at https://github . com/AraiKensuke/PP-AR and https://github . com/AraiKensuke/pyPG , respectively .
The stochastic oscillation for the mth trial wm is generated by first generating a stochastic phase tm and stochastic amplitude Am as t m , n + 1 = t m n + Δ t ( 1 + ξ m n C ξ ) w m n = 1 + A m n C A sin ( 2 π ν t m n ) . ( 44 ) where ξm and Am are generated by AR ( 1 ) processes , and tm0 ∈ [0 , 1] a uniform random number . These are then used to generate the irregular oscillation Cξ and CA control the size of deviation from uniformity , while the timescale of the AR ( 1 ) processes controls the timescale of variation of amplitude and phase in the oscillation . AR ( 1 ) s with timescales on the order of the oscillation period T = 1/ν produce fluctuations that causes considerable variability of period and amplitude in w , which we quantify with the oscillator irregularity , the oscillatory coefficient of variation ( OCV ) of the instantaneous periods T of oscillation , defined as the ratio of the standard deviation to the mean of time intervals between phase 0 crossings of an oscillatory signal . Fig 1 shows 2 example oscillations generated by this procedure , their OCVs and their power spectral densities . Irregular oscillations have a higher OCV and a less peaked spectral density . To generate spikes , we employ an inhomogeneous renewal process or the LIF model , driven by a stochastic model of oscillation to modulate the firing of spikes . For the inhomogeneous renewal process , for each trial m and time n , if for a uniform random number rmn ∈ [0 , 1] , r m n < Δ t ( e μ m + w m n + λ l ( m , n ) G ) , we generated a spike , with λ l ( m , n ) G the user-defined ground truth refractory history function . For the LIF model , spikes were generated using Δ V m n = ( - V m n τ + f n + μ m + w m n + χ m n ) Δ t , ( 45 ) where the Gaussian random variable χ m n ∼ N ( 0 , σ b 2 ) represents irregular arrival times of afferent spikes , τ = 0 . 2 the membrane time constant , μm a baseline DC current and σ b 2 = 210 the amplitude of background fluctuation . Spikes are elicited when Vmn ≥ 1 passes the threshold value of 1 , which then causes a reset to Vm , n+1 = 0 . LIF have a refractory period that depends on the model parameters , and therefore the CIF naturally is dependent on the last spike time . We analyze neurons from M1 of rat performing a self-paced lever push-hold-pull task [13 , 41] that have been found to be significantly modulated to the theta rhythm in the LFP . These 2 neurons were recorded on separate electrodes , with “neuron 1” in Figs 10 and 11 recorded and spike sorted from a tetrode on a siliconprobe , and “neuron 2” recorded using juxtacellular ( cell-attached ) recording , where the spike trains did not need to be sorted . Both neurons were located in layer 1/2 , and “neuron 2” identified morphologically as an interneuron , while “neuron 1” is likely to be an interneuron based on spike shape . We identified trials by selecting lever hold periods lasting more than 1 second followed by a large-amplitude pull , and analyzed the 1 . 2 second period encompassing the hold-pull period .
We have defined the LOST model , together with accompanying posterior simulation technology , in order to detect the presence of oscillatory firing-rate modulation in a spike train , and infer its phase of oscillation in the presence of a variety of non-stationarities in firing rate that are present in experimental data . Previous methods have assessed the oscillatory content in spike trains by comparing the spiking to a known oscillatory signal like a band-passed LFP [42 , 43] , by detecting oscillation directly from the spike train [12 , 18 , 44–46] , or by point process regression using the oscillatory signal as a covariate [47] . The LOST model not only detects oscillatory modulation from the spike train itself , but does so in the presence of both spiking noise and oscillatory irregularity , and also allows extensions to inferring additional latent structure , such as non-stationarities in the modulational strength across trials . In addition , LOST is able to separately account for modulational signals of different frequency band , which has proven to be vital in the analysis of real spike trains . Structural priors on the latent state dynamics together with explicit consideration of spiking noise and oscillatory irregularity allows LOST to uncover oscillatory structure even when the firing rate is low , the modulation weak or when the oscillation is irregular , compared to methods that directly regressed the spiking probability on the raw spiking history itself without an intermediary latent state [12 , 18 , 19] . LOST uses the intermediary latent state to addresses the irregularity characteristic of neural oscillations . Another approach to modeling the spectral features of time series using Gaussian processes has recently been developed by Wilson et al [48] , where different classes of covariance kernels , the SE and SM , respectively , model non-oscillatory and oscillatory structures , analogous to the real and imaginary roots of the characteristic polynomials . It would be interesting to compare the two approaches in future work . The increased sensitivity and flexibility in specifying latent structures , should allow LOST to be used in investigating the role of oscillations in cognitive functions in the cortex . Investigators are increasingly interested in characterizing the change in neural responses to time-varying stimulus or behavior [49 , 50] . Theta and gamma oscillations in hippocampus change their coupling structure and prevalence during learning and memory acquisition [51 , 52] . The inclusion of trial-specific structure independently of the LFP in the LOST model may also allow detection of increased recruitment of a given neuron into cell assemblies during periods when oscillations in the LFP are changing . Further , the variability of the timing and presence of oscillations in the LFP seen in many areas of the cortex and hippocampus [2 , 13 , 53] , suggests oscillatory modulation in single neurons may likewise exhibit finer structure on a per-trial basis . Use of the LOST model in the analysis of such systems may reveal richer dynamics of recruitment into cell assemblies , and a better understanding of the role of single neurons in cognition . | Oscillatory modulation of neural activity in the brain is widely observed under conditions associated with a variety of cognitive tasks and mental states . Within individual neurons , oscillations may be uncovered in the moment-to-moment variation in neural firing rate . This , however , is often challenging because many factors may affect fluctuations in neural firing rate and , in addition , neurons fire irregular sets of action potentials , or spike trains , due to an unknown combination of meaningful signals and extraneous noise . We have devised a statistical Latent Oscillatory Spike Train ( LOST ) model with accompanying model-fitting technology , that is able to detect subtle oscillations in spike trains by taking into account both spiking noise and temporal variation in the oscillation itself . The method couples two techniques developed for other purposes in the literature on Bayesian analysis . Using data simulated from theoretical neurons and real data recorded from cortical motor neurons , we demonstrate the method’s ability to track changes in the modulatory structure of the oscillation across experimental trials . | [
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] | 2017 | Inferring oscillatory modulation in neural spike trains |
Paratransgenesis , the genetic manipulation of insect symbiotic microorganisms , is being considered as a potential method to control vector-borne diseases such as malaria . The feasibility of paratransgenic malaria control has been hampered by the lack of candidate symbiotic microorganisms for the major vector Anopheles gambiae . In other systems , densonucleosis viruses ( DNVs ) are attractive agents for viral paratransgenesis because they infect important vector insects , can be genetically manipulated and are transmitted to subsequent generations . However , An . gambiae has been shown to be refractory to DNV dissemination . We discovered , cloned and characterized the first known DNV ( AgDNV ) capable of infection and dissemination in An . gambiae . We developed a flexible AgDNV-based expression vector to express any gene of interest in An . gambiae using a two-plasmid helper-transducer system . To demonstrate proof-of-concept of the viral paratransgenesis strategy , we used this system to transduce expression of an exogenous gene ( enhanced green fluorescent protein; EGFP ) in An . gambiae mosquitoes . Wild-type and EGFP-transducing AgDNV virions were highly infectious to An . gambiae larvae , disseminated to and expressed EGFP in epidemiologically relevant adult tissues such as midgut , fat body and ovaries and were transmitted to subsequent mosquito generations . These proof-of-principle data suggest that AgDNV could be used as part of a paratransgenic malaria control strategy by transduction of anti-Plasmodium peptides or insect-specific toxins in Anopheles mosquitoes . AgDNV will also be extremely valuable as an effective and easy-to-use laboratory tool for transient gene expression or RNAi in An . gambiae .
Transmitted by Anopheles mosquitoes , malaria is a disease responsible for inordinate mortality , morbidity and economic loss worldwide [1] . Failure of traditional control methodologies has stimulated efforts to develop novel genetic strategies to control the mosquito vectors , particularly An . gambiae . Transgenic manipulation of An . gambiae has proven to be especially challenging , with few published successes [2]–[3] . Paratransgenesis , the genetic manipulation of insect symbiotic microorganisms , is being considered as an alternative to traditional transgenic strategies [4]–[5] . Microorganisms associated with Anopheles could be manipulated to alter the mosquito's ability to become infected with and transmit the malaria parasites , or reduce mosquito fecundity or lifespan . A suitable microbial candidate for paratransgenic malaria control would have a symbiotic ( mutualistic , commensal or parasitic ) relationship with the vector , be readily propagated and stably engineered to express the gene ( s ) of interest without compromising microorganism fitness , and be easily delivered to wild mosquito populations [4] . Ideally , the engineered microbe would also be maintained in the environment , be passed to subsequent mosquito generations and have limited effects on non-target species . Densonucleosis viruses , or “densoviruses” ( DNVs ) , are non-enveloped single-stranded DNA icosahedral viruses in the family Parvoviridae ( subfamily Densovirinae ) that infect arthropods such as mosquitoes . Mosquito DNVs have narrow host ranges and are maintained in natural populations by a cycle that includes both horizontal and vertical transmission from infected adults to larvae . DNVs possess some of the smallest known viral genomes ( 4–6 kb ) , a trait that makes them highly amenable as molecular tools because the entire genome can be placed into an infectious plasmid , manipulated by standard cloning techniques , and used to express foreign genes ( i . e . anti-parasite or toxin ) upon infection in cell cultures or live mosquitoes [6] . DNV infectious clones , expression systems , and lethal biocontrol agents ( based on the Aedes aegypti densovirus; AeDNV ) have been developed and show promise for Aedes mosquitoes [6]–[8] . When injected into larvae , AeDNV virions can infect An . gambiae [9] , but when infection by larval exposure to virions is attempted , AeDNV does not disseminate in An . gambiae [8] . Similar results were observed when researchers could only infect An . gambiae with TaDNV ( isolated from a Toxorhynchites amboinensis cell line ) by adult injection but not larval exposure [10] . Thus , DNVs have previously not been considered useful for paratransgenic manipulation or control of An . gambiae . We serendipitously discovered a novel DNV capable of infection and dissemination in An . gambiae larvae ( AgDNV ) while investigating a PCR artifact in an unrelated experiment . AgDNV is highly infectious to An . gambiae larvae , disseminates to adult tissues , and is passed on to subsequent generations . Recombinant AgDNV genomes were able to transduce expression of an exogenous transgene ( enhanced green fluorescent protein; EGFP ) in cultured An . gambiae cells and mosquitoes and were transmitted to subsequent mosquito generations . AgDNV will form the foundation for the development of much-needed tools for routine manipulation of An . gambiae and paratransgenic malaria control .
In the course of verifying Wolbachia infection of An . gambiae cell line Sua5B [11] , we observed a weak band at approximately 400 bp instead of the expected ∼600 bp fragment using the putatively Wolbachia-specific primers 81F and 691R [12] . We isolated the band from the gel for cloning and sequencing . We compared the 358 bp sequence to the BLAST database where it hit with high homology ( 87% ) to a portion of the NS1 gene of the Aedes aegypti densovirus ( AeDNV ) ( GenBank #M37899 ) [13] , indicating that there was a DNV present in our Anopheles cell culture which we termed AgDNV . We used a densovirus-specific immunofluorescence assay ( IFA ) to visualize AgDNV infection in Sua5B cells , which confirmed localized AgDNV infection in cell nuclei [6] ( Figure 1A ) . We then determined that AgDNV virions isolated from Sua5B cells were highly infectious to An . gambiae larvae in vivo . In order to evaluate both viral infection efficiency and lethality , we infected naïve first instar larvae ( Keele strain ) by either allowing larvae to feed on infected Sua5B cell cultures or by adding filtered infected Sua5B cell lysate to the larval rearing water . Both methods resulted in similarly high infection levels in emerging adults as determined by PCR ( whole cells: 62% , N = 39; lysate: 57% , N = 53; Fishers Exact P = 0 . 67 ) . Quantitative PCR indicated that larvae were exposed to approximately 2 . 1×1011±0 . 97×1011 viral genome equivalents per ml , which is well within the range that causes significant mortality for other DNV isolates [14] . However , we observed no difference in survival to adulthood between the controls and either infection treatment ( control: 34% , N = 50; whole cells: 26% , N = 150 , lysate: 35% , N = 150 , chi-square = 3 . 27 , d . f . = 2 , P = 0 . 195 ) , possibly due to adaptation of the virus to cell culture conditions . To test for transtadial transmission and dissemination of AgDNV in adult mosquitoes , we infected first-instar An . gambiae larvae , transferring them to clean virus-free water after 2 days . Uninfected control larvae were exposed to culture media . After adult emergence , we dissected adult tissues and performed densovirus-specific immunofluorescence microscopy . AgDNV clearly disseminates and infects adult midgut and ovary ( Figure 2 ) . We then assessed whether AgDNV could be transmitted to subsequent generations . We treated mosquitoes for 24 hours as larvae with AgDNV , which were reared to adulthood , bloodfed , allowed to oviposit , and their offspring reared to adulthood and assayed for AgDNV by PCR . Fifty percent of treated mosquitoes were positive for virus by PCR ( N = 42 ) . Twenty-eight percent ( N = 71 ) of their offspring were positive for infection , indicating that AgDNV was transmitted between generations , either by vertical transmission or by horizontal transmission from adults to larvae . To purify AgDNV particles for microscopy and isolation of the viral genome , we fractionated crude Sua5B cell lysates in a cesium chloride gradient and examined fractions for viral particles by negative-stain transmission electron microscopy . We isolated numerous icosahedral , non-enveloped particles of the expected size ( 20 nm ) ( Figure 1B ) . We extracted the viral DNA from this gradient fraction and cloned the entire viral genome into the pBluescript S/K ( - ) cloning vector ( denoted pBAg; Figure 3 , Text S1 ) . The cloned AgDNV genome is typical of mosquito DNVs . It is 4139 nt ( GenBank #EU233812 ) in length and has 3 overlapping reading frames: the viral capsid and 2 non-structural ( NS ) proteins . The 5-prime and 3-prime ends of the genome consist of inverted hairpin repeats and are predicted to fold into perfect Y-shaped hairpin structures ( Figure 4 ) . Phylogenetic analysis of the entire AgDNV genome indicated that AgDNV falls within the “Asian” clade of known mosquito densoviruses [6] . Within the coding region , it is most closely related to a recently-described cluster of DNVs isolated from Culex pipiens pallens ( CppDNV ) in China [15] ( Figure 5 ) . To confirm infectiousness of pBAg to An . gambiae cells , we transfected it into the An . gambiae cell line Moss55 ( which lacks endogenous densovirus infection; Figure 1C ) and observed DNV-specific signal in transfected cell nuclei by IFA ( Figure 1D ) . However , when purified from the cell culture , virions produced from pBAg were unable to infect An . gambiae larvae in vivo . By sequencing fragments of directly-cloned viral DNA isolated from Sua5B cells , we identified multiple clones with point mutations in the 5-prime UTR and non-synonymous point mutations in the NS1 and NS2 genes ( Table 1 ) , suggesting that AgDNV was not homogeneous within Sua5B cells , but rather exists as a heterogeneous population of viral genomes that may differ in their ability to infect Anopheles larvae . To select for the viral genotype ( s ) that were infectious to An . gambiae larvae , we infected larvae as first-instars with virus isolated from Sua5B cells , reared them to adulthood and sequenced most of the coding portion of the AgDNV genome ( nucleotides 403–3709 ) from 5 infected females . All 5 sequences were identical , indicating that within the viral population in Sua5B cells only one genotype was infectious to larvae . This genotype differed from pBAg at 3 sites: A636G ( Lys to Glu in NS1 ) , A1174C ( Asp to Ala in NS1 and Ile to Leu in NS2 ) and A3399T , ( Asn to Ile in capsid ) ( no synonymous mutations were detected ) . We used site-directed mutagenesis to reproduce these three mutations in pBAg ( denoted pBAgα ) . Virions produced from pBAgα in Moss55 cells had similar infectivity to An . gambiae larvae as wild-type AgDNV from Sua5B cells as determined by both PCR and IFA . We used pBAg to create a flexible gene transduction construct by deleting most of the viral genome between the hairpin sequences and inserting a multiple cloning site ( pBAgMCS; Figure 3 , Text S1 ) . Using pBAgMCS , we can easily construct viral transducing genomes carrying any gene-promoter combination of interest , and by supplying the missing viral proteins in trans with pBAgα or wild-type virus , we can express the gene in An . gambiae mosquitoes simply by adding the virions to the larval rearing water . As proof-of-concept , we inserted the enhanced green fluorescent protein ( EGFP ) under control of the constitutive Drosophila actin5C promoter into the multiple cloning site of pBAgMCS ( pAgActinGFP; Figure 3 , Text S1 ) . When pBAgα and pAgActinGFP were simultaneously transfected into Moss55 cells , we observed cytoplasmic EGFP expression 24–48 hours post-infection ( Figure 1E , F ) . We observed fluorescent cells in the culture even after 10 passages ( approximately 2 months ) , indicating that the helper and transducing virions were replicating in the cells . We do not believe that these results are due to integration of the viral genome into the host genomic DNA , as integration is not known to occur for DNVs in the genus Brevidensovirus ( the genus AgDNV belongs to ) , although integration does occur for other DNV genera [6] . We purified helper and EGFP-transducer virions from transfected Moss55 cells , exposed first-instar An . gambiae larvae to them and assayed emerged adults for EGFP expression by fluorescence microscopy . EGFP expression was observed in approximately 50% of adults ( N>100 ) . We observed similar results when virus from Sua5B cells rather than pBAgα was used as helper . EGFP expression was first observed in the fat body , later disseminating to other tissues such as the eye , midgut , hindgut , malpighian tubules and ovaries ( Figures 6 and 7 ) . EGFP-positive mosquitoes were allowed to reproduce . We observed EGFP expression in approximately 20% of F1 offspring ( N>50 , Figure 8 ) and detected EGFP DNA by PCR and sequencing from EGFP-expressing F1 mosquitoes ( N = 8 ) . We continued to breed the offspring and again assessed EGFP expression in the F3 generation , where 20% of the mosquitoes had observable EGFP fluorescence ( N = 20 ) . These data indicate that AgDNV can be used to drive expression of exogenous transgenes in An . gambiae and that transducing virions are transmitted to subsequent generations , similar to wild-type virus . While it is not clear at this point whether offspring are infected by transovarial transmission or horizontal transmission from adults to larvae , we detected EGFP in both developing ovarioles and in mature oocytes ( Figure 7 ) suggesting that transovarial transmission may be involved . The development of novel , efficacious malaria control methods is critical to reduce the enormous public health and economic burdens experienced in affected areas . Densovirus-based tools for control of Anopheles mosquitoes are very attractive for this purpose due to their specificity , stability , ease in engineering , ability to spread horizontally and vertically and accumulate in natural environments , and recent advances in large-scale production and purification methods [16]–[17] . Recombinant AgDNV could potentially be used to control malaria by transduction in An . gambiae of anti-Plasmodium peptides to block parasite transmission or insect-specific toxins to reduce mosquito population density or mosquito lifespan . AgDNV will also be extremely valuable as an effective and easy to use laboratory tool for transient gene expression or RNAi [6] in An . gambiae .
The Anopheles gambiae Keele strain was used for experiments in 30 cm cube cages kept in a walk-in insectary at 28°C and 80% relative humidity . Mosquitoes were allowed access to a cotton wick soaked in 20% sucrose as a carbohydrate source . Adults were allowed to bloodfeed on an anesthetized mouse 5 days post-emergence . Two days after bloodfeeding , an oviposition substrate ( consisting of a filter paper cone inside a 50 ml beaker half-filled with water ) was introduced into cages and filter papers containing eggs removed the next day , placed into a 41×34×6 cm rearing tray half-filled with distilled water and one pellet dry cat food , with one additional food pellet added daily after day 3 . Pupae were picked with an eye-dropper , placed in a cup and introduced into cages ( ∼200 pupae/cage ) to begin the next generation . The Anopheles gambiae cell lines Sua5B and Moss55 were grown at room temperature in Schneider's medium ( Sigma ) supplemented with 10% fetal bovine serum . DNAs used for transfection were prepared using a QIAGEN Plasmid Purification Kit ( Qiagen , Valencia , CA ) according to the manufacturer's protocol . For the transfection of cells with different plasmids , one µg of total plasmid DNA ( 0 . 5 µg vector and 0 . 5 µg helper ) was used with Effectene® Transfection Reagent ( QIAGEN ) according to the manufacturers suggested protocol . Genomic DNA was extracted from Sua5B cells using DNEasy kits ( QIAGEN , Valencia , CA ) according to the manufacturer's suggested protocol . Unexpected PCR amplification of an approximately 400-bp fragment of AgDNV was amplified using Wolbachia primers wsp81F ( 5′-TGG-TCC-AAT-AAG-TGA-TGA-AGA-AAC-3′ ) and wsp691R ( 5′-AAA-AAT-TAA-ACG-CTA-CTC-CA-3′ ) [12] . PCR amplicons were separated by 1% agarose gel electrophoresis , stained with ethidium bromide , and visualized with UV light . PCR fragments were cloned into the pCR4-TOPO vector and sequenced . We detected AgDNV infection in infected mosquitoes using primers DensoVF ( 5′-CAG-AAG-GAT-CAG-GTG-CAG-3′ ) and DensoVR ( 5′-GCT-ACT-CCA-AGA-GCT-ACT-C-3′ ) using Sua5B as a positive control and water as a negative control . Cells were grown overnight in 8-well chamber slides , then fixed with 4% paraformaldehyde . Fixed cells were washed 3 times with PBS , permeabilized with 0 . 01% Triton X-100 in PBS , and washed 3 times in PBS . Cells were incubated in 1% BSA , PBS pH 7 . 4 for 30 min to block non-specific antibody binding . Cells were incubated with primary antibody ( 1∶1000 ) in 1% BSA , PBS pH 7 . 4 for 60 min and washed for 10 minutes three times with PBS pH 7 . 4 . Cells were incubated with goat anti-rabbit IgG FITC conjugate ( Sigma ) ( 1∶500 ) , Evans Blue ( 1∶1000 ) , in 1% BSA , PBS pH 7 . 4 for 60 min at RT , then washed for 10 minutes three times with PBS pH 7 . 4 . Cells were stained with DAPI , mounted and visualized by epifluorescent microscopy . First-instar larvae were either introduced directly into culture flasks containing Sua5B cells or were infected by adding Sua5B cell lysate to the rearing water . In this case , Sua5B cells were pelleted in a 50 ml conical tube by centrifuging for 10 minutes at 2 , 500 G , 4°C . The pellet was resuspended in 20 ml PBS . Cells were lysed by vortexing with sterile 3 mm borosilicate glass beads for 5 minutes . Approximately 20 ml cell lysate was added to 20 ml ddH20 with approximately 50 first-instar An . gambiae larvae Keele strain ( 4 replicates ) . Larvae were exposed to virus for 24 hours , then were transferred to clean water with larval food . First-instar larvae were infected with Sua5B lysate , reared to adulthood , allowed to bloodfed on an anesthetized mouse approximately one week post-emergence , and offspring produced as described above . Adults and offspring were tested for AgDNV by PCR using primers DensoVF and DensoVR , using Sua5B as a positive control and water as a negative control . Sua5B cells were pelleted and lysed as described above . The supernatant was removed to a new tube and cellular debris pelleted by centrifuging for 20 minutes at 10 , 000 G , 4°C . The supernatant was centrifuged at 35 , 000 rpm for 75 minutes , 4°C to pellet virion particles . The virion pellet was removed and further purified by 1 M sucrose cushion centrifugation for 120 minutes at 39 , 000 rpm , 4°C . The final pellet was fractionated in a CsCl ( 0 . 3 g/ml ) gradient at 60 , 000 rpm overnight at 8°C . The virion band was removed from the gradient for DNA extraction and TEM . Purified virus particles were applied to glow-discharged carbon-coated grids and negatively stained with 2% ( w/v ) uranyl acetate . Electron micrographs were recorded on Kodak SO-163 film using a Philips CM12 electron microscope at nominal magnifications of 37 , 000× to 52 , 000× . Pure virion particles isolated from the gradient were incubated in 300 µl buffer ( 100 mM EDTA , 10 mM Tris-HCl , 0 . 1% SDS , 100 µg/ml proteinase K , pH 8 . 0 ) overnight at 55°C . The next day , the mixture was centrifuged at 14 , 000 rpm for 2 minutes to pellet debris . DNA was extracted from the supernatant twice using 1 volume of phenol∶chloroform ( 1∶1 ) . One tenth volume of 3 M sodium acetate and 2 . 5 volumes of cold ethanol were added to precipitate viral DNA . DNA was pelleted by centrifugation at 14 , 000 G for 20 minutes , washed with 70% cold ethanol , air dried and resuspended in 5 µl 10 mM Tris-HCl ( pH 8 . 5 ) . 600 ng AgDNV genomic DNA was blunt-ended by incubating for 15 minutes at room temperature with 10 units Klenow fragment . Viral DNA was ethanol precipitated , cloned into the EcoRV site of plasmid pBluescript S/K ( - ) and transformed into SURE® competent cells ( Stratagene ) . 20 clones were selected and sequenced to confirm viral inserts . We were unable to clone the entire AgDNV genome in one step , and thus assembled the genome from two clones that , together , contained the entire AgDNV genome . These clones were digested with NcoI and XbaI and ligated together to build a full-length infectious clone ( pBAg ) . pBAg infectivity in Moss55 cells was confirmed by transfection and IFA as described . pBAg plasmid was used as a copy-number standard for viral genome quantification as previously described [14] . The plasmid has an estimated mass of 7 . 78×10−18 g/copy . Plasmid concentrations were determined using an ND-1000 NanoDrop spectrophotometer ( Thermo Fisher Scientific ) , and serial dilutions were made from 50 µM to 5×10−8 µM to generate a standard curve that ranged from 6 . 4×1010 viral genome equivalents/µL ( geq/µL ) to 6 . 4×100 geq/µL in ten-fold increments . Primers were designed based on regions within the overlapping NS1 and NS2 genes that were highly conserved amongst all known mosquito densovirus isolates , as previously described [14] . The forward primer ( 5′-CAT-ACT-ACA-CAT-TCG-TCC-TCC-ACA-A-3′ ) and reverse primer ( 5′-CTT-GGT-GAT-TCT-GGT-TCT-GAC-TCT-3′ ) produce an 183 bp amplicon . The Quantitect SYBR Green Kit ( Qiagen ) was used in a 25 µL reaction containing 0 . 3 µM of each primer , and 5 µL of a 1/100 dilution of the Sua5B viral infection prep . Real-time PCR was performed on an ABI Prism model 7300 using 96-well reaction-plates ( ABI ) and MicroAmp Optical Adhesive Film ( ABI ) with a program of: ( 1 ) 50°C for 2 min , ( 2 ) 95°C for 15 min , ( 3 ) 45 cycles of i ) 94°C for 15 sec , ii ) 55°C for 30 sec , iii ) 72°C for 30 sec . Data was collected each cycle at step 3iii , and the 45th cycle was followed by a dissociation program to verify specific amplification . Virions produced by pBAg were not infectious to An . gambiae larvae . We infected larvae with virus isolated from Sua5B cells , reared larvae to adulthood and screened for infected mosquitoes by PCR as described . We selected 5 individual infected mosquitoes , sequenced the coding region of the virus that infected them and identified 3 mutations that all had in common as described in the text . We reproduced these mutations in pBAg by site-directed mutagenesis using the QuikChange Multi-Site Directed Mutagenesis Kit ( Stratagene ) with the manufacturer's protocol . pBAgMCS carries a multiple cloning site ( MCS ) flanked by the 5-prime and 3-prime AgDNV hairpin sequences . The MCS possesses 5 common unique cloning sites: NsiI , NcoI , MluI , EcoRV and BglII ( and several other less common cut sites , Figure 5 ) . NsiI , NcoI , MluI , and BglII produce sticky ends for directional subcloning , while EcoRV produces blunt ends for blunt-end ligation procedures . We used pBAg as template for PCR using primers MCSF3 ( 5′- CCC-AAA-CCT-ATA-TAA-GGC-AAC-TGG-AAT-CGA-AGG-A -3′ ) and MCSR2 ( 5′- CCA-ATG-CAT-CCA-TGG-ACG-CGT-GAT-ATC-AGA-TCT-TGT-ATT-GTC-TCG-GTG-CA-3′ ) to amplify part of the 3-prime UTR , adding the MCS to the amplicon as part of the primer . The resultant product and pBAg were double-digested with NsiI and EcoNI . The digested pBAg was CIP-treated to prevent autoligation , and the 2 products ligated together with T4 ligase . The construct was transformed into SURE Competent cells ( Stratagene ) , clones screened and proper vector construction confirmed by sequencing . The actin5C-EGFP-SV40 cassette was PCR-amplified from pHermes[act5C:EGFP] using primers Actin5CegfpF ( 5′-CCC-AAA-GAT-ATC-CGA-TCG-CTC-CAT-TCT-TG-3′ ) and Actin5CegfpR ( 5′-CCC-AAA-GAT-ATC-CGC-TTA-CAA-TTT-ACG-CC-3′ ) using pfuUltra II Fusion HS DNA Polymerase ( Stratagene ) with the manufacturers suggested protocol . The PCR product was digested with EcoRV . pBAgMCS was digested with EcoRV , and CIP-treated to prevent autoligation . The 2 products were ligated together with T4 ligase and the construct transformed into SURE competent cells . Clones were screened and proper insert confirmed by sequencing . A combination of pBAgα and pAgActinGFP were transfected into Moss55 cells ( or pAgActinGFP into Sua5B cells ) as described . EGFP expression was monitored by fluorescence microscopy daily beginning 24 hours post-transfection . For mosquito infections , virion particles were purified from cells 1–2 weeks post-transfection by glass bead lysis/filtration and first-instar larvae infected directly as described above . EGFP expression in cells , dissected tissues and mosquitoes was monitored using an Olympus BX41 epifluorescent compound microscope . Images were captured using a Macrofire monochrome digital camera ( Optronics ) . Mosquitoes which had observable EGFP expression were allowed to oviposit , offspring reared and EGFP expression in offspring assessed as described above . DNA was extracted from positive offspring and EGFP DNA detected using primers egfpF2 ( 5′-TGA-AGT-TCA-TCT-GCA-CCA -3′ ) and egfpR2 ( 5′-CAG-CAG-GAC-CAT-GTG-ATC-3′ ) . PCR was conduced using pAgActinGFP as a positive control and water as negative control . Amplicons were gel purified and directly sequenced . | Paratransgenesis , the genetic manipulation of mosquito symbiotic microorganisms , is being considered as a potential strategy to control malaria . Microorganisms associated with Anopheles mosquitoes could be manipulated to alter the mosquito's ability to become infected with and transmit the malaria parasites , or reduce mosquito fecundity or lifespan . We identified the first potential microorganism ( An . gambiae densovirus; AgDNV ) for paratransgenesis of the major malaria vector Anopheles gambiae . AgDNV is highly infectious to An . gambiae larvae , disseminates to adult tissues and is transmitted vertically to subsequent generations . Recombinant AgDNV was able to transduce expression of an exogenous gene ( EGFP ) in An . gambiae cells and mosquitoes . EGFP-transducing virions infected mosquitoes , expressed EGFP in epidemiologically relevant tissues and were transmitted to offspring in a similar manner to wild-type virus . AgDNV could be used as part of a paratransgenic malaria control strategy by transduction of anti-Plasmodium genes or insect-specific toxins in Anopheles mosquitoes , as well as an easy-to-use system for transient gene expression and RNAi for basic laboratory research . | [
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] | [
"virology/virus",
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"biology"
] | 2008 | Viral Paratransgenesis in the Malaria Vector Anopheles gambiae |
Neurological impairments are frequently detected in children surviving cerebral malaria ( CM ) , the most severe neurological complication of infection with Plasmodium falciparum . The pathophysiology and therapy of long lasting cognitive deficits in malaria patients after treatment of the parasitic disease is a critical area of investigation . In the present study we used several models of experimental malaria with differential features to investigate persistent cognitive damage after rescue treatment . Infection of C57BL/6 and Swiss ( SW ) mice with Plasmodium berghei ANKA ( PbA ) or a lethal strain of Plasmodium yoelii XL ( PyXL ) , respectively , resulted in documented CM and sustained persistent cognitive damage detected by a battery of behavioral tests after cure of the acute parasitic disease with chloroquine therapy . Strikingly , cognitive impairment was still present 30 days after the initial infection . In contrast , BALB/c mice infected with PbA , C57BL6 infected with Plasmodium chabaudi chabaudi and SW infected with non lethal Plasmodium yoelii NXL ( PyNXL ) did not develop signs of CM , were cured of the acute parasitic infection by chloroquine , and showed no persistent cognitive impairment . Reactive oxygen species have been reported to mediate neurological injury in CM . Increased production of malondialdehyde ( MDA ) and conjugated dienes was detected in the brains of PbA-infected C57BL/6 mice with CM , indicating high oxidative stress . Treatment of PbA-infected C57BL/6 mice with additive antioxidants together with chloroquine at the first signs of CM prevented the development of persistent cognitive damage . These studies provide new insights into the natural history of cognitive dysfunction after rescue therapy for CM that may have clinical relevance , and may also be relevant to cerebral sequelae of sepsis and other disorders .
Malaria , together with tuberculosis and human immunodeficiency virus/acquired immunodeficiency syndrome ( HIV/AIDS ) , is one of three most important infectious diseases worldwide , with devastating morbidity and mortality and deleterious economic consequences [1] . More than 400 million people suffer from malaria , which causes over two million deaths annually , mainly among African children [2] . Cerebral malaria ( CM ) is the most severe neurological complication of infection with Plasmodium falciparum and is the main cause of acute non-traumatic encephalopathy in tropical countries . Mortality is high . In addition , physical and neurologic deficits are frequently seen at the time of hospital discharge in children surviving CM , although most resolve within 6 months after discharge [3] . Nevertheless , several retrospective studies suggest that cognitive deficits in children with CM are more frequent , and persist far longer than physical and neurologic deficits [4] , [5] , [6] , [7] , [8] . Boivin et al . [4] reported that 21% of children >5 years old with CM have cognitive deficits 6 months after discharge , and that increased seizure frequency and prolonged coma duration are associated with persistent cognitive deficits . Desruisseaux and coworkers [9] reported cognitive dysfunction in the acute phase of experimental infection with Plasmodium berghei ANKA in mice . A test of work memory performed at the 7th day of infection demonstrated significant impairment in visual memory in C57BL/6 mice associated to significant histological alterations as well as hemorrhage and inflammation [9] . Although the pathogenesis of CM has been extensively investigated , many aspects of the cellular and molecular pathogenesis remain incompletely defined [10] . This is in part due to the complexity of the host-pathogen interaction , which includes intricate biologic and inflammatory responses , variations in immune status and genetic background of the host , and factors unique to the malarial parasites [1] . This complexity has been revealed by clinical and experimental observations that have recently included informative mouse models [11] , [12] , [13] . Biochemical features also influence the natural history and complications of CM [14] . For example , there is evidence that oxidative stress mediates some of the tissue damage caused by experimental malarial infection and in cultured human cells [15] , [16] . Until recently physicians have focused on survival of patients with CM and not on long-term outcomes and sequellae and , as a result , the incidence and impact of chronic neurocognitive dysfunction have been underestimated and underreported [17] . Similarly , there has been little inquiry into these issues in experimental CM . Here , we establish and characterize permissive and resistant murine models that clearly demonstrate sustained cognitive dysfunction due to CM . In addition , we demonstrate that a component of the cognitive dysfunction is related to oxidative stress and that this can be favorably modified by an interventional strategy that includes antioxidants in addition to specific rescue chemotherapy aimed at the malarial pathogen . Because oxidative stress is a pathogenetic mechanism in other syndromes of neurocognitive injury and neurodegeneration , the findings may also be relevant to other systemic inflammatory syndromes with cerebral involvement .
In order to establish the clinical course of neurobehavioural complications of CM , mice from diverse genetic backgrounds were infected with different strains of Plasmodium . Mortality , parasitemia and behavior alterations ( detected by the SHIRPA protocol – see below ) were recorded . First , we compared C57BL/6 and BALB/c mice infected with PbA ( Figure 1A , C ) . Ninety five percent of C57BL/6 mice died between 7 and 10 days ( with an average of 7 . 7 days of survival , Figure 1C ) after infection , with cerebral manifestations including convulsion , paralysis , and coma . Mean parasitaemia was 23% . Seventy percent of BALB/c mice died within 15 days after infection with severe anemia and overwhelming parasitemia ( ∼80% ) , but no signs of cerebral malaria . C57BL/6 mice had 40% mortality within 15 days after infection with Pch ( Figure 1C ) . These animals showed high parasitemia ( average of 46% ) on day 7 and profound anemia , but no signs of CM were observed at anytime during the experiment . In this model , parasitemia at day 10 post infection was an average of 11% in surviving mice . In an additional model of infection , used to examine the effect of genetic background and parasite variables , SW mice infected with PyXL had 100% lethality ( Figure 1D ) within 8 days , surviving an average of 7 . 25 days and displaying clear signs of CM at day 6 associated with substantial parasitemia ( approximately 32% ) . In contrast , when SW mice were infected with the non-lethal PyNXL , 100% of the animals survived for at least 15 days post infection with parasitemia over 20% and no signs of CM . These results are in agreement with others in the literature [12] , [13] , [18] and confirm that C57BL/6 and SW mice are susceptible to CM when infected with PbA or the lethal strain of Py , respectively . The summary results of primary screening by SHIRPA on days 3 and 6 post-infection are shown in Table 1 . On day 3 post-infection , no alterations were observed in any of the groups tested ( C57BL/6 versus BALB/c infected with PbA; C57BL/6 infected with PbA versus Pch; or SW mice infected with PyNXL versus PyXL ) ( Table 1 ) . On day 6 , however , C57BL/6 mice infected with PbA and SW infected with PyXL displayed significant alterations of reflex and sensory function , motor behavior , and autonomic function . On day 7 , the animals demonstrated additional alterations of muscle tone and strength ( not shown ) . BALB/c mice showed minor alterations of motor behavior and autonomic function ( only in two tests in this protocol , while susceptible C57BL6 infected with PbA and SW mice infected with PyXL displayed positive findings in three and four assessments , respectively ) . Previously , Lackner et al . [19] reported that alterations of autonomic function and muscle tone and strength are specific and early signs of CM . Using these criteria , the SHIRPA protocol was prospectively applied on day 6 to identify CM . Positive results diagnosing CM were then taken as an indication to start chloroquine treatment , and to conduct further assessment of cognitive function in CM-positive animals . Interestingly , when we started treatment with chloroquine at 25 mg/kg on day 6 , signs of neurological involvement were rapidly responsive and were abolished by day 7 post infection ( data not shown ) . To investigate the occurrence of late cognitive impairment , PbA-infected C57BL/6 mice that had early signs of CM as detected by the SHIRPA protocol were treated from day 6 to 12 with chloroquine and submitted to the open field-task analysis at day 15 post infection . Chloroquine treatment was very effective in controlling parasitemia , since infected red blood cell counts were reduced to 0 . 66±0 . 6% at day 16 and parasites were not recovered at day 30 ( 1 . 1±0 . 56% ) post infection . There were no differences in the numbers of crossings and rearings observed when groups of PbA-infected C57Bl/6 and BALB/c mice subjected to the same rescue treatment with chloroquine were studied in the training session ( Figure 2 ) . In the test session , non-infected C57BL/6 mice treated with chloroquine or saline demonstrated a significant decrease in the numbers of crossings and rearings , indicating intact cognitive skills . In contrast , there was no reduction in crossings or rearings in PbA-infected C57BL/6 mice rescued with chloroquine ( Figure 2A , B; right bars ) , indicating diminished cognitive capacity [20] . Importantly , the decrease in cognitive ability was persistent for at least 30 days indicating a long lasting dysfunction ( Figure 2C , D ) . In parallel , PbA-infected BALB/c mice that did not have CM based on SHIRPA analysis ( Table 1 ) , but were , nonetheless , treated with chloroquine showed a significant reduction in both crossings and rearings ( Figure 2E , F , p<0 . 05 , Student's T Test ) that was not different from what was observed in non-infected controls . Thus , despite being infected with PbA , as confirmed by parasitological examinations , BALB/c mice do not develop CM and its sequelae , i . e . , late cognitive impairment . Importantly , when C57BL6 mice were infected with Pch , a Plasmodium strain that does not induce CM [13] ( Table 1 ) , the pattern was similar to that of uninfected animals and CM-resistant BALB/c mice ( Figure 3A , B ) . Therefore , even though C57BL/6 mice are susceptible to CM , when animals of this genetic background are infected with a Plasmodium strain that does not cause central nervous system involvement they do not develop signs of CM or consequent cognitive impairment based on our tools of detection . Conversely , cognitive impairment identified by our analytic instruments was not restricted to the C57BL/6 background since it was also observed in SW mice . SW mice infected with lethal strain PyXL [21] developed early signs of cerebral dysfunction that was not detected after infection with a non-lethal PyNXL strain ( Table 1 ) . SW mice infected with PyNXL showed a significant reduction in the numbers of test events when training and testing sessions were compared and the pattern was not different from non-infected control animals ( Figure 3C , D ) . Nevertheless , when SW mice infected with PyXL were subjected to testing there was no reduction in test events in training and testing sessions ( Figure 3C , D , right bars ) . A similar pattern was observed in PbA-infected C57BL/6 animals . Finally , we also performed experiments on PbA infected C57BL/6 animals that were depleted of CD8+ lymphocytes by treatment with anti-CD8 monoclonal antibody . CD8+ cells were previously shown to have an important role in CM [22] . In agreement with previous reports , single dose treatment with anti-CD8 temporarily reverse or stabilize the progression of CM [22] , [23] . However , parasitemia and , consequently , anemia , are persistent in anti-CD8 treated mice and probably contribute to late deaths observed in these animals [22] , [23] . In our hands , the first death in the anti-CD8 treated group was observed on day 13 , but the majority of deaths occurred later on days 16–18 . Importantly , the results from an open-field test can be altered if the mice are seriously ill , since the motor activity and general behavior are usually affected under this condition , interfering with the performance of the animals during the test . Therefore , to ensure that the results of the cognitive tests were not reflecting compromised behavior due to an ongoing severe systemic illness we decided to perform the experiments on animals that were treated both with chloroquine and anti-CD8 . In fact , combined treatment with chloroquine and anti-CD8 monoclonal antibody prevented the occurrence of cognitive damage in these animals ( reduction in crossings/rearings between training and testing sessions in untreated animals 34 . 0/32 . 5% versus reduction in crossings/rearings between training and testing sessions in anti-CD8 treated animals 13 . 0/0 . 0% ) . Together , these results indicate a clear correlation between the occurrence of CM and the development of late cognitive impairment . To determine if CM differentially influences memory skills , we submitted mice to different cognitive tasks including step-down latency and inhibitory avoidance , continuous multiple-trials step-down inhibitory avoidance and object recognition task . For this purpose , we elected to use PbA infection in C57BL/6 and BALB/c strain as positive and negative comparative models , respectively . The step-down latency and inhibitory avoidance in the test session at day 15 post infection ( Figure 4 ) was not different from training and test in PbA-infected C57BL6 mice treated with chloroquine ( mean of latency of 9 and 9 . 5 s , training and test sessions respectively; Z = −1 . 075; p = 0 . 282 , Wilcoxon's Test ) , suggesting impairment in aversive memory . On the contrary , non-infected mice treated with chloroquine or saline showed an increase in step-down latency , indicating intact aversive memory , when comparing their behavior in training and test sessions . A similar pattern was seen with Pb-infected BALB/c mice , where comparisons between infected and non-infected mice were not statistically different ( Figure 4 ) . When we applied the continuous multiple trials step-down inhibitory avoidance task analysis ( Figure 5 ) , we observed a significant increase in the number of training trials required to reach the acquisition criterion ( 50 sec on the platform ) with PbA-infected C57Bl/6 mice treated with chloroquine as compared to the non-infected controls ( f ( 5–54 ) = 8; p = 0 . 0001 , Wilcoxon's Test ) . The results of this task suggest that PbA-infected C57Bl/6 mice required approximately two times more stimulation to reach the acquisition criterion compared to non-infected animals receiving the same treatment , indicating learning impairment after recovery from CM [24] . As expected , PbA-infected BALB/c mice did not show any differences in the number of training trials required to reach the acquisition criterion when compared to non-infected controls . In the retention test , there was no difference between groups at all the time points tested . Therefore , learning ability , but not long term aversive memory retention skills , is impaired in PbA-infected C57BL/6 mice . PbA-infected C57Bl/6 mice treated with chloroquine showed an impairment of novel object recognition memory , i . e . , they did not spend a significantly higher percentage of time exploring the novel object during short ( Z = −1 . 782; p = 0 . 075 , Kruskal-Wallis's Test ) or long-term ( Z = −1 . 753; p = 0 . 080 , Kruskal-Wallis's Test ) retention test sessions in comparison to the training trial ( Figure 6 ) . In contrast , this pattern was not reproduced in PbA-infected BALB/c mice ( Figure 6 ) . This result indicates that , as in other memory tasks , CM is associated with late deficits in cognition and memory skills that are not shared by infected animals that did not have clinical or neurobehavioural evidence for CM . Oxidative stress is thought to be an important mechanism in the pathogenesis of neurodegenerative diseases and in sepsis-associated encephalopathy [25] , [26] . To examine this issue in experimental CM , we measured lipid peroxidation by the production of MDA , and the formation of diene conjugated species . On day 3 post infection , no significant differences in lipid peroxidation were detected in brains of C57BL/6 mice infected with PbA compared to those inoculated with control RBC ( Figure 7A , C ) . On day 6 post infection , however , the amount of both MDA and diene conjugates ( Figure 7B , D , p<0 . 05 , Student's T Test ) were increased in brain tissue from PbA-infected mice when compared to the RBC group . Conversely , C57BL/6 mice infected with Pch and BALB/c mice infected with PbA , which do not develop CM , did not show increased production of MDA ( Figure 7E , F ) or diene conjugates ( data not shown ) . These data identify oxidative stress in the brains of C57BL/6 mice infected with PbA but not in non-infected controls or mice infected with Pch , a Plasmodium strain that does not cause CM , suggesting that oxidative injury is a component of neurological impairment and , potentially , cognitive dysfunction in murine CM . Taoufiq and coworkers [27] proposed that the protection of the endothelium by antioxidant delivery may constitute a relevant strategy in CM . Therefore , we asked if antioxidants used as an additive together with antimalarials therapy would reduce subsequent cognitive impairment in mice that developed early clinical signs of CM . We treated PbA-infected C57BL/6 that showed signs of CM , detected by the SHIRPA protocol , with chloroquine plus a combination of desferoxamine and N-acetylcysteine treatment starting when antimalarial treatment was initiated on day 6 post-infection and continuing for 7 days . As described previously , treatment with chloroquine alone dramatically reduced mortality and parasitemia , but did not prevent cognitive damage ( Figure 2 ) . On the other hand , treatment with desferoxamine or N-acetylcysteine alone or in combination had no effect on the parasitemia curve ( data not shown ) . We found that the treatment with a combination of chloroquine , desferoxamine and N-acetylcysteine ameliorated cognitive impairment in infected mice . Importantly , combination of chloroquine , desferoxamine and N-acetylcysteine was equally effective in controlling parasitemia as the treatment with chloroquine alone ( 0 . 66%±0 . 65 in chloroquine treated animals vs 0 . 71%±0 . 49 in animals with combination treatment , ns ) . Figure 8 shows that there was a significant reduction in numbers of crossing and rearing events when analysis in test and training sessions of mice treated with anti-parasitic and an antioxidant drugs ( p<0 . 05 ) was compared to analysis of animals given chloroquine alone . The combined administration of desferoxamine and N-acetylcysteine is a necessary condition , since when chloroquine was given with either desferoxamine or N-acetylcysteine we did not see protection against the cognitive damage ( Figure 8A , B ) . Combination therapy was also able to abolish microvascular congestion and plugging detected by histological examinations of the cortex , hippocampus and cerebellum of treated mice ( Figure 8 , panels G , J and M ) at day 7 post-infection , histologic features that were present in untreated mice with clinical signs of CM ( Figure 8 , panels F , I and L ) . Administration of desferoxamine plus N-acetylcysteine without chloroquine did not protect animals from early death with high parasitemia and therefore could not be tested as a treatment for cognitive impairment . The protection of cognitive function by chloroquine together with desferoxamine and N-acetylcysteine was seen both in C57BL/6 mice infected with PbA ( Figure 8A , B ) and SW mice infected with PyXL and ( Figure 8C , D ) , indicating that the additive therapy with antioxidants is able to prevent cognitive impairment due to CM in relevant models of the disease and diverse genetic backgrounds . Because artesunate has become the standard therapy to treat P . falciparum malaria in humans [28] , we also performed an experiment in which the animals were treated with a combination of artesunate ( 100 mg/kg , b . w . , p . o . ) plus desferoxamine and N-acetylcysteine following the same protocol described above . As seen with chloroquine , combination therapy with artesunate was able to prevent the cognitive damage observed in untreated C57BL/6 mice infected with PbA ( reduction in crossings/rearings between training and testing sessions in untreated animals 14 . 0/13 . 3% versus reduction in crossings/rearings between training and testing sessions in artesunate together with deferoxamine and N-acetylcysteine treated animals 32 . 8/23 . 8% ) .
More than 500 , 000 children develop CM in sub-Saharan Africa each year , of whom 110 , 000 die [29] . Additionally , survivors may not fully recover from CM since long-term cognitive impairment is observed in 12–14% of those individuals [6] . In a study conducted by Dugbartey and coworkers [7] , children with a history of CM performed significantly poorer than those without previous CM in bimanual tactile discrimination , accuracy of visual scanning , visual memory , perceptual abstraction and rule learning skill , right ear auditory information processing , and dominant-hand motor speed . The social and economic burden of persistent cognitive dysfunction is not yet fully clear . Nevertheless , these residual deficits may affect future cognitive development in children , and this establishes the potential for devastating impact in adulthood . CM may thus be the chief cause of cognitive impairment in children in Sub-Saharan Africa and an important cause of cognitive impairment in adults in this region . Additional insights regarding the pathogenesis of cognitive deficits in CM and strategies for effective therapy to prevent this devastating complication are urgently required . The natural history of cognitive dysfunction in experimental CM and its response to rescue therapy with antimalarial are unknown . Here we addressed these issues and provide new insights that may have clinical relevance . In the present work we demonstrated cognitive damage in animals rescued from CM by treatment with the antimalarial drugs chloroquine and artesunate in the early phase of the disease . In addition , we found that antioxidant agents that have previously been used in clinical regimens reduce cognitive dysfunction when given as additive to antimalarial therapy . Experimental CM is characterized by brain edema , parenchymal lesions , blood brain barrier breakdown , and reduced cerebral blood flow . These pathophysiological responses are associated with impaired brain metabolism reflecting cellular injury and bioenergetic disturbances [30] . Magnetic resonance imaging studies suggest lesions in the corpus callosum and striatum [30] . The corpus callosum is one of the most prominent fiber systems of the mammalian brain . Patients with callosal damage cannot read text presented in the left visual field , and animals in which the callosum is divided , and sensory input restricted to one hemisphere , fail to show interhemispheric transfer of learning [31] . Taken together , these date suggest that damage in specific regions of the brain due to CM could generate cognitive damage as well as lack of memory or learning , similar to what was observed in neurocognitive impairment following CM in African children [4] . Additional studies are required to elucidate the mechanisms of central nervous system injury in children with CM as a necessary precursor to the development of interventions to prevent consequent long-term cognitive impairment [32] . We developed surrogate models that mimic clinical CM and its cognitive sequelae after parasitic cure by chloroquine , establishing invaluable tools to study mechanisms and consequences of cerebral involvement in malaria . We found that distinct cognitive abilities are affected in this condition , and that the use of antioxidant therapy concomitant with anti-malarial drugs was an effective therapy to prevent late cognitive damage to the host . In experimental malaria , infection can vary in severity depending on the species and strain of Plasmodium , the dose of parasites and the mouse genetic background . We chose our innoculum based on previous work on experimental CM in the literature [12] , [19] , [33] , [34] , [35] , but we recognize the possibility that different results could have been obtained if we had used a mild infection model . In non-lethal infections , such as those caused by Pch and Py 17XNL , resolution generally results in immunity to a second challenge with the same strain , but not to a heterologous parasite . Some parasite strains are lethal only to a particular strain of mice ( for instance Pch to 129sv , A/J and DBA/2 mice ) and some are uniformly lethal ( P . berghei ANKA , Py 17XL or YM ) , indicating that parasite associated factors as well as the host genetic background interact to determine lethality [13] . In the PbA model , the genetic background of the murine host is extremely important and modulates the disease outcome . For instance , the Th-1 biased C57BL/6 mouse is susceptible to the development of CM , whereas the Th-2 biased BALB/c mouse is resistant [12] . Although PbA infection is regarded as a standard model of experimental CM , there have been conflicting results using the Py 17XL parasite as a CM model . Contrary to PbA , Py 17XL has been described to induce high parasitemia , massive anemia and kidney failure without CM ( for review see Engwerda et al . , [11] ) . On the other hand , other studies report that Py 17XL induces clear signs of CM and is a useful model of this condition in the laboratory setting [13] , [21] , [36] . We detected high parasitemia ( 32% ) at day 7 after Py 17XL infection and these animals exhibited signs of cerebral dysfunction when submitted to the SIRPA protocol . Because we were able to establish sensitive and reproducible methods by which CM could be unequivocally demonstrated by performing tests described in the SHIRPA protocol [19] , [37] , our findings are consistent with previous literature indication that Py 17XL induces important dysfunctions in the central nervous system . Based on previous studies [19] , C57BL/6 mice with CM develop a wide range of behavioral and functional alterations as the syndrome progresses , and significant impairment in all functional categories when assessed 36 hours prior to death . Reflex , sensory function and neuropsychiatric state are altered in the early phase of malaria infection , and muscle tone , strength and autonomic functions are affected in animals with CM exclusively . We confirmed these findings in several models of CM . We also observed that C57BL/6 mice treated with chloroquine are rescued to basal locomotor activity when tested by the SHIRPA protocol ( data not shown ) . Nevertheless , a cognitive deficit persists and was clearly demonstrated when the animals were subjected to specific tasks , such as the memory habituation open-field test performed 15 and 30 days after CM , indicating that cure of the parasitic infection does not prevent the development of late cognitive sequelae once CM is established . Furthermore , C57BL/6 mice infected with Pch developed clinical signs of infection but failed to develop CM and cognitive damage , indicating that cognitive impairment is not an unavoidable consequence of systemic malarial infection in C57BL/6 mice , but rather is associated with the development of clinically detected CM . We also found that severe infection without clinically established CM is not sufficient to trigger cognitive impairment using the PbA-infected BALB/c mice model . Taken together , these data document a strict correlation between development of CM and long-lasting cognitive impairment in surrogate models of malaria . It is our working hypothesis that acute cerebral malaria that leads to death in the absence of rescue therapy and long term cognitive dysfunction in animals that have been rescued with chloroquine share some of the same cellular and molecular mechanisms , and that the substrate for long term cognitive dysfunction is initiated by cerebral injury during the acute period of untreated cerebral malaria . We do not exclude , however , the possibility that long term cognitive dysfunction may also have complex mechanisms that are independent of those that cause neurologic injury and death during acute cerebral malaria , and that these undefined mechanisms only operate if the animal survives . Future investigations are aimed to clarify this point . Memory function is vulnerable to a variety of pathological process including neurodegenerative diseases , strokes , tumors , head trauma , hypoxia , cardiac surgery , malnutrition , attention-deficit disorder , depression , anxiety , medications , and normal aging [38] . One of the most elementary nonassociative learning tasks is that of behavioral habituation to a novel environment [39] . We identified deficits in memory habituation in open-field test analysis , which revealed long-term memory defect in mice with experimentally-induced CM . This deficit was unrelated to changes in basic exploratory or motor processes . Rather , it is likely to be directly related to impaired hippocampus-dependent memory processes [40] , [41] . Additional target areas such as prefrontal cortex could also be involved , since reduced density of neuronal cells in this area is known to lead to orientation disturbances and memory problems in complex tasks [42] , [43] . Memory habituation impairment was not the only late consequence of CM in our models as , in fact , several other cognitive deficits were documented in PbA-infected C57BL/6 mice . Step-down inhibitory avoidance learning triggers biochemical events in the hippocampus that are necessary for the retention of this task . The events are similar in many ways to those described for different types of long-term potentiation and other forms of neural plasticity [44] , [45] . They are triggered by glutamate receptor activation and involve at least four different cascades led by different protein kinases ( PK ) , including protein kinase G , PKC , calcium-calmodulin-dependent protein kinase II ( CaMKII ) , and PKA . Several steps in these cascades have been implicated in other forms of learning that also involve the hippocampus ( reviewed by Izquierdo & Medina [45] ) . Step-down inhibitory avoidance involves learning , acquired generally in one single trial , and long-term aversive memory retention . C57BL/6-infected mice lack long-term memory retention ability ( 24 hours post-stimulus ) ( Figure 4B ) and have deficits in learning even when they are submitted to multiple trials ( Figure 5A ) . The inhibitory avoidance task relies heavily on the dorsal hippocampus , but also depends on the entorhinal and parietal cortex and is modulated by the amygdala [44] , [45] . CM may , therefore , be affecting distinct areas in the brain to interrupt different facets of memory and task performing ability . We found that object recognition is also impaired after CM . This task , originally developed by Ennaceur and Delacour [46] , is based on the tendency of rodents to explore a novel object more than a familiar one . Because no rewarding or aversive stimulation is used during training , the learning occurs under conditions of relatively low stress or arousal [46] . We observed that PbA-infected C57BL/6 mice rescued from CM with chloroquine had significant impairment in novel object recognition memory compared with sham-infected mice . These findings are important since the novel object recognition task in rodents is a nonspatial , nonaversive memory test , in contrast to other tests performed in this study ( habituation and aversive memories ) [47] . Recognition of objects is thought to be a critical component of human declarative memory that is mainly dependent on the hippocampus . Object recognition is commonly impaired in human patients affected by neurodegenerative diseases , or who have suffered brain injury [47] , [48] . We do not know if the cognitive defects are reversible , but our experiments indicate that they persist for at least 30 days after rescue from CM with chloroquine alone . Experiments are in progress to determine the duration of CM induced cognitive deficiency imposed by CM in these models . The mechanisms for cognitive impairment in CM are not completely characterized , but inflammation and vascular dysfunction appear to be the basis of cerebral involvement [9] . During malarial infection , the host and parasites are under severe oxidative stress with increased production of reactive oxygen species ( ROS ) and NO by activated cells in the host [14] . When produced in large amounts , ROS and nitrogen intermediates may cause damage to the host tissue including the vascular endothelium . Endothelial damage may lead to increased vascular permeability and leukocyte and platelet adherence , all seen in cerebral malaria [49] . Despite being generally accepted , this view has been challenged by observations showing that gp91phox deficient mice and inhibitors of iNOS fail to modify the development of cerebral malaria in appropriate murine models [50] , [51] . We have performed preliminary experiments using NOS deficient mice and observed that those animals , despite being susceptible to high parasitemia and early death with CM symptoms , were protected from the cognitive damage if treated with chloroquine at day 6 post infection . Together , these observations may suggest that the pathology leading to mortality during CM may occur via different mechanisms than that leading to cognitive dysfunction after successful rescue therapy . In this pathophysiologic milieu , antioxidants may be an effective strategy to counteract damage in CM , and metal chelators may be of particular interest [52] . Products of lipid peroxidation are markers for oxidative stress in several diseases and experimental models [53] . To characterize the oxidative damage during early events of CM we measured TBARS and conjugated diene formation on days 3 and 6 post infection . Our findings indicate a significant increase in oxidative stress in the brains of PbA-infected mice on day 6 post-infection , further suggesting antioxidants as a potential additive therapy to reduce cerebral damage and cognitive dysfunction during CM . Oxidative stress is associated with the development of neurodegenerative diseases and is important to the development of multiple organ dysfunction syndromes during sepsis [25] , [26] , providing a precedent for this approaches . In fact , combined antioxidant therapy with N-acetylcysteine and desferoxamine improves survival in sepsis induced by cecal ligation and puncture ( CLP ) in rats by decreasing oxidative stress and limiting mitochondrial dysfunction [54] . Barichello and coworkers [55] showed that the combined therapy also prevents late memory impairment in experimental sepsis . N-acetylcysteine is a well-known thiolic antioxidant that acts as a precursor for glutathione synthesis [56] . The reducing thiol group in N-acetylcysteine also reacts directly with ROS , leading to cellular protection against oxidative damage in vitro and in vivo [57] . Desferoxamine is a powerful iron chelator that can inhibit iron dependent free radical reactions and has been shown to diminish oxidant damage in several animal model of human disease [58] , [59] . Previous studies have demonstrated that desferoxamine protects against brain ischemic injury in neonatal rats when administered after an ischemia-reperfusion insults [60] . In adult rats , desferoxamine protects against focal cerebral ischemia when given as a preconditioning stimulus 72 h before the ischemic insult [61] . In agreement with the protective effect of antioxidants in sepsis-induced brain dysfunction , we found that combined treatment with N-acetylcysteine , desferoxamine and chloroquine in PbA-infected C57BL/6 mice or Swiss mice infected with PyXL prevented cognitive damage as detected by the open-field task test , further indicating a role for oxidative stress in the development of cognitive dysfunction in experimental CM and providing an approach to modify this consequence of cerebral injury . In addition , our initial experiments indicate that antioxidants are effective as additive treatment in combination with artesunate as well . Because N-acethylcysteine and desferoxamine have been used in clinical treatment of human subjects and their pharmacologic profile and side effects are known , we suggest that these drugs should be examined as additive therapy for antimalarial drugs in clinical trials to investigate their potential to decrease , or prevent , cognitive damage after CM .
6–8 weeks old C57BL/6 , BALB/c ( n = 10/group per experiment ) and Swiss webster ( SW , n = 8/group ) mice from the Oswaldo Cruz Foundation breeding unit , weighing 20 to 25 g , were used for the studies . The animals were kept at constant temperature ( 25°C ) with free access to chow and water in a room with a 12 hour light/dark cycle . The experiments in these studies were approved by the Animal Welfare Committee of the Oswaldo Cruz Foundation under license number L033/09 ( CEUA/FIOCRUZ ) . The guidelines followed by this Committee were created by the same institution that provided ethical approval . N-acetylcysteine ( Zambom Group S . p . A . , Italy ) , desferoxamine ( Novartis Bioscience S . A . , Brazil ) , and chloroquine ( Farmanguinhos , Oswaldo Cruz Foundation , Brazil ) were directly dissolved in saline solution ( NaCl 0 . 9% , w/v ) . The solutions were prepared immediately before use and were protected from the light before administration to the animals . Uncloned parasite lines of Plasmodium berghei ANKA , Plasmodium chabaudi chabaudi and Plasmodium yoelii were used in this study . Plasmodium berghei ANKA ( PbA ) parasitized red blood cells ( PRBC ) from BALB/c or C57BL/6 mice , Plasmodium chabaudi chabaudi ( Pch ) in C57BL/6 PRBC , Plasmodium yoelii non-lethal ( PyNXL ) , and Plasmodium yoelii lethal ( PyXL ) in Swiss Webster PRBC donor strains were kept in liquid nitrogen and were thawed and passed into normal mice that served afterwards as parasite donors . 6–8 weeks old C57BL/6 , BALB/c and Swiss webster ( SW ) mice were inoculated intraperitoneally with 0 . 2 mL suspension of 106 parasitized red blood cells ( n = 8–10/group ) . As a control group for infection , mice were inoculated with 106 non parasitized red blood cells ( RBC ) . Parasitaemia on days 3 , 5 , 7 and 10 and survival rate were recorded . On day 7 post-infection , animals of different groups ( control , PbA-infected , and PbA-infected rescued with chloroquine and antioxidant; n = 3 per group ) were transcardially perfused with 0 . 9% saline solution and 4% paraformaldehyde in PBS . The brains were carefully dissected , cryoprotected in 10% , 20% , and 30% sucrose at 4°C , and embedded in O . C . T . ( Tissue-Tek ) for frozen sectioning on a cryotome ( Leica Microsystems ) . Parasagittal sections were cut at 12 µm and placed on slides for staining with haematoxylin and eosin ( H&E – VETEC , Rio de Janeiro ) for histological analysis by a blinded expert pathology . Sections were examined on an Axioplan light microscope ( Zeiss , Germany ) . Mice were infected as described above . On day 3 and 6 , they were subjected to SHIRPA protocol testing ( see below ) to identify neurobehavioral signs of CM . Animals that were positive for clinical signs of CM detected in this fashion were immediately started on chloroquine treatment ( 25 mg/kg b . w . , orally ) and were treated daily for 7 days ( 15 days analisis ) or 21 days ( 30 days analysis ) . At day 15 post infection , the animals were subjected to a battery of behavioral tests to access cognitive function . As a control , uninfected mice received saline ( when indicated ) or chloroquine . An additional group of animals received additive antioxidant therapy with desferoxamine and N-acethylcisteine , ( each 20 mg/kg b . w . , intraperitoneally ) from day 6 to 12 post infection , concomitant with chloroquine , and then were subjected to behavioral tasks on day 15 . In an additional experiment , mice were infected with 106 PbA-PRBC . At day 6 mice were intraperiotoneally treated with a single dose of 0 . 5 mg anti-CD8 Mab obtained from Hybridomas 53-6 . 7 [62] and orally treated with chloroquine ( 25 mg/kg b . w . ) during 7 days . The behavioral testing was performed according to the SHIRPA protocol [63] , 1997 ) . The primary screen was performed as described for detection of CM by Lackner and coworkers [19] . Individual tests are described in Table 1 . Habituation to an open-field was carried out as described by Vianna and coworkers [39] . Animals were gently placed on an open field apparatus and allowed to explore the arena for 5 minutes ( training session ) . 24 h later they were submitted to a similar open-field session ( test session ) . Crossing of the black lines and rearing performed in both sessions were counted . The step-down inhibitory avoidance test was performed as described by Quevedo et al . , [64] . In the training trial , animals were placed on the platform and their latency to step down on the grid with all four paws was measured with an automatic device . Immediately after stepping down on the grid , the animals received a 0 . 4 mA , 2 . 0 seconds foot shock . A retention test trial was performed 24 h after training and permanence on the grid is recorded . Continuous multiple-trials step-down inhibitory avoidance task testing was performed in the same step-down inhibitory avoidance apparatus , however , in the training session , animals were placed on the platform and immediately after stepping down on the grid , received a 0 . 3 mA , 2 . 0 seconds foot shock . 1 h 30 min later , this procedure was repeated until the mice remained on the platform for 50 seconds and the number of training trials required was recorded . On the following day the retention test was performed and the result was given by latency period on the platform , with a cut-off at 180 seconds [65] , [66] . The object recognition task was carried out as described in previous studies [67] . Briefly , animals had the opportunity to explore the open field for 5 min . On the following day , a training session was conducted by placing individual mice for 5 min into the field in the center of the arena , in which two identical objects ( object A1 and A2; Double Lego Toys ) were positioned in two adjacent corners at 10 cm from the walls . In a short-term memory ( STM ) test ( 1 . 5 h after training ) , the mice explored the open field for 5 min in the presence of one familiar ( A ) and one novel ( B ) object . In a long-term memory ( LTM ) test ( 24 h after training ) , the mice explored the field for 5 min in the presence of the familiar ( A ) and different novel ( C ) object . Objects had only distinction in shape . The exploratory preference was defined as percentage of the total exploration time animal spent investigating the object A or the novel object and calculated for each animal by the ratio TB or C/ ( TA+TB or C ) [TA = time spent exploring the familiar object A; TB or C = time spent exploring the novels objects B or C ) . To characterize the oxidative stress in murine brains , lipid peroxidation levels were measured by assays of thiobarbituric acid reactive species - TBARS [68] - and the formation of diene-conjugated species [69] . Brains from animals dying of CM were homogenized in cold phosphate buffer , pH 7 . 4 with BHT ( final concentration 0 . 2% ) . Briefly , the samples ( 0 . 5 mL ) were mixed with equal volume of thiobarbituric acid 0 . 67% ( Sigma Chemical , St . Louis , MO ) and then heated at 96°C for 30 min . TBARS were determined by the absorbance at 535 nm . To analyze diene-conjugate formation , lipids were extracted by partition on chloroform∶methanol ( 2∶1 , v∶v ) and the organic phase was submitted to espectrophotometric analysis at 234 nm . Results were expressed as malondialdehyde ( MDA , ε = 1 , 56×105M−1cm−1 ) and diene equivalents ( ε = 2 , 95×104M−1cm−1 ) per milligram of protein ( BCA assay ) [68] . Data were expressed as mean ± SEM . Statistical significance of survival curves were evaluated by Log-rank ( Mantel-Cox ) and Gehan-Breslow-Wilcoxon tests . Statistical analysis from SHIRPA was performed by nonparametric test ( Wilcoxon rank-sum test ) . Data from the open-field task were analyzed with ANOVA followed by Tukey post hoc and Student's T tests and expressed as mean ± SEM . Data from the inhibitory avoidance task , object recognition task and the number of training trials from continuous multiple trials step-down inhibitory avoidance are reported as median and interquartile ranges and comparisons among groups were performed using Mann–Whitney U tests . The variations within individual groups were analyzed by Wilcoxon's test . Difference in amounts of MDA and diene-conjugates were evaluated by Student's T test . In all comparisons , p<0 . 05 or less was taken as statistical significance . | Cerebral malaria ( CM ) is a deadly consequence of Plasmodium falciparum infection . Severe neurologic deficits are frequent during CM . Although most resolve within 6 months , several retrospective studies have described high frequencies of long-lasting cognitive impairment after an episode of CM . We developed behavioral tests to identify cognitive impairment due to experimental CM . During infection with Plasmodium berghei ANKA ( PbA ) , mice susceptible to CM ( C57BL/6 ) developed long-lasting cognitive impairment in contextual and aversive memory . The same profile was seen in Swiss Webster mice infected with Plasmodium yoelii XL , a lethal strain that also induces neurological dysfunctions in susceptible mice strains , confirming that the cognitive dysfunction is closely associated to the development of CM . Reactive oxygen species are described as mediators of neurological and cognitive impairment associated to sepsis and Alzheimer's disease . Here we found enhanced production of malondialdeyde and conjugated dienes in brains of PbA-infected C57BL/6 mice , indicating oxidative stress . Antioxidant therapy with N-acetylcisteine and desferroxamine , as an additive to chloroquine , prevented the cognitive impairment , confirming the importance of oxidative stress in CM-associated cognitive sequellae . Administration of additive antioxidants may be a successful therapeutic strategy to control long-lasting consequences of CM and in other severe systemic inflammatory syndromes with neurological involvement . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"infectious",
"diseases/protozoal",
"infections",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"immunology/immunity",
"to",
"infections",
"immunology/innate",
"immunity"
] | 2010 | Cognitive Dysfunction Is Sustained after Rescue Therapy in Experimental Cerebral Malaria, and Is Reduced by Additive Antioxidant Therapy |
In mammalian meiosis , homologous chromosome synapsis is coupled with recombination . As in most eukaryotes , mammalian meiocytes have checkpoints that monitor the fidelity of these processes . We report that the mouse ortholog ( Trip13 ) of pachytene checkpoint 2 ( PCH2 ) , an essential component of the synapsis checkpoint in Saccharomyces cerevisiae and Caenorhabditis elegans , is required for completion of meiosis in both sexes . TRIP13-deficient mice exhibit spermatocyte death in pachynema and loss of oocytes around birth . The chromosomes of mutant spermatocytes synapse fully , yet retain several markers of recombination intermediates , including RAD51 , BLM , and RPA . These chromosomes also exhibited the chiasmata markers MLH1 and MLH3 , and okadaic acid treatment of mutant spermatocytes caused progression to metaphase I with bivalent chromosomes . Double mutant analysis demonstrated that the recombination and synapsis genes Spo11 , Mei1 , Rec8 , and Dmc1 are all epistatic to Trip13 , suggesting that TRIP13 does not have meiotic checkpoint function in mice . Our data indicate that TRIP13 is required after strand invasion for completing a subset of recombination events , but possibly not those destined to be crossovers . To our knowledge , this is the first model to separate recombination defects from asynapsis in mammalian meiosis , and provides the first evidence that unrepaired DNA damage alone can trigger the pachytene checkpoint response in mice .
The genesis of gametes containing an intact , haploid genome is critical for the prevention of birth defects , and is highly dependent upon the fidelity of chromosome dynamics before the first meiotic division . Homologous chromosomes must pair , synapse , undergo recombination , and segregate properly to opposite poles . Recombination , which repairs repair double strand breaks ( DSBs ) that are genetically induced in leptonema , is coupled with synapsis in budding yeast and mammals . While our knowledge of the assembly and nature of recombination machinery is extensive , little is known about the disassembly of recombination intermediates , recruitment of DNA replication machinery during recombinational repair , and how the choice between different repair pathways is made . Defects in recombination can preclude homologous chromosome pairing , leave unrepaired chromosome breaks , and cause aneuploidy by abrogating crossing over . To avoid such deleterious outcomes , surveillance systems ( “checkpoints” ) exist to sense meiotic errors and eliminate cells containing unresolved defects . In many organisms , including S . cerevisiae , Drosophila melanogaster , C . elegans , and mice [1–4] , meiocytes with defects in recombination and/or chromosome synapsis trigger meiotic arrest in the pachytene stage of meiotic prophase I . This response to meiotic defects is referred to as the “pachytene checkpoint” ( reviewed in [5] ) . Genetic experiments in S . cerevisiae have identified elements of the pachytene checkpoint machinery ( reviewed in [5] ) . In addition to meiosis-specific proteins , these include factors that play roles in DNA damage signaling in mitotic cells [6–10] . Arabidopsis thaliana does not appear to have a pachytene checkpoint akin to that in yeast [11] , nor do male Drosophila . The pachytene checkpoint is known to monitor two aspects of meiotic chromosome metabolism in S . cerevisiae and C . elegans: ( 1 ) DSB repair and ( 2 ) chromosome synapsis [2 , 12] . In mice , both spermatocytes and oocytes harboring mutations that disrupt DSB repair ( such as Dmc1 , Msh5 , and Atm ) are efficiently eliminated in pachynema , but spermatocytes are much more sensitive to DSB repair–independent synapsis defects than oocytes [13–15] . However , because recombination is required for synapsis in mice ( mutations in recombination genes such as Dmc1 cause extensive asynapsis [16] ) , it has remained formally uncertain whether there is a distinct pachytene checkpoint that responds to defects in meiotic recombination , and if so , whether it would be identical to that used in somatic cells . The mechanisms of putative pachytene checkpoint control remain unknown in mammals , since no mutations have been identified that abolish it . PCH2 , encoding a nucleolar-localized AAA-ATPase that was originally identified in an S . cerevisiae genetic screen for mutants that relieve pachytene arrest of asynaptic zip1 mutants [8] , was recently determined to be an essential component of the pachytene synapsis ( but not DSB repair ) checkpoint in yeast and worms [2 , 12] . PCH2 orthologs are present in organisms that undergo synaptic meiosis , but not asynaptic meiosis , prompting the suggestion that a Pch2-dependent checkpoint evolved to monitor synaptonemal complex ( SC ) defects from yeast to humans [12] . Here , we generated mice deficient for the Trip13 , the ortholog of PCH2 , and evaluated whether it also plays a role in the pachytene checkpoint . Surprisingly , while we found no evidence for checkpoint function , we did uncover a potential role for this protein in noncrossover ( NCO ) repair of meiotic DSBs .
The mammalian ortholog of PCH2 , Trip13 ( thyroid hormone receptor interacting protein 13 ) , encodes a protein with extensive amino acid homology in regions alignable to the yeast and worm orthologs ( Figure S1 ) [12] ) . Interestingly , phylogenetic analysis of TRIP13/Pch2p shows that the mammalian protein clusters more closely to plants than it does to the evolutionarily more closely related worms and flies ( Figure 1A; see Discussion ) . Semi-quantitative reverse-transcriptase PCR ( RT-PCR ) analysis showed Trip13 mRNA to be expressed in a variety of embryonic and adult tissues , including testis ( Figure 1B ) , consistent with mouse and human EST data summarized in Unigene ( http://www . ncbi . nlm . nih . gov/UniGene ) . It is also highly expressed in human and mouse oocytes [17] . To explore the function of TRIP13 in mammals , we generated mice with a gene trap-disrupted allele , Trip13RRB047 ( Figure 1C; abbreviated as Trip13Gt ) . Heterozygotes were normal in all respects , but homozygotes were present at ∼2/3 the expected ratio from intercrosses between heterozygotes ( 91 Trip13+/+ , 183 Trip13Gt/+ , and 61 Trip13Gt/Gt ) . Since >90% of prewean mice that died were mutant homozygotes , this discrepancy is apparently due to a partially penetrant lethality . Most surviving Trip13Gt/Gt animals were grossly normal . However , homozygotes that were semi-congenic ( N4 ) on the C57BL/6J strain were often markedly smaller and/or had kinked or shorter tails ( Figure 2A and 2B ) . RT-PCR analysis of Trip13Gt expression ( Figure 1D ) revealed a low level of normally spliced transcripts in testes of homozygotes that is presumably a consequence of incomplete usage of the gene trap's splice acceptor . Western blot analysis , using a polyclonal antibody raised against a peptide encoded by exon 3 , revealed multiple species in wild-type and heterozygous testes , one of which corresponds to the expected size of 48 kDa ( Figure 1E ) . This and three other species were undetectable in homozygous mutant testes , but a reduced amount of an intense ∼38 kDa smaller band was present . It is not clear if this corresponds to TRIP13 . The greatly decreased Trip13 mRNA and predicted correct-length protein in mutants indicate that the Trip13RRB047 allele is severely hypomorphic . To determine the germ cell types in which TRIP13 is expressed , and to assess possible expression in the mutant by means other than Western analysis , testis sections were immunolabeled for TRIP13 using a polyclonal chicken antipeptide antibody ( see Materials and Methods ) . The most intensely labeled cells in control testes were Type B spermatogonia and leptotene spermatocytes ( Figure 1F ) . Zygotene/pachytene spermatocytes stained less strongly , and there was no detectable staining in late pachytene spermatocytes . TRIP13 appeared to be nuclear localized . There was no such staining of nuclei in mutant seminiferous tubules ( Figure 1F ) . To further assess the nuclear localization , TRIP13 was used to probe meiotic chromosomes prepared by surface spreading of spermatocyte nuclei . In wild type , there was diffuse nuclear staining , and no evidence of concentration on SC cores ( marked by the axial element protein SYCP3 ) at any meiotic substage ( Figure 1G ) . TRIP13 signal was noticeably absent in mutant meiotic nuclei . Homozygotes of both sexes had small gonads ( Figure 2C; see below ) and were invariably sterile . Ovaries of adult Trip13Gt/Gt females were severely dysmorphic and had few or no follicles ( Figure 3A and 3B ) . The majority of oocyte loss occurred in late embryogenesis or early in postnatal development , since 2 d postpartum ovaries were markedly smaller than those of control littermates , and were lacking oocytes or newly forming follicles ( Figure 3C and 3D ) . Thus , oocytes failed to progress to the dictyate ( resting ) phase . Since we observed oocytes with pachytene stage chromosomes in 17 . 5 d Trip13Gt/Gt embryonic ovaries ( unpublished data ) , this indicates that oocytes were eliminated somewhere between pachynema and dictyate . Histological sections of mutant testes revealed a lack of postmeiotic cell types that are characteristic of wild-type seminiferous tubules ( Figure 3E ) . The most developmentally advanced seminiferous tubules contained adluminal spermatocytes with condensed chromatin characteristic of pachynema ( Figure 3F ) . The absence of coordinated spermatogenic progression beyond this stage is indicative of a pachytene arrest . This was revealed more clearly by chromosome analysis ( see below ) . Some sections of adult seminiferous tubules contained postmeiotic spermatids ( Figure 3G ) , although we saw no motile epididymal sperm . These drastic meiotic defects stand in contrast to yeast and C . elegans , in which deletion of Pch2 alone has minor effects on spore/gamete development [2 , 8] . To better characterize the degree of meiotic progression in Trip13Gt/Gt spermatocytes , we immunostained chromosome spreads for SYCP3 and SYCP1 , components of the axial/lateral elements and transverse filaments , respectively , of the synaptonemal complex ( SC ) . Pachytene spermatocyte nuclei from postpubertal mutant testes could assemble normal SC cores and exhibited full synapsis of chromosomes as judged by colabeling of SYCP1 and SYCP3 along the full lengths of all autosomes ( Figure 4A ) . Additionally , the X and Y chromosomes were normally synapsed at their pseudoautosomal region . More prepubertal ( 17 . 5 d postpartum ) mutant spermatocytes contained asynaptic or terminally asynapsed chromosomes than age-matched controls ( 62 . 5% versus 25% , respectively; Figure 4B ) . We attribute this to a delay in the first wave of postnatal spermatogenesis ( Figure 2D and 2E ) , likely related to systemic developmental retardation ( Figure 2A and 2B ) . Nevertheless , since Trip13Gt/Gt spermatocytes progress to pachynema with no gross SC abnormalities , and oocytes were eliminated soon after birth ( a characteristic of DNA repair mutants [13] ) , this suggested that unrepaired DSBs are responsible for eventual meiotic arrest and elimination . To elucidate the cause of meiotic arrest , we analyzed meiotic chromosomes with a variety of markers that are diagnostic of recombination and synapsis . Recombination in Trip13Gt/Gt spermatocytes appeared to initiate normally as judged by the presence of γH2AX in leptonema ( Figure S2A and S2B ) , which reflects the presence of meiotically induced DSBs [18] . RAD51 and/or DMC1 , components of early recombination nodules ( ERNs ) , was also present as abundant foci in Trip13Gt/Gt zygotene spermatocytes ( unpublished data; the anti-RAD51 antibody cross-reacts with DMC1 ) , indicating that recombinational repair of DSBs is initiated . The cohesin complex , which is essential for completion and/or maintenance of synaptic associations , appeared to assemble normally as judged by immunolabeling for the meiosis-specific cohesins STAG3 ( Figure S2C and S2D ) and REC8 ( unpublished data ) . Because yeast PCH2 localizes to telomeres in a Sir3p-dependent manner , we tested for possible telomere defects by immunolabeling for TRF2 , a component of a protein complex that plays an essential role in telomere protection [19] . It was localized to telomeres of both fully synapsed and telomerically asynaptic mutant chromosomes ( Figure S2E and S2F ) . Defects in DSB repair became apparent in pachynema upon probing of mutant spermatocyte nuclei with antibodies against molecules involved in various stages of recombination . In >99% of Trip13Gt/Gt chromosome spreads , BLM helicase ( Figure 4C and 4D ) , RAD51/DMC1 ( Figure 4E and 4F ) , γH2AX ( Figure 4G and 4H ) , and TOPBP1 ( Figure 4I and 4J ) all persisted abnormally on synapsed chromosomes . For RAD51/DMC1 , mutant pachytene spermatocytes contained 138 ± 6 foci ( compared to 11 ± 3 foci in wild type , most of which were on the XY body ) , down from 218 ± 13 in zygonema ( compared to 220 ± 13 foci in wild type ) . TOPBP1 is a DNA damage–checkpoint protein involved in ATM protein–dependent activation of ATR protein [20 , 21] . It binds sites of DSBs and unsynapsed regions of meiotic chromosomes [22 , 23] . BLM has been reported to colocalize with markers ( RPA and MSH4 ) of recombination at sites distinct from those that become resolved as crossovers ( CO ) [24] . We therefore assessed the distribution of RPA , the ssDNA binding protein , which is normally present at focal sites of synapsing meiotic chromosomes before disappearing in mid-pachynema [25] . It is thought to bind D-loops of recombination intermediates [26] . RPA also persisted on pachytene mutant chromosomes ( Figure 4K and 4L ) . These data indicate that unrepaired DSBs , or unresolved recombination intermediates , remain in pachynema and activate a DNA damage checkpoint system . It should be noted that chromosomes affected by meiotic sex chromosome inactivation ( MSCI ) and meiotic silencing of unpaired chromatin ( MSUC ) are heavily stained by antibodies for several DSB repair-associated molecules , including γH2AX . H2AX phosphorylation due to MSCI and MSUC is conducted by ATR , not ATM [27–29] . Since mutant chromosomes are fully synapsed , and MSUC is known to occur only as a result of asynapsis , the decoration of Trip13Gt/Gt chromosomes with DNA repair markers is probably attributable to incomplete DNA repair rather than transcriptional silencing . Consistent with the presence of rare ( <1% ) Trip13Gt/Gt pachytene spermatocytes devoid of persistent DNA repair markers , and testis histology showing some degree of postmeiotic progression ( Figure 3G ) , we observed both diplotene nuclei that lacked autosomal RAD51/DMC1 and γH2AX ( Figure S3A–S3D ) , and also metaphase I spreads with 20 bivalents ( Figure S3E–S3F ) . Since Trip13Gt may not be a complete null , these diplotene and metaphase I spermatocytes might arise by virtue of having sufficient wild-type TRIP13 . The persistence of BLM on Trip13Gt/Gt spermatocyte chromosomes suggests that at least a subset of the unrepaired DSBs correspond to sites of defective NCO recombinational repair . To assess whether CO recombination occurs in the mutant , we examined the distribution of the mismatch repair proteins MLH1 and MLH3 , which are normally detectable as foci in mid-late pachynema and mark the locations of chiasmata [30 , 31] . Remarkably , MLH1/3 foci were formed; we observed 1–2 foci/chromosome as in wild type and at typical overall levels ( MLH3 = 23 ± 2 , N = 10; [30 , 32] ) on mid-late pachytene chromosomes ( Figure 4M and 4N; MLH1 not shown ) . Since <1% of Trip13Gt/Gt pachytene nuclei had normal repair ( as judged by absence of persistent DSB repair markers; see above ) , but most of the pachytene nuclei had MLH1/3 foci , it was unlikely that the MLH1/3 foci formed only on chromosomes with fully repaired DSBs . To test this directly , we conducted double staining for MLH1 and RAD51/DMC1 . MLH1 foci were present on chromosomes that also contained numerous RAD51/DMC1 foci ( Figure 4O and 4P ) . To assess whether these MLH1/3 foci in Trip13Gt/Gt pachytene spermatocytes represent CO events completed to a point where they could maintain interhomolog attachments though the end of prophase I , we treated testicular cells from 17 . 5–20 . 5-d-old control ( +/+ ) , Trip13Gt/Gt , and Dmc1−/− mice with the protein phosphatase inhibitor okadaic acid ( OA ) , a chemical that induces degradation of the SC , chromosome condensation , and premature progression to metaphase I [33] . Fifteen metaphase spreads were identified for each genotype . Whereas all of the Dmc1−/− spreads had ∼35 or more condensed chromosomes , all of the +/+ and Trip13Gt/Gt spreads had 20–25 , suggesting that the MLH1/3 foci in Trip13Gt/Gt pachytene spermatocytes represent sites of completed , or near-completed , COs . Because the preparations were made from whole testes , it is possible that the univalent-containing metaphases from Dmc1−/− mice were from spermatogonia , not spermatocytes . To determine if TRIP13 deficiency prevents apoptosis triggered by asynapsis as in C . elegans , we analyzed mice that were doubly mutant for Spo11 and Trip13 . SPO11 is a transesterase that is essential for the creation of genetically programmed DSB during leptonema of many organisms , including mice [18] . In C . elegans , spo-11 mutant gametes have extensive asynapsis , which triggers PCH-2 dependent apoptosis in pachynema [2] . In mice , Spo11−/− spermatocytes are severely defective in homologous chromosome synapsis [34 , 35] , and arrest with chromosomes in a state characteristic of the zygotene/pachytene transition ( Figure 3H ) . Spermatocytes in Trip13Gt/Gt Spo11−/− testes progressed maximally to that point before undergoing death ( Figs 3I ) , well before the spindle checkpoint that eliminates achiasmate spermatocytes [36] . There was no evidence of metaphase I spermatocytes or postmeiotic spermatids in these testes , unlike those seen in Trip13 single mutants ( Figure 3G ) . In contrast to the complete synapsis in Trip13Gt/Gt pachytene spermatocytes ( Figure 5A ) , in which SPO11 is available in leptonema to initiate ( via DSB induction , Figure S2A and S2B ) a recombination-driven homolog search , chromosome synapsis in doubly mutant spermatocytes was highly disrupted as in Spo11 single mutants ( Figure 5B and 5C ) . Identical studies were performed with mice deficient for Mei1 , a vertebrate-specific gene also required for DSB formation and chromosome synapsis [37] , with similar results ( Figure 3J and 3K; immunocytology not shown ) . In yeast , deletion of PCH2 alleviates the pachytene arrest caused by asynaptic mutants zip1 and zip2 [8] . Although mouse SYCP1 might be a functional equivalent of Zip1p , because Sycp1 mutant spermatocytes arrest at approximately the same point as Trip13 mutants , there would be no opportunity to observe bypass of Sycp1−/− . Since Zip2p is present at sites of axial associations , even in zip1 mutants , it has been suggested that Zip2p promotes initiation of chromosome synapsis [38] . These observations raise the possibility that in yeast , Pch2p responds to synapsis polymerization rather than initiation . To test this , we performed epistasis analysis with a Rec8 allele ( Rec8Mei8 , abbreviated as Rec8− ) . Meiotic chromosomes of Rec8 mutant spermatocytes undergo apparent homolog pairing and interhomolog synaptic initiation , but are defective in DSB repair and fail to maintain interhomolog synapsis [39 , 40] . Rather , sister chromatids appear to synapse and are bound by SYCP1 along their axes . Rec8 mutants do not progress to diplonema or metaphase I . Double mutant analysis indicated that Rec8 is epistatic to Trip13 . As in the Spo11 and Mei1 experiments , histology of testes deficient for both REC8 and TRIP13 resembled the Rec8 mutant , with no evidence of progression to metaphase I that occurs in Trip13Gt/Gt mice ( Figure 3L and 3M ) . Immunocytological analysis of spread chromosomes showed a failure of homologous chromosome synapsis in both the Rec8−/− and Rec8−/− Trip13Gt/Gt spermatocytes , as previously reported for Rec8 mutants ( Figure 5D and 5E ) [39 , 40] . Although subsequent reports indicate otherwise [10 , 12] , deletion of PCH2 in yeast was originally reported to alleviate meiotic arrest caused by deficiency for the meiosis-specific RecA homolog DMC1 [8] . To investigate this relationship in mice , we constructed animals doubly mutant for Trip13 and Dmc1 . As in Dmc1−/− mice , in which spermatocytes undergo meiotic arrest from defective DSB repair and failed chromosome synapsis [16] , spermatogenesis in Dmc1−/− Trip13Gt/Gt testes was uniformly arrested at the point where spermatocytes contained chromatin characteristic of zygonema/pachynema ( Figure 3N ) . Immunocytological analysis indicated that both Dmc1−/− and Dmc1−/− Trip13Gt/Gt chromosomes had extensive asynapsis compared to Trip13Gt single mutants ( Figure 5F–5H ) , and all had persistent RAD51/DMC1 foci and phosphorylated H2AX ( γH2AX; Figure 5I–5L ) , confirming that Dmc1 is epistatic to Trip13 . Doubly mutant females had residual ovaries , phenocopying Dmc1−/− and Trip13Gt/Gt single mutants ( unpublished data ) . Epistasis analysis of females was insightful with respect to the cause of arrest in Trip13 mutants . Both Mei1−/−/Trip13Gt/Gt and Spo11−/−/Trip13Gt/Gt females had ovaries with numerous follicles , identical to Mei1 and Spo11 single mutants ( Figure 3O–3R ) . Thus , Spo11 and Mei1 are epistatic to Trip13 in oogenesis , just as they are to Dmc1 [13 , 41] . This demonstrates that oocyte loss in Trip13Gt/Gt females is dependent on DSB formation . In conjunction with the immunohistochemical data , these data provide strong evidence that meiotic arrest in Trip13 mutant mice is due to defects in DSB repair . As expected , ovaries of Rec8 Trip13 double mutants were devoid of oocytes as were those from either single mutant ( Figure 3B , 3S , and 3T ) .
Genetic experiments in S . cerevisiae provided evidence that the pachytene checkpoint monitors and responds to recombinational DSB repair and synapsis independently . Wu and Burgess concluded that the repair checkpoint is RAD17-SAE2 dependent , while the synapsis checkpoint is PCH2-ZIP1 dependent [12] . Of these four genes , SAE2 and ZIP1 do not have clear mammalian orthologs ( although SYCP1 may be a functional ortholog of ZIP1 ) , and mutation of the mouse RAD17 ortholog , Rad1 , presumably causes embryonic lethality [42] . Thus , mutational analysis of mouse Pch2 ( Trip13 ) , which is also critical for the synapsis checkpoint in C . elegans [2] , was the best remaining option to evaluate potential functional conservation in mammalian meiotic checkpoint control . Our results demonstrate that in mice , the primary meiotic function of TRIP13 is in recombination itself . We found no evidence that it is involved in pachytene checkpoint control . Our data suggest that while recombination events destined to be resolved as COs can proceed normally in Trip13 mutants , DSBs that enter the NCO repair pathway are incompletely resolved or processed inefficiently . This hypothesis is compatible with current knowledge of meiotic recombination pathways . In S . cerevisiae , CO and NCO pathways are distinct [43]; they have different recombination intermediates , and are dependent upon different proteins [44 , 45] . Mice also appear to have independent CO versus NCO recombination pathways [46] . As in yeast , both require SPO11-induced breaks , but only the CO pathway requires MLH1 . Both types of recombinant products are formed by mid-late pachynema . Another possibility is that the recombination defects are a result of defective intersister recombination . However , this type of DSB repair is suppressed in meiotic cells . Ablation of RAD54 , which mediates intersister recombination in yeast , does not significantly disrupt meiosis in either yeast or mice [47 , 48] . Interestingly , RAD54-deficient spermatocytes display abnormal persistence of RAD51 foci on pachytene chromosomes , similar to those in TRIP13 mice , but there are no deleterious effects on meiotic progression or fertility [49] . Data from budding yeast also indicate that Pch2p functions in recombination . Deletion of PCH2 delays meiotic progression by ∼2 h in SK1 yeast , and causes a minor decrease in ascus formation [50] . DSBs persist >2 h longer in pch2Δ yeast than in wild type , and hyperresection of DSBs in dmc1Δ pch2Δ double mutants is lower than in dmc1Δ cells [10] . Additionally , it was reported that pch2Δ yeast had a meiosis I delay dependent on the RAD17–SAE2 checkpoint that monitors recombination intermediates [12] . However , the exact role of TRIP13 ( or Pch2 ) in recombination is unclear . Because synapsis occurs in TRIP13-deficient spermatocytes and is dependent on DSB formation ( activity of SPO11 and MEI1 ) , we suggest that TRIP13 functions after homology recognition and strand exchange , and that recombination events entering the CO repair pathway are either completed or nearly so ( because OA treated resulted in bivalent chromosomes ) . One possibility for TRIP13′s role in recombination is that it is directly involved in a step specific to resolution of NCO recombination intermediates . Another possibility is that TRIP13 is required for disassembly of NCO recombinational repair complexes [51] containing those proteins that persist abnormally on Trip13Gt/Gt pachytene chromosomes . Notably , TRIP13 has two putative ATPase domains , a signature of AAA-ATPase ClpA/B chaperones that perform protein or protein/DNA complex disassembly [52] . These potential recombination roles might not be limited to meiosis , since Trip13 is widely transcribed and the mutant animals exhibited developmental defects . Finally , TRIP13 might play an indirect role , such as providing a “licensing” signal for the resolution of NCO intermediates and completion of meiosis . Regarding the cause of cell death in Trip13 mutants , our data indicate that this is triggered by defective DSB repair rather than asynapsis . We base this conclusion on two observations: ( 1 ) oocyte elimination is dependent upon DSB formation and ( 2 ) synapsis is normal in spermatocytes of adult testes . Indeed , this mutant is unique in that recombination defects occur in the absence of asynapsis ( e . g . , as in Dmc1 knockouts ) . Thus , the Trip13 mutant provides the first evidence that unrepaired DNA damage alone can trigger the mammalian pachytene checkpoint response . Furthermore , our results allow us to conclude that oocytes and spermatocytes share a similar , if not identical , DNA damage pachytene checkpoint that is decoupled from a synapsis checkpoint . Interestingly , we found that OA treatment of Trip13Gt/Gt spermatocytes could propel them into MI , despite a report that the same did not occur when wild-type pachytene spermatocytes were treated with the DSB-inducing agents gamma radiation or etoposide [53] . It is possible that the nascent induction of DSBs in pachynema evokes a checkpoint response that cannot be bypassed by OA , whereas the post-strand invasion lesions in TRIP13-deficient spermatocytes do not . TRIP13 was originally discovered to be an interactor with rat thyroid receptor beta ( THRB; [54] ) , but the relationship between THRB and TRIP13 in meiosis is unknown . Interestingly , we observed that THRB is distributed diffusely throughout wild-type spermatocyte nuclei but is excluded from the XY ( sex ) body ( unpublished observations ) , a compartmentalized nuclear domain beginning in pachynema , in which the sex chromosomes become heterochromatinized and transcriptionally silenced in the process of MSCI [55] . However , the XY body appeared intact in most mutant spermatocytes upon probing with several markers of XY heterochromatinization ( unpublished observations ) . Considering that THRB knockout mice are viable and fertile [56] , the functional relationship between TRIP13 and its receptor THRB in meiosis is unclear . Given the high similarity of PCH2 orthologs throughout the eukaryotic world , one or more essential functions of this protein must be conserved . Since TRIP13 does not exhibit checkpoint function in mice , we surmise that the TRIP13/PCH2 ancestral protein had a function in recombination that persists to the present . Notably , A . thaliana does not appear to have a meiotic checkpoint activity that eliminates mutant meiocytes in a manner analogous to organisms such as mice , budding yeast , and female Drosophila [11 , 57] , and mammalian TRIP13 is more similar to Arabidopsis PCH2 than the fly or worm proteins ( Figures 1A and S1 ) . The unusual relatedness between mammalian and plant PCH2 may therefore be attributable to both the presence of a common conserved function ( namely recombination , although the role of PCH2 in plants has yet to be determined ) , and the absence of checkpoint function . Nevertheless , the evolutionary relationships between animals , fungi , and plants ( which are discordant with PCH2 sequence phylogeny ) do not allow parsimonious models addressing the points in time that checkpoint functions in PCH2 were gained or lost . It is possible that its checkpoint function evolved independently in worms and budding yeast . The picture will become clearer as the function of PCH2 in other organisms is elucidated . The nature of the synapsis checkpoint in male mice remains unidentified . One possible candidate is Dot1 ( PCH1 in yeast ) , a histone methyltransferase silencing factor that is required for pachytene arrest of zip1 and dmc1 mutants in yeast [58] , and for preventing RAD54-mediated recombinational DSB repair between sister chromatids . However , DOT1 acts upstream of PCH2 . Given that TRIP13 doesn't have checkpoint function in mice , a potential role for mammalian DOT1 in the pachytene checkpoint is dubious but awaits investigation . Recently , it was shown that the TRP53 homolog TRP63 is required for DNA damage–induced death of dictyate-stage primordial oocytes , leading to the suggestion that it is involved in monitoring genome integrity [59] . However , this activity occurs subsequent to a pachytene checkpoint . As alluded to earlier , a complicating problem for studying potential meiotic checkpoint genes in mice is that as in yeast , such genes often have mitotic functions ( such as RAD24 [7] ) , and their ablation can cause lethality [42] . Unless mammalian pachytene checkpoint components have orthologs with similar functions in organisms such as yeast , their identities are likely to remain elusive .
Trip13 was amplified from samples of Clontech's Mouse Multiple Tissue cDNA Panel I ( http://www . clontech . com ) , using the following primers: 5′-GCACCATTGCACTTCACATC-3′ ( TRP3-6F ) and 5′-TGACCATCAGACTGTCGAGC-3′ ( TRP3-6R ) . These primers correspond to exons 3 and 6 , respectively , and amplify a 330-bp cDNA product . The cDNAs in this panel are equalized to allow quantitative analysis by RT-PCR . The mouse embryonic stem cell line RRB047 ( strain 129/Ola ) containing a gene trap insertion in Trip13 was obtained from BayGenomics ( http://www . baygenomics . ucsf . edu/ ) . The gene-trapping vector used to create this line , pGT1lxf , was designed to create an in-frame fusion between the 5′ exons of the trapped gene and a reporter , βgeo ( a fusion of β-galactosidase and neomycin phosphotransferase II ) . The gene-trapped locus creates a fusion transcript containing exons 1–3 of Trip13 and βgeo . To identify the exact insertion site within intron 3 , PCR was performed using one primer within the gene trap vector , and the other primer at various positions in intron 3 pointing towards the 3′ end of the gene . Product from a productive reaction was sequenced , revealing that the insertion site was 445 bp into intron 3 . Three primers were used to distinguish wild-type and mutant alleles of Trip13: primer 1 , 5′-CGTCGCTCCATTGCTTTGTGC-3′; primer 2 , 5′-AGTAGTGGTACACTGTATTTTTGCTTTCATTGA-3′; and primer 3 , 5′-GTAGATCCCGGCGCTCTTACCAA-3′ . Primers 1 and 2 are located upstream and downstream , respectively , of the gene trap insertion within the intron 3 . Primer 3 corresponds to pGTlxf sequence . Primers 1 and 2 amplify a 700-bp band from a wild-type allele; primers 1 and 3 amplify a 540-bp fragment from a mutant allele . Separate reactions were used to assay the presence or absence of each amplicon from a DNA sample . The cycling conditions were: 94 °C 2 min; 35 cycles of 94 °C 30 s , 57 °C 45 s , and 72°C 50 s; and 72 °C 2 min . Total RNA was isolated from adult testes with the RNeasy Mini Kit ( Qiagen , http://www . qiagen . com ) , and 4 . 0 μg was oligo dT–primed and reverse-transcribed with Superscript II ( Stratagene , http://www . stratagene . com ) . The entire Trip13 protein-coding sequence was amplified with primers 5′-ATGGACGAGGCGGTG-3′ and 5′-TCAAACATAAGCTGAAAGTT-3′ . The cycling conditions were: 94 °C 2 min; 94 °C 30s , 55 °C 45 s , and 72 °C 80 s for 35 cycles; and 72 °C 2 min . The primers for amplifying the Med31 coding sequence as control were : 5′-ATGGCCGCGGCCGTCGCTATGG-3′ and 5′-TCATTTCCCTGCTGTGTTATTCTGCTGCTGCTGC-3′ . The cycling conditions were: 94 °C 2 min; 94 °C 30 s , 55 °C 30 s , and 72 °C 35 s for 35 cycles; and 72 °C 2 min . A peptide corresponding to amino acids 25–40 of TRIP13 , VLQRSGSTAKKEDIK , was conjugated to KLH and used to immunize chickens ( done by Sigma Genosys , http://www . sigmaaldrich . com ) . Polyclonal IgY was isolated from eggs with the Eggcellent Chicken IgY Purification kit ( Pierce , http://www . piercenet . com ) . IgY antibodies were then affinity purified using the immunizing synthetic peptide . 50 μg of testis extract in RIPA buffer was separated by 8% SDS-PAGE and electrotransferred onto a Pure Nitrocellulose membrane ( Bio-Rad , http://www . biorad . com ) . The membrane was incubated with a polyclonal rabbit anti-human TRIP13 antibody ( 18-003-42687; Genway , http://www . genwaybio . com ) . According to the manufacturer , the immunogen was a synthetic peptide embedded in sequence we deduced to correspond to exon 3 . Binding was detected by chemiluminescence ECL kit ( Pierce ) using a rabbit anti-chicken IgG horseradish peroxidase conjugate ( Pierce ) . Testes or ovaries were fixed in Bouin's , embedded in paraffin , sectioned at 6 μm , and stained by hematoxylin and eosin . Antigen retrieval for immunohistochemistry of testis sections was as described [60] . Oocyte and follicle numbers were counted as described [61] . Only follicles containing an oocyte with a clearly visible nucleus were scored . Immunolabeling of surface-spread spermatocytes and oocytes was performed as described [39 , 62] . To reach conclusions on the pattern of staining for various proteins , 30 ( unless otherwise indicated ) well-spread nuclei of particular meiotic stages were first identified under the fluorescent microscope on the basis of SYCP3 or STAG3 staining , then imaged at both appropriate wavelengths to determine the pattern of second proteins with focal patterns such as RAD51 or RPA . Unless otherwise indicated , the panels shown in the figures were the exclusive or predominant patterns seen . The exception for this approach was in the case of staining for MLH1 or MLH3 plus RAD51 ( in which case SYCP3 or STAG3 was not available to find chromosome cores ) . Nuclei in this situation were identified first by MLH1/3 foci clustering , then imaged for both fluorescent wavelengths . Primary antibodies used in this study were as follows: mouse anti-SCP3 ( 1:500; Abcam , http://www . abcam . com ) ; rabbit anti-SYCP1 ( 1:1 , 000; a gift from C . Heyting ) [63]; rabbit anti-REC8 ( 1:100; a gift from C . Heyting ) ; rabbit anti-RAD51 ( 1:250 , this polyclonal antibody recognizes both RAD51 and DMC1; Oncogene Research Products , http://www . merckbiosciences . co . uk ) ; rabbit anti-γH2AX ( 1:500; Upstate Biotechnology , http://www . upstate . com/ ) ; rabbit anti-STAG3 ( 1:1 , 000; a gift from R . Jessberger ) ; rabbit anti-MLH3 ( 1:400; a gift from P . Cohen ) ; mouse-anti-human MLH1 ( 1:50; BD Biosciences , http://www . bdbiosciences . com ) ; rabbit-anti-TopBP1 ( 1:100; a gift from J . Chen ) [22]; mouse-anti-ubiquityl-histone H2A ( 1:200; Upstate Biotechnology ) ; rabbit-anti-TRF2 ( 1:500; a gift from T . de Lange ) ; and rabbit-anti-BLM ( 1:50; a gift from R . Freire ) . All secondary antibodies conjugated with either Alexa Fluor 488 or 594 ( Molecular Probes , http://probes . invitrogen . com/ ) were used at a dilution of 1:1 , 000 . All images were taken with a 100× objective lens under immersion oil . Metaphase fixed spermatocytes from 8-mo-old Trip13RRB047 homozygotes , using 23-d-old wild-type mice as control , were prepared and stained with Giemsa as described [64] . For OA treatment , cells were exposed to 5 μM OA ( Calbiochem , http://www . emdbiosciences . com ) for 6 h at 32 °C in a humidified environment of 5% CO2 before spreading [65] . These preparations were stained with DAPI to visualize metaphase nuclei and chromosomes . TRIP13 orthologs were identified by BLASTP searches of Genbank and other sources providing gene models such as Ensembl . The selected orthologs can be found in Table S1 . Amino acid alignments were done with Clustal W , using the default settings with and without removing the regions outside of the AAA-ATPase central domain . The trees were constructed by using the neighbor-joining method with Poisson correction . The reliability of internal branches was assessed by using 500 bootstrap replicates , and sites with gaps were ignored in this analysis . Neighbor-joining searches were conducted by using the computer program MEGA3 [66] . | It is critical that the chromosomes carried by sperm and eggs contain faithful representations of the genome of the individual that produced them . During the process of meiosis , the maternal and paternal copies of each chromosome “synapse” with each other ( become tightly associated ) , exchange genetic material via the process of recombination , then separate into daughter cells in the first of two meiotic cell divisions . The intricate chromosome behavior is subject to errors , so most organisms have evolved meiotic “checkpoints” that monitor fidelity of chromosome synapsis and repair of DNA damage . These checkpoints cause defective cells to self destruct rather than generate defective sperm or eggs . We studied the effects of deleting mouse Trip13 , a gene that in distant organisms plays a key role in meiotic checkpoint control . These experiments revealed that instead of having a checkpoint role , Trip13 is required for one of the two major classes of recombination in meiosis that is required for repairing broken DNA molecules . The chromosomes still synapsed normally , but animals were sterile due to massive death of oocytes and spermatocytes . These results indicate that , in addition to a checkpoint that responds to failed synapsis , one exists to specifically detect unrepaired DNA damage that is due to failed recombination . | [
"Abstract",
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] | 2007 | Mouse Pachytene Checkpoint 2 (Trip13) Is Required for Completing Meiotic Recombination but Not Synapsis |
DNA double-strand breaks ( DSBs ) are potent sources of genome instability . While there is considerable genetic and molecular information about the disposition of direct DSBs and breaks that arise during replication , relatively little is known about DSBs derived during processing of single-strand lesions , especially for the case of single-strand breaks ( SSBs ) with 3′-blocked termini generated in vivo . Using our recently developed assay for detecting end-processing at random DSBs in budding yeast , we show that single-strand lesions produced by the alkylating agent methyl methanesulfonate ( MMS ) can generate DSBs in G2-arrested cells , i . e . , S-phase independent . These derived DSBs were observed in apn1/2 endonuclease mutants and resulted from aborted base excision repair leading to 3′ blocked single-strand breaks following the creation of abasic ( AP ) sites . DSB formation was reduced by additional mutations that affect processing of AP sites including ntg1 , ntg2 , and , unexpectedly , ogg1 , or by a lack of AP sites due to deletion of the MAG1 glycosylase gene . Similar to direct DSBs , the derived DSBs were subject to MRX ( Mre11 , Rad50 , Xrs2 ) -determined resection and relied upon the recombinational repair genes RAD51 , RAD52 , as well as on the MCD1 cohesin gene , for repair . In addition , we identified a novel DNA intermediate , detected as slow-moving chromosomal DNA ( SMD ) in pulsed field electrophoresis gels shortly after MMS exposure in apn1/2 cells . The SMD requires nicked AP sites , but is independent of resection/recombination processes , suggesting that it is a novel structure generated during processing of 3′-blocked SSBs . Collectively , this study provides new insights into the potential consequences of alkylation base damage in vivo , including creation of novel structures as well as generation and repair of DSBs in nonreplicating cells .
DNA double-strand breaks ( DSBs ) are important sources of genome instability , giving rise to chromosomal aberrations and severe biological consequences including tumorigenesis and cell death [1] , [2] . We and others also showed that regions adjacent to DSBs are prone to mutagenesis through a variety of mechanisms [3]–[7] . DSBs can be induced directly by exposure to DNA-damaging agents such as ionizing radiation ( IR ) and radiomimetic chemicals . While there is a great deal of information about direct DSBs , little is known about the contribution of single-strand lesions to the production of DSBs , although single-strand lesions are generally accepted to be a source of DSBs via replication fork collapse in regions of single-strand DNA [8] . A common single-strand lesion that is generated during normal cell metabolism and repair is an apurinic/apyrimidic ( AP ) site , one of the most abundant DNA lesions in the cell [9] , [10] . As many as 10 , 000–200 , 000 single-strand lesions appear each day in mammalian cells [11] , [12] . Most of these are subject to base excision repair ( BER ) , a highly coordinated process initiated by a lesion-specific glycosylase removing damaged bases and forming AP sites . Removal of AP sites by AP endonucleases or AP lyases involves the generation of single-strand breaks ( SSBs ) with blocking groups at their 3′ or 5′-ends that cannot be joined by DNA ligases [13]–[15] . Subsequent SSB end-processing involves a diverse set of enzymes/pathways to deal with the termini [16] . Single strand lesions which are produced by many mutagens are also potential sources of DSBs if they are processed to form closely-opposed SSBs . Closely-opposed SSBs could result in derived DSBs simply through loss of pairing of short DNA duplex regions bounded by the SSBs , as shown by in vitro analysis [17]–[19] and a limited number of in vivo studies [20] , [21] . A DSB could also be generated if two more distant SSBs are processed to form closely-opposed SSBs . This second category of derived DSBs have been proposed following induction of methyl methanesulfonate ( MMS ) lesions and subsequent processing of AP sites to 5′-blocked SSB termini in rad27/FEN1 and pol32 mutants [20] . Removal of these 5′-blocked SSB ends involves DNA synthesis and strand displacement that can move distant SSBs closer [20] , [22] . However , there is little information about in vivo generation of derived DSBs from nearby opposed SSBs with 3′-blocked termini . Such termini are a challenge to the repair machinery since they must be removed to enable repair synthesis at 3′-OH ends [23] , [24] . Besides being formed directly from sugar damage , SSBs with 3′-blocked termini , the α , β-unsaturated aldehyde ( 3′-dRP ) , can be generated during incision at the 3′-side of AP sites by AP lyase [14] . In the budding yeast Saccharomyces cerevisiae , the AP endonucleases Apn1 and Apn2 , which have 3′-phosphodiesterase activity , are responsible for removing most 3′-dRP ends as well as other blocking groups [25]–[27] . Previously , we found that deletion of both AP endonucleases appears to lead to accumulation of chromosome breaks in nongrowing G1 haploid yeast [28] and the number of chromosome breaks increased with time of liquid-holding in buffer . However , it was possible that the DSBs were not formed in vivo but actually appeared during subsequent pulsed-field gel electrophoresis ( PFGE ) processing . Therefore , while those findings highlighted the potential for single-strand damage to generate DSBs , they did not definitively show that the derived DSBs were generated in vivo or that they could be generated later in the cell cycle . Importantly , there was no evidence of repair of the DSBs , which is not surprising since the haploid cells were in the G1 phase of the cell cycle when there would be no recombinational partner . Our previous study also showed that non-homologues recombination ( NHEJ ) has little if any role in dealing with the derived “DSBs” caused by MMS in G1 cells based on deletion of yku70 , especially for the apn1/2 mutant which accumulated DSBs even though it has the wild type NHEJ machinery [28] . Although there is abundant genetic evidence for homologous recombinational ( HR ) repair proteins dealing with MMS damage-induced lesions in a variety of systems , there has been no direct demonstration of MMS-induced DSBs being formed or subsequent repair in vivo . It is generally assumed , though not proven , that recombinational repair deals with DSBs generated during replication fork collapse following induction of MMS lesions . Here , we demonstrate MMS can generate derived DSBs within G2/M arrested cells and that these DSBs are processed and undergo repair . Utilizing our recently developed PFGE assay [29] , we establish that MMS-derived DSB ends are subject to resection , one of the earliest steps in DSB repair . In addition we identify a novel repair intermediate detected as slow mobility chromosomal DNA during PFGE , providing additional insights into the processing of 3′-blocked groups in vivo .
We previously described a system using PFGE for analyzing in vivo repair of alkylation base damage caused by MMS [28] in yeast that is based on detection of chromosome breaks . Though MMS does not cause SSBs directly [28] , [30] , they can arise as repair intermediates during BER . If the SSBs are closely-spaced on complementary DNA strands , they are detected as “DSBs” with PFGE . Most of the closely-opposed single strand lesions were shown to be efficiently repaired in stationary G1 haploid wild type cells by BER [28] , thereby preventing the formation of derived DSBs in vivo . We now extend this system to a characterization of derived DSBs in G2 cells where there is the opportunity for recombinational repair between sister chromatids . Haploid yeast were grown to log phase in rich medium ( YPDA ) , arrested in G2/M with the microtubule and mitotic spindle disrupter drug nocodazole and treated with 0 . 1% MMS ( 11 . 8 mM ) for 15 min in PBS . They were subsequently incubated in YPDA+nocodazole to prevent G2 cells from progression into the next cell cycle stage . Changes in chromosomes at various times after treatment were determined using PFGE . The treatment of WT cells with MMS did not cause fragmentation of chromosomes ( which range in size from ∼200 kb to ∼2 . 5 Mb; Figure 1 ) , suggesting that there is efficient repair of single strand damage and , therefore , no apparent generation of DSBs . There was also no apparent reduction in survival ( Figure 2 ) . However , MMS treatment of apn1 apn2 ( apn1/2 ) cells led to loss of all but the smaller chromosome bands ( Chr I , 230 kb and Chr VI , 270 kb ) as well as decreased survival ( Figure 2 ) . At later times , there is “restitution” of the broken chromosomes ( i . e . , formation of full size chromosomes ) as shown in the ethidium bromide stained gel ( indicated in Figure 1 ) , and the survival is somewhat higher for MMS-treated apn1/2 after 8 hour G2/M holding . Surprisingly , there was also a rapid accumulation of slow-moving DNA ( SMD ) that appeared below the well . These results differ from those with MMS-treated stationary arrested G1 apn1/2 cells which did not give rise to SMD although chromosome breakage was detected by PFGE [28] . The amount of SMD decreased after 4 hours , at which time restituted chromosomes were detected . The mechanism ( s ) of induction and disappearance of DSBs and SMD , as well as possible relationship , is investigated below . In yeast , the first step in the BER of MMS-induced lesions requires Mag1 glycosylase which removes damaged bases and forms abasic sites [31] . To confirm that the DSBs as well as SMD resulted from BER , the MAG1 gene was deleted in the apn1/2 background . As shown in Figure 3 , the appearance of DSBs and SMD requires at least the first step in BER in G2 arrested cells since there was no apparent change in chromosomes and no SMD formation in the triple apn1/2 mag1 mutants following MMS treatment . Although methylated bases can be spontaneously depurinated to form AP sites , the impact of this process to DSB formation is limited as indicated by the limited appearance of DSBs in the apn1/2 mag1 mutant ( Figure S3 ) . The formation of AP sites and subsequent DSBs were also prevented in the triple mutant arrested as G1 stationary cells [28] . The absence of Mag1 also resulted in a considerable increase in toleration of MMS damage ( Figure 2 ) . We examined further the role of AP sites by including methoxyamine ( MX ) during the MMS treatment and subsequent incubation of the apn1/2 . Methoxyamine covalently binds to AP sites , preventing subsequent BER processing [32] . The MX results shown in Figure 3 were similar to those observed with the mag1 apn1/2 mutant . Thus , the appearance of DSBs and SMD in the G2/M cells lacking the Apn1 and Apn2 endonucleases , along with increased MMS hypersensitivity , requires the generation of AP sites by BER and/or repair events downstream of AP sites . To further address how DSBs are generated by MMS in the apn1/2 mutant and their repair in G2/M cells as well as the mechanism of SMD appearance and loss , we first investigated whether HR has a role in these processes . Deletions of key genes involved in HR including RAD50 , -51 , 52 , -54 and MRE11 were generated in the apn1/2 background . While HR mutants are MMS sensitive even in an APN+ background [33] , they do not affect the appearance of MMS-induced chromosomal damage or repair in G1 stationary cells as compared to wild type cells [28] because of efficient BER . The reports of MMS sensitivity of HR mutants are likely due to small number of lesions that remain unrepaired when cells pass into S-phase [34] . Nothing is known about the induction and repair of MMS derived DSBs in G2 cells where there are opportunities for recombinational repair between sister chromatids . Similar to results in G1 cells , there appeared to be little or no induction of derived DSBs when rad52 APN+ cells were treated in the G2/M phase of the cell cycle ( Figure S1 ) . However , efficient restitution of full-length chromosomes in the apn1/2 cells treated with MMS in G2/M does require components of the recombinational repair pathway as shown in Figure 4A ( rad52 and rad51 ) , Figure 5A ( mre11 and rad50 ) and Figure 6A ( mre11 and rad54 ) . The small amount of chromosome restitution in some of the mutants might be due to some sort of microhomology mediated end-joining given the RAD51 independence and RAD52 dependence . Thus , in contrast to the situation in G1 cells , where there are no opportunities for recombination , derived DSBs created in G2 cells by MMS can undergo recombinational repair between sister chromatids ( also addressed below using an mcd1 cohesin mutant ) . The gain and loss of SMD in the apn1/2 mutant appeared to parallel the timing of chromosome breakage and restitution , as shown in Figure 4A . However , SMD formation is not related to recombination since additional deletion of RAD52 , RAD51 ( Figure 4A ) , RAD50 , MRE11 ( ( Figure 5A ) , or RAD54 ( Figure 6A ) did not significantly alter the appearance of SMD . To compare the levels of SMD in response to MMS and between strains , we determined the ratios between amount of material in the SMD region to DNA in the small chromosomes that experienced little breakage . As shown at the bottom of panel A in each figure ( “SMD/Chr I+VI” ) , the ratios were similar between the apn1/2 and the various triple mutants over the first hour of treatment . In all cases , there was considerable reduction in SMD by 4 hours . The decreased amount of SMD in rad52 or mre11 ( in Figure 4A , Figure 5A ) could be due to delays in S-phase arising from repair defects which would lead to an overall reduction in DNA entering into the gel since the mutants have a somewhat reduced growth rate . We also investigated the 5′ to 3′ exonuclease I ( EXO1 ) since it can resect at random DSBs and appears to enlarge single-strand gaps during nucleotide excision repair [35] that could lead to reduced chromosomal DNA mobility during PFGE . As shown in the Figure S2 , exonuclease 1 does not influence either the repair of the MMS induced DSBs or the appearance of SMD . The role of HR in repair of the MMS-derived DSBs and the lack of contribution to the appearance and loss of SMD is investigated further in Figure 6B . Using a LEU2 probe for Chr II+III , it is clear that nearly all the DNA of Chr II appears as SMD within 30 minutes after treatment . ( This probe , which identifies Chr II , also hybridizes with circular and broken Chr III molecules , as described in Ma et al . [28] and is discussed below . ) The amount of SMD started to decrease at 2 hours after MMS exposure and a significant amount of SMD is lost by 4 hours , where at the same time there is a reappearance of full size Chr II . However , there is substantial restitution of Chr II starting at 4 hours in the apn1/2 mutant , but not in the mre11 and rad54 derivatives . Thus , while the appearance and loss of SMD are not influenced by HR , based on the Southern blotting results with the apn1/2 mre11 and apn1/2 rad54 , the reappearance of full-size Chr II requires HR . While a role for recombinational repair of MMS-associated DSBs has been proposed for S-phase cells and demonstrated above for G2/M cells , there has been no direct demonstration of DSB processing or generation of recombinants . This is due in part to the difficulties of characterizing events associated with random DSBs ( discussed in [29] ) . In addition , opportunities to examine MMS-induced events in S-phase cells using PFGE are limited because of the structures created in the replicating DNA which results in most of the DNA being retained in the starting wells [36] . Recently , we described an assay involving PFGE and circular chromosomes for characterizing resection and recombination at random DSBs [29] . Since a single DSB in a circular chromosome results in a unit length linear molecule , the direct or derived induction of random DSBs can be followed by the appearance of the corresponding band with PFGE ( as described in [28] , [29] , [37] ) . Importantly , resection at the DSB ends leading to the generation of single-strand tails could be detected by reduced mobility of the unit length molecules ( i . e . , “PFGE-shift” ) . Previously , we showed that MMS treatment of apn1/2 stationary G1 cells led to the linearization of the circular Chr III . However these “DSBs” could have arisen from molecules with closely spaced-SSBs during preparation of chromosomal material for PFGE analysis . The resection in the G2 cells , as well as subsequent repair , establishes that MMS-induced DSBs actually occur in vivo in G2 cells . Similar to the results with the G1 stationary cells , MMS treatment of apn1/2 cells resulted in the rapid appearance of linear Chr III molecules in Southern blots using a probe specific to this chromosome ( Figure 4B and Figure 5B ) . However , unlike observations with apn1/2 cells treated in G1 [28] , the linearized molecules from the G2/M cells exhibited the PFGE-shift similar to that found previously for direct DSBs induced by IR [29] , suggesting that the MMS derived DSBs are subject to resection . The PFGE shift which appeared by 1 hour after treatment was also found in the apn1/2 rad52 and rad51 triple mutants ( Figure 4B ) . The bulk of PFGE-shift required MRX ( Mre11 , Rad50 , Xrs2 ) since there was no apparent resection with the apn1/2 rad50 or mre11 mutants ( Figure 5 and Figure 6 ) . The PFGE profiles of DSBs induced by MMS in the triple mutants of apn1/2 combined with deletions of the recombinational repair genes were similar to patterns found for rad52 , rad51 , rad50 and mre11 single mutants exposed to IR [29] . Thus , the MMS derived DSBs are subject to resection; MRX plays an important role , presumably through initiation of 5′ to 3′ resection . The processing of DSBs induced by IR appears different from that found for MMS-derived DSBs in that the PFGE shifted band is smeared as compared to a narrow shifted band for IR damage ( e . g . , the rad52 mutant; [29] ) . The smearing of the band after MMS treatment might be due to the formation of single-stranded tails with variable lengths or the timing of DSB formation and resection ( Figure 4B ) . Similar to our findings with radiation-induced direct DSBs [29] , the MMS treatment of apn1 , 2 cells leads to the appearance of linear Chr III molecules at 2 to 4 hours post-treatment that are twice the size of the broken Chr III ( Southern blots in Figure 4B and Figure 5B ) . Based on our previous studies [29] , these dimers are likely the product of recombination between full size Chr III sister chromatids ( discussed in [29] ) . The role of recombination is supported by the observed dependence on Rad51 and Rad52 ( Figure 4A ) . Overall these results provide the first direct physical evidence of i ) MMS lesions being processed to DSBs in G2 cells , ii ) resection of the ends , and iii ) MMS generation of recombinant molecules . In the absence of the Apn1 and Apn 2 endonucleases , AP sites can be nicked at the 3′ side by the bifunctional DNA N-glycosylases/AP lyases Ntg1 and Ntg2 that convert AP sites into 3′-blocked SSBs [13] . Additionally , the bifunctional 8-oxyguanine glycosylase Ogg1 can nick an AP site that is opposite a cytosine [38] . Mutants of these genes were created in the apn1/2 background to identify possible contributors to the appearance of DSBs and SMD . As shown in Figure 7 , the apn1/2 ntg1/2 quadruple mutant also exhibited appearance and loss of SMD . However , there was less chromosome breakage and nearly full chromosome restitution by 4 hours , as compared to the incomplete chromosome restitution in the apn1/2 , even after 8 hours . The additional ntg1/2 mutations increased the time required for maximal appearance of SMD and there is less material lost in the full-size chromosome bands , consistent with a decreased likelihood of single-strand break generation . The increased resistance of apn1/2 ntg1/2 compared to apn1/2 cells suggests that DSBs rather than SMD are the major contributor to loss of survival with or without arrest in G2 following MMS treatment ( Figure 2; the dose-modifying factor is ∼3 ) . The combination of apn1/2 ntg1/2 does not totally block formation of DSBs . While there appears to be less processing of ends , recombinants can still be formed based on the formation of Chr III dimers ( Figure 7B ) . A similar finding of dimer generation , independent of resection , was reported for MRX mutants following IR [29] . Further deletion of OGG1 ( i . e . , apn1/2 ntg1/2 ogg1 ) greatly reduced the appearance of SMD formation and decreased DSB induction ( Figure 7 ) . Survival was also improved compared to the apn1/2 ntg1/2 mutant ( Figure 2 ) . Thus , SSBs generated at AP sites are a likely source of SMD consistent with the above findings with the apn1/2 mag1 mutants as well as the effect of MX ( Figure 3 ) where AP sites are prevented or blocked . Furthermore , these results demonstrate a role for OGG1 in the general processing of methylated base damage or imply that MMS causes additional types of damage that are substrates for Ogg1 . The reduced amount of overall chromosome breakage observed with the ethidium bromide stained gel ( Figure 7A ) is expected if there are less SSBs to generate derived DSBs . Surprisingly , some linearized Chr III molecules and dimers were generated , based on Southern blotting with a probe specific to Chr III , even though there is no resection . Possibly they were formed through additional mechanisms for processing abasic sites and/or in vitro during PFGE of DNAs with opposed SSBs that are sufficiently close . While the results with the various RAD mutants demonstrate that neither the appearance nor the disappearance of SMD is dependent on HR , there is still the possibility of SMD arising through unknown interactions with sister chromatids . To address this , a temperature-sensitive cohesin mutant mcd1-1 was generated in both a WT and apn1/2 background . Cohesin is required to hold sister chromatids together and is essential for efficient repair of radiation induced DSBs [39]–[41] . The mcd1-1 single mutant grows well at permissive temperature of 23°C but is not viable at 37°C [42] , consistent with our observation of no growth of the apn1/2 mcd1-1 triple mutant at the elevated temperature . In preliminary experiments we found that apn1/2 cells exhibited less repair of MMS lesions at 37°C . This may be due to more closely-opposed lesions being converted into DSBs , since methylated bases are heat-labile . Therefore , cells were incubated at 37°C for 3 hours to inactivate the temperature-sensitive cohesin; during this period cells were arrested in G2/M by nocodazole . Cells were then shifted to the semi-permissive temperature of 30°C for MMS treatment as well as post-MMS incubation in YPDA with nocodazole . As shown in Figure 8 , MMS did not lead to the appearance of DSBs or loss of chromosomes in the mcd1-1 single mutant , similar to results with wild type cells ( Figure 1 ) . As expected , derived DSBs were detected in the apn1/2 mcd1-1 triple mutant . However , unlike observations with the apn1/2 mutant ( Figure 1 ) there was much less overall restitution of chromosomes at 4 and 8 hours , more similar to the apn1/2 rad51 mutant ( Figure 4 ) . Yet , the formation and disappearance of SMD was not affected . Most chromosome bands , especially the larger chromosomes , were lost from the PFGE gels in the apn1/2 mcd1-1 triple mutant but not in the mcd1-1 single mutant ( Figure 8 ) , which is comparable to results with other HR mutants . These findings suggest that cohesin is not required during SMD formation and that SMD is not a consequence of sister chromatid interaction . As described above , the generation of SSBs at abasic sites is required for the generation of SMD . However , the SMD molecules do not rely on sister chromatid interactions , recombination or DSB ends , based on a lack of SMD at later times in the HR mutants ( Figure 4 , Figure 5 , and Figure 6 ) . Examination of several of the ethidium bromide gels , reveal that SMD is dependent on chromosome size ( see , for example , Figure 1 and Figure 4A ) . In several of the experiments there is little disappearance of the smallest chromosomes ( I and VI ) . This is confirmed in Figure 6 where Chr II ( 800 kb ) and linearized Chr III can be detected with a common probe . Nearly all the larger Chr II molecules appear in SMD , while there is little change in the amount of the smaller Chr III . This is further substantiated in Figure 4 and Figure 5 , where only Chr III is probed and there is little , if any SMD . With a decrease in SSBs , there is less SMD with only the large chromosomes being affected ( Figure 7 ) . Thus , the SSB related lesion ( s ) or combination of lesions leading to SMD appear to be less than an average of 1 per few hundred kb . Given the retardation of much of the chromosomal DNA in PFGE , we investigated various enzymes that recognize structural changes in DNA to help discern the nature of SMD . ( We also considered proteins bound to DNA that could give rise to SMD; however , we found that extending the proteinase K treatment beyond that normally used in preparation of plugs for PFGE did not change the SMD as noted in “Material and Methods” . ) Mung bean nuclease had been used to demonstrate resected DNA at radiation induced DSB ends [29] . However , for the DNA obtained after MMS treatment , there was general degradation by this nuclease of the chromosomal DNA treated in plugs before PFGE . This extensive digestion is likely due to this nuclease acting at SSBs and possibly gap-like structures . We also investigated bacteriophage T7 endonuclease I because of its ability to recognize and cleave at a variety of structures including DNA mismatches , nicks as well as branch molecules such as Holliday type junctions [43]–[45] . As shown in Figure 9 , endonuclease I treatment of the chromosomal DNAs following MMS treatment of apn1/2 cells eliminated much of the SMD leading to the appearance of small DNA fragments ( ∼50 to 100 kb ) . This enzyme specifically acted on SMD since there was little effect on the chromosomal DNA from untreated cells or the chromosomal DNA following 4 hour to 8 hour of repair . The cutting of SMD into small molecules by T7 endonuclease I ( i . e . , smaller than the Chr I and Chr VI which exhibit little SMD ) suggests the presence of few endonuclease responsive substrate structures in the smaller chromosomes . Interestingly , the use of T7 endonuclease I to remove the SMD enabled us to establish further that repair of derived DSBs does occur between 2 and 4 hours ( as previously suggested in Figure 4 , Figure 5 , Figure 6 ) . The SMD structures sensitive to T7 endonuclease I are unlikely to be due to recombinational intermediates because SMD was observed in the various HR mutants as described above . While they could be related to branched molecules produced from persisting nicks , other possibilities exist given the various types of structures susceptible to this endonuclease .
BER is critical for dealing with a variety of single strand lesions . Many enzymes in this pathway are conserved from microorganisms to humans and serve as antimutators , especially in terms of tumor suppression and preventing hereditary neurodegenerative disease [46] , [47] . Aberrant BER processes might result in the eventual appearance of DSBs , which are a major source of genome instability . MMS-induced lesions are considered a source of DSBs as a result of collapsed replication forks at the lesions or processed intermediates . Based on genetic evidence , these replication-associated DSBs have been considered to be repaired by HR mechanisms . Previously , we showed that MMS-induced single-strand damage in G1 arrested cells had the potential for generating derived DSBs and highlighted the role that Rad27 and Pol32 play in preventing such breaks [20] . We had concluded that closely-spaced opposing lesions could be a source of the derived DSBs and that well-coordinated BER assures prevention of these downstream DSBs . The present study using G2/M cells is the first to characterize directly the generation , processing and repair of derived DSBs following treatment by an alkylating agent . While two closely-opposed SSBs with 5′-blocked termini could be “moved closer” to form a DSB during repair-associated DNA synthesis and strand displacement [17] , [18] , [19] , [20] , [21] , this is not expected to be the case for SSBs with 3′-blocked termini since they would not support DNA synthesis directly . The present results are consistent with derived DSBs resulting from generation of SSBs at closely opposed lesions . It is also possible that derived DSBs could arise through processing of more distant SSBs with 3′-blocked ends in cells lacking AP endonucleases , in essence the breaks are “moved closer , ” as discussed below . Furthermore , we have described a novel repair intermediate , SMD , which can be generated if abasic sites are nicked by AP lyases . While the APE1 gene , which codes for the major mammalian AP endonuclease ( the APE1 homologue APE2 has only weak endonuclease activity , and its role in human BER is not clear ) , is essential for human cell survival and results in embryonic lethality when knocked out in mouse [48]–[51] , yeast can survive the deletion of both AP endonucleases with almost no growth defect . It is , therefore , possible to study alternative mechanisms/pathways that deal with AP sites and 3′-blocked SSBs in vivo and their role in generating DSBs . Earlier studies had demonstrated that MMS does not cause DSBs directly [28] , [30] . With an assay that can specifically monitor the processing of closely-opposed single strand lesions , our previous study showed that PFGE-detected DSBs were accumulated in G1 apn1/2 haploid yeast after MMS damage . However , since closely-opposed SSBs might lead to chromosome DNA breakage during in vitro handling , the extent to which the PFGE-detectable DSBs were actually formed in vivo remained a question . Here , we confirm that DSBs do appear after MMS treatment of G2 cells lacking AP endonucleases , as demonstrated by i ) resection , ii ) a requirement for HR components to reconstitute chromosomes , and by iii ) the formation of Chr III dimers . While resection is generally considered essential for DSB repair mechanisms [52] , [53] , we have demonstrated that it also occurs at the MMS-derived DSBs and like radiation-induced DSBs they are subject to MRX control . As in the case of randomly generated radiation-induced DSBs [29] , we aimed to determine other factors affecting resection at the MMS-derived DSBs , especially factors that may lead to increased resection . We have recently shown that UV as well as MMS damage to single-strand DNA formed at site-specific DSBs cause high level of mutagenesis [4] , [5] . Increasing resection at MMS derived breaks could further enhance its mutagenic potential . The current results further confirm that DSBs can be derived from AP sites arising during BER , since the appearance of DSBs could be blocked either at the step in which methylated bases are removed or if cleavage of AP sites is prevented by MX ( Figure 3 ) . It is clear that DSBs were generated by the bifunctional glycosylases because deletion of NTG1 , NTG2 and OGG1 along with APN1 and APN2 blocked the formation of DSBs as well as resection . The targets of these enzymes are limited to AP sites instead of methylated bases based on efficient DSB inhibition following deletion of MAG1 ( Figure 3 ) . Though OGG1 is known to deal primarily with oxidative damage [14] , [54] , we have shown that this bifunctional glycosylase provides a backup for cleavage at AP sites following induction of MMS damage since derived DSBs that appeared in the apn1/2 ntg1/2 mutant were prevented by a further ogg1 mutation ( Figure 7 ) . This is the first direct demonstration for the Ogg1 glycosylase dealing with lesions other than oxidative damage in vivo , suggesting a potentially more general role for this gene in repair . Considering that the predominant lesions induced by MMS are N7-methylguanine and N3-methyladenine [55] , the function of Ogg1 in the development of DSBs and SMD is likely due to its action on AP sites derived from N7-methylguanine . There was still a small amount of DSBs after removal of all the bifunctional glycosylases/lyases ( Figure 7 ) which might be due to NER or some other enzymes . It was shown that DNA Topoisomerase I ( Top1 ) forms DNA-protein adducts with nicked and gapped DNA structures [56] , [57] . Possibly the AP sites could also be processed by yeast topoisomerases to generate DSBs . This might explain the small amount of SMD presented in apn1/2 ntg1/2 ogg1 mutants ( Figure 7 ) . As summarized in Figure 10 , the generation of derived DSBs would require that opposed AP sites either be sufficiently close ( left side of figure ) so that DSBs are created directly in vivo or there is a nick-processing mechanism that “moves” the relatively distant opposing-nicks closer ( central part of figure ) to form a DSB . Considering that MMS is an SN2 type of alkylating agent that methylates DNA bases in a random manner with a limited ability to produce closely-spaced lesions under the conditions used in this study ( in contrast to ionizing radiation [58] ) , many of the MMS-derived DSBs might be generated from distant single-strand breaks during processing/repair of the end-blocking groups as also suggested from our previous study with rad27 and pol32 mutants [20] . Since AP lyases generate blocked 3′-ends ( 3′-dRP ) while repair of either SSBs or DSBs requires an unblocked 3′-OH end for repair synthesis or ligation , we suggest that both the formation of DSBs and SMD are related to the processing of 3′-blocking groups . Either or both might be generated through development of 3′-flaps , possibly by helicases or nucleases . For example , opposing SSBs could be “moved” together to form a DSB if 3′-flaps are generated toward each other . Possibly it is the generation of multiple flaps that leads to the reduced mobility of large DNAs on PFGE , and the SMD molecules; however , the reduction in mobility is not as great as observed with replicating chromosomes , which remain in the well during PFGE . Although exonuclease 1 generated gaps at UV-damage sites can lead to reduced mobility , they are unlikely to be the source of SMD in the present experiments , While we have shown that the DSBs and SMD arise from a common BER intermediate , their subsequent appearance and disappearance are genetically separable . Importantly , we have established that SMD does not involve the HR pathway . The derived DSBs are subject to processing by MRX and the subsequent DSB repair as well as the appearance of dimer recombination products requires HR . Regardless , there are limitations on the appearance of SMD . The loss of chromosomal DNA along with the appearance of the wide band of SMD following MMS treatment of apn1/2 cells is dependent on the size of the chromosome . SMD was substantially greater for larger chromosomes than smaller ones ( Figure 6 ) . This is clearly shown in a Southern blot comparison of linearized Chr III with Chr II and in comparisons of the 230 kb Chr I and 270 kb VI with the larger chromosomes where there was little if any loss of the smaller chromosome bands ( Figure 6 ) . Based on our previous results [28] , we anticipate ∼0 . 4 SSBs/kb which would lead to considerable damage in even the smallest chromosomes ( ∼100 SSBs/Chr I ) . Thus , while SMD requires the generation of SSBs , other factors determine its appearance . Possibly , the appearance of SMD depends simply on the likelihood of producing some minimum amount of lesions or certain types of structures ( i . e . , sensitive to T7-endonuclease ) that are stable in vitro . The requirement for generation of a 3′-flap to remove 3′-blocked termini had been proposed previously to explain the synthetic lethality between apn1/apn2 and rad1 or rad10 [25] , [59] . Although in vitro studies demonstrated that a 3′-flap can be removed by Rad1/Rad10 proteins [60] , direct evidence for flap removal in vivo has been lacking . The observation of SMD in our current study fits well with this hypothesis though the actual mechanism for its formation and release might be more complex than previously proposed . It is interesting that while we have eliminated SMD as a recombination product , it is sensitive to the T7 endonuclease I which can cleave structures that might arise during recombination as well as branched molecules containing single strand regions ( possibly as a result of 3′-flap formation as proposed in Figure 10 ) . In conclusion , our study identifies new mechanisms for processing abasic sites and provides the first direct demonstration in nonreplicating G2/M cells of MMS-derived DSBs and that the DSBs are subject to recombinational repair . In addition , we identify and characterize the generation of SMD . While not previously described , possibly because of the techniques used to assess DNA damage and repair , SMD might be a general repair intermediate for various types of DNA damage , a view that we are currently pursuing . Interestingly , there has been an indication , though not directly addressed , of SMD-like material in exonuclease 1 defective yeast cells during excision repair of UV damage [35] . The combination of genetics and systems for detection of novel structures has provided a unique opportunity to address processed events at intermediates in repair of DNA lesions . While the derivation and repair of derived DSBs has been addressed as well as the generation of SMD , it will be interesting to determine the specific nature of the actual DNA changes that lead to SMD and the eventual resolution including the genetic controls . To our knowledge , this is the first report of a novel branched repair intermediate being generated during the processing of 3′-blocked termini . These findings are expected to expand our understanding of mechanism for repair of 3′-blocked ends as well as their impact on genome stability .
All strains are haploid derivatives of two isogenic haploid yeast strains MWJ49 and MWJ50 ( MATα leu2-3 , 112 ade5-1 his7-2 ura3D trp1-289 ) which contain a circularized chromosome III and has the construct lys2::Alu-DIR-LEU2-lys2D5′ on Chr II [28] . The construction of strains with circular Chr III was described in [28] . Deletion strains of apn1 , apn2 , rad50 , rad51 , rad52 , mre11 , mag1 , ntg1 , ntg2 , ogg1 and derived multiple mutants were created by replacement of the relevant open reading frame with selectable markers by PCR [61] . Temperature-sensitive mutants of mcd1-1 in wild type or apn1/2 background were generated using plasmid pVG257 [41] . Experiments were done at 30°C , unless specifically stated at a different temperatue . The generation of G2 arrested cells was described in [62] . Briefly , logarithmically growing cells in YPDA medium ( 1% yeast extract , 2% Bacto-Peptone , 2% dextrose , 60 mg/ml adenine sulfate ) were incubated with nocodazole at a final concentration of 15 µg/mL . After 3 hours , most cells are arrested in G2/M as determined microscopically by the presence of large budded cells and verification using flow cytometry . Cells were then harvested by centrifugation , washed and resuspended in phosphate-buffered saline ( PBS , 10 mM phosphate , 0 . 138 M NaCl; 0 . 0027 M KCl , pH 7 . 4 ) . MMS treatment was performed as described in [28] with modification . Cells in PBS were incubated with 11 . 8 mM ( 0 . 1% ) MMS for 15 or 30 min at 30°C with vigorous shaking , and then neutralized by mixing 1∶1 ( v/v ) ratio with 10% Na2S2O3 . After washing with dH20 , a portion of the MMS-treated cells was immediately resuspended in ice-cold cell suspension buffer ( 10 mM Tris ( pH 8 . 0 ) , 100 mM EDTA ) to prepare DNA-agarose plug for pulsed-field gel electrophoresis ( PFGE ) as described below . Other portions of the MMS-treated and control cells were resuspended in YPDA media containing nocodazole and incubated at 30°C with constant shaking . Cells were collected up to 16 hours after MMS treatment , centrifuged , wash with dH20 and resuspended in cell suspension buffer for PFGE DNA-agarose plug preparation . Nocodazole-arrested G2 cells were first incubated with MX ( final concentration 100 mM ) in YPDA for 15–30 min to first allow MX diffuse into cells . Then MMS treatment and post-treatment incubation were as described above with MX ( final concentration 100 mM ) present during the whole procedure . Cells were then collected at various times for plug preparation and PFGE analysis . Detection of DSBs and repair intermediates ( such as resected DNA molecule ) were based on PFGE analysis as described [28] . PFGE was performed using a Bio-Rad CHEF-Mapper XA system ( Bio-Rad , Hercules , CA ) . Preparation of agarose-embedded DNA ( DNA plug ) was described in [28] . Briefly , control and MMS-treated cells collected at different times following MMS treatment were embedded in 0 . 6% agarose with 1 mg/ml Zymolyase ( 100 U/mg , MP Biochemicals , Solon , OH ) . The plug was incubated for 1 h at 30°C in a “spheroplasting” solution ( 1 M sorbitol , 20 mM EDTA , 10 mM Tris pH 7 . 5 ) to remove the cell wall . This was followed by digestion with proteinase K ( 10 mM Tris , pH 8 . 0 , 100 mM EDTA , 1 . 0% N-lauroylsarcosine , 0 . 2% sodium deoxycholate , 1 mg/ml proteinase K ) for 24 hours at 30°C . Increasing the time of proteinase treatment did not influence the DNA mobility characteristics on PFGE . The parameters for CHEF gel separation of yeast chromosomes in a 1% agarose gel were 6 V/cm for 24 hours with a 10–90 sec switch time ramp and 120° switch angle ( running buffer at the 14°C ) . Subsequently , the DNA was analyzed by Southern blotting as described in [28] . Hybridization was carried out with a probe for the CHAI gene to detect specifically Chr III material or a probe to the LEU2 gene that marked both Chr III and Chr II . Autoradiographs were digitized and densitometric analysis was performed using Kodak MI software ( version 5 . 0 ) . DNA was digested in agarose plugs with T7 endonuclease I ( New England Biolabs , Beverly , MA ) . A 50 µl plug slice was equilibrated 3 times for 20 minutes at room temperature in 150 µl of TE ( 10 mM Tris , pH 7 . 4 , 1 mM EDTA ) , followed by 30 minute incubation at room temperature with 30 units T7 endonuclease in 150 µL reaction buffer , and stopped by washing 3 times with ice-cold Tris-EDTA ( 10 mM Tris , 50 mM EDTA , pH 8 . 0 ) . PFGE analysis was performed as described above . | DNA double-strand breaks ( DSBs ) are an important source of genome instability that can lead to severe biological consequences including tumorigenesis and cell death . Although much is known about DSBs induced directly by ionizing radiation and radiomimetic cancer drugs , there is a relative dearth of information about the formation of derived DSBs that arise from processing of single-strand lesions . Since as many as 10 , 000–200 , 000 single-strand lesions have been estimated to occur each day in mammalian cells , conversion of even a small percentage of such lesions to DSBs could dramatically affect genome stability . Here we addressed the mechanism of formation and repair of derived DSBs in vivo during the processing of DNA methylation damage in yeast that are defective in base excision repair ( BER ) due to a lack of AP endonucleases . Armed with a technique developed in our lab that detects resection at DSBs , a first step in DSB repair , we demonstrated formation of DSBs in G2 cells and the role of recombinational repair in subsequent chromosome restitution . Furthermore , we have identified a novel repair intermediate that can be generated if abasic sites are nicked by AP lyases , providing additional insights into the processing of 3′-blocked groups at single-strand breaks . | [
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] | 2011 | Alkylation Base Damage Is Converted into Repairable Double-Strand Breaks and Complex Intermediates in G2 Cells Lacking AP Endonuclease |
Inclusion body myopathy with Paget's disease of bone and frontotemporal dementia ( IBMPFD ) is caused by mutations in Valosin-containing protein ( VCP ) , a hexameric AAA ATPase that participates in a variety of cellular processes such as protein degradation , organelle biogenesis , and cell-cycle regulation . To understand how VCP mutations cause IBMPFD , we have established a Drosophila model by overexpressing TER94 ( the sole Drosophila VCP ortholog ) carrying mutations analogous to those implicated in IBMPFD . Expression of these TER94 mutants in muscle and nervous systems causes tissue degeneration , recapitulating the pathogenic phenotypes in IBMPFD patients . TER94-induced neurodegenerative defects are enhanced by elevated expression of wild-type TER94 , suggesting that the pathogenic alleles are dominant active mutations . This conclusion is further supported by the observation that TER94-induced neurodegenerative defects require the formation of hexamer complex , a prerequisite for a functional AAA ATPase . Surprisingly , while disruptions of the ubiquitin-proteasome system ( UPS ) and the ER–associated degradation ( ERAD ) have been implicated as causes for VCP–induced tissue degeneration , these processes are not significantly affected in our fly model . Instead , the neurodegenerative defect of TER94 mutants seems sensitive to the level of cellular ATP . We show that increasing cellular ATP by independent mechanisms could suppress the phenotypes of TER94 mutants . Conversely , decreasing cellular ATP would enhance the TER94 mutant phenotypes . Taken together , our analyses have defined the nature of IBMPFD–causing VCP mutations and made an unexpected link between cellular ATP level and IBMPFD pathogenesis .
IBMPFD is a progressive autosomal dominant disorder , characterized by the adult onset of muscle degeneration , abnormal bone metabolism , and drastic behavior changes . This disease has been linked to mutations in VCP ( known as p97 in mouse or CDC48 in yeast ) , a hexameric AAA ( ATPase associated with various cellular activities ) ATPase known to participate in numerous cellular events [1] , including ubiquitinated protein processing [2] , [3] , homotypic membrane fusion [4]–[6] , nuclear envelope reconstruction [7] , and cell cycle regulation [8] . Despite these advances in understanding VCP functions , it is unclear which of the aforementioned VCP roles are critical for causing IBMPFD . VCP protein contains an N-terminal CDC48 domain and two ATPase domains ( D1 and D2 ) . In VCP hexamers , the D1 and D2 domains of each monomer align in a head-to-tail manner [9] , [10] . It is thought that ATP hydrolysis causes major conformational changes to the hexamer [11] , [12] , thus generating the mechanical force required for VCP function . The ATPase activity of VCP appears to be mediated mainly by D2 , whereas D1 contributes to heat-induced activity [13] . In support of this , mutations disrupting the residues required for the ATP-binding and ATP-hydrolysis in D2 have been used to dominantly interfere with endogenous VCP function [14]–[16] . The N-terminal CDC48 domain has been shown to bind cofactors and ubiquitin . As VCP is implicated in numerous processes , the cooperation with different cofactors may account for its functional diversity . For instance , VCP associates with p47 in mediating homotypic Golgi membrane fusion [6] , [17] , with p37 in ER and Golgi biogenesis [18] , and with Ufd1/Npl4 in ERAD [19] and nuclear envelope reassembly [7] . Nearly all of the VCP mutations implicated in IBMPFD are located in the N-terminal CDC48 domain , the N-D1 linker , and the D1 ATPase domain . The R155 residue in the CDC48 domain has been mutated into different amino acids ( C , H , and P ) in 14 familial IBMPFD cases [20] . Mutations disrupting R191 ( in N-D1 linker ) and A232 ( linker-D1 junction ) are also implicated in familial IBMPFD cases , and it has been shown that individuals carrying the VCPA232E mutation exhibited more severe symptoms [20] . It is peculiar that the D2 domain , while essential for VCP function in vitro , has not been disrupted by any of the currently known IBMPFD-causing mutations ( Figure S1 ) . It is possible that , as VCP functions as hexamers , mutations in the D2 domain will dominantly deplete the pool of functional VCP hexamers , resulting in early demises of heterozygous individuals . Alternatively , the integrity of the D2 domain may be required for VCP mutants to cause IBMPFD . To decipher the mechanistic link between IBMPFD and VCP mutations , efforts have been made to establish animal models expressing VCP mutants . Overexpression of VCPR155H in mice caused accumulation of ubiquitinated proteins , implying that UPS is an underlying cause for IBMPFD [21] . However , recent reports showed that cells expressing VCPR155H also exhibited impaired ERAD [22] and autophagy [23] , [24] , indicating that the expression of IBMPFD-causing VCP mutants could hamper multiple cellular pathways . Redistribution of TAR DNA-binding protein-43 has also been implicated as a cause for VCP-induced toxicity [25] . Biochemical analysis showed that IBMPFD-causing VCP mutants have elevated ATPase activity [15] , although the significance of this finding is unclear . Drosophila contains a single VCP homolog TER94 [26] , which shares ∼83% protein sequence identity with human VCP . Of the twelve known VCP amino acid substitutions in IBMPFD , nine residues are conserved in TER94 ( Figure S1 ) , suggesting that Drosophila is a suitable model for IBMPFD . Here we show that expression of TER94 carrying mutations analogous to those implicated in IBMPFD mutants could disrupt muscle integrity and cause progressive neurodegenerative defects . Genetic evidence suggests that IBMPFD-causing VCP mutations are dominant active alleles . Mutational analysis shows that the ability of TER94 to form hexamers is essential for the mutant proteins to induce neurodegenerative defects , further suggesting that the IBMPFD-causing VCP mutations are not simple loss-of-function mutations . Using reporters specific for ERAD and UPS , we found the disruptions of these pathways are unlikely to be the underlying cause for the neurodegenerative defects in our model . Instead , the TER94-dependent neurodegenerative defects correlate with cellular ATP reduction , and could be suppressed by increasing cellular ATP level and enhanced by decreasing cellular ATP level . These observations , along with earlier report that IBMPFD-linked VCP mutants have elevated ATPase activity , suggest that depleting cellular ATP is a contributing factor for VCP mutant–induced tissue degeneration .
To establish a Drosophila model for IBMPFD , we introduced amino acid substitutions at three residues in TER94: R152H , R188Q and A229E , to simulate the human VCP mutations R155H , R191Q and A232E respectively ( Figure 1A ) . The R152H mutation is expected to affect the N-terminal CDC48 domain , whereas R188Q and A229E are located in the linker 1 ( L1 ) region and L1-D1 junction respectively . As human versions of these Drosophila alleles have been linked to IBMPFD , they will be referred as “IBMPFD mutants” hereafter . To investigate the importance of ATPase activity in VCP function , we also generated flies expressing mutant TER94 defective in ATP-binding ( K248A & K521A; K2A for short ) or ATP-hydrolysis ( E302Q & E575Q; E2Q for short ) ( Figure 1A ) . As disruption of the nucleotide hydrolysis cycle of protomer can affect the configuration and function of VCP hexamer [14] , expression of these ATPase mutants is expected to dominantly impair the activity of endogenous TER94 . While IBMPFD affects multiple tissues , the most prevalent pathology is myopathy . To ask if our model could recapitulate this defect , we utilized 24B-GAL4 [27] ( an early driver active in myoblasts ) and Mhc-GAL4 ( a muscle-specific driver active from larval stage onward ) to examine the effect of mutant TER94 expression on muscle development and maintenance respectively . The muscle fibers , visualized using mCD8-GFP ( a membrane-bound GFP ) , were organized in segmental fashion in wild type at late embryonic stage ( Figure 1B ) . In 24B>TER94wt embryos , mCD8-GFP localization appeared comparable to wild type , indicating that muscle fibers developed normally up to this stage ( Figure 1C ) . In contrast , muscle fibers in embryos expressing TER94 IBMPFD mutants appeared disorganized and loss of GFP signals was seen in some segments ( Figure 1D–1F ) . Among the three IBMPFD mutants , there appeared to be a difference in phenotypic severity , as the disruption of muscle fiber development seemed strongest in 24B>TER94A229E , coinciding with the observation that patients bearing VCPA232E allele had more severe symptoms [20] . This difference in phenotypic severity was not caused by differences in TER94 transgene expression , as quantitative Western blots showed comparable levels of TER94 proteins in lines used in our analysis ( Figure S2 ) . Furthermore , TER94 expression from transgenes inserted at identical genomic location ( generated by integrase-mediated transformation [28] to eliminate positional effect ) showed that TER94A229E caused the strongest photoreceptor degeneration among the three IBMPFD mutants ( Figure S3; see below ) . Compared to IBMPFD mutants , the phenotype in 24B>TER94K2A was even more severe , as developing muscle fibers were completely absent ( Figure 1G ) . This strong phenotype in 24B>TER94K2A is consistent with the hypothesis that mutations disrupting the ATPase activity are more debilitating than the disease alleles . To determine the effect of TER94 alleles on mature muscles , adult flies expressing TER94 mutants under the control of Mhc-GAL4 [29] were subjected to flight tests . Those lacking functional muscles , as caused by mutant TER94 expression , were expected to perform poorly in this simple behavior assay . Indeed , flies expressing all TER94 mutants tested showed disrupted flight behavior ( Figure 1H ) . Interestingly , Mhc>TER94wt also exhibited impaired flight ( Figure 1H ) , suggesting that excessive TER94 activity could disrupt muscle function . To ensure that the flightless phenotype was caused by muscle defect , phalloidin staining was performed to determine the integrity of indirect flight muscles ( IFMs , Figure 1I–1L ) . In Mhc>lacZ control , typical pattern of myofibril with clear A-bands was seen ( arrowheads in Figure 1J ) , indicating that IFMs were normal . In contrast , myofibril from Mhc>TER94A229E showed disrupted sarcomeres without repetitive A-bands ( compare Figure 1J to Figure 1L ) , suggesting that the flightless phenotype was caused by structural defects in IFMs . To monitor TER94 localization in IFMs , we stained Mhc>lacZ and Mhc>TER94A229E tissues with an αVCP antibody ( Cell Signaling ) that could recognize Drosophila TER94 ( Figure S2 ) . Interestingly , while endogenous TER94 was localized diffusely in Mhc>lacZ , Mhc>TER94A229E myofibrils contained TER94-positive inclusion-like structures ( arrows in Figure 1L ) , reminiscent of the rimmed vacuoles found in IBMPFD patient's muscles [1] , [30] . To ensure that this flight impairment was caused by muscle degeneration , temperature-sensitive GAL80 ( GAL80ts ) was used to prevent GAL4 from activating TER94 expression until adulthood . Mhc>tub-GAL80ts >TER94A229E flies raised at 25°C exhibited normal flight , demonstrating that muscles had formed normally at permissive temperature . However , the same flies lost their ability to fly after being shifted to 29°C for 10 days . In comparison , such temperature shift did not affect the flight ability of Mhc>tub-GAL80ts >LacZ flies ( data not shown ) . Together , our results suggest that expression of the TER94 IBMPFD mutants can affect both the development and maintenance of muscles . In addition to myopathy , mutations in VCP have been implicated in frontotemporal lobar degeneration [31] . To examine the effect of VCP mutations in brain , we used elav-GAL4 , a driver active in all neuronal cells , to express TER94 IBMPFD mutants . In addition , UAS-mCD8-GFP was included to label the plasma membrane of Elav-positive neurons . While mCD8-GFP localization remained normal in elav>LacZ and elav>TER94wt brains ( Figure 2A and 2B ) , elav>TER94 IBMPFD mutant brains had a midline-crossing phenotype in the β/γ lobes of the mushroom body ( arrows in Figure 2C–2E ) . The penetrance of this defect was lowest for elav>TER94R152H ( 22% , n = 18 ) , but higher for elav>TER94R188Q ( 80% , n = 15 ) and elav>TER94A229E ( 62% , n = 21 ) . The midline-crossing phenotype was seen in newly eclosed elav>TER94 IBMPFD mutant adults ( data not shown ) , indicating that expression of TER94 IBMPFD mutants in neuronal cells may disrupt axonal guidance during brain development . The midline-crossing phenotype observed in elav>TER94 IBMPFD mutants is reminiscent of those described for linotte , a Drosophila memory mutant [32] . We thus performed olfactory learning tests to see if elav>TER94 IBMPFD mutants had learning deficit . elav>TER94R152H , which had the lowest penetrance of midline-crossing defects , did not exhibit detectable deficit in olfactory learning . On the other hand , elav>TER94R188Q , the mutant that exhibited the highest penetrance of midline-crossing defects , had the most severe learning deficit ( Figure 2F ) . elav>TER94A229E flies showed intermediate decline in both the midline-crossing assay and the learning tests ( Figure 2F ) . To further understand the effect of TER94 IBMPFD mutants on neuronal cells , we used GMR-GAL4 driver , which is active in photoreceptor cells ( R cells ) in the developing eye . The development and maintenance of R cells in fly eye has been a powerful model for analyzing genes contributing to human neurodegenerative diseases [33] . As shown in Figure 2G and 2H , external morphologies of GMR>lacZ ( a negative control ) and GMR>TER94wt eyes were normal . Phalloidin staining showed that GMR>lacZ and GMR>TER94wt retina both had normal arrangement of rhabdomeres ( the light-sensing organelles in R cells ) ( Figure 2Gi and 2Hi ) , indicating the presence of normal complement of R cells . In contrast , the rhabdomere arrangement was disorganized in animals expressing TER94 IBMPFD mutants under the control of GMR-GAL4 . Similar to muscle formation , expression of TER94R152H had the weakest effect on eye formation , as GMR>TER94R152H eyes actually appeared normal externally ( Figure 2I ) . However , the rhabdomeres in GMR>TER94R152H retina were noticeably disorganized ( Figure 2Ii ) , and many clusters contained fewer rhabdomeres , indicative of loss of R cells . On the other hand , GMR>TER94R188Q and GMR>TER94A229E both exhibited noticeable eye roughness ( Figure 2J and 2K ) . Furthermore , the severe defect in their rhabdomere organization suggested that both GMR>TER94R188Q and GMR>TER94A229E retina also lost significant numbers of R cells ( Figure 2Ji and 2Ki ) . In addition to disruption in rhabdomere organization in tangential sections , GMR>TER94R152H , GMR>TER94R188Q , and GMR>TER94A229E retinas all showed reduced thicknesses and disorganizations in the longitudinal sections ( Figure 2Iii–2Kii ) . This defect further strengthens the notion that expression of TER94 IBMPFD mutants could interfere with R cell development . To understand the effect of VCP mutations on R cell maintenance , we used Rh1-GAL4 to express TER94 IBMPFD mutants . Rh1-GAL4 becomes active in the outer R cells ( those with large rhabdomeres; R1-6 in Figure 3A ) , but not in the inner R cells ( those with small rhabdomeres; R7 in Figure 3A ) , at late pupal stage . Thus , this driver will allow us to circumvent the potentially detrimental effect of TER94 on development and focus on its effect on mature neurons , and ask if the neurodegenerative defect is cell autonomous . Retina from young Rh1>TER94 ( in all tested transgenic alleles ) adults all showed normal rhabdomere organization ( Figure 3B–3E ) . However , in the retina of 28 day-old TER94 IBMPFD mutants , rhabdomere staining was disorganized and significant loss of R cells was evident ( Figure 3H–3J ) . It should be noted that only the outer R cells suffered degeneration , indicating that the effect of TER94 on neurodegeneration is cell autonomous . Furthermore , the fact that older flies , but not the young ones , displayed loss of photoreceptors demonstrates that expression of TER94 IBMPFD mutants could cause progressive degeneration of R cells . The presence of ubiquitinated inclusions , which also contain VCP mutant proteins , has been suggested as the underlying mechanism of IBMPFD pathogenesis [21] , [34] . To test whether TER94 associates with aggregates in our model , retina of various genotypes were stained with αVCP antibody to monitor its localization . We reasoned that , if the formation of VCP-containing aggregates causes IBMPFD , a strong correlation between the extent of aggregate formation and the severity of neurodegenerative defects should be observed . While rhabdomere organization was normal in young Rh1>TER94 ( both wild type and IBMPFD mutants; Figure 3B–3E ) , scattered large structures with intense TER94 staining could be seen ( arrowheads in Figure 3B–3E ) . Immunostaining with FK2 antibody ( specific for polyubiquitin ) suggests that most of these structures did not contain polyubiquitinated proteins ( data not shown ) . In 28 day-old flies , progressive degeneration of R cells was observed , along with these TER94-containing structures ( Figure 3G–3J ) . However , while the TER94-induced degeneration could be suppressed by increasing cellular ATP level ( see below ) , the level of large TER94-positive structures was unaffected ( compare Figure 9E to Figure 9Ei , and Figure 10D to Figure 10Di ) . Similarly , while R cell formation was unaffected in GMR>TER94wt retina , large TER94-positive structures were detected ( arrows in Figure 2Hi ) . Thus , in this fly IBMPFD model , it appears that the phenotypes of large TER94-positive structures and TER94-induced neurodegeneration could be uncoupled . It has been suggested that impaired UPS , caused by VCP mutations , results in the deposition of proteinaceous aggregates and IBMPFD [21] . To test whether the expression of TER94 IBMPFD mutants disrupts UPS , UAS-CL1-GFP ( kindly provided by Dr . Paul Taylor ) [35] was co-expressed with UAS-TER94A229E ( the IBMPFD mutant that exhibited strongest muscle and R cell defects ) in the eye discs . To demonstrate that CL1-GFP could monitor proteasome function , GMR>CL1-GFP eye discs were treated with lactacystin , a proteasome inhibitor . As shown in Figure 3K and 3L , 1 mM of lactacystin could elicit robust GFP signal , whereas DMSO alone had no effect , indicating that this reporter responded to disruption of proteasome function . Furthermore , GFP intensity increased in GMR>CL1-GFP when one copy of the 20S proteasome α1 subunit gene ( Prosα1l ( 2 ) SH2342 ) was mutated ( Figure 3N ) . However , although this reporter appeared to be sensitive , no GFP signal was detected in GMR>TER94A229E larval eye discs ( Figure 3P ) . In pupal GMR>TER94A229E eye where large TER94-containing structures and R cell disruption were evident , CL1-GFP signal remained undetectable ( compare Figure 3Q to Figure 3R , and Figure 3Qi to Figure 3Ri ) . Thus , expression of TER94 IBMPFD mutants does not appear to cause UPS impairment in our model . Impairment of ERAD , caused by VCP mutants , has also been suggested as a mechanism for IBMPFD pathogenesis [22] . To test whether expression of TER94 IBMPFD mutants disrupts ERAD , we generated transgenic flies carrying UAS-CD3δ-YFP , a well-established ERAD reporter in cell culture and mouse [36] , [37] . To test if CD3δ-YFP could be a potent ERAD reporter in fly , GMR>CD3δ-YFP eye discs were treated with 5 mM dithiothreitol ( DTT ) , a reducing agent capable of eliciting ER stress . While no YFP signal was seen in untreated tissues , intense YFP signal was observed in DTT-treated GMR>CD3δ-YFP eye discs ( Figure 4A and 4Ai ) . Moreover , GFP intensity increased in GMR>CD3δ-YFP when Sip3 , the Drosophila homolog of a key ERAD component Hrd1 , was knockdown by dsRNA-mediated RNA interference ( RNAi ) ( Figure 4B ) . It is worth noting that expression of toxic polyglutamine ( Q108 ) , which does not have apparent role in ERAD , did not elicit CD3δ-YFP signals ( Figure 4Bi ) . These data demonstrated that CD3δ-YFP , a mammalian T-cell receptor subunit , is specific and capable of detecting ERAD in Drosophila . Consistent with the notion that TER94 has a role in ERAD , robust YFP signals were detected in GMR>TER94K2A and GMR>TER94E2Q larval eye discs ( Figure 4C and 4Ci ) . However , in larval eye discs expressing TER94 IBMPFD mutants , the CD3δ-YFP signal was nearly undetectable ( Figure 4D–4F and data not shown ) . Subsequent examinations of two pupal stages revealed that GMR>TER94A229E retina exhibited aberrant arrangement of R cells , whereas GMR>TER94wt had normal complement of R cells . Yet the CD3δ-YFP signals in both GMR>TER94A229E and GMR>TER94wt were comparable ( compare Figure 4Fi to Figure 4Di and 4Ei , and Figure 4Fii to Figure 4Dii and 4Eii ) . This lack of correlation between the CD3δ-YFP signal and the photoreceptor defects suggests that impaired ERAD is not a major factor for the R cell loss . To independently confirm this finding , we used xbp1-EGFP ( kindly provided by Dr . Hermann Steller ) to detect the unfolded protein response ( UPR ) in TER94-expressing eye discs [38] . Similar to CD3δ-YFP , robust xbp1-EGFP signal was detected when eye discs were treated with DTT ( Figure S4B ) or expressing TER94K2A ( Figure S4G and S4Gi ) . In contrast , no detectable xbp1-EGFP signal was seen in GMR>TER94 IBMPFD mutants ( Figure S4D , S4E , S4F ) . These observations suggest that overexpressing TER94 IBMPFD mutants does not trigger ERAD impairment or UPR in our fly model . VCP is known to act as hexamer , and a recent report showed that p97R155P and p97A232E are capable of forming hexamer in vitro [15] . To ask if the ability of these IBMPFD mutants to disrupt R cells requires hexameric formation , we made monomeric forms of TER94 ( mTER94 ) by mutating R356 and R359 , two positively charged arginine residues in D1 domain , to negatively charged glutamic acids ( Figure 1A ) . Although these mutations were chosen based on literature [39] , blue native polyacrylamide gel electrophoresis ( BN-PAGE ) was performed to ensure that these mTER94 mutants were indeed monomers . In GMR-GAL4 extract under native conditions , αVCP antibody detected a band migrating at ∼676 kDa , corresponding to the hexameric TER94 ( Figure 5A ) . In extracts from GMR>TER94wt , GMR>TER94A229E , and GMR>TER94R188Q , the intensity of this ∼676 kDa band became elevated , indicating that TER94 proteins made from the transgenes were capable of forming hexamers ( Figure 5A ) . In contrast , in extracts from GMR>mTER94wt , GMR>mTER94A229E , and GMR>mTER94R188Q , the intensity of ∼676 kDa band was reduced to the GMR-GAL4 level ( Figure 5A ) . Immunoblots of the same extracts in SDS-PAGE detected ∼95 kDa monomer in mTER94 lines with intensity that was significantly higher than endogenous protein in GMR-GAL4 control ( Figure 5B ) , demonstrating that these mTER94 proteins were expressed , but incapable of forming hexamers . Although overexpressed mTER94 proteins were seen in SDS-PAGE , we did not detect mTER94 in the native PAGE in several attempts ( Figure 5A and data not shown ) . It is possible that the epitope is masked in native mTER94 proteins . To ask whether the monomeric TER94 IBMPFD mutants could still elicit neurodegenerative defects , we expressed these transgenes with Rh1-GAL4 and examined R cell organization . In young ( 5-day old ) Rh1>mTER94A229E or Rh1>mTER94R188Q , the number and arrangement of R cells was completely normal ( Figure 5C–5F ) . In 28 day-old flies , the R cells remained normal in Rh1>mTER94A229E or Rh1>mTER94R188Q eyes; however , the R cells were severely degenerated in eyes expressing TER94R188Q or TER94A229E ( compare Figure 5Ci to Figure 5Di , Figure 5Ei to Figure 5Fi , and Figure 5G ) . These observations suggest the ability to form hexamers is essential for TER94-dependent R cell degeneration . While the inheritance of IBMPFD is dominant , the nature of these VCP mutations remains unclear . To distinguish whether these mutations were dominant-active or dominant-negative , we examined the effect of altering TER94 gene dose on the eye phenotypes of TER94 IBMPFD mutants . We reasoned that if TER94 IBMPFD mutations are dominant-active , the rough eye phenotypes of GMR>TER94 IBMPFD mutants should be suppressed by inactivating one copy of the endogenous TER94 and enhanced by overexpressing wild type TER94 . Conversely , if TER94 IBMPFD mutations act as dominant negatives , the rough eye phenotypes of GMR>TER94 IBMPFD mutants should be enhanced further by inactivating one copy of the endogenous TER94 and suppressed by overexpressing wild type TER94 . As shown in Figure 6 , the rough eye and R cell defects of GMR>TER94R152H , GMR>TER94R188Q , and GMR>TER94A229E were suppressed by inactivating one copy of TER94 and noticeably enhanced by overexpressing TER94wt transgene . To demonstrate the specificity of these genetic interactions , we tested the effect of TER94wt overexpression on GMR>TER94K2A , a known dominant negative . GMR>TER94K2A animals died at larval or early pupal stage ( possibly due to GMR-GAL4 leaky expression elsewhere ) ; however , this lethality could be rescued by overexpressing TER94wt ( Figure 6J and 6Ji ) . Together , these results strongly suggest that IBMPFD mutations are dominant actives , and TER94 IBMPFD mutants and TER94K2A confer cytotoxicity through distinct mechanisms . Hexamers consisted of VCP disease proteins have recently been shown to possess increased ATPase activity in vitro [15] and in vivo [40] . As IBMPFD seems to preferentially affect tissues with high energy-demand , we speculated that this elevated ATPase activity of VCP mutants might contribute to IBMPFD pathogenesis by depleting cellular ATP . To test whether TER94 IBMPFD mutants could deplete ATP , cellular ATP levels from thorax ( eyes were not used because eye pigment would interfere with the ATP assay ) of flies expressing TER94 IBMPFD mutants driven by hs-GAL4 driver were measured . After three cycles of induction by heat shocks , hs>TER94wt and hs>TER94R152H did not show significant reduction in ATP level when compared to hs>LacZ control ( Figure 7 ) . However , significant reduction in ATP level was seen in hs>TER94A229E and hs>TER94R188Q , the alleles associated with strong defects in muscles and photoreceptors . While this reduction in ATP level appears modest , it is worth mentioning that mouse kidney cells undergo apoptosis when cellular ATP is depleted to ∼70% of normal level [41] . Thus it is entirely possible that similar level of ATP reduction could cause muscle and R cell degeneration in flies . In any case , our results are consistent with the notion that TER94 IBMPFD mutants could alter cellular ATP level . If this ATP depletion contributes to the neurodegenerative defects of TER94 IBMPFD mutants , it should be possible to suppress the phenotypes by boosting cellular ATP production or reducing energy consumption . To test this , we subjected TER94 flies to dietary restriction ( DR ) , a regimen known to boost energy production by either increasing the number of electron transport chains during mitochondrial biogenesis [42] , or enhancing the metabolic adaptation to reduce overall energy expenditure [43] . Freshly eclosed Rh1>TER94 flies were raised on either normal food or DR food ( reduction of two-third of sucrose and yeast ) . In support of the notion that energy expenditure plays a role in IBMPFD pathogenesis , the progressive degeneration of R cells was markedly mitigated for all tested TER94 IBMPFD mutants raised under DR condition for 10 days ( data not shown ) . This diet-dependent suppression of R cell degeneration was still seen in most of the TER94 IBMPFD mutants raised under DR condition for 20 days ( Figure 8 ) . The only exception was Rh1>TER94A229E , which had the most severe R cell degeneration and showed only mild response to DR condition at this age ( Figure 8 ) . This suppression appears specific to IBMPFD , as DR had no evident effect on expanded polyglutamine-induced neurodegeneration ( Figure 8E–8F ) . To independently verify this energy expenditure idea , we took advantage of R cell physiology to assess the role of ATP in IBMPFD . It is known that fly R cells consume at least 5-fold more ATP under illuminated condition than in the dark [44] . Freshly eclosed ( <12 h ) Rh1>TER94 and controls flies were raised under a normal 12 hours light/12 hours dark cycles ( L/D , light intensity ∼520 lux ) , constant light ( L/L ) , or completely dark ( D/D ) conditions , and the presence of the R cells in these flies were compared . The progressive neurodegeneration of R cells was easily seen in TER94 IBMPFD mutant-expressing flies raised under the L/D condition ( Figure 9 ) . This loss of R cells was rescued when these flies were raised under the D/D condition ( compare Figure 9C to Figure 9Ci , Figure 9D to Figure 9Di , Figure 9E to Figure 9Ei , and Figure 9I ) . Although this darkness treatment rescued the R cell defect in all TER94 IBMPFD mutants , it failed to restore the degeneration caused by overexpressing TER94K2A ( compare Figure 9F to Figure 9Fi , and Figure 9I ) or two expanded polyglutamine disease models ( compare Figure 9G to Figure 9Gi , Figure 9H to Figure 9Hi , and Figure 9I ) [45]–[47] . This suggests that the reduction of ATP expenditure is not a universal antidote for neurodegeneration , but specific to these TER94 IBMPFD mutants . The R cell degeneration was strongly enhanced under L/L condition for all TER94 IBMPFD mutants ( data not shown ) . However , L/L condition also caused mild R cell degeneration in control and flies expressing TER94wt , likely due to retinopathy induced by continuous light exposure [48] . In any case , data from both DR and light conditioning experiments support energy expenditure as a pathophysiological mechanism in this Drosophila IBMPFD model . To further strengthen our energy expenditure hypothesis , we used genetic approaches to modulate ATP levels in fly IBMPFD model . As the electron transport chain of mitochondria generates the majority of ATP in animal cells , we found that RNAi knockdown of components in the catalytic core of F1 ATP synthase of complex V could significantly enhance R cells degeneration in Rh1>TER94A229E flies ( Figure S5 ) . Conversely , reducing ATP consumption through RNAi knockdown of two ATPases , ATP-citrate lyase [49] or a putative human copper transporter DmATP7 [50] , could suppress Rh1>TER94A229E-induced R cell degeneration ( Figure S5 ) , further supporting the notion that ATP level is a key to the disease . To genetically boost cellular ATP production , we took advantage of an observation that RNAi knockdown of mitochondrial phosphatase PTPMT1 ( a protein tyrosine phosphatase localized to the mitochondrion ) could markedly increase ATP production in mouse cells [51] . We thus expressed RNAi transgenes that target plip , the Drosophila homolog of PTPMT1 , to analyze its effect on our disease model ( Figure 10A ) . To avoid off-target effect , two independent plip-RNAi constructs v104774 and v47624 , which target partially overlapped plip mRNA sequence , were used [52] . Knockdown with v104774 and v47624 both decreased the level of plip mRNA ( Figure 10A ) and elevated cellular ATP level ( Figure 10B ) , although the increase in ATP level was higher with v47624 . Expression of either v104774 or v47624 in outer R cells using Rh1-GAL4 had no effect on R cells ( data not shown ) . However , expression of these plip-RNAi lines could significantly suppress the R cell degeneration in RH1>TER94R188Q and RH1>TER94A229E ( compare Figure 10C to Figure 10Ci and Figure 10Cii , Figure 10D to Figure 10Di and Figure 10Dii , and Figure 10E ) . This suppression of TER94 IBMPFD mutants by plip-RNAi is specific , as knockdown of plip had no significant effect on the R cell degeneration in Rh1>TER94K2A or mutant MJDtr-Q78 ( Figure 10E ) . To determine whether the suppression of TER94-dependent R cell degeneration correlates with ATP level , we measured ATP content in flies that co-express plip-RNAiv47624 with TER94R188Q or TER94A229E . Compared to TER94R188Q and TER94A229E alone , co-expressing plip-RNAiv47624 showed elevated ATP content ( Figure 10F ) , further supporting a role of cellular ATP level in IBMPFD mutant-induced neurodegeneration .
In this study , we introduced IBMPFD-causing mutations in Drosophila TER94 to elucidate the pathogenesis of VCP mutants . Expression of these TER94 IBMPFD mutants in mature muscle cells causes loss of muscle tissues . Similarly , expression of these TER94 mutants in mature photoreceptor cells causes progressive neurodegeneration . Moreover , the observation that TER94A229E consistently had the strongest phenotypes correlates well with the allelic strength of its counterpart in human . Thus , although the fly model differs from human IBMPFD in the sense that TER94 mutant proteins are overexpressed , the fact that the expression of these TER94 mutants could recapitulate phenotypic and genetic features of IBMPFD suggests that our fly model is an appropriate system to analyze VCP mutations . Using early muscle- and R cell-specific drivers , we show that overexpression of TER94 IBMPFD mutants during development can interfere with the formation of muscle and neuronal cells . Thus , although IBMPFD is an adult onset disease , the cytotoxic effect of IBMPFD-causing VCP mutant proteins may not be restricted to mature muscle and neuronal cells . It is likely that if expressed at high level , these VCP mutant proteins can cause deterioration of other cell types and manifest their cytotoxicity at earlier stages . Given the onset age of IBMPFD varies broadly ( range 20 to >60 yrs ) [20] , [53] , it seems plausible that subtle defects may have occurred in the development of muscle and neuronal tissues in IBMPFD individuals . What is the nature of these IBMPFD-causing VCP mutations ? The autosomal dominant inheritance of IBMPFD suggests that these mutations are not simple loss-of-function mutations . One possible scenario is that TER94 IBMPFD mutants interfere with wild type TER94 by forming non-functional hexamers . We showed that expression of TER94K2A , a known dominant negative , could disrupt muscles and R cells . However , while TER94K2A expression readily elicits ERAD and UPR responses , expression of TER94 IBMPFD mutants has little effect on these processes , suggesting that TER94K2A and TER94 IBMPFD mutants cause cytotoxicity through distinct mechanisms . Indeed , several results argue that TER94 IBMPFD alleles are dominant actives . First , disruptions of flight and R cell organization , phenotypes associated with TER94 IBMPFD mutants , could be mimicked by elevated expression of TER94wt . Moreover , IBMPFD-causing VCP mutants are known to possess elevated ATPase activity [15] , [40] and we demonstrated that hexameric formation is critical for the IBMPFD mutants to disrupt photoreceptors . Most importantly , we showed that the eye defects of GMR>TER94 IBMPFD mutants could be enhanced by additional wild type TER94 expression . Taken together , these data argue strongly that IBMPFD-causing VCP alleles are gain-of-function mutations . It is well documented that VCP participates in UPS and ERAD [19] , [54] . The phenotypes of TER94 mutants defective in ATP-binding or ATP-hydrolysis also demonstrated that this AAA ATPase is indispensible for ERAD in Drosophila . The key question , however , is whether the disruption of UPS or ERAD is the cause that VCP mutations induce IBMPFD . Using reporters capable of monitoring UPS and ERAD , we showed that expression of TER94 IBMPFD mutants had little , if any , effect on both processes . The disconnect between the ability of TER94 IBMPFD mutants to induce neurodegeneration and their ability to impact UPS and ERAD is surprising because expression of pathogenic VCP mutants has been shown to disrupt ERAD and caused the accumulation of ubiquitinated aggregates in mice [21] , [22] , [34] . It is not clear why this difference exists between the two model systems . One possible explanation is that the reporters we used were not sensitive enough . Although we cannot formally exclude this possibility , CL1-GFP was capable of detecting a 50% reduction of gene dose in the 20S proteasome α1 subunit gene , suggesting that the UPS reporter is sensitive . It should also be mentioned that in mammals , expression of VCP mutant does not always result in disruption of UPS and ERAD . Analysis of primary IBMPFD myoblasts ( VCPH155C ) and cells transfected with IBMPFD mutants did not reveal obvious increase of polyubiquitinated aggregates [55] . In cells expressing VCP mutant R155H or A232E , UbG76V-YFP and CD3δ-YFP reporters did not detect impairment to UPS and ERAD respectively [56] . Moreover , biochemical analysis showed that wild type and three disease proteins had identical binding affinity to cofactors Ufd1 , Npl4 , and Ataxin-3 [55] . In support of this , we have been unable to detect any genetic interaction between TER94 IBMPFD mutants and the VCP cofactors Ufd1 , Npl4 , and Eyc ( Chang and Sang , unpublished data ) . It is possible that VCP mutant proteins can cause IBMPFD through multiple mechanisms , and in the Drosophila model , impairment of UPS and ERAD is not the main cause for TER94-induced R cell degeneration . All VCP mutants implicated in IBMPFD hold increased ATPase activity [15] , [40] . In addition , the symptoms of IBMPFD appear to manifest in tissues with high energy-demands . These observations prompt us to examine whether there is a mechanistic link between the TER94 mutant-induced neurodegenerative defects and the cellular ATP level . We show that expression of TER94A229E and TER94R188Q could reduce cellular ATP level . More strikingly , the neurodegenerative defects in TER94 IBMPFD mutant expressing eyes could be modified by perturbations in ATP level . Increasing ATP level by dietary restriction , dark conditions , or plip knockdown could all suppress the TER94 IBMPFD mutant-induced R cell degeneration . We further demonstrated that plip-dependent suppression of TER94A229E-induced neurodegeneration coincides with increase in cellular ATP level . Conversely , decreasing ATP level could enhance these degenerative defects . This phenotypic modulation by ATP level is specific , as these treatments had no impact on the degeneration caused by ATP-binding defective TER94 and polyglutamine expansion-containing proteins . Several other observations lend support to this energy expenditure hypothesis . We showed that TER94 IBMPFD mutant-induced neurodegeneration requires hexameric formation , which is necessary for a functional ATPase . As the model stipulates that the ATPase activity would be essential for the pathogenesis , it makes sense that no IBMPFD-causing mutation resides in D2 . Taken together , these results suggest that elevated ATPase activities may have a prominent role in IBMPFD pathology . It is imaginable that pathogenic VCP mutants may spend excessive amount of energy in performing their functions , thereby gradually deteriorating other energy-dependent pathways and causing progressive tissue degenerations . In addition to improve our understanding of how VCP mutations cause IBMFPFD , another major goal of establishing a Drosophila IBMPFD model is to facilitate the identification of targets for designing therapeutic agents . Our demonstration that RNAi-mediated knockdown of plip could suppress TER94-induced neurodegeneration clearly suggests that this is a viable strategy . Furthermore , our demonstration that dietary restrictions and illumination conditions could modify TER94 phenotypes suggests a potential avenue of treating IBMPFD using environmental approach .
The Drosophila TER94 cDNA clone ( GM02885 ) , obtained from the Drosophila Genomics Resource Center ( Indiana , USA ) , was subcloned into the transformation vectors pUAST and pattB-UAST as an EcoRI-XhoI fragment . Mutations in the TER94 cDNA were introduced using QuikChange ( Stratagene ) , according to instructions from the manufacturer , and primers used for the site-directed mutagenesis of TER94 are listed in Table S1 . CD3δ-YFP cDNA was purchased from Addgene [37] , and subcloned into pUAST as an EcoRI-NotI fragment . All constructs were verified by sequencing prior to transgenic fly production . Flies carrying pUAST-based transgenes were generated by P element-mediated transformation , and flies carrying pattB-UAST-based constructs were generated by phiC31-mediated integration ( Bestgene , California , USA ) . Flies were raised in standard cornmeal food at 25°C in 12 hours light/12 hours dark cycles unless otherwise noted . 24B-GAL4 and elav-GAL4c155 were obtained from the Drosophila Stock Center ( Bloomington , Indiana , USA ) . GMR-GAL4 has been described previously [46] . Rh1-GAL4 and UAS-mCD8-GFP stocks were originally obtained from Dr . Larry Zipursky . An enhancer trap line l ( 2 ) SH2342 that allelic to CG18495 in which encodes Proteasome α1 subunit was acquired from Szeged Drosophila Stock Center ( Hungary ) . All transgenic RNAi lines were provided by the Vienna Drosophila RNAi Center . The UAS-CL1-GFP was a generous gift from Dr . Paul Taylor . Dr . Hermann Steller kindly provided the UAS-xbp1-EGFP stocks . Standard genetic markers and balancer chromosomes were used to generate specific genotypes . Analyses of eye morphology using scanning electron microscopy and whole-mount preparation of fly eyes were performed as described previously [46] . Primary antibodies used were the following: anti-VCP ( 1∶100 , Cell Signaling ) , anti-Elav ( 1∶20 , Developmental Studies Hybridoma Bank , DSHB ) , anti-LaminDm ( 1∶20 , DSHB ) . FITC , Cy3 , and Cy5 conjugated secondary antibodies ( Jackson ImmunoResearch Laboratories ) were used at 1∶100 dilutions . F-Actin enriched rhabdomere was labeled by Rhodamine-conjugated phalloidin ( Sigma ) . Whole-mount brain preparation was performed as described previously [57] . The mushroom body was examined as a projected image that covered the whole structure . Zeiss LSM-510 or 710 confocal microscopes were used for collecting all fluorescent images . Photoshop CS was used for image processing . For experiments that comparing fluorescent-labeled probes among different genotypes , the sample preparation and image processing was performed in the same procedure and setting . For the learning test , the classical T-maze paradigm ( Pavlovian ) conditioning procedure was followed [58] . Briefly , flies were trained by exposure to electroshock paired with one odor of either 3-octanol ( OCT ) or 4-methycyclohexanol ( MCH ) for 1 minute , and subsequent exposure to the second odor ( either OCT or MCH ) without electroshock . Immediately after training , learning was measured by allowing flies to choose between the two odors used during training . Avoidance of the odor previously paired with electroshock produced a performance index . For each test , more than a hundred flies from each genotype were analyzed . For measuring flight ability , a classical flightless assay was adopted [59] . 30–40 flies aged to 3 to 5 days were gently empted into a 2-liter graduated cylinder that coated with vacuum pump oil through a funnel at the top . Flies with normal flight ability were trapped in oil on the wall at higher cylinder marks , whereas flightless flies landed on the lower level or fell to the bottom . The numbers of flies stuck on the cylinder wall was counted into 10 divisions with higher index that indicates normal flight behavior and vise versa . Protein preparation for SDS-PAGE from Drosophila heads followed the procedures described previously [46] . For the BN-PAGE experiment , the proteins extraction and separation followed manufacture's direction ( Invitrogen ) . Primary antibodies were used as the following dilutions: anti-VCP ( 1∶3000 , Cell Signaling ) and anti-β-Actin ( 1∶5000 , Abcam ) . Secondary antibodies conjugated with HRP ( Jackson ImmunoResearch Laboratories ) were used in 1∶5000 dilutions . All loading controls were prepared by stripping off the reagents from the original membrane and then re-immunoblotting with anti-β-Actin following the standard procedures . ImageJ was used to quantify bands intensity . A naïve examiner who was blinded to the genotype counted the number of rhabdomeres in each unit eye . At least six individual eyes were scored in each experimental group . Data plotting and statistics were processed using Prism ( GraphPad software ) or SigmaPlot 10 ( Systat software ) . Adult eyes from GMR>LacZ , GMR>plip-RNAiv47624 and GMR>plip-RNAiv104774 were dissected for RNA isolation using TRI Reagent ( Sigma ) . 2 µg RNA was used for reverse transcription ( SuperScript First Strand , Invitrogen ) following the manufacture's direction . Subsequent PCR amplification was performed with about 1 . 5 µg cDNA and specific primer pairs for plip ( forward: 5′-CATGTTCGCACGCGTTTC-3′ , reverse: 5′-GGTCATGATTTCGTCTCCAC-3′ ) and internal control G3PDH ( forward: 5′-CCACTGCCGAGGAGGTCAACTA-3′ , reverse: 5′-GCTCAGGGTGATTGCGTATGCA-3′ ) . The amplification was done for 24 cycles ( 95°C 30 sec , 52°C 45 sec , 72°C 1 min ) . Drosophila ATP assay was performed as following a previous report [60] . Briefly , the fly thoraces were dissected and homogenized in 6 M guanidine hydrochloride in extraction buffer ( 100 mM Tris and 4 mM EDTA , pH7 . 8 ) to denature endogenous ATPases . The supernatant fraction of the homogenate was collected after centrifugation at 16 , 100 g and then diluted in 1/750 with extraction buffer . The protein concentration was determined using the Bradford protein assay system ( Bio-Rad ) . The ATP level was determined by ATP determination kit ( Invitrogen ) and measured by Victor 3 plate reader ( PerkinElmer ) . The relative ATP level was then calculated by dividing the luminescence readout with the protein concentration and then normalized to control . | Inclusion body myopathy with Paget's disease of bone and frontotemporal dementia ( IBMPFD ) is a progressive autosomal dominant disease , characterized by the adult onset of muscle degeneration , abnormal bone metabolism , and drastic behavior changes . IBMPFD is caused by specific mutations in the highly conserved VCP gene , an ATPase known to participate in numerous cellular functions . Because of its diverse functions , it has been difficult to decipher how VCP mutations cause this debilitating disorder . To understand how these specific mutations in VCP lead to IBMPFD , we have developed a Drosophila IBMPFD model by introducing analogous mutations in TER94 , the fly VCP homolog . We show that TER94 carrying these specific mutations can disrupt the fly muscle and nervous systems , similar to the symptoms of IBMPFD in humans . These phenotypic similarities suggest that information gained from our analysis of TER94 will enhance our understanding of how VCP mutations cause IBMPFD . By subjecting our fly IBMPFD model to various physiological and genetic manipulations , we have uncovered a novel link between the disease progression and cellular ATP level . Thus , in addition to establishing a fly model for further analysis of this disease , our finding should suggest new therapeutic strategies for IBMPFD . | [
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"pathology/pathophysiology",
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] | 2011 | Pathogenic VCP/TER94 Alleles Are Dominant Actives and Contribute to Neurodegeneration by Altering Cellular ATP Level in a Drosophila IBMPFD Model |
Large numbers of gross chromosomal rearrangements ( GCRs ) are frequently observed in many cancers . High mobility group 1 ( HMG1 ) protein is a non-histone DNA-binding protein and is highly expressed in different types of tumors . The high expression of HMG1 could alter DNA structure resulting in GCRs . Spt2p is a non-histone DNA binding protein in Saccharomyces cerevisiae and shares homology with mammalian HMG1 protein . We found that Spt2p overexpression enhances GCRs dependent on proteins for transcription elongation and polyadenylation . Excess Spt2p increases the number of cells in S phase and the amount of single-stranded DNA ( ssDNA ) that might be susceptible to cause DNA damage and GCR . Consistently , RNase H expression , which reduces levels of ssDNA , decreased GCRs in cells expressing high level of Spt2p . Lastly , high transcription in the chromosome V , the location at which GCR is monitored , also enhanced GCR formation . We propose a new pathway for GCR where DNA intermediates formed during transcription can lead to genomic instability .
Maintaining genomic stability is crucial for cell survival and normal cell growth . Different genetic disorders , including cancers , display different forms of genomic instabilities . There is growing evidence supporting the hypothesis that gross chromosomal rearrangements ( GCRs ) found in different cancers are caused by the pre-acquisition of mutator mutations [1]–[4] . Identification of such mutator mutations could help to identify more genes participating in carcinogenesis . Multiple mutator mutations that facilitate GCRs were identified by using the yeast Saccharomyces cerevisiae as a model system [2] , [5]–[8] . There are multiple pathways for the suppression of genomic instability . The importance of these pathways in human cancer development has been uncovered by observations of mutations in their human homologous genes in many cancers or cells from cancer pre-disposed syndrome patients [1] , [2] , [4] , [9] . Chromatin structure is important for almost all DNA metabolism including replication , transcription , recombination , and repair . Nucleosome , a basic unit of chromatin is composed of 146 base pairs of DNA wrapped with octameric histones [10] . Other non-histone DNA binding proteins participate in the structure of chromatin [11] . Spt2p , also known as Sin1p is a non-histone DNA binding protein and was first identified by mutations suppressing Ty and Δ insertion mutations in the HIS4 gene in Saccharomyces cerevisiae [12] . In addition , the spt2Δ mutation suppresses the abnormal initiation of transcription conferred by mutations that cause defects in Swip/Snfp [13] or in the SAGA complex [14] , [15] as well as by the rpb1Δ mutation that shortens the Rpb1p carboxyl-terminal domain [16] . The synthetic lethal interactions between spt2Δ and cdc73Δ , a member of the PAF complex , which accompanies RNA polymerase II during elongation and has an important function in polyadenylation , suggested that Spt2p could function in transcription elongation and polyadenylation [17] , [18] . In addition , the functional interaction between Spt2p and Hpr1p further supported the putative role of Spt2p in transcription elongation and polyadenylation because Hpr1p is part of THO complex as well as Fir1p that is a positive regulator of RNA polyadenylation [17] , [19] . Recent molecular evidence including chromatin immunoprecipitation data and the effect on polyadenylation of the spt2Δ mutation confirmed that Spt2p indeed functions in both transcription elongation and polyadenylation [17] , [20] . In addition to its role in transcription , the spt2Δ mutation enhances recombination where transcription is active [17] and causes defects in chromosome segregation [21] . These data strongly suggest that Spt2p has a role in maintaining general genomic integrity , presumably where transcription is active . Spt2p has two domains that have high homology to the high mobility group 1 ( HMG1 ) protein in higher eukaryotes , as well as an acidic domain and a C-terminal polar helical domain [21]–[23] . Three of these domains can bind four-way junction DNA . Its DNA binding activity seems to induce specific changes in chromatin structure , thereby allowing the assembly of proteins involved in transcription and recombination [23] . In the present study , we demonstrate that excess Spt2p induces a high degree of GCR formation in Saccharomyces cerevisiae . The C-terminal polar helical domain ( amino acids 303 to 333 ) , which is required for DNA binding is necessary and sufficient for enhancing GCR formation . GCRs enhanced by excess Spt2p were due to an increase of single stranded DNA ( ssDNA ) , presumably through the collision of transcription-dependent R-loops and replication forks . These findings demonstrate that defects in tight regulation between replication and transcription could lead genomic instability .
Structural changes of chromosomes induced by overexpression of DNA binding proteins alter multiple DNA metabolisms including replication , repair , and transcription . Such changes in chromosome might lead to GCR . Dramatic increase of HMG1 expression has been documented in many tumors [24]–[26] . Yeast Spt2p has an HMG1-like motif and functions to change structure of chromosomes that affects transcription presumably through its DNA binding activity [15] , [17] , [20] , [23] . We hypothesized that Spt2p overexpression could lead to GCR . To test this hypothesis , we overexpressed Spt2p for two hours under a galactose-inducible promoter . High expression of Spt2p enhanced GCR up to 1 , 600 fold compared to normal level of expression even without treatment with DNA damaging agents ( Figure 1A ) . To determine whether the level of Spt2p expression affects GCR formation , the GCR frequencies were measured after inducing Spt2p expression for different lengths of time . A slight increase in Spt2p expression after 30 minutes was enough to increase GCR formation ( Figure S1A ) . The maximum increase in GCRs was achieved after two hours of induction and started to decrease after four hours . When we chronically overexpressed Spt2p , cells did not grow well ( Figure S1B ) . Therefore , the decrease in GCRs after four hours seems to be due to growth defects caused by excess Spt2p . The rearrangement structures from sixty independent clones containing GCRs induced by excess Spt2p were all broken chromosomes healed by the addition of telomere sequence through de novo telomere addition , a class of GCR known as de novo telomere addition . Consistent with this observation , mutations in yKU80 , RAD10 , RAD1 , or TLC1 that are required for de novo telomere addition almost completely abolished GCRs caused by excess Spt2p ( Figure 1B ) . This reduction in GCRs is unlikely due to a reduction in Spt2p expression , as levels of this protein were similar across all strains ( Figure 1C ) . Spt2p has two mammalian HMG1-like domains in its N terminal half and two C-terminal acidic domains , which are often found in HMG-like proteins ( Figure 2A ) . Because all four domains have been shown to bind DNA [23] and thus potentially affect GCR formation , we cloned each domain individually and overexpressed them in the same manner as the wild type protein , and monitored GCR frequencies . Overexpression of each domain enhanced GCR in different extent . The highest enhancement was observed when the C-terminal thirty amino acids were overexpressed ( Figure 2B ) . The last thirty amino acids of Spt2p have a cluster of positively charged amino acids that is important for the binding of Spt2p to four-way junction DNA [23] and suppression of a swi1 phenotype [27] , [28] . A single amino acid substitution at Lysine 325 to Arginine completely abolished the binding activity of Spt2p to four-way function DNA and the ability of Spt2p to suppress swi1 phenotype . To test whether DNA binding of Spt2p is important for GCR formation , we overexpressed full length Spt2p with the K325R mutation . Although the mutant protein was expressed at a level similar to wild type , the overexpression of K325R Spt2p mutant protein did not cause any GCR formation ( Figure 2C and D ) . Therefore , the GCR formation enhanced by excess Spt2p requires the C-terminal DNA-binding domain . The lack of GCR promoting activity of the full length Spt2p with the K324R mutation even though it has other domains that could enhance GCR separately ( Figure 2B ) could be due to structural differences . To investigate whether GCR enhancement by excess Spt2p has any genetic interaction with known GCR pathways , the spt2Δ mutation was added in mre11Δ , mec1Δ sml1Δ , or pif1-m2 strains and GCR rates were determined . The additional spt2Δ mutation did not cause any change in GCR rates compared to parental strains ( data not shown ) . Therefore , GCR enhancement by excess Spt2p seems to be promoted by a different mechanism . Spt2p functions in transcriptional elongation and polyadenylation [17] , [20] , [29] . We hypothesized that GCR induced by excess Spt2p could be due to defects in transcription . To test this hypothesis , we mutated different genes functioning in elongation and polyadenylation of transcription . The Bur1p/Bur2p complex is a cyclin-dependent protein kinase involved in the regulation of transcription elongation [30] . The Bur1p/Bur2p complex phosphorylates the serine 120 of Rad6p that activates Rad6p and Bre1p to monoubiquitinate H2B for transcription elongation . Because BUR1 is an essential gene , we deleted the BUR2 , RAD6 , and BRE1 genes and monitored GCR formation by excess Spt2p . Although the mutation of BUR2 , RAD6 , or BRE1 did not cause significant change of the GCR rate ( Table 1 ) , these mutations completely abolished the increase of GCRs caused by excess Spt2p ( Figure 3A ) . Western blots showed that Spt2p expression was not affected by the rad6Δ , bre1Δ , or bur2Δ mutation ( Figure 3A ) . BUR kinase is functionally linked to histone H2B ubiquitination and K4 trimethylation . Recently , synthetic genetic arrays and DNA microarrays demonstrated that a functional link between the BUR kinase complex and histone modification was achieved by its ability to regulate PAF recruitment selectively to genes for histone K4 trimethylation and H2B ubiquitination [30] , [31] . Histone H3 is methylated at lysines 4 and 79 positions by Set1p and Dot1p , respectively , and it is dependent on a preexisting mark on the ubiquitination of K123 on H2B [32] . We hypothesized that GCR enhanced by excess Spt2p would be dependent on the methylation of Histone H3 by Set1 and Dot1 . To test this hypothesis , we compared GCR frequencies upon Spt2 overexpression in set1Δ and dot1Δ strains with wild type . Consistent with our hypothesis , the set1 and dot1 mutations clearly reduced GCR frequencies enhanced by excess Spt2p ( Figure 3B ) . We observed a slight reduction of Spt2p expression in the set1Δ strain ( Figure 3B ) . Similar to other mutations affecting transcription , the set1Δ and dot1Δ mutations did not increase GCR rates ( Table 1 ) . The PAF transcription elongation complex is composed of Cdc73p , Ctr9p , Leo1p , and Rtf1p [33]–[35] . Although the exact role of the PAF complex is still unclear , defects caused by the mutation of these genes clearly indicate that the PAF complex is involved in transcription elongation . Another complex known as the HIR1/HPC complex is composed of Hir1p , Hir2p , Hir3p , and Hpc and is involved in several chromatin-related processes , including regulation of histone genes , chromatin assembly , kinetochore function , and transcription elongation [17] , [36] . To confirm the dependence of GCR induced by excess Spt2p on transcription elongation , GCR frequencies caused by excess Spt2p in cdc73Δ and hir1Δ strains were measured . Although there was no significant difference in GCR rates in cdc73Δ and hir1Δ strains compared to wild type ( Table 1 ) , cdc73Δ and hir1Δ mutation completely abolished the enhancement of GCR formation by excess Spt2p ( Figure 3C ) . We observed slight reduction of Spt2p in cdc73Δ and hir1Δ strains . Swr1p is a member of the Snf2 family ATPases . A complex containing Swr1p incorporates the histone H2A variant Htz1 into chromatin to change chromatin structure in favor of transcription [37] . To investigate whether GCR formation by excess Spt2p could be suppressed by the swr1Δ mutation by lowering transcription , we measured the GCR frequency in the swr1Δ strain upon Spt2p overexpression . Although there was no significant change in GCR rate in the swr1Δ strain ( Table 1 ) , the enhanced GCR caused by excess Spt2p was completely reduced by the swr1Δ mutation ( Figure 3C ) . We also tested whether its inactivation of a general transcription elongation factor Dst1p could reduce GCR formation enhanced by excess Spt2p . Similar to swr1Δ , even though there was no significant change of GCR rate by the dst1Δ mutation ( Table 1 ) , the dst1Δ mutation completely blocked the GCR enhancement by excess Spt2p ( Figure 3C ) . The galactose-induced Spt2p expression was not significantly affected by either the swr1Δ or the dst1Δ mutation ( Figure 3C ) . Spt2p interacts with Fir1p , a component of the RNA cleavage/polyadenylation complex [20] , [29] . Proper RNA cleavage and polyadenylation are also dependent on Hpr1p , which has been implicated in the modification of chromatin structure and in the removal of Spt2p from chromatin [19] . We hypothesized that GCR enhanced by excess Spt2p would be dependent on proper polyadenylation . To test this hypothesis , we measured GCR frequencies upon Spt2p overexpression in hpr1Δ and fir1Δ strains compared to wild type . Consistent with our hypothesis , the enhanced GCRs caused by excess Spt2p were substantially reduced by these mutations ( Figure 3D ) even though these mutations did not significantly affect the expression of Spt2p ( Figure 3D ) . The hpr1Δ and fir1Δ mutations did not cause significant changes in GCR rates as compared to wild type ( Table 1 ) . Therefore , GCRs enhanced by Spt2p depend on proper transcription elongation and termination . The reduction of Spt2p-induced GCR by mutations inhibiting proper transcription suggested that abnormal transcription would produce Spt2p-induced GCRs . During transcription , the transcription machinery unwinds the DNA double helix and occupies the noncoding strand to use it as a template for transcription . In addition , transcription produces a transient DNA-RNA hybrid ranging 9 to 12 nucleotides and the coding strand becomes single stranded DNA ( ssDNA ) . It has been shown that the hyper-recombination observed in hpr1Δ was due to the induction of the DNA-RNA hybrid with the R-loop formation and could be suppressed by the overexpression of RNase H [38] . We therefore hypothesized that abnormal transcription induced by excess Spt2p could increase the number of DNA-RNA hybrids and create larger ssDNA . RNase H can remove RNA from DNA-RNA hybrids . Therefore , we first tested whether removing RNA from DNA-RNA hybrids by RNase H overexpression could reduce GCRs produced by excess Spt2p . Overexpression of RNase H in addition to Spt2p substantially reduced GCR formation as compared to Spt2p overexpression alone ( Figure 4A ) . We then compared the quantity of ssDNA when there was an excess Spt2p . Spt2p overexpression caused a high level of ssDNA that was also substantially reduced by RNase H co-overexpression ( Figure 4B ) . Therefore , GCRs caused by excess Spt2p seemed to be produced by higher levels of ssDNA , presumably due to an abnormal transcription . High levels of ssDNA activate a cell cycle checkpoint [39] . To test whether ssDNA created by excess Spt2p also activates cell cycle checkpoints , we investigated cell cycle profiles of cells after chronic Spt2p overexpression . In contrast to control , where there is no protein induction , Spt2p overexpression caused a significant population of cells to arrest in S phase ( Figure 4C ) and also arrested cell growth ( Figure S1B ) . Consistent with a reduction of ssDNA by RNase H and Spt2p co-overexpression , the S phase population was substantially reduced by RNase H overexpression together with Spt2p overexpression . Therefore , excess Spt2p induced ssDNA presumably due to high transcription and as a result , a cell cycle checkpoint was activated . However , we could not detect Rad53 phosphorylation after the induction of Spt2p for four hours ( data not shown ) . In addition , the rad24Δ mutation could not restore the growth defect of cells chronically overexpressing Spt2p ( data not shown ) . Long ssDNA caused by excess Spt2p ( Figure 4B ) could be an easy target for multiple enzymatic reactions . Cytosines in ssDNA can be modified through deamination and changed to Uracil . Such modification by activation-induced deaminase ( AID ) in immunoglobulin genes causes somatic hypermutation and class switch recombination [40] . Uracil produced by deamination results in error prone hypermutation or strand breaks . We hypothesized that long ssDNA produced by excess Spt2p would be modified by AID-like enzymes in yeast to induce strand breaks for GCR formation . To test this hypothesis , we expressed the human AID enzyme in yeast and measured GCR frequency . The human AID enzyme has been shown to cause a hyper-mutation and hyper-recombination phenotype in yeast similar to in human B cells [41] , [42] . Consistent with our hypothesis , the induction of human AID expression increased GCR frequency as compared to control ( Figure 5A ) . Ung1p , a uracil DNA glycosylase , removes uracil from DNA in yeast [43] . The removal of uracil from DNA could generate nicks in DNA . We hypothesized that strand breaks by Ung1p would be a necessary step for GCR formation by excess Spt2p . To test this hypothesis , we knocked out UNG1 and measured GCR frequency upon Spt2p overexpression . Consistent with our hypothesis , the inactivation of Ung1p significantly reduced GCRs produced by excess Spt2p ( Figure 5B ) . Therefore , GCRs by excess Spt2p are dependent on Ung1p that presumably creates breaks at modified uracils in ssDNA . As an independent method to investigate whether transcription is a factor that enhances GCR formation , we treated yeast strain overexpressing Spt2p with 6-Azauracil ( AU ) and monitored the GCR formation . 6-AU is an inhibitor of enzymes involved in nucleotide biosynthesis and causes change in nucleotide pool levels . It has been shown that the treatment of 6-AU on yeast diminished transcription elongation [44] . The treatment of 6-AU significantly reduced GCRs produced by excess Spt2p ( Figure 6A ) . Therefore , transcription elongation is an important factor for increased levels of GCR by excess Spt2p expression . To investigate the direct involvement of transcription in GCR formation , we inserted the TRP1 gene under the control of strong TEF promoter between two negative selection marker genes , CAN1 and URA3 for GCR assay . The TEF-TRP1 gene was inserted in two different orientations; one transcribing the TRP1 gene from centromeric to telomeric direction ( CEN to TEL ) and the other transcribing the TRP1 gene from telomeric to centromeric direction ( TEL to CEN ) ( Figure 6B ) . The GCR rate of the TEL to CEN strain was significantly higher than the rates of wild type or of the CEN to TEL strain ( Figure 6C ) . In addition , when we measured the GCR frequencies after 0 . 1% MMS treatment , the TEL to CEN strain had significantly increased GCR frequency when compared to the no TRP1 insertion ( WT ) or the CEN to TEL strain . Because the GCR assay marker genes seem to preferentially replicate from centromeric to telomeric direction , the collision between transcriptions and stalled forks might be the major cause of the high induction of GCR frequency in the TEL to CEN strain . Spt2p overexpression would further enhance GCR formation in these strains because it would modify the transcription rate of TEF-TRP1 . Indeed , Spt2p overexpression further enhanced GCR formation ( Figure 6D ) . Intriguingly , when 0 . 1% MMS was treated together with Spt2p overexpression , GCR frequency was reduced . To further support that transcription caused GCR formation , we constructed another strain having a TRP1 gene expressed under the galactose inducible promoter in the TEL to CEN direction ( Figure 6E ) . When this strain was cultured in media having galactose that induced the expression of TRP1 gene , GCR formation was enhanced ( Figure 6F ) . Therefore , a high level of transcription promotes GCR formation .
Spt2p binds DNA and regulates transcription elongation and chromatin structure [15] , [17] , [20] , [23] . The synthetic lethality of spt2Δ with other transcription elongation genes strongly suggests that Spt2p functions in transcription through its sequence non-specific DNA binding activity [17] . In addition , the absence of Spt2p caused a loss of histone H3 in transcribed regions and increased recombination between inverted repeats [17] . Therefore , Spt2p's sequence non-specific DNA binding activity seems to contribute to genomic integrity , presumably through the regulation of chromatin structure in the transcribed region . Complete suppression of GCR caused by excess Spt2p by mutations affecting transcription ( Figure 3 ) strongly demonstrates that excess Spt2p might alter transcription and result in GCR formation . The suppression of GCR by these mutations was specific for excess Spt2p-directed GCRs because the fir1Δ , set1Δ , or cdc73Δ mutation did not suppress the mre11Δ mutation-directed GCR formation ( data not shown ) . It should be pointed out that a set1Δ or cdc73Δ mutation caused a slight growth defect in the mre11Δ strain . Transcription synergistically increases the hyper-recombinogenic effect of methyl methane-sulfonate ( MMS ) , suggesting that transcription makes DNA more accessible to genotoxic agents [45] . Transcription also introduces topological change that could lead to transient accumulation of ssDNA . The changes in topology and chromatin structure caused by excess Spt2p could produce ssDNA because more RNA polymerase II could occupy the transcribed strands and result in the enlargement of R loops ( Figure 7 ) . In addition , excess Spt2p could bind to the collided junction between the DNA replication fork and transcription that mimics a four-way junction structure through its binding activity to four-way junction structure . The longer un-transcribed ssDNA by excess Spt2p is supported by the high-levels of ssDNA , which produced by excess Spt2p ( Figure 4 ) and the decrease in ssDNA and GCR formation by RNase H , which removes DNA-RNA hybrids ( Figure 4A and B ) . It has been known that ssDNA is a better substrate for many chemical reactions than double-stranded DNA [46] , [47] . Long ssDNA can easily be targeted by many modifications including deamination , oxidation as well as simple breaks . Uracil introduced by the deamination of cytosine in ssDNA could be one of the intermediates for GCR formation by excess Spt2p , because expression of human AID that deaminates cytosine increased GCR formation ( Figure 5A ) and Ung1p , an enzyme responsible for removal of Uracil from DNA is required for GCR caused by excess Spt2p ( Figure 5B ) . Similar to what we observed , the hyper-recombination in the hpr1Δ strain was caused by the increase of DNA-RNA hybrid with the R-loop formation [38] . In addition , large R loops could be mis-recognized as an intermediate in nucleotide excision repair ( Figure 7 ) . When there is DNA damage caused by ultra-violet radiation , nucleotide excision repair proteins denature damaged DNA and create bubble structure . Each end of the bubble is targeted by endonucleases to remove the damaged strand . Yeast Rad1p-Rad10p endonuclease that is homologous to human ERCC1-XPF , makes a nick in the bubble [48] . Indeed , ERCC1-XPF could cleave R loops formed in the switch regions during immunoglobulin heavy chain switch recombination in vitro [49] . Strong suppression of excess Spt2p-dependent GCR by the rad1Δ or the rad10Δ mutation ( Figure 1B ) suggests that large R-loops could be targeted by Rad1p-Rad10p endonuclease . However , we could not detect any significant difference in the level of overall transcription in microarray experiments ( data not shown ) , which could be due to subtle difference in the level of transcription . Defects in proper DNA replication seem to be a major source of spontaneous GCRs because GCRs accumulate in eukaryotes when S phase checkpoints are abrogated [50]-[52] . High levels of transcription may cause more collisions between transcription and DNA replication ( Figure 7 ) . Recombination at stalled DNA replication forks increases if there is transcription colliding with it [53] . Excess Spt2p increased the population of cells highly in S phase and RNase H could partially reverse this effect ( Figure 4C ) . Therefore , large R loops produced by excess Spt2p could be caused mainly in S phase during DNA replication , presumably due to increased collision between transcription complexes and stalled DNA replication forks . The high increase in GCR in the TEL to CEN strain , containing the highly transcribed TRP1 gene between two negative selection marker genes CAN1 and URA3 supports the collision model for GCR ( Figure 6C ) . This model is further supported by a GCR increase observed in the GAL-TRP1 strain only when the expression of the TRP1 gene was induced by galactose ( Figure 6F ) . Interestingly , the CEN to TEL strain containing the same high transcription TRP1 gene in a reverse orientation did not cause any significant increase in GCR . It might be due to higher preference of DNA replication in this region of chromosome V from centromeric to telomeric by using ARS507 even though there are multiple late origins at the end of chromosome V . Alternatively , the TEL to CEN strain might have more susceptible chromosome structure for GCR because different orientation of TRP1 gene could produce different chromosome structures . The Rad5p-Rad18p dependent post-replication repair pathway suppresses GCR formation [8] , [54] . In contrast to Rad18p-Rad6p that monoubiquitinates proliferating cell nuclear antigen ( PCNA ) and suppresses GCR formation , Bre1p-Rad6p that monoubiquitinates histone H2B , is required to promote GCR formation in the rad5Δ , rad18Δ , or mec1Δ strains [54] . In the present study , we found that GCRs produced by excess Spt2p were also suppressed by the rad6Δ or bre1Δ mutation ( Figure 3A ) . GCRs from each individual clone carrying a GCR were all broken chromosomes healed by de novo telomere addition requiring telomerase and the yKu70-yKu80 heterodimer ( Figure 1B ) . The same type of GCR was observed in rad5Δ , rad18Δ , or mec1Δ strain [8] , [52] . Therefore , it is possible that certain types of GCR could be preferentially generated when DNA damage at stalled forks collide with transcription complexes . Further investigations are necessary to elucidate mechanisms . Intriguingly , Rad5p has a Swi2/Snf2 domain that has been suggested to function in altering chromatin structure . Although there is no direct evidence that yeast Rad5p functions in transcription , it might modulate transcription of genes near the stalled DNA replication forks . The HMG1 protein is a non-histone DNA binding protein and regulates the transcription of many genes through its interaction with other proteins involved in transcription . Transcription profiling showed a dramatic increase of HMG1 expression in more than 80% of gastric cancers [24] . In addition , various cancer cells including melanoma expressed higher levels of HMG1 protein [25] , [26] . Therefore , high levels of HMG1 protein seem to be closely linked to carcinogenesis . Previous studies of HMG1 overexpression in cancers mainly revealed its role in activating transcription of certain genes such as the Melanoma Inhibitory Activity ( MIA ) for the progression of carcinogenesis [25] . Our novel discovery demonstrating the high enhancement of GCR formation by an HMG-like protein suggests that Spt2p can add new mechanistic detail to carcinogenesis linked to transcription imbalance and genomic integrity .
The strains used in this study were isogenic to S288c background RDKY3615 ( MATa ura3-52 leu2Δ1 trp1Δ63 his3Δ200 lys2-Bgl hom3-10 ade2Δ1 ade8hxt13::URA3 ) . All strains were generated using standard PCR-based gene disruption methods and correct gene disruptions were verified by PCR as described previously [8] , [52] . The sequences of primers used to generate disruption cassettes and to confirm disruption of indicated genes are available upon request . The detailed genotypes of strains are listed in Table S1 . Media for the propagation of strains were as previously described [8] , [52] . All S . cerevisiae strains were propagated at 30°C . Yeast transformation , yeast chromosomal DNA isolation for use as PCR template in and PCRs were performed as previously described [8] , [52] . 400 ml of overnight cultured yeast in selective synthetic drop-out ( SD ) media and containing 2% glucose was inoculated into 10 ml fresh media and grown at 30°C to a cell density of 1–2×107 cells/ml . Cells were washed twice with 10 ml distilled water and resuspended in10 ml of selective SD media with 2% ( w/v ) glycerol and 1% Succinic acid and cultured at 30°C overnight . Freshly prepared galactose was added to a final concentration of 2% to induce the expression of Spt2p . After 2 hours , cells were harvested from 1 ml of culture , resuspended in 10 ml of yeast extract-peptone media containing 2% glucose ( YPD ) , and incubated overnight until the culture reached saturation . The cells were plated onto YPD plates and plates containing both 5-fluoroorotic acid ( 5-FOA ) and canavanine ( FC ) for selection of clones with GCRs . The GCR frequency was calculated by dividing the number of colonies resistant to both drugs with actual plated cell numbers that were deduced from the number of colonies on YPD plates . Five independent cultures of each strain were used in each experiment and each experiment was performed at least twice . The average fold increases in the GCR frequency of treatment relative to that of each control were calculated . All GCR rates were determined independently by fluctuation analysis using the method of the median with at least two independent clones two or more times using 5 to 11 cultures for each clone . The average value is reported as previously described 6 , 55 . The breakpoint spectra from mutants carrying independent rearrangements were determined and classified as described [6] , [8] , [55] . The full-length SPT2 gene was amplified from yeast chromosomal DNA by PCR with the primers PRKJM804 ( 5′ggatccGTGAAATATTTTAGTTATGAGTTTTCTTTCC3′ ) and PRKJM805 ( 5′ctcgagCAAAACATATATCAATATTCCTTAGCG3′ ) . The sequences in lower case are additional sequences for restriction enzyme digestion for cloning purposes . The amplified SPT2 gene was first cloned in the PCR 2 . 1 vector and ( Invitrogen ) and named pKJM371 ( SPT2 ) . The SPT2 gene was sequenced to confirm that there was no mutation and then subcloned into the pYES3CT plasmid ( Invitrogen ) , which allows the SPT2 gene to be expressed under the GAL1 inducible promoter . This plasmid was named pKJM378 and transformed into different yeast strains for induction of Spt2 expression . As a control , the pYES3CT empty vector was transformed into the same yeast strains for comparison . The N-terminal cDNA of the SPT2 gene encoding amino acids from 1 to 96 was PCR amplified by using PRKJM1790 ( 5′ccccggatccATGAGTTTTCTTTCCAAACTT3′ ) and PRKJM1791 ( 5′ ccccgcggccgcccTTAAAGGCCACCTTCATCATCGTCAT3′ ) . The middle portion of the cDNA of the SPT2 gene encoding amino acids from 100 to 162 was PCR amplified by using PRKJM1792 ( 5′ ccccggatccATGTTTAAGAGGTCTATTGGAGCA3′ ) and PRKJM1793 ( 5′ ccccgcggccgcccTTAGAAACCTGGCTTGTTAAAATGTG3′ ) . The PCR amplification of the SPT2 cDNA from amino acids 225 to 304 was achieved using primers PRKJM1794 ( 5′ ccccggatccATGAGATACCAGGATGACTATGAT3′ ) and PRKJM1795 ( 5′ ccccgcggccgcccTTATCTTGCCATTTCCTCCTCTTCC3′ ) . The PCR amplification of the SPT2 gene from amino acids 304 to 333 was performed using primers PRKJM1796 ( 5′ ccccggatccATGAGAAAAATGGCAAGGTTAGAG3′ ) and PRKJM1797 ( 5′ ccccgcggccgcTTAGCGTATGCCCTTCTTACGG3′ ) . The SPT2 cDNA from amino acids 1 to 303 was PCR amplified with primers PRKJM1790 ( 5′ccccggatccATGAGTTTTCTTTCCAAACTT3′ ) and PRKJM1795 ( 5′ ccccgcggccgcccTTATCTTGCCATTTCCTCCTCTTCC3′ ) . The single amino acid substitution mutant Spt2p ( K325A ) was generated by site-directed mutagenesis with primers , PRKJM1872 ( 5′ AGCATGAAGAGGAGgcGAGACGCCGTAAGAA 3′ ) and PRKJM1873 ( 5′ TTCTTACGGCGTCTCgcCTCCTCTTCATGCT 3′ ) . The lower case sequences indicate mutations incorporated to make the K325A mutation . The pKJM378 plasmid was used as a template for all PCR amplifications . All amplified PCR products were first subcloned into the PCR 2 . 1 vector and sequenced to confirm that there was no mutation . The plasmids carrying the SPT2 cDNA 1–96 , 100–162 , 224–304 , 304–333 1–303 , and the K325A mutation in pCR2 . 1 backbone were named pKJM916 , pKJM918 , pKJM922 pKJM920 pKJM970 and pKJM980 , respectively . All inserts were moved to the pYES3CT and named as pKJM924 , pKJM926 , pKJM928 pKJM930 pKJM972 and pKJM985 , respectively , and were used to transform different yeast strains . The RNH1 gene was amplified with the primers PRKJM1891 ( 5′ gggaattcATGGCAAGGCAAGGGAACTTCTACGCGG ) and PRKJM1892 ( 5′ ggctcgagTTATCGTCTAGATGCTCCTTTCTTCGCC 3′ ) from the yeast chromosomal DNA and subcloned into the pYX243 vector in the same manner with the construction of plasmids expressing its insert under GAL1 promoter and named pKJM1011 . To measure the level of the Spt2 protein expression , the SPT2 gene was tagged at the N terminus . The FLAG tag was added into the N-terminus of the SPT2 gene through PCR amplification of the SPT2 gene with primers PRKJM1859 ( 5′ ggggatccATGGACTACAAAGACCATGACGGTGATTATAAAGATCATGACATCGATTACAAGGATGACGATGACAAGAGTTTTCTTTCCAAACTTTCCCA 3′ ) and PRKJM1797 ( 5′ ccccgcggccgcTTAGCGTATGCCCTTCTTACGG3′ ) . The sequences in lower case are additional sequences for restriction enzyme digestion for cloning purposes . The amplified FLAG tagged SPT2 gene was cloned in the PCR 2 . 1 vector and the insert was sequenced to confirm that there was no mutation . The SPT2 gene was moved into pYES3CT and named pKJM989 . Each mutation used in the study was amplified similarly by using the same primers with different templates . GCR frequencies were not affected by FLAG tag . To determine the cell cycles of yeast strains , FACS analysis was performed . Indicated yeast strains were grown in 2 ml of synthetic drop-out media with 2% glucose . Tryptophan or Leucine was omitted from media to support plasmids . One milliliter of the overnight cultured yeast was collected and washed three times with sterile water . Cells were resuspended in 1 ml of synthetic drop-out media with 2% galactose and allowed to grow for an additional 24 hours for induction of the Spt2 gene . Cells ( 0 . 5 ml; 1–2×106 ) were washed and resuspended in cold 70% ethanol followed by 2 hour incubation on ice . Cells were then incubated with 0 . 5 ml of 1 mg/ml RNase containing 50 mM Tris HCl ( pH 7 . 4 ) and 15 mM NaCl overnight at 37°C . Cells were harvested and resuspended in 0 . 5 ml of 50 mM Tris HCl ( pH 7 . 4 ) and 50 µl of cell suspension was placed into 1 ml of SYTOX Green solution ( 1 µM SYTOX Green in 50 mM Tris HCl pH 7 . 4 ) , was sonicated at low power , and was analyzed by standard flow cytometry methods . For this study , cells were analyzed on a FACScalibur ( Becton Dickinson Immunocytometry Systems ) , with an argon laser tuned to 488 nm . The FL1 detector with a standard 530/30 band pass filter was used in the acquisition of SYTOX Green fluorescence and the FL3 detector with a 670 nm long pass filter was used to collect PI fluorescence . Cell extracts were prepared by a standard trichloroacetic acid ( TCA ) method . Briefly , cells were washed with 20% trichloroacetic acid and broken with glass beads . Cell extracts were collected and resuspended in 1X SDS loading buffer . Samples were boiled and centrifuged before being loaded onto a 7–12% SDS PAGE ( Bio-Rad ) . Proteins separated by SDS PAGE were transferred to a PVDF membrane and FLAG-Spt2 was detected by standard western hybridization with an anti-FLAG HRP antibody ( Sigma ) and Western Blotting Detection Reagents ( GE Healthcare ) . Cells were prepared by using the same method described in FACS analysis . Chromosomal DNA was prepared using Gentra Puregene yeast chromosomal DNA isolation kit ( Qiagen ) following the manufacturer's protocol . The same quantity of chromosomal DNA was spotted on nitrocellulose membrane in duplicate . DNA in one membrane was denatured via incubation with the denaturation solution ( 0 . 5M NaOH , 1 . 5M NaCl ) for 30 minutes followed by incubation with the neutralization solution ( 0 . 5M Tris-HCl , 3M NaCl , pH7 . 4 ) for thirty minutes at room temperature . After UV crosslinking of DNA , membranes were hybridized with radio-labeled DNA by random priming with Prime II ( Stratagene ) . The DNA used for probe was PCR amplified from yeast chromosome V ( 31121–31859 ) that covers part of two ORFs , AVT2 and CAN1 with primers PP1-1 ( 5′-CCTTGGCTTCCGTCATCGGAGTCGTTATCAG-3′ ) and PP1-2 ( 5′-GCTTTGCTGCCGCCTATATCTCTATTTTCCTG-3′ ) . | Transmitting genetic information without creating deleterious genetic alternations is one of the cell's most important tasks . When cells cannot repair DNA damage properly , it leads to genomic instability and results in genetic disorders , including cancer . Many studies , including ours , have started to uncover pathways suppressing one type of genomic instability , gross chromosomal rearrangement ( GCR ) . However , the pathogenic mechanism to promote GCR that could mimic the hyper-activation of oncogenes during tumorigenesis is not clearly understood . The high expression of HMG1 has been documented many times as a putative oncogene . Therefore , we tested whether high expression of its yeast homologue , Spt2p , could induce pathogenic effect including GCR formation . Excess Spt2p expression indeed induced GCR formation dependent on its role in transcription elongation and polyadenylation . Further studies to find mechanisms resided in GCR formation by Spt2p revealed that excess Spt2p increased single-stranded DNA to produce GCR . Our studies provide a mechanistic bridge between transcription and genomic instability . | [
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] | 2008 | Spt2p Defines a New Transcription-Dependent Gross Chromosomal Rearrangement Pathway |
Distinct transcriptional states are maintained through organization of chromatin , resulting from the sum of numerous repressive and active histone modifications , into tightly packaged heterochromatin versus more accessible euchromatin . Polycomb repressive complex 2 ( PRC2 ) is the main mammalian complex responsible for histone 3 lysine 27 trimethylation ( H3K27me3 ) and is integral to chromatin organization . Using in vitro and in vivo studies , we show that deletion of Suz12 , a core component of all PRC2 complexes , results in loss of H3K27me3 and H3K27 dimethylation ( H3K27me2 ) , completely blocks normal mammary gland development , and profoundly curtails progenitor activity in 3D organoid cultures . Through the application of mammary organoids to bypass the severe phenotype associated with Suz12 loss in vivo , we have explored gene expression and chromatin structure in wild-type and Suz12-deleted basal-derived organoids . Analysis of organoids led to the identification of lineage-specific changes in gene expression and chromatin structure , inferring cell type–specific PRC2-mediated gene silencing of the chromatin state . These expression changes were accompanied by cell cycle arrest but not lineage infidelity . Together , these data indicate that canonical PRC2 function is essential for development of the mammary gland through the repression of alternate transcription programs and maintenance of chromatin states .
A central question in biology is how different cell types maintain distinct cell fates despite containing the same genetic material . Organization of DNA into open or closed chromatin states by posttranslational modifications ( PTMs ) of histones has emerged as a critical mechanism underpinning cell diversity and reflecting lineage-specific gene expression , developmental programs , or disease processes [1] . The highly conserved Polycomb repressive complex 2 ( PRC2 ) , which catalyzes trimethylation of histone 3 on lysine 27 ( H3K27me3 ) , is associated with global gene repression and suppression of alternative differentiation programs . The canonical PRC2 complex is composed of the intimately associated core proteins histone methyltransferase Enhancer of Zeste homolog 2 ( Ezh2 ) or an alternative related subunit Ezh1 [2] , embryonic ectoderm development ( Eed ) , Suppressor of Zeste 12 protein homolog ( Suz12 ) , and histone-binding protein accessory proteins [3] . Upon recruitment of PRC2 to chromatin , Ezh2/Ezh1 deposits the H3K27me3 mark associated with chromatin compaction [3] . PRC2 is required for deposition of H3K27me3 and for maintenance of this PTM upon cell division [4] . Additionally , the requirement of PRC2 activity for H3K27 mono- and dimethylation ( H3K27me1 and H3K27me2 ) remains unclear [5] . Early studies showed that both Suz12 and Eed are nonredundant and essential for a functional PRC2 complex [6] . Deletion of Suz12 or Eed resulted in elevated expression of Hox genes in Drosophila and mammalian cells and marked increases in gene networks that control developmental lineages [7 , 8] because of loss of PRC2 integrity and H3K27me3-mediated repression [9] . In contrast to Eed and Suz12 , Ezh2 function can be compensated , partially [10] or completely [11] , by Ezh1 . Nevertheless , like mice lacking Suz12 or Eed , Ezh2-deficient mice are not viable and die during early implantation stages [6] . Members of PRC2—in particular , Ezh2—are often found dysregulated in human cancers . In breast cancer , Ezh2 overexpression is associated with aggressive breast cancers and poor prognosis and inversely correlated with H3K27me3 expression [12] . It remains unclear whether Ezh2 overexpression is a consequence or cause of breast oncogenesis [13] . High levels of Ezh2 may not be sufficient to induce mammary tumors in mice [14] , suggesting additional driver mutations are required . To further understand the basis of this dysregulation in cancer , it is imperative to determine the normal functions of PRC2 and Ezh2 in maintaining gene expression programs in the mammary gland . The mammary gland in both humans and mice is a bilayered structure composed of two cellular lineages: an inner luminal layer and an outer myoepithelial layer that contacts the basement membrane [15] . There is increasing evidence for a differentiation hierarchy composed of stem cells , committed progenitors , and mature epithelial cells [15] . In the steady state , mouse mammary stem cell ( MaSC ) /basal , luminal progenitor , and mature luminal cell subsets display distinct patterns of H3K27me3 [16] . The MaSC/basal subset demonstrates the lowest levels of H3K27me3 . Higher levels of H3K27me3 correlate with reduced gene expression and increase upon cell specialization [16] . These data support a model whereby mammary epithelial cell ( MEC ) differentiation requires narrowing of transcriptional programs and suppression of alternate cell fates . Accordingly , genetic ablation or knock-down of Ezh2 in the mammary gland resulted in a developmental delay [16 , 17] but did not entirely prevent mammary gland development . This is likely due to residual H3K27 methylation , presumably established through compensatory methyltransferase activity of Ezh1 . It is therefore probable that studies to date have underestimated the importance of PRC2 in directing mammary cell fate . To reexamine the contribution of the canonical PRC2 complex to mammary gland development , we deleted Suz12 in vivo and in mammary organoids [18] . Here , we show a nonredundant function for Suz12 in mammary progenitor cells due to loss of PRC2 function . Similar findings were made upon deletion of Eed . Through the application of assay for transposase-accessible chromatin using sequencing ( ATAC-seq ) to probe chromatin accessibility in Suz12-deficient organoid cultures , we have identified regions of PRC2-dependent chromatin compaction and consequent changes in gene expression . Interestingly , the chromatin state in basal-derived organoids was reliant on PRC2 function , and Suz12 deletion led to gene de-repression , thus highlighting a crucial role for PRC2 in the maintenance of chromatin states within the mammary epithelial hierarchy .
Mammary gland development proceeds through distinct phases that include puberty and cycles of pregnancy , lactation , and involution . Suz12 expression , like Ezh2 [16 , 17] , was detected at all stages of mammary gland development ( S1A Fig ) but was particularly high during puberty ( 4–6 week old mice ) . To examine the role of Suz12 in the mammary gland , we crossed mice bearing floxed Suz12 alleles with Cre transgenic mice that express Cre under control of the mouse mammary tumor virus promoter ( MMTVcre ) . Genotyping of offspring revealed that two-thirds of MMTVcreT/+Suz12fl/f mice did not survive to weaning ( S1 Data ) , and examination of newborn pups revealed abnormal lung development ( S1B Fig ) , likely due to activity of MMTVcre in this tissue [19] , and consistent with the reported role for PRC2/Ezh2 in lung [20] . MMTVcreT/+Suz12f/f mice that survived beyond birth appeared normal and did not differ from wild-type ( Wt ) mice with respect to bodyweight ( S1C Fig ) . Examination of whole mounts and histological sections of mammary glands from MMTVcreT/+Suz12f/f mice during puberty revealed a heterogeneous phenotype . Some glands were indistinguishable from Wt or heterozygous littermates , while others comprised a small ductal tree , characteristic of the rudimentary ductal tree found in newborn mice ( n = 4 ) ( Fig 1A and S1D Fig ) . These small but otherwise normal mammary glands indicate that gene deletion leads to severe impairment of ductal growth during puberty [21 , 22] . Notably , Suz12 mRNA expression in MMTVcreT/+Suz12f/f and MMTVcreT/+Suz12f/+ glands was found to be comparable ( S1E Fig ) , demonstrating that these ductal structures were derived from epithelial cells in which Suz12 had not been deleted . Moreover , protein expression of Ezh2 , Suz12 , and H3K27me3 was retained in sections from 5–6 week old MMTVcreT/+Suz12f/f mammary glands , indicative of strong selection against Suz12-deleted cells and retention of a functional PRC2 complex in the mammary epithelium ( Fig 1B ) . Cell fate was also retained as assessed by immunostaining for estrogen receptor ( ER ) , progesterone receptor ( PR ) , and forkhead box A1 ( Foxa1 ) ( S1F Fig ) . We next determined whether deletion of the second core subunit of canonical PRC2 member Eed produced a similar phenotype to deletion of Suz12 . As seen with Suz12 deletion , conditional deletion of Eed with MMTVcre resulted in perinatal lethality of mice ( S1 Data ) , and surviving mice appeared normal , except for a pronounced absence or delay in growth of the ductal tree ( S2A Fig ) . Similar to Suz12 deletion , Eed mRNA expression was found to be indistinguishable between mammary epithelium from MMTVcreT/+Eedf/f and Wt mice ( S2B Fig ) . In addition , Eed , Ezh2 , and H3K27me3 proteins were detected by immunofluorescence ( IF ) and immunohistochemistry ( IHC ) at comparable levels to those in Wt mammary glands ( S2C and S2D Fig ) . Taken together , these results suggest that canonical PRC2 complexes are critical for the proliferation and/or survival of MECs , and cells deleted for PRC2 function cannot contribute to the developing mammary gland . To investigate which MEC subsets were affected by Suz12 loss , we acutely deleted Suz12 using the inducible and conditional Rosa26-creERT2 ( R26creERT2 ) mouse model . R26creERT2KI/+Suz12f/f mice injected with tamoxifen die within 2 weeks because of hematopoietic failure ( personal communication , S . Lee to E . Michalak ) , thus precluding the use of this model for in vivo studies . Basal/MaSC-enriched ( basal , CD29hiCD24+ ) , committed luminal progenitor ( CD29loCD24+CD14+ ) , and mature luminal ( CD29loCD24+CD14− ) cell subsets were sorted from R26creERT2KI/+Suz12f/f and control mice and subjected to in vitro colony-forming assays to determine the effect of Suz12 deletion on the activity of mammary progenitor cells following induction by 4-hydroxytamoxifen ( 4OHT ) . Consistent with expression of Suz12 in all mammary epithelial subsets ( Fig 2A ) , we observed fewer colonies in 2D colony forming assays on irradiated feeder cells ( Fig 2B ) upon induction of Suz12 deletion . In contrast to the hematopoietic system , in which loss of one allele of Suz12 [14] or Eed [23] enhances the activity of stem cells , deletion of one Suz12 allele did not affect the clonogenic activity of progenitor cells ( Fig 2B ) . Unsorted MECs efficiently deleted Suz12 and yielded sufficient protein for western blot analysis ( Fig 2C ) . As expected , Ezh2 protein was completely lost upon Suz12 deletion , while Ezh2 mRNA levels were not reduced ( S3A Fig ) , suggesting rapid Ezh2 protein degradation leads to the absence of functional PRC2 upon Suz12 deletion . This is supported by the loss of H3K27me2 and H3K27me3 that accompanies Suz12 deletion ( Fig 2C ) . Conversely , expression of the active H3K27me1 mark [4] did not change . No change in cleaved poly ADP ribose polymerase ( PARP ) was detected , suggesting that Suz12-deleted MECs do not undergo appreciable apoptosis . However , we observed increased expression of cyclin-dependent kinase p16 and p19 alternate reading frame ( p19Arf ) , consistent with a known role for Ezh2/PRC2 in regulating cdkn2a gene expression ( S3B Fig ) . These data suggest that Suz12 exerts an essential function in the mammary gland through maintenance of functional tri- as well as dimethylation of target loci . To develop a system more amenable to molecular studies , we employed a 3D mammary organoid system , in which organoids grown from single cells in defined medium recapitulate many features of mammary tissue architecture and function [18] . Single basal or luminal progenitor cells sorted from R26creERT2KI/+Suz12f/f mice were plated , and deletion of Suz12 was induced by addition of 4OHT on day 1 . Suz12 loss resulted in diminished numbers and smaller basal- and luminal progenitor–derived organoids over a 2 week culture period . Notably , PCR analysis of the resulting organoids revealed that selection against cre-mediated excision of both floxed alleles of Suz12 had occurred ( S3C Fig ) . To circumvent this issue , small organoids were allowed to form before induction of deletion . Indeed , addition of 4OHT on day 4 resulted in smaller cystic-like organoids ( Fig 3A and 3B ) composed of Suz12-deleted cells ( Fig 3C and S3D and S3E Fig ) that had markedly reduced levels of H3K27me3 ( S3E Fig ) . There were no notable differences in cleaved caspase 3 ( CC3 ) in 2 week old Suz12-deleted organoids , but rather , a striking decrease in proliferative ( Ki67+ ) cells was evident ( Fig 3D ) . Consistent with the idea that cells lacking Suz12 are nonproliferative , repassaging resulted in the expansion of rare cells that had escaped Suz12 deletion ( S3F and S3G Fig ) . Together , these data suggest that loss of Suz12 reduces the fitness and proliferation of mammary organoids . To determine potential target genes that are dependent on Suz12 and therefore canonical PRC2 function , we performed RNA sequencing ( RNA-seq ) analysis of Suz12 Wt or Suz12-deleted basal- and luminal-derived mammary organoids grown from R26creERT2KI/+Suz12f/f mice . Suz12 was significantly down-regulated in both populations ( Fig 4A , S4A Fig and S2 Data ) , and deletion of exon 5 was confirmed at the targeted locus ( S4B Fig ) . In both basal- and luminal-derived organoids , the majority ( >86% ) of differentially expressed ( DE ) genes were up-regulated rather than down-regulated upon Suz12 deletion , consistent with the repressive role of PRC2 ( Fig 4A , S4A and S4C Fig ) . A large overlap was apparent between up- and down-regulated DE genes ( S4C Fig ) , as well as a strong correlation with gene expression changes ( S4D Fig ) in Suz12-deleted basal- versus luminal-derived organoids . This is consistent with a global effect of Suz12 deletion on PRC2 stability in MECs . Gene ontology ( GO ) enrichment analysis found that common DE down-regulated genes were significantly enriched for metabolic processes and H3K27 methylation , while common DE up-regulated genes were enriched for development and morphogenesis GO terms ( S4C Fig ) . To explore whether the changes in gene expression accompanying loss of Suz12 reflect alterations in chromatin compaction associated with loss of repressive H3K27me2/me3 marks , we performed global mapping of chromatin accessibility using ATAC-seq . Differential accessibility analysis identified 2 , 767 windows , of which 2 , 415 ( 87 . 3% ) were more accessible upon Suz12 deletion ( Fig 4B and S3 Data ) . Further , 1 , 377 of these windows overlapped with the transcription start site or gene body of 766 independent genes in Suz12-deleted basal organoids ( referred to as differentially accessible [DA] genes from hereon ) . Nevertheless , the frequency of reads mapping to nucleosome-free regions versus mononucleosomes was not significantly altered . We observed only minor changes to insert length , suggesting no gross changes in nucleosome occupancy ( S5A and S5B Fig ) , and a comparison of GC enrichment indicated a minor increased frequency of highly GC-rich regions , usually found at regulatory elements within promoters ( Fig 4C ) . Moreover , model-based analysis of ChIP-seq ( MACS ) peaks analysis showed a small number of unique peaks upon Suz12 deletion ( Fig 4D ) . Next , we compared the DA windows identified by ATAC-seq with gene expression from their associated gene to determine if changes in chromatin compaction elicited by Suz12 deletion were sufficient to induce changes in gene transcription . A strong correlation was evident between open chromatin and transcriptional up-regulation in Suz12-deleted basal-derived organoids ( Fig 4E ) . However , analysis of Wt organoids did not reveal a correlation between genes assigned from ATAC-seq analysis and changes in their expression . Almost one-third ( 28% ) of genes that became more accessible upon Suz12 deletion were significantly up-regulated by RNA-seq ( Fig 4F ) . These 218 common genes included Cdkn2a ( S6A Fig and S4 Data ) , a known target of Ezh2-mediated repression that encodes p16 and p19Arf , and transcription factors ( TFs ) involved in diverse lineage commitment , including Foxd1 and homeobox TFs ( S6B and S6C Fig ) , and accordingly , represented sequence-specific DNA-binding GO terms ( S5 Data ) . Notably , confocal imaging of organoids confirmed the persistence of basal and luminal cells in Suz12-deleted organoids ( S6D Fig ) , suggesting lineage fidelity was maintained . A similar ATAC-seq analysis was applied to Suz12-deleted luminal-derived organoids ( S7A and S7B Fig ) , which revealed an increase in reads associated with nucleosomes ( S5A and S5B Fig ) . These longer fragments were enriched in promoter-flanking and transcribed regions [24] consistent with increased expression of DE genes . Despite detecting many unique DA peaks ( S7C Fig ) , particularly associated with promoters and intragenic/gene bodies ( S7D Fig ) , the corresponding windows were less strongly associated with DE genes found by RNA-seq ( S7E and S7F Fig ) . Comparison of the Suz12-deleted gene expression signatures derived for both basal and luminal organoids showed that they were most similar to claudin-low and normal-like breast tumors ( S7G Fig ) , similar to that observed for the basal/MaSC cell signature [25] . Likewise , the gene expression profile of Ezh2-deleted MECs was most concordant with claudin-low breast tumors [16] . The striking similarity between gene expression signatures of organoids lacking key PRC2 complex genes and claudin-low breast tumors—which exhibit a metaplastic , clinically aggressive phenotype in patients—suggests that PRC2 may play a role in tumor phenotype and behavior .
Using in vivo and in vitro studies , we show that deletion of a core component ( either Suz12 or Eed ) of the canonical PRC2 complexes leads to a complete block in mammary gland development in vivo and markedly curtails progenitor cell activity in vitro . Strikingly , Suz12- or Eed-deleted cells were strongly selected against and could not contribute to the developing mammary gland . This resulted in very small glands consisting of Wt cells . These findings are consistent with previous reports for nonredundant genes that are required for stem cell function [26] . Assays using primary MECs and organoids suggest that this is due in part to de-repression of cdkn2a , resulting in increased p16 and p19 expression and reduced proliferation owing to cell cycle arrest . Thus , previous studies [16 , 17] have underestimated the importance of PRC2 in governing mammary progenitor activity and differentiation , owing to some functional redundancy between Ezh1 and Ezh2 . Moreover , these results are consistent with PRC2 promoting mammary epithelial expansion , rather than inhibiting it [27] . By exploiting mammary organoids to bypass the severe developmental phenotype associated with loss of Suz12 , we examined changes in chromatin structure in the presence and absence of PRC2 and correlated these with gene expression changes . PRC2 complexes are reported to colocalize with H3K27me3 on the promoters of around 10% of all genes [5] . Upon Suz12 deletion , we observed comparable changes in gene expression ( 7%–11% ) in both basal- and luminal-derived organoids . There was significant overlap between the up-regulated genes in basal- and luminal-derived organoids , and the magnitude of change was highly correlated . Assessment of chromatin accessibility with ATAC-seq indicated that the chromatin of basal-derived organoids is more open upon Suz12 loss . Recent findings in human breast epithelial subsets [28] predict that bivalent promoters and primed enhancers would be the most affected by loss of H3K27me3 in Suz12-deleted basal-derived organoids . Since we did not observe a significant increase in GC content of DA reads identified by ATAC-seq , the mechanism is most likely to involve enhancers . Coupling RNA-seq with ATAC-seq analysis in basal-derived organoids revealed that deletion of Suz12 led to more than one quarter of genes becoming significantly transcriptionally active coincident with more accessible chromatin . We have previously shown that the MaSC/basal subset demonstrates the lowest levels of H3K27me3 across transcriptional start sites ( TSSs ) [16] while also producing slightly less RNA overall [29] . This observation supports the notion that this gene subset marked by H3K27me3 is transcriptionally regulated through H3K27me3-mediated repression accompanied by chromatin compaction and is dependent on canonical PRC2 function . Our results indicate that open chromatin is a good predictor of gene activation in basal-derived organoids . In the luminal compartment , there is an increased level of H3K27me3 [16] , while these cells are more transcriptionally active overall [29] . In luminal-derived organoids , loss of PRC2 resulted in increased chromatin accessibility and an increase in reads mapping to regions with high GC content . In contrast to basal-derived organoids , the low correlation with gene expression in luminal-derived organoids suggests that additional mechanisms beyond chromatin accessibility serve to ensure proper gene repression . This may be due to DNA methylation or the combination of additional activating and repressive histone marks that maintain gene repression in this more committed cell type , as observed for human luminal cells [28] . Our studies indicate that PRC2 is responsible for both H3K27me3 and H3K27me2 marks in MECs , as evidenced by their loss in Suz12-deleted cells . Notably , we show here that the absence of these repressive marks through deletion of a single protein results in large changes in gene expression , consistent with the notion that chromatin states are the sum of a limited repertoire of PTM combinations on histone tails . While these data do not exclude noncanonical functions in the presence of a functional PRC2 complex [30] , they suggest that Suz12 is a limiting factor for expression of Ezh2 protein and thus a functional PRC2 complex in the mammary epithelium . In the context of breast cancer , our data indicate that Suz12-deleted organoids , like Ezh2-null basal cells [16] , displayed similarities with signatures of claudin-low breast cancers , which are thought to arise from MaSCs . While Ezh2 up-regulation was initially associated with aggressive breast cancers , several studies now indicate that Ezh2 overexpression may be a consequence rather than a cause of breast cancer [13] . Indeed , deletion of Ezh2 accelerated tumors in a mouse model of Brca1-deleted breast cancer [31] and in a breast cancer model of Notch activation [13] . Additionally , the status of the cdkn2a locus of an individual tumor might predict its response to Ezh2 loss . The observation that Suz12 deficiency in normal mammary cells leads to de-repression of cdkn2a , paralleling that seen with loss of Ezh2 [32] , would predict that loss of PRC2 function in breast cancer would result in decreased tumor proliferation via up-regulation of p16Ink4a and p14Arf expression . However , if this locus is perturbed , as is seen in many breast cancers [33] , then loss of Ezh2 might have quite different consequences . Further work will be required to determine if loss rather than overexpression of PRC2 contributes to tumor progression in other cellular contexts . In summary , our findings establish an essential role for PRC2 in the maintenance of mammary progenitor function and point to a critical function for PRC2 in maintaining chromatin states to ensure appropriate gene expression in MECs .
All animal experiments were conducted using mice bred at and maintained in our animal facility according to the Walter and Eliza Hall Institute of Medical Research Animal Ethics Committee guidelines , approval number 2017 . 002 . Suz12f/f [34] , Eedf/f [35] , Ezh2f/f [36] , MMTV-cre ( line A ) [37] , and R26creERT2 [38] gene-targeted mice have been described previously . CD4cre-Eedf/f T lymphocytes [39] were a gift from R . Allan . MMTV-cre mice were maintained as a pure strain on an FVB/N background , R26creERT2 mice were on a C57BL/6 background , and Ezh2 , Eed , and Suz12 conditional knockout mice were on either a C57BL/6 or mixed FVB/N and C57BL/6 background . For analysis of postnatal day 1 lungs , adult female mice were subjected to timed pregnancies and injected with 0 . 2 mg progesterone on days 17 and 18 of pregnancy to delay parturition . At 19 . 5 days of pregnancy , pups were recovered by caesarian section and monitored for breathing for 1 hour with constant stimulation before collection of lungs into Bouin’s solution . Primary mouse MEC cultures were isolated from both inguinal and/or thoracic mammary glands and prepared as described [17] . MEC suspensions and flow cytometry were performed as previously described [40] . Antibodies against mouse antigens were purchased from Biolegend and included CD24-PB ( #101820 ) , CD31 ( #102410 ) , CD45 ( #103112 ) , and Ter119 ( #116212 ) conjugated to APC , CD29–FITC ( #102206 ) , and CD14-PE ( #123309 ) . Cells were sorted on a FACSAria or FACSDiva ( BD PharMingen ) and manually counted prior to plating on irradiated fibroblast feeders as described [40] . After 7 days , colonies were fixed and stained with Giemsa and manually counted . To induce R26creERT2-mediated deletion , 0 . 1 μM 4OHT was added to culture medium for 20 hours on day 1 of culture . Organoids were cultured as described previously [18] in 8 μl BME ( Cultrex ) drops ( 70 basal and 60 luminal progenitor cells per drop ) on nontreated 24 well plates and covered by Advanced DMEM/F12 supplemented with growth factors excluding Wtn3a or FGF2 . Rock inhibitor ( Y-27632 ) was added to culture medium for the first 4 days , and the medium was refreshed every 2–3 days . To induce R26creERT2-mediated deletion , 0 . 1 μM 4OHT was added to culture medium for 16–20 hours on day 1 or day 4 of culture . Organoids were photographed using best focus projection images on the Nikon TiE System software after 12–14 days in culture . Prior to proliferation measurements , RNA-seq , or ATAC-seq analysis , 12–16 day old organoids were dissociated into single cells using TrypLE express ( Thermo Fisher Scientific ) . Cell suspensions were mixed 1:1 with CellTiter-Glo Luminescent Cell Viability Assay ( Promega ) prior to detection of luminescence . Genomic DNA was extracted from organoids using DNeasy Blood and Tissue kit ( Qiagen ) and PCR used to distinguish the Wt , floxed , and recombined ( deleted , referred to as “del” ) Suz12 alleles as described [34] . Mice were injected with BrdU Cell Labeling Reagent ( 0 . 5 mg/10 g body weight , Amersham Biosciences ) 1 . 5 hours prior to collection . For histology , tissues were fixed in 4% paraformaldehyde overnight and embedded in paraffin . Sections ( 5 μm ) were stained with hematoxylin–eosin ( HE ) . For whole-mount analysis , mammary glands were harvested and fixed in Carnoy’s solution ( 6:3:1 of 100% ethanol , chloroform , and glacial acetic acid ) and stained with Carmine alum . The extent of ductal outgrowth was measured on inguinal whole mounts as the distance from the center of the lymph node to the leading edge of the ductal mass . IHC and IF were performed as described [16] . Paraffin-embedded sections ( 5 μm ) were dewaxed in xylene and rehydrated through an alcohol series , blocked with 3% hydrogen peroxide , and subjected to antigen retrieval by boiling in 10 mM citrate buffer pH 6 . 0 for 30 seconds using a DAKO pressure cooker . Immunostaining was performed using the streptavidin-biotin peroxidase detection system as per the manufacturer’s instructions ( ABC reagent , Vector Laboratories ) and 3 , 3-diaminobenzidine was used as substrate ( DAKO ) . In all cases , an isotype-matched control IgG was used as a negative control . The following antibodies were used: anti-BrdU ( Bio Rad OBT0030 ) , anti-Ezh2 ( BD Biosciences #612667 ) , anti-ERα ( Santa Cruz sc-543 ) , anti-PR ( Santa Cruz sc-538 ) , anti-Foxa1 ( Abcam Ab23738 ) , anti-H3K27me3 ( Millipore #07–449 ) , anti-Suz12 ( Diagenode pAB-029-050 ) , anti-Eed ( R&D Systems AF5827-SP ) , anti-CC3 ( Cell Signaling #9664L ) , and anti-Ki67 ( Cell Signaling #12202S ) . Secondary antibodies were biotin-conjugated anti-rabbit IgG , anti-rat IgG and anti-mouse IgG ( Vector Laboratories ) , anti-mouse alexa-488 ( Invitrogen ) , and anti-rabbit alexa-647 ( Invitrogen ) , and DAPI was used to detect nuclei ( Thermo-Fischer Scientific ) . As described in [18] , 3D imaging of organoids was performed on a SP8 Confocal microscope after staining with anti-cytokeratin 14 ( Thermo-Fisher Scientific ) and anti-cytokeratin 8/18 ( TROMA-I , DSHB ) antibodies . Lysates were prepared in RIPA buffer [16] , and western blotting was performed as described [17] . The following antibodies were used for western blot analysis: anti-Ezh2 ( BD Biosciences #612666 ) , anti-Suz12 ( Cell Signaling 3737S ) , anti-Eed ( Millipore 05–1320 ) , anti-H3K27me1 ( Millipore 07–448 ) , anti-H3K27me2 ( Millipore 07–452 ) , anti-H3K27me3 ( Millipore 07–449 ) , anti-Histone 3 ( Millipore 07–690 ) , anti-β-actin ( Sigma A5441 ) , anti-Gapdh ( Sigma G8795 ) , anti-ERα ( Millipore 07–690 ) , anti-Ezh1 ( Millipore 07–690 ) , anti-PARP ( Cell Signaling #9542 ) , anti-p16 ( Santa Cruz M-156 ) , and anti-p19 ( Rockland Immunochemicals #200-501-891 ) . Secondary antibodies included HRP-conjugated anti-rabbit and anti-mouse ( Southern Biotech ) , both 1:10 , 000 . ATAC-seq was performed as described [24] with the following adaptations . Organoid cultures were dissociated , and 50 , 000 single cells were lysed and nuclei collected at 500 g for 10 minutes in lysis buffer containing 0 . 1% NP-40 . Pelleted nuclei were tagmented with Nextera Tn5 Transposase ( TDE1 , Illumina FC-121-1030 ) for 20 minutes at 22 °C and 30 minutes at 37 °C . Transposed DNA was purified using Qiagen MinElute kit ( 28204 ) and fragments PCR amplified as described [24] . ATAC libraries were sequenced on a NextSeq using a 150H kit with 75 bp paired-end reads , and total reads were collated from 2 runs to obtain approximately 60 × 106 reads per sample . Sequencing runs were pooled for each replicate and sample , and adapters were trimmed with Trim Galore ! ( http://www . bioinformatics . babraham . ac . uk/projects/trim_galore/ ) and mapped to the mm10 mouse genome using bowtie2 [41] , allowing for fragments <2 , 000 bases in length . Mitochondrial and duplicate reads were removed using Picard-Tools ( http://broadinstitute . github . io/picard/ ) , and bam files were run through macs2 ( doi: 10 . 1186/gb-2008-9-9-r137 ) for peak calling using these parameters: “—nomodel—shift—75—extsize 150—qvalue 0 . 05 . ” For DA analysis , bam files were loaded in SeqMonk ( v1 . 37 . 1 , https://www . bioinformatics . babraham . ac . uk/projects/seqmonk/ ) , and probes were created using a tiling approach with 150 bp windows end to end across the genome . Raw counts were then processed through the differential gene expression pipeline within edgeR [42] , and DA regions were called with an exact test and FDR < 0 . 05 . Reads were quantified by reads per million and log2 transformed for visualizing graphically . For each gene , the 5 Kb region upstream of intron 1 was used in combination with the gene body to define the TSSs plus gene body . GC enrichment was quantified on mapped bam files using DeepTools [43] computeGCBias with an effective genome size of 2 , 150 , 570 , 000 . These data have been deposited in the Gene Expression Omnibus ( accession code GSE116431 ) . Total RNA was extracted from basal- or luminal-derived organoids grown from single sorted luminal or basal populations from the mammary glands of R26creERT2/Suz12f/f female mice using the RNeasy Mini kit ( Qiagen ) . Two biological replicates were prepared of the basal-derived organoids and 3 biological replicates of luminal-derived . RNA-seq was carried out on an Illumina Nextseq 500 to produce 20–65 million 80 bp reads per sample . Read pairs were mapped to the mouse genome ( mm10 ) using the subread aligner [44] implemented in the Rsubread software package . Read counts for Entrez Genes were obtained using featureCounts [45] and its inbuilt mm10 annotation . Gene information was downloaded from the NCBI on 1 February 2017 . Statistical analysis used the limma [46] and edgeR [42] software packages . Genes with at least 0 . 5 read counts per million ( cpm ) in at least 2 samples were considered to be expressed and were kept in the analysis . Immunoglobulin receptor segments , ribosomal genes , predicted and pseudogenes , and obsolete Entrez IDs were filtered out . Trimmed mean of M-values ( TMM ) scale normalization [47] was applied , and read counts were transformed to log2-cpm with a prior count of 3 . Linear models were used to test for expression differences between 4OHT treated versus untreated samples from luminal cells and from basal cells . Each organoid sample was treated as a random block , allowing for correlation between repeats [48] . Differential expression was assessed using the Treat method [49] , computing empirical Bayes moderated t statistics relative to a fold change threshold of 1 . 5 , and allowing for an abundance trend in the standard errors and for robust estimation of the Bayesian hyperparameters [50] . The Benjamini and Hochberg method was used to control the FDR . These data have been deposited in the Gene Expression Omnibus ( accession code GSE116431 ) . Microarray expression profiles of breast tumors were downloaded from Gene Expression Omnibus series GSE18229 [51] . Probe intensities were normexp background corrected with offset 50 [52] and loess normalized using the limma package . Mouse Entrez Gene IDs were mapped to human using HUGO Gene Nomenclature Committee orthology predictions downloaded November 2016 . Suz12-deficient expression signatures were computed for each tumor using a previously described method [25] . Briefly , the sum of products of RNA-seq log2-fold-changes with microarray log2-normalized intensities was computed for all genes DE in the Suz12 deficient basal- or luminal-derived organoids . For qRT-PCR analysis , total RNA from MECs was reverse-transcribed using Superscript III ( Invitrogen ) , and cDNA were analyzed on a LightCycler 480 ( Roche ) . Input cDNA concentrations were normalized to GAPDH . Product accumulation was evaluated using the comparative Ct method ( 2−ΔΔCT ) . Primer sequences were: Ezh2 For: 5′-ACATCCCTTCCATGCAACACC-3′; Ezh2 Rev: 5′-TCCCTCCAGATGCTGGTAACA-3′; Eed For: 5′-GTGAACAGCCTCAAGGAAGAT-3′; Eed Rev: 5′- ATAAGGTTACTCTGTGCTTC-3′; Gapdh For: 5′-TGACATCAAGAAGGTGGTGAAG; Gapdh Rev: AAGGTGGAAGAGTGGGAGTTGC-3′ . | The formation of mammary glands requires the tight regulation of many genes that govern cell fate decisions in the cells that form them . However , most of these genes remain undefined . The Polycomb repressive complex 2 ( PRC2 ) has a role in gene silencing , and it is comprised of several subunits , which include either Enhancer of Zeste homolog 2 ( Ezh2 ) or Ezh1 in combination with Suppressor of Zeste 12 protein homolog ( Suz12 ) and embryonic ectoderm development ( EED ) . Dysregulation of these subunits can lead to breast cancer . Although previous studies have analyzed the contribution of these complexes in mammary epithelium , failure to inactivate all canonical PRC2 complexes has made this task difficult . Deletion of Suz12 resulted in nonfunctional PRC2 and led to growth defects in mammary epithelial cells in vivo and in vitro . Here , we have used a 3D mammary organoid system to circumvent the lethality associated with Suz12 loss and studied chromatin dynamics and gene expression of PRC2 complexes . Our results suggest that loss of all canonical PRC2 complexes results in failure to repress transcriptional programs associated with early commitment and differentiation of mammary epithelial cells . | [
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] | 2018 | Canonical PRC2 function is essential for mammary gland development and affects chromatin compaction in mammary organoids |
Meiotic recombination safeguards proper segregation of homologous chromosomes into gametes , affects genetic variation within species , and contributes to meiotic chromosome recognition , pairing and synapsis . The Prdm9 gene has a dual role , it controls meiotic recombination by determining the genomic position of crossover hotspots and , in infertile hybrids of house mouse subspecies Mus m . musculus ( Mmm ) and Mus m . domesticus ( Mmd ) , it further functions as the major hybrid sterility gene . In the latter role Prdm9 interacts with the hybrid sterility X 2 ( Hstx2 ) genomic locus on Chromosome X ( Chr X ) by a still unknown mechanism . Here we investigated the meiotic recombination rate at the genome-wide level and its possible relation to hybrid sterility . Using immunofluorescence microscopy we quantified the foci of MLH1 DNA mismatch repair protein , the cytological counterparts of reciprocal crossovers , in a panel of inter-subspecific chromosome substitution strains . Two autosomes , Chr 7 and Chr 11 , significantly modified the meiotic recombination rate , yet the strongest modifier , designated meiotic recombination 1 , Meir1 , emerged in the 4 . 7 Mb Hstx2 genomic locus on Chr X . The male-limited transgressive effect of Meir1 on recombination rate parallels the male-limited transgressive role of Hstx2 in hybrid male sterility . Thus , both genetic factors , the Prdm9 gene and the Hstx2/Meir1 genomic locus , indicate a link between meiotic recombination and hybrid sterility . A strong female-specific modifier of meiotic recombination rate with the effect opposite to Meir1 was localized on Chr X , distally to Meir1 . Mapping Meir1 to a narrow candidate interval on Chr X is an important first step towards positional cloning of the respective gene ( s ) responsible for variation in the global recombination rate between closely related mouse subspecies .
Meiotic recombination of homologous chromosomes enhances genetic diversity of species and safeguards proper segregation of chromosomes into gametes . In the mouse the process begins at the leptotene stage of the first meiotic prophase with chromatin modification by PRDM9-directed trimethylation at lysine-4 of histone H3 . Of approximately 4700 PRDM9-modified , nucleosome-depleted sites present in an average meiosis [1] , ~250 are targeted by the SPO11 protein to induce programmed DNA double-strand breaks ( DSBs ) detectable by immunofluorescence as RAD51/DMC1 foci [2–4] . The foci represent single-stranded 3' DNA intermediates generated by 5'-strand resection of DSBs and bound by RAD51 and DMC1 strand exchange proteins . The resulting nucleoprotein filaments invade nearby DNA molecules in search of homologous DNA sequences and initiate synapsis of homologous chromosomes ( but see [5] ) . In mice , approximately 90% of these DSBs are repaired by non-reciprocal recombination ( NCO ) and about 10% convert to reciprocal crossovers ( COs ) , which can be traced in meiotic spreads as the MLH1 foci at mid- and late pachytene or as chiasmata at diplotene—metaphase I [6] . Meiotic COs are regulated at several levels . At the DNA sequence small scale , the distribution of DSBs and COs is highly nonrandom . The majority of DNA breaks occur in a subset of approximately 15 000 hotspots defined as 1 to 2kb long genomic intervals , with dramatically enhanced cM/DNA length ratio . The chance of a DSB to arise in a particular hotspot varies between 10–0 . 01% in a given cell , but is dramatically lower or zero outside the hotspots [7] . The nonrandom localization of hotspots is almost exclusively determined by the sequence-specific DNA binding of the zinc-finger array of PRDM9 meiosis-specific protein [8–11] . In Prdm9-null mutants the number of activated H3K4me3 hotspots remains constant but they move towards gene promoters and to other PRDM9-independent H3K4me3 sites [12] , and probably cause male and female infertility [13] . The regulation of meiotic recombination also operates at the chromosome level , since at least one CO site has to occur per chromosome pair to secure proper synapsis and segregation of homologous chromosomes ( "obligatory CO" ) [14] . Location of any additional CO on the same chromosome is constrained by positive interference [14–16] . The third and the least understood level of regulation operates at the genome-wide frequency of meiotic recombination , designated as the recombination/CO rate . The CO rate is under the genetic control in all properly analyzed species . More than 40 years ago we reported significant differences of male meiotic chiasma frequency between A/Ph , C57BL/10ScSn ( high ) and C3H/Di ( low ) inbred strains and suggested genetic control of CO rate in mice [17 , 18] . Recently , a variation in the global recombination rate has been reported in whole-genome F2 genetic linkage maps in the mouse [19] , in a panel of human two-generation families [20] and in a large dairy cattle pedigree [21] . Detailed information on quantitative trait loci ( QTL ) controlling the meiotic CO rate in mice was obtained by counting autosomal MLH1 foci , the cytological counterparts of meiotic COs [22] . The counts varied between 20 and 30 foci per single meiosis in male mice , corresponding to 1000 cM and 1500 cM of the genome size on genetic maps [3 , 23–26] . It is important to note that besides specifying the position of recombination hotspots , Prdm9 functions as a hybrid sterility gene in mouse intersubspecific PWD/Ph x C57BL/6J F1 hybrids . PWD/Ph ( henceforth PWD ) and C57BL/6J F1 ( B6 ) inbred strains represent Mus musculus musculus ( Mmm ) and Mus musculus domesticus ( Mmd ) subspecies of the house mouse [27] . Sterility of male hybrids is controlled by the interaction between Prdm9 and the X-linked Hybrid sterility X chromosome 2 locus , Hstx2 , another major hybrid sterility factor [28] . The incompatibility between both hybrid sterility genes results in abnormal synapsis of homologous chromosomes , failure of sex body formation and consequent sterility of male F1 hybrids; all other allelic combinations yield fertile or semifertile phenotypes [29 , 30] . One of the probable explanations of disrupted synapsis between homologs in the first meiotic prophase could be the failure of proper repair of programmed DSBs caused by Dobzhansky-Muller incompatibility between Prdm9 and Hstx2 . Thus Hstx2 might at some level participate in genetic control of meiotic recombination . This line of thought was inspired by the finding of an X-linked transgressive quantitative trait locus ( QTL ) controlling the global meiotic CO rate in crosses of another mouse subspecies , Mus m . castaneus ( Mmc ) , with Mmm . The QTL was 18 . 8 Mb long , overlapping the 4 . 7 Mb long Hstx2 genomic locus [24] . To inquire into the effect of PWD X-chromosome and individual PWD autosomes ( Chrs ) or their parts on global CO rate in the context of B6 genome [31] we estimated the average counts of MLH1 foci per pachynema in a panel of 26 chromosome substitution ( consomic ) strains C57BL/6-Chr#PWD/Ph/ForeJ ( hereafter B6 . PWD-Chr# ) [32] . Chromosome substitution strains are generated by transfer of an entire chromosome from the donor inbred strain , in our case PWD , into the genetic background of the recipient strain ( B6 ) by repeated backcrosses and selection at each backcross generation for a nonrecombinant donor chromosome [33] . In agreement with our prediction , the strongest , male-specific modifier was mapped on the X chromosome into the 4 . 7 Mb Hstx2 genomic locus . In female meiosis , a strong modifier of global CO rate mapped also on Chr X , but distally to Hstx2 . Two PWD autosomes also displayed a significant impact on the meiotic recombination rate . We further examined the meiotic CO rate in Prdm9 deficient males and in males with Prdm9 transgenes in order to compare the effect of Prdm9 gene dosage on meiotic recombination and hybrid sterility .
To evaluate the role of individual PWD autosomes in the control of CO frequency we determined the mean number of MLH1 foci per pachynema in males of individual intersubspecific B6 . PWD-Chr# chromosome substitution ( consomic ) strains and compared them with the parental B6 recipient strain or among themselves ( Fig 2 , S1 Table ) . Chromosome substitution strains fractionate phenotypic and genomic variation between the donor and the recipient inbred strain chromosome-wise , due to the fact that each strain of the panel carries one chromosome or its part from the donor strain , in our case PWD , on the genetic background of the recipient strain–B6 [32–34] . Altogether we examined 21 autosomal consomic strains for the male CO rate . Strains B6 . PWD-Chr 8 and B6 . PWD-Chr 10 . 1 were lost before the experiment had started; hence the effect of Chr 8PWD and the proximal part of Chr 10PWD could not be evaluated . The variance of MLH1 counts showed positive correlation with the average MLH1 counts at both intra-individual ( Spearman´s rho = 0 . 20 , p = 0 . 031 ) and intra-genotype levels ( Spearman´s rho = 0 . 58 , p = 0 . 003 , S1 Fig ) . Comparison of individual consomic strains with the B6 parental strain revealed a significant increase of recombination rate in males carrying Chr 7PWD ( 27 . 04 , CI 26 . 63–27 . 46 , p<0 . 001 , Dunnet's post-hoc test , S2 Table ) . The PWD allele of genetic factor ( s ) on Chr 7 responsible for elevation of the recombination rate is recessive because five B6 . PWD-Chr 7PWD/B6 heterozygotes displayed 25 . 15 ( CI 24 . 63–25 . 70 ) foci per cell , a value not significantly different from the B6 parent ( p = 1 , S2 Table ) . The proximal and middle part of Chr 11PWD were associated with a significantly lower mean count of MLH1 foci ( 23 . 79 , CI 23 . 46–24 . 16 , p = 0 . 027 , and 23 . 36 , CI 23 . 03–23 . 72 , p = 0 . 001 ) , than the mean counts of B6 parental strain , pointing to the transgressive effect of the underlying modifiers . At least two modifiers may be envisaged to control the suppressive effect of Chr 11PWD on meiotic CO rate , the first one localized in the overlapping interval of PWD sequence in B6 . PWD Chr 11 . 1 and B6 . PWD Chr 11 . 2 ( Chr 11 , 44 . 0–75 . 6 Mb , GRCm38 ) and the second one in the 4 . 2 Mb interval of PWD sequence present in B6 . PWD Chr 11 . 2 but absent in B6 . PWD-Chr 11 . 1 and B6 . PWD-Chr 11 . 3 males ( Chr 11 , 75 . 6–79 . 8 Mb , S2 Fig ) . Prdm9 determines localization of hotspots of meiotic COs in most studied mammalian genomes [11] and shows a minor effect on the male recombination rate in humans [35] . Previous studies of two F2 populations of mouse strains , ( PWD x CAST ) [24] and ( B6 x CAST ) [26] , reported no effect of Chr 17 carrying Prdm9 on the global recombination rate . Since the change of Prdm9 dosage partially rescues hybrid sterility of PWD x B6 F1 males [27 , 36] , we analyzed the effect of a Prdm9 null allele and of two extra transgenic copies of Prdm9 on the meiotic CO rate ( Fig 3 , S3 Table ) . The mean number of MLH1 foci per cell in four B6 males ( 24 . 52 , CI 24 . 02–25 . 06 ) was not significantly different from knockout heterozygotes B6-Prdm9tm1Ymat/wt [27] ( 24 . 34 , CI 23 . 78–24 . 92 ) and from B6 males carrying two copies of the BAC5 transgene containing Prdm9C3H ( 24 . 20 , CI 23 . 71–24 . 73 ) [27 , 30] . After transferring the Prdm9tm1Yma knockout to the PWD genetic background by 10 generations of backcrosses , Prdm9wt/- heterozygotes displayed high counts of MLH1 foci , 29 . 61 , CI 28 . 87–30 . 40 per cell , value not significantly different from that shown by PWD wildtype males ( 29 . 31 , CI 28 . 58–30 . 10 ) . To conclude , neither the extra Prdm9 copies nor the deficiency of Prdm9 in the PWD genome alters the global recombination rate when monitored by the mean number of MHL1 foci per meiosis , strongly indicating an independent control of global crossover rate variation and genomic crossover placement . Previous studies have shown the fundamental impact of genetic factors located on mouse Chr X not only on the meiotic CO rate [24–26 , 29 , 37] , but also on the inter-subspecific reproductive isolation [28 , 38–43] . In ( B6 x CAST ) F2 males , the strongest meiotic CO rate QTL was mapped on Chr X ( 25 . 4–42 . 4 cM , the highest LOD score Chr X:106 . 8 Mb ) [26] and the analysis of the ( PWD x CAST ) F2 population revealed a strong QTL within the 65 . 5–84 . 5 Mb interval . In both studies the X-linked QTLs displayed transgressive effects , since the allele derived from the high CO rate parent acted in the opposite direction and caused decreased recombination frequency in the F2 hybrids [24] . We characterized the effect of Chr XPWD on meiotic CO rate using the same four B6 . PWD-Chr X # subconsomic strains that we developed for mapping the Hstx2 hybrid sterility gene [28] ( Fig 4 , S4 and S5 Tables ) . The B6 . PWD-Chr X . 1 strain , which carries 64 . 9 Mb of the PWD sequence starting from the centromeric end of Chr XPWD displayed 23 . 74 ( CI 23 . 30–24 . 44 ) MLH1 foci per cell , a value not significantly different from the B6 parent ( 24 . 40 , CI 23 . 87–24 . 94 , p = 0 . 71 , Tukey's post-hoc test , S5 Table ) . Also B6 . PWD-Chr X . 3 males with the PWD sequence at the telomeric end did not differ from B6 ( 25 . 34 , CI 24 . 56–25 . 98 , p = 0 . 357 ) . However , B6 . PWD-Chr X . 1s ( 69 . 6 Mb of proximal PWD sequence ) and B6 . PWD-Chr X . 2 displayed a significantly lower number of MLH1 foci , 22 . 16 ( CI 21 . 85–22 . 66 ) and 22 . 96 ( CI 22 . 69–23 . 44 ) , respectively ( p<0 . 001 ) . We designated the X-linked genetic factor present in the 4 . 7 Mb interval in B6 . PWD-Chr X . 1s and B6 . PWD-Chr X . 2 but absent in B6 . PWD-Chr X . 1 as Meiotic recombination 1 , Meir1 . The same 4 . 7 Mb locus ( X:64 , 880 , 641–69 , 584 , 093 , GRCmm38 , see S1 Table in [28] ) harbors hybrid sterility genes Hstx1 and Hstx2 [28 , 44] . Interestingly , the transgressive effect of the Meir1PWD allele coming from the parent with high recombination rate but acting in the opposite way on B6 background parallels the effect of Hstx2PWD , which causes small testis size and lack of sperm in ( PWD x B6 ) F1 hybrids [45] . Moreover , Hstx1PWD causes sperm malformations on the B6 background [44] , a phenotype not seen in either parent . Meir1 maps to the same genomic locus as hybrid sterility gene Hstx2 , and both genetic factors operate in a transgressive mode , early at the first meiotic prophase . Hstx2 interacts with Prdm9 to control hybrid sterility of inter-subspecific hybrids in a male-specific manner [28 , 45] . These facts and the divergent modes of global CO rate regulation in males and females [24 , 37] led us to investigate the possible activity of Meir1 in female meiosis . We estimated the mean MLH1 counts in pachytene oocytes from 18 . 5–19 . 5 dpc female fetuses of four B6 . PWD-Chr X . # strains and PWD and B6 parents ( Fig 5 , S6 and S7 Tables ) . In agreement with previous findings [24] , B6 females displayed an average 4 . 7 MLH foci per cell more than males ( 29 . 61 , CI 28 . 92–30 . 33 vs 24 . 87 , CI 24 . 29–25 . 47 ) , but the opposite effect was seen in PWD mice showing higher CO rate in males ( 29 . 58 , CI 28 . 66–30 . 56 MLH1 foci per meiosis ) than in females ( 25 . 40 , CI 24 . 82–26 . 00 ) . The MLH1 frequency in B6 . PWD-Chr X . 1s females showed significant depression compared to B6 females ( 27 . 56 , CI 26 . 98–28 . 19 vs 29 . 61 , CI 28 . 92–30 . 33 , p<0 . 001 Tukey's post-hoc test , see Fig 5 and S7 Table ) . However , in contrast to the transgressive effect of Meir1 in males , the change in B6 . PWD-Chr X . 1s females was in the same-sense direction . These findings support the male-specific activity of Meir1 . PWD sequence X:69 . 58 Mb—98 . 15 Mb in B6 . PWD-Chr X . 2 subconsomic females caused the strongest reduction of meiotic CO rate ( 25 . 00 , CI 24 . 49–25 . 54 ) to the level seen in PWD females , pointing to the presence of one or more major female-specific modifiers . Contrary to the transgressive effect of male X-linked modifiers ( [24 , 26] and this study ) , this female-specific factor acted in accord with its PWD origin by suppressing the global CO rate . Recent intraspecific comparisons of the crossover rate at pachytene stage of meiosis indicated direct proportionality of MLH1 foci variation to the DNA DSB frequency [3 , 46] . Nevertheless , a simple formula for direct conversion of DSB frequency into CO rate seems unlikely because the Spo11 gene dosage variation does not affect the MLH1 counts , although it changes the DSB frequency , a phenomenon known as homeostatic control of recombination [14] . Comparison of B6 and PWD mid-zygonemas did not show a significant difference in the frequencies of Rad51/DMC1 foci ( 212 . 48 , CI 208 . 09–216 . 87 vs 220 . 56 , CI 215 . 33–225 . 78 , p = 0 . 227 , robust linear mixed model , Fig 6 , S9 and S10 Tables ) . However , one B6 male was an outlier with high RAD51/DMC1 counts . Accidentally , this animal was from a different breeding room than the remaining five males . Excluding the outlier , the PWD zygonemas would show significantly higher counts ( p< 0 . 001 ) of cytologically detected DSBs than those of B6 origin . However , regardless of whether the B6 outlier is included or not , the two consomics , namely B6 . PWD-Chr 7 , showing a significantly enhanced rate of MLH1 foci , and B6 . PWD-Chr X . 1s , showing a strong depression of meiotic recombination rate , displayed significant decrease of RAD51/DMC1 counts ( Fig 6 , S9 and S10 Tables ) . Thus the inter-subspecific comparisons do not support direct proportionality between the DSB frequency and the meiotic recombination rate and may indicate uncoupling of DSB programming from global crossover rate control in inter-subspecific hybrids .
Analysis of MLH1 profiles of PWD and B6 parental strains revealed an increase of 4 . 7 MLH1 foci per cell in PWD male meiosis . Individual chromosome substitution strains showed a significant effect of PWD chromosomes in the case of Chr 7PWD ( +2 . 17 MLH1 foci per cell ) , middle region of Chr 11PWD ( -1 . 51 foci ) and Hstx2 genomic locus of Chr XPWD ( -2 . 24 foci ) . Information on Chr 8PWD and the proximal part of Chr 10PWD is missing because the strains carrying them were not available . In humans and mice , the variation in CO rates is associated with SUMO ligase RNF212 and ubiquitin ligase HEI10 [20 , 35 , 49]; however , the mouse Chrs 5 and 14 , which carry their orthologs , showed no effect . The effect of the PRDM9 gene on human genome-wide CO rate [35] and global recombination rate in cattle [21] was not paralleled by a change of CO rate in B6 . PWD-Chr17 males . Since the copy-number variation of Prdm9 gene alters hybrid sterility , we evaluated its effect on MLH1 rate . Two additional copies of functional Prdm9 had no effect , but more remarkably , PWD males homozygous for Prdm9 deletion kept the same high frequency of MLH1 foci as shown by PWD wild-type or hemizygous males . Recently , a decrease by about 11% DSBs determined as RAD51 foci in late leptonema was found in PRDM9-deficient versus PRDM9B6- carrying males on MmdB6 background [50] . We can conclude that , at least on the MmmPWD genetic background , the functional PRDM9 protein is not essential for meiotic recombination and setting up the appropriate level of meiotic CO rate . Two genetic studies on the meiotic CO rate showed partially overlapping autosomal QTLs in Mmm , Mmd and Mmc subspecies [24 , 26] . The strongest QTLs in ( Mmc x Mmm ) F2 population were revealed on Chr 7 and Chr X , the same two chromosomes involved in the CO rate control of Mmm/Mmd in the present study . Three weaker QTLs were mapped on Chr 3 , Chr 15 and Chr 17 in both F2 crosses and three others were specific for a particular cross . We can conclude that the variation of the meiotic recombination rate between representatives of three mouse subspecies is controlled by a discrete number of genetic loci , some of which may be shared in different subspecies combinations . The Chr X harbored the strongest modifiers with transgressive effect in all three inter-subspecific comparisons . We localized the underdominant ( transgressive ) Meir1 into the 4 . 7 Mb interval on Chr X ( X:64 , 880 , 641–69 , 584 , 093 ) previously shown to carry hybrid sterility Hstx1 and Hstx2 genetic factors [28 , 44] . Hstx1 controls reduced fertility due to abnormal spermiogenesis in B6 . PWD-Chr X . 1s males , as independently confirmed by backcross mapping [44] . The Hstx2PWD allele is associated with intrameiotic arrest and full sterility in PWD x B6 F1 hybrid males [28] . ( B6 . PWD-Chr X . 1s x PWD ) F1 males are completely sterile in contrast to ( B6 . PWD-Chr X . 1 x B6 ) F1 males equipped with the Hstx2B6 allele [28] . All three genetic factors act in spermatogenesis in a sex-specific mode , and show transgressive effects on their respective phenotypes in inter-subspecific interactions . Such coincidence is remarkable but could have a simple explanation if all three phenotypes posed the pleiotropic effects of the same gene . The ultimate solution will require completion of positional cloning of these three factors and identification of appropriate candidate genes . There are six known protein-coding genes expressed in spermatogenesis in the 4 . 7 Mb interval; however , none of them has a known DNA DSB repair or other meiotic recombination function . 1700030B21Rik and 4933436I01Rik show only postmeiotic expression and the remaining four ( Slitrk2 , Fmr1 , Fmr1nb and Aff2 ) are transcribed at early meiotic prophase I [51] , thus being more likely candidates . The interval also harbors RNA genes including a cluster of miRNA genes , some of which show differences in meiotic expression between PWD and B6 males [28] . Uncovering the candidate gene for Meir1 and evaluation of its relation to Hstx1 and Hstx2 could be facilitated if the candidate region could be shortened by recombination . Our attempts to further genetically dissect the 4 . 7 Mb genomic locus have so far been unsuccessful . The suspicion that the Hstx2 locus lies in a recombination coldspot has strengthened after inspecting the same interval in recombinant inbred lines of Collaborative Cross , ( http://csbio . unc . edu/CCstatus/index . py ? run=CCV ) . In contrast with adjacent sites on Chr X , the Hstx2 interval ( 62 . 1–66 . 8 Mb , GRCm37 ) carried no crossovers in 71 CC lines ( S3 Fig ) constructed from eight founder mouse strains by 20 generations of brother-sister inbreeding [52 , 53] . Dumont and Payseur [24] mapped the strongest QTL for F2 variation in the MLH1 foci count in crosses of PWD ( Mmm ) and CAST ( Mmc ) to the 68 . 5 Mb—87 . 3 Mb ( 95% CI , GRCm38 ) region overlapping the Meir1 candidate region reported here . The highest LOD score was also exhibited by the QTL on Chr X in the F2 population of CAST and B6 inbred strains and again , the QTL from higher recombination B6 strain was associated with low recombination rate in F2 males [26] . This QTL was mapped more distally , the peak location being 55 . 6 cM or 106 . 8 Mb . Of interest in this context may be the recent finding of the effect of Tex11 dosage , the meiosis-specific gene situated at 100 . 8 Mb on mouse Chr X , which significantly affected the number of MLH1 foci in both sexes [54] . The possible role of Chr X in the control of recombination rate variation was shown in a series of inter-subspecific reciprocal crosses of inbred strains derived from Mmm ( PWD , CZECHI ) , Mmd ( WSB , PERA ) and Mmc ( CAST , CIM ) . In all combinations tested the X chromosome from a low recombination strain was associated with higher recombination in F1 hybrids and vice versa [25] . Similarly , analysis of G1 generation males from the Collaborative Cross project revealed that CAST Chr X ( coming from the low recombination rate strain ) is associated with expansion of the male genetic map , while the Mmm Chr XPWK is associated with contraction of the map , and the Mmd Chr X yields intermediate results [37] . The genetic mapping studies of mice and humans show that beside recombination hotspots common for male and female meiosis , evidence is available for male- and female-specific hotspots or for sex-specific quantitative differences in the hotspot activity ( citace ) . At the chromosomal level , the male-specific subtelomeric enhancement of CO frequency has been described in mice and humans [37 , 46 , 55 , 56] . Considering the global recombination level , the genetic length of the female genome is generally higher than that of males . The comparison of reciprocal F1 hybrids between mouse subspecies indicated the major role of the X chromosome in global recombination rate in males [25] . Here we mapped the transgressive genetic factor Meir1 , responsible for Mmm—Mmd intersubspecific variation , to a 4 . 7 Mb genomic locus and showed its male-specific effect . The analysis of female meiosis revealed a major female-specific genetic factor located on Chr X distally from Meir1 and acting in opposite , same-sense , direction . Testis-expressed gene 11 , Tex11 ( X:100 . 8 Mb ) [54] , considerably modifies the recombination rate in both sexes depending on its copy number , but is situated 2 . 7 Mb outside of the border of PWD sequence in the B6 . PWD- Chr X . 3 subconsomic ( X:98 . 1 Mb ) . The transgressive effect of Meir1 and its mapping to the same 4 . 7 Mb genomic interval as hybrid sterility Hstx2 provides indirect evidence in favor of the identity of both loci . However , no direct evidence is yet available in favor or against the hypothesis that the same genetic factor controls the recombination rate and hybrid male sterility . To conclude , several observations link hybrid sterility to meiotic recombination . Prdm9 histone H3K4 trimethyltransferase determines localization of recombination hotspots and simultaneously functions as a major hybrid sterility gene . Meir1 controls the meiotic recombination rate and is localized in the same 4 . 7 Mb interval as Hstx2 , the second major hybrid sterility gene . Both , Meir1 and Hstx2 , act in an underdominant manner on F1 hybrid background and both are sex-specific . The intimate relation between meiotic recombination and reproductive isolation was reported in yeast inter- and intra-specific hybrids , where an overall genomic sequence divergence and the anti-recombination action of the mismatch repair system was shown to account for hybrid sterility [57–59] . In the mouse , variation in the genome-wide recombination rate was shown to initiate at the onset of the first meiotic prophase [3] The zygotene/pachytene stage of prophase I is the time point when the meiosis fails in PWD x B6 F1 hybrids , showing many homologs unsynapsed and spermatocytes predestined to apoptosis . Mapping the Meir1 gene to a narrow candidate interval on Chr X is an important first step towards its positional cloning and elucidation of its relevance for reproductive isolation between closely related mouse subspecies .
Mice of the C57BL/6J inbred strain and Mus m . m . -derived PWD/Ph inbred strain , together with C57BL/6J-Chr #PWD chromosome substitution ( consomic ) and subconsomic strains [32] , were maintained in the Specific Pathogen-Free Facility of the Institute of Molecular Genetics in Prague . Male mice were sacrificed by cervical dislocation and whole testes were dissected at the age of 13–18 weeks for the MLH1 and RAD51/DMC1 immunostaining . Ovaries were removed from 18–19 days old female embryos . The mice were maintained in accordance to animal care protocols approved by the Committee on the Ethics of Animal Experiments of the Institute ( No . 141/2012 ) . The animal care obeyed the Czech Republic Act for Experimental Work with Animals ( Decree No . 207/2004 Sb and Acts Nos . 246/92 Sb and 77/2004 Sb ) , fully compatible with the corresponding regulations and standards of European Union ( Council Directive 86/609/EEC and Appendix A of the Council of Europe Convention ETS123 ) . The project number is 141/2012 . The head of the Committee for Animal Wefare and protection is MVDr . Jan Honetschläger , MBA . Spreads of meiocytes were prepared as described [60] with modifications , and immunostained with the following primary antibodies; rabbit anti-SYCP1 ( Abcam , # ab15087 ) diluted 1:500 , mouse anti-MLH1 ( Abcam , # ab14206 ) diluted 1:20 , human anti-centromere protein ( AB-Incorporated #15–235 ) diluted 1:300 , rabbit anti-RAD51 ( Santa Cruz , SC-8349 ) diluted 1:300 , rabbit anti-DMC1 ( Santa Cruz , SC-22768 ) diluted 1:300 . Secondary antibodies: anti-Rabbit IgG AlexaFluor 488 ( Molecular Probes , # A-11034 ) diluted 1:400 , anti-Mouse IgG AlexaFluor 568 ( Molecular Probes , # A-11031 ) diluted 1:400 , anti-Human IgG AlexaFluor 647 ( Molecular Probes , # A-21445 ) diluted 1:400 . After adding of primary antibodies in 100 μl of MAH buffer ( 1 . 5% BSA , 5% goat serum , 0 . 05% Triton X-100 in PBS , 0 , 2x cocktail of protease inhibitors ) slides were incubated overnight in a cold room covered with a cover glass and washed 3x in PBS 10 minutes each . Secondary antibodies in MAH buffer were applied and slides were incubated under a cover glass for 1 hour in a cold room in the dark . Slides were washed 3x in PBS 10 min each , air dried for 15 min in the dark at room temperature and mounted in Vectashield medium with DAPI . Images were observed in a Nikon Eclipse 400 epifluorescence microscope equipped with single-band pass filters for excitation and emission of infrared , red , blue , and green fluorescence ( Chroma Technologies ) and Plan Fluor Objective 60x ( MRH00601; Nikon ) . The images were captured with a DS-QiMc mono-chrome CCD camera ( Nikon ) and NIS-Elements imaging software . Pictures were adjusted for evaluation and foci were counted using the NIS-Elements program . For counting MLH1 foci only autosomal MLH1 foci were scored in mid- to late pachynemas . To determine the RAD51/DMC1 foci count , we scored zygotene spermatocytes ( predominantly in mid-zygonema ) with bright signal of SCP3 and RAD51/DMC1 . RAD51 and DMC1 proteins were labeled with the same secondary antibody and counted together . Altogether 4–5 animals per strain and 20 or more cells per mouse were evaluated for both MLH1 and RAD51/DMC1 . All calculations were performed in the statistical environment R 3 . 2 . 2 [61] and using packages lme4 [62] , robustlmm [63] and multcomp [64 , 65] . The MLH1 counts were not normally distributed , exhibited heteroscedasticity and were typically clustered for individual mice . Moreover , the numbers of evaluated cells were mostly 20 , but for a few individuals they were much higher . All these features of the data were properly modeled by generalized linear mixed model ( GLMM ) or generalized linear model ( GLM ) . The data analyzed using GLMM with Poisson error distribution with a log-link function were fitted using restricted maximum likelihood ( REML ) and Gauss-Hermite quadrature with 20 quadrature points . The logarithm of the intensity parameter was fitted as dependent on the genotype ( fixed effect ) and individual mouse ( random effect nested within genotype fixed effect ) plus 19 because every cell has at least 19 foci , Yij∼Po ( λij ) , log ( λij ) =19+α ( genoi ) +β ( mouseij ) Since the β ( mouse ) effect was missing for Prdm9 copy number variation data the GLM was fitted instead . All consomic and subconsomic strain effects were compared to B6 control group . The individual members within the sets of subconsomic strains for Chr 11 and Chr X were compared to each other . The data from mouse strains differing in the copy number of Prdm9 were compared to each other with Tukey’s method as a multiple-testing procedure . RAD51/DMC1 counts were analyzed using the robust linear mixed model [60] with the mean dependent on the genotype ( fixed effect ) and individual mouse ( random effect nested within genotype fixed effect ) . The Bonferonni correction was applied to the set of comparisons: B6 to PWD and B6 and PWD to other genotypes–to achieve family-wise error rate of 0 . 05 . | During differentiation of germ cells into gametes , a maternal and a paternal copy of each chromosome have to find each other , pair , and synapse in order to ensure proper chromosome segregation into the gametes . Because of the unique ability to identify homologous DNA sequences between homologous chromosomes , meiotic recombination is an essential step in proper chromosome pairing and synapsis in the majority of species . However , when the paternal and maternal sets of chromosomes come from different ( sub ) species , the recognition of homologs can be disturbed and result in sterility of male hybrids . In this study we investigated the genetic control of variation in the global recombination rate between two closely related mouse subspecies with regard to the known infertility of their F1 hybrids . We show that the variation in the global recombination rate between both subspecies is under the control of three genomic loci . The strongest one appeared within the hybrid sterility X2 genomic locus on Chromosome X . Our findings will allow positional cloning of the gene and will shed new light on the role of meiotic recombination in reproductive isolation between closely related species . | [
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] | 2016 | Hybrid Sterility Locus on Chromosome X Controls Meiotic Recombination Rate in Mouse |
The conserved target of rapamycin complex 1 ( TORC1 ) integrates nutrient signals to orchestrate cell growth and proliferation . Leucine availability is conveyed to control TORC1 activity via the leu-tRNA synthetase/EGOC-GTPase module in yeast and mammals , but the mechanisms sensing leucine remain only partially understood . We show here that both leucine and its α-ketoacid metabolite , α-ketoisocaproate , effectively activate the yeast TORC1 kinase via both EGOC GTPase-dependent and -independent mechanisms . Leucine and α-ketoisocaproate are interconverted by ubiquitous branched-chain aminotransferases ( BCAT ) , which in yeast are represented by the mitochondrial and cytosolic enzymes Bat1 and Bat2 , respectively . BCAT yeast mutants exhibit severely compromised TORC1 activity , which is partially restored by expression of Bat1 active site mutants , implicating both catalytic and structural roles of BCATs in TORC1 control . We find that Bat1 interacts with branched-chain amino acid metabolic enzymes and , in a leucine-dependent fashion , with the tricarboxylic acid ( TCA ) -cycle enzyme aconitase . BCAT mutation perturbed TCA-cycle intermediate levels , consistent with a TCA-cycle block , and resulted in low ATP levels , activation of AMPK , and TORC1 inhibition . We propose the biosynthetic capacity of BCAT and its role in forming multicomplex metabolons connecting branched-chain amino acids and TCA-cycle metabolism governs TCA-cycle flux to activate TORC1 signaling . Because mammalian mitochondrial BCAT is known to form a supramolecular branched-chain α-keto acid dehydrogenase enzyme complex that links leucine metabolism to the TCA-cycle , these findings establish a precedent for understanding TORC1 signaling in mammals .
The Target of Rapamycin Complex 1 ( TORC1 ) is functionally and structurally conserved throughout eukaryotes and senses and responds to nutrients to promote cell growth and inhibit catabolic processes such as autophagy . Amino acids , particularly the branched-chain amino acid leucine , control TORC1 activity by affecting the nucleotide binding status of the Exit from G0 Complex ( EGOC ) GTPase subunits Gtr1 and Gtr2 in Saccharomyces cerevisiae ( or the Rag GTPases in mammalian cells ) [1–3] . Under leucine-starvation conditions , the yeast SEA ( Seh1-associated ) complex and its mammalian ortholog GATOR activate the GTPase activity of , and thereby inhibit , the EGO and Rag GTPase complexes [4–6] . Conversely , the EGO and Rag GTPase complexes are positively regulated in leucine-replete conditions by leucyl-tRNA synthetase ( LeuRS ) in yeast and mammals ( although this model has been contested in mammals [7] ) , and the vacuolar ATPase in mammalian cells [8–10] . Amongst other TORC1-stimulating amino acids , arginine abundance in mammalian cells is proposed to be sensed by the lysosomal transporter SLC38A9 and conveyed to mTORC1 via the Rag GTPases [11 , 12] , while glutamine levels appear to be transduced to control both yeast and mammalian TORC1 independently of the EGO/Rag GTPases [13 , 14] . In turn , activation of TORC1 controls yeast growth via phosphorylation of three major effector branches: activation of ribosome biogenesis via the protein kinase Sch9 , and repression of autophagy , nitrogen , and stress responses via Atg13/Atg1 and Tap42-PP2A [15–19] . Furthermore , ammonium starvation , heat , oxidative , and osmotic stresses , and also low levels of carbon , phosphate , and energy , control yeast TORC1 activity by additional mechanisms involving Rho1 , the AMP-regulated , MAPK , PAS , and Hog1 kinases , and stress granule sequestration [20–23] . Despite considerable focus , the mechanisms by which leucine and other nutrient signals are transduced to control TORC1 activity remain incompletely defined . Plausible mechanisms through which leucine could be sensed are as follows . First , the leucine signal may be elicited via an enzyme for which leucine is a substrate . Candidates in yeast include: 1 ) the key controllers of leucine metabolism , the BCATs [24]; 2 ) amino acid transporters , of which significant precedent already exists for acting as signal transducers [11 , 12 , 25–27]; 3 ) LeuRS , already implicated in TORC1 control [8 , 9]; and 4 ) importantly , amino acid limitation is in general sensed through the binding of uncharged tRNAs to the Gcn2 kinase which , in conjunction with Gcn2 dephosphorylation , contributes to Gcn2 activation . However , because inactivation of TORC1 with rapamycin contributes to Gcn2 dephosphorylation , the current model is that TORC1 negatively controls Gcn2 and there is no evidence that the Gcn2 amino acid sensing mechanism impinges upon TORC1 signaling [28] . Second , leucine may serve as an allosteric activator or inhibitor of an enzyme that directly or indirectly controls TORC1 . In yeast , leucine feedback-inhibits α-isopropylmalate synthase ( Leu4 ) , thereby controlling pathway flux and reducing α-isopropylmalate levels , which in turn downregulates the expression of a set of genes regulated by the Leu3-α-isopropylmalate complex [24] . Significantly , leucine is an allosteric activator of mammalian glutamate dehydrogenase ( Gdh1 ) , important for driving mTORC1 activity via glutaminolysis [29] . Third , leucine could be metabolized to a signaling compound . Leucine is metabolized by BCATs to KIC , which has been shown to support mTORC1 activity [30–33] . In yeast KIC is further metabolized to fusel alcohols that can serve as signaling molecules [34–37] . Here we investigated roles for leucine metabolites and metabolic enzymes in the control of TORC1 activity . We show that KIC is capable of stimulating TORC1 activity following leucine starvation . TORC1 stimulation by leucine or KIC is only partially reduced by leucyl-tRNA synthetase inhibition or EGOC disruption , suggesting EGOC-independent and -dependent routes of TORC1 regulation . Our studies indicate that the BCATs Bat1 and Bat2 are critical to activate TORC1 signaling . We provide evidence that Bat1 governs TCA-cycle flux via both its enzymatic activity and by signaling leucine and KIC availability through formation of a supramolecular complex with the key TCA-cycle enzyme Aco1 . A mammalian BCAT-GDH metabolon is known to connect α -ketoglutarate and glutamate production that could fuel the TCA-cycle [38 , 39] , and thus our findings establish a foundation for understanding control of mTORC1 signaling by metabolism .
To elucidate the mechanisms via which leucine controls TORC1 activity , we investigated if any products of branched-chain amino acid ( BCAA ) metabolism ( Fig 1A ) are capable of stimulating TORC1 activity following leucine starvation ( Fig 1B ) . Surprisingly , we found that the leucine α-ketoacid KIC was as effective as leucine in activating TORC1 ( Fig 1B ) . The other BCAAs isoleucine and valine , and their respective α-ketoacids , α-keto-β-methylvalerate and α-ketoisovalerate , all failed to stimulate TORC1 activity , as monitored by phosphorylation of Sch9-Thr737 . Leucine is reversibly metabolized to KIC via the BCATs Bat1 ( mitochondrial ) and Bat2 ( cytoplasmic ) , in a reaction coupled to the transamination of α-ketoglutarate to glutamate ( Fig 1A ) . We observed very modest TORC1 stimulation by dimethyl α-ketoglutarate ( d-KG , a membrane-permeable derivative of α-ketoglutarate ) , but not by glutamate addition . KIC is further metabolized via Thi3/Aro10 to isovaleraldehyde ( IVA ) and isoamyl alcohol ( IAA ) , both of which also failed to stimulate TORC1 activity . Furthermore , thi3 aro10 mutants were not rapamycin hypersensitive ( Fig 1C ) and showed wild-type levels of TORC1 activity ( Fig 1D ) . Therefore , conversion of KIC to IVA or IAA was not required for stimulation of TORC1 activity . To determine if KIC stimulation of TORC1 following leucine starvation is biologically relevant and not merely due to transamination back to leucine , we tested the ability of KIC to stimulate TORC1 activity in bat1 bat2 mutants . Mutation of BAT1 or BAT2 individually did not perturb either TORC1 activity or the ability of KIC and leucine to stimulate TORC1 activity following leucine starvation ( Fig 1E ) . In contrast , we observed a striking ~73% reduction in TORC1 activity in the bat1 bat2 mutant compared to the WT ( Fig 1E and 1F ) . However , leucine and KIC were still similarly effective in stimulating TORC1 ( albeit to reduced levels; 20–30% of the level observed in the WT strain ) in the bat1 bat2 mutant ( Fig 1E and 1F ) . Thus the ability of KIC to stimulate TORC1 is , in part , unrelated to its conversion back to leucine . When combinations of leucine or KIC with glutamine , d-KG , or glutamate were added to leucine-starved cells they did not stimulate TORC1 activity above KIC or leucine addition alone , and in contrast to the WT , no stimulation by d-KG was observed ( Fig 1E ) . Moreover , we found that the bat1 bat2 mutant was hypersensitive to rapamycin and recovered less well from rapamycin-induced growth arrest than the WT and bat1 or bat2 single mutants ( Fig 1C ) . Taken together , these results support a role for BCATs in promoting robust TORC1 activity . To further substantiate a role for BCATs in control of TORC1 activity , we investigated whether the TOR1-LM allele , which contains a mutation in the kinase domain ( Tor1L2134M ) rendering TORC1 independent of upstream activation [23] , would suppress bat1 bat2 TORC1-signaling defects . Expression of TOR1-LM partially suppressed the rapamycin hypersensitivity and defects in recovery from rapamycin-mediated growth arrest of the bat1 bat2 mutants ( Fig 2A ) . Thus , these results support a role for BCAT upstream of TORC1 . We also investigated if BCAT control and KIC stimulation of TORC1 activity requires EGOC . Expression of GTR1GTP GTR2GDP-locked alleles that activate TORC1 independent of leucine levels partially suppressed the rapamycin hypersensitivity and recovery defects of bat1 bat2 mutants ( Fig 2A ) . Stimulation of Sch9 phosphorylation by KIC and leucine addition to leucine-starved cells was similarly only partially reduced by gtr1 or ego1 mutations ( Fig 2B ) . These results support that the BCATs and their metabolites leucine and KIC control and stimulate TORC1 activity via EGOC and EGOC-independent inputs . The editing activity of leucyl-tRNA synthetase ( LeuRS ) is implicated in sensing and signaling leucine availability to TORC1 in yeast , acting upstream of the EGOC [8] . Exposure of leucine-starved WT cells to the LeuRS inhibitor DHBB , which traps tRNA-LEU in the LeuRS editing site [8] , reduced both KIC and leucine stimulation of TORC1 activity at comparable levels ( Fig 2C ) . These results support that similar to leucine , KIC stimulates TORC1 activity partially via the LeuRS . Remarkably , the reduced stimulation of TORC1 activity elicited by leucine and KIC addition to leucine-starved bat1 bat2 cells was nearly blocked by DHBB ( Fig 2C ) . These findings support a model in which both BCATs and the LeuRS-EGOC module contribute independently to activate TORC1 . In our experiments , leucine starvation is achieved via a leu2 mutation and incubation in media lacking leucine . Under these conditions cells accumulate the intermediate 3-isopropylmalate ( 3-IPM ) , which is subsequently converted to 3-IPM methyl ester , an invasive growth signaling molecule [40] . However , 3-IPM accumulation does not influence TORC1 activity because the TORC1 activity response to leucine starvation and leucine or KIC readdition was similar in leu4 leu9 , leu1 , or leu2 mutants , which each accumulate different pathway intermediates ( S1 Fig ) . Isoleucine and valine auxotrophy , or elevation of the respective isoleucine and valine α -ketoacids and their fusel alcohol degradation products ( Fig 3A ) caused by BCAT mutation , could potentially affect TORC1 signaling . We found that ilv2 and ilv3 mutants , which are BCAA auxotrophs , were rapamycin-sensitive , but less hypersensitive than bat1 bat2 mutants ( Fig 3B ) . Similarly , TORC1 activity was reduced in ilv2 mutants , but not to the low level observed for bat1 bat2 mutants ( Fig 3C ) . Furthermore , ilv2 bat1 bat2 , and ilv3 bat1 bat2 triple mutants , which cannot accumulate isoleucine and valine α-ketoacids and their degradation products , were as rapamycin-hypersensitive as bat1 bat2 double mutants ( Fig 3B ) . These results support that the effects of BCAT mutation in perturbing TORC1 activity are not mediated by BCAA intermediate accumulation in the bat1 bat2 mutant , and that there are both BCAA biosynthesis-dependent and -independent roles for BCATs in TORC1 signaling . To further test whether a BCAT enzymatic role is required to activate TORC1 signaling , we mutated the conserved pyridoxal phosphate-binding site [41] of BAT1 ( BAT1K219R/A ) and BAT2 ( BAT2K202R/A ) to eliminate BCAT activity . BAT1K219R/A and BAT2K202A were as stably expressed as the WT BAT1 and BAT2 respectively ( although BAT2K202R was less well expressed ) ( Fig 4A ) . Loss of BCAT activity of plasmid-expressed BAT1K219R/A was confirmed by an inability to utilize KIC to supplement the leucine auxotrophy ( Fig 4B ) , and an inability to complement the bat1 bat2 strain leucine , isoleucine and valine auxotrophy ( Fig 4C ) . Surprisingly , the BAT1K219R/A alleles , but not the equivalent BAT2K202R/A alleles , were nearly as effective as WT BAT1 or BAT2 at partially suppressing the rapamycin recovery and TORC1 activity defects of the bat1 bat2 mutant ( Fig 4C and 4D ) . Taken together , these results suggest that both Bat1 and Bat2 contribute to TORC1 signaling , likely through their BCAT biosynthetic role , and that in addition Bat1 plays a non-enzymatic role . We next tested the model that Bat1 plays a structural , non-enzymatic role mediated via protein-protein interactions in controlling TORC1 . We employed a proteomics approach to identify Bat1-interacting proteins . To this end , Bat1-FLAG was expressed in the bat1 mutant strain and immunoprecipitated , and Bat1-coimmunoprecipitated proteins were identified by mass-spectrometry . Specific proteins identified in the Bat1-FLAG immunoprecipitates ( which were absent in control immunoprecipitates from FLAG-untagged cells ) were , like Bat1 , mitochondrial proteins . Interestingly , these Bat1-interacting proteins included BCAA biosynthetic and metabolic enzymes ( Leu4 , Ilv5 , Ilv3 , Ape2 ) and TCA-cycle enzymes including pyruvate dehydrogenase subunits ( Lat1 and Pdb1 ) and aconitase ( Aco1 ) ( Fig 5A ) . Our proteomic results support the intriguing model that Bat1 may comprise a central component of a metabolon linking BCAA biosynthesis to energy metabolism via the TCA-cycle . To test this model , we sought to validate the Bat1-FLAG interactions using GFP-tagged TCA-cycle pyruvate dehydrogenase and aconitase alleles . First , we confirmed that Lat1-GFP , Pdb1-GFP , and Aco1-GFP were correctly localized to the mitochondria , and had wild type function with respect to growth on respiratory carbon sources ( S2A and S2B Fig ) . Because we observed a low signal for Lat1-GFP by Western blot analysis , only Aco1-GFP and Pdb1-GFP interactions were tested ( S2C Fig ) . We were unable to demonstrate binding between Bat1-FLAG and Pdb1-GFP; thus this interaction may be weak , consistent with a low number of spectral reads identified by mass spectrometry ( Fig 5A ) . Remarkably , Aco1-GFP readily coimmunoprecipitated with Bat1-FLAG under conditions that promote robust TORC1 activity ( leucine or KIC addition ) , while the interaction was disrupted by leucine starvation ( Fig 5B ) . The Bat1-Aco1 interaction was not perturbed by rapamycin treatment , consistent with leucine or KIC triggering this interaction upstream of TORC1 . Furthermore , Bat1K219R-FLAG retained the ability to interact with Aco1-GFP , indicating that the interaction does not depend on pyridoxal binding or catalysis but does depend on the presence of substrate ( leucine or KIC ) binding ( Fig 5B ) . We also employed a bimolecular fluorescence complementation ( BiFC ) approach [42 , 43] to test if Bat1 ( fused at the N-terminus with the N-terminal-half-Venus; VC-Bat1 in MATa ) and Aco1 ( fused at the C-terminus with the C-terminal-half-Venus; Aco1-VC in MATα ) interact with each other . Consistent with Bat1 and Aco1 interacting or occurring in close proximity to each other ( within a ~7 nM distance ) , we observed a robust fluorescent signal that co-localized with the mitochondria when diploid cells expressing both VC-Bat1 and Aco1-VN were grown in leucine-replete media ( Fig 5C ) . The signal was noticeably brighter than when control cells expressing either Aco1-VC or VC-Bat1 individually were visualized under identical conditions ( Fig 5C ) . Moreover , the signal was reduced following two hours of leucine starvation , further supporting that the Bat1-Aco1 interaction is dependent on leucine ( Fig 5C ) . To determine whether Bat1 interaction with TCA-cycle enzymes is physiologically relevant , we analyzed the levels of TCA-cycle organic acid , amino acid , and acetyl CoA metabolites in the WT and bat1 bat2 strains . Samples were prepared from control , rapamycin-treated , leucine-starved , and leucine- , KIC- , glutamate- or d-KG-addition to leucine-starved cells . We found that in general , the levels of most amino acids detected were higher in the WT compared with bat1 bat2 mutant cells in most conditions examined , in particular upon rapamycin treatment , which blocks translation ( Fig 5D , S4 Table ) . Because bat1 bat2 mutation confers valine , leucine , and isoleucine auxotrophy , these amino acids were supplemented in the growth media; however , they are not metabolized in the bat1 bat2 strain and thus their levels were increased in this mutant compared to the WT under most conditions tested . Interestingly , levels of acetyl CoA and the organic acids pyruvate and citrate were higher in the WT and lower in the bat1 bat2 mutant . In contrast , succinate , fumarate , and malate were lower in the WT and higher in the bat1 bat2 mutant . These results are consistent with a model in which the lack of BCAT results in a block in TCA-cycle flow at the entry point of the pyruvate dehydrogenase product acetyl CoA and near the point of Aco1 action . In accord with this interpretation , leucine starvation resulted in acetyl CoA and citrate accumulation concomitant with decreased levels of all of the other TCA-cycle intermediates in the WT strain . Therefore , BCATs are required to maintain metabolite homeostasis and TCA-cycle flux; likely , at least in part through their BCAA biosynthetic role , and potentially via interactions that affect activity of TCA-cycle enzymes , such as Aco1 . We next tested if inhibition of TCA-cycle flux perturbs TORC1 signaling . First , we determined the effects of disruption of genes encoding Bat1-interacting pyruvate dehydrogenase subunits ( pda1 , pdb1 , lat1 , lpd1 ) or aconitase ( aco1 ) on TORC1-related phenotypes . Compared with the WT , all mutants were rapamycin hypersensitive and had impaired recovery from rapamycin-induced growth arrest ( Fig 6A ) . Furthermore , the pda1 pdb1 mutant also had reduced TORC1 activity under all conditions tested; for example , Sch9 phosphorylation levels were reduced to 50% of WT in cells grown under control conditions ( Fig 6C ) . The aco1 mutant also had substantially reduced TORC1 activity ( 15–20% of WT levels ) following leucine or KIC addition to leucine-starved cells . Mutation of ACO1 renders cells auxotrophic for glutamate [44 , 45]; thus , we reasoned that glutamine levels should also be reduced in these mutants . Accordingly , simultaneous addition of glutamine with leucine or KIC resulted in TORC1 activation to a level comparable to the WT strain ( Fig 6C ) . We also assessed the effect of inhibitors of glycolysis ( 2DG , which reduces pyruvate feeding into the TCA-cycle ) and the TCA-cycle ( sodium ( meta ) arsenite ) on TORC1 activity . Both 2DG and sodium ( meta ) arsenite inhibited growth at the concentrations tested ( Fig 6B ) . TORC1 activity was then assessed for WT cells in which inhibitors were added during control-growth conditions and during leucine starvation 30 min prior to addition of leucine or KIC . Sodium ( meta ) arsenite and 2DG markedly reduced levels of TORC1 activity under all conditions tested ( Fig 6D ) . To determine if TCA-cycle flux controls TORC1 via increased flux through the electron transport chain , we also assessed the effect of electron transport chain inhibitors ( rotenone , antimycin A ) on TORC1 activity . Antimycin A only inhibited growth in non-fermentable carbon sources ( ethanol glycerol medium ) , while rotenone had little effect on growth ( Fig 6B ) , consistent with a rotenone-insensitive NADH:Q6 oxidoreductase identified previously in yeast [46] . In accord with these results , neither rotenone nor antimycin A perturbed TORC1 activity ( Fig 6D ) indicating that flux through the TCA-cycle , but not the respiratory chain , controls TORC1 signaling under our experimental conditions . We considered three hypotheses to explain the mechanism by which the integration between BCATs and TCA-cycle fluxes could influence TORC1 signaling . First , we reasoned that acetyl CoA could affect TORC1 activity by fueling the TCA-cycle . In mammalian cells , KIC can be converted to acetyl CoA via branched-chain ketoacid dehydrogenase complex ( BCKDC ) activity . However , no BCKDC enzymes or activity have been identified in yeast . Although acetyl CoA levels decreased upon leucine starvation ( Fig 5D ) , addition of acetate , which increases cytosolic acetyl CoA levels [47] , failed to stimulate TORC1 activity in leucine-starved WT cells ( S1C Fig ) . Second , TCA-cycle fluxes may signal to TORC1 by affecting glutamine levels , which in turn activates TORC1 independently of the EGOC [14] . Glutamine addition greatly but not completely overcame the TORC1 signaling defects in the aco1 mutant , and also increased TORC1 activity when added with leucine or KIC to WT cells following leucine starvation ( Fig 6C ) . However , glutamine did not elevate TORC1 activity in either the pda1 pdb1 ( Fig 6C ) or bat1 bat2 ( Fig 1F ) mutants when supplemented in the same conditions . These results suggest that while glutamine provides a contribution , there must be additional inputs . Third , reduced TCA-cycle flux should result in increased AMP:ATP or ADP:ATP ratios , which in turn trigger TORC1 inhibition by a cascade involving activation of the AMP-activated kinase ( AMPK ) Snf1 [21] via Thr210-phosphorylation . Indeed , we found that bat1 bat2 mutants growing in leucine-replete conditions had reduced ATP levels compared with the WT ( Fig 7A ) . We tested if the observed perturbations in ATP levels were sufficient to foster activation of Snf1 phosphorylation at residue Thr210 by employing a phospho-specific antibody against mammalian AMPK Thr172 that effectively cross-reacts with phospho-Snf1 Thr210 [48] . Interestingly , in general we observed markedly elevated Snf1 phosphorylation in bat1 bat2 cells compared with the WT under all conditions tested in glucose-containing media ( Fig 7B upper panel ) and a moderate elevation of Snf1 phosphorylation in the WT strain upon leucine starvation ( Fig 7B lower panel ) . Consistent with a TORC1-inhibitory role of Snf1 , mutation of SNF1 led to rapamycin resistance compared with the WT ( Fig 7C panel 3 ) . Conversely , mutation of the Glc7 protein phosphatase regulator REG1 , which promotes Snf1 dephosphorylation [49] , resulted in rapamycin hypersensitivity ( Fig 7C ) , confirming earlier reports [50 , 51] . Moreover , snf1 disruption partially suppressed the rapamycin sensitivity and recovery ( Fig 7C ) and TORC1 activity defects ( Fig 7D ) of the bat1 bat2 mutant strain . These results support a model whereby BCATs control TCA-cycle flux and thereby high ATP levels to promote robust TORC1 activity . BCAT disruption leads to an elevated ADP:ATP ratio , which in turn results in Snf1 activation and TORC1 inhibition ( Fig 7E ) .
The amino acid leucine is a potent inducer of both yeast and mammalian TORC1 activity , but the mechanisms by which regulation occurs are not fully understood . Here , we demonstrate that , similar to leucine , KIC is able to activate TORC1 via an EGOC-dependent mechanism . In addition leucine and KIC , in combination with BCAT , play novel EGOC-independent roles in activating TORC1 signaling . Consistent with an EGOC-dependent role , we provide evidence that KIC is sensed by the leucyl-tRNA synthetase ( LeuRS ) in a mechanism analogous to leucine and leucinol sensing ( Fig 2C ) , reported to act upstream of EGOC or Rag GTPases to signal leucine availability to TORC1 [8 , 9] . Our demonstration of KIC stimulation of yeast TORC1 activity has precedent in controlling mTORC1 . KIC , but not other BC-α-ketoacids , activates mTORC1 similar to leucine in different mammalian models [30–33] and , like leucine , KIC partially rescues development in a zebrafish model of Cornelia de Lange syndrome in a TORC1-dependent manner [52] . Compared with leucine , KIC stimulates mTORC1 activity more efficiently in rat skeletal muscle than in liver , which contains considerably lower mammalian BCAT activity levels . These results were interpreted as consistent with a requirement of BCAT-mediated conversion of KIC to leucine for promoting mTORC1 activity [31] . In contrast , our experiments show KIC and leucine activate yeast TORC1 via both BCAT-dependent and independent mechanisms , and it would be of interest to investigate if a similar BCAT-independent TORC1 activation mechanism occurs in mammals . We present evidence for both enzymatic and structural roles for BCATs in controlling TORC1 activity . Mitochondrial Bat1 is preferentially expressed during the logarithmic phase of growth when energy is produced by glycolysis while cytosolic Bat2 is expressed during stationary growth phase when energy is obtained via respiration [41 , 53] . Given the differential Bat1 and Bat2 subcellular localization and temporal expression , we propose BCAT enzymatic activity could control TORC1 signaling by perturbing the levels of BCAA and central metabolites , glutamate and α-ketoglutarate , in the cytosol and the mitochondria during specific growth phases . Our results suggest that the TORC1-relevant structural role of Bat1 , but not of Bat2 , involves a direct physical interaction with other BCAA-biosynthetic enzymes ( Ilv5 , Ilv3 , Leu4 ) as well as the TCA-cycle PDH and aconitase Aco1 . Such a multiprotein metabolon may provide an assembly line to couple leucine metabolism and glycolysis products to TCA-cycle fluxes , energy production , and TORC1 signaling ( Fig 5E ) . Strikingly , we find the interaction of Bat1 and Aco1 is disrupted by leucine starvation , and reestablished following either leucine or KIC readdition ( conditions that also support TORC1 activity ) . Although the physiological effects of Bat1 interaction on Aco1 activity remain to be determined , we show that BCAT mutants have perturbed TCA-cycle intermediate levels consistent with a block in the TCA-cycle pathway at the step of pyruvate and acetyl-CoA incorporation . Taken together , we propose that BCATs may affect TORC1 activity by controlling TCA-cycle flux via physical interactions with Aco1 and possibly PDH , and by contributing metabolites to fuel the cycle . Multiprotein complexes consisting of mitochondrial BCAT ( BCATm ) and TCA-relevant enzymes , as reported here for yeast Bat1 , have also been identified in mammals [38 , 54] . BCATm interacts with pyruvate carboxylase and the branched-chain α-keto acid dehydrogenase enzyme complex ( BCKDC ) , which converts BCAA-derived α-ketoacids to TCA-cycle intermediates including branched-chain acyl-CoAs , acetyl-CoA , or succinyl-CoA [38 , 54] . Although yeast lacks BCKDC and BC-α-ketoacid degradation instead occurs via the Ehrlich pathway [37] ( in a reaction where Bat2 , as opposed to Bat1 , is more predominant [55] ) , the BCKDC-related PDH complex , which our results suggest interacts with Bat1 ( Fig 5 ) , catalyzes an analogous set of reactions as the BCKDC , except with pyruvate instead of α-keto acids as substrates [56] . BCATm-pyridoxal-5-phosphate ( reduced ) interacts with BDKDC , while BCATm-pyridoxamine 5-phosphate ( oxidized by the transamination reaction , which requires substrate presence ) binds and activates Gdh1 , thereby coupling the BCAA-mediated amination of α-ketoglutarate to form glutamate with the regeneration of BCATm-pyridoxal-5-phosphate and α-ketoglutarate [38 , 39 , 54] , and this reaction which may also contribute to mTORC1 activation [29] . Interestingly , while pyridoxal/pyridoxamine-phosphate binding defines BCATm interactions with BCKDC and Gdh1 [41 , 57–59] , Aco1-Bat1 interaction is not governed by pyridoxal phosphate-binding and our results show that this interaction is triggered by leucine and KIC . We propose two mechanisms via which Bat1 stimulation of TCA-cycle flux controls TORC1 activity independently of the EGOC ( Fig 7E ) . First , increasing α-ketoglutarate levels may sustain glutamine production , which in turn activates TORC1 activity in an EGOC-independent fashion [14] . In contrast to proliferating mammalian cells wherein glutaminolysis predominates over glutamine and glutamate synthesis to produce α-ketoglutarate that sustains TCA-cycle flux [60] , the equilibrium in glucose-grown yeast instead favors glutamate and glutamine synthesis from α-ketoglutarate for anabolic reactions [61 , 62] . Furthermore , while glutaminolysis drives mTORC1 activity [29] , glutamine synthesis and accumulation activate yeast TORC1 [14] . However , whereas glutamine addition restored TORC1 activity to an aco1 mutant ( Fig 6C ) , it failed to stimulate TORC1 activity in the bat1 bat2 mutant ( Fig 1F ) , suggesting another TORC1-inhibitory mechanism occurs in this strain . This BCAT-dependent mechanism likely involves a role for maintenance of ATP levels to sustain TORC1 activity . Previously , mTORC1 was proposed to act directly as an ATP sensor by virtue of its unusually high Km for ATP ( >1 mM ) [63] compared with most kinases ( 10–20 μm ) [64] , although others have argued that the intracellular ATP concentration is normally higher than 1 mM , and a substantial change in ATP levels would be required to alter mTORC1 activity [65] . A high ratio of AMP:ATP in mammals or ADP:ATP in yeast , reflecting low energy production , activates the conserved AMPK/Snf1 by phosphorylation to induce pathways for energy production and downregulate pathways for energy consumption [66–68] . In mammals , AMPK is thought to be a more sensitive ATP sensor than mTORC1 because the intracellular AMP concentration is significantly lower than ATP , and thus , small changes to ATP levels could substantially affect the ATP:AMP ratio [69] . We find that BCAT disruption led to both reduced ATP levels , and increased Snf1 phosphorylation . Substantial research demonstrates that AMPK activation inhibits mTORC1 by phosphorylation of the mTORC1 subunit RAPTOR and the negative mTORC1 regulator tuberous sclerosis complex 2 [70–72] . Recent research also supports a role for Snf1 involvement in yeast TORC1 inhibition [20 , 21] . Upon glucose starvation , Snf1 phosphorylates and activates PAS kinase signaling , which in turn phosphorylates Pbp1 and this event correlates with TORC1 inhibition [21] . Alternatively , Snf1 could control TORC1 by phosphorylation of Kog1 and Tco89 , both of which were recently identified as Snf1 targets [73] . Our results support the model that the AMPK/Snf1 cascade inhibits TORC1 in yeast BCAT mutants . This mechanism for TORC1 inhibition could operate at the diauxic shift and during stationary growth phase when glucose is exhausted and Bat1 is poorly expressed [53] , likely resulting in AMPK/Snf1 activation , which in turn could fine-tune gene expression necessary for energy saving ( notably , downregulation of TORC1-controlled ribosome biogenesis ) while promoting respiratory capacity ( induction of genes for utilization of alternative carbon sources and aerobic growth [74] . However , further experimentation will be required to test this model . As in yeast , both the cytoplasmic BCATc and mitochondrial BCATm isoenzymes are present in mammals , differentially expressed , and control different aspects of leucine metabolism . BCATc expression is restricted to selected neurons but induced in regulatory T-cells , skin grafts , proliferating embryonic and cancer cells [75–78] and may be a prognostic marker for aggressive glioblastomas carrying WT IDH [79] . Most notably , BCAT overexpression has been associated with brain , urothelial , bladder , and breast cancers , for which treatment or clinical trials with the rapamycin analog everolimus have been approved or are underway , respectively , highlighting the coincidence of BCAT and TORC1 defects in human disease [79–81] . Interestingly , mutation of BCATc increased glycolytic metabolism , leucine levels , and mTORC1 activity in activated T-cells [82] . In contrast to BCATc , metabolon-forming BCATm is expressed in most body tissues with the exception of the liver and coincident with BCKDC presence [76] . Furthermore , evidence exists that α-ketoglutarate generated from glutaminolysis , governed by the interaction between BCATm and leucine-allosterically activated Gdh1 [38 , 39] , stimulates mTORC1 , thereby also integrating both glutamine and leucine signals [29] . Therefore , BCATc and BCATm may differently contribute to control mTORC1 . Given the ubiquitous distribution of TORC1 and BCATs in yeast and mammals and the conserved role of BCATm in forming multicomplex metabolons connecting BCAA , glycolysis , and TCA-cycle metabolism , we predict that our findings in yeast will have implications for mTORC1 signaling in human health and disease .
Media consisted of Yeast Extract Peptone Dextrose ( YPD ) , Synthetic Complete ( SC ) , Synthetic Dextrose ( SD ) , Synthetic Ethanol Glycerol ( SEG ) or synthetic medium with amino acids and supplements omitted or added to complement auxotrophies or select for plasmid maintenance [83 , 84] . Synthetic ( S ) -Raffinose contained 20 g/L raffinose as the sole carbon source . When required , media was supplemented with rapamycin ( LC Laboratories ) , 100 μg/ml nourseothricin ( ClonNAT , Werner BioAgents ) , 200 μg/ml G418 ( AG Scientific ) , 200 μg/ml hygromycin ( Calbiochem ) , and 2 mM α-ketoisocaproate , 2 mM dimethyl α-ketoglutarate ( d-KG ) , 2 mM isovaleraldehyde , 2 mM isoamyl alcohol , 10 μM 1 , 3-dihydro-1-hydroxy-2 , 1-benzoxaborole ( DHBB ) , 50 μM antimycin A , 10 mM 2-deoxyglucose ( 2DG ) , 5 mM sodium ( meta ) arsenite , 100 μM rotenone , or potassium acetate ( Sigma ) . All cultures were incubated at 30°C . Strains used in this study are derived from BY4742 or BY4741 [85] and are listed in S1 Table . To construct strains employed in the BiFC assay , the N-terminal of Bat1 was fused with the N-terminal-half-Venus ( VN ) ( under the control of the mid-strength CET1 promoter ) in BY4741 ( MATa ) , and the C-terminal of Aco1 was fused with the C-terminal-half-Venus in BY4742 ( MATα ) . Following PCR-confirmation , strains were crossed to generate a diploid co-expressing both fusion proteins , or control diploid strains expressing either fusion individually . Plasmids and oligonucleotides used in this study are listed in S2 and S3 Tables , respectively . To construct pPC02 , the BAT1 gene was PCR-amplified from BY4742 genomic DNA using oligonucleotides that contained XbaI and BamHI sites ( MPDC01 and MPDC02 ) and the XbaI/BamHI-digested PCR product was cloned into the XbaI/BamHI-digested vector , p416ADH . In an analogous fashion , BAT2 , amplified using oligonucleotides MPDC13 and MPDC14 , was cloned in p416ADH to produce plasmid pJK51 . Construction of the BAT1K219R and BAT1K219A alleles was performed as follows using mutagenic oligonucleotides . The 5’ and 3’ ends of BAT1K219R were amplified using oligonucleotide pairs MPDC01+JK46 and MPDC02+JK44 , and of BAT1K219A using MPDC01+JK47 , MPDC02+JK45 , respectively . Based on the complementarity of oligonucleotides JK44 with JK46 , and JK45 with JK47 , purified PCR products were used as templates in an overlap PCR reaction ( oligonucleotides MPDC01+MPDC02 ) . The final overlap BAT1K219R and BAT1K219A-containing PCR products were XbaI/BamHI-digested and cloned into XbaI/BamHI-digested p416ADH to yield pJK12 and pJK15 , respectively . Similarly , the 5’ and 3’ ends of the BAT2K202A and BAT2K202R alleles were amplified using oligonucleotide pairs MPDC13+JK372 and MPDC14+JK371 , and MPDC13+JK370 and MPDC14+JK369 , respectively . Fusion products amplified using oligonucleotides MPDC13+MPDC14 were cloned into p416ADH to create pJK48 ( BAT2K202A ) and pJK50 BAT2K202R . To construct the FLAG-tagged BAT1 and BAT1K219R plasmids pJK47 and pJK59 , BAT1-FLAG and BAT1K219R-FLAG were PCR-amplified from genomic or pJK12 DNA using oligonucleotides MPDC01+JK376 , and XbaI/BamHI-digested products were ligated into XbaI/BamHI-digested p416ADH . All plasmids were confirmed by restriction digest analysis and sequencing of the insert . BiFC Venus signal images were taken using a standard fluorescein isothiocyanate filter with identical exposure settings for each strain and condition , and processed using identical levels of image contrast using ImageJ software . To visualize mitochondria , 100 nM MitoTracker Red CMXRos ( Molecular Probes ) was added to cells for the final hour of incubation . Twice-washed cells were imaged using a Zeiss Axioskop 2 Plus microscope and AxioVision 4 . 6 image acquisition software . Protein extract preparation , Sch9 phosphorylation , and FLAG coimmunoprecipitation assays were performed as described previously [86] . Western blot analysis employed Sch9 and phospho-Thr737-Sch9 [86] , Bat1 and Bat2 [41] , FLAG ( Sigma ) , GFP ( Roche ) , Snf1 ( Santa Cruz Biotechnology , Inc . ) , and phospho-Thr172-AMPKα ( Cell Signaling Technology ) antibodies . Protein mass spectrometry was performed by the Duke University Medical Center Proteomics and Metabolomics Core Facility , as follows . The eluents from FLAG-affinity immunoprecipitates were subjected to a 3 min SDS-PAGE separation on an Invitrogen NuPAGE 4–12% gel for desalting purposes and stained with colloidal Coomassie stain . Gel bands were excised and subjected to in-gel reduction with 5 mM dithiothreitol and alkylation with 10 mM iodoacetamide . Trypsin digestion ( sequencing grade , Promega Corp ) was allowed to proceed overnight at 37°C . Following peptide extraction , peptides were vacuum centrifuged to dryness and resuspended in 12 μl of 1% TFA/2% acetonitrile . LC/MS/MS was performed on 2 μl of each sample using a nanoAcquity UPLC system ( Waters Corp ) coupled to a Thermo QExactive Plus high-resolution accurate mass tandem mass spectrometer ( Thermo ) via a nanoelectrospray ionization source . Briefly , the sample was first trapped on a Symmetry C18 300 mm × 180 mm trapping column ( 5 μl/min at 99 . 9/0 . 1% v/v water/acetonitrile ) , after which the analytical separation was performed using a 1 . 7 μm Acquity BEH130 C18 75 mm × 250 mm column ( Waters Corp ) using a 90 min gradient of 5 to 40% acetonitrile with 0 . 1% formic acid at a flow rate of 400 nL/min with a column temperature of 55°C . Data collection on the QExactive Plus mass spectrometer was performed in a data-dependent acquisition ( DDA ) mode of acquisition with a r = 70 , 000 ( at m/z 200 ) full MS scan from m/z 375–1600 with a target AGC value of 1e6 ions followed by 10 MS/MS scans at r-17 , 500 ( at m/z 200 ) at a target AGC value of 5e4 ions . A 20s dynamic exclusion was employed to increase depth of coverage . Raw LC-MS/MS data files were processed in Proteome Discoverer and then submitted to independent Mascot searches ( Matrix Science ) employing the SwissProt database ( Yeast taxonomy ) containing both forward and reverse entries of each protein . Search tolerances were 5 ppm for precursor ions and 0 . 02 Da for product ions using trypsin specificity with up to two missed cleavages . Carbamidomethylation ( +57 . 0214 Da on C ) was set as a fixed modification , whereas oxidation ( +15 . 9949 Da on M ) and deamidation ( +0 . 98 Da on NQ ) were dynamic modifications . All searched spectra were imported into Scaffold ( v4 . 4 , Proteome Software ) and scoring thresholds were set to achieve a protein false discovery rate of 1 . 0% using the PeptideProphet algorithm . Amino acids and organic acids were extracted from 8 OD600nm units of cells grown to OD600nm~0 . 8 in SD+ile+leu+val+gln , and subjected to various treatments . Cells were filtered on 1 . 2 μm nitrocellulose filters ( Millipore ) , washed twice with 5 ml sterile dH20 , resuspended in 1 ml ice-cold methanol , and then pellets were dried on a Speed-vac at room temperature . Pellets were resuspended in ice-cold methanol or 50% acetonitrile/0 . 3% formic acid for analysis of amino and organic acids , respectively . ATP and acetyl CoA were extracted from 8 OD600nm units of cells as described previously [87] , with modifications [88] , and pellets for acetyl CoA analysis were resuspended in 50% acetonitrile/0 . 3% formic acid for analysis . Metabolomic analyses were performed by the Sarah W . Stedman Nutrition and Metabolism Center , Duke University Medical Center , as follows . Amino acids , organic acids , and acetyl CoA were analyzed using stable isotope dilution techniques . Amino acid measurements were made by flow injection tandem mass spectrometry using sample preparation methods described previously [89 , 90] . The data were acquired using a Waters Acquity UPLC system equipped with a TQ ( triple quadrupole ) detector and a data system controlled by MassLynx 4 . 1 operating system ( Waters , Milford , MA ) . Organic acids were quantified using methods described previously [91] employing Trace Ultra GC coupled to ISQ MS operating under Xcalibur 2 . 2 ( Thermo Fisher Scientific , Austin , TX ) . Acetyl CoA was extracted and purified as described previously [92 , 93] , and analyzed by flow injection analysis using positive electrospray ionization on Xevo TQ-S , triple quadrupole mass spectrometer ( Waters , Milford , MA ) . ATP concentration was determined using the ATP Colorimetric/Fluorometric Assay Kit ( Sigma ) from extracts resuspended in 100 μl ATP Extract Buffer , as recommended by the manufacturer . | In all organisms from yeasts to mammals the target of rapamycin TORC1 pathway controls growth in response to nutrients such as leucine , but the leucine sensing mechanisms are only partially characterized . We show that both leucine and its α-ketoacid metabolite , α-ketoisocaproate , are similarly capable of activating TORC1 kinase via EGOC GTPase-dependent and -independent mechanisms . Activation of TORC1 by leucine or α-ketoisocaproate is only partially mediated via EGOC-GTPase . Leucine and α-ketoisocaproate are interconverted by ubiquitous branched-chain aminotransferases ( BCAT ) . Disruption of BCAT caused reduced TORC1 activity , which was partially restored by expression of BCAT active site mutants , arguing for both structural and catalytic roles of BCAT in TORC1 control . We find BCAT interacts with several branched-chain amino acid metabolic enzymes , and in a leucine-dependent fashion with the tricarboxylic acid ( TCA ) -cycle enzyme aconitase . Both aconitase mutation or TCA-cycle inhibition impaired TORC1 activity . Mutation of BCAT resulted in a TCA-cycle intermediate profile consistent with a TCA-cycle block , low ATP levels , activation of AMPK , and TORC1 inhibition . Our results suggest a model whereby BCAT coordinates leucine and TCA cycle metabolism to control TORC1 signaling . Taken together , our findings forge key insights into how the TORC1 signaling cascade senses nutrients to control cell growth . | [
"Abstract",
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"Results",
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"Methods"
] | [] | 2015 | Branched-Chain Aminotransferases Control TORC1 Signaling in Saccharomyces cerevisiae |
While the importance of transmission of pathogens is widely accepted , there is currently little mechanistic understanding of this process . Nasal carriage of Streptococcus pneumoniae ( the pneumococcus ) is common in humans , especially in early childhood , and is a prerequisite for the development of disease and transmission among hosts . In this study , we adapted an infant mouse model to elucidate host determinants of transmission of S . pneumoniae from inoculated index mice to uninfected contact mice . In the context of co-infection with influenza A virus , the pneumococcus was transmitted among wildtype littermates , with approximately half of the contact mice acquiring colonization . Mice deficient for TLR2 were colonized to a similar density but transmitted S . pneumoniae more efficiently ( 100% transmission ) than wildtype animals and showed decreased expression of interferon α and higher viral titers . The greater viral burden in tlr2−/− mice correlated with heightened inflammation , and was responsible for an increase in bacterial shedding from the mouse nose . The role of TLR2 signaling was confirmed by intranasal treatment of wildtype mice with the agonist Pam3Cys , which decreased inflammation and reduced bacterial shedding and transmission . Taken together , these results suggest that the innate immune response to influenza virus promotes bacterial shedding , allowing the bacteria to transit from host to host . These findings provide insight into the role of host factors in the increased pneumococcal carriage rates seen during flu season and contribute to our overall understanding of pathogen transmission .
The bacterial pathogen Streptococcus pneumoniae ( the pneumococcus ) robustly colonizes the upper respiratory tract of humans and is commonly carried asymptomatically . Colonization rates are highest in early childhood , where they can exceed 80% [1]; in crowded environments such as daycare centers [2]; and when viral respiratory infections are prevalent [3] . From its niche in the nasopharynx , the bacterium can invade other host sites , and as a result the pneumococcus is a leading cause of otitis media , pneumonia , and septicemia . Importantly , colonization of the nasopharynx is the reservoir for pneumococcal disease [4]–[7] and transmission between hosts [8] , [9] . While pneumococcal colonization and disease have been well-studied using animal models ( for review see [10] , [11] ) , transmission of the bacterium remains poorly understood . In humans , close contact is required for transmission , which is thought to occur via respiratory secretions , but the specific host and bacterial factors contributing to this process have not been elucidated [12] , [13] . Recently , an infant mouse model of pneumococcal transmission has been described [14] . In this model , ‘index’ mice are given Streptococcus pneumoniae intranasally and co-housed with uninoculated ‘contact’ littermates . Subsequently , all pups are inoculated with influenza A virus and , following an exposure period , transmission from index to contact mice is assessed by enumerating bacteria in the nasopharynx . Using this model , this group demonstrated that both increasing the bacterial titer in the index mice and inducing inflammation in the contact mice led to more efficient transmission [15] . Given the timing of these experiments , these studies suggest a role for the innate immune response in the setting of co-infection in transmission of the pneumococcus from host to host . The host immune response to influenza has been extensively reviewed [16] , [17] and is briefly summarized here to highlight key elements . After passing through the mucus layer lining the respiratory tract , influenza A virus is recognized by several pattern recognition receptors ( PRRs ) expressed by epithelial cells , including the Toll-like receptors ( TLRs ) , nucleotide oligomerization domain-like receptors ( NLRs ) , and retinoic acid-inducible gene-I receptors ( RIG-I ) . Engagement of these receptors induces a signaling cascade that culminates in expression of interferons ( IFNs ) and pro-inflammatory cytokines and chemokines , resulting in recruitment and activation of immune cells , such as neutrophils and macrophages . A recent study has shown that influenza infection also leads to increased mucin expression by epithelial cells in the respiratory tract [18] . TLR2 has been implicated in promoting clearance of the pneumococcus [19] , [20] , and stimulation of this receptor has been shown to protect against influenza infection [21] . Additionally , recent work has shown that signaling through TLR2 can induce anti-viral responses after stimulation by viral and synthetic ligands . [22] , [23] Thus , we hypothesized that mice deficient for TLR2 would display altered transmission of the bacterium in this model . We found that mice deficient in TLR2 show heightened acute inflammation in index mice and enhanced transmission due to an increase in number of bacteria shed via nasal secretions . This model recapitulates many facets of human-to-human transmission of pneumococcal carriage , such as concurrent viral infection and close contact between infants/children , and these findings provide insight into how innate immune responses to infection promote the spread of pathogens from host to host .
Our adaptations to the previously reported infant mouse model of pneumococcal transmission are outlined in the schematic in Figure 1A [14] . In these experiments , four-day-old pups were intranasally inoculated with pneumococci ( index mice only ) and on day 8 all mice in the litter ( both index and contacts ) were intranasally inoculated with Influenza A/HKx31 ( mouse-adapted H3N2 ) virus . On day 14 , the pups were sacrificed and bacterial loads were enumerated in nasal lavages . In wildtype mice , acquisition of colonization was detected in approximately half ( 47% ) of the contact pups ( Figure 1B , flu ) . In contrast , without influenza ( PBS administered at day 8 ) , none of the contact pups acquired S . pneumoniae ( Figure 1B , mock ) . Moreover , transmission was not simply due to increased bacterial load in the index mice , as there was no significant difference between the mock or flu index groups . This phenotype makes this model particularly useful for probing the factors that limit and promote bacterial transmission because transmission can be either increased or decreased by experimental manipulations . As this infection model utilizes a six-day exposure period , it is possible that contact mice infected early in that window could then go on to spread the bacteria to other contacts . In order to test this possibility , we repeated the initial experiment but gave S . pneumoniae to the index ( “inoculated contact” ) on day 9 instead of day 4 , to simulate acquisition from an index mouse . These inoculated contacts were able to spread infection effectively , with four out of five “uninoculated contact” mice acquiring colonization ( Figure S1 ) . The observed transmission among contact mice makes it unlikely that the ratio of index to contact mice was a significant factor in overall rates of transmission . We then repeated the transmission experiment using tlr2−/− mice . All tlr2−/− contact mice acquired pneumococcal colonization and were colonized at high levels ( Figure 2A ) . As TLR2 deficiency led to increased transmission , we reasoned that stimulation of TLR2 could limit bacterial spread . To test this , we performed a transmission experiment with wildtype mice and intranasally administered the TLR2 agonist Pam3Cys three times over the course of the exposure period ( days 8 , 10 , and 12 ) . As depicted in Figure 2B , transmission was significantly less efficient in treated animals than in control litters , with only one mouse out of sixteen acquiring colonization . We hypothesized that the increase in transmission efficiency seen in tlr2−/− animals could be due to either increased spread by the index mice or increased susceptibility in the contact mice . To address this question , we assessed whether the transmission phenotype was dependent on the index or contact mice in a mixed litter experiment . Age-matched litters of wildtype and tlr2−/− mice were inoculated with S . pneumoniae as described above , but the index mice from each litter were switched , such that tlr2−/− index were co-housed with wildtype contacts and wildtype index were co-housed with tlr2−/− contacts . After the six-day exposure period , only 39% of the tlr2−/− contacts housed with wildtype index mice had detectable levels of colonization ( Figure 2C ) . This was not significantly different from the wildtype contact group in the previous experiments , but was significantly different from the tlr2−/− contact group ( p = 0 . 0014 ) . On the contrary , 89% of wildtype contacts housed with tlr2−/− index mice became infected ( Figure 2C ) . This was not significantly different from the tlr2−/− contact group , but was significantly different from the wildtype contact group ( p = 0 . 0013 ) . These results indicate that the increased transmission phenotype seen in the tlr2−/− mice is linked to the index mouse , which we postulated was due to increased spread of the bacterium from these mice . However , this effect was not due to an increased bacterial load in the tlr2−/− mice compared to the wildtype . We thus concluded that TLR2 deficiency likely did not cause an increase in susceptibility to bacterial acquisition in this model . We next assessed the innate immune response to infection with influenza and pneumococcus , both separately and in the context of co-infection , in our infant mouse model . To test this , we infected wildtype mice as index pups according to the schematic in Figure 1A , giving PBS doses when appropriate for single or mock-infected groups . Nasal lavage was performed on day 14 , and a sample of lavage fluid was stained with antibodies against a panel of immune cells ( Ly6G , CD11b , F/480 and CD4 ) . While no significant influx of macrophages or T cells was seen ( data not shown ) , we noticed significant differences in neutrophil populations , as seen in Figure 3A . Neutrophils ( Ly6G+ and CD11b+ events ) comprised very little of the total cell count for both mock-infected mice and those given S . pneumoniae alone . However , when influenza was present , both in single infection and in co-infection with the pneumococcus , we observed a significant neutrophil influx , comprising 62 . 6% and 74 . 7% of the total cell infiltrate , respectively ( Figure 3A ) . We also visualized neutrophils and bacteria in the lavage fluid via immunofluorescence microscopy . The representative image in Figure 3B shows multiple intact pneumococci associated with a cluster of neutrophils . These clusters of neutrophils were not seen in mice that were mock infected or infected with the pneumococcus alone ( data not shown ) . Taking these results together , we posit that influenza infection sparks neutrophil influx into the nasopharynx , and that this acute inflammatory response is ineffective at clearing the bacteria , but facilitates the spread of bacteria amongst littermates . Based on the observation that TLR2 deficiency led to increased transmission , we hypothesized that the innate immune response to co-infection with the pneumococcus and influenza differed between these two groups . When we analyzed the neutrophil content of lavage fluid from co-infected tlr2−/− mice , we found neutrophils to make up a significantly higher percentage of the total cells than in wildtype mice ( mean 86 . 1% , Figure 4A ) . This was not due to a difference in response to the pneumococcus , as these percentages were not different between wildtype and tlr2−/− infected with S . pneumoniae alone ( Figure 4A ) . As this result suggested that there was more inflammation in co-infected tlr2−/− mice , we also compared mucus production by analyzing relative expression levels of Muc5ac , the primary secreted mucin of the nasopharynx , by qRT-PCR [24] . We found that in co-infected tlr2−/− mice , Muc5ac transcript levels were on average 2 . 8-fold higher than in co-infected wildtype mice ( Figure 4B ) . Recent work has shown that TLR2-dependent signaling can induce anti-viral responses in the form of type I IFN production [22] , [23] . Considering these findings , we hypothesized that tlr2−/− mice display a weakened anti-viral response , rendering them more susceptible to influenza infection leading to heightened inflammation . To assess the anti-viral responses produced by the mice used in our model , we examined IFNα expression levels by qRT-PCR at an early time point after influenza inoculation ( 3 days ) and found that co-infected tlr2−/− mice showed a 2 . 6-fold reduction in IFNα transcript compared to wildtype ( Figure 4C ) . Additionally , levels of viral RNA were 5 . 4-fold higher in tlr2−/− mice than wildtype , suggesting that the increased inflammation observed could be due to increased viral titers in the tlr2−/− host ( Figure 4D ) . This increase in viral levels was not dependent on the presence of pneumococcus , as evidenced by the ∼3-fold increase in viral RNA in tlr2−/− mice infected with influenza alone . Thus , increased inflammation appears to be associated with increased transmission of S . pneumoniae by infected hosts . Supporting this , mice treated with the TLR2 agonist Pam3Cys displayed both lower viral titers ( Figure 4E ) and subsequently , less of an inflammatory response , with fewer total cells in the nasopharyngeal infiltrate ( Figure 4F ) . Taken together , our data have suggested that inflammation promotes bacterial spread , and thus we hypothesized that increases in inflammation and mucus production lead to increased bacterial shedding in the form of nasal secretions . In order to determine the number of bacteria being shed in this manner , we adapted a method in which the nose of the mouse is gently pressed onto a nutrient agar plate and exhaled bacteria are then quantified [25] . We observed that in both wildtype and tlr2−/− co-infected mice , detectable levels of bacteria were shed throughout the experiment , starting on day 10 ( Figure 5A , B ) . When bacterial counts from days 10–14 are compared between groups , the tlr2−/− mice shed significantly more bacteria then the wildtype group over the course of the exposure window ( p<0 . 0001 ) . As TLR2 deficiency led to increased shedding , we reasoned that stimulation of this receptor could limit shedding , and thus we repeated the wildtype shedding experiment , including three intranasal administrations of Pam3Cys over the exposure period . The treated animals shed significantly fewer bacteria than the control litter ( Figure 5E ) , demonstrating that TLR2 stimulation can limit shedding . Bacterial shedding was dependent on influenza co-infection for both groups , as mice infected with S . pneumoniae alone did not shed appreciable amounts of bacteria at any point in the experiment ( Figure 5C , D ) . To determine if the numbers of bacteria shed were sufficient to infect contact mice , we intranasally administered 500 CFU of S . pneumoniae ( ∼ID50 for adult mice ) to 7-day-old pups and assessed colonization levels the following day . We found that this was a sufficient dose to establish colonization with a robust increase in bacterial density over the inoculum size in the infant mouse nasopharynx , with 100% of pups colonized . Most adult mice , in contrast , were not consistently colonized by this low dose and those that became colonized had bacterial numbers below that of the inoculum – a result that correlated with the lack of pneumococcal transmission observed among adult mice ( Figure 5F ) . In co-infected infant pups , from days 10–14 , approximately 14% of wildtype mice regularly shed >500 colonies , while 43% of tlr2−/− mice consistently shed at this level or higher , corresponding to the rates of transmission in these two groups . In contrast , none of the mice given PBS instead of influenza shed above this level . These experiments suggest that TLR2-dependent inflammation induced by influenza infection promotes shedding of S . pneumoniae through nasal secretions , and the contact between infected and uninfected infant mice is sufficient to mediate bacterial transmission from host to host .
This study aimed to expand existing knowledge of the transmission of respiratory bacterial pathogens by specifically analyzing spread of Streptococcus pneumoniae in an infant mouse model . Transmission of bacterial pathogens is critical to their success but has long been a black box in the study of pathogenesis due to a lack of tractable animal models . An infant mouse model utilizing influenza co-infection has been recently introduced [14] , [15] . These studies have established a preliminary link between inflammation induced by infection and spread of the bacterium . Here , we sought to identify specific host factors that contribute to this process and to gain further insight into the mechanisms responsible for transmission in this model . Our results demonstrate a role for the innate immune receptor TLR2 in transmission of the pneumococcus in a flu-dependent manner . The findings in our report are consistent with previous studies in adult mice , which suggested that TLR2 stimulation by either commensal bacteria [26] or a synthetic agonist [21] is protective against flu infection . Previous in vitro studies have also demonstrated a role for TLR2-mediated signaling in induction of type I IFNs [22] , [23] . Our work adds in vivo data to this model , demonstrating that expression of IFN α , a key component of the anti-viral response , was diminished in tlr2−/− mice compared to wildtype mice . These findings solidify a link between TLR2 activity and type 1 IFN expression . Importantly , influenza co-infection was required for transmission to occur; this was true for both wildtype and tlr2−/− experimental groups . Both infection with influenza alone and co-infection resulted in a significant inflammatory influx to the nasopharynx , with the largest proportion of the cellular infiltrate comprised of neutrophils . In tlr2−/− mice , this response was even more pronounced , with a significantly higher percentage of neutrophils present than in the wildtype samples , while we did not observe any differences in macrophages or other cell types recruited . The host response to increased viral titers also correlated with higher expression of the mucin Muc5ac . We hypothesize that these higher levels of virus stimulate an exaggerated acute inflammatory response that drives an increase in nasal secretions , consisting of mucus and inflammatory cells , providing an exit vehicle for the pneumococcus . As the presence of pneumococcus adds relatively little to inflammation , our findings suggest that it is primarily the host response to the virus that provides this vehicle for shedding , with the bacterium acting as a passenger . The pneumococcus can thus take advantage of the heightened inflammatory state present in co-infected index animals . The observation that there is very little inflammation seen in the context of a pneumococcal single infection explains why transmission is not detected when the animals are not given influenza . The few neutrophils that are recruited to the airway lumen in response to pneumococcal colonization have a limited ability to take up the organism in the absence of specific antibody , and depletion of neutrophils has no effect on bacterial clearance in adult singly infected mice [27] . Additionally , the neutrophil influx observed in influenza-infected lavages did not appear to be effective at lysing the bacterium , as indicated by the predominance of intact bacterial cells in immunofluorescence images . Thus , the secretions resulting from robust inflammation are required for transmission , but this inflammatory response must also be ineffective at killing the bacterium . We also demonstrate here that the inflammation induced by influenza infection promotes bacterial shedding from index mice at or above a level sufficient to infect uninoculated contact mice . Transmission could be a consequence of bacterial shedding above this threshold level . We conclude that the increased inflammation in tlr2−/− mice is due mostly to diminished sensing of the virus and inability to control viral infection . Another consideration is that while tlr2−/− genotype itself does not bias the composition of the host microbiota [28] , it likely causes differences in sensing of the flora . Although previous studies have shown that the microbiota can affect the immune response to influenza , the contribution of the colonizing pneumococci to the inflammatory response was small in comparison to influenza . This was the case even though pneumococcal colonization itself stimulates TLR2 signaling [19] . Thus , it appears that the increased viral load observed in tlr2−/− mice was sufficient to lead to a heightened inflammatory response through sensing by other viral PRRs resulting in more copious purulent mucus secretions . These secretions carry live bacteria , which are then shed in increased numbers . As the nursing mother piles infant mice in close proximity to one another , there are thus ample opportunities for the bacteria to spread from one host to another . These studies also help to explain the transmission differences between adult and infant mice , namely that transmission has not been observed in similarly treated adults because of the higher inoculum required to establish robust colonization in adults . This is the first study to implicate a specific host factor in transmission of the bacterial pathogen S . pneumoniae . While stimulation of TLR2 limits transmission , approximately half of wildtype contact mice acquire the bacterium , indicating that other components of the innate immune system must contribute to the inflammation and shedding necessary for this process . For instance , stimulation of other pattern recognition receptors that respond to influenza , such as TLR3 , TLR7 , and RIG-I , could affect pneumococcal transmission , as shown for TLR2 . Signaling downstream of other viral PRRs has yet to be fully explored in the context of transmission . The model detailed here thus shows much promise for investigating these additional microbial and host factors to determine the complete mechanism behind bacterial shedding and its consequences for host-to-host transmission .
This study was conducted according to the guidelines outlined by National Science Foundation Animal Welfare Requirements and the Public Health Service Policy on the Humane Care and Use of Laboratory Animals . The protocol was approved by the Institutional Animal Care and Use Committee , University of Pennsylvania Animal Welfare Assurance Number A3079-01 , protocol number 803231 . S . pneumoniae strains were grown statically in tryptic soy broth ( BD , Franklin Lakes , NJ ) at 37°C in a water bath . All studies described here utilized strain P1121 , a serotype 23F isolate that has been previously used for human carriage studies [29] . Bacteria were stored in 20% glycerol at −80°C . Influenza A/HKx31 ( H3N2 ) was grown in the allantoic fluid of 10-day embryonated chicken eggs ( B&E Eggs ) and stored at −80°C . Viral concentrations for infection were determined by titration in Madin-Darby Canine Kidney cells , as described previously [30] . All experiments using animals were approved by the Institutional Animal Care and Use Committee of the University of Pennsylvania , and mice were housed in accordance with IACUC protocols . Wildtype C57BL/6 mice were originally obtained from The Jackson Laboratory ( Bar Harbor , ME ) . The tlr2−/− mice have been described previously [19] . All mice were bred and maintained in a conventional animal facility , and both male and female pups were used . Mice were bred under specific pathogen-free conditions at the University of Pennsylvania . Four days after pups were born , 1–2 index mice ( such that ratio of index∶contact was always 1∶3 to 1∶4 ) were randomly selected from each litter and inoculated with 2000 CFU of S . pneumoniae suspended in 3 µl PBS intranasally . Inoculation was performed atraumatically with a blunt pipette tip without anesthesia , and index pups were returned to the litter . When pups were 8 days old , all infants were given 2×102–2×104 TCID50 Influenza A/HKx31 in 3 µl PBS intranasally . This H3N2 isolate was chosen because it replicates well in the mouse upper respiratory tract without causing disease [30] . For mock infections , sterile PBS was given . When indicated , pups were treated with the TLR2 agonist Pam3Cys on days 8 , 10 , and 12 . Pam3Cys ( Invivogen ) was resuspended to a concentration of 2 mg/ml in sterile water , and 10 µg doses were given intranasally . On day 14 , all pups were euthanized by CO2 asphyxiation . To quantify bacteria , the trachea was exposed , cannulated , and flushed with 200 µl sterile PBS . PBS lavages were serially diluted and plated on tryptic soy agar containing neomycin ( 20 µg/ml ) to minimize the growth of contaminants . To obtain RNA from the epithelium , a second lavage was performed with 600 µl of RLT lysis buffer ( QIAGEN ) and stored at −80°C until needed . Nasal lavage samples ( 100 µl per mouse ) were stained with the following fluorescent antibodies: CD4-FITC , Ly6G-PE , CD11b-perCP and F4/80-APC ( eBioscience ) after blocking with FC Block at 4°C . Cells were then fixed with 1% paraformaldehyde and assayed using a BD FACSCalibur the following day . Data were gathered using CellQuest Pro software ( BD ) , analyzed using FlowJo software ( TreeStar ) , and graphed with Prism 5 ( GraphPad ) . Undiluted nasal lavage fluid was spotted onto glass microscope slides and allowed to air-dry , and then fixed and stained essentially as described [19] , using rabbit serum against type 23F pneumococcus ( 1∶5000 ) and rat anti-mouse α-Ly6B ( 1∶100 , AbD serotec ) primary antibodies with anti-rabbit-Cy3 and anti-rat-FITC conjugated secondary antibodies ( both 1∶600 ) , respectively , along with DAPI staining . Immunofluorescence images were collected using a Nikon Eclipse E600 ( Nikon Instruments Inc . ) equipped with a liquid crystal ( Micro*Color RGB-MS-C; CRi Inc . ) and a charge-coupled device digital camera . RNA was isolated from the epithelium lining the mouse nasopharynx following lavage with 600 µl RLT lysis Buffer using an RNeasy Mini Kit ( QIAGEN ) according to the manufacturer's instructions . For all experiments except IFNα analysis , lavages were collected at day 14 . IFNα levels were measured in lavage collected on day 11 . cDNA was generated from each sample using a high-capacity reverse transcription kit ( Applied Biosystems ) . Approximately 10 ng cDNA was used as a template in reactions with forward and reverse primers ( 0 . 5 µM ) and SYBR Green ( Applied Biosystems ) , according to the manufacturer's instructions . Reactions were carried out using the StepOnePlus Real-Time PCR system , and fold changes were calculated using the ΔΔCT method ( Applied Biosystems ) . GAPDH was used as an endogenous control . The following primers were used in reactions: influenza nucleoprotein – F 5′-CAGCCTAATCAGACCAAATG-3′ , R 5′-TACCTGCTTCTCAGTTCAAG-3′; MUC5AC – F 5′-CCATGCAGAGTCCTCAGAACAA-3′ , R 5′-TTACTGGAAAGGCCCAAGCA-3′; GAPDH – F 5′-TGTGTCCGTCGTGGATCTGA-3′ , R 5′-CCTGCTTCACCACCTTCTTGAT-3′ , IFNα – F 5′-TCTGATGCAGCAGGTGGG-3′ , R 5′-AGGGCTCTCCAGACTTCTGCTCTG-3′ . Infant mice were infected as “index mice” as described above for the transmission model . From day 8 to day 14 , daily sampling was performed for each mouse , in which the nose of the mouse was gently pressed onto tryptic soy agar containing neomycin ( 20 µg/ml ) 10 times to obtain a representative sample . The mouse was then returned to the cage , and exhaled bacteria were spread across the surface of the plate with a polyester-tipped swab . Plates were grown overnight at 37°C with 5% CO2 and colonies were enumerated the following day . Six to eight-week-old ( adult ) and seven-day-old ( infant ) unanesthetized wildtype C57BL/6 mice were intranasally inoculated with 500 CFU of S . pneumoniae P1121 . Twenty-four hours later , mice were sacrificed by CO2 asphyxiation , and tracheas were exposed and cannulated , then flushed with 200 µl PBS . Lavage fluid was collected from the nares , serially diluted , and plated on tryptic soy agar . Colonies were enumerated after overnight incubation at 37°C with 5% CO2 . | In this study , we sought to identify factors contributing to the transmission of the bacterial pathogen Streptococcus pneumoniae ( the pneumococcus ) , a major cause of otitis media , pneumonia , and septicemia . Often found as a co-infection with other bacterial and viral pathogens , the pneumococcus is commonly carried by young children and is spread by close human contact , most likely through large droplet respiratory secretions . The specific determinants of bacterial transmission , however , have not been identified . This report details our use of an infant mouse model of transmission , which includes influenza A co-infection , to elucidate the mechanism of host-to-host transmission . We found that the inflammatory response to influenza , which is aggravated in the context of weakened host defense , promotes transmission by inducing bacterial shedding from the mouse nose . These results show how a bacterial pathogen exploits the host immune response to spread from one host to the next . | [
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] | 2014 | TLR2 Signaling Decreases Transmission of Streptococcus pneumoniae by Limiting Bacterial Shedding in an Infant Mouse Influenza A Co-infection Model |
Genome-wide association studies ( GWAS ) have detected many disease associations . However , the reported variants tend to explain small fractions of risk , and there are doubts about issues such as the portability of findings over different ethnic groups or the relative roles of rare versus common variants in the genetic architecture of complex disease . Studying the degree of sharing of disease-associated variants across populations can help in solving these issues . We present a comprehensive survey of GWAS replicability across 28 diseases . Most loci and SNPs discovered in Europeans for these conditions have been extensively replicated using peoples of European and East Asian ancestry , while the replication with individuals of African ancestry is much less common . We found a strong and significant correlation of Odds Ratios across Europeans and East Asians , indicating that underlying causal variants are common and shared between the two ancestries . Moreover , SNPs that failed to replicate in East Asians map into genomic regions where Linkage Disequilibrium patterns differ significantly between populations . Finally , we observed that GWAS with larger sample sizes have detected variants with weaker effects rather than with lower frequencies . Our results indicate that most GWAS results are due to common variants . In addition , the sharing of disease alleles and the high correlation in their effect sizes suggest that most of the underlying causal variants are shared between Europeans and East Asians and that they tend to map close to the associated marker SNPs .
Genome-wide association studies ( GWAS ) have detected hundreds of risk alleles [1] , generating novel biological knowledge and widening the range of diagnostic and treatment tools [2] . However , the reported effect sizes of these variants are small and their impact in individual risk prediction remains modest , raising doubts about the relevance of GWAS results [1] , [3]–[6] . Some of the most hotly debated topics are how to account for the unexplained risk [4]; what may be the role of rare variants as a source of synthetic GWAS results [7]–[10]; and up to what extent GWAS results are portable between populations [11]–[15] . Answering to these questions is pressing for two reasons . First , the description of the genetic architecture of disease lies at the foundation of personalized medicine and , in particular , finding predictors of individual disease risk that could be applicable to different ancestries would be a major step forward [1] and would also allow the development of prioritizing strategies to identify disease-associated loci . Second , if sharing of causal variants across populations were common , it would suggest trans-ethnic mapping as a powered tool that would take profit of population heterogeneity in LD and allele frequencies to identify the causal variants underlying disease susceptibility [1] , [15] . The available reports on the allele frequency distribution of GWAS risk variants point at an excess of common variants [16] that , at least for some particular diseases [17] , present consistent effects across populations . If repeated , these observations constitute empirical evidence against rare alleles as a source of synthetic associations and would point at common variants that are in LD with the associated tagSNPs in all populations . However , such studies have not been generalized across different diseases and , currently , most evidence accumulating in the field comes from either re-sequencing efforts aimed to capture rare variants [18] or multi-ethnic replication efforts for a few risk variants [13] , [15] , [17] , [19] . In addition , most meta-analysis of GWAS data , that could shed light on these issues , either have ignored population heterogeneity [2] , [20] or have focused on a limited set of traits [21] and GWAS [22] . By compiling data from 299 GWAS for 28 different diseases , we build a large database of discovery-and-replication patterns of SNPs associated with complex disease . We evaluate the extent to which risk variants discovered in Europeans replicate in posterior studies performed on individuals of European , East Asian and African ancestries and compare the risk effect sizes found across populations . We also examine the extent up to which statistical power and differences in Linkage Disequilibrium among populations explain replication failures . Our results describe the patterns of replicability of GWAS across disease , evaluate how transportable these results are across populations and allow for inferences about the relative roles of rare and common variants in explaining current GWAS results .
Replicability rates are high within Europeans , with 155 successful out of 181 attempts ( 85 . 6% ) , when only 9 positive replications ( ∼5% ) would be expected under the null hypothesis of no association ( binomial test , P<10−16 ) . This excess was robust to the significance threshold ( e . g . 122 observed vs . 0 . 18 expected if only replication attempts achieving P<0 . 001 are considered successful and 56 observed vs . 1 . 8×10−5 expected for a threshold of P<10−7 , Table S5 ) . Moreover , replicability rates within Europeans approach 100% when accounting for statistical power . For the 168 attempts for which we could calculate the power to replicate the original finding ( Table S5 ) , we observed 147 positive replications , which is almost identical to the expectation of 149 . 1 positive replications given that average power is 89 . 1% ( see Materials and Methods ) . This is expected , since most GWAS already contain an internal replication phase [1] , [24] . Interestingly , all diseases presented similarly high replicability patterns , with no traces of heterogeneity in replicability ( Table S6 ) . These results were consistent with previous partial reports of replication for individual diseases [17] , [19] and confirmed that the subset of 190 genome-wide significant SNPs map in loci truly associated with disease in peoples of European ancestry . Next , we considered the replication attempts in East Asian populations . Out of 225 replication attempts , 103 were successful at a P<0 . 05 threshold ( 45 . 8% ) . This replicability departs significantly from the null expectation ( 103 vs . 11 . 3 expected , P<10−16 ) and is robust across replication thresholds ( e . g . 49 observed vs . 0 . 23 expected for P<0 . 001 and 19 observed vs . 2 . 3×10−5 expected for P<10−7 ) . Nevertheless , that figure is smaller than for Europeans , which can be expected since East Asian GWAS tend to have smaller sample sizes and , thus , less power [15] . We tested this hypothesis by calculating replicability rates after controlling for statistical power . First , we focused on the 81 attempts with ≥80% power to replicate the Odds Ratio ( OR ) found in Europeans ( Table S5 and Materials and Methods ) . For that subset , replicability increases dramatically to 76 . 5% ( 62 out of 81 attempts are successful with a P<0 . 05 threshold ) . Second , we calculated that at most 132 positive replications would be expected out of statistical power ( 59% on average for the 225 attempts in East Asians , Table S5 ) . The 103 observed replications thus correspond to an effective replicability rate of 77 . 9% , which suggests that a noticeable fraction of GWAS associations are shared across Eurasians . Again , we found no heterogeneity across diseases ( Table S7 ) . Finally , we considered replication attempts performed upon individuals of African ancestry . Even if GWAS on individuals of non-Eurasian ancestry are scarce , we could find 16 GWAS performed on African Americans from which we gathered a total of 73 replication attempts ( 61 and 12 for SNPs discovered in Europeans and East Asians , respectively; see Materials and Methods and Table S1 ) . Overall , we observed a low replicability rate ( 9 . 6% , 7 out of 73 attempts ) that was not attributable to lack of statistical power ( 59 . 2% on average , see Table S5 ) . This figure would cast doubts about the sharing of causal variants between Eurasians and Africans , but the inherent limitations of this part of the analysis warrant for caution . For instance , lower levels of LD in Africans than in Eurasians make it difficult to ensure that potentially shared causal variants are tagged by the same marker SNP [25] . Additionally , the 16 African-American GWAS form a rather small dataset corresponding to only five diseases ( asthma , cardiovascular disease , hypertension , prostate cancer and type 2 diabetes ) . A complete study of African replicabilities will be possible when more studies are available . In the meantime , we focused on data gathered from European and East Asian GWAS . The observed rates of trans-ethnic replication between Europeans and East Asians indicate that a considerable fraction of risk loci associated with the 28 diseases is shared between the two Continental groups . As to the sharing and frequency of risk variants , it can be explored even if the causal variants themselves remain undiscovered . First , the possibility that the same causal variants underlie association in the two continents is reinforced by the strong correlation between the ORs for SNPs discovered in European GWAS and their replication OR in the largest East Asian study ( Spearman's ρ = 0 . 82 , P<10−16 , Figure 1; we used the log ( OR ) to ensure symmetry around 1 ) . Also , the slope of the linear regression of the two log ( OR ) is very close to 1 ( 1 . 03 , SE = 0 . 064 , P<10−16 ) , which indicates that the log ( OR ) found in Europeans is the best predictor of the East Asian log ( OR ) . These figures would be unexpected if GWAS hits were synthetically generated by population-specific rare causal variants , as their effect size and Linkage Disequilibrium ( LD ) with the replicated SNP would be different in each population . Moreover , when considering only replication attempts that did not achieve P<0 . 05 in East Asians , there is still a strong and significant correlation between the two ancestries' OR ( Spearman's ρ = 0 . 53 , P<2·10−9 ) , which suggests that a fraction of these associations might also be shared even if not successfully replicated in East Asians . A final further piece of evidence indicates that causal alleles behind non-replicated SNPs might actually be common and shared . Since most rare variants occurred after the split of Europeans and East Asians [4] , [12] , [26]–[28] , they would have accumulated randomly in the genealogy of each allele of the tagSNP used in GWAS . Therefore , if causal variants were rare , risk alleles would not be necessarily shared even if discovered through the same tagSNP . Strikingly , when considering the direction of effects of SNPs non-replicated in East Asians instead of only their significance , the same risk allele as in Europeans was observed for 73 . 6% of attempts . This proportion clearly departs from the 50% expectation in a scenario of independent sets of rare variants generating different synthetic associations if each continent ( P<10−16 , binomial test ) . Publication bias could have inflated our replicability estimates [29] , [30] . Due to the large number of SNPs that are tested , the usual practice when publishing a GWAS has been to report all newly discovered associations , plus the replication status of previously associated SNPs . However , this is not always the case and , in some cases , not all previous results are discussed in each publication . Therefore , our ability to gather replication attempts depends on how many of them are explicitly reported , which presents enormous variability among papers . This opens the possibility of a reporting bias , in which GWAS authors could prioritize mentioning successful replication attempts , while overlooking failed replications . If so , our chance of gathering a replication attempt might be heavily biased towards positive results , thus inflating our estimates of replicability [30] . In the most extreme version of this scenario , the 103 observed replications in East Asians at P<0 . 05 that find the same risk allele that had been previously discovered in European studies would be the result of type I error with a P = 0 . 05 threshold . In that case , the 103 positive replications would be just the 2 . 5% ( = 5% type I error×50% probability of the same risk allele ) of a large pool of 4 , 120 replication attempts in East Asians ( 95% C . I . = 3 , 418–4 , 959 , assuming a Poisson distribution ) . In other words , 4 , 017 ( = 4 , 120−103 ) associations failing to find the same risk allele at P<0 . 05 would have remained unreported . Given the huge amount of unpublished GWAS that this scenario would imply , we discard a big impact of publication bias in our analysis . To obtain a more precise assessment of the potential size of reporting bias in East Asian GWAS , we estimated the maximum number of failed ( P>0 . 05 ) but unreported replication attempts that is actually possible in our database [30] . Specifically , and for each East Asian GWAS , we computed the number of disease-associated SNPs discovered in Europeans that were not mentioned in East Asian studies ( neither a p-value nor any other information was reported in the main text or in the supplemental information ) . In total , we found 416 such instances . Most of these cases may not constitute reporting bias at all , since the SNPs in question may not be included in the array used for the East Asian GWAS , may be monomorphic in the studied population , may have been filtered out during QC and so on . Still , making the extreme assumption that all these 416 cases are failed replications , they constitute the maximum number of biased reports that we could have not included in our database ( Table S8 ) . Under this extreme scenario , the 103 positive replications would not have been drawn from the 225 replication attempts gathered in our database , but from a larger set of 641 ( = 225+416 ) replication attempts in East Asians . This calculation allows us to estimate the lower-bound replicability rate at 16 . 1% ( 103 out of 641 ) , which still departs from the null expectation of 5% ( P = 10−16; Binomial test ) . In other words , the figure of 416 ungathered replication attempts is about an order of magnitude lower than the total number of unreported cases needed to explain all East Asian replications as type I errors ( 4 , 017 , see previous paragraph ) and , therefore , it is very unlikely that systematic reporting bias accounts for our results . A clear prediction can be made if , as our results suggest , a substantial fraction of associations reported by European GWAS are caused by common variants with similar effect sizes across the two ancestral groups: whenever associations were successfully replicated , the frequencies of tagSNPs and causal variants and their LD relationships should be similar in the two groups . In other words , levels of heterozygosity and LD patterns should be more similar between populations in the genomic regions that contain successfully replicated SNPs than in the genomic regions with European-associated SNPs that have not reached significance in East Asians . To test this prediction , we compared the inter-continental similitude of heterozygosity and LD in genomic regions harboring two different groups of disease-associated SNPs: the 47 SNPs discovered in Europeans that have been successfully replicated in every attempt with East Asians and the 65 SNPs that have never been positively replicated . We compared heterozygosity patterns by measuring the differences in average heterozygosity between Europeans and East Asians . We measured these differences between the two ancestry groups in a 600-SNP region around each SNP under study . We used sliding windows of 50 consecutive SNPs , with a step of 5 SNPs . As predicted , windows immediately centered on non-replicated SNPs presented significantly larger differences in average heterozygosity across populations than windows centered on replicated SNPs ( 0 . 048 vs . 0 . 019 , P<0 . 009 , Figure 2 ) . Analogous patterns were observed when comparing the differences in LD . To assess differences in LD between populations , we computed the varLD score [31] in the same 50-SNP sliding windows we used for heterozygosity . As predicted , differences in LD were significantly larger for the windows centered in non-replicated than in replicated SNPs ( varLD = 17 . 64 vs . 12 . 66 , P<0 . 002 ) . Indeed , varLD differences are only significant in the immediate vicinity of the associated SNP and they quickly cancel out as the distance for the associated allele increases ( Figure 3 and Table S9 ) . We obtained the same result when using only attempts with ≥80% statistical power and contrasting 39 replicated versus 14 non-replicated SNPs ( varLD = 20 . 42 vs . 12 . 49 , P = 0 . 045 ) . To study how differences in LD patterns compare with the genome-wide average , we focused on the region immediately adjacent to the marker SNPs . For a window of 50 SNPs around the marker , we compared differences between Europeans and East Asians in LD patterns around replicated and non-replicated SNPs to genome-wide average differences for random SNPs . We used the permutation method included in varLD to assign an empirical p-value to the observed differences in LD for each analyzed window ( see Materials and Methods ) . We considered three different sets of 50-SNP windows centered on each of the ( i ) 47 replicated SNPs , ( ii ) 64 non-replicated SNPs , and ( iii ) 100 groups of 47 SNPs randomly selected from across the genome . Because the two populations differ in their LD patterns , we observed a trend towards significant differences in LD ( empirical P<0 . 05 ) for the three datasets . However , the proportion of significant windows was larger for non-replicated ( 78% ) than for replicated ( 62% ) and random genomic SNPs ( 66% ) . Figure 4 shows the cumulative distributions of empirical p-values for the three groups of SNPs . The cumulative distribution of p-values correspondent to replicated SNPs does not depart from genome-wide expectations , while non-replicated SNPs clearly map into regions of the genome with extreme differences in LD between Europeans and East Asians . In other words: our observations on LD differences suggest that a proportion of associations would have failed to replicate in East Asians because of population heterogeneity in LD between causal variants and tagSNPs . Yet , the possibilities of heterogeneity across populations in the effect size of causal variants themselves ( see [22] , [32] ) or the presence of European-specific causal rare variants in some associations cannot be discarded as a source of lack of replication . Our results indicate that many causal variants underlying GWAS results are common and shared between Europeans and East Asians , extending the observation of reports that focused in single traits [17] , [19] . This would seem to contradict results by us and others that highlighted heterogeneity in the genetic etiology of disease across human populations [14] , [21] , [22] . This observation contrasts with the large replicability and large correlation in OR that we observe , as well as with the suggested role of differences in LD in explaining associations non-replicated in East Asians . The apparent contradiction between the present and previous papers can be explained by two facts . First , our previous results focused on candidate-gene studies , which have been largely dominated by false positives [14]; and , second , studies that considered GWAS data addressed different questions , used different approaches and gathered different sets of traits [21] and/or associations [22] . An examination of previous datasets confirms a general trend to consistency of GWAS results across continents and emphasizes the benefits of incorporating as many associations as possible . Fu et al . [21] focused on associated SNPs discovered in East Asian GWAS . Although they used only four traits and 47 SNPs ( 43 loci ) , they demonstrated the challenges of multi-ethnic studies , and provided a framework to cope with these difficulties . As discussed by the authors , caution is warranted as to whether the disease loci and/or causal variants are population-specific . For instance , they suggested that two signals for type 2 diabetes located in 9p24 . 1 ( PTPRD , rs17584499 ) and 17p13 . 3 ( SRR , rs391300 ) could be East Asian-specific , as they fail to replicate in a well-powered study in Europeans . However , we gathered several replication attempts of these two signals in more recent East Asian GWAS ( Table S4 ) , and out of 8 replication attempts only one was successful ( for PTPRD , rs17584499 ) at P<0 . 05 , when a total of 7 . 49 replications would be expected by power alone ( 4 for PTPRD and 3 . 49 for SRR , see Table S5 ) . Also , in only 4 out of 8 cases the risk allele was the same ( two for each gene ) . Overall , the replication attempts gathered in our database suggest that both associations were false positive findings in East Asians . These results make it clear that Fu et al . [21] were right in asking for caution , since putative population-specific associations may well turn out to be false positives . Moreover , the inclusion of more recent studies in our dataset helps discarding the population-specific status of some true associations . For instance , the association of 10p13 ( CAMK1D , rs12779790 ) to type 2 diabetes was considered as European-specific , but it has been eventually replicated in East Asians [33] . Ntzani et al . [22] examined differences in effect sizes from a set of 108 associations discovered by GWAS and for which data for various ancestries was available . Because of the sophistication of their approach , they had to focus on 12 diseases and 4 anthropometric traits , as well as on a relatively short ( ∼30 ) list of GWAS that either use samples with different ancestries in the replication stage or compare their own results with previous papers using different ancestries [22] . In contrast , we took the simpler approach of studying replicability in the studies with largest sample size , so we could gather attempts from multiple GWAS on the same diseases and were able to construct a larger database . Ntzani et al . [22] found overall consistency in effect direction across ancestries ( ∼82% , similar to ours of 85% ) , but with modest correlations in effect sizes , ( rho≈0 . 33 ) that would seem contradictory with the large correlation in odds ratios we report here . Nevertheless , an almost identical correlation in OR would have been observed if limiting the study to the 22 SNPs that are shared between Ntzani et al . [22] and our dataset ( rho = 0 . 58 and 0 . 53 , respectively ) . Barring possible differences due to the different nature of the anthropometric traits analyzed by Ntzani et al . [22] , the previous results stress the importance of continuously updating the list of replication attempts to increase the statistical power upon which inferences can be based . Of course , the finding of shared variants underlying GWAS results holds only for associations that have been published so far . Ongoing efforts to join cohorts into large consortia [34] ensure steady progress in the field and guarantee the discovery of new genetic associations to complex disease [6] , [35] . It is tempting to make inferences about what may be the results of future , much larger , association studies; particularly about the frequency and degree of trans-ethnic sharing of as yet undiscovered variants . We approximated this question by considering the allele frequencies and effect sizes of associated SNPs along with their patterns of replicability across time . Specifically , it is clear that if the GWAS with larger sample sizes that have been published recently for peoples of European ancestry had discovered variants with lower frequencies ( variants that should be increasingly population-specific ) , their results should be less likely to replicate across populations . If this observation were made , it would predict decreased replicabilities in future , even larger GWAS with increased power to discover lower-frequency risk variants . As observed in Figure 5 , more recent GWAS have gathered larger sample sizes and unveiled associations with lower ORs . Although more recent GWAS present decreased replicability rates , an interesting inference can be made by observing effective replicability rates , the ratio between the proportion of positive replications and their statistical power . Effective replicability would be expected to decrease if the lower ORs detected by GWAS were due to lower-frequency ( and thus increasingly population-specific ) causal variants . In contrast , we observed a constant effective replicability rate of ∼80% that was independent of the OR reported in the European discovery GWAS ( red line in Figure 5 ) , indicating that the associations discovered by larger GWAS present similarly high replicability rates regardless of their weaker effect size . Changing focus to minor allele frequencies , it is possible that , regardless of the reported OR , genotyped marker SNPs with lower MAFs are more efficient in tagging low frequency causal variants . If that were the case , patterns of replicability may change as a function of the MAF of associated SNPs . Nevertheless , we observed similar rates of effective replicability across all the frequency spectrum of disease-associated SNPs , with no drastic decrease for markers of increasingly smaller MAFs ( Figure 6 ) . All these inferences are confirmed after categorizing European discovery GWAS into two groups using a threshold of 10 , 000 individuals to distinguish between “small” and “large” studies . First , we did not observe differences in the MAF distribution of associated SNPs according to the discovery sample size ( average MAF of 0 . 301 vs . 0 . 333 for “small” and “large” GWAS respectively; P = 0 . 12 , Wilcoxon test ) . Second , even if larger GWAS do indeed detect associations with smaller ORs ( average OR 1 . 15 vs . 1 . 28; P<3×10−7 ) , the trans-continental correlation of ORs between Europeans and East Asians was the same for “small” and “large” GWAS ( Figure 7 ) . Both results show , yet again , that causal variants of different effect sizes are equally shared across populations , independently of the sample size of the discovery GWAS . GWAS focus on describing new variants across the genome rather than validating the findings from previous GWAS . Instead , many replication attempts consist on genotyping limited sets of SNPs previously discovered by GWAS in independent samples . They tend to be published independently from GWAS and , hence , our replicability database may have failed to gather many replication attempts that occur outside the setting of genome-wide studies . Since endeavoring to analyze all the literature available for the 28 diseases in our database would have required a massive effort , we randomly selected six diseases to address this possibility ( Table S10 ) . For each disease , we searched all the publications citing each of the disease-associated variants present in our database , as well as for the original GWAS publications initially describing them . In total , we looked at 1 , 706 and 6 , 068 citations available at PubMed and Google Scholar by December 2012 , respectively ( Table S10 ) . In doing so , we gathered a total of 59 replication attempts from 38 candidate studies targeting GWAS variants discovered in Europeans ( 40 and 19 attempts used individuals of European and East Asian ancestry , respectively , see Table S11 ) . Nonetheless , the observed effective replicability rates after accounting for statistical power of attempts gathered from GWAS and non-GWAS studies are very similar in both Europeans and East Asians ( 93 . 8% vs . 89 . 5%; P = 0 . 20 and 80 . 6% vs . 88 . 4% , P = 0 . 69; respectively ) . Thus , the inclusion of replication attempts that occur outside from the setting of GWAS should not have affected the patterns of replicability we report in the present study . The relevance of our findings is that they allow for three inferences . First , they contribute to the debate on the possible synthetic origin of GWAS associations [7] , [8] , [10] , since trans-continental replicability confirms that most –even if not all– of the associations detected by GWAS are not caused by population-specific , rare variants . Second , they clarify the contribution of common variants to extant GWAS results , since practically all GWAS have delivered precisely what they were designed to detect: associations with common variants [1] . Third , our results show that a substantial proportion of causal variants are shared across European and East Asian populations and that they probably lie in the regions close to marker SNPs , which may allow leveraging on the increasingly varied ancestries of GWAS to track them down [25] , [36]–[38] . Finally , since larger GWAS did not detect variants with lower frequencies , our findings support a model of common variants of varying effect sizes , closer to the infinitesimal model than to a pure rare variant model of the genetic architecture of disease [4] . However , it is not simple to extrapolate our results to associations that so far remain undiscovered . Whether the heritability that is not yet explained by GWAS will be partly due to risk variants in insufficient LD with common SNP markers , as suggested by some authors [6] , [39] or whether this heritability exists at all [40] will only be resolved by further empirical research .
We considered the 1 , 171 studies indexed in the catalog of Published Genome-Wide Association Studies as to February 2012 ( http://www . genome . gov/26525384 , last accessed 14th February 2012 ) and classified them according to the trait under study . Each study was classified according to the genetic ancestry of the samples , considering only individuals used in the GWAS stage . Studies performed on a mixed panel were considered only if separate ancestry-specific analyses were provided and we recorded them as independent studies . We observed a strong bias towards GWAS performed with “European” ( 78 . 4% ) and “East Asian” ( 14 . 9% ) individuals , while much fewer studies are available for “African” ( 4 . 3% ) , “Hispanic” ( 1 . 2% ) , “Middle Eastern” ( 0 . 5% ) , “Native American” ( 0 . 4% ) and “Oceanian” ( 0 . 3% ) ancestries . Therefore , and to increase the reliability of our results , we focused on GWAS performed with peoples of European and East Asian ancestry to select frequently studied diseases . We ascertained only dichotomous disease traits , avoiding anthropometric traits such as height . To produce reliable replicability estimates across ancestries we included in our analysis the 28 diseases for which two or more GWAS were available in any of the two ancestral groups and at least one in the other group ( e . g . 11 GWAS for lung cancer in Europeans and 5 in East Asians; 4 GWAS for Kawasaki Disease , 1 in Europeans and 3 in East Asians ) . Finally , we also added GWAS performed upon individuals of African ancestry for any of the 28 selected diseases . We built a database with 28 dichotomous disease phenotypes ( Table S1 ) , with data coming from 206 European , 71 East Asian and 16 African GWAS . Several features of interest were recorded for each GWAS: first author , journal , year of publication , genetic ancestry , sample size in GWAS stage , total sample size in replication stage , array genotyped , genomic control factor in GWAS stage ( if available ) , use of imputed SNPs ( Y/N ) and number of genomic regions achieving genome-wide significance in the initial and final stage ( Table S2 ) . The publications corresponding to each GWAS were downloaded from the respective journals . To explore the full range of published GWAS , we performed a comprehensive independent search for studies not gathered in the Catalog . For each of the 28 diseases , we mined three resources: ( i ) the PubMed database of biomedical literature , ( ii ) the HuGE Navigator tool available at the Human Genome Epidemiology Network [41] and ( iii ) specific reviews available in the literature . Specifically , we searched the PubMed to identify potential new GWAS ( i . e . “Asthma AND genome-wide” ) and the “HuGE Literature Finder” available at the HuGE Navigator . Finally , we used the PubMed ( “Review” tool in Article types ) to identify 59 reviews covering the literature available for each disease ( ∼2 . 4 reviews per disease ) . After examination of all these sources , we complemented the list of 277 GWAS with six new genome-wide studies performed on Europeans that had remained unnoticed in the Catalog ( Tables S2 and S3 ) . For each disease , the selected studies were sorted per date of publication regardless of the population of study . Starting for the first study , we built a cumulative database of disease-associated SNPs and their replicability in successive studies . After excluding GWAS with pooled DNAs or focusing on CNVs , each GWAS publication was visually screened for two kinds of association data: the report of a new disease-associated SNPs ( discovered SNPs ) ; and the replication status of disease-associated SNPs discovered in previous GWAS ( replicated SNPs ) . In both cases , we recorded three features from each association: ( i ) Odds Ratio ( OR ) ( ii ) confidence interval of the OR and ( iii ) the p-value . We used several conservative criteria to include newly discovered SNPs in our database . First , to avoid the winner's curse bias , we used the OR and p-value from the replication stages of the discovery GWAS . Second , when several replication stages from the same GWAS were available , the OR from the stage with largest sample size was recorded . Only when no replication stages were available did we use the OR from the GWAS stage . Third , SNPs associated uniquely in sex-specific analyses were excluded . Fourth , ORs coming from allelic tests and additive models were prioritized over genotypic tests and other genetic models . Fifth , the genome-wide significance level for a newly discovered SNP to be included in our analysis was set at P<5×10−7 , unless imputed SNPs were used in the GWAS , in which we toughened up the threshold to P<5×10−8 . Sixth , for genomic regions with several genome-wide significant SNPs ( SNPs less than 200 Kb from each other ) , we included in the study the SNP with lowest p-value . Finally , disease-associated SNPs from the MHC region and HLA alleles were not included in the study . In several analyses , we used the log ( OR ) to ensure symmetry , which does not happen if using OR ( i . e . an OR of 2 is equivalent to an OR of 0 . 5 ) . For replication attempts to be included in our database , several conservative conditions had to be met . We only recorded attempts in which exactly the same SNP than in the discovery GWAS had been genotyped . Moreover , and to avoid any bias towards associations that replicate across ancestries , we did not gather any replication attempt from the same “discovery” GWAS in which a new disease-associated SNP is described . Third , in all these cases , the p-value considered for the replication report was the one from the GWAS stage . Finally , the OR for each disease-associated SNP was referenced for the allele that had been the risk allele in the discovery study . Thus , OR<1 ( and log ( OR ) <0 ) means that the minor allele was found as protective in the discovery study , while OR>1 ( and log ( OR ) >0 ) means that the minor allele appeared as the risk allele . For SNPs with different minor alleles across populations , OR were referenced to the minor allele specific for each population . Instances of the latter are indicated in column “Shift” in Table S5 and the shifted OR is represented in all Figures except when otherwise indicated . A total of 419 discovered SNPs from 337 genomic regions were found to be associated with disease , 320 of those SNPs being reported for the first time in Europeans , 97 in East Asians and 2 in Africans ( Table S4 ) . In total , we gathered 543 replication reports , dealing with 227 out of the 419 discovered SNPs ( Table S5 ) . Out of the 543 replication reports , 210 , 260 and 73 corresponded , respectively , to attempts performed on Europeans , East Asians and on Africans . Since East Asian and African GWAS are more recent , most of the replication attempts ( 465 out of 536 , 87% ) reported the replication status of discovered SNPs that had been reported for the first time in Europeans . Therefore , we focused on the subset of 465 replication attempts gathered for 190 associated SNPs discovered in European GWAS . Out of these , a total of 181 , 225 and 61 replication attempts had been reported for Europeans , East Asians and Africans , respectively . The 225 replication attempts in East Asians aimed to replicate a total of 131 SNPs associated with disease with genome-wide significance in Europeans , which results in an average of 1 . 75 replication attempts per associated SNP ( range = 1–7 ) . Thus , our estimates of replicability could be biased if replicated SNPs gathered more replication attempts per SNP , or more associated SNPs in European populations . During the analysis , and as noted in the text , we applied an additive filtering to ensure no bias in the estimates of replicability and correlations between European and East Asian OR . Specifically , we repeated the analysis selecting only the largest replication attempt per SNP , resulting in a filtered set of 123 attempts . The SNPs ascertained for the filtering are indicated in Table S5 . Polymorphism data was downloaded from HapMap Project Phase 2 ( release 24 , November 2008 ) . For each ascertained SNP , we downloaded two data sets: ( i ) genotypes for the associated SNP and ( ii ) genotypes for a 600-SNP window centered on the associated SNP . We downloaded all genotypes for all unrelated samples from the three populations of European and East Asian ancestry ( CEU , JPT and CHB ) . JPT and CHB samples were clustered together due to their close genetic relationship . Population differences in local patterns of Linkage Disequilibrium ( LD ) around disease associated SNPs were measured with the varLD software ( www . nus-cme . org . sg/software/varld . html ) [3] , using the targeted option for 50-SNP windows . For each population and genomic region , varLD builds a matrix of pairwise signed r2 values among all the SNP pairs and provides a raw score corresponding to the absolute difference in the eigen-decompositions between two matrices . This score is a summary measure of the overall LD levels in a given genomic region between two populations . We used it to measure the extent of differences in local LD between two kind of genomic regions: these containing replicated and non-replicated SNPs . To rule out the possibility that differences in LD between replicated and non-replicated SNPs are not related to the presence of the disease associated SNP , we scanned varLD differences in consecutive windows of the same size ( 50 SNP ) , starting 300 SNPs upstream of the disease associated SNP and finishing 300 SNPs downstream , with an step of 5 SNPs . In total , we checked 121 consecutive windows around the disease associated SNP . On average , we were examining a window of 503 . 61 Kb centered on each associated SNP . We used a similar sliding window approach to summarize the differences in allele frequencies between Europeans and East Asians . Again , we did it for each SNP , calculating the average heterozygosity in each window for replicated and non-replicated SNPs . Differences in heterozygosity are simply the result of subtracting the average heterozygosity in East Asians from that in Europeans ( Figure 2 ) . To compare LD differences of associated SNPs to the genome-wide background , we used the varLD targeted option that tests the null hypothesis that the correlations in LD between SNPs from a given window are equal in both populations . We implemented 1 , 000 permutations to calculate the empirical p-value for each 50-SNP window . Then , we built three cumulative distributions correspondent to each of the three sets of SNPs: replicated ( n = 47 ) , non-replicated ( n = 64 ) and random genomic ( n = 4 , 700 ) SNPs . The SNPs selected for the latter dataset were ascertained from HapMap Phase 2 in order to randomly match the minor allele frequencies of replicated and non-replicated SNPs in Europeans . Finally , we randomly created 100 groups of 47 genomic SNPs to calculate the median and 95% empirical CI of permutation p-values available at Figure 4 . As noted in the text , for some analysis we focused on the attempts that had >80% power to replicate the effect size found in Europeans . Statistical Power was calculated with the CaTS Power Calculator ( www . sph . umich . edu/csg/abecasis/CaTS/ ) [42] . For each replication attempt we checked the power under a log-additive model to find the same effect size as in the discovery European GWAS , given the sample size of the replication GWAS and the allele frequency of the risk allele in East Asians . The number of expected replications was approached by multiplying the total number of replication attempts with the statistical power to replicate averaged for all attempts ( see Table S5 ) Statistical analyses were performed using standard R procedures . The significance of the replicability estimates was checked by means of a binomial test , with an expected replicability rate of 0 . 05 under the null hypothesis of no shared associated SNPs between Europeans and East Asians or Europeans and Africans . Similarly , the significance in the risk allele direction was checked by means of a binomial test , using a null expected ratio of 0 . 5 . As indicated in the first section , differences in LD between replicated and non-replicated SNPs were checked by means of Mann-Whitney tests comparing the distributions of varLD scores for sliding 50-SNP windows centered on the disease-associated SNPs . The same procedure was used for the average difference in heterozygosity and distributions of OR found by “small” and “large” GWAS . | Describing and identifying the genetic variants that increase risk for complex diseases remains a central focus of human genetics and is fundamental for the emergent field of personalized medicine . Over the last six years , GWAS have revolutionized the field , discovering hundreds of disease loci . However , with only a handful of exceptions , the causal variants that generate the associations unveiled by GWAS have not been identified , and their frequency and degree of sharing across populations remains unknown . Here , we present a comprehensive comparison of GWAS results designed to try to understand the nature of causal variants . By examining the results of GWAS for 28 diseases that have been performed with peoples of European , East Asian , and African ancestries , we conclude that a large fraction of associations are caused by common causal variants that should map relatively close to the associated markers . Our results indicate that many of the disease risk variants discovered by GWAS are shared across Eurasians . | [
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] | 2013 | High Trans-ethnic Replicability of GWAS Results Implies Common Causal Variants |
The SAFE strategy aims to reduce transmission of Chlamydia trachomatis through antibiotics , improved hygiene , and sanitation . We integrated assessment of intestinal parasites into large-scale trachoma impact surveys to determine whether documented environmental improvements promoted by a trachoma program had collateral impact on intestinal parasites . We surveyed 99 communities for both trachoma and intestinal parasites ( soil-transmitted helminths , Schistosoma mansoni , and intestinal protozoa ) in South Gondar , Ethiopia . One child aged 2–15 years per household was randomly selected to provide a stool sample of which about 1 g was fixed in sodium acetate-acetic acid-formalin , concentrated with ether , and examined under a microscope by experienced laboratory technicians . A total of 2 , 338 stool specimens were provided , processed , and linked to survey data from 2 , 657 randomly selected children ( 88% response ) . The zonal-level prevalence of Ascaris lumbricoides , hookworm , and Trichuris trichiura was 9 . 9% ( 95% confidence interval ( CI ) 7 . 2–12 . 7% ) , 9 . 7% ( 5 . 9–13 . 4% ) , and 2 . 6% ( 1 . 6–3 . 7% ) , respectively . The prevalence of S . mansoni was 2 . 9% ( 95% CI 0 . 2–5 . 5% ) but infection was highly focal ( range by community from 0–52 . 4% ) . The prevalence of any of these helminth infections was 24 . 2% ( 95% CI 17 . 6–30 . 9% ) compared to 48 . 5% as found in a previous study in 1995 using the Kato-Katz technique . The pathogenic intestinal protozoa Giardia intestinalis and Entamoeba histolytica/E . dispar were found in 23 . 0% ( 95% CI 20 . 3–25 . 6% ) and 11 . 1% ( 95% CI 8 . 9–13 . 2% ) of the surveyed children , respectively . We found statistically significant increases in household latrine ownership , use of an improved water source , access to water , and face washing behavior over the past 7 years . Improvements in hygiene and sanitation promoted both by the SAFE strategy for trachoma and health extension program combined with preventive chemotherapy during enhanced outreach services are plausible explanations for the changing patterns of intestinal parasite prevalence . The extent of intestinal protozoa infections suggests poor water quality or unsanitary water collection and storage practices and warrants targeted intervention .
An integrated strategy of surgery , antibiotics , facial cleanliness , and environmental improvement – the SAFE strategy in short – is recommended to eliminate blinding trachoma in endemic countries by the year 2020 [1] . The F and E components aim to reduce the transmission of Chlamydia trachomatis via flies , fingers , and fomites within the community [2] . Face washing is promoted specifically to keep faces free of infectious ocular and nasal discharge , and make them less attractive to eye-seeking flies . The construction and use of latrines are promoted as a form of fly control to reduce fly-to-eye contact [2] , [3] . Improved accessibility to clean water is also promoted , but whether or not water is used for hygiene is more important than absolute access to clean water in trachoma prevention . Where water is not readily accessible , household use of a limited supply of water may not be prioritized for bathing [4]–[6] . These aims of the F and E components go beyond trachoma control and align with other major initiatives , such as the WASH program of UNICEF and Millennium Development Goal ( MDG ) 7c which , by 2015 , aim to provide access to clean water and sanitation to all children and to reduce by half the proportion of households without access to basic sanitation [7] , [8] . Improved hygiene , sanitation , and water have a positive and sustained impact on several diseases , including many of the neglected tropical diseases [9] , [10] . Trachoma was eliminated from the United States of America ( USA ) primarily through sustained social and economic development [11] . The Rockefeller Foundation noted the pivotal role sanitation played in the elimination of hookworm in the southern parts of the USA some 100 years ago [12] . Improving water supply and sanitation have been recommended after noting the reduction in the prevalence and incidence of parasitic worms such as dracunculiasis and soil-transmitted helminthiasis , and diarrhea as well as an increase in child survival [13] . A systematic review and meta-analysis of studies reporting the effects of sanitation on soil-transmitted helminth infections ( Ascaris lumbricoides , Trichuris trichiura , and hookworm ) found that having access and using sanitation was associated with an approximately 50% lower odds of any soil-transmitted helminth infection even after accounting for random effects between studies [10] . These are assumed ancillary benefits of the activities promoted by the F and E components of the SAFE strategy , yet these have not been fully documented in the context of an ongoing trachoma control program . The purpose of this study was to determine the prevalence of intestinal parasites ( soil-transmitted helminths , Schistosoma mansoni , and intestinal protozoa ) among children aged 2–15 years to complement a large trachoma impact survey in 2011 . The data also allowed to study changing patterns of parasitic worm infections in the school-aged population by comparing our findings to those obtained in a survey conducted in the mid-1990s [14] . We aimed also to determine whether improvements in household-level access to water and sanitation have occurred in this zone of the Amhara National Regional state in Ethiopia after the SAFE strategy had been fully implemented for at least 5 years .
The study protocol was reviewed and approved by the ethical review committee of the Amhara National Regional State Health Bureau . Additionally , the study activities , including oral consent , were approved by Emory University Institutional Review Board ( protocol no . 079-2006 ) . According to the principles of the Helsinki Declaration , informed consent for the interview and for stool examinations was sought . Due to the high rate of illiteracy , oral informed consent was obtained from the parent or guardian and recorded in the electronic survey form . Additionally verbal assent was obtained from children aged 7 years and above and also recorded in the electronic survey form . Each selected child , regardless of participation , was offered a single dose of albendazole ( 400 mg ) during the household visit . The study was conducted in South Gondar zone of the Amhara regional state of Ethiopia in the rainy season from late June to early August 2011 , covering all 10 rural woredas ( districts ) in the zone . The two semi-urban woredas excluded from the survey were the zonal capital , Debra Tabor Town and Woreta Town . The total population of South Gondar is approximately 2 . 05 million people with 1 . 86 million living in the 10 surveyed woredas [15] . The elevation in the zone ranges from 600 to >4 , 000 m above sea level and is geographically diverse with areas of lake shore , lowlands , highland plateaus , rugged mountain peaks , and valleys . People are primarily engaged in subsistence agriculture; rice in the lake shore areas , wheat and teff in hill and mountain sides , and animal husbandry in all areas . We assumed a null hypothesis of no change in the prevalence of infection with any of the following helminths , A . lumbricoides , T . trichiura , hookworm , and S . mansoni , as assessed in a cross-sectional survey of school-aged children of South Gondar in 1995 , when it was estimated at 49% [14] . In order to detect at least a 20% decline in prevalence ( from 49% to 29% ) at the 5% level of significance and power of 90% , stool specimens from 800 school-aged children ( 7–15 years ) needed to be examined assuming a design effect of 4 for the multi-stage cluster random sampling methodology implemented . We oversampled and included children 2–6 years of age to assess the prevalence of helminths and intestinal protozoa infections in this age group currently receiving preventive chemotherapy with albendazole during biannual campaigns known as enhanced outreach services ( EOS ) [16] . Additionally , given the focal nature of some helminth infections ( e . g . S . mansoni ) , we aimed to select a geographically representative sample from each of 10 woredas by systematically selecting 10 gotts ( communities ) from a random starting gott from woreda-specific lists arranged geographically . In each gott , one child aged 2–15 years was selected randomly in each of 30 surveyed households and asked to provide a single stool sample . Households were selected randomly using a modified segmentation design , and children were selected randomly by an electronic data collection device ( see below ) after enumerating all residents , both present and absent , of the selected household [17] . Household sanitation characteristics were determined and recorded at each consenting household by observing the presence of a used latrine and hand washing container noted with or without water . A used latrine was defined as directly observing feces in the pit with the use of a torch if needed . The head of household or adult representative was interviewed about access to , and use of , water . The selected child and the parent/guardian were shown the albendazole tablets distributed during EOS campaigns and were asked whether the child had received and taken the drug . Using small portable scales to measure submitted stool samples , field teams recorded the exact weight and fixed approximately 1 g of stool in 10 ml of sodium acetate-acetic acid-formalin ( SAF ) solution [18] . Fixed specimens were labeled with unique identification numbers ( IDs ) , transferred to a central storage area at room temperature , and shielded from direct sunlight [19] . Upon completion of the field data collection , all specimens were processed at the Amhara Regional Research Laboratory using an ether-concentration method that has shown good reliability among European reference laboratories [20] . The entire sediment was assessed systematically for helminth eggs and intestinal protozoa cysts . For helminths , the number of eggs identified were counted and recorded ( 1 up to 100 eggs ) . Counting stopped above 100 eggs and was recorded as 100+ . The frequency of intestinal protozoa cysts were recorded as none , rare ( 1–5 parasites per slide ) , frequent ( 1 parasite per observing field ) , and very frequent ( >1 parasite per observing field ) . Prior to the field data collection , teams participated in a 7-day , applied training for data collectors ( health facility-based laboratory technicians ) , which consisted of classroom instruction and field practice where the protocol and data collection tools were refined , and adapted to the local context . Technicians processing the stool specimens were trained in the ether-concentration method , reading slides , and identification of parasites at species level . Every tenth negative specimen and every specimen where a helminth was identified by a technician was reexamined by a senior laboratory technician . Survey data were collected electronically using tablet computers operating on the Android ( Google Inc . ; Mountain View , CA , USA ) platform , and were linked to results of processed specimens via the unique ID on each specimen . Laboratory results were recorded on paper forms by technicians and then double-entered in Microsoft Access by separate entry clerks , compared for discordance , and corrected with the original hard-copy . Data were analyzed using SAS version 9 . 3 ( SAS Institute Inc . ; Cary , NC , USA ) . Selection probabilities were calculated and used to weight the data in the analysis . Additionally , the variance of the estimates was adjusted to account for clustering . To measure differences in household-level access to , and use of , water and sanitation , the current survey data were compared to household survey data collected in 2000 and 2003 prior to any interventions , and 2006 after interventions in only three of 13 districts . All surveys were conducted by the Amhara National Regional State Health Bureau and The Carter Center using the same cluster , randomized survey methodology [21] , [22] .
Figure 1 shows the geographical distribution of the 99 surveyed communities across 10 woredas as well as the woredas where schools were surveyed in 1995 . A total of 2 , 355 stool samples were provided and processed from 2 , 657 randomly selected children aged 2–15 years in surveyed households ( 88 . 6% ) . Figure 2 shows the resulting sample sizes used in the analysis . Mean age of children submitting samples was 6 . 8 years ( standard deviation ( SD ) 3 . 6 years ) and 48 . 0% of specimens were from boys . Helminth eggs and intestinal protozoa cysts were examined in 2 , 338 processed stool specimens ( Figure 3 ) . The prevalence of any intestinal protozoa infection ( 76 . 8%; 95% confidence interval ( CI ) 73 . 2–80 . 4% ) was higher than the prevalence of any helminth infection ( 23 . 0%; 95% CI 18 . 7–27 . 4% ) in this study . The prevalence of the two intestinal protozoa Giardia intestinalis and Entamoeba histolytica/E . dispar was 23 . 4% ( 95% CI 20 . 7–26 . 1% ) and 11 . 1% ( 95% CI 8 . 9–13 . 2% ) , respectively . The prevalence of infections where cysts of any intestinal protozoa were identified as very frequent was 11 . 0% ( 95% CI 9 . 5–12 . 6% ) and the majority of these were Entamoeba coli . Among the helminths , A . lumbricoides and hookworm were the most frequently observed with point prevalence of 9 . 9% ( 95% CI 7 . 2–12 . 7% ) and 9 . 7% ( 95% CI 5 . 9–13 . 4% ) , respectively . Including T . trichiura and Strongyloides stecoralis ( larvae ) , the prevalence of infection with any of these soil-transmitted helminths was 19 . 5% ( 95% CI 15 . 6–23 . 4% ) . At the woreda level , the prevalence of any soil-transmitted helminths ranged from 7 . 9% to 34 . 6% ( Table 1 ) . The prevalence of any soil-transmitted helminth among preschool-aged children ( Table 2 ) was 17 . 4% ( 95% CI 13 . 0–21 . 7% ) and was not significantly different from prevalence among school-aged children ( 21 . 4% , 95% CI 16 . 5–26 . 4%; p = 0 . 106 ) . The prevalence of S . mansoni was 2 . 9% ( 95% CI 0 . 2–5 . 5%; range by woreda 0–12 . 5% ) . In surveyed gotts , the proportion of children with S . mansoni ranged from nil to 52 . 4% . A comparison of household-level indicators revealed statistically significant differences in household latrine ownership ( Χ2 = 32 . 47 , p<0 . 001 ) , use of an improved source of water for drinking ( Χ2 = 6 . 31 , p = 0 . 012 ) , reported access to water within 30 min collection time ( Χ2 = 34 . 44 , p<0 . 001 ) , and reported frequency of washing faces of children under the age of 6 years ( Χ2 = 23 . 28 , p<0 . 001 ) compared to the baseline household surveys for trachoma done in 2000 and 2003 ( Figure 4 ) . In 3 . 8% ( 95% CI 2 . 6–5 . 0% ) of households , the presence of a container outside of the latrine to hold water for washing hands was observed in 2011 , but this indicator was not assessed in prior surveys . There has been a 14-fold increase in household latrine ownership , a 69 . 4% increase in reported household use of an improved water source , and a 71 . 3% increase in household access to water as defined by the reported round trip time of less than 30 min to collect water from the source . Among families with children younger than 6 years of age , the proportion reporting to wash the child's face at least once per day has increased by 81 . 2% since the implementation of the SAFE strategy . The estimated drug coverage with albendazole is presented in Table 3 . The proportion of children aged 2–6 years ( preschool age ) reported to have taken albendazole in the past year was 14 . 9% ( 95% CI 9 . 3–20 . 5%; range by woreda 0 . 8–33 . 0% ) and 35 . 1% ( 95% CI 24 . 3–45 . 8%; range by woreda 10 . 3–68 . 4% ) reported to have ever taken albendazole . The proportion of school-aged children reported to have ever taken albendazole was 33 . 2% ( 95% CI 22 . 9–43 . 5%; range by woreda 12 . 4–65 . 7% ) . The estimated prevalence of each , A . lumbricoides , T trichiura and S . mansoni , infection was considerably lower than reported in 1995 ( Figure 5 ) . The prevalence of hookworm infection was not different from the previous estimate . Table 4 presents a comparison of the historical survey to data in the current study , restricted to children aged 7–15 years both within only the six woredas represented in the 1995 study ( column 2 ) and within all 10 woredas covered in 2011 ( column 3 ) . For each of the helminths compared , infections were identified in a smaller proportion of communities in the current survey than observed in 1995 . A . lumbricoides was the only helminth infection for which more than 100 eggs were counted per specimen , representing a prevalence of 1 . 9% ( 95% CI 0 . 8–2 . 9% ) . Without counting all the eggs identified in those specimens , a classification as moderate or high intensity using the standardized eggs per gram of stool ( EPG ) thresholds frequently employed when using the Kato-Katz thick smear method is not possible [23] . Even after adjusting for the exact weight of stool preserved , all other infections identified would be classified as low intensity infections in contrast to the 1995 findings ( Table 2 ) .
Our study done in 2011 revealed a considerably different epidemiological portrait of soil-transmitted helminths and S . mansoni in South Gondar zone of central Ethiopia than the one painted in 1995 . Indeed , the infection prevalence of compared helminths has declined substantially , except for hookworm , and infection intensities have concurrently declined for all the identified helminths . These changes have occurred in the context of the health extension program ( HEP ) , the implementation of the SAFE strategy for the control of trachoma , and EOS . The HEP is a major undertaking since 2004 to provide access to preventive health services to the rural communities of Ethiopia and serves as the backbone of SAFE implementation in the communities [24] . The SAFE strategy was implemented by the Amhara National Regional State Health Bureau in pilot areas of South Gondar starting in 2003 and , by 2006 , the program was operating at scale in all woredas due to simultaneous scale-up of HEP having in place at least one health extension worker in each kebele ( village ) . In addition to the ongoing promotion of behavior change communication in 337 kebeles of South Gondar , a total of 339 , 913 household latrines have been reported to be constructed since pilot interventions in 2003 [South Gondar Zonal Health Department reports , unpublished data] . We have presented evidence ( Figure 4 ) from a series of cross-sectional surveys indicating statistically significant improvements in reported hygiene behavior ( e . g . , washing faces of young children ) , use of an improved water source , improved access to water , and household-level access to basic sanitation ( e . g . , presence of a used latrine ) . If each of the 339 , 913 households , latrines reported to be constructed were first latrines of households . Hence , the corresponding latrine coverage should be as high as 72 . 6% , which is considerably higher than the 42 . 2% coverage identified in this study . There are several possible explanations for the discordance , which may contribute to the difference independently or in combination: health workers double-counted latrines or reported them as complete before they were , the reports from the districts were inflated to exaggerate progress , the collation of reports at district level was not accurate , a proportion of the new latrines reported were actually new replacements for households that already had one and therefore would not add to the numerator of households with a latrine , or the number of household units and population has grown significantly so as to increase the denominator – as previously highlighted to be a challenge to meeting the MDG 7c target [25] . Whatever the reasons , the discordance outlines the importance of periodic household surveys to serve as an independent monitor of the uptake of promoted interventions . Needless to say , despite improvements in access to sanitation ( from 3% in 2003 to 42% in 2011 ) , interventions are still needed , as more than half of households surveyed were without a toilet and using an unimproved source of water . While the presence of a water container for hand washing was observed outside a latrine in only a small proportion of households , some people are adopting a recently promoted health message even though there is a long way to go . These findings might explain the frequent intestinal protozoa infections identified . We have no background data to assess any change in intestinal protozoa infection prevalence , but presence of these infections suggests that the water being used for drinking is of poor quality . Whether contamination is occurring at the source , collection , or storage should be further investigated , so that adequate mitigation strategies can be implemented . Albendazole was distributed to children aged 2–5 years every 6 months in EOS campaigns since 2004 . At the national level , it has been reported that up to 9 million doses of albendazole have been distributed per round [16] . The program was originally targeted to children in woredas labeled as “food insecure” , but has since been expanded . Coverage surveys to evaluate the EOS have reported achievement of 93 . 8% of targeted children for albendazole in 2006 and 92 . 1% for vitamin A in 2008 [26] . Since 2009 in South Gondar , a cumulative 945 , 991 doses of albendazole have been distributed to approximately 213 , 000 preschool-aged children during such campaigns with a reported coverage of 100% ( range by year 98 . 3–106% ) [South Gondar Zonal Health Department , unpublished data] . The reported coverage figures are in sharp contrast to our survey estimates . Coverage estimates of mass drug administration ( MDA ) programs are commonly lower than administrative reports [27] , [28] . At the most , only one out of every three preschool-aged children reported taking the drug within the past year . From Table 3 , it is evident that the food insecure woredas were likely to be Dera , Ebinat , and Farta , which had the highest proportions of children in both age groups reporting ever having taken albendazole . If the distribution decisions were determined at a level below the woreda , then we may have misrepresented albendazole coverage by aggregating the results from non-targeted communities with targeted communities . Nonetheless , the findings confirm the importance of independently assessing MDA coverage with household surveys . Our survey has some limitations . First , the prevalence estimates of helminths and intestinal protozoa are based on a small amount of stool from a single specimen . Helminth egg output varies from one day to another and within each stool specimen , hence it is probable that we have underestimated the ‘true’ prevalence , although less likely intense infections [29] , [30] . However , this variation should be comparable to the single stool results presented by Jemenah in the mid-1990s which had the same limitation [14] . Second , we avoided calculating percent decreases in prevalence due to the different diagnostic techniques used in 1995 ( fresh stool samples using the Kato-Katz technique [31] ) and the current study ( SAF-fixed stool samples subjected to an ether-concentration method [18] , [20] ) . Using the Kato-Katz technique may have allowed a more direct comparison to the baseline data and a more precise measure of infection intensity , but it was not feasible given the logistical challenges posed by community-based surveys in the remote settings surveyed here . Our comparison of intensity of infection was limited , but in determining prevalence , fixing of stool samples in SAF and employing an ether-concentration method is as sensitive as the Kato-Katz technique [32] . Previous studies consistently revealed low sensitivity of the Kato-Katz technique in detecting hookworm infections , particularly those of low-intensity [33]–[35] . Third , we did not assess the cleanliness of the observed latrines . While improved sanitation is protective against soil-transmitted helminthiasis , a latrine with feces around the drop hole , in theory , may serve as a source of hookworm transmission [36] , [37] . Fourth , our albendazole coverage estimates are subject to recall bias . However , we took steps to minimize recall bias by showing the albendazole tablets distributed during EOS campaigns and the most recent round of EOS was implemented less than one month prior to the survey . Additionally , other MDA participation studies reported that individuals are capable of recalling whether they have taken a drug during the distribution [38] , [39] . Albendazole coverage , even in targeted woredas , was very low , which we feel provides stronger support to the hypothesis that improvements in F and E were largely responsible for the decline in helminth infection prevalence . However , this study was cross-sectional and therefore inherently has the inability to link causal associations with improvements in the sanitation due to SAFE and preventive chemotherapy due to unmeasured confounding factors . An alternative hypothesis is that the recorded , significant improvements in latrines , water access , and face washing have had minimal impact on intestinal parasites and the decline is due to secular variation . The two surveys compared were conducted nearly 16 years apart . We cannot rule out a secular decline in the prevalence and intensity of helminth infections , but a national survey of school-aged children in 2006 reported a prevalence of A . lumbricoides of 28 . 0% and of any soil-transmitted helminth of 37 . 7% , perhaps indicating that from 1995 to 2006 there may have been little change in prevalence due to secular variation in South Gondar [40] . Hence , further investigation of predictive factors of the observed infections is warranted . This study demonstrates the feasibility and success of an integrated neglected tropical disease assessment for programmatic decision making . Despite the low intensity of identified helminth infections , infection with any of the helminths targeted for control was identified in over 80% of the communities surveyed . The woreda-level prevalence of any soil-transmitted helminth exceeded 20% in East Estie , West Estie , Dera , and Fogera woredas , and hence , according to WHO guidelines , warrants preventive chemotherapy targeting school-aged children [41] . Additionally , preventive chemotherapy using praziquantel against schistosomiasis is warranted in Fogera woreda and other communities where the proportion of children infected with S . mansoni was greater than 10% . Through this assessment , we were able also to identify several intestinal protozoa infections , some of which contribute to morbidity [42] . At the least , the high prevalence of these infections indicates contamination of water at the point of the source or use and warrants further investigation and setting-specific interventions . It also suggests that there is much more work to be done in improving water quality , hygiene , and sanitation in these mostly rural areas of Ethiopia . While we cannot directly attribute the decline in helminth prevalence and intensity directly to the SAFE strategy , the documented increase in hygiene and sanitation offer both a biologically plausible and parsimonious explanation for the decline which is consistent with our understanding of the epidemiology of helminth and intestinal protozoa infections . Preventive chemotherapy in national helminth control programs has been shown to significantly reduce prevalence and intensity of helminth infections and has likely contributed to the observed decline [43]–[45] . However , without environmental changes , there is potential for rapid reinfection and continued transmission [46] , [47] . Additionally , participation , defined as ever taking albendazole , among the targeted population as reported in this survey was much lower than administrative records suggest . Given the simultaneous scaling up of both F and E from the SAFE strategy and de-worming in EOP since 2006 , one has to consider that there has been a synergistic effect of these ongoing interventions even though coverage ( both household latrine ownership and preventive chemotherapy with albendazole ) has been below target . There remain opportunities for integrated neglected tropical disease control throughout Ethiopia [48] . These results are encouraging and present a portrait of what might be expected within an integrated , multi-sectoral package of interventions for neglected tropical disease control . | Part of the SAFE strategy ( surgery , antibiotics , facial cleanliness , and environmental improvement ) to eliminate blinding trachoma involves improving access to , and use of , water and sanitation . We combined the assessment of parasitic worm and intestinal protozoa infections with surveys of trachoma in an area of Ethiopia where the SAFE strategy , together with enhanced outreach services and the health extension program , had been implemented for more than 5 years . We compared our findings with results from a survey conducted in the mid-1990s . We documented significant increases in household access and use of latrines and clean water: the F and E components of the SAFE strategy as promoted by the health extension program . We found considerably lower levels of parasitic worm infections than those reported previously . Moreover , we documented , for the first time in this zone , pathogenic intestinal protozoa infections , which indicate poor water quality and unhygienic water collection and storage practices in the communities surveyed . A plausible hypothesis for the decline in parasitic worm infections might be the combined impact of ongoing simultaneous health programs: SAFE strategy for trachoma control alongside the health extension program and regular deworming of preschool-aged children . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"ophthalmology",
"medicine",
"public",
"health",
"trachoma"
] | 2013 | Intestinal Parasite Prevalence in an Area of Ethiopia after Implementing the SAFE Strategy, Enhanced Outreach Services, and Health Extension Program |
Given the relevance of beige adipocytes in adult humans , a better understanding of the molecular circuits involved in beige adipocyte biogenesis has provided new insight into human brown adipocyte biology . Genetic mutations in SLC39A13/ZIP13 , a member of zinc transporter family , are known to reduce adipose tissue mass in humans; however , the underlying mechanisms remains unknown . Here , we demonstrate that the Zip13-deficient mouse shows enhanced beige adipocyte biogenesis and energy expenditure , and shows ameliorated diet-induced obesity and insulin resistance . Both gain- and loss-of-function studies showed that an accumulation of the CCAAT/enhancer binding protein-β ( C/EBP-β ) protein , which cooperates with dominant transcriptional co-regulator PR domain containing 16 ( PRDM16 ) to determine brown/beige adipocyte lineage , is essential for the enhanced adipocyte browning caused by the loss of ZIP13 . Furthermore , ZIP13-mediated zinc transport is a prerequisite for degrading the C/EBP-β protein to inhibit adipocyte browning . Thus , our data reveal an unexpected association between zinc homeostasis and beige adipocyte biogenesis , which may contribute significantly to the development of new therapies for obesity and metabolic syndrome .
Obesity and its associated metabolic diseases are caused by a long-term imbalance between energy intake and energy expenditure . Adipose tissue , a major factor in controlling the balance of energy , is composed of white and brown adipocytes , which have two distinct functions: white adipocytes store excess energy , whereas brown adipocytes specialize in expending energy . The unique metabolic properties of brown adipocytes depend on their mitochondrial density , fuel oxidation capacity , and exclusive expression of uncoupling protein-1 ( UCP1 ) . Inducible brown fat-like cells , named beige adipocytes , have also been identified in white adipose tissue ( WAT ) . Beige adipocytes are induced by various external cues , such as chronic cold exposure , long-term treatment with a peroxisome proliferator-activated receptor ( PPAR ) -γ agonist , cancer cachexia , and bariatric surgery [1–3] . The presence and activity of thermogenic adipocytes ( brown and beige adipocytes ) are associated with improved global metabolic fitness , such as improvements in insulin resistance and glucose homeostasis [2 , 4 , 5]; conversely , thermogenic adipocytes decrease with age and obesity in mice and humans [6 , 7] . Recent studies indicate that adult human brown adipose tissue ( BAT ) in the supraclavicular region has beige-like characteristics [8–11] . These findings indicate the potential importance of beige fats in human obesity and metabolic disease . Therefore , identifying a selective molecular pathway that regulates the acquisition of beige adipocyte properties will enable us to selectively and preferentially promote beige adipocyte biogenesis and thermogenesis as a therapy for obese or older subjects who do not have active BAT depots [1] . Identifying and implementing therapies based on beige fat requires a detailed understanding of the differences in the developmental mechanisms and functions of white , brown , and beige adipocytes . Differentiation mechanisms of all of these fat cell types share many transcriptional regulators , such as PPARγ and members of the CCAAT/enhancer binding protein ( C/EBP ) family of transcription factors [12] . C/EBP-β is induced in the early phase of adipogenesis and is crucial for activating PPARγ expression . PPARγ collaborates with C/EBP-α to bind and regulate the expression of most adipocyte-associated genes , including aP2 , in fat cells [13] . Many transcription factors that direct cells toward a brown/beige adipocyte identity rather than a white adipocyte identity act by modulating the core adipogenic transcriptional machinery . PRDM16 , which is one of the most important transcriptional co-regulators of brown/beige adipocyte differentiation [14] , promotes a brown fat-selective gene program , mainly via protein-protein interactions with transcriptional factors such as C/EBP-β , PPARγ , and zinc finger protein 516 ( Zfp516 ) [3 , 15 , 16] . Zinc is an essential nutrient for all living organisms and is required for the structure and function of a wide range of proteins; 10% of all human proteins have the potential to bind zinc [17] . Zinc acts as both an intracellular and extracellular signaling effector; zinc signaling is mediated by zinc transporters and metallothioneins that regulate various cellular functions [18] . Cellular zinc homeostasis is tightly regulated by two families of zinc transporter proteins , namely , the zinc transporter ( ZnT ) family , which controls zinc efflux out of the cytosol , and the Zrt/Irt-related protein ( ZIP ) family , which controls zinc influx into the cytosol [19] . Dysfunction of zinc signaling leads to physiological disturbances . For example , our group and others showed that extracellular zinc signaling via the zinc transporter ZnT8 regulates hepatic insulin clearance , and that altered ZnT8 function is involved in type 2 diabetes pathogenesis [20 , 21] , indicating that the precise control of zinc homeostasis is crucial for maintaining health and preventing diseases , including lifestyle-associated diseases . Intriguingly , zinc deficiency significantly reduces the DNA-binding activity of PPARs [22] , suggesting that some zinc-containing proteins that participate in brown/beige adipocyte differentiation and function ( e . g . , PRDM16 , Kruppel-like factor 11 ( KLF11 ) , and Zfp516 ) might be dysregulated by changes in zinc homeostasis . In this study , we focused on the zinc transporter ZIP13 because Zip13-deficient ( Zip13-KO ) mice and humans with spondylocheirodysplastic Ehlers-Danlos syndrome who carry a loss-of-function mutation in SLC39A13 have been reported to have a significantly decreased white fat mass [23] . We show that ZIP13 is a crucial regulator of beige adipocyte differentiation , and negatively regulates C/EBP-β protein levels , illustrating the physiological relevance of the ZIP13-C/EBP-β axis in beige adipocyte biogenesis and thermogenesis , and its therapeutic potential in obesity treatment .
We previously reported that ZIP13 may be involved in adipose tissue homeostasis [23] . To clarify this point , we first weighed the WAT and BAT of Zip13-KO mice and their wild-type ( WT ) littermates ( control ) . As shown in S1A Fig , the interscapular BAT and inguinal WAT ( iWAT ) weights were similar in Zip13-KO and WT mice , but the epididymal WAT ( eWAT ) weight was significantly lower in Zip13-KO mice . Zip13 expression was higher in the eWAT and BAT than in the iWAT ( S1B Fig ) . Surprisingly , hematoxylin and eosin ( H & E ) staining of the iWAT in Zip13-KO mice showed large clusters of cells with multilocular lipid droplets , which are characteristic of the browning of iWAT depots ( Fig 1A ) . Consistently , immunohistochemical staining revealed a high number of UCP1-positive cells in the Zip13-KO iWAT ( Fig 1B ) ; gene expression analyses confirmed that brown fat genes were significantly upregulated in the Zip13-KO iWAT ( Fig 1C ) . There were no significant differences between WT and Zip13-KO mice in eWAT and BAT morphology , or in the expression of various brown adipocyte markers in BAT ( Fig 1A and 1D , S1C Fig ) ; however , we observed that the expression of brown adipocyte markers in the eWAT of Zip13-KO mice tended to be higher than that of WT mice , although statistical significance was not observed ( S1D Fig ) . We assessed overall gene expression changes in the iWAT by microarray analyses of RNAs isolated from the iWATs of Zip13-KO mice and their WT littermates , and identified differentially expressed genes ( 1 , 260 upregulated and 1 , 082 downregulated genes in Zip13-KO mice compared with WT mice ) ( S2A Fig and S1 Table ) . Gene Ontology Biological Process ( GOBP ) analysis and Kyoto Encyclopedia of Genes and Genomes ( KEGG ) pathway enrichment analysis revealed that genes involved in inflammatory responses were downregulated , whereas genes involved in fatty acid metabolism and mitochondrial function were upregulated in the Zip13-KO iWAT , suggesting that an accelerated adipocyte-browning process occurs in the iWAT of Zip13-KO mice ( S2B and S2C Fig , S2 Table ) . Pan et al . identified brown fat-specific , white fat-specific , and common fat genes by the RNA-sequencing of BAT , eWAT , and soleus-muscle tissue [24] . We used these criteria to profile the gene expression patterns in the Zip13-KO iWAT , and demonstrated the upregulation of a broad spectrum of brown fat-specific genes , a slight upregulation in common fat genes , and no change in white fat-specific genes ( Fig 1E ) . Furthermore , the oxygen consumption rate ( OCR ) was significantly higher in the inguinal fat tissue of Zip13-KO mice than that of WT mice ( S1E Fig ) , although there was no significant difference of OCR in brown fat tissue between the two groups . These results indicate that there was an increase in the number of functional beige adipocytes in Zip13-KO inguinal fat tissue . The increase in beige adipocyte characteristics in Zip13-KO mice prompted us to examine the metabolic profiles of these mice . Indeed , we observed a significantly higher oxygen consumption ( VO2 ) rate in Zip13-KO mice than in WT mice under both light and dark conditions at 23°C ( Fig 1F ) , without any changes in food intake ( S3A Fig ) . Furthermore , the locomotor activity of the Zip13-KO mice tended to decrease ( S3B Fig ) , although this change was not statistically significant . These results suggested that the increase in VO2 in Zip13-KO mice was not due to hyperactivity or impaired food intake . To further investigate the role of ZIP13 in thermogenesis , we measured VO2 in thermoneutral conditions ( 30°C ) after activation of the β3-adrenoreceptor agonist [25] . As shown in S3C Fig , VO2 levels of Zip13-KO mice were significantly increased after the administration of β3-adrenoreceptor agonist CL316 , 243 , suggesting that ZIP13 might play a role in thermogenesis . We subsequently examined whether Zip13-KO mice acquired resistance to high-fat diet ( HFD ) -induced obesity . Body weights of WT mice were significantly increased by the HFD compared with a standard diet ( STD ) ; however , Zip13-KO mice were resistant to HFD-induced obesity primarily due to low fat mass gain ( Fig 1G , S4A and S5A–S5E Figs ) . Furthermore , Zip13-KO mice appeared to have an improved glucose tolerance and insulin tolerance , compared to WT mice ( S4B and S5D Figs ) . Taken together , these results indicated that Zip13 deletion in vivo promotes beige adipocyte biogenesis and energy expenditure , and thereby reduces diet-induced obesity and insulin resistance . To clarify whether the increase in iWAT browning in Zip13-KO mice occurs in a cell-autonomous manner , primary white preadipocytes isolated from the iWAT of WT or Zip13-KO mice were cultured to induce their differentiation into adipocytes under defined conditions [26–28] . We found higher gene expression levels of several brown adipocyte markers and an adipogenesis marker ( aP2 ) ( Fig 2A and 2B ) , and higher total and oligomycin-insensitive cellular respiration in Zip13-KO cells than in WT cells ( Fig 2C ) , suggesting that preadipocytes from Zip13-KO mice cell-autonomously accelerate adipocyte browning . Furthermore , treatment with the cAMP-inducer forskolin also increased the expression of thermogenic genes , including Ucp1 and Pgc1α , in Zip13-KO cells ( Fig 2B ) , indicating that the differentiated cells were functional . Importantly , the exogenous expression of ZIP13 in Zip13-KO cells efficiently repressed the expression of brown adipocyte markers and the adipogenesis marker ( Fig 2D , S6A Fig ) . Taken together , these results clearly demonstrate that ZIP13 negatively and cell-autonomously regulates adipocyte browning . To investigate the mechanism by which ZIP13 regulates adipocyte browning , we immortalized white preadipocytes from Zip13-KO and WT mice for further experiments . Stimulation with a cocktail that induces browning of adipocytes increased the expression of the adipogenic transcription factor PPARγ in Zip13-KO cells ( Fig 2E ) . Since PPARγ is positively regulated by the transcription factors C/EBP-β and C/EBP-δ [29] , we next examined C/EBP-β and C/EBP-δ gene and protein expression levels in these cells . Although the mRNA levels of C/EBP-β and C/EBP-δ genes were expressed comparably in Zip13-KO and WT cells ( Fig 2E , S6B Fig ) , the C/EBP-β protein level in Zip13-KO cells was higher than that in WT cells at 2 days after inducing differentiation , before the upregulation of PPARγ protein ( Fig 2F ) . The expression of other early transcriptional regulators , such as Krox20 , did not differ between WT and Zip13-KO cells ( S6B Fig ) . C/EBP-β is not only important in adipogenesis , but is also essential for brown fat development; C/EBP-β cooperates with the coregulatory protein PRDM16 to act as a crucial molecular switch in determination of brown fat cell fate [15] . Intriguingly , PRDM16 protein levels also increased in Zip13-KO cells after 4 days of differentiation ( Fig 2F ) , suggesting that ZIP13 is involved in the homeostatic regulation of C/EBP-β and the PRDM16 proteins , both of which are essential for adipocyte browning at either the post-transcriptional or translational level . Since C/EBP-β is required in both white and brown adipocyte differentiation [12] , we next investigated whether the regulatory expression of C/EBP-β in WT white preadipocytes might affect white versus beige adipocyte differentiation . When white preadipocytes stably expressing C/EBP-β ( WPreCβ cells ) were differentiated using a cocktail that induces white adipocytes , several white adipocyte markers and the adipogenesis marker aP2 were significantly increased ( Fig 3A and 3B , S7 , S8A and S8B Figs ) . However , this effect was due to the enhancement of adipogenesis per se , since white adipocyte marker genes were expressed at almost the same ( or at a decreased ) level between control and WPreCβ cells when the mRNA levels of these genes were normalized to that of aP2 ( Fig 3C , S8C Fig ) . In contrast , when these cells were exposed to a brown adipogenic differentiation cocktail , brown adipocyte markers were significantly increased in WPreCβ cells ( Fig 3D , S8B Fig ) , and this was not likely to be due to enhanced adipogenesis , since this upregulation of brown adipocyte markers was still evident after normalizing the mRNA levels of these genes to those of aP2 ( Fig 3E , S8C Fig ) . These results indicate that C/EBP-β , an intrinsic transcription factor that positively regulates adipogenesis , facilitates beige adipocyte differentiation . These observations led us to further assess and compare the characteristics of Zip13-KO and WPreCβ cells , since Zip13-KO cells accumulate the C/EBP-β protein ( Fig 2F ) . In fact , we found that both white and brown adipocyte markers were increased in Zip13-KO cells ( Fig 3F–3G and 3I , S8D and S8E Fig ) , and the enhanced expression of brown adipocyte markers in Zip13-KO cells was still evident when normalized to the expression of aP2 ( Fig 3H and 3J ) . Taken together , these results suggest that Zip13-KO cells have similar features to those of preadipocytes , which contain high levels of the C/EBP-β protein . To further validate the intrinsic role of ZIP13 in adipocyte browning , we used C3H10T1/2 cells , which differentiate into brown/beige adipogenic lineages when exposed to a brown adipogenic cocktail . Depleting ZIP13 by RNAi significantly increased the expression of brown adipocyte markers ( Fig 4A and 4B ) , which was further confirmed using another Zip13 siRNA ( S9A–S9D Fig ) . C/EBP-β protein levels were also increased by Zip13 knockdown in C3H10T1/2 cells at the indicated time points ( Fig 4C ) . The C/EBP-β protein level was investigated by a cycloheximide ( CHX ) chase experiment ( Fig 4D and 4E ) , which suggested the possible involvement of ZIP13 in C/EBP-β protein stability . We next investigated whether C/EBP-β ubiquitination was increased in Zip13-KO cells . Ubiquitinated C/EBP-β was detected by immunoprecipitation with the HA antibody , followed by immunoblotting with an anti-ubiquitin antibody . The level of ubiquitinated C/EBP-β protein was reduced by approximately half ( 0 . 48-fold decrease ) in Zip13-KO cells compared with those in WT cells ( Fig 4F ) , suggesting that C/EBP-β is resistant to ubiquitination in Zip13-KO cells , which might account for the increased adipocyte browning in these cells . To examine the biological relevance of C/EBP-β upregulation in Zip13-KO cells , we used retroviruses expressing short hairpin ( sh ) RNAs targeting C/EBP-β ( shβ-1 and shβ-2 ) to downregulate C/EBP-β ( Fig 4G ) , which efficiently blocked brown adipocyte differentiation ( Fig 4H ) . These results indicated that the enhanced adipocyte browning due to loss of ZIP13 is caused by C/EBP-β accumulation . We next investigated whether the zinc-transporting activity of ZIP13 is necessary for moderate adipocyte browning . The most highly conserved portions among the ZIP-family proteins are reported to be in transmembrane domains ( TMDs ) IV and V , which both contain common amino acids required for zinc binding , such as His ( Fig 5A ) [30] . To address whether these residues of ZIP13 contribute to intracellular zinc homeostasis , we generated a series of ZIP13 mutants ( H229A and H254A ) in which the His residues were replaced with Ala in TMDs IV and V ( Fig 5B ) . As shown in Fig 5C , cells with exogenously expression of ZIP13 ( WT ) showed significantly upregulated MT1A mRNA levels , which correlates with cytosolic zinc levels , compared to the control ( Ctrl ) ; this result is consistent with previous reports [31 , 32] ( Fig 5C ) . In contrast , exogenous expression of H229A or H254A mutant ZIP13 decreased the mRNA level of MT1A compared with ZIP13 ( WT ) ( Fig 5C ) . Homophilic interactions ( Fig 5D ) and intracellular localization of these mutants were similar to those in the WT ( Fig 5E ) , as previously reported [31] . Together , these findings indicated that H229 and H254 are important for increasing cytosolic zinc levels . We next ectopically expressed the loss-of-function ZIP13 mutants in Zip13-KO cells in rescue experiments to determine whether they suppress the adipocyte browning induced by ZIP13 deficiency . Interestingly , expression of these loss-of-function ZIP13 mutants ( H229A or H254A ) could not suppress the adipocyte-browning phenotype ( Fig 5F ) or decrease C/EBP-β protein levels in Zip13-KO cells ( Fig 5G ) . Finally , we examined whether zinc ion treatment could rescue the adipocyte-browning phenotype of Zip13-KO cells . Exogenous zinc ion treatment increased MT1A levels ( S10A and S10B Fig ) but did not reverse the adipocyte browning induced by ZIP13 deficiency ( S10C Fig ) , indicating that specific zinc transport mediated by ZIP13 is indispensable for the proper homeostasis of adipocyte browning .
The iWAT in Zip13-KO mice showed a browning phenotype that reflected increased energy expenditure ( Fig 1A–1C and 1F , S1E Fig ) . Gene expression profiling showed that common fat genes were slightly upregulated in the iWAT of Zip13-KO mice , the expression of brown fat-specific genes were more notably increased , and the expression of white fat-specific genes was minimally affected ( Fig 1E ) . These unbiased analyses demonstrated that ZIP13 is unlikely to be involved in the fate decision of white versus beige fat , but is likely involved in the inhibition of beige fat differentiation . The differentiation of mesenchymal stem cells into adipocytes is regulated by a series of transcription factors that determine the sequence of events , such as commitment , differentiation , and activation [1] . For example , BMP7 and EBF2 are crucial for mesenchymal progenitor cells to commit to a brown or beige fat lineage [33 , 34] . Therefore , if these factors are activated in Zip13-KO cells , the expression of adipogenic regulators ( such as C/EBP-β , C/EBP-δ , and Krox20 ) at the early stages should be upregulated . However , there was no change in the expression of these genes between WT and Zip13-KO cells ( Fig 2E , S6B Fig ) . Instead , C/EBP-β protein levels were increased in Zip13-KO cells ( Fig 2F ) , causing accelerated beige adipocyte differentiation . Exposure to cold , norepinephrine , or forskolin activates brown/beige adipocytes to express high levels of thermogenic genes [1] . In fact , we found that Zip13-KO cells exhibited increased thermogenic gene expression upon exposure to forskolin ( Fig 2B ) , indicating that functional beige adipocytes are increased . However , the rate of increase in thermogenic gene expression was similar between Zip13-KO and WT cells ( Fig 2B ) . These results suggested that ZIP13 is mainly involved in the differentiation rather than the activation of beige adipocytes . We demonstrated that preadipocytes from Zip13-KO mice accelerate adipocyte browning at a higher rate than those from WT mice ( Fig 2A–2C ) , and Zip13 knockdown experiments showed similar results ( Fig 4A and 4B ) . Furthermore , the exogenous expression of ZIP13 in Zip13-KO cells efficiently repressed adipocyte browning ( Fig 2D ) . These results suggest that ZIP13 negatively regulates adipocyte browning in a cell-autonomous manner . We also showed that functional beige fat cells were increased in inguinal fat tissue of Zip13-KO mice ( Fig 1A and 1C , S1E Fig ) , which might contribute to an increase in whole body VO2 of Zip13-KO mice ( Fig 1F ) . Furthermore , the observation that the β3-adrenoreceptor agonist treatment increased whole-body energy expenditure even under thermoneutrality , indicated that beige fat of Zip13-KO mice at least partially contributed to the whole-body energy expenditure even if Zip13-KO mice were global KO mice . However , we cannot rule out the possibility that tissues other than beige fat might contribute to the increased in whole-body VO2 of Zip13-KO mice since Zip13-KO mice do have dermal defects [35] , and this could play an important role in the increase in beige fat activity , as reported by Cannon and Nedergaard [36] . Further investigation is necessary to test this possibility , by analyzing tissue-specific Zip13-KO mice . C/EBP-β is involved in both adipogenesis and brown/beige adipocyte differentiation [12] . During adipogenesis , C/EBP-β induces the expression of C/EBP-α and PPARγ , the two major transcriptional inducers of adipogenic gene expression . C/EBP-β-KO mice have severely impaired brown fat development and reduced Ucp1 expression [37] . Overexpression of C/EBP-β induces Ucp1 expression in 3T3-L1 white adipocytes [38] . Upstream activators of C/EBP-β induce brown/beige fat differentiation [26 , 39–41] . In fact , browning is accelerated not only in cultured white but also brown Zip13-KO adipocytes , and in brown fat tissue of Zip13-KO mice fed a HFD ( Fig 2 , S4D , S4F and S11 Figs ) . Furthermore , Zip13 knockdown increased adipocyte browning in C3H10T1/2 cells , which are capable of differentiating into the beige or brown adipocyte lineage when exposed to a brown adipogenic cocktail ( Fig 4A–4C ) . These results suggest that Zip13 may contribute to the browning of both white and brown adipocytes via the accumulation of C/EBP-β . These findings raised the fundamental question of whether C/EBP-β stabilization provides a plausible explanation for the Zip13 deficiency phenotype . We noted that white preadipocytes overexpressing C/EBP-β phenocopied Zip13 deficiency with regard to accelerated adipocyte browning ( Fig 3E and 3J , S8C and S8F Fig ) . Furthermore , the enhanced browning in Zip13-KO adipocytes was almost completely eliminated by C/EBP-β knockdown ( Fig 4H ) . These results , obtained from overexpression or knockdown experiments , indicated that C/EBP-β stabilization is a crucial step for the enhanced browning resulting from ZIP13 deficiency . An interesting observation in the present study was that C/EBP-β played specific roles in promoting beige adipocyte differentiation ( Fig 3B–3E ) . C/EBP-β and PRDM16 comprise a transcriptional unit crucial for brown/beige fat differentiation; therefore , C/EBP-β protein induction might stabilize or recruit PRDM16 [42] and accelerate adipocyte browning . Indeed , PRDM16 expression was significantly increased in Zip13-KO cells compared with WT cells 4–6 days after the induction of differentiation ( Fig 2F ) , supporting the idea that C/EBP-β protein accumulation stabilizes PRDM16 . ZIP13 is shown to transport zinc from the Golgi apparatus to the cytoplasm in mammalian cells [23 , 43] . Thus , we investigated whether this zinc-transporting ability of ZIP13 is required for inhibiting adipocyte browning . The conserved His residues in the TMDs are required for zinc transport in other ZIP-family proteins [44] , and we demonstrated that these His residues in ZIP13 were crucial for ZIP13-mediated zinc transport for the inhibition of adipocyte browning ( Fig 5F–5G ) . One possible explanation is that zinc transport elicited by a specific zinc transporter contributes to the stability of a particular protein , hence enabling it to perform its physiological function . C/EBP-β is regulated by several post-translational modifications that are crucial for proper activation of the adipogenic program , such as phosphorylation , ubiquitination , and sumoylation [45 , 46] . We showed that the amount of ubiquitinated C/EBP-β was decreased in Zip13-KO cells ( Fig 4F ) , causing accelerated adipocyte browning . There are two possible mechanisms underlying this observation . The first is that the expression or activity of deubiquitinating enzymes is upregulated; the second is that the ubiquitin system is downregulated . In fact , zinc blocks the activity of these deubiquitinating enzymes , including cysteine protease [47] , and zinc is required for the activity of ubiquitin system-related proteins , including the RING-finger family E3 ligase [48] . Therefore , the activity of these enzymes may be altered in Zip13-deficient cells . Further analysis of Zip13-deficient cells will clarify the specific roles of C/EBP-β in beige adipocyte differentiation . We also demonstrated that ZIP13-mediated zinc transport , but not a sufficient zinc supply in the form of zinc ions , is required for stabilizing C/EBP-β ( Fig 5G , S10 Fig ) , suggesting that ZIP13-mediated zinc transport plays a specific role in clearing C/EBP-β proteins to inhibit adipocyte browning . ZIP13-mediated zinc transport might specifically determine the molecular fate of C/EBP-β partners , such as deubiquitinating enzymes or the RING-finger family E3 ligase , thereby affecting C/EBP-β stability and the rate of adipocyte browning . These hypotheses should be further verified by identifying the binding partners of ZIP13 via proteomic studies . In this study , while investigating the role of a causative gene for a human disease , we unexpectedly found a novel molecular adipocyte-browning mechanism regulated by the ZIP13-C/EBP-β signaling cascade ( Fig 6 ) . This system might be conserved in humans , considering the lean phenotype observed in a patient with a loss-of-function mutation of ZIP13 [23] . Elucidating the ZIP13-regulated adipocyte-browning pathway may contribute to the development of new therapeutics against obesity .
All mice were housed in specific pathogen-free barrier facilities , maintained under a 12-h light/dark cycle , given water ad libitum , and fed standard rodent feed ( Oriental Yeast , Tokyo , Japan ) or rodent feed containing 60% fat ( Research Diet , New Brunswick , NJ , USA ) from 8 to 14 weeks of age . As Zip13-KO mice show hypodontia [23] , all mice used in the experiments were fed powdered feed to ensure adequate nutrition . All mice were backcrossed onto C57BL/6J mice for more than seven generations . The whole-body energy expenditure of Zip13-KO or WT mice at 10 weeks of age was measured using an ARCO 2000 mass spectrometer ( Arco System , Chiba , Japan ) . Immortalized white or brown preadipocytes were isolated from the iWAT or BAT of WT and Zip13-KO mice ( 6–8 weeks ) by collagenase digestion , as described previously [49] . Preadipocytes were immortalized by retroviral transduction with the SV40T antigen and selection with puromycin ( 2 mg/mL ) . Immortalized preadipocytes were a mixed population and two cell lines were examined . Preadipocytes were seeded into collagen-coated dishes ( Corning , Kennebunk , ME , USA ) in DMEM/F12 ( Gibco , Carlsbad , CA , USA ) with 10% FCS . C3H10T1/2 cells were obtained from American Type Culture Collection . For white adipocyte differentiation , cells were induced with induction medium containing 10% FBS , 5 μg/mL insulin , 250 μM isobutylmethylxanthine ( IBMX ) , and 2 μg/mL dexamethasone in DMEM . Two days after induction , the culture medium was changed to a maintenance medium containing 10% FBS and 5 μg/mL insulin . For the brown adipocyte cocktail , we used a formula described in previous reports [26–28] . Briefly , when the cells reached confluency , brown/beige adipocyte differentiation was induced by treating cells with DMEM containing 10% FBS , 250 μM IBMX , 2 μg/mL dexamethasone , 125 μM indomethacin , 5 μg/mL insulin , 1 nM T3 , and 0 . 5 μM rosiglitazone . Two days after induction , the culture medium was changed to a maintenance medium containing 10% FBS , 5 μg/mL insulin , 1 nM T3 , and 0 . 5 μM rosiglitazone . For cAMP treatment , cells were incubated with 10 μM forskolin for 4 h . RNAi-mediated gene knockdown was performed as described previously [8]; siRNAs for Zip13 were obtained from Invitrogen ( Carlsbad , CA , USA ) ( silencer select siRNA [s206098] and stealth siRNA[MSS229105] ) . Immunoblotting and immunoprecipitation were performed as described previously [50] . The following antibodies were used for immunoblotting: anti-C/EBP-β ( 1:1 , 000; Cell Signaling , Danvers , MA , USA ) , anti-PRDM16 ( 1:1 , 000 ) [51] , anti-PPARγ ( 1:1 , 000; Cell Signaling ) , anti-Rpb1CTD ( RNA polymerase II CTD ) ( 1:1000; Cell Signaling ) , anti-tubulin ( 1:3 , 000; Sigma-Aldrich , St Louis , MO , USA ) , or anti-β-actin ( 1:3 , 000; Sigma ) . Total RNA was isolated from tissues using QIAzol ( Qiagen , Valencia , CA , USA ) following the manufacturer’s protocol . Reverse transcription reactions were performed using High Capacity cDNA Synthesis Kit ( Applied Biosystems , Foster , CA , USA ) . The sequences of the primers used in this study are shown in S3 Table . Quantitative reverse transcriptase PCR ( qRT-PCR ) was performed with SYBR green fluorescent dye using an ABI 7500 Fast Real-Time PCR System . Relative mRNA expression was determined by relative standard curve methods using 18S as an internal control to normalize samples . Samples were hybridized onto an array ( Agilent SurePrint G3 Mouse GE 8x60K ) . The gene expression data set was deposited in the Gene Expression Omnibus database ( GSE77933 ) . Microarray analysis and functional enrichment analysis were as described in S1 Text . C-terminally HA-tagged plasmids expressing mouse ZIP13 ( mZIP13-HA ) were constructed by inserting cDNA into a pcDNA3 . 1 , pMX-IRES-GFP , or pBabe-puro vector . Plasmids expressing zinc transport-incompetent ZIP13 mutants were constructed using two-step PCR . The plasmid expressing HA-C/EBP-β was kindly provided by Dr . Y . Kido ( Kobe University ) [52] . All constructs were verified by sequencing . Phoenix packaging ( PLAT-E ) cells , provided by Dr . T . Kitamura ( Tokyo University ) , were transfected with retroviral vectors by lipofection [53] . After 48 h , the viral supernatant was collected and filtered . Cells were incubated for 6 h with the viral supernatant supplemented with 10 μg/mL polybrene . Immunohistochemical analysis was performed as described previously [20 , 54 , 55] , using an anti-Ucp1 antibody ( 1:250 dilution , Abcam , St . Charles , MO , USA ) . Immunocytochemistry was performed as described previously [50] . Anti-HA ( 1:100; MBL , Nagoya , Japan ) and anti-GM130 antibodies ( 1:250; Transduction Lab , Lexington , KY , USA ) were used for staining . The protocol for animal experiments was approved by the Ethics Review Committee of Animal Experimentation of Juntendo University and Gunma University . All quantitative data were reported as the mean ± SEM . The Student’s t-test was performed for the comparison of two groups . For multiple comparisons , analysis of variance was performed by Two-way ANOVA followed by Bonferroni’s multiple comparison test or One-way ANOVA followed by Bonferroni’s multiple comparison test . A p-value of less than 0 . 05 was considered to indicate a statistically significant difference between two groups . All mice were housed and cared for according to guidelines approved by the Animal Care and Use Committee of Juntendo University ( 280216 ) , and by the Committee for Institutional Animal Care and Experimentation Committee at the Gunma University ( 16–048 ) . | Inducible brown fat-like cells , named beige adipocytes have recently been a topic of great interest , mainly because they are induced in response to external cues , and are closely associated with adult human brown adipocyte . Therefore , the identification of selective molecular circuits involved in beige adipocyte biogenesis and thermogenesis will enable the selective induction of white adipocyte browning as a therapy for obesity . Here , we show that zinc homeostasis , which is controlled by ZIP13 , a protein associated with human disease , is essential for the accurate regulation of beige adipocyte differentiation . Inhibition of ZIP13 function enhances beige adipocyte biogenesis and thermogenesis , highlighting the potential of ZIP13 as a therapeutic target for obesity and metabolic syndrome . | [
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"elements"
] | 2017 | Zinc transporter ZIP13 suppresses beige adipocyte biogenesis and energy expenditure by regulating C/EBP-β expression |
Urochordates are the closest relatives of vertebrates and at the larval stage , possess a characteristic bilateral chordate body plan . In vertebrates , the genes that orchestrate embryonic patterning are in part regulated by highly conserved non-coding elements ( CNEs ) , yet these elements have not been identified in urochordate genomes . Consequently the evolution of the cis-regulatory code for urochordate development remains largely uncharacterised . Here , we use genome-wide comparisons between C . intestinalis and C . savignyi to identify putative urochordate cis-regulatory sequences . Ciona conserved non-coding elements ( ciCNEs ) are associated with largely the same key regulatory genes as vertebrate CNEs . Furthermore , some of the tested ciCNEs are able to activate reporter gene expression in both zebrafish and Ciona embryos , in a pattern that at least partially overlaps that of the gene they associate with , despite the absence of sequence identity . We also show that the ability of a ciCNE to up-regulate gene expression in vertebrate embryos can in some cases be localised to short sub-sequences , suggesting that functional cross-talk may be defined by small regions of ancestral regulatory logic , although functional sub-sequences may also be dispersed across the whole element . We conclude that the structure and organisation of cis-regulatory modules is very different between vertebrates and urochordates , reflecting their separate evolutionary histories . However , functional cross-talk still exists because the same repertoire of transcription factors has likely guided their parallel evolution , exploiting similar sets of binding sites but in different combinations .
Gene regulation is facilitated by the binding of transcription factors to specific sites in genomic DNA . Consequently , accurate control of gene expression in any cell is largely influenced by two variables; the presence of the transcription factor proteins themselves and accessibility to regulatory sites . During animal development , a highly complex and dynamic set of regulatory interactions must be precisely articulated in order to accurately direct the patterning of the embryo . This has resulted in the establishment of stable and robust , scale free gene regulatory networks ( GRNs ) [1] , with high information content encoded into cis-regulatory modules ( CRMs ) , where cohorts of transcription factors bind combinatorially to define a regulatory state [2] , [3] . As a result of this , the largest and most highly conserved cis-regulatory sequences identified in vertebrate genomes are associated with transcription factor genes that regulate development [4] , [5] , reflecting both the complexity and precision required to co-ordinate common patterning mechanisms during embryogenesis . Furthermore , the vast majority of these conserved non-coding elements ( CNEs ) are not conserved at the sequence level in invertebrate genomes , where parallel sets of cis-regulatory sequences have evolved [6] , [7] . Interestingly , a tiny handful of vertebrate CNEs do share some sequence similarity with amphioxus elements [8] , a more distant [cephalo]chordate relative , and even with elements in protostomes [9] . Recently , a number of shorter regions of sequence homology ( av . 45 bp at 55% identity ) have been identified between Ciona and vertebrates , although they are not generally associated with orthologous genes in the two lineages , and a majority are transcribed [10] . Nevertheless , urochordates must exploit genomic sequence , in the form of CRMs , to orchestrate their own development , deploying a similar repertoire of genes to vertebrates and other animal lineages . Indeed , patterning of the early vertebrate embryo and Ciona larva bear a strong resemblance to each other , suggesting that the many aspects of urochordate development are very similar to that of vertebrates [11] , even if the rate at which their genome sequence has evolved is relatively rapid compared with amphioxus [12] . Two important questions therefore are how , and when , did complex CRMs for embryonic patterning become established in the chordate lineage . Are similarities in urochrodate and vertebrate patterning orchestrated by long established CRMs pre-dating the divergence of the chordate lineages , or have entirely different genomic sequences been recruited and deployed as CRMs in urochordates and vertebrates ? In order to address these questions we have identified a large set of urochordate ( Ciona ) specific CNEs ( ciCNEs ) through comparison of the highly diverged C . intestinalis and C . savignyi genomes , and compare them with vertebrate CNEs . The evolutionary distance between the two Ciona genomes is considered to be greater than the distance between human and chick , providing a very low background of unconstrained conservation [12] . Support for this comes from a genomewide study of vertebrate and ciona species which showed that Ciona species evolve about 50% faster than vertebrates [13] , with a genomewide average amino acid distance between intestinalis and savignyi of 0 . 3349 ( compared with values of 0 . 3258 and 0 . 3735 for human∶chick and human∶frog respectively ) . Many of the ciCNEs are associated with developmental regulator genes; in some cases the same genes that harbour CNEs in vertebrates , despite an absence of identifiable sequence similarity between the CNEs themselves . We test a number of these ciCNEs using two independent transgenic reporter assays in zebrafish embryos , and find that a small number drive highly specific and reproducible patterns of reporter expression . We then examine the relationship between enhancer sequence and function by further dissecting these sequences . We also assay a number of ciCNEs in C . intestinalis embryos . Our findings suggest that despite a degree of regulatory cross-talk , there is little evidence to suggest that the majority of CNEs in urochordates and vertebrates share sequence ancestry . Although it remains possible that binding site reorganization and sequence drift have resulted in very diverged homologous vertebrate and urochordate sets of CNEs , an alternative simple explanation for our findings is that the two sets of CNEs have been recruited and hardwired into the genome independently , after their divergence from a common chordate ancestor , albeit shaped by a similar repertoire of transcription factors . Functional characterization of a larger set of chordate and vertebrate CNEs would likely prove useful in distinguishing between these two scenarios .
We compared the assembled genomes of C . intestinalis and C . savignyi to identify conserved non-coding DNA sequences ( Methods ) . Our analysis is quite different from a previous whole genome comparative analysis performed on these two genomes to identify highly conserved non-coding sequences [14] in that we removed any sequences that overlapped with known transcripts or non-coding RNAs . Consequently our dataset of 2 , 336 sequences ( Dataset S1 ) represents predominantly Ciona conserved non-coding elements ( ciCNEs ) . The length distributions of both C . intestinalis and human CNEs are skewed to the right , with a few very long CNEs in both sets ( Figure S1 ) . ciCNEs are on average 181 . 6 bp long , ranging from 94 bp to 1 , 883 bp , with median 156 bp . For comparison , the lengths of the 1 , 373 human CNEs defined by alignment of the human and Fugu genomes [5] range from 93 bp to 737 bp , with a median of 177 bp . The distribution of ciCNEs is slightly more skewed than the vertebrate CNEs , reflecting a large set of short CNEs together with some extreme cases of very long CNEs . A majority of the extremely long ciCNEs overlap predicted exons ( data not shown ) . Therefore , we expect that the extremely long ciCNEs are most likely to be at least partly un-annotated coding sequences . By comparing the sequence conservation between the two sets of CNEs , we find that ciCNEs are also less conserved than vertebrate CNEs . ciCNEs range from 71 . 0% to 96 . 8% sequence identity , with a median of 81 . 7% , while vertebrate CNEs range from 67 . 8% to 97 . 9% , with a median of 84 . 6% ( based on human-Fugu pairwise comparisons ) . Therefore , ciCNEs are both shorter and less conserved than vertebrate CNEs , possibly reflecting a lower sequence constraint , or a simpler regulatory module structure in urochordates than in vertebrates . We then tested whether CNEs cluster near developmental regulatory genes in the C . intestinalis genome as they do in vertebrate and nematode genomes . By assigning 2 , 146 ciCNEs to their closest protein coding genes ( 190 ciCNEs are on unplaced contigs containing no genes ) , we identified 1 , 289 ciCNE-associated genes ( on average 1 . 7 ciCNEs per gene ) . Using the same approach , 1 , 373 human CNEs [5] are assigned to 397 CNE genes ( on average 3 . 5 CNEs per gene ) . For this genome-wide comparison of human and Ciona CNEs we used proximity to assign genes to CNEs , however we expect that the numbers of CNE-associated genes are over-estimated as it is known that enhancers ( and CNEs ) can lie far from their targets . The number of CNE-associated genes in Ciona is likely to be exacerbated by the fact that the Ciona genome is highly fragmented . Nevertheless , in common with vertebrate CNEs , we found that ciCNE-genes are enriched for homeodomain-like ( log-odds ratio = 2 . 03 , p-value<2 . 2e-16 ) , winged helix repressor ( log-odds ratio = 1 . 71 , p-value = 6 . 72e-11 ) , HMG1/2 ( log-odds ratio = 1 . 49 , p-value = 1 . 43e-3 ) and zing finger C2H2 ( log-odds ratio = 0 . 71 , p-value = 1 . 52e-3 ) domains . In addition , we also found enrichment for several signalling domains that we previously saw overrepresented among nematode but not vertebrate CNE-associated genes . These domains include EGF-like ( log-odds ratio = 0 . 68 , p-value = 2 . 46e-3 ) , laminin G ( log-odds ratio = 1 . 89 , p-value = 1 . 70e-4 ) , cadherin ( log-odds ratio = 1 . 71 , p-value = 4 . 35e-4 ) and pleckstrin-like ( log-odds ratio = 0 . 95 , p-value = 7 . 92e-4 ) . Using a compiled set of transcription factors and signalling genes in the C . intestinalis genome [15] , we found that both types of genes are highly enriched in the ciCNE-associated gene set ( log-odds ratio = 1 . 78 , p-value<2 . 2e-16 and log-odds ratio = 1 . 43 , p-value = 1 . 60e-10 , respectively ) . Therefore , in terms of the protein domains they encode , the types of genes associated with CNEs in the C . intestinalis genome are consistent with the genes associated with CNEs in both non-chordate invertebrates [7] and vertebrates [5] , perhaps reflecting the evolutionary position of C . intestinalis as an invertebrate chordate . We then looked to see if the same genes are associated with CNEs in both urochordates and vertebrates . Among the ciCNEs-associated genes there are 32 Ciona genes orthologous to 38 human genes also associated with CNEs ( orthology was determined using EnsemblCompara [16] ) ( Table S1 ) . Interestingly , several of the C . intestinalis genes associated with multiple CNEs are orthologous to human genes also associated with multiple CNEs . For example , human PTCH1 is associated with 3 CNEs and its C . intestinalis orthologue is associated with 4 ciCNEs . We note that most ( 21/32 ) ciona genes have multiple orthologues in human . So for example , two paralogous human genes , MEIS1 and MEIS2 , are associated with 10 and 42 CNEs respectively whilst their C . intestinalis orthologue is associated with 10 ciCNEs . The fact that orthologous genes in human and Ciona are associated with multiple CNEs further suggests that CNEs are associated with specific regulatory genes . Finally , we identified at least 45 ciCNEs that overlap with a limited number of functionally annotated cis-regulatory regions in the ANISEED database [17] . ANISEED is a database of genomic and functional information , such as gene expression patterns of genes , for ascidian genomes including those of Ciona intestinalis and Ciona savignyi . It is intriguing that in many cases , CNEs are found next to the same gene in vertebrates and Ciona and yet they bear no observable sequence similarity to each other , despite being highly conserved within their respective lineages . Furthermore , their spatial organisation is very different . The Ciona Meis gene has 10 proximal CNEs of which 4 are upstream and the remaining 6 are dispersed across the first seven introns of the gene ( Figure 1 ) . This is in contrast with the distribution of CNEs around vertebrate MEIS1 and MEIS2 , where a majority of CNEs in each case are positioned in introns towards the end of the gene or downstream of the coding sequence . In the case of the human genes , the CNEs are often hundreds of kilobases from the coding sequence . This suggests that CNEs might have evolved independently in the two lineages but have then become fixed relatively early in their history , particularly in vertebrates . Nevertheless , the genes they co-associate with play very similar roles in each lineage and so we were interested to see if ciCNEs could function as spatio-temporally specific enhancers in zebrafish embryos , a model vertebrate for this type of study . To select a subset of ciCNEs for experimental testing of enhancer activity , we first identified all ciCNEs that are associated with genes where the orthologous human gene is also associated with CNEs . We then narrowed down the list of candidate ciCNEs by considering only those associated with genes that have known and specific expression profiles during development according to the ANISEED database [17] . We also avoided the cases where the human CNE cluster is close to multiple candidate target genes and the cases where the predicted target gene ( from Woolfe et al , [5] ) is not the nearest gene to the CNE . From the remaining , we selected a subset of 22 candidate ciCNEs associated with nine different Ciona genes for experiments ( Table 1; Text S1 ) . We independently tested 21 out of the 22 Ciona CNEs ( one CNE failed to amplify during PCR ) , firstly exploiting a co-injection strategy using a minimal beta-globin promoter [5] , and secondly through direct cloning into a Tol2 vector with a c-fos promoter [18] . Whilst levels of GFP reporter expression were generally stronger using the Tol2 vector , presumably due to more efficient integration and therefore reduced mosaicism , the results were highly reproducible between the two approaches . Four out of the 21 CNEs give robust and reproducible patterns of restricted GFP expression at either 24 or 48 hours post fertilisation ( hpf ) using both methods ( Table 1 ) . Two of these CNEs were from the Meis gene locus , one was from the Pax6 region and the other resides within the only intron of the Hhex gene in both Ciona and vertebrates . A further four elements were able , in around 5% of embryos , to drive reporter expression in Tol2 constructs only , but these were considered too weak to merit further analysis . We looked for consistent and reproducible patterns of GFP reporter expression in cell types other than muscle ( we routinely see muscle expression in transient analyses with Tol2 ) in at least 10% of embryos screened for any particular ciCNE ( Table 1 ) . At 48 hpf , Pax6_ciCNE2 drives GFP expression in cranial ganglia and sensory neurons ( Figure 2A , C ) in 12% of screened embryos . More specifically , GFP is detectable in the sensory neurons innervating the tail fin ( Figure 2B ) and along the spinal cord ( Figure 2D ) . The two positive ciCNEs associated with the Meis gene drive very different patterns of GFP expression . Meis_ciCNE10 drives GFP expression in neuronal cells ( Figure 2E ) in 20% of embryos screened . At 48 hpf , GFP is readily detected in Rohon-Beard neurons ( Figure 2F ) , including those innervating the tail fin ( Figure 2G ) as well as in trigeminal ganglion neurons ( Figure 2H ) . GFP is observed in both cell bodies and axonal projections . A more detailed confocal analysis shows strong GFP fluorescence into the projections of the Rohon-Beard neurons and trigeminal ganglion , extending to the hindbrain ( Figures 2I , J ) . Injection of Meis_ciCNE1 drives a very robust pattern of GFP expression ( Figure 3A–I ) . Remarkably , in over 50% of embryos screened , GFP expression is detected in motor neurons ( Figure 3A , C ) and interneurons ( Figure 3B , D ) . Confocal microscopy allowed us to identify morphological subtypes of interneurons and motor neurons . Two classes of descending interneurons ( Figure 3E ) , one class of ascending interneurons ( Figure 3F ) and one class of bifurcating interneurons ( Figure 3G ) were GFP positive , as were as at least two subtypes of primary motor neurons ( Figure 3H , I ) . It should be noted that meis1 has been identified as a gene potentially involved in interneuron migration [19] . Finally , in embryos injected with the Hhex_ciCNE1 GFP expression was detected in cells of the hematopoietic lineage ( Figure 3J ) The size and morphology of the cells resemble macrophages ( Figure 3K , L ) . In zebrafish , a specific lineage of early macrophages differentiate in the yolk sac before the onset of blood circulation [20] . Previously we have shown that evolutionarily conserved aspects of enhancer function often reside in core regions of a CNE sequence [21] . In order to examine whether sub-regions of ciCNEs are sufficient to drive GFP expression in zebrafish , we carried out an extensive functional analysis of sub-sequences of the pax6 ciCNE and the two meis ciCNEs . Pax6_ciCNE2 is 413 nucleotides ( nt ) in length and was initially divided into three non-overlapping segments ( Figure 4A ) and the relative activity of each sub-region compared with the whole ciCNE ( Figure 4B ) . Only the first two regions are able to activate GFP expression ( Figure 4C , D ) , with the first 171 nt being most active . A further delineated region spanning nt 88–244 is able to drive the same patterns of GFP reporter expression as the entire ciCNE , but in a little under half the number of embryos ( Figure 4E ) . Meis_ciCNE1 , a particularly strong enhancer in zebrafish , is 457 nt in length and was similarly initially divided into three non-overlapping segments ( Figure 5A ) . Only the most 3′ region shows any activity ( Figure 5C ) and this is both anteriorly restricted and observed in ten times fewer embryos than the full length element ( Figure 5B ) . Whilst fusing the middle and 3′ regions together gave a small increase in the number of embryos driving GFP ( Figure 5D ) , a larger central core region , encompassing nt 97–384 , is able to drive more robust and comprehensive expression , re-capitulating the pattern driven by the full-length ciCNE ( Figure 5E ) . Meis_ciCNE10 is a relatively short element , just 108 nt in length . Initially , this element was divided into two overlapping sub-regions ΔCNE10-1 and ΔCNE10-2 ( Figure 6A ) , where all the detectable enhancer activity was confined to the second segment ( Figure 6B , C ) . Further definition of the ciCNE resulted in a 3′ fragment encompassing nt 71–108 ( ΔCNE10-2-2 ) which retains the same enhancer potential as the full element ( Figure 6D , E ) . Deletion of a putative Pbx-Hox site at nt 71–79 from ΔCNE10-2 ( ΔCNE10-2-1 ) or from the full length ciCNE ( ΔCNE10-3 ) results in loss of enhancer potential . However , enhancer activity is also lost on deletion of nt 83–92 from the full length ciCNE ( ΔCNE10-4 ) . Further synthetic constructs were then made by annealing complementary oligonucleotides representing nt 71–108 ( ΔCNE10-2 oligo1 ( Figure 6F ) ) , nt 71–94 ( ΔCNE10-2 oligo2 ( Figure 6G , H ) ) and nt 82–108 ( ΔCNE10-2 oligo3 ) resulting in the delineation of a minimal sequence of just 24 nucleotides ( nt 71–94 , 5′ tgattaatatttcataatgcacta 3′ ) that is sufficient to re-capitulate both the strength and varied pattern of GFP expression of the full length element . Trinucleotide site-directed mutagenesis across this region ( Figure 7 ) identifies a critical 12 nucleotide motif , ( 5′ ttaatatttcat 3′ ) rich in A+T , and containing strong binding sites for helix-turn-helix homeodomain transcription factors , a diverse group of proteins that play important roles in developmental patterning . However , expression is considerably weaker at all mutated positions across the 24mer , suggesting that , as is generally the case , any homeodomain binding protein might be binding co-operatively alongside other factors across this site . Of particular note is the fact that the first 8 nucleotides of the 24mer sequence represent a perfect canonical Pbx/Hox site ( tgatnnat ) , a bipartite site that itself forms a close relationship with meis proteins , and a motif that is strongly enriched in vertebrate CNEs [22] . We searched for sequence similarity to the 24 nt sequence ( 5′ tgattaatatttcataatgcacta 3′ ) that drives highly specific neuronal expression in zebrafish embryos and found no identical sequences in any of the organisms in Ensembl [23] except for the known match close to the Meis gene in C . intestinalis . We also profiled the 24mer for transcription factor binding sites in JASPAR [24] and TRANSFAC [25] , predicting a large number of possible sites , including a 13 nt match to the binding site of the Oct domain binding transcription factor POU3F2 , a protein known to be involved in neurogenesis in the central nervous system ( CNS ) [26] . The above experiments demonstrate that despite the absence of sequence conservation between ciona and vertebrate CNEs , 4 out of 21 ciona elements can act as enhancers in zebrafish . Extensive analysis of subsequences of these elements shows that in all cases the minimal functional CNE is at least 12 nt long . This suggests that these ciona elements are recognized and co-ordinately bound by more than one transcription factor in order for them to act as robust developmental enhancers in zebrafish . We next assessed the activity of selected ciCNEs in Ciona embryos . We focused on well-annotated genes , particularly those with known expression patterns at the tailbud stage of development when major tissue types have been established and transgene assays are viable . Seventeen ciCNEs were assessed , three that had shown activity in zebrafish assays ( Pax6_ciCNE2; Meis_ciCNE1; Meis_ciCNE10 ) and fourteen others ( only Pax6_ciCNE1 , Meis_ciCNE7 , Zfhx_ciCNE1 and Hhex_ciCNE1 were not tested ) . These were cloned into the reporter vector pCES and electroporated into Ciona zygotes . At the tailbud stage Ciona Pax6 is expressed in the central nervous system , including both the brain and spinal cord [27] . Pax6_ciCNE2 drove reporter expression into a subset of this domain in the ventral sensory vesicle , a part of the brain ( Figure 8A ) . Both Meis_ciCNE1 and Meis_ciCNE10 drove expression into the ventral sensory vesicle ( Figure 8B , C ) and anterior tail epidermis ( Figure 8D ) , in a pattern similar to the endogenous expression pattern of the Ciona Meis gene [15] , [17] . All three transgenes also drove expression into the endoderm located to the posterior ventral part of the trunk . This is a common ectopic site of expression observed with the pCES vector , reflecting the expression of the gene from which the minimal promoter is derived . The remaining ciCNEs had little or no activity in tail bud stage Ciona embryos: only Nkx2 . 2/2 . 4_ciCNE2 showed reproducible expression , with this in the posterior ventral trunk endoderm as described above ( data not shown ) . These cells are distinct from the cells to which the mRNA for this gene localises [15] . These results show that the Pax6 and Meis elements , which drive transgene expression in zebrafish , are capable of driving reporter expression in Ciona in a pattern reflecting the endogenous mRNA domain . Although Nkx2 . 2/2 . 4_ciCNE2 was able to increase the residual activity of the basal promoter , it did not drive expression in a pattern related to the expression of Ciona Nkx2 . 2/2 . 4 . Other ciCNEs failed to activate expression . These may be CNEs associated with gene expression at other points in the life cycle , and hence not active in tailbud stage embryos .
We have identified a genome-wide set of non-coding elements that are conserved between two representatives of the urochordates , C . intestinalis and C . savignyi . Due to the rapid rate of neutral evolution of their genomes , these two species are ideal candidates for the identification of functionally constrained sequences , and have enabled the generation of a valuable data set for comparison with vertebrate CNEs . ciCNEs are slightly shorter and less well conserved on average than vertebrate CNEs , despite the divergence between the Ciona genomes being somewhat less than that between fish and mammals [12] . This suggests , given that these regions are in general candidate cis-regulatory elements , that the complexity of cis-regulation ( as measured by the numbers of transcription factors that bind combinatorially to an element at any one time ) might be greater in vertebrates than urochordates . This in turn may reflect the increased number of paralogous transcription factors in vertebrate genomes generated through gene/genome duplications and a greater number of tissue types . A further indication of increased vertebrate complexity , at least associated with developmental regulation , is the larger numbers of CNEs that cluster around individual gene loci; for example there are 10 ciCNEs , compared with a total of 52 vertebrate CNEs , associated with the Meis genes ( Figure 1 ) . To try to further understand the relationship between ciCNEs and vertebrate CNEs from a functional perspective , we assayed a set of C . intestinalis CNEs located adjacent to genes that have orthologues in vertebrates that also harbour CNEs . We first adopted a co-injection strategy that has been used previously to characterise vertebrate CNEs in zebrafish embryos , using a minimal beta globin promoter fused to the GFP gene . We then re-assayed all 21 ciCNEs using the well-established Tol2 transgenesis approach , using a vector containing a cfos promoter , again fused to GFP . Although the co-injection strategy resulted in highly mosaic and consequently rather weak GFP expression , we found the same four elements to be active using the Tol2 approach plus another four ciCNEs that drive weaker expression in a small number of embryos . Thus , we believe the results obtained , at least for the four ciCNEs positive in both assays , are robust and reliable and independent of promoter used . Notably we routinely obtained some non-specific ‘ectopic’ muscle expression using the Tol2 vector , but this has been previously documented [28] . The positive ciCNE from the pax6 locus ( Pax6_ciCNE2 ) drives expression in sensory neurons in the spinal cord and cranial ganglia in zebrafish embryos at 48 hpf . GFP expression extends caudally as far as sensory neurons innervating the tail . In zebrafish , pax6 ( represented by two genes , pax6a and pax6b ) is expressed in sensory placodes , the eye and throughout the CNS during neurogenesis although not specifically in cranial ganglia [29] . Furthermore , whilst pax6 expression is strongest in the ventral spinal cord , sensory neurons tend to originate more dorsally [30] . Similarly , in Ciona , Pax6 is expressed throughout the CNS at early tailbud stage [27] , [31] . When the Pax6_ciCNE2 was electroporated in Ciona embryos , expression was observed in the ventral sensory vesicle , the most anterior portion of the Ciona CNS , and related to the vertebrate forebrain . Thus Pax6_ciCNE2 drives reporter expression consistent with the endogenous pattern of expression of the Ciona Pax6 gene , and in a pattern that partially overlaps pax6a expression in zebrafish embryos . However , the same element drives somewhat different patterns of reporter gene expression in the two different organisms . Pax6_ciCNE2 is a relatively large sequence ( 413 nt ) and efforts to dissect it were largely unproductive , although a core region encompassing nt 88–244 is able to drive the same pattern of expression as the whole element but in a smaller proportion of injected embryos , suggesting that this core region is either less stable or a weaker enhancer . Interestingly , Pax6_ciCNE2 has been identified in Ciona previously but was only tested as part of a larger fragment that encompasses the entire intron 4 region in C . intestinalis and as such it does not possess enhancer activity [31] . The Ciona Meis gene has 10 CNEs , and two of these exhibit strong and specific enhancer activity in zebrafish embryos . Although the expression patterns driven by Meis_ciCNE1 and Meis_ciCNE10 in zebrafish embryos are very different , both sequences activate expression in neuronal cells . Meis_ciCNE1 in particular activates reporter gene expression in at least four different classes of interneurons and two classes of motor neurons throughout the CNS and is by some margin the strongest enhancer in either assay . Meis genes act as Hox/Pbx co-factors [reviewed in 32] and whilst particularly associated with hindbrain development in vertebrates [33] , zebrafish meis genes are expressed throughout the brain and spinal cord as well as in the developing eye [34] . Ciona Meis is expressed in the ventral sensory vesicle and the anterior epidermis of the tail and posterior trunk at the tailbud stage [15] , [35] and the two Meis ciCNEs direct patterns of reporter gene expression consistent with this pattern . Dissection of Meis_ciCNE1 resulted in the identification of a large core region ( nt 97–384 out of 457 ) of 288 nt that is sufficient to activate the same pattern of reporter gene expression as the whole element , despite a smaller core region comprising nt 156–310 having no enhancer activity . Similar to Pax6_ciCNE2 , the larger core region appears to be a weak enhancer , driving expression in less than half the number of embryos than when the whole element is injected . Both the whole element and core region ( nt 97–384 ) are highly active throughout the spinal cord and hindbrain , consistent with a prominent role for Meis genes in hindbrain development , although the core region activates a smaller percentage of injected embryos . Note that there is very limited reporter expression more rostrally in the mid- or forebrain . Contrastingly , the 3′ region of Meis_ciCNE1 ( nt 311–457 ) is able to activate reporter expression more rostrally in the mid-to-forebrain region of the embryo yet not in the hindbrain or spinal cord . A construct combining the middle and 3′ regions of the ciCNE ( nt 156–457 ) however , results in loss of the rostral expression and restoration of primarily the hindbrain , but also the spinal cord expression patterns . Thus it would appear that this ciCNE has the potential to drive expression in the fore- and midbrain encoded in the 3′ region but that this is repressed by upstream sequences in the ciCNE . Meis_ciCNE10 is already a comparatively small element at just 108 nt in length and as a consequence was initially dissected into two overlapping regions of approximately 70 nt . Firstly , it was apparent that a majority of the activation potential of this ciCNE was located in the 3′ region . Attention focused on a small core region where loss of different motifs resulted in loss of enhancer activity . Strikingly , a minimal region of just 24 nucleotides ( nt 71–94 ) is able to drive reporter expression in the same pattern as the full length element . However this minimal region was no longer able to activate expression in Ciona tailbud embryos . This suggests that mechanisms of activation are subtly different between Ciona and zebrafish . Hhex_ciCNE1 is located in the single intron of the Ciona Hex gene . This ciCNE drives reporter expression very specifically ( in both assays ) in a small population of cells located either in the yolk sac or in the circulatory system , with a morphology reminiscent of monocytes or macrophages . This would reflect a role for Hhex_ciCNE1 consistent with that of zebrafish hhex in early haematopoeisis [20] , [36] . We also note that the three ciCNEs that tested positive in Ciona tailbud embryos also showed the strongest phenotypes in zebrafish embryos , while the ciCNEs that were negative in Ciona tailbud embryos had limited or no impact in zebrafish . Whether this apparent association is meaningful is difficult to determine , as Ciona transgenesis only assesses construct activity up to a specific point in the life cycle , the tailbud stage . However this stage does present the canonical chordate bodyplan and active neuronal differentiation . One possibility is that Ciona enhancers active at this time point are more likely to also be active cross-species , for example reflecting constraint on underlying regulatory circuitry imposed by the use of similar suites of transcription factors to establish the conserved chordate body plan in the two lineages . In this respect we note that one of the few previous studies to demonstrate cross-species enhancer activity between Ciona and vertebrates also found tailbud stage enhancers to be active in vertebrates , in this case for two enhancers associated with the Ciona Hox1 gene [37] . However , we cannot unequivocally conclude this without exhaustive testing for activity amongst the other ciCNEs at other life cycle stages in both Ciona and zebrafish , and as such it must remain speculative . In summary , these results demonstrate that at least some of the regulatory logic encoded in ciCNEs can be recognised and deciphered in a vertebrate embryo , directing specific and reproducible patterns of expression in distinct populations of cells during early development . This is in agreement with other studies that have shown that developmental enhancers can function in heterologous contexts in different species ( e . g . [38] ) . However , as we would predict if there has been extensive CRM remodeling , the patterns of expression activated by ciCNEs in zebrafish embryos are not wholly consistent with the endogenous expression of their associated gene , and can in at least one case be driven by a very small sub-region within a ciCNE . Furthermore , it has been established that trans-regulatory changes ( i . e . the ability of one species to interpret the cis-regulatory code from another species ) also play a role in the reproducibility of enhancer activity [39] . Our data are supported by another recent study that assayed three putative Ciona regulatory elements in zebrafish embryos [10] , and suggests that the CRM architecture of vertebrate and urochordate CNEs is very different . We speculate that control is mediated by regulatory cross-talk via a limited number of transcription factors , rather than accurate deciphering of whole ciCNEs as CRMs . In the vertebrate lineage it is now well established that the most highly conserved regulatory elements are associated with developmental transcription factors , remaining largely unchanged at least since the divergence of cartilaginous fish around 500 million years ago ( MYA ) . However , with just a few exceptions [8] , [9] , vertebrate CNEs do not show strong sequence similarity to non-vertebrate sequences . In this paper we have tried to examine the reasons for this paradox . Recently , a comparison of vertebrate and Ciona conserved non-coding sequences identified between 150 and 200 short stretches of conservation , termed oCNEs ( av . 45 bp at 55% identity ) [10] . Surprisingly , oCNEs are not found in syntenic locations in vertebrates and urochordates , but are located close to different developmental regulator genes , suggesting they have been co-opted into novel CRMs and regulatory networks , possibly as a result of major re-arrangement events . 65 oCNEs are embedded in our ciCNE set ( we would expect no overlap by chance ) , in agreement with our hypothesis that CRMs have been extensively re-modeled , and that even the small minority of shared sequence ancestry has been re-deployed into new regulatory elements and networks . Indeed , these two complementary datasets hint at the extent of re-structuring of gene regulatory networks early in chordate history , and contribute to our understanding of the processes of evolution within gene regulatory networks in different lineages . A second important contributing factor to CRM re-modeling might be the result of the continued and rapid evolution of ancestral chordate CNE sequences in the urochordate lineage but many more urochordate genome sequences are necessary to measure this . Finally , the location and spatial organisation of multiple CNEs around genes , such as at the meis loci , also show no obvious relationship between lineages . A majority of CNEs are downstream of vertebrate meis1 and meis2 genes or in 3′ introns , whereas the Ciona meis gene has no downstream CNEs and all are either upstream or in 5′ introns . If vertebrate and urochordate CNEs have evolved from the same ancestral sequences then there must have been a great deal of local rearrangement of these sequences in early urochordate evolution ( vertebrate CNEs remain co-linear across all species and the ciCNEs are co-linear between C . intestinalis and C . savignyi ) . Given these observations , we conclude that urochordate and vertebrate CNEs emerged and evolved largely independently . Conservation of CRM function in the absence of sequence conservation or ancestry is not surprising . There are many well-documented examples of regulatory conservation with low or no sequence conservation [40] , [41] . Because transcription factor binding sites are small and degenerate , they can easily arise by chance within a short stretch of genomic sequence thereby making existing binding sites redundant [42] , [43] . In this way , previously established regulatory regions can become highly divergent , or new sequence regions may be recruited as regulatory sites , without an overall change in function . Alternatively , extensive re-wiring of the regulatory code can create a new set of CRMs that still co-operate within the GRN to achieve the same output . This is supported by the fact that divergent expression profiles of orthologous sets of zebrafish and Ciona genes can still result in similar body plans [44] . Despite an apparent lack of direct sequence ancestry , CNEs from vertebrate and urochordate genomes will not have evolved completely independently . They are associated with the same genes and regulatory networks . Consequently , as we demonstrate here , a number of ciCNEs tested are recognised , at least in part , by specific developmental regulatory states ( i . e . a set of transcription factors ) when injected into the genome of a species that has been evolving independently for over 500 million years . In essence , this reveals that in several cases vertebrate and urochordate CNEs represent different solutions to the same problem , ensuring that similar cohorts of transcription factors active in a particular cell type switch on the same target gene .
The C . intestinalis repeat-masked genome ( version v2 . 0 ) was retrieved from the Joint Genome Institute website ( http://genome . jgi-psf . org/Cioin2/Cioin2 . info . html ) . The C . savignyi repeat-masked genome ( version v2 . 1 ) was retrieved from the Sidow lab website ( http://mendel . stanford . edu/sidowlab/Ciona . html ) at Stanford University Medical Centre [45] . For the BLAST similarity search , the C . savignyi scaffolds were split into 500 kb fragments overlapping by 200 bp using the EMBOSS [46] program splitter . The C . savignyi fragments were then searched for similarity against the sequence of the C . intestinalis genome using MegaBLAST [47] . MegaBLAST was run with word seed length 20 bp , mismatch penalty -2 and e-value threshold 0 . 001 , as described previously for the Fugu-human whole genome comparison [5] . This search returned 177 , 708 matches between the C . savignyi sequence fragments and the C . intestinalis genome . In line with Fugu∶human comparisons [5] , only alignments at least 100 bp long were retained , thus reducing the set to 73 , 728 sequence hits . The C . intestinalis conserved sequence elements were first screened for evidence of transcription according to Ensembl C . intestinalis annotation ( release v40 ) using Ensembl Perl API [23] . Elements overlapping exons or containing in total more than 10 bp of repeats were removed . Conserved elements were further filtered by searching for similarity against the EMBL EST , Rfam and the microRNA registry using MegaBLAST ( e-value cut-off 0 . 001 ) . All elements with matches to the non-coding RNA databases were removed and elements with more than three matches to expressed transcripts from EMBL were also removed . In addition , because analysis of duplicated elements was beyond the scope of this manuscript , C . intestinalis elements matching multiple locations in C . savignyi were removed , too . The final set consists of 2 , 336 C . intestinalis CNEs ( ciCNEs ) , where for 1 , 817 ciCNEs there is no evidence of transcription and for 519 there is little evidence of transcription ( up to 3 matches to expressed transcripts from EMBL ) The nearest protein-coding genes ( i . e . genes with the nearest TSS ) to ciCNEs were retrieved using Ensembl Perl API . 190 of the 2 , 336 cCNEs were in sequence fragments that did not contain any genes . The remaining 2 , 146 ciCNEs were assigned to 1 , 289 protein-coding genes . The human orthologs of the ciCNE-associated genes were retrieved using Ensembl Perl API accessing the Ensembl Compara database , C . intestinalis Ensembl Core and H . sapiens Ensembl core database ( Ensembl release v43 ) . This was performed as previously described for nematode CNEs [7] . In brief , we downloaded InterPro domains of all human and ciona genes from Ensembl [22] . Using a custom Perl script we converted all domains to their top-level parent domain based on InterPro annotation hierarchy [48] . We removed domains present in fewer than 10 genes . We calculated the enrichment of each domain in CNE-associated genes versus the rest using the log-odds ratio test in R and accounted for multiple testing using the Benjamini and Hochberg method [49] . CNEs were amplified from Ciona genomic DNA by PCR and assayed in zebrafish using the Tol2 system [50] . The PCR products were cloned into the pCR8/GW/TOPO vector ( Invitrogen ) and then into a Tol2GFP construct [51] , using the Gateway LR Clonase II enzyme ( Invitrogen ) . Transient transgenic zebrafish embryos were screened for GFP expression at 24 hpf and 48 hpf . Mutations in the 24 nt sequence of Meis_ciCNE10 were generated by mutating the wild type sequence already inserted into the tol2 vector using the ‘QuickChange II Site-Directed Mutagenesis Kit’ ( Agilent Technologies ) . Putative ciCNE fragments were directionally cloned in 5′ to 3′ orientation into the β-galactosidase based reporter vector pCES , which uses a minimal promoter derived from the C . intestinalis FoxAa gene [52] . Adult C . intestinalis type B were collected from marinas on Hayling Island , South England , and maintained in a re-circulating sea water aquarium at 12°C . Gametes were removed separately by dissection , eggs fertilised in vitro and the chorion removed chemically [53] within 15 mins of fertilisation . Electroporation of fertilised eggs was carried out as described , [54] , with modifications [55] , using 40 µg of construct DNA . Embryos were cultured until the tail bud stage before fixation in 0 . 2% glutaraldehyde for 30 minutes in sea water , two washes in PBS and transfer to staining buffer ( 3 mM K5Fe ( CN ) 6 , 3 mM K3Fe ( CN6 ) , 1 mM MgCl2 ) . They were stained in staining buffer containing 4 mg ml−1 Xgal at 37°C for 12 to 72 hours . All experiments included a negative control ( the pCES vector without an enhancer inserted ) and a positive control ( the Ciona βγ-crystallin enhancer [55] in pCES ) . All negative controls showed no reporter expression , and positive controls showed at least 50% of embryos with palp and/or sensory vesicle expression , reflecting a typical rate of successful electroporation by this method [56] . | Vertebrates share many aspects of early development with our closest chordate ancestors , the tunicates . However , whilst the repertoire of genes that orchestrate development is essentially the same in the two lineages , the genomic code that regulates these genes appears to be very different , even though it is highly conserved within vertebrates themselves . Using comparative genomics , we have identified a parallel developmental code in tunicates and confirmed that this code , despite a lack of sequence conservation , associates with a similar repertoire of genes . However , the organisation of the code spatially is very different in the two lineages , strongly suggesting that most of it arose independently in vertebrates and tunicates , and in most cases lacking any direct sequence ancestry . We have assayed elements of the tunicate code , and found that at least some of them can regulate gene expression in zebrafish embryos . Our results suggest that regulatory code has arisen independently in different animal lineages but possesses some common functionality because its evolution has been driven by a similar cohort of developmental transcription factors . Our work helps illuminate how complex , stable gene regulatory networks evolve and become fixed within lineages . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2013 | Parallel Evolution of Chordate Cis-Regulatory Code for Development |
Harlequin Ichthyosis ( HI ) is a severe and often lethal hyperkeratotic skin disease caused by mutations in the ABCA12 transport protein . In keratinocytes , ABCA12 is thought to regulate the transfer of lipids into small intracellular trafficking vesicles known as lamellar bodies . However , the nature and scope of this regulation remains unclear . As part of an original recessive mouse ENU mutagenesis screen , we have identified and characterised an animal model of HI and showed that it displays many of the hallmarks of the disease including hyperkeratosis , loss of barrier function , and defects in lipid homeostasis . We have used this model to follow disease progression in utero and present evidence that loss of Abca12 function leads to premature differentiation of basal keratinocytes . A comprehensive analysis of lipid levels in mutant epidermis demonstrated profound defects in lipid homeostasis , illustrating for the first time the extent to which Abca12 plays a pivotal role in maintaining lipid balance in the skin . To further investigate the scope of Abca12's activity , we have utilised cells from the mutant mouse to ascribe direct transport functions to the protein and , in doing so , we demonstrate activities independent of its role in lamellar body function . These cells have severely impaired lipid efflux leading to intracellular accumulation of neutral lipids . Furthermore , we identify Abca12 as a mediator of Abca1-regulated cellular cholesterol efflux , a finding that may have significant implications for other diseases of lipid metabolism and homeostasis , including atherosclerosis .
Harlequin ichthyosis ( HI , OMIM 242500 ) is a rare and devastating congenital disorder characterised by premature delivery and thick , hyperkeratotic , ‘armour’-like skin plaques . This immobile skin or ‘collodion membrane’ constricts the embryo causing odema , limb contractures and eversion of the eyelids and lips . Despite the provision of neonatal intensive care to ameliorate dehydration and the application of high-dose retinoid therapy [1] , many infants die from respiratory distress , bacterial infections and feeding difficulties [2] . In surviving patients , the skin barrier dysfunction remains , leading to excessive transepidermal water loss , impairment of thermal regulation and an increased risk of cutaneous infection . The gross phenotypic and barrier defects in HI are thought to primarily result from abnormal lipid metabolism in the epidermis . In mammalian skin the outer layer , or stratum corneum , maintains barrier function . Within this layer , corneocytes are embedded in a lamellar intercellular lipid complex of cholesterol , phospholipids and ceramides . Small , specialised vesicular structures known as lamellar bodies ( LBs ) are thought to traffic many of these components to the surface of differentiating keratinocytes [3] . Ceramides contribute to both lamellar extracellular lipids and to a covalently attached lipid layer known as the corneocyte lipid envelope ( CLE ) [4] . They are derived primarily from the conversion of glucosylceramides through the action of β-glucocerebrosidase [5] and to a lesser extend by the conversion of sphingomyelin by sphingomyelinase [6] . Most ceramide processing in the stratum corneum is thought to occur extracellularly after docking of the LBs with the cell surface , however significant levels of glucosylceramides and ceramides are found within the cell and in other layers of the epidermis [7] . Two independent studies have established that mutations in the ATP binding cassette A12 ( ABCA12 ) gene cause HI [8] , [9] . The ABC proteins are thought to act primarily as transporters of molecules across cellular membranes and like other family members ABCA12 encodes a polytopic transmembrane ( TM ) protein comprising at least 12 TM domains and 2 ATP binding cassettes . Mutations in ABCA12 are also associated with a less severe disease known as lamellar ichthyosis-2 ( LI2 , OMIM 601277 ) [10] . Initial studies of these conditions indicate that LI2 is caused by missense , potentially hypomorphic , mutations in or near the first ATP binding domain ( NBD1 ) whereas HI is associated with mutations that either abolish ABCA12 protein production or produce a protein with severely impaired function [8]–[10] . The co-localisation of ABCA12 with LBs [11] , the common malformation of these organelles in HI [12] , the mis-localisation of glucosylceramide in HI keratinocytes and the correction of this abnormality by ABCA12 expression [8] present prima facie evidence that the protein plays an active role in trafficking lipids into LBs . More specifically , the abnormal LBs in HI granular layer keratinocytes and lack of extra-cellular lipid lamellae in patients imply that lipid transport to the intercellular lamella is disrupted . Despite these observations the nature and scope of Abca12's involvement in lipid homeostasis remain unclear . Several of the 48 member ABC protein family are known to play critical roles in controlling lipid levels , primarily by mediating their efflux from the cell . Cholesterol metabolism is perhaps the best studied of these pathways , as defects in clearance of cholesterol from vascular cells constitute a key element in the development of atherosclerosis . ABCA1 , in particular , is considered the primary mediator of cholesterol efflux and mutations in the gene are associated with reduced cholesterol efflux and absent reverse cholesterol transport in both humans ( Tangier disease ) and in animal models [13]–[15] . Genetic studies in the mouse have proven to be a very powerful approach to understanding human diseases that affect embryonic development . We have undertaken a genotype driven ENU screen which identifies pedigrees in which mice die embryonically or neonatally , irrespective of the cause or timing of death , and simultaneously maps the causative mutations within the genome . Using this strategy we have identified a pedigree carrying a mutation in one of the transmembrane domains of Abca12 . Pups homozygous for the mutation die shortly after birth and show hallmarks of HI including hyperkeratosis , abnormal extracellular lipid lamellae and defects in cornified envelope processing . We have used this model to follow disease progression in utero and we report profound defects in lipid homeostasis demonstrating the extent to which Abca12 plays a pivotal role in maintaining the skin's lipid balance . Our study identifies Abca12 as a key regulator of lipid transport and homeostasis , and describes specific lipid efflux functions , including that of cholesterol , with broader implications for other lipid-related metabolic disorders .
Mutations that cause recessive lethality in embryos or neonates ( and markers to which they are closely linked ) are homozygous at reduced frequency among adults . This banality formed the basis of a genetic screen to identify genes required for mouse development ( Figure 1A ) . Briefly , 129/Sv male mice were injected with ENU and mated to C57BL/6 females . Their first-generation ( G1 ) male progeny were again crossed to C57BL/6 females , and then backcrossed to one of their second-generation ( G2 ) daughters to yield a third-generation ( G3 ) . For those pedigrees in which 20 or more G3 mice were generated , the sperm of the founding G1 mouse was frozen . Adult G3 mice were genotyped with a panel of simple sequence length polymorphic markers and regions in which no animals showed homozygosity of the 129/SV alleles , despite both parents being heterozygous , were highlighted as being linked to a potential recessive lethal ENU-induced mutation . The presence of recessive lethal mutation was then confirmed by generating and genotyping a second cohort of G3 animals from the frozen sperm of the founding G1 male . In our initial screen , we set up 40 G1 male mice to breed and generated 18 pedigrees that contained more than 20 G3 mice . To prove the principle of the approach , we have proceeded with one pedigree , Embryonic Lethal 12 ( EL12 ) . In this pedigree , we found 129/SV alleles that were absent in all of the adult G3 mice . Notably , among 34 G3 EL12 mice , we observed none that were homozygous for the129/Sv allele of D1Mit156 , even though both parents were heterozygous for the 129/Sv allele of this marker . This was confirmed in a second cohort of 31 G3 mice . Using a total of 463 mice and 13 polymorphic markers , we refined the interval harboring the lethal mutation to 4 . 7 Mb between D1Mit178 and D1Mit482 ( Figure 1B ) . We sequenced the exons and intron/exon boundaries of the 13 genes in the candidate interval and found a single G to A transition of exon 41 of Abca12 ( Figure 1C ) . Abca12 is a member of the ABC transporter family of proteins , and the mutant allele ( Abca12el12 ) results in a point mutation ( G1997D ) in the first helix of the protein's second transmembrane array ( Figure 1D ) which is highly conserved in a diverse range of organisms ( Figure 1E ) . Consistent with the results of the genetic screen , at weaning no Abca12el12/el12 mice were detected from heterozygous crosses however examination of litters at E18 . 5 found normal mendelian ratios of viable but phenotypically abnormal Abca12el12/el12 embryos ( n = 17/57 embryos ) . Abca12el12/el12 pups were occasionally found in the first few hours after birth but were often dead or severely dehydrated and had failed to suckle normally . Recent studies by Yanagi et al . , indicate a role for Abca12 in lung development and defects in this organ may contribute to neonatal death [16] . To follow the development of the phenotype we examined cohorts of embryos from various developmental stages . At E14 . 5 and E15 . 5 homozygous embryos appeared normal; however from E16 . 5 onwards they were characterised by an absence of normal skin folds around the trunk and limbs . As development progressed , Abca12el12/el12 embryos developed a taut , thick epidermis and multiple contractures affecting the limbs ( Figure 2A , 3A ) . Late stage Abca12el12/el12 embryos were also found to be smaller than their wild type or heterozygous littermates ( Figure 2A , 3A ) , a phenotype we assayed in newborn mice ( p = 0 . 0023 , data not shown ) . Skin sections from affected embryos revealed a hyperkeratotic phenotype from E16 , and confirmed the absence of normal folding ( Figure 2B ) . Histologically all epidermal cell layers were apparent in Abca12el12/e1l2 embryos , although the size of the granular layer progressively increased at the expense of the spinous layer ( Figure 2B , 5A ) . By parturition the cornified layers had coalesced into thick sheets of 20–30 enucleate corneocytes . The basal layer in Abca12el12/el12 mice also lost the dense palisaded nuclear organisation apparent in wild type and Abca12el12/+ mice ( Figures 2B , 4A ) . Consistent with the apparently restrictive nature of the cornified layer , the epidermis as a whole was 30% thinner at E17 . 5 and P1 in Abca12el12/el12 animals ( data not shown ) . Despite this constriction , hair follicles formed and differentiated relatively normally ( Figure 2B , 3F ) and complete histological examination of E18 . 5 embryos did not identify overt anomalies in other organs . Adult and embryonic Abca12el12/+ mice had no overt phenotype , no obvious histological abnormalities and were healthy and fertile . As the Abca12el12/el12 mice apparently died from dehydration , we tested skin barrier function which normally initiates in the mouse from E16 , and acquires almost full adult function by E18 . 5 [17] . We measured permeability of E18 . 5 embryos against the dye toluidine blue and found that Abca12el12/el12 embryos had uniform absence of barrier function ( Figure 3A ) . To determine if this defect contributed to the dehydration observed in homozygous animals we harvested the dorsal epidermis from E18 . 5 embryos and measured the ability of the skin to retain water using a trans-epidermal water loss ( TEWL ) assay over a 5 hour time course . A significant difference in TEWL from Abca12el12/el12 embryos was observed as early as 60 minutes ( Figure 3B ) , confirming that mutations in Abca12 in mice also lead to the defects in barrier formation that are observed in HI patients . HI patients develop a thick armour like stratum corneum and a suite of defects in the biochemistry of this layer . To examine the stratum corneum we harvested cornified envelopes ( CE ) from E18 . 5 embryonic skin . In wild type mice , large squames were present in expected numbers whereas Abca12el12/el12 mice were found to have sparse CEs which were both small and unable to structurally withstand the purification procedure ( Figure 3C , D ) . While the levels of filaggrin in the epidermis of E18 . 5 Abca12el12/el12 mice were slightly increased , its processing into a functional 27 kDa monomer was ablated ( Figure 3E ) as has previously been observed in HI patients [18] , indicating that normal LB and Abca12 function is required for this process . The level of other barrier proteins such as loricrin was unaffected ( Figure 3E , data not shown ) . While keratin VI expression in interfollicular keratinocytes has been noted in some studies of HI skin [18] , no aberrant expression of this hyperproliferative marker was detected in Abca12el12/el12 mice ( Figure 3F ) . These observations suggest that the hyperkeratotic phenotype in these animals is not a result of increased cell proliferation in the basal epidermal layer . To confirm these findings we surveyed cell proliferation and apoptosis from E17 . 5 to P1 ( using Ki67 , PCNA , phospho-histone H3 and TUNEL staining ) and found no significant differences ( Figure 3G , data not shown ) . Many defects of barrier function have profound impacts on the epidermis as a whole . We were able to show by histology that alterations in both nuclear organisation and cellular architecture of the basal cell layer characterises Abca12el12/el12 embryos and postpartum epidermis . We investigated expression of markers of basal and differentiating keratinocytes during this period and observed normal levels of Abca12 staining in epidermal cells in the uppermost granular and cornified layers of the epidermis ( Figure 4A ) , in a pattern similar to that observed in developing human skin [19] . Expression of filaggrin , which usually marks the granular layer of the epidermis , was detected in keratinocytes juxtaposed to the basal layer itself and in some cells expressing keratin 14 ( Figure 4B ) . Additionally , we demonstrated a significant increase in basal ( and spinous ) layer keratinocytes dually expressing keratins 10 and 14 ( Figure 4C , D ) . These observations indicate that keratinocytes in affected epidermis undergo premature differentiation , either as a result of defects in the cornifying layer which overlies them or as a consequence of defects in the balance of intracellular lipids in these cells . Thin sections of affected epidermis highlighted the striking hyperkeratosis in Abca12el12/el12 epidermis ( Figure 5A ) . To investigate Abca12 mediated alterations in epidermal lipid composition we stained the epidermis with the lipophilic dye Nile Red . Abca12el12/el12 mice displayed very little of the normal lipid deposition in intercellular spaces of the cornified envelope ( Figure 5B ) . To confirm these effects at an ultrastructural level we performed transmission electron microscopy on epidermal tissue at E18 . 5 , utilising ruthenium tetroxide postfixation of thin epidermal sections [20] to investigate the lamellar lipids which normally surround cells of the stratum corneum . We demonstrated an absence of these elements in the spaces between the corneocytes and cornified/granular layer ( Figures 5C–F ) , although the CLE was still apparent in Abca12el12/el12 tissues , an observation previously observed in HI biopsies [21] ( data not shown ) . The absence , relative scarcity or malformation of LBs is particularly characteristic of HI [18] , [22]–[25] . While relatively scarce structures resembling LBs were apparent within the granular layers in Abca12el12/el12 animals ( Figure 5E , F ) , most lacked the multilayered lamellar cargos present in control skin ( Figure 5D ) . Fusion of LBs with the surface of the normal granular cells was commonly observed in wild type skin ( Figure 5C ) and occurred occasionally in affected epidermis ( Figure 5F ) . Normally , corneocytes are filled with uniformly opaque keratin-filaggrin protein , however in affected epidermis they contained numerous vesicular and lamellar structures ( Figure 5H , I ) , defects found in both in situ and reconstituted HI epidermis [19] , [21] , [26] . Whilst our cellular and biochemical analysis suggested that some aspects of cornified cell envelope formation were disrupted in homozygous mice we did not observe overt differences in this structure during our EM studies . Indeed EM studies show that as well as a normally formed cornified envelope ( Figure 5G–I ) there is increased retention of corneodesmosomes in the distal layers of the stratum corneum ( Figure 5G , H ) . The persistence of these structures provide a mechanistic basis for the hyperkeratotic phenotype in our animals and serves to explain the relative decrease in extraction of CE's as noted above . The epidermis of the Abca12 homozygous mice bear many if not all of the features of HI , establishing them as an excellent model in which to study the biochemical basis of this disease . While previous studies of cultured human HI keratinocytes have identified defects in the traffic of glucosylceramides , global analysis of defects in lipid homeostasis , either in vitro or in vivo , have not been performed . We therefore utilised the Abca12el12 model to examine whether defects in lipid homeostasis were apparent in our mice . We harvested whole epidermis from the mid-dorsum of E18 . 5 homozygous , heterozygous and wild type littermates and empirically assayed for levels of a panel of thirteen different lipid species . Consistent with reports that Abca12 regulates the trafficking of glucosylceramide we detected greater than 2-fold increases in this lipid species in Abca12el12/el12 epidermis ( Figure 6A ) . We also detected striking increases in the relative levels of all ceramide species in affected versus wild type skin with highest proportional differences in C18 , C20 and C22 species , indicating that their transport is also reliant on Abca12 function ( Figure 6A ) . Sphingosine , a breakdown product of ceramide , was also markedly increased . This was in spite of the fact that we were unable to resolve intercellular lipid lamellae in the cornified envelope indicating an intracellular build-up of these species . Furthermore , significantly increased levels of cholesterol were observed in the epidermis of Abca12el12/el12 mice ( Figure 6A ) . No differences were observed in total levels of phosphatidylinositols , phosphatidylethanolamines , phosphatidylcholines , acyl- and lyso-phosphatidylcholines and sphingomyelin , suggesting that the defects apparent in our mice , while more broad and widespread than previously appreciated , were not a consequence of universal dysregulation of lipid homeostasis . Notably , we failed to identify any significant differences in lipid levels in Abca12el12/+ skin , confirming the absence of a haplo-insufficient phenotype highlighted by our histological survey . Having established a role for Abca12 in lipid metabolism in keratinocytes we wondered whether the protein might be more widely involved in this process . To assess the generality of the involvement of Abca12 in lipid metabolism , we investigated lipid efflux from Abca12el12/el12 , Abca12+/el12 and Abca12+/+ mouse skin fibroblasts [27] . To establish the specific involvement of ABC transporters , cholesterol efflux was compared with or without activation of LXR , which greatly increases expression of most ABC transporters including Abca12 [28] . As expected , in wild type cells activation of LXR resulted in a more than doubling of cholesterol efflux to apolipoprotein A-I ( apoA-I ) ( Figure 6B ) . In Abca12+/el12 cells the effect was less pronounced , but there was still a statistically significant increase of the efflux from activated versus non-activated cells . In contrast , in Abca12el12/el12 cells activation of LXR did not result in elevation of cholesterol efflux . Specific , ABC-dependent cholesterol efflux ( i . e . the difference in the efflux with and without activation ) was virtually zero ( Figure 6B ) . Current models suggest that the cholesterol efflux to apoA-I is fully controlled by ABCA1 [29]; however , in Abca12el12/el12 cells there was no ABC-dependent efflux to apoA-I despite the animals having functional Abca1 . Further , when phospholipid efflux was compared in Abca12el12/el12 and Abca12+/+ fibroblasts , activation of cells with LXR agonist resulted in a 25% increase in phospholipid efflux in Abca12+/+ cells ( p<0 . 05 ) , but no increase in Abca12el12/el12 ( not shown ) . To determine whether the loss of Abca12 was affecting the production and abundance of the Abca1 protein in cells from Abca12el12/el12 mice we performed western blotting for Abca1 . Strikingly , loss of Abca12 , even in a heterozygous state , led to concomitant decreases in Abca1 protein , providing a functional link between loss of Abca12 and impairment of cholesterol efflux ( Figure 6C , upper panel ) . Analysis of transcription of Abca1 in these cells highlighted 5 fold less expression in mutant versus wild type fibroblasts but no significant difference between wild type and heterozygotes ( Figure 6C , lower panel ) . Impairment of cholesterol efflux is a frequent cause of excessive accumulation of neutral lipids in cells , especially when exposed to acetylated low density lipoprotein ( AcLDL ) , a cholesterol donor for poorly regulated cholesterol uptake pathways . We compared accumulation of neutral lipids in Abca12el12/el12 , Abca12+/el12 and Abca12+/+ fibroblasts treated or not treated with AcLDL by staining lipids with Oil Red O . Wild type fibroblasts did not accumulate lipids independently of the presence of AcLDL indicating that lipid homeostasis pathways successfully cope with excessive lipid delivery ( Figure 6D ) . Abca12+/el12 fibroblasts also did not accumulate lipids in the absence of AcLDL , but there was visible lipid accumulation in the presence of AcLDL . Abca12el12/el12cells accumulated lipids both in the absence and presence of AcLDL , the accumulation being more severe in the presence of AcLDL . Thus , lipid homeostasis is severely impaired in Abca12el12/el12 fibroblasts .
Forward genetic screens in mice remain an important source of models of genetic disorders in humans . In this report we have used a forward genetic approach to identify a model of harlequin ichthyosis which has allowed us to characterise Abca12's function as a key regulator of lipid homeostasis and cholesterol transport . Current recessive ENU mutagenesis approaches to identify embryonic lethal mutations in the mouse either require the analysis of large numbers of embryos to identify defects , or the use of mice carrying engineered balancer chromosomal rearrangements tagged with visible phenotypic markers [30] . While the latter approach can very efficiently identify all the mutations that cause lethality between conception and weaning , and has the advantage of simultaneously isolating and mapping mutations , the genomic region screened is restricted to that delimited by the balancer chromosome . We have developed a simple genome-wide approach which obviates the requirement to dissect embryos and which simultaneously isolates and maps mutations . We inter-crossed two inbred mouse strains , one of which was mutagenised with ENU , established pedigrees from the resultant offspring , and screened these for regions of the genome under-represented for the mutagenised genetic background . As a consequence we simultaneously identified and mapped lethal mutations in an unbiased genome wide manner . Using this approach we have isolated a mouse model of Harlequin Ichthyosis , a hyperkeratotic and often lethal disease of the epidermis . We observed many HI features in our Abca12el12/el12 mice including severe hyperkeratosis , LB defects , absence of intercellular lamellae , aberrant filaggrin processing , neonatal death , defects in lipid metabolism , congenital contractures and the absence of skin barrier function . Studies of HI pathology suggest that the disease may be grouped into 3 subtypes [18] . The altered LB structure , absence of keratin VI expression and defects in filaggrin processing indicate that our mutant is equivalent to Type 1 HI proposed by this scheme although recent genotype/phenotype analysis suggests no correlation between mutation and phenotype [31] . Defects in the CE are characteristic of LI [32] , [33] , but our EM investigation showed no obvious deficiencies in this structure . A missense mutation similar to that of our mouse ( glycine to charged amino acid in a highly conserved TM domain residue ) has been shown to cause severe HI [31] . It is highly unlikely that our model is of LI2 , in which missense mutations have only been found within or near the first nucleotide binding domain of the protein [10] . This , coupled with the severity of disease in our mouse , suggests the G1997D mutation severely affects Abca12 activity although it remains to be determined whether it mis-localises or has altered transport function , as both have been observed in TM mutations in ABC family members causing severe disease [34]–[36] . The Abca12el12/el12 phenotype closely matches a targeted deletion of exon 10 generated by Lexicon Genetics , an allele in which postnatal lethality and absence of heterozygous effects were noted ( Abca12tm1Lex , www . informatics . jax . org ) . However , the Lexicon study undertook no characterisation of homozygous animals beyond noting lethality . A recent similar study by Yanagi et al . , demonstrated barrier defects in mice lacking Abca12 and suggest that postnatal death is a result of defects in lung function in newborn animals [16] . Our mouse parallels HI in almost every respect and has allowed us to investigate several aspects of disease which have been impossible in the limited patient samples available . We first examined the temporal progress of disease . Abca12el12/el12 mice displayed severe hyperkeratosis around the time of stratification of the cornified epidermal layer ( E16 . 5 ) . This phenotype increased in severity as development progressed to the point where the epidermis restricts the normal growth of the embryo . Abca12el12/el12 skin progressively enters a state of premature differentiation characterised by loss of normal basal cell architecture , mis-expression of differentiated keratins in basal keratinocytes , reduction in the size of the spinous cell layer and expansion of granular layers . We show that the retention of cornified squames in the upper layers of the epidermis which contribute to this restrictive epidermis is not due to hyper-proliferation or alteration in apoptosis . Instead we observe defects in the deposition of extra-cellular lipid lamellae and in proteolytic activity in the epidermis , indicating that the hyperkeratosis in our mice is due to failure to form and shed cornified envelopes from the skin surface . Our EM studies indicated that this retention was in part due to persistence of corneodesmosomes into the distal layers of the epidermis . This retention defect may explain why the CE's isolated directly from the skin surface by detergent extraction were relatively sparse and also exhibit fragility . Our results are consistent with previous studies indicating that the defects in LB loading can result in decreases in co-transport of proteases required for normal desquamation [37] and which has been suggested as a mechanism by which HI hyperkeratosis might occur [38] . Our results lend weight to this hypothesis . These defects also contribute to the loss of barrier function of mutant epidermis . As with human HI patients [18] , defects in proteolytic cleavage of filaggrin characterise the mice . These defects in the proteolytic processing of components are almost certainly reflected in the unusual presence of inclusions and vesicles within the normally uniform cells of the stratum corneum when examined by EM . In addition to these defects in the cornified layer , our observation of differentiation defects in Abca12el12/el12 mice indicates that defects in the HI epidermis affect all layers of the skin . Insights into the mechanisms by which loss of Abca12 function might affect the skin was revealed by our analysis of lipid species present in the epidermis . Previous studies have shown that Abca12 is important in controlling glucosylceramide trafficking in keratinocytes [8] , where it localises to the golgi and lamellar bodies [11] , an observation which correlates well with the striking increase in levels of glucosylceramide in the Abca12el12/el2 epidermis . However , our investigations of the Abca12el12/el12 mice revealed that defects in lipid homeostasis in the skin extend well beyond glucosylceramide . Despite the absence of intercellular lipid lamellae we detected significant increases in both ceramide and free cholesterol in the epidermis . We propose that increases in ceramide ( and indeed sphingosine , a ceramide breakdown product ) might reflect continuing unchecked de-novo synthesis and accumulation of this species , because of the absence of an Abca12 mediated trafficking mechanism to remove glucosylceramide from the cell . Cholesterol is also a known cargo of lamellar bodies [3] and its increased concentration in the epidermis probably reflects defects in trafficking of LBs or of a failure of loading this component as a consequence of loss of Abca12 . The ratio of ceramide , cholesterol and fatty acids in the epidermis is also a key determinant of barrier function in the skin , and normal LB formation [39] and induction of the synthesis machinery for these compounds is an early response to compromises in barrier function [40] , [41] . Consequently the defects in lipid levels in Abca12 mutant skin might actually be exacerbated by these positive feedback loops beyond primary defects related to Abca12 transport dysfunction . We find that the effects of Abca12 mutation are not limited to keratinocytes . Skin fibroblasts isolated from mutant mice showed an impairment of their ability to maintain cholesterol efflux to apoA-I proportional to gene dose . Cholesterol efflux to apoA-I is a key pathway responsible for maintaining cellular cholesterol homeostasis and is believed to be fully controlled by another ABC transporter , ABCA1 [42] . Here we demonstrate that this is not the case , and that Abca12 is also essential for cholesterol efflux to apoA-I . Phospholipid efflux was also impaired , consistent with the currently adopted view of the mechanism of ABCA1-dependent cholesterol efflux [43] . We demonstrate that in primary cells from Abca12el12/el12 mice , loss of Abca12 function results in decreased transcription of Abca1 . The basis of this association remains unclear but the alteration in transcription may not be the only explanation of significant ablation of ABCA1-dependent cholesterol efflux observed in these cells . This is particularly notable in fibroblasts heterozygous for the el12 mutation , which have normal levels of Abca1 transcription but which display significant decreases in Abca1 abundance and in efflux of cholesterol to apoA-1 . Given that many ABC transporters form homo- or hetero-oligomers and that oligomerisation of Abca1 is important for its function [44] we speculate that a direct association between Abca12 and Abca1 might additionally be essential for functional stabilization of Abca1 and normal cholesterol efflux to ApoA1 . The exact mechanism by which this occurs remains to be determined . Significantly , fibroblasts lack the classic LB organelles observed in keratinocytes . Our results therefore indicate that the Abca12 protein is capable of regulating the accumulation and efflux of lipids without this highly specialized organelle . Impairment of cholesterol efflux led to intracellular accumulation of neutral lipids , most likely cholesteryl esters; in Abca12el12/el12 cells . These lipids accumulated even in the absence of a challenge with extra-cellular lipid delivery through AcLDL . This finding has implications for another important pathology , atherosclerosis . Accumulation of cholesterol is a crucial element of the pathogenesis of atherosclerosis and impairment of cholesterol efflux , especially against a background of hypercholesterolemia , is a key contributor to the risk of atherosclerosis and coronary heart disease . Polymorphisms in ABCA1 are one of the strongest factors affecting plasma levels of high density lipoprotein [29] and risk of cardiovascular disease [45] . Our findings suggest that Abca12 is also required for the cholesterol efflux pathway to function and therefore should be taken into account when investigating mechanisms of atherosclerosis or considering targets for its treatment . Abca12 is expressed in primary macrophages at levels approximately 10 fold greater than the fibroblasts in which we have demonstrated cholesterol efflux defects in this study ( unpublished observations ) . The severity and rarity of HI have precluded studies addressing associations between HI and heart disease , but the availability of this mouse model will allow us to investigate this relationship . We have detailed a genetic screening protocol which concurrently identifies and maps postnatal or embryonic lethal mutations in an unbiased genome wide manner . This approach has allowed us to characterise an animal model of HI , providing a unique avenue by which to pursue therapeutic interventions for this and other ichthyoses . Our results demonstrate that HI should be viewed as a disease in which defects in Abca12 function lead to profound dysregulation of lipid metabolism in the epidermis . Furthermore we show in fibroblasts that the protein is a key regulator of cholesterol efflux , an observation with direct relevance to other defects of lipid homeostasis , including atherosclerosis .
Male 129/Sv mice were injected with a total dose of 200–400 mg/kg of N-Ethyl-N-Nitrosourea ( ENU ) in 3 weekly doses . ENU-treated males were mated with a C57BL/6 female and male G1 mice were crossed to C57BL/6 females . A G2 daughter was then backcrossed to her G1 father to produce G3 progeny for typing using 139 polymorphic markers spaced evenly throughout the genome [46] . Genotyping the G2 female allowed us to identify those markers that were heterozygous and hence informative in the final screen of G3 mice . Embryonic lethal mutations therefore manifest as a reduction in the expected frequency of 129/Sv homozygosity in adult animals . Those pedigrees in which no 129/Sv homozygosity of an informative SSLP was observed in G3 mice at weaning were recovered by performing IVF using cryopreserved G1 male sperm and eggs from C57BL/6 females . G2 mice heterozygous for the region of interest were then inter-crossed and their progeny analysed to determine whether 129/Sv homozygosity of linked markers was also absent in the second cohort . In these cases , the location of the embryonic lethal mutation was refined by genotyping key recombinants with additional polymorphic markers . Genomic DNA was extracted from tail biopsies and subjected to PCR amplification with oligonucleotide primers designed using the GABOS/GAFEP program ( http://bioinf . wehi . edu . au/gabos/index . php ) . To remove primers and unincorporated nucleotides , post-PCR reactions were treated with ExoSAP-IT ( USB ) according to the manufacturer's instructions and filtered through Sephadex columns . Amplicons were then sequenced directly using BigDye Terminator v3 . 0 ( Applied Biosystems ) . Assays of epidermal barrier function were performed essentially as previously described [17] . Gravimetric TEWL assays were performed using skin samples excised from the lateral thoracolumbar region of E18 . 5 embryos . Embryos and skin were photographed with a Zeiss Axiocam camera mounted on a Zeiss Stemi microscope . Comparison of TEWL was made using logistic regression models . All other statistical analyses were performed using the statistical software package STATA Version 7 ( Stata Corporation USA ) . Cornified envelopes and epidermal protein samples were prepared as previously described [47] , [48] . Size calculations were performed using the Image J software package ( NIH ) . Nile Red staining was performed as previously described [49] . IHC and IF were performed on citrate antigen retrieved paraffin embedded tissues or on frozen sections . Antibodies used were: rabbit anti-cytokeratin 14 , -cytotkeratin 10 , -cytokeratin 6 , -filaggrin and -loricrin ( Covance , 1∶1000 ) ; mouse anti-keratin 14 ( 1∶200 , LL002 , gift from Fiona Watt ) ; goat anti-Abca12 ( Santa Cruz Biotechnology , 1∶50 ) . Secondary antibodies were from Molecular Probes . Samples were imaged by epifluorescence on an Olympus Provis AX70 or by confocal , using a Lecia SPE microscope . Cell proliferation and differentiation were assayed by counting phospho-histone H3+ and K14+/K10+ positive basal interfollicular keratinocytes in multiple fields under 20× magnification ( n = 15 and 7 respectively ) . Mid-dorsum skin was processed for EM as described [20] with minor modifications . Following fixation and cryoprotection , samples were OCT embedded , frozen on dry ice and 40 µm sections cut using a Leica CM 3050 S cryostat . Washed samples were post-fixed with 0 . 2% ruthenium tetroxide ( Polysciences , USA ) , 0 . 5% potassium ferrocyanide in 0 . 1 M sodium cacodylate , pH 7 . 4 in the dark for 60 min . Following rinsing in water , samples were dehydrated in an alcohol series and embedded in Spurrs resin . Sections were cut using a Leica Ultracut S ultra-microtome , mounted on copper grids and contrasted with methanolic uranyl acetate and aqueous lead citrate before imaging in a JEOL 1011 TEM with a MegaView III CCD cooled digital camera ( Soft Imaging Systems , Germany ) . All solvents were of HPLC grade and were used without further purification . N-Palmitoyl-d3-glucosylceramide ( GC 16:0 ( d3 ) ) and N-palmitoyl-d3-lactosylceramide ( LC 16:0 ( d3 ) ) were from Matreya Inc . ( Pleasant Gap , USA ) . Sphingosine ( Sph , 17:1 base ) , ceramide ( Cer ) 17:0 , sphingomyelin ( SM ) 16:0 ( d31 ) and phosphatidylcholine ( PC ) 17:0/17:0 were from Avanti Polar Lipids ( Alabaster , USA ) , Cholesteryl ester ( CE ) 17:0 was from Mp Biomedicals ( Seven Hills , NSW , Australia ) . Lipid analysis was performed independently on 7 Abca12+/+ , 8 Abca12el12/+ and 6 Abca12el12/el12 embryos . E18 . 5 fetus skins were incubated in phosphate buffered saline containing 5 mM EDTA for 1 h at 37°C . The epidermal layer was then peeled from the skin with tweezers , weighed and homogenized in 1 . 0 ml of PBS using a dounce homogenizer . Protein determination was performed using the Micro BCA Protein Assay Kit ( Pierce , Rockford , Il , USA ) . Total cholesterol was determined using the Amplex Red Cholesterol Assay Kit ( Invitrogen , Mount Waverly , Vic , Australia ) . Total lipids were extracted from tissue homogenates ( 100 µL containing approximately 100 µg protein ) according to established methods [50] , incorporating 400 pmol of each of the following internal standards: GC 16:0 ( d3 ) , LC 16:0 ( d3 ) Sph ( 17:1 base ) , Cer 17:0 , SM16:0 ( d31 ) , PC 14:0/14:0 and CE 17:0 . Lipid extracts were reconstituted in 200 µL 10 mM , NH4COOH in methanol . Lipid analysis was performed by liquid chromatography , electrospray ionisation-tandem mass spectrometry ( LC ESI-MS/MS ) using a HP 1100 liquid chromatography system combined with a PE Sciex API 2000 Q/TRAP mass spectrometer with a turbo-ionspray source ( 250°C ) and Analyst 1 . 4 . 2 data system . LC separation of lipids was performed on an Alltima C18 , 3 um , 50×2 . 1 mm column using the following gradient conditions; 70% A reducing to 0% A over three minutes followed by 5 minutes at 0% A , a return to 70% A over 0 . 1 minute then 1 . 9 minutes at 70% A prior to the next injection . Solvent A and B consisted of tetrahydrofuran∶methanol∶water in the ratios ( 30∶20∶50 ) and ( 70∶20∶10 ) respectively , both containing 10 mM NH4COOH . Quantification of individual species of Sph , Cer , GC , LC , SM , PC and CE was performed using multiple-reaction monitoring ( MRM ) in positive ion mode . MRM product ions used were m/z 264 [sphingosine–H2O]+ for sphingosine , Cer , GC and DHC , m/z 184 [phosphocholine]+ for SM , PC and m/z 369 [cholesterol-H2O]+ for CE . Each ion pair was monitored for 50 ms with a resolution of 0 . 7 amu at half-peak height and averaged from continuous scans over the elution period . Lipid concentrations were calculated by relating the peak area of each species to the peak area of the corresponding internal standard . The skin was separated from the mouse embryos ( last week of gestation ) . Skin tissue was finely minced , resuspended in 0 . 05% Trypsin/EDTA solution , incubated for 30 min at 37°C , vigorously shaken , incubated for another 10 min at 37°C and neutralized with medium containing 10% FBS . Cells were seeded and incubated overnight in CO2 incubator before unattached cells and debris were washed out . Quantitative expression of Abca1 was determined by qPCR from cDNA transcribed from Trizol prepared sample total RNA and amplification using SYBR GreenER PCR mix ( Invitrogen ) by primer sequences previously optimized for this approach [51] . Assays were performed in triplicate and standardized to an internal 18S rRNA control . Human HDL ( 1 . 085<d<1 . 21 ) and apoA-I were isolated from pooled normolipidemic human plasma supplied by Red Cross as described previously [52] . LDL was purified from human plasma by sequential centrifugation and acetylated as described by Basu et al . [53] . Cholesterol and phospholipid efflux were assessed as described previously [54] . Briefly , fibroblasts were incubated in labeling medium containing [3H]cholesterol ( 75 kBq/ml ) or [methyl- 14C] choline ( 0 . 2 MBq/ml ) for 48 hours . Cells were then incubated for 18 hr in serum-free medium in the presence or absence of the LXR agonist TO-901317 ( final concentration 4 µM ) to stimulate expression of ABC transporters and cholesterol efflux . Cells were then washed with PBS and incubated for 2 h in either serum-free medium alone ( blank ) or in serum-free medium supplemented with 30 µg/ml of lipid-free apoA-I . For cholesterol efflux analysis , aliquots of medium and cells were counted . For phospholipid efflux lipids were extracted from cells and medium [50] and counted . The efflux was calculated as radioactivity in the medium/ ( radioactivity in the medium+radioactivity remaining in the cells ) ×100% . Non-specific efflux ( i . e . the efflux in the absence of acceptor ) was subtracted . Cells were incubated in the presence of TO-901317 ( final concentration 4 µM ) and in the presence or absence of AcLDL ( 10 µg/ml ) in serum-containing medium for 18 hrs . After washing with PBS , cells were fixed in 3 . 7% formaldehyde for 2 min , washed with water , and incubated at room temperature for 1 h with Oil Red O working solution ( Fisher Biotech ) . | Harlequin Ichthyosis is a severe inherited disease in which the skin develops as thick armour-like plates . While many HI patients die at birth , those who survive are subject to dehydration and infection . The disease is caused by defects in a protein called ABCA12 , which is thought to function by transporting lipids within the cells of the skin . Here , we describe a new genetic screen that we have used to identify a mouse model that develops the hallmarks of HI and carries a mutation in Abca12 . We have used this model to elucidate Abca12's significant role in the transport of lipids within the skin , and we demonstrate that the loss of these lipids contributes to the dehydration in affected embryos and newborns . We attribute specific transport functions to the protein and show that it can mediate the efflux of a number of different lipids from the cell including , importantly , cholesterol . Cholesterol transport by proteins related to Abca12 plays a critical role in the development of a number of diseases , including heart and peripheral vascular disease , and the description of these functions for Abca12 suggest that it may play a wider role in controlling lipid metabolism . | [
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] | 2008 | A Mouse Model of Harlequin Ichthyosis Delineates a Key Role for Abca12 in Lipid Homeostasis |
Indirect reciprocity , besides providing a convenient framework to address the evolution of moral systems , offers a simple and plausible explanation for the prevalence of cooperation among unrelated individuals . By helping someone , an individual may increase her/his reputation , which may change the pre-disposition of others to help her/him in the future . This , however , depends on what is reckoned as a good or a bad action , i . e . , on the adopted social norm responsible for raising or damaging a reputation . In particular , it remains an open question which social norms are able to foster cooperation in small-scale societies , while enduring the wide plethora of stochastic affects inherent to finite populations . Here we address this problem by studying the stochastic dynamics of cooperation under distinct social norms , showing that the leading norms capable of promoting cooperation depend on the community size . However , only a single norm systematically leads to the highest cooperative standards in small communities . That simple norm dictates that only whoever cooperates with good individuals , and defects against bad ones , deserves a good reputation , a pattern that proves robust to errors , mutations and variations in the intensity of selection .
Indirect Reciprocity ( IR ) , which involves reputation and status [1] , constitutes , perhaps , the most elaborated and cognitively demanding mechanism of cooperation discovered so-far [2] . Unlike other mechanisms of cooperation , IR has been heralded as providing the biological basis of our morality [1] . Whereas under direct reciprocity one expects to receive help from someone we have helped before , under IR one expects a return , not from someone we helped , but from someone else: In this sense , helping the “right” individuals may increase the chance of being helped by someone else at a later stage . Seminal work carried out since the mid eighties [1–35] has shown how IR can lead to the emergence and sustainability of cooperation . Most theoretical models employed to date ( for exceptions , see [10 , 26] ) have considered infinite populations . In this context , the work of Ohtsuki and Iwasa [13] became an inspiring and influential framework on top of which many other models were built , and led to the identification of the so-called leading eight social norms of cooperation [13–15] . But what about small-scale societies , e . g . , Hunter-Gatherers where reputation is paramount [36 , 37] ? Indeed , and despite other forms of reciprocity or kinship relations that may also play a co-evolutionary role , reputations easily diffuse in small communities and influence individuals’ choices . In this context , it remains an open question which norms are able to promote cooperation in small societies . Here we shall investigate to which extent norms found to promote cooperation in large populations will remain effective in small societies , and also to which extent the capacity of a social norm to foster cooperation depends on the community size . In small populations , stochastic finite size effects are not only important , but may even render analyses based on concepts originating from infinite populations misleading . In the context of direct reciprocity , for instance , it was shown that individuals in finite populations select reciprocation , while defection is selected in infinite populations [38] . In general , it is also well-known that strict Nash Equilibria and Evolutionary Stable Strategies may not prevail in finite populations [39 , 40 , 41 , 42] . In this paper we address this problem by studying the stochastic dynamics of different strategies ( also called action or behavioral rules ) when reputation assignment is governed by second order social norms ( defined below ) . Consider a finite population comprised of Z individuals who may opt to help one another ( that is , to Cooperate , C ) or not ( to Defect , D ) . Random pairs of individuals are chosen and play the donation game , one being the potential provider of help ( donor ) to the other ( recipient ) . The donor may cooperate and help the recipient at a cost c to herself/himself , conferring a benefit b to the recipient ( with b > c ) . The donor may also decide not to help , in which case no one pays any costs nor distributes any benefits . In line with previous work , this donation game characterizes the interactions between pairs of individuals in the population . We further assume that individuals have a public reputation that can only have 2 attributes: Good ( G ) or Bad ( B ) . It is worth pointing out that , to begin with , G and B reputations are mere labels with no a-priori meaning . Their significance will eventually emerge in association with individual behavior in connection with the donation game . Indeed , it is the structure of the donation game , in which help implies engaging in a costly action to confer a higher benefit to someone else , that ultimately assigns a meaning to the reputation labels . Decision is an individual attribute , encoded in a behavioral rule specified by the duple p = ( pG , pB ) that defines the probability of an individual to opt for C when facing a G and a B opponent , respectively . The reputation of each individual is public and ( errors apart , see Methods ) is attributed by a bystander who witnesses a pairwise interaction; in doing so , she/he identifies the action ( C or D ) of the donor , as well as the reputation ( B or G ) of the recipient , based on which she/he attributes a new reputation to the donor . To perform this task , the bystander uses a social norm , that is , a rule that converts the combined information stemming from the action of the donor and the reputation of the recipient into a new reputation for the donor . Social norms encoding this type of information are classified as second-order norms [13–15 , 26] . In this hierarchy , first-order norms convert the action of the donor into a new reputation for her/him , whereas third-order norms use , besides the information used in second-order norms , the reputation of the donor at the time of engaging in the donation game . Likewise , the complexity of behavioral rules varies concomitantly . In the space of second order norms we shall consider here , the duple p suffices to unambiguously define a strategy , leading to the following 4 possible strategies: unconditional Defection ( AllD , p = ( 0 , 0 ) ) , unconditional Cooperation ( AllC , p = ( 1 , 1 ) ) , Discriminator strategy ( Disc , p = ( 1 , 0 ) ) , that is , cooperate with those in good standing , and defect otherwise ) , and paradoxical Discriminator strategy ( pDisc , p = ( 0 , 1 ) , the opposite of Disc ) . This simplified societal structure has been very influential in studying the evolution of cooperation under indirect reciprocity [4 , 7 , 9 , 10 , 13–15 , 17–19 , 22 , 23 , 25–27] . Unlike previous analytical studies , however , we shall investigate the evolutionary dynamics of small-scale societies by means of stochastic birth-death processes , monitoring explicitly to which extent a social norm fosters cooperation . Let us assume that all individuals start with the same reputation ( say , G ) , and that some of them ( k ≤ Z ) adopt the behavioral rule p while the rest of the population ( Z-k ) adopts another behavioral rule p’ . By interacting with each other , it may happen that individual reputations change in time . If no one changes their behavioral rule , there will be a characteristic time after which the distribution of reputations in the population will stabilize . This stable distribution can be determined by computing the limiting distribution of the 2-dimensional Markov chain described in the Methods section . Given a ( stationary ) distribution of reputations , we can compute the fitness of an individual using behavioral rule p ( p’ ) by determining the average payoff of such an individual in the population . Knowledge of the fitness of each type of individual in the population allows us now to study the evolution of behavioral rules in the population . To this end we define a stochastic birth-death process . Analytically , we shall restrict the number of behavioral rules present in the population , at any time , to be at most two . In other words , we assume that no new behavior rule appears in the population before one of the 2 existing ( p and p’ ) rules goes extinct . Such a Small Mutation Approximation ( SMA ) [43] , which has been employed in the past with great success [40–45]—albeit not in the context of IR—allows us to compute , for a population under a given social norm i ) the stationary distribution of behavioral rules and , from it , ii ) the cooperation index ( η , a real number between 0 and 1 , defined in Methods ) of that population , measuring the average fraction of donations observed in a community evolving under a given social norm . Computer simulations , in which all behavior rules are allowed to co-evolve , allow us to show that the intuitive analytical results extracted from the SMA do actually remain valid in a surprisingly wide parameter range ( see S1 Text ) .
In Fig 1 we calculate analytically the cooperation index ( η ) for different social norms as a function of ( small ) population size . Out of a total of 16 second order social norms [15] , only 10 are truly distinct , and of these , four have been given special attention: Stern-judging [14 , 26 , 33] ( SJ , also known as Kandori , which assigns a good reputation to a donor that helps a good recipient or refuses help to a bad one , assigning a bad reputation in the other cases ) ; Simple-Standing ( SS ) [5] , similar to SJ , but more “benevolent” by assigning a good reputation to any donor that cooperates; Shunning ( SH ) [6 , 18 , 22] , similar to SJ but less “benevolent” , by assigning a bad reputation to any donor that defects; and Image Score [12 , 21] ( IS , a first order norm ) where all that matters is the action of the donor , who acquires a good reputation if playing C and a bad reputation if playing D . The results in Fig 1 show that SJ is able to foster the highest values of the cooperation index η , independently of the ( finite and small ) population size . Large-scale agent-based computer simulations confirm these results ( see S1 Text ) . Despite leading to cooperation index values systematically lower than SJ in small-scale societies , SS is capable of providing significant levels of cooperation . The fact that SS is more benevolent than SJ towards unconditional cooperators prevents it from sustaining levels of cooperation comparable to SJ in small-scale societies . Conversely , SH harms cooperation ( by being too strict compared to SJ ) due to the abusive widespread assignment of bad labels . The right balance of SJ , in turn , proves robust to variations in population size and different error rates , as shown in Fig 2 , where the robustness with respect to errors is investigated for each of the four social norms explicitly defined in Fig 1 . As also shown in Fig 1 , for large populations , the levels of cooperation obtained under SS smoothly converge to the levels obtained with SJ , confirming these two social norms as the leading-two in promoting cooperation [15] . Fig 2 allows to further capture the robustness of each social norm in the presence of noise . We consider errors of assignment , of execution and of private assessment . The disadvantages of having a norm that is more ( SS ) or less ( SH ) benevolent than SJ are highlighted by the impact that each kind of error has on it . SS benefits from assignment and execution errors . It happens because those specific errors allow to disambiguate between an unconditional and a conditional cooperator . For example , in a population governed by SS and solely composed by AllC and Disc , everyone would be regarded as G . Mistakenly failing i ) to donate ( execution error ) or ii ) to assign a good reputation ( assignment error ) , leads to an increase of B individuals , providing an advantage to Disc individuals . On the contrary , the lack of benevolence of SH is alleviated by assignment errors , as G individuals will now increase ( by mistake ) . Execution errors , in turn , do not promote cooperation under SH , as they act to further increase the number of B individuals ( specially in populations dominated by Disc ) , or to explicitly decrease the number of donations . While Figs 1 and 2 provide aggregate information regarding the performance of each social norm , they do not reveal the interplay between strategies that is on the basis of the cooperation indexes observed . Such an interplay is detailed in Fig 3 , where we resort to directed graphs in which each vertex corresponds to one of the four possible monomorphic states and respective strategies: AllC , AllD , pDisc and Disc . The radius of each vertex corresponds to the prevalence of each strategy in time , whereas orange/dark-gray pies represent the level of cooperation/defection , while blue/light-gray pies display the stationary fraction of G and B reputations at each monomorphic state . Arrows represent the fixation probabilities of one individual ( with a strategy located at the vertex of origin of the arrow ) in a population of individuals ( with a strategy located at the vertex at the end of the arrow ) . The values , computed analytically in the SMA , are only shown whenever the fixation probability is larger than neutral fixation , given by 1/Z , with values reported relative to the neutral fixation value . Fig 3 shows that , in accord with previous studies [13–15] , all the so-called leading 2nd order norms—SJ and SS—are able to promote Disc to an evolutionary robust strategy [46 , 47] , defined as strategies for which no mutant , adopting any other strategy , has a selective advantage . To these leading 2nd order norms , one may also add SH , which , despite not being a leading norm , can also make Disc an evolutionary robust strategy . This norm , however , is unable to support the good standing of Disc individuals , a fact that is stressed by execution errors and alleviated by the assignment ones ( see Fig 2 ) . IS , in turn , is dominated by the AllD state , despite the inexistence of any evolutionary robust strategy . Thus , only SJ and SS are able to combine a high prevalence of an ALL-Disc configuration with the incidence of G reputations in this configuration , efficiently fostering high levels of cooperation . This said , SS cannot preclude strong transitions from both AllC and pDisc into AllD , with a significant impact on the overall levels of cooperation ( see Fig 1 ) . As a side remark , for large populations , the relative magnitude of these two transitions is reduced in comparison with what is observed in small communities , while the transition from AllC to Disc is strengthened , leading to the result pictured in Fig 1 . The opposite will happen for low execution errors ( see S1 Text for details ) . Furthermore , SJ is the only social norm that profits from the existence of a pDisc strategy . Indeed , the population spends roughly half of the time in an ALL-pDisc configuration and the other half in an ALL-Disc configuration . The symmetry of SJ , however , dictates that , in both cases , individuals end up cooperating ( apart from errors ) : cooperate and remain good in the ALL-Disc configuration , and cooperate and remain bad in the ALL-pDisc configuration . However , as stated before , the labels G and B have no pre-determined meaning in our formulation . What is remarkable with SJ is that it is the only social norm that successfully fosters cooperation in the donation game , irrespectively of the labeling adopted . Indeed , pDisc is the equivalent to Disc when the labels good and bad are swapped . The specific labeling , in turn , is irrelevant: All that matters—and ultimately defines a moral system—is what is achieved through the donation game . Finally , but importantly , i ) the advantages of SJ remain valid for different values of errors and selection strength , and , in the presence of errors of execution , ii ) such advantage is emphasized in small scale societies , as shown already . It is also noteworthy that the analytical results discussed above , obtained in the limit where mutations rarely occur [43] , remain valid for a wide range of mutation probabilities , as we show explicitly in the S1 Text via comparison with results from numerical simulations . Additionally , in the S1 Text , we also show that the analytical results remain valid for a wide interval of reputation assignment time-scales , as we abandon the time-scale separation ansatz that sits at the heart of the analytical treatment adopted .
We have investigated the stochastic dynamics of different strategies ( behavioral rules ) as a function of population size , when reputation assignment is governed by second order social norms . In our model , where the reputation dynamics is also the outcome of a stochastic process , the four social norms among first and second-order norms that lead to a cooperation index η higher than 0 are SJ , SS , SH and IS . From these , SJ clearly stands out for small population sizes , dominating with SS for large population sizes , ensuring high values of η that are robust to parameter variations and errors . Interestingly , the fingerprint of both leading norms SJ and SS is consistent with recent findings showing that toddlers not only positively evaluate those who treat others prosocially [48–51] , but also positively evaluate those who behave negatively towards those who have acted antisocially [50] . Moreover , in Ref . [50] it is specifically pointed out that toddlers clearly prefer characters that harm ( rather than help ) antisocial puppets which fits nicely with the assessment of SJ . On the other hand , the relative importance of SS and SH depends on the amount and nature of noise . For cases in which individuals often make errors when donating , benevolent social norms are appropriate , and thus , SS prevails over SH . If execution errors are rare , larger populations and a larger selection pressure ( high β ) allows SH to prevail over SS , and benevolent social norms become less capable of promoting cooperation . SS and IS , in turn , benefit from noise , as is the case when populations are very small or when the exploration rate μ is large . Clearly , to assess the effect of a particular social norm regarding the promotion of cooperation in a finite population , it is not enough to require the evolutionary stability or robustness of the discriminating strategy ( Disc ) , as addressed in previous works on IR [9 , 15] . When population sizes grow from 5 to 130 , a range that includes typical community sizes of hunter-gatherer societies , and in which one expects stochastic effects to play a sizable role , we find that , under SS , SH and SJ , Disc is evolutionary robust [46 , 47] . However , for cooperation to emerge , strategies and reputations must be coordinated: under SH , and despite the prevalence of the Disc strategy , defection still prevails over cooperation since individuals are mostly regarded as B; SS , in turn , fails to prevent transitions into AllD in small populations; SJ fosters an ideal coordination between strategy and prevailing reputations , leading individuals to cooperate in the donation game . The framework developed here has the advantage of being naturally extendable to social norms of higher order . Research carried out to date led to the discovery of SJ in a multi-level selection model in which an exhaustive search was carried out in the space of all third order norms [10 , 26] . Thus , it would not be surprising if SJ still promotes cooperation when this formalism is extended to third order norms . Work along these lines is in progress .
The actions employed in each interaction depend on the known reputation of the opponent . In a world of binary reputations ( Good , G or Bad , B ) , the strategy ( also called action or behavioral rule ) used by each player is a 2-bit string that prescribes an action ( C or D ) given the reputation of the opponent ( G or B ) . Following the notation in [13–15] , we denote a strategy by the duple p = ( pG , pB ) , in which pG and pB represent , respectively , the probability of cooperating when the opponent is G or B . There are thus 4 different strategies: ( 1 , 1 ) , ( 1 , 0 ) , ( 0 , 1 ) and ( 0 , 0 ) which are traditionally called AllC , paradoxical Discrimination ( pDisc ) , Discrimination ( Disc ) , and AllD [15] . We consider the existence of execution errors ( ε ) that simulate the inability of individuals to act in the way that their strategy dictates [11] . It is common practice to consider errors in the form of failed intended cooperation [15 , 31] , due , for instance , the lack of “resources , time or energy” to donate [52] . Our results , however , remain valid even if the execution error would also induce defectors to involuntarily cooperate . We assume that the donation game described in the main text is observed by a third party that will update the reputation of the players according to a social norm that is common to the entire population . The social norms prescribe a new reputation to a potential donor given the action employed ( C or D ) and the reputation of the opponent ( the potential receiver of the donation ) . These second order social norms are defined as a bit-string with length 4 , d = ( dG , C , dG , D , dB , C , dB , D ) , in which di , j denotes the probability of assigning a good reputation to an individual that employed action j towards an opponent with reputation i . There are 16 different second order social norms [15] , which reduce to 10 if we take into consideration that the labels B and G can be swapped and the same results would ensue . In other words , norms d1 = ( dG , C , dG , D , dB , C , dB , D ) and d2 = ( 1-dB , C , 1-dB , D , 1-dG , C , 1-dG , D ) are equivalent due to a mirror symmetry [13] . We consider the existence of assignment errors , α [14] . They model the fact that the bystander observing the interaction may fail to attribute an accurate reputation to the donor , due to a myopic assess of the reputation of the potential receiver or due to a misinterpretation of the action employed . Following [10 , 13 , 14 , 22 , 23 , 26] , and given that we are dealing with small communities , we assume that , once the reputation of an individual is assigned , it is widely and faithfully disseminated throughout the population , so that everyone shares the same opinion regarding the reputation of others . In the SMA , we assume a maximum of two strategies ( p and p' ) to be present , at any time , in the population . We assume that p already includes the execution error ( i . e . , p→ ( 1−ε ) p ) and d already includes the assignment error ( i . e . , d→ ( 1–2α ) d+α ) . There are private errors , occurring with a probability χ , in assessing the actual reputation of an opponent . Consequently , denoting X = ( 1−χ , χ ) and Χ¯ = ( χ , 1−χ ) , the probability that someone using strategy p cooperates when meeting a good opponent is given by CGp= ( 1−χ ) pG+χpB=X . pT , and the probability of cooperating with a bad opponent is given by CBp=χpG+ ( 1−χ ) pB=X¯ . pT . The probability that one observer assigns a good reputation to an individual using p and interacting with a good opponent is given by GGp= ( 1−χ ) ( CGpdG , C+ ( 1−CGp ) dG , D ) +χ ( CGpdB , C+ ( 1−CGp ) dB , D ) = ( X⊗ ( CGp , 1−CGp ) ) ⋅dT where ⊗ is the Kronecker product ( ( a1 , a2 ) ⊗ ( b1 , b2 ) = ( a1b1 , a1b2 , a2b1 , a2b2 ) ) . The probability of assigning a good reputation to an individual using p and interacting with a bad opponent is given by GBp= ( X¯⊗ ( CBp , 1−CBp ) ) ⋅dT . Given the expressions above , we now define birth and death probabilities [53] for good individuals . We use h and h’ to denote the number of good individuals using strategies p and p’ . For a population with size Z , where k individuals use strategy p ( and Z-k use p’ ) , the probability of having one more good individual using strategy p is given by , Hp+ ( h , h′ ) =k−hZ ( h+h′Z−1GGp+Z−h−h′−1Z−1GBp ) whereas the probability of having one more bad individual using strategy p is given by , Hp− ( h , h′ ) =hZ ( h+h′−1Z−1 ( 1−GGp ) +Z−h−h′Z−1 ( 1−GBp ) ) with analogous expressions for the birth and death probabilities associated with good individuals using the strategy p’ ( i . e . the expressions Hp′+ and Hp′− ) . To that end , one only has to substitute k for Z-k , h for h’ and p for p’ . For a fixed value of k , the expressions Hp+ , Hp− , Hp′+ , Hp′- define the stochastic process with which we may evolve the reputation dynamics in the population . Indeed , those probabilities define a two-dimensional Markov chain whose states , ( h , h’ ) , are defined by the number of good individuals using strategies p and p’ . In total , there are S = ( k+1 ) ( Z-k+1 ) different states . The entry ( i , j ) of the underlying transition matrix ( H ) represents the transition probability from state ( hi , h′i ) to state ( hj , h′j ) . Consequently , the entries of matrix H are given by Hi , j{Hp+ ( hi , h′i ) , hj=hi+1∧h′j=h′iHp− ( hi , h′i ) , hj=hi−1∧h′j=h′iHp′+ ( hi , h′i ) , hj=hi∧h′j=h′i+1Hp′− ( hi , h′i ) , hj=hi∧h′j=h′i−1H= ( hi , h′i ) , i=j0 , otherwhise where H= ( hi , h′i ) =1−Hp+ ( hi , h′i ) −Hp− ( hi , h′i ) −Hp′+ ( hi , h′i ) −Hp′− ( hi , h′i ) is the probability of keeping the same reputation distribution . From H , one can now compute the stationary ( or limiting ) distribution σ , defined as the eigenvector of matrix H , associated with eigenvalue 1 [54] , σH=σ The evolution of strategies in the population is determined by a birth death process with imitation [55] , in which those strategies that fare better are imitated more often [56 , 57] . This probabilistic imitation ( i . e . , the probability of strategy p being imitated by an individual previously adopting p' , P ( p'→p ) is accomplished through the Fermi ( also known as pairwise comparison ) update rule [55 , 58] , P ( p′→p ) =1/ ( 1+e−β Δfp , p′ ) , where Δfp , p′ ( k ) =f¯p ( k ) −f¯p′ ( k ) is the difference of average fitness between p and p’ and β controls the selection strength: whenever β→0 imitation approximates the neutral drift; on the other hand , whenever β→+∞ the imitation occurs deterministically and selection pressure is maximal . To this end we compute the average payoff ( fitness ) of individuals employing a given strategy in the following way: The frequency-dependent fitness of strategy p , when k individuals are using it ( and thereby Z-k are using p’ ) , is composed by two terms: one positive corresponding to the received benefit ( b ) , and another negative that translates the donations made ( c ) when individuals using p cooperate: fp ( k , h , h' ) = bRp ( h , h' ) −cDp ( h , h' ) . Rp ( h , h’ ) stands as the probability that a p strategist receives a donation , Rp ( h , h′ ) =hk ( k−1Z−1CGp+Z−kZ−1CGp′ ) +k−hk ( k−1Z−1CBp+Z−kZ−1CBp′ ) Dp ( h , h’ ) , in turn , stands as the probability that a p donates , Dp ( h , h′ ) =hk ( h−1+h′Z−1CGp+Z−h−h′Z−1CBp ) +k−hk ( h+h′Z−1CGp+Z−h−1−h′Z−1CBp ) Provided a distribution of reputations σ is known , the average fitness is then calculated as f¯p ( k ) =∑0<h<k∑0<h′<Z−kσh , h′fp ( k , h , h′ ) , where σh , h' = σh ( Z−k+1 ) +h' ) is the stationary distribution over the state in which there are h and h’ individuals labeled G and using , respectively , action rules p and p’ . The fixation probability ( ρp'→p ) of a unique mutant p in a population where Z-1 individuals use p’ can be written [4 , 53 , 55 , 59] , ρp′→p= ( 1+∑i=1Z−1∏j=1iT− ( j ) T+ ( j ) ) −1 Using the pairwise comparison rule ( introduced above ) to model the probability of imitation [55] this expression simplifies to ρp′→p= ( 1+∑i=1Z−1∏j=1ie−β Δfp , p′ ) −1 With these definitions for the fixation probabilities , we setup now an embedded Markov chain whose state-space is composed by all the possible monomorphic states , Ti , j=ρi→j3 ( i≠j ) Ti , i=1−∑k=1 , k≠i3ρi→k3 Following a procedure similar to that employed in the derivation of the stationary distribution of reputations , the stationary distribution of strategies is unique to the extent that the underlying Markov chain is irreducible , and given again by the eigenvector associated with the eigenvalue 1 of the transition matrix [41 , 45 , 54] ) . The cooperation index ( η ) is computed , for a given social norm , by taking the weighted average of the fraction of cooperative acts that take place in each of the monomorphic configurations of the population; for weights , we use the fraction of time the population spends in each of these configurations , provided by the stationary distribution of strategies . Denoting by λpi the fraction of time spent in the monomorphic configuration where all individuals adopt pi , and denoting by σd ( pi , h ) the probability of having h good individuals within the monomorphic configuration pi ( calculated with d as the underlying social norm ) , the cooperation index ( η ) is given by η=∑pi∈{AllC , AllD , Disc , pDisc}λpi∑j=0ZDpi ( j , 0 ) σd ( pi , j ) | The prevalence of cooperation among human societies is a puzzle that has caught the eye of researchers from multiple fields . Why is that people are selfless and often incur costs to aid others ? Reputations are intimately linked with the answer to this question , and so are the social norms that dictate what is reckoned as a good or a bad action . Here we present a mathematical framework to analyze the relationship between different social norms and the sustainability of cooperation , in populations of arbitrary sizes . Indeed , it is known that cooperation , norms , reciprocity and the art of managing reputations , are features that go along with humans from their pre-historic existence in small-scale societies to the contemporary times , when technology supports the interaction with a large number of people . We show that population size is relevant when evaluating the merits of each social norm and conclude that there is a social norm especially effective in leveraging cooperation in small populations . That simple norm dictates that only whoever cooperates with good individuals , and defects against bad ones , deserves a good reputation . | [
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] | 2016 | Social Norms of Cooperation in Small-Scale Societies |
Mechanical force plays an important role in the physiology of eukaryotic cells whose dominant structural constituent is the actin cytoskeleton composed mainly of actin and actin crosslinking proteins ( ACPs ) . Thus , knowledge of rheological properties of actin networks is crucial for understanding the mechanics and processes of cells . We used Brownian dynamics simulations to study the viscoelasticity of crosslinked actin networks . Two methods were employed , bulk rheology and segment-tracking rheology , where the former measures the stress in response to an applied shear strain , and the latter analyzes thermal fluctuations of individual actin segments of the network . It was demonstrated that the storage shear modulus ( G′ ) increases more by the addition of ACPs that form orthogonal crosslinks than by those that form parallel bundles . In networks with orthogonal crosslinks , as crosslink density increases , the power law exponent of G′ as a function of the oscillation frequency decreases from 0 . 75 , which reflects the transverse thermal motion of actin filaments , to near zero at low frequency . Under increasing prestrain , the network becomes more elastic , and three regimes of behavior are observed , each dominated by different mechanisms: bending of actin filaments , bending of ACPs , and at the highest prestrain tested ( 55% ) , stretching of actin filaments and ACPs . In the last case , only a small portion of actin filaments connected via highly stressed ACPs support the strain . We thus introduce the concept of a ‘supportive framework , ’ as a subset of the full network , which is responsible for high elasticity . Notably , entropic effects due to thermal fluctuations appear to be important only at relatively low prestrains and when the average crosslinking distance is comparable to or greater than the persistence length of the filament . Taken together , our results suggest that viscoelasticity of the actin network is attributable to different mechanisms depending on the amount of prestrain .
Actin is the most abundant intracellular protein in eukaryotic cells and plays an important role in a wide range of biological and mechanical phenomena [1] . Monomeric actin ( G-actin ) self-assembles to a filamentous form , F-actin , which is crosslinked into the actin cytoskeleton by various actin crosslinking proteins ( ACPs ) . It has been known that mechanical force plays a crucial role in the physiology of eukaryotic cells [2] , and therefore appropriate functions of living cells are attributable to the rigorous control of their rheological properties [3] . Thus , investigating rheological properties of actin networks is indispensable for elucidating the mechanics of cells as well as for understanding a wide variety of cellular processes . Experiments have been conducted to probe viscoelastic properties of cells and reconstituted actin gels using a variety of techniques such as microbead rheology , magnetic bead cytometry , and bulk rheology [4]–[23] . In experiments , discrepancies have been observed among measurements using dissimilar methodologies , and many of the observed features are not well understood . For example , viscoelastic moduli measured by single-bead passive microbead rheology are much smaller than those determined by 2-point microrheology or bulk rheology [4]–[6] . Also , although distinct power law responses of the storage modulus have often been observed in vivo and in vitro [7]–[11] , their origin is not yet clearly understood . Concurrently , characteristics of semi-flexible polymer networks have been studied theoretically and computationally [24]–[34] . Two- [24] , [25] and 3-dimensional computational models [26] studying affine and nonaffine deformations of semi-flexible networks responding to large shear strain revealed two regimes dominated by bending or stretching of filaments , respectively . Recently , using a microstructure-based continuum mechanics approach , Palmer and Boyce reproduced many of the rheological properties of actin networks observed in experiments [27] . The viscoelastic behavior of semi-flexible networks was also investigated using dissipative particle dynamics and the concept of microbead rheology , where scale-free behavior of the bead displacement was observed [28] , [29] . To date , however , most of these models neither explicitly take into account ACP mechanics nor systematically account for thermal fluctuations , nor have they been used to explore the effects of finite prestress on viscoelasticity , all of which are potentially important factors governing matrix viscoelasticity . With the objective of extending these previous works and providing new insights into underlying mechanisms , we develop a Brownian dynamics ( BD ) model of the actin network that includes features such as steric interaction among filaments , the usage of explicit crosslinkers , a more realistic morphology , and the consideration of crosslinker stiffness . By measuring stress in response to applied oscillatory shear strain ( “bulk rheology” ) and thermal fluctuations of individual segments in the polymeric chain ( “segment-tracking rheology” ) , we investigate viscoelastic properties of actin-like networks . Throughout this paper , for convenience , the term , “actin network” is used to refer to the network being simulated . It should be noted , however , that some of the properties employed in our model , especially for ACPs , were estimated since they are not well-known experimentally . Due to simplifications in the model and parameter uncertainty , the results should therefore be viewed as representative of a generic crosslinked network , but lack a quantitatively precise correspondence to actin networks . Nevertheless , we found features that semi-quantitatively capture experimentally observed behaviors of actin networks . The storage and loss moduli , G′ and G″ , followed power laws as functions of the oscillation frequency . As the prestrain increased , the network became increasingly elastic . Bending and extensional stiffnesses of actin filaments and ACPs played an important role depending on the degree of prestrain . We found that the mechanical response of the network is dominated by a percolating ‘supportive framework , ’ while other actin filaments contribute little to the viscoelastic moduli . Surprisingly , in typical physiological conditions where the distance between crosslinking points along F-actin is much shorter than the actin persistence length , we found that thermal fluctuation plays little role in viscoelasticity , so that the network consisting of crosslinked F-actins can be viewed essentially as a deterministic overdamped system in a viscous medium . In sum , our computational model elucidates how various mechanical responses ( thermal forces and the bending and stretching of actin filaments and ACPs ) govern viscoelastic properties of the network under different conditions .
To create an actin network , we began with a uniform distribution of actin monomers with ACPs dispersed randomly , and allowed the network to polymerize until 99% of the monomers were incorporated into filaments [35] . Motion of the monomers , filaments , and ACPs followed Brownian dynamics , and bonding occurred according to a first order irreversible process . After polymerization , we applied a coarse-graining procedure explained in Methods to cover a larger system size . In the resulting networks , the filament length distribution and the network morphology [35] bear a closer resemblance to reconstituted actin gels ( Figure 1 ) than those generated by the random placement of equal-length filaments [24]–[26] , [29] , [32] . F-actins and ACPs in our model are characterized by introducing bending and extensional stiffnesses ( Table 1 ) . Two types of ACPs were used , depending on whether they form bundles ( ACPB ) or orthogonal networks ( ACPC ) during the polymerization process with equilibrium crosslinking angles of 0 and π/2 , respectively , between the two crosslinked actin filaments . Viscoelasticity of the network was probed in two ways: 1 ) In segment-tracking rheology , we analyzed the random thermal motion of many individual actin segments in the network [36] . 2 ) In bulk rheology , we applied an oscillatory shear strain on the top surface of the system while holding the bottom surface fixed . The measured viscoelastic moduli may depend on detailed geometry and the extent of percolation , especially if the network is small . Therefore , the use of a geometrically identical network for all simulations enables us to systematically control and isolate the effects of a given parameter . We thus used the two representative networks shown in Figure 1 for our measurements . The filament length ( Lf ) was 1 . 5 µm±0 . 65 µm ( average±standard deviation ) , and the actin concentration ( CA ) was 12 . 1 µM . We randomly removed ACPs from the networks to change R , the ratio of ACP concentration to CA ( parameters are defined in Table 1 ) . Actin filaments longer than the width of the simulation domain ( 2 . 8 µm ) were severed to reduce finite size effects . While recognizing the limitations of any direct quantitative comparisons between our model predictions and experiments , we conducted one set of experimental measurements under conditions similar to those of the simulation ( mean filament length <Lf> = 1 . 5 µm . , R = 0 . 01 , and CA = 12 . 1 µM ) and compared viscoelastic moduli . In order to match <Lf> , gelsolin was added to the sample; the length distribution was determined by fluorescence imaging . One difficulty in matching simulation and experimental conditions originates from the fact that R in the experiment corresponds to the total amount of ACPs in the sample , including both those in active and inactive ( partially bound or free ) states , whereas in our simulation , it indicates the net amount of active ACPs that crosslink or bundle two filaments . In addition , due to computational constraints , the oscillation frequency tested in the simulation overlaps that of the bulk rheology experiment only in a narrow range . Despite these difficulties , as seen in Figure 2 , the values of G′ and G″ computed using the model are in reasonable agreement with the experiment , both qualitatively and quantitatively , perhaps better than one might have expected given the range of uncertainty of some of the parameters . We computed G′ and G″ of structures crosslinked via ACPC ( R = 0 , 0 . 01 , and 0 . 021 ) and those bundled via ACPB ( R = 0 , 0 . 01 , 0 . 02 , and 0 . 04 ) using both bulk rheology and segment-tracking rheology . Networks without ACPs exhibit a slope of G′ close to 0 . 75 , as indicated by the black solid line in Figures 3A and 4A . This value has been observed in various experiments [8] , [37]–[39] , and it is known to originate from transverse thermal undulations of actin filaments [30] . Interestingly , when repulsive forces between the filaments are eliminated , the slope of G′ estimated via segment-tracking rheology approaches unity ( data not shown ) , implying that the volume exclusion effect of neighboring filaments creates a tube-shaped space that hampers free translation and rotation of the filament [31] . Although the filament confined in this way can perform reptation , it is operative on long time scales that are beyond those attainable in these simulations , so the mean square displacement ( MSD ) observed here primarily reflects transverse thermal motions , resulting in the 0 . 75 slope . On the other hand , the plateau in G′ often exhibited by experiments [6] , [12] , [40] was not observed within the frequency range of these simulations . Note that the plateau modulus is induced by entanglement effects that become more pronounced at longer time scales . The combination in these simulations of relatively short filaments leading to longer entanglement time [12] and computational constraints precluding simulations for longer times limited our ability to observe a plateau . Values of viscoelastic moduli attained using bulk rheology and segment-tracking rheology exhibit surprisingly good agreement ( Figure 3 ) even though segment-tracking rheology ( Equation 11 ) was originally developed for a test particle much larger than the meshwork of filaments [36] . ACPC elevates the magnitude of G′ and reduces its slope ( Figure 3A ) , implying that the frequency dependence of G′ is reduced as networks incorporate more ACPC . G′ follows a power law , G′∼fs0 . 3 ( dashed line ) , for fs<100 Hz at the highest crosslink density , R = 0 . 021 , which is within the range of powers observed in cells , 0 . 15–0 . 3 [9]–[11] . However , the magnitude of G′ from these simulations was much lower than in vivo values . This is likely due to many factors , notably the absence of prestrain . G″ increases slightly as the amount of ACPC is increased , but the slope remains similar ( Figure 3B ) . In addition , a decrease in the phase delay , tan−1 ( G″/G′ ) ( Equation 10 ) , accompanies the increase in R , indicating that ACPC elevates the elasticity of the network . At fs∼103 Hz , the phase delay depends only weakly on R , but as frequency decreases , it decreases more quickly with higher R , implying a greater effect by crosslinking at lower frequencies . Networks bundled by ACPB exhibit a behavior distinctly different from that of ACPC ( Figure 4 ) . Large differences in G′ and G″ were observed between segment-tracking rheology and bulk rheology . This originates from the heterogeneity of the bundled network attributable to the small computational domain , for which viscoelastic moduli measured by bulk rheology depend strongly on whether or not there are bundles that percolate between the top and bottom boundaries . We thus discuss results of only segment-tracking rheology for networks formed by ACPB . ACPB increases G′ but has little effect on its slope in contrast to ACPC ( Figure 4A ) , whereas the phase delay is only slightly influenced by R . ACPC is able to form a well-percolated network even at relatively low R , so that the network gels more efficiently [14] and acts like a single-body elastic object in response to shear stress or strain . By contrast , ACPB bundles filaments together , resulting in the relatively low level of percolation for the same value of R as reflected by its low connectivity [35] . Although the diffusivity of bundled filaments is lower , the lack of connectivity leads to the absence of elastic behavior at long time scales . In order for ACPB to increase network elasticity to a similar extent by ACPC , its concentration may have to be much higher . Only bulk rheology is employed here since the application of prestrain leads to a highly nonuniform distribution of the load , with a small fraction of highly tensed filaments and a larger number of filaments under little or no stress . Due to such heterogeneity , segment tracking rheology underestimates G′ and G″ as it randomly traces NMSD segments from the entire network . The effect of prestrain on G′ is analogous to that of ACPC . As seen in Figure 5A , G′ increases and produces a weaker dependence on frequency at higher prestrain ( γ ) ; at γ = 0 . 55 , G′ is virtually independent of frequency and is nearly 100-fold larger than that at γ = 0 for fs<10 Hz . This means that large prestrain transforms the network into a highly elastic one that exhibits a phase delay close to 0 at all frequencies and results in G′ comparable to in vivo values [9]–[11] , wherein it has been postulated that prestress or prestrain plays a significant role [14] . G″ also exhibits interesting behavior; at high prestrain , it increases slightly at low frequency , similar to in vitro observations using heavy meromyosin ( HMM ) [18] , [41] , and a similar increase in G″ was also observed in vivo [19] , [38] . This suggests that at low frequencies , viscous effects play an important role . Tharmann and coworkers argued that this trend of G″ may be due to the unbinding of HMM . However , since unbinding was not permitted in these simulations , the increase in the low-frequency G″ with prestrain must originate from a different mechanism . Note that such a tendency may have been more evident if the simulations were capable of reaching even lower frequencies . Also , the relation between G′ at fs = 3 . 16 Hz and prestress ( τ0 ) was investigated . It remains relatively constant until a threshold prestress ( τ0∼0 . 1 Pa , Figure 5B ) beyond which it increases following a power law , G′∼τ00 . 85 . The exponent of 0 . 85 is close to the value of ∼1 found in in vitro experiments under similar conditions [14] . To illustrate how prestrain transforms a network into a more elastic one , we display the network using a color scale depending on bond length averaged for duration of 0 . 1 ms . Only a small number of actin filaments aligned in the x-z direction are highly stretched ( Figure 6A , B ) . As mentioned above , this heterogeneity precludes using segment-tracking rheology which measures thermal motions of randomly selected segments , many of which are not a part of the highly stretched filaments in prestrained networks . The mean filament length of the entire network increased by only 0 . 5% with γ = 0 . 55 . However , due to the large value of κs , A ( Table 1 ) , this results in large spring forces that contribute significantly to the high magnitude of G′ . We also performed simulations with different extensional stiffness ( κs , A = 0 . 0338 and 0 . 0068 N/m ) for networks with γ = 0 . 55 ( Figure 6C ) . G′ and G″ decreases with lower κs , A , but its phase delay is virtually unchanged ( data not shown ) . When γ = 0 , however , variation in κs , A has little or no effect since most actin filaments are not highly stretched ( Figure 6D ) . Therefore , κs , A affects viscoelasticity only under high prestrain . We also studied the influence of κs , A on G′ ( fs = 3 . 16 Hz ) at different levels of prestress ( Figure 6E ) . Interestingly , when κs , A is reduced to 0 . 0068 N/m , the previous slope of 0 . 85 decreases to about 0 . 7 , suggesting that the slope of the curve in the nonlinear regime increases with κs , A . Note that we used a value of κs , A that is 1/40 of the experimentally measured value due to computational efficiency . Our result is thus consistent with a larger slope observed in experiments , ∼1 [14] . To address factors affecting viscoelasticity at low prestrain , we probed the influence of actin filament bending stiffness , κb , A , by using three different values ( κb , A = 1 . 056×10−18 , 1 . 056×10−19 , and 1 . 056×10−20 Nm ) at γ = 0 ( Figure 7A ) and 0 . 4 ( Figure 7B ) . At high prestrain , γ = 0 . 4 , variations in κb , A have little effect on G′ and only minor effects on G″ , suggesting that in the high prestrain regime , actin bending stiffness does not play a major role in viscoelastic properties ( Figure 7B ) . By contrast , at γ = 0 , κb , A influences G′ ( Figure 7A ) , although perhaps not to the extent that one might expect for reductions in κb , A by factors of 10 and 100 . We also studied the effect of thermal fluctuation of actin filaments using separate simulations with or without the Langevin force term , FiB ( Equation 1 ) , at various γ and lp ( ∼κb , A/kBT , with T = 300 K ) ( Figure 7C ) . Without FiB , the temperature of the system drops to ∼10 K , as measured by applying the equipartition theorem , where the nonzero temperature is due to the externally imposed oscillation . Figure 7C shows the ratio of G′ without thermal fluctuation ( TF ) to that with TF at fs = 10 Hz under each condition . At high prestrain ( γ≥0 . 4 ) , the elimination of TF does not affect G′ regardless of lp . Surprisingly , for filaments with bending stiffness comparable to that of actin ( lp = 10–20 µm ) , elimination of thermal effects had no significant effect on G′ at any value of γ . Only when κb , A was decreased ( lp≤3 µm ) did thermal fluctuations have a noticeable influence on G′ . Under these conditions , lp became comparable to the average distance between crosslinks , lc , ( 0 . 393 µm at R = 0 . 021 ) , in which entropic effects would be expected to play a role [15] , especially with a low prestrain . However , it is not clear whether this range of conditions can be attained in vivo . This finding that the relation between lp and lc determines the importance of thermal fluctuations is consistent with previous qualitative predictions [24] . Yet , our result is at odds with a previous view that entropic effects due to thermal undulations of individual filaments are responsible for the elasticity of scruin-crosslinked [15] , [16] and HMM-crosslinked [18] networks even in the case when lc≪lp . In previous numerical studies [25] , [26] , thermal fluctuations were applied only before applying shear strain , not during the measurement of stress , and thus they cannot be used as a clear demonstration that thermal fluctuations are important . Further experimental , numerical , and theoretical investigations are necessary to clarify the role of thermal fluctuation and entropic elasticity . For instance , if network elasticity is mainly governed by enthalpy , and if thermal fluctuation plays little role , we expect that G′ will be minimally affected upon adding crowding agents to increase solvent viscosity and to provide steric barrier to conformational motion . However , crowding agents may affect organization of filaments , such as enhanced bundling [42] . Thus , such experiments will need to be carefully interpreted . Also , the vibrational motion of actin filaments depending on lc measured in a computational study can be compared to theoretical predictions in [24] . Previous studies showed that the detailed structure of ACPs strongly influences viscoelastic moduli of actin networks . In [20] , each synthetically constructed crosslinking molecule of a different length produced distinctive macroscopic mechanical behaviors . In addition , Gardel et al . observed significantly different stress-strain relationships between a reconstituted actin network with intact filamin A ( FLNa ) and one with mutated hingeless FLNa [14] . Since these previous results led us to anticipate substantial effects of ACPs in our actin networks , we investigated the influence of bending stiffness of ACPC , κb , ACP , 1 and κb , ACP , 2 . As described in Methods , κb , ACP , 1 acts between two arms of the ACP , whereas κb , ACP , 2 limits bending between one arm of the ACP and the axis of actin filament to which it is attached , with an equilibrium value of π/2 . To study their effects , we decreased both κb , ACP , 1 and κb , ACP , 2 10 fold , 100 fold , and to zero . This had a significant effect on G′ and G″ at all prestrains ( Figure 8 ) , but especially so for the magnitude of G′ in the highly prestrained case with γ = 0 . 4 . At small prestrain ( γ = 0 ) , deformation is mostly associated with the bending of actin filaments , and the ACP angles remain close to their equilibrium values . As prestrain increases ( to 0 . 4 ) , the actin filaments are progressively straightened , especially those that support the bulk of the load . Since this is accompanied by changes in the angle between crosslinked actin filaments , bending stiffness of ACPs becomes an important determinant of G′ . As prestrain further increases ( to 0 . 55 ) , ACPs are maximally bent , and stretching of actin filaments is the dominant mechanism for resisting deformation since changing the extensional modulus has the greatest influence on G′ . It should be noted , however , that though the geometry of ACPC mimics that of FLNa , the large values of κb , ACP are closer to that of stiff scruin . Studies involving mutant ACPC ( e . g . FLNa ) that differs in bending stiffness would further clarify our observation . A change in extensional stiffness of ACPC , κs , ACP , results in little change in G′ and G″ ( data not shown ) at low γ , but plays a significant role at high γ . This tendency is not surprising as unbending of the V-shaped ACPC should precede stretch of ACPC arms responding to the load . Based on the effects of bending and extensional stiffnesses of actin filaments and ACPs as well as thermal fluctuations discussed above , we can estimate the relative importance of each factor over a wide range of prestrain and identify regimes where different phenomena dominate the viscoelastic behavior . For this purpose , each stiffness was decreased by 25-fold from the standard case , and the ratio of G′ with the decreased stiffness to that with the normal stiffness was computed at fs = 10 Hz ( Figure 9 ) . A fall in extensional stiffness of both actin filaments ( κs , A ) and ACPs ( κs , ACP ) decreases G′ more as γ increases , implying that the stretch of filaments and ACPs plays an important role in strain-stiffening behavior at high γ , at least at the exaggerated levels of extensional compliance used in the simulations . Bending stiffness of ACPs , κb , ACP , is significant at all tested γ , but has the largest effect on G′ for γ∼0 . 3–0 . 4 . The influence of bending stiffness of actin filaments , κb , A , on G′ is interesting , passing through a minimum at γ = 0 . 2 . We measured G′ under the same conditions but without thermal fluctuation , and the minimum at γ = 0 . 2 disappeared; the ratios at γ = 0–0 . 2 are similar to each other . Therefore , thermal fluctuation of actin filaments contributes to an increase in G′ at low γ when lp is comparable to lc . Based on these observations , we propose three distinct regimes: i ) A low γ regime where κb , A is dominant , with thermal fluctuations playing a substantial role for lp≤lc . ii ) An intermediate γ regime in which the effect of κb , ACP becomes dominant . iii ) A high γ regime where κs , A and κs , ACP are the predominant factors . Transition from one regime to another , however , is not sharply defined . Others [25] , [26] have also argued that bending stiffness of filaments would dominate in the low γ regime , and extensional stiffness of filaments in the high γ regime , but they did not consider stiffness of ACPs as a parameter . Also , since these previous simulations lacked thermal fluctuations , their relative effects could not be assessed . Figure 6B shows that only a subset of the entire network supports the dominant portion of the stress in prestrained networks . While there could be many different criteria for identifying such a ‘supportive framework , ’ we considered two , based either on the stretch of actin filaments or on the bending of ACPs . In the first case , we deleted filaments that were not highly stretched . The remaining network , however , when oscillated at the same strain amplitude , produced very low stress levels , suggesting that a significant fraction of the stress-supporting elements had been removed by this process . In the second case , we use bending force on ACPs as a selection criterion since the change in κb , ACP had a strong effect on viscoelastic moduli ( Figure 8 ) . First , the sum of magnitudes of all bending forces applied on each ACP in a prestrained state was calculated during 0 . 1 ms , the portion of ACPs with the highest bending forces were selected , and the remaining ACPs were deleted . All actin filaments not connecting between a pair of these highly strained and bent ACPs were removed . We compared stresses exerted on networks in which 25% , 50% , and 75% of ACPs remain under the same imposed prestrain . The network containing only 25% of initial ACPs ( Figure 10A ) consists of filaments oriented mostly in the diagonal direction on the x-z plane ( cf . , Figure 6B ) . Moreover , as the orientational distribution shows ( Figure 10B , inset ) , there are ( less stretched ) filaments oriented in other directions that also help to transmit the applied load , by bending of connecting ACPs , which are not selected when a criterion based on stretch of actin filaments is used . The mean stress and oscillation amplitude of the reduced network remain nearly at original levels ( Figure 10B ) . For example , the network containing only ∼28% of the original actin filaments and ∼25% of ACPs produced ∼70% of the original mean and amplitude of oscillating stress at the same levels of strain . Thus , bending force on ACPs is a major determinant of the elasticity of prestrained networks . We also confirmed that at high prestrain , ACPs experiencing large bending forces tend to displace in a manner consistent with the deformations being affine , as measured by the S parameter used in [43] ( data not shown ) . This means that parts of the supportive framework that bear greater levels of force experience more affine deformation . It is well known that most cells are in a prestressed state largely due to the actomyosin contractile apparatus [9] , [44] , [45] . By analogy , we hypothesize that the large G′ ( ∼103 Pa ) measured in cells is mostly due to the supportive framework composed of the contractile apparatus while the rest of cytoskeletal structures play comparatively little role in the macroscopic viscoelasticity of cells . Most ACPs have finite binding lifetimes leading to dissociation and reformation of crosslinks , which is known to play an important role in the viscoelastic moduli and rheology of actin networks [22] , [46] , [47] . However , ACPs in our present simulation do not undergo dynamic rearrangement . This artifact renders the level of stress responding to prestrain unreasonably high . While stress catastrophically drops beyond 1∼30 Pa in experiments [14] , [16] , [20] , [48] , it can increase over 100 Pa in our simulation . The absence of crosslink dynamics causes G′ in Fig . 5A to be much higher and to exhibit a lower power compared to experiments . Nevertheless , our present results provide valuable insights as well as benchmarks whereby stress relaxation or dynamic reorganization of networks can be compared as an extension of this work . Using Brownian dynamics simulations , we systematically investigated viscoelastic moduli of actin-like networks . First , viscoelastic moduli measured in our model compared favorably with those of experiments conducted using a bulk rheometer under similar conditions . Then , effects of the kind and concentration of ACPs on G′ and G″ were investigated . With no ACP , G′ exhibited a power law slope of 0 . 75 against frequency , reflecting the transverse thermal motion of actin filamets . ACPC tended to produce networks that were more elastic with a weaker dependence of G′ on frequency and smaller phase delay than ACPB . The influence of prestrain on G′ and G″ was also studied . High prestrain makes the network more elastic mainly via the stretch of a small fraction of aligned actin filaments . In addition , the effects of extensional and bending stiffnesses of actin filaments and ACPs on viscoelasticity were tested individually . We found that for viscoelastic moduli , three regimes are evident , each governed by different mechanisms depending on the amount of prestrain . At low prestrain , bending stiffness of ACPs and actin filaments plays an important role in determining viscoelasticity . At intermediate prestrain , bending stiffness of ACPs affects viscoelastic moduli the most . At high prestrain , the extensional stiffness of actin filaments and ACPs becomes dominant . We also found that entropic effects due to thermal fluctuations of filaments are important only when the prestrain is low and the average distance between active ACPs is comparable to the filament persistence length . Last , we identified the supportive framework that largely accounts for the high elasticity of prestrained networks , as that associated with strained ACPs rather than stretched actin filaments . Even after 75% of the network components are deleted , stress remains at 70% of the original value . Our computational model using the discrete network of crosslinked F-actins provides insights into the microscopic origin of the viscoelastic behavior of the actin cytoskeleton . Importantly , our findings regarding the limited role of thermal fluctuation , as well as the different contributions to viscoelastic responses by the bending or stretching of F-actin or ACPs , require additional experiments for further investigation . The present computational framework can be further developed to study phenomena associated with ACP unbinding , such as stress relaxation , network reorganization , and plastic deformation by shear .
In our previous model , we treated a segment of F-actin as a spherical particle representing two G-actins . To simulate larger length and time scales , we introduced coarse-graining in which a cylindrical segment represents several monomers . We kept thermal forces on ACPs and actin segments in a form similar to our previous model , but incorporated a cylindrical geometry for calculating repulsive forces . As seen in Figure 11 , the points of two adjacent elements on a filament correspond to the ends of one cylindrical segment representing NC actin monomers of the previous model . Accordingly , the diameter of the cylindrical segment , σC , A , is the same as the diameter of actin monomer of the previous model , σA = 7 nm , and its length , LC , A , is NC·σA . By letting each monomer of the polymerized network evolve into a cylindrical segment , a larger network is created , albeit with a lower concentration of ∼1–100 µM . In addition , one point on a filament axis and the other point representing the center of an ACP determine the two end points of each arm of the ACP , indicating that ACPs have a structure with two cylindrical arms whose length and diameter are LC , ACP and σC , ACP respectively . In this study , NC was set to 10; this degree of coarse-graining is appropriate since the length of one rigid cylindrical segment , LC , A = 70 nm , is still much shorter than the persistence length of an actin filament , ∼10 µm . One consequence of this coarse-graining technique is that it increases the arm length of ACPs . That is , since the redundant volume of the original monomers is neglected , the arm length of ACPs is extended by ( LC , A−σC , A ) /2 . This produces a network for which the shortest distance between two bundled filaments is ( LC , A−σC , A ) /2 ( 63 nm in this case ) that exceeds the typical spacing formed by many of the bundling ACPs ( e . g . fascin , fimbrin , and α-actinin ) . However , it is not expected that this will significantly alter the qualitative behavior of bundled networks since the change in the ACP arm length is much shorter than the length of F-actin . Previous experiments where different bundling ACPs led to distinct macroscopic behaviors [21] , [22] are more likely due to the different stiffness and dissimilar binding affinities of the ACPs rather than their physical size . Repulsive forces are computed according to the following harmonic potential depending on the minimum distance , , between two cylindrical segments , 1 and 2: ( 4 ) where and are diameters of the cylindrical segments ( i = A or ACP ) , and is the strength of repulsive effects . Forces calculated from the potential ( Equation 4 ) , , are distributed onto the two end points constituting the cylindrical segment , α and β , via the following equations: ( 5 ) where y represents the location on the segment at which the minimum distance , r12 , is measured ( Figure 11B; 0≤y≤LC , i ) . Monomer assembly and disassembly are important determinants of the morphology of actin networks . Given the rate constants obtained in recent in vitro experiments [49] , it would take ∼100 s for an actin filament 1 . 5 µm in length to completely depolymerize , or ∼1–10 s to polymerize the same filament with CA = 12 . 1 µM . Though in vitro depolymerization of F-actin is very slow , various ACPs can accelerate it in vivo . However , we assume here for simplicity that neither polymerization nor depolymerization of actin filaments occurs within the time scale of interest in this study , ∼1 s . Unlike our previous model [35] , an ACP can bind to any point along an actin filament in any circumferential direction . The measurement of viscoelastic moduli can be influenced by the detailed geometry and the extent of percolation , especially if the network is small . Therefore , the use of a geometrically identical network for all simulations enables us to systematically control and isolate the effect of a given parameter . In other studies , actin networks have been generated by the random placement of equal-length filaments [24]–[26] , [29] , [32] . However , we generated more realistic networks using the previous model that incorporates both polymerization and crosslinking . A somewhat heterogeneous network bundled by ACPB ( Figure 1A ) and a well-percolated , homogeneous network crosslinked by ACPC ( Figure 1B ) were prepared . In Figure 1A , ladder-like structures consisting of two actin filaments and multiple ACPB are evident , in contrast to thick bundles that are often observed in experiments . The relative absence of thicker bundles in these simulations is attributable to the small domain size compared to an average filament length . With NC = 10 , the filament length ( Lf ) is 1 . 5 µm±0 . 65 µm ( average±standard deviation ) , and the actin concentration ( CA ) is 12 . 1 µM . We randomly removed ACPs to change R ( Table 1 ) , while maintaining the overall network geometry . In addition , actin filaments longer than the width of the simulation domain ( 2 . 8 µm ) were severed to minimize finite size effects . Upon coarse-graining , several parameters need to be adjusted . For the cylindrical geometry of the segments , approximate forms for friction coefficients are [50]: ( 6 ) where η is the viscosity of the surrounding medium . However , since the mostly crosslinked cylindrical segments move predominantly in the transverse direction , and considering that is only 1 . 64 times higher than , was used in all directions for simplicity . To test this assumption , we compared simulations with and without the anisotropic friction coefficient at zero prestrain . At fs = 10 Hz , we obtained G′ = 5 . 07 Pa ( isotropic ) and 5 . 00 Pa ( anisotropic ) , and G″ = 2 . 02 Pa ( isotropic ) and 2 . 39 Pa ( anisotropic ) . Since motion along the filament axis is more suppressed with prestrain , the effect of anisotropic friction coefficient will be even less . We also assumed a constant friction coefficient regardless of the filament length to which the segment belongs . Hydrodynamic interactions between filaments are expected to play little role and were ignored since the volume fraction of actin is low ( ∼0 . 1% ) , and because the filaments have high aspect ratio ( long thin rod ) [51] . Since the geometry of ACPs changes after coarse-graining , the following equilibrium values for additional harmonic potentials were used for ACPB: ( 7 ) and for ACPC: ( 8 ) where and are the length and diameter of a cylindrical arm of ACP , is the equilibrium angle between two arms of ACP , and is the angle formed by an arm of ACP and the axis of the actin filament to which the ACP is bound . , , and are stiffness constants related to , , and , respectively ( Figure 11A ) . The value of is the same as in the previous model , and of ACPC was adjusted to maintain an equilibrium minimal distance of 70 nm between two crosslinked filaments . was set to be one fortieth that of an actin filament due to computational efficiency . and of ACP were estimated to be similar to the bending stiffness of an actin filament . Other parameters were also modulated according to the altered scale , as listed in Table 1 . In our previous model by which the network was generated [35] , a crosslink was allowed only if the torsional angle between two filaments was close to the equilibrium value ( 0 for ACPB and π/2 for ACPC ) , and a finite stiffness was assigned to the torsional angle between crosslinked filaments . However , because the other two bending forces can preclude free torsional rotation , the torsional force is neglected here for simplicity . The concept of a strain-controlled bulk rheometer used in experiments was adopted in order to measure the viscoelastic moduli of the generated networks . First , all actin filaments were severed at the upper and lower boundaries , and the periodic boundary condition on those surfaces was deactivated . Cylindrical actin segments within 70 nm from the bottom surface were fixed , whereas those within the top 70 nm were forced to move following an imposed strain . For the application of prestrain , the top boundary was translated at a constant strain rate , , up to the desired strain . To measure differential viscoelastic moduli as in [14] , a small sinusoidal strain ( 5% ) was superposed on top of the finite prestrain . The sum of forces on the ends of filaments attached to the top boundary was calculated , and divided by the area of the top surface to determine stress . Only the force component parallel to the surface was considered . In addition , due to the small dimension of the system , the time scale for water diffusion through the computational domain is of order 10 µs . Since this is smaller than the smallest period of oscillatory strain , we assumed that the imposed shear strain immediately induces a linear velocity profile within the fluid . Consequently , after calculating the stress due to filament forces , we added a shear stress expressed as: ( 9 ) where τzx is the shear stress exerted in the x-direction on a plane perpendicular to the z-direction ( pointing from the bottom to the top face ) , and γx is the shear strain applied in the x-direction . The induced velocity of medium affects the movement of elements via the v∞ term of Equation 2 . Finally , dividing the measured stress ( τzx ) by the differential strain ( γx ) , viscoelastic moduli , G′ and G″ , can be evaluated: ( 10 ) where is phase delay between strain and stress , and and are the amplitude of the differential stress and differential strain , respectively . Note that is zero for a perfectly elastic material and equals for a perfectly viscous one . We also measured the mechanical properties of in vitro F-actin networks with a rheometer ( AR-G2 , TA Instruments ) using a 40 mm parallel plate geometry . Lyophilized actin monomer from rabbit skeletal muscle was purchased from ( Denver , CO ) . To minimize artifacts caused by sample preparations [52] , the actin was stored at high concentration ( 10 mg/ml ) at −80°C and thawed rapidly at 37°C before each experiment . Recombinant filamin A was purified from Sf9 cell lysates , and recombinant human gelsolin was produced in Escherichia coli . Solutions of gelsolin , filamin , actin polymerization buffer , and actin were gently mixed . The solutions were then loaded within 10 s into a rheometer to form a crosslinked F-actin network . After 2 hr of polymerization at room temperature , frequency-dependent shear moduli , G′ and G″ , were measured in the range of 0 . 1–10 Hz . To obtain mechanical properties in the linear elastic regime , strain was maintained below 2% . Many experiments have used microbead rheology to probe viscoelastic moduli of actin gels based on the concept that the thermal motion of the bead is reflective of the gel's viscoelastic properties . Here , we used a variation of this approach , and tracked thermal fluctuations of individual actin segments . First , the domain was divided into a cubic lattice comprised of NMSD cells ( NMSD = 512 ) of equal volume . Then , one cylindrical element was randomly selected per cell , and MSDs of the center of these elements were recorded over time . Using a well-known approximate method [36] , G′ and G″ were calculated from the MSDs . Considering that this method was initially designed for a spherical bead , it was appropriately modified for cylindrical elements: ( 11 ) where rb is the effective radius of an actin segment , which satisfies . is the gamma function , where is the power law exponent describing the logarithmic slope of at , . In spite of the coarse-graining using cylindrical segments , the length and time scales that our model can attain are still much smaller than those of usual experiments . For example , Lf in this computational model is a few microns at most , and the width of the 3-D domain is less than 5 µm . In such a small domain , it is difficult to investigate the effects of a wide range of Lf or CA on viscoelastic moduli since Lf cannot be longer than the width of the domain to avoid artifacts associated with self-repulsion , and because an increase in CA was achieved by a decrease in domain size with a constant number of molecules . If we increase the number of molecules , simulation time significantly increases . On the other hand , many in vitro studies have used quite long actin filaments ( ∼20 µm ) with relatively large systems of size ∼O ( 1 mm ) . It takes about 16 days to reach 1 s in typical conditions using an Intel Xeon 2 . 66GHz CPU , but experimental results span up to 100–1000 s . Thus , an exhaustive comparison between computational and experimental results was not possible . | The actin cytoskeleton provides structural integrity to a cell , is highly dynamic , and plays a central role in a wide variety of phenomena such as migration and the sensation of external forces . For years , researchers have studied the mechanics of the cytoskeleton by creating actin gels in the laboratory in combination with proteins that bridge between and reinforce the actin gel found inside cells . These gels , however , failed to replicate many aspects of cell behavior . Recent studies have shown that tension within the cytoskeleton contributes to the observed stiffness of cells . Still , our understanding of cytoskeletal mechanics is incomplete , and many observed phenomena cannot be explained by existing models . Here , we simulate a three-dimensional network containing actin filaments linked together by other proteins . We studied the relative contributions of thermal fluctuations of the network and the stiffness of filaments and linking proteins . Under conditions that replicate those in a cell , properties of the linking proteins are surprisingly significant , as is the stiffness of the actin filament to stretching . Thermal fluctuations are relatively unimportant , but become more so at low levels of resting tension . At high tensions , a small fraction of filaments support a majority of the load . | [
"Abstract",
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] | [
"biophysics/theory",
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] | 2009 | Computational Analysis of Viscoelastic Properties of Crosslinked Actin Networks |
Contemporary computational accounts of instrumental conditioning have emphasized a role for a model-based system in which values are computed with reference to a rich model of the structure of the world , and a model-free system in which values are updated without encoding such structure . Much less studied is the possibility of a similar distinction operating at the level of Pavlovian conditioning . In the present study , we scanned human participants while they participated in a Pavlovian conditioning task with a simple structure while measuring activity in the human amygdala using a high-resolution fMRI protocol . After fitting a model-based algorithm and a variety of model-free algorithms to the fMRI data , we found evidence for the superiority of a model-based algorithm in accounting for activity in the amygdala compared to the model-free counterparts . These findings support an important role for model-based algorithms in describing the processes underpinning Pavlovian conditioning , as well as providing evidence of a role for the human amygdala in model-based inference .
Neural computations mediating instrumental conditioning are suggested to depend on two distinct mechanisms: a model-based reinforcement learning system , in which the value of actions are computed on the basis of a rich knowledge of the states of the world and the nature of the transitions between states , and a “model-free” reinforcement learning system in which action-values are updated incrementally via a reward prediction error without using a rich representation of the structure of the decision problem [1]–[6] . Accumulating evidence supports the existence of model-based representations during instrumental conditioning in a number of brain regions , including the ventromedial prefrontal cortex , striatum and parietal cortex [7]–[9] . However , instrumental conditioning is not the only associative learning mechanism in which model-based computations might play a role . Pavlovian conditioning can also be framed as a model-based learning process , in which the animal begins with a model of the possible structure of the world: the stimuli within it , and sets of possible contingencies that could exist between conditioned stimuli and unconditioned stimuli , as well as assumptions about how these contingencies might change over time . In essence , learning within such a system corresponds to determining the statistical evidence for which structure out of the set of possible causal structures best describes the environment , as well as determining whether or when the relevant causal processes have changed as a function of time . Model-based approaches to classical conditioning to date have used Bayesian methods to yield inference over structure space [10] . Very little is known about the extent to which such model-based algorithms are implemented in the brain during Pavlovian conditioning . The aim of the present study was to address this question using computational fMRI . Human participants were scanned while undergoing a Pavlovian conditioning procedure with a sufficiently complex structure to enable the predictions of model-based and model-free algorithms to be compared and contrasted ( see Figure 1 ) . We then constructed a Bayesian algorithm incorporating a model of the structure of the learning problem and compared the predictions of this algorithm against two widely adopted prediction-error driven “model-free” algorithms for Pavlovian conditioning: the Rescorla-Wagner ( RW ) learning rule [11] and the Pearce-Hall ( PH ) learning rule [12] as well as a recently developed model which combines the two: the Hybrid model [13] . In order to test for model-based signals in the brain we focused on the amygdala , a structure heavily implicated in Pavlovian conditioning in both animal and human studies [14]–[17] . To obtain signals from this region with sufficient fidelity , we used a high-resolution fMRI protocol in which we acquired images with more than four times the resolution of a standard 3 mm isotropic scan , alongside an amygdala specific normalization procedure [18] . We hypothesized that the model-based algorithm would account better for both behavioral and fMRI data acquired during both the appetitive and aversive conditioning phases than would the models of Pavlovian conditioning which do not contain such structured knowledge .
We report results from our analyses based on our model-based learning algorithm ( the HMM model ) within the amygdala using a height threshold of p<0 . 005 , with an extent threshold significant at p<0 . 05 corrected for multiple comparisons . We first report signals correlating with signals generated by our model-based HMM , and then we compare the performance of our model-based algorithm against its model-free counterparts in terms of the capacity of these models to account for BOLD activity in the amygdala .
In this study , we used a Pavlovian conditioning task with a rudimentary higher-order structure in both appetitive and aversive domains to investigate whether neural activity in the human amygdala reflects learning that requires access to model-based representations . By comparing neural activity correlating with expected value signals generated by model-based versus model-free learning algorithms using a Bayesian model selection ( BMS ) procedure , we have been able to show that in at least some parts of the human amygdala activity during Pavlovian conditioning is better accounted for by a model-based algorithm rather than by prediction error driven model-free algorithms . One of the critical distinctions between the prediction error driven model-free and model-based learning algorithms in the present study is that while the expected value of a stimulus previously paired with the unpleasant outcome is still low following reversal of contingencies because that was the value it had before reversal in a model-free system , the expected value of this stimulus will become high in a model-based system because it incorporates the knowledge that after a reversal , stimulus values switch ( i . e . there is full resolution of uncertainty when a reversal occurs ) . We have captured model-based representations in formal terms using an elementary Bayesian hidden Markov computational model that incorporates the task structure ( by encoding the inverse relationship between the cues and featuring a known probability that the contingencies will reverse ) . Our behavioral analysis demonstrated that participants showed evidence of conditioned responses to the conditioned stimuli and thus successfully learnt the associations between the different cues and outcomes . In a trial-by-trial analysis in which we correlated reaction times against the model predictions , we found that the HMM model predicted changes in reaction times over time as a function of learning better than the prediction-error driven model-free alternatives , and that the prediction error model-free algorithms did not predict variation in reaction times significantly better than chance . In the neuroimaging data , we found trial-by-trial positive correlations of model-based expected values in an area consistent with the basolateral complex of the amygdala according to the Mai atlas in the appetitive session , and in areas in the likely vicinity of the centromedial complex in the aversive session [25] . It is interesting to note that activity in these same areas ( i . e . basolateral versus centromedial complex ) has been found to correlate with expected value signals generated by a simple RW model in a recent reward versus avoidance instrumental learning task ( in an appetitive versus aversive context respectively ) [18] . Using a BMS procedure , we found that amygdala activity correlating with expected value was best explained by the model-based than by the prediction error driven model-free learning algorithms . Whereas the model-free system has received considerable attention in the past [26] , the more sophisticated and flexible model-based system , has been more sparsely studied particularly in relation to its role in Pavlovian learning . Thus , our results point to the need for integrating model-based representations and their rich adaptability into our understanding of Pavlovian conditioning in general , and of the role of the amygdala in implementing this learning process in particular . Another important feature of the model-based algorithm featured in this study , is that as well as keeping track of expected value , this model also keeps track of the degree of precision in the prediction of expected value over the course of learning . This precision starts off low at the beginning of a learning session with a new stimulus because the expected value computation is very uncertain at this juncture , but once outcomes are experienced in response to specific cues , the precision in the estimate quickly increases . However , this precision lessens again as the trial progresses because a reversal in the contingencies is increasingly expected to occur ( hence the expected value becomes more and more uncertain ) . Signals correlating with precision were found to be located in the vicinity of the centromedial complex in both the appetitive and aversive sessions . Precision signals might play an important role in the directing of attentional resources toward stimuli in the environment . The presence of a precision signal in the centromedial amygdala in the present paradigm could be a key computational signal underpinning the putative role of this structure in directing attention and orienting toward affectively significant stimuli . The presence of a precision-related signal in the amygdala during Pavlovian conditioning may relate to other findings in which the amygdala has been suggested to play a role in “associability” as implemented in a model-free algorithm such as the Pearce-Hall learning rule [13] , [27] . Associability as defined in such a model is essentially a model-free computation of uncertainty , the inverse of precision: associability is maximal when the absolute value difference between expected and actual rewards is greatest . However , in our case , an associability signal is clearly distinct from the signal we observe in the amygdala in the centromedial complex ( even leaving aside the fact the signal we found is negatively as opposed to positively correlated with uncertainty ) . First of all , because the signal in our HMM is model-based , it changes to reflect anticipated changes in task structure ( such as a reversal ) , whereas Pearce-Hall associability does not change to reflect anticipated changes in task structure , both rather changes only reflexively once contingencies have reversed . Further evidence that the amygdala is involved in model-based computations came from an additional analysis in which we compared the signals generated by our model-based HMM against signals generated by a reduced version of our HMM in which knowledge of when contingencies are expected to reverse was not incorporated . Although this reduced model still generated very similar expected value signals as the model-based HMM and thus made similar predictions about behavior , the precision signals generated by these two algorithms are quite distinct and can therefore be compared against neural activity in the amygdala . In a direct comparison , activity in the amygdala was best accounted for by the precision signal generated by the full HMM . It is interesting to note that evidence for model-based processing in the amygdala was more robust in the aversive case given the traditional view of the amygdala as being associated especially with aversive processing . However , it is unlikely that this pattern of results reflects a qualitative difference in the way that appetitive and aversive learning is mediated by the amygdala , particularly in the light of considerable evidence implicating this structure in both reward-related as well as aversive-learning [28] , [29] . Finally , we checked the correlation between the precision signal we found here and an associability signal generated by the Pearce-Hall learning rule , and we found the correlation between these signals to be essentially negligible ( with r ranging from −0 . 06 to −0 . 14 ) , as opposed to being strongly negatively or positively correlated as would be anticipated were these signals to tap similar underlying processes . The fact that in the present study we found model-based signals in the amygdala does indicate that this structure is capable of performing model-based inference even during Pavlovian conditioning . However , it is important to note that the findings of the present study do not rule out a role for this structure in prediction error driven model-free computations during Pavlovian conditioning . Indeed , while the prediction error driven model-free learning rules we used did not work very well in accounting for behavior on the task ( as indexed by changes in reaction times ) , we did find some evidence ( albeit weakly ) of model-free value signals in the amygdala as generated by either a Rescorla-Wagner , a Pearce-Hall or a Hybrid learning rule . Indeed , while using our HMM model we did not find evidence for aversive-going expected value signals in the aversive session ( i . e . by showing an increase in activity the more the unpleasant tasting liquid was expected ) , we did find such a signal correlating with expected value as computed by a Pearce-Hall learning rule . As a consequence , we cannot rule out a contribution for the amygdala in model-free computations . It is important to note however , that in many tasks in which neuronal activity was found in the amygdala to correlate with the predictions of model-free learning algorithms [18] , [30]–[32] , such tasks were either not set up to discriminate the predictions of model-free versus model-based learning rules , or else the relevant model comparisons were not performed . Thus , it is entirely feasible that many of the computations found in the amygdala in previous studies correspond more closely to model-based as opposed to model-free learning signals . More generally , if indeed , both model-based and model-free signals are present in the amygdala during Pavlovian conditioning , then an important question for future research will be to address how and when these signals interact with each other . To conclude , we have found in the present study evidence for the existence of model-based learning signals in the human amygdala during performance of a Pavlovian conditioning task with a simple task structure . These findings provide an important new perspective into the functions of the amygdala by suggesting that this structure may participate in model-based computations in which abstract knowledge of the structure of the world is taken into account when computing signals leading to the elicitation of Pavlovian conditioned responses . The findings also resonate with an emerging theme in the neurobiology of reinforcement learning whereby value signals are suggested to be computed via two mechanisms: a model-based and a model-free approach [1] , [3] . Whereas up to now , theoretical and experimental work on this distinction has tended to be focused on the domain of instrumental conditioning [4] , [7] , [8] , the present study illustrates how similar principles may well apply even at the level of Pavlovian conditioning . Thus the distinction between model-based and model-free learning systems may apply at a much more general level across multiple types of associative learning in the brain . Furthermore , the present results provide evidence that model-based computations may be present not only in prefrontal cortex and striatum , but also in other brain structures such as the amygdala .
Nineteen right-handed subjects ( 8 females ) with a mean age of 22 . 21±3 . 47 participated in the study . All subjects were free of neurological or psychiatric disorders and had normal or correct-to-normal vision . Written informed consent was obtained from all subjects , and the study was approved by the Trinity College School of Psychology research ethics committee . Subjects participated in a Pavlovian task where they had to learn associations between different cues ( fractal images ) and a pleasant ( blackcurrant juice [Ribena , Glaxo-Smithkline , UK] ) , affectively neutral ( artificial saliva made of 25 mM KCl and 2 . 5 mM NaHCO3 ) or unpleasant ( salty tea made of 2 black tea bags and 29 g of salt per liter ) flavor liquid . The task consisted of two sessions lasting approximately 22 minutes each . Each session was composed of 120 trials , leading to a total of 240 trials . In one of the sessions , subjects underwent an appetitive Pavlovian conditioning procedure whereby they were presented with cues leading to the subsequent delivery of either the pleasant flavor , or the affectively neutral one , while in the other aversive conditioning session , subjects underwent an aversive conditioning procedure whereby they were presented with cues leading to the subsequent delivery of either the unpleasant flavor stimulus , or else the affectively neutral stimulus . The rationale for including the appetitive and aversive conditioning procedures in separate sessions as opposed to including both conditions intermixed within the same sessions was to avoid contrast effects observed in prior behavioral piloting whereby cues signaling the aversive outcome tended to overwhelm cues signaling the pleasant one such that both the pleasant and the neutral cue stimuli were viewed as relief stimuli ( contrasted against the aversive outcome ) [33] . Performing the appetitive and aversive conditioning procedures in separate sessions ensured robust behavioral conditioning in both the appetitive and aversive cases and largely avoided contrast effects between the appetitive and aversive conditions . For both sessions , on each trial , a cue was displayed randomly on either the left or right side of a fixation cross for 4 seconds . Following a well-established Pavlovian conditioning protocol [34]–[36] , subjects were also instructed to indicate on which side of the screen the cue was presented by means of pressing the laterally corresponding button on a response box , yet they were also instructed that the subsequent outcomes were not contingent on their responses . This serves two purposes: it allows one to monitor the extent to which participants are paying attention to the cues on each trial , as well as offering a response time measure which can serve as an index of conditioning . The offset of the cue ( after 4 seconds ) was followed by delivery of one of the liquid flavor stimuli with a probability of 0 . 6 , or else no liquid stimulus was delivered . The next trial was triggered following a variable 2–11 secs inter-trial interval . At the beginning of each session , subjects were presented with two novel fractal cues ( not seen before in the course of the experiment ) : which we will denote as cue 1 and cue 2 . In the appetitive session , cue 1 predicted the subsequent presentation of the pleasant liquid 60% of the time ( or no liquid delivery 40% of the time ) , while in the aversive session cue 1 predicted the delivery of the aversive liquid 60% of the time ( or no liquid delivery 40% of the time ) . Cue 1 and cue 2 trials were presented in a randomly intermixed order . After 16 trials ( 8 trials of each type ) , a reversal of the cue-outcome associations was set to occur with a probability of 0 . 25 on each subsequent trial . The probabilistic triggering of the reversal after the 16th trial ensured that the onset of the reversal was not fully predictable by subjects . Once a reversal was triggered , cue 1 no longer predicted the appetitive or aversive outcome but instead was associated with delivery of the neutral outcome , while cue 2 now predicted the appetitive or aversive outcome . After another 16 trials ( 8 trials of each type ) following the onset of the reversal , another event was triggered to occur with probability 0 . 25 on one of the subsequent trials: this time instead of a reversal , a completely novel pair of stimuli was introduced . One of these , cue 3 , was now paired with the appetitive or aversive outcome , while cue 4 was now paired with the neutral outcome . These new cues were presented for a further 16 trials , and followed again after a probabilistic trigger of p = 0 . 25 on each subsequent trial with a reversal of the associations . After the reversal , a new set of cues were introduced according to the same probabilistic rule and this was followed again by a reversal . Thus in total , 3 unique pairs of stimuli were used in each session and each of these pairs underwent a single reversal ( Figure 1a , b ) . A completely different set of cues were used for each session , so that subjects experienced a total of 6 pairs of fractal stimuli throughout the whole experiment . Within each session , the presentation order of the affective and neutral cue presentations was randomized throughout , with the one constraint that the cue predicting the neutral tasting liquid delivery had to be delivered twice every four trials . This ensured that the appetitive and neutral cues , and aversive and neutral cues were approximately evenly distributed in their presentation throughout the appetitive and aversive sessions respectively . All fractal images were matched for luminance . The order of the sessions was counterbalanced across subjects so that half of the subjects started the experiment with the appetitive session and half of the subjects with the aversive session . Before the conditioning session , subjects received the following task instructions: “In each trial , an image will appear on the screen and may be followed by some liquid delivery . There are six different images per session . Each image will lead to either a pleasant , neutral or unpleasant tasting liquid . You will have to learn these associations . However , during the experiment , this may change ( or reverse ) , making image 1 associated with the liquid of image 2 and image 2 associated with the liquid of image 1 . This reversal may actually happen more than once during the experiment and you have to fully pay attention and realize that it has happened . These cues may change during the experiment , so that you will have to learn these associations again with these new cues ( which may also reverse ) . At the beginning of each trial , the image will either appear on the left or right side of the screen . You will have to press the left button of the response pad if the image appears on the left side , or the right button if it appears on the right side . It is important that you press the button because we need to record your response times , although the trial will carry on if you don't press any button . At the beginning and end of each session , we will ask you to rate different images and liquids . You will also have to rate these images in the middle of each session . ” The pleasant , neutral and unpleasant tasting liquids were delivered by means of three separate electronic syringe pumps positioned in the scanner control room . These pumps pushed 1 mL of liquid to the subject's mouth via ∼10 m long polyethylene plastic tubes , the other end of which were held between the subject's lips like a straw , while they lay supine in the scanner . Functional imaging was performed on a 3T Philips scanner equipped with an 8-channel SENSE ( sensitivity encoding ) head coil . Since the focus of our study was on the amygdala , we only acquired partial T2*-weighted images centered to include the amygdala while subjects were performing the task . These images also encompassed the ventral part of the prefrontal cortex , the ventral striatum , the insula , the hippocampus , the ventral part of the occipital lobe and the upper part of the cerebellum ( amongst other regions ) . Nineteen contiguous sequential ascending slices of echo-planar T2*-weighted images were acquired in each volume , with a slice thickness of 2 . 2 mm and a 0 . 3 mm gap between slices ( in-plane resolution: 1 . 58×1 . 63 mm; repetition time ( TR ) : 2000 ms; echo time ( TE ) : 30 ms; field of view: 196×196×47 . 2 mm; matrix: 128×128 ) . A whole-brain high-resolution T1-weighted structural scan ( voxel size: 0 . 9×0 . 9×0 . 9 mm ) and three whole-brain T2*-weighted images were also acquired for each subject . To address the problem of spatial EPI distortions which are particularly prominent in the medial temporal lobe ( MTL ) and especially in the amygdala , we also acquired gradient field maps . To provide a measure of swallowing motion , a motion-sensitive inductive coil was attached to the subjects' throat using a Velcro strap . The time course derived from this measure was used as a regressor of no interest in the fMRI data analysis . Finally , to account for the effects of physiological noise in the fMRI data , subjects' cardiac and respiratory signals were recorded with a pulse oximeter and a pressure sensor placed on the umbilical region and further removed from time-series images . We discarded the first 3 volumes before data processing and statistical analysis to compensate for the T1 saturation effects . All EPI volumes ( ‘partial’ scans acquired while subjects were performing the task and the three whole-brain functional scans acquired prior to the experiment ) were corrected for differences in slice acquisition and spatially realigned . The mean whole-brain EPI was co-registered with the T1-weighted structural image , and subsequently , all the partial volumes were co-registered with the registered mean whole-brain EPI image . Partial volumes were then unwarped using the gradient field maps . After the structural scan was normalized to a standard T1 template , the same transformation was applied to all the partial volumes with a resampled voxel size of 0 . 9×0 . 9×0 . 9 mm . In order to maximize the spatial resolution of our data , no spatial smoothing kernel was applied to the data . These preprocessing steps were performed using the statistical parametric mapping software SPM5 ( Wellcome Department of Imaging Neuroscience , London , UK ) . To test whether amygdala activity was better explained by model-based or model-free learning algorithms , we correlated brain activity in this region with expected value signals estimated by a number of different computational models . In model-free learning algorithms , the agent is surprised when a reversal occurs and starts learning again after it happens , whereas in model-based learning algorithms , the agent expects the reversal and considers it as resolution of uncertainty and does not need to relearn . The two modes of learning are diametrically opposed in the current task , therefore allowing us to test whether amygdala is tracking model-based or model-free computations . To perform a formal model comparison on the behavioral conditioning data , we used the trial-by-trial reaction time data ( measuring the length of time taken on each trial for participants to press a button to indicate which side of the screen the Pavlovian cue stimulus had been presented ) . Many previous studies have shown that changes in RTs to a Pavlovian cue are correlated with changes in associative encoding between cues and behaviorally significant outcomes [13] , [34] , [36] , [42] . For each session separately , we log transformed and adjusted the RT data to account for a linear trend in RTs over time independently of trial type , as well as to remove the effects of changes in reaction time related to switching responses from one side of the screen to the other . This was done by regressing the log transformed RTs against a matrix containing a column of ones , a column accounting for the linear trend over time and a column indicating whether participants switched their response from left to right or vice versa between the current and previous trial using the function regress in Matlab . Using the same function , we then regressed these adjusted response times against the expected values generated by our model-based HMM our model-free RW , PH and Hybrid algorithms and our baseline model ( for the baseline model , a small amount of noise was added to each expected value in order to compute the regression; without any noise the regression would not be calculable ) . This second regression analysis was run for each of these models , and cycled through all the possible learning rate parameters for the RW model and CS intensity parameters for the Pearce-Hall and hybrid models between 0 and 1 , with increments of 0 . 001 . This method returned Sum Squared Error ( SSE ) values for each of these parameter values thereby allowing us to obtain the best fitting value for the free parameter for the appetitive and aversive sessions ( i . e . the free parameter associated with the lowest SSE value ) . In order to compare the model goodness between these four different algorithms , we converted the best SSE value of each session ( appetitive and aversive ) and each model into a Bayesian information criterion ( BIC ) value . The BIC adds a penalty proportional to the number of additional free parameters to the SSE value of each model , depending also on the number of degrees of freedom which in this case , is the total number of trials per session across all subjects [43] . Using this procedure , we found that in both the appetitive and aversive sessions , the model-based HMM outperformed the prediction-error driven model-free algorithms ( Table 1 ) . In the model validation analyses , where we compared the prediction-error driven models against a random baseline model , only the model-based HMM fit our behavioral data significantly better than the baseline model ( Table 2 ) . Hence , unlike RW , PH and the Hybrid model , the model-based HMM predicted RTs better than chance performance . Note that we did not regress the expected values generated by our reduced HMM since they were highly correlated with that of our model-based HMM . The event-related fMRI data were analyzed by constructing sets of δ ( stick ) functions at the time of cue presentation and at the time of outcome for the appetitive and aversive sessions . For our main GLM ( illustrated in Figures 4 and 5 ) , additional regressors were constructed by using the expected values and the precision values generated by the model-based HMM as modulating parameters at the time of cue presentation . In order to compare model-based versus model-free learning algorithms in the amygdala , we ran three additional GLMs . For RW , the regressors were similar to our model-based HMM except that we did not have a regressor for precision which is not estimated by RW , and we added a modulating parameter for prediction error at the time of outcome . The regressors used in the GLM computed using PH model were the same as the ones used in our model-based HMM , except that the precision modulating parameter was replaced with an associability modulating parameter at the time of cue presentation ( note that similar regressors were used for the Hybrid model ) . Finally , we ran an analysis using our reduced model HMM using the same regressors as for our model-based HMM . All of these regressors were convolved with a canonical hemodynamic response function ( HRF ) . The six scan-to-scan motion parameters derived from the affine part of the realignment procedure were included as regressors of no interest to account for residual motion effects . To account for motion of the subjects' throat during swallowing , we added a regressor of no interest for swallowing motion . Finally , we also included thirteen additional regressors to account for physiological fluctuations ( 4 related to heart rate , 9 related to respiration ) which were estimated using the RETROICOR algorithm [44] . Six of the 38 ( 2 sessions×19 subjects ) log files could not be used to estimate these regressors due to a technical problem during data collection , and the missing physiological regressors were simply omitted for those sessions . All of these regressors were entered into a general linear model and fitted to each subject individually using SPM5 . The resulting parameter estimates for regressors of interest were then entered into second-level one sample t-tests to generate the random-effects level statistics used to obtain the results shown in figures 4 and 5 . All reported fMRI statistics and p values arise from group random-effects analyses . We present our statistical maps at a threshold of p<0 . 005 , corrected for multiple comparisons at p<0 . 05 . To correct for multiple comparisons , we first used the 3dFWHMx function in AFNI to estimate the intrinsic smoothness of our data , within the area defined by a mask corresponding to our amygdala template . We then used the AlphaSim function in AFNI to estimate via Monte Carlo simulation an extent threshold for statistical significance that was corrected for multiple comparisons at p<0 . 05 for a height threshold of p<0 . 005 within the amygdala ROI . In order to test whether amygdala activity was better accounted for by the model-based than model-free learning algorithms , we used a Bayesian model selection procedure ( BMS ) [45] . For both the appetitive and aversive sessions , we included in this model comparison individual betas averaged across voxels within a 4 mm sphere centered on the peak voxels of the amygdalar activities correlating with either expected value signals for the HMM or the model-free algorithms using the leave-one out method , thereby avoiding a non-independence bias in the voxel selection [46] . Using the spm_BMS function in SPM8 , we compared expected value signals across all model-based ( HMM ) and model-free models separately for the appetitive and aversive sessions . We used a similar approach to compare neural activity pertaining to precision signals estimated by our model-based and reduced model HMMs . The difference between these two HMMs is that the model-based HMM does not allow for a reversal without moving from a “non-reversal state” to a “possible reversal state” . As a consequence , the precision values generated by these models are clearly distinguishable and thus easily comparable using a BMS ( whereas the estimated expected rewards are strongly correlated ) . Again , we included in this model comparison voxels within a 4 mm sphere centered on the peak voxels of the amygdalar activities correlating with precision signals for either the model-based HMM or the reduced model HMM using the leave-one out method . Here , we compared activity correlating with precision signals between the model-based and reduced HMM separately for the appetitive and aversive sessions ( see Results section for the exceedance probabilities ) . Functional regions of interest ( ROIs ) were defined using the MarsBaR toolbox ( http://marsbar . sourceforge . net/ ) . Beta estimates were extracted for each subject from the functional clusters of interest as they appeared on the statistical maps of a given contrast using the leave-one out method to avoid a non-independence bias . They were then averaged across subjects to plot expected reward ( Figure 4b ) and precision ( Figure 5b ) according to 3 categories ( category 1 corresponding to the lowest values and category 3 corresponding to the highest values ) . | A hot topic in the neurobiology of learning is the idea that there may be two distinct mechanisms for learning in the brain: a model-based learning system in which predictions are made with respect to a rich internal model of the learning environment , versus a “model-free” mechanism in which trial-and-error learning occurs without any rich internal representation of the world . While the focus in the literature to date has been on the role of these mechanisms in instrumental conditioning , almost nothing is known about whether more fundamental kinds of learning such as Pavlovian conditioning also involve model-based processes . Furthermore , nothing is known about the extent to which the amygdala , which is known to be a core structure for Pavlovian learning , contains neural signals consistent with a model-based mechanism . To address this question , we used a novel Pavlovian conditioning task and scanned human volunteers with a special high-resolution fMRI sequence that enabled us to obtain signals within the amygdala with over four times the resolution of conventional imaging protocols . Using this approach in combination with sophisticated computational analyses , we find evidence to suggest that the human amygdala is involved in model-based computations during Pavlovian conditioning . | [
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] | 2013 | Evidence for Model-based Computations in the Human Amygdala during Pavlovian Conditioning |
Human cytomegalovirus ( HCMV ) forms two gH/gL glycoprotein complexes , gH/gL/gO and gH/gL/pUL ( 128 , 130 , 131A ) , which determine the tropism , the entry pathways and the mode of spread of the virus . For murine cytomegalovirus ( MCMV ) , which serves as a model for HCMV , a gH/gL/gO complex functionally homologous to the HCMV gH/gL/gO complex has been described . Knock-out of MCMV gO does impair , but not abolish , virus spread indicating that also MCMV might form an alternative gH/gL complex . Here , we show that the MCMV CC chemokine MCK-2 forms a complex with the glycoprotein gH , a complex which is incorporated into the virion . We could additionally show that mutants lacking both , gO and MCK-2 are not able to produce infectious virus . Trans-complementation of these double mutants with either gO or MCK-2 showed that both proteins can promote infection of host cells , although through different entry pathways . MCK-2 has been extensively studied in vivo by others . It has been shown to be involved in attracting cells for virus dissemination and in regulating antiviral host responses . We now show that MCK-2 , by forming a complex with gH , strongly promotes infection of macrophages in vitro and in vivo . Thus , MCK-2 may play a dual role in MCMV infection , as a chemokine regulating the host response and attracting specific target cells and as part of a glycoprotein complex promoting entry into cells crucial for virus dissemination .
Herpesviruses enter their host cells either by fusion of the viral envelope with the plasma membrane or with membranes of endocytotic vesicles . The fusion process is promoted by a concerted action of the conserved viral glycoproteins gB , gH , and gL [1] of which gH and gL consistently form a tight heterodimer [2] , [3] . These three glycoproteins can promote receptor recognition and subsequent fusion as has been shown for the entry of Epstein-Barr virus ( EBV ) into epithelial cells [1] . Often , gB and gH/gL are not sufficient to promote receptor recognition . For instance , entry may depend on further envelope glycoproteins , as has been shown for gD of Herpes simplex virus [1] , or on gH/gL forming tight complexes with additional viral proteins , as has for example been shown for the gH/gL/gp42 complex of EBV [4]–[6] , the gH/gL/Q1/Q2 complex of HHV6 [7] , or the gH/gL/pUL ( 128 , 130 , 131A ) complex of HCMV [8]–[10] . For HCMV , two gH/gL complexes have been identified . In vitro , formation of gH/gL/gO ensures efficient production of infectious supernatant virus and promotes entry into a restricted set of cells by fusion at the plasma membrane [11] , [12] . In the absence of gO , HCMV spreads in a cell-associated manner [11] . A restriction of cell tropism for mutants lacking gO has not been observed . The second complex , gH/gL/pUL ( 128 , 130 , 131A ) promotes entry into a broad range of HCMV host cells including endothelial , epithelial , and dendritic cells [13] , [14] by using endocytotic pathways [15]–[17] . Data published recently strongly suggest that gH/gL/gO and gH/gL/pUL ( 128 , 130 , 131A ) promote virus entry through distinct cellular receptors [18] , [19] . Depending on the HCMV strain analyzed , gO has been found to be incorporated into the virion or not [20]–[22] . The UL128 , UL130 and UL131A gene products have consistently been shown to be incorporated into the virion [8] , [9] , [20] , [21] , [23] , [24] . Their precise functions in the entry process have not yet been determined . It is also not known what the exact functions of gH/gL/gO and gH/gL/pUL ( 128 , 130 , 131A ) are in the infection of humans . In a recent publication , we could show that the gH/gL complexes of HCMV are distributed to distinct virus populations which consequently differ in their cell tropism . In vitro , host cells like fibroblasts and endothelial cells either released or retained the population promoting infection of endothelial cells . We have proposed that , by determining the target cells of their virus progeny , host cells may route infection in vivo [23] . Infection of mice with MCMV serves as an animal model for the HCMV infection . We have recently identified the m74 ORF of MCMV as a functional homolog of HCMV gO [15] . Although the MCMV genome does not contain sequence homologs for HCMV UL128 , UL130 , and UL131A , the relative positions of the MCMV m130 , m131/129 , and m133 ORFs within the MCMV genome are comparable to the positions of the UL128 , UL130 , and UL131A ORFs in the HCMV genome . The m130 gene product has not been characterized . Deletion of m133 has been shown to result in reduced virus growth in salivary glands in vivo [25] , [26] . The ORFs of m131 and m129 are fused by a splicing event which results in a protein product designated MCK-2 [27] , [28] . The m131-derived part of MCK-2 contains , like the UL128 protein of HCMV , a CC ( ß ) chemokine domain . Besides that , MCK-2 does not show further sequence homologies to the UL128 gene product . MCK-2 and synthetic peptides of the m131 ORF or the complete MCK-2 have been shown to attract monocytes confirming its predicted chemokine activity [29] , [30] . When mice are infected with MCMV mutants lacking MCK-2 the most apparent phenotype is a reduced virus production in salivary glands [28] , [31] , [32] . MCK-2 knock-out mutants are impaired in recruiting leukocytes which might serve as vehicles for virus dissemination [29] , [30] , [32] . Some populations of the attracted leukocytes have been shown to control virus specific CD8+ T cell immunity [33] . Yet , these populations differ from the myelomonocytic cells which are infected at the site of virus entry [30] , [33] . Notably , MCK-2 knock-out viruses have additionally been shown to exhibit a 10-fold lower capacity to infect attracted myelomonocytic leukocytes [30] . Here , we report a completely new role for MCK-2 , namely , as part of a gH/gL/MCK-2 complex promoting entry into macrophages . This offers an explanation for the hitherto unexplained low infection capacities of MCK-2 knock-out viruses for leukocytes in vivo [30] . The gH/gL/MCK-2 complex can complement the function of the gH/gL/gO complex of MCMV with respect to virus spread in vitro and strongly increases the efficiency of MCMV in infecting macrophages in vitro and in vivo . We propose that MCK-2 might have a dual role in infection , one as a chemokine attracting cells regulating the host immune response or attracting MCMV target cells and one in infecting viral target cells promoting subsequent virus dissemination .
HCMV and MCMV lacking gO both show the same spread phenotype in vitro , namely , strongly reduced titers of infectious virus in supernatants of infected cells and a focal spread pattern . For HCMV , we could show that the residual focal spread of mutants lacking gO is dependent on the alternative gH/gL/pUL ( 128 , 130 , 131A ) complex [15] . To find out whether MCMV also forms an alternative gH/gL complex , we infected cells with bacterial artificial chromosome ( BAC ) -derived wildtype MCMV and precipitated gH-associated proteins from extracts of virus released into the supernatant by using an antibody specific for MCMV gH [34] . The precipitates were separated on SDS-polyacrylamide gels , proteins extracted from gel slices and then analyzed by liquid chromatography-tandem mass spectrometry . The obtained peptides were compared to MCMV gene translations . One prominent hit was a LLCLVR peptide which matches the C-terminus of the m131 ORF which together with the m129 ORF forms the MCMV MCK-2 protein ( data not shown ) . On Western blots , MCK-2 appears as multiple glycosylated forms running between 30 and 45 kDa ( [27] and ( Fig . 1A ) ) . When we prepared extracts of supernatant virus , MCK-2 ran at a slightly higher molecular weight than MCK-2 from extracts of infected cells ( Fig . 1A ) . This points towards a differentially modified protein . A similar pattern has been shown for MCK-2 secreted from transfected cells [27] . MCK-2 could also be detected in extracts of gradient purified virus which strongly suggests that it is incorporated into virions ( Fig . 1B ) . Under non-reducing conditions , MCK-2 migrated at a molecular weight of about 180 kDa ( Fig . 1B ) , which argues for MCK-2 forming a tight complex with other viral proteins in virions . As there is no antibody available which recognizes MCMV gH in Western blots , we constructed an MCMV BAC which expresses a C-terminally HA-tagged gH ( Fig . S1 ) which grew like wildtype virus ( Fig . S2 ) . gH-HA could easily be detected in extracts of supernatant virus ( Fig . 1C ) . Under reducing conditions it migrated at the expected molecular weight of about 85 kDa [34] . When supernatant virus from cells infected with MCMV-gH-HA was analyzed under non-reducing conditions , two prominent high molecular weight bands , one running slightly above and one running below the 180 kDa marker could be detected for gH-HA . The upper band co-migrated with MCK-2 ( Fig . 1C ) . Whereas an anti-gH antibody could precipitate all gH bands visible in extracts of supernatant virus , an anti-MCK-2 antibody specifically precipitated the band co-migrating with MCK-2 ( Fig . 1C ) . This band very likely represents a gH/gL/MCK-2 complex . The prominent gH-HA positive band below 180 kDa could represent a gH/gL complex consisting of gH-HA and the 274 amino acid long gL . We could also show that the upper band represents a complex containing gH and MCK-2 by using an MCMV-gH-HA mutant which carries a disrupted MCK-2 ORF ( MCMV-gH-HA/129stop ) ( Fig . S1 ) . This mutant does not express MCK-2 ( Fig . 1D , upper panel ) and lacks the upper gH-HA band under non-reducing conditions ( Fig . 1D , right lower panel ) . The protein extracts of the gH-HA/m129stop mutant were at least five times more concentrated than the extracts of the gH-HA virus which can be seen from the strength of the gH-HA band under reducing conditions and the lower gH-HA band under non-reducing conditions ( Fig . 1D , lower panel ) . Thus , it could be excluded that the gH/MCK-2 band had escaped detection . To show that the anti-MCK-2 antibody specifically co-precipitates gH and to confirm the co-precipitation of MCK-2 which we had found by mass spectrometry analysis of proteins precipitated with an anti-gH antibody , we performed the reverse co-immunoprecipitation using an anti-MCK-2 antibody to precipitate MCK-2 associated proteins . An extract was prepared from supernatant virus of cells infected with MCMV-gH-HA and aliquoted . Anti-gH and anti-HA antibodies readily precipitated gH-HA from this extract ( Fig . 1E ) . The anti-MCK-2 antibody clearly co-precipitated gH-HA , whereas a mouse control IgG antibody did not ( Fig . 1E ) . To show that gH/gL/gO and gH/gL/MCK-2 are alternative complexes , we used an MCMV BAC expressing HA-tagged gO . This BAC was generated by adding an HA-tag to the 3′ end of the m74 ORF and by introducing a duplication of the overlapping C-terminus of the m73 ORF ( gN ) to preserve the function of gN ( gO-HA , Fig . S1 ) . MCMV-gO-HA grew like wildtype virus in fibroblasts ( data not shown ) . We have previously shown that HA-tagged gO expressed in the virus context forms a complex of more than 200 kDa which can be precipitated with anti-gH and anti-HA antibodies [15] . To show that this complex is different from the complex formed by gH and MCK-2 , we compared extracts from supernatant virus from MCMV-gH-HA and MCMV-gO-HA . gO-HA could easily be detected in extracts of supernatant virus ( Fig . 2A ) . Under reducing conditions it migrated at the expected molecular weight of about 70 kDa [15] . Under non-reducing conditions a weak band representing the gH/gL/gO complex could only be detected after a very long exposure of the Western blots ( Fig . 2A ) . In Figure 2A extracts of supernatant virus from cells infected with either MCMV-gH-HA or MCMV-gO-HA are depicted side by side and stained for the HA-tag . To show the position of the gH/gL/gO complex more clearly , an anti-HA immunoprecipitation from extracts of infected cells was included [15] . The comparison of extracts from MCMV-gO-HA and MCMV-gH-HA shows that the gO and MCK-2 complexes clearly have different molecular weights . Additionally , anti-MCK-2 which had co-precipitated gH from lysates of supernatant virus ( Fig . 1 E ) , did not co-precipitate gO-HA from extraxts of infected cells , whereas anti-gH antibodies clearly co-precipitated gO-HA ( Fig . 2B ) . These findings strongly support that gH/gL/gO and gH/gL/MCK-2 are indeed distinct complexes . In total cell extracts ( data not shown ) and in extracts of supernatant virus infected with the gH-HA virus , the anti-HA antibody could not detect a complex corresponding to the gH/gL/gO complex ( Fig . 1C and Fig . 2A ) , a failure which might be due to a loss of the accessibility of the HA-tag of gH when the gH/gL/gO complex is formed . To analyze the role of MCK-2 within a glycoprotein complex promoting entry , we constructed BAC-derived MCMV mutants in which the m131/129 reading frame was disrupted by stop cassettes and which do not express MCK-2 ( Fig . S1 ) . MCK-2 knock-out mutants previously constructed by others have been extensively studied in vivo and shown to exhibit defects in recruitment of leukocytes , in virus dissemination , and in growth in salivary glands [28] , [30] , [32] . We have recently shown that an MCK-2 mutant of the MCMV strain Smith which was cloned as a BAC also showed a reduced growth in salivary glands in vivo [31] . None of these mutants has been reported to show attenuation in vitro [28] , [29] , [31] . We could confirm this for two clones of the 131stop mutant ( Fig . 3 ) . Multistep growth curves even exhibited a marginal growth advantage for the MCK-2 knock-out mutants at days 4 and 5 after infection , yet , even three independent growth curves could not show that these differences were statistically significant . For HCMV , it has been shown that the inability to form the alternative gH/gL/pUL ( 128 , 130 , 131A ) complex abolishes the tropism for cells like endothelial and epithelial cells . Thus , we infected primary ( MEF ) and immortalized fibroblasts ( NIH3T3 ) , endothelial cells ( MHEC-5T ) , and epithelial cells ( TCMK-1 ) with wildtype virus and 131stop mutants and compared the infection capacities by staining the cells for expression of the immediate early 1 ( IE1 ) protein of MCMV . The numbers of infected MEF cells were set to 100% and numbers of infected NIH3T3 , MHEC-5T , and TCMK-1 were expressed as percent of MEF infection ( Fig . 4A ) . No significant differences in infection capacities for fibroblasts or endothelial cells could be detected when wildtype and 131stop MCMV were compared . Only infection of TCMK-1 epithelial cells was slightly but significantly enhanced . As staining for expression of IE1 reflects successful entry , but not the ability to replicate in certain cell types , we also tested virus production of wildtype and 131stop viruses in these cell types but could not detect any differences ( data not shown ) . When MCK-2 knock-out mutants were analyzed in vivo , reduced capacities to infect myelomonocytic leukocytes were observed when compared to wildtype infections [30] . To find out whether this is also observed when closely related cells like macrophages are infected in vitro , we infected macrophage cell lines like ANA-1 or J774 , primary bone marrow derived macrophages ( BMDM ) , or macrophages directly from peritoneal exudates ( PEC/M ) . For the latter , infection was studied for cells in the macrophage-enriched gate of peritoneal exudate cells ( PEC ) from untreated BALB/c mice ( [35] and Fig . S3 ) . ANA-1 cells were infected with wildtype virus , two clones of the 131stop mutant , and a gH-HA/129stop mutant ( Fig . 4B ) . J774 cells were infected with wildtype virus , a 131stop mutant , and a pSM3fr BAC-derived virus which carries a stop mutation in m129 and shows the typical reduction of growth in salivary glands after in vivo infection [31] ( Fig . 4B ) . BMDM and PEC were infected with wildtype virus and a 131stop mutant ( Fig . 4C ) . For all macrophages tested , all mutants unable to express an intact MCK-2 showed a strongly and significantly reduced capacity to infect macrophages ( Fig . 4B and C ) . To exclude that the differences in infection capacities are due to soluble MCK-2 produced by cells infected with wildtype virus and not to the presence of a gH/gL/MCK-2 complex promoting infection , virus pelleted from supernatants of infected cells and purified by centrifugation through sucrose cushions was used . When mice were infected with the 131stop mutant , a reduced virus production in salivary glands was observed ( Fig . S4 ) as described for vpSM3fr [31] and for other MCK-2 mutants [28] , [32] . To study macrophage infection in vivo , we infected adult BALB/c mice with wildtype and 131stopD MCMV and analyzed F4/80- and CD11b-double-positive macrophages from the peritoneal cavity 6 hours post infection . We observed a more than 50% reduction of the percentage of MCMV-infected macrophages when mice were infected with the 131stopD mutant ( Fig . 5A ) . A significant reduction of normalized numbers of MCMV-infected macrophages could also be observed when immunocompromised BALB/c mice were infected via the footpad . Here , liver tissue sections were stained for F4/80 and MCMV IE1 protein 10 days after infection . Infected F4/80+IE1+ liver macrophages were counted and the numbers normalized to the numbers of all F4/80+ macrophages and all IE1+ cells present in the same tissue sections to take account of differences in the overall levels of infection and macrophage recruitment ( Fig . 5B and C ) . Thus , very consistently , infection of immortalized macrophages , of primary bone marrow-derived macrophages , of macrophages ex vivo , and of macrophages in vivo were impaired when infected with MCMV lacking MCK-2 . MCK-2 knock-out mutants only show very subtle phenotypes when their growth behavior is studied in vitro ( Fig . 3 and 4 ) . To evaluate the mechanism how MCK-2 controls infection of cells , it would be of advantage to analyze effects on a strong infection phenotype . For HCMV , it has been shown that knock-out of both , gO and pUL ( 128 , 130 , 131A ) abolishes the capacity of the virus to infect cells [11] . Assuming that also MCMV either uses gH/gL/gO or gH/gL/MCK-2 for promoting entry into cells , knock-out of both proteins should also abolish its capacity to infect cells . To test this , we constructed double mutants lacking gO ( Δm74 ) and additionally carrying stop cassettes either in the m129 ORF ( 129stop ) or the m131 ORF ( 131stop ) . Reconstitution of both double mutants resulted in infected cells from which infection could barely spread ( data not shown ) . Release of infectious virus into the supernatants could never be detected ( data not shown ) . Yet , infectious supernatant virus carrying double mutations could readily be produced by virus reconstitution in NIH3T3 cells expressing gO ( NIH3T3-gO ) or MCK-2 ( NIH3T3-MCK-2 ) ( data not shown ) . Infection of these trans-complementing cell lines with the double mutants did not result in detectable levels of recombination between the mutated loci and the wildtype m74 or m131/129 ORFs of the trans-complementing cells ( data not shown ) . We reconstituted the Δm74/131stop double mutant in NIH3T3-gO cells and used this virus to infect NIH3T3 , NIH3T3-MCK2 , or NIH3T3-gO cells . Double mutant virus produced in NIH3T3-gO cells should be gO-positive and MCK-2-negative and , after infection of new cells , virus progeny will be gO- and MCK-2-negative . If the new target cells are expressing gO then virus progeny will be gO-positive and MCK-2-negative . If the target cells are expressing MCK-2 , virus progeny will be gO-negative and MCK-2-positive . Thus , we could study spread of a virus with an identical genetic backbone , but a different protein complementation . We either infected cells at a very low m . o . i . to study spread ( Fig . 6A ) , or infection was enhanced by a centrifugation step to initially infect about 10% of cells to study virus production ( Fig . 6B ) . Spread of the double mutant in NIH3T3 cells and , thus , in the absence of MCK-2 and gO was highly restricted ( Fig . 6A , upper panel ) . Release of infectious virus , which was tested by titration of supernatants on NIH3T3-gO cells , could not be observed ( Fig . 6B ) . Spread of the double mutant in cultures of NIH3T3-MCK-2 cells was predominantly focal ( Fig . 6A , middle panel ) , and production of infectious supernatant virus was reduced when compared to the production by the double mutant growing in NIH3T3-gO cells ( Fig . 6B ) . Thus , complementation of the MCK-2 defect of the double mutant resulted in a growth pattern comparable to the growth pattern observed for Δm74 or m74stop mutants [15] . In NIH3T3-gO cells the double mutant readily spread ( Fig . 6A , lower panel ) and produced infectious virus like wildtype virus ( Fig . 6B ) . Thus , both , gO and MCK-2 could restore the spread deficiency of the double mutant , and for the first time we could show that MCK-2 is indeed promoting virus spread . MCK-2 restored not only efficient focal spread but also production of infectious virus . When supernatants where tested for DNAse-resistant viral DNA by real-time PCR , which should be an equivalent for DNA in virus particles , we found that independent of production of infectious virus , comparable numbers of viral DNA copies were released into the supernatants of NIH3T3 , NIH3T3-MCK-2 , and NIH3T3-gO cells ( Fig . 6C ) . DNA copy numbers in the cell culture supernatants were identical at time points 6 and 24 hours after infection and reflect leftovers of the input supernatant ( Fig . 6C ) . After 48 hours , the first round of replication was completed which was reflected by an increase in DNA copy numbers . In supernatants from infected NIH3T3 cells , the copy numbers were higher than in supernatants from trans-complementing cells . Very likely , this indicates that particles produced by NIH3T3 cells are not infectious , cannot enter new cells , and are accumulated , whereas particles from trans-complementing cells are infectious and infect new cells . At 96 h after infection , only supernatants from NIH3T3-gO and NIH3T3-MCK-2 cells which support efficient virus spread showed a further increase in DNA copy numbers mirroring a second round of infection ( Fig . 6C ) . In summary , the experiments with the double mutant showed that even in the absence of gO and MCK-2 , virus particles are produced and released , but they are not infectious . If the mutant is trans-complemented either with MCK-2 or gO , comparable numbers of virus particles are produced , but the infection capacities for fibroblasts seem to be lower when they are trans-complemented with MCK-2 . If gH/gL/MCK-2 is the alternative complex to gH/gL/gO with respect to promoting infection of host cells , antibodies directed against the MCK-2 complex should inhibit infection with MCMV lacking the gH/gL/gO complex but not infection with MCMV expressing gH/gL/gO . To study this , virus preparations were preincubated with a rabbit antiserum specific for MCK-2 or with a control rabbit antiserum . Then , cells were infected with these virus-antibody mixtures and infected cells were detected by staining the cells for expression of MCMV IE1 . Numbers of infected cells were expressed as percent of infected cells obtained with mock-treated virus . Infection of MEF and ANA-1 cells with a Δm74 mutant ( Fig . S1 ) , could be strongly and specifically inhibited when virus was preincubated with the anti-MCK-2 antiserum , whereas infection with a 131stop mutant which expresses gO , but lacks MCK-2 , could not be inhibited ( Fig . 7A ) . Thus , in the absence of gO , infection is MCK-2-dependent . If MEF cells were infected with the Δm74 mutant trans-complemented in NIH3T3-gO cells , the inhibition by anti-MCK-2 antibodies was abrogated although not completely ( Fig . 7A ) . This partial abrogation indicates that infection with the trans-complemented Δm74 mutant depends on gO and also on MCK-2 . We have shown before that infection of fibroblasts with MCMV lacking gO , but not with MCMV expressing gO , is energy- and pH-dependent [15] . To find out whether MCK-2 is promoting an energy- and pH-dependent entry pathway , we infected fibroblasts with a Δm74/m129stop mutant trans-complemented with gO or MCK-2 in the presence of inhibitors of ATP depletion or inhibitors of endosome acidification like bafilomycin A1 and NH4Cl [15] . Infection with MCK-2-complemented Δm74/m129stop MCMV was inhibited by all three inhibitors and inhibition was significantly different from inhibition of gO-complemented Δm74/m129stop MCMV ( Fig . 7B ) . Bafilomyin A1 even increased infection of gO-complemented Δm74/m129stop MCMV . The inhibitor studies clearly indicate that MCK-2 promotes an energy- and pH-dependent entry pathway which is different from entry promoted in the presence of gO .
gH/gL complexes of herpesviruses have been extensively studied over the past years . The major function attributed to gH/gL associated proteins is receptor recognition . For HCMV , two gH/gL complexes , gH/gL/gO and gH/gL/pUL ( 128 , 130 , 131A ) have been identified . gH/gL/gO determines entry into a restricted set of cell types and ensures efficient production of infectious supernatant virus in vitro [21] . The gH/gL/pUL ( 128 , 130 , 131A ) complex determines the broad cell tropism characteristic for HCMV , very likely by recognizing a receptor found on many different cell types . Infection of the mouse with MCMV has been shown to be a model for the HCMV infection in many , although not all , aspects [36]–[38] . We have recently characterized a functionally homologous gH/gL/gO complex of MCMV [15] . As MCMV mutants lacking gO can still infect cells and spread in cell culture , it was obvious that MCMV may also form a second gH/gL complex . The role of the chemokine homolog MCK-2 of MCMV has been studied in vivo by using viruses in which the MCK-2 gene was deleted . Reduced salivary gland titers and reduced numbers of infected peripheral blood leukocytes have been attributed to the missing chemokine function of MCK-2 . The observed phenotypes were explained by a role of MCK-2 in attracting myelomonocytic leukocytes to the site of infection which are then infected and promote dissemination and finally efficient infection of salivary glands [28]–[30] , [32] . Recently , it has been shown that MCK-2 also attracts inflammatory monocytes which down-modulate antiviral CD8+ T cell responses [33] . Yet , these monocytes are not targets of infection . Additionally , it has been described that MCK-2 knock-out mutants not only recruit less myelomonocytic leukocytes to the site of infection but are also highly impaired in infecting them [30] . This pointed to an additional protein function of MCK-2 which drives infection efficiencies . However , this putative function has never been addressed . Here , we propose a new function of MCK-2 which could explain the reduced infection efficiencies described above . We could show that MCK-2 forms a complex with gH which is incorporated into virions . It is known from crystal structures of other herpesviruses that gH and gL usually form tight heterodimers [2] , [3] , thus , the high molecular weight complex of gH and MCK-2 very likely is a gH/gL/MCK2 complex . In SDS-polyacrylamide gels , the complex showed a different size than the gH/gL/gO complex and anti-MCK-2 antibodies did not co-precipitate HA-tagged gO from extracts of cells infected with a virus expressing gO-HA indicating that the gH/gL/MCK-2 complex indeed is an alternative complex to gH/gL/gO . It is difficult to study how MCK-2 promotes infection of cells in vitro , as spread and virus production of MCK-2 knock-out mutants are not drastically affected . Therefore , we used MCMV mutants lacking gO or double mutants lacking both , gO and MCK-2 to evaluate the contribution of MCK-2 to infection . Infection of cells with gO knock-out mutants could be blocked with anti-MCK-2 antibodies which demonstrated that gH/gL/MCK-2 can act as an alternative mediator of virus spread when gH/gL/gO is not formed . Trans-complementation of Δm74/131stop double mutants with MCK-2 showed that MCK-2 promotes mainly focal spread . Supernatants of cells infected with this virus only showed low titers of infectious virus , although high numbers of virus particles were released . This suggests that virions complemented with MCK-2 , but lacking gO , are less efficient in infecting cells . In contrast to gO , MCK-2 promoted entry through a pH- and energy-dependent entry pathway as has been observed for MCMV mutants lacking gO . It is noteworthy in this context that in contrast to HCMV , where double mutants lacking gO and pUL ( 128 , 130 , 131A ) are lethal [11] , the MCMV double mutant can be reconstituted and spread in cell culture , although to a very limited degree and without producing free infectious virus . We do not know whether this residual spread occurs only by direct cell-to-cell transmission . It will have to be determined in the future whether MCK-2 and gO are directly involved in the entry process or whether they just promote infection as cofactors rendering target cells more susceptible for infection . Whether gH/gL/MCK-2 is a tripartite complex or can associate with additional proteins is currently not known . Potential candidates would be the m130 and m133 genes which neighbor the m131/129 ORF . In an analysis of the MCMV transcriptome , we found that the putative m130 ORF which lies on the opposite strand and overlaps with m131/129 is not transcribed ( data not shown ) . This is in line with data from Saederup et al . [32] who showed that interruption of the m130 ORF does not affect the phenotype of an m131/129 deletion mutant . It is intriguing that mutants lacking the m133 gene show , like MCK-2 mutants , reduced titers in salivary glands of infected mice [25] , [26] . We could not detect peptides derived from the m133 ORF by mass spectrometry of anti-gH precipitates ( data not shown ) . As this failure is not an absolute criterion to exclude that the m133 gene product is part of a gH/gL/MCK-2 complex , we deleted m133 and additionally m74 . In contrast to MCK-2stop/Δm74 double mutants , the 133stop/Δm74 double mutant grew like a Δm74 mutant ( data not shown ) . We observed a slight growth advantage for MCK-2 mutants in fibroblasts with respect to production of supernatant virus which was not detected before [28] , [31] . Interestingly , this finding is reminiscent of what was observed for UL131A mutants of HCMV [9] , [39] , and it might explain why isolates of MCMV do , just as isolates of HCMV , loose their capacity to form the second gH/gL complex during passage in fibroblasts [31] , [40] . HCMV , which cannot form a gH/gL/pUL ( 128 , 130 , 131A ) complex , completely loses its broad cell tropism in vitro , including its tropism for monocytes and macrophages , but can still infect fibroblasts like wildtype virus [41] , [42] . This is a strong phenotype and it implies that for HCMV , infection of most cells types depends on the gH/gL/pUL ( 128 , 130 , 131A ) complex . In contrast , deletion of MCK-2 was associated with a more restricted phenotype in vitro , namely , the loss of its capacity to efficiently infect macrophages . Additionally , an increased capacity to infect TCMK-1 epithelial cells was observed . This implies that the MCMV gH/gL/MCK-2 complex rather modulates infection capacities . Whether these differences reflect completely different roles for the gH/gL/MCK-2 complex of MCMV and the gH/gL/pUL ( 128 , 130 , 131A ) complex of HCMV or are due to different in vitro culture systems is not known . Comparable to HCMV , rhesus CMV ( RhCMV ) lacking its gH/gL/pUL ( 128 , 130 , 131 ) complex shows reduced infection capacities for endothelial and epithelial cells [43] , [44] but not for fibroblasts . Guinea pig CMV lacking its gH/gL/GP ( 129 , 131 , 133 ) complex loses its capacity to efficiently infect both , endothelial cells and fibroblasts [45] . Currently , it is not clear how these in vitro phenotypes translate to the in vivo infection . All CMV mutants , which lack the gH/gL complex containing a chemokine homolog , share one phenotype in vivo , namely , the loss of their capacity to efficiently establish infection in salivary glands [28] , [32] , [45]–[47] . We found that infection of mice with MCK-2 knock-out mutants results in reduced numbers of infected macrophages due to an impaired capacity of the mutants to infect the macrophages . How and whether this defect contributes to MCK-2 knock-out phenotypes like reduced viral titers in the salivary gland or elevated CD8+ T cell responses is currently not clear . It is also not known whether it is true for other cytomegaloviruses . Reduced virus replication of RhCMV mutants lacking gH/gL/pUL ( 128 , 130 , 131 ) is not restricted to salivary glands [47] . Yet , all in vivo studies performed so far used a RhCMV mutant which not only lacked a functional gH/gL/pUL ( 128 , 130 , 131 ) complex , but also additional viral genes coding for alpha chemokine-like proteins . When infection of different cell types in skin biopsies was tested for this RhCMV mutant a strong reduction in infection of endothelial cells and a slight , but not significant reduction in the numbers of infected macrophages was observed [48] . Both , MCMV gH/gL/MCK-2 and HCMV gH/gL/pUL ( 128 , 130 , 131A ) contain potentially functional CC chemokines . Recombinant UL128 protein can interfere with the chemokine responsiveness of monocytes [49] , and also isolated MCK-2 can act as a chemokine [29] , [30] . The r129 gene product of rat CMV which is homologous to HCMV UL128 has also been shown to induce migration of lymphocytes as a recombinant protein [50] . At the moment it is not known whether MCK-2 and UL128 promote infection as gH/gL complex constituents and exert their chemokine functions only as free proteins or whether both functions can be complex-associated . Co-immunoprecipitation of gH and MCK-2 was only possible using an antibody recognizing the m131 ORF , but not the m129 derived protein part which indicates that the latter is involved in complex formation , whereas the part containing the CC chemokine domain is accessible . Thus , complex formation might still allow chemokine function of MCK-2 . This also raises the question whether the chemokine function of MCK-2 and entry promoted by MCK-2 are both transmitted by the same cellular receptor . It will be of particular interest to find out whether it is possible to make MCK-2 mutants which are active chemokines , but no longer promote infection in the absence of gO or vice versa and to study them in vivo .
Primary mouse embryonal fibroblasts from BALB/c mice ( MEF ) , NIH3T3 cells ( ATCC: CRL-1658 ) , the endothelial cell line MHEC5-T [51] , the epithelial cell line TCMK-1 ( ATCC: CCL-139 ) , the macrophage cell line J774 ( ATCC: TIB-67 ) , and peritoneal exudates cells ( PEC ) from BALB/c mice were maintained in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% fetal calf serum . The macrophage cell line ANA-1 [52] was maintained in RPMI medium supplemented with 10% fetal calf serum . BMDM were prepared from BALB/c mice . Femurs and tibias were removed and cleaned , and bone marrow was flushed through with DMEM supplemented with 10% FCS , 2 mM L-glutamine , 100 U/mL penicillin , 100 µg/mL streptomycin and 50 µM 2-mercaptoethanol . To remove stromal cells , bone marrow cell suspensions were first seeded in 10 cm tissue culture dishes for four hours . Then , non-adherent cells were collected , resuspended in complete medium additionally containing 20 ng/ml murine recombinant M-CSF ( Peprotech ) , and cultivated for 7 days in 10 cm tissue culture dishes . During this time , non-adherent cells were removed daily and half of the medium was replaced by fresh , M-CSF containing medium . At day 7 , cells were harvested and used for FACS analysis and subsequent experiments . More than 95% of the cells generated by this method stained positive for the macrophage marker F4/80 ( data not shown ) . As wildtype MCMV , a BAC-derived virus ( pSM3fr-MCK-2fl ) cloned from MCMV strain Smith was used [31] . pSM3fr BAC-derived virus was used as an additional m129 stop mutant [31] , [53] . For infection experiments , supernatants from infected cells showing complete cytopathic effect ( CPE ) and precleared at 3 , 500× g were used . For production of supernatant virus for protein analysis , NIH3T3 cells were infected at an m . o . i . of 0 . 1 . Media were collected when a full CPE was observed , cleared at 6 , 000× g for 10 min and then pelleted for 4 h at 20 , 000× g . Virus stocks for analysis of macrophage infection efficiencies were prepared as described recently [31] . Virus titers were determined by a TCID50 assay performed in 96 well plates on MEF or on NIH3T3-gO . Monoclonal mouse anti-MCK2 antibodies 5A5 and 2H9 , rabbit anti-MCK2 antiserum WU1073 [27] , and rabbit anti-pUL131A antiserum [9] have been described before . HA-tagged proteins were detected with rat anti-HA antibody ( 3F10 , Roche Diagnostics ) . Mouse macrophages were stained with rat anti-F4/80 antibody ( BM8 , BioLegend ) . Mouse anti-MCMV gH ( 8D122A ) was kindly provided by Lambert Loh , University of Saskatchewan , Canada . Mouse anti-MCMV immediate early protein 1 ( IE1 ) antibody ( Croma101 ) was kindly provided by Stipan Jonjic , University of Rijeka , Croatia . An NIH3T3 cell line stably expressing gO has been described before [15] . For NIH3T3 cells stably expressing MCK-2 , the complete m131/129 ORF was amplified by PCR from a pCR3-MCK-2 expression vector and cloned in a modified pEPi-luc vector [54] following the same strategy as used for pEPi-gO [15] . The resulting plasmid pEPi-MCK-2 was transfected into NIH3T3 cells using Fugene ( Promega ) , and MCK-2 expressing cell clones isolated by limiting dilution under blasticidin S selection ( 10 µg/ml , Invivogen ) . MCK-2 expression was tested by staining cell extracts in the Western blot using an anti-MCK-2 antibody . For indirect immunofluorescence , adherent cells were fixed in 50% acetone-50% methanol and stained using anti-IE1 antibody and Fluor488-coupled goat anti-mouse antibody ( Invitrogen ) . For counterstaining of cell nuclei , cells were incubated in PBS containing 5 µg/ml Hoechst 333258 ( Invitrogen ) . For intracellular FACS staining , cells were detached with 0 . 5 mM Na-EDTA , fixed with 1% paraformaldehyde for 10 min and then stained in PBS containing 0 . 3% Saponin and 1% BSA using the antibodies described above . Cells were washed with PBS containing 0 . 03% Saponin . After staining , cells were resuspended in 1% paraformaldehyde and analyzed on a FACSCalibur using CellQuest software ( BD Biosciences ) . Cells or virus pellets were lysed in RIPA buffer ( 50 mM Tris ( pH 7 . 4 ) , 150 mM NaCl , 1 mM EDTA , 1% NP-40 , 0 . 1% SDS , 0 . 5% deoxycholate ) . Lysates were precleared with Sepharose G beads ( GE Healthcare ) and then , beads with antibody bound were added to the precleared lysates and coincubated for 4 h at 4°C . The beads were washed , proteins released in reducing sample buffer ( 0 . 13 M Tris-HCl ( pH 6 . 8 ) , 6% SDS , 10% α-thioglycerol ) or in non-reducing sample buffer without α-thioglycerol and subjected to SDS-PAGE , followed by either Western blot analysis or LC-MS/MS . For preparation of peptides for LC-MS/MS , gel slices were chopped from the SDS-PAGE , treated with water and ammonium bicarbonate , and afterwards dehydrated using acetonitrile . Samples were reduced in DTT buffer ( 10 mM DTT , 40 mM ammonium bicarbonate ) for 1 h and then alkylated with iodoacetamide buffer ( 55 mM iodoacetamide , 40 mM ammonium bicarbonate ) for another 30 min in the dark . After washing in 40 mM ammonium bicarbonate , gel slices were dehydrated again in acetonitrile and soaked in 40 mM ammonium bicarbonate containing sequencing grade modified trypsin ( Promega ) . Samples were incubated overnight at 37°C and resulting peptides were extracted by 5% formic acid , dried in a SpeedVac concentrator , resuspended in 15 µl 0 . 1% formic acid and analyzed in a nano-ESI-LC-MS/MS . Here , each sample was first separated on a C18 reversed phase column via a linear acetonitrile gradient ( UltiMate 3000 system , Dionex ) and column ( 75 µm i . d . ×15 cm , packed with C18 PepMap , 3 µm , 100 Å; LC Packings ) , before MS and MS/MS spectra were acquired on an Orbitrap mass spectrometer ( Thermo Scientific ) . Recorded spectra were analyzed via Mascot Software ( Matrix Science ) using an MCMV protein database . Markerless BAC mutagenesis was performed to introduce stop cassettes in the m131/129 ORF , to delete 532 bp at the N-terminus of the m74 ORF , to introduce a C-terminal HA-tag to the M75 ORF and to introduce a C-terminal HA-tag to the m74 ORF in the pSM3fr-MCK-2fl BAC as described previously [11] , [55] . A schematic presentation of the pSM3fr-MCK-2fl mutants is depicted in Figure S1 . For the pSM3fr-m129stop BACmid ( virus: 129stop ) , the primers m129stop-for ( 5′- GTACCGTTCCCGACCCAGGTGATCTCACAGACACACTCTATCCAGTTTTCGGCTAGTTAACTAGCCAGGATGACGACGATAAGTAGGG-3′ ) and m129stop-rev ( 5′-AATCGCCACGCATCACGGTGGGCAAGTACCCCTACGAGGTGAAGGACGGTGGCTAGTTAACTAGCCGAAAACTGGATAGAGTGTGTCAACCAATTAACCAATTCTGATTAG-3′ ) were used . For the pSM3fr-m131stop BACmid ( virus: 131stop ) , the primers m131stop-for ( 5′-TGACCAGACACAAGAGTCTGTCCGACCACCAGGCCCGCTTAGCGCACACCGGCTAGTTAACTAGCCAGGATGACGACGATAAGTAGGG-3′ ) and m131stop-rev ( 5′-AACACTTCGTGCGGACGAGAGGTGGTTTTCACTACCTTCTCTGGGATGAGGGCTAGTTAACTAGCCGGTGTGCGCTAAGCGGGCCTCAACCAATTAACCAATTCTGATTAG-3′ ) were used . For the pSM3fr3-Δm74 BACmid ( virus: Δm74 ) , the primers deltam74-for ( 5′-TTTAAAATATTTGGCGGTGATGTTACTTTTCGGGGTGATGAGGTCTCTCCAGGATGACGACGATAAGTAGGG-3′ ) and deltam74-rev ( 5′-AGAGCCGCGATTAATGTCCGCTGTATTCAACGCGGAGATCAGCCCTCCCGGGAGAGACCTCATCACCCCGAAAAGTAACATCACCGCCAAATATTTTAAACAACCAATTAACCAATTCTGATTAG-3′ ) were used . For the pSM3fr-M75-HA BACmid ( virus: gH-HA ) , the primers M75HA-for ( 5′-TAGCGATCCTCATGGCGCTAGGGCTGTACCGGCTGTGCCGGCAAAAAAGATACCCATACGACGTCCCAGACTACGCTAGGATGACGACGATAAGTAGGG-3′ ) and M75HA-rev ( 5′-GACGCAATAAAGAATCTTTTCTTTCTTCATTCACCTCGCGTGTGTCCTTACTAAGCGTAGTCTGGGACGTCGTATGGGTACCGACACGGCCGTTTTTTCTCAACCAATTAACCAATTCTGATTAG-3′ ) were used . For the pSM3fr-m74-HA BACmid ( virus: gO-HA ) , the primers m74HAfor ( 5′-AGAAACCACAACAACACGTACCGTCTCTGCCCCACAAAAGGCGCACCGGCTCAATATCCTTTAGCCGTGTCTACCCATACGACGTCCCAGACTACGCTAGGATGACGACGATAAGTAGGG-3′ ) and m74HA-rev ( 5′-GGCACTGGTGTTACAAGGCCTTCACCTCAGACACGGCTAAAGGATATTGACTAAGCGTAGTCTGGGACGTCGTATGGGTAGACACGGCTAAAGGATATTGAGCCGGTGCGCCTTTTGTGGGCAACCAATTAACCAATTCTGATTAG-3′ ) were used . This BAC also has a duplication of 18 C-terminal base pairs of m73 which overlapped with the C-terminus of m74 . The sequences of the stop cassettes and the HA-tags in all primers are highlighted . Deletions and insertions of stop cassettes or HA-tags were controlled by restriction pattern analysis and subsequent sequencing . BACs were reconstituted to virus by transfection of BAC DNA into MEF using Superfect transfection reagent ( Qiagen ) according to the manufacturer's instructions . Transfected cells were propagated until viral plaques appeared , and supernatants from these cultures were used for further propagation . Virus particles were purified from supernatants of MCMV-infected cells by Nycodenz-gradient purification [56] . Briefly , supernatants were cleared at 6 , 000× g for 10 min to remove cell debris , and then virions were pelleted by centrifugation at 20 , 000× g for 4 h . The resulting pellet was resuspended in VS-buffer ( 0 . 05 M Tris , 0 . 012 M KCl , 0 . 005 M EDTA ( pH 7 . 8 ) ) and free DNA removed by overnight treatment with 625 U/ml Benzonase ( Novagen ) at 4°C . Then , the suspension was loaded onto a continuous 10–40% Nycodenz ( Axis-Shield ) density gradient and separated at 20 , 000× g for 105 min at 4°C , and the band corresponding to virus particles was collected . 100 µl supernatant from infected cells was pretreated with 75 U Benzonase for 20 min at RT to remove free DNA , and then DNA was extracted using the DNeasy blood and tissue kit ( Qiagen ) . 1/20th of the extracted DNA was used for real-time PCR which was performed on a Light Cycler ( Roche Molecular Biochemicals ) as described recently [57] . Primers used were specific for the MCMV M54 gene [15] . Viral DNA copy numbers/ml were calculated by comparing the amplification to standard curves using pSM3fr-LBR BAC DNA . For energy depletion , cells were preincubated in energy depletion medium ( glucose-free DMEM with 2% bovine serum albumin , 50 mM 2-deoxy-D-glucose , 0 . 1% sodium azide ) for 1 h followed by coincubation with virus for 90 min in the presence of energy depletion medium . Virions that had not penetrated were inactivated by washing the cells two times with PBS pH 3 . 0 . For inhibition of pH-dependent endocytosis , cells were pretreated with medium containing NH4Cl or bafilomycin A1 ( Sigma ) for 1 h at 37°C . Infection ( 90 min ) and further incubations were all performed in the presence of the respective inhibitors . For all inhibitions , infection was monitored by staining cells for IE1 expression three hours after removing supernatant virus . Female BALB/c mice were housed and bred under specified-pathogen-free conditions at the Central Animal Facility of the Medical Faculty , University of Rijeka , in accordance with the guidelines contained in the International Guiding Principles for Biomedical Research Involving Animals . The approval of animal protocols has been obtained from the authorised Ethics Committee of the Croatian Ministry of Agriculture , Veterinary Department ( Class: UP/I-322-01/13-01/31; No . : 525-10/0255-13-2 ) . The animal care authorisation for the Central Animal Facility of the Medical Faculty , University of Rijeka has been issued by the Croatian Ministry of Agriculture , Veterinary Department ( authorisation number: HR-POK-004 ) . Eight- to 12-week-old mice were used in all experiments . The mice were infected intraperitoneally ( i . p . ) with 5×105 PFU of wildtype or 131stopD in a volume of 500 µL . PEC collection: Mice were sacrificed 6 h p . i . and PEC were collected by washing the peritoneal cavity with 10 ml cold PBS . Erythrocytes were lysed , cells counted and 1×106 cells stained for surface markers with the following antibodies: anti-F4/80-APC ( BioLegend , BM8 ) , anti-CD11c-PE ( eBioscience , N418 ) , anti-CD19-PerCP-Cy5 . 5 ( eBioscience , eBio1D3 ) , anti-CD11b-PECy7 ( eBioscience , M1/70 ) . Cells were then fixed using Cytofix/Cytoperm solution ( BD ) and Perm/Wash ( BD ) was used to dilute Abs for IC staining as well for washing . Cells were first incubated with CROMA229 ( anti-m06 ) antibody and then with FITC-labeled rat anti-mouse IgG1 mAb ( BD , A85-1 ) . F4/80+CD11b+ macrophages were gated according to a recently published strategy [58] and analyzed for m06 expression . Flow cytometry was performed on FACSAria ( BD Bioscience; San Jose , CA ) , and data were analyzed using the FlowJo software ( Tree Star ) . Female BALB/c mice were immunocompromised by total-body γ-irradiation with a dose of 6 . 5 Gy and infected in the left hind footpad with 105 PFU of the indicated viruses . Mice were bred and housed under specified-pathogen-free conditions in the Central Laboratory Animal Facility ( CLAF ) at the University Medical Center of the Johannes Gutenberg-University , Mainz . Animal experiments were approved according to German federal law , permission numbers 23 177-07 and G10-1-052 . Two-color immunohistochemical analysis ( IHC ) was performed on liver tissue sections at day 10 after infection . Macrophages were labeled specifically with a rat mAb directed against antigen F4/80 ( Ly71; clone BM8 , Acris antibodies ) . Black staining was achieved by using biotin-conjugated polyclonal anti-rat Ig ( BD ) and the peroxidase-coupled avidin-biotin complex ( Vectastain Elite ABC kit , Vector Laboratories ) with DAB as substrate and ammonium nickelsulfate hexahydrate for color enhancement . Infected cells were then labeled specifically with murine mAb CROMA 101 , directed against viral protein IE1 , and stained red with goat polyclonal alkaline phosphate-conjugated anti mouse IgG ( AbD Serotec ) and a fuchsin substrate-chromogen kit ( Dako-Cytomation ) . Light blue counterstaining was performed with hematoxylin . | Several human herpesviruses form alternative gH/gL complexes which determine the tropism for different cell types . For murine cytomegalovirus ( MCMV ) , a gH/gL/gO complex has recently been characterized . Here , we present the identification and characterization of an alternative gH/gL/MCK-2 complex which promotes MCMV spread and is important for efficient infection of macrophages in vitro and in vivo . Association of the MCMV CC chemokine MCK-2 with a glycoprotein complex promoting virus entry is a novel function for the well-characterized MCK-2 . Virus mutants lacking MCK-2 have been shown to exhibit a reduced capacity to attract leukocytes and a disregulated T cell control of the MCMV infection in vivo . These defects can be attributed to the chemokine function of MCK-2 . Yet , the observation that MCK-2 knock-out mutants additionally are impaired in infecting leukocytes in vivo is consistent with our new finding that MCK-2 forms a glycoprotein complex promoting entry into monocytic cells . gH/gL complexes associating with multifunctional proteins add a new level of complexity to the interpretation of infection phenotypes of the respective knock-out herpesviruses . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"infectious",
"diseases",
"virology",
"biology",
"microbiology",
"viral",
"diseases"
] | 2013 | The Viral Chemokine MCK-2 of Murine Cytomegalovirus Promotes Infection as Part of a gH/gL/MCK-2 Complex |
Pathophysiological explanations of epilepsy typically focus on either the micro/mesoscale ( e . g . excitation-inhibition imbalance ) , or on the macroscale ( e . g . network architecture ) . Linking abnormalities across spatial scales remains difficult , partly because of technical limitations in measuring neuronal signatures concurrently at the scales involved . Here we use light sheet imaging of the larval zebrafish brain during acute epileptic seizure induced with pentylenetetrazole . Spectral changes of spontaneous neuronal activity during the seizure are then modelled using neural mass models , allowing Bayesian inference on changes in effective network connectivity and their underlying synaptic dynamics . This dynamic causal modelling of seizures in the zebrafish brain reveals concurrent changes in synaptic coupling at macro- and mesoscale . Fluctuations of both synaptic connection strength and their temporal dynamics are required to explain observed seizure patterns . These findings highlight distinct changes in local ( intrinsic ) and long-range ( extrinsic ) synaptic transmission dynamics as a possible seizure pathomechanism and illustrate how our Bayesian model inversion approach can be used to link existing neural mass models of seizure activity and novel experimental methods .
Epileptic seizures are transient disturbances in the brain’s electrical activity causing changes of patients’ behaviours or perceptions . Seizures have different causes , from gene mutations to acquired brain injuries [1] . The effects of particular pathologies on neuronal dynamics have been studied using animal models , where different interventions ( e . g . chemoconvulsant exposure ) can be evaluated in vivo [2–4] . Zebrafish in particular have been of recent interest for epilepsy research because they ( i ) are a vertebrate organism , ( ii ) allow the introduction of genetic mutations [5] and large-scale drug screening [6 , 7] , and ( iii ) allow recording of neuronal function at high resolution across distributed brain networks [8 , 9] . There are now several studies of epileptic seizures in zebrafish [10–13] and recent imaging studies have captured network-wide changes in zebrafish brain activity during seizures [14 , 15] . However , a detailed mapping of how localised activity is integrated across the brain as a functional network during seizures is still missing . Insights into seizure dynamics have largely been derived from computational modelling of EEG [16–18] . Using population models of neuronal activity allows the systematic description of the relationship between local brain circuit function and neuronal dynamics [19] . Combining novel empirical data and in silico models in this way has the potential to lead to an in-depth understanding of how specific disruptions at the microscale lead to whole brain phenotypes recognisable as epilepsy . One strategy to combine computational modelling with imaging is dynamic causal modelling ( DCM , [20] ) . Here , Bayesian model inversion is used to fit neuronal models to empirical data . This approach combines ( i ) widely-used neural mass models , and ( ii ) Bayesian model inversion algorithms . It is formally related to existing work on neural mass models in epilepsy [21–24]; as well as Bayesian inference approaches [25 , 26] . DCM has been widely applied to scalp EEG [27] , invasive recordings in patients [28] , and in invasive recordings from in vivo animal models [29] . Both EEG and LFP recordings are spatially sparse samples of distributed neuronal activity . Yet most modelling approaches assume measurable oscillations to represent homogeneous averages of population activity . Such averages can now be accessed more directly using light sheet microscopy , providing summaries of neuronal population activity that closely adhere to the modelling assumptions . In this report we model empirical recordings of epileptic seizures in zebrafish across spatial and temporal scales using hierarchical DCM analysis: Spatial scales range from regional microcircuit neural mass models ( mesoscale ) to dynamic whole-brain networks ( macroscale ) . Neuronal states of the underlying biophysical models capture fast oscillatory neuronal dynamics ( millisecond temporal scale ) , whilst slowly varying model parameters capture the slow changes in the dynamic behaviour that occur over time ( seconds to minutes temporal scale ) . Seizures were induced with pentylenetetrazole ( PTZ ) in healthy larval zebrafish and recorded in vivo with light sheet microscopy of a single slice through the zebrafish brain capturing five main bilateral brain regions . PTZ is a well-characterised chemoconvulsant and acts as a GABA antagonist , thus disrupting inhibitory synaptic transmission . Acute seizures are believed to be associated with changes in ( i ) local microcircuit dynamics that allow for a ( phase ) transition between resting and seizure activity [19 , 30] , and ( ii ) changes in whole-brain connectivity [31–33] . DCM allows concurrent testing of the following emerging hypotheses across these different spatial scales: ( 1 ) seizures lead to a measurable reorganisation of effective connectivity between regions [34] , ( 2 ) local excitation-inhibition imbalance explains associated regional spectral changes [35] , ( 3 ) in addition to changes in connection strengths , seizures are also associated with changes in synaptic transmission dynamics [29] .
In the analysis presented here , we used electromagnetic neural mass models originally designed to explain data features observed in LFP recordings . First , we confirmed the construct validity of this approach–i . e . applying DCM for local field potentials to time traces derived from light sheet imaging–by applying the analysis to synthetic data . These were derived from a neural mass undergoing predefined parameter changes: Using a single ‘source’ consisting of three coupled neuronal populations , we generate noisy LFP-like data . These are then convolved with a composite exponential decay kernel modelling calcium probe dynamics [36] . These surrogate fluorescence time traces are then downsampled to the sampling frequency achieved in the single-slice light sheet imaging ( 20Hz ) . This linear convolution equates to a simple addition of the signals in ( log ) frequency space . Because of the simple frequency composition of the calcium imaging kernel , this linear transformation preserves much of the spectral features in the underlying LFP like signal ( Fig 1A ) . The variations in the single neural mass model parameter introduces spectral changes in both the surrogate LFP and fluorescence time traces ( Fig 1B ) . We fitted a three-population neural mass model ( of the kind used to generate the LFP traces , Fig 1C ) separately to each of the fluorescence time traces . This yielded six separate dynamic causal models ( DCMs ) , one each fitted to the six timeseries generated using variations in a single parameter as shown in Fig 1D . Using a hierarchical parametric empirical Bayesian model , we then identified which parameter could best explain the differences in these DCMs ( fitted to fluorescence signals ) . This successfully identified variations in the correct parameter ( an intrinsic connectivity parameter H1 ) as the most likely cause for the differences in time series . Furthermore , the estimated between-DCM differences in H1 values also capture the direction of the linear change introduced in the original simulated LFP . In order to elicit epileptic seizures , PTZ was infused in the bath of n = 3 zebrafish larvae . The resultant seizure activity was recorded with light sheet imaging utilising a genetically encoded calcium sensor ( GCaMP6F ) . Neural activity was recorded in vivo in agarose immobilised larvae capturing a single slice of the intact brain . The changes in activity within the whole imaged slice was readily apparent in the fluorescence images ( Fig 2A ) . We divided the slice into 5 bilateral regions of interest to extract fluorescence time series from the recording . These showed distinctive features consistent with highly correlated epileptic seizure activity ( Fig 2B ) . Using a sliding window ( length: 60s , step: 10s ) we could estimate the time-changing frequency content using a Fourier transform , which demonstrate a particular increase in low frequency power after PTZ infusion ( Fig 2C ) , with additional intermittent bursts of broadband activity seen . Estimating correlations between the regional power-frequency distributions across different time windows reveals apparently distinct phases of PTZ induced seizures ( Fig 2D ) : A baseline that is stable over time ( 0–30 minutes ) , an initial ictal period that differs most from the baseline state ( 30–70 minutes ) , and a late ictal period where time periods of apparent similarity ( i . e . high correlation ) to the baseline are interrupted by intermittent different ( i . e . low correlation ) segments ( 70–150 minutes ) . We employed Bayesian model comparison to identify the effective connectivity network that best explains the baseline data . In brief , baseline activity was modelled as spontaneous activity arising from a coupled network of neuronal sources . Each source is made up of a three-population neuronal microcircuit ( excitatory and inhibitory interneuron populations , as well as a main projection neuronal population ) that is fitted to a cross-spectral density summary of the fluorescence signal at baseline . A single fully connected network was fitted to an average of the baseline activity by inverting a single fully connected dynamic causal model ( DCM ) . Using Bayesian model reduction and Bayesian model selection we compared models , where specific sets of between-region reciprocal effective connections were either present or absent . These sets of connections were ( 1 ) hub-like connectivity between any one region and all other regions; and ( 2 ) short range connection between neighbouring , and homotopic brain regions ( Fig 3A ) . Bayesian model comparison across the reduced models in this model space provided evidence that the baseline configuration can best be described as a network of neighbouring connected nodes with the tectum acting as a network-wide hub ( Fig 3B ) . Notably in this mesoscale modelling , such directed connectivity is understood to be the average influence one region has over another–this may be mediated monosynaptically or through additional ( hidden ) network nodes . In the model each source contains a simply parameterised steady state noise input function that is updated as part of the model inversion–therefore synchronous oscillations between different nodes could possibly be explained away during the inversion by fitting identical input functions to each source . Where more complex models with specific connectivity patterns are identified as the most parsimonious explanations for the particular spontaneous activity , this suggests that not all aspects of the complex cross-spectral densities ( which include phase differences between sources ) can be explained by common input alone . Using this model architecture , individual DCMs are fitted to the sequence of sliding-window derived cross-spectral density summaries of the original data . Spectral changes were found to be consistent across the fish used for this study ( S2 Fig ) . All seizure effects are subsequently assumed to arise from variations in the model parameters that were estimated from the baseline architecture . Thus the seizure activity may ‘switch off’ connections ( through reduction of the particular parameter ) , or silence a node in the network ( through increases in self-inhibition ) , but no new connections or nodes are added to explain data features that arise during the seizure . At this stage ( i . e . first level models ) , each time window is modelled as an independent DCM . The model fits show that these independently inverted models recreate the dynamic fluctuations of spectral composition observed during a seizure very well and thus provide a good representation of the original data features ( Fig 4A ) . Across all complex cross spectra ( for all time window and all animals ) , the model fits explain 74 . 6% of the variance in the original data ( R2 = 0 . 746 ) . Parametric empirical Bayes ( PEB ) can be employed to identify parameters across individual DCMs that vary systematically with specified experimental variables . In brief , PEB allows one to invert hierarchical models where , in this instance , the first level of the model corresponds to a sequence of time windows . The second level of the model then uses the posterior densities over the first level parameters to model changes ( here fluctuations ) in the first level parameters . We modelled PTZ induced changes as a mixture of four effects: ( 1 ) a simple model of PTZ bioavailability as first order pharmacokinetics with a maximum effect achieved at 30 minutes , ( 2 ) a tonic effect switched on for the duration of PTZ exposure , ( 3 ) a monotonically increasing effect representing the influence of prolonged seizure activity , ( 4 ) oscillatory effects at different slow frequencies represented by a set of discrete cosine transforms [37] . This approach provides a single model at the group level ( i . e . across all time windows , and all individual fish ) and parameter changes are modelled as a mixture of experimental and random effects . The estimated mixture of parameter effects yielded consistent spectral changes across the individual fish used for the study ( S3 Fig ) . This type of modelling assumes that discrete oscillatory neuronal states ( e . g . apparently distinct states during the seizure with very different neuronal signatures ) arise from mostly smooth fluctuations in the underlying parameters ( smooth transitions are tracked in Fig 5; as well as S4 Fig ) . This indeed is a feature of the types of models at the heart of the dynamic causal modelling ( i . e . neural mass models ) and their nonlinear mapping between parameters and states . This has been exploited extensively in the past to link apparently sudden transitions in neuronal dynamics to slow synaptic or neurochemical changes [19] that could cause them . These second level inversions also provide an estimate of the model evidence , so that different models can be tested against each other . In the first instance , we compared models where only subsets of between region connections were allowed to vary between time points . Bayesian model comparison shows that only changes in the forward connections to the network hub ( i . e . bilateral tectum ) are required to explain the spectral changes during seizure activity ( Fig 4B ) . Model comparison was also used to test for PTZ induced changes in the intrinsic coupling parameters in individual regions . There was strong evidence for an involvement of all measured brain regions ( Fig 4C ) . Note that among the models where seizure activity only affects intrinsic connections of a single node , the tectum and rostral hind brain emerge as the most likely models–suggesting that variations in both have a particular impact on the seizure dynamics . The estimated parameter changes induced by PTZ were varied between different brain regions , but overall showed a relative reduction in excitatory time constants ( suggesting faster responses ) , reduction in inhibitory intrinsic connections , and a reduction the influence of other regions on the optic tectum ( i . e . a reduction in forward connections; estimates of the time varying parameters shown here summarise effects at the assumed peak PTZ effect time window early in the seizure Fig 4D ) . Most of the largest effects in terms of intrinsic model parameters affect the rostral hind brain and the optic tectum , with at times apparently opposing effects ( e . g . opposing changes in excitatory time constant changes ) . Future studies may explore the differences in dynamic responses to PTZ stimulation across these regions . Note that each value on this plot represents a posterior density that consists of both the estimated parameter value for the particular parameter , and a posterior covariance that represents the uncertainty around that parameter estimate . In dynamic causal modelling , inferences are made via model comparison ( i . e . , log evidence or odds ratios provided in Fig 4 ) . Thus Fig 4D provides a quantitative characterisation of the underlying effect sizes in terms of posterior densities , under the best model . The values in Fig 4 shows the effects and between region differences ( the scatter of the dots reflects precise and systemic inter-regional differences , not random effects ) . Whilst similar parameters are grouped in the scatter plot for visualisation purposes , they represent different aspects of the same model inversion and thus the optimal model fit , given the data . As such their mean or median value is only informative to provide an intuition as to the overall direction of the effect . The intuition of how individual parameter changes relate to spectral output is characterised in more detail for one region ( the optic tectum ) below ( Fig 5 ) . In a next step , we quantified the temporal evolution of the parameter changes in one example region so as to map ( smooth ) parameter changes to associated changes in the spectra over time . For this we collated all the parameters intrinsic to that region ( i . e . intrinsic coupling parameters and time constants ) and simulated the associated spectral output from a single three-population source . This was done for a range of different parameter values informed by the empirically-derived posterior parameter estimates from the PEB analysis above ( Fig 5A ) . We then extracted the parameter estimates for time constants and intrinsic connectivity within the right tectum over time across all components of the PEB model ( i . e . tonic seizure effects , monophasic PTZ effect , prolonged seizure effect , discrete cosine transforms , random between-animal effects ) . In order to visualise the parameter changes over time , we derived a low-dimensional representation of the data: We extracted the first principal component of the posterior estimates of the intrinsic connectivity parameters , and the time constants over time ( Fig 5B ) . The first principal component of the time constant changes explains 70 . 9% of their variance; the first principal component of the intrinsic connection changes explains 49 . 2% of their variance . Plotting each time window onto this reduced parameter space containing most of the variance in the coupling parameters represents the seizures as a spiral path through parameter space . We can apply the parameter combinations at each point in the parameter space to a microcircuit model and predict the spectral output . Here we show log delta band power as a heat map , with log gamma band power superimposed as isoclines ( Fig 5C ) . This forward modelling approach shows that during the seizure , the model enters a section of parameter space characterised by both high delta and gamma power components , which is also seen in LFP recordings during seizures in zebrafish reported in previous studies [6] . In S4 Fig , we added an additional component for the intrinsic connection as a third dimension , with the two combined now explaining 80 . 0% of the intrinsic connectivity variance , and revealing a separation of the distinct seizure phases identified from the spectral analysis alone ( Fig 2 ) .
Calcium imaging time series are highly correlated with concurrent LFP recordings [41] . Whilst LFP generally allows measuring of neuronal population activity at a higher temporal resolution ( including activity >100Hz ) , calcium imaging is more limited due to both the sampling frequency [42] , and the fluorescence decay dynamics of the calcium-sensitive probe [36] . The predominant frequency components of both resting brain activity and seizure activity in the larval zebrafish brain are in the delta ( <4Hz ) and theta ( 4-8Hz ) band [10] . Neuronal fluctuations in these frequency bands are largely preserved in calcium imaging , and apparent even at sampling frequencies as low as 20Hz . Our simulations illustrate the construct validity of using neural mass models that generate electrophysiological responses to explain calcium imaging data: DCM allows correct causal inference from calcium fluorescence time series to underlying coupling parameters . This approach provides deeper neurobiological insights than functional connectivity approaches alone . Furthermore , our hierarchical modelling allows tracking of slowly varying model parameters [29] , offering explanations for qualitatively very sudden changes in oscillatory behaviour ( represented by the output of individual DCM models ) emerging from gradual changes in model parameters ( represented across-DCM parameter changes estimated in the PEB approach ) . The DCM analysis of simulated data here only recovered the trend of the activity ( not the actual value ) . We use a convolution kernel to simulate the effects of calcium imaging on time series , but we inverted the models with a DCM without a kernel , hence some difference is anticipated . However , in the time-resolved analysis of connectivity changes during a seizure , we are interested in the relative change of model parameters over time more than the background setup ( which we account for as an additional group-mean effect in the hierarchical modelling with PEB-DCM ) . This analysis harnesses specific advantages of regionally averaged calcium imaging: Light-sheet microscopy samples in a spatially unbiased fashion , thus providing a closer approximation to the assumptions underlying neural mass models [43] . Heuristically , this spatial averaging suppresses local fluctuations in the same way that averaging over time in event related potential studies ( in electrophysiology ) reveals dynamics that are conserved over multiple realisations . Furthermore epileptic seizures are an emergent property at the level of neuronal populations , and computational models specifically addressing this ‘mesoscale’ may yield important insights about emergent population-wide features less readily apparent from microscale modelling of individual neurons [44] . Furthermore , our analysis allows inferences to be linked back to established knowledge about the anatomical regions included in the study . However , we do not fully exploit the spatial resolution offered by the calcium imaging data , which will need to be addressed in the future with scalable custom approaches to modelling of individual neurons [45 , 46] . One strategy to exploit the resolution of light sheet images is through definition of ‘regions’ based on microscale neuronal properties ( e . g . correlated activity , distribution of neurotransmitter receptors [47] ) –whilst the same model inversion technology illustrated here remains applicable , the data features selected for DCM would be informed by the neuroanatomical and neurophysiological information in the light-sheet imaging data and thus exploit the spatial resolution . DCM allows for the estimation of network coupling parameters underlying neurophysiological recordings , within the constraints of the available data and the hypothesised model space . The first step of our analysis thus aims to explain the pre-ictal baseline fluctuations in 5 bilateral brain regions of the zebrafish brain through any one of the proposed model architectures . Both changes in the data used for further analysis ( e . g . changes in the regional divisions , or extension beyond a single imaging plane ) and changes in model space ( e . g . inclusion of another possible hypothesis ) may therefore impact on the inference drawn . However , both the data included in this study and the model space explored reflect the types of hypotheses we sought to explore . Early during zebrafish development , retino-tectal connections develop , and stereotyped but effective visuomotor behaviour is established [48–51] . This is associated with distributed network activity involving information flow from the optic tectum to other brain areas . This visually-dominated early network activity is also apparent in the DCM analysis , where the tectum has been identified as a hub with widespread connectivity to the rest of the larval zebrafish brain from resting state light sheet recordings at baseline . This network organisation is modulated during seizure activity , where our modelling identifies a reduction of the effective forward connections from other brain areas to the optic tectum . This asymmetric shift in connectivity ( with only forward , but not backward connections affected ) , may be indicative of a key role of the optic tectum—as a central network hub at baseline—in driving network-wide synchronisations during an epileptic seizure . The selective reduction in effective connectivity corresponds to previously reported seizure-related changes in functional connectivity estimated from human EEG recordings , where increased clustering during a seizure has been described [52] . Fluctuations in effective connectivity between regions is usually thought of as resulting from changes in direct synaptic connectivity [53] . Where all connections towards a single brain region are involved , this may be due to ( i ) specific synaptic mechanisms affecting synaptic receptors at this particular region , or ( ii ) changes in local excitability . However , the asymmetric involvement of a single brain region–where only effective connectivity to ( and not from ) the optic tectum is reduced–suggests that local microcircuitry changes may underlie the macroscale changes . The relationship between local and macroscale network changes in epilepsy in the context of hierarchically coupled brain areas is discussed elsewhere [31] . This phenomenon has been formally described in other modelling work through a slow local permittivity variable that governs synchronisation between different brain regions and represents different slowly unfolding changes in local energy and metabolic milieu [54] . PTZ acts as an acute chemoconvulsant in a range of different model organisms , likely due to allosteric inhibition of GABA-A receptors [38] . Previous work on a PTZ rat model showed dose-dependent regionally specific cellular activation [33] , suggesting differential susceptibility of different brain regions to PTZ effects . Bayesian model comparison of seizures recorded from the zebrafish in this report indicate that PTZ-induced changes of intrinsic neuronal population coupling were required in each of the brain regions . From the free energy distribution across models with different single regions affected by seizure changes , we found relatively high model evidence for models comprising seizure-related parameter changes in the optic tectum , or in the rostral hindbrain , suggesting that there is heterogeneity in the contribution of individual brain regions to the evidence for the winning model . PTZ-related seizure effects here are modelled under the assumption that they arise from changes in the existing extrinsic ( between-region ) connections and intrinsic coupling parameters . We expected most of the interesting effects to occur on the coupling parameters within regions ( as most of the PTZ effect will affect local inhibitory interneuron connectivity [55] ) . Whilst epileptogenesis in the brain ( i . e . developing the propensity for recurrent seizures ) may require the establishment of novel , pathological connectivity , acute seizure activity most likely will not . Thus , our modelling approach has the ability to account for most neurobiologically plausible mechanisms underlying acute seizures . However , these effects varied widely between regions . This in part reflects different baseline configurations of the regional source models , which in turn require different shifts in coupling parameters . Yet , overall the PTZ-related changes are broadly consistent with our current understanding of its effects at the neuronal membrane . Specifically , PTZ is expected to cause a relative decrease of inhibitory connectivity compared to excitatory connectivity; and preferential blockade of fast GABA-A ( and not GABA-B ) mediated transmission would be expected to cause an increase in the relative inhibitory transmission time constants ( i . e . slowing down ) , compared to excitatory synaptic dynamics–both of these effects are observed in the parameters estimated across the whole brain slice here ( noting that population-level time constants are likely a product of several convergent synaptic effects [38 , 56 , 57] . Left-right asymmetries in the intrinsic estimated connectivity in the optic tectum is most likely secondary to differences in light stimulation received by either eye . Further exploration of individual parameter effects at a single brain region supports the notion that seizure dynamics in this recording are largely caused by two main effects: a relative disturbance in excitation / inhibition balance with increased excitation and decreased inhibition , and a reciprocal disturbance in the dynamics of excitatory and inhibitory connectivity with slower inhibition and faster excitation . Because we have fitted fully generative neural mass models , we can make predictions about the spectral output caused by particular parameter combinations beyond the measured ≤10Hz frequency range . This approach reveals that particularly the time points where both connectivity and time constant effects changes reach their respective extremes , the typical seizure spectral output containing high amplitudes in both low ( i . e . delta ) and high ( i . e . gamma ) frequency components emerges . The addition of PTZ causes an increase in broadband activity , with particularly high predicted power in the gamma band early after PTZ administration , and more pronounced increases in slow frequency power as the seizures evolve . This is consistent with previous studies that have separately recorded LFP traces during seizures in zebrafish [6 , 14] . Recurrent neuronal loops with a close balance of overall excitation and inhibition underlie spontaneous brain activity . The brain is believed to operate near a transitional state from which both subcritical , random dynamics and supercritical , ordered dynamics can emerge ( i . e . self-organised criticality , cf . [58] ) . Blocking of the largely GABA-A mediated local recurrent inhibition shifts this balance and allows ordered , seizure-like activity to occur [59] . In our model the emergence of seizure dynamics requires changes in both connection strengths and their temporal dynamics . Future research will address how different pathologies may converge on the mechanisms that underlie observable seizure dynamics . The analysis presented here illustrates the use of computational modelling to explain neuronal dynamics in the larval zebrafish brain during acutely induced seizures . This approach exploits the spatial independence of single plane in vivo light-sheet recordings of brain regions and uses dynamic causal modelling to identify the mechanisms underlying seizure dynamics . Our Bayesian model inversion scheme allows translating observations from whole-network novel light sheet imaging to the concepts and models used to explain electrophysiological abnormalities observed during seizures . Seizures in this model are associated with an asymmetric decoupling of the network hub , and changes in excitation/inhibition balance that crucially also involve the temporal dynamics of excitatory and inhibitory synaptic transmission . Mapping the expected spectral changes along both the connection strength and time constant domains of the model within the pathophysiological range estimated from acute seizures allows us to delineate the independent contribution of changes in either type of parameter to the overall dynamics . This is the first step to establishing network-wide mechanisms that underlie seizures and may be targeted with novel treatments for epilepsy . Like all Bayesian modelling approaches , DCM only provides estimates of the likelihood of individual models in direct comparison to a larger model space . As the model space evolves , and other plausible hypotheses are being tested , a new model may offer an overall better solution to the inverse problem . Furthermore , as our understanding about the underlying neurophysiology progresses , prior knowledge can be incorporated into the model inversion ( quantitatively in terms of changes in the prior parameter expectations ) and thereby nuance Bayesian model comparison . It is also worth noting , that the DCM results are ‘true’ in that they represent the mathematically simplest approximation of a given dataset under specific assumption–a more complex model may be biologically implemented but not emerge as the winning model because the added complexity is not required to explain the particular data features at hand . The approach presented here illustrates how light-sheet imaging in zebrafish larvae can offer an insight into the kind of mesoscale dynamics that are also observable ( and of interest to the modelling communities ) in electrophysiological recordings . The type of modelling and inversion scheme used here is flexible enough to ultimately accommodate data that contain some of the microscale information about the neuronal ensemble ( e . g . by defining ‘regions’ through molecular markers present on individual neurons rather than gross anatomy ) , however this was beyond the scope of the current–proof of concept–paper . Furthermore , our imaging protocol was optimised to capture widespread activity changes at high sampling frequencies ( e . g . by only imaging a single plane ) , assuming that activity in this plane reflects the dynamics of the whole region , whilst excluding non-imaged regions ( that are situated above or below to the plane ) from the analysis . Whilst only a small number of fish were included in this analysis , the effects at the level of the recordings are large and consistent between fish . For future studies on more subtle effects and observations ( e . g . the topological organisation of spontaneous seizures ) , a higher number of fish is likely to be required .
This work was approved by the local Animal Care and Use Committee ( King’s College London ) and was performed in accordance with the Animals ( Scientific Procedures ) Act , 1986 , under license from the United Kingdom Home Office . | We show that Bayesian inversion techniques used in electrophysiological data are applicable to calcium imaging data derived from light sheet microscopy in the zebrafish brain . Using this approach we can now make inference on the underlying large-scale connectivity changes underlying pathological states such as seizures , and translate findings from zebrafish directly into the modelling frameworks utilised in human patients . Ultimately this modelling approach can be used to integrate evidence across different models of abnormal neuronal dynamics to facilitate a mesoscale understanding of seizure dynamics . | [
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] | 2018 | Calcium imaging and dynamic causal modelling reveal brain-wide changes in effective connectivity and synaptic dynamics during epileptic seizures |
Certain strains of the endosymbiont Wolbachia have the potential to lower the vectorial capacity of mosquito populations and assist in controlling a number of mosquito-borne diseases . An important consideration when introducing Wolbachia-carrying mosquitoes into natural populations is the minimisation of any transient increase in disease risk or biting nuisance . This may be achieved by predominantly releasing male mosquitoes . To explore this , we use a sex-structured model of Wolbachia-mosquito interactions . We first show that Wolbachia spread can be initiated with very few infected females provided the infection frequency in males exceeds a threshold . We then consider realistic introduction scenarios involving the release of batches of infected mosquitoes , incorporating seasonal fluctuations in population size . For a range of assumptions about mosquito population dynamics we find that male-biased releases allow the infection to spread after the introduction of low numbers of females , many fewer than with equal sex-ratio releases . We extend the model to estimate the transmission rate of a mosquito-borne pathogen over the course of Wolbachia establishment . For a range of release strategies we demonstrate that male-biased release of Wolbachia-infected mosquitoes can cause substantial transmission reductions without transiently increasing disease risk . The results show the importance of including mosquito population dynamics in studying Wolbachia spread and that male-biased releases can be an effective and safe way of rapidly establishing the symbiont in mosquito populations .
Mosquito-borne parasites and viruses cause some of the world's most important diseases , disproportionately affecting poor communities and representing a major public health challenge . Biological control techniques aimed at suppressing mosquito populations or reducing their capacity to transmit disease may be a useful addition to traditional vector control strategies , especially if resistance to chemical insecticides in mosquito populations continues to rise [1] . Recently there has been increased interest in the use of certain strains of Wolbachia bacteria to reduce transmission by mosquito vectors of human diseases [2]–[7] . Wolbachia are maternally-inherited endosymbiotic bacteria that are common in many insect species including mosquitoes . Wolbachia spread in mosquito populations by manipulating the host's reproduction using a mechanism known as cytoplasmic incompatibility ( CI ) [8] . CI occurs when Wolbachia in infected males modify the sperm of their host such that arrest of embryonic development occurs unless the egg also carries the bacterium . Uninfected females are therefore at a disadvantage , and the Wolbachia spreads by a process of positive frequency-dependent selection . Models of Wolbachia dynamics show that spread will occur if the proportion of infected hosts exceeds a threshold that is higher for Wolbachia that cause stronger reductions in host fitness [9] . Recent studies indicate that infecting mosquito populations with certain strains of Wolbachia may lower their rates of disease transmission for two reasons . First , the bacteria may reduce mean adult lifespan [6] , [10] . Because most vector-borne pathogens have a relatively long extrinsic incubation period in the mosquito a reduction in average longevity disproportionately affects infectious individuals , with beneficial consequences for disease transmission [11] , [12] . However , a reduction in longevity also lowers the fitness of Wolbachia carriers and hence increases the threshold infection frequency required for spread to occur [13] . An ideal strain would increase mortality only late in life as this would ( i ) particularly affect pathogen-carrying individuals; ( ii ) have a lesser effect on host fitness and thus require fewer individuals to be introduced to pass the threshold infection frequency; and ( iii ) lead to less selection for modulation of the harmful effects of these Wolbachia . Second , Wolbachia can inhibit the development , replication or dissemination of important mosquito-borne pathogens , including filarial nematode parasites [5] and dengue and chikungunya viruses in Aedes aegypti [2] , [7] , and Plasmodium malaria parasites in Aedes aegypti [7] and Anopheles gambiae [5] . The capacity of Wolbachia-infected mosquitoes to transmit these diseases may thus be much reduced . However , the ability of Wolbachia to assist in the control of mosquito-borne diseases will depend on their dynamics in natural mosquito populations . Understanding the ecology of Wolbachia infections in mosquito populations is important as programmes to establish Wolbachia in wild Ae . aegypti are currently under consideration [3] . Recently we developed a modelling framework that allows the spread of Wolbachia that reduce the longevity of their insect hosts to be analysed [14] . The models allow the study of the demographic consequences of releasing the significant numbers of individuals often needed to breach the threshold for Wolbachia to spread . They can be used to explore different schedules of Wolbachia introduction ( for example few large or many small introductions of infected insects ) , the effects of different types of density-dependent mortality in the host population on Wolbachia dynamics and the timing of introductions in a seasonal environment . Here we employ this modelling approach to investigate practical questions concerning the use of Wolbachia for mosquito-borne disease management . Because female mosquitoes bite people and so constitute a nuisance , and because they can potentially transmit disease , it is desirable that only a minimum number of female insects are released as part of a Wolbachia introduction . This may be possible by applying methods of sex separation by pupal size sorting to reared insects to create releases with a highly male-biased sex-ratio [15] , [16] . We develop theory for male-biased release strategies and explore their feasibility and how releases may be optimised when the mosquito population size shows strong seasonal fluctuations . We then extend the model to include a simple representation of a mosquito-borne disease . The model is sufficiently general to represent a wide range of mosquito species and the diseases they transmit; here we chose parameters derived from the literature on Anopheles mosquitoes for illustration . This is used to estimate how the rate of disease transmission changes over time following male-biased Wolbachia releases . Different assumptions about mosquito population dynamics and the effects of Wolbachia on vectorial capacity are explored .
The model of mosquito and Wolbachia dynamics used here is an extension of that in [14] with separate adult sexes and the inclusion of egg and pupal stages ( Figure 1 ) . It is phrased as a system of integral equations describing the numbers of infected and uninfected larvae and adults of different ages; full details are given in Text S1 . The mosquito life cycle is divided into three juvenile stages ( egg , larva and pupa ) and an adult stage . The population is assumed to be regulated by density-dependent mortality experienced during the larval stage described by a power function , , where is larval density and and β are constants . Higher values of the parameter β denote a steeper response to increasing density ( which we shall refer to as strong density dependence ) . Mortality in adults is assumed to be age-dependent and is modelled by a Weibull function whose parameters may depend on infection status ( see Text S3 and [14] ) . Adult fecundity is assumed to be constant with age ( but see the Discussion ) . Wolbachia may increase adult mortality , particularly in older age-classes , and the proportional reduction in average adult longevity caused by Wolbachia is denoted sg . Wolbachia-infected individuals may also have reduced fecundity ( by a proportion sf ) . Mating is assumed to occur at random , and an uninfected female mating with an infected male will lose a fraction sh of her offspring . Infected females fail to transmit Wolbachia to their offspring with probability . We assume here that Wolbachia does not affect survival during , or length of , the juvenile stages . For a closed population ( no immigration , deliberate introduction , or emigration ) , the position of the equilibrium threshold frequency above which Wolbachia spreads through the population depends on the magnitude of the fitness effects of the bacterium on its host , and the probability of non-transmission ( see Figure 2 ) . For the basic model analysed here , Hancock et al . [14] showed that the threshold frequency p*is ( 1 ) where and . This expression is closely related to the classic condition for spread derived for discrete-generation , purely genetic models by Turelli and Hoffmann [17] . Mosquito populations are very sensitive to patterns of seasonal rainfall , and often show strong annual fluctuations in abundance [18]–[20] . We model this by assuming that larval carrying capacity ( the parameter in the expression for larval density dependent mortality ) varies over the year . Two seasonal abundance patterns are considered which we refer to as A and B . These patterns were chosen to represent attributes of mosquito population dynamics that we have found to be important to Wolbachia spread; strong temporal variation in adult abundance and varying rates of seasonal population growth and decline . In pattern A there is a six-month season of high mosquito abundance generated by setting = 0 . 05 for six consecutive months and = 0 . 1 for the rest of the year ( Figure 3A; solid line ) . In pattern B the seasonal increase and decline in mosquito abundance is more gradual ( Figure 3B; solid line ) . This pattern is produced by setting the larval carrying capacity to α = 0 . 055 , 0 . 055 , 0 . 05 , 0 . 05 , 0 . 053 and 0 . 06 respectively for the six months of the year when mosquitoes are abundant and = 0 . 1 otherwise . In exploring different release strategies , for operational reasons we restrict deliberate introductions to the wet season . Although the size of the resident population is lowest in the dry season , which would appear to facilitate population replacement , Anopheles and other mosquitoes are highly sensitive to desiccation . Mosquitoes may aestivate during the dry season [19] , or rest in microhabitats with higher than average humidity . It is likely that if introductions were made at this time of year any introduced mosquitoes would experience very high mortality before locating relatively rare , suitable resting sites ( or conspecifics with which to mate ) . We extend the age-structured model of mosquito and Wolbachia dynamics to include a vector-borne pathogen . The infected and uninfected adult classes are divided into susceptible , exposed and infectious ( SEI ) stages , with the exposed stage assumed to be of fixed duration ( the extrinsic incubation period ) . A fraction x of the human population is assumed to be infectious , and this parameter as well as the total number of humans is assumed to be constant over time . The model makes assumptions about the frequency of blood feeding , and the probability of the mosquito being infected during a blood meal . Our treatment of the adult stages is based on Hancock et al . [21] ( a model of the interaction between Anopheles , Plasmodium and a pathogenic fungus ) . Full details of the model are given in Text S2 . We assume here that a proportion cw of mosquitoes that are infected with both Wolbachia and the pathogen do not become infectious . This represents the reduction in disease transmission that has been shown to occur in mosquitoes infected with Wolbachia . A critical quantity in the epidemiology of mosquito-borne diseases is the Entomological Inoculation Rate ( EIR ) , the number of bites on humans by infectious mosquitoes per person per day . This is simply the total number of infectious mosquitoes ( both carrying and not carrying Wolbachia ) per human multiplied by the daily rate of biting [22] . Analytical expressions for the equilibrium EIR can be obtained for a constant environment where Wolbachia is absent or at equilibrium frequencies ( Text S2 ) . The different parameters included in the models and their default values are shown in Table 1 . Parameter estimates obtained in the field for Anopheles mosquitoes have been used where possible , though for some such as those governing density dependent mortality little information is available . Data on age-dependent mortality rates of laboratory colonies of Anopheles were used to parameterise the Weibull function describing adult age-dependent mortality [23] . We assume that mosquitoes experience additional age-independent background mortality at rates observed in field populations ( see Text S3 ) . Parameters describing the effect of Wolbachia on longevity derive from field cage studies of the life-shortening Wolbachia strain wMelPop infecting Ae . aegypti [6] . We calculated the age-dependent increase in the rate of adult mortality caused by wMelPop infection in Ae . aegypti and assumed that it would have a similar proportional effect on Anopheles ( Text S3 ) . Mosquitoes in cages tend to live longer than those in nature and this can lead to overestimation of the fitness consequences of late-acting mortality . Including background field mortality , the overall reduction in average adult lifespan caused by Wolbachia infection is assumed to be 16% ( sg = 0 . 16 ) ( Text S3 ) . Both strains of Wolbachia that have to date been successfully introduced into Ae . aegypti , wMelPop and wAlbB , inhibit the development and transmission of human pathogens in this host [2] , [4] , [7] , and unlike wMelPop the wAlbB transinfection had no observable impact on longevity in the lab [2] . We also explore the effects of introducing a Wolbachia that causes a 5% reduction in adult lifespan in the field ( sg = 0 . 05 ) . We address the implications of our imperfect knowledge of different parameters in the Results and Discussion .
When transmission is perfect ( ) , Wolbachia spreads when it reaches an infection frequency in the population such that an average infected female has more offspring than an average uninfected female . The latter are disadvantaged through mating with infected males which causes them to lose a fraction sh of their offspring . This picture is slightly more complex when transmission is not perfect , or when immigration , introductions or emigration are occurring [14] , but again spread is caused by the presence of infected males giving an indirect , relative advantage to Wolbachia-bearing females . This advantage can be made greater simply by increasing male ( and not female ) infection frequency . Of course infected females must be present for the infection ( which is not transmitted through males ) to spread , but once the threshold is exceeded the frequency in females will increase from an arbitrarily low start . An infection can thus be established even though relatively few females are released . A simple way to model sex-biased releases is to assume that newly-emerged infected males and females are introduced into an uninfected population at constant rates IM and IF . For simplicity we assume that the larval carrying capacity does not vary with time ( no seasonality ) and that the uninfected population is at equilibrium prior to the introduction . Figure 2 illustrates how introducing males at a relatively high rate allows the infection to invade when females are introduced at a much lower rate ( 1% of the rate of male introduction ) . The time it takes for the infection to be established is longer when introduction rates are low . When the rate at which infected females are introduced is very small ( ) , it is possible to calculate the unstable equilibrium male infection frequency above which Wolbachia spreads , and the threshold rate of male introduction required to exceed this frequency ( Figure 2 and Text S1 ) . However the expressions are complicated because the introduction of infected males reduces the fecundity of resident uninfected females and this lowers the density dependent mortality experienced by the juvenile population . The effect of the introduction on the rate of recruitment of uninfected adults will thus depend on the strength of juvenile density dependence . This is illustrated by comparing the threshold male introduction rates required for Wolbachia spread to occur in populations with relatively strong ( = 0 . 3 , = 0 . 05 ) and weak density dependence ( = 0 . 1 , = 0 . 2 ) . Values of the larval carrying capacity were chosen so that the equilibrium adult abundance in the absence of Wolbachia is the same in both cases . The required rate of male introduction is approximately 50% higher in the case of strong ( IM = 0 . 44 day−1 ) as opposed to the weak ( IM = 0 . 28 day−1 ) density dependence . This occurs because the reduction in density-dependent mortality caused by the introduction of males is greater when density dependence is stronger , and so less suppression of the ( uninfected ) adult population occurs . It can thus be important to consider demographic as well as genetic processes in models of Wolbachia dynamics . A more realistic scenario for the release of Wolbachia-infected mosquitoes is that the insects are released in separate batches rather than continuously , and that mosquito population size fluctuates seasonally . The total and relative numbers of male and female mosquitoes that need to be released for spread to occur were studied in a population whose seasonal dynamics are described by pattern A . We compare releases consisting of equal numbers of the two sexes and 95% males and calculate the minimum numbers that have to be liberated at different times of the season to ensure Wolbachia becomes established . The release strategy we model is of 30 daily releases , each containing the same number of mosquitoes . The results are shown in Figure 3A . First note that for all strategies releases early in the season when the resident population is small require fewer mosquitoes to be introduced , a result we explore in more detail below . Overall , for any particular release date , the total required release size is 3–4 times larger for the 95% male-biased strategy compared to the equal sex-ratio strategy , and so the number of mosquitoes that must be reared ( prior to separation of the sexes ) assuming a 50∶50 sex-ratio is 6–8 times greater . However , although more mosquitoes in total must be produced , fewer females need to be released with the male-biased strategy . In the present example , which is typical of others we have explored , the total number of females introduced is approximately ⅓–½ the numbers required in the equal sex-ratio strategy . There are two reasons why the male-biased strategy requires the release of fewer females . First , releasing a large number of males causes a high frequency of incompatible matings and so reduces the size of the resident ( uninfected ) population . Second , the high frequency of infected males means that infected females have a strong relative fitness advantage . However , the dynamics of releasing mosquitoes in a finite number of separate batches are not the same as those assuming introduction at a constant rate . Figure 4 shows the male infection frequency as a function of time over a 3 year period following 30 daily 95% male releases made in the second month of the season of high mosquito abundance . Although male infection frequencies are initially very high they decline rapidly after the final release as the introduced males die . At this stage there are still relatively few infected females present and hence recruitment of Wolbachia-carrying individuals is low . To prevent the loss of Wolbachia in this transient period , the releases must attain a temporary male infection frequency that is considerably higher than the threshold calculated in the continuous release case . Enough females must also be introduced so that they produce sufficient infected sons that the male infection frequency does not fall below the threshold following the final release . As in the case of continuous release , we found that the minimum required number of insects for Wolbachia establishment depended on the assumed form of juvenile density-dependent mortality . Further details are given in Text S4 but we again found that larger releases were needed when density dependence was strong . However , for all the forms of density dependent mortality we studied , consistently ⅓–½ the number of females was required for male-biased ( 95% ) compared to equal sex-ratio releases . These results suggest that the establishment of Wolbachia using highly male-biased releases is feasible , provided comparatively large numbers of mosquitoes can be reared and the sexes separated . We explore this issue further in the Discussion . We explored the effect of seasonal variation in mosquito abundance on the release size necessary for Wolbachia to spread for the two seasonal patterns described in the Model Development . Again we assume that 95% of the insects introduced are males , and that 30 daily releases of the same size are made . Figures 3A & B show the minimum required release size for different release times . In both cases releases early in the season require fewer mosquitoes . At this time the resident population is small and so a fixed number of introduced infected insects constitute a greater proportion of the total . Releases made towards the end of the season when the population is beginning to decrease may also require fewer insects , but this depends on the rate of population decline . In case A the decline is abrupt and the size of releases required for spread increases steadily through the season . Late season releases here are a poor strategy because the decline in larval carrying capacity drastically reduces recruitment to the adult stage so that the proportion of infected individuals is chiefly determined by adult mortality rates that with our parameter assumptions particularly penalise Wolbachia-carrying individuals . However , in case B , where the population declines more gradually , the required release size decreases towards the end of the season . Insight into the seasonal dynamics of Wolbachia spread following the releases can be gained by plotting the temporal change in male infection frequency for seasonal pattern A ( Figure 4 ) . The male infection frequency falls following the final release and then starts to rise again towards the end of the season as the progeny of the first female introductions reach the adult stage . However , the collapse in carrying capacity acts to reduce the infection frequency as the season ends , for the reasons described above . Wolbachia only becomes established if the infection frequency towards the end of the season is high enough that it does not drop below the threshold ( eqn . 1 ) when the population enters the steep decline . This is a further reason why Wolbachia strains that reduce host longevity require large releases before they can become established . Models of the introduction of Wolbachia with equal numbers of males and females show that the number of releases made can significantly affect the total number of insects that need to be introduced to establish Wolbachia in the population [14] . In particular , introducing large numbers of females at one time that then reproduce can increase the juvenile density-dependent mortality , which disadvantages the progeny of these females . Introducing the insects in a larger number of smaller releases is therefore sometimes more effective , particularly for Wolbachia strains that incur a high fitness cost and thus require large releases to allow spread . In the case of highly male-biased releases , this effect does not occur , because relatively few females are added and the number of larvae declines due to the high frequency of incompatible matings . However multiple releases may still be beneficial because they prolong the period over which the male infection frequency is artificially elevated , so sustaining the fitness advantage of infected females and allowing their numbers to increase from an initial low level . For seasonal pattern A , we compared the minimum number of introduced insects required for spread for strategies where different numbers of equal-sized , daily releases are made , again assuming that the sex-ratio of the releases is 95% male ( see Text S5 ) . If releases are made towards the middle of the season , after the period of rapid population growth , the total required number of introduced insects is smaller if the insects are distributed across a larger number of batches . This is not the case for releases made close to the start of the season , when there is a slight advantage in releasing the mosquitoes in a single batch . At the start of the season the benefit of prolonging the elevation of the male infection frequency over multiple releases is lost because the population is increasing rapidly and so later releases cause a smaller increase in the proportion infected . These results indicate that seasonal changes in mosquito abundance are a much stronger determinant of the required release size than the number of releases made ( Text S5 ) . Here we examine the effects of Wolbachia introduction on the abundance of female mosquitoes and the rate of disease transmission for a release strategy that introduces the minimum number of mosquitoes required for spread in 30 daily equal-sized batches , and a strategy that releases a larger number , three times the minimum required , in 90 daily equal-sized batches . The sex-ratio of the releases is 95% male . We assume that seasonal abundance dynamics follow pattern A , and that releases are made one month into the season of high mosquito abundance . Figure 4 shows the daily entomological inoculation rate ( EIR ) , the female population size , and the male infection frequency for a three year period where releases of the minimum size required for spread are made in the second year . During the releases the total number of females ( including wild and released individuals ) , and likewise the EIR , are quickly reduced compared to the level in the previous year , although at the start of the releases there are slightly more females present than there would be in the absence of the intervention . The reduction in both quantities is due mainly to the suppression in population abundance caused by the high frequency of incompatible matings between uninfected females and infected males . The reduction also partly results from the lower fitness of Wolbachia-infected females , although this does not have a strong effect in the year of release because the Wolbachia infection frequency in females remains relatively low . In this example the Wolbachia does not reach its stable frequency until the year following the releases and its establishment results in much greater reduction in EIR than in the female population size ( Figure 4 ) . This is because the reduction in longevity brought about by Wolbachia infection causes a disproportionate reduction in the abundance of individuals that live long enough to transmit the pathogen . We now compare these dynamics to those produced when the total number of insects released is three times the minimum required ( Figure 5A ) . In this case the Wolbachia spreads much more rapidly and reaches its final frequency in the year of release . Although more insects are introduced there is still only a very slight initial increase in the female population size at the start of releases , followed rapidly by a net reduction in both population size and EIR . An advantage of releasing more mosquitoes is that the EIR declines more quickly due to faster Wolbachia spread . In addition to reducing adult longevity , Wolbachia can also directly inhibit pathogens within the mosquito . The insets in Figure 5 show the combined effects of life-shortening and pathogen inhibition on the EIR once Wolbachia has become established . We assume that Wolbachia reduces average adult longevity by either 16% ( Figure 5A ) or 5% ( Figure 5B ) . Reducing longevity has a major impact on the EIR but in both cases direct pathogen inhibition gives a further substantial decrease in the EIR . Our results show that a 16% reduction in longevity with no effect on transmission is similar to the joint effect of a 5% reduction in lifespan with a 50% reduction in transmission . However , it would require far fewer mosquitoes to be released to establish a Wolbachia with the second phenotype . We explored how these conclusions were affected by the nature of the assumed density-dependence ( see Text S6 ) . When density-dependence is strong the releases cause less reduction in the female population size , both transiently due to incompatible matings and in the long term due to the fitness costs of Wolbachia infection . However the reduction in the EIR was similar for all the forms of density dependence we considered , particularly in the longer term once the Wolbachia has reached a high infection frequency . This is because the EIR is much more sensitive to changes in adult mortality than to changes in the rate of adult recruitment .
Introducing Wolbachia into mosquito populations can lead to a reduction in the transmission of mosquito-borne diseases . The normal way in which establishment has been envisioned is through the release of equal numbers of male and female mosquitoes [3] . However , as females transmit disease and are responsible for nuisance biting , it is important to minimise the numbers of females released , and this may be critical in obtaining regulatory permission and public support for introductions . It is shown here that establishment can occur following releases composed very largely of males provided this causes the Wolbachia infection frequency in males in the field to exceed a threshold . The numbers of females in the population decline rapidly following the initial male-biased releases , and only for a relatively brief period at the commencement of releases are female numbers slightly higher than they would have been in the absence of the intervention . However , for male-biased releases the numbers of insects that must be reared is considerably higher than when releases are composed of equal numbers of the two sexes , and a reliable method must exist for separating males and females . These may not be major barriers to the strategy . Some mass-rearing facilities have the capacity to produce over 1 million mosquitoes per week [15] , which is more than 30 times the estimated size of some village-scale natural mosquito populations [3] , [24] . For Aedes aegypti , male and female pupae can be rapidly separated by size and when larval rearing conditions are optimal over 99% males can be achieved [15] , [16] . Transgenic sex separation methodologies also exist ( e . g . Alphey et al . [15] ) , although their use would lose the advantages of Wolbachia intervention not involving genetically modified organisms . However in situations where the number of insects that can be reared and released is more strongly limited , such as in isolated rural areas , it may be preferable to use equal sex-ratio releases which will provide more rapid Wolbachia spread for a given available release size and number . This would improve the likelihood of achieving Wolbachia establishment , for example in the case of unexpected losses of released insects . Equal sex-ratio releases result in considerably less suppression of female numbers as well as of the EIR during the release period compared to male-biased releases . They also lead to higher densities of biting females , though the increase over natural levels is not always very marked ( see Text S7 ) . Release programmes that have the capacity for bigger release sizes and greater numbers of releases are likely to gain stronger benefit from using male-biased releases to limit the addition of females and suppress the vector population . Engagement with local communities will also reveal the extent to which the possibility of modest and temporary increases in biting female numbers would be a significant impediment to their support for the program . A further strategy for reducing the risk of increased biting or disease transmission associated with the introduction of females is artificially to suppress the mosquito population prior to release , for example by insecticidal fogging or larval control . Fewer released mosquitoes would then be required to surpass the threshold infection frequency that allows Wolbachia to spread . Suppression measures would be stopped immediately prior to mosquito release so as to minimise the time for population numbers to rebound , and so as not to affect the introduced insects . The efficacy of different types of pre-release suppression will depend on the population dynamics of the mosquito species , in particular the form of density dependence , and can be explored using the type of model developed here ( see Text S8 for examples ) . Once Wolbachia becomes established in the population , the model indicates that the rate of disease transmission can be substantially reduced due to the bacteria both reducing adult mosquito longevity and inhibiting pathogen transmission . The pathogen inhibition phenotype of Wolbachia described for the wMelPop strain in Ae . aegypti [5] , [7]and An . gambiae [4] is also produced by some , but not all , other strains of the bacterium , both in Drosophila [25]–[27] and Ae . aegypti [2] . To date only wMelPop has been shown to produce a significant reduction in lifespan , but it seems reasonable to predict that other strains will also produce some degree of life shortening when moved into a naïve mosquito host , particularly if costly immune pathways are activated [4] , [5] , [7] . Our results indicate that even a small reduction in adult longevity acts together with the direct effects of Wolbachia on the pathogen to produce a considerably greater reduction in pathogen transmission . In general Wolbachia strains that induce strong pathogen inhibition with minimal or no associated life-shortening would be the optimal choice for use in disease control strategies , since this would reduce the level of releases that are required , improve spread dynamics , and minimize selective pressure for modulation of phenotypes that reduce pathogen transmission . Whether the additional Wolbachia-associated mortality occurs only in late life or throughout adulthood will also determine the strength of the selective pressure for modulation of the phenotype , and thus how long-lasting the strategy is likely to be in providing disease control . Sub-lethal effects could for example affect the capacity of young adults to escape predation in the wild . Ultimately it may only prove possible to obtain a full understanding of how cage survival dynamics translate to natural conditions , and relative mortalities in captive-bred versus wild insects , once field releases are actually underway . Other aspects of mosquito biology that have not been considered here may also be important to Wolbachia spread dynamics . For example , in Aedes aegypti old females have been shown to have lower fecundity [28] , [29] , and this may reduce the fitness costs of Wolbachia infection if its effects on the host are strongest late in life . An extended version of the model that incorporates age-dependent fecundity can be analysed using the methods presented in Text S1 . However we currently know little about the interactions between mosquito age and the effects of Wolbachia infection on fecundity for any mosquito species . This emphasises the need for detailed empirical study of the effects of Wolbachia on mosquito demography . In conclusion , our results show that the establishment of Wolbachia in natural mosquito populations using male-biased releases is feasible provided that the mass-rearing capacity is available for the larger number of insects that need to be reared . Successful establishment of Wolbachia strains which reduce mosquito longevity or interfere with the pathogen in its vector are predicted to have substantial long-term benefits in terms of reduced disease transmission , and employing male-biased introductions minimises the risk of any biting or disease transmission during the release period . | Wolbachia are symbiotic bacteria that are found in many insect species . Recent laboratory studies show that certain strains of Wolbachia can reduce the capacity of mosquito species to transmit diseases such as dengue fever and malaria , either by directly inhibiting the pathogen or by shortening lifespan . However , little is known about how easily these bacteria will spread in natural mosquito populations or the impact of deliberate Wolbachia introduction on disease transmission . We use a simple model of Wolbachia-mosquito interactions to explore the design of field releases of infected mosquitoes to initiate symbiont spread . A particular concern is how Wolbachia can be introduced while releasing only small numbers of female mosquitoes which may bite humans and transmit disease . The models include explicit mosquito population dynamics including seasonal fluctuations in population size and different forms of population regulation . We find that rapid Wolbachia establishment is possible by releasing predominantly male mosquitoes , though the number of insects introduced may need to be large . This strategy requires the introduction of considerably fewer females compared to equal sex-ratio releases and is unlikely to increase disease transmission throughout the intervention . We demonstrate that once Wolbachia has become established , substantial reductions in disease transmission are possible . | [
"Abstract",
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] | 2011 | Strategies for Introducing Wolbachia to Reduce Transmission of Mosquito-Borne Diseases |
A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes . Currently , available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models . In this study , we use the recently proposed computational approach iSCHRUNK ( in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models ) , which combines Monte Carlo parameter sampling methods and machine learning techniques , in the context of Bayesian inference . Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws . The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive posterior distributions of kinetic parameters consistent with the observed physiology . In this work , we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are their values that give rise to a desired metabolic behavior ? Such information is important for a wide variety of studies ranging from biotechnology to medicine . To illustrate the proposed methodology , we performed Metabolic Control Analysis , computed the flux control coefficients of the xylose uptake ( XTR ) , and identified parameters that ensure a rate improvement of XTR in a glucose-xylose co-utilizing S . cerevisiae strain . Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology , and ultimately to design and control the desired responses of the metabolism . This framework paves the way for a new generation of methods that will systematically integrate the wealth of available omics data and efficiently extract the information necessary for metabolic engineering and synthetic biology decisions .
Kinetic models are one of the cornerstones of rational metabolic engineering as they allow us to capture the dynamic behavior of metabolism and to predict dynamic responses of living organisms to genetic and environmental changes . With reliable kinetic models , metabolic engineering and synthetic biology strategies for improvement of yield , titer , and productivity of the desired biochemical can be devised and tested in silico [1] . The scientific community has acknowledged the utility and potential of kinetic models , and efforts towards building large- and genome-scale kinetic models were recently intensified [2–9] . Nevertheless , the development of these models is still facing challenges , such as partial experimental observations and large uncertainties in available data [10–12] . The major difficulty in determining parameters of kinetic models are uncertainties associated with: ( i ) flux values and directionalities [13–16]; ( ii ) metabolite concentration levels and thermodynamic properties [13–16]; and ( iii ) kinetic properties of enzymes [2 , 17] . As a result of interactions of metabolite concentrations and metabolic fluxes through thermodynamics and kinetics , these uncertainties make parameter estimation difficult . Quantifying these uncertainties and determining how they propagate to the parameter space is essential for identification of parameters that should be measured or estimated to reduce the uncertainty in the output quantities such as time evolution of metabolites or control coefficients [18 , 19] . In biological systems , large uncertainties and partial experimental data commonly result in a population instead of in a unique set of parameter values that could describe the experimental observations . Such population of parameter sets is typically computed using Monte Carlo sampling techniques [3–5 , 8 , 9 , 11 , 20–28] . However , the problem is when certain properties differ among models in a model population . For example , one such property is flux control coefficients ( FCCs ) [18 , 19 , 29] . In [30] , we used the ORACLE ( Optimization and Risk Analysis of Complex Living Entities ) framework [3 , 4 , 8 , 10 , 11 , 31 , 32] to compute a population of kinetic models along with the corresponding flux control coefficients with the aim of improving xylose uptake rate ( XTR ) of a glucose-xylose co-utilizing S . cerevisiae strain . We have found that in the same population of models that are consistent with the observed physiology FCCs can be different due to lack of data about kinetic parameters . This can lead to erroneous or conflicting conclusions and decisions about the system in metabolic engineering and synthetic biology studies . In this contribution , to resolve such issues , we propose to formulate these problems as parameter classification: identify which of the parameters , if any , should be constrained so that the values of studied properties , such as FCCs , are in predefined ranges . For this purpose , we extended the capabilities of iSCHRUNK ( in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models ) , a recently introduced machine learning approach that characterizes uncertainties in parameters of kinetic models , and identifies accurate and narrow ranges of parameters that can describe a studied physiological state [17] . In iSCHRUNK , machine learning is combined with methods that generate populations of kinetic models [3–5 , 8 , 9 , 11 , 20–28] to data-mine the integrated data and observed physiology together with the kinetic parameters . The extended iSCHRUNK workflow is amenable for identifying parameters that give rise to a wide variety of properties of metabolic responses . The identified parameters can further be refined in an iterative way using the stratified sampling . Moreover , a set of improvements in the parameter classification procedure was introduced to improve the classification accuracy and to allow for dealing with uncertainties in alternative physiologies , e . g . , when multiple metabolite concentrations vectors are consistent with the observed physiology . As an illustration of the capabilities of the extended iSCHRUNK , we identified the enzymes and their kinetic parameters that determine consistent FCC values related to XTR . Our results showed that by constraining only three parameters , corresponding to xylose reductase ( XRI ) and ATP synthase ( ASN ) , consistent FCCs can be obtained for models computed around multiple steady-state metabolite concentrations . We further showed how the parameter classification can be improved to more accurately identify the parameter subspaces that lead to well-determined model properties .
In [30] , we analyzed the improvement of the xylose uptake rate ( XTR ) during mixed glucose-xylose utilization in a recombinant Saccharomyces cerevisiae strain . Here , we revisited that study and built the kinetic model of S . cerevisiae metabolic network around the reference steady-state of metabolic fluxes and metabolite concentrations ( Methods ) . The model contains 258 parameters and describes 102 reactions and 96 intracellular metabolites distributed over cytosol , mitochondria and extracellular environment . The experimentally determined values of kinetic parameters were missing , and the analyzed system was underdetermined , i . e . , we had 102+96 computed values for steady-state fluxes and metabolite concentrations versus 258 unknown parameters . This meant that a multitude of parameter sets could reproduce the observed physiology , and we used the ORACLE framework that employs Monte Carlo sampling to generate a population of 200’000 kinetic models . We computed the flux control coefficients ( FCCs ) of the metabolic network and used them to rank enzymes according to their control over XTR , i . e . , the highest ranked enzymes were the ones with the largest magnitude FCCs with respect to XTR . Among the top ranked enzymes , hexokinase ( HXK ) , non-growth associated maintenance ( ATPM ) , and NADPH reductase ( NDR ) had ambiguous control over XTR ( Fig 1A ) . The distributions of the control coefficients of XTR with respect to HXK , ATPM and NDR ( CHXKXTR , CATPMXTR , and CNDRXTR , respectively ) were extensively spread around zero , and we could not deduce with certainty whether the control of these enzymes over XTR was positive or negative . The population of control coefficients CHXKXTR was nearly symmetric around zero with a mean of 0 . 005 and 47% of samples had negative values ( Fig 1B ) . We split the population of kinetic models based on the sign of CHXKXTR , and we analyzed the two populations with a negative ( Fig 1C , left ) and a positive ( Fig 1C , right ) control of HXK over XTR . The split in the population did not have a substantial effect on the majority of the control coefficients . Interestingly , the exceptions were precisely the other enzymes with the ambiguous control over XTR , i . e . , ATPM and NDR , which exhibited a negative correlation with HXK ( Fig 1C ) . This suggested that there were two distinct populations of kinetic models . The fact that models within these two populations have several common metabolic responses further implied that each of these two populations of models had distinct values of some kinetic parameters that determined such metabolic responses . We used the Classification and Regression Trees ( CART ) algorithm [33 , 34] to identify significant parameters that determine responses of XTR to changes in HXK activity . The CART algorithm partitions the parameter space into hyper-rectangles determined by the ranges of parameters that satisfy the studied property . Here , we used as parameters the degree of saturation of the enzyme active site , σA [10] , because this quantity is constrained in a well-defined range between 0 and 1 ( Methods ) , and the desired property was the negative control of HXK over XTR . The inputs of parameter classification were: ( i ) the information for each out of 200’000 parameter sets whether or not it gave rise to the negative control of HXK over XTR; and ( ii ) parameter values of 200’000 parameter sets . Subsequently , we will refer to hyper-rectangles computed by the CART algorithm as rules . To measure the performance of parameter classification we defined the performance index ( PI ) , which quantifies a portion of parameter sets giving rise to the studied property . In this work , out of all parameter sets that satisfy rules ( or a rule ) inferred by parameter classification , PI quantifies how many of them are giving rise to the negative control of HXK over XTR . For example , within a population of models satisfying a rule , if 40% of models give rise to the negative control of HXK over XTR , then PI of this rule is 0 . 4 . To combine the distributions of top 3 parameters that ensure a high PI in an unbiased way , we performed another parameter classification ( Methods ) . The parameter classification algorithm inferred 66 rules on these three parameters , and the top rule enclosed 9389 samples with PI of 0 . 73 ( S1 Table ) . The PI value of 0 . 73 was close to the maximal PI value of 0 . 78 , which was computed for the rules formed with all parameters . As expected , the ranges of the three parameters defined by the top rule ( Fig 4C ) were consistent with the analysis presented in the previous section . We proceeded with the validation of the ranges of the top 3 parameters on a new population of models . We imposed the ranges of the top 3 parameters derived from the top rule of the parameter classification and generated a population of 100’000 models ( Methods ) . We then computed the control coefficients of the top enzymes over XTR ( Fig 4 ) . The control coefficient CHXKXTR was distinctively negative with a mean value of -0 . 09 ( Fig 4C ) , and its distribution was clearly shifted toward negative values compared to that of the original population of models ( Fig 1B ) . More than 72% of models had negative values of CHXKXTR compared to 47% in the original population of models . The value of PI of 0 . 72 obtained from the validation set was strikingly close to the predicted value of 0 . 73 from the second tree training . In Metabolic Control Analysis ( MCA ) , it is considered that the control coefficients depend only on elasticities , however this holds only when the reactions are irreversible and there are no conserved moieties . It has been shown that metabolite concentrations affect displacements of reactions from thermodynamic equilibrium , which in turn influence the control over fluxes and concentrations in the network [3 , 16 , 32 , 37] . Therefore , when there is uncertainty in physiology , e . g . , when several alternative concentration profiles correspond to experimental observations , the control coefficients derived from the kinetic models computed for these concentration profiles can be significantly different . iSCHRUNK can resolve this kind of problems by identifying the parameter values that give rise to well-determined control coefficients of the metabolic network for multiple alternative physiologies . As an illustration , we analyzed three alternative physiologies characterized with three alternative concentration profiles ( Reference , Extreme1 and Extreme2 ) and a common flux profile ( Methods ) . We have undertaken to identify significant parameters that ensure a well-determined control over XTR for these physiologies . For this purpose , we constructed two populations of 200’000 kinetic models for the Extreme1 and Extreme2 physiology ( Methods ) . Overall , together with 200’000 parameter sets computed previously for the reference physiology ( Reference ) , we had 600’000 parameter sets for parameter classification . In the three populations of models , 47% ( Reference ) , 46% ( Extreme1 ) and 39% ( Extreme2 ) of the models had a negative CHXKXTR . We found no rule with PI equal to 1 in performed parameter classification studies . This suggested that the parameter subspaces leading to a negative and a positive control of HXK over XTR were not distinctly separated . To improve the parameter classification for the problems where the separation between the classes is fuzzy , we propose to employ the k-nearest neighbors ( k-NN ) algorithm ( Methods ) . The k-NN algorithm allows us to identify the parameter sets from one class that are surrounded by the parameter sets of the other class and reassign them to the latter . In the context of finding parameter values that give rise to a certain property , this means that the parameter classification algorithm will find only those parameter sets that are surrounded by a majority of the parameter sets of the same class . This way , the separation between the classes will be increased at the expense of neglecting parameter sets from the regions with a heavy overlap of the classes . We reconsidered the classification for parameters determining a negative control of HXK over XTR in the Reference case , and we applied the k-NN algorithm with k = 10 over the set of initial 200’000 parameters in order to find the surrounding for each of parameter sets , and to perform the reassignment ( Methods ) . If in the group of 10 closest neighbors of a parameter set the percentage of parameter sets from the same class was less than a reassignment threshold , r , then that parameter set was reassigned . We performed two parameter classification studies for two different reassignment thresholds , r , of 30% and 50% ( Methods ) . We found that as the reassignment threshold was increasing the tree training algorithm was inferring a smaller number of rules ( 73 for r = 30% versus 31 for r = 50% ) . Furthermore , the inferred rules were enclosing a smaller number of parameter sets for higher values of r , i . e . , for r = 30% and 50% , the top rules enclosed respectively 13427 and 1339 parameter sets ( Fig 5 and S1 Table ) . In contrast , the obtained PIs , were higher for r = 50% than for r = 30% ( Fig 5 ) . For example , PI of the top rule for r = 50% was 0 . 83 , whereas the one for r = 30% was 0 . 73 ( Fig 5 and S1 Table ) . A comparison between the original method with preselection , which is identical to the reassignment method with r = 0% ( corresponding to no reassignment ) , and the reassignment methods for r = 30% and 50% showed a general tendency of the latter for obtaining rules with improved PI and that enclose a smaller number of parameter sets ( Fig 5 ) . We also tested the reassignment procedure for parameters determining a positive control of HXK over XTR in the case of the reference metabolite concentration with k = 10 and r = 60% . The classification algorithm inferred 19 rules with PIs ranging from 0 . 75 to 0 . 90 . The rules were defined by only 28 parameters ( S1 Table ) . The top rule enclosed 1711 parameter sets with PI of 0 . 90 , and it was defined by 6 parameters . To validate the proposed improvement to the parameter classification , we imposed the distributions of the parameters defined by the top rules for the negative control case with r = 50% , and for the positive control case with r = 60% ( S1 Table ) . We generated for each study a population of 100’000 models , and we computed the control coefficients in the network . In the case of negative control , the distribution of the control coefficient CHXKXTR was biased toward negative values with mean -0 . 13 ( Fig 6A and 6B ) . More than 79% of the computed control coefficients CHXKXTR were negative . ( Table 4 ) . Similarly , in the case of positive control , the distribution of the control coefficient CHXKXTR was shifted toward positive values with the mean of 0 . 21 and a remarkable PI of 0 . 89 ( Fig 6D and 6F , and Table 4 ) . For the negative and positive cases , the top rules were defined by 6 parameters each , where three parameters , σnadh_cXRI , σpi_mASN , and σt3p_cTPI , were common for both cases ( Fig 6C and 6E ) . These three parameters were also ranked as the top 3 parameters in the parameter classification with the original algorithm ( Fig 4 ) . Moreover , the range of σnadh_cXRI was constrained toward low values for the negative control and toward high values for the positive control . In contrast , the parameters σpi_mASN and σt3p_cTPI were constrained toward high values for the negative control , and toward low values for the positive control . These patterns suggest that these three parameters are crucial for determining the sign of the control of HXK over XTR , whereas the remaining parameters , σdhap_cGPD1 , σatp_mASN , and σxlt_cXRI for the negative control case , and σnad_cXRI , σatp_cATPM , and σnadp_cNDR for the positive control case , are likely having a minor effect on the PI . This result clearly demonstrated that the reassignment procedure allows for more precise identification of the subspaces leading to a desired control of HXK over XTR . We observed improvement of both PI and the mean CHXKXTR value compared to the results obtained with the unaltered parameter classification algorithm .
Machine learning methods [38–42] have found applications in a large number of biological and biomedical areas such as cancer research [43–45] , population genetics [46 , 47] , protein structure and function prediction and phylogenomic mapping [48–52] , protein-protein interactions [53–55] , medical imaging [56–60] , gene expression and microarray data analysis [61–64] , regulatory interactions [65 , 66] , metabolic pathway dynamics [67] , biomarker discovery and analysis of metabolomics and proteomics data [68–71] . However , the potential of these methods for detecting patterns in parameters of kinetic models of metabolism and uncovering hidden relationships between kinetic parameters , omics data , and observed phenotypes remained largely unexploited . Machine learning methods require large sets of training data for their successful application and methods for generating kinetic metabolic models that use Monte Carlo sampling offer an unprecedented opportunity for employing machine learning to advance our understanding of metabolic processes in cellular organisms . Kinetic models are usually built around a metabolic steady-state , which is characterized by the metabolite concentrations and metabolic fluxes , and the generated populations of kinetic parameters together with the observed steady-state data contain implicit information about the studied physiology . This information , if extracted systematically , can be used as guidance for the design of metabolic engineering and synthetic biology strategies that ensure the desired metabolic responses of studied organisms . In this work , we have extended iSCHRUNK functionalities to data-mine this information and systematically reduce uncertainties in the values of kinetic parameters that give rise to the desired metabolic behavior . As a demonstration , we reduced the uncertainties in the kinetic parameters that ensure that values of flux control coefficients remain within a pre-specified range . iSCHRUNK lends itself to a broad scope of applications ranging from sustainable production of biochemicals to medicine and regarding both the analysis and design of metabolism . It allows us to analyze the relationships between the inferred parameter ranges and the measurements acquired on the actual biological system , and , consequently , to create hypotheses regarding the operating states of enzymes and provide information about saturations of all enzymes in the network . This information is crucial for biotechnology studies where living cells need to be engineered for improved performance , or for drug discovery studies where , e . g . , we want to overproduce a compound that is toxic to a pathogen . The method can be applied not only to identify distributions of kinetic parameters but also to determine distributions of the metabolic fluxes and metabolite concentrations satisfying given requirements . It can also be used for guaranteeing both qualitative and quantitative features of metabolism , and several requirements can be combined simultaneously . For example , iSCHRUNK can be used to identify and quantify the parameters that maintain a redox potential while enforcing the desired level of yield and specific productivity of a compound of interest . Provided that the desired properties are biologically feasible , the method can be used to guarantee an arbitrary number of requirements . Finally , iSCHRUNK can be used to alleviate issues with high computational requirements of Monte Carlo sampling of kinetic parameters in large- and genome-scale metabolic networks . As the size of the models and complexity of studies increases , sampling a kinetic space becomes increasingly difficult and even intractable . However , iSCHRUNK allows us to identify relevant kinetic parameters that correspond to the observed physiology . The key finding of the current and previous studies [17] is that only a small set of parameters corresponding to a few enzymes is sufficient to characterize the observed physiology . Therefore , once we identify the most relevant parameters , it suffices to densely sample the identified parameters while fixing the remaining parameters at arbitrary feasible values . This way , iSCHRUNK dramatically reduces the sampling space , thus enabling computational analyses of large-scale and genome-scale dynamic metabolic systems .
The computational method for characterization and reduction of uncertainty , iSCHRUNK , was proposed in [17] . iSCHRUNK involves a set of successive computational procedures that can help us to ascertain and quantify the kinetic parameters that correspond to a given physiology . iSCHRUNK can be used with any method that generates populations of kinetic models describing given physiology such as ensemble modeling [24] or ORACLE [3 , 4 , 8 , 10 , 11 , 31 , 32] . Here , we extended the original iSCHRUNK workflow ( 17 ) by an iterative loop that uses parameter classification to perform stratified sampling of the kinetic parameters , i . e . , it allows identifying refined sets of parameters that lead to the desired metabolic behavior ( Fig 7 ) . We used the extended iSCHRUNK to identify the distribution of kinetic parameters that determine the sign in ambiguous distributions of control coefficients as follows: We carried out the parameter classification in several steps ( Fig 7B ) . We first removed from the consideration the parameters that were not affecting the control over the analyzed flux . We then used the CART algorithm with the preselected parameters for three populations of kinetic models where each population was computed with a different metabolite concentration vector ( see Step II of the framework discussed above ) . In the third step , we ranked the parameters over three concentrations , and we chose the top parameters to continue . We next refined the distributions of the top parameters for each concentration individually , and we then used this information to determine the consistent distributions of top parameters over all concentrations . We detail the parameter classification steps below . In the cases when the space of parameter sets leading to a negative and the one leading to a positive control over analyzed quantities are overlapping , the separation between parameter classes is fuzzy . To enhance the separation between the classes , we propose here utilization of the k-nearest neighbors ( k-NN ) algorithm in the parameter classification as follows [87] . For each of the parameter vectors , we first assessed whether or not they were determining , e . g . , a negative control , and we assigned them to two distinct sets . The first set , SN , contained parameter vectors that gave rise to a negative control , whereas the second set , SP , contained the ones that gave rise to a non-negative control . We then ran the k-nearest neighbors ( k-NN ) algorithm , and for each parameter vector from the set SN , we computed how many out of its k-nearest neighbors belonged to the same set ( SN ) . For each of these parameter vectors , if the percentage of k-nearest neighbors that belonged to the set SN was higher than a pre-specified reassignment threshold , r , we then retained that vector in the set SN . For instance , for r = 50% , if more than 50% of k-nearest neighbors of the analyzed parameter set belonged to the set SN , that parameter set remained in the set SN . Otherwise , we re-assigned that parameter vector to the set SP . With the proposed reassignment procedure , we emphasized the regions of the parameter space that have a higher proportion of parameter vectors belonging to the set SN . The reassignment procedure introduced two new parameters: the reassignment threshold , r , and the number of nearest neighbors , k . The values of r were chosen on the basis of the initial , unbiased , sampling that was performed in Step III . Specifically , from the initial sampling we could assess the average percentage of SN parameter vectors in the set of all vectors . We then set r to be a larger than the average percentage so that the parameter classification algorithm could identify the regions in the parameter space with the above than average proportion of SN vectors . Assuming that the parameter space was sampled uniformly , we use the parameter k to choose the larger or smaller part of the parameter space around the analyzed parameter vector for a possible reassignment . Very large values of k are not recommended as the reassignment procedure would consider the overall parameter space and no samples would be retained in the set SN as r is chosen to be larger than the average percent of SN vectors in the overall set of parameter vectors . Bayesian inference relies on use of Bayes theorem to compute the conditional distribution of a parameter vector θ given observed data x: p ( θ|x ) =p ( x|θ ) p ( θ ) p ( x ) where p ( θ|x ) is the posterior distribution of the parameters θ , p ( θ ) is the prior distribution of parameters , p ( x|θ ) is the likelihood , and p ( x ) is the evidence . In computing the posterior distribution p ( θ|x ) , the evidence can be ignored as it represents a normalizing constant . It is often computationally prohibitive to explicitly evaluate the likelihood function and Approximate Bayesian Computation ( ABC ) methods are used for approximating this function by simulations [88] . For this type of studies , the ABC rejection algorithm [89] can be used as follows . First , the prior distribution of kinetic parameters is generated using the ORACLE framework or any other method that uses Monte Carlo sampling of uncertain parameters for constructing populations of kinetic models [3–5 , 8 , 9 , 11 , 20–28] . The corresponding control coefficients are next computed , and the parameter classification algorithm is then used to discard parameter vectors from the prior that gave rise to ambiguous control over analyzed quantities . As a result , the retained samples are distributed according to the approximate posterior distribution of kinetic parameters that give rise to well-determined control over analyzed quantities . The simulations in this study were performed in MATLAB using an Apple MacPro Workstation with 2 . 7 GHz 12-Core Intel Xeon E5 processor and 64 GB of RAM memory . The required time to generate a set 200’000 kinetic models was ~12 . 5h , whereas one run of the parameter classification algorithm required several minutes . | Kinetic models are the most promising tool for understanding the complex dynamic behavior of living cells . The primary goal of kinetic models is to capture the properties of the metabolic networks as a whole , and thus we need large-scale models for dependable in silico analyses of metabolism . However , uncertainty in kinetic parameters impedes the development of kinetic models , and uncertainty levels increase with the model size . Tools that will address the issues with parameter uncertainty and that will be able to reduce the uncertainty propagation through the system are therefore needed . In this work , we applied a method called iSCHRUNK that combines parameter sampling and machine learning techniques to characterize the uncertainties and uncover intricate relationships between the parameters of kinetic models and the responses of the metabolic network . The proposed method allowed us to identify a small number of parameters that determine the responses in the network regardless of the values of other parameters . As a consequence , in future studies of metabolism , it will be sufficient to explore a reduced kinetic space , and more comprehensive analyses of large-scale and genome-scale metabolic networks will be computationally tractable . | [
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] | 2019 | Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties |
The value of rewards arises from multiple hedonic and motivational dimensions . Reward-encoding brain regions such as the ventral striatum ( VS ) are known to process these dimensions . However , the mechanism whereby distinct reward dimensions are selected for neural processing and guiding behavior remains unclear . Here , we used functional imaging to investigate how human individuals make either hedonic ( liking ) or motivational ( wanting ) evaluations of everyday items . We found that the two types of evaluations were differently modulated depending on whether participants won or lost these items . Neural activity in the VS encoded both hedonic and motivational dimensions of reward , whereas ventromedial prefrontal activity encoded primarily motivational evaluations and central orbitofrontal activity encoded predominantly hedonic evaluations . These distinct prefrontal representations arose regardless of which judgment was currently relevant for behavior . Critically , the VS preferentially processed the reward dimension currently being evaluated and showed judgment-specific functional connectivity with the dimension-specific prefrontal areas . Thus , our data are in line with a gating mechanism by which prefrontal cortex ( PFC ) –VS pathways flexibly encode reward dimensions depending on their behavioral relevance . These findings provide a prototype for a generalized information selection mechanism through content-tailored frontostriatal communication .
Reward is central for goal-directed behavior . However , reward is not a unitary concept but characterized by multiple dimensions . Activity in reward-processing regions such as the ventral striatum ( VS ) correlates with various reward dimensions , including gains and losses [1] , pleasantness [2] , hedonic value [3] , motivational value [4 , 5] , expected value [6 , 7] , received value [8] , decision value [9] , and salience [10] . Some of these different reward dimensions can be separated at the behavioral level [11 , 12] . This raises an important yet unresolved question: does the VS process these dimensions simultaneously and in parallel , irrespective of which dimension is currently relevant for behavior ? Alternatively , if the VS processes only one dimension at a time , how does the VS selectively and flexibly gate access to the behaviorally relevant signals ? Here , we focus on two common reward dimensions [13–15] that overlap anatomically in the VS [12 , 13 , 16]: the motivational drive to obtain rewards ( wanting ) and the hedonic pleasure associated with rewards ( liking; please note that we use the terms “wanting” and “liking” in their everyday meaning , i . e . , as measured by self-report [11 , 12] ) . We used a behavioral task in which participants indicated how much they wanted or liked various nonconsumable reward items , and we aimed to dissociate the motivational and hedonic reward dimensions by having participants win or lose these items in a game . Given the VS’s central position at the center of corticostriatal loops [17] , the VS could participate in largely separate and parallel wanting and liking loops , passing on information received from distinct regions in the medial prefrontal cortex ( mPFC ) and orbitofrontal cortex ( OFC ) . This possibility mirrors traditional views of cortical and basal ganglia architecture [18 , 19] and predicts that VS activity should scale with wanting or liking ratings irrespective of whether the current judgment is a wanting or a liking judgment . In contrast , based on the anatomical convergence of prefrontal projections in the VS [20 , 21] , the VS could dynamically interact with cortical wanting and liking regions depending on which dimension is currently required for guiding behavior . In this view , VS activity should reflect primarily wanting ratings during wanting judgments and primarily liking ratings during liking judgments . In line with the second mechanism , we find evidence compatible with the idea of striatal gating of hedonic and motivational reward dimensions . In contrast to the judgment-specific coding observed in the VS , distinct regions in the mPFC and OFC encoded wanting or liking regardless of judgment type . Finally , frontostriatal connectivity varied as a function of judgment type , supporting the idea that access to the currently relevant reward dimension is gated in the striatum .
Participants rated everyday items in the scanner according to how much they wanted and how much they liked them ( Fig 1A and 1B ) . The ratings in the scanner were collected twice—once before and once after participants played a game in which they won half of the items . Won items were handed over to participants at the end of the game . The game allowed us to separate wanting and liking behaviorally while also making the task more engaging . Participants differentiated between wanting and liking judgments in terms of both response times and ratings ( Fig 1C–1E ) . Analyzing response times using an ANOVA with repeated-measures factors Session ( pre- or postgame ) , Judgment Type ( wanting or liking rating ) , and Stimulus Type ( won or lost item ) revealed a main effect of Session ( F ( 1 , 27 ) = 29 . 94 , p < 0 . 0001 ) , as well as a main effect of Judgment Type ( F ( 1 , 27 ) = 41 . 10 , p < 0 . 0001 ) . Participants took significantly more time to make liking judgments than wanting judgments ( t ( 27 ) = 6 . 39 , p < 0 . 001; Fig 1E ) , and response times correlated ( positively ) with ratings only for wanting ( r = 0 . 33 , p = 0 . 04 ) but not for liking ( r = −0 . 09 , p = 0 . 56 ) judgments . Together , these findings suggest that participants treated the two judgment types differently . Furthermore , even though they remained significantly correlated overall ( before game: r = 0 . 79; after game: r = 0 . 78 , both p < 0 . 001 ) , wanting and liking ratings changed differentially from before to after the game depending on whether the item was lost or won . An ANOVA served to analyze the change in ratings , with repeated-measures factors Judgment Type ( wanting or liking rating ) and Stimulus Type ( won or lost item ) . We found both main effects of Judgment Type ( F ( 1 , 27 ) = 10 . 49 , p < 0 . 005 ) and Stimulus Type ( F ( 1 , 27 ) = 21 . 40 , p < 0 . 0001 ) , as well as an interaction between Stimulus and Judgment Type ( F ( 1 , 27 ) = 34 . 50 , p < 0 . 0001 ) . Wanting ratings decreased specifically for won items ( change in wanting won versus lost items: t ( 27 ) = −5 . 28 , p < 0 . 001; wanting won pre versus won post: t ( 27 ) = 4 . 81 , p < 0 . 001; wanting lost pre versus lost post: t ( 27 ) = −0 . 16 , p = 0 . 873; Fig 1C ) . In contrast , liking ratings decreased specifically for lost items ( change in liking won versus lost items: t ( 27 ) = 2 . 79 , p < 0 . 05; liking won pre versus won post: t ( 27 ) = 0 . 52 , p = 0 . 609; liking lost pre versus lost post: t ( 27 ) = 4 . 50 , p < 0 . 001; Fig 1D ) . Taken together , these differences in response times and ratings provide evidence that the participants differentially processed the hedonic and motivational dimension of items . We next assessed which neural systems encoded wanting and liking . Using a parametric general linear model ( GLM ) , we identified regions where activity was parametrically associated either with wanting or with liking ratings ( Table 1 and Fig 2 ) . In this GLM , we pooled data from both liking and wanting trials , resulting in one onset regressor , which was modulated by three parametric modulators ( PMs ) : the individual average wanting rating of the presented item , the individual average liking rating of the presented item , and the trial-specific response time ( serial orthogonalization of parametric regressors was turned off for these analyses [22] ) . In a whole-brain ( voxel-level ) corrected analysis , we found that wanting was related to prefrontal activations , including medial parts of the OFC ( z = 5 . 03 , family-wise error ( FWE ) -corrected , p < 0 . 05 , peak [0 , 50 , −5]; Fig 2A ) , and the mPFC ( z = 5 . 21 , FWE-corrected , p < 0 . 05 , peak [−3 , 44 , −2] ) . In contrast , liking-related responses were more focal and limited to the central OFC ( z = 4 . 86 , FWE-corrected , p < 0 . 05 , peak [−24 , 47 , −14]; Fig 2D ) and posterior cingulate ( z = 4 . 92 , FWE-corrected , p < 0 . 05 , peak [0 , −34 , 25] ) . These results suggest that neural activity in anatomically segregated regions of the prefrontal cortex ( PFC ) track either wanting or liking . To further characterize the degree to which these responses are specific to wanting or liking judgments , we employed two post hoc region-of-interest ( ROI ) analyses . First , we extracted individual liking- and wanting-related responses in the ROIs associated with wanting and liking ratings ( 6 mm spheres around the peak voxels; Table 1 ) and assessed the difference between these responses . To minimize bias , the ROIs were defined using data from all subjects except the one for whom the neural responses were being extracted ( leave-one-subject-out cross-validation procedure ) . This allowed us to determine whether different regions encoded wanting and liking differently or similarly . While wanting- and liking-related responses in the posterior cingulate ROI did not differ significantly ( t ( 27 ) = 1 . 66 , p = 0 . 108 ) , those extracted from the OFC and mPFC ROIs did . Responses in the central OFC showed significantly stronger associations with liking than wanting ( t ( 27 ) = 2 . 35 , p = 0 . 026 ) . In contrast , the medial OFC cluster as well as the mPFC cluster showed stronger responses for wanting than liking ( medial OFC: t ( 27 ) = −2 . 07 , p = 0 . 048; mPFC: t ( 27 ) = −1 . 99 , p = 0 . 056 ) . Second , we performed an ROI analysis with entirely independent ROIs from a meta-analysis of reward activity in the medial and lateral OFC [23] . This analysis yielded similar findings as the previous one: main effects of PM Type ( F ( 1 , 27 ) = 4 . 59 , p = 0 . 034 ) and ROI ( F ( 1 , 27 ) = 12 . 43 , p < 0 . 001 ) and a significant interaction of PM Type with ROI ( F ( 1 , 27 ) = 8 . 90 , p = 0 . 004 ) . Pairwise comparisons showed significant coding of wanting ( t ( 27 ) = 5 . 35 , p < 0 . 001 ) but not liking ( t ( 27 ) = 1 . 62 , p = 0 . 116 ) and stronger coding of wanting than liking ( t ( 27 ) = 2 . 53 , p = 0 . 018 ) in the medial OFC . Conversely , the central OFC showed significant coding of liking ( t ( 27 ) = 2 . 92 , p = 0 . 007 ) but not wanting ( t ( 27 ) = 1 . 20 , p = 0 . 239 ) , although the difference between liking and wanting ( t ( 27 ) = 0 . 95 , p = 0 . 349 ) was not significant . Together , these data suggest that wanting and liking tend to be processed in anatomically distinct regions in the PFC but overlap in the posterior cingulate . Previous animal work has implicated the VS ( nucleus accumbens ) and the pallidum in encoding both motivational and hedonic reward dimensions [24] . Based on these findings , we examined the role of these two areas in more detail . We analyzed data in two a priori anatomically defined ROIs encompassing these two regions ( Table 1 , Fig 2 ) . In the pallidum , activity was parametrically associated only with liking ratings ( z = 3 . 97 , FWE-small volume correction ( SVC ) , p < 0 . 01 , peak [−15 , 5 , −2] ) . In the VS , we found parametric wanting-related activations ( z = 4 . 06 , FWE-SVC , p < 0 . 01 , peak [−6 , 11 , −2]; Fig 2G ) , as well as more confined parametric liking-related activations ( z = 3 . 79 , FWE-SVC , p < 0 . 05 , peak [− 9 , 14 , −5]; Fig 2J ) . Thus , in line with previous animal studies , the VS encoded both wanting and liking , whereas the pallidum processed primarily hedonic evaluations . To more systematically assess the relation of these striatal and pallidal responses to wanting and liking , we extracted and compared both wanting- and liking-related responses from 6 mm sphere ROIs in the VS and pallidum , again using a leave-one-subject-out cross-validation procedure ( Table 1 ) . In contrast to the PFC clusters , comparable wanting- and liking-related responses were found in the VS ROI associated with liking ( t ( 27 ) = −0 . 50 , p = 0 . 622 ) as well as the VS ROI associated with wanting ( t ( 27 ) = −1 . 02 , p = 0 . 315 ) . While the pallidum ROI associated with liking showed no difference to wanting ( t ( 27 ) = −1 . 12 , p = 0 . 906 ) , it is worth keeping in mind that we found no significant relation to wanting in the pallidum to start with . In line with an overlap of both reward dimensions primarily in the VS , a formal conjunction analysis [25] revealed common wanting and liking areas in the VS ( z = 3 . 97 , FWE-SVC , p < 0 . 05 , peak [− 9 , 11 , −5]; Fig 3A ) but not in the pallidum and the posterior cingulate . Thus , while prefrontal responses appear to be specific to either wanting or liking and exhibit a regional dissociation between the two , responses in the VS ( and to a lesser degree in the pallidum and posterior cingulate ) seem to encode both reward dimensions . To investigate the effects of game outcome , we assessed mean neural activity elicited by item onset ( irrespective of trial-specific rating ) in exploratory analyses of the ROIs identified by the parametric analyses reported above . Central OFC activity decreased more for lost than won items ( t ( 27 ) = 2 . 60 , p = 0 . 015 ) and mPFC activity decreased more for won than lost items in wanting trials ( t ( 27 ) = 2 . 83 , p = 0 . 009 ) . Finally , the VS showed decreases in activity for both won and lost items ( t ( 27 ) > 3 . 77 , p < 0 . 001 ) . These findings are consistent with coding of mean behavioral liking decreases by central OFC , mean behavioral wanting decreases by mPFC , and coding of both of these effects by the VS . The results reported above suggest that wanting and liking are encoded in overlapping regions in the striatum but in separate regions in the PFC . We next assessed whether encoding of these two dimensions in the VS depends on which dimension is currently relevant for behavior . We therefore tested whether the responses identified by the parametric GLM were independent of the type of judgment participants made in a given trial or whether the VS switched between coding wanting and liking as a function of judgment type . For this analysis , we used a second parametric GLM that distinguished between trials with different judgement types ( two regressors corresponding to trials in which liking and wanting judgments were made , respectively ) . Each of these regressors was again parametrically modulated by the individual average wanting rating of the presented item , the individual average liking rating of the presented item , and the trial-specific response time ( serial orthogonalization of parametric regressors was again turned off for these analyses [22] ) . These analyses were performed in ROIs of 6 mm spheres around the peak voxels from the first parametric GLM ( Table 1 ) . We extracted and compared wanting-related responses during wanting and liking trials as well as liking-related responses during wanting and liking trials . This allowed us to assess whether responses were specific to the currently performed judgment ( e . g . , for wanting , specificity would be reflected in significantly stronger encoding of wanting ratings during wanting judgments compared to liking judgments ) . For both liking- and wanting-related responses , areas in the PFC and posterior cingulate encoded reward dimensions irrespective of judgment type . Specifically , we found that liking-related responses within the central OFC ROI were significant during both liking and wanting judgments ( liking trials: t ( 27 ) = 2 . 83 , p = 0 . 009; wanting trials: t ( 27 ) = 2 . 15 , p = 0 . 041 ) and did not differ significantly between judgment types ( liking versus wanting trials: t ( 27 ) = 0 . 45 , p = 0 . 655 ) . Likewise , liking-related responses in the posterior cingulate were significant during both judgment types ( liking trials: t ( 27 ) = 4 . 41 , p = 0 . 0001; wanting trials: t ( 27 ) = 4 . 14 , p = 0 . 0003 ) and did not differ significantly ( liking versus wanting trials: t ( 27 ) = 0 . 23 , p = 0 . 823 ) . Moreover , wanting-related responses in the mPFC and medial OFC were significant during both wanting and liking trials and did not differ significantly between judgment types ( mPFC: wanting trials t ( 27 ) = 4 . 83 , p = 0 . 00005; liking trials t ( 27 ) = 4 . 57 , p = 0 . 0001; wanting versus liking trials t ( 27 ) = 0 . 12 , p = 0 . 903; medial OFC: wanting trials t ( 27 ) = 5 . 33 , p = 0 . 00001; liking trials t ( 27 ) = 4 . 15 , p = 0 . 0003; wanting versus liking trials t ( 27 ) = 0 . 51 , p = 0 . 613 ) . Thus , beyond exhibiting regional specificity for motivational versus hedonic reward dimensions , these anatomically segregated cortical regions also appear to consistently track wanting or liking regardless of which judgment is currently being made . In contrast , responses in the VS strongly depended on the current judgment type . Parametric liking-related responses in the VS were only significant during liking judgments ( liking trials: t ( 27 ) = 4 . 85 , p = 0 . 00005; wanting trials: t ( 27 ) = 1 . 49 , p = 0 . 15 ) and significantly stronger during liking than wanting judgments ( liking versus wanting trials: t ( 27 ) = 2 . 32 , p = 0 . 028 ) . Conversely , parametric wanting-related responses in the VS were only significant during wanting judgments ( wanting trials: t ( 27 ) = 3 . 61 , p = 0 . 001; liking trials: t ( 27 ) = 1 . 27 , p = 0 . 216 ) and significantly stronger for wanting than liking judgments ( wanting versus liking trials: t ( 27 ) = 2 . 80 , p = 0 . 009 ) . Focusing on the activation pattern of the common overlapping voxels in the VS ( Fig 3A ) mirrored this finding . We compared wanting-related and liking-related signals in the VS cluster defined by the conjunction analysis using an ANOVA with repeated-measures factors Judgment Type ( wanting or liking trial ) and PM Type ( wanting or liking ) . In line with selective processing of the currently relevant reward dimension , we observed a significant interaction ( F ( 1 , 27 ) = 7 . 17 , p = 0 . 012; Fig 3B ) . Specifically , the VS showed stronger parametric wanting-related responses during wanting judgments than liking judgments ( t ( 27 ) = 2 . 53 , p = 0 . 018 ) and stronger parametric liking-related responses during liking than wanting judgments ( t ( 27 ) = 2 . 28 , p = 0 . 031 ) . Taken together , while the frontal ROIs ( OFC and mPFC ) exhibit regional specificity for wanting and liking regardless of judgment type , the striatum flexibly encodes wanting or liking depending on whether wanting or liking judgments are required . These findings imply that VS activity is closer to behavioral responses than central OFC and mPFC activity . To directly test this prediction , we extracted subject-wise time series from the VS , mPFC , and central OFC , z-scored them , and used them to predict trial-wise ratings irrespective of judgment type . The participant-specific regression model also included motion parameters . We then used paired t tests to compare the mean regression coefficients between brain regions . We find that VS activity is a significantly better predictor of trial-by-trial ratings than activity in mPFC ( t ( 27 ) = 5 . 97 , p = 0 . 000003 ) or central OFC ( t ( 27 ) = 2 . 47 , p = 0 . 02 ) . These data corroborate the notion that VS activity is closer to behavior than medial prefrontal and central orbitofrontal activity . Lastly , we explored the mechanism by which activity in the VS switched between encoding of different reward dimensions . One possible mechanism could be to flexibly enhance the cross-talk between the VS and the cortical region that processes the currently relevant dimension proportional to the current level of this reward dimension . To examine this possibility , we performed a psychophysiological interaction ( PPI ) analysis and tested whether functional coupling ( fMRI signal coherence ) between the VS and wanting and liking regions in the PFC depended on the type and level of the current judgment . We used the overlapping voxels in the VS as a seed region to extract the physiological signal . Psychological factors were liking and wanting judgment trials , each parametrically modulated by the average wanting and liking ratings of the current item . The PMs were multiplied by the physiological variable to generate a total of four psychophysiological regressors ( liking-trial liking rating , liking-trial wanting rating , wanting-trial liking rating , wanting-trial wanting rating ) . As target regions , we focused on the same ROIs in the central OFC and mPFC defined above that processed wanting and liking ratings irrespective of the current judgment . During liking judgments , we found that VS connectivity with the central OFC was more strongly related to levels of liking than levels of wanting ( z = 3 . 26 , FWE-SVC , p < 0 . 05 , peak [−21 , 44 , −11]; Fig 3C ) . Conversely , during wanting judgments , we found that VS connectivity with the mPFC was more strongly related to levels of wanting than levels of liking ( z = 3 . 10 , FWE-SVC , p < 0 . 05 , peak [−6 , 44 , 4]; Fig 3C ) . Together , these results suggest that flexible processing of reward dimensions in the VS may be realized by selectively gating input from prefrontal regions that encode the reward dimension that is currently relevant for behavior . However , it should be kept in mind that a gating mechanism is only one possible interpretation of our functional coupling data . In any case , the degree of this connectivity modulation is directly related to the level of the currently relevant reward dimension .
We find anatomically segregated wanting- and liking-related signals in the PFC , as well as overlapping wanting- and liking-related responses in the VS . Our results are consistent with the idea that hedonic and motivational reward dimensions from the cortex converge in the striatum and are passed on from the striatum in a condensed and focused manner . We propose that this selection process is mechanistically implemented through frontostriatal gating of different reward signals . In the PFC , motivational and hedonic dimensions of reward are encoded in a parallel and anatomically separated manner , while the VS flexibly encodes only the reward dimension that is currently relevant for behavior . Thereby the striatum acts as a detector for behaviorally relevant reward dimensions and enables selective processing of reward information required for guiding ongoing actions appropriately . Thus , our findings show how the VS reduces the multiplexed nature of reward information and enables adaptive action selection . More generally , we demonstrate that besides selecting actions that provide the highest ( decision ) value within a given situation , the brain can also contextually select value representations . Finally , our data suggest situation-adapted modulation of connectivity as one possibility of achieving selection .
All participants provided informed written consent . The study complied with the Declaration of Helsinki and was approved by the ethics committee of the Canton of Zurich ( protocol 2010-0327/3 ) . We studied 28 right-handed participants aged 20–29 years ( 22 . 8 ± 0 . 5 years , mean ± SEM; 14 females ) . All participants were recruited from the Laboratory for Social and Neural Systems Research participant pool . Forty nonconsumable everyday items were used as rewards in the study ( S1 Table for a full list ) . Items were selected based on prior pilot experiments so that initial mean liking and wanting ratings were similar . Before scanning , we physically presented all items to participants in real life , which ensured that they recognized and were familiar with each item . Moreover , participants learned to separately consider hedonic and motivational dimensions of a good that they did not possess , such as an expensive sports car . The task was implemented with Matlab ( The MathWorks , Natick , MA , United States ) and the Cogent 2000 toolbox ( http://www . vislab . ucl . ac . uk/cogent . php ) . In the scanner , participants were asked to rate each item according to how much they wanted to have it , as well as how much they liked the item at that moment . In each trial ( Fig 1B ) , participants first saw a cue indicating the type of rating trial ( 1 s ) , followed by an image of the item ( 3 s ) , and finally the rating screen ( 3 . 5 s ) . Ratings were provided on a continuous scale using a trackball . Trials were separated by a variable intertrial interval ( mean 3 s ) . Each item was rated twice for wanting and twice for liking , resulting in 160 trials split into 4 runs before the game and the same again after the game . Participants performed the rating task in two sessions , which were separated by a game in which participants could win the items outside of the scanner ( Fig 1A ) . The game consisted of a perceptual task in which participants had to indicate whether the item was presented to the left or the right of the midpoint of the screen . Participants won items that they classified correctly . The difficulty of the game was calibrated so that participants won and lost 50% of the items . To make the items more salient and thereby enhance the memorability of winning and losing the items , participants were seated at a table with the items set up next to them while they performed the task on a computer . Additionally , immediately after the game , participants packed up the items they won in a bag , which they later took home . Whole-brain scanning was performed with a Philips Achieva 3T whole-body MRI scanner equipped with an 8-channel head coil ( Philips , Amsterdam , the Netherlands ) . For each of the 8 scanning runs , 227 T2*-weighted whole-brain EPI images were acquired in ascending order ( 33 transverse [axial] slices per volume , field of view 192 mm × 192 mm × 108 mm , slice thickness 2 . 6 mm , 0 . 7 mm gap , in-plane resolution 2 mm × 2 mm , matrix 96 × 96 , repetition time [TR] 2 , 000 ms , echo time [TE] 25 ms , flip angle 80° ) . Additionally , a T1-weighted turbo field echo structural image was acquired in sagittal orientation for each participant with the same angulation as applied to the functional scans ( 181 slices , field of view 256 mm × 256 mm × 181 mm , slice thickness 1 mm , no gap , in-plane resolution 1 mm × 1 mm , matrix 256 × 256 , TR 8 . 4 ms , TE 3 . 89 ms , flip angle 8° ) . Preprocessing and statistical analysis of the MRI data were performed using SPM8 ( http://www . fil . ion . ucl . ac . uk/spm; Wellcome Trust Centre for Neuroimaging , London , United Kingdom ) . All EPI images were temporally corrected to the middle slice , realigned to the mean image , normalized ( resampling to 3 mm × 3 mm × 3 mm voxels ) to the standard EPI template of the Montreal Neurological Institute ( MNI ) , and smoothed using a Gaussian kernel with 4 mm full width at half maximum ( FWHM ) . We chose a relatively small smoothing kernel because we were particularly interested in the VS , and a recent meta-analysis found that in order to avoid bias against subcortical activations , applying minimal smoothing is recommended [68] . To detect activity related to wanting or liking , we used a parametric analysis . The first GLM pooled data from wanting and liking judgments into one judgment-type–unspecific regressor , time locked to the onset of each trial . This regressor was modulated by 3 PMs: within-session normalized item-specific average wanting ratings , within-session normalized item-specific average liking ratings , and response times . Importantly , to ensure that all regressors explain only independent components of variance , serial orthogonalization of parametric regressors ( as implemented in SPM ) was turned off [22] . Moreover , the GLM contained the 6 nuisance movement parameters . The duration of the onset regressor was 7 s , which corresponds to the time participants had to view and rate each image ( Fig 1B ) . We report whole-brain results ( p < 0 . 05 , voxel-level FWE corrected ) as well as activations in the a priori ROIs , VS , and pallidum ( p < 0 . 05 , voxel-level FWE corrected ) . The VS ROI was based on earlier studies and included the nucleus accumbens , ventral caudate nucleus , and putamen rostral to the anterior commissure [69] . The pallidum ROI was derived from the automatic anatomical labeling ( AAL ) atlas incorporated in the WFU-PickAtlas Tool in SPM [70 , 71] . To determine whether responses were specific or common to wanting and liking , we used an ROI analysis . We checked for specificity by extracting parameter estimates for each of the wanting and liking ROIs identified in the parametric contrast and using paired t tests that determined whether parameter estimates of one PM were significantly higher than those of the other PM . In order to minimize bias , we used two approaches . First , we performed a leave-one-subject-out cross-validation procedure , in which we extracted the neural data for each subject from ROIs consisting of 6 mm spheres around the peak of the activations identified by a group analysis in which this subject was left out . By iterating over all participants , this allowed us to extract relatively unbiased parameter estimates for all participants . Second , we performed the analysis in entirely independent 6 mm spheres centered on coordinates reported by a meta-analysis of reward activity in the medial ( 4 , 54 , −4 ) and lateral ( −18 , 40 , −16 ) OFC [23] . To determine common areas of wanting and liking , we used an inclusive masking procedure , which identifies areas significantly associated with both wanting and liking PMs [25] . We used a second GLM to investigate judgment-specific and judgment-unspecific activations . In this model , we separated wanting and liking trials so that there were two onset regressors corresponding to judgment type ( wanting trial or liking trial ) , each of which had three PMs associated with it ( within-session normalized average wanting ratings of the presented item , within-session normalized average liking ratings of the presented item , and trial-specific response times ) , as well as the six nuisance movement parameters . Again , serial orthogonalization of parametric regressors was turned off . We then used an ROI analysis to investigate whether responses to wanting and liking identified by the first GLM depended on judgment type . ROIs were 6 mm spheres around the peak of the activations identified by the first GLM . We used Marsbar [72] ( http://marsbar . sourceforge . net/ ) to extract parameter estimates for each of the PMs split by judgment type , which were then tested using repeated-measures ANOVAs and paired t tests . We performed a PPI analysis [73] with the VS ( showing common coding of wanting and liking ) as the seed region and Judgment type ( wanting versus liking ) and Level ( parametric regressors for wanting versus liking ratings ) as psychological factors . We used the generalized form of the PPI model [74] to test whether the strength of the functional connectivity between the VS and the cortical regions showing specific coding of either wanting or liking depended on the type and level of the judgment performed on a given trial . The seed region was defined by the overlap of the wanting- and liking-related activations ( Fig 3A ) . For each subject , we estimated a PPI model with the activity in the seed region included as the physiological regressor and Judgment type ( wanting trial or liking trial ) , modulated by the within-session normalized item-specific average wanting ratings , as well as the within-session normalized item-specific average liking ratings included as the psychological regressors . The four PMs were multiplied with the physiological variable to create the psychophysiological regressors of interest ( liking-trial liking rating , liking-trial wanting rating , wanting-trial liking rating , wanting-trial wanting rating ) . The two critical comparisons of the PPI regressors were: wanting rating versus liking rating during wanting trials and liking rating versus wanting rating during liking trials . Please note that because the PPI model included the psychological and parametric rating regressors , any rating-level–dependent increases in connectivity are independent of the linear effects of these rating levels on activity . Thus , any significant interaction would show increased functional coupling between seed and other regions with increasing wanting/liking ratings rather than simple rating-induced activity changes in region pairs . We focused our analysis on the prefrontal clusters in the mPFC and OFC that were identified by the first GLM . | People and animals typically both want and like rewards . Here , we show that these two dimensions of value can be dissociated at both the behavioral and the neural level . In keeping with rodent findings , our human neuroimaging data indicate that the ventral striatum—a part of the reward system in the basal ganglia—encodes both dimensions . However , it does so depending on the judgment being made: during wanting judgments , activity in the ventral striatum increases with the degree of wanting significantly more than with the degree of liking , and vice versa during liking judgments . Accordingly , activity in the ventral striatum expresses the value dimension currently needed for behavior . In contrast , distinct regions of the prefrontal cortex encode either the degree of wanting or the degree of liking , irrespective of judgment type . Functional coupling analysis suggests that the ventral striatum preferentially communicates with wanting- or liking-related regions in the prefrontal cortex according to the type of expressed judgment . These findings suggest that flexible frontostriatal coupling can serve a gating mechanism to achieve behaviorally relevant selection of value dimensions . | [
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] | 2018 | Frontostriatal pathways gate processing of behaviorally relevant reward dimensions |
Kaposi sarcoma is a tumor consisting of Kaposi sarcoma herpesvirus ( KSHV ) –infected tumor cells that express endothelial cell ( EC ) markers and viral genes like v-cyclin , vFLIP , and LANA . Despite a strong link between KSHV infection and certain neoplasms , de novo virus infection of human primary cells does not readily lead to cellular transformation . We have studied the consequences of expression of v-cyclin in primary and immortalized human dermal microvascular ECs . We show that v-cyclin , which is a homolog of cellular D-type cyclins , induces replicative stress in ECs , which leads to senescence and activation of the DNA damage response . We find that antiproliferative checkpoints are activated upon KSHV infection of ECs , and in early-stage but not late-stage lesions of clinical Kaposi sarcoma specimens . These are some of the first results suggesting that DNA damage checkpoint response also functions as an anticancer barrier in virally induced cancers .
Recent findings suggest that DNA damage checkpoints become activated in early stages of human tumorigenesis , leading to growth arrest or apoptosis and thereby hindering tumor progression . Likewise , very recent reports have indicated that oncogene-induced senescence triggered by DNA replication stress also has a role as a tumorigenesis barrier . DNA damage checkpoint markers like phosphorylated ATM and Chk2 kinases and phosphorylated histone H2AX and p53 are activated in precancerous lesions ( early stages of tumorigenesis ) of several different human cancers , including bladder , breast , colon , and lung cancer [1 , 2] . These checkpoint responses precede p53 mutations and the appearance of gross chromosomal abnormalities . The tumorigenic events early in the progression of major human cancer types activate the ATR/ATM-regulated checkpoint as a guard against tumor progression and genetic instability . Candidate inducers of the response include oncogenes such as Myc [3 , 4] , Ras [5] , Cdc6 [1] , Cdc25A , E2F1 , or overexpressed cyclin E [6] . Kaposi sarcoma herpesvirus ( KSHV , or human herpesvirus 8 [HHV8] ) is a γ-2 herpesvirus implicated in all subtypes of Kaposi sarcoma ( KS ) , in multicentric Castleman disease , and in primary effusion lymphoma ( PEL ) [7–9] . KSHV establishes a latent infection in host cells , where only a subset of viral genes is expressed , while viral replication is not activated [10] . KS is an angiogenic tumor that consists of proliferating infected cells that form irregular microvascular channels and extravasated infiltrating inflammatory cells . Tumor cells in KS lesions are characterized by spindle-like morphology , and they express endothelial cell ( EC ) markers but also have features of other cell lineages , like macrophages and smooth muscle cells [11–14] . In KS lesions , all tumor cells are latently infected by KSHV and express latent genes , such as the viral cyclin ( v-cyclin ) , the viral FLICE inhibitory protein ( vFLIP ) , and the latency associated nuclear antigen ( LANA ) . These latent proteins are known to impinge in the regulation of the cell cycle , cell survival , and the major tumor suppressor pathways p53 and pRb , which suggests that they are important for viral pathogenesis ( reviewed in [15] ) . v-cyclin is structurally similar to cellular D-type cyclin and forms an active kinase complex with cellular CDK6 . v-cyclin also associates with CDK4 and CDK2 , but the binding does not lead to significant activation of these kinases . As its cellular counterpart , v-cyclin–CDK6 also phosphorylates pRb and induces accelerated S-phase entry in cultured cells , but it has a remarkably broader substrate range than the cellular D-type cyclin–CDK4/6 complexes ( reviewed in [16–18] ) . v-cyclin–CDK6 is resistant to inhibition by CDK inhibitors . Interestingly , both p27Kip1 and p21Cip1 are phosphorylated by v-cyclin–CDK6 , which leads to their inactivation [19–22] . These properties of v-cyclin suggest that it can function as an oncogene . However , v-cyclin expression in primary cells was shown to induce not only DNA synthesis but also a p53-dependent growth arrest and cytokinesis defects . These growth-restricting steps were overcome by the loss of p53 , which exposed the oncogenic potential of v-cyclin [23] . Previous reports have demonstrated that ECs are susceptible to both latent and productive infection by KSHV , and therefore represent a good model system for studies of the pathogenesis of this endothelial neoplasm [11 , 24–27] . However , the effect of KSHV infection on the growth properties and tumorigenic conversion of infected cells has remained obscure . In this study , we have assessed the consequences of v-cyclin expression and de novo KSHV infection in human ECs . Our results show that expression of v-cyclin in ECs induces senescence and strong DNA damage response , leading to centrosome amplification and growth arrest . Moreover , we have analysed the major tumor suppressor pathways upon de novo KSHV infection of ECs , and our data indicate that antiproliferative checkpoints are activated during the initial stages of KSHV infection . To determine whether DNA damage response is also associated with the development of KS , we examined early and late KS lesions for expression of the checkpoint markers .
To analyse the consequences of v-cyclin expression in a cell type naturally infected by KSHV , we used both primary human dermal microvascular ECs ( HDMECs ) and their telomerase ( hTERT ) –immortalized derivatives ( hT-HDMECs ) . The cells were transduced with retroviruses expressing Flag-tagged v-cyclin protein ( see Text S1 for the vectors used ) . To explore the proliferation capacity of v-cyclin–expressing hT-HDMECs ( v-cyclin–ECs ) , freshly transduced cells were grown for 48 h and then subjected to a 3-d selection in puromycin , after which proliferation was analysed by an MTT assay . As depicted in Figure 1A , cells transduced with the control virus ( vector only; mock ) and nontransduced cells proliferated during the follow-up period , while the v-cyclin–ECs did not . The specific v-cyclin–associated kinase activity in these cells was comparable to v-cyclin kinase activity in PEL cells , which are naturally infected , KSHV-positive cells expressing v-cyclin ( unpublished data ) . However , during 2-wk culture of the v-cyclin–ECs , v-cyclin protein was lost from the cells ( as analysed by indirect immunofluorescence and Western blotting ) , and the cells started to proliferate again ( unpublished data ) . For comparison , ECs transduced with a retrovirus expressing a cellular homolog to v-cyclin , cyclin D3 , had full proliferative capacity comparable to that of the controls ( Figure 1A ) . Thus the proliferation arrest in the v-cyclin–ECs was a specific property of this viral cyclin and not just a consequence of cyclin overexpression . To examine whether the v-cyclin–induced growth arrest in ECs was p53- dependent , we transduced hT-HDMECs constitutively expressing a dominant-negative p53 ( p53CTer; encoding the C-terminal amino acids 302–390 of murine p53 ) . These hT-HDMECs–p53CTer were transduced with the v-cyclin or control retrovirus . Expression of p53CTer in the ECs disturbs the wild-type ( wt ) p53 functions , which was observed as a growth advantage over the parental hT-HDMECs ( Figure 1A ) . Indeed , cells coexpressing v-cyclin and p53CTer had full proliferative capacity , suggesting that the growth arrest was p53-dependent ( Figure 1A ) . To determine at which stage of the cell cycle v-cyclin–ECs are arrested , we analyzed their cell-cycle profile by flow cytometry 7 d after transduction . According to the DNA profile , a significantly higher proportion of v-cyclin–ECs were in the S-phase ( 36% ) compared with the mock-transduced cells ( 15%; Figure 1B , top panels ) . Similar cell-cycle distributions were observed already at day 2 after transduction ( unpublished data ) . Since the v-cyclin–ECs lacked proliferative capacity , the observed increase in the number of S-phase cells could result either from re-replication of nuclear DNA without subsequent mitosis ( endoreduplication ) or an intra–S-phase arrest . To determine precisely the nature of this cell-cycle arrest , we performed a pulse-chase experiment with BrdU . A 2-h pulse of BrdU ( 15 μM ) , followed by 5-h and 10-h chase periods , was analysed by multiparameter flow cytometry . By plotting BrdU incorporation versus DNA content , we were able to detect both BrdU-labeled and unlabeled cells . After the initial 2-h labeling period , 10% of the mock-transduced cells and 8% of the v-cyclin–ECs had incorporated BrdU ( Figure 1B , bottom panels ) . However , the total fraction of cells in S-phase ( both BrdU-positive and -negative ) was higher in the v-cyclin–ECs ( 24% ) than in the mock ECs ( 10% ) . After the 5-h and 10-h chase periods , the population of BrdU-positive , mock-transduced cells had shifted towards the G2/M-phase , while the profile of BrDU-positive cells in v-cyclin–ECs remained unchanged ( unpublished data ) . The v-cyclin–ECs with an S-phase DNA content but negative for BrdU incorporation most likely represent cells that arrested at the S-phase before the BrdU pulse . This data indicates that the v-cyclin–ECs are not undergoing endoreduplication , but are arrested at the S-phase . The intra–S-phase checkpoint is activated as a result of DNA replication stress [28] . Previous studies have demonstrated that v-cyclin–CDK6 interacts with and phosphorylates components of the origin recognition complex such as Orc1 and Cdc6 [29] . Recent observations have established Cdc6 as a key replication initiation protein whose stability is increased by CDK phosphorylation [30] . The accumulated Cdc6 binds to the origin recognition complex , and promotes the initiation of DNA replication [31] . To explore if v-cyclin expression in the ECs induces premature or aberrant firing of the replication origins , we analysed the protein level of Cdc6 in the v-cyclin–ECs . v-cyclin–ECs had highly elevated levels compared with the mock-ECs , demonstrating that v-cyclin expression induces accumulation of the essential licensing factor Cdc6 in ECs ( Figure 1C ) . Similar accumulation of Cdc6 has also been seen in response to expression of other oncogenes such as Mos [1] . Oncogene-induced DNA replication stress induces senescence in human diploid cells [1 , 5] . To address whether a viral oncogene can also induce senescence , we stained the v-cyclin–ECs for the senescence-associated beta-galactosidase [32 , 33] ( see Text S1 ) . H-RasV12–expressing ECs were used as a positive control . Both the H-RasV12– and v-cyclin–ECs stained positive for senescence-associated beta-galactosidase , while the mock-transduced ECs remained negative ( Figure 1D ) . In addition to the positive signal from the senescence marker , the v-cyclin–ECs appeared significantly larger and flatter than cells expressing the mock virus . In response to replication stalling and DNA damage , checkpoint pathways are activated . We therefore analysed whether v-cyclin expression was evoking a DNA damage checkpoint in ECs . The occurrence of a DNA damage response can be ascertained by monitoring the appearance of specific markers such as phosphorylated histone H2AX ( γ-H2AX ) , phosphorylation of the ATM kinase ( on Ser1981; pS-ATM ) , or Chk2 kinase ( on Thr68; pT-Chk2 ) , focal staining of p53-binding protein 1 ( 53BP1 ) , or increase in Ser15-phosphorylated p53 ( pS-p53 ) . Cytological and biochemical analyses of v-cyclin–ECs ( both immortalized and primary ECs ) showed strong induction of all the above-mentioned DNA damage markers starting at 2–3 d after transduction ( Figure 2 for hT-HDMECs and Figure S1 for the primary HDMECs ) . Although 53BP1 expression was detected as uniform nuclear staining in the mock-virus–transduced ECs , its signal in the v-cyclin–ECs appeared dramatically different , and localized into bright intranuclear foci ( Figure 2A , bottom panel ) . These results suggested that v-cyclin activates the ATM–Chk2 pathway in ECs . In accordance with previous studies [23 , 34] , the v-cyclin–ECs also showed p53 stabilization and p21 induction ( Figure 2B ) . In contrast , little or no signal for the DNA damage markers was detected in ECs transduced with the mock-virus ( Figure 2A and 2B ) or with retrovirus expressing cyclin D3 ( unpublished data ) , consistent with the absence of a DNA damage response . Quantitation of the number of cells expressing markers for activated DNA damage response confirmed a significant increase in their expression in the v-cyclin–ECs compared with the mock-transduced ECs ( Figure 2C ) . The pronounced induction of γ-H2AX and the S-phase promoting capacity ( Figure 1B and 1C ) suggested that the DNA damage checkpoint induced by v-cyclin expression was provoked by DNA replication stress in the ECs . Expression of v-cyclin in mouse embryonic fibroblasts ( MEFs ) has been shown to induce centrosome amplification [23] , which prompted us to investigate whether the v-cyclin–ECs also displayed supernumerary centrosomes . We analysed the number of centrosomes in v-cyclin–expressing primary HDMECs , hTERT-HDMECs , or endothelial EA . hy926 cells by indirect immuofluorescence . Anti–γ-tubulin labeling of the cells revealed centrosomal amplification either as clustered centrosomes in HDMECs and hT-HDMECs ( Figure 3A , left and middle panels ) or as multiple centrosomes surrounding the nucleus in EA . hy926 cells ( Figure 3A , right panel ) . Amplification of centrosomes was often accompanied with bi- or multinucleation . Quantitation of these results depicted in Figure 3B for the hT-HDMECs shows a significant increase ( 4 . 2-fold ) in the proportion of the v-cyclin–ECs with more than two centrosomes . To investigate whether abrogation of the ATM-Chk2–dependent checkpoint would facilitate proliferation of the v-cyclin–ECs , we treated the cells with caffeine and wortmannin to inhibit ATM and ATR kinases ( Figure 3C , left graph ) , or with a specific ATM inhibitor KU-55933 ( Figure 3C , right graph ) . The inhibition was analysed by immunofluorescence analysis , which showed reduced phosphorylation of Chk2 as well as reduction of focal staining of 53BP1 in the KU-55933–treated cells . Treatment ( unpublished data ) with all of the inhibitors partially rescued the centrosomal aberrations in the v-cyclin–ECs ( Figure 3C ) , but did not lead to an increase in the net cell numbers ( unpublished data ) . However , inhibition of ATM–Chk2 by the chemical inhibitors led to an increase in multinucleated syncytia-like cells ( Figure 3C , insert ) , suggesting that the S-phase arrest was released , but this resulted in aberrant mitosis and subsequent “mitotic catastrophe . ” In order to explore if the centrosome amplification in v-cyclin–ECs was dependent on p53 , we analysed the centrosome numbers in hT-HDMECs constitutively expressing the dominant-negative p53CTer together with v-cyclin , and compared them with the v-cyclin–ECs . Inactivation of p53 by the C-terminal mutant led to a 40% decrease in the cells with abnormal centrosome numbers ( unpublished data ) , suggesting that the amplification of centrosomes by v-cyclin is p53-dependent . About one-third of the cells harboring more than two centrosomes were either binucleated ( as in Figure 3A ) , multinucleated , or had micronuclei , indicating that karyokinesis ( nuclear division ) had occurred without concomitant cytokinesis . Yet , the proportion of cells undergoing mitosis in the v-cyclin–ECs was very low as detected by an antibody against serine 10–phosphorylated histone H3 ( unpublished data ) . Interestingly , the v-cyclin–ECs showed increased cyclin B1–associated kinase activity when compared with the mock-transduced cells as well as an increase in the nuclear localization of cyclin B1 ( Figure 3D and 3E ) . This suggests that the cells were unable to switch off the cyclin B1 activity , which in turn can lead the cells to aberrant mitosis ( karyokinesis ) . Next , we sought to determine whether CDK activity is required for the observed DNA damage response and centrosome amplification in v-cyclin–ECs . To this end , we depleted CDK2 , CDK4 , or CDK6 expression in EA . hy926 cells using lentivirus-mediated RNA interference . EA . hy926 cells stably expressing small hairpin RNA ( shRNA ) specific for the above-mentioned kinases or control shRNA ( Scramble ) were first subjected to immunoblotting , which indicated very efficient downregulation ( 90%–95% ) of the target proteins . β-tubulin was used as a loading control for the CDK2 immunoblots ( Figure 4 ) . Silencing specificity for CDK4 and CDK6 was controlled by reciprocal immunoblotting ( i . e . , CDK6 for CDK4 shRNA-expressing cells and vice versa; Figure 4B ) . The CDK-downregulated cells were transduced with the v-cyclin–expressing retrovirus , and analyzed 5 d after transduction for DNA damage markers and centrosome numbers by indirect immunofluorescence . Cytological analysis using antibodies against pS-ATM and pT-Chk2 indicated that the DNA damage response was readily activated in the control EA . hy926 cells upon expression of v-cyclin ( Figure 4A , left panels ) . Depletion of CDK6 resulted in a significant reduction of phosphorylation on ATM and Chk2 , indicating that v-cyclin–induced DNA damage checkpoint was dependent on CDK6 expression ( Figure 4A and 4C ) . This is in accordance with previous studies demonstrating that CDK6 is the in vivo catalytic subunit of v-cyclin in the patient-derived PEL cells [35] . Accordingly , CDK6 expression was also required for the v-cyclin–induced centrosome amplification ( Figure 4D ) . In contrast , depletion of CDK4 or CDK2 did not decrease the DNA damage response or centrosome numbers , indicating that v-cyclin–induced DNA damage and centrosome amplification are not dependent on either of these cyclin-dependent kinases ( Figure 4C and 4D ) . Interestingly , after CDK6 knockdown , the morphology of most of the v-cyclin–ECs returns to normal , i . e . , the nuclei are of normal size and the cells do not display a flattened phenotype ( Figure 4A , shCDK6 panels ) . We then addressed the effects of KSHV infection on the DNA damage response of ECs . To analyse proliferation , we generated de novo KSHV-infected ECs ( KSHV-ECs ) by infecting the hT-HDMECs with a recombinant KSHV expressing GFP ( rKSHV . 219 [36] ) . The establishment of KSHV latent infection was confirmed by immunofluorescence using antibodies against the latent nuclear antigen , LANA ( unpublished data ) . Proliferation was analysed by the MTT assay . The KSHV-ECs proliferated at a very slow rate compared with the noninfected , passage-matched parental cells ( Figure 5A ) , suggesting that growth-limiting mechanisms were activated upon KSHV infection . To investigate potential growth-suppressive pathways , we took the hT-HDMEC–p53CTer , and also prepared hT-HDMECs stably transduced with retroviruses expressing SV40 large T antigen ( hT-HDMEC-LT ) or the human papillomavirus oncogenes E6 and E7 ( hT-HDMEC-E6/E7 ) to abrogate the function of both the p53 and pRb tumor-suppressor pathways . Expression of all of these genes led to a growth advantage over the parental hT-HDMECs ( Figure 5A ) . After infection with KSHV , all these cell lines proliferated faster than the KSHV-infected normal hT-HDMECs ( Figure 5A ) , indicating that the growth arrest was overcome . The proliferation rate was similar in cells expressing p53CTer and those expressing the multifunctional viral oncogenes . Although the presence of hTERT in these cells may alter the experimental conditions , this is suggesting that the p53 pathway primarily restricted the proliferation of KSHV-ECs . Interestingly , the KSHV-ECs started to spontaneously grow at a faster rate approximately 3–4 wk after infection ( Figure S2A ) . The recovery from this crisis period was accompanied by an increase in LANA signal in the KSHV-infected cells ( Figure S2B ) . A previous report [37] showed that KSHV infection of human umbilical vein ECs ( HUVECs ) induced abnormal centrosome duplication and multinucleation . We therefore sought to determine whether centrosome aberrations occurred also in our KSHV-ECs by labeling them with the γ-tubulin antibodies at 7 d after infection . This revealed that the KSHV-infected ECs had a marked increase ( >4-fold ) in the proportion of cells with more than two centrosomes ( Figure 5B , lower right panel ) . Furthermore , several of the cells with amplified centrosomes were also binucleated ( Figure 5B , top two panels ) . Centrosome aberrations may arise as a result of DNA damage-induced checkpoints , and recent data indicate the involvement of the checkpoint kinases Chk1 and Chk2 in this process ( reviewed in [38 , 39] ) . To explore the involvement of the ATM–Chk2 pathway in the KSHV-induced centrosome amplification , we treated the cells with pathway inhibitors followed by analysis of the centrosome numbers . This led to a marked decrease in the population of cells with abnormal centrosome numbers ( Figure 5C ) , suggesting the involvement of the ATM–Chk2 pathway in the centrosome amplification of KSHV-infected ECs . We did not observe the multinucleated syncytia-like cells as seen in the v-cyclin–ECs ( Figure 3C , insert ) , suggesting that expression of the other KSHV latent genes attenuated the strong oncogenic stress elicited by v-cyclin . Cytological analysis showed no increase in the DNA damage markers ( γ-H2AX , p-ATM , pT-Chk2 , 53BP1 , or pS-p53 ) during the growth arrest phase of KSHV-infected ECs ( early KSHV-ECs ) compared with the parental noninfected hT-HDMECs ( Figure 5D ) . However , after overcoming the crisis period , the proliferating KSHV-ECs ( late KSHV-ECs ) displayed an increased DNA damage response as indicated by the appearance of intranuclear 53BP1 foci ( Figure 5D ) . Nutlins are recently discovered small-molecule inhibitors that competitively bind MDM2 at the p53-binding pocket and prevent the destabilization of p53 . This leads to activation of the p53 pathway [40] . Recent results suggest that the cytotoxic effect of Nutlin-3a in cancer cells is enhanced by intrinsic DNA damage signaling in the cells [41] , and establish the 53BP1 protein as a critical mediator of Nutlin-3a cytotoxicity [42] . To provide further evidence of the involvement of DNA damage signaling in KSHV infection of ECs , we treated the proliferating , post-crisis KSHV-ECs and their parental noninfected cells with Nutlin-3a for 24 , 48 , or 96 h , and analysed them for cell viability by trypan blue exclusion . As shown in Figure S3 , Nutlin-3a treatment specifically increased apoptosis of KSHV-infected ECs , but had a minimal effect on the viability of the parental noninfected cells . These data provide additional evidence that DNA damage signaling is activated in KSHV-infected ECs . We have recently shown that Nutlin-3a also dissociates p53 interaction with LANA in KSHV-infected patient-derived lymphoma cells [41] , providing evidence that p53-dependent apoptosis is restrained in KSHV-infected cells , most probably by LANA-mediated binding to p53 . Our results from Nutlin-3a–treated KSHV-ECs further support the fact that KSHV-infected cells cannot proliferate or survive in the presence of active p53 . Our results suggest that KSHV infection and , more specifically , the expression of a viral latent gene can induce the activation of DNA damage signaling . To determine whether DNA damage response is activated in KS tumors as it is in several human cancers and especially in premalignant lesions [2 , 6] , we analysed expresssion of the activated Chk2 in early-stage ( patch ) and late-stage ( nodular ) cutaneous lesions of KS ( Figure 6 ) . All KS lesions used in the study were confirmed for LANA expression by staining with anti-LANA antibodies ( Figure 6B and unpublished data ) . The early-stage lesions are characterized by proliferating ECs forming irregular vascular spaces , associated with extravasation of red blood cells [43] . In the late nodular stage , the tumor consists of bundles of spindle cells with irregular slit-like vasculature , which is filled with erythrocytes . Immunohistochemistry for pT-Chk2 showed speckled nuclear staining in all early-stage KS skin lesions ( n = 5 ) . This staining could be blocked by a specific peptide ( Figure S5 ) . The signal was primarily detected in the patch-like tumor islets in the dermis ( Figures 6A and S4A , top two panels ) , but , interestingly , it was significantly weaker for eight out of nine nodular KS cases ( n = 9; Figures 6A and S4A , bottom two panels ) . The data suggested that ATM–Chk2 signaling is activated in the early KS lesions . To expand these studies using other DNA damage markers , we examined the phosphorylation status of H2AX ( γ-H2AX ) and the intranuclear localization of 53BP1 , both markers of the DNA double-strand break ( DSB ) checkpoint . Immunohistochemistry of early ( n = 3 ) and late KS ( n = 5 ) lesions revealed expression of γ-H2AX in both stages ( Figures 6C and S4B ) . However , the signal intensity in the late-stage KS tumors was weaker than in the early KS tumors ( Figure 6C , inserts ) . The infiltrated erythrocytes , typical for especially late-stage KS skin tumors , gave a very prominent autofluorescence signal , which is , however , easily separatable from the specific signal due to the absence of Hoechst staining ( arrows in Figures 6C and S4B ) . Intriguingly , 53BP1 was mostly localized to discrete nuclear foci in the early patch KS sections ( n = 4; Figure 6D , top panels ) , thus resembling the DNA damage foci in irradiated cells in culture . In two of five late-stage KS samples analyzed , 53BP1 gave a very strong but more uniform nuclear signal ( Figure 6D , bottom panels ) than in the early lesions . Taken together , the data suggests that DNA damage checkpoint is activated especially in the early stages of KS tumorigenesis .
The strong association between KSHV infection and development of KS , PEL , and multicentric Castleman disease has provided compelling evidence that KSHV is a tumorigenic virus . KSHV is well-equipped to engage important cellular signaling pathways such as cell cycle , apoptosis , angiogenesis , and immune evasion ( reviewed in [15] ) . However , direct cellular transformation by KSHV in cell culture occurs only rarely , suggesting that additional genetic alterations are required for KSHV tumorigenesis . We show here that KSHV infection does not confer a growth advantage to endothelial cells , and that intrinsic DNA damage signaling is activated in the KSHV-ECs . This is further supported by our recent finding of activated DNA damage response in KSHV-induced lymphomas ( PELs; [41] ) . Our results suggest that the oncogenic stress elicited by the latent viral protein v-cyclin promotes deregulated entry into the S-phase , thereby inducing DNA damage . The resulting checkpoint activation leads to growth arrest and senescence in the EC . Interestingly , overexpressed cyclin E , Cdc6 , and Ras , which all share the ability to promote unscheduled S-phase entry , have recently been shown to induce cellular senescence and DNA damage response due to deregulated DNA replication [1 , 5 , 6] . Activated DNA damage signaling in KSHV-infected ECs was observed only in the proliferating “post-crisis” population of cells . It is possible that in the very early stages of de novo KSHV infection , when the cells do not grow at all , there can be other growth-restraining mechanisms related to the establishment of latent infection . These may initially affect v-cyclin function and thereby suppress the activation of the DNA damage checkpoint . Although the KSHV-ECs start to proliferate after overcoming the growth arrest , they still grow slower than noninfected cells . This is accompanied by induction of markers for the activated DNA damage checkpoint . The induction of these markers was less pronounced in KSHV-infected ECs than in the v-cyclin–ECs , which may be due to expression of other latent KSHV genes , such as LANA , that can interfere with the p53 and pRb tumor suppressor pathways [44 , 45] . However , inhibition of p53 function by LANA did not seem to be sufficient initially after infection and establishment of the latency , since an increase in the net propagation of KSHV-infected cells required abrogation of p53 signaling . Nevertheless , the increased proliferative potential of KSHV-infected ECs after the initial crisis period correlated with an increase of the LANA signal of infected cells , suggesting that p53 function was suppressed when critical levels of LANA expression were achieved . This underscores the importance of p53 in constraining KSHV pathogenesis . It is intriguing to speculate that the accumulation of the key replication initiation protein , Cdc6 , would cause untimely initiation of DNA replication and lead to formation of DNA DSBs . This is supported by the strong induction of phosphorylated ATM and Chk2 kinases in these cells along with the positive signals from the ATM substrates , γ-H2AX , and p53 phosphorylated on Ser 15 as well as focal staining of 53BP1 . On the other hand , it is possible that v-cyclin deregulates cellular DNA replication or simply mimics DSBs by upregulating certain cellular factor ( s ) involved in the DNA damage pathway . There is growing evidence that several DNA viruses especially trigger DNA damage response in infected cells [46] . In most cases , the DNA damage response is involved in viral replication processes , and therefore the viruses have developed ways to inhibit or circumvent the host cell [47] . Human cytomegalovirus has been shown to mislocalize ATM , ATR , and Chk1 to limit the function of DNA repair proteins [48] . Interestingly , recent reports identify activated DNA damage signaling as an important mechanism both in Epstein-Barr virus lytic replication [49] and in Epstein-Barr virus oncogenesis [48] . Chk2 is normally activated as a result of DNA damage to arrest the cell cycle . It functions as an effector kinase , and prevents the activation of the cyclin B1–CDK1 complex required for the entry into mitosis . Inhibition of Chk2 and checkpoint signaling in the growth-arrested v-cyclin–ECs by treatment with chemical inhibitors induced mitotic defects ( syncytia formation ) and concomitant cell death ( unpublished data ) . This together with the increased cyclin B1–CDK1 activity in v-cyclin–ECs suggest that v-cyclin was capable of eliciting also mitotic signals , but in the absence of activated Chk2 , v-cyclin–induced signaling resulted in mitotic catastrophe . This is consistent with the previously identified role of Chk2 as the negative regulator of mitotic catastrophe [50] . In accordance with the previous findings by Verschuren and coworkers [23] as well as Pan and colleagues [37] , we observed induction of supernumerary centrosomes and multinucleation both by v-cyclin expression and KSHV infection . These results suggest that , like other viral oncogenes ( E7 , E1A , and LT [51] ) , v-cyclin can promote chromosomal instability by disrupting the centrosome cycle . Abnormal , oncogene-induced centrosome duplication has recently been shown to require CDK2 activity [52] . The v-cyclin–induced centrosome amplification required the v-cyclin kinase partner CDK6 , but not CDK2 , which suggests that v-cyclin–CDK6 activity may directly drive the aberrant centrosome duplication . Growing evidence indicates that alterations in centrosome number or function contribute to genomic instability and aneuploidy in advanced human cancers . Our results suggest that v-cyclin may contribute to the centrosomal abnormalities in KSHV-infected cells , which could lead to the induction of genomic instability and thereby predispose the cells to malignant transformation . Recently , KSHV LANA was shown to induce centrosome aberrations and increased multinucleation , indicating that other viral genes can also increase the incidence of genomic instability and promote KSHV-mediated tumorigenesis [53] . The finding of activated DNA damage signaling predominantly in the early-stage KS lesions suggests that this recently discovered anticancer barrier is also important in KS tumorigenesis . This inducible anticancer mechanism is activated in the premalignant lesion subject to “oncogenic stress” [2 , 6] , and it imposes a selective pressure to gain mutations that compromise the checkpoint . The observed checkpoint activation in the early KS lesions correlates with their relatively low proliferation index [54] and the reduction of apoptosis in the late-stage nodular KS lesions [55 , 56] . Moreover , recent analysis of chromosomal abnormalities in KS shows increased numbers of recurrent ( and sporadic ) chromosomal alterations in nodular KS compared with the early cases [57] . We thus propose that the early steps of KSHV tumorigenesis involve activation of the DNA damage checkpoint . This enforces selective pressure for mutations abrogating the checkpoint , and provides an advantage for cells with defective DNA damage response components . KSHV v-cyclin–induced DNA damage , particularly the DSBs , may be one of the factors enhancing genomic instability and progression to KS . Whether it is the DNA damage checkpoint reported here that predominantly limits neoplastic transformation by KSHV remains to be determined .
Amphotropic retroviruses were produced by transfection of Phoenix-Ampho retrovirus–producing cells ( a kind gift from G . Nolan , Stanford University , Stanford , California , United States ) with retroviral vectors using Lipofectamine 2000 reagent ( Invitrogen , http://www . invitrogen . com/ ) . After 48 h , viral supernatants were harvested and filtered through a 0 . 45-μm filter ( Millipore , http://www . millipore . com/ ) , and fresh media was added to Phoenix cells to produce more viral supernatant for a second round of virus production for 96 h . Target cells were plated 1 d before , and spin-transduced using 8 μg/ml polybrene ( Sigma , http://www . sigmaaldrich . com/ ) by centrifugation ( 2500 rpm; Heraeus Multifuge 3 S-R; Thermo Scientific , http://www . thermo . com/ ) for 30 min at room temperature . Cells were then returned to 37 °C , 5% CO2 , and after 1 h of incubation viral supernatant was removed and replaced with fresh complete media . The transduction was highly efficient ( 80%–90% ) as determined by flow cytometric analysis . HDMECs ( Promocell , http://www . promocell . com/ ) transduced with pWZLblast-hTERT were subjected to blasticidin selection ( 5 μg/ml ) 2 d after transduction . Cells transduced with pBabe , 2FkpBabe , D3pBabe , or pBabepuro-H-RasV12 retrovirus were subjected 2 d after transduction to selection with 1 μg/ml puromycin , whereas cells transduced with pBabe-Hygro-p53Cter and pBabe-Hygro-SVLT retrovirus were subjected to selection with 200 μg/ml hygromycin B ( Invitrogen ) . Retroviruses encoding the human papillomavirus oncogenes E6 and E7 ( pLXSN-E6/E7 ) were produced as described earlier [58] , and 2 d after transduction cells were subjected to selection with 700 μg/ml G418 . To construct CDK-silencing lentiviruses , shRNA-oligos to CDK2 ( 5′-GATCCGCACGTACGGAGTTGTGTATTCAAGAGATACACAACTCCGTACGTGCCCTTTTTTGGAAA-3′ ) , CDK4 ( 5′-GATCCGCACTTACACCCGTGGTTGTTTC AAGAGAACAACCACGGGTGTAAGTGCCTTTTTTGGAAA-3′ ) , CDK6 ( 5′-GATCCGAGTAGTGCATCGCGATCTTTCAAGAGAAGATCGCGATGCACTACTCGGTTTTTTGA-3′ ) , and a noncoding , random sequence ( Scramble; 5′-ATCCGTTCTCC GAACGTGTCACGTTTCAAGAGAACGTGACACGTTCGGAGAATTTTTTGGAAA-3′ ) were recombined from the pENTR-H1-BgH- vector ( provided by the Biocentrum Helsinki SYSBIO initiative ) by Gateway cloning technology ( Invitrogen ) into the lentiviral vector pDSL_hpUGIH ( LGC Promochem , http://www . lgcpromochem . com/ ) according to the manufacturer's protocol . Viruses were produced by transfecting Invitrogen ViraPower viral packaging plasmids with pDSL_hpUGIH into 293FT cells with Lipofectamine 2000 reagent according to the manufacturer's protocol ( Invitrogen ) . Lentiviral supernatants were collected 72 h after transfection , filtered through a 0 . 45-μm filter , and used for spin transduction of EA . hy926 cells as described above . Transduced cells were subjected for selection 72 h after transduction with 50 μg/ml hygromycin . Infectious recombinant KSHV virus ( rKSHV . 219 ) was produced from Vero cells latently infected with rKSHV . 219 ( a kind gift from J . Vieira , University of Washington , Seattle , Washington , United States ) , as described in [36] . hT-HDMECs , hT-HDMECs-p53CTer , hT-HDMECs-SVLT , or hT-HDMECs-E6/E7 were plated 1 d before , and were infected by the rKSHV . 219 virus supernatant by spin-infection as described above for spin-transduction . At 2 d after infection , the cells were subjected to selection with 1 μg/ml puromycin .
The Genbank ( http://www . ncbi . nlm . nih . gov/Genbank/ ) accession numbers for the genes and gene products discussed in this paper are ATM ( NM 000051 ) , ATR ( NM 001184 ) , Cdc6 ( NM 001254 ) , Cdc25A ( NM 001789 ) , CDK1 ( NM 033379 ) , CDK2 ( NM 001798 ) , CDK4 ( NM 000075 ) , CDK6 ( NM 001259 ) , Chk2 ( NM 007194 ) , cyclin B1 ( NM 031966 ) , cyclin D3 ( NM 001760 ) , cyclin E ( NM 001238 ) , E2F1 ( NM 005225 ) , E1A ( AY147066 ) , histone H2 ( XM 636495 ) , histone H3 ( NM 003493 ) , hTERT ( NM 198253 ) , human papillomavirus E6 ( EF424414 ) , human papillomavirus E7 ( AF478148 ) , K-Ras ( M54968 ) , LANA ( AF305694 ) , Myc ( NM 012333 ) , Orc 1 ( U40152 ) , pRb ( AF109873 ) , p21Cip1 ( NM 000389 ) , p27Kip1 ( NM 004064 ) , p53 ( NM 000546 ) , SV40 large T antigen ( AF168998 ) , v-cyclin ( U79416 ) , vFLIP ( U90534 ) , and 53BP1 ( NM 005657 ) . | Recent findings have indicated that DNA hyper-replication triggered by oncogenes can induce cellular senescence , which together with the oncogene-induced DNA damage checkpoint confers a barrier to tumorigenesis . Kaposi sarcoma herpesvirus ( KSHV ) can infect human dermal microvascular endothelial cells ( ECs ) in vitro , but KSHV infection does not seem to provide growth advantage to the cells , but rather leads to retarded growth . Moreover , the proliferative index has long been known to be low in KSHV-infected spindle cells in Kaposi sarcoma ( KS ) tumors . Our results provide an explanation for these observations by showing that activation of the DNA damage response , exerted by KSHV and a latent viral protein v-cyclin , functions as a barrier against transformation of KSHV-infected cells . Interestingly , the antiproliferative checkpoints are activated during the initial stages of KSHV infection and KS tumorigenesis . During the course of infection , the infected cells are imposed to overcome the checkpoint , and oncogenic stress elicited by the expression of v-cyclin may further contribute to the induction of genomic instability and malignant transformation . | [
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] | 2007 | Viral Oncogene–Induced DNA Damage Response Is Activated in Kaposi Sarcoma Tumorigenesis |
Bacterial pathogens utilize pore-forming toxins or sophisticated secretion systems to establish infection in hosts . Recognition of these toxins or secretion system by nucleotide-binding oligomerization domain leucine-rich repeat proteins ( NLRs ) triggers the assembly of inflammasomes , the multiprotein complexes necessary for caspase-1 activation and the maturation of inflammatory cytokines such as IL-1β or IL-18 . Here we demonstrate that both the NLRP3 and NLRC4 inflammasomes are activated by thermostable direct hemolysins ( TDHs ) and type III secretion system 1 ( T3SS1 ) in response to V . parahaemolyticus infection . Furthermore , we identify T3SS1 secreted effector proteins , VopQ and VopS , which induce autophagy and the inactivation of Cdc42 , respectively , to prevent mainly NLRC4 inflammasome activation . VopQ and VopS interfere with the assembly of specks in infected macrophages . These data suggest that bacterial effectors interfere with inflammasome activation and contribute to bacterial evasion from the host inflammatory responses .
The innate immune responses play important roles in host defense against the infection by microbial pathogens . Several nucleotide-binding , oligomerization domain ( NOD ) leucine-rich repeat proteins ( NLRs ) and PYHIN proteins , such as NLRP1 , NLRP3 , NLRC4 , AIM2 , and IFI16 , form inflammasomes: the multiprotein complexes that induce caspase-1 activation by functioning as sensors of pathogen-associated molecular patterns ( PAMPs ) or danger-associated molecular patterns ( DAMPs ) [1] , [2] , [3] . Inflammasome assembly is necessary for caspase-1 activation , and active caspase-1 then induces the processing of pro-IL-1β and pro-IL-18 and the secretion of mature active proinflammatory cytokines . Caspase-1 activation also triggers a rapid proinflammatory cell death known as pyroptosis . The NLRC4 inflammasome is activated in response to bacterial flagellin or rod protein , an essential component of the type III secretion system ( T3SS ) of Gram-negative bacteria . Flagellin that has been delivered into the cytoplasm of infected cells by the T3SS or type IV secretion system ( T4SS ) can bind to NAIP5 and facilitate NAIP5-NLRC4 interaction following the triggering of NLRC4 inflammasome assembly . On the other hand , rod protein such as PrgJ of Salmonella enterica delivered by the T3SS can bind to NAIP2 and promote NAIP2-NLRC4 interaction [4] , [5] . The NLRP3 inflammasome is activated in response to a wide variety of stimuli , such as bacterial pore-forming toxins , ionophores , and noninfectious crystals or materials [1] . The stimulants trigger the cellular signals responsible for NLRP3 inflammasome activation , including a change in the intracellular potassium concentration , the generation of reactive oxygen species , lysosomal disruption or mitochondrial dysfunction [1] . A recent report has shown that oxidized mitochondrial DNA released from damaged mitochondria can bind NLRP3 and trigger inflammasome activation [6] . However , whether the released mitochondrial DNA is definitively triggered by the cellular signals for NLRP3 activation listed above remains to be elucidated . Upon bacterial infection , the inflammasome is triggered as a proinflammatory response in host cells by sensing the pore-forming toxins or virulence-associated secretion systems . On the other hand , bacterial virulence strategies can interfere with essential components of immune signaling pathways such as NF-κB activation by secreting effectors that dampen cellular signals [7] . Also , bacteria use strategies that manipulate inflammasome activation , in most cases interfering with the production or recognition of bacterial ligands that trigger inflammasomes [3] . A recent study has shown that YopK , one of the T3SS effectors of Yersinia pseudotuberculosis , interacts with the T3SS translocon , thereby blocking the inflammasome from sensing the pathogen [8] . However , how bacterial pathogens interfere with inflammasome-mediated recognition of their secretion systems is largely unknown . Vibrio parahaemolyticus is a gram-negative halophilic bacterium that is a leading cause of seafood-borne gastroenteritis [9] . As virulence factors for infection , this bacterium produces pore-forming toxins known as thermostable direct hemolysins ( TDHs ) and has two sets of T3SS: namely T3SS1 and T3SS-2 [10] . Histopathological study of the acute stage of infection with V . parahaemolyticus in humans has shown inflammatory responses with PMN infiltration , edema of the lamina propria and hemorrhage . Also , the secretions of TNF-α and IL-1β , two pro-inflammatory cytokines were markedly induced [11] . These results suggested that infection with V . parahaemolyticus induces inflammatory responses in the intestinal mucosa . However , how host cells recognize infection with V . parahaemolyticus and regulate the inflammatory responses remain largely unknown . Since pore-forming toxins or virulence-associated secretion systems in bacterial infection mediate caspase-1 activation [12] , [13] , [14] , we considered the possibility that V . parahaemolyticus might induce caspase-1 activation . Here we demonstrate that TDHs trigger NLRP3 inflammasome activation and that both the NLRP3 and NLRC4 inflammasomes are triggered in response to the T3SS1 in infected macrophages . We further show that two T3SS1-secreted effector proteins , VopQ and VopS , induce autophagy and Cdc42 inactivation , respectively; these processes consequently interfere mainly with NLRC4 inflammasome activation . Our data demonstrate that recognition of the activities of pore-forming toxins and the T3SS1 contribute to the host proinflammatory responses , and define the effector proteins that dampen the host responses by interfering with inflammasome activation . Preventing inflammasome activation by the T3SS1 effectors appears to be one strategy for bacterial evasion of the host proinflammatory responses .
Mouse bone marrow-derived macrophages ( BMMs ) were primed with LPS to induce proIL-1β ( 35 kDa ) expression and infected with a wild-type ( WT ) V . parahaemolyticus strain . We observed that infected BMMs underwent lytic cell death with membrane swelling by 3 hours post infection ( hpi ) ( Figure 1A ) . Furthermore , infection with V . parahaemolyticus induced caspase-1 activation ( production of p10 fragment by processing procaspase-1 ( 45 kDa ) ) and the processing ( production of 17 kDa mature form ) /release of proIL-1β ( Figure 1B and 1C ) as well as the release of LDH , a marker of lytic cell death ( Figure 1D ) . These results indicated that infection with V . parahaemolyticus induces the pyroptosis of macrophages , a form of proinflammatory cell death . The processing/release of IL-1β induced by V . parahaemolyticus was caspase-1-dependent ( Figure 1B and 1C ) , and caspase-1 activation in turn was dependent on ASC , an adaptor protein of NLRs ( Figure 1B and 1C ) . Meanwhile , LDH release from infected cells was partly inhibited and delayed in both caspase-1- and ASC-deficient macrophages , suggesting the partial contributions of caspase-1 and ASC in triggering cell death . The NLR family member proteins NLRP3 and NLRC4 are known to recognize infection with various pathogens and to mediate caspase-1 activation by forming a complex with adaptor protein ASC [1] . To further investigate the mechanisms responsible for caspase-1 activation by V . parahaemolyticus , we next examined caspase-1 activation in NLRP3- or NLRC4-deficient macrophages in response to infection with V . parahaemolyticus . The caspase-1 activation and IL-1β processing/release were significantly inhibited in NLRP3-deficient BMMs , but not in NLRC4-deficient cells ( Figure 1E and 1F ) . In NLRP3-deficient BMMs , a small amount of activated p10 fragment of caspase-1 was still detected . To clarify whether NLRC4 is involved in residual caspase-1 activation in NLRP3-deficient cells , we then generated NLRP3/NLRC4-double deficient mice . Consequently , caspase-1 activation in NLRP3/NLRC4-double deficient macrophages was hardly detected , but the processing and release of IL-1β were not completely inhibited , suggesting that some molecules , such as other NLRs , may trigger weak caspase-1 activation at an almost undetectable level by immunoblotting with anti-caspase-1 antibody . Alternatively , some proteases may be activated to cleave proIL-1β in infected NLRP3/NLRC4 double-deficient macrophages . On the other hand , the release of LDH induced by infection with V . parahaemolyticus was partially inhibited in infected NLRP3-deficient cells ( Figure 1G ) . Our collective findings from the infection with WT bacteria suggest that NLRP3 , but not NLRC4 , plays a major role in inducing caspase-1 activation . We also examined caspase-1 activation in infected wild-type and NLRC4-deficient macrophages in the presence of a high concentration of extracellular potassium ( Figure S1A and S1B ) , which is known to inhibit NLRP3 inflammasome activation [15] . Interestingly , the processing/release of IL-1β was also almost completely inhibited in KCl-treated NLRC4-deficient cells . Very high concentrations of extracellular potassium also block the activation of NLRP1 , NLRC4 and AIM2 inflammasomes [16] . Our data may raise the alternative possibility that potassium can block some activities of other NLRs or proteases in NLRC4-deficient macrophages . To identify the bacterial factors that induce caspase-1 activation in infected macrophages , we first focused on the major virulence factors of V . parahaemolyticus , thermostable direct hemolysins ( TdhA and TdhS ) and two type III secretion systems ( T3SSs ) . A series of virulence factor gene-deletion mutants were constructed and their abilities to trigger caspase-1 activation were analyzed . Caspase-1 activation and IL-1β processing were markedly attenuated after infection with the TdhA mutant ( ΔtdhA ) ( Figure 2A and 2B ) . The deletion of TdhS did not affect caspase-1 activation . The attenuation of the activation by infection with a double mutant of TdhA and S ( ΔtdhAS ) was observed , but caspase-1 activation and IL-1β processing persisted , suggesting that additional bacterial stimulators are involved in caspase-1 activation . V . parahaemolyticus has two T3SS coded in separate loci on its genome [10] . We generated deletion mutants lacking vcrD1 or vcrD2 , essential components of T3SS1 or T3SS2 , respectively . The single T3SS1 mutant ( ΔvcrD1 ) and single T3SS2 mutant ( ΔvcrD2 ) , as well as a mutant lacking both T3SS1 and T3SS2 ( ΔvscN1N2 ) , triggered caspase-1 activation and IL-1β processing/release . However , a triple mutant of TdhAS and T3SS1 ( ΔtdhASΔvcrD1 ) did not induce either caspase-1 activation or the processing/release of IL-1β ( Figure 2A and 2B ) . By contrast , the ΔtdhASΔvcrD2 mutant continued to induce caspase-1 activation . LDH release was completely inhibited only when the cells were infected with ΔtdhASΔvcrD1 , suggesting that the lytic cell death of BMMs is dependent upon both TdhAS and T3SS1 ( Figure 2C ) . Collectively , these data suggest that TdhAS and the T3SS1 of V . parahaemolyticus are essential bacterial factors for activation of caspase-1 , IL-1β release and cytotoxicity in infected macrophages . Notably , we observed the enhancement of IL-1β release after infection with a T3SS1 mutant expressing TdhA and S ( ΔvcrD1 ) , compared with that after infection with WT bacteria ( Figure 2B ) , raising the possibility that T3SS1 is involved in the inhibitory function in addition to triggering caspase-1 activation . We further examined whether V . parahaemolyticus requires an intracellular localization to trigger T3SS1-mediated caspase-1 activation . Cytochalasin D is an inhibitor of phagocytosis that disrupts filamentous actin in host cells and has previously been shown to inhibit pyroptosis induced by Salmonella pathogenicity island 1 ( SPI-1 ) [17] , [18] . The cytochalasin D treatment inhibited internalization of WT V . parahaemolyticus or ΔtdhAS as well as Salmonella ( Figure S2A and S2B ) . In contrast to the effect seen during Salmonella infection , caspase-1 activation was not inhibited by cytochalasin D during infection of BMMs with the WT V . parahaemolyticus or ΔtdhAS mutant ( T3SS1+ ) . Similarly , the stimulation with ATP triggered NLRP3 inflammasome activation in phagocytosis-independent manner ( Figure S2C and S2D ) . These data suggest that the phagocytosis of bacteria is not necessary for V . parahaemolyticus T3SS1-inducing inflammasome activation . We next examined the NLR function in caspase-1 activation triggered by TDHs or T3SS1 , respectively . Using the ΔvcrD1 mutant , we examined TDHs-triggered caspase-1 activation and the processing/release of IL-1β in infected wild-type , ASC- , caspase-1- , NLRP3- or NLRC4-deficient BMMs . As shown in Figure 3A , 3B , 3D , and 3E , caspase-1 activation and the processing/release of IL-1β were completely inhibited in the infection of NLRP3- and ASC-deficient BMMs , whereas inhibition was marginal in NLRC4-deficient cells . Thus , these results indicate that NLRP3 and ASC are essential host factors for caspase-1 activation induced by TDHs , and also partially are involved in LDH release from infected BMMs ( Figure 3C and 3F ) . The NLRs involved in T3SS-triggering caspase-1 activation were analyzed using ΔtdhAS mutant . Caspase-1 activation was apparently abrogated in infected NLRP3-deficient BMMs , but residual cleavage and the release of IL-1β were detected ( Figure 3G , 3H , 3J and 3K ) . Wild-type and NLRC4-deficient cells supported T3SS1-mediated caspase-1 activation . In contrast , caspase-1 activation but not IL-1β processing/release was almost suppressed in NLRP3/NLRC4-double deficient BMMs , suggesting that NLRP3 and in part NLRC4 are involved in T3SS1-triggered caspase-1 activation . LDH release was partially dependent on caspase-1 , ASC , and NLRP3 ( Figure 3I and 3L ) , suggesting that T3SS1-mediated cytotoxicity is in part dependent on NLRP3 inflammasome activation . Consistent with these results , both caspase-1 activation and the processing/release of IL-1β were not detected in KCl-treated NLRC4-deficient BMMs ( Figure S1C and S1D ) . The NLRP3/NLRC4-independent weak activation of caspase-1 or caspase-1-independent processing/release of IL-1β may be triggered by T3SS1 , and this activity can be blocked by high concentration of extracellular potassium . The established genome analysis suggests that several T3SS1 effectors are coded in an 8320-bp gene fragment designated as the h1 region in the T3SS1 gene cluster ( Figure 4A ) [19] , [20] . The h1 region contains the putative 11 open reading frames including the established 2 effectors ( VopQ and VopS ) and their chaperons ( VP1682 and VP1687 , respectively ) . To examine the relevance of T3SS1 effectors of V . parahaemolyticus , the abilities of ΔtdhAS or ΔtdhASΔh1 mutants to trigger caspase-1 activation were compared . Surprisingly , when the h1 region was deleted , T3SS1-mediated caspase-1 activation and IL-1β processing/release were significantly enhanced ( Figure 4B and 4C ) . To clarify whether inflammasome activation via NLRP3 or NLRC4 is enhanced by the deletion , we compared caspase-1 activation in wild-type , NLRP3- or NLRC4-deficient BMMs . The enhancement of caspase-1 activation was remarkable in infected NLRP3-deficient BMMs , compared with the other BMMs ( Figure 4B and 4C ) , suggesting that T3SS1 effectors mainly suppress the activation of the NLRC4 inflammasome . The LDH release from wild-type or NLRP3-deficient BMMs was not strongly affected by the h1 deletion ( Figure 4D ) . However , LDH release from NLRC4-deficient cells was considerably decreased by infection with ΔtdhASΔh1 , raising the possibility that some effectors encoded in the h1 region affect NLRP3-mediated LDH release in a caspase-1 activation-independent manner . Infection with the ΔtdhASΔh1 mutant confirmed that NLRP3/NLRC4-inflammasomes activation is triggered by T3SS1 ( Figure S3 ) . Notably , caspase-1 activation and processing/release of IL-1β were almost abrogated in NLRP3/NLRC4-double deficient BMMs . The residual processing/release of IL-1β triggered by T3SS1 in NLRP3/NLRC4-double deficient cells ( Figure 3J and 3K ) may result from some functions of the effectors coded in the h1 region , but more detailed mechanisms are unclear . Furthermore , to examine the functional involvement of V . parahaemolyticus flagellin in triggering the NLRC4 inflammasome , we introduced the deletion of two flagellin genes ( lafK and flaK ) in the genome of ΔtdhASΔh1 mutant [21] , [22] , [23] . The enhancement of caspase-1 activation and IL-1β processing/release by h1 deletion mutant were significantly decreased by introducing deletions of the flagellin genes in NLRP3-deficient BMMs ( Figure S4 ) , suggesting that flagellins are predominantly involved in NLRC4 inflammasome activation . To clarify how h1 deletion affects caspase-1 activation upon infection by V . parahaemolyticus , we examined the amounts of released IL-1β and IL-18 from the infected BMMs with or without LPS pretreatment . The released cytokines were significantly enhanced infected with Δh1 mutant compared with those from BMMs infected with WT bacteria ( Figure S5A and S5B ) . The enhancement was observed in LPS priming-independent manner . These results suggest that the effectors coded in the h1 region suppress caspase-1 activation triggered by TDHs or T3SS1 during the infection . Our data suggest that the T3SS1 effectors coded in the h1 region have inhibitory effects on NLRC4 inflammasome activation . To identify the effector genes , we further introduced a deletion in the h2 , h3 or h4 region ( Figure 4A ) and examined NLRC4 inflammasome activation after infection with the h2 , h3 , or h4 mutants as well as the h1 mutant in NLRP3-deficient BMMs . The deletion of the h2 region did not enhance caspase-1 activation , whereas the deletions of the h3 and h4 regions partially enhanced activation ( Figure 5A and 5B ) , suggesting that the effectors coded in the h3 and h4 regions act additively to inhibit the inflammasome activation . Two effectors of T3SS1 , VopQ ( also called as VP1680 or VepA ) and VopS ( VP1686 or VepB ) were previously identified in the h3 and h4 regions , respectively ( Figure 4A ) . VopQ induces phosphatidylinositol-3-kinase ( PI3K ) -independent autophagy in infected HeLa cells [24] , whereas VopS induces the AMPylation of Rho GTPases such as Cdc42 [25] . Both effectors are thought to be involved in the cell death of infected HeLa cells . We focused on VopQ and VopS and constructed deletion mutants for these effectors in a ΔtdhAS background to examine IL-1β release from infected NLRP3-deficient BMMs . The single mutation of VopQ or VopS revealed the partial enhancement of IL-1β secretion ( Figure 5C ) . However , when infected with double mutant of VopQS ( ΔtdhASΔvopQS ) , the IL-1β release in infected NLRP3-deficient macrophages was enhanced to a similar extent as that upon infection with ΔtdhΔh1 . These results suggest that caspase-1 activation-inhibiting factors coded in the h1 region can be attributed to the two effectors , VopQ and VopS . To investigate the effect of VopQ function on the suppression of inflammasome activation , we first examined whether VopQ induce autophagy in infected macrophages . The wild-type or NLRP3-deficient BMMs were infected with ΔtdhAS or ΔtdhASΔvopQ and immunostained with anti-LC3 antibody . As shown in Figure 6A , VopQ-dependent autophagosome accumulation was induced in infected wild-type and NLRP3-deficient BMMs . Also , the conversion of LC3-I to LC3-II was induced upon infection with ΔtdhAS but not with ΔtdhASΔvopQ ( Figure 6B ) , suggesting that VopQ-dependent autophagy is induced in both wild-type and NLRP3-deficient BMMs . To further analyze the formation of autophagosomes by VopQ , we examined the conversion of LC3 in infected BMMs with ΔtdhAS in the presence of bafilomycin A1 , an inhibitor of autophagosome–lysosome fusion [26] according to recent criteria [27] . Unexpectedly , the amounts of LC3-II in both WT and NLRP3-deficient BMMs infected with ΔtdhAS were unaffected by bafilomycin A1 , in contrast to rapamycin-induced LC3-II production , which was enhanced by the addition of bafilomycin A1 ( Figure 6B ) . These data suggest that VopQ induces autophagosome accumulation by inhibiting autophagic degradation rather than by enhancing autophagic flux . Next , to examine whether VopQ-induced autophagy suppresses the NLRC4 inflammasome , we performed the short hairpin RNA ( shRNA ) knockdown of ATG5 , one of the components of autophagy in NLRP3-deficient BMMs and assessed inflammasome activation upon bacterial infection . By the knockdown of ATG5 , the rapamycin-induced conversion of LC3-I to II was down-regulated ( Figure 6C and 6D ) . Caspase-1 activation and the processing/release of IL-1β upon infection with ΔtdhAS were partially but significantly enhanced by the knockdown of ATG5 ( Figure 6E and 6F ) . We also observed a marginal increase in IL-1β release from Atg5-knockdown BMMs compared with that from control cells after infection with ΔtdhASΔvopQ . It is possible that the induction of autophagy by additional factors may be involved to some extent in the regulation of NLRC4 inflammasome activation . These results suggest that VopQ-mediated autophagy interferes with the NLRC4 inflammasome . On the other hand , infected NLRP3-deficient macrophages treated with rapamycin did not affect either caspase-1 activation or processing/release of IL-1β by bacterial infection which trigger NLRC4 inflammasome ( Figure S6 ) , suggesting that PI3K-mediated signaling for autophagy is unassociated with the suppression of the NLRC4 inflammasome , consistent with the finding that VopQ induces PI3K-independent autophagy induction [24] . To investigate the function of VopS in infected macrophages , we assessed the VopS-mediated inactivation of Rho GTPases such as Cdc42 . Since we could not detect the activation of endogenous Cdc42 triggered by infection with V . parahaemolyticus using a pull-down assay ( Figure 7A ) , the amounts of active Cdc42 in infected BMMs were quantified by loading with GTP-γS followed by a pull-down assay . As shown in Figure 7A , the VopS-mediated inactivation of Cdc42 was definitely observed in the cell lysates of infected NLRP3-deficient BMMs . The inactivation of Cdc42 by VopS was confirmed by the transcomplementation of vopS gene in the ΔtdhASΔvopS mutant ( Figure 7B ) , whereas the inhibitory effect was cancelled by introducing an amino acid substitution H348A in VopS , which is an essential amino acid residue for Rho GTPases inactivation [25] . Using cells infected with the ΔtdhASΔvopS mutant and transcomplemented by wild-type VopS or mutated VopS-H348A , we analyzed the effects of VopS-mediated Cdc42 inactivation on caspase-1 activation and the processing/release of IL-1β in infected NLRP3-deficient BMMs . VopS-H348A , in addition to wild-type VopS , was secreted into the bacterial culture supernatants ( data not shown ) . The wild-type VopS but not VopS-H348A inhibited caspase-1 activation and the processing/release of IL-1β in infected NLRP3-deficient cells ( Figure 7C and 7D ) . The VopS-mediated suppression of the NLRC4 inflammasome was more significant when the macrophages were infected with vopS-complemented ΔtdhASΔvopQS mutants ( Figure 7E ) , probably because of the elimination of the suppression of inflammasome activation by VopQ . These data suggest that VopS-mediated inactivation of Cdc42 ( probably also Rac and Rho ) is involved in the inhibition of NLRC4 inflammasome activation . To clarify how VopQ or VopS inhibit NLRC4 inflammasome at the molecular level , we first examined the effect of VopQ or VopS on the formation of the NLRC4 inflammasome complex using co-expression in 293T cells and the co-immunoprecipitation of NLRC4 with other components . As shown in Figure S7A , and consistent with previous reports [4] , [5] , the interaction of NAIP2-NLRC4 or NAIP5-NLRC4 was hardly detected , but the co-expression of PrgJ from Salmonella enterica serovar Typhimurium significantly enhanced the NAIP2-NLRC4 interaction . Also , the C-terminal portion of FlaA ( FlaA-C ) of Legionella pneumophila enhanced the NAIP5-NLRC4 interaction . We analyzed the proteins that co-precipitated with NLRC4 after infection with the ΔtdhASΔvopQ mutant or the ΔtdhASΔvopS mutant . The infected 293T cells caused cell rounding after 1 hpi in a VopQ or VopS-dependent manner ( data not shown ) , but NLRC4-NAIP2-PrgJ or NLRC4-NAIP5-FlaA-C complex formation was not affected by VopQ or VopS ( Figure S7A ) . We next introduced ASC ( as ASC-GFP ) to examine the interaction with NLRC4 . The co-precipitation of ASC-GFP with NLRC4 also increased in the presence of PrgJ or FlaA-C . However , the ASC-NLRC4 interaction was not affected by infection with the ΔtdhASΔvopQ mutant or the ΔtdhASΔvopS mutant ( Figure S7B ) . Consistent with this , the ΔtdhAS mutant ( expressing both VopQ and S ) did not affect NLRC4 inflammasome complex formation ( Figure S7C ) . Based on these results , the inflammasome complexes formed by NLRC4 and the other components may not be influenced by VopQ and VopS . However , since the experiments were performed using overexpression systems in 293T cells , it remains possible that VopQ or VopS do block oligomerization physiologically in infected macrophages . We next examined speck formation in infected macrophages , a critical step in caspase-1 activation induced by inflammasome activation . NLRP3-deficient BMMs were infected with a series of VopQ or VopS mutants and the specks that formed in the infected cells were analyzed by immunostaining with anti-ASC antibody . Compared with the ΔtdhAS mutant , the numbers of speck-forming cells after infection with ΔtdhASΔvopQ mutant or ΔtdhASΔvopS mutant were slightly increased . Furthermore , over 30% of the infected cells contained specks , including ASC , in the absence of VopQ and VopS after infection with ΔtdhASΔvopQS mutant ( Figure 8A and 8B ) . These results suggest that VopQ and vopS inhibit speck formation induced by NLRC4 inflammasome activation in infected macrophages but do not interfere with the formation of the NLRC4 inflammasome complex .
A wide variety of human pathogens , including bacteria , trigger caspase-1 activation via NLRP1 , NLRP3 , NLRC4 or AIM2 inflammasomes . The key factors in the activation of caspase-1 in bacterial infection are mainly pore-forming toxins or virulence-associated secretion systems . Here , we demonstrate that infection with V . parahaemolyticus triggers both NLRP3- and NLRC4-inflammasome activation . The pore-forming toxins TdhA and TdhS induce NLRP3 activation , whereas NLRP3/NLRC4 inflammasomes are triggered by T3SS1 . TdhA and S have been considered to be the major virulence factors in V . parahaemolyticus infection [28] . We provide experimental evidence that these toxins are the bacterial stimulators of NLRP3 inflammasome activation as targets of the innate immune system of the host . In macrophages infected with WT bacteria , TdhA and S play major roles in triggering NLRP3 inflammasome activation . We further found that the T3SS1 effectors VopQ and VopS induce autophagy and the inactivation of Cdc42 , respectively , thereby mainly preventing speck formation driven by NLRC4 inflammasome activation . Several bacterial pathogens have been reported to interfere with inflammasome activation [3] . In most cases , bacteria interfere with the production or recognition of bacterial ligands that trigger inflammasomes . For example , YopK of Yersinia pseudotuberculosis blocks the inflammasome from sensing pathogens by interacting with the T3SS translocon [8] . Similarly , in cases with a systemic infection by Salmonella , the expressions of flagellin and SPI-1 T3SS are down-regulated to evade the NLRC4 inflammasome , with the converse up-regulating of SPI-2 T3SS [29] , [30] . The enzymatic activities of bacterial effectors secreted via T3SS also modulate caspase-1 . The phospholipase ExoU , the Rho GTPase-activating protein ( GAP ) ExoS of Pseudomonas aeruginosa [31] , [32] , the Rho GAP YopE , and the cysteine protease YopT of Yersinia enterocolitica negatively regulate caspase-1 activation [33] . However , what NLRs are affected and modulated of inflammasome activation by these bacterial effectors remain to be elucidated . We show that VopQ , a PI3K-independent autophagy inducer , and VopS , a Rho GTPase inactivator of V . parahaemolyticus , mostly suppress NLRC4 inflammasome activation . Although the functions of VopQ and VopS in infected macrophages differ , they additively prevent NLRC4 inflammasome activation . Neither VopQ nor VopS interferes with complex formation by NLRC4 , NAIP2 ( or NAIP5 ) , PrgJ ( or flagellin ) , and the ASC proteins in a co-immunoprecipitation assay . Instead , speck formation in infected macrophages is negatively regulated in a VopQ-dependent and VopS-dependent manner . Specks are also known to be induced by NLRP3 and AIM2 inflammasomes , resulting in the recruitment and aggregation of ASC in the stimulated cells . The reasons why NLRP3 inflammasomes triggered by TDHs or T3SS1 are much less affected than NLRC4 inflammasomes are not yet understood , but we speculate that different signaling mechanisms and functions in the cells support speck formation in response to the activation of different inflammasomes . On the other hand , we cannot exclude the possibility that the inhibition of the NLRC4 inflammasome may result from cell death induced by VopQ or VopS , since these effectors induce cell rounding and the death of infected HeLa cells [24] , [25] . It is also possible that the secreted VopQ or VopS may be able to capture the flagellin subunits or rod proteins and down-regulate the activation of NLRC4 inflammasomes . Alternatively , induced autophagosome by VopQ may degrade intracellular flagellin or rod protein delivered via T3SS . Further studies are needed to determine the precise mechanisms responsible for the inhibition of speck formation by VopQ-mediated autophagy induction or VopS-mediated Rho GTPase inactivation . VopQ induces PI3K-independent autophagy in infected HeLa cells [24] . We demonstrate that VopQ induces autophagosome accumulation by inhibiting autophagic degradation rather than by enhancing autophagic flux , and subsequently inhibits NLRC4 inflammasome-mediated caspase-1 activation . The regulation of inflammasome by autophagy has been recently reported [34] , [35] . Basal autophagy prevents the release of mitochondrial DNA sensed by NLRP3 from damaged mitochondria in response to NLRP3 stimulators [34] . Also , the activation of AIM2 or NLRP3 inflammasomes triggers autophagosome formation by recruiting autophagic adaptor p62: conversely , stimulating autophagy limits inflammasome activation [35] . On the other hand , the autophagy induced by VopQ inhibits caspase-1 activation via the NLRC4 inflammasome by preventing speck formation but does not affect the formation of the protein complex , suggesting that the autophagy induced by VopQ acts on later events associated with the NLRC4 inflammasome . Moreover , rapamycin-induced autophagy does not inhibit NLRC4 activation stimulated by V . parahaemolyticus or Salmonella infection: thus the mTORC1-PI3K pathway driven by rapamycin might not be involved in interference with the NLRC4 inflammasome . Although the manner in which VopQ-mediated autophagosome accumulation prevents speck formation in macrophages is still unexplained , it is most appropriate to speculate that the heterotopic autophagosome accumulation induced by VopQ is able to block the machinery required for speck formation . The further characterization of the VopQ-mediated autophagy signaling pathway may shed light on the mechanisms responsible for the assembly of ASC specks triggered by NLRC4 inflammasome . The inactivation of Rho GTPase , including Cdc42 , by VopS inhibits NLRC4-mediated caspase-1 activation , suggesting that Cdc42 is involved in NLRC4 inflammasome activation . A previous report has shown that the SopE effector secreted via SPI-1 of Salmonella triggers caspase-1 activation through the activation of Cdc42 [36] , but the molecular mechanisms of Cdc42-mediated inflammasome activation are still largely unknown . In this context , the GAP activity of ExoS or YopE in the inactivation of Rho GTPases is involved in the inhibition of caspase-1 activity , but how GAP activity negatively controls the inflammasome activation remains unknown . We demonstrate that VopS inhibits ASC speck formation , suggesting that the basal activity of Cdc42 , Rac or Rho supports the assembly of specks in infected cells . As the activation of endogenous Cdc42 is not observed upon infection with V . parahaemolyticus , we speculate that the basal activity of small GTPases is sufficient for maintaining the speck formation . Another explanation for the suppression of inflammasome activation is that the effect of Cdc42 inhibition may cause differential uptake of WT or VopS mutant and results in less contact between bacteria and macrophages , and thus a decrease in total translocation by the T3SS . However , this possibility can be excluded since inflammasome activation by V . parahaemolyticus is not affected by cytochalasin D which blocks phagocytosis of macrophages . In the absence of priming with LPS , caspase-1 activation is readily induced during the early stage of infection with an h1 region deletion mutant in BMMs , suggesting that VopQ and VopS are capable of partially suppressing caspase-1 activation in wild-type bacterial infections leading to both NLRP3 and NLRC4 inflammasomes . The biological importance of effector-mediated NLRC4 suppression during in vivo infection with V . parahaemolyticus remains to be elucidated because of the lack of an oral infection model in mice capable of evoking bacterial colonization and inflammation in intestine . We are attempting to establish an infection model in mice . A recent report has shown that intestinal mononuclear phagocytes in the mouse colon predominantly express NLRC4 , but not NLRP3 , and do not respond to TLR ligands [37] . In these cells , the NLRC4 inflammasome is activated upon infection with Salmonella , whereas the activation of NLRP3 inflammasome is not triggered by the NLRP3 stimulators such as ATP [37] . These finding lead us to consider what cell lineages are involved in caspase-1 activation upon infection with V . parahaemolyticus in vivo . In in vitro studies using BMMs infected with WT V . parahaemolyticus , our results suggest that NLRP3 plays a major role in inducing caspase-1 activation , whereas NLRC4 plays a minor role . However , this conclusion may be limited to particular cell lines , such as BMMs that express both NLRP3 and NLRC4 . In in vivo infection , the bacteria are thought to colonize the epithelia of the intestine and to cause epithelial injury through the secretion of TDHs or T3SS-mediated effectors . In such situations , the bacteria are likely exposed to attack from intestinal mononuclear phagocytes that express NLRC4 but not NLRP3 [37] , and the inhibition of NLRC4 inflammasome by VopQ and VopS may confer an advantage to invading V . parahaemolyticus . Although establishing the biological significance of the VopQ and VopS-mediated inhibition of the NLRC4 inflammasome will require further studies , the elucidation of effector-based suppression of inflammasome activation may provide important insights into bacterial strategies for evading inflammasome-mediated host immune responses .
All animal studies were carried out in strict accordance with the Guidelines for Animal Experimentation of the Japanese Association for Laboratory Animal Science . The protocols were approved by the Animal Care and Use Committee of the University of the Ryukyus , Okinawa , Japan ( Permit Number: 5350 and 5351 ) . The wild-type V . parahaemolyticus strain RIMD2210633 ( KP positive , serotype O3:K6 ) was clinical isolate [38] . Isogenic V . parahaemolyticus mutants were constructed using allele replacement strategies and the suicide vector pYAK1 [14] . For complementation in bacteria , the shuttle vector pSA19Cm-MCS was used as described previously [19] . The site directed mutagenesis was performed using QuikChange site-directed mutagenesis kit ( Stratagene ) . The wild-type Salmonella enterica serovar Typhimurium SL1344 was described previously [14] . The expression plasmids of FLAG-NLRC4 , HA-NAIP2 , HA-NAIP5 , Myc-PrgJ or Myc-FlaA ( C-terminal truncated ) were kindly provided from Dr . Shao ( National Institute of Biological Sciences , Beijing , China ) . For expression of ASC-GFP , ASC gene was cloned into pEGFP-N1 ( Clontech ) . C57BL/6 mice were purchased from Japan SLC ( Tokyo , Japan ) as wild-type mice . C57BL/6 background caspase-1-deficient [39] , NALP3-deficient ( Nlrp3−/− ) [40] , NLRC4-deficient ( Nlrc4−/− ) [41] and ASC ( Asc−/− or Pycard−/− ) -deficient mice [42] were housed in a pathogen-free facility . Caspase-1-deficient mice also lack caspase-11 [43] . Mice doubly deficient in NLRP3 and NLRC4 ( Nlrp3−/− Nlrc4−/− ) were generated from the single gene-deficient mice . BMMs were prepared from the femurs and tibias of the above mice and cultured for 5–6 days in 10% FCS-RPMI 1640 supplemented with 30% mouse L-cell supernatant . The ultrapure LPS and cytochalasin D were purchased from Invivogen and Sigma-Aldrich , respectively . Rapamycin was from LC Laboratories . The following antibodies were obtained commercially: rabbit anti-mouse caspase-1 ( sc-514 , Santa Cruz ) , goat anti-mouse IL-1β ( AF-401-NA , R & D Systems ) , rabbit anti-mouse IL-18 ( 5180R-100 , BioVision ) , rabbit anti-mouse LC3 ( PM036 and M152-3 , MBL ) , rabbit anti-mouse ATG5 ( #8540 , Cell Signaling ) , rabbit anti-mouse Cdc42 ( 21010 , New East Biosciences ) , rabbit anti-FLAG M2 ( F3165 , Sigma ) , mouse anti-HA ( MMS-101P , Covance ) , rabbit anti-Myc tag ( #2278 , Cell Signaling ) , anti-V . parahaemolyticus ( O3 , Eiken Chemical ) , and anti-S . enterica ( multiple O-antigens , Eiken Chemical ) . The rabbit anti-VopS antibody and rat anti-mouse ASC antibody were generated previously [44] , [45] . BMMs were seeded at 5×105 cells in 24-well plates containing 10% FCS-RPMI 1640 and primed with LPS ( 1 µg/ml ) for 3 h to induce the expression of proIL-1β prior to bacterial infection . The cells were infected with V . parahaemolyticus grown to mid-log phase at multiplicity of infection ( MOI ) of ∼10 per cell . The plates were centrifuged at 600 g for 10 min to synchronize the stage of infection and incubated . At the times indicated after infection without antibiotic treatment , the lactate dehydrogenase ( LDH ) activity of the culture supernatants of infected cells was measured by using a CytoTox 96 assay kit ( Promega ) according to the manufacturer's protocol . The cytokines released in culture supernatants were quantified by ELISA ( R &D systems ) . The end point of time course experiments was limited as 3 hours post infection ( hpi ) , since Vibrio are extracellular pathogen and multiply fast in the cell culture media . BMMs were seeded at a density of 2×106 cells per well in 6-well plates and infected with bacteria . The cells were lysed and combined with the supernatant precipitated with 10% trichloroacetic acid . The samples were loaded onto 15% SDS-PAGE , and the cleaved form of caspase-1 , IL-1β , was detected using anti-caspase-1 , anti-IL-1β or anti-IL-18 antibody , respectively . To compare the intensity of the bands on immunoblots , band density was analyzed using ImageJ densitometry software . For immunofluorescence study , the infected cells were fixed and immunostained as described previously [46] , and they were analyzed with a confocal laser-scanning microscope ( TCS-SPE , Leica-Microsystems ) . We used lentiviral pLKO . 1-puro vector coding shRNA targeting Atg5 , CCGGCCTTGGAACATCACAGTACATCTCGAGATGTACTGTGATGTTCCAAGGTTTTTG ( RCN0000099431 , Sigma ) and negative control vector including non-target shRNA ( SHC002 , Sigma ) . Each plasmid was transfected together with two packaging plasmids ( pMISSION GAG POL and pMISSION VSV-G , Sigma ) into 293T cells . The packaging and lentiviral infection were performed following the manufacturer's instruction . Lentivirus expressing shRNA was collected and infected to BMMs . The puromycin resistant cells were used in bacterial infection . Knockdown efficiency was examined by immunoblotting analysis . The cells were washed once in cold TBS and samples were collected by scraping into lysis/wash buffer ( 50 mM Tris-HCl , pH 8 . 0 , 150 mM NaCl , 10 mM MgCl2 , 1 mM EDTA , 1% Triton X-100 ) supplemented with a protease inhibitor mixture ( Roche ) . The precleared samples were used for loading with GDP ( 1 mM ) or GTP-γS ( 0 . 1 mM ) for 30 min at 30°C . GST-pull-downs were performed with 5 µg of purified GST-PAK PBD ( Thermo Scientific ) on glutathione beads for one hour at 4°C and then washed three times in lysis/wash buffer . The samples on the beads were boiled in SDS sample buffer for 5 min . 293T cells were transfected with indicated plasmids . Cells were harvested and lysed in a buffer containing 50 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl and 1% Triton X-100 supplemented with a protease inhibitor mixture . The precleared lysates were subjected to anti-FLAG M2 immunoprecipitation by following the manufacturer's instruction . The beads were boiled in SDS sample buffer followed by immunoblotting analysis . All data are presented as the mean and standard deviation of at least three determinations per experimental condition . All experiments were performed at least three times and representative results are shown in the figures . Statistical analyses were performed using unpaired two-tailed Student's t tests . Differences were considered significant at a p value of <0 . 05 . | V . parahaemolyticus is Gram-negative pathogen that causes a food poisoning in human . To date , a number of bacterial factors that play a role in V . parahaemolyticus virulence have been characterized , yet little is known about the host factors contributing to the disease process and susceptibility to these pathogens . IL-1β , in addition to TNF-α , is thought to be involved in inflammatory responses and disease development during infection with the pathogen , but the mechanisms of IL-1β production remain poorly defined . In this work we found that both the NLRP3 and NLRC4 inflammasomes are activated by thermostable direct hemolysins ( TDHs ) and type III secretion system 1 ( T3SS1 ) in response to V . parahaemolyticus infection . The activated inflammasomes then triggers the activation of caspase-1 , a cysteine protease that is essential for IL-1β processing and release . Furthermore , we identified T3SS1 secreted effector proteins , VopQ and VopS , which prevent mainly NLRC4 inflammasome activation . VopQ and VopS induce autophagy and the inactivation of Rho GTPases , including Cdc42 , respectively , and these cellular events interfere with the assembly of specks , the platform of inflammasome activation . Collectively , T3SS1 effector-based suppression of inflammasome activation may provide important insights into bacterial strategies for evading inflammasome-mediated host immune responses . | [
"Abstract",
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] | [
"medicine",
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] | 2013 | Vibrio parahaemolyticus Effector Proteins Suppress Inflammasome Activation by Interfering with Host Autophagy Signaling |
Infection of host tissues by Staphylococcus aureus and S . epidermidis requires an unusual family of staphylococcal adhesive proteins that contain long stretches of serine-aspartate dipeptide-repeats ( SDR ) . The prototype member of this family is clumping factor A ( ClfA ) , a key virulence factor that mediates adhesion to host tissues by binding to extracellular matrix proteins such as fibrinogen . However , the biological siginificance of the SDR-domain and its implication for pathogenesis remain poorly understood . Here , we identified two novel bacterial glycosyltransferases , SdgA and SdgB , which modify all SDR-proteins in these two bacterial species . Genetic and biochemical data demonstrated that these two glycosyltransferases directly bind and covalently link N-acetylglucosamine ( GlcNAc ) moieties to the SDR-domain in a step-wise manner , with SdgB appending the sugar residues proximal to the target Ser-Asp repeats , followed by additional modification by SdgA . GlcNAc-modification of SDR-proteins by SdgB creates an immunodominant epitope for highly opsonic human antibodies , which represent up to 1% of total human IgG . Deletion of these glycosyltransferases renders SDR-proteins vulnerable to proteolysis by human neutrophil-derived cathepsin G . Thus , SdgA and SdgB glycosylate staphylococcal SDR-proteins , which protects them against host proteolytic activity , and yet generates major eptopes for the human anti-staphylococcal antibody response , which may represent an ongoing competition between host and pathogen .
Staphylococcus aureus and S . epidermidis are successful human commensals that primarily colonize the nares and skin . S . aureus can also invade a variety of tissues , leading to life-threatening infections . Recently emerged strains of S . aureus show increased virulence and enhanced ability to cause disease in otherwise healthy individuals . In addition , the recent development of resistance to antibiotics , in particular methicillin , have made S . aureus infections more difficult to treat . Currently , the most prevalent and most virulent clinical strain of methicillin resistant S . aureus ( MRSA ) is USA300 , which has the capacity to produce a large number of virulence factors and cause mortality in infected individuals [1] . S . epidermidis , which is closely related to S . aureus , is often associated with hospital-acquired infections , and represents the most common source of infections on indwelling medical devices . The factors mediating colonization of human tissues by S . aureus and S . epidermidis are complex and not yet fully elucidated , but have been studied in many animal models of S . aureus infection . Tissue colonization involves interactions of several S . aureus surface proteins with host cells and extracellular matrix . Using in vitro models , several S . aureus surface proteins , including clumping factor ( Clf ) A and ClfB , are important for adherence to mammalian cell lines and purified extracellular matrix proteins [2] . In addition , it is believed that ClfA is a key factor in triggering sepsis [3] . ClfA and ClfB are members of a family of cell wall proteins , characterized by a large stretch of serine-aspartate dipeptide ( SDR ) repeats , that is present in staphylococci [4] . In addition to ClfA and ClfB , S . aureus also expresses three SDR-proteins , SdrC , SdrD and SdrE , which are organized in tandem in the genome . These proteins are also thought to be involved in tissue colonization , and elimination of any of them decreases bacterial virulence [5] . Three additional members of this family , SrdF , SdrG and SdrH , are present in most S . epidermidis strains [6] . In each of these proteins , the SDR-region , which contains between 25 and 275 SD-dipeptide repeats , is located between the N-terminal ligand-binding A-domain and a C-terminal LPXTG-motif , which mediates anchoring to the cell wall by the transpeptidase sortase A . The function of the SDR-domain remains unknown , although it has been proposed to act as a cell wall spanning domain allowing exposure of the N terminal ligand binding sites of these proteins [7] . Serine rich glycoproteins have been identified in several other pathogenic bacteria , with demonstrated roles in bacterial adhesion . As yet , it remains unknown if S . aureus and S . epidermidis SDR-proteins are sugar modified and whether the SDR-domain contributes to virulence of staphylococci . In the present study , we have discovered that SDR-domains of all SDR-proteins of S . aureus and S . epidermidis are heavily glycosylated by two novel glycosyltransferases , SdgA and SdgB . These glycosylation events prevent degradation of these proteins by host proteases , thereby preserving bacterial host tissue interactions . These sugar modifications also represent a dominant antibody epitope .
We isolated several S . aureus-reactive monoclonal antibodies ( mAb ) from memory B cells from peripheral blood of MRSA-infected donors . When characterizing these antibodies , we identified one IgG1 mAb ( hereafter referred to as rF1 ) with broad reactivity to a panel of S . aureus strains that induced robust opsonophagocytic killing ( OPK ) by human polymorphonuclear leukocytes ( PMN ) . Maximum binding of mAb rF1 to bacteria from clinical MRSA strain USA300 was approximately 10 fold higher than that of an isotype-matched anti-ClfA mAb ( Figure 1A ) . Consistent with increased binding , opsonization with rF1 resulted in increased uptake ( Figure 1B ) and killing ( Figure 1C ) of USA300 by PMN . In contrast , preopsonization with human anti-ClfA had no effect on bacterial viability ( Figure 1C ) . The rF1 antibody did not affect viability of USA300 in the absence of PMN ( not shown ) . Thus , rF1 is a mAb with the capacity to bind MRSA and induce potent killing of MRSA by PMN . Flow cytometry ( FCM ) analysis showed potent binding activity of rF1 to all 15 S . aureus strains tested ( Figure S1 ) . These strains were broadly distributed across the S . aureus phylogeny [8] . As expression levels of bacterial cell surface antigens might differ between in vitro and in vivo growth , we also tested the ability of rF1 to recognize USA300 isolated from various mouse tissues after systemic infection . The rF1 mAb strongly bound to USA300 derived from infected mouse kidneys , livers and lungs ( Figure 1D ) . The binding rF1 to USA300 from mouse kidneys was sustained until at least 8 days after infection ( not shown ) , suggesting robust long-term expression of the rF1 epitope during infection . In addition , rF1 strongly bound to MRSA COL bacteria from heart vegetations in a rabbit model of infectious endocarditis . Treatment with vancomycin did not affect the reactivity of rF1 with MRSA ( Figure 1D ) . Thus , the antigen recognized by rF1 is conserved across various strains and stably expressed in various growth and infection conditions . Given the ubiquitous nature of rF1-reactivity across all S . aureus strains , we further queried if such reactivity is extended to other gram-positive bacteria . Notably , rF1 binding was detectable only for the coagulase-negative human pathogen S . epidermidis ( Figure 1E ) . The rF1 mAb did not bind to any other staphylococcal species tested , including S . saprophyticus , S . lugdunensis , S . simulans and S . carnosus , or other Gram-positive species such as Streptococcus pyogenes , Bacillus subtilis , Enterococcus faecalis , and Listeria monocytogenes ( Figure 1E ) . Thus , rF1 is a human antibody that binds to stably-expressed surface antigen ( s ) on human-adapted staphylococcal pathogens and promotes bacterial killing by human PMNs . We next sought to identify the S . aureus antigen ( s ) responsible for rF1 reactivity . Immunoblotting of cell wall preparations ( CWPs ) with rF1 revealed that the mAb binds to a group of high molecular weight entities . Treatment of CWP with proteinase K completely eliminated rF1 reactivity , suggesting that the rF1 antigens are cell wall-associated proteins ( Figure 2A ) . Furthermore , rF1 reactivity was not altered in CWPs derived from MRSA strains lacking some of the most abundant surface-expressed carbohydrate antigens , such as wall teichoic acids ( ΔtagO mutant ) and poly-N-acetyl glucosamine ( ΔicaA mutant ) ( not shown ) . As many as 21 proteins are attached to the cell wall by the sortase A enzyme [9] . Sortase A-anchored proteins include an interesting class of proteins that each contain a long tract of serine-aspartate ( SD ) repeats near the C-terminus; five of these proteins are present in S . aureus [10] and three in S . epidermidis . rF1 reactivity was abolished in a ΔsrtA S . aureus strain [11] ( not shown ) and in a ΔpanSDR S . aureus strain [12] , in which all five SDR-proteins were deleted ( Figure 2B ) . Binding of rF1 was significantly reduced in a ΔclfA S . aureus mutant , reflecting the abundance of cell wall-associated ClfA . We also probed the identity of rF1-reactive proteins in S . epidermidis , by immunoprecipitation of S . epidermidis lysates with rF1 followed by immunoblotting . Three unique rF1-reactive bands were present in S . epidermidis lysates , and mass spectrometry analysis of these bands identified peptides corresponding to SdrF , SdrG and SdrH ( Figure 2C; Figure S2 ) . These data suggest that the rF1 epitope is present on SDR proteins in both S . aureus and S . epidermidis . To determine which specific region of SDR-proteins is recognized by rF1 , we designed three different recombinant maltose binding protein ( MBP ) -tagged constructs based on the sequence of the S . aureus ClfA protein as a model antigen . These constructs contained either the N-terminal A-domain of ClfA , or the first 58 amino acids ( SDR560-618 ) or the entire length of 150 amino acids ( SDR560-709 ) of the SDR-domain of ClfA all under tet-inducible promoters ( Figure 2D ) . Blotting with anti-MBP antibodies confirmed that each of these three recombinant proteins was expressed in S . aureus ( Figure 2E , left panel ) . Only proteins containing SDR-domains were reactive with rF1 by immunoblotting , with increased binding in the strain expressing the longer stretch of SD-repeats ( Figure 2E , right panel ) . Collectively , these observations indicate that rF1 epitopes reside on the SDR region of the SDR family of proteins of S . aureus and S . epidermidis . Since rF1 recognized S . aureus and S . epidermidis , but not other staphylococcal species or other genera of Gram positive organisms ( Figure 1 ) , we next analyzed rF1 reactivity with recombinant SDR560-709 domain of ClfA , containing N-terminal MBP and C-terminal His tag ( MBP-SDR-His ) , which was expressed in ΔpanSDR S . aureus , B . subtilis or E . coli to prevent reactivity of rF1 with endogenous SDR-family proteins . Immunoblot analysis using an anti-His antibody confirmed expression of MBP-SDR-His in all three bacterial species ( Figure 3A , bottom panel ) . Surprisingly , we observed that rF1 reactivity with MBP-SDR-His could only be detected when the protein was expressed in S . aureus , but not when expressed in E . coli or B . subtilis ( Figure 3A , upper panel ) . Notably , the recombinant protein also showed a size increase when expressed in S . aureus ( Figure 3A , bottom panel ) . Given that the rF1-reactive recombinant SD560-709 proteins appeared larger than their predicted sizes , and that rF1 reactivity is specific to S . aureus and S . epidermidis ( Figure 1 ) , we hypothesized that rF1 reactivity with SDR-proteins requires species-specific post-translational modification . Next , we investigated whether all five S . aureus SDR-proteins , ie . CflA , CflB , SdrC , SdrD and SdrE , are subjected to the same post-translational modification . Therefore , we expressed each of these five SDR-proteins ( tagged with His ) in E . coli , purified the proteins and incubated them with whole-cell lysates of ΔpanSDR S . aureus . Expression of these five unmodified SDR-proteins in E . coli was confirmed using anti-His antibody ( Figure 3B , bottom panel ) . Each of the five SDR-proteins , when expressed in E . coli , was devoid of rF1 reactivity . However , incubation of these proteins with S . aureus lysates completely restored rF1 reactivity ( Figure 3B , top panel ) , supporting the requirement of S . aureus-specific post-translational modification to generate rF1 reactivity on SDR-proteins . Heat inactivation of the ΔpanSDR S . aureus lysates abolished this effect ( not shown ) , raising the possibility that SDR-protein modification is mediated by enzymatic activity . To identify the putative enzyme factor ( s ) affording rF1 reactivity to the SDR-proteins , ΔpanSDR S . aureus protein lysate was fractionated by size exclusion chromatography . The fractions were incubated with ClfA expressed in and purified from E . coli , and analyzed for the ability to induce rF1 reactivity by immunoblotting . B . subtilis lysate was added to this reaction to provide putative factor ( s ) necessary for efficient post-translational modifications , such as energy resources ( ATP ) and/or potential sugar building blocks , which were likely absent in the fractions of the ΔpanSDR S . aureus lysates . As shown in Figure 3A , B . subtilis itself does not have the ability to generate rF1-reactive modifications , presumably due to the lack of the necessary enzyme ( s ) . Using this in vitro reconstitution assay , we obtained strong enzymatic activity in fractions 35–38 ( Figure 3C , upper panel ) . Fractions 35–38 were pooled and fractionated by ion-exchange chromatography to further enrich for the enzyme ( s ) capable of modifiying the SDR-proteins . Only fraction 16 showed significant enzymatic activity ( Figure 3C , bottom panel ) , and this fraction was subjected to mass spectrometry analysis to identify the enzyme ( s ) responsible . Four glycosyltransferases ( Gtfs ) were enriched only within this fraction , three of which belong to the Group 1 family: TarM ( SAUSA300_0939 ) and two novel Gtfs ( SAUSA300_0549 and SAUSA300_0550 ) ; and one from the Group 2 family , TarS ( SAUSA300_0252 ) [13] ( Figure S3 ) . These glycosyltransferases were not detected in the negative control mass spectrometry analysis of fraction 25 . TarM and TarS have been shown to function as glycosyltransferases appending N-acetylglucosamines ( GlcNAcs ) to the phospho-polyribitol backbone of wall teichoic acids in alpha- and beta- enantiomeric configurations , respectively [13] , [14] . The other putative glycosyltransferases have not been characterized . Interestingly , the glycosyltransferases SAUSA300_0549 and SAUSA300_0550 are encoded by two divergently transcribed genes adjacent to the sdrCDE locus ( Figure 3D ) and this genetic arrangement is completely conserved across all sequenced S . aureus strains . Homologs of SAUSA300_0549 and/or SAUSA300_0550 are also present in S . epidermidis strains where they are also found near loci encoding for SDR-proteins . Based on their genomic locations and their potential roles in glycosylation of SDR-proteins ( see below ) , we named SAUSA300_0549 and SAUSA300_0550 , sdgA and sdgB , respectively , for SD-repeat glycosyltransferases . To verify which of these putative glycosyltransferases were responsible for modifying the SD-repeats , we generated sdgA , sdgB , tarM , or tarS deletion mutants in USA300 , and assayed cell wall lysates from these mutants for reactivity with various antibodies including rF1 . Strikingly , while all of these mutants remained proficient at expressing SDR-proteins , including ClfA and SdrD ( Figure 4A , two middle panels ) , rF1 reactivity was completely lost in the absence of sdgB ( Figure 4A , upper panel ) . Thus , rF1 reactivity with SDR-proteins requires SdgB . An antibody against unmodified SD-repeats reacted only with CWP from bacteria containing the ΔsdgB mutation , but not with WT and ΔsdgA bacteria ( Figure 4A , bottom panel ) . This suggests that the SDR-domains are heavily decorated by SdgB-dependent sugar modifications , which disguise the protein backbone epitopes recognized by this antibody . Complementation of ΔsdgB mutant with exogenous SdgB restored rF1 binding , and led to a concomitant loss of reactivity with the antibody against unmodified SD-repeats ( Figure 4B ) . Overexpression of SdgB in the ΔsdgB mutant resulted in SDR-proteins migrating slower than those made by WT cells , presumably due to exaggerated sugar modification on these proteins by the transgene . Moreover , deletion of SdgB , abolished both binding of rF1 to whole bacteria , and rF1-induced bacterial killing by human PMN ( Figure 4C and Figure 4D ) . Together , these data suggest that SdgB is the main protein that modifies SDR-proteins to enable recognition by rF1 . The apparent molecular weights of the SDR-proteins obtained from ΔsdgA were smaller compared to wild-type bacteria , and those from ΔsdgB were smaller than from ΔsdgA bacteria , as judged by immunoblotting ( Figure 4A and 4B , top and middle panels ) . These differences likely reflect changes in relative molecular mass ( Mr ) caused by sugar modifications . If the sugar modifications of SdgA and SdgB occured independent of each other , one expectation would be that SDR-proteins from ΔsdgAΔsdgB bacteria would run faster than from ΔsdgB bacteria . However , ClfA from ΔsdgB and ΔsdgAΔsdgB mutants showed a similar Mr ( Figure 4A and 4B , middle panels ) , suggesting that the sugar modification appended by SdgA depends on SdgB activity . Next , we tested the hypothesis that SdgA acts after SdgB , by immunoblotting whole cell lysates from wild-type , ΔsdgA , ΔsdgB , or ΔsdgAΔsdgB strain that expressed MBP-SDR-His under the control of a tet-inducible promoter . In both the ΔsdgB and ΔsdgAΔsdgB mutants , we observed reactivity with antibodies to unmodified SD-repeats ( Figure 4E , bottom panel ) , but not with rF1 ( Figure 4E , upper panel ) . The MBP-SDR-His proteins expressed in ΔsdgB and ΔsdgAΔsdgB mutants had a similar Mr , as shown by both anti-SDR ( Figure 4E , bottom panel ) and anti-His antibodies ( Figure 4E , middle panel ) . This supports the notion that no carbohydrate modification of the SDR-domains occurs in the absence of SdgB . In the ΔsdgA mutant , rF1 reactivity was unaffected; however , the SDR protein had a lower Mr than in wild-type cells . Together , these data suggest that SdgB and SdgA add sugar modifcations onto SDR-domains in a sequential manner , with SdgB appending the sugar residues proximal to the target Ser-Asp repeats , followed by additional modification by SdgA ( see model , Figure 4F ) . Next , we determined the nature of the sugar epitope that is added by SdgB and recognized by mAb rF1 using mass spectrometry . First , we co-expressed SdgA or SdgB together with MDP-SDR-His fusion protein as substrate in E . coli . Immunoblot analysis of the whole-cell lysates showed that rF1-reactivity on the MDP-SDR-His protein required expression of SdgB ( Figure 5A , upper panel ) . The lack of reactivity of the E . coli-expressed protein with mAb against unmodified SDR proteins suggests that glycosylation by SdgB masks the backbone epitope for this antibody ( Figure 5A , middle panel ) . Mass spectrometry of the purified MDP-SDR-His proteins from these E . coli bacteria suggested that SdgB was appending GlcNAc on these SDR-domains ( data not shown ) . When recombinant MBP-SDR-His fusion protein , purified from E . coli , was incubated with purified SdgA or SdgB , in the presence or absence of UDP-GlcNAc , the MBP-SDR-His substrate gained rF1-reactivity only in the presence of SdgB and UDP-GlcNAc ( Fig . 5B ) . Alternative sugar resources such as UDP-Glucose and GDP-mannose did not confer rF1-reactivity ( data not shown ) . As was shown in E . coli , SdgA alone had no effect on rF1 immunoreactivity or on the Mr of the recombinant substrate , but the combination of SdgB and SdgA resulted in more protein modifications than SdgB alone , as suggested by the higher Mr of SDR-substrate in rF1 and anti-His immunoblots ( Figure 5B ) . Since this effect occurred when UDP-GlcNAc was the only sugar source , it is likely that SdgA as well as SdgB can append GlcNAc residues to SDR proteins , but SdgA does so only after SdgB modification has occurred ( Figure 5C ) . To determine the extent and complexity of SdgA and SdgB-mediated GlcNAc modification , enzyme-modified MDP-SDR substrates from the cell-free glycosylation reconstitution system were enriched by liquid phase chromatography and subjected to mass-spectrometric analysis ( LC-MS ) . Compared to the unmodified MDP-SDR-recombinant substrate ( Figure 5D , top panel ) , MDP-SDR-recombinant substrate that was modified by SdgB showed several peaks , with each peak separated from the others by mass of one additional GlcNAc residue ( Figure 5D , middle panel ) . Here , the largest mass observed ( 70708 Da ) , represents the addition of 59 GlcNAc residues . In the MDP-SDR-recombinant substrate there are a total of 60 “Asp-Ser-Asp” ( DSD ) motifs . These data suggest that under these conditions , almost all the serines in the SDR-domain were modified with a GlcNAc residue . When the MDP-SDR-recombinant substrate was incubated with both SdgB and SdgA , up to an additional 47 GlcNAc residues were added ( Figure 5D , bottom panel ) . These data suggest that most serines in the DSD-motifs are modified by disaccharide GlcNAc moieties through the sequential activity of SdgB and SdgA . Given our discovery of rF1 recognizing the SdgB-dependent glycosylation on SDR-proteins , we next determined whether this reflects a unique or rather common epitope specificity during anti-staphylococcal immune responses in humans . First , we analyzed the binding of human IgG from four different sources to cell wall preparations from either WT or ΔsdgB USA300 by ELISA . We tested ( 1 ) purified human IgG , ( 2 ) Gammagard , an intravenous immunoglobulin preparation purified from a plasma pool of ∼10 , 000 healthy donors [15] , ( 3 ) serum pooled from healthy donors , and ( 4 ) serum pooled from MRSA patients . All four IgG preparations exhibited a significant reduction in reactivity with ΔsdgB CWP as compared to WT CWP ( Figure 6A ) . Here , approximately 160 µg/mL of IgG in healthy serum and 140 µg/mL of IgG in patient serum accounted for the difference in reactivity with ΔsdgB compared to WT CWP . Given that the total IgG concentration in human serum fluctuates around 12 mg/mL , this indicates that the proportion of Sdg-dependent IgG can represent up to approximately 1% of total human serum IgG . This observation suggests that SdgB-dependent antibodies constitute a substantial proportion of the total anti-staphylococcal IgG content in humans , likely resulting from previous exposure to staphylococci . Secondly , we were able to isolate three additional IgG mAb clones from three different S . aureus infected patients , which showed the same pattern of antigen recognition with wild-type or ΔsdgA USA300 CWP as mAb rF1 , and which also showed absence of reactivity using ΔsdgB CWP ( Figure 6B ) . Thus , we have found four independent IgG clones from four different patient donors , showing the same SdgB-dependent epitope specificity . Together , these data suggest that the SdgB-dependent glycosylation on SDR proteins reflects an immunodominant epitope for human anti-staphylococcal antibody responses . For successful colonization and invasion of host tissues , S . aureus bacteria must evade attack by a variety of host immune mechanisms , including proteolytic enzymes derived from neutrophils . In this context , we analyzed the physiological roles of the GlcNAc modification of SDR proteins during interaction of the bacteria with neutrophil lysosomal enzymes . A large cleaved fragment of ClfA was released when intact ΔsdgB bacteria was incubated with a human neutrophil lysosomal extract , but this fragment was not cleaved from WT USA300 or from ΔsdgB bacteria complemented with exogenous SdgB ( Figure 7A ) . Similar results were observed using lysosomal extracts derived from the human monocytic cell line THP1 , but not from mouse monocytic cell line RAW ( Figure 7B ) . Mass spectrometry analysis revealed that lysosomal extracts from both human neutrophils and human THP-1 cells were highly enriched for cathepsin G . In contrast , mouse RAW cells showed abundant expression of other cathepsin family members , but not cathepsin G ( not shown ) . Thus , these data suggested that SdgB-mediated glycosylation protects ClfA against proteolytic activity of the human neutrophil lysosomal enzyme cathepsin G . To determine which protease ( s ) is responsible for cleaving unglycosylated ClfA , we incubated WT or ΔsdgB cells with each of the four main human neutrophil-derived serine proteases , ie . neutrophil elastase , cathepsin G , proteinase-3 and neutrophil serine protease-4 . Of these four proteases , only cathepsin G was able to induce cleavage of unglycosylated ClfA from ΔsdgB cells ( Figure 7C ) . In addition , none of the serum serine proteases α-thrombin , plasmin , kallikrein , factor Xa , or factor X1a , were able to induce cleavage of unglycosylated ClfA ( not shown ) . A biochemical inhibitor of cathepsin G abrogated cleavage of unglycosylated ClfA by neutrophil lysosomal extracts ( Figure 7D ) , confirming that cathepsin G is the predominant enzyme mediating this effect . S . aureus expressing SdgB but lacking SdgA treated with cathepsin G showed less ClfA cleavage than ΔsdgB bacteria ( Figure 7E ) , indicating that GlcNAc disaccharides may provide more protection from proteolysis than GlcNAc monosaccharide modifications . Next , we analyzed the extent of reduction in the amount of cell surface-associated ClfA resulting from cathepsin G-mediated cleavage in the absence of glycosylation . The release of cleaved ClfA into the supernatant of ΔsdgB cells correlated with an almost complete loss of cell-associated ClfA ( Figure 7F , upper two panels ) . We also observed that this effect is not limited to ClfA , since SdrD , another member of the SDR family , was similarly sensitive to cathepsin G-induced cleavage and release from ΔsdgB , but not WT S . aureus cells ( Figure 7F , middle two panels ) . However , this cathepsin G-mediated cleavage appeared restricted to the Sdr-family proteins , since IsdA , another sortase-A anchored cell surface protein , was not affected ( Figure 7F , bottom two panels ) . Together , these data demonstrate that cell surface-bound SDR proteins are sensitive to proteolysis by human cathepsin G , and that glycosyl modification , primarily by SdgB , protects against this activity . Finally , we hypothesized that the observed cathepsin G-mediated disappearance of unglycosylated SDR proteins from the bacterial surface would have biological consequences , such as the capacity of the bacteria to adhere to host tissues . To test this hypothesis , we analyzed the effects of cathepsin G on the adherence of SDR glycosylation-competetent or -deficient bacteria to human fibrinogen , a well-known host ligand for SDR proteins including ClfA [2] . Treatment with cathepsin G inhibited the adherence of ΔsdgB , but not WT bacteria to human fibrinogen ( Figure 7G ) . Thus , SdgB-mediated glycosylation of SDR proteins likely represents a bacterial mechanism of protection against host innate immune responses to safeguard successful tissue colonization .
The data presented in this study highlight three novel findings: ( 1 ) the identification and characterization of two novel glycosyltransferases , designated SdgA and SdgB , which glycosylate serines in the Ser-Asp motifs of all SDR-proteins in S . aureus by appending GlcNAc moieties; ( 2 ) the elucidation of a protective role for these glycosylation events against proteolysis of SDR-proteins by human neutrophil cathepsin G; and ( 3 ) the discovery of highly opsonic human antibodies that are directed against these GlcNAc-modifications . The glycosyltransferases SdgA and SdgB identified here function sequentially to append GlcNAc moieties onto SDR-proteins . Based on reactivity of the patient-derived , glycosylation-specific monoclonal antibody rF1 with ΔsdgA , ΔsdgB and ΔsdgAΔsdgB mutant bacteria , we propose a step-wise model , in which SdgB first appends GlcNAc on serine residues within the SD-repeats of SDR ( Figure 5C ) . The first SdgB-dependent GlcNAc modification forms the minimal epitope recognized by rF1 . This was confirmed in a cell-free glycosylation reconsititution assay using UDP-GlcNAc as the sole sugar donor; the nature of the modified substrate was confirmed by LC-MS analysis . The SdgB-dependent modification is followed by the addition of GlcNAc to the glycoproteins by the second enzyme , SdgA , yielding disaccharide GlcNAc moieties on SDR proteins . This is consistent with our observation that SDR-proteins bind succinylated wheat germ agglutinin ( sWGA ) lectin , which is known to require divalent GlcNAcs [16] , only when both SdgB and SdgA are expressed ( data not shown ) . Among all the identified glycosyltransferases ( Gtfs ) , SdgB and SdgA are the most homologous to each other ( 43% identity; 63% similarity ) . TarM is another glycosyltransferase that appends GlcNAcs to wall teichoic acids; interestingly , TarM is the second most homologous protein to both SdgB ( 23% identity; 46% similarity ) and SdgA ( 24% identity; 46% similarity ) . In S . aureus , the genes for SdgA and SdgB are located adjacent to the SdrC/D/E locus . Some S . aureus strains , which lack some of the individual Sdr genes ( SdrC or SdrD ) or all three of them , still contain SdgA and SdgB . In addition to appending GlcNAcs to SdrC , D and E proteins , SdgA and SdgB append similar modifications to ClfA and ClfB , and these two genes are independent of each other and away from the SdrC/D/E locus . sdgA and sdgB genes are intact in all sequenced S . aureus; all 15 S . aureus and S . epidermidis strains that we have analyzed express rF1 antigen , suggesting that these genes are expressed and that this is a highly conserved property of these bacteria . Interestingly , as in S . aureus , the sdgA and sdgB homologs in S . epidermidis are usually adjacent to genes encoding SDR-proteins . The Sdg orthologue that is encoded adjacent to S . epidermidis SdrG , is most homologous to S . aureus SdgB ( 56% identity ) , which further explains why rF1 antibody reacts to modifications that are tailored by very similar Gtfs in both species . In some S . epidermidis strains , a sgdA homolog appears to be absent , suggesting that the second glycosylation step may be less conserved in S . epidermidis , perhaps consistent with its decreased invasiveness and consequent decreased need to evade neutrophil proteases . rF1 reactivity is only found in S . aureus and S . epidermidis , but not other staphylococci or gram-positive or -negative strains , and homologs of SdgA and SdgB are absent from all staphylococcal strains that were negative for rF1 binding . These lines of evidence support the notion that the sugar modifications of SDR-proteins may be specific for human-adapted staphylococci . Cells of the innate immune system such as neutrophils and macrophages play a vital role in immediate defense against invasive staphlylococal infections . For successful establishment of mucosal colonization and for deeper infection S . aureus must be able to survive attack by a variety of potent antimicrobial substances , including reactive oxygen and nitrogen species , lysozyme , defensins and cathepsins . While S . aureus has mechanisms for evasion of each of these phagocyte host defenses , the work in this paper describes a novel one , since glycosylation of SDR proteins is also important in this regard . The glycosylation mediated by SdgA and SdgB protects SDR-proteins from proteolytic cleavage by cathepsin G produced by neutrophils and macrophages . SDR-proteins belong to a family of bacterial proteins collectively reffered to as MSCRAMMs ( microbial surface components recognizing adhesive matrix molecules ) which mediate bacterial adherence to host extracellular matrix components including fibrinogen , fibrinectin and collagen . Cleavage of SDR-proteins from the bacterial cell surface in this environment is likely to compromise successful establishment of S . aureus colonization of and proliferation in its exclusively human niche . Glycosylation of adhesins of gram positive and gram negative pathogens is an emerging theme [17] . Glycosylation is necessary for the maturation and function of the SraP-like glycoproteins of gram-positive pathogens , and of the E . coli adhesins TibA and adhesin involved in diffuse adherence ( AIDA ) [17] , [18] , [19] . Similar to S . aureus SdgB and SdgA , the E . coli glycosyltransferase genes are found directly adjacent to the genes encoding their specific target proteins . The S . aureus SDR-proteins are distinct from several other glycosylated serine-rich adhesins of gram-positive bacteria , such as fimbriae-associated protein Fap1 of Streptococcus parasanguinis [20] , SrpA of Strep . sanguinis [21] , GspB of Strep . gordonii [22] and SraP of S . aureus [23] . Unlike these serine-rich proteins , the SDR-proteins have only one single serine repeat region , almost always with aspartate as the alternate amino acid , and they do not cluster with the accessory Sec apparatus . SgdA and SgdB do not have signal sequences or transmembrane domains , suggesting that they function in the cytoplasm , prior to the export of glycosylated SDR-proteins to the cell surface by an as yet unknown mechanism . In some of these bacterial species , glycosylation of serine-rich repeats , for example in S . parasanguinis Fap1 protein [24] or in the pneumococcal protein PsrP [25] , has been associated with biofilm formation . In S . aureus , ClfB has recently been implicated in biofilm formation as well [26] . However , we have not observed attenuation in in vitro biofilm formation by the ΔsdgAΔsdgB S . aureus strain ( not shown ) , which may be explained by functional redundancy with other glycosyl transferases appending similar sugar moieties on other cell wall associated molecules . Examples of this include TarM and TarS that append similar GlcNAcs on WTA molecules . However , glycolsyation events may also protect other bacterial functions . For instance , SdrE has been shown to bind complement factor H as an immune evasion mechanism [27] , which may well be preserved by glycosylation-mediated protection of SdrE . The precise roles of SDR glycosylation during infection remain to be defined . Since cathepsin G-mediated degradation of unglycolsylated SDR proteins results in the release of a proteolytic fragment containing the adhesive A-domain of SDR-proteins , we proposed that this cleavage could compromise the capacity of S . aureus to colonize host tissues . Supporting this hypothesis , we demonstrated that in the absence of glycosylation of SDR-proteins , human cathepsin G inhibits bacterial adhesion to human fibrinogen . Thus , we propose that Sdg-mediated glycosylation of SDR proteins is a bacterial mechanism of protection against host innate immunity , to safeguard efficient host tissue colonization . In a systemic mouse infection model , we have observed a minor , though non-significant , reduction in bacterial burden in kidneys for USA300 deletion strains lacking SdgA and SdgB ( not shown ) . However , the significance of these glycosyl modifications may be underappreciated in mouse models . We showed that unglycosylated SDR-proteins in SdgA- or SdgB-deficient bacteria are susceptible to degradation by purified human cathepsin G , or by lysosomal extracts from human neutrophils or from a human monocytic cell line , but not from a mouse monocytic cell line . Thus , the lack of a strong phenotype of SdgA/B deficient S . aureus bacteria in mouse infection may be explained by the possibility that mouse cathepsin G is either less effective in degrading unglycosylated SDR proteins or present at lower levels than in human phagocytes . This is in agreement with previous studies showing divergence of human and mouse cathepsin G with respect to their substrate specificities [28] , and normal infection of S . aureus in cathepsin G-deficient mice [29] . Since S . aureus and S . epidermidis are typical human pathogens , it is thus conceivable that the functions of these glycosylation events are important for infection in humans but not in mice . This is reminiscent of recent studies showing essential S . aureus virulence mechanisms , mediating specific interactions between S . aureus and host factors , to be specific for human and not occur in mice [30] , [31] . A definite analysis of our hypothesis of a role for glycosylation in tissue colonization is technically difficult , due to the lack of appropriate in vitro models for S . aureus colonization of relevant human tissues , such as nasal or skin epithelium . We have observed that the purified bacterial protease Asp-N , a metallo-endoprotease from either Pseudomonas fragi or Flavobacterium menigosepticum , that hydrolizes the N-terminal side of aspartic acids shows enhanced cleavage of A-domains of SDR proteins from ΔsdgB mutants , compared to the wildtype S . aureus ( data not shown ) . In this example , the track of “naked” Ser-Asp repeat residues on ΔsdgB mutants is a clear target for these proteases . This suggests that , in addition to interfering with host proteases , the glycosylation may protect S . aureus from proteases secreted by other bacteria competing for the same ecologic niche . We demonstrated that the SdgB-mediated GlcNAc modification generates an epitope on SDR proteins that is specifically recognized by a patient-derived mAb rF1 . This antibody induced robust opsonophagocytic killing by human neutrophils , much more potent than another human mAb that recognizes the A-domain of ClfA . The superior capacity of rF1 to induce killing can likely be explained by a higher abundance of glycosyl groups on the SDR-proteins . We also found that the SdgB-dependent GlcNAc-containing epitope on SDR-proteins is recognized by a significant proportion of the total amount of anti-staphylococcal IgG in humans , and that multiple patients are able to recognize this epitope . Thus , the SdgB-dependent GlcNAc modification of SDR-proteins represents an immunodominant epitope during human anti-staphylococcal immune responses . The SDR-glycosylation may be used by the host immune system as an important recognition factor to detect S . aureus in infected tissues . However , the potential importance of glycosylation of SDR proteins in protecting against host proteases and successful tissue colonization may outweigh the need of the bacteria to go undetected from the host antibody response , thus representing a balance between host and pathogen .
Informed written consent was obtained from all donors and was provided in accordance with the Declaration of Helsinki . Approval was obtained from the health research ethics committee of Denmark through the regional committee for The Capital Region of Denmark , and the medical ethical committee of Academic Medical Center , Amsterdam . All animal procedures were conducted under a protocol ( #08-1990 ) approved by the Genentech's Institutional Animal Care and Use Committee in an AAALAC-accredited facility in accordance with the Guide for the Care and Use of Laboratory Animals and applicable laws and regulations . Bacterial strains are listed in Table 1 . For generation of mutant strains , we used protein A deficient ( Δspa ) USA300 as parental strain , being referred to as wild-type USA300 , in order to minimize non-specific antibody binding . Single SgdA and SdgB mutants were generated by transduction using phage Φ85 ( ATCC , Manassas , VA ) of transposon-inserted SdgA NE381 and SdgB NE105 genes from Nebraska Transposon Library strains ( NE381 and NE105 , respectively; obtained from NARSA ) into Δmcr USA300 NRS384 [32] or Δspa USA300 NRS384 . The SdgAB double mutant was generated by allelic exchange using the pIMAY plasmid ( a generous gift from Dr . T . Foster ) as previously described [32] . Briefly , a targeting construct including ∼600 bp of the upstream and downstream flanking sequences of the two adjacent SdgA and SdgB genes was cloned into pIMAY , which was electroporated into Δmcr USA300 NRS384 or ΔmcrΔspa USA300 NRS384 . To complement the SdgB gene inΔspaΔSdgB USA300 NRS384 , a construct containing the ribosomal binding site of the SodA gene followed by a His-tag fused in frame with the full length SdgB gene was cloned downstream of a constitutive SarA promoter in the pSarA . MK4 plasmid ( ATCC ) . This plasmid was electroporated into ΔspaΔSdgB USA300 NRS384 , and complementation of the SdgB gene was confirmed by PCR . Bacteria were grown on tryptic soy agar plates supplemented with 5% sheep blood ( TSA plates ) for 18 h at 37°C . For liquid cultures , single colonies from TSA plates were inoculated into tryptic soy broth ( TSB ) and incubated at 37°C while shaking at 200 rpm for 18 h; 100 fold dilutions of these cultures in fresh TSB were further subcultured for various times . For generation of mAb rF1 , CD19+CD3−CD27+IgD−IgA− memory B cells were isolated from peripheral blood of an MRSA-infected donor using a FACSAria cell sorter ( BD , San Jose , CA ) . Before viral transduction with B-cell lymphoma ( Bcl ) -xL and Bcl-6 genes , the memory cells were activated on CD40L-expressing mouse L fibroblasts in the presence of interleukin-21 , as described previously [33] . Transduced B cells were maintained in the same culture system . The use of donor blood was approved by the institutional committee . rF1 was selected from culture supernatants by reactivity with lysates of MSSA strain Newman by ELISA; positive wells were subcloned and re-tested by ELISA twice . Recombinant rF1 was generated by cloning the heavy and light chain variable regions with human IgG1 kappa constant regions using pcDNA3 . 1 ( Invitrogen ) and transfection into 293T cells ( ATCC ) . Purified IgG was obtained from culture supernatants using protein A-coupled sepharose ( Invitrogen ) . The human IgG antibodies 4675 , SD2 , SD3 and SD4 were cloned from peripheral B cells from patients post S . aureus infection using a monoclonal antibody discovery technology which conserves the cognate pairing of antibody heavy and light chains [34] . Both plasma and memory B-cells were used as genetic source for the recombinant full length IgG repertoires ( manuscript in preparation ) . Individual antibody clones were expressed by transfection of mammalian cells [35] . Supernatants containing full length IgG1 antibodies were harvested after seven days and used to screen for antigen binding by ELISA . Antibodies 4675 , SD2 , SD3 and SD4 were positive for binding to cell wall preparations from USA300 or Newman S . aureus strains . Antibodies were subsequently produced in 200-ml transient transfections and purified with Protein A chromatography ( MabSelect SuRe , GE Life Sciences , Piscataway , NJ ) for further testing . Isolation and usage of these antibodies were approved by the regional ethical review board . Mouse mAb against ClfA ( 9E10 ) , ClfB , ( 10D2 ) , SdrD ( 17H4 ) , IsdA ( 2D3 ) and non-modified SDR proteins ( 9G4 ) were generated by immunizing mice with the respective recombinant proteins , which were purified after expression in E . coli , using standard protocols; hybridoma supernatants were purified by protein A affinity chromotography . Rabbit mAb 28 . 9 . 9 was generated by immunizing rabbits with peptidoglycan ( PGN ) -derived peptide CKKGGG- ( L-Ala ) - ( D-gamma-Glu ) - ( L-Lys ) - ( D-Ala ) -D-Ala ) followed by cloning of the IgG . CWP were generated by incubating 40 mg of pelleted S . aureus or S . epidermidis per mL of 10 mM Tris-HCl ( pH 7 . 4 ) supplemented with 30% raffinose , 100 µg/ml of lysostaphin ( Cell Sciences , Canton , MA ) , and EDTA-free protease inhibitor cocktail ( Roche , Pleasanton , CA ) , for 30 min at 37°C . The lysates were centrifuged at 11 , 600 x g for 5 min , and the supernatants containing cell wall components were collected . For immunoprecipitation , CWP were diluted 4 times in NP-40 buffer ( 120 mM NaCl , 50 mM Tris-HCl pH 8 . 0 , 1% NP-40 , complete protease inhibitor cocktail ( Roche ) and 2 mM dithiothreitol ) containing 1 µg/mL of indicated primary antibodies and incubated for 2 h at 4°C , followed by a 1 h incubation with Protein A/G agarose ( Thermo , Waltham , MA ) . Whole cell lysates ( WCL ) were generated by a 30 min incubation at 37°C in 20 mM Tris-HCl ( pH 7 . 4 ) , 150 mM NaCl , 100 µg/ml of lysostaphin , 1% Triton-X100 ( Thermo ) and EDTA-free protease inhibitor cocktail . For immunoblot analysis , proteins were separated on a 4-12% Tris-glycine gel , and transferred to a nitrocellulose membrane ( Invitrogen , Carlsbad , CA ) , followed by blotting with indicated primary antibodies ( 1 µg/mL ) . Antibodies used are listed in Table 1 . Lectin studies were performed by immunoprecipitating filtered ( 0 . 2 micron ) overnight culture supernatants with concanavalin A ( ConA ) - or sWGA-agarose beads ( Vector Labs , Burlingame , CA ) supplemented with 0 . 1 mM CaCl2 and 0 . 01 mM MnCl2 . ELISA experiments were performed using standard protocols . Briefly , plates which were pre-coated with CWP were reacted with human IgG preparations , ie . purified human IgG ( Sigma ) , intravenous immunoglobulin Gammagard Liquid ( Baxter , Westlake Village , CA ) , pooled serum from healthy donors or from MRSA patients ( both generated in-house ) . The concentrations of anti-staphylococcal IgG present in the serum or purified IgG were calculated by using a calibration curve that was generated with known concentrations of mAb 28 . 9 . 9 against peptidoglycan . For inducible expression in S . aureus or B . subtilis , constructs of maltose binding protein ( MBP ) were fused to various PCR-amplified clumping factor A ( CflA ) domains ( listed in Table 1 ) and cloned into vector pTet . MK4 , which was modified from pMK4 ( ATCC ) . The pTet . MK4 constructs were electroporated into Δmcr USA300 NRS384 ( including SdgA/B mutant strains ) , Δspa RN4220 , or B . subtilis . Protein expression was induced by culture in TSB for 2 . 5 h at 37°C with 200 ng/ml of anhydrotetracycline ( Sigma ) . For inducible expression in E . coli , constructs were cloned into expression vector pMal . c5x ( New England Biolabs , Ipswich , MA ) , and expression was induced by culture in the presence of 0 . 3 mM IPTG ( Sigma ) . The cultures were resuspended in lysis buffer ( 1% TritonX-100 , 150 mM NaCl , 20 mM Tris pH 7 . 5 , EDTA-free protease inhibitor cocktail [Roche] ) , and bacteria were lysed by incubation at 37°C for 30 minutes with either 100 µg/ml of lysostaphin ( S . aureus ) , or with 6 kU/ml of lysozyme ( B . subtilis and E . coli ) , followed by mechanical disruption using a Mini-Beadbeater ( Biospec Products , Bartlesville , OK ) . The lysates were centrifuged at 18 , 000 x g for 10 minutes , and the supernatants were incubated with Ni-Nta resin ( Qiagen , Valencia , CA ) for 1 . 5 h at 4°C or with amylose agarose resin ( New England Biolabs ) for 1 . 5 hrs at 4°C . The resins were washed 3 times with PBS containing 10 mM imidazole and 1% NP40 , and samples were analyzed by immunoblotting . The clfA , clfB , sdrC , sdrD and sdrE genes were PCR amplified from the mature start of the protein to the glycine in the LPXTG motif from USA300 genomic DNA and ligated in frame with an NT Unizyme tag . The constructs were transformed into E . coli for protein expression . Proteins were purified from E . coli lysates using a Ni-NTA resin ( Qiagen ) in PBS with protease inhibitors ( Roche ) for 1 . 5 h at 4 °C . These resin-bound SDR-proteins were subjected to in vitro glycosylation by S . aureus lysates as follows . First , ΔpanSDR mutant S . aureus from a log-phase culture were lysed by incubation in PBS containing 200 ug/ml lysostaphin and 250 U/ml of benozase nuclease ( Novagen , Madison , WI ) for 30 min at 37°C , followed by centrifugation at 18 , 000 x g to remove debris . Next , in vitro glycosyl modification of resin-bound E . coli SDR-proteins was induced by addition of ΔpanSDR mutant S . aureus lysates and incubation for 1 h at 37°C , followed by 3 washes with a buffer containing 50 mM NaH2PO4 , 300 mM NaCl , 10 mM imidazole ( pH 8 . 0 ) . For in vitro glycosylation by purified glycosyltransferases , the full lengths of the indicated S . aureus glycosyltransferase genes were PCR amplified and ligated in frame with an N-terminal 6x His-tag into the pET15b E . coli expression vector , and transformed into E . coli , followed by protein expression and purification . MBP-SDR709 fusion protein was cloned into pMal . c5x , and expressed and purified as described above . Purified MBP-SDR709 fusion protein was incubated with amylose agarose ( New England Biolabs ) for 1 . 5 h at 4°C . To provide necessary building sugar blocks , a B . subtilis lysate was prepared by resuspension of a logarithmic culture of B . subtilis in PBS with protease inhibitors ( Roche ) and mechanical disruption using a Mini-Beadbeater , followed by removal of debris by centrifugation . To induce glycosylation , the amylose captured MBP-SDR709 fusion protein was incubated with the B . subtilis lysate in the presence of 10 µg/ml of purified glycosyltransferase for 1 h at 37°C , followed by 3 washes in 1% NP-40 buffer . Binding of glycosyltransferases to MBP-SDR709 fusion protein was assessed as follows . Two µg of purified MBP-SDR709 protein was incubated with 10 µg of a purified His-tagged glycosyltransferase in 1% NP-40 buffer and protease inhibitors for 2 h at 4°C . This was followed by incubation with amylose agarose for 1 h at 4°C , and 3 washes with 1% NP-40 buffer . For co-expression of SDR protein with Sdg enzymes in E . coli , a NT-His tag fused to the full length of either SdgA or SdgB was cloned into the arabinose inducible expression vector pBad33 ( ATCC ) , and transformed into the E . coli strain harboring the inducible MBP-SDR709 fusion protein ( expressed in the pMal . c5x expression vector; see above ) . Expression of both SDR protein and Sdg enzyme was induced by addition of 0 . 2% L- ( + ) arabinose ( Sigma ) and 0 . 3 mM IPTG . Whole cell lysates were prepared by incubation in 100 mM Tris ( pH 7 . 5 ) with 4% SDS , 1 mM EDTA , and 100 mM DTT for 5 min at 100°C . For cell-free reconstitution of SDR-protein glycosylation , 100 µg of purified MBP-SDR709 protein , purified after expression in E . coli , was mixed with 30 µg of UDP-GlcNAc ( Sigma ) and 4 µg of purified SdgA and/or SdgB enzyme in 100 mM Tris . This mixture was incubated for 1 h at 37°C for 1 h , and subsequently analyzed by immunoblotting or mass spec . All in vitro glycosylated SDR-proteins were analyzed by immunoblotting as described above . For the identification of the SDR-modifying glycosyltransferases , lysates of ΔpanSDR mutant S . aureus were prepared as described above and fractionated by sequential size exclusion and anion exchange chromatography . Aliquots of fractions were separated on a 4–20% Tris-glycine gel , and fractions which stained positive with rF1 by immunoblotting were again separated by SDS-PAGE , subjected to in-gel trypsin ( Promega , Madison , WI ) digestion , followed by mass spectrometric analysis as previously described [36] . Tandem mass spectra were submitted for database searching using the Mascot program version 2 . 2 . 06 ( Matrix Science ) against a concatenated NCBInr target-decoy database consisting of staphylococcus aureus proteins and common laboratory contaminants . Peptide and protein identifications were validated with the Scaffold program version 2 . 06 . 01 ( Proteome Software ) . Thresholds for accepting peptide and protein identifications were set at greater than 90% and 99% respectively , using the Peptide Prophet and Protein Prophet algorithms [37] . For the identification of the sugar moieties on SDR proteins , the MBP-SDR-His protein was expressed in E . coli and purified on a dextrin affinity column . Protein was analyzed by LC-MS using a PLRP-C ( Agilent , Santa Clara , CA ) reversed phase column connected to the electrospray orifice of an Agilent 6520 TOF run in positive ion mode . Proteins were chromatographed in 0 . 05% trifluoroacetic acid buffer and eluted with a gradient of acetonitrile . Whole bacteria were harvested from TSA plates or TSB cultures and washed with HBSS without phenol red supplemented with 0 . 1% IgG free BSA ( Sigma ) and 10 mM Hepes , pH 7 . 4 ( HB buffer ) Bacteria ( 20×108 CFU/mL ) were incubated with 300 µg/mL of rabbit IgG ( Sigma ) in HB buffer for 1 h at room temperature ( RT ) to block nonspecific IgG binding . Bacteria were stained with 2 µg/mL of primary antibodies , including rF1 or isotype control IgG1 mAb mAb gD:5237 [38] , and next with fluorescent anti-human IgG secondary antibodies ( Jackson Immunoresearch , West Grove , PA ) . The bacteria were washed and analyzed by FACSCalibur ( BD ) . For antibody staining of bacteria from infected mouse tissues , 6–8 weeks old female C57Bl/6 mice ( Charles River , Wilmington , MA ) were injected intravenously with 108 CFU of logphase-grown USA300 in PBS . Mouse organs were harvested two days after infection . Rabbit infective endocarditis ( IE ) was established as previously described [39] . Rabbits were injected intravenously with 5×107 CFU of stationary-phase grown MRSA strain COL , and heart vegetations were harvested eighteen hours later . Treatment with 30 mg/kg of vancomycin was given intravenously b . i . d . 18 h after infection with 7×107 CFU stationary-phase COL . To lyse mouse or rabbit cells , tissues were homogenized in M tubes ( Miltenyi , Auburn , CA ) using a gentleMACS cell dissociator ( Miltenyi ) , followed by incubation for 10 min at RT in PBS containing 0 . 1% Triton-X100 ( Thermo ) , 10 µg/mL of DNAseI ( Roche ) and Complete Mini protease inhibitor cocktail ( Roche ) . The suspensions were passed through a 40 micron filter ( BD ) and bacteria were stained with mAbs as described above . Bacteria were differentiated from mouse organ debris by double staining with 20 µg/mL mouse mAb 702 anti-S . aureus peptidoglycan ( abcam , Cambridge , MA ) and a fluorochrome-labeled anti-mouse IgG secondary antibody ( Jackson Immunoresearch ) . During flow cytometry analysis , bacteria were gated for positive staining with mAb 702 from double fluorescence plots . All animal experiments were approved by the Institutional Review Boards of Genentech and the University of California , San Francisco . Peripheral leukocytes were enriched for polymorhonuclear cells ( PMN ) by dextran sedimentation . Briefly , equal volumes of healthy donor peripheral blood collected in EDTA tubes were mixed with a 0 . 9% NaCl solution containing 3% 500 kD dextran ( Sigma ) , and left to sediment for 20 min at RT . The leukocytes in the upper layer were washed twice with HB buffer and resuspended at 3×16/mL . USA300 bacteria were washed , resuspended at 30×16/mL in HB , and preopsonized for 30 min at 37°C with rF1 , human mAb 4675 anti-ClfA , or human IgG1 control mAb gD:5237 . Bacteria were spun down at 2500 rpm for 5 min , and PMN were added at a bacteria:PMN ratio of 10∶1 . After incubation for 90 min at 37C , the suspensions were diluted 10-fold in water and left for 1 min at RT to lyze the PMN . Serial 10-fold dilutions in PBS containing 0 . 05% Tween-20 were cultured on TSA plates to determine numbers of viable CFU . Lysosomal extracts were isolated from human neutrophils , THP-1 cells , and RAW cells , using a Lysosome Enrichment kit ( Thermo ) . A total of 5×107 cells was used to obtain 300 to 500 microgram of total proteins in the lysosomes . Protease inhibitors were omitted from all steps to maintain protease activity in the lysosomes . The plasma membranes of the cells were disrupted by 30 strokes using a dounce homogenizer ( Wheaton , Millville , NJ ) . The homogenate was centrifuged at 500 x g for 5 min to obtain postnuclear supernatant , which was loaded onto the top of a gradient of 8% , 20% , 23% , 27% and 30% ( from top to bottom ) of iodixanol . After ultracentrifugation at 145 , 000 x g for 2 h at 4°C , we obtained the lysosomes layered between 8% and 20% iodixanol . This lysosomal fraction was diluted into PBS and pelleted by centrifugation at 18 , 000 x g for 30 min at 4°C . The lysosomal pellets were washed with PBS and lysed in 2% CHAPS with Tris-buffered saline to obtain lysosomal extracts . To analyze the cleavage of SDR proteins by host proteases , S . aureus bacteria were treated with 50 nM of purified human neutrophil serine proteases or 0 . 1 mg/ml of neutrophil lysosomal extracts in 50 mM Tris ( pH 8 . 0 ) with 150 mM NaCl and 2 mM CaCl2; or with 0 . 1 mg/ml of RAW or THP-1 lysosomal extracts in 50 mM NaCitrate with 100 mM NaCl and 2 mM DTT ( pH 5 . 5 ) . Cathepsin G inhibitor ( Calbiochem , Billerica , MA ) was added at 100 µg/ml . These mixtures were incubated at 37°C for 30 minutes when using purified proteases or for 1 h when using lysosomal lysates , and centrifuged to pellet bacteria . The supernatants were analyzed by immunoblotting to detect cleavage products . In some experiments , cell wall preparations were obtained from the remaining bacterial pellets and also analyzed by immunoblotting . Bacteria from 4 h logphase culture in TSB were washed by centrifugation and resuspended at a concentration of 3×108/mL in 50 mM TrisHCl ( pH 8 . 0 ) with 150 mM NaCl and 2 mM CaCl2 . Human cathepsin G ( Athens , Athens , GA ) was added at a variety of concentrations . The bacteria were incubated for 30 min at 37°C , followed by one centrifugation and resuspension at 3×108/mL in PBS with 2% IgG-free BSA ( Sigma ) . These suspensions were transferred to 2HB Immunon 96-well plates ( Thermo ) which were precoated with human fibrinogen ( Fluka/Sigma ) at 20 µg/mL in carbonate buffer ( pH 9 . 0 ) . The plates were gently rocked for 1 h at room temperature , and washed three times using 10 mM Tris ( pH 8 . 0 ) containing 150 mM NaCl and 0 . 1% Tween-20 . To quantify the number of bacteria remaining adherent to the plate-bound fibrinogen , the amount of bacterial ATP was measured using the BacTiterGlo kit ( Promega ) , and fluorescence was expressed in arbitrary units ( AU ) as a measure for bacterial adherence . | Staphylococcus aureus and S . epidermidis are major bacterial pathogens that can cause life-threatening human diseases . Following entry into the circulation , S . aureus can infect virtually any organ . However , it must first counter antibacterial mechanisms of the innate immune system , including those involving macrophages and neutrophils . Important for staphylococcal adhesion to and successful colonization of host tissues , is a family of bacterial surface proteins containing multiple repeats of serine-aspartate repeats ( SDR ) adjacent to an adhesive A-domain . The biological functions of the SDR-domain of these SDR proteins remain elusive . We found that the SDR-domain of all staphylococcal SDR proteins is heavily glycosylated . We identified two novel glycosylases , SdgA and SdgB , which are responsible for glycosylation in two steps , and found that this glycosylation protects the adhesive SDR proteins against proteolytic attack by human neutrophil cathepin G . Since pathogen binding to human tissues , including the extracellular matrix protein fibrinogen , depends on SDR proteins , this glycosylation may be important for successful colonization of the human host . We also show that the SdgB-mediated glycosylation creates an immunodominant epitope for highly opsonic antibodies in humans . These antibodies account for a significant proportion of the total anti-staphylococcal IgG response . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Novel Staphylococcal Glycosyltransferases SdgA and SdgB Mediate Immunogenicity and Protection of Virulence-Associated Cell Wall Proteins |
The global distribution map of schistosomiasis shows a large overlap of Schistosoma haematobium- and S . mansoni-endemic areas in Africa . Yet , little is known about the consequences of mixed Schistosoma infections for the human host . A recent study in two neighboring co-endemic communities in Senegal indicated that infection intensities of both species were higher in mixed than in single infections . Here , we investigated the relationship between mixed Schistosoma infections and morbidity in the same population . So far , this has only been studied in children . Schistosoma infection was assessed by microscopy . Schistosoma-specific morbidity was assessed by ultrasound according to WHO guidelines . Multivariable logistic regression models were used to identify independent risk factors for morbidity . Complete parasitological and morbidity data were obtained from 403 individuals . Schistosoma haematobium-specific bladder morbidity was observed in 83% and S . mansoni-specific hepatic fibrosis in 27% of the participants . Bladder morbidity was positively associated with S . haematobium infection intensity ( OR = 1 . 9 ( 95% CI 1 . 3–2 . 9 ) for a 10-fold increase in intensity ) . Moreover , people with mixed infections tended to have less bladder morbidity than those with single S . haematobium infections ( OR = 0 . 3 ( 95% CI 0 . 1–1 . 1 ) ) . This effect appeared to be related to ectopic S . mansoni egg elimination in urine . Hepatic fibrosis on the other hand was not related to S . mansoni infection intensity ( OR = 0 . 9 ( 95% CI 0 . 6–1 . 3 ) ) , nor to mixed infections ( OR = 1 . 0 ( 95% CI 0 . 7–1 . 7 ) ) . This is the first population-wide study on the relationship between mixed Schistosoma infections and morbidity . Mixed infections did not increase the risk of S . mansoni-associated morbidity . They even tended to reduce the risk of S . haematobium-associated morbidity , suggesting a protective effect of S . mansoni infection on bladder morbidity . These unexpected results may have important consequences for schistosomiasis control in co-endemic areas and warrant further investigation .
Worldwide more than 207 million people are infected with Schistosoma , 85% of whom live in Africa [1] . Due to the large overlap in Schistosoma haematobium- and S . mansoni-endemic areas [2]–[4] many people are at risk of co-infection . Yet , little is known about mixed Schistosoma infections and their impact on host morbidity . Clinical manifestations of schistosomiasis are associated with the species-specific oviposition sites [2] . Both S . haematobium and S . mansoni mature in the portal vein of the human host and form male-female pairs . Subsequently , the S . mansoni male carries the female to the mesenteric plexus whereas the S . haematobium couple continues its way to the veins of the pelvis . In these respective sites , they lay eggs , which are eventually eliminated from the body via the urine ( S . haematobium ) or feces ( S . mansoni ) . About half of the eggs , however , are carried away with the blood stream and/or trapped in the tissues . These retained eggs provoke inflammatory and granulomatous immune responses [2] . For S . haematobium , this can lead to inflammation , ulceration and pseudopolyposis of bladder and ureteral walls , and in children this is often accompanied by hematuria . Chronic lesions may evolve to fibrotic and sandy patches with severe sequelae such as hydroureter , hydronephrosis , and squamous bladder cancer [2] , [5] , [6] . For S . mansoni , egg deposition can lead to inflammatory hepatic schistosomiasis and hepatosplenomegaly in children and adolescents [7] . Trapped schistosomal eggs are gradually replaced by fibrotic deposits , and give rise to chronic hepatic schistosomiasis [2] . In mixed S . mansoni and S . haematobium infections , the above-described processes act in parallel , which may result in more severe or other abnormalities than in single infections . So far , only two studies have investigated the relationship between mixed infections and host morbidity . In Zimbabwe , a positive association between S . mansoni egg output and liver size was found in the presence but not in the absence of S . haematobium . Yet , this effect was very little [8] . In a study in Mali , mixed infection was associated with reduced hepatic morbidity on one hand and increased bladder morbidity on the other [9] . Both studies were performed in schoolchildren , in whom severe hepatic schistosomiasis is unlikely to have developed already [2] , [7] , [10] . Community-wide studies would therefore be more appropriate to investigate mixed infections and morbidity . In northern Senegal , many communities have in the past decades become co-endemic for S . mansoni and S . haematobium [11]–[16] . Schistosoma mansoni was introduced in Richard-Toll in 1988 upon construction of the Diama dam and rapidly spread throughout the region [17] , [18] . By 1994 , virtually the entire Guiers Lake ( ‘Lac de Guiers’ ) area had become exposed to this species [19] . Today , both S . mansoni and S . haematobium are widely spread , resulting in a large number of people with mixed infections in the communities around the lake . Recently , we reported on the distribution of and risk factors for mixed Schistosoma infections in two communities on the banks of Lake Guiers . Individuals with mixed infections were found to have higher infection intensities than those with single infections [20] . In the present study , we set out to investigate the relationship between mixed Schistosoma infections and morbidity in the same communities . We studied the patterns of S . haematobium-specific bladder morbidity and S . mansoni-specific hepatic morbidity in this co-endemic focus , and compared morbidity in people with mixed Schistosoma infections to those with single infections .
This study was part of a larger investigation on the epidemiology of schistosomiasis and innate immune responses ( SCHISTOINIR: www . york . ac . uk/res/schistoinir ) for which approval was obtained from the review board of the Institute of Tropical Medicine , the ethical committee of the Antwerp University Hospital and ‘Le Comité National d'Ethique de la Recherche en Santé’ in Dakar . Informed and written consent was obtained from all participants prior to inclusion into the study . This study was conducted from July until November 2009 in Ndieumeul ( also known as Thiekène ) and Diokhor Tack , two neighboring communities on the Nouk Pomo peninsula in Lake Guiers . Details on the study area have been described elsewhere [20] . Two urine and two feces samples were collected from each participant on consecutive days for microscopic analysis [21] , [22] . Per feces sample , two Kato-Katz slides of 25 mg fecal material each were prepared and microscopically examined for Schistosoma species [22] . Schistosoma mansoni infection intensity was expressed as the number of eggs detected per gram of feces ( epg ) . Urine filtration was performed using a filter of 12 µm pore-size ( Isopore ) according to standard procedures [21] . Schistosoma haematobium infection intensity was expressed as the number of eggs detected per 10 ml of urine ( ep10ml ) . Ectopic eggs were measured qualitatively ( positive/negative ) . Ectopic egg elimination refers to elimination of schistosomal eggs via the unusual route – i . e . S . mansoni eggs in urine or S . haematobium eggs in feces . Single infection was defined as passing eggs of only one species , and mixed infection as passing eggs of both S . mansoni and S . haematobium , regardless of the route of egg elimination [20] . Microhematuria was determined in a subsample using Combur 7 dipsticks ( Roche ) on the first urine sample . All community members were offered praziquantel ( one dose of 40 mg/kg body weight ) and mebendazole ( one dose of 500 mg ) treatment after the study according to WHO guidelines [23] . Participants were examined using a portable ultrasonography device with convex transducer . Pathologic lesions associated with S . haematobium or S . mansoni infection were recorded according to the Niamey guidelines [10] . All examinations were performed by the same clinician who was blind to the participant's infection status . Participants with severe pathology that needed further treatment were referred to the appropriate health authority . For S . haematobium-specific morbidity , the urinary bladder score was determined [10] . A score of ≥1 was considered as S . haematobium-specific urinary bladder morbidity in accordance with previous studies [9] , [24]–[30] . Individuals with a score of 0 were categorized as controls . For S . mansoni-specific morbidity , the liver image pattern was determined [10] . Additional measurement of periportal thickening was not included as this approach has been shown to be not reproducible [31] . Liver image patterns of C to F were categorized as S . mansoni-specific hepatic morbidity [31]–[35] . Individuals with liver image pattern A or B are not likely to have periportal fibrosis [10] and were therefore categorized as controls . Individuals with signs of hepatic morbidity that were not specific for S . mansoni ( e . g . hepatitis , cirrhosis or fatty liver ) were excluded [10] . IBM SPSS 19 . 0 ( SPSS , Inc . ) was used for statistical analysis . Results were considered significant when the p-value was <0 . 05 . As egg outputs showed skewed distributions , data were normalized by log ( base 10 ) -transformation after adding half of the detection limit to allow for zeros . Differences between groups were determined by the Pearson Chi-square test for community and gender , and by the Mann-Whitney U test for age . Furthermore , the Pearson Chi-square test was used to determine the association between bladder morbidity and microhematuria , as well as between bladder and liver morbidity . Because of the non-linear trend of morbidity over age , the population was divided into four age groups ( 0–9 , 10–19 , 20–39 and ≥40 years ) for multivariable regression analysis . Multivariable logistic regression models were used to identify independent risk factors for S . haematobium-specific bladder morbidity and S . mansoni-specific hepatic fibrosis , respectively . Age , gender , community of residence , S . haematobium infection intensity and S . mansoni infection intensity were included as potential risk factors . Moreover , significant interaction terms with age ( p<0 . 05 ) were added . Similar models were used to assess the independent effect of mixed infection ( as compared to single infection ) on S . haematobium-specific bladder morbidity and S . mansoni-specific hepatic fibrosis , respectively . Among S . haematobium-positive subjects , the association between bladder morbidity and mixed infection was investigated using a dummy variable for mixed infection ( 1 = mixed , 0 = single ) , and age , gender , community of residence and S . haematobium infection intensity as other determinants . Likewise , the association between hepatic fibrosis and mixed infection was investigated in S . mansoni-positive subjects with a dummy variable for mixed infection and upon correction for age , gender , community of residence and S . mansoni infection intensity .
Schistosoma haematobium-specific bladder morbidity was observed in 83% of the study population ( 334/403; Table 1 ) . Most common lesions concerned multifocal or diffuse bladder wall thickening ( n = 189 ) , irregularities ( n = 94 ) or a single mass ( n = 19 ) . Microhematuria was twice as prevalent among those with bladder morbidity as compared to those without ( 44% versus 21% , p = 0 . 002 ) . S . mansoni-specific fibrosis was present in 27% ( 109/403 ) of the population ( Table 2 ) . Liver image patterns up to F were observed , but the large majority had pattern C ( 89/109 ) . Morbidity of both liver and bladder was observed in 24% ( 93/391 ) of the study participants ( Table 3 ) . Those who had bladder morbidity tended to be more at risk for hepatic fibrosis and vice versa ( odds ratio ( OR ) = 1 . 3 ( 95% confidence interval ( CI ) 0 . 7–2 . 4 ) ) . Figure 1 shows that bladder morbidity was mainly observed in children ( <20 years ) , with a peak in 10-to-19-year-olds . The age-related distribution of bladder morbidity coincided with that of S . haematobium infection intensity and microhematuria , although the peak was slightly later in adolescence , and the subsequent decline in adults less pronounced . The prevalence of hepatic fibrosis increased from 6% in 0-to-9-year-olds to 44% in those ≥20 years old . The more severe forms of hepatic fibrosis ( liver image patterns D , E and F ) only became apparent in adults ( ≥20 years old ) . The peak in the age-related distribution of hepatic fibrosis occurred more than 10 years later in life than the S . mansoni infection intensity peak ( Figure 1 ) . Multivariable analysis showed age to be a significant risk factor for bladder morbidity as well as hepatic fibrosis ( Table 4 ) . However , age-related patterns of hepatic fibrosis differed between the two communities ( p = 0 . 033 ) : while the ORs for hepatic fibrosis increased with age in Diokhor Tack ( p<0 . 001 ) , they did not vary with age in Ndieumeul ( data not shown ) . Individuals from Diokhor Tack were significantly more at risk for hepatic fibrosis but tended to be less at risk for bladder morbidity than their counterparts from Ndieumeul ( Table 4 ) . Furthermore , females were less at risk for both forms of morbidity than males . Neither S . mansoni intensity nor S . haematobium intensity was identified as an independent risk factor for hepatic fibrosis . On the other hand , S . haematobium ( but not S . mansoni ) infection intensity was a strong risk factor for bladder morbidity . After including mixed infection into the multivariable model , the above-described trends remained the same ( Table 5 ) . The risk of hepatic fibrosis did not differ between subjects with single S . mansoni and those with mixed infections . Interestingly however , mixed infection tended to be negatively associated with S . haematobium-specific bladder morbidity , suggesting a protective effect of current S . mansoni infection ( p = 0 . 068 ) . Ectopic S . haematobium eggs were found in one ( 0 . 6% ) and ectopic S . mansoni eggs in 23 ( 13% ) out of 176 individuals with mixed infections . Table 6 illustrates the importance of the route of S . mansoni egg elimination in the development of bladder morbidity . Those who eliminated S . mansoni via both urine and feces ( n = 17 ) had highest S . haematobium infection intensities and prevalences of bladder morbidity . Lowest prevalences of morbidity were observed in those who exclusively eliminated S . mansoni eggs via the urine ( and not via the feces; n = 6 ) , despite relatively high S . haematobium infection intensities .
Up to now , the relationship between mixed Schistosoma infection and morbidity has only been studied in schoolchildren . This population-wide study is the first to include adults . Mixed infections were not associated with an increased risk of S . mansoni-associated morbidity and even tended to reduce the risk of bladder morbidity . These unexpected results warrant further investigation of a possible protective effect of S . mansoni on bladder morbidity . Especially the role of interspecies interactions and ectopic S . mansoni egg elimination should be studied in more detail , as these phenomena may have important consequences for schistosomiasis morbidity and control in co-endemic areas . | In the developing world , over 207 million people are infected with parasitic Schistosoma worms . Schistosoma haematobium and S . mansoni are the most abundant species in Africa and many people carry both . Yet , little is known about the consequences of such mixed infections . In general , S . haematobium affects the urinary tract of the host and S . mansoni the liver . Here , we investigated the effect of mixed Schistosoma infection on these health problems . We examined 403 people from northern Senegal for Schistosoma infections as well as for abnormalities of the urinary bladder and liver . Recently , we observed that people with mixed Schistosoma infections had generally higher infection intensities than those with single infections . The present study showed that abnormalities of the urinary bladder were more common in heavy than in light S . haematobium infections . Also , they were more common in single S . haematobium than in mixed infections . So far , only two studies have looked into the relationship between mixed Schistosoma infection and abnormalities of the bladder and liver , but only investigated children . Our findings suggest a possible protective effect of S . mansoni on bladder disease , in children as well as in adults . This may have important consequences for schistosomiasis control in co-endemic areas . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
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] | 2012 | Bladder Morbidity and Hepatic Fibrosis in Mixed Schistosoma haematobium and S. mansoni Infections: A Population-Wide Study in Northern Senegal |
The activation of several transcription factors is required for the elimination of infectious pathogens via the innate immune response . The transcription factors NF-κB , AP-1 , and STAT play major roles in the synthesis of immune effector molecules during innate immune responses . However , the fact that these immune responses can have cytotoxic effects requires their tight regulation to achieve restricted and transient activation , and mis-regulation of the damping process has pathological consequences . Here we show that AP-1 and STAT are themselves the major inhibitors responsible for damping NF-κB–mediated transcriptional activation during the innate immune response in Drosophila . As the levels of dAP-1 and Stat92E increase due to continuous immune signaling , they play a repressive role by forming a repressosome complex with the Drosophila HMG protein , Dsp1 . The dAP-1– , Stat92E- , and Dsp1-containing complexes replace Relish at the promoters of diverse immune effector genes by binding to evolutionarily conserved cis-elements , and they recruit histone deacetylase to inhibit transcription . Reduction by mutation of dAP-1 , Stat92E , or Dsp1 results in hyperactivation of Relish target genes and reduces the viability of bacterially infected flies despite more efficient pathogen clearance . These defects are rescued by reducing the Relish copy number , thus confirming that mis-regulation of Relish , not inadequate activation of dAP-1 , Stat92E , or Dsp1 target genes , is responsible for the reduced survival of the mutants . We conclude that an inhibitory effect of AP-1 and STAT on NF-κB is required for properly balanced immune responses and appears to be evolutionarily conserved .
The innate immune response triggered by pathogen infection activates signal transduction pathways and elicits diverse humoral and cellular responses [1 , 2] . This response requires the activation of several transcription factors to remodel the gene expression pattern of cells [3] . NF-κB plays a key role in the synthesis of antimicrobial peptides and cytokines [4 , 5] , whereas the transcription factors AP-1 and STAT regulate genes involved in phagocytosis and melanization [6–8] . Nuclear receptors and SMAD proteins also influence the expression of inflammatory cytokines [9 , 10] . In many cases , the functions of these transcription factors do not appear to be limited to the synthesis of particular effector molecules , but also regulate the activities of other transcription factors involved in such biological processes as immune responses [11 , 12] . For example , AP-1 transcription factors are reported to interact with the histone deacetylase complex [13] and to suppress Smad2 transcription [14] . Stat1 is required for the interferon-γ suppression of c-myc expression in mouse embryo fibroblasts [15] . In addition , NF-κB–dependent Fas transcription is down-regulated by the suppressive action of c-Jun and STAT3 in human melanoma-derived cell lines [16] , and NF-κB bound to the Il6 and Il12b promoters is gradually replaced by ATF3 , which interacts with AP-1 and STAT [17] . These interactions play key roles in the proper maintenance and termination of immune responses . However , the precise nature of the positive or negative cross-talk between these transcription factors is still unclear , as is the physiological role of such cross-talk in the innate immune response . To address these issues , we examined positive and negative interactions between transcription factors during the response to lipopolysaccharide/peptidoglycan ( LPS/PGN ) . We found that two transcription factors , dAP-1 and Stat92E , which are activated by LPS/PGN-induced signal transduction pathways , form a repressosome complex together with the Drosophila HMG protein , Dsp1 ( dorsal switch protein ) and histone deacetylase , and this then inhibits transcription of diverse immune effector genes activated by Relish . We also found that mis-regulation of negative cross-talk increased the lethality of bacterial infection in Drosophila , as has been noted in mammals with over-activated NF-κB–mediated immune responses . Therefore , the inhibitory effect of this repressosome complex on NF-κB plays an important role in maintaining properly balanced immune responses and appears to be evolutionarily conserved .
To test for the presence of regulatory cross-talk between the signaling pathways of innate immunity , we examined the involvement of key transcription factors ( Relish , Jra , Stat92E , Mad , and EcR ) in LPS/PGN-induced immune responses in Drosophila SL2 cells ( Figure 1 ) . To this end , we knocked down each transcription factor by RNA interference ( RNAi ) , and examined its effect on the LPS/PGN-induced transcriptional activation of Attacin-A , Puckered , and CG15097 , the known targets of transcription factors Relish , dAP-1 and Stat92E , respectively . LPS/PGN-induced transcriptional activation of Attacin-A , Puckered , and CG15097 was abolished only by depletion of the corresponding transcription factor , and no obvious transcriptional defect was observed as a result of depletion of Mad or EcR ( the Drosophila SMAD and nuclear receptor , respectively ) . Intriguingly , Relish-dependent transcriptional activation of Attacin-A was hyperactivated in the absence of Jra or Stat92E . The repressive effect of Stat92E on Relish-dependent transcription required the activated form of Stat92E , because knock-down of Hopscotch resulted in an increase of LPS/PGN-induced Attacin-A expression comparable to that in the Stat92E-depleted cells ( Figure S1 ) . Therefore , the Relish-dependent transcriptional activation of Attacin-A appears to be down-regulated by activated Stat92E as well as Jra during the innate immune response . To examine whether the activated form of Stat92E exerts its repressive role directly by binding to the promoter of Attacin-A , we examined the upstream regions of the Attacin-A genes of a number of Drosophila species to identify evolutionarily conserved Stat92E and other transcription factor binding motifs ( Figure S2 ) . Sequence alignment revealed several strongly conserved regions: in addition to the core promoter elements ( TATA and initiator motifs ) , we identified a Relish-binding motif ( −140 bp ) , a GATA motif ( −130 bp ) , and a dAP-1–binding motif ( −90 bp ) , along with a highly conserved region ( region Y at −45 bp ) upstream of the TATA box . Intriguingly , this region contains an Relish-binding motif [18] that overlaps with a sequence showing weak homology to the STAT consensus binding sequence [19] in the opposite strand ( Figure 2A ) . To test for binding of these motifs by the corresponding transcription factors , we performed electrophoretic mobility shift assays ( EMSAs ) with a probe spanning region Y , and we compared the results with those obtained with a probe for the distal Relish-binding motif . LPS/PGN treatment of SL2 cells led to strong mobility shifts of both probes , and these were competed out by an excess of cold probe ( unpublished data ) . Because the region Y probe contained both the Relish- and Stat92E-binding motifs , the addition of a specific antibody against one or other of these transcription factors resulted in supershifting only a portion of the shifted bands ( Figure S3 and unpublished data ) . Therefore , we confirmed the identity of the protein ( s ) bound to each probe by repeating the EMSAs after depleting Relish or Stat92E , or both , by RNAi ( Figure 2B ) . The LPS/PGN-induced mobility shift of the distal Relish probe ( Relish1 ) was lost when Relish was depleted but not when Stat92E was depleted , whereas the shift of the region Y probe was only abolished when both Relish and Stat92E were depleted , indicating that both transcription factors bind to their putative binding sites in this probe . This interpretation was confirmed by showing that the LPS/PGN-induced mobility shift was not affected by mutations affecting only the Relish or the Stat92E binding sequence , but was abolished when both binding sequences were mutated ( Figure 2C ) . Moreover the Stat92E mutant probe was not shifted in Relish-depleted extracts , and the Relish2 mutant probe was not shifted in Stat92E-depleted extracts . These results establish that region Y contains genuine Relish- and Stat92E-binding sites . Although the putative GATA-binding sequence in the Attacin-A promoter is also strongly conserved ( Figure S2B ) , Drosophila GATA homologs do not appear to play a major role in LPS/PGN-induced Attacin-A transcription , at least in SL2 cells , since depletion of the Drosophila GATA factor Serpent by RNAi had no discernable effect on LPS/PGN-induced Attacin-A transcription ( unpublished data ) . We also generated luciferase reporters under the control of the mutant versions of the Attacin-A promoter and examined LPS/PGN-induced luciferase activities after transient transfection of the reporters ( Figure 2D ) . Mutation of either of the Relish-binding motifs inhibited transcription from the Attacin-A promoter such that no ( Relish1 mutation ) or only weak ( Relish2 mutation ) luciferase activity was detected . In contrast , mutation of the Stat92E- or dAP-1-binding motifs resulted in at least 3-fold higher luciferase activities than obtained with the wild-type promoter . This result , along with the result from EMSAs , demonstrates that the defects in the binding of Relish , Jra , and Stat92E to their binding motifs in the promoter result in altered Attacin-A transcription . Therefore , both Jra and Stat92E appear to down-regulate genes activated by Relish in response to pathogen-associated molecular patterns . To test whether similar transcription factor binding occurs in a chromosomal context , we examined the recruitment of transcription factors by means of chromatin immunoprecipitation ( ChIP ) assays . These experiments showed that LPS/PGN treatment induced the synthesis and nuclear translocation of Relish , Jra , and Stat92E and their binding to the promoter ( Figure 3 ) . Relish knock-down reduced Relish without affecting the binding of the other transcription factor . On the other hand , depletion of Jra caused the loss not only of its own binding but also of that of Stat92E , and vice versa , indicating that Jra and Stat92E bind synergistically to the Relish target promoter . This co-occupancy of the promoter by Jra and Stat92E may elicit the repressive function of the transcription factors . As shown previously , dHDAC1 is also recruited to the Attacin-A promoter after LPS/PGN-treatment , and this also requires concurrent binding of Stat92E and Jra . Knock-down of EcR or Mad had no effect on recruitment of Relish , Jra , Stat92E , and dHDAC1 ( unpublished data ) . The recruitment of dHDAC1 to the promoter bound by both transcription factors suggested that some factor , such as an HMG protein , is required to mediate the interactions between the transcription factors and histone deacetylase . We therefore examined the requirement for Drosophila HMG proteins ( Dsp1 , HmgD , and HmgZ ) for the Jra- and Stat92E-mediated down-regulation of Attacin-A transcription . Depletion of Dsp1 mimicked the effect of Jra knock-down , whereas knock-down of HmgD or HmgZ had no detectable effect ( Figure 4A ) . In addition , depletion of Dsp1 prevented the LPS/PGN-induced binding of Jra , Stat92E , and dHDAC1 ( Figure 4B ) . Interestingly , ChIP with antibody to Dsp1 revealed that Dsp1 was recruited to the Attacin-A promoter by LPS/PGN treatment , and this was completely dependent on Jra and Stat92E , suggesting that Dsp1 is specifically localized to the Attacin-A promoter region in response to LPS/PGN ( Figure 4C ) . To test this idea , we performed ChIP assays with antibodies against Dsp1 , Relish , and Jra , monitoring the upstream region of Attacin-A from −1 . 2 kb to the coding sequence . The amount of chromatin co-precipitated with anti-Dsp1 , anti-Relish , or anti-Jun antibodies reached a peak in the region 150 base pairs ( bp ) upstream from the transcription initiation site where the canonical Relish and dAP-1 binding motifs are located ( Figure 4D ) . Moreover we found a putative Dsp1 binding site ( GAAAA ) within this region ( Figure S2 ) . Therefore , Dsp1 appears to be required for the interaction between the transcription factors and histone deacetylase on the Attacin-A promoter In order to prove that Jra , Stat92E , Dsp1 , and dHDAC1 form a repressosome complex , we also examined the physical interactions between them . Immunoprecipitation experiments demonstrated that Jra , Stat92E , and Dsp1 , but not Relish , were co-precipitated from LPS/PGN-treated SL2 cells in a dHDAC1-containing complex by antibody against any one of them ( Figure 5A ) . These interactions do not appear to be mediated by DNA , because the addition of a high concentration of ethidium bromide to the extracts did not affect their co-immunoprecipitation ( unpublished data ) . Similar immunoprecipitation experiments with nuclear extracts of non–LPS/PGN-treated SL2 cells brought down only trace amounts of the corresponding transcription factors . We conclude that LPS/PGN treatment induces activation and nuclear transport of Relish , as well as the formation of a complex containing Jra , Stat92E , Dsp1 , and dHDAC1 . The formation of a dHDAC1-containing complex with Stat92E and Jra prompted us to examine the possibility that Jra , Stat92E , or Dsp1 are modified in some way during LPS/PGN signaling . However , we failed to detect any LPS/PGN-induced mobility shift of these factors by two-dimensional electrophoresis followed by Western analysis ( unpublished data ) . To investigate whether an increase in the levels of Jra , Stat92E , and Dsp1 upon LPS/PGN treatment is instead the major determinant of repressosome formation , we set up a system in which Attacin-A transcription was driven by overexpression of the Relish N-terminal domain ( Rel-ΔC ) without the need for LPS/PGN treatment . We found that in this system , ectopic overexpression of Jra and Stat92E down-regulated Attacin-A transcription ( Figure 5B ) , and ChIP with antibody to dHDAC1 revealed that histone deacetylase was only recruited to the Attacin-A promoter when Jra and Stat92E were overexpressed ( Figure 5C ) . In addition , the inhibitory effect of the exogenous Jra and Stat92E was also dependent on the presence of Dsp1 . These results suggest that elevated concentrations of Jra , Stat92E , and Dsp1 lead to the formation of the repressosome . To investigate the relationship between occupation of the Attacin-A promoter by the repressosome complex and by Relish , we examined the binding kinetics of these transcription factors to the promoter ( Figure 6A ) . The ChIP results showed that both Relish and the repressosome complex were recruited to the Attacin-A promoter by 15 min after LPS/PGN treatment . Sustained incubation with LPS/PGN ( 8 h ) resulted in loss of the Relish binding without loss of Jra and Stat92E binding . To examine whether Relish co-occupies the promoter with the repressosome complex during the early stage of LPS/PGN induction , we analyzed the chromatin fragments precipitated with antibody to Relish for the presence of the repressosome complex by means of a second ChIP ( Figure 6A ) . Sequential ChIPs revealed that the Relish-bound Attacin-A promoter was devoid of Jra , Stat92E , and Dsp1 , and that the Jra-associated Attacin-A promoter was devoid of Relish . Therefore , Relish and the Jra/Stat92E/Dsp1 repressosome complex occupy the Attacin-A promoter in a mutually exclusive fashion . These results indicate that Relish is displaced from the Attacin-A promoter by the repressosome complex , and that this results in the termination of transcriptional activation . Based on these observations , we postulate that at the outset of LPS/PGN-induced activation , only processed Relish is available to activate transcription; but as the newly synthesized and translocated Jra , Stat92E , and Dsp1 accumulate inside the nucleus , they form a repressosome complex and displace Relish from the promoter to terminate transcription . The Jra/Stat92E/Dsp1/dHDAC1-mediated down-regulation of Relish-driven transcription does not appear to be limited to the Attacin-A . Among the seven additional antimicrobial peptide ( AMP ) genes ( Attacin-B , Cecropin A1 , Cecropin A2 , Cecropin C , Drosomycin , Drosocin , and Metchnikowin ) tested , four AMP genes ( Attacin-B , Cecropin A1 , Drosocin , and Metchnikowin ) showed an identical pattern of regulation to Attacin-A ( Figure 6B ) . Although the suppressive effect on the expression of other AMP genes was relatively small , the repressosome complex appears to down-regulate their expression in some degree . Considering that Drosomycin is activated mainly by Dif rather than Relish , this result suggests that the repressosome activity may vary depending on the types of NF-κB homologs present at the target gene promoters . Consistently , we found that both Jra and Stat92E binding sites as well as the NF-κB binding sequence exist on the promoter regions of these antimicrobial peptide genes ( Figure S4 ) . Therefore , the competitive binding of the Jra/Stat92E/Dsp1-containing repressosome complex to the Relish-binding site appears to be responsible for the termination of Relish-dependent gene transcription . In order to confirm the physiological relevance of the Jra/Stat92E/Dsp1 repressosome complex , we tested for defects in transcription of the Relish-dependent antimicrobial peptide genes during bacterial infection in mutant flies in which Stat92E , Jra , or Dsp1 levels were significantly reduced . To this end , we generated transgenic flies in which Stat92E RNAi was induced conditionally under the control of either daughterless or the minimal heat shock promoter . These mutant flies showed no obvious developmental defects; however quantitative reverse-transcriptase ( RT ) -PCR and Western blot analysis revealed that upon induction of the RNAi , the level of Stat92E declined significantly in the mutant flies ( Figure S5A–S5D ) . The heterozygous Jra mutant flies and the homozygous Relish or Dsp1 mutant flies were viable , and Western blot analysis with the corresponding antibodies revealed a significant reduction ( Jra ) or almost complete loss ( Relish and Dsp1 ) of the corresponding proteins in the mutant flies ( Figure S5E and S5F ) . In the absence of bacterial infection , all the mutant and wild-type flies appeared to be normal and did not make the Attacin-A transcript ( unpublished data ) . Upon bacterial infection , Attacin-A transcription was induced more than 3–5-fold in the wild-type flies ( w1118 ) but not in the Relish homozygotes ( Rel/Rel ) . Consistent with the in vitro result , the Stat92E RNAi mutants ( UAS-shStat92E/+; da-Gal4/+; and UAS-hs-shStat92E/UAS-hs-shStat92E ) , the Jra heterozygotes ( Jra1A109/CyO , and the Dsp1 homozygotes ( Dsp1[EP355]/Y ) contained several-fold higher levels of Attacin-A transcripts than bacterially infected wild type flies ( Figure 7A and Figure S6 ) , and a similar result was obtained from an analysis of Drosocin transcripts ( unpublished data ) . This hyperactivation of Attacin-A transcription in the mutant flies was rescued by introducing a copy of the Relish mutant allele , indicating that Stat92E , Jra , and Dsp1 are required for the down-regulation of Relish during infection . Reflecting this result , the Relish mutants were defective in clearing the infecting bacteria , whereas the Stat92E , Jra , and Dsp1 mutants had even higher bacterial clearance activities than wild type flies did ( Figure 7B–7G ) . Continuous activation of NF-κB after clearance of infected pathogens in mammals usually causes damage and , in severe cases , septic injury [20 , 21] . Therefore , down-regulation of NF-κB target genes by a repressosome complex should play an important role in maintaining a proper balance between immune responses . To determine whether mis-regulated expression of Relish results in an excessive immune response that may be harmful to Drosophila , we examined the survival rates of these mutant flies after bacterial infection ( Figure 8 ) . Under conditions of bacterial infection that enabled most wild-type flies to survive but killed most Relish homozygotes within 4 d , the Stat92E RNAi mutant flies , the Jra heterozygotes , and the Dsp1 homozygotes all displayed reduced survival comparable to Relish heterozygotes ( 50% survived beyond 4 d ) . The increased mortality of the mutants appears to result from the immune response of the flies against the bacterial infection . No obvious survival defect was observed when phosphate-buffered saline ( PBS ) was injected instead of bacteria ( Figure S7 ) . Most importantly , combining Relish heterozygosity with mutations of Stat92E , Jra , or Dsp1 overcame the lethal consequence of bacterial infection rather than aggravating them . Hence , the increased vulnerability to infection of the Stat92E , Jra , and Dsp1 mutants is due to excessive activation of Relish target genes rather than to reduced activation of Stat92E , Jra , or Dsp1 target genes . Therefore , we conclude that down-regulation of NF-κB by the Jra/Stat92E/Dsp1-containing repressosome complex also occurs under physiological conditions and plays an important physiological role .
An excessive inflammatory response is harmful to the host; it can even be fatal [22–24] and must be prevented by negative-feedback mechanisms . Several such mechanisms , which mainly function by reducing NF-κB activation , have been identified [21–25] , but little is known of their underlying mechanisms . Our demonstration that AP-1 and STAT are directly involved in down-regulating NF-κB illustrates the context-dependent use of transcription factors to achieve fine control of gene expression . Ligand-induced conformational changes have been implicated in switching nuclear receptors from activators to repressors; however , it is not clear what makes other types of transcription factors act sometimes as activators and other times as repressors . We have shown above that a specific HMG protein functions as a core element nucleating the assembly of a repressor complex of AP-1 , STAT , and HDAC1 . The mammalian homolog of Dsp1 , HMGB1 , binds to a negative regulatory element adjacent to the NF-κB binding site in the interferon-beta enhancer [26] , and Dsp1 is a co-repressor that converts Dorsal from activator to repressor by binding to G ( A ) motifs adjacent to Dorsal binding sites [27–-29] . Dsp1 is also required for correct expression of homeotic genes , and to recruit polycomb group proteins to polycomb and trithorax response elements [30] . Evidently , the role of Dsp1 is to facilitate the formation of repressor complexes on NF-κB–dependent promoters . It is noteworthy that a different HMG protein , HMG-Y/I , forms repressosomes with the transcription factors NF-Y and BTEB-1 that represses transcription of a growth hormone receptor gene by recruiting the histone deacetylase complex [31] . This suggests that different HMG proteins associate with different transcription factors to regulate particular groups of genes [32 , 33] . Thus , the role of Dsp1 in forming a repressosome complex with AP-1 and STAT that inhibits specific types of NF-κB target promoters is one instance of an evolutionarily conserved mechanism . It may be a key NF-κB pathway regulatory mechanism , assuring an appropriate immune response . It is well established that JNK and JAK/STAT signaling are involved in innate Drosophila immune responses [11 , 12 , 34 , 35] . Because the JNK pathway is primarily involved in cellular processes such as phagocytosis , wound healing , melanization , and defense against extracellular pathogens [36–39] , our observation of increased lethality of the Jra mutant upon bacterial infection is unlikely to be due exclusively to malfunction of the repressosome complex . Nevertheless , the repressosome complex may well be an important component of Drosophila immune responses , since many Stat92E and Dsp1 mutant flies die upon bacterial infection , and this enhanced lethality was reversed by reducing Relish copy number . We propose that JNK participates in cellular immune responses and also forms a repressosome complex with Stat92E , Dsp1 , and dHDAC1 that restricts the production of antimicrobial peptides . Recently , Delaney et al . [40] have claimed that JNK is required for the synthesis of antimicrobial peptide genes upon bacterial infection of Drosophila . This claim conflicts with our results and also with a report that JNK activity is reduced by activation of NF-κB [11] . In the latter work [11] , it was shown that expression of Attacin-A was enhanced by knock-down of JNK . The discrepancy between Delaney's result and ours may be due to differences between the mutants or methods used in the two studies . First , the extent of knock-down of gene activities differed: in our study , we reduced the expression of various genes ( Jra , dJNK , and Stat92E ) by conditional knock-down or by reducing copy number ( Jra ) , whereas Delaney et al . clonally deleted dJNK and Jra and overexpressed a JNK inhibitor ( Puc ) , and these procedures may have affected an essential function of the JNK pathway required for Relish-mediated transcriptional activation . Another possible explanation of the discrepancy derives from the use of different Jra alleles in the two studies . Unlike flies carrying the Jra1A109/CyO allele used by us , the heterozygous mutant flies carrying the Jra1/CyO allele used by Delaney et al . did not show any defect in Relish-dependent transcriptional activation of AMP genes even when they were examined under our experimental conditions . According to FlyBase , the truncated protein is stopped at the 177th amino acid in the Jra1/CyO allele and at the 72nd amino acid in the Jra1A109/CyO allele . The reason of the different lesions between two alleles needs further investigation . With regard to the role of the JAK/STAT pathway in innate immunity in Drosophila , JAK/STAT pathway mutants have only been reported to have defects in antiviral responses and hemocyte function [34 , 35] . Though there have been efforts to identify the role of JAK/STAT in the innate immune response by genome-wide RNAi screening [41 , 42] , the basis of the precise regulation of immune responses by this essential transcription factor remains unclear . Ours is the first evidence , to our knowledge , that the JAK/STAT pathway regulates the synthesis of antimicrobial peptide genes in Drosophila . Intriguingly , Agaisse and co-workers [34] found that expression of Drosomycin was enhanced upon bacterial infection in a loss-of- function mutant of Hopscotch . This finding is supported by our observation that functional Stat92E negatively regulates the synthesis of Relish-dependent antimicrobial peptides by forming a complex with Jra and Dsp1 .
Double-stranded RNA ( dsRNA ) was prepared as described previously [12] . Drosophila SL2 cells ( 1 × 106; CRL-1963 , American Type Culture Collection ) were washed with serum-free Drosophila medium ( Welgene; http://www . welgene . com ) and treated with specific dsRNAs ( 20 μg ) for 3 h . Serum was added back to the culture medium to 10% final concentration and the cells were incubated for an additional 72 h . The primers used for making the dsRNAs are listed in Table S1 . The efficiency of knock-down in each RNAi experiment was confirmed by RT-PCR or Western blotting . Total RNA was isolated from SL2 cells with Trizol reagent ( Invitrogen; http://www . invitrogen . com ) and used for cDNA synthesis with Superscript II reverse transcriptase ( Invitrogen ) . The abundance of transcripts in each cDNA sample was measured by real-time PCR using a Lightcycler ( Roche; http://www . roche . com ) . The PCR reactions contained 1 × SYBR Green mix ( Applied Biosystems; http://www . appliedbiosystems . com ) or 1 × Taqman probe ( Applied Biosystems ) , 10 pmol of forward and reverse primers , and cDNA corresponding to 0 . 1 μg of total RNA . The reactions were subjected to 40 cycles of PCR amplification ( 95 °C for 10 s , 55 °C for 20 s , and 72 °C for 30 s ) , and analyzed with Lightcycler Software 4 ( Roche ) . All results were normalized to the level of RpL32 mRNA in each sample . The real-time PCR analyses were repeated at least three times independently , and the means and standard deviations were calculated . The primers used in the real time PCR analyses are listed in Table S1 . ChIP experiments were performed as described previously [12] . For most of the ChIP experiments in this study , 500–1 , 000-bp-length chromatin fragments were used except for the ChIP used to scan the Attacin-A promoter region , in which 200–300-bp fragments were used ( Figure 4D ) . The antibodies were either raised in rats using recombinant Relish [from 270 amino acids ( aa ) to 540 aa] , Stat92E ( from 1 aa to 267 aa ) , Dsp1 ( from 1 aa to 393 aa ) , and dHDAC1 ( from 114 aa to 504 aa ) , or purchased from Santa Cruz Biotechnology ( anti-Jun rabbit antibody; http://www . scbt . com ) . Dissociated DNA fragments were recovered with a QIAquick purification kit ( Qiagen; http://www . qiagen . com ) and subjected to 30 cycles of PCR ( 94 °C for 1 min , 55 °C for 1 min , and 72 °C for 1 min , followed by one cycle of 72 °C for 5 min ) with specific primers . For ChIP-ChIP experiments , the complexes immunoprecipitated with the first antibody were eluted from the antibody beads by incubation with 10 mM dithiothreitol ( DTT ) at 37 °C for 30 min , diluted 1:50 in buffer ( 1% Triton X-100 , 2 mM ethylene diamine tetraacetic acid [EDTA] , 150 mM NaCl , 20 mM Tris , pH 8 . 0 ) , and immunoprecipitated with the second antibody [43] . All subsequent procedures were essentially as for the primary ChIPs . The precipitated chromatin fragments were quantitated by real-time PCR with a Lightcycler with the primers listed in Table S1 . The amount of precipitated chromatin measured in each PCR was normalized with the amount of chromatin present in the input of each immunoprecipitation . All experiments were repeated at least three times . Whole-cell extracts of SL2 cells were prepared in lysis buffer ( 20 mM Tris , pH 7 . 6 , 150 mM NaCl , 10% glycerol , 1% Triton X-100 , 25 mM β-glycerophosphate , 1 mM DTT , 2 mM EDTA , and protease inhibitors ) . Aliquots of the extracts ( 30 μg ) were transferred to nitrocellulose membranes after resolving them by SDS-PAGE and probed with anti-Relish ( 1:1000 ) , anti-Stat92E ( 1:1000 ) , anti-Dsp1 ( 1:500 ) , anti-dHDAC1 ( 1:1000 ) , anti-Jun ( 1:1000 ) , and anti-γ tubulin ( 1:1000 ) . The antibodies were diluted in TBST ( 40 mM Tris , pH 7 . 4 , 200 mM NaCl , 0 . 1% Tween20 ) by the factors shown in the parentheses , and the complexes were visualized with an ECL plus Detection System ( Amersham Biosciences; http://www . amersham . com ) after reaction with appropriate peroxidase-conjugated secondary antibody ( 1:10000; Sigma; http://www . sigmaaldrich . com ) . For co-immunoprecipitation , nuclear extracts were prepared from SL2 cells incubated with or without 10 μg/ml LPS/PGN ( Sigma ) as described previously [44] . After pre-clearing with Protein G beads ( Invitrogen ) , antibody ( 5 μg ) was added and the mixtures were incubated at 4 °C for 1 h followed by the addition of 50 μl of a 50% slurry of Protein G beads . Complexes were eluted with SDS loading buffer ( 50 mM Tris , pH . 6 . 8 , 2% SDS , 0 . 1% bromophenol blue , 10% glycerol and 100 mM DTT ) , loaded on 10% SDS-PAGE gels and analyzed by Western blotting . The Attacin-A sequences of five Drosophila species were obtained from the UCSC genome database and their promoter regions ( from −2 , 000 bp to +1000 bp around the transcription initiation site ) were retrieved . Putative binding sequences for each transcription factor were identified using the Transfac Professional 7 . 3 program ( Biobase; http://www . biobase-international . com/ ) , and sequences were aligned with vector NTI ( Informax; http://www . informax . com ) . The default alignment parameters ( gap opening penalty: 15; gap extension penalty: 6 . 66; gap separation penalty range: 8; score matrix: swgapdnamt ) were used for alignment . The wild-type pGL3-AttA plasmid was constructed by cloning the PCR-amplified sequence −2 , 400 to +32 of the Attacin-A promoter into XhoI/HindIII-digested pGL3 Basic vector ( Promega; http://www . promega . com ) . A series of plasmids containing various mutations of the Attacin-A promoter were constructed from the pGL3-AttA wild-type plasmid . To generate pGL3-AttA Relish1m with a mutant Relish-binding site 1 , we performed site-directed mutagenesis using a QuikChange Site-Directed Mutagenesis Kit ( Stratagene; http://www . stratagene . com ) . To construct other mutant promoters , we introduced a suitable restriction site to mutate the transcription factor binding sequence . To this end we generated both upstream and downstream fragments of the transcription factor binding sites by PCR using specific primers in which the transcription factor binding sequence was replaced by a restriction enzyme recognition sequence . These fragments were digested with restriction enzyme and ligated to pGL3 Basic vector to generate a luciferase reporter construct under the control of the mutant promoter . pGL3-AttA dAP-1m , pGL3-AttA Relish2m-Stat92Em , pGL3-AttA Stat92Em and pGL3-AttA Relish2m reporters were constructed by replacing the relevant target sequences with EcoRI , SpeI , BglII , and SpeI sites , respectively . All the mutations introduced were confirmed by sequencing . The primers for these constructs are listed in Table S1 . Promoter constructs ( 100 ng ) were transfected into SL2 cells ( 1 × 106 ) using Cellfectin Reagent ( Invitrogen ) . After 2 d , the transfected cells were treated with LPS/PGN ( 10 μg/ml ) for various times and lysed with lysis solution ( Tropix; http://www . appliedbiosystems . com/tropix ) . Firefly luciferase and β-galactosidase activities were analyzed with the dual-light luciferase assay system ( Tropix ) on an Infinite 200 instrument ( Tecan; http://www . tecan . com ) . To normalize transfection efficiencies , a CMV-lacZ construct ( 20 ng ) was co-transfected with each mutant reporter construct . Each reaction was assayed in duplicate , and the reporter analysis was repeated a minimum of three times independently . EMSA experiments were performed as described previously [12] . To prepare SL2 nuclear extracts depleted for a given transcription factor , the cells ( 1 × 106 ) were pre-treated with dsRNA for Relish , Stat92E , or both; 20 μg each [or Luciferase as a control] . After three days , the cells ( 3 × 107 ) were lysed with 100 μl of NE buffer #1 ( 10 mM HEPES , pH 7 . 9 , 10 mM KCl , 0 . 1 mM EDTA , 0 . 1 mM ethylene glycol tetra acetic acid [EGTA] , 1 mM DTT , and protease inhibitors ) for 10 min at 4 °C; 6 μl of 10% IGEPAL CA-630 was added and the suspension was centrifuged at 12 , 000g for 15 min to pellet nuclei . The supernatant was removed , and the pellet was resuspended in 50 μl of NE buffer #2 ( 20 mM HEPES , pH 7 . 9 , 0 . 4 M NaCl , 1 mM EDTA , 1 mM EGTA , 10% glycerol , 1 mM DTT , and protease inhibitors ) . After further centrifugation at 12 , 000g for 20 min , the supernatant was obtained . To make the probes , 26-bp oligonucleotides containing the transcription binding sites were labeled with [γ-32P] ATP ( Amersham Bioscience ) . The sequences of the oligonucleotides used are listed in Table S1 . 10-μg samples of nuclear extract in a final volume of 20 μl EMSA binding buffer ( 100 mM NaCl , 15 mM HEPES , pH 7 . 5 , 0 . 75 mM EDTA , 1 mM DTT , 1 μg poly dI-dC , and 8% glycerol ) were chilled on ice for 15 min and then incubated for 30 min at room temperature with labeled probe . After addition of sucrose loading solution ( 40% sucrose , 0 . 25% bromophenol blue , and 0 . 25% xylene cyanol ) , the samples were loaded onto a 4% native polyacrylamide gel and run at 200 V for 80 min . The dried gel was exposed on an image plate and analyzed with a Cyclon Phosphor Screen ( Packard; http://www . perkinelmer . com ) . The expression construct for the N-terminal half of Relish was obtained from J . M . Park ( Massachusetts General Hospital , United States ) . The full length Jra and Stat92E coding regions were cloned into the SRT-tagged SL2 cell expression vector , pSRT-MK33 [45] . The expression constructs ( 2 μg ) were transfected into SL2 cells ( 3 × 106 ) , pre-treated with the corresponding dsRNA ( Dsp1 and Luciferase [control]; 20 μg each ) , and incubated for 2 d , then incubated with 0 . 7mM CuSO4 for 6 h to induce the production of recombinant proteins . The level of recombinant protein expression was analyzed by Western blotting with SRT monoclonal antibody ( Daeil; http://www . daeil21 . com/ ) . Transgenic Stat92E RNAi fly lines were obtained using an inducible RNAi method [46] . To construct the Stat92E RNAi element , two Stat92E fragments ( one from 287–1 , 418 nucleotides ( nt ) including an intron and the other from 986–487 nt in the opposite direction ) were amplified with the specific primers listed in Table S1 and ligated together into EcoRI/NotI-digested pBluescript vector ( Stratagene ) to yield pBS-shSTATi . The EcoRI/NotI fragment of pBS-shSTATi was inserted into EcoRI/NotI-digested pUAST P element transformation vector to yield pUAST-shSTATi . Germline transformation of Drosophila embryos with pUAST-shSTATi and identification of the P-element–integrated chromosome in each transgenic line was carried out as described previously [47] . A transgenic line with pUAST-shSTATi on the X chromosome was named UAS-shStat92E , and used in the analysis . To activate transcription of the hairpin-encoding transgene , flies carrying a copy of both UAS-shStat92E and a da-Gal4 driver were generated by crossing homozygous UAS-shStat92E female flies with homozygous male flies carrying the da-Gal4 driver ( which induces strong and ubiquitous expression of Gal4 protein ) on the third chromosome ( gift from B . Lemaitre , Centre National de la Recherche Scientifique , France ) . We also tried to generate Stat92E knocked-down transgenic flies controlled by the hs-Gal4 driver ( UAS-shStat92E/+; hs-Gal4/+ ) . However , the shStat92E RNA expression in this mutant was too strong , and most of the flies died before infection , probably because of defects caused by the lack of Stat92E-dependent responses . Because the minimal heat shock promoter used in the UAS-shStat92E flies can be activated by heat shock treatment as shown in Figure S5 , we exposed the UAS-shStat92E flies to heat shock ( 37 °C ) three times a day to induce the Stat92E knock-down condition . To examine the effect of Stat92E knock-down in Relish heterozygotes , a RelE20 homozygote was crossed with UAS-shStat92E homozygous flies to generate UAS-shStat92E/+; Rel/+ double heterozygous progeny . UAS-shStat92E/+; Rel/+ double heterozygous females were crossed with da-Gal4 homozygous males to generate UAS-shStat92E/+; Rel/da-Gal4 triple heterozygous progeny or exposed to heat shock to generate UAS-hs-shStat92E/+; Rel/+ mutants . Knock-down of Stat92E expression in the transgenic flies was confirmed by real-time PCR analysis and immunoblotting with Stat92E antibody . Jra mutants flies ( Jra1A109/CyO ) were obtained from D . Bohmann ( University of Rochester Medical Center , United States ) . Because the Jra mutation was homozygous-lethal , Jra heterozygotes ( Jra1A109/CyO ) were crossed with Rel homozygotes ( Rel/Rel ) to generate Jra1A109/+; Rel/+ flies . Dsp1 mutant flies ( Dsp1[EP355]/Dsp1[EP355] ) were obtained from the Bloomington stock center . A Dsp1 homozygous female ( Dsp1[EP355]/Dsp1[EP355] ) was crossed with a Rel homozygous male ( Rel/Rel ) to generate Dsp1[EP355]/Y; Rel/+ male flies . Knock-down of Jra or Dsp1 expression in the transgenic flies was confirmed by Western blotting with Jun or Dsp1 antibody . The systemic response was triggered by pricking adult flies in the thorax with a thin tungsten needle dipped in a concentrated culture of Escherichia coli , or by injecting an E . coli suspension into adult flies with a pulled glass needle using a Picospritzer III injector ( Parker Hannifin; http://www . parker . com ) . The glass needle was placed on the ventrolateral surface of the anterior abdomen as previously described [48] . For infection with bacteria , 3- to 4-d-old adult flies ( 15 males and 15 females ) were anesthetized with CO2 and injected with a concentrated E . coli culture resuspended ( 1–5 nl; optical density = 200 ) in phosphate-buffered saline ( 137 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , 2 mM KH2PO4 ) . To study survival after infection , the injected flies were kept at 25 and the number of surviving flies was scored at 1-d intervals . Survival curves were plotted as Kaplan-Meier plots . Statistical significance was tested using log-rank analysis with MedCare software ( http://www . medcare . be ) . To study bacterial clearance , flies after infection were homogenized in 100 μl of LB containing 1% Triton X-100 with a small pestle , and the homogenate was assayed by plating on LB-agar . These experiments were repeated at least four times . | The immune response is designed to target foreign infectious elements , not self , but it can become destructive when it fails to discriminate self from nonself . Therefore , it is important to restrain the magnitude and duration of the immune response by several mechanisms including receptor down-regulation and inhibitor synthesis . Here , focusing on the immune system of Drosophila , we present a mechanism of control that relies on the transcription factors AP-1 and STAT to prevent the excessive activation of the NF-κB–mediated immune response . Thus , AP-1 and STAT , renowned for their role in activating the NF-κB–mediated immune response , appear also to participate in its attenuation . In their role as negative regulators , AP-1 and STAT form a complex with HMG protein and HDAC . This complex is then recruited to the promoter regions of NF-κB target genes , causing the chromatin structure near the NF-κB target genes to contract and the expression of NF-κB target genes to shut down . Mis-regulation of this negative-feedback process , we found , increased the lethality of bacterial infection in Drosophila . A similar scenario has been noted in mammals with over-activated NF-κB–mediated immune responses , which has been implicated in autoimmune disease . Thus , feedback inhibition of NF-κB appears to be evolutionarily conserved to maintain properly balanced immune responses . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"drosophila",
"immunology"
] | 2007 | Down-Regulation of NF-κB Target Genes by the AP-1 and STAT Complex during the Innate Immune Response in Drosophila |
The envelope ( E ) protein from coronaviruses is a small polypeptide that contains at least one α-helical transmembrane domain . Absence , or inactivation , of E protein results in attenuated viruses , due to alterations in either virion morphology or tropism . Apart from its morphogenetic properties , protein E has been reported to have membrane permeabilizing activity . Further , the drug hexamethylene amiloride ( HMA ) , but not amiloride , inhibited in vitro ion channel activity of some synthetic coronavirus E proteins , and also viral replication . We have previously shown for the coronavirus species responsible for severe acute respiratory syndrome ( SARS-CoV ) that the transmembrane domain of E protein ( ETM ) forms pentameric α-helical bundles that are likely responsible for the observed channel activity . Herein , using solution NMR in dodecylphosphatidylcholine micelles and energy minimization , we have obtained a model of this channel which features regular α-helices that form a pentameric left-handed parallel bundle . The drug HMA was found to bind inside the lumen of the channel , at both the C-terminal and the N-terminal openings , and , in contrast to amiloride , induced additional chemical shifts in ETM . Full length SARS-CoV E displayed channel activity when transiently expressed in human embryonic kidney 293 ( HEK-293 ) cells in a whole-cell patch clamp set-up . This activity was significantly reduced by hexamethylene amiloride ( HMA ) , but not by amiloride . The channel structure presented herein provides a possible rationale for inhibition , and a platform for future structure-based drug design of this potential pharmacological target .
Coronaviruses ( family Coronaviridae , genus Coronavirus [1] ) are enveloped viruses that cause common colds in humans and a variety of lethal diseases in birds and mammals [2]–[4] . The virus species in the genus Coronavirus have been organized into 3 groups , using genetic and antigenic criteria [5] . Group 1 is subdivided into two groups , 1a and 1b . Group 1a includes the porcine Transmissible gastroenteritis virus ( TGEV ) , whereas group 1b includes Human coronaviruses 229E ( HCoV-229E ) or NL63 ( HCoV-NL63 ) . Group 2 is also subdivided in groups 2a , e . g . , Murine hepatitis virus ( MHV ) and Human coronavirus OC43 ( HCoV-OC43 ) and 2b , e . g . , the virus responsible for the severe acute respiratory syndrome ( SARS-CoV ) [6] , [7] . Group 3 includes the avian Infectious bronchitis virus ( IBV ) and the turkey coronavirus ( TCoV ) . SARS-CoV produced a near pandemic in 2003 [8] , with 8 , 096 infected cases and 774 deaths worldwide ( http://www . who . int/csr/sarsarchive/2003_05_07a/en/ ) . SARS-CoV was enzootic in an unknown animal or bird species , probably a bat [9] , before suddenly emerging as a virulent virus in humans . A similar crossing of the animal-human species barrier is thought to have occurred between the bovine coronavirus ( BCoV ) and human coronavirus OC43 ( HCoV-OC43 ) more than 100 years ago [10] . Such coronavirus interspecies jumps , from animal hosts to humans , are likely to reoccur in the future . There is therefore an urgent need to know more about the coronavirus life cycle , and about new ways to battle infection . Protective efficacy of candidate vaccines against coronaviruses in humans has been mainly studied in animals so far , and only few vaccines have entered Phase 1 human trials [11] . Other compounds [12]–[17] have shown activity against SARS-CoV and HCoV-229E , but there is no data from animal studies or clinical trials [18] . Studies of antiviral therapy against coronaviruses other than SARS-CoV have been scarce; in vitro data show that several chemicals may have inhibitory activities on HCoV-NL63 and HCoV-229E [19] , [20] , but there have not been clinical trials on therapy of infections caused by human coronaviruses HCoV-OC43 , HCoV-229E , HCoV-NL63 and HCoV-HKU1 . All coronaviruses express the envelope ( E ) protein , a typically short polypeptide that in SARS-CoV is 76 amino acids long , and which contains at least one α-helical transmembrane domain ( ETM ) . In SARS-CoV E the transmembrane domain spans ∼25 residues [21] , approximately from residue 10 to 35 . Coronavirus E proteins are incorporated into the virion lipidic envelope , along with the spike protein ( S ) and the membrane protein ( M ) . While the S protein is involved in fusion with host membranes during entry into cells , and the M protein is important in envelope formation and budding , E protein is not essential for in vitro and in vivo coronavirus replication . However , its absence results in an attenuated virus , as shown for SARS-CoV [22] . Recently , using a transgenic mouse model expressing the SARS-CoV receptor human angiotensin converting enzyme-2 ( hACE-2 ) , SARS coronavirus lacking gene E was shown to be attenuated and , in contrast to the wild type virus , did not grow in the central nervous system [23] . In other coronaviruses , E protein affects viral morphogenesis , i . e . , virus-like particle ( VLP ) formation and release [24]–[29] . Indeed , mutations in the extramembrane domain of E protein impaired viral assembly and maturation in MHV [30] . In TGEV , the absence of E protein resulted in a blockade of virus trafficking in the secretory pathway and prevention of virus maturation [31] , [32] . In addition to the aforementioned roles of E protein in morphogenesis and tropism , enhanced membrane permeability has been observed in bacterial and mammalian cells expressing MHV E [33] or SARS-CoV E [34] . It has also been reported that synthetic E proteins of SARS-CoV , HCoV-229E , MHV , and IBV , have in vitro cation-selective ion channel activity in planar lipid bilayers , and this activity has been shown to be localized at the transmembrane domain [35]–[37] . It was also shown that the drug hexamethylene amiloride ( HMA ) , but not amiloride , inhibited in vitro conductance of synthetic MHV E and HCoV-229E E , and decreased viral replication of MHV and HCoV-229E in infected cells [36] . To determine if this channel activity is biologically relevant , this function must be associated to a structural organization compatible with an ion channel , together with electrophysiological studies performed using the complete polypeptide . Lastly , a correlation between inhibition and a molecular description of drug-channel interaction must be obtained . The data currently available , however , ( see above ) was obtained using synthetic transmembrane peptides or unpurified synthetic E proteins in non-physiological environments [35]–[37] , or using qualitative permeability assays [33] , [34] , and the target of HMA was not unequivocally determined [36] . The fact that SARS-CoV ETM forms only pentamers in dodecylphosphocholine ( DPC ) and perfluorooctanoic ( PFO ) micelles [38] , strongly suggests that the ion channel activity of coronavirus E proteins is caused by a pentameric ion channel . Therefore , in the present work our aim was ( i ) to use NMR to determine the structure of the pentameric oligomer formed by a selectively labeled SARS-CoV ETM ( residues 8 to 38 ) when reconstituted in DPC micelles , ( ii ) to characterize the interaction of HMA or amiloride with this channel , and ( iii ) to test if this data is still relevant in a more physiological environment , using patch clamped mammalian cells expressing full length SARS-CoV E . The structural model described for this channel provides a valuable insight into coronavirus envelope ion channel activity , ion selectivity and channel inhibition , and could serve as a platform for the development of novel anti-viral drugs .
The 3D structure of the pentameric channel formed by the transmembrane domain of SARS-CoV E ( ETM ) was reconstructed in several stages ( Fig . S1 , A–C ) . In a first stage , the structure of the ETM monomer was calculated using the constraints derived from 492 NOEs . For a set of 20 ETM monomeric conformers , the backbone root-mean-square deviation ( RMSD ) was less than 1 Å , or 1 . 5 Å after including side chain heavy atoms ( see statistics in Table S1 ) . ETM forms a continuous α-helix encompassing all residues ( Fig . 1 , A–C ) , including both N- and C-termini , showing no signs of terminal fraying [39] . Similar results were obtained in the presence of the drugs HMA and amantadine ( AMT ) ( Fig . S2 ) . The latter drug was shown to inhibit in vitro channel activity of a transmembrane domain of SARS-CoV ETM flanked by two N- and C-terminal lysines [37] . In a second stage , a representative ETM conformer was selected , and threaded through the pentameric scaffold of ETM [38] , [40] while monitoring inter-monomer constraints; out of possible 9 inter-monomer constraints ( Fig . 2 ) , only 5 were finally used ( Table S2 ) . NOEs were added sequentially , and upon fulfillment of the NOE , the next NOE was added . Ambiguity due to overlap of resonances from 1Hβ2 of L19 and 1Hβ2/1Hγ of L21 was resolved by molecular dynamics ( MD ) and energy minimization to adjust side chain orientations of residues forming the inter-helical interface . To validate independently our reconstructed pentameric ETM model , the orientation of the ETM helices relative to the DPC molecules in the micelle was determined using “dipolar waves” [41] , i . e . , oscillations in the longitudinal relaxation of protons due to the periodically variable proximity of ETM 1HN to 16-DSA , a hydrophobic paramagnetic probe confined to the DPC environment . The observed paramagnetic relaxation enhancement ( PRE ) of the six isotopically labeled residues in ETM ( Fig . 3A ) was compared with the PRE calculated from our model according to Protocol S1 ( and see Fig . S3 ) . The good fit between observed and expected values validates the proposed orientation of the ETM helices in the α-helical bundle . This orientation was further confirmed by the observed broadening of the NOESY crosspeaks from aromatic side-chains of F20 , F23 and F26 to aliphatic protons of DPC after addition of 3 mM 16-DSA ( not shown ) . Cross-peaks from ETM N- and C-terminal residues , E8-T11 and T35-R38 , remained unaffected , indicating that these residues are exposed to the aqueous environment . Consistent with this , we observed broadening of NOESY cross-peaks from E8-L12 and A36-R38 when 1 . 5 mM of the water soluble paramagnetic probe gadodiamide was added to a fresh sample ( not shown ) . Residual dipole couplings ( RDCs ) were also measured ( Fig . 3B ) using two different polyacrylamide concentrations and methods of compression . A 4% gel was subjected to axial compression ( its lower density allows the application of greater compressive forces ) while an 8% gel was subjected to radial compression using a gel press assembly . In both RDC measurements , a sinusoidal wave of residue periodicity of ∼3 . 6 could be observed from residues 19 to 25 , consistent with α-helical periodicity . The RDC of residue L18 could not be fit to this periodicity , due to either deviation from ideal α-helical geometry or to conformational dynamics . The RDCs were used to determine the alignment tensors of the helix , where one of the tensors coincided with the axis of symmetry of the helical bundle , consistent with the helix forming part of an oligomeric complex , as shown previously by other techniques [38] . Thus , to summarize , the present pentameric α-helical bundle model was built using ( i ) NOE constraints , ( ii ) paramagnetic relaxation data , ( iii ) the obtained alignment tensor/axis of symmetry from RDCs , and ( iv ) the known oligomeric size of the ETM α-helical bundle . To gain a further insight on the compactness of the channel structure , we monitored the deviation of the observed chemical shifts from those expected in a random coil structure . The periodicity in these chemical shifts was analyzed using wavelets ( Fig . S4 ) . For residues 8–18 the periodicity was 2 . 8 residues per cycle , close to that of a 310 helix ( 3 residues per turn ) , for residues 19–30 the periodicity was 6 . 2 , and for residues 32–38 the periodicity was 3 . 8 residues per cycle , i . e . , close to that of a canonical α-helix . We interpret the low periodicity in the central part of the α-helix as due to a tighter packing of the oligomer , i . e . , lumenally oriented ETM residues are expected to experience a less hydrophilic environment in this region than in the less compact ends of ETM , leading to a more uniform hydrophobicity around the helix . The lumen of the pentameric ETM assembly ( Fig . 4A ) adopts a distinct hour-glass shape . The polar side chains of N15 are oriented towards the lumen and , from the MD simulations , they form a ring with an inner diameter of about 4–5 Å ( Fig . 4B , C ) . The hydrophobic side chains of L18 and A22 line a more spacious region where the diameter reaches ∼7 . 3 Å . The most constricted part is located between residues V25 and L28 with diameters of 2 . 0 and 2 . 3 Å , respectively ( Fig . 4B , C ) . It has been reported that the drug HMA , but not amiloride , inhibited in vitro conductance of synthetic MHV E and HCoV-229E E [36] , which are close homologs to SARS-CoV E . Therefore , we tested the effects of both drugs on the ETM channel . When ETM in DPC micelles was exposed to HMA , changes in 1HN chemical shift were observed throughout the peptide , with most affected ETM amide protons clustering at both ends of ETM , L19 exhibiting the largest chemical shift ( Fig . 5A ) . The NOEs observed between HMA and ETM ( Fig . S5 ) suggest the presence of two binding sites , one near R38 and another near N15 . This figure also shows that a protonated form of HMA at nitrogen-5 is bound to the channel . This form may be stabilized by hydrogen bonding to the side-chain carbonyl of N15 and the guanidinium moiety of R38 , resulting in an observable 1HN5 signal at 10 . 7 ppm ( Table S3 ) . In the absence of ETM , this HMA resonance was only observed when the pH was lower than 3 . 5 , indicating a possible role of ETM in the stabilization of this HMA protonated state . The relative intensities of the cross-peaks assigned to HMA protons indicate that at the N-terminal binding site , near N15 , HMA∶ETM stoichiometry approaches 1∶5 , i . e . , one HMA molecule per ETM pentamer . In contrast , at the C-terminal binding site , near R38 , the HMA∶ETM stoichiometry was 1∶2 suggesting for this site a rapid ( in the chemical shift time scale ) exchange between ETM-bound and micelle-bound forms of HMA . We note that DPC micelles and HMA exhibited identical diffusion rates , indicating that HMA partitions into the detergent phase . The shifts in the [1H , 15N]-HSQC spectrum after addition of HMA are apparent ( Fig . S6 , AC ) . Amiloride , in contrast , did not produce significant chemical shift changes ( Fig . S6 , BD ) , even at an ETM∶drug molar ratio ten times higher than for HMA ( not shown ) . For comparison , addition of AMT at ten times more concentration than HMA also produced similar chemical shifts as those observed for HMA ( not shown ) . However , in contrast to HMA , no NOEs between AMT and ETM were detected . It is interesting to note that L19 , which was present at a discontinuity point in chemical shift periodicity ( Fig . S4 , B ) , also showed a significantly broadened cross-peak in the [1H , 15N]-HSQC spectrum due to conformation exchange processes . By elevating the temperature from 30°C to 37°C , this exchange increased , resulting in sharpening of the L19 cross-peak ( Fig . S6 , EF ) . Broadening was also reduced by addition of HMA at 30°C ( Fig . S6 , G ) , suggesting stabilization of one of the ETM exchanging conformers by bound HMA . Incidentally , increasing the temperature from 30°C to 37°C also resulted in sharpening of the L18 cross-peak ( not shown ) , indicating that both residues may be involved in a hinge-like motion . The proposed two binding sites of HMA in the ETM channel are shown in Fig . 6 . In one binding site , HMA may be stabilized by a hydrogen bonding network to the Asn 15 side chains , with the cyclohexamethylene ring pointing away from the center of the channel ( Fig . 6 , AC ) . The second binding location for HMA was observed near the C-terminus of ETM , around residue R38 , where the amiloride group of HMA is likely to be involved in interactions with the guanidinium groups of R38 ( Fig . 6 , BD ) . The cyclohexamethylene ring was in van der Waals contact with methyl groups of residue T35 , i . e . , oriented towards the center of the membrane . To confirm the relevance of this pentameric structure and the effect of HMA and amiloride on the channel activity , results were obtained by transient expression of SARS-CoV E in human embryonic kidney 293 ( HEK-293 ) cells . Transfected cells produced significantly higher channel activity than the controls ( Fig . 7A ) . The whole-cell patch clamp recording ( Fig . 7B ) reveal moderate inward ( negative current ) and large outward ( positive current ) conductance . The same figure shows significantly smaller ‘control’ currents obtained with cells transfected with the vector alone , or non-transfected HEK-293 cells . ACSF ( artificial cerebro-spinal fluid ) was used as bath solution , which contained a high concentration of NaCl ( 124 mM ) , whereas the internal solution contained a high concentration of potassium ion ( 145 mM ) , close to the intracellular medium under physiological conditions . Under our recording conditions , the estimated equilibrium potentials , ENa and Ek , were 65 mV and −87 mV , respectively . Strong selectivity for either of these cations would produce a reversal potential ( i . e . , zero current ) near their corresponding equilibrium potential . If the channel was poorly selective , the reversal potential would have a value somewhere in between ENa and Ek , whereas no selectivity would produce a reversal potential in the mid point between these values ( ∼−10 mV ) . The observed value of reversal potential at ∼0 mV ( Fig . 7B ) indicates low selectivity between sodium and potassium , with perhaps a mild preference for sodium . This is consistent with previous results performed in planar lipid bilayers with synthetic E proteins [35] , [36] . To test the inhibitory effect of HMA , cells were exposed to 10 µM HMA in the bath solution . This significantly reduced the whole cell current flowing through SARS-CoV E protein; indeed , the mean peak current at 70 mV was reduced by ∼60% ( P<0 . 02 , unpaired t-test ) ( Fig . 7C ) . Amiloride , in contrast , reduced the mean peak current by only ∼25% , although this difference was not statistically significant ( P>0 . 05 , unpaired t-test ) ( Fig . 7D ) . In this figure , we note that the peak current for transfected cells recorded in panel B , ∼600 pA , is larger than that in panels C and D , of ∼200 pA . We attribute these differences to variation in cDNA preparation , transfection , and the time of recording following transfection .
ETM shows a sufficiently resolved 1H NMR spectra . However , to facilitate resonance assignment and to unequivocally identify inter-monomer NOEs , six labeled amino acids were chemically incorporated near the center of the ETM α-helix . Selection of appropriate specific labels is facilitated by prediction of likely inter-monomer interactions using other lower resolution biophysical techniques . In particular , the model reconstructed here with NMR data is consistent with a model that was derived from the analysis of evolutionary conservation of ETM in coronavirus envelope proteins [40] . The latter approach is data independent , and only relies on the reasonable assumption that all homologues share the same backbone structure [42] . Because by definition conservative mutations that appear during evolution should not destabilize the correct model of transmembrane interaction , but may destabilize incorrect low energy models that appear during the simulations along with the correct model , these mutations act effectively as an in silico filter [43] . The inter-helical orientation obtained for the ETM α-helices , and their orientation respect to the lumen of the ETM channel and detergent phase , is also in agreement with previous ETM helix rotational orientation measurements obtained by infrared linear dichroism [38] . Our model shows a 2–2 . 3 Å wide constriction formed by the side-chains of V25 and V28 . This is probably not wide enough for the passage of sodium ions , which suggests this represents a closed state of the ETM channel . The 1H-15N dipolar couplings from the six labeled backbone amides exhibited a periodicity of 3 . 6 , consistent with a canonical α-helical periodicity , except for L18 which was found to be an outlier , i . e . , its 1H-15N vector points in a direction not consistent with the other labeled residues . Additionally , the amide groups of L18 and L19 showed significant line broadening , which was reduced at more elevated temperature likely due to acceleration of the exchange rates . We interpret this as a conformational exchange-induced transverse relaxation at these residues , and we speculate that these conformational dynamics may be required for the channel's function . Similar band narrowing was observed after addition of the drug HMA ( see below ) . In a previous report [36] , it was suggested that synthetic CoV E proteins have cation selective channel activity , with selectivity ( PNa/PK ) of 0 . 25 for HCoV-229E , 69 for MHV E , 10 for SARS-CoV E and 3 for IBV . In the present work , we observed a very mild preference for sodium over potassium . According to these data , only the apparent selectivity of HMV for sodium appears to be significant . The diameter of naked Na+ is around 2 Å , and that of K+ is 2 . 66 Å , and the diameter of the ETM pore at the level of N15 ( 4 to 5 Å ) is sufficient to accommodate a single dehydrated Na+ or K+ ion . Hence it may be speculated that N15 , or its polar equivalent in other sequences , form a selectivity filter for cations . The equivalent residue to SARS-CoV E N15 in MHV E is Gln ( Fig . S7 ) , which has a one methylene longer side chain . This may lead to further occlusion of the channel at this position , and may explain the observed higher selectivity for sodium in MHV E . We also note that the lumen-facing orientation of Asn and Gln may also have a structural role , as these residues are known to stabilize transmembrane interactions [44]–[46] . In the present work , we localized two binding sites for HMA . We speculate that the localization of HMA near N15 could be similar in other CoV E proteins because this position ( lumen-exposed ) is always occupied by a polar residue in other CoV E sequences ( N , Q , S , T ) ( Fig . S7 ) . However , HMA sensitivity has only been shown for E proteins that contain a long polar side chain at this position , e . g . , N ( SARS-CoV E ( this paper ) , HCoV-229E [36] ) or Q ( MHV E [36] ) ; IBV E , which contains a smaller polar side chain ( Thr ) was HMA-insensitive [36] . It would be interesting to test if E proteins containing a small polar side chain at this position , e . g . , S or T , are generally HMA insensitive . Similarly , at the C-terminal end of ETM , in the position equivalent to R38 in SARS-CoV E , a basic residue is often found in other E sequences ( Fig . S7 ) . Additionally , in HMA-sensitive CoV E proteins , at least one of the lumen-facing residues immediately below R38 is polar: TA in SARS-CoV E , AS in MHV E , and KL in HCoV-229E . For IBV E ( group 3 ) which was reported to be HMA insensitive [36] there is no polar residue at this position ( AF pair ) . We note , however , that R38 is the C-terminal residue in ETM , and may not be involved in HMA binding in the context of the full length protein . Experiments to clarify this point using an extended ETM or full length SARS-CoV E are in progress . For cells infected with MHV , the EC50 for HMA was ∼4 µM , whereas in an E-deleted virus ( MHVΔE ) , no effect was observed after HMA addition , pointing to E protein as the HMA target [36] . HMA also inhibited HCoV-229E replication in cultured cells , with an EC50 of ∼1 µM . In neither case , however , did amiloride have antiviral activity on replication in cultured cells . Consistent with these results , we show that channel activity of transfected mammalian cells expressing SARS-CoV E is inhibited by extracellular HMA , but not by amiloride , suggesting a specific activity . SARS-CoV E in plasma membranes is oriented with the N-terminus facing the cytoplasm [47] , whereas the C-terminus of the ETM would face the extracellular domain . The latter therefore would be the likely HMA binding site in our patch clamp experiment , although the fact that HMA partitions into detergent micelles , and presumably into lipid bilayers , suggests that both N and C-termini of ETM could be accessible to the drug . The weak inhibition observed for amiloride is consistent with our NMR data , because addition of amiloride to ETM showed an increase in line broadening , but only small changes in peak positions , suggesting a global perturbation of protein structure but not a specific interaction . Finally , although the chemical shifts induced by AMT ( not shown ) were similar to those observed for HMA , we did not observe NOEs between AMT and ETM . This is not unexpected; in contrast with what we observed in a lysine-flanked ETM peptide [37] , [38] , the in vitro ion channel activity observed for ETM without flanking lysines , i . e . , like the one used herein , was not inhibited by AMT [38] . The flexibility encountered around residues 18–19 , which was reduced by temperature or by addition of HMA , is reminiscent of the changes observed in the influenza A channel M2 after addition of AMT . The M2 open state ( low pH ) has been shown to be dynamic or heterogeneous [48] , as opposed to the less flexible closed state ( high pH ) . Addition of AMT to M2 caused substantial narrowing of 15N spectra [48] and a reduced M2 conformational distribution in a MAS 13C and 15N NMR study [49] , both indicative of a more rigid M2 channel in the presence of the drug . The latter studies conform to a model where M2 accesses several conformational states , and AMT would stabilize a ‘closed’ conformation . In SARS-CoV E , a similar rigidization of ETM may be partly responsible for inhibition , although physical blockage to ion passage is also possible . A more complete ETM labeling approach , which is in progress , will undoubtedly shed more light on the nature of this inhibition . Another important issue is the effect of the extramembrane domain on channel function and stability . For example , in M2 proton channel from influenza A , truncating the cytoplasmic tail alters ion channel activity when M2 is expressed in oocytes of Xenopus laevis [50] , and analytical ultracentrifugation showed that the full-length protein stabilizes the M2 tetramer by approximately 7 kcal/mol [51] . In SARS-CoV E , preliminary sedimentation equilibrium experiments ( unpublished data ) suggest a slightly lower association constant in a monomer-pentamer equilibrium for full length SARS-CoV E protein , or for a synthetic ETM spanning residues 7–42 ( Ka∼1015 ) , when compared to ETM8–38 ( Ka∼1017 ) [38] . Thus , the extramembrane residues may be slightly destabilizing for the SARS-CoV E pentamer . In recent years , several viral proteins have shown membrane permeabilization properties or ion channel activity , e . g . , poliovirus 2B , alphavirus 6 K , HIV-1 Vpu , and influenza virus M2 , and have been named collectively as ‘viroporins’ [52] . However , the physiological relevance of this activity has only been shown conclusively for M2 , a well known pharmacological target that is inhibited by AMT for which a detailed structure is available [53]–[55] . Electrophysiological data , as well as detailed structural information is lacking for most of these proteins . We show in the present work that SARS-CoV E possesses channel activity not only in vitro , but also when expressed in mammalian cells , and we have structurally characterized the homo-pentameric transmembrane domain ( ETM ) responsible for this activity , when solubilized in DPC micelles in the absence or presence of small drugs . Although the precise role of this proposed channel activity is not known , it is possible that it leads to a subversion of ion homeostasis in the host cell that could account for the observed attenuation of E-deleted coronaviruses ( see above ) . For example , in hepatitis B virus , calcium homeostasis regulation by HBx protein has been shown to be essential for replication [56] . It is also possible that the observed pro-apoptotic effect of SARS-CoV E protein in T cells [57] could be mediated by a disruption of cell ion homeostasis and membrane depolarization , a general marker of apoptosis [58] .
Isotopically labeled amino acids were derivatized with 9-fluorenyl-methyloxycarbonyl ( FMOC ) [59] . The ETM peptide , corresponding to the transmembrane domain of SARS-CoV E ( residues 8–38 ) , E8TGTLIVNSVLLFLAFVVFLLVTLAILTALR-NH2 , was synthesized using standard solid phase FMOC chemistry ( Intavis Respep peptide synthesizer ) . The peptide was cleaved from the resin with trifluoroacetic acid ( TFA ) . The lyophilized peptide was purified by HPLC , as described previously [21] . Lyophilization was performed in the presence of HCl ( at the molar ratio of 20∶1 , HCl∶peptide ) in order to avoid formation of peptide-TFA adducts; consequently , the TFA band at ∼1685 cm−1 was absent in the infrared amide I region ( not shown ) . Peptide purity was further confirmed by electrospray ionization ( ESI ) mass spectrometry . During ETM synthesis , 15N-labeled amino acids were introduced at positions A22 , V24 , V25 , and 13C , 15N-labeled amino acids at positions L18 , L19 and L21 . Approximately 1 . 6 mg of lyophilized ETM peptide was solubilized in phosphate-buffered saline ( PBS , 10 mM Na2HPO4·NaH2PO4 ) containing 17 mg of DPC ( Avanti Polar Lipids ) , to a molar ratio of 1∶100 ( peptide∶DPC ) . Under these conditions , sedimentation equilibrium studies have shown ETM to be pentameric [38] . For AMT binding experiments , the NMR sample was titrated stepwise with AMT ( 1 amino-adamantane ) hydrochloride powder ( Fluka ) dissolved in PBS , pH 5 . 5 , up to a final molar ratio of 1∶100∶100 ( peptide∶DPC∶AMT ) . For HMA ( 5-N , N-Hexamethylene amiloride , Sigma ) and amiloride ( amiloride hydrochloride , Sigma ) binding experiments , aliquots of HMA ( solubilized in D6-DMSO , Cambridge Isotopes ) or amiloride ( solubilized in water ) were added to an empty NMR tube . In both cases , solvent was removed by lyophilization followed by addition of ETM/DPC solution to a molar ratio of 1∶100∶10 ( peptide∶DPC∶drug ) , i . e . , ten times less than for AMT ( see above ) . The resulting mixture was heated to 40°C for 30 min , vortexed and equilibrated at 30°C for a few hours before collecting NMR spectra . For the sample preparation in the presence of 16-doxyl stearic acid ( 16-DSA ) , the desired amount of 16-DSA was first dissolved in methanol . The aliquots of 16-DSA corresponding to 1 mM , 3 mM and 5 mM of 16-DSA in final NMR samples were added to an empty NMR tube and dried under a stream of dry N2 gas . The NMR sample containing ETM/DPC was added to the NMR tube containing the dry 16-DSA and was left to equilibrate for a few hours . Gadodiamide ( OMNISCAN; gadolinium chelated with 2-[bis[2-[ ( 2-methylamino-2-oxoethyl ) - ( 2-oxido-2-oxoethyl ) amino]ethyl]amino]acetate , GE Healthcare ) was used from a 0 . 5 M stock solution and was diluted to 1 . 5 mM . Weakly aligned samples were prepared by soaking a 1 mM solution of selectively labeled ETM in 100 mM DPC into polyacrylamide gels . Two different acrylamide concentrations , 4% and 8% , at axial and radial compression , respectively , were used to independently verify the experimental results . Gels were prepared from stock containing 36% w/v acrylamide ( Bio-Rad Laboratories ) and 0 . 94 w/v N , N-methylenebisacrylamide ( Bio-Rad Laboratories ) which yields an acrylamide/bisacrylamide molar ratio of 83∶1 . 4% acrylamide gels were cast in 4 . 2 mm inner diameter ( ID ) glass tubes , while 8% gels were cast in a gel chamber of 5 . 4 mm ID ( New Era Enterprise , Inc ) . After complete polymerization , gels were washed in large excess of H2O overnight to ensure removal of un-reacted components . Gels were then dried to completeness at 37°C . Peptide solutions were soaked into the dried gels overnight to ensure complete re-hydration . The 4% gel was carefully added into a 4 . 2 mm ID Shigemi tube ( Shigemi Co . Ltd . ) and compressed axially using the supplied Shigemi plunger; the 8% gel was radially compressed into a 4 . 2 mm ID open-ended tube using the gel press assembly ( New Era Enterprise , Inc ) , and secured using the supplied support rod and end gel plug . NMR experiments were performed at 30°C using Bruker Avance-II 700 and 600 NMR spectrometers equipped with cryogenic probes ( Bruker BioSpin ) . Complete sequence-specific assignment of backbone 1HN was achieved using 2D homonuclear 1HN , 1Haromatic band-selected NOESY ( Fig . S8 ) , 3D 15N resolved NOESY-HSQC , 3D 13C resolved NOESY-HSQC and 3D 15N HSQC-NOESY . Intra-monomer NOEs involving both backbone and side-chain protons were assigned using the same set of 2D and 3D NOESY spectra . Mixing time for all NOESY spectra was set to 200 ms . To identify inter-monomer contacts , we constructed a difference between two 2D 1HN , 1Haromatic band-selected NOESY spectra , acquired with and without 13C decoupling during the t1 chemical shift evolution period . Based on the reconstructed secondary structure of ETM , NOEs were identified as inter-monomeric , i . e . , between 1H covalently bound to L18 , L19 or L21 and other proximal 1H spins , if they could not be explained by intra-monomer distances . The amplitudes of inter-monomer NOEs were used to define the corresponding upper limit constraints . Two sets of HMA 1H resonances were assigned using 2D TOCSY and 2D NOESY spectra . NOEs between ETM and HMA were identified by direct comparison of NOESY spectra , measured with and without the presence of the drug ( Fig . S5 ) . The orientation of the ETM α-helices with respect to the lipid hydrocarbon phase was verified by the paramagnetic enhancement induced by 16-DSA in the longitudinal 1HN relaxation of the six labeled amino acids . The saturation recovery method in a series of [1H , 15N]-HSQC experiments with a variable inter-scan delay was employed . Using two different approaches , the experimental data obtained was compared to the expected paramagnetic relaxation enhancement ( PRE ) from our proposed pentameric structure ( Protocol S1 and Fig . S3 ) . DSS ( sodium 2 , 2-dimethyl-2-silapentane-5-sulfonate ) was used as the internal reference for 1H nuclei . The chemical shifts of 13C and 15N nuclei were calculated from the 1H chemical shifts [60] . 1H-15N residual dipolar couplings ( RDCs ) were determined using TROSY-anti-TROSY spectra . The acquired data was analyzed with MODULE [61] . The structure of the ETM monomer in DPC micelles was calculated using the site-specific assignment of 1H , 13C and 15N resonances and unassigned NOEs as input for the program CYANA [62] , [63] . Structure calculations started from 100 random conformers , using the standard simulated annealing protocol in CYANA . The statistics of meaningful NOE distance constraints in the final CYANA cycle ( Table S1 ) showed a high density of structural constraints per amino acid . Seven cycles of NOE assignment and structure reconstruction resulted in a bundle of 20 conformers with the average target function values below 0 . 15 . A symmetrical ETM homo-pentameric structure was reconstructed ( Fig . S1 , A–C ) starting with a backbone model based on orientational data from site-specific infrared dichroism ( SSID ) , which defined helix tilt and rotational pitch angles for residues L21 , A22 , F23 , and V24 [38] . The ETM α-helix built from NMR data was superimposed onto the pentamer skeleton to obtain the full atomic description of the model . This model was subjected to energy minimization to resolve steric clashes , following which , inter-helical NOEs ( Table S2 ) were used as constraints in molecular dynamics ( MD ) simulations to refine the structure . Inter-helical NOE constraints were applied one by one; only when the system reached equilibrium , another constraint was added . Upon inclusion of all inter-helical constraints , the refined final model was compatible with both site specific infrared dichroism and NMR data . The energy minimization and all the restrained MD simulations were performed using GROMACS [64] at an atomistic level of detail , using the OPLS-AA [65] force field . Atomic charges were assigned on the basis of the default atomic charge values specified in the OPLS-AA force field . The Van der Waals interactions were modeled using a cut-off distance of 9 . 0 Å . In the simulation , the cell temperature was maintained at 298 . 15 K using the Berendsen temperature coupling algorithm . The Berendsen pressure coupling algorithm was applied to maintain the pressure of 1 . 0 bar . With backbone positions restrained , each inter-helical distance constraint includes a 500 ps simulation , enough for the side chains to move into a conformation that is constraint allowed . The lumenal dimensions for the pentameric model were calculated using HOLE [66] and were visualized using VMD [67] . According to the chemical shift changes observed after addition of HMA , two ETM pentameric models were obtained . For one model , residues 8–12 and 17–38 did not change after exposure to HMA . For the second model , residues 8–30 did not change . Docking of HMA to these two models was performed using Glide [68] , [69] with standard parameters , guided by NOE constraints , and allowing for HMA flexibility . The binding site was defined in terms of two concentric cubes: the bounding box , which contains the center of any acceptable ligand pose , and the enclosing box , which contains all ligand atoms of an acceptable pose . Upon completion of each docking calculation , the best docked structure was chosen using a Glidescore ( Gscore ) function , a modified and extended version of the empirically based Chemscore function . The full-length SARS-CoV E protein gene was cloned into pIRES-AcGFP1 ( Clonetech ) vector by using the restriction enzymes BglII and PstI . The identity of the insert was confirmed by DNA sequencing . In a 35 mm Petri dish , 1 . 65 µg of human SARS-CoV E cDNA was transiently transfected into HEK-293 cells using the standard calcium phosphate method [70] . The vector pIRES-AcGFP1 was also transiently transfected in separate experiments as a control . Another control was the use of untransfected HEK-293 cells . The cells were grown for 36–48 h in a 5% CO2 incubator at 37°C before whole-cell patch clamp recordings . Whole-cell current was recorded at room temperature using the standard patch clamp technique , 48–72 h after transfection . The bath solution contained the following ( mM ) : 124 . 0 NaCl , 3 . 5 KCl , 1 . 0 NaH2PO4 , 26 . 2 NaHCO3 , 1 . 3 MgSO4 , 2 . 5 CaCl2 and 10 . 0 D ( + ) -glucose; gassed with a mixture of 95% O2 and 5% CO2; pH 7 . 4 , and an osmolarity of 300 mOsmkg−1 . The internal solution ( pipette solution ) contained the following ( mM ) : 135 . 0 potassium gluconate , 10 . 0 KCl , 10 . 0 Hepes buffer , 0 . 5 EGTA , 2 Mg-ATP ( pH adjusted to 7 . 3 with KOH; osmolality 275–285 mOsmkg−1 ) . The voltages were uncorrected for a −9 mV junction potential , and actual voltage is obtained by subtracting 9 mV from the reported values . Whole-cell currents , obtained under voltage clamp with an Axopatch Multiclamp700B amplifier ( Axon Instruments ) , were filtered at 1–5 kHz and sampled at 5–50 kHz . The access resistance Ra ( usually less than 20 MΩ ) and the capacitive transients were not compensated . Stock solutions of amiloride and HMA ( Sigma ) at 100 mM were prepared in 50% DMSO∶50% methanol . To determine if the amiloride derivatives blocked SARS-CoV E protein ion channel conductance in HEK-293 cells , after ion channel currents were detected , 10 µM of the drug diluted in bath solution was applied to the cell . The accession numbers for the proteins in this paper are SARS-CoV E , NP_828854; TGEV E , AAZ91440; HCoV-229E E , NP_073554; MHV E , O72007 and IBV E , P05139 . | Coronaviruses are viral pathogens that cause a variety of lethal diseases in birds and mammals , and common colds in humans . In 2003 , however , an animal coronavirus was able to infect humans and produced severe acute respiratory syndrome ( SARS ) , causing a near pandemic . Such events are likely to reoccur in the future , and new antiviral strategies are necessary . A small coronavirus protein called ‘envelope’ is important for pathogenesis , affecting the formation of the viral envelope and the distribution of the virus in the body . In vitro studies have shown that synthetic coronavirus envelope proteins have channel activity that in some cases has been inhibited by the drug hexamethylene amiloride , but not by amiloride . In the present paper , we have characterized the structure responsible for this channel activity . We have also determined the binding site of the drug hexamethylene amiloride in the channel , and shown that amiloride has only a mild effect on the NMR signals from the protein . The validity of these results is supported using mammalian cells expressing full length SARS-CoV E , where channel activity was inhibited by hexamethylene amiloride , but only mildly by amiloride . The structural model described for this channel provides a valuable insight into coronavirus envelope protein ion channel activity , and could serve as a platform for the development of novel anti-viral drugs . | [
"Abstract",
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"Results",
"Discussion",
"Methods"
] | [
"biophysics",
"biophysics/experimental",
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"genomics",
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] | 2009 | Structure and Inhibition of the SARS Coronavirus Envelope Protein Ion Channel |
Pili of pathogenic Neisseria are major virulence factors associated with adhesion , twitching motility , auto-aggregation , and DNA transformation . Pili of N . meningitidis are subject to several different post-translational modifications . Among these pilin modifications , the presence of phosphorylcholine ( ChoP ) and a glycan on the pilin protein are phase-variable ( subject to high frequency , reversible on/off switching of expression ) . In this study we report the location of two ChoP modifications on the C-terminus of N . meningitidis pilin . We show that the surface accessibility of ChoP on pili is affected by phase variable changes to the structure of the pilin-linked glycan . We identify for the first time that the platelet activating factor receptor ( PAFr ) is a key , early event receptor for meningococcal adherence to human bronchial epithelial cells and tissue , and that synergy between the pilin-linked glycan and ChoP post-translational modifications is required for pili to optimally engage PAFr to mediate adherence to human airway cells .
Type IV fimbriae , or pili , are long filamentous structures that extend from the bacterial surface and primarily consist of the monomer pilin protein [1] . Pili are shown to play a major role in promoting colonization of the mucosal epithelium by several human pathogens and , thus , are generally considered to be major virulence determinants for bacterial pathogens . Roles attributed to the Type IV pili expressed by the pathogenic Neisseria ( N . gonorrhoeae and N . meningitidis ) include: adhesion , cytotoxicity , twitching motility , auto-aggregation , and DNA transformation [2] [3] [4]; [5] . Pili of the pathogenic Neisseria are post-translationally modified . These post-translational modifications ( PTMs ) include: a glycan [6] [7] [8] , phosphorylcholine ( ChoP ) [9] , and/or a phosphoglycerol [10] . Roles for these PTMs in the pathogenesis of N . gonorrhoeae and N . meningitidis are proposed . For example , during N . gonorrhoeae challenge of primary human cervical epithelial cells , the pilin glycan mediates the activation state of complement receptor 3 ( CR3 ) ; this , in turn , modulates gonococcal adherence , invasion , and ultimately their intra-epithelial cell survival [11] . Additionally , the pili-linked phosphoglycerol is proposed to trigger N . meningitidis dissemination [10] . The role of the ChoP PTM has not been previously determined and is the subject of this study . In N . meningitidis strain C311#3 , pilin is glycosylated at Ser63 with the trisaccharide , Gal ( β1-4 ) Gal ( α1-3 ) 2 , 4-diacetimido-2 , 4 , 6-trideoxyhexose ( DATDH ) [6] . We previously describe a series of gene products ( PglB , PglC , PglD ) involved in the biosynthesis of DATDH [12] . Once formed , DATDH serves as the base for the stepwise development of the trisaccharide , in which PglA transfers the first galactose ( Gal ( α1-3 ) ) to the DATDH [13] that is followed by the PglE-mediated addition of a second ( terminal ) galactose ( Gal ( β1-4 ) ) to form the mature trisaccharide [12] . Not all strains of N . meningitidis make this same glycan . Neisseria are capable of the biosynthesis of a wide range of glycans that can be transferred to protein targets , which is dependent upon the absence , or presence , of various glycosyltransferases in any particular strain ( e . g . , PglG , PglH , PglE ) ; and also because many of these glycosyltransferase genes ( pglA , E , I , G , and H ) are subject to phase variation ( random ON/OFF switching of expression ) [13] [14] [8] [15] . ChoP is another phase variable PTM made to the pilin of the pathogenic Neisseria [16] [9] [17] . Several microorganisms of the human respiratory tract express ChoP on their surface where it serves as a ligand to mediate an association with a host cell [18] [19] [20] [21] [22] . In both non-typeable Haemophilus influenzae ( NTHi ) and the commensal Neisseria , ChoP is important in adherence via the platelet activating factor receptor ( PAFr ) as well as in the signalling cascades that ultimately result in invasion of some epithelial cells [23] , [24] . Alternatively , ChoP can also serve as a target for bacterial killing by C-Reactive Protein ( CRP ) . For example , CRP binds to ChoP present on Streptococcus pneumoniae [25] , H . influenzae [22] , commensal Neisseria [24] , and N . meningitidis [26] . Although not yet show for Neisseria spp . , within the nasopharynx , CRP binding to ChoP can decrease bacterial adherence to the PAFr [27] . Within the blood , opsonization with CRP can activate complement by the classical pathway , resulting in a bactericidal effect [22] . Therefore , the phase variable expression of ChoP is a seemingly critical mechanism in the colonization and pathogenesis of bacteria commonly found within the human airway [9] . In this study , we hypothesized that the phase variable exposure of ChoP on pili of N . meningitidis may be of functional importance in aiding colonization and in immune evasion . To test this hypothesis , we first set out to define the surface accessibility of ChoP and the precise location of the ChoP PTM on pilin . We then examined the role of the pilin-linked glycan and ChoP as contributors to meningococcal adherence to the PAFr on a PAFr-expressing cell line and , of substantive importance to human disease , to the PAFr present on human bronchial epithelial cells and on human bronchial tissue .
We screened a representative collection of 32 N . meningitidis strains that were chosen for temporal , geographic and genetic diversity ( from a WHO collection plus our own clinically isolated bacteria; [28] ) . All of the strains tested contained the gene , pptA ( NMB0415 ) , encoding the transferase responsible for the addition of ChoP to pili ( for pathway see [29] ) ; thus , every strain examined had the potential for pili to be post-translationally modified by ChoP [17] . However , the gene encoding PptA contains a homopolymeric tract of guanosine ( G ) residues within its coding region ( encoding poly-glycine , see Fig . 1A ) , which are hypermutagenic , leading to frame-shift mutations and , in turn , the reversible ON/OFF switching of gene expression ( phase variation ) and , thus , of ChoP addition to pili [17] . Pili may also phase vary ON and OFF at a high frequency , and although essential for adherence in colonisation of the host , may phase vary to OFF during in vitro culture ( reviewed by [30] ) . Analysis of the pili expressing strains within the WHO and clinical isolate collection revealed that the majority had pptA genes possessing 8G residues , which is in-frame for expression of this gene ( Fig . 1A ) . Of those strains that did not express ChoP on their pili , the majority possessed homopolymeric tract numbers ( 7G , 9G , 10G ) that were out-of-frame for expression ( Fig . 1A , Tbl . S3 ) . Strains containing an 11G tract in pptA had no detectable expression of ChoP on their pili ( see Fig . 1B for example ) . This was surprising because 11G is in-frame for PptA expression . Within the PptA polypeptide , an 11G homopolymeric region should result in the generation of a full-length protein with only one additional glycine , compared to those strains harbouring an 8G pptA homopolymeric region ( Fig . 1A ) . To determine whether the extra glycine , resulting from the difference in the 8G vs . 11G tract length within the pptA homopolymeric region , was responsible for the observed loss of PptA protein function; the number of Gs comprising the pptA homopolymeric region of strains C311#3 ( pptA8G , ChoP+; our model strain [17] ) and 8013SB ( pptA11G , ChoP−; another model strain [10] ) were genetically altered such that C311#3 now contained 11Gs ( i . e . , C311#3pptA11G ) and 8013SB now contained 8Gs ( i . e . , 8013SBpptA8G ) . We found that , although both 8G and 11G versions of pptA are in-frame for PptA expression , ChoP was only detectable on pili when pptA contained 8Gs ( Fig . 1C ) . This demonstrated that the 8013SB pptA gene is functional when modified to contain one less glycine , and , therefore , that the number of glycines expressed is critical for PptA function . Colony immunoblot screening of approximately 80 , 000 wildtype ( WT ) 8013pptA11G colonies failed to yield any phase variants that had reverted to a ChoP positive phenotype . This suggested that a phase variation event that would allow ChoP expression to switch back ON ( pptA11G to pptA8G ) is rare . Such an event would require 3 independent mutational events; 11G to 10G , 10G to 9G , and then 9G to 8G; to achieve expression of an active PptA . In contrast , strains with the out-of-frame forms , pptA7G or pptA9G , require only a single mutational event to switch to the in-frame , active , pptA8G form . Thereby , the latter pptA7G or pptA9G out-of-frame form is easily reversible , with ON/OFF switching occurring at the approximate rate of 1 in 100 colonies . Our analysis of “G” repeat numbers within pptA indicated that the pptA11G strains; which express the inactive PptAGly+1 form of the enzyme and , therefore , do not express ChoP on their pili; are uncommon and represent only 6% of the clinical isolates surveyed ( See Table S3 ) . This , thereby , suggests that the ChoP PTM is an important aspect of pili function . Using the ChoP-specific , monoclonal antibody , TEPC-15 , we assessed the surface accessibility of ChoP on native pili . ChoP was detectable on the majority of N . meningitidis strain C311#3 with some colonies exhibiting a much higher level of TEPC-15 binding ( Fig . 2A ) . This suggested that either more ChoP was present on the surface of some bacterial cells or that the altered presentation of ChoP on the surface of some bacteria resulted in an increase in antibody accessibility . Therefore , we isolated these highly TEPC-15-reactive colonies to determine whether the increase in TEPC-15 reactivity was attributable to an increase in ChoP on each pilin subunit . Western Blot analysis of denatured pili revealed equivalent TEPC-15 reactivity among the various pili tested ( Fig . 2B ) . This indicated that the amount of ChoP present on pili was not responsible for the increase in TEPC-15 reactivity observed by Colony Blot analysis . Rather , these data suggested that high antibody reactivity likely resulted from differences in ChoP accessibility to TEPC-15 in the context of the properly folded , native pili polymer . Such differences in ChoP accessibility could have resulted from variation in the pilin primary amino acid sequence and/or from variation in other , additional , pilin PTMs reported for meningococci . To determine the mechanism ( s ) of altered ChoP accessibility , both of these possibilities were examined . The gene encoding pilin , pilE , can alter its sequence at high frequency by recombination with silent pilin gene copies , called pilS [30] . To determine whether changes in the amino acid sequence of PilE contributed to increased TEPC-15 reactivity , the pilE sequences from three ( C311#3TEPC+1 , C311#3TEPC+2 , and C311#3TEPC+3 ) high TEPC-15 reactive colonies were examined . Sequencing of pilE revealed that nucleotide variation , consistent with amino acid alterations , in pilE had occurred in two variants , C311#3TEPC+1 and C311#3TEPC+2 ( Fig . S1 ) . No changes had occurred in the expression of phase variable pgl genes in these two strains and pili-linked trisaccharide was evident in Western blot analysis using anti-trisaccharide sera ( result not shown ) . Upon mapping these variations onto the described structural model of the pilus fiber [31] , we found that , within the assembled pilus fibre , these changes occurred near the site of pilin glycosylation . Conversely , no amino acid changes were observed for C311#3TEPC+3 , indicating that an additional mechanism ( s ) of altered accessibility to ChoP existed . Altered accessibility to ChoP could result from variations in other PTMs , e . g . , phase variation of the glycosyltransferases involved in the biosynthesis and , thus , the expression , of the pilin-linked glycan . To determine if the phase variable expression of the pilin-linked glycan also contributed to the accessibility of ChoP on the pilus fiber , we sequenced the repeat tract regions that control phase variation of the pilin glycosylation genes , pglA [13] and pglE [32] . The repeat region of pglE from strain C311#3TEPC+3 had indeed lost a repeat unit leading to a frame-shift mutation and an inactive PglE . As phase variation of pglE to an “off” state results in the loss of the terminal galactose from the pilin-linked trisaccharide ( Fig . 2C ) , these data provided the first evidence that alterations to the trisaccharide structure may influence the accessibility of ChoP on pili . Using defined pilin glycosylation mutants , we then analysed native pili for the effect of glycan truncation on ChoP exposure by colony immunolabeling . N . meningitidis strains tested included C311#3 [6] and its previously described mutants , C311#3pglE [12] and C311#3pglA [13] . The C311#3pptA mutant ( lacking ChoP; [17] ) and C311#3ΔpilE mutant ( lacking pili [33] ) were included as negative controls . TEPC-15 bound only weakly to C311#3 WT colonies and did not bind to the negative control strains , C311#3pptA and C311#3ΔpilE ( Fig . 2C ) . The remaining strains exhibited an increase in the level of TEPC-15 bound , and this was inversely proportional to the length of the pilin-linked glycan expressed by each mutant strain . The C311#3pglE mutant ( possessing a pili-linked disaccharide ) displayed a slight increase in TEPC-15 binding when compared to the WT . There was a further increase in TEPC-15 reactivity for the C311#3pglA mutant ( glycosylated with a monosaccharide ) [13] . To confirm that the observed hierarchy of TEPC-15 binding to ChoP resulted from interactions present in the pili polymer , pilin was isolated from each of the mutants and examined under denaturing conditions by western blotting . No difference in TEPC-15 binding was observed between C311#3 WT , C311#3pglE , and C311#3pglA mutants ( Fig . 2B ) . However , we observed a hierarchy in TEPC-15 binding to ChoP under native conditions ( Fig . 2C ) . We propose that these two PTM structures are closely associated and that masking of ChoP by the pili-linked glycan alters the accessibility of ChoP in the native pilus fibre . Our data indicated that the ChoP and glycan structures are likely juxtaposed on the mature pilus fibre . Within the gonococcal pilus structure [34] , Ser 34 , 45 , 68 , 69 , 70 , 157 , and 160 are all located near the site of glycan addition . In that the site of ChoP addition to pilin is unknown , we made Ser/Ala conversion mutants in strain C311#3 by site-directed mutagenesis . Each of these mutants was then analysed by western blotting for the presence of ChoP . TEPC-15 antibody still recognized each isolated mutant pilin form , indicating ChoP was still present . Hence , we then generated and purified ( C311#3 WT and C311#3pptA mutant ) pilin fused to a FLAG-tag ( Fig . S2 ) to enable identification of the ChoP PTM site by LC-MS/MS ( Fig . 3A ) . LC-ESI/MS analysis of trypsin-digested , FLAG-tagged pilin revealed that no molecular ion was present at the calculated mass for the pilin C-terminal trypsin-digested peptide , 155DASDASDYKDDDDKLEF170 ( 1947 . 8 Da ) . Instead , an unassigned LC-ESI/MS [M+3H]3+ signal at m/z of 760 . 3 was observed for C311#3WT pilin; whereas , 650 . 2 m/z was observed for the pilin from the C311#3pptA mutant strain ( Fig . 3A ( panel I ) ) . The neutral mass of [M+3H]3+ at m/z of 760 . 3 is 2277 . 9 Da . The difference between the observed mass ( 2277 . 9 ) and the predicted mass ( 1947 . 8 ) is 330 . 1 Da , or the equivalent of two molecules of 165 Da . When the covalent bond between ChoP and a serine residue breaks , the mass of ChoP then becomes 184 . 1+ Da ( 162Da+18Da ( H2O ) +1Da; ChoP tends to exist as positive ion ) ( Fig . 3A , panel II ) . The LC-ESI/MS result of trypsin-digested pilin from C311#3pptA lacking ChoP showed an unmodified 155DASDASDYKDDDDKLEF170 peptide [M+3H]3+ ion at m/z of 650 . 2 ( neutral mass of 1947 . 6 Da ) and no observation of an [M+3H]3+ ion at m/z 760 . 3 . In Fig . 3A ( panel III ) , the MS/MS data confirmed the sequence of a ChoP unmodified 155DASDASDYKDDDKLEF170 peptide . There was a two ChoP mass difference between the modified ( 760 . 33+ m/z ) and unmodified ( 650 . 23+ m/z ) peptides , which indicates that ChoP is attached to both Ser 157 and Ser 160 . LC-MS/MS analyses indicated that a ChoP is attached to both Ser157 and Ser160 . However , as pilin containing a FLAG-tag at its C-terminus ( i . e . , after Ser160 ) was used for these experiments , we wanted to confirm that data obtained were attributable to pilin ( not the FLAG motif ) , as well as to ensure that Ser157 and Ser160 were the only sites of ChoP modification . To this end , we generated a S157A/S160A mutant strain of C311#3 , C311#3S157A/S160A , by site-directed mutagenesis . Pilin of C311#3S157A/S160A was then isolated and analysed by probing Western Blots with anti-pilin or anti-ChoP ( TEPC-15 ) antibody . Although membranes probed with the anti-pilin serum showed the presence of pilin in each sample , ChoP was not detectable on strain C311#3S157A/S160A ( Fig . 3B ) . These data confirmed that ChoP modifications occurred only on Ser 157 and Ser 160 of the pilin protein . We have proposed that masking of ChoP by the pili-linked glycan alters the accessibility of ChoP ( to TEPC-15 ) within the pilus fibre . Having defined the sites for pilin ChoP modification , we next wanted to investigate how ChoP and the pilin-linked glycan may be potentially associated in the context of the pilus polymer . Although the structure of N . meningitidis pilus is not available , it has been modelled based on the published N . gonorrhoeae pilus crystal structure [34] . Based on this same model , and by using the molecular modelling program Insight II ( Accelrys ) , we altered the C-terminal region of N . gonorrhoeae pilin to possess a RDASDAS motif ( consistent with C311#3 pilin ) with two serine-linked ChoP modifications . Additionally , a trisaccharide structure , also consistent with C311#3 , was added to Ser 63 . The structure was calculated in the minimal energy condition . As shown in Fig . 3C ( panel I ) and in supporting information ( Movie S1 ) , a long distance exists between the glycan present on Ser 63 and the ChoP moieties present on the C-terminus . Further , the glycan and ChoP modifications are not on the same side of pilin protein . However , when multiple pilin subunits come together to form a pilus fiber , the pilin glycan is proximal to the ChoP in the adjacent pilin subunit ( Fig . 3C , panel II and supporting information Movie S2 ) . Therefore , the observed altered surface accessibility to ChoP on the C-terminus of any one pilin monomer most likely results from variation in the glycan on the adjacent pilin subunit . The above data support the hypothesis that the ChoP and glycan PTMs are closely associated in the native pilus fibre . Thus , investigations of the biological role of ChoP must take into account , phase variable , glycan structural variations . To determine the biological significance of the ChoP PTM of pili , we investigated the interaction of meningococci with the PAFr on human bronchial epithelial cells , as there are ample data to indicate that the PAFr serves as a docking site for molecules on which ChoP is exposed [23]; [24] . Confocal microscopy revealed that , after 15 minutes incubation , 100% of cell associated N . meningitidis C311#3 were co-localized with the PAFr on 16HBE14 human airway epithelial cells ( Fig . 4A ) . Similar data were observed upon the analysis of human bronchial tissue that had been challenged for 30 min with WT meningococci ( Fig . 4B ) . Co-localization was not observed in tissue sections in which the primary anti-meningococcal and -PAFr antibodies were omitted during processing ( negative control; Fig . 4B ) . These data were supported by further quantitative investigations to determine whether a pili-linked ChoP-PAFr interaction accounted for the co-localisation of N . meningitidis with the PAFr that we observed by confocal microscopy . To this end , PAFr and pilus immune complexes were captured from uninfected 16HBE14 cells by immunoprecipitation ( IP ) , as well as from 16HBE14 cells that were challenged for 15 min ( Fig . S3 ) or 30 min with N . meningitidis strains C311#3 WT , C311#326A ( trisaccharide , no ChoP ) , or C311#3pglA ( disaccharide , ChoP+ ) ( Fig . 4C ) . Western Blots in which pilus-associated complexes were probed with an anti-PAFr antibody ( C-20 ) , revealed a prominent band at approximately 39 kDa , as well as a fainter band of approximately 69 kDa , consistent with the PAFr ( Fig . 4C ) . Reciprocal experiments , in which anti-PAFr IPs were probed for the presence of pilus , demonstrated similar results in that an approximate 18 kDa band , suggestive of pilus , was readily evident ( Fig . 4C and S3 ) . The intensity of PAFr- and pilus-corresponding bands in each Western Blot described above was highly variable among the C311#3 mutant and variant strains we examined . One explanation for this observation is that differences in the ability to adhere to 16HBE14 cells via the PAFr existed among the pilus glycosylation and ChoP mutants we examined . For example , in the anti-PAFr IP Western Blots; whereas an intense pilus-associated band was observed in the lane corresponding to the C311#3 WT infection , a band of moderate intensity was observed in the lane corresponding to infection with strains expressing pilus lacking either ChoP or glycan ( Fig . 4C and S3 ) . Therefore , to confirm and to quantitate data obtained above , as well as to determine the biological contribution of the glycan and ChoP pilus moieties to the PAFr-pilus association; we performed a fluorometric adherence assay . To this end , 16HBE14 cells were challenged with an expanded panel of C311#3 mutant and variant strains that varied in their expressed pilus structure ( Tbl . S1 ) . Antibodies capable of competing with N . meningitidis for PAFr binding were included or omitted from each assay ( Fig . 5 ) . These data strongly supported a role for both the pilus glycan and ChoP moieties in mediating N . meningitidis adherence to the PAFr in that: 1 ) anti-PAFr , -pilus , and –ChoP; but not anti-CD46; antibody competimers significantly decreased the association of C311#3WT with 16HBE14 cells , 2 ) glycan and ChoP C311#3 mutant and variant strains exhibited decreased adherence to 16HBE14 cells when compared to C311#3WT meningococci , and 3 ) the greatest decrease in adherence of meningococci to 16HBE14 cells occurred when the glycan and ChoP moieties both were absent from the pilus structure . Under these latter conditions , recorded fluorescence approached background levels , and adherence could not be further impaired by the presence of antibodies capable of competitively blocking a PAFr-pilus interaction . To further examine the ability of meningococci to bind to the PAFr in the absence of extraneous factors , as well as to confirm the contribution of pili-linked glycan and ChoP PTMs in modulating PAFr binding; we performed comparative , quantitative fluorometric adherence assays using Chem-1 cells , which do not naturally express the PAFr , as well as Chem-1 cells in which the human PAFr was over expressed ( Chem-1-PAFr ) . Chem-1 or Chem-1-PAFr cells were seeded to microtiter plates and meningococcal adherence was quantitated fluorometrically , as described in the Materials and Methods . Consistent with data obtained by IP , a significantly different ( p≤0 . 001 ) , glycan- and ChoP-dependent , hierarchical pattern of adherence was observed as: C311#3 WT≫C311#3pglL>C311#3pptA>>C311#3pglLS157/160A ( Fig . 6 ) . Only background levels of fluorescence ( indicative of adherence ) were detectable for uninfected cells or for Chem-1 cells that were infected in parallel with the Chem-1-PAFr cells ( Fig . 6 ) . Fluorescence units recorded for assays performed using C311#3pilE were only modestly significantly different ( p≤0 . 04 ) than that recorded for uninfected Chem-1-PAFr cells , and there was no significant difference ( p≤0 . 08 ) in C311#3pilE adherence to Chem-1-PAFr cells when compared to the Chem-1 parental cells ( p≤0 . 08 ) , demonstrating that the residual adherence observed occurred independently of a pilus-PAFr interaction . Thus , taken together , the above data strongly suggest that the PAFr serves as a receptor for pilus binding during meningococcal infections . To directly assess the interaction of the pili-linked ChoP and glycan moieties in mediating an association with the PAFr , we next performed modified enzyme-linked immunosorbent assays ( ELISAs ) using purified components and as described in the “Materials and Methods” . In that the PAFr is not commercially available as an independent protein , two forms of stabilized PAFr were tested in our analyses: 1 ) recombinant human PAFr coupled to the G-protein , Giα2β1γ2 ( PAFr-G2 ) as a stabilizing agent and 2 ) recombinant human PAFr coupled to the G-protein , Giα3β1γ2 ( PAFr-G3 ) as a stabilizing agent . The wells of microtiter plates were lined with PAFr-G2 or -G3 to which pili isolated from a panel of N . meningitidis WT and mutant strains were added , as noted . Subsequent ELISA analyses again demonstrated a hierarchical pattern of pili adherence to PAFr-G2 and to PAFr-G3 that was consistent with that observed with the use of WT , mutant , and variant strains of C311#3 in IP ( Fig . 4C and S3 ) and antibody inhibition ( Fig . 5 ) assays using 16HBE14 cells , as well as in adherence assays performed using Chem-1-PAFr cells ( Fig . 6A ) The pilus-PAFr interaction was greatest for pili isolated from C311#3 WT ( glycan+ , ChoP+ ) , and this interaction was diminished in those wells in which pili isolated from strains lacking the glycan and/or ChoP PTMs ( i . e . , C311#3pglL , C311#3pptA , C311#3pglL S157A/S160A ) were added ( Fig . 7 ) . To demonstrate that this interaction was not limited to strain C311#3 , parallel assays were performed using the low passaged , clinically isolated; MPJ11 , MPJ24 , MPJ26 , and MPJ50; WT and pglL , pptA , and pglL/pptA mutant strains . These assays yielded comparable data to that obtained with the C311#3 laboratory strains , indicating that the PAFr-meningococcal interaction is not a strain-specific phenomenon . Additionally , statistically significant differences were observed when comparing the use of the pglL mutant pili to the pptA mutant pili ( p≤0 . 0001 for all meningococcal strains tested ) as well as when comparing the use of pptA mutant pili to pili isolated from C311#3 S157A/S160A ( p≤0 . 0001 ) or from the “MPJ” pglL/pptA double mutant strains ( p≤0 . 0001 for all strains tested ) , demonstrating that synergy exists between the glycan and ChoP moieties in mediating adherence to the PAFr . Further , in that comparable data were obtained with the use of PAFr-G2 or PAFr-G3 ( Fig . 7 ) , in which distinct G-proteins were used to stabilize recombinant human PAFr , the interaction observed between pili and PAFr-G2 or -G3 can be concluded to result from a specific interaction with the PAFr and not the stabilizing G-protein component . To determine whether strains that express the pptAG11 form of pptA ( and , therefore , express the inactive PtaAGly+1 protein ) are capable of an interaction with the PAFr in the rare event that they under-go phase variation to produce an active form of PptA and , thus , add ChoP to their pili , we genetically modified strain 8013SB WT to contain pptA8G ( strain 8013SBpptA8G ) and , thus , express an active form of PptA ( Fig . 1 ) . Strains 8013SB WT , 8013SBpglL , and 8013SBpptA8G were then examined for their ability to interact with Chem-1 and Chem-1-PAFr cells , as described above . Although each of the 8013SB WT and mutant strains examined displayed a PAFr-dependent association with the Chem-1 cells , the presence of ChoP on strain 8013SBpptA8G pili resulted in a greater than 2-fold increase in the ability of this strain to adhere to Chem-1-PAFr cells , when compared to 8013SB WT bacteria . Additionally , although 8013SB WT ( ChoP− ) bacteria were significantly less adherent to Chem-1-PAFr cells , than that observed for C311#3 WT ( ChoP+ ) bacteria ( p≤0 . 0001 ) , adherence levels for strain 8013SBpptA8G ( ChoP+ ) were not significantly different ( p≥0 . 081 ) from C311#3 WT ( Fig . 6B ) . Support for these data is obtained by the inclusion of pili isolated from 8013SB , and its derivatives , in the ELISA assays described above , in which the pili-PAFr-G2 or -PAFr-G3 interaction was examined ( Fig . 7B ) . A direct interaction between pili isolated from these strains and the PAFr is readily observed , as is the involvement of both ChoP and the pilin-linked glycan in this interaction . In this strain , the pptA gene is heterologously expressed from the porA promoter , resulting in the deletion of the porA gene , and the loss of PorA expression . A control strain , in which the porA gene was independently inactivated , revealed that the absence of PorA had no impact on 8013SB adherence to Chem-1 or Chem-1-PAFr cells . Similarly , the absence of PorA did not effect the ability of 8013SB-derived pili to adhere to PAFr-G2 or -G3 by ELISA . In all experiments , the 8013SBporA mutant strain behaved as did the 8013SB WT strain , indicating that data obtained with the use of strain 8013SBpptA8G were not the result of a genetic aberration introduced in generating the latter strain . Thus , collectively , the above data provide strong support for a key role for the PAFr in mediating adherence of meningococci to bronchial epithelial cells by a mechanism in which the glycan and ChoP moieties present on the core pilus structure act synergistically .
The presence of ChoP on pilin is subject to phase variation , and this results from alterations in the length of the homopolymeric guanine tract in pptA , the gene encoding the transferase responsible for linking ChoP to pilin [17] . The presence of surface exposed , phase variable ChoP is not unique to Neisseria . For example , ChoP is a covalently linked , post-translational modification found on the surface of predominate respiratory pathogens , e . g . H . influenzae [23] , S . pneumoniae [19] , and Pseudomonas aeruginosa [35] [24] [36] for which a role in mediating adhesion and invasion to host epithelia is described for ChoP . Additionally , C-reactive protein ( CRP ) present in blood has the potential to bind to ChoP that , in turn , can result in complement-mediated killing of H . influenzae bearing ChoP on their surface [22] . In this scenario , expression of ChoP would serve as a selective disadvantage to the microorganism . This has led to the idea that phase variation of ChoP provides one mechanism by which invasive pathogens adapt to a transitional lifestyle that occurs with respect to colonization of the respiratory mucosa and , subsequent , survival during systemic infections [22] . We could not demonstrate CRP/complement-mediated killing of strain C311#3 using the same methodology as described for H . influenzae ( 20 ) ; however , Casey et al . ( 2008 ) have shown ChoP-dependent opsonophagocytic killing of strain C311#3 , suggesting that expression of ChoP by meningococci may be disadvantageous in some environments . As ChoP is clearly important in the pathobiology of other respiratory pathogens , we sought to analyse its role in pili-related functions in N . meningitidis . When this work commenced , although neisserial pilin was known to be modified with ChoP , the location of ChoP on N . meningitidis pilin was undefined . Therefore , we initiated our investigation by asking whether ChoP was surface exposed in N . meningitidis strain C311#3 . Colony immunoblotting performed using the ChoP-specific antibody , TEPC-15 , revealed that ChoP was indeed surface exposed in this strain . However , they also revealed that a small number of colonies were hyper-reactive to antibody TEPC-15 , hinting that ChoP may be aberrantly presented or exposed in these hyper-reactive bacterial colonies . Additional Colony Blot analysis of isolated TEPC-15 hyper-reactive C311#3 variants confirmed that the accessibility of ChoP increased as the length of the pilin-linked glycan was shortened . However , this hierarchy of ChoP exposure was only seen for pili in its native , polymerised , state and not for denatured pilin examined by western blotting . The presentation and exposure of ChoP on meningococcal pili has the potential to affect the receptor-ligand interactions with host cells . Therefore , we sought to investigate the underlying mechanism ( s ) for TEPC-15 hyper-reactivity by sequencing the pilE and phase variable pilin glycosylation genes of C311#3 strains TEPC+1 , +2 , and +3 . Two of these TEPC-15 hyper-reactive variants , C311#3 TEPC+1 and C311#3 TEPC+2 , displayed changes in their respective pilE sequences . These sequence variations occurred near the site of PilE glycosylation or near the 3′ end of the pilE gene . The other variant , C311#3 TEPC+3 , had a frame shift in the phase variable pilin glycosylation gene , pglE . Collectively , these data provided evidence that , in addition to pilin amino acid changes , alterations to the trisaccharide structure also had the potential to influence the accessibility of ChoP on pili . In determining the site of ChoP modification to N . meningitidis pili , we hypothesised that ChoP would be O-linked to a serine residue because 1 ) ChoP is shown to be O-linked to sugar residues on the surface of other bacteria [17] , 2 ) serine and threonine residues are preferred sites for O-linked ( protein ) modification , 3 ) the primary sequence of meningococcal pilin contains a DASDAS motif at its C-terminus that could serve as a potential site ( s ) for O-linked ChoP addition , and 4 ) sequence variations occurring near the site of pilin glycosylation or near the 3′ end of the pilE gene altered the accessibility of antibody TEPC-15 to pilin-linked ChoP . To test this hypothesis , we initially targeted seven serine residues ( Ser34 , Ser45 , Ser68 , Se69 , Ser70 , Ser157 and Ser160 ) for conversion to alanine residues by site-directed mutagenesis . These seven serine residues were chosen for mutagenesis as they were located near the O-linked glycosylation site ( Ser63 ) in the mature pilus fibre of N . meningitidis , and during the course of our investigations , it was reported that ChoP is linked to Ser 68 in N . gonorrhoeae strain MS11 ( Hegge et al . , 2004 ) . In contrast to what was observed for N . gonorrhoeae , none of our N . meningitidis mutants , including a mutant harbouring a S68A mutation , showed the loss of ChoP . LC-MS/MS and mutagenesis analyses defined the location of ChoP as the pilus C-terminus . Upon consideration of the relative position of the ChoP and glycan modifications of pilin , it was difficult to resolve how the O-linked pilin glycan present on Ser63 could affect ChoP accessibility ( on Ser157 and Ser160 ) ; however , these data are consistent with our present model of the meningococcal pilus quanternary structure . That is , we have presented a model in which the pilin-linked glycan and ChoP moiety are remotely associated in the pilin monomer but reside in proximity in the context of the polymeric pilus fiber , and the surface accessibility to ChoP mediated by glycan structure described above may have functional consequences in host-pathogen interactions . In N . gonorrhoeae MS11 , Ser68 is reported to be the site for ChoP addition [37] , which is in proximity to the Ser63 site of glycan linkage . The same group has also reported a phosphoethanolamine modification at Ser156 of MS11 pilin [38] . In contrast , N . meningitidis C311#3 pilin has ChoP on both Ser 157 and 160 . Changing both of these residues to alanine resulted in the complete loss of TEPC-15 immunoreactivity . This suggests that the C-terminus of meningococcal pilin is the only site of ChoP modification , and further , modification by phosphoethanolamine was not observed in our study . This fundamental difference in meningococcal and gonococcal PTM by phosphate moieties may reflect the different lifestyles associated with these two Neisseria spp . Support for this idea is derived from studies examining ChoP expression by H . influenzae in which it is demonstrated that changing the site of ChoP addition on the surface of this bacterium corresponds to differential CRP-mediated bactericidal activity [39] . In this regard , H . influenzae seems to have developed methods to balance the beneficial and detrimental effects of ChoP exposure by varying both the presence/absence of ChoP displayed on its surface as well as the specific site of ChoP addition . In contrast to the systemic disease caused by N . meningitidis , in which CRP would be expected to play a role , N . gonorrhoeae is rarely associated with systemic infection . Colonization of the nasopharnyx precedes meningococcal disease and requires adhesion to the mucosal epithelium . Previous invasion data had implicated CD46 as the pilus receptor for the pathogenic Neisseria [40] . However , CD46 is basolaterally located on the mucosal epithelium [40] and an increasing body of evidence has ruled out a role for this host cell molecule as serving as the initial pilus receptor mediating adherence to host cells by N . gonorrhoeae [41] [42] [43] [44] . Although several pilus receptors are described for the gonococcus [45] [42] [43] , the pilus-host cell first point of contact in the airway has not been identified for N . meningitidis . Given that the ChoP moiety on platelet activating factor serves as the primary ligand for the PAFr and that several respiratory pathogens are able to bind to the PAFr through an interaction that involves surface exposed ChoP . We hypothesized that a pilin-linked ChoP-PAFr interaction could exist as one mechanism mediating N . meningitidis colonization of the human airway . Evidence we provide to support this hypothesis include data obtained from confocal microscopy and IP assays in which we observed co-localization of meningococci or meningococcal pilin with the PAFr on 16HBE14 SV40-transformed , human bronchial epithelial cells as well as on infected human bronchial tissue . We further show that the presence of anti-CD46 antibody competimers had no effect on the adherence of C311#3 WT and mutant bacteria to 16HBE14 cells; whereas , parallel assays performed using anti-PAFr , -pili , and -ChoP antibody competimers resulted in a significant ( P≤0 . 001 ) decease in meningococcal adherence . Additionally , we demonstrate N . meningitidis adherence to human PAFr-expressing Chem-1 cells as well as the direct association of pili isolated from a panel of laboratory and low passage , clinically isolated meningococci to stabilized recombinant human PAFr ( PAFr-G2 and PAFr-G3 ) . Taken together these data provide strong evidence that the PAFr serves as a key receptor mediating the initial , pilus-dependent , interaction of N . meningitidis with the airway epithelium . Furthermore , evidence to indicate that both the pilin-linked glycan and ChoP contribute directly to PAFr-mediated adherence to 16HBE14 cells are found in the following observations: ( 1 ) ChoP dependent adherence is directly related to the length of the pilin-linked glycan due to altered accessibility ( trisaccharide; ChoP+ ( C311#3 WT ) ≫disaccharide; ChoP+ ( C311#3pglE ) >monosaccharide; ChoP+ ( C311#3pglA ) >no glycan; ChoP+ ( C311#3pglL ) ) and ( 2 ) glycan dependent adherence is further decreased ( but not abolished ) when ChoP is absent ( ( trisaccharide; ChoP+ ) C311#3>>> ( trisaccharide; ChoP− ) C311#3 S157A/S160A ) . This same hierarchy of interaction was observed in 4 independent N . meningitidis clinical isolates . To our knowledge , this situation , in which dual post-translational modifications of a bacterial adhesion mediate interactions with the host cell receptor , is unique in biology . The pivotal steps in the transition of some N . meningitidis-infected individuals from a state of harmless , asymptomatic carriage to rapidly progressing , often fatal , sepsis are poorly understood . A series of recent studies focusing on the interactions of meningococci with endothelial cells have reported that type IV pili can mediate adhesion to brain endothelial cells allowing bacteria to cross the blood-brain barrier via ß2-adrenoceptor/ß-arrestin signalling pathway [46] [47] . This signalling pathway does not occur in adhesion to epithelial cells [48] . In particular , Chamot-Rooke et al . ( 2011 ) reported that the addition of phosphoglycerol to meningococcal pilin by PptB plays a role in initiating disseminated infections . These authors propose that , upon contact with the human airway , pptB is up-regulated; pilin is decorated with phosphoglycerol , that then causes the dissociation of bacterial colonies with the airway epithelium and , in turn , disseminated infection . The phosphoglycerol PTM study described above raises several key mechanistic questions ( for commentary see [49] ) , and focuses on a PTM distinct from those described herein . However , both studies address pili PTM-mediated host-pathogen interactions , so it is import to consider the key differences between our investigations and those of Chamot-Rooke et al . ( 2011 ) . Our investigations were aimed at identifying the crucial , initial , events mediating meningococcal adherence at the airway interface , including the identification of the previously unknown receptor involved in this process . Our on-going studies are focused on elucidating the events mediating pathogenesis following that particular mechanism of adhesion . The Chamot-Rooke et al . ( 2011 ) study involved the use of a human endometrial cell line for adhesion assays . This cell type is not relevant to the initial contact step occurring within the airway . For initial attachment studies , we used physiologically relevant human bronchial cells as well as human bronchial tissue . Secondly , we identified the key , initial , receptor in cell attachment , whereas Chamot-Rooke et al ( 2011 ) did not characterise the human cell receptor that mediates adherence observed in their studies . Rather , they focused on the bacteria to bacteria interaction mediated by pili ( pili bundling ) with the distinct , pptB mutant generated , phosphoglycerol PTMs phenotype . Further , it is important to note that in strain 8013SB ( used in the Chamot-Rooke et al , 2011 , study ) the PptA ChoP transferase that is expressed from pptA11G is the inactive PptAGly+1 form ( see Fig . 1 ) . Strain 8013SB is atypical of most meningococcal clinical isolates in that it does not express ChoP on pili . Therefore , the rapid and early engagement of the PAFr described herein would not have been be possible to observe in the strain ( 8013SB ) used in the Chamot-Rooke et al study , or , indeed , in the other endothelial cell signalling studies noted above , which also used strain 8013SB [46] , [47] , [48] . However , by altering the homopolymeric “G” region of this same strain such that a ChoP+ phenotype would be expressed ( i . e . , strain 8013SBpptA8G ) , we have shown that adherence to Chem-1-PAFr cells as well as PAFr protein is increased greater than 2-fold . Nevertheless , it is presently not clear if a meningococcal ChoP-PAFr interaction would result in the complex bacterial detachment phenotype observed by Chamot-Rooke et al ( 2011 ) . Similarly , it is not clear if the pili bundling phenotype ( suggested to result from the negative charge associated with the phosphoglycerol PTM in strain 8013 ) would , indeed , occur with strain 8013SB were it capable of naturally undergoing pilin ChoP ( positive charge ) PTM , as is true for the vast majority ( >94% ) of meningococcal clinical isolates . Although pili are critical to N . meningitidis colonization in being able to promote the adherence of capsular , as well as acapsular organisms; the host surface molecule recognized , and bound , by pili had previously remained elusive despite extensive study . In this regard , we have provided the first evidence demonstrating a role for both the pilin-linked glycan and ChoP as important contributors in the adherence of N . meningitidis to human bronchial epithelial cells . However , of greater importance is our identification of the PAFr , a G-protein coupled receptor , in mediating the earliest contact with human bronchial cells and tissue in that the immunomodulatory properties of the PAFr are well-characterized and include a specific role in promoting polymicrobial-induced sepsis [50] [51] . Whether the engagement of the PAFr within the human airway by the pilin glycan and/or ChoP results in signalling events consistent with an acute inflammatory response remains to be determined . Similarly , it is not known if the expression of ChoP and/or a particular pilin glycan are predominate in N . meningitidis isolated from the human airway , human blood , or human cerebral spinal fluid . These and other unanswered questions are the basis for on-going studies in our lab .
Meningococcal strains used in this study are listed in Tbl S1 . Meningococcal strains were grown on Brain Heart Infusion ( BHI ) -1% agar-10% ( both from Oxoid ) - Levinthals Base medium at 37°C with 5% CO2 for 16–18 hrs . Escherichia coli strain DH5α was used to propagate cloned plasmids and were grown at 37°C in Lauria-Bertani ( LB ) broth supplemented with either ampicillin ( 100 µg/ml ) or tetracycline ( 5 µg/ml ) . All primers used for the following procedures are listed within Tbl . S2 . The plasmid , pGEMTetMBpilE::His/lpxC [52] , was modified using splice overlap PCR [53] to construct the different serine mutants . The upstream region of pilE was amplified using the primer PilE-NotI and the relevant reverse primer . The downstream region was amplified using the Tet-HindIII primer and the relevant forward primer ( incorporating the desired change ) . The mutated full-length product was then amplified by a reaction consisting of an equal mix of upstream and downstream DNA and by using the primers PilE-NotI and Tet-HindIII . This amplicon was digested using the restriction endonucleases NotI and HindIII and ligated into the plasmid backbone of NotI- and HindIII-digested pGEMTetMB::lpxC [52] . A FLAG-tagged pilE was constructed by fusing the FLAG sequence to pilE using the primers PilE-NotI and FLAG-XhoI . The primer , FLAG-XhoI , comprises the FLAG-tag and an XhoI digestion site extension that allows in-frame tag incorporation at the C-terminus of pilE , and , therefore , does not interfere with secretion of the mature pilin peptide . Following digestion , the resulting pilE::FLAG DNA fragment was directionally ligated into pGEMTetMBlpxC . The resulting plasmid was sequenced to ensure the desired change had occurred and transformed into acapsular N . meningitidis C311#3 , as previously described [14] . The pptA gene in N . meningitidis C311#3 was inactivated by the insertion a tetracycline ( tetM ) cassette as described in [17] . The pptA genes from C311#3 and 8013SB were amplified by primers ( pptA_EagI and pptA_NcoI Table S2 and cloned into the EagI and NcoI sites in plasmid pCO14k [54] which can be used for the heterologous expression of genes from the porA locus [55] . The plasmids of pptA gene with various polyG numbers were constructed by inverse PCR using the primers described in Table S2 . The various pCO14kpptA plasmids were linearized and transformed into the C311#3pptA::tet and 8013SB . C311#3pilE::FLAG were grown overnight on BHI agar , harvested into TE buffer and heat-killed for 1 h at 56°C . The bacterial cells were lysed by a French-press ( 5 times , 1000 psi ) after which the lysate was centrifuged ( 14 , 000 g , 30 min , 4°C ) . The supernatant was then loaded onto an affinity gel column with ANTI-FLAG M2 Affinity Gel ( Sigma ) . FLAG-tagged pilin was purified per the manufacturer's instructions ( Sigma ) . Column purified , FLAG-tagged pilin was then processed for spectrometric analysis , as outlined within the text . In separate experiments , pili from WT and mutant meningococci were isolated as described by Power et al . [56] and analysed by western blotting , also as outlined within the text . Purified pilin was reduced and then alkylated with 50 mM dithiothreitol and 100 mM iodoacetamide , respectively . Four volumes of 1∶1 acetone∶methanol were added , incubated ( −20°C for 16 h ) and centrifuged ( 18 , 000 rcf , 10 min ) to collect alkylated pilin . This protein was then dried at room temperature ( RT ) before resuspension in 50 µL of 50 mM NH4HCO3 and digestion ( 37°C , 16 h ) with 1 µg trypsin ( Sigma ) . Digested pilin peptides then were analysed by LC–ESI/MS/MS using an API QSTAR Pulsar i LC/MS/MS system ( Applied Biosystems ) . Samples were separated on a ZORBAX SB-C18 5 µm , 150×0 . 5 mm column previously equilibrated with 5% acetonitrile-0 . 1% formic acid in water , after which they were eluted over 45 min using a gradient from buffer A ( 0 . 1% formic acid ) to buffer B ( 90% acetonitrile with 0 . 1% formic acid ) . Analyst QS 1 . 1 software was used to manually examine LC–MS and MS/MS data for the presence of predicted peptides and for ChoP-modified peptide . WT , mutant , and derivative C311#3 strains were transformed with the green fluorescent protein ( GFP ) expressing plamid pCmGFP [57] , as previously described [58] . Confocal microscopy of GFP-expressing C311#3-infected and uninfected 16HBE14 , SV40-transformed , human airway epithelial cells [59] was conducted as previously described [23] following immunolabeling with rabbit anti-PAFr immune sera , Alexa 647:Goat anti-rabbit secondary antibody , and staining with CellTracker Red ( Invitrogen ) . The 16HBE14 cell line was originally derived by SV-40 transformation of a polyclonal population of cells obtained from human bronchial epithelium and thus is heterogeneous . A Zeiss 510 Laser scanning confocal microscope located at the Central Microscopy Research Facility ( University of Iowa , Iowa City USA ) was used to evaluate co-localization of C311#3 with the PAFr . Immunolabeling of frozen infected bronchial tissue sections was performed as described by Edwards et al . ( 2000 ) . Primary antibodies used for immunolabeling were specific for the PAFr ( H-300 , rabbit ) or N . meningitidis PIB ( pS-20 , goat; both from Santa Cruz Biotechnology , Santa Cruz , CA ) . Rhodamine- or FITC-conjugated secondary antibodies ( Jackson ImmunoResearch Laboratories , West Grove , PA ) were applied to tissue sections , cell monolayers , and bacteria , as noted . Omission of the primary antibodies served as a negative control for non-specific labelling and autofluorescence . Immunolabeled tissue cryosections and cell monolayers were viewed using the Zeiss 510 Laser Scanning Confocal viewing system located at the Research Institute at Nationwide Children's Hospital ( Columbus , OH ) . Western or colony blotting was performed using standard protocols and the following primary antibodies: 1 ) mouse anti-ChoP , TEPC-15 , ( Sigma , St . Louis MO ) , 2 ) mouse anti-pilin , SM1 , [33] , 3 ) rabbit polyclonal anti-pilin , or 4 ) goat anti-PAFr antibody , C-20 ( Santa Cruz Biotechnologies , Santa Cruz , CA ) . Colorimetric/chemiluminescent detection of pili or pilin-associated immune complexes was obtained , as noted , following the application of a phosphatase- or a peroxidase-conjugated secondary antibody and the addition of the appropriate enzyme substrate . In separate experiments; bacterial colonies , purified pili , as well as anti-PAFr or anti-pilin immunoprecipitates served as substrates for immunolabeling . For colony immunoblotting , overnight cultures of meningococci were subcultured on BHI agar and plated to allow single colony formation . Immunoprecipitation was performed as described by Wen et al . [60] . Rabbit immune serum to C311#3 pilus or goat anti-PAFr antibody , C-20 , were used to capture immune complexes . C311#3-infected , 16HBE14 cell lysates in which the anti-PAFr or –pilus primary , or agarose-conjugated secondary , antibodies were omitted ( negative controls ) were treated in parallel with uninfected and infected cell lysates to which antibodies had been added . The ability of WT and mutant N . meningitidis strains to adhere to the PAFr was determined using modified fluormetric ELISAs . 16HBE14 , Chem-1 , or PAFr-expressing Chem-1 ( Chem-1-PAFr; EMD Millipore , Billerica , MA ) cells were used to assess the ability of bacteria to adhere to the PAFr on a cellular surface . 16HBE14 cells were passed to 96-well microtiter plates and allowed to grow to confluence in Airway Medium [61] . Chem-1 and Chem-1-PAFr cells were seeded to microtiter plates at 104 cells per well using ( DMEM-based ) medium supplied by the manufacturer . At least 24 h before use in infection studies , culture media were replaced with antibiotic- and serum-free medium . For antibody competiton assays , anti-PAFr [23] , -ChoP ( XC10 ) , or - pilin rabbit immune serum was added to select microplate wells to yield a final antibody dilution of 1∶50 . Anti-CD46 antibody , H-294 , ( 20 µg/ml , Santa Cruz ) served as an arbitrary antibody competimer control . Immediately following antibody addition , GFP-expressing meningococci were used to challenge 16HBE14 cells at a multiplicity of infection of 100 . Blank wells , devoid of human cells , also were inoculated with bacteria and served as controls for potential fluorescence intensity differences among the bacterial strains examined . Chem-1 and Chem-1-PAFr cells were challenged with 106 N . meningitidis WT or mutant ( non-GFP-expressing ) bacteria per well , as noted . For either assays , infections were stopped after 30 min by the removal of the infection medium , extensively rinsing the cell monolayers with phosphate buffered saline , and cell fixation . Following fixation , were noted N . meningitidis were immunolabeled using monoclonal antibody 2C3 , which recognizes the conserved H . 8 outer membrane protein of the pathogenic Neisseria , and a FITC-conjugated secondary antibody . Infected , control 16HBE14 cell assays ( devoid of antibody competitors ) and uninfected , control cell assays ( with antibody competimers ) were treated in parallel with competitive antibody inhibition assays . Uninfected and infected , Chem-1 and Chem-1-PAFr cells were , similarly , treated in parallel within the confines of the same microtiter plate . Fluorescence intensity , corresponding to bacteria adherence , was recorded using a Synergy HT Multi-mode Microplate Reader ( BioTek Instruments , Winooski , VT ) . Each assay set was performed in triplicate on 3 separate occasions . A Kruskal-Wallis k-sample analysis of variance was used to determine the statistical significance of the calculated mean adherence ( recorded as fluorescence units ) for each of the bacterial strains examined under each assay condition . No significant difference ( p≤0 . 02 ) was observed among the bacteria inocula used to challenge 16HBE14 cells , as determined by a Student-t Test . Modified ELISAs were also used to examine the direct interaction of N . meningitidis pili with the PAFr . Purified PAFr , as a single protein entity , is not commercially available; therefore , 2 sources of stabilized human PAFr were comparatively evaluated for their ability to interact with meningococci or isolated meningococcal pili . These comprised recombinant PAFr coupled to the G-proteins Giα2β1γ2 ( PAFr-G2 ) or Giα3β1γ2 ( PAFr-G3; Axxora , Farmingdale , NY ) . In separate studies , 96-well microtiter plates were coated with 1 µg PAFr-G2 or PAFr-G3 . Plates were then incubated with 10 µg pili isolated from each meningococcal strain ( as described in 59 ) . Plates were washed ( 6× ) before immunolabeling an anti-pilin ( rabbit ) primary antibody and a FITC-conjugated secondary antibody . The PAFr-N . meningitidis pili interaction was subsequently recorded as arbitrary fluorescence units ( FUs ) at 485 nm excitation/528 nm emission using a Synergy HT multimode plate reader . Assays were performed in quadruplicate on three separate occasions . Statistical significance of the data obtained was determined using paired Student-t Tests . | Neisseria meningitidis is an important human pathogen that can cause rapidly progressing , life threatening meningitis and sepsis in humans . There is no fully protective vaccine against this pathogen in current use and the key processes that dictate the transition from harmless carriage of the bacterium in the airway ( the case for the vast majority of colonised hosts ) to invasive disease are largely undefined . A key missing link in this organism's interaction with the human host is the identity of the receptor that is the first point of contact for the organism within the airway . In this study , we report that the receptor for this important human pathogen on airway epithelial cells is the platelet activating factor receptor ( PAFr ) , an immunomodulatory molecule shown by others to play a role in promoting bacterial sepsis . We also show that two post-translational modifications , glycosylation and phosphorylcholine , are subject to phase-variation ( high frequency , reversible switching of gene expression ) . They are closely associated on adjacent pilin subunits , and synergy between both are required for the efficient engagement with the PAFr . These data define a new role for these post-translational modifications in meningococcal adherence and also provide an insight into the selective pressures that underlie their phase variable expression . | [
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"medicine",
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] | 2013 | Dual Pili Post-translational Modifications Synergize to Mediate Meningococcal Adherence to Platelet Activating Factor Receptor on Human Airway Cells |
Dietary restriction ( DR ) is the most consistent means of extending longevity in a wide range of organisms . A growing body of literature indicates that mitochondria play an important role in longevity extension by DR , but the impact of mitochondrial genotypes on the DR process have received little attention . Mitochondrial function requires proper integration of gene products from their own genomes ( mtDNA ) and the nuclear genome as well as the metabolic state of the cell , which is heavily influenced by diet . These three-way mitochondrial-nuclear-dietary interactions influence cellular and organismal functions that affect fitness , aging , and disease in nature . To examine these interactions in the context of longevity , we generated 18 “mito-nuclear” genotypes by placing mtDNA from strains of Drosophila melanogaster and D . simulans onto controlled nuclear backgrounds of D . melanogaster ( Oregon-R , w1118 , SIR2 overexpression and control ) and quantified the lifespan of each mitonuclear genotype on five different sugar:yeast diets spanning a range of caloric and dietary restriction ( CR and DR ) . Using mixed effect models to quantify main and interaction effects , we uncovered strong mitochondrial-diet , mitochondrial-nuclear , and nuclear-diet interaction effects , in addition to three-way interactions . Survival analyses demonstrate that interaction effects can be more important than individual genetic or dietary effects on longevity . Overexpression of SIR2 reduces lifespan variation among different mitochondrial genotypes and further dampens the response of lifespan to CR but not to DR , suggesting that response to these two diets involve different underlying mechanisms . Overall the results reveal that mitochondrial-nuclear genetic interactions play important roles in modulating Drosophila lifespan and these epistatic interactions are further modified by diet . More generally , these findings illustrate that gene-by-gene and gene-by-environment interactions are not simply modifiers of key factors affecting longevity , but these interactions themselves are the very factors that underlie important variation in this trait .
The extension of longevity by dietary or caloric restriction ( DR or CR ) has been demonstrated in a wide range of organisms , suggesting evolutionarily conserved pathways that regulate this response [1] , [2] , [3] , [4] , [5] . However , dissecting the genetic and cellular mechanisms of DR remains a great challenge . Several pathways have been identified that mediate the DR response such as the insulin [6] , [7] , mTOR [8] , [9] , [10] , AMPK [11] , [12] , [13] , [14] and sirtuin pathways [15] , [16] , [17] , [18] . These pathways intersect through downstream targets and involve mechanisms of regulation and feedback . Thus , the control of DR involves a network of pathways rather than any one critical pathway , which underlies the challenge of identifying singular genetic and biochemical mechanisms for the DR response . While efforts to identify single genes , pathways or nutrients that extend longevity have been productive , studies that focus on the interaction among relevant factors have received relatively little attention [19] . Longevity , and its extension by genetic or dietary interventions , are complex phenotypes , and it is increasingly apparent that gene-by-gene ( G×G ) and gene-by-environment ( G×E ) interactions are fundamental components of these kinds of traits [20] , [21] . Given the complexity of the genetic , nutritional and physical environments in which most organisms live , including humans , explicit studies of the role of these interaction effects are critically important to determine the generality of single factor analyses . The mitochondrion has received increasing attention as a nexus for regulation of the longevity extending effects associated with DR [10] , [22] , [23] , [24] , [25] . Mitochondria generate critical energy stores in the form of ATP and NADH that can promote cellular maintenance and longevity , but also generate reactive oxygen species ( ROS ) that can cause cellular damage and senescence . As a hub for input from multiple pathways affecting longevity , mitochondria provide a compelling target for studies seeking to understand gene-by-gene and gene-by-environment effects in aging . Mitochondrial function and biogenesis are dependent on genes encoded in both the mtDNA and the nuclear chromosomes . Animal mtDNA encodes 13 protein coding subunits of oxidative phosphorylation complexes of the electron transport chain ( ETC ) with more than 70 remaining subunits encoded by nuclear genes and imported in to the mitochondrion [26] , [27] . mtDNA also encodes a minimal set of RNAs ( 2 rRNAs and 22 tRNAs ) that comprise the translation machinery inside the mitochondrion , with the remaining protein components , such as ribosomal proteins and tRNA synthetases , encoded by nuclear genes and imported into the organelle . Thus , mitochondrial function depends critically on the coordination of mitochondrial and nuclear encoded components and this co-dependence provides the basis of mitochondrial-nuclear interactions ( hereafter mitonuclear interactions ) , which is one mode of gene-by-gene interaction , or epistasis , that may affect fitness , disease and aging [28] , [29] , [30] . Specific genes have been identified that play important roles in modifying mitochondrial function in response to DR . The TOR pathway regulates nutrient sensing and protein translation [31] , [32] , and 4E-BP regulates differential translation of mRNAs encoding proteins targeted for mitochondria vs . cytosolic function [10] . The PGC-1α family of transcriptional co-activators also plays a critical role in regulating mitochondrial biogenesis with systemic and tissue-specific effects on longevity [22] , [33] . Altered expression of specific subunits of electron transport chain ( ETC ) complexes can reduce the efficacy of longevity extension by DR [10] , [34] , suggesting that the coordination of nuclear and mtDNA-encoded components of mitochondrial function are important in mediating a proper response to DR . Genetic analyses of longevity extension by DR are examples of the more general problem of genotype-by-environment interaction , as the question concerns the identification of genes or alleles that generate a novel phenotype ( longer life ) in a novel environment ( reduced protein or calories ) . Thus , mitochondrial regulation of DR or CR not only requires the complex mitonuclear interactions , but also involves the impact of the dietary environment on this gene-by-gene interaction . Collectively , this network can be summarized as a three-way interaction between mitochondrial genotype , nuclear genotype and dietary environment denoted as G×G×E in quantitative and ecological genetics . In this study we explicitly unite all three of these issues: mtDNA variation , nuclear gene variation and dietary variation to test hypotheses about the generality of single-factor effects on longevity under DR and CR . By employing a 5-diet design , we attempt to make the distinction between CR and DR . In caloric restriction ( CR ) only the caloric content of the diet is reduced while the relative composition of macro-nutrients is kept untouched . In dietary restriction ( DR ) , one focal nutrient of the diet , such as the proportion of protein or sugar , is restricted with the caloric content kept constant ( see Figure 1 ) . We address the questions of main effects and interaction effects directly using Drosophila genetic tools to jointly manipulate mitochondrial and nuclear gene mutations , and examine these individual and combined effects on lifespan in response to dietary alterations . We demonstrate that mtDNA genotype alters the dietary effect on lifespan in each of two commonly used nuclear genetic backgrounds of D . melanogaster . We also find that mtDNA haplotypes can alter the effect of SIR2 overexpression on life span in a diet dependent manner . These results demonstrate the three-way mito-nuclear-diet interaction as an important factor in shaping longevity outcomes . These results imply that an accurate assessment of mitochondrial factors cannot be made without the context of nuclear genetic background and dietary regime , each of which has been implicated as a single important factor influencing longevity .
For each of two axes of dietary manipulation , both the concentration axis and the composition axis ( see Figure 1 ) , there was a robust lifespan response in each of two standard D . melanogaster strains , w1118 and OreR ( Figure 2 and Table 1 ) . The concentration axis is made of food type I , II and III ( Figure 2A and C ) . There is a clear pattern that mean lifespan declines with food concentration . Strain w1118 is shorter-lived than OreR ( comparing Figure 2A , B to Fig . 2C , D ) , which also agrees with earlier work where w1118 is generally found to be a highly fecund but short-lived strain [35] , [36] . Food type V , II and IV make up a composition axis , where the sugar:yeast ratio is 3∶1 , 1∶1 and 1∶3 , respectively . In both nuclear genetic backgrounds , it is apparent that the low protein and the balanced diets results in long lifespan , with the high protein diet resulting in reduced lifespan ( Figure 2B , D ) . Pooling data crosses all mitotypes and just focusing on the dietary affect , the estimates of hazard ratio of different diets are summarized in Table 1 . It shows that compared to the median level diet ( type II ) , the high yeast diet ( type IV ) and high caloric diet ( type III ) roughly double the chance of death ( see Table 1 , column ‘hazard ratio’: for w1118 the risks of death for diet types III and IV are 1 . 9442 and 2 . 0007 relative to type II; for OreR these values are 1 . 6809 and 2 . 1390 ) . In Drosophila studies , yeast is usually the only protein source , and increase in yeast concentration leads to high fecundity and short lifespan [36] . It has been further demonstrated that rather than yeast concentration alone , sugar/yeast ratio is what leads to the longevity and reproductive changes [37] , [38] . Here we have a high sugar food , a high yeast food and a balanced food with their caloric content roughly being equal . While altering either the diet composition or concentration significantly affects lifespan ( P<0 . 001 for both strains ) , different nuclear genotypes appear to have different optimal sugar/yeast ratios that result in maximum longevity ( compare w1118 to OreR in Figure 2B and D; Note that this figure presents data for multiple mtDNA types , described in the following section ) . To examine the effects of mtDNA genotype on longevity , we generated 18 ‘mito-nuclear’ genotypes by placing mtDNA from strains of Drosophila melanogaster and D . simulans onto controlled nuclear backgrounds of D . melanogaster ( Oregon-R , w1118 , SIR2 overexpression and control; see Methods ) . In each diet regime , on each nuclear background , the individual mitotypes show significant variation in longevity ( P<2×10−16; Table 2 , effect of mito; note: ‘nucleartype’ = nuclear genetic background; ‘mitotype’ = individual mtDNA genotype ) . Mitochondrial effects are largely due to variation among individual mitotypes while species-level mtDNA sequence divergence surprisingly contributes very little ( compare D . simulans mitoypes siI , sm21 , w501 to D . melanogaster mitotypes OreR and Zim; Figure 2 A–D ) . Although in some cases it appears D . simulans mitotypes are shorter lived than D . melanogaster mitotypes on specific diets ( w1118 background under CR , Figure 2A ) , this species divergence effect is not significant across all diets given the magnitude of within-species variation observed . Comparing a model where individual mitotypes are treated separately to a model where mtDNA variation is treated as a species divergence effect with individual mitotypes nested within species divergence , the latter model is not significantly better in the OreR nuclear background ( table 2 , χ2 = 0 . 1113 , n . s ) , indicating the lack of support for an mtDNA species-divergence effect . In the w1118 nuclear background , the species effect ( χ2 = 3 . 7314 , P = 0 . 053 ) is marginally significant . While the support for a species-level effect of mtDNA on longevity is again not strong , this effect does appear to be stronger in w1118 than in the OR nuclear background . The variation among mitotypes for longevity is , however , very significant in each nuclear background ( table 2 , χ2 = 156 . 07 for OreR and χ2 = 556 . 76 for w1118 , d . f . = 2 , P<0 . 001 for both ) . Dietary effects are modulated by mitochondrial genotypes and this genotype-by-environment interaction ( G×E; technically a mitotype-by-environment ( M×E ) interaction ) is also a big contributor to survivorship . We found dietary effects to be variable in different mitotypes ( Table 2 , χ2 = 149 . 68 in the OreR nucleartype background and χ2 = 45 . 813 in the w1118 nucleartype background , d . f . = 1 , both P<0 . 001 ) . However , the same is not true for species mtDNA divergence: dietary effect is virtually not altered by the species-level mtDNA divergence effect on either nuclear background ( table 2 , χ2 = 0 . 0129 for OreR and χ2 = 0 . 0001 for w1118 , d . f . = 1 , both insignificant ) . The use of independent contrast approaches , or scaling for mtDNA branch length does not alter the outcome of these analyses ( data not shown ) . Together these results indicate that mtDNA polymorphisms have a more pronounced effect on longevity than the species-level mtDNA divergence of ∼100 amino acids and a total of 571 fixed nucleotide substitutions between the mtDNAs of these two species . In the statistical analyses for the previous section , mitotype w501 was excluded from the results reported in Table 2 as it is known to have a strong negative epistatic interaction with the Oregon-R nuclear background [29] . The D . simulans w501 and sm21 mtDNAs differ from each other by six mutations , 5 of which are synonymous or indels in non-coding DNA , and the only putative functional mutation is in the anticodon stem of the mitochondrial tRNATyr gene . Further genetic mapping shows that the w501 mtDNA interacts negatively with a mutation in the nuclear-encoded gene for mitochondrial-tRNATyr synthetase [29] . This gene is polymorphic in D . melanogaster: the OreR genetic background carries the mutant allele and w1118 carries the wildtype allele for mitochondrial tRNATyr synthetase on chromosome 2 . Transgenic analyses of this single nucleotide show that it is responsible for the negative interaction with the point mutation in the tRNATyr gene of w501 mtDNA [29] . Our longevity results show that the w501 mitotype is generally shorter-lived than sm21 in OreR but not in w1118 , which carries the wild type allele of the nuclear-encoded tRNATyr synthetase ( Figure 2 a–d ) . Therefore , the lifespan difference caused by the mt-tRNATyr mutation between w501 and sm21 appears to be strongly dependent on nuclear background , as is true for several other phenotypes [29] , [39] . This empirical pattern has very strong statistical support . The w501 mtDNA -by-nuclear term is highly significant ( χ2 = 132 . 98 on 1 degrees of freedom , P<0 . 001 ) . It can be further determined that in the OreR genetic background , the sm21 mitotype has about 53 . 8% risk of death relative to the w501 mitotype , which is highly significantly lower ( Table 3 , P<0 . 001 ) . In contrast , in the w1118 genetic background , the risk of death in sm21 and w501 mitotypes are not significantly different ( Table 3 , P = 0 . 952 ) . The strong mitonuclear interaction for longevity with the w501 mtDNA is further modified by dietary environment . To quantify this , a series of contrasts were performed where the w501 and sm21 mtDNAs were evaluated as either two separate mitotypes or as one pooled class of mitotype , based on the fact that w501 is most closely related to the sm21 mtDNA ( both are siII mtDNA haplotypes of D . simulans differing by only 6 nucleotides; see above ) . Two groups of models were evaluated ( see Table 4 ) . The first model evaluates main effects of mitotype and diet , and contrasts a model of five mitotypes ( si1 , sm21 , w501 , OreR , and Zim ) vs . a four-mitotype model ( si1 , sm21 and w501 pooled , OreR and Zim ) , to test whether w501 has a significant main effect . The second set of contrasts was performed between nested models ( mitotype effect nested within diet effect ) , and again we compared the models containing 5 mitotypes versus the reduced models of 4 mitotypes ( w501 and sm21 pooled ) to test whether the w501-by-diet effect is significant . These contrasts were run using data for all five diets , just the DR conditions ( diets IV , II , V ) , or just the CR conditions ( diets I , II , III ) . The top half of Table 4 shows the results of the main-effect models . In the w1118 nuclear background the sm21/w501 distinction is only significant under the DR conditions , whereas in the OreR nuclear background the sm21/w501 distinction significantly improves the models under all diet conditions ( see Table 4 , top half ) . These analyses confirm that the w501 effect on longevity is greater on the OreR background , as expected from the known epistatic interaction [29] . However , the nested models reveal that even though w501 does not have a significant main effect in the w1118 background , the diet-by-w501 effect improves the model greatly compared to the main effect model ( compare entries from top half and bottom half of Table 4 ) . In the OreR background where the w501 main effect is significant , the diet-by-w501 effect greatly improves the model when all diets are analyzed , and under CR diets . Notably , in the OreR nuclear background under DR diets , there is little improvement in the model fit between main-effect and nested models ( χ2 = 198 . 32 vs . 201 . 24 ) . These analyses provide a specific example of G×G×E interactions: a particular mtDNA with distinct effects on longevity in alternative nuclear backgrounds generates unique responses to the dietary environments that modify longevity . Mitochondrial DNA copy number also shows a w501-by-nuclear epistatic effect . We observed that the OreR nucleartype carrying the w501 mtDNA has a significant increase in mtDNA/nDNA copy number ratio over the sm21 mitotype and such an increase is not present in w1118 nuclear background ( Figure 3 ) ( F2 , 30 = 9 . 79 , P = 5 . 34×10−4 , General Linear Model: mitotype effect nested within nuclear background effect ) . Given that mtDNA/nDNA ratio is a good indicator of mitochondrial biogenesis , this pattern may be due to the compensatory response in the presence of a w501 mito OreR nuclear incompatibility . Two mitotypes of each species ( OreR , Zim , sm21 siI ) were placed on to SIR2 overexpression and control nuclear backgrounds to test the hypothesis of a SIR2-mediated mitonuclear interaction , and to examine the response of these genotypes to DR and CR dietary environments . The SIR2 overexpression genotypes show similar mean lifespans on each of the diets of the caloric restriction gradient ( diet type I , II and III ) which displays a lack of CR response , while the SIR2 control genotypes display a robust CR effect ( compare the slopes of the lines in Figures 4A and 4C ) . The reduced response of the SIR2 overexpression genotypes compared to the controls is consistent across different mitotypes . Statistical analyses of these data reveal that increase of caloric content increases the risk of death , however , the magnitude of increase is modulated by SIR2 overexpression . A very strong dietary effect on survivorship is observed in the control genotypes , ( Table 5 , 60 . 1% increase in the risk of death with each diet level , P = 5 . 29×10−103 ) . In the SIR2 overexpression genotypes , this increase in risk is small but remains significant ( Table 5 , Caloric manipulations , hazard ratio column , 5 . 51% increased risk with each diet level , P = 0 . 0132 ) . Compared to the distinct responses of SIR2 overexpression and control genotypes to the CR gradient , these same genotypes have clearly parallel responses to the DR gradient , i . e . when the animals receive dietary manipulation in a form of food composition change ( sugar:yeast ratio axis , diet type IV , II and V; compare the slopes of the lines in Figures 4B to 4D ) . Overall , a diet rich in yeast ( S/Y = 1∶3 ) reduces longevity whereas a diet with relatively low yeast ( S/Y = 3∶1 ) extends longevity , relative to the balanced diet . This dietary gradient has similar effects in both SIR2 overexpression and the control genetic background: the higher yeast diet causes a 54 . 9% increased risk of death in the control and 67 . 8% in the overexpression genotypes , P<0 . 001 for both; see Table 5 , S/Y manipulations , column hazard ratio ) . The observation that SIR2 overexpression reduces the response to caloric restriction agrees well with the original report in D . melanogaster in which SIR2 overexpression extended life span on high caloric diet but gains no additional extension on CR diet [40] . The new finding that SIR2 overexpression has relatively little impact on longevity changes across a diet composition gradient , while diet V of this gradient does result in longevity extension ( Figures 4B and D ) suggests that the mechanisms of nutrient sensing of S/Y ratio are either independent of , or abrogate , the main effects of SIR2 . Our results provide a clear example that CR and DR are very different processes and reveal considerable complexity underlying diet-genotype interactions that modify longevity . The plots of mean lifespan indicate that the variance among mitotypes is lower in the SIR2 overexpression background than in the control background ( Figure 4 ) . The random effect Cox model confirms this observation . Across the CR axes ( diet I II and III ) , the variance of the mitochondrial effect is 0 . 4216 in the control genotype and drops to 0 . 1506 with SIR2 overexpression ( Table 6 ) . Across the DR axes ( diet V , II and IV ) , our results show the mitotype effect to be larger than across the CR axes . Moreover , SIR2 overexpression reduces the mitotype effect from 0 . 8138 to 0 . 3858 ( Table 6 ) . Figure 4 shows that the impact of SIR2 overexpression is evident for mitotypes OreR , Zim and sm21 , with mitotype si1 showing less response . With the si1 mitotype removed , the variance component among mitotypes diminishes ( Table 6 ) . The different responses of the two D . simulans mtDNAs ( sm21 and si1 ) result in little or no impact of species-level mtDNA divergence on longevity . It is apparent that the SIR2 overexpression reduces longevity of each mitotype on the CR and DR axes ( Table 5 ) , but the si1 mitotype is less sensitive to this effect than the other three ( OreR , Zim , sm21 ) . The distinct response of the si1 mitotype to these SIR2 and diet experiments , and the minor role for species-level divergence , indicates that nucleotide polymorphisms between sm21 and si1 mtDNAs play a role in mediating the combined effects of SIR2 overexpression and diet on longevity . It is possible that the strong si1-SIR2 effect is due to a very different expression level of the UAS-SIR2 construct in the si1 mitotype background , rather than a specific mito-SIR2 interaction . We quantified the overexpression level of UAS-SIR2 in the siI and OreR mitotype backgrounds to test this possibility . The expression level of SIR2 is not significantly different between siI and the most-divergent OreR ( Figure 5 ) . We note that only females were included in our demography experiment and the overexpression level is about 5× in females ( Figure 5 ) . While the 40× overexpression shown in males would likely be deleterious , we did not use males for longevity assays . Based on these results we conclude that mitotype itself does not affect the level of overexpression , indicating that the SIR2-siI interaction is not likely due to a spurious differential over expression mediated by mtDNA-effects on the GAL4-UAS system . Overall , we found strong support for the effects of nuclear genotypes ( SIR2 overexpression ) , diet , and mitochondrial genotype on lifespan . As summarized in Table 7 , all three of these terms are highly significant ( P<0 . 001 ) . We found the diet effect to be significantly altered by SIR2 overexpression ( Table 7 χ2 = 233 . 94 P<0 . 001 ) on the CR axis and not significantly modified by SIR2 on the DR axis ( χ2 = 3 . 09 P = 0 . 0793 ) . An epistatic interaction between mitotype and SIR2 is well supported ( χ2 = 77 . 272 and 64 . 074 for CR and DR respectively , P<0 . 001 for both ) . A mitotype-by-diet interaction is also found to be significant ( χ2 = 53 . 423 and 305 . 95 for CR and DR respectively , P<0 . 001 for both ) . As shown in the w1118 and OreR experiments , we again see little support for the effect due to species level mtDNA divergence ( Table 7 ) . In summary , the results indicate that in addition to diet , mitochondrial and nuclear genotypes are important factors modulating lifespan , and that the epistatic interaction between them also has substantial effects .
A general result regarding mtDNA effects on longevity is the different impacts of point mutations versus deeper mtDNA divergence . The high mutation rate of mtDNA results in high levels of mtDNA variation in most animals , including humans . These new mutants are actively tested for function with nuclear genetic variation across each generation , and chance combinations of nuclear and mitochondrial alleles can generate novel phenotypes ( epistatic interactions ) that are not predicted from the main effects of either genome . The large number of nuclear-encoded subunits of the mitochondrial electron transport chain and mitochondrial translation machinery make a particularly large mutational target for novel variants that must interact with the mtDNA encoded subunits that contribute to these critical cellular processes . One result of particular interest is the effect of the tRNATyr mutation in w501 mtDNA , which reduces lifespan on the OreR nuclear background but can show average longevity on the w1118 background . This demonstrates a particular case of 2-nucleotide mitonuclear incompatibility due to mis-match between mitochondrial- and nuclear-encoded components . In this particular case of w501 mtDNA , the OreR and w1118 nuclear backgrounds carry different tRNA-synthetase alleles that functionally interact with the w501 tRNATyr mutation . Although , admittedly , a D . simulans mitochondrial-D . melanogaster nuclear genome combination is unlikely in nature , mitochondrial dysfunction due to tRNA mutation is not at all rare . Notably , exercise intolerance in humans results from a mutation in this same mt-tRNATyr gene [41] , [42] , [43] . Indeed , mitochondrial tRNA diseases involving similar mitochondrial-nuclear dysfunction in human are common , such as MERRF syndrome ( Myoclonic Epilepsy with Ragged Red Fibers ) [44] , which is caused by mutation in mitochondrial tRNALys . Therefore , the principle of mitonuclear epistatic interactions is a general problem that has received little attention until recently [28] , [29] , [30] . One rather unexpected finding is that the w501- OreR interaction appears to result in small , yet significant , upregulation of mitochondrial copy number . The regulatory mechanism of steady level of mtDNA copy number is thought to be a complex network consisting of both mitochondrial and nuclear encoded components , with many details yet to be elucidated . The major nuclear encoded components includes mitochondrial transcription factor A ( TFAM ) , mitochondrial DNA polymerase γ ( PLOG ) , mitochondrial single strand binding protein ( mtSSB ) [45] . The sequence of the mtDNA in the origin of replication region of the D-loop , presumably also plays an important role affecting mtDNA level [46] . Our result suggests that mitochondrial translational deficiency , such as the one leading to the w501- OreR interaction , may also be a part in the mtDNA level regulatory network , possibly acting though a compensatory mechanism . In both the wild type nuclear backgrounds , w1118 and OreR , we demonstrated that dietary restriction effect is highly dependent on mitotype ( Table 2 ) . These genome-wide patterns provide strong motivation to test the hypothesis that epistasis also exists between mitotypes and genetic pathways known to influence dietary restriction . We tested this hypothesis using a mito-SIR2 interaction . The up-regulation of SIR2 leads to lifespan extension in many model organisms [15] , [18] , [40] , [47] , [48] , [49] and there are sound reasons why this process may involve a mito-SIR2 interaction . First , SIR2 is an NAD+ consuming deacetylase [50] , [51] and mitochondria oxidize NADH to NAD+ during oxidative phosphorylation by NADH dehydrogenase ( Complex I ) . Interaction between SIR2 and mitochondria can stem from this sharing of the same cofactor pool . Second , SIR2-mito interactions may also arise through the upregulation of mitochondrial function by mimicking DR , which also involves other members of the sirtuin family [27] , [52] . This pattern of epistatic interaction is consistent with a number of studies showing that mitochondrial function is enhanced , not only by sirtuins , but also other pathways that mimic DR [10] , [14] , [22] , [23] , [34] , [53] . Due to the sequence divergence of the mitotypes included in this study , different mitotypes are expected to cause different levels of mitonuclear incompatibility , some mild while others may be severe . To the extent that overexpressing SIR2 mimics DR and enhances mitochondrial function , the distinct set of mtDNA haplotypes used in our study has the potential to identify novel functional interactions between SIR2 and mitochondria . Our initial expectation was that the divergent D . simulans mtDNA would generate higher levels of incompatibility and show greater differences in longevity . However , it was a particular mitotype , not the species-level divergence that is most informative ( Figure 4 , siI mitotype ) . Further genetic mapping and biochemical analyses are required to understand how siI mitotype mediates a unique response to SIR2 , but the mutations that are unique to the siI mitotype provide a list of candidates that could guide these studies ( see Table S1 ) . Our results have consistently falsified the hypothesis that divergent mtDNAs from a different species represent ‘strongly incompatible’ mtDNA genotypes . In most nuclear backgrounds , and across most diet treatments , our data show that the species-level divergence of mtDNAs ( ∼100 amino acids , see supplementary Table S1 ) has little phenotypic effect , while individual mtDNAs can have pronounced effects ( e . g . , w501 and siI mtDNAs; Figures 2 and 4 , Tables 2 and 7 ) . If purifying selection has been a general force during the divergence of mtDNAs , the large sequence divergence between species may be accompanied by little accumulated functional divergence . Likewise , the nature of purifying selection will determine the mutation-selection balance that permits individual mutant mitotypes carrying deleterious mutations to persist in populations , with measurable effects on longevity and other performance traits . These results suggests that additional screens of very closely related mtDNA may uncover informative SNPs showing strong epistatic interactions with nuclear factors affecting longevity . Our results also imply that the responses to food concentration or composition changes involve different mechanisms . Altering caloric content and changing yeast concentration are currently two main approaches of caloric vs . dietary restriction ( CR and DR , respectively ) in D . melanogaster . The former changes diet concentration while keeping sugar/yeast ratio constant [37] , [54] . The latter approach can alter the sugar/yeast ratio while keeping total calories constant [37] , or restrict the amount of yeast , resulting in both sugar/yeast ratio and total caloric changes [55] . We found that the SIR2 control genotype shows a strong response to CR , but SIR2 overexpression does not respond to CR , and lifespan changes little on different diets as long as yeast:sugar ratio is unchanged ( compare Figure 4A to 4C ) . In contrast , the lifespan of SIR2 overexpression still displays response to isocaloric DR involving sugar/yeast ratio changes ( compare Figure 4B to 4D ) . These observations collectively suggest that SIR2 is involved in sensing food concentration changes but has less of a role in sensing composition changes or yeast:sugar ratio . Thus , our results are consistent with the hypothesis that SIR2 regulates lifespan , but clearly show that SIR2's manner of longevity regulation is dependent on diet , which is indeed a case of genotype × diet interaction . A possible model to explain this result is that SIR2 is only involved in CR but not DR . When SIR2 is already overexpressed , additional CR can't result in further extension lifespan . However under DR , which may be SIR2 independent , this dietary modification remains effective regardless of SIR2 level . Therefore in both control and SIR2 overexpression condition , DR results in similar extension of lifespan . In contrast to other reports , we found overexpression of SIR2 overall decreases life span . SIR2 is an important gene in aging research as it has suggested a link between metabolism and longevity [16] , [56] . A polyphenol resveratrol , found in red wine , was suggested to be a SIR2 activator and has been demonstrated to lead to variety of benefits that are associated with DR [18] , [47] . However , the roles of both SIR2 and resveratrol have been challenged [57] , [58] , [59] . Absence of life span extension by SIR2 overexpression has also been reported before [60] , [61] , [62] , [63] . In our case , we suspect the lifespan decrease we observe is due to the expression level of SIR2 that is not optimal for longevity extension or related to developmental effects in larval life . Here we found the expression of SIR2 driven by a strong ubiquitous da-GAL4 driver to be about 5 times that of controls in females and 40 times that of controls in males ( we used females for all longevity experiments ) . Previously , the similar ∼5× over-expression of SIR2 has been shown , in two studies , to increase or have no effect on longevity in Drosophila [61] , [64] . It is known that SIR2 expression is high in the embryo stage but moderate in adult with high expression in brain , salivary gland , female ovary and spermatheca [65] , [66] , displaying a highly tissue specific pattern . Therefore , the absence of lifespan extension in our experiments is also possibly due to SIR2 expression in tissues and stages where overexpression can become detrimental . We note that one goal of this study was to identify genetic interactions between SIR2 and mtDNA variants as they affect the response to DR and CR , and the si1 mtDNA-SIR2 interaction is a promising result . However , we acknowledge further studies using stage- and tissue-specific SIR2 overexpression with moderate dosage are needed to resolve the details of SIR2-mtDNA genetic interactions . Aging is a complex process and extending life span requires the coordination of a large number of genes , pathways , and environments , among which dietary regime is probably specially important . The interaction between them is an intrinsic nature of the aging process . These interactions may often appear as annoying sources of experimental complication that reduces the repeatability in particular experiments . For example , different mouse genotypes respond very differently to the same dietary environment [36] , [21] , which is essentially a form of G×E interaction . Moreover , alternative combinations of alleles from different loci can lead to different phenotypic response [40] , [61] , which is in fact an epistatic or G×G interaction . Both G×E and G×G interactions have been demonstrated and are beginning to draw attention in aging research . However , the greater challenge , as we have elucidated in this work , is a more general G×G×E interaction . That is , the effects of mutations that affect lifespan are heavily dependent on the interaction between genetic background and dietary regime simultaneously . If a goal in aging research is to understand the complexity of factors that may help ensure increased healthspan in humans , we must confront the complexity of interactions presented by genetically variable humans living in the variety of dietary environments so common today . Here we have sought to provide an example of such an effort in a model organism where we can begin to manipulate these factors and eventually elucidate the mechanisms of the interactions that have undoubtedly been important throughout evolution .
Longevity analyses were carried out on lines of D . melanogaster carrying alternative mtDNAs from D . melanogaster and its sister species D . simulans . We generated three sets of lines on each of three different nuclear genetic backgrounds , Oregon R ( OreR ) , w1118 ( Bloomington stock number 6326 ) and daughterless-GAL4 ( da-GAL4 , Bloomington stock number 5460 ) , each carrying alternative mtDNAs . The D . melanogaster mitochondria are from the Oregon R ( OreR ) strain and Zim53 ( Zim ) , from Zimbabwe [67] , [68] . Also included are Drosophila simulans mitochondrial haplotypes si1 from Hawaii , sm21 from strain C167 . 4 ( Drosophila Genetic Resource Center stock number 107850 ) , and w501 , from strain white501 ( w501 , Drosophila Species Stock Center stock number 14021-0251 . 195 ) . Introgression of D . simulans mitochondria into flies carrying D . melanogaster nuclear genome is made possible by a rescue cross involving D . simulans strain C167 . 4 as females and D . melanogaster strain In ( 1A ) B as males [28] , [39] , followed by balancer chromosome replacement to ensure the mito-replaced strains have the same set of D . melanogaster nuclear chromosomes of the original strain ( Supplementary Figure S1 ) . For simplicity , we will refer to different mitochondrial haplotypes as “mitotypes” ( cf . mitochondrial genotypes ) and nuclear chromosomal composition as “nucleartype” . When contrasting mitotypes in different nucleartypes , the following terminology will be used: ‘the OreR mitotype has different longevity on alternative nuclear backgrounds’; or the w1118 nucleartype shows different longevities on different mtDNA backgrounds' . We use the term “genotype” or “mitonuclear genotype” to refer to the combination of mtDNA and nuclear chromosomes that are constructed in a particular genetic line , using the notation: mtDNA; nuclearDNA . For example , the mitonuclear genotype w501; w1118 carries the D . simulans w501 mtDNA and the D . melanogaster w1118 nuclear chromosomes . The mitotype panel covers a wide range of mitochondrial mutations: the OreR mitotype differs from the D . melanogaster Zim mitotype by 18 amino acid substitutions , and by 103 amino acid substitutions from the most diverged mitotype , D . simulans si1 . The mitotypes are all fully sequenced and details of the molecular evolutionary divergence and the phylogenetic relationships can be found in Ballard [68] , and Montooth et al . [39] . The Genebank sequence accession numbers for the sequences of mitotypes are: OreR AF200828 , Zim AF200829 , siI AF200835 , sm21 KC244283 and w501 KC244284 . Overexpression of SIR2 was achieved by driving the expression of UAS-SIR2 via a ubiquitous daughterless-GAL4 driver . UAS-SIR2 construct was kindly provided by Drs . Jason Wood and Stephen Helfand . Each mitotype described above was introduced into the da-GAL4 construct using balancer chromosome substitution . SIR2 overexpression in different mitotype backgrounds is made possible by the mito-replaced da-GAL4 strains . Overexpression genotypes were obtained by crossing UAS-SIR2 males to mitotype-replaced da-GAL4 females . The control genotypes are generated by crossing the w- strain , in which UAS-SIR2 is constructed , to da-GAL4 females . To keep UAS-SIR2 and its control w- strain ( + ) has the same genetic background , they were maintained as UAS-SIR2/+ heterozygote and sorted by eye color to get UAS-SIR2/UAS-SIR2 and +/+ males to minimizing the chance of possible fixed genetic divergence that may arise between the SIR2 and the + genotypes if both were to be maintained as individual homozygote strains . Additional control genotypes , such as a control for the GAL4 line , would have been informative . However , the primary goal is to determine the reality ( and magnitude ) of interaction effects . Therefore , the alternative design is not employed . In our approach the over-expression genotype ( da-GAL4/UAS-SIR2 ) and the non-overexpression genotype ( da-GAL/+ ) both carry the same whole genome heterozygous background ( da-GAL4 background/UAS-SIR2 background ) . Therefore the whole genome heterozygous background will minimize the effect of any fixed mutations on either the da-GAL4 background or the UAS-SIR2 background , and will reveal a more general picture how SIR2 and mtDNA will interact in any randomly chosen genetic background from a wild population . A five-diet design was used to study the effects of both food composition and food concentration on life span , termed diet I , II , II , IV , V ( see Figure 1 ) . The ingredients of the five diets ( all in grams per liter ) ; type I: 50 sucrose + 50 yeast , type II: 100 sucrose + 100 yeast , type III: 150 sucrose + 150 yeast , type IV: 50 sucrose + 150 yeast and type V: 150 sucrose + 50 yeast . All of these are prepared with 15 g/dl agar and supplemented with 0 . 2 g/dl Tegosept as fungal suppressor . The food making procedure follows the protocol described in [35] . Briefly , ingredients are well mixed in hot water and then autoclaved at liquid cycle ( 120°C for 25 min ) to achieve sterility and uniform cooking conditions . The food mix is then allowed to cool down before addition of Tegosept using 20 g/dl stock solution . Note that type I , II and III are exactly the same as 0 . 5 N , 1 . 0 N and 1 . 5 N food used by other groups [35] , [36] , [40] . Type I , II and III form a concentration gradient where the yeast sugar ratio is constant and the total concentration and caloric content varies . Mair et al . reported very similar caloric contents for carbohydrate and autolysed yeast powder , 4 . 0 and 4 . 02 kcal/g respectively [37] . Therefore , type V , II and IV form a composition gradient where the caloric content is approximately constant but the food composition ( sugar/yeast ratio ) varies . “Standard food” is the common cornmeal food for general stock keeping and is made of 10 g/l agar , 100 g/l sucrose , 50 g/l cornmeal and 50 g/l cornmeal yeast . Fly lifespan was measured by counting individual deaths in demography cages . Briefly , newly eclosed flies were collected over a 24 hour period , held on regular food for 4 days as mixed sex adults , and then sorted by sex . Demography experiments were only conducted on females . 100 females were placed in 1 liter cages in triplicate and were supplied with different types of food . On Mondays , Wednesdays and Fridays , food was renewed and death recorded . Cages were kept in climate controlled chambers maintaining a 12/12 hours light/dark cycle at 25°C . Quantification of the ratio of mitochondrial to nuclear DNA ( mtDNA/nDNA ) and SIR2 expression level were carried out by a quantitative PCR ( qPCR ) based method . To avoid amplification bias due to relative high A/T richness of mtDNA compared to genomic DNA we chose a G/C rich region in mtDNA to make mtDNA and nDNA primer sets comparable . Both mtDNA primers and nDNA primers were aligned against available sequence data in the NCBI database to ensure they map to highly conserved regions and are not likely to be affected by mismatch due to sequence polymorphism . We followed the DNA extraction method suggested by Guo et . al [69] to avoid underestimating the abundance of mitochondrial DNA . The primer sequences are as follows: mtDNA: 5′-GATTAGCTACTTTACATGGAACTC-3′ and 5′-CTGCTATAATAGCAAATACAGCTC-3′ adjacent to the mitochondrial genomic region of cytochrome c oxidase subunit I gene; nDNA: 5′-AACTCTGCTGCTACTTATCG-3′ and 5′-CAGGATCAGGATGGAATAGTATC-3′ adjacent to the nuclear region of NADH dehydrogenase 51 kDa subunit gene . SIR2 expression level is quantified using ddCT method with a pair of primers within SIR2: 5′-TCATGAAGCCGGATATCGT-3′ and 5′-GGTATGCTGCTGGGAATG-3′ together with the reference primers in house keeping gene GAPDH: 5′-CCACTGCCGAGGAGGTCAACTAC-3′ and 5′-CATGCTCAGGGTGATTGCGTATGC-3′ . Both qPCR experiments are done with 10-day-old whole flies reared on regular food . mtDNA/nDNA was measured in males only while SIR2 expression levels were assayed in both males and females . For both experiments , 3 biological replicates were assayed with 3 technical replicates . To test the effect of mitotype , nuclear genotype and diet on lifespan , we analyzed the demography data using a mixed-effect Cox proportional hazard model . The mixed-effect Cox model is an extension to the popular Cox proportional hazard model [70] to include random effects . The Cox model estimates the log ( hazard ratios ) from survivorship of different groups . Two groups having a hazard ratio equal to 1 ( or equivalently log ( hazard ) equal to 0 ) suggests the groups are equally likely to die . Similarly , negative log ( hazard ) or positive log ( hazard ) suggests one group has higher or lower probability to die than the other group and subsequently survives shorter or longer , respectively . In a mixed-effect Cox model , random effects are included . Random effects are modeled assuming their log ( hazard ratio ) are drawn from Gaussian distributions with zero mean and unknown variances . The unknown Gaussian variances are numerically solved by finding the maximum of integrated partial likelihood using an iterative method [71] . The model was originally developed for clinical application . For example , a type of drug is tested in multiple hospitals in order to determine its ability to extend patient survival ( by comparing treatment group survival to control group in each hospital ) . However , it is also important to know the amount of variation among hospitals especially how big the variation is in relation to the treatment effect . The among-hospital variation is considered as a random effect as it estimates the variation among all hospitals represented by the hospitals sampled . The treatment effect is , on the contrary , considered as a fixed effect . The mixed-effect Cox model enabled us to examine both fixed terms ( such as diet type analogous to drug effect in this example ) and random terms ( such as genetic background effect and replicate noise , analogous to variation among hospitals ) simultaneously . It also made examining variance components possible such that we can determine the relative magnitude of different random effects . Therefore their relative importance can be evaluated . In more practical terms , variance components estimated by the mixed-effect Cox proportional hazard model is a metric of the magnitude of effects , with a larger value indicating an effect is substantial and a small value indicating an effect is negligible . These tests are not possible with a classic Cox proportional hazard model with support for only fixed effects . The mixed effect Cox model is implemented using R package coxme ( ) ( http://cran . r-project . org/web/packages/coxme/ ) . Additionally , the significance tests of overall contributions of each term are conducted by likelihood ratio test by comparing full models versus reduced models . Log-likelihood ratio tests are implemented using R Analysis of Deviance package anova ( ) ( http://stat . ethz . ch/R-manual/R-patched/library/stats/html/anova . glm . html ) . | It is widely recognized that mitochondrial function plays an important role in longevity and healthy aging . Considerable attention has been focused on the extension of longevity by caloric or dietary restriction and mutations that alter this process , and these interventions commonly are associated with shifts in mitochondrial function . While the genetic bases of these effects are the focus of much interest , relatively little effort has been directed at understanding the role that mitochondrial DNA ( mtDNA ) polymorphisms play in the diet restriction response . This work presents a comprehensive effort to quantify the effects of mtDNA variants , nuclear genetic variants and dietary manipulations on longevity in Drosophila , with a focus on testing for the importance of the interactions among these factors . We found that mitochondrial genotypes can have significant effects on longevity and the diet restriction response but these effects are highly dependent on nuclear genetic ( G ) background and the specific diet environment ( E ) . For example , a mitochondrial haplotype that shortens lifespan in one nuclear background or diet regime shows no such effect when the genetic background or diet regime is changed . Our experiments indicate that identifying individual mitochondrial , nuclear or dietary effects on longevity is unlikely to provide general results without quantifying the prevalent mitochondrial × nuclear × diet ( G×G×E ) interactions . | [
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] | 2014 | G×G×E for Lifespan in Drosophila: Mitochondrial, Nuclear, and Dietary Interactions that Modify Longevity |
Almost all animals show sex differences in body size . For example , in Drosophila , females are larger than males . Although Drosophila is widely used as a model to study growth , the mechanisms underlying this male-female difference in size remain unclear . Here , we describe a novel role for the sex determination gene transformer ( tra ) in promoting female body growth . Normally , Tra is expressed only in females . We find that loss of Tra in female larvae decreases body size , while ectopic Tra expression in males increases body size . Although we find that Tra exerts autonomous effects on cell size , we also discovered that Tra expression in the fat body augments female body size in a non cell-autonomous manner . These effects of Tra do not require its only known targets doublesex and fruitless . Instead , Tra expression in the female fat body promotes growth by stimulating the secretion of insulin-like peptides from insulin producing cells in the brain . Our data suggest a model of sex-specific growth in which body size is regulated by a previously unrecognized branch of the sex determination pathway , and identify Tra as a novel link between sex and the conserved insulin signaling pathway .
Drosophila is a well-established model to study the mechanisms that control animal growth [1 , 2] . Drosophila body size is determined by various developmental cues that coordinate tissue patterning with growth , and by environmental cues such as nutrients and oxygen , that regulate whole body metabolism . One important , but often overlooked , determinant of size in Drosophila is sex–adult females are significantly , and visibly , larger than males [3 , 4] . This sexual size dimorphism ( SSD ) arises due to differences in larval growth: males and females have a similar overall duration of larval development , but females achieve critical weight at a larger size and grow more during the terminal growth period [5] . While over two decades of genetic research have identified many conserved signaling pathways that link developmental and environmental cues to the control of tissue and body size [6–9] , the genetic and physiological mechanisms that account for the larger female body size remain unclear . In flies , sex is determined by the ratio of sex chromosomes to autosomes ( X:A ) [10] . In females , the X:A ratio is 1 , and a functional protein is produced from the Sex-lethal ( Sxl ) locus [11 , 12] . In males , the X:A ratio is 0 . 5 , and no Sxl is produced . Sxl is a master regulator of female sexual development ( eg . sexual differentiation , reproduction ) , and Sxl mutant females are smaller than wild-type females . This is due , in large part , to the sex-specific splicing of its downstream target gene transformer ( tra ) [13–16] . As a result of this Sxl-dependent splicing , a functional Tra protein is produced in females , but not males . Tra is a splicing factor , and has only two known direct targets: doublesex ( dsx ) and fruitless ( fru ) [17–22] . While Tra is thought to mediate most of Sxl’s effects on sex determination , the control of sex differences in body size is thought to be independent of the Tra/Dsx/Fru branch of the sex determination pathway [23] . Here , we identify for the first time a role for Tra as a key regulator of SSD in Drosophila . Further , we show that Tra’s effects on SSD are mediated by a novel pathway that is independent of dsx and fru , and of other aspects of sexual dimorphism .
Female and male Drosophila larvae show no difference in their rate of development [5] . However , by the end of larval life , female body size is approximately 30% larger than male body size ( Fig 1A and 1B ) . These differences are not due to sex differences in food intake or feeding behaviour ( S1A and S1B Fig ) . Although the prevailing view is that tra does not regulate sex differences in body size [24] , one study showed that adult weight in tra mutant females was reduced compared to wild-type females [25] . However , this weight reduction can be explained by the lack of ovaries in tra mutant females . We therefore tested whether the decreased weight was due to an effect of tra on growth by measuring pupal volume in tra mutant animals . We found that body size was significantly reduced in tra mutant females compared to wild-type females ( Fig 1C and 1D ) . Thus while wild-type females are 30% larger in body size than males , tra mutant females are only 10% larger than males . This suggests that tra contributes to establishing SSD in Drosophila . Body size was unchanged in tra mutant males , consistent with the lack of a functional Tra protein in males ( Fig 1D ) . We also performed loss-of-function experiments with tra , using an RNAi transgene directed against tra’s splicing co-factor tra2 ( UAS-tra2-RNAi ) . We found that ubiquitous expression of the UAS-tra2-RNAi transgene using the Act5c-GAL4 driver successfully transformed female animals into phenotypic males ( S1C Fig ) , and led to a reduction in body size ( S1D Fig ) . We next examined whether lack of Tra expression in males could explain their smaller body size . Ubiquitous expression of a UAS-tra transgene using the daughterless ( da ) -GAL4 driver led to a significant increase in body size in males ( Fig 1E and 1F ) . Interestingly , overexpression of Tra also stimulated growth in females , showing Tra has growth-promoting effects in both sexes . While previous studies have shown that high levels of Tra expression can cause artifacts such as lethality [26] , this is the first report of an alternative splicing factor promoting body growth in Drosophila . To further confirm these Tra-dependent changes in body size , we measured adult weight . In order to ensure that tra’s effects on adult weight are not confounded by its effects on ovary or testis development , we weighed 5-day-old animals from which the gonads were removed by dissection . As with pupal volume , we found that tra mutant females , but not tra mutant males , were significantly smaller than controls ( S2A Fig ) . Overexpression of UAS-tra caused an increase in body weight in males ( S2B Fig ) . Together , these results suggest that male-female differences in body size are created in part by the presence of Tra in females , and the absence of Tra in males . This defines a new role for Tra in the regulation of sex differences in body growth . As with body size , female cell size in the wing [27] , and the fat body ( Fig 2A ) are larger . Since one previous study showed that wing cell size in tra mutant females was intermediate in size between female and male cells [25] , we tested whether Tra expression could mediate cell-autonomous effects on growth by expressing either UAS-tra or the UAS-tra2-RNAi transgenes in the fat body ( polyploid cells ) and the wing disc cells ( mitotic cells ) of developing larvae . We found that flp-out-mediated mosaic expression of UAS-tra2-RNAi in female fat body caused a significant reduction in cell size ( Fig 2B ) . Consistent with the lack of a functional Tra protein in males , similar UAS-tra2-RNAi expression in males did not affect cell size ( Fig 2B ) . In contrast , overexpression of UAS-tra in fat body cells was sufficient to increase cell size in both sexes ( Fig 2C ) . We next examined whether Tra expression could affect sex differences in another larval tissue , the wing disc . Using engrailed ( en ) -GAL4 , we expressed either UAS-tra2-RNAi or UAS-tra transgenes in the posterior compartment of the wing , and measured compartment size . We found a significant reduction in compartment size in female wings when we knocked down Tra function by UAS-tra2-RNAi expression ( Fig 2D ) . This effect was also seen when we used a second UAS-tra2-RNAi transgene ( S3A Fig ) . In contrast , male compartment size was unaffected ( Fig 2D ) . To determine whether this reduction in compartment size was due to a decrease in cell number or cell size , we counted wing hairs in a fixed area in the posterior compartment in females . Each wing cell secretes one hair , thus by counting wing hairs we can accurately determine how many cells are present in a specific area . We found that the number of cells in the counting area was significantly increased in the compartments expressing UAS-tra2-RNAi compared to controls ( Fig 2E ) . This suggests that the reduction in compartment size is due to a reduction in cell size , rather than cell number . Indeed , the estimated cell number in the posterior compartment of the wing was not significantly altered by expression of UAS-tra2-RNAi ( Fig 2F ) . Overexpression of UAS-tra using en-GAL4 caused no significant increase in compartment size in either males or females ( S3B Fig ) . Together , these results demonstrate a cell-autonomous requirement for Tra in females to promote increased female cell size in both mitotic and endoreplicating cells . This result supports previous findings from early gynandromorph studies , where sex differences in cell size were regulated in a cell-autonomous manner [28] . More recently , Sxl expression in the wing disc was shown to promote growth [29]; however , altering either the X:A ratio or Sxl expression also affects the process of dosage compensation . Since Tra does not affect this process [30] , our results demonstrate that sex differences in cell size can be uncoupled from dosage compensation . An emerging literature in Drosophila has highlighted the importance of noncell-autonomous signaling in the control of body growth [31–34] . This signaling relies on organ-to-organ endocrine communication and is particularly important in controlling body growth in response to dietary nutrients . We therefore tested whether sex differences in overall body size may also involve non-autonomous effects of Tra function specific tissues . We used a number of tissue-specific GAL4 drivers to express the UAS-tra2-RNAi transgene in larvae , and measured pupal volume in these animals . We found that loss of Tra in the fat body caused a significant reduction in female body size ( Fig 2G ) . This reduction in body size was also observed in females expressing a second UAS-tra2-RNAi transgene in the fat body ( S3C Fig ) . Expression of UAS-tra2-RNAi in neurons , glia , ring gland or in muscle did not reproduce the female-specific effects of the fat body ( S4A Fig ) , and male body size was unaffected by expression of the UAS-tra2-RNAi transgene in any tissue ( S4B Fig ) . We next asked whether expression of UAS-tra in specific tissues was sufficient to drive an increase in body size . Tissue-specific expression of UAS-tra using a panel of GAL4 drivers did not significantly affect body size in wild-type females or males ( S5A and S5B Fig ) . We then asked whether tissue-specific expression of Tra could rescue the body size defects of tra mutant females . We found that ubiquitous , or fat-specific , expression of Tra was sufficient to rescue a normal body size to tra mutant females ( Fig 2H ) . Together , these results suggest that the decreased body size in tra mutant females is due to fat-specific loss of Tra function . Tra controls many aspects of sexual differentiation , including gonad and germline differentiation , and previous studies in C . elegans showed that the germline can influence body growth [35] . We therefore tested whether Tra expression in these tissues could explain its effects on female body size . However , we found that gonad- or germline-specific expression of a UAS-tra2-RNAi transgene using the c587-GAL4 or nanos ( nos ) -GAL4 drivers , respectively , caused no significant reduction in body size in females ( S6A and S6B Fig ) . Similarly , body size was unaffected in males and females completely lacking a germline ( the progeny of tudor1 homozygous mutant females crossed to wild-type males; S6C Fig ) . Decreased body size in tra mutant females is therefore not due to the presence of a male gonad or germline . Instead , our results suggest the sex of the fat body , as determined by Tra expression , controls body growth in a non cell-autonomous manner . Tra is a splicing factor , and has only two known direct targets: doublesex ( dsx ) and fruitless ( fru ) [17–22] . Dsx is expressed in a handful of tissues throughout the body and in a restricted expression pattern in the central nervous system ( CNS ) in both males and females [36–40] . In females , Tra binding to dsx pre-mRNA causes a female-specific Dsx isoform to be produced ( DsxF ) . In males , which express no functional Tra protein , a default splice in dsx pre-mRNA generates a male-specific isoform of Dsx ( DsxM ) [19 , 20] . Tra binding to the pre-mRNA of transcripts from the fru P1 promoter causes the introduction of a stop codon , and no Fru P1 protein is expressed in females . In males , the lack of Tra leads to the use of a default splice in fru P1 transcripts , generating a male-specific Fru P1 protein ( FruM ) [17 , 18] . FruM expression is limited to males in approximately 2000 neurons in the CNS and peripheral nervous system ( PNS ) [18 , 41] . Importantly , dsx and fru are thought to mediate most , if not all , effects of Tra on sex determination and behaviour [42 , 43] . We therefore tested whether either gene was required for Tra’s effects on growth . We first examined whether mutants lacking Dsx or FruM expression phenocopied any of Tra’s effects on growth . We found that dsx mutant animals ( genotype dsx1/Df ( 3R ) dsx15 ) , had no significant difference in pupal volume compared to controls in either males or females ( Fig 3A ) . We also examined the effect of Dsx knockdown in the fat body using a UAS-dsx RNAi line . Using the flp-out system , we found that mosaic expression of UAS-dsx RNAi led to reduced fat cell size in both males and females , suggesting that dsx regulates cell size in this tissue ( Fig 3B ) . However , expression of UAS-dsx-RNAi throughout the fat body using r4-GAL4 did not recapitulate the non cell-autonomous reduction of body size observed upon Tra inhibition in this tissue ( Fig 3C ) . While it seems counterintuitive that Dsx causes a reduction in size in the fat body , but does not affect body size , dsx expression is restricted to specific tissues in larvae [37 , 39] . Thus while loss of dsx may affect cell size in a relatively small number of tissues , the expression may not be broad enough to cause a reduction in overall body size . Also , in the context of our findings with tra , loss of dsx throughout the fat body does not phenocopy the non cell-autonomous effects of loss of Tra on body size . Similar to dsx , we found that males lacking FruM expression , or females ectopically expressing FruM proteins [44] , showed no difference in body size compared to controls ( Fig 3D and 3E ) . We next asked whether dsx was required for Tra-induced growth . Using da-GAL4 to overexpress Tra in a dsx mutant background , we found that Tra’s ability to drive body growth was unaffected by loss of dsx ( Fig 3F ) . Our results suggest Tra controls growth in a pathway that is independent of its effects on sexual differentiation and behaviour . In Drosophila , the conserved insulin/insulin-like growth factor signaling ( IIS ) and Target-of-Rapamycin ( TOR ) pathways are two main regulators of tissue and body growth [7 , 45 , 46] . Both pathways play a central role in linking dietary nutrients to regulation of larval metabolism and growth [34 , 47–49] . We therefore tested whether IIS/TOR also plays a role in creating sex differences in body size . We first measured pupal volume in males and females grown in either nutrient-rich food ( which promotes high levels of IIS/TOR signaling ) , or in food with reduced nutrition ( which inhibits IIS/TOR signaling ) . We found that sex differences in body size were abolished in low nutrient conditions ( Fig 4A ) . Since previous studies have shown that IIS/TOR can act in separate pathways to activate downstream effectors [48 , 50] , we wanted to specifically inhibit the TOR pathway , and examine the effects on body size . When we grew larvae on food containing rapamycin , a specific TOR inhibitor , we found an overall reduction in body size in both sexes; however , the SSD between males and female remained at 25% ( S7A Fig ) . Thus IIS , but not TOR , is required for male-female body size differences . This finding is consistent with a recent study that showed sex differences in adult body weight were eliminated in animals heterozygous for two hypomorphic mutations in the insulin receptor ( InR ) gene [5] . We next examined whether we could detect any sex differences in IIS activity during development . The serine/threonine kinase Akt is phosphorylated and activated downstream of IIS . Measuring the ratio of the phosphorylated active form of Akt ( P-Akt ) to total Akt therefore provides a read-out of IIS activity . When we compared male and female larvae collected 96 hr and 120 hr after laying ( AEL ) at 25°C , we found that females had a significantly higher ratio of P-Akt:Akt at 120 hr AEL ( S7B and S7C Fig ) . To further confirm higher IIS activity in females during development , we used an antibody staining in the larval fat body to detect the subcellular localization of the transcription factor FOXO . When IIS activity is high , FOXO is phosphorylated by P-Akt , and is evenly distributed throughout the cytoplasm and nucleus . When IIS is inhibited , FOXO re-localizes to the nucleus , where it regulates expression of its target genes [51] . We found the nuclear:cytoplasmic ratio of FOXO was significantly higher in males than in females , further suggesting that males have lower levels of IIS activity ( S7D and S7E Fig ) . We found no significant male-female differences in mRNA levels of previously described foxo targets such as InR , 4E-BP , or dilp6 ( S7F Fig ) [52–54] . This may be due to additional FOXO-independent factors required for their expression [55 , 56] . While the differences in IIS we report here are not as dramatic as seen with genetic or starvation-mediated perturbation of IIS , they are consistent with females having a modest increase in IIS activity compared to males . To understand how females achieve a modest increase in IIS compared to males , we examined insulin-like peptide ( ILP ) expression in larval insulin-producing cells ( IPCs ) . The IPCs express three Drosophila ILPs ( dilps 2 , 3 and 5 ) [57 , 58] . Nutrients have been shown to regulate both mRNA transcription and secretion of these dilps [33 , 57 , 58] . Thus in response to amino acid input to the fat body , an as-yet-unidentified secreted factor is released that acts upon the IPCs in the brain to trigger dILP2 and dILP5 release into the larval hemolymph . These dILPs bind to the insulin receptor on target cells to activate IIS and promote body growth . In contrast , when nutrient abundance is low , the fat-to-brain signal is reduced and secretion of dILPs is inhibited , leading to decreased systemic IIS and body growth . Given our finding that Tra function in the fat body is required for normal growth in females , we examined whether Tra expression in the fat body influences brain dILPs . We first examined dILP transcript levels and release in wild-type males and females . Using qRT-PCR , we found that only dilp3 transcript levels were different between the sexes , where males had a significant increase in dilp3 compared to females ( Fig 4B ) . We next wanted to determine whether we could detect any differences in dILP secretion . This can be assayed by immunostaining for dILP2 expression in the IPCs . When dILP secretion is high , dILP2 levels seen in IPC are low . Conversely , when secretion is decreased , dILP2 levels in the IPCs are higher [33] . When we compared males and females , we found that male IPCs had a significantly higher average pixel intensity with anti-dILP2 ( Fig 4C ) . Since dilp2 transcript levels are not different between males and females ( Fig 4B ) , this result suggests that dILP2 secretion is higher in females than males . Given the importance of the fat body as a regulator of IPC dILP release , we tested whether male-female differences in dILP2 secretion occur as a result of Tra expression in the female fat body . Using r4-GAL4 , we expressed the UAS-tra2-RNAi transgene to inhibit Tra function specifically in the fat body , and measured dilp transcript levels ( in the larval carcass that was devoid of fat body ) , or ILP2 staining intensity in the IPCs . We found that loss of Tra in the fat body did not affect transcript levels of dilp2 , dilp3 or dilp5 in either males or females ( S7G Fig ) . However , the average pixel intensity of dILP2 staining in the IPCs was significantly higher in r4>tra2-RNAi females compared to control females ( Fig 4D and 4E ) . Male dILP2 levels were unchanged by loss of Tra function in the fat ( Fig 4E ) . These results suggest that Tra expression in the female fat body can enhance levels of dILP secretion compared to males , to control systemic insulin signaling and consequently body size . To test this , we measured pupal volume in tra mutants in which we genetically increase IIS activity via heterozygous loss of PTEN , a known inhibitor of IIS . We found that loss of one copy of PTEN rescued the decreased body size in tra mutant females ( Fig 4F ) . We next wanted to test whether a reduction in IIS could suppress the ability of Tra to drive growth . Using da-GAL4 to drive ubiquitous expression of the UAS-tra transgene , we measured body size in larvae heterozygous for null or hypomorphic alleles of the InR . This genetic inhibition of IIS blocked Tra-induced overgrowth ( Fig 4G ) . Together , these results support a model of sex-specific growth in which Tra function in the female fat body stimulates the release of dILPs from the IPC . Higher dILP levels stimulate IIS activity to promote increased body growth in females . Overall , our results identify sex determination gene Tra as an additional regulator of the highly conserved IIS pathway .
In almost all animals , sex is an important determinant of body size [4] . While sex hormones have been shown to control the rate and duration of growth in mammals to achieve SSD , the mechanisms underlying male-female differences in growth in invertebrates are less clear [3 , 59] . We therefore used Drosophila larvae as a model to study the mechanisms underlying SSD . We identified clear male-female differences in the control of cell and body size that precede the differentiation of adult sexual morphology ( eg . sex combs , abdominal pigmentation , genitalia ) . Since the duration of larval growth does not significantly differ between male and female larvae [5] , these results implicate the sex-specific regulation of growth as a key determinant of SSD in Drosophila . While previous studies showed that master sex determination gene Sxl contributes to sex differences in body size [23] , it was unclear whether these effects on growth were mediated by Sxl’s regulation of the sex determination pathway , or the process of dosage compensation . Also , Sxl’s role as a master sex determination gene is not conserved in all insects [60] , suggesting other genes may contribute to SSD in these other species . In our study , we show that sex determination gene tra contributes to SSD in Drosophila . This suggests that the sex-specific regulation of growth is at least partly independent of dosage compensation , as Tra does not regulate this process [30] . Since tra’s role in sex determination is widely conserved in insects , many of which show SSD , the sex-specific regulation of growth by Tra may be a conserved mechanism to create dimorphic body size across many insect species [61 , 62] . It is important to note , however , that in spite of our results demonstrating an important role for Tra in creating SSD in Drosophila , loss of Tra function in females does not fully ‘masculinize’ body size , as tra mutant females remain significantly larger than wild-type males . There must therefore be other genes that contribute to increased female body size . One obvious candidate is Sxl , where Sxl mutant females have a male-like body size . The tra-independent effects of sex on body size may therefore be regulated by Sxl . This could occur in one of two ways: first , by Sxl acting on targets in addition to Tra , or second , by the effects of Sxl on dosage compensation . A recent study by Evans and Cline [63] showed that one female-specific behaviour , ovulation , was controlled by Sxl in a tra-independent manner . This ‘tra-insufficient feminization’ branch of the pathway does not cause any misregulation of the dosage compensation pathway , providing strong evidence that additional , as yet unknown , targets of Sxl mediate its effects on ovulation . In the case of SSD , then , other targets of Sxl may explain the tra-independent effects of sex on size . In addition to potential targets other than tra , the effects of Sxl on SSD may alternatively be mediated by its regulation of dosage compensation . In females , the presence of Sxl prevents the activation of the dosage compensation complex , whereas absence of Sxl in males allows dosage compensation to be activated to promote male development [11] . Loss of Sxl in females causes the inappropriate activation of this complex . Thus the decreased body size of Sxl mutant females may be explained by the ectopic activation of the dosage compensation complex . In the future , it will be interesting to dissect the individual contributions of Sxl , tra , the dosage compensation complex , and additional Sxl targets , to the control of male-female differences in body size in Drosophila . Further , it will be interesting , where possible , to determine whether these genes perform similar roles in SSD in other insect species . One key finding from our work is that sex differences in body growth are regulated by Tra independently of sexual differentiation , behaviour and reproduction . To date , most studies have shown that Tra’s effects on sexual development are mediated by its known targets dsx and fru [43] . Together , dsx and fru regulate most aspects of sexual development and behaviour . However , our data shows that Tra’s effects on body size are independent of dsx and fru . Combined with our data showing that masculinizing or feminizing the gonads or germline has no effect on body size , this shows that that sex differences in body size are not simply a consequence of sexual differentiation , reproduction and behaviour . Instead , SSD in Drosophila is regulated by Tra in a separate pathway , separate from other sex determination genes and aspects of sexual dimorphism . One possible explanation for SSD to be regulated separately of other aspects of sexual development is that while increased female body size is an important sexual trait , as it is related to fecundity [64] , the inability to adjust body size in response to environmental factors such as low nutrition can compromise survival during larval life [47] . Therefore , unlike aspects of sexual dimorphism that must be fixed to permit reproduction ( eg . gonad and germline differentiation , female neural circuits for egg-laying ) , body size must show a higher degree of plasticity . Indeed , studies have shown that male genital discs in Drosophila are less sensitive to growth perturbation than other imaginal discs [65] . We therefore propose that sexually dimorphic body growth is regulated independently from other aspects of sexual differentiation to allow body size to be co-ordinated with environmental conditions . Another finding from our work is that Tra function in the fat body can regulate the growth of other tissues to influence body size in a non cell-autonomous manner . Previous studies have also identified non cell-autonomous interactions that determine the sex of the genital disc , the development of the male-specific muscle of Lawrence , or sexual dimorphism in the gonad [66–69] . Combined with our data that sex differences in body size are also regulated in a non cell-autonomous manner , this suggests that in Drosophila , like in mammals , some aspects of sex determination and sexual dimorphism are regulated in a non cell-autonomous manner . Our identification of sex differences in the secretion of dILP2 suggest that this conservation extends to the cell-cell signaling pathways that mediate growth , as sex hormones in mammals are known to control male-female differences in body size via regulation of the growth hormone ( GH ) /insulin-like growth factor 1 ( IGF1 ) axis [59] . Several recent studies have shown that higher levels of circulating dILPs can increase body growth by augmenting IIS activity [70 , 71] . Our findings therefore suggest a model of sex-specific growth in Drosophila in which the sex of the fat body , as determined by the presence ( females ) or absence ( males ) of Tra , is one contribution to the sex differences in body size via regulation of dILP secretion . Higher dILP secretion in females leads to elevated IIS activity , and consequently an increase in body size . This model of increased female body size is supported by data that flies lacking all three IPC-derived dILPs ( dilp2-3 , 5 triple mutants ) show a 40% reduction in body weight in females , but no effect on body weight in males [72] . Similarly , female body size is more strongly affected than in males in animals with loss-of-function mutations in components of IIS such as chico or InR [73 , 74] . Together , these findings highlight the importance of sex as a critical determinant of dILP secretion , and IIS-mediated body growth . During larval development , the fat body responds to a variety of extrinsic and intrinsic cues such as nutrients and hormones to control body growth . For example , in response to nutrient input , the fat body releases an as-yet-unidentified factor into the larval hemolymph [33] . This secreted factor acts in an endocrine manner to control the release of dILPs from the IPC in the brain . Our studies have identified sex as an additional factor that alters the function of the fat body to influence body growth in a non cell-autonomous manner . In particular , we identified a role for the function of sex determination gene Tra in the fat body as one factor influencing SSD in flies . Yet it is unclear how Tra function in the fat body influences the molecular and physiological properties of this tissue to influence body size . Tra is a member of the conserved family of SR proteins . These proteins play well-characterized roles in the regulation of alternative splicing , and have also been shown to influence other aspects of RNA metabolism , such as regulation of mRNA translation [75–77] . Tra may therefore act in two ways in the fat body to control dILP2 release: 1 ) via sex-specific splicing to facilitate production or secretion of the secreted factor ( s ) , or 2 ) in a more general mechanism by influencing mRNA translation to elevate production or secretion of these fat-to-brain signals . Although the regulation of dILP secretion is an established mechanism to regulate body growth , the molecules that are released by the fat body to control dILP release are only beginning to be identified . For example , the cytokine-like molecule unpaired 2 ( upd2 ) , and the peptide hormone CCHa2 play roles in coupling fat body function to regulation of dILP secretion and body size [32 , 78] . Similarly , Hedgehog was also identified as a factor that can control dILP secretion in an endocrine manner [31] . In adults , fat body-derived dILP6 or dawdle , an Activin-like ligand in the TGF-β superfamily , could both influence the secretion of IPC-derived dILPs [79 , 80] . In addition , several neuropeptides and neurotransmitters have also been shown to regulate IPC activity and dILP release [81] . In the future , it will be interesting to determine whether Tra directly regulates any of these known secreted factors to control dILP2 release . However , an additional possibility is that tra does not directly regulate any secreted factors; instead , tra’s effects on growth may be mediated by effects on mRNA translation . Many studies have identified the regulation of mRNA translation in the fat body as a limiting factor for growth during development . For example , two studies identified significant effects of TOR and Myc in the fat body in promoting dILP release [33 , 82] . TOR is an important regulator of mRNA translation , and Myc’s effects were thought to involve elevated levels of ribosome synthesis . A more recent study showed that stimulation of tRNA synthesis , and consequently mRNA translation , in the fat body could drive increased body growth [83] . Future studies will allow us to determine which of Tra’s molecular functions ( splicing vs . mRNA translation ) determine its contribution to fat body function and consequently growth . Given the increasing awareness of functional similarities between the fly fat body and mammalian liver/adipose tissue , our results suggest the intriguing possibility that the function of these important endocrine organs may be similarly regulated by sex to control systemic growth and physiology in mammals . In addition to Tra’s non cell-autonomous effects on body size , we found that Tra also has cell- and organ-autonomous effects on size . While our data suggests that Tra’s effects on body size are independent of fru and dsx , since loss of neither gene affects overall body growth or non cell-autonomous growth , it is possible that Tra’s cell-autonomous effects on cell size are mediated by dsx . In the larval fat body , we identified a cell-autonomous requirement for dsx in both males and females to promote growth in fat body cells . We believe the reason that these cell-autonomous effects of dsx on cell size do not affect overall body size is due to the restricted nature of Dsx expression in larvae . Indeed , two studies showed that Dsx expression in larvae is limited to the fat body , CNS , gonads , some regions of the gut , and subsets of imaginal discs [37 , 39] . However , in spite of the lack of effect on overall body size , previous studies in Drosophila have identified a role for dsx in regulating organ size in other tissues . For example , expression of the male- or female-specific isoforms of Dsx ( DsxM and DsxF , respectively ) control the sex-specific growth of the genital disc via Wingless and Decapentaplegic signaling [84] . In addition , DsxF has been shown to promote sex-specific programmed cell death in both the larval ventral nerve cord , and in male-specific gonadal precursor cells [85–87] . Our findings identify an additional mechanism by which Dsx controls organ size: regulation of cell growth . While the molecular mechanism by which Dsx controls cell size is unclear , Dsx has been shown to control horn size in stag beetles by regulating tissue sensitivity to a circulating hormone , juvenile hormone [88] . Interestingly , a recent paper identified the insulin receptor ( InR ) and the ecdysone receptor ( EcR ) as potential Dsx targets [89] . Since both pathways have been shown to control fat body cell size [47 , 82] , Dsx may influence fat body cell growth by regulating tissue sensitivity to circulating dILPs or the steroid hormone ecdysone , integrating signals from both the primary sex-determining signal ( X:A ) and circulating hormones to control tissue growth . In the future , it will be interesting to determine whether the integration of sex and environmental cues is a general feature of Dsx-mediated tissue growth , or whether this mechanism is limited to specific tissues , such as the fat body . In conclusion , our studies identify Tra as one regulator of sex differences in growth and body size . Moreover , we provide the first link between Tra and IIS in the control of sex differences in body growth . Interestingly , sexual dimorphism in phenotypes such as stress resistance , immune responses and lifespan have been noted in Drosophila [90–95] . These phenotypes are also affected by altering IIS [96–99] . Tra may therefore control sexual dimorphism in a wide variety of phenotypes via regulation of dILP secretion and IIS activity . Deregulation of insulin secretion and IIS activity have been implicated in diseases such as diabetes and cancer [45 , 100] . Interestingly , sex differences in incidence have been previously reported for both diabetes and some forms of cancer [101 , 102] . Thus future studies on the link between sex and insulin secretion/IIS activity may explain why one sex is predisposed to these diseases .
Larvae were raised on food at a density of 50 larvae per vial at 25°C [83 , 103] . The following fly GAL4 stocks were used in this study: da-GAL4 , r4-GAL4 ( fat body ) , cg-GAL4 ( fat body ) , elav-GAL4 ( neurons ) , repo-GAL4 ( glia ) , P0206-GAL4 ( ring gland ) , Mef2-GAL4 ( muscle ) , Act5c-GAL4 ( ubiquitous ) , en-GAL4 ( posterior compartment of the wing ) , nos-GAL4 ( germline ) , c587-GAL4 ( gonad ) . We used the following UAS lines: UAS-tra2-RNAi ( TriP ) , UAS-tra2-RNAi ( VDRC ) , UAS-tra , UAS-dsx-RNAi ( TRiP ) . The following mutant strains were used: w1118 , foxoΔ94 , tra1/TM6B , Df ( 3L ) st-j7/TM6B , dsx1/TM6B , Df ( 3R ) dsx15 , tud1/CyO::GFP , fruF/TM6B , fru4-40/TM6B , fruΔtra/TM6B , pten100;CyO::GFP;MKRS/TM6B . We used the following stocks for flp-out experiments: hsflp;;UAS-tra2-RNAi , hsflp;UAS-tra , hsflp;;UAS-dsx-RNAi , act>stop>CD2>stop>GAL4 . Larvae were sexed using gonad size . Where gonad size could not be used to sex larvae ( eg . dsx or tra mutants , da>UAS-tra , Mef2>UAS-tra , Act5c>tra2-RNAi or Act5c>UAS-tra ) , males with a GFP on the X chromosome ( Ubiquitin-GFP ) were crossed to the virgin females of the correct genotype . In the progeny of the cross , females were GFP-positive and males were GFP-negative [5] . Pupal volume was measured as previously described [82] . n>60 per genotype . Measured as previously described [83 , 103] . n>40 per genotype . Five-day-old adult flies lacking gonads were weighed in groups of six in 1 . 5 ml tubes on an analytical balance . The gonads were removed prior to weighing by dissection; n>30 per genotype . 96 hr larvae were fed for the indicated amounts of time on yeast paste containing 0 . 05% Bromophenol blue . After feeding for the desired amount of time , ten larvae were isolated in a 1 . 5 ml tube , with eight tubes per sex collected in total . 250 μl of PBS was added to the tube and the larvae were homogenized with a micropestle . The lysate was cleared by centrifugation at 5000 rpm for 1 min , then the absorbance at 595 nm was measured in a spectrophotometer . Total RNA was extracted from larval tissues , then DNase-treated and reverse transcribed using Superscript II , as previously described [83 , 103] . Whole larval extracts were prepared as previously described [83 , 103] . The P-Akt and total Akt antibodies were obtained from Cell Signaling ( #4054 and #9272 ) . Anti-FOXO antibody was applied to fat bodies dissected from larvae 110 hr AEL ( 25°C ) at a dilution of 1:500 , as previously described [80] . Larvae were grown on rich food containing either DMSO or rapamycin , as previously described [83 , 103] . All data were analyzed using R Studio using the code described below . Student’s t-test: a <- filename$genotype1 b <- filename$genotype2 t . test ( a , b ) One-way ANOVA: aov . PV <- aov ( Pupal_Volume ~ Genotype , data = filename ) ls ( aov . PV ) summary ( aov . PV ) TukeyHSD ( aov . PV ) Two-way ANOVA with interaction term: int <- aov ( Pupal_Volume ~ Sex + Treatment + Sex*Treatment , data = filename ) summary ( int ) TukeyHSD ( int ) | Female-biased sexual size dimorphism is common in invertebrates , yet the mechanisms underlying increased female body size remain unclear . We uncovered a key role for sex determination gene transformer ( tra ) in promoting increased growth in females . Interestingly , we found that sex differences in body size are regulated by Tra in a pathway that is separate of the canonical sex determination pathway , and of other aspects of sexual dimorphism . Instead , Tra function in the fat body regulates growth in a non cell-autonomous manner by regulating the secretion of insulin-like peptides from the brain . This novel Tra-insulin link we describe may have implications for other sexually dimorphic phenotypes in Drosophila ( eg . lifespan , stress resistance ) , many of which are also regulated by insulin . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"Methods"
] | [] | 2015 | The Sex Determination Gene transformer Regulates Male-Female Differences in Drosophila Body Size |
Dengue is the most extensively spread mosquito-borne disease; endemic in more than 100 countries . Information about dengue disease burden , its prevalence , incidence and geographic distribution is critical in planning appropriate control measures against dengue fever . We conducted a systematic review and meta-analysis of dengue fever in India We searched for studies published until 2017 reporting the incidence , the prevalence or case fatality of dengue in India . Our primary outcomes were ( a ) prevalence of laboratory confirmed dengue infection among clinically suspected patients , ( b ) seroprevalence in the general population and ( c ) case fatality ratio among laboratory confirmed dengue patients . We used binomial–normal mixed effects regression model to estimate the pooled proportion of dengue infections . Forest plots were used to display pooled estimates . The metafor package of R software was used to conduct meta-analysis . Of the 2285 identified articles on dengue , we included 233 in the analysis wherein 180 reported prevalence of laboratory confirmed dengue infection , seven reported seroprevalence as evidenced by IgG or neutralizing antibodies against dengue and 77 reported case fatality . The overall estimate of the prevalence of laboratory confirmed dengue infection among clinically suspected patients was 38 . 3% ( 95% CI: 34 . 8%–41 . 8% ) . The pooled estimate of dengue seroprevalence in the general population and CFR among laboratory confirmed patients was 56 . 9% ( 95% CI: 37 . 5–74 . 4 ) and 2 . 6% ( 95% CI: 2–3 . 4 ) respectively . There was significant heterogeneity in reported outcomes ( p-values<0 . 001 ) . Identified gaps in the understanding of dengue epidemiology in India emphasize the need to initiate community-based cohort studies representing different geographic regions to generate reliable estimates of age-specific incidence of dengue and studies to generate dengue seroprevalence data in the country .
Dengue is the most extensively spread mosquito-borne disease , transmitted by infected mosquitoes of Aedes species . Dengue infection in humans results from four dengue virus serotypes ( DEN-1 , DEN-2 , DEN-3 , and DEN-4 ) of Flavivirus genus . As per the WHO 1997 classification , symptomatic dengue virus infection has been classified into dengue fever ( DF ) , dengue haemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) . The revised WHO classification of 2009 categorizes dengue patients according to different levels of severity as dengue without warning signs , dengue with warning signs ( abdominal pain , persistent vomiting , fluid accumulation , mucosal bleeding , lethargy , liver enlargement , increasing haematocrit with decreasing platelets ) and severe dengue [1 , 2 , 3] . Dengue fever is endemic in more than 100 countries with most cases reported from the Americas , South-East Asia and Western Pacific regions of WHO [1] . In India , dengue is endemic in almost all states and is the leading cause of hospitalization . Dengue fever had a predominant urban distribution a few decades earlier , but is now also reported from peri-urban as well as rural areas [4 , 5] . Surveillance for dengue fever in India is conducted through a network of more than 600 sentinel hospitals under the National Vector Borne Disease Control Program ( NVBDCP ) [6] , Integrated Disease Surveillance Program ( IDSP ) [7] and a network of 52 Virus Research and Diagnostic Laboratories ( VRDL ) established by Department of Health Research [8] . In 2010 , an estimated 33 million cases had occurred in the country [9] . During 2016 , the NVBDCP reported more than 100 , 000 laboratory confirmed cases of dengue [6] . It is therefore possible that dengue disease burden is grossly under-estimated in India . High dengue disease burden and frequent outbreaks result in a serious drain on country’s economy and stress on the health systems . In India , case detection , case management , and vector control are the main strategies for prevention and control of dengue virus transmission [6] . A new dengue vaccine is now available and several vaccines are in the process of development [10 , 11 , 12] . Information about dengue disease burden , its prevalence , incidence and geographic distribution is necessary in decisions on appropriate utilization of existing and emerging prevention and control strategies . With this background , we conducted a systematic review and meta-analysis to estimate the disease burden of dengue fever in India . We also reviewed serotype distribution of dengue viruses in circulation , and estimated case fatality ratios as well as proportion of secondary infections .
This systematic review is registered in PROSPERO ( Reg . No . CRD 42017065625 ) . We searched Medline ( PubMed ) , Cochrane Central , WHOLIS , Scopus , Science Direct , Ovid , Google Scholar , POPLINE , Cost-Effectiveness Analysis ( CEA ) Registry and Paediatric Economic Database Evaluation ( PEDE ) databases for articles published up to 2017 . The main search terms included incidence , prevalence , number of reported cases , mortality , disease burden , cost of illness , or economic burden of dengue in India . The complete search strategy is described in S1 Appendix . Back referencing of included studies in bibliography was also done to identify additional studies . The search results were initially imported to Zotero software ( Version 4 . 0 . 29 . 5 ) and duplicate records were removed . During title screening , we examined relevant studies from various databases . Our inclusion criterion was studies reporting dengue infection in India , not restricted to setting , design , purpose and population . Titles thus selected were subjected to abstract screening . Studies were considered eligible for further examination in full text if their abstracts reported incidence , prevalence , number of reported cases , mortality or the burden of dengue fever anywhere in India . Studies reporting complications of dengue , serotype details of dengue virus as well as seroprevalence of dengue were also included . Using a pre-designed data extraction form , two reviewers extracted details from selected studies independently . The data , which differed between the reviewers , were resolved by consensus . Information about the year of publication , study setting ( hospital/laboratory based , or community-based ) , study location , study period , laboratory investigations , number of suspected patients tested and positives , age distribution of cases , and details of dengue serotypes were abstracted ( S1 Dataset ) . The primary outcome measures of interest were ( a ) prevalence ( proportion ) of laboratory confirmed dengue infection among clinically suspected patients in hospital/laboratory based or community-based studies , ( b ) seroprevalence of dengue in the general population and ( c ) case fatality ratio among laboratory confirmed dengue patients . The diagnosis of acute dengue infection among the clinically suspected patients was based on any of the following laboratory criteria: ( a ) detection of non-structural protein-1 ( NS1 ) antigen , ( b ) Immunoglobulin M ( IgM ) antibodies against dengue virus ( c ) haemagglutination inhibition ( HI ) antibodies against dengue virus , ( d ) Real-time polymerase chain reaction ( RT-PCR ) positivity or ( e ) virus isolation . Seroprevalence of dengue was based on detection of IgG or neutralizing antibodies against dengue virus . Studies providing prevalence ( proportion ) of laboratory confirmed dengue infection among clinically suspected patients were classified into ( a ) hospital/laboratory-based surveillance studies and ( b ) outbreak investigations or hospital/laboratory-based surveillance studies when the outbreak was ongoing in the area , as mentioned in the original research paper . Studies regarding outbreak investigations considered an increase in number of reported cases of febrile illness in a geographical area , as the criteria for defining an outbreak . The outbreak investigations included one or more of the following activities: active search for case-patients in the community , calculation of attack rates for suspected case-patients , confirmation of aetiology and entomological investigations . For the case fatality ratio , the numerator included reported number of deaths due to dengue and denominator as laboratory confirmed dengue patients . Our secondary outcomes of interest were the following: ( a ) proportion of primary and secondary infections among the laboratory confirmed dengue patients . This classification was made based on the information about dengue serology provided in the paper . Primary dengue infection was defined as acute infection , as indicated by qualitative detection of NS1 antigen , and/or IgM or HI antibodies or RT-PCR positivity and absence of IgG antibodies against dengue virus . A case of acute infection as defined above , in presence of IgG antibodies , was considered as secondary dengue infection [2 , 13 , 14] . Some of the studies used the ratio of IgG to IgM antibodies as the criteria for differentiating primary and secondary infections [14]; ( b ) distribution of predominant and co-circulating dengue virus serotypes; ( c ) proportion of severe dengue infections based on WHO 1997 or WHO 2009 criteria [1 , 2] . The category of severe dengue infection included patients with DHF and DSS as per the WHO 1997 classification as well as severe dengue infections classified as per the WHO 2009 classification and ( d ) cost of illness , which included reported direct and indirect costs associated with dengue hospitalization . The risk of bias was assessed using a modified Joanna Briggs Institute ( JBI ) appraisal checklist for studies reporting prevalence data [15] and essential items listed in the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) checklist [16] . The criteria for assessing bias primarily included methods for selecting participants , methods for laboratory testing , and outcome variables ( Supplementary file S2 Appendix ) . We conducted quantitative synthesis to derive meta-estimates of primary and secondary outcomes ( severity of disease and primary/ secondary infections ) and qualitative synthesis to describe the serotype distribution and economic burden due to dengue . We followed Meta-analysis of Observational Studies in Epidemiology ( MOOSE ) guidelines [17] . For each study , primary outcomes ( prevalence of acute infection , seroprevalence and CFR ) were summarized as proportion and their 95% confidence intervals were computed . We used logit and inverse logit transformations for variance stabilization of proportions [18] . Binomial–Normal mixed effects regression model was used to estimate the pooled proportion of dengue infections . Forest plots were used to display pooled estimates . Heterogeneity was tested using likelihood ratio test . Funnel plots with logit prevalence on x-axis and standard errors on y-axis and Egger’s test were used to evaluate publication bias . Independent variables potentially associated with the prevalence of laboratory confirmed dengue were included as fixed-effects in univariate and multivariate binomial meta-regression models . P <0 . 05 was considered statistically significant . Sensitivity analysis was carried out by leaving out one study at a time in the order of publication to check for consistency of pooled estimates . Analyses were performed in the R statistical programming language using the ‘metafor’ package [19 , 20] .
The search strategy initially identified 2 , 285 articles from different databases . After removal of duplicates , 1 , 259 articles were considered for title and abstract screening . Seven hundred and forty-six articles were excluded for reasons provided in Fig 1 . Thus , 513 articles were found to be eligible for full-text review . After the review of full-text articles , 233 studies were included for the analysis [21–253] . The details of the studies included in the review are provided in the PRISMA flowchart ( Fig 1 ) . None of the studies reported incidence of dengue fever . Funnel plots and Egger’s test revealed no publication bias in the estimates of dengue prevalence in hospital-based surveillance studies , hospital-based surveillance studies during outbreaks and outbreak investigations . CFR estimates , however , showed a significant publication bias , and studies with high prevalence were more likely to be published . In the sensitivity analysis , the estimated pooled proportions were found to be consistent for all study outcomes . ( S3 Appendix )
The present study has estimated the burden of dengue fever based on published literature from India spanning over five decades . Most of the published literature included in the analysis were hospital/ laboratory-based surveillance studies or reports of dengue outbreak investigations . Additionally the published data from VRDL network has been included in the analysis [65 , 96] . The data from the other two nationally representative surveillance platforms could not be used for the analysis because surveillance data from NVBDCP only reports the number of laboratory confirmed dengue cases , while the IDSP data is not available in the public domain . There was no community-based epidemiological study reporting the incidence of dengue fever . Our analysis revealed that among the clinically suspected dengue fever patients , the estimated prevalence of laboratory-confirmed dengue infection was 38% . The burden of dengue was also variable in studies conducted in different settings . Our findings indicated that most of the laboratory confirmed dengue cases in India occurred in young adults . Dengue positivity was higher between the months of August and November , corresponding to monsoon and post-monsoon season in most states in India . In the meta-regression , studies that had used WHO/NVBDCP case definitions and the hospital based studies conducted during outbreaks or studies reporting outbreaks were more likely to have laboratory confirmation of dengue . The odds of laboratory confirmation were also higher among studies conducted during the period of 2011 to 2017 , as compared to studies conducted prior to the year 2000 . Information about seroprevalence of dengue in the general population is a useful indicator for measuring endemicity of dengue fever . The dengue vaccine ( CYD-TDV ) manufactured by Sanofi Pasteur has been introduced in two sub-national programs in Philippines and Brazil [254] and it has been suggested that vaccine acts by boosting the naturally acquired immunity [255] . WHO SAGE conditionally recommends the use of this vaccine for areas in which dengue is highly endemic as defined by seroprevalence in the population targeted for vaccination [12 , 256] . The results of the two vaccine trials and mathematical modelling suggest that optimal benefits of vaccination if seroprevalence in the age group targeted for vaccination was in the range of ≥70% [255 , 256] . In 2018 , WHO revised the recommendation from population sero-prevalence criteria to pre-vaccination screening strategy [257] . The pooled estimate based on the seven studies conducted in India indicated a dengue seroprevalence of 57% . However , this estimated seroprevalence is not representative of the country , as these studies were conducted only in 12 Indian states , and some had used a convenience sampling method [201] . The computed pooled estimate of case fatality due to dengue in India was 2 . 6% with a high variability in the reported CFRs . The CFR estimated in our study was higher than the estimate of 1 . 14% ( 95% CI: 0 . 82–1 . 58 ) reported in the meta-analysis of 77 studies conducted globally; in the 69 studies which adopted WHO 1997 dengue case classification , the pooled CFR was 1 . 1% ( 0 . 8–1 . 6 ) while the pooled CFR for 8 studies which used the WHO 2009 case definition , the pooled CFR was 1 . 6% ( 95% CI: 0 . 64–4 . 0 ) [258] . Higher CFR observed in our analysis could be due to smaller sample sizes as 14 of the 35 studies that reported CFR of 2 . 6 or higher had a sample size of 100 or less , while in the remaining 21 studies the denominator ranging between 101 and 400 . Also , we only considered laboratory confirmed dengue cases in the denominator for the calculation of CFR . As per the NVBDCP surveillance data , a total of 683 , 545 dengue cases and 2 , 576 deaths were reported in India during 2009–2017 giving a CFR of 0 . 38% [6] . The lower CFR estimates from NVBDCP data could probably be on account of under-reporting of deaths due to dengue , or inclusion of higher number of mild cases in the denominator [259] . As per the NVBDCP surveillance data , an average of 28 , 227 dengue cases and 154 deaths were reported annually during 2009–2012 . The number of dengue cases reported increased thereafter , with an average of 100 , 690 cases per year during 2013–2017 . However , the reported number of deaths did not increase proportionately . The information about severity of dengue cases is not available from NVBDCP surveillance data . The published studies from India indicated circulation of all the four-dengue serotypes , with DEN-2 and DEN-3 being the more commonly reported serotypes . Two third of the studies reported circulation of more than one serotype . Co-circulation of multiple serotypes was particularly evident from the published studies in Delhi . More than two third ( 16/19 ) studies from Delhi reported circulation of more than one serotype; and most of the studies conducted in the last 10 years identified co-circulation of more than one serotype [Table 3] . Our review also revealed that more than two-fifth of the laboratory confirmed infections were secondary dengue infections and nearly one-fourth of the cases were severe in nature . Circulation of numerous dengue serotypes is known to increase the probability of secondary infection , leading to a higher risk of severe dengue disease [260] . Our systematic review has certain limitations . First , our study included only peer-reviewed literature from selected databases and we excluded grey literature which may have provided additional data . Second , most of the studies on disease burden were hospital-based , with no community-based studies estimating incidence . Hospital-based studies do not provide any information about the community level transmission as hospitalization is a function of health-seeking behaviour of the population . In absence of the information about health seeking behaviour provided in these studies , we estimated the prevalence of dengue using number of patients tested in the hospitals as the denominator . Third , the hospital-based studies used varying case definitions and laboratory tests to confirm dengue infection . Fourth , information about the type of health facility ( public or private ) , or residential status of patients ( urban or rural ) , and age was not uniformly reported and hence we did not estimate the dengue prevalence by these variables . In conclusion , the findings of our systematic review indicate that dengue continues to be an important public health problem in India , as evidenced by the high proportion of dengue positivity , severity and case fatality as well as co-circulation of multiple dengue virus serotypes . Our review also identified certain research gaps in the understanding on dengue epidemiology in the country . There is a need to initiate well planned community-based cohort studies representing different geographic regions of the country in order to generate reliable estimates of age-specific incidence of dengue fever in India . As such studies are cost intensive , a national level survey to estimate age-stratified dengue seroprevalence rates could be an alternative . Such estimates could be used to derive the relative proportions of primary and secondary infections using mathematical models [261] . Well planned studies in different geographic settings are also needed to generate reliable data about economic burden from India . Although the existing dengue surveillance platforms of NVBDCP , IDSP and VRDL are generating data about dengue disease burden , these systems could be strengthened to also generate data about dengue serotypes , severity , and primary and secondary infection from India . | Dengue fever , an extensively spread mosquito-borne disease , is endemic in more than 100 countries . Information about dengue disease burden , its prevalence and incidence and geographic distribution is necessary to guide in planning appropriate control measures including the dengue vaccine that has recently been licensed in a few countries . We performed a systematic review and meta-analysis of published studies in India on dengue . The overall estimate of the prevalence of laboratory confirmed dengue infection based on testing of more than 200 , 000 clinically suspected patients from 180 Indian studies was 38 . 3% . The pooled estimate of dengue seroprevalence in the general population and CFR among laboratory confirmed dengue patients was 56 . 9% and 2 . 6% respectively . There were no community-based studies reporting incidence of dengue . Our review also identified certain knowledge gaps about dengue epidemiology in the country . Identified gaps in the understanding of dengue epidemiology in India emphasize the need to initiate community-based cohort studies representing different geographic regions to generate reliable estimates of age-specific incidence of dengue and studies to generate dengue seroprevalence data in the country . | [
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] | 2018 | Dengue infection in India: A systematic review and meta-analysis |
Protection at the peak of Plasmodium chabaudi blood-stage malaria infection is provided by CD4 T cells . We have shown that an increase in Th1 cells also correlates with protection during the persistent phase of malaria; however , it is unclear how these T cells are maintained . Persistent malaria infection promotes protection and generates both effector T cells ( Teff ) , and effector memory T cells ( Tem ) . We have previously defined new CD4 Teff ( IL-7Rα- ) subsets from Early ( TeffEarly , CD62LhiCD27+ ) to Late ( TeffLate , CD62LloCD27- ) activation states . Here , we tested these effector and memory T cell subsets for their ability to survive and protect in vivo . We found that both polyclonal and P . chabaudi Merozoite Surface Protein-1 ( MSP-1 ) -specific B5 TCR transgenic Tem survive better than Teff . Surprisingly , as Tem are associated with antigen persistence , Tem survive well even after clearance of infection . As previously shown during T cell contraction , TeffEarly , which can generate Tem , also survive better than other Teff subsets in uninfected recipients . Two other Tem survival mechanisms identified here are that low-level chronic infection promotes Tem both by driving their proliferation , and by programming production of Tem from Tcm . Protective CD4 T cell phenotypes have not been precisely determined in malaria , or other persistent infections . Therefore , we tested purified memory ( Tmem ) and Teff subsets in protection from peak pathology and parasitemia in immunocompromised recipient mice . Strikingly , among Tmem ( IL-7Rαhi ) subsets , only TemLate ( CD62LloCD27- ) reduced peak parasitemia ( 19% ) , though the dominant memory subset is TemEarly , which is not protective . In contrast , all Teff subsets reduced peak parasitemia by more than half , and mature Teff can generate Tem , though less . In summary , we have elucidated four mechanisms of Tem maintenance , and identified two long-lived T cell subsets ( TemLate , TeffEarly ) that may represent correlates of protection or a target for longer-lived vaccine-induced protection against malaria blood-stages .
Malaria accounts for an estimated 438 , 000 deaths annually , with over 3 billion people at risk of infection [1] . Plasmodium infection can be considered chronic both for the repetitious exposure in hyperendemic areas [2] , as well as for the ability of both P . falciparum and P . vivax infections to persist for years even in the absence of parasite transmission [3 , 4] . P . chabaudi infection lasts up to 90 days in mice [5] , making it a unique and well-accepted model to study the chronic phase of malaria infection . CD4 T cells play a central role in protection of chronic infections such as malaria , LCMV and Leishmania in mice , but the protection established wanes on cure of the infection . In P . chabaudi infection , complete protection from secondary parasitemia decays by 200 days post-infection [6] . This is accompanied by a decay in proliferation of CD4 T cells in response to parasite antigens in vitro , but not a decay in antibody titers , suggesting that T cell function mediates decay in protection . Chronic infection has also been shown to improve T cell-mediated protection [5–7] . Protection by the RTS , S vaccine , which will likely be implemented in some countries soon , varies from 12 to 68% , depending on the context and what outcomes are measured [8] . Although many malaria cases are likely to be averted with this first vaccine , total decay of the efficacy of the RTS , S vaccine occurs in just four years [9] . Even with the newer whole-parasite vaccines , which have higher reported efficacy , the phenotype of the T cells generated suggests that they may also be short-lived effector T cells [10] . Strategies that include treatment of infection with anti-malarial drugs may generate more long-lived T cell responses [11] . The specific CD4 T cell population observed after P . chabaudi infection is comprised of a mixture of effector ( Teff ) and memory ( Tmem ) phenotype T cells [7] . We showed that specific T cells in the memory phase do not re-expand in response to a second P . chabaudi infection [12] . While this could be explained by either Teff or Tem , it has been experimentally challenging to distinguish the phenotype of these two populations . In a recent elegant study , protective Teff in Leishmania infection were identified as proliferating , terminally differentiated cells expressing effector molecules , while effector memory T cells ( Tem ) were defined only at later timepoints as memory T cells expressing migration markers and effector molecules [13] . In our work , we have used the observation that IL-7Rα is completely but transiently downregulated on effector T cells , and re-upregulated on Tem , to distinguish two different populations with unique survival and protection capacity [7 , 12 , 14] . Tem do not undergo homeostatic proliferation [15] , suggesting that they may not survive as long as Tcm in the absence of antigen . However , the actual survival potential of Tem is unknown , as studies to date do not distinguish Tem from Teff [16] . Even less is known about mechanisms of survival of Teff and Tem cells in chronic infections . We have started to narrow down the characteristics of T cells that are most protective in malaria . Using adoptive transfer of MSP1-specific TCR Transgenic CD4 T cells to study the effect of chronic P . chabaudi infection on protection , we showed that the memory population ( CD4+CD44hiCD25- ) of previously activated T cells from chronically infected mice protected better than T cells from mice that were treated with chloroquine , an anti-malarial drug previously shown to clear P . chabaudi ( AS ) , one month after infection [7] . We observed that the T cell population from chronically infected animals at the memory timepoint contained more Teff ( CD127- ) , and more IFN-γ+TNF+IL-2- cytokine producing T cells than those from treated animals . These Th1 cells were CD44hi CD62Llo , indicating that they are either Teff or Tem maintained by chronic infection . More recently , we showed that most Ifng+ Teff do not maintain Ifng expression to the memory timepoint . However , all Ifng+ T-bet+ T cells derived from Ifng+ Teff that survive until d60 , proliferate extensively between the peak of infection and the memory timepoint [14] . Collectively , these data suggest that maintenance of potentially protective Th1 cytokine production and Teff/Tem cells themselves is linked to persistent infection . In order to define the precursors of Tem , we previously identified three subsets of Teff ( CD127- ) representing different stages of activation which are generated sequentially in P . chabaudi infection [12] . By day 5 post-infection ( p . i . ) , early effector T cells ( TeffEarly , CD62LhiCD27+ ) are detectable , as they have down-regulated IL-7Rα/CD127 , but have not yet lost CD62L expression . Strikingly , though some TeffEarly express IFN-γ , the majority of CD127- TeffEarly have not expressed CD11a or proliferated . TeffEarly still have not proliferated , even on day 9 p . i . , when T cell numbers peak in this infection . The majority of Teff at day 7 p . i . are CD62LloCD27+ intermediate effector T cells ( TeffInt ) , which are the first proliferating subset . TeffInt express PD-1int and high levels of Th1 cytokines . Finally , the majority of Teff lose both CD62L and CD27 expression , and become TeffLate ( CD62LloCD27- ) by day 9 p . i . TeffLate have high levels of phosphatidyl serine in their outer plasma membrane leaflet , indicating susceptibility to apoptosis , as previously reported for CD27- CD8 T cells [17] . All three Teff subsets contain cells expressing the Th1- cytokine , IFN-γ , and transcription factor , T-bet . Notably , upon transfer into recipients at the peak of infection , TeffEarly survive the T cell contraction phase better than the intermediate and late Teff subsets [12] . The increased survival of TeffEarly is supported by their higher transcription of the pro-survival genes Bcl2 , Mcl1 , Pim2 , and Pim3 . These anti-apoptotic molecules are down-regulated concomitant with CD62L down-regulation , which also signals terminal differentiation and expression of PD-1 and Fas . Identification of these Teff subsets allowed purification of activation intermediates and facilitates the study of effector and memory T cell differentiation and maintenance in vivo . To determine the pathway of differentiation of effector memory T cells ( Tem ) in chronic infection , we used these three Teff subsets in adoptive transfer experiments in P . chabaudi infection [18] . We found that TeffEarly can generate central memory T cells ( Tcm ) in uninfected recipients . We have shown in the past that Tcm , in turn , can generate Tem during the high-level chronic infection of RAG2o animals without B cell transfer [7 , 12] . In contrast , TeffLate , which share the CD62loCD27- phenotype , are generally short-lived , though they have the plasticity to survive and expand highly in infected RAG2o animals . Therefore , our data supports a model where Tem are generated from Tcm , not Teff , as often proposed , suggesting the possibility of a longer lifespan [19] . In the current study , we purified Tmem and Teff subsets from Merozoite Surface Protein-1 ( MSP-1 ) -specific T cell receptor transgenic ( B5 TCR Tg ) mice , and tested their ability to survive in uninfected and/or chronically-infected recipients . We also tested their ability to protect immunodeficient animals in coordination with B cells . In the course of this work , we observed four mechanisms for promoting Tem survival in chronic infection . These mechanisms are: 1 ) Tem can survive in the absence of antigen; 2 ) some Teff may survive to generate Tem , as Teff are capable of re-upregulating CD127 over time , and remain protective; 3 ) Tcm derived from P . chabaudi-infected animals continue to generate Tem , even after infection is cleared; 4 ) low-level persistent infection promotes proliferation of Tem . On testing the Teff and Tmem for protection of immunodeficient animals from malaria , we show that among Tmem subsets , only Late effector memory T cells ( TemLate , CD127hi CD62Llo CD27- ) reduce both pathology and parasitemia slightly . In contrast , all Teff subsets strongly reduce parasitemia , though they lose this ability over time . Interestingly , TeffEarly both protect well and survive in these assays , while other Teff are shorter-lived . These results have the potential to both explain poor , short-lived protection from malaria and to inform novel methods to drive long-lived protection by vaccination .
While the current paradigm holds that Tmem survive longer than Teff , there is little data in the literature on the relative lifespans of Tem and Teff , particularly after clearance of antigen or pathogen . Therefore , we investigated the decay of polyclonal T cells responding to P . chabaudi infection upon clearance of infection . We tracked potential decay of three memory T cell ( CD127hi ) subsets ( Tcm , CD62LhiCD27+; TemEarly , CD62LloCD27+ , TemLate , CD62LloCD27- ) , and three effector T cell ( CD127- ) subsets ( TeffEarly , CD62LhiCD27+; TeffInt , CD62LloCD27+; TeffLate , CD62LloCD27- ) after treatment of infection , as shown schematically in Fig 1A–1F . Infection was stopped by day 34 using the anti-malarial drug , mefloquine ( MQ ) . We gated on CD11a+ cells and determined memory ( CD44hiCD127hi ) , Tmem subsets , and proliferation using BrdU ( Fig 1B ) , then quantified the number and proportion of proliferating Tmem ( CD4+ CD44hi CD127hi CD11a+ , Fig 1C and 1D ) in the spleen of C57BL/6 animals using CD11a , as it was recently described to be upregulated only on MHC/peptide-stimulated T cells , and not on T cells activated by cytokines [20] . We have studied the lymph nodes in this infection , and they contain few T cells responsive to malaria , and equal ratios of Tcm and Tem at memory timepoints [7] . We quantified the number of polyclonal Tcm , TemEarly , and TemLate over a 30-day period by flow cytometry , after clearance of parasite in the spleen with MQ treatment ( Fig 1E ) . We found relatively stable numbers of all Tmem subsets . In addition , we tracked the survival of Tmem after the clearance of parasite by labelling Tmem that had proliferated late in infection and then administering anti-malarial drug . These Tmem may have proliferated specifically in response to the low levels of parasite , or homeostatically , which would increase Tcm labelling . To quantify proliferation , we administered 5-Bromodeoxyuridine ( BrdU ) , which is incorporated into the DNA of proliferating cells , on days 24–30 p . i . , just before anti-malarial treatment . Strikingly , the proportions of BrdU+ Tem measured over the 20-day period did not decay ( Fig 1E , left ) . The percent of BrdU+ Tcm out of Tmem does decay initially; however , none of the Tmem subsets decay significantly in cell number over this time period ( Fig 1E , right ) . We also tracked the survival of polyclonal Teff ( IL-7Rα/CD127- ) after clearance of parasite by treating P . chabaudi infected mice with mefloquine on days 10–14 p . i ( Fig 1F ) . The number of polyclonal effector T cells ( CD4+ CD127- ) in the spleen of mefloquine-treated mice decayed significantly in the 20 days post-treatment ( day 30 p . i . , Fig 1G ) , suggesting an intermediate lifespan . The decay in number of the largest Teff subset , TeffInt ( CD62Llo CD27+ ) , is easily seen , while TeffEarly ( CD62Lhi CD27+ ) remained stable ( Fig 1H ) , showing strong survival as we previously showed in the T cell contraction phase from days 8–11 p . i . [12] . The number of TeffLate , the most terminally differentiated subset , also remains stable . This stability likely represents TeffInt transition to the TeffLate population and then die [12] . Similar observations have also been suggested for CD8 CD27- [17] . When we gated on CD11a+ T cells that had divided in response to infection ( BrdU+ ) , the decline of Teff numbers was variable , but rapid ( Fig 1I and 1J ) . This decline was similar in the proportions and numbers of all the BrdU+ Teff subsets ( Fig 1K ) . The difference between the decay in the number of TeffEarly by day 30 when gated on divided cells ( CD127-CD11a+ BrdU+ ) , and the better maintenance of undivided ( BrdU- ) TeffEarly is interesting . This difference suggests that while the TeffEarly population survives better than other Teff subsets [12] , there is a fraction of TeffEarly that are CD11a+ and proliferate , and then decay like the other proliferative Teff subsets . Taken together , this data suggests that Teff generated in malaria infection decay over 20 days in the absence of parasite , while Tmem , including Tem , are more stable and long-lived . Teff and Tmem survival cannot be understood only from studying the polyclonal response due to the changing phenotypes of activated T cells , particularly the re-upregulation of CD127 on Teff . Therefore , we tested the ability of highly-purified subsets of MSP1-specific B5 TCR Tg T cells to survive in uninfected Thy1 . 1 congenic recipients after adoptive transfer of a physiological number ( 5 x 104 ) . T cells from infected B5 Tg donors of each Teff ( CD127- , d8 p . i . ) and Tmem ( CD44hi CD127hi , d60 p . i . ) subset were transferred into groups of uninfected congenic ( Thy1 . 1 ) recipients , as shown schematically in Fig 2A . Donors were age matched , and Teff and Tmem sort was completed on the same day so that recipient flow cytometry could be performed on the same day . Recovered B5 TCR Tg T cells ( Thy1 . 2+ CD4+ ) were counted after two months ( Fig 2B ) . We observed significantly more TeffEarly cells than terminally differentiated TeffLate that survived for two months . These results confirm our previous studies which demonstrated a clear survival advantage for TeffEarly compared to other Teff subsets over two weeks [12] . Recipients of both Tem subsets had significantly higher numbers of Thy1 . 2+ T cells by day 60 post-transfer than recipients of CD127- Teff cells with similar CD62L and CD27 phenotypes , TeffInt and TeffLate . We concluded that survival was more robust for all of the memory T cell subsets , including Tem , compared to survival of the terminal Teff . Importantly , survival of TeffEarly ( CD62Lhi CD27+ , CD127- ) was not significantly different than survival of Tcm ( CD62Lhi CD27+ , CD127hi ) , consistent with our previous studies suggesting that Tcm and TeffEarly are closely related [12] . The surviving TeffEarly population re-upregulated CD127 and became largely CD127hi in uninfected Thy1 . 1 recipients over two months ( Fig 2C ) . CD127 re-upregulation starts on day 14 post-transfer , as we previously reported [12] . While transition to CD127hi is not definitive evidence of a memory phenotype , TeffEarly do survive in similar numbers as Tcm , suggesting that surviving TeffEarly cells become Tcm , and that the few surviving mature Teff can become Tem . Taken together , these experiments suggest that Tem survive in the absence of chronic infection , and that even in malaria infection , where immunity decays , memory T cells are longer-lived than terminally differentiated effector T cells . Durability of memory T cells is a critical feature for their function . However , there is relatively little that is known about the ability of CD4 Tem to survive in the absence of antigen [16 , 21] . Therefore , we tested the phenotype of surviving Tmem generated in this infection after transfer and competition with endogenous T cells in congenic mice . Memory T cell subsets were sorted from B5 TCR Tg animals infected two months earlier , using the gating strategy shown in S1A Fig . These highly-purified populations were generated from untreated B5 TCR Tg donors , or donors treated with chloroquine ( CQ ) on days 30–34 p . i . to eliminate persistent parasite . Each of three sorted Tmem subsets were transferred ( 2 . 5 x 105 ) into a group of uninfected Thy1 . 1 recipients , as shown schematically in Fig 3A . B5 TCR Tg T cells ( Thy1 . 2+ CD4+ ) were recovered and analyzed by flow cytometry after two weeks . On recovery , all Tmem subsets maintained their original memory phenotype ( CD44hiCD127hi , Fig 3B ) . The number of T cells recovered after two weeks from each group of recipients was similar ( Fig 3C ) , and the number of cells in each recipient was clearly detectable above the limit of detection ( L . O . D . ) in all animals . Interestingly , the T cells recovered from recipients of Tcm had progressed to include some TemEarly and TemLate phenotype cells ( Fig 3D ) , while the TemEarly recipients also generated TemLate cells in some animals ( 3/5 ) , potentially reflecting the individual variation in time to clearance of parasite . The majority of TemLate maintained their original phenotype . We previously described this differentiation pathway in the context of a chronic infection with high parasitemia in immunodeficient RAG2o animals [7] , but it was unexpected to see this progression in immunocompetent and T cell replete recipient animals , especially in the absence of parasite antigen in the recipient . We hypothesized that the continued differentiation of memory T cell subsets from Tcm to Tem in the uninfected recipient animals could be due to T cell “programming” in the context of chronic infection , which can last up to day 90 p . i . , in donor animals . Therefore , to test if progressive differentiation of memory T cells was due to continuous exposure to infection , Tmem donor mice were infected with P . chabaudi ( 1 x 105 iRBCs ) , and then treated with chloroquine ( CQ ) starting at day 30 . Chloroquine completely clears low levels of parasitemia in P . chabaudi ( AS ) infection [5 , 22] , therefore , parasite is eliminated in +CQ donors for the month prior to Tmem sorting and transfer . Memory T cell subsets were sorted on d60 , 26 days after final chloroquine treatment of the donor , and they were transferred into uninfected Thy1 . 1 hosts . Similar numbers of T cells were recovered 14 days post-transfer regardless of the chloroquine treatment of the donor animals ( Fig 3E ) . While the pattern of Tmem subsets of B5 T cells recovered from Tcm transfer was similar after chloroquine treatment of donors , there was a significant reduction in progression of TemEarly to TemLate when TemEarly had not been exposed to parasite for several weeks before transfer ( Fig 3F , p = 0 . 0499 ) , with rested ( +CQ ) cells recovered from all animals ( 4/4 ) exhibiting little progression to TemLate . These data indicate that Tcm cells in this infection can continue to progress towards more highly differentiated Tmem subsets for some time after clearance of parasite . However , in the recent absence of persistent infection , the TemEarly do not make TemLate . Tem are generally found associated with chronic infection [23 , 24]; however , we observed similar survival of Tem and Tcm even in the absence of antigen . Therefore , we tested if transferring Tem into the environment of low-level chronic infection would improve Tem numbers compared to uninfected hosts . Tem subsets were sorted from infected ( d60 p . i . ) B5 TCR Tg mice , and transferred into infection-matched ( d60 p . i . ) Thy1 . 1 recipients with sub-patent ( below-detectable ) parasitemia , or uninfected hosts , as shown schematically in Fig 4A . The number of B5 T cells recovered from infection-matched recipients after two months compared to uninfected recipients was significantly higher in TemEarly recipients ( Fig 4B ) . Interestingly , most B5 T cells recovered from TemEarly recipients maintained their high level of CD127 expression ( Fig 4C ) , even in the presence of sub-patent levels of parasite . T cells from all infection-matched recipients showed a distinct peak of divided T cells ( CFSE- ) ; however , this difference ( %CFSE- TemEarly infected and uninfected recipients ) did not reach statistical significance . The majority of TemEarly maintained their phenotype; however , some of the recovered cells progressed , or re-upregulated CD62L . TemLate do not accumulate in low-level chronic infection . Similar to TemEarly , TemLate from infection-matched recipients showed a distinct peak of divided T cells , however , this difference ( %CFSE- TemEarly infected and uninfected recipients ) did not reach statistical significance ( Fig 4E , top panels ) . The TemLate recovered from uninfected recipients retained their original phenotype remarkably well in all recipients ( Fig 4E , bottom panels ) . Therefore , it appears that Tem survive similarly to Tcm in the absence of infection , but Tem have additional mechanisms to promote their longevity in chronic infection accounting for their increased fraction compared to Tcm . In the long-term , P . chabaudi infection primarily drives generation of effector memory T cells . In order to understand the potential role of Tem in protection from high parasitemia and pathology , we compared the effects of Tcm and Tem cells on survival , parasitemia , and pathology in infected immunocompromised mice . Using P . chabaudi infection of RAG animals to study the contribution of adaptive immunity to protection , we previously established that the peaks of parasitemia and pathology are only controlled by activated T cells; yet , the full clearance of parasite depends on high levels of antibody [7 , 25] . As in the studies above , to test the potential of Tmem subsets to protect , we used subsets we have previously established in this model for memory T cells ( CD127hi ) : Tcm ( CD62LhiCD27+ ) , TemEarly ( CD62LloCD27+ ) , and TemLate ( CD62LloCD27 ) [7] . The same number of B5 TCR Tg donor CD4+ T cells ( 2 x 105 ) from each subset and immune BALB/c B cells ( CD19+ , 2 x 107 ) were both transferred into groups of RAG2o mice , which were then infected the following day with P . chabaudi , as shown schematically in Fig 5A . Infected RAG2o mice that received B cells but no T cells were used as controls to measure changes induced by T cells at the peak of infection . Another appropriate control could have been naïve T cells , but we have previously shown that there is no significant difference in the peak parasitemia or pathology between infected RAG2o mice with naïve T cells and B cells , or B cells alone , so we chose to use the no T cell control in each experiment as a universal control [7] . Parasitemia levels , weight loss , temperature , and cytokine levels were measured over the course of infection ( Fig 5B–5E ) . We report an average of each animal’s maximal change , as each animal can exhibit peak parasitemia and pathology on a different day between days 8–10 , as we have reported before [7] . Comparing the average peak parasitemia , TemLate was the only subset that significantly reduced parasitemia , compared to the universal control group that received immune B cells but no T cells ( by an average of 8 . 7% iRBC/RBC , or 19% of peak RAG2o parasitemia ) , while TemEarly and Tcm did not reduce parasitemia ( Fig 5B ) . TemLate and Tcm also significantly reduced peak hypothermia compared to control ( Fig 5C ) . Transferred T cells expanded dramatically in all groups after 38 days of infection , as evidenced by the high numbers of recovered cells ( Fig 5D ) . Interestingly , this suggests similar abilities by all memory subsets to expand in the context of high parasitemia and leukopenia . Similar results were observed when cells were taken out on day 14 post-infection . While there were no differences in IFN-γ production by the T cells recovered from RAG recipients on day 38 ( Fig 5E ) , we have previously shown that the three Tmem subsets transferred into congenic mice , instead of RAG2o mice , exhibit strikingly different cytokine profiles . For instance , TemLate contain discreet IL-10+ and IFN-γ+ populations by two months after infection [7] . In summary , these data show that TemLate generated by chronic infection are able to contribute to reduction of peak parasitemia , though further study is required to determine the effector mechanisms responsible for the differences . Effector T cells are responsible for clearance of primary infection , but are thought to become terminally differentiated and die in the process . However , the lifespan of CD4 Teff populations during specific infections has rarely been determined . We sought to determine if the degree of maturation of the effector T cells affects their ability to provide protection from P . chabaudi infection . The CD127- Teff Early ( CD62LhiCD27+ ) , TeffInt ( CD62LloCD27+ ) , and TeffLate ( CD62LloCD27- ) subsets were sorted from B5 TCR Tg donors on day 8 post-infection as shown with the gating strategy in ( S1B Fig ) , and transferred ( 5x105 ) into immunodeficient RAG2o mice together with B cells ( 1x107 ) from immune BALB/c mice , as in previous work [7 , 25] . Recipient mice were infected with P . chabaudi one day post-transfer and parasitemia , weight loss , and hypothermia were monitored over 14 days , as shown schematically in Fig 6A . The average peak parasitemia showed that all of the effector T cell subsets significantly protected the recipient mice by an average of 28% iRBC/RBC , or 59% of peak RAG2o parasitemia , compared to the control group ( Fig 6B ) . Weight loss and hypothermia were measured at the peak of infection for each mouse ( d8-10 ) as indicators of pathology . Weight loss , but not hypothermia , showed a significant difference between Teff groups and the control ( Fig 6C ) . After 14 days of infection , all Teff populations were recovered in similar numbers ( Fig 6D ) . Further , all the Teff subsets responded to the infection by producing cytokines on day 14 p . i ( Fig 6E ) . The TeffLate population produced higher proportions of all three cytokines ( p = 0 . 0002 ) . However , the number of triple-cytokine producers ( TNF+IFN-γ+IL-2+ ) was similar in all recipient groups , suggesting a mechanism for the equal protection provided , as multi-cytokine producers correlate with protection in several infections [26] . As Teff protect so well , we next tested the possibility that Teff can be intermediate-lived and/or become memory T cells in the absence of exposure to parasite , and possibly contribute to protection . To this end , the three Teff subsets ( TeffEarly , TeffInt , TeffLate ) were sorted and transferred into uninfected RAG2o animals for two weeks , as shown schematically in the top panel of S2A Fig . As previously shown in wildtype congenic recipients [12] , more TeffEarly cells than the mature Teff subsets were recovered after two weeks of “resting” ( S2B Fig ) . Our previous study showed that the TeffEarly subset also contains precursors to memory T cells , while the TeffInt and TeffLate subsets decay [12] . Consistent with this observation , T cells recovered after 14 days from TeffEarly recipients showed re-upregulation of CD127 ( S2C Fig ) . As we demonstrated previously in wildtype recipients , we observed that TeffEarly progressed along the pathway of differentiation and generated all of the Teff and Tmem subsets ( S2D Fig ) , while recipients of TeffInt and TeffLate had too few cells at this timepoint to determine their phenotypes . While the purpose of this experiment was to test protection , the potential of TeffEarly to make both Teff and Tmem subsets in this second model confirms the differentiation pathway largely defined in our previous work [12] , and now summarized in our final figure . To test the functional ability of Teff cells over time , we determined the relative ability of T cells surviving from each Teff subset to contribute to protection after 2 weeks in uninfected recipients ( S2A Fig , bottom ) . Teff subsets ( TeffEarly , TeffInt , TeffLate ) were sorted from infected B5 TCR Tg donors on d8 p . i . and transferred into RAG2o mice . These mice were then given B cells , and infected with P . chabaudi two weeks later . All Teff subsets still showed a trend of reducing peak parasitemia within 14 days , though this was only significant for TeffInt ( S2E Fig ) . Both TeffEarly and TeffInt significantly protected recipients from weight loss , while TeffLate significantly protected from hypothermia . ( S2F Fig ) . We also recovered equally high numbers of all Teff subsets when recipient mice were sacrificed 14 days after challenge ( S2G Fig ) . The numbers of Teff surviving after challenge were about three logs higher than before challenge , as depicted in S2B Fig , suggesting that B5 TCR Tg Teff only proliferate in RAG2o mice in the presence of antigen , and not homeostatically . Teff subsets had an average of 77% Teff ( CD127- ) phenotype on recovery ( TeffEarly shown in S2H Fig ) , compared to 37% in uninfected RAG2o recipients ( S2C Fig , right plot ) , and were primarily TeffInt and TeffLate . A substantial fraction of all T cells recovered after 2 weeks post-infection also produced IFN-γ ( S2I Fig ) , demonstrating that all Teff subsets maintain the ability to perform effector functions even after two weeks without antigen . In order to test the possibility that Teff can be intermediate-lived and contribute to protection even after a long absence of infection , we transferred the three Teff subsets ( TeffEarly , TeffInt , TeffLate ) into RAG2o recipients and infected the mice with 105 parasitized RBCs after two months , as diagrammed in Fig 7A . Parasitemia and pathology were monitored for 14 days , and splenocytes were tested for cytokine production . Strikingly , all the three Teff subsets reduced peak parasitemia significantly ( Fig 7B ) . Interestingly , weight loss ( Fig 7C ) and hypothermia ( Fig 7D ) were significantly lower in recipients of all three Teff subsets after infection . All Teff subsets were recovered in similar large numbers after infection ( Fig 7E ) . On day 14 p . i . , there was no difference in IFN-γ production observed between the groups ( Fig 7F ) . Taken together , these data suggest that dwindling Teff numbers are still able to protect from both parasitemia and pathology , plausibly by re-expanding , though we have not documented proliferation per se . This work has demonstrated mechanisms of Tem survival , and also contributed to our understanding of mechanisms of Tem differentiation , which we have diagrammed in Fig 8 . We propose a model of T cell activation with the power to explain many aspects of how CD4+ effector memory T cells are generated in malaria infection . The model illustrates that TeffEarly generate Tcm , which become Tem in conditions of chronic infection . Important support for the model suggesting that Tmem differentiation occurs early after T cell activation is that while chronic P . chabaudi generates predominantly Tem cells in the long-term , the dominance of Tem over Tcm is actually determined between days 3 and 5 post-infection [12] . After this window , the ratio of these two subsets can no longer be reversed by parasite clearance to favor Tcm . Previously , we showed that TeffEarly could re-express CD127 and generate both Tcm and Tem in uninfected wildtype recipients , and that Tcm could make Tem subsets in the presence of high parasitemia [7]; however , it was unclear if Tcm made Tem subsets during low-level chronic infection . We confirmed that TemEarly can make TemLate ( Figs 3D and 4C ) and expanded on our previous observations by showing that Tcm can be programmed by low-level chronic infection to sustain Tem cell numbers ( Fig 3D ) . We also demonstrated that continuous parasite exposure after this early period further promotes Tem accumulation . Dotted arrows in the model schematic indicate plasticity in the directionality of the pathway , and these conversions are often dependent on the presence or absence of infection . For example , we previously showed transfer of Tem into infected RAG2o animals with high parasitemia induced downregulation of CD127 , or secondary Teff from Tem [7]; here , we showed that mature Teff do not survive well , but are nevertheless capable of re-upregulating CD127 , and re-expanding to protect . In future studies , we hope to use this model to understand the mechanisms driving differentiation of protective T cell subsets .
A critical feature of protective T cells generated by a successful vaccination is their ability to survive long-term . To our knowledge , there is only one study in the literature that directly compares survival of CD4 central and Tem in vivo , and this study suffers from the difficulty of separating effector T cells from Tem resulting in the observation of a short half-life for the mixed population [21] . Using the transient but complete downregulation of CD127 by Teff has allowed us to make progress on this question with important implications for vaccine development for chronic infections . Similarly , there are no studies to our knowledge directly comparing Teff and Tem survival , though there is elegant work comparing effector function and protection of activated subsets in various models [13] . The current paradigm suggests that Tmem , even CD62Llo Tem , might survive more durably than CD127- Teff in the absence of persistent infection , and indeed our data support this hypothesis . Interestingly , CD27- CD8+ T cells have been previously reported to be protective , intermediate-lived , and proliferate to self-renew [17 , 27] . However , our data show that the protective CD4+ CD127hi CD27- TemLate definitely survives longer than CD127- CD27- Teff . This suggests that the shorter half-life reported could be due to the inclusion of both CD127- CD27- Teff and CD127+ CD27- T cells . Nevertheless , our data are compatible with the interpretation that in the long-term , both TeffLate and TemLate decay faster than other Teff and Tmem T cell subsets , respectively . Tcm are known to survive through homeostatic proliferation [15] . However , Tem are not perpetuated by this slow , cytokine-driven turnover mechanism . On the other hand , T resident memory cells ( Trm ) , which have a similar surface phenotype to Tem , but do not recirculate , clearly do survive in peripheral tissues for prolonged periods in the absence of antigen [27 , 28] . The current paradigm for Tem survival is that Tem predominate in chronic infections in an antigen-dependent manner , suggesting a requirement for antigen [7 , 29] . However , we show here that Tem were able to survive in uninfected recipients similar to Tcm , suggesting a variety of unknown survival mechanisms . Our previous study showed that Tem are preferentially generated ( over Tcm ) during P . chabaudi infection lasting longer than three days , showing that Tem generation is not only a result of long-term infection [12] . In the current studies , we have added three additional later mechanisms that promote a high Tem to Tcm ratio after the initial activation event in addition to unexpected Tem survival . We have also shown that CD4 Tcm can generate Tem in the presence of high-level chronic infection in immunodeficient animals [7] . Here , we observed that Tcm purified from persistently infected donors can also differentiate into Tem in uninfected recipients ( Fig 3D ) . This transition of Tcm to Tem subsets occurs even when the donors are treated to clear the infection . Therefore , progressive differentiation of memory T cells from Tcm to TemLate can be pre-programmed during chronic infection and continue even after clearance . It is formally possible that the CD62Llo and CD27- cells observed in these experiments are the result of surface cleavage of these two molecules , which are regulated by proteolytic cleavage; however , samples were always handled on ice as a technical precaution to avoid this artifact . In addition , similar forward differentiation of Tcm into Tem has been reported in studies of CD8 T cells [30] . Some studies have also proposed the reverse direction of differentiation of CD8 T cells back from Tem into Tcm , which we also see in some experiments , though to a lesser degree , and this reversion is not the result of enzymatic shedding of CD62L [31–33] . In our work , this conclusion is further strengthened by the observation that reducing the length of time of exposure of Tcm cells to persistent infection , by treatment of donors with an anti-malarial drug , significantly reduces progression of TemEarly to TemLate . There is significant regulation of lifespan and proliferation in the downregulation of CD27 in CD8 T cells , particularly memory [34] . The second additional mechanism of Tem predominance that we observed is that persistent infection increased the overall survival of TemEarly by inducing proliferation and expansion , without differentiation of these cells into CD127- Teff ( Fig 4B and 4C ) . This may help to explain the poor protection of the natural memory T cell population . Interestingly , TemEarly represent about half of the memory T cells late in infection , while TemLate are fewer [7] . The higher representation of TemEarly supports the trend apparent in our data that TemLate do not persist as well as TemEarly , even during persistent infection . Therefore , while we show that chronic infection is not required for the survival of Tem , we have also uncovered three potential mechanisms explaining how pathogen persistence promotes an increased representation of CD62Llo CD127hi ( CD27- ) CD44hi Tem in the Tmem pool . The third mechanism of Tem maintenance in chronic infection suggested by our data , is the potential for mature Teff to contribute to the generation of Tem , which is suggested by the CD127hi phenotypes seen on recovery of all Teff subsets from uninfected Thy1 . 1 hosts after 60 days ( Fig 2C ) . We also previously showed an intermediate level of CD127 re-upregulation on TeffEarly recovered from uninfected congenic hosts after two weeks [12] . This observation supports the dominant paradigm that Tem are also generated from Teff that survive after contraction [35] , though highly differentiated Teff lose their Tmem potential [12 , 36] . The caveat to the interpretation that CD127 ( IL-7Rα ) re-upregulation indicates Tmem differentiation , is that CD127 downregulation is known to be transient . However , re-upregulation of CD127 in CD8 Teff was recently shown to correspond with an epigenetic program of de-differentiation at some loci of Teff into a more resting state [33] . Furthermore , we showed here that Teff still have the potential to protect recipients after two months , a feature of memory T cells . However , it is important to note that survival of Teff is much lower than Tem , which along with the early timepoint of the programming of Tem suggests an important contribution to Tem generation from CD62Lhi precursors . The traditional target for vaccination has been to generate long-lived Tmem . However , the protection provided to the host by a single infection is not complete . This is particularly obvious in people living in malaria endemic areas who are exposed to sequential heterologous Plasmodium infections , but remain susceptible to malaria . Poor immunity suggests that either generation of memory B or T cells , or the quality of the adaptive immune response is sub-optimal . We have studied the functionality of the fairly protective mixed effector/memory T cell population present two months after chronic P . chabaudi infection [37] , and showed that they do not re-expand on homologous re-infection , but that they can make cytokines . In order to understand the potential of Teff and Tmem cells to contribute to protection , we tested the protective potential of CD127hi Tmem subsets , including Tem . Upon P . chabaudi infection of recipients of the Tmem subsets , we observed a small reduction in parasitemia only in animals that received the TemLate ( CD127hiCD62LloCD27- ) subset , but not Tcm or TemEarly . Both Tcm and TemLate reduced hypothermia in infected recipients . Other studies have shown that CD8+ CD27- , similar to the TemLate phenotype , can protect mice from Listeria infection , though it is not clear if these are primarily Teff or Tmem [38] . Significantly , TemEarly , the cell type making up the largest fraction of Tmem after P . chabaudi infection [7] , had no beneficial effect on either parasitemia or pathology , but still promoted survival of the hosts . This could contribute to the poor protection most notable on re-infection with heterologous parasite . Therefore , our data suggest that while specific Tmem may not be very efficient at controlling acute infection , the right types of Tem can reduce clinical manifestations of disease and allow the host to survive . Though we have previously shown that each Tmem subset has a unique cytokine profile [7] , it is not yet clear what effector functions of TemLate contribute to their unique protective phenotype . In wildtype mice and humans , levels of pre-existing antibodies are very likely to contribute to the effector functions promoted by memory CD4 T cells , suggesting important interactions . However , we do not have a model system at this time to evaluate the contribution of T cells in the context of pre-existing serum antibody or pre-activated innate cells . Strikingly , all CD127- effector T cell subsets contribute to a striking reduction in peak parasitemia in infected hosts . The TeffLate population produced higher proportions of all three cytokines . However , the number of triple-cytokine producers ( TNF+IFN-γ+IL-2+ ) was similar in all recipient groups , suggesting a mechanism for the equal protection provided , as multi-cytokine producers correlate with protection in several infections [26] . Overall , we conclude from our studies that effector T cells provide better protection compared to the universal standard , than memory T cells . Several other studies have shown important protective effects of Teff , especially in chronic infection [13 , 38 , 39] . An important conclusion from earlier studies is that chronic infection promotes maintenance of Teff cell numbers . Teff protect well , but have a shorter half-life , potentially explaining the decay of T cell immunity seen over time as parasitemia decays in this infection and other persistent infections [6]; however , this hypothesis remains to be tested in vivo . In that context , it is notable that not all Teff subsets decay at the same rate . The TeffEarly subset is particularly interesting , as this subset protects like other Teff , but TeffEarly also survive in higher numbers than terminally differentiated TeffLate in replete hosts without infection over two months . We previously showed that TeffEarly can generate all other Teff and Tmem subsets [12] . In contrast , the more mature or activated TeffInt and TeffLate progressively lose the potential to generate memory T cells in both RAG2o and T cell replete hosts . Progressive loss of memory potential with increasing time of exposure to inflammation has been hypothesized for CD8 effector T cells [40] . In conclusion , the current study defines two CD4 T cell populations ( TeffEarly and TemLate ) that are both long-lived and protect against malaria infection , though to different degrees . These phenotypes could represent correlates of immunity , or targets for vaccination . The observation that short-lived Teff present at the peak of malaria infection protect better than long-lived Tmem that predominate at later timepoints suggests a decay of effector function that could explain the decline in clinical immunity especially in the absence of exposure . Therefore , a better understanding of the mechanisms for the survival of these subsets , and their effector functions in situ , is critical . Better understanding of the functions and generation of the diverse T cell populations in the memory phase of this infection could help us design more effective vaccines that generate long-lived , protective T cells in chronic infection .
All animal experiments were carried out according to protocol number 1006031A , as reviewed and approved by the University of Texas Medical Branch Institutional Animal Care and Use Committee ( IACUC ) . The studies were performed in accordance with the guidelines in the Guide for the Care and Use of Laboratory Animals , 8th edition ( Institute of Laboratory Animal Resources , National Academies Press , Washington , DC ) and regulatory document from Public Health Service ( PHS ) Policy on the Humane care and use of Laboratory Animals . Thy1 . 1 BALB/cByJ were backcrossed to BALB/cJ ( N4; The Jackson Laboratory , Bar Harbor , ME ) . B5 TCR Tg mice , a kind gift from J . Langhorne ( Francis Crick Institute , London , UK ) , were generated as previously described [25] and backcrossed to BALB/cJ ( N7-10 ) and maintained in the UTMB Animal Resources Center . The B5 TCR recognizes MSP-1 ( 1157–1171 , ISVLKSRLLKRKKYI/I-Ed ) ; B5 TCR Tg mice were typed using primers Vα2 , 5’- gaacgttccagattccatgg-3’ and 5’-atggacaagatcctgacagcatcg-3’ , and Vβ8 . 1 , 5’-cagagaccctcaggcggctgctcagg-3’ and 5’- atgggctccaggctgttctttgtggttttgattc-3’ . RAG20 ( Taconic , Germantown , NY ) were used at 9–12 weeks old . All other mice were used at 6–12 weeks old and infected with 105 ( expect in RAG20 infection 5x104 ) Plasmodium chabaudi chabaudi ( AS ) -infected erythrocytes i . p . ( kind gift of J . Langhorne ) . Parasites were counted by light microscopy in thin blood smears stained with Giemsa ( Sigma-Aldrich , St . Louis , MO ) [41] . In some experiments , mice were treated with a dose of 50 mg/kg of the antimalarial drug chloroquine ( CQ , i . p . ) on days 30–34 post-infection ( p . i . ) . C57BL/6 mice were purchased from Jackson Labs ( Bar Harbor , ME ) and were treated with a dose of 20 mg/kg off the antimalarial drug mefloquine chloride ( MQ , i . t . , from 4 mg/ml stock ) on days 10–14 for the effector time-course study and days 30–34 for the memory time-course study . All adoptive transfer experiments were done using age-matched donors . Single-cell suspensions of splenocytes were made in HEPES-buffered HBSS ( Mediatech , Manassas , VA ) , then depleted of erythrocytes by incubation in RBC lysis buffer ( eBioscience , San Diego , CA ) . For all Figures except Fig 1 , Thy1 . 2+ T cells were enriched by positive selection using Miltenyi Thy1 . 2 microbeads ( San Diego , CA ) , double stained and analyzed using the “dump” gate to get maximal resolution . Cells were then stained in PBS with 2% FBS ( Sigma , St . Louis , MO ) and 0 . 1% sodium azide with anti-CD16/32 ( 2 . 4G2 ) supernatant ( BioXCell , West Lebanon , NH ) for Fc receptor blocking , followed by double staining for Thy1 . 2 in both -FITC , -PE , and a combination of other PerCP-Cy5 . 5 , PE/cyanine 7 ( Cy7 ) , PE/Cy5 , Allophycocyanin ( APC ) , or APC/efluor780–conjugated Abs ( all from eBioscience ) ; or CD62L PE-Texas Red ( Invitrogen , Life Technologies ) . A combination of CD11b , F4/80 , and Ter119 biotin antibodies followed by streptavidin-PerCyP-Cy5 . 5 , were used as a “dump” channel for analysis of donor cells in RAG2o mice . Cells were collected on a LSRII Fortessa using FACSDiva software ( BD Biosciences , San Jose , CA ) and analyzed in FlowJo ( version 9 . 7 , Tree Star , Ashland , OR ) . Compensation was performed in FlowJo using single-stained splenocytes ( using CD4 in all colors ) . Data from each mouse was analyzed and averages and SEM calculated in Excel ( Microsoft ) . Data from three to four mice are concatenated in some figures to achieve sufficient cell numbers for presentation . For intracellular cytokine staining , 5 x 106 cells/ml were stimulated with PMA and ionomycin at 37°C for 5 hours and Brefeldin A was added ( all from Sigma ) for the last two hours . Cells were harvested and processed for surface staining as described above . Cells were fixed with 2% paraformaldehyde and permeabilized using BD perm buffer ( BDbiosciences ) . Cells were washed 3 times and stained for IFN-γ , IL-2 or TNF or isotype controls . Cell Trace Violet ( CFSE , Invitrogen , Carlsbad , CA ) was used according to manufacturers’ instructions . Sorted cells were washed twice with PBS without calcium and magnesium and incubated with 1x dilution of cell trace violet at 37°C in a water bath for 10 minutes while shaking . Labeled cells were transferred into recipient mice at 5x104–2 . 5x105 cells ( see Results and Legends for number of cells in each experiment ) . Except for Fig 5C , which is calculated as the % recovered Tg cells/lymphocytes x the number of lymphocytes , recovered cells were counted by inclusion of counting beads ( AccuCheck , Molecular Probes , ThermoFisher Scientific ) in the FACS sample according to manufacturer’s instructions and calculated using the equation: Total cell number = ( ( all cell event count / bead event count ) x Bead conc . ( per ml ) x ( Bead volume / cell volume ) ) x Volume of original sample . This formula only works if the whole volume of FACS tube is collected . Single cell suspensions of splenic CD4+ T cells from ( 10–12 ) infected ( d8 for Teff; d60 for Tmem ) B5 TCR Tg donors were enriched using negative selection with EasySep biotin microbeads ( Stemcell Technologies , Vancouver , BC , Canada ) with biotinylated anti-CD8a ( 55–6 . 7 ) , B220 ( RA3-6B2 ) , CD11b ( MI/70 ) , CD11c ( N418 ) , F4/80 ( BM8 ) , and Ter119 ( eBioscience , San Diego , CA ) . Enriched T cells were then stained with anti-CD4-FITC , CD44-APC/Cy7 , CD127-PE , CD62L-Texas Red and CD27-APC for Teff or Tmem subset sorts . Cells were sorted on a FACSAria with FACSDiva software ( BDbiosciences ) to >99% purity ( as shown in S1 Fig ) . B cells were purified from BALB/cJ mice that were infected with P . chabaudi twice over a two-month period using CD19 microbeads from Miltenyi ( San Diego , CA ) ( 98% pure ) . For protection assays , RAG2o mice ( Jackson Laboratory ) were given sort-purified T cells ( 2 . 5x105 ) together with immune B cells ( 1–2 x 107 , i . p . ) , and were infected with 5x104 P . chabaudi infected RBCs . Weight and body temperature were measured every other day using a portable Ohaus balance ( Parsippany , NJ ) , and temperature microchip transponders ( IPTT-300 , BMDS , Seaford , DE ) Where indicated , experiments were analyzed by one-way ANOVA , followed by Tukey’s for nonparametric data or the Student’s t test for parametric data in Prism ( GraphPad , La Jolla , CA ) : *p ≤ 0 . 05 , **p ≤ 0 . 01 , ***p ≤ 0 . 001 . Limit of detection is defined as three times the standard deviation of the blank . Therefore , the limit of detection for the transferred Thy1 . 1+ cells in all experiments was calculated using all available data from animals from all experiments that did not receive transferred T cells . | Malaria causes significant mortality but current vaccine candidates have poor efficacy and duration , as does natural immunity to malaria . T helper cells ( CD4+ ) are essential to protection from malaria , but it is unknown what kinds of T cells would be both protective and long-lasting . Here , we explored the mechanisms of survival used by memory T cells in malaria , and their ability to protect immunodeficient animals from malaria . We identified four mechanisms by which memory T cells are maintained in chronic infection . We also showed that highly activated effector T cells protect better than memory T cells in general , however , effector T cells have a shorter lifespan suggesting a mechanism for short-lived immunity . In total , we identified two protective T cell subsets that are long-lived . Unfortunately , the memory T cell subset that protects , is not the predominant memory T cell population generated by natural infection , suggesting a mechanism for the poor immunity seen in malaria . Our work suggests that vaccines that induce these two T cell subsets may improve on current immunity from malaria infection and disease . | [
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] | 2018 | Protection by and maintenance of CD4 effector memory and effector T cell subsets in persistent malaria infection |
Vasopressin neurons , responding to input generated by osmotic pressure , use an intrinsic mechanism to shift from slow irregular firing to a distinct phasic pattern , consisting of long bursts and silences lasting tens of seconds . With increased input , bursts lengthen , eventually shifting to continuous firing . The phasic activity remains asynchronous across the cells and is not reflected in the population output signal . Here we have used a computational vasopressin neuron model to investigate the functional significance of the phasic firing pattern . We generated a concise model of the synaptic input driven spike firing mechanism that gives a close quantitative match to vasopressin neuron spike activity recorded in vivo , tested against endogenous activity and experimental interventions . The integrate-and-fire based model provides a simple physiological explanation of the phasic firing mechanism involving an activity-dependent slow depolarising afterpotential ( DAP ) generated by a calcium-inactivated potassium leak current . This is modulated by the slower , opposing , action of activity-dependent dendritic dynorphin release , which inactivates the DAP , the opposing effects generating successive periods of bursting and silence . Model cells are not spontaneously active , but fire when perturbed by random perturbations mimicking synaptic input . We constructed one population of such phasic neurons , and another population of similar cells but which lacked the ability to fire phasically . We then studied how these two populations differed in the way that they encoded changes in afferent inputs . By comparison with the non-phasic population , the phasic population responds linearly to increases in tonic synaptic input . Non-phasic cells respond to transient elevations in synaptic input in a way that strongly depends on background activity levels , phasic cells in a way that is independent of background levels , and show a similar strong linearization of the response . These findings show large differences in information coding between the populations , and apparent functional advantages of asynchronous phasic firing .
Magnocellular vasopressin neurons produce and secrete the antidiuretic hormone vasopressin in response to increases in the osmotic pressure of extracellular fluid [1] . They form a key part of the highly robust homeostatic system which maintains osmotic pressure within narrow bounds . Each of the neurons independently encodes and responds to an input signal , but they must also coordinate as a population , making the vasopressin neurons a prime example of a distributed control system [2] . The vasopressin cell bodies , located in the supraoptic and paraventricular nuclei of the hypothalamus , project axons to the posterior pituitary gland , and receive synaptic input from osmosensitive neurons located near the third ventricle , as well as responding through depolarising currents generated by the vasopressin neurons' own osmosensitive ion channels [2]–[4] . The generated action potentials ( spikes ) propagate down the axons to trigger hormone secretion from the axonal terminals into the blood . When osmotic pressure rises , the secreted vasopressin acts at the kidneys to reduce the amount of water lost in urine . After chronic water deprivation , many vasopressin neurons respond to the increased osmotic input by shifting from slow irregular firing into a distinctive phasic pattern , consisting of long bursts and silences lasting tens of seconds . As input increases the bursts lengthen , eventually shifting to fast continuous firing [3] , [4] . Unlike the rhythmic activity observed in many other neural systems , phasic firing is generated by an intrinsic mechanism , rather than by network activity [5] . The vasopressin neurons have no synaptic interconnections , and they fire asynchronously; accordingly , the hormone output reflects a smooth population signal rather than the fluctuating activity at individual cells . Activity-dependent secretion is characterised by a combination of frequency facilitation , whereby disproportionately more vasopressin is secreted at higher frequencies of stimulation , and fatigue , whereby peak secretion rates can only be sustained transiently . As a result , phasic firing patterns are optimally efficient [6] , [7] , in the sense of maintaining a given level of secretion with the fewest spikes . However , phasic firing is efficient only because the properties of the neurosecretory terminals make it so , and those properties are not universal – in particular the properties of oxytocin terminals in the posterior pituitary gland are different from those of vasopressin terminals , and seem to be adapted to the different firing properties of oxytocin neurons . Both cells' secretion mechanisms are subject to facilitation , and oxytocin cells can also show an enhanced response to phasic firing compared to a continuous pattern [8] . However , the phasic pattern is not optimal as it is for vasopressin cells . The oxytocin cells have a much greater facilitation frequency range , and are not subject to short timescale ( 10s of seconds ) fatigue [9] . It seems that the secretion mechanism is as much adapted to the spike patterning as vice versa , thus while phasic firing is efficient for secreting vasopressin , we must look deeper to fully understand why these neurons fire phasically . To address this , we need a model that can accurately reproduce the range of firing response observed in vivo , which we can then systematically interrogate to understand how phasic firing affects the signal processing properties of the neurons . This requires a model which includes the essential elements of the neuronal firing mechanism , but which is still simple enough to manipulate and be well understood . The core of the phasic firing mechanism is a slow depolarising after potential ( DAP ) , acting on a timescale of several seconds . This activity-dependent current increases neuronal excitability , generating a positive feedback effect . When a vasopressin cell is in a silent phase , a few random close spikes will generate the beginnings of a depolarised ‘plateau potential’ that can self-sustain , resulting in a prolonged burst of spikes . However , during this burst , firing also triggers the dendritic release of the opioid peptide dynorphin [10] , which acts back on the cell of origin in an autocrine manner to progressively attenuate the DAP [11] . The cumulative effect of activity-dependent dynorphin secretion eventually causes the plateau to fail , and the cell begins a new silent phase [5] . There are two current theories for the slow DAP . Li and Hatton [12] suggest that it is caused by the removal of a hyperpolarising K+ leak current , whereas Bourque et al [13] , [14] suggest that a depolarising non-specific cation current is responsible . Using a Hodgkin-Huxley based model fitted to in vitro data , Roper et al [15] , [16] argue that the generation of both a plateau , and a silent period , is more easily explained by the K+ leak based mechanism . The Roper model uses only one compartment , but includes a dynorphin mechanism , and was the first published model to demonstrate bursting . However , the Roper model is based on data recorded in vitro . Vasopressin cells recorded in vitro are largely denuded of afferent input , and accordingly have a high input resistance; this directly impacts upon membrane time constants , and all activity-dependent potentials are amplified [17] . In particular , the DAP following single spikes in vitro is so large that it can produce regenerative spiking , while perturbations produced by synaptic input are relatively sparse . For vasopressin cells in vitro , bursts comprise spikes that occur at a relatively constant inter-spike interval , giving a symmetrical distribution of intervals with a modal value that is the inverse of the mean intraburst firing rate . This distribution implies regenerative spiking . By contrast , in vivo , bursts in vasopressin cells comprise inter-spike intervals that have a very skewed distribution , with a mode that is disproportionately short for the mean firing rate – and which is largely independent of the mean firing rate . At the same time , the distributions have a very long tail , and this tail can be fit by a single negative exponential . From these features it can be deduced that , in vivo , neuronal excitability after a spike follows a sequence of hypoexcitability – consistent with an HAP , followed by hyperexcitability , consistent with a subthreshold DAP peaking at ∼40 ms . However , most spikes occur at longer intervals , and the negative exponential distribution suggests that their arrival is the result of a Poisson process – and is random in being independent of prior activity . Thus in vivo , spiking is not regenerative , and spike patterning is dominated by the stochastic effects of synaptic input [18]; these effects not only increase variation and noise in the output but can also change the qualitative behaviour [19] . In phasic cells in particular , small variations in firing activity can trigger the starting and stopping of bursts [20] , [21] . Nadeau et al . [22] extended the Roper model by adding synaptic input and a simulation of direct osmosensitivity . In this model , synaptic noise produces some variability in spike timing , but the burst mechanism remains essentially regenerative: bursts , once triggered , can be sustained in the absence of synaptic input , and firing within bursts has the extreme regularity typical of in vitro data but very different to in vivo data . Thus intraburst frequency is largely unaffected by EPSP or IPSP rates , except at high frequencies of synaptic input , which give reasonable matches to ISI distributions observed in vivo but produce continuous , rather than phasic , firing . Clayton et al . [23] took a different approach; they use an integrate-and-fire model and ask what is the simplest model that can fit in vivo spike data to a point whereby data from a model cell cannot be distinguished statistically from data from a target vasopressin cell ? In this model , the combination of a slow DAP and the opposing action of dynorphin is represented by an explicit bistable mechanism which drives phasic firing . Using automated parameter fitting , this model produces extremely close fits to in vivo spike patterns , and can be fitted well to cells firing phasically , or firing continuously . However , we observed that , when a model cell with parameters that fit a phasically firing cell is challenged with increasing input , it fails to shift to continuous firing . Thus the Clayton model's explicit bistable mechanism captures the neuron's behaviour concisely , but within only a limited range . This suggests that some of the fitted parameters , particularly those accounting for bistability , are activity-or input dependent , and rather than being parameters , need to be incorporated into the model's dynamics . Here we simulate vasopressin neurons in a model that displays emergent bistable behaviour , combining the best elements of previous models . The model gives a more complete match to vasopressin neuronal firing activity , while being simpler and more directly related to the physiology . We then use this model to explore how vasopressin cell activity encodes afferent signals , by comparing a population of phasically firing model neurons with an otherwise identical non-phasic population . We show that bistability and phasic firing gives neurons acting as a population several important signal processing properties that non-phasic neurons lack . They can produce a strongly linear response to both a constant and transient input signal , and they produce a consistent response to transient signals , independent of background activity . These are important properties that have been identified in the vasopressin response in vivo [1] , and may also apply more generally to neural signal processing .
The model takes as its base the oxytocin cell model of [19] , [24] . This is a leaky integrate-and-fire ( IGF ) model driven by Poisson random decaying input perturbations simulating synaptic input . A fast large hyperpolarising afterpotential ( HAP ) and a slow small after-hyperpolarising potential ( AHP ) , are simulated as exponentially decaying variables , incremented ( using a negative value for hyperpolarisation ) when a spike is fired . These are summed with a fixed resting potential and the synaptic input to give the membrane voltage . When this crosses the spike threshold a spike is recorded , followed by an absolute refractory period of 3 ms . This gives a close match to the in vivo spike patterning in oxytocin neurons , and by adding a simple fast DAP , using the same decaying exponential form , a similar model can closely match the intraburst activity of vasopressin neurons . These representations of post-spike potentials were developed to match the spike-dependent changes in excitability deduced from the interspike interval ( ISI ) distributions and hazard functions of oxytocin and vasopressin cells recorded in vivo , based on the decaying post-spike depolarisation and hyperpolarisations observed in in vitro intracellular recordings . They are comparable to the forms used in Roper's Hodgkin-Huxley based model [15] , [16] , which represents the HAP , AHP , and DAP as separate compartments of intracellular [Ca2+] , ( [Ca2+]i ) driving Ca2+ sensitive currents . The varied decay time courses used in the IGF model are similar to the corresponding compartmental [Ca2+] half-lives . We explored whether adding a second , slower , simple DAP could generate quantitatively realistic burst firing in the IGF model . A sustained plateau could be achieved if the DAP half life was >2 s , and combined with saturation to limit the DAP magnitude . Given the ability to sustain a plateau , an activity-dependent mechanism is required to terminate the bursts . Physiologically , this involves spike-dependent release of dynorphin which inhibits the DAP . Using a slow spike-dependent exponentially decaying variable to inhibit the DAP , combined with a hyperpolarised resting potential ( −75 mV ) , we could produce bursts , but could not achieve sharp bistable switches in activity , and could not produce in vivo comparable silent periods , only periods of slower activity . The Roper model [15] , [16] uses a different DAP mechanism to solve these problems; the burst plateau is generated by fully suppressing a hyperpolarising K+ leak current that is partially active at resting potential , and silences are periods where the K+ leak current is fully active , suppressing firing . This single mechanism can generate both activity dependent depolarisation and hyperpolarisation . Its model form , fitted to in vitro data , includes saturation and a simple relation between competing spike-triggered increases in [Ca2+]i and dynorphin , allowing dynorphin accumulation to eventually switch off a burst and generate a prolonged silence . This mechanism was simplified and integrated into the IGF model to produce the design illustrated in Figure 1 . Twin Poisson random processes generate excitatory and inhibitory post-synaptic potential ( EPSP and IPSP ) counts en and in at each 1-ms time step , using mean rates Ire and Iri; the IPSP frequency Iri is defined as a proportion of Ire given by Iratio . All of the results here use Iratio = 1 so that input rate is controlled by using just Ire . The input potentials have fixed amplitudes eh = 2 mV and ih = −2 mV and are summed to give the input: ( 1 ) This is summed with the synaptic component of the membrane potential , Vsyn , decaying exponentially with half life λsyn: ( 2 ) Time constants are calculated from half-life parameters by:where x is the variable concerned . Variables for the HAP , AHP and the fast DAP decay exponentially , controlled by half-life parameters , λHAP , λAHP , and λDAP , and are incremented by kHAP , kAHP , and kDAP when a spike is fired . The AHP also depends on [Ca2+]i , so that only fast spiking substantially activates the AHP . ( 3 ) ( 4 ) ( 5 ) where s = 1 if a spike is fired at time t , and s = 0 otherwise . Variables for [Ca2+]i , C and dynorphin , D , use similar forms: ( 6 ) ( 7 ) The slow DAP ( L ) uses a simplified version of the DAP conductance equations in the Roper model [14] , [15] , defined as two components: ( 8 ) where kL is a scaling parameter . The function tanh , previously used in the fit to in vitro data [15] , ensures that the activation of L is sigmoidal , with half maximal activation at 0 . This is used to inhibit the K+ leak potential , VL , scaled by conductance parameter gL: ( 9 ) Finally , these components are summed with the resting potential , Vrest , to give the membrane potential V: ( 10 ) When V exceeds the spike threshold , Vthresh , a spike is fired , though its form is not modelled . The parameter values for the figures in this paper are given in tables 1 and 2 . We also tested a more complex model of dynorphin dynamics where release depends on the availability of dynorphin for activity-dependent release , which in turn depends on a slow activity-dependent mechanism , T , possibly representing vesicle translocation from some reserve pool to the releasable pool close to the membrane , similar to the mechanism suggested for vesicles at the terminal release sites [25]: ( 11 ) The releasable store ( Dstore ) accumulates at a rate determined by T , and is capped at parameter Dstorecap ( Dstore increases only if Dstore<Dstorecap ) . Dynorphin release ( and store depletion ) per spike is independent of the store , unless Dstore is too low for one unit of release , defined by parameter Dspike: ( 12 ) ( 13 ) where r = 1 if s = 1 and Dstore>Dspike , and r = 0 otherwise . This is incorporated into the model by replacing eqn . 7 with eqn . 13 . To simulate the effect of an acute systemic intraperitoneal injection of hypertonic saline , which has a delayed effect on osmotic pressure as the hypertonic saline enters the blood [26] , we added an osmotic pressure variable O which shifts towards parameter Oinject with time constant τO: ( 14 ) where initially Oinject = O . Injection is simulated by changing Oinject at a specified time . Synaptic input is then generated assuming a simple linear relation: ( 15 ) which with Iratio = 1 , gives a parallel increase in excitatory and inhibitory synaptic input with increased osmotic pressure . The values 20 and 280 scale Ire so that the physiological 1–8spikes/s range corresponds to osmotic pressures of ∼290–320 mOsmol/l . The data from the model and from experimental recordings consist of series of ISIs . These are used to calculate mean firing rates and to generate ISI histograms and hazard plots , as described in [18] . A burst detection algorithm is used to detect and measure bursts , with a burst defined as a train of >25 spikes with no interval >1500 ms . Burst measures include spike count , burst duration , silence duration , and intra-burst firing rate . The in vivo spike data for model fitting are from extracellular recordings of magnocellular vasopressin neurons , recorded from the supraoptic nucleus in urethane-anaesthetised rats . The method is detailed in Sabatier et al . [18] . To test the model neuron response to transient changes in input we used a fixed synaptic input rate ( Ire ) as a background and added four identical 1-s duration changes to this rate at intervals of 100 s . We measured the response as the mean firing rate over the four 1-s pulses . To simulate in vivo data gathered from multiple cells with varied spiking patterns , we took a set of parameters based on an existing fit of the model , and added random variation to the parameter subset which we vary to fit recorded cells ( see Results below ) . Each varied parameter was defined by a fixed mean and standard deviation , using these to generate normally distributed random values .
To match the firing activity of a cell with a model in a quantitatively robust way , we need to define the features of that activity in a way that we can statistically compare recorded and model generated data . Spike activity comprises the overall firing pattern , shown by spike rate as measured in bins of varying width; the bursting characteristics , using burst detection to quantify bursts and silences and plot the mean burst profile; and short term spike patterning and excitability , contained in the ISI distribution and related hazard function ( Figure 2 ) . Together , these measures capture the important features of phasic firing , and describe the variation between individual cells . They also relate closely to many of the underlying mechanisms , such as the post-spike potentials . These burst measures differ considerably between cells , and in any one cell , as synaptic input increases , bursts lengthen , silences shorten and the intra-burst firing rate increases [3] , [4] . The burst profile of a cell typically shows a distinctive peak in firing rate at the start of the burst which rapidly rises and then declines to a plateau ( Figure 2B ) . In vitro experiments have shown that this decline requires an AHP [27] . The ISI histogram and hazard plots ( Figure 2C ) , used to examine spike patterning within bursts , show a relative refractory period of 30–50 ms followed by a period of increased firing excitability , which slowly decays , consistent with the superposition of an HAP and a slower DAP . The long exponentially decaying tail of the histogram suggests that after about 150 ms has elapsed since a spike , spikes arise randomly , indicating that there is no patterning in the synaptic input activity . Our previous in vivo modelling work [23] used automated parameter fitting based on a genetic algorithm . The present model can be fitted with the same technique ( not shown ) , but is also simple enough to fit manually . Using manual fitting helps to understand how each parameter effects spiking behaviour , and also how parameters interact and how independent they are . To fit the model to data , we began with the ISI distribution and hazard function , and then moved to the burst features . The basic membrane parameters , for Vrest , Vthresh , PSP magnitude , and PSP half life , are derived from the earlier oxytocin cell model [19]; oxytocin cells are closely related to vasopressin cells but lack a DAP and a bistable burst mechanism . The fitting for each in vivo recording began with gL = 0 , to switch off bursting and get an initial approximate fit for the synaptic input rate , HAP , AHP and fast DAP parameters; the cell's intra-burst ISI distribution , hazard function and firing rate can be closely fit without producing bursting . The burst mechanism was then turned on by setting gL , and the full model fitted to the mean firing rate , mean burst duration , mean silence duration , and mean burst profile . To understand which parameters are most important to variation between cells , we attempted to fit different cells by varying as few parameters as possible . This process identified the dynorphin parameters kD , λD , calcium parameter kC , and gL as those required to fit the burst measures . Parameter kD dominates the burst duration , while λD and gL can be adjusted to match varied silence durations . The HAP , AHP and DAP parameters were further adjusted to compensate for the K+ leak current's additional effect on short term spike patterning . The AHP parameter kAHP dominates the size of the peak in firing rate at the start of each burst , consistent with experimental evidence that this current is responsible [27] . Five representative fits to recorded phasic cells are shown in Figure 3 , using the parameters given in Tables 1 and 2; all give a close match to the firing rate , hazard , and burst measures ( Table 3 ) . The bistable burst firing mechanism is based on opposing effects of [Ca2+]i and dynorphin , acting on different timescales . These do not act deterministically , but shift the probability of a burst starting or stopping , subject to the stochastic synaptic input . When rapid successive spikes arise during a silent period , they cause an increase in [Ca2+]i which begins to suppress the hyperpolarising K+ leak current . This triggers activity-dependent positive feedback , increasing firing rate and hence [Ca2+]i . This feedback becomes self-sustaining , maintaining the suppression of the leak current and hence allowing spike activity to be sustained at a relatively stable level ( Figure 4 ) . As a burst begins , the AHP begins to accumulate , but this rises more slowly than the burst initiates , allowing a peak in firing rate at the head of the burst , before the AHP slows the intra-burst firing . Bursts reach a stable state when activity-dependent inhibition reaches an approximate equilibrium with activity-dependent disinhibition of the leak current . As bursts continue , the effects of dynorphin accumulate , with a small increase per spike but a long half life ( ∼10 s ) , while [Ca2+]i remains stable . Dynorphin weakens the ability of [Ca2+]i to suppress the leak current , and , as a result , the burst becomes sensitive to small fluctuations in firing rate . A small drop in firing rate , within the range of random variation , can be sufficient to cause the plateau to collapse , causing the leak current to switch back on , silencing firing . The silent period depends on the reverse order of effect between [Ca2+]i and dynorphin . [Ca2+]i decays more rapidly , so that the more prolonged effect of dynorphin is left unopposed; this causes a large leak current that hyperpolarises the cell and prevents burst initiation and most spike firing until dynorphin has sufficiently decayed . The cycle then repeats when enough spikes occur to initiate a new burst . The key to the model parameters is to balance dynorphin's increase per spike and half life such that it does not quite reach equilibrium at the plateau firing rate . This leaves a gradual increase which eventually terminates the burst . More dynorphin release per spike or a slower decay will cause faster accumulation and terminate the burst more quickly . Too little per spike or too fast a decay and the effects of dynorphin will not continue to increase , so that the burst never terminates . Equally , [Ca2+]i parameters must be such that [Ca2+]i is sufficient to initiate and sustain a burst , but not so strong that it cannot be eventually overcome by dynorphin . In vivo experiments have shown that bursting can be both initiated and terminated by triggering increased spike firing , either by evoking spikes antidromically by electrical stimulation of the axons [4] , or by stimulating increased synaptic input [20] . It is an important test of the model to be able to reproduce this , as these effects have clear implications for information coding . Figure 5 shows simulated antidromic spikes in a typical model cell . The model has the advantage that its noisy input activity can be repeated precisely , so that the effects of interventions can be tested against known times of burst initiation and termination . In the model cell illustrated , stimulating at 10 Hz for 0 . 5 s has no effect early in the silent period , but more intense stimulation ( 10 Hz for 2 s ) or stimulation later in the silent period , causes early burst initiation . Triggering early burst termination requires stronger stimulation than burst initiation , but shows a similar pattern , requiring either a more intense stimulation ( 20 Hz for 2 s ) or stimulation at a later point in the burst . There is still a delay before the burst stops , due to the opposing effects of [Ca2+]i and dynorphin: the activity-induced increase in [Ca2+]i sustains the burst before the longer lasting activity-dependent dynorphin increase causes termination . Such delayed terminations are an experimentally-observed feature of bursts that are truncated by modest stimulation ( see Figure 3A of [20] ) . Applying a very intense stimulation ( 50 Hz for 2 s ) causes a more rapid termination by increasing the AHP sufficiently to block spike firing . In vivo , when osmotic input is increased , an increased proportion of vasopressin neurons fire phasically , shifting from slow irregular firing . Phasic neurons also show longer bursts , shorter silences , and higher intra-burst firing rates [3] , eventually shifting to continuous firing . We tested this in the model by increasing the synaptic input rate . Similarly , model cells progress from slow sparse firing to short irregular bursts and then full phasic firing with bursts increasing in duration and firing rate and shortening silent periods , until they eventually shift to continuous firing ( Figure 6 ) . The increase in intraburst firing rate is more linear than the increase in burst duration due to the additional opposing effect of the AHP on firing rate . The experimental study of Brimble and Dyball [26] reports the responses of vasopressin cells to a systemic injection of hypertonic saline , which triggers a rapid and prolonged increase in osmotic pressure . In that study , relatively slow phasic neurons shifted to longer , faster bursts , and faster firing phasic neurons shifted to continuous firing , similar to our results above . However , non-phasic slow , irregular neurons when challenged go through a transitional period of ∼10 min of continuous firing before shifting to stable phasic firing ( see Figure 5 in [26] ) . The firing rate of the neurons rises much more quickly than the shift to bursting , suggesting that the change in firing pattern is determined by the state of the burst mechanism rather than the input per se . We simulated the delayed effect of hypertonic saline injection on the rise in osmotic pressure , but could not reproduce these experimental results with our basic model . However , the pool of readily-releasable vesicles in the dendrites of magnocellular neurones is labile , and regulated in an activity-dependent manner . We therefore hypothesised that , in the absence of activity-dependent replenishment of the readily releasable pool , the initial lack of burst termination might be due to insufficient readily releasable stores of dynorphin . Out first attempt at modelling this used a simple mechanism where spike-triggered dynorphin release was directly dependent on a dynorphin store charged by spike activity . However this resulted in shorter , not longer , bursts as input activity increased , and so we developed a more complex mechanism where spike triggered dynorphin release is partially decoupled from the dynamics of the releasable dynorphin store , and using this mechanism we were able to reproduce the initial period of continuous firing before onset of phasic firing ( Figure 7 ) . In this mechanism , a slowly accumulating measure of spike activity ( T , hypothesised to represent slow activity-driven vesicle translocation ) determines the rate at which the readily releasable pool of dynorphin is replenished . While T is too low , the release decoupling element ( r in eqn . 12 and 13 ) makes some spikes fail to trigger dynorphin release , slowing the increase in dynorphin while store replenishment is too slow to keep up with the spike rate . This results in a gradual increase in the amount of dynorphin available for activity-dependent release until it reaches an equilibrium with activity-dependent depletion , at which point it is sufficient to sustain phasic firing . In the model , one of the main elements which generates the inter-burst silence is a dynorphin driven hyperpolarisation . In addition to dynorphin , silence duration is also determined by the AHP and the synaptic input rate . However , Brown et al [28] studied the effect of the competitive dynorphin antagonist nor-BNI , combining analysis of in vitro and in vivo results to suggest that dynorphin does not affect inter-burst activity . We used the model to test this by simulating their in vivo results . As a competitive antagonist , nor-BNI reduces rather than blocks dynorphin's effect and we simulated this in the model by reducing parameter kD ( eqn . 7 ) sufficiently to produce a similar increase in burst duration as they observe . Their in vivo data is taken from multiple cells , and to simulate cell variation we generated 100 model cells based on the fit to cell v1 , with small random variations in the parameter subset we varied to fit different in vivo cells ( Table 4 ) . We ran each cell for 3000 s with and without reduced kD , generating a total of 3032 bursts under simulated control , and 1239 bursts under simulated nor-BNI . We then generated the same collected data burst and silence duration histograms and hazards as shown in Figure 5 of Brown et al 2006 [28] ( Figure 8 ) . Our results show a similar strong effect of reduced dynorphin action increasing burst duration , but despite our dynorphin-based mechanism , we also show very little effect on silence duration , similar to the result of Brown et al [28] . Further reductions in dynorphin effect ( not shown ) do show a reduction in the mean silence duration , but it is much smaller than the effect on increased burst duration , and in a varied population results in many cells shifting to continuous firing . We want to understand what use asynchronous phasic firing is , in particular what advantage it gives to information processing in a neuronal population . We can use the model to study this , running duplicates of the single neuron model in parallel , and taking the summed spike activity as the population output . To study population response , we compare the summed activity of 100 model cells in two conditions: one where all cells were made phasic by setting gL = 8 . 5 , and one where the cells were made non-phasic by setting gL = 0 , but with no other change ( Figure 9 ) . To maintain an asynchronous population , we use independently generated input PSPs for each model cell , using a common input rate , parameter Ire . We first measured the mean spike rate in the population in response to constant input rates over the range 100–1000 Hz ( beyond this range the phasic cells begin to fire continuously and behave as non-phasic cells ) , running the model cells for 2000 s of activity ( Figure 9A ) . The non-phasic cells show a very non-linear relationship between input and output rates , with an initially steep relationship at low input rates that flattens at higher input rates . By contrast , the phasic cells show very little response until the input is sufficient to trigger bursting , but then show a much more linear average increase in response . In the overall output range of ∼1–8 spikes/s ( including mean intraburst rates as high as 15spikes/s ) , which corresponds to the normal physiological dynamic range of firing activity of vasopressin cells in vivo , the relationship is particularly linear for phasic cells compared to non-phasic cells . At high input rates , the phasic population shifts to continuous firing , at which point they give a non-linear response that is similar to that of the non-phasic cells . Thus overall , the phasic cells show a strong linearization of the response to increasing input , compared to the non-phasic cells , which show a typical non-linear neuronal response . To study how the population responds to transient pulses , we challenged the two populations with four 1-s duration periods of increased input by setting parameter Ire to 1000 Hz at 100-s intervals , testing over the same range of background input as Figure 9A . The phasic population shows more variability in its average output activity , but the response to the input pulses is similar to that of the non-phasic population ( Figure 9B ) . However , unlike the non-phasic population , the phasic population responds to pulses in a way that is relatively independent of the background input , producing a consistent response that is largely independent of background rate ( Figure 9C ) . By contrast , for the non-phasic population , the mean response to pulses reduces as the background input increases , as the constant firing increases the amount of time that cells are refractory due to activity-dependent hyperpolarisation . Finally , we tested the response of the two populations to transient input of varying amplitude . For this , we fixed the background input ( 255 Hz for non-phasic , 560 Hz for phasic ) so that the two populations were firing at the same mean rate ( 5 Hz ) and tested the effects of input pulses in the range 100–1000 Hz . The phasic population ( Figure 9D ) responds to increasing pulse amplitudes with a linear average increase in firing rate ( measured during the pulses ) . By contrast , the non-phasic population again shows a non-linear response . The response of the phasic population to transient pulses is also smaller than that of non-phasic cells , supporting the previous suggestion [20] that the asynchronous phasic cells function as a low-pass filter .
The present model is relatively simple; it has 21 parameters , but five of these define the synaptic input , and only one of these , Ire , is varied here , to control the input rate . Parameter Crest is only included to scale [Ca2+]i to in vivo units , and can be removed by setting to 0 . Several others can be fixed , and only eight varied parameters are required to fit the model to a wide range of recorded cells . The one element of the model which was predicted , rather than directly based on experimental data , is the fast DAP , which is required to fit the short term spike patterning detected in the ISI histogram and hazard function . It turns out that such a current has already been found in recent in vitro work of Armstrong et al [29] . Our predicted 150 ms half life is a close match to their measured value of 200 ms . Both a medium-duration ( ∼500 ms half life ) and a slow AHP have been found in vasopressin cells [27] , [30] . We represent the AHP in the model using only a single slow AHP . We tested whether adding a second AHP would alter the model behaviour ( not shown ) but found no clear effects of this . The very long half life ( 10s . ) is necessary to fit the duration of the peak at the head of each burst . The Roper model uses a single medium-duration AHP but also shows burst peaks which are much shorter than in vivo . One of the distinctions of the model from previous published attempts is that it works with very simple dynorphin dynamics . The Nadeau spiking model was unable to reproduce the increase in burst duration with increased input activity , and they corrected this by adding frequency-dependent fatigue to the dynorphin signal . The Roper model itself added spike rate based facilitation to dynorphin accumulation , attempting to make burst duration less regular . Our model is able to produce the increased burst duration with a simple fixed rate of dynorphin accumulation per spike , suggesting that the more complex dynamics are not required . It will require further work , attempting to map between the models , to understand what the important difference is . We did use more complex dynorphin dynamics when attempting to simulate the effect of sudden changes in osmotic input , showing that the observed switch to continuous firing from slow irregular firing , before settling into phasic firing , can be explained by activity-dependent upregulation of releasable dendritic dynorphin stores . In vasopressin neurons , dynorphin is co-packaged with vasopressin in large dense-cored vesicles , and these vesicles can be released from the dendrites by calcium-dependent exocytosis . Electrical activity can induce dendritic release through voltage-gated calcium entry through mainly N-type calcium channels [31] , but the amount of release in response to electrical activity depends on other factors . In particular , dendrites possess a readily-releasable pool of vesicles close to the plasma membrane [32] , and recruitment of vesicles into this pool from deeper reserve stores is regulated by the cytosolic actin cytoskeleton in a calcium-dependent way [33] , allowing for a slow activity-dependent augmentation of dendritic release . Adding this mechanism only affects model behaviour in response to sudden and prolonged changes to input activity . We don't require the more complex mechanism to simulate other behaviour and so consider this an optional extension for the purposes of further work . It is an advantage to maintain a model which is as simple as possible in order to understand its behaviour . The obvious simplification in our model , compared to the Hodgkin-Huxley based models , is that we don't have voltage dependency . During the model's development , voltage dependency was tested with the K+ leak current , but was unable to produce proper burst activity . Experience suggests that an incomplete implementation of voltage dependence does not work well in neural models . The HAP for example , modelled as a decaying exponential , requires a very large initial magnitude ( 60 mV ) when the spike is just a point event . In an integrate-and-fire model this doesn't matter – the HAP is unrealistically large initially , but only at a time when cells are refractory , and so has no effect on spike patterning . However , if other voltage-dependent elements are added then the large voltage perturbation can cause unrealistic behaviour . We would argue that it is more important for an in vivo model to match spike patterning , rather than the detailed dynamics of individual spikes – as generally the experimental data in vivo essentially capture only spike events . Obviously spike events and membrane voltage changes are related , but the detailed parameters are more difficult to measure in vivo , and voltage dependence in vivo is much weaker than in vitro [17] . The deafferentation of neurons that is inevitable in the preparation of hypothalamic slices for in vitro recordings leaves the cells relatively denuded of synaptic input; accordingly the cell input resistance is inevitably higher due to the lack of activation of neurotransmitter-gated ion channels , and the higher input resistance amplifies the effects of conductance changes on membrane potential . Comparing in vivo and in vitro recordings of vasopressin cells shows a large difference in post-spike excitability [18] , that cannot be accounted for by the membrane voltage effects of a different level of background synaptic input , indicating that channel dynamics are very different in vivo . The combination of losing the stochastic element of the synaptic input and the larger post-spike potentials makes in vitro spiking slower and more regular , as observed in the ISI histograms [18] . In addition , the slower , larger DAP makes bursting regenerative . Bursts become self-sustaining , and not subject to external input . In vivo , bursts are generated by the same intrinsic mechanism , but the smaller DAP is not sufficient on its own , requiring synapse driven depolarisation to maintain a burst . Thus in vivo bursting characteristics depend on both synaptic input and the intrinsic bursting mechanism . Vasopressin neurons display diverse patterns of spontaneous spike activity; some cells are relatively silent or irregular , some are continuous , and the rest show variations of the phasic pattern , with varying intraburst rates , burst durations and silence durations . We have shown ( Figure 6 ) that these different modes of spiking can be produced in a single model neuron by varying the synaptic input rate . However , other parameter changes were required to reproduce the different phasic firing characteristics observed between cells . To determine the subset of parameters essential for capturing the full heterogeneity seen in vivo , we attempted to fit different cells changing as few parameters as possible ( Table 2 ) . In addition to the synaptic input rate ( Ire ) , the HAP and fast DAP parameters were required to fit the short-term spike patterning reflected in the ISI histogram and hazard function . The most important parameters however were kAHP , and the K+ leak parameters , in particular kD , which determines dynorphin's effect on the slow DAP . The AHP is essential to determining the intraburst spike rate , balancing against the depolarising effects of the DAPs . Dynorphin's most sensitive effect is on limiting burst duration , which in turn defines how much input is required to shift a cell to continuous firing , but in the model it is also essential to determining mean silence duration , as further discussed below . To fit the cells with longer mean silence , we had to use a longer dynorphin half-life value , λD . Nadeau et al [34] have recently extended their spiking model to include the vasopressin secretion response , and have used their model to explore what properties of the spiking mechanism might underlie the heterogeneity observed in the cell population . The core of their secretion model is based on an interpolation of in vitro data which demonstrated the frequency facilitation effect [9] , assuming that this is sufficient to also represent the fatigue effect on secretion . The important test of a secretion model should be whether it can reproduce the enhanced secretion response to phasic firing compared to continuous firing at the same mean spike rate [6] . This is thought to depend on both the facilitation and the fatigue elements of the secretion mechanism . Their spiking model , like our present model , includes a dynorphin mechanism that plays a role in cell heterogeneity , and which is a key element in determining whether cells fire irregularly , phasically or continuously . They suggest however that synaptic input in the normal frequency range is not a factor in determining firing mode , and their model shows very little response to increases in synaptic activity while in the phasic firing mode . The differences between the Nadeau model and the present model mirror the differences between vasopressin cell activity in vitro and in vivo: the Nadeau model is a regenerative spiking model , in which a relatively very low level of synaptic activity provides a limited variability in spike timing , while the present model displays spike activity that is wholly dependent on a relatively high level of synaptic input . In response to increased osmotic pressure , their individual cells produce a step-like increase in spike rate and secretion . They suggest that the linear population response is based on inter-cell variation in the dynorphin parameters and resting potential , so that the proportion of active cells gradually increases with increased osmotic input . Such a non-linear increase is not observed in our model , nor in the responses of individual vasopressin cells to progressive osmotic pressure changes in vivo . We further tested our model by using it to simulate various in vivo experiments which have studied the spiking activity and the underlying mechanisms . We predict that some slow activity driven dendritic vesicle translocation is responsible for the delayed shift to phasic firing in response to a sudden rise in osmotic input . We do not require this mechanism for other results , and so retain it as an optional extension to the model , but it may play an important role when further considering the effects of dendritic release in future work , and has parallels to the mechanisms postulated for the axonal secretion , the idea of a releasable and a reserve store [25] . It is now widely accepted that dynorphin is an essential element of the phasic mechanism , and determines burst duration , but there is debate on what role it plays in determining inter-burst silence . Combining in vitro and in vivo analysis of the response to a dynorphin antagonist , Brown et al [28] suggested that dynorphin does not play a role . However , our model , in which dynorphin is a part of the silence generating mechanism , is able to replicate the results of their in vivo analysis . We show that reducing the effect of dynorphin has a much larger effect on burst duration than silence duration . It does reduce silence duration , but to show this significantly requires a dose of the antagonist which will turn most cells continuous . The cells selected for their analysis were by nature those which retained bursting , and likely to show less effect on silence duration . Their in vitro data shows no change in time course of the inter-burst hyperpolarisation with reduced dynorphin . We would suggest that this is due to the slow AHP acting on a similar time course . More recent in vivo results [35] do show a reduction in silence duration in response to the same dynorphin antagonist , supporting the hypothesis that dynorphin drives a post-burst hyperpolarisation . | Vasopressin is a hormone secreted from specialised brain cells into the bloodstream , acting at the kidneys to control water excretion , and thereby help regulate osmotic pressure . This is a cell membrane property determined by the ratio between body salt and water , and its maintenance is essential to the function of all our cells and organs , which depend on a stable fluid volume and extracellular salt concentration . Specialised cells in the brain sense osmotic pressure and generate electrical signals , which the thousands of vasopressin neurons process and respond to by producing and secreting vasopressin . The individual vasopressin cells generate an interesting phasic pattern of electrical activity in response to rises in osmotic pressure – they fire in long bursts , separated by long silences . In our project we're using modelling to simulate this phasic pattern of electrical activity and how it relates to the input signals , trying to understand exactly why vasopressin cells generate this kind of pattern and exactly what advantages it offers to signal processing and the control of vasopressin secretion . | [
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] | 2012 | Phasic Firing in Vasopressin Cells: Understanding Its Functional Significance through Computational Models |
The Kato-Katz technique is widely used for the diagnosis of Schistosoma mansoni , but shows low sensitivity in light-intensity infections . We assessed the accuracy of a commercially available point-of-care circulating cathodic antigen ( POC-CCA ) cassette test for the diagnosis of S . mansoni in preschool-aged children before and after praziquantel administration . A 3-week longitudinal survey with a treatment intervention was conducted in Azaguié , south Côte d'Ivoire . Overall , 242 preschoolers ( age range: 2 months to 5 . 5 years ) submitted two stool and two urine samples before praziquantel administration , and 86 individuals were followed-up posttreatment . Stool samples were examined with duplicate Kato-Katz thick smears for S . mansoni . Urine samples were subjected to POC-CCA cassette test for S . mansoni , and a filtration method for S . haematobium diagnosis . Before treatment , the prevalence of S . mansoni , as determined by quadruplicate Kato-Katz , single CCA considering ‘trace’ as negative ( t− ) , and single CCA with ‘trace’ as positive ( t+ ) , was 23 . 1% , 34 . 3% and 64 . 5% , respectively . Using the combined results ( i . e . , four Kato-Katz and duplicate CCA ( t− ) ) as diagnostic ‘gold’ standard , the sensitivity of a single Kato-Katz , a single CCA ( t− ) or CCA ( t+ ) was 28 . 3% , 69 . 7% and 89 . 1% , respectively . Three weeks posttreatment , the sensitivity of a single Kato-Katz , single CCA ( t− ) and CCA ( t+ ) was 4 . 0% , 80 . 0% and 84 . 0% , respectively . The intensity of the POC-CCA test band reaction was correlated with S . mansoni egg burden ( odds ratio = 1 . 2 , p = 0 . 04 ) . A single POC-CCA cassette test appears to be more sensitive than multiple Kato-Katz thick smears for the diagnosis of S . mansoni in preschool-aged children before and after praziquantel administration . The POC-CCA cassette test can be recommended for the rapid identification of S . mansoni infections before treatment . Additional studies are warranted to determine the usefulness of POC-CCA for assessing drug efficacy and monitoring the impact of control interventions .
Recognizing the public health impact of schistosomiasis and soil-transmitted helminthiasis , the World Health Organization ( WHO ) has set a minimum target for the control of morbidity due to these parasitic worm infections , urging member states to regularly treat at least 75% and up to 100% , of all school-aged children at risk of morbidity [1] , [2] . As a result , many African countries have set up national plans of action for the control of schistosomiasis and soil-transmitted helminthiasis , and pursue school-based deworming campaigns [3] , [4] . Experience and lessons from these programs are that they significantly reduce the prevalence and intensity of infection , and thus morbidity [5]–[8] . There is growing evidence that soil-transmitted helminths ( Ascaris lumbricoides , hookworm , and Trichuris trichiura ) and schistosome infections are acquired already in early childhood [9]–[14] . Hence , there is a need for effective and safe treatment of preschool-aged children , as their inclusion in preventive chemotherapy is being discussed [10] , [11] , [15] , [16] . The intensity of infection with soil-transmitted helminths and schistosomes is age-dependent , usually showing a peak in school-aged children and adolescents [17] , [18] . For schistosomiasis this might be due to cumulative and increasing water contacts of the school-aged child , combined with the maturation and increasing egg-laying capacity of schistosome worm pairs [11] . Hence , the majority of infected young children might excrete only a few eggs with their feces ( for soil-transmitted helminths and Schistosoma mansoni ) and their urine ( for S . haematobium ) [9] , [11] , [13] , [17] . It is important to note that the Kato-Katz technique , which is widely used in endemic countries for the diagnosis of S . mansoni and soil-transmitted helminths , lacks sensitivity , particularly in areas of low endemicity , and for low-intensity infections ( i . e . , in young children or after treatment interventions ) [19]–[22] . Hence , improved diagnostic methods for the accurate detection of S . mansoni in preschool-aged children , assessment of drug efficacy , and monitoring progress of control programs are desirable . Recent studies have shown that indirect diagnostic tests ( e . g . , point-of-care circulating cathodic antigen ( POC-CCA ) ) have become valuable alternatives to direct parasitological methods for the diagnosis of S . mansoni [13] , [23] . Note that the POC-CCA cassette test detects the presence of CCA ( a schistosome glycoprotein ) in host urine , after being regurgitated into the bloodstream by actively feeding worms , and successive clearance in the host's kidneys . Schistosome antigens ( CCA and circulating anodic antigen ( CAA ) ) can be detected in the serum and urine of infected individuals and their levels are sensitive and specific markers for the presence and intensity of infection [13] , [23]–[26] . Circulating antigens disappear from serum and urine of schistosomiasis patients within a couple of weeks after successful treatment [24] , [27] . Studies assessing a CCA urine dipstick and a POC-CCA cassette test in preschool-aged children in Uganda and Kenya , respectively , recommended these rapid tests as a useful technique for the detection of S . mansoni in that age group [11] , [28] , [29] . In our own research , conducted with school-aged children in south Côte d'Ivoire , we found that a single POC-CCA cassette test was similarly sensitive as triplicate Kato-Katz thick smears for the diagnosis of S . mansoni [26] . However , the physiological development and biological processes , such as absorption , distribution , metabolism , toxicity and , particularly , excretion are all age and setting dependent [30] . Moreover , the effect of geographical variations of S . mansoni strains on the performance of POC-CCA cassette test is poorly understood . Hence , there is a need to determine the accuracy of the POC-CCA cassette test in preschoolers from different settings as a diagnostic tool for S . mansoni , including its potential for drug efficacy evaluation , and monitoring of community effectiveness of control interventions . The current study was designed to assess the accuracy of the commercially available urine POC-CCA cassette test for the diagnosis of S . mansoni in preschool-aged children . We designed a 3-week longitudinal study with a treatment intervention , and determined the accuracy of the POC-CCA cassette test before and after the administration of praziquantel .
Our study received ethical clearance from the Ministry of Health and Public Hygiene of Côte d'Ivoire ( reference no . 4248/2010/MSHP/CNER ) . Local authorities in the study area ( Azaguié , south Côte d'Ivoire ) were informed about the objectives , procedures , and potential risks and benefits of the study . At study onset , a door-to-door information campaign was conducted , and all households in the area informed about the aims and procedures of the study . Written informed consent ( or fingerprints of illiterate people ) was obtained from parents/guardians of participating preschool-aged children . Treatment was administered to all preschool-aged children and their mothers , irrespective of their infection status . Participating preschool-aged children were treated with crushed praziquantel tablets at a dose of 40 mg/kg and the efficacy and safety of this intervention have been described elsewhere [31] . At the end of the study , anthelmintic treatment ( single 40 mg/kg oral dose of praziquantel against schistosomiasis , and single 400 mg oral dose of albendazole against soil-transmitted helminthiasis ) was offered to all villagers free of charge . The study pursued a 3-week longitudinal design with a treatment intervention and was conducted between August and November 2011 in two villages located in the Azaguié district in south Côte d'Ivoire . The two villages , Azaguié Makouguié ( geographical coordinates , 05°37′33″N latitude , 04°09′04″W longitude ) and Azaguié M'Bromé ( 05°39′42″N , 04°08′38″W ) are co-endemic for S . mansoni and S . haematobium [26] , [31] . Subsistence farming is the main economic activity in both villages . Unprotected surface water bodies are frequently contacted due to the lack of tap water and other sources of clean water . Improved sanitary facilities are the exception rather than the norm . Our door-to-door census conducted in June 2011 revealed total populations of 931 people in Azaguié M'Bromé , and 783 people in Azaguié Makouguié . For the current study , emphasis is placed on preschool-aged children younger than 6 years in both villages ( n = 367 ) . Using records obtained from the mid-2011 census , a list of all children aged <6 years ( considered at preschool-age ) was prepared and all of them were invited to participate in our study . Two cross-sectional parasitological surveys were implemented; at baseline and 3 weeks after the administration of praziquantel in order to study the epidemiology of schistosomiasis in preschool-aged children , assess the efficacy and safety of praziquantel in this age group , and determine the diagnostic accuracy of the POC-CCA casette test before and after treatment . Mothers/guardians of participating preschoolers were provided with two plastic containers labeled with unique identification numbers ( IDs ) at the first day of the respective survey . Mothers/guardians were instructed to collect a morning stool and urine sample of the child , each in one of two separate containers . After sample collection , the mothers were invited to submit the filled containers until noon to fieldworkers stationed at a central location ( the primary school ) in each village . Upon submission of the specimens , mothers were handed out a second set of two containers for stool and urine sample collection the next day . Stool and urine samples were transferred to a nearby laboratory located in the district town Azaguié and processed on the same day . For the diagnosis of S . mansoni , duplicate Kato-Katz thick smears were prepared from each stool sample , using 41 . 7 mg templates [32] . Kato-Katz thick smears were allowed to clear for at least 30 min before examination under a microscope by experienced laboratory technicians . The number of S . mansoni eggs was counted and recorded . Additionally , eggs of soil-transmitted helminths were counted and recorded for each species separately . For the diagnosis of S . haematobium , urine samples were subjected to a filtration method , as described elsewhere [1] , [12] . In brief , 10 ml of vigorously shaken urine were gently pressed through a filter mesh ( 30 µm; Sefar AG , Heiden , Switzerland ) . The filter mesh was placed on a microscope slide and a drop of Lugol's iodine solution added before quantitative examination under a microscope for S . haematobium eggs by experienced technicians . For quality control , 10% of the Kato-Katz and the urine filtration slides were re-examined by a senior technician . In case of disagreement with the initial readings , the results were discussed with the concerned technicians and the slides read a third time until agreement was reached . Urine samples were additionally subjected to a commercially available POC-CCA cassette test ( batch no . : 33112; Rapid Medical Diagnostics , Pretoria , South Africa ) . The POC-CCA tests were performed as follows: one drop of urine was added to the well of the testing cassette . Once fully absorbed , one drop of buffer ( provided with the CCA test kits ) was added and the test results were read 20 min after adding the buffer . In case the control bands did not develop , the test was considered invalid and the urine sample was retested with a new POC-CCA cassette . Valid tests were scored as either negative or positive , the latter further stratified into trace , 1+ , 2+ , or 3+ according to the visibility of the color reaction and the manufacturer's instructions . All tests were read independently by two investigators . In case of discordant results , a third independent investigator was consulted , and the results were discussed until agreement was reached [26] . Stool and urine samples collected 3 weeks after the administration of praziquantel ( single oral dose of 40 mg/kg using crushed tablets ) were subjected to the same diagnostic tests as during the pretreatment cross-sectional survey . Data were double entered into an Excel spreadsheet , transferred into EpiInfo version 3 . 2 ( Centers for Disease Control and Prevention; Atlanta , United States of America ) , and cross-checked . In case of discrepancies , the results were traced back to the original data records . Statistical analyses were done using Stata version 10 ( Stata Corp . ; College Station , United States of America ) . Only children who had complete data records from the baseline surveys ( i . e . , quadruplicate Kato-Katz thick smears , two POC-CCA cassette tests , and two urine filtrations ) were included in the final analysis . Helminth species-specific fecal egg counts ( FECs ) as recorded by the microscopists were transformed into numbers of eggs per gram of stool ( EPG ) , multiplying the FEC of each Kato-Katz reading by a factor 24 . To assess the infection intensity of each individual , we calculated the arithmetic mean EPG value of quadruplicate Kato-Katz thick smear readings and categorized them according to thresholds given by WHO [1] . The three infection intensity classes for S . mansoni are ( i ) light ( 1–99 EPG ) ; ( ii ) moderate ( 100–399 EPG ) ; and ( iii ) heavy ( ≥400 EPG ) . Means were compared by Wilcoxon signed rank test and proportions by Pearson's χ2 test . Based on POC-CCA test scores , the infection intensity of S . mansoni was categorized into light ( trace or 1+ ) , moderate ( 2+ ) and heavy ( 3+ ) . To investigate the infection intensity of all infected individuals , we calculated the group arithmetic mean of the individual arithmetic mean EPG values . When using the combined results of the POC-CCA tests from days 1 and 2 , discordant scores were redefined to provide a single infection intensity measure , as shown in Table 1 . For determining the POC-CCA test accuracy , ‘trace’ results were considered as negative in our ‘gold’ standard , due to the fact that ‘trace’ can indicate false positivity . Thus , the accuracy of the Kato-Katz and POC-CCA tests ( considering trace results as negative ( t− ) ) for the diagnosis of S . mansoni was determined . As diagnostic ‘gold’ standard before and after treatment we considered the combined results of quadruplicate Kato-Katz thick smears and duplicate CCA ( t− ) , resulting in a positive case as both or either of the tests was positive ( see also Midzi et al . [33] ) . This assumes an ( almost ) 100% specificity for the CCA ( t− ) test . Based on this ‘gold’ standard , sensitivity , specificity , and negative predictive value ( NPV ) were calculated . The strength of agreement between quadruplicate Kato-Katz thick smears and the POC-CCA test before treatment was assessed by kappa statistics ( κ ) , as follows: κ = 0 indicating no agreement; κ = 0–0 . 2 indicating poor agreement; κ = 0 . 21–0 . 4 indicating fair agreement; κ = 0 . 41–0 . 6 indicating moderate agreement; κ = 0 . 61–0 . 8 indicating substantial agreement; and κ = 0 . 81–1 . 0 indicating almost perfect agreement [34] , [35] . Differences of p<0 . 05 were considered as statistically significant . A univariable logistic regression was performed to assess the association between POC-CCA cassette test results , expressed as binary outcome variable ( negative/positive ) , and a schistosome infection , with separate models for S . mansoni and S . haematobium . Hence , egg counts from each schistosome species were utilized as explanatory variable ( eggs per gram of stool ( EPG ) for S . mansoni and eggs per 10 ml of urine for S . haematobium ) .
Figure 1 shows that a total of 367 preschool-aged children were enrolled in the two study villages , 200 girls ( 54 . 5% ) and 167 boys . Complete parasitological data ( i . e . , quadruplicate Kato-Katz thick smears , duplicate POC-CCA cassette tests , and duplicate urine filtrations ) at the baseline survey before treatment were available for 242 children , 133 from Azaguié M'Bromé ( 55 . 0% ) and 109 from Azaguié Makouguié . There were 127 girls ( 52 . 5% ) and 115 boys with a mean age of 3 . 2 years ( range: 2 months to 5 . 5 years ) . Three weeks after the administration of praziquantel , only 86 out of the 242 children had complete parasitological data . There were 43 girls ( 50 . 0% ) and 43 boys with a mean age of 3 . 6 years ( range: 15 months to 5 years ) . The two population groups ( children with complete data records before and after treatment ) were similar in terms of average age , sex , arithmetic mean FECs of S . mansoni , and co-infection status ( all p>0 . 05 ) . Table 2 shows the baseline prevalence and intensity of S . mansoni infection , as assessed by Kato-Katz and POC-CCA . Among the 242 children with complete data records , 56 ( 23 . 1% ) were found positive for S . mansoni by quadruplicate Kato-Katz thick smears . Most infections were of light intensity ( n = 40; 71 . 4% ) , whereas 12 children ( 21 . 4% ) had a moderate ( 100–399 EPG ) and four children ( 7 . 1% ) had a heavy infection ( ≥400 EPG ) . The group arithmetic mean FEC was 23 . 4 EPG ( 95% confidence interval ( CI ) : 13 . 0–33 . 7 EPG ) . A single CCA ( t− ) test identified 83 children ( 34 . 3% ) harboring active schistosome infections . The youngest child infected with S . mansoni , as determined by the presence of S . mansoni eggs in stool using the Kato-Katz technique , was 8 months . According to the CCA ( t− ) test results , the earliest infection was observed in a child aged 3 months . According to quadruplicate Kato-Katz thick smears before treatment , among the 242 preschool-aged children with complete data records , 22 ( 9 . 1% ) , 15 ( 6 . 2% ) and nine ( 3 . 7% ) were positive for T . trichiura , hookworm and A . lumbricoides , respectively ( Table 2 ) . Hookworm and T . trichiura infections were exclusively of light intensity ( <2 , 000 EPG and <1 , 000 EPG , respectively ) , whereas a third of the A . lumbricoides infections were of moderate intensity ( 5 , 000–49 , 999 EPG ) . Among the 40 children who were infected with S . mansoni according to a single CCA ( t− ) test , but showed no S . mansoni eggs in any of the four Kato-Katz thick smears , three ( 7 . 5% ) , two ( 5 . 0% ) , and two ( 5 . 0% ) children were positive for T . trichiura , S . haematobium and A . lumbricoides , respectively . None of these children were infected with hookworm . Among 242 children at the baseline survey , 26 were infected with S . haematobium , giving a prevalence of 10 . 7% ( Table 2 ) . Only one child , a 5-year-old girl , had a heavy infection ( 128 eggs/10 ml of urine ) . There was no significant association between CCA ( t− ) results expressed as binary variable ( presence/absence of disease ) and S . haematobium egg counts ( OR = 1 . 2; p = 0 . 81 ) . Similarly , no significant association was found between CCA ( t+ ) results expressed as binary variable ( presence/absence of disease ) and S . haematobium egg counts ( OR = 1 . 2; p = 0 . 11 ) . Figure 2 shows the correlation between the intensity of S . mansoni infection determined by quadruplicate Kato-Katz thick smears , as expressed in EPG , and the CCA ( t− ) test shown in color scores . We observed a correlation between the color intensity of CCA ( t− ) test bands and EPG values ( odds ratio ( OR ) = 1 . 2 , p = 0 . 04 ) . Comparing the two different methods used for the diagnosis of S . mansoni , we found moderate agreement between a single CCA ( t− ) test and quadruplicate Kato-Katz thick smears ( κ = 0 . 47 , p<0 . 001 , Table 3 ) . The agreement between duplicate CCA ( t− ) and quadruplicate Kato-Katz thick smears was only fair ( κ = 0 . 36 , p<0 . 001 ) . Agreement between the two methods was weaker when considering trace results as positive in the urine CCA cassette test . According to our ‘gold’ standard , the sensitivity of a single CCA ( t− ) test ( 69 . 7% ) was considerably higher than that of a single ( 28 . 3% ) or quadruplicate Kato-Katz thick smears ( 47 . 5% , Table 4 ) . Also the NPV of a single CCA ( t− ) test ( 77 . 4% ) was higher than that of a single ( 59 . 1% ) or quadruplicate Kato-Katz ( 65 . 9% ) . The sensitivity and NPV of a single CCA ( t+ ) test were higher than those of quadruplicate Kato-Katz and single CCA ( t− ) ( sensitivity: 89 . 1%; NPV: 84 . 9% ) . The specificity of the Kato-Katz technique and CCA ( t− ) was 100% by definition , whereas the specificity of a single CCA ( t+ ) was considerably lower ( 59 . 3% ) . Among the 86 individuals who had complete data records after treatment , S . mansoni eggs were detected by Kato-Katz from 22 ( 25 . 6% ) individuals during the baseline cross-sectional survey . A single POC-CCA , considering trace results as negative , revealed 34 preschoolers ( 39 . 5% ) with an infection . Considering trace results as positive , then a considerably higher number of preschoolers were classified as positive ( n = 56 , 65 . 1% ) . After treatment , among these 86 children , eggs of S . mansoni were only found in two ( 2 . 3% ) individuals . A single urine CCA ( t− ) cassette test revealed 20 children ( 23 . 3% ) with S . mansoni , whereas CCA ( t+ ) found 35 ( 40 . 7% ) infections . At the 3-week posttreatment evaluation , and considering our ‘gold’ standard ( combined results of quadruplicate Kato-Katz thick smears plus duplicate urine CCA ( t− ) cassette tests ) , a single CCA ( t− ) revealed a sensitivity and NPV of 80 . 0% and 92 . 4% , respectively ( Table 4 ) . Single and even quadruplicate Kato-Katz thick smears showed very low sensitivity ( 4 . 0% and 8 . 0% , respectively ) and only moderate NPV ( 71 . 8–72 . 6% ) . In our cohort of 86 children , when considering the combined results from both sampling days , 27 children had a positive POC-CCA cassette test result , traces included . Among these children , 20 were S . mansoni egg-negative at the baseline survey , whereas the seven infected children had baseline FECs ranging between 6 and 450 EPG . When considering POC-CCA trace results as negative , 24 children were still found with a positive POC-CCA cassette test . Among them , 21 children were egg-negative , whereas the three infected children showed baseline FECs ranging between 132 and 588 EPG . Hence , regardless of whether POC-CCA trace results were considered positive or negative , more than three-quarter of the children found positive with the POC-CCA cassette test at the posttreatment follow-up were egg-negative at the baseline survey . Table 5 shows the day-to-day variability of the POC-CCA cassette test scores before ( n = 242 ) and 3 weeks after the administration of praziquantel ( n = 86 ) . At baseline 156 ( 64 . 5% ) and 145 ( 59 . 9% ) were found CCA positive on day 1 and day 2 , respectively . After treatment , 35 ( 40 . 7% ) children on day 1 and 32 ( 37 . 2% ) children on day 2 showed a positive POC-CCA test . Comparing POC-CCA cassette test results from both days , revealed no statistically significant difference in test results before ( p = 0 . 619 ) and after ( p = 0 . 756 ) treatment . There was relatively little day-to-day variation , both before and after treatment . For example , before treatment , about half of the paired POC-CCA test results showed the same scores , whereas 127 ( 52 . 5% ) children had discordant scores , with the highest discrepancy observed between negative and trace results . Considering trace results as negative , the percentage of discordant results decreased to 22 . 7% . In the posttreatment survey , none of the children with duplicate POC-CCA cassette tests performed showed 3+ scores on both days . Discordant POC-CCA test scores between days 1 and 2 were found in slightly more than half of the children ( n = 44 , 51 . 2% ) with the highest number of discordant results between negative and trace results . The concordance between POC-CCA cassette test scores from days 1 and 2 increased with infection intensity ( based on POC-CCA cassette test band color ) , both before and after treatment ( Table S1 ) . Among those 86 preschool-aged children who had complete data records before and after treatment , and considering the higher of the two color reactions in the duplicate POC-CCA tests as the final score showed that the number of tests scored 3+ before treatment decreased by 76 . 5% following treatment . A decrease of 22 . 5% of POC-CCA tests scored as trace was observed 3 weeks posttreatment . Among seven preschool-aged children scored as trace-positive before treatment , four became CCA-negative following treatment , whereas the remaining three were diagnosed CCA-positive ( two children with 1+ and one child with 2+ ) . Nine ( 16 . 1% ) children among the 56 children detected with CCA ( trace included ) had unchanged test scores after treatment . The number of children found CCA-negative increased sharply 3 weeks after a single dose of praziquantel , with a particularly steep decrease of heavy infections ( χ2 = 6 . 50 , p = 0 . 011 ) ( Figure 3 ) . Table 6 summarizes key test requirements and compares them between Kato-Katz ( standard test ) and POC-CCA ( newly developed test ) for the diagnosis of S . mansoni . Important test requirements include the ease of obtaining and analyzing the samples , cost considerations , and diagnostic accuracy .
There is growing awareness that in high endemicity settings , schistosomiasis already affects preschool-aged children , and hence these young children might need to be included in deworming campaigns [11] , [13]–[16] . The Kato-Katz technique has been the backbone of intestinal schistosomiasis ( and soil-transmitted helminthiasis ) diagnosis in epidemiological studies for decades . However , it shows a low sensitivity for detecting low-intensity infections , which are commonly seen in young children and in communities undergoing regular treatment [21] , [28] , [36] . Recent studies have shown that a commercially available , urine-based POC-CCA cassette test is a promising method for the diagnosis of S . mansoni in preschoolers and school-aged children [13] , [23] , [26] , [28] , [29] , [37] , [38] . In the present work , we investigated the accuracy of this POC-CCA cassette test in preschool-aged children from south Côte d'Ivoire before and after administration of a single oral dose of praziquantel ( 40 mg/kg ) and compared its performance to that of multiple Kato-Katz thick smears . We found that a single POC-CCA is more sensitive than quadruplicate Kato-Katz thick smears before and 3 weeks after praziquantel treatment . The intensity of a positive CCA test band reaction was significantly correlated with the S . mansoni egg burden quantified by the Kato-Katz technique . There was a sharp decrease of CCA tests scored 3+ after treatment and an increase in tests scored negative or trace . The youngest child identified as infected with S . mansoni applying the POC-CCA cassette test was 3 months old . Eggs in stool examined with the Kato-Katz method were only detected in children aged 8 months and above . Our results corroborate recent findings from Kenya and Uganda , where CCA tests detected S . mansoni infections in preschool-aged children considerable earlier and at higher frequency than the Kato-Katz technique and an enzyme-linked immunosorbent assay ( ELISA ) kit to test for host antibodies to soluble egg antigens [13] , [28] , [29] . The results reported here also extend on our own recent observations in the same study area and that of other groups made elsewhere that urine POC-CCA tests show a considerably higher sensitivity than the widely used Kato-Katz technique for the diagnosis of S . mansoni in school-aged children [23] , [26] , [38] . As confirmed in the present study , the prevalence and intensity of Schistosoma infections in preschool-aged children is rather low [9] , [11] , [13] , [31] . Hence , the Kato-Katz and other direct diagnostic methods have limitations when it comes to accurate individual diagnosis . Moreover , the consistency of stools in very young children is mostly diarrheic what renders the preparation of Kato-Katz thick smears difficult , which further challenges an accurate diagnosis . The constrains of using diarrheic stool as well as stool of breastfed infants for helminth diagnosis has been reported elsewhere [39] , [40] . In that respect , one needs to consider that in the humid tropics , viral , bacterial , and multiple species parasitic infections causing diarrhea are very common [41]–[43] , and that preschool-aged children are particularly prone to such infections [42] , [44] . Hence , the Kato-Katz technique has shortcomings for helminth diagnosis in this age-group . The implementation of large-scale schistosomiasis control programs that are based on preventive chemotherapy reduces the prevalence and , most importantly , the intensity of Schistosoma infections [4] , [5] , [45] . Hence , the endemicity is lowered , which goes hand-in-hand with a reduced accuracy of the Kato-Katz technique [22] , [46] . In view of recent discussions regarding schistosomiasis elimination [47] , the need for highly sensitive and specific diagnostic tools for the diagnosis of S . mansoni and other Schistosoma species after extensive preventive chemotherapy campaigns and additional interventions cannot be emphasized enough . However , some weaknesses seem to go against the use of POC-CCA as a diagnostic tool for control programs . First , the Kato-Katz method allows for diagnosis of other helminth infections ( e . g . , soil-transmitted helminthiasis ) , which commonly co-exist where schistosomiasis is endemic . Second , the Kato-Katz technique provides a quantitative measure to the infections , which guide the control program interventions . Third , the cost of a single POC-CCA cassette ( approximately US$ 1 . 75 ) is similar to the total costs of performing a single Kato-Katz thick smear in epidemiological surveys ( US$ 1 . 7 ) [48] , [49] . Hence , the costs for individual diagnosis currently limit the use and attractiveness for program managers for larger-scale applications . For individual diagnosis , however , it should be noted that the costs largely depend on the patient's economical situation . Our finding of very young children diagnosed with S . mansoni when using the urine POC-CCA cassette test ( 3 months old ) , and only 5 months later when using the Kato-Katz technique raises an alarm bell . Current control programs focus on the school-aged population ( usually starting at an age of 5–6 years ) , and hence a considerable number of infected children might be restrained from treatment for perhaps 3–4 years . Recent studies discussed the potential impact of early infections that remain untreated for several years on child health due to the cumulative effect of repeated infections [23] , [50]–[52] . Our observations are also important from a surveillance point of view . Indeed , first the POC-CCA test revealed the age of first S . mansoni infection several months earlier than the Kato-Katz technique and , second , we found that three-quarter of the people who were CCA-positive at follow-up were egg-negative at baseline . It seems that these children were infected with immature worms that praziquantel was not able to kill . Hence , despite the aforementioned limits of the POC-CCA cassette test , some advantages deserve to be highlighted . First , POC-CCA is based on simple-to-use urine test , which can be performed by non-specialized personnel . Hence , it can be employed in remote rural areas that lack access to the power grid by minimally trained people ( Table 6 ) . Second , collection of urine samples for POC-CCA is more straightforward and less invasive than collection of stool for Kato-Katz thick smears . The time spent from the field ( sample collection; urine for POC-CCA cassette test versus stool for Kato-Katz thick smears ) to the laboratory ( implementation; at least 25 min for POC-CCA cassette test versus several hours for Kato-Katz thick smears ) places the POC-CCA in a favorable position . Third , a POC-CCA test is able to detect prepatent infections , whereas the Kato-Katz technique can only detect patent infections . Note that de Water and colleagues , in the mid-1980s , studying ultrastructural localization of CCA in the digestive tract of various life-cycle stages of S . mansoni showed that the antigens are present in the gut of adult worms , as well as in the primordial gut cells of cercariae aged 3 . 5 weeks [53] . In addition , a study implemented by van Dam and colleagues 10 years later on in vitro and in vivo excretion of CAA and CCA by developing schistosomula and adult worms showed that during the first days of S . mansoni development more CAA than CCA was excreted , while after one week the trend was reversed [25] . Taken together , the POC-CCA cassette test is an adequate and most useful tool for rapid identification of infected individuals and high-risk communities that warrant interventions at the individual patient level and at the community level with the goal to lower morbidity and transmission of schistosomiasis . Efforts might thus be warranted by the United Nations through its agencies to allow extension of the use of POC-CCA tests in schistosome-endemic areas where financial resources are often limited . Our study shows that the number of positives determined by POC-CCA after treatment is considerably higher than that revealed by quadruplicate Kato-Katz thick smears . Indeed , the Kato-Katz technique found a very low prevalence after treatment ( 2 . 3% ) , whereas POC-CCA test results revealed several-fold higher prevalences ( 23 . 3% considering trace results as negative and 40 . 7% considering trace results as positive ) . These differences might be explained by the following reasons . First , the Kato-Katz technique is underestimating the prevalence due to very low infection intensities after treatment [54] . Second , in the current study , Kato-Katz thick smears were read shortly after slide preparation ( within 30–60 min ) . Prompt microscopic examination of Kato-Katz thick smears is recommended for the concurrent diagnosis of soil-transmitted helminths , particularly hookworm [20] , but the optimal detection of S . mansoni eggs is after clearing the slides for 24 hours [55] . On the other hand , the POC-CCA test might overestimate the prevalence ( i . e . , in case CCA is still excreted in urine more than 3 weeks after treatment despite the death of adult worms ) . Studies conducted to date are inconclusive on when exactly CCA is eliminated from urine below detection limits [25] , [56] , [57] . In view of the aforementioned limitations of our direct parasitological approach , it is conceivable that CCA-positive , egg-negative cases are false-negatives based on the Kato-Katz technique [33] . Assessing the converting proportion of POC-CCA test color band scores after treatment , we observed a significant increase of negative scores and decrease of trace and 3+ scores , despite only considering the combined score per individual over two test days . Since we also found that the FECs detected with the Kato-Katz method correlate with the test band color intensity , the POC-CCA test might indeed reveal formerly heavily infected individuals as still positive . In light of the absence of a real ‘gold’ standard in our study , future investigations using highly sensitive and specific diagnostic methods ( i . e . , a polymerase chain reaction ( PCR ) [58] , or detection of CAA by an up-converting phosphor technology ( UPT ) -based lateral flow ( LF ) assay [59] ) are of need to investigate the true accuracy of a urine CCA cassette test after treatment , and hence its applicability for monitoring the success of schistosomiasis control programs . In conclusion , a single POC-CCA urine cassette test appears to be more sensitive than multiple Kato-Katz thick smears for the diagnosis of S . mansoni in preschool-aged children . It is therefore an appropriate tool for the rapid identification of S . mansoni-infected individuals , including preschool-aged children , and of high-risk communities before the onset of control interventions . Its applicability to accurately assess infections a few weeks after praziquantel administration needs further investigation and comparison with highly sensitive and specific diagnostic tools . | The strategy to control morbidity due to infection with the blood fluke Schistosoma mansoni is to regularly treat school-aged children with the drug praziquantel . Recent studies suggest that in highly endemic areas preschoolers might need to be included in such deworming campaigns . An accurate diagnosis is important to assess how many preschool-aged children need treatment , but the widely used Kato-Katz technique does not detect all infections . We assessed the accuracy of a point-of-care ( POC ) test that is based on the detection of the fluke's circulating cathodic antigen ( CCA ) in children's urine . We obtained two stool and two urine samples from 242 preschoolers in Côte d'Ivoire before and from 86 of these children after praziquantel treatment . Stool samples were examined with the Kato-Katz technique and urine samples with the POC-CCA test for S . mansoni . The sensitivity of one POC-CCA was much higher than a single Kato-Katz for S . mansoni diagnosis before ( 69 . 7% versus 28 . 3% ) and after treatment ( 80 . 0% versus 4 . 0% ) . The POC-CCA therefore is useful for the rapid identification of S . mansoni-infected preschoolers who need treatment . The application of the POC-CCA test for monitoring of schistosomiasis control interventions needs further investigation . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"biology"
] | 2013 | Accuracy of Urine Circulating Cathodic Antigen Test for the Diagnosis of Schistosoma mansoni in Preschool-Aged Children before and after Treatment |
Plant innate immunity is mediated by Resistance ( R ) proteins , which bear a striking resemblance to animal molecules of similar function . Tobacco N is a TIR-NB-LRR R gene that confers resistance to Tobacco mosaic virus , specifically the p50 helicase domain . An intriguing question is how plant R proteins recognize the presence of pathogen-derived Avirulence ( Avr ) elicitor proteins . We have used biochemical cell fraction and immunoprecipitation in addition to confocal fluorescence microscopy of living tissue to examine the association between N and p50 . Surprisingly , both N and p50 are cytoplasmic and nuclear proteins , and N's nuclear localization is required for its function . We also demonstrate an in planta association between N and p50 . Further , we show that N's TIR domain is critical for this association , and indeed , it alone can associate with p50 . Our results differ from current models for plant innate immunity that propose detection is mediated solely through the LRR domains of these molecules . The data we present support an intricate process of pathogen elicitor recognition by R proteins involving multiple subcellular compartments and the formation of multiple protein complexes .
Plants , like animals , are able to launch successful defense responses against invading microorganisms . For this purpose , plants have developed a variety of strategies that include molecular , chemical , and physical barriers to infection . One of the most important of these defense systems relies on germline-encoded molecules that can specifically recognize a particular pathogen or strain of a given pathogen . These molecules are encoded by Resistance ( R ) genes , and each R protein typically initiates a defense response in the presence of one pathogen-derived elicitor protein that is termed the Avirulence ( Avr ) determinant [1] . The genetic relationship between R and Avr proteins was elegantly stated in the gene-for-gene hypothesis [2] , and this type of plant defense is now described as the plant innate immunity . Over the last several years , approximately 40 R genes have been cloned [1] . These genes confer resistance to several classes of pathogens , including viruses , bacteria , fungi , oomycetes , insects , and even nematodes . Surprisingly , the protein products of these R genes are structurally similar to each other and contain a few , conserved domains . The leucine-rich repeat ( LRR ) domain is the most common domain among R proteins , and it is also found in animal innate immunity molecules , including Toll from Drosophila and Toll-like receptors ( TLRs ) , and nucleotide binding-oligomerization domain proteins ( NODs ) from mammals [3 , 4] . Members of the largest class of R proteins possess , in addition to the LRR , a central nucleotide binding site ( NB ) domain that is similar to the NB of the NODs and the animal cell death effector proteins Apaf1 and CED4 [1 , 5] . The NB-LRR class of R proteins is further subdivided according to the N-terminal domain of these proteins . Some proteins contain a Toll-interleukin 1 receptor homology region ( TIR ) domain , whereas others possess a coiled-coil ( CC ) domain . Like the LRR and NB domains , the TIR domain is found in animal innate immunity proteins , specifically Toll and the TLRs [6] . In recent years , extensive molecular and genetic analyses have been performed in a number of R-Avr systems . One interesting aspect of R protein function is its localization . These proteins have been found in a variety of cellular locations , depending on the localization of the eliciting pathogen or its Avr determinant . For example , the tomato Cf proteins , which recognize extra-cellular Cladosporium fulvum Avr proteins , are localized to the plasma membrane [7] . Interestingly , Arabidopsis RPM1 and RPS2 are associated with cellular membranes although they do not possess any canonical membrane-targeting domains [8 , 9] . This subcellular localization is consistent with the membrane localization of their corresponding Avr elicitors , AvrRpm1 and AvrRpt2 , respectively [9 , 10] . Apart from the plasma membrane , R proteins may also be found in the nucleus of plant cells , as is the case with RRS1-R in the presence of its bacterial Avr elicitor , PopP2 [11] . However , many NB-LRR R proteins do not carry recognizable subcellular targeting signatures and so are believed to be cytoplasmic . However , a cytoplasmic localization has only been demonstrated for the Solanaceae R protein Bs2 [12] and for barley Mla1 [13] . One caveat to most of these studies is that they involved the generation of biochemical extracts or artificial systems like protoplasts . A more nearly ideal approach for this analysis should use nondisruptive techniques to examine localization in intact , living tissue . In addition to studies on localization , researchers have also identified some of the host proteins that are involved in R protein activation and signaling downstream of the activation event . Recent work from several groups has attempted to address a central issue in plant innate immunity: how R proteins recognize pathogen-derived Avr proteins and initiate a defense response . Early models of the R-Avr relationship proposed a direct interaction between the host and pathogen proteins based on the gene-for-gene hypothesis . However , a direct interaction has been demonstrated for few R-Avr pairs: [11 , 14–18] . It should be noted that these interactions have been demonstrated in heterologous systems like yeast and in vitro binding assays . The paucity of detectable R-Avr interactions has led to the hypothesis that other host proteins may facilitate the association of R and Avr proteins and that these accessory host proteins are critical for the activation of the resistance protein . This idea is articulated in the guard hypothesis , which proposes that the Avr protein induces a change in a host protein that is normally recruited by the pathogen via its Avr protein to establish a successful infection , and it is this change that is sensed by the R protein ( guard ) , leading to the activation of the R protein and subsequent defense signaling [19] . This model for R protein activation is supported by evidence from several R-Avr systems . In Arabidopsis , both RPM1 and RPS2 and their cognate Avr elicitors interact with a host protein RIN4 [9 , 20] . RIN4 is modified in the presence of these Avr proteins , and it is believed that this modification is a key step in the activation of the R proteins . Additional support for the guard hypothesis comes from tomato Cf2 and the host protease Rcr3 [21] , as well as from Arabidopsis RPS5 and PBS1 [22] . Thus , data suggest that some R proteins may indirectly recognize Avr proteins through other host proteins . This mode of activation is in contrast to mammalian TLR function in which TLRs directly recognize pathogen-associated molecular patterns ( PAMPs ) through their extracellular LRR domains [23] . Interestingly , genetic analyses of plant R proteins have identified a crucial role for the LRR in conferring the specificity of R-Avr systems [14 , 24] , To date , an in vivo association between an R protein and its corresponding Avr protein has not been demonstrated . This is true even for cases in which a direct interaction has been demonstrated in yeast two-hybrid assays or in vitro . There is no obvious biological explanation for this seeming anomaly . One possibility is that detection has been technically challenging , presumably because R proteins are present at relatively low levels in plant cells [25] . Attempts to increase R protein levels using strong viral promoters like the Cauliflower mosaic virus ( CaMV ) 35S promoter have failed to yield a wild-type resistance response [26] , suggesting that R protein levels are fine-tuned within a cell and that an effective resistance response is dependent on these levels . We have optimized the expression and detection of several R proteins by standard microbiology and molecular biology techniques in an effort to directly address the issues of pathogen recognition and R protein activation . One of the classic model systems for studying plant-virus interactions involves Tobacco mosaic virus ( TMV ) infection of Nicotiana tabacum ( tobacco ) plants . Tobacco and other Nicotiana species carrying the N gene are resistant to infection by all strains of TMV , except the Ob strain [27] . The N gene has been cloned , and it encodes a TIR-NB-LRR R protein [28] . The first visible outcome of infection of N-containing plants by TMV is the formation of necrotic lesions at the infection sites [28 , 29] . These necrotic lesions are part of the stereotypical R gene–dependent defense response that is called the hypersensitive response ( HR ) . The helicase domain of the TMV replicase proteins , termed the p50 protein , is necessary and sufficient to elicit an HR in N-containing Nicotiana plants [30 , 31] . The HR resulting from N–p50 interaction is generally assumed to indicate N function [31] . In this work , we have used the N-TMV system to investigate how an R protein recognizes its pathogen-derived elicitor protein . We set out to determine the subcellular localization of N and its Avr elicitor , p50 . Using biochemical approaches and fluorescence microscopy , we show that N and p50 are both cytoplasmic and nuclear proteins . We also investigated whether nuclear localization was important for a defense response and found that N's presence in the nucleus was indeed required for its function . However , p50 could elicit N-mediated responses even when expressed exclusively in the cytoplasm . Once we determined that N and its elicitor are present in the same subcellular compartments , we then tested the association between N and p50 . We show by co-immunoprecipitation and fluorescence microscopy-based assays in intact , living tissue that N and p50 associate in planta . This is the first report of R-Avr association occurring in living tissue undergoing a defense response . Another interesting finding is that the TIR domain of N is critical for this association to occur . These results propose additional functions for the TIR domain in addition to its known role as an adaptor for signaling in animal innate immunity . We propose that the N TIR domain acts as an adaptor between the pathogen Avr protein and the signaling function of the R protein .
In order to localize the N protein , we fused a tandem affinity purification ( TAP ) tag containing nine copies of the MYC epitope ( 9xMYC ) to the C terminus of N [32] . The full genomic clone of N including its endogenous 5′ and 3′ regulatory sequences and introns was used for this purpose , and the tagged N construct was called gN-TAP . This genomic construct was used to drive expression of N and to facilitate the alternative splicing that is required for N function [33] . To investigate the localization of the TMV elicitor , p50 , we used the p50 sequence from the U1 strain of the virus . Two tandem copies of the HA epitope were fused to the C-terminus of p50 . Expression of p50-U1-HA was driven by the strong CaMV 35S promoter in an effort to mimic the high levels of viral replicase that accumulate during TMV infection . To determine whether gN-TAP and p50-U1-HA were functional , we co-infiltrated Nicotiana benthamiana leaves with Agrobacterium cultures expressing gN-TAP and/or p50-U1-HA . Tissue co-expressing the two proteins exhibited cell death typical of the HR 2 d after co-infiltration ( Figure 1A ) . Tissue expressing either gN-TAP or p50-U1-HA alone did not show the HR cell death response ( Figure 1A ) . The expression of gN-TAP ( Figure 1B , lane 1 ) and p50-U1-HA ( Figure 1C , lane 1 ) was confirmed by Western blot analysis . The subcellular localization of N and p50 was first examined by cell fractionation . Tissue transiently expressing gN-TAP was collected , and protein extracts were ultra-centrifuged to produce crude soluble ( S100 ) and membrane ( P100 ) fractions . The proteins in the fractions were separated by SDS-PAGE and analyzed by Western blot with anti-MYC antibodies . gN-TAP was found in the S100 soluble fraction of protein extracts in the absence of TMV or p50-U1 ( Figure 1D , panel 1 ) , and its localization did not change when co-expressed with its Avr elicitor , p50-U1-HA ( unpublished data ) or in the presence of TMV itself ( Figure 1D , panel 2 ) . Similar analysis for p50-U1-HA showed it to be associated primarily with the S100 fraction ( Figure 1E , panel 1 ) . These results suggest that both N and p50-U1 are soluble proteins . We then used the noninvasive technique of fluorescence microscopy on intact , living leaf tissue as an independent approach to confirm the localization of N and p50-U1 . This method avoids the tissue disruption and possible introduction of artifacts that could have occurred during preparation of protein extracts used in our biochemical cell fractionation . The advent of newer forms of fluorescent molecules that give stronger emissions allows the imaging of fusion proteins expressed at fairly low levels [34] . For fluorescence detection , the Citrine variant of enhanced yellow fluorescent protein ( EYFP; [35] ) was fused to the C-terminus of N . Again , N was in its full genomic context including its endogenous 5′ and 3′ regulatory sequences and introns , and Citrine-tagged N was called gN-Citrine . p50-U1 was tagged at its C-terminus with the Cerulean variant of enhanced cyan fluorescent protein ( ECFP; [36] ) to generate p50-U1-Cerulean . Again , the 35S promoter was used to drive expression of p50-U1-Cerulean . We confirmed that gN-Citrine and p50-U1-Cerulean were functional by assessing their ability to produce HR-associated cell death when co-expressed ( Figure 2A ) . Expression of either construct alone did not result in HR-associated cell death ( Figure 2A ) . gN-Citrine ( Figure 2B , lane 1 ) and p50-U1-Cerulean ( Figure 2C , lane 1 ) were readily detected by Western blot analysis . Citrine alone or gN-Citrine was transiently expressed in N . benthamiana leaves by agroinfiltration . Sections were cut from the infiltrated leaves and observed under the confocal microscope . As expected , Citrine alone , driven by the strong 35S promoter , was localized to the cytoplasm and nuclei of cells ( Figure 2D , column 1 ) . Interestingly , gN-Citrine ( Figure 2D , column 2 ) produced a similar pattern of fluorescence to that of Citrine alone ( Figure 2D , column 1 ) . These data are consistent with the biochemical analysis of gN-TAP , and show that N is a cytoplasmic protein . However , unexpectedly , gN-Citrine was also detected in the nuclei of most cells examined ( Figure 2D , column 2 ) . For p50-U1 localization , a similar pattern of fluorescence was obtained for Cerulean alone ( Figure 2E , column 1 ) and p50-U1-Cerulean ( Figure 2E , column 2 ) . Together , these biochemical and cell biological analyses indicate that N and p50-U1 are cytoplasmic and nuclear proteins . As a control , we were interested in examining a p50 from a TMV strain that could not elicit N-mediated defense . The Ob strain of TMV does not elicit N-mediated responses at temperatures above 20 °C [27] . As expected , the p50 from the Ob strain of TMV ( p50-Ob ) does not cause HR-associated cell death when expressed in N-containing plants [37] . The amino acid sequences of p50-Ob and p50-U1 are 64% identical and 80% similar [38] . We therefore attempted to characterize p50-Ob . For this purpose , two copies of the HA epitope tag were fused to the C-terminus of p50-Ob to generate p50-Ob-HA . Interestingly , p50-Ob-HA was not detected with antibodies in Western blot analyses ( unpublished data ) . Following the approach used for p50-U1 , p50-Ob was tagged with Cerulean to produce p50-Ob-Cerulean . Like p50-Ob-HA , p50-Ob-Cerulean was not detected by Western blot ( unpublished data ) . Surprisingly , we detected low levels of p50-Ob-Cerulean in chloroplasts ( Figure 2E , column 3 ) . The fluorescence signal aligns with the stroma of the chloroplasts , and in some cases , stromules ( stroma-filled tubules that connect plastids ) were identified . It must also be noted that the signal generated by p50-Ob-Cerulean was very weak , even though the 35S promoter was used to drive its expression . It is possible that the failure of p50-Ob to elicit N-mediated defense is due to its localization to chloroplasts and exclusion from the cytoplasm or nucleus where N is found . Analyses of the N-terminus of p50-Ob indicated that it contains a chloroplast localization signal ( unpublished data ) . Therefore , we decided to use a chimeric p50 containing the first 192 amino acids from p50-U1 and the remaining sequence from p50-Ob ( p50-U1-Ob ) [38] . The p50-U1-Ob chimera fails to elicit N-mediated resistance [38] . To investigate the localization pattern of p50-U1-Ob , two tandem copies of the HA epitope tag were fused to the C-terminus of p50-U1-Ob . p50-U1-Ob-HA expression was driven by the 35S promoter , and the protein was detected by Western blot analysis ( Figure 1C , lane 2 ) . We examined the localization of the p50-U1-Ob chimera by cell fractionation . p50-U1-Ob-HA was found in the S100 fraction of cell extracts ( Figure 1E , panel 2 ) . To determine p50-U1-Ob localization in intact , living leaf tissue , a C-terminal Cerulean tag was attached to this fusion to generate p50-U1-Ob-Cerulean , and it was transiently expressed in N . benthamiana leaves . Unlike p50-Ob-Cerulean , the p50-U1-Ob-Cerulean chimera was detectable by Western blot analysis ( Figure 2C , lane 2 ) . p50-U1-Ob-Cerulean was detected in the cytoplasm and nucleus of transfected cells by fluorescence microscopy ( Figure 2D , column 4 ) . Thus , although it does not elicit N-mediated resistance , the p50-U1-Ob chimera has an identical subcellular localization pattern to p50-U1 that elicits N-mediated defense . Moreover , the p50-U1-Ob chimera provides us with a suitable control for our experimental system . Since N's nuclear localization was unexpected , we investigated whether it was important for a defense response . For this , we prevented N's nuclear accumulation by fusing a nuclear export signal ( NES ) to the C-terminus of gN-Citrine . The NES sequence was derived from the human immunodeficiency virus-1 ( HIV-1 ) Rev protein [39] . As expected , gN-Citrine-NES was excluded from nuclei and found only in the cytoplasm of plant cells when examined by fluorescence microscopy ( Figure 3A , column 2 ) . A mutant NES ( NESmut ) in which critical leucine residues have been substituted with alanine , fails to prevent N's nuclear localization ( Figure 3A , column 3 ) , and in these instances , gN-Citrines-NESmut's localization is identical to gN-Citrine ( Figure 3A , column 1 , and Figure 2D , column 2 ) . We then co-expressed gN-Citrine , gN-Citrine-NES , or gN-Citrine-NESmut and p50-U1-HA and examined whether an HR occurred . As expected , gN-Citrine and p50-U1-HA produced an HR ( Figure 3B , column 1 ) . Interestingly , gN-Citrine-NES and p50-U1-HA co-expression did not result in HR ( Figure 3B , column 2 ) , whereas the ability to produce an HR was restored in gN-Citrine-NESmut and p50-U1-HA ( Figure 3B , column 3 ) . This suggests that N's nuclear localization is required for a defense response . To identify which domain of N directed its intracellular distribution , we used previously described mutants of N that carry deletions of the TIR , NB , or LRR domains ( [26]; see below ) . None of these mutants produces an HR cell death in the presence of TMV [26] , indicating that all three domains are necessary for mounting a successful defense response . Each of these N deletion mutants was tagged at its C-terminus with Citrine for localization by fluorescence microscopy . Again , N mutants were created in their full genomic context including N's endogenous 5′ and 3′ regulatory sequences and introns . Surprisingly , all three N mutants retained their nuclear and cytoplasmic localization ( Figure 3C ) , although lower levels of the LRR deletion mutants appeared to accumulate in nuclei ( Figure 3C , column 3 ) . It should be noted that the TIR , NB , and LRR domains do not together constitute the entire N protein , and that there are regions outside these domains that are unaffected in each of the three deletions . Our data suggest that subcellular distribution of N is determined by amino acid sequences outside of the TIR , NB , and LRR domains , because the distribution of the deletion mutants is similar to that of gN-Citrine . Similarly , we wanted to determine whether the nuclear localization of p50 was necessary for a defense response . Using the same strategy we had employed to investigate the function of nuclear N , we attached a C-terminal NES to p50-U1-Cerulean to determine its intra-cellular distribution and examine whether an HR still occurred in the absence of nuclear p50-U1 . As expected , the NES prevented the nuclear accumulation of p50-U1-Cerulean-NES ( Figure 3D , column 2 ) , and the fusion protein was able to enter the nucleus when the NES was mutated ( Figure 3D , column 3 ) . When p50-U1-Cerulean , p50-U1-Cerulean-NES , or p50-U1-Cerulean-NESmut was infiltrated into N-containing N . bethamiana plants , HR was observed ( unpublished data ) . These results suggest that the nuclear localization of p50-U1 is not required for recognition by N and a subsequent defense response . Taken together , our data indicate that recognition may occur in the cytoplasm of plant cells , supporting the hypothesis that nuclear N has another function in addition to pathogen recognition . Given that N and p50 were found in the same subcellular compartments , we decided to investigate their association . For this we attempted to co-immunoprecipitate gN-TAP and p50-U1-Cerulean . N . benthamiana plants were infiltrated with a mixture of Agrobacterium cultures expressing gN-TAP and p50-U1-Cerulean or Cerulean . We were unsure when these proteins would associate , but we assumed that it would be before HR lesions became visible at 48 h post-infiltration ( hpi ) . Therefore , we allowed sufficient time for Agrobacterium to establish a successful infection and T-DNA integration , and then collected samples over a time course from 16 to 48 hpi . Extracts were tumbled with anti–green fluorescent protein ( GFP ) antibodies to immunoprecipitate p50-U1-Cerulean or Cerulean . Isolated immunocomplexes were analyzed by Western blot , and gN-TAP was detected with anti-MYC antibodies . We found that at 46 hpi , we were able to detect gN-TAP in immunoprecipitated p50-U1-Cerulean complexes ( Figure 4 , lane 2 ) but not with those that contained only Cerulean ( Figure 4 , lane 1 ) . Interestingly , p50-U1-Ob-Cerulean , which does not produce HR when co-expressed with N , was unable to pull down gN-TAP ( Figure 4 , lane 3 ) . These data demonstrate that N and p50 associate with each other prior to the observation of a visible defense response . As a second , independent method for assessing the association detected by co-immunoprecipitation , a bimolecular fluorescence complementation ( BiFC ) assay was used . BiFC , as originally described , splits a fluorescent molecule into two parts that are then individually fused to two proteins whose association is being investigated [40] . If the proteins associate , then the portions of the fluorescent molecule are brought into close proximity with each other , and the fluorescent molecule is reconstituted . It should be noted that BiFC does not require that the associating proteins make direct contact with each other and therefore cannot distinguish between direct and indirect associations [40] . However , BiFC has the distinct advantage of using intact , living tissue to study associations . It does not involve the chemical fixation or physical disruption of tissues and is therefore less likely to produce artifacts . We used Citrine for the BiFC analysis of N and p50-U1′s association . One tag contained the amino-terminal 155 amino acids ( YN155 ) , and the other contained the remaining Citrine sequence ( YC155 ) . As a control to show that the two portions of Citrine could reconstitute fluorescence , published interactions using the 14-3-3 protein , T14-3c , which is known to homodimerize , were repeated [41] ( Figure S1A ) . N , in its full genomic context ( endogenous 5′ and 3′ regulatory sequences and introns ) , was then tagged with YN155 to produce gN-YN . p50-U1 was tagged with YC155 to give p50-U1-YC , and its expression was driven by the 35S promoter . The tags did not interfere with the activity of N and p50-U1 , and they were able to produce HR cell death when transiently co-expressed in N . benthamiana leaves ( unpublished data ) . Samples were collected from plants expressing gN-YC and/or p50-U1-YC at 46 hpi , and observed under the confocal microscope . As expected , gN-YC and p50-U1-YC did not produce any fluorescence when expressed individually ( Figure 5A , columns 1 and 2 ) . However , when gN-YN and p50-U1-YC were co-expressed , fluorescence was detected in both cytoplasm and nuclei of cells ( Figure 5A , column 3 ) . When we co-expressed gN-YN and a widely used reporter gene , β-glucoronidase ( GUS ) , tagged with YC155 ( GUS-YC ) , we were not able to detect any fluorescence ( Figure 5A , column 4 ) . Our GUS-YC fusion protein is functional ( Figure S1B and S1C ) , and as expected , GUS-YC alone did not generate fluorescence ( Figure S1D ) . This indicates that the fluorescence we detected with gN-YN and p50-U1-YC was due to a specific association of these proteins . We also examined the association between N and the p50-U1-Ob chimera . YC155 was fused to p50-U1-Ob to produce p50-U1-Ob-YC , and as expected , it did not produce fluorescence when expressed alone ( Figure 5B , column 1 ) . Consistent with our co-immunoprecipitation findings , p50-U1-Ob-YC was not able to complement gN-YN , and fluorescence was not observed when they were co-expressed ( Figure 5B , column 2 ) . Thus , the failure of this p50 chimera to elicit N-mediated responses may be due to its inability to associate with N . Taken together with the co-immunoprecipitation assays , these BiFC results demonstrate that N and p50 associate in plant cells . Having determined that N and p50 associate , we wanted to examine whether the interaction was direct or indirect . For this , we transcribed and translated N in vitro and performed a co-immunoprecipitation assay with recombinant ( HIS ) 6-p50-HA purified from Escherichia coli . We failed to pull down N with p50 in this direct binding assay ( Figure S2 ) . A recent publication has shown that full-length N and p50 interact directly in yeast two-hybrid assays , but interestingly , this interaction was not demonstrated by in vitro pull down [18] . Since N and p50 associate in vivo , we wanted to determine which domain of N was responsible for association . For this , we used previously described mutants of N that carry deletions of the TIR , NB , or LRR domain ( Figure 6A ) . To investigate the ability of the mutants to associate with p50-U1 , we fused the TAP tag to their C-termini as described for tagging with Citrine . gN-mutant-TAP constructs were co-expressed with p50-U1-Cerulean in N . benthamiana leaves . At 46 hpi , tissue was collected and protein extracts prepared . Anti-GFP antibodies were used to immunoprecipitate p50-U1-Cerulean from extracts , and the precipitate was probed with anti-MYC antibodies after separation by SDS-PAGE . Surprisingly , mutants missing the P-loop of the NB , the entire NB , or the LRR retained the ability to co-immunoprecipitate with p50-U1-Cerulean ( Figure 6B , lanes 4 , 5 , and 6 ) . Unexpectedly , the mutant lacking the TIR domain did not co-immunoprecipitate with p50-U1-Cerulean ( Figure 6B , lane 3 ) . These findings were corroborated by the BiFC assay . N mutants lacking the TIR , NB , or LRR domain were tagged with YN155 , and expression was confirmed by Western blot analysis ( unpublished data ) . Each tagged mutant was then co-expressed with p50-U1-YC or GUS-YC as a control , and tissue was monitored for fluorescence at 46 hpi . The loss of the NB or LRR domain did not disrupt the ability of N and p50-U1 to associate , and fluorescence was detected in those samples ( Figure 7A , columns 1 and 3 ) . Again , as in the co-immunoprecipitation assays , the TIR-deletion mutant failed to complement p50-U1 , and fluorescence was not observed ( Figure 7B , column 1 ) . As expected , none of the mutants gave BiFC with GUS-YC ( Figure 7A , columns 2 and 4; Figure 7B , column 2 ) . The TIR domain of N is therefore necessary for association with the Avr elicitor , p50-U1 . It was possible that removing the TIR domain had disturbed the remaining portion of N and this was responsible for the loss of association , not the loss of the TIR domain per se . To examine this possibility , mutants carrying point mutations in their TIR domains were assessed for their ability to associate with p50-U1 . Both mutants used , N ( D46H ) and N ( W141S ) , disrupt N-mediated resistance [26] . TAP-tagged point mutants could not be co-immunoprecipitated with p50-U1-Cerulean ( Figure 6B , lanes 7 and 8 ) . Similarly , when they were tagged with YN155 , they failed to complement p50-U1-YC by BiFC ( Figure 7C , columns 1 and 3 ) . We had confirmed expression of these mutants by Western blot ( unpublished data ) . These results suggest that the point mutants disturb the function of N by interfering with the ability to associate with its Avr elicitor . Thus , the wild-type TIR domain of N is necessary for association with p50-U1 . The TIR domain of N was then directly tested for its ability to associate with p50-U1 . Using the same strategy applied to full-length N , the TIR domain was TAP-tagged in N's genomic context including its endogenous 5′ and 3′ regulatory sequences to give N ( TIR ) -TAP . N ( TIR ) -TAP and p50-U1-Cerulean or p50-U1-Ob-Cerulean were co-transfected into N . benthamiana plants . Tissue was collected 46 hpi , and protein extracts were prepared . Co-immunoprecipitation using anti-GFP antibodies was performed , and the precipitate probed with anti-MYC antibodies . N ( TIR ) -TAP was found in complexes containing p50-U1-Cerulean ( Figure 8A , lane 1 ) , but not in those isolated using p50-U1-Ob-Cerulean ( Figure 8A , lane 2 ) . For the BiFC assay , N-TIR was tagged with YN155 to produce N ( TIR ) -YN , which was then co-expressed with p50-U1-YC or p50-U1-Ob-YC . Consistent with the co-immunoprecipitation results , fluorescence was detected when N ( TIR ) -YN complemented p50-U1-YC ( Figure 8B , column 1 ) . No fluorescence was observed between N ( TIR ) -YN and p50-U1-Ob-YC ( Figure 8B , column 2 ) . As an additional control , we checked for complementation between N ( TIR ) -YN and GUS-YN , and observed none ( Figure S3 , column 1 ) . The TIR domain of N is therefore both necessary and sufficient for association with p50-U1 . We then investigated the specificity of the N ( TIR ) -p50-U1 association . To do this , we chose the TIR domains from two R proteins closely related to N , tomato BS4 and Arabidopsis RPP5 , and examined their association with p50-U1 . N and BS4 share 54% identity and are most similar at the TIR domain [42] , whereas the TIR domains of N and RPP5 share 52% identity [43] . The TIR domains of BS4 and RPP5 were each placed under the control of N's 5′ and 3′ endogenous regulatory regions . Thus , the only difference between the N ( TIR ) and BS4 ( TIR ) and RPP5 ( TIR ) constructs used in our analysis are the coding sequences . We examined the association between BS4 ( TIR ) or RPP5 ( TIR ) and p50-U1 by the BiFC assay . For this , BS4-TIR and RPP5 ( TIR ) were tagged with YN155 to produce BS4 ( TIR ) -YN and RPP5 ( TIR ) , respectively . The expression of these constructs was confirmed by Western blot ( unpublished data ) . No Citrine fluorescence was observed in tissue co-expressing BS4 ( TIR ) -YN and p50-U1-YC ( Figure 8B , column 3 ) or RPP5 ( TIR ) -YN and p50-U1-YC ( Figure 8B , column 4 ) . As expected , BS4 ( TIR ) -YC and RPP5 ( TIR ) -YC expressed with p50-U1-Ob-YC or GUS-YC do not produce fluorescence ( Figure S3 , columns 2–5 ) . Thus , TIR domains of BS4 and RPP5 do not associate with p50-U1 . Thus , the observed association between N-TIR and p50 is specific and depends on the sequence of the TIR domain . We examined whether the association we observed between N ( TIR ) and p50-U1 was a direct interaction by pull-down assays , using in vitro transcribed and translated N ( TIR ) and ( HIS ) 6-p50-HA purified from E . coli . N ( TIR ) did not precipitate p50-U1 in this assay ( Figure S2 ) , and this is consistent with recent findings [18] . This suggests that the association between N ( TIR ) and p50-U1 is indirect and may involve other proteins .
Here we show that the tobacco R protein N and its cognate Avr determinant from TMV , p50 , are cytoplasmic and nuclear proteins . N's nuclear localization is required for a defense response . Further , N and p50 associate in living plant cells as determined by both biochemical and non-destructive microscopic analysis . We have also identified the domain of the R protein that mediates this association . Based on the results of genetic analyses of alleles of R genes and several studies of direct interactions by yeast two-hybrid assays , we expected the LRR domain of N to mediate the association with p50 . Surprisingly , we found that the TIR domain was not only necessary for association with p50 , but also sufficient . We propose a new model for how N may recognize the presence of its elicitor in a plant cell ( Figure 9 ) . We have examined the localization of N and p50 by both biochemistry and fluorescence microscopy . Biochemical assays have been successfully used to localize multiple R proteins [8 , 9 , 12 , 13] . In addition , we utilized confocal fluorescence microscopy to observe the proteins in their native state and subcellular location with minimal disruption to the tissue . For this , we used improved EYFP and ECFP variants , Citrine and Cerulean , respectively , which produce greater fluorescence signal and hence allow easier detection of our fusion proteins [35 , 36] . Both N and p50 from the U1 strain of TMV are cytoplasmic proteins . This finding was largely expected for N , given that it possesses no obvious subcellular targeting signatures in its sequence and shares sequence similarity to other predicted cytoplasmic R proteins , including BS4 [42] and RPP5 [43] . Surprisingly , in addition to being cytoplasmic , both N and p50-U1 show localization to nuclei . Another R protein , barley MLA1 , also shows apparent localization to two subcellular compartments [13] . N's nuclear localization is required for a defense response since preventing its nuclear accumulation of N disrupts the production of an HR . What is the possible significance of N's nuclear localization in mediating a defense response ? Interestingly , we had previously identified plant-specific transcription factors as proteins that interact with N [32] . Further , nuclear-localized Arabidopsis RRS1 possesses a WRKY DNA-binding domain as a C-terminal extension to its TIR-NB-LRR core structure [11 , 44] . Taken together , these findings hint at a previously undescribed role for R proteins regulating nuclear events , possibly gene transcription . It will be interesting to determine whether this is indeed the function of N and other nuclear R proteins . Although p50-U1 is also nuclear , we determined that its nuclear localization was not required for a defense response . In the context of TMV replication , the helicase domain of the viral replicases , p50 , is not likely to have access to the nucleus , suggesting that recognition of p50 may occur in the cytoplasm . Thus , our results may indicate that the first phase of defense , recognition of p50 , occurs in the cytoplasm while a second , important phase responsible for the actual signaling response occurs in the nucleus . However , we do not yet know how cytoplasmic recognition is communicated to the nucleus . Despite the availability of cloned R and Avr gene sequences for the past decade or so , and the demonstration that some R and Avr proteins interact directly with each other in vitro , an association between an R protein and its cognate Avr elicitor had not been previously demonstrated in intact plant tissue . Here we used co-immunoprecipitation and BiFC to show that N and p50 associate in N . benthamiana . It should be noted that co-immunoprecipitation and BiFC do not conclusively distinguish between a direct or indirect association . Therefore , we cannot rule out the possibility that the N-p50 association may be mediated by other host factor ( s ) . Consistent with this idea , host proteins like Arabidopsis RIN4 that interact with both an R protein and its corresponding Avr elicitor have been identified [9 , 20] . Indeed , we failed to demonstrate a direct interaction between N and p50-U1 by in vitro pull-down assay . Genetic analyses of flax L alleles , which encode TIR-NB-LRR R proteins , have pointed to the LRR as being critical for determining the specificity of L-Avr recognition [24] . L alleles that differ solely in the sequence of their LRRs confer resistance to different Avr determinants . This suggests that the specificity is derived from the ability of the LRR to associate with , and hence recognize , an Avr protein . This is supported by a recent report of direct L–AvrL interactions in yeast [14] . The LRR domain of the rice R protein Pi-ta has also been shown to interact with its corresponding Avr protein [17] and , interestingly , the NB-LRR region of N interacts with p50 in yeast and in vitro [18] . It is important to note , however , that further analysis of the flax alleles also found that other regions of the L proteins in addition to the LRR domain , particularly the TIR and NB domains , were involved in conferring specificity to L-Avr recognition [45] . Our studies have determined that the TIR domain of N is necessary and sufficient for association with the p50 Avr elicitor ( Figure 7A and 7B ) . Also , the association we observed between N ( TIR ) and p50 was specific since BS4 ( TIR ) and RPP5 ( TIR ) could not associate with p50 despite their similarity . Our data therefore support a critical role for the TIR domain in mediating the R–Avr interaction . Interestingly , results from other R proteins support a possible role for domains other than the LRR in association with pathogen-derived elicitors . For example , Arabidopsis RPM1 interacts with the host protein RIN4 through its amino-terminus [20]; RIN4 in turn also interacts with RPM1′s Avr elicitor AvrRpm1 . Further , tomato Pto , a kinase that acts as an R protein , interacts with the N-terminus of Prf , a CC-NB-LRR protein required for Pto function [46] . Pto and Prf act closely to regulate not only recognition of pathogen elicitor molecules , but also subsequent defense signaling , and their coordination function depends on their interaction in the plant cell [46] . Thus , it appears that multiple regions of R proteins are involved in interacting with pathogen-derived elicitor molecules , suggesting a complex mode of R protein activation prior to initiating a defense response . At first glance , our findings appear to contradict two recent papers that investigate the N-p50 association . The first suggests that N and p50 do not associate in plant tissue [47] , whereas the other found that N and p50 directly interact in yeast two-hybrid assays , and further , that this interaction occurred through N's NB-LRR region [18] . However , a closer examination reveals that these findings can be assimilated into a coherent model for N's function . In the initial phase of recognition , N and p50 associate through N's TIR domain , most likely through the involvement of other host proteins , because we found this association is indirect ( Figure 9A ) . This is the association that we have detected by co-immunoprecipitation and BiFC in living tissue . The absence of other host proteins from the yeast two-hybrid system may explain why Ueda and co-workers failed to observe the association between TIR and p50 [18] . This N ( TIR ) –p50 association is possibly the event that leads to the observed oligomerization of N that is proposed to be mediated by N's TIR domain [47] . Next , there is a direct interaction between N and p50 that occurs through N's NB and LRR domains ( Figure 9B ) . This interaction is facilitated through conformational changes of N that result from the disruption of the interaction between the TIR-NB and LRR domains by p50 [18] . This interaction may be weaker than the first , accounting for our failure to observe it in plant tissue . Both interactions likely occur in the cytoplasm because p50 is not needed in the nucleus to initiate a defense response . Subsequently , recognition is communicated to the nucleus , and signaling leading to a defense response follows ( Figure 9C ) . Nuclear-localized N is critical to this process by an as-yet-unknown mechanism , but it may involve changes to the conformation of N , re-distribution of N between the nucleus and cytoplasm , or biochemical modification such as phosphorylation . We have proposed a complex model for p50 recognition by the N protein that involves different multiple compartments and different regions of N interacting with p50 at different times during the recognition event . Our model also explains the findings from flax ( discussed above ) that both the LRR and the TIR domains contribute to the specificity of R–Avr interactions . Although most of these findings were unexpected , they are consistent with the emerging view that pathogen recognition is a complex process involving players other than the R and elicitor proteins . An intricate recognition system allows for the fine control of the output of this initial event in defense , an important consideration when the outcome for most cells that detect the presence of a pathogen-derived elicitor is death . We expect that our model for N , although differing in the small details , will hold true for other R proteins and their elicitors . It will be interesting to see whether other R proteins are also nuclear-targeted and if so , to determine what function these proteins perform in the nucleus . Also , the investigation of the formation of multiple complexes by different R proteins with their cognate elicitors in addition to high-resolution structures for R proteins will be most useful in explaining these interactions and how they culminate in defense . Finally , it will be exciting to investigate the nuclear–cytoplasmic partitioning of N and other R proteins , and determine the role of these proteins in the nucleus .
To generate N-TAP and N deletion mutants-TAP constructs , the TAP tag consisting of 9xMYC-3xHis-3C protease cleavage site-2xIgG binding domain from the vector pYL436 [32] was cloned into the unique SacI site at the 3′ end of the N , NΔTIR , NΔP-loop , NΔNB , NΔLRR2–14 , N ( D46H ) , and N ( W141 ) S constructs described in [26] . The Citrine sequence was amplified by polymerase chain reaction ( PCR ) and was cloned in place of the TAP tag to generate gN-Citrine . The HIV Rev NES sequence ( LQLPPLERLTL ) and NES mutant sequence ( LQAPPAERATL ) [39] were included in the downstream PCR primer to amplify Citrine , and cloned into gN to create gN-Citrine-NES and gN-Citrine-NESmut . The N-terminal 465 nucleotides of Citrine sequence were amplified by PCR , and cloned into N and N deletion mutants in place of the TAP tag to generate N-YN and N deletion mutants-YN constructs . To generate the TIR domain fused to the TAP and YN155 tags , the TIR sequence was PCR amplified and cloned between the NcoI and SacI sites of the gN-TAP and gN-YN constructs . The TIR region of BS4 and RPP5 was PCR amplified from tomato and Arabidopsis cDNA , respectively , and cloned between NcoI and SacI sites of gN-TAP . The p50 region of TMV-U1 and Ob replicases was amplified using a primer containing 2xHA sequence , and cloned into pYL400 , a T-DNA vector containing the 35S promoter and the NOS terminator cassette . Cerulean and the C-terminal 255 bases of Citrine were cloned into the 3′ end of p50 to generate the p50-Cerulean and p50-YC constructs , respectively . The HIV Rev NES sequence and NES mutant sequence [39] were included in the downstream PCR primer to amplify Cerulean , and cloned into 35S-p50-U1 to create p50-Cerulean-NES and p50-Cerulean-NESmut . To produce p50-U1-Ob constructs , p50-U1 sequence ( nucleotides 1–576 ) and p50-Ob sequence ( nucleotides 577–1 , 338 ) were amplified and inserted in place of p50-U1 sequence in p50-U1-HA , p50-U1-Cerulean , and p50-U1-YC plasmids . PCR-amplified p50-U1-HA was recombined into DEST17 vector ( Invitrogen , Carlsbad , California , United States ) to generate ( HIS ) 6-p50-HA . To generate GUS-YC , the GUS sequence was amplified from pCAMBIA3301 and was inserted in place of p50-U1 in the p50-U1-YC construct . All constructs were confirmed by DNA sequencing . Agrobacterium cultures were grown overnight in LB medium containing appropriate antibiotic selections . Cells were pelleted at 3 , 000 rpm and resuspended in infiltration medium containing 10 mM MgCl2 , 10 mM 2-morpholinoethanesulfonic acid ( MES ) , and 150 μM acetosyringone , and incubated at room temperature for 2–3 h . Strains containing N-derived constructs were infiltrated into N . benthamiana leaves at an optical density ( OD600 ) = 1 . 8 , and those containing p50-derived constructs were infiltrated at OD600 = 1 . 0 . For co-infiltration , equal volumes of Agrobacterium were mixed . Cultures were infiltrated into leaves with a 1-ml needleless syringe . N . benthamiana plants were grown on light carts under 24 h of light , and 4–5-wk-old seedlings were used for all assays . Protein was extracted from ground tissue with buffer containing 150 mM NaCl , 20 mM Tris/HCl , 1 mM EDTA , 1% Triton X-100 , 0 . 1% β-mercaptoethanol , 1 mM PMSF , and complete protease inhibitors ( Roche , Indianapolis , Indiana , United States ) . Protein concentrations were determined by Bradford assay ( Bio-Rad , Hercules , California , United States ) , and equal amounts were loaded onto polyacrylamide gels . Proteins were transferred to PVDF membrane ( Millipore , Billerica , Massachusetts , United States ) for Western blot analysis . Antibodies used were as follows unless otherwise stated: mouse anti-MYC ( Santa Cruz Biotechnology , Santa Cruz , California , United States ) , rat anti-HA ( Roche or Covance , Berkeley , California , United States ) , mouse anti-GFP ( Covance ) , anti-mouse horseradish peroxidase conjugate ( Sigma , St . Louis , Missouri , United States ) , and anti-rat IgG peroxidase ( Roche ) . Samples were ground in liquid nitrogen , and protein was extracted in buffer containing 50 mM HEPES ( pH 7 . 5 ) , 150 mM NaCl , 500 mM sucrose , 10 mM EDTA , 1 mM DTT , 1 mM PMSF , and complete protease inhibitors ( Roche ) . Cell debris was spun down at 10 , 000×g at 4 °C . Extracts were ultra-centrifuged at 100 , 000×g at 4 °C for 1 h . The supernatant or soluble fraction ( S100 ) was collected , and the pellet ( P100 ) was washed with extraction buffer before resuspension in an equal volume of buffer as the S100 . Western blot analysis was carried out as described above . Plant tissue expressing proteins of interest was collected and ground in liquid nitrogen . Protein was extracted with IP buffer containing 100 mM NaCl , 20 mM Tris/HCl ( pH 7 . 5 ) , 1 mM EDTA , 0 . 1 % Triton X-100 , 10 % glycerol , 1 mM DTT , 2 mM NaF , 1 mM PMSF , and complete protease inhibitors ( Roche ) . Cell debris was pelleted at 20 , 000×g , and extracts were incubated with 50-μl Protein A bead slurry ( GE Healthcare , Piscataway , New Jersey , United States ) equilibrated in IP buffer . Samples were tumbled at 4 °C for at least 1 h . Protein A beads were spun down at 600×g and the supernatant collected . A total of 1-μl rabbit anti-GFP antibodies ( Abcam , Cambridge , Massachusetts , United States ) were added to each 1-ml sample , and the mixture was tumbled at 4 °C for 2 h . A total of 50 μl Protein A bead slurry equilibrated in IP buffer was added to each sample , and the antibodies were allowed to couple to the beads with rotation for 1 h at 4 °C . Beads were spun down at 600×g and the supernatant discarded . Beads were washed three times with IP buffer . A total of 25-μl 4xSDS loading buffer was added to each sample and boiled for 4 min . Immunoblotting was carried out as described previously . ( HIS ) 6-p50-HA protein was produced in C43 ( DE3 ) cells ( Lucigen , Middleton , Wisconsin , United States ) and affinity purified using nickel-NTA resin ( Qiagen , Valencia , California , United States ) . Approximately 1 μg of purified protein was used to pull down 35S-Met-labeled in vitro–translated ( TNT; Promega , Madison , Wisconsin , United States ) N and N ( TIR ) as described in [32] . TNT mixture was supplemented with 1 . 5 mM magnesium chloride and 0 . 2 mM potassium acetate . Live plant imaging was performed on a Zeiss Axiovert 200M light microscope equipped with a LSM 510 NLO confocal microscope ( Carl Zeiss , Thornwood , New York , United States ) using either a 40× or 63× C-Apochromat water immersion objective lens ( numerical aperture [NA] 1 . 2 ) . Tissue samples were cut from N . benthamiana leaves at approximately 46 hpi and infiltrated with water . The 458-nm and 514-nm laser lines of a 25-mW argon laser ( Coherent , Santa Clara , California , United States ) and the 543-nm laser line of a 1-mW helium neon laser ( LASOS Lasertechnik , Jena , Germany ) with appropriate emission filters were used to image Cerulean , Citrine , and chloroplast autofluorescence , respectively . In some instances , 488-nm and 568-nm laser lines of a 15-mW argon:krypton laser ( Coherent ) were used for Citrine and chloroplast autofluorescence . All images were acquired in fastline switch mode and processed with the Zeiss LSM 510 ( Ver . 3 . 2 ) channel unmixing algorithm to eliminate crosstalk . | Each year , up to 10% of world agricultural production is lost to pests and diseases caused by a variety of pathogens including bacteria , fungi , nematodes , and viruses . Scientists have understood for nearly a century that plants carry their own immune system that actively engages pathogens and prevents many infections . One aspect of the plant immune system is defined by the gene-for-gene hypothesis: a plant Resistance ( R ) gene encodes a protein that specifically recognizes and protects against one pathogen or strain of a pathogen carrying a corresponding Avirulence ( Avr ) gene . In tobacco and its relatives , the N resistance protein confers resistance to infection by the Tobacco mosaic virus ( TMV ) . We have used N , and the TMV elicitor , p50 , to investigate the mechanism of gene-for-gene resistance . We show that N and p50 associate in the cytoplasm and nucleus of plant cells and that this association is mediated by N's TIR domain , which is structurally similar to animal innate immunity molecules . Our findings provide novel insight into how R proteins recognize pathogen Avr proteins , and should help in long-term efforts to enhance crop yield . | [
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] | 2007 | A Novel Role for the TIR Domain in Association with Pathogen-Derived Elicitors |
Using DNA sequences 5′ to open reading frames , we have constructed green fluorescent protein ( GFP ) fusions and generated spatial and temporal tissue expression profiles for 1 , 886 specific genes in the nematode Caenorhabditis elegans . This effort encompasses about 10% of all genes identified in this organism . GFP-expressing wild-type animals were analyzed at each stage of development from embryo to adult . We have identified 5′ DNA regions regulating expression at all developmental stages and in 38 different cell and tissue types in this organism . Among the regulatory regions identified are sequences that regulate expression in all cells , in specific tissues , in combinations of tissues , and in single cells . Most of the genes we have examined in C . elegans have human orthologs . All the images and expression pattern data generated by this project are available at WormAtlas ( http://gfpweb . aecom . yu . edu/index ) and through WormBase ( http://www . wormbase . org ) .
Determining when and where genes are expressed is often key to determining their function . Although expression profiling of genes using Serial Analysis of Gene Expression ( SAGE ) and microarrays is now routine , we still have complete developmental expression profiles for only a small fraction of all genes expressed in any metazoan . The spatial resolution of these two techniques is limited unless purified cell populations can be isolated in sufficient abundance to provide the necessary RNA ( for examples , see [1–3] ) . How then do we gain expression information on the thousands of human genes that are still largely uncharacterized ? One approach is to use high-throughput RNA in situ hybridization as has recently been done for brain tissue in the mouse [4] . In this study , 20 , 000 genes were assayed in the adult male mouse brain , and their distribution in many cases was resolved to the level of a single cell . Another complementary approach involves employing green fluorescent protein ( GFP ) [5] as a marker to monitor gene expression in a specific cell or tissue . The GenSAT project [6] uses Bacterial Artificial Chromosomes ( BACs ) with GFP-marked genes in transgenic mice to monitor tissue and cell expression . About 2 , 000 gene expression patterns are described at the GenSAT site ( http://www . gensat . org/ ) . Because gene functions were largely maintained during evolution , yet another possible approach is to first study orthologs of these genes in less complex organisms . Knowing what tissue or cell type expresses a particular gene in a simpler system such as Caenorhabditis elegans or Drosophila melanogaster could help drive the analysis of this gene in a more complex tissue or organ system , as is found in mice and humans . In Drosophila , a large-scale in situ hybridization study has now documented the expression pattern of close to 3 , 000 genes in the developing embryo ( [7]; http://www . fruitfly . org/cgi-bin/ex/insitu . pl ) . The goal of our study was to characterize the temporal and spatial expression pattern of human orthologs in the nematode C . elegans down to the resolution of a single cell . Specifically , we determined the expression profile of individual genes throughout the whole organism and across all life stages . Independent of the biomedical aspects of our approach , the analysis of complex expression patterns of many genes may not only facilitate functional analysis in C . elegans and other organisms , but also create a foundation for decoding the informational hierarchies governing gene expression . C . elegans has several advantages as a venue for expression studies at this resolution . The main advantages are that it is one of the simplest multicellular organisms with a complete genome sequence available [8] and a completely documented cell lineage [9 , 10] . In addition , the small size , transparency , and limited cell number of the worm allow for the easy observation of many complex cellular and developmental processes that are difficult to observe in higher eukaryotes , and morphogenesis can be observed at the level of a single cell [11] . Besides ourselves , only two groups have attempted large-scale expression profiling in C . elegans at this resolution . Hope and colleagues in the past have used lacZ reporters and currently are using the newly developed “promoterome” to characterize gene expression [12–14] . Another approach , developed by Yuji Kohara's group in Japan , uses in situ hybridization to fixed animals at different developmental stages ( http://nematode . lab . nig . ac . jp ) . Our approach was to examine expression in living animals transformed with GFP fused to DNA 5′ of genes with human orthologs . For gene fusion and amplification , we used “PCR stitching” [15] , which proved to be a fast , efficient , and economical method for obtaining such constructs , and we have demonstrated that the method is scalable [1] . Because of the relatively small intergenic regions in the C . elegans genome , typically less than 3 kb , PCR stitching did not have to be done over large intervals . These small intergenic intervals illustrate yet another advantage of doing this type of study in the nematode . This is a key advantage that sets our project apart from previous high-throughput expression projects done in other organisms . Our overall approach takes advantage of the transparency of the nematode and allows us to visualize gene expression in vivo , in real time , in a living animal . This method allowed us to determine the temporal and spatial distribution of the expressed GFP in close to 10% ( 1 , 886 ) of all genes identified in this organism .
Expression patterns analyzed for the 1 , 886 genes in this study were primarily , but not exclusively , from nematode orthologs of human genes ( >80% ) . Our target genes were drawn from nematode–human ortholog groups in the InParanoid database [16] ( http://inparanoid . sbc . su . se ) , selecting primarily genes for which no function is known . To analyze the in vivo spatial and temporal expression profiles of thousands of genes , we needed a high-throughput approach for GFP fusion constructs . GFP has been shown to be an effective cell marker in C . elegans [5 , 17] , and because of the need for cost-effectiveness and scalability , we chose to use the promotor::GFP fusion technique “PCR stitching” [15] . The 5′ regulatory regions examined in this study extend a maximum of 3 kb upstream of the predicted ATG initiator site for a targeted gene . Most often , an upstream gene was nearer than 3 kb and we did not extend our analysis into or past this adjacent gene . As a benchmark and internal control , 10% of our analysis included genes with expression annotation in WormBase . We used half of these benchmark genes and found that 80% of our observations on expression matched the annotated expression patterns . For another 10% of the benchmark genes , we found some overlap , and for about 10% , we found little or no agreement with expression patterns compiled at WormBase . ( Table S1 ) . Transformants carrying GFP fusions were subject to detailed in vivo analysis as outlined in Figure 1 . We have observed GFP expression for 1 , 886 genes . Because we only sampled 10% of the genes in this organism , we wanted to ensure that specific functional categories were not overrepresented in our dataset . We used Gene Ontology ( GO ) annotation to examine the genes in our set relative to the whole genome and found that the representation of most functional groups reflected their frequency within the genome ( Figure S1 ) . Besides the genes for which we detected expression , there were another 516 genes for which we did not detect any expression ( see Discussion ) . At present , only 15% of the strains exhibiting expression are in stable strains ( possibly chromosomal integrants ) . As is usual for microinjected transgenes , most strains carry unincorporated concatamer arrays , and we detected mosaicism in many of these strains . To compensate for this mosaicism , and to ensure that we did not miss expressing cells , at least 20 replicates were analyzed for each developmental stage . Only GFP-expressing cells and tissues that showed consistent expression in 50% of the animals at any given developmental stage were recorded . Two subclasses of expressing strains were further analyzed: ( 1 ) those with rare or complex expression patterns and ( 2 ) those that showed embryonic expression before the comma stage of embryogenesis . In the former case , the strains underwent their final analysis via 2-D and 3-D imaging on a confocal microscope before being submitted to the public Web site . In the latter case , the embryonic strains were first integrated ( see Materials and Methods ) and then recorded during development using a four-dimensional ( 4-D ) microscope system ( multifocal , time-lapse video recording system ) developed for the purpose of tracking embryonic cell identities and movements [18 , 19] . Since the cell lineage of C . elegans is invariant [10] , we could use these recordings in conjunction with Simi BioCell software [19] to retrace the cell lineages and determine the identity of the cells expressing GFP . This has resulted in 95 embryonic recordings , two examples of which are illustrated in Figure 2 . In the first , pC45G9 . 13 ( Figure 2A–2D ) , expression is initially detected in three cells , ABprappppa , M5 , and MSpapaapa , but later expands to include several other cells . In the second example , pZK637 . 11 ( Figure 2E–2H ) , expression is detected early during embryogenesis , and includes the AB and MS lineages . At present , only a portion ( 10% ) of the embryonic recordings have been completely analyzed and the lineage of all GFP expressing cells determined . The data from this project are publicly available at WormBase and interactively at WormAtlas ( http://gfpweb . aecom . yu . edu/index ) . All strains are available from the Caenorhabditis Genetics Center ( http://www . cbs . umn . edu/CGC/CGChomepage . htm ) ( currently , strain requests go through R . Johnsen [bjohnsen@gene . mbb . sfu . ca] ) . Our Web site ( http://gfpweb . aecom . yu . edu/index ) provides the user with two formats for accessing the data: ( 1 ) a Browse page ( Figure 3 ) to display all strains and data , with a search option for stage or tissue , and ( 2 ) a Gene Search page ( Figure 4 ) that enables the user to recover selected information on specific genes of interest , or identify a subset of genes from the entire dataset ( e . g . , show genes that are unc and have associated movies ) . Each gene displayed has links through the gene name and location to WormBase's Gene Summary and mapping pages ( Figures 3C and 4C ) . The strain name has a link to a comprehensive summary page containing all data relevant to that strain ( Figure 3D ) . Along with the data present on the initial search readout page , other information included are the primers used to amplify the promoter , whether the strain is stabilized , and links to additional images of the strain . A survey of temporal and spatial GFP expression patterns for all 1 , 886 genes is shown in Table 1 and Figure 5 , and some illustrative examples in different tissues are displayed in Figure 6 . We have detected GFP at all developmental stages and have identified expressed GFP in all major tissues except the germinal gonad . Most GFP fusions express across all developmental stages with 1 , 781 ( 95% ) showing expression in adults , 1 , 835 ( 97% ) in larval animals , and 1 , 556 ( 83% ) expressing during embryogenesis . A majority of the 5′ regulatory DNA sequences examined drive GFP expression in the nervous system ( 63% ) , the intestine ( 63% ) , the pharynx ( 40% ) , and the body-wall muscle ( 32% ) ( Table 1 ) . Subsets of cells and tissues within these broad categories are also delineated; we have observed GFP expression specific to the nerve ring , sensory neurons , ventral nerve cord , pharynx , seam cells , the excretory canal and excretory gland cells , the spermatheca , and coelomocytes , to list a few . Over the course of our analysis , we observed GFP expression in 38 tissues and cell types throughout all developmental stages: embryo , larval ( L1–L4 ) and adult ( Figure 6; Table 1 ) . We observed many examples of temporal expression stability and examples where the expression pattern changed during development . For example , pF26F4 . 6::GFP exhibited hypodermal expression during the larval stages , but no GFP was detectable in adult hypodermis; pY61A9LA . 10::GFP showed intestinal and neural expression during early developmental stages , whereas adults lacked any GFP expression at all . Conversely , we observed cases where GFP expression was turned on later in development , as in the case of pF11F1 . 1::GFP , where no GFP was detected until the animals matured to adults , at which point hypodermal and intestinal expression were observed . Examples of changing patterns of expression formed a minority of our dataset . This , in some respects , was to be expected because we used the enhanced form of GFP ( EGFP ) , which has a long half-life . Early expressed embryonic GFP could persist through the 14 h ( 22 °C ) duration of embryogenesis [20] and possibly past hatching . Similarly , GFP expressed during larval development may persist in adult tissues . Also , embryonic expression was never detected earlier than the 50–100 cell stage of embryogenesis , possibly a consequence of our inability to detect maternal RNA contributions to the developing embryo [21] ( see Discussion ) . Although the concatameric arrays may have led to germline silencing in the gonad [21] , they may also have contributed to increasing the sensitivity of detecting an expression signal in other tissues . As described in Materials and Methods , each array has several copies of the fusion GFP construct . Several of these GFP fusions can express simultaneously in a particular cell . As a test of the sensitivity of GFP fusions , we used them to see if we could detect expression from genes with low numbers of SAGE tags . Specifically , we were able to detect a GFP signal for 232 genes that only had a single tag in either the embryo or one of the following tissues: neurons , hypodermis , intestine , or muscle . In each case , GFP expression was detected in the tissue for which only a single SAGE tag had been recorded ( Table S2 ) . The SAGE data can be viewed at http://tock . bcgsc . ca/cgi-bin/sage170 and WormBase . The source of this material is from different developmental stages and different purified tissue and cell populations during early development ( [1] and unpublished data ) . To measure the reliability and accuracy of the reporter expression patterns described in this study , we took further advantage of the existing SAGE data for specific tissues . We have compared the intersects between our GFP expression patterns for muscle , gut , and the nervous system against SAGE data for stage-specific purified cells populations for each of these tissues . In each case , we can identify about 70% of GFP reporter genes in the corresponding SAGE library ( e . g . , genes for 71% of GFP reporters expressed in muscle are detected in the muscle SAGE library; unpublished data ) . Considering that the SAGE libraries are limited to embryonic tissue only and that half of the SAGE tags are present in single copies , we believe this is a reasonable validation of the GFP reporter expression patterns observed . Of the 1 , 886 genes with analyzable expression , only one in five was found to be tissue specific and only a very few were found to be cell specific ( Figure 5; Table 1 ) . Cell-specific promoters were found in a few special cases , as in the excretory cell in which we identified six 5′ regulatory regions that drove expression in only this cell ( Figure 5 ) . In another example , we found four specific cases of 5′ sequences limiting expression to the head mesodermal cell ( Figure 5 ) . In this study , we did not find any examples where individual cells belonging to a larger tissue group such as body-wall muscle , or hypodermis , or the intestine expressed by themselves . Tissue-specific GFP expression accounted for 20% of our samples , and all major tissues in this organism are represented in our dataset ( Figure 6; Table 1 ) . Of the 414 tissue-specific regulatory regions identified , the majority are expressed exclusively either in neural ( l55; Figure 7 displays several examples of the complexity of the nervous system ) or intestinal tissue ( 136 ) . Other tissues or cell groupings that exhibited exclusive expression include the pharynx ( 40 ) , body-wall muscle ( 18 ) , reproductive system ( 14 ) , hypodermis ( 10 ) , hypodermal seam cells ( 8 ) , pharyngeal gland cells ( 4 ) , and the arcade cells ( 2 ) . When we examine the remaining genes , we observe that 321 of these regulatory regions drive expression in only two tissues . In the majority of these examples ( 72% ) , one of the two tissues involved is neural . We detected no bias for specific combinations of tissues or specific exclusions ( Figure 8 ) . Co-expression in nerve and muscle ( 604 examples ) , nerve and intestine ( 698 examples ) , or intestine and muscle ( 532 examples ) are all roughly equivalent , with relatively little contribution from hypodermal expression . Cell- and tissue-specific regulatory regions clearly account for a minority of our expression examples , because the majority of 5′ regulatory regions we have analyzed , 1 , 151 ( 61% ) , drive expression in several tissues . ( This is reflected in the Venn diagram of Figure 8 in which 493 examples express in at least three of the tissues being examined ) . A portion of this last group may represent ubiquitous expression , but it is not always possible to conclude that every cell expresses GFP . Widespread expression in an animal can make it extremely difficult to detect expression in each cell . In these cases , mosaicism of expression , rather than a hindrance , can be helpful . Figure 9 illustrates how mosaic expression can be used to advantage to obtain images of structures within the somatic gonad ( Figure 9A , 9B , 9C , 9E , and 9G ) and individual cells of the gonad ( Figure 9D , 9F , 9H , and 9I ) . All of the examples in this figure are for genes that show expression in many different cells and tissues ( see database at http://gfpweb . aecom . yu . edu/index for details on each gene . ) A large source of regulatory sequences and expression data permits investigation of regulatory sequences required to drive expression in a specific cell or tissue type . We use muscle as an example of how this dataset can be employed . We first identified several 5′ sequences capable of driving expression of GFP in body-wall muscle . We next took a subset of these sequences ( four ) and mapped out the region responsible for muscle expression by constructing a deletion series ( Table S3 lists primers used for this deletion series ) . These deletion constructs determined the minimal 5′ DNA sequence required to drive muscle expression . The four gene promoters analyzed in our study were those of F15G9 . 4a , C34E10 . 6 , T04A8 . 4 , and T27A1 . 4 ( Figure 10 ) . From the deletion series , we found that the minimal length required for muscle expression varied between the promoters , the longest being 326 bp ( Figure 10B ) , whereas the shortest was only 143 bp ( Figure 10D ) . When compared to each other , except for T27A1 . 5 which contains an E box consensus sequence , the minimal promoters were found to contain neither any shared motifs nor any of the previously identified muscle motifs [22 , 23] ( unpublished data ) .
Within this database , there are representatives of many of the expression patterns that are possible in this organism . We have identified 5′ DNA sequences that drive expression in single cells , in single tissues , in multiple tissues , and in all tissues . The dataset is large enough so that one can make some general statements about patterns of expression in this organism . One conclusion from these data is that expression within only a single cell using extant 5′ sequences is rare . The examples that exist in our dataset are usually examples in which a single cell is equivalent to a tissue , as in the case of the excretory cell . However , tissue-specific 5′ regulatory regions are abundant . We found many examples of expression limited to a single tissue , and this included such tissues as the intestine , muscle , and the nervous system , the primary tissues arising from the three primordial germ layers , of endoderm , mesoderm , and ectoderm ( Figure 8 ) . There are also expression patterns that represent subsets of these tissues and expression patterns that are specific to organs or specialized groupings of cells within these broader tissue categories , for example , expression in the pharynx , but not other muscle , or expression in the amphids/phasmids , but not other cells of the nervous system . We also identified 5′ regulatory regions that are not limited to regulating expression in a single tissue , but may include two or more tissues and even cells from several tissues . We also identified several 5′ sequences that apparently permit ubiquitous expression ( at least 1% ) . Finally , we observed 516 5′ regulatory regions that did not exhibit any detectable expression . Although there are several trivial explanations for why these regions do not promote expression , there is also the possibility that these are conditional promoters . Several laboratories have requested these strains to test for expression in different genetic ( male vs . hermaphrodite ) or environmental backgrounds . So far , none have been shown to be conditional promoters . The paucity of 5′ regulatory regions that drive expression in a single cell is perhaps disappointing , but it should not be a surprising result . At least one quarter of the genome is expressed in any particular tissue or cell type ( [1] , unpublished data; http://tock . bcgsc . ca/cgi-bin/sage170 ) which , as we observed , suggests even tissue-specific control regions will be relatively infrequent . To identify regulatory regions that drive expression in only single cells in this organism may require other approaches . In our experience , single unique genes predominantly express in multiple cell types . One possible way to identify cell-specific control elements may be to focus attention on gene families or alternative splice forms of a single gene . The seven transmembrane domain and guanyl cyclase gene families of receptors are excellent examples of gene families in which isoforms are specific for separate sensory neurons [24 , 25] . In this study , we did not focus on gene families , but it may be the approach one should take if the objective is to identify cell-specific markers . The database should not be viewed as the final arbiter of complete expression for any specific gene . As we have only included DNA 5′ of a particular ORF , we may not have the complete “promoter” or all possible “enhancer” elements that impinge on the regulation and expression of this gene when located at its proper location within the chromosome . Our analysis misses any downstream , intronic , or more than 3-kb upstream elements important for proper gene expression . Because of this , a gene's complete expression pattern may differ from that observed using our reporter constructs . As well , 85% of the strains we examined had concatamer arrays with multiple copies of the regulatory region of the gene . This led to mosaic expression when the concatamer was lost , which meant that we had to be sure to examine several animals to ensure that we described all expression patterns possible using this stretch of DNA as a control element . Stably inherited constructs were made for about 15% of the samples , including those from which we desired to make an embryo 4-D recording . Note that the aforementioned caveats are not unusual , as most single gene studies reported in most C . elegans publications work with the same limitations ( see expression report summaries in WormBase ) . If one uses reproducibility as a benchmark , then the data reported here are quite reliable . First , we compared our GFP expression data to expression data using SAGE to detect tissue-specific transcripts and found that about 70% of the genes found expressed in a particular tissue by our GFP reporter assay were also detected using SAGE analysis . We also included in our analysis several genes whose expression was previously characterized , either by GFP promoter constructs or protein fusions or by antibodies . For more than 80% of the previously characterized genes we examined , the expression pattern is in good agreement with published observations . In some cases , we observed a wider range of expression , and in some cases , we observed less . In less than 10% of cases , our observations were completely at odds with what has previously been published . Due to the possible differences in size of 5′ promoter regions , differences in concatamer arrays , or even entirely different methodologies , these discrepancies should not be too surprising . In regard to this benchmark set of genes , often it is not clear whether our observations are the correct ones , or whether previous observations are correct , or if neither reflect the full range of expression of the gene in question . What we are certain of is that the annotation of tissue and cell identity is correct in our study . We have called upon experts within the C . elegans community and the staff of WormAtlas in every instance in which there was a question of cell identity . If cell identity could still not be resolved , this was indicated in the annotation . If there are errors , they are errors of omission , not errors of commission . With almost 2 , 000 expression profiles , the database is an excellent resource for examining the expression profile of a previously uncharacterized gene , even with the caveats stated above . However , we do not feel this is the only possible use of the data . The data reflect expression from less than 5 Mb of DNA , less than 5% of the genome of this organism , and yet we see expression in almost every tissue and cell type in the organism . We think this is fertile ground for researchers interested in identifying motifs regulating gene expression . In many cases , the DNA segment regulating precise cellular and temporal expression is considerably shorter than our maximum size fragments of 3 kb . The ability to search this database for short DNA sequences controlling specific expression patterns should make it easier to identify transcription factor binding sites for a particular organ , tissue , or cell type . Our survey of a few regions determining expression within muscle serves as a case study . We first identified several genes expressed within body-wall muscle . We then picked a subset of 5′ regions and did promoter deletions in order to map essential sites for muscle expression . Curiously , we did not find any single motif , but in fact , found several potential sequences that each could direct expression in muscle ( unpublished data ) . The implication of these observations is that different 5′ sequences can lead to expression in the same tissue , in this case muscle , and we suspect this multiplicity of transcriptional control regions may occur in other tissues as well . This adds a level of complexity to gene regulation that many researchers fail to take into consideration . Our findings of multiple different sequences controlling muscle expression are similar to results reported previously [22 , 23] , but the sequences we have identified are different from those reported in these earlier studies . Even though a MyoD homolog ( hlh-1 ) [26–28] is expressed in C . elegans muscle , it does not seem to be the major transcription factor , because no MyoD binding site has been found in three of four control regions we analyzed . Recently , it has been shown that MyoD acts as part of a trio of transcription factors to regulate muscle differentiation in C . elegans [29] . Many of the genes in this expression database have human orthologs , and for a number of these genes , these expression data are the first indication of where these genes may be expressed in humans . We think this is an important resource to help direct studies of these genes in mammals . Considering the complexity of the mammalian nervous system , any gene that we can identify in a particular subset of neurons may be especially useful . Another use of the database has been to confirm an expression profile of a specific gene identified by other methods . Studies of adult intestine and ciliated neurons have used the GFP strains described in this database as confirmation of tissue-specific expression of genes identified by SAGE tags found in these tissues [30 , 31] . The GFP constructs described in this study are relatively easy to make and thus lend themselves to a high-throughput strategy . The PCR-stitching strategy we used [15] has proven robust and efficient . This approach has at least one advantage over the newly developed “promoterome” [12] , which is that significantly larger 5′ regions can be used for stitching when necessary . Many regulatory regions are close to the ATG start site , as shown for the four genes we analyzed for muscle expression ( Figure 10 ) , but this is not always the case . A further complication with plasmids is that they often contain cryptic promoter elements , which one can avoid by using the PCR-stitching approach . The use of freely segregating concatamer arrays for this study had three implications . It appears from a comparison of GFP expression with low tag-frequency SAGE data that concatamer arrays of GFP may be a sensitive tool for detecting genes with a low level of transcription . We also demonstrated that mosaicism due to loss of the array often led to expression in small groups of cells or single cells , and thus allowed us to obtain a detailed image of these cells . This has been an invaluable aid to the WormAtlas project ( http://www . wormatlas . org/ ) . On the other hand , an unfortunate consequence of using a concatamer array was that it excluded us from recording germline expression and thus monitoring the maternal contribution to early development . Germline silencing of genes is well documented [21] , and this silencing led to us not detecting germline expression in any of the genes we tested . It also meant that we could not detect expression in the early embryo ( before 50 cells ) in most cases . In addition to the approaches described in this study , other approaches to monitor gene expression will be required if we are to monitor gene expression for the whole genome throughout all of development . The technique of homologous recombination in E . coli called recombineering [32–36] is a promising approach because it allows the modification and manipulation of large genomic clones . Larger DNA clones would remove some of the doubt about whether all control elements for transcription regulation are included . Recombineering in bacteria to construct GFP::protein fusions using fosmids with 35- to 40-kb DNA inserts should cover all control elements for most genes in C . elegans . We have built a C . elegans fosmid library , and clones from this library are being used for recombineering ( http://elegans . bcgsc . bc . ca/perl/fosmid/CloneSearch ) ( [33] and unpublished data ) . If these GFP-engineered fosmids are introduced to the worm using a Biolistic gun [37 , 38] , there is a higher probability of generating a transformed animal with a single or low copy number level of the gene . This should allow expression in the germline and the early embryo of any gene in which these are the normal sites of expression . Coupling these strategies to the newer methods of lineaging early cell division [39] should cover the stages in development overlooked in our study .
Our list of target genes was based on the 4 , 367 C . elegans proteins identified from a comparison of C . elegans and human predicted proteomes with InParanoid [16] ( http://inparanoid . sbc . su . se ) , Most of the genome annotations used in the selection of our list of target genes were obtained from WormBase [40 , 41] ( http://www . wormbase . org ) . The list was filtered to remove rRNA genes and genes with SL2 trans-splice acceptor sites , which are associated with operons [42 , 43] . Also removed were genes with characterized mRNAs , an indication that the gene was already well studied . Preference was given to genes with EST-confirmed 5′ ends and those identified as embryonically expressed in Intronerator [44] . We kept genes for which other researchers have constructed reporter fusions as a control set for our study . Our final set of targets consisted of a gene pool enriched for , although not exclusive to , human orthologs with unknown function . The promoter::GFP fusion constructs were generated using the PCR stitching method from Hobert [15] . The PCR experiments were designed to capture putative 5′ DNA regions by amplifying about 3 kb of genomic DNA sequence immediately upstream of the predicted ATG initiator site . When an upstream gene was within 3 kb , the size of the amplicon was adjusted downward . We set the maximum primer length to be 25 nucleotides , and in order to eliminate false-positive PCR products , we designed a nested primer immediately downstream from the most 5′ primer for the second-round reaction . Where the primer encompassed the ATG initiator site , the G was mutated to a C , to ensure there was only one start codon in the promoter::GFP fusion . Early PCR experiments were designed semimanually with the aid of primer3 [45] . To facilitate scale-up , we used Perl and AcePerl [46] to extract C . elegans genomic DNA sequence , and annotations from WormBase to tie them together with the primer design and validation programs primer3 and e-PCR [47] . An interactive version of the GFP primer design program is available at http://elegans . bcgsc . bc . ca/promoter_primers . We used pPD95 . 67 variant S65C ( developed by Dr . Andrew Fire , Carnegie Institution , http://www . addgene . org/pgvec1 ? f=c&cmd=showcol&colid=1 ) as our GFP source because it contains a GFP-cassette and a region that has sequence overlap with the 3′ primer , thus allowing for PCR stitching . 5′ DNA regions from target genes were amplified from C . elegans N2 ( Bristol ) genomic DNA . DNA amplification mixtures consisted of Mix 1: 0 . 5-μl dNTP ( 10 mM ) , 1-μl N2 genomic DNA , 21 . 5-μl double-distilled H2O ( ddH2O ) , 5′ and 3′ primers ( 1 μl of 12 . 5 μM each ) ; and Mix 2: 0 . 75-μl Long Taq ( Expand Long Template PCR System made by Roche Diagnostics , http://www . roche . com ) , 5-μl 10× Long PCR buffer ( #2 from kit ) , 19 . 25-μl ddH2O . Mix 1 and Mix 2 were combined , and PCR was carried out for 30 cycles under the following conditions . Step 1: ( 1 cycle ) 94 °C for 1 min . Step 2: ( 30 cycles ) denaturation at 94 °C for 10 s , anneal at 56 °C for 30 s , and elongation at 68 °C for 2 . 5 min ( depending on amplification fragment size ) . Step 3: 68 °C for 5 . 5 min . Stitched PCR product was constructed as follows: Mix 3: 5′ and 3′ primers ( 1 μl of 12 . 5 μM ) ; 0 . 5-μl 5′ regulatory DNA PCR product , 0 . 5-μl GFP PCR product , 1 . 5-μl dNTP 10 mM , 21-μl ddH2O , and Mix 4: 5-μl 10× Long PCR buffer , 20-μl ddH20 . Mix 3 and Mix 4 were combined . PCR was done as follows . Step 1: ( 1 cycle ) 94 °C for 1 min . Step 2: ( 18 cycles ) denaturation at 94 °C for 10 s , anneal at 56 °C for 30 s , and elongation at 68 °C for 2 . 5 min . Step 3: ( 10 cycles ) 94 °C for 10 s , 56 °C for 30 s , and 68 °C for 2 . 5 min ( increased by 10 s each cycle ) . The PCR product was stored at 4 °C . Nematode strain maintenance and culture were carried out as described by Brenner [48] . Strains were maintained at 15 °C on OP50 plates unless otherwise specified . Strains used include dpy-5 ( e907 ) and wild-type N2 Bristol [48] . At the beginning of the project , we injected a number of strains , with-gel purified DNA , and came to a similar conclusion as Hobert [15] , that gel purifying DNA for injection did not significantly change the results . Transgenic worms were generated by a modification of the method described by Mello et al . [49] . 5′ regulatory DNA::GFP constructs and dpy-5 ( + ) plasmid ( pCeh-361 ) ( kindly provided by C . Thacker and A . Rose; [50] ) were used to construct transgenic strains . Transformants were identified by rescue of the dpy-5 mutant phenotype . The 5′ regulatory DNA::GFP fusions were co-injected with wild-type dpy-5 plasmid DNA into P0 Dpy-5 ( e907 ) gonads using one of these systems: a Olympus BH2-HLSK with a Leitz Westlab injection needle manipulator , or a Zeiss 47 3016 microscope ( Carl Zeiss , http://www . zeiss . com ) with a Leitz Westlab injection needle manipulator ( http://www . leitz . org/leitz_english/index . html ) , or a MINJ-7 microinjection system with an Olympus CK40 microscope from Tritech Reseach ( http://www . tritechresearch . com ) . Injection mixture included ddH2O , 10× TE , dpy-5 plasmid ( pCeh361 , concentration 5–80 ng/μl ) , and 5′ regulatory DNA::GFP fusion construct ( concentration 50 ng/μl ) . A total of 1 nl of the final mix ( 80–90 ng/μl pCeh361 and 5–20 ng/μl DNA::GFP fusion ) was microinjected into P0 worms using 1 . 0-mm , 6” filamented capillary tubes from World Precision Instruments ( http://www . wpiinc . com ) pulled on a Sutter P-97 needle puller . P0 worms were set up for microinjection on agarose pads ( 2%–3% agarose flattened on cover slips ) in either mineral oil ( Sigma ) or in halocarbon oil #700 grade ( Lab Scientific , http://www . labscientific . com ) . An injection set consists of 25–50 P0 worms injected with a given 5′ regulatory DNA::GFP construct . Wild-type F1s were set up individually and their progeny were screened for wild-type animals in the F2 generation . One or two lines yielding at least 30% wild-type progeny were maintained as transformed stocks . For promoter analysis , DNA was injected at 40–60 ng/μl for both subcloned constructs and PCR fusions , using rol-6 as an injection marker . To determine the size of the concatemeric arrays in vivo , we used quantitative PCR to estimate the copy number of the 5′ DNA::GFP constructs and plasmids in 20 different transgenic strains . We estimated that there were about 5–10 copies of promotor::GFP and 100–600 copies of the dpy-5 plasmid in the heritable arrays , which was sufficient for the sensitivity of our GFP assay . We constructed chromosomal integrant strains for a subset of the GFP constructs ( 1% ) using a modified version of M . Koelle's method ( http://info . med . yale . edu/mbb/koelle/ ) . Young adult transgenic ( wild-type ) P0 hermaphrodites were treated with low-dose X-ray irradiation ( 1 , 500 R ) . After 1 h , the P0 animals were transferred to 90-mm OP50 plates—one P0 worm/plate for 12 plates for each strain . The P0 animals were allowed to lay eggs for 18–24 h and then were removed in order to limit the number of F1s laid . Seven days later , mid to late larval wild-type F2 animals were picked and set up ( one/plate , 12 from each of the 90-mm plates ) at room temperature ( 20–22 °C ) . Four to 5 d later , the F2 plates were screened for the absence of Dpy-5 animals , indicating stable inheritance of the array . Strains intended for embryo recordings were outcrossed using an unc-32 marker . P0 GFP-expressing hermaphrodites were crossed with N2 males , F1 GFP males were crossed with unc-32 hermaphrodites , and then F2 and F3 GFP hermaphrodites were individually plated . Lastly , the F4 populations were screened for exclusively wild-type animals . Outcrossing was done at 15 °C . General classification and imaging of GFP expression was done initially with a low-power GFP dissecting microscope ( Zeiss stereomicroscope fitted with Kramer epifluorescence ) , before moving to either a Zeiss Axioplan or a Zeiss Axiophot microscope . Images were captured using a digital camera ( QICAM; QImaging , http://www . qimaging . com/products/cameras/scientific/ ) and QCapture software . This was the first pass , where we determined the developmental stage , tissues , and , where possible , the individual cells expressing GFP . Both stable and unstable strains were evaluated on expression pattern complexity and frequency of occurrence . Unusual or complicated expression patterns , or neural expression , would undergo further analysis using an inverted Zeiss Axiovert LSM 5 confocal microscope equipped with epifluorescence , Nomarski optics , and LSM 5 Pascal software . If we detected pre-comma nonubiquitous expression , strains were put in queue for stabilization and/or outcrossing , and 4-D recording and analysis . The results of all analyses , excepting of the embryos , were curated by hand and uploaded to the project Web site ( http://gfpweb . aecom . yu . edu/index ) and WormBase ( http://www . wormbase . org ) , and the strains were sent to the stock centre ( http://www . cbs . umn . edu/CGC/CGChomepage . htm ) and are available by request from R . Johnsen ( bjohnsen@gene . mbb . sfu . ca ) ( see expression pipeline in Figure 1 ) . The images and movies were processed using Adobe PhotoShop 7 . 0 ( http://www . adobe . com ) and the LSM 5 Pascal volume-rendering software . Single images were normalized and placed into image panels before exporting to the public domain . Movies were obtained from Z-stacks comprised of 20–60 * . lsm images , taken 0 . 5–1-μm intervals apart , the specifics of which were dependant on the age of the worm , the tissue of interest , and the intensity of the GFP . These stacks were then optionally volume-rendered and/or converted into QuickTime movies , normalized , and exported to the Web site . In some cases , embryonic expression was determined without difficulty . However , in many cases , the patterns were determined to be too complex , and it was deemed necessary to have a 4-D recording and to lineage the embryo . Embryos for 4-D analysis were obtained from gravid hermaphrodites . Two embryos at the 2–4 cell stage , were transferred to a 5% agar pad and manipulated into adjacent positions with the same orientation . Detailed expression patterns and gene activation in the embryos were captured with live , two-channel , four-dimensional microscopy , on a Zeiss Axioplan microscope . The fourth dimension being time , Z-stacks ( 25 Z-images ) of developing embryos were recorded at 25 °C using Nomarski microscopy every 30–45 s over a 7-h time course . Interspersed with the normal Z-stacks , we recorded several Z-stacks of GFP fluorescence in specific cells , which were then mapped and identified relative to the Nomarski images . Software that supports this type of microscopy recording and analysis has been developed [19 , 51–53] . We used programs derived from the study by Schnabel et al . [19] and the program Simi Biocell to lineage the embryos [19] . The data have not been posted to the Web site , but are available from the authors . All strain data are in a mySQL database . All primer designs relative to genes and all annotation of genes on the Web site are based on WormBase version 140 . The functionality of the Web site is based on perl/CGI and perl modules for the queries , which provide the user with three formats for accessing the data: ( 1 ) the display of all strains and data for browsing , ( 2 ) the selection of specific genes and the information the user wants to see for each gene , and ( 3 ) a gene search , based on tissue expression pattern . All of the data can be downloaded in . tab or . csv format from WormAtlas ( http://gfpweb . aecom . yu . edu/index ) . The data are also available at WormBase ( http://www . wormbase . org ) . | Knowing where a protein is expressed provides an important clue about its potential function . As critical as this information is , we have complete developmental expression profiles for only a small fraction of all genes expressed in any metazoan . Here , we have generated spatial and temporal tissue expression profiles for 10% of all genes in the nematode Caenorhabditis elegans . Worms expressing putative gene regulatory elements fused with green fluorescent protein were analyzed at each stage of development from embryo to adult . Among the regulatory regions identified are sequences that regulate expression in all cells , in specific tissues , in combinations of tissues , and in single cells . Most of the genes we have examined in C . elegans have human orthologs . Our analysis of complex expression patterns for so many genes may not only facilitate functional analysis in C . elegans , but also create a foundation for decoding the informational hierarchies governing gene expression in all organisms . | [
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] | 2007 | High-Throughput In Vivo Analysis of Gene Expression in Caenorhabditis elegans |
Chagas disease kills approximately 45 thousand people annually and affects 10 million people in Latin America and the southern United States . The parasite that causes the disease , Trypanosoma cruzi , can be transmitted by insects of the family Reduviidae , subfamily Triatominae . Any study that attempts to evaluate risk for Chagas disease must focus on the ecology and biogeography of these vectors . Expected distributional shifts of vector species due to climate change are likely to alter spatial patterns of risk of Chagas disease , presumably through northward expansion of high risk areas in North America . We forecast the future ( 2050 ) distributions in North America of Triatoma gerstaeckeri and T . sanguisuga , two of the most common triatomine species and important vectors of Trypanosoma cruzi in the southern United States . Our aim was to analyze how climate change might affect the future shift of Chagas disease in North America using a maximum entropy algorithm to predict changes in suitable habitat based on vector occurrence points and predictive environmental variables . Projections based on three different general circulation models ( CCCMA , CSIRO , and HADCM3 ) and two IPCC scenarios ( A2 and B2 ) were analyzed . Twenty models were developed for each case and evaluated via cross-validation . The final model averages result from all twenty of these models . All models had AUC >0 . 90 , which indicates that the models are robust . Our results predict a potential northern shift in the distribution of T . gerstaeckeri and a northern and southern distributional shift of T . sanguisuga from its current range due to climate change . The results of this study provide baseline information for monitoring the northward shift of potential risk from Chagas disease in the face of climate change .
Climate change has been implicated in shifts of the geographic distribution of many species[1] , enabling some taxa to increase their distributions into northern latitudes [1] , [2] . Thus , changes in climate can potentially alter the spatial range of vector-borne diseases through shifts in geographical distributions of their vectors [3] , [4] , [5] . Despite some positive developments such as better access to clean drinking water , lower exposure to insect vectors , and higher-quality housing , the projected changes in climate over the next decades may exacerbate infectious disease incidence even in developed regions such as North America [6] . Habitat changes , alterations in water storage and irrigation habits , pollution , development of insecticide and drug resistance , globalization , tourism and travel are additional factors that may help to aggravate this threat [4] . The southern United States is highly vulnerable to outbreaks of vector-borne diseases due to many factors , including poor housing conditions , suboptimal drainage , lack of electricity in some areas , the presence of feral dogs , and human migration [7] , [8] , [9] . Moreover , that some southern states , such as Texas , share a legacy of neglected tropical diseases ( NTDs [9] ) with Mexico , increases the urgency of the development and deployment of active surveillance programs necessary for optimal management and control of vector-borne diseases including Chagas disease [7] , [9] and leishmaniasis [5] . Chagas disease is a zoonosis caused by Trypanosoma cruzi , a flagellated protozoan parasite . Trypanosoma cruzi is transferred from mammalian reservoirs ( e . g . , Neotoma woodrats ) to humans through a triatomine vector [7] . These vectors are insects from the family Reduviidae , sub-family Triatominae [7] , [10] . Trypanosoma cruzi is most characteristically transmitted by infected feces of triatomines entering the human bloodstream . However , it can also be transmitted through blood transfusion , organ transplants and ingestion of infected food; congenital parasite transmission has also been demonstrated [7] . After contamination with the parasite , Chagas disease develops from an acute phase ( period during which the parasites can be found easily in the blood ) followed by an asymptomatic period of varying length; this stage is called the indeterminate phase . During the indeterminate phase , the parasites disappear from the blood . A chronic phase can be followed after 5 to 40 years , and ∼30% of infected people develop the disease [11] , [12] . Chagas disease kills approximately 45 , 000 people annually [13] and affects 10 million people in several countries of Latin America [14] . In the United States around 300 , 000 individuals could be infected with T . cruzi , causing a considerable disease burden [15] . Several factors might influence the geographical distribution of Trypanosoma cruzi vectors and reservoirs ( e . g . , historical presence , the existence of barriers and dispersal capabilities ) , but anthropogenic factors play a fundamental role in the spread of the disease ( e . g . , through habitat changes , globalization , and travel [4] ) . The geographical distribution of Chagas disease has increased beyond regions of endemic occurrence during the last half-century and is now considered a worldwide problem [10] . Species distribution models ( SDMs ) based on machine-learning algorithms and Geographic Information Systems ( GIS ) platforms have been used to predict areas of potential distribution of Trypanosoma cruzi vectors [7] , [16] , [17] , [18] , [19] . These analyses typically show that climatic factors significantly influence the potential geographic distributions of vector ( and reservoir ) species . Additionally , temperature may have a particularly strong influence on the behavior of triatomine species [20] , [21] . For instance , temperatures exceeding 30°C combined with low humidity , cause insects toincrease their feeding rate to avoid dehydration . In addition , in domestic life cycles , when indoor temperatures increase , the insects may develop shorter life cycles and higher population densities [20] . High temperatures can also speed up the development of T . cruzi in vectors [22] . In this paper , we forecast the future ( 2050 ) distribution in North America of Triatoma gerstaeckeri and T . sanguisuga , two of the most commonly found triatomine species and important vectors in the southern United States [7] . Triatoma gerstaeckeri is one of the most widely distributed Triatoma species in Texas [7] , occurring mainly in the southern areas of the state . It is also found in New Mexico and in northeast Mexico [7] . Triatoma gerstaeckeri is more frequently found in economically poorly-developed areas; though it is naturally found in sylvan environments , it is able to disperse to human dwellings [23] . Triatoma sanguisuga can be found in several environments similar to T . gerstaeckeri , including domestic surroundings [24] . Triatoma sanguisuga has been found in several states across United States including Alabama , Arizona , Florida , Georgia , Kansas , Kentucky , Louisiana , Maryland , Mississippi , Missouri , New Jersey , New Mexico , North Carolina , Ohio , Oklahoma , Pennsylvania , South Carolina , Tennessee , Texas , and Virginia [24] . The species has also been found near the Canadian border in Illinois and Indiana [20] . We used geographic information ( longitude/latitude distributional data ) ( Tables S1 and S2 ) and explanatory climatic variables ( temperature , precipitation , etc . , Table 1 ) to produce Species Distribution Models ( SDMs ) using a maximum entropy algorithm . Current SDMs were projected to 2050 using three different Global General Circulation models ( the Canadian Centre for Climate Modelling and Analysis ( CCCMA ) , the Commonwealth Scientific and Industrial Research Organization ( CSIRO ) and the Hadley Centre for Climate Change ( HADCM3 ) . We used two scenarios A2A and B2A from the International Panel on Climate Change [1] . Our aim was to analyze how climate change might affect the future spread of Chagas disease in North America .
For modeling purposes , geographic data ( i . e . , longitude and latitude ) were gathered from data bases from museum collections , voluntary collectors , and through field work by members of our team in South Texas . For the original field work reported here , insects were collected either from public lands or donated by the owners of private lands . As a pilot study , field work was conducted in one sylvatic area , “La Sal del Rey” , Texas ( 26° 31′ N and 98° 03′ W ) , on 8 July 2011 . We did not collect insects in domestic areas , we only included the La Sal del Rey locality in the model construction . To collect the insects , we used suspended dark ultraviolet light traps with a white background sheet and baited with carbon dioxide from dry ice . All geographic localities for both species are reported in Supplemental files ( Tables S1 and S2 ) . Following the methodology of Sarkar et al . [7] , only post-1980 records with an estimated error of <1 . 0 km were used; these choices ensured compatibility between the resolution of the occurrence data and the spatial and temporal resolution of the environmental layers . The study area includes the continental portions of Mexico and the United States and was delimited in the south by the 14°55′S line of latitude and to the north by the 49° 38′N line of latitude , continued by the lines −66° 97′E boundary and −124° 71′W . It was divided into 14 520 497 cells with an average area of 1 . 03 km2 ( SD = 0 . 27 ) . This ensured the enclosure of all points used in the analysis . Present and projected future potential distributions for the target species were computed using presence records for the species ( longitude/latitude ) and with climatic parameters as exploratory variables , using a maximum entropy algorithm incorporated in the Maxent software package [11] , [25] . Maxent predicts probability values ( thresholds ) from 0 ( least suitable ) to 1 ( most suitable ) of habitat suitability over the study area [11] , [25] . We used Maxent Version 3 . 3 . 3k ( http://www . cs . princeton . edu/~schapire/maxent/ ) with the default modeling parameters ( convergence threshold = 105 , maximum iterations = 500 , regularization value β = auto ) [26] . Climatic variables were selected from the 19 WorldClim variables [27] available at WorldClim database . Following Sarkar et al . [7] , four climatic variables were eliminated from the analysis since these variables have presumed artifactual discontinuities for Texas ( mean temperatures of the wettest quarter , driest quarter , warmest quarter , and coldest quarter; Table 1 ) . These climatic variables have a resolution of approximately 1×1 km2 ( more accurately , 30 arc-seconds ) . Twenty models were developed and evaluated via cross-validation per species . The final model presented is the average of the replicates . Model results were processed and visualized using ArcGIS 10 . For the future climate projections we used three GCMs: the Canadian Centre for Climate Modelling and Analysis ( CCCMA ) , the Commonwealth Scientific and Industrial Research Organization ( CSIRO ) and the Hadley Centre for Climate Change ( HADCM3 ) . We used two scenarios of climate change , A2A and B2A , from the International Panel on Climate Change ( IPCC 2007 ) . Both scenarios assume a more heterogeneous world and are oriented toward regionalization . The A2A scenario assumes an increase in population , economic development , regionally oriented and per capita economic growth and technological change that is more fragmented than the scenario B2A . The focus of this scenario is more economic . On the other hand , the B2A scenario describes a world in which the emphasis is on local solutions to economic , social and environmental sustainability . It assumes a constant increase of population , but at a rate lower than A2A and intermediate levels of economic development as well . This scenario is oriented towards environmental protection and social equity . We calculated the Area Under the Curve ( AUC ) of Receiver Operating Characteristic plots ( ROC ) ; [28] to evaluate the models by cross-validation of the 20 replicates using the training and test data as described above . Receiver Operating Characteristic is a threshold–independent measure that evaluates the sensitivity ( probability that the model produces a positive result in a positive locality ) versus the specificity ( probability that the model produces a negative result in a negative locality ) of a model when presented with new data . A ROC plot is obtained by plotting the sensitivity on the y–axis versus one minus specificity for all available decision thresholds on the x–axis . The theoretically perfect result is AUC = 1 , whereas a test performing no better than random yields AUC = 0 . 5 . The AUC was calculated internally by Maxent . The final AUC is the average AUC for all maps . The averaged habitat suitability spatial distributions were converted into binary maps for further analysis using two thresholds: a “minimum training presence threshold” and a 0 . 5 habitat suitability threshold . A “minimum training presence threshold” is a threshold in which at least one known presence for the target species was found; therefore it guarantees that all presences are predicted as suitable [29] . Shifts on suitable habitat were calculated in km2 . Percentage of change in suitable habitat comparing present and future projections was calculated using the formula ( ( future gain - future loss ) *100 ) /present area .
A total of 84 unique geo-referenced localities , i . e . , one locality per cell , were used to develop models of present and future suitable habitat for Triatoma gerstaeckeri and 24 for T . sanguisuga ( Tables S1 and S2 ) . Table 2 shows AUC values . For T . gertaeckeri the averages AUC were 0 . 9857 ( SD = 0 . 0015 ) and 0 . 9738 ( SD = 0 . 0279 ) for training and testing data , respectively; for T . sanguisuga the corresponding numbers were 0 . 9680 ( SD = 0 . 0026 ) and 0 . 9323 ( SD = 0 . 0982 ) . Figures 1 and 2 show models of present and future distributions for both species . Models of future distribution for the suitable habitat of T . gerstaeckeri show a shift to northern areas in USA , with projected suitable habitat in Michigan and in New York ( Fig 1B-E ) . However , distributional shifts northward showed marked differences in habitat suitability between different climate change models and scenarios . For example , CCCMA-A2A and CCCMA-B2A models showed wide regions of unsuitable habitat between extant distributions and future northward shifts ( Fig 1B–C ) . Conversely , CSIRO-A2A and CSIRO-ABA models showed contiguous suitable habitat between extant distribution and future northward shifts ( Fig 1D–E ) . No shifts were observed between extant and future distributions with HADCM3_A2A and HADCM3_B2A models ( Fig 1F–G ) Increases in future suitable habitat can be also observed for T . sanguisuga through the northeast and northwest of the USA . In all models , north-east shifts showed contiguous habitat suitability . This was not the case for future northwest shifts , where regions of unsuitable habitat were observed between extant and future shifts , except for the CCCMA-A2A model ( Fig 2B ) . In just one model , CCCMA-A2A , the suitable habitat for this vector extended to Florida ( Fig 2B ) . For this species , a shift of suitable habitat to South Texas ( Lower Rio Grande Valley ) and North Mexico in the State of Tamaulipas is observed using the HADCM3 model for both A2A and B2A ( Fig . 2F–G ) scenarios of the IPCC , while the CCCMA and CSIRO models ( Fig . 2B–E ) showing lower suitability habitat compared with the model of present distribution for this region ( South Texas-northern Mexico ) ( Fig . 2A ) For both triatomine species , the variable that contributed the most to the distribution of the species was annual mean temperature ( Figs . 1-H and 2-H ) . The minimum training presence threshold value for T . gerstaeckeri was 0 . 017 and for T . sanguisuga 0 . 068 . For T . gerstaeckeri , the 0 . 5 threshold predicted loss on suitable habitat in 2050 compared with the minimum presence threshold for climatic change scenarios , A2A and B2A , and the three general circulation models ( CCCMA , CSIRO , and HADCM3 ) ( Table 3 ) . For T . sanguisuga , both thresholds predicted an expansion of the suitable habitat by 2050 ( Table 3 ) .
Although we acknowledge several important shortcomings discussed below , our study emphasizes one issue that has not been previously considered: the importance of climate change in the transmission of T . cruzi . The transmission of T . cruzi includes several vectors and hosts in domestic , peri-domestic , and sylvatic cycles . Trypanosoma . cruzi has three infective forms capable of infecting its host , and currently 6 DTUs ( discrete typing units ) are recognized in the taxon . These DTUs establish with mammalian hosts peculiar interactions in distinct time-space scales . Thus , the transmission of T . cruzi is a complex system for its non-linearity , unpredictability and also for being multivariable . Ideally , the potential distribution of most hosts should be included in the modeling exercises . We know relatively little about which mammal species are confirmed hosts of T . cruzi . To include simply a large list of mammals into the modeling approach without the certainty of being confirmed hosts of this parasite will add confusion into our understanding of this crucial biotic interaction . More studies are needed to produce a comprehensive list of confirmed hosts for T . cruzi as well as time-space scales for the operative interactions of hosts , vectors , and parasites . Novel modeling techniques developed to provide a predictive list of potential hosts for other emerging diseases , such as leishmaniasis [40] , can be applied for T . cruzi . Landscape and ecotypic scenarios under climate change are also needed to refine distribution shifts of species at finer spatial scales . This information should be associated with data on the salient features of landscape diversity , roles of extant members of regional mammalian faunas , local cultural , social and economic diversity , as well as the land use practices . This information will provide a more comprehensive understanding of the complexity in the transmission of T . cruzi . | Chagas disease kills thousands of people annually . Triatomine insects ( family Reduviidae , sub-family Triatominae ) , can be potential vectors of the parasite ( Trypanosoma cruzi ) that causes the disease . There are often no symptoms until cardiac and digestive system dysfunction ( possibly including heart failure ) after 10 to 30 years of infection . Climate change can shift the distribution of triatomine insects , favoring the spread of the disease to non-original areas . We used distributional information on the most commonly found triatomine species and the most important vectors of Trypanosoma cruzi in South Texas and North Mexico ( T . gerstaeckeri and T . sanguisuga ) , and explanatory climatic variables to forecast the potential distribution of the insects in the year 2050 . We used two different scenarios of climate change and three different general circulation models . Our results showed that the triatomine species studied will likely shift their distribution northwards in the future . There is thus a need to monitor areas that are not currently endemic for Chagas disease but may potentially be affected in the future due to climate change . | [
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] | 2014 | Projected Future Distributions of Vectors of Trypanosoma cruzi in North America under Climate Change Scenarios |
The type of food source has previously been shown to be as important as the level of food intake in influencing lifespan . Here we report that different Escherichia coli food sources alter Caenorhabditis elegans lifespan . These effects are modulated by different subsets of sensory neurons , which act with nmur-1 , a homolog of mammalian neuromedin U receptors . Wild-type nmur-1 , which is expressed in the somatic gonad , sensory neurons , and interneurons , shortens lifespan only on specific E . coli food sources—an effect that is dependent on the type of E . coli lipopolysaccharide structure . Moreover , the food type-dependent effect of nmur-1 on lifespan is different from that of food-level restriction . Together our data suggest that nmur-1 processes information from specific food cues to influence lifespan and other aspects of physiology .
The sensory systems of Caenorhabditis elegans and Drosophila melanogaster have been shown to modulate the lifespan of these animals [1]–[4] . This sensory influence involves subsets of gustatory and olfactory neurons [2] , [3] that either shorten or lengthen lifespan , which suggests that ( i ) some of the cues that affect lifespan are food-derived and that ( ii ) these cues can exert different effects on lifespan . Since a reduction in food levels can increase lifespan [5] , it is possible that the sensory system influences lifespan by simply regulating the animal's general food intake , and , indeed , the sensory system has been implicated in the lifespan effects of food-level restriction in Drosophila [3] . On the other hand , the sensory influence on lifespan , at least in C . elegans , can be uncoupled from the sensory effects on feeding rate , development , and reproduction [1] , [2] . Since the lifespan effect of food-level restriction has been linked to changes in feeding rates and decreased development and reproduction [5] , this suggests that the sensory system also affects lifespan through other mechanism ( s ) . The C . elegans hermaphrodite has 60 sensory neurons with dendrites that terminate in ciliated endings [6] . These specialized structures contain dedicated sensory receptors [7] , [8] and are thus the sites of recognition for different types of environmental cues , including gustatory , olfactory , thermal , and mechanical stimuli [9] . Within its natural environment , C . elegans encounters various types of bacteria that can serve as food sources . Similar to the sensory influence on lifespan , some of these food sources have been shown to alter lifespan independently of development and reproduction [10] . At the same time , not all but only a subset of food-sensing neurons influence the lifespan of C . elegans grown on the standard laboratory food source [2] , Escherichia coli OP50 [11] . Together these data raise the possibility that sensory neurons promote the lifespan effects of different food sources through a mechanism distinct from that of food-level restriction . In this study , we have investigated the role of the sensory system in the food-source influence on C . elegans lifespan and the signaling pathway ( s ) that might be involved in this process . We show that the C . elegans sensory system recognizes food types to affect longevity . We also identify ( i ) the neuromedin U receptor nmur-1 as a neuropeptide signaling pathway involved in this process and ( ii ) a food-derived cue , the E . coli lipopolysaccharide ( LPS ) structure , which elicits the nmur-1 response .
Wild-type C . elegans have altered lifespan on different E . coli strains ( Figure 1A and 1B ) . Indeed , we found that at 25°C the mean lifespan of wild-type worms is shorter on OP50 than on HT115 ( Figure 1A and 1B ) , another food source that is widely used [12] , [13] . To test the hypothesis that sensory perception contributes to these food source-dependent effects , we measured the lifespan of sensory mutants on OP50 and HT115 at this temperature . The gene daf-10 encodes an ortholog of an intraflagellar transport complex protein that is required for cilia formation in a subset of sensory neurons ( Table S1 ) [14] , [15] . We observed that the lifespan of daf-10 mutants is extended to the same extent ( 44% versus 46%; Table 1 ) compared to that of wild type when grown on either OP50 or HT115 ( Figure 1C ) , which suggests that some sensory neurons shorten lifespan independently of these two food sources . In contrast , worms that carry mutations in osm-3 , which encodes a kinesin motor protein required for cilia formation in a different subset of sensory neurons ( Table S1 ) [16] , [17] , live long relative to wild type only when grown on OP50 , but not when grown on HT115 ( Figure 1D; Table 1 ) . This implies that at least some of the osm-3-expressing neurons sense the lifespan-influencing difference ( s ) between these food sources . Since osm-3 functions in cilia structure formation rather than in directly sensing or translating food-derived cues , we searched for non-structural genes that would act with the sensory system to regulate the food source-dependent effects on lifespan . Candidate genes would include those encoding sensory receptors and downstream signaling molecules , like neuropeptides and their receptors , which help transmit or modulate sensory information . Unlike individual sensory receptors specific for single cues , a single downstream factor may affect the integration of several cues , which would make the effects of this class of genes more readily detectable . The C . elegans genome has more than 75 neuropeptide-like genes and more than 1 , 000 G-protein-coupled receptors , some of which function as neuropeptide receptors [9] , [18]–[21] . We focused on a subset of these genes based on the availability of mutations and on the evidence that their homologs in other animals regulate feeding and metabolism [19] , [21]–[25] . We compared the lifespan of the different mutants on OP50 and HT115 . While most neuropeptide signaling pathways had no effect on lifespan on the two food sources tested ( Table S2 ) , we found that animals carrying the deletion mutation ok1387 within the gene C48C5 . 1 live long on OP50 but not on HT115 ( Figure 2A and 2B; Tables 1 , S2 , and S3 ) . C48C5 . 1 is predicted to encode a seven-transmembrane neuropeptide receptor ( Figure S1 ) with homology to mammalian neuromedin U receptors ( NMURs ) , whose peptide ligand , neuromedin U ( NMU ) , has been shown to regulate food intake [24] . We renamed C48C5 . 1 as nmur-1 , since our study makes it the first phenotypically characterized member of the worm NMUR family , of which there are at least three other members—nmur-2 ( K10B4 . 4 ) , nmur-3 ( F02E8 . 2 ) , and nmur-4 ( C30F12 . 6 ) . As a confirmation that the wild-type function of nmur-1 is to shorten lifespan in a food source-dependent manner , we were able to rescue the long-life phenotype of the nmur-1 mutation on OP50 with the wild-type nmur-1 genomic locus , without shortening lifespan on HT115 ( Figure 2G; Tables 1 and S3 ) . Next , we asked whether sensory neurons regulate the food source-dependent effects on lifespan through nmur-1 . We found that loss of nmur-1 still considerably increases the lifespan of daf-10 sensory mutants on OP50 ( Figure 3A; Table 1 ) , which indicates that nmur-1 acts in parallel at least to some daf-10-expressing neurons . Surprisingly , loss of nmur-1 extends the lifespan of daf-10 mutants also on HT115 ( Figure 3B; Table 1 ) , which may suggest that the lifespan of nmur-1 mutants becomes food source-independent in the absence of daf-10 activity . Thus , nmur-1 appears to be subject not only to activation by certain environmental cues but also to inhibition by others . In contrast , animals that carry both nmur-1 and osm-3 mutations have a lifespan phenotype similar to that of nmur-1 single mutants on OP50 and HT115 ( Figure 3C and 3D; Table 1 ) . This suggests that nmur-1 acts with osm-3 either in a subset of osm-3-expressing sensory neurons or in downstream cells . We observed expression of a gfp reporter for nmur-1 in the spermathecae of the somatic gonad , in several different types of sensory neurons , some of which co-express osm-3 ( Table S1 ) [16] , and in interneurons ( Table 2 ) , some of which receive inputs from , or modulate the activity of , osm-3-expressing sensory neurons [6] . This expression pattern , together with the genetic interaction between the mutations in nmur-1 and osm-3 , suggests that nmur-1 plays a role in the processing of sensory information derived by the worm from various food sources . We then explored the possible differences between OP50 and HT115 , which might be recognized by the worm . OP50 is derived from an E . coli B strain [11] , whereas HT115 is from an E . coli K-12 strain [26] , [27] . To determine whether nmur-1 affects lifespan only on B strains but not on K-12 strains , we measured the lifespan of nmur-1 mutants on other bacteria derived from these two lineages . Interestingly , we found that nmur-1 mutants live long consistently on the B strain BL21 [28] and on HB101 ( Figure 2C and 2E; Tables 1 and S3 ) , a K-12 strain that contains a large stretch of B strain genomic DNA [29] . In contrast , the nmur-1 long-life phenotype is absent on another K-12 strain , DY330 [30] , and only occasionally present on the K-12 strain DH5α ( Figure 2D and 2F; Tables 1 and S3 ) [31] . Together these data suggest that nmur-1 affects lifespan in a largely B strain-dependent manner . Although the B and K-12 strains clearly would have many differences , one of the few well-characterized molecular differences between these strains lies in the LPS structures ( Figure 4A ) on their outer membranes [32]–[34] . Since the LPS of the K-12 strain [33] , [34] has a longer outer core than the LPS of the B strain [32] , we tested whether LPS structure influences lifespan . We compared wild-type and nmur-1 mutant worms on E . coli K-12 mutants that have truncated LPS to worms grown on the corresponding K-12 parent strain . We found that wild-type worms live shorter on the LPS truncation mutants CS2198 and CS2429 [35] , [36] than on the isogenic parent strain CS180 ( Tables 1 and S3 ) , which expresses wild-type K-12 LPS [35] . On the other hand , nmur-1 mutants live long compared to wild type only on the LPS truncation mutants ( Figure 4C and 4D; Tables 1 and S3 ) , but not on the K-12 parent strain ( Figure 4B; Tables 1 and S3 ) . To exclude the possibility that all changes to the LPS will elicit the nmur-1 response , we also measured the lifespan of worms grown on the K-12 strain CS1861 that expresses the Shigella dysenteriae 1 O Antigen fused to the end of the full-length K-12 LPS [36] . We observed no lifespan difference between wild type and nmur-1 mutants on this strain ( Figure 4E; Table 1 ) . Together our data suggest that a short E . coli LPS structure can shorten worm lifespan in an nmur-1-dependent manner . In contrast to nmur-1 , we found that osm-3 can affect lifespan independently of the E . coli LPS structure ( Figure 4F and 4G; Table 1 ) , which indicates that at least some of the osm-3-expressing neurons detect other food-derived cues . However , even on the CS180 and CS2429 bacterial food sources , nmur-1 and osm-3 appear to act together in influencing lifespan , since osm-3; nmur-1 double mutants have the same lifespan phenotype as osm-3 or nmur-1 single mutants ( Figure 4F and 4G; Table 1 ) . Food type [37] and sensory neurons [3] , [38] have been shown to mediate the lifespan extension induced by dietary restriction ( DR ) , which is commonly studied through restriction of food levels . Thus , the food type-dependent effects on lifespan we observe might reflect different levels of DR experienced by wild-type and mutant worms on the various food sources . To address this possibility , we measured the feeding rates , speed of development , total progeny , and the rates of reproduction of wild-type and nmur-1 mutant worms on five different E . coli strains ( Figures S2 and S3 ) , since restricting food levels is known to change these parameters [5] . For comparison , we used a genetic model for food-level restriction [39] , a mutation in eat-2 that impairs pharyngeal function [40] , which leads to decreased feeding rates on both OP50 and HT115 ( Figure S4A ) . Unlike the nmur-1 mutation , we found that the eat-2 mutation increases lifespan on both food sources ( Figure S4C ) , which suggests that the food-type effects of nmur-1 are not the same as those of food-level restriction . Moreover , we observed no correlation between lifespan and feeding rates or lifespan and development of wild-type or nmur-1 mutant worms on the different food sources ( Figures 5A , 5B , S2A , S2B , S2C , S2D , S3A , and S3B ) , which is also unlike the reported effects of restricting food levels [5] . As expected for a genetic model for food-level restriction , we found that the lifespan extension conferred by the eat-2 mutation is accompanied by a decrease in total progeny on OP50 and HT115 ( Figures 5C and S4B ) . Surprisingly , we also found that wild-type worms grown on different food sources do exhibit an inverse correlation between lifespan and number of progeny but that nmur-1 mutants can still live long without a proportionate decrease in total progeny ( Figures 5C , S2E , and S3C ) . This suggests that the food source-dependent effects on lifespan have reproduction-dependent and reproduction-independent components , the latter of which is uncovered by the nmur-1 mutation . Interestingly , we also observed that food sources that increase wild-type lifespan induce the animals to reproduce faster ( Figure 5D ) , which not only differs from eat-2 mutants ( Figure 5D ) but is also the inverse of the effects shown for food-level restriction on rates of reproduction [5] , [41] . At the same time , we again saw no correlation between the nmur-1 mutant lifespan and its rates of reproduction on the different E . coli strains ( Figures 5D , S2F , and S3D ) . Since our data show that the effects of eat-2 on C . elegans physiology differ from those of nmur-1 or the different food sources , this suggests that the effects of the food sources and nmur-1 on lifespan can be distinct from food-level restriction . Consistent with this idea , we observed that , unlike long-lived , food level-restricted animals that have decreased lipid storage [5] , nmur-1 mutants do not exhibit gross changes in fat storage compared to wild type on either OP50 or HT115 ( Table S4 ) . Next , we asked whether nmur-1 acts through the insulin/IGF-1 daf-2 pathway [42] , which has been shown to mediate a large part of the sensory influence on lifespan [1] . For example , the increased lifespan of osm-3 sensory mutants on OP50 has been shown to be partly dependent on daf-16 [1] , a FOXO transcription factor that acts downstream of and is negatively regulated by daf-2 [43]–[45] . Accordingly , we found that removing nmur-1 does not significantly increase the lifespan of insulin/IGF-1 receptor daf-2 mutant worms ( Figure 6A and 6B; Table 1 ) , but loss of nmur-1 can still extend the lifespan of worms carrying a null mutation in daf-16 ( Figure 6C; Table 1 ) . Thus , our data suggest that nmur-1 , like at least some osm-3-expressing neurons , acts either with daf-2 but at least partly independently of daf-16 , or in parallel to the daf-2/daf-16 pathway . To identify other factors required for nmur-1 to affect lifespan , we tested how removal of nmur-1 would affect the short lifespan caused by mutations in genes proposed to act independently of daf-16 [46]–[48] . We found that loss of nmur-1 can still extend the lifespan of animals with a mutation in either ( i ) the AMP-dependent kinase aak-2 ( Figure 6D; Table 1 ) , which regulates energy metabolism [46]; ( ii ) the heat shock transcription factor hsf-1 ( Figure 6E; Table 1 ) , which regulates stress response [47] , [49] , [50]; or ( iii ) the p38 MAPK pmk-1 ( Figure 6F; Table 1 ) , which regulates innate immunity [48] , [51] . Although none of these factors appears essential for nmur-1 function , we did observe partial suppression of the nmur-1 phenotype in the hsf-1 mutant background . This could suggest that nmur-1 affects lifespan by acting through several parallel pathways that include hsf-1 and/or daf-16 .
If food-derived cues alter lifespan through the sensory system , then it is likely that impairment of a specific set of sensory neurons that detect a given set of cues would affect lifespan only on some food sources . In this study , we provide a detailed investigation of the interdependence between food and sensory perception in regulating C . elegans longevity . We show not only that wild-type lifespan is modulated by different E . coli food sources ( Figure 1A and 1B ) but also that three genes , which have been shown to be expressed and/or act in sensory neurons , have food source-dependent effects on lifespan . Mutations in two of these genes—osm-3 and nmur-1—increase lifespan on OP50 but not on HT115 ( Figures 1D and 2; Tables 1 and S3 ) . Since the effects of these mutations are non-additive ( Figure 3C and 3D; Table 1 ) , this suggests that osm-3 and nmur-1 influence lifespan through a common mechanism . On the other hand , a mutation in the third gene , daf-10 , not only extends lifespan on both OP50 and HT115 ( Figure 1C; Table 1 ) but also alters the food type-dependence of the nmur-1 effect on lifespan ( Figure 3A and 3B; Table 1 ) . Together with their requirement in the formation of the sensory cilia in subsets of neurons [15] , [16] , the osm-3 and daf-10 data are consistent with a role for sensory perception in the food source-dependent effects on lifespan . In addition , the identification of a neuropeptide receptor gene , nmur-1 , that interacts with osm-3 and daf-10 ( Figures 3 , 4F and 4G; Table 1 ) suggests a mechanism through which the sensory system mediates the effects of specific food cues on lifespan . The nmur-1 expression in sensory neurons and interneurons ( Table 2 ) suggests that nmur-1 modulates the transduction of signals downstream of the sensory receptors . Based on the observed interactions among these three genes , we propose the following model: ( i ) osm-3-expressing sensory neurons detect the presence of certain food-derived cues and transmit this information through an nmur-1-dependent pathway , and ( ii ) a different set of daf-10-expressing neurons detects other food cues , some of which inhibit nmur-1 activity . According to this model , the expression patterns ( Table S1 ) of osm-3 [16] and daf-10 [15] should help define the candidate sensory neurons that might recognize the food cues that shorten or extend lifespan through nmur-1 . daf-10 is necessary for proper cilia morphology in the mechanosensory CEP neurons and some unidentified neurons in the head and tail sensory organs called the amphids and phasmids , respectively [15] . Several amphid neurons also express osm-3 [16]: these include two pairs of gustatory neurons , ASI and ASG , that have been found to shorten lifespan on OP50 [2] , and two other gustatory neuron pairs that co-express nmur-1—ADF , which by itself has no lifespan effect on OP50 [2] , and ADL . In addition , osm-3 is expressed outside of the amphid organs in the IL2 inner labial head neurons and in the phasmid tail neurons [16] , all of which have been proposed to have chemosensory function [15] , [52] . Our discovery of a food source-dependent function for the C . elegans nmur-1 gene is consistent with the known food-associated activities of other members of the NMUR signaling pathway in mammals [24] , [53] and insects [54] , [55] . In mammals , NMUR2 , the receptor isoform expressed in the central nervous system , and its ligand , the octapeptide NMU-8 , have been implicated in the regulation of food intake and energy expenditure [24] , [53] . In Drosophila , the gene hugin encodes two of the peptide ligands , PK-2 and HUG-γ , recognized by two of four NMUR isoforms [54]–[56] . hugin regulates not only the food-seeking behavior and feeding rate of larvae but also affects the rate of food intake of adult flies in a food type-dependent manner [54] . Like hugin , we find that nmur-1 exerts food-type specific effects on feeding rate ( Figure S2A and S2C ) , although the nmur-1 regulation of this process appears to be parallel to its regulation of lifespan ( Figure 5A ) . Similar to the neuronal expression of nmur-1 , Drosophila hugin is expressed in interneurons that appear to relay gustatory information [54] . At present , a potential role for the fly or mammalian NMUR signaling pathways in the regulation of lifespan has not been reported . However , the evolutionary conservation of several aspects of NMUR signaling leads us to speculate that the effects on lifespan by this system might also be conserved across species . The Drosophila NMU signaling system also includes a second neuropeptide precursor gene , capability , that encodes three other peptide ligands , CAPA-1 , CAPA-2 , and CAPA-3 ( also called PK-1 ) , that can activate three of the fly NMUR isoforms [56] . The C . elegans homolog of capability , nlp-44 , has recently been identified [57] . Like capability , it is predicted to give rise to three peptides , one of which activates the receptor encoded by nmur-2 [57] . A mutation of nmur-2 gives no lifespan phenotype on the food sources we have tested ( Table S2 ) , but it will be interesting to determine whether peptides derived from nlp-44 can also activate NMUR-1 . A role of nmur-1 in the sensory influence on lifespan is supported by its expression in a number of sensory neurons and interneurons ( Table 2 ) . However , it remains possible that sensory cues regulate nmur-1 activity at the level of the somatic gonad , which is the only non-neuronal tissue that expresses the nmur-1 reporter gene ( Table 2 ) . At the same time , the expression of nmur-1 in a relatively large number of cells also makes it likely that the parallel effects of nmur-1 on lifespan , feeding rate , development , and reproduction ( Figures 5A–5D , S2 , and S3 ) are mediated by its activity in different subsets of cells . The food source-dependent activities of nmur-1 raise the possibility that other neuropeptide signaling pathways—many of which are associated with the sensory system [18]–[20] , [25] —will also affect lifespan or other aspects of physiology only under specific conditions . Although most of the neuropeptide signaling pathways we have screened so far on two food sources show no effect on lifespan ( Table S2 ) , it remains possible that they will have effects on other food types . Thus , the large repertoire of neuropeptides and their receptors in C . elegans might serve to translate environmental complexity into appropriate physiological responses . We find that wild-type worms live shorter on the E . coli B strains BL21 and OP50 than on K-12 strains , like HT115 and DY330 ( Figure 1A and 1B ) . Conversely , the nmur-1 mutation causes reproducible lifespan extensions on the B strains but not on the K-12 strains ( Figure 2; Tables 1 and S3 ) . Since B and K-12 strains differ in their LPS structure , we have tested the lifespan effects of specific alterations in the K-12 LPS that mimic aspects of the B strain LPS ( Figure 4A ) . Although the effect of LPS on wild-type lifespan is not large , wild-type worms do live longer on full-length than on truncated forms of the K-12 LPS ( Tables 1 and S3 ) . We also find that the nmur-1 effect on lifespan is LPS-dependent and suppressed by full-length K-12 LPS but not by its truncated versions ( Figure 4; Tables 1 and S3 ) . Although the LPS experiments were carried out in isogenic bacterial backgrounds , the effects of the LPS alterations might be indirect since they could lead to secondary changes in bacterial metabolism or surface structure . Indeed , LPS truncations have been shown to interfere with the expression of outer membrane proteins , increase capsule polysaccharide levels , and redistribute phospholipids from the inner to the outer leaflet of the outer membrane ( [58] and references therein ) . However , these secondary changes have only been observed with mutations that disrupt the inner core of the LPS , like the mutation present in the CS2429 strain ( Figure 4A ) , and thereby compromise the integrity of the outer membrane [58] . No such effects have been reported for truncations that affect only the LPS outer core , like the mutation in CS2198 ( Figure 4A ) . Thus , the observation that nmur-1 extends lifespan on both CS2429 and CS2198 argues for a direct effect of the bacterial LPS on worm lifespan . Direct recognition of LPS is biologically plausible: LPS is the predominant component of the outer membrane of gram-negative bacteria and is consequently used by multicellular organisms from diverse phyla to recognize bacteria in the context of defense against pathogens [59] , [60] . Nevertheless , the LPS structure is clearly only one of potentially many food-derived cues that influence worm lifespan . This is most evident from the LPS-independent lifespan phenotype of osm-3 mutants ( Figure 4F and 4G; Table 1 ) , and from the fact that the lifespan extension by the nmur-1 mutation is greater on OP50 than on any other strain with a similar , short LPS ( Figure 2; Tables 1 and S3 ) . Thus , changes in lifespan are likely triggered by different sets of sensory neurons in response to a variety of food-derived cues , and loss of nmur-1 interferes with the detection of several of these cues . The LPS dependence of the nmur-1 phenotype makes it conceivable that nmur-1 may regulate stress-related and innate immune responses elicited by different food sources . We find that nmur-1 can still affect lifespan in the absence of either of three genes , daf-16 , hsf-1 , and pmk-1 , all of which have major roles in stress responses and innate immunity [47]–[51] , [61]–[63] . However , the mutations in daf-16 and hsf-1 can partly suppress the nmur-1 lifespan phenotype ( Figure 6; Table 1 ) , which makes it possible that the nmur-1 influence on lifespan requires a combination of mechanisms that involve daf-16 , hsf-1 , and/or other factors . We find that the food-source influence on wild-type lifespan is strongly correlated with reproductive effects ( Figure 5C and 5D ) , in that increases in lifespan are accompanied not only by a decreased number of total progeny but also a faster rate of reproduction . One possible interpretation of these data is that the different reproductive profiles cause the food source-dependent differences in wild-type lifespan . Indeed , with the exception of BL21 , the bacterial diets we have tested seem to affect initial survival more than late-age survival . This is supported by age-specific force of mortality plots ( Figure S5A ) : the different food sources alter wild-type mortality primarily before day 10 of adulthood but have little effect thereafter . It is conceivable that damage inflicted on somatic tissues [64] or neglect of somatic maintenance and repair during reproduction [65] are important determinants of early mortality . In agreement with this idea , we find that long-lived glp-1 mutant worms [66] , which are sterile because they generate few or no germ cells [67] , have very similar lifespan , at least on OP50 , HT115 , CS180 , and CS2429 ( W . M . , unpublished data ) . This suggests that the food type-dependent effects on wild-type lifespan are indeed germline-dependent . Interestingly , a recent study [68] has shown that different E . coli food sources can differentially affect fat storage in C . elegans . Wild-type worms grown on HB101 or HT115 are found to have lower triacylglyceride ( TAG ) levels than wild-type worms grown on OP50 [68] . Although that same study and another report [68] , [69] question the reliability of fat stains with vital dyes , we also observe a slightly reduced fat storage in wild type on HT115 compared to wild type on OP50 , using lipid labeling with a lipophilic fluorophore ( Table S4 ) . Thus , a correlation may exist not only between lifespan and reproduction but also between lifespan and TAG levels of wild-type worms . Since germline signals have been proposed to regulate both lifespan and intestinal fat storage [70] , the food-type and reproduction-dependent effects on wild-type lifespan may also be mediated by changes in TAG levels . In contrast , we find that nmur-1 exerts an additional effect on lifespan that is largely independent of reproduction ( Figure 5C and 5D ) and also appears to be independent of glp-1 on OP50 and CS2429 ( B . A . and W . M . , unpublished data ) and fat storage on OP50 and HT115 ( Table S4 ) . Accordingly , the nmur-1 mutation can affect mortality prior to day 10 of adulthood ( OP50 and CS180; Figure S5B ) on the food sources that significantly reduce the total progeny of nmur-1 mutants ( compare OP50 and CS180 in Figures 5C , S2E , and S3C ) . At the same time , nmur-1 mutants show reduced mortality after day 10 , but not past day 16 , of adulthood on the short LPS strains OP50 and CS2429 ( Figure S5B ) , the latter of which has no effect on the nmur-1 mutant number of progeny ( Figure S3C ) . Thus , our findings imply that food sources affect lifespan through both reproduction-dependent and reproduction-independent mechanisms , with the second being uncovered by the nmur-1 mutation . Unlike the longevity-promoting effect of food-level restriction [5] , [41] , the food type-dependent effects on lifespan that we observe not only have reproduction-independent and fat storage-independent components ( Figure 5C and 5D; Table S4 ) but are also independent of alterations in feeding rate and developmental rate ( Figure 5A and 5B ) . In addition , our data show that different food types and nmur-1 affect initial mortality without decreasing late-age mortality ( Figure S5 ) , again unlike food-level restriction , which decreases the slope of the mortality trajectory and thus slows the rate of aging [71] . These data lead us to propose that these two forms of dietary influence on lifespan employ distinct , but possibly overlapping , mechanisms . Another recent study [72] has shown that different DR regimens for C . elegans require different signaling pathways to affect lifespan . However , some of these regimens altered not only food levels but also the nature of food sources . In fact , at least one of these protocols , which lowered protein levels , does not decrease but increase reproduction ( [72] and references therein ) , which suggests that the lifespan effect of protein restriction , unlike that of other DR protocols , could be partly reproduction-independent . Our data might help explain some of these findings , if one assumes that the net consequence on lifespan of some DR protocols represents a mix of independent effects from food-level restriction and food-type dependence . In the future , it would be of interest to determine whether the food type-dependent effects on lifespan will also require the activities of genes , e . g . , the NFE2-related protein skn-1 [38] and the FOXA transcription factor pha-4 [73] , that have been implicated in the longevity-promoting effects of DR .
All worm mutant strains used in this study were backcrossed six times to our lab wild-type ( N2 ) strain , with the exception of nmur-1 ( ok1387 ) , which was backcrossed eight times , and eat-2 ( ad1116 ) , which was outcrossed once , before generation of different mutant combinations and any phenotypic analysis . The different worm mutant alleles used are indicated within the figures , supplementary tables , and their legends . Worms were grown for at least two generations at 25°C on the same food source used in a given phenotypic analysis , unless otherwise stated . The E . coli strains used were: OP50 [11] , HT115 [rnc14::ΔTn10 λ ( DE3 ) of W3110] [13] , [26] , [27] , BL21 ( DE3 ) [28] , DY330 ( DE3 ) [Δ ( argF-lacZ ) U169 gal490* ( IS2 ) pglΔ8 rnc<>cat λcI857 Δ ( cro-bioA ) of W3110] [30] , HB101 [29] , DH5α [31] , CS180 [rfa+] [35] , CS2198 [rfaJ19::Tnlac Δlac pyrD+ of CS180] [35] , CS2429 [rfaC− of CS180] [36] , and CS1861 ( CS180 transformed with a plasmid that confers chloramphenicol resistance and encodes the proteins required for the expression of Shigella dysenteriae 1 O Antigen fused to the parent strain K-12 LPS ) [36] . We generated two independent rescue lines using standard methods: nmur-1 ( ok1387 ) ; jxEx12[nmur-1p::nmur-1+myo-3p::rfp] and nmur-1 ( ok1387 ) ; jxEx40[nmur-1p::nmur-1+myo-3p::rfp] . The rescue fragment , which is a 7 . 96 kb-long PCR fragment of the wild-type nmur-1 genomic locus ( injected at 100 ng/µl ) , includes the 2 . 9 kb sequence upstream of the nmur-1 start codon and the 1 kb sequence downstream of the correct stop codon ( see Figure S1 ) . The myo-3p::rfp ( gift of Cori Bargmann ) was used as a coinjection marker ( injected at 100 ng/µl ) . As controls , we also generated wild-type and nmur-1 mutant worms that carry the myo-3p::rfp coinjection marker alone . We observed that the extrachromosomal array jxEx12 has a large number of arrested embryos and larvae , whereas the extrachromosomal array jxEx40 produces ∼13% arrested larvae ( 25 arrested worms/196 total worms ) . These additional phenotypes might be due to a hyperactive NMUR-1 pathway caused by overexpression of the gene from its extrachromosomal copies . To determine the expression pattern of nmur-1 , we generated a transcriptional gfp reporter construct ( nmur-1p::gfp; based on the pPD117 . 01 vector; gift from A . Fire ) , in which the gfp is flanked by the 2 . 9 kb sequence upstream of the nmur-1 start codon and by the 1 kb sequence downstream of the correct stop codon , including the newly identified 3′ UTR ( see Figure S1 ) . In addition , sequences from the four largest introns , 1 , 4 , 8 , and 10 , which may contain regulatory sequences required for expression , were fused downstream of the 1 kb 3′ cis sequences . This construct was injected into wild-type worms at a concentration of 100 ng/µl , and two independent transgenic lines , jxEx36 and jxEx37 , were recovered , which show identical patterns of gfp expression . All bacterial strains were grown from single colonies in Luria-Bertani medium overnight at 37°C . However , the medium used to grow the chloramphenicol-resistant strain CS1861 was supplemented with 100 µg/ml chloramphenicol . Nematode-growth agar plates ( 6 cm in diameter; [11] ) were seeded with 100 µl bacterial culture and were allowed to dry at room temperature ( 23°C ) . Seeded plates were stored at room temperature and used within 5 d . The survival analyses of all worm strains on the different bacteria were initiated on the first day of adulthood and carried out at 25°C . Throughout their reproductive period , the worms were transferred daily to new plates to separate them from their progeny . We used the JMP 5 . 1 ( SAS ) software to determine Kaplan-Meier estimates of survival probabilities and mean lifespan , and for all statistical comparisons . p values were determined by the logrank and Wilcoxon tests . The logrank test , which places more weight on larger survival times , is appropriate when comparing differences between groups of animals whose ratio of hazard functions ( ratio of mortality rates ) stays approximately constant over time [74] . However , when the hazard ratios do not stay constant with time , as when one survival curve shows more early deaths than another ( e . g . , wild type on OP50 versus wild type on HB101 or HT115 in Figure 1A and 1B ) , the Wilcoxon test is more appropriate for comparing differences between groups [74] . We found that the Wilcoxon test is more sensitive to the lifespan differences we see in most of our experiments , since the nmur-1 mutation and most bacterial food sources clearly affect mean lifespan more than the maximum lifespan , which in fact violates the logrank test assumption of constant hazard ratios . Here we refer to a Wilcoxon p value of ≤0 . 01 as a significant difference between the various groups of animals . For comparison , we report both the Wilcoxon and logrank test results in all tables . For mortality plots , the age-specific force of mortality was calculated as Fx = −ln ( 1−Dx ) , where Dx is the probability of death between day x−1 and x of adulthood [75] . At least five independent trials of a given lifespan experiment were used to calculate means and standard errors of Fx , which were plotted on a log scale against age . Feeding rates were determined on the first and fourth days of adulthood at 25°C by measuring the animals' pharyngeal pumping rates , which reflect the rates at which they eat bacteria [76] . The pumps of the pharyngeal bulbs of individual worms were counted 3 to 5 times over periods of 30 s . Each resulting mean value was then doubled to get “pumps per minute . ” A two-way ANOVA test was used to compare the different genotypes on different food sources and p values were calculated with the Tukey post-test . Developmental rate differences were determined through a population-based assay at 25°C . First-stage ( L1 ) larvae that had hatched within a 2-h time window were collected and allowed to develop for 36 . 5 h . At this point , the number of second-stage ( L2 ) , third-stage ( L3 ) , and fourth-stage ( L4 ) larvae , as well as of young adult ( YA ) or gravid adult ( GA ) worms were counted . The chi-square test was used to compare the resulting stage distributions across food sources or worm genotypes . Total progeny and temporal profiles of egg-laying were determined at 25°C by culturing L4 larvae singly on plates of the appropriate food source . The worms were then transferred to new plates regularly until they stopped laying eggs . The eggs were allowed to hatch and the larval progeny were then counted . Two-way ANOVA and the Tukey post-test were used to compare the total number of progeny of genotypes across food sources . To ensure that the data followed a normal distribution , it was necessary to incorporate a statistical censoring procedure to exclude outliers ( worms with a very low number of progeny ) from the data set before the ANOVA test . Briefly , this involved the tentative identification of outliers and calculation of standard deviation ( SD ) for the remaining set . Then , from the full data set , we excluded worms that had produced less progeny than the mean minus 2 . 5 times SD . In general , this procedure led to exclusion of worms with a progeny number smaller than 90 , which corresponded to ∼4% of the total data set . The exception is nmur-1 mutant worms feeding on HB101 , for which two classes of worms seem to exist: one with a large number of progeny and another with a small number of progeny . In this particular case , censoring caused 25% of worms to be excluded from the analysis and the remaining data set to be biased considerably towards a larger progeny number , as can be seen in Figure S2E . The temporal profiles of egg-laying were determined from the same statistically censored populations of worms . The Hill function , P ( t ) = Pmax * tn/ ( tn+t50n ) , was used to fit the cumulative number of progeny over time , where t denotes time , Pmax is the total number of progeny , n the Hill coefficient , and t50 the time until half of the progeny is produced . In Figures S2F and S3D , the data were normalized to Pmax . For statistical assessments of correlations between mean lifespan and feeding , development , or reproduction on different food sources , we used the Pearson Product Moment test . To determine the correlation between lifespan and development , the stage distributions of the original data were used to calculate “speed of development” values , which are the percentages of worms scored as either young or gravid adults in the corresponding assays . eat-2 mutants have a value of zero on this scale because no mutant worms reached adulthood within 36 . 5 h after egg-laying . To correlate lifespan and rate of reproduction , the t50 of the fitted temporal reproduction profiles was used . | Work on the model organisms C . elegans and D . melanogaster has contributed important and often surprising insights into the factors that determine lifespan . One intriguing finding is that lifespan in both animals can be extended or shortened by interfering with the function of neurons that smell or taste food . Indeed , specific taste neurons in C . elegans are required for the lifespan extension due to the restriction of the animals' level of food intake , while certain olfactory neurons in Drosophila inhibit this effect . Here we provide evidence that the sensory system also alters lifespan in response to specific food types as opposed to different food levels . C . elegans that feed on different E . coli strains can have different lifespans , which is not only dependent on the activities of a subset of sensory neurons but can also occur independently of food level restriction . We also show that the neuropeptide receptor NMUR-1 acts with the sensory system to affect lifespan in a manner dependent on the bacterial lipopolysaccharide structure . Thus , we identify both a food-derived factor and a component of a signaling pathway involved in the food-type effects on worm lifespan . | [
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] | 2010 | A Neuromedin U Receptor Acts with the Sensory System to Modulate Food Type-Dependent Effects on C. elegans Lifespan |
Post-translational modification of proteins by small ubiquitin-related modifier ( SUMO ) is reversible and highly evolutionarily conserved from yeasts to humans . Unlike ubiquitination with a well-established role in protein degradation , sumoylation may alter protein function , activity , stability and subcellular localization . Members of SUMO-specific protease ( SENP ) family , capable of SUMO removal , are involved in the reversed conjugation process . Although SUMO-specific proteases are known to reverse sumoylation in many well-defined systems , their importance in mammalian development and pathogenesis remains largely elusive . In patients with neurodegenerative diseases , aberrant accumulation of SUMO-conjugated proteins has been widely described . Several aggregation-prone proteins modulated by SUMO have been implicated in neurodegeneration , but there is no evidence supporting a direct involvement of SUMO modification enzymes in human diseases . Here we show that mice with neural-specific disruption of SENP2 develop movement difficulties which ultimately results in paralysis . The disruption induces neurodegeneration where mitochondrial dynamics is dysregulated . SENP2 regulates Drp1 sumoylation and stability critical for mitochondrial morphogenesis in an isoform-specific manner . Although dispensable for development of neural cell types , this regulatory mechanism is necessary for their survival . Our findings provide a causal link of SUMO modification enzymes to apoptosis of neural cells , suggesting a new pathogenic mechanism for neurodegeneration . Exploring the protective effect of SENP2 on neuronal cell death may uncover important preventive and therapeutic strategies for neurodegenerative diseases .
Emerging evidence suggests the importance of protein modification by Small Ubiquitin-related Modifier ( SUMO ) in neural development and function [1]–[3] . Abnormal SUMO modification has been found in several neurodegenerative diseases , characterized by progressive loss or dysfunction of neurons [4]–[6] . Unlike ubiquitin with a well-established role in protein degradation , SUMO is involved in protein trafficking , cell proliferation and survival , as well as ubiquitin-mediated proteolysis [7]–[11] . Covalent conjugation of SUMO to protein substrates , also known as sumoylation , is a reversible process catalyzed by SUMO ligases [12] , [13] . The removal of SUMO , also known as desumoylation , is mediated by SUMO proteases [14] , [15] . Although these proteins have been shown to reverse sumoylation in various physiological systems , their roles in mammalian development and disease remain largely unknown . SUMO-specific protease 2 ( SENP2 ) is found in three alternatively spliced forms exhibiting differential subcellular localizations [16] . Genetic inactivation of Senp2 reveals its requirement in development of trophoblast stem cell niches and lineages during development of the placenta [17] . Although SENP2 mutants display embryonic defects including brain and heart abnormalities , they are likely associated with placental insufficiency which requires further investigation [17] , [18] . Enhanced sumoylation and accumulation of SUMO-conjugated proteins have been widely observed in patients with various neurodegenerative disorders [19]–[22] . Among the most notable ones are polyglutamine disorders , including Huntington's disease ( HD ) caused by a trinucleotide expansion , and neuronal intranuclear inclusion disease ( NIID ) . The encoded CAG expansions result in production of toxic proteins carrying extended glutamine repeats . In HD , SUMO1 conjugation of the disease protein Huntingtin ( Htt ) contributes to the disease pathology possibly by stabilizing the toxic Htt [20] . SUMO-modified targets/substrates also accumulate in the nuclear aggregates of NIID , a multisystem neurodegenerative disease characterized by large intranuclear inclusions in neurons of the central and peripheral nervous systems [21] . In autosomal recessive juvenile parkinsonism , the SUMO pathway might affect protein degradation mediated by the disease protein Parkin , an E3-ubiquitin ligase [23] . Targeting the SUMO pathway may offer new strategies for disease prevention and therapy . However , there is no evidence indicating a direct involvement of SUMO modification regulators/enzymes in neurodegenerative disease . Information providing a causal link of SUMO dysregulation to neural cell survival is also very limited .
We previously created a mouse strain carrying a null allele of SENP2 [17] . The knockout of SENP2 led to severe developmental abnormalities in trophoblast stem cell niches and lineages during placentation [17] . Although brain and heart deformities were also detected in the SENP2-null embryos ( Figure S1 , Maruyama et al . , unpublished , and [18] ) , we speculated these are secondary defects due to placental insufficiencies [17] . To analyze the involvement of SENP2 and the importance of SUMO modification in neural development and disease , we first examined its expression pattern . In situ hybridization detected the presence of SENP2 mRNA in the developing mouse brain at embryonic day 14 . 5 ( E14 . 5 ) and postnatal day 0 ( P0 ) , P7 and P14 ( Figure 1A ) . SENP2 was expressed in subventricular neural progenitors and differentiated cells of the cerebral cortex ( Figure 1A ) . To definitively assess our speculation on the contribution of placental deficiencies to the embryonic deformities , we took a genetic approach by creating a mouse model deficient for SENP2 during neural development . A new mouse strain carrying a SENP2ΔSUMOFx allele , permitting removal of the protease core domain using the Cre-loxP system , was generated ( Figure S2 ) . The presence of Cre caused an in-frame deletion , resulting in production of a SENP2 mutant deficient for the SUMO protease activity . Using EIIa-Cre to remove the protease core domain , we generated a mouse strain carrying SENP2ΔSUMO mutant allele expressing the truncated SENP2 ( Figure S3 ) . The SENP2ΔSUMOΔ/Δ embryos were significantly smaller or underdeveloped compared to their SENP2ΔSUMO+/+ and SENP2ΔSUMO+/Δ littermates at E10 . 5 ( Figure S3A–B ) . Development of all three trophoblast layers was severely impaired in the homozygous mutants ( Figure S3C–J ) . These extraembryonic and embryonic defects are highly reminiscent to the SENP2 nulls [17] , suggesting that the protease core domain deletion results in a loss of function mutation . We also were able to obtain mice heterozygous for the deleted allele without any noticeable abnormality , further suggesting that there is no dominant phenotype associated with the mutation . Next , we generated a SENP2ΔSUMO-Nes model , in which SENP2 is ablated in the neural progenitor cells by Nestin-Cre ( Figure S4 ) . At newborn , no obvious defects associated with the deletion could be detected , including neuronal differentiation ( Fu and Hsu , unpublished ) , indicating that SENP2 is not essential for embryonic neural development . The embryonic deformities observed in the SENP2 nulls were attributed to placental insufficiency . However , the SENP2ΔSUMO-Nes mice displayed movement difficulties at P10 . They developed paralysis around P16 ( Figure 1B and Supplementary Video S1; 100% penetrance , n = 20 SENP2ΔSUMO-Nes mutants collected from 10 litters ) , and died at the age of 3 weeks . The size of the mutant brains was slightly smaller comparable to the control at P0 , but later on exhibited a gradual reduction ( Figure 1B ) . At P14 , the mutant brain looked transparent , and was much smaller than the control ( Figure 1B; * , p<0 . 05; ** , p<0 . 01 , n = 3 ) . Histology revealed no obvious defects at P0 but severe brain abnormalities at P7 and P14 associated with the SENP2 deficiency ( Figure 1C ) . The cerebral cortex of SENP2ΔSUMO-Nes became significantly thinner and malformed . Other CNS regions , e . g . midbrain , cerebellum , hippocampus and spinal cord were also affected by the mutation although the phenotypes were less severe ( Figure S5 ) . The results suggested an essential role of SENP2 in neural development at postnatal , but not prenatal , stages . The neurodegenerative phenotype of SENP2ΔSUMO-Nes prompted us to examine programmed cell death affected by the mutation . Immunostaining of active Caspase 3 and TUNEL analysis revealed that abnormal apoptosis is , not detectable at P0 , but increased at P4 ( Caspase 3: 0 . 46±0 . 12% in control vs . 1 . 52±0 . 33% in mutant ) and highly enhanced at P7 ( Caspase 3: 0 . 82±0 . 08% in control vs . 9 . 4±0 . 59% in mutant; TUNEL: 0 . 91±0 . 17% in control vs . 12 . 09±0 . 87% in mutant ) ( Figure 2A , p<0 . 01 , ∼700 cells were counted in each of 3 independent experiments , mean ± SEM ) . The apoptotic abnormality , albeit less severe at this stage , was also observed in other CNS regions ( Figure S6 ) . To further elucidate the mechanism underlying the neural cell death of SENP2ΔSUMO-Nes , we examined expression of the activated form of Bak , a proapoptotic effector which promotes programmed cell death through modulation of mitochondrial morphogenesis [24] , . In the SENP2ΔSUMO-Nes cerebral cortices , Bak activation is stimulated at P0 ( 1 . 39±0 . 41% in control vs . 3 . 03±0 . 17% in mutant ) and P7 ( 3 . 4±0 . 36% in control vs . 8 . 21±0 . 59% in mutant ) , suggesting an association of mitochondrial dysfunction with the SENP2 mutation ( Figure 2B , p<0 . 01 , ∼700 cells were counted in each of 3 independent experiments , mean ± SEM ) . Neurons derived from the cerebral cortices of mouse embryonic brains were then cultured in vitro for examination of mitochondrial dynamics . Fluorescent labeling of the mitochondria revealed a more than 2 . 5-fold increase of neurons containing fragmented , but not tubular/rod-like , mitochondria in the cell body and neurite caused by the mutation ( 20 . 8±4 . 4% in control vs . 55 . 3±7 . 8% in mutant ) ( Figure 2C , p<0 . 002 , ∼200 neurons were counted in each of 3 independent experiments , mean ± SEM ) . Electron microscopy analysis of the P7 brain sections further identified fragmentation of the mitochondria in the cerebral cortical neurons of SENP2ΔSUMO-Nes ( Figure 2D ) . The mitochondrial cisternae are generally intact although few of them show alterations on the inner membrane . The results thus suggested a protective effect of SENP2 on neuronal cell survival . SENP2 plays an essential role in the regulation of mitochondrial dynamics during postnatal development of CNS . We then examined whether the SENP2 deficiency causes imbalances of sumoylation , resulting in accumulations of SUMO-conjugated proteins . Immunostaining of SUMO1 showed increased levels of the sumoylated proteins ( 26 . 1±1 . 5% in control vs . 39 . 4±4 . 5% in mutant ) , indicating that SENP2 deficiency induces hyper-sumoylation ( Figure 3A , p<0 . 01 , ∼700 cells were counted in each of 3 independent experiments , mean ± SEM ) . Although SENP2 was shown to regulate the Mdm2-p53 pathway [16] , [17] , the expression and the activity of p53 and Mdm2 were not altered in these mutants ( Fu and Hsu , unpublished ) . The neural defects caused by the SENP2 deletion most likely were not associated with p53-induced apoptosis , which is a mitochondrial independent event . Examination of protein extracts isolated from the P7 cerebral cortices revealed an elevation of SUMO1 association in the mutants ( Figure 3B ) . The loss of SENP2 activated Bak ( Figure 2B ) , which has been shown to promote sumoylation of Dynamin regulated protein 1 ( Drp1 ) and its association with mitochondria during programmed cell death [24] , [25] . Therefore , we tested if Drp1 is affected in the SENP2ΔSUMO-Nes mutants . Not only the stability ( 1 . 9-fold ) , but also SUMO1 association with Drp1 ( 2 . 7-fold ) , was enhanced by the mutation while RanGAP1 , a known substrate of SENP2 , did not appear to be affected ( Figure 3B ) . We then examined the mitochondrial association of Drp1 in primary neurons derived from the cerebral cortices of mouse embryonic brains . The mutation apparently promoted Drp1 association with the mitochondria ( Fig . S7 ) . The results implied that dysregulation of Drp1 may cause mitochondrial defects , leading to the development of neurodegeneration in the SENP2ΔSUMO-Nes mutants . Drp1 has been implicated in neural degenerative diseases with disruption of mitochondrial dynamics [26] , [27] . To test if Drp1 plays a role in this pathogenic process , we investigated its regulation by SENP2 . Our previous report showed that three gene products of SENP2 ( SENP2 , SENP2M and SENP2S ) , generated by alternative splicing , leading to the use of distinct translation initiation sites , exhibit distinct subcellular localizations and functions [16] . The SENP2 , SENP2M and SENP2S isoforms are predominately located to the nucleus , cytoplasmic vesicles and perinuclear region , and cytoplasm , respectively [16] . First , we examined which of these isoforms might be involved in the regulation of Drp1 using a parental cell line and its stably transformed variants , which express high levels of different isoforms [16] . Whole cells or mitochondria only prepared from these cell lines were used to isolate extracts , followed by protein analysis . Overexpression of a HA tagged SUMO1 led to hyper-sumoylation of total as well as the mitochondrial proteins in the parental cells which occurs less effectively in all SENP2 variants ( Figure 4A ) . SUMO1 also promotes total cell , cytoplasmic and mitochondrial accumulation of Drp1 , suggesting that its stability is modulated by sumoylation . However , this regulatory process , not affected by SUMO2 and SUMO3 , is apparently a SUMO1-specific regulation ( Figure S8A , B ) . Moreover , high levels of SENP2S , but not SENP2 and SENP2M , prevented the SUMO1-induced accumulation of Drp1 to the mitochondria ( Figure 4A ) . SENP2S also decreased the SUMO1-induced accumulation of Drp1 in the cytoplasm . Thus suggests that the Drp1 reduction mediated by SENP2S is caused by protein degradation rather than decreased targeting to the mitochondria ( Figure 4A ) . Immunoprecipitation-immunoblot analysis further showed that the SUMO1-association of endogenous Drp1 is eliminated by SENP2S , but not other isoforms ( Figure 4B ) . Although certain levels of reduction were detected in the SENP2 and SENP2M analyses , these might be attributed to the disruption of cellular compartmentalization in vitro . To further examine the ability of SENP2 to remove SUMO1 from Drp1 , we used in vitro reconstitution analysis ( Figure 4C ) . Recombinant enzymes , including Ubc9 and SAE1/2 , were first utilized to perform the SUMO1 conjugation of Drp1 . The addition of purified SENP2 efficiently was able to reverse this sumoylation process ( a ∼3 . 8-fold decrease ) , suggesting Drp1 as a direct substrate of SENP2 ( Figure 4C ) . Because of differential subcellular distributions of the SENP2 isoforms ( SENP2 in nucleus; SENP2M in Golgi; SENP2S in cytoplasm ) [16] , their co-localizations with Drp1 were then investigated . Double labeling analysis indicated an extensive co-localization between Drp1 and SENP2S ( Figure 4D ) . Using a proximity ligation assay examining protein-protein association within the cells , we found that SENP2S exhibited an isoform-specific interaction with Drp1 ( Figure 4E ) . The interaction apparently took place in the mitochondria and cytoplasm ( Figure 4F ) . Furthermore , using siRNA specifically knocking down SENP2 to an expression level at ∼17% ( Figure 4H ) , we found that its reduction promotes Drp1 association with the mitochondria ( Figure 4G ) , resulting in a 2 . 2-fold increase compared to the control ( Figure 4H ) . A mitochondrial protein with higher molecular mass , which is probably the SUMO1-associated Drp1 , was also increased in the SENP2 siRNA treated cells . Consistent with our analysis in the primary neuron ( Figure 2C ) , the knockdown of SENP2 also enhanced mitochondrial fragmentation in the cell line ( Figure 4I , p<0 . 01 , ∼200 cells were counted in each of 3 independent experiments , mean ± SEM ) . These results imply an isoform-specific effect of SENP2 on Drp1 stabilization and mitochondrial accumulation through modulation of SUMO1-specific conjugation . The isoform-specific regulation of Drp1 by SENP2S suggests its potential involvement in modulating mitochondrial dynamics . Using DsRed2-Mito labeling , mitochondrial morphology was examined in HCT116 and HCT116-SENP2S cells . Similar to previous findings [28] , overexpression of Drp1 and SUMO1 caused fragmentation of the mitochondria in these cells ( Figure 5A–B , A′–B′ , I ) . However , the SUMO1-induced mitochondrial fission was prohibited by high levels of SENP2 ( Figure 5C , C′ , G , G′ , I , p<0 . 01 , ∼100 cells were counted in each of 3 independent experiments , mean ± SEM ) . This might be attributed to the regulatory effects of SENP2 on Drp1 sumoylation and stability . Therefore , we examined if Drp1 is involved in the SENP2-mediated protection of mitochondrial fragmentation . High levels of Drp1 were able to overcome the protective effect of SENP2 on the SUMO1-induced mitochondrial fission ( Figure 5D , D′ , H , H′ , I , p<0 . 01 , ∼300 cells counted , n = 3 , mean ± SEM ) . In contrast , high levels of SENP2S did not seem to affect the Drp1-induced mitochondrial fission , suggesting that Drp1 acts downstream of SENP2S in the regulatory pathway . These results not only indicated a role of SENP2 in controlling mitochondrial dynamics but also suggested that SENP2 exerts its effects through modulation of Drp1 .
This study demonstrates that SENP2 controls the SUMO1-mediated modification of Drp1 essential for the regulation of mitochondrial dynamics . Targeted disruption of SENP2 induces neurodegeneration through promotion of Drp1 sumoylation and mitochondrial fragmentation . Impaired desumoylation results in neural cell death suggesting a new pathogenic mechanism for neurodegenerative diseases . Dysregulation of several aggregation-prone proteins which are sumoylation substrates have been implicated in neurodegeneration [19] , [20] , [22] , [29] , [30] . However , there is no evidence showing a direct involvement of SUMO modification enzymes in human diseases . Our findings suggest that enhanced sumoylation may also be attributed to mutations in the SUMO regulators in addition to the substrates . A balanced sumoylation is pivotal for neuronal cell survival . Hyper-sumoylation resulting from stimulation of SUMO ligases or disruption of SUMO proteases can lead to neural cell death . Our findings imply that targeting the SUMO protease may correct an imbalance of sumoylation and desumoylation . The SENP2ΔSUMO-Nes mice might have potential in modeling human diseases associated with the SUMO pathway . An association of the SUMO pathway with the regulation of mitochondrial dynamics has also been demonstrated in this study . Mitochondrial dysfunction has a strong association with neurodegenerative diseases [31]–[33] . Mitochondria possess a highly dynamic nature , undergoing frequent fusion and fission [34] . Due to large energy demands and long extended processes of the neurons , they are particularly sensitive and vulnerable to mitochondrial abnormalities . Enhanced mitochondrial fission induces apoptosis during neurodegeneration [31]–[33] . Mitochondrial dynamics is regulated by the GTPase dynamin-related protein Drp1 , whose function is modulated by SUMO modification . In cells , overexpression of SUMO1 prevents Drp1 degradation , resulting in its stabilization and activation [35] . The SUMO1-induced Drp1 promotes mitochondrial fission which can be altered by manipulating the SENP activity [36] , [37] . Data presented in this study strongly suggest that SENP2 is the physiological enzyme essential for this regulation . SENP2 controls mitochondrial dynamics through modulation of Drp1 in neural development and disease . Furthermore , Drp1 regulation by the SUMO pathway is causally linked to neural degeneration . The SENP2 deficiency causes cell survival issues through increases in mitochondrial fission , leading to the development of neurodegeneration . As Drp1 appears to be a direct substrate of SENP2 , dysregulation of mitochondrial dynamics is likely the primary cause of defects induced by the SENP2 disruption . Further study of mice with aberrant expression of Drp1 in the neural cells promises new insight into this regulatory mechanism . It remains possible that the aberrant mitochondrial phenotype is one of the main causes , which acts parallel with another cellular abnormality or is a consequence of other cellular abnormalities , e . g . failure of neural connection . Therefore , it is interesting to test if prevention of mitochondrial apoptosis can alleviate the defects caused by SENP2 deficiency . Further examination on the role of mitochondria dynamics promises new insight into the SENP2-mediated neuronal cell death . The involvement of SENP2 in neural development and degeneration opens new opportunities to develop therapeutic targets in the SUMO pathway . As sumoylation has been shown to counter against ubiquitination , manipulation of the SUMO pathway may also alter the ubiquitination-mediated degradation for the prevention and treatment of neurological disorders . Although SENP2 may have a general effect on the neurons , it remains possible that a specific subtype is more sensitive to the loss of SENP2 . In the SENP2 mutants , we identify different degrees of neurodegeneration in the cerebral cortex , hippocampus , cerebellum and spinal cord with the cerebral cortex being most severely affected . A disruption of SENP2 in a specific neuronal subtype may further divulge its role in neurodegenerative diseases . Testing the protective role of SENP2 in neural cell survival in disease conditions is also likely to gain a knowledge base of neurodegenerative diseases , leading to new therapeutic strategies .
The pCS2-SENP2 , pHASUMO1 , pGEX-4T-SAE1/2 , pGEX-2T-Ubc9 , pCS2-SENP2 , pCS2-SENP2M and pCS2-SENP2S DNA plasmids were described previously [16] . The pGEX-2T-Drp1 clone was generated by inserting a DNA fragment encoding Drp1 into the pGEX-2T vector ( GE HealthCare ) . The SRa-HA-SUMO2 , pcDNA3-HA-SUMO3 and pDsRed2-Mito clones were from Addgene or Clontech Laboratories . C3H10T1/2 and HCT116 cells and their derivatives were cultured in DMEM media with 10% fetal bovine serum and antibiotics [16] , [38] . Isolation , culture and differentiation of primary neural progenitor cells were performed as described [38] , [39] . The SENP2ΔSUMOFx ES cell lines were generated by electroporation of a targeting vector , containing the insertion of an orphan loxP site in intron 15 and another loxP site and a pgk-neo cassette flanked by two FRT sites in intron 16 , into CSL3 ES cells [17] , [40] , [41] . Twenty mouse ES cell clones heterozygous for the targeted allele were obtained by homologous recombination ( targeting efficiency: 23/112 ) . Two independent clones were injected into blastocysts to generate chimeras which were bred to obtain mice carrying the targeted allele . These mice were then crossed with the R26Flp mice to remove the pgk-neo cassette to obtain the SENP2ΔSUMOFx mouse strain . Mice were genotyped by PCR analysis using primers ( 5′-TCTCACTTGAAACCGTAGGGACC-3′ and 5′-GAAGGAAGGACTGGAGGAGAGAAG-3′ ) to identify the 5′ loxP locus , primers ( 5′-TTGTCAGAAGCAGTGTCCTGCG-3′ and 5′-GACTGGGAAGATATGAACTCGGC-3′ ) to identify the 3′ loxP locus . The deleted allele was identified using primers ( 5′-TCTCACTTGAAACCGTAGGGACC-3′ and 5′-GACTGGGAAGATATGAACTCGGC-3′ ) . The PCR was performed by denaturation at 95°C for 5 min and 35 cycles of amplification ( 95°C for 30 s , 67°C for 30 s , and 72°C for 60 s ) , followed by a 7-min extention at 72°C . The SENP2lacZ and Nestin-Cre mouse strains and genotyping methods were reported previously [17] , [40] . To generate the SENP2ΔSUMO mouse strain expressing a deficient protein , mice carrying the SENP2ΔSUMOFx allele were crossed with EIIa-Cre transgenic mice to delete the protease core domain in the germ cells [41] . To examine the production of SENP2 transcript , the reverse transcription products were subject to PCR amplifications using primers 5′-CAGTCTCTACAATGCTGCC-3′ and 5′-CTGTCACTCTGATCTTTGG-3′ ( exons 3–5 ) , primers 5′-GTGAGCTCATGAGTTCTGG-3′ and 5′-GTCGCTCCAATAACTTTCG-3′ ( exons 5–7 ) , primers 5′-GGAGGAGCAGAATCATGG-3′ and 5′-CTCAAAATCTCATCTGGTGG-3′ ( exons 8–11 ) and primers 5′-AGGTACATTGGAGCCTGGTG-3′ and 5′-AGCAACTGCTGGTGAAGGAT-3′ ( exons 13–17 ) . The PCR reaction was performed by denaturation at 94°C for 5 min and 30 cycles of amplification ( 94°C for 30 s , 53°C for 30 s , and 72°C for 45 s ) , followed by a 7-min extension at 72°C . Care and use of experimental animals described in this work were approved by and comply with guidelines and policies of the University of Committee on Animal Resources at the University of Rochester . Samples were fixed , paraffin embedded , sectioned and stained with hematoxylin/eosin for histological evaluation [17] , [42] . The in situ hybridization was performed as described [17] , [38] , [43] , [44] . In brief , sections were incubated with the digoxygenin labeled RNA probes generated by in vitro transcription [17] , [43] , followed by recognition with an alkaline phosphatase conjugated anti-digoxygenin antibody and visualization with BM-purple [38] , [44] . TUNEL staining was performed with ApopTag ( Millipore ) as described [45] , [46] . For electron microscopy , mice were fixed by perfusion with fixative ( 2% paraformaldehyde , 2 . 5% Glutaraldehyde , 0 . 1M sodium cacodylate , 6 . 8% sucrose ) . The dissected tissues were then fixed with 1% osmium tetroxide , embedded in EPON/Araldite resin and cut in seventy nm sections , followed by staining with aqueous uranyl acetate and lead citrate and examined using Hitachi 7650 transmission electron microscope . Proximity ligation assay ( PLA ) was performed using Duolink In Situ reagents ( Duolink Bioscience ) . Briefly , cells were fixed and incubated with rabbit anti-Drp1 and mouse anti-myc tag antibodies . Two oligonucleotide-labeled anti-rabbit and anti-mouse PLA probes , which bind to each other when they are in close proximity , were then used to generate fluorescent signals . Immunostaining of cells [47] and tissue sections [48]–[50] were performed by incubation with primary antibodies , followed by detection with fluorescence-conjugated or horseradish peroxidase-conjugated secondary antibodies . Images were taken using Zeiss Axio Observer microscope equipped with deconvolution analysis . To determine the mitochondrial morphology , cells were either stained by MitoTracker or transient expression of DsRed2-Mito . For statistical analysis , cells containing MitoTracker or DsRed2-Mito positive mitochondria were counted and scored for tubular/rod-like or fragmented mitochondria . Immunoblot was performed by isolation of protein extracts from mitochondria , cells or tissues using M-PER ( Pierce ) in the presence of protease inhibitor cocktail , followed by electrophoresis as described [16] , [17] , [38] , [48] . Isolation of mitochondria was performed using a Mitochondria Isolation Kit ( Thermo Fisher ) according to the manufacture's description . Immunoprecipitation was performed using Pierce Classic IP Kit . Briefly , cells were lysed in buffer containing 0 . 025M Tris , 0 . 15M NaCl , 0 . 001M EDTA , 1% NP-40 , 5% glycerol . Approximately 500 µg of protein lysates were mixed with 2 µg of antibodies overnight , followed by incubation with Protein A/G agarose for 1 hour at 4°C . The antibody-bound complex was then incubated with elution buffer for 5 min at 100°C , and collected by centrifugation for SDS-PAGE analysis . Mouse monoclonal antibodies , Actin ( Thermo Fisher ) , HA ( Cell Signaling ) , and myc tag ( Santa Cruz ) ; rabbit polyclonal antibodies Bak ( Novus Biologicals ) , caspase-3 ( BD Biosciences ) , Cox IV ( Cell Signaling ) , Drp1 ( Novus Biologicals ) , and SUMO1 ( Cell Signaling ) , were used in these analyses . Recombinant GST-SAE1/SAE2 , GST-Ubc9 , GST-Drp1 , HA-SUMO1 and myc tagged ( MT ) -SENP2 proteins expressed in Escherichia coli were affinity purified . The 20-µl reaction buffer containing 50 mM Tris-HCl ( pH 7 . 6 ) , 5 mM magnesium chloride , 10 mM ATP , 1 µg of GST-ASE1/2 , 2 µg of GST-Ubc9 , 10 µg of GST-HA-SUMO1 and 200 ng of GST-Drp1 with the presence of protease inhibitor cocktail was incubated for 3 h at 37°C . The desumoylation reaction was then carried out in 10 µl of the above sumoylation mixture with the addition of purified MT-SENP2 for overnight at 37°C . The samples were then analyzed by SDS-PAGE and immunoblot analysis of Drp1 and SUMO1 . | Protein modification by SUMO is a reversible and evolutionarily conserved process . Members of the SUMO-specific protease ( SENP ) family are known to reverse SUMO-conjugation in many defined systems , but their importance in mammalian development and pathogenesis remains largely elusive . Although SUMO-conjugated proteins have been shown to aberrantly accumulate in patients with neurodegeneration , there is no evidence supporting a direct involvement of SUMO modification enzymes in human diseases . This study reveals that disruption of SENP2 causes neurodegeneration through modulation of mitochondrial morphogenesis . Our findings provide a causal link of SUMO modification enzymes to cell survival , suggesting a new pathogenic mechanism for neurodegeneration . Exploring the protective effect of SENP2 on neuronal cell death may uncover important preventive and therapeutic strategies for neurodegenerative diseases . | [
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] | 2014 | Disruption of SUMO-Specific Protease 2 Induces Mitochondria Mediated Neurodegeneration |
It is widely suspected that gene regulatory networks are highly plastic . The rapid turnover of transcription factor binding sites has been predicted on theoretical grounds and has been experimentally demonstrated in closely related species . We combined experimental approaches with comparative genomics to focus on the role of combinatorial control in the evolution of a large transcriptional circuit in the fungal lineage . Our study centers on Mcm1 , a transcriptional regulator that , in combination with five cofactors , binds roughly 4% of the genes in Saccharomyces cerevisiae and regulates processes ranging from the cell-cycle to mating . In Kluyveromyces lactis and Candida albicans , two other hemiascomycetes , we find that the Mcm1 combinatorial circuits are substantially different . This massive rewiring of the Mcm1 circuitry has involved both substantial gain and loss of targets in ancient combinatorial circuits as well as the formation of new combinatorial interactions . We have dissected the gains and losses on the global level into subsets of functionally and temporally related changes . One particularly dramatic change is the acquisition of Mcm1 binding sites in close proximity to Rap1 binding sites at 70 ribosomal protein genes in the K . lactis lineage . Another intriguing and very recent gain occurs in the C . albicans lineage , where Mcm1 is found to bind in combination with the regulator Wor1 at many genes that function in processes associated with adaptation to the human host , including the white-opaque epigenetic switch . The large turnover of Mcm1 binding sites and the evolution of new Mcm1–cofactor interactions illuminate in sharp detail the rapid evolution of combinatorial transcription networks .
The recent genome sequencing and annotation of the major model organisms established that organismal complexity does not scale in a simple way with gene count . This discordance is consistent with earlier proposals that “tinkering” with gene regulation may be a particularly powerful mode of evolution [1–3] . In principle , changes in when and where , and thereby in what combinations , genes are expressed can help to explain changes in organismal complexity over longer time scales . Over shorter time scales , the contributions of changes in gene regulation to phenotypic variation have been clearly demonstrated [4 , 5] . For example , small changes in gene regulation underlie the gain and loss of wing spots in Drosophila species [6] and armor in stickleback fish [7] . The plasticity of gene regulatory networks is of interest because it presumably relates directly to the ability of these networks to generate phenotypic novelty [8] . The potential for rapid turnover ( gains and losses ) of transcription factor binding sites was predicted on theoretical grounds [9–11] and was supported by comparisons of cis regulatory sequence both within and between species [12–15] . Recently , experimental localization of four transcription factors across the mouse and human genomes revealed that binding sites have diverged appreciably between these two species [16] . Analogous experiments performed on two transcription factors from closely related yeasts led to similar conclusions [17] , although in this case , it was not clear how the differences in binding related to gains and losses of cis-acting sequences . The ascomycete lineage , which includes the model yeast S . cerevisiae , serves as a powerful framework for investigating the general impact of regulatory evolution , because several of its members are particularly easy to study experimentally . These include the model yeast S . cerevisiae , the dairy yeast K . lactis , and the human pathogen C . albicans . S . cerevisiae and K . lactis diverged more recently than either did from C . albicans; the divergence of S . cerevisiae and C . albicans is thought to have occurred on the order of 300 million years ago [18] . To date , only a handful of comparative gene regulation studies have been carried out in fungi . These include a few large-scale analyses of changes in gene expression [19] and cis regulatory motifs [20 , 21] as well as some smaller-scale studies [22–24] focusing on sets of co-regulated genes . Whereas the whole-network studies have generally uncovered an abundance of divergence , the smaller-scale studies have characterized this divergence in greater detail or provided mechanistic insight into transcriptional rewiring . Here we take an approach intermediate in scale and attempt to characterize the evolution of a large combinatorial circuit comprised of the MADS-box transcriptional regulator Mcm1 and each of its cofactors . Mcm1 has been intensively studied in S . cerevisiae and , in most cases , it is found as a homodimer that binds DNA cooperatively with other sequence-specific DNA binding cofactors to regulate sets of genes , which we refer to here as regulons . Five regulons have been identified in S . cerevisiae where Mcm1 acts in combination with a second transcriptional regulator . Mcm1 joins with the following: ( 1 ) MATα2 to turn off the a-specific genes ( asgs ) , ( 2 ) MATα1 to turn on the α-specific genes ( αsgs ) , ( 3 ) Fkh2 and Ndd1 to activate G2/M-specific genes , ( 4 ) Yox1 to repress the M/G1-specific genes , and ( 5 ) Arg80 and Arg81 to either repress or activate the arginine metabolic genes [25] . Because Mcm1 itself is not generally regulated , it is typically the regulation of its cofactors that produces the effect of differential gene regulation at each of the Mcm1–cofactor regulons [25] . For example , it is the regulated binding of Mcm1′s cofactor Yox1 that leads to the M/G1-specific expression of genes in the Mcm1-Yox1 regulon [26] . At these Mcm1–cofactor regulons , Mcm1 is thought to increase specificity through added protein–DNA and protein–protein interactions [27] . Previously we showed Mcm1 to be at the center of a rewiring event that led to the replacement of one cofactor ( MATa2 ) with another ( MATα2 ) [24] . In principle , the free energy gain contributed by the interaction between Mcm1 and its flanking cofactor could catalyze evolutionary change by expanding the space of cis-regulatory sequences that yield appropriate gene regulation . For instance , mutations that strengthen Mcm1′s interaction with its cofactor or with DNA can compensate for mutations to the cofactor–DNA interaction , thereby expanding the possibilities for cross-reaction with a new DNA binding protein . This idea bears at least a formal similarity to the neutral networks in RNA sequence space studied by Fontana and colleagues [28] . Because Mcm1 participates in many combinatorial interactions in S . cerevisiae , and because it regulates a large number of genes , we felt that Mcm1 provided a particularly strong entry point to study the evolution of combinatorial networks . To study this problem we performed ChIP-Chip ( chromatin immunoprecipitation , analyzed genome-wide using microarrays ) on Mcm1 in three species ( S . cerevisiae , K . lactis , and C . albicans ) and combined this data with informatics analyses across 32 fungal species . We found that all five Mcm1–cofactor regulons currently characterized in S . cerevisiae are present at least in limited form in K . lactis and C . albicans , suggesting an ancient origin of these regulons . Although the Mcm1–cofactor interaction is typically conserved and a small set of core target genes remains part of the regulon in each species , most of these regulons have undergone substantial divergence through gain and loss of cis-acting sequences . On the global level , substantial gain and loss of Mcm1 binding sites is also evident . Although some of this , as discussed above , is due to target genes moving in and out of existing regulons , much of it is due to the evolution of entirely new Mcm1–cofactor regulons . We highlight two specific instances in which combinatorial regulation by Mcm1 and a cofactor is gained; in one case , we observe the large-scale convergent evolution of regulation at the ribosomal genes and in the other , we describe a very recent gain of regulation that was likely shaped by the selective pressures of the human host . The picture that emerges from this study is one of massive transcriptional rewiring in species that span approximately the same range of protein sequence divergence as human , fish , and sea squirt [29 , 30] . This rewiring consists of both rapid turnover of cis-acting sequence and the formation of new combinations of regulatory proteins .
Mcm1 was chromatin immunoprecipitated ( ChIP-ed ) from S . cerevisiae , K . lactis , and C . albicans a cells using peptide antibodies custom designed for the Mcm1 ortholog of each species . To maximize the detection of Mcm1 binding , each strain was grown under two different conditions known to stimulate binding of Mcm1: yeast extract peptone dextrose ( YEPD ) medium and pheromone-inducing medium with α pheromone ( details in the Text S1 ) . Immunoprecipitate and whole-cell extract samples were competitively hybridized to custom-designed Agilent microarrays that tile 60mer probes at a median spacing of 66 , 59 , and 79 base pairs ( bp ) across the genomes of S . cerevisiae , K . lactis , and C . albicans , respectively ( Figure S1 ) . For each species/condition , the ChIP-Chip was performed twice and the two biological replicates were combined in downstream data processing . Data were processed by a variety of methods , and it was determined empirically that the Joint Binding Deconvolution ( JBD ) algorithm [31] provides the best combination of consistency across species and accuracy on a test set of previously characterized S . cerevisiae binding sites ( see Text S1 ) . Complete ChIP profiles for all experiments can be viewed at: http://genome . ucsf . edu/mcm1_evolution/ . The majority of regions that JBD called as bound by Mcm1 contained at least one instance of the well-characterized Mcm1 binding motif [32 , 33] . We therefore decided to incorporate motif information into our final criteria for Mcm1-bound segments . De novo motif finding by MEME [34] on a set of high-confidence bound regions from JBD yields Mcm1 binding site motifs that are roughly the same in each species ( Figure 1 ) ; the motif deduced de novo from S . cerevisiae closely resembles previously described Mcm1 recognition sequences . In C . albicans , there was a large subset of bound regions without a canonical Mcm1 motif . These regions are largely explained by the appearance of a noncanonical motif ( Figure 1 ) , discussed later . Parameter cutoffs for JBD statistics and the motif p-value were chosen that correctly call 85% ( 28 of 33 genes ) of our S . cerevisiae test set as positives while also calling an additional 219 of 5 , 769 genes as bound . Details regarding test set selection are provided in the Text S1 along with a discussion of false-positive rates and receiver operator characteristic plots ( Figure S8 ) evaluating a variety of parameter value choices . These same cutoffs used for the S . cerevisiae data yield 626 of 5 , 327 genes bound in K . lactis and 761 of 6 , 090 genes bound in C . albicans ( gene lists in Table S1 ) . For these and all subsequent calculations , Mcm1 targets from the two growth conditions examined have been pooled . After defining Mcm1 targets in each species , we sought to evaluate the overlap of these targets between species . We mapped orthologs using an existing algorithm [24] , which was modified to reduce directional bias ( Text S1 , section titled: “Mapping orthologous gene sets” ) , on an updated database of open reading frame ( ORF ) sequences from 32 fully sequenced genomes ( Table S2 ) . Genes bound by Mcm1 in each species A were then “mapped to” one of the other two species B via our ortholog map . The number of genes “mapped from” A and also found to be in the Mcm1 bound gene set of B was counted and is displayed as a fraction of the total genes bound in species A that can be mapped to species B ( Figure 2A ) . Note the lack of symmetry; comparing the Mcm1 bound gene set of species A to that of B is not the same as comparing the bound gene set of species B to that of A because of the different total number of Mcm1-bound genes in the different species . Overlap p-values were also calculated for each species pair by using the hypergeometric distribution ( Figure 2B ) . There is significant overlap in Mcm1-targeted genes between each pair of species ( p < 10−3 ) . As might be expected , conservation is strongest between the two more closely related species , S . cerevisiae and K . lactis , with 42% of mapped S . cerevisiae Mcm1 targets also bound by K . lactis Mcm1 . However , as the lower frequency ( 16% ) of K . lactis Mcm1-mapped targets bound by S . cerevisiae Mcm1 indicates , the K . lactis Mcm1 target set is much larger . Interestingly , the C . albicans Mcm1 target set overlaps more significantly with the K . lactis Mcm1 set than it does with the S . cerevisiae Mcm1 set , indicating that a sizeable fraction of the extra genes bound by K . lactis Mcm1 are shared with C . albicans Mcm1 and are therefore likely to have been lost as Mcm1 targets on the branch leading to S . cerevisiae ( see next section ) . For simplicity , we have focused here on only those genes that can be mapped in a 1:1 fashion between species . However , similar results are obtained when genes with more complex interspecies mappings ( e . g . , 2:1 ) are included . To rule out the possibility that our results were biased by the exact parameters chosen , we repeated the analysis with a variety of parameter choices and obtained similar results ( Figure S9 ) . To assess the prevalence of gain and loss of Mcm1 binding sites across the three-species phylogeny , we constructed a model with nine parameters: four gain rates and four loss rates , corresponding to each of the four branches of the rooted tree , and a single parameter representing the probability of an Mcm1 binding site at the root of the tree ( Figure 2C ) . We take as our dataset the Mcm1-binding occurrence patterns at each of the 2 , 766 genes that can be mapped between S . cerevisiae , K . lactis , and C . albicans in a 1:1:1 fashion via our ortholog mapping . There are eight such patterns , e . g . , the pattern “101” for hypothetical gene X indicates an Mcm1 binding site is present upstream of gene X in S . cerevisiae and C . albicans , but not in K . lactis . We devised a modified maximum-likelihood algorithm to infer the gain and loss rates on each branch of the three-species phylogeny . A more thorough description of this procedure is given in the Text S1 . The results show a high degree of binding site turnover on all branches of the tree . For example , we estimate that the last common ancestor of S . cerevisiae and K . lactis had Mcm1 binding sites at 156 genes . Since divergence , Mcm1 binding sites were gained at 44 genes and lost at 109 genes in the S . cerevisiae lineage . Likewise , Mcm1 binding sites were gained at 128 genes and lost at 38 genes in the K . lactis lineage . Thus , present day S . cerevisiae and K . lactis have only 38 Mcm1-targeted genes in common . We do not believe that this analysis is biased by any systematic failures to detect Mcm1 binding sites in our ChIP experiments—either through experimental biases or because the growth conditions chosen did not promote Mcm1 binding . In cases where Mcm1 is bound upstream of a gene in one species but not in the other two species , the Mcm1 motif is generally not present in those other two species as well ( Text S1 , section titled: “Mcm1 DNA motifs are not present at genes that are not bound” ) . In the sections that follow , we will further dissect the changes in combinatorial regulation that give rise to the conservation and divergence summarized in Figure 2C . If we consider just the subset of genes that has a 1:1:1 mapping in our ortholog table , only 12 genes ( ∼13% of the genes bound in S . cerevisiae ) are part of the Mcm1 circuit in all three species ( Figure 3 ) . If gene duplications are allowed , the number of genes in S . cerevisiae with at least one “ortholog” bound in K . lactis and C . albicans is 45 ( ∼18% of the genes bound in S . cerevisiae ) . Presumably this conserved set of target genes reflects a conserved role played by Mcm1 in the common ancestor as well as in the three modern species . The set of ancestral Mcm1-bound genes is clearly enriched for genes regulated by the cell cycle ( Figure 3 , shaded orange ) and mating type ( Figure 3 , shaded blue ) . The latter is confirmation of results from our previous study [24] describing the conservation of membership within the a-specific gene regulon despite the dramatic switch from positive regulation by MATa2 to negative regulation by MATα2 . In S . cerevisiae , the cell cycle genes listed are regulated by the Mcm1 cofactors Fkh2/Ndd1 and Yox1 . The conservation of these genes as targets of Mcm1 prompted us to inquire whether combinatorial control by Mcm1 and each of its S . cerevisiae cofactors was also conserved since the time when S . cerevisiae , K . lactis , and C . albicans diverged from a common ancestor . The Mcm1–cofactor regulons of S . cerevisiae were mapped to Mcm1-bound regions in K . lactis or C . albicans , and motif finding was performed to identify cis-regulatory elements controlling the orthologous regulons ( details presented in Text S1 ) . The results of this analysis ( Figure 4A and 4B ) demonstrate that most known Mcm1–cofactor interactions from S . cerevisiae are present in K . lactis and C . albicans and are therefore likely of ancient origin . In the description that follows , we first compare the cis-regulatory motifs of the more closely related S . cerevisiae and K . lactis and then compare these to the motifs of the more divergent C . albicans . Here we use the term “interaction” to refer to both demonstrated protein–protein interactions as well as those inferred from the co-occurrence in cis of two or more regulatory motifs . One caveat of this approach is that co-occurrence of motifs can arise from cooperative as well as competitive binding of two transcription factors . However , we think the latter is unlikely for most cases documented in this work , because the spacing of the motifs tends to be highly constrained and nonoverlapping , a feature typically observed for cooperative binding with Mcm1 . In general the cis-regulatory elements of the K . lactis and S . cerevisiae Mcm1–cofactor regulons are similar , suggesting that the corresponding Mcm1–cofactor interactions have changed little since these two lineages split . Notable exceptions are the changes seen at asgs discussed previously [24] and the apparent added Fkh2 specificity flanking several of the Yox1-Mcm1 sites in K . lactis ( Figure 4B ) . Although the latter is seen in at least a few genes in S . cerevisiae [26] , this Yox1-Mcm1-Fkh2 architecture appears much more prominent in K . lactis . Comparison of K . lactis and S . cerevisiae to the more divergent C . albicans reveals that a number of changes to cis-regulatory motifs have occurred over longer time scales . At the Fkh2-Mcm1 regulon , there is a shift in the placement of the Fkh2 site relative to the Mcm1 site by 1 base pair , which occurs across the entire regulon . We note that species with the tighter Fkh2-Mcm1 spacing have clear orthologs to Ndd1 , a protein which in S . cerevisiae binds the Fkh2-Mcm1 complex periodically , thereby driving G2/M-specific expression [35] , while those with the lengthened spacing do not . It is not known how the Fkh2-Mcm1 complex of C . albicans would function to drive G2/M-specific gene expression without an Ndd1 ortholog , although this altered spacing may provide a clue . It is also noteworthy that Fkh2 is related to another protein , Fkh1 , which is derived from the yeast whole-genome duplication event [36] , meaning that these two genes found in S . cerevisiae map to a single gene in K . lactis and C . albicans . It is known that Fkh2 binds DNA cooperatively with Mcm1 , but that Fkh1 does not [37] . Given the evidence for the Fkh2-Mcm1 motif in K . lactis and C . albicans , we infer that this interaction is ancestral to the species under study and that after duplication , only Fkh2 retained the ability to bind cooperatively with Mcm1 . The cis-regulatory motif at the MATα1-Mcm1 regulon has clearly changed as well , indicating that MATα1 , despite its obvious conservation , recognizes distinct DNA motifs in different species . However , the altered MATα1 motif observed in C . albicans is not necessarily incompatible with the S . cerevisiae protein , a surmise based on previous mutagenesis studies [38] . Despite this change in motif , experimental evidence indicates that MATα1′s function as an activator of αsgs is the same in S . cerevisiae and C . albicans [39] . Once it was determined that most Mcm1–cofactor pairings are conserved across the species we examined , we then determined to what extent the set of genes in their corresponding regulons was also conserved . The motif matrices for each of the Mcm1–cofactor pairs were used to score the entire set of Mcm1-bound sequences in each species and thus to define the members of each Mcm1–cofactor regulon in each species ( Text S1 ) . We found that the number of targets in each regulon is roughly the same across the three species , but the precise set of members is not . However , within each regulon , there is a small , core set of conserved genes ( Figure 4C ) . For example , the Fkh2-Mcm1 regulon consists of roughly 20 genes in each species , but only three genes are part of the regulon in all three species . Previously we showed that for the asg regulon at least , this core is conserved throughout the yeasts spanning the lineage of S . cerevisiae and C . albicans [24] . A similar promoter sequence analysis with the Mcm1-Fkh2 matrices supports a conserved core within this regulon as well ( unpublished data ) . For example , the promoters of BUD4 and CDC20 have strong matches to the Fkh2-Mcm1 matrix in most species within the lineage spanning S . cerevisiae and C . albicans . Thus , turnover within these regulons is not a purely stochastic process , but rather is constrained in some respects by purifying selection . As summarized in Figure 2C , this study revealed many specific instances of gains and losses of Mcm1 regulation across the ascomycete lineage . The large number of changes seen at the global level , however , can not be fully accounted for by binding site turnover within the ancestral Mcm1–cofactor regulons alone ( Figure 4C ) . In the following section , we highlight three examples of large-scale rewiring events , chosen for their particular clarity and their relevance to well-developed systems .
In this work , we have tracked the evolution of combinatorial gene regulation by the highly conserved transcriptional regulator Mcm1 and each of its known cofactors across the ascomycete fungal lineage . Our analysis shows that the genes regulated by Mcm1 have changed considerably over the evolutionary time scales represented by this lineage; our results reveal many more differences than similarities in the Mcm1 circuitry . Regulation by Mcm1 is more pervasive in K . lactis and C . albicans , where 12% of all genes are bound , than in S . cerevisiae , where 4% of genes are bound . The fraction of genes shared as targets between all three species is very low ( 13%–18% ) , and we have demonstrated that this is due to both substantial gain and loss of Mcm1 binding sites along each branch of this phylogeny ( Figure 2B ) . The extensive amount of gain and loss observed is consistent with recent studies in mammals [16] and closely related yeasts [17] and suggests the following three possibilities: ( 1 ) there is a richness of selective advantages offered in the dynamic rewiring of gene regulatory networks , ( 2 ) there are a large number of neutral alternatives to gene regulation by Mcm1 , or ( 3 ) selection on gene expression is weak . The latter possibility seems at odds with other observations such as the large fraction of genes devoted to transcriptional regulation in S . cerevisiae ( ∼3% ) , the greater-than-expected number of transcriptional regulators retained after the whole genome duplication ( ∼6% versus ∼3% ) , and the considerable conservation found in many S . cerevisiae promoters [54 , 55] . Additionally , the fact that many of the Mcm1 sites are enriched at functionally related genes and often found in tandem with cofactor motifs argues strongly against the hypothesis that a large number of these sites are fortuitous and nonfunctional . Gauging the relative contributions of selection versus neutral drift on the gene regulatory networks will be an exciting challenge for future research [56] . Despite the highly dynamic nature of evolution of Mcm1 regulation , we find evidence that most Mcm1–cofactor interactions characterized in S . cerevisiae are also present in K . lactis and C . albicans ( Figure 4B ) . Although the Mcm1–cofactor pairings are conserved , the set of genes that each regulates has diverged considerably across species . Nonetheless , each Mcm1–cofactor pair targets a small core of genes conserved as part of the regulon . These regulon cores are enriched for genes functioning in the cell cycle and mating . Thus it would seem that Mcm1′s role in these processes evolved prior to the split of the species we have chosen to study . Nevertheless , even at these conserved regulons , there are many species-specific differences . For example , across an entire regulon , the spacing between Fkh2 and Mcm1 binding sites has changed in S . cerevisiae and K . lactis relative to C . albicans , as have the DNA recognition sequences of MATα1 . This latter observation is particularly interesting because it suggests that the specificity of MATα1 has evolved without an accompanying gene duplication . In addition to the conservation of Mcm1–cofactor interactions associated with cell cycle and mating , we see the evolution of new Mcm1–cofactor regulons . For example , Mcm1 binding sites are gained at the majority of ribosomal genes in K . lactis in close proximity to binding motifs for another transcription factor , Rap1 ( Figure 5C and 5D ) . The evolution of ribosomal gene regulation has been studied previously [21] , but a role for Mcm1 was not discussed . Our new results support the idea , first proposed by Tanay et al . [21] , that while the protein sequence of this critical macromolecular machine has remained nearly constant , its regulation has undergone substantial diversification in yeasts . What is perhaps most surprising is our finding that the set of species that contain Mcm1 binding motifs upstream of ribosomal genes ( Figure 5A and 5B; C . glabrata , K . lactis , Y . lipolytica , and the A . nidulans lineage ) do not cluster phylogenetically . From this we inferred that Mcm1 binding at ribosomal genes likely evolved on four separate occasions . If further genome sequencing continues to support this result , this will serve as the largest instance of convergent regulatory evolution yet reported . The relatively sudden appearance of Mcm1 binding sites in close proximity to Rap1 sites at roughly 70 ribosomal genes in K . lactis raises another important question: Can the commonly accepted mutational processes , such as point mutation and recombination , support this scale of concomitant changes—or must some alternative mechanism for moving promoters around the genome be invoked [57 , 58] ? One can argue that , without a redundant mechanism in place , loss or gain of Mcm1 regulation of even a single gene means losing precise control over one component of a macromolecular complex that is thought to need tight stochiometric control [40] . With further sequencing and characterization of Mcm1′s functional role at the ribosomal genes , it may become clear how such a massive regulatory change can take place at a set of genes encoding such highly conserved , tightly regulated and essential proteins . In C . albicans , we identified the presence of Mcm1 at a noncanonical motif upstream of roughly 110 genes . The noncanonical motif differs significantly from the canonical Mcm1 motif ( Figure 1 ) , although in both cases GC-rich regions flank an AT-rich core . To our knowledge no MADS-box domain has ever been shown to bind a sequence this far diverged from the canonical Mcm1 motif . Even so , we find that noncanonical motifs tend to be centered with respect to peaks of ChIP-Chip enrichment and thus conclude that Mcm1 either binds this motif directly with some unknown cofactor or some unknown transcriptional regulator recognizes this motif and interacts strongly with Mcm1 . The set of genes at which Mcm1 binds the noncanonical motif is enriched for processes such as adhesion and contains three of four known regulators of the white-opaque phenotypic switch [45] . The white-opaque switch is of considerable interest because the white and opaque states are heritable and because the two states are thought to allow adaptation to different niches within a human host [49 , 50] . In this vein , the evolution of regulation associated with the switch deserves special attention too , because the changes seen here represent , to our knowledge , the first gene regulatory changes to be associated with a heritable biological process and one of only a few instances implicated to play an adaptive role in fungal biology [59] . The results of our comparative analysis of 32 yeast species demonstrate that Mcm1 binding at the noncanonical motif is found only in two very closely related species , C . albicans and C . dubliniensis , and thus likely arose only very recently ( Figure 7D ) . Moreover white-opaque switching has been described only in these two species [60] , which are both pathogens of humans . Thus , the evidence so far suggests that the white-opaque switch may have arisen just prior to the divergence of C . albicans and C . dubliniensis and that the emergence of the noncanonical Mcm1 motif at white-opaque regulators was crucial to this development . Alternatively , the white-opaque switch may have arisen earlier , and the addition of Mcm1 regulation may have refined it in some way , affecting heritability , for example . The picture that emerges from this study is one of massive transcriptional rewiring in species that span approximately the same range of divergence as human , fish , and sea squirt [29 , 30] . Mcm1 regulates hundreds of genes in S . cerevisiae , K . lactis , and C . albicans , but less than 20% of Mcm1–target gene connections are preserved in all three species . The differences arise from target genes moving in and out of ancient Mcm1–cofactor regulons , but also from the formation of new Mcm1–cofactor interactions and the loss of ancient ones . Taken together with our previous work [24] , we have now provided evidence for the gain of three interactions: Mcm1 with MATα2 , Mcm1 with Rap1 , and Mcm1 with Wor1 . We have also described loss of an interaction between Mcm1 and MATa2 and the loss of an interaction between Mcm1 and Arg81 that was preserved in an Mcm1 duplicate . In attempting to judge the relative contributions of combinatorial control per se to the evolution of transcriptional circuits , we acknowledge that the ideal “control” datasets do not exist . For example , data collected from a large noncombinatorial circuit ( should one even exist ) over several species would allow an objective assessment of the special contribution of combinatorial control to circuit evolution . Nonetheless , our results provide experimental and informatic support for the idea that combinatorial networks are highly evolvable [61–64] , and perhaps more importantly , they document specific mechanisms by which one large combinatorial circuit has evolved .
Detailed methods can be found in Text S1 , Figure S1–S10 , and Table S1–S3 . The information can also be found in one complete file , Protocol S1 . | In explaining the diversity of organisms on Earth , it is increasingly evident that evolutionary changes in when and where genes are expressed provide a crucial source of variation . By using genome-wide transcription factor localization experiments in S . cerevisiae , K . lactis , and C . albicans , combined with comparative genomics across many more yeast species , we examined how a large combinatorial transcription circuit evolves over the course of hundreds of millions of years . Combinatorial regulation is pervasive in eukaryotic organisms and is thought to allow for increased specificity and integration of multiple signals in the control of gene expression . Our studies focused on one prolific combinatorial regulator , Mcm1 , which , in combination with five cofactors , binds and regulates genes functioning in a diverse range of cellular processes in S . cerevisiae . We found evidence of massive network rewiring , including high rates of gain and loss of Mcm1 binding sites and the formation of new Mcm1–cofactor combinations and the breaking of old ones . We propose that the multiple protein–protein and protein–DNA interactions that specify transcription in combinatorial circuits allow for a richness of compensatory mutations and thereby provide ample opportunity for both adaptive and neutral evolution . | [
"Abstract",
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] | 2008 | The Evolution of Combinatorial Gene Regulation in Fungi |
Human cytomegalovirus ( HCMV ) , a herpesvirus , is a ubiquitously distributed pathogen that causes severe disease in immunosuppressed patients and infected newborns . Efforts are underway to prepare effective subunit vaccines and therapies including antiviral antibodies . However , current vaccine efforts are hampered by the lack of information on protective immune responses against HCMV . Characterizing the B-cell response in healthy infected individuals could aid in the design of optimal vaccines and therapeutic antibodies . To address this problem , we determined , for the first time , the B-cell repertoire against glycoprotein B ( gB ) of HCMV in different healthy HCMV seropositive individuals in an unbiased fashion . HCMV gB represents a dominant viral antigenic determinant for induction of neutralizing antibodies during infection and is also a component in several experimental HCMV vaccines currently being tested in humans . Our findings have revealed that the vast majority ( >90% ) of gB-specific antibodies secreted from B-cell clones do not have virus neutralizing activity . Most neutralizing antibodies were found to bind to epitopes not located within the previously characterized antigenic domains ( AD ) of gB . To map the target structures of these neutralizing antibodies , we generated a 3D model of HCMV gB and used it to identify surface exposed protein domains . Two protein domains were found to be targeted by the majority of neutralizing antibodies . Domain I , located between amino acids ( aa ) 133–343 of gB and domain II , a discontinuous domain , built from residues 121–132 and 344–438 . Analysis of a larger panel of human sera from HCMV seropositive individuals revealed positivity rates of >50% against domain I and >90% against domain II , respectively . In accordance with previous nomenclature the domains were designated AD-4 ( Dom II ) and AD-5 ( Dom I ) , respectively . Collectively , these data will contribute to optimal vaccine design and development of antibodies effective in passive immunization .
Human cytomegalovirus ( HCMV ) is an important , ubiquitously occurring , human pathogen in immunocompromised hosts . The virus can cause severe disease in transplant recipients [1] . In large parts of the world HCMV is also the most common viral infection acquired in utero . In the USA and Europe an estimated 0 . 2%–1 . 2% of all live born infants are infected with HCMV [2] , [3] . Congenital HCMV infection is a leading cause of sensorineural hearing loss in children and the leading infectious cause of central nervous system damage in children [4] , [5] . As a consequence of the importance of congenital HCMV infection for public health , the Institute of Medicine of the National Academy of Sciences , USA , has ranked the development of a HCMV vaccine as a top priority [6] . As with all successful antiviral vaccines , induction of an efficient antibody response will be crucial for the success of such a vaccine [7] . Importantly , an effective anti-HCMV vaccine will need to protect the vaccine from HCMV infection/disease as well as in the case of pregnant women , the developing fetus . Transfer of protective maternal antibodies to the fetus will be critical in this respect and a study of passive transfer of immunoglobulins to pregnant mothers has supported a role of antibodies in reducing the risk for congenital infection [8] , [9] . Also , naturally acquired maternal immunity contributes to prevention of congenital HCMV infection [10] . Although correlates of protection from HCMV infection are poorly understood , it can be predicted that humoral immune responses to the envelope glycoproteins will be particularly important since antibodies directed against these antigens can neutralize virus infectivity directly and/or induce immunoglobulin Fc-receptor mediated effector functions such as antibody dependent cytotoxicity and/or complement mediated effects which can lead to elimination of infected cells [11] . HCMV is a highly complex virus harboring more than 20 different glycoproteins in its envelope [12] , [13] . With respect to induction of neutralizing antibodies during natural infection , the glycoprotein ( g ) B dominates , but additional antigens such as the gM/gN complex and the gH/gL complex have also been identified as highly immunogenic [14]–[16] . Antibodies directed against gB can be detected in all naturally infected individuals [17] . Moreover , a major fraction of neutralizing antibodies in human sera seems to be directed against gB and the overall neutralizing capacity in sera from HCMV-seropositive donors correlates with anti-gB antibody titer [18] . In addition , anti-gB antibodies are effective in preventing cell-to-cell spread [19] . In the guinea pig CMV model , immunization with gB DNA vaccines confers protection from infection [20] . In a recent study protection from brain pathology in murine cytomegalovirus ( MCMV ) infected mice was accomplished by passive transfer of a gB-specific monoclonal antibody ( mab ) [21] . Thus , gB is an attractive antigen for inclusion in a human vaccine and has been part of a number of experimental vaccines [22] , [23] . In fact , a recent phase 2 trial using recombinant gB as vaccine has shown significant protection from infection [24] . However , the spectrum of anti-gB antibodies developing during infection remains poorly defined . HCMV gB is an essential viral protein which is involved in the early events of infection . It has been shown to bind to a variety of cell surface molecules such as heparan sulfate proteoglycans , integrin heterodimers and platelet-derived growth factor-α receptor [25]–[27] . In addition , HCMV gB has been shown to mediate fusion of viral and cellular membranes [28]–[30] . The protein is essential for viral entry and cell-to-cell spread but not for virion attachment , assembly or egress [31] . Three antigenic domains ( AD ) have been described previously . AD-1 consists of approximately 80aa between positions 560 and 640 of gB of HCMV strain AD169 [32] . It is the immunodominant region of gB since nearly all sera from HCMV-infected individuals recognize AD-1 [33] . Antibodies that bind to AD-1 can have virus neutralizing capacity as indicated by the fact that a number of AD-1-specific human mabs have been isolated which show various degrees of neutralizing activity [34] , [35] . Polyclonal AD-1-specific antibodies , purified from human serum , are incapable of completely neutralizing HCMV even at high concentration , indicating that AD-1 is bound by antibodies with widely differing neutralizing activity [36] . It has been suggested that the competitive binding of neutralizing and non-neutralizing antibodies to AD-1 may represent a mechanism to evade efficient neutralization of cell free virus [36] . AD-2 , located at the extreme amino terminus of the protein , consists of at least two distinct sites between aa 50 and 77 of gB . Site I is common to all HCMV strains and induces neutralizing antibodies , whereas the aa sequence of site II differs between strains and is recognized by strain specific antibodies which are incapable of neutralization in vitro [37] . The overall immunogenicity of AD-2 is lower than that of AD-1 since only about 50% of human sera from HCMV-infected donors have antibodies against this determinant [17] . An additional linear aa sequence , AD-3 , recognized by gB-specific antibodies in human sera includes epitopes at the intraluminal/intraviral part of the molecule [38] , [39] . However , antibodies binding to these determinants are non-neutralizing as can be expected from the localization of AD-3 within the molecule . Additional protein domains which are bound by murine mabs have been identified , but whether these regions are relevant in the context of antibody response during natural infection is unknown [40] . The gB-specific human monoclonal antibodies for which the binding sites has been identified react with either AD-1 or AD-2 [35] , [41] . Overall , there are significant gaps in our knowledge of antibody epitopes on gB . Given the size of the gB protein it seems highly likely that additional antigenic domains exist on gB . Defining these sites will not only provide an antigenic map of this important protein , it will also be helpful for monitoring the response to vaccination for production of antibodies with binding profiles similar to natural infection . We comprehensively analyzed the human antibody repertoire against gB as it is developed during infection . To this end we isolated gB-specific memory B cells from different healthy HCMV-seropositive donors and activated the cells at the clonal level to immunoglobulin production . The produced antibodies were tested for reactivity with gB and parts thereof and in in vitro neutralization assays . Our results revealed that most of the anti-gB antibodies produced during infection failed to neutralize cell-free virus . In addition , and perhaps more importantly , we find that the vast majority of anti-gB antibodies with potent neutralizing capacity recognize two protein domains which have not been identified previously as target sites .
We intended to comprehensively analyze the human IgG anti-gB memory B-lymphocyte repertoire established by healthy HCMV infected individuals in terms of epitope specificity as well as neutralizing capacity . To this end we used the complete extraviral part of gB , as it is used in vaccination trials [24] , for sorting of IgG positive memory B-lymphocytes ( CD19+/CD27+ ) binding to fluorochrome-labeled gB by flow cytometry ( Fig . S1A ) . In a first set of experiments we analyzed the possibility to identify gB-specific memory B cells in 15 seropositive individuals . Frequencies of gB-binding , IgG-positive memory B cells among all IgG-positive memory B cells ranged from 0 . 33 to 1 . 4% , being in the range of frequencies of IgG memory B-lymphocytes against other viral antigens [42] ( Fig . S1B ) . Among HCMV seronegative individuals , gB-binding memory B cells were detectable but with considerably lower frequency . These cells might bind gB unspecifically or may represent part of the natural antibody repertoire [43] . Sorted gB-binding B cells were activated and immortalized at the clonal level by an in vitro culture system using CpG oligonucleotides and EBV [44] . Among clonal cultures with IgG secretion 40–95% ( mean 63% ) of cultures showed IgG binding to gB in ELISA , substantiating a high degree of specificity in the cell sorting process ( data not shown ) . Seven donors were selected for further analysis . The anti-gB antibody titer was comparable in this group ( Fig . S2A ) while the neutralization titer varied significantly , which is not uncommon for HCMV-infected individuals ( Fig . S2B ) . From these 7 donors we were able to analyze a total of 888 clonal IgG gB-binding culture supernatants for epitope specificity towards the well-known antigenic domains AD-1 and AD-2 [45] as well as neutralizing capacity against HCMV AD169 on fibroblasts . With respect to binding of gB protein domains we found a high frequency of antibodies binding to the AD-1 epitope ( mean 38 . 1% ) in all individuals correlating with earlier findings that up to 50% of gB-specific IgG in sera might be directed against the AD-1 epitope ( Table 1 ) [38] . Interestingly , only few of the AD-1-specific antibodies were able to neutralize HCMV in vitro ( mean 2 . 0% , range 0–6%; Table 1 ) . The frequency of clones producing AD-2-specific IgG was low ( Table 1 ) and in only 3 out of 7 individuals were we able to retrieve AD-2-specific memory B cells , correlating with earlier data that this specificity is found only in approximately 50% of HCMV infected individuals [17] . None of the rare AD-2-specific antibodies was neutralizing in vitro . A high frequency of clones from all individuals did not react with either AD-1 or AD-2 ( range 40–86%; Table 1 ) . Importantly , among these antibodies a significant number was able to neutralize HCMV in vitro ( 17% out of 429 clones; Table 1 ) . A summary of the neutralizing capacity of the gB-specific B-cell supernatants of 5 donors from which we were able to isolate B cells that secreted neutralizing antibodies is shown in Fig . S2C . We conclude from this analysis that the memory B-cell repertoire against gB is dominated by antibodies that do not neutralize the virus and that most neutralizing antibodies bind to a so far unknown antigenic site . Next , we attempted to map the target sites of antibodies that did not react with the known antigenic sites on gB . To obtain a consistent supply of mabs , the Ig-genes from 10 selected B-cell clones that secreted neutralizing antibodies unreactive with AD-1 and AD-2 were cloned and expressed in recombinant systems as IgG1 molecules ( Table 2 ) . We choose the IgG1 subtype since 8 of the 10 selected B-cell clones secreted IgG1 , whereas 2 secreted IgG3 . The recombinant antibodies showed comparable neutralizing activity to the B-cell supernatants when tested on fibroblasts as target cells indicating that the Fc-part of the IgG is not significantly contributing to the in vitro neutralizing activity of the antibodies ( Fig . 1 ) . 50% neutralization was achieved at concentrations of 0 . 2–1 . 3 µg/ml ( Table 2 ) . Importantly , the recombinant antibodies were also able to neutralize virus on endothelial , epithelial and dendritic cells with comparable activities . Representative results for Group A mabs ( see next paragraph ) on endothelial cells and Group B mabs on epithelial cells are shown in Fig . 1 and the data for all mabs are summarized in Table 2 . The fact that all tested antibodies were not reactive in western blot analyses using extracellular HCMV particles as antigen indicated that the intact three-dimensional protein conformation was crucial for binding ( data not shown ) . Therefore , we used indirect immunofluorescence of transiently expressed fragments of gB in Cos7 cells to obtain further information on the binding sites of the mabs . Two distinct reactivity patterns were observed for the mabs ( Group A and Group B in Fig . 2 ) . Full length gB 1-906 and fragments as short as gB 1-447 were reactive with the entire set of mabs ( Fig . 2 ) . For Group A mabs gB residues 100–342 were sufficient for binding whereas for Group B antibodies a larger fragment of gB comprising residues 100–448 was required for binding , indicating that the gB region between aa 100–448 harbors at least two distinguishable antibody target sites . In order to obtain further information on potential structural domains of gB in the region between aa 100–448 , we generated a three dimensional model of the trimeric conformation of the ectodomain of HCMV gB strain AD169 based on the crystal structure of HSV-1 gB [46] ( Fig . 3 ) . According to the model , both the overall structure of the HCMV gB monomer and the organization of the subunits in the trimer were highly similar to that of HSV-1 gB , as expected from the degree of sequence similarity of gB between human herpesviruses ( 28% identity and 40% similarity between HSV-1 gB and HCMV strain AD169 gB ) . The individual domains I to V , which were previously defined based on the HSV-1 gB structure , can be clearly identified from the homology model of HCMV gB . With respect to potential antibody binding structures , the protein domains ( Dom ) I and II were of particular interest since they are located within aa 100–448 of gB . Dom I ( Ile133 to Thr343 ) constitutes part of the trimer interface and is located proximal to the membrane , potentially containing the fusion domain [47] . The discontinuous Dom II is composed of two separate segments comprising residues Leu121 to Asn132 and Cys344 to Ser438 ( Fig . 3 ) . Either domain contains a single disulfide bond which helps to stabilize the conformation of the respective protein domain [48] . To investigate Dom I and Dom II for antibody binding , expression plasmids were constructed which allowed for the synthesis of either domain in eukaryotic cells . In both cases the cloning strategy involved the attachment of a HA epitope tag at the amino terminus of the respective peptide in order to facilitate detection . Dom I comprised aa 133–343 of gB . The Dom II-specific peptide consisted of the gB-specific residues 112–132 and 344–438 joined by a 5 aa ( Ile-Ala-Gly-Ser-Gly ) synthetic linker sequence . Following transient expression of the peptides in Cos7 cells , antibody binding was analyzed by indirect immunofluorescence . All four antibodies from Group A were reactive with the Dom I-specific peptide , whereas all Group B mabs recognized Dom II ( Fig . 4 and Table 2 ) . The crystal structure of HSV-1 gB as well as the model of HCMV gB predict that for Dom I and Dom II discontinuous sequence stretches are essential for formation of either domain . Shorter protein fragments would be expected not to fold correctly . We tested this assumption by expressing a Dom II variant with five amino acids deleted at the carboxyl-terminus and observed complete loss of binding of all Group A-specific antibodies . Likewise , the continuous part of Dom I ( residues 140–255 ) showed no antibody binding capacity ( data not shown ) . Thus , further resolution of antibody epitopes will be possible only by generation of point mutants in Dom I and Dom II , respectively . Having identified Dom I and Dom II as new targets for neutralizing antibodies , we re-tested clonal antibody supernatants from 4 individuals to obtain information about the overall frequency of Dom I- and Dom II-specific antibodies in HCMV infected individuals . As shown in Table 1 , the frequency of Dom I and Dom II specific memory B cells was variable among different donors and in most cases considerably lower as compared to AD-1-specific B cells . In summary , the repertoire analysis revealed that most anti-gB IgG antibodies derived from memory B cells are non-neutralizing . Among those antibodies that neutralized HCMV in vitro , most antibodies reacted against two previously uncharacterized regions of the gB protein . Addition of complement had no influence on the neutralizing capacity of the recombinant antibodies . Moreover , when a selected set ( n = 10 ) of non-neutralizing antibodies directed against different antigenic domains of gB was tested in concentrations up to 5 µg/ml in the presence of complement we observed no significant increase in neutralization capacity which is in agreement with previous reports on gB-specific human mabs ( data not shown ) [35] . However , we cannot completely rule out a moderate enhancing effect of complement for some antibodies , especially those of the IgG3 subtype . gB has been postulated to be involved in receptor binding of HCMV and entry [26] , [49] , [50] . We therefore analyzed at which stage of the infection the mabs exerted their action . To assay influence on attachment , virus/antibody mixtures were added to target cells at 4°C and the number of HCMV DNA copies attached to the cells was determined by quantitative real time PCR . Neither Dom I- nor Dom II-specific antibodies inhibited attachment of virions , indicating that the mabs did not block receptor binding of HCMV ( Fig . 5A ) . In accordance with previous reports , heparin almost completely prevented virus attachment , whereas the AD-2-specific antibody C23 had no effect on virus binding [27] , [51] . The slight increase of bound virus in the presence of some antibodies as compared to control might reflect deposition of antibody/virus aggregates on the surface of cells . We also determined activity of the mabs towards virus that is already adsorbed to cells . To this end , virus was preadsorbed to cells for 1 h at 4°C before antibody was added . Both Dom I- and Dom II-specific antibodies were capable of neutralizing HCMV at a postadsorption step , whereas non-neutralizing antibodies had no effect ( Fig . 5B ) . The higher antibody concentration that was required to completely neutralize adsorbed virus is explained by the need to block fusion of an already adsorbed virus particle . Competitive binding of neutralizing and non-neutralizing human mabs has been described for gB [35] . We tested whether a similar phenomenon occurs for Dom I- or Dom II-specific neutralizing antibodies . A total of 14 non-neutralizing mabs , directed against different antigenic regions of gB was analyzed in neutralization assays for competition with Dom I- or Dom II-specific mabs ( SM10 , 1G2 , SM5-1 ) . In no case did we observe a reduction in neutralizing capacity by addition of non-neutralizing mabs . The result of a representative analysis is shown in Fig . 6A . Although these data indicated that reduction of neutralizing activity by competing non-neutralizing mabs is not common for Dom I- and Dom II-specific antibodies , it is difficult to assess the relevance of this finding for the situation in human sera . For AD-1-specific antibodies it has clearly been shown that the sum of antibodies , as present in human sera , is not capable of completely neutralizing infectious virus , indicating that non-neutralizing antibodies can constitute a significant fraction of the entire AD-1-specific IgG fraction [36] . To obtain more information on the neutralizing capacity of Dom II-specific antibodies in human sera , we purified Dom II as a GST fusion protein following expression in E . coli ( Fig . S3A ) . The bacterially derived peptide retained the capacity to bind all Group B mabs and the affinity of mab SM5-1 was similar for gB and Dom II ( Fig . S3B ) . Circular dichroism spectra indicated a homogeneous three-dimensional structure ( Fig . S3C ) . Using this protein , polyclonal Dom II-specific antibodies were affinity-purified from pooled human sera ( Fig . S4 ) . The affinity-purified IgG preparation was then tested in neutralization assays . 50% neutralization of input viral infectivity was achieved with an IgG concentration of approximately 0 . 2 µg/ml which is within the same concentration range as the Dom II-specific mabs ( Fig . 6B ) . In summary , these data provided evidence that gB Dom II not only represents an immunogenic domain on gB , but also that antibodies binding to it in general have potent neutralizing capacity . Similar experiments using Dom I could not be carried out since the Dom I domain does not fold correctly after prokaryotic expression and thus antigen for affinity purification of antibodies could not be generated . Previous analyses of human sera for recognition of gB domains have revealed differential rates of antibody reactivity for the individual antigenic domains . Whereas almost 100% of infected individuals develop antibodies against AD-1 , only 50% show reactivity against AD-2 [17] . To obtain information on the frequency of recognition of Dom I and Dom II we determined antibody reactivity against these domains in a larger serum panel of HCMV infected individuals and compared it to the known antigenic domains . A total of 80 randomly selected sera from HCMV seropositive individuals , as determined by a commercially available ELISA test , was analyzed . Ten sera from HCMV negative donors served as controls . Within the serum panel from HCMV-seropositive individuals , reactivity for gB was 100% , underscoring the antigenicity of this protein ( Fig . 7 ) . In accordance with our previous observations , positive reaction with AD-1 was also 100% and 57% of the sera contained antibodies against AD-2 [17] . Dom I was recognized by 55% whereas 94% of the specimens reacted with Dom II . Thus , Dom I and Dom II represent antigenic domains on gB which induce antibodies with high frequency during infection . For the sake of consistency in nomenclature of gB antigenic domains , they were designated AD-4 ( Dom II ) and AD-5 ( Dom I ) .
We have used recombinant gB to analyze the antibody repertoire derived from activated memory B cells of healthy HCMV seropositive individuals . The donors were not selected for hight titers of anti-gB or neutralizing antibodies against HCMV in order to obtain an unbiased result . Our results reveal a number of new findings with respect to the anti-gB response in humans . The vast majority of anti-gB antibodies did not show antiviral activity in in vitro assays . Among the seven donors that were tested comprehensively , the percentage of neutralizing antibodies among the gB binders ranged from 0% to 11% ( mean 3% ) . Previous studies using adsorption of antibodies to gB have noted that in some individuals the overall neutralization capacity cannot be reduced , indicating that in these cases non-gB specific antibodies are major components of the neutralizing antibody response [18] . Repertoire analysis in other viral systems have also noticed an excess of binding versus neutralizing antibodies [44] , [52] , [53] . At this time we can only speculate on a potential role of the non-neutralizing antibodies on the infection in vivo . Besides being irrelevant for the infection , non-neutralizing antibodies might have positive or negative effects . Effector functions mediated via the Fc-portion of the antibodies such as ADCC and/or complement fixation may contribute to elimination of infected cells and thus could lead to enhanced clearance of these cells . That such mechanisms are operative in vivo has been shown in different viral systems , including herpesviruses , but not for HCMV [54] , [55] . On the other hand , infection-enhancing effects could be contributed by mechanisms such as competition of non-neutralizing antibodies for binding to neutralizing epitopes or enhanced infection of Fc-receptor bearing cells such as monocytes , which is one of the major target cell population for HCMV infection in vivo [56] . Whereas all anti-gB mabs recognized mammalian cell expressed gB aa 1-687 , only 40% to 50% of anti-gB antibodies were reactive with a shorter fragment expressing aa 1–447 indicating the presence of epitopes in gB between aa 448 and 687 . These antibodies were negative for recognition of the bacterial fusion protein containing AD-1 ( aa 484–650 ) . Thus , the location of the antibody binding site ( s ) remains unknown . There is the possibility that residues 447–484 and 650–687 contain additional epitopes since they are not represented by the AD-1 fusion peptide . However , this seems unlikely . The more plausible explanation for our finding is that the region between aa 447–687 contains additional conformational epitopes that are not formed in the bacterial fusion protein . Our previous analyses have shown that AD-1 induces a multitude of antibodies with different binding characteristics , even when assayed as bacterial fusion proteins or synthetic peptides [33] . Thus , it would be not surprising that this region of gB contains additional epitopes which are present only on the native protein . The large fraction of antibodies falling in this category also supports this possibility . A corresponding protein region of HSV-1 gB was found to contain a “pseudocontinuous” epitope , further supporting our interpretation [57] . However , no matter what the underlying mechanism of this finding is , antibodies binding within the 447–687 region of gB were non-neutralizing . Antibodies which show potent in vitro neutralizing activity were found to bind to two previously unknown protein domains , namely AD-4 and AD-5 . Because this was demonstrated in most donors , it is unlikely a sampling artifact . The antigenicity of these domains is also indicated by the fact that in randomly selected serum samples from HCMV-seropositive donors we found positivity rates of >90% for AD-4 and >50% for AD-5 , which identified both domains as strongly antigenic structures on gB . Comparable positivity rates among sera from HCMV infected individuals have been reported for AD-1 and AD-2 , respectively , and were confirmed in the current analysis [17] . A distinct difference between AD-4/AD-5 and AD-1/AD-2 is the functional antiviral activity of the domain specific antibodies . AD-1 is bound by virus neutralizing and non-neutralizing monoclonal antibodies which can compete for binding to the domain [35] , [58] . Affinity purified AD-1-specific IgG fractions from pooled human sera were shown to have neutralizing capacity not exceeding 50% , indicating that during natural infection a considerable proportion of competing non-neutralizing antibodies are induced [36] . The incomplete neutralizing capacity of polyclonal anti-AD-1 antibodies has been suggested to provide the virus with an effective mechanism to evade the humoral immune response . Likewise , AD-2 harbors two different antibody binding sites which are bound by neutralizing and non-neutralizing antibodies , respectively [37] . The situation seems to be different for AD-4 and AD-5 in that almost all human monoclonal antibodies that have been obtained so far have potent virus neutralizing activity . Moreover , the affinity-purified AD-4-specific polyclonal human IgG fraction had a neutralizing titer that was comparable to the monoclonal antibodies with 50% neutralization in the low nanomolar range . Thus , it seems unlikely that AD-4 induces significant concentrations of non-neutralizing antibodies that compete with neutralizing antibodies for binding to the domain . Whether this correlation also holds true for AD-5 needs to be determined in further studies . AD-4- and AD-5-specific mabs did not prevent virus attachment to fibroblasts indicating that neither type of antibody interferes with receptor binding of HCMV . However , they were capable of neutralizing infectious virus at a postadsorption step . In the case of HSV-1 it has been shown that neutralizing anti-gB murine mabs have different effector mechanisms . Antibodies that bind to the HSV-gB domain IV ( corresponding to AD-1 in HCMV gB ) block fusion of viral and cellular membranes but do not interfere with interaction of gB with gH/gL , the second constituent of the minimal herpesviral fusion complex . Murine mabs specific for the HSV-gB domains I and II ( corresponding to AD-4 and AD-5 in HCMV gB ) block interaction of gB with gH/gL and thereby inhibit fusion [59] , [60] . Whether similar effector mechanisms apply for HCMV needs to be determined in further studies . HCMV isolates from clinical samples exhibit extensive genetic variation and HCMV reinfections have been demonstrated to occur in seropositive individuals . In immunocompromised hosts , when the development of a de novo humoral immune response is impaired , reinfection with a different HCMV isolate might result in clinical symptoms , due to unrestricted replication of the ‘new’ virus strain [61]–[65] . However , we have no information on whether reinfection of seropositive individuals is inevitable upon contact with a different HCMV strain or whether some infected hosts develop an immune response which prevents reinfection . It will be important to analyze whether such “absolute controllers” exist and what the correlate of protection is . In the HIV field it has clearly been shown , that a small fraction of infected individuals can develop broadly neutralizing antibodies which control the viral mutants that develop during infection and prevent progression to disease [53] , [66] . In the case of HCMV , it will be interesting to determine whether production of antibodies against the individual antigenic domains of gB or , for that matter additional envelope glycoproteins , correlates with reduced susceptibility to reinfection . Of note , the gB protein region harboring AD-4 and AD-5 is highly conserved between different HCMV isolates ( Fig . S5 ) and is situated well outside the polymorphic protein segments that have been defined [67] . In summary , we have investigated the human antibody repertoire against gB using a recombinant protein that is currently used in vaccine trials . Our data reveal new antigenic sites on the protein , which are immunogenic during infection and , more importantly , target of potent antiviral antibodies . It will be interesting to compare our findings to the B-cell repertoire against gB as it is produced during infection since this may enable us to draw conclusions about the structural integrity of the gB vaccine antigen . Improving our understanding of the antigenic map of gB will be of value in the rational design of future vaccine antigens . Lastly , human mabs with potent and broadly neutralizing activity might be useful as biologicals in prophylaxis and/or therapy of HCMV infections .
Ethics approval for the sample collection has been obtained from the Ethics Committee of the Medical Faculty of the Friedrich-Alexander Universität Erlangen-Nürnberg . Written informed consent was obtained from all donors . African green monkey kidney cells ( Cos7 ) were cultured in Dulbecco’s modified Eagle’s medium ( DMEM ) ( Invitrogen , Germany ) supplemented with 10% fetal calf serum ( FCS ) , glutamine ( 100 mg/l ) and gentamicin ( 350 mg/l ) . HCMV was propagated in human fetal lung fibroblasts ( MRC-5 ) or human foreskin fibroblasts ( HFF ) grown in DMEM supplemented with 10% FCS , glutamine and gentamicin as above . For immunofluorescence cells were grown on 13-mm glass coverslips in 24-well plates . HB15-UL84prluc represents a recombinant AD169-based HCMV which expresses the firefly luciferase gene under the control of the HCMV UL84 promoter . Peripheral blood mononuclear cells ( PBMCs ) were isolated from peripheral blood of healthy , HCMV-positive volunteers using Ficoll-density gradient centrifugation . After B-cell enrichment using anti-human CD22-microbeads ( Mitenyi Biotec , Bergisch Gladbach , Germany ) , B cells were labeled with the following reagents: 1 . Anti-human CD19-PerCP ( Dianova , Germany ) ; 2 . Anti-human CD27-PE ( BD Bioscience Pharmigen , Switzerland ) ; 3 . anti-human IgG-FITC ( Dianova , Germany ) ; 4 . Cy5-labeled glycoprotein B ( Sanofi Pasteur , 100 ng per 1×106 B cells ) . The gB protein was labeled with Cy5 using the FluoroLink-Ab Cy5 labelling kit ( Amersham Pharmacia Biotech , Germany ) . gB-specific , IgG-positive memory B cells were either analyzed using FACSCalibur ( Becton Dickinson , Germany ) or isolated by sorting cells that fulfilled the criteria PerCP+/PE+/FITC+ and Cy5+ . Cells were sorted at 5 cells/well , in 96 F-bottom microplates containing a confluent layer of irradiated feeder cells ( HFF ) , using a MoFlo cell sorter ( Cytomation , Germany ) . Sorted cells were grown in complete RPMI-1640 medium supplemented with 2 mM glutamine , 100 IU/ml penicillin , 100 µg/ml streptomycin , 50 µM 2-mercaptoethanol and 10% FCS ( heat-inactivated ) ( PAN-Biotech , Germany ) in the presence of EBV and CpG ODN 2006 as previously described [44] . After 3 weeks , the culture supernatants were screened for antigen recognition and/or virus neutralization . Supernatants were classified as neutralizing if at a 1:1 dilution the neutralization capacity exceeded 70% of input virus . To produce selected recombinant human monoclonal antibodies the Ig heavy and corresponding light chains were amplified by RT-PCR from clonally expanded activated memory B cells and cloned into eukaryotic expression vectors exactly as described by Tiller et al . [68] . The respective cloning vectors were kindly provided by H . Wardemann , Berlin . V gene usage and CDR sequences are supplied in Table S1 . Fibroblasts ( 1×104 ) in a volume of 100 µl were seeded in 96-well plates . HB15-UL84prluc ( 275 pfu ) was preincubated with serial log2 dilutions of complement inactivated serum or mab in a volume of 50 µl in culture medium for 1 h at 37°C . The mixture was added to fibroblasts for 4 h . The inoculum was removed and the cells were incubated at 37°C for 48 h . Cells were lysed using 100 µl Glo Lysis Buffer ( Promega , USA ) per well . 30 µl of each cell lysate was placed in white 96-well LIA-plates . Per well , 50 µl assay buffer ( 15 mM KH2PO4 , 25 mM glycylglycine , 1 M MgSO4 , 0 . 5 M EGTA , 5 mM ATP , 1 mM DTT ) was added . Luciferase activity was measured by injection of 50 µl D-luciferin ( P . J . K . , Germany ) solution per well ( in 25 mM glycylglycine , 1 M MgSO4 , 0 . 5 M EGTA , 2 mM DTT and 0 . 05 mM D-luciferin ) and detection of chemiluminescence was performed by an Orion Microplate Luminometer ( Berthold Technologies , Germany ) . Neutralization assays using endothelial and epithelial cells were performed using the HCMV isolate TB40E as described [69] . The influence of complement on neutralization capacity of monoclonal antibodies was tested by inclusion of 5% rabbit complement ( Cedarlane Labs , Canada ) in the neutralization assay . Fibroblasts were seeded at 3×104 cells per well in 96-well plates . HB15-UL84prluc was preincubated with individual mabs for 1 h at 37°C at concentrations ensuring complete neutralization . Cells and the virus/mab mixture were cooled to 4°C and the virus/mab mixture was added to the cells at a multiplicity of infection ( m . o . i . ) of 0 . 5 . Following incubation for 1h at 4°C , cells were washed three times with ice-cold PBS and cell lysates were prepared by freezing/thawing . DNA was extracted from the lysates using a MagNA Pure LC ( Roche , Germany ) instrument and quantitative real-time PCR was performed on an ABI PRISM 7500 . To control for recovery of cells , copy numbers of albumine DNA was determined in parallel to HCMV and HCMV copies were calculated per 1000 copies albumine . Primers : CMV 5′:GAGCAGACTCTCAGAGGATCGG; CMV 5′: AAGCGGCCTCTGATAACCAAG; Albumine 5′: GTGAACAGGCGACCATGCT; Albumine 3′: GCATGGAAGGTGAATGTTTCAG . For the penetration assay , precooled cells were preincubated with virus at a m . o . i . of 0 . 2 for 1 h at 4°C , washed twice with ice-cold PBS and incubated for 2 h at 37°C with log10 dilutions of mabs . After incubation , remaining mabs were removed by washing twice with PBS before cells were incubated for 48 h at 37°C . Subsequent steps were carried out according to the virus neutralization assay . Chemiluminescence of virus only was set to 100% . Construction of the expression plasmid coding for complete gB has been described previously [32] . Carboxyterminal truncated forms of gB were expressed using pcDNA3 as plasmid . Aminoterminal truncations were expressed using the vector pcUL132sigHA . This pcDNA3 . 1 based plasmid contains the coding sequence of the HCMV gpUL132 authentic signal sequence aa 1-27 , followed by the coding sequence for the influenza hemagglutinin ( HA ) epitope YPYDVPDYA [70] . To express Dom II ( AD-4 ) , the nucleotide sequence coding for aa 112–132 and 344–438 was chemically synthesized by GeneArt , Germany . The two parts were joined by a nucleotide linker coding for the sequence Ile-Ala-Gly-Ser-Gly and inserted in pcUL132sigHA to give rise to pcAD-4 . To express Dom I ( pcAD-5 ) the nucleotide sequence coding for aa 133-343 were inserted into pcUL132sigHA . To generate plasmids for expression of AD-4-GST ( Glutathion-S-transferase ) fusion proteins in E . coli we used the expression vector pGEX-6P-1 ( Pharmacia Biotech , Germany ) . Cos7 cells grown on glass coverslips in 24-well plates were transfected with 0 . 8 µg of plasmid DNA using Lipofectamine ( Invitrogen , Germany ) . 48 hours after transfection the cells were fixed and permeabilized with ice cold methanol . Primary antibodies were then added . Unbound primary antibody was removed by three washing steps using PBS . Binding of the primary antibody was detected with the appropriate secondary antibody conjugated with FITC ( fluorescein isothiocyanate ) ( Dako , Germany ) . Counterstaining of cell nuclei was done with DAPI ( 4' , 6-diamidino-2-phenylindole ) . Images were collected using a Zeiss Axioplan 2 fluorescence microscope fitted with a Visitron Systems charge-coupled device camera ( Puchheim , Germany ) . Images were processed using MetaView software and Adobe Photoshop . Antibodies: gB-specific human mab C23 ( TI-23 ) [41] , gN-specific murine mab 14-16A [71] , gH-specific murine mab SA4 [72] , murine anti-HA ( Sigma Aldrich , Germany ) and murine anti-GST ( BIOZOL , Germany ) . Plasmid DNA was used to transform E . coli DH10B for expression of GST fusion proteins . The respective fusion proteins were induced and the soluble form of the protein was purified from E . coli lysates according to the manufacturer’s instructions . To prepare an affinity matrix , 2 . 6 mg of purified AD-4-GST fusion protein was dialysed against coupling buffer and conjugated to AminoLink Plus Coupling Resin ( Thermo Fisher Scientific , USA ) according to the manufacturer's instructions . Four ml of a HCMV hyperimmune globuline preparation , diluted 1∶3 ( v/v ) with PBS , was passed over 2 ml antigen-coupled beads , followed by extensive washing with PBS . Bound IgG was eluted with 0 . 2 M Glycin-HCl , pH 3 . 0 , in 1 ml fractions and fractions were dialysed against PBS . Total IgG concentration was determined by an ELISA . In brief , polystyrene 96-well plates were coated with 100 ng AffiniPure goat anti-human IgG , Fcγ-specific ( Jackson Immuno Research , USA ) in 0 . 5 M carbonate buffer , pH 9 . 6 , overnight at 4°C . Serial log2 dilutions of the eluted fractions in a volume of 50 µl were added and bound IgG was detected by using a polyclonal peroxidase-conjugated goat F ( ab ) 2-fragment anti-human IgG , Fcγ-specific ( Jackson Immuno Research , USA ) . A human IgG preparation ( Jackson Immuno Research , USA ) with known concentration was used as standard . The following gB-specific antigens were used: gB , AD-1 , containing aa 484–650 of gB , AD-2 , containing aa 68–80 and AD-4-GST . Proteins were diluted between 25 ng and 200 ng ( depending on antigen ) in 0 . 5 M sodium carbonate buffer , pH 9 . 6 , or in 6 M urea ( AD-1 ) and 50 µl was used to coat microtiter plates overnight at 4°C . All subsequent steps were carried out at room temperature . Reaction wells were rinsed with PBS supplemented with 0 . 1% Tween 20 and blocked for 2 h with PBS containing 2% FCS . Plates were again rinsed with PBS supplemented with 0 . 1% Tween 20 and incubated with mabs , human serum or polyclonal eluted antibody fractions ( 50 µl/well ) for 2 h . Unbound antibody was removed by washing and peroxidase-conjugated anti-human or anti-mouse IgG ( Dako , Germany ) was added at an appropriate dilution for 1 h . The plate was washed and 100 µl TMB ( tetramethylbenzidine ) peroxidase substrate , diluted 1∶1 in peroxidase substrate solution B ( KPL , USA ) , was added for 5 min . The reaction was stopped by the addition of 100 µl 1 M H3PO4 and the OD450 was determined using Emax microplate reader ( Eurofins MWG Operon , Germany ) . Dilution of all antibodies was done in PBS with 2% FCS . In all assays involving gB fusion proteins , the respective prokaryotic fusion partner was assayed in parallel and the optical density subtracted from values obtained with the gB fusion protein . AD-5-specific antibodies in human sera were measured in a capture ELISA . 96 well plates were coated with 125 ng/well of an anti-HA monoclonal antibody ( Sigma , Germany ) and blocked for 2 h with PBS containing 2% FCS . The plates were incubated with culture supernatant from cells that had been transfected with pcAD-5 six days before . Plates were rinsed and incubated with human sera in a 1∶50 dilution for 2 h at 37°C . The plates were washed and developed as described above . The model of the HCMV gB structure was generated by standard homology modelling procedures using the program MODELLER [73] , based on a sequence alignment with the template structure of HSV-1 gB [46] . Two loop regions ( Val306 to Glu317 and Leu439 to His468 of HCMV gB ) were not resolved in the reference structure and could therefore not be modelled . All images were generated with Accelrys DS Visualizer v2 . 0 . 1 . The quality of the model was validated using ProSA [74] and PROCHECK [75] . ProSA analysis reveals that the overall model quality ( Z-score = −6 . 22 ) is quite similar to that of the template crystal structure ( Z-score = −7 . 84 ) . Both values are within the range of z-scores typically found for crystal structures of proteins of similar size . In addition , the residue energy profiles of template and target structure are very similar indicating that the modelling did not place amino acids in an unfavourable environment . In addition , analysis of the backbone geometry shows that that 87 . 5% of all residues of HCMV gB are located in the most favourable regions of the Ramachandran Plot . This value corresponds to a crystal structure with 2 . 0–2 . 5 Å resolution . The HCMV gB model will be made available by the authors upon request . GenBank accession numbers for the individual heavy and light chain nucleotide sequences of the recombinantly expressed IgG molecules are JF806449-JF806467 . | The development of antibodies is a major defense mechanism against viruses . Understanding the repertoire of antiviral antibodies induced during infection is a necessary prerequisite to defining the protective activities of an antiviral antibody response . The isolation of antigen specific memory B cells and subsequent stimulation to antibody producing cells provides a powerful tool to study the antibody repertoire in infected individuals . We have used this approach to analyze the antibody repertoire against glycoprotein B ( gB ) of human cytomegalovirus ( HCMV ) , a major antigen for the induction of antiviral antibodies during infection and a constituent of experimental vaccines in humans . We find in different infected individuals that the vast majority of gB-specific B cells produce antibodies that cannot neutralize free virus . Antibodies with antiviral capacity target two domains of gB that have not been previously identified . The identification of these new antigenic domains was possible with the aid of a 3D molecular model of HCMV gB . Our results will be useful for vaccine development since comparison of the immune response after natural infection with that induced by vaccination can be readily accomplished . Moreover , neutralizing human monoclonal antibodies could constitute powerful therapeutics to combat the infection in populations at risk for HCMV disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"viral",
"vaccines",
"virology",
"immunology",
"biology",
"microbiology",
"immune",
"response",
"immunoglobulins"
] | 2011 | B Cell Repertoire Analysis Identifies New Antigenic Domains on Glycoprotein B of Human Cytomegalovirus which Are Target of Neutralizing Antibodies |
The efforts to control and eradicate polio as a global health burden have been successful to the point where currently only three countries now report endemic polio , and the number of cases of polio continues to decrease . The success of the polio programme has been dependant on a well-developed network of laboratories termed the global polio laboratory network ( GPLN ) . Here we explore collaborative opportunities with the GPLN to target two of the 18 diseases listed as a neglected tropical diseases ( NTD ) namely soil transmitted helminthiasis ( STH ) and Schistosomiasis ( SCH ) . These were chosen based on prevalence and the use of faecal materials to identify both polio , STH and SCH . Our study screened 448 faecal samples from the Ghana GPLN using three triplex TaqMan assays to identify Ascaris lumbricoides , Necator americanus , Ancylostoma spp , Trichuris trchiura , Strongyloides stercoralis and Schistosoma spp . Our results found a combined helminth prevalence of 22% . The most common helminth infection was A . lumbricoides with a prevalence of 15% followed by N . americanus ( 5% ) , Ancylostoma spp . ( 2 . 5% ) , Schistosoma spp . ( 1 . 6% ) and S . stercoralis ( 1% ) . These results show that it is possible to identify alternative pathogens to polio in the samples collected by the GPLN platform and to introduce new diagnostic assays to their laboratories . The diagnostic methods employed were also able to identify S . stercoralis positive samples , which are difficult to identify using parasitological methods such as Kato-Katz . This study raises the possibility of collaboration with the GPLN for the surveillance of a wider range of diseases which would both benefit the efforts to control the NTDs and also increase the scope of the GPLN as a diagnostic platform .
In 1988 the WHO set out to eradicate polio after the successful development of effective polio vaccines and since then the eradication campaign has reduced the number of countries reporting endemic polio from 125 to three in 2016 . Control of polio has been a co-ordinated effort involving two main arms; the delivery of vaccination alongside establishing an effective laboratory network for monitoring and surveillance . This surveillance arm is comprised of 145 labs spread throughout the world which taken as a whole forms the Global Polio Laboratory Network ( GPLN ) . The network receives samples from local health clinics where individuals have presented with clinical signs of the disease , typically acute flaccid paralysis ( AFP ) , with the need to confirm or exclude an aetiology of polio . Thus , investigation of AFP initially involves collection of a faecal sample ( s ) which is then transferred from the regional clinic thence to the central laboratory to undergo a culture screen for polio virus and if found positive is then followed up with a real-time PCR analysis with diagnostic primers able to identify and discriminate if the sample is wild type , vaccine strain or a vaccine-derived virus . Across Africa there are 16 GPLN labs and these have received , in total , an average of 22 , 017 samples per year in the past 5 years with Ghana contributing , on average , 350 samples per year . These 16 laboratories are divided into three regional reference laboratories ( RRLs ) and 13 intratypic differentiation laboratories ( ITD ) . The ITD laboratories are responsible for the isolation of poliovirus , molecular characterization of isolates and referral of critical samples to a sequencing laboratory [1] . Currently faecal samples collected by the GPLN are only screened for polio and non-polio enteroviruses; here we explore the potential of the GPLN to screen for other pathogens of public health importance allowing for co-investigation . Across the world , but especially in Africa , the Neglected Tropical Diseases ( NTDs ) are an umbrella group of diseases that afflict the poor and retain a cycle of poverty . In total , approximately a billion people from the poorest communities across the globe are infected with at least one NTD [2] . There are currently 18 diseases listed as NTDs [3] with seven of these diseases caused by parasitic helminths . The intestinal nematodes , often referred to as soil transmitted helminths ( STH ) contribute the greatest number of infections and highest number of DALYs lost for any NTD [4] closely followed by schistosomiasis ( SCH ) , a waterborne trematode infection [5] . Recently the importance of control of these diseases has been recognised by policy makers and steps have been taken to develop cost effective strategies in managing them [6] . Although there are WHO guidelines for classic parasitological surveillance , there is no equivalent for a molecular diagnostic platform of these infections . A key block in doing so is the cost of setting up a standard surveillance platform , de novo , it would therefore be sensible to expand and strengthen existing surveillance structures . The GPLN is a good example and is maintained with substantial annual investments [7] . Thus being able to augment or ‘piggyback’ appropriate NTD surveillance onto the GPLN could have the necessary ‘kick-starting’ effect to provide better access to diagnostic tools needed for control and elimination of STH and SCH [8–12] . Addressing the need for a molecular diagnostics platform for NTDs , in this investigation we explore and develop synergies and necessary steps with the GPLN , taking advantage of its accumulated experience and resources , to include pilot screening for STH ( A . lumbricoides , N . americanus , Ancylostoma spp , T . trchiura , S . stercoralis ) and SCH ( S . mansoni and S . haematobium ) . The Ghanaian National Polio laboratory based at the Noguchi Memorial Institute for Medical Research was selected to carry out this assessment determining the suitability of the GPLN faecal collections with multiplex TaqMan diagnostic assays .
Ethics applications were approved by LSTM ( Research protocol 16-007 ) and Noguchi Scientific and Technical Committee ( Study number 065/16-17 ) followed by the Institutional Review Board . To obtain approval , initial patient collection forms were amended to later facilitate expanded diagnostic testing with the results made available to the national NTD programme . All participants were anonymised for the final study . Prior to the work being carried at the Ghanaian GPLN a workshop was carried out to train the staff in the methods used to extract DNA from faecal samples and the subsequent optimisation and running of the qPCR TaqMan assays . The workshop included both practical and theoretical training , this gave the staff of the GPLN a good background knowledge and practical experience in the methods they would use [13 , 14] . The samples used in this study were faecal samples sent to the Ghanaian GPLN laboratory from individuals presenting with acute flaccid paralysis . The samples were sent via courier from local health clinics and were kept at 4°C until it reached the GPLN laboratory at which point they were stored at -20°C . The age of the patients that supplied the sample as well as the district from which it originated from were available to this study . Faecal samples were removed from the -20°C freezer and allowed to defrost at room temperature , once defrosted ~0 . 1g of faeces was removed and placed into a 2mL screw cap sample tube that was preloaded with 0 . 9g of 1 . 4mm ceramic beads . To this 250μL of a 2% PVPP/PBS suspension was added and the sample vortexed for 5-10 seconds . The faecal suspension was then frozen at -20°C overnight . The following day the samples underwent bead-beating at 3000rpm for 30 seconds using the MagnaLyser system . DNA extraction was carried out using the QIAamp DNA Mini kit per the manufacturer’s instructions with the following two modifications: i ) an aliquot of phocine herpes virus-1 was added to the AL buffer to act as an internal positive control for the subsequent TaqMan assays , ii ) the DNA was eluted in 200μL of nuclease free water . As well as introducing Phocine Herpes Virus ( PhHV ) into each sample to act as an internal positive control a DNA extraction negative control was introduced after every 47th sample , in total 448 samples were processed [15] . The six helminth types were screened using previously described primers and probes [8–10 , 12 , 16 , 17] and these were used in three triplex reactions , each targeting two helminth types and the internal positive control . The first of these targeted S . stercoralis and N . americanus; the second targeted Ancylostoma spp . and a generic Schistosoma spp . ( S . mansoni , S . haematobium , S . intercalatum ) ; the third triplex reaction targeted A . lumbricoides and T . trichiura ( Table 1 ) . The primer concentrations were determined individually through primer limiting assays and then tested in the final triplex concentrations using mono , double and triple target DNA assays to ensure there was no internal competition within a reaction . The final volume for each triplex reaction was 20μL , consisting of 12 . 5μL of iQ supermix , 2μL of DNA template and a final helminth primer concentration of 200nM except for Ancylostoma spp . which ran at 300nM; the concentration of all probes and the PhHV primers was 100nM . All assays were processed using the same ABI 7500 qPCR thermocycler . Each qPCR run consisted of the following cycle , an initial holding step at 95°C for three minutes followed by 50 cycles of 95°C for 15s , 60°C for 30s , 72°C for 30s and a final extension step at 72°C for two minutes .
The 10 regions of Ghana were used to categories the origins of the samples collected by the Ghanaian GPLN . This was then used to determine how evenly across Ghana the origins of the samples were distributed . The region that contributed the most samples was Brong Ahafo , where 23% of the samples originated from following this was the Western region , supplying 15% of the samples . The Ashanti , Central , Greater Accra , Northern , Upper East and Volta regions all contributed a similar amount of between 7-10% . The regions that contributed the least number of samples were the Eastern and Upper West Regions . The distribution of the participants that supplied the samples can be shown to have come from across the country with most regions contributing a similar number of samples . Due to the anonymity of the samples the sex of the participants was unknown and could not be included as a risk factor , however their age was recorded . It was possible to observe the age range of samples as this would affect the suitability of samples for STH screening . Across the 10 regions the average age ranged from 5 to 6 years and an analysis using ANOVA resulted in a P = 0 . 16 , indicating there was no significant difference in participants age across the six regions of Ghana . Breaking down the ages of participants into pre-school age ( PSAC , 0-4 yrs ) , school age ( SAC , 5-16yrs ) and adults ( 17+ ) the following percentages were found for each group: 60% , 30% and 10% respectively . The qPCR assay was successful in identifying positive samples for A . lumbricoides , N . americanus , A . duodenale , Schistosoma and S . stercoralis . A total of 102 out of 448 samples were found to be positive for one or more helminth types tested , giving an overall prevalence of 22 . 7% with 92 of these being single helminth infections and 10 being double infections ( Table 2 ) The proportion of samples positive for the different helminth types is shown in Fig 1 , A . lumbricoides was found to be the most prevalent helminth , being found in 16% of all samples . The two-hookworm species followed with N . americanus found in 6% of samples and Ancylostoma spp . found in 3% . The prevalence of Schistosoma spp . and S . stercoralis was 2% and 1% respectively whilst no samples were found to be positive for T . trichiura . The distribution of helminth species across the different regions of Ghana varied with Brong Ahafo having the highest proportion of positive samples and the central region having the lowest proportion of positives . The distribution of helminth species across these regions was also not even with the Upper West , Northern and Eastern regions only being positive for two of the helminth types ( Fig 2 ) . Other regions contained samples positive for multiple species of helminth .
The purpose of this study is to demonstrate the suitability of adapting a GPLN laboratory for the detection of STH and SCH . It is not within the scope of this study to infer anything from the epidemiological data as the sample size is too small to be representative of the different administrative regions of Ghana . Similarly the samples collected by the GPLN will not be representative of the communities they originate from as they are from individuals that have presented with specific clinical symptoms , notably acute flaccid paralysis . There were a total of 102 helminth positive samples detected out of a total of 448 samples screened , of which 92 were single infections comprising A . lumbricoides ( 59 ) , N . americanus ( 15 ) and Ancylostoma spp . ( 10 ) respectively . Schistosoma spp . ( 5 ) was the next most common helminth although surprisingly no cases of Schistosoma spp . were detected in samples from the Volta . S . stercoralis is perhaps the least understood of the intestinal helminths [19] and is difficult to detect with traditional techniques , despite this our study was able to identify five cases of S . stercoralis , three single infections and two co-infections with A . lumbricoides . The total number of samples positive for co-infections was 10 of which half were a co-infection of A . lumbricoides and N . americanus . The results show that the average age of participants falls between five to six years which means they fall within the SAC age group which is the usual target group for STH and SCH prevalence surveys . The sample contribution from each region varied from 23% to 3% however seven out of the 10 regions contributed a similar percentage of samples . Surprisingly no SCH positives were found in samples from the Volta . The reason for the lack of SCH positives from the Volta region is not yet clear and could be due to insufficient samples from this region , although this is unlikely as regions contributing fewer samples were still found to have SCH positives . An alternative explanation for the lack of Schistosoma s . l . positives is that the method described in this paper is more suited to the detection of S . mansoni than it is for S . haematobium , whose eggs are typically passed in stool samples , whereas S . haematobium predominantly pass their eggs in urine [20] . The scientific community has long acknowledged the likely high burden of disease and morbidity that is caused by S . stercoralis [21] . Current estimates of global infection range from 30 to 100 million [22 , 23] although these estimates are based on imperfect sampling techniques and a more recent study has proposed a higher prevalence of 370 million people infected world-wide [24] . This wide range in prevalence estimates highlights the variable reliability of different screening methods . The most common method of screening for helminth infections , Kato-Katz , is poorly suited to the detection of S . stercoralis [25] . The success of the methodology used in this paper to detect S . stercoralis alongside the other STH species and Schistosoma spp . demonstrate the versatility of using qPCR to detect a wider range of helminth infections than more traditional methods . Currently there is no data regarding the distribution of S . stercoralis in Ghana however by screening the samples from the GPLN we were able to identify five samples positive for S . stercoralis . The current design of the GPLN is not yet suited for its samples to be used to infer the distribution of STH and SCH as the samples are too few in number and are from a specific group within the population , those presenting with clinical signs of polio . The introduces confounding factors and does not provide a representative cross section of the communities at risk , notably adults and school age children were fewer in number than pre-school age children . To become an adequate surveillance platform the range of clinical symptoms for sample collection would need to be widened to include those presenting with STH and SCH symptoms . This would no doubt increase the number of samples being sent in for analysis and subsequently improve the surveillance capabilities of the system . However , this would no doubt incur a greater cost , a possible solution would be to incorporate other pathogens for screening to attract extra funding to cover these costs . In conclusion , the findings of this study show that it is possible to identify STH and SCH positives in the faecal samples collected by the GPLN and that new diagnostic techniques can be introduced to compliment the work currently being carried out . The current narrow clinical symptoms required to qualify a sample to be sent to the GPLN limits their epidemiological use , a change in sample submission policy would be required to improve their epidemiological relevance . Despite this the study demonstrates a potential way forward in the monitoring and control of NTDs that could be included in the legacy plan of the GPLN . | The successful campaign being waged against polio has eliminated the disease from most countries where it was once endemic . With this success , it is anticipated that the disease will be eradicated in the coming years with only 37 cases being reported in 2016 . Although the efforts to control polio are successful there are a number of low-profile , but no less serious disease , that are still highly prevalent throughout the world . These diseases have been termed the neglected tropical diseases ( NTD ) and this study aims to test the suitability of the Global Polio Laboratory Network ( GPLN ) as a platform to screen for two of the NTDs , soil transmitted helminthiasis ( STH ) and schistosomiasis ( SCH ) . To test the suitability of the samples collected by the GPLN and the suitability of the laboratories themselves 448 samples from the Ghanaian GPLN laboratory were screened with multiplex TaqMan assays for the following six helminth types: Ascaris lumbricoides , Necator americanus , Ancylostoma spp , Trichuris trchiura , Strongyloides stercoralis and Schistosoma spp . Using this method this study was able to identify a prevalence of 22% for the combined helminth infection . The most common infection was A . lumbricoides with a prevalence of 15% followed by N . americanus ( 5% ) , Ancylostoma spp . ( 2 . 5% ) , Schistosoma spp . ( 1 . 6% ) and S . stercoralis ( 1% ) . The success of this study indicates that this may be a cost-effective method to passively screen a country for STH and SCH and its success in identifying S . stercoralis infections makes it especially useful as this parasite is hard to identify using traditional surveillance techniques . | [
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] | 2018 | Expanding molecular diagnostics of helminthiasis: Piloting use of the GPLN platform for surveillance of soil transmitted helminthiasis and schistosomiasis in Ghana |
Small bowel adenocarcinoma ( SBA ) is an aggressive disease with limited treatment options . Despite previous studies , its molecular genetic background has remained somewhat elusive . To comprehensively characterize the mutational landscape of this tumor type , and to identify possible targets of treatment , we conducted the first large exome sequencing study on a population-based set of SBA samples from all three small bowel segments . Archival tissue from 106 primary tumors with appropriate clinical information were available for exome sequencing from a patient series consisting of a majority of confirmed SBA cases diagnosed in Finland between the years 2003–2011 . Paired-end exome sequencing was performed using Illumina HiSeq 4000 , and OncodriveFML was used to identify driver genes from the exome data . We also defined frequently affected cancer signalling pathways and performed the first extensive allelic imbalance ( AI ) analysis in SBA . Exome data analysis revealed significantly mutated genes previously linked to SBA ( TP53 , KRAS , APC , SMAD4 , and BRAF ) , recently reported potential driver genes ( SOX9 , ATM , and ARID2 ) , as well as novel candidate driver genes , such as ACVR2A , ACVR1B , BRCA2 , and SMARCA4 . We also identified clear mutation hotspot patterns in ERBB2 and BRAF . No BRAF V600E mutations were observed . Additionally , we present a comprehensive mutation signature analysis of SBA , highlighting established signatures 1A , 6 , and 17 , as well as U2 which is a previously unvalidated signature . Finally , comparison of the three small bowel segments revealed differences in tumor characteristics . This comprehensive work unveils the mutational landscape and most frequently affected genes and pathways in SBA , providing potential therapeutic targets , and novel and more thorough insights into the genetic background of this tumor type .
The gastrointestinal tract , a continuous passageway , includes the main digestive organs: the stomach , the small bowel , and the large bowel . The small bowel makes up 75% of the length of the gastrointestinal tract , yet small bowel tumors constitute only approximately 3% of gastrointestinal tumors [1] . The major histological types of primary small bowel cancers are carcinoids , adenocarcinomas , lymphomas , and sarcomas . Small bowel adenocarcinomas ( SBAs ) account for around one third of the tumors and are most often found in the duodenum , the first section of the small bowel [2] . SBAs are often sporadic , however , several factors such as inflammatory bowel disease ( IBD; Crohn's disease and ulcerative colitis ) and hereditary syndromes such as familial adenomatous polyposis ( FAP ) and Lynch syndrome ( LS ) are known to predispose to these tumors [3] . Patients with celiac disease are also at a greater risk of developing SBA compared to general population . Other risk determinants include lifestyle factors , such as alcohol use , obesity , and consumption of red meat [4] . Although diagnostic tools such as imaging and endoscopy have improved , SBAs are often advanced at the time of diagnosis and sometimes found incidentally . The estimated five-year relative survival rate for SBA is 40% , indicating a worse prognosis than for colorectal adenocarcinomas ( hereinafter referred as CRC ) [2] . The incidence of SBA has also increased over the past decades . This combined with the scarcity of evidence-based treatment recommendations underlines a dire need for knowledge on the biology of these tumors . To date , there have been relatively few large studies on SBA that have either screened a set of known mutation hotspots or cancer genes [5–7] , along with two exome sequencing efforts on small sets of duodenal adenocarcinomas [8 , 9] . The most commonly mutated genes in SBA include TP53 , KRAS , SMAD4 , and APC [3 , 7] . The fraction of microsatellite unstable ( MSI ) tumors in SBA has been reported to vary between 5–35% [10] . These tumors have a defective DNA mismatch repair ( MMR ) system and thus , compared to microsatellite stable ( MSS ) tumors , exhibit a remarkably high mutation burden . SBAs share many of the above-mentioned features with CRC . They also share similar carcinogenic pathways; e . g . they are thought to arise through an adenoma-to-carcinoma transition [11] . Regardless , large bowel tumors are much more frequent . Factors that could contribute to the difference include protective factors of the small bowel environment . Due to alkalinity , fewer bacteria , liquid nature of small bowel contents , and shorter transit time , there is less exposure to carcinogens [3] . The difference in cancer incidence between the small and large bowel could also be related to a slower rate of stem cell divisions in the small bowel [12] . Since there are limited data available to guide treatment decisions , our aim was to characterize the somatic mutational landscape of SBAs using exome sequencing to gain new insights into the SBA biology and identify potential therapeutic targets .
Clinicopathologic features of the 106 SBA patients are listed in Table 1 . Of the 106 tumors , 26 ( 25% ) were duodenal , 52 ( 49% ) jejunal , 18 ( 17% ) ileal , and 10 ( 9 . 4% ) resided in an unspecified location . The male-to-female ratio was 1 . 1 , and the median age at diagnosis 62 years ( range , 24 to 86 years ) . Median age at diagnosis was lowest for patients with jejunal tumor ( 59 . 5 years for jejunum versus 71 . 0 for duodenum and 63 . 0 for ileum; P = 0 . 00108 , Kruskal-Wallis test ) . Fifteen tumors were designated as MSI based on the exome sequencing data ( see below ) . Ten patients in the cohort had been diagnosed with celiac disease , five with IBD , and six with hereditary syndromes ( LS or FAP ) . The causative germline mutations in LS patients occurred in MLH1 or MSH6 , and in FAP patients in APC . All tumors from patients with IBD were MSS , whereas all LS-associated tumors were MSI , and the tumors from the two FAP patients were either MSS or MSI . Evaluation of the clinicopathologic characteristics revealed enrichment of celiac patients amongst the SBA patients: 9 . 4% compared to 2 . 4% in the general Finnish population ( P = 2 . 48x10-4 , exact binomial test ) [13] . Five of 10 tumors from patients with celiac disease were microsatellite-unstable , and thus celiac disease was associated with MSI ( odds ratio ( OR ) , 8 . 31; 95% confidence interval ( CI ) , 1 . 62–43 . 6; P = 4 . 83x10-3 ) , which corresponds to previous literature [14] . None of the tumors related to celiac disease resided in ileum . Otherwise the celiac disease-related tumors did not notably differ from other tumors in terms of the characteristics in Table 1 . Disease-specific survival was superior for patients with microsatellite-unstable tumors after adjustment for sex , tumor stage , and age at diagnosis ( hazard ratio ( HR ) , 0 . 111; 95% CI , 0 . 0292–0 . 419; P = 1 . 20x10-3 ) ( Table a in S1 Table; S1 Fig ) . Also , male patients had a worse disease-specific survival , although the difference was not formally significant . Exome sequencing analysis identified 75 , 993 somatic mutations across all samples . Of these , 29 , 120 were non-synonymous and 9 , 415 synonymous ( S2 Table ) . Fifteen out of 106 ( 14% ) samples were classified as MSI based on high mutation load and overrepresentation of insertions and deletions ( indels ) at microsatellite loci obtained from Hause et al . [15] . The classification was confirmed by signature analysis ( see methods ) . The average mutation burden in the whole target region was 4 . 30 mutations per megabase ( mut/Mb ) per MSS and 63 . 6 mut/Mb per MSI sample ( S2 Fig ) . The median number of non-synonymous mutations per sample was 88 in MSS ( interquartile range ( IQR ) , 64 . 5–114 ) and 1 , 266 in MSI tumors ( IQR , 666–1 , 738 ) . The median number of missense mutations was 79 ( IQR , 56 . 5–105 ) in MSS and 812 ( IQR , 518–1 , 209 ) in MSI tumors . For nonsense mutations , the median mutation counts were 10 ( IQR , 6 . 5–15 ) in MSS and 429 ( IQR , 210–498 ) in MSI tumors and for frameshift mutations 4 ( IQR , 2–6 ) in MSS and 286 ( IQR , 180–397 ) in MSI tumors . In MSS tumors , 6 , 214 genes harbored a non-synonymous mutation in at least one tumor and 1 , 921 genes in two or more tumors as compared to 10 , 716 and 5 , 055 in MSI tumors , respectively . In MSS tumors , the most frequently mutated known cancer genes were TP53 ( 44/91 , 48% ) , KRAS ( 43/91 , 47% ) , APC ( 20/91 , 22% ) , SMAD4 ( 14/91 , 15% ) , SOX9 ( 11/91 , 12% ) , BRAF ( 10/91 , 11% ) , and ERBB2 ( 10/91 , 11% ) . In MSI tumors , among the most frequently mutated genes were known driver genes ACVR2A ( 13/15 , 87% ) , BMPR2 ( 9/15 , 60% ) , KRAS ( 8/15 , 53% ) , and APC ( 7/15 , 47% ) . TP53 , the most frequently mutated gene in MSS tumors , was also frequently mutated ( 6/15 , 40% ) in MSI tumors . Next , we sought to identify genes showing statistical evidence of positive selection for mutations in SBA . We applied OncodriveFML to detect candidate driver genes in MSS tumors . In total , 44 genes displayed a nominally significant P-value ( <0 . 05 ) ( Table a in S3 Table ) . Seven genes remained significant after correction for multiple testing ( false discovery rate ( FDR ) , q-value <0 . 1 ) . However , genes with P<0 . 05 were also considered as being of potential interest . The most significant genes in MSS tumors consisted of known cancer genes such as TP53 , KRAS , APC , SOX9 , SMAD4 , BRAF , and ACVR2A . ( Fig 1 , Table a in S3 Table ) . The twenty-five highest-ranking driver candidates included also recently reported ( ATM and ARID2 ) and novel candidate drivers such as ACVR1B , BRCA2 , and SMARCA4 that ( to our knowledge ) have not been implicated in SBA before . More information on the mutation content of the genes ( P<0 . 05 ) is displayed in Table b in S3 Table . In addition to KRAS , APC was designated as one of the most significant genes in MSS tumors ( 20/91 , 22% ) and was also frequently mutated in MSI ( 7/15 , 46 . 7% ) tumors . Of note , 37 of 42 ( 88% ) APC mutations were protein-truncating ( 22 nonsense and 15 frameshift ) . Of the five patients with IBD , two ( 40% ) harbored an APC nonsense mutation . BRAF was mutated in 11 tumors ( 11/106 , 10 . 4% ) : 10 MSS and one MSI ( Fig 2 ) . We did not observe any V600E mutations . Instead , we identified an atypical mutation pattern with two known , less studied hotspots: G469A with two and D594A/G/N with three hits . In addition , we observed other known mutations near these hotspots ( G466E , G596R , and K601N ) . All above-mentioned mutations resided in exons 11 or 15 and have been designated as somatic hotspots in various cancers [16] . In read level inspection , we identified one additional tumor ( SIA56 ) displaying a hotspot mutation in G469A supported by four mutant reads which had not been called . This tumor also harbored one missense mutation in BRAF ( T241M ) . Furthermore , two tumors harbored protein-truncating BRAF variants: Q257X ( SIA214 ) and A404fs ( SIA53 ) . Except for one frameshift mutation , all other mutations occurred in MSS tumors . We compared tumor and patient characteristics according to BRAF mutation status , no significant differences were detected ( Table a in S4 Table ) . BRAF V600E and KRAS mutations are generally mutually exclusive . Regarding atypical hotspot mutations , however , we identified four out of 11 BRAF mutants where BRAF and KRAS mutations co-occurred: KRASA146T+BRAFD594A ( SIA121 ) , KRASG12R+BRAFG469A ( SIA228 ) , KRASG12D+BRAFQ257X ( SIA214 ) , and KRASG12D+BRAFA404fs ( SIA53 ) . We identified 18 ERBB2 mutations in 15 tumors ( 15/106 , 14% ) : 10 MSS and five MSI ( Fig 3 ) . ERBB2 did not reach significance in the OncodriveFML analysis; however , it is a known therapeutic target frequently mutated in many tumors of the digestive system , including those of the small bowel [5 , 7 , 17 , 18] . The majority ( 14/18 , 78% ) of the mutations clustered into four known hotspots ( Fig 3 ) [16] . One of the hotspots , L755S , was mutated exclusively in MSI tumors , whereas the other hotspots , S310F/Y , R678Q , and V842I , were found both in MSS and MSI tumors . Two samples harbored concurrent hotspot mutations , L755S+V842I and R678Q+V842I . Such co-occurrence has been reported previously at least once in SBA [5] . In addition to the hotspot mutations , three single mutations were identified in MSS ( S250F , V777L , and T862A ) and one in MSI tumors ( P1209T ) . We compared tumor and patient characteristics of ERBB2 mutant and wild-type cases ( Table b in S4 Table ) . We detected a statistically significant difference in the MMR status ( OR , 3 . 98; 95% CI , 0 . 886–16 . 4; P = 0 . 0368 ) , ERBB2 mutation frequency being higher in MSI tumors . The ERBB family comprises of four receptor tyrosine kinases encoded by EGFR ( also known as ERBB1 ) , ERBB2 , ERBB3 , and ERBB4 . Albeit with lower frequencies , also ERBB3 and ERBB4 displayed hotspot mutations in our data . We identified 10 ERBB3 mutations in nine tumors , revealing two hotspots: V104M/L in one MSS and in two MSI and S846I in two MSS tumors . These affected either the extracellular domain ( V104M/L ) or the kinase domain ( S846I ) . We also observed 10 ERBB4 mutations in nine tumors . ERBB4 displayed one mutation hotspot , L798R/P in the protein tyrosine kinase domain , supported by two MSS tumors . Moreover , we detected one EGFR mutation ( R977C ) . Thus , there were altogether 29 samples ( 27% ) with a mutation in at least one of the ERBB genes ( Fig 3 ) . Of these , four tumors exhibited mutations in more than one of these three genes . All hotspot mutations in different ERBB genes were mutually exclusive . We performed an allelic imbalance ( AI ) analysis for the whole data set of 106 tumors . The analysis revealed 1 , 541 loss and 840 gain events across all samples . The number of AI events in MSI tumors ( median , 5; IQR , 4–8 ) was significantly lower compared to that of MSS tumors ( median , 22; IQR , 13–35 ) ( P = 1 . 95x10-9 ) , see Table b in S1 Table . The number of AI events did not differ significantly between tumors from different small bowel segments . The most frequent AI event was partial or whole loss of chromosome 17 short arm ( p ) harboring TP53 , detected in 62/106 ( 58 . 5% ) samples ( Fig 4; S3 Fig ) . Non-synonymous variants in TP53 co-occurred with loss events in 41/50 ( 82 . 0% ) of mutated cases ( OR , 7 . 43; 95% CI , 2 . 86–21 . 1 , P = 4 . 02x10-6 ) ( S4 Fig ) . We also observed a high frequency of chromosomal losses in two other significantly mutated known cancer genes: SMAD4 ( n = 46 ) and SOX9 ( n = 44 ) . Chromosome or arm level losses were observed at high frequency ( n>30 ) at chromosomes 3p , 8p , 9q , 12q , 15 , 17 , 18q , 19 , and 22 ( Fig 4; S3 Fig ) . Gain events were observed at high frequency at chromosomes 13 and 8q ( with MYC as a possible target ) . In addition , known oncogenes , such as KRAS , BRAF , and PIK3CA that were amongst the highest-ranking genes , were clearly amplified in 20/106 ( 18 . 9% ) , 19/106 ( 17 . 9% ) , and 16/106 ( 15 . 1% ) samples , respectively . We observed also localized and strong amplification at the ERBB2 locus in 4 samples , two of which had a hotspot mutation in ERBB2 ( S3–S5 Figs ) . First , we performed mutational signature analysis for all 106 samples . A known MSI signature ( signature 6 ) was identified in 15 tumors ( Fig 5; Table a in S5 Table ) . The signature analysis was then performed separately for the 91 MSS SBAs . This process yielded three mutational signatures ( 1A , 17 and U2 ) corresponding to known signatures reported by Alexandrov et al . ( Fig 5; Tables b and c in S5 Table ) [19] . Signature U2 has not been validated previously due to lack of available biological samples and access to BAM files for the samples . We were able to inspect read sequences in our data set and validate mutations in this signature class . Mutational signatures were studied using multivariable-adjusted negative binomial regression ( Table c in S1 Table ) . Similar to other cancers , the frequency of mutations attributable to signature 1A increased with age at diagnosis ( increase per 10 years , 20%; 95% CI , 10–32%; P = 4 . 32x10-5 ) [19] . Exposure to signature 1A was highest in jejunal tumors; compared to duodenal tumors , there was an increase of 66% in expected mutation count ( 95% CI , 29%-110%; P = 7 . 17x10-5; see S6 Fig ) . No notable difference in signature 1A was observed between ileal and duodenal tumors ( P = 0 . 348 ) . Also , tumors from female patients showed an increase of 26% in the number of mutations attributable to signature 1A ( 95% CI , 2 . 9%-54%; P = 0 . 0259 ) . We characterized most frequently affected cancer signalling pathways in MSS SBA , focusing on mutations in known pathways—Wnt/β-catenin , TGF-β , PI3K/AKT , ERBB , ERK/MAPK , and p53 signalling ( S6 Table ) . The most frequently mutated pathway was PI3K/AKT , where at least one gene was mutated in the majority of tumors ( 77/91 , 84 . 6% ) . This pathway includes the two most frequently mutated genes , KRAS and TP53 . The PI3K/AKT pathway was followed by ERBB ( 73/91 , 80 . 2% ) , ERK/MAPK ( 72/91 , 79 . 1% ) , and Wnt/β-catenin ( 70/91 , 76 . 9% ) signalling pathways . Also , TGF-β and p53 signalling were affected in many tumors ( 66/91 ( 72 . 5% ) and 63/91 ( 69 . 2% ) , respectively ) . Eighty-four out of 91 tumors ( 92 . 3% ) harbored at least one non-synonymous mutation in one of these six known cancer pathways . We compared tumor characteristics between the three small bowel segments . Although the tumors displayed rather similar numbers of mutations , mutated known cancer genes , and MSI frequencies ( S7 Table ) , some differences existed . In MSS tumors , the APC mutation frequency varied between segments; it was the lowest in jejunal tumors ( 13 . 6% ) , followed by ileal ( 31 . 3% ) and duodenal tumors ( 37 . 5% ) . The TP53 mutation frequency was lower in duodenum ( 29 . 2% ) than other segments: jejunum ( 56 . 8% ) and ileum ( 56 . 3% ) . The differences between the segments were also reflected in the frequencies at which major signalling pathways were mutated . In duodenal tumors , the most frequently affected pathway was ERBB signalling ( 20/24 , 83 . 3% ) . Whereas , both in jejunal and ileal tumors the most frequently affected pathway was PI3K/AKT ( 42/44 , 95 . 5% and 12/16 , 75 . 0% , respectively ) . The most notable differences between segments were seen in ERBB signalling which was less frequently mutated in ileal tumors ( 9/16 , 56 . 3% ) compared duodenal and jejunal tumors ( 20/24 , 83 . 3% and 38/44 , 86 . 4% , respectively ) ( P = 0 . 0463 ) and in ERK/MAPK signalling , most frequently affected in jejunal tumors ( 40/44 , 90 . 9% ) compared to duodenal and ileal tumors ( 18/24 , 75 . 0% and 9/16 , 56 . 3% , respectively ) ( P = 9 . 06x10-3 ) .
Through large-scale utilization of archival tissue from nationwide population-based material , we conducted a comprehensive study of the somatic mutational landscape of primary SBA including all three small bowel segments . To our knowledge , this is the largest exome sequencing study on SBA to date . Most MSS tumors had a mutational burden of <10 mut/Mb . The median mutational burden in the whole SBA set was 3 . 96 mut/Mb which is in agreement with previously published results on SBA and corresponds to the mutation rates reported in CRC and gastric cancer [7 , 20] . The assessment of relevant genes in SBA indicated that TP53 and KRAS were the most significantly mutated genes in MSS tumors , the mutation frequencies corresponding to previous reports [5–7] . The high frequency of losses in TP53 and gains in KRAS provided further support for these observations . Thus , our results strengthen the pivotal roles of these genes in SBA genesis . Of note , KRAS was also frequently mutated in MSI tumors and its mutation status holds therapeutic value , since tumors with mutant KRAS do not respond to EGFR inhibitors [21] . Interestingly , TP53 mutation frequency in the duodenum was lower than in other regions of the small bowel , a similar trend as reported by Laforest et al . [5] . APC reached an equally high level of significance with TP53 and KRAS in the analysis of MSS tumors . The role of mutant APC in the pathogenesis of SBA has been under debate . Some have proposed , in contrast to colorectal carcinogenesis , that APC would not play such an essential role in SBA [14 , 22 , 23] . Especially , a lack of nonsense mutations has been noted . In our data , APC was relatively frequently mutated ( 27/106 , 25 . 5% ) , as reported in recent studies [7 , 8] . Furthermore , the majority of APC mutations in our data were protein-truncating , and the mutation frequency varied between the small bowel segments . We also detected 21 deletions overlapping the APC locus solely in MSS tumors , three of which co-occurred with a truncating mutation . Although the overall mutation rate was lower than in CRC , our results support the importance of APC also in the pathogenesis of SBA . Additionally , APC has been reported to be less frequently mutated in MSI than in MSS CRC [17] , whereas in our set APC was more frequently mutated in MSI SBAs . Recently , APC mutations were reported to occur exclusively in SBA patients without IBD [7] . Our results indicate , however , that a subset of SBA patients with IBD have inactivating APC mutations . Among the most significantly mutated genes was also BRAF , a well-known oncogene mutated in various cancers , such as melanoma ( 44% ) , CRC ( 10% ) , and lung adenocarcinoma ( 10% ) [24] . In our study , BRAF was mutated in 11 tumors ( 11/106 , 10 . 4% ) , which is consistent with current literature [6–8 , 22] . Instead of the most common activating mutation , V600E , we identified two atypical mutation hotspots , G469A and D594A/G/N , the first having been shown to activate and the latter to inhibit BRAF kinase activity [25 , 26] . These hotspot mutations were present exclusively in MSS tumors , as indicated previously [27] . In addition , the observed surrounding mutations were also either activating ( K601N ) or inactivating ( G466E & G596R ) . Like activating BRAF mutations , the inactivating mutations are also thought to activate the MEK/ERK pathway , albeit through activation of the related family member CRAF [28] . Heidorn et al . showed that the kinase-dead BRAF needs activated RAS to induce BRAF binding to CRAF [26] . This could explain the co-occurrence of mutant KRAS with the kinase-silencing and truncating BRAF mutations . Co-occurrence of kinase-impaired BRAF with mutant KRAS has been reported in various malignancies [25–27] . A recent study on SBA reported only 10 . 3% of BRAF mutations to be V600E , whereas we identified none , together highlighting the importance of atypical BRAF mutations in SBA [7] . Of note , metastatic CRCs harboring non-V600 BRAF mutations have been shown to display distinct clinicopathologic features and an improved overall survival compared to V600E mutated CRCs [29] . These non-V600 mutations are also common e . g . in lung adenocarcinomas and melanomas [25] . Investigation is undergoing to elucidate how different non-V600 BRAF mutants respond to therapy . These tumors are unlikely to respond to selective BRAF inhibitors but might respond to MEK or pan-RAF inhibitors [26 , 30] . Our results suggest that screening for atypical BRAF mutations may be clinically relevant , since they can be at least as frequent as BRAF V600E and help guide personalized treatment choices . Exome data analysis also revealed other significantly mutated genes previously linked to SBA ( e . g . SMAD4 ) , recently reported potential driver genes ( e . g . SOX9 , ATM , and ARID2 ) , and novel candidates ( e . g . ACVR2A , ACVR1B , BRCA2 , and SMARCA4 ) that have not previously been linked to SBA [5 , 7] . For example ATM , one of the recently reported potential SBA driver genes , was ranked the 10th most significant gene in our MSS tumor set , with half of the mutations being truncating . ATM is also significantly mutated in lung adenocarcinomas , kidney clear cell carcinomas , and prostate adenocarcinomas [7 , 31] . ATM has been implicated as a barrier to dysplastic growth in bowel tumors [32] . It has also potential clinical relevance as a biomarker to predict PARP inhibitor sensitivity . The novel candidate SBA driver genes , ACVR2A , ACVR1B , BRCA2 , and SMARCA4 , have been previously implicated as drivers in various other human malignancies . ACVR2A , a known MSI target gene , encodes for a type II activin receptor that is involved in activin-mediated signalling [33] . Indeed , ACVR2A was the most frequently mutated known cancer gene in our MSI SBAs . ACVR2A was also among the significantly mutated genes in MSS tumors with mutations affecting the TGF-β receptor and the protein kinase domains . ACVR2A forms an activin receptor complex with ACVR1B . ACVR1B encodes for a type I activin receptor that regulates many biological processes , including extracellular matrix production and cell growth inhibition [34] . All the observed ACVR1B mutations , except one in TGF-β receptor GS domain , hit the protein kinase domain . ACVR1B has been shown to be significantly mutated in CRC , for instance [31] . It has also been indicated in vivo as a tumor suppressor in pancreatic cancer [35] . Our results implicate both ACVR2A and ACVR1B as candidate therapeutic targets in SBA . BRCA2 encodes for a known tumor suppressor that is involved in the repair of double-strand breaks in DNA by homologous recombination [36] . Over a thousand mutations have been found throughout this gene . Inactivating germline mutations in this gene are associated with the hereditary breast-ovarian cancer syndrome [37] . Somatic BRCA2 mutations have been found e . g . in melanoma , where these mutations have been found to correlate with anti-PD-1 responsiveness [38] . The mutations observed here , the majority of which protein-truncating , were scattered along the gene . The gene has also further clinical relevance since drugs targeting BRCA1 and BRCA2 mutations are being developed [39] . SMARCA4 encodes for one of the main catalytic subunits of mammalian SWI/SNF chromatin remodelling complex [40] . Here , most SMARCA4 mutations located in known gene domains with a mutation hotspot in helicase C-domain . SMARCA4 has been suggested to be a tumor suppressor , but some studies have reported SMARCA4 overexpression in advanced cancers , proposing SMARCA4 to be pro-oncogenic [41] . Additionally , the loss of SMARCA4 seems to attenuate aberrant Wnt signalling in APC-deficient small bowel epithelium in mice [42] . SMARCA4 has been shown to be significantly mutated in lung adenocarcinomas and esophageal cancer [31] . Chromatin regulators , in general , have been suggested as biomarkers for drug response and therapeutic targets [43] . ERBB2 was mutated in altogether 14% of the tumors ( 15/106 ) . We identified four known mutation hotspots ( S310F/Y , R678Q , L755S , and V842I ) , of which R678Q has not been previously shown to be mutated in SBA [5–7] . These mutation hotspots have also been detected in other cancer types , such as breast and bladder cancer [16] . Of these , S310F , L755S and V842I are associated with drug sensitivity [44] . One of R678Q mutations co-occurred with another ERBB2 hotspot mutation in our set . This phenomenon has been reported previously , suggesting that in these cases R678Q might provide additional selective value [44] . In addition to activating point mutations , oncogenic activation of ERBB2 can occur through amplification and overexpression . We detected localized and strong amplification of ERBB2 in four samples , two of which co-occurred with a hotspot mutation . Consequently , the prevalence of ERBB2 alterations in SBAs is likely to be even higher . The other members of the ERBB family are also commonly overexpressed , amplified , or mutated in various cancers [45] . We detected hotspot mutations in ERBB3 ( V104M/L and S846I ) and ERBB4 ( L798R/P ) , albeit with lower frequency than in ERBB2 . These hotspots have been previously reported in e . g . gastric adenocarcinomas ( GA ) and CRC but , to our knowledge , not in SBA [46] . Of these , V104M/L has been shown to be a statistically significant mutation hotspot and , along with S846I , to promote oncogenic signalling [16 , 46] . Many approved therapies targeting ERBB2 and EGFR receptors are in clinical use [45] . Multiple ERBB family members have potential clinical relevance , as therapies targeting them are currently being developed [46 , 47] . Particularly , ERBB2 can be considered as a potential therapeutic target in SBA . In addition to identifying possible single therapeutic target genes , we examined the essential pathways in SBA . Of the well-known cancer related pathways , PI3K/AKT and ERBB signalling were affected in most of MSS tumors . Comparison between the small bowel segments uncovered shared mutated pathways although there was some variability in the order of mutation frequencies . For example , in our set of duodenal SBAs , ERBB signalling was the most frequently affected pathway , followed by ERK/MAPK signalling . In jejunal and ileal tumors the most frequently mutated pathway was PI3K/AKT signalling , followed by ERK/MAPK signalling in jejunal and Wnt/β-catenin signalling in ileal tumors . Though these results may reflect variation between the tumor subgroups , more work is still needed to robustly elucidate the differences . We performed , to our knowledge , the first comprehensive signature analysis of SBA and identified four mutational signatures: 1A , 6 , 17 , and U2 . Signature 1A is proposed to be a result of spontaneous deamination of 5-methylcytosine , whereas the process causing signature 17 is still unknown [19] . The observed association of signature 1A with older age at diagnosis has been reported in other tumor types , such as medulloblastoma and gastric cancer [19] . We also observed a previously unreported association between increased signature 1A exposure and jejunal tumor location , even though patients with jejunal tumors were , on average , younger than those with duodenal and ileal tumors . This may suggest regional differences in DNA methylation or in the rate of cell division between different segments . Signature U2 has been reported in liver , prostate , and kidney chromophobe cancers , but thus far has been unvalidated . However , we were able to inspect read sequences in our dataset and thus validate mutations in this signature class . These results revealed that SBA , CRC , and gastric cancer share features in their signature content . Signatures 1A and 17 have been reported in both CRC and gastric cancer studies [19 , 48] . Signature-wise SBA seems to closely resemble CRC , since the majority of associated signatures overlap . Although the small and large bowel represent different environments , they may share comparable exposures that could explain similarities in the tumors’ signature content . Many additional signatures have been associated with gastric cancer , and thus signature-wise they differ from SBAs . Compared to GAs and CRCs , SBAs displayed similar mutation frequencies of certain driver genes , such as TP53 , SMAD4 , and PIK3CA [17 , 18] . Additionally , the proportions of MSI tumors were similar in these tumor types . Thus , MSI testing should be also considered in SBA in view of benefit from immunotherapy [49] . We also found that , as in CRC , patients with MSI tumors had a longer disease-specific survival than patients with MSS SBA [50] . On the contrary , many notable differences between SBA and GA/CRC were observed . For instance , the frequency of KRAS mutants resembled that of CRC , but was clearly higher than that in GAs . The APC mutation frequency differed between the three malignancies , and seemed to increase along the GI-tract , confirming previous results [7] . Also , the BRAF mutation spectrum varied markedly , since SBA was the only one where BRAF mutations consisted mainly of atypical mutations . Our results support the notion that SBA is a distinct entity with a unique set of significantly mutated genes . Despite our large population-based dataset , no obvious genetic reason for the low incidence of SBA compared to CRC was detected . The Finnish Cancer Registry allowed us to collect information on all SBA cases in Finland . Due to insufficient tumor material or low tumor percentage in some cases , we were unable to include every patient diagnosed during the selected years . However , we believe that the sample material is approximately representative of the population . Duodenum has been reported to be the most common location of SBAs . Duodenal tumors were slightly underrepresented due to: 1 ) exclusion of tumors from the papillary region ( which are classically grouped together with duodenal tumors ) , and 2 ) the fact that some duodenal tumors were only biopsied and had too little material for exome sequencing . Besides this , all segments were fairly well-represented . Due to the lack of corresponding normal samples , we used strict filtering methods for somatic variant calling . However , we recognize that the data may contain some rare germline variants . Despite exome sequencing being highly informative , we acknowledge that non-coding genetic driver mechanisms remain currently unaddressed . This large population-based study elucidated the molecular basis of SBA through exome sequencing . The results singled out many potential therapeutic targets that could be exploited when developing treatments for SBAs . These include both currently targetable genes ( BRAF , ERBB2 , and BRCA2 ) and novel candidates including ERBB3 , ERBB4 , PIK3CA , KRAS , ATM , ACVR2A , ACVR1B , and SMARCA4 . In addition to KRAS , we detected multiple genes that may predict resistance to anti-EGFR therapy , such as BRAF , ERBB2 , and PIK3CA . Additionally , this was the first large-scale pursuit to compare the primary tumors from all three small bowel segments . Although the tumors shared somewhat similar characteristics , differences were noted . The results presented here provide further evidence that SBA is a genetically distinct tumor entity . Observed heterogeneity in the mutational landscape indicates that several driver genes play a role in the biology of SBA . These results take forward our understanding of the pathogenesis of SBA and ultimately should be useful for the management of the disease .
The study has been reviewed and approved by the Ethics Committee of the Hospital District of Helsinki and Uusimaa , Finland ( 408/13/03/03/2009 ) . Authorisation from the National Supervisory for Welfare and Health was obtained for genetic studies on the samples , as determined in the National legislation . This study has been conducted according to the Declaration of Helsinki . We compiled from the Finnish Cancer Registry information on all patients diagnosed with SBA in Finland during years 2003–2011 . This registry maintains a nationwide database on all cancer cases diagnosed since 1953 , and has almost complete coverage [51] . In order to focus solely on small bowel tumors , we excluded tumors of the papillary region ( n = 31 ) since they might have originated in the pancreas or the biliary tract . Cases reported only by autopsy ( n = 20 ) and cases without histopathological confirmation of small bowel primary tumor ( n = 25 ) were also excluded from the study , and 162 cases remained . From these we selected all cases with available tumor material and tumor content of at least 50% . In total , 55 SBA cases were excluded due to these reasons and one due to low sequencing depth . The final set consisted of 106 out of 162 ( 65% ) confirmed SBA cases ( excluding autopsies ) . All relevant medical records , including follow-up information for survival analysis , were available for all the cases . Hematoxylin-eosin staining was performed to estimate tumor percentages . To reach maximal tumor percentage , macrodissection was conducted , when possible , to remove non-malignant tissue . Genomic DNA extractions from formalin-fixed and paraffin-embedded ( FFPE ) blocks were performed using either a standard phenol-chloroform isolation method or GeneRead FFPE-kit according to manufacturer’s instructions ( QIAGEN , Hilden , Germany ) . DNA concentration was determined with Qubit double-stranded DNA BR Assay Kit ( Thermo Fisher Scientific , Waltham , MA , USA ) and purity with NanoDrop8000 ( Thermo Fisher Scientific ) . Exome libraries were prepared with KAPA Hyper Prep Kit ( Kapa Biosystems , Wilmington , MA , USA ) . Coding exons and untranslated regions ( UTRs ) of the genome ( 94 megabases ) were enriched with NimbleGen SeqCap EZ Exome Library v3 Kit ( Roche NimbleGen , Madison , WI ) . Paired-end sequencing with read lengths of 75 base pairs with a median depth of 40x ( range , 33x to 62x ) was performed with Illumina HiSeq 4000 ( Illumina Inc . , San Diego , CA ) in Karolinska Institutet , Sweden . At least 85% of the exome target was covered by a minimum of 10 reads in all except two samples ( SIA137 , 82%; SIA196 , 83% ) . The quality of raw sequencing data was examined with FastQC v . 0 . 10 . 0 ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) and QualiMap v . 2 . 1 ( http://qualimap . bioinfo . cipf . es/ ) [52] . Trim Galore ! v . 0 . 3 . 07 ( http://www . bioinformatics . babraham . ac . uk/projects/trim_galore/ ) was used to remove the 3’ ends of reads with high adapter similarity . The trimmed reads were then mapped to the integrated 1000 Genomes Phase 2 GRCh37/hg19 reference assembly with Burrows-Wheeler Aligner ( BWA ) –MEM v . 0 . 7 . 12 ( http://bio-bwa . sourceforge . net/ ) [53] . BamUtil v . 1 . 0 . 13 ( http://genome . sph . umich . edu/wiki/BamUtil#Releases ) ClipOverlap was used to clip overlapping read pairs . Duplicate reads were removed using Samtools version 1 . 0 ( http://www . htslib . org/ ) rmdup on both paired-end and single-end reads [54] . Aligned reads were locally realigned with the Genome Analysis ToolKit ( GATK ) v . 3 . 5 ( https://www . broadinstitute . org/gatk/ ) IndelRealigner [55] . GATK BaseRecalibrator was utilized to recalculate base scores . After realignment the final indel and single nucleotide variant ( SNV ) calls were produced with the GATK HaplotypeCaller using a Phred-scaled confidence threshold ( stand_call_conf ) of 1 . 0 . Since our exome data consisted of only tumors , we utilized methods similar to those in Hiltemann et al . for discriminating somatic variants; the approach removed >96% of the germline variants when corresponding normals were not available [56] . However , additional filtering steps as well as larger population and sequencing pipeline specific datasets were used in this study . Putative somatic variants were extracted by filtering SNV and indel calls against whole-genome and exome samples of the GnomAD dataset ( n = 138 , 632 ) [57] . First , we excluded all variants found in Finnish whole-genome samples ( n = 1 , 747 ) . This set was utilized separately to remove common as well as population-specific variation equally at the whole targeted region , as the exome data did not fully cover our targets . Then we applied the full GnomAD dataset ( exomes and genomes ) using allele frequency threshold; variants with allele frequency more than 0 . 0001 were excluded . For SNVs , matching chromosomal position and base change were required for exclusion . Indels were excluded in cases of overlapping occurrences . Additional filtering was performed with 183 in-house whole-genome sequencing samples ( normal solid tissue or peripheral blood ) to remove sequencing platform and variant calling pipeline specific errors . We refined remaining variant calls against a pooled set of whole-genome sequencing data ( median ~40x coverage/sample ) from 10 blood samples by excluding any SNV call which was found in three or more reads in the pooled data . Indel calls were filtered out if two or more samples had more than three reads calling an indel at 100 base pairs ( read length ) from the indel locus . This step was done to exclude low allelic fraction artefacts in regions prone to sequencing errors . Only variants within the targeted region of NimbleGen SeqCap EZ Exome Library v3 Kit were analyzed . BasePlayer [58] was utilized to visualize and analyze the data ( allele frequency and quality filtering , allelic imbalance , gene annotation , and calculation of variant statistics ) . Variant filtering parameters are listed in S8 Table . Ensembl version 87 ( GRCh37 ) was used for gene annotation . Mutation calls have been deposited in the EGA database ( EGAS00001002559 ) . We used OncodriveFML v . 2 . 0 . 2 [59] to perform significance analysis for somatic mutations within the coding DNA sequence ( CDS ) . OncodriveFML is a permutation-based method that compares a region’s mean functional impact score to its null distribution by randomizing observed mutations . Protein-coding CDS regions were obtained from Gencode release 19 ( http://www . gencodegenes . org/ ) . The resulting regions were then merged using bedtools ( v . 2 . 25 . 0 ) . The method's default scoring framework , CADD [60] , was used . OncodriveFML’s default configurations were applied , with the genomic elements file defined as “coding” and the sequencing type defined as “whole exome sequencing” . The focus was , solely , on genes mutated in at least four tumors . Quantile-quantile plots are presented in S7 Fig . Inflation factors for P-value distributions were estimated using the R package GenABEL v . 1 . 8–0 . The Benjamini-Hochberg method was applied to adjust for false discovery rate ( FDR ) . All non-synonymous mutations in the novel candidate genes ( ACVR2A , ACVR1B , BRCA2 , and SMARCA4 ) used in OncodriveFML analysis and genes with a clear mutation hotspot pattern ( ERBB2 and BRAF ) were selected for validation with Sanger sequencing . Primers were designed using Primer3Plus [61] . Each PCR reaction was performed in triplicates to ensure consistency of the observations . Sequencing reactions were carried out with the Big Dye Terminator v . 3 . 1 kit ( Applied Biosystems , Foster City , CA , USA ) on an ABI3730 Automatic DNA Sequencer ( FIMM Technology Center and DNA sequencing and Genomics laboratory , Institute of Biotechnology , Helsinki , Finland ) . The sequence graphs were analyzed both with the Mutation Surveyor–software ( version v4 . 0 . 8 , Softgenetics , State College , PA ) and manually . Validation was successfully performed for altogether 49/54 mutations . From two tumors ( SIA137 and SIA98 ) no DNA material was left for validation . For 47/49 mutations , we had just enough DNA material from the corresponding normal samples to validate their somatic status . All except two mutations in BRCA2 were validated as somatic . These two rare germline variants ( ExAC MAF = 0 . 00002 & 0 . 00005 ) were excluded from the whole study . Even after the removal of these two mutations , BRCA2 remained in the top 25 genes in the OncodriveFML re-run . AI regions were called using germline SNVs of the whole sample set of 106 SIA tumors . We selected SNVs for the analysis based on following criteria: B allele frequency segmentation ( BAFsegmentation ) algorithm ( described in Staaf et al . [62] ) was utilized to call AI regions with parameters: non_informative = 0 . 97 , ai_threshold = 0 . 6 , ai_size = 4 , triplet_threshold = 0 . 8 . BAF value was calculated from allelic depth fields of VCF file ( ALT calls / total coverage ) . First , we performed control analysis with 80 normal exomes using the same parameters to detect possible technical artefacts caused by low-complexity genomic loci and usage of exome variant data , which has limited power to detect AI . Control analysis revealed genomic regions more prone to false calls ( e . g . centromeres and chromosome ends ) . Variants overlapping these regions were excluded from the tumor analysis . In addition , we observed median coverage differences between chromosomes ( e . g . median coverages across all samples in chromosome 1 and 16 was 38 and 30 , respectively ) . This information was used for chromosome-specific coverage normalization in calculation of log-R ratios for tumor variants . Median coverage for X chromosome was calculated by using only female samples . We ran BAFsegmentation for tumor samples twice . The first run was performed to detect AI regions to get as accurate median coverage for all samples as possible . Median coverages were calculated using all variants , which did not overlap with called AI regions . Variant-specific log-R ratios were calculated using following formula ( 1 ) : log2 ( varCoverage/ ( sampleMedian*chromNormalize[chr] ) ) ( 1 ) varCoverage was obtained from coverage field ( DP ) in VCF-file . sampleMedian is sample-specific median coverage value of all chromosomes ( AI regions excluded ) . chromNormalize[chr] corresponds to chromosome specific coverage normalization coefficient , which was processed in control analysis . Second , and last BAFsegmentation run was performed using refined log-R ratios . AI events with median log-R ratios higher than 0 . 1 were considered as gains and events equal or less than 0 . 1 were considered as losses ( including copy number neutral loss of heterozygosity ) . First , we performed signature analysis on 106 SBAs , as in Katainen et al . , using non-negative matrix factorization of six substitution types in 5′-Xp ( C/T ) pY-3′ for any nucleotides X and Y [48 , 63] . All variants within exome target regions were used , including UTRs . We computed the exposure of extracted signatures for each 106 SBAs as a projection of the mutation matrix to the signature weight matrix . The obtained signatures ( p ) were compared to the published signatures ( q ) of Alexandrov et al . by mean Kullback-Leibler divergence ( DKL ( p||q ) +DKL ( q||p ) ]/2 . Fifteen samples displayed the MSI signature ( Signature 6 ) , consistent with the division of tumors based on the exome data ( S2 Table ) . Mutation signature analysis was subsequently performed in 91 MSS SBAs . Ingenuity Pathway Analysis ( IPA ) version 39480507 was used to determine the frequency of known cancer pathways affected in the MSS tumors . IPA was utilized to define genes linked to each pathway . All genes with at least one non-synonymous mutation were included in the analysis . We used R v . 3 . 4 . 1 to analyze clinical variables . Fisher’s exact test was used to test for independence of categorical variables . Differences in continuous variables were assessed with the Mann-Whitney U test . Disease-specific survival was analyzed by Cox proportional hazards regression with Firth's penalized likelihood ( coxphf package v . 1 . 12 ) . Per-tumor mutation counts attributable to mutational signatures were estimated in MSS tumors , and their associations with clinical features were modeled using negative binomial regression ( MASS package v . 7 . 3–47 ) . All P-values are two-sided and unadjusted for multiple comparisons . P-value <0 . 05 was regarded as statistically significant . | Small bowel adenocarcinoma is a rare but aggressive disease with limited treatment options . Of gastrointestinal tumors , small bowel tumors account for 3% , of which around one third are adenocarcinomas . Due to the scarcity of evidence-based treatment recommendations there is a dire need for knowledge on the biology of these tumors . Here , we performed the first large exome sequencing effort of 106 small bowel adenocarcinomas from a Finnish population-based cohort to comprehensively characterize the genetic background of this tumor type . The set included tumors from all three small bowel segments allowing us to also compare the genetic differences between these subsets . We defined significantly mutated genes and frequently affected pathways , providing potential therapeutic targets , such as BRAF , ERBB2 , ERBB3 , ERBB4 , PIK3CA , KRAS , ATM , ACVR2A , ACVR1B , BRCA2 , and SMARCA4 , for this disease . | [
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"types"
] | 2018 | Exome-wide somatic mutation characterization of small bowel adenocarcinoma |
Differentiated mammary epithelium shows apicobasal polarity , and loss of tissue organization is an early hallmark of breast carcinogenesis . In BRCA1 mutation carriers , accumulation of stem and progenitor cells in normal breast tissue and increased risk of developing tumors of basal-like type suggest that BRCA1 regulates stem/progenitor cell proliferation and differentiation . However , the function of BRCA1 in this process and its link to carcinogenesis remain unknown . Here we depict a molecular mechanism involving BRCA1 and RHAMM that regulates apicobasal polarity and , when perturbed , may increase risk of breast cancer . Starting from complementary genetic analyses across families and populations , we identified common genetic variation at the low-penetrance susceptibility HMMR locus ( encoding for RHAMM ) that modifies breast cancer risk among BRCA1 , but probably not BRCA2 , mutation carriers: n = 7 , 584 , weighted hazard ratio ( wHR ) = 1 . 09 ( 95% CI 1 . 02–1 . 16 ) , ptrend = 0 . 017; and n = 3 , 965 , wHR = 1 . 04 ( 95% CI 0 . 94–1 . 16 ) , ptrend = 0 . 43; respectively . Subsequently , studies of MCF10A apicobasal polarization revealed a central role for BRCA1 and RHAMM , together with AURKA and TPX2 , in essential reorganization of microtubules . Mechanistically , reorganization is facilitated by BRCA1 and impaired by AURKA , which is regulated by negative feedback involving RHAMM and TPX2 . Taken together , our data provide fundamental insight into apicobasal polarization through BRCA1 function , which may explain the expanded cell subsets and characteristic tumor type accompanying BRCA1 mutation , while also linking this process to sporadic breast cancer through perturbation of HMMR/RHAMM .
The mammary gland is composed of two epithelial cell lineages that form an inner apicobasal-polarized luminal layer surrounded by an outer , or basal , layer of contractile myoepithelial cells [1] . Epithelial cell subsets are likely maintained through a differentiation hierarchy supported by an estrogen receptor ( ER ) -negative mammary stem cell population enriched at the basal compartment [2]–[7] . Cytoskeletal structures , including actin and intermediate filament content , identify differentiated cells [8] and may therefore contribute to differentiation . For example , the organization of microtubules at adherens junctions is essential for the maintenance of cell-to-cell contacts in apicobasal-polarized epithelial [9] . This involves centrosome-dependent microtubule assembly followed by release and capture at non-centrosome sites [10] . Therefore , dynamic cytoskeletal reorganization may be critical to the terminal differentiation of breast luminal epithelium . However , the molecular determinants of this process and the link with carcinogenesis remain unknown . The common pathological features of breast tumors arising in breast cancer 1 , early onset ( BRCA1 ) gene mutation carriers , including the basal-like phenotype and ER negativity [11] , [12] , led to the proposition that BRCA1 function regulates stem/progenitor cell proliferation and differentiation [13] . Recent evidence supports this hypothesis . Cell proliferation and differentiation are altered with BRCA1 depletion in the non-tumorigenic MCF10A breast cell line [14] and with ex vivo culture of primary mammary epithelial cells from BRCA1 mutation carriers [15] . Xenografts of primary mammary epithelial cells depleted of BRCA1 show expansion of stem cells with impaired luminal differentiation [16] . Expanded luminal progenitor populations have also been detected in breast tissue from BRCA1 mutation carriers [17] and , subsequently , proposed as the target of transformation leading to basal-like tumors [18] . A more recent study has shown expanded basal progenitor cells but also defects in luminal progenitor differentiation in these carriers [19] . While it has been postulated that stem/progenitor cells may have stringent requirements for high-fidelity DNA damage repair [17] , the potential contribution of BRCA1 to other molecular events fundamental in differentiation remains to be elucidated . BRCA1-dependent ubiquitination , functioning as a heterodimer with BRCA1-associated RING domain 1 ( BARD1 ) , down-regulates assembly of centrosome microtubules in a mammary-specific manner [20] , [21] . Xenopus brca1-bard1 attenuates the function of a microtubule-associated protein called Xenopus receptor for hyaluronan-mediated motility ( xrhamm ) [22] . Xrhamm is the ortholog of a candidate low-penetrance breast cancer susceptibility gene product ( RHAMM , HMMR gene ) [23] whose over-expression in tumors is associated with poor prognosis and early age at diagnosis [23]–[25] . While xrhamm regulates microtubule organization during meiosis [26] , RHAMM controls γ-tubulin ( TUBG1 ) recruitment [27] and interphase microtubule dynamics [28] . Together , these observations suggest that BRCA1 might be involved in epithelial differentiation by down-regulating centrosome microtubule assembly , through RHAMM and TUBG1 , and promoting the cytoskeletal reorganization necessary for apicobasal polarization . Conversely , loss of BRCA1 function might impair structural cues of terminal differentiation and , consequently , increase risk of breast cancer characterized by the basal-like tumor type . Here , we conduct complementary analyses to demonstrate genetic , molecular , and functional interactions between BRCA1/BRCA1 , HMMR/RHAMM , and additional centrosome components that orchestrate cytoskeletal reorganization critical for epithelial apicobasal polarization . These new insights may enhance our understanding of mammary epithelial differentiation and the link with breast carcinogenesis .
Although BRCA1 and BRCA2 function coordinately during DNA damage response , genomic , transcriptomic , molecular , and pathological features of breast tumors arising in BRCA1 and BRCA2 mutation carriers suggest that carcinogenesis may occur through perturbation of shared and distinct biological processes [13] , [29] . Previous analysis of candidate genomic regions using a linkage approach suggested specific modification of breast cancer risk among BRCA1 mutation carriers by common genetic variation at chromosome 5q33-34 [30] . Extension of this study supports the original conclusion: a haplotype analysis in 27 families with BRCA1 mutations revealed a nonparametric linkage score peak of 4 . 24 at the 5q34 region containing HMMR ( Table S1 ) ; in contrast , no evidence of linkage was observed among 16 families with BRCA2 mutations ( only a suggestive signal at 20 centiMorgans distal of HMMR was detected , D5S408 nonparametric linkage score = 1 . 91 ) . Common breast cancer-predisposition alleles may differentially modify breast cancer risk among BRCA1 and BRCA2 mutation carriers [31]–[33] . To complement the linkage approach , we evaluated the effect of common HMMR genetic variation [23] on breast cancer risk in BRCA1 and BRCA2 mutation carriers . Following a pilot study in Italy and Spain , analysis of carriers ( n = 11 , 609 ) collected through 24 study groups participating in the Consortium of Investigators of Modifiers of BRCA1/2 ( CIMBA ) detected significant modification of breast cancer risk by HMMR rs299290 variant among BRCA1 , but not BRCA2 , mutation carriers: BRCA1 mutation carriers n = 7 , 584 , Cox proportional-hazards regression model , hazard ratio ( HR ) = 1 . 08 ( 95% confidence interval ( CI ) 1 . 02–1 . 13 ) , ptrend = 0 . 004 ( p2df = 0 . 014 ) , in the same direction as originally detected in Ashkenazi Jewish populations [23]; BRCA2 mutation carriers n = 3 , 965 , HR = 1 . 03 ( 95% CI 0 . 96–1 . 10 ) , ptrend = 0 . 42 ( p2df = 0 . 67 ) . For BRCA1 mutation carriers , consistent effects were observed across centers with larger sample sizes ( Figure 1 ) . We performed a number of sensitivity analyses to investigate the robustness of our results . First , since prophylactic oophorectomy reduces the risk of breast cancer in BRCA1 mutation carriers by up to 50% [34] , we included this observation as a time-dependent covariate in the analysis , and a significant association similar to the one shown above was revealed: HR = 1 . 09 ( 95% CI 1 . 03–1 . 16 ) , ptrend = 4 . 5×10−4 . Second , a non-significant association , but in the same direction , was identified when prevalent cases ( defined as those diagnosed with breast cancer more than five years before recruitment ) were excluded from the analysis: HR = 1 . 06 ( 95% CI 0 . 99–1 . 15 ) , ptrend = 0 . 10 . Finally , to investigate whether the retrospective study design and the non-random sampling of affected and unaffected mutation carriers introduce bias into the HR estimates , the data were also analyzed using a weighted cohort approach [35] , which yielded similar results to those shown above: BRCA1 mutation carriers wHR = 1 . 09 ( 95% CI 1 . 02–1 . 16 ) , ptrend = 0 . 017 ( p2df = 0 . 041 ) ( wHR per study centre are detailed in Table S2 ) ; BRCA2 mutation carriers wHR = 1 . 04 ( 95% CI 0 . 94–1 . 16 ) , ptrend = 0 . 43 ( p2df = 0 . 68 ) . Examination of heterogeneity in risk estimates across groups did not show significant differences under the multiplicative model ( phet≥0 . 3 ) . The association was then evaluated according to the predicted functional consequences of BRCA1 mutation type [36]–[40] . This analysis suggested an effect in carriers of loss-of-function mutations expected to result in a reduced transcript or protein level due to nonsense-mediated RNA decay ( n = 4 , 636 , wHR = 1 . 08 ( 95% CI 0 . 99–1 . 19 ) ) , whereas carriers of mutations likely to generate stable proteins with potential residual or dominant negative function might not be influenced ( n = 1 , 380 , wHR = 1 . 00 ( 95% CI 0 . 85–1 . 18 ) ) . While studies have identified low-penetrance alleles that associate with breast cancer risk in carriers of BRCA1 mutations and carriers of BRCA2 mutations [32] , [33] , specificities have also been detected [31] , [33] , [40] . Here , the results of linkage and association studies support a potential , specific genetic interaction between BRCA1 and HMMR ( high- and low-penetrance mutations , respectively ) , which could highlight a BRCA1-RHAMM function altered in familial and sporadic breast carcinogenesis . Analysis of public gene expression datasets suggests that the rs299290 risk allele is associated with HMMR germline over-expression ( see also Table S3 ) [23] . However , while the rs299290 variant represents a missense change predicted to be benign ( V368A; concordant predictions for PolyPhen-2 [41] and SIFT [42] were obtained ) , it is in linkage disequilibrium ( according to HapMap Caucasians data: D′ = 1 and r2 = 0 . 48 ) with rs299284 ( R92C in Entrez accession number NP_036616 ) , which is predicted to be damaging . The minor allele frequencies of rs299290 and rs299284 in HapMap Caucasian individuals are 29% and 16% , respectively . Since rs299284 is at the fourth base position of HMMR exon 5 , we evaluated the potential alteration of the splicing pattern of this exon or the ratio of the alternative exon 4 . Notably , exon 4 spans the microtubule-binding domain and has been shown to be skipped with progression of myeloma and breast cancer [43] , [44] . However , no differences were observed when analyzing the splicing pattern of both exons in lymphocytes from 10 BRCA1 mutation carriers ( Figure S1 ) and in public transcriptome sequence datasets ( unpublished data ) . Therefore , further work may be warranted to conclusively define the causal mutation ( s ) and its potential alteration of RHAMM levels or function . Breast tumors arising in BRCA1 mutation carriers are typically ER-negative , whereas most tumors in BRCA2 mutation carriers and sporadic cases are ER-positive [11] , [12] . Given the evidence above , we next evaluated whether HMMR variation was associated with ER tumor status in BRCA1 and/or BRCA2 mutation carriers . In data provided by several CIMBA groups ( Text S1 ) , no ER-positive tumors were observed among rare rs299290 homozygotes in BRCA1 mutation carriers ( pinteraction = 0 . 006 ) , whereas this bias was not observed in BRCA2 mutation carriers ( pinteraction = 0 . 95 ) ( Table S4 ) . That is , despite the expected differences in the frequency of tumor types between the two sets of carriers , heterogeneity was observed in the distribution of rs299290 genotypes in BRCA1 , but not BRCA2 , mutation carriers . This result further suggests an interaction between BRCA1 and HMMR that influences or regulates differentiation of breast luminal epithelium . On the basis of these observations and the published data presented above , we next investigated the relationship between BRCA1/BRCA1 and HMMR/RHAMM regulating apicobasal polarization ( hereafter polarity/polarization ) . The growth of nonmalignant human mammary epithelial cells , such as MCF10A and HMT3522 S1 , within three-dimensional cultures containing reconstituted basement membrane ( rBM ) recapitulates aspects of the terminal differentiation of mammary luminal epithelia , including apicobasal polarization , growth arrest , and milk production [45] , [46] . The cyst-like polarized structures ( hereafter termed acini ) formed by these cell types may , however , vary in the nature or degree of polarization and tight junction formation and , unlike heterotypic cultures of stromal and epithelial cells [47] , do not form bilayered cellular organizations [48] . Importantly , disruption of BRCA1 function through shRNA-mediated depletion impairs differentiation and promotes proliferation of MCF10A cells within rBM [14] . This seminal observation has been supported by evidence from other models for differentiation [15] , [16] and the examination of human mammary epithelial cell populations [17] . However , to date , the molecular contributions of BRCA1 to apicobasal polarization are largely unknown . Thus , we utilized the growth of MCF10A cells in rBM as a model for polarization , as determined by the apical localization of centrosomes , basal deposition of CD49f ( also known as α6-integrin ) and reduced expression of vimentin ( VIM ) , an intermediate filament associated with the basal lineage [49] . These attributes were also captured through quantitation of acini size and circularity or shape factor ( Figure S2 ) . As BRCA1 and RHAMM functions may intersect at the organization of microtubules and centrosomes , these structures were first examined in MCF10A cells grown on two-dimensional ( i . e . , plastic ) versus three-dimensional ( i . e . , rBM ) cultures . In plastic , microtubules were assembled at centrally located centrosomes ( Figure 2A ) . During polarization in rBM , however , microtubule organization transitioned from centrosome-dependent assembly in early stages of culture to concentrate at non-centrosome sites , such as regions of cell-to-cell contact , in late stages ( Figure 2B ) . Centrosomes were repositioned from the outside of cell clusters to apical surfaces and the eventual site of the lumen ( Figure 2B ) . This organization was maintained in polarized acini ( Figure 2B ) and is comparable to the apical position of centrosomes in mammary epithelial cells in vivo ( see also Figure S3A ) [50] . Thus , polarization of MCF10A is associated with a transition in the organization of microtubules from centrosome to non-centrosome sites , consistent with observations in other epithelial cells or tissues [9] , [10] . Complementary to the study of microtubules , the dynamics of VIM were also examined during polarization . In accordance with a shift from basal to luminal cytoskeletal structures , VIM abundance was reduced concurrent with the transition to non-centrosome-dependent microtubule organization ( Figure 2C ) and the deposition of CD49f ( Figure S2 ) . Therefore , polarization requires dynamic cytoskeletal organization . However , the mechanistic contribution of BRCA1 to this process remains unknown . As BRCA1 down-regulates centrosome microtubules by targeting microtubule-associated factors for proteasome-dependent degradation [20] , [21] , we hypothesized that this activity may be important for the transition to non-centrosome-dependent assembly that is essential for polarity [9] , [10] . To evaluate this hypothesis , we first examined the impact of BRCA1 depletion on polarization and cytoskeletal structures . In agreement with a previous report [14] , transduction of lentiviral-based shRNAs against BRCA1 expression ( shRNA-BRCA1 ) impaired polarization; observed acini in this condition were , on average , significantly larger and less circular than controls ( Figure 3A ) . Results were similar following transduction of individual ( two different sequences ) or pooled shRNAs , with transient or stable shRNA expression assays , and over a time course of one or two weeks ( Figures S4 and S5 ) . In addition , VIM and CD49f expression were increased and reduced , respectively , in acini depleted of BRCA1 relative to controls ( Figure 3A and S6 ) . Thus , loss of BRCA1 function may impair polarization by altering intracellular cytoskeletal organization , resulting in intermediate filament content consistent with the characteristic basal-like tumor type . While BRCA1 haploinsufficiency does not preclude the formation of a functional luminal layer , the cytoskeletal structure within luminal epithelia from BRCA1 mutation carriers might be compromised . Accordingly , histologically normal breast tissue from BRCA1 mutation carriers revealed elevation of ALDH1-positive cells with reduced expression of cytoskeletal markers ( cytokeratins 18 and 14 ) and ER [16] . Given these observations , we evaluated TUBG1 staining , as a centrosome marker , in breast tissue paraffin sections from four affected BRCA1 mutation carriers . Three hyperplastic lesions were identified that showed abnormal localization of the centrosome when considering their respective nuclei and lumen ( Figure S3B ) . Although the number of samples is limited , these results agree with the loss of polarity observed in MCF10A cells after BRCA1 depletion . Next , we used chemical and biological tools to dissect the mechanistic contribution of BRCA1 to MCF10A polarization . Should polarization require BRCA1-mediated reduction in microtubule assembly at the centrosome , proteasome inhibition may disrupt this transition , even in the presence of BRCA1 . When grown in rBM , the major phenotypic response of MCF10A cells to proteasome inhibition ( MG132 , see Materials and Methods ) was growth ablation and/or retardation ( unpublished data ) . However , exposure to 100 nM of MG132 for short periods of time resulted in abnormal acini that deviated from circularity with impaired centrosome apical polarity ( Figure 3B ) . Additionally , proteasome inhibition altered centrosome structures , resulting in diffuse and enlarged pericentrin ( PCNT ) organization ( Figure 3B , arrows ) . Thus , proteasome inhibition phenocopies aspects of BRCA1 depletion , which suggests that proteolytic degradation of BRCA1-target ( s ) , such as RHAMM [23] , may be critical for polarization . To further evaluate this , we examined the influence of BRCA1 depletion and proteasome inhibition on the abundance of RHAMM and aurora kinase A ( AURKA ) , a defined proteasome target [51] . Importantly , both proteasome inhibition and BRCA1 depletion increased the abundance of RHAMM ( Figure S7 ) , which is also consistent with observed RHAMM over-expression in breast cancer cell lines derived from BRCA1 mutation carriers [23] . BRCA1 depletion , however , did not alter AURKA levels ( Figure S7 ) . Thus , RHAMM abundance , which is responsive to both BRCA1 depletion and proteasome inhibition , may play a pivotal role in the polarization necessary for differentiation . One critical role of RHAMM/xrhamm may be the accumulation of TUBG1/tubg1 at the centrosome to influence microtubule assembly [26] , [27] and interphase microtubule dynamics [28] . To further determine whether accumulation of microtubule-associated factors was sufficient to disrupt polarization , RHAMM and TUBG1 , tagged with the green-fluorescent protein ( GFP; TUBG1-GFP ) , were constitutively over-expressed in MCF10A cultures . Even in the presence of BRCA1 , over-expression of RHAMM produced significantly larger and less circular acini ( Figure 3C ) . Accordingly , over-expression of TUBG1-GFP ( but not GFP alone ) impaired centrosome apical localization and resulted in grape-like cell clusters with aberrant mitotic spindles ( Figure 3D ) . Therefore , increases in microtubule-associated factors–through BRCA1 depletion , proteasome inhibition , or over-expression of centrosome proteins targeted by BRCA1-dependent ubiquitination–impair polarization . If decreased microtubule assembly at centrosomes is fundamental to BRCA1-mediated polarization , concurrent depletion of BRCA1 and associated factors may recover this process . Active AURKA phosphorylates BRCA1 to influence interphase microtubule assembly at the centrosome [52]; in turn , AURKA is activated by a complex with targeting protein for Xenopus kinesin-like protein 2 ( TPX2 ) [53] . Therefore , to comprehensively examine the molecular determinants of BRCA1-mediated polarization , we evaluated the consequences of single and concurrent depletions of AURKA , BRCA1 , RHAMM , and TPX2 expression . As with experiments targeting BRCA1 expression , depletion of AURKA , RHAMM , and TPX2 was performed using individual and pooled shRNAs , with transient or stable shRNA expression assays , and over a time course of one or two weeks ( Figures S4 and S5 ) . Note that depletions were not complete for any target , so results should be interpreted in the context of partial loss-of-function . Depletion of TPX2 did not impair growth , did not disrupt polarization , and only slightly reduced the average acini area ( Figures 4A , B , S4 , and S5 ) . However , depletion of AURKA significantly reduced two- and three-dimensional cellular growth ( Figures 4A , B , S4 , and S5 ) , which parallels the effect of a small molecule inhibitor [54] . Finally , depletion of RHAMM induced visible scattering in two-dimensional growth ( Figure S4B ) and increased the area and altered the circularity of acini ( Figures 4A , B , S4 , and S5 ) . These results were further supported by observations of VIM and CD49f immunostaining in acini ( Figure S6 ) . Thus , alteration of RHAMM levels by over-expression or depletion impairs polarization in a similar manner to BRCA1 depletion , which suggests critical regulation of RHAMM in this process . Having established the effects of single depletions , we investigated the genetic interactions that regulate polarization . Using concurrent , transient assays with pooled shRNAs , we identified interactions between AURKA and HMMR ( type double nonmonotonic [55] ) , BRCA1 and TPX2 ( type suppressive [55] ) , and HMMR and TPX2 ( type suppressive [55] ) that regulate polarization ( Figure 4C ) . Notably , simultaneous depletion of BRCA1 and RHAMM did not rescue the polarity defects of the corresponding single depletion assays ( Figure 4C , 4F , and 4G ) . In fact , equivalent acini alterations were observed . As down-regulation of a microtubule-associated factor ( i . e . , RHAMM ) did not recover BRCA1 depletion , a more complex regulation of cytoskeletal reorganization during polarization may exist . In contrast to single depletions , simultaneous reduction of AURKA and RHAMM levels recovered normal acini formation ( Figure 4C–G ) , possibly implying a negative regulatory relationship between RHAMM abundance and AURKA activity . Although mechanistic insight into this relationship is lacking , RHAMM depletion also protects against small-molecule inhibition of AURKA in a different cell model [56] . Notably , depletion of TPX2 , the major activator of AURKA [53] , recovered normal acini formation with concurrent depletion of either BRCA1 or RHAMM ( Figure 4C , 4F , and 4G ) . Together , these genetic interactions suggest that a balance between AURKA-TPX2 and BRCA1-BARD1 activities , mediated by RHAMM , may determine proliferation and polarization . Should AURKA antagonize BRCA1-BARD1 ubiquitination activity to promote centrosome-dependent microtubule assembly [52] , AURKA depletion may amplify the degradation of BRCA1-targeted molecules . As presented above , we confirmed this relationship by examining RHAMM abundance , which was augmented by BRCA1 depletion ( Figure S7B and S7C ) . Consistently , AURKA depletion reduced RHAMM levels ( Figure S7C ) , while simultaneous depletion of AURKA and BRCA1 recovered RHAMM to control levels ( Figure S7C ) . Taken together , these data indicate a critical relationship between AURKA and BRCA1 in regulating RHAMM abundance and , thus , polarization . Complementary analyses suggest a BRCA1-HMMR interaction linked to early-onset , ER-negative breast tumorigenesis , while polarization studies suggest that RHAMM abundance is central to BRCA1 and AURKA activities . As AURKA function relies upon a physical association with TPX2 [53] , we next investigated protein complexes through the cell cycle to determine the relationship between RHAMM abundance and AURKA activity . Consistent with prior reports [26] , [27] , co-immunoprecipitation assays confirmed strong reciprocal interactions between RHAMM and TPX2 during periods of microtubule re-organization ( G2/M , spindle assembly , and M/G1 , spindle disassembly ) ( Figures 5A and S8 ) . Importantly , immunoprecipitation of BRCA1-associated or TPX2-associated protein complexes revealed mobility-shifted RHAMM species suggestive of phosphorylation ( Figure S8 ) . Threonine 703 ( T703 ) is an evolutionarily conserved phosphorylated residue in RHAMM [57] similar to a consensus aurora kinase Ipl1p site [58] . We carried out complementary analyses to test this site as an AURKA substrate . Ectopic expression of GST-AURKA increased levels of phosphoT703-RHAMM ( pT703-RHAMM ) ( Figure 5B ) , as detected by a novel polyclonal antibody ( Figure S9 and Materials and Methods ) . In MCF10A cells , AURKA abundance and activity determined total RHAMM as well as pT703-RHAMM levels ( Figure S10A ) . An in vitro kinase assay with recombinant AURKA confirmed T703-RHAMM site-specific activity ( Figure 5C ) . Finally , pT703-RHAMM was reduced in a dose-dependent manner with AURKA inhibition ( Figures 5D and S10B ) and with mitotic progression ( Figure 5E ) , which is consistent with AURKA degradation in anaphase [51] . Importantly , while total RHAMM was predominantly cytoplasmic with enrichment at microtubules and centrosomes , pT703-RHAMM localized to interphase nuclei ( Figure 5F ) . This observation prompted the hypothesis that pT703-RHAMM maintains homeostasis of AURKA activity by sequestering TPX2 in the nucleus . Consistent with this hypothesis , pT703-RHAMM immunoprecipitated with TPX2 during periods of high AURKA activity ( G2/M as previously described [52] ) ( Figures 5A and S8 ) , while RHAMM depletion not only redistributed TPX2 to the cytoplasm and nuclear envelope ( Figure 6A ) but also increased the level of TPX2 immunoprecipitated with AURKA ( Figure 6B ) . In addition , RHAMM depletion increased AURKA activity as measured by an in vitro kinase assay with beads from AURKA and TPX2 immunoprecipitations ( Figure 6C ) . Collectively , these data indicate that RHAMM maintains AURKA homeostasis as a kinase substrate that , when phosphorylated , negatively regulates AURKA-TPX2 complex formation . Moreover , these results illustrate how depletion of RHAMM alone , or in combination with BRCA1 , impairs polarization through augmentation of AURKA activity . The data above indicate that a balance between BRCA1-mediated turnover and AURKA-mediated phosphorylation of RHAMM regulates polarization versus proliferation . To evaluate the link with carcinogenesis , pT703-RHAMM immunochemistry was performed in BRCA1 mutant breast cancer cells , HCC1937 line , their wild-type reconstituted counterparts , and in primary breast tumors . As a result , pT703-RHAMM staining was revealed to be strong at the nuclear envelope of HCC1937 cells but homogenous and less intense in the nucleus of the reconstituted cells ( Figure 7A ) . Subsequently , high expression of pT703-RHAMM was scored in 58% ( n = 11 ) and 50% ( n = 4 ) of BRCA1 mutation carriers and sporadic ER-negative tumors , respectively , but in 36% ( n = 5 ) and 30% ( n = 10 ) of BRCA2 mutation carriers and sporadic ER-positive tumors , respectively ( Figure 7B ) . Although this dataset is limited , the results support the indication of an interplay between BRCA1 and RHAMM , which is altered in breast carcinogenesis . Our data delineate a model in which different types of relationships between high- and low-penetrance breast cancer susceptibility genes and their products regulate the polarization necessary for terminal differentiation of luminal epithelia . That is , BRCA1 and AURKA activities , as regulated by RHAMM and TPX2 , control this transition and regulate cellular proliferation and differentiation ( Figure 8 ) . In this model , concurrent depletion of BRCA1 and RHAMM does not recover normal acinar morphogenesis because target degradation of RHAMM may be restricted to late phases of polarization . This model is consistent with reduced expression of AURKA , TPX2 , and HMMR , but to a lesser extent BRCA1 , with polarization and growth arrest of nonmalignant mammary epithelial cells , as measured by gene expression profiling ( Figure S11A ) [59] . Deviation from this pathway , through loss of BRCA1 function or augmentation of microtubule-associated factors , may impair terminal differentiation of luminal epithelia and promote tumorigenesis . Consistently , HMMR over-expression might be detectable as early as the transition from normal breast tissue to hyperplasia ( Figure S11B ) [60] . In our cellular assays for polarization , however , concurrent BRCA1 depletion and RHAMM over-expression did not result in an additive disruption of polarity , perhaps due to the non-additive alteration of RHAMM abundance and variable BRCA1 depletion ( Figure S12 ) . According to the model and as stated above , analysis of public gene expression datasets suggests that the rs299290 risk allele is associated with HMMR germline over-expression ( Table S3 ) [23] . As the potential splicing alteration by rs299284 might be tissue specific and RHAMM-R92 was used in the over-expression assays , further work may be warranted to define the causal mutation ( s ) and the alteration of RHAMM function and/or expression level according to the depicted model .
We have investigated gene and protein interactions in a centrosome-cantered module , including BRCA1/BRCA1 and HMMR/RHAMM , across biological systems ranging from breast cancer risk estimates to cellular phenotypes and cytoskeletal structures . Consistent findings between these systems provide insights into diverse processes and conditions . First , the key role of this module in epithelial apicobasal polarization suggests that genetic variation in its components might influence risk of breast cancer . Accordingly , a common candidate breast cancer-predisposition allele in HMMR , originally identified in an Ashkenazi Jewish study [23] , may specifically modify breast cancer risk among BRCA1 mutation carriers . Population-discordant results for HMMR [61] , and possibly for other components of this module ( i . e . , AURKA [62] ) , might be due to genetic differences between populations . A recent report has suggested that common genetic variation in genes encoding for centrosome pathway components ( excluding AURKA and HMMR ) may frequently influence risk of breast cancer and , notably , includes variants in TACC3–a proposed HMMR homolog [63]–TUBG1 , and TPX2 loci [64] . Our results highlight the importance of conducting comprehensive evaluations of the interactions between cancer susceptibility genes and their products across systems to delineate the potential relationship with carcinogenesis [65] . A unifying mechanism of breast carcinogenesis linked to BRCA1 loss-of-function should provide a comprehensive explanation for the observed accumulation of stem and luminal progenitor cells [17]–[19] , and for the characteristic pathological features of the corresponding tumors [11] , [12] . The results of our study suggest that BRCA1 promotes the polarization necessary for luminal differentiation , in part , by orchestrating the dynamic transition to microtubule assembly at non-centrosome sites ( i . e . , cell-to-cell contacts ) . Transition to microtubule anchorage at adherens junctions regulates epithelial cell-to-cell contacts [9] that , in turn , instruct mammary stem cell fate and differentiation [66] . Thus , the switch to non-centrosome-dependent assembly of microtubules may be essential in discriminating between proliferating and differentiated cells , as also observed recently in myoblasts [67] and neurons [68] . Should BRCA1 function ( s ) promote this transition in mammary stem/progenitor cells , impaired luminal differentiation in BRCA1 mutation carriers and a propensity to develop basal-like tumors with elevated proliferative capacity would be expected . Further studies using different polarization and/or differentiation cellular models may be warranted to corroborate the depicted mechanism . Proliferation is promoted by activated AURKA but , as occurs in tightly synchronized cell cycle events [69] , possibly regulated through a negative feedback loop as identified in this study . In preparation for mitotic spindle assembly , AURKA promotes centrosome-dependent microtubule assembly by suppressing BRCA1-dependent ubiquitination [52] , which involves BRCA1 phosphorylation at S308 [70] . Subsequently , terminal differentiation is proposed to be mediated by BRCA1 activation and RHAMM degradation . Accordingly , BRCA1 depletion increases the clonogenic potential of mammary epithelia [14]–[16] , while a BRCA1 S308A mutant alters embryonic stem cell differentiation [71] . Moreover , RHAMM abundance may be central to the balance between AURKA-TPX2 and BRCA1-BARD1 activities during polarization; consistently , depletion of RHAMM also impairs ciliary differentiation of human respiratory epithelial cells [72] . However , key components of the depicted molecular wiring diagram are probably missing , such as a phosphatase that regulates AURKA-mediated modification of BRCA1 and RHAMM . Additionally , loss of BRCA1 function is likely to alter complementary pathways such as the regulation of epithelial-mesenchymal transition [19] and androgen receptor signaling [73] . A key question remains regarding the significance of BRCA1 function to stem/progenitor differentiation and BRCA1 haploinsufficiency . Examination of histologically normal breast tissue in BRCA1 mutation carriers revealed cellular foci expressing stem cell markers and lacking cytoskeletal structures characteristic of luminal epithelia [16] . Here , we suggest an alteration of polarization in preneoplastic lesions . Neither of these presentations is as severe as those observed in murine mammary epithelial tissues reconstituted from human cells depleted of BRCA1 [19] . Thus , BRCA1 dose or mutation type may distinctly affect the tissue architecture and function , leading to differences in the accumulation of stem or progenitor cells , and the resulting tumor type [74] . More detailed examination of mammary gland histology and function may reveal specificities in BRCA1 mutation carriers reflective of a gradient in the disruption of luminal differentiation . To summarize , this study describes a mechanistic model in which high- and low-penetrance breast cancer susceptibility genes and their products are connected through a series of genetic , molecular , and functional interactions that , when perturbed , alter proper epithelial apicobasal polarization and may lead to an increased risk of breast cancer .
BRCA1 and BRCA2 mutation carriers were recruited under the CIMBA initiative following approval of the corresponding protocols by institutional review boards or ethics committees at each participating centre , as described [32] , [33] . Study acronyms are detailed in Table S2 . The NICCC centre in Israel followed similar protocols and similar approval processes . Deviation from Hardy-Weinberg equilibrium was evaluated among unrelated participants separately for each study . Risk estimates and significance testing were computed using standard and weighted Cox regression models [35] that included centre , country , and birth cohort ( <1940 , 1940–1949 , 1950–1959 , and ≥1960 ) as stratification factors and ethnicity as the covariate for adjustment . A robust variance estimate was used to account for familial correlation . Time to diagnosis of breast cancer from birth was modeled by censoring at the first of the following events: bilateral prophylactic mastectomy , breast cancer diagnosis , ovarian cancer diagnosis , death , and last date known to be alive . Participants were considered affected if they were censored at breast cancer diagnosis and unaffected otherwise . The weighted cohort approach involves assigning weights separately to affected and unaffected individuals such that the weighted observed incidences in the sample agree with established estimates for mutation carriers [35] . This approach has been shown to adjust for the bias in the HR estimates resulting from the ascertainment criteria used , which leads to an over-sampling of affected women . Weights were assigned separately for carriers of mutations in BRCA1 and BRCA2 and by age interval ( <25 , 25–29 , 30–34 , 35–39 , 40–44 , 45–49 , 50–54 , 55–59 , 60–64 , 65–69 , ≥70 ) . Polymorphism data were analyzed as a three-group categorical variable ( codominant model ) and using restricted inheritance models ( log-additive , dominant and recessive ) . The p values were derived from the robust score test . All statistical analyses were carried out using R software . Linkage analysis was performed with GENEHUNTER version 2 . 1 [75] . HeLa ( American type culture collection , ATCC ) , 293FT ( Invitrogen ) , and MCF10A ( ATCC ) were cultured in media as recommended . For growth factor-reduced experiments , media ( HuMEC from Invitrogen or HMEC from Lonza ) contained 1/3 recommended hEGF . Growth in rBM ( Cultrex from Trevigen or Geltrex from Invitrogen ) followed embedded or on-top techniques as described [45] . MCF10A were embedded in rBM for proteasome inhibition ( MG132; Sigma-Aldrich ) experiments; for other endpoints , embedded and on-top conditions were equivalent . For rBM growth , MG132 or equivalent DMSO volumes were added to media at seeding , or as indicated , for two days . For proteolysis protection , MG132 ( 1 . 5 µM ) was added for 3 h prior to lysis . For AURKA inhibition , a commercially available AURKA inhibitor ( C1368; Sigma-Aldrich ) was titrated and used at 100 nM . For TUBG1-GFP expression , MCF10A were transduced with a lentiviral-based vector , sorted , and selected for blasticidin resistance ( pLenti6 . 2/EmGFP-DEST , Invitrogen ) . For expression in MCF10A , RHAMM and TUBG1-GFP were subcloned into pDONR223 ( Invitrogen ) , sequenced , and transferred to pLenti6 . 2/V5-DEST following the manufacturer's instructions ( Invitrogen ) . All constructs maintained native stop codons . Depletion assays used MISSION shRNA sequences ( Sigma-Aldrich ) , shown in Table S5 . The lentiviral packaging , envelope , control , and GFP expression plasmids ( psPAX2 , pMD2 . G , non-hairpin-pLKO . 1 , scrambled-pLKO . 1 , and pWPT-GFP ) were purchased from Addgene . Production and collection of lentiviral particles followed a modified Addgene protocol . Initial viral titres >5×105/ml were confirmed by Lenti-X GoStix ( Clontech ) and supernatants were then concentrated by ultracentrifugation or Lenti-X Concentrator ( Clontech ) and stored at −80°C . Concentrated viral supernatants were titrated for optimal inhibition of target gene products , by immunoblot at 5 d , and MCF10A survival . For shRNA-mediated depletion of BRCA1 , four shRNA species were purchased and tested; these sequences were distinct from that previously described [14] . For depletion of AURKA , RHAMM , and TPX2 , five shRNA constructs were purchased for each gene ( Sigma-Aldrich ) . Initial experiments used combinations of shRNAs targeting individual genes ( up to five sequences per gene ) at a multiplicity of infection of five . For confirmation experiments , individual and redundant constructs were identified with high knockdown efficacy . Two shRNA sequences effectively reduced the expression of AURKA ( 5′-ACGAGAATTGTGCTACTTATA-3′ and 5′-CCTGTCTTACTGTCATTCGAA-3′ ) , BRCA1 ( 5′-CACCTAATTGTACTGAAT-3′ and 5′-TACAAGAAAGTACGAGAT-3′ ) , and RHAMM ( 5′-CGTCTCCTCTATGAAGAACTA-3′ and 5′-GCCAACTCAAATCGGAAGTAT-3′ ) , respectively . These shRNAs have also been independently validated for reduction in mRNA levels by the manufacturer ( 67%–87% reduction , Sigma-Aldrich ) . Only one sequence efficiently reduced expression of TPX2 ( 5′-CCGAGCCTATTGGCTTTGATT-3′ ) . Transient transfection of GST-AURKA in HeLa and MCF10A followed the manufacturers' suggested protocols for Lipofectamine 2000 ( Invitrogen ) or FuGENE ( Roche ) . Synchronization and immunoprecipitation , and immunofluorescence of cells and acini , were performed as described previously [27] , [45] . For immunofluorescence analysis , cells were mounted in 90% glycerol/PBS and counterstained with DAPI or TOPRO . The in vitro kinase assay with recombinant HIS-AURKA ( PTP055 , Cell Science ) followed the protocol for the PKLight HTS Protein Kinase Assay Kit ( Lonza ) , as suggested by the manufacturer . Reactions were performed in triplicate . Luminescence values were normalized to the mean value for no-substrate ( HIS-AURKA alone ) reactions . The activity of endogenous AURKA was determined by performing the kinase assay with ATP , substrate , and immunoprecipitation beads―IgG ( negative control ) , anti-AURKA ( positive control ) , or anti-TPX2 from MCF10A lysates but without recombinant AURKA . Consumption of ATP was determined after incubation for 30 min . For total RHAMM , a previously developed and characterized polyclonal antibody ( originally named anti-IHABP ) was used [24] , [76] . The specificity of this antibody has been further evaluated elsewhere ( see also Figure S9 ) [23] , [27] . The phosphorylation-specific polyclonal antibody against pT703-RHAMM is a custom reagent generated by New England Peptide . For this antibody , unpurified and purified sera were tested for specificity relative to the RHAMM-total antibody defined above . These assays included immunoblots with shRNA-mediated depletion of RHAMM ( Figure S9 ) . Other antibodies included anti-ACTB ( A5060 , Sigma-Aldrich ) , anti-AURKA ( 1G4 , Cell Signaling Technology ) , anti-BRCA1 ( SD118 , Calbiochem ) , anti-CD49f ( 4F10 , Millipore ) , anti-CDH1-Alexa 488 ( 24E10 , Cell Signaling Technology ) , anti-GST ( GE healthcare ) , anti-MYC ( 9E10 , Sigma-Aldrich ) , anti-PCNT ( Covance ) , anti-TUBA ( B512 , Sigma-Aldrich ) , anti-TUBB-Alexa 647 ( 9F3 , Cell Signaling Technology ) , anti-TUBG1 ( GTU88 , Sigma-Aldrich ) , and anti-VIM ( V9 , Sigma-Aldrich; or R28 , Cell Signaling Technology ) . Secondary antibodies for immunofluorescence ( Alexa ) were obtained from Molecular Probes ( Invitrogen ) and GE Healthcare for immunoblot analysis ( HRP-conjugated ) . Blind deconvolution with AutoQuant ( AutoQuant Imaging Inc . ) was performed on images from an Axiovert microscope with Plan-Apochromat 63× objective ( Zeiss ) ( numerical aperture ( NA ) 1 . 25 ) with Z-steps from 0 . 5–1 . 0 µm . Alternatively , a Leica DMI 6000 laser scanning confocal microscope equipped with a Leitz HCX Pl-Apo CS 40× oil objective ( 1 . 25 NA ) captured images as indicated . Epifluorescence images were acquired with an Olympus BX-60 using a Spot camera and Spot3 . 2 . 4 software ( Diagnostic Instruments ) . For quantitation of growth in rBM , bright-field images of acini were analyzed for size and shape with ImageJ software ( National Institutes of Health ) . For shape analysis , the square of the inverse of circularity was plotted . The position of the centrosome relative to the lumen , or centre of the cellular cluster , was measured using pericentrin immunofluorescence or TUBG1-GFP in image stacks . In order to reconstitute HCC1937 cells with wild-type BRCA1 , the corresponding full-length open-reading frame was cloned into a retroviral vector S11N and transduced . Assays with the empty vector were used as controls . Cells were fixed in 2% paraformaldehyde and immunochemistry carried out following a standard labelled streptavidin biotin ( LSAB ) method . For tumors , immunohistochemical staining was performed by the Envision method ( Dako , Glostrup , Denmark ) , with a heat-induced antigen retrieval step . Sections from the tissue array were immersed in 10 mM boiling sodium citrate at pH 6 . 5 for 2 min in a pressure cooker , and antibodies were used at dilution of 1∶1 , 500 and 1∶1 , 000 for pT703-RHAMM and TUBG1 , respectively . Scoring for pT703-RHAMM was performed in a blind and independent manner by two pathologists with an initial correlation value of 0 . 75 . Discordant results were then assessed jointly but blind from the genetic status of the samples . Hyperplastic lesions in BRCA1 mutation carriers were also assessed by both pathologists . | Mutations in two genes that were initially identified as predisposing carriers to early-onset breast cancer , BRCA1 and BRCA2 , cause similar perturbations in cellular responses to DNA damage but predispose carriers to distinct tumor types . Thus , the two genes may trigger different carcinogenic processes . We have used genetic analyses of affected families to uncover additional genetic variation that is linked to the risk of developing cancer for carriers of BRCA1 mutations . This variation falls within a centrosomal gene , named HMMR . The protein product of HMMR , which is called RHAMM , works in concert with BRCA1 to regulate the structure of normal breast cells as they grow and become polarized . This polarization process depends upon a balance between the activities of BRCA1 and the Aurora kinase A , with the kinase opposing BRCA1 function and promoting growth . Our findings provide new insights into the mechanism through which BRCA1 may promote commitment of initially bipotent mammary cells towards the luminal lineage , and how loss of this function may predispose cells to become breast tumors of a basal-like type . | [
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] | 2011 | Interplay between BRCA1 and RHAMM Regulates Epithelial Apicobasal Polarization and May Influence Risk of Breast Cancer |
Malaria-protective CD8+ T cells specific for the circumsporozoite ( CS ) protein are primed by dendritic cells ( DCs ) after sporozoite injection by infected mosquitoes . The primed cells then eliminate parasite liver stages after recognizing the CS epitopes presented by hepatocytes . To define the in vivo processing of CS by DCs and hepatocytes , we generated parasites carrying a mutant CS protein containing the H-2Kb epitope SIINFEKL , and evaluated the T cell response using transgenic and mutant mice . We determined that in both DCs and hepatocytes CS epitopes must reach the cytosol and use the TAP transporters to access the ER . Furthermore , we used endosomal mutant ( 3d ) and cytochrome c treated mice to address the role of cross-presentation in the priming and effector phases of the T cell response . We determined that in DCs , CS is cross-presented via endosomes while , conversely , in hepatocytes protein must be secreted directly into the cytosol . This suggests that the main targets of protective CD8+ T cells are parasite proteins exported to the hepatocyte cytosol . Surprisingly , however , secretion of the CS protein into hepatocytes was not dependent upon parasite-export ( Pexel/VTS ) motifs in this protein . Together , these results indicate that the presentation of epitopes to CD8+ T cells follows distinct pathways in DCs when the immune response is induced and in hepatocytes during the effector phase .
Immunization with irradiated Plasmodium sporozoites to induce sterile protection against live parasite challenge is a powerful model for malaria vaccination [1] . Protective immunity is mediated in part by CD8+ T cells specific for the circumsporozoite ( CS ) protein of Plasmodium [2] , [3] . Plasmodium specific CD8+ T cells have been shown to be primed by dendritic cells ( DCs ) [4] , [5] , [6] , [7] . In particular , we have found that after sporozoite inoculation into the dermis by infected mosquitoes , antigen is presented by DCs in the skin-draining lymph node to initiate the CD8+ T cell response [4] . Primed CD8+ T cells then exit the priming site and migrate to the liver where they can eliminate infection after recognizing antigen presented by hepatocytes [4] . Thus CD8+ T cell mediated immunity requires antigen presentation by two different cell types – DCs and hepatocytes . Determining how DCs and hepatocytes process and present Plasmodium antigens is essential for the rational identification of vaccine candidates . Since immunization with irradiated sporozoites represents the gold standard for malaria vaccination it is important to know which sporozoite antigens are presented by DCs . Perhaps more vital still , is to understand which molecules are presented by hepatocytes , as only those molecules presented to effector cells can be the targets of protective immunity . Microbial and tumor epitopes presented by MHC class I usually derive from proteins in the cytosol that are proteolytically cleaved into small peptides by the proteasome . These peptides are translocated from the cytosol into the ER by the TAP transporter for loading onto class I MHC molecules , which then traffic towards the cell surface ( reviewed in [8] ) . Many parasites , however , reside within a parasitophorous vacuole ( PV ) and their proteins are not necessarily secreted into the host cytosol . The processing and presentation of intracellular parasite antigens is therefore complex and still poorly understood . Toxoplasma gondii antigens have been reported to reach the cytosol for class I processing via fusion of the PV and the host ER; from the host ER antigens may be retrotranslocated into the host cytosol for processing [9] . Leishmania major antigens may bypass the host cytosol altogether as antigen presentation appears to be TAP independent . Instead it is believed that L . major-derived peptides are directly loaded onto MHC Class I in the phagolysosome [10] . The in vivo processing of Plasmodium sporozoite or liver stage antigens has not been studied . Unlike Toxoplasma or Leishmania , Plasmodium does not infect professional APCs and it is not known how DCs acquire sporozoite antigen . Likewise , the presentation of antigens by hepatocytes to effector cells is also poorly understood . In-vitro evidence suggests that hepatocytes are capable of presenting Plasmodium antigen and that this may be proteasome dependent [11] , requiring the export of parasite antigen to the hepatocyte cytosol by unknown mechanisms . It has been proposed that Pexel/VTS motifs , known to be important for the export of proteins out of the PV in Plasmodium blood stages [12] , [13] , could also be involved in the transport of liver stage antigens to the hepatocyte cytosol for processing and presentation by class I MHC [14] . In this study we aimed to identify key cellular and molecular features of the antigen processing pathways employed by DCs and hepatocytes . We aimed to determine if Plasmodium CS processing requires the use of the cytoplasmic TAP dependent pathway to transport the processed epitope from the cytosol to the ER and allow binding of the peptide to class I MHC . In addition , we wanted to investigate whether the CS antigen is phagocytosed by presenting cells or if it is directly deposited or secreted into the cytosol of DCs or hepatocytes . To address these questions we generated P . berghei parasites that express a mutant CS protein containing the model SIINFEKL H-2Kb restricted epitope . Using this parasite in conjunction with knockout and mutant mice we have been able to generate the clearest picture to date of the processing of the CS protein from both sporozoite and liver stages .
A major obstacle to determining how Plasmodium antigens are presented to T cells is the lack of defined H-2b restricted epitopes which severely limits in vivo studies , as many transgenic mice , which are critical to study basic aspects of immunology , are generated on a C57Bl/6 ( H-2b ) background . To overcome this , we generated P . berghei CS5M parasites in which the endogenous CS gene was replaced with a modified CS gene carrying 5 mutations that changed the natural H-2Kd restricted epitope SYIPSAEKI to SIINFEKL , an H-2Kb restricted epitope ( Figure 1A and B ) . P . berghei CS5M parasites were apparently normal as they infected mosquitoes and mice similarly to parental P . berghei ANKA ( Figure S1 ) . Most importantly P . berghei CS5M parasites stimulated a robust SIINFEKL specific response in C57Bl/6 mice upon immunization ( Figure 1C ) , and activated SIINFEKL-specific CD8+ T cells from previously generated TCR transgenic mice [15] were able to eliminate the liver stages of P . berghei CS5M ( Figure 1D ) . It is important to emphasize that our approach differs significantly from the more common strategy of inserting an entire foreign gene into a parasite and then tracking the immune responses to the foreign molecule . In the P . berghei CS5M parasite SIINFEKL is inserted in place of a well-defined natural epitope , leaving intact the neighboring residues to ensure correct proteasomal processing , thus the model epitope is presented exactly as the natural CS epitope . This makes the P . berghei CS5M parasite an excellent system in which to study antigen processing and presentation . Moreover , we anticipate that P . berghei CS5M will be a powerful tool for use in future studies of antigen specific immune responses to malaria sporozoites . We initiated our studies on the presentation of Plasmodium antigen by investigating whether DCs present irradiated sporozoite antigen via the canonical TAP dependent pathway . Wild type and TAP-1 deficient mice [16] were immunized intra-dermally in the ear with sporozoites and 2 days later CD11c+ DCs were isolated from the draining lymph nodes . To assess antigen presentation the DCs were co-cultured with CFSE-labeled SIINFEKL specific transgenic cells . Antigen presentation was quantified by measuring the expansion of the transgenic cell population 3 days after immunization . While DCs isolated from wild type animals induced extensive proliferation of the SIINFEKL specific cells , DCs from immunized TAP-1 deficient animals were unable to induce proliferation ( Figure 2A ) . The failure of TAP-1 deficient DCs to induce proliferation could only be due to a processing defect as TAP-1 deficient DCs pulsed with exogenous SIINFEKL peptide were fully capable of inducing antigen specific T cell proliferation ( Figure S2 ) . To determine if TAP-1 is required in vivo after immunization via the natural route of infection , wild-type and TAP-1 deficient animals that had received SIINFEKL specific TCR transgenic CD8+ T cells were immunized by the bites of irradiated mosquitoes infected with P . berghei CS5M parasites . We observed a robust antigen specific CD8+ T cell response after immunization of wild type mice; however , immunized TAP-1 deficient animals failed to mount a significant CD8+ T cell response in either the draining LN , spleen or liver ( Figure 2B ) . Together these data indicate that the presentation of the CS protein by DCs is strictly TAP dependent . Given that the priming of sporozoite specific T cells is TAP dependent , the CS protein must reach the cytosol of the DC for antigen processing . Since Plasmodium parasites have not been observed to productively infect DCs [17] , [18] it is not obvious how sporozoite antigen accesses the DC cytosol . One possibility is that CS antigen from sporozoites is cross-presented via an endosome-to-cytosol pathway in which sporozoite antigen is phagocytosed and then retrotranslocated into the cytosol [19] . Alternatively , CS may be deposited in DCs during the process of cell traversal - a process in which sporozoites pass through the cytosol of cells , without forming a vacuole around themselves [20] , [21] , [22] . To distinguish between these possibilities we evaluated the induction of CD8+ T cell responses in animals which have a single-point mutation in the molecule Unc93B1 ( 3d mice ) . This mutation causes several impairments to endosome function including defects in signaling via the endosomal TLRs and in cross presentation [23] . We reasoned that if there were defects in T cell priming in these animals it would strongly indicate a role for endosomes in antigen processing by DCs . We found that DCs isolated from immunized 3d mice were less capable of priming SIINFEKL specific T cells in vitro compared to wild type controls ( Figure 3A ) . This defect appears to be in the processing of antigen , as exogenous peptide is efficiently presented by DCs from 3d mice ( Figure S2 ) . Nonetheless , ex vivo antigen presentation assays provide only a snapshot of sporozoite antigen presentation at a single time point whereas we have recently shown that prolonged antigen presentation is required for full T cell priming [24] . Thus we assessed T cell priming in vivo after immunization by mosquito bites . We found that the difference observed in ex vivo experiments was amplified in vivo as 3d mice had severely decreased SIINFEKL specific responses in the spleen and liver compared to wild type mice ( Figure 3B ) . The role of endosomes in the presentation of sporozoite antigen by DCs was further confirmed in experiments in which cross-presenting DCs subsets were depleted in vivo by treatment with cytochrome c ( cyt c; Figure S3 ) [25] , [26] , [27] . Upon taking up cyt c cross-presenting DCs retrotranslocate it into the cytosol where it can induce apoptosis . In contrast non cross-presenting cell subsets are unaffected as they break down any cyt c that has been taken up in lysosomes . In agreement with the data from 3d mice we found significant reductions in the priming of SIINFEKL specific T cells in cyt c treated animals after immunization via mosquito bites ( Figure 3C ) . Together these data demonstrate that the majority of sporozoite antigen is probably processed via the endosome-to-cytosol pathway . Given that the presentation of sporozoite antigen by DCs occurs via the endosome , we hypothesized that opsonization of parasites might enhance the priming of CD8+ T cells [28] , [29] . Accordingly we incubated parasites with the anti-CS mAb 3D11 [30] prior to immunization . Unexpectedly , we found that opsonized parasites induced much reduced proliferation of CD8+ T cells compared to sporozoites treated with irrelevant antibody [31] ( Figure 4 ) . This intriguing result indicates that opsonization inhibits rather than potentiates the delivery of sporozoite derived CS protein to the DC class I processing pathway . This surprising result is not completely unprecedented – opsonized T . gondii parasites appear to be taken up by DCs via complement and Fc receptors and directed away from the cross presenting pathway and towards break down by lysosomes [9] . To determine if this occurs after opsonization of Plasmodium sporozoites we also treated sporozoites with F ( ab′ ) 2 fragments of the 3D11 mAb which cannot be recognized by Fc receptors and do not efficiently fix complement . However 3D11 F ( ab′ ) 2 fragments were as efficient as intact antibody at inhibiting T cell priming . Thus it may be that opsonization ( and F ( ab′ ) 2 treatment ) affect T cell priming by immobilizing parasites [32] and thus interfering with a number of processes which may be important for T cell priming . These include parasite migration to the skin draining lymph nodes , invasion of cells in the skin and the shedding of antigen from the sporozoite surface [4] , [17] , [33] . Because effector cells must kill infected hepatocytes , it is also required that hepatocytes present processed antigen to CD8+ T cells . Therefore , in addition to DCs , we were also interested in determining how hepatocytes process antigen for presentation to effector cells . To determine if antigen is processed by hepatocytes via the same endosome-to-cytosol pathway employed by DCs , activated SIINFEKL specific CD8+ T cells were transferred to TAP-1 deficient , 3d and cyt c treated mice that were subsequently infected with P . berghei CS5M parasites . The read-out for epitope presentation is T-cell mediated inhibition of liver stage development i . e . if the epitope is presented , activated CD8+ T cells will recognize it and will eliminate liver stage parasites . We also tried to visualize antigen presentation by immuno-fluorescence with the mAb 25-D1 . 16 which recognizes Kb-SIINFEKL complexes [34]; however , in common with other researchers we found that this technique was not sensitive enough to detect epitopes on the surface of parasite infected cells [35] . Using our in vivo functional assay we found that effector CD8+ T cells had no inhibitory effect on parasite development in the livers of TAP-1 deficient animals while they were fully capable of eliminating parasites in wild type mice ( Figure 5A ) , clearly indicating that in hepatocytes , as in DCs , CS must reach the cytosol for antigen processing . However , in sharp contrast to DCs , we found that hepatocytes do not process antigen via endosomes since effector CD8+ T cells were capable of efficiently eliminating parasites from the livers of 3d or cyt c treated mice ( Figure 5B and C ) . Thus hepatocytes unlike DCs do not appear to process antigen by an endosome to cytosol pathway , rather , hepatocytes present antigen that has been deposited or secreted by the parasite directly into the cytosol . Our findings that antigen presentation in hepatocytes requires CS to enter the host cytosol but is independent of the endosomal pathway , raise the question as to how CS traffics to the hepatocyte cytosol . A previous report in which the 2 Pexel/VTS motifs in the N terminal domain of CS were mutated , suggested that CS export to the cytosol was eliminated in the absence of functional Pexel/VTS motifs [14] . To determine whether Pexel/VTS motifs are critical for the entry of CS into the class I processing pathway of infected hepatocytes we generated P . berghei CS5M parasites that carried mutations in key residues of both Pexel/VTS motifs as well as the SIINFEKL epitope ( P . berghei CS5MΔP1–2; Figure S4 ) . We mutated the Pexel/VTS sequences to the sequence that was previously suggested to abolish CS export into the cytoplasm of infected hepatocytes [14] . In fact we were able to observe punctate staining of CS in the cytosol of both P . berghei CS5M and P . berghei CS5MΔP1–2 infected Hepa1-6 cells ( Figure 6A and B ) , and more importantly , we found that the P . berghei CS5MΔP1–2 parasites were killed as efficiently as P . berghei CS5M by effector CD8+ T cells ( Figure 6C ) . This indicates that Pexel/VTS motifs are not required for the entry of CS into the cytosol of hepatocytes for antigen presentation to effector CD8+ T cells . However , in agreement with the previous study we did observe that parasites with mutated Pexel/VTS motifs in the CS protein have a ∼10-fold decrease in infectivity ( Figure 6C ) . Finally we found that DCs efficiently present the epitope from the CS protein of parasites lacking the Pexel/VTS motifs ( Figure 6D ) . This was not entirely unexpected as our previous findings suggested that DCs likely acquire the CS antigen by phagocytosis which is unlikely to be affected by host cell targeting sequences .
In this study we demonstrate that the process of antigen presentation required for the priming of sporozoite specific T cells and for the elimination of liver stage parasites are distinct . The difference in antigen presentation between DCs and hepatocytes has important consequences for malaria vaccine development based on irradiated sporozoites . If other Plasmodium antigens are processed similarly to CS , it is likely that DCs , which acquire antigens by phagocytosis , could stimulate T cell responses to a broad range of secreted and non-secreted antigens . In contrast hepatocytes can only present antigens that are secreted into the cytosol of infected or traversed cells; these antigens are , however , the potential targets of protective immunity as they induce effector cells to eliminate liver stage parasites . Thus , irradiated sporozoites may induce a range of irrelevant as well as protective immune responses . Moreover it is possible that irradiated sporozoites will fail to induce protective responses to various liver stage antigens presented by hepatocytes , that are not expressed by sporozoites . This appears to be the case for the liver stage antigen Hep17: irradiated sporozoites do not induce detectable Hep17 specific CD8+ T cells; however , vaccine-induced T cells specific for this antigen are protective against Plasmodium liver stages [36] . We observed that both T cell priming and parasite elimination by T cells were strictly TAP dependent . Thus in both DCs and hepatocytes antigen must reach the cytosol for presentation . In DCs this appears to occur via an endosome-to-cytosol pathway as determined by two independent in vivo methodologies: the use of 3d mice and treatment of mice with cyt c . However , unlike the defect in TAP1 deficient mice , the reduction in T cell priming in both 3d and cyt c treated mice was not complete . This may indicate that a small amount of antigen is directly deposited in the cytosol of DCs by traversing sporozoites . Alternatively cross-presentation may not be fully ablated in these models . 3d mice carry a single point mutation in one molecule ( Unc93B1 ) which may retain some residual functionality [23] , while the depletion of cross-presenting DCs by cyt c may not be absolute , particularly in the lymph nodes . The function of Unc93B1 in antigen presentation is not clear , though it may be involved in translocating elements of the cross-presentation machinery to the endosome similar to the way it mediates the movement of TLRs to endosomes [37] . An intriguing recent study showed that 3d mice were highly susceptible to T . gondii infection [38] . The authors suggest that this was not due to an impairment of CD8+ T cell control of parasites as the activation of CD8+ T cells appeared normal in 3d mice – however they were only able to look at bulk T cell populations and not antigen specific cells . Further research will be required to determine what receptors DCs use to take up sporozoites and which pattern recognition molecules interact with sporozoites to facilitate cross presentation . One unexpected finding was that opsonization of sporozoites did not enhance the presentation of the CS antigen by DCs . One hypothesis is that opsonization may immobilize parasites [32] and thus interfere with a variety of processes that may be important for T cell priming including antigen shedding , and migration to the draining lymph nodes for presentation [4] , [17] , [33] . Alternatively opsonization may prevent parasites from infecting cells in the skin where they could continue to provide antigen to the immune system [18] , [39] . The inability of DCs to present antigen from immobilized parasites may explain why irradiated parasites are capable of inducing a protective CD8+ T cell response , but heat killed parasites are not [3] , [40] . These data also have important implications for vaccine design since they imply that there would be difficulties in priming or boosting sporozoite specific CD8+ T cell responses in individuals with high anti-CS antibody titers . Thus it may be hard to induce effective CD8+ T cell responses in individuals who have already been naturally exposed to parasites or immunized with vaccines such as RTS , S that are designed to induce strong anti-sporozoite antibody responses [41] . Using the 3d and cyt c treated mice we showed that in contrast to T cell priming , parasite elimination was unaffected in mice with reduced capacity to cross-present antigen . This is in agreement with the findings of a previous in vitro study [11] which found no evidence for endosomes having a role in antigen presentation by infected cells . The previous study also showed that proteasome and Golgi inhibitors blocked antigen presentation , which is compatible with our finding that antigen presentation occurs via the classical TAP-dependent pathway [11] . Together these data suggest that cell killing occurs only after direct antigen presentation by the infected hepatocyte itself . A key direction for future research will be to identify how antigens enter the host cell for presentation . We were unable to find a role for Pexel/VTS motifs in targeting the CS protein to the host cell cytosol as suggested by a previous study [14] . Our data are based on fluorescence microscopy 6 hours post-infection when the highest amounts of CS can be observed in the cytosol [42] , [43] and , more importantly , our functional assay to measure the elimination of parasites by T cells . The fact that Pexel/VTS motifs are not required for the entry of CS to the class I processing pathway suggests that liver stage proteins may be exported to the hepatocyte by other mechanisms . In particular , it suggests that the CS protein may contain another motif that facilitates its export out of the PV into the infected host cell . Alternatively , liver-stage antigens might also be exported to the class I processing pathway if the Plasmodium PV can fuse with the hepatocyte ER as appears to occur in Toxoplasma infected DCs [9] . Together our data provide the most complete description to date of the processing of sporozoite and liver stage antigen . Using the P . berghei CS5M parasite we have demonstrated that DCs cross-present sporozoite antigen via an endosome-to-cytosol pathway . Of most importance , we show that CS must be delivered to the hepatocyte cytosol for presentation to effector cells . If this is true for other antigens , it is likely that antigens secreted into the hepatocytes of either infected or traversed cells constitute the major targets of anti-liver stage CD8+ T cell mediated immunity . Secretion to the hepatocyte is likely a complex process given our finding that Pexel/VTS motifs are not required for the entry of CS to the class I processing pathway; however , unraveling this process will be key to the identification of vaccine candidates .
All animal procedures were approved by the Institutional Animal Care and Use Committee of the Johns Hopkins University ( Protocol Number MO09H41 ) following the National Institutes of Health guidelines for animal housing and care . 5–8 week old female C57Bl/6 were purchased from NCI ( Frederick , MD ) . TAP-1 deficient animals were purchased from Jackson ( Bar Harbor , ME ) . Unc93B13d mice were obtained from the Mutant Mouse Resource Center ( University of California , Davis , CA ) . OT-1 mice ( carrying a transgene specific for the SIINFEKL epitope ) were kindly provided by David Sacks ( Laboratory of Parasitic Disease , National Institute of Allergy and Infectious Disease , Bethesda , MD ) . P . berghei CS5M parasites were generated by transfection of P . berghei ANKA with the linearized pR-CS5M plasmid as previously described [44] . pR-CS5M was derived from the plasmid pR-CSwt [45] as follows . A Kpn1-Xho1 fragment including the entire CS gene was excised from pR-CSwt into a pBluescript SK- ( Stratagene ) backbone to generate the plasmid pIC-CSwt . A SexA1 site was introduced by mutation of G to A at position 714 in the CS gene ( silent in Gln238 ) and a BsmF1 site was introduced by a mutation of T to C at position 810 ( silent in Asp270 ) using the QuikChange XL site directed mutagenesis kit ( Stratagene ) . The SexA1-BsmF1 fragment was excised and replaced with a ∼100 bp insert including the SIINFEKL epitope in place of the SYIPSAEKI sequence ( formed from the oligos S8ins F and S8insR; see Table S1 ) to generate the plasmid pIC-CS5M . The Kpn1-Xho1 fragment from pIC-CS5M was excised and ligated into the backbone of pR-CSwt to generate the pR-CS5M plasmid . pR-CS5M was linearized with the enzymes Kpn1 and Sac1 . P . berghei CS5MΔP1–2 parasites were generated similarly to P . berghei CS5M ( Figure S1 ) . The plasmid pR-CS5MΔP1–2 was generated as follows . Arg32 and Leu34 in the CS gene on the pIC-CS5M plasmid were mutated to Alanines by using the QuikChange site directed mutagenesis kit with the primers PEXEL1 F and PEXEL1 R ( see Table S1 ) , which include a Bsm1 site . Arg66 and Leu68 were mutated similarly with the primers PEXEL2 F and PEXEL2 R that include an ApaB1 site . The resulting plasmid was designated pIC-CS5MΔP1–2 . The Kpn1-Xho1 fragment of the pIC-CS5MΔP1–2 plasmid was ligated into the pR-CSwt backbone to generate the pR-CS5MΔP1–2 plasmid used for transfection . Lymph node and spleen myeloid DCs were prepared essentially as described [46] . Briefly , spleens or lymph nodes from immunized mice or naive mice were taken , chopped finely and digested with 1 mg/ml collagenase . The single cell suspension of spleen cells was then separated over a Nycodenz gradient ( density , 1 . 075 g/ml ) and the DC-rich low-density fraction was taken . To further enrich the DC population , negative selection was performed on the collected fraction using magnetic bead separation with anti-CD3 , anti-GR1 , anti TER119 , anti-B220 and anti-Thy1 . 2 antibodies . Final purity of CD11c+ DC was about 70% . To assess Ag presentation ex vivo , splenic myeloid DCs ( 1×105 ) were mixed with 5×104 purified naive CFSE-labeled CD8+-transgenic cells in a single V-bottom well of a 96-well plate . 60–65 h later , the cells were harvested , and CFSE dilution in the transgenic cell population was used as a measure of Ag presentation . Where possible SIINFEKL-specific T cell priming was measured after immunization by the bites of 10–20 irradiated mosquitoes . Prior to biting , a low number ( 2×103 ) of CD45 . 1+ OT-1 cells were transferred to mice and the expansion of the CD45 . 1+ CD8+ ( SIINFEKL-specific ) population were measured by flow cytometry 10 days later to allow time for the responses to reach detectable levels . In some experiments it was necessary to perform immunizations with needle injected sporozoites ( e . g . where the sporozoites were treated with antibodies prior to immunization ) . In these experiments 5×105 congenic CD45 . 1+ OT-1 cells were adoptively into mice , which were immunized the following day . The cells would be labeled with 0 . 6 µM CFSE using the Vybrant Cell Tracker kit according to the manufacturer's instructions ( Invitrogen Life Technologies ) , and antigen presentation was inferred from proliferation of CD45 . 1+ CD8+ cells in the draining lymph nodes after 3 days . Use of a high number of transgenic cells is acceptable in these experiments as we are using the cells as a readout of antigen presentation not measuring particular T cell phenotypes . ELISPOTs to measure peptide-specific IFN-γ secreting cells were performed as described [47] and used to detect endogenous SIINFEKL responses . F ( ab′ ) 2 fragments from the 3D11 mAb ( class: mouse IgG1 ) were prepared by incubation with immobilized Ficin in the presence of 4 mM cysteine according to the manufacturer's instructions ( Pierce ) . F ( ab′ ) 2 fragments were isolated from intact antibody and Fc fragments by passing twice over a Protein A column . Purity of F ( ab′ ) 2 fragments was verified by SDS-PAGE under non-reducing conditions . SIINFEKL-specific effector cells were purified from mice that had received 5×105 naïve CD45 . 1+ OT-1 cells and then been immunized with 2×106 pfu VV-OVA [48] . 8–10 days later spleens were taken from the immunized mice and the lymphocytes were purified by spinning over lympholyte M ( Cedarlane Laboratories ) . A total of 2×106 effector/SIINFEKL specific CD8+ T cells were transferred to each recipient mouse . Quantification of liver stage parasites was performed as previously described [49] . Briefly , 40 hours after challenge , livers were excised and parasite load was determined by quantitative PCR for P . berghei 18S rRNA using SYBR Green ( Applied Biosystems ) . Single cell suspensions of lymphocytes were obtained by grinding spleen cells or lymph node cells between the ground ends of two microscope slides and filtering twice through 100 µm nylon mesh . Liver lymphocytes were isolated from perfused livers by grinding , filtration through a 70 µm mesh and separation over a 35% percol gradient as described [50] . Hepa1-6 cells were grown on coverslips in a 48 well plate and allowed to reach ∼80% confluence prior to infection with ∼3×104 parasites . 6 hours later the slides were washed and fixed for 15 minutes with 4% formaldehyde prior to permeablilization with 100% methanol for 10 minutes . The cells were then blocked with 3% BSA for 45 minutes . The parasite cytosol was labeled with anti-Plasmodium HSP70 mAbs [51] followed by secondary staining with Alexa594 anti-mouse IgG ( Molecular Probes ) . The cells were then stained with anti-P . berghei CS mAb ( 3D11 ) directly conjugated to FITC . Slides were mounted with ProLong antifade with DAPI ( Molecular Probes ) . Images were acquired on a Nikon Eclipse 90i microscope with a Hamamatsu Orca-ER camera attachment using Volocity software ( Perkin Elmer ) . Images were analyzed and assembled using ImageJ software ( open source from NIH ) . Statistical analysis was performed using Prism 4 software ( GraphPad Software ) , unless otherwise stated , means were compared by two-tailed Student's t tests . Analysis of all flow cytometry data was performed using FlowJo software ( TreeStar ) . | Malaria causes the deaths of 0 . 5–2 million people each year , mainly in Africa . A safe and effective vaccine is likely needed for the control or eradication of this disease . Immunization by irradiated malaria-infected mosquitoes has been shown to protect people against malaria . Irradiated parasites do not divide and cause infection but are capable of activating specialized killer cells called CD8+ T cells , which can protect against live parasites . Because vaccinating people with irradiated mosquitoes is not practical , we wanted to understand which parasite molecules are targeted by CD8+ T cells . These molecules may then be formulated into a safe and effective vaccine . CD8+ T cells do not automatically recognize every parasite molecule , but instead fragments of parasite proteins must be displayed on the surface of infected cells to be seen by CD8+ T cells . Our data show that CD8+ T cells recognize parasite proteins secreted by the parasite into the infected cell . This suggests that such proteins could be important components of malaria vaccines . | [
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] | [
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] | 2011 | Dendritic Cells and Hepatocytes Use Distinct Pathways to Process Protective Antigen from Plasmodium in vivo |
Hosts may mitigate the impact of parasites by two broad strategies: resistance , which limits parasite burden , and tolerance , which limits the fitness or health cost of increasing parasite burden . The degree and causes of variation in both resistance and tolerance are expected to influence host–parasite evolutionary and epidemiological dynamics and inform disease management , yet very little empirical work has addressed tolerance in wild vertebrates . Here , we applied random regression models to longitudinal data from an unmanaged population of Soay sheep to estimate individual tolerance , defined as the rate of decline in body weight with increasing burden of highly prevalent gastrointestinal nematode parasites . On average , individuals lost weight as parasite burden increased , but whereas some lost weight slowly as burden increased ( exhibiting high tolerance ) , other individuals lost weight significantly more rapidly ( exhibiting low tolerance ) . We then investigated associations between tolerance and fitness using selection gradients that accounted for selection on correlated traits , including body weight . We found evidence for positive phenotypic selection on tolerance: on average , individuals who lost weight more slowly with increasing parasite burden had higher lifetime breeding success . This variation did not have an additive genetic basis . These results reveal that selection on tolerance operates under natural conditions . They also support theoretical predictions for the erosion of additive genetic variance of traits under strong directional selection and fixation of genes conferring tolerance . Our findings provide the first evidence of selection on individual tolerance of infection in animals and suggest practical applications in animal and human disease management in the face of highly prevalent parasites .
Hosts of gastrointestinal nematodes vary widely in the number of parasites they harbour [1] and the severity of symptoms they experience at a given parasite burden [2] . Explaining such variation is a major challenge with practical implications for biomedicine and agricultural sciences , but also represents a challenge for evolutionary biologists aiming to determine how host and parasite strategies influence life history evolution ( e . g . , [3]–[6] ) . Hosts combat the adverse effects of parasites with two broad strategies: resistance and tolerance . Resistance is defined as the ability to avert infection , reduce parasite burden , or recover from infection , and the extent and causes of between-individual variation in resistance have been relatively well-studied in natural animal populations [7] . Tolerance , by contrast , is defined by evolutionary ecologists [8]–[10] , livestock scientists [11]–[13] , and plant pathologists [3] , [4] as the ability to limit the damage caused by a given parasite burden , and is less well studied . Understanding natural selection on both resistance and tolerance is crucial , because they jointly determine the health and fitness of hosts [14] , [15] and because genetic and epidemiological predictions arising from theories about resistant and tolerant host populations must be tested if we are to determine the practical applicability of theory [5] , [6] , [16] , [17] to disease management in wildlife , livestock , and human populations [5] , [6] , [18] . Here , we report substantial between-individual variation in tolerance of gastrointestinal nematodes that covaries with fitness in a wild mammal population . Determining the role of tolerance in host defence against nematodes under natural conditions has important implications for fundamental and applied science . First , it advances our understanding of parasite-mediated selection on hosts , for some of the most prevalent and abundant parasite taxa on Earth [1] . Second , quantifying variation in tolerance may inform management of livestock to enhance productivity during nematode infection [19]–[21] . Third , the quantitative approach to studying variation in nematode tolerance applied here , in a natural animal population , may also prove useful in future studies of how variation in human health with increasing gastrointestinal nematode burdens [1] , [2] , [22] is generated and/or maintained . Gaining insight into how tolerance varies and affects host fitness under natural conditions ( e . g . , limited food , natural infection rates , diverse host and parasite genetics ) requires individual-based study of a wild population . Tolerance may be measured at the level of a population or genotype [3] , [11] , [23] , [24] , but for studies of both natural and artificial selection , it is best measured at the individual level , as the rate of decline in an individual's health or fitness as parasite burden increases [8] , [12] , [13] . This latter definition of tolerance , based on a rigorous statistical framework applied to longitudinal data on health and infection levels [8] , [12] , [13] and used throughout this article , is similar to disease phase curves [25] in the focus on decoupling health and parasite burden of individual hosts . Despite this similarity , disease phase curves also have an explicit temporal component over the course of a microparasite infection , whereas we consider tolerance to be the health changes in an individual host across macroparasite infections of varying intensity . The statistical apparatus for dealing with tolerance in this way is very well-developed [8] , [11] , [12] , [26] , whereas disease-phase curves , while currently a very useful conceptual tool [25] , have not yet , to our knowledge , been statistically characterized . Indeed , the difficulty of measuring health or fitness in known individuals across a range of parasite infection intensities has meant that there have been no empirical demonstrations of the operation of tolerance in wild animal populations . Similarly , despite evidence that parasites exert selection on their hosts for resistance [27] , [28] , the possibility of selection on tolerance has not been explored . Wild systems enable study of natural selection in action and can also provide insights of direct relevance to the practical management of medically and economically important parasites [29] . Until now , wild studies have been hindered by the unavailability of suitable datasets and statistical tools [11]–[13] , [30]–[32] . The Soay sheep ( Ovis aries ) population of St . Kilda has been a model system in which to explore heterogeneity in a wide array of quantitative traits [33] . The sheep harbour gastrointestinal nematodes , and several causes of heterogeneity in nematode resistance , including host genetics [34] , [35] , sex [36] , age [37] , [38] , and body weight [39] , [40] , have been identified . However , the degree of variation in host tolerance of gastrointestinal nematodes and any associations of tolerance with fitness are unknown . Here , we study tolerance in terms of changes in body weight with increasing parasite burden . Because body weight is the single biggest predictor of annual fitness [41] through positive effects on survival [42] and reproductive success [43] , [44] in this population , it is an appropriate proxy ( sensu [45] ) for host fitness in our analyses . We expected that any weight-associated tolerance variation predictive of fitness of sheep would also be relevant to parasite fitness , as assumed by theory [5] , [46] , due to the persistent shedding of parasite transmission stages by tolerant individuals that survive despite high parasite burdens . With these motivations , we used longitudinal sampling of known individuals , a population pedigree , and a novel statistical workflow ( see below ) to quantify ( i ) the average association between body weight and parasite burden in the population; ( ii ) between-individual variation in tolerance , quantified as the slope of body weight on parasite burden; ( iii ) the additive genetic basis of tolerance; and ( iv ) the strength and direction of selection on tolerance . Our results reveal that individuals vary in their tolerance of nematode infection and that tolerance is under positive phenotypic selection through lifetime breeding success ( LBS ) .
Our data were collected from 1988 to 2012 and consist of complete demographic data ( on annual survival and reproductive success ) plus faecal egg counts ( FECs ) of highly prevalent gastrointestinal strongyle nematodes as a measure of parasite burden and body weight from 4 , 934 captures of 2 , 438 individuals of known age and sex born between 1980 and 2012 . Around 50% of the study population are captured and sampled each August , though not necessarily the same 50% . Many of our individuals were captured across many years ( up to 12 ) , whereas some were captured only once , for instance as lambs . Data from once-captured individuals are essential because they enhance estimation of the model intercept and the statistical power for our random regression analyses [47] . A comprehensive genetic pedigree has been constructed using data on 315 highly informative SNPs , allowing us to determine the genetic basis of body weight and of tolerance to infection . Breeding success in females is evaluated by behavioural observations of lambs and ewes and confirmed using genetic markers , whereas breeding success in males is evaluated using genetic markers ( see Materials and Methods for further details on all aspects of data collection ) . Longitudinal multivariate data are required to address questions pertaining to individual variation in tolerance and its fitness consequences , but determining the most appropriate statistical framework for such analyses has proved challenging [12] , [13] . Random regression models are mixed-effects models that include one or more random slope terms alongside standard random intercept terms . These random slope terms allow estimation of the between-individual variance in a linear function: for example , tolerance may be defined as the slope of individual health or fitness on parasite burden . These models have recently been advocated as a means of quantifying and exploring individual variation in tolerance [8] , [11] , [31] . Combining this approach with widely used pedigree-based “animal models” allows further separation of individual variation in tolerance slopes into additive genetic and environmental components [12] , [13] , allowing us to estimate genetic variance for tolerance . Multivariate versions of these models can estimate the covariance between a measured trait and an index of fitness and thus the strength and direction of selection [26] , [48] , allowing selection on tolerance to be estimated as the covariance between the slope of health ( estimated as body weight; [45] ) on parasite burden and lifetime fitness . Finally , the results of these analyses allow calculation of selection gradients [49] , a measure of the strength of natural selection on a trait that is broadly used in evolutionary biology that quantifies the relative strength of selection on each trait in question . Here , we utilise this workflow to determine the extent of phenotypic and genetic variance in nematode tolerance and whether it is under natural selection in a wild mammal population . We began by investigating the mean association between August body weight and August strongyle FEC , using LMMs with weight as a response variable , in order to determine how body weight changed with infection intensity at the population level . We controlled for age at measurement and sex as fixed effects and individual identity , mother's identity , and year of measurement as random effects , to account for repeated measures across these scales , as well as maternal effects [50] and between-year variation in nematode transmission intensity ( Materials and Methods , model 1 ) [51] , [52] . Individual identity also accounts for sources of between-individual variation , including behaviour and spatial variation in habitat quality and exposure . Body weight declined with increasing strongyle FEC in a linear fashion ( estimate = −0 . 0011±0 . 0001; Figure 1 ) : A model of body weight with a linear fixed effect of FEC was a significantly better fit than one without a FEC term ( χ2 ( d . f . = 1 ) = 211 . 22 , p<0 . 001 ) . On average , after accounting for variation in age and sex , this equates to a loss of 2 . 2 kg of body weight across the range of FEC shown in Figure 1 ( note that the figure is drawn from the raw population-level data and therefore does not account for age and sex differences ) . More complex polynomial functions of FEC did not significantly improve model fit ( quadratic , χ2 ( 1 ) = 0 . 34 , p = 0 . 560; cubic , χ2 ( 2 ) = 2 . 50 , p = 0 . 287 ) , confirming that the association was linear . Addition of fixed interaction terms between age and sex groups and the linear FEC term revealed no difference in the slope of body weight on FEC between the sexes ( addition of sex-by-FEC interaction , χ2 ( 1 ) = 0 . 42 , p = 0 . 517 ) but did reveal differences in the slope among age classes ( age class-by-FEC interaction , χ2 ( 2 ) = 15 . 96 , p<0 . 001 ) . This effect was due to the body weight of adults and yearlings declining at a faster rate with increasing FEC ( −0 . 0015±0 . 0001 ) than that of lambs ( −0 . 0009±0 . 0001 ) . This is likely to be because the individuals with the highest FEC in the adult-and-yearling group will be yearlings , which will have considerably lower body weight than the average adult . Thus , the difference in body weight between the lowest FEC individuals in this class ( mature adults ) and the highest FEC individuals in this class ( immature yearlings ) will be greater than it is for lambs , which vary less in body weight ( Figure S1 ) . We next fitted quantitative genetic “animal models” in ASReml 3 . 0 [53] in order to determine the additive genetic basis of body weight and to estimate its heritability ( Materials and Methods , model 2 ) , as a prerequisite for determining the heritability of the slope of body weight on FEC ( i . e . , tolerance ) . There was significant additive genetic variance for body weight , as previously reported in this population [39] , [54]: The pedigree random effect to separate among-individual variation in body weight into additive genetic and permanent environment components resulted in a significant improvement in model fit ( χ2 ( 1 ) = 55 . 7 , p<0 . 001 ) . Heritability was 0 . 16 ( ±0 . 03 SE ) , with the permanent environment effect explaining a further 0 . 38 ( ±0 . 03 ) of the overall phenotypic variation , after conditioning on fixed effects of age and sex ( see Materials and Methods ) . Other random effects explained smaller but significant proportions of the variation in body weight ( year of measurement , 0 . 13±0 . 03; maternal effect , 0 . 05±0 . 02; residual , 0 . 29±0 . 02 ) . Having established that there was a significant negative linear relationship between body weight and strongyle FEC across the population , and that body weight was significantly heritable , we next examined individual and genetic variation in tolerance , defined as the slope of body weight on strongyle FEC . We estimated the amount of between-individual variation in the rate of change in body weight with increasing strongyle FEC using the random regression approach advocated by evolutionary ecologists [8] and animal breeders [12] . This was accomplished by fitting a random interaction term between individual identity and strongyle FEC , to estimate variation in individual body weight-on-FEC slopes ( tolerance ) . We fitted a similar interaction between individual identity and age to account for between-individual variation in the change in weight with age ( Materials and Methods , model 3 ) [55] . Crucially , these models revealed variation among individuals in the rate at which body weight declined with increasing FEC , suggesting variation in tolerance: addition of an interaction between individual and strongyle FEC in the random effects compartment of the model of body weight significantly improved model fit ( χ2 ( 2 ) = 34 . 36 , p<0 . 001 ) . Addition of a random interaction between individual and age to this model further improved model fit ( χ2 ( 3 ) = 142 . 36 , p<0 . 001 ) . Inclusion of separate residual variance terms for each strongyle FEC quartile ( heterogeneous residuals ) further improved model fit ( χ2 ( 3 ) = 42 . 02 , p<0 . 001 ) and the individual-by-strongyle FEC and individual-by-age slope terms retained their explanatory power even in this model with heterogeneous residuals ( FEC slope , χ2 ( 3 ) = 16 . 82 , p<0 . 001; age slope , χ2 ( 3 ) = 143 . 62 , p<0 . 001 ) . Full details of the final model are presented in Table 1A . The association between body weight and strongyle FEC thus varied substantially among individuals ( Figure 2A ) , with variation in both weight intercepts ( Figure 2B ) and slopes of body weight on FEC ( Figure 2C ) . These predicted slopes were always negative , and back-transforming these model predictions to the original scale showed that , across the range of FECs shown in Figure 1 , the most tolerant individuals lost 0 . 36 kg in body weight , whereas the least tolerant lost 4 . 52 kg . To put this amount of weight loss into perspective , the commonest class of individuals in our study are adult females ( aged 2–11 , contributing 1 , 877/4 , 934 samples ) , with a mean weight of 21 . 61 kg: thus , a highly tolerant adult female would lose <2% of her body weight , whereas a low-tolerance female would lose ∼20% across the range of FEC shown in Figure 1 . We next extended this model by separating the between-individual variance in intercept for body weight and the between-individual variance in slopes of body weight on FEC and age into additive genetic and nongenetic components ( Materials and Methods , model 4 ) . Separation of the individual slopes of weight on strongyle FEC into additive genetic and permanent environment components did not improve model fit ( χ2 ( 2 ) = 3 . 50 , p = 0 . 174 ) , suggesting that there was not a significant additive genetic basis to tolerance . However , the same separation for the slope of body weight on age did improve the model ( χ2 ( 2 ) = 18 . 42 , p<0 . 001 ) and so the change in body weight with age was heritable , as has been found previously [55] . Full details of the model incorporating additive genetic effects for both slopes are presented in Table 1B . Finally , we tested for natural selection on tolerance by estimating the individual-level covariance between the slope of body weight on strongyle FEC and LBS following the standard approach in evolutionary biology [26] , [48] , [56] . This was accomplished using bivariate random regression models: We fitted LBS ( the number of lambs born to a female or sired by a male ) as a second response variable in the random regression model ( combining model 3 with model 5 , Materials and Methods ) . LBS for each individual was divided by sex-specific mean breeding success in order to calculate selection gradients ( β ) [49] . Selection gradients ( β ) measure the strength of directional selection acting directly on the trait of interest and crucially can take into account linear selection on correlated characters [49] . These models estimate selection in terms of effects on relative fitness in units of phenotypic standard deviations of the trait , providing a measure that is directly comparable across traits , aspects of fitness , populations , and species [57] . The random effect estimates of our bivariate model of body weight and LBS are presented in full in Table 2 . We ran the model using the Bayesian mixed-effects model R package MCMCglmm [58] . There was a positive individual-level correlation between body weight intercept ( i . e . , estimated weight at the population mean FEC ) and relative LBS ( relLBS ) . The 95% highest posterior density ( HPD ) intervals of the correlation did not cross zero ( ρ = 0 . 1939; HPD interval = 0 . 1521–0 . 2474 ) . This confirmed previous reports of positive selection on body weight in the population [41] . The correlation between the body weight intercept and the slope of body weight on FEC ( tolerance ) terms was weak , and the 95% HPD intervals overlapped zero ( ρ = −0 . 0963; HPD interval = −0 . 3069–0 . 0951; Tables 1 and 2 ) , suggesting no consistent relationship between an individual's weight at the average FEC and the rate at which their body weight changed with FEC ( i . e . , their tolerance of infection ) . Thus , the results of this analysis suggested that our measure of tolerance was independent of the intercept of body weight: In Figure 2 , individuals with high predicted weight at FEC = 0 did not have a stronger or weaker tolerance slope that those with low predicted weight at FEC = 0 . Importantly , we found a positive correlation between the individual slope of body weight on FEC and relLBS , with the lower boundary of the 95% HPD interval above zero ( Table 2 ) , suggesting that tolerance is under positive selection at the individual level ( ρ = 0 . 3101; HPD interval = 0 . 1583–0 . 4192 ) . We used these results to calculate selection gradients ( see Materials and Methods ) [49] . The posterior mean of the selection gradients β for the slope of body weight on FEC ( tolerance ) was +0 . 7559 ( HPD interval = 0 . 4555–1 . 0693; Figure 3B ) , suggesting that , having accounted for selection on body weight ( at mean age and FEC; β = +0 . 4826; HPD interval = 0 . 2190–0 . 7627 ) and the slope of body weight on age ( β = +0 . 0925; HPD interval = −0 . 2371–0 . 4318 ) , there was evidence for strong positive phenotypic selection on our measure of tolerance .
The negative association between strongyle nematode burden and body weight is likely to arise from parasite-induced anorexia and parasite- and immune-mediated damage to the intestinal wall that causes diarrhoea and/or decreased absorption of protein [59] . Thus , Soay sheep that lost weight slowly with increasing strongyle burden ( the more tolerant individuals ) may be able to maintain feeding and/or digestive efficiency in the face of increasingly heavy infections , and/or to repair damage to the gut wall . Our models control for variation in body weight due to skeletal size associated with age and sex . This means that our estimate of weight loss associated with heavier strongyle infections is likely to be due to a loss of body condition , reflected in nutritional state or fatness . In any case , the observed variation in the slope of body weight on FEC was substantial , with the most tolerant individuals losing approximately 18 g of body weight per 100 eggs per gram of faeces , and the least tolerant losing 226 g per 100 eggs per gram , a 13-fold difference . Although this is an observational study , we did account statistically for temporal [51] and individual differences ( e . g . , in behaviour and heft/spatial allegiance ) [60] affecting strongyle exposure risk in this population . This was accomplished by fitting random effects of individual identity and year in our models , and by collecting all samples at the same time of the year . In addition , we accounted for age in all models , which is the key driver of between-individual variation in parasite infracommunity ( the species composition of a host individual's parasite fauna ) in this population [61] . There is also evidence in this population that coinfection with prevalent Eimeria protozoan parasites does not affect the association between body weight and strongyle FEC [37] . Finally , as is discussed in more detail below , all the evidence collected thus far suggests that the relatively intolerant individuals identified by this analysis are not merely paying a cost of resistance: analysis has revealed that body weight is either not significantly associated or is positively associated with antibody responses , including those specific to Teladorsagia circumcincta [52] , [62] , suggesting that nematode-resistant sheep do not pay a cost in terms of reduced body weight . We are therefore able to report that the interindividual variation in tolerance reported here is unlikely to be attributable to variation in exposure or to costs of resistance . What might then cause the observed variation ? Several empirical studies have shown that variation in tolerance has a genetic basis: Host strains differ in their slopes of fitness or health on infection intensity [24] , [63]–[65] . We found that variation in tolerance ( the slope of body weight on FEC ) does not appear to be due to additive genetic effects . Indeed , epidemiological feedbacks and positive frequency dependence , all else being equal , are expected to purge genetic variation for tolerance [6] , [16] , [17] . Theoretical work predicts that other causes of tolerance variation may include phenotypic tradeoffs with heterogeneous resistance [5] , [16] , and empirical studies suggest variation in health or fitness at the individual level may be affected by defence against coinfections [66] , [67] and/or nonadditive genetic effects [68] . In the Soay sheep , however , we have thus far found no evidence for any of these factors , including covariance between individual tolerance and resistance . Using a bivariate analytical framework such as that outlined here , we estimated the covariance between individual tolerance slope and strongyle-specific antibody titre , and we found that the covariance was low and did not differ statistically from zero [18] . Strongyle-specific antibody titre is strongly negatively associated with strongyle FEC [52] , and thus , this result suggests that there is no association between our measure of tolerance and our best measure of immunological resistance in this population . In addition , mean tolerance ( the association between strongyle burden and body weight ) is independent of the burden of coinfecting intestinal protozoa [37] . The diverse effector mechanisms of T-helper 2–mediated immunity , which include anthelmintic and tissue-repair processes [69] , [70] , suggest that resistance and tolerance to nematode infections may occur in concert . As the molecular and cellular mechanisms of tolerance in animals are elucidated—and we expect that they will be , given the recent surge in interest [9] , [15] , [71]–[73]—we will gain greater insight into the causes of this variation . A major challenge for the future will be to determine the contributions of variation in the parasite infracommunity and parasite as well as host genetics to variation in defence strategies . Variation in the rate at which individuals lose weight with increasing strongyle FEC appears to have important selective consequences in this population . Tolerance was under positive selection in the population , with more tolerant individuals having higher LBS . Previous work on the population shows comparable positive selection for higher body weight [48] and greater strongyle resistance [27] . Together , these selection analyses reveal that in this population , greater weight , resistance , and tolerance are all independently associated with greater LBS . These results clearly demonstrate that tolerance plays a major role in defence against parasite infection in wild vertebrates , varies considerably between individuals , and that this variation is under relatively strong selection through LBS . The finding that , in this naturally infected population , there was significant between-individual variation in tolerance that was associated with higher fitness has practical relevance to management of parasitic diseases in livestock . Selective breeding for resistance to helminths is considered both profitable and sustainable [74] , [75] . However , breeding for tolerance ( a slow rate of health or productivity loss with increasing infection intensity ) may be desirable where prevalence of infection is high , as it is for gastrointestinal nematodes in domesticated sheep [76] , or where resistant breeds show lower productivity due to high investment in immunity [77] . Trypanotolerant goats and sheep , which naturally maintain productivity in the face of infection with Trypanosoma spp . [78] and the gastrointestinal nematode Haemonchus contortus [79] , are crucial to rural populations in Sub-Saharan Africa and illustrate the potential benefits of breeding for improved tolerance . Despite this , if the lack of genetic variance for our measure of tolerance proves general and tolerance is largely underpinned by environmental factors , artificial selection for tolerance would be futile; management efforts should instead focus on nutritional or other interventions to promote tolerance . However , if tolerance in domesticated populations does have an additive genetic basis , or if tolerance in both wild and domestic livestock has an epistatic genetic basis , individual breeding values for tolerance may be predicted [12] , [76] , [80] using methods such as ours and those outlined by Kause [11] , facilitating breeding for tolerance [81] , [82] . Our methods and results may also prove relevant to management of human helminth infections , the chronicity of which suggests that tolerance may be important in maintaining health . The word “tolerance” has only recently become widely applied at the organismal , as opposed to genotypic or immunological , level for such infections , but the importance of varying symptom severity at a given infection intensity for host health and nematode epidemiology has been acknowledged for some time [14] , [22] . Research has understandably focused on eliminating parasites from human hosts , but the extent of between-individual variation in nematode tolerance and the implications for epidemiology and medical interventions are poorly understood . Nematodes typically only cause disease in heavily infected human hosts [1] , [2] , [22] , suggesting that hosts can tolerate infection up to a point . Indeed , hookworm-infected children can tolerate burdens generating up to 2 , 000 nematode eggs per gram of faeces before pathology sets in [83] . However , the tolerance heterogeneity predicted by theoretical work on human helminthiasis [22] has not yet been quantified in any human population [1] , [2] . It would be of interest to determine whether the observed heterogeneity in the health of nematode-infected people is due to varied resistance , tolerance , or both . For example , application of our methods to data from human populations may reveal variation in linear tolerance and/or in the threshold of infection intensity at which people begin to experience a decline in health . If , as in Soay sheep , human tolerance of nematodes is variable but is not heritable , then environmental , behavioural , and nutritional interventions may enhance tolerance . Such efforts could enhance the health impact of helminth control programs , especially in areas of high transmission . Here , we have defined tolerance at the individual level as the rate at which body weight declines with increasing strongyle nematode burden , in line with recent conceptual developments [8] , [10] , [12] . We used the random regression modelling approach to study tolerance using such longitudinal data , which has been advocated for the study of tolerance in evolutionary ecology and veterinary science [8] , [11]–[13] . However , there are several important caveats of these analyses . First , the lack of evidence for additive genetic variance for tolerance may simply reflect a lack of statistical power to distinguish pedigree- from non-pedigree-associated between-individual variation . However , simulations run on a model dataset [84] suggest that our total sample size of almost 5 , 000 should give us sufficient power to accurately detect between-individual variation in slopes of body weight on FEC . Second , a nonzero correlation between the intercept ( body weight ) and slope ( body weight on FEC ) in a random regression model can increase the power to detect significant slope variance [47] , potentially resulting in a type I error . The lack of significant covariance between body weight and tolerance ( Table 1A ) suggests that our estimate of individual variance in tolerance is robust . Importantly , we were able to account for selection on potentially correlated traits ( i . e . , body weight and the slope of body weight on age ) in our selection analysis , suggesting that our selection gradients are accurate . Finally , our models assume that the association between weight and strongyle FEC is due to heavy infections causing weight loss ( i . e . , weight is dependent , FEC is independent ) . If part of the association is due to low body weight reducing investment in immunity and leading to higher infection intensity , or due to some other unmeasured variable , FEC may not be truly independent . Where the assumption of independence is violated , this may create biased patterns of observed covariance between intercept ( weight ) and slope ( body weight on FEC; that is , tolerance ) that could inflate estimates of the tolerance slope variance as described above [47] . In addition , it may create biased conclusions , if we assume a causal relationship ( strongyles reduce body weight , which reduces fitness ) , which may not be entirely responsible for the observed covariance . It is uncertain to what extent this may affect the results presented here , although weight loss is dependent on nematode dose in experimental studies in domesticated sheep [85] , suggesting that strongyle infection has a causal negative effect on body weight in sheep populations . Here , we report novel evidence for between-individual variation in tolerance to parasite infection , which is under positive phenotypic selection . Much remains to be determined about how this variation is generated and how it contributes to epidemiology and trajectories of host–parasite coevolution [9] , [10] , [86] . Such analyses will require a rigorous statistical framework in the absence of controlled infections , but that currently developed [11] , [12] , [26] , [48] , [49] has already enabled us to ( i ) show that host body weight declines with increasing infection intensity; ( ii ) reveal between-individual variation in the decline in weight with infection intensity , and therefore among-individual variation in tolerance slopes; ( iii ) demonstrate that among-individual variation in tolerance does not have an additive genetic ( heritable ) basis; and ( iv ) reveal that individual tolerance is associated with LBS and , having accounted for selection on other correlated traits , is under relatively strong positive selection . Thus , tolerance varies between individuals and natural selection can act upon it in the wild . Determining the evolutionary , ecological , and physiological mechanisms responsible for variation in tolerance is now a priority . Selection patterns on allocation to different life history components ( e . g . , growth , maintenance , reproduction ) may be understood more clearly if nutritional physiology can be monitored . Measuring nutritional indices across the lifespan of individuals , for example , would enable estimation of “point tolerance” [10] to determine how individual tolerance varies across ontogeny . Linking these to the components of fitness ( annual survival and fecundity ) would help to determine the origin of the positive association between tolerance and lifetime fitness detected here . Deeper understanding of the physiological mechanisms underpinning tolerance will only be possible with controlled experiments [71]–[73] . Ultimately , collection of mechanistically informed longitudinal data on tolerance in natural systems will enable empirical tests of the predictions of epidemiological and evolutionary theory ( e . g . , [5] , [6] , [16] , [18] , [22] ) . Data on parasitology , immunology , condition/health , genetics , and fitness from longitudinally monitored wild populations will create powerful opportunities to explore the effects of tolerance on natural host–parasite dynamics , building on this demonstration of natural selection on tolerance in the wild .
The St Kilda archipelago ( 57°49′N 08°34′W ) lies 70 km NW of the Outer Hebrides , NW Scotland , and consists of four islands , the largest of which are Hirta ( 638 ha ) and Soay ( 99 ha ) . Soay has been home to a population of sheep ( Ovis aries ) , originating from early domesticated breeds , for several thousand years [33] . In 1932 , 107 sheep were moved from Soay onto Hirta , from which the current , unmanaged population has grown . The population inhabiting the Village Bay area of the island has been the subject of a longitudinal individual-based study since 1985 [33] . The majority of lambs are born in April , and around 95% are captured within a week of birth , given individual identification tags , blood sampled , and weighed [33] . Each August , around 50% of the study population are captured and weighed to the nearest 0 . 1 kg and sampled for blood and faeces . Body weight is positively associated with survival [41] , [87] and reproductive success [43] , [44] . Thus , it is strongly associated with fitness and has the added advantage of being repeatedly estimable across a range of parasite burdens per individual , making it a suitable correlate of fitness to use in analysis of individual tolerance estimated as a reaction norm , or “range tolerance” [8] , [10] , [12] , [13] , [76] . Faecal samples are stored at 4°C until examination for helminth parasite eggs using a modified version of the McMaster egg counting technique to provide an estimate of individual parasite burden [61] . In this study , we use counts of strongyle nematode eggs per gram of faeces , or strongyle FEC , which consist largely of the species Teladorsagia circumcincta , Trichostrongylus vitrinus , and Trichostrongylus axei [61] . Postmortems have revealed FEC to be positively and linearly correlated with strongyle infection intensity in this population [59] , [88] . Molecular parentage assignment used 315 highly informative SNPs to simultaneously infer both parental identities for sheep born between 1980 and 2012 using the R package MasterBayes [89] . Sheep were included in the list of candidate parents if alive in the year before the focal individual's birth; they were discarded if they mismatched at more than eight loci . This pedigree inferred a total of 5 , 981 maternities and 4 , 371 paternities with 100% confidence [90] . Not all candidate fathers had been genotyped using SNPs . Thus , an additional 319 paternities were recovered using a panel of 18 microsatellite markers [48] , and 416 paternity estimates were gained using CERVUS with at least 80% pedigree-wide confidence [91] . The pedigree used for all analyses is shown in Table S3 . It contains records for all of the individuals analysed in this study and all of their known relatives . | Animals can defend themselves against parasites through either resistance ( reducing parasite numbers , for example , by killing them ) or tolerance ( maintaining health as infections levels increase , for example , by repairing damage ) . Resistance has been well-studied in wild animals , but tolerance has been less so . We analysed data on body weight collected over 25 years on a natural population of Soay sheep , infected with parasitic gut worms . As parasite burden increased , sheep lost weight . Crucially , there was variation among individuals: some lost weight rapidly with increasing infections ( i . e . , showed “low tolerance” ) , whereas others lost weight slowly ( i . e . , showed “high tolerance” ) . The least tolerant individuals lost 4 . 5 kg of body weight across the range of parasite burdens that we saw , whereas the most tolerant lost only around 0 . 36 kg . However , variation in tolerance did not have a heritable genetic basis , so that although tolerance varied between individuals , this was not due to genetic differences . Further analysis revealed that there was natural selection on tolerance . Individuals who were more tolerant of infection produced more offspring over the course of their lives . This study shows that natural selection can act upon resistance and tolerance simultaneously in nature , a result that has implications for both human health and livestock management . | [
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] | 2014 | Natural Selection on Individual Variation in Tolerance of Gastrointestinal Nematode Infection |
The constant removal of deleterious mutations by natural selection causes a reduction in neutral diversity and efficacy of selection at genetically linked sites ( a process called Background Selection , BGS ) . Population genetic studies , however , often ignore BGS effects when investigating demographic events or the presence of other types of selection . To obtain a more realistic evolutionary expectation that incorporates the unavoidable consequences of deleterious mutations , we generated high-resolution landscapes of variation across the Drosophila melanogaster genome under a BGS scenario independent of polymorphism data . We find that BGS plays a significant role in shaping levels of variation across the entire genome , including long introns and intergenic regions distant from annotated genes . We also find that a very large percentage of the observed variation in diversity across autosomes can be explained by BGS alone , up to 70% across individual chromosome arms at 100-kb scale , thus indicating that BGS predictions can be used as baseline to infer additional types of selection and demographic events . This approach allows detecting several outlier regions with signal of recent adaptive events and selective sweeps . The use of a BGS baseline , however , is particularly appropriate to investigate the presence of balancing selection and our study exposes numerous genomic regions with the predicted signature of higher polymorphism than expected when a BGS context is taken into account . Importantly , we show that these conclusions are robust to the mutation and selection parameters of the BGS model . Finally , analyses of protein evolution together with previous comparisons of genetic maps between Drosophila species , suggest temporally variable recombination landscapes and , thus , local BGS effects that may differ between extant and past phases . Because genome-wide BGS and temporal changes in linkage effects can skew approaches to estimate demographic and selective events , future analyses should incorporate BGS predictions and capture local recombination variation across genomes and along lineages .
The causes of the variation observed within natural populations have been a long-standing question in evolutionary and genetic studies . Particular insight into these causes can be gained by analyzing the distribution of nucleotide diversity across genomes , where species- and population-specific parameters such as the number of individuals , environmental factors , or demography are constant . A number of population genetics models have been put forward to explain this intra-genomic variation in diversity , often including the predicted consequences that selection acting at a genomic site impinges on genetically linked sites , either neutral or under selection themselves ( i . e . , models of ‘selection at linked sites’; [1]–[4] and references therein ) . Although there is general agreement that selection at linked sites can play a role shaping levels of variation , there is still intense debate and research on the selective nature of the mutations causing such effects ( e . g . , beneficial or deleterious ) and whether the same causes can be applied to different species [4]–[6] . Strongly beneficial mutations rise rapidly to fixation and hitchhike adjacent sites , causing a fingerprint of reduced intra-specific variation around the selected site known as a ‘selective sweep’ ( the HHss model; [2] , [3] , [7]–[11] ) . A qualitatively similar outcome can be generated by another model of selection and hitchhiking effects at genetically linked sites without requiring adaptive changes , just as a result of the continuous input of strongly deleterious mutations and their removal by natural selection ( the background selection ( BGS ) model [1] , [4] , [12]–[16] ) . Both models also predict that the consequences of selection removing adjacent diversity diminish when genetic recombination increases , a general pattern that has been observed in many species when comparing genomic regions with high and low ( or zero ) recombination rates ( reviewed in [1] , [3]–[5] ) . The magnitude and distribution of recombination rates across genomes play key roles in predicting the consequences of selection on adjacent variation . In humans , for instance , the presence of large recombination cold spots raised the possibility that BGS could reduce polymorphism levels at specific genomic regions . In agreement , recent analyses using models of purifying selection rather than purely neutral ones suggest that patterns of nucleotide diversity across the human genome are consistent with BGS predictions [17]–[21] . In the model system D . melanogaster , low-resolution recombination maps described limited or absent recombination near sub-telomeric and –centromeric regions whereas recombination outside these sub-telomeric and –centromeric regions ( i . e . , across trimmed chromosome arms ) has been often assumed to be both high and homogeneously distributed . As a consequence , variation in nucleotide diversity across trimmed chromosome arms has been mostly attributed to positive selection and selective sweeps ( [4] , [5] , [22]–[29]; but see [30] ) . There are , however , several reasons to believe that BGS effects could be significant in D . melanogaster as well . First , compared with humans , D . melanogaster has a more compact genome and a larger effective population size ( Ne ) , predicting tighter genetic linkage between genes and stronger purifying selection , respectively , and both factors forecast greater BGS effects . Second , recent whole-genome studies of recombination rates in D . melanogaster exposed extensive heterogeneity in the distribution of crossover rates even after removing sub-telomeric and centromeric regions [31] . This high degree of variation in recombination rates across D . melanogaster chromosomes is observed when recombination is obtained from a single cross of two specific strains [31] , [32] as well as when analyzing a species' average obtained from combining genetic maps from crosses of several natural strains [31] . The presence of coldspots of recombination embedded in chromosomal regions assumed to have high recombination rates , therefore , provides the opportunity for BGS to play a more significant role across broader genomic regions than previously anticipated [31] . Finally , Charlesworth [33] has recently showed that BGS effects are predicted to be detectable in the middle of recombining chromosome arms in D . melanogaster . The consequences of BGS at a given nucleotide position in the genome ( focal point ) can be described by the predicted level of neutral nucleotide diversity when selection at linked sites is allowed ( π ) relative to the level of diversity under complete neutrality and free recombination between sites ( π0 ) , with B = π/π0 [12]–[15] . Therefore , B∼1 would indicate negligible BGS effects whereas B<<1 would suggest very strong BGS and a substantial reduction in levels of neutral diversity . B can also be understood in terms of a reduction in Ne , and variation in B forecasts differences in levels of diversity within species but also differences in the efficacy of selection , which can be approximated by the product of Ne and the selection coefficient s . Note , however , that the prediction about reduced efficacy of selection is a qualitative one since there is no simple scalar transformation of Ne influenced by selection at linked sites that allows estimating probabilities of fixation of selected mutations [34] , [35] . Thus , a comprehensive study of the predictive power of BGS to explain natural variation across genomes needs to show that , 1 ) conditions exists across a genome to generate significant overall effects reducing B , 2 ) B varies across the genome , and 3 ) regions with reduced B are associated with reduced levels of polymorphism and efficacy of selection ( e . g . , detectable on rates of protein evolution ) . Here , we investigated what is the fraction of the D . melanogaster genome that is influenced by BGS and how much of the observed variance in patterns of intra-specific variation and rates of evolution across this genome can be explained by BGS alone . Importantly , to obtain a sensible BGS baseline that could be used to test for positive selection and other departures from neutrality , we investigated BGS models that are purposely simple and independent of nucleotide variation data . Additionally , we studied whether our conclusions are sensitive to parameters of the BGS model . To this end , we expanded approaches previously applied to investigate human diversity [17] , [18] to now estimate BGS effects across the D . melanogaster genome . In all , we generated a detailed description of the consequences of purifying selection on linked sites at every 1 kb along D . melanogaster chromosomes under a variety of BGS models . Our results show that BGS likely plays a detectable role across the entire genome and that purifying selection alone can explain a very large fraction of the observed patterns of nucleotide diversity in this species . Notably , we show that these conclusions are robust to different parameters in the BGS models . The use of a BGS baseline also uncovers the presence of regions with the signature of a recent selective sweep and , less expected , numerous instances of balancing selection . Furthermore , analyses of rates of protein evolution suggest that the recombination landscape has changed recently along the D . melanogaster lineage thus generating disparity between short- and long-term Ne at many genomic positions . We discuss the advantages of incorporating BGS predictions across chromosomes and the potential consequences of temporal variation in recombination landscapes when estimating demographic and selective events .
BGS expectations ( i . e . , estimates of B ) were obtained for every 1-kb region across the whole genome as the cumulative effects caused by deleterious mutations at any other site along the same chromosome ( see Materials and Methods for details ) . These estimates of B were based on BGS models that include our current knowledge of genome annotation at every nucleotide site of the genome and high-resolution recombination landscapes in D . melanogaster that distinguish between crossover and gene conversion rates [31] . These models also incorporate the possibility that strongly deleterious mutations occur at sites that alter amino acid composition as well as at a fraction of sites in noncoding sequences . The inclusion of deleterious mutations in noncoding sequences allows taking into account the existence of regulatory and other non-translated functional sequences , either in introns and 5′- and 3′-flanking UTRs , or in intergenic regions [22] , [33] , [36]–[38] . For each category of selected sites ( nonsynonymous , intronic , UTR , or intergenic ) we used the proportion of constrained sites ( cs ) estimated for D . melanogaster [22] , [37] , [38] as the fraction of sites with deleterious fitness consequences when mutated [33] . In terms of recombination rates , we studied BGS predictions following the standard approach of including crossover as the sole source of recombination ( hereafter models MCO ) and also when combining the effects of crossover and gene conversion events ( models MCO+GC ) to better quantify the true degree of linkage between sites in natural populations . The distribution of deleterious fitness effects ( DDFE ) and the diploid rate of deleterious mutations per generation ( U ) are parameters that have direct implications on estimates of B but are more difficult to establish experimentally . Although a gamma distribution has been proposed a number of times for deleterious mutations [39]–[44] , a log-normal DDFE allows capturing the existence of lethal mutations and fits better D . melanogaster polymorphism data [45] , [46] . Additionally , a log-normal DDFE predicts a higher fraction of mutations with minimal consequences removing linked variation than a gamma DDFE and , ultimately , weaker BGS effects ( see Materials and Methods ) . Therefore , the use of a log-normal DDFE can be taken as a conservative approach when inferring the magnitude of BGS effects . Direct estimations of deleterious mutation rates are still fairly limited . In D . melanogaster , initial analyses of mutation accumulation lines estimated a mutation rate for point mutations and small indels ( u ) of ∼8 . 4×10−9/bp/generation and a diploid rate of deleterious mutations per generation ( U ) of ∼1 . 2 [47] . Nevertheless , one of the lines used in this study had an unusually high mutation rate [48] and more recent studies suggest u∼4–5×10−9 ( U∼0 . 6 ) for point mutations and small indels [48]–[50] . These lower estimates , however , do not include the possible presence in natural populations of genotypes with high mutation rates or the deleterious consequences of transposable element ( TE ) insertions . In fact , TEs are very abundant in natural populations of D . melanogaster [51]–[60] and have been proposed to be an important source of BGS in this species [30] . Therefore , U∼0 . 6 represents a lower boundary for the deleterious mutation rate when inferring the consequences of BGS . To include the consequences of TE insertion in our BGS models , we obtained an approximate diploid insertion rate of UTE≥0 . 6 based on a detailed description of TE distribution in D . melanogaster [60] and mutation-selection balance predictions ( see Materials and Methods for details ) . Thus , a genome-wide diploid deleterious mutation rate of ∼1 . 2 per generation is a reasonable approximation that captures the consequences of point mutations , small indels and the insertion of transposable elements . To assess how robust our results and conclusions are to the parameters of the BGS model , we obtained genome-wide landscapes for B under eight different models , with DDFE following a log-normal or a gamma distribution ( models MLN and MG , respectively ) , with deleterious mutations rates that include or not TE insertions ( models MStdMut and MLowMut , respectively ) , and with recombination taking into account crossover and gene conversion events or only crossovers ( models MCO+GC and MCO , respectively ) . Unless specifically noted , we report results based on the BGS model that is most consistent with our current knowledge of gene distribution across the genome , a log-normal DDFE , a genome-wide diploid deleterious mutation rate of U = 1 . 2 , and recombination rates that include crossover and gene conversion events ( i . e . , our default model is MLN , StdMut , CO+GC ) . Table S1 summarizes the results from the BGS models and Table S2 provides the full distribution of B estimates across all chromosomes . Genome-wide estimates of B show a median of 0 . 591 and indicate that the predicted influence of BGS across the D . melanogaster genome would reduce the overall Ne substantially relative to levels predicted by evolutionary models with free recombination ( see Figure 1A ) . The study of the distribution of B along chromosomes shows that the reduction in neutral diversity is severe in a large fraction of the genome , with 19% of all 1-kb regions with B<0 . 25 and a lower 90% CI for B of 0 . 005 ( Figure 1B and Figure 2 ) . Importantly , the distribution of B across trimmed chromosomes is also highly heterogeneous . As expected , estimates of B are strongly influenced by variation in local crossover rates ( c ) , with a Spearman's rank correlation coefficients ( ρ ) between B and c of 0 . 792 for trimmed chromosomes . As shown in Figure 3 , however , there is detectable variance in B for a given local c that exposes the additional effects of long-range distribution of recombination rates and genes when estimating B at a focal point . Median B across trimmed chromosome arms is 0 . 643 , with a minimum estimate of 0 . 19 . Significant and variable BGS effects are , therefore , expected in D . melanogaster not only due to sub-telomeric and -centromeric regions but also across trimmed chromosomes ( see also [33] ) . This general conclusion does not vary qualitatively when considering BGS models with other parameters ( Table S1 ) . As expected , a model with a DDFE following a gamma distribution ( MG ) predicts stronger BGS effects and lower estimates of B across the genome than when the DDFE follows a log-normal ( as in our default model ) . Under model MG , CO+GC the median B is 0 . 428 and the lower 90% CI for B is 0 . 001 ( median B across trimmed chromosomes is 0 . 493 , with a minimum estimate of 0 . 007 ) . Also anticipated , models with a lower deleterious mutation rate ( models MLowMut ) generate higher estimates of B than when TE insertions are taken into account ( models MStdMut ) . For instance , median B increases from 0 . 591 ( MLN , StdMut , CO+GC ) to 0 . 769 ( MLN , LowMut , CO+GC ) , and from 0 . 428 ( MG , StdMut , CO+GC ) to 0 . 654 ( MG , LowMut , CO+GC ) . In addition , the comparison of predictions under models with and without gene conversion shows that the standard approach of considering crossover as the only source of recombination between sites would overestimate linkage effects . Median estimates of B are 20 and 21% lower for models MLN , CO and MG , CO than for MLN , CO+GC and MG , CO+GC , respectively . The use of only crossover rates in BGS models skews estimates of B particularly in regions with intermediate rates ( ∼0 . 2–2 cM/Mb ) , mostly across trimmed chromosomes . Both crossover and gene conversion data , therefore , need to be considered to obtain accurate estimates of the consequences of selection on linked sites and , in this case , the magnitude of BGS effects . Finally , it is worth noting that although the different BGS models predict different point estimates and ranges of B across the chromosomes , all models generate B estimates across the genome that have virtually the same relative ranking ( i . e . , monotonically related; Table S3 ) . Pairwise Spearman's ρ between estimates of B from different BGS models range between ρ = 0 . 9856 ( comparing the two most distinct models MLN , StdMut , CO and MG , LowMut , CO+GC ) and ρ>0 . 9999 ( for the four comparisons between models differing in the deleterious mutation rate ) . In this study , all sites along a chromosome were allowed to potentially play a role adding up BGS effects at any focal region of the same chromosome . To investigate the size of the genomic region causing detectable BGS effects in D . melanogaster , we estimated the size of the region surrounding a focal 1-kb needed to generate 90% of the total BGS effect obtained when considering the complete chromosome ( DB90 in either genetic or physical units ) . Equivalently , we also obtained DB75 and DB50 as the size of the genomic region needed to generate 75 and 50% , respectively , of the total BGS effect obtained when considering the whole chromosome . The study of complete chromosomes shows a median genetic DB90 , DB75 and DB50 of 5 . 5 , 1 . 2 and 0 . 15 cM , respectively . In terms of physical distance , the median DB90 , DB75 and DB50 is 2024 , 477 and 76 kb , respectively ( Figure 4A ) . Although the overall effects of BGS are reduced along trimmed chromosomes compared to whole chromosomes , the size of the region playing a significant role in the final magnitude of B at a focal point is fairly equivalent , with 6 . 9 , 1 . 8 and 0 . 21 cM for DB90 , DB75 and DB50 , respectively ( 2 , 412 , 640 and 84 kb for DB90 , DB75 and DB50 , respectively ) . This genetic and physical scale , moreover , increases with crossover rates ( Figure 4B ) . This analysis , therefore , suggests that the extent of BGS at most genes and intergenic sites across the D . melanogaster genome is influenced by the cumulative effects of many sites and include numerous other genes . Thus , accurate estimates of B in D . melanogaster require the study of genomic regions at the cM or Mb scale , ideally full chromosomes . Otherwise , BGS can be severely underestimated , and inferences about demographic events or other types of selection may be unwarranted . These results are also in agreement with the previous observation that all intergenic sequences and introns across the genome are predicted to be influenced by BGS . A second goal of this study was to estimate how much of the observed levels of neutral diversity across the D . melanogaster genome can be explained by a BGS landscape obtained independently from variation data . A strong positive correlation would not only indicate that BGS should not be ignored in population genetic analyses but also that our estimates of B are likely suitable as baseline to infer additional types of selection and/or demographic events . Because our best experimentally-obtained whole-genome recombination maps for crossover and gene conversion have a maximum resolution and accuracy at the scale of 100-kb [31] , the predictive nature of the B baseline was first investigated at this physical scale . To obtain levels of neutral diversity across the D . melanogaster genome , nucleotide diversity per bp ( πsil ) at introns and intergenic sequences was estimated from a sub-Saharan African population ( Rwanda RG population [62]; see Materials and Methods for details ) . The comparison of estimates of B generated by our BGS models and levels of πsil across the genome reveals a strikingly positive association ( Table 1 and Figure 5 ) . For autosomes , the correlation between B and πsil is ρ = 0 . 770 ( 965 non-overlapping 100-kb regions , P<1×10−12 ) , and increases up to ρ = 0 . 836 ( P<1×10−12 ) along individual autosome arms . Equivalent results are obtained when silent diversity is analyzed separately at intergenic and intronic sites , with ρ = 0 . 736 between B and πintergenic , and ρ = 0 . 741 between B and πintron ( P<1×10−12 in both cases ) . The study of individual autosome arms shows a positive association up to ρ = 0 . 799 and 0 . 800 for intergenic and intronic sites , respectively ( P<1×10−12 in both cases ) . The predictive nature of the B landscape in D . melanogaster remains remarkably high along trimmed autosomes where BGS has been often assumed to play a minor role explaining variation in levels of polymorphism . The correlation between B and πsil is ρ = 0 . 529 , ranging up to ρ = 0 . 655 when trimmed chromosome arms are analyzed separately ( P<1×10−12 in all cases ) . Additionally , the BGS models investigated generate a stronger association between B and πsil than between estimates of local crossover ( c ) and πsil , particularly along trimmed chromosomes ( ρ = 0 . 677 and ρ = 0 . 397 for complete and trimmed autosomes , respectively ) . This last result exposes the limitations of using local c as an estimate of the overall strength of linked selection at a given genomic position , and highlights the importance of including long-range information of recombination rates and gene structures . Altogether , these results show the high predictive value of simple BGS models , with almost 60% of the observed variance in πsil across 100-kb autosomal regions explained by BGS , a percentage that is as high as ∼70% when investigating variation in nucleotide diversity along individual chromosome arms ( see Table 1 ) . The robustness and high predictive power of the BGS models to explain qualitative trends of nucleotide diversity across the genome , suggest that we can investigate the presence of other forms of selection by searching for regions that depart from BGS expectations . We , therefore , compared observed πsil and levels of diversity predicted by B , and parameterized departures by using studentized residuals ( πsil-R; see Material and Methods ) . Overall , the distribution of πsil-R does not show a significant departure from normality ( χ2 = 28 . 9 , d . f . = 23 , P = 0 . 183 ) thus validating the approach . Nevertheless , there are 58 outlier regions with nominal P<0 . 05 , 24 regions with significantly negative πsil-R ( revealing a deficit in πsil relative to BGS expectations ) and 34 regions with significantly high πsil-R ( revealing a relative excess of πsil ) . Regions with a relative deficit of πsil are candidate regions for recent adaptive events [2] , [8] , [63] and our data confirms the presence of several regions with the fingerprints of a recent selective sweep across the D . melanogaster genome [22] , [23] , [25] , [26] , [29] . The strongest signal of selection at this 100-kb scale , and the only region that shows a departure that remains significant after correction for multiple tests [P<1 . 6×10−6 , with false discovery rate ( FDR ) q-value<0 . 10] , suggests a recent selective sweep at position 8 . 0–8 . 1 Mb along chromosome arm 2R . This genomic region includes gene Cyp6g1 and also showed the strongest signal of directional selection and selective sweep in large-scale population genetic analyses of North American [26] and Australian D . melanogaster [64] populations . Not all regions with a severe reduction in πsil across the trimmed genome , however , may need recent adaptive explanations . A number of regions with πsil much smaller than the median ( e . g . , 0 . 002 relative to a median of 0 . 008; see Figure 6A ) also show estimates of B of 0 . 25 or smaller and , thus , the observed πsil would be close to the predicted level of neutral diversity when a BGS context is taken into account . Because the genome-wide recombination maps used in this study were generated to have good accuracy at the scale of 100-kb , our BGS models assumed homogeneous rates within each 100-kb region . Notably , these models predict variation in B across 100-kb regions due to the heterogeneous location of genes and exons within these regions and the differential effects of proximal and distal flanking regions . Nevertheless , detailed analyses of a few small genomic regions have revealed recombination rate variation at a smaller scale in Drosophila [32] , [68] . Therefore , outliers from BGS expectations at scales smaller than 100-kb can reveal the localized fingerprints of other types of selection ( directional or balancing selection ) but the possibility of uncharacterized heterogeneity in recombination within these regions cannot be formally ruled out . That said , the study of the relevant size of the genomic region adding up BGS effects at a given focal point ( with DB75>200 kb; see above ) suggests that very local recombination variation may play a limited role influencing B at a focal point . With these caveats and considerations in mind , we investigated the presence of outliers at the scale of 10 and 1-kb to identify candidate regions under positive or balancing selection using the approach discussed for 100-kb regions . The strong relationship between B and the observed level of silent diversity is maintained when analyzing smaller regions ( Figure 5 ) . At 10-kb scale , B remains a very good predictor of πsil along complete autosomes ( ρ = 0 . 678 , 8 , 883 regions; Figure 6B ) whereas ρ is 0 . 551 ( 55 , 467 regions ) at the finest scale of 1-kb ( P<1×10−12 in both cases ) . The use of BGS models with different parameters ( MG , StdMut , MG , LowMut or MLN , LowMut ) generate virtually equivalent results , with ρ between estimates of B and πsil ranging between 0 . 678 and 0 . 682 for analyses at the 10-kb scale , and with ρ ranging between and 0 . 551 and 0 . 554 for analyses at the 1-kb scale . As observed before , B along the X chromosome shows reduced association with πsil than for autosomes also at 10- and 1-kb resolution . For X-linked regions , the correlation between B and πsil is ρ = 0 . 397 ( 1 , 979 regions ) and 0 . 295 ( 12 , 680 regions ) for 10- and 1-kb regions , respectively ( P<1×10−12 in both cases ) . Another prediction of the models of selection and linkage ( either HHss or BGS ) is that , parallel to a reduction in intra-specific variation , there will be a reduction in efficacy of selection ( i . e . , the Hill-Robertson effect [10] , [70]–[76] ) . This general prediction has been previously confirmed in Drosophila using local low-resolution crossover rates as indirect measure for the magnitude of Hill-Robertson effects acting on a gene . These studies showed weak but highly significant associations between crossover rates and estimates of codon usage bias or rates of protein evolution [71] , [74] , [75] , [77]–[85] . To investigate whether B landscapes also capture differences in efficacy of selection , we focused on selection against amino acid substitutions along the D . melanogaster lineage , after split from the D . simulans lineage ( less than 5 mya [86] ) . To this end , we obtained the ratio of nonsynonymous to synonymous changes ( ω , ω = dN/dS ) for 6 , 677 protein encoding genes and , more informatively , the variation in ω after controlling for selection on synonymous mutations based on residual analysis ( ωR; see Materials and Methods for details ) . When each gene is analyzed as a single data point , there is a negative association between B and ωR ( ρ = −0 . 086 , P = 2×10−12; Table 2 ) . Interestingly , and contrary to the results of nucleotide diversity , the X chromosome shows a tendency for a stronger effect of B on ωR than autosomes: ρ = −0 . 189 ( P = 3 . 4×10−8 ) and ρ = −0 . 071 ( P = 5 . 7×10−8 ) for X-linked and autosomal genes , respectively . An equivalent but more clear pattern is observed at the scale of the resolution of our recombination maps ( 100 kb ) , where estimating the average ω and ωR for all genes within each region also allows for reducing idiosyncrasies of different genes influencing rates of protein evolution ( e . g . , gene expression breadth and levels , protein length , etc . ; see [84] ) . At this scale , variation in B is negatively associated with ωR along autosomes ( ρ = −0 . 160 , P = 6 . 1×10−6 ) and the X chromosome ( ρ = −0 . 367 , P = 1 . 5×10−6; Table 2 ) . Again , the association between estimates of B and rates of protein evolution is robust to different BGS models and parameters . Equivalent ρ are obtained for all eight BGS models investigated , and this is observed when analyzing individual genes ( ρ between B and ωR ranging between −0 . 0856 and −0 . 0874; P≤3×10−12 ) and when using the average ωR for genes within 100-kb regions ( ρ ranging between −0 . 187 and −0 . 193; P≤5 . 9×10−9 ) across the whole genome . The association between B and rates of amino acid substitution along the D . melanogaster lineage , although highly significant in terms of associated probability , is much weaker than that for levels of polymorphism at silent sites . Heterogeneity in overall evolutionary constraints among proteins is expected to add substantial variance when investigating the consequences of B on ω relative to studies of B on πsil because πsil is only influenced by local Ne and the mutation rate . Nevertheless , temporally variable recombination rates at a given genomic location would also reduce the association between B and ω . Indeed , the high degree of intra-specific variation in crossover genetic maps within current D . melanogaster populations [31] together with differences in genetic maps between closely related Drosophila species [87]–[89] support the notion that recombination landscapes vary within short evolutionary scales , at least across trimmed chromosomes . Under this scenario , extant recombination rates and the corresponding estimates of linkage effects would be poor predictors of interspecific rates of protein evolution , even between closely related species . In this study across the D . melanogaster genome , the use of recombination rates obtained experimentally would provide only an approximation for the relevant B along the lineage leading to D . melanogaster populations [84] . These estimates of B would be an even weaker predictor of ωR ( or ω ) along the D . simulans lineage after split from the D . melanogaster lineage . In agreement , we observe no significant relationship between B and ωR estimated along the D . simulans lineage ( ρ = −0 . 009 based on the default BGS model whereas the other BGS models generate ρ ranging between −0 . 014 and +0 . 011; P>0 . 25 in all cases ) . A similar result has been obtained in comparisons of local crossover rates and rates of protein evolution between two other closely related Drosophila species , D . pseudoobscura and D . persimilis [88] . In species where BGS plays a significant role , temporal fluctuations in recombination landscapes could influence a number of analyses of selection that assume constancy in Ne , including estimates of the fraction of adaptive substitutions ( α [6] , [90]–[92] ) . Several studies have shown that the bias in estimating α can rapidly reach considerable values as a consequence of demographic changes , with α being overestimated when Ne influencing polymorphism is larger than Ne influencing divergence ( Ne_Pol>Ne_Div; [34] , [91] ) . We propose that temporal changes in recombination at a given genomic position would generate an equivalent scenario , with a B influencing polymorphism ( B_Pol ) that differs from long-term B influencing divergence ( B_Div ) , or the corresponding terms for local Ne . Because long term Ne can be approximated by its harmonic mean [93] , temporal fluctuations of recombination landscapes ( and of local B and , therefore , local Ne ) would also predict a tendency for local Ne_Pol≥Ne_Div . Such scenario would allow amino acid changes to make a larger relative contribution to divergence than to polymorphism , particularly in regions where recombination has recently increased , and thus bias estimates of α upward . Precise quantitative predictions of the potential bias in α would minimally depend on the rate , magnitude and physical scale of the variation in recombination landscapes along lineages . To obtain initial insight into the effects of temporal changes in recombination rates on estimates of α , we investigated a rather simple and conservative scenario with forward population genetic simulations . In particular , we used the program SLIM [94] to capture the consequences of temporal changes in linkage effects on estimates of α when only neutral and deleterious mutations occur along an archetypal 1-Mb region for D . melanogaster that includes 100 protein coding genes ( see Materials and Methods for details ) . Figure 7 shows the results of estimating α at selected sites under fluctuating recombination rates , with cycles of moderately high recombination for 1N generations ( with N indicating the diploid population size ) followed by moderately low ( not zero ) recombination for 3N generations . Estimates of α based on models that assume constant population size [34] , [91] , [95] overestimate the true α particularly when extant recombination is high ( α>0 . 3 ) , with an overall α∼0 . 15 when the data is combined from all temporal points . As expected , models allowing for population size change [91] generate more unbiased estimates of α that show , nonetheless , a tendency upward , likely due to limitations assessing older population size changes .
Discerning the relative contribution of different types of selection to patterns of intra- and interspecific variation is not trivial , in part because essential population and demographic parameters are often not known and the potential interactions among them are still poorly characterized . Here , we obtained the baseline of diversity across the genome predicted by BGS models completely independent of the data on nucleotide variation . The results of this study suggest that there might be no euchromatic region completely free of linkage effects to deleterious mutations in D . melanogaster . Instead , there are only genomic sites ( neutral or under selection ) associated with diverse degrees of BGS effects and thus under highly variable local Ne even across the recombining regions of the genome . In this regard , the heterogeneity in local Ne may bias population genetic estimates of selection or recombination if such variation is not taken into account . The pervasive presence of BGS has also potential consequences on demographic studies because BGS is known to generate a moderate excess of low-frequency variants [96]–[102] . As a result , demographic inferences based on allele-frequencies may be skewed , with consistent patterns suggestive of a recent population expansion . The next question investigated was how much of the patterns of variation in D . melanogaster could be explained by purifying selection alone . The results are consistent with BGS playing a major role in explaining the observed heterogeneity in nucleotide diversity across the entire D . melanogaster genome . At 100-kb scale , BGS can explain ∼58% ( ρ = 0 . 749–0 . 773 for different BGS models ) of the variation of πsil across the genome . The study of smaller regions reduces the statistical association between B and πsil , but even when analyzing 10- and 1-kb regions , B explains ∼46% ( ρ = 0 . 678–0 . 682 ) , and ∼30% ( ρ = 0 . 551–0 . 554 ) of all the observed variance in πsil , respectively . These percentages increase up to ∼70% ( ρ = 0 . 836 ) for 100-kb regions , ∼53% ( ρ = 0 . 728 ) for 10-kb regions , and ∼36% ( ρ = 0 . 599 ) for 1-kb regions , across individual chromosome arms . Importantly , these conclusions are robust to differences in parameters of the BGS model ( e . g . , deleterious mutation rates , DDFE , or recombination ) even though the precise magnitude of BGS effects does depend on these parameters . Median estimates of B range between 0 . 337 and 0 . 769 for different BGS models , but the distribution of B along the chromosomes maintains the same rank order ( pairwise ρ between estimates of B generated by the BGS models is ≥0 . 9856 ) and is similarly associated with the observed distribution of nucleotide diversity . Taken together , the results and conclusions of this study imply that null expectations based on models that ignore linkage effects to deleterious mutations ( or assume homogeneous distribution of Ne across the genome ) are likely inaccurate in D . melanogaster , even across trimmed chromosomes . The fact that deleterious mutations are more frequent than mutations involved in balancing selection or adaptive events , together with the robustness of our conclusions to reasonable ranges of selection and mutation parameters , suggest that BGS predictions may be adequate as a baseline of diversity levels and can be used detect outlier regions subject to other selective regimes . The use of B as baseline reveals several regions with signal of recent directional selection and associated selective sweeps , where levels of variation are lower than those predicted by BGS alone ( e . g . , near gene Cyp6g1 [26] , [64] ) . In agreement with expectations after a selective sweep , these regions also show an excess of variants at low frequency . A number of other regions with reduced levels of variation , however , are located in recombining genomic regions where BGS is predicted to have strong effects . These results , therefore , are consistent with a detectable but limited incidence of recent classic ‘hard sweeps’ ( where diversity is fully removed near the selected sites ) caused by beneficial mutations with large effects . Still it must be acknowledged that older selective sweeps , sweeps caused by weakly beneficial mutations , or ‘soft sweeps’ associated with standing genetic variation or polygenic adaptation [103] , [104] would not be detected in this study . In fact , estimates of the proportion of adaptive substitutions indicate that beneficial mutations are not rare in Drosophila [6] , [90]–[92] . Moreover , analyses of nucleotide diversity around amino acid substitutions suggest that a majority of these beneficial mutations involve small effects on fitness [25] , [105] , [106] and cause detectable but very localized reduction in adjacent diversity ( at the scale of 25 bp; [105] , [106] ) . Therefore , a bulk of beneficial mutations in Drosophila may be difficult to detect in genome-scans of variation but could contribute significantly to differences between species and overall adaptive rates of evolution . BGS baselines , however , are particularly adequate to detect genomic regions under balancing selection because the predicted genomic signature of an excess of diversity may not be always evident when compared to genome-wide averages or purely neutral expectations ( see Figure 6 ) . Indeed , the use of a B baseline across the whole D . melanogaster genome provides the adequate local Ne context caused by purifying selection and corresponding linkage effects . In all , our study uncovers numerous candidate regions for balancing selection , identifying genes involved in sensory perception of chemical stimulus , antibacterial humoral response , olfactory behavior , inter-male aggressive behavior , defense response , etc . These genomic regions not only show a relative excess of polymorphic sites but also have segregating variants at higher frequency than the rest of the genome ( another telltale sign of balancing selection ) . The results based on the study of a single population are appealing since heterogeneous environments or temporal changes predict the maintenance of local polymorphism through balancing selection [107]–[109] , and are consistent with analyses of clines in D . melanogaster that detected the signature of local adaptation and spatially varying selection between populations [64] , [110] , [111] . Additionally , the results evidence a clear difference between autosomes and the X chromosome in terms of the consequences of variable B on levels of variation . While the X chromosome exhibits a reduced association between B and neutral diversity than autosomes , it shows a better fit between B and rates of protein evolution and efficacy of selection . These patterns are predicted by higher rates of recombination and a higher fraction of deleterious mutations with minimal role generating BGS effects in the X ( see Material and Methods ) . Another factor possibly influencing this difference between X and autosomes is stronger efficacy of selection in the X chromosome [112] . Indeed , events of adaptive and/or stabilizing selection would distort levels of neutral diversity at linked sites beyond BGS predictions and stronger selection in the X would explain a reduced association between B and levels of diversity along the X relative to autosomes . Such combined scenario of reduced BGS effects and stronger selection would also explain a number of patterns observed in Drosophila , including an increased degree of synonymous codon usage bias [112]–[115] , stronger purifying and positive selection acting at the level of protein evolution in X-linked genes [115] , [116] , and the ‘faster-X’ effect [117] , [118] reported at both protein and gene expression levels [119]–[123] . Of interest will be the study of species with higher average recombination rate in autosomes than in the X , thus partially uncoupling differences in linkage effects and X-specific patterns . Moreover , the results of this and previous studies [26] , [87]–[89] suggest that recombination rates change frequently in the Drosophila genus , and often involve differences in the distribution of recombination rates across the genome ( i . e . , a change of the recombination landscape ) . Very recent changes in recombination landscapes would uncouple extant recombination rates and polymorphism patterns generated during the last few ∼Ne generations and , therefore , the reported contribution of BGS to patterns of diversity across the genome may be underestimated . On the other hand , changes in the recombination environment in species where BGS plays a significant role would also predict temporal variation in local B and impact a number of studies of selection that assume constancy of Ne along lineages or that changes in Ne should be equivalent across the genome . Temporally variable recombination landscapes can generate , for instance , spurious evidence for multimodality in the distribution of fitness effects , or lineage-specific physical clustering of amino acid changes ( such clustering has been observed among Drosophila species [124] , [125] ) . Another consequence of temporally variable recombination landscapes ( and local B and Ne ) would be gene- or region-specific inequality of short- and long-term Ne . Regions that have recently increased in recombination would show patterns of variation suggesting population expansion or bias estimates of α upward , making it less negative or even positive with no adaptive mutations [126] . Moreover , these changes in recombination landscape would also forecast substantial between-gene variation in α without requiring adaptive evolution . At this point , therefore , there is the open possibility that positive estimates of α in Drosophila and other species with large population size ( see [6] , [22] and references therein ) may be influenced , to an unknown degree , by temporal changes in recombination rates and landscapes . Future analyses designed to estimate population size changes or the strength and frequency of adaptive events would , therefore , benefit from including variable BGS effects across genomes and along lineages , ideally discerning local variation in BGS ( and local Ne ) from genome-wide patterns that may represent true demographic events . Genome-wide analyses may also need to consider the non-negligible presence of regions under balancing selection to prevent overestimating the extent of recent sweeps based on a relative reduction in levels of diversity . Finally , a number of limitations and areas for future improvement need to be mentioned . In terms of selection , we investigated BGS models based on either a log-normal or a gamma DDFE [39]–[43] , [46] , [127] . Trying to include all possible mutations with deleterious effects across a genome into a single distribution is , however , a clear oversimplification . A combination of different DDFEs for different groups of genes and/or sites would be a more fitting approach , ideally based on a priori information independent of levels of variation ( e . g . , based on amino acid composition , expression levels and patterns , connectivity , etc . [84] ) . To gain initial insight into the potential consequences of more realistic models , we obtained estimates of B under BGS models that allow for two DDFEs ( one for nonsynonymous changes and one for changes at constrained noncoding sites; see Materials and Methods for details ) . We then investigated whether such models would alter our conclusions , mostly in terms of the proposed adequacy of using BGS predictions as baseline to detect outliers . The results show that models with two DDFEs depart only marginally from the models with a single DDFE: rank correlations between estimates of B predicted by these hybrid models and all eight previous models show ρ ranging between 0 . 946 and 0 . 998 . The association between predicted B and the variation of πsil across the genome is also very high ( albeit slightly lower than for models based on a single DDFE ) , with ρ≥0 . 711at 100-kb scale and ρ≥0 . 504 at 1-kb scale . Notably , the comparison of outliers generated by these 2-DDFE models with those obtained by the models described above reveals few differences . For instance , 50 out of the 52 significant ( FDR q-value<0 . 10 ) 1-kb outlier regions based on the default model are also predicted to be equally significant outliers by a model assuming a log-normal DDFE for nonsynonymous mutations and a gamma for deleterious noncoding mutations ( the other two regions show departure with P<0 . 0001 ) . Overall , 87 . 1% of the 1 , 213 1-kb regions showing departure at P<0 . 01 using the default BGS model are also detected as outliers ( P<0 . 01 ) under this hybrid model . These results further support the robustness of the proposed approach to detect outlier regions based on BGS baselines . Another line of future improvement is the physical resolution of our recombination maps and , perhaps more important , how well these maps represent the recent history of a population or species . To generate B landscapes , we used recombination maps that were experimentally obtained ( independent of nucleotide variation data ) and capture some intra-specific variation in recombination landscapes ( see [31] for details ) . The fact that these B landscapes explain a very large fraction of the variance in nucleotide diversity across the genome suggests that these recombination maps represent quite accurately the recombination landscape in the recent history of D . melanogaster . Future recombination maps obtained from additional natural strains and populations , or under different biotic/abiotic conditions , can only improve the confidence in outlier regions and , ultimately , our understanding of selective and demographic events in this species .
The consequences of BGS at a given nucleotide position in the genome ( focal point ) can be described by B [12]–[15] , the predicted level of neutral nucleotide diversity under circumstances where selection and linkage are allowed ( π ) relative to the level of diversity under complete neutrality and free recombination ( π0 ) . Following [15] , [16] , we havewhere ui is the deleterious mutation rate at the i-th selected site out of the n possibly linked sites , si indicates the selection coefficient against a homozygous mutation and ri is the recombination frequency between the focal neutral site and the selected i-th site . Note that under a BGS scenario B can only be equal ( no BGS effects ) or lower than 1 , and B<<1 indicates strong BGS effects . Molecular evolutionary analyses indicate variable fitness effects of deleterious mutations [44] , [121] , [127] , [128] and , therefore , a probability distribution of deleterious fitness effects ( DDFE ) of mutations at site i , ϕ ( si ) needs to be included , generatingB , therefore , is predicted to vary across the genome as consequences of the known difference in recombination rates along chromosomes as well as due to the heterogeneous distribution of sites under selection across genomes ( genes , exons , etc . ) . However , and because we are allowing the selection coefficient s to vary according to a distribution ϕ ( s ) , some sites under selection may have selection coefficients too small to play any BGS effect , thus not all sites under section need to be included in the study [33] . We truncate the distribution of selection coefficients at sT ( sT∼1/Ne ) because mutations with s<sT are effectively neutral and do not contribute to BGS . Different DDFEs , therefore , will generate a different fraction of deleterious mutations with s>sT and , therefore , different BGS effects ( see below ) . We investigated a model that assumes the possibility of strongly deleterious mutations at nonsynonymous sites as well as at a fraction of noncoding sites , either untranslated genic ( introns and 5′- and 3′-flanking UTRs ) or intergenic regions . B , therefore , can be estimated at any focal neutral site of the genome as the cumulative effects of deleterious mutations at any other nonsynonymous , untranslated genic and intergenic site along the same chromosome . For simplicity¸ and based on analyses of rates of evolution between Drosophila species [22] , [37] , [38] , [44] , [91] , we assumed that the distribution and magnitude of selection on intergenic or UTR noncoding strongly selected sites [] is equivalent to that on nonysynonymous sites [] . We can estimate B at any focal neutral site of the genome using the equation described above , , the actual genome annotation ( D . melanogaster annotation release 5 . 47 , http://flybase . org/ ) and high-definition recombination maps for crossing over and gene conversion events [31] . The analysis of every nucleotide sites along a single chromosomal arm taking into account all other sites under selection of this same chromosome would , however , require >1×1013 pair-wise nucleotide comparisons , each one requiring a numerical integration . To speed up the process we followed [17] and first obtained the integralalong a continuum for possible recombination rates between two sites , in our case for r ranging between 1×10−10 ( equivalent to c = 0 . 01 ) to 1; we assumed r = 0 whenever the recombination distance between two nucleotides is smaller than 1×10−10 . Because he recombination distance between any pair of nucleotides can be obtained , the generation of complete maps of B is now more tractable although still computationally very intensive . We then made the simplifying assumption of ignoring variation within 1-kb windows when estimating B at any another 1-kb window along the chromosome . That is , for the purpose of generating BGS effects , all sites within a 1-kb window have an equivalent recombination rate with sites within any other 1-kb window along the chromosome . This is a reasonable approximation at this time due to the fact that the resolution of our best genome-wide recombination maps is 100-kb and differences in recombination due to a few nucleotides play a minor role when comparing sites separated by tens of hundreds of kbs . By dividing a chromosome arm into L adjacent 1-kb windows , B at a focal neutral site located at the center of the j-th window ( Bj ) , can be estimated by using:where Naai , Nutr_ssi and Nnc_ssi are the number of nonsynonymous , UTR and intergenic sites possibly under strong selection within window i , respectively , and rji is the recombination between the center of the focal window j and the center of window i , with rji increasing in 1-kb intervals . , , and are the deleterious mutation rate at nonsynonymous , UTR and intergenic sites , respectively . Because all sites in the genome are actually being taken into account , this approach avoids the need to interpolate B estimates and generates a very detailed B landscape . We initially investigated eight BGS models using the formulas described above , with two DDFEs ( log-normal or gamma; models MLN and MG , respectively ) , two deleterious mutations rates ( U = 1 . 2 or 0 . 6; models MStdMut and MLowMut , respectively ) , and two recombination scenarios ( with only crossovers or crossovers and gene conversion events; models MCO and MCO+GC , respectively ) . For instance , the full notation of a model that uses a log-normal DDFE , a deleterious mutation rate of U = 0 . 6 , and recombination that only considers crossovers is MLN , LowMut , CO . To obtain the number of sites Naa , Nutr_ss and Nnc_ss for each 1-kb region , we followed the approach described by Charlesworth [33] . We assume 0 . 75 as the fraction of coding sites that alter amino acid sequences and correct this fraction by the proportion of constrained nonsynonymous sites ( cs ) to focus only on the fraction of deleterious mutations . In D . melanogaster , cs for amino acid sites is ∼0 . 92 [22] , [37] and , thus , Naa for a regions is Lcoding×0 . 75×0 . 92 . To obtain Nutr_ss , we correct the number of noncoding genic sites using the proportion of constrained sites ( 0 . 56 for introns and 0 . 81 for flanking UTRs [22] ) and , equivalently , we use the proportion of constrained sites at intergenic sequences ( ∼0 . 5 ) [22] , [37] to obtain Nnc_ss . The deleterious mutation rate per bp ( u ) will be different for different models , due to the overall mutation rate and because different DDFEs predict a different fraction of deleterious mutations with s<sT that will not contribute to BGS . The two mutation rates investigated here , U = 0 . 6 ( MLowMut ) and 1 . 2 ( MStdMut ) , represent a neutral mutation rate of 4 . 2×10−9 and 8 . 4×10−9/bp/generation . Assuming Ne = 1 , 000 , 000 for D . melanogaster , the log-normal and gamma DDFEs described above predict 15 . 3 and 7 . 4% of deleterious mutations with s<sT , respectively . Therefore , the deleterious mutation rate relevant for BGS is 84 . 7 ( MLN ) and 92 . 6% ( MG ) that of the mutation rate . Nucleotide diversity across the D . melanogaster genome was estimated from a sub-Saharan African population ( Rwanda , RG , population of the Drosophila Population Genetics Project , DPGP; www . dpgp . org/ and [62] ) . D . melanogaster is thought to have originated in sub-Saharan Africa , and eastern Africa—including Rwanda—in particular [134] . Our use of the RG population , therefore , minimizes the non-equilibrium effects caused by recent expansion observed in western Africa and non-African D . melanogaster populations [62] , [134] . Additionally , the RG population combines a relatively large sample ( n = 27 ) , and low and well characterized levels of admixture [62] . We followed Pool et al . ( 2012 ) and used updated assemblies from the DPGP2 . v3 site ( see http://www . dpgp . org/dpgp2/DPGP2 . html and [62] for details ) . We analyzed these assemblies with , 1 ) sites putatively heterozygous or with quality value smaller than Q31 masked to ‘N’ , and 2 ) putatively admixed regions of African genomes filtered to “N” based on the description of admixed regions from [62] ( also available from http://www . dpgp . org/dpgp2/DPGP2 . html ) . Finally , we only investigated non-N sites that were present in a minimum of 10 sequences . Equivalent results were obtained when the analyses were restricted to sites with a minimum of 15 sequences , or after removing regions showing excess admixture as well as regions showing excess long-range identity-by-descent ( IBD ) [62] ( data not shown ) . Neutral ( silent ) diversity was estimated as pairwise nucleotide variation per site at intergenic sequences and introns ( πsil ) and following the approach described in [31] . In short , we first annotated all gene models , transposable elements and repetitive sequences onto the reference sequence allowing for overlapping annotation . Intergenic sites correspond to sites between gene models ( excluding annotated UTRs ) , transposable elements or repetitive sequences . Intronic sites are analyzed as such only when they never overlap with another annotation ( e . g . , with alternatively spliced exons or other elements within introns ) . After this filtering approach , intergenic and intronic sites show similar levels of silent diversity , with a very weak tendency for πsil in introns ( πintrons = 0 . 0085 ) to be higher than in intergenic ( πintergenic = 0 . 0082 ) sites ( Sign test , P = 0 . 034 and P = 0 . 056 , along autosomes and X chromosome , respectively ) . Unless indicated otherwise , diversity analyses were performed using all 100-kb non-overlapping windows across the genome whereas analyses of 10-kb and 1-kb regions were limited to regions with more than 1 , 000 and 500 silent sites , respectively . Inferences about the frequency of segregating variants within the population were based on estimates of Tajima's D [69] after normalizing by Dmin ( D/Dmin ) following Schaeffer ( 2002 ) [135] . Equivalent conclusions were obtained when using DGRP sequenced strains [136] from a North American natural population ( Raleigh , NC , USA ) . We used the program SLIM [94] to capture the consequences of temporal changes in recombination rates on estimates of α . Simulations followed a panmictic population of 10 , 000 diploid individuals ( N ) and a chromosome segment of 1 Mb that contained 100 protein encoding genes evenly distributed , one every 10 kb . Each 10-kb region included a typical Drosophila gene: a 1 , 000 bp 5′ UTR , a first short 300-bp exon , a 1 , 000 bp first intron , two additional 600 bp exons , a short 200-bp internal intron , and a 300 bp 3′-UTR , followed by a 5 , 000 bp intergenic sequence . Mutations were assigned to have the same parameters than those in the BGS models described above , with two mutation types: neutral and deleterious . The population mutation rate was set to Nu = 0 . 005 and deleterious mutations were assumed to follow a gamma DDFE ( h = 0 . 5 ) with average Ns = −2 , 500 [33] , [38] . The proportion of deleterious mutations at the different genomic elements was set to 0 . 92 at first and second codon positions , 0 . 81 at UTRs , 0 . 56 at introns , and 0 . 5 at intergenic sites [22] , [37]; all third codon positions evolved neutrally . Note that the simulation of a smaller population would prevent the study of deleterious mutations with the appropriate scaled selection for Drosophila ( Ns = −2 , 500 ) while the simulation of shorter genomic sequences would severely underestimate BGS effects in regions with non-reduced recombination . Simulations followed 10 independent populations a minimum of 50 N generations after reaching equilibrium ( >10 N generations ) . Recombination rates were uniformly distributed , with a population crossover rate ( /bp ) of NrCO = 0 . 04 and NrCO = 0 . 0025 for periods of high and low recombination , respectively . To prevent overestimating BGS effects we also included a constant rate of gene conversion initiation ( /bp ) of Nγ = 0 . 05 and average gene conversion tract length of LGC = 518 [31] . After the initial 10N generations , we sampled the population every 0 . 1N generations , obtained polymorphism data from 20 randomly drawn chromosomes , and compared them to a sequence that evolved independently for 20N generations to obtain divergence ( d ) values . These levels of polymorphism and divergence are equivalent to those observed within D . melanogaster for polymorphism and between D . melanogaster-D . simulans for divergence , where d/θ = ∼6 for neutral sites [95] . Estimates of α at selected sites were restricted to the 100-kb central region to capture long-range BGS effects . Following Eyre-Walker and Keightley [91] , α was obtained by maximum likelihood ( ML ) to capture the presence of strongly deleterious mutations in the simulations , with and without the possibility of population size change , and with and without correcting for the contribution of polymorphism to divergence [95] . Estimates of α were obtained using the DFE-alpha server ( http://lanner . cap . ed . ac . uk/~eang33/dfe-alpha-server . html ) . | The removal of deleterious mutations from natural populations has potential consequences on patterns of variation across genomes . Population genetic analyses , however , often assume that such effects are negligible across recombining regions of species like Drosophila . We use simple models of purifying selection and current knowledge of recombination rates and gene distribution across the genome to obtain a baseline of variation predicted by the constant input and removal of deleterious mutations . We find that purifying selection alone can explain a major fraction of the observed variance in nucleotide diversity across the genome . The use of a baseline of variation predicted by linkage to deleterious mutations as null expectation exposes genomic regions under other selective regimes , including more regions showing the signature of balancing selection than would be evident when using traditional approaches . Our study also indicates that most , if not all , nucleotides across the D . melanogaster genome are significantly influenced by the removal of deleterious mutations , even when located in the middle of highly recombining regions and distant from genes . Additionally , the study of rates of protein evolution confirms previous analyses suggesting that the recombination landscape across the genome has changed in the recent history of D . melanogaster . All these reported factors can skew current analyses designed to capture demographic events or estimate the strength and frequency of adaptive mutations , and illustrate the need for new and more realistic theoretical and modeling approaches to study naturally occurring genetic variation . | [
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] | 2014 | Background Selection as Baseline for Nucleotide Variation across the Drosophila Genome |
Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market . An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug’s benefits and risks . Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects , our knowledge of these pathways is incomplete . To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules , we created a novel method , PathFX , a non-commercial entity , to identify these pathways and drug-related phenotypes . We benchmarked PathFX by identifying drugs’ marketed disease indications and reported a sensitivity of 41% , a 2 . 7-fold improvement over similar approaches . We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System ( FAERS ) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease . By discovering molecular interaction pathways , PathFX improved our understanding of drug associations to safety and efficacy phenotypes . The algorithm may provide a new means to improve regulatory and therapeutic development decisions .
The drug discovery process is long , difficult , and expensive . Only ~10% of drugs entered in human studies make it to the market[1] because many drugs have insufficient efficacy[2] or significant safety issues[3 , 4] . The lack of efficacy may be related to poor bioavailability , incomplete inhibition of the target , or selection of a target that is not a central driver of the disease . Adverse events can occur through primary effects of a drug on the intended targets or respective biological pathways , or secondary effects that can occur with off-target binding [3 , 5] . For these reasons , understanding a drug’s phenome–the collection of clinical characteristics that are related to a drug target or pathway–is integral to validating and prioritizing drug targets for development , and identifying other potential ( adverse ) drug effects that may occur by perturbing a particular biological network . However , the available tools for characterizing drug pathways during target optimization and regulatory review catalogue varied aspects of the cellular response to drug and integration across these resources is often incomplete . Many data sources containing relevant information are available to characterize pharmacological drug effects . However , the vast amount of information is siloed into separate databases , creating a patchwork of information that is difficult and tedious to easily integrate for regulatory review . To illustrate , in evaluating a drug that inhibits a specific enzyme , one might look to Mendelian disease as an indicator of what clinical manifestations are related to loss of that enzyme’s function; this could inform what toxicities or benefits might arise from pharmacologically interrupting the enzyme’s function ( e . g . , as for the case of PCSK9 ) . This exercise can then be repeated by evaluating the genetic epidemiology of more common variations in the enzyme , and so on . Relevant resources that could collectively inform a drug target’s phenome include Pharmacogenomics Knowledgebase ( PharmGKB ) [6] , genome-wide association studies ( GWAS ) [7] , Online Mendelian Inheritance in Man ( OMIM ) [8] , disease-gene associations such as DisGeNet[9 , 10] , and phenotype-gene association studies ( PheWAS ) [11 , 12] . In addition , focusing on a single gene or protein does not always provide a complete view of the biological milieu , or full pathway context , relevant to a drug target . Protein interaction databases , such as STRING[13 , 14] and iRefWeb[15] , relate drug targets to signaling intermediates to provide context for single gene effects , though , these are not easily linked to phenotype information . Network methods can be useful for identifying mechanistic interactions that relate drug targets to adverse reactions , and are under-utilized in understanding drug adverse effects[5] . A network approach uncovered interaction intermediates between drug targets associated with peripheral neuropathy[16] , drug-induced rhabdomyolysis[17] , drug-induced severe cutaneous stevens-johnson syndrome[18] , drug-induced lung injury[19] , and drug-induced contraction-related cardiotoxicity[20] . A further meta-analysis of networks for these toxicities discovered protein mediators that are common among pairs of toxicities[20]; for instance , they discovered that drugs associated with peripheral neuropathy and drugs associated with Stevens-Johnson syndrome had nine protein targets in common . Another study merged protein-protein interactions , gene-to-adverse events ( AEs ) associations , and knowledge of drug-protein targets to train a random forest model that identified drugs with the greatest connectivity to AEs[21] . Their analysis showed improved prediction of AEs when combining their approach , SubNet , with medication-wide association studies ( MWAS ) assessing genes associated with four AEs[21] . Another network based approach used a shortest-path technique for in silico predictions for drug repurposing[22] . A network propagation technique created drug pathways and collapsed these pathways into phenotype vectors , though , they did not use this paradigm to be predictive of drug safety and efficacy[23] . These foundational studies demonstrated that interaction networks are a rich source of pathway information that can identify molecular mechanisms for drug safety and efficacy . Here we constructed drug pathways using protein-protein interactions , and we annotated these pathways with the phenotypes–diseases and off-target effects–associated with the pathway genes using a novel algorithm–PathFX . We demonstrated the utility of PathFX by creating pathways for marketed drugs and identified interaction paths from the drug’s target ( s ) to genes associated with the marketed indication of the drug . We benchmarked PathFX’s performance using a published set of marketed drugs and quantified our ability to relate a drug to its disease indication . We applied the algorithm to two tasks . First , we strengthened adverse event signals in the FDA Adverse Event Reporting System ( FAERS ) by searching for drug pathways containing an association to a reported adverse event . Second , we identified repurposing opportunities for marketed drugs and tested these identifications using existing off-label drug use and clinical trial data . We created a tool for better understanding drug safety and efficacy and PathFX may have the potential to aid in regulatory review and therapeutic development decisions .
Recent work in identifying a drug’s marketed disease , or indication , from protein interactions found that this identification was maximized by considering protein interactions that are in close proximity to a drug’s target [22 , 23 , 24] . Thus , we hypothesized that protein-protein interactions that are proximal to a drug’s target ( s ) could provide insight into mechanisms of drug safety and efficacy . To create drug interaction pathways , we pulled interaction data from iRefWeb[15] , Reactome[25] , PharmGKB[6] , and a curated set of predicted drug-protein binding data ( see Methods section ) . We merged and scored ( explained in methods ) these data to yield an interaction network of 25 , 604 nodes and 318 , 644 edges . The number of interactions and interaction score distributions are in S1 Fig . Our algorithm , PathFX , selects a drug target’s most relevant interaction edges ( local interaction neighborhoods ) , merges neighborhood networks from all drug targets , and then identifies enriched phenotypes–which could represent either safety or efficacy phenotypes–in the interaction neighborhood ( Fig 1 , and usage summary in S4 Fig ) . In this context , safety phenotypes included associations such as adverse events or side effects ( e . g . “pancreatitis” , “adverse weight gain” ) , and efficacy phenotypes included disease associations ( e . g . “diabetes” , “major depressive disorder” ) ; some phenotypes ( e . g . “hypertension” ) could belong to both of these groups . To identify which phenotypes are associated with the drug target network , we merged data from multiple sources: DisGeNet[9 , 10] , Phenotype Genotype Integrator ( PheGenI ) [26] , ClinVar [27] , OMIM [8] , and PheWas [11 , 12] . In this process we controlled for multiple biases as follows: PathFX uses a threshold parameter for selecting proteins included in each drug network . We derived this threshold based on the available data and did not tune the parameter to improve identification accuracy ( explained in An optimal threshold parameter for the tissue non-specific network in Methods ) . We first applied the PathFX method to the diabetic medication , metformin . DrugBank[29] listed five protein targets for metformin—SLC22A2 , SLC22A3 , PRKAB1 , SLC47A1 , and SLC29A4 –for metformin that were in our interactome ( note that some of these are transport proteins that may be included because metformin inhibits them , not as a pharmacological effect ) . We created a drug interaction network pathway based on all listed proteins using PathFX . This yielded a 25-protein final neighborhood ( 20 proteins + 5 drug targets ) significantly associated with 18 phenotypes ( Fig 2A , S1 Table ) . Diabetes mellitus type 2 and diabetes mellitus type 1 are both associated with the metformin pathway via interactions with 12 genes ( Fig 2B , S1 Table ) . Metformin’s protein targets were not sufficient to describe the association to diabetes mellitus type 1 and diabetes mellitus type 2 when we analyzed phenotypic associations with these targets . However , with the full 25 protein network identified by PathFX , we recovered the association to diabetes mellitus type 1 and diabetes mellitus type 2 . We collected a benchmarking set of approved drugs to test our algorithm’s utility in accurately identifying diseases that the drug is known to effectively treat . This set included marketed drugs with approved disease indications . We first started with marketed , non-palliative drugs analyzed in [22] to compare our performance with this seminal work . This data set included 238 drugs associated with one or more disease indications , yielding a total of 403 drug-indication pairs . We augmented this dataset by using repoDB[30] to add additional approved indications for the original drug set ( we excluded data from repoDB where the trial was terminated or ongoing ) ; using repoDB , we added 1353 drug-indication pairs , yielding a total of 1756 drug-indication pairs for testing . The full list of drugs and approved indications are included in S1 File and consists of a list of drugs associated with one or more disease indications . Most drugs were approved for fewer than 10 indications , though , prednisolone and hydrocortisone are used to treat 105 and 98 indications respectively ( S1 File , Fig 3A & 3B ) . The dataset included drugs used to treat 594 indications . We binned these 594 indications into 62 clusters based on the semantic similarity of the disease indications . We used the interactive mmlite interface to metamap[31] , a highly configurable program developed to map biomedical text to the UMLS Metathesaurus , to map diseases to the nearest Unified Medical Language System ( UMLS ) CUI ( Concept Unique Identifier ) identifier . We selected the UMLS terminology because this system had the greatest coverage of phenotypes in our dataset and contained mappings from many popular languages ( such as MedDRA ) . We then clustered these diseases based on ontological , semantic similarity using the UMLS::Similarity package in Perl[32] ( cluster membership in S1 File , Fig 3C ) . For instance , cluster four contained two CUI terms–C0497327 , C0002395 –that mapped to 24 Alzheimer’s and dementia phenotypes ( S1 File ) . Cluster five contained five CUI terms–C0042842 , C0042875 , C0030783 , C0016412 , C0936215 –that mapped to nine diseases associated with vitamin deficiency . When testing PathFX , we analyzed and reported whether the algorithm identified the drug’s original , un-clustered indication , and also reported results based on the indications’ cluster to assess trends in the types of diseases where we had better identification capacity . We used the UMLS::Similarity tools for determining if PathFX identified phenotypes that matched the drug’s marketed indication . In this case , we regarded a match as any phenotype significantly associated to the network; for most drugs , PathFX identified multiple phenotypes as statistically significantly associated to the drug’s network . For each drug , we pulled these significant phenotypes from our PathFX analysis as above , and matched these identified phenotypes to CUI identifiers , also using mmlite[31] . We measured semantic similarity using Lin distance in the UMLS::Similarity package[32] . For example , the drug enoxaparin is indicated for deep vein thrombosis and myocardial infarction; our algorithm identified that enoxaparin’s drug pathway was significantly associated with “deep venous thrombosis” ( direct match , semantic similarity = 1 . 0 ) , and other similar diseases such as “thrombosis” ( semantic similarity = 0 . 8615 ) , “venous thromboembolism” ( semantic similarity = 0 . 6853 ) , and “myocardial infarction” ( semantic similarity = 0 . 9377 ) ( full results in S1 File , Fig 4A ) . Of the 1756 drug-indication pairs , PathFX could not create an interaction network for two pairs ( tolazamide + diabetes type 1 , and tolazamide + diabetes type 2 ) because this drug’s target was not mapped to a gene symbol in our interactome . For 389 drug-indication pairs , metamap was unable to map the marketed indication to a CUI term so we could not assess whether the PathFX identified indications matched the marketed indications ( cluster number 58 in Fig 4C and in S1 File ) . This left 1366 pairs for further analysis . For this analysis , PathFX found pathway information for 171 of the 403 drug-indication pairs ( 42 . 4% ) from Guney et al[22] and 558 of the 1364 ( 40 . 9% ) eligible drug-disease pairs in our expanded set of benchmarking drugs; this is our best estimate of PathFX sensitivity . At the drug level , 141 out of 236 drugs ( there were two of the original 238 drugs without sufficient binding information in DrugBank ) had at least one identified phenotype that was similar to one of the drug’s marketed indication ( s ) ( 59 . 8% ) . Guney et al[22] reported a sensitivity of 15 . 4% ( they matched 62 of 403 drugs-indication pairs ) , demonstrating improved sensitivity from our drug-target-centric approach . For comparison with PathFX , we analyzed the disease associations of the drug targets alone without the local interaction information . Using only drug targets , we identified statistically significant associations between 751 drug and disease pairs ( 54 . 98% sensitivity ) . Of these pairs , 409 were also identified using PathFX pathway information , leaving 342 drug-disease pairs that were identified by targets alone and 147 drug-disease pairs that were only identified when pathway information was included from PathFX ( S1 File ) . After merging gene-to-phenotype associations from multiple data sources , each phenotype had a set of associated genes and the size of this gene set distinguished which diseases each method detected ( Kruskal-Wallis statistics = 33 . 6 , p-value = 5 . 04x10-8 ) ( Fig 4B ) . PathFX was biased towards selecting phenotypes with fewer genes ( median gene set size of 90 genes ) ; targets-only analysis was biased to selecting phenotypes with more associated genes ( median gene set size of 342 . 5 genes ) ( Mann-Whitney-U statistic: 33089 , p-value 1 . 43x10-8 ) ( Fig 4B ) . The median gene set size where both methods detected the phenotype was 257 genes . For completeness , we calculated positive and negative predictive values ( PPV , NPV ) ( S2 File ) . To calculate PPV and NPV , we made a conservative assumption that any phenotype associated with a drug that was not a marketed disease indication was a false positive . Because PathFX was designed to search broadly for drug-associated phenotypes , the PPV and NPV were deflated and inflated respectively ( S3 Fig ) . We analyzed PathFX identifications in the context of the 62 disease clusters ( Table 1 , Fig 4C ) . For 58 of the 62 clusters , PathFX found pathway evidence supporting the drug’s marketed indication for at least one of the drug-disease pairs assigned to that cluster ( Table 1 , S1 File ) . For instance , the top cluster contained three CUI terms which mapped to five disease phenotypes diseases: inappropriate adh syndrome , acromegaly somatic , hyperprolactinemia , acromegaly , and prolactin excess ( CUI terms C0021141 , C0001206 , and C0020514 ) ( Table 1 ) . There were five drug-indication pairs for these diseases: tolvaptan ( inappropriate adh syndrome ) , octreotide ( acromegaly somatic ) , bromocriptine ( hyperprolactinemia ) , bromocriptine ( acromegaly somatic ) , and cabergoline ( hyperprolactinemia ) . PathFX identified phenotypes for all five drug-disease pairs . The cluster with the second highest identification rate contained four CUI terms that mapped to seven disease terms , which are pathophysiologically distinct: alcohol withdrawal delirium , restless legs syndrome , premenstrual dysphoric disorder , insomnia , nicotine dependence , late insomnia , sleeplessness . There were seven drug-indication pairs included in this cluster: gabapentin ( restless legs syndrome ) , ropinirole ( restless legs syndrome ) , rotigotine ( restless legs syndrome ) , diphenhydramine ( late insomnia ) , estradiol ( premenstrual dysphoric disorder ) , nicotine ( nicotine dependence ) , and diazepam ( alcohol withdrawal syndrome ) . PathFX identified the original , un-clustered phenotype for six of the seven pairs ( PathFX did not identify estradiol’s association to premenstrual dysphoric disorder ) ( Table 1 ) . For the remaining four clusters , PathFX did not identify the drugs’ marketed indication for any of the drug-indication pairs assigned to these clusters ( S1 File ) . These clusters are numbered 7 , 13 , 35 , and 1 . Additionally , cluster 58 contained 216 disease indications , of which 123 diseases were not mapped to a CUI term . Understanding and prioritizing drug safety signals are important regulatory concerns[33 , 34] . The FDA Adverse Event Reporting System ( FAERS ) is a repository of voluntarily submitted case reports of adverse events that occur when a patient is on a particular medication . Multiple confounding variables , including comorbidities , incomplete reports , and polypharmacy[35] , make it difficult to determine when a drug is causative for an AE . This makes signal detection and triaging reports difficult . Further , when associations are not directly explained by drug targets , pathway models may provide additional justification for why a drug is associated with an AE . Anticipating and identifying drug-induced AEs are critical for novel therapeutics in development as well; and thus , further characterizing which drug targets and interacting proteins may cause AEs is an important goal of this work . Designated medical events ( DMEs ) are serious , significant adverse drug events of special concern to regulators . As such , DMEs , selected based on expert medical review , were evaluated in this study . To assess the utility of PathFX for signal detection , we extracted drug-adverse event pairs from FAERs for 1906 drugs across 35 DMEs and ran PathFX on these 1906 drugs . Case reports were associated with one of the 35 DMEs if the adverse event mapped to the preferred MedDRA DME term . Close synonyms were used for some DMEs to better capture reporting ( e . g . , “pancreatitis” was captured by “pancreatitis” and “pancreatitis acute” ) . PathFX identified the adverse event phenotype for the input drugs between 0 . 24% - 39 . 57% ( Table 2 and S3 File ) , depending on the DME . For instance , in this noisy data set , 1045 drugs were reported as having an adverse association with pancreatitis . PathFX identified that 282 of these drugs were associated with pancreatitis ( 26 . 99% ) . Similarly , 1150 drugs were reported to have an association with myocardial infarction and PathFX identified 391 ( 34 . 00% ) of these drug-DME associations . For eight of the DME phenotypes , we found no pathways associations to the reported drugs ( S3 File ) . We used this FAERS dataset to estimate a lower bound on the specificity of PathFX . Because FAERS contains many more drug-DME associations than are real , we treated any drugs without a reported DME association as silver-standard negatives; reasoning that if a noisy sampling of the FAERS system contained no association between the drug and the DME , that these pairs were sufficient negatives . We asked how often PathFX associated a drug with a DME when no case report existed to calculate the specificity rate for the 35 DMEs in this analysis; the rate varied from 80 . 84%-99 . 91% ( Table 2 , S3 File ) . For instance , 1261 drugs were reported to have an association with hypertension , leaving 645 of our original 1906 drugs without an association to hypertension . Of these 645 silver-standard negatives , PathFX identified an association with hypertension for 112 ( 17 . 4% ) of the drugs ( 82 . 64% specificity ) . For cardiac arrest , 1211 drugs were reported to have an association , leaving 695 drugs without an association . Of these drugs , PathFX identified seven drugs to have an association , estimating a specificity of 98 . 99% ( Table 2 ) . PathFX identified multiple phenotypes for each drug even if the drug only has a single approved indication . We sought support for the additional identified phenotypes from two data sets: ( 1 ) a list of off-label drug uses extracted from the electronic medical record[36] and ( 2 ) drugs currently in clinical trials . In the EMR data , we found support for six drugs applied in 11 off-label indications ( Table 3 , listed as ‘Jung CUI’ and ‘Jung Disease’ ) . For instance , telmisartan is indicated for hypertension , though PathFX and the EMR dataset supported the use of this drug as an anti-diabetic; this conclusion is further supported in the literature[37] . PathFX identified that thiothixene would be broadly applicable to depressive disorders beyond the indicated use for schizophrenia . PathFX also identified that etanercept , an anti-TNF-alpha drug used in auto-immune disorders , would be applicable to colitis and this identification was supported by the Jung dataset ( Table 3 ) . PathFX identified associations for 4 drug-indication pairs supported by additional clinical trials ( Table 4 ) : sunitinib for viral infections[38] , erlotinib for viral infections[38] , ketoprofen for lymphoedema[39] , and sirolimus for dystrophic bullosa [40] . Given evidence that EMR and clinical trial data supported PathFX predictions , we further scrutinized PathFX identifications to identify drug repurposing opportunities ( Fig 5A ) ; we inferred that a non-marketed indication could be a repurposing opportunity if the interaction path was found in the network of a drug marketed for this indication . We demonstrated an example with leuprolide and triptorelin , both gonadotropin-releasing hormone ( GnRH ) agonists ( Fig 5B ) . PathFX also identified that the triptorelin pathway is enriched for associations with endometriosis , and these interaction pathways are supported by leuprolide’s pathway . PathFX also identified that the antispasmodic , flavoxate , could be indicated for urticaria based on interaction paths shared with cyproheptadine and promethazine , two anti-histamines already approved for urticaria . In total , we identified 2 , 043 new drug-disease associations for 215 drugs ( S4 File ) . We ranked these predictions based on the number of diseases identified for a drug ( top 20 in Table 5 , remainder in S4 File ) , and the number of interaction paths supporting a drug-disease association ( top 20 in Table 6 , remainder in S4 File ) .
Here we presented PathFX , a phenotypic pathways approach for characterizing drug efficacy and safety based on molecular interactions around the drug target . The algorithm characterizes the phenome around drug targets by integrating several data repositories relevant to regulatory review and therapeutic discovery . We supported our hypothesis that molecular interactions contribute to a drug’s safety and efficacy phenotypes . We successfully detected pathways that confirmed a drug’s association with its marketed disease indication and strengthened signals from adverse event reports in FAERS . In the analysis of marketed drugs , we benchmarked against a published dataset and additionally expanded this dataset to reflect updated uses of this drug set . We further discovered that PathFX identified drug phenotypes when the phenotype had relatively fewer genes associated in our data sources compared to analyzing phenotypes using the drug targets alone . We tested additional PathFX predictions using clinical trial and EMR data , and further identified novel uses for marketed drugs using networks from PathFX . We identified associations for some phenotypes more than others . This suggested a few possibilities: incomplete data–gene-disease annotations , molecular interactions , or drug-protein binding–may prohibit the creation of pathways relating drug targets to disease associations; or marketed drugs may impact a clinically-relevant outcome to earn approval but may not be supported by genetic epidemiology studies . Incomplete knowledge of all drug-binding proteins limited our ability to construct complete drug pathways . In our database of drug-binding proteins taken from DrugBank [29] , the mean and median number of proteins bound by a drug is 2 . 66 and 1 . 0 respectively . Indeed , more recent work estimates the mean and median number of proteins bound by a drug to be 329 and 38 respectively [41] , suggesting that incorporation of more drug-protein interactions could improve our network predictions . Standardized drug-binding profiling could greatly improve the predictions from these algorithms . PathFX did not identify mechanism of action for all disease clusters . It is not surprising that PathFX did not identify bacterial infections , given that we are using a human protein-protein interactome . Cluster 13 contained brain cancer indications and suggested that the drug-target centric approach is not sufficient for describing efficacy for these anti-brain cancer therapies . There are some limitations of our method: the model does not consider tissue specificity and is biased to selecting phenotypes with fewer gene annotations . Future work will consider incorporating tissue-specific interaction networks such as the GIANT networks [42] and consider screening drugs for binding across these tissues . PathFX quantifies the overlap between drug pathways and disease phenotypes but does not indicate directionality ( helpful or harmful ) between the drug and the pathway . Using a non-directional analysis enabled a broader discovery process given fewer directional molecular interaction networks . Compared to analyzing phenotypic associations to drug targets alone , PathFX was biased to select phenotypes with fewer genes associated and this is likely due to the statistical approach of our method: Starting with a smaller list ( e . g . just the drug targets ) increases the chance of finding a statistically significant association to phenotype for which there are many associations in the whole network . Conversely , starting with a larger list of proteins ( e . g . using proteins from PathFX networks ) , decreases the chance of finding a statistically significant association to a phenotype with many proteins distributed in the interactome network . PathFX identified phenotypes where there is significant overlap with the network and where there are relatively fewer associations to the phenotype in the entire interactome network . We estimated algorithm specificity with a silver-standard data set of drugs not reported in FAERS . We recognize the limitation of this assumption because sampling biases and drug usage affect whether or not a drug is reported in FAERS in addition to our assumption that the drug does not cause an adverse event . However , we lacked sufficient gold-standard , true negatives with which to estimate specificity . Our approach is not the first network biology tool for describing drug function , though , it does have different downstream applications . The comparator approach [22] reported lower sensitivity than PathFX . This could reinforce the role of incomplete data in creating pathways for marketed drugs or drug effects beyond the underlying disease pathway , as mentioned previously . Additionally , our improved performance could have resulted from the more permissive approach of our algorithm . The motivating question for regulatory review was “what biological evidence supports the validity of an observed adverse event ? ” in the post-market setting , and “what clinical trial assessments might be needed ? ” for vigilant detection of safety issues in the pre-market , investigational drug setting . In this paradigm , PathFX sampled disease signals around a drug target and was not constrained to find the right answer such as in the shortest-path method in Guney et al[22] . For our regulatory context , our expansive search was a positive design feature for understanding biological evidence supporting adverse events . We demonstrated utility of PathFX in a pertinent regulatory context . Our analysis of FAERS data proved useful in detecting if and how a drug could cause a particular DME . We did not apply all available statistical filters to the FAERS data before analysis . Our intention was to apply PathFX as a biologically-motivated tool for signal detection and to develop PathFX as a complementary approach to other statistical methods; in practice , filtering using statistical approaches prior to PathFX application is a viable research strategy as well and using PathFX alone is not sufficient for identifying a causative relationship between a drug and AE . There were multiple DMEs for which we could not identify an interaction pathway . Though , for some of these diseases , further structured expansion of acceptable terms could improve prediction accuracy . For instance , the Ontology of Adverse Events ( OAE ) [43] provides a structured language for relating symptoms , findings , and measurements to disease and could be a useful tool for expanding the capacities of PathFX . In practice , FAERS reports do not contain research-ready disease language , and thus it is important to map and relate changes in symptoms to relevant diseases . For example , the DME , suicide attempt , would be difficult to identify with our method directly , but is likely related to depression and anxiety . Structured language relating these phenotypes would improve prediction accuracy for these phenotypes . Improved ontology incorporation is an aspiration for the project but was beyond the scope of this work . Our findings reflect a trend in network biology to leverage drug pathways for repurposing approaches , though , the techniques are imperfect for describing all drugs . PathFX identified additional diseases beyond the marketed indications . Without a data set of true negatives ( e . g . the drug was tested in condition X and did not work ) , it is difficult to systematically test and reject network predictions . Instead , we leveraged molecular interaction paths relating marketed drugs to their relevant disease genes and used these paths to identify possible repurposing opportunities . This prudent approach limited repurposing hypotheses to indications for which drugs already exist . Further , literature evidence supports some of the associations such as tetracycline and hypertension [44] , but tetracycline induces an undesirable hypertension phenotype . We discovered these associations because we do not yet have a means for discerning directionality ( e . g . a drug improves the phenotype , or a drug aggravates the phenotype ) . Future work will address this question . The PathFX paradigm may be useful for both regulatory and pharmaceutical industry stakeholders to validate targets and enhance pharmacovigilance activities . We designed and tested our algorithm’s utility for one regulatory task: strengthening signals from adverse event reports in FAERS . Additionally , the drug-target-centric approach is useful for drug targets in development and may be used as a filter for identifying potential safety concerns and for confirming a sufficient association with disease . In contrast to therapeutic development through high-throughput screening , PathFX epitomizes the paradigm of identifying drug candidates based on biological rationale and supports the pathway relevance of a drug target .
We downloaded data from iRefWeb version 13 . 0 human , Reactome , and PharmGKB . We chose iRefWeb because the source contains interactions from BIND , BioGRID , CORUM , DIP , IntAct , HPRD , MINT , MPact , MPPI , and OPHID . We extracted protein-protein interactions from http://irefindex . org/wiki/index . php ? title=iRefIndex , drug-variant interactions from PharmGKB , and protein-protein interactions from Reactome . We scored iRefWeb protein-protein interactions using the MIScore framework[45] and removed low-scoring interactions below the median score value , 0 . 244 , to save memory in later computations . We kept the relative weighting of each score component equal ( i . e . Km = Kp = Kt = 1 ) . The scoring framework represents the amount of evidence supporting the interaction of two proteins: the Sm represents the method used to detect the interaction and is higher for more dedicated experimental techniques . Sp reflects the number of publications supporting an interaction . This score increases with the number of publication and plateaus . St reflects the interaction type . Because we only used ‘direct’ interactions , this score is always 1 . We adapted the MIScore framework for PharmGKB data and used publication , and ‘clinical evidence’ to score drug-variant relationships . Whenever an interaction with a variant was added to our network , we also added an interaction edge from the variant to the gene and scored this interaction as 0 . 99 , the maximum possible score in the interactome . We used the following equation where Kp = Ke = 1 , Sp was the same as published in [45] . Se reflects the clinical level evidence available from PharmGKB and we crafted a scoring framework similar to [45] . Where scv’1A’ = 0 . 99 , scv’1B’ = 0 . 86625 , scv’2A’ = 0 . 7425 , scv’2b’ = 0 . 61875 , scv’3’ = 0 . 495 , and scv’4’ = 0 . 2475 . Because interactions in PharmGKB only receive one level of clinical evidence , a and b collapse to: a=scvi;b=a+∑scvi We adapted this scoring framework for Reactome pathways using the following equation: SMI=KmSm ( cv ) +KpSp ( n ) +KtSt ( cv ) Km+Kp+Kt Sp was the kept the same as in [45] . We derived a scheme for the method component , Sm , where scv’direct_complex’ = 1 . 00 , scv’neighboring_reaction’ = 0 . 66 , scv’indirect_complex’ = 0 . 8 , scv’reaction’ = 1 . 0 , and scv’unknown’ = 0 . 05 . Because the two maximum scoring categories were ‘direct_complex’ and ‘reaction’ , the Max ( Gscvi ) term = 2 . 0 . Because Reactome interactions did not include a method of detection but are curated interactions , we gave these interactions a cvi = 0 . 8 . Because they all received the same ‘method’ score , calculating a and b yields a Sm = 0 . 615 . We lastly incorporated predicted drug to protein binding data based on PocketFEATURE [46] where drug-protein pairs were scored based on the similarity between the drug’s known targets and other protein targets from the Protein Data Bank[47] ( See methods below ) . PocketFEATURE has been extensively validated on predicting drug protein interactions in multiple applications [46 , 48 , 49] . In all cases , we estimated interaction scores based on the quality of evidence available; these edge scores were fixed before applying PathFX and we did not alter these parameters to improve prediction accuracy . We downloaded variant and phenotype association data from PheWAS[11 , 12] , disease to gene associations from DisGeNet[10 , 11] , Phenotype-Genotype Integrator ( PheGenI ) [26] , ClinVar[27] , and OMIM[8] , and eQTL data from the GWAS catalogue[7] . We collapsed all phenotype names to CUI identifiers using MetaMap lite and took the union of all data sources to create our source of gene to phenotype annotations . This yielded a database associating 29785 genes to 20524 phenotypes . To look for enriched phenotypes , we used a Fisher’s exact test and Benjamini-Hochberg multiple hypothesis correction to assess whether a disease or phenotype had more associations to the network genes relative to the total number of associations in the interactome . We filtered out disease or phenotypes associated with fewer than 25 genes . Recent work suggested that current interactomes are insufficient for analyzing disease pathways with fewer than 25 genes[28] . More specifically , interactomes are incomplete and these missing interactions reduce the ability to find pathways between smaller disease modules . This recent estimate discovered that missing interactions disproportionally affect phenotype pathways with fewer than 25 genes and thus , we eliminated these phenotypes knowing that our interaction network was insufficient for studying these phenotypes . We further filtered phenotypic predictions by deriving a p-value threshold from networks created with randomly-selected , druggable proteins . We realized that if any random set of input drug-targeting proteins could discover a statistically-significant association to a given phenotype , that associating a real set of drug proteins with this phenotype would reflect bias in our data instead of a biologically-meaningful result . To assess this bias in our data , we created 100 networks using randomly selected drug targets from the intersection of all drug-targeting proteins in DrugBank and ran PathFX using these targets as inputs ( i . e . using the same statistical approaches to assess phenotypes that are significantly associated to networks created with random inputs ) . Because the distribution of p-values from the 100 randomizations was not normally distributed , we used the median value as the threshold p-value for determining if a drug network was associated to a phenotype . The number of randomly selected input proteins matched the number of targets of the drug of interest . PathFX retained a phenotype if the association is more significant than the p-value threshold for that phenotype run with the same number of random , input protein targets . When analyzing drug targets alone , we again used the Fisher’s exact text , the Benjamini-Hochberg correction , and filtering relative to the expected p-value threshold . We created a depth-first search tool which ‘walked’ away from a drug’s protein target through the interactome and evaluated these paths using the multiplicative sum of all interaction edges between a gene and the target . Genes with a path score above the threshold were retained in the drug pathway . We expedited the search with “fast-tracking” . This process reflects the fact that molecules exist in highly interconnected pathways and assumed that we could reduce the searchable interaction spaces by looking for molecular redundancies . As the search explored an interaction path , fast-tracking searched the remaining que of interaction paths for genes that had already been added to the network and added these interaction paths to the network . Interaction edge scores were used from the scoring system above . We used specificity analysis to determine an optimal threshold for the interactome ( S2 Fig , explained below ) . In the case of multiple protein targets , we created a pathway around each protein target , and merged these neighborhoods to create the full protein network . We evaluated the interactome specificity by comparing a gene’s path score to all possible path scores for that gene . To measure all path scores , we created pathways for all genes in the interactome network , treating each molecular entity as a drug target and creating a pathway as described above . We used these empirically-derived scores to calculate an enrichment score for an entity in the pathway of a real drug target by subtracting the average path score to that gene from the gene’s score in the drug pathway ( Fig 1 , ‘Interaction Specificity Analysis’ ) . We selected an optimal threshold by evaluating gene specificity at threshold values from 0 . 7 to 0 . 9 . At each of these values we created a drug pathway around the drug’s protein target ( s ) , calculated the gene specificity , and then tabulated the fraction of genes that are specific to a drug target ( i . e . have a specificity score > 0 ) . We plot the normalized histograms of specificity values in S2A Fig and a distribution of the proportion of specific paths at each threshold value in S2B Fig . The PathFX code is available at: https://github . com/jenwilson521/PathFX . Using the algorithm requires minimal inputs and creates a network and several association files as depicted in S4 Fig . The user provides three inputs: 1 . an analysis name . 2 . the name of the drug . 3 . an optional list of proteins ( if the drug-binding proteins are not in DrugBank or the user wishes to complete a more specific analysis ) . The algorithm creates a set of output files: 1 . networks for individual target proteins and a merged interaction network combining networks from each target proteins . These files are tab-delimited files with one interaction per line and the score for that interaction . 2 . An association table containing one significantly-associated network phenotype , a p-value for that association , and network genes associated with that phenotype . 3 . A table listing the database source for individual phenotype-gene associations . For all diseases , these phenotypes were mapped to CUI terms using Metamap lite[31] . This was the same process used in assembling the phenotype dataset . We downloaded the UMLS Metathesaurus , version 2017AA and used the Perl packages UMLS::Interface[32] and UMLS::Similarity[32] to measure the lin distance between diseases in a set . For the gold-standard drug set , we calculated a matrix of similarity values for all approved indications and we used SciPy in Python to perform hierarchical clustering . We identified 62 as the optimal number of clusters using the elbow method . For visualization of the dendrogram , we counted the top five disease-associated words in each cluster . To determine how well PathFX identified a drug’s approved indication , we again used the umls-similarity . pl scripts to calculate similarity between the approved indication and the PathFX identified phenotypes . Because the number of true positives and true negatives varied for each drug and for each phenotype , we calculated the PPV and NPV separately for each drug and for each phenotype . To calculate PPV , we assumed that false positives were any PathFX identified phenotype that was not a marketed indication . The PPV was the ratio of correctly identified marketed indications to the total of marketed indications and additional PathFX phenotypes . To calculate NPV , we considered any phenotype from our dataset that was not a marketed disease indication to be a true negative . The NPV was the ratio of these true negatives to the sum of the true negatives and the unidentified , marketed indications . When calculating PPV for each phenotype , we assumed that a false positive was any drug identified by PathFX to be associated to the phenotype but was not marketed for that phenotype . When calculating NPV for each phenotype , we considered as true negatives any drugs not marketed for or identified by PathFX to be associated with the phenotype . We calculated the number of genes associated with the original marketed indication for drug-disease pairs identified by PathFX only , targets only , or identified by both methods . We first used a Kruskal-Wallis test implemented in the Python package , SciPy , to determine that these populations were not from the same distribution . We tested the hypothesis that the targets-only analysis was biased towards diseases with more genes using the Mann-Whitney-U statistic implemented in the Python package , SciPy . The FDA Adverse Event Reporting System ( FAERS ) data was extracted using the Oracle Health Sciences Empirica Signal software . Although , the software is not publicly available , we used only publicly-available data at the time of analysis ( 2004-2017Q3 ) . All drugs that had at least one case reported for the 35 MedDRA Preferred Terms identified as Designated Medical Events by review and medical experts at the FDA were included in this analysis . From the successfully matched drug-indications pairs , we created a catalogue of interaction paths that supported the association between these drugs and their approved indications . We searched through remaining drug pathways and asked if any associations with the drugs’ non-marketed indications were supported by the catalogue of interaction paths . These non-marketed associations became our cohort of repurposing predictions . The Drug-binding Dataset collects 984 high-quality 3D structures ( x-ray resolution higher than 2 . 5 Å ) that co-crystalized with FDA approved small molecule drugs ( non-nutraceuticals ) , representing binding environments of 284 distinct drugs [50] . The Human Off-target Dataset comprises 2271 proteins representing a non-redundant representative set ( 90% percent identity ) of human proteins and their close homologs that have high quality 3D structures ( x-ray resolution higher than 2 . 5 Å ) in PDB[51] . We have applied PocketFEATURE[46] to predict the probability of binding between the 284 drugs and the 2271 potential off-targets . PocketFEATURE uses the FEATURE representation to calculate site similarities by aligning microenvironments between two sites . A more negative score suggests binding site similarity and thus a higher probability of drug binding to a site similar ( off-target ) to its known binding site . Given a pair of drug and off-target , we used an average score of similarity between the binding sites and the off-target . For each drug , we generated a profile of its binding probability to each of the 2271 potential off-targets . | Many drugs fail to reach the market because they are not sufficiently efficacious for their disease indication or they cause intolerable side-effects . To understand drug efficacy and safety , we created an algorithm , PathFX . The algorithm identified relationships between drugs and diseases , and drugs and side-effects . We tested PathFX’s ability to identify the disease for which the drug was developed . We applied PathFX to post-marketing reports of drug side effects and identified drug side effects where regulatory review was ambiguous . Finally , we identified novel diseases for which marketed drugs could treat . The method has the potential to be a tool for assessing drug safety and efficacy during development and may have utility for regulators and industry scientists . | [
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] | 2018 | PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development |
Segregation of homologous chromosomes during meiosis I depends on appropriately positioned crossovers/chiasmata . Crossover assurance ensures at least one crossover per homolog pair , while interference reduces double crossovers . Here , we have investigated the interplay between chromosome axis morphogenesis and non-random crossover placement . We demonstrate that chromosome axes are structurally modified at future crossover sites as indicated by correspondence between crossover designation marker Zip3 and domains enriched for axis ensemble Hop1/Red1 . This association is first detected at the zygotene stage , persists until double Holliday junction resolution , and is controlled by the conserved AAA+ ATPase Pch2 . Pch2 further mediates crossover interference , although it is dispensable for crossover formation at normal levels . Thus , interference appears to be superimposed on underlying mechanisms of crossover formation . When recombination-initiating DSBs are reduced , Pch2 is also required for viable spore formation , consistent with further functions in chiasma formation . pch2Δ mutant defects in crossover interference and spore viability at reduced DSB levels are oppositely modulated by temperature , suggesting contributions of two separable pathways to crossover control . Roles of Pch2 in controlling both chromosome axis morphogenesis and crossover placement suggest linkage between these processes . Pch2 is proposed to reorganize chromosome axes into a tiling array of long-range crossover control modules , resulting in chiasma formation at minimum levels and with maximum spacing .
During meiosis , a single round of DNA replication is followed by two rounds of chromosome separation , with homologous chromosomes ( homologs ) segregating during meiosis I and sister chromatids during meiosis II . Homolog segregation critically depends on formation of crossovers ( COs ) between homologs . COs , cytologically detectable as chiasmata , in combination with sister chromatid connections , mediate the correct positioning of homolog pairs in the meiosis I spindle . Without COs , homologs frequently fail to segregate , resulting in formation of aneuploid gametes , i . e . gametes with a chromosome surplus or deficit . Aneuploid gametes are one of the major causes for stillbirths and birth defects in humans [1] . CO formation occurs via a carefully orchestrated program during prophase of meiosis I entails close homolog juxtaposition , followed by reciprocal exchange of chromosome arms through homologous recombination [2] . On the DNA level , meiotic recombination is initiated by formation of programmed double strand breaks ( DSBs ) at multiple genome positions [3]–[5] . A non-random subset of DSBs undergoes stable interaction with a homologous chromatid , giving rise to COs , while the remainder of DSBs progress to alternative fates , including non-crossovers ( NCOs ) , i . e . recombination events without exchange of flanking chromosome arms , as well as repair events with the sister chromatid [6]–[8] . Studies in fungi including S . cerevisiae have provided an understanding of meiotic recombination at the molecular level . Processing of meiotically induced DSBs depends on numerous proteins with related roles in mitotic DSB repair , but there are also prominent differences between these processes: First , during meiosis , homologs rather than sister chromatids serve as partners for homologous recombination [6] . Second , CO formation is enhanced over that of NCOs [7] . Following 5′ resection , DSBs undergo strand invasion of intact non-sister homologous chromatids . Pathways leading to COs and NCOs appear to bifurcate no later than the stage of strand invasion: Single end invasions ( SEIs ) emerge as the first CO-specific intermediate , subsequently giving rise to double Holliday junctions which are specifically resolved as COs [9]–[11] . NCOs likely arise via an alternative pathway characterized by a more transient strand invasion [7] . Notably , only COs provide interhomolog connections as required for homolog segregation . Recombination is temporally and spatially coordinated with dramatic changes in global chromosome structure culminating in the assembly of the synaptonemal complex ( SC ) . The SC , a widely conserved proteinaceous structure , stably juxtaposes homologs along their entire lengths during the pachytene stage [12] . SC formation is initiated during the leptotene stage when axial elements first form between and along sister chromatids . During the zygotene stage , axial elements of homologous chromosomes become closely juxtaposed via the SC central element which starts polymerizing from discrete sites; achieving full length homolog synapsis during the pachytene stage . Recombination is initiated via induction of DSBs during the leptotene stage , followed by onset of strand invasion at the transition from the leptotene to the zygotene stage [9] . During the pachytene stage , in the context of fully formed SC , double Holliday junctions are formed and resolved into COs , with NCOs emerging somewhat earlier [9]–[11] . Morphogenesis of the SC and recombination are highly interdependent , as indicated by ( i ) requirements for recombination proteins for SC assembly , and ( ii ) functions of SC components in recombination . In S . cerevisiae , DSBs are introduced by the widely conserved topoisomerase homolog Spo11 [5] . Spo11-dependent DSB formation is also required for SC assembly . Prominent components of yeast axial elements include Hop1 and its binding partner Red1 , as well as meiosis-specific cohesin Rec8 and cohesin-associated proteins , e . g . Spo76/Pds5 [13]–[16] . Hop1 and Red1 further mediate normal DSB formation and preferential interaction of DSBs with homologs rather than sister chromatids [6] , [17]–[19] . Zip1 is a prominent component of the SC central element . Zip1 starts polymerizing from both centromeres and from positions of designated CO sites [20]–[24] . Prior to assembly into full length SC , Zip1 mediates timely and efficient CO-specific strand invasion during recombination [11] . Two ZMM proteins , Zip2 and Zip3 , are required for formation of most COs and also mediate normal SC assembly . Zip3 is present along fully formed SC with the number and distribution expected for CO designated sites , in S . cerevisiae and C . elegans [21] , [22] , [25] , [26] . Finally , regions surrounding emerging COs are structurally modified as suggested by localized separation of sister chromatids at sites of ongoing recombination [16] . Later , when chiasmata emerge , they are characterized by extended regions of sister axis separation flanking the position of an established CO ( see example in ref . [27] ) . CO placement along homolog pairs is non-random at several levels: First , CO assurance guarantees formation of at least one CO per bivalent ( e . g . ref . [28] ) . Second , CO homeostasis enhances CO formation at the expense of NCOs when initiating DSBs are artificially reduced [29] . Third , CO interference reduces the frequency of COs in regions adjacent to established COs resulting in maximally spaced COs [30] . The three levels of CO control indicate communication along chromosomes between sites of ongoing recombination . CO interference reduces CO frequencies over large physical distances , >100 kb in yeast and >100 Mb in higher eukaryotes [8] , [31] . CO assurance and CO homeostasis suggest mechanism ( s ) that sense overall CO and/or DSB levels , affecting the outcome of ongoing recombination events . Timing , mechanism and the functional relationship between CO control and meiotic recombination pathway ( s ) are poorly understood . CO control is thought to operate on randomly distributed recombination interactions , a non-random subset of which become designated as future COs with the remainder progressing to NCOs . CO designation likely occurs no later than zygotene , as suggested by occurrence of cytological markers of CO-designation at this stage , and by concurrent appearance of CO specific recombination intermediates [11] . Linkage between CO assurance and CO interference was inferred from coordinate loss or retention of both features in certain mutant situations [29] , [32] . Conversely , CO interference is retained in two zmm mutants ( zip4Δ , spo16Δ ) despite apparent loss of CO assurance , indicating that separable pathways contribute to CO control [28] . Structural chromosome components responsible for CO control also remain unknown . Normal interference distribution of CO-designation marker Zip2 in zip1Δ suggests that the SC central element is not required for crossover interference [22] . Notably , in zip1Δ , CO designation sites/Zip2 foci exhibit interference distribution , while CO interference is defective , indicating uncoupling between chromosome morphogenesis and events on the DNA level [22] . The widely conserved AAA+ ATPase Pch2 performs important functions in cell cycle control , recombination and chromosome morphogenesis during mutant and WT meiosis . Identified as a yeast mutant that bypasses meiotic arrest in zip1Δ , Pch2 also mediates mutant delay/arrest in C . elegans and Drosophila [33]–[38] . During yeast WT meiosis , Pch2 mediates timely resolution of double Holliday junctions and formation of COs and NCOs [35] , [37] . Processing of a subset of recombination intermediates also depends on Pch2 in mouse [36] . In yeast , Pch2 further mediates assembly of structurally normal SC , controlling installation of axis component Hop1 and SC central element protein Zip1 along meiotic chromosomes in a pattern of alternating hyperabundance [37] . This pattern likely arises due to uniform loading of Hop1 and Zip1 at base levels along the length of the SC , corresponding to the uniform appearance of the SC detected by electron microscopy , in combination with additional domainal loading of either protein . Absence of Pch2 results in uniform localization patterns of Hop1 and Zip1 along the length of meiotic chromosomes [37] . Here , we have investigated the interplay between meiotic chromosome morphogenesis and CO control in yeast . We demonstrate intimate coordination between controlled CO distribution and axial element morphogenesis , as suggested by frequent association between Zip3-marked CO-designation sites and domains of preferential Hop1/Red1 loading . Association between Zip3 and Hop1/Red1 becomes detectable prior to substantial SC polymerization , consistent with axis differentiation at future CO sites early during meiosis . Furthermore , Hop1-Zip3 association is detected in ndt80Δ-arrested cells indicating its establishment independent of and prior to double Holliday junction resolution . Pch2 controls chromosome axis status by ( i ) specifying amount and pattern of chromosomal Hop1 , ( ii ) limiting Zip3 positions along pachytene chromosomes and ( iii ) mediating global axis shortening . While competent for CO formation at normal levels , pch2Δ is defective in controlling the distribution of COs along chromosome arms . In pch2Δ , ( i ) CO interference is defective , and ( ii ) spore viability is drastically reduced upon global reduction of initiating DSBs . The pch2Δ phenotype is dramatically modulated by incubation conditions , including temperature , suggesting the existence of Pch2-independent back-up systems for crossover interference and for maintenance of normal spore viability despite reduced DSB levels . We propose a model where Pch2 mediates establishment of multiple CO control modules along each chromosome , with potential effects on CO interference and chiasma function .
Pch2 mediates domainal hyperabundance of axis protein Hop1 along pachytene chromosomes [37] . Loss of domain structure in pch2Δ during zygotene suggests Pch2 functions at or before this stage . To examine Pch2 localization throughout meiosis I prophase , an isogenic SK1 strain homozygous for N-terminally 3×HA-tagged Pch2 ( = HA-Pch2 ) was induced to undergo synchronized meiosis at 33°C . The 3×HA-tagged Pch2 construct used here complements Pch2 function as suggested by its ability to confer arrest in zip1Δ . It is identical to a construct previously examined in a different strain background ( data not shown , A . Hochwagen , personal communication , see Materials and Methods for details; [33] ) . Pch2 localization was examined at all stages of meiosis I prophase . At specified time points , cells were surface spread and immunodecorated with antibodies against the HA-epitope , and SC central element component Zip1 [20] . Cells progressed through meiosis with appropriate timing [11]: Late leptotene nuclei , containing <10 Zip1 staining foci , first appear at 2 hrs ( Figure S1 ) . Zygotene nuclei carrying multiple Zip1 foci ( “early zygotene” ) and/or Zip1 in partial lines ( “late zygotene” ) are prominent at 4 to 5 hrs ( Figure 1A , 1AE , and 1I ) . Pachytene cells exhibiting mostly continuous lines of Zip1 along most of the 16 homolog pairs reach peak levels between 4 to 7 hours and disappear shortly before the onset of nuclear divisions ( Figure 1M and Figure S1 ) . In pachytene nuclei , multiple Pch2 foci of comparable intensities are detected on most of the chromatin mass ( Figure 1M–1P ) . The majority of Pch2 foci is associated with Zip1 , but cells frequently also contain ∼five grouped foci in a crescent-shaped , Zip1-free chromatin region , likely corresponding to the nucleolus ( Figure 1K and 1O; see ref . [33] ) . In pachytene nuclei , 21 ( ±7 S . D . ) Pch2 foci residing outside the nucleolus ( referred to as chromosomal Pch2 hereafter ) are detected ( n = 149 nuclei; Figure 1O ) . Pch2 is also present at chromosomal and presumed nucleolar positions in early and late zygotene nuclei where 16 ( ±5 S . D . ) Pch2 foci are detected ( n = 25 nuclei ) , some of which localize to several Zip1-free regions , suggesting localization to unsynapsed chromosomes . Together , these data indicate that Pch2 starts localizing abundantly to chromosome arms during the early zygotene stage , reaching maximum levels during the pachytene stage . Nucleolar and chromosomal Pch2 staining exhibits comparable intensities here , yet appears more prominent in the nucleolus in an earlier report [33] . Such differences could be due to effects of different spreading protocols and/or imaging systems . Pch2 promotes timely formation of recombination products [37] , and plays roles in CO control ( see below ) . Zip3 is a cytological marker for CO-designated sites , forming interference-distributed foci along pachytene chromosomes with numbers corresponding to COs [22] . To examine localization of chromosomal Pch2 with respect to ongoing recombination interactions , meiosis was induced in strains homozygous for HA-Pch2 and C-terminally GFP-tagged Zip3 . ( ZIP3-GFP complements ZIP3 function as suggested by spore viabilities >85% , normal CO levels by physical analysis and WT-like progression through meiosis ( G . V . B . and O . Nanassy , unpublished ) ) . Cells from a synchronous time course carried out at 33°C were spread and stained with appropriate antibodies . Anti-Zip1 antibody was used to determine stages of cells . Number and localization patterns of Zip3 in zygotene and pachytene nuclei correspond well with earlier reports [21]–[23] . Pachytene nuclei contain 61 ( ±6 S . D . ) Zip3 and 31 ( ±12 S . D . ) Pch2 foci ( n = 42 nuclei; see Figure 2E–2H ) . Importantly , a substantial number of Pch2 foci colocalizes with Zip3: In pachytene nuclei , 54% ( ±17% S . D . ) of Pch2 foci are associated with Zip3 foci , compared to 18% ( ±10% S . D . ) fortuitous colocalization ( n = 17 nuclei; see Materials and Methods for details on analysis of fortuitous colocalization ) . Colocalization of Pch2 with Zip3 is also observed in zygotene nuclei where 44 ( ±16 S . D . ) Zip3 and 25 ( ±12 S . D . ) Pch2 foci are detected ( n = 28 nuclei; Figure 2A–2D ) . Of the Pch2 foci detected , 58% ( ±18% S . D . ) colocalize with Zip3 , compared to 13% ( ±10% S . D . ) fortuitous colocalization ( n = 13 nuclei ) . Similar localization patterns are observed in the same strain incubated at 30°C ( data not shown ) . Together , these data demonstrate that chromosomal Pch2 partially and/or transiently associates with Zip3-marked CO-designated sites . This association could be related to Pch2's function in CO placement and/or CO-associated domain organization ( see below ) . To gain insights into positional identities of Hop1-enriched axis domains , Zip3 and Hop1 localization were examined in a synchronous WT time course at a time when pachytene cells are abundant [11] . In WT , at t = 7 hrs , >50% of undivided nuclei are at the pachytene stage , as indicated by Zip1 staining patterns ( data not shown ) . In the same cell population , Hop1 and Zip3 localization are remarkably similar in number and position: Hop1 localizes to 55 ( ±13 S . D . ) foci while Zip3-GFP localizes to 56 ( ±13 S . D . ) foci per nucleus ( Figure 3A–3D; n = 68 nuclei ) . When Hop1 and Zip3 localization patterns in the same nuclei are compared , a striking correspondence in position emerges: 72% ( ±10% S . D . ) of Zip3 foci colocalize with Hop1 , and 73% ( ±15% S . D . ) of Hop1 foci colocalize with Zip3 . Fortuitous colocalization in the same nuclei is 17% ( ±7% S . D . ) and 17% ( ±8% S . D . ) , respectively . These results suggest that CO-designated recombination interactions frequently localize to chromosome domains enriched for Hop1 . To examine Zip3 localization relative to another axis protein and to determine the stage of meiosis in the same cells , spread nuclei were triple-stained for Red1 , Zip3 , and Zip1 in a strain homozygous for C-terminally HA-tagged Red1 ( Red1-HA ) [17] and Zip3-GFP . High levels of colocalization between Zip3 and Red1 were observed at both the zygotene and pachytene stages ( Figure 3E–3H ) . In zygotene nuclei , 57 ( ±16 S . D . ) Red1 foci and 50 ( ±19 S . D . ) Zip3 foci are detected: 65% ( ±10% S . D . ) of Zip3 foci colocalize with Red1 , and 55% ( ±14% S . D . ) of Red1 foci colocalize with Zip3 ( n = 72 nuclei; Figure 3I–3M ) . In pachytene nuclei , Red1 localizes to 53 ( ±11 S . D . ) foci , and Zip3 to 55 ( ±15 S . D . ) foci; 59% ( ±10% S . D . ) Zip3 foci colocalize with Red1 , and 60% ( ±14% S . D . ) Red1 foci colocalize with Zip3 ( n = 57 nuclei; Figure 3N–3R ) . Thus , Red1 is also frequently associated with chromosome regions designated to undergo CO formation . Together , these results have two key implications: Association of Zip3 and Hop1/Red1 ( i ) at the same sites along pachytene chromosomes suggests spatial linkage between Hop1-enriched domains and CO placement , and ( ii ) temporal coincidence with CO/NCO differentiation during the zygotene stage [10] , [11] . We note that not all Zip3 foci are associated with Hop1/Red1 in every cell . This association may be transient and/or only a subset of Zip3 associates with Red1/Hop1 . Furthermore , Zip3 occupies presumed CO designation sites only during the pachytene stage , while it localizes to centromeres in pre-zygotene cells [22] , [24] . In pre-zygotene cells , Zip3 is detected at small numbers and rarely colocalizes with abundantly staining Hop1 or Red1 ( data not shown ) . We next investigated Hop1 and Zip3 localization in dmc1Δ and ndt80Δ , two meiotic mutants exhibiting distinct recombination blocks: In the absence of Rad51-paralog Dmc1 , hyperresected DSBs accumulate , and COs and NCOs are eliminated , consistent with a role of Dmc1 in strand invasion ( G . V . B . , unpublished data; ref . [39] ) . Without transcription factor Ndt80 , NCOs appear normally , but double Holliday junctions accumulate and COs are reduced accordingly [10] . Cells further arrest in ndt80Δ at a bona fide normal pachytene stage , as suggested by formation of viable spores upon induction of Ndt80 [40] . In dmc1Δ at 33°C , Zip3 localizes to nuclei abundantly , although at reduced numbers . At a time when most cells have completed DSB formation ( t = 5 hrs; G . V . B . , unpublished data ) , 33 ( ±7 S . D . ) Zip3 foci are detected ( n = 58 nuclei ) , compared to ∼50 Zip3 foci in WT cells ( Figure 4A–4C and 4P ) . In dmc1Δ , maximum Zip3 localization is reached at t = 5 hrs , as indicated by comparable numbers of foci at t = 4 and t = 6 hrs ( data not shown ) . Thus , Dmc1 is required for association of Zip3 with meiotic chromosomes at normal levels . Hop1 also localizes at high levels to meiotic chromosomes in dmc1Δ , but poor spreading in these cells interferes with quantitation of Hop1 foci . Of the Zip3 foci detected in dmc1Δ , 77% ( ±11% S . D . ) colocalize with Hop1 , compared to 26% ( ±8% S . D . ) fortuitous colocalization . About 10% of dmc1Δ nuclei further exhibit WT-like patterns of Zip3 staining , with several Zip3 foci located in a linear array , consistent with staining along condensed chromosome axes . In these nuclei , Zip3 again colocalizes with-Hop1 at high levels ( Figure 4A–4C ) . In summary , Zip3 foci form with reduced numbers in dmc1Δ , but tend to be associated with Hop1 . In ndt80Δ at 33°C , at a time when most cells have undergone pachytene arrest ( t = 8 hrs ) , Zip3 and Hop1 localize to meiotic chromosomes with patterns and numbers similar to wild-type pachytene nuclei ( compare Figure 4J and 4K with Figure 4D and 4E ) . Both Zip3 and Hop1 are detected as foci , with 50 ( ±9 S . D . ) Zip3 foci and 63 ( ±8 S . D . ) Hop1 foci detected ( n = 51 nuclei ) . Colocalization between Hop1 and Zip3 is also high in ndt80Δ , with 76% ( ±11% S . D . ) of Zip3 foci colocalizing with Hop1 , similar to the WT pachytene stage ( n = 51 nuclei; Figure 4F and 4L ) . We conclude that Dmc1 is required for normal levels of both Zip3 localization and Hop1-Zip3 co-staining domains . Importantly , association of Zip3 and Hop1 is independent of NDT80 , indicating that it is established prior to and independent of double Holliday junction resolution into COs . Pch2's role in chromosome morphogenesis was examined in more detail by analyzing patterns and levels of Hop1 localization in ndt80Δ-arrested cells . Detection of 63 ( ±8 S . D . ) Hop1 foci in ndt80Δ confirms that Hop1 localizes as foci rather than in lines along pachytene-arrested chromosomes ( Figure 4K ) . Conversely , in both NDT80 and ndt80Δ nuclei , with maximized visualization of near-background signals , Hop1 foci frequently coalesce into lines , consistent with continuous localization of Hop1 at base levels along pachytene chromosomes ( data not shown ) . Absence of Pch2 affects Hop1 patterns similarly in ndt80Δ and NDT80 ( compare Figure 4H and 4N; ref . [37] ) : Hop1 localizes as continuous , mostly uniform lines along the 16 homolog pairs . Quantitative analysis further identifies roles of Pch2 in controlling both Hop1 loading levels and patterns: Hop1 signal intensities are ∼three-fold increased in pch2Δ ( p<0 . 0001; see Figure S2A for details ) . The Hop1 staining observed in pch2Δ could be due to a uniform increase exclusively or concurrent Hop1 redistribution . To examine this question , number and contour length of high intensity Hop1 signals were determined in 20 WT and pch2Δ nuclei ( see Materials and Methods for details ) . In pch2Δ , strong Hop1 signals exhibit ∼two-fold increased average contour lengths and are present at reduced numbers ( see Figure S2B , S2C; p<0 . 0001; two-sided Wilcoxon rank sum test ) . If extra loading had occurred universally , patterns of strong Hop1 signals would be similar in WT and pch2Δ . We conclude that the more uniform Hop1 signal in pch2Δ is due to an overall increase in Hop1 signal intensities concurrent with changes in Hop1 loading patterns . Together , these findings have four important implications . ( i ) Hop1 is a prominent component of pachytene SC . ( ii ) In WT , Hop1 is present along chromosome axes in a mostly continuous pattern at base levels , with hyperabundance at distinct chromosome domains [37] . ( iii ) Pch2 controls both overall levels and patterns of Hop1 localization along meiotic chromosomes . ( iv ) Changes in Hop1 localization are caused by absence of Pch2 , rather than being an indirect result of meiotic arrest . Next , the role of Pch2 in controlling Zip3 association with chromosomes was investigated in WT and pch2Δ at a time point exhibiting maximum levels of pachytene cells ( >50%; t = 7 hrs ) as well as in ndt80Δ-arrested cells ( T = 33°C ) : In WT , Zip3 and Hop1 predominantly localize as distinct foci ( Figure 4D–4F; see above ) . In pch2Δ ( t = 7 hrs ) , by contrast , Hop1 localizes in continuous lines and Zip3 foci are occasionally not well separated ( Figure 4G; see above; ref . [37] ) . Further , in pch2Δ , Hop1 ( but not Zip3 ) is detected in the nucleolus ( Figure 4H ) [33] . In WT nuclei , 56 ( ±1 . 7 S . E . ) Zip3 foci are detected along meiotic chromosomes ( n = 68 , see above ) , while in pch2Δ , the average number of Zip3 foci per nucleus is 62 ( ±1 . 5 S . E . ) ( n = 64 nuclei; Figure 4D , 4G , and 4P ) . Accordingly , the number of Zip3 foci in pch2Δ is significantly increased ( P = 0 . 0026 , two-sided Wilcoxon rank sum test ) . To exclude possible effects of differences in meiotic progression , the number of Zip3 foci in WT and pch2Δ was also examined in the ndt80Δ background . In PCH2ndt80Δ , 50 ( ±1 . 3 S . E . ) Zip3 foci are detected , compared to 67 ( ±1 . 3 S . E . ) Zip3 foci pch2Δndt80Δ , reflecting an increase by 34% ( Figure 4P ) . Again , this increase is statistically significant ( p<0 . 0001 , two-sided Wilcoxon rank sum test ) . Thus , Pch2 controls the number of Zip3 foci along pachytene chromosomes . Notably , increased numbers of Zip3 foci are not caused by accumulation of cells at the pachytene stage in pch2Δ: In ndt80Δ arrested cells , the number of Zip3 foci is substantially increased in pch2Δ compared to the corresponding PCH2 strain , indicating that Pch2 controls the number of Zip3 association sites . To examine effects of pch2Δ on chromosome axis length , SC contour length was measured by staining for Hop1 and Zip1 . In WT pachytene nuclei ( identified based on Zip1 staining ) , Hop1 and Zip1 preferentially localize to alternating domains , whereas largely overlapping localization patterns are observed in pch2Δ pachytene cells ( Figure 4S and 4V ) [37] . The combined Hop1/Zip1 SC contour length of an entire chromosome complement is 34 µm ( ±0 . 9 µm S . E . ) in WT , in accordance with published results ( see ref . [41] ) . In pch2Δ , the axis length is increased by 18% to 40 µm ( ±1 . 2 µm S . E . ) , representing a significant increase ( p = 0 . 00023 , two-sided Wilcoxon rank sum test; see Figure 4W ) . Thus , homolog axes fail to shorten appropriately in the absence of Pch2 . Roles of Pch2 in controlling the number of Zip3-marked presumed CO-designated sites , Hop1's localization to the same regions , and meiotic chromosome axis length as well as Pch2's role in CO interference have important implications for the mechanism of CO control ( Discussion ) . We examined the roles of Pch2 in recombination in an interference tester strain carrying twelve pairs of heterozygous markers , defining nine genetic intervals along three homologs ( designated as intervals 1 to 9 in Figure 5A ) , [42] . Chromosomes III , VII , and VIII represent small , large and intermediately sized yeast chromosomes , respectively . Marked regions span physical distances of 132 kb , 229 kb and 106 kb , corresponding to WT map distances of 43 cM , 66 cM and 47 cM , respectively ( Figure 5A; below ) . Map distances were determined using tetrads with four viable spores and Mendelian ( i . e . 2∶2 ) segregation at a given pair of markers: Three types of tetrads can be distinguished ( Figure 5B , boxed region ) : ( i ) All four spores exhibit parental marker combinations , giving rise to a parental ditype ( PD ) ; ( ii ) two spores are parental and two recombinant , constituting a tetratype ( TT ) ; ( iii ) all four spores carry nonparental marker configurations , constituting a nonparental ditype ( NPD ) . The majority of PDs are derived from tetrads where no CO has occurred . TTs preferentially arise from tetrads that have undergone a single CO , while NPDs are derived from double COs involving all four chromatids within an interval ( Figure 5B ) . Double COs involving two or three chromatids give rise to PDs or TTs , respectively , and are indistinguishable from tetrads involving a single or no CO ( Figure 5B , lower part ) . Thus , total frequencies of double COs are extrapolated from NPD frequencies [43] . Note that this formula assumes absence of chromatid interference which has been validated for WT and pch2Δ ( data not shown ) . Following meiosis at 33°C , tetrads from WT and pch2Δ strains were dissected and markers were scored . Dissection of WT and pch2Δ asci gave rise to >1200 four spore-viable tetrads for each strain . In WT , genetic distances are similar to those previously reported ( Figure 5C; Table 1 ) , [42] . Map distances are remarkably similar between WT and pch2Δ , with two of nine intervals in pch2Δ exhibiting a significant increase ( intervals 1 and 3 ) . We note an apparent increase in NPD frequencies in pch2Δ . Accordingly , in pch2Δ double COs when calculated separately , contribute disproportionally to total map distances in seven intervals ( see Figure 5C , no differences in intervals 4 and 9 ) . Thus , Pch2 is not required for formation of COs at normal levels in most genome regions , consistent with prior physical analysis at a particular recombination hotspot [35] , [37] . However , Pch2 appears to limit the occurrence of closely spaced double COs . Increased levels of double COs in pch2Δ raise the question of Pch2's role in CO control . Modified coincidence analysis and analysis of NPD frequencies were used to determine effects of pch2Δ on CO interference using the tetrad set generated at 33°C . In modified coincidence analysis , map distances for each test interval are determined for two distinct tetrad subsets [31]: Subset P includes tetrads with parental marker configuration at an adjacent reference interval ( PD; Figure 6A , left column ) . Subset N includes tetrads with non-parental marker configuration at the reference interval ( TT or NPD; Figure 6A , right column; Table S1 ) . Map distances derived from subset P are remarkably similar between WT and pch2Δ , with only a single interval exhibiting a significant increase in pch2Δ CO frequencies ( interval 5P6; Figure 6A , left panel ) . In contrast , map distances derived from subset N are strikingly different between WT and pch2Δ ( Figure 6A , right panel ) : In six out of 12 adjacent interval pairs , map distances are significantly increased in pch2Δ compared to WT . Thus , Pch2 has no detectable effect on CO frequencies along an interval when the adjacent interval is parental , but suppresses CO formation in the same interval when the adjacent interval is recombinant . Notably , total map distance increases in intervals 1 and 3 in pch2Δ can entirely be attributed to subset N tetrads , while map distances in subset P tetrads are indistinguishable between WT and pch2Δ ( Figure 5C; Figure 6A , left panel ) . Numerous mutants defective for CO interference also exhibit intermediate to severe defects in CO assurance , as suggested by frequent occurrence of tetrads with two viable or zero viable spores due to homolog nondisjunction ( e . g . [28] , [32] ) . Such patterns of spore viability are frequently associated with elevated levels of non-exchange chromosomes [32] . WT-like patterns of spore viability in pch2Δ provide no indication of increased homolog nondisjunction: Overall spore viability in pch2Δ at 33°C is 84 . 0% compared to WT viability of 82 . 6% , consistent with normal homolog disjunction in pch2Δ . WT like levels of spore viability are also observed in pch2Δ at 30°C ( see Figure 7 and Figure 8B: SPO11/” , black bars; [33] ) . Low levels of non-exchange homolog pairs could be rescued by a backup system that mediates disjunction of non-exchange chromosomes reducing the reliability of spore viability as a measure for CO assurance [45] . To directly evaluate whether chromosomes receive similar numbers of COs in WT and pch2Δ , tetrads formed at 33°C were therefore individually inspected for the number of COs along three pairs of homologs . Tetrads with no CO in the monitored interval are only marginally increased in pch2Δ , by 1% , 6% and 8% , suggesting that similar numbers of COs are formed along a given interval in WT and pch2Δ ( Figure S3 ) . Taken together , patterns of spore viability and WT-like levels of COs across the chromosome segments examined suggest that CO assurance is functional in pch2Δ . These findings raise the possibility that CO assurance and CO interference can be separated ( discussion ) . Non-Mendelian marker segregation during meiosis ( e . g . , 3∶1 or 1∶3 ) occurs due to gene conversion of markers in association with meiotic recombination [31] . Gene conversion frequencies are increased in pch2Δ at eight markers , 1 . 2- to 2 . 0 fold over WT ( Figure 5D; Table S2 ) . Thus , Pch2 plays a role in suppressing gene conversion . A gene conversion may be flanked by parental or recombined chromosome arms , suggesting association with a NCO or a CO , respectively . In WT , at the assayable six central markers , gene conversions are associated with COs and NCOs at similar frequencies . In pch2Δ , at markers where gene conversion is substantially increased ( ade2 , met13 , cyh2 ) , such events are also flanked by COs and NCOs with similar frequencies ( Figure S4 ) . Thus , pch2Δ increases occurrence of gene conversions in both CO and NCO interactions . Increased gene conversion could be due to changes in the length of heteroduplex in recombination intermediates , repair defects , and/or region-specific changes in DSB levels ( see discussion ) . Notably , pch2Δ does not affect DSB levels at a hotspot of recombination and does not change global DSB patterns along the majority chromosomal loci ( A . Hoachwagen , personal communication; [35] , [37] ) . Meiotic phenotypes in several mutants are dramatically modulated by incubation temperature , with prominent effects on processing of recombination intermediates and formation of CO products ( e . g . , [11] , [37] ) . In pch2Δ , temperature modulates defects in recombination progression , but not those in chromosome domain organization [37] . To examine whether the interference defect observed in pch2Δ at 33°C is affected by temperature , we investigated crossover formation and interference also at 30°C . Surprisingly , this minor temperature change results in a drastic improvement in CO interference in pch2Δ . At 30°C , map distances along three chromosomes are similar between pch2Δ and WT ( i ) for total tetrads , without increases in NPDs ( Figure S5 ) and ( ii ) for subset P tetrads ( Figure 8A , left panel ) . Also , and in sharp contrast to observations at 33°C , map distances in subset N tetrads exhibit only minor differences between pch2Δ versus WT . Only interval pairs 8N9 and 9N8 exhibit significantly higher CO frequencies in pch2Δ ( Figure 8A , right panel ) . Modified coincidence analysis suggests that in pch2Δ at 30°C , interference is lost in only two interval pairs ( Figure 8A; interval pairs 2-1 and 5-6 ) . NPD frequencies further indicate loss of interference in pch2Δ at 30°C in only one interval ( Table S4 ) . Thus , defects in crossover interference can be suppressed by incubation at lower temperatures . The role of Pch2 in meiosis when DSBs are limiting was examined in hypomorphic spo11 strain backgrounds . In WT meiosis , normal homolog segregation is maintained despite reduction of initiating DSBs to ∼20% , of normal levels , likely due to preferential processing of DSBs into COs versus NCOs [29] . Levels of initiating DSBs are reduced to ∼80% , ∼30% or ∼20% of normal levels in strains homozygous for spo11-HA , heterozygous for alleles spo11yf-HA ( = spo11yf ) and spo11-HA or homozygous for spo11da-HA ( = spo11da ) , respectively [29] . Patterns of tetrad viability in PCH2 and pch2Δ strains were determined following sporulation at 30°C on solid medium ( n≥97 tetrads ) . Frequencies of four spore-viable tetrads in WT indicate normal chromosome segregation in >58% of cells despite DSB reduction to ∼20% of WT levels consistent with earlier findings ( Figure 7; Table S3 ) [29] . Frequency of four spore-viable tetrads decreases dramatically in pch2Δ strains hypomorphic for spo11 , in particular when DSBs occur are reduced below levels occurring in a homozygous spo11-HA strain . Chromosome segregation occurs normally in only 18% and 8% of meioses in spo11yf/spo11-HA and homozygous spo11da/” strains , respectively , and >50% of meioses in the same strains generate zero spore-viable tetrads . Such viability patterns can occur when≥two homolog pairs missegregate . We conclude that Pch2 plays a critical role for spore viability when DSBs are reduced . Thus , although Pch2 does not play a role in spore viability at normal DSB levels , it is essential under conditions of reduced DSB formation . Following observation of temperature-modulated interference in pch2Δ , we next examined whether incubation conditions also affect spore viability in pch2Δ at reduced DSB levels . Examining effects of hypomorphic spo11 on spore viability at 33°C on solid medium , we find , surprisingly , that spore viabilities are high in the pch2Δ strain , a drastic deviation from observations at 30°C ( compare Figure 7 and Figure 8B ) . Notably , in spo11yf/spo11-HApch2Δ at 33°C , 75% of tetrads undergo normal meiotic chromosome segregation as suggested by levels of 4 spore-viable tetrads , compared to 18% 4 viable spore tetrads in the same strain sporulated at 30°C in parallel ( see Figure 7 , orange bars ) . Similar results are obtained for pch2Δ strains homozygous for spo11da/” ( compare Figure 7 and Figure 8 , yellow bars ) or for spo11da/spo11yf ( data not shown ) : At 33°C , these strains give rise to 37% and 81% four spore-viable tetrads , compared to frequencies of 8% and 2% , respectively , at 30°C . ( In spo11daPCH2/spo11yfPCH2 ∼48% of tetrads give rise to four viable spores at both 33°C and 30°C ) . In subsequent experiments , we also discovered that spore viability patterns in pch2Δ strains hypomorphic for spo11 are also affected by culture conditions ( see below ) . In summary , higher versus lower temperatures oppositely modulate pch2Δ defects in CO interference and spore viability at reduced DSB levels . Conditions that improve spore viability weaken or eliminate interference and vice versa . Together , these results have several implications: ( i ) CO interference and factors affecting spore viability at reduced DSB levels can be uncoupled in pch2Δ . ( ii ) Effects of temperature on CO interference and the process that mediates normal spore viability at reduced DSB levels suggest linkage via Pch2 between both processes . ( iii ) Pch2 stabilizes both CO interference and spore viability over a wide range of DSB levels , temperatures , and possibly other environmental conditions ( see below ) . Additional effects of incubation conditions in pch2Δ were revealed during our investigation of recombination defects at reduced DSB levels . Physical recombination analysis is routinely performed in liquid medium , while spore viability is determined following sporulation on solid medium . To ascertain correspondence between these conditions , asci from parallel cultures incubated at 30°C with solid or liquid medium were dissected and viability patterns were compared . Surprisingly , WT and pch2Δ strains carrying spo11-da/” formed four viable spore tetrads at much higher levels when sporulated at 30°C in liquid versus solid medium ( Figure 9A , compare pink and yellow bars ) . To examine CO formation in pch2Δspo11da/” under conditions that result in low levels of four viable spore tetrads , sporulation in liquid medium was analyzed at 27°C and 30°C: In pch2Δspo11da/” , incubation of parallel cultures results in a dramatic decrease in spore viability at 27°C versus 30°C , while viability patterns are similar under both conditions in PCH2 ( Figure 9A , pink and blue bars ) . Accordingly , in pch2Δ hypomorphic for spo11 , spore viability is modulated not only by temperature , but also by the exact nature of the sporulation medium . Final CO levels were examined in PCH2 and pch2Δ in a spo11da/” background at the HIS4LEU2 hotspot of recombination ( see ref . [11] for details ) . Surprisingly , CO levels in PCH2 and pch2Δ were extremely similar; both at 27°C and 30°C , in four independent WT and pch2Δ strains ( Figure 9B and data not shown ) . Thus , differences in CO formation , at least at the HIS4LEU2 recombination hotspot , are not responsible for the loss in spore viability in pch2Δ at reduced DSB levels . In summary , pch2Δ is defective in ensuring normal spore viability when overall DSB levels are reduced , with viability patterns suggestive of homolog disjunction defects . pch2Δ may affect genome-wide levels or distribution of initiating DSBs , the efficient designation of DSBs as future COs in genomic regions outside of the HIS4LEU2 hotspot or the formation of functional chiasmata ( see Discussion ) . Importantly , our results identify Pch2 as a protein that ensures normal homolog segregation at reduced DSB levels . Reduction of DSBs or absence of Pch2 alone have marginal or no effects on homolog segregation , yet both mutant conditions combined synergistically affect spore viability .
Pachytene chromosomes in yeast display a domainal organization , where Hop1/Red1 or Zip1-enriched regions occur in an alternating pattern [37] . Our analysis supports the idea that hyperabundance domains are layered over a base level of Hop1 ( and Zip1 ) along the lengths of synaptonemal complexes . Correspondence between Hop1 hyperabundance domains and CO-designation marker Zip3 establishes a link between domainally modified chromosome axes and positions of future COs [21]–[23] . Crossovers and CO designation markers Zip2/Zip3 occur at different sites in each cell and exhibit interference distribution [22] , [46] . By implication , Hop1 hyperabundance domains likely also form at different positions in different cells along a given chromosome . Together , these findings suggest that chromosome axes undergo a differentiation process that is spatially coordinated with crossover placement . During WT meiosis , association between Zip3 and Hop1 reaches maximum levels in pachytene nuclei . High levels of Zip3-Hop1-association are also detected in ndt80Δ cells arrested at the pachytene stage ( Figure 4L ) , indicating that association is established prior to and independent of double Holliday junction resolution , a recombination step blocked in ndt80Δ [10] . Thus , Hop1-Zip3 association is completed prior to and independent of completion of the majority of COs . Our findings further suggest that association between Hop1/Red1 and Zip3 is established during the zygotene stage and can occur independently of stable strand invasion . Zip3 is the earliest known marker for designated CO sites [22] . In pre-zygotene cells , Zip3 localizes to paired centromeres of yeast chromosomes [24] . During the zygotene stage , Zip3 appears to localize abundantly to additional sites , a process that is completed at the pachytene stage when Zip3 is found at interference-distributed CO designation sites , while it is absent from centromeres [24] . In the population of zygotene nuclei analyzed here , ∼50 Zip3 foci are detected , suggesting that Zip3 occupies multiple non-centromeric positions at this stage . Zip3 foci colocalize at high levels with Red1 in the same zygotene cells ( Figure 3I–3M ) . Thus , association between Red1/Hop1 and Zip3 at designated CO sites appears to be established during the zygotene stage . In dmc1Δ , a mutant defective for strand invasion , Zip3 localizes to chromosomes with numbers substantially lower than those observed in normal zygotene nuclei . A subset of these cells , however , exhibit Zip3 localization with WT-like numbers and patterns ( Figure 4C ) . A high percentage of Zip3 foci is associated with Hop1 in such nuclei , raising the possibility that Zip3-Hop1 association can occur independent of Dmc1-mediated strand invasion . Whether Zip3 localizes to its normal sites in dmc1Δ cells is presently unknown . Hop1/Red1 localize to meiotic chromosomes prior to and independent of DSB formation , consistent with functions at earlier stages ( G . V . B . , unpublished; [13] , [17] ) . Several scenarios can explain the transition between early , pre-DSB association of Red1/Hop1 with meiotic chromosomes and their association with Zip3 marked designated CO sites at later stages: Red1/Hop1 may ( i ) become associated with future CO sites as an outcome of CO designation , following relocalization from a more dispersed ( pre- ) leptotene localization pattern; ( ii ) initially be present at all nascent recombination interactions and later undergo selective stabilization at future CO sites; ( iii ) preferentially localizes to future CO sites prior to CO designation , possibly participating in CO designation itself . ( iv ) Finally , it is possible that Zip3 preferentially localizes to Hop1/Red1 hyperabundance domains due to preferential recombination initiation in such domains [17] . Further work is required to determine whether Hop1/Red1 hyperabundance domains assemble at CO designated chromosomal positions before or after CO designation , and whether CO designation is a requirement for association between Hop1 and Zip3 . Spatial association between structural axis modifications and markers of nascent recombination interactions have also been observed in other organisms . ( i ) In Sordaria , cohesin associated protein Spo76/Pds5 is depleted from Msh4-marked recombination sites in a mutant deficient for the meiotic cohesin Rec8 . Local splitting of sister chromatids at the corresponding sites also occurs in the WT , and it was proposed that intersister connections may become destabilized as part of the normal process of chiasma formation [16] . ( ii ) In an ATM−/− mouse , SC proteins Sycp3 and Sycp1 are absent from sites of ongoing recombination [47] . ( iii ) In C . elegans , SC component SYP-1 and axis proteins HTP-1/2 are removed from reciprocal chromosome arms directed by and dependent on designation of a given recombination interaction as future CO [48] . These data indicate that locally weakened sister cohesion and enhanced interhomolog interactions may both contribute to preferential interhomolog recombination . Such modifications may be especially important for CO formation which entails long-lived strand invasion intermediates [10] , [11] . In yeast , axis ensemble Hop1/Red1 plays a central role in directing meiotically induced DSB processing towards homologous chromosomes , with Mec1-dependent Hop1 phosphorylation constituting a key event in establishing this interhomolog bias [19] . Our results further provide insights into likely dynamics of SC assembly . SC initiation occurs at Zip3 foci , some of which correspond to CO-designated sites [21] ( N . J . , unpublished data ) . Conversely , domains enriched for Zip1 alternate with Hop1/Red1 ( and Zip3 ) enriched domains when SC assembly is complete [37] . We propose that in early zygotene nuclei , Hop1/Red1 and Zip1 are present at Zip3-marked sites ( see Figure 3I–3L ) . During SC polymerization , Zip1 is preferentially deposited in axis regions distal from Zip3 , giving rise to the alternating Zip1/Hop1 pattern of pachytene chromosomes . Our results are not compatible with a model of SC assembly where Hop1 is displaced from chromosome axes as Zip1 polymerizes [13] . Hop1 in association with Zip3 , is present at substantial levels along pachytene chromosomes , both in WT and in ndt80Δ , indicating that Hop1 is an integral component of pachytene chromosomes ( this work; [37] ) . Pch2 plays key roles in establishing and/or maintaining the distribution of at least two proteins along meiotic chromosome axes . First , Pch2 controls overall levels and localization patterns of Hop1 . Accordingly , Hop1-enriched domains appear as multiple discrete foci along WT pachytene chromosomes , while in pch2Δ , Hop1 spreads into fewer , more extended structures ( Figure S2B , S2C ) . Importantly , changes in Hop1 loading in pch2Δ are not an indirect consequence of e . g . a delay in meiotic progression: In ndt80Δ arrested cells , Hop1 forms distinct foci , while in the ndt80Δpch2Δ double mutant , Hop1 loads at increased levels and localizes uniformly along chromosomes . Pch2 may control Hop1 association with chromosome axes via its chromosomal localization . Consistent with this idea , in zip1Δ , a mutant condition that eliminates Pch2 specifically from chromosome arms , Hop1 and Red1 load in a continuous pattern along chromosome axes , reminiscent of patterns observed in pch2Δ [13] , [21] , [33] . Increased numbers of Zip3 foci along pch2Δ pachytene chromosomes further identify a function of Pch2 in controlling association of Zip3 with meiotic chromosome axes . Increased numbers of Zip3 foci in pch2Δ may indicate defects in CO designation , possibly indicating an increase in the number of CO designation sites with associated inefficiencies to form functional chiasmata . Notably , Zip3 represents a CO designation marker in WT . In several mutants exhibiting reduced CO levels and loss of CO interference , Zip2 foci form with apparently normal numbers and distribution , indicating that CO designation on the cytological level can be uncoupled from the execution of CO formation [22] . Chromosome axis defects in pch2Δ are indicated by increased numbers of Zip3 foci and a failure to undergo appropriate axis shortening . Axis shortening , too , may be an outcome of normal CO designation . Coordinate increases in axis length and number of CO designation sites as well as loss of interference in pch2Δ support a linkage between axis length and CO control . SC length and CO numbers are closely correlated in many taxa , including mammals [41] , [49] , consistent with the idea that CO number and distribution are controlled via the status of the chromosome axis . In a mutant situation such as pch2Δ , changes in axis status may indicate defects in chromosome axis status , with possible effects on CO placement and/or formation of functional chiasmata . Pch2 suppresses COs in adjacent chromosome regions without being required for normal CO formation . In pch2Δ , map distances tend to be increased when flanking intervals are nonparental , yet are at WT levels when neighboring intervals are parental . The major implication of these results is that functions in CO interference can be separated from those in CO formation . While Pch2 is required for timely CO formation , COs form at normal levels in a pch2Δ mutant , both at a hotspot of recombination and in the genetic intervals examined here [35] , [37 , this work] . Conversely , we consider it as unlikely that Pch2 changes the overall distribution of COs rather than affecting CO interference: An overall change in CO distribution should change individual map distances independent of the presence or absence of a CO in an adjacent interval . Pch2 further does not exert a general inhibitory effect on recombination: ( i ) DSBs form at normal levels in pch2Δ when analyzed at a recombination hotspot or by a genome-wide approach ( A . Hochwagen , personal communication; [35] , [37] ) . ( ii ) Absence of Pch2 does also not compensate for low DSB levels in hypomorphic spo11 mutants , e . g . by improving spore segregation . Thus , Pch2 performs a function in CO control without playing a role in overall CO levels . While CO levels in pch2Δ are normal in the intervals examined here , the number of Zip3 foci is substantially increased . We interpret this discrepancy as indicating that CO designation is increased in pch2Δ , yet does not result in a corresponding increase in completed COs . At the same time , we cannot exclude that somewhat different incubation conditions result in actual increases in CO levels in pch2Δ . Cytological studies presented here were performed in liquid medium , but CO levels along the three chromosomes were determined following sporulation on solid medium . Such minor differences may have major effects on CO levels and distribution in pch2Δ . Notably , an independent study from the Alani lab observed increased CO levels in pch2Δ ( see accompanying paper ) . Two classes of COs , one that exhibits interference and the other that does not exhibit interference , have been proposed to contribute to total CO levels in the WT [e . g . , 11] , [32] , [50] , [51] . Accordingly , in certain mutants , CO reduction is accompanied by defective interference among residual COs [32] . Identification of pch2Δ as a mutant that forms COs at normal levels but is defective for interference suggests that interference is superimposed on basic recombination pathways , and that it can be eliminated without loss of COs . Absence of Pch2 further results in increased levels of gene conversion . Association of increased gene conversion levels with parental and non-parental configuration of flanking chromosome arms suggests that Pch2 affects this process prior to bifurcation of the CO and NCO pathways . Increased gene conversion without increases in DSB levels could occur due to an increased length of heteroduplexes in ongoing recombination interactions and/or mismatch repair defects in recombination intermediates . Such defects could be an outcome of spatial changes in axis juxtaposition , or due to elimination of other factors . The biological function of interference is presently mysterious . Several ideas have been put forward to explain this conserved phenomenon [e . g . , 52]: ( i ) Closely spaced double COs provides insufficient sister cohesion resulting in chromosome missegregation [53] . ( ii ) Interference is a byproduct of the CO assurance system [54] . Linkage between defects in interference , loss of CO assurance and/or normal homolog segregation and reduced spore viability in most yeast mutants with interference defects has complicated our understanding of the function of interference [e . g . , 28] , [32] , [55] , [56] . The current study indicates that short range interference is not required for normal chromosome segregation and/or the formation of functional gametes . Notably , all intervals tested along chromosome III exhibit interference defects here , yet chromosome segregation ( including chromosome III ) is normal in pch2Δ , as suggested by high spore viability patterns . Based on this finding , we present a model postulating that interference is a byproduct of the CO assurance system ( see below; [54] ) . Full levels of interference appear dispensable for meiotic chromosome segregation , yet we demonstrate that a mechanism compensating for reduced DSBs is critical for viable gamete formation . Segregation of the 16 homolog pairs is mostly normal during WT meiosis , even when DSBs are reduced to <20% of WT levels ( in spo11da; this work; [4] , [29] ) . By contrast , in a pch2Δ background , DSB reduction to <80% of WT levels ( in spo11yf/spo11-HA ) , results in catastrophic reduction of spore viability , identifying essential functions for mechanisms that compensate for reduced DSBs during yeast meiosis . In a pch2Δ mutant hypomorphic for spo11 , two- and zero viable spore tetrads are highly abundant , a pattern suggestive of defects in homolog disjunction . Such defects are frequently attributed to a failure of homolog pairs to acquire sufficient COs for homolog disjunction . Our analysis of CO levels at the HIS4LEU2 recombination hotspot does not provide evidence for substantial CO defects in pch2Δ at reduced DSB levels . CO levels at HIS4LEU2 may not be representative for genome-wide CO levels in pch2Δspo11da . Notably , unlike other genome regions , HIS4LEU2 does not exhibit CO homeostasis [29] . Alternatively , COs may form efficiently along the entire genome in pch2Δspo11da , but fail to undergo appropriate chiasma maturation . Such defects could affect intersister connections near crossovers resulting in a failure to maintain cohesion along chromosome arms until onset of anaphase . Severe defects in spore viability despite substantial CO formation have also been demonstrated for pch2Δrad17Δ double mutants , and may be related to the results reported here [35] . Importantly , results presented here define a Pch2-dependent mechanism that assures homolog segregation at reduced DSB levels . In yeast , on average>five COs/chiasmata form per homolog pair ( 90 COs distributed among 16 homolog pairs ) . Accordingly , stabilizing functions in homolog segregation and/or CO assurance may only manifest themselves when initiating DSBs are reduced . In organisms with lower wild-type COs levels , similar defects in chiasma function may result in homolog nondisjunction at normal DSB levels due to a failure to acquire sufficient COs ( e . g . the XY pair in mammals [47] ) . Unexpectedly , pch2Δ defects in CO interference and in spore viability at reduced DSB levels are partially rescued under certain conditions . For example , at 33°C , despite loss of most short range CO interference , some long-range interference ( >100 kb ) is retained . Furthermore , at 30°C , pch2Δ is mostly proficient for interference , yet reduced DSB levels result in formation of inviable spores . Pch2 appears to stabilize CO control and spore viability over a wide range of conditions , including different temperatures and low DSB levels . In the absence of Pch2 , a temperature decrease of only 3°C results in catastrophic chromosome missegregation , with no comparable effects in WT highlighting the necessity of Pch2-mediated stabilization of CO control . Oppositely stabilizing and destabilizing effects of temperature on interference and viable spore formation are consistent with the idea that interference and homolog disjunction assuring chiasma formation are the outcome of two antagonistically-acting pathways . Thus , these functions are separable based on their different dependence on incubation temperature . One explanation is that temperature oppositely modulates two chromosome components , e . g . chromosome axes and chromatin fiber ( see below ) . We infer the existence in the absence of Pch2 of one or several default systems that provide partially functional CO control and/or mechanisms for maintaining high levels of spore viability . Backup systems for CO control may e . g . utilize basic organizational features shared with mitotic chromosomes . Roles in CO control of general structural chromosome components have been demonstrated in C . elegans [57] . We note that Pch2-independent backup systems appear to function independent of a properly structured chromosome axis: Incubation conditions modulate pch2Δ defects in crossover placement and spore viability/chromosome segregation , but not chromosome axis defects . More uniform Hop1 association in pch2Δ occurs over a wide range of conditions , at 33°C and 23°C , and is also evident at 30°C in a different strain background [33] . Drastically different defects in interference and CO homeostasis are observed under the respective conditions ( this work; [37] ) . Together , these data indicate that uniform Hop1 association with chromosome axes is a consequence , not a cause of the initial CO control defect . The current work identifies functions of Pch2 during WT meiosis in chromosome morphogenesis , CO placement and spore viability/homolog segregation when DSBs are reduced . In C . elegans and Drosophila , Pch2 prevents meiotic progression in mutant meiosis when chromosomal events independent of recombination initiation are defective . No role of Pch2 in WT meiosis has been detected in these organisms [34] , [38] . In mouse WT meiosis , Pch2 is required for efficient completion of recombination , but no role in mutant meiosis as a checkpoint is apparent [36] . Accordingly , Pch2 has been described as a checkpoint or a factor required for normal meiotic progression . Pch2 modulates axis status , recombination progression and SC morphogenesis [37 , this work] . Thus , Pch2 affects all processes in WT meiosis that it is proposed to monitor as a checkpoint during mutant conditions . We propose that control of chromosome axis status constitutes Pch2's primary function , with secondary effects on recombination progression , CO placement and homolog segregation . Accordingly , changes in axis status may result in destabilized homolog juxtaposition , with downstream effects such as delayed double Holliday junction turnover , delayed CO/NCO formation and aberrantly high levels of non-Mendelian segregation events [37 , this work] . pch2Δ induced changes in axis status would likely also affect axis-associated processes under mutant conditions , with possible consequences for checkpoint activation . Modulation of underlying defects rather than compromised monitoring represents an attractive explanation for diverse Pch2 functions in mutant and WT meiosis . Alternatively , Pch2 may perform unrelated functions in meiotic cell cycle control , CO placement and gamete viability . We note that in yeast , pch2Δ defects in WT meiosis are relatively subtle , and detectable only under certain conditions ( see above ) . Corresponding defects in other organisms may also be difficult to detect and/or become manifest only under certain conditions . Consistent with this idea , in Drosophila , a synergistic effect of pch2Δ on CO levels has recently been demonstrated in combination with another mutant [38] . One key outcome of the current study is that Pch2 appears to suppress or enhance formation of functional chiasmata at normal or reduced DSB levels , respectively . Here , we propose a model integrating these apparently opposing functions of Pch2 . Pch2 is proposed to reorganize chromosome axes into long range CO control modules , hereafter referred to as ‘One Crossover Modules’ ( OCMs ) . Key features of OCMs include assurance to undergo one CO , and suppression of additional COs within the same module . Modules are proposed to tile each bivalent , resulting in formation of as many COs as OCMs . Pch2-mediated CO control is proposed to occur in two-steps: ( i ) bivalents become organized into a tiling array of OCMs . ( ii ) CO designation and interference occur . Cytologically , each OCM would correspond to a centrally localized Zip3 focus with associated Hop1 hyperabundance domain extending to both sides into Hop1-poor regions , reflecting the reach of interference . Pch2 is proposed to function as a determinant for OCM installation . The stress hypothesis of CO control provides a mechanistic explanation of how chiasma assurance/maturation and interference might be linked along each OCM [27] , [54] . We propose that each OCM constitutes an independent stress module . Stress and stress relief along the axis are hypothesized to mediate crossover designation and interference , respectively . Specifically , compression stress along the axis would result in localized axis deformation with two important consequences , stress relief and CO designation . Stress relief prevents additional axis deformation events along each OCM , effectively establishing interference . By setting a module for stress transmission , Pch2 would promote CO progression of a DSB proximal to the deformed axis segment , and coordinately prevent additional DSBs from undergoing the same fate . OCMs may become installed de novo , or , more likely , be specified via modification of preexisting chromosome features . Available data are easily integrated with this model: Pch2 associates with chromosome axes during the early zygotene stage . At the same stage , CO/NCO differentiation is finalized , axis domains associated with future COs appear , and the interference distribution of Zip2/Zip3 becomes established [9] , [11] , [22] . Programmed axis deformation at CO sites associated with stress relief and CO designation could further contribute to axis shortening . When defective , this may result in aberrantly long axes ( this work ) . Moreover , axis deformation may promote assembly of Red1/Hop1 and Zip3 at CO-designated sites , with aberrant CO designation/axis deformation resulting in uniform Hop1 axis association . Default modules that provide some CO control in pch2Δ may result in suboptimal CO designation , aberrant CO positioning , or increased sensitivity to incubation conditions . Such effects may be particularly detrimental when DSBs are limiting . Under such conditions , DSBs normally ensured to become COs may now fail to induce steps in chromosome morphogenesis associated with normal chiasma formation . Control of CO numbers via one-crossover modules provides an attractive way how recombination frequencies can be controlled in different organisms and even different sexes . Accordingly , in C . elegans , each chromosome would be organized as a single module , while in e . g . mouse , there would be one or two OCMs per homolog pair . In many species , CO levels between males and females differ for identical homolog pairs . Setting differently sized OCMs represent an attractive way to control CO levels in a chromosome-wide manner . Levels and distribution of COs are dramatically modulated by temperature and other environmental factors in many eukaryotes ( e . g . , [58] ) . Such sensitivity along WT chromosomes may be related to the mutant sensitivities revealed by the current work . In summary , we have demonstrated here that chromosome axes undergo programmed changes in their global structure that strikingly parallel the non-random positioning of chiasmata during meiosis . Unlike other cases of cytologically detectable chromosome domain organization , including heterochromatin assembly , such domainal organization is determined individually for each cell , in accordance with non-random meiotic crossover distribution . Close functional and temporal coordination between assured crossover formation and chromosome domain organization identify potential functions for chromosome axis status in faithful meiotic homolog segregation .
Strains were of the SK1 background ( Table S5 ) . Markers were introduced by transformation or crossing and were verified by Southern blot . N-terminally HA-tagged PCH2 was transferred from the BR strain background [33] by insertion of the URA3 marker 300 bp upstream of PCH2 followed by PCR amplification of the tagged construct including the marker and transformation into SK1 ( strain gift from A . Hochwagen ) . In the resulting strain , the 3×HA-tag encoding sequence is flanked by 15 and 14 polylinker-encoded amino acids , respectively ( N . J . , unpublished data ) . Haploids mated overnight on supplemented YPD were transferred to identical batches of sporulation medium ( 0 . 5% potassium acetate , 0 . 02% raffinose ) and incubated at 33°C or 30°C for 72 hrs . Asci were incubated with zymolyase , dissected on supplemented YPD and replica-printed to appropriate media to determine marker status . Tetrads exhibiting non-Mendelian segregation of ≥5 markers were assumed to be false tetrads and omitted from further analysis . For calculations of map distances and NPD frequencies , see text . Standard error calculations were performed using the Stahl Lab Online Tools . Tetrads with non-Mendelian segregation for either marker of an interval were omitted for calculations for that interval . Chi square values were used to calculate P-values using the Vassar College webpage . Time courses and meiotic spreads were prepared and immunostained as described [11] . Chromatin was stained using DAPI . Hop1 and Zip1 were stained with rabbit anti-Hop1 ( F . Klein ) and rabbit anti-Zip1 ( S . Keeney ) antibodies at 1∶300 to 1∶1000 , except for nuclei shown in Figure 4Q–4V in which mouse anti-Zip1 antibody ( P . Moens ) was used at 1∶500 dilution . GFP- and HA fusion proteins were detected with goat anti-GFP ( Rockland ) at 1∶400 or mouse anti-HA ( Covance ) antibodies at 1∶1000 dilution , followed by incubation secondary antibodies conjugated to Alexa 488- , Alexa 594- , or Alexa 680 ( Molecular Probes ) at 1∶2500 dilution . All antibodies were tested for epitope specificity using appropriate deletion/untagged strains . Images were captured by a computer-assisted fluorescence microscope system ( DeltaVision , Applied Precision ) . The objective lens was an oil-immersion lens ( 100× , NA = 1 . 35 ) . Image deconvolution was carried out using an image workstation ( SoftWorks; Applied Precision ) . In double staining experiments , real colocalization was scored by counting the number of overlapping foci in composite images . Fortuitous colocalization was evaluated by the misorientation method where one of the two images is rotated by 180° , ensuring maximum nucleus overlap , and colocalization is determined by counting the number of overlapping foci [59] . ImageJ was used for processing and quantitative analysis of images saved as 16bit TIFF files in SoftWorks . To analyze Hop1 distribution patterns , threshold levels were visually adjusted to maximize signal detection within the DAPI staining area , followed by measurements of the Hop1 positive area and the mean signal intensities at above background levels . Subsequently , threshold levels were set to the mean signal intensity for each image , and a mask was generated for the Hop1 positive signals exhibiting≥average signal intensities . Masks were transferred into MicroMeasure to determine the number and maximum lengths of individual Hop1 signals ( below ) . Total SC length in WT and pch2Δ strains were measured by a “blind” observer , using MicroMeasure . MStat 5 . 1 was used for data plotting and statistical analysis . Rasband , WS , ImageJ , U . S . National Institutes of Health , Bethesda , Maryland , USA , http://rsb . info . nih . gov/ij/ , 1997–2008 Drinkwater N Mstat Statistical Software: http://mcardle . oncology . wisc . edu/mstat/ Stahl Lab Online Tools: http://www . molbio . uoregon . edu/~fstahl/ MicroMeasure , version 3 . 3: ( http://www . colostate . edu/Depts/Biology/MicroMeasure | In the germ line of sexually reproducing organisms , haploid gametes are generated from diploid precursor cells by a specialized cell division called meiosis . Reduction by half of chromosome numbers during the first meiotic division depends on genetic exchange , resulting in the formation of crossovers . Without crossovers , pairs of homologous chromosomes frequently fail to separate , resulting in unbalanced gametes with a surplus or deficit of individual chromosomes . Along a given chromosome , crossovers form in different locations in different cells , but distribution of crossovers within each cell is controlled in two ways: first , at least one crossover is formed along each homolog pair , irrespective of size; second , a crossover in a given interval reduces the frequency of crossovers in adjacent chromosome regions . Here , we identify functions of the evolutionarily conserved protein Pch2 in suppressing additional crossovers in adjacent regions and ensuring homolog segregation under certain conditions . Pch2 further controls the assembly of chromosome axis protein Hop1 at future crossover sites . Our findings reveal that chromosome axes undergo structural changes at the same positions where crossovers occur . Thus , axis remodeling and crossover placement are linked via Pch2 . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"molecular",
"biology/recombination",
"molecular",
"biology/chromosome",
"structure"
] | 2009 | Pch2 Links Chromosome Axis Remodeling at Future Crossover Sites and Crossover Distribution during Yeast Meiosis |
Enterotoxigenic Escherichia coli ( ETEC ) cause severe diarrhoea in humans and neonatal farm animals . Annually , 380 , 000 human deaths , and multi-million dollar losses in the farming industry , can be attributed to ETEC infections . Illness results from the action of enterotoxins , which disrupt signalling pathways that manage water and electrolyte homeostasis in the mammalian gut . The resulting fluid loss is treated by oral rehydration . Hence , aqueous solutions of glucose and salt are ingested by the patient . Given the central role of enterotoxins in disease , we have characterised the regulatory trigger that controls toxin production . We show that , at the molecular level , the trigger is comprised of two gene regulatory proteins , CRP and H-NS . Strikingly , this renders toxin expression sensitive to both conditions encountered on host cell attachment and the components of oral rehydration therapy . For example , enterotoxin expression is induced by salt in an H-NS dependent manner . Furthermore , depending on the toxin gene , expression is activated or repressed by glucose . The precise sensitivity of the regulatory trigger to glucose differs because of variations in the regulatory setup for each toxin encoding gene .
ETEC are Gram negative bacteria that cause severe diarrhoea , known as non-vibrio cholera , in humans [1] , [2] . First isolated in 1971 , ETEC are responsible for 210 million infections annually , mostly in developing countries , leading to 380 , 000 deaths [3] . Disease results primarily from the action of two enterotoxins . The heat-labile toxin ( LT ) is similar in structure and function to cholera toxin [4] , [5] . The heat-stable toxin ( ST ) mimics the human hormone guanylin [6] . Both toxins are secreted by ETEC during infection . Made up of two subunits , encoded by the eltAB operon , LT has the configuration AB5 [5] , [7] . In the gut , LT binds to host cell GM1 gangliosides and is endocytosed [8] , [9] . This triggers constitutive cAMP production in the affected cell [8] . The ST toxin , encoded by the estA gene , also interferes with cell signalling [6] . Hence , ST binds to the guanylate cyclase C receptor and stimulates overproduction of cGMP . The combined actions of LT and ST cause loss of H2O , and electrolytes , from epithelial cells into the gut lumen [4] . Oral Rehydration Therapy ( ORT ) is used to redress the resulting electrolyte imbalance and rehydrate the patient [10] . In its most simple form , ORT requires only an aqueous solution of glucose and salt . Hence , the availability of metabolites and cations are a central theme of ETEC mediated disease . The effect of ORT on human physiology is well understood: glucose and Na2+ are transported across the epithelial membrane , along with water , to promote rehydration [11] . Surprisingly , despite the existence of molecular mechanisms that allow bacteria to respond to these signals , the consequences for ETEC are unknown . In E . coli , the transcriptional response to glucose is controlled by cAMP receptor protein ( CRP ) [12] . In the absence of glucose , intracellular cAMP levels increase and CRP binds DNA targets with the consensus sequence 5′-TGTGA-n6-TCACA-3′ [13] . Subsequently , gene expression is reprogrammed to make use of alternative carbon sources [14] . Note that the gene regulatory network managed by CRP includes many indirect pathways [14] , [15] . Hence , CRP is also a pleiotropic regulator of transcription . Whilst indirect regulatory effects are difficult to characterise , genes that are directly controlled by CRP can be divided into distinct classes [12] . At Class II targets , CRP binds to a site overlapping the promoter -35 element and interacts directly with both the N-terminal and C-terminal domains of the RNA polymerase α subunit ( αNTD and αCTD ) . At Class I targets , CRP binds further upstream and interacts only with αCTD . This interaction can be further stabilised by UP-elements , AT-rich DNA sequences , adjacent to the CRP site , that facilitate αCTD-DNA interactions [12] . At both classes of promoter , the various contacts enhance gene expression by stabilising the transcription initiation complex . Unsurprisingly , most genes regulated by CRP encode proteins involved in metabolism . However , in some bacteria , CRP has been co-opted as a virulence regulator [16] . The Histone-like Nucleoid Structuring ( H-NS ) factor is a component of bacterial nucleoprotein . Consequently , H-NS also influences gene expression on a global scale [17] . Briefly , H-NS targets sections of the genome with a low GC content [17] . Depending on H-NS conformation , the resulting nucleoprotein complexes can be filamentous or bridged in organisation [18] . Filamentous complexes favour gene regulation by excluding RNA polymerase , and transcriptional regulators , from their targets [19] , [20] . Bridged complexes favour RNA polymerase trapping [21] . In all scenarios , it is thought that H-NS acts primarily to silence transcription [22] . The conformation of H-NS , and hence the way in which it modulates DNA topology , can be controlled by divalent cations . Consequently , H-NS mediated repression can be relieved by increased osmolarity [23] . Like CRP , H-NS has been incorporated into the virulence gene regulatory networks of many bacteria [17] . In this work we define the molecular trigger that controls toxin expression in ETEC . We show that CRP and H-NS are key regulatory factors . Strikingly , this allows ETEC to integrate extracellular signals of osmolarity and metabolism to control toxin production . Hence , we propose that ETEC toxicity responds directly to osmo-metabolic flux . Interestingly , the precise regulatory settings are different for each toxin encoding gene . The differences result from i ) varying promoter configurations and ii ) competition between CRP and H-NS for overlapping DNA targets . This is significant since fluctuations in osmolarity , and changes in the availability of metabolites , are central to ETEC infection and its treatment .
The prototypical ETEC strain H10407 reproducibly elicits diarrhoea in human volunteers and has a well-defined genome that shares 3 , 766 genes with E . coli K-12 [1] . Pathogenicity arises from 599 ancillary genes encoded by 25 discrete chromosomal loci and 4 plasmids . The plasmids , named p948 , p666 , p58 and p52 , encode the enterotoxins . Derivatives of the estA gene are found on plasmids p666 ( estA1 ) and p948 ( estA2 ) . A single copy of the eltAB operon is encoded by plasmid p666 . We used Chromatin Immunoprecipitation ( ChIP ) coupled with next-generation DNA sequencing ( ChIP-seq ) to map CRP and H-NS targets across the ETEC H10407 genome . The binding profiles are shown in Fig . 1A . In each plot genes are illustrated by blue lines ( tracks 1 and 2 ) , DNA G/C content by a cyan and pink graph ( track 3 ) , H-NS binding is in green ( track 4 ) and CRP binding is shown in orange ( track 5 ) . As expected , H-NS binding is inversely correlated with DNA G/C content ( compare tracks 3 and 4 ) . Similarly , CRP binding occurs in expected locations; 96% of the CRP binding sites are associated with the DNA logo shown in Fig . 1B ( i . e . the known CRP consensus sequence ( 13–15 ) ) . We identified a total of 111 high-confidence CRP targets ( Table 1 ) . Of these targets 93% were present in the genome sequences of both ETEC H10407 and E . coli K-12 . The most common location for CRP sites was in intergenic regions ( 66% of targets ) whilst a smaller number of targets were found within genes ( 34% ) . Consistent with expectations , CRP sites were most frequently located ∼40 . 5 bp , or ∼92 . 5 bp , upstream of experimentally determined transcription start sites ( TSSs ) . Surprisingly , CRP binding was restricted to the ETEC chromosome ( Fig . 1Ai ) . Conversely , H-NS bound to chromosomal and plasmid loci ( Fig . 1Ai ) , including all toxin encoding genes ( Fig . 1Aii ) . To better understand the lack of CRP binding to p948 and p666 we took a bioinformatic approach . CRP targets were aligned to generate a position weight matrix ( PWM ) . The PWM was then used to search p948 and p666 for CRP sites . A continuum of over 100 potential CRP targets was identified . However , we recognise that the vast majority of these are likely to be false positives . Hence , we next sought to differentiate between genuine CRP sites and spurious predictions . To do this , predicted sites were scored , grouped , and ranked on the basis of their match to the PWM ( Fig . 2A , S1 Table ) . Electrophoretic mobility shift assays ( EMSA ) were then used to measure binding of CRP to a target from each group so that a meaningful cut-off could be established . The result is illustrated graphically in Fig . 2B . The raw data are shown in S1A Fig . We found that predicted sites with a score<10 did not bind CRP . To assess the affinity of CRP for all predicted targets scoring >10 a second set of EMSA experiments was done ( S1B Fig . ) . Hence , we identified a total of 5 potential CRP targets on p666 and p948 . Interestingly , the estA1 and estA2 genes , which both encode ST , were amongst the 5 targets ( Fig . 2C ) . Remarkably , all 5 of the plasmid borne CRP targets identified in silico , and bound tightly by CRP in vitro , were occupied by H-NS in vivo ( Fig . 2C ) . To understand if CRP could regulate ST production we focused first on estA2 . This derivative of the toxin is more commonly associated with human disease and ETEC H10407 is somewhat unusual in also encoding estA1 [24] . The sequence of the estA2 regulatory region is shown in Fig . 3A . A 93 bp DNA fragment , containing the regulatory region , was cloned into the lacZ reporter plasmid pRW50 to generate a lacZ fusion ( S2A Fig . ) . The estA2 TSS was then determined using mRNA primer extension analysis . We detected a single extension product , of 109 nucleotides ( nt ) in length ( Fig . 3B ) . The position of the TSS is labelled “+1” in Fig . 3A . Promoter -10 ( 5′-TTAAAT-3′ ) and -35 ( 5′-TTGCGC-3′ ) elements were observed at the expected positions upstream of the TSS . Throughout this work we refer to this promoter , highlighted purple in Fig . 3A , as PestA2 . To confirm CRP binding at the predicted site we used DNase I footprinting ( Fig . 3C ) . As expected , CRP protected the predicted target from digestion . Additionally , CRP induced DNase I hypersensitivity in the centre of the site . Note that the CRP site is centred 59 . 5 bp upstream of the TSS and adjacent to an AT-rich sequence that may be an UP element ( Fig . 3A ) . Thus , we hypothesised that PestA2 is a class I CRP activated promoter . To test our hypothesis we first determined whether CRP could indeed activate PestA2 . To do this , we compared LacZ expression in M182Δlac and M182ΔlacΔcrp cells carrying the PestA2::lacZ fusion . The data show that loss of CRP results in a 3-fold decrease in LacZ expression from PestA2 ( Fig . 3D ) . We next tested the ability of CRP to activate PestA2 in vitro . The 93 bp DNA fragment was cloned upstream of the λoop terminator in plasmid pSR . In the context of this construct a 112 nt transcript is generated by RNA polymerase from PestA2 in vitro . The amount of transcript can then be quantified by electrophoresis . The result of the analysis , with and without CRP , is shown in Fig . 3E . As expected , an intense band corresponding to the 112 nt transcript was observed . Production of the transcript was stimulated by CRP . Note that CRP had no effect on production of the 108 nt control RNAI transcript from the plasmid replication origin . Finally , we examined the AT-rich DNA sequence ( highlighted blue in Fig . 3A ) located between the CRP site and the promoter -35 element . We found that increasing the GC content of the putative UP-element altered migration of the 93 bp DNA fragment on an agarose gel , consistent with a change in DNA topology ( S3A Fig . ) . Moreover , these changes to the UP-element rendered PestA2 insensitive to CRP in vivo and in vitro ( S3B Fig . ) . Promoters can be liberated from H-NS repression if separated from flanking , H-NS bound , DNA [25] . We reasoned that this might be why , when isolated on the 93 bp fragment , PestA2 was active and dependent on CRP . To test this logic we generated a further two PestA2::lacZ fusions using the pRW50 plasmid system . The additional PestA2 DNA fragments were both 460 bp in length and include the full estA2 gene that was entirely bound by H-NS in our ChIP-seq assay ( Fig . 2C ) . The CRP site was ablated in one of the additional fragments by introducing point mutations that are predicted to disrupt CRP binding . The sequence of the DNA fragments is shown in S2A Fig . The lacZ fusions are illustrated graphically in Fig . 4A . Our expectation was that the longer 460 bp fragment would bind H-NS whilst the starting 93 bp fragment would not . To test this prediction we used ChIP . Thus , we compared H-NS binding to the different PestA2 containing fragments in vivo . Fig . 4B shows results of a PCR analysis to measure enrichment of the PestA2 locus . As expected , PestA2 was only enriched in anti-H-NS immunoprecipitates when in the context of the 460 bp fragment . Crucially , enrichment is specific because , in a set of control PCR reactions , there was no enrichment of the yabN locus in any immunoprecipitate . Our ChIP analysis suggests that the 460 bp fragment containing PestA2 is subject to regulation by H-NS . To confirm that this was the case , the various pRW50 derivatives were used to transform M182Δlac and M182ΔlacΔhns cells . We then measured LacZ activity , driven by PestA2 , in the transformants . Consistent with our expectations the data show that PestA2 is repressed 5-fold by H-NS only in the context of the 460 bp DNA fragment ( Fig . 4C ) . Importantly , mutations in the CRP binding site abolish PestA2 activity in the absence of H-NS . Hence , the measured LacZ expression must be driven by PestA2 rather than any spurious promoters located within the estA2 gene . Taken together our ChIP-seq and LacZ activity data show that H-NS prevents CRP from activating PestA2 . The estA1 regulatory region , located on plasmid p666 , contains a sequence similar to PestA2 ( Fig . 5A ) . We expected that this sequence would be the estA1 promoter ( PestA1 ) . To test this expectation we created a 92 bp PestA1::lacZ fusion , equivalent to the 93 bp PestA2::lacZ fusion described above , and mapped the 5′ end of the resulting mRNA . As expected , the primer extension product was 109 nt in length ( Fig . 5B ) . Hence , PestA1 and PestA2 use equivalent TSSs . However , we were surprised that the intensity of the PestA1 primer extension product increased in cells lacking CRP ( Fig . 5B ) . Closer examination of the alignment in Fig . 5A shows that , whilst PestA1 and PestA2 are similar , there are differences in the sequence and position of key promoter elements . To try and understand which changes result in the aberrant behaviour of PestA1 we made a set of hybrid promoters . The hybrid constructs are derived from the CRP-activated estA2 promoter . In each hybrid , named PestA2 . 1 through PestA2 . 7 , a region of PestA2 was replaced with the equivalent region from PestA1 ( see underlined sequences in Fig . 5C ) . The ability of the different hybrid promoters to drive lacZ expression , with and without CRP , was then tested . The results are shown in Fig . 5D . Note that , in Fig . 5D , the composition of each hybrid promoter is indicated in the grid below the graph . For example , PestA2 . 1 is derived from PestA2 but contains the PestA1 CRP site . As expected , both PestA1 and PestA2 were able to drive lacZ expression but CRP had opposite effects . Moreover , maximal expression from PestA1 was 3-fold lower than from PestA2 . Only PestA2 . 3 and PestA2 . 5 , which both carried the same changes in the promoter -35 element , exhibited a reversed dependence on CRP . Hence , the PestA1 -35 element must be responsible for the altered CRP dependence . All other hybrid promoters exhibited an overall reduction in activity compared to the parent PestA2 construct . We conclude that this combination of changes results in the lower activity of PestA1 . Note that both PestA1 and PestA2 were bound by H-NS in our ChIP-seq analysis ( Fig . 2C ) . We reasoned that cloning PestA1 , with flanking DNA , would reveal H-NS mediated repression . We generated a derivative of the PestA1::lacZ fusion where the downstream boundary was extended to include the entire estA1 gene ( S2B Fig . , Fig . 5Ei ) . As expected , transcription from PestA1 was repressed by H-NS in the presence of downstream DNA ( Fig . 5Eii ) . We next turned our attention to the LT toxin promoter ( PeltAB ) [26] , [27] . Previously , Bodero and Munson [27] showed that transcription from this promoter was repressed by CRP . A mechanism for repression was proposed whereby CRP acted directly by binding three DNA targets overlapping PeltAB [27] . Even so , no CRP binding at PeltAB was identified by our ChIP-seq analysis ( Fig . 6A ) . It is possible that this is because H-NS also excludes CRP from this locus ( Fig . 6A ) . However , we also failed to identify CRP targets at PeltAB in our bioinformatic screen , even below the stringent cut-off ( Fig . 2 , S1 Table ) . In retrospect , this appears to be because all of three PeltAB CRP binding sites contain at least 4 mismatches to the consensus for CRP binding ( Fig . 6A ) . Hence , we measured the affinity of CRP for PeltAB using EMSA assays . In parallel , we tested CRP binding to PestA2 as a control . As expected , CRP bound tightly to PestA2 at low concentrations ( Fig . 6B , lanes 1–6 ) . At high CRP concentrations further non-specific binding was observed ( evidenced by a conspicuous “smear” in DNA migration in lane 7 ) . In the equivalent experiment , with PeltAB , no specific binding of CRP was observed ( lanes 8–13 ) . However , non-specific CRP binding was again detectable at high protein concentrations ( lane 14 ) . Hence , CRP does not bind specifically to PeltAB . We hypothesised that previously observed changes in PeltAB activity , in cells lacking CRP , may occur indirectly . To test this , we cloned a 359 bp DNA fragment , containing PeltAB , into our pRW50 lacZ expression system . We also made a truncated 118 bp derivative of this construct where two of the three putative CRP targets were removed . A derivative of the truncated 118 bp construct , where the remaining CRP site was completely ablated by point mutations , was also made . The DNA sequences of the different constructs are shown in S2C Fig . They are illustrated graphically in Fig . 6Ci . Consistent with previous measurements , we found that transcription from PeltAB increased 2 . 5 fold in the absence of CRP . However , the response of PeltAB was identical when the CRP binding sites were removed ( Fig . 6Cii ) . Hence , although CRP represses transcription from PeltAB , this must occur indirectly . Given the configuration of H-NS binding at the eltAB locus ( Fig . 6A ) we reasoned that PeltAB would be repressed by H-NS in the presence of sufficient flanking DNA . As we had done previously for PestA1 and PestA2 , we compared the binding of H-NS to PeltAB in the presence and absence of the downstream flanking sequence . The different DNA constructs are illustrated in Fig . 7A and results of ChIP experiments to measure H-NS binding are shown in Fig . 7B . As predicted , enrichment of PeltAB , in immunoprecipitations with anti-H-NS , was only observed in the presence of downstream DNA . Importantly , this enrichment was specific to PeltAB and not observed for the control locus yabN . Corresponding LacZ activities , for the different DNA constructs , measured in M182 or the Δhns derivative , are shown in Fig . 7C . Incorporation of flanking DNA downstream of PeltAB resulted in a 15-fold reduction in LacZ activity that was largely relieved in the absence of H-NS . Given the established regulatory connections between CRP and glucose , and between H-NS and salt , we next measured changes in the activity of PestA1 , PestA2 and PeltAB in response to glucose and salt . A complete description of assay conditions is provided in the Materials and Methods section . Briefly , to establish the range of conditions across which the promoters were able to respond , we examined the effect of titrating glucose or salt into the growth medium individually . In all experiments , we used the promoter::lacZ fusions that included downstream flanking DNA . This was to ensure that signals sensed by both CRP and H-NS could be integrated . As expected , the activity of PestA1 was low . Consequently , the effects of glucose and salt were negligible ( S4A Fig . ) . Conversely , the activity of PestA2 was sensitive to both glucose and salt ( S4B Fig . ) . Thus , lacZ expression driven by PestA2 was repressed by glucose ( orange line ) and enhanced by salt ( green line ) . As expected , PeltAB activity increased in the presence of both salt and glucose , but induction by salt was more prominent ( S4C Fig . ) . We hypothesised that , for PestA2 , the inhibitory effect of glucose should override the stimulatory effect of salt . Our reasoning was that , although H-NS can repress PestA2 , the promoter is ultimately dependent on CRP for activity . Hence , we examined the effect of adding salt and glucose , to cells carrying the PestA2::lacZ fusion , separately and in combination ( Fig . 8A ) . As predicted , the inhibitory effect of glucose was dominant ( Fig . 8Ai ) and was still observed in the absence of H-NS ( Fig . 8Aii ) . Conversely , the stimulatory effect of salt required H-NS ( compare green bars in Fig . 8 ) . Importantly , in a separate experiment , we also showed that the effect of glucose on PestA2 activity requires that the CRP site is intact ( S4D Fig . ) . The combined effect of salt and glucose on PeltAB was more difficult to predict because CRP acts via an undefined , and indirect , mechanism . The result of the analysis ( Fig . 8B ) shows that the stimulatory effects of salt and glucose on transcription from PeltAB are not additive . Moreover , the stimulatory effect of glucose requires H-NS . Examination of all sequenced ETEC genomes reveals slight variations in the sequence of the eltAB and estA2 promoter sequences ( recall that ETEC H10407 is somewhat anomalous in also encoding estA1 ) . Thus , we next sought to understand if our model for regulation of LT and ST expression was broadly applicable . We focused our efforts on ETEC E24377A since i ) the genome has been sequenced and ii ) a vast array of independently generated transcriptomic data are available for this organism [28] , [29] . Using ETEC E24377A DNA as a template , we generated a 460 bp PestA2 , and 1126 bp PeltAB DNA fragment . The sequences are shown in S2D Fig . The DNA fragments were cloned into pRW50 and the ability of the promoters to drive lacZ expression in response to CRP and H-NS was measured . As expected , transcription from PestA2 was repressed by H-NS and activated by CRP whilst PeltAB was repressed by H-NS ( Fig . 9A ) . We observed no effect of CRP on transcription from PeltAB in the context of the 1126 bp ETEC E24377A fragment . This is not unexpected because CRP acts indirectly and these indirect CRP effects have only previously been observed in the context of short DNA fragments containing PeltAB that are not subject to direct repression by H-NS . We note that Sahl and Rasko previously examined the global transcriptome response of E24377A to glucose levels and bile salts [28] . In exact agreement with our model for toxin regulation , and the data in Fig . 9A , this study confirmed that i ) salt induced expression of both toxins and ii ) glucose inhibited expression of estA2 [28] . Fortuitously , changes in the ETEC E24377A transcriptome , prompted by ETEC attachment to human gut epithelial cells , have also been quantified comprehensively [29] . Briefly , in these experiments , ETEC were added to sets of Caco-2 intestinal epithelial cell tissue cultures . Over a time course , ETEC that had adhered to host cells were separated from non-adhered ETEC . The transcriptomes of adhered and non-adhered ETEC were then compared . By mining these data , we next sought to determine if our model was consistent with observed changes in the transcription of crp , hns , eltA and estA during host cell attachment . Briefly , our data predict that changes in estA expression should be directly correlated to changes in the level of CRP and inversely correlated with changes in levels of H-NS . Conversely , levels of eltA expression should be inversely correlated with levels of H-NS . The result of the analysis is illustrated in Fig . 9B . The data show that the relative levels of crp transcription in attached and unattached cells are similar ( orange line ) . However , levels of hns transcription change dramatically ( green line ) 60 minutes after host cell attachment . As predicted by our model , levels of estA2 and eltA transcription ( dashed lines ) inversely track changes hns transcript levels . When undertaking this analysis we noticed that , although there was little change in the relative level of crp mRNA between attached and unattached ETEC cells , the absolute level of crp mRNA did fluctuate across the time course of the experiment and between biological replicates . Strikingly , when these absolute mRNA levels are compared there is a clear linear relationship between crp and estA2 expression ( Fig . 9C ) . Note that in Fig . 9C the absolute level of hns mRNA has been added in parenthesis for each data point . Remarkably , the only two outlying data points in this plot correspond to the two samples with increased hns expression . We conclude that regulation of estA2 and eltA by CRP and H-NS is important during the attachment of ETEC to human intestinal epithelial cells , and that the regulatory control of ETEC toxins is conserved across different strains . Taken together , our data suggest that CRP and H-NS form a regulatory switch that controls ETEC toxicity . We next sought to examine the effect of disabling the switch on virulence . This is not straightforward because no animal model faithfully mimics the disease caused by ETEC in humans . However , intranasal mouse models have been used as a proxy for measuring E . coli pathogenicity [30] . Importantly , pathogenic E . coli cause more severe disease in this model than non-pathogenic strains [30] . Furthermore , ETEC strains lacking genes encoding toxins and known colonisation factors are less virulent in this model [31] . We opted to disrupt the regulatory switch by removing the crp rather than the hns gene . This was a deliberate decision since E . coli strains lacking hns are severely attenuated for growth in laboratory conditions . Conversely , the crp null derivative of ETEC H10407 was only mildly compromised for growth in liquid culture . Hence , we compared pathogenicity of ETEC H10407 , and the crp derivative , using the intranasal mouse model [30] . Note that the outcome of this experiment is difficult to predict since the effects of CRP on pathogenicity likely go far beyond the control of toxin expression . However , it is reasonable to assume that ETEC virulence should differ in cells lacking crp . The median survival of mice challenged with wild type ETEC was 53 hours and the mortality rate was 100% . Conversely , the median survival of mice challenged with Δcrp ETEC was 72 h and 20% of the mice survived ( Fig . 9D ) . Thus , whilst the full extent to which CRP co-ordinates the ETEC virulence programme remains to be determined , CRP is clearly central to the pathogenic response .
We propose that toxin expression in ETEC can be controlled by osmo-metabolic flux . This is relevant to conditions in the small intestine ( osmolarity equivalent to 300 mM NaCl ) disease symptoms ( the extrusion of cations and cAMP into the gut lumen ) and treatment ( the ingestion of solutions containing glucose and salt ) [7]–[11] , [32] . A molecular model , describing how the different signals are integrated , is illustrated in Fig . 10 . Two gene regulatory proteins , CRP and H-NS , are central to our model . Hence , H-NS directly represses the expression of eltAB , estA1 and estA2 ( pathways “a” and “b” in Fig . 10 ) . For estA2 and eltAB this repression can be relieved , in an H-NS dependent manner , by increased osmolarity . At PestA2 CRP directly activates transcription by a Class I mechanism ( pathway “c” ) . H-NS can interfere with this process by competing with CRP for binding at PestA2 ( pathway “d” ) . Finally , CRP can indirectly repress expression of eltAB via an unknown pathway that is influenced by H-NS ( “e” ) . Both pathways “c” and “e” are sensitive to glucose availability because of their dependence on CRP . We speculate that pathway “e” may include H-NS since the effects of salt and sugar on eltAB expression were epistatic ( Fig . 8 ) . Our model for H-NS repression of eltAB is consistent with previous work [26] . However , our conclusion that eltAB is indirectly repressed by CRP disagrees with a previous study [27] . Even so , we were able to faithfully reproduce most of the observations previously described by Bodero and Munson [27] . We note that Bodero and Munson previously suggested that CRP may bind targets at PeltAB with a 7 , rather than 6 , base pair spacer between the two CRP half sites . Such CRP targets have never been described amongst hundreds of known CRP regulated promoters . Furthermore , we found no such CRP sites in our ChIP-seq analysis . Given that these DNA sequences can be deleted , without negating the effect of CRP on PeltAB activity , the regulatory effect of CRP must be indirect . Our model for regulation of ST and LT expression is pertinent to both ETEC mediated disease and its treatment . ST and LT trigger the extrusion of H2O , cations , and cAMP ( the cofactor for CRP ) from the small intestine into the gut lumen [4]–[9] . Furthermore , solutions of salt and glucose are consumed by patients to reverse this process [10] , [11] . We speculate that , during infection , extrusion of electrolytes and cAMP into the gut lumen could create a positive feedback loop to drive toxin expression . Importantly , our model also suggests that ORT may provide benefits beyond stimulating rehydration of the patient . The concentration of glucose used in ORT is ∼10-fold higher than is required to repress estA2 expression . Hence , even if 90% of glucose present in ORT solutions is absorbed before reaching the site of infection , sufficient glucose should be present to down regulate toxin expression . Furthermore , even though salt is able to induce expression of estA2 and eltAB , the effect is only observed at concentrations far higher than those found in ORT solutions . Our observation that estA1 and estA2 are oppositely regulated by CRP is intriguing given the similarities between the promoter sequences of these genes . Differential regulation is dependent on the promoter -35 element ( Fig . 5 ) . At Class I CRP regulated promoters an αCTD protomer sits between CRP and domain 4 of the RNA polymerase σ subunit , which is bound to the promoter -35 element [12] . Thus , one possible explanation is that changes in the -35 element result in subtle repositioning of σ . This could result in unproductive interactions between αCTD and σ when CRP is present . Our data indicate that several strong CRP binding sites in the H10407 genome are occluded by H-NS . This strongly suggests that the CRP regulon has evolved to incorporate additional environmental signals through the action of H-NS . The repressive effect of H-NS on transcription has been widely described [23] . H-NS represses transcription predominantly by occluding the binding of RNAP or by trapping RNAP at promoters [20] . Recently , it was shown that H-NS occludes many binding sites for the CRP homologue , FNR , in E . coli [21] . Thus , occlusion of transcription factor binding sites appears to be a major function of H-NS , especially for CRP family proteins . Note that , in order to exclude CRP from target promoters , sites of H-NS nucleation and CRP binding need not overlap precisely . For example , at both estA1 and estA2 , maximal H-NS binding is observed within the coding sequence of the gene ( Fig . 2C ) . Despite this , H-NS oligomerisation across adjacent DNA is sufficient to prevent CRP binding . In summary , our model provides a framework for better understanding ETEC mediated disease and its treatment . Moreover , our catalogue of CRP and H-NS binding targets provide a useful community resource for further studies of all E . coli strains . In particular , our ChIP-seq data for CRP report >50 targets not identified previously in E . coli K-12 and 8 ETEC-specific targets . Finally , our data show how very small changes in the organisation of gene regulatory regions can have major effects on gene expression , such that transcription responds differently to the same environmental cues .
ETEC strain H10407 is described by Crossman et al . [1] . The C-terminal crp-3×FLAG tag was introduced into the H10407 chromosome using the recombineering method of Stringer et al . [33] . Wild type E . coli K-12 strains JCB387 and M182 have been described previously [34] , [35] . The Δhns M182 derivative was generated by P1 transduction of hns::kan from E . coli K12 derivative YN3144 ( a gift from Ding Jin ) . Plasmids pRW50 and pSR are described by Lodge et al . [36] and Kolb et al . [37] . More detailed descriptions of strains and plasmids , along with the sequences of oligonucleotides , are provided in S2 Table . Cultures were grown to mid-log phase in M9 minimal medium with 1% ( w/v ) fructose at 37°C . Targeted ChIP experiments ( Fig . 4 and 6 ) were done exactly as described by Singh and Grainger [38] using PestA2 or PeltAB fragments cloned in pRW50 carried in strain M182 . The ChIP-seq was done as described extensively by Singh et al . [25] using strain H10407 . Briefly , H-NS and CRP-3×FLAG were immunoprecipitated using protein A sepharose ( GE Healthcare ) in combination with 2 µL of anti-H-NS or 2 µl of anti-FLAG respectively . After immunoprecipitation and washing , beads were resupended in 100 µL 1× Quick Blunting Buffer ( NEB ) with dNTPs ( as specified by the manufacturer ) and 2 µL Quick Blunting Enzyme Mix , and incubated for 30 minutes at 24°C with gentle mixing . After being collected by centrifugation , the beads were again washed and the associated DNA was A-tailed by resuspension of beads in 100 µL 1× NEB buffer #2 supplemented with 2 mM dATP and 10 units of Klenow Fragment ( 3′→5′ exo-; NEB ) . Following incubation for 30 minutes at 37°C , with gentle mixing , the beads were again collected and washed . Illumina adapters ( 1 µl NEXTflex ChIP-seq barcoded adapters; BioO Scientific ) were added to beads resuspended in 100 µL 1× Quick Ligation reaction buffer and 4 µL Quick T4 DNA Ligase ( NEB ) , and incubated for 15 minutes at 24°C with gentle mixing . After washing the beads , the DNA was the eluted into a fresh tube by addition of 100 µL ChIP elution buffer ( 50 mM Tris–HCl , pH 7 . 5 , 10 mM EDTA , 1% SDS ) and incubation at 65°C for 10 minutes . The eluate was collected by centrifugation for one minute at 4000 rpm . Crosslinks were reversed by incubation for 10 minutes at 100°C . Samples were purified by phenol extraction and precipitated with ethanol , 40 µg glycogen and 8 . 3 mM sodium acetate . DNA was pelleted for 15 minutes at 4°C at top speed in a microcentrifuge , washed with 70% ethanol , dried and resuspended in 11 µL H2O . After quantification by PCR each library was amplified , purified and resuspended in 20 µL H2O . Libraries were the sequenced using a HiSeq 2000 sequencer ( Illumina; University at Buffalo Next Generation Sequencing Core Facility ) . Sequence reads were aligned to non-repetitive sequences in the E . coli H10407 genome using CLC Genomics Workbench and overall coverage was determined using custom Python scripts . Sequence reads have been submitted to the EBI ArrayExpress database and can be accessed using accession number E-MTAB-2917 . ChIP-seq peaks were identified as described previously [25] . We refer to these peaks as “high stringency” peaks . A second round of peak calling was performed in which the sequence read threshold values ( i . e . the minimum number of sequence reads at a given genomic position that is required for a peak to be called ) was reduced by 20% . We refer to these peaks as “low stringency” peaks . MEME [39] was used to identify enriched sequence motifs in the sequences from 50 bp upstream to 50 bp downstream of the high stringency peak centres . Thus , we identified a motif closely resembling the known CRP consensus site in many of the regions surrounding high stringency ChIP-seq peaks . These CRP site sequences are included in Table 1 . Those high stringency peaks for which MEME did not identify a motif were used for a second round of analysis using MEME . This also identified a motif closely resembling the known CRP consensus site . These CRP site sequences are also included in Table 1 . We used MEME to identify enriched sequence motifs in the low stringency peak list . This also identified a motif closely resembling the known CRP consensus site . These CRP site sequences are also included in Table 1 . “High-confidence” ChIP-seq peaks listed in Table 1 include all the high stringency peaks but only those low stringency peaks for which we identified a motif using MEME . A complete list of all peaks , including low stringency peaks for which a motif was not identified by MEME , is provided in S3 Table . In order to assess the location of CRP sites with respect to TSSs we used the targets listed in Table 1 . For each target the predicted sequence from MEME was used in a BLAST search against the E . coli K-12 MG1655 genome . All but 11 CRP sites in ETEC had a single perfect match in the E . coli K-12 chromosome . For each perfect match the distance from the centre of the CRP site to all transcription start sites was calculated . Transcription start site coordinates are from Kim et al . [40] and Cho et al . [41] . Distances between −200 and +100 were selected and all other distances were discarded . Distances were then grouped in bins of 5 bp each and the most common distance bins were identified . Note that , because the position of the CRP site was transposed onto the E . coli K-12 genome , the distance between CRP sites and TSSs The PWM describing CRP binding sites was generated using the PREDetector software package and our previous list of 68 CRP binding sites in the E . coli K-12 genome [15] , [42] . Subsequent bioinformatic screens of plasmids p666 and p948 were done by importing the relevant genbank files into PREDetector and running a binding site search with a cut-off of 7 using settings that did not exclude CRP sites within genes . The “score” for each site predicted by PREDetector increases if a closer match to the PWM is found . To generate the chromosome and plasmid maps shown in Fig . 1 we used DNA plotter software [43] . Data shown in Fig . 9B–C were extracted from the publically available datasets of Kansal et al . [29] that measure changes in the ETEC E24377A transcriptome upon contact with Caco-2 intestinal epithelial cells . The data are hosted under the GEO accession code GSE40427 . For each assay condition ( planktonic and attached ETEC cells ) we extracted the signal intensity for microarray probe sets A1527 ( crp ) , UTI189_C1433 ( hns ) , D4754 ( eltA ) and D4048 ( estA ) . The average signal intensity was calculated and the fold change in transcription in attached compared to planctonic ETEC cells was determined for each time point . The data in Fig . 9C show a comparison of absolute signal intensities for probe sets A1527 ( crp ) and D4048 ( estA ) compared for each of the two replicates obtained at 30 , 60 or 120 minutes after attachment to host cells . Signal intensities obtained after 30 minutes growth in LB medium ( three replicates ) are also included in this analysis . The CRP and σ70 purification was done exactly as described previously [44] , [45] . RNA polymerase core enzyme was purchased from Epicenter . RNA polymerase holoenzyme was generated by incubating the core enzyme with an equimolar concentration of σ70 at room temperature for 20 minutes prior to use . H-NS was overexpressed in T7 express cells from plasmid pJ414hns . After overexpressing H-NS , cells were collected from the culture by centrifugation and resuspended in buffer A ( 20 mM Tris-HCl pH 7 . 2 , 1 mM EDTA and 10% ( v/v ) glycerol ) containing 100 mg/ml PMSF . Cells were lysed by sonication and the sample was cleared by centrifugation . The supernatent was loaded directly onto a Heparin column ( Amersham ) pre-equilibrated with buffer A . A linear NaCl gradient was applied and H-NS was found to elute at approximately 500 mM NaCl . The peak fractions were pooled and diluted 3-fold with buffer A . The sample was then loaded onto an S-FF column ( Amersham ) pre-equilibrated with Buffer A . A NaCl gradient was applied and H-NS eluted at approximately 550 mM NaCl . The H-NS containing fractions were then dialysed against a buffer containing 20 mM Tris HCl ( pH 7 . 2 ) , 300 mM KCl and 10% Glycerol ( v/v ) for storage at −80°C . DNA fragments for DNAse I footprinting or EMSA assays were excised from pSR by sequential digestion with HindIII and then AatII . After digestion , fragments were labelled at the HindIII end using [γ-32P]-ATP and T4 polynucleotide kinase . DNAse I footprints and EMSA experiments were then done as described by Grainger et al . [45] except that cAMP was added to reactions at a concentration of 0 . 2 mM . Radio-labelled DNA fragments were used at a final concentration of ∼10 nM . Note that all in vitro DNA binding reactions contained a vast excess ( 12 . 5 µg ml−1 ) of Herring sperm DNA as a non-specific competitor . Footprints were analysed on a 6% DNA sequencing gel ( molecular dynamics ) . The results of all footprints and EMSA experiments were visualized using a Fuji phosphor screen and Bio-Rad Molecular Imager FX . Transcript start sites were mapped by primer extension , as described in Lloyd et al . [46] using RNA purified from strains carrying the 92 bp PestA1 or 93 bp PestA2 fragment cloned in pRW50 . The 5′ end-labelled primer D49724 , which anneals downstream of the HindIII site in pRW50 , was used in all experiments . Primer extension products were analysed on denaturing 6% polyacrylamide gels , calibrated with size standards , and visualized using a Fuji phosphor screen and Bio-Rad Molecular Imager FX . The in vitro transcription experiments were performed as described previously Savery et al . [35] using the system of Kolb et al . [38] . A Qiagen maxiprep kit was used to purify supercoiled pSR plasmid carrying the different promoter inserts . This template ( ∼16 µg ml−1 ) was pre-incubated with purified CRP in buffer containing 0 . 2 mM cAMP , 20 mM Tris pH 7 . 9 , 5 mM MgCl2 , 500 µM DTT , 50 mM KCl , 100 µg ml−1 BSA , 200 µM ATP , 200 µM GTP , 200 µM CTP , 10 µM UTP with 5 µCi [α-32P]-UTP . The reaction was started by adding purified E . coli RNA polymerase . Labelled RNA products were analysed on a denaturing polyacrylamide gel . β-Galactosidase assays were done using the protocol of Miller [47] . All assay values are the mean of three independent experiments with a standard deviation <10% of the mean . Cells were grown aerobically at 37°C to mid-log phase in LB medium unless stated otherwise . For all experiments investigating the effects of glucose and salt M9 minimal medium was used so that the glucose and salt concentrations could be controlled more accurately . The amount of glucose is shown as percentage w/v . The addition of “salt” refers to a 3∶1 molar ration of NaCl to KCl . We have arbitrarily described 30 mM NaCl and 10 mM KCl as being a “1%” salt solution . Strains of ETEC were grown in Luria Broth ( LB ) to an OD600 of 1 . 0 . Groups of 10 mice ( 8–10 week old BALB/c ) were infected intranasally with approximately 1×109 colony forming units of bacteria in 100 µl of inoculums according to Byrd et al . [30] . Mice were monitored daily for 6 days post-infection for weight and morbidity . The protocol 12-02-015IBT “Oral Immunization of Mice with Enterotoxigenic: E coli ( ETEC ) ” has been approved by the Noble Life Sciences IACUC committee . All animal care and use procedures adhere to the guidelines set by the Public Health Service Policy , U . S . Dept . of Agriculture ( USDA ) and the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . | Diarrheagenic illness remains a major disease burden in the developing world . Enterotoxigenic Escherichia coli ( ETEC ) are the leading bacterial cause of such disease; hundreds of millions of cases occur every year . The severe watery diarrhoea associated with ETEC infections results from the action of enterotoxins . The toxins target human gut epithelial cells and trigger the loss of water and electrolytes into the gut lumen . Oral rehydration therapy can counteract this process . Hence , glucose and salt solutions promote rehydration of the patient . In this work we show that the gene regulatory mechanisms controlling toxin expression respond directly to sugar and salt . Furthermore , we describe a molecular mechanism to explain these effects . Hence , we provide a starting point for the optimisation of oral rehydration solutions to reduce toxin expression over the course of an ETEC infection . | [
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] | 2015 | The Molecular Basis for Control of ETEC Enterotoxin Expression in Response to Environment and Host |
Associations between repeated ocular infections with Chlamydia trachomatis in childhood and conjunctival scarring in adulthood are well established . Trachomatous scarring ( TS ) in children has also been observed in hyper-endemic areas , but data are scant regarding childhood scarring in areas where trachoma has been reduced to hypo-endemic levels . In this cross-sectional study , a random sample of children , ages 1–9 years , were selected from 38 communities in the formerly hyper-endemic district of Kongwa , Tanzania . Each participant received an ocular examination and eye-swab test for C . trachomatis infection . Conjunctival photographs were taken and analyzed at 5x magnification to determine scarring presence and severity . Community-level case clustering was assessed using intra-class correlation coefficients , and associations between TS presence and demographic/clinical factors were assessed using contingency table analyses . 1 , 496 children ( 78% of eligible ) participated in this study . The mean age was 5 . 5 years and 51% were female . Scarring prevalence was 2 . 1% ( 95% CI: 1 . 5%– 3 . 0% ) . The prevalence of follicular trachoma and ocular C . trachomatis infection were 3 . 2% and 6 . 5% , respectively . Most TS cases ( 68 . 7% ) fell into the mildest category , grade S1 . 18 . 7% were grade S2; 12 . 6% were grade S3 . No significant associations were seen between TS presence and age , sex , follicular trachoma , or active ocular C . trachomatis infection ( p-values: 0 . 14 , 0 . 48 , 0 . 27 , 0 . 15 , respectively ) . Thirty communities ( 78 . 9% ) had 0–1 TS cases , and the most seen in any single community was four cases . Three years ago , follicular trachoma prevalence averaged 4 . 9% in communities with 0–1 TS cases , but 7 . 6% in communities with 2–4 TS cases ( p-value: 0 . 08 ) . In this formerly hyper-endemic district of Tanzania , TS was rare in 1–9 year-olds and usually mild when present . Communities with higher rates of follicular trachoma in the past were more likely to have ≥2 cases of scarring , but the association was not statistically significant .
Trachoma results from repeated ocular infection with Chlamydia trachomatis and is the leading infectious cause of blindness worldwide . [1] While the conjunctival inflammation associated with active infection is predominantly seen before 10 years of age , the resultant conjunctival scarring usually presents in adulthood and is associated with age both in prevalence and severity . [2–4] Nevertheless , conjunctival scarring has been documented in children in regions where trachoma prevalence is 30% or higher , and in one longitudinal study , children who were observed to have severe trachomatous inflammation on multiple occasions were most at risk of having scarring . [2 , 5 , 6] There is no animal reservoir for C . trachomatis , and transmission is from person to person through infected ocular and nasal secretions . The World Health Organization has endorsed the SAFE strategy for trachoma control , which consists of Surgery for end-stage trichiasis , and Antibiotic use , Facial cleanliness , and Environmental improvements to reduce the active , infectious stage of the disease . At present , there is no direct intervention to decrease scarring , except to reduce the frequency/severity of the repeated episodes of infection . [7] In the context of trachoma-reduction programs like GET 2020 ( Global Elimination of Blinding Trachoma by 2020 Initiative ) , trachoma prevalence has decreased significantly in a number of areas that were once hyper-endemic . [8–10] Yet there is a lack of data describing the prevalence or severity of trachomatous scarring ( TS ) in children in these formerly hyper-endemic areas . This study’s primary objective is to determine the prevalence of TS among children , ages 1–9 years , in the formerly hyper-endemic district of Kongwa , Tanzania . Secondary aims include determining the severity of TS , assessing associations between TS and various demographic or clinical factors , and evaluating community-level case clustering .
This study complied fully with the Declaration of Helsinki and the guardians of all participants provided informed , written consent . The Johns Hopkins Institutional Review Board and the National Institute of Medical Research of the United Republic of Tanzania approved of this study . Kongwa district has been known for its high trachoma prevalence and scarring risk in children , as documented in previous studies . [6 , 11] In 1986 , the prevalence of trachoma in children ages 1–7 years ( pre-school age ) was 60% . By 2010 , Kongwa district had an overall prevalence in children of 17% . [11 , 12] Recent data suggest that trachoma has declined to less than 10% in children age 1 to 9 years in this district , suggesting there may be a declining risk of the scarring complications of trachoma . [13] Participants age 1–9 years were randomly selected based on a census conducted in 38 communities in Kongwa that were already involved in a separate study . [13] Fifty children were randomly selected from each community; two alternates were also randomly selected to ensure the goal of 50 children would be achieved . Data collection took place at central sites within participants’ villages . Participants received an ocular examination of the right upper tarsal conjunctiva by a trained grader to assess for follicular trachoma using 2 . 5x loupe and a flashlight . The grader also collected an eye swab from the child’s right eye to assess for C . trachomatis infection using the Aptima Combo at Johns Hopkins International Chlamydia Laboratory . A 5% sample of “air swabs” was also collected to assess contamination . Digital photographs of each child’s right upper tarsal conjunctiva were captured ( D40; Nikon , Tokyo , Japan ) . Two trained graders at Johns Hopkins Hospital examined these photographs at 5x magnification to determine scarring severity . They were masked to one another’s scores , and scores were determined using a validated , five-point scale developed by Wolle et al . [14] S1 is the presence of at least one linear scar of size 3 mm or less , with or without stellate scars , S2 is multiple lines of scarring of more than 3mm that occupy up to 1/8 of the upper tarsus; S3 is a pattern of scarring that occupies up to 1/3 of the eyelid and S4 is >90% of the tarsus obliterated by scarring . S0 is an eyelid that does not meet the criteria for S1 or worse . Any discordant grades were openly adjudicated with a senior grader . Before starting , inter-grader agreement was evaluated using 60 images from a separate dataset , and each grader was required to achieve a kappa 0 . 70 or greater relative to the senior grader . Descriptive statistics were used to characterize the study population . Contingency table analyses were used to evaluate differences between participants and non-participants . The TS prevalence with exact 95% confidence intervals ( 95% CIs ) is presented . The confidence interval was not adjusted for clustering , as there was no evidence for clustering by community . Due to the small number of TS cases , the data was collapsed from an ordinal scale to a simple presence or absence . Chi-square and Fisher’s exact testing were used , as appropriate , to assess for associations between TS presence and age , sex , follicular trachoma , or ocular C . trachomatis infection . To assess the community-level clustering of TS cases , the intra-class correlation coefficient and corresponding 95% CIs were estimated . Follicular trachoma prevalence rates three years prior to the TS assessment were compared between communities with zero or one TS case and communities with 2 or more cases based on medians and interquartile ranges using a Wilcoxson 2-sample test . All analyses were run using SAS 9 . 2 software ( SAS Institute , Cary , NC ) . Data are available in supplementary file 1 ( S1 Data ) . For the STROBE checklist , please see S2 Data .
A total of 1 , 505 children ( 78 . 4% of invitees ) participated in this study . ( Fig 1 ) Of these , nine participants ( 0 . 6% ) had un-gradable photographs and were excluded from subsequent analysis . As shown in Table 1 , the only significant difference between participants and non-participants was the availability of a household latrine ( p-value < 0 . 001 ) . The remaining factors assessed showed no significant difference between participants and non-participants . None of the air controls for the test of infection demonstrated evidence of contamination . The prevalence of TS was low at 2 . 1% ( 95% CI: 1 . 5%– 3 . 0% ) ( Fig 2 ) . When present , TS was usually mild , with 68 . 8% of cases ( 22/32 cases ) falling into the mildest category , grade S1 . Fig 3A–3C illustrates examples of scarring of differing severities that were observed in our study . The prevalence of follicular trachoma and C . trachomatis infection were 3 . 2% and 6 . 5% , respectively , at the time of the survey ( Table 2 ) . TS was more prevalent among older children , males , those with active follicular trachoma , and those with C . trachomatis infection , but none of these associations were statistically significant ( p-values: 0 . 14 , 0 . 48 , 0 . 27 , 0 . 15 , respectively ) . The inferences did not change after using a multivariate approach ( p-values: 0 . 09 , 0 . 34 , 0 . 59 , 0 . 31 ) . Nearly half of all communities had no TS present ( 47 . 4% , 18/38 communities ) , and only 21 . 1% of communities had multiple cases ( Fig 4 ) . The most seen in any single community was four cases . The intraclass correlation coefficient was suggestive of an association between TS prevalence and village-level clustering ( ICC: 0 . 10; 95% CI: -0 . 09–0 . 29 ) but did not reach statistical significance ( p-value: 0 . 29 ) . We wanted to assess the relationship between past trachoma prevalence in these communities to the current number of childhood TS cases . We dichotomized communities into having 0–1 TS cases or 2–4 TS cases , and averaged the community prevalence of TF as assessed 3 years prior to the current survey ( Table 3 ) . Both follicular trachoma and C . trachomatis infection were more prevalent in communities with 2–4 TS cases than those with the 0–1 TS cases , but these associations did not achieve statistical significance ( p-values: 0 . 08 and 0 . 17 , respectively ) . Similarly , the prevalence of any active trachoma ( follicular trachoma and/or intense trachomatous inflammation ) was higher in communities with multiple TS cases , but this was not statistically significant ( p-value: 0 . 12 ) .
In this large study of children ( 1 , 496 participants , 38 communities ) we assessed the prevalence and severity of trachomatous scarring among 1–9 year-olds in the formerly hyper-endemic district of Kongwa , Tanzania . The prevalence of TS within this population was low , at 2 . 1% ( 95% CI: 1 . 5%– 3 . 0% ) . Scarring was generally mild when present , with 69% of cases falling under the mildest category , grade S1 , and no cases falling under the most severe category , grade S4 . Our previous work found that children with a constant , severe trachomatous inflammation are at particularly high risk of developing childhood TS . [6] Given the trajectory of decline in trachoma in Kongwa , we now see low prevalence and severity of TS , suggesting that Kongwa’s trachoma elimination efforts may have been particularly effective in decreasing the prevalence of these cases with constant severe inflammation . This contrasts with findings by Ngondi et al . in a hyper-endemic area of southern Sudan , in which the prevalence of trachomatous trichiasis was 1 . 4% among 1–14 year-olds . [15] Trachomatous trichiasis is a consequence of severe scarring from trachoma , and no such cases of trichiasis were seen in our cohort of 1 , 496 children . In the aforementioned study by Ngondi et al . , trichiasis was significantly associated with increasing age , female sex , and the presence of inflammatory trachoma within the household . [15] The association of TS with age and female sex have been demonstrated consistently in numerous studies , and association with sex seems to hold true in all age groups: children , adolescents , and adults . [2 , 16] It is interesting to note , however , that we found no significant association with these factors in our study , although we did not study scarring in children past the age of 9 years . It is possible that had we included older children and adolescents , a more apparent effect of age would have been seen . The reasons for an increase in scarring with age have to do with cumulative risk of exposure to repeated infection , but clearly , for the age group 1 to 9 years , that has not happened in these communities . It will be fruitful to follow these children longitudinally and observe if scarring rates increase as they age . Gender differences are observed when females have higher rates of repeated exposures to infection compared to males , but again that appears not to have happened in these communities . While the likelihood of more cases of TS in a community appeared to be associated with greater community prevalence of trachoma as measured three years ago , this is not marked and indeed there was no clustering of cases by community . This is likely due to the fact that even 3 years ago , the community-level prevalence of follicular trachoma rarely exceeded 10% . [13] It is possible that some of our cases of conjunctival scarring were due to causes other than trachoma , such as adenovirus or trauma , which we would have been unable to distinguish . With such low levels of scarring present , scarring due to other factors might reduce the precision with which we can detect associations between age and gender and scarring due to trachoma . The data did suggest an increase in TS with age , isolation of infection on a swab , and active trachoma , but the associations were not significant . However , the prevalence of conjunctival scarring due to other causes is likely to be less than that due to trachoma in this formerly endemic district . One limitation of this study was non-participation , which was low at 22% . The most common cause of non-participation was travel outside the village and reflects the highly mobile nature of this population . We found a significant difference with greater latrine ownership in our participants compared to non-participants . This proxy for socio-economic and hygiene status has been linked to greater trachoma prevalence in prior studies[15 , 17] , and suggests that our estimate of scarring may be lower than the true prevalence of scarring in children . However , there was no significant difference between these two groups in the remaining five factors that were assessed , suggesting that participants and non-participants likely were similar populations . Another potential limitation is the fact that the majority of TS was grade S1 , which is the most difficult to grade reliably . Our use of multiple graders who had a high inter-rater reliability and were masked to each other’s scores should have increased the reliability of all scoring , including grade S1 . This research study used high quality image acquisition and two graders with adjudication to determine scarring . Although we found a low rate of scarring in children , which suggests an important outcome from the reduction of active trachoma , at present the difficulties in assuring quality assessment of scarring in children do not suggest it would be a useful tool for surveillance of trachoma for the global elimination program . In conclusion , the prevalence of TS among 1–9 years was low in the formerly hyper-endemic region of Kongwa , Tanzania . [14] When present , scarring severity was usually mild . Our findings suggest that the trajectory of decline in trachoma over several years has resulted in a very low risk of scarring for these communities , which is not increasing with age or gender . There is a need for longitudinal follow up to monitor the incidence and progression of scarring in this cohort as they age in communities with low rates of trachoma . | Trachoma is a disease in which the conjunctiva of the eye is repeatedly infected by the intracellular organism Chlamydia trachomatis . This can cause scarring of the eyelid’s inner surface , which in turn can cause the eyelashes to turn inwards , scratching the eye and leading to blindness . This scarring process is associated with age and usually does not present until adulthood . However , in communities with very high trachoma rates , it has also been reported in children . There are scant data addressing the presence and nature of such scarring among children in areas where elimination efforts have reduced trachoma from high to low prevalence . In this study , we found that even though interventions in Kongwa , Tanzania , have successfully reduced trachoma levels in recent years , 1–9 year-olds can still be found with eyelid scarring . However , this scarring is rare and generally mild . Eyelid scarring from trachoma is known to increase with age , but interestingly , that was not seen in this cohort . This suggests that not only have interventions reduced eyelid scarring in children to low levels , but that this protective affect may be sustained into adolescence and adulthood . | [
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] | 2017 | Trachomatous scarring among children in a formerly hyper-endemic district of Tanzania |
Rhythms with time scales of multiple cycles per second permeate the mammalian brain , yet neuroscientists are not certain of their functional roles . One leading idea is that coherent oscillation between two brain regions facilitates the exchange of information between them . In rats , the hippocampus and the vibrissal sensorimotor system both are characterized by rhythmic oscillation in the theta range , 5–12 Hz . Previous work has been divided as to whether the two rhythms are independent or coherent . To resolve this question , we acquired three measures from rats—whisker motion , hippocampal local field potential ( LFP ) , and barrel cortex unit firing—during a whisker-mediated texture discrimination task and during control conditions ( not engaged in a whisker-mediated memory task ) . Compared to control conditions , the theta band of hippocampal LFP showed a marked increase in power as the rats approached and then palpated the texture . Phase synchronization between whisking and hippocampal LFP increased by almost 50% during approach and texture palpation . In addition , a greater proportion of barrel cortex neurons showed firing that was phase-locked to hippocampal theta while rats were engaged in the discrimination task . Consistent with a behavioral consequence of phase synchronization , the rats identified the texture more rapidly and with lower error likelihood on trials in which there was an increase in theta-whisking coherence at the moment of texture palpation . These results suggest that coherence between the whisking rhythm , barrel cortex firing , and hippocampal LFP is augmented selectively during epochs in which the rat collects sensory information and that such coherence enhances the efficiency of integration of stimulus information into memory and decision-making centers .
Fluctuations in local field potential ( LFP ) affect a neuronal population’s likelihood of spike output and its sensitivity to synaptic inputs [1–3] ) . When the oscillations of two brain regions are coherent , the respective neuronal populations might be functionally coupled to allow efficient transmission of signals [4] . Moments of high coherence could permit target neurons to fire at low energy cost [5] . The degree of LFP coherence between two populations depends on the animal’s state [6 , 7] , and heightened coherence has been observed in various cognitive tasks [8–10] . Oscillations in the theta frequency range ( 5–12 Hz ) are a prominent feature of hippocampal spiking and LFP as rats engage in a wide range of behaviors [11 , 12] . The effects of manipulating the theta rhythm suggest specific roles of different theta phases in encoding versus decoding processes [13] . In rats , the hippocampus and prefrontal cortex undergo intervals of coherent LFP oscillation during spatial navigation [14] . We hypothesize that not only prefrontal but also primary sensory regions of neocortex might be linked to hippocampus by coherent oscillations . The rat whisker sensorimotor system offers intriguing opportunities for testing this hypothesis because the system is entrained by a periodic rhythm , the forward-backward sweeping of the whiskers [15–17] . Though whisking and theta reside within the same 5–12 Hz frequency band , whisking does not depend on theta—ablation of the medial septum abolishes the theta rhythm while whisking remains intact [18 , 19] . A specific phase relationship between the hippocampal theta rhythm and whisking has been posited [20] , yet coherence between sensorimotor system and hippocampus was not found to be higher than would be expected for independent oscillators when rats whisked in the air under conditions that involved no memory component [21] . These results , taken together , suggest independent rhythmic generators for whisking and the theta rhythm . Whether these generators become coherent , and if so , under what circumstances , remains to be elucidated . Here we address this issue in the context of a whisker-mediated perceptual task . While rats carried out a texture discrimination task , we examined the relationships between three variables: ( i ) the LFP oscillation of the CA1 region of hippocampus , ( ii ) the cyclical forward and backward “whisking” motion , and ( iii ) the firing of neurons in barrel ( primary somatosensory ) cortex . Barrel cortex is the main gateway by which signals from the whiskers enter neocortex; from there , signals are relayed to the hippocampus [22] where they are integrated with spatial and other sorts of information [23] . We hypothesize that coherence between the whisking rhythm , barrel cortex firing , and hippocampal theta might be enhanced selectively during epochs in which the rat collects sensory information whose destination is the hippocampus .
We trained seven rats to perform a tactile discrimination task ( Fig 1A ) in which they classified textured plates ( Fig 1B , see Materials and Methods ) touched by their whiskers . One of the plates , selected randomly , was presented on each trial , and the rat collected a water reward if it correctly turned to the side ( left or right ) associated with that plate ( Table 1 ) . Once rats performed correctly at least 65% of the time on three consecutive sessions , we implanted two arrays of tetrodes in order to measure LFP from dorsal hippocampus ( CA1 region ) and the spiking activity of barrel cortex neurons simultaneously . We also monitored whisking motion . On each trial , the rat’s actions were recorded ( Fig 1C ) and four behavioral episodes were extracted based on triggering of optic sensors and video analysis: “approach , ” “touch , ” “turn , ” and “reward . ” The mean value of touch episode length was 540 ms , and so 500 ms was selected as the analysis interval length for the episodes included in the analysis: “approach , ” “touch , ” and “reward” ( see Materials and Methods ) . We plotted the mean angle of the full set of whiskers [24] ) across each trial ( Fig 1D ) . This allowed us to compare the phase of whisking angle to the phase of hippocampal LFP ( Fig 1E ) . Neurons in barrel cortex , recorded simultaneously , typically showed an increase in firing rate during texture exploration ( Fig 1F ) . Performance during recording sessions was above 70% and better than chance ( randomization test for every session of every rat , p < 10−10 , see Materials and Methods for details ) for all rats ( Fig 2A ) . Tissue sections at the end of the experiment confirmed tetrodes position ( Fig 2B ) . Two of the animals ( Rats 5 , 6 ) were monitored outside the context of the texture discrimination task; they were placed on a square platform and foraged for cereal flakes . In these sessions , we recorded hippocampal LFP and whisking simultaneously and later classified each time period as an instance of walking or resting ( see Materials and Methods ) . We first examined how the hippocampal LFP varied according to the animal’s ongoing behavior . Fig 3A shows key events of an exemplar trial , aligned to the time of first whisker contact with the stimulus . Error bars represent variable durations for touch and turn episodes across sessions and rats . Fig 3B shows hippocampal LFP aligned to the time of first whisker contact for a randomly selected set of trials . By visual inspection , a theta rhythm of about 10 Hz is evident around contact time . To better quantify the time course of LFP modulation through one trial , we measured the power spectrogram for the period extending 3 s on either side of first whisker contact . Fig 3C shows the LFP power spectrogram of the same session , averaged across trials , and normalized to the power at 4 Hz during the baseline interval ( from 2 . 5 to 3 s before first contact ) . The spectrogram shows that the distribution of power across frequencies was related to the rat’s actions , as previously reported [23] ) . The theta band showed a marked increase in power late in the approach , remained high during touch , and dropped suddenly as the rat turned to the reward spout; theta power fell below baseline levels during reward consumption . Next we considered the LFP power spectrum separately for the four behavioral episodes—baseline , approach , touch , and reward ( Fig 3D ) . In all episodes , the peak of the LFP power spectrum occurred within the theta range of 5–12 Hz [5] ) . At baseline , the peak frequency of theta rhythm was 7 . 87 ± 0 . 25 Hz ( n = 4 rats ) . Peak frequency was slightly higher during approach ( 8 . 25 ± 0 . 29 Hz , n = 4 rats ) and touch ( 8 . 75 ± 0 . 5 Hz ) although only during touch was the peak frequency significantly higher than during baseline . Reward episodes showed a significantly lower peak frequency ( 7 . 25 ± 0 . 28 Hz ) compared to baseline , approach , and touch ( all comparisons using one-way ANOVA p = 0 . 0004 , with multiple comparison test ) . Moreover , mean theta power during touch and approach was significantly higher than during baseline and reward periods ( randomization test for each rat , with Bonferroni correction , p < 0 . 01; see Materials and Methods ) . We hypothesize that , during approach , animals began to attend to incoming signals from their whiskers in preparation for the texture discrimination; the LFP changes might reflect an engagement of the hippocampus in the rhythm of the sensorimotor system in order to optimize the intracortical transfer of relevant sensory inputs . Fig 3C shows that task events also modulated the LFP spectrogram in other frequency ranges ( e . g . , from 20–40 Hz ) ; however , this report focuses only on the theta range . We also computed the LFP power spectrum when the rats were positioned in an open arena; this served as a control condition because the rats , whether resting ( immobile and whisking ) or active ( walking and whisking ) , did not need to identify tactile objects or to compare incoming signals to a memorized spatial rule , as they did during the texture task . The peak frequency of theta rhythm as rats walked ( 8 . 50 ± 0 . 01 Hz , n = 2 rats ) was higher than during rest episodes ( 7 . 80 ± 0 . 35 Hz , paired t test , p < 0 . 05 ) , but there was no significant difference between theta peak frequency during walking versus that during the touch epoch of the texture discrimination task ( 8 . 75 ± 0 . 5 Hz , t test , p = 0 . 54 ) . Moreover , theta power was higher during walking episodes than it was during rest and touch episodes ( randomization test with Bonferroni correction , p < 0 . 01 for each rat , Fig 3E ) , confirming that movement through the environment is accompanied by increased theta rhythm frequency and power [25 , 26] . An issue of interest was the distribution of whisking frequency during the tactile task ( Fig 3F ) . During approach to the texture , whisking frequency was broadly distributed between 6 and 12 Hz . During actual texture contact , whisking frequency was concentrated in a narrower band with peak at 10 Hz ( n = 4 rats ) . Moreover , whisking power during touch was higher than during approach ( randomization test with Bonferroni correction , p < 0 . 01 for each rat ) . A previous study [21] in which rats explored a runway in dimmed light did not find significant phase coherence between hippocampal theta rhythm and whisking; however , the authors allowed that “…it is possible that the theta rhythm and whisking will phase lock under certain circumstances , such as when a rat learns to discriminate an object with the vibrissas , as opposed to whisk in air” ( p . 6 , 522 ) , a prediction reiterated later [27] . The present behavioral task involved associating incoming sensory signals with a stored spatial rule—the reward location associated with that stimulus—a process that engages the hippocampus [23] . We hypothesize that the coherence between CA1 theta and the whisking rhythm might depend on behavioral context; more precisely , coherence could emerge during whisker-mediated texture identification . We tested this hypothesis by measuring the Phase Synchronization Index ( PSI ) between whisking cycles and hippocampal LFP when rats performed the discrimination task versus when they walked through the open arena , a condition in which signals from the whiskers had no explicit connection to information stored in hippocampus . PSI captures the degree of phase synchronization in short time windows without amplitude confounds ( see Materials and Methods ) . Fig 4A illustrates five samples of simultaneously recorded whisking and hippocampal LFP recorded during the discrimination task , aligned to contact onset . Whisking-to-hippocampal LFP phase synchronization during the tactile discrimination task was highly significant in the four rats examined ( Fig 4B ) . The observed PSI averaged across rats was 0 . 71 during approach to the texture and 0 . 74 during touch ( 915 approach and touch episodes ) . Both values were above the upper bound of the 95% confidence limit ( PSI = 0 . 507 , labeled “shuffled” in Fig 4B ) derived through the bootstrap method ( see Materials and Methods ) . The PSI was significantly higher during touch than during approach ( n = 915 paired episodes , two-sample randomization test , p < 0 . 0001 ) . It is informative to compare the observed coherence values to an estimate for their upper limit—the average coherence between the whisking motion of left and right whisker pad , a value of 0 . 82 . During the approach phase and the touch phase , coherence was on average 64% and 74% as large , respectively , as its upper limit ( i . e . , proportion of the distance from chance level to the upper limit ) . In contrast , as the animals foraged in the open arena , the level of coherence compared to that derived from the bootstrap value was reduced during rest ( n = 163 episodes; PSI = 0 . 48 , p = 0 . 03 ) and was unaltered during walking ( n = 175 episodes; PSI = 0 . 52 , p = 0 . 79 ) . Absence of coherence could not be due to theta phase resolution , as theta power during foraging was as high as during the texture discrimination task ( Fig 3E ) . The average PSI during approach and touch was 46% greater than during foraging . In sum , when the rat approached and contacted the stimulus in the context of the tactile classification task , the level of coherence between hippocampal theta and whisking increased markedly with respect to the chance level and to that observed in the control condition . We examined the phase relationship between whisking and hippocampal theta during approach and touch . Fig 4C ( upper panel ) shows the phase delay ( Φ ) distribution in polar coordinates taken from all correct trials with significant value of coherence . Both during approach and during touch , the theta-whisking phase delay showed a borderline-significant degree of clustering ( approach: n = 714 , Rayleigh test , p = 0 . 06; touch: n = 776 , Rayleigh test , p = 0 . 04 ) . Though the phase delay was widely distributed across trials , the mean values are indicated by arrows: 4 . 25 radians during approach and 1 . 63 radians during touch . We posited that the efficiency of integration of stimulus information into memory and decision making centers is augmented when the rat’s sensorimotor rhythms are coherent with central oscillations , including that of the hippocampus . The prediction ensues that the likelihood of an incorrect choice would be greater on trials with low theta-whisking synchronization . Although an error could originate at any stage of processing , we hypothesize that some proportion of incorrect trials arose as a consequence of reduced theta-whisking synchronization . Consistent with the prediction , on incorrect trials mean PSI during the touch interval was 0 . 69 , significantly lower ( two-sample randomization test , 10 , 000 iterations , p = 0 . 0018 ) than the PSI of 0 . 74 on correct trials , when controlled for touch duration ( touch duration of correct versus incorrect trials , t test , p > 0 . 05 ) . The distribution of PSI values was more dispersed on incorrect trials , in accordance with the idea that not all errors happened for reasons related to the integration of sensory information into hippocampus . Our view of the functional role of sensorimotor/hippocampal coherence also led us to examine the relationship between PSI and trial duration ( the inverse of trial speed ) , measured as the time elapsed between first texture contact and withdrawal from the stimulus . We created a normalized trial duration measure such that a speedier trial gave a higher value , as follows: normalized trial duration = maximum trial duration for that rat − single-trial touch duration . The distribution of normalized trial duration values for all rats and all sessions is plotted in Fig 5A . On correct trials ( 915 trials , four rats ) , as expected , the value of PSI-touch was significantly and positively correlated with trial speed ( Pearson correlation , r = 0 . 136 and p = 3 . 8*10−5 ) . Unexpectedly , however , on these same correct trials the value of PSI-approach was negatively correlated with trial speed ( Pearson correlation , r = -0 . 068 and p = 0 . 040 ) . From these seemingly contradictory findings , we were drawn to consider an alternative sequence of events that might encompass both the approach and touch PSI observations: we asked whether the change in value of PSI from approach to touch ( PSI-touch—PSI-approach ) might be a more robust predictor of task performance than PSI-touch or PSI-approach taken separately . The chain of events could be as follows . Initiation of the approach toward the stimulus entails an increase in hippocampal theta power and phase-coherent whisking . Later , the afferent sensory volley arising from whisker contact with the plate acts as a timing signal to sharpen the sensorimotor/hippocampal synchronization and thus facilitates hippocampal processing of texture information; however , the timing signal is effective only on those trials in which hippocampus is receptive to phase modulation . In contrast , when the hippocampal oscillation is less receptive to the timing signal , sensorimotor/hippocampal synchronization upon contact may remain constant or even decline , thus slowing the execution of the task . As a first test , we looked for a correlation between within-trial PSI change ( = PSI-touch—PSI-approach ) and normalized trial duration . Fig 5B demonstrates that the change in PSI from approach to touch was significantly and positively correlated with faster trial execution ( Pearson correlation , r = 0 . 146 , p < 0 . 0001 , slope of linear fit = 0 . 16 ) . Next , we divided the set of trials of each rat into three groups , according to touch duration: fast , intermediate , and slow ( see Fig 5 legend for details ) . Fig 5C presents the paired PSI-approach and PSI-touch values for all trials of each group . Fast trials were characterized by an increase in PSI from approach to touch ( n = 329 , p < 0 . 001 ) , while slow trials were characterized , on average , by a decrease in PSI from approach to touch ( Wilcoxon signed rank test , n = 258 , p = 0 . 0017 ) . Intermediate trials showed no change in PSI ( n = 328 , p = 0 . 64 ) . Thus , on trials in which texture contact led to an increase in sensorimotor/hippocampal phase synchronization , rats turned toward the reward spout earlier , suggesting that they could convert sensory input to decision more rapidly . To confirm that PSI change was a more robust determinant of trial speed than were PSI-approach or PSI-touch alone , we grouped trials according to PSI-approach ( low , intermediate , high; details in figure legend ) and according to PSI-touch ( low , intermediate , high ) . PSI change predicted trial speed ( Fig 5D ) whether PSI-approach alone was low ( upper panel ) or intermediate ( middle panel ) ( Pearson correlation for low group: n = 248 , r = 0 . 233 , p = 2 . 16*10−4; for intermediate group: n = 290 , r = 0 . 129 , p = 0 . 027 ) . However , when PSI-approach was high ( lower panel ) , there was no significant effect of PSI change ( n = 377 , r = 0 . 043 , p = 0 . 403 ) . This result can be attributed to the scarcity of trials in which a high PSI-approach was followed by a positive PSI change—the scatter plot reveals PSI change truncated just above 0 and all points clustered to the left . There may be a PSI-approach threshold above which the tactile contact volley can provide no additional timing signal and hence no further boost in synchronization between the sensorimotor system and hippocampus . Likewise , PSI change predicted trial speed ( Fig 5E ) , whether PSI-touch was low or high ( Pearson correlation for low group: n = 232 , r = 0 . 136 , p = 0 . 037; for high group: n = 380 , r = 0 . 133 , p = 0 . 0091 ) . In the case of intermediate PSI-touch , PSI change was not well-correlated with trial duration ( n = 303 , r = 0 . 0591 , p = 0 . 301 ) . Taken together , these data suggest that even when conditional on restricted ranges of PSI-approach and PSI-touch , PSI change remains a predictor of the rat’s efficiency in collecting sensory data and acting upon its choice . As an additional test of the hypothesis that approach-to-touch PSI change was a primary factor underlying the rat’s performance , we selected trials in which PSI change was minimal ( -0 . 15 < PSI change < 0 . 15 ) . If PSI change predicts trial speed , then among these trials variation in PSI-approach ( Fig 5F , upper plot ) and variation in PSI-touch ( Fig 5F , lower plot ) should have little additional effect . As predicted , neither showed significant correlation with speed ( Pearson correlation , PSI-approach: n = 405 , r = 0 . 0517 , p = 0 . 299 . PSI-touch: n = 405 , r = 0 . 075 , p = 0 . 128 ) . The analysis above considered only correct trials . A further prediction is that PSI change , from approach to touch , may be lower or perhaps negative on error trials; this is confirmed in Fig 6 , which shows that PSI change was 0 . 024 averaged across all correct trials and -0 . 043 averaged across all incorrect trials ( correct versus incorrect PSI change , one-tail t test , p = 0 . 0073 ) . Next , we measured the PSI change values on correct and incorrect trials for three groups based on trial duration: slow , intermediate , and fast . Confirming the results of Fig 5C , on correct trials mean PSI change was negative , near zero , and positive on slow , intermediate , and fast trials , respectively ( Fig 6 ) . Statistical details are given in the figure legend . On incorrect trials PSI change tended to be negative , with no significant variation according to trial duration . This is consistent with the notion that poorer integration of sensory signals into hippocampus occurred when vibrissal contact with the stimulus failed to evoke a boost in coherence between the sensorimotor system and hippocampus , leading to slower execution of the behavior and even to errors . In conclusion , numerous analyses indicate that the efficiency with which the rats integrated sensory signals and converted the percept into an action was related to the magnitude of change in coherence between the sensorimotor systems and the hippocampus evoked by the rats’ contact with the stimulus . Having evaluated the relation between the whisking rhythm and hippocampal theta , in the following sections we consider the coherence between barrel cortex and these same two rhythms . The analysis is organized as barrel cortex coherence with whisking ( Fig 7A and 7B ) and barrel cortex coherence with hippocampal theta ( Fig 7C and 7D ) . We tested each barrel cortex neuron for non-uniformity in the phase distribution of its spikes with respect to the whisking cycle ( Rayleigh test , p < 0 . 05 ) . In accordance with previous studies [28 , 29] ) , the spiking of most barrel cortex neurons was phase-locked to whisking during the tactile discrimination task ( approach: 45 out of 51 [88%] , and touch: 43 out of 51 [84%] ) . To visualize phase relationships , we pooled all the spikes fired from the set of significantly phase locked neurons; in Fig 7A the black-outlined circle sectors represent the spikes’ distribution in relation to the whisking cycle during approach and touch . The radial extent of each sector indicates the proportion of all spikes that occupy that bin of the cycle ( bin width: 10 degrees ) . The underlying gray bins indicate the phase angle histograms obtained when the phase relationship between spike occurrence and instantaneous whisking phase was shuffled across time , which effectively assigned one random phase value to each spike . The arrow in each plot indicates the circular mean phase of spiking , proportional in length to the concentration parameter κ from the von Mises distribution ( see Materials and Methods ) . While , as expected , the shuffled spikes ( gray ) were distributed uniformly across the whole whisking cycle ( Rayleigh test , p > 0 . 1 ) , the spikes emitted during both approach and touch were significantly modulated in relation to whisking phase ( Rayleigh test , p < 0 . 0001 ) . The mean phase of spikes shifted from approach ( 2 . 09 radians ) to touch ( 3 . 56 radians ) : during approach spikes were concentrated late in the phase of protraction , whereas during touch spikes were concentrated around the start of retraction . The phase distribution of spikes from the entire set of neurons ( including those not significantly phase locked ) yielded similar results ( S1A Fig ) . The pooling of spikes , as above , provides an estimate of the message received by an observer ( target population of the brain ) that integrates all incoming signals with equal weights , ignoring the identity of the neuron emitting the spike; neurons with higher firing rate would have a larger influence on the observer . Next we considered the same dataset but , instead of pooling spikes , we examined the phase preference of each neuron with respect to whisking . By doing this , we examined the phase entrainment of the whole population , giving equal weight to each neuron and neglecting the heterogeneity in the number of spikes fired by each neuron . Cell-by-cell averaging of neuronal responses has been proved to be an effective method to decode information from a population of neurons [30 , 31] ) . Fig 7B shows the mean preferred phase of each neuron ( significantly phase locked neurons [Rayleigh test , p < 0 . 05] given by black delimited dots; non-significantly phase locked neurons by gray dots ) : angle denotes phase preference and distance from the origin denotes κ parameter . As was seen for pooled spikes ( Fig 7A ) , the phase preferences of neurons significantly entrained to the whisking cycle were clustered around late protraction during approach ( Rayleigh test: p < 0 . 000001 ) , whereas during touch preferred phases were clustered around early retraction ( Rayleigh test: p < 0 . 00001 ) . Mean angles across neurons with significant phase locking ( black arrows ) during approach and touch were 2 . 24 radians and 3 . 62 radians , respectively , and thus well aligned with directions derived from all pooled spikes ( Fig 7A ) . Arrows are proportional in length to the concentration of units around the mean phase value , where the maximum length is normalized to the radial axis , 0 . 5 . In spite of the increase in firing rate for most units upon touch , the strength of phase locking , per neuron , did not change significantly ( κapproach = 0 . 339 ± 0 . 087 , κtouch = 0 . 348 ± 0 . 112 , Wilcoxon rank sum test , p > 0 . 05 , see Materials and Methods ) . Similar results were obtained when neurons without significant phase-locking were included in the analysis ( gray arrows , Fig 7B ) . The whisking phase preference of all significantly phase-coherent neurons is also illustrated by the phase angle histogram in Supporting Information ( S2A Fig ) . Since coherence between hippocampal theta and whisking were significantly higher during tactile discrimination than during foraging ( Fig 4 ) , we expected barrel cortex units to be increasingly phase locked to hippocampal theta during the discrimination task . We explored phase relationships between barrel cortex and theta by the same procedures described above . Since this measurement did not rely on high-speed video of the whiskers , we could extend the analysis to the pre-trial onset epoch , as the rats waited for the foot bridge and texture plate to be presented ( waiting period ) . By direct observation , we noted that they actively whisked over the edges of the platform to detect the positioning of the bridge . First we tested each barrel cortex neuron for non-uniformity in the phase distribution of its spikes with respect to hippocampal theta ( Rayleigh test , p < 0 . 05 ) . Though the concentration of single cell’s preferred phases did not change from the waiting period ( n = 136 , κ = 0 . 242 ± 0 . 080 ) to the approach ( n = 161 , κ = 0 . 235 ± 0 . 064 ) and touch ( n = 163 , κ = 0 . 238 ± 0 . 088 , Kruskal-Wallis test , p > 0 . 05 ) , a larger proportion of barrel cortex neurons became phase locked to hippocampal theta , around their mean preferred phase . In the pre-trial period , 37 out of 136 barrel cortex neurons ( 27% ) were significantly coherent with theta . Once the bridge was in place , rats approached the texture and phase coherence with theta increased significantly: 92 out of 161 neurons ( 57% ) were coherent . During the touch epoch , the proportion of neurons coherent with theta decreased to 64 out of 163 ( 39% ) . In summary , during approach , the proportion of neurons phase-coherent with hippocampal theta exceeded the proportions during the waiting period ( two-proportion z-test , z = 5 . 1858 , p = 0 ) and the touch period ( z = 3 . 2204 , p = 0 . 001 ) . The percent of neurons with theta phase locking might have diminished from approach to touch because spike times were dictated less by internal brain dynamics and more by vibrissal kinematic events [32] , which could be distributed along both protraction and retraction . Likewise , a possible explanation for the preferred phase change is that as retraction began , neuronal firing was evoked by the translation of the whiskers along the textured surface . To examine phase coherence more directly , all the spikes fired by the significantly phase-locked neurons in each epoch were pooled and plotted in relation to the theta cycle . The results are given as phase angle histograms during waiting , approach , and touch in Fig 7C . The black-outlined circle sectors represent the spikes’ distribution in relation to the theta cycle during waiting , approach , and touch . The radial extent of each sector indicates the proportion of all spikes that occupy that bin of the cycle ( bin width: 10 degrees ) . The underlying gray bins indicate the phase angle histograms obtained when the phase relationship between spike occurrence and instantaneous theta phase was shuffled across time . As expected , spiking in the shuffled data was distributed uniformly across the whole theta cycle ( gray bins , Rayleigh test , p = 0 . 8 ) . All three conditions yielded significant modulation of spiking phase ( Rayleigh test , p < 0 . 00001 ) , although during approach the modulation was strongest . The arrow in each plot indicates the circular mean phase of spiking , proportional in length to the concentration parameter κ from the von Mises distribution . The mean preferred phase during waiting , approach , and touch were 1 . 80 , 3 . 18 , and 2 . 94 radians , respectively . The phase distribution of spikes from the entire set of neurons ( including those not significantly phase locked ) yielded similar results ( S1B Fig ) . Next we considered the same dataset but , instead of pooling spikes , we examined the phase preference of each neuron with respect to theta . Fig 7D shows the mean preferred phase of each neuron ) during pre-trial waiting , approach , and touch ( significantly phase locked neurons ( Rayleigh test , p < 0 . 05 ) given by black delimited dots; non-significantly phase locked neurons by gray dots ) : angle denotes phase preference and distance from the origin denotes κ parameter . Only approach and touch yielded significant clustering of neurons’ preferred theta phase of spiking ( Rayleigh test , p = 0 . 097 during pre-trial waiting , p < 0 . 00001 during approach , p < 0 . 05 for touch ) . Mean direction across neurons with significant phase locking during approach and touch , given by black arrows ( 3 . 18 and 2 . 70 radians , respectively ) , was in accordance with those derived from the analysis of all spikes ( Fig 7C ) . Length of the arrows , similar to Fig 7B , is proportional to the concentration of units around the mean direction . Similar results were obtained when neurons without significant phase-locking were included in the analysis ( gray arrows , Fig 7D ) . Arrows are not given in the left plot ( waiting for bridge ) because preferred theta phase of spiking was not significant . The theta phase preference of all significantly phase-coherent neurons is also illustrated by the phase angle histogram in Supporting Information ( S2B Fig ) .
Each trial began when the foot bridge rotated into the position whereby the rat could perch on it and approach the texture plate . Coherence between whisking and hippocampal theta augmented as the rat leaned forward ( “approach” ) , about 200–300ms before whisker contact occurred ( Fig 4B and 4C ) . In the dark , the noise produced by the motor that moved the bridge was a predictor of forthcoming stimulus availability . We suggest that external sensory inputs such as the motor sound , together with proprioceptive inputs such as leaning and reaching , triggered synchronization between sensory and memory structures . Before making their choice , rats typically make just two to four whisks upon the texture for a total duration of about 500 ms ( Fig 1; also see [35–37] ) . For this reason , preparatory entrainment would be important in permitting even the first whisker-to-discriminanda touch to be optimally processed . During texture palpation , whisking–theta synchronization increased with respect to the approach episode , indicating that coherence was also affected by extrinsic signals , namely , contact with the plate; a larger increase in synchronization predicted a faster correct trial ( Fig 5 ) and failure to increase synchronization was on average more associated with incorrect trials ( Fig 6 ) . This increase was accompanied by other changes: ( i ) CA1 theta peak frequency increased with respect to baseline and approach ( Fig 3D ) , ( ii ) whisking frequency became concentrated in a narrower band with peak at 10 Hz ( Fig 3F ) , and ( iii ) the phase difference between theta and whisking shifted and mean phase delay of most trials became as small as a quarter cycle ( Fig 4C ) . The increase in peak frequency of the theta rhythm ( from 7 . 87 ± 0 . 25 Hz at baseline to 8 . 75 ± 0 . 5 Hz during touch ) is the first finding , to our knowledge , of a rise in theta frequency related to sensory sampling in the context of a memory task . In contrast , earlier studies showed a downward shift in theta frequency during novelty detection and odor sampling onset in the learning phase [38 , 39] . Hippocampal theta is known to increase in power and frequency during locomotion through an environment , as compared to inactivity [26 , 40] . Inasmuch as the rats in our task moved only a few cm as they reached forward and sampled the stimulus , locomotion is not the best explanation for the increase in theta power and frequency . It has been suggested that the triggering of phase reset of hippocampal theta band by stimulus onset [41–43] , may ensure that sensory input is integrated at an optimal phase of the oscillation . This may have important implications for memory-related mechanisms that are associated with a specific theta phase [42 , 44–46] ) . For two independent oscillators , phase coherence can be achieved by resetting the phase of just one oscillator . In our dataset , we found indications consistent with hippocampal phase resetting by touch onset; however , the number of trials available was not sufficient to support robust statistical testing . Yet , the shift in preferred phase between theta and whisking at the junction from approach to touch ( Fig 4C ) is in line with these observations . During approach , most barrel cortex neurons tended to spike late in the protraction phase of the whisking cycle ( Fig 7A and 7B ) , consistent with earlier studies of rats whisking in the air ( reviewed by [47]; one study , however , found no marked phase preference [28] . During touch , preferred phases were clustered around the start of whisker retraction ( Fig 7A and 7B ) , the instant when whisker kinematic events are most likely to evoke spikes [37] . Moreover , because the timing of neuronal spikes in hippocampus is entrained to theta [48] , theta could act to organize the routing of information during sensory processing and memory retrieval [49 , 50] . Thus , the entrainment to theta of multiple brain regions [6 , 14 , 51] ) would augment the temporal precision by which neurons from downstream areas ( higher sensory and association areas ) would process rhythmic primary somatosensory spiking activity . In moments in which the rat’s attention is not directed to the identity of objects contacted through whisking , hippocampal theta may be coherent with non-tactile sensory systems . Recent work has suggested that cognitive operations including memory [6 , 14 , 52] , perception [53] ) , and decision making [50 , 54] are mediated by rhythmic modulation of cortical local field potential ( LFP ) . If synchronization between oscillators plays a functional role in information processing , the animal’s behavior must reflect variations in synchronization . In a spatial memory task , the degree of coordination between prefrontal cortex and hippocampus theta rhythms was correlated with behavioral performance [6] ) . During performing a Y-maze rule-learning task , theta coherence between prefrontal cortex and hippocampus was significantly higher after animals acquired the rule [14] . Moreover , trials with high coherence were , on average , associated with higher levels of performance . The degree of synchronization of the olfactory bulb and hippocampal theta rhythms in an odor-discrimination task was positively correlated with performance [55] , suggesting a functional significance of synchronized oscillation not only within widely distributed , multimodal spatial navigation systems but also within individual sensory systems . Still , the generality of coupling brain regions by coherent oscillation remains to be demonstrated . Many species , including rats , mice , flying squirrels , gerbils , chinchillas , hamsters , shrews , porcupines , and opossums whisk , yet their theta frequency bands vary much less than do their whisking frequency bands [56] . The coherence between whisking and theta might be an incidence in a particular species , under particular conditions . Furthermore , whisking is correlated with nose , head , and sniffing movements in some but not in other species [57] . Overall , there is not adequate evidence in the literature to specify to what extent the brain’s multiple sensory and motor systems are entrained to theta . Our finding of augmented PSI between the sensorimotor plant ( expressed through the whisking oscillation ) and the hippocampus during approach and touch raises the question of whether the sensorimotor system entrains hippocampal theta , or vice versa . Early lesion studies [18] showed that whisking is generated independently from the septo-hippocampal system; instead , the intermediate band of the ventral intermediate reticular formation ( vIRt ) is critical in generating the whisking pattern [58] . vIRt functions as the premotor pattern generator for rhythmic whisking and is part of a larger circuit devoted to inspiration ( pre-Bötzinger complex ) . The pattern generator is situated within nested , closed loops [59 , 60] that , as a whole , mediate vibrissae-based sensation and motor control . Hippocampal theta has been recorded in immobile rats prior to the initiation of lateral dodging movements in response to conspecific rats attempting to steal their food [61] . Following infusion of atropine into the medial septum , theta recorded during immobility is abolished and the rats are severely impaired at initiating movements . Thus , the trigger to the rat’s goal-directed actions ( reaching forward and palpating the texture ) might originate within the hippocampal complex and associated nuclei . However , once the task is initiated , sensory inputs may provide positive feedback: nucleus reticularis pontis oralis ( RPO ) and peduculopontine tegmental nucleus ( PPT ) —brainstem regions involved in the regulation of cortical arousal—receive somatosensory input [62 , 63] and have direct and indirect projections to the medial septal region of the hippocampal formation [61] . Electrical stimulation of pathways from RPO and PPT elicits theta oscillations in the hippocampal formation [61] . Based on the work summarized above , we suggest that the entrainment between hippocampus and the sensorimotor system may proceed by a sequence of events that includes the following elements: Hippocampal theta power increases prominently as the rat leans forward during approach ( Fig 3C ) . The boost in hippocampal theta facilitates the concentration of whisking power in the theta band ( note the change in whisking power from approach to touch in Fig 3F ) . Sensory inputs generated by contact of the whiskers provide positive feedback to the generation of hippocampal theta through the ascending brainstem pathways . The state of the hippocampal networks at the onset of approach and touch could predispose the system towards more or less efficient phase synchronization; we found that on trials when phase synchronization between whiskers and hippocampus increased upon touch , the trial was executed more efficiently and also there was a higher chance for the rat to perform correctly . In summary , we argue that there is no single direction of entrainment , but a reciprocal interplay in both directions . The present work confirmed earlier notions and added several insights to existing knowledge . Our study emphasizes the task-dependence of coherence: hippocampal theta rhythm was coherent with the vibrissal sensorimotor rhythm only during moments when information about tactile object identity was integrated into the hippocampus . Rhythmic activity extended to the barrel cortex , where the spiking of most neurons became phase-locked to whisking as the rat approached the texture . High levels of coherence between the whisking rhythm and hippocampal theta were associated both with better performance and a more rapid response .
Seven Wistar male rats ( Harlan Italy , S . Pietro al Natisone , Italy ) weighing about 300 g were housed in pairs and maintained on a 14/10-h dark/light cycle . All experiments were conducted during the dark phase of the daily cycle . Rats were habituated to handling for five days before experiments started . Food was offered ad lib throughout the experiment . Water was given during training as a reward and was also available ad lib for 10 min after training . The rats were under the care of a consulting veterinarian . Protocols followed the guidelines of EU Directive 2010/63/EU , established as Italian decree 26/2014 , and were approved by the SISSA Ethics Committee and the Italian Ministry of Health license numbers 569/2015-PR and 570/2015-PR . Seven rats were trained to perform a tactile discrimination task [23] . The training setup consisted of a 10 cm x 30 cm elevated platform with two lateral water spouts , a movable bridge , and a motor that positioned one of the stimuli , in random order , in front of the platform just prior to trial onset ( Fig 1A ) . Four 10 ( width ) x 3 ( height ) cm textured Plexiglas rectangles , designated S1–S4 , were used ( Fig 1B ) as stimuli . S1 was smooth . S2 was an irregular texture made by pressing P100 sandpaper into heat-softened Plexiglas . S3 and S4 were pieces of Plexiglas with vertical grooves of 1 mm width and depth cut at intervals of 2 mm ( S3 ) and 4 mm ( S4 ) . Each stimulus was associated uniquely with one of the two lateral and opposite spouts ( see Fig 1A for position of the spouts ) ; rats learned to identify the texture plates and turn to the associated spout in order to receive a water drop as reward . If , after touching the plate , rats turned to the incorrect spout , no water was delivered and a timeout was given during the inter-trial interval before a next trial could take place . Only the first choice of the animal was considered as valid . All rats except one were trained to discriminate two of the four textures; Rat 3 was trained to discriminate three textures , i . e . , to associate two of the stimuli with one water spout and the third stimulus with the opposite spout . Associations between texture and reward location were fixed for each animal but were varied across rats ( see Table 1 for stimulus/reward location pairings ) . Substitution of several different exemplars of S1–S4 ensured that rats did not use specific cues attached to one particular object . Moreover , potential olfactory cues were removed by cleaning with 75% alcohol at least once every session . The rat self-initiated each trial by perching on the bridge and extending forward to contact the stimulus . Once it identified the texture and turned toward one of the spouts , the bridge retracted , signaling the start of the inter-trial interval ( duration from 9 to 12 s ) . At the end of the inter-trial interval , the bridge came out again and the central motor positioned one of the stimuli in front of the platform , signaling the onset of the next trial . Central and lateral light sensors were used to monitor the rat’s position so that its behavior could be synchronized with electrophysiological recordings and high-speed video . The central sensor , positioned directly in front of the texture plate , signaled the start ( contact onset ) and end of touch , whereas sensors positioned adjacent to the lateral spouts signaled the end of turn and start of reward consumption . A high-speed video camera ( Optronis CamRecord 450 , Optronis ) was positioned above the stimulus to capture whisker movements with 512 x 512 pixel-images at 1 , 000 frames per second ( Fig 1C ) . The entire set up was commanded with LabVIEW software ( National Instruments ) . The rats’ performance in each recording session was above 70% correct ( Fig 2A ) . To verify that this performance was above chance , we simulated the percent correct by randomly shuffling texture labels across trials . Repeating the shuffling procedure for 500 iterations provided a distribution of performance values that could be expected by chance . Compared to the chance distribution , performance of each rat exceeded a threshold of p < 10−10 . Video clips during the discrimination task were recorded in a dim ambience using infrared light reflected in a circular mirror positioned under the rat; the head and whiskers were dark against a bright background . When the rat entered the field of view and approached the texture , it triggered the central sensor , which in turn triggered the video clip recording . The video clip lasted 750 ms and included the period from approach until withdrawal . We extracted whisker movements ( mean angle of all detected whiskers in each frame ) with the Standard Tracker free toolbox [24] . In addition to the discrimination task , two of the animals were also trained to walk across a 40 x 40 cm platform in which cereal flakes were delivered at random times and locations . A high-speed video recorder ( Motionpro 2000; Redlake ) was positioned above the platform to capture 800 x 600 pixel images of the whiskers at 200 frames per second as the rat foraged for a total of 30 min in each session ( Rat 5: five sessions , Rat 6: eight sessions ) . The platform had a red glass floor to filter bright white light and was illuminated from below so that the animal’s head and whiskers were dark against a light background . In each session we collected an average of 150 seconds of high-speed video . Video segments were cut into pieces termed “trials” with duration of 500 ms , equal to the mean duration of the texture palpation episodes analyzed during the discrimination task . We took only trials where the animal was continuously whisking and extracted whisker movement manually because image contrast was below threshold for the automated tracking program . Manual tracking consisted of detecting the retraction/protraction start and end frame , and reconstructing the whisking cycle by modeling it as a sinusoid . Trials were divided into two groups according to the rat’s actions: “walk” and “rest . ” Animals were anaesthetized with isofluorane or with a mixture of Zoletil ( 30 mg/kg ) and Xylazine ( 5 mg/kg ) delivered i . p . A craniotomy was made above left dorsal hippocampus and barrel cortex , centered 2 . 76 mm posterior to bregma and 3 mm lateral to midline [64] . A 12-tetrode microdrive ( Neuralynx ) was implanted ( hippocampal bundle: -3 mm AP , -2 mm L; barrel cortex bundle: -2 . 7 mm AP , 5 . 5 mm L ) and fixed by dental cement . Rats were given the antibiotic enrofloxacin ( Baytril; 5 mg/kg delivered through the water bottle ) and the analgesic caprofen ( Rimadyl; 2 . 5 mg/kg , subcutaneous injection ) for 1 wk after surgery . For 10 d after surgery , rats had unlimited access to water and food . Recording sessions in the apparatus began thereafter . Tetrodes were moved individually until they reached CA1 ( DV: 3 . 12 mm ) and barrel cortex layer 4–5 ( DV: 0 . 9–1 . 1mm ) . To acquire action potentials , the signal was amplified ( 1 , 000–5 , 000 times ) , bandpass filtered ( 300–6 , 000 Hz ) , and digitized ( 32 kHz ) . Spikes were sorted offline using MClust [65 , 66] ) . Most electrodes yielded multiunit neuronal clusters , but in some cases , we could isolate single units with a pronounced refractory period . After the first encounter with neuronal populations at the desired depth , electrodes were advanced in steps of 40 um . Only neurons with consistent action potential shape and interspike interval histogram throughout the session were included in the analysis . Data recorded in different sessions from the same tetrode were always considered to be different clusters . LFP was obtained from CA1 by treating the raw signal with amplification ( 1 , 000–2 , 000 times ) , bandpass filtering ( 1–475 Hz ) , and digitization ( 2 kHz ) . The reference electrode was the stainless steel guide tube touching the surface of the brain , plus a tetrode located in white matter as local reference . Online visual inspection of prominent theta waveforms in addition to histology confirmed the position of the tetrodes ( Fig 2C ) . Using optical position sensors and video analysis , we divided each trial into four episodes: ( i ) “approach” ( the rat reached the platform edge , perched on foot rest , and leaned forward to reach the texture plate ) , ( ii ) “touch” ( from onset of first stimulus contact to offset of final contact , provided by the central light sensor ) , ( iii ) “turn” ( from offset of final stimulus contact until arrival at the reward spout , given by the lateral sensors ) , and ( iv ) “reward” ( from arrival until departure from reward spout ) . Although well-trained rats carried out the task with stereotypical actions and there was no systematic difference across animals , the duration of these four episodes varied ( Fig 3A ) . Spectral analyses required all epochs to be of equal length . To that end , we set 500 ms , close to the 540 ms mean value of the touch interval across rats and sessions , to be the standard duration . For the reward episode , we selected an interval of 500 ms aligned to arrival at the reward spout . For approach we selected an interval of 500 ms extending back in time from touch onset . The fifth episode used for comparison was “baseline , ” taken as the interval from 2 . 5 to 3 s before first contact . The turn episode was not included in the spectral analysis . Power spectra in the time and frequency domains ( 4–20 Hz ) for whisking and hippocampal LFP were computed using a fast Fourier transform in the Fieldtrip toolbox [67] . Spectrograms were examined for the entire period extending 3 s on either side of first whisker contact , yielding a period that covered pre-trial baseline , approach , stimulus palpation , turn , and reward consumption . To inspect the time course of frequency changes , we computed the power spectrogram using an adaptive sliding time window eight cycles long ( Δt = 8/f ) multiplied by Slepian tapers [68] . Power was averaged over all trials of all recording sessions and normalized relative to the baseline ( taken 2 . 5 to 3 s before first contact ) . Each animal’s log-transformed power value ( 10 * log10 ( LEPpower ) ) from all conditions was divided by 4 Hz baseline LFP power ( 10 * log10 ( 4HZ ) ) , and then the mean and standard deviation of this normalized power were computed across animals for all conditions . For statistical analysis of LFP power change across episodes , we computed the power spectrum in the frequency domain in each behavioral epoch ( approach , touch , and reward ) with equal length of 500 ms . Two other episodes were included for comparison; ( i ) resting and ( ii ) walking , both in the open arena . We applied a Fourier transform to segments previously multiplied by a Hanning taper . The null hypothesis was that the power spectrum in the frequency domain was the same in each behavioral episode . To robustly estimate the sampling distribution of the test statistic , mean power value , we needed many samples . If the null hypothesis were true , then randomly changing the designation of the episodes would have no effect on the outcome . By randomly shuffling all trials we could generate an arbitrarily large number of datasets . After each of 1 , 000 shuffles , we calculated the desired test statistics in order to build a probability distribution for the null hypothesis . The ranking of the real test statistic among the shuffled test statistics gave a p-value , namely , the proportion of random partitions with mean power value exceeding the observed one . We applied Bonferroni correction for multiple comparisons of different frequencies . We calculated the mean and standard deviation of the peak frequency ( maximum value in the theta range ) across all rats and computed a one-way ANOVA with multiple comparison tests of all episodes during the discrimination task . The same analysis was done for whisking data . We measured phase synchronization between oscillations by the Phase Synchronization Index ( PSI ) [69] ) during approach and touch . We first bandpass filtered LFP ( x ) and whisking ( y ) signals in the range 5 to 12 Hz . We use notation of theta for filtered LFP . By applying the Hilbert transform [70 , 71] ) to the filtered data , we extracted instantaneous phase ( ϕxH , ϕyH ) . Asymmetries in wave shape of theta oscillation can vary in time and depend on filter settings , instantaneous theta power and frequency . To account for these asymmetries , a conservative approach was taken by testing the distribution of phases in each session for uniformity prior to unit analysis . This distribution then was corrected for the bias with a Ψ-transform [6] . We computed the phase difference between the signals , ΦxyH ( t ) -m≡nΦxH ( t ) ΦyH ( t ) . x and y will be synchronized if the phase difference of their analytic signals remains bounded for all t . Because theta and whisking have prominent spectral peaks at a similar frequency , we focused only on the 1:1 ( n:m ) locking condition Phase Synchronization Index ( PSI ) is defined as: PSI=〈cosθxy ( t ) 〉2+〈sinθxy ( t ) 〉2 where the bracket denotes average over time . The PSI varies from 0 ( the two signals are not synchronized ) to 1 ( constant phase difference ) . For the two behavioral episodes in which whisking video records were acquired ( approach and touch ) , PSI was computed on every trial , for equal lengths of approach and touch episodes ( 250 ms approximately ) across trials . We generated the confidence limit by a bootstrap method; we computed the PSI of 2 , 000 sets of theta and whisking data after shuffling signals across trials and , from this , calculated the 95% confidence interval about the population mean ( mean PSIshuffle = 0 . 507 ) . By similar tests , we measured PSI during two episodes—resting and walking—when the rat foraged along an open floor . We tested the consistency of phase delay across trials by the Rayleigh test . Once the Hilbert transform assigned instantaneous phase to whisking and to hippocampal theta , barrel cortex units’ spikes could also be assigned both a whisking and theta phase . The principal model against which our data was tested was the von Mises distribution , a unimodal distribution with probability density function f ( Φ|θ , k ) ) = ekcos ( Φ−θ ) / ( 2πIo ( k ) ) , and parameters κ ( concentration ) , θ ( mean angle ) and Io ( modified Bessel function ) . The function has maximum value at Φ = θ . Parameters θ and κ are analogous to the mean and variance in the linear normal distribution . For κ = 0 the von Mises distribution takes the form of a uniform distribution; f ( Φ ) = 12π . The larger the κ the more the distribution is concentrated around the mean angle , θ . To test data against the uniform distribution we applied the Rayleigh test , using statistic Z defined as Z = 2nr2 , where r = ∑j=1ncos ( θj ) ²+∑j=1nsin ( θj ) 2 Given the sum of n vectors representing the preferred directions , Z tests whether the mean length r is sufficiently large to indicate a non-uniform distribution . The large-sample asymptotic distribution of Z under uniformity is χ2 with two degrees of freedom [72] ) . Uniformity is rejected when Z is far from 0 . The alternative hypothesis is that the population has a von Mises distribution with small κ . We note that as mean vector length r depends on the sample size , so do all measures derived from r . Indeed , the statistical distribution under the null hypothesis is only true asymptotically . For small datasets , the null distribution of Z is typically broad , and therefore the null hypothesis is harder to reject; whereas with a large dataset the distribution can narrow . By varying sample size from 100 to 10 , 000 , values of κ were found to vary from 0 . 1 to 1 . 5 . We did a simulation [51] to see how the value of κ converges to the asymptote with increasing sample size and settled on the following criteria: neurons emitting fewer than 1 , 000 spikes per condition were not included in the analysis unless the Rayleigh test was significant . Because sample size has a large effect on the parameters computed , we compared phase modulation between conditions by taking samples of equal size . To avoid any sampling bias , we applied a bootstrap procedure by randomly sampling the original spike pool of each neuron for 500 iterations . In each iteration we calculated the statistics Z and the concentration parameter , k; then we calculated the median values using CircStat toolbox [73] . | Many regions of the mammalian brain exhibit oscillations in electrical activity . In rats , the 5–12 Hz theta rhythm is present in the hippocampus and in diverse areas of the cerebral cortex . What is the function of this rhythm ? One proposal is that the exchange of information between two brain regions is facilitated whenever their respective oscillations are coherent . To test this idea , we ask whether theta oscillation in the hippocampus , a crucial memory structure located deep in the brain , is coherent with the rat’s rhythm of moving its whiskers and sensing the physical environment with them . We acquired hippocampal local field potentials ( LFP ) —extracellular voltage fluctuations within a small volume—while rats classified textures using cyclical whisker motion ( “whisking” ) . At the moment of texture palpation , coherence between whisking and hippocampal theta oscillations increased by nearly 50% . At the same time , neuronal firing in sensory cortex became more phase-locked to the hippocampal theta oscillations . Rats identified the texture more rapidly and with lower error likelihood on trials characterized by an increase in hippocampal theta-whisking coherence during texture palpation . These results suggest that , as rats collect touch signals , enhanced coherence between the whisking rhythm , sensory cortex , and hippocampal LFP facilitates the integration of sensory information into memory and decision-making centers in the brain . | [
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] | 2016 | Coherence between Rat Sensorimotor System and Hippocampus Is Enhanced during Tactile Discrimination |
The in vivo kinetics of antigen-presenting cells ( APCs ) in patients with advanced and convalescent tuberculosis ( TB ) is not well characterized . In order to target Mycobacterium tuberculosis ( MTB ) peptides- and HLA-DR-holding monocytes and macrophages , 2 MTB peptide-specific CD4+ T-cell receptor ( TCR ) tetramers eu and hu were successfully constructed . Peripheral blood ( PBL ) samples from inpatients with advanced pulmonary TB ( PTB ) were analyzed using flow cytometry , and the percentages of tetramer-bound CD14+ monocytes ranged from 0 . 26–1 . 44% and 0 . 21–0 . 95% , respectively; significantly higher than those measured in PBL samples obtained from non-TB patients , healthy donors , and umbilical cords . These tetramers were also able to specifically detect macrophages in situ via immunofluorescent staining . The results of the continuous time-point tracking of the tetramer-positive rates in PBL samples from active PTB outpatients undergoing treatment show that the median percentages were at first low before treatment , increased to their highest levels during the first month , and then began to decrease during the second month until finally reaching and maintaining a relatively low level after 3–6 months . These results suggest that there is a relatively low level of MTB-specific monocytes in advanced and untreated patients . Further experiments show that MTB induces apoptosis in CD14+ cells , and the percentage of apoptotic monocytes dramatically decreases after treatment . Therefore , the relatively low level of MTB-specific monocytes is probably related to the apoptosis or necrosis of APCs due to live bacteria and their growth . The bactericidal effects of anti-TB drugs , as well as other unknown factors , would induce a peak value during the first month of treatment , and a relatively low level would be subsequently reached and maintained until all of the involved factors reached equilibrium . These tetramers have diagnostic potential and can provide valuable insights into the mechanisms of antigen presentation and its relationship with TB infection and latent TB infection .
With approximately one-third of the world's population infected with Mycobacterium tuberculosis ( MTB ) , tuberculosis ( TB ) continues to persist as a major infectious disease that significantly contributes to global morbidity and mortality [1] . However , 5–10% of infected individuals will eventually develop an active form of the disease . During TB infection , cellular immune responses are a critical part of the host's defense mechanisms [2]–[3] . Although the mechanisms of protection against TB are not completely understood , many studies have indicated the predominately protective role of CD4+ T cells [4]–[6] . MTB is endocytosed and survives in antigen-presenting cells ( APCs ) , such as macrophages , monocytes , and dendritic cells . Some APCs present antigens in association with major histocompatibility complex ( MHC ) class II molecules that then stimulate CD4+ T cells . This process is essential to MTB infection [7] , but the in vivo kinetics of APCs in patients with advanced and convalescent TB is not well characterized . Many methods are available for studying the interactions between the T-cell receptors ( TCR ) on epitope-specific T cells and the epitopes and MHCs on APCs . Fluorescence-labeled , tetrameric MHC-peptide complexes have been widely used to detect and quantify antigen-specific T-cell populations via flow cytometry . Since Altman et al . first described the use of peptide/human leukocyte antigen ( HLA ) tetrameric complexes to directly visualize antigen-specific cytotoxic T lymphocytes ( CTLs ) using flow cytometry in 1996 [8] , tetramerized MHC I and II complexes have been extensively used to quantify and characterize antigen-specific T cells [9]–[11] and probe TCR-MHC interactions . In 2004 , Subbramanian et al . extended the tetrameric technique to TCR and successfully constructed high-affinity TCR tetramers [12] . In 2008 , Wei H et al . developed γδ TCR tetramers in order to investigate the molecular mechanisms of the presentation of MTB-phospho-antigen to Vγ2Vδ2 T cells [13]–[14] . Soluble TCR tetramers have been utilized in a variety of functional assays , including the specific detection of target cells that have been pulsed with cognate peptide , discrimination between the quantitative changes that occur in antigen display at the cell surface , the identification of virus-infected cells , the inhibition of antigen-specific CTL activation , and the identification of cross-reactive peptides [13]–[19] . Until now , no MTB-specific CD4+ TCR tetramers have been reported . In the present study , we describe how we successfully constructed tetrameric CD4+ TCR complexes . Their binding specificities to monocytes obtained from peripheral blood ( PBL ) samples and macrophages in lung and lymph node sections from pulmonary TB ( PTB ) or lymph node TB patients and the inhibition of peptide-specific CD4+ T cells in PBL samples from patients with PTB were evaluated; In addition , any changes in tetramer-bound CD14+ monocytes from advanced and convalescent PTB outpatients were also tracked . MTB-specific TCR tetramers may provide a useful methods for detecting target cells and identifying specific , high-affinity interactions between HLA and peptides .
In our previous studies , MTB peptides E6 and E7 from early secreted antigenic target-6 ( ESAT-6 ) and C14 from culture filtrate protein-10 ( CFP-10 ) were confirmed as HLA-DR-restricted and specific TCR ligands of CD4+ T cells , while C5 from CFP-10 was identified as a specific TCR ligand of both CD4+ T cells that is restricted by HLA-DR and CD8+ T cells by testing PBL and pleural fluid ( PLF ) samples from active TB patients using the IFN-γ-enzyme-linked immunospot ( IFN-γ-ELISPOT ) assay , lymphocyte-proliferation and -blocking tests , and intracellular cytokine staining ( ICS ) . In this study , human MTB peptide-specific CD4+ T cells were obtained in order to access specific TCR tetramers . Mononuclear cells in PLF samples from patients with active tuberculous pleuritis were first analyzed using ELISPOT . Over 96% of the PLF samples reacted with the 4 peptides mentioned above , and 12–20% of enriched peptide-specific T cells were positively stained with the anti-CD4 monoclonal antibody ( MAb ) . After separation of the CD4+ T cells using magnetic beads , the cells were stained with carboxyfluorescein succinimidyl ester ( CFSE ) , and then allowed to proliferate in vitro by incubating them with the peptide for 9 days . After staining with anti-CD4-phycoerythrin ( PE ) , >98% of the pure , expanded , peptide-responsive CD4+ T cells were obtained following cell sorting . The CD4+ TCR α and β chain genes were successfully amplified from expanded peptide-responsive CD4+ T cells , each about 0 . 8 kb and 0 . 9 kb , respectively . Seventy-nine TCR α and β chain gene clones were isolated from 4 active TB patients . As shown in Table 1 , 2 CD4+ TCR tetramers , eu and hu , were constructed using 2 different TCR α chains ( e , accession number: HE862272 and h , accession number: HE862271; http://www . ebi . ac . uk/ena/ ) and the same TCR β chain ( u , accession number: HE862270 ) that contained the high-frequency VDJ repertoire ( AV12-3*01-J29*01/BV29-1*01-D2*01-J2-5*01 and AV1-2*01-J33*01/BV29-1*01-D2*01-J2-5*01 ) and complementarity-determining region 3 ( CDR3 ) amino acid sequences ( AMSARSGNTPLV/SLRDAKETQY and AVRDQNYQLI/SLRDAKETQY , respectively ) , which are the wild-type TCR α/β chains that were mainly cloned from subpopulations of C14- and E7-responsive CD4+ T cells that were obtained from an active TB patient ( patient 11 ) with an HLA background of HLA-DRB1*1503/*1504 and HLA-DRB1*08032 . Three other active TB patients shared the VDJ repertoire , the common CDR3 amino acid motifs of the α/β chains ( AV12-3*01/AMSA of patient 10 with the TCR α chain of the eu-tetramer and AV12-3*01/AVRD of patients 5 and 10 with the TCR α chain of the hu-tetramer , respectively , as well as BV29-1*01/TQY of patient 9 and BV29-1*01/ETQY of patient 10 with the TCR β chains of the eu- and hu-tetramers , respectively ) , and HLA-DRB1 alleles ( DRB1*150101 and DRB1*0818/*0806 , DRB1*1503/*1504 and DRB1*03 , and DRB1*1503/*1504 and DRB1*02023 in patients 5 , 9 and 10 , respectively ) . The expression of monomeric TCR complexes in the culture supernatant of Drosophila Schneider 2 cells ( S2 cells ) was verified by detecting the corresponding tags . The target protein in the supernatant was purified by Ni-NTA agarose , and the purified sample was concentrated . Small aliquots of the purified samples were monitored using SDS-PAGE , dot-blot and Western blot assays . These assays confirmed that about 60 kDa of the soluble , biotinylated TCR α/β monomer was obtained , similar to our previous study [20] . A panel of the MTB peptide/HLA-DR molecules that are displayed in the S2 cell lines ( i . e . , previously constructed , artificial APC lines ) were used to determine the affinity of the constructed TCR tetramers for different MTB-peptide/HLA-DR molecules . After 48 hours of induction using CuSO4 , the cells were incubated with PE-labeled TCR tetramer at 4 °C for 20 minutes and analyzed using flow cytometery . In order to determine the expression of HLA-DR in each cell lines , limited anti-HLA-DR antibody ( L243-fluorescein isothiocyanate [FITC]; BD Pharmingen , San Jose , CA , USA ) was co-incubated with the cells . Because the TCR-MHC-peptide interaction can be competitively blocked by L243 , the percentage of tetramer positivity does not represent the tetramer-positive staining of all HLA-DR-peptide complexes , but instead reflects the affinity of TCR tetramers for different HLA-DR-peptides . TCR tetramers were able to bind to MTB peptide C14/HLA-DRB1*08032 displayed in S2 cells , while only background staining was accomplished in cells without induction ( Figure 1B ) . The positive-detection rates ( Figure 1A ) of eu-tetramer staining in the cell lines that expressed peptides C14/HLA-DRB1*08032 , C14/HLA-DRB1*150101 , C5/HLA-DRB1*0404 , E6/HLA-DRB1*090102 , C5/HLA-DRB1*090102 , and C5/HLA-DRB1*150101 on the cell membrane were 18 . 65% , 10 . 90% , 7 . 30% , 6 . 40% , 6 . 32% , and 5 . 71% after induction , respectively . As for the hu-tetramer , the rates were <2 . 7% , except in C14/HLA-DRB1*08032 ( 3 . 13% ) and E7/HLA-DRB1*160201 ( 3 . 12% ) . The 2 tetramers did not react with non-induced cells or cell lines that only expressed the HLA-DR molecules ( Figure S1 ) . CD4+ T cells can be activated when TCRs on the cells were occupied by immunogenic peptide bound to an HLA II molecule , together with a co-stimulatory signal from the APC . Activation leads to cell proliferation which can be identified using CFSE T cell proliferation assay . Along with the cells that are labeled with CFSE on day 0 , upon cell division each CFSE-high cell will lose half of its CFSE labeling , so that the populations of CFSE-low daughter cells can be visualized using flow cytometry . On the other hand , when co-incubated with peptide-specific TCR tetramer , the tetramer competitively inhibits the binding of peptide-HLA to the TCR on CD4+ T cell . As a result , the proliferation of CD4+ T cells is suppressed . A single dose of TCR tetramer was added to the peripheral blood mononuclear cells ( PBMCs ) that were co-cultured with peptide on day 0 . After 10 days of culturing , the divided ( i . e . , low CFSE fluorescent ) CD4+ T cells were quantified . As shown in Figure 2 , the percentage of low-CFSE CD4+ cells was significantly lower in cells cultured with TCR tetramer and peptide than cells cultured with only peptides E7 , C5 , E6 , and C14 , respectively . However , there were no significantly differences between the cells incubated with or without TCR tetramer when the cells were stimulated with oncopeptide . This indicates that the eu- and hu-tetramers inhibit , to various degrees , the proliferation of CD4+ T cells that is induced by peptides E7 , C5 , E6 , and C14 , respectively , but do not inhibit oncopeptide-induced CD4+ T cells proliferation . Above all , the results show that the 2 pure , MTB-specific CD4+ TCR tetramers could be used as staining reagents to analyze tetramer-bound ( CD14+ ) APCs in clinical samples . In 76 active PTB inpatients , a median of 0 . 60% ( range: 0 . 26–1 . 44% ) of the CD14+ monocytes was positively stained with the eu-tetramer , while a median of 0 . 45% ( range: 0 . 21–0 . 95% ) was positively stained with the hu-tetramer in 104 active PTB inpatients . Some positively stained CD14+ monocytes were detected in a few samples from the healthy donor and umbilical cord blood groups , though there were definite and significant differences in the median percentages of tetramer-bound CD14+ monocytes between the PTB patient group and each of the control groups ( p<0 . 01 ) , as determined using the Mann-Whitney U test ( Table 2; Figures 3 and 4A [clustered bar graph] ) . The actual distribution of the percentage of tetramer-bound CD14+ monocytes in each sample is shown in Figure 4B ( scatter graphs ) . A few positive stained samples were found in the healthy donor group , which may have been related to latent TB infection . Nonetheless , high tetramer-positive samples were only apparent in the PTB patients . The PBL samples , which either demonstrated a high affinity for TCR tetramers or negative double-labeling staining to both tetramers , were selected for HLA-DR typing . The results show that the eu-tetramer has a high affinity for HLA-DRB1*13 , *16 , *11 , *07 , *14 , and *15 , while the hu-tetramer has a high affinity for HLA-DRB1*16 , *14 , *09 , *15 , and *04 ( Table 3 ) . These results indicate that the 2 TCR tetramers interact with multiple HLA-DR molecules . This is because these 2 tetramers consisted of TCR β chains with the same amino acid sequence and obtained from the same patient with an HLA-DRB1 background who shared some similar VDJ repertoires , common CDR3 amino acid motifs , and overlapping HLA-DRB1 backgrounds with other patients , as well as 2 similar α chains that consisted of the 2 TCR tetramers . In a follow-up study , continuous time point-tracked PBL samples from 9 active PTB outpatients ( Table 4 and Figure 5 ) were drawn every month during regular , 6-month-long , anti-TB treatments in order to assess the changes in the percentage of TCR tetramer-bound CD14+ monocytes using eu- and hu-tetramer staining and flow cytometric analysis . Figure 5 shows that the percentage changes in all patients followed roughly the same trends , except patient 7 . In patients 2 , 3 , 6 , and 9 , along with the amendment of TB symptoms ( although there were some small undulations ) , the median percentages were at first low before treatment , increased to their highest levels during the first month , and then began to decrease during the second month until finally reaching and maintaining a relatively low level after 3–6 months of treatment . These relatively low and small percentage changes were observed in patients 1 , 4 , 5 , 7 , and 8 and might be related to the different HLA backgrounds or immunity and disease statuses of the individual patients . Furthermore , group PBL samples from 7 continuous time point-tracked PTB outpatients groups , which included time points recorded before treatment and monthly samples obtained during regular 6-month-long anti-TB treatment periods , were detected using flow cytometry and eu- and hu-tetramer staining . Figure 6 shows that the median percentages of tetramer-bound CD14+ monocytes were 0 . 43% , 0 . 90% , 0 . 59% , 0 . 70% , 0 . 49% , 0 . 64% , and 0 . 74% at months 0 , 1 , 2 , 3 , 4 , 5 , and 6 , respectively , according to eu-tetramer staining , and were 0 . 31% , 0 . 62% , 0 . 20% , 0 . 39% , 0 . 26% , 0 . 43% , and 0 . 33% according to hu-tetramer staining , respectively . Similarly , as shown in Figure 5 which depicts the continuous time point-tracking results of 9 active PTB outpatients , the median percentages were at first low before treatment , increased to their highest levels during the first month , and then began to decrease during the second month until finally reaching and maintaining a relatively low level after 3–6 months of treatment ( although there was some undulation ) ; however , all patients demonstrated relatively higher levels than the healthy donor and umbilical cord blood groups . As shown in Table 2 , 0 . 35% and 0 . 35% of samples demonstrated positive eu-tetramer staining and 0 . 14% and 0 . 16% of samples demonstrated positive hu-tetramer staining , respectively . However , the Mann-Whitney U test determined that the statistical differences were mainly found between the treatment groups during the first month of treatment and both the healthy donors and umbilical cord blood groups ( 0 . 86% , 0 . 35% and 0 . 14%; p<0 . 001 and p<0 . 009 for eu-tetramer staining , respectively; and 0 . 62% , 0 . 14% and 0 . 16%; p<0 . 000 and p<0 . 000 for hu-tetramer staining , respectively ) ; no significant differences were observed between any PTB groups in terms of the results of the either of the tetramer tests , as determined by the Kruskal-Wallis H test ( p = 0 . 585 and p = 0 . 141 , respectively ) . Because TCR tetramers are HLA II-dependent , only MTB-specific APCs with matching HLA background can be detected by the 2 TCR tetramers . Therefore , the differences between the positive rates of the samples from different treatment periods would be disguised . Earlier researches have reported that APC apoptosis is an important process in TB [21] , [22] . We speculate that the relatively low level of tetramer-positive monocytes in blood from untreated TB patients is probably related to the apoptosis of APCs due to live bacteria and their growth; on the other hand , the increase in the first month after treatment may be related to the decrease in APC apoptosis . In order to verify these , MTB H37Ra-induced apoptosis of human acute monocytic leukemia cells ( THP-1 cells ) was examined by flow cytometry using Annexin V plus PI staining in vitro . The results show that the apoptosis of the THP-1 cells decreased following treatment with isoniazid ( INH ) ( Figure 7 ) . Analysis of CD14+ cells obtained from healthy donors , untreated or continuously treated PTB patients also showed that the percentages of apoptosis dramatically decreased following treatment ( Figure 8 ) . To determine the distribution and number of tetramer-bound and antigen-specific CD14+ macrophages in local TB lesions , fresh-frozen , 8-µm-thick sections of lung and lymph node from active TB patients were probed using the anti-MTB antibody and TCR tetramer or the anti-CD14 antibody and TCR tetramer , respectively , followed by nuclear staining with 4′ , 6-diamidino-2-phenylindole ( DAPI ) and observation with confocal laser-scanning microscopy . The results of both staining strategies demonstrated double-positive stainings in the lung and lymph node sections from 2 active TB inpatients with HLA-DRB1*040601/DRB1*110103 and HLA-DRB1*1202/DRB1*1202 backgrounds , respectively , and negative responses to the TCR tetramers and anti-TB antibodies in the lung and lymph node sections of 2 non-TB patients with HLA-DRB1*1504/DRB1*1202 and HLA-DRB1*1202/DRB1*0406 backgrounds , respectively ( Figure 9 ) . These results suggest that there are TCR tetramer-bound and MTB antigen-positive CD14+ macrophages ( i . e . , APCs ) in the local TB tissues .
In this study , we amplified TCR α and β chains from expanded peptide-responsive CD4+ T cells that were separated from ELISPOT-positive PLF mononuclear cells that were obtained from patients with active tuberculous pleuritis . High-frequency α and β chain families were analyzed and selected . The full-length TCRs were expressed and biotinylated using insect cells . The TCR heterodimers were purified and tetramerized . The affinities and specificities of the 2 selected TCR tetramers ( eu and hu ) for binding to MTB peptide/HLA-DR molecules were confirmed using a series of artificial APCs that expressed different MTB peptide/HLA-DR molecules in S2 cells . Although CD4+ TCR chain sequences are highly diverse , we found that MTB peptide-responsive CD4+ T cells derived from different individuals shared the exact same TCR α and β chain sequences , CDR3 sequences , genetic families , or common CDR3 amino acid motifs . The TCR α/β chain families that were present at a high frequency in the PLF mononuclear cells that responded to the same or different MTB peptides were selected for the construction of TCR tetramers . As shown in Table 1 , 2 peptide C14-responsive α chains from different CD4+ T-cells clones and 1 C14-responsive β chain from the same CD4+ T-cell clone ( these chains share the same or similar VDJ repertoire and CDR3 amino acid motifs ) were selected for the preparation of the eu- and hu-tetramers . Our data clearly demonstrate that these 2 tetramers are capable of recognizing MTB peptides in the context of multiple HLA-DR molecules , which is consistent with the results of earlier studies [23]–[25] . Meanwhile , both eu- and hu-tetramers could inhibit CD4+ T-cell proliferation at different levels , which is induced by a variety of peptides ( E7 , C5 , E6 , and C14 ) . These results indicate the different detection efficiencies and specificities of these 2 tetramers . On the other hand , because of the limited numbers of cases enrolled in this study , we were unable to determine all of the HLA-DR alleles that interacted with the tetramers . TB is characterized by the formation of local granulomas , caseous necrosis , and cavities where macrophages , their derived cells ( e . g . , Langhans-type multinucleated giant cells ) , and a variety of lymphocytes are recruited . Macrophages are believed to differentiate from the recruited monocytes in circulation . Both macrophages and monocytes are crucial cells involved in immune defense , in which bacteria grow and survive [26] , [27] . To investigate MTB-specific APCs in vivo in patients with advanced and convalescent TB , we evaluated the tetramer-positive monocytes in a series of clinical samples using flow cytometry . Inpatients with advanced PTB , both those who were untreated and those who had just begun treatment , were recruited to participate in the present study . In their PBL samples , the percentages of tetramer-bound CD14+ monocytes ranged between 0 . 26–1 . 44% and 0 . 21–0 . 95% by according to the results of eu- and hu-staining respectively . The percentage of tetramer-positive cells was significantly higher than those measured in samples obtained from non-TB patients , healthy donors , and the umbilical cord groups . The 2 tetramers could also specifically detect macrophages in situ in lungs and lymph nodes sections from untreated and advanced TB patients using immunofluorescentce staining . Surprisingly , continuous time-point tracking of the 2 tetramers in the PBL samples obtained from active PTB outpatients undergoing treatment demonstrated that the median percentages were at first low before treatment , increased to their highest levels during the first month , and then began to decrease during the second month until finally reaching and maintaining a relatively low level after 3–6 months of treatment; however , all demonstrated relatively higher levels than the healthy donor or umbilical cord groups . Higher detection rates were measured in the PBL samples obtained from the PTB inpatients group than samples obtained from the PTB outpatients group before anti-TB treatment . This may have been due to the fact that the former group contained some patients in the initial treatment stage ( usually within the first 20 days of treatment ) . Also , the inpatients were sicker than the outpatients and carried more MTB . These results suggest that there is a relatively low level of MTB-specific monocytes in the circulation of advanced and untreated PTB patients . The quantity of MTB-specific monocytes is probably related to the local recruitment of APCs , focus formation , and APC apoptosis or necrosis due to live bacteria and their growth . Earlier studies have reported that APC apoptosis is an important process and major event that is necessary to produce caseous necrosis in granulomas and other lesions during mycobacterial infection [21] , [22] and is associated with live mycobacteria and mycobacterial molecules [28]–[39] . On the other hand , MTB can also induce macrophage necrosis by inhibiting the repair of plasma membranes [40] . Placido et al . found that by using a virulent strain MTB H37Rv , apoptosis was induced in a dose-dependent fashion in macrophages that were obtained by broncho alveolar lavage from patients with TB [41] . In our research , when THP-1 cells were co-cultured with MTB H37Ra in vitro , apoptosis decreased when INH was added . In addition , we found that early apoptosis of CD14+ cells dramatically decreased after treatment . Because APC apoptosis decreased , the quantity of APC increased . Also , during the early stages of treatment , bacteria are killed or a large amount of MTB peptides are picked up and processed by APCs , which expands the T-cell population [42] . That , in turn releases IFN-γ , other cytokines and lymphokines then activate macrophages . Pedroza-Gonzalez et al . found that CD14+ cells were recruited into lungs by day 14 after MTB infection , significantly increased by day 21 ( approximately 16-fold over the control group ) , and elevated during the chronic phase of infection [43] . So , the decrease in the local recruitment and consumption of APCs and the bactericidal effects of anti-TB drugs would induce a peak in the quantity of monocytes for a short period of time during the first month and , subsequently , a relatively lower level would be reached and maintained due to equilibrium between the various factors involved . Perhaps in the later periods of treatment the peptide levels would decrease to the background level along with the clearance of bacteria . In our data , although the frequency of TCR tetramer-positive cells in PBL samples was low , the frequency of positive cells in the artificial APCs was high ( up to 18 . 65% ) . This implies a high affinity for the tetramers . Although there are no data on the frequency of TCR tetramer-positive cells in TB patients , low levels of peptide/MHC tetramer-positive cells in PBL samples obtained from TB patients have been reported in many studies [44]–[46] . In addition , low numbers of peptide/MHC II tetramer-positive cells have been reported in recent studies on the detection of CD4+ T cells in response to infectious agents , autoantigens , allergens and tumour antigens , with frequencies generally ranging from 0 . 02 to 0 . 6% of the total number of CD4+ T cells [47]–[54] . The low frequency of tetramer-positive cells may be due to the fact that most tetramer-staining studies on humans have relied on the enumeration of the T-cell populations that are present in circulating PBL not at the primary site of inflammation . Higher frequencies probably exist in the compartments that are more directly affected by the immune response of interest . Meyer et al . found a nearly 33-fold increase in the abundance of outer-surface protein A-specific CD4+ T cells at the primary site of inflammation [55] . Mice infected with the sendai virus demonstrated a remarkably high frequency ( 13% ) of activated CD4+ T cells in the lung sections using specific MHC-immunoglobulin multimers [56] . Moreover , in PBMCs obtained from TB patients or MTB-infected animals , the 10-fold expansions of peptide/HLA-DR tetramer-bound epitope-specific CD4+ T cells were seen after specific peptide stimulations in vitro [20] , [44] . Because the affinities of the CD4+ TCR tetramers are correlated with the patient's HLA II background to some degree , negative staining does not necessarily indicate that the sample came from a non-TB patient; however the sample may be from an TB patient with an unmatched HLA II background . The same level of tetramer staining reflects different amounts of the peptide presenting to the APCs due to the different HLA backgrounds of the patients . This HLA II restriction may be detrimental to the successful and extensive use CD4+ TCR tetramers , but perhaps we can solve the problem by mixing different MTB peptide-specific tetramers that match different HLA-DR backgrounds in a single reagent or arrange them into a protein array in order to reduce the false-negative rate . The exquisite binding sensitivity and specificity exhibited by these multimeric TCRs allows us to monitor quantitative modifications in the antigens displayed on the APCs and investigate the binding parameters of TCRs with cross-reactive HLA-DR . We carried out a small-scale study that consisted of monitoring PLF and cerebrospinal fluid ( CSF ) samples from patients with tuberculous pleuritis and tuberculous meningitis , respectively , using eu- and hu-tetramer stainings and flow cytometry , but were unsuccessful due to very low ratios or low numbers of monocytes and macrophages in these samples ( data not shown ) . Perhaps this problem can be solved by using a Ficoll-Hypaque density gradient , slide smears , and staining . Further studies are needed to obtain additional information about that how many HLA-DR-restricted TCR tetramers can bind with peptide/HLA-DR , the affinities between TCR tetramers and the different forms of HLA-DR , how in vivo dynamic changes in APCs are related to TB infection and latent TB infection , and the effects of anti-TB treatment . Our data are the first description of MTB-specific human CD4+ TCR tetramers . These soluble CD4+ TCR tetramers demonstrate great diagnostic potential and provide valuable insights into the mechanisms of antigen presentation and its relationship with TB infection and latent TB infection , and can potentially be used to develop revolutionary immunotherapies by enabling the targeted delivery of drugs .
The collection , delivery use of clinical samples obtained from TB patients and other control donors and the experimental procedures were approved by the Medical Ethics Committee of Zhongshan School of Medicine , Sun Yat-sen University , the Biosafety Management Committee of Sun Yat-sen University , and the Medical Ethics Committee of Guangzhou Chest Hospital , respectively . All of the patients and healthy donors gave written , informed consent before enrollment in this study . Patients were recruited from the Guangzhou Chest Hospital , Guangzhou , China between October 2009 and February 2011 . A diagnosis of active TB was made based on the following: ( 1 ) positive sputum smear or culture results for MTB; ( 2 ) the detection of active PTB lesions or extrapulmonary TB lesions by X-ray examination or the detection of active PTB or lymph node TB in tissue sections by MTB antigen-specific immunohistochemistry; and ( 3 ) the presence of typical symptoms such as cough , expectoration , bloody sputum or hemoptysis , chest distress , chest pain , short breath , and lymphadenovarix . The possibility of malignant lesions in the lung or lymph node sections was ruled out using these criteria . PBL samples , PLF samples , and frozen 8-µm-thick sections of lung and lymph node granuloma and cavernous tissues were obtained from the inpatients , who were either untreated or in the initial stages of treatment ( within the first 20 days ) , and used in this study , in addition to PBL samples that were collected from PTB outpatients during TB development and treatment . The PBL samples from the non-PTB patients and tissue sections from non-TB patients with pulmonary or lymph node infections were collected from the Guangzhou Chest Hospital . PBL samples from healthy donors and umbilical cord blood samples were collected from the Guangzhou Blood Center and Guangzhou Women and Children's Medical Center , respectively , and used as the study controls . The IFN-γ-ELISPOT assay was used to screen for CD4+ T cells that were secreting IFN-γ in response to MTB-specific peptides E6 , E7 , C5 , and C14 in PLF samples obtained from patients with tuberculous pleuritis , as described in previous studies [20] , [57] . Briefly , MultiScreen ELISPOT plates ( Millipore , Bedford , MA , USA ) were coated with 5 µg/mL mouse anti-human IFN-γ capture antibody ( eBioscience , San Diego , CA , USA ) and stored at 4 °C overnight . After blocking , 2 . 0–5 . 0×105 mononuclear cells from the PLF samples were co-incubated with the peptide at a final concentration of 10 µg/mL in a total volume of 200 µL per well for 16–18 hours in the culture medium ( complete RPMI with 10% fetal calf serum [FCS; Hyclone , Logan , UT , USA] ) . After washing , the wells were incubated with 250 µg/mL biotinylated mouse anti-human IFN-γ antibody ( eBioscience ) for 2 hours at room temperature . After washing again , the plates were incubated with 1∶10000 diluted streptavidin-conjugated alkaline phosphatase ( AP ) ( Pierce , Rockford , IL , USA ) for 2 . 0–2 . 5 hours . IFN-γ-specific spots were developed by adding BCTP/NBT solution ( Pierce ) into each well after washing . The reaction was stopped after 15 minutes by rinsing the wells with distilled water . Spots were counted using an ELISPOT reader ( Cellular Technology Ltd . , Shaker Heights , OH , USA ) . The CD4+ T cells were separated from the ELISPOT-positive PLF mononuclear cells using immunomagnetic anti-human CD4 particles-DM beads ( BD Biosciences , Franklin Lakes , CA , USA ) , according to the manufacturer's protocol , and resuspended in RPMI-1640 medium at a concentration of 1 . 0×107 cells/mL . CFSE ( Enzo Life Sciences , Lausen , Switzerland ) was added to the cell suspension to reach a final concentration of 5 µM . The cell suspension was incubated for 10 minutes at room temperature in the dark . Labeling was terminated by adding the same volume of 100% FCS in order to quench the free CFSE for 10 minutes at room temperature . The labeled cells were washed 3× with sterile phosphate buffer saline ( PBS ) containing 10% FCS , and then counted . Approximately 2 . 0–5 . 0×106/mL cells were seeded into 1 . 5 mL of culture media ( complete RPMI with 10% FCS ) that was supplemented with 10 µg/mL of the specific peptide and recombinant interleukin ( rIL ) -2 ( 30 IU/mL; PeproTech , Rocky Hill , NJ , USA ) per well in 24-well tissue culture plates ( Becton Dickinson , San Jose , CA , USA ) , then incubated at 37 °C in an atmosphere of 5% CO2 in order to allow the peptide-specific CD4+ T cells to expand . Two days later , the cells were administered 30 IU/mL rIL-2 and 10 µg/mL of the same peptide every 2 days for 9 days , then harvested and stained with PE-labeled mouse anti-human CD4 antibody ( CD4-PE; Ancell , Bayport , MN , USA ) . Finally , the peptide-responsive and expanded CD4+ T cells were sorted using a Coulter EPICS ALTRA cell sorter ( Beckman Coulter , Fullerton , CA , USA ) . The purity of the cells was always >98% . Total RNA was extracted from the peptide-responsive CD4+ T cells using the TRIzol one-step method , then reverse transcripted to cDNA using the BD SMART Polymerase Chain Reaction ( PCR ) cDNA Synthesis Kit ( BD Biosciences ) . The complete code sequences of TCR α and β chains were amplified using the multiplex PCR method and different primer groups . The specific product was purified , digested with restriction enzymes , and ligated using the pGEM-7Zf ( + ) vector ( Promega , Madison , WI , USA ) , then transferred to competent Escherichia coli DH5α cells . Positive recombinant clones were screened and identified by restriction-endonuclease digestion and sequencing . The frequencies of the TCR VA/VB and JB subfamilies and CDR3 sequences were analyzed using software provided with the International ImMunoGeneTics Information System® ( IMGT/V-QUEST , http://www . imgt . org/IMGT_vquest/share/textes/ ) . TCR α and β chains containing high-frequency CDR3 sequences were selected to construct the TCR tetramers . In order to construct the soluble biotinylated TCR molecules in the expressed cell lines , the transmembrane domain and intracellular fragments of TCR α and β chains were cut and the remaining parts were inserted into FB-pGEM-7Zf ( + ) or JB-pGEM-7Zf ( + ) vectors , respectively . These vectors were obtained in previous studies and contain complementary , hydrophilic , and polar amino acid fragments ( Fos and Jun ) and the BirA-dependent biotinylation substrate peptide ( BSP ) . TCRα-FB-pGEM-7z ( + ) and TCRβ-JB-pGEM-7zf ( + ) plasmids were constructed . The modified TCR α and β chains , which have the same CDR3 domain and amino acid sequences in many CD4+ T-cell clones , were chosen and subcloned into a eukaryotic expression vector , pMT/V5-His ( Invitrogen , Carlsbad , CA , USA ) , which consisted of a metal promoter , multiple cloning sites , the V5 epitope , and 6-histidine tags . pMT-TCRα , pMT-TCRβ , pMT/Bip-BirA , and pCoHygro ( 7∶7∶1∶1 molar ratio ) were cotransfected into S2 cells ( Invitrogen ) by calcium phosphate precipitation . The pMT/Bip-BirA plasmid contains the BirA gene that encodes biotin-protein ligase ( BirA enzyme ) . The pCoHygro vector ( Invitrogen ) expressing hygromycin was used to select stable transfected cell lines . Cells were cultured at 28 °C overnight in Schneider's Drosophila medium ( Invitrogen ) containing 270 µg/mL hygromycin-B and 10% FCS . After 4–5 weeks of hygromycin-B selection , a stable co-transfected cell line was established . Then , limited dilution cultivaions were processed until a stable monoclonal cell line was obtained , which was identified by PCR amplification of the sequences of TCR α , TCR β , and BirA carried by the corresponding recombinant vectors . The following specific primers of the C-region of TCR and BirA were used as follows: forward primer , 5′-ACTGTGCTAGACATGAGGTC-3′ , and reverse primer , 5′-CGCGTCGACTGACAGGTTTTGA-3′ , for the TCR α chain; forward primer , 5′-AGATACTGCCTGAGCAG-3′ , and reverse primer , 5′-CGCGTCGACCTCATAGAGGATG-3′ , for the TCR β chain; and forward primer , 5′-AAGGATAACACCGTGC-3′ , and reverse primer , 5′-TTATTTTTCTGCACTACGCA-3′ , for BirA . The selected stable S2 cell line that was cotransfected with pMT-TCRα , pMT-TCRβ , pMT/Bip-BirA , and pCoHygro was added to serum-free Schneider's Drosophila medium containing 160 µg/mL hygromycin-B . The expanded cells were transferred to 1 . 0-L conical flasks ( Wheaton , Millville , NJ , USA ) for large-scale culturing at 28 °C , placed on a 120–140-rpm rotary shaker , and secretory expression was induced using 500 mM CuSO4 . Heterodimers were directly biotinylated by cell cultivation using 1 . 0 µg/mL d-biotin for 72 hours . The clarified supernatants were collected by centrifugation . Free Cu2+ and other impurities in the medium were removed and the proteins were precipitated from the supernatant by adding polyethylene glycol 6000 ( PEG 6000; Sigma-Aldrich , St . Louis , MO , USA ) until the final concentration was 12% . The sediment was resuspended by constant stirring in PBS ( pH 8 . 0 ) containing 10 mM imidazole . The sample was applied to a Ni-NTA agarose affinity column ( Qiagen , Valencia , CA , USA ) , washed with PBS ( pH 8 . 0 ) containing 20 mM imidazole , and then recombinant biotinylated proteins with 6-histidine tags were eluted out using 250 mM imidazole . After dialyzation with PBS ( pH 8 . 0 ) , the purified samples were concentrated by ultrafiltration using a 30-kDa molecular weight cutoff concentrator ( Millipore ) . At each purification step , small aliquots were subjected to SDS-PAGE , dot blot and Western blot assays . Protein concentrations were determined using the Coomassie brilliant blue method . For the dot blot assay , the cultured supernatant or the purified solution was bound to polyvinylidene fluoride ( PVDF ) membranes ( Bio-Rad , Hercules , CA , USA ) . The membrane was blocked overnight at 4 °C in PBS containing 5% skim milk , then incubated overnight at 4 °C with a 1∶3000 dilution of the anti-TCR α/β antibody ( eBioscience ) or anti-V5 antibody ( BD Pharmingen ) , After washing with PBS containing 0 . 05% Tween 20 ( PBST ) , the membrane was incubated with AP-conjugated goat anti-mouse IgG antibody ( 1∶3000 dilution; Bethyl , Montgomery , TX , USA ) for 1 hour at room temperature . After intensive washing with PBST , the membrane was developed using BCIP/NBT solution for 15 minutes . To confirm biotinylation of the expressed TCR monomers , another blocked membrane was incubated with streptavidin-AP ( 1∶10000 dilution ) for 1 hours and developed using the BCIP/NBT solution . For SDS-PAGE , the purified protein samples in Ni-NTA agarose were run in 12% SDS-PAGE under denaturing conditions and stained with coomassie blue ( Bio-Rad ) in order to calculate the molecular weight of the protein by comparing its migration rate with that of standard protein markers . The expression and purified efficiency of recombinant TCR α , TCR β , and their heterodimers were determined by observing the bands . For the Western blot assay , proteins from the cultured supernatants or the purified solution were separated on 5–12% SDS-PAGE gels and transferred to PVDF membranes using Trans-Blot SD Cell ( Bio-Rad ) . Membranes were blocked overnight at 4 °C in PBS containing 5% skim milk . The TCR α/β chain was identified by overnight incubation in a 1∶3000 dilution of the anti-TCR α/β antibody in PBS at 4 °C . The membranes were washed 3× ( 5 minutes per wash ) in PBST and incubated for 1 hour with AP-conjugated goat anti-mouse IgG antibody in PBS at 1∶3000 dilution . Then , the membranes were washed 5× and detection was performed using the BCIP/NBT solution for 15 minutes . The purified biotinylated TCR monomers were mixed with one-eighth of the molar amount of streptavidin by repeatedly adding streptavidin 8× , and each reaction mixture was incubated for 5 minutes at room temperature . Subsequently , the final reaction mixture was incubated for 30 minutes at room temperature , purified , and concentrated by ultrafiltration and buffer exchange with PBS using a 100-kDa molecular weight cut-off concentrator ( Millipore ) . For staining and flow cytometric analysis of the PBL samples , the purified biotinylated TCR monomers were tetramerized with PE-conjugated streptavidin ( eBioscience ) ; as well as with pure streptavidin ( eBioscience ) for proliferation and blocking expansion assays of CD4+ T cells and with FITC-conjugated streptavidin ( eBioscience ) for in situ staining of the tissue sections , respectively . To compare the binding affinities of the TCR tetramers to specific HLA class II molecules , the S2 cell lines expressing different MTB peptide/HLA-DR molecules on the cell membranes ( using the previously constructed artificial APCs ) were cultured and screened for CD4+ TCR tetramers by flow cytometry . After 48 hours of induction by CuSO4 , the cells were incubated with streptavidin to block endogenous biotin , and then stained with the PE-labeled TCR tetramer and the FITC-conjugated mouse anti-human HLA-DR antibody ( L243-FITC; BD Pharmingen ) at 4 °C for 20 minutes before beginning flow cytometric analysis . S2 cells expressing only HLA-DR and non-induced S2 cells served as the controls . PBMC were isolated from the PBL samples obtained from the PTB patients and resuspended in RPMI-1640 medium at a concentration of 1 . 0×107 cells/mL . CFSE-labeled cells were stained and counted as described above . Approximately 3 . 0–5 . 0×105 cells/mL were seeded in 0 . 2 mL of culture media ( complete RPMI with 10% FCS ) per well in 96-well tissue culture plates ( Becton Dickinson ) in the presence of 10 µg/mL of the specific peptide in order to expand the peptide-specific lymphocytes . Peptide-specific expansion was performed either in the presence or absence of a single dose of the non-labeled TCR tetramer ( 1 µg ) and cultured for 9 days in complete RPMI medium containing 30 IU/mL IL-2 . Parallel lymphocytic expansion in the presence of an unrelated peptide ( oncopeptide ) was also concurrently monitored to determine the blocking specificity of the TCR tetramer . Peptide-specific and TCR tetramer-blocking expansion of the CD4+ T cells were monitored by CD4-PE ( Ancell ) staining and flow cytometric analysis . The tetramers were routinely titrated in order to obtain the optimal concentration for nonspecific binding . Antibodies and tetramers were added to freshly isolated PBMCs from PTB patients at different stages and control donors under optimized conditions: 1 . 0×106 mixed cells were stained with 1 . 25 µg of PE-labeled TCR tetramer in 80 µL PBS for 20 minutes at 4 °C in the presence of 1 . 3 µL FITC-labeled mouse anti-human CD14 antibody ( CD14-FITC; Ancell ) . The stained samples were analyzed using a Coulter EPICS XL-MCL flow cytometer ( Beckman Coultronics , Margency , France ) and FCS Express V3 software ( De Novo , Los Angeles , CA , USA ) . THP-1 cells were co-cultured with 3×105 MTB H37Ra . A single dose of INH ( Sigma-Aldrich; 30 µg/mL , 150 µg/mL and 250 µg/mL , respectively ) was added . Apoptotic cells were observed by annexin-V-FITC/PI ( BD Pharmingen ) staining and flow cytometric analysis at 24 and 48 hours . CD14+ cells obtained from PBL samples from healthy donors , and untreated- and treated- ( <5 days , 15–30 days and >30 days from the initiation of treatment ) TB patients , were sorted using CD14 immune magnetic beads ( Miltenyi Biotec , Freiburg , Germany ) , and early apoptosis was immediately detected using annexin-V-FITC/PI staining and flow cytometric analysis as described above . Two staining strategies were used to verify the specific binding affinities of the TCR tetramers . The first staining strategy was to identify the location of the MTB specific antigen and tetramer-positive cells in the lung and lymph node sections from TB patients; tissue sections from non-TB patients with pulmonary or lymph node infections were used as controls . Briefly , frozen sections were fixed in 4% polyformaldehyde , washed with PBST , and blocked using PBS containing 2% BSA or the avidin/streptavidin-biotin blocking kit for 30 minutes at room temperature , respectively . Then the sections were incubated in a moist container at 4 °C overnight with 50 µL of the FITC-labeled TCR tetramer ( final concentration: 10 µg/mL ) and rabbit anti-MTB antibody ( 1∶300 dilution; Abcam , Cambridge , United Kingdom ) , followed by incubation with mouse anti-FITC antibody ( 1∶300 dilution; Sigma-Aldrich ) for 30 minutes at room temperature . After washing with PBST to remove the unbound primary antibodies , the sections were incubated with Alexa 555-labeled goat anti-rabbit antibody ( 1∶2000 dilution; Invitrogen ) and Alexa 488-labeled goat anti-mouse antibody ( 1∶2000 dilution; Invitrogen ) for 1 hour at room temperature , then incubated with 1∶3000 DAPI for 5 minutes at room temperature . After washing with PBST , the sections were observed in situ using confocal laser-scanning microscopy to determine the distribution of the MTB-specific antigen cells that were bound to the tetramer . The second staining strategy was to determine if the tetramer-positive cells were APCs . The anti-CD14 antibody was used to identify monocytes and macrophages in fresh-frozen 8-µm-thick sections of lung and lymph node tissues from TB patients; the same controls described above were also used . Briefly , frozen sections were fixed in 4% polyformaldehyde , washed with PBST , and blocked with PBS containing 2% BSA or the avidin/streptavidin-biotin blocking kit for 30 minutes at room temperature , respectively . Then , the sections were incubated in a moist container at 4 °C overnight with 50 µL FITC-labeled TCR tetramer ( 10 µg/mL ) followed by the rabbit anti-FITC antibody ( 1∶300 dilution: Invitrogen ) for 30 minutes . CD14 was detected following incubation with mouse anti-human CD14 MAb ( 1∶200 dilution; Invitrogen ) for 1 hour . After removing the unbound primary antibodies and washing with PBST , the sections were incubated with Alexa 555-labeled goat anti-rabbit antibody ( 1∶2000 dilution ) and Alexa 488-labeled goat anti-mouse antibody ( 1∶2000 dilution ) for 1 h , then incubated with DAPI ( 1∶300 dilution ) for 5 minutes at room temperature . After washing with PBST , the sections were monitored in situ using confocal laser-scanning microscopy to determine the distribution of the tetramer-bound CD14+ macrophages . Clinical samples from the TB patients with high-affinity reactions to the TCR tetramers and samples from donors with negative reactions to the TCR tetramers on double-label staining were chosen for HLA genotyping . DNA was extracted from whole blood samples using the QIAamp DNA kit ( Invitrogen ) . The HLA type of each individual was determined by sequence-specific primer PCR ( SSP-PCR ) according to manufacturer's protocol . Data were statistically analyzed by using SPSS software ( version 16 . 0 for Windows; SPSS , Inc . , Chicago , IL , USA ) . Abnormally distributed data obtained from the flow cytometric analyses were presented using median values and the 25th and 75th percentiles . The results were compared using the Kruskal-Wallis H test for multiple ( i . e . , ≥3 ) independent samples , while the Mann-Whitney U test was used to compare 2 independent samples . Any p-values<0 . 017 and <0 . 05 were considered statistically significant , respectively . | Mycobacterium tuberculosis ( MTB ) is one of the most dangerous pathogens in the world . It is estimated that one-third of the world population contracts the bacteria during their lives . Approximately 5–10% of infected individuals will eventually develop an active form of the disease . Cellular immunity plays an important role in immunity against tuberculosis ( TB ) ; however , the host's defense mechanisms are not completely understood . Here , we developed a novel tool: MTB antigen-specific tetrameric CD4+ T-cell receptor ( TCR ) complexes that can detect MTB peptide-specific antigen presenting cells ( APCs ) in blood and local tissues . We found that a relatively low level of antigen-specific monocytes ( i . e . , APCs ) was detected in peripheral blood ( PBL ) samples from untreated TB patients , and then increased to their peak levels during the first month after treatment , which probably had something to do with the decrease in APC apoptosis . Our research provides a new method for tracking dynamic changes in APCs that are associated with TB infection and latent TB infection , and an additional tool for the studies of TB immunity and its pathogenesis . | [
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] | 2012 | Relatively Low Level of Antigen-specific Monocytes Detected in Blood from Untreated Tuberculosis Patients Using CD4+ T-cell Receptor Tetramers |
The de novo origin of a new protein-coding gene from non-coding DNA is considered to be a very rare occurrence in genomes . Here we identify 60 new protein-coding genes that originated de novo on the human lineage since divergence from the chimpanzee . The functionality of these genes is supported by both transcriptional and proteomic evidence . RNA–seq data indicate that these genes have their highest expression levels in the cerebral cortex and testes , which might suggest that these genes contribute to phenotypic traits that are unique to humans , such as improved cognitive ability . Our results are inconsistent with the traditional view that the de novo origin of new genes is very rare , thus there should be greater appreciation of the importance of the de novo origination of genes .
The origin of new genes has always been an intriguing evolutionary question [1] . New genes play significant roles in the evolution of lineage specific phenotypes and adaptive innovation [2] . The origin of genes can involve gene duplication , exon shuffling , retroposition , mobile elements , lateral gene transfer , gene fusion/fission , and de novo origination [1] . The mechanisms for many of these processes have been extensively studied; however , studies focused on de novo origination are few , and it is commonly considered to be a very rare process [3] , [4] . In 1970 , Susumu Ohno proposed that new genes arise from existing genes , and that the de novo gene origination of a gene from a random sequence would be highly unlikely [3] . Francois Jacob even claimed that “the probability that a functional protein would appear de novo by random association of amino acid is practically zero” in a paper he published in 1976 [4] . Today , we know that this evolutionary process is not impossible . For the de novo origin of a protein-coding gene two steps are needed [2] , [5]: ( 1 ) , the DNA must be transcriptionally active , and ( 2 ) it must evolve a translatable open reading frame; however , these two steps can occur in either order . Pioneering research in 2006 clearly showed that new genes could originate from non-coding sequences in Drosophila . Levine et al . identified five novel genes in Drosophila melanogaster that were derived from non-coding DNA [6] . These Drosophila genes were found to be expressed predominantly in the testes , and four of them were X-linked [6] . Similarly , Begun et al . found that the Acp genes , which code for small proteins in Drosophila , originated from noncoding DNA [7] . Over the next few years , there were several additional reports of the characterization of de novo-originated Drosophila genes [8]–[10] . In particular , Zhou et al . ( 2008 ) identified nine genes that originated de novo through a systematic search strategy , and proposed that the de novo origin of genes plays an important role in the origination of new genes , and estimated that about 11 . 9% of the new genes that originated in the Drosophila lineage had arisen de novo [10] , however , it is unclear whether all of these new Drosophila genes encode proteins . In 2009 , Knowles and McLysaght identified three putative protein coding genes: CLLU1 , c22orf45 , and DNAH10OS , which had a de novo origin in the human genome . These genes were identified by employing a straightforward , but rigorous , procedure which provided transcriptional and translational evidence , and allowed them to estimate that about 0 . 075% of the human protein coding genes may have originated de novo from noncoding regions [5] . Li et al . ( 2010 ) described another de novo protein-coding gene: C20orf203 , which is associated with brain function in humans [11] . Additional searches for de novo genes have resulted in the identification of two protein coding genes by Cai et al . [12] and Li et al . [13] in the Saccharomyces cerevisiae genome , a gene by Heinen et al . in Mus musculus that arose de novo within the past ∼2 . 5–3 . 5 million years in a large intergenic region [13] , a gene in rice [14] , at least 13 protein-coding genes by Yang and Huang in the Plasmodium vivax genome [15] , and a Drosophila gene , Noble , in a recent study by Gontijo et al . ( 2011 ) [16] . Despite all of these studies , the de novo origin of new protein-coding genes from non-coding DNA region in the genome is still considered to be a very rare event . The advent of large-scale genome sequencing has resulted in the bioinformatic prediction of many lineage-specific genes in genomes , suggesting that there may be a significant rate of de novo origin for genes . A large proportion of these genes , however , are likely falsely predicted genes [17] , [18] and the true numbers of functional de novo originated genes remains unclear . While gene duplication certainly plays a role in the origin of new genes [3] , we hypothesized that the rate of de novo gene origination is not extremely low and also plays an important role in the origin of new genes . Here by comparing genomes among primate species we identified 60 de novo-originated protein-coding genes in the human lineage , including 27 genes identified based only on genes found in Ensembl version 56 , and 33 genes identified based on the genes that were now excluded in version 56 of Ensembl , but were present in versions 40–55 of the human genome . Each of these new genes has both transcriptional and proteomic evidences supporting their functionality . The number of de novo genes that we found in the human genomes is much higher than that expected based on previous estimates of the rate of de novo origination , therefore , we suggest that a greater appreciation of de novo origination of genes is needed .
We performed a simple , conservative , but systematic pipeline to search for genes that originated de novo in the human genome since divergence from the chimpanzee ( Figure 1 ) . All human protein sequences were searched using BLASTP against the protein databases of other primates , i . e . chimpanzee , orangutan , rhesus macaque , and marmoset , with orthologs identified using an E-value threshold of 10−10 . After the BLAST procedure and excluding proteins shorter than 100 amino acids and short protein sequences from alternatively spliced genes , we retrieved 584 genes from the human genome that did not have a hit in other primates . Human sequences that did not have a start ( i . e . , ATG ) or stop codons were excluded and the remaining 352 genes were searched using BLAT against the chimpanzee and orangutan genomes in the UCSC database ( http://genome . ucsc . edu/ , [19] ) to identify orthologous sequences . In addition to the bioinformatic analyses all of the sequences underwent extensive manual checks . Human genes for which an orthologous gene region ( i . e . , highly similar sequences ) could not be identified in the chimpanzee or orangutan were discarded . Genes that had many duplicates in the human genome were also discarded . To be a candidate de novo originated gene , in addition to having a potentially translatable open reading frame in the human genome , the gene must have been present , and disrupted ( i . e . , non-translatable ) , in both the chimpanzee and orangutan genomes , e . g . , the chimpanzee and orangutan sequences must lack an ATG start codon or have frameshift-inducing indels or nucleotide differences that result in a premature stop codon . Chimpanzee and orangutan sequences lacking only an ATG start codons were searched to determine whether they had alternative start codons , either upstream or downstream of the human ATG that could generate frame complete translatable open reading frames . Chimpanzee or orangutan genes that possessed premature stop codons but retained predicted protein lengths longer than 80% of the human proteins were discarded for analysis , while those with predicted proteins that were shorter than 80% of the size of the human proteins were kept for the analysis of human de novo genes ( see Dataset S1 ) . To exclude the possibility that the new gene had been generated in the primate ancestor and then lost in parallel in both the chimpanzee and orangutan lineages we searched for human specific mutations that were responsible for generating the completed protein-coding open reading frame . Only those genes that had a human specific mutation that generates an open reading frame and where both the chimpanzee and orangutan retained the ancestral state at these positions , thus disrupting the open-reading frame , were kept ( see Dataset S2 ) . These stringent criteria yielded a set of 46 genes . Lastly , the coding sequences of these 46 putative de novo human genes were used as queries in searches of databases for evidence of expression at the mRNA and protein level . Expression at the mRNA level was assessed by BLASTN searches of the NCBI ( http://www . ncbi . nlm . nih . gov/ ) nr ( non-redundant ) database , to search the corresponding matched expressed mRNA sequence , and the UCSC ( http://genome . ucsc . edu/ ) EST database , to search for short expressed sequence tags . Evidence for the existence of the protein was obtained through searches of two proteomic databases , PRIDE [20] and PeptideAtlas [21] ( Dataset S3 ) . The PRIDE and PeptideAtlas databases are composed of peptide sequences derived from proteomic experiments . Searches of these databases resulted in the identification of 27 novel human genes that have matching expressed mRNA sequences in the GenBank or UCSC databases , thus must be transcribed , and also have evidence for being translated as they have matching peptides from the proteomic databases ( Table S1 ) . The mRNA evidence suggests that none of these human genes have splice variants . Intriguingly , CLLU1 , c22orf45 , and DNAH10OS , three human genes identified as having a de novo-origin by Knowles and McLysaght [5] were not found by our search . Knowles and McLysaght [5] had used protein data from version 46 of Ensembl for their study while we use sequence data from version 56 . c22orf45 and DNAH10OS were no longer annotated as genes in version 56 of Ensembl , however CLLU1 still was . The peptide , PAp00140670 ( HIIYSTFLSK ) , that supported the translation of CLLU1 , though , is no longer present in the current build of PeptideAtlas [21] , yet the peptides that support the translation of c22orf45 and DNAH10OS still remain in the proteomic database . Thus the absence of a supporting peptide , for CLLU1 , and the absence of annotated genes , for c22orf45 and DNAH10OS , prevented our approach from identifying these three previously identified genes as having a de novo origin . Given the differences in protein content between versions 46 and 56 of Ensembl , we therefore identified protein sequences that had been present in previous versions of the human genome ( Ensembl versions 40–55 ) but were no longer annotated as gene products in version 56 . These human protein sequences were then used in BLASTP searches against other primate protein databases , adopting the same pipeline that we described above , resulting in the identification of an additional 33 de novo-originated protein coding genes that are supported by human expression and proteomic data ( Figure S1 , Table S2 , Dataset S1 , Dataset S4 and Dataset S5 ) . Of the three de novo genes , CLLU1 , c22orf45 , and DNAH10OS , identified by Knowles and McLysaght [5] , only DNAH10OS ( ENSG00000204626 ) was identified in our study . As described above , peptide PAp00140670 ( HIIYSTFLSK ) that supported the translation of CLLU1 is no longer present in the current build of PeptideAtlas , thus does not meet our criteria of a de novo gene with transcription and translation evidence . The orangutan genome predicts a gene sequence orthologous to c22orf45 that has a complete translatable open reading frame , suggesting that it has a much earlier origin . It is important to note that the sequences of all of our 60 predicted de novo genes , 27 from the original screen and 33 from our subsequent screen are present in the most current version of the human genome ( GRCh37/hg19 ) , thus all 60 genes were kept for our subsequent analyses . We identified a total of 60 protein-coding genes that originated de novo on the human lineage since divergence from chimpanzee . Each of these new genes is found as a single copy coding gene , with no other highly similar coding sequence in the human genome , indicating that they were not generated by gene duplication in the human genome . In addition , the orthologous sequences in the chimpanzee and orangutan genomes are found as single copies ( except ENSG00000230294 which has two orthologous copies in the orangutan , but both of these sequences are disrupted , see Dataset S2 for sequence alignment ) . Pairwise divergences between the sequences were consistent with the accepted one-to-one orthologous relationships between human , chimpanzee , and orangutan . All of the de novo genes were found to be composed of a single exon , with the exception of ENSG00000204292 , which has two . Only one of the genes is located on the X-chromosome; the remainders appear to be distributed randomly to the autosomes . To determine whether these new genes are fixed in human population , we searched the human population polymorphism data in HapMap ( Phases I , II , and III , http://hapmap . ncbi . nlm . nih . gov/ ) . There was no evidence for deletion or insertion of any of the genes from the HapMap data . Only one of the genes , ENSG00000206028 , was found to have a SNP causing a premature translation stop . This observation suggests that ENSG00000206028 has not been fixed in the human population . Our finding of 60 de novo genes , 59 of which are fixed in the human population , suggests that the de novo origin of protein coding genes on the human lineage is not a rare event . Since the chimpanzees and humans shared a common ancestor ∼5–6 million years ago , this indicates that the rate of origin of de novo genes is ∼9 . 83–11 . 8 genes per million years , an estimate that is much higher than previously reported [5] , [10] , [22] . To gain insight into the potential functions of these de novo originated genes we examined the expression of these genes using RNA-seq data . RNA-Seq is a recently developed approach for transcriptome profiling using high-throughput sequencing technologies , and is powerful for detecting the expression of genes [23] . Here , we examined the expression of the de novo originated genes using previously described RNA-seq align data [22] , [23] from 11 human tissues: adipose , whole brain , cerebral cortex , breast , colon , heart , liver , lymph node , skeletal muscle , lung and testes . Since the exact transcripts for the de novo genes had not been defined , we defined the expression level of these genes as the numbers of unique RNA-seq reads that map to the coding region divided by the length of the coding region , instead of typically used number of reads mapping to a transcript divided by transcript length . Evidence for expression , i . e . , the mapping of reads , was found in the RNA-seq data for 53 of the 60 genes . Expression data for the 7 genes not represented in RNA-seq data had been found from other sources ( e . g . , EST data ) in the NCBI database . Of these seven genes , three had evidence of expression in tissues other than the 11 tissues represented by the RNA-seq data , and four had evidence for expression in the brain , testis or lung . The failure to find evidence for expression of these four genes with RNA-seq data , despite evidence from the NCBI data , may suggest that these genes are expressed are a very low level in these tissues , or the site of expression of the NCBI data may be incorrect ( e . g . , due to contamination by other tissue ) . Typically , the expression levels of the de novo originated genes are very low . The mean level of gene expression , as defined by the number of reads mapping to these genes divided by the total length of their coding sequences , is highest in the testes , and second highest in the cerebral cortex ( Figure S2 ) . After normalizing for the numbers of valid reads , highest expression was still found in the testes , and the second in the cerebral cortex ( Figure 2A ) . Interestingly , the tissue that had the largest proportion of the de novo genes expressed was the cerebral cortex , with the second being the testes ( Figure 2B ) . Normalized expression levels of the 53 genes with RNA-seq expression data for the 11 tissues were sorted from highest to lowest . The proportion of genes having highest expression level in the tissue , which was defined as the numbers of genes having highest expression level in the tissue divided by total gene number ( i . e . 53 ) , was highest in cerebral cortex followed by the testes among these 11 tissues ( Figure 2C ) ; however , a similar pattern was not observed for the proportion of genes having second , third , or fourth highest levels of expression ( Figure S3 ) . In addition , we also obtain these patterns of the genome wide genes , and normalized these values of de novo genes by dividing the values of genome wide genes . In consistent , the level of gene expression , normalized expression level and the proportion of genes having expression evidences are still highest in the cerebral cortex and testes , except the proportion of genes having highest expression level ( Figure S4 ) . Several genes were found to have intriguing expression patterns ( Figure S5 ) . For example , gene ENSG00000187488 is highly expressed in the testes and thus we speculate that this gene may have a role in reproduction . ENSG00000206028 is highly , and specifically , expressed in the cerebral cortex , suggesting that this gene may contribute to the development of the human brain and associated cognitive abilities . To determine whether the de novo genes had come under selective constraints , which would indicate that they had acquired a function , we examined the rate of sequence evolution of these genes . Substitution rates for these sequences were calculated for both the human and chimpanzee lineages and these rates were compared to the genome-wide average rate for genes . The substitution rate for de novo genes was found to be higher than the genome-wide average rate on both the human and chimpanzee lineages ( Figure 3 ) , with the chimpanzee sequences evolving at the highest rate . The chimpanzee sequences were expected to evolve at a high rate , as these sequences should act as non-coding sequences rather than genes . The human sequences also evolved at an elevated rate , but at a rate that was slightly lower than that seen on the chimpanzee lineage . This observation is not an unexpected result if these had become functional genes as these new genes originated very recently from non-coding regions on the human lineage , and thus should have been under selection for only part of the time since divergence from chimpanzee , and thus should have a rate higher than the genome-wide average , but lower than the chimpanzee lineage . In addition , young genes have been found to tend to be the subject of weaker purifying selection [24] , thus should have higher substitution rates .
Here , we discovered 60 genes that originated de novo on the human lineage , with 59 of them being fixed in the human population . This number of genes implies a rate of de novo generation of ∼9 . 83–11 . 8 genes per million years , a rate much higher than previously proposed rates [5] , [10] , [22] . Despite this high rate , when the rate is expressed in terms of per gene , ∼0 . 00033–0 . 00039 per gene per million years , it is still a lower rate than the rate of new gene origin by gene duplication [25] , [26] . Our estimated rate , though , for de novo origin may be underestimated due to the conservativeness of our pipeline . First , as described above , in our pipeline , translatable open reading frames must have been complete in the human genome and disrupted in both the chimpanzee and orangutan genomes to be candidates as a de novo gene . Genes that did not have a clear ortholog ( i . e . , a sequence with very high similarity ) in either the chimpanzee or the orangutan genomes ( both of which are less complete than the human genome , and thus could be a missing genes ) were not used . It is also often difficult to determine whether a protein-coding gene originated specifically on the human lineage or if it originated in a primate ancestor but was then lost on both the chimpanzee and orangutan lineages . The conservativeness of our pipeline thus only allowed us to accept genes where we could clearly show human specific mutations generated complete protein-coding reading frames , and that these were conserved for disrupting state in both the chimpanzee and orangutan genomes . As both the chimpanzee and orangutan sequences should be non-functional sequences , and thus not under selection , there is a reasonable likelihood that a second mutation , in addition to the human open reading frame completing mutation , could have occurred in the chimpanzee or orangutan that would prevent us for identifying these genes as having a de novo origin on the human lineage . A total of 69 genes ( 20 shown in Figure 1 and 49 in Figure S1 . were excluded from our analysis as the ancestral state of the human specific mutation was not conserved in chimpanzee and orangutan . Second , we used only two proteomics databases: PRIDE [20] and PeptideAtlas [21] to show that these genes were translated; however , proteomic data is still limited in terms of tissues and developmental stages sampled and evidence for the protein products of some genes is likely lacking from the current versions of these databases . Here , 56 genes having human specific mutations but no supported peptide evidence were excluded . More diverse proteomic datasets may demonstrate that additional de novo originate genes are indeed protein coding . RNA-seq expression data suggest potential functions for some of the de novo originated protein-coding genes . De novo genes show higher expression in the cerebral cortex relative to other examined tissues . The brain is responsible for cognitive abilities that occur primarily in the cerebral cortex which is the furrowed gray matter covering the cerebral hemispheres [27] . The cerebral cortex plays key roles in learning , memory , language , thought , emotion , perceptual awareness , and consciousness [27] . Great efforts have been made to explore the origin and evolution of human cognitive ability [28] , including examining the contributions of positive natural selection on brain development genes [29] and changes in the expression [30]–[32] and alternative splicing of genes expressed in the brain [33] . Our results provide new information for the field and suggest that de novo originated genes may also be responsible for some of these characters . Many new genes , generated by diverse mechanisms including gene duplication , chimeric origin , retrotransposition , and de novo origin , are specifically expressed or function in the testes [6] , [34]–[38] ( reviewed in [2] ) . Henrik Kaessmann hypothesized that the testis is a catalyst and crucible for the birth of new genes in animals [2] . First , the testes is the most rapidly evolving organ due in part to its roles in sperm competition , sexual conflict , and reproductive isolation [2] . Second , Henrik Kaessmann speculated that the chromatin state in spermatocytes and spermatids should facilitate the initial transcription of newly arisen genes [2] . The reason for this is that there is widespread demethylation of CpG enriched promoter sequences and the presence of modified histones in spermatocytes and spermatids [39] , causing an elevation of the levels of components of the transcriptional machinery , permitting promiscuous transcription of nonfunctional sequences , including de novo originated genes . While this study has resulted in the identification of 60 novel human genes , and emphasized the underappreciated role of de novo origin of genes , there are several important caveats to our study . First , the protein evidence is based on only two proteomic databases: PRIDE [20] , and PeptideAtlas [21] , both of which have many limitations . For example , the sampling of proteomic databases are still limited to a small number of tissues and developmental stages , and problems with sample contamination still need to be resolved [40] . As larger and better proteomic databases become available the evidence in support of the translation of these novel genes will be strengthened . Second , many of these new genes are expressed at very low levels in the 11 tissues that had available RNA-seq data . These results indicate that many of these genes may play only weak biological roles , or that their functions are not well established .
Human protein sequences from Ensembl version 56 were used as queries for BLASTP [21] searches against the proteins of chimpanzee , orangutan , rhesus macaque , and marmoset with significant hits being those with an E-value lower than 10−10 . The coding sequences of the human proteins that did not record a significant BLAST hit against any of the other primate genomes were used as queries in BLAT searches of the chimpanzee , orangutan genomes to identify orthologous sequences . Non-human primate sequences that contained a frame-shift or premature stop codon that prevented the translation of a protein of at least 80% of the size of the human predicted proteins were considered to be non-protein coding . BLASTN searches with the human coding sequences against the nr ( non redundant ) database in the NCBI were used to identify matching expressed mRNA sequences . EST database download from UCSC ( http://genome . ucsc . edu/ ) was also searched by BLASTN for expression evidence . We searched two proteomics databases: PRIDE [20] and PeptideAtlas [21] ( 2010-05 ) , to determine whether a candidate de novo-originated gene had known exact match peptide data . The peptides in these proteomic databases had been identified by a variety of methods from diverse healthy cells , tissues , and fluids . The recently developed RNA-seq technique has proven to be a powerful approach to detect the expression of genes [23] . RNA-seq data from 11 human tissues: adipose , whole brain , breast , colon , heart , liver , lymph node , skeletal muscle and testes were obtained [41] and downloaded from NCBI with accession code GSE12946 , and from cerebral cortex and lung from [42] with NCBI accession code GSE13652 . Only reads that mapped to a unique location in the genome were considered . Since the exact transcriptional units of these new genes has not been defined , the expression level of the genes was defined as the numbers of unique reads mapping to the coding region divided by the length of the coding region . Expression levels were normalized by dividing by the total number of valid reads in the samples . Expression levels of 19 , 800 human genes evaluated by RNA-seq data described above in the 11 tissues , which were obtained from study [43] , were used to evaluate genome wide expression pattern . In the study the expression level of a gene in a tissue was defined by the number of valid hits to the gene divided by the effective length of the gene , then was normalized by dividing the total number of valid hits in the tissue [43] . To calculate the evolutionary rates of sequence we used an approach similar to that used in a previous study [44] . Human protein sequences were used to identify one-to-one orthologous genes with BLASTP searches against the chimpanzee and orangutan protein sequences . Reciprocal searches were performed using the chimpanzee and orangutan proteins to query the human proteins to confirm orthology . A total of 16 , 126 proteins with reciprocal best hits in both human/chimpanzee and human/orangutan searches were retained for further analysis . Orthologs with sequences containing “X” amino acid for “N” in the coding sequences were excluded . Sequences of orthologs were aligned by ClustalW [45] . To exclude incorrect alignments and nonorthologus regions from alignments , we used a sliding window of 5 amino acids , moved the sliding window by one codon for each step , to examine the quality of the alignments . If the aligned human and chimpanzee sequences within a window have a similarity ≤20% , then the orthologs were discarded . Finally , protein sequence with the longest amino acid alignments were retained for each gene , and alignments containing <100 amino acids were discarded . A total of 14 , 050 one-to-one orthologous genes among human , chimpanzee , and orangutan were identified . The baseml program , implemented in the PAML package , with the HKY85 substitution model was used to calculate the substitution rates in the human and chimpanzee lineage for each gene [46] . Genes that had a substitution rate on the human or chimpanzee lineage of greater than 0 . 1 were discarded . | The origin of genes can involve mechanisms such as gene duplication , exon shuffling , retroposition , mobile elements , lateral gene transfer , gene fusion/fission , and de novo origination . However , de novo origin , which means genes originate from a non-coding DNA region , is considered to be a very rare occurrence . Here we identify 60 new protein-coding genes that originated de novo on the human lineage since divergence from the chimpanzee , supported by both transcriptional and proteomic evidence . It is inconsistent with the traditional view that the de novo origin of new genes is rare . RNA–seq data indicate that these de novo originated genes have their highest expression in the cerebral cortex and testes , suggesting these genes may contribute to phenotypic traits that are unique to humans , such as development of cognitive ability . Therefore , the importance of de novo origination needs greater appreciation . | [
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] | 2011 | De Novo Origin of Human Protein-Coding Genes |
West Nile virus ( WNV ) replicates in a wide variety of avian species , which serve as reservoir and amplification hosts . WNV strains isolated in North America , such as the prototype strain NY99 , elicit a highly pathogenic response in certain avian species , notably American crows ( AMCRs; Corvus brachyrhynchos ) . In contrast , a closely related strain , KN3829 , isolated in Kenya , exhibits a low viremic response with limited mortality in AMCRs . Previous work has associated the difference in pathogenicity primarily with a single amino acid mutation at position 249 in the helicase domain of the NS3 protein . The NY99 strain encodes a proline residue at this position , while KN3829 encodes a threonine . Introduction of an NS3-T249P mutation in the KN3829 genetic background significantly increased virulence and mortality; however , peak viremia and mortality were lower than those of NY99 . In order to elucidate the viral genetic basis for phenotype variations exclusive of the NS3-249 polymorphism , chimeric NY99/KN3829 viruses were created . We show herein that differences in the NS1-2B region contribute to avian pathogenicity in a manner that is independent of and additive with the NS3-249 mutation . Additionally , NS1-2B residues were found to alter temperature sensitivity when grown in avian cells .
West Nile virus ( WNV ) is the most widely distributed flavivirus in the world , occurring on all continents except Antarctica [1 , 2] . Recent human disease outbreaks in Europe and North America have brought increased scientific and public health attention to WNV; however , WNV may also cause significant underreported disease in developing countries [1 , 3–8] . Despite some advances , significant gaps remain in our knowledge of the ecological and genetic determinants of WNV transmission and disease . WNV is maintained in avian reservoir hosts and is transmitted by Culex spp . mosquitoes [9 , 10] . Infection rates of mosquito vectors with WNV are proportionate to the virus titer in the infectious blood meal , with host sources generating titers below approximately 105 plaque-forming units ( pfu ) /ml sera considered to be poorly infectious to mosquitoes [11–14] . In contrast , birds of the family Passeridae can develop very high viremia titers , up to approximately 1010 pfu/ml in some corvids , and are considered to be the most relevant reservoir hosts that drive the force of epizootic/epidemic transmission [15–17] . For maximum transmissibility , WNV strains must be able to replicate at a variety of temperatures , from approximately 14°C external temperatures experienced by mosquitoes to 45°C body temperatures of febrile avian hosts [18–22] . Strains that cannot withstand the high temperatures experienced by febrile birds are expected to be at a competitive disadvantage for viremogenesis and subsequent transmission [18 , 23] . Indeed , flavivirus strains and mutants that are temperature sensitive ( ts ) in vitro are frequently also attenuated in vivo [23–27] . WNV , like other members of the Flavivirus genus , encodes a polyprotein that is post-translationally processed into three structural proteins ( the capsid protein C and envelope proteins prM and E ) and seven nonstructural proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B , and NS5 ) . WNV is phylogenetically divided into at least five lineages , with the majority of circulating and epidemic strains belonging to lineage 1 [2] . WNV was isolated in the Americas for the first time in New York in 1999 , and rapidly spread across the continent [28] . The NY99 strain , representative of the East Coast genotype of lineage 1a WNVs , has been extensively studied and is widely used as a model strain for WNV studies . The current strains circulating in North America represent a different genotype that is derived from the NY99 ancestor [29] . An alternative lineage 1a WNV strain that was isolated in Kenya , KN3829 , shares a high genetic identity with NY99 with a total of 11 amino acid differences between the two strains ( Table 1 ) ( Genbank: AF196835 [NY99] and AY262283 [KN3829] ) . American crows ( AMCRs; Corvus brachyrhynchos ) infected with North American strains of WNV exhibit high levels of mortality and high viremia titers [15 , 30–32] . In laboratory infection studies , WNV NY99 typically elicits a viremia of over 108 plaque forming units ( pfu ) /ml sera , and 100% mortality within approximately 6–7 days [15 , 18 , 26 , 30 , 33] . Due to their high susceptibility and visibility , AMCRs have been used as a sentinel species for WNV circulation in North America [34] . Despite the high genetic relatedness with NY99 , KN3829 exhibits a strikingly different avian virulence phenotype , eliciting very low viremia and limited mortality in AMCRs [18 , 30 , 33] . Previous research has demonstrated that a single , positively-selected amino acid substitution at residue 249 in the NS3 helicase gene of WNV is strongly associated with virulence in AMCRs [30] . The NY99 strain encodes a proline residue at this position , while KN3829 encodes a threonine residue ( Table 1 ) . Introduction of an NS3-P249T mutation into the NY99 backbone reduced AMCR viremia by almost 106-fold , while the reciprocal mutation in the KN3829 virus ( KN3829-NS3-T249P ) increased viremia to a similar degree [26 , 30] . However , a residual difference in virulence between the two strains was not attributable to the NS3-249 amino acid difference . KN3829 and KN3829-NS3-T249P elicited approximately 10-fold lower viremia than NY99-NS3-P249T and NY99 , respectively [30] . Mortality was also reproducibly lower with the NS3-249T mutant virus created in the NY99 backbone . Therefore , we hypothesized that other amino acid polymorphisms , or differences in the 3′ untranslated region ( UTR ) , could account for this difference in pathogenesis and/or be associated with stabilization of the KN3829 virus . To test this hypothesis , we generated chimeric virus constructs between the infectious clones of NY99 and KN3829 , and used the resulting viruses for evaluation of pathogenic potential in AMCRs and growth at standard and elevated temperatures in avian cell culture .
Infectious clones of NY99 and KN3829 were described previously [18] . To create chimeric constructs , we divided the viral genome into segments based on conveniently located restriction sites: NgoMIV at nucleotide ( nt ) 2495 ( in NS1; used for ligation of the two-plasmid system during virus rescue ) ; KpnI at nt 5341 ( in NS3 ) ; KpnI at nt 7762 ( beginning of NS5 ) ; and AatII at nt 10203 ( end of NS5 ) . Segments from the wild-type KN3829 and NY99 infectious clones , as well as the KN3829-NS3-T249P mutant virus , were interchanged using these restriction sites . Chimeric virus strains were named based on the KN3829-specific genome segments they contained ( Fig 1 ) . NS1-2B point mutations were created in the KN-IC ( CG plasmid ) infectious clone by site-directed mutagenesis as previously described [23] . Rescue of infectious clone-derived virus was described previously [18] . Briefly , the 5′ and 3′ plasmids of NY99 , KN3829 , and mutant and chimeric viruses were digested with NgoMIV , ligated , and linearized with XbaI ( New England Biolabs ) before in vitro transcription with the Ampliscribe High-Yield T7 Transcription kit ( Epicentre Biotechnologies ) . Viral RNA was transfected into BHK-21 cells by electroporation . When >50% of cells displayed cytopathic effect , supernatant was harvested , centrifuged to remove cellular debris , and stored at -70°C until titration by plaque assay . RNA was extracted from stocks from individual clone-derived viruses and viral genomes were sequenced as described previously [26] . Vero , BHK-21 , and duck embryonic fibroblast ( DEF ) cells were maintained in DMEM containing 10% FBS , 100 U/ml penicillin , and 50 μg/ml streptomycin . For determination of growth kinetics , DEF cells were inoculated with virus at an MOI of 0 . 1 . After a one hour adsorption at 37°C , cells were washed three times with Dulbecco’s PBS ( Life Technologies ) , growth medium was replaced , and cells were placed in incubators at either 37° or 44°C . Supernatant was sampled daily for five days . 30 μl of each sample was added to 270 μl of fresh medium containing 20% FBS , frozen , and stored for titration as above . AMCR peripheral blood mononuclear cells ( PBMCs ) were isolated using Histopaque-1077 ( Sigma-Aldrich ) and maintained in RPMI containing 10% FBS , penicillin/streptomycin as above , and 1 μg/ml Fungizone ( Life Technologies ) , as described previously [35] . PBMCs were inoculated with virus at an MOI of 10 , incubated for one hour at 37°C or 42°C , and then centrifuged at 1500×g , resuspended in fresh growth medium , and incubated at the same temperature . One third of the well volume was sampled and replaced daily for six days , and samples were stored as described above . After-hatch year AMCRs were trapped using cannon nets in Bellvue , Colorado between 2004–2007 . Crows were banded , bled , and tested for pre-existing immunity to WNV and St . Louis encephalitis virus using plaque reduction neutralization tests as previously described [33] . AMCRs were housed at Colorado State University in groups of 2–3 in 1-m3 cages and fed an ad libitum mixture of dry dog and cat food . Groups of 16 AMCRs were inoculated subcutaneously with 1500 pfu of parental , chimeric , or point mutant WNV in a 100 μl volume . Inoculated AMCRs were bled by jugular venipuncture daily for seven days . Whole blood was diluted 1:10 in DMEM containing 10% FBS and penicillin/streptomycin . Blood samples were allowed to coagulate at room temperature before centrifugation for 10 min at 4000 × g , and were stored at -70°C until titration by plaque assay . AMCRs were monitored daily for 14 days and any birds displaying signs of WNV disease , such as ataxia , incoordination , or difficulty feeding , were euthanized by intravenous phenobarbital overdose . All surviving birds were euthanized at day 14 in the same manner . RNA was extracted from selected samples of PBMC culture supernatant or AMCR blood using a Viral RNA mini kit ( Qiagen ) as described previously [26] . RT-PCR was performed using a SuperScript III One-Step RT-PCR kit ( Life Technologies ) and primers WNV5032F ( 5′-GGAACATCAGGCTCACCAATAGTGG-3′ ) and WNV5497R ( 5′-CTTTGTGGAAATGTAACCTCTTGCTGC-3′ ) . The resulting RT-PCR product was sequenced with the same primers . All statistical calculations were performed using GraphPad Prism v . 6 . 04 or R v3 . 2 . 2 . Statistical analysis of in vivo data was performed by synonymizing groups based on NS1-2B genotype ( NY or KN ) and NS3-249 genotype ( Pro or Thr ) . Survival curves were compared using a log-rank test . Viremia was regressed on dpi assuming polynomial trend and normal errors . The model includes a fixed effect for each modified region of the two viruses , and a random effect for replicates . Times at which peak viremia occurred were estimated from the fit . Standard errors for differences in peak viremia were computed using the delta method and incorporate uncertainty from estimating both time of peak viremia and value of peak viremia . Results were adjusted to account for multiple comparisons , achieving an overall Type I error rate of 0 . 05 . For temperature-sensitivity data in DEF cells , a semiparametric , mixed model was fit to the titer data . The model includes a fixed effect for each modified region of the two viruses , a random effect for replicates , and temperature-specific mean titer curves . The temperature-specific components were characterized by second degree penalized splines with truncated power basis . The solution to the fit and estimated variances were obtained by computing the best , linear , unbiased predictors of the penalized spline’s representation as a linear , mixed model [36] . Times at which peak titer occurred were estimated from the fit . Standard errors for differences in peak titer were computed using the delta method and incorporate uncertainty from estimating both time of peak titer and value of peak titer . Results were adjusted to account for multiple comparisons , achieving an overall Type I error rate of 0 . 05 . Trapping of AMCRs was performed under US Fish and Wildlife Scientific Collecting Permit MB-032526 and MB-082812 . Birds were collected under US Fish and Wildlife Services and Colorado Parks and Wildlife permits with permission of private land owners as well as the managers of the Colorado State Fisheries Unit in Bellvue , CO . Field studies did not involve endangered or protected species . All animal studies presented herein were approved by Institutional Animal Care and Use Committees at the University of California , Davis ( approval number 12874 ) and Colorado State University ( approval number 10-2078A ) . All protocols and practices for the handling and manipulation of crows were in accordance with the guidelines of the American Veterinary Medical Association ( AVMA ) for humane treatment of laboratory animals as well as the ‘‘Guidelines to the Use of Wild Birds in Research” published by the ornithological council 3rd edition ( 2010 ) .
We constructed chimeric virus constructs between NY99 and KN3829 to determine which differences between the two strains , other than the previously described NS3-249 site [26 , 30] , contribute to avian virulence and pathogenesis ( Table 1; Fig 1 ) . All chimeric viruses could be grown in vitro in rodent ( BHK-21 ) and primate ( Vero ) cell lines to titers comparable to those attained by the wild-type parental infectious clone viruses ( at least 7 log10 pfu/ml ) . AMCRs were inoculated with virus derived from infectious clones of WNV NY99 , KN3829 , or chimeric plasmids with proline or threonine residues present at the NS3-249 locus . As described previously [18 , 26 , 30 , 33] , viremia in NY99-inoculated AMCRs peaked at approximately 109 pfu/ml serum , while KN3829 elicited only approximately 105 pfu/ml serum ( Fig 2A ) . NY99 infection induced 100% mortality within six days post-infection ( dpi ) , whereas 13/16 ( 81% ) of AMCRs infected with clone-derived KN3829 virus survived to 14 dpi ( Fig 2E ) . As expected , viruses encoding a proline residue at the NS3-249 locus elicited higher viremia ( 95% CI for difference in peak viremia 0 . 3 logs to 2 . 0 logs ) ( Fig 2A and 2B ) and mortality ( Fig 2E and 2F ) rates than those containing a threonine residue . However , within both Pro and Thr-containing groups , constructs containing the NS1-2B region of NY99 induced statistically higher peak viremia and mortality than those containing the NS1-2B region from KN3829 ( Fig 2B and 2F ) . When groups were synonymized based on genotype at NS1-2B ( NY or KN ) and NS3-249 ( P or T ) , survival distributions were significantly different ( p < 0 . 001 ) among all groups ( Fig 2F ) . The peak viremia was significantly different between all pairs of groups except KN/Pro and NY/Thr ( Fig 2C and 2D ) . The structural genes of WNV did not have an apparent effect on pathogenesis in AMCRs . Mortality did not differ between strains that were identical with the exception of their structural genes ( p > 0 . 05 ) ( e . g . compare KN-str/KN-NS3-4B and NY-str/KN-NS3-4B ) . The difference in mean peak viremia titers between these strains was 0 . 31 logs ( 95% CI of -0 . 1 logs to 0 . 7 logs ) . Similarly , the 3′ UTR did not have a detectible effect on viremia or mortality ( p > 0 . 05 ) ( i . e . KN-str/KN-NS1-2B and KN-str/KN-NS1-2B 3’ ) . We hypothesized that , given the importance of the NS3-249 position for viral replication in AMCRs , infection with Thr-containing viruses may have imposed selective pressure , leading to potential mutations at this site . Therefore , viral RNA extracted from sera collected at 4 dpi from AMCRs infected with KN-str/KN-NS3-4B and NY-str/KN-NS3-4B ( 8 AMCRs each ) was spot sequenced . These viruses were chosen because they grew relatively well in AMCRs compared to other Thr-containing constructs . Of the 16 samples sequenced , only three maintained a Thr residue at NS3-249 with no detectable mutations in the viral population . Eight had mutated to contain an alanine residue at this position . One had mutated to an asparagine residue . The other four contained mixed sequences at the locus . Two had a mixture of alanine and threonine , one had a mixture of alanine and proline , and the last sample contained a mixture of alanine , aspartic acid , and threonine . No other mutations were detected in the surrounding NS3 region . To further analyze the effects of the differences between NY99 and KN3829 , AMCR PBMCs were inoculated with the chimeric virus constructs . As described previously [35] , replication in AMCR PBMCs at 37°C correlated with NS3-249 genotype ( Fig 3A ) . However , when the temperature was increased to 42°C , the body temperature of AMCRs , only viruses containing both a proline residue and the NS1-2B region from NY99 were able to replicate ( Fig 3B ) . High variability was observed among the three replicate infections with the KN-str/KN-NS3-4B and NY-str/KN-NS1-4B viruses grown at 37°C . One replicate of the KN-str/KN-NS3-4B culture attained a final titer that was over 100-fold higher than the titers attained by the other two replicates ( Fig 3C ) . Similarly , one replicate of the NY-str/KN-NS1-4B culture attained a titer at 5 dpi that was over 10-fold higher than the other two cultures . Viral RNA was isolated from these two culture supernatants and the region surrounding the NS3-249 region was sequenced [35] . No mutations were found in the NS3-249 residue . However , mutations were found in nearby residues in single high-titered replicates . Specifically , a NY-str/KN-NS1-4B sample contained a mixed population of wild-type and NS3-E251K mutant virus , while a KN-str/KN-NS3-4B sample contained a mixed population of wild-type and NS3-T246I mutant virus . The proximity of these mutations to the NS3-249 site , which modulates PBMC replication , suggests that they may be the cause of the improved growth in these replicates . There are three amino acid differences between NY99 and KN3829 in the NS1-2B region ( Fig 1 ) . In order to determine the relative contribution of these three differences to the observed changes in AMCR virulence , we created single NS1-S70A , NS2A-A52T , and NS2B-A103V point mutants in the KN3829 infectious clone backbone . Inoculation of AMCRs with these point mutants led to viremia and mortality that were not distinguishable from wild-type KN3829 ( Fig 5 ) .
We show here that replication and virulence of WNV in American crows are modulated by the NS1-2B region of the genome in addition to the previously described effect of the NS3-249 residue . The effects of these two genomic regions are independent and additive in vivo . Although the effect of the NS1-2B region is relatively subtle ( approximately 10-fold ) compared with the effect of NS3-249 , it is reproducible and statistically significant . This finding underscores the importance of the flaviviral nonstructural proteins for virulence and viral replication in the natural reservoir host . As previously described , the NS3-249 site evidently modulates replication in leukocytes [35] . Viral constructs containing a threonine at this position consistently failed to replicate in PBMC culture , while those containing a proline replicated well . No other determinants detectably affected growth in PBMCs at 37°C . However , constructs containing the NS1-2B region from KN3829 were unable to replicate at 42°C . Thus , we conclude that the NS3-249 residue is a determinant of replication in AMCR PBMCs , while the NS1-2B region is a determinant of temperature sensitivity . As febrile AMCRs typically attain body temperatures above 42°C [18] , this suggests a possible explanation for the decreased in vivo viremia observed in AMCRs infected with these constructs . The combination of leukocyte replication and temperature sensitivity effects may explain the relative in vivo virulence of the various constructs . These results suggest that temperature sensitivity may play an important role in WNV pathogenesis in birds . Interestingly , in North American WNV strains , an NS1-K110N mutation , in combination with a mutation in NS4A , has been associated with in vitro temperature sensitivity in DEF cells [23] . Although these findings are not directly comparable to those shown here , they also point to a potential role for NS1 in mediating temperature sensitivity in WNV . Interestingly , temperature sensitivity of WNV in DEF cells was not directly correlated with temperature sensitivity in AMCR PBMCs . Although previous work found a slight effect of the residue at position NS3-249 on temperature sensitivity in DEF cells in the NY99 genetic background , this effect was not evident in the KN3829 and chimeric genetic backbones assessed here [26] . Instead , the structural genes , NS1-2B region , and NS3-4B region exclusive of NS3-249 all appeared to modulate the differences in DEF cell temperature sensitivity between NY99 and KN3829 . This is consistent with chemical mutagenesis studies in dengue virus , in which temperature sensitivity was conferred by mutations in a variety of positions throughout both the structural and nonstructural regions [37] . Temperature sensitivity of flaviviruses is evidently a complex phenotype that can be conferred by a variety of mutations , likely with different underlying mechanisms . These mechanisms may include protein stability and protein-protein interactions , among others . The difference between AMCR PBMCs and DEF cells also indicates that temperature sensitivity results may be dependent on the system used for testing . Although the use of DEF cells for temperature sensitivity testing is convenient , AMCR PBMCs are likely more phenotypically relevant . None of the three individual amino acid differences in the NS1-2B region between NY99 and KN3829 could individually explain the effect of the overall region on temperature sensitivity or in vivo virulence . Thus , the overall effect of this region evidently requires two or more of the amino acid differences . This is consistent with previous results in a dengue-2 vaccine virus study , which showed that a combination of mutations at NS1-53 and NS3-250 was required to make the vaccine virus fully temperature sensitive [38] . The single NS3-250 substitution did not increase temperature sensitivity of the dengue vaccine virus , while the NS1-53 substitution alone only caused subtle temperature sensitivity . Future studies will be required to fully understand the effects of these NS proteins on pathogenesis and temperature sensitivity . Alternatively , the synonymous nucleotide changes in the NS1-2B region could have an effect at the RNA level , which would not be captured by amino acid point mutations . An RNA secondary structure motif is required for the production of the frameshifted NS1′ protein in WNV and closely related flaviviruses [39] . Mutations that alter this secondary structure can change the ratio of full-length to frameshifted polyprotein , affecting WNV pathogenesis in mice and house sparrows [40 , 41] . Both NY99 and KN3829 encode the frameshift motif and would be expected to produce NS1′ . There are two amino acid differences between the NS1′ coding sequences of NY99 and KN3829 , one of which is encoded by the same nucleotide polymorphism that encodes the tested NS2A-A52T mutation . The role of the amino acid sequence of NS1′ is not well understood , and it is possible that the polymorphism not tested here could play a role in pathogenesis and temperature sensitivity . Other cryptic RNA motifs that have not yet been described could also play a role . The functions of the flaviviral NS1 , NS2A , and NS2B proteins are not fully understood , making it difficult to determine why these proteins apparently affect temperature sensitivity in PBMC culture and replication and virulence in vivo . The NS1 and NS2A proteins , in particular , have apparent roles in immunomodulation and immunopathogenesis , in addition to their roles in viral replication [42–47] . A silent mutation in WNV-Kunjin virus NS2A that affects the NS1′ frameshift motif also has been shown to alter interferon induction , and an amino acid change at the same position affects apoptosis in vitro and virulence in mice [41 , 48] . Given that the differences in avian pathogenesis observed here appear to be modulated at least in part by replication in immune cells , these immunomodulatory functions may be relevant . Further research on these nonstructural proteins will aid in understanding their role in temperature sensitivity and avian virulence . Subtle effects of differences among viral strains could have an amplified effect on a larger scale . Although the addition of the NY99 NS1-2B region to virus backbones containing the NS3-249-Thr residue only increased peak viremia titers by approximately 100-fold , AMCRs infected with these viruses experienced viremia titers above 105 pfu/ml for 3–4 days . In contrast , AMCRs infected with the corresponding strains containing the KN3829 NS1-2B region experienced 0–1 days of viremia above 105 pfu/ml . As 105 pfu/ml is the approximate titer required for infection of mosquitoes , this relatively subtle difference could lead to an increased chance of transmission to a mosquito [11–14] . Furthermore , if these determinants in NS1-2B are present in non-North American or alternative lineage WNV strains , increased viremia titers could weaken the potential selective pressure for development of NS3-249P mutations . These observations highlight the importance of understanding of the determinants of WNV replication and pathogenesis in relevant avian reservoir hosts , including the AMCR . Unraveling the viral genetic factors influencing the infection of different avian species will provide insight into emergence mechanisms of WNV and related flaviviruses . This behavior cannot be predicted based on studies of mammals such as mice , which exhibit physiological , immunological and cytological differences from birds that preclude use as a relevant model system for the selective pressure these viruses undergo during enzootic/epizootic transmission cycles . | West Nile virus ( WNV ) is a mosquito-borne virus that has caused outbreaks in humans in many regions of the world . Birds are the natural hosts for WNV . However , different strains of WNV cause different disease outcomes in birds . Here , we compared two WNV strains , one of which causes higher mortality and generates more virus in American crows than the other . Previous research has shown that this difference is due in large part to a difference between the two strains at a single amino acid in the NS3 gene; however , this difference does not completely explain the observed effect . Here we show that another region of the viral genome also affects disease outcomes in American crows , and changes the sensitivity of the virus to temperature when grown in bird cells . These findings help us to understand the genetic features that affect WNV infection and disease outcomes in its natural host . Detection of such features in new strains of WNV and related viruses could help to understand and predict future outbreaks . | [
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] | 2016 | West Nile Virus Temperature Sensitivity and Avian Virulence Are Modulated by NS1-2B Polymorphisms |
Hemorrhagic fever with renal syndrome ( HFRS ) , an infectious disease caused by hantaviruses , is endemic in China and remains a serious public health problem . Historically , Shandong Province has had the largest HFRS burden in China . However , we do not have a comprehensive and clear understanding of the current epidemic foci of HFRS in Shandong Province . The incidence and mortality rates were calculated , and a phylogenetic analysis was performed after laboratory testing of the virus in rodents . Spatial epidemiology analysis was applied to investigate the epidemic foci , including their sources . A total of 6 , 206 HFRS cases and 59 related deaths were reported in Shandong Province . The virus carriage rates of the rodents Rattus norvegicus , Apodemus agrarius and Mus musculus were 10 . 24% , 6 . 31% and 0 . 27% , respectively . The phylogenetic analysis indicated that two novel viruses obtained from R . norvegicus in Anqiu City and Qingzhou City were dissimilar to the other strains , but closely related to strains previously isolated in northeastern China . Three epidemic foci were defined , two of which were derived from the Jining and Linyi epidemic foci , respectively , while the other was the residue of the Jining epidemic focus . The southeastern and central Shandong Province are current key HFRS epidemic foci dominated by A . agrarius and R . norvegicus , respectively . Our study could help local departments to strengthen prevention and control measures in key areas to reduce the hazards of HFRS .
Hemorrhagic fever with renal syndrome ( HFRS ) , a global infectious disease caused by hantaviruses ( HV ) in the Bunyaviridae family , is characterized by fever , hemorrhage , kidney damage and hypotension . All the currently known HV genotypes can cause HFRS , including Hantaan virus ( HTNV ) , Seoul virus ( SEOV ) , Puumala virus ( PUUV ) and Dobrava/Belgrade virus ( DOBV/BGDV ) [1 , 2 , 3] . In China , most cases are caused by two HV genotypes , HTNV and SEOV , which are mainly carried by the rodents Apodemus agrarius and Rattus norvegicus , respectively . According to previous studies , over 90% of all cases of HFRS in the world have occurred in China in recent decades[4 , 5] . Although the harm caused by HFRS in humans has declined in recent years due to the development of preventive measures and medical capabilities , HFRS is still a serious public health threat in China today . Shandong Province , which has the second largest population among all the provinces in China , has been suffering from a large HFRS burden for several decades . In the past decade , Shandong Province remained in the top five of all provinces in China regarding the number of HFRS cases[1] . Changes in the epidemic foci ( areas in which some wild animals have a long-term preservation of infectious pathogen ) of infectious diseases are caused by many factors , including natural , human and viral host factors[6] . In recent years , Shandong Province developed an R . norvegicus-dominated mixed viral genotypes epidemic focus . However , the characteristics of the HFRS epidemic foci in Shandong Province have been constantly changing , and the health threat to humans has been constantly changing as well . Adjustments to policies and practices should be performed in a timely manner . However , although many studies have analyzed the HFRS epidemic situation in Shandong Province[4 , 6 , 7] , we do not have a comprehensive and clear understanding of the current epidemic foci of HFRS . For this reason , we conducted a systematic analysis of surveillance information on HFRS in Shandong Province from 2012 to 2015 to assess the severity of the situation and determine the new epidemic foci in Shandong Province .
This study was reviewed and approved by the Ethical Review Board , Science and Technology Supervisory Committee at the Beijing Institute of Microbiology and Epidemiology . The animal work described here adhered to the guidelines of the Animal Subjects Research Review Boards at the Beijing Institute of Microbiology and Epidemiology . The study area is located in the North China Plain , in eastern China ( 34°22’to 38°23’north latitude , 114°19’ to 122°43’ east longitude , Fig 1 ) , with a population of more than 99 million and an area of 158 , 000 km2 . Shandong Province has a large population of agricultural workers and high land cultivation rate in China . The central region is mountainous and hilly , which is surrounded by plains . The records of HFRS cases between 2012 to 2015 were obtained from the China Information System for Disease Control and Prevention and all data analyzed were anonymized . All patients were diagnosed as HFRS clinically . The diagnostic criteria were the Diagnostic Standards for Epidemic Hemorrhagic Fever issued by the National Health and Family Planning Commission of the People's Republic of China ( http://www . nhfpc . gov . cn/zwgkzt/s9491/200802/39043 . shtml ) . Based on historical HFRS surveillance data from Shandong Province , 14 representative counties with serious epidemics were selected as the surveillance sites in this study . The 14 surveillance sites including 4 fixed surveillance sites mandated by Shandong Provincial CDC and 10 flexible surveillance sites set up in this study . All surveillance sites were established with consideration of the severity of the local HFRS epidemic and geographic landscape differences . Rodents surveillance data were also collected from the China Information System for Disease Control and Prevention . Lung samples of the captured rodents were obtained for laboratory testing . The 2012–2015 records of HFRS cases covering the whole of Shandong Province were used to calculate the monthly incidence rate ( per 100 , 000 population ) . The proportions of cases in different demographic groups , based on gender , age and occupation were calculated . To observe the temporal distribution of cases more intuitively , we divided all cases into three groups ( Feb to May , June to Sept and Oct to Jan ) based on the two annual HFRS incidence peaks . The night trapping method was used to capture rodents with peanuts as bait . The wild and residential areas were both taken into account when placing the rat clips and spring and autumn were selected as the sampling time for each surveillance site . The sampling process of each surveillance site was carried out by professionals use same method to ensure the representativeness of results . According to the information on the captured rodents , the proportion of each rodent species out of all the rodents captured was calculated . Lung samples of the captured rodents were screened using the reverse transcription-polymerase chain reaction ( RT-PCR ) . Total RNA , which was extracted from 20–50 mg of lung tissue using the TRIzol reagent ( Invitrogen , USA ) , was reverse-transcribed using M-MLV reverse transcriptase ( Invitrogen , USA ) and the primer P14: 5’-TAGTAGTAGACTCC-3’[8] . The PCR was performed to amplify a partial L sequence , using primer pairs as previously described[9] . Amplification result was sequenced and compared with data from the National Center for Biotechnology Information ( NCBI ) database using the Basic Local Alignment Search Tool ( BLAST ) to determine which genotypes they were[10] . It was found that the strains of same surveillance sites stacked in same small branch based on the results of preliminary phylogenetic analysis . Therefore , we screened the strains with low homology in the same county every year . Eventually , the partial L segments of 29 strains from the 14 surveillance sites were selected ( S1 Table and S1 File ) . Additionally , we searched GenBank and downloaded data on 18 relevant HV strains isolated in other regions of China , the Korean Peninsula and Europe . A phylogenetic tree was then constructed , using the Maximum likelihood ( ML ) method and bootstrap testing ( 1000 replicates ) , in MAGE 7 . 0 software ( Pennsylvania State University , PA , USA ) [11] . According to the HFRS cases distribution , molecular epidemiology and rodent surveillance data , the epidemic foci of HFRS in Shandong Province during the study period were defined . The Kriging interpolation method was applied to display the extent of the epidemic foci more clearly . Data were analyzed using SAS 9 . 4 ( SAS Institute Inc . , Cary , NC , USA ) . All maps were mapped by using a geographical information system ( GIS ) technique in ArcGIS 10 . 2 software ( ESRI Inc . , Redlands , CA , USA ) . MEGA 7 . 0 was used to conduct the phylogenetic analysis .
From 2012 to 2015 , 6 , 206 HFRS cases and 59 related deaths were reported in Shandong Province , and the annual incidence rate , mortality rate and case fatality rate ranged from 1 . 15 to 1 . 87 per 100 , 000 , 0 . 01 to 0 . 02 per 100 , 000 , and 0 . 80% to 1 . 07% , respectively ( Table 1 ) . During the study period , the annual HFRS incidence rate in Shandong Province peaked in 2013 , then decreased year by year , and reached its lowest point in 2015 . In addition , there were two incidence peaks each year , the small peak was from April to June and the larger one was from October to January . However , mortality rates and case fatality rates declined year by year . The deaths mainly occurred from September to January ( Fig 2 ) . As for spatial distribution , significant dynamic variation was found in that the high-risk areas were concentrated in a single large region in central and southeastern Shandong Province in 2012 , which gradually separated into two regions , and then eventually formed two relatively independent high-risk regions in 2015 ( Fig 3 ) . Regarding the demographic characteristics , 4465 males accounted for 72% of all cases . We found that 81% of all cases occurred in individuals aged 30–70 years , with the 41–50-year-old age group having the highest proportion of all the 10-year age groups . The three occupations with the highest incidence rates were farm workers ( 85% ) , urban workers ( 5% ) and students ( 3% ) . The results clearly showed that middle-aged male farm workers are the main population to experience HFRS in Shandong Province . ( Table 2 ) . A total of 1 , 798 rodents were captured in 14 rodent surveillance sites in Shandong Province from 2012 to 2015 , including 908 R . norvegicus , 373 Mus musculus , 269 A . agrarius , 95 Cricetulus barabensis , 73 Sorex araneus , 31 Cricetulus triton , 25 Niviventer confucianus , 23 Rattus flavipectus and 1 Miorotus fortis . The proportions of different rodent species indicated that the dominant species in the surveillance sites was R . norvegicus , followed by Mus musculus and A . agrarius . The laboratory testing results revealed that 111 of the captured rodents were positive for HV , of which 93 were R . norvegicus , 17 were A . agrarius and 1 was Mus musculus . The mean HV carriage rate among all rodents was 6 . 17% , and the rates of R . norvegicus , A . agrarius and Mus musculus were 10 . 24% , 6 . 32% and 0 . 27% , respectively ( Table 3 ) . It can be seen that the HV carriage rates in this study were highest for R . norvegicus and A . agrarius among all the rodent species . However , it is worth noting that one HV strain was also isolated from a single Mus musculus in this study . We constructed a geographic map of the different HV-infected rodent species . This indicated that the HV-infected dominant rodents in central Shandong Province were R . norvegicus , followed by Mus musculus , while A . agrarius was the dominant HV-infected rodent in southeastern Shandong Province ( Fig 4A ) . Additionally , the HV carriage rates of rodents in each surveillance site were calculated and plotted on another map ( Fig 4B ) . This showed that the surveillance sites with high HV carriage rates were mainly concentrated in central Shandong Province ( Guangrao county had the highest HV carriage rate , at 16 . 67% ) , while the surveillance sites in southeastern Shandong Province had lower HV carriage rates ( Pingdu city had the highest HV carriage rate , at 6 . 67% ) . Obviously , there were distinct differences in the spatial distributions of different HV-infected rodent species and HV carriage rates in Shandong Province . A total of 111 samples generated the PCR products of expected size for the partial L sequence . from the rodents captured in Shandong Province from 2012 to 2015 . The Blast results using GenBank revealed that the numbers of SEOV and HTNV strains were 91 and 18 , respectively , and two strains with novel genotypes were also found . To construct a phylogenetic tree , partial L segment sequences from 47 HV strains were used: 29 strains from our samples and 18 strains from GenBank ( which had been isolated in mainland China , the Korean Peninsula and Europe ) . As shown in Fig 5 , the branches of the phylogenetic tree revealed that the HTNV strains obtained in this study consisted of two clades from a bigger branch . They shared a common ancestor with strains isolated in Hubei and Xi'an . The SEOV strains obtained in this study constituted one branch , except for one strain that was obtained in Jiaxiang County ( in the southwest of Shandong Province ) . Almost all the SEOV strains were closely related to viruses isolated in Fujian , Beijing and Zhejiang . However , the SEOV strain obtained in Jiaxiang County was dissimilar to all other strains and formed a separate branch . In addition , the genetic diversity associated with the SEOV branches indicated that they were more homogeneous than the HTNV branches , which had a higher degree of genetic diversity . We obtained two viruses with novel genotypes from R . norvegicus in Anqiu City and Qingzhou City that were different from the other strains . After the alignment analysis in GenBank , it was found that they were closely related to the strain previously isolated in Russia ( Jewish Autonomous Oblast ) , which has not been classified and named . Then together with strains isolated in Fusong County and Shenyang City ( northeastern China ) , forming a single branch in the phylogenetic tree . Previous studies on the complete S segments of strains isolated in Fusong County and Shenyang City have shown that they are closely related to a virus isolated in Vladivostok in the Russian Far East , forming a single branch . The researchers believed that they belong to the same HV genotype , the Vladivostok virus ( VLAV ) , which host are Microtus fortis[12] . Therefore , our two novel viruses appear to be similar to the VLAV genotype [12 , 13 , 14] . We also obtained HTNV ( 1584101 HD 2015 ) from the wild-collected R . norvegicus in Huangdao . This result suggested that HTNV in A . agrarius has spilled over to R . norvegicus , which was previously not common in Shandong Province . To more intuitively identify the current HFRS epidemic foci in Shandong Province , the distribution of HV genotypes in each surveillance site was mapped . Additionally , the time distribution of HFRS throughout Shandong Province has a seasonal periodicity obviously . Peaks generally occur in spring or autumn and winter . Based on the two annual incidence peaks , we divided the 2012–2015 cases into three groups according to the month of their occurrence ( Feb to May , June to Sept , and Oct to Jan ) and mapped them with the 2012–2015 annual incidence rates on a single map . Finally , as shown in Fig 6 , we summarized the 2012–2015 HFRS case spatial epidemiology , molecular epidemiology and rodent surveillance results in Shandong Province , indicating the epidemic foci during the study period . The epidemic foci comprised two major foci , designated Regions a and b , which is located in southeastern and central Shandong Province , respectively , and a smaller one designated Region c in southwestern Shandong Province ( S2 Table ) . Among them , Region a had the largest coverage area and the highest incidence rate , with most cases occurring in the autumn and winter , and the rodents carrying the virus were mainly A . agrarius . The viral genotypes in Region a were mainly HTNV , which constituted an A . agrarius-dominated mixed-type epidemic focus . The coverage and incidence rates of Region b were smaller than in Region a , the temporal distribution of cases in Region b was relatively uniform , and the infected rodents were mainly R . norvegicus . The viral genotypes in Region b were mainly SEOV , which constituted an R . norvegicus-dominated mixed-type epidemic focus . The coverage and incidence rates of Region c were much smaller than in Regions a and b . The cases in this area occurred mostly in late spring and early summer , and the species of infected rodents and viral genotypes were more similar to those of Region b , involving an R . norvegicus-dominated mixed-type epidemic area .
As a natural epidemic disease , HFRS has a prominent position in the history of infectious disease prevention and control in Shandong Province . In Shandong Province , HFRS case peaked in the mid-1980s , then declined gradually and then became stable[6 , 15] . Based on the 2012–2015 HFRS data from Shandong Province , the characteristics of the disease , virus host and laboratory testing were analyzed in this study , and the nature and characteristics of current epidemic foci were identified . Overall , the annual HFRS incidence rate exhibited small fluctuations during the study period , peaking in 2013 , then decreasing year by year , and reaching its lowest point in 2015 . There were still annual incidence peaks , which comprised a small peak in late spring to early summer and a big peak in autumn to winter . The small peak is associated with indoor-related infections linked to R . norvegicus breeding activity , while the big peak is mainly due to the increased contact between people and wild rats when they work in fields , and the migration of wild rats from fields to residential areas for foraging in the later period[15] . It is worth noting that 88% of all deaths occurred in September to January . Previous studies have suggested that the clinical symptoms of HFRS caused by HTNV are often more serious than those caused by SEOV[16] . Additionally , human immunity decreases in autumn and winter when it is cold . Therefore , the deaths may be the result of a combination of HTNV and seasonal factors . Additionally , male farmers represented the biggest HV-susceptible population because of their increased contact with rodents due to working in the fields and poor residential sanitation conditions[17] . Therefore , HFRS prevention and control in Shandong Province should continue to be focused on controlling the rodents’ population and strengthening the immunization and education interventions for farmers in key epidemic foci . To reduce the mortality rate , we should also pay more attention to the treatment of cases occurring in autumn and winter . We found that although the HV carriage rate among rodents was lower in southeastern Shandong Province than in central Shandong Province , the epidemic situation and clinical symptoms of cases were worse . This suggests that more attention should be paid and more interventions should be undertaken in the southeastern region . Additionally , the proportion of Mus musculus carrying HV is very low in this study and other researches . Therefore , determining whether Mus musculus can carry HV normally and pass on the infection to humans should be a key task of future research . [18 , 19] . HTNV and SEOV were still the main HV genotypes in Shandong Province . Although we inferred the patient's situation according to the HV genotype from rodents , studies have shown that HV isolated from patient's serum in Shandong Province was associated with local dominated rodent species . For example , the serum test results of Zhaoyuan patients showed that the area belongs to Apodemus-type epidemic focus , while Jining , Zibo , Weifang belong to Rattus-type epidemic foci . These conclusions are consistent with this study[20 , 21] . But two points are worth noting . First , we obtained HTNV from R . norvegicus captured in the wild , indicating that HTNV from A . agrarius spilled over to R . norvegicus . Previous studies also showed that some HV obtained from R . norvegicus seemed to be HTNV[13 , 22 , 23] . However , each HV genotype is associated primarily with a specific rodent host species that it coevolved with[18 , 24 , 25] . The phenomenon of viruses spilling over to non-traditional specific rodent hosts can allow them to find more suitable surroundings for a higher survival rate and more efficient infection and replication[26 , 27] . Huangdao is one of the most serious HFRS epidemic foci , which is located in southeastern Shandong . This phenomenon may promote the epidemic situation in this region . It is perhaps one of the reasons for migration of geographic boundaries of HFRS endemic areas . Second , we found two novel strains obtained from R . norvegicus in Anqiu City and Qingzhou City and they were closely related to strains from northeastern China , which reportedly belong to the VLAV genotype . This phenomenon of potential VLAV strains in Shandong Province has not previously been reported . Therefore , determining whether the viruses were transmitted from northeastern China , or simply involve indigenous genetic mutations , requires further research . In the 1970s , HFRS was in completely Apodemus-type natural focal state in Shandong . Then the Apodemus-type focus in the southeastern part of Linyi and the Rattus-type focus in the southern part of Jining began to expand and merge with each other , and formed two foci in the 1980s , one mixed focus dominated by Apodemus-type in the hilly area of the southern and middle part of Shandong , another one dominated by the Rattus-type in the Yellow River valley of the northwestern part of Shandong . In the 1990s , Shandong Province formed a mixed focus dominated by Rattus-type , and entered a stable stage[15] . The results of Liu and Fang showed that the epidemic focus of HFRS in Shandong Province before this study was mainly concentrated in the northern part of Linyi in southeastern Shandong Province with a dominance of spring peak of incidence[6 , 7] . The whole landscape of Shandong Province became a uniform mixed HTN-type and SEO-type endemic focus with dominant rodents R . norvegicus and Mus musculus . However , the current epidemic foci of HFRS in Shandong Province shows changes both in its distribution and the composition of the HV host . In terms of the temporal distribution , the influence of Region a , which involved an A . agrarius-dominated mixed-type epidemic focus , on HFRS epidemic situation in Shandong Province mainly occurred in the autumn and winter . The coverage of Region a was more extensive than the coverage of Regions b and c , and the epidemic situation was also more serious . We inferred from previous studies that Region a is derived from the epidemic foci previously centered on Linyi City . Conversely , Regions b and c were more similar , involving R . norvegicus-dominated mixed-type epidemic foci . In Regions b and c , the temporal distribution of HFRS cases was more uniform and the epidemic situation was more moderate than in Region a . It was inferred that Region b is derived from the epidemic foci previously centered on Jining City , while Region c is the residue of the epidemic foci centered on Jining City[7] . Although the HV carriage rates among rodents in Regions b and c were higher than that associated with Region a , the severity of the epidemic situation was less severe , suggesting that the HV-susceptible population appears to be more susceptible to the HV strains in Region a ( which mainly belong to the HTNV genotype ) . Interestingly , the distance between Regions b and a is much smaller than that between Regions b and c . Despite this , the epidemic characteristics of Region b are quite different from those of Region a , and more similar to those of Region c . Although previous studies have shown that HTNV ( which was the main genotype in Region a , where the epidemic situation was more serious ) is more harmful to humans than SEOV ( which was the main genotype in Regions b and c ) , local geographic conditions , climate , HFRS prevention and control measures and safety awareness among susceptible populations are also important factors[16 , 28–32] . Therefore , we believe that a further study should be performed to explore the factors underlying our result . Several limitations of this study should be noted . First , the HV genome can be divided into S , M and L segments . A comprehensive phylogenetic analysis of the three segments would give us a clearer understanding of the genetic diversity and evolutionary trajectory of HV . The data on S and M segments in this study were insufficient , so the phylogenetic analysis of HV may be biased . Second , there are many factors that can influence the distribution and severity of infectious disease , such as natural environmental factors , urbanization processes and local prevention efforts[32–34] . However , this research lacked information on these factors , so the HFRS spatio-temporal distribution analysis is inadequate . In conclusion , the southeastern and central Shandong Province are current key HFRS epidemic foci dominated by A . agrarius and R . norvegicus , respectively . Although the overall epidemic intensity was lower than the previous intensity in Shandong Province , it was still relatively high compared to the rest of the country . The spatio-temporal distribution of HFRS within Shandong Province indicated different epidemic situations in different regions . Local departments still need to strengthen their prevention and control measures to reduce the hazards of HFRS . | Hemorrhagic fever with renal syndrome ( HFRS ) is a global infectious disease , which is still a serious public health threat in China today . The reported HFRS cases in Shandong Province accounted for approximate one third of total cases in the whole country . HFRS is a zoonosis mainly caused by Hantaan virus ( HTNV ) and Seoul virus ( SEOV ) , which natural rodent hosts are A . agrarius and R . norvegicus , respectively . To explore the current HFRS epidemic foci based on patients , rodents and molecular epidemiology characteristics in Shandong Province , we collected the records of HFRS cases from whole province and the rodents captured in 14 surveillance sites . We found that the epidemic situation of HFRS is quiet different in temporal and spatial distribution . Three epidemic foci were defined based on patients , rodents and molecular epidemiology characteristics . The situation of HFRS epidemic foci in Shandong Province was clear . Our study provides a reference for relevant departments to develop key prevention strategies . | [
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] | 2019 | The characteristics of current natural foci of hemorrhagic fever with renal syndrome in Shandong Province, China, 2012-2015 |
Recent neural ensemble recordings have established a link between goal-directed spatial decision making and internally generated neural sequences in the hippocampus of rats . To elucidate the synaptic mechanisms of these sequences underlying spatial decision making processes , we develop and investigate a spiking neural circuit model endowed with a combination of two synaptic plasticity mechanisms including spike-timing dependent plasticity ( STDP ) and synaptic scaling . In this model , the interplay of the combined synaptic plasticity mechanisms and network dynamics gives rise to neural sequences which propagate ahead of the animals’ decision point to reach goal locations . The dynamical properties of these forward-sweeping sequences and the rates of correct binary choices executed by these sequences are quantitatively consistent with experimental observations; this consistency , however , is lost in our model when only one of STDP or synaptic scaling is included . We further demonstrate that such sequence-based decision making in our network model can adaptively respond to time-varying and probabilistic associations of cues and goal locations , and that our model performs as well as an optimal Kalman filter model . Our results thus suggest that the combination of plasticity phenomena on different timescales provides a candidate mechanism for forming internally generated neural sequences and for implementing adaptive spatial decision making .
Neural sequences have been widely observed in many brain areas including the cortex [1 , 2 , 3 , 4] , and the hippocampus [5 , 6 , 7 , 8 , 9 , 10 , 11] . Based on how sequences are initialized , they can be placed into two broad categories , namely externally and internally generated sequences [12] . Externally generated sequences ( EGS ) are those which directly reflect an ongoing behavioural sequence such as actions [13] or positions visited [12 , 14]; whilst internally generated sequences ( IGS ) arise either spontaneously or by being triggered by non-sequential external cues [12] . IGS have been argued to underlie predictions [15] , goal-directed planning and decision making [6 , 12 , 16 , 17] . One area where IGS have been extensively examined is the rodent hippocampus during navigational tasks [6 , 18 , 11 , 15] . Most of these tasks follow a similar basic procedure; rodents are introduced to a maze and must navigate towards goal locations [6 , 18 , 15] . Recent experimental studies with multi-electrode array recordings have revealed that when the animals rest between goal-directed spatial navigation episodes , neural ensemble activity propagates forward towards potential goal locations [15] . Such recordings of rodents trained on spatial decision tasks have also found that when rodents paused around the decision point , forward sweeping IGS were formed [6] . Reconstructed locations from these IGS were found predominately forward of the animal’s position , indicating that these IGS are related to representation of future paths rather than pinpointing the current location or being a replay of recent history . Furthermore , the IGS appears to be used for making a goal-related choice , as the path chosen by the animal through the T-maze was strongly correlated with the path reconstructed from the IGS . Despite the importance of IGS for goal-directed behaviours such as spatial decision making , the neural mechanism underlying the formation of these IGS and their general computational roles remain unclear . To address these issues , we build a spiking neural circuit model endowed with a combination of STDP and synaptic scaling , and show that the model is able to reproduce the dynamical properties of IGS and the behavioural response of correct rates of binary choices as reported in [6] . As in previous modelling studies [19] , STDP in our model can learn the paths taken by moving rodents . Synaptic scaling , however , can prevent a positive feedback loop caused by STDP , and provides a separation of temporal scales needed for adaptive choice under uncertainty . We show that STDP complemented with slower homeostatic synaptic scaling is necessary to account for the properties of forward sweeping IGS recorded in [6] , thus unravelling a mechanism for IGS propagation in the spatial decision making circuit . To further study the general computational role of IGS in spatial decision making , we go beyond the deterministic association of cue and goal as used in [6] , considering cases where the association between cue and goal is stochastic and varies over time . For these cases , our results are primarily focused on correct decisions on a trial basis; we find that the correct choice made by the model based on IGS can effectively track the time-varying cue-goal association , and that this process can be described as a recursive probabilistic inference . We show that the performance of this inference process implemented by our spatial decision making neural circuit is comparable to that of a Kalman filter [20] , which is optimal for the cases we consider . In our model , the interplay of spiking sequences and the combined synaptic plasticity rules lead to constant changes of synaptic strengths which are proportional to the probability of cue-goal association . These changes can serve as a posterior for test trials which can then be exploited by IGS to make a choice; the IGS , combined with plasticity mechanisms , are essential for implementing this optimal inference . Our model offers an explicit formal characterization of probabilistic inference involved in the IGS-forming spiking neural circuit , thus establishing a link between such an inference and spatial decision making [12 , 21] .
We begin by briefly describing the T-maze based decision task used in [6] , which we aim to reproduce in a spiking neural circuit . In this task , rats were made to run laps through a T-maze , augmented with return arms , as shown in Fig 1 . When the rats approached the decision point of the T-maze ( A in Fig 1 ) , one of two sound cues was played . These two cues were differentiated by their frequencies , and provided information to the rats about the location of a reward within the T-maze . For example , when the low or high-frequency cues were applied , the reward was on the left or right arm of the T-maze , respectively . Training began with a directed pre-training phase , in which the rats were prevented from choosing the incorrect arm of the T-maze . Training then continued following a similar procedure , except that the rats were free to travel down the incorrect arm of the T-maze . The rate at which the rats correctly identified the rewarded arm corresponding to the applied cue was monitored during this second training phase , and the activity of neural ensembles from the CA3 region was recorded . Analysis of the neural activity revealed a transient , but repeatable , phenomenon: as a rat approached the decision point , the neural representation of the rat’s location swept forward , creating a sequence of neural activity . These sequences were coherent and preferentially swept ahead of the animal rather than behind , implying that they represented future choices rather than recently travelled paths [6 , 12] . We use a two-dimensional spiking neural circuit to reproduce the forward-sweeping sequences observed in [6] . In our model , we consider synaptic plasticity dynamics , which are determined by a combination of STDP and synaptic scaling . The external cues are applied by activating the cue neurons , which are randomly coupled to the other neurons in the two-dimensional network . For a full description of our neural circuit model , see Materials and methods . To investigate the neural mechanisms of forward-sweeping neural sequences and their functional roles in learning and implementing goal-directed choice , we apply the same task and training protocol as in [6] . During training , when a model rat moves along a T-maze , as in Fig 1 , a localized activity pattern surrounding its position is initialized , corresponding to the behaviour of place fields in the hippocampus [22] . As the model rat approaches the decision point of the T-maze , one of a pair of cues is activated . The cue corresponding to the low-frequency sound signal is denoted by C1 and that for the high-frequency by C2 , with C1 and C2 indicating that the goal is located on the left and right arm of the T-maze , respectively . As the model rat is restricted to move along the T-maze , we similarly restrict neurons from firing if their place fields are outside the T-maze . For a training trial in our neural circuit model , as in [6] , one arm of the T-maze is blocked . This is implemented by fixing the potential of the neurons in one arm of the T-maze; this forces the EGS to travel down the non-blocked arm . For instance , when the cue C1 is applied , the right arm of the T-maze is blocked , i . e . , the model rat can only take the left arm . On each training trial , the sequence reaches the goal location , i . e . , the end of the arm . After each training trial , a test trial is run , in which neither arm of the T-maze is blocked . By running a test trial after each training trial , it is possible to monitor in detail the behaviour of the neural circuit during the training process . For a test trial , the cue is activated as the model rat approaches the decision point . Under this protocol , if a forward-sweep sequence is formed and then travels down the arm of the T-maze corresponding to the goal location associated with the applied cue , whilst not reaching the goal location associated with the other cue , a successful choice is made by the network . In our scheme , we do not model reward explicitly . Fig 2 shows such a forward-sweeping sequence shortly after the activation of the cue C2; this sequence in not related to the sequential movement of the model rat , so it is an IGS [12] . Note that the EGS is similar to the IGS except that the EGS is achieved by forcing the sequence to travel along a specific path . For the training trials , both STDP and synaptic scaling are enabled , whilst test trials are run with both synaptic rules disabled to prevent the test trials from affecting the training process . As in [6] , the number of trials with the goal on the left and right is equal; this is achieved by alternating between C1 and C2 on successive training trials . We now demonstrate that our model is able to reproduce forward-sweeping neural sequences as observed in [6] , and that such sequences can be used to make a correct choice of the goal location based on an external cue . In experimental studies , multiple animals were typically used to obtain average response rates [6 , 18 , 11] . In a similar way , we use multiple realizations of our neural circuit model and track average response rates during the training process . For each realization of the neural circuit , the connections between the cue neurons and the neurons with the 2D network and their coupling weights are randomly selected . As shown in Fig 3 , in our models , the proportion of correct responses increases rapidly during the initial period of the training process . It eventually saturates at a threshold which is close to , but less than 1 , meaning that even after extensive training , the neural circuit does not always make a correct choice; in other words , the IGS does not always travel to the goal location associated with the cue . In experimental studies in which rats learn to navigate a maze under a similar training paradigm , the rate at which rats correctly identify the rewarded arm of a T-maze shows similar behaviour , with an initial low correct response rate which increases before saturating at a level slightly under 1 ( see Figure 4 in [6] ) . There are three possible ways in which the neural circuit fails to makes a correct choice of the goal location . In the first failure type , the sequence fails to propagate down either arm of the T-maze; this failure occurs most commonly in the early trials because the model rat has not learned the environment , in particular the goal location . For the second failure type , there are sequences propagating down both arms of the T-maze simultaneously , indicating that the network has been unable to make a correct choice about the goal location; this failure mode only occurs rarely in our model . In [6] , it was found that the place fields of cells involved in an IGS tended to belong to one arm or the other , and IGS which activated neurons corresponding to both arms was rare . Finally , the sequence may simply propagate along the wrong arm of the T-maze; in experimental studies this failure type has been reported and has been correlated with the animal choosing the non-rewarded arm of the T-maze [6] . We now consider how forward-sweeping IGS can emerge from the network and why they can generate correct responses as shown in Figs 2 and 3 . To this end , we consider a simple case , in which a spiking sequence is evoked by a model rat moving along a straight line , as shown in Fig 4a; i . e . neurons y1 , y2 , … , y7 are activated sequentially . Without loss of generality , we use a representative neuron , y4 , to study the change of coupling strength to the other neurons due to the interplay between this evoked sequence and STDP . Later , we will discuss the effect of synaptic scaling . We let Δti be the time between when neuron yi spikes and when our reference neuron ( y4 ) spikes . Based on the propagation of the evoked spiking sequence , it is clear that Δt1 < Δt2 < Δt3 < Δt4 = 0 < Δt5 < Δt6 < Δt7 . The change in coupling strengths , ΔWi , from y4 to the other neurons can be calculated by using the STDP window function ( see Eq 22 in Materials and methods ) : Δ W i = sgn ( Δ t i ) A exp ( - | Δ t i | / τ ) , ( 1 ) where the sign function , sgn ( x ) = −1 if x < 0 , sgn ( 0 ) = 0 and sgn ( x ) = 1 if x > 0 . It follows that ΔW5 > ΔW6 > ΔW7 > ΔW4 = 0 > ΔW1 > ΔW2 > ΔW3 . In other words , connection strengths in the direction in which the model rat moves are increased while those in the reverse direction are decreased . Suppose then that this procedure is repeated many times , so that the path taken by the model rat is learned by the network: when neuron y4 is activated again , due to the directionality of the coupling strength changes , the membrane potential of neuron y5 will be greater than y3 , and so on . Now , consider a group of neurons arranged as in the decision making task shown in Figs 1 and 4b: neurons y1 , … , y4 are placed along the center stem of the T-maze , while neurons y5 , 1 , y6 , 1 , y7 , 1 are along the left arm , and y5 , 2 , y6 , 2 , y7 , 2 are along the right arm . As outlined earlier , an equal number of training trials go along each arm . As a result , the connections from y4 to y5 , 1 and y5 , 2 must have their strengths changed in an identical way by STDP . This means that the connection strengths from neurons in the center stem to those on the left and right arm of the T-maze are equal , resulting in no preference for either path . To show that this is indeed the case in the full spiking network , we calculate the bias of connections strengths from neurons in the center stem to all other neurons ( see Materials and methods ) ; Fig 5a shows that this bias value is small for neurons in the center stem , indicating that changes in coupling strength for these neurons are symmetric . These results indicate that the changes of coupling strengths due to STDP can learn the paths of the model rat , but that these changes have not broken the symmetry between the two paths in the network . The changes of coupling strengths between the cue neurons and the neurons within the 2D spiking neural circuit , however , are asymmetrical , with connections to one arm of the T-maze increased whilst those to the other arm are unchanged . As before , we consider a group of neurons which are activated sequentially by a moving rat , but we now examine the changes of the connection strengths from the cue neuron C2 to the neurons in the two-dimensional network ( Fig 4c ) , with the cue neuron firing at the same time as the neuron y4 . For this case , the interplay between STDP and the evoked firing sequence changes the coupling strengths in an asymmetrical way , as shown in Fig 4c; this occurs because for each training trial as C2 fires , the model rat is forced to travel along the right arm of the T-maze . As a result , the connection strengths from C2 to the neurons along the right arm of the T-maze are increased , whilst connections from C2 to the neurons along the left arm are unmodified by STDP and maintain their original values . Likewise , the connections from C1 to the neurons along the left arm of the T-maze will have their strengths increased , whilst connections from C1 to the neurons along the right arm of the T-maze are not changed . On each successive training trial , this symmetry breaking becomes stronger as these differences in the connection strengths to the two different paths are magnified . We have verified that this mechanism indeed results in such asymmetric changes to the coupling strengths from the external cue neurons to the neurons within the 2-D spiking neural circuit ( Fig 5b ) . These changes in synaptic coupling strengths due to the interactions between the sequence evoked by the moving rat and STDP can be shown to eventually result in the formation of a forward-sweep IGS when the rat approaches the decision point . Suppose that the trained model rat is at the decision point ( Fig 1 ) , and generates a bump of activity corresponding to this location . As we have demonstrated above , the connections from the decision point to the nearby neurons along both arms of the T-maze have had their strength increased by STDP ( Fig 5a ) . When an external cue is applied , the membrane potentials of the neurons along one side of the T-maze are increased more than on the other side , because the connections from the external cue are asymmetrical , as shown in Fig 5b . These increased membrane potentials cause these neurons to fire before those on the other arm , therefore leading to the formation of a spiking neural sequence sweeping away from the decision point of the T-maze towards the goal location . We note that without training , the asymmetry in the connection strengths does not exist , and the rat is unable to make a decision about which goal is correct given the cue . In our model without STDP , the paths of the model rat cannot be learned and as a result the IGS cannot make a correct choice . To understand the contribution of synaptic scaling to the choice made by the neural circuit shown in Fig 3 , we now study the behaviour of the network in the absence of synaptic scaling . We find that for the first 40 trials the proportion of correct responses when C2 is applied is almost identical in the model with and without synaptic scaling , as shown in Fig 3 . However , after 40 trials , the synaptic behaviour of the two models is no longer similar . The major difference is that in the model without scaling , it becomes increasingly common for the neural sequence to split and travel along both paths . After 80 trials , this has become the dominant response; this behaviour is inconsistent with experimental results [6] . As described earlier , the strength of synapses along the direction of propagation of sequences are increased . As training trials occur with the goal on both the left and right arm of the T-maze , connections from neurons in the center stem to those on the left and right arm are both increased , as in Fig 4b . After sufficient training , the additional excitation generated from these synapses may be sufficient to cause a neuron on one of the arms of the T-maze to spike without any input from the external cue . As a result , the sequence splits and travels down both arms of the T-maze simultaneously . Synaptic scaling , however , causes coupling strengths to gradually return to their original values , effectively reducing the excitation received by neurons on the arms of the T-maze , and preventing the sequence from splitting and propagating along both arms of the T-maze . In addition , due to synaptic scaling , recent changes to synaptic strength have more impact on the current synaptic strength than those that occurred earlier in the training process . As illustrated in the later sections , this temporal property is crucial for the network to generate adaptive choices in response to time-varying and stochastic cue-goal associations . To study the general computational roles of the IGS that emerge from our spiking neural circuit model , we now consider the cases in which the cue-goal associations can be both probabilistic and time-varying . We introduce gn , which is the probability that on trial n , the goal location is on the right arm when C2 is the supplied cue . To maintain symmetry between C1 and C2 , whilst ensuring that the number of left and right trials is the same , the probability that on the nth trial the goal location is on the left arm when C1 is the supplied cue is also gn; note that for the cue-goal association considered in the previous section , gn = 1 for both C1 and C2 . We first consider a simple case of time-varying cue-goal association in which an initial association between the cue and the goal is first learned and then switched . This switch takes the form of a step-like change in gn after a certain number of trials; in this study , we have gn = 0 when n ≤ 100 , followed by gn = 1 for n > 100 . Such a switching change has been used to study the behaviour of rats in a T-maze goal-directed choice protocol [23] , similar to ours . Fig 6 shows the correct response rate ( blue line ) when C2 is used as the test cue under this protocol . When we compare the results from the experimental study [23] to our modelling study , we find that significant similarities exist . In particular , after the switch of the cue-goal association , in both cases the correct response rate increases quickly . This increase then slows before a saturation level is reached . In our study this takes around 60 trials , whilst in [23] , 40 training trials were necessary for all rats to learn the cue-goal relationship . The similarity between the correct response rate from the neural circuit model and the experimental data provides further evidence that our IGS-based model is able to respond to the switching change in the cued-choice task . Again , we find that the model without synaptic scaling cannot respond to this change , in particular , after 200 trials , responses almost exclusively consist of the non-physiological splitting behaviour and the correct response rate is very low . It has been proposed that goal-directed decisions and planning can be understood as an implementation of probabilistic inference [21 , 12] . We shall show how such probabilistic inference can be related to our IGS-based decision making model . In general , the problem of probabilistically estimating the value of gn is an example of a hidden variable problem , since the variable of interest ( i . e . , gn ) cannot be measured directly , but instead its value is estimated based on observations . In the context of the problem we are considering , these observations come from the training trials . We label the observation obtained on the nth training trial as yn , which may take one of two values , namely L and R , corresponding to the goal location being on the left and the right arm of the T-maze , respectively . We assume that yn is a martingale , i . e . , it is unaffected by the value of y1 , … , yn−1 , so that p ( yn|y1 , … , yn−1 ) = p ( yn ) . The sequence of these observations is denoted by Yn = y1 , y2 , … , yn . In a Bayesian context , to solve the hidden variable problem , we need to calculate the conditional posterior distribution function ( pdf ) p ( gn|Yn ) . We now provide a brief overview of how this calculation can be performed . Using the notation we have outlined above , the conditional pdf can be calculated in the following way [24]: p ( g n | Y n ) = p ( Y n | g n ) p ( g n ) p ( Y n ) = p ( y n , Y n - 1 | g n ) p ( g n ) p ( y n , Y n - 1 ) = p ( y n | Y n - 1 , g n ) p ( Y n - 1 | g n ) p ( g n ) p ( y n | Y n - 1 ) p ( Y n - 1 ) = p ( y n | Y n - 1 , g n ) p ( g n | Y n - 1 ) p ( Y n - 1 ) p ( g n ) p ( y n | Y n - 1 ) p ( Y n - 1 ) p ( g n ) = p ( y n | g n ) p ( g n | Y n - 1 ) p ( y n | Y n - 1 ) , ( 2 ) where p ( yn|gn ) is the likelihood function , p ( gn|Yn−1 ) is the prior and p ( yn|Yn−1 ) is a normalizing factor . Calculating p ( gn|Yn ) gives the conditional probability density function for the goal being located on the right arm when C2 is applied , for the sequence of observations Yn . Assuming that gn is constant , i . e . gn = gn−1 , Eq 2 can be simplified to: p ( g n | Y n ) = β n p ( y n | g n ) p ( g n - 1 | Y n - 1 ) , ( 3 ) where βn is an appropriate normalization constant which is independent of gn . This recursive formula for the posterior pdf treats the posterior pdf from the previous trial as a new prior , which is then updated by the observation on the current trial . To complete this formulation , we require both the initial prior and the likelihood function p ( yn|gn ) . We choose p ( g0 ) = 1 as a simple uninformative prior . The likelihood function calculates the likelihood of the observation L or R , given a value of gn . The likelihood function is then given by: p ( L | g n ) = g n , p ( R | g n ) = 1 - g n . ( 4 ) From this formulation , it can be shown that [25]: p ( g n | Y n ) = ( α + β - 1 ) ! ( α - 1 ) ! ( β - 1 ) ! g n α - 1 ( 1 - g n ) β - 1 , ( 5 ) where α is the number of L observations and β is the number of R observations . To make a concrete choice for the goal location , we use a maximum likelihood estimator , which gives an estimate g ^ n for gn by choosing the gn that maximizes the value of p ( gn|Yn ) . For the case gn = gn−1 , it can be shown that g ^ n = β / ( α + β ) = β / n . ( 6 ) It is clear that as n increases , g ^ n will tend towards gn , at least in the case where gn is constant . We now compare the choices made using IGS in our neural circuit model with a recursive Bayesian inference approach outlined above . We find that estimated values of gn based on Eq 3 do not match the true values; for example , the prediction g ^ 200 = 0 . 5 compares poorly to the true value of g200 = 1 . However , our neural circuit generates a more accurate estimate of gn ( Fig 6 ) . To understand how this discrepancy between the result from this probabilistic inference and our neural circuit model occurs , we consider whether both models are sensitive to the order of the training trials . As we described in Eq 6 , the estimate g ^ n made by recursive Bayesian inference depends only on the total number of trials with the goal location on the left and right arm , and is independent of their temporal order . In contrast , we have found that our neural circuit model is sensitive to the order of the training trials; that is , if we reverse the order of the training trials ( i . e . 100 R , which is followed by 100 L ) , the resulting estimate for gn on the 200th trial is significantly different than the original case ( i . e . 100 L , which is followed by 100 R ) , as shown in Fig 6 . This sensitivity to the order of training trials in our spiking neural circuit happens because synaptic connection strengths learned by STDP can be gradually reset by synaptic scaling . This gradual resetting process means that more recent trials have a more significant effect on the coupling strengths than those in the distant past . As accurate predictions for a changing gn require sensitivity to the order of the training trials , we propose an extension to Eq 3; inspired by the effect of synaptic scaling on synaptic coupling strengths , we incorporate a similar resetting mechanism to Eq 3 and obtain the following: p ( g n | Y n ) = β n p ( y n | g n ) p ( g n | Y n - 1 ) α , ( 7 ) where 0 < α < 1 is a parameter that controls the contribution of the previous probabilistic estimate for gn ( i . e . p ( gn|Yn−1 ) ) to the new one ( i . e . p ( gn|Yn ) ) . Expanding this formula , it becomes clear how this change is sensitive to the order of trials , similar to the effect of synaptic scaling: p ( g n | Y n ) = β n p ( y n | g n ) p ( y n - 1 | g n ) α p ( y n - 2 | g n ) 2 α … p ( y 0 | g n ) n α . ( 8 ) Eq 8 indicates that p ( gn|Yn ) can be viewed as a product of the likelihood functions from the previous trials i . e . , p ( yn−1|gn ) α for the n − 1th trial , p ( yn−2|gn ) 2α for the n − 2th trial , etc . As α < 1 , we have p ( yn|gn ) < p ( yn|gn ) α < p ( yn|gn ) 2α < ⋯ < 1 . This relationship indicates that more recent trials have a more significant contribution to the estimate of the current value of gn; this is necessary for capturing the temporal order effect of responding to the switching change in the cued-choice task . We find that by choosing an appropriate value of α ( α = 0 . 99 in this case ) , the estimated value of gn calculated using a maximum likelihood method from the modified recursive Bayesian inference method indeed matches that generated from our spiking neural circuit and the true value of gn ( Fig 6 ) . We have shown that our neural circuit model with IGS is able to adaptively respond to a switch in the cue-goal association , and that this behaviour is well approximated by a simple approach based on probabilistic inference . Now , we demonstrate that our IGS-based model can make real-time choices when gn changes randomly , and that the performance of our model is close to the optimal choices implemented by a Kalman filter . We first describe a Kalman filter in the context of our problem and then compare its performance with that from our IGS-based model . The basic operation of the Kalman filter can be understood as follows: the Kalman filter contains some internal state which incorporates the history of past measurements , each new observation is then incorporated into this internal state and used to make a prediction of the hidden variable; for a detailed discussion see [24] . The 1-dimensional Kalman filter for estimating gn is given by the following set of equations: g^n+1 = ( 1−K ( n ) ) g^n+K ( n ) yn , ( 9 ) Σn+1 = ( 1−K ( n ) ) 2Σn+K ( n ) 2Q+Z , ( 10 ) K ( n ) = ΣnΣn+Q , ( 11 ) where g ^ n + 1 is the estimate of the hidden variable , i . e . the estimate for the current value of gn+1 , yn is the nth measurement as defined above , Q is the covariance of the measurement error , Z is the covariance of the variation in gn , Σn is the estimate of the covariance of g ^ n + 1 and K ( n ) is the Kalman gain . In several cases , it can be shown that the estimate g ^ n + 1 given by the Kalman filter is optimal . One of these cases is the Gaussian random walk , where the dynamics of the hidden variable and the measurement process are given by the following equations: g n + 1 = g n + ξ 1 ( n ) , y n = g n + ξ 2 ( n ) , ( 12 ) where ξ1 ( n ) and ξ2 ( n ) are independent zero-mean Gaussian noise processes , with variance Q and Z , respectively . In the Kalman filter , the Kalman gain , K ( n ) , controls how much each individual observation , yn affects the internal state of the Kalman filter . If each individual observation is unreliable due to noise , i . e . , Z is large , the Kalman gain is small and it takes many trials for the Kalman filter to change its estimate of gn . On the other hand , if each observation is accurate , i . e . , Z is small , the Kalman gain is large and the Kalman filter will quickly change its estimate . In our study , to be consistent with the Gaussian random walk , we use gn given by Eq 12 and g0 = 1/2 . As our neural circuit adapts slowly to changes in gn , it is necessary that Q is small; we have used Q = 2 × 10−5 . Satisfying the condition on yn is more complex , to this end we must verify that the measurement error , gn − yn has a Gaussian distribution with mean 0 . Numerically , we have found that the distribution of measurement errors is well approximated by a Gaussian distribution . As a result , we have shown that when gn is given by Eq 12 , the requirements for the Kalman filter to produce optimal estimates of gn have been satisfied . As noted earlier , the neural circuit estimates of gn saturate at a level below 1 , even when gn = 1 , as shown in Fig 3 . To allow for a direct comparison with the Kalman filter , we thus use a linear map that scales the estimate of gn to match the scale of the output of the Kalman filter; namely , we use g ^ n ′ = a g ^ n + b , where g ^ n is the proportion of correct responses on the nth trial from our network model , with a and b chosen to minimize the mean squared difference between g ( n ) and g ^ n . Fig 7a shows the estimated values of gn from the spiking neural circuit , the modified recursive Bayesian inference procedure described by Eq 7 and the Kalman filter . We note that the parameters of the Kalman filter must be tuned to match both the measurement noise and the noise in the random walk , as they are crucial parameters describing the behaviour of the Kalman filter . The parameters of the neural circuit , however , are identical to those used in the earlier sections . It is apparent that all methods closely follow the changes in gn when it undergoes a Gaussian random walk . To enable a more quantitative comparison between the models , we calculate a cumulative distribution function for the absolute error , i . e . | g n - g ^ n |; as shown in Fig 7b , the neural circuit model and Eq 7 have a similar accuracy to the Kalman filter , specifically , the estimate for gn from the neural circuit was within 3% of the correct value on 50% of trials , within 5% on 80% of trials and within 6% on 90% of trials . The fact that our neural circuit model achieves accuracy comparable to the Kalman filter suggests that our neural circuit model is indeed capable of making a real time estimate of gn . It may be possible to further increase accuracy by incorporating a reward signal . For example , in [26] , a synaptic update rule was obtained from the theoretical consideration of maximizing the probability of receiving a reward event , but in our study we use biologically plausible plasticity mechanisms ( i . e . , STDP and synaptic scaling ) . Additionally , in our model we study a general goal-location decision task by considering time varying cases of cue-goal association , which was not studied in [26] . We now illustrate how the combined synaptic plasticity rules ( i . e . STDP and synaptic scaling ) enable the changes to coupling strengths to encode time-varying cue-goal associations; this mechanism underlies the adaptive choice results reported above . In the previous sections , we showed that connections from the external cue to neurons on the left or right arms of the T-maze such as y5 , 2 ( Fig 4c ) are crucial for determining which path the neural sequence would choose to travel down . We now consider the dynamics of the synapse connecting C2 to neuron y5 , 2 , which are driven by both STDP and synaptic scaling , in order to understand the choice mechanism within our neural circuit , although our analysis does not crucially depend on the precise location of the neuron . In particular , the increase in coupling strength due to STDP in a single trial is A+ exp ( −Δt5/τ+ ) ( Eq 22 ) . However , it is not necessarily true that on every training trial , the model rat travels along the right arm of the maze , where the sample neuron is located . In fact , the probability of this event is given by gn , by its definition . It follows that the average increase in coupling strength per trial is A+gn exp ( −Δt5/τ+ ) . As we described in the Materials and Methods section , synaptic scaling causes a gradual resetting in coupling strengths towards their initial values . It follows that over the time period of a learning trial , some fraction of the changes in coupling strength caused by STDP will be lost by this resetting process . We can find the value of this loss as follows: if at the start of the trial , the change in the connection strength from its initial value is ΔW; at the end of the trial , synaptic scaling will have reduced it to rΔW ( see Materials and methods ) . This decay occurs on every trial , regardless of whether the rat travels down the left or right arm of the T-maze . This gives a recursive equation for the change in coupling strength of the synapse from the external cue to our sample neuron after the nth trial , ΔWn , as follows: Δ W n = A + g n exp ( - Δ t 5 / τ + ) + r Δ W n - 1 . ( 13 ) Analyzing this equation directly is difficult , so we consider a simplification based on the assumption that gn is either constant or varying slowly , as in Figs 3 and 7 . To this end , rather than using the value of gn , we use g ¯ n = ∑ i = 1 N g n - i / N where N is chosen depending on both the dynamics of gn and the neural circuit . We have chosen a value of N = 50 based on the time it takes for the correct response rate to saturate , as shown in Fig 3 . By setting ΔWn = ΔWn−1 = ΔWss , we can find the value of the steady state coupling strength change ΔWss: ( 1−r ) ΔWss =g¯nA+exp ( −Δt5/τ+ ) . ( 14 ) This then gives an explicit description of ΔWss: Δ W s s = g ¯ n A + exp ( - Δ t 5 / τ + ) 1 - r . ( 15 ) As all variables other than g ¯ n are fixed parameters of the model , we have: Δ W s s = F g ¯ n , ( 16 ) where F = A + exp ( - Δ t 5 / τ + ) 1 - r . It is clear from Eq 16 that the change in coupling strength is proportional to g ¯ n; this point is further illustrated in Fig 8 in which ΔWss and g ¯ n are compared . These results thus indicate how the synaptic strengths mediated by the combination of STDP and synaptic scaling to some extent are able to adaptively trace the time-varying gn . As the synaptic strengths are the neural basis for forming the IGS , as shown above , our model is able to respond correctly even when gn is changing ( Fig 7 ) .
In this study , we have developed a spiking neural circuit to study the formation mechanisms of forward sweeping , internally generated sequences and their function in goal-directed spatial decision making . We have demonstrated that in our spiking neural circuit , the interplay between the network dynamics and the combined synaptic dynamics of STDP and synaptic scaling is essential for the formation of such neural sequences . Our model can capture the salient properties of these sequences and yields behavioural performance comparable to data obtained from behaving animals [6] . In addition , we have demonstrated that STDP when complemented by slower synaptic scaling enables the neural sequences generated in the network model to adaptively respond to changing cue-goal associations . Our model of spatial decision making shares a resemblance with other spreading-activation or propagating wave-based models for path planning [27 , 28 , 29] in that propagating sequential activity is a key feature used for making choices . However , unlike these models , but consistent with experimental data , there is no backwards diffusion of activity from the goal to the current state during the learning or planning process . Furthermore , by implementing goal directed decision making in probabilistic terms , our model also extends to the cases in which uncertainty in the cue-goal association exists , which has not been generally addressed by previous spreading activation models . Goal directed learning has previously been studied in spiking networks [26 , 30 , 31 , 32] . However in these studies , goal-directed decision making with time-varying cue-goal association has not been considered , and how this can be implemented by biologically realistic synaptic mechanisms has not been addressed . As we have demonstrated , STDP causes the coupling strengths along the direction of the model animal’s movement to increase and those in the opposite direction to decrease on each training trial . Such positive feedback effects from STDP alone would eventually result in the saturation of synaptic coupling strengths , so that the formed neural sequence propagates towards all possible goals , rather than toward one goal at a time . As a result , the spiking network neither can give rise to correct choice rates as reported in [6] , nor make flexible , time-varying choices of goal locations . The homeostatic synaptic scaling rule , however , plays a role in preventing saturation of synaptic coupling strengths as the training process proceeds . In our model , this scaling process happens at a temporal scale which is around 350 times longer than that of STDP , therefore enabling the network to intrinsically possess distinct temporal scales . We have found that the behaviour of our model is not dependent on the precise values of the time constants chosen , as long as the timescale of STDP is shorter than that of a single learning trial and the timescale of synaptic scaling is much longer than both of these . In addition , changing the size of the neural circuit and T-maze does not significantly change our results; we have found that similar results can be found with T-mazes which are either twice or half the size of the T-maze that we have used in this study . A combination of plasticity rules with separate temporal scales enables the network to maintain the trace of the moving paths of the model rat during the training process , and it provides the needed flexibility to allow the network to generate time-varying choices . Both STDP [33 , 34 , 35] and synaptic scaling [36] have been widely observed in the brain , and our model results show that the loss of either mechanism would impair the normal functions of the brain . In previous modelling studies , the combination of STDP and synaptic scaling has been mainly used to achieve specific network states , such as balanced states [37] . Our study , however , relates this combination and the resultant separation of time scales to network dynamics in terms of emergent IGS , and further to goal-directed spatial decision making . Other synaptic mechanisms such as short-term depression [38] and homeostatic synaptic plasticity described by the BCM rule [39] when combined with STDP may produce a similar behaviour , whilst also avoiding the saturation of coupling strength . The IGS in our model capture the characteristic dynamics of IGS as observed in [6] , including their forward propagation , and propagation toward one goal at a time rather than towards all goals simultaneously . Some previous modelling studies used spreading activation or propagating wave fronts to model path planning or goal directed decision making [27 , 28 , 29]; in these models , however , neural activity propagates backwards from the goal to the current location . Importantly , by reproducing animals’ behavioural responses , i . e . , the correct decision rates as measured in [6] , our modeling study directly demonstrates that such forward-sweeping sequences could be the neural substrate for implementing learning and implementing goal-directed decision making . In our study , this computational role of forward-sweeping IGS has been further illustrated by extending the deterministic cued-choice task studied in [6] to tasks with time-varying and probabilistic associations between cues and goals . This extension allows us to show that IGS-implemented functionality can be generally understood in terms of probabilistic inference . The general computational role of IGS revealed in our model is consistent with the proposal that IGS can implement probabilistic inference that optimizes goal acquisition for real time choice and learning [12 , 21] . Our study thus provides a basis for further extensions considering reward learning and multiple choices . In our model , when the probabilistic associations of the cues and goals undergoes a Gaussian random walk , such IGS-based decision making works in a near optimal way; the estimates are within 3% of the correct value for 50% of the time , largely comparable to those obtained by a Kalman filter that is optimal for this case . The Kalman filter has been well studied in relation to optimal decision making in prediction and motor control , however , neural representations of Kalman filters are largely unknown . By comparing our model with the working mechanism of the Kalman filter , we can understand how it is able to make accurate choices . In particular , in our IGS-based model for the spatial decision task , the interplay of spiking sequences and the combined synaptic rules lead to constant changes of synaptic coupling strengths . As we have demonstrated , these changes are proportional to the probability of cue-goal associations , as long as this probability varies slowly . Thus these changes to coupling strength can serve as the posterior for test trials , which can then be exploited by the sequences to make a choice . The ratio between the strength of STDP and synaptic scaling plays a similar role as the Kalman gain , as it controls how quickly new training trials are incorporated into the internal model . Specifically , STDP controls the strength of the trace left by the previous training trials , whilst synaptic scaling controls the speed with which they are reset to their original values . This ratio is fixed in our case , but Kalman gain is dynamical; this difference suggests that including other synaptic mechanisms such as meta-plasticity to the model may give rise to a dynamical ratio with multiple timescales [40] , which may make the spiking model perform optimally like a Kalman filter . Nevertheless , our spiking neural circuit model suggests that the IGS and the combined synaptic plasticity rules are candidate neural implementations of a Kalman filter-based estimation of the changing cue-goal associations .
Analyzing the overall effect of plasticity is difficult due to the large number of synaptic connections in our model . To simplify this analysis , we construct a per-neuron measure which determines the bias in connection strengths emanating from that particular neuron . In particular we are interested in the directional bias of connection strengths to understand how decisions can be made in the cued-choice task . If an excitatory neuron in the center stem is more strongly coupled to neurons on the left arm of the T-maze , the sequence will be more likely to travel along the left arm of the T-maze . We construct the following measure of the bias in connection strengths inspired by a center of mass calculation as follows: B i j ( t ) = | ∑ i ′ j ′ ( i - i ′ ) W i j , i ′ j ′ ( t ) | ( 27 ) This measure will be small if connections from the neuron to the left and right have similar strength , and large otherwise , allowing simple analysis of any bias present in the network . | Adaptive goal-directed decision making is critical for animals , robots and humans to navigate through space . In this study , we propose a novel neural mechanism for implementing spatial decision making in cued-choice tasks . We show that in a spiking neural circuit model , the interplay of network dynamics and a combination of two synaptic plasticity rules , STDP and synaptic scaling , gives rise to neural sequences . When a model rat pauses around a decision point , these sequences propagate ahead of the animal’s current location and travel towards a goal location . The dynamical properties of these forward-sweeping sequences and the rate of correct responses made by them are consistent with experimental data . In addition , we demonstrate that STDP when complemented by slower synaptic scaling enables neural sequences to make adaptive choices under probabilistic and time-varying cue-goal associations . The adaptive performance of our sequence-based network is comparable to a mathematical model , namely the Kalman filter , which is optimal for this adaptive task . Our results thus shed new light on our understanding of neural mechanisms underlying goal-directed decision making . | [
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] | 2017 | Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits |
Subversion of host immune surveillance is a crucial step in viral pathogenesis . Epstein-Barr virus ( EBV ) encodes two immune evasion gene products , BCRF1 ( viral IL-10 ) and BPLF1 ( deubiquitinase/deneddylase ) ; both proteins suppress antiviral immune responses during primary infection . The BCRF1 and BPLF1 genes are expressed during the late phase of the lytic cycle , an essential but poorly understood phase of viral gene expression . Several late gene regulators recently identified in beta and gamma herpesviruses form a viral pre-initiation complex for transcription . Whether each of these late gene regulators is necessary for transcription of all late genes is not known . Here , studying viral gene expression in the absence and presence of siRNAs to individual components of the viral pre-initiation complex , we identified two distinct groups of late genes . One group includes late genes encoding the two immunoevasins , BCRF1 and BPLF1 , and is transcribed independently of the viral pre-initiation complex . The second group primarily encodes viral structural proteins and is dependent on the viral pre-initiation complex . The protein kinase BGLF4 is the only known late gene regulator necessary for expression of both groups of late genes . ChIP-seq analysis showed that the transcription activator Rta associates with the promoters of eight late genes including genes encoding the viral immunoevasins . Our results demonstrate that late genes encoding immunomodulatory proteins are transcribed by a mechanism distinct from late genes encoding viral structural proteins . Understanding the mechanisms that specifically regulate expression of the late immunomodulatory proteins could aid the development of antiviral drugs that impair immune evasion by the oncogenic EB virus .
Late genes represent more than one third of the herpesvirus genome . The functions of many of these genes are indispensable for the life cycle of the virus . Late genes encode structural proteins that form the viral capsid , and glycoproteins that mediate virus attachment , fusion and entry during primary infection . Other late proteins also mediate essential events during virion assembly and maturation such as viral DNA cleavage and packaging into pre-formed capsids , capsid envelopment , and egress of infectious particles . Furthermore , late proteins play an integral role in suppressing the immunogenicity of infected cells . Here we investigate the expression of late genes in Epstein-Barr virus ( EBV ) , an oncogenic gamma herpesvirus associated with several forms of cancer including Burkitt lymphoma [1] , nasopharyngeal carcinoma [2 , 3] , Hodgkin lymphoma [4] , gastric carcinoma [5 , 6] , post-transplant lymphoproliferative disease [7 , 8] , and AIDS-associated lymphoma [9] . Currently , there are no drugs or vaccines that can interfere with EBV primary infection . Studying the mechanisms that regulate the various phases of the virus life cycle is crucial to generate new means to control EBV infection and its associated diseases . While late products play an essential role in the life cycle of EBV , key players regulating their expression have only been recently identified . We described two EBV-encoded proteins that regulate synthesis of late mRNAs [10] . These two late gene regulators are BGLF3 , an early protein that has no cellular homologs or identifiable domains , and BGLF4 , a serine/threonine protein kinase conserved in all herpesviruses . Knockdown of BGLF3 or BGLF4 selectively abolished expression of late genes independent of any effect on expression of early genes or viral DNA replication [10] . The mechanism by which BGLF4 regulates expression of late genes is not clear; however , the kinase activity of BGLF4 is indispensable for accumulation of late products . BGLF3 is part of a viral pre-initiation complex ( vPIC ) dedicated for transcription of late genes [11] . The complex contains at least five additional early lytic proteins . These proteins are: BcRF1 , BDLF3 . 5 , BDLF4 , BFRF2 , and BVLF1 . Knockout of BcRF1 , BDLF4 , BVLF1 , BDLF3 . 5 and BFRF2 disrupts expression of late genes [11–14] . However , the exact function of several of these late gene regulators is still unknown . The current model suggests that late gene regulators assemble together to form a viral pre-initiation complex ( vPIC ) that mediates recruitment of the RNA polymerase II complex ( RNAPII ) to late promoters . In support of this model , disrupting expression of the murine gamma herpesvirus BGLF3 or BDLF3 . 5 orthologs reduced association of the large catalytic subunit ( RPB1 ) of RNAPII to late promoters [15] . Selective recognition of late promoters is mediated by the late gene regulator BcRF1 , a viral protein predicted to have a saddle-like protein-fold that is characteristic of the cellular TATA-box binding protein ( TBP ) [13 , 16] . The binding specificity of BcRF1 ( vTBP ) is different than cellular TBP . BcRF1 preferentially binds to a TATT element ( with a T rather than A at the fourth position ) that is present in most late promoters [13 , 17] . BcRF1 directly interacts with subunits of RNAPII and recruits the polymerase complex to late promoters [11] . Similar observations were reported for ORF24 , the ortholog of vTBP in Kaposi’s sarcoma-associated herpesvirus [18] . Mutations in ORF24 that disrupt its interaction with RPB1 or the TATT sequence present in late promoters abolished synthesis of late transcripts [19] . HCMV UL79 , the ortholog of EBV BVLF1 , functions as an elongation factor of RNAPII during the late phase of viral gene expression [20] . Sub-nuclear localization studies revealed that late gene regulators assemble in replication compartments and that true late genes are transcribed from newly replicated viral DNA [14 , 21 , 22] . Formation of replication compartments might increase the local concentration of these late gene regulators and promote their interaction with replication proteins to stimulate synthesis of late transcripts . Despite recent advances in identifying virally encoded late gene regulators , many questions remain . One specific question that we address here is whether the same set of late gene regulators activates expression of all late genes . In this study we began by knocking down expression of BGLF3 and examined the EBV transcriptome during the late phase of lytic infection . We found that transcript levels of several late genes were not altered by the absence of BGLF3 . We focused on four of these BGLF3-independent genes that are expressed with true late kinetics . We demonstrated that expression of this subclass of late genes is regulated by the BGLF4 protein kinase but not by other known late gene regulators . Interestingly , two of the BGLF3-independent late genes , BCRF1 and BPLF1 , function as immunomodulators . BCRF1 , a homolog of human IL10 ( vIL10 ) , and BPLF1 , a deneddylase/deubiquitinase ( vDUB ) protein , suppress immune recognition during the productive lytic cycle and viral de novo infection [23–27] . Our findings show that the mechanism regulating expression of EBV antigenic late structural proteins differs from that of late immunosuppressants .
Recently we identified BGLF3 as a lytic product essential for transcription of late genes from the endogenous viral genome [10] . Silencing expression of BGLF3 selectively reduced the level of three late transcripts , BFRF3 ( minor viral capsid protein ) , BcLF1 ( major viral capsid protein ) and BdRF1 ( scaffold protein ) , but had no effect on the level of an early transcript , BMRF1 ( the polymerase processivity factor ) , or the capacity of the virus to amplify its genome [10] . To determine which lytic genes require the function of BGLF3 for their expression , we studied the EBV transcriptome in the absence and presence of siRNA to BGLF3 ( siBGLF3 ) . We transfected 2089 cells , 293 cells harboring wild type EBV bacmid , with an expression vector encoding ZEBRA , the lytic cycle activator , either alone or together with 50 nM siBGLF3 . A Western blot analysis was performed 48h after transfection to confirm induction of the lytic cycle in cells transfected by ZEBRA . Co-transfection of siBGLF3 markedly reduced the level of late FR3 protein , in accordance with our previous data [10] . Total RNA was purified and subjected to RNA-seq analysis . The relative abundance of each transcript was estimated as the number of reads mapping to a particular gene normalized to the total number of viral reads [28 , 29] . Fig 1 compares the abundance of each transcript in the absence and presence of siBGLF3 . The change in expression is represented as a log base 2-fold . The kinetic classes of EBV genes were assigned based on viral gene classification reported by Yuan et al [30] . Viral transcripts with a negative fold-change represent EBV genes whose expression is dependent on the presence of BGLF3 , and vice versa . We found that silencing of BGLF3 reduced expression of 23 late genes . This result supports our previous conclusion that BGLF3 is a late gene regulator [10] . However , the level of several late transcripts , e . g . BTRF1 ( tegument protein ) , BPLF1 ( vDUB ) , BCRF1 ( vIL10 ) , and BSRF1 ( tegument protein , homolog of HHV1 UL51 , an egress protein ) , was not affected by the lack of BGLF3 ( Fig 1 and S1 Fig ) . We refer to these genes as the BGLF3-independent late genes . This finding suggests that some late genes might be expressed in a manner independent of BGLF3 . Thus , expression of late genes could be regulated by more than one mechanism . Our RNA-seq data suggests that BGLF3 is dispensable for expression of several lytic genes that were previously categorized as late ( Fig 1 ) [30] . To verify this result we used RT-qPCR to compare expression of four BGLF3-independent genes ( BTRF1 , BPLF1 , BCRF1 , and BSRF1 ) with two early genes , BMRF1 and BRLF1 ( transcription activator ) , and five BGLF3-dependent late genes , BdRF1 ( scaffold protein ) , BLLF1 ( glycoprotein gp350 ) , BFRF3 ( minor viral capsid protein ) , BDLF1 ( triplex capsid protein ) , and BLRF2 ( tegument protein ) , with and without siBGLF3 . Selection of the four BGLF3-independent late genes was based on our capacity to establish their true late kinetics using RT-qPCR . The comparison was performed in 2089 cells transfected with empty vector ( CMV ) , ZEBRA ( Z ) , or ZEBRA plus siBGLF3 ( 50 nM ) . Expression of ZEBRA in 2089 cells triggered the lytic cycle leading to accumulation of the early BMRF1 and the late FR3 proteins ( Fig 2A lane 2 ) . Silencing expression of BGLF3 reduced the level of FR3 but had no effect on the protein level of BMRF1 ( Fig 2A lane 3 ) . To demonstrate that the effect of siBGLF3 on late gene expression is specific to silencing BGLF3 rather than an off-target activity , we inserted silent mutations to generate a form of BGLF3 that is resistant to the siRNA ( rBGLF3 ) . Co-transfection of rBGLF3 in lytic 2089 cells suppressed the effect of siBGLF3 on synthesis of late products and restored expression of the late FR3 protein ( Fig 2A lane 4 and [10] ) . Fig 2B compares expression of eleven lytic transcripts in the absence and presence of BGLF3 . Change in expression was calculated as the amount of mRNA detected in cells transfected with ZEBRA or ZEBRA plus siBGLF3 ( 50 nM ) relative to empty vector . The relative concentration of each transcript was corrected according to the level of GAPDH mRNA measured in the same sample . Knockdown of BGLF3 expression did not reduce the level of the two early transcripts , BRLF1 and BMRF1 ( Fig 2B ) ; instead a slight increase was detected . In agreement with the RNA-seq data presented in Fig 1 , knockdown of BGLF3 significantly reduced the level of the five BGLF3-dependent late genes but did not significantly decrease expression of the four BGLF3-independent late genes ( Fig 2B and S1 Table ) . This result supports the model for the presence of at least two subclasses of late genes that differ in their mechanisms of expression based on the requirement of BGLF3 . BGLF3 reportedly functions in a viral pre-initiation complex dedicated to transcription of late genes [11] . Thus , it was imperative to assess the kinetics of expression of the BGLF3-independent genes . Typically , EBV late genes are synthesized only after the onset of viral DNA replication . To examine temporal expression of the BGLF3-independent genes , we transfected 2089 cells with ZEBRA in the absence or presence of an inhibitor of viral DNA replication , phosphonoacetic acid ( PAA ) [31] . Addition of PAA to 2089 cells transfected with ZEBRA blocked expression of the true late FR3 protein ( Fig 3A , compare lanes 2 and 4 ) . However , PAA treatment did not reduce the level of ZEBRA or the early BMRF1 protein . Using RT-qPCR , we compared expression of eleven lytic genes: four BGLF3-independent , two early and five BGLF3-dependent late genes ( Fig 3B ) . Fold expression was calculated as the ratio of a particular transcript in lytic 2089 cells transfected with ZEBRA versus non-lytic cells transfected with empty vector . The level of a particular mRNA in the absence or presence of PAA was corrected to the level of GAPDH in the same sample . PAA treatment reduced the level of all eleven mRNAs regardless of their kinetic class . However , the effect of PAA on expression of BGLF3-dependent late genes was drastically more reduced than its effect on early genes . Addition of PAA to lytic cells decreased early gene expression to an average of 52% but BGLF3-dependent late genes to an average of 2% relative to lytic cells without PAA . Expression of the four BGLF3-independent late genes ( BTRF1 , BPLF1 , BCRF1 and BSRF1 ) was markedly reduced to an average of 4% relative to ZEBRA-transfected 2089 cells not treated with PAA . These results with PAA strongly indicate that at least four of the BGLF3-independent genes were expressed with true late kinetics . To confirm the late kinetics of BTRF1 , BPLF1 , BCRF1 and BSRF1 , we studied their expression in delta BMRF1 cells ( 293 cells carrying EBV-bacmid with deletion in the BMRF1 gene ) . BMRF1 is a component of the viral DNA polymerase holo-enzyme [32] and is indispensable for viral genome amplification ( S2A Fig and [33] ) . Expression of ZEBRA in delta BMRF1 cells induced synthesis of the two early transcripts BRLF1 and BBLF2/3 ( S2B and S2C Fig ) , but failed to induce synthesis of late BLLF1 gene ( S2D Fig ) and the four BGLF3-independent genes ( S2E–S2H Fig ) . Co-expression of ZEBRA and BMRF1 restored viral DNA replication , expression of the late FR3 viral capsid protein ( S2A Fig ) and synthesis of late transcripts including the four studied BGLF3-independent genes ( S2D–S2H Fig ) . These results provide strong evidence that BTRF1 , BPLF1 , BCRF1 and BSRF1 are true late genes expressed in the absence of the BGLF3 late gene regulator . In addition to BGLF3 , EBV encodes five other late gene regulators that were previously shown to be essential for activation of late promoters in reporter assays [11] . These proteins are: BcRF1 , BDLF4 , BFRF2 , BVLF1 and BDLF3 . 5 . All six late gene regulators are thought to assemble on late promoters to form a distinct viral pre-initiation complex ( vPIC ) for transcription of late genes [11] . As illustrated in Fig 4 , we used siRNAs specific to each of the five late gene regulators to study their roles in activation of late genes from the endogenous viral genome . The capacity of these siRNAs to disrupt expression of late genes was studied in 2089 cells transfected with ZEBRA ( Z ) , or ZEBRA plus siRNA . We selected siRNAs that had no effect on expression of the early BMRF1 protein but markedly reduced expression of the late FR3 protein ( Figs 4A and S3 ) . Using RT-qPCR , we examined the capacity of the selected siRNAs to knockdown expression of their corresponding mRNA ( S4 Fig ) . The specificity of the selected siRNAs was further assessed by generating siRNA-resistant forms of BcRF1 ( rBcRF1 ) , BDLF4 ( rBDLF4 ) , BFRF2 ( rBFRF2 ) , and BVLF1 ( rBVLF1 ) . Expression of rBcRF1 , rBDLF4 , rBFRF2 , and rBVLF1 suppressed the effect of the corresponding siRNA on synthesis of the late FR3 protein ( S5 Fig ) . The specificity of siBGLF3 and siBGLF4 was previously established using a similar approach ( Fig 2A and [10] ) . These results demonstrate that the effect of each of the selected siRNAs on the late phenotype was specific to silencing expression of its corresponding late gene regulator . Altogether , our data suggest that BcRF1 , BDLF4 , BFRF2 , BVLF1 , and BDLF3 . 5 are individually essential for expression of late genes from the endogenous virus genome; none of these regulators play a redundant role in synthesis of late products . To determine whether any of the late gene regulators affect the process of viral genome amplification , we purified DNA from 2089 cells transfected with ZEBRA alone or together with 80 nM of each siRNA . Using qPCR , we found that transfection of ZEBRA increased the level of intracellular viral DNA by 286-fold relative to cells transfected with empty vector . None of the siRNAs to late gene regulators compromised the capacity of the virus to amplify its genome ( Fig 4B ) . In the previous experiment ( Fig 4 ) , we demonstrated that disrupting expression of any of the late gene regulators reduced the level of late transcripts without affecting expression of early genes or amplification of the viral genome . To determine whether such reduction in late transcripts impacts the amount of virus particles released , we purified DNA encapsidated in virions from culture medium of the same samples that were used to generate the data in Fig 4 . Quantitative PCR was employed to assess the amount of extracellular virion-protected DNA . The level of extracellular viral DNA detected in the culture medium of 2089 cells transfected with a ZEBRA expression vector was approximately 150-fold higher relative to cells transfected with a control plasmid . Co-transfection of ZEBRA plus siBcRF1 , siBDLF4 , siBFRF2 , siBVLF1 , siBDLF3 . 5 , or siBGLF3 reduced the level of extracellular encapsidated viral DNA to 16% , 7 . 8% , 13% , 1 . 4% , 23 . 8% , and 6 . 9% compared to ZEBRA alone , respectively ( Fig 5 ) . In conclusion , while none of the siRNAs diminished the level of intracellular EBV DNA , all siRNAs significantly reduced the level of encapsidated viral DNA released from lytic infected cells . These results suggest that knockdown of late gene regulators affects expression of late genes and subsequent events but has no effect on early events and viral DNA replication . BcRF1 is a TATA box-binding protein that was previously reported to be required for efficient expression of late gene transcripts in the EBV lytic cycle [13] . Expression of the viral late genes BcLF1 , BDLF1 and BLLF1 , all of which encode virion structural proteins , was attenuated in the absence of BcRF1 [13] . To determine the effect of BcRF1 on the newly identified BGLF3-independent late genes , we induced lytic infection in 2089 cells by transfecting ZEBRA alone or ZEBRA plus siRNA against BcRF1 . Fig 6A shows the level of transcriptional expression of each gene in the absence of BcRF1 compared to levels measured from samples expressing BcRF1 . When BcRF1 was knocked down , the early genes BMRF1 and BRLF1 were not significantly affected: BMRF1 was reduced by 1 . 3-fold ( 24% ) while BRLF1 increased by 1 . 3-fold ( 25% ) . However , the levels of the late BcLF1 and BLLF1 transcripts significantly decreased by 5 . 6-fold ( 82% ) and 5 . 0-fold ( 80% ) , respectively , compared to the levels detected in samples without siBcRF1 ( Fig 6A ) . Three additional BGLF3-dependent late genes were also significantly impacted; BdRF1 , BFRF3 and BLRF2 decreased 10 . 8-fold ( 91% ) , 5 . 8-fold ( 83% ) , and 5 . 6-fold ( 82% ) , respectively , compared to samples not treated with siBcRF1 . When fold-changes of early and BGLF3-dependent late genes were averaged and statistically analyzed as a group , the difference between early and late transcripts was significant ( p = 0 . 002 ) ( Fig 6B ) . The BGLF3-independent subset of late genes was only modestly affected by knockdown of BcRF1; BPLF1 , BCRF1 and BSRF1 were reduced by 1 . 2-fold ( 14% ) , 1 . 6-fold ( 37% ) , and 1 . 7-fold ( 42% ) , respectively . Due to the co-terminal nature of the BcRF1 and BTRF1 transcripts , siBcRF1 reduced the level of the BTRF1 transcript by 2 . 3-fold ( 57% ) . Unlike the effect of siBcRF1 on expression of BGLF3-dependent late genes , changes in expression of the BGLF3-independent late genes , excluding BTRF1 , were not statistically significant among biological replicates ( S1 Table ) . Thus , the expression pattern of the BGLF3-independent late genes in the presence of siBcRF1 was similar to early genes and different than late genes encoding structural proteins ( Fig 6A and S1 Table ) . The average fold-change between BGLF3-dependent late and BGLF3-independent late genes upon exposure to siBcRF1 is significant ( p = 0 . 0007 ) , whereas the change between early and BGLF3-independent late genes is not significant ( p = 0 . 13 ) ( Fig 6B ) . These results demonstrate that while BcRF1 is , in fact , necessary for the expression of a number of late genes , the BGLF3-independent subset of late genes do not require the virally encoded TBP . Similar findings showing that vTBP is dispensable for expression of BCRF1 ( vIL10 ) were also observed in EBV-infected HH514-16 Burkitt lymphoma cells ( S6 Fig ) . First , we examined whether BCRF1 is expressed with late kinetics by treating ZEBRA-transfected HH514-16 cells with two concentrations of PAA , 0 . 3 and 0 . 4 mM . We found that PAA treatment had no effect on synthesis of BMRF1 mRNA and protein relative to untreated cells ( S6A and S6D Fig ) but markedly reduced expression of the two late genes , BFRF3 ( S6B and S6D Fig ) and BCRF1 ( S6C Fig ) . Second , we knocked down the late gene regulator vTBP and assessed expression of BMRF1 , BFRF3 , and BCRF1 as examples of early , BGLF3-dependent late and BGLF3-independent late genes , respectively . We found that expression of BMRF1 and BCRF1 was not affected by siBcRF1 relative to cells transfected with ZEBRA alone , yet expression of BFRF3 was markedly reduced . These results suggest that BCRF1 is a late gene that is expressed in the absence of vTBP BcRF1 , one of the main components of the viral pre-initiation complex . Four EBV lytic proteins ( BDLF4 , BFRF2 , BVLF1 and BDLF3 . 5 ) in addition to the viral BcRF1 function as regulators of transcription of EBV late genes ( [11 , 12] and Fig 4 ) . In order to determine if these regulators also impact expression of the BGLF3-independent subset of late genes , we activated the lytic cycle in 2089 cells by transfecting ZEBRA along with siRNA against each of the four late gene regulators . RNA was purified and expression of viral genes was studied using RT-qPCR . We investigated the effect of siBDLF4 on expression of early , BGLF3-dependent late , and BGLF3-independent late genes . Knockdown of BDLF4 did not reduce transcription of early genes; on the contrary , the level of the BRLF1 early transcript slightly increased compared to samples not treated with siBDLF4 ( Fig 7A ) . Significant decrease in expression of the BGLF3-dependent late transcripts , ranging from 5 . 1-fold ( 81% ) to 9-fold ( 89% ) , was detected in cells transfected with ZEBRA plus siBDLF4 relative to cells solely transfected with ZEBRA ( Fig 7A and S1 Table ) . However , expression of the BGLF3-independent late gene transcripts was not significantly altered in the absence of BDLF4 ( S1 Table ) . The level of the late BTRF1 transcript increased by 1 . 3-fold ( 32% ) relative to cells transfected with ZEBRA alone , whereas the level of the three other BGLF3-independent late transcripts , BPLF1 , BCRF1 and BSRF1 , decreased by 1 . 4-fold ( 29% ) , 1 . 3-fold ( 22% ) , and 1 . 5-fold ( 35% ) , respectively ( Fig 7A ) . Similarly , silencing BFRF2 , BVLFI , and BDLF3 . 5 did not markedly affect accumulation of early or BGLF3-independent late genes but significantly reduced expression of BGLF3-dependent late genes ( Figs 7C , 8A and 8C ) . A marginal effect of all three siRNAs on the level of early transcripts was observed , ranging from a 1 . 2-fold ( 15% ) decrease to a 1 . 3-fold ( 32% ) increase relative to expression in cells transfected with ZEBRA alone . Likewise , knockdown of the three late gene regulators , BFRF2 , BVLFI , and BDLF3 . 5 only modestly changed the level of expression of the BGLF3-independent late genes resulting in 1 . 2-fold ( 21% ) to 1 . 5-fold ( 48% ) increase in the amount of BTRF1 mRNA; 1 . 4-fold ( 29% ) to 1 . 7-fold ( 40% ) decrease in the level of BPLF1 mRNA , and no-change to 1 . 3-fold ( 22% ) and 1 . 4-fold ( 29% ) reduction in the levels of BCRF1 and BSRF1 transcripts , respectively . In contrast , transfection of siBFRF2 , siBVLF1 , or siBDLF3 . 5 resulted in significant reduction in expression of the BGLF3-dependent late genes that ranged from 5 . 6-fold ( 82% ) to 10 . 9-fold ( 91% ) , 6 . 6-fold ( 85% ) to 11 . 7-fold ( 91% ) , and 4 . 8-fold ( 79% ) to 7 . 2-fold ( 86% ) , respectively ( S1 Table ) . A statistical approach averaging fold-changes in expression of the three categories of lytic genes revealed similar outcomes among the four siRNAs targeting late gene regulators ( Figs 7B , 7D , 8B and 8D ) . Variations in averaged fold-changes indicated that differences in expression between early and BGLF3-dependent late genes were statistically significant for each of the used siRNAs ( siBDLF4 p = 0 . 0014 , siBFRF2 p = 0 . 0008 , siBVLF1 p = 0 . 0002 , and siBDLF3 . 5 p < 0 . 0001 ) . Differences in the averaged fold-changes between BGLF3-dependent and BGLF3-independent late genes were statistically significant ( siBDLF4 p = 0 . 0016 , siBFRF2 p = 0 . 0012 , siBVLF1 p = 0 . 0014 , and siBDLF3 . 5 p = 0 . 0004 ) . However , no statistical significance was observed when comparing the averaged fold-changes of early and BGLF3-independent late genes . These results reinforce the conclusion that the pattern of expression of the BGLF3-independent late genes is similar to early genes and is independent of the action of the conventional late gene regulators . Recently we showed that BGLF4 plays an indispensable role in regulation of late gene expression [10] . To determine whether knockdown of BGLF4 disrupts expression of the BGLF3-independent late genes , we transfected 2089 cells with empty vector , or ZEBRA expression vector in the absence and presence of siRNA to BGLF4 . We used Western blot analysis to assess expression of BMRF1 as a marker for induction of the lytic cycle by ZEBRA . BMRF1 is also a bona fide substrate of BGLF4 . Silencing expression of the BGLF4 kinase abolished the hyperphosphorylated form of BMRF1 as well as expression of the late BFRF3 gene encoding the small viral capsid protein ( Fig 9A ) . These results confirm our previously published findings showing that BGLF4 is necessary for late gene expression . Using RT-qPCR we found that silencing expression of BGLF4 marginally reduced the level of the two early transcripts , BMRF1 and BRLF1 , by 1 . 1- and 1 . 5-fold but markedly reduced the transcript level of both the BGLF3-dependent and the BGLF3-independent subclasses of late genes ( Fig 9B and 9C ) . Our findings demonstrate that the viral BGLF4 protein kinase is the only known late gene regulator necessary for expression of the BGLF3-independent late genes . In an attempt to understand the mechanism that regulates expression of the BGLF3-independent late genes , we searched the primary sequence of BTRF1 , BPLF1 , BSRF1 , and BCRF1 promoters for common potential binding sites of viral and cellular transcription factors . We found that all four BGLF3-independent late promoters contained putative Rta response elements ( RREs ) that conform to the GNCC ( N ) 9GGNG consensus sequence [34 , 35] . To determine whether Rta binds to these RREs in vivo , we performed chromatin immunoprecipitation using a polyclonal antibody against Rta in delta Rta/ZEBRA cells ( 293 cells harboring EBV-bacmid with deletions in the genes encoding the lytic cycle activators , Rta and ZEBRA ) . Ectopic expression of both ZEBRA and Rta in delta Rta/ZEBRA cells is necessary to induce the lytic cycle and to activate viral DNA replication . The cells were transfected with CMV ( empty vector ) , or Rta plus ZEBRA , and harvested after 48h . Rta occupancy was assessed using next generation DNA sequencing of the chromatin-immunoprecipitated DNA . Among the four BGLF3-independent late genes , the promoters of two genes , BCRF1 and BSRF1 , exhibited Rta binding peaks that met our criteria for significance ( see methods ) . Three Rta binding peaks were observed upstream of the BPLF1 gene that coincided with predicted Rta binding sites; however , none of the three peaks showed significant binding of Rta when compared to input samples ( Fig 10 ) . No obvious peaks were detected upstream of the BTRF1 gene ( S7 Fig ) . To further confirm association of Rta with the BGLF3-independent late promoters , we repeated the ChIP experiment using quantitative PCR to determine association of Rta with the promoters of BCRF1 , BSRF1 , and BPLF1 ( Fig 11 ) . As a control , we assessed binding of Rta to the upstream and enhancer regions in the origin of lytic replication ( oriLyt ) . Previous reports , and our current ChIP-seq data ( Fig 10 ) demonstrated that Rta binds to two RREs present in the enhancer element but does not bind to the upstream region of oriLyt [36–38] . Relative association of Rta with BCRF1p , BSRF1p , BPLF1p and the enhancer region of oriLyt increased by 14- , 4- , 2 . 5- and 5- fold , respectively , in cells transfected with Rta plus ZEBRA relative to cells transfected with empty vector ( Fig 11 ) . However , similar to the ChIP-seq data , Rta association to the upstream region of oriLyt was low , 1 . 4-fold higher in cells transfected with ZEBRA and Rta relative to empty vector ( Fig 11 ) . These results demonstrate that Rta specifically associates with at least two of the four BGLF3-independent late promoters in vivo . In addition to the BGLF3-independent late genes , our ChIP-seq analysis revealed that Rta selectively binds to the promoter region of six other late genes , namely BORF1 ( triplex capsid protein ) , BLRF1 ( glycoprotein N ) , BLRF2 ( tegument protein ) , BBRF3 ( glycoprotein M ) , BGLF2 ( tegument protein ) , and BXRF1 ( homolog of HHV1 UL24 , a nuclear egress protein ) . Relative association of Rta with these promoters ranged from 3 . 1- to 7 . 7-fold ( Table 1 ) . In addition , we observed several Rta binding peaks upstream to known Rta-responsive early genes , e . g . BHRF1 , BMRF1 , BMRF2 , SM , and BGLF3 ( Table 1 ) . In conclusion , our findings demonstrate the presence of at least two independent mechanisms for transcription of late genes . One mechanism , involving the conventional late gene regulators , is responsible for transcription of BGLF3-dependent late genes encoding structural proteins . The other mechanism , involving Rta and the BGLF4 protein kinase , regulates transcription of four BGLF3-independent late genes two of which encode viral immunoevasins .
The current model for regulation of late gene expression suggests that all late genes are regulated by a single common mechanism involving viral DNA replication and the functions of seven late gene regulators [10 , 11 , 13 , 15 , 16 , 18–21 , 39–42] . Recent identification of these key regulators of late gene expression prompted us to ask whether there are different subclasses of late genes depending on their mechanism of expression . For example , each subclass of late genes might require the activity of a unique set of late gene regulators . To address this question we studied the EBV transcriptome during the late phase of the viral lytic cycle under conditions in which expression of BGLF3 , a component of vPIC , was silenced ( Fig 1 ) . This set of experiments led to the identification of four BGLF3-independent genes that were expressed with true late kinetics ( Figs 1–3 and S2 ) . To determine the importance of the other components of vPIC in expression of the BGLF3-independent late genes , we generated specific siRNAs to BcRF1 ( vTBP ) , BDLF4 , BFRF2 , BDLF3 . 5 , and BVLF1 . Knockdown of each of these late gene regulators was dispensable for expression of the four BGLF3-independent late genes ( Figs 6–8 and S6 ) . On the contrary , elimination of any one of the late gene regulators significantly reduced the level of several late transcripts encoding structural proteins , an effect which was manifest as a decreased level of extracellular encapsidated viral DNA ( Fig 5 ) . siRNAs to the late gene regulators did not significantly reduce expression of early genes or the level of intracellular viral DNA , a measure of the process of viral genome amplification ( Figs 4–8 and S1 Table ) . These findings demonstrate the presence of at least two subsets of late genes , vPIC-dependent and vPIC-independent . In addition , our results establish the essential and non-redundant role of each of the conventional late gene regulators in transcription of late genes encoding the major and minor capsid proteins ( BcLF1 and BFRF3 ) , the scaffold protein ( BdRF1 ) , and the major glycoprotein gp350 ( BLLF1 ) . At the mechanistic level we are just beginning to understand some of the functions of the late gene regulators . BcRF1 and its orthologs function as TATA box binding proteins that recognize late promoters and recruit RNA polymerase II to late promoters through direct protein-protein interactions [11 , 16 , 18] . Based on our findings , BcRF1 is dispensable for transcription of the BGLF3-independent late genes ( Fig 6 ) . BcRF1 recognizes late promoters that contain a distinct TATT element with a thymidine at position 4 [13 , 17] . However , this feature is not preserved in all late genes . At least six late gene promoters contain a canonical TATA box: BCRF1 , BBRF3 , BLRF1 , BRRF2 , BDLF3 , and BXLF2 . Whether BcRF1 binds to these promoters is still unknown . Among the BGLF3-independent late genes , the promoter of the BCRF1 ( vIL10 ) gene harbors a canonical TATA box while the promoters of BPLF1 and BSRF1 contain the unconventional TATT element . No TATA box element has been assigned for the BTRF1 gene . Knockdown of BcRF1 did not compromise transcription from the BPLF1 or the BSRF1 late genes suggesting that the TATT sequence in the promoters of these two genes is likely to be recognized by cellular or viral proteins other than the vTBP ( Fig 6 ) . The BGLF4 protein kinase is the only late gene regulator essential for transcription of the two subclasses of late genes . BGLF4 is likely to phosphorylate substrates that regulate transcription of both groups of late genes . For example , BGLF4 might phosphorylate a component of the general transcription machinery or one of the late gene regulators to promote recruitment of RNA polymerase II to late promoters or to facilitate the transcription elongation phase during synthesis of late mRNAs . BGLF4 is also known to phosphorylate proteins involved in activation of DNA damage response , e . g . the histone acetyltransferase TIP60 , which is involved in chromatin remodeling [43] . Activation of late gene expression may be linked to the DNA damage response pathway through the capacity of BGLF4 to phosphorylate TIP60 which then alters chromatin structure around late promoters [44 , 45] . Transcription of the vPIC-independent late genes is likely to be regulated by viral proteins that could be subject to phosphorylation by BGLF4 . One potential candidate is Rta . Our chromatin immunoprecipitation data indicate that Rta strongly binds to the promoters of two vPIC-independent late genes , BCRF1 and BSRF1 . Binding of Rta to these vPIC-independent late promoters suggests that Rta might function as a late gene activator . Rta is a phosphoprotein; phosphorylation occurs late during the lytic cycle [46 , 47] . The kinase that phosphorylates Rta and the Rta phosphorylation sites are yet to be identified . A possible scenario is that Rta becomes phosphorylated by BGLF4 late during lytic infection . This scenario is further supported by findings demonstrating that the kinase activity of BGLF4 is essential for transcription of late genes [10] . Rta and BGLF4 co-localize in viral replication compartments , a locale where BGLF4 might promote phosphorylation of Rta during the late phase [48–50] . Our ChIP-seq data ( Table 1 ) shows that Rta also binds upstream of six other late genes: BORF1 , BLRF1 , BLRF2 , BBRF3 , BGLF2 , and BXRF1 , that are dependent of vPIC ( Table 1 ) . These results confirm previous reports implicating Rta in regulation of late gene expression . For instance , Rta induces expression of the late BLRF2 gene by directly binding to its promoter [34 , 37 , 51 , 52] . Our ChIP experiments were performed in delta Rta/ZEBRA cells , which require ectopic expression of both Rta and ZEBRA to activate viral genome amplification . Experiments assessing whether the presence of ZEBRA or the onset of viral DNA replication are necessary for the capacity of Rta to bind to late promoters are currently underway . While several cellular transcription factors , such as TBP , TFIIB , TAF4 , Sp1 , ATF2 , and TSG101 , mediate Rta transcriptional activity [53–58] , the mechanism regulating temporal activation of late genes by Rta has not been elucidated . Our data demonstrate that transcription of BLRF2 is regulated by vPIC ( Figs 2 and 6–8 ) . Therefore , Rta might interact with one or more components of the vPIC to activate transcription of the late BLRF2 gene . Such protein-protein interaction might regulate the capacity of Rta to temporally activate transcription of a subset of late genes . Alternatively , phosphorylation of Rta by BGLF4 during the late phase of the lytic cycle might also regulate the temporal role of Rta in activation of a subset of late genes . Viral infection is often accompanied by triggering of innate immune responses . However , incoming virus particles are usually equipped with a mechanism that subverts the immune response to allow infection to proceed . In the case of EBV , several virally encoded proteins were shown to interfere with host immune defense mechanisms to promote progress of lytic replication and primary infection [59] . Two of these viral immune suppressants are the products of the BCRF1 and the BPLF1 genes , which encode a homolog of human IL-10 ( vIL-10 ) and a viral deubiquitinase ( vDUB ) , respectively [23 , 26] . BCRF1 and BPLF1 are expressed in the late phase of the lytic cycle [23 , 30 , 60 , 61]; the mechanism regulating their expression has not been studied . Our findings demonstrate that both genes belong to the vPIC-independent subclass of late genes ( Figs 1 , 2 and 3 ) . The BCRF1 transcript and the BPLF1 protein are packaged into virus particles and delivered to newly infected cells [23 , 62] . Both vIL-10 and vDUB are essential to subvert the immune response during primary infection [23 , 26 , 27 , 63 , 64] . vIL10 impairs NK cell-mediated elimination of newly infected B cells; disrupts secretion of antiviral cytokines , and interferes with the antiviral activity of CD4+ effector T cells [26] . BPLF1 counteracts innate antiviral immune responses by inhibiting NF-κB activation . Through its DUB activity , BPLF1 removes K48- and K63-ubiquitin moieties from TRAF6 , NEMO , and IκBa and hence blocks the Toll-like receptor-signaling pathway [23] . Lack of vDUB also interferes with establishment of EBV infection and virus production [24 , 65–67] . An interesting question is why a subclass of late genes is transcribed by a distinct mechanism relative to other late genes . Considering the fact that two of the vPIC-independent late genes ( BCRF1 and BPLF1 ) encode viral immunoevasins , it is possible that expression of this subclass of late proteins precedes expression of the late antigenic structural proteins to prevent immune recognition of late lytic cells . However , this difference in timing would still be within the late phase of the lytic cycle as both classes of genes are sensitive to inhibition of viral DNA replication ( Figs 3 and S2 ) . Alternatively , the presence of distinct mechanisms that regulate transcription of these two subclasses of late genes might be attributed to the level of their expression . In conclusion , in this study we demonstrate that the mechanism regulating expression of the late immunomodulators vIL10 and vDUB differs from that regulating synthesis of late structural proteins . While transcription of the late genes encoding structural proteins is dependent on the six components of vPIC , transcription of BCRF1 , BPLF1 , BSRF1 , and BTRF1 is vPIC independent . Delineating mechanisms regulating expression of the vPIC-independent late genes and selectively inhibiting their expression has the potential to promote immune recognition of EBV during the late phase of the productive lytic cycle and during de novo infection . The studies described here might have relevance for vaccine development .
The ZEBRA and Rta protein expression vectors were prepared as previously described [68 , 69] . Expression vectors of BGLF3 , BVLF1 , BDLF4 , and BDLF3 . 5 were cloned into the eukaryotic pCMV6-Entry vector using the SgfI and MluI unique sites ( Origene ) . Expression vectors of the two late gene regulators , BcRF1 and BFRF2 , were kind gifts from Dr . Eric Johannsen . The construct expressing the BGLF4 protein kinase protein was a kind gift from Dr . Mei-Ru Chen [70] . siRNA-resistant forms were produced by inserting silent mutations in the region of the late gene regulator mRNA that is recognized by the siRNA . These silent mutations disrupt the complementarity between the siRNA and its target late gene regulator without affecting the amino acid sequence of the protein . 2089 cells are 293 human embryonic kidney ( HEK ) cells stably transfected with a bacmid containing wild-type EBV B95-8 genome [71 , 72] . Delta Rta/ZEBRA and delta BMRF1 cells are 293 HEK cells containing EBV-bacmid in which the BZLF1 and BRLF1 genes encoding ZEBRA and Rta or the BMRF1 gene , respectively , were inactivated by insertion of a kanamycin resistance gene . All three types of cells were cultured in Dulbecco’s modified Eagle medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) , and penicillin-streptomycin at 50 units/ml . Hygromycin B ( Calbiochem ) 100 μg/ml was added to the medium to select for 293 cells containing the EBV bacmid . Transfection of eukaryotic plasmids was performed in 25 cm2 flasks using Lipofectamine 2000 ( Invitrogen ) following the manufacturer’s protocol . Transfections were carried out in OPTI-MEM medium . HH514-16 cells were derived from the P3J-HR1K Burkitt lymphoma cell line [73] . The cells were cultured in RPMI 1640 medium containing 10% fetal bovine serum and antibiotics . To induce the lytic cycle , HH514-16 cells were transfected with 4μg empty vector ( CMV ) or ZEBRA expression vector using Ingenio nucleofection reagent according to the manufacturer’s protocol ( Mirus ) . Cells were incubated at 37°C in 5% CO2 incubator and harvested 48 h after transfection . For each late gene regulator , two siRNAs were designed and separately tested for their efficiencies to disrupt expression of the late FR3 protein without affecting expression of the early BMRF1 protein ( S3 Fig ) . The selected siRNAs were then tested for their capacities to knockdown the levels of their corresponding late gene regulator transcripts ( Table 2 and S4 Fig ) . The specificities of these siRNAs were also examined by generating siRNA-resistant forms of each late gene regulator ( S5 Fig ) and [10] . Cells were re-suspended in sodium dodecyl sulfate ( SDS ) sample buffer; 106 cells/sample were loaded onto 10% SDS-polyacrylamide gels and electrophoresed . The separated proteins were transferred to nitrocellulose membranes ( Bio-Rad ) and the membranes were blotted with specific antibodies to cellular and viral proteins . The ZEBRA and BFRF3 antibodies were described previously [38] . The EA-D ( BMRF1 ) monoclonal antibody ( R3 . 1 ) was provided by G . Pearson [74] . GAPDH and FLAG-tagged BGLF3 were detected using mouse monoclonal antibodies ( Sigma Aldrich ) . Antigen-antibody complexes were detected by autoradiography using 125I-protein A or chemiluminescence ( GE Healthcare Life Sciences ) . RNA was prepared from cells using the Qia-shredder and the RNeasy Plus products from Qiagen . The concentration of RNA in each sample was determined by measuring the optical density at 260 nm . Viral transcript levels were assessed from 100 ng of total RNA using iTaq Universal SYBR Green One-Step Kit ( Bio-Rad ) in a total volume of 25 μl . The level of GAPDH RNA was measured to normalize for the total amount of RNA . Each sample was analyzed in triplicate; the fold change in expression was calculated using the ΔΔCT formula . The efficiency of the primers used in the RT-qPCR was determined against 10-fold increasing concentrations of viral DNA . The sequences of the primers are provided in Table 3 . Three strand-specific sequencing libraries were generated using total RNA purified from 2089 cells transfected with CMV , ZEBRA , or ZEBRA plus siBGLF3 . The libraries were sequenced using the Illumina HiSeq 2500 system . The reads were single-end and 150bp long . The first 6 nucleotides and the last 60 nucleotides in each read were trimmed to remove low quality bases using FASTX toolkit ( http://hannonlab . cshl . edu/fastx_toolkit/index . html ) . Reads were mapped to the human reference genome ( hg19 ) with a known transcriptome index ( UCSC Known Gene annotation ) using Tophat v2 . 0 . 8 [75] . Reads that did not map to the human genome were later mapped to the EBV genome ( GenBank accession number NC_007605 . 1 excluding the B95-5 deletion ) with a known transcriptome annotation [76] . ZEBRA and ZEBRA plus siBGLF3 had over 450 , 000 reads mapping to the EBV genome . We used the Expectation-Maximization ( EM ) algorithm in RSEM [77] with Bowtie 2 [78] to map and estimate gene expression levels . EBSeq within RSEM pipeline was used to identify differentially expressed genes [79] . Viral DNA was purified from cell pellets as described [10] . Total DNA concentration was determined by measuring absorbance at 260 nm . Viral genome amplification was measured using the iQ SYBR Green Supermix kit ( Bio-Rad ) and primers targeting oriLyt ( Table 2 ) . Relative concentrations of DNA were calculated based on a standard curve of known concentrations of oriLyt DNA . Levels of viral DNA were normalized to a negative control sample transfected with the empty vector CMV . Supernatant from transfected cells was collected 48 h after initial transfection and spun twice at 1500 rpm to remove cell debris . The collected supernatant was treated with DNase-I ( 7 μg/ml ) and RNase ( 7 μg/ml ) for 30 min at 37°C in the presence of 3 mM MgCl2 and CaCl2 . This mixture was centrifuged at 77 , 000 xg for 30 min at 4°C to pellet viral particles . The pellet was resuspended in TE buffer containing 0 . 5% SDS . Pronase ( 1 . 2 mg/ml ) was added and the solution was incubated at 60°C for 2 h to digest the viral capsid . Phenol-chloroform extraction was used to remove proteins and the DNA was precipitated using 5M potassium acetate and 2 . 5 volumes of ethanol . The DNA precipitate was washed with 70% cold ethanol and re-suspended in TE buffer . The level of extracellular viral DNA was determined by qPCR using primers towards the upstream region of oriLyt . Immunoprecipitation of viral DNA was performed by chemically crosslinking DNA-protein complexes formed in 2089 cells using 1% formaldehyde . The cells were incubated for 10 min at 37°C and then washed once in phosphate-buffered saline containing protease inhibitors ( Thermo Scientific ) . Cells were re-suspended in SDS lysis buffer ( 50 mM Tris-HCl [pH 8 . 1] , 1% SDS , and 10 mM EDTA ) and sonicated four times , 10s each , using Sonifier 450 apparatus ( Branson ) . Cell lysates were cleared by centrifugation and the collected supernatants were diluted 10-fold in chromatin immunoprecipitation ( ChIP ) dilution buffer ( 16 . 7 mM Tris-HCl [pH 8 . 1] , 0 . 01% SDS , 1 . 1% Triton X-100 , 167 mM NaCl , and 1 . 2 mM EDTA ) . Rta-associated DNA was immunoprecipitated using a rabbit polyclonal antibody ( S2454 ) generated against the full-length protein [38] . The immune complexes were collected on protein G agarose beads ( Millipore ) . Binding of Rta to viral DNA was assessed by quantitative PCR and by next generation sequencing . Sequencing of ChIP and Input samples was performed using an Illumina HiSeq 2500 sequencer generating 27 and 40 million reads for the CMV ChIP sample and its input control , respectively , and 22 and 30 million reads for the Rta plus ZEBRA ChIP sample and its input control , respectively . The generated reads were single-end and each read was 76bp long . The first and the last nucleotides for each read were trimmed with fastx-toolkit ( http://hannonlab . cshl . edu/fastx_toolkit/index . html ) to remove low quality bases . Trimmed reads were mapped to the human reference genome ( hg19 ) using BWA-MEM v0 . 7 . 12 [80] . Only reads with mapping quality scores equal or higher than 20 were kept . Those reads that did not map to the human genome were later mapped to the EBV genome ( GenBank accession number NC_007605 . 1 ) also using BWA-MEM . Peak finding was performed using HOMER [80] . For peak finding , we used transcription factor mode requiring each putative peak to have at least 3-fold normalized tags than input controls . Assigning Rta peaks to viral gene promoters was based on the presence of a peak within 1kb from an open reading frame . Filtering for local and clonal signal was set to off . | Late proteins are expressed during the productive cycle of Epstein-Barr virus ( EBV ) after the onset of viral DNA replication . Many late proteins serve structural functions; they form the capsid shell around the viral genome or mediate attachment and fusion of the virus to the host cell . EBV also encodes two late proteins that suppress the immune system during primary infection . The current model suggests that transcription of all late genes is regulated by a common mechanism involving seven late gene regulators . Here , we demonstrate that late genes encoding two viral immune suppressants are transcribed by a mechanism different from that regulating late genes encoding structural proteins . Abolishing expression of the late immunomodulators without disrupting expression of the antigenic viral structural proteins could serve as an approach to block EBV de novo infection and its associated malignancies . | [
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] | 2016 | The Epstein-Barr Virus Immunoevasins BCRF1 and BPLF1 Are Expressed by a Mechanism Independent of the Canonical Late Pre-initiation Complex |
Dengue is a major public health problem worldwide . Although several drug candidates have been evaluated in randomized controlled trials , none has been effective and at present , early recognition of severe dengue and timely supportive care are used to reduce mortality . While the first dengue vaccine was recently licensed , and several other candidates are in late stage clinical trials , future decisions regarding widespread deployment of vaccines and/or therapeutics will require evidence of product safety , efficacy and effectiveness . Standard , quantifiable clinical endpoints are needed to ensure reproducibility and comparability of research findings . To address this need , we established a working group of dengue researchers and public health specialists to develop standardized endpoints and work towards consensus opinion on those endpoints . After discussion at two working group meetings and presentations at international conferences , a Delphi methodology-based query was used to finalize and operationalize the clinical endpoints . Participants were asked to select the best endpoints from proposed definitions or offer revised/new definitions , and to indicate whether contributing items should be designated as optional or required . After the third round of inquiry , 70% or greater agreement was reached on moderate and severe plasma leakage , moderate and severe bleeding , acute hepatitis and acute liver failure , and moderate and severe neurologic disease . There was less agreement regarding moderate and severe thrombocytopenia and moderate and severe myocarditis . Notably , 68% of participants agreed that a 50 , 000 to 20 , 000 mm3 platelet range be used to define moderate thrombocytopenia; however , they remained divided on whether a rapid decreasing trend or one platelet count should be case defining . While at least 70% agreement was reached on most endpoints , the process identified areas for further evaluation and standardization within the context of ongoing clinical studies . These endpoints can be used to harmonize data collection and improve comparability between dengue clinical trials .
Dengue is a major public health problem worldwide , with an estimated 3 . 9 billion people at risk globally [1] . While most dengue virus ( DENV ) infections are asymptomatic or result in a self-limited acute febrile illness ( AFI ) , some are life-threatening due to severe plasma leakage , severe bleeding , or , less frequently , severe organ impairment [2 , 3] . Although several antiviral and immunomodulatory drug candidates have been evaluated in randomized controlled trials , none have been shown to be effective in treating dengue or preventing its severe manifestations [4 , 5] . At present , early recognition of severe dengue and timely supportive care are used to reduce mortality [6–8] . However , the first dengue vaccine was recently licensed , and there are several other dengue vaccine candidates in late-stage clinical trials [9] . Future decisions regarding the use of candidate dengue vaccines and therapeutics will require evidence of product safety and efficacy , as well as demonstration of vaccine effectiveness in reducing disease burden . The current guidelines for evaluation of dengue vaccines in endemic areas recommend that the primary efficacy endpoint be prevention of virologically-confirmed dengue of any severity [10] . However , these guidelines also recommend that secondary endpoints be developed to measure outcomes such as the vaccine’s effect on severity of disease , clinical presentation , and atypical cases . In recent years , there has been increasing recognition among the dengue scientific community that establishing standard , quantifiable clinical endpoints that can be applied across a range of research activities are an important step towards ensuring that study methodology and implementation are reproducible [11–13] . Adoption of standard endpoints should greatly facilitate comparison between interventional trials conducted in diverse clinical settings , may help prevent biased reporting of selective outcomes or post hoc analyses , and could also prove useful for studies focused on understanding disease pathogenesis . In January 2015 , the National Institute of Allergy and Infectious Diseases ( NIAID ) , part of the National Institutes of Health , and the Partnership for Dengue Control ( PDC ) convened an expert working group to develop standard clinical endpoints to measure moderate and severe manifestations of dengue in clinical research studies . The primary aim was to improve comparability of severity of dengue disease assessments among clinical trials . Importantly , the clinical endpoints developed as part of this endeavor are intended only for use as research tools and are not meant to replace current World Health Organization ( WHO ) case classification for dengue [3] . The WHO 2009 classification is intended to be broadly applicable in clinical settings and for disease surveillance , and differentiates between dengue , dengue with warning signs , and severe dengue , based on readily accessible clinical information . However , it is of limited use in clinical research because criteria for severe disease are not well-defined [14] . This could affect reproducibility between research sites , even within the same country . Further refinement and granularity is needed to develop international standards for the detailed discrimination of clinical phenotypes for use in interventional trials and pathogenesis studies [12] . In addition , severe dengue is thought to be an infrequent event , and several less severe manifestations that require medical intervention contribute disproportionately to disease burden [15]; therefore , inclusion of an intermediate endpoint , “moderately severe dengue” , was felt to be important . A group of 27 dengue researchers from academia , government , industry and public health specialists from 14 countries were invited to participate in at least one group by task: 1 . ) clinical endpoint development , 2 . ) endpoint validation , and 3 . ) development of a tool to characterize febrile illness experience . In this paper , we summarize the progress to date of the clinical endpoint development group .
Twenty-two of the 27 participants volunteered to be part of the clinical endpoint development group which was divided into three work groups tasked with reviewing the literature to identify potential research endpoints from publications describing clinical dengue studies , and those developed by international medical organizations for more general use . The three groups were asked to develop endpoints for moderate and severe plasma leakage , bleeding , thrombocytopenia , liver disease , neurologic disease , and myocarditis , that were measurable , reproducible , and implementable in diverse settings . Work groups met by phone to review results from their literature review for each endpoint and discuss potential endpoint definitions . Work groups then presented and led a discussion on their proposed endpoints during a 2-day workshop with 56 participants from 16 countries , at two large international meetings , and at a final 1-day workshop with participants from the first workshop . Following these discussions , the Spiral Research Center at the University of Liège in Belgium queried participants using Delphi methodology to refine and operationalize the clinical endpoints . The Delphi methodology is a structured tool that creates conditions that are favorable for a convergence of opinions while allowing moderators to discern points of dissent [16] . The process consisted of three rounds of inquiries , which were administered electronically using Mesydel software [17] . In the first round , participants were asked to select the best endpoint among a list of proposed endpoints to characterize disease outcomes among clinical trial participants with laboratory-confirmed dengue ( Table 1 ) . They were given the opportunity to propose another endpoint or offer ideas on how to improve their preferred endpoint in an open-ended question . Questions about endpoints were paired with questions about how to operationalize the endpoint in terms of timing and type of data to be collected including clinical laboratory tests , physical exams , and medical procedures . The software automatically centralized all data for tabulation and a qualitative content analysis was done for open-ended questions and a quantitative analysis for closed-ended questions . Anonymous responses were used to modify the endpoint and operational recommendations in successive rounds until at least 70% agreement , a pre-specified cut-off , of those responding to a given question was reached . If an endpoint definition proposed in round 1 did not reach at least 70% agreement , suggestions from the paired open-ended round 1 question on how to improve the endpoint were used to modify the endpoint . The original endpoint and the modified endpoint ( s ) would be offered in round 2 . This allowed a focus on more uncertain or problematic endpoints in the next round . At the start of each new round , participants were provided a summary of the results from the previous round . Participants were given 2 to 3 weeks to respond in each round . Reminder emails were sent about one week before the close of each round and again 24 hours before the deadline . The Humanities and Social Sciences Ethics Committee at the University of Liège reviewed and approved the study protocol .
Most symptomatic dengue patients start to recover around the time of defervescence , usually between days 3–7 of illness . However , in a small proportion of cases , an increase in vascular permeability becomes clinically apparent around this time , marking the onset of the critical phase for complications . The altered permeability results in plasma leakage , evidenced variously by pleural effusions , ascites , hemoconcentration and/or hypoproteinaemia . In severe cases and in the absence of appropriate fluid resuscitation , leakage may compromise the circulating plasma volume so that the patient develops potentially life-threatening hypovolemic shock [18] . Based on a literature review , definitions for moderate and severe plasma leakage were developed and presented to participants ( Table 1 ) . To operationalize the endpoints , hemoconcentration was defined as a >15% or >20% change in hematocrit [19 , 20] . In round 1 , the majority ( 73% ) of participants preferred moderate plasma leakage Definition A , which relies on a hematocrit change of >15% , over Definition B which relies on a hematocrit change of >20% ( Table 1 and S1 Table ) . While 64% selected severe plasma leakage Definition A in round 1 ( Table 1 and S1 Table ) , by round 3 the majority ( 72% ) of participants felt that different hematocrit cut-offs should be used for moderate versus severe plasma leakage and that a >20% cut-off should be used for severe plasma leakage . When asked to select other case-defining factors that should be captured as “evidence of plasma leakage , ” which was part of severe plasma leakage Definition A , most selected pleural effusion ( 79% ) and ascites ( 79% ) . The majority of participants felt that gallbladder wall thickening on its own was insufficient evidence of plasma leakage ( 74% ) and that hypoproteinemia should not be case-defining ( 72% ) . Options were given to refine the endpoint definitions , and most ( 83% and 78% , respectively ) agreed to add two caveats to the definitions: 1 . ) “pleural effusion , ascites , or cardiac effusion is a new clinical finding and unrelated to another cause” , and 2 . ) “if a cardiac effusion is detected without concurrent pleural effusion or ascites then another diagnosis should be considered—such as myocarditis” . Lastly , most participants agreed with the proposed definitions of respiratory compromise ( 83% ) and hemodynamic instability ( 78% ) ( Table 1 ) . Hemoconcentration . A change in hematocrit from a baseline value is required to define hemoconcentration . The majority ( 77% ) of participants chose baseline hematocrit definitions B or D , which differed only by allowing use of population standards for hematocrit ( Table 1 and S1 Table ) . However , in round 2 no agreement was reached regarding whether population standards can be used when no other value is available , so this was not included in the final definition ( Table 2 ) . After two rounds of discussion , most ( 83% ) participants agreed with defining maximum hematocrit as the peak value recorded during the acute illness taken during a period consistent with expected plasma leakage ( day 4–8 from fever onset ) or within 48 hours of defervescence ( S1 Table and Table 2 ) . Bleeding is a common manifestation in dengue patients , with cutaneous bleeding , epistaxis or gum bleeding being more common than gastrointestinal or vaginal hemorrhage [6 , 20–27] . The frequency and severity of clinically significant bleeding is thought to vary by patient age and disease severity such that adult cases and those with dengue shock syndrome are more likely to have severe spontaneous bleeding than pediatric cases and those without shock [6 , 20 , 26–29] . Several grading systems for bleeding exist [30 , 31] and were used to derive criteria for moderate and severe bleeding that were presented to participants ( Table 1 ) . Bleeding that requires a local intervention but does not result in shock or hemodynamic instability were the two key criteria that define moderate bleeding . Proposed severe bleeding included any bleeding that involves a critical organ; leads to hemodynamic instability; results in death or permanent disability; or requires a red blood cell transfusion and more intensive monitoring in an intensive care unit or high dependency unit . In round 1 , more than 70% of participants agreed that Definitions A-D and F represented moderately severe bleeding , while Definitions E ( macroscopic hematuria ) and G ( eye bleeding that does not affect vision ) did not get this level of agreement ( Table 1 and S2 Table ) . In subsequent rounds , the majority ( 74% ) agreed that the finding that a blood type and cross match was ordered should not be an indicator of severity on its own and moderate bleeding definitions A and F were modified accordingly ( Table 2 ) . In round 1 , most ( >90% ) selected Definitions A through D as severe grade bleeding , while 64% preferred Definition E for severe bleeding ( Table 1 and S2 Table ) . In round 2 , most ( 79% ) participants agreed that if there is no need for blood transfusion , the bleeding described in Definition E should define moderate bleeding . In addition , 79% agreed with the statement that “need for blood transfusion” be defined as meaning need for whole blood or packed red blood cells . Thrombocytopenia is a common finding in patients with dengue . Although pathogenesis is incompletely understood , it likely includes direct and indirect viral-mediated mechanisms [32 , 33] . Traditionally , thrombocytopenia has been used as a criterion for dengue disease severity [34] . However , as a predictive marker for bleeding , the speed of the platelet count decrease may be more important than the absolute number . Based on a review of the literature and discussions , definitions for moderate and severe thrombocytopenia were developed for participants to consider ( Table 1 ) . In round 1 , a majority ( 68% ) of participants chose moderate thrombocytopenia Definitions B or C , which specified a 50 , 000 to 20 , 000 mm3 platelet count range as case defining ( Table 1 and S2 Table ) . While participants were more divided over the four severe thrombocytopenia endpoints , 55% of participants chose severe thrombocytopenia Definitions B or C which specified a <20 , 000 mm3 platelet count cut-off . Participants remained divided through round 3 on whether moderate and severe thrombocytopenia Definition B or C was best for use in clinical trials . The difference between these definitions is that both moderate and severe thrombocytopenia Definition B specify the need to detect a decrease in the platelet count within the specified range in a 24-hour period , while both Definition C specify that one platelet count within the range is case defining ( Tables 1 and 2 ) . The most common reason for support of Definition B was that a single value could be a spurious measurement , whereas a fall within a 24-hour period is more meaningful . In contrast , those who chose Definition C felt it was easier to operationalize because only one platelet measurement is needed each day and any value within the range would be considered case defining . Last , 72% felt that one platelet count measurement done daily during the critical phase is adequate to determine if a case-patient had severe or moderate thrombocytopenia . Operationally , this is more consistent with Definition C . Liver involvement in dengue patients is relatively common and serum aminotransferases are elevated in most hospitalized dengue patients , with higher levels observed among severe dengue cases [35–42] . Prospective studies have found that acute hepatitis occurs in 2–6% of dengue inpatients when defined by an ALT >10 times the upper limit of normal [37 , 43] . Acute liver failure ( ALF ) is also known to occur among confirmed dengue patients [38–42] , even among patients without shock , but ALF is uncommon [39] . A review of the literature found several definitions for ALF [44] , however , most included acute liver dysfunction with change in mental status and new onset coagulopathy defined by an international normalization ratio ( INR ) ≥1 . 5 as case-defining criteria [45–48] . There were fewer definitions for acute viral hepatitis [35–40 , 43] , and distinguishing criteria included use of an ALT cut-off alone versus AST and/or ALT , and presence of jaundice . The working group recommended the use of an ALT cut-off because of its greater specificity for liver involvement than AST , and the use of an ALT >10 times the upper limits of normal cut-off , which corresponds to Grade 4 FDA toxicity table level ( Table 1 ) . Presence of jaundice or elevated bilirubin were not recommended because of difficulties in evaluating jaundice in populations with dark skin pigmentation . In round 1 , most ( 77% ) participants selected Definition A for acute hepatitis and Definition A for ALF ( Table 1 and S3 Table ) . In round 2 , 84% agreed that ALF should be the severe liver disease endpoint while acute hepatitis should be the moderately severe liver disease endpoint , and most ( 84% ) participants felt that ALT should be evaluated for all trial participants with an acute febrile illness . In round 3 , 78% agreed that , “at least two ALT levels should be done depending on the severity of illness with a third ALT ( or more ) recommended if a case is severe; the second ALT is elevated; or local clinical practice indicates more ALT measurements be done” . Last , participants were asked whether there should be a recommendation that trial participants with an AFI have an INR measured , and if so , the timing of the measurement . Many ( 61% ) participants disagreed with making the recommendation . Reports of neurologic disease in dengue patients are rare despite a high burden of dengue . However , neurologic disease has been described in laboratory-confirmed dengue cases involving all DENV types , all age groups , and in all parts of the world [49–51] . Dengue encephalitis [52–64] and encephalopathy [63–66] are most commonly described followed by Guillain-Barré Syndrome and aseptic meningitis; however , other neurologic conditions have been reported [64 , 65 , 67–69] . The incidence of neurologic disease among dengue patients is difficult to determine because case definitions , study population and methods vary among studies , but estimates have ranged from 0 . 5–20% [49 , 64 , 68 , 70] . Based on a literature review , it was proposed that laboratory-confirmed dengue cases in clinical trials with an abnormal neurologic examination be defined as moderate or severely affected and that these cases then be classified using established case definitions ( Table 1 ) . Further , given the past difficulties in attribution , it was proposed that alternative etiologies , including concurrent metabolic abnormalities and co-infections with other neurotropic flaviviruses and pathogens be assessed . In round 1 , 68% of the participants chose moderate neurologic disease Definition C; however , some participants commented that a specific Glasgow Coma Score ( GCS ) should be added to the definition to make it more measurable instead of including it as a footnote ( Table 1 and S3 Table ) . In round 2 , the majority ( 74% ) agreed that a specific GCS be added to moderate neurologic disease Definition C ( S3 Table and Table 2 ) . In round 1 , the majority ( 73% ) of participants chose severe neurologic disease Definition C , and 63% agreed to add GCS to the severe neurologic disease definition in round 2 . Reasons cited for not adding GCS included inconsistent use of the score depending on where and how participants are treated , and poor interrater reliability . Some participants felt that capturing GCS was inconsequential compared with need for neurologic intervention and the duration of neurologic impairment . However , in round 3 , most ( 83% ) agreed to a modified severe neurologic disease Definition C in which use of a high dependency unit was added ( Table 1 ) . Last , when asked questions about how to operationalize the neurologic disease endpoints , a majority ( 68% ) participants recommended that evidence-based definitions such as those developed by the Brighton Collaboration be used to further classify identified neurologic cases . When asked to select data to be collected for trial participants with neurologic disease ( Tables 1 and 2 ) , the majority ( >70% ) agreed that items 1 and 2 should be required . False positive rates of PCR , difficulty in collecting cerebral spinal fluid ( CSF ) on dengue patient with bleeding tendencies , lack of EMG at some facilities , and lack of availability of post-mortem tissue were cited by participants as reasons items 3 through 8 should be optional . However , these items could be collected if readily feasible at the participating trial site or if clinical presentation warrants further investigation ( e . g . , electromyography in suspected Guillain-Barré Syndrome case ) . Reduced cardiac output and myocarditis have been reported in dengue-infected patients although the incidence is unknown , and the severity of cardiac involvement has not been well-characterized [71–76] . A direct myodepressive effect of dengue virus has been difficult to determine because cardiac function is preload-dependent and dengue patients can have reduced intravascular volume secondary to a vascular leakage syndrome [72 , 77] . Electrocardiogram ( ECG ) abnormalities , bradycardia , and conduction abnormalities have been described , however , cardiac enzymes are not commonly elevated in dengue , even in cases with suspected myocarditis [72 , 75 , 77 , 78] . Most cardiac involvement in dengue patients appears to be transient as the ejection fraction and conduction abnormalities return to normal during convalescence [75 , 78 , 79] . Because of the difficulty in discerning a true viral-induced myodepressive effect from that due to reduced intravascular volume , myocarditis was chosen as the clinically-relevant cardiac endpoint in clinical trial participants with laboratory-confirmed dengue . Based on a review of the literature , proposed definitions for moderate and severe myocarditis were developed for participants to consider ( Table 1 ) . In round 1 , the more sensitive endpoint definitions were selected more often with 55% of participants choosing Definition A for moderate myocarditis and a similar proportion ( 59% ) choosing Definition A for severe myocarditis ( Table 1 and S3 Table ) . In round 2 , the majority ( 74% ) agreed that we should specify that the arrhythmia in the definition should be a “new onset” arrhythmia , and many ( 58% ) of the participants felt that we should recommend that ECGs be done only for those with clinical findings consistent with cardiac involvement . In round 2 , 68% of participants agreed that the phrase: “has evidence of myocardial dysfunction on the echocardiogram , that is , reduced left ventricular function , despite adequate filling of left ventricle ( normal left ventricle end diastolic diameter ) and adequate volume status” be added to the definition . Less than half ( 47% ) of participants preferred that the caveat , “need for inotropic support AND has evidence of myocardial dysfunction from echocardiogram” be added to criteria 3 in severe myocarditis Definition A . In the end , participants were divided on whether the revised severe myocarditis Definition A ( 39% ) or the newly revised severe myocarditis Definition A ( 39% ) should be used in clinical trials . However , in round 3 , participants were given a scenario and asked how they would classify a trial participant with laboratory-confirmed dengue who has ST elevation on an ECG , elevated cardiac enzymes , and need for inotropic support who did not have an ECHO done . A majority ( 72% ) of participants stated that they would classify this case as having severe myocarditis , which is consistent with the newly revised Definition A ( Tables 1 and 2 ) . That is , a case would qualify as severe if they receive inotropic support , and/or they have evidence of myocardial dysfunction from echocardiogram .
We set out to define separate endpoints for plasma leakage , bleeding and organ impairment , to more precisely characterize clinical phenotypes among research study participants with laboratory-confirmed dengue . There was at least 70% agreement on eight of the 12 clinical endpoint definitions that we addressed , including moderate and severe plasma leakage , moderate and severe bleeding , moderate and severe liver disease , and moderate and severe neurologic disease . Not surprisingly , there was less agreement among participants on the definitions for myocarditis , an endpoint which is an uncommon manifestation of dengue . In addition , for moderate and severe thrombocytopenia , although consensus was reached on certain parameters , some issues , primarily practical , remain to be addressed . Although further work is needed to finalize these remaining endpoints , we feel the proposed definitions as described in Table 2 should now be made available to interested groups to begin the process of evaluation . We envisage that achieving any one severe or moderate endpoint during a laboratory-confirmed dengue illness would be sufficient to designate the case as severe or moderate , respectively . However , we suggest that the research community might benefit if data on all endpoints were to be presented in reports , thereby describing the clinical outcomes more fully and allowing detailed comparisons between studies . Importantly , researchers should understand that these endpoints are intended to represent the level of severity experienced by a study participant over the course of their laboratory-confirmed dengue illness . For example , to assess a participant for moderate or severe plasma leakage , the investigator should evaluate hematocrit at different times during the illness to evaluate hemoconcentration . With respect to thrombocytopenia , although we attempted to characterize severe and moderate thrombocytopenia , it was not our intention for a case with severe thrombocytopenia alone , in the absence of any other severe criterion , to be classified as a severe dengue case . Although historically thrombocytopenia was part of the definition of dengue hemorrhagic fever and considered to be an indicator of disease severity , in the WHO 2009 classification , thrombocytopenia alone is not included as part of the definition for severe dengue . Agreement on moderate and severe thrombocytopenia endpoint definitions did not reach the level of agreement attained by plasma leakage , bleeding , and liver and neurologic disease endpoints . However , a clear majority ( 68% ) of participants agreed on a 50 , 000–20 , 000 mm3 platelet count range for moderate thrombocytopenia , and more than half ( 58% ) agree to a <20 , 000 mm3 platelet count cut-off for severe thrombocytopenia in round 1 . By round 3 , the only remaining issue which divided respondents was whether one platelet count or a decreasing trend within the cut-off range was case-defining . However , in the end the majority felt that one platelet count measurement done daily during the critical phase is adequate to determine if a case-patient had severe or moderate thrombocytopenia which is , operationally , more consistent with Definition C ( i . e . , needing one platelet count within a cut-off range ) . Over 70% agreement was reached early on most types of moderate and severe bleeding . Eye bleeding not affecting vision was dropped from the list of types of moderate bleeding; however , eye bleeding resulting in permanent disability will be captured under Definition C for severe bleeding . Macroscopic hematuria nearly had reached 70% agreement; however , it was rejected by some because no intervention was mentioned in the definition and all the other types of moderate bleeding specified a need for an intervention . Similarly , Definition E for severe bleeding was thought not to be a severe because there was no need to give whole blood or packed red blood cells , and most participants agreed that it be included as moderate bleeding . Lastly , the majority of participants agreed that , “need for blood transfusion” meant need for whole blood or packed red blood cells . This is important as studies have found that prophylactic platelet transfusions are not uncommonly given to dengue patients [80–82] . In addition , the sentiment among many participants was if fresh frozen plasma , platelets or factor concentrates were given without whole blood or packed red blood cells , then it was unlikely that the bleeding was clinically severe . Patients with dengue may have significant organ involvement , with the most commonly affected organ being the liver . Most participants agreed early on to definitions for acute hepatitis and acute liver failure without changes to the proposed definitions . However , it was more difficult to reach agreement on operational items , including timing and number of ALT measurements and need for INR . In the end , most recommended that two or more ALT levels be obtained during the clinical course . While neurologic disease is thought to be infrequent , it is part of the current WHO classification for severe dengue and an outcome that may be associated with significant morbidity and mortality . One of the issues with determining the incidence of neurologic disease among laboratory-confirmed dengue cases has been the inconsistent use of case definitions and shortcomings with regards to data collection and methodology [49–51] . We therefore sought to propose a sensitive clinical endpoint definition and systematic collection of data that would enable the description of a broad spectrum of moderate and severe neurologic outcomes among clinical trial participants with laboratory-confirmed dengue . Once a case is identified with moderate or severe neurologic disease , cases can be classified using evidence-based definitions such as those developed by the Brighton Collaboration [83] . We reached agreement on endpoint definitions for moderate and severe neurologic disease . However , there was less agreement on recommendations for data to be collected for neurologic disease cases . Many of the proposed items for data collection depend on feasibility and clinical presentation . While the process enabled us to reach agreement on most endpoints from a geographically dispersed group of experts , there were some challenges . First , participants were extremely busy individuals and even with frequent reminders , participation decreased by 18% between the first and third round . We tried to minimize drop-out by using an easily accessible online platform to query participants that allows direct communication with participants for technical support . We also sent email reminders between rounds . Second , early on it became clear that there were a few dominant individuals in the working group . By using a Delphi methodology-based query , participants do not interact with each other thus allowing all expert opinion to be heard . In addition , the online platform allowed for more in-depth responses to the open-ended questions since participants were able to save and revisit their answers during the round . In this way , we were able to get more agreement than would have been possible in additional face-to-face meetings . Third , while Delphi methodology-based queries have the potential to create a bottleneck towards convergence of opinions [84] , we felt we were able to prevent this from happening by using a combination of close and open-ended questions to capture opinions . Last , operational considerations were discussed at length throughout the process , and in the end , our endpoints can be used in all settings . However , further classification of moderate and severe neurologic cases may be a challenge in settings with limited resources ( e . g . , Guillain–Barré case ) . Outlook: With over 70% agreement on most clinical endpoint definitions , a group of dengue experts is working to validate the endpoints using several large existing prospective data sets . Specifically , they will evaluate endpoint accuracy in identifying moderate and severe disease , endpoint reproducibility in diverse clinical settings , and ease of use . In addition , they will assess how many dengue cases could not be included because necessary components of the endpoint definition , such as repeat clinical laboratory tests or clinical assessments , were not performed . For example , multiple data points may not be available for non-hospitalized dengue cases identified in community-based clinical studies . Such cases are likely to be non-severe; however , there is currently insufficient data to rule out moderate severity . Last , more work is needed to finalize the myocarditis endpoints , and evaluate the utility of one versus two or more platelet count measurements within a 24-hour period to identify cases with moderate and severe thrombocytopenia . The proposed clinical endpoints can be used to harmonize data collection and improve comparability between dengue clinical trials . | Dengue is a major public health problem worldwide . Although several drug candidates have been evaluated in randomized controlled trials , none has been effective , and early recognition of severe dengue and timely supportive care remain the only means to reduce mortality . While the first dengue vaccine was recently licensed , and several other candidates are in late stage clinical trials , future decisions regarding deployment of such vaccines or therapeutics will require evidence of product safety , efficacy and effectiveness . Standard , quantifiable clinical endpoints are needed to ensure reproducibility and comparability of research findings . To address this need , we established a working group of dengue researchers , vaccine developers , and public health specialists to develop endpoints . After two working group meetings and discussions at international meetings , the Delphi methodology was used to clarify and further develop endpoints such that 70% or greater agreement was reached on most endpoint definitions including moderate and severe plasma leakage , moderate and severe bleeding , acute hepatitis and acute liver failure , and moderate and severe neurologic disease . The process identified areas for further evaluation and standardization within the context of ongoing clinical studies . The endpoints can be used to harmonize data collection and improve comparability between dengue clinical trials . | [
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] | 2018 | Development of standard clinical endpoints for use in dengue interventional trials |
Sixty cases of human rabies in international travelers were reviewed from 1990–2012 . A significant proportion of the cases were observed in migrants or their descendants when emigrating from their country of origin or after a trip to visit friends and relatives or for other reasons ( 43 . 3% ) . The cases were not necessarily associated with long-term travel or expatriation to endemic countries; moreover , cases were observed in travelers after short trips of two weeks or less . A predominance of male patients was observed ( 75 . 0% ) . The proportion of children was low ( 11 . 7% ) . Cases from India and Philippines were frequent ( 16 cases/60 ) . In a significant proportion of cases ( 51 . 1% ) , diagnosis was challenging , with multiple missed diagnoses and transfers from ward to ward before the final diagnosis of rabies . Among the 28 patients whose confirmed diagnosis was obtained ante-mortem , the mean time between hospitalization and diagnosis was 7 . 7 days ( median time: 6 . 0 days , range 2–30 ) including four cases with a diagnosis delayed by 15 or more days . In five cases , a patient traveled through one or more countries before ultimately being hospitalized . Three factors played a role in delaying the diagnosis of rabies in a number of cases: ( i ) a low index of suspicion for rabies in countries where the disease has been eradicated for a long time or is now rare , ( ii ) a negative history of animal bites or exposure to rabies , and ( iii ) atypical clinical presentation of the disease . Clinical symptomatology of rabies is complex and commonly confuses physicians . Furthermore , failure in diagnosing imported cases in more developed countries is most likely related to the lack of medical familiarity with even the typical clinical features of the disease .
Rabies is readily diagnosed when it presents in the classic furious form . The paralytic and atypical forms can pose significant problems in diagnosis , particularly when found in rabies-free countries in travelers who acquired the disease abroad . The discussion of geographical and post-exposure prophylaxis issues of travel-associated human rabies has focused on preventive measures including pre-travel vaccination but has been limited to certain regions [1] , [2] . An analysis of the compiled cases of rabies in travelers from a clinician's perspective is therefore absent . Since reports concerning these cases have been published , new cases have been documented and published . In this work , we present an updated analysis of travel-associated cases of human rabies with the objective of identifying potential risk factors and describing the procedures of clinicians with the aim of highlighting potential problems in the diagnosis and management of patients .
To retrieve information on human rabies cases in travelers , we first conducted a literature search using the PubMed ( MEDLINE ) and Scopus databases ( http://www . ncbi . nlm . nih . gov/pubmed; www . scopus . com/scopus/home . url ) , from 1980 to December 2012 , cross-referencing the following terms: “rabies” , “imported” and “travel” . Relevant systematic and narrative reviews were also utilized to obtain useful background information . The reference lists of the systematic reviews and other identified papers were scanned for potentially relevant primary studies that could be considered for inclusion in the review . Additional searches were conducted using the ProMED-mail ( http://www . promedmail . org/ ) , Google ( http://www . google . fr/ ) , and Yahoo ( http://fr . yahoo . com/ ) general search engines . Miscellaneous articles from Rabies Bulletin Europe were systematically scanned ( www . scopus . com/scopus/home . url ) . The inclusion criteria were all available publications written in European languages on human rabies cases in individuals who crossed a national border between the times of infection and diagnosis . Reports with insufficient clinical description were only included in the epidemiological analyses .
Sixty cases met the inclusion criteria ( see Supporting Text S1 ) . The epidemiological data are summarized in figures 1 and 2 . The description of travelers , data on clinical findings , laboratory results , diagnosis methods and treatments are shown in tables 1 to 3 and figure 3 . An average of 2 . 6 cases were documented per year over the 23 years of the study with a slight increase from 1990–2003 to 2004–2012 ( 1 . 9 to 3 . 7 cases per year ) . The mean age of the patients was 37 . 7 years ( range , 3–73 years ) and the ratio of males to females was 3 . 5 . Most cases were diagnosed in Europe ( 56 . 7% ) , notably in France ( eight cases ) and the United Kingdom and Ireland ( six cases ) , and in the US ( 26 . 7% , n = 16 ) . High income countries accounted for 56 . 7% of the cases in individuals travelling for tourism , business or expatriation . Migrants originating from low income countries and their descendants accounted for 43 . 3% of cases when taking their first trip abroad , visiting friends and relatives in their country of origin , or traveling to seek care , for business or for other undocumented reasons . Most exposures occurred in Asia ( 40 . 0% ) , notably in India ( 10 cases ) and the Philippines ( six cases ) ; in Central America and the Caribbean ( 13 . 3% ) , notably in Mexico ( five cases ) ; and in North Africa ( 10 . 0% ) , notably in Morocco and Algeria ( six cases ) . Travel duration was not documented in the majority of the reports . Two cases were recorded in tourists taking two-week trips to India and Kenya . The vast majority ( 85 . 0% ) of cases resulted from exposure to dogs . Three cases resulted from bat-related injuries , including one case of a Dutch tourist returning from Kenya [3]–[5] , one case of a US citizen injured in the US who developed rabies symptoms while expatriated in Iraq and was subsequently evacuated to Switzerland [6] , and one case of a Mexican citizen who developed rabies in the US where he was involved in seasonal work [7] , [8] . One case was observed in Russia following a fox bite that occurred in Ukraine [2] . The incubation time was documented in 47/60 records ( Figure 3 ) with a mean incubation time of 273 . 6 days ( median time: 80 days , range , 12–3600 days ) , including nine cases with an incubation time of 30 days or less . Very short incubation times were observed in two cases . A 50-year-old French female tourist sustained multiple deep dog bites on the legs during a trip in India and developed rabies 12 days later while returning to France [9] , [10] . A 19-year-old male Mexican seasonal worker was bitten by a bat on his leg and developed rabies due to a variant virus of vampire bat rabies 15 days later in the US [7] , [8] . In three cases , very long incubation times were recorded . A 10-year-old female migrant from Vietnam who stayed 1 . 5 years in Hong Kong prior to immigrating to Australia developed rabies more than five years after she had lived continuously in Australia . The sequence of the rabies virus isolated post-mortem was closely related to a subgroup of viruses found in China . No history of animal bites was documented [11]–[13] . A 40-year-old man developed rabies in the US due to a canine rabies virus variant associated with dogs in Brazil , which was identified by sequence analysis of viral amplicons . After the diagnosis was established , interviews with family members indicated a history of contact with a “rabid-acting” dog while living in Brazil , approximately 8 years prior to becoming ill . An investigation of the patient's travel history did not identify any intermittent travel to Brazil since that time [14] . An 18-year-old male recent migrant , originating from Myanmar , developed rabies in Thailand . He gave a history of dog bites 10 years before , and he denied any recent animal bites or contact with bats [15] . In 46 records , information about whether or not a history of animal bite was investigated at initial presentation was available . Only 26/46 ( 56 . 2% ) patients reported a history of animal bite at first medical encounter . Number of health care provider consulted before a diagnosis of rabies was made was available in 44 records . 31/44 ( 70 . 5% ) patients consulted several health care providers before a diagnosis of rabies was obtained . In these patients , an incorrect primary diagnosis was given including acute psychiatric illness , anxiety , depression , influenza like illness , meningitis , cervical radiculopathy , Guillain-Barré syndrome , Bickerstaff's encephalitis , angina pectoris , pharyngitis , lumbago , and constipation . In 45/60 ( 75 . 0% ) patients the acute neurological signs were furious while they were paralytic in 6/60 ( 15 . 0% ) ; the clinical features were not documented in nine records . Among patients with furious form of rabies , 11/45 ( 24 . 4% ) received a diagnosis of rabies at first medical consultation of which 9/11 ( 81 . 8% ) reported a history of animal bite , 27/45 ( 60 . 0% ) consulted several health care providers before a diagnosis of rabies was made , of which 8/27 ( 29 . 6% ) reported a history of animal bite at first medical encounter . The number of health care providers consulted was not documented in 7 records . Most cases resulted from bites inflicted by dogs , and in 26 patients whose information was available , wounds were located on the upper limbs in most cases . A confirmed diagnosis of rabies was obtained post-mortem in one-third of the cases . In 4 instances , a diagnosis of rabies was only considered after death , including one case in an organ donor whose death was not considered to be related to rabies at the time of death . Overall , the mean time between hospitalization and suspected diagnosis of rabies based on clinical features was 3 . 9 days ( median time: one day , range , 1–30 ) . The time between hospitalization and a confirmed diagnosis of rabies was documented in 27 out of 28 patients whose diagnosis was confirmed ante-mortem . The mean time was 7 . 7 days ( median time: 6 . 0 days , range 2–30 ) , including four cases with diagnoses delayed by 15 , 16 , 18 and 30 days [7] , [8] , [14] , [16]–[18] . Reasons for delayed diagnosis were an absence of a history of animal bites at presentation in three cases and a paralytic form mimicking Guillain-Barré syndrome in three cases . In patients whose rabies diagnosis was confirmed post-mortem , a suspected diagnosis of rabies was delayed by 22 days because of the absence of a history of animal bites at presentation [6] . Hydrophobia and aerophobia were present in 60 . 0% and hyper-salivation was present in 78 . 6% of the patients whose clinical records were available . Several atypical cases were described , including a UK tourist with a history of dog bites in India and an initial presentation of lower back pain radiating in the leg . The patient was first hospitalized in orthopedics , and then referred to a medical ward where a provisional diagnosis of Guillain-Barré syndrome was made because of the appearance of flaccid weakness in both legs and arms . A few days later , due to absent oculocephalic reflexes and unreactive pupils , a diagnosis of Bickerstaff's encephalitis was considered . Subsequently , the infectious diseases unit and specialist neurology center were contacted for advice , and rabies was suspected [16] , [17] . A Nigerian visitor to the UK who developed fever , exhibited altered behavior , and manifested malarial parasites in blood films was diagnosed with cerebral malaria before rabies was diagnosed post-mortem [19]–[22] . A migrant from Myanmar presented in Bangkok , Thailand with fever and dysphagia . There was a history of fluctuating consciousness and aerophobia , but they were absent or could not be demonstrated at the time of admission . He exhibited subcutaneous chest wall emphysema and was found to have pneumomediastinum , which resulted in surgical intervention . He developed paralysis followed by seizures during the postoperative period . Diagnosis was confirmed during the preterminal phase [15] . Among the 60 cases presented in this review , confirmation of rabies was assessed by reverse transcriptase polymerase chain reaction ( RTPCR ) in most cases , primarily from salivary gland biopsy or saliva and skin biopsy . Computed tomography scans of the brain were reported in 16 cases , none of which contributed to the diagnoses . Cerebral magnetic resonance imaging ( MRI ) was reported in 14 cases of which three showed typical alterations: high signal intensity on T2-weighted images bilaterally in the hippocampal gyri and the head of the caudate nucleus in one case [16] , [17]; hyperintense signal changes bilaterally in the caudate nucleus , thalamus , mesencephalon , pons and medulla oblongata in the second case [22]–[25] and T2-weighted images in the posterior part of the medulla oblongata and pons in the third case [3]–[5] . In these three cases , the diagnosis of rabies was assessed before the MRIs were obtained . Rabies post-exposure prophylaxis was provided in the country of infection in less than 8% of cases . Interestingly , two cases were recorded in medical doctors who did not receive post-exposure prophylaxis following animal-related injuries [3]–[5] , [26] . All therapeutic attempts that were conducted after the onset of disease were unsuccessful , including seven patients who underwent the experimental “Milwaukee” protocol including induction of coma with pentobarbital , midazolam and ketamine and the use of the antivirals amantadine and ribavirin [3]–[5] , [26]–[33] . The mean time between hospitalization and death was 14 . 4 days ( median time: 12 . 0 days , range , 1–40 ) with no significant differences between those who underwent the “Milwaukee” protocol and those who did not .
It is very likely that a number of confirmed travel-associated rabies cases were unpublished or published in journals not indexed in PubMed and Scopus . Moreover , rabies may be misdiagnosed , notably when death occurs abroad . The figure of two-four cases per year of travel-associated cases of rabies is most an underestimate of true incidence . Risk factors cannot be extrapolated from our results because we lack denominators . However , several points need to be stressed: ( i ) a significant proportion of cases were observed in migrants or their descendants when emigrating from their country of origin or following a trip with the purpose of visiting friends and relatives ( 43 . 3% ) , ( ii ) cases were not necessarily associated with long-term travel or expatriation to endemic countries; rather , cases were observed in travelers undergoing short trips of 2 weeks or less , ( iii ) a predominance of male patients was observed ( 75 . 0% ) , ( iv ) children , although typically accounting for a large proportion of cases in people living in rabies-endemic areas were rare among travelers ( 11 . 7% ) , ( v ) cases from India and the Philippines were frequent ( 16 cases/60 ) , and ( vi ) 85% of cases had dog as a source of infection . An important finding of this study concerning rabies in travelers is that in a significant proportion of cases , diagnosis was challenging with multiple missed diagnoses and transfers from ward to ward before a diagnosis of rabies was finally assessed or clinical diagnosis was delayed ( more than seven days post-hospitalization ) with post-mortem biological confirmation and/or delayed ante-mortem biological diagnosis ( more than seven days post-hospitalization ) . In five cases , patients traveled through one or more countries before ultimately being hospitalized . The diagnosis was challenging in 24 out of 47 cases ( 51 . 1% ) in which such information was available . Three factors played a role in delaying the diagnosis of rabies in a number of cases: ( i ) a low index of suspicion for rabies in countries where the disease has been eradicated for a long time or is now rare , ( ii ) a negative history of animal bites or exposure to rabies , and ( iii ) atypical clinical presentation of the disease including paralytic form , and furious form initially mimicking sore-throat infection , orthopedic or acute psychiatric disorder . A delayed diagnosis of rabies can have adverse public health consequences including multiple transmissions of rabies via transplanted solid organs from a single infected donor whose diagnosis of rabies was retrospectively assessed [22] , [34]–[36] . It may also lead to the need for risk assessment in a large number of patient contacts , as recently exemplified in Louisiana where 204 individuals were investigated by public health officials and hospital infection control staff , resulting in 95 requiring post-exposure prophylaxis , including 68 healthcare workers [7] , [8] . In the present work , we observed a large heterogeneity in the use of laboratory techniques , which was due to the length of the study period and differences between countries . The clinical symptomatology of rabies is complex and commonly causes confusion to physicians . Furthermore , failure to diagnose imported cases in more developed countries is likely to be related to the lack of medical familiarity with even the typical clinical features of the disease . Rabies should be suspected , even when a history of animal bites is missing , in patients with encephalitis or paralysis who originate or return from rabies-enzootic countries , notably in male adult patients , in migrants visiting countries of origin and in the context of a travel to India or the Philippines . The analysis of three serially collected saliva samples and one skin biopsy taken from the nape of the neck offers the highest level of sensitivity when using appropriate molecular techniques for viral RNA detection [37] . Rabies can be efficiently averted by preexposure immunization , avoidance of contact with animals , and postexposure prophylaxis ( PEP ) . Preventive vaccination against rabies has to be considered for travelers to rabies endemic areas because it simplifies PEP from five vaccine doses over 28 days to two vaccine doses over three days , and it eliminates the need for immunoglobulin administration . In the present work , many cases were associated with short duration travel which challenges the common view that preventive vaccination against rabies should be preferentially given to long-term travelers to high risk areas . Many of the rabies cases were in migrants traveling to their origin country who may lack the budget for pre-travel vaccination . The intradermal vaccination route has been proven economical , safe , and immunogenic in the population of rabies-endemic areas , and this route of administration has been recently used in travelers from developed countries [1] , [38] . Alternatively , initial short schedules induce rapid and sufficient antibody responses provided that a booster vaccination is provided [39] . The immunity provided by the gold-standard three-dose series is long-lasting and can be maintained over at least a decade , thus preventive vaccination against rabies should be considered an investment for future travel [38] . | Rabies is readily diagnosed when it presents in the classic furious form . The paralytic and atypical forms can pose significant problems in diagnosis , particularly when found in rabies-free countries in travelers who acquired the disease abroad . We systematically reviewed the existing literature and collected 60 cases of rabies in travelers and expatriates from non-endemic countries and in migrants from endemic countries during their first migration trip or following subsequent trip to their origin country . We observed that the disease may have an atypical presentation and/or long incubation time resulting in delayed or missed diagnosis with adverse public health consequences including multiple transmissions of rabies via transplanted solid organs from a single infected donor whose diagnosis of rabies was retrospectively assessed or need for risk assessment in a large number of patient contacts . Rabies should be suspected , even when a history of animal bites is missing , in patients with encephalitis or paralysis who originate or return from rabies-enzootic countries , notably in male adult patients , in migrants visiting countries of origin , and in the context of travel to India or the Philippines . The analysis of three serially collected saliva samples and one skin biopsy taken from the nape of the neck offers the highest level of sensitivity when using appropriate molecular techniques for viral RNA detection . | [
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] | 2013 | Imported Human Rabies Cases Worldwide, 1990–2012 |
During central nervous system ( CNS ) development neural stem cells ( Neuroblasts , NBs ) have to acquire an identity appropriate to their location . In thoracic and abdominal segments of Drosophila , the expression pattern of Bithorax-Complex Hox genes is known to specify the segmental identity of NBs prior to their delamination from the neuroectoderm . Compared to the thoracic , ground state segmental units in the head region are derived to different degrees , and the precise mechanism of segmental specification of NBs in this region is still unclear . We identified and characterized a set of serially homologous NB-lineages in the gnathal segments and used one of them ( NB6-4 lineage ) as a model to investigate the mechanism conferring segment-specific identities to gnathal NBs . We show that NB6-4 is primarily determined by the cell-autonomous function of the Hox gene Deformed ( Dfd ) . Interestingly , however , it also requires a non-cell-autonomous function of labial and Antennapedia that are expressed in adjacent anterior or posterior compartments . We identify the secreted molecule Amalgam ( Ama ) as a downstream target of the Antennapedia-Complex Hox genes labial , Dfd , Sex combs reduced and Antennapedia . In conjunction with its receptor Neurotactin ( Nrt ) and the effector kinase Abelson tyrosine kinase ( Abl ) , Ama is necessary in parallel to the cell-autonomous Dfd pathway for the correct specification of the maxillary identity of NB6-4 . Both pathways repress CyclinE ( CycE ) and loss of function of either of these pathways leads to a partial transformation ( 40% ) , whereas simultaneous mutation of both pathways leads to a complete transformation ( 100% ) of NB6-4 segmental identity . Finally , we provide genetic evidences , that the Ama-Nrt-Abl-pathway regulates CycE expression by altering the function of the Hippo effector Yorkie in embryonic NBs . The disclosure of a non-cell-autonomous influence of Hox genes on neural stem cells provides new insight into the process of segmental patterning in the developing CNS .
The Drosophila central nervous system ( CNS ) consists of 20 segmental units ( neuromeres ) , the sizes and composition of which are specifically adapted to the functional requirements of the respective body parts in the head , thorax and abdomen . Thus , neural stem cells ( called neuroblasts , NBs ) , although showing serial homologies among segments , generate distinct cell lineages in correspondence to their segmental assignment [1] . This segmental identity is conferred to NBs already in the embryonic neuroectoderm and persists during the generation of their larval and adult sublineages . Therefore , it is convenient to study mechanisms regulating the segmental specification of neural stem cells in the embryo . Most studies on segmental specification of embryonic NBs in Drosophila were focused so far on thoracic ( T1-T3 ) and abdominal ( A1-A10 ) segments of the ventral nerve cord ( VNC ) . Neuromeres T1-A7 are built by a stereotype pattern of approximately 30 NBs per hemisegment . Individual identities and serial homology of segmentally repeated NBs is reflected by position , marker gene expression [2 , 3] and composition of their lineages [4–6] . However , some of the serially homologous NB-lineages exhibit specific differences between thoracic and abdominal segments , which are conveyed to NBs already in the neuroectoderm by Bithorax-Complex ( Bx-C ) Hox genes [7–9] . While thoracic identities seem to represent a ground state ( T2 , no input of Hox genes; [10] ) , identities of consecutive posterior segments are established by adding the function of Bx-C Hox genes Ultrabithorax ( Ubx ) , abdominal-A ( abdA ) and Abdominal-B ( AbdB ) , an evolutionary highly conserved phenomenon described as posterior dominance or prevalence of Hox genes [10–12] . The terminal abdominal neuromeres A8-A10 exhibit a progressively derived character regarding size and composition . In these segments , NB patterns and segmental identities are controlled by combined action of the Hox gene AbdB and the ParaHox gene caudal [13 , 14] . The Drosophila head consists of seven segments ( 4 pregnathal and 3 gnathal ) all of which contribute neuromeres to the CNS [15 , 16] . The brain is formed by approximately 100 NBs per hemisphere , which have been individually identified and assigned to specific pregnathal segments [17 , 18] . As judged from comparison of the combinatorial codes of marker gene expression only few brain NBs appear to be serially homologous to NBs in the thoracic/abdominal ventral nerve cord , reflecting the highly derived character of the brain neuromeres [19] . The connecting tissue between brain and the thoracic VNC consists of three neuromeres formed by the gnathal head segments named mandibular ( mad ) , maxillary ( max ) and labial ( lab ) segment , but the number and identity of the neural stem cells and their lineage composition in these segments is still unknown . Compared to the thoracic ground state the segmental sets of gnathal NBs might be reduced to different degrees , but are thought to be less derived compared to the brain NBs . Therefore , to fully understand segmental specification during central nervous system development , it is important to identify the neuroblasts and their lineages in these interconnecting segments . Assuming that most NBs in the gnathal segments still share similarities to thoracic and abdominal NBs , we searched in these segments for serially homologous NB-lineages , which are suitable for genetic analyses . Using the molecular marker eagle ( eg ) , which specifically labels four NB-lineages in thoracic/abdominal hemisegments [20 , 21] we identified three serial homologs ( NB3-3 , NB6-4 and NB7-3 ) in the gnathal region . To investigate the mechanisms conferring segmental identities , we focused on one of them , the NB6-4 lineage , which shows the most significant segment-specific modifications . Our analysis reveals a primary role of the Antennapedia-Complex ( Antp-C ) Hox gene Deformed ( Dfd ) in cell-autonomously specifying the maxillary fate of NB6-4 ( NB6-4max ) . Surprisingly , we uncovered an additional , non-cell-autonomous function of the Antp-C Hox genes labial ( lab , expressed anterior to Dfd ) and Antennapedia ( Antp , expressed posterior to Dfd ) in specifying NB6-4max . In a mini-screen for downstream effectors we identify the secreted protein Amalgam ( Ama ) to be positively regulated by lab , Dfd and Antp and negatively regulated by the Antp-C Hox gene Sex combs reduced ( Scr ) . Loss of function of Ama and its receptor Neurotactin ( Nrt ) [22–25] as well as the downstream effector kinase Abelson tyrosine kinase ( Abl ) [23] lead to a transformation of NB6-4max similar to Dfd single mutants . Thus , in parallel to the cell-autonomous role of Dfd , a non-cell-autonomous function of Hox genes lab and Antp , mediated via the Ama-Nrt-Abl pathway , is necessary to specify NB6-4max identity . Disruption of either of these pathways leads to a partial misspecification of NB6-4max ( approx . 40% ) , whereas simultaneous disruption of both pathways leads to a complete transformation ( approx . 100% ) of NB6-4max to a labial/thoracic identity . We further show that both pathways regulate the expression of the cell cycle gene CyclinE , which is necessary and sufficient to generate labial/thoracic NB6-4 identity . Whereas Dfd seems to directly repress CyclinE transcription ( similar to AbdA/AbdB in the trunk ) [26] , we provide indications that the Ama-Nrt-Abl pathway prevents CyclinE expression by altering the activity of the Hippo/Salvador/Warts pathway effector Yorkie ( Yki ) .
To identify serially homologous NBs in the gnathal segments , we used the transcription factor Eagle ( Eg ) as a marker . In the well-studied thoracic and abdominal segments Eg is expressed in four NBs and their lineages: NBs 2–4 , 3–3 , 6–4 and 7–3 [20 , 21] . Staining WT embryos at stage11 ( st11 ) with Eg and the NB-marker Deadpan ( Dpn ) ( Fig 1A ) [27] reveals a reduced number of Eg-positive NBs in gnathal segments . Using Eg and Dpn to identify NB2-4 [28] we observed , that NB2-4 is missing in all three gnathal segments ( Fig 1 ) . Co-staining of Eg and Runt at st12 identifies NB3-3 [29] in labial and maxillary segments , but not in the mandibular segment ( Fig 1B ) . Eg and Engrailed ( En ) co-expression and its position in a typical dorso-posterior area of the En stripe indicates the presence of NB7-3 [20 , 30] in all three gnathal segments ( Fig 1A and 1B ) . NB6-4 is identifiable by combined expression of Eg , En and Gooseberry ( Gsb ) [31 , 32] in labial and maxillary segments but is missing in the mandibular segment ( Fig 1A and 1C ) . Next we used Eg in combination with various other markers at st13 to st16 to describe the composition of the Eg-positive lineages ( Figs 1D , 1E and S1 ) . NB3-3 generates a lineage of approximately 9 neurons , which is less than described for thoracic/abdominal lineages with 10–13 cells [6] . Among these we observed 7 neurons expressing Even-skipped ( Eve ) in maxillary and labial segments ( S1A Fig ) , compared to 5 ( on average ) in the thoracic and 9 ( on average ) in the abdominal lineages [6 , 14 , 33] . The early NB7-3 lineage consist of six to seven cells , expressing the marker Eyeless ( Ey; Figs 1E and S1B ) [13 , 34] ) , but due to segment-specific cell death at later stages [35 , 36] the cell numbers in each segment are distinct . At st16 we find the mandibular NB7-3mad lineage to consist of two cells , the maxillary NB7-3max of three cells and the labial NB7-3lab of three to five cells ( Fig 1E ) . In comparison , in T1 and T2 the late embryonic NB7-3 lineage consists of four , in T3-A7 of three , in A8 of two or three , and in A9 and A10 NB7-3 is not formed [14 , 36] . NB6-4 generates a mixed neuronal/glial lineage ( 4–5 neurons , 3 glia ) in labial segments as revealed by staining against Eg and the glial marker Repo ( Fig 1E–1G ) [37] . Its division pattern is identical to thoracic NB6-4 , in which the first division separates a glial from a neuronal daughter precursor and factors like Prospero or glia cells missing ( gcm ) ( S1C Fig ) are asymmetrically localized to the glial progenitor [7 , 38–40] . In contrast , similar to NB6-4 in abdominal segments [6] , NB6-4max is a pure glioblast , generating four glial progeny cells ( Fig 1E–1G ) . To our surprise , we observed more Eg-positive cell clusters in gnathal segments from st12 onwards ( Figs 1D , 1E , S1 and S2 ) , that we could identify as progeny of the Midline Neuroblast ( MNB , in mad and max segments ) , progeny of the NB5-3 ( in all three segments ) [41–45] and from stage 14 onwards we could identify Eg-positive late progeny of NBs 6–2 , 4–3 , 4–4 and 5–6 ( in mad and max segments ) . Further details can be found in Figs 1D , 1E , S1 and S2 . Taken together , we show that ( 1 ) despite of its complex expression pattern in the late embryo , Eg is a reliable marker to identify serially homologous NBs and their progeny in gnathal segments , ( 2 ) whereas NB2-4 is missing in all gnathal segments , NB3-3 and NB6-4 are present in labial and maxillary segments , and NB7-3 is present in all three gnathal segments , ( 3 ) segment-specific differences occur in the NB7-3 lineage with regard to neuronal cell numbers , and in the NB6-4 lineage with regard to cell types: While NB6-4lab gives rise to a mixed neuronal/glial lineage corresponding to the thoracic homologs , NB6-4max is a pure glioblast corresponding to the abdominal homologs . In the following we will focus on the NB6-4 lineage to elucidate the mechanism conferring segmental specificities in the gnathal CNS . Since it was shown for thoracic/abdominal segments that Bx-C Hox genes convey the regional specification on the progenitor level [9 , 13 , 46] , we next analyzed the mRNA expression pattern of Antp-C genes labial ( lab ) , Deformed ( Dfd ) , Sex combs reduced ( Scr ) and Antennapedia ( Antp ) in the presumptive neuroectoderm of maxillary and labial segments from st4 onwards . The widths of the mRNA stripes were measured in whole mount embryos using the distance from the anterior pole in relation to the length of the whole egg ( EL; see material and methods; S3 Fig ) . Fig 2A shows the average expression domains in st6 , st7 and st8 schematically . In contrast to earlier reports [47 , 48] , we could detect an overlap of Dfd and Scr-mRNA expression from st6 onwards ( due to tissue invagination in the cephalic furrow at st7 this overlap is hardly visible , compare to Fig 2B ) in the presumptive neuroectoderm of NB6-4max . The presumptive neuroectoderm of NB6-4lab shows Antp-mRNA expression only . In st7/8 it seemed that lab-mRNA expression was overlapping with Dfd-mRNA ( Fig 2A ) , but close investigation showed , that due to the morphogenetic movements during cephalic furrow formation two epithelial sheets are overlapping but no co-staining can be found in cells of the neuroectoderm from which NB6-4max will delaminate ( Fig 2B ) . We confirmed our observations using antibody staining against Lab , Dfd and the segmental marker En and could not observe a co-staining of Lab and Dfd in the maxillary stripe at st8/9 , which is prior to NB6-4 delamination from the neuroectoderm ( Fig 2C , see also [49] ) . Next we performed antibody staining for Dfd , Scr and Antp together with Eg and could indeed verify that NB6-4max co-expresses Dfd and Scr ( Fig 2D and 2E ) , whereas NB6-4lab expresses Antp ( Fig 2F ) and no Scr . We also tested for Proboscipedia ( Pb ) expression and could observe single cell clusters distributed over the whole CNS [47] but we could not detect Pb expression in NB6-4 in gnathal segments ( S4O Fig ) . Therefore , of all Antp-C genes tested on mRNA and protein level , Dfd and Scr are expressed in NB6-4max , whereas NB6-4lab expresses Antp . labial and Antp are neither expressed in the maxillary neuroectoderm nor in the neuroblast NB6-4max itself . To analyze the influence of Hox genes on the segmental identity of NB6-4 in gnathal segments , we next tested different single or double mutants and overexpression of Antp-C genes ( Fig 3A shows wild type ) . We first analyzed Antp loss-of-function ( LoF ) alleles Antp25 and Antp11 and could not observe a change in the segmental identity of both NB6-4max and NB6-4lab ( Antp25 , n = 50 maxillary hemisegments ( mHs ) ; Antp11 , n = 50 mHs; Fig 3B shows Antp25; since different mutant Hox gene alleles revealed similar phenotypes we show in this and the following experiments the results for only one allele ) . Furthermore , overexpression of Antp has no effect on the NB6-4 lineage identity in any segment ( n = 50 mHs; Fig 3C ) . Therefore , similar to the thoracic NB6-4 lineages [46] the labial NB6-4 identity represents a ground state , which does not require Hox gene function . Next , we tested the LoF alleles Dfd16 , Dfd12 and Dfd11 for their impact on NB6-4max . Double staining against Eg and the glial cell marker Repo revealed that loss of Dfd leads to a homeotic transformation of NB6-4max into a mixed lineage comprising neurons and glial cells ( corresponding to NB6-4lab lineage ) in approximately 43% of all hemisegments ( Dfd16 n = 650 mHs , Dfd12 n = 650 mHs; Dfd11 n = 650 mHs; Fig 3D shows Dfd16 ) . Since NB6-4max expresses Dfd and Scr , we analyzed two LoF alleles of Scr ( Scr17 ( n = 500 mHs ) and Scr11 ( n = 500 mHs ) ) . In 10% of the mutant hemisegments NB6-4max produces neurons and glial cells ( Fig 3E shows Scr17 ) . As this suggests that Dfd and Scr might act synergistically , we analyzed the effect of double LoF of Dfd16 and Scr4 ( n = 500 mHs ) . Surprisingly , instead of an increase in the transformation rate we could observe a reduced transformation rate in NB6-4max ( 17%; Fig 3F ) compared to single Dfd LoF situation ( Fig 3D , 43% ) . In addition , whereas ectopic expression of Scr ( n = 50 mHs ) has no effect on labial ( S4A Fig ) and thoracic NB6-4 ( Fig 3G ) , the ectopic expression of Dfd leads to a transformation of labial ( S4B Fig ) and thoracic NB6-4 ( 74% of all hemisegments ( n = 160 mHs ) , Fig 3H ) towards a maxillary identity with four glial cells and no neurons . Thus , Dfd seems to be the major Hox gene influencing the NB6-4max identity cell-autonomously . Since loss of function only leads to a partial transformation rate ( 43% ) , we wondered if any of the other Antp-C genes , although not expressed in NB6-4 , might be involved in specifying NB6-4max . Surprisingly , we observed an increase of the transformation rate in double LoF of lab1/Dfd12 ( 100% , n = 650 mHs , Fig 3I ) and Dfd16/Antp7 ( 87% , n = 650 mHs , Fig 3J ) compared to 43% in single Dfd mutants ( Fig 3D ) . None of the single mutant alleles of lab4 ( n = 50 mHs , S4C Fig ) or Antp25 ( see above ) or the double LoF of lab1 and Antp25 ( n = 100 mHs , S4D Fig ) showed any change of the identity of NB6-4max . To show that loss of Hox gene function leads to a transformation of the maxillary NB6-4 on the progenitor level , we stained lab1/Dfd12 mutants for Eg and gcm-mRNA . Whereas the wild type NB6-4max distributes gcm-mRNA equally to both daughter cells ( see S1C Fig ) , the transformed NB6-4 in maxillary segments of lab1/Dfd12 mutants shows an asymmetrically distribution of gcm-mRNA to the glial sublineage only ( S4E Fig ) , like wild type labial ( see S1C Fig ) or thoracic [7 , 38–40] progenitors do . Furthermore , the loss of Scr together with Antp increased the transformation rate to 33% ( n = 100 mHs; S4F Fig ) compared to 10% for Scr LoF alone . Since neither Antp nor lab is expressed in wild type NB6-4max , we next investigated if the expression pattern of Hox genes is altered in mutant background . In Dfd mutants we could not detect any changes in Hox gene expression ( single or double mutants , S4G–S4N Fig ) with the exception of a slight extension of the lab expression domain towards the anterior mandibular segment ( see also [50] ) and a loss of Scr protein in NB6-4max . This protein loss seems to be due to a translational repression of Scr , since we detected normal expression of Scr-mRNA in Dfd mutants ( S4H Fig ) . Taken together , our expression and mutant analyses reveals a cell-autonomous function of Dfd and Scr in specifying NB6-4max ( Fig 3K ) . Dfd seems to be the major cell-autonomously acting Hox gene in NB6-4max , since only ectopic expression of Dfd , but not Scr , can transform NB6-4lab/thoracic into a maxillary identity . Surprisingly , we detected a non-cell-autonomous effect of the more anterior expressed lab and the more posterior expressed Antp gene . Since these two genes are normally not expressed in NB6-4max nor in the neuroectodermal primordium and we did not observe mis-regulation of other Hox genes in NB6-4max in any Antp-C-mutant situation , we conclude that Lab and Antp act in a non-cell-autonomous manner to contribute to the determination of the maxillary segmental specificity of NB6-4 . To identify potential effectors downstream of the non-cell-autonomously acting Hox genes lab and Antp in NB6-4max specification , we next conducted a mini-screen . Since we uncovered a non-cell-autonomous function of lab and Antp in specifying the segmental fate of NB6-4max , we performed a mini-screen to identify candidate genes ( Fig 4A ) affected by and downstream of Hox genes . Candidate genes were selected based on existing studies on Hox downstream targets [51 , 52] as well as previously identified potential modifiers of Hox genes [53–57] . We also analyzed a number of non-homeotic genes that are located in the Antp-gene cluster on chromosome 3R . Considering that Hox genes convey a non-cell-autonomous function we speculated that genes involved in signaling pathways , secreted molecules or genes known to interact with secreted molecules might be promising candidates . These criteria supplied us with 17 candidate genes , and we tested their LoF with Eg/Repo double labeling for defects in the specification of NB6-4max identity . Whereas most of the candidates did not show any abnormal phenotype of the NB6-4 lineage ( Fig 4A ) , the LoF of two of these genes resulted in a transformation of NB6-4max to NB6-4lab/thoracic identity: the secreted adhesion molecule Amalgam ( Fig 4B , 29% , n = 160 mHs ) , and its potential receptor Neurotactin ( Nrt , Fig 4C , 15% , n = 120 mHs ) [22–25] . Amalgam ( Ama ) is located in the Antp-C on chromosome 3R [24] . All available Ama mutant stocks are combined with the mutant allele of the Ableson tyrosine kinase Abl1 ( Abl1 allele alone has no effect on NB6-4max , see below ) . To test whether Ama is regulated by Hox genes , we analyzed the expression of Ama on mRNA level using in situ hybridization . Ama-mRNA is expressed from early st10 onwards with strongest expression in a repetitive pattern at early st11 ( Fig 4D ) in the neuroectoderm and cells of the nervous system . At late st11 , Ama remains strongly expressed in lateral areas , while decreasing expression in the nervous system becomes restricted to the midline ( Fig 4D ) . Throughout the neuroectoderm we observed reduced expression in the En domain until mid st11 ( Fig 4D lower panels; also [24] ) . Surprisingly , we found that the posterior part of the maxillary ( En-expressing cells ) and the anterior part of the labial segment ( corresponding to parasegment 2 ) does not express Ama-mRNA , perfectly matching the Scr expressing region ( Fig 4D lower panel , yellow box ) . Thus , we analyzed , whether Scr might repress Ama , and indeed in mutants for Scr we could observe an up-regulation of Ama expression in parasegment 2 , the Scr expression domain ( Fig 4E yellow box compare to Fig 4D middle lower panel , for quantification see S6B Fig ) . Since we find that Ama is a positive regulator of NB6-4max identity , this might explain why removing Scr has only minor effects on NB6-4max specification ( 10% ) . Next we tested the dependency of Ama expression on lab , Dfd and Antp . Indeed , removing the function of labial , Dfd ( Fig 4F ) , labial/Dfd ( Fig 4G ) or Dfd/Antp ( Fig 4H ) leads to a significant reduction of Ama expression in the corresponding segments ( compare to wild type Fig 4D , middle panel , for quantification see S6B Fig ) . To further validate whether Ama is a potential transcriptional target of Hox genes , we screened the enhancer region 3kb upstream of the coding region for the existence of conserved Hox binding motifs ( described in [58–61] ) . We could identify Lab , Dfd , Scr and Antp binding sites that are highly conserved down to Drosophila pseudoobscura ( 25–50 mio . years distance to D . melanogaster , S5 Fig ) . To show whether Hox genes actively regulate the Ama transcription , we ectopically expressed Scr , Dfd and Antp using the scaGal4 line and monitored the Ama mRNA expression using in situ hybridization ( S6C Fig ) . Whereas Scr or Antp did not influence the wild type expression of Ama , Dfd was able to strongly upregulate Ama expression in ectopic positions . Taken together our analysis shows that the secreted molecule Ama is a transcriptional downstream target of Hox genes . Therefore , we assume that Ama transcriptional regulation by Lab and Antp can non-cell-autonomously influence the segmental specification of NB6-4max . In wild type Lab , Dfd and Antp positively regulate Ama expression in gnathal segments , ensuring together with the cell-autonomous function of Dfd the correct specification of NB6-4max . Loss of Ama regulation due to loss of lab or Antp function ( in single and in double mutants ) appears to be compensated by Dfd function and Dfd-regulated Ama expression . Conversely , in Dfd single mutants the Lab- and Antp-dependent expression of Ama can rescue the loss of Dfd function in approximately 50% of all hemisegments . Finally , in lab/Dfd or Dfd/Antp double mutants lacking all sources of Ama induction in the area of maxillary NB6-4 leads to an increase in the transformation rate to nearly 100% . Using antibody staining for Ama , we were able to recapitulate this on the protein level ( Figs 4I–4K and S6E ) . In the wild type CNS Ama-protein is detectable in st10 and is strongly expressed in the lab expression domain ( Fig 4I left panel , orange box ) . During st11e strong Ama signal also becomes detectable in the maxillary segment ( area of NB6-4max , Fig 4I middle panel , red box ) whereas labial/thoracic segments start expression from mid/late st10 onwards ( Fig 4I ) . To analyze , which cells express Ama in the nervous system and whether the receptor Nrt is expressed in NBs at the required time point we performed antibody staining of Ama and Nrt proteins and could observe Ama and Nrt proteins in both neuroectodermal cells and Neuroblasts in stage 10 ( S6D Fig ) . In Dfd single mutants Ama is still visible in the area of NB6-4max ( red box ) presumably due to invading Ama protein from anterior and posterior sources ( Fig 4J; Lab- and Antp-dependent ) , whereas double mutation for Dfd/Antp ( S6E Fig ) or lab/Dfd ( Fig 4K ) lead to a complete loss of the Ama signal in the maxillary segment ( red box ) . A further proof for our assumption of a non-cell-autonomous component of NB6-4max specification via the secreted protein Ama would be a double mutant for Dfd and Ama . In this mutant , both pathways would be depleted . Since both genes are located in close proximity in the Antp gene cluster on chromosome 3R , we were not able to establish a double mutant fly stock . We therefore followed an alternative approach of removing Ama function in a heterozygous Dfd situation by crossing a deficiency covering Dfd and Ama ( Df ( 3R ) BSC467 ) to the Abl1 , AmaR1 allele ( Fig 4L ) . In this transheterozygous situation of complete LoF of Ama and heterozygosity for Dfd we could observe an increase in the transformation rate to 50% ( n = 50 mHs ) of all hemisegments compared to 8% in Dfd heterozygotes ( n = 50 mHs , S6G Fig ) or 29% in Ama single mutants ( Fig 4B ) , reflecting the synergistic effect of both pathways . Finally , we tested whether heatshock-induced expression of Ama in the lab1/Dfd12 double mutant background can rescue the 100% penetrant transformation phenotype in NB6-4max . Indeed , inducing Ama expression at stage 9 rescued the transformation of NB6-4max in 40% of all analyzed hemisegments ( Fig 4M ) . Taken together , we show that the specification of segmental identity of NB6-4max depends on two pathways . First , Dfd acts cell-autonomously and removing Dfd function leads to a loss of maxillary identity and a transformation into labial/thoracic identity in 43% of all maxillary hemisegments . The remaining cases of correctly specified NB6-4max appear to be determined by a second Dfd-independent pathway , in which the secreted Ama protein , expressed under the control of lab and Antp , invades the maxillary segment from adjacent regions . Next , we wanted to understand how the non-cell-autonomous pathway of lab and Antp via Ama acts to specify the segmental identity of NB6-4max . Ama and its receptor Neurotactin ( Nrt ) have been identified as dominant enhancers of the Abelson tyrosine kinase ( Abl ) mutant phenotype in axon pathfinding [23] . Ama bound to Nrt can potentially transduce signals to the tyrosine-phosphorylated adapter protein Disabled ( Dab ) that genetically acts upstream to Abl [62] . Other identified factors in the Abl pathway are the antagonist Enabled ( Ena ) [63] and a cooperating factor named Trio [64] . We tested all factors ( Dab , Abl , Ena and Trio ) for a possible role in NB6-4max specification . Ena and Trio act downstream of Abl and were identified as cytoskeleton modulators via Actin regulation [65–67] and as such could influence NB divisions per se , e . g . in changing the mode of division ( asymmetric versus symmetric ) . In the case of NB6-4max , we could not observe a transformation phenotype in ena or trio mutants ( for both n = 50 mHs; Fig 4A ) , and thus we assume that the cytoskeleton-associated function of Abl is not responsible for defects in NB6-4max . Since Ama was found in a modifier screen of the Abl mutant phenotype , all available Ama mutants also harbor the mutant allele of Abl1 [23] that encodes a truncated protein with residual kinase activity [68] . Testing the Abl1 allele alone did not result in a transformation of NB6-4max ( n = 50 mHs; Fig 5A ) . Moreover , comparing the double mutant stock Abl1 , AmaM109 with a fly stock that includes an Abl rescue construct ( Abl1 , AmaM109 , Abl+ ) ( Fig 5B ) showed the same transformation rate ( both 10%; both n = 50 mHs ) , strengthening our finding that a ) AmaM109 shows a transformation phenotype of NB6-4max and b ) the Abl1-allele has no influence on the specification of NB6-4max . To test whether Abl kinase itself has an effect on NB6-4max specification , we analyzed the Abl4 mutant allele that produces a protein with catalytically inactive kinase domain [68 , 69] . This allele exhibited a transformation of NB6-4max in 41% of all hemisegments ( n = 400 mHs; Figs 4A and 5C ) . Additionally , analysis of the Abl kinase interacting protein Disabled ( Dab1 ) mutation also showed a transformation of NB6-4max in 40% of all hemisegments ( n = 60 mHs; Figs 4A and 5D ) . Thus , our analysis of the Hox-mediated non-cell-autonomous pathway of NB6-4max specification shows that Ama/Nrt possibly act via Dab to regulate the Abl kinase . Intriguingly , both intracellular components Dab and Abl show a similar transformation rate like Dfd single mutants ( all approximately 40% ) , again arguing for the existence of a second Dfd-independent pathway . Accordingly , a Abl4 , Dfd16 double mutant indeed shows an increase in the transformation rate to 95% of all hemisegments ( n = 150 mHs; Fig 5E ) , strongly supporting our hypotheses that a ) two independent , parallel pathways ensure the proper specification of the NB6-4max identity , and b ) Ama/Nrt act through regulating the Abl kinase activity . To further prove this , we wanted to assess whether the activity change of the Abl kinase can be monitored on the protein level in WT and lab/Dfd-mutants ( impairing Hox and Ama pathway ) , since it was shown that Abl exhibits its kinase function when localized and concentrated at the cell cortex , whereas cytoplasmic Abl acts via a kinase-independent function [70–72] . In WT embryos at st11 ( Fig 5F left panels ) , we can observe a cytoplasmic localization of Abl that shows cortical enhancement in a lot of NBs ( white arrow heads ) including NB6-4max ( magenta arrow head ) , suggesting an active Abl kinase . This cortical localization is not observable in NB6-4max in lab/Dfd double mutants in which both pathways are disrupted ( Fig 5F right panels , see also tracks of pixel intensities in lower panels ) . On the other hand , Abl function could impinge on Dfd localization or expression . We investigated Abl4 mutants and we could observe nuclear Dfd expression in the glial and the neuronal precursor of transformed NB6-4max ( Fig 5G ) . Finally , we wanted to analyze whether re-expression of Abl in the lab1/Dfd12 or Dfd16/Antp7 double mutant background could rescue the transformation phenotype . Indeed , using the scaGal4 line to express Abl in the double mutant situation rescued the strong transformation phenotypes of NB6-4max in 20% of all hemisegments in the lab1/Dfd12 or 40% in the Dfd16/Antp7 mutants ( Fig 5H ) . Taken together , our analysis of the Dfd-independent pathway suggests , that Ama/Nrt ensure the proper specification of NB6-4max by positively regulating the kinase function of Abl . We conclude that nuclear located Dfd and cortical located Abl act synergistically to regulate NB6-4max identity . Therefore , we next wanted to understand if Dfd and Abl act in parallel on the same target to ensure proper specification of NB6-4max . We have previously shown , that abdA and AbdB specify abdominal ( glioblast ) identity of NB6-4 by repressing the cell cycle gene CyclinE ( CycE ) , while CycE is expressed in thoracic NB6-4 neuroglioblasts ( ground state identity ) and becomes asymmetrically distributed to the neuronal daughter precursor during first division [7] . Since the loss of Dfd or Abl function leads to a transformation of NB6-4max glioblast fate into a neuroglioblast fate , we next wanted to address whether Dfd and/or Abl also modulate CycE expression or function . Therefore , we first studied CycE-mRNA expression in Dfd- and Abl-deficient NB6-4max ( Fig 6A and 6B ) . Indeed , in transformed NB6-4max generating neurons and glial cells of Dfd- and Abl-mutants , we could observe deregulation and thus an increased expression of CycE-mRNA in neuronal daughter cells ( Fig 6A–6C ) indicating that both Dfd and Abl repress CycE expression in wild type NB6-4max . Furthermore , ectopic expression of CycE in NB6-4max was sufficient to generate a labial/thoracic neuroglioblast fate in 12% of all maxillary hemisegments ( n = 140 mHs; Fig 6D ) , whereas loss of CycE in labial NB6-4 leads to a loss of the neuronal sub-lineage in 64% of all hemisegments ( n = 100mHs; Fig 6E ) , similarly to CycE loss in thoracic NB6-4 ( Fig 6G and 6H and [7] ) . This shows that CycE is an important target in gnathal NB6-4 that has to be regulated in order to decide whether to generate neurons or not . Since CycE seems to be repressed in NB6-4max by Dfd and Abl , we wanted to test , whether Dfd or Abl can repress CycE in labial/thoracic NB6-4 lineages , thereby repressing the ground state identity and leading to the generation of pure glial NB6-4 lineages . Ectopic expression of Dfd leads to a severe loss of neuronal sublineages in labial/thoracic segments in 74% of all hemisegments ( n = 100Hs ) and suppression of CycE mRNA expression in the transformed NB6-4 lineages ( Figs 3H and 6F ) . Moreover , in the case of labial or thoracic NB6-4 expressing ectopic Dfd , we could observe a perfect transformation towards maxillary fate , with 4 glial cells ( instead of two in abdominal hemi-segments ) and no neurons . In contrast , ectopic expression of Abl did not result in a repression of CycE in labial/thoracic NB6-4 and did not lead to a transformation towards maxillary fate . This might argue for an indirect regulation of CycE by Abl through Abl-downstream components . If Dfd and Abl act both upstream of CycE , we reasoned that CycE should be epistatic to Dfd and Abl . Therefore , we combined the CycEAR95 mutation with the single mutation Dfd11 and the double mutant of Abl4/Dfd16 . Indeed , loss of CycE leads to a rescue of the transformation phenotype in 77% of NB6-4max in Dfd11 single mutants or 85% in Abl4/Dfd16 double mutants , showing that CycE is epistatic to Dfd and Abl ( Fig 6G and 6H ) . We conclude , that both pathways ensure the proper segmental specification of NB6-4max through repression of the cell cycle gene CyclinE . Since Abl is a cytoplasmically/cortically localized kinase , we next wanted to address how Abl might repress CycE transcription . In addition to the higher proliferation rate of NB6-4max in Abl mutants ( 4–5 neurons and 3 glial cells versus 4 glial cells in the wild type ) , we observed a statistically significant increase in nuclear size ( S7A–S7C Fig ) of the Eg-positive NBs in gnathal segments . A number of Abl mutant embryos displayed an overproliferation phenotype with many supernumerous Eg-positive cells at the end of embryogenesis ( S7D–S7F Fig ) . To circumvent the problem of second site mutations on the mutant chromosome , we also tested transheterozygous mutations and could observe similar results in different allelic combinations ( S7A and S7B Fig ) . Interestingly , the same phenotype could be observed in lab/Dfd double mutants ( S7G Fig ) , also disrupting the Ama/Abl pathway , or Nrt1 mutants ( S7H Fig ) , but never in any of the single Hox mutants , in which Abl function is normal . Since this implies that Abl might regulate a pathway involved in proliferation and growth affecting CycE , and since a possible connection of the vertebrate homologue c-Abl with the Hippo/YAP pathway was recently suggested [73 , 74] , we wondered whether the highly conserved Hippo/Salvador/Warts ( HSW ) pathway might be involved in this process . At the center of the HSW pathway are the two core kinases Hippo ( Hpo ) and Warts ( Wts ) that phosphorylate and thereby inhibit the transcriptional regulator Yorkie ( Yki ) [75–77] . When Hpo is active it phosphorylates Salvador ( Sav ) that becomes stabilized [78 , 79] and leads to the phosphorylation of the downstream kinases Mats and Wts . Wts finally phosphorylates Yki that upon phosphorylation retains in the cytoplasm and is inactive [80] . As soon as Hpo/Wts are inactive , Yki is no longer phosphorylated and can enter the nucleus , complex with other transcription factors like Scalloped ( Sd ) and start transcription of its target genes like CycE [80 , 81] . Since so far the HSW pathway has not been implicated in embryonic NBs development , we first wanted to show the presence of the active Hpo kinase in early Drosophila NBs . One way to monitor Hpo activity in tissues is the Salvador ( Sav ) protein , since Sav is stabilized only in the presence of active Hpo via phosphorylation , whereas absence of Hpo or loss of Hpo activity leads to the destabilization of Sav and its subsequent targeting for degradation [78 , 79] . We used antibody staining to visualize the levels of Sav . In wild type we can find a strong staining for Sav in nearly all embryonic NBs ( Fig 7A ) suggesting an active Hpo kinase . Moreover , active Hpo should lead to the cytoplasmic retention of Yki , and indeed using antibody staining , we could observe a predominant cytoplasmic localization of Yki in NB6-4max with only minor signal in the nucleus , prior to its first division ( Fig 7D ) . Next we tested , whether Hpo activity is impaired in Abl-single ( Fig 7B ) or lab/Dfd-double mutants ( Fig 7C ) . We were not able to monitor Yki nuclear localization in the mutant situation , but observed a clear reduction of the Sav protein ( Fig 7B and 7C ) , suggesting that the loss of Abl function might lead to the destabilization and degradation of Sav . Since it was shown that c-Abl phosphorylates and activates vertebrate MST1 and MST2 ( homologues of Hpo ) and Drosophila Hpo [74] , the loss of Sav in the Abl LoF situation implies an inactive Hpo kinase . Although we were not able to show a change in the subcellular localization of Yki , we still wanted to examine the function of Yki in NB6-4max lineage development . To test if Yki is sufficient to generate neurons in NB6-4max and therefore to transform its identity , we ectopically expressed a constitutive active from of Yki ( YkiS168A , YkiCA ) [82] ( Fig 7E ) . This Yki protein can no longer be phosphorylated by Warts and as such is nuclear and active [82] . Expression of YkiCA leads to a phenocopy of the Abl4 mutant phenotype , with NB6-4max transformed to a thoracic/labial identity in 29% ( n = 60 mHs; Fig 7E ) and a general increase in nuclear sizes and cell proliferation ( S7I Fig ) . Finally , we tested the effect of ykiB5 mutation alone or in combination with Abl4/Dfd16 or lab1/Dfd12 mutants . YkiB5 mutants are predominantly embryonic lethal , but a few escapers also develop into larvae . In ykiB5 single mutants we could not observe a loss of neuronal cells in labial or thoracic segments ( n = 25 mHs ) . Nevertheless , double or triple mutants for ykiB5 and Abl4/Dfd16 or ykiB5 and lab1/Dfd12 ( both n = 35 mHs; Figs 7F and S7J ) showed a rescue of the transformation phenotype in 31% or 34% , respectively ( a drop from 100% transformation to 69% or 66% , respectively ) . This indicates that Yki is necessary for the generation of neurons in the transformed NB6-4max of Abl4/Dfd16 or lab1/Dfd12 mutants , and suggests that Abl kinase specifies NB6-4max identity potentially by activating the HSW pathway and thereby repressing the expression of CycE .
Using the well-established molecular marker Eagle ( Eg ) which labels four embryonic NB-lineages ( NB2-4 , NB3-3 , NB6-4 , NB7-3 ) in all thoracic and most of the abdominal segments [20 , 21] we identify serially homologous lineages of NB3-3 , NB6-4 and NB7-3 in gnathal segments . The embryonic NB7-3 lineage shows segmental differences as it comprises increasing cell numbers from mandibular ( 2 cells ) , maxillary ( 3 cells ) to labial ( 3–5 cells ) segments , while cell numbers are decreasing from T1-T2 ( 4 cells ) , T3-A7 ( 3 cells ) to A8 ( 2–3 cells ) [14 , 35] . Reduced cell numbers in the mandibular and maxillary NB7-3 lineages depend on Dfd and Scr function , respectively ( S8A–S8E Fig ) . While NB7-3 appeared in all three gnathal segments , NB3-3 and NB6-4 was only found in labial and maxillary segments , and NB2-4 was not found in any of them . Our preliminary data suggest that the missing NBs are not generated in these segments , instead of being eliminated by apoptosis . For the terminal abdominal neuromeres ( A9 , A10 ) it has recently been shown that the formation of a set of NBs ( including NB7-3 ) is inhibited by the Hox gene Abdominal-B[13] . Similarly , in Dfd mutants we occasionally observed the formation of a NB with NB6-4 characteristics in mandibular segments ( 10% , S8F Fig ) , in which it is never found in wild type . Similar to the thoracic and abdominal segments [6] NB6-4 showed dramatic differences between maxillary and labial segments . NB6-4max produces glial cells only ( like abdominal NB6-4 ) , whereas the labial homolog produces neurons in addition to glial cells ( like thoracic NB6-4 ) . The number of glial cells produced by the glioblasts NB6-4max ( 4 cells ) and abdominal NB6-4 ( 2 cells ) and by the neuroglioblasts NB6-4lab ( 3 glia ) and thoracic NB6-4 ( 3 glia ) is segment-specific . Thus segment-specific differences among serially homologous lineages may concern types and/or numbers of specific progeny cells and may result from differential specification of NBs and their progeny , differential proliferation and/or differential cell death of particular progeny cells . It has been shown that the segment-specific modification of serially homologous lineages is under the control of Hox genes and that during neurogenesis Hox genes act on different levels , i . e . they act in a context-specific manner at different developmental stages and in different cells ( reviewed in [86] ) . In the thoracic/abdominal region segmental identity is conferred to NBs early in the neuroectoderm by cell-autonomous function of Hox genes of the Bithorax-Complex [8 , 9 , 46] . In this study we used the NB6-4 lineage to clarify mechanisms of segmental specification in the gnathal segments . In segments of the trunk , the action of Hox genes strictly follows the rule of the posterior prevalence concept [12 , 87]: More posterior expressed Hox genes repress anterior Hox genes and thereby determine the segmental identities . In the gnathal segments we could not observe this phenomenon on the level of the nervous system . Removing Hox genes of the Antp-C had no or only minor impact on the expression domain of other Antp-C Hox genes ( see S4 Fig ) . Similar results were also obtained in a study that analyzed cross-regulation of Hox genes upon ectopic expression [88] . Moreover , it seems that at least in the case of the differences monitored between labial and maxillary segments Hox gene function has to be added to realize the more anterior fate . Antennapedia has no impact on NB6-4 identity in the labial segment , but specification of the maxillary NB6-4 requires the function of Deformed and Sex combs reduced . These two Hox genes are not repressed or activated by Antp ( see also [88] ) . Also , cross-regulation between Dfd and Scr seems to be unlikely or is very weak since we observed only mild effects on the protein level and on the phenotypic penetrance . In principle Scr can repress Dfd , but it was suggested that this occurs only when products are in sufficient amounts [89] . In NB6-4 Dfd and Scr are co-expressed , but Scr levels appear to be insufficient to repress Dfd . Dfd seems to be the major Hox gene that cell-autonomously confers the maxillary NB6-4 fate , since the loss of Dfd showed the highest transformation rate and , more importantly , ectopic expression of Dfd in thoracic segments leads to a robust transformation towards maxillary fate . Scr does not act redundantly since in double mutants Dfd/Scr we did not find a synergistic effect . It might have a fine-tuning effect , as we could show that Scr influences Ama by repressing its transcription , whereas all other Antp-C Hox genes seem to activate Ama . However , since we could find only minor changes in cell identities and numbers in Scr LoF background , the role of Scr in NB6-4max stays enigmatic . To our surprise cell-autonomous Hox gene function was not the only mechanism that confers segmental identity in NB6-4max . Loss of Dfd showed an effect in approx . 43% of all segments . Moreover , mutations of the adjacently expressed Hox genes labial and Antennapedia in combination with Dfd LoF showed a dramatic increase in the transformation rate of NB6-4max . We carefully studied their expression patterns on the mRNA and protein level in wild type and Hox mutant background . In no case we could find these genes to be expressed in NB6-4max or in the neuroectodermal region from which NB6-4max delaminates . This indicates that labial and Antennapedia influence NB6-4max fate in a non-cell-autonomous manner . That Hox genes can act non-cell-autonomously on stem cells was recently shown in the male germ-line , were AbdB influences centrosome orientation and the proliferation rate through regulation of the ligand Boss in the Sevenless-pathway [90] . In our study Antp-C Hox genes control the expression of the secreted molecule Amalgam , which spreads to adjacent segments and ensures segmental specification of NB6-4max in a parallel mechanism to the cell-autonomous function of Dfd . Thus , we provide first evidence for parallel non-cell-autonomous and cell-autonomous functions of Antp-C genes during neural stem cell specification in the developing CNS . Abelson kinase ( Abl ) was shown to be required for proper development of the Drosophila embryonic nervous system . In neurons Abl interacts with proteins like Robo [91] or Chickadee [92] and influences the actin cytoskeleton in the growth cone [93] to regulate axonogenesis and pathfinding . In this system it was also demonstrated that Ama and Nrt are dominant modifiers of the Abl phenotype [23] . In our model ( Fig 7G ) we propose that the interaction of secreted Ama and the membrane-bound Nrt regulates Abl function in NBs . This leads to the correct segmental specification of NB6-4max . Antp-C Hox genes lab , Antp and Dfd regulate the expression of Ama and in mutants for theses Hox genes expression of Ama is severely reduced , which leads to the transformation of NB6-4max due to missing Abl function and de-repression of the cell cycle gene CyclinE . That Abl can influence the expression of CyclinE was also demonstrated in a modifier-screen in the Drosophila eye , but the mechanism remained unclear [94] . Our genetic analysis now suggests that in NBs this might occur via the regulation of the highly conserved HSW pathway [75–77] and its downstream transcriptional co-activator Yki , which is known to regulate CyclinE expression [80] . The HSW pathway controls organ growth and cell proliferation in Drosophila and vertebrates but so far has not been implicated in embryonic NB development . We could observe Yki cytoplasmic localization in wild type NB6-4max prior to division suggesting the active Hippo pathway . Although we could not detect nuclear localization of Yki in Abl mutants , the loss of Yki activity in the Abl mutant background leads to a significant reduction in the strength of the Abl single mutant phenotype showing their genetic interaction and therefore supporting our proposed model in which Abl influences Yki activity . Moreover , expression of constitutive active Yki also lead to the transformation of NB6-4max and phenotypes that were similar to those observed in Abl mutants . We tried to assess how Abl might influence Yki activity . Work in vertebrates suggests that this could be at least on two levels: first , c-Abl was shown to directly phosphorylate and activate the vertebrate MST1 and MST2 ( Hpo homologue ) and the Drosophila Hpo on a conserved residue ( Y81 ) [74] and second , c-Abl can also phosphorylate YAP1 , which changes its function to become pro-apoptotic [73] . Our analysis suggests that in NBs Abl might regulate Hpo , since we could find changes in the stability of Salvador , which is used as a Hpo activity readout [78 , 79] , but we can not rule out a parallel direct regulation of Yki , since it was recently shown that other pathways like the AMPK/LKB1 pathway can directly influence Yki activity [95 , 96] . Since we could observe severe over-proliferation in Abl or lab/Dfd mutants , that have an impaired Ama-Nrt-Abl pathway , or upon overexpression of YkiCA , future studies need to elucidate whether and how the proto-oncogene Abl kinase and Hox genes act on growth and proliferation or even tumor initiation through regulation of the Hippo/Salvador/Warts pathway .
Oregon R ( used as wild type ) , lab4 , pb17 , Scr11 , Scr17 , Dfd11 , Dfd12 , Dfd16 , Antp25 , Antp11 , Dfd16/Scr4 , Dfd16/Antp7 , lab1/Dfd12 , Scr4/Antp25 , CycEAR95 , Abl4 , Abl1 , Nrt1 , hdFf , disco1 , dpphr27 , ems4 , Dab1 , ena23 , trio8 , sas4s2214 , grappa61A , zen1 , bcd12 , ftz11 , Alhr13 , wg8 ( all strains are balanced with TM6b ) , iab2-lacZ Antp-Hu e , UAS-CyclinE , UAS-n-lacZ , UAS-ykiS168A ( ykiCA ) , scabrous-Gal4 , engrailed-Gal4 , Hsp70-Ama , UAS-Abl ( all from Bloomington ) ; AmaM109/Abl1 , AmaM109/Abl1 , Abl+ AmaR1/Abl1 and NrtM54/Abl1 ( gifts from E . Liebl ) [23]; tsh/tio 8and tsh 2757 ( gifts from L . Fasano ) [97–99] , ykiB5 ( gift from J . Knoblich , [80] , gbb1 and gbb2 ( gifts from K . Wharton ) [100] , mspoc26 ( gift from A . Nose ) [101] , wgcx4 ( gift from J . Ng ) [102] , UAS-Dfd , UAS-Scr , UAS-Antp , UAS-lab ( gifts from A . Percival-Smith ) [88 , 103–105] . To identify conserved Hox binding sites in the Ama locus we used the EvoPrinterHD ( http://evoprinter . ninds . nih . gov/ ) and compared Drosophila melanogaster to Drosophila pseudoobscura and Drosophila simulans . Entry sequence was the D . melanogaster Ama gene locus plus 3kb of upstream sequence from the 5’UTR of Flybase release 2015_01 . Following dechorionization in 7 . 5% bleach , embryos from overnight collections were devitellinized and fixed in heptane with 4% formaldehyde in 0 . 3% PBT buffer ( 1x PBS with 0 . 3% Triton , C . Roth ) for 20 minutes . Dechorionisation and dehydration was accomplished by vortexing and 10 min wash in methanol . The heatshock of Hsp70-Ama in lab1/Dfd12 double mutants was induced at embryonic stage 9 for 1 hour at 37°C . Afterwards embryos were allowed to develop until stag 13 at 25°C before fixing and staining . Primary antibodies used: mouse ( m ) α-Eg ( 1:100 , gift from C . Q . Doe ) [106] , rabbit ( rb ) α-Eg ( 1:500 ) [20] , rb α-Repo ( 1:500 , gift from T . Halter ) [37] and guinea pig ( gp ) α-Repo ( 1:1000 , gift from B . Altenhein ) [107] , gp α-Dfd ( 1:100 , gift from W . McGinnis ) [108] , rb α-En ( 1:100 ) , rb α-Dfd ( 1:20 , both Santa Cruz ) , m α-Engrailed ( 1:5 ) , m α-Nrt ( 1:100 ) , m α-Ena ( 1:1000 ) , m α-Dab P4 ( 1:50 ) , m α-Wrapper ( 1:20 ) , m α-Antp ( 1:20 ) , m α-Scr ( 1:20 ) , m α-Prospero ( 1:10 , all DSHB ) , rb α-Pb ( 1:50 , gift from T . Kaufman ) [109] , rat α-Lab ( 1:10 , gift from F . Hirth ) and rb α-Lab ( 1:100 , gift from H . Reichert ) , rb α-Ama ( 1:500 , gift from I . Silman ) [25] , rb α-Abl ( 1:500 , gift from E . Giniger ) [62] , m α-beta-gal ( 1:750 , Promega ) , chicken α-beta-gal ( 1:1000 , Cappel ) , rb α-Castor ( 1:500 , gift from M . Odenwald ) [110] , m α-GFP ( 1:500 , Covance ) , rat α-Gooseberry distal and rat α-Gooseberry proximal ( 1:2 , gift from R . Holmgren ) [111] , gp α-Zfh1 ( 1:500 ) , gp α-Deadpan ( 1:1000 , both gift from J . Skeath ) , gp α-Runt ( 1:500 , gift from J . Reinitz ) [112] , rb α-Ey ( 1:1000 , gift from Uwe Walldorf ) [34] , rb α-Ind ( 1:3000 , gift from T . von Ohlen ) [113] , rb α-Yki ( 1:200 , gift from K . Irvine ) [82] , rb α-Sav ( 1:100 , gift from J . Jiang ) [114] , rb α-pH3 ( 1:1000 , Abcam ) . The secondary antibodies used were α-mouse-Cy3 , α-rabbit-FITC , α-rabbit-Cy3 , α-guinea pig-Cy5 , α-rat-Cy5 ( 1:500 , all from donkey , all Jackson Immunoresearch Laboratories ) and donkey α-mouse-Alexa488 ( 1:500 , Molecular Probes ) . In situ probes against Ama- Dfd- , Scr- , Antp- , gcm- and CycE-mRNA were generated by PCR using the DIG-RNA and FITC-RNA labeling kit ( Roche Applied Science ) from an embryonic cDNA library and genomic DNA . The probe for labial-mRNA was generated by R . Urbach . In situ hybridizations were performed according to standard procedures using a 40% formamide hybridization solution . The Leica TCS SP2 and SP5 confocal microscope was used for fluorescent imaging , and images were processed using Leica Confocal software , Adobe Photoshop and Adobe Illustrator . Pixel intensities and tracks of pixel intensities were measured using the Volocity software . Statistical analysis of the nuclear size was analyzed using the sigmaBlot v . 11 software . Embryos from stage 5 to stage 9 ( according to Campos-Ortega and Hartenstein 1997 ) where stained with Alkaline Phosphatase ( AP ) and in-situ RNA-probes for lab , Dfd , Scr and Antp mRNA . Whole-mount embryos where documented with a Zeiss Axioplan microscope using a 40x objective and pictures where digitalized with a Kontron Progress3012 camera . The measurements of the Hox gene expression domains were taken with the Adobe Photoshop CS4 measurement tool . The total length of the embryo was taken as 100% value and the extent of the expression domain calculated accordingly from the anterior pole . All single values where evaluated statistically with Microsoft Excel to attain a median for each stage and Hox gene . | The central nervous system ( CNS ) needs to be subdivided into functionally specified regions . In the developing CNS of Drosophila , each neural stem cell , called neuroblasts ( NB ) , acquires a unique identity according to its anterior-posterior and dorso-ventral position to generate a specific cell lineage . Along the anterior-posterior body axis , Hox genes of the Bithorax-Complex convey segmental identities to NBs in the trunk segments . In the derived gnathal and brain segments , the mechanisms specifying segmental NB identities are largely unknown . We investigated the role of Hox genes of the Antennapedia-Complex in the gnathal CNS . In addition to cell-autonomous Hox gene function , we unexpectedly uncovered a parallel non-cell-autonomous pathway in mediating segmental specification of embryonic NBs in gnathal segments . Both pathways restrict the expression of the cell cycle gene CyclinE , ensuring the proper specification of a glial cell lineage . Whereas the Hox gene Deformed mediates this cell-autonomously , labial and Antennapedia influence the identity via transcriptional regulation of the secreted molecule Amalgam ( and its downstream pathway ) in a non-cell-autonomous manner . These findings shed new light on the role of the highly conserved Hox genes during segmental patterning of neural stem cells in the CNS . | [
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] | 2016 | Cell-Autonomous and Non-cell-autonomous Function of Hox Genes Specify Segmental Neuroblast Identity in the Gnathal Region of the Embryonic CNS in Drosophila |
Interferon-inducible GTPases of the Immunity Related GTPase ( IRG ) and Guanylate Binding Protein ( GBP ) families provide resistance to intracellular pathogenic microbes . IRGs and GBPs stably associate with pathogen-containing vacuoles ( PVs ) and elicit immune pathways directed at the targeted vacuoles . Targeting of Interferon-inducible GTPases to PVs requires the formation of higher-order protein oligomers , a process negatively regulated by a subclass of IRG proteins called IRGMs . We found that the paralogous IRGM proteins Irgm1 and Irgm3 fail to robustly associate with “non-self” PVs containing either the bacterial pathogen Chlamydia trachomatis or the protozoan pathogen Toxoplasma gondii . Instead , Irgm1 and Irgm3 reside on “self” organelles including lipid droplets ( LDs ) . Whereas IRGM-positive LDs are guarded against the stable association with other IRGs and GBPs , we demonstrate that IRGM-stripped LDs become high affinity binding substrates for IRG and GBP proteins . These data reveal that intracellular immune recognition of organelle-like structures by IRG and GBP proteins is partly dictated by the missing of “self” IRGM proteins from these structures .
Many intracellular pathogens including the bacterium C . trachomatis and the protozoa T . gondii co-opt the host cell endomembrane system to enclose themselves inside membrane-bound vacuoles . Within the confines of these remodeled PVs , microbes acquire nutrients and replicate [1] . To combat these pathogens , the mammalian host has evolved a large repertoire of cell-autonomous defense mechanisms that kill or restrain the replication of microbes residing within vacuoles [2] , [3] . While these defense mechanisms are effective at targeting foreign or “non-self” vacuoles , they also have the potential to cause organelle damage and must therefore be tightly regulated . Control over these host defenses is executed at two critical steps: ( i ) induction of genes encoding host resistance factors in the context of an infection and ( ii ) targeting of these resistance factors to the appropriate intracellular location , for example to PVs . These two modes of regulation are exemplified by the induction and execution of cell-autonomous defenses by the cytokine Interferon-γ ( IFNγ ) . The importance of IFNγ in host immunity is demonstrated by the severe immuno-deficiencies observed in genetically engineered mouse strains lacking IFNγ or its receptor and in patients carrying rare mutations in genes critical for IFNγ signal transduction [4] , [5] . IFNγ is produced by immune-activated lymphocytes and exerts its antimicrobial effects by dramatically remodeling the transcriptional expression profile of target cells bearing the IFNγ receptor [3] . IFNγ-induced resistance genes include members of two IFN-inducible GTPase families named IRGs and GBPs . Members of both GTPase families have the ability to translocate and to adhere specifically to PVs in order to inhibit intracellular pathogen growth . Although the specificity of this intracellular targeting event is well documented [3] , [6] , the underlying mechanism is unclear . Once docked to PVs , GBP proteins recruit antimicrobial protein complexes that include the NADPH oxidase NOX2 , the autophagy apparatus and the inflammasome [3] . IRG proteins on the other hand can directly disrupt PV membranes , thereby releasing vacuolar pathogens into the cytosol where they can be removed through autophagy [6] , [7] . IRG GTPases are divided into two categories: ( i ) the predominantly cytosolic GKS proteins constitute the most abundant group and harbor a conserved GX4GKS sequence in the first nucleotide-binding motif ( G1 ) , ( ii ) the predominantly membrane-bound IRGM proteins instead contain a non-canonical P-loop sequence GX4GMS [6] . Both GKS and IRGM proteins are essential for cell-autonomous resistance to infections with C . trachomatis and T . gondii in mice but fulfill distinct functions in this process [6] . Whereas GKS proteins directly target and eliminate C . trachomatis and T . gondii PVs , IRGM proteins appear to orchestrate the targeting of GKS proteins to PVs by an incompletely understood mechanism [6] . In addition to their role as regulators of GKS protein function , IRGM proteins also exert antimicrobial activities independently of GKS proteins . Both mouse and human IRGM proteins promote the formation of autophagosomes upon IFNγ stimulation [8]–[10] . Additionally , murine Irgm1 loads onto early phagosomes containing beads or live bacteria [11]–[13] . Vacuolar Irgm1 interacts with target SNARE protein complexes and through these interactions can facilitate the rapid fusion of Irgm1-coated phagosomes with degradative lysosomes . Accelerated lysosomal maturation was shown to result in the destruction of the attenuated pathogen Mycobacterium bovis BCG contained within Irgm1-positive phagosomes in mouse macrophages [13] . Similar to Irgm1 , Irgm3 was implicated as a mediator of direct antimicrobial activities towards T . gondii [14] . To initially establish vacuoles permissive for microbial survival in IFNγ-activated cells , pathogens must have evolved strategies to evade the direct , fast-acting immune responses mediated by membrane-bound Irgm1 and Irgm3 proteins . In agreement with the existence of such evasion mechanisms , we observed that Irgm1 and Irgm3 failed to robustly associate with PVs formed by either C . trachomatis or T . gondii . The absence of substantial amounts of Irgm1/m3 from PVs contrasted with the abundant localization of Irgm1/m3 to “self” structures like LDs . We found that Irgm1/m3-decorated LDs are largely devoid of GKS and GBP proteins , whereas Irgm1/m3-deficient PVs are targets for GKS and GBP proteins . These observations led us to hypothesize that the absence of Irgm1/m3 proteins marked intracellular structures as targets for a “second line of defense” mediated by GKS and GBP proteins . In support of this hypothesis , we demonstrated that stripping LDs of Irgm1/3 resulted in mistargeting of GKS and GBP proteins to LDs independently of an infection . Because IRGM proteins were previously shown to inhibit GKS protein oligomerization [15] , we propose a model in which the missing of “self” IRGM proteins from “non-self” PVs results in the formation of GKS ( and GBP ) protein oligomers with high avidity for membrane binding .
GKS proteins in their GTP-bound state form dimers [6] . Dimerization occurs at the G domain interface and is a prerequisite for the formation of higher order GKS protein oligomers . Mutations that diminish guanine nucleotide binding or disrupt the G domain interface eliminate both protein oligomerization and targeting to T . gondii PVs [16] , [17] . To determine if these findings extended to other PVs , we first tested whether guanine nucleotide binding of the GKS protein Irgb10 was essential for their targeting to “inclusions , ” the PVs formed by C . trachomatis . We replaced the serine on position 82 of Irgb10 in the conserved P-loop GKS motif with asparagine ( Irgb10S82N ) . This mutation is analogous to the Irga6S83N mutation that abrogates guanine nucleotide binding and T . gondii PV localization [16] . We found that Irgb10-GFP-fusion proteins harboring the S82N mutation or a deletion of the central G-domain ( Irgb10ΔG ) failed to localize to C . trachomatis inclusions in infected mouse embryonic fibroblasts ( MEFs ) ( Figure 1A ) . Combined with previous results in T . gondii [16] , our data suggested that protein oligomerization of GTP-bound Irgb10 is essential for tethering this GKS protein to C . trachomatis inclusion membranes . To test whether protein oligomerization of the N- and C-terminal domains of Irgb10 was sufficient to target inclusion membranes , we replaced the G domain of Irgb10 with alternative protein oligomerization domains and monitored the subcellular localization of these protein chimeras . We first substituted the G domain of Irgb10 with the tetramer-forming protein dsRED [18] , which emits red fluorescence exclusively in the oligomerized form [19] . Insertion of dsRED between the N-terminal domain ( NTD ) and C-terminal domain ( CTD ) of Irgb10ΔG ( Irgb10NTD-dsRED-CTD ) restored the association of a GFP-tagged fusion protein with C . trachomatis inclusions ( Figure 1A ) . Inserting dsRED in between the N-terminal myristoylation motif ( Myr ) and the CTD of Irgb10 ( Irgb10Myr-dsRED-CTD ) similarly redirected the mutant variant Irgb10Myr-CTD to inclusions ( Figure 1A ) . Tetramerized Irgb10 localized to IncG-positive inclusion membranes ( Figure 1B ) . Inclusion targeting required the presence of both the Irgb10 myristoylation motif and the C-terminal amphipathic helix αK ( Figure 1A , Figure 2A and data not shown ) . As an alternative mediator of protein oligomerization , we used the highly oligomeric cytoplasmic yeast protein TyA [20] . Insertion of TyA in between the myristoylation domain and the C-terminus of Irgb10 ( Irgb10Myr-TyA-CTD ) similarly re-localized these fusion proteins towards inclusions . Myristoylated-TyA ( Myr-TyA ) localized to microvesicles , as described [21] , but failed to associate with inclusions ( Figure 1C ) , demonstrating that the C-terminus of Irgb10 containing the αK amphipathic helix is essential for inclusion targeting . In summary , these data show that the oligomerization of the N- and C-terminal lipid binding domains of Irgb10 was sufficient to drive localization to inclusions . The targeting of GKS proteins to T . gondii is substantially diminished in the absence of the IRGM proteins Irgm1 and Irgm3 [16] , [22] . We found that the association of Irgb10 and other GKS proteins with C . trachomatis inclusions was similarly reduced in infected MEFs derived from Irgm1−/− , Irgm3−/− and Irgm1/m3−/− mice ( Figure 3 ) . These data indicate that Irgm1 and Irgm3 either directly or indirectly promote the delivery of GKS proteins to inclusions . Because IRGM proteins physically interact with GKS proteins at the G domain interface [16] , we hypothesized that IRGM proteins facilitate the delivery of GKS proteins to inclusions through their interactions with the G domain of GKS proteins . In such a scenario , artificially oligomerized Irgb10 lacking a G domain should target inclusions independently of IRGM proteins . To test the hypothesis , we expressed two tetramerized , chimeric Irgb10ΔG proteins , Irgb10NTD-dsRED-CTD and Irgb10NTD-dsRED-αK , in wildtype and Irgm3−/− MEFs and scored the frequency of dsRED signal on inclusions . We chose Irgm3−/− MEFs for these experiments , because they displayed the most pronounced defect in targeting endogenous Irgb10 to inclusions ( Figure 3 ) . In contrast to endogenous Irgb10 ( Figure 2B and Figure 3 ) , tetramerized Irgb10 lacking a G domain targeted inclusions with the same efficiency in wildtype and Irgm3-deficient cells ( Figure 2A ) . These data suggest that Irgm3 regulates the targeting of Irgb10 to inclusions through its interaction with the G domain of Irgb10 . It is currently unknown where inside a cell IRGM proteins interact with GKS proteins to regulate their function . To determine whether IRGM proteins regulate GKS proteins directly at PV membranes , we first monitored the subcellular localization of IRGM proteins in cells infected with either T . gondii or C . trachomatis . As reported previously [14] , [23] , we found that endogenous Irgm3 but not Irgm1 associated with T . gondii PVs , albeit only weakly relative to its association with endogenous , puncta-like structures ( Figure 4A and data not shown ) . These results are also in agreement with a previous report demonstrating that Irgm3 associates with T . gondii PVs at a lower frequency than GKS proteins do [24] . Next we examined the subcellular localization of endogenous Irgm1 and Irgm3 in C . trachomatis-infected cells . In agreement with a previous report [25] , we detected association of Irgm3 with C . trachomatis inclusions at 2 hpi . However , Irgm3 associated only weakly with inclusions relative to its interactions with endogenous structures ( Figure 4B ) . Similar to the staining pattern of T . gondii PVs , we failed to detect the presence of Irgm1 on inclusions ( data not shown ) . To determine whether Irgm1 or Irgm3 could target established inclusions , we infected MEFs with C . trachomatis and subsequently treated cells with IFNγ at 3hpi . Under these experimental conditions endogenous as well as ectopically expressed Irgm1 and Irgm3 were not present at inclusion membranes in detectable amounts at 20 hpi ( Figure 4C , Figure 5B and data not shown ) . Collectively , these data show that PVs formed by either C . trachomatis or T . gondii are devoid of substantial amounts of Irgm1 and Irgm3 proteins . Because established PVs lack sizeable amounts of Irgm1/m3 , we considered the hypothesis that IRGM proteins regulate Irgb10 and other GKS proteins at sites distinct from PVs . It is known that IRGM proteins localize to various endomembranes , including LDs , a neutral lipid storage organelle [6] . Specifically , Irgm3 was shown to localize to LDs in IFNγ-treated dendritic cells [26] . To determine whether or not Irgm3 also localizes to LDs in IFNγ-treated MEFs , we induced the formation of LDs by supplementing the growth media with oleic acid ( OA ) and subsequently stained these cells with the neutral lipid dye BODIPY493/503 and with anti-Irgm3 antibody . We found that Irgm3 co-localized with the BODIPY dye in IFNγ-treated MEFs ( Figure 5A ) . To determine whether additional IRGM proteins localize to LDs , we monitored the localization of C-terminally V5-tagged Irgm1 , Irgm2 and Irgm3 inside OA-treated MEFs . In addition to Irgm3-V5 , Irgm1-V5 and Irgm2-V5 co-localized with a subset of LDs but not with inclusions ( Figure 5B and Figure S1 ) . Staining for endogenous protein confirmed the presence of Irgm1 but not Irgm2 on a subset of LDs ( Figure S2 and data not shown ) . We next asked if GKS proteins were also found on LDs by immunostaining IFNγ-activated MEFs with antibodies directed against three representative GKS proteins Irga6 , Irgb6 and Irgb10 . We were unable to detect co-localization of these proteins with BODIPY-labeled LDs in wildtype MEFs by immunofluorescence ( Figure 5C and D ) . We independently confirmed these observations by assessing the levels of IRG proteins on purified LDs . LDs purified from IFNγ-treated , wildtype MEFs by sucrose gradient centrifugation displayed significant levels of Irgm1 and Irgm3 ( Figure 5E ) , and relatively small amounts of Irgb10 ( Figure 5E ) , suggesting possible transient interactions between IRGM proteins and Irgb10 on the surface of LDs . In summary , these data indicate that LDs of wildtype cells are decorated with Irgm1 and Irgm3 but only weakly associate with GKS proteins . Although LDs could play an essential role in guiding GKS proteins to inclusions , we thought this was unlikely , because LD-deficient cells lines still target Irgb10 to inclusions ( H . A . S . and R . H . V . , unpublished data ) . Because IRGM proteins inhibit GTP acquisition by GKS proteins and are believed to thereby block the ability of GKS proteins to bind lipids [15] , [16] , we formed an alternative hypothesis in which LD-resident IRGM proteins would prevent GKS proteins from binding to LDs . To test our hypothesis , we examined the localization of Irgb10 in Irgm1/m3−/− MEFs that contain IRGM-deficient LDs . We found that the LDs of Irgm1/m3−/− MEFs were heavily decorated with Irgb10 ( Figure 5D ) . Targeting of Irgb10 to LD in Irgm1/m3−/− MEFs was primarily due to the absence of Irgm3 , because Irgm3−/− MEFs but not Irgm1−/− MEFs displayed a substantial increase in the number of Irgb10-positive LDs ( Figure 5D ) . The simultaneous deletion of both Irgm3 and Irgm1 , however , exacerbated the association of Irgb10 with LDs ( Figure 5D ) suggesting that these proteins fulfill partially redundant functions in protecting LDs against Irgb10 targeting . The role of Irgm1 and Irgm3 in guarding LDs was not limited to Irgb10 but extended to other GKS proteins including Irga6 and Irgb6 ( Figure 5C ) . Again , Irgm3 was predominantly responsible for guarding LDs , because ectopic expression of Irgm3 in either Irgm3−/− or Irgm1/m3−/− MEFs prevented deposition of Irga6 on LDs ( Figure S3 ) . Irgm1 and Irgm3 were also required to prevent Irgb10 accumulation on LD in primary macrophages , indicating that the observed phenomenon is not cell type specific ( Figure S4 ) . Furthermore , endogenous LDs found infrequently in MEFs not treated with OA also acquired Irgb10 in the absence of Irgm1 and Irgm3 ( Figure S5A ) , demonstrating that the aberrant localization of Irgb10 was not induced by OA treatment . Lastly , consistent with our immunofluorescence observations , we detected a robust increase in the amount of Irgb10 protein present in the LD fraction derived from Irgm1/m3−/− MEFs compared to wildtype MEFs ( Figure 5E ) . These data combined demonstrate that GKS proteins target LDs in the absence of Irgm1/m3 . Because IRGM proteins can act as positive regulators of autophagy [8] , [10] , [27] , we also considered the possibility that the mislocalization of GKS proteins to LDs in Irgm1/m3−/− cells was a consequence of disrupted autophagy . To test this hypothesis , we examined the subcellular localization of Irgb10 in autophagy-deficient Atg5−/− MEFs . We did not observe an increase in the association of Irgb10 protein with LDs in Atg5−/− MEFs ( Figure 5E and Figure S6 ) , indicating that a defect in autophagy is not the underlying cause for the mislocalization of GKS proteins to LDs in Irgm1/m3−/− MEFs . Next , we asked whether IRGM proteins exclusively guard LDs . We observed that Irgb10 formed “aggregate-like structures” in Irgm1/m3−/− cells that did not identify as LDs ( Figure S5A ) , suggesting that GKS protein could target additional “self” structures in the absence of Irgm1/m3 . In support of this hypothesis , we found that GKS proteins also targeted mitochondria ( Figure S5B ) and peroxisomes ( Figure S5C ) in Irgm1/m3−/− cells . In wildtype cells mitochondria are decorated with Irgm1 ( G . A . T . , manuscript in preparation ) and subsets of peroxisomes stain positive for Irgm3 ( Figure S5D ) . In summary , our data suggest that IRGM proteins guard LDs and other organelles against the stable association with GKS proteins . We demonstrated that endogenous GKS proteins like Irgb10 stably associate with LDs in the absence of IRGM proteins ( Figure 5 ) . Similarly , ectopically expressed Irgb10 frequently targets LDs in Irgm1/m3−/− MEFs ( Figure S7A ) but not in wildtype MEFs ( Figure 6A ) . Two distinct models could explain the differential targeting of Irgb10 and other GKS proteins to IRGM-deficient but not IRGM-positive LDs: in the first model , the presence of IRGM proteins alters the molecular properties of LDs such that LDs do not serve as binding substrates for GKS proteins; in the second model , IRGM proteins directly interact with GKS proteins on the surface of LDs and block lipid binding . Previous studies have shown that IRGM proteins can transiently interact with GKS proteins and thereby retain GKS proteins in the GDP-bound , inactive state [15] , [16] , thus supporting the second model . We therefore predicted that an Irgb10 variant locked in the active , GTP-bound state should be able to overcome IRGM protein mediated restrictions on lipid binding and be able to target IRGM-positive LDs . To generate a GTP-locked Irgb10 mutant , we replaced the lysine residue of the conserved GKS motif with alanine ( Irgb10K81A ) , as homologous mutations in Irga6 or Irgb6 interfere with GTP hydrolysis and force these GTPases into a GTP-locked state [16] . Similar to previous observations demonstrating the targeting of GTP-locked Irga6 to T . gondii vacuoles [16] , we found that Irgb10K81A co-localized with C . trachomatis inclusions ( Figure 6 ) . However , in contrast to Irgb10WT , Irgb10K81A co-localized with Tip47-positive LDs ( Figure 6A ) and Irgm3 ( Figure 6B ) in wildtype MEFs . In contrast to Irgb10WT and Irgb10K81A , a mutant with low affinity binding for GTP ( Irgb10S82N ) failed to associate with either inclusions or LDs ( Figure S7 ) . Overall , these data are consistent with a model , in which IRGM proteins on LDs block the activation of endogenous Irgb10 on the surface of LDs and prevent their stable association of Irgb10 with LDs . GBP proteins constitute a second large family of IFNγ-inducible GTPases known to target PVs and to provide resistance to infections with vacuolar pathogens [3] . Because we previously observed that the subcellular location of the GBP protein Gbp2 is altered in the absence of IRGM proteins [27] , we hypothesized that IRGM proteins could guard self-membranes against the improper deposition of not only GKS but also GBP proteins . Consistent with this , we found that Gbp2 co-localized with LDs in Irgm1/m3−/− but not in wildtype MEFs ( Figure 7A ) and was enriched in LD fractions obtained from Irgm1/m3−/− cells ( Figure 7B ) . Irgm1 and Irgm3 appeared to fulfill partially redundant functions in guarding LDs against Gbp2 targeting , because Gbp2 localization to LDs was more pronounced in Irgm1/m3−/− MEFs than in Irgm1−/− or Irgm3−/− single gene deletion cells ( Figure 7C and Figure S8 ) . Because both Irgm1 and Irgm3 regulate the formation and/or maturation of autophagosomes [8] , [27] , it was formally possible that the mislocalization of Gbp2 to LDs in Irgm1/m3−/− cells resulted from a defect of these cells in autophagy . However , it is unlikely that defective autophagy is the primary cause for mislocalization of Gbp2 to LDs , because LDs inside autophagy-deficient Atg5−/− MEFs remained exempt from Gbp2 targeting ( Figure 7A and B ) . Similar to endogenous Gbp2 , we found that ectopically expressed , N-terminally tagged FLAG-Gbp1 protein was redirected to LDs in the absence of Irgm1 and Irgm3 proteins ( Figure 7D ) . To determine whether activation of Gbp1 was critical for targeting IRGM-deficient LDs , we expressed a FLAG-Gbp1K51A mutant form that has previously been shown to be defective for nucleotide binding and protein oligomerization [28] . In contrast to wildtype FLAG-Gbp1 , we found that FLAG-Gbp1K51A failed to associate with LDs in Irgm1/m3−/− cells ( Figure 7D ) . These data suggest that the active form of Gbp1 associates with IRGM-deficient LDs . Our data demonstrated that Gbp1 and Gbp2 localized to IRGM-deficient LDs . One of the known effector molecules of Gbp1 is the autophagic adaptor protein p62/sequestosome-1 [29] . We therefore hypothesized that Gbp1 proteins residing on IRGM-deficient LDs would be able to recruit p62 to LDs . In support of our hypothesis we found that 4–5% of LDs in IFNγ-treated Irgm1/m3−/− cells stained positive for p62 ( Figure 8A ) . In contrast to Irgm1/m3−/− cells , we were unable to detect p62 on LDs of IFNγ-treated wildtype cells using immunofluorescence microscopy ( Figure 8A ) . A critical function of p62 is to bind to macromolecular cargo that is destined for autophagic destruction [30] . To deliver its cargo to autophagosomes , p62 also binds directly to the ubiquitin-like protein LC3 , a maker of autophagosomes . To determine whether IRGM-deficient LDs are delivered to autophagosomes upon IFNγ activation , we incubated both wildtype and Irgm1/m3−/− MEFs with OA and IFNγ and subsequently stained cells with anti-LC3 and BODIPY . In these experiments , we frequently observed LDs that were engulfed within ring-like LC3-positive structures in Irgm1/m3−/− but not in wildtype MEFs ( Figure 8B ) . Similarly , LDs purified from Irgm1/m3−/− cells were enriched for LC3-II , the lipidated form of LC3 that is associated with autophagosomes ( Figure 8C ) . Collectively , these data strongly suggested that IRGM-deficient LDs were captured inside autophagosomes upon IFNγ activation . To test this model further , we treated cells with the lysosomotropic H+-ATPase inhibitor bafilomcyicn ( BAF ) , a known inhibitor of autophagic flux [31] . We observed a substantial increase in the number of p62-positive LDs in Irgm1/m3−/− MEFs upon combined treatment with IFNγ and BAF ( Figure 8A ) . BAF treatment also resulted in the appearance of p62-positive LDs in IFNγ-treated wildtype MEFs , however , at a frequency significantly lower than what we observed in BAF-treated Irgm1/m3−/− MEFs ( Figure 8A ) . These data indicated that the targeting of p62 to IRGM-deficient LDs resulted in the degradation of LD-bound p62 . Furthermore , our observations excluded an alternative model in which the increase in the number of p62-positve LDs in Irgm1/m3−/− MEFs was due to a defect in autophagosome maturation in these cells . We then asked whether the increased association of p62 and LC3 with IRGM-deficient LDs would affect the total mass of LDs . To quantify LD mass , we used a flow cytometry approach using BODIPY staining , as previously described [26] . We found that IFNγ treatment resulted in an increase in the BODIPY signal in wildtype MEFs , similar to the observations previously made in dendritic cells [26] . In contrast to the increase in the BODIPY signal observed in IFNγ-treated wildtype cells , the BODIPY signal decreased in IFNγ-treated Irgm1/m3−/− MEFs ( Figure 9 and Figure S9 ) , suggesting increased rates of LD degradation in IRGM-deficient cells . To determine whether the decrease in LD mass in Irgm1/m3−/− MEFs was due to autophagy ( = lipophagy ) , we treated cells with BAF . BAF treatment blocked the IFNγ-induced decrease in LD mass in Irgm1/m3−/− MEFs ( Figure 9 ) . In sum , these data strongly support a model in which p62 targets IRGM-deficient but not IRGM-guarded LDs and delivers IRGM-deficient LDs to autophagosomes for degradation . It has previously been reported that targeting of GBP proteins to T . gondii PVs is facilitated by unknown IFNγ-inducible factors [32] , [33] . Because our data had already established functional interactions between GBP and IRGM proteins , we asked whether IRGM proteins could act as IFNγ-inducible co-factors promoting the recruitment of Gbp2 to PVs . We found that ectopically expressed Gbp2-GFP fusion proteins failed to localize to C . trachomatis inclusions in the absence of IFNγ treatment or in IFNγ-activated Irgm1/m3−/− cells ( Figure 10A ) . Similarly , we observed that both Irgm1 and Irgm3 played critical roles in facilitating targeting of endogenous Gbp2 protein to inclusions ( Figure 10B and C ) . Similar to Gbp2 , recruitment of FLAG-Gbp1 to inclusions was also dependent on IRGM proteins ( Figure 10D ) . To determine whether the regulatory role of IRGM proteins extends to the recruitment of GBP proteins to PVs formed by pathogens other than C . trachomatis , we monitored co-localization of both Gbp2 and , as a control , Irgb10 with T . gondii vacuoles in wildtype , Irgm1−/− , Irgm3−/− and Irgm1/m3−/− MEFs . We observed that the deletion of both Irgm1 and Irgm3 caused a near complete defect in the recruitment of Irgb10 ( Figure 10E ) and Gbp2 to T . gondii PVs at 0 . 5 hpi ( Figure 10F ) . These observations demonstrate that the expression of IRGM proteins is critical for the efficient delivery of GBP proteins to vacuoles formed by distinct intracellular pathogens .
The data presented in this study support a model in which Irgm1 and Irgm3 proteins act as “guard molecules” that block GKS and GBP proteins from stably associating with “self” structures ( Figure 11 ) . On PVs , however , guarding Irgm1 and Irgm3 proteins are present at such low levels that GKS and GBP proteins can firmly attach to these unprotected membranes . In support of our model we found that Irgm1 and Irgm3 , but not GKS and GBP proteins , are present in LDs of wild type cells ( Figure 5 and Figure 7 ) . In the absence of Irgm1 and Irgm3 , however , normally GKS-/GBP-deficient LDs become decorated with various GKS and GBP proteins . We provide evidence that GKS and GBP proteins assemble on IRGM-deficient LDs in their GTP-bound , i . e . “active” state ( Figure 7 and Figure S7 ) . According to our model GKS-/GBP-decorated LDs should resemble GKS-/GBP-decorated PVs and would therefore be expected to become targets of GKS-/GBP-solicited immune responses . Consistent with such a scenario , we demonstrate that the Gbp1 effector protein p62 is recruited to IRGM-deficient LDs . The targeting of p62 to LDs in Irgm1/m3−/− MEFs likely accounts for the enhanced association of IRGM-deficient LDs with the autophagic marker LC3-II and the decrease in LD mass upon IFNγ activation that we observed in Irgm1/m3−/− MEFs . Our observations are consistent with a previous report that showed that the number of LDs is significantly reduced in IFNγ-activated Irgm3−/− dendritic cells compared to IFNγ-activated wildtype dendritic cells [26] . Whereas the authors of this previous study speculated that Irgm3 could play a role in the neoformation of LDs triggered upon IFNγ receptor signaling , our data strongly suggest that the decrease in LDs observed in IRGM-deficient cells primarily results from GBP-mediated autophagy of LDs . However , because our experiments were conducted in MEFs , additional studies are needed to determine whether Irgm3 may also play a role in LD neoformation in dendritic or other cell types . While we propose that IRGM proteins guard self-organelles against misdirected attacks by GKS and GBP proteins , our studies do not exclude additional roles for IRGM proteins in organelle homeostasis . For example , human IRGM protein translocates to mitochondria and induces mitochondrial fission [10] . Because mitochondrial fission not only results in the production of radical oxygen species and the induction of antimicrobial autophagy , but also contributes to the isolation and removal of damaged segments of mitochondria , IRGM proteins may indeed regulate the homeostasis of specific organelles like mitochondria . The question now arises as to why the “guarding” Irgm1 and Irgm3 proteins are present on “self” membranes but largely absent from “non-self” PVs . The answer to this question may be quite obvious , if one considers that IRGM proteins can exert antimicrobial activities directly , once localized to PVs [3] . To escape from IRGM-mediated antimicrobial activities like lysosomal targeting , we propose that vacuolar pathogens have evolved strategies to actively avoid co-localization with IRGM proteins . The absence of Irgm1/m3 from PVs would initially allow pathogens to establish a vacuolar niche permissive for microbial replication . However , the evasion of IRGM proteins would simultaneously mark PVs for immune targeting by GKS and GBP proteins . The principle underlying this type of intracellular immune recognition is similar to the extracellular immune recognition process by which NK cells detect transformed and/or virus-infected cells [34] . NK cells express inhibitory receptors on their cell surface . The ligands for one set of inhibitory NK receptors are MHC class I molecules displayed on the surface of host cells . The primary function of the MHC class I molecules is to display viral or tumor antigens to cytotoxic T cells . To avoid immune recognition by cytotoxic T cells , many tumor cells and viruses have evolved mechanisms to downmodulate MHC class I surface expression . However , the failure of MHC class I-deficient cells to provide an inhibitory signal to NK cells , allows NK cells to recognize the missing of “self” MHC class I in transformed or infected cells . In this analogy IRGM proteins resemble MHC class I molecules: just like MHC class I molecules , IRGM proteins fulfill dual functions in that they can promote antimicrobial activities directly and simultaneously act as inhibitory molecules that block the activation of an alternative defense system . How do Irgm1 and Irgm3 proteins guard membranes against GKS and GBP proteins ? Studies performed by Howard and colleagues indicate that IRGM proteins act as Guanine nucleotide Dissociation Inhibitors ( GDI ) for GKS proteins [15] , [16] . Based on these findings , Howard and colleagues proposed that by maintaining GKS GTPases in the GDP-bound , monomeric state , IRGM proteins reduce the lipid binding capacity of GKS proteins and block their stable association with IRGM-coated membranes . Here , we provide direct evidence in support of this model . As originally proposed by Hunn et al . , our data indicate that the absence of IRGM proteins from PVs promotes the transition of GKS proteins into the GTP-bound , active state and their stable association with IRGM-deficient PVs . We show here that IRGM-deficient membranes are also targets for GBP proteins ( Figure 7 ) . Whether IRGM proteins act as GDIs for GBP proteins or block the ability of GBP proteins to associate with LDs by an alternative mechanism will need to be elucidated in future studies . The lipid binding substrates for GKS and GBP proteins are currently unknown . However , lipid components that are present in LDs as well as in T . gondii parasitophorous and Chlamydia inclusion membranes are obvious candidates to act as GKS- and GBP-interacting molecules . It is tempting to speculate that GKS and GBP proteins might have evolved to preferentially bind to lipids that are frequently found in PVs but infrequently found on the cytosolic face of most endomembranes [35] . According to this model , most “self” structures would be protected against the erroneous attack by GKS and GBP proteins for two reasons: 1 ) the presence of guarding IRGM proteins and 2 ) the relative sparsity of lipid binding substrates on the cytosolic leaflet of “self”-membranes . This model would suggest that GKS and GBP proteins tether specifically to PVs due to the missing of “self” IRGM proteins from PVs and the presence of an unknown “second signal” on PVs . As suggested above , a unique pattern of lipids may provide such a second signal , although other molecules may also be involved . Albeit speculative at this point , we propose that LDs feature such a second signal and therefore become primary targets for GKS and GBP proteins in the absence of IRGM guard molecules . The requirement of a second signal for GKS/GBP membrane targeting as proposed in the model outlined above could in part explain why Irgm1/m3−/− mice and cells are viable in spite of lacking two critical “guard” proteins . Alternatively , expression of Irgm2 in Irgm1/m3−/− cells may provide sufficient protection to assure survival of Irgm1/m3−/− cells upon immune activation . This second model would necessitate that Irgm2 like Irgm1/m3 guards “self” membranes against GKS and GBP proteins . In addition to guarding self-structures , expression of IRGM proteins is required for the efficient targeting of endogenous GKS proteins to C . trachomatis inclusions ( Figure 3 ) and T . gondii PVs [16] . However , tetramerized Irgb10-dsRED , when overexpressed , targets PVs efficiently in IRGM-deficient cells ( Figure 2A ) . These data argue against a direct role for IRGM proteins in delivering GKS proteins to PVs . We therefore propose a model in which IRGM proteins fulfill an indirect role in targeting endogenous GKS proteins to PVs: in this model GKS proteins can bind to an excess of unguarded “self” membranes in IRGM-deficient cells . Consequently , the cellular pool of available GKS proteins is diminished in IRGM-deficient cells and the efficiency of PV targeting is reduced . In addition to GKS proteins , GBP proteins also bind to PVs . The delivery of GBP proteins to PVs requires the presence of a previously unknown IFN-inducible cofactor ( s ) [32] , [33] . Here , we identify IRGM proteins as one such co-factor . We propose that IRGM proteins promote the recruitment of GBP proteins to PVs by a mechanism similar to that which regulates the subcellular localization of GKS proteins . GBP proteins bind to lipids as activated oligomers [36] , [37] and GBP mutants deficient in GTP binding fail to localize to PVs [29] , [33] . In this study , we demonstrate that IRGM proteins on LDs prevent GBP recruitment , suggesting that IRGM proteins interfere with the ability of GBP proteins to transition into the GTP-bound , oligomeric state . In support of this hypothesis , we found that Gbp2 forms high molecular weight aggregates in the absence of Irgm1/m3 ( A . S . P . and J . C . , unpublished results ) . Therefore , IRGM proteins may promote GBP recruitment to PVs by maintaining a pool of GDP-bound , monomeric GBP proteins that are able to diffuse to their target sites . Additional evidence for functional interactions between the GBP and IRG protein families comes from the observation that one or more members of GBP protein family associate with Irgb6 in complexes [38] . Deletion of the chromosomal region containing the genes Gbp1-3 , Gbp5 and Gbp7 causes a partial defect in the recruitment of Irgb6 and Irgb10 but not Irga6 to T . gondii PVs [38] , suggesting that physical interactions between specific GBP proteins and Irgb6/b10 promote targeting of Irgb6/b10 to PVs . In contrast to the partial GKS targeting defects of Gbp-deficient cells , Irgm1/m3−/− cells display a nearly complete deficiency in recruiting either Irgb10 ( Figure 10E ) or Gbp2 protein to T . gondii PVs ( Figure 10F ) . The combined results from both studies suggest that Irgm1 and Irgm3 regulate the recruitment of both GKS and GBP proteins to PVs , while one or more PV-targeted GBP proteins augment the recruitment of a subset of GKS proteins through direct physical interactions . In summary our data demonstrate that IRGM proteins orchestrate the proper targeting of antimicrobial GBP and GKS proteins away from “self” membranes and towards “non-self” PVs .
MEFs derived from wildtype , Irgm1−/− , Irgm3−/− and Irgm1/m3−/− mice were previously described [39] , [40] . MEFs and African green monkey kidney Vero cells were cultured in Dulbecco's modified Eagle's medium supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) ( Denville and Life Technologies ) . Primary murine bone marrow macrophages were isolated from the tibia and femurs of 2- to 4-months-old mice as described before [41] . C . trachomatis LGV-L2 was propagated as described [39] . A previously described GFP-expression vector [42] was introduced into LGV-L2 for visualizing C . trachomatis at 2 hpi . GFP-expressing Toxoplasma gondii tachyzoites of the type II strain Prugniaud A7 were a generous gift from Dr . John Boothroyd ( Stanford University , Stanford , CA ) [43] . Infections with C . trachomatis were performed at a nominal multiplicity of infection of 1–5 as described [39] . For T . gondii infections cells were incubated overnight with or without 200 U/ml of IFNγ and asynchronously infected with tachyzoites at a nominal multiplicity of infection of 5–10 for thirty minutes . For lipid loading experiments , OA ( Sigma ) was precomplexed with fatty acid-free BSA ( Sigma ) in PBS and emulsified by sonication . OA was added to growth media at final concentration of 100 µM for immunofluorescence experiments . LDs were isolated from MEFs as described before [44] with minor modifications as outlined here . Cells were grown in 150 mm dishes in DMEM +10% FBS and incubated with OA at 300 µM in the presence or absence of 100 U/mL of IFNγ for 14 h before harvesting LDs . Cells were washed with PBS and collected in 5 ml TNE buffer [20 mMTris-HCl ( pH 7 . 5 ) , 0 . 15 M NaCl , and 1 mM EDTA] containing protease inhibitors ( Roche Diagnostics ) . Cells were lysed on ice with ∼30 strokes/150 mm dish in a Dounce homogenizer and 80 µl of total lysates were collected from each sample and stored at −20°C for Western blotting . Cell lysates were then adjusted to 0 . 45 M sucrose , overlaid with 2 ml each of 0 . 25 M , 0 . 15 M , and 0 M Sucrose/TNE and centrifuged at 30 , 000 rpm for 90 min at 4°C in an SW41 rotor ( Beckman Coulter ) . The floating LD-enriched fat layer was collected , diluted in TNE , and refloated at 47 , 000 rpm for 45 min in a TLA55 rotor ( Beckman Coulter ) . LDs were collected , and lipids were extracted with 4 volumes of diethyl ether . Delipidated proteins were precipitated with ice-cold acetone for 1 h , solubilized in 0 . 1%SDS and 0 . 1 N NaOH , and normalized for total protein content by Bradford assay before SDS-PAGE and immunoblot analysis . Following protein transfer to nitrocellulose membranes , membranes were incubated with antibodies as listed below . Densitometric analyses for protein quantification in Western blots were carried out using Image J 1 . 45 s software . Immunocytochemistry was performed essentially as described previously [39] , [44] . Cells were washed thrice with PBS , pH 7 . 4 prior to fixation . Cells were fixed with 3% formaldehyde and 0 . 025% glutaraldehyde for 20 min at room temperature ( RT ) in all experiments that visualized LDs . T . gondii-infected cells were fixed with 4% paraformaldehyde in PBS , pH 7 . 4 , cells for 20 min at RT . In all experiments involving LD staining , fixed cells were permeabilized/blocked with 0 . 05% ( v/v ) saponin and 2% BSA/PBS ( SBP ) for 30 min at RT . When preserving LD structures was not required , fixed cells were permeabilized in 0 . 1% ( v/v ) Triton X-100 in PBS for ten minutes , blocked for 1 h with 2% ( w/v ) BSA ( Equitech-Bio Inc . ) in PBS , and then stained with various primary antibodies , followed by Alexa Fluor-conjugated secondary antibodies ( Molecular Probes/Invitrogen ) . Working solutions of antibodies and BODIPY 493/503 ( 10 µg/ml ) ( Invitrogen ) for immunofluorescence were prepared in SBP ( for LD visualization ) or in 2% ( w/v ) BSA/PBS ( for all other experiments ) . Nucleic and bacterial DNA were stained with Hoechst 33258 according to the manufacturer's protocol . Mitochondria were visualized using MitoTracker Red CMXRos ( Invitrogen ) according to the manufacturer's instructions . Stained cells were washed with PBS , mounted on microscope slides with FluorSave ( Calbiochem ) or ProLong Gold ( Invitrogen ) , and allowed to cure overnight . Cells were imaged using either a Zeiss LSM 510 inverted confocal microscope or a Zeiss Axioskop 2 upright epifluorescence microscope . Co-localization of proteins with PVs was quantified in at least 3 independent experiments . In each experiment at least ten randomly selected fields were imaged for each condition for each cell type . Differential interference contrast images were used to identify extracellular T . gondii tachyzoites because the vacuoles typically contained only one parasite under the experimental conditions used . The fraction of Gbp2- or Irgb10-positive vacuoles was determined for each field by dividing the number of Gbp2- or Irgb10-labeled vacuoles by the total number of vacuoles . Co-localization with C . trachomatis inclusions was quantified using the identical approach . Co-localization of Irgb10 and Gbp2 with LDs was quantified using MBF ImageJ software ( developed by Wayne Rasband , National Institutes of Health , Bethesda , MD; available at http://rsb . info . nih . gov/ij/index . html ) . Images were pre-processed to correct uneven illumination and to minimize noise and background . The co-localization rates were measured based on Manders' coefficient , which varies from 0 to 1 . A coefficient value of zero corresponds to non-overlapping images while a value of 1 reflects 100% co-localization between the images being analyzed . To perform line tracings , i . e . analyze the fluorescence signal intensity profiles of pixels along a selection from images , we used ImageJ software . Cells were treated with or without IFNγ ( 200 U/ml ) in the absence or presence of OA for 20 to 24 hours . Where indicated , BAF was supplemented at a final concentration of 100 nM at 12 hours post IFNγ activation . LD mass was determined by Flow Cytometry as described elsewhere [26] . Briefly , after fixing the cells with 2% PFA , cells were stained with BODIPY 493/503 at 5 µg/ml in FACS buffer ( PBS , 1% BSA and 0 . 1% NaN3 ) for 30 minutes and washed with FACS buffer prior to analysis . The primary antibodies used included anti-Irgm1 mouse monoclonal antibody 1B2 [11] at 1∶10; anti-Irga6 mouse monoclonal antibody 10D7 [12] at 1∶10; anti-Irgb10 rabbit polyclonal antiserum [39] at 1∶1000; anti-Irgb6 rabbit polyclonal antisera [27] at 1∶1000; anti-Irgm3 rabbit polyclonal antisera [45] at 1∶1000; mouse monoclonal anti-Irgm3 antibody ( BD-Transduction Labs ) at 1∶300; FITC-labeled mouse monoclonal anti-C . trachomatis MOMP [39] at 1∶200; rabbit anti-IncG [46] at 1∶50; anti-V5 mouse monoclonal antibody ( Invitrogen ) at 1∶1000; anti-FLAG mouse monoclonal antibody F1804 ( Sigma ) at 1∶500 , rabbit anti-Pmp70 ( abcam ) at 1∶500; anti-TIP47 polyclonal antisera ( Proteintech ) at 1∶1000; anti-p62/SQSTM1 rabbit polyclonal antibody ( MBL International ) at 1∶500; and anti-LC3 rabbit polyclonal antibody ( MBL International ) at 1∶1000 . An affinity-purified polyclonal rabbit anti-Gbp2 antibody was generated against the peptide EVNGKPVTSDEYLEHS of Gbp2 and used at 1∶1000 . An Irgb10-GFP expression construct has been previously described [39] . Site-directed mutagenesis was performed using the QuikChange II Site-Directed Mutagenesis Kit ( Agilent Technologies Inc . ) to introduce the listed point mutations and deletions into the same construct . Standard cloning techniques were used to generate to insert DNA encoding dsRED and the yeast protein TyA into the listed GFP expression constructs . DNA oligonucleotides used for cloning are listed in Table 1 . A previously described TyA expression construct [21] , a kind gift from Dr . Stephen Gould , was used as a template for DNA amplification . The C57BL/6J-derived cDNAs of Irgm1 , Irgm2 and Irgm3 were cloned into pcDNA3 . 1/V5-His-TOPO ( Invitrogen ) following the manufacturer's instructions . FLAG-tagged and GFP-tagged expression constructs of Gbp1 and Gbp2 and Gbp1 mutant variants have been previously described [33] . MEFs were transduced using the MSCV-based delivery system ( Clontech ) or transfected using Attractene ( Qiagen ) following the manufacturers' instructions . Results are represented as means ± SD . All comparisons were evaluated for statistical significance through the use of unpaired two-tailed t tests . When necessary , significant differences between data points were highlighted and the level of significance was depicted as: * , p<0 . 05; ** , p<0 . 01; and *** , p<0 . 005 . | Cell-autonomous host defense pathways directed against vacuolar pathogens constitute an essential arm of the mammalian innate immune defense system . Underlying most of these defense strategies is the ability of the host cell to recognize foreign or pathogen-modified structures and to deliver antimicrobial molecules specifically to these sites . Specific targeting of molecules to pathogen-containing vacuoles ( PVs ) requires host cells to recognize PVs as “non-self” structures that are distinct from intact “self” structures like organelles and other endomembrane components . In this work , we develop a new framework for understanding a critical principle that guides the mammalian immune system in the recognition of PVs as “non-self” structures . Our data indicates that so-called IRGM proteins function as markers of “self” compartments . We find that IRGM proteins act as “guards” that prevent a set of antimicrobial GTPases from stable association with “self” membranes . Because IRGM proteins are largely absent from “non-self” PVs , we propose that intracellular immune recognition of PVs can occur via the missing of “self” IRGM proteins . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
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"Methods"
] | [
"immunity",
"microbial",
"pathogens",
"immunology",
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] | 2013 | IRG and GBP Host Resistance Factors Target Aberrant, “Non-self” Vacuoles Characterized by the Missing of “Self” IRGM Proteins |
The TGF-β/Smad signaling system decreases its activity through strong negative regulation . Several molecular mechanisms of negative regulation have been published , but the relative impact of each mechanism on the overall system is unknown . In this work , we used computational and experimental methods to assess multiple negative regulatory effects on Smad signaling in HaCaT cells . Previously reported negative regulatory effects were classified by time-scale: degradation of phosphorylated R-Smad and I-Smad-induced receptor degradation were slow-mode effects , and dephosphorylation of R-Smad was a fast-mode effect . We modeled combinations of these effects , but found no combination capable of explaining the observed dynamics of TGF-β/Smad signaling . We then proposed a negative feedback loop with upregulation of the phosphatase PPM1A . The resulting model was able to explain the dynamics of Smad signaling , under both short and long exposures to TGF-β . Consistent with this model , immuno-blots showed PPM1A levels to be significantly increased within 30 min after TGF-β stimulation . Lastly , our model was able to resolve an apparent contradiction in the published literature , concerning the dynamics of phosphorylated R-Smad degradation . We conclude that the dynamics of Smad negative regulation cannot be explained by the negative regulatory effects that had previously been modeled , and we provide evidence for a new negative feedback loop through PPM1A upregulation . This work shows that tight coupling of computational and experiments approaches can yield improved understanding of complex pathways .
Transforming Growth Factor-β ( TGF-β ) , a regulator of cell migration and cell fate , is a pharmaceutical target for the treatment of metastatic cancer and fibrotic diseases [1] . Signal transduction from extracellular TGF-β to the cell nucleus through the Smad pathway is well documented [2]–[7] . The TGF-β ligand binds sequentially to the type II TGF-β receptor , a constitutively active kinase , and then to the type I receptor , to form a ligand-receptor complex ( LRC ) . The type I receptor is activated by the type II receptor and then phosphorylates the R-Smads ( Smad2 and Smad3 ) at two C-terminal serine residues . Upon phosphorylation , R-Smads form a homomeric complex or a heteromeric complex with Co-Smad ( Smad4 ) . The key outcome of the Smad cascade is the accumulation of phosphorylated R-Smad ( phospho-R-Smad ) in the nucleus , affecting the transcriptional regulation of many genes[7] , [8] . Smad signaling is known to decrease quickly after TGF-β stimulation , causing rapid decline of phospho-R-Smad after its initial peak . The HaCaT cell line has been adopted as an experimental model system for quantifying the detailed signaling of the TGF-β/Smad system . In HaCaT cells , there is a rapid decline of phospho-R-Smad after short exposure to TGF-β stimulation ( 30–45 min ) [9] , [10] , and a gradual decline after long exposure to TGF-β ( 6–24 hr ) [9] , [11] . The duration of phospho-R-Smad activation could be crucial for regulation of different genes [12] . The self-limiting behavior of Smad signalling ( i . e . , negative regulation ) may be caused by ligand-induced receptor inhibition [13]–[19] , phospho-R-Smad dephosphorylation [9] , phospho-R-Smad degradation [11] , [20]–[23] , or other effects . Extensive experimental evidence has documented multiple modes of negative regulation , but the relative roles and combined effects are not well understood . Previous computational models of TGF-β/Smad signaling have contributed important biological insights , but they have only simulated some selected negative regulatory effects . Vilar et al . built a model of TGF-β receptor trafficking dynamics , including ligand-induced receptor degradation , which was able to simulate some key dynamic observations such as the peak and decline of phospho-R-Smad levels [24] . Models by Klipp and co-workers extended the work of Vilar et al . to include Smad phosphorylation and nuclear translocation [25] , and to include transient versus sustained Smad signaling [12] . These models used simple representations for negative regulation , and gave a strong role to receptor degradation . The model by Schmierer et al . provided important insights into the Smad nucleo-cytoplasmic shuttling [10] , but the only negative regulatory effect in this model was dephosphorylation . Other modeling studies have focused on robustness and in silico perturbation analysis [26] , [27] . Mathematical models have yielded important insights , but they have not represented TGF-β/Smad negative regulation with enough detail for analyzing the contributions of different negative regulatory effects , nor for evaluating alternative hypotheses . In this work , we developed a series of computational models , representing individual and combination effects of R-Smad negative regulation . Comparisons between models and observations revealed negative regulation to occur at more than one time-scale . We classified negative regulatory effects into fast-mode ( 5–240 min ) and slow-mode ( 1–24 hr ) , depending on how quickly they act ( and how quickly they equilibrate to steady state ) . Models then showed that at least one fast-mode and one slow-mode effect would be required for a model to fit the phospho-R-Smad dynamics in both short-exposure and long-exposure experiments . R-Smad Dephosphorylation was a fast-mode effect and it was strong enough to explain the fast-mode observations . Receptor Degradation and P-Smad Degradation were slow-mode effects , but they were too weak to explain the observed slow-mode decline . With a shortfall in explaining why R-Smad continues to decline hours after TGF-β stimulation , we sought a novel slow-mode effect . A second key finding of this work is a novel negative feedback effect , confirmed experimentally , in which the phosphatase PPM1A is upregulated after TGF-β stimulation . A final model hypothesizes how PPM1A might be upregulated with delayed activity , based on previously published molecular mechanisms for regulating PPM1A degradation [28] , [29] . Another final contribution we provide is an explanation for a previous controversy about proteasomal degradation of phospho-R-Smad [9] , [11] , [20] , [21] . Previous experiments inhibiting proteasomal degradation showed either strong effects [11] , [20] , [21] or no effects [9] on phospho-R-Smad levels . These seemingly contradictory trends were both mathematically consistent with our model , and the disparity could be explained by different durations of TGF-β exposure .
Smad signaling is enormously complex , as proven by a vast literature of previous work . We first considered negative regulatory effects from the published literature , for the purpose of selecting a set of effects relevant to our studies . R-Smad dynamics depend on the duration of TGF-β stimulation . When TGF-β is administered in excess ( 2 ng/ml ) [44] , [45] for 24 hrs , phospho-R-Smad peaks at about 1 hr and then decays for 24 hrs [9] . When TGF-β is administered for 30 min and then removed ( by washing following by receptor inhibition with the compound SB-431542 ) , phospho-R-Smad is eliminated within 4 hrs [9] , [10] . Our first modeling studied the kinetics of the three negative regulatory effects selected from the literature review . We were curious whether they would have different kinetic implications for the system . Both short-exposure and long-exposure TGF-β treatment datasets ( Figure 2 ) were utilized when building the models of negative regulation ( Table 1 , Table S2 ) . The models were simulated to obtain the dynamics of their effects and to estimate their potential contributions to the down-regulation of phospho-R-Smad ( 0 . 5-24 hr ) . Model 1 , with R-Smad Dephosphorylation , was able to recapitulate the short-exposure TGF-β treatment experiment , as dephosphorylation is a fast process . This dephosphorylation model could turn off the signal once the stimulus was cut off ( Figure 2A red curve ) , but it reached a steady state at about 1 hr and was not able to recapitulate the extended 24 hr decline of phospho-R-Smad in long-exposure TGF-β treatment ( Figure 2A blue curve ) . Thus we describe R-Smad Dephosphorylation as a “fast-mode” effect . To explain the prolonged decline during long-exposure experiments , a complementary “slow-mode” might be provided by cumulative processes such as degradation . Model 2 combines Receptor Degradation and R-Smad Dephosphorylation . It succeeded in recapitulating the short-exposure TGF-β treatment very well , and it had moderately good agreement with the long-exposure dataset ( Figure 2B ) . As a control , we modeled Receptor Degradation alone ( Model 3 ) , but it could not provide an early decline in the short-exposure experiment ( Figure 2C ) . Thus , Receptor Degradation serves as a slow-mode effect as it was able to explain the gradual and protracted decline of phospho-R-Smad in the long-exposure experiment but not the steep decline of phospho-R-Smad in the short-exposure experiment . Another cumulative process of decline is P-Smad Degradation . A model with P-Smad Degradation alone ( Model 4 ) achieved significant negative regulation for the long-exposure case ( Figure 2D ) , because P-Smad Degradation would persist for many hours . However , Model 4 had difficulty explaining both the short-exposure and long-exposure datasets simultaneously . If P-Smad Degradation is strong , it could recapitulate the steep decline after short-exposures , and if it is weak effect , it could recapitulate the gradual decline after long-exposures . Since it cannot be both strong and weak , it cannot explain both behaviors . Note that previous experimental evidence showed that P-Smad Degradation is not responsible for fast-mode effects in short-exposure conditions [9] . Having simulated each of the three negative regulatory effects in isolation , we could conclude that no single negative regulatory effect was able to explain phospho-R-Smad dynamics . We infer that the experimentally observed levels of phospho-R-Smad arise from a combination of fast-mode and slow-mode effects ( or from higher-order combinations of effects ) . Many models have omitted P-Smad Degradation from simulations [10] , [12] , [24] , [25] , perhaps because this effect was found to be insignificant in the experiments of Lin et al . [9] . Noting that the Lin experiments used short-exposure conditions , we asked whether P-Smad Degradation , a slow-mode effect , might have greater significance during the negative regulation induced by long-exposure treatments . Model 5 incorporated R-Smad Dephosphorylation , Receptor Degradation , and P-Smad Degradation ( Figure 2E ) . P-Smad Degradation was significant in this model ( Figure 2F-G ) when its impact was measured after more than 1 hr of TGF-β treatment . We also fitted a variety of models to the short-exposure and long-exposure experiments . The cumulative difference in phospho-R-Smad between +MG132 and -MG132 was minor in the short-exposure experiment and significant in the long-exposure experiment ( Figure 2H ) . As yet , we have no basis for knowing which type of slow-mode degradation would be most important in R-Smad signaling . We next tried to assess the relative impact of two slow-mode effects , Receptor Degradation and P-Smad Degradation , on the dynamics of phospho-R-Smad in long-exposure TGF-β treatment . The rate constant for Receptor Degradation and the rate constant for P-Smad Degradation were varied in silico ( Figure 3A ) , showing that many ratios were equally good at fitting the observed dynamics . Several of the successful models exhibited a strong decline in T1R , the type I receptor ( Figure 3B ) . Moreover , the degree of T1R decline was correlated with the rate of Receptor Degradation and the rate of P-Smad Degradation ( Figure 3C ) . Thus , to quantify the relative contribution of Receptor Degradation and P-Smad Degradation in HaCaT cells , we measured T1R experimentally at 9 time points ( from 15 min to 24 hr ) after TGF-β stimulation ( with n = 3 replications and significance determined by Student's t-test ) . Surprisingly , there was no significant loss of T1R ( type-I receptor ) observed in experiments ( Figure 3D–E ) , even at late time points . ( As positive control , phospho-R-Smad time series concentrations were measured in Figure 4C ) . Previous work has already shown that T2R ( type II receptor ) shows no decrease after 2 ng/ml of TGF-β treatment in HaCaT cells [46] . Unchanged receptor levels indicate that Receptor Degradation is very weak in HaCaT cells . A weak role for Receptor Degradation has also been suggested by the experimental work of Clarke et al . [47] . Other forms of receptor inactivation or sequestration may occur without changing the total T1R concentration , but there is less published evidence for these possibilities ( modeling analysis rejected these possibilities as well , in Text S2 ) . Note that the set of models ( Figure 3A ) capable of explaining the dynamics of phospho-R-Smad decline all exhibited a negative correlation between the degree of Receptor Degradation and the degree of P-Smad Degradation ( Figure 3F ) , suggesting that these two effects would be balanced alternatives . In light of our experimental finding that Receptor Degradation is a very weak effect , we next turned to P-Smad Degradation as the alternative slow-mode effect to explain the long-term decline of phospho-R-Smad . A model with R-Smad Dephosphorylation and P-Smad Degradation ( Model 6 , without Receptor Degradation ) provided an excellent fit to both the long-exposure and short-exposure treatment data ( Figure 4A ) . However , an unavoidable consequence of this model was dramatic decline of total R-Smad ( Figure 4B ) . Previous experiments in HaCaT cells failed to observe a large fold-change of total R-Smad [9] but the amount of decline was not quantified . To clarify this potential conflict , we repeated the experimental measurement of total R-Smad levels after TGF-β treatment , using ELISA assays , a more quantitative method . Measurements of total R-Smad at 7 time points during 24 hrs of TGF-β treatment showed no significant decrease of total R-Smad ( Figure 4B–C ) . There is an apparent conflict between the constant level of total R-Smad ( observed experimentally ) and the significant degradation of R-Smad induced by TGF-β ( according to Model 6 ) . Degradation might be more difficult to rule out if we consider TGF-β-stimulated degradation in combination with Endogenous Synthesis and Degradation of R-Smad . If endogenous R-Smad is synthesized in an unphosphorylated form , and targeted by Smurf2 for degradation only in its phosphorylated form , then can P-Smad Degradation explain the decline of phospho-R-Smad despite the constant levels of total R-Smad ? We therefore expanded the model to include Endogenous Synthesis and Degradation of R-Smad ( Model 7 ) . However , Model 7 diverged strongly from the observed dynamics of phospho-R-Smad , when constrained to maintain a constant level of total R-Smad . To summarize these results , P-Smad Degradation can only affect the shape of the phospho-R-Smad curve if it is not balanced by synthesis , in which case it would cause an unrealistic decline in the total Smad levels . If P-Smad Degradation is balanced by Smad synthesis , then it can only affect the height but not the shape of the phospho-R-Smad curve . Therefore we can rule out strong P-Smad Degradation ( not balanced by synthesis ) as an explanation for the later decline in the phospho-R-Smad curve shape . We cannot rule out the presence of significant P-Smad Degradation accompanied by Smad synthesis . Hence , our model-driven experimental tests , sensitivity analysis ( Text S3 ) , and modeling analysis showed that P-Smad Degradation and Receptor Degradation were not sufficient to explain the 1–24 hr decline in phospho-R-Smad dynamics . We next sought some other negative regulatory effect that could help explain the peak and decline of phospho-R-Smad after a long exposure to TGF-β . After excluding the three well-accepted effects of Smad negative regulation , we then examined possible alternative influences at different steps along the Smad pathway , seeking quantitative consistency with the observed peak and decline of phospho-R-Smad . One scenario that could not be rejected on kinetic grounds was upregulation of PPM1A , the phosphatase targeting phospho-R-Smad . If PPM1A were to be upregulated by TGF-β signaling , this could help explain the decline of phospho-R-Smad after long exposure to TGF-β ( Text S4 ) . To test this possibility , we performed Western blots of the PPM1A protein after TGF-β treatment . HaCaT cells were treated with 2 ng/ml of TGF-β and measured after 0 . 25 , 0 . 5 , 1 , 2 , 4 , or 8 hr . We found that the intensity of the PPM1A western blot band increased 2 . 4-fold after 1 hour of TGF-β treatment ( p<0 . 05 , Figure 5A-B ) . To the best of our knowledge , this is the first study to report that TGF-β causes upregulation of the PPM1A phosphatase . The increased abundance of PPM1A after TGF-β stimulation could be due to some type of decreased degradation and/or increased production . To aid future studies in investigating how the upregulation occurs , we have constructed a hypothetical mechanism , PPM1A Stabilization , in which we speculate that PTEN may be involved . Model 8 includes PPM1A Stabilization plus all the mechanisms of Model 7 ( R-Smad Dephosphorylation , P-Smad Degradation , and Endogenous Synthesis and Degradation of R-Smad ) . Text S5 provides a full specification of Model 8 . In previous studies , Bu et al . found that PTEN can bind to PPM1A and protect it from degradation [28] . These studies of PPM1A stability occurred in fibroblasts , where TGF-β caused dissociation of PTEN and PPM1A , leading to downregulation , not upregulation of PPM1A . In other words , they found PTEN to be a negative regulator of Smad signaling , but in their fibroblasts , TGF-β decreased this negative effect causing self-perpetuation ( positive feedback ) rather than self-limitation ( negative feedback ) of the Smad signal . The binding of PTEN in response to TGF-β is known to differ between fibroblasts and HaCaT keratinocytes . Hjelmeland et al . found that in HaCaT cells , TGF-β stimulation caused formation of a PTEN-Smad complex [29] , not dissociation of the PTEN complex [28] . They did not measure participation of PPM1A in that complex , but based on our analysis of the trends from [28] and [29] , we propose a scaffolding role for phospho-R-Smad to promote association between PTEN and PPM1A in HaCaT cells . In other words , Model 8 speculates that TGF-β stimulation would induce PTEN association to stabilize PPM1A . This implies that there is some new or unknown mechanism upstream of PTEN , to explain why TGF-β signaling would promote PTEN-PPM1A association in one cell type and dissociation in another cell type . Model 8 assumes that PTEN and PPM1A would have a low on-rate for binding each other in HaCaT cells without phospho-R-Smad , but they would readily form a ternary complex in the presence of phospho-R-Smad . Thus , PPM1A would not be strongly stabilized in unstimulated HaCaT cells . After TGF-β stimulation , the phospho-R-Smad mediated association between PTEN and PPM1A would protect PPM1A from degradation and create negative feedback in the system . Note that Model 8 does not imply any alteration of total PTEN protein levels , merely the recruitment of PTEN by phospho-R-Smad into complexes with PPM1A . Simulations of Model 8 in Supplementary Figure S9 confirm that total PTEN levels could in theory remain constant ( as observed in [29] ) while levels of the PTEN-PPM1A complex could change over time . Simulations of Model 8 were consistent with all the observed dynamics for the impact of TGF-β on HaCaT cells . This model was sufficient to explain the complete dynamics of phospho-R-Smad after short or long exposures to TGF-β ( Figure 5C ) , the dynamics of PPM1A ( Figure 5B ) , and the unchanged levels of T1R and total R-Smad ( Figure 5D-E ) . With the key experimental trends satisfied , we next tested Model 8 against another dataset , obtained from combination treatment with TGF-β and a chemical inhibitor MG132 . Previous studies assessed P-Smad Degradation using MG132 to inhibit proteasomal degradation , but with conflicting conclusions: Massague et al . saw a strong impact , implying an important role for degradation [11] , [21] , while Lin et al . found negligible impact from MG132 [9] . Both protocols measured the long-term dynamics of phosphorylated Smad2 , but the Lin protocol triggered phosphorylated Smad2 using a 30 min exposure to TGF-β , while the Massague protocol used a 6 h exposure . Simulations of Model 8 with MG132 inhibition of proteasomal degradation show that MG132 would have minimal impact on Smad signaling , when triggered by short exposure to TGF-β ( Figure 5F ) . In surprising contrast , MG132 would have a strong impact on Smad signaling , when phospho-R-Smad is triggered by longer exposures to TGF-β ( Figure 5G ) . Figure 5H compares the P-Smad2 Change calculated from Figure 5F ( red curve ) and Figure 5G ( blue curve ) with experimental data from Lin et al . ( Figure 1C in [9] ) ( red dots ) and Alarcon et al . ( Figure 2G in [21] ) ( blue dots ) . The P-Smad2 Change was calculated as Eq . 1 . ( Eq . 1 ) Model 8 shows , mathematically , that the Lin observations and the Massague observations can be generated from the same system . Model 8 contains hypothetical mechanisms ( e . g . , PPM1A Stabilization ) and imperfect parameters ( e . g . , reaction rate constants ) , but it suffices to prove that the seeming conflict between Lin et al . and Massague et al . is not necessarily a contradiction . In summary , the combination of several negative regulatory effects was consistent with , and sufficient to explain , the observed nuances of negative regulation and degradation in the Smad signaling system .
Several negative regulatory effects in the Smad signaling pathway have been identified and individually studied [9] , [11] , [13] , [14] , [17] , [21] , [32] , [48] . We focused our modeling and experiments on these specific effects with published evidence . R-Smad Dephosphorylation by PPM1A is widely recognized to be a strong form of negative regulation , having significant fast-mode impact . However , the known slow-mode effects could only recapitulate phospho-R-Smad dynamics at the expense of very strong , cumulative degradation; as much as 90% decrease of T1R at 24 hr ( Figure 3B ) , or 90% decrease in total R-Smad at 24 hr ( Figure 4B ) . Our experimental measurements in HaCaT found that total T1R protein levels did not decline significantly ( Figure 3B , 3D ) , nor did total R-Smad ( Figure 4B ) . This contrasts with previous work in 293T and COS-1 cells [17] , [19] . In [17] , 293T cells were transfected with I-Smad which was able to induce significant receptor degradation . The significant degradation seen in [17] may be due to transfection [47] or may be due to cell line differences . Although most dynamic models of signal transduction represent an amalgam of findings from multiple cell lines , our model ( and the previous models we rely on ) are specific to the HaCaT cell line . Thus a discrepancy with [17] is not necessarily a flaw of our model . In light of our experimental measurement that TGF-β treatment does not cause any significant drop in total R-Smad levels , and the evidence showing no significant decline in type I or type II receptor levels , we conclude that degradation effects , if they occur in HaCaT , must be counterbalanced by endogenous synthesis . Model 7 simulated a balance of synthesis and degradation ( Endogenous Synthesis and Degradation of R-Smad ) such that phospho-R-Smad was degraded while unphosphorylated Smad was synthesized; this model was not able to induce the observed decline of phospho-R-Smad in long-exposure experiments . The first key contribution of our work was to conclude that degradation of R-Smad or T1R , with or without endogenous synthesis , is not sufficient to explain the slow-mode of Smad negative regulation in HaCaT cells . Degradation with synthesis remains a plausible effect of negative regulation , but it must occur alongside other effects . Figure 6 shows the relative contributions of different negative regulatory effects in our final model ( Model 8 ) : R-Smad Dephosphorylation was crucial for maintaining a limited level of phospho-R-Smad ( compare red versus yellow curves ) ; PPM1A Stabilization was capable of explaining the decline after the peak of phospho-R-Smad ( compare yellow versus green curves ) ; and P-Smad Degradation could further adjust the absolute level of phospho-R-Smad ( compare green versus blue curves ) . The second key contribution of our work was the discovery of a novel feedback loop in which the PPM1A protein is significantly upregulated after TGF-β treatment . Feedback loops have crucial importance in dynamical systems because they create nonlinear responses and permit self-regulation ( by converting a directed subgraph into a connected subgraph ) . In our study , the new feedback loop via PPM1A was significant enough to allow the model to finally explain the observed trends of phospho-R-Smad decline after TGF-β treatment ( Figure 6 , yellow versus green curves ) . Because PTEN is known to stabilize PPM1A against degradation [28] , we built a model to illustrate hypothetical dynamics of PTEN-induced PPM1A sequestration , including delayed enzymatic activity for PPM1A . Note that in previous experiments , the influence of PTEN served as a positive feedback loop ( PTEN-induced stabilization was inhibited by TGF-β [28] ) , not negative feedback . HaCaT cells are an accepted model system for understanding how epithelial cells respond to TGF-β; and it will be interesting for future work to test which cell types utilize PPM1A regulation for negative feedback . Model 8 shows that PPM1A Stabilization , with delayed nuclear import , was sufficient to reconcile the early upregulation of PPM1A total protein with later decline of phospho-R-Smad . Our theoretical model could be useful for the design of experiments to determine how the upregulation actually occurs . Future work should test whether PTEN stabilizes and/or sequesters PPM1A in HaCaT after TGF-β treatment , as illustrated in Model 8 . Our model would recommend testing for PPM1A-PTEN binding at 30 min–1 hr to catch the peak interaction , but testing for increased PPM1A activity at 4 hr , significantly later than the upregulation . Careful examination of a broader set of previous work reveals some issues that appear to be discrepancies . The steepness of phospho-R-Smad decline in HaCaT appears to differ slightly between the experiments of Massague and colleagues in [11] , [21] versus the experiments of Lin et al . [9] , which are similar to our results ( Figure 4C ) and similar to the results of [12] . One possible explanation is a difference in the effective concentrations of TGF-β . TGF-β has a very short half-life , and the dissolving conditions , such as carrier protein concentration , can alter the effective concentration of TGF-β . Previous authors did not report how their TGF-β was dissolved , but we found that dissolving TGF-β without carrier protein led to a steeper decline of phospho-R-Smad , similar to Massague et al . [11] , [21] ( data not shown ) . We believe this slight discrepancy in slope is a technicality of the experiments and not fundamental to the pathway analysis . Recent work has shown the importance of TGF-β depletion as a determinant of Smad signaling kinetics , for cells treated with low doses of TGF-β ( 10pM and 25pM ) [47] . Our work did not emphasize low-dose contexts , but our models are consistent with observed TGF-β depletion behaviors . Figure S6 ( Text S6 ) shows simulations of our final model , Model 8 except with lower doses of TGF-β treatment . Smad signaling was indeed dominated by TGF-β scarcity . When the Smad system was externally limited by TGF-β availability , self-limiting mechanisms and negative regulatory effects were not apparent . Negative self-regulation of the Smad system was strongly apparent in treatments with 2 ng/ml ( 80pM ) of TGF-β , which is the dose studied in most previous experimental and computational studies of Smad dynamics . After successfully predicting PPM1A upregulation and achieving recapitulation of the available datasets , our final contribution was to address an existing controversy about the role of proteasomal degradation in Smad signaling . We discovered that an apparent conflict about the role of degradation was in fact a mutually consistent set of trajectories that can both emerge from a single model . Degradation is intuitively understood to be a cumulative effect seen in long-term observations , but in this case the duration of observation was irrelevant , and the crucial variable for degradation was the duration of the TGF-β stimulus . MG132 ( an inhibitor of proteasomal degradation ) caused negligible change in pSmad2 levels ( at 1 , 2 , 4 , 6 hr ) , in a system triggered with 30 min exposure to TGF-β , but MG132 caused a significant change in pSmad2 levels ( at 1 , 2 , 4 , 6 hr ) , in a system triggered with long exposure to TGF-β . In other words , the importance of degradation in Smad signaling depended not on the time point at which pSmad2 was measured , but rather on the duration with which the Smad system had been induced . The consistency between the two experiments can be rationalized in retrospect because degradation depends on the area under the curve , which is large in systems with prolonged stimulus , and very small in systems with short stimulus . However , the consistency between Lin et al . and Massague et al . was not apparent prior to modeling , and mathematical inference of kinetic implications is dramatically different from the interpretations provided by the previous authors . Computational modeling of any biochemical pathway involves several caveats and approximations , particularly when the system is as complex as Smad signaling . For our modeling of the Smad system , many interaction partners and post-translational modifications have been neglected , and some highly complex processes have been described as two-species reactions with simple mass action kinetics . Few of the rate constants have been determined from direct experiments and therefore , many parameters have been estimated by optimizing the fit between the model and the available datasets . Despite these limitations , we believe mathematical modeling provides valuable insights . Our modeling provides a consistent , quantitative , and fine-grained integration of available information about the negative regulation of phospho-R-Smad , both from published literature and from our experiments . Our combination of modeling and experiments showed that previous negative regulatory effects such as Receptor Degradation have a minor effect , and led us to introduce a negative feedback loop with upregulation of PPM1A . Modeling can make additional predictions ( e . g . , future experiments should test for peak perturbation of PPM1A binding and activity ) . Also , modeling has provided a new and non-obvious interpretation for the effects of MG132 treatment . When interpreting the biological meaning of observed kinetics , informal intuition can unwittingly lead to flawed conclusions . Our modeling of Smad signaling may in the future be useful to other researchers interpreting data , designing experiments , or strategizing therapeutic perturbations . | TGF-β signaling pathway regulates a variety of cellular responses , such as differentiation , migration and apoptosis . Phosphorylated R-Smad , the central signaling protein in this pathway , exhibits self-limiting behaviors: it not only decreases quickly after TGF-β is removed , but it also decreases slowly when TGF-β remains abundant . These two self-limiting behaviors are important to understand clearly because diseases such as cancer and fibrosis might benefit from treatments to decrease Smad signaling . Several negative regulatory effects have been reported previously , and we studied the dynamics of these effects with computational modeling . Analyzing the timing of negative regulation revealed that the three most widely accepted effects were not sufficient to explain the observed declines . After considering and excluding several alternative models , we arrived at a model in which TGF-β upregulated the phosphatase PPM1A . We tested for PPM1A upregulation in cell culture experiments . In addition , our model was able to explain why different durations of TGF-β exposure could cause seemingly opposite results about the importance of Smad degradation . | [
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] | 2014 | The Self-Limiting Dynamics of TGF-β Signaling In Silico and In Vitro, with Negative Feedback through PPM1A Upregulation |
Phenotypic plasticity is the ability of a given genotype to produce different phenotypes in response to distinct environmental conditions . Phenotypic plasticity can be adaptive . Furthermore , it is thought to facilitate evolution . Although phenotypic plasticity is a widespread phenomenon , its molecular mechanisms are only beginning to be unravelled . Environmental conditions can affect gene expression through modification of chromatin structure , mainly via histone modifications , nucleosome remodelling or DNA methylation , suggesting that phenotypic plasticity might partly be due to chromatin plasticity . As a model of phenotypic plasticity , we study abdominal pigmentation of Drosophila melanogaster females , which is temperature sensitive . Abdominal pigmentation is indeed darker in females grown at 18°C than at 29°C . This phenomenon is thought to be adaptive as the dark pigmentation produced at lower temperature increases body temperature . We show here that temperature modulates the expression of tan ( t ) , a pigmentation gene involved in melanin production . t is expressed 7 times more at 18°C than at 29°C in female abdominal epidermis . Genetic experiments show that modulation of t expression by temperature is essential for female abdominal pigmentation plasticity . Temperature modulates the activity of an enhancer of t without modifying compaction of its chromatin or level of the active histone mark H3K27ac . By contrast , the active mark H3K4me3 on the t promoter is strongly modulated by temperature . The H3K4 methyl-transferase involved in this process is likely Trithorax , as we show that it regulates t expression and the H3K4me3 level on the t promoter and also participates in female pigmentation and its plasticity . Interestingly , t was previously shown to be involved in inter-individual variation of female abdominal pigmentation in Drosophila melanogaster , and in abdominal pigmentation divergence between Drosophila species . Sensitivity of t expression to environmental conditions might therefore give more substrate for selection , explaining why this gene has frequently been involved in evolution of pigmentation .
Phenotypic plasticity , “the property of a given genotype to produce different phenotypes in response to distinct environmental conditions” [1] , is a widespread phenomenon . Phenotypic plasticity can be adaptive if different but optimal phenotypes are produced by a given genotype in distinct environments [2] . Furthermore , phenotypic plasticity could facilitate evolution [3–6] . In particular , Conrad Waddington showed that changes in environmental conditions can reveal cryptic genetic variation that can be selected , allowing to fix a phenotype initially observed only in particular environmental conditions [7 , 8] . Waddington called this process “genetic assimilation” . Analysis of phenotypic plasticity and morphological complexity in an evolutionary framework supports indeed the idea that phenotypic plasticity increases evolutionary potential . For example , a recent study on feeding structure evolution in nematods revealed that phenotypic plasticity correlates with morphological diversification [9] . The question then arises whether the same genes are involved in phenotypic plasticity and in phenotypic variation within and between species . To address this question , the molecular mechanisms underlying phenotypic plasticity need to be identified . Several examples show that environmental factors can strongly affect the transcriptome [10] through modification of chromatin structure by DNA methylation [11] , histone mark apposition [12] or nucleosome remodelling [13] . In Drosophila melanogaster , female abdominal pigmentation is a plastic trait as it is darker in females grown at 18°C than at 29°C [14] . As low temperature leads to darker pigmentation , which increases body temperature , the thermal plasticity of female abdominal pigmentation is thought to be adaptive [14] . Abdominal pigmentation in drosophilids is a particularly appropriate model to study phenotypic plasticity , as the genes involved in abdominal pigmentation are well known . Indeed , abdominal pigmentation has been used as a model to dissect the genetic bases of sexual dimorphism and of variation within or between species [15–23] . In none of these studies , which focussed on genetic factors and were performed in standard conditions ( usually at 25°C ) , was the effect of the environment taken into account . However , Drosophila melanogaster can develop between 12°C and 30°C [24] . As temperature varies spatially and temporally in the wild , taking it into account is paramount to understand the development and evolution of abdominal pigmentation . Using mainly genetics approaches , we previously showed that temperature acts on melanin production by modulating a chromatin regulator network , but we did not further dissect the underlying molecular mechanisms [25] . Here , we identify the pigmentation gene tan ( t ) as the major structural gene involved in female abdominal pigmentation plasticity and we show that chromatin structure at this locus is modulated by temperature . Temperature dramatically modulates t expression in the female abdominal epidermis and this modulation plays a major role in female abdominal pigmentation plasticity . Temperature modulates the activity of an enhancer of t , t_MSE [17] , but had no detectable effect on its chromatin structure . By contrast , the active histone mark H3K4me3 is strongly enriched on the t promoter at low temperature . The H3K4 methyl-transferase responsible for this effect is likely Trithorax ( Trx ) . Indeed , we show that Trx regulates t expression and the level of H3K4me3 on the t promoter , and is involved in abdominal pigmentation as well as in its plasticity . As t has been linked to pigmentation divergence within or between Drosophila species [17 , 19 , 20 , 26] , t is listed among hotspot loci of evolution [27] . Our study therefore suggests that the sensitivity of particular genes to environmental changes could turn them into evolutionary hotspots by giving more substrate for selection .
To focus on the effect of temperature , we quantified abdominal pigmentation in females from an inbred w1118 line , the wild-type stock commonly used in our laboratory for molecular experiments ( Fig 1 ) . As previously described for other D . melanogaster lines [14] , flies raised at 18°C were darker than flies raised at 25°C or 29°C ( Fig 1A ) . Female pigmentation plasticity was observed in the whole abdomen but was particularly pronounced in posterior abdominal segments A5 , A6 and A7 ( Fig 1B , A5: p<0 . 001; A6: p = 0 . 001; A7: p<0 . 001 ) . Furthermore , statistical analyses revealed that temperature accounted for most of the variation of pigmentation ( Eta-squared , A5: 0 . 91; A6: 0 . 93; A7: 0 . 95 ) . Cuticle pigmentation is a complex trait that involves the coordinated expression of many pigmentation enzyme coding genes , expressed from the second half of pupal life to the beginning of adulthood depending on the gene [28 , 29] ( Fig 2A ) . To test whether the expression of these genes was modulated by temperature , we performed RT-qPCR experiments on epidermes of A5 , A6 and A7 segments from w1118 females grown at 18°C or 29°C and collected at late pupal stage ( pharates , Fig 2B left ) , or within two hours after eclosion , i . e . when cuticle tanning occurs ( young adults , Fig 2B right ) . In pharates , the expression of tan ( t ) , ebony ( e ) , Dopa Decarboxylase ( DDC ) , yellow ( y ) and black ( b ) was moderately modulated by temperature ( less than 2 times ) . In young adults , among all genes tested , only t showed a significant modulation of expression by temperature . This modulation was very strong as t was expressed 7 times more at 18°C than at 29°C ( Fig 2B , p<0 . 01 ) . We therefore focused on t and we analysed its spatial expression by in situ hybridization in D . melanogaster female abdominal epidermis ( line w1118 ) ( Fig 2C and 2D ) . t was strongly expressed in the posterior abdomen of females grown at 18°C , as previously shown for D . yakuba females whose abdomen is darkly pigmented [30] . However , in D . melanogaster , t expression was strongly reduced at 29°C , which correlates with the lighter pigmentation of adult females . As t activity increases melanin production ( [31] and Fig 2A ) , its changing expression with temperature might be directly linked to abdominal pigmentation plasticity of females . If modulation of t expression by temperature were necessary and sufficient for thermal plasticity of female abdominal pigmentation , then manipulating t expression should counteract the effect of temperature . To test this hypothesis , we down-regulated or over-expressed t throughout development using the pannier-Gal4 driver [32] ( pnr-Gal4 ) combined with a UAS-RNAi-t ( [33] ) or a UAS-t ( [31] ) transgene ( Fig 3A ) . As pnr is expressed only in the dorsal region of the body [32] , the lateral regions serve as internal controls . t down-regulation at 18°C was sufficient to reduce pigmentation , which shows that high t expression at low temperature is required for dark pigmentation . Conversely , t over-expression at 29°C was sufficient to increase pigmentation , proving that at high temperature the lower level of t expression is limiting for melanin production . Similar results were obtained with yellow-wb-Gal4 ( y-Gal4 ) , a driver expressed in wing and body epidermes at the late pupal stage ( Fig 3B ) . These results show that modulation of t expression by temperature plays a major role in thermal plasticity of female abdominal pigmentation . In the pigment synthesis pathway , e encodes the enzyme that synthesizes the substrate of Tan ( Fig 2A ) . We thus wondered whether a functional e gene was required to observe the effect of t modulation on pigmentation . To test this , we manipulated t expression in an e loss-of-function mutant background ( e1 allele ) . t mis-regulation had no phenotypic consequence on pigmentation in this background ( S1 Fig ) , showing that e is epistatic over t . Hence , a functional e gene is required to observe the phenotypic effect of t expression modulation . This result again points towards t as the major effector of pigmentation thermal plasticity . Involvement of a gene in thermal plasticity is quantified by the effect of the interaction between genotype and temperature . To further establish the role of t in thermal plasticity of female abdominal pigmentation , we compared the reaction norms [pigmentation = f ( temperature ) ] of control flies and of t loss-of-function mutant flies ( td07784 allele ) ( Fig 4 , S2 Fig ) . We observed a very strong effect of temperature ( T , p<0 . 001; Eta-squared = 0 . 38 ) and of genotype ( G , p<0 . 001; Eta-squared = 0 . 49 ) alone . As t is involved in abdominal pigmentation [31] , this result was expected . In addition , the effect of the interaction between genotype and temperature was also very strong ( GxT , p<0 . 001; Eta-squared = 0 . 08 ) . Hence , td07784 females are less plastic than wild type females , thus corroborating the role of t in thermal plasticity of abdominal pigmentation . The effect of temperature on t expression could be mediated by its cis-regulatory sequences . An enhancer essential for driving t expression in the epidermis of abdominal segments A5 and A6 in males , t_MSE , was previously mapped upstream of t , between the genes CG15370 and Gr8a [17] ( Fig 5A ) . We analysed the activity of a t_MSE-nEGFP reporter transgene [17] in young females grown at 18°C and 29°C . Quantification of nEGFP in segments A5 , A6 and A7 showed that this enhancer was also active in female abdominal epidermes . Furthermore , its activity was modulated by temperature , as nEGFP was between 1 . 3 and 2 times more expressed at 18°C than at 29°C , depending on the segment ( Fig 5B and 5C , p<0 . 001 ) . When using an ebony-nEGFP transgene in which nEGFP is under control of the regulatory sequences of ebony [34] , a pigmentation gene not modulated by temperature in the posterior abdominal epidermis of young females ( Fig 2B and S3A Fig ) , we observed no higher nEGFP expression at 18°C as compared to 29°C ( S3B and S3C Fig ) . This indicates that transcription of nEGFP and not stability of the nEGFP protein was responsible for the effect observed with the t_MSE-nEGFP transgene . Interestingly , the fold change observed with the t_MSE-nEGFP transgene between 18°C and 29°C was lower than that of t expression ( Fig 2B ) . This could be due to the genetic background . Alternatively , additional regulatory sequences of t may be important to mediate the effect of temperature . In conclusion , these results show that the effect of temperature on t expression is mediated , at least partly , by t_MSE . Modulation of t_MSE activity by temperature prompted us to analyse the chromatin structure of this enhancer in epidermes of female abdominal segments A5 , A6 and A7 at 18°C and 29°C ( Fig 6 ) . As nucleosome depletion characterizes active regulatory chromatin regions [35 , 36] , we performed Formaldehyde Assisted Isolation of Regulatory Elements ( FAIRE ) -qPCR experiments , a methodology allowing detection of open chromatin [37 , 38] . FAIRE experiments have previously shown that the VG01 enhancer of vestigial ( vg ) , which recapitulates vg expression in wing and haltere imaginal discs , was specifically open in these tissues , but not in leg imaginal discs where vg is not expressed [38] . As vg is not expressed in the abdominal epidermis either ( S4A Fig ) , we used VG01 as a negative control . FAIRE signal was significantly higher on t_MSE , showing that t_MSE was less compact than VG01 at 18°C and 29°C ( 18°C: p<0 . 01; 29°C: p<0 . 05 ) ( Fig 6A ) . However , compaction of t_MSE was similar at 18°C and 29°C . Similar conclusions were drawn from analysis of total histone 3 ( panH3 ) enrichment by chromatin immunoprecipitation experiments ( ChIP-qPCR ) , which showed a higher nucleosome concentration on the VG01 enhancer than on t_MSE , but no difference between 18°C and 29°C for both enhancers ( S5A Fig ) . We then analysed the enrichment of t_MSE in H3K27ac , a histone mark characteristic of active enhancers [39] . t_MSE was enriched in H3K27ac compared to VG01 enhancer at both 18°C ( p<0 . 001 ) and 29°C ( p<0 . 05 ) . However , we detected no significant H3K27ac enrichment on t_MSE at 18°C compared to 29°C ( Fig 6B and S5B Fig ) . This result indicates that t_MSE is active at 18°C and at 29°C . Furthermore , temperature affects neither the compaction of t_MSE nor the apposition of H3K27ac . However , other histone marks on t_MSE might be modulated by temperature . Alternatively , the effect of temperature on chromatin structure could target another region of t , for example its promoter . We thus studied chromatin compaction and the H3K4me3 active mark at the t promoter . The 500 base pair region upstream of the transcription start site ( TSS ) of active genes , which includes the promoter , is known to be depleted in nucleosomes [40] . FAIRE-qPCR experiments showed that chromatin upstream the t TSS ( t-TSS-up , -253 to -151 bp ) tended to be less compact at 18°C than at 29°C ( Fig 6A , p = 0 . 087 ) , which correlated with the higher expression of t at 18°C compared to 29°C . No such difference was observed for CG12119 ( Fig 6A , CG12119-TSS-up , -266 to -200 bp ) , a gene nearby t ( Fig 5A ) that was expressed at the same level at 18°C and 29°C ( S4B Fig ) , or for an untranscribed region between CG12119 and t ( Fig 6A , NC ) . Highly transcribed genes are enriched in H3K4me3 , with a maximum of enrichment 50–750 bp downstream of the TSS [41] . We found that H3K4me3 was strongly enriched at 18°C as compared to 29°C both downstream of t TSS ( Fig 6C , t-TSS down , 193 to 288 bp , p<0 . 01; S5C Fig ) and on t exon 2 ( Fig 6C , t-ex2 , p<0 . 05; S5C Fig ) . Such a difference between 18°C and 29°C , which correlates with higher t expression at 18°C , was not observed for CG12119-TSS-down ( 204 to 256 bp ) or for NC ( Fig 6C and S5C Fig ) . In conclusion , our results show that temperature modulates chromatin compaction and H3K4me3 enrichment on the t promoter in the posterior abdominal epidermis of females . As temperature modulates deposition of the H3K4me3 active mark on t , we addressed the role of genes involved in H3K4 methylation in pigmentation and its plasticity . In D . melanogaster , H3K4 mono- , di- and tri- methylations are catalysed by three complexes of the COMPASS family called Trithorax ( Trx ) , Trithorax-related ( Trr ) and Set1 . These complexes are characterised by their histone methyl-transferase subunit encoded by the genes trx , trr and Set1 , respectively [42] . The histone methyl-transferase Trx was also purified previously from another complex , TAC1 [43] . Whereas Trr is involved in H3K4 mono-methylation [44] , Set1 is responsible for the bulk of H3K4 di- and tri-methylation [45] . Independent studies indicate a role for Trx in H3K4 mono- and tri-methylation [45–47] . We first down-regulated trx , trr or Set1 using UAS-RNAi transgenes and the late pupal driver y-Gal4 to analyse their implication in abdominal pigmentation ( Fig 7A and 7B ) . Down-regulation of trr and Set1 using two different RNAi lines for each gene induced no changes in pigmentation . By contrast , trx down-regulation induced strong depigmentation of all abdominal segments . A similar phenotype was obtained by inducing trx down-regulation at late-pupal life with the pnr-Gal4 driver combined with Gal80ts ( Fig 7C ) . These results show that Trx , but neither Trr nor Set1 , participates in the late steps of female abdominal pigmentation establishment . As the level of H3K4me3 on the t promoter was modulated by temperature , we wondered whether Trx participates in t regulation . We thus quantified t expression in abdominal epidermes of y-Gal4>UAS-RNAi-trx females raised at 18°C , a temperature at which loss of pigmentation induced by trx down-regulation was very strong ( Fig 8A ) . trx down-regulation induced a significant decrease in t expression ( Fig 8B , 2 . 1 fold down , p<0 . 05 ) , showing that trx is required for the strong expression of t in abdominal epidermes at 18°C . In addition , down-regulation of trx in abdominal epidermes significantly reduced H3K4me3 on the t promoter ( Fig 8C , 3 . 7 fold down , p<0 . 05 ) , which suggests that Trx participates in H3K4me3 deposition on t . Interestingly , trx RNAi females exhibited stronger loss of melanin than td07784 mutants suggesting that Trx controls the expression of other pigmentation genes . Therefore , we analysed the expression of pigmentation genes in the abdominal epidermis of y-Gal4>UAS-RNAi-trx females raised at 18°C ( S6 Fig ) . In addition to t , TH , DDC and b were down-regulated showing that Trx also participates in their regulation . We then investigated the involvement of trx in thermal plasticity of pigmentation . As we could not use a trx UAS-RNAi transgene since the UAS/Gal4 system is temperature-sensitive , we established the pigmentation reaction norms of trxj14A6 heterozygous mutant females ( Fig 8D and S7 Fig ) . The effect of this allele on pigmentation was not as strong as the one of the trx UAS-RNAi transgene , probably because only heterozygous females could be studied ( the trxj14A6 allele is lethal homozygous ) . Nevertheless , the interaction between genotype and temperature was highly significant ( Fig 8D , GxT , p<0 . 01 ) , indicating that trx is involved in thermal plasticity of pigmentation .
We show here for the first time that thermal plasticity of female abdominal pigmentation in D . melanogaster involves strong modulation of the expression of the pigmentation gene t . Furthermore , our results demonstrate that this modulation plays a major role in female abdominal pigmentation plasticity . Interestingly , a previous study analysing thermal plasticity of gene expression in the whole body of three days old D . melanogaster females showed that t expression diminishes when temperature increases [48] . However , as the abdominal pigmentation pattern is already established at this stage , it is likely that , in these experiments , other tissues contribute to the variation of t expression . As t is expressed in photoreceptors and plays a role in vision [31] , it would be interesting to test whether its expression varies with temperature in adult eyes . In young adults , t is the only pigmentation gene among those tested which is significantly modulated by temperature . However , we observed a trend towards a weaker e expression at 18°C than at 29°C , although not statistically significant ( Fig 2B , p = 0 . 06 ) . In pharates , several pigmentation genes , including t and e , are moderately modulated by temperature . In addition , we observed a weaker expression of e-nEGFP in A6 and A7 at 18°C than at 29°C ( S3 Fig ) . These findings agree with our previous data showing the qualitative analysis of e expression at different temperatures using an e-lacZ transgene [25] . In this previous publication we showed that e mutants remain dark at all temperatures and concluded that "a functional e gene is required for the plasticity of pigmentation" . Our present data complete this conclusion . Indeed , we show here that e is epistatic over t . This explains why e mutants lose abdominal pigmentation plasticity , as a functional e gene is required to observe plasticity induced by modulation of t expression . Furthermore , our data show that the expression of e , DDC , y and b is modulated by temperature in pharates . This could explain the residual pigmentation plasticity observed in t mutants . Lastly , spatial analysis of e expression by in situ hybridization reveals a stronger expression at 29°C than at 18°C in anterior abdominal segments . This observation suggests that the reduced but observable plasticity of these anterior segments might be due to e temperature sensitive expression . The effect of temperature on t expression is mediated , at least partly , by the t_MSE enhancer . Thus , this enhancer may have particular properties making it temperature sensitive . Indeed , recent data showed that the number of redundant binding sites for a particular transcription factor in an enhancer could influence its temperature sensitivity [49] . Another , non-exclusive , explanation could be that temperature affects the expression or the activity of regulatory factors upstream of t . We detected no chromatin modification of t_MSE at different temperatures , possibly because this enhancer is active , although at different levels , at the temperatures we tested . The level of H3K27ac could therefore be saturated and the chromatin on t_MSE decompacted at both temperatures . By contrast , the effect of temperature on t expression is correlated with the modulation of H3K4me3 deposition on the t promoter . As this histone mark correlates with active transcription [50] , the strong accumulation of t transcripts at 18°C is more likely caused by a transcriptional response to temperature than by modulation of a post-transcriptional mechanism that would stabilize them . Interestingly , deposition of H3K4me3 can also be modulated by environmental conditions such as diet in mouse liver [51] , drought stress in plants [52 , 53] or chemical stress in yeast [54] . This histone mark emerges therefore as a general mediator of environmental impact on the genome . We show that the H3K4 methyl-transferase Trx is involved in t regulation , but also in the regulation of other pigmentation genes . As the level of H3K4me3 on the t promoter decreases when trx is inactivated , it is tempting to speculate that Trx directly regulates t . However , Trx might also indirectly control t expression through the regulation of genes upstream t . Furthermore , as Trx has no intrinsic DNA binding activity , its recruitment on t or on upstream regulators must depend on specific transcription factors . Thus , it would be interesting to identify the upstream regulators of t controlled by Trx as well as the transcription factors recruiting Trx on t or on its upstream regulators . Trx also participates in the thermal plasticity of female abdominal pigmentation . This confers to Trx a very specific role as compared to other H3K4 methyl-transferases . Indeed , Set1 has been described as the main H3K4 di- and tri- methyl-transferase during Drosophila development [45] . However , our results demonstrate for the first time that Trx is involved in the thermal plasticity of female abdominal pigmentation . Modulation of pigmentation by environmental conditions is observed in many insects [55 , 56] . Interestingly , t expression is strongly modulated by environmental conditions in the developing wings of Junonia coenia , a butterfly with contrasting seasonal morphs [57] . The involvement of t in pigmentation plasticity might therefore be widespread in insects . Several studies have also linked t to pigmentation variation within or between Drosophila species . Modulation of t expression through modification of t cis-regulatory sequences has been implicated in evolution of abdominal pigmentation between species [17 , 19 , 26] . Remarkably , in D . santomea , independent mutations in t_MSE have generated three distinct loss-of-function alleles involved in the reduced pigmentation of this species [17] . Furthermore , SNPs associated with variation of abdominal pigmentation in D . melanogaster females have been identified in t_MSE [20] . Interestingly , abdominal pigmentation dimorphism in female Drosophila erecta was recently shown to be caused by sequence variation in t_MSE maintained by balancing selection [58] . The recurrent implication of t in pigmentation evolution has led to list this gene among hotspots of evolution [27] . In other organisms , genes sensitive to environment and involved in phenotypic plasticity are also responsible for differences within or between species . For example , in Brassicaceae , the reduced complexity locus ( RCO ) that participates in leaf margin dissection is modulated by temperature and has been repeatedly involved in leaf shape evolution through cis-regulatory sequence variation or gene loss [59] . Therefore , sensitivity of particular genes to environmental conditions might turn them into evolutionary hotspots . Indeed , this broadens the range of phenotypes produced by a particular allele , providing more substrate for natural selection .
We used a w1118 inbred line as wild-type . The UAS-t line was a gift from Dr . Nicolas Gompel , whereas t_MSE-nEGFP was from Dr . Sean Carroll's lab . The ebony-nEGFP line ( ebony- ( ABC+intron ) -nEGFP ) was from Dr . Mark Rebeiz . The UAS-RNAi-t ( GD18124 ) and UAS-RNAi-Set1 ( GD40683 ) lines were from the VDRC Stock Center . The pnr-Gal4 ( BL3039 ) , y-Gal4 ( BL44267 ) , P ( XP ) td07784 ( BL19282 ) , e1 ( BL1658 ) , trxj14A6 ( BL12137 ) , as well as the VALIUM UAS-RNAi lines ( Transgenic RNAi Project at Harvard Medical School ) against trx ( BL33703 ) , trr ( BL29563 and BL36916 ) , Set1 ( BL33704 ) and GFP ( BL41556 ) were from the Bloomington Stock Center . The homozygous lethal trxj14A6 allele that corresponds to an insertion of a w+ P transposon was used in this study . This allowed us to introgress this allele in the w1118 background ( ten generations ) , so that the mutation is in the same genetic background as the control . Complementation test with a well characterized trx loss-of function allele ( trxE2 [60] ) indicated that trx j14A6 is a genuine loss-of-function allele of trx . To control the expression of RNAi transgenes during development , we combined the pnr-Gal4 driver with the tub-Gal80ts transgene from the Bloomington Stock Center ( BL7019 ) . Gal80 inactivation was performed by shifting the progeny at late pupal stage from 18°C to 29°C . We tested that all lines allowing trx , trr or Set1 down-regulation induced lethality with the ubiquitous daughtherless-Gal4 ( da-Gal4 ) driver . Efficiencies of BL29563 ( UAS-RNAi-trr ) and GD40683 ( UAS-RNAi-Set1 ) were previously published [42 , 45] . For the UAS-RNAi-trx line ( BL33703 ) , quantification of trx expression level in da-Gal4>UAS-RNAi-trx embryos showed a 1 . 5 fold down regulation as compared to control embryos , thus proving its efficiency . Adult females between 3 and 5 days old were stored for 10 days in ethanol 75% before dissection . Abdominal cuticles were cut just beyond the dorsal midline , which was therefore entirely included in each preparation . After dissection , cuticles were dehydrated 5 minutes in ethanol 100% and mounted in Euparal ( Roth ) . For nEGFP observations , abdomens were dissected in PBS , fixed 20 minutes in 3 . 7% paraformaldehyde in PBS , washed twice 10 minutes in PBS and mounted in Mowiol . Fragments of cDNAs from t ( 611 bp ) and e ( 639 bp ) were amplified by PCR ( primer sequences are listed in S1 Table ) and cloned by Topo-Cloning and LR-Recombination ( Gateway ) in pBlueScript vector ( Invitrogen ) . Sense and antisense DIG-labelled RNA probes were synthesized using the appropriate RNA polymerase . In situ hybridizations were performed according to the Carroll's lab protocol ( http://carroll . molbio . wisc . edu ) . Specificity of the antisense probe was assessed by comparison with signal from the sense probe . For t , we also performed in situ hybridization with the t antisense probe on UAS-RNAi-t/pnrGal4 females and observed a strong decrease of the signal in the pnr domain ( Fig 2D ) . Adult cuticles and abdominal in situ hybridizations were imaged with a binocular equipped with a Leica DC480 digital camera using the Leica IM50 Image Manager software . They were imaged using identical settings and an annular lamp to ensure homogeneous lighting . To quantify pigmentation , each entire hemi-segment was circled by hand . For A5 and A6 , the melanic line at the dorsal limit of each hemi-segment ( i . e the dorsal midline ) separates the two hemi-segments . Cuticle pigmentation in hemi-tergites A5 , A6 or A7 was measured as mean grey value using ImageJ . This value was subtracted from 255 to get a final pigmentation value comprised between 0 ( white ) and 255 ( black ) . Abdominal epidermes of t_MSE-nEGFP and ebony-nEGFP females were imaged using a Macro-Apotome ( Zeiss ) . nEGFP intensity was measured in hemi-tergites A5 , A6 or A7 using ImageJ in Maximum Intensity projections of 40 picture stacks . RNA was extracted from pools of dissected female posterior abdominal epidermes ( A5 , A6 and A7 ) with the RNAeasy Mini kit ( Qiagen ) ( 50 abdominal epidermes for pharates , 30 for young adults ) . We could not use developmental time to stage pharates as it is temperature sensitive . We therefore used morphological markers ( wing colour , abdominal bristles , localisation of the meconium in anterior abdomen ) to collect pharates grown at 18°C or 29°C at a similar developmental stage . This stage corresponds to the stage P12 ( i ) described by Bainbridge and Bownes [61] . For each experiment three independent replicates were analysed for each genotype and each temperature except for S6 Fig ( two replicates ) . After treatment of RNA with Turbo DNAse ( Ambion ) , cDNA were synthesized with the SuperScript II Reverse transcriptase kit ( Invitrogen ) using random primers . RT-qPCR experiments were carried out in a CFX96 system ( Biorad ) using SsoFast EvaGreen Supermix ( Biorad ) . Expression levels were quantified with the Pfaffl method [62] . The geometric mean of two reference genes ( Fig 2B and S4 Fig: rp49 and Act5C; Fig 8B: rp49 and eIF2 , ) was used for normalization [63] . Primers used are listed in S1 Table . Chromatin immunoprecipitation ( ChIP ) experiments were performed as previously described [64] with minor modifications . For each experiment , 50 posterior abdominal epidermes ( A5 , A6 and A7 ) of females between 0 and 2h after hatching and 3μg of antibody were used . Results present the mean of three independent experiments for each antibody . Tissue disruption was performed before cell lysis using the FastPrep technology ( MP Biomedicals , Lysis matrix D , 20 seconds at 4m/s ) . Chromatin sonication was performed in a Bioruptor sonifier ( Diagenode ) ( 16 cycles of 30'' ON , 30'' OFF , High power ) . Input and immunoprecipitated DNA were purified with the Ipure kit ( Diagenode ) in 70μl of water and 4μl were used per qPCR reaction . qPCR experiments were carried out in a CFX96 system ( Biorad ) using SsoFast EvaGreen Supermix ( Biorad ) . Primers used are listed in S1 Table . Data were normalized against input chromatin or panH3 ChIP . Antibodies used were anti-H3K4me3 ( C15410003 , Diagenode ) , anti-H3K27ac ( C15410174 , Diagenode ) , anti-panH3 ( C15310135 , Diagenode ) . Rabbit IgGs ( Diagenode ) were used as negative control ( Mock ) . 75 posterior abdominal epidermes ( A5 , A6 and A7 ) of females between 0 and 2h after hatching were used for each FAIRE experiment . Fixation and lysis protocols were similar to those used for ChIP except that fixation was performed for 5 minutes at room temperature in PBS-1% paraformaldehyde with gentle shaking . Chromatin sonication was performed in 300μl in a Bioruptor sonifier ( Diagenode ) with 8 cycles of 30'' ON , 30'' OFF , High power , allowing to obtain chromatin fragments between 300–400 bp . 100μl of chromatin preparation was kept as the input ( total chromatin ) . The rest ( 200μl ) was submitted to phenol-chloroform extraction and the aqueous phase containing the decompacted chromatin ( FAIRE chromatin ) was kept . Input and FAIRE DNA were purified with the Ipure kit ( Diagenode ) in 150μl of water and 4μl were used per qPCR reaction . qPCR experiments were carried out in a CFX96 system ( Biorad ) using SsoFast EvaGreen Supermix ( Biorad ) . Primers used are listed in S1 Table . Data were normalized against input chromatin . Results present the mean of three independent experiments . To analyse the effect of temperature on A5 , A6 and A7 pigmentation , we performed a one-way ANOVA ( or Welch’s ANOVA when variances were heterogeneous ) with temperature as factor . To analyse the effect of t ( Fig 4 ) or trx ( Fig 8D ) on pigmentation plasticity , we used a two-way ANOVA with genotype and temperature as factors . The variable analysed was the first component of a Principal Component Analysis of pigmentation in A5 , A6 and A7 conducted on correlations , which captures more than 95% of total variation in both cases . ANOVAs and Welch’s ANOVA were performed using the OpenStat software ( W . G . Miller , http://statprogramsplus . com/OpenStatMain . htm ) . Normality of the residual distributions was checked with a Shapiro-Wilk test ( Anastats; http://anastats . fr ) . For t-tests , we checked first homogeneity of variance using a Levene Test ( Anastats; http://anastats . fr ) and then used the appropriate option of t-test . | Environmental conditions can strongly modulate the phenotype produced by a particular genotype . This process , called phenotypic plasticity , has major implications in medicine and agricultural sciences , and is thought to facilitate evolution . Phenotypic plasticity is observed in many animals and plants but its mechanisms are only partially understood . As a model of phenotypic plasticity , we study the effect of temperature on female abdominal pigmentation in the fruit fly Drosophila melanogaster . Here we show that temperature affects female abdominal pigmentation by modulating the expression of tan ( t ) , a gene involved in melanin production , in female abdominal epidermis . This effect is mediated at least partly by a particular regulatory sequence of t , the t_MSE enhancer . However we detected no modulation of chromatin structure of t_MSE by temperature . By contrast , the level of the active chromatin mark H3K4me3 on the t promoter is strongly increased at lower temperature . We show that the H3K4 methyl-transferase Trithorax is involved in female abdominal pigmentation and its plasticity and regulates t expression and H3K4me3 level on the t promoter . Several studies have linked t to pigmentation evolution within and between Drosophila species . Our results suggest that sensitivity of t expression to temperature might facilitate its role in pigmentation evolution . | [
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] | 2016 | Phenotypic Plasticity through Transcriptional Regulation of the Evolutionary Hotspot Gene tan in Drosophila melanogaster |
Viruses infecting prokaryotic cells ( phages ) are the most abundant entities of the biosphere and contain a largely uncharted wealth of genomic diversity . They play a critical role in the biology of their hosts and in ecosystem functioning at large . The classical approaches studying phages require isolation from a pure culture of the host . Direct sequencing approaches have been hampered by the small amounts of phage DNA present in most natural habitats and the difficulty in applying meta-omic approaches , such as annotation of small reads and assembly . Serendipitously , it has been discovered that cellular metagenomes of highly productive ocean waters ( the deep chlorophyll maximum ) contain significant amounts of viral DNA derived from cells undergoing the lytic cycle . We have taken advantage of this phenomenon to retrieve metagenomic fosmids containing viral DNA from a Mediterranean deep chlorophyll maximum sample . This method allowed description of complete genomes of 208 new marine phages . The diversity of these genomes was remarkable , contributing 21 genomic groups of tailed bacteriophages of which 10 are completely new . Sequence based methods have allowed host assignment to many of them . These predicted hosts represent a wide variety of important marine prokaryotic microbes like members of SAR11 and SAR116 clades , Cyanobacteria and also the newly described low GC Actinobacteria . A metavirome constructed from the same habitat showed that many of the new phage genomes were abundantly represented . Furthermore , other available metaviromes also indicated that some of the new phages are globally distributed in low to medium latitude ocean waters . The availability of many genomes from the same sample allows a direct approach to viral population genomics confirming the remarkable mosaicism of phage genomes .
Prokaryotic viruses , often referred to as phages , are one of the largest reservoirs of underexplored genetic diversity on Earth . They are more numerous than any other biological form on the planet , and the astronomical values put forward for their numbers are to the tune of 1030 , difficult to comprehend even by metaphoric abstractions [1] . Such estimates have contributed to an increasing appreciation of the role of this poorly charted component in the global carbon and energy cycling in the oceans [2] , [3] . The high prevalence of phages in the environment also raises important questions about their local and global population diversity , the dynamics of interaction within themselves and their hosts , and the evolutionary implications of these relationships [4] , [5] . A critical bottleneck for the study of phages is the need to obtain their hosts in axenic cultures before they themselves can be cultured . Consequently , as most marine bacteria remain uncultured [6]–[8] , so too do their phages . Obtaining genomic DNA for uncultured microbes has been relatively easy , and sequencing of numerous oceanic metagenomes and single cell genomes have provided an extraordinarily detailed view of the real world of marine microbes [9]–[14] . Similar progress , though , has been elusive for marine phages . Even though they are estimated to be 10-fold as numerous as prokaryotic cells , recovering viral DNA in amounts sufficient for sequencing has proven difficult although recently methods have been devised to improve the process [15]–[17] . Phage genomes are much smaller than cellular ones and total phage DNA per volume is relatively low [18] compared to their cellular hosts . As a result , DNA amplification is normally a necessary step before metavirome sequencing , which probably biases the product significantly [19] , [20] . Still , with all these caveats , the nascent field of marine metaviromics has provided an insight into the marine viral world [21]–[26] . Therefore , most of our knowledge about complete marine phages' genomes stems from cultured representatives [27]–[30] , isolated only because of success in culturing the host , which themselves , in several cases took painstaking years to be adapted for growth in the laboratory e . g . Ca . Pelagibacter [31] , [32] . Cloning of environmental DNA into fosmids , used successfully for studying prokaryotic genomic fragments of uncultured microbes [9] , [10] , has opened an alternative to obtain complete genomes of phages [18] , side-stepping completely the previously mandatory availability of a cultivated host . This has been possible due to the observation that inserts cloned in fosmids from metagenomic DNA have a significant representation of phage genomic fragments [9] , [10] . Actually , a replicating phage in the course of its natural lytic cycle in a cell , provides a natural amplification that is reminiscent of laboratory cloning or other methods of genome amplification , such as multiple displacement amplification ( MDA ) [33] . Formerly , metagenomic fosmids have been shown to capture major marine phage lineages like cyanophages [10] , [18] and the SAR11 viruses [32] . The deep chlorophyll maximum ( DCM ) is the site of maximal phototrophic cell density in oligotrophic open ocean waters . It is a seasonal phenomenon in temperate waters forming at the middle of the photic zone during the summer stratification of the water column [34] , [35] and a permanent feature in tropical latitudes . Supported by the high number of microbial cells , the number of infecting phages is also expected to be high . We have sequenced and assembled ∼6000 metagenomic fosmids obtained from the Mediterranean DCM ( MedDCM ) cellular fraction ( >0 . 2 µm ) . Among them more than a thousand genomic contigs were derived from marine phages that were actively replicating and are described here . Two hundred and eight of them represented novel complete genomes , and some were very different from any phage known to date . Furthermore , the examination of the genomes has allowed assigning putative hosts to many of these previously unknown phages . This collection also provides a unique opportunity to examine concurrent phages from the same natural habitat , en masse . The sequences reveal the existence of multiple , highly related coexisting lineages for each phage type , likely matching or exceeding the multiple prokaryotic lineages of their host genomes [36] . From the same site a metavirome ( from the viral size fraction ) has also been directly sequenced by Illumina ( MedDCM-Vir ) to assess the relevance of these phages in the viral sized fraction .
From the sequenced fosmids , we manually selected 1148 virus-like contigs ( size range 5–48 Kb , average size 23 Kb , GC% range 27–57 ) based on their resemblance to known phages and/or presence of key phage genes using the Phage Orthologous Groups [37] ( see Materials and Methods ) . As is typical for viral genomes , nearly half of the proteins from this collection of fosmid contigs ( 40% ) did not present any hits to the NR database , reflecting the novelty of the phage genomes described here . Thirty six percent were similar only to hypothetical proteins . Of the remaining 24% that could be attributed a function , most ( 19% ) were clearly phage-related , 1% were cellular-like ( host ) proteins and 4% were unclassified . Among host-related genes , we identified auxiliary metabolic genes ( AMGs ) commonly found in phages such as photosystem related genes ( psbA , psbD ) , 6-phosphogluconate dehydrogenase ( gnd ) , Glucose-6-phosphate 1-dehydrogenase ( zwf ) and transaldolase ( talC ) [30] , [38] , [39] . The presence of a large number of predicted proteins characteristic of tailed phages ( e . g . terminase , tape measure protein , tail formation and baseplate related proteins ) indicated that 935 contigs clearly originated from the order Caudovirales . For the remaining contigs , in which these specific genes could not be identified , further comparisons suggested that they were also tailed bacteriophages . Given that these contigs could be reliably assigned to head-tail phages and are derived from the cellular fraction ( between 5 and 0 . 2 µm ) selective for prokaryotic cells , we have focused this work only on tailed phages and not on other types of viruses ( e . g . eukaryotic viruses ) that might also be present in the fosmid library . Phylogeny of the essential terminase gene has been used to resolve different phage groups and define new ones [18] , [40] . We found a remarkable diversity of this phage packaging gene in our assembled contigs . A phylogenetic analysis showed that these contigs not only recaptured several known lineages ( e . g . T4-like or T7-like ) but also defined many novel major branches ( Figure 1 ) . We organized the 1148 contigs into sequence identity clusters ( see Materials and Methods ) to group together genomic fragments of the same or highly related phage lineages ( more than 95% nucleotide identity over at least 20% overlap ) . It seems apparent from the examination of the contigs that the DNA from which they derive are not individual phage genomes but the concatamer that appears as an intermediate stage during the replication of most Caudovirales [41] . This has allowed us to assess genome completeness when one fosmid covered more than one complete genome in the cellular concatamer or two identical clones overlapped ( Figure S1 ) . Even though the insert size of fosmid clones ( 30–40 kb ) limits the maximum size , two hundred and eight such complete genome representatives ( henceforth referred to as CGRs ) could be recovered . We have largely focused on their analyses , although the other contigs have been also used when they could provide additional information . All contigs were named in a way to reflect their origin , completeness , and sequence similarity amongst themselves ( see Materials and Methods for details ) . To establish the relationships of the novel phage genomes with known phages , we performed a large-scale whole genome comparison with several reference genomes , including all marine phages . A purely genomic approach to classify phages has been proposed before and actually recapitulates several features of traditional phage classification [42]–[44] . Our slightly modified genomic approach similarly agrees well with both methods ( Figure S2 , S3 and S4 ) . The whole genome comparison of the 208 CGRs shows that while some of them cluster with known phages , there are several instances of completely novel phage groups ( Figure 2 , Figure S5 ) as already hinted by the terminase phylogeny ( Figure 1 ) . Using the tree obtained , we have organized these CGRs into 21 sequence groups ( G1 to G21 ) ( Figure 2 , Table 1 ) . Within each group there was also a large degree of variation , showing protein identities typically in the range of 50–70% ( see below ) , in effect akin to different genera of phages , i . e . within each group there was more than one phage genus . As an example , G21 groups together different genera of phages from the marine Bacteroidetes Cellulophaga [45] and Persicivirga [46] ( Figure S5 ) . Another way to classify phages is by the host upon which they prey . Although the identification of hosts of uncultured viruses is non-trivial , phage genomes sometimes display features that divulge the identity of the host . Well known amongst such features are AMGs , metabolic genes that are frequently phage versions of host genes , e . g . photosystem genes carried by cyanophages that help boost phage replication during infection [47]–[50] . Actually , the photosystem genes ( psbA and psbD ) , apart from unequivocally linking a phage to cyanobacteria , have been shown to discriminate not only between phages of different environments ( e . g . marine or freshwater ) , but even different phage types ( e . g . podoviruses or myoviruses ) [51] , [52] . In absence of such signature genes , another tell-tale feature may be simply high sequence identity to phages with known hosts , which is likely only if the phages share a common host species . For some phages , presence of CRISPR spacers in uncultured phage genomes and concordant matches in a host genome may also be used as evidence of a phage-host relationship [53] . In our case this last approach did not help , probably due to the scarcity of CRISPR systems among marine microbial genomes . A less explored link between phages and their hosts is related to the putative temperate nature of several phages , particularly their integration into host tRNA genes . Integration into a host genome requires that the phage carries an integrase , an excisionase and a repressor [54] , [55] . Phages integrating into tRNAs carry a phage attachment site ( attP ) that is an exact match of a host tRNA gene ( bacterial attachment site , attB ) . For example , the Prochlorococcus phage P-SS2 contains an integrase gene , and an attP site ( 53 bp ) , which is an exact match of 36 bp to the host tRNA ( attB ) of Prochlorococcus MIT9313 [56] . Along these lines , a phage carrying an integrase and a putative attP site identical to a host tRNA gene fragment is highly suggestive of a host-phage relationship . As proof of principle for this method , we used two cyanophage contigs from our collection identified clearly due to presence of photosystem genes ( psbA in this case ) . Both these contigs also carried an integrase gene and a partial tRNA gene . Comparisons to the cyanobacterial genomes of Prochlorococcus and Synechococcus revealed that the tRNA gene fragment in both of these contigs was identical to the tRNA-Leu of Prochlorococcus marinus MED4 ( 42 bp exact match ) and Synechococcus CC9605 ( 39 bp exact match ) , linking them to these putative hosts . Phylogenetic analysis of the psbA gene additionally supported this specific prediction ( Figure S6 and Figure S7 ) . Another such prediction could be made for a CGR that was >80% identical ( in nucleotides ) along its entire length to pelagiphage HTVC019P ( Figure 3 ) . Such high sequence identity already suggests that this CGR represents a novel pelagiphage . This CGR also carries an integrase gene and a fragment of a tRNA-Leu gene that is identical ( 46 bp ) to the tRNA-Leu gene in Ca . Pelagibacter HTCC7211 . It is important to emphasize that , given the high conservation of the tRNA gene among closely related species , the predictions based on this method alone are expected to provide only a broad taxonomic assignment , i . e . the phylum or class ( e . g . SAR11 cluster or Verrucomicrobia ) . However , when supplemented with supporting evidence in the form of characteristic host genes ( e . g . psbA for cyanophages ) , or high nucleotide identity to cultivated phages , it may be possible to be more specific in the predictions . Using a combination of these approaches , applied to all the phage contigs , we were able to assign putative hosts to 527 contigs ( Data S1 ) . Several CGRs could be associated with a known host ( Figure 2 , Table 1 , Data S1 ) ( see below ) . Many of which are as yet uncultured microbes known only by their genome sequences . They represent a wide variety of important marine microbes like Cyanobacteria ( Prochlorococcus and Synechococcus ) , members of the SAR11 clade ( Ca . Pelagibacter and the Alpha proteobacterium HIMB114 ) , SAR116 representatives ( Ca . Puniceispirillum ) , Verrucomicrobia and the recently described low-GC clade of marine Actinobacteria [57] . However , it is important to underscore that host-association of a single CGR in a group in no way implies that the entire group to which it belongs infects the same host . With all these caveats and after comparison of the closest known phages for each group in Figure 2 , inferences regarding putative hosts could be made for 64 of the 208 CGRs . Using these host assignments and the genomic properties of the phages we classified them as follows . Group G2 contains CGRs that appear to be cyanophages , likely infecting Prochlorococcus . They are related ( >75% nucleotide identity in several regions ) to the known Prochlorococcus phages MED4-117 and MED4-184 , both dwarf myoviruses . Groups G7 , G8 and G9 were closely related and actually all belong to the subfamily Autographivirinae . They all possess an RNA polymerase that is the hallmark gene of this family , among other characteristic structural and replication genes [43] ( Figure 3 ) . All RNA polymerase containing CGRs could be classified in one of these three groups . G7 contains a novel CGR that , from the psbA gene phylogeny ( Figure S6 ) and similarity to Synechococcus phage RIP2 ( >75% identity across the genome ) , likely preys on Synechococcus . Group G8 contained a CGR that could be classified as a new pelagiphage ( infecting SAR11 ) by both sequence similarity ( >75% nucleotide identity along the entire genome ) to HTVC019P [32] and the integrase/att relationship . CGRs in group G9 are novel phage genomes for which no host assignment was possible . Of the 31 CGRs in group G15 , nine seem to be related to the recently cultured Pelagibacter phage , HTVC010P , which was shown to be the most abundant phage in the oceans [32] . These CGRs shared high nucleotide identities ( >80% ) in large regions with HTVC010P , suggesting they are also pelagiphages . In particular , two of these CGRs are highly similar along their entire lengths to HTVC010P , effectively making them Mediterranean variants of this phage , which was isolated from Bermuda ( Hydrostation S ) ( Figure 4 ) . Additionally , several of these new phage genomes were linked to the SAR11 cluster by the integrase/att relationship ( Table 1 , Data S1 ) . We defined several distinct groups of phages for which there are no known related genomes available . However , it was still possible to predict hosts for several CGRs in these novel phage groups . For example , one of the seven CGRs in group G11 could be linked to Verrucomicrobia using evidence from integrase/att identity to the single-cell amplified genome ( SAG ) SCGC AAA300-K03 [14] recently described as belonging to this phylum . The GC content of this phage genome ( 43 . 8% ) also matches very well the cellular genome GC content ( 42 . 3% ) . To our knowledge this is the first report of a marine Verrucomicrobia phage . G17 and G19 contained CGRs that were putative pelagiphages unlike any others known before . There is evidence for them infecting SAR11 cluster microbes from both integrase/att relationship and small regions of high nucleotide identity with HTVC010P . In addition , some of the CGRs in G19 were nearly fully syntenic to a prophage locus in the genome of the SAR11 alpha proteobacterium HIMB114 , albeit at a protein sequence identity in the range of 40–50% ( Figure S8 ) . Along the same lines , several of the CGRs from the group G16 ( Table 1 , Figure 2 ) could prey upon SAR116 . The first SAR116 phage ( HMO-2011 ) has been recently described [58] . However , these CGRs are unrelated to HMO-2011 , which is related to group G12 instead ( Figure 2 ) . Only the integrase/att relationship connected these CGRs of group G16 to Candidatus Puniceispirillum marinum [59] and other uncultured SAR116 representatives [14] . One phage genomic fragment ( not a CGR ) could be putatively assigned as an actinobacterial phage , the most likely host being Ca . Actinomarina minuta , the smallest free-living microbial cells described so far [57] . The fragment carries an integrase and also a 43 bp att site that is 100% identical to tRNA-Val of the putative host genome . This match is so specific that the att site sequence only retrieves Ca . Actinomarina minuta sequences from the complete GenBank collection . In addition , a WhiB transcriptional regulator found only in Actinobacteria , was also found in this phage fragment . This gene has been found previously in mycobacterial phages ( e . g . TM4 ) , where it has been shown to have a growth inhibitory and a super-exclusion effect [60] . This phage genomic fragment appeared most closely related by sequence ( Figure 2 ) to the G13 group of CGRs to which no other host could be assigned . An essential question is how relevant are the phages represented by our CGRs in a DCM phage population . To this end , a different DCM sample from the same location ( and retrieved four years later ) has been processed to generate a metavirome ( MedDCM-Vir ) . The DNA from the viral fraction in the sample was amplified by MDA and sequenced by Illumina to provide nearly 18 Gb of sequence data . We used this metavirome , along with several others [21] , [22] , and some representative metagenomes , to assess relative abundance of known marine phages ( 133 reference genomes ) and the CGRs . The most abundantly recruiting genomes are shown in Figure 5 and Figure S9 . As expected , recruitment from metagenomes is much less than from metaviromes , reasserting the viral nature of CGRs . Among the top recruiting genomes there is a large representation of the CGRs , with only a few cultivated Ca . Pelagibacter and Prochlorococcus phages reaching comparable values . Although most CGRs recruited more in their habitat of origin ( MedDCM-Vir ) they also recruited very well in other datasets , such as the Sargasso Sea . Reciprocally , several phages isolated from the Sargasso ( e . g . P-SSP2 , P-GSP1 and P-SSP7 ) , not only recruited a high number of reads from the Sargasso Sea metavirome , but also from the MedDCM-Vir . How much of the viral diversity at the DCM was recovered in our fosmids ? To answer this question , we have used very relaxed criteria for recruitment ( BLASTN , minimum alignment length 50 bp and e-value 0 . 01 ) . As a control , we used multiple genomes of several abundant DCM microbes ( Prochlorococcus , Synechococcus , Ca . Pelagibacter , SAR86 , Group II Euryarchaeota , adding up to a total of 75 Mb sequence data ) to recruit reads from the metavirome MedDCM-Vir . Only 0 . 14% of reads could be matched indicating a negligible contamination with cellular DNA . On the other hand , the 1148 phage contigs described here recruited about 1 . 54% of all the reads of the MedDCM-Vir , an order of magnitude more , but still suggesting they represent a small minority in the Mediterranean DCM virome . The 133 reference genomes recruited even less ( only about 0 . 36% ) . It has been recently suggested , using microscopic techniques , that natural marine viral populations may be dominated ( up to 92% ) by non-tailed phages [61] , providing a potential explanation for such low recruitment levels . It is important to underscore here that the metaviromes are always amplified by MDA . There is evidence that MDA acts much more efficiently with single stranded DNA so that extant metaviromes could be over representing ssDNA viruses [19] , [20] and are consequently biased against dsDNA genomes . However , even the recently described 608 genomes of marine , circular ssDNA viruses [26] recruited only 1 . 5% of the MedDCM-Vir reads . Such results are not restricted to the Mediterranean DCM metavirome , as they recruited similarly low levels from the Sargasso metavirome ( 0 . 89% ) [21] , and nearly nothing from the Pacific Ocean Virome [22] . Therefore , it appears that the vast majority of the marine virome sequence space is as yet unsampled . With this large collection of complete phage genomes from the same place and time , it becomes possible to examine concurrent diversity , and patterns of variability , that have traditionally been analyzed by repeated and independent phage isolation in culture . Firstly , we have found several examples of nearly identical phage genomes . Using very restrictive similarity criteria ( 95% nucleotide identity over 95% overlap ) we identified 519 contigs ( out of the total of 1148 ) that clustered in groups of 2 to 22 members . Analysis of these highly similar clusters revealed several examples of nearly identical phage genomes with minor differences only , clearly showing that there are numerous recently diverged concurrent variants . For example , cluster C12B ( Figure S10 ) contains nine contigs that were >98% identical over the overlapping regions . In this comparison , some contigs are nearly identical with only minor indels , such as contigs 6 and 7 in Figure S10 . However , other contigs/regions diverged much more ( similarity down to 75–80% , for example contigs 7 and 8 ) . This is reminiscent of the flexible genomic islands of the prokaryotic genomes [62] and had previously being shown for cultivated phages . In a number of published studies [27] , [53] , tail proteins and other host recognition structures have been described as highly variable . This phenomenon was attributed to diversity of host recognition specificity among different phage lineages . However , the variations that we have found in the closely related genomes , although including structural host recognition features , do not appear to be restricted to any specific functional role . For example , internal virion proteins , terminases and capsid proteins were all observed within variable regions . Another frequent pattern is the presence of a hybrid architecture in which large divergent regions are present together with regions of high identity . An example is shown in Figure S11 . Such genomes clearly belong to phages infecting a common host that have exchanged genomic fragments during a mixed infection . Moreover , given the identical nature of several of these regions , it does appear that these exchanges are recent events . Similar results have been obtained by comparing cultured phage isolates . For example , the study of several isolated staphylococcal phages strongly suggested the exchange of large segments of genomes among them [63] . Whether or not such recombinations are facilitated by the presence of linker regions [64] or are random rearrangements followed by selection for function has not been established . The sheer amount of phage infections occurring in the marine habitat at any given time [1] , [2] makes it likely that any of these events are feasible . Given the high sequence identities found between the Mediterranean pelagiphages and the first pelagiphage HTVC010P isolated from the Sargasso Sea ( Figure 4 ) , we searched for more such examples in our contig collection . Identical phage sequences have been found before in geographically distant marine samples , but these were based on small genomic fragments ( 200–600 bp ) [65] , [66] . We found two cyanophage contigs from our collection that were >97% identical along their entire lengths to cyanophages isolated as far as the Pacific Ocean ( Figure S12 ) . Both of these contigs were nearly 40 Kb long ( nearly complete fosmids ) and originate from myoviruses that are >170 Kb long . These are remarkable examples of global distribution of viruses that suggest a rapid global phage circulation , likely along with oceanic currents .
It has been clear for some time that culture , although instrumental in the development of Microbiology , cannot provide an adequate representation of the real diversity of prokaryotic microbes and their phages in a sensible timeframe . New technologies based on high-throughput sequencing and direct nucleic acid retrieval from communities or single cells provide critical short-cuts for advancing in the discovery of the cellular microbes . However , an equivalent short-cut for the phage sequence space has been missing . Phage isolation and sequencing is very important in studying the natural diversity of phage populations , yet it is tightly constrained by the burden of obtaining host cultures . These limitations are not only relevant for the study of biodiversity alone . The population genomics of prokaryotic microbes and their phages , i . e . their evolution and microdiversity , suffer from similar handicaps . Here we have provided the largest collection of concurrent phage genomes ever described for any habitat so far by using metagenomic fosmids . This opens a route towards phage population genomics that can be based on complete genomes , rather than small genomic fragments . It appears to be the simplest and most effective high-throughput method to obtain complete phage genomes from a natural habitat yet . In addition , we have been able to assign putative hosts to many by using sequence based criteria that appear quite reliable and could prove instrumental as the field of metaviromics evolves further . On the other hand , it is quite obvious that we have only retrieved a small fraction of the full diversity of phages living in the habitat of choice ( the Mediterranean DCM ) . First of all our method is limited by the size of fosmid clones so that large viral genomes could not be retrieved . We could have tried to use overlapping fosmids but they would probably lead to unreliable chimeric assemblies . The genuine examples of mosaicism detected here , make artifactual assemblies from metaviromes a possibility that needs to be considered . For now we decided to focus mostly on the bona fide complete genomes ( CGRs ) . One possible way to bypass the size limitation would be to use larger insert vectors such as BACs [67] , or apply long-read sequencing directly to the samples when it is available [68] . Another obvious limitation of our method is that only replicating viruses , and apparently , those using the concatamer mode of replication , have been captured . Although it appears to narrow the window of the kinds of phages detected , it provides a confirmation of their active role in the ecology of the environment . It is possible that some phage particles are just remnants of past lytic events [1] , which are not relevant for the current habitat ecosystem functioning despite their presence in the metavirome . Those are excluded in our methodology . Finally , both single stranded DNA or RNA phages [69] are obviously omitted from detection by our technique . As mentioned before , traditional metaviromes might also be highly biased [19] , [20] , [70] , and in this sense both methods might be complementary . In spite of all these caveats , we nearly tripled the number of marine phage genomes . Given the recovery of nearly identical genomic fragments across the globe , it is already evident that there is very weak ( if any ) phage biogeography in temperate and tropical latitudes . Therefore , an in depth study of a single location can contribute enormously to our knowledge of phage biodiversity . Furthermore , coming from a single sample , we have shed light into the dynamics of genome change in concurrent phages . Using phage contigs highly related to the globally distributed pelagiphage HTVC010P , remarkable sequence conservation and variation patterns were discernible . There are aspects that are reminiscent of similar phenomena in prokaryotes [71]–[73] , such as the flexible genomic islands , i . e . the presence of several concurrent lineages that differ only in small genomic regions . Some of these regions are probably involved in host specificity at the level of clonal lineages [5] , [27] . However , some unexpected genes were subjected to high microdiversity ( capsid protein and terminases ) , the reasons for which are for the moment obscure . Capsid proteins could be involved in host recognition but it is not likely that the terminase has any connection with such specificity . In addition , swapping of genome fragments amongst phage lineages appears to be a central theme in phage evolution . Overall there seems to be more creativity in concurrent , highly identical ( over 95% ) , phage genomes compared to cellular genomes , that sometimes involves the replacement of large genomic segments , likely by recombination with distant lineages that share the same host . Similar phenomena had been detected before in cultivated phages [63] . Not being strictly fitness constrained as the cellular compartment , phages might embark onto more adventurous evolutionary trajectories . Actually , there is little doubt that phages may represent a significant part of the prokaryotic pan-genome [74] that could outsource risky , but highly innovative , evolutionary paths to their accompanying phage populations . The availability of large numbers of closely related genomes and the discernible patterns in their diversity and distribution increases our appreciation towards the enormous variety that exists , much of which was only partially captured before by isolated phage genomes . Importantly , it opens up a view of the phage world where instead of observing phage genomes as discrete entities , we can begin to look upon them as vast , constantly churning global continuums .
The sample from which the fosmid library was constructed was taken on October 15 , 2007 from the DCM ( 50 m depth ) off the coast of Alicante , Spain ( 38°4′6 . 64″N 0°13′55 . 18″W ) with a Niskin bottle . The sample was filtered through 5 µm polycarbonate and 0 . 22 µm Sterivex filters . DNA from 0 . 22 µm filters was used to create a fosmid library of ∼13000 clones . A 454 metagenome from the same filter , and results of sequencing of ∼1000 fosmids have been described previously [10] . For this work , DNA from ∼6000 metagenomic fosmids was extracted and pooled in 24 batches , with ∼250 fosmids in each batch . These were sequenced using Illumina PE 300 bp reads in a single lane ( ∼175× coverage for each fosmid ) . Each batch was assembled independently using Velvet [75] ( k = 51 ) . Several criteria were employed to identify phage genomic fragments , for example , multiple hits to all known phages , presence of key phage genes using Phage Orthologous Groups [37] , availability of multiple related fragments , and manual examination of each contig . POGs are clusters of orthologous genes from bacteriophages that can be used to identify viral genes and a virus quotient ( VQ ) quantifies the phage specificity of each gene ( the closer it is to 1 , more phage specific the gene is ) . The VQ profile of the POGs of selected MedDCM contigs was very similar to the one obtained for the known phage genomes ( majority of the POGs with VQ equal to 1 ) , suggesting that those contigs indeed represent true phage genome fragments . A total of 1148 ( lengths ranging from 5–48 kb ) contigs were finally selected for the final analysis . The presence of the vector sequence ( ranging to 16–67 bp ) on both sides of 139 assembled contigs indicated that these contigs represented the complete fosmid sequence . The lengths of the majority of the complete fosmids were between 30–40 kb . Genes were predicted using prodigal [76] , and annotated using BLAST against the NR database , Pfam [77] , COGs [78] , TIGRfams and POGs [37] . All complete genome representatives were manually examined and annotated using the HHpred server [79] . All contigs were named according to the nomenclature described below . Seawater ( 20 L ) collected from the DCM of the Mediterranean Sea ( 65 m deep ) on August 29th , 2011 , was filtered through a 0 . 2 µm filter ( Millipore GVWP2932A ) . Subsequently , phages were concentrated using tangential flow filtration ( TFF ) with a 30 kD polyethersulfone membrane from Vivaflow ( VF20P2 ) . The resulting phage concentrate was ultracentrifuged ( Optima XL 1000K Ultracentrifuge , Beckman ) for 1 h at 4°C using a Type 70 Ti rotor ( Beckman ) at 30 , 000 rpm ( 92 , 600 g ) . The pellet was resuspended in 1 mL of the seawater supernatant and treated with 2 . 5 units DNase I at 37°C for 1 hr , and 70°C for 10 min to remove bacterial DNA . The phages were then lysed in 0 . 50 mg/mL Proteinase K and 1 . 0% SDS at 56°C for 1 h followed by two rounds of phenol/chloroform/isoamyl alcohol extraction . The aqueous phase was then chloroform/isoamyl alcohol extracted and ethanol precipitated and resuspended in sterile water . DNA quantity and quality was determined using gel electrophoresis and Picogreen . Multiple amplification displacement ( Illustra GenomiPhi V2 DNA Amplification Kit , GE Healthcare ) was performed using ca . 30 ng of DNA for each of five reactions . The resulting DNA ( ca . 5 µg ) was sequenced in one third of an Illumina lane , yielding approximately 18 Gb of sequenced data ( paired end reads , 300 bp insert size ) with a total of ∼180 million reads . An all-vs-all comparison , using BLASTN [80] was performed for all contigs . Only >95% identical hits and with lengths >50 bp were retained . Overlapping hits , if any , were merged together using the mergeBed program in the BEDtools package [81] . The total length of these hits was then used to compute percentage coverage of the contig length . All pairs of contigs selected satisfying the coverage criteria ( of 20% in the first round of clustering and 95% in the second round ) were visualized in Cytoscape as a connected network [82] . Groups of connected contigs in these networks were considered as valid clusters . The 1148 contigs were clustered first using a criterion of >20% coverage but with very high nucleotide sequence identity ( >95% ) . 117 clusters ( containing 914 contigs ) were obtained , and 236 contigs remained unclustered . In the next step , the contigs in each cluster were clustered at an even stricter criterion of at >95% coverage and >95% nucleotide identity to identify nearly identical contigs . Further examination of the 102 subclusters obtained after this second step , allowed us to identify 208 complete phage genomes indicated by the circular-like organization of two or more contigs of a cluster . Similarly , end redundancy in contigs that were unclustered was used to identify complete genome representatives . As described above , an all-vs-all nucleotide comparison was used first to cluster all viral contigs using cut-off of 95% sequence identity and 20% coverage . Contig clusters formed in this step were named given a cluster number ( e . g . C1 , C2 etc ) . Unclustered contigs were tagged with a “U” , for “unclustered” . In the second round of clustering , we used the same sequence identity ( 95% ) but a higher cut-off to coverage ( 95% ) to identify the most highly related and syntenic contigs within each cluster . At this stage , if multiple clusters were obtained within a single cluster ( say C1 ) , they were tagged alphabetically , e . g C1A , C1B , C1C etc . Contigs within a cluster ( C1 ) , but not part of any further subclusters were not tagged again . All clusters ( both clusters and subclusters ) were examined manually for completeness . For example , if a complete genome representative ( CGR ) was identifiable in subcluster C1A , it was tagged as a CGR-C1A . If a CGR was identifiable in a cluster , it was tagged as CGR-C1 . If a CGR was found in a cluster , all other contigs that were not identified as CGRs , were tagged as CGF ( complete genome fragment ) . The naming scheme is described in detail below . Following the suggestions made for the nomenclature of viruses , we have used the following procedure for the nomenclature of uncultured viruses described in this work: ( 1 ) uv - uncultured virus ( 2 ) MED - three letter abbreviation in capitals indicating origin of the sample ( 3 ) CGR/CGF/GF - field indicating if the contig refers to a complete genome , a fragment of a complete genome , or just a genomic fragment . CGR ( complete genome representative ) is a contig that is assumed to be a complete phage genome . There may be more than one CGR in a cluster . CGF ( complete genome fragment ) is a contig that cannot be inferred to represent a complete phage genome , but is part of a cluster that contains a CGR ) . GF ( genomic fragment ) is similar to a CGF but without any CGR . Such GF contigs can have an extra name field ( see below ) indicating they are unclustered ( U ) . ( 4 ) C1/C1A/C1B/U - indicates the clustering status of the contig . ( 5 ) Field containing the contig identifier , e . g . MedDCM-OCT-S14-C437 . An example of a complete identifier is uvMED-CGR-C1-MedDCM-OCT-S17-C19 , enabling quick identification of several key features of a phage genome/contig . Several well-classified reference phage genomes , identified using the ICTV classification ( http://www . ictvonline . org ) were downloaded from NCBI . In addition , all known marine phage genomes were included in the comparison . Each genome was compared to another using TBLASTX [80] using the BLOSUM45 matrix . A hit was considered significant if it had >30% sequence identity , a minimum length of 30 aa and an e-value of at least 0 . 01 . The bit score of all such selected hits in a comparison was summed up to give a comparison score for a pair of genomes . Closely related genomes get higher comparison scores . To normalize for different genome sizes each phage genome was also compared to itself to obtain a self-score . The Dice coefficient , which is a similarity metric ranging from 0 to 1 , was computed as follows , Dice = ( 2*AB ) / ( AA+BB ) , where AB is the comparison score of phage A with phage B , AA and BB are the comparison scores of phages A and B with themselves respectively . This metric was transformed to a dissimilarity metric by subtracting it from one . A neighbor joining tree was constructed from the complete distance matrix using the PHYLIP package [83] . Separate initial comparisons were run for well classified podoviruses , myoviruses and siphoviruses ( classification obtained from http://www . ictvonline . org ) to examine the validity of the approach ( See Figure S2 , Figure S3 and Figure S4 ) . The tree shown in Figure 2 was created using a comparison of all reference phages and with the complete genome representatives ( 208 CGRs ) identified in this study . In the comparison of all tailed phages to each other ( Figure 2 ) , several well described phage groups are separable , e . g . Autographivirinae , Tevenvirinae , Spounavirinae etc . For the terminase tree , Pfam domains , COGs , POGs , TIGRfams were searched using hmmsearch program in the HMMER3 package [84] ( evalue 1e-5 ) , in addition to NCBI BLAST [80] to identify large subunit terminase sequences in the entire dataset . 401 unique sequences were identified in the contigs . In addition , 125 reference sequences and 105 terminase sequences from marine phages were included . A total of 631 terminase sequences were used for the alignment . For the phylogenetic trees of photosystem genes psbA and psbD , protein sequences were extracted from the annotated metagenomic fosmids and compared to NCBI NR database using BLASTP to recover additional sequences . Several previously described sequences were also used . All alignments were created using Muscle [85] , manually inspected and trimmed as necessary , and maximum likelihood trees were constructed using the program FastTree2 [86] using a JTT+CAT model and an estimation of the gamma parameter . Bootstrapping was performed using the Seqboot program in the PHYLIP package [83] . We used both metagenomes ( MedDCM [10] , Global Ocean Sampling [11] ) and metavirome datasets from the Sargasso Sea , British Columbia coastal waters , Gulf of Mexico , Arctic Ocean [21] and the Pacific Ocean [22] . In addition , the metavirome ( MedDCM-Vir ) constructed in this study was also used . For depicting comparative recruitment across metaviromes and metagenomes ( as shown in Figure 5 ) , a hit was considered if it was at least 50 bp long , had an e-value of less than 1e-5 and more than 95% identity . The number of hits to each phage contig was divided by the length of the contig ( in kb ) and also by the size of the database ( number of reads recruited per kb of contig/size of the database in Gb ) , which provides a normalized measure to compare recruitments by differently sized contigs versus several metagenomes . This measure is abbreviated as RPKG ( Reads per Kb per Gb ) . All 1148 contigs assembled in this study have been submitted to DDBJ and are available using the accession numbers AP013358-AP014505 . The metavirome has been deposited in NCBI SRA with the Bioproject number PRJNA210529 . | Prokaryotic species contain extremely large gene pools ( pan-genome ) the study of which has been constrained by the difficulties in getting enough cultivated representatives of most of them . The situation of their viruses , also known as phages , that provide part of this genomic diversity and preserve it , is even worse . Here we have found a way to bypass the limitation imposed by pure culture to retrieve phage genomes . We obtained large insert clones ( fosmids ) from natural communities that are undergoing active viral attack . This has allowed us to triple the number of genomes of marine phages and could be similarly applied to other habitats , shedding light into the biology of the most numerous and least known biological entities on the planet . They exhibit a remarkable degree of variation at one single geographic site but some seem also to be prevalent worldwide . Their frequent mosaicism indicates a high level of promiscuity that goes beyond the already remarkable hybrid nature of prokaryotic genomes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Expanding the Marine Virosphere Using Metagenomics |
Protein-RNA docking is hampered by the high flexibility of RNA , and particularly single-stranded RNA ( ssRNA ) . Yet , ssRNA regions typically carry the specificity of protein recognition . The lack of methodology for modeling such regions limits the accuracy of current protein-RNA docking methods . We developed a fragment-based approach to model protein-bound ssRNA , based on the structure of the protein and the sequence of the RNA , without any prior knowledge of the RNA binding site or the RNA structure . The conformational diversity of each fragment is sampled by an exhaustive RNA fragment library that was created from all the existing experimental structures of protein-ssRNA complexes . A systematic and detailed analysis of fragment-based ssRNA docking was performed which constitutes a proof-of-principle for the fragment-based approach . The method was tested on two 8-homo-nucleotide ssRNA-protein complexes and was able to identify the binding site on the protein within 10 Å . Moreover , a structure of each bound ssRNA could be generated in close agreement with the crystal structure with a mean deviation of ~1 . 5 Å except for a terminal nucleotide . This is the first time a bound ssRNA could be modeled from sequence with high precision .
RNA participates in most processes leading to genome expression and its regulation [1 , 2] , mainly in association with proteins [3 , 4] . Protein-RNA interactions are also involved in several neurodegenerative diseases [5] and cancers [6] . Understanding such interaction and the design of drug molecules requires the three-dimensional structure of protein-RNA complexes [7] . In many cases the protein bound RNA molecule is able to adopt a great variety of conformations . In particular , the structure determination of complexes containing flexible single-stranded ( ss ) RNA is a major challenge . Protein-RNA docking methods could help to generate at least models of such interactions . The first task in docking is to sufficiently sample the space of possible conformations and relative orientations ( i . e . poses ) of the components so as to include near-native structures . Similar to existing protein-protein docking methods [8 , 9] , most current protein-RNA docking methods consist of docking rigid structures of unbound RNAs or their domains [10 , 11] , with no or very limited conformational sampling of the RNA conformations prior to docking . Recently , some efforts have been made to model RNA flexibility , by use of ( i ) coarse-grained models to account for atomic-scale inaccuracies [12] , ( ii ) normal modes analyses and elastic network models [13 , 14] to explore large linear global motions , ( iii ) local backbone perturbations modeling non-linear deformation [14 , P . Setny , I . Chauvot de Beauchêne and M . Zacharias , in prep . ] , or ( iv ) comparison to a template library of protein-RNA complex structures [15] . Such semi-rigid-body methods can perform well when moderate or predictable conformational changes occur [8 , 9] . However , RNA conformational changes upon association with protein can involve global rearrangements , changes of secondary structure elements and/or flipping-out of bases from intra- to extra-helical position , which most docking methods fail to model [16] . The limits of current methods have been illustrated by the 15th round of the Critical Assessment of PRedicted Interactions ( CAPRI ) experiment [17] . The first and so far unique CAPRI target consisting of a protein-RNA complex to be modeled from unbound structures has been largely unsuccessful [16] . The accuracy of current protein-RNA docking methods is limited especially when some single-stranded loops participate in the binding [13] . Even RNA molecules that are otherwise well-structured contain such single-stranded regions , which are highly flexible or even disordered in the unbound form but carry the specificity of most protein-RNA recognition processes [18 , 19] . Moreover , many RNA-binding proteins bind only single-stranded RNA ( ssRNA ) , for which no unbound structures are available [20 , 21] . For docking a highly flexible ligand , fragment-based docking forms an alternative approach to docking . It consists of cutting the ligand into fragments , docking them separately on the receptor followed by assembling the compatible poses . The main advantage is that no unbound structure of the whole ligand is required . However , a structure of the fragments themselves is still needed: if the fragments are themselves flexible , a structural library that samples the possible conformations of each fragment is required . The second limitation is that all fragments must participate in binding within the native complex , making enough favorable contacts with the receptor for this position to be sampled . In contrast , in rigid-body docking , if the unbound conformation of the ligand is accurate enough , only part of the ligand needs to make specific contacts with the receptor , the position of the rest of the ligand being fully determined by the position of the interacting part . Fragment-based docking has been successfully applied to protein-ligand docking , especially for drug design [22] . In this case , the fragments are typically small ( ring , linker or side-chain [23] ) and the number of fragments to be joined is small ( 2–5 fragments ) [24] . A first attempt to model protein-bound ssRNA has recently been made by the RNA-Lim method [25] . Tested on one 6-nucleotide ssRNA–protein complex , and restricting the search on the known binding site , RNA-Lim achieved only limited success , with a 5 Å precision on the nucleotide placement in ~ 10% of the proposed solutions . RNA-Lim does not predict the orientation of nucleotides , and so far , no fragment-based docking/modeling method has been developed that allows modeling ssRNA bound to a protein with high precision . This highlights the highly challenging difficulty of this biologically relevant problem . In the present study , we present a proof-of-principle of a fragment-based approach for ab initio modeling of a protein-bound ssRNA at an unprecedented level of detail . Assuming that all nucleotides bind the protein , our method does not require any structural information on the ssRNA , assembling it from the sequence alone . Moreover , in contrast to previous methods , no prior knowledge on the binding site is required . Each fragment is approximated by a conformational ensemble generated by exhaustive docking of all conformers in a structural library of trinucleotides , built from all the existing experimental structures of protein-ssRNA complexes . Ensembles corresponding to each trinucleotide sequence present in the RNA sequence are docked all around the protein , and spatially overlapping poses corresponding to overlapping sequences are selected to build the RNA chains . In the current study , the scope is limited to complexes containing the most abundant RNA-binding domain in proteins: the “RNA recognition motif” ( RRM ) [20 , 21] . RRMs are present in 2% of all proteins in the human genome , and 44% of RRM-containing proteins contain two or more RRMs [21] . In particular , we consider complexes where the RNA binds two RRMs , each nucleotide interacting with the protein , and nucleotides outside the binding site are discarded . The exhaustive docking of a single trinucleotide sequence results in a very large number of poses ( 35–40 million ) . To reduce computational costs , we focus here on homopolymer RNAs , allowing us to perform only one docking predicting the structure of all RNA fragments simultaneously . The PDB contains two complexes corresponding to those criteria ( two RRMs + homopolymer ssRNA ) : one poly ( U ) and one poly ( A ) 8-nucleotide ssRNA , bound to two different proteins . For those two test-cases , we perform thorough analyses of different docking regimes with different amounts of structural knowledge . We first validate the fragment-based approach by docking and assembling the bound RNA fragments on the bound protein . Then we assess the cost on the sampling accuracy of the use of sub-optimal fragments conformations , by docking and assembling only the closest-to-bound conformers in our library . The problem of combinatorial explosion due to usage of the whole library is then addressed and its impact on the results evaluated . Finally , we explore and discuss the correlation between the precision of the best docking solution and the number of incorrect decoys . For each complex , the native structure of seven consecutive nucleotides was sampled in close agreement with the published crystal structure ( ~ 1 . 5 Å RMSD ) , a precision never reached so far . Such a limited benchmark does not allow us to claim any generality of our method . However , it provide a convincing proof-of-principle for fragment-based docking of protein-ssRNA complexes , pushing farther the limits of modeling RNA flexibility in docking .
To validate the fragment-based approach , we took the ssRNA conformation from each complex and cut it into 6 overlapping trinucleotides . We used those fragments as a “bound library” to perform fragment-based docking . This was compared to a standard rigid-body docking of the bound ssRNA as a whole onto the protein . For bound docking , we would not expect a fragment-based approach to outperform traditional rigid-body docking . In general , assuming a reasonably accurate scoring function , a large number of favorable contacts highly favors the native pose . In fragment-based docking , however , the favorable contacts are split among all fragments , making the docking of each fragment more difficult in terms of scoring . Even more limiting is the possibility that some fragments establish no or few favorable contacts with the protein , their position in the native complex being only constrained by the favorable interactions established by the adjacent fragments . In such cases , the sampling of these fragments is not possible within a reasonable number of poses . Standard rigid-body bound docking reproduced the complex structure with 0 . 2 and 0 . 9 Å RMSD for 1B7F and 1CVJ , respectively . To assess the effect of switching to a fragment-based approach , we performed separate docking for each fragment with the same protocol as for rigid-body bound docking . After discarding the redundant poses , we selected the 20% top-ranked poses in ATTRACT force-field . The fragment docking proved of comparable efficiency to the rigid-body docking , with very little loss in accuracy of the sampling: all fragments were sampled with a precision of 0 . 5–1 . 2 Å ( Table 1 ) . Therefore , the process of cutting the RNA into fragments does not lead to a significant loss in accuracy or precision , at least in our two test-cases . Also , the scoring was accurate enough to keep almost all generated hits ( RMSD < 2 Å ) in the 20% top-ranked poses . These results suggest that , in the native form of our test complexes , each fragment establishes enough favorable contacts with the protein to participate in the positioning of the whole RNA , independent of the constraints applied by the adjacent fragment . However , the number of hits and near-hits ( RMSD < 5 Å ) formed only a small fraction within the pool of selected poses . To enrich this fraction , we performed a position-specific filtering based on the propensity of each pose to form ssRNA chains . With six different pools , we tested all possible chain connections between the poses in pool n and in pool n+1 . Connectivity was defined by an overlap criterion , based on a strict upper distance limit between the atoms in the last nucleotides of n and the first nucleotides of n+1 . Based on these connectivities , all possible six-fragment chains were enumerated . By selecting only chain-forming poses , the large majority of hits was kept and essentially all wrong poses were eliminated ( Table 1 ) . For each complex , the complete RNA chain was modeled with sub-angstrom resolution ( geometric mean of CG RMSD over the 6 fragments ) . This method proved highly selective: more than 99% of the 6 . 105–4 . 105 chains built for 1B7F - 1CVJ respectively had an RMSD under 2 Å , and 75–96% under 1 . 5 Å . More importantly , we found that position-specificity is not a requirement for the chain propensity filter . In a second chain assembling test , all six bound-docking fragment pools were merged into a single pool , equivalent to performing a single docking run with a library of six ( bound ) conformers . Connectivity was considered between all poses within the pool ( a pose from the conformer corresponding to fragment 1 could thus be placed at any position in the chain , not only 1st position ) , and all poses with a propensity to form chains of at least five fragments were kept . As shown in Table 1 , this chain propensity filter , used in all subsequent experiments , performs as well in terms of selectivity as the position-specific filter . More than 81% of the hits are kept by the chain propensity filter , whereas only ~1% of the total poses are kept . Cutting the RNA into fragments allows us to model flexibility at the fragment level . To do so at the trinucleotide level , the conformational space for each possible trinucleotide sequence ( in our case , AAA and UUU ) must be sampled . In the absence of a bound structure , conformational sampling can be provided by using a generic library for single-stranded , protein-bound ssRNA trinucleotides that occur in nature . However , to the best of our knowledge , no such library exists . The RNA fragment library used by FARNA for de novo prediction of RNA was built “from a single crystal structure containing just over 2 , 700 ribonucleotides from the large ribosomal subunit from Haloarcula marismortui [1FFK]” [29] , which is mainly double-stranded . The libraries used by MC-Fold/MC-Sym [30] ModeRNA [31] or RNA-MoIP [32] represent only fragments that are partially or fully double-stranded fragments ( “Nucleotide Cyclic Motifs” ) [33] or internal loops ( which limit the backbone conformations sampling by a loop closure constraint ) . Therefore , for the current approach we extracted all trinucleotide structures from ~500 ssRNA-protein complexes available in the PDB ( July 2014 ) and built exhaustive non-redundant libraries of 1305/1140 UUU/AAA protein-bound fragments . The two test-case complexes ( 1B7F and 1CVJ ) were excluded from the library building process . We computed the RMSD of the best-fitting conformer of the library with respect to each fragment in our test cases . Our library proved exhaustive enough to approximate each bound trinucleotide fragment within 2 Å , and in the great majority of cases ( 75% ) within 1 Å ( Table 2 ) . In the future , to further increase the accuracy of the docking , one should regularly update the library with new resolved structures of protein-RNA complexes . As a next step , we evaluated the effect of the inaccuracy of even the best conformations ( closest to the bound fragments ) in our library on the docking results . When docking the whole UUU/AAA libraries and assembling the poses into chains , the best solutions ( smallest RMSD toward native form ) are likely to be formed by a chain of poses of the library conformers that are similar to the bound form . For a first evaluation of the capacity of our library to sample near-native solutions , with a reduced computational cost , we performed a biased docking test for each complex: prior to docking , we selected for each bound fragment the best fitting conformer in our library , resulting in six UUU/AAA conformers out of 1305/1140 . After docking , we retained the 20% best poses for each conformer and merged them into a unique pool , ending up with a total of 19 , 293 and 17 , 345 non-redundant poses for 1B7F and 1CVJ respectively . For each complex , all poses were compared to each of the bound fragments , and the number of poses close to each fragment was assessed . Hits were found for 75% of the fragments , and near-hits for all fragments ( Table 2 col . I ) . As expected , the RMSD of the best pose is linearly correlated to the accuracy of the best-fitting conformer ( Pearson coeff 0 . 72 , p-val 0 . 008 ) . The most inaccurately docked fragments are frag1 in 1B7F , and frag6 in both 1B7F and 1CVJ ( 2 . 3 Å , 2 . 0 Å and 2 . 9 Å respectively ) . The first one corresponds to the most deeply buried fragment in the binding site . The structures of the two frag6 correspond to conformations that are less well-approximated in the fragment library: the best conformers display 1 . 8 Å RMSD when fitted to the bound form , versus 0 . 3 Å to 1 . 1 Å for the other fragments ( Table 2 ) . Additionally in 1CVJ , the nucleotides from fragment 6 establish interactions not only with the protein but also with the RNA of symmetrical units in the crystal ( fragment 6 1st and 3rd nucleotides ) , and with a soluble adenosine-5'-monophosphate ( fragment 6 2nd nucleotides ) . This makes it more difficult to sample the correct pose of this fragment on the protein alone . All nucleotides in both complexes establish H-bonds via their bases and/or phosphates , except the 3rd nucleotide of 1B7F frag6 . This nucleotide binds the protein by H-bonds via its O3' and O2' oxygens , which position in the coarse grain representation is more loosely defined than of the other partially charged atoms . This could also contribute to a worse sampling of that fragment . Apart from these limitations , our docking results indicate that ATTRACT was able to sample and rank solutions close to the optimal position of each conformer in the 20% top-ranked poses . To account for a decreased sampling quality of the terminal fragments , we decided to build 5-fragment chains for the docking poses . We applied the same chain-propensity filter as for bound docking . Even more so , the filter eliminated virtually all incorrect poses , ending up with 53 and 24 poses out of 19293 and 17345 , respectively ( Table 2 ) . Again , the procedure proved highly selective: 38–63% of the retained poses were hits for 1B7F and 1CVJ , respectively , compared to 0 . 4–0 . 1% before filtering ( Table 2 ) . Moreover , for all fragments for which a hit was in the top 20% , one or more hits were kept after filtering , usually the ones with the best RMSD . Finally , the filtered poses were assembled into all possible 5-fragment chains ( 166–69 chains ) and compared to the bound ssRNA chain ( nucleotide 1–7 ) . The best chain had an average RMSD of 1 . 6–1 . 0 Å for 1CVJ and 1B7F , respectively ( Fig 3 ) . More importantly , this RMSD was representative for the whole result . For 1CVJ , 64% of the chains had an overall RMSD of better than 2 Å , and all chains were within a 5 Å deviation . For 1B7F , there was a little more diversity: 21% of the chains within a 2 Å RMSD of the native geometry , and 27% within 5 Å . We clustered the poses at the 5 Å ( 1B7F ) or 0 . 5 Å ( 1CVJ ) level , in order to get similar numbers of clusters despite the higher diversity in poses on 1B7F . The correct cluster was the 1st-largest for 1CVJ and the 5th-largest for 1B7F , with all chains in this cluster within 1 . 1–1 . 8 Å respectively . In conclusion , using approximately correct conformations for the fragments , the correct chain was one of the very few possible ways to build a poly-U/A hexanucleotide onto the protein . A similar procedure was applied considering not only the best conformers but the whole UUU/AAA sub-library ( 1305/1140 conformers ) . This should in principle not modify the sampling compared to biased docking , as the poses obtained by biased ( subset of the library ) docking will constitute a subset of the poses obtained by unbound ( whole library ) docking . However , the inclusion of the other library conformers results in a large number of mostly inaccurate decoys with a potential impact on the ranking of the correct solutions . In addition , compared to biased docking , the very high number of poses generated by unbound docking ( 30 , 000 * 1305 = ~40 million , compared to just 180 , 000 for biased docking ) causes considerable additional numerical demand and we had to adapt our protocol accordingly . First , to take into account the redundancy induced by close conformers in the library , we selected only the 5% top-ranked non-redundant poses for each conformer , instead of 20% as for our biased docking . Second , processing such a large pool of poses was not possible in terms of computational memory . Therefore , we assembled first a small sub-pool of poses , retained the chain-forming fragments , and selected all related poses ( close in RMSD ) from a larger sub-pool , in an iterative procedure that eventually kept about two-thirds of all top 5% poses before the final filtering . The docking produced poses within 3 Å RMSD toward all bound fragments but frag6 in 1B7F , similarly to what was obtained by biased docking ( Table 3 ) . Interestingly , despite the reduced percentage of poses kept per fragment compared to our biased docking ( 5% vs 20% ) , the quality of our sampling was not significantly changed . The best sampling of some fragments was even improved ( see 1B7F frag5 and 1CVJ frag4-5 , Tables 2–3 ) , which means that a conformer close to the best-fitting conformer was docked better than the best-fitted conformer itself . Thus , the redundancies in the poses induced by the use of the full library made the 5% top-ranked poses sufficient to reach a good sampling . However , a notable exception is 1B7F fragment 2 , for which hits were no longer among the top-ranked poses . In addition , for all fragments , the large increase in the number of candidate poses reduced the fraction of hits among the top-ranked poses by an order of magnitude or more . The large increase in the number of candidate poses in unbound compared to biased docking ( in the previous paragraphs ) made it much more difficult to select hits and near-hits . Among all fragments , the best docking solution was kept in the filtered solutions for only 3 of the 12 cases ( Table 3 , Fig 4 ) . For 1CVJ , the chain-propensity filter performed well: it kept almost half of the hits while selecting only 0 . 6% of all the poses , leading to a 78 fold enrichment . Still , because the chain-propensity filter selected a few thousand structures , rather than a few dozen for biased docking , the hits represented only 0 . 4% of all selected poses ( compared to 64% for biased docking ) . The procedure performed less well for 1B7F , selecting no hits at all , which might be explained by the reduced sampling for fragment 2 at the docking stage ( no hit , versus 3 hits for biased docking ) . However , for both 1CVJ and 1B7F , the procedure led to a significant enrichment of near-hits , increasing their percentage from 3–4% to 10–13% respectively , while keeping less than 1% of the docking poses . For all experiments , the chain-propensity filter was shown to be highly selective in eliminating incorrect solutions ( that cannot form an ssRNA chain ) . For biased docking , we found it to be rather sensitive to the chain length parameter . With a chain-propensity filter based on the capacity of each pose to participate in 6-fragments chains instead of 5-fragments chains , no chains were formed at all ( causing all fragments to be eliminated ) . In contrast , for unbound docking , changing the chain length to 4 or 6 had little effect: the fraction of near-hits among the selected poses remained at ~14% for both test cases ( supplementary material , S1 Table ) . Despite the still high number of decoys after filtering , the unbound docking permitted to exactly delineate the binding site ( Fig 5 ) without taking this information into account prior to docking: The worst pose after filtering was at only 16 . 7–14 . 9 Å from the closest fragment in 1B7F and 1CVJ respectively; for each complex , more than 65% of the poses were under 10 Å and more than 95% under 15 Å . So , our procedure for fragments assembly proved an efficient method to discard remote poses . These results also suggest that the method could be used for binding site prediction . The novelty compared to existing methods is that is does not use any information from sequence conservation or homology . But as we tested it only on a very well conserved pattern , where homology-based methods for binding-site prediction should work very well , this direction would need farther investigation on other patterns . To reduce further the number of solutions to consider for each fragment , we clustered the 7863–3268 filtered poses at 3 Å , and selected the best-ranked pose in each of the 287–440 clusters obtained for 1B7F - 1CVJ respectively . By assembling these fragments into chains , with weaker overlap-restraints , a total of 242 and 334 chain-forming poses were selected , among which 24% close-hits ( RMSD < 6 Å ) , all fragments in frag1-5 being well sampled ( Table 3 ) . These poses could be assembled into 10064–4413 chains , with 3–2% close-hits ( geometric mean over frag1-5 RMSD < 6 Å ) . Measured over the whole chain , the best precision was 5 . 7–3 . 6 Å , and this was sufficient to define both position and orientation of most of the 7 nucleotides in each complex ( Fig 6 ) . The chain propensity filter selects an ensemble of poses or chains that is still rather large to carry out subsequent refinement steps . Therefore , to reduce this number , we investigated if it is possible to assign a ranking to the poses within the selected ensembles . We used combinations of three statistics: chain-propensity , the number of chains a pose participates in; ATTRACT rank , the rank of the pose according to the ATTRACT force field; and , for chains , overlap , the violation of the harmonic distance restrains between two consecutive poses in a chain . We tried to rank the poses and chains obtained by biased and unbound docking , according to the scoring functions Sposes and Schains , based on these statistics . Since we have only two test cases , we emphasize that the performance of such scoring functions should be considered as a proof-of-principle , and should be trained on a much larger benchmark before any predictive power can be credited . Still , given these caveats , we found that the following scoring functions worked well: Sposes=log ( chain−propensity ) ATTRACTrank ( 1 ) Schains=∑0<i<NfragmentsOverlap ( posei , posei+1 ) ∑poses ( ATTRACTrank ) 2Nfragments ( 2 ) For biased docking , the ranking proved efficient in selecting the best solutions , both at the poses and chains levels , for both complexes ( Fig 7 , S1 File ) . The ranking was less efficient in selecting the best solutions for unbound compared to biased docking , as expected by the use of non-correct conformers in the docking . Yet , it still achieved a statistically significant enrichment of good solutions in the best-ranked solutions , both at the poses and chains levels , for both complexes . At the poses level: 178 of the 1038 poses with RMSD < 2 Å were ranked in the top 1000 out of 7862 for 1B7F and 5 of the 7 poses with RMSD < 1 . 5 Å were ranked in the top 1000 out of 3268 for 1CVJ ( p-value 6x10-6 and 0 . 03 ) . At the chains level , 74 out of 309 chains with RMSD < 6 Å were ranked in the top 2000 out of 13693 for 1B7F , and 53 out of 119 in the top 2000 out of 6190 for 1CVJ ( p-values 4x10-5 and 0 , 001 ) . Although below usual precision in classical whole-body docking , these results constitute a considerable improvement compared to the poor success that had been achieved so far in docking protein-ssRNA complexes . To the best of our knowledge , essentially all current methods are limited to structured RNA . Only the RNA-lim method [25] has attempted to predict protein-ssRNA structures based on fragments . The authors dock and assemble ultra-coarse-grained nucleotides ( one bead per nucleotide ) in a pre-defined binding site . The very small size and the simplistic model of the fragments greatly limit the accuracy of the results , partially compensated by the limitation of the search to the known binding site . They achieved very limited success , sampling center-of-mass ( COM ) positions ( not orientation ) of six RNA mono-nucleotide fragments with ~10% “coarse starting estimates” ( ~ 5 Å on COM ) on a single test-case . In contrast , our method correctly sampled both position and orientation for most nucleotides in an heptamer RNA on two test-cases . Our method worked very well when the conformer library was biased towards the closest-to-bound conformers , achieving a best precision of ~1 . 5 Å at both the fragment level ( best fragment among dozens of poses ) and the chain level ( among a hundred of chains ) . With fully unbound docking , this precision could only be achieved when large numbers ( 105–106 ) of poses where considered . Filtering the number of poses down to a few thousands worked well for 1CVJ ( ~1 . 5 Å best precision ) but less so for 1B7F ( ~4 Å best precision ) . At the chain level , our method achieved a best precision of 3 . 6–5 . 7 Å ( among thousands of chains ) . In real cases , the chains could be further filtered by experimental data on specific contacts ( e . g . from protein mutagenesis or from RNA sequence specificity ) , especially at the extremities of the chains were the diversity in positioning among the chains is the highest . To be successful , the building of chains needs each of the fragments to be correctly sampled . But a correct sampling is possible only if the fragment establishes sufficient contacts with the protein . To get an idea if the contacts of each fragment in a test-case are sufficient to make the correct sampling possible , one can perform a quick docking test with the bound form of each fragment ( without usage of the library nor chain building ) . To evaluate the applicability of our methods to other protein families and other contact patterns , we performed such bound-bound fragment docking tests on 5 other RNA-protein complexes ( S2 File ) from different RNA-binding protein families and with different RNA binding modes ( S3 Fig , S2 File ) . The docking was successful ( best docking pose within 4 . 0 Å RMSD ) for all fragments in all complexes , but for frag-1 in 3V6Y and 4KRF ( S2 Table ) . Still , for those two complexes , 6 consecutive fragments could be well sampled . Noteworthy , in 4PMW , the presence of a Mg2+ ion participating in the binding of frag-12 , which is not taken into account in our current method , did not affect the quality of the sampling . In 3V6Y , the only failure is due to symmetries in the system ( S3 Fig , S2 File ) , the poses for frag-1 having RMSD < 4 . 0 Å when compared to the bound form of other fragments . In 3V6Y , the presence of a bulged out nucleotide at n7 , in the center of the RNA strand , had no impact on the quality of the sampling for that fragment . Poses were found with RMSD in 0 . 4–0 . 9 Å for the 3 fragments containing n7 . Therefore , the absence of contacts at least for one nucleotide can be compensated by contacts made by the adjacent nucleotides . When using a conformational library instead of the bound form , the bulged-out nucleotide is likely to be less well sampled , but our tests show that this would not affect dramatically the adjacent nucleotide , and consequently the building of chains . However , the current state of our method is not able to distinguish binding from non-binding RNA fragments , but only to sample the correct positioning of a fragment , assuming that it does bind to the protein . Therefore , the docking should be limited to the binding fragments . In a real case , this data could be obtained experimentally , e . g . by comparing the in vitro affinity of RNA with different sequences or with modified bases , or by NMR data ( e . g . intermolecular NOE , differences in bound-unbound chemical shifts , H/D exchange rates ) . In the present study , we focused on homopolymers for the convenience of rather short CPU time needed for the fragment docking as well as the selection of overlapping poses . The total procedure took around 14h for each case , the docking of one unique conformational ensemble ( for UUU or AAA sequence ) being run on 8 CPU and the chains built on 1 CPU . The two test-cases provide an important proof-of-principle for RNA-protein modeling , and the method is in principle extendable to arbitrary sequences . The extension toward heteropolymer will require more CPU time , as each trinucleotidic sub-sequence will require the docking of the corresponding conformational ensemble . However , the docking of the fragments are independent and can therefore be run in parallel . The building of chains begins with the identification of pairs of compatible poses , which can be run in parallel for each pair of fragments . The absolute time required by the method should therefore not be increased when applied on an heteropolymer rather than homopolymer sequence , if running on 8 CPU * Nb ( fragment ) , but would increase linearly with the length of the chain . Specific binding of proteins to ssRNA participates in key post-transcriptional regulatory processes . We developed a method to predict the structure of an RNA homopolymer complexed to a RRM-containing protein , based on the structure of the protein and the sequence of the RNA , using a fragment-based approach . For the first time , we were able to predict the structure of such a complex at high precision . The largest difficulty of fragment-based docking is the large number of fragment decoys to consider for chain-building , due to the intrinsically difficult scoring of small fragments . We showed that filtering docking poses of fragments by their chain-forming capacity can reduce the number of poses by two orders of magnitude . It significantly enriches the part of correct solutions ( poses/chains ) , while maintaining a good precision for the best solution . Moreover , we showed that scoring functions can further improve this enrichment . Our results provide an encouraging proof-of-principle for ab initio fragment-based docking of ssRNA on protein . The scoring functions will be further developed using a larger benchmark , for both before and after merging the RNA fragments into RNA chains . To reduce further the number of chains to build with the selected fragments , before refinement and scoring of the whole complex , we will test the usage of insights of specific protein-RNA contacts from conserved RNA-binding motifs in proteins in future studies . Finally , our method was tested on cases of a binding ssRNA that is uniform in sequence . The method is in principle extendable to arbitrary sequence , and this will be a direction of further research .
We extracted the structures of all trinucleotides from the ~500 ssRNA-protein complexes available in the pdb ( July 2014 ) , and sorted them by sequence . Adjacent nucleotides in a RNA strand can establish stacking interactions between their cycles . The conformation of a trinucleotides depends , apart from its contacts with a protein , from the arrangement of the cycle ( s ) of its nucleotides . Two pyrimidines ( C or U ) having the same cycles , but different substitutes , trinucleotides with three pyrimidines ( UUU , UCU… ) should have similar conformational spaces . Therefore , to increase the number of 'UUU' conformations in our libraries , we selected all fragments composed of three pyrimidine and converted them into UUU , by modifying the substitutes without changing the overall conformation of the fragment . We repeated the process for fragments made of three purines and mutated them into AAA . We ended up with ensembles of 1305/1140 UUU/AAA fragments . For each docking of RNA fragments , both the bound protein and the fragment were in coarse-grained representation . Each pyrimidine/purine was represented by 6 or 7 beads and each amino-acid by 3 or 4 beads [12 , 34] . For each docked conformer , 30 , 000 starting positions and respective orientation of the two partners ( protein and fragment ) were produced by the “randsearch” procedure of ATTRACT [35] , generating random starting positions . The positions of the center of mass ( COM ) of the fragment at each starting position are equidistant on a unit sphere of 75 Å radius centered on the protein . The fragments are attracted to the protein by a distance restraint toward the COM of the protein with harmonic constant of 0 . 0015 kcal/mol/Å . For each fragment , 1000 minimization steps were performed in ATTRACT coarse-grained force-field [14] , the long-range pairwise interactions between ligand and receptor being approximated on a pre-calculated receptor grid . A final re-scoring was performed without grid , pairwise interactions being considered until a squared distance of 50 Å . The final poses were sorted by ATTRACT score , and the redundant poses ( within 0 . 05 Å from a better scored pose ) were discarded . The overlap of two fragments was evaluated using ATTRACT scoring function with harmonic distance restraints and no force-field . The restraints were defined between the 2nd and 3rd nucleotides of the 1st fragment and the 1st and 2nd nucleotides of the 2nd fragment , such that each coarse-grained bead must occupy the same position , with some margin . The margin was defined with smaller values for the backbone than for the base ( 2 . 3 Å and 2 . 8 Å respectively ) , to account for the necessity to further link the backbone atoms in a chains refinement procedure . The harmonic constant was set to 100 kcal/mol/Å2 , and an overlap was considered satisfying when the total violation energy was below 2 kcal/mol . For assembling the hundreds of poses obtained by unbound docking with chain-propensity filtering and 3 Å -clustering , the distance restrains were enlarged to 5 Å , with no violation allowed . The chain-propensity of a pose is defined as the percentage of total possible chains it participates in . A list of possible combinations of poses was built for each pair of poses corresponding to frag ( i ) -frag ( i+1 ) . Then was attributed to each pose for frag ( i+1 ) the sum of the number of chains {frag1 , … , frag ( i ) } were the compatible poses for frag ( i ) participate in . Going backward , we attribute to each pose for frag ( i ) the sum of the number of chains {frag ( i+1 ) , … , frag ( 6 ) } were the compatible poses for frag ( i+1 ) participate in . We finally multiply the two indexes attributed to each pose to get the number of chains it participate in . In all the docking protocols , the chain-propensity filtering kept poses present in at least one out of 10 , 000 chains . The 0 . 5% top-ranked poses obtained by unbound docking were filtered in term of chain-forming propensity , then the poses in 1% top-ranked poses within 5 Å from at least one previously selected pose were added to the pool . The procedure was repeated with the 2% top-ranked poses , then with the 5% top-ranked poses . For the unbound docking , the 20% best-scored poses for each conformer were selected and grouped , then clustered by 2 Å . The centers of the 2Å-clusters were clustered at 3Å-clusters , and the centers of the 3Å-clusters at 4 Å . The overlap between the center of mass of the central structure of the 4Å-clusters were evaluated , and the pairs of clusters with low overlap-energy were stored . The same procedure was applied inside each pair of overlapping 4Å-clusters at the 3Å-clusters level , with a lower margin . Each center of 3Å-cluster belonging to the first 4Å-cluster was assembled wit each center of 3Å-cluster in the second 4Å-cluster . Same with each pair of overlapping 3Å-clusters at the 2Å-clusters level , then at the level of individual fragments , with decreasing overlap margins . According to optimization tests , we used distance restraints of 2 . 23 Å for backbone and 2 . 83 Å for side chain , with decreasing margins for overlap-energy for the different clustering levels . We chose a representative set of 5 complexes corresponding to different RNA-binding protein families and with different RNA binding modes regarding the length and sequence of the RNA , the shape of the protein binding site , the main parts of the nucleotides interacting and the main types of interactions ( hydrogen bond , electrostatics , stacking ) ( S2 File , S3 Fig ) . For each fragment , we docked its bound form , starting from 200 , 000 random positions and orientations . The 20% best-scored poses were retained and their position compared to the position of the corresponding fragment in the experimental complex . | Protein-RNA interactions fulfill a large variety of fundamental cellular functions , in particular for regulation of genome expression . A full understanding of these interactions requires an atomistic description of the interface in the complex . It can aid in silico design of new therapeutics to modulate these functions . However , structure determination of these complexes can be costly and in many cases difficult due to the transient nature of many protein-RNA interactions . Computational docking can help to generate structural models of protein-RNA interactions . Traditional rigid body docking methods largely fail due to the flexibility especially of single-stranded ( ss ) RNA that often forms the binding region in protein-RNA complexes . We developed an original approach to cope with ssRNA flexibility by assembling them from small structural fragments . Tested on two known complexes , our method could model the ssRNA at a level of detail never reached so far . These results constitute a proof-of-principle and major step towards designing a fully flexible RNA-protein docking methodology with a wide range of possible applications . | [
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] | 2016 | Binding Site Identification and Flexible Docking of Single Stranded RNA to Proteins Using a Fragment-Based Approach |
Genome integrity depends on correct chromosome segregation , which in turn relies on cohesion between sister chromatids from S phase until anaphase . S phase cohesion , together with DNA double-strand break ( DSB ) recruitment of cohesin and formation of damage-induced ( DI ) cohesion , has previously been shown to be required also for efficient postreplicative DSB repair . The budding yeast acetyltransferase Eco1 ( Ctf7 ) is a common essential factor for S phase and DI-cohesion . The fission yeast Eco1 ortholog , Eso1 , is expressed as a fusion protein with the translesion synthesis ( TLS ) polymerase Polη . The involvement of Eso1 in S phase cohesion was attributed to the Eco1 homologous part of the protein and bypass of UV-induced DNA lesions to the Polη part . Here we describe an additional novel function for budding yeast Polη , i . e . formation of postreplicative DI genome-wide cohesion . This is a unique Polη function not shared with other TLS polymerases . However , Polη deficient cells are DSB repair competent , as Polη is not required for cohesion locally at the DSB . This reveals differential regulation of DSB–proximal cohesion and DI genome-wide cohesion , and challenges the importance of the latter for DSB repair . Intriguingly , we found that specific inactivation of DI genome-wide cohesion increases chromosomal mis-segregation at the entrance of the next cell cycle , suggesting that S phase cohesion is not sufficient for correct chromosome segregation in the presence of DNA damage .
Correct chromosome segregation is fundamental for genome integrity , and facilitated by the cohesin complex , that tethers sister chromatids from S phase until anaphase , a function known as cohesion [1] , [2] , [3] . Cohesin consists of four subunits: Smc1 , Smc3 , Scc1 ( also called Mcd1 ) , and Scc3 and associates with DNA prior to replication [4] , [5] , [6] , [7] . In all organisms analyzed to date , loading of cohesin onto chromosomes requires a complex formed by the Scc2 and Scc4 proteins [8] , [9] . However , loading alone is not sufficient for actual sister chromatid cohesion to commence . Cohesion is established during S phase in an incompletely understood process that is closely connected with replication and depends on acetylation of Smc3 by the highly conserved acetyltransferase Eco1 ( also called Ctf7 ) [10] , [11] , [12] . Several proteins have been shown to be important for cohesion establishment , including Ctf18 , a subunit of an alternative replication factor C ( RFC ) complex and the proliferating cell nuclear antigen ( PCNA ) , [13] , [14] , [15] , [16] . Once established , cohesion is maintained until anaphase , when it is dissolved through cleavage of Scc1 by the enzyme separase ( for a review see [3] ) . DNA double strand breaks ( DSBs ) can arise during normal cellular processes such as replication stress and replication fork collapse , as well as programmed genomic rearrangements including yeast mating-type switching , immunoglobulin class-switch recombination and DSB induction during meiotic prophase [17] , [18] . Evidently , DNA damage can also be a consequence of exposure to DSB inducing agents such as ionizing radiation and various chemicals [17] . Regardless , correct repair of damaged DNA is vital for genome integrity . Cohesion formed during S phase is required for postreplicative repair of DSBs via homologous recombination ( HR ) [19] , [20] . In addition to S phase cohesion , recruitment of cohesin to the region around the DSB and formation of cohesion genome-wide , a phenomenon called damage induced ( DI ) -cohesion , has been shown to be important for DSB repair [21] , [22] , [23] . The establishment of DI-cohesion requires a number of proteins , such as the cohesion regulatory factors Scc2 and Eco1 , the Smc5/6 complex , the DNA-damage sensing protein Mre11 , the checkpoint kinases Mec1/Tel1 , phosphorylated H2A , and activation of the Mec1 target Chk1 [24] . In addition , DI-cohesion has been proposed to depend specifically on acetylation of Scc1 via Eco1 [25] . Of the factors required for both S phase and DI-cohesion , Eco1 has been shown to be a limiting component [23] . Interestingly , in the fission yeast Schizosaccharomyces ( S . ) pombe , the Eco1 ortholog Eso1 is expressed as a fusion protein with the translesion synthesis ( TLS ) polymerase Polη [26] . Eso1 is required for the establishment of cohesion but deletion mutants of Eso1 that lack the Polη-containing N-terminal part are effectively S phase cohesion proficient [26] . An additional link between Polη and cohesin is the S phase cohesion establishment factor Ctf18 that has been shown to exclusively activate Polη in Saccharomyces ( S . ) cerevisiae [16] , [27] . TLS polymerases are found in all domains of life [28] , [29] and are best known for their ability to bypass DNA damage that blocks the replication fork progression [30] . Since TLS polymerases have active sites with more open structures than the replicative DNA polymerases , they can bind to and replicate past DNA with aberrant structures [31] . S . cerevisiae has three TLS polymerases Rev1 , Polζ ( Rev3/7 ) and Polη [28] , [29] . The gene encoding Polη is in S . cerevisiae called RAD30 . This gene nomenclature was proposed based on the original finding that a RAD30 deletion causes UV-sensitivity [32] . Both Polη and Rad30 are seen as names for the protein in S . cerevisiae , while the human ortholog is called Polη . Here we are therefore using Polη as a common name for the protein while RAD30 is used when we are discussing the yeast gene . The function of Polη has been best characterized in the process that inserts appropriate nucleotides opposite UV induced cis-syn cyclobutane pyrimidine dimers ( CPDs ) , a type of DNA damage that typically blocks the progression of the replication fork since the highly stringent replicative DNA polymerases are unable to bypass it [33] , [34] , [35] , [36] . Patients with the Xeroderma pigmentosum variant disease ( XP-V ) , caused by loss of Polη function , display a higher rate of UV-induced mutations and a greatly increased incidence of skin cancer [37] , [38] . Here , we investigated the functional relationship between Polη and Eco1 in S . cerevisiae . We found that in the absence of Polη , the establishment of DI-cohesion is abolished . This deficiency could be counteracted by overexpression of ECO1 , as well as by an acetyl-mimic version of one of the Eco1 acetylation targets , SCC1 ( pGAL-scc1-K84Q , K210Q ) , indeed suggesting that Polη is important for the function of Eco1 . Despite the importance for DI-cohesion , RAD30 deleted cells are fully capable of postreplicative DSB repair during G2 . This could be explained by the findings that Polη is essential explicitly for DI genome-wide cohesion and not for loading of cohesin to the break , or for formation of DI-cohesion close to the actual DSB . In summary , this not only reveals that cohesion in response to DNA damage is regulated differently close to the break and genome-wide , but it also challenges the functional importance of the genome-wide form . Our study indicates that lack of DI genome-wide cohesion causes a predisposition to chromosomal mis-segregation at the entrance to the next cell cycle , which after exposure to repeated DSB inductions seems to have negative consequences for survival .
To investigate whether Polη is important for formation of DI-cohesion , the S . cerevisiae RAD30 gene that encodes Polη was deleted ( rad30Δ ) in strains harboring systems that allow distinction between S and G2 phase established cohesion , described in Figure 1A , 1C and 1E and below . All the assays used for detection of DI-cohesion are based on the Tet-repressor-GFP/Tet-operators ( TetR-GFP/Tet-O ) system for sister chromatid separation . This system utilizes the insertion of an array of Tet-operators at the URA3 locus , 38 kb from the centromere of Chromosome ( Chr . ) V , to which the endogenously expressed GFP-tagged Tet-repressor will bind . This results in one GFP focus in cells where the sisters are cohered and two foci where they are separated [21] , [22] , [23] . Initially we tested whether Polη was required for the formation of DI-cohesion in response to γ-irradiation ( γ-IR ) . Log phase yeast cells harboring a temperature sensitive ( ts ) SMC1 allele ( smc1-259 or smc1ts ) were arrested in G2/M by addition of benomyl . Expression of pGAL-SMC1 ( Smc1WT ) was initiated at permissive temperature in one half the cultures and DNA damage was induced by γ-IR , the cells were then allowed one hour to recruit cohesin , containing either smc1ts or Smc1WT , to DNA and to establish DI-cohesion . Thereafter , incubation at restrictive temperature caused degradation of the cohesion formed by smc1ts ( Figure 1A ) . As seen in Figure 1B , and as shown previously [21] , DI-cohesion was formed in response to γ-IR only when Smc1WT was expressed . However , DI-cohesion was strongly disabled in the absence of Polη . DI-cohesion has also been shown to arise genome-wide in response to a single DSB [22] , [23] . To examine whether lack of Polη would also prevent establishment of cohesion under these conditions , we used the smc1ts/Smc1WT system ( Figure 1A ) in combination with expression of the galactose-inducible , site specific , HO-endonuclease ( pGAL-HO ) , which induced a single DSB at the MAT locus on Chr . III ( Figure 1C ) . Because the Tet operators are located on Chr . V , any DSB-dependent cohesion that is observed must be genome-wide . Indeed , in the absence of Polη establishment of DI-cohesion was impaired also in response to one single DSB ( Figure 1D ) . To exclude that the effect seen on DI-cohesion was due to the combination of rad30Δ and smc1-259 , we took advantage of a noncleavable version of the Scc1 subunit ( Scc1NC ) of the cohesin complex which cannot be cleaved off the chromosomes by separase ( Figure 1E ) [39] . In this system for detection of DI-cohesion , cells are arrested in G2/M , and expression of pGAL-HO and pGAL-scc1NC are initiated by addition of galactose in half of the cultures . After 90 minutes , when cohesin has been loaded and cohesion formed in response to the DSB , the cells are released from the G2 arrest , by transfer into YEPD media , whereby also the break formation is stopped . The single DSB can then be repaired and the cells re-enter the cell cycle , go through anaphase and separate their sisters , unless DI-cohesion has been established using Scc1NC . Again , in wild-type cells DI-cohesion is established after DSB induction and expression of Scc1NC , while in rad30Δ cells it is not . We could also conclude that lack of DI-cohesion in the absence of Polη was not due to deficient S phase cohesion , caused by the combination of the smc1ts allele and the rad30Δ ( Figure 1F ) . Further evidence for an unperturbed S phase progression , was given by FACS analyses and determination of cell population doublings in comparison with WT cells ( data not shown and Figure S1 ) . From these results we propose that functional Polη is indeed required for DI-cohesion . Based on previous experiments where the function of Eco1 was inactivated , DI-cohesion was concluded to be important for efficient repair of DSBs in G2 [22] , [23] . After observing that Polη was required for establishment of DI-cohesion , we analogously wanted to investigate whether it was also important for postreplicative DSB repair . G2/M arrested WT and rad30Δ cells were exposed to γ-IR and thereafter allowed time for repair of induced damage . The dosage applied to the cells caused an approximate 70% reduction of the signal of intact Chr . XVI immediately after γ-IR ( Figure 2A–2B ) , as analyzed by pulse field gel electrophoresis ( PFGE ) and Southern blotting with a radioactive probe hybridizing to the left arm of Chr . XVI [19] . Over time this signal was restored to the same extent in WT and rad30Δ cells indicating that Polη , despite its importance for formation of DI-cohesion , was of no significance for repair of the damage that activates the genome-wide cohesion ( Figure 2A–2B ) . We then wanted to exclude that the repair was performed via Non-Homologous End Joining ( NHEJ ) , as an alternative to HR in the absence of DI-cohesion . The LIG4 gene encoding ligase 4 , essential for NHEJ [40] , was deleted ( lig4Δ ) , either alone or in combination with rad30Δ . In both cases DSB repair was as efficient as in WT cells indicating that postreplicative DSB repair can occur via HR also in the absence of Polη ( Figure 2A–2B ) . As a control we also analyzed the level of repair in cells where the RAD52 gene , absolutely required for HR [41] , was deleted ( rad52Δ ) either alone or in combination with rad30Δ , which caused a complete absence of repair as expected ( Figure 2A–2B ) . To exclude that the HR mediated repair performed in the absence of Polη was the result of a defective mode of repair incompatible with life , we analyzed survival after γ-IR by the ability to form colonies and found that >80% of both WT and rad30Δ cells survived . This was in strong contrast to the less than 1% of cells that survived in the rad52Δ population after exposure to the same radiation dosage ( Figure 2C ) . Thus , despite its importance for establishment of DI-cohesion , Polη was dispensable for repair of DSBs induced by γ-IR in the G2 phase . The assays used for detecting DI-cohesion are based on induction of either a single DSB on Chr . III by expression of pGAL-HO , or multiple DSBs randomly distributed throughout the genome by γ-IR , but causing approximately one break/Chr . V . Given that Chr . V is 540 kbp , a break would only rarely be formed in the direct vicinity of the TetR-GFP/Tet-O system at the URA3 locus , which is used for determining cohesion ( Figure 1A , 1C and Figure 3A ) . Thus , one possible explanation for why absence of Polη did not lead to DNA repair deficiency , despite causing defects in formation of DI-cohesion , could be that DI-cohesion genome-wide and close to the actual break are regulated differently . If this is true , cohesion that could be used for HR-based repair would still be established locally around each DSB in the absence of Polη . To test this , we inserted the recognition sequence for the HO enzyme 4 kb from the Tet-O array , and simultaneously deleted the endogenous recognition sequence at the MAT locus on Chr . III , in cells harboring the smc1ts/Smc1WT system for detecting DI-cohesion ( Figure 3A ) . In the presence of Smc1WT , cohesion was now established both in WT and rad30Δ cells in response to , and close to a specific DSB ( Figure 3B ) . DI-cohesion was formed only in the presence of Smc1WT confirming that this was due to presence of Smc1WT , rather than to sisters being kept together due to lack of DSB repair ( Figure 3C ) . Since DI-cohesion around the break and genome-wide seems to be regulated differently , from now on we will call them DSB-proximal cohesion and DI genome-wide cohesion respectively . We next asked whether also cohesin loading genome-wide and close to the break were regulated differently . In line with the lack of importance for establishment of DSB-proximal cohesion , recruitment of cohesin to the break in the absence of Polη was not affected ( Figure 3D ) . This was shown using chromatin immunoprecipitation combined with microarrays ( ChIP on chip ) on FLAG-tagged Scc1 , as described previously [21] . Furthermore , in the same type of experiments FLAG-tagged Polη could not be detected at or close to the DSB between 10 and 120 minutes after break induction ( data not shown ) . This was not because Polη could not be detected by ChIP on chip since Polη binding was detected at some replication origin sites during S phase arrest ( Figure S2 ) . We then examined possible differences in loading of cohesin to regions distant from a DSB , in WT and rad30Δ cells . To be able to distinguish between S phase and G2 phase loaded cohesin we performed ChIP-sequencing experiments on HA-tagged Scc1 , expressed from the GAL promoter , specifically in G2 . The genome-wide binding pattern of G2 loaded Scc1 was virtually identical in WT and rad30Δ cells and overlapped with the known binding pattern of cohesin , both in the presence and absence of DSB induction ( Figure 3E , and Figure S3A ) [42] . Recent data suggested that DNA damage causes an approximately 30% enhancement of cohesin binding genome-wide in human cells [43] . To exclude quantitative differences in cohesin binding between WT and rad30Δ cells , G2 loaded Scc1 binding was determined at a number of known cohesin association sites ( CARs ) in two selected regions of the genome , as well as at one non-binding site , using ChIP in combination with real time quantitative PCR ( qChIP ) . At the previously identified cohesin non-binding site , significantly less cohesin was detected than at CARs . However , we could not detect any significant changes in cohesin binding at the known CARs on Chr . V and VIII that we analyzed , neither in WT nor in rad30Δ cells before or after induction of a single DSB on Chr . III ( Figure S3B ) . Thus , despite that DSB-proximal and DI genome-wide cohesion are differently regulated , the basis for this disparity cannot be attributed to differences in loading of cohesin . Given that Polη is required for DI genome-wide cohesion , we decided to test whether this is a common function for TLS-polymerases . Strains were created with deletions of the REV1 or the REV3 gene ( which encodes the catalytic subunit of Polζ ) and DI-cohesion experiments were performed using the Scc1NC system and induction of one break by HO ( Figure 1E ) . As seen in Figure 4A , neither the Rev1 ( rev1Δ ) nor the Polζ ( rev3Δ ) polymerase was of any importance for DI genome-wide cohesion in response to a single DSB . In addition , none of the TLS polymerases were required to establish cohesion during S phase , as seen by lack of sister chromatid separation on arrival in G2/M . The possibility that Rev1 and/or Polζ could replace Polη at the break for DSB-proximal cohesion was excluded by the fact that even in a triple-deletion strain ( rev1Δ rev3Δ rad30Δ ) efficient DSB repair was observed ( Figure 4B–4C ) . Thus the function for Polη in DI genome-wide cohesion is not shared between the TLS polymerases . We next sought to understand the mechanism by which Polη supports formation of DI genome-wide cohesion . Since Polη is defined as a polymerase we naturally started by testing whether the TLS polymerase activity was required [28] , [29] . Plasmids harboring either the RAD30 gene or one of three different polymerase region mutations , D30A , E39A and D155A , known to completely abolish the polymerase function of Polη in vitro ( Figure 5A ) , were introduced into rad30Δ yeast strains containing the Scc1NC system ( Figure 1E ) . Deletion of RAD30 renders the cells UV sensitive , which was overcome by expression of the pRAD30-plasmid , but not by any of the plasmids with a mutated RAD30 gene , indicating that they are polymerase dead also in vivo ( Figure 5B ) . In Figure 5C we show that the pRAD30-plasmid in addition restores the DI-cohesion formation in the rad30Δ strain . In strains containing the D30A and E39A mutants , the same was found despite their UV sensitivity . However , the D155A mutation left the cells incapable of forming DI genome-wide cohesion . The possibility that the observed differences in cohesion were due to timely variations in entry into the cell cycle after the G2 arrest was excluded by the fact that the level of Pds1 , the protein that keeps separase inactive until anaphase , and thereby prevents chromatid separation , declined simultaneously in all four strains ( Figure 5D ) . Cohesion proficiency could also depend on variable protein levels caused by the mutations . To analyze this , mutated versions of RAD30 fused to a myc13 tag were introduced into the endogenous RAD30 loci . Whole cell extracts were prepared from equal numbers of wild-type cells without any myc-tagged protein ( RAD30 ) , cells harboring RAD30-myc13 or the various mutated versions of rad30-myc13 , and protein levels were examined by Western blotting . As can be seen in Figure 5E the differences between the differently mutated rad30 alleles turned out to be rather modest ( Figure 5E ) . The lower levels of the Polη-D155A and -E39A mutant proteins compared with Polη -D30A could most likely not explain why the Polη-D155A mutant is unable to form DI-cohesion , since the Polη-E39A mutant is expressed to a similar level as Polη-D155A but despite this proficient in DI genome-wide cohesion . It has been suggested that PCNA is important for establishing cohesion during S phase through physical interaction with Eco1 [14] , [44] . PCNA is also known to interact with Polη , via Rad6-Rad18-mediated monoubiquitination of the PCNA K164 lysine residue , in response to UV induced DNA damage . This interaction has been proposed to be fundamentally important for the optimal function of Polη in replication past UV-induced CPDs [33] , [45] , [46] . Interaction between PCNA and Polη has been shown to occur via the ubiquitin-binding motif ( UBZ domain ) and the ( PCNA-binding ) PIP box in Polη ( Figure 5A ) [47] , [48] . By introducing point mutations , at the PCNA K164 ( K164R ) and the Polη UBZ ( D570A ) or PIP ( F627A , F628A ) residues , we analyzed the requirement for interaction between PCNA and Polη for establishing DI genome-wide cohesion in three ways , using the Scc1NC system ( Figure 1E ) . As seen in Figure 5F , none of the mutations caused any defect in the formation of DI-cohesion . Thus , unlike the Polη function during replication bypass of UV damage , which is strongly dependent on PCNA , the function during DI genome-wide cohesion seems to be independent of the same . The possible synergistic functionality of Eco1 and Polη , based on the fact that their orthologs in S . pombe are expressed as the fusion protein Eso1 [26] , suggested that they are reciprocally dependent on each other for efficient formation of DI genome-wide cohesion . If Polη is supporting the function of Eco1 , previously shown to be the limiting factor for DI-cohesion in general [23] , excess amounts of Eco1 should rescue the DI genome-wide cohesion defect caused by rad30Δ . To test this , we introduced a plasmid containing ECO1 under control of the GAL promoter into strains void of Polη that contain the smc1ts/Smc1WT system for analyzing DI-cohesion ( Figure 1C ) . With this experimental setup , we not only corroborated the notion that excess amounts of Eco1 can induce cohesion in G2 in the absence of DNA damage ( Figure 6A ) [23] , but also showed that in this situation Polη was no longer required for formation of cohesion , neither in the presence nor in the absence of damage ( Figure 6B ) . Cohesion establishment is normally inactivated after S phase is completed , presumably by the degradation of Eco1 . However , in response to DNA damage , this inhibition is overcome by stabilization of Eco1 [49] . We thus tested whether this was the mechanism by which Polη influences the establishment of cohesion in response to DNA damage . WT and rad30Δ cells containing myc-tagged Eco1 ( Eco1-myc13 ) were arrested in G1 by alpha factor ( αF ) and synchronously released into a subsequent G2 arrest ( Figure 6C ) . Samples for preparation of protein extracts were withdrawn at indicated time points , and analyzed by Western blotting ( Figure 6C ) . Upon entry into S phase the levels of Eco1 markedly increased ( Figure 6C , 6D ) . These were reduced again at entry into G2 and continued to decline during G2 arrest unless damage was induced . The level of stabilization did not , however , differ between WT and rad30Δ strains ( Figure 6C , 6D ) . Thus , the functional importance of Polη for DI genome-wide cohesion is not based on Eco1 stabilization . Acetylation of the cohesin components , Smc3 during S phase and Scc1 during G2 , is potentially the main function of Eco1 during cohesion establishment [10] , [11] , [12] , [25] . We therefore wanted to investigate the Eco1 dependent acetylation events in rad30Δ cells . Using an Smc3-acetylation specific antibody [50] , [51] , we saw , as expected from the finding that Polη seems to be dispensable for S phase cohesion , an upregulation of Smc3 acetylation at the entrance to S phase in both WT and rad30Δ cells . The Smc3 acetylation level was in essence maintained during a G2 arrest , both in the absence and in the presence of γ-IR induced DSBs ( Figure S4A , S4B , and data not shown ) . Acetylation of Scc1 in G2 , in response to damage has , to our knowledge , not been demonstrated directly [25] . However , an acetyl-mimic version of SCC1 ( pGAL-scc1-K84Q , K210Q ) has been shown to enable cohesion formation genome-wide during G2 in the absence and presence of DSB induction [25] . Importantly , pGAL-scc1-K84Q , K210Q also rescued the DI genome-wide cohesion defect in rad30Δ strains , while overexpressed SCC1 ( pGAL-SCC1 ) did not ( Figure 7A , 7B ) , indicating that Polη is indeed vital for the acetylation of Scc1 by Eco1 . A possible function for DI genome-wide cohesion , if not important for DSB repair , could be to reinforce the cohesion in undamaged regions of the genome , during an extended G2 arrest as part of a checkpoint response induced by DNA damage , potentially to ensure proper chromosome segregation at anaphase . This issue has been addressed previously , whereby different Eco1 mutated strains that were also defective in S phase cohesion and in DSB repair were analyzed . When a ts allele of ECO1 ( eco1-1 ) that has a very high background of precocious sister separation was used it was concluded that the absence of functional Eco1 during G2 did not cause increased mis-segregation in the subsequent cell cycle . The substantial background level of mis-segregation could however have masked limited differences . In a different eco1 ( ecoack- ) mutated strain , a threefold increase in loss of unbroken chromosomes after break induction was found [10] , [22] , [23] , [52] , [53] . In rad30Δ cells there is no deficiency in S phase cohesion and therefore we have the opportunity to specifically measure the effect of absent DI genome-wide cohesion . In WT or rad30Δ cells arrested in metaphase by nocodazole a DSB was induced at the MAT locus on Chr . III by activation of pGAL-HO or not . After one hour the cells were released from the G2/M arrest and subsequently arrested in G1 by addition of αF ( Figure 8A ) . When 90–100% of the cells had reached G1 ( Figure 8B ) , chromosome segregation was determined using the TetR-GFP/Tet-O system on Chr . V . In unchallenged cells , no significant difference between WT and rad30Δ cells was apparent . However in response to DSB , rad30Δ cells displayed a small but statistically significant increase in chromosomal mis-segregation ( Figure 8C ) . This did not have an immediate negative consequence for survival , but when the mis-segregated population of cells was re-exposed to multiple rounds of induction of a single DSB , we found that after the fourth repetition of damage induction the survival rate of Polη deficient cells compared with WT cells had reduced significantly ( Figure 8D ) . This indeed suggests that the DI genome-wide cohesion has an important function for maintenance of genome integrity and that S phase cohesion is insufficient for correct chromosome segregation in the presence of DNA damage .
It is becoming clear that the cohesin protein complex and its cohesive function are important for genome integrity in multiple ways . Thus , S phase cohesion between sister chromatids is essential both for correct chromosome segregation at anaphase and for postreplicative DSB repair in G2 [3] , [19] . It has also been shown that in response to damage , so called DI-cohesion forms in G2 de novo that was suggested to be important for repair [21] , [22] , [23] . In this study we demonstrate that the specialized TLS polymerase , Polη is important specifically for establishment of DI genome-wide cohesion , which is a unique feature of Polη among the translesion synthesis polymerases . The finding that Polη , despite its absolute requirement for formation of DI genome-wide cohesion , was dispensable for postreplicative DSB repair via HR , may be explained by the discovery that Polη is not required for loading of cohesin to the break or for formation of DSB-proximal cohesion . Thus , DI-cohesion is regulated differently in the vicinity of the break compared to genome-wide . Contrary to a recently published study on human cells , where cohesin binding was reinforced after irradiation [43] , we noted no significant changes in cohesin binding in WT or rad30Δ cells , neither in the absence nor the presence of damage . This either reflects a difference between mammalian and yeast cells , or the fact that we induce a single DSB as opposed to the induction of multiple lesions by γ-IR in the human cells . Regardless , the differential regulation of DSB-proximal cohesion and DI genome-wide cohesion in yeast occurs on another level than cohesin loading . So , what is the function of Polη during formation of DI genome-wide cohesion ? An initial hypothesis was that Polη does for DI-cohesion in G2 what the replicative polymerases do for S phase established cohesion [15] . Polη has been reported to fill in gaps left after bulk replication is finished via its TLS function [54] . This type of DNA synthesis , together with a cohesion machinery reactivated by damage , could possibly establish DI-cohesion . However , two main arguments make this scenario unlikely . Firstly , DI-cohesion is independent of ubiquitination of the PCNA K164 residue , which has been shown to be required for TLS by Polη [33] , [45] , [46] . Secondly , since two out of three tested Polη polymerase dead mutants are proficient in DI genome-wide cohesion we have no indication that it is the polymerase activity that is required for establishing DI-cohesion . It is however intriguing that the D155A mutation in the active site of Polη is sufficient to disable DI-cohesion formation , despite equal UV sensitivity . One explanation for this could be that the mutations affect protein stability , resulting in different Polη protein levels . However , when determining the resulting levels of myc-tagged Polη in cells harboring the differently mutated rad30-myc alleles , they were all expressed at comparable levels . The lower levels of the Polη -D155A and -E39A mutant proteins compared with Polη -D30A could not explain why the Polη -D155A mutant is not able to form DI-cohesion , since the Polη -E39A mutant is expressed to a similar level as Polη -D155A but despite this proficient for DI genome-wide cohesion . Interestingly , the D155 amino acid is responsible for liganding one of the two essential Mg2+ ions in the active site of the polymerase , which could be crucial for association with chromatin [55] . It is possible that Polη in this manner enables recruitment of Eco1 to chromatin , which could facilitate acetylation events of importance for DI-cohesion [25] . In line with this we found that an acetyl-mimic version of Scc1 was capable of rescuing the DI genome-wide cohesion defect in rad30Δ cells , suggesting that Polη is important for the acetylation of Scc1 by Eco1 . Including Polη , a significant number of proteins have now been shown to be important for DI-cohesion without influencing the ability to repair DSBs in G2 [24] . We showed that DSB-proximal cohesion and DI genome-wide cohesion are regulated differently and that it is presumably the DSB-proximal cohesion that is important for DSB repair . This could explain why the exclusive lack of DI genome-wide cohesion , in the absence of Polη , does not affect HR based postreplicative DSB repair . The specific relevance for DI genome-wide cohesion , in contrast to DSB-proximal cohesion , then had to be redefined . Possibly it is formed in response to a DSB activated checkpoint and is important to prevent precocious sister chromatid separation during a prolonged G2/M arrest . Alternatively it is activated to prevent recombinational repair between homologous chromosomes , a risk caused by the increased movements of both damaged and undamaged chromosomes that occur after DNA damage [56] . In line with this , we showed that absence of DI genome-wide cohesion , results in an increased level of chromosomal mis-segregation at entry of the following cell cycle , indicating that DI genome-wide cohesion is important for correct segregation of chromosomes at anaphase . The consequences of aneuploidy are dramatic since aneuploidy is a hallmark of malignant cells . Indeed , when analyzing the outcome of repeated DSB induction in DI genome-wide cohesion deficient cells it became apparent that this has implications on viability . This parallels the recent finding that not only can reduced genome integrity lead to aneuploidy , but aneuploidy in itself can also cause genome instability [57] . Our results demonstrate a novel function for Polη , but also provide important evidence for differential regulation of DSB-proximal and DI genome-wide cohesion . Furthermore , they suggest a functional interaction between budding yeast Eco1 and Polη as implied by the homologous fission yeast fusion protein between the two .
All strains used are haploid and of W303 origin ( ade2-1 , trp1-1 , can1-100 , leu2-3 , leu112 , his3-11 , 15 , ura3-1 , RAD5 ) . Genetic modifications and names of the individual strains are listed in Table S1 . Deletions of genes were performed by conventional one-step replacement of the open reading frame in question , with kanamycin ( kanMX6 ) , hygromycin ( hphMX4 ) or nourseothricin ( natMX4 ) resistance , or the HIS3 gene [58] . Insertion of the HO cut-site at selected positions in the genome was performed as described [21] . For integration close to the TetR-GFP/Tet-O system on Chr . V , the HO cut-site was amplified using primers with restriction sites for HindIII and BamHI , for subsequent ligation into the pAG32-plasmid ( Euroscarf ) with the hphMX4 selection marker . The cut-site sequence was then amplified using primers with homologies 4 kb downstream of the URA3 locus on Chr . V . Presence of a single copy of the HO cut-site at the desired position was confirmed by PCR . Introduction of plasmids into yeast strains was done using standard yeast transformation protocols . SMC1-myc13 was cloned by PCR and inserted downstream of the GAL1-10 promoter in the YIPlac128 vector [59] , and introduced into the LEU2 locus of smc1-259 cells as described [21] . For further details on strains used for DI-cohesion detection see below and Figure 1 . To generate strains containing the rad30-F627A , F628A and rad30-D570A mutations we used a two-step PCR-based method [60] . The ORF of RAD30 was amplified from genomic DNA with HindIII and SalI 5′ flanking primers and cloned into pAG25 plasmid ( Euroscarf ) digested with above-mentioned enzymes . RAD30 was then amplified from the vector using a forward gene internal primer that bears the desired mutation . The reverse primer is targeted to the plasmid backbone sequence , amplifying the natMX4 ORF and bears a 3′ flanking sequence targeted to the RAD30 3′ untranslated region . The PCR fragment containing the mutant version of rad30 linked to natMX4 was then transformed into appropriate yeast strains . Transformants were selected on YEPD plates containing 10 µg/ml nourseothricin ( Jena Bioscience/Sigma ) . Correct integration was confirmed by PCR and the presence of mutation was verified by sequencing . POL30 containing the K164R mutation was amplified from genomic DNA from cells carrying the pol30-K164R allele ( a kind gift from H . Ulrich ) with BamHI and SacI 5′ flanking primers and cloned into the YIplac211 plasmid . The YIplac211-pol30-K164R:URA3 vector was linearized by ClaI digestion and transformed into appropriate cells . Ura− cells were selected on 5-fluoroorotic acid ( 5-FOA ) plates for selection of URA3 pop-out clones . Correct integration at the POL30 gene locus and the removal of the URA3 gene were confirmed by PCR and the presence of pol30 -K164R mutation was screened for by increased UV-sensitivity and confirmed by sequencing . Plasmids ( kindly provided by L . Prakash ) harboring either the RAD30 gene in the WT version ( pR30 . 382:LEU2 ) or three different polymerase region mutations , D30A ( pR30 . 12:LEU2 ) , E39A ( pR30 . 127:LEU2 ) or D155A ( pR30 . 138:LEU2 ) were transformed into rad30Δ yeast strains . Transformants were selected on plates lacking leucine , and were then crossed with yeast strains containing the Scc1NC system ( Figure 1E ) . The experimental strains were tested for UV-sensitivity and the presence or absence of mutation was verified by sequencing of the plasmids rescued from each strain . Myc-tagged variants of rad30 mutants were generated by PCR amplification of the ORF excluding the stop codon of the rad30 gene from the above mentioned plasmids; pR30 . 126:LEU2 , pR30 . 127:LEU2 and pR30 . 138:LEU2 and integrated into the pFA6a-myc13-kanMX6 vector . The presence of D30A , E39A or D155A mutations was confirmed by sequencing . The variant rad30-myc13:kanMx6 mutants were then amplified from the vectors using a gene internal forward primer . The reverse primer is targeted to the plasmid backbone sequence , amplifying the kanMX6 ORF and bears a 3′ flanking sequence targeted to the RAD30 3′ untranslated region . Resulting PCR fragments were transformed into appropriate yeast strains . The correct integration of respective rad30 construct was confirmed by PCR and the presence of mutation was screened for by increased UV-sensitivity and confirmed by sequencing . All PCR amplifications were performed using the proofreading enzyme included in the Long Range dNTP Pack ( Roche ) . The YIplac112 plasmid containing the pGAL-ECO1 construct ( a kind gift from K . Nasmyth ) was introduced into strains containing the smc1ts/Smc1WT system for detection of DI-cohesion ( Figure 1C ) . At the end of the experiments performed on strains containing plasmids expressing pGAL-ECO1 , or different versions of RAD30 under its endogenous promoter , plasmids were rescued/purified from the cells using miniprep kit ( Qiagen ) according to manufacturer's recommendations . Thereafter plasmids were transformed into chemically competent DH5α bacteria and purified ( Invitrogen quick plasmid miniprep kit ) . To confirm WT or mutated genotypes the purified plasmids were sequenced . To generate strains harboring either the wild-type or the acetyl-mimic version of Scc1 under the galactose promoter , pGAL-SCC1-HA6 and pGAL-scc1-K84Q , K210Q-HA6 were amplified from pPCM87 and pPCM87-K84Q , K210Q respectively ( kind gifts from D . Koshland ) and integrated into the YIplac128 plasmid . The YIplac128-pGAL-SCC1-HA6:LEU2 and YIplac128-pGAL-scc1-K84Q , K210Q-HA6:LEU2 vectors were linearized by EcoRV digestion and transformed into appropriate cells . Strains harboring the rad30-D30A , -E39A , -D155A or RAD30 in the low-copy number CEN LEU2 vector YCplac111 were grown to logarithmic phase . Cells were resuspended in YEP-media at identical densities and plated in 10-fold dilutions on YEP plates containing 2% glucose ( YEPD ) . The plates were left untreated or exposed to 35–50 J/m2 UV irradiation and incubated at 25–30°C in the dark for 3–4 days . Three types of experimental systems were used to detect formation of DI-cohesion , one based on the expression of pGAL-SMC1 in cells where the endogenous SMC1 allele is temperature sensitive ( smc1-279 ) , one based on the expression of pGAL-SCC1 in cells where the endogenous SCC1 allele is temperature sensitive ( scc1-73 ) , and one based on expression of an noncleavable version of Scc1 , pGAL-scc1NC . DNA damage was then induced either by γ-IR ( 350 Gy , using a Cs137 source with a dose rate of 6 Gy/min ) , or expression of pGAL-HO that creates a DSB at the MAT locus on Chr . III or an inserted recognition sequence on Chr . V . Break formation on Chr . III or V , as well as break induction and repair after γ-IR were analyzed by Southern blotting after separation of chromosomes by Pulse Field Gel Electrophoresis , using probes against Chr . III or Chr . XVI as described [21] . During the course of the experiments samples were collected at indicated time points for detection of sister separation at the URA3 locus on Chr . V by the TetR-GFP/Tet-O system , and separation scored in ≥200 cells/time point . The cell cycle distribution was analyzed by FACS . In all experiments the levels of Smc1WT or Scc1NC were confirmed by in situ immuno-fluorescence . For more detailed information of each separate experiment se the main text , figures and figure legends . Pds1-myc13 , Smc1-myc13 or Scc1NC−HA6 levels were analyzed by in situ immuno-staining with anti-myc or anti-HA ( Roche ) antibodies as previously described [21] . FACS samples were in essence prepared as described [5] , and analyzed with a Becton Dickinson FACSCalibur , ensuring 10 , 000 events per samples . Pulse field gel electrophoresis was used for verification of induction of a DSB at HO cut-sites on Chr , III , V and VI as well as efficiency of damage induction by γ-IR . Chromosomes were prepared and separated on 1% agarose gel by Pulse Field Gel Electrophoresis as described [19] ( Biorad , Chef DRIII ) . For best separation in the size range of Chr . III and VI the gel was run at 14°C for 24 hr at 6 V/cm with a 35 . 4–83 . 55 s switch time and an included angle of 120° . For separation of Chr . V and XVI the gel was run at 10°C with 90 s switch time , otherwise as for Chr . III . Wild-type and mutant cells were grown in YEPD and arrested in G2/M by benomyl . Arrested cells were exposed to γ-IR ( 150 Gy ) . Samples were isolated before and at indicated time points after γ-IR . Cell cycle distribution was analyzed by FACS and efficiency of DNA damage and repair by PFGE and Southern blotting as described [19] , [61] . The ratio of the Chr . XVI remaining at specified times were compared with the Chr . XVI signal measured before γ-IR . Cells grown in YEPD at 25°C were arrested in G2/M , by addition of benomyl . The cultures were split in two , and one half was exposed to 150 Gy of γ-IR . 300 of both irradiated and control cells were plated on YEPD plates and colonies counted after two days . ChIP-on-chip was performed on FLAG-tagged Scc1 using anti-FLAG ( Sigma ) in essence as described [42] , [62] , using S . cerevisiae whole-genome tiling 1 . 0F arrays ( Affymetrix ) . For analysis of G2 expressed cohesin binding , ChIP was performed on HA-tagged pGAL-SCC1 with anti-HA ( Abcam ) essentially as described [63] , but after 30 min fixation with 1% formaldehyde at room temperature . The samples were then processed either for ChIP sequencing ( ChIP seq ) or quantitative real time pcr ( qRT-PCR ) . ChIP seq was performed as described [63] , and qRT-PCR was performed using SYBR green ( Applied Biosystems ) according to manufacturer's instructions with primers for the positions indicated in Figure S3B ( Chr . V: 520fo 5′-TCGCGTTTCTTACAGTGGCT-3′ , 520re 5′-CAGGTCGCCTAATGAAACAG-3′ , 529fo 5′-CAATGTCTGGGGAGAGTACT-3′ , 529re 5′-CTCCAAACAGATACACCCTC-3′ , 534fo 5′-ACAAGCATCATTCATAGCCT-3′ , 534re 5′-ATCGTGGCTAGGACATTTTG-3′ , 548fo 5′-GAAAATAGCCGCCCAAGGAT-3′ , 548re 5′-CTGTGTATATCCCACCAGAC-3′ , ChrVIII: 110fo 5′-CCGACCTCTTCTAATCCAAG-3′ , 110re 5′-AGAGATGAGGCTCTCAGACA-3′ ) . Samples were then analyzed on ABI Prism 7000 sequence detection system ( Applied Biosystems ) . Chromosome segregation was analyzed after activation of pGAL-HO using the TetR-GFP/Tet-O system . Cells grown in YEPR at 25°C were arrested in G2/M , by addition of nocodazole . Thereafter 2% galactose was added to half of the cultures to induce pGAL-HO for one hour . Cells were then washed and released into YEPD containing alpha factor ( αF ) for G1 arrest . When 90–100% of the cells were in G1 , the percentage of arrested cells with a double GFP signal , representing mis-segregation of Chr . V was determined . Cell cycle progression was checked by FACS , break induction by PFGE and Southern blotting using a radioactive probe hybridizing to Chr . III . Cells grown in YEPR at 25°C were arrested in G2/M by addition of benomyl . A single DSB was introduced at the MAT locus on Chr . III , by expression of pGAL-HO for 90 min in half of the cultures . The cells were then washed once in PBS and 300 of both damaged and control cells were plated on YEPD plates and incubated at 25°C . After two days the numbers of colonies were counted for determination of survival . All surviving cells of each population were collected and resuspended at identical densities in YEPR for continued cultivation . This entire experimental procedure was repeated four times in sequence . Protein lysates were prepared with lysis buffer ( 20 mM Tris-HCl pH 8 . 0 , 10 mM MgCl2 , 1 mM EDTA , 0 . 3M ( NH4 ) 2SO4 and 5% glycerol ) supplemented with 1 mM DTT , 1 mM PMSF and 1× Complete EDTA-free protease inhibitor cocktail tablets ( Roche ) using glass beads vortexing . Proteins were separated by SDS-PAGE using NuPAGE Bis-Tris Gels ( Life Technologies ) and transferred by Western Blotting using the same system . Antibodies against myc ( Roche ) , cdc11 y-415 ( Santa Cruz Biotechnology ) and acetylated Smc3 [50] , [51] were used . Antibody detection was done using the Odyssey Infrared Imaging System and quantifications were done using Image Studio 2 . 0 Software ( LI-COR Biosciences ) . | Correct chromosome segregation requires that sister chromatids are held together by the protein complex cohesin , from S phase until anaphase . This S phase established cohesion is , together with DSB recruitment of cohesin and formation of damage-induced ( DI ) cohesion , also important for repair of DSBs . Eco1 is a common essential factor for S phase and DI-cohesion . The fission yeast Eco1 ortholog , Eso1 , is important both for S phase cohesion and for bypass of UV-induced lesions , and is expressed as a fusion protein with Polη . The cohesion function has been attributed solely to Eso1 and the lesion bypass function to the Polη part of the protein . As we found the interaction between the two proteins intriguing , we decided to look for a functional connection also in budding yeast . Indeed , despite being dispensable for S phase cohesion , budding yeast Polη is required for formation of DI genome-wide cohesion . However , Polη-deficient cells are DSB repair competent , revealing differential regulation of DI-cohesion at the break and genome-wide . This finding challenges the importance of DI genome-wide cohesion for DSB repair , and based on our findings we suggest that S phase cohesion is not sufficient for correct chromosome segregation in the presence of DNA damage . | [
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] | 2013 | Importance of Polη for Damage-Induced Cohesion Reveals Differential Regulation of Cohesion Establishment at the Break Site and Genome-Wide |
Drosophila melanogaster is emerging as an important model of non-pathogenic host–microbe interactions . The genetic and experimental tractability of Drosophila has led to significant gains in our understanding of animal–microbial symbiosis . However , the full implications of these results cannot be appreciated without the knowledge of the microbial communities associated with natural Drosophila populations . In particular , it is not clear whether laboratory cultures can serve as an accurate model of host–microbe interactions that occur in the wild , or those that have occurred over evolutionary time . To fill this gap , we characterized natural bacterial communities associated with 14 species of Drosophila and related genera collected from distant geographic locations . To represent the ecological diversity of Drosophilids , examined species included fruit- , flower- , mushroom- , and cactus-feeders . In parallel , wild host populations were compared to laboratory strains , and controlled experiments were performed to assess the importance of host species and diet in shaping bacterial microbiome composition . We find that Drosophilid flies have taxonomically restricted bacterial communities , with 85% of the natural bacterial microbiome composed of only four bacterial families . The dominant bacterial taxa are widespread and found in many different host species despite the taxonomic , ecological , and geographic diversity of their hosts . Both natural surveys and laboratory experiments indicate that host diet plays a major role in shaping the Drosophila bacterial microbiome . Despite this , the internal bacterial microbiome represents only a highly reduced subset of the external bacterial communities , suggesting that the host exercises some level of control over the bacteria that inhabit its digestive tract . Finally , we show that laboratory strains provide only a limited model of natural host–microbe interactions . Bacterial taxa used in experimental studies are rare or absent in wild Drosophila populations , while the most abundant associates of natural Drosophila populations are rare in the lab .
The genetic and experimental tractability of Drosophila melanogaster often overshadows the phenotypic , evolutionary and ecological diversity of its relatives . Over 3000 species of Drosophila and related genera inhabit all continents except Antarctica , occur in practically every type of habitat , and show a great variety of morphological , behavioral , and life-history traits [1] . In particular , the feeding and breeding substrates vary tremendously within the Drosophilids . While the well-known cosmopolitan species are considered generalists , as decaying fruit of many different plants makes for an acceptable substrate , dietary specialization has evolved many times within Drosophila . A well-known example is D . sechellia , which specializes on the Morinda fruit , a resource that is toxic to most other animals [2] . Other Drosophila species use flowers , mushrooms , sap fluxes , cambium , decaying vegetation , and cacti as feeding and breeding sites [3] , [4] . Importantly , dietary shifts have occurred numerous times within the genus , and closely related species are known to utilize different types of food sources [5] , [6] , [7] . At the same time , it is common to find phylogenetically distant species using the same food source . In almost all of these cases , the biotic environment that Drosophila are interacting with , especially the microbial communities associated with these flies , is unknown . The importance and ubiquity of microbial associates of animals is only beginning to be appreciated . Although most attention has been devoted to pathogenic bacteria , pathogens are a small minority of animal symbionts . Bacteria can play beneficial , and often essential , roles in the lives of their hosts . In animals that carry vertically transmitted , intracellular bacteria , the host and its symbiont community form an inseparable holobiont with shared metabolism and evolutionary fate [8] , [9] . However , symbionts need not be intracellular or completely dependent on the host to shape host physiology and evolution . Most animal-microbial interactions are flexible and facultative , where the symbionts can exist without the host and the host can carry different symbionts at different times . It is likely that every animal is associated with a complex and ever-changing microbial community that consists predominantly of non-pathogenic , free-living bacteria [10] . Nowhere is this more evident than in intestinal microbiology . In humans , bacterial gut fauna is composed of more than a thousand taxa and certain aspects of human health , such as obesity , are associated with an altered intestinal community [11] . Bacterial gut symbionts are equally prevalent in other mammals [12] and in insects [13] , [14] . In many insects , gut symbionts are essential for survival and form the core of host physiology and ecological adaptation [15] , [16] , [17] . Even when not strictly essential for survival , experimental evidence suggests that insect gut fauna affects many aspects of host phenotype [18] and can mediate interactions between the host and potential pathogens [19] . The composition of bacterial symbiont communities is shaped both by host genotype and its diet . In mice and fruit flies , mutations in a single host gene can be sufficient to alter microbiome composition [20] , [21] . Reciprocal transplants of intestinal microbiomes between zebrafish and mice reveal that the gut habitat of these hosts selects for different communities [22] . These differences are smaller at shorter evolutionary time scales , as species that are more closely related often share more similar bacterial communities . This trend has been observed in stinkbugs [23] , termites [24] , and mammals [12] . Diet also plays an important role in shaping the intestinal bacterial microbiome in many systems . When humans are shifted onto a low carbohydrate , low fat diet , their intestinal communities shift towards a higher percentage of the phylum Bacteroidetes [11] . The gut communities of European and African human populations are shaped , at least in part , by their different diets [25] . D . melanogaster is naturally emerging as a model of host-microbe interactions . Genetic experiments have identified some of the genes contributing to intestinal community homeostasis . The gene PIMS actively suppresses immune response when flies are exposed to commensal , non-pathogenic intestinal communities [26] . Similarly , downregulation of caudal significantly alters this bacterial community , allowing normally rare bacteria to increase in abundance [21] . However , little is known about the effects of gut bacteria on Drosophila physiology . Axenic strains of D . melanogaster are viable , at least on rich media . Although some studies suggested that gut symbionts increase life span in D . melanogaster [27] , other studies failed to replicate this effect [21] , [28] . Commensal bacteria can even affect mate choice in D . melanogaster in the lab [29] , although the evolutionary significance of this effect in the wild is not clear . In contrast to our increasing understanding of Drosophila-microbe interactions in the lab , little is known about the microbial communities associated with natural Drosophila populations . In other insects , laboratory-reared larvae have been shown to harbor significantly less diverse bacterial microbiomes than their wild counterparts [30] , [31] . Laboratory strains of D . melanogaster have been reported to carry the bacterial genera Lactobacillus , Acetobacter and Enterococcus [21] , [27] , [28] , [32] . Although these taxa are present in most studies , there is also a possible “lab effect” where different labs have different bacteria [28] . Many of the same bacterial genera ( although not always the same species ) were found in natural D . melanogaster populations in the eastern United States [32] , [33] . However , given the worldwide distribution of Drosophila and the tremendous variation in Drosophila ecology , these taxa may represent only a small fraction of the bacterial communities associated with flies in the wild . A better knowledge of these communities is necessary to understand the role of symbiosis in Drosophila physiology , ecology , and evolution . To explore the bacterial communities associated with this speciose and ecologically diverse lineage , and to identify the factors shaping these communities , we surveyed natural populations of 14 species of Drosophila and two closely related genera ( Scaptodrosophila and Microdrosophila ) . Although we acknowledge that non-bacterial microbes such as archaea and yeasts are likely associated with these hosts , we focused our survey on the bacterial portion of the microbiome because of its known importance to animal and Drosophila biology . We shall use the term “bacterial microbiome” to refer to what was sampled in this study . We used culture-independent 16S ribosomal DNA ( rDNA ) polymerase chain reaction ( PCR ) amplification and sequencing to characterize the bacterial communities associated with each population . To sample the widest spectrum of fly-associated bacteria , collections were selected from as large a swath of Drosophila ecology , phylogeny , and geography as possible . Flies were collected directly from their natural feeding substrates including rotting fruit , flowers , mushrooms , and cacti , without the use of any artificial baits , from locations on both coasts of North America , Hawaii , Australia , Southeast Asia , and Seychelles , Africa . In addition to the natural survey , controlled laboratory experiments were performed to further determine the role of environment and host species in shaping the bacterial communities . This combined approach allows us to address several previously unexplored questions . Do the bacterial communities associated with Drosophila exhibit the same diversity as their hosts ? What factors are most important in shaping the differences between symbiont communities of different host species ? How does the composition and structure of these communities compare to the bacterial microbiomes of other taxa , particularly mammals ? Finally , is the bacterial microbiome of lab strains used in experimental research representative of natural bacterial communities ?
Drosophila samples were collected with the help of many colleagues around the world ( see Acknowledgments , Table 1 and Dataset S1 ) . Flies were either washed in sterile water ( to remove cuticular bacterial cells ) or were dissected to obtain just their crops and digestive tracts . After DNA extraction , rDNA PCR amplification was done with bacterial specific primers . The 16S rDNA amplicons were cloned , transformed , and Sanger sequenced from both ends . For 50% of the clones , the two reads did not overlap and therefore a concatenated read was made by inserting gap characters in the space between the two reads . After all preliminary filtering , our dataset consisted of 3243 nearly full-length high quality sequences representing 39 host samples ( which we refer to as libraries ) ( Table 1 and Dataset S1 ) . This dataset excluded 421 clones that were only sequenced from one end , 65 sequences with fewer than 300 non-gap characters , 76 sequences that were identified as chimeric , 9 that appeared to be chimeric based on conflicting taxonomy assignments of the 3′ and 5′ reads , 3 chloroplast sequences , and 351 sequences of likely endosymbionts such as Wolbachia and Spiroplasma ( which will be addressed in a separate section ) . Because small sample sizes can lead to inaccurate diversity measures [34] , two libraries containing a total of 28 sequences were removed completely . The 39 remaining libraries vary in size from 26 to 223 sequences , with an average of 83 . 2 ± 37 . 4 . Most libraries ( 29 of 39 ) contain between 63 and 97 sequences . 20 libraries containing 1850 total sequences are from wild-caught hosts , while the remaining libraries and sequences came from laboratory samples and experiments . Full tables containing each library's identifier , size , the host species from which it was collected , location and date of collection , and other information are given in Table 1 and Dataset S1 . Clustering with mothur [35] using the average neighbor algorithm with 0 . 03 cutoff ( corresponding to 97% sequence similarity ) creates 139 operational taxonomic units ( OTUs ) , the largest of which contains 638 sequences . 66 OTUs are singletons ( i . e . , there is only a single sequence in the OTU ) and 110 OTUs contain 10 or fewer sequences . The average OTU contains 23 . 3 sequences ( standard deviation = 78 ) . Phylogenetic analysis was performed using FastTree [36] . Included in this analysis ( and many other comparisons throughout this study ) were many previously identified Drosophila-associated bacteria [21] , [32] , [33] , [37] . Four bacterial families representing three orders make up 90% of all sequences within our dataset . These include Enterobacteriales: Enterobacteriaceae ( 60% ) , Rhodospirillales: Acetobacteraceae ( 9% ) , and Lactobacillales: primarily Lactobacillaceae and Enterococcaceae ( 21% ) ( Figure 1A and Table 2 ) . 14 other orders comprise the remaining 10% of the dataset . All wild populations are dominated by at least one of the three major clades , and many Drosophila species carry all three of them ( Figure 1B and 1C ) . Although no core bacterial microbiome ( a set of taxa present in all samples ) emerges , Enterobacteriaceae and Lactobacillales come close , being found in 18 and 17 out of 20 wild Drosophila populations , respectively ( Figure 1B ) . There is an interesting reciprocal relationship between these two taxa ( Figure 1C ) . Each of the five host samples which lacks one of these groups is dominated ( >84% ) by the other one . In only two populations ( ELA and SCA ) do Lactobacillales and Enterobacteriaceae each make up at least 15% of the bacterial microbiome; both of these are flower-feeding flies with highly diverse bacterial microbiomes . In all the other samples , the abundance of the more dominant microbe is , on average , 44 times greater than the other one . OTU richness , evenness , and overall diversity vary widely among host samples ( Table S1 ) . As many as 30 OTUs were present in some samples such as D . falleni collected on Russula mushrooms , while five or fewer OTUs were found in 5 different samples . For example , D . hydei collected from either citrus fruit or prickly pear are found with four or less bacterial OTUs , and a single Enterobacteriaceae OTU represents at least 85% of each of these bacterial microbiomes . Similarly , D . sechellia collected on Morinda fruit is dominated by a single Lactobacillales OTU ( 84% ) , leading to very low bacterial community richness and evenness ( Dataset S2 ) . Rarefaction analysis , which helps determine how close the sampling effort came to fully describing the community , shows that different host communities differ greatly in richness and were sampled at different depths ( Figure 2A ) . The least diverse samples are those collected from fruit-feeding hosts , while the flower- and mushroom-feeders tend to have more diverse bacterial communities . For the communities that have not been sampled to completion , the situation exists in which rare , and potentially important , taxa have not been identified . Community similarity ( beta-diversity ) between samples was calculated for each of the 190 comparisons between the 20 wild populations ( Dataset S4 ) . In 27% of these comparisons , no OTUs are shared between the two samples . The two Drosophila that share the highest proportion of their bacterial microbiomes are D . hydei collected from citrus fruit and prickly pear fruit ( samples HCF and HPP , respectively , Dataset S4 ) . In contrast to the bacterial communities associated with wild populations , laboratory samples are much less diverse and so were sampled nearly to completion ( Figure 2B ) . Chao1 analysis [59] predicts an average of 6 . 3 OTUs per sample , and most libraries have >80% coverage ( Table S2 ) . It is interesting to note that some of the most OTU-rich communities are present on the culture media and on the external surfaces of flies ( MED and XYX ) ( Table S2 ) . This suggests that flies are able to exclude many of the external microorganisms present on the feeding substrate , allowing only a subset to persist in their digestive tract . In both wild and lab host samples , most of the bacterial diversity is found at short phylogenetic distances , since most samples share the same dominant orders and families ( Figure 1 ) . This distribution produces a typical “hockey stick” pattern found in many animal-associated microbial communities ( Figure S6 ) [60] . To put the Drosophila bacterial microbiome in perspective , we compared the 20 wild-caught samples to published mammalian datasets [12] and previous studies of naturally isolated D . melanogaster [33] . These studies are well suited for effective comparison to our data because they use culture-independent , long-read Sanger sequencing that allows closely related OTUs to be resolved , and because they represent a large taxonomic breadth and/or include many samples from a wide geographic area . Principal component analysis ( PCA ) shows that the Drosophila bacterial microbiome from our study is similar to previous D . melanogaster samples , but is clearly distinct from the microbiome found in the mammalian orders Artiodactyla , Carnivora , and Primates ( Figure 3B ) . Despite the relatively tight clustering of Drosophila samples , some differences between separate studies are apparent ( Table S3 ) . Notably , the Enterobacteriaceae , which are the dominant taxon in our global survey , are almost absent from two previous Drosophila studies [21] , [33] . Although Enterobacteriaceae comprise a large proportion of the bacterial microbiome within a single Massachusetts population [32] , the dominant genera in that sample were Enterobacter and Klebsiella , which are not present in our survey . The high abundance of Acetobacteraceae in the Massachusetts population may be caused by the fruit bait used during sample collection in that study [32] . The dominant bacterial order in all three mammalian orders is the strictly anaerobic Clostridiales , which is rarely found in Drosophila ( Table S4 ) . The Enterobacteriaceae are not found or are minimal residents of the Artiodactyla and Primate guts , respectively . While this family is present in high amounts within the Carnivora , the dominant genera , Escherichia and Shigella , are not common in flies ( Dataset S2 ) . A similar pattern is found for the Lactobacillales . This order is found in relatively high numbers in the Carnivora and Primates ( 20% and 9% respectively ) [12] , but the major genus in mammals ( Streptococcus ) is found at less than 0 . 5% abundance in wild flies ( Dataset S2 ) . Finally , Acetobacteraceae are not present in any of the three mammalian orders [12] . The only bacterial genus present in appreciable numbers in both mammals and Drosophila is Lactobacillus . This genus is found in Artiodactyla ( 2% ) , Carnivora ( 3% ) , Primates ( 2% ) , and Drosophila ( 3% ) [12] ( Dataset S2 ) . Flies also differ from mammals in the overall patterns of bacterial microbiome diversity . The richness of Drosophila bacterial communities is dramatically lower than in mammals , although community evenness is comparable ( Table 3 ) . Additionally , we find that many OTUs are present in taxonomically and ecologically diverse Drosophila populations ( Dataset S2 ) and that the proportion of bacterial OTUs that are unique to a single host sample is consistently lower in Drosophila than in mammals ( Figure S7 ) . To estimate the role of host diet in shaping bacterial microbiome composition , we compared taxonomically diverse Drosophila species collected from different types of food sources . Our survey contains 11 samples of fruit-feeding flies and 6 samples of flower-feeders . UniFrac analysis [61] , [62] shows that flies subsisting on these two diets have significantly different bacterial microbiomes ( p<0 . 01 ) . One major difference is the absence of Acetobacteraceae in flower-feeding flies ( Table 4 ) . This may be due to the fact that Acetobacteraceae can thrive under the low pH and high ethanol conditions present in fermenting fruit . The same argument can be made for Lactobacillus , an acidophilic genus associated with high resource habitats , which is present at higher abundance in fruit-feeding flies ( Table 4 ) . In contrast , the genera Serratia and Pantoea ( Enterobacteriaceae ) are found in much higher proportions in flower-feeders ( Table 4 ) . Many of the largest OTUs are found only , or mainly , in association with one diet type ( Figure 4A ) . Of the 14 largest OTUs , which contain 75% of all sequences , 10 derive >95% of their sequences from a single diet type ( Figure 4A ) . In general , the difference between fruit- and flower-feeders is consistent and can be attributed to multiple host samples within each category . An exception to this pattern is Shigella , whose apparent abundance in fruit-feeding flies is due almost entirely to a single library ( D . melanogaster from rotting grapes , Sample MAH ) ( Dataset S2 ) . Similarities among the bacterial communities of wild populations and laboratory strains were summarized with PCA using UniFrac ( Figure 3A ) . We find that the majority of fruit feeding flies occupy a distinct region within PC space , while the two mushroom feeders are mostly separated from the other samples . In congruence with the taxonomic similarity between the cactus feeding population and the fruit feeders , we find that the D . mojavensis sample clusters near the fruit associated flies . Some differences are also apparent within diet types . In particular , D . elegans was collected simultaneously from Alpinia and Brugmansia flowers ( Samples ELA and ELD ) . These collections were made less than 10 meters apart and almost certainly represent a single fly population . Therefore , any differences in their bacterial communities are most likely due to the different food sources . We find that D . elegans collected on Alpinia has a much higher amount of Leuconostocaceae and Streptococcaceae ( phylum Firmicutes ) , while those collected on Brugmansia are dominated by Enterobacteriaceae ( phylum Proteobacteria ) ( Dataset S2 ) . Alpinia-collected flies also show much higher bacterial microbiome diversity than Brugmansia-collected flies ( Chao1 = 23 vs 7 . 5 ) ( Table S1 ) . Although it is possible that individual flies travel between host plants , these switches are clearly insufficient to overcome the effect of diet . Both mushroom-feeding populations were associated with a high amount of Lactobacillales , specifically D . falleni had 30% Vagococcus and Microdrosophila sp . had 57% Enterococcus ( Dataset S2 ) . D . falleni is also notable because its bacterial microbiome contains 16% each of both Bacillales and Burkholderiales , two orders that are otherwise rare in Drosophila bacterial microbiomes ( Figure 1C ) . The mushroom-feeding species are also marked by relatively high community richness and diversity , especially compared to fruit-feeding Drosophila ( Table S1 and Figure 2A ) . The single cactus-associated population is very similar to many fruit feeders both in composition ( 84% Enterobacteriaceae Group Orbus ) ( Dataset S2 ) and diversity ( Table S1 ) . A major benefit of the Drosophila model is the experimental flexibility it provides in a laboratory setting . However , OTU classification and rarefaction analysis show that lab-raised flies contain a much lower richness and diversity of bacteria compared to wild-caught flies ( Figure 2 , Table 3 , Table S1 and Table S2 ) . At the broadest level , the wild and laboratory samples are similar in that both are composed mainly of Enterobacteriaceae , Lactobacillales , and Acetobacteraceae ( Table 2 ) . However , 90 of the 139 total OTUs are present only in wild samples , while six are found only in lab samples ( Figure 4B ) . Most of these OTUs are rare , so that the majority of sequences in our survey belong to OTUs that are found in both wild and lab hosts ( Figure 4B ) . The four largest OTUs , which together comprise over half of the entire dataset , are composed of both wild and laboratory sequences . It should be noted , however , that each of these four OTUs is composed primarily ( >95% ) of either wild or laboratory sequences ( Figure 4B ) . PCA ( Figure 3A ) further emphasizes the reduced diversity and distinct composition of the bacterial microbiome of laboratory flies . We find the laboratory populations in a subset of the total PC space occupied by the wild populations . Specifically , the laboratory samples' PC space is near that of the fruit feeding Drosophila , which could be explained by the nutritional similarity of these sugar rich diets . Many of the bacterial strains found in this study are closely related to those from previous laboratory studies of Drosophila . Five strains that are common in our lab samples ( Acetobacter malorum , A . pomorum , Commensalibacter intestini , Lactobacillus brevis , and L . plantarum ) are >99% identical to previously indentified cultured isolates of D . melanogaster [21] ( Figure S3 and Figure S4 ) . A notable difference between our results and another previous study is that Enterococcus is virtually absent in our lab samples ( Table S3 ) , but comprises nearly 50% of the laboratory bacterial microbiome in that study [32] . Our survey of natural bacterial communities suggests that host diet may be an important determinant of bacterial microbiome composition . We tested this hypothesis using laboratory experiments where diet and rearing conditions were carefully controlled . Starting with a large pool of isogenic D . melanogaster , we transferred 25 flies each to a different sterile diet and examined the resulting changes in their gut bacterial communities . We find that the high yeast diet , which is most similar in composition to our standard lab media , induced a similar bacterial microbiome with a high abundance of Enterobacteriaceae Group Orbus ( Table 5 ) . In contrast , the high ethanol and sugar-only diets resulted in a bacterial microbiome dominated by Providencia . Flies on the no-nutrient ( agar-water ) diet contained appreciable levels of both of these groups , but a quarter of their bacterial microbiome was composed of Commensalibacter intestini ( Table 5 ) . Flies kept on standard lab media showed little change in their bacterial microbiome after three days , suggesting that diet has a consistent effect on the bacterial microbiome . UniFrac analysis confirms a significant overall effect of diet in this experiment ( p<0 . 01 ) . In a reciprocal experiment , we tested whether different host species develop different bacterial microbiomes when feeding on the same diet . Three distantly related Drosophilids that feed on different food sources in the wild , D . melanogaster ( fruits ) , D . elegans ( flowers ) , and D . virilis ( sap fluxes and cambium ) , were reared together on the same media . We found that all three species had similar bacterial microbiomes at the end of this experiment ( Table 6 ) . The digestive tracts of each species contained between 72% and 94% Providencia . UniFrac analysis does not show significant differences between host species ( p = 0 . 54 ) . However , some differences between these species could be masked because the strains used in this experiment have been adapting to the laboratory environment for many generations . Additionally , laboratory Drosophila are likely exposed to a lower overall diversity of possible symbionts than their wild counterparts , further masking possible differences between host species . Our study spanned two years and used flies from two different labs at UC-Davis . The Kimbrell and Kopp lab flies had significantly different bacterial microbiomes , despite obtaining the same type of media from the same kitchen ( p<0 . 01 ) . The three dominant taxa in the Kopp lab are Enterobacteriaceae Group Orbus , Providencia , and Lactobacillus , while all three are at minimal amounts within the Kimbrell lab ( all three combined equal 9% of the bacterial microbiome within Drosophila from the Kimbrell lab ) ( Table S5 ) . Conversely , the dominant taxa in the Kimbrell lab are Shigella and Variovorax , which are not present in the Kopp lab . Even within the Kopp lab , the bacterial microbiome was different in experiments performed at different times ( p<0 . 01 ) ( Table S6 ) . We propose that these inter- and intra-lab differences are the result of different sets of environmental communities that inhabit the labs and inoculate the fly stocks during routine maintenance . These observations suggest that some of the conflicting phenotypic results reported by different labs [27] , [28] may be the result of different bacterial communities . The bacterial microbiome of Drosophila is likely environmentally acquired since , with the exception of Wolbachia and Spiroplasma , no evidence exists that bacterial communities are transmitted vertically within the egg . To ask whether the gut bacterial microbiome differs from the external bacterial community , we examined external washes of adults and their culture media ( Table S7 ) . UniFrac analysis shows a significant difference ( p<0 . 01 ) between the external and internal samples of D . melanogaster grown on unsterilized media . Larvae also differ significantly from the media they feed on ( p<0 . 01 ) . The larval bacterial microbiome consisted entirely of Enterobacteriaceae Group Orbus , while the media also contained Serratia , Providencia , and Lactobacillus .
Natural Drosophila populations have a remarkably restricted bacterial microbiome . Despite the phylogenetic , ecological , and geographical diversity of the hosts we surveyed , only a few bacterial clades are associated with all these flies . The families Enterobacteriaceae and Acetobacteraceae and the order Lactobacillales make up over 85% of natural Drosophila bacterial microbiome ( Figure 1A ) . All Drosophila populations are dominated by at least one of these clades , and many host isolates have all three of them ( Figure 1B ) . Although we find no strict core bacterial microbiome , Enterobacteriaceae and Lactobacillales are found in 18 and 17 of the 20 wild Drosophila populations , respectively . Each of the five samples that lack either of these groups is dominated by the other , and the two groups generally show a pattern of reciprocal abundance ( Figure 1C ) . One possible explanation is that competitive interactions between the two groups allow only one of them to persist at a detectable level within the host digestive tract . These three bacterial taxa are emerging as common microbial associates of insects . The Acetobacteraceae ( Acetobacter sp . ) have been found with bees , olive fruit flies , parasitic wasps and mealybugs [44] , [63] , [64] , [65] , [66] . Likewise , the Lactobacillales ( such as Lactobacillus ) are common symbionts of insects , notably bees and beetles [43] , [65] , [67] , [68] . Finally , the most common Enterobacteriaceae found with Drosophila ( Enterobacteriaceae Group Orbus ) has found with numerous insect species , but especially bees ( Figure S2 ) [43] , [44] , [45] , [46] , [69] , [70] , [71] , [72] . This taxonomically restricted bacterial microbiome leads to interesting patterns of bacterial diversity . Many samples have very low observed and expected ( Chao1 ) species richness ( Table S1 ) . These results stand in contrast with the highly diverse bacterial communities found in mammals [12] ( Table 3 ) . There is an important difference in sampling procedures: the mammalian samples each come from a single individual [12] , while the Drosophila samples were isolated from multiple individuals . However , this difference would be expected to bias the results in the opposite direction , since different individuals are likely to carry slightly different bacterial communities . Our laboratory studies show that the intestinal bacterial microbiome represents only a subset of the external bacterial communities ( Table S7 ) . This suggests that although the gut bacterial microbiome is environmentally acquired , the host exerts significant control over its composition . Since most environmental samples are composed of many phyla and are rarely dominated by just one or two lineages [73] , we suggest that the low-diversity communities of Drosophila reflect the effects of strong host filtering . Whether this filtering is an adaptive function of the immune system or simply a by-product of the physiological conditions in the gut remains to be determined , but host control has previously been demonstrated in genetic experiments [21] , [26] . The importance of bacterial microbiome restriction for host fitness is yet to be investigated , as well . Analysis of OTU-level data shows that individual OTUs are not specific to a single host species , diet type , or location , but are typically associated with many Drosophila populations . Although most OTUs ( 91 out of 127 ) present in wild flies are each found in one host sample , all these OTUs represent only a small percentage of the total fly bacterial microbiome ( 16% ) . Conversely , the dominant OTUs from each host population are usually found in other populations as well . In fact , we find that the most common OTU in 19 out of 20 populations is also found in other , often geographically distant , hosts . Several particularly wide-ranging OTUs are found in nearly half of all populations . In comparison with mammalian bacterial microbiomes [12] , the fraction of OTUs unique to a single host sample is much lower ( Figure S7 ) . The closest relatives of many bacterial lineages found in our survey were also detected in previous studies of D . melanogaster . For several common taxa ( Commensalibacter , A . malorum , A . pomorum , L . plantarum , and L . brevis ) , the closest sequences in GenBank were isolated from D . melanogaster . Since few Drosophila-associated 16S sequences are available in GenBank , compared to the much greater number of non-host associated and mammalian-associated sequences , these similarities imply a pervasive association of these lineages with Drosophila . Overall , these patterns suggest that the bacteria associated with Drosophila display some level of host specificity . Since far-flung , ecologically diverse flies are associated with a common set of bacteria , “Drosophila” can be considered a selective environment that allows only certain taxa to persist . Previous studies have shown that the mammal-associated bacterial microbiome is shaped by both host phylogeny and host diet , while sampling location has little or no effect on community composition [12] , [74] . Diet has also been shown to influence the bacterial composition of gypsy moth [30] and cotton bollworm [31] larval midguts . We find that host diet plays a substantial role in shaping bacterial microbiome composition in Drosophila , as well . This conclusion is supported both by the survey of natural communities and by controlled laboratory experiments . Although we were unable to quantify the role of host species in natural populations because many species were only represented by a single collection , laboratory populations of multiple co-habitating species showed no significant differences between their bacterial microbiomes . These results suggest two possible hypotheses regarding the assembly of Drosophila–associated bacterial communities . One possibility is that the guts of different host species inhabiting the same food source provide suitable environments for the same bacteria . These bacteria could provide specific benefits to their hosts on that diet , so that phylogenetically distant Drosophila species evolve to allow the persistence of the same , diet-specific , bacteria . Alternatively , different substrates may harbor different bacterial communities and environmental acquisition of these bacteria may simply overwhelm any potential control by the host . As these hypotheses suggest different roles for the host ( adaptive vs . passive ) , future experiments should take care to sample the bacterial community of the environment the host is interacting with . If environmental acquisition is indeed the most important factor determining Drosophila bacterial microbiome composition , then two general observations are expected . First , patterns of host and symbiont co-speciation seen in closely related insect and mammalian groups should not be observed within Drosophila [17] , [74] . Second , the genetic complementarily commonly found in tightly associated symbionts should be harder to evolve [8] . Drosophila has recently emerged as a powerful model for studying non-pathogenic host-microbe interactions . Several important genes that control host interactions with commensal intestinal bacteria have been identified , including caudal and PIMS ( Lhocine et al . , 2008; Ryu et al . , 2008 ) . Another study has shown that Lactobacillus plantarum can affect mating preferences ( Sharon et al . , 2010 ) . Cox and Gilmore , 2007 , have suggested that D . melanogaster is naturally colonized by the commensal/opportunistic pathogen Enterococcus faecalis , and can serve as a good model for E . faecalis pathogenesis . In all these studies , laboratory experiments serve as a proxy for the natural ecology of Drosophila-microbe interactions . However , in order to serve as an ideal model system , the lab bacterial microbiome should be a subset of the wild bacterial microbiome , and the most common wild taxa should be found in the lab . We find that these conditions are only partially satisfied . The putative commensal bacterial genera studied by Ryu et al . , 2008 are members of the family Acetobacteraceae ( Acetobacter , Glucoacetobacter , Commensalibacter ) and the genus Lactobacillus ( L . plantarum and L . brevis ) . Ren et al . , 2007 also identified Acetobacter and Lactobacillus as commensal bacteria in laboratory-reared flies . While all of these bacteria are present in some Drosophila populations , their abundance in wild samples is low and none are ubiquitous . In D . melanogaster samples L . plantarum and L . brevis comprise 7 . 7% and 9 . 7% of the total bacterial microbiome , respectively , whereas Enterococcus , Commensalibacter and Glucoacetobacter are not found at all . Only L . plantarum is found in all wild D . melanogaster samples . Drosophila has been used for decades as a model for pathogenic bacterial infections . In some cases , it was applied to study bacteria that pose important threats to human health , such as Bacillus anthracis [75] , Vibrio cholerae [76] , [77] , Salmonella typhimurium [78]–[80] , Pseudomonas aeuruginosa [81]–[86] and Burkholderia cepacia [78] . Other studies focused on elucidating the molecular mechanisms of fly immunity using known or suspected entomopathogens or phytopathogens , including species of Serratia [86] , [87] , Erwinia [88] , [89] , Micrococcus [90] , and Pseudomonas [91] , [92] . We find that , collectively , the above 8 microbes make up less than 10% of the total Drosophila microbiome , and none constitutes more than 3 . 5% individually . This indicates that they are relatively rare in wild Drosophila populations on the whole , although we cannot rule out the existence of some unsampled , heavily infected individuals . While most of the well-studied lab bacteria are rare in natural populations , the reciprocal is also true – the most common bacteria in wild populations are not the most abundant Drosophila associates in the lab ( Figure 4B ) , and have not been used as model bacteria in laboratory studies . A single group , Enterobacteriaceae Group Orbus , represents over 21% of all bacteria present with natural Drosophila populations and is nearly twice as abundant as the next most common genus . This clade is present in over half of all Drosophila populations , but has not been used in any laboratory studies . The second most common bacterium in wild Drosophila , a strain of Vagococcus ( 15% of total bacterial microbiome , present in 9 populations ) , has also never been used in Drosophila host-microbe studies . One final consideration for laboratory studies concerns the lab- and time-dependent variation in bacterial communities . It has been previously suggested that discrepancies between reported phenotypes may be due to different bacterial communities present in different labs [28] . Indeed , we find that different laboratories at UC-Davis are home to completely different bacterial communities despite using the same media ( Table S5 ) . Even when genus-level taxonomies agree ( as in Serratia ) , OTU clustering shows that different strains are present in different laboratories . Moreover , we find that bacterial community composition can change in the same lab over time ( Table S6 ) . Despite these caveats , laboratory strains of Drosophila can still serve as a useful model of host-microbe interactions . For example , conclusions from the natural survey mesh well with laboratory experiments in highlighting the importance of diet in shaping the bacterial microbiome . We suggest that many experimental projects would benefit from determining and monitoring the composition of bacterial communities associated with fly strains . Awareness of this important aspect of host biology will lead to a better understanding of Drosophila physiology , ecology , and evolution . Our results suggest a model where the composition of gut bacterial communities is determined by three separate factors: diet , host physiology , and chance . Since all gut bacteria must first be ingested , bacterial taxa that thrive on the feeding substrates of the host species will have the greatest chance of colonizing the gut . The aerobic , and often high-nutrient environments frequented by Drosophila may present taxonomically and geographically distant fly populations with similar “source” bacterial communities . Furthermore , the quantitative differences between Drosophila feeding upon different food sources may be the result of exposure to different diet-specific bacterial communities . Next , within the range of microbes presented by the diet , some properties of the Drosophila intestinal environment determine which bacteria are allowed to persist . These properties may reflect conserved features of the Drosophila immune system as well as the physico-chemical conditions in the gut lumen – such as pH or the simple fact that , unlike the mammalian digestive tract , the Drosophila gut is most likely an aerobic environment . This may explain why the closest relatives of the dominant OTUs in our survey come from other insects , and why bacteria commonly associated with flies are very rare in diverse mammalian species and vice versa . At this time , it is not clear whether genetic variation between or within species can further bias the acquisition of symbionts . Although we do not detect an effect of host species in our study , it is possible that deeper sequencing will uncover quantitative effects of the host genotype , especially under controlled environmental conditions . Finally , within the boundaries set by the host diet and subject to host filtering , the microbiome of each population is likely determined by chance environmental encounters between flies and bacteria . This factor may explain both the lab effect and the change in bacterial communities over time observed in our lab samples . In the simplest scenario , each individual host would collect a random sample of permissible bacteria available in its environment . A further level of complexity may be added if one considers the interactions between bacterial taxa or their order of colonization . The reciprocal dominance of Enterobacteriaceae and Lactobacillales in Drosophila samples suggests that one or both of these processes may be important . This model of microbiome assembly , while consistent with all our data , remains to be tested by more systematic environmental sampling and experimental analyses . It is also unclear whether it applies to other Drosophila-associated microbes such as yeast . Repeated sampling of multiple co-occurring species from the same feeding sources , analysis of individual variation in natural populations and laboratory settings , and characterization of bacterial communities native to the diet of each population will all be necessary to determine the relative importance of source bacterial communities , host control , and the vagaries of chance in shaping the gut microbiome . The gut bacterial communities of Drosophila are likely to represent the most common type of animal microbiomes , where symbionts are free-living and horizontally transmitted and the host-symbiont associations are flexible and facultative . If this model is confirmed by future work , it may serve as a paradigm for the assembly of other animal microbiomes in nature . This framework may help us understand both the ecology of host-symbiont interactions and the functional impact of these interactions on the host .
Drosophila samples were collected with the help of many colleagues around the world ( see Acknowledgments , Table 1 and Dataset S2 ) . All samples were obtained from naturally occurring substrates and no artificial baits were used to attract flies . For collections done in Northern California , adults were immediately transferred to sterile no-nutrient media ( agar-water ) and transported to UC-Davis for dissection , which occurred within 2 hours of collection . For more remote field collections , flies were stored in 100% ethanol for transport . Freshly collected flies were washed twice in 2 . 5% bleach and twice in sterile water . The entire gut was dissected in sterile insect saline and placed in sterile TES buffer ( 10 mM Tris-HCl [pH = 7 , 5] , 1 mM EDTA , 100 mM NaCl ) . For flies stored in ethanol , dissection was not feasible because weakening of the fly tissues caused the gut to fragment . For these samples , the entire fly body was used after three washes with sterile water . To ensure adequate removal of external bacteria , each final wash was confirmed to be free of bacterial cells by PCR with universal bacterial primers and by plating onto rich media . In no case did the final wash show evidence of bacterial contamination . For a single sample ( D . melanogaster reared in the Kopp laboratory ) , the first wash was saved for DNA extraction to characterize the external bacterial community . Seven to 20 fly bodies or guts were combined for most samples . In one exception ( D . melanogaster bodies collected from rotting grapes , sample MAW ) only a single body was used . On a single occasion , the bacterial community of laboratory media within the Kopp laboratory was sampled using 1 ml of media that had been inhabited by D . melanogaster for 7–10 days . Further details regarding sample collection dates , locations , and contents can be found in Dataset S1 . DNA was extracted from samples using a modification of the Bead Beater protocol [93] . The tissue was homogenized by grinding and three freeze/thaw cycles on dry ice . Samples were then incubated with 50 units/ml of lysozyme for 15 minutes . Next , physical disruption was performed in a Bead-Beater ( BioSpec Products , Inc . , Bartlesville , OK ) on the homogenize setting for three minutes . An overnight incubation with 1% SDS and 2 mg/ml Proteinase K was followed by extraction with an equal volume of 25∶24∶1 phenol:chloroform:isoamyl alcohol . The aqueous phase was incubated at room temperature for 30 minutes with 2 . 5 volumes of 100% isopropanol and 0 . 1 volumes of 3 M sodium acetate before centrifugation at 16 , 000 g for 30 minutes at 4°C . The DNA pellet was washed with cold 70% ethanol and allowed to air dry before resuspension in TE ( 10 mM Tris-HCl pH 7 . 5 , 1 mM EDTA . ) . Approximately 100 ng of DNA was used as template for small-subunit rDNA ( 16S ) amplification . Bacterial universal primers 27F ( 5′- AGAGTTTGATCCTGGCTCAG ) and 1492R ( 5′-GGTTACCTTGTTACGACTT ) were used to amplify a ∼1450 bp fragment ( Lane , 1991 ) . These primers were chosen for three reasons . First , although they are not truly universal , they are specific to a region that is conserved in many groups of bacteria [94] . Second , they allow for the amplification of nearly the full length of the gene , therefore providing consistent comparisons to previous studies of 16S rDNA diversity [95] . Finally , both of these primers have been used in many similar surveys of bacterial diversity , including a previous study of bacterial diversity in Drosophila melanogaster [32] . Using these primers allows our results to be directly comparable to those previous studies . The PCR conditions were as follows: initial denaturation for 5 minutes at 95°C; 30 or 35 cycles at 95°C for 30 seconds , 55°C for 30 seconds , and 72°C for 2 minutes; final extension for 10 minutes at 72°C . These PCR conditions were used for all samples , with an annealing temperature of 55°C chosen from a temperature gradient study of 48°C to 58°C because it produced the maximum product yield . The 16S rDNA amplicons were cloned into the pCR4-TOPO vector using the TOPO TA Cloning Kit . Clones were transformed chemically into One Shot TOP10 chemically competent E . coli cells or via electroporation into ElectroMAX DH10B E . coli cells ( Invitrogen , Carlsbad , CA ) and plated onto agar plates with X-gal and either 50 mg/mL Kanamycin or 50 mg/mL Ampicillin . Colony PCR ( 20 colonies ) was used to verify a <10% insertless rate and ∼1 . 5 kb insert size . White colonies were arrayed into 384-well plates . Prior to sequencing , plasmids were amplified by rolling circle amplification using the TempliPhi DNA Sequencing Amplification Kit ( Amersham Biosciences , Piscataway , NJ ) and sequenced from both ends using the M13 ( −28 or −40 ) primers with the BigDye kit ( Applied Biosystems , Foster City , CA ) . Sequencing reactions were purified using magnetic beads and run on an ABI PRISM 3730 ( Applied Biosystems ) sequencing machine . Vector and primer sequences were removed with cross_match , a component of the Phrap software package [96] , [97] , and bases with a PHRED quality score of Q> = 15 were converted to “N”s using JAZZ , the Joint Genome Institute's in-house assembly algorithm . When possible , overlapping regions from the forward and reverse reads of each clone were used to assemble a single contiguous sequence for each clone . In cases where the overlap was not sufficient for assembly , custom perl scripts were used to concatenate the forward and reverse reads with gaps inserted between them ( see below ) . All sequence data are available via BioTorrents ( http://biotorrents . net/details . php ? id=143 ) and have been submitted GenBank under the accession numbers JN420379 through JN426767 . We used the Infernal 1 . 0 software [98] to create a single multiple sequence alignment for all of our samples . Infernal creates a Hidden Markov Model ( HMM ) based on a high-quality reference alignment with a fixed length of 1532 . 2078 of the 4198 clones consisted of non-overlapping paired reads; for those we created a 5′-alignment ( of reads beginning with the 27F primer sequence ) and a 3′-alignment ( of reads ending with the reverse complement of the 1492R primer sequence ) , and merged the two alignments , inserting gaps into the intervening columns , based on positions in the reference alignment . The concatenated sequences from this “merged” alignment were combined with the successfully-assembled , full-length clones to create a single multiple sequence alignment . This alignment is available via BioTorrents ( http://biotorrents . net/details . php ? id=143 ) . For the purposes of OTU ( operational taxonomic units [99] ) definition and phylogenetic inference , this multiple sequence alignment was further refined to remove column blocks that contained >80% gaps . This resulted in the removal of the first 11 ( 1–11 ) and last 132 ( 1400–1532 ) positions , as well as positions 642–806 ( 164 positions ) from the middle of the alignment ( which primarily corresponded to the regions of non-overlap between reads ) . A custom perl script was used to remove sequences with fewer than 300 remaining nucleotides from this trimmed alignment . Chimeric sequences were identified and removed using the chimera . slayer function within mothur v . 1 . 11 [35] . We submitted our sequences to the Ribosomal Database Project ( RDP10 ) Classifier for taxonomic assignment [100] to the genus level . We were unable to submit a single , full-length sequence for every 16S clone that was sequenced , because for 50% of our clones , there was no overlap between the forward and reverse reads . For each clone , we assigned taxonomy independently to the 5′ read , the 3′ read , and to the full-length or concatenated reads ( with intervening gaps inserted , as described above ) , and then selected a single taxonomy assignment for each 16S clone . We used the measures of confidence ( bootstrap values ) that are associated with the RDP taxonomy predictions to guide the selection process . Most investigators agree that >70% bootstrap support is indicative of strong support for a phylogenetic clade [101] . In order to arrive at taxonomy predictions with very high confidence , we only considered taxonomy assignments that had bootstrap values of >75% at the genus level , >80% at the family level , >95% at the order level , and 100% at the class level . Strongly supported disagreements between the 5′ , 3′ , and combined data sets were rare ( 72 total sequences ) . These were handled in one of two ways: 1 ) if the conflict was at the level of family or above , they were considered likely to be chimeric sequences and excluded from further analysis , or 2 ) if the conflict was within a single family , the genus name was changed to “unclassified” . We used the mothur program [35] to generate a distance matrix using our trimmed Infernal alignment of 3243 sequences as input . Using the distance matrix created by mothur , sequences were clustered using the average neighbor algorithm . Using the 0 . 03 cutoff option ( 97% sequence similarity ) , all sequences fell into 139 OTUs . The average OTU abundance was 23 . 3 sequences ( Min = 1 , Max = 638 ) . A representative sequence from each OTU was selected using the get . oturep function within mothur . This representative sequence and the dist . seqs command in mothur was used to calculate genetic distances between OTUs and representative sequences throughout this study . Taxonomy predictions generated by RDP were mapped onto each sequence within an OTU . In many cases , this led to a clear reassignment of “unclassified” sequences to the genus level based on the dominant genus present in that OTU . In other cases , the entire OTU was comprised of “unclassified” sequences . These OTUs were assigned names based on their phylogenetic position relative to the reference sequences included , either from the RDP type strains , from other Drosophila bacterial microbiome studies , or from GenBank . All 7448 good quality 16S sequences longer than 1200 bp from bacterial type strains were downloaded from the RDP website on 8/22/10 [100] . These representatives are usually the first identified or most fully characterized strains within a bacterial lineage . Although closely related bacteria may differ substantially in genome content , inclusion of these type strains provides important phylogenetic landmarks during tree building . All 7448 strains were aligned using Infernal , and the resulting alignment was trimmed to remove the first 11 ( 1–11 ) and last 132 ( 1400–1532 ) positions , as well as positions 642–806 ( 164 positions ) from the middle of the alignment as described above . All sequences from previous studies of Drosophila bacterial communities [32] , [33] ( Corby-Harris , unpublished ) were downloaded from GenBank . Mothur was used to create a distance matrix , and OTUs were created at a 97% similarity cutoff . The get . oturep function was used to pick a representative sequence for each OTU . Additional Drosophila-associated sequences were also included [21] , [37] . Finally , for OTUs in our study that do not have any closely related sequences within the RDP database ( such as Enterobacteriaceae Group Orbus ) the closest BLAST hits from GenBank were included . A list of the RDP and GenBank accession numbers for sequences used in the final tree are found in the Dataset S5 . To compare our results to mammalian studies , the 17 , 504 ultraclean sequences from [12] were analyzed . To obtain a sample that was roughly equal to our data in taxonomic breadth , only the sequences from Artiodactyla , Carnivora , and Primate samples were analyzed . These sequences were aligned and trimmed as above and a full PCA analysis was performed using the Fast UniFrac Interface [62] . Taxonomic classifications were done with RDP [100] . Diversity measurements were calculated for each library from both Drosophila [33] and mammalian [12] datasets using mothur [35] . The proportion of OTUs unique to each library was calculated for each group ( as in Ley et al . , 2008a ) [12] . Using representative sequences from our dataset , previously identified Drosophila-associated bacteria , representative type strains from the RDP database , and sequences obtained from GenBank ( see previous section ) , a phylogenetic tree was created with FastTree [36] . Default settings with the GTR ( generalized time-reversible ) model were used . The entire tree was rooted using Thermus thermophilus ( RDP identifier S000381199 ) . After an initial run with all 8 , 407 sequences , many clades were removed from the alignment ( for example , bacterial Phyla in which no Drosophila associated sequences were present ) . The remaining 1349 aligned sequences were then re-run on FastTree using the settings described above . Final publication quality images were prepared using Dendroscope [102] . Tests of significance of differences between samples were performed using UniFrac and FastUniFrac [61] , [62] . The low depth of coverage provided by the sequencing method used is sufficient to find significant results using UniFrac [103] . Because of the correction for multiple comparisons , pairwise comparisons for each library were not feasible with the amount of data collected . We therefore parsed all data into bins representing different host diets to estimate the overall effect of this factor . The effect of different experimental treatments was determined similarly . All comparisons of bacterial communities are given in the Text S1 . Co-occurrence tests were also performed ( as in [104] ) , but inadequate power precluded the finding of any significant co-occurring pairs of taxa ( additional details in Text S1 ) . Unless explicitly stated , flies were fed unsterilized standard lab media ( Text S1 ) . All transfer steps were performed near a Bunsen burner flame and all surfaces and instruments were frequently sterilized with 70% ethanol . For bacterial DNA extraction , flies were washed to remove external bacterial cells and their intestines dissected as described above . All negative controls were confirmed to be bacteria-free by plating onto MRS media and PCR with universal bacterial primers . Separate libraries were created from adult Canton-S males , Oregon-R males , and Oregon-R females for Deborah Kimbrell's lab ( UC-Davis , CA ) . In addition , a large population of wild D . melanogaster originally collected from Winters , CA was established in the Kopp lab for use in dietary treatment experiments ( strain WO ) . For diet experiments , approximately 25 flies were transferred to each of 5 separate diets , with one vial per treatment . The diets included standard lab media , high yeast media , high yeast supplemented with 6% ethanol , sugar-agar , and agar only ( see Text S1 for media composition ) . All media were initially sterilized in an autoclave , with the exception of the standard lab media . Ethanol was added to the ethanol treatment after media cooled below 55°C . To reduce the effect of the media-dwelling bacterial population that arose after contact with non-sterile flies , cultures were transferred daily to fresh sterile media , with the exception of the standard lab diet . These transfers continued for three days on all media except the agar-only diet , where starvation-induced death limited the experiment to two days . Four hours after the final transfer , the intestines of 10 flies per treatment were dissected for analysis . For the multiple species experiment , approximately 25 adults each of D . melanogaster , D . elegans , and D . virilis were combined on sterilized high yeast media . After three days of daily transfers as above , 10 males per species were dissected for analysis . | All animals are associated with large consortia of non-pathogenic microbes . Most of these “microbiomes” are not well characterized despite their importance for many aspects of host biology including human and animal health and the agricultural impact of pest species . The fruit fly Drosophila melanogaster provides a powerful experimental model for investigating the dynamics and consequences of animal–microbial interactions . However , it is not clear whether the model bacteria studied in the lab are representative of natural microbial consortia . To establish an ecological and comparative background for experimental studies , we have conducted a global survey of bacterial communities associated with natural populations of 14 species of Drosophila and related genera . Despite the taxonomic and ecological diversity of these species , we find that they are associated with the same dominant bacterial groups . Based on our results , we propose a model of microbiome assembly where its composition is circumscribed by host diet and physiology but , within those limits , is highly dependent on chance environmental encounters . Consistent with this model , the microbiomes of wild flies differ significantly from those of laboratory strains , suggesting that experimental studies should be extended to include the bacteria that are most prevalent in natural communities . | [
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] | 2011 | Bacterial Communities of Diverse Drosophila Species: Ecological Context of a Host–Microbe Model System |
Bacteria suffer various stresses in their unpredictable environment . In response , clonal populations may exhibit cell-to-cell variation , hypothetically to maximize their survival . The origins , propagation , and consequences of this variability remain poorly understood . Variability persists through cell division events , yet detailed lineage information for individual stress-response phenotypes is scarce . This work combines time-lapse microscopy and microfluidics to uniformly manipulate the environmental changes experienced by clonal bacteria . We quantify the growth rates and RpoH-driven heat-shock responses of individual Escherichia coli within their lineage context , stressed by low streptomycin concentrations . We observe an increased variation in phenotypes , as different as survival from death , that can be traced to asymmetric division events occurring prior to stress induction . Epigenetic inheritance contributes to the propagation of the observed phenotypic variation , resulting in three-fold increase of the RpoH-driven expression autocorrelation time following stress induction . We propose that the increased permeability of streptomycin-stressed cells serves as a positive feedback loop underlying this epigenetic effect . Our results suggest that stochasticity , pre-disposition , and epigenetic effects are at the source of stress-induced variability . Unlike in a bet-hedging strategy , we observe that cells with a higher investment in maintenance , measured as the basal RpoH transcriptional activity prior to antibiotic treatment , are more likely to give rise to stressed , frail progeny .
Microbial phenotypic heterogeneity , defined as variability of a given trait in a genetically identical population in a homogeneous environment , has been repeatedly observed [1] , [2] . It is manifest , for example , in the broad distributions of individual gene expression levels recorded in studies of both prokaryotic and eukaryotic cells [3] , [4] . Stress conditions may induce further differentiation of clonal cells , in agreement with the observed higher variability of stress response genes' expression in comparison with other gene families [5] . At the extreme , initial stochastic variability is funneled into bistable states via positive feedback mechanisms that persist through generations [6] , [7] . Recent evidence suggests that fate decisions can be partly made even before cells experience an environmental change [8] , [9] , [10] . A cell's ultimate fate depends on its historical state , indicating that phenotypic variability is shaped by pre-disposition factors [11] . It has been proposed that population heterogeneity increases fitness in unpredictable environments [12] , [13] . This may work as a kind of bet-hedging [7] , [14] , [15] , allowing a given genotype to express multiple phenotypes of differing viability . One phenotype may be better adapted to the current environment while others are prepared for future environmental changes under which they may gain higher fitness . On the other hand , heterogeneous populations may simply undergo performance-based selection , in which fitter cells always perform better despite an environmental change . Stress-responsive genes show greater expression variability than genes from other classes [5] , suggesting the hypothesis that variability arises as an anti-stress adaptation evolutionary strategy . Among the stresses that bacteria face , antibiotics are prominent and widespread [16] . Yet the consequences of low-grade antibiotic stress are rather poorly understood . Our interest here is to characterize the dynamic process of stress-induced phenotypic heterogeneity . Specifically , we address the following questions: Will sub-inhibitory antibiotic concentrations further amplify phenotypic variation to the extent of producing persistant and sensitive sub-populations ? Are there any predetermining factors that modulate the response ? How does this variation propagate through the bacterial lineage ? To this end , we followed the growth of micro-colonies from single Escherichia . coli ( E . coli ) cells under microfluidic control . We exposed cells to sub-inhibitory concentrations of the aminoglycoside antibiotic streptomycin and tracked their responses at the single-cell level . We find that mild antibiotic treatment results in rapid generation of increased phenotypic variability in terms of stress-induced gene expression , growth rate , survival and death . Stochastic events leading to differentiated outcomes may precede the application of stress , propagating in a more deterministic fashion within the lineage as the stress persists . Counter-intuitively , progenitors that exhibit relatively higher maintenance activity prior to stress are not primed for survival , but are rather more likely to develop frail progeny .
Streptomycin penetrates aerobically growing bacteria and targets the ribosome , causing mistranslation of nascent proteins [17] . These in turn may misfold , resulting in the induction of RpoH-mediated heat-shock-responsive gene expression [18] . We monitored the heat-shock response using a chromosomal transcriptional fusion of the yellow fluorescent protein ( YFP ) to the RpoH-driven ibpAB promoter [19] , [20] . This construct was found to be a highly sensitive reporter ( Figure S1 ) . We found streptomycin concentrations ( <4 µg/ml ) , lower than the minimal inhibition concentration ( MIC ) , where significant induction of the heat-shock response can be detected with minimal perturbation to bacterial population growth rate ( Figure S2 ) . The survival rate in these conditions , as determined by plating experiments , is 100% ( see Materials and Methods ) . We followed the outcome of low-dose streptomycin treatment at the single-cell level within its lineage context by time-lapse fluorescence microscopy . This allowed us to determine the extent to which a cell's stress state depends on its ancestors and life history . From a single cell exposed to antibiotics , large variations in fluorescence and growth rate phenotypes were found to propagate through the lineage ( Video S1 ) . As can be seen in this typical movie , cells may either survive or die . As early as the first division , the two daughter cells differentiate into sub-lineages: one with higher fluorescence signal , visible inclusion bodies , slower growth and fewer total divisions before the ultimate death of all its descendants . Here ‘death’ is defined as prolonged arrest in cell growth and gradual loss of contrast in phase contrast images . The other sub-lineage grows faster ( engulfing the dead cousins ) , exhibits lower fluorescence and further develops variation in fluorescence signal and growth rate . Periodic ‘switch on’ events , characterized by increased fluorescence and slowed growth , recur within this sub-lineage ( Video S1 ) . Thus , in response to stress induction , single cells give rise to progeny of diverse phenotypes . Other examples of stressed 2D colonies can be found in Figure S3 . We further studied the emergence of variability using a microfluidic setup allowing controlled environmental changes while following micro-colony growth with time-lapse microscopy [8] . In this setup , single cells were grown without stress for four generations prior to streptomycin treatment . The micro-colonies were monitored by phase contrast and fluorescence time-lapse microscopy ( Video S2 , Video S3 as representative examples ) . The time-series images were analyzed by our custom-made open-source software ‘Cellst’ [21] to segment the cells , quantify their growth rate and fluorescence intensities , and reconstruct their lineage ( Materials and Methods ) . Under induced stress conditions , the pibpAB-YFP signal was found to negatively correlate with growth rate ( Figure 1A ) . In contrast , in absence of stress , a positive correlation prevails ( Figure S4A ) . Therefore , the promoter fusion is a valid reporter for the protein quality , streptomycin-induced stress response . Notably , when the stress is so severe that cells stop growing , overall promoter activity diminishes . As shown in Figure 1a , the correlation saturates at low growth rates . Single cell growth rates exhibit a bimodal distribution ( Figure 1B ) , with one sub-population identified as death-prone ( Figure 1A , data points in red ) . The commitment to eventual cell death can be traced back as early as one generation ( 30 minutes , Figure S6 ) after induction , even though the actual death may take up to 3 generations to occur ( Figure 1C , Figure S6 ) . Staining with Propidium iodide ( PI ) , a widely used death marker that fluoresces upon intercalation between DNA bases yet can diffuse only through depolarized cellular membranes , supports our conclusion that growth-arrested cells are indeed killed by continuous antibiotic exposure ( Figure S5 ) . While a significant ( >5 hours ) delay occurs between growth-arrest and PI signal , all growth-arrested cells are eventually marked . We quantified phenotypic variation as the sub-lineage coefficient of variation ( SLCV ) and individual coefficient of variation ( IDCV ) of cellular fluorescence intensity or growth rate across time ( Text S1 ) . The IDCV measures the phenotypic heterogeneity among a population of single cells regardless of their lineage relation , while the SLCV quantifies the differences among sub-populations of cells grouped according to common progenitors . For example , a single cell may divide twice to form a four-cell micro-colony . These four cells continue to divide respectively . Under normal conditions , the four subsequent sub-lineages are expected to have similar phenotypes , with relatively small differences . However , if the four sub-lineages show significantly large variation in phenotype , we would conclude that differentiation had occurred in the four-cell micro-colony , leading to significantly different sub-lineages . It follows , as depicted theoretically below , that large SLCV with respect to IDCV , indicates occurrence of differentiation . Consider a micro-colony originated from a single cell . At time s , the micro-colony reaches Ns cells . At a later time point t>s , each cell from time s has produced ni progeny , whose fluorescence intensity or growth rate are denoted as xik ( i = 1∼Ns; k = 1∼ni ) . Therefore , the total number of cells at time t is Nt = n1+n2…+nNs The SLCV for a starting point s and end point t is calculated aswhere is the average phenotype among cells within the same sub-lineage and is the average across all ( see Text S1 for the precise definitions ) . Let IDCV be the coefficient of variation among all individual cells in the micro-colony . where is defined as the overall average of single cell phenotypes xik at given time ( Text S1 ) . We expect that if no differentiation occurs between the sub-lineages ( see Text S1 for the derivation ) :While if differentiation occurs:In support of this statistical model , we performed a mathematical simulation reflecting the lineage dynamics in response to the streptomycin-induced stress . A set of stochastic differential equations were constructed to describe reporter gene expression and cell division . We account for the possible positive feedback between stress level and reporter gene expression . The reporter gene expression , in turn , inversely correlates with the cellular growth rate ( Figure 1A ) . Such feedback and correlation can lead to extended cell memory . Model parameters were set to fit the mean and variance of single cell phenotypes measured from the experimental data . As shown in Figure S7 , in agreement with our expectation , the simulation results show that IDCV and SLCV are comparable in the non-stressed condition , while the extended cell memory effect leads to significantly increased SLCV in stress response . We then calculated the actual SLCV and IDCV curves from the growth rate and fluorescence signal experimental data with different starting points Ns = 4 , 8 , 16 , 32 , 64 ( eg . 2∼6 generations ) under induced and non-induced conditions ( Figure 2 ) . The IDCV and SLCV values are similar and stable through 8 generations of micro-colony growth without induction , indicating no differentiation ( Figure 2C and 2D ) . In contrast , when streptomycin is added at the 8–16 cell stage , both values increase ( Figure 2A and 2B ) . The SLCV increases faster than the IDCV , indicating differentiation . The SLCV curves with a starting point prior to induction ( Ns = 4 , 8 ) also increase relative to the IDCV , indicating that differentiation potentially occurs among sibling cells even before they encounter the stress condition . This suggests that the stress has revealed a pre-existing difference in physiological states among the non-induced cells . In other words , there may exist pre-disposition factors in non-induced cells that prime the stress-induced differentiation . We used data randomization to assess the significance of these experimental results . Randomly-chosen cells were switched within the lineage tree as follows: For a micro-colony with final population of N cells , N pairs of cells were chosen for switching to achieve sufficient mixing . Only cells born after stress induction were selected . In order to preserve the time course profile , switching was only allowed between cells of the same generation . As expected , while IDCV remains unchanged , SLCV decreases and is indistinguishable from IDCV ( Figure S8 ) . This result highlights the existence of extended memory effect in the original data . We could exclude a genetic component to the observed variability increase under stress . Identical variability emerged by repeating the above microfluidics experiments with cells from an exponential phase culture , initially stressed ( 2 hours , 3 ug/ml Streptomycin ) , washed , and recovered for 4 hours in absence of stress ( data not shown ) . Indeed , mutations would not be expected to reproducibly manifest these phenotypic effects given the rapid emergence of variability by 4–16 cell stage . In agreement with the SLCV analysis , the detailed view of the induction phenotype within the lineage context reveals significant sub-lineage divergence as well as clustering of stress induction ( Figure 3; Video S2 ) . To highlight the existence of pre-disposition factors in single cells , we compared the RpoH-driven stress response and growth rate of the descendants of each sister cell at the tree nodes prior to induction . In most cases , there was a significant difference between the mean fluorescence ( T-test , p-value <0 . 01; circled nodes , Figure 3 ) and mean growth rate ( Figure S10 ) of the two progeny groups . To assess the significance of this result , we randomly exchanged progeny measurements in the experimentally derived tree . For each pair of sister cells born prior to induction ( 15 nodes ) , we generated 500 randomized trees where progeny were randomly re-assigned . In the stark majority of the runs , no significant difference was detected between the descendants of the pre-induction sister cells . At most , fewer than two percent of the runs per node were statistically significant ( p value <0 . 01 ) . This is in contrast with the experimental data ( Figure 3 ) where the majority of these events ( 12 of 15 ) are significant indicating a 15 ! /12 ! /3 ! ) *0 . 02∧12 = 2E-18 probability of generating our experimental tree by chance . This suggests that differentiation between progenitor sister cells occurred prior to stress induction . In search of a marker for pre-disposition , we considered differentiation events occurring within the time-scale of the stressed cellular phenotype memory half-life time ( 90 minutes , see below , Figure 4 ) . That is , we compared sibling progeny at 90 minutes after induction . We found no global correlation in the comparison of fluorescence intensity , promoter activity , or growth rate between the non-induced progenitor cells and their induced progeny ( Figure S9 ) . However , for the specific identified differentiation events ( T-test , p-value <0 . 01 , Figures S11 , S12 , S13 , S14 ) , there is a clear bias ( p-value of binomial distribution test <0 . 003 Figures S11 , S12 , S13 , S14 ) that the more fluorescent sister gives rise to a sub-lineage with more stressed siblings . The memory or epigenetic effect was further quantified with a gene expression level auto-correlation function . In its simplest form ( i . e . stable gene product and constant production rate ) , this auto-correlation is expected to decrease exponentially with half-life equal to the cellular doubling time ( [22]; Text S1 and Figure S19 ) . In case of nonlinear regulation , such as a positive feedback loop , the half-life will be longer than the doubling time . To this end , we calculated the auto-correlation function of the fluorescence signal , representing the RpoH-driven gene expression level . As expected , before induction , the auto-correlation function decreases exponentially with a half-life close to the cells' doubling time ( 23 Minutes; Figure 4 ) . However , a significant delay of the auto-correlation function decrease is observed after induction ( Figure 4 ) . Note that the auto-correlation function half-life increases after induction to as long as three times the cell doubling time ( 140 minutes , Figure 4 red line ) . This is indicative of epigenetic effects that last longer than a generation . It is thus likely that nonlinear effects such as positive feedback contribute to the delayed decrease in auto-correlation . It was previously proposed that streptomycin exposure could induce further streptomycin uptake by damaging the bacterial cytoplasmic ( inner ) membrane [23] . Such a positive feedback loop could be responsible for the epigenetic effects described above . Upon streptomycin treatment , the cytoplasmic membrane integrity is challenged by mistranslated periplasmic [23] and membrane proteins [18] . However , despite reports of increased secretion of small molecules [24] , direct evidence for increased membrane permeability after streptomycin treatment is scarce . If streptomycin ( molecular weight MW = 581 g/mol ) treatment increases the membrane permeability , it should also increase permeability of other molecules with similar size . Therefore , controlled gene expression by transcriptional inducers such as anhydrotetracycline ( ATC , analog of tetracycline , MW = 463 g/mol ) should function as indicators of a parallel increase in streptomycin uptake . Similar to streptomycin , tetracycline ( MW = 444 g/mol ) can penetrate the outer membrane through porins [25] and diffuse across the cytoplasmic membrane . The latter step is rate limiting for both streptomycin [26] and tetracycline , with half-equilibration time of 35±15 minutes [27] . Such slow permeation rates produce detectable variation in the intracellular inducer concentration . Consider cells co-induced by streptomycin and ATC , a positive correlation between the heat-shock reporter and an ATC-inducible reporter is expected if the higher stress level induced by streptomycine leads to higher membrane permeability , with a corresponding influx of ATC molecules . To test this hypothesis , a tetR-controlled fluorescence reporter was chromosomally integrated in the ibpAB-promoter-driven fluorescence reporter strain . When the strain was co-induced with both ATC and streptomycin , a positive correlation between two reporters was observed ( Figure 5 ) . Furthermore , compared to ATC induction alone , the expression level of the tetR reporter is stronger in the presence of streptomycin . The possibility that the positive correlation is due to elevated global protein expression level in higher stressed cells was excluded as no positive correlation was found between prrna promoter ( e . g . a constitutive promoter ) and pibpAB activity after stress ( Figure S15 ) . These results suggest that cells accumulate higher concentration of ATC under streptomycin stress , supporting the hypothesis that streptomycin stress increases the cytoplasmic membrane permeability . Such increased membrane permeability is likely to allow higher uptake of streptomycin as well , closing a positive feedback loop of stress induction which leads to the observed epigenetic effect ( Figure 4 ) . As control , we tested two other antibiotics at sub-inhibitory concentrations: Mitomycin C ( a DNA cross-linker ) and Nalidixic acid ( topoisomerase inhibitor ) that are not expected to significantly impact translational fidelity . Indeed , while these antibiotics induced the SOS response ( judged by characteristic filamentation ) they did not induce the ibpAB promoter and did not enhance but rather reduced the ATC induction levels ( Figure S16 ) .
We demonstrated that sibling E . coli cells diverge in their response to a sub-inhibitory concentration of streptomycin , to the extent that sub-populations may die and others survive within the same growing micro-colony in a homogeneously defined environment ( Figure 1 and Figure S5 ) . Upon induction , phenotypic differentiation events occurred , manifested as a stronger increase of the coefficient of variation among sub-lineages as compared to that of the coefficient of variation among individual cells ( Figure 2 ) , leading to significant differences between sister's progeny ( Figure 3 ) . Increased phenotypic variation upon stress is coupled with transient epigenetic inheritance that lasts for up to three generations , as opposed to a typical autocorrelation half-life of one generation time in absence of stress ( Figure 4 ) . Our results indicate the existence of nonlinear feedbacks that prolong the memory lifetime . The correlated expression of an ATC-induced tetR promoter and a streptomycin-induced ibpAB promoter ( Figure 5 ) agrees with the hypothesis that streptomycin treatment leads to higher cellular membrane permeability , allowing more streptomycin as well as ATC molecules to enter the cell . Such feedback could be triggered by random events such as bursts of membrane damage by nascent mistranslated proteins or the asymmetric segregation of damaging factors during cell division [28] . While some cells are induced earlier and pass on the stressed state to descendants , others stay relatively healthy for a longer time , resulting in sustained diversification of cell fate . Therefore , we argue that positive feedback and stochasticity are responsible for the differentiation and increased variation . Apart from membrane permeability , there may be other feedback pathways that can affect cell fate . For example , streptomycin may lead to production of ribosomes with lower accuracy , which in turn produce more dysfunctional ribosomes [29] . Or the amount of misfolded protein in the cell could exceed the capacity of the chaperone system , preventing the latter from maintaining protein homeostasis [30] . Recent work presents a revised view of the antibiotic mode of action , showing that apart from targeting a single entity , antibiotics broadly effect on the global metabolism of the cell [31] , [32] . It is well established that sub-inhibitory concentration of antibiotics can directly or indirectly interact with different functional modules in the cell [33] . In the presence of stochastic events , such complex response processes are expected to produce diverse phenotypic outcomes . The process described here may play a role in other systems , since stochastic fluctuation and positive feedback are common . Our methodology could be applied to test other stresses , where incurred damage weakens the defense system of cells , leading to further damage accumulation . The early occurrence of differentiation events ( Figure 2 and Figure 3 ) indicates that the stress condition can reveal differences in cellular physiological state existing prior to induction . Some cells are intrinsically more resistant to stress than others . After induction , the difference is amplified and passed on in the respective sub-lineages , resulting in differentiation and increased variability . Similar pre-disposition phenomena have been reported in other induction-response systems . For example , the probability of lysogeny during phage infection is determined by both the number of infecting phage and the size of the host cell [9] . In the lactose switch , the bacterial growth rate and basal LacI level are highly predictive of switching outcome after induction [8] . In Bacillus subtilis , the decision to form an endospore is made two generations before encountering starvation conditions [10] . Whether these pre-disposition factors are a consequence of natural selection for bet hedging is mostly unclear . In the case of ampicillin ‘persisters’ , a sub-population of bacteria transiently enter a dormant state in a non-stressing environment and can thus survive ampicillin treatment that attacks only growing cells [14] . Such persister cells pay a cost to express a phenotype which is less fit in the current environment but more fit for a particular environmental change . This example was interpreted as a bet-hedging strategy anticipating the arrival of future stress conditions . Yet whether it is beneficial to apply a bet-hedging strategy depends on the phenotypic switching rate , the time scale of environmental change and the fitness cost [15] , or on rather stochastic events inherent to cellular physiology rather than resulting from a positive evolutionary fitness gain . In our observations , there is no sign of a pre-disposition factor working to hedge phenotypic bets . Higher stress responses prior to induction do not prime our cells for the stress to come . Instead , cells with relatively higher basal RpoH transcriptional activity are more likely to give rise to more stressed progeny ( Figure S11 , S12 , S13 , S14 ) . This suggests that under non-stressed conditions , cells with a higher basal stress level may be paying a cost which will not help them to survive the upcoming stress . It is the weaker cells that simply suffer more , while fitter cells prevail , suggesting a simple performance-based selection . It was recently shown that antibiotic-resistant mutants can emerge rapidly in a structured environment with a gradient of antibiotic concentrations , even from small population of 100 cells [34] . The fact that a single bacteria can generates highly variable progeny at sub-inhibitory antibiotic concentrations may facilitate this process , as it has been shown theoretically that higher variation in cellular growth rate indicates higher selection pressure [35] . Our findings may have clinical relevance as it is common that pathogens encounter sub-lethal doses of antibiotics , due either to disruptions in the prescribed medication regime or limited diffusion through structured niches such as biofilms .
All strains were derived from the wild-type strain E . coli MG1655 [36] . The YFP gene was integrated downstream of the ibpAB promoter with the ibpAB operon [19] . The strain with pibpAB-RFP and ptetR-YFP is from [37] . The strain with prrna-CFP and pibpAB-RFP is from [19] . E . coli were cultured overnight at 37°C in Luria-Bertani ( LB ) medium ( Bacto ) . The cell culture was diluted and plated on LB-agar plates with different concentrations of streptomycin ( 0–4 µg/ml ) . The number of colonies on the plates were counted following overnight incubation at 37°C . A detailed description of the microfluidic setup can be found in [8] . In short , cells were plated on a thin agarose pad ( 1 . 5% agarose in LB medium ) . The agarose pad was then inverted and laid on a cover slide with cells contacting the glass . A block of crosslinked poly ( dimethylsiloxane ) ( PDMS RTV615 , General Electric ) with the feeding channel structures is exposed to air plasma ( HARRICK PLASMA ) and then placed over the agarose pad with the rest of surface area sticking to the cover-slide . LB medium or LB supplemented with 3 µg/ml streptomycin ( Sigma ) is injected into the feeding channel ( 2 ml per hour ) and diffuses through the agarose pad to feed the cells . With this setup , it is possible to switch the medium on the spot with <1 minute homogenisation time [8] . We controlled for positional effects and no difference in cellular growth rate was found at different locations within a micro-colony ( Figure S17 and S18 ) . Additional controls on homogeneous permeability of the agarose layer have already been reported [8] . All the experiments are run at 37°C using a Zeiss automated microscope ( Axio Observer Z1 , HXP 120 , 63× objective ) with a temperature-controlled chamber ( Live Imaging Services ) . For each media condition ( with or without streptomycin ) , four single cells were chosen to be followed . Phase contrast photos were taken every 90 seconds while fluorescence photos were taken every 180 seconds ( 2% lamp energy , 3 second exposure ) . Overnight cultures in LB 37°C were diluted 200 fold into fresh LB and agitated at 37°C for 2 hours . 1 µl of cell culture was dropped onto an agarose pad ( 1 . 5% agarose in LB medium with or without 3 µg/ml streptomycin or 25 ng/ml ATC ) . The agarose pad was covered with a cover-slide and the border sealed with nail polish [38] , [39] . Phase contrast images were analyzed by customized software “Cellst” [21] for cell segmentation and micro-colony lineage reconstruction . The cell border was then projected onto the corresponding fluorescence image to determine the fluorescence intensity of the cells , defined as the mean grey level ( background subtracted ) of the pixels inside cell border . The exact location of a cell was set as the pixel coordinate of the centre of mass of the cell area . The length of a cell is measured as the long axis of the cell area . | Individual organisms of identical genetic background , living in a homogeneous constant environment , may nonetheless exhibit observable differences dubbed phenotypic plasticity or variability . When such a population is challenged with an unforeseen stress , the disparity among individuals may increase , yielding different strategies in response . This work addresses the occurrence and propagation of phenotypic variation as it affects bacterial survival in response to mild antibiotic treatments . We recorded images of single bacterial cells as they divide prior to and during exposure to a sub-lethal level of streptomycin , a ribosome-targeted antibiotic . We found that individual differences increase upon stress to the extent that cells may either die or survive the treatment . Differentiation events were traced back prior to exposure . We suggest that a positive feedback loop , governed by increased membrane permeability , underlies the transient cell memory observed . Cells with relatively high basal stress-response levels prior to stress are not primed for better survival , but are rather more likely to succumb to antibiotic treatment . As pathogens commonly encounter sub-lethal doses of antibiotics , their survival may be better understood in light of this study . | [
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] | 2012 | Pre-Disposition and Epigenetics Govern Variation in Bacterial Survival upon Stress |
RNA interference ( RNAi ) pathways are widespread in metaozoans but the genes required show variable occurrence or activity in eukaryotic microbes , including many pathogens . While some Leishmania lack RNAi activity and Argonaute or Dicer genes , we show that Leishmania braziliensis and other species within the Leishmania subgenus Viannia elaborate active RNAi machinery . Strong attenuation of expression from a variety of reporter and endogenous genes was seen . As expected , RNAi knockdowns of the sole Argonaute gene implicated this protein in RNAi . The potential for functional genetics was established by testing RNAi knockdown lines lacking the paraflagellar rod , a key component of the parasite flagellum . This sets the stage for the systematic manipulation of gene expression through RNAi in these predominantly diploid asexual organisms , and may also allow selective RNAi-based chemotherapy . Functional evolutionary surveys of RNAi genes established that RNAi activity was lost after the separation of the Leishmania subgenus Viannia from the remaining Leishmania species , a divergence associated with profound changes in the parasite infectious cycle and virulence . The genus Leishmania therefore offers an accessible system for testing hypothesis about forces that may select for the loss of RNAi during evolution , such as invasion by viruses , changes in genome plasticity mediated by transposable elements and gene amplification ( including those mediating drug resistance ) , and/or alterations in parasite virulence .
In metazoans , RNAi interference and related pathways play many key roles including regulation of mRNA levels and translation , chromatin silencing , programmed DNA rearrangements , genome surveillance , and defense against invading viruses . The phylogenetic distribution of key genes required for RNA interference such as Argonaute and Dicer suggests that this pathway may have been present in the common eukaryote ancestor [1] . However the situation for eukaryotic microbes is complex: some have active RNAi pathways , others lack RNAi genes and activity , and demonstration of RNAi has proven elusive in some species bearing reasonable homologs of canonical genes such as Argonaute [2]–[7] . The trypanosomatid protozoa comprise three major lineages , broadly grouped as the African trypanosomes ( Trypanosoma brucei ) , South American trypanosomes ( T . cruzi ) and a lineage encompassing a number of genera associated with insects or plants , ultimately leading to the mammalian parasite Leishmania [8] . Functional and genome sequencing data have shown that species within the African trypanosome lineage such as T . brucei contain an active RNAi pathway and genes , including an Argonaute “slicer” ( AGO1; [2] ) and two Dicers ( DCL1 and DCL2; [9] , [10] ) . In contrast , T . cruzi , L . major and L . donovani lack these activities and associated genes [11]–[14] . However the genome of L . braziliensis ( subgenus Viannia ) contains orthologs of T . brucei AGO1 , DCL1 and DCL2 [15] , suggesting this group might retain a functional RNAi pathway . Given the uncertainties of extrapolating from RNAi genes to functions noted in other eukaryotic microbes [2]–[4] , we sought to establish whether the RNAi machinery functions in L . braziliensis , and explored its utility as a genetic tool . Furthermore , we made evolutionary comparisons to map when the RNAi pathway was lost , and we discuss potential selective forces impacting on the parasite that may have contributed to the demise of RNAi during Leishmania evolution .
Dicer is required to process long dsRNA to small interfering RNAs ( siRNAs ) , which in trypanosomes are 24–26 nt long [16] . A convenient marker of RNAi activity is siRNA formation from endogeneous retroelements [17] , and Northern blot analysis of L . braziliensis RNAs revealed the presence of small RNAs of the expected sizes arising from the retroelement SLACS , similar to T . brucei siRNAs ( Fig . S1; [16] ) . We then developed a green fluorescent protein ( GFP ) -based RNAi reporter assay for siRNA formation , as well as target mRNA and protein levels . Initially we experienced unexpected difficulty in L . braziliensis transfection , when using episomal constructs previously developed in one of our labs that function effectively in many Leishmania species , and in many laboratories [18] . The basis for this effect is not definitively known , as addressed in the discussion , but we suspect it is due to the tendency of episomal vectors to be transcribed from both strands , which in an RNAi-proficient species would strongly inhibit episomal gene expression [11] , [13] . Thus in all studies reported here , transfection was accomplished following integration of DNA constructs into the ribosomal small subunit RNA ( SSU ) locus , using the appropriately digested DNA from pIR1SAT-based vectors , or derivatives thereof [19] . In trypanosomatids , processing of polycistronic RNA precursors by 5′ trans-splicing and 3′ polyadenylation produces capped mRNAs that can direct protein synthesis [20] . First we generated a GFP ‘stem-loop’ ( long hairpin ) construct , containing two copies of an AT-rich GFP reporter ( GFP65 ) in an inverted orientation separated by a short loop ( Fig . 1A ) . This GFP stem-loop construct ( GFP65-StL ) was flanked by Leishmania sequences required for efficient 5′ and 3′ end mRNA formation , and was expressed following integration into the parasite small subunit ribosomal RNA locus ( SSU rRNA; Fig . 1A ) in L . braziliensis strain M2903 . Northern blot analysis with a GFP65 probe showed that expression of GFP65-StL gave rise to a variety of products ( Fig . 1D , lane 2 ) . The largest of these likely correspond to unprocessed transcripts , while the smaller ones likely correspond to degradation products , which could occur irrespective of whether RNAi pathways are active . Importantly , abundant levels of 24–26 nt siRNAs were seen ( Figs . 1B and 1E ) . In contrast , similarly small RNAs were not detected with probes to the SAT drug resistance marker , which is not found in an inverted repeat ( data not shown ) . These data suggested that L . braziliensis expresses a robust Dicer-like activity . We used two GFP reporters , one encoded by the AT-rich ORF ( GFP65 ) used in the GFP65-StL construct above , and the second by a GC-rich ORF ( GFP+ ) . These genes differ in most 3rd codon positions , but their protein products only differ by a single amino acid . Alignment of these genes showed that the longest tracts of identical nucleotides were less than 14 nt ( Fig . S2 ) . GFP65 or GFP+ was then expressed separately following integration into the SSU rRNA locus , in wild-type ( WT ) L . braziliensis or the GFP65-StL transfectant that produces GFP65 siRNAs . As expected , expression of GFP65 or GFP+ led to high levels of GFP mRNA and protein in WT lines , as did expression of GFP+ within the GFP65-StL transfectant ( Fig . 1D , F , G ) . In contrast , clonal lines arising from introduction of GFP65 into the GFPST-StL transfectant showed only trace amounts of GFP65 mRNA ( Fig . 1D ) , and the level of GFP protein was below the limit of detection by western blotting ( <1% in these studies; Fig . 1G ) or flow cytometry ( Fig . 1C ) . These data established that GFP65-derived dsRNA mediated selective ablation of the AT-rich GFP65 but not the GC-rich GFP+ . Similar studies were carried out with a luciferase ( LUC ) reporter , expressed alone or in combination with a LUC stem-loop construct , revealing strongly-reduced LUC expression ( 90–300 fold; Fig . S3 , and other studies below ) . We then tested the activity of the RNAi pathway on several endogenous genes . In transient transfections performed using several protocols and dsRNAs synthesized in vitro against the L . braziliensis α-tubulin , Northern blot analysis showed at best a 63% decrease in α -tubulin mRNA ( Fig . 2A ) . This contrasts with T . brucei where such protocols readily yield >95% reduction in tubulin mRNA expression [21] . This perhaps reflects the lower efficacy of transient transfection attained thus far in Leishmania [11] . Since inducible expression systems were unavailable , we focused on stably expressed ‘stem-loop’ constructs targeting a panel of nonessential genes in L . braziliensis , including ones mediating synthesis of the abundant glycoconjugate lipophosphoglycan ( LPG1 , LPG2 , LPG3; [22] ) , hypoxanthine-guanine phosphoribosyltransferase ( HGPRT ) , or the genes PFR1 and PFR2 , which encode major components of the paraflagellar rod , a component of the trypanosomatid flagellum required for motility [23] . These StL-transfectants showed a variable decrease in mRNA levels when estimated by qPCR , ranging from no effect ( LPG1 ) to more than 10-fold reduction ( LPG2 , LPG3; Fig . 2B ) . However , Northern blot analysis showed a nearly complete absence of LPG2 mRNA ( Fig . 2C ) , suggesting that the qPCR values are likely underestimates , possibly due to the presence of RNA degradation intermediates able to act as templates ( these are evident in Fig . 2C ) . Despite the reductions in mRNA levels , LPG levels were at best only 3-fold lower in the LPG2-StL or LPG3-StL transfectants , with considerable clonal variability ( Fig . 2E; data for LPG3-StL not shown ) . This suggests that L . braziliensis requires only low levels of LPG biosynthetic proteins , similar to the relatively small effects of RNAi on trypanosome glycoconjugate biosynthetic genes [24] . Both HGPRT mRNA and protein levels showed 3–4 fold decreases in HGPRT-StL transfectants ( Fig . 2B , D ) . One of the earliest reports of stable phenotypic modulation by RNAi in trypanosomes involved down regulation of a paraflagellar rod protein [25] , [26] . The paraflagellar rod is a complex assembly of proteins required for motility , which in trypanosomatids includes two major proteins , termed PFR1 and PFR2 in Leishmania [23] , [27] , [28] . Introduction of PFR1-StL or PFR2-StL constructs into L . braziliensis yielded viable transfectants that grew normally , but lacked the paraflagellar rod , as visualized in longitudinal or transverse EM sections , and exhibited motility defects ( Fig . 3 ) . These phenotypes closely resemble those seen in L . mexicana PFR1 and PFR2 gene deletion mutants [23] . Multiple attempts to introduce ‘stem-loop’ α- or β-tubulin constructs were unsuccessful , as anticipated for essential genes ( not shown ) . Collectively , the strength of the RNAi effect for these phenotypic reporters suggests that RNAi may function sufficiently well to assess the functions of many genes in L . braziliensis . In other organisms RNAi is mediated by the combined activity of a number of proteins , ultimately converging on the endonucleolytic ‘slicer’ activity of the Argonaute protein , which is encoded by the single AGO1 gene in trypanosomes and L . braziliensis [15] , [17] . To establish a critical role for L . braziliensis AGO1 in RNAi , we employed the seemingly counterintuitive approach of ‘RNAi of RNAi genes’ , where introduction of dsRNAs targeting RNAi pathway genes inhibits RNAi activity , albeit not to the same level seen in null RNAi pathway gene knockouts [17] , [29]–[31] . To facilitate comparisons of the efficacy of RNAi , we developed a single RNAi ‘self reporter’ construct which simultaneously expressed two mRNAs , one encoding a luciferase ORF ( LUC ) and a second encoding a luciferase ORF stem-loop ( LUC-StL ) . This minimized experimental variability and the number of transfections required , allowing the assessment of RNAi efficacy by the introduction of a single construct . When introduced into WT L . braziliensis , the ‘LUC RNAi self reporter’ ( LUC-SR ) showed low levels of luciferase activity , about 4-fold over background and comparable to that obtained with lines expressing LUC and LUC-StL independently after successive transfections ( Fig . 4 ) . In contrast , introduction of the LUC reporter alone resulted in activities nearly 1000-fold over background ( Fig . 4 ) . We then introduced a construct expressing an AGO1 stem-loop ( AGO1-StL ) into the LUC RNAi reporter line ( LUC-SR ) . These transfectants showed an average of 100-fold increased luciferase expression relative to LUCSR transfectants , signifying a considerable reduction in the efficiency of RNAi ( Fig . 4 ) . As expected from studies in other organisms cited above , inhibition of RNAi activity was partial , as these values were still about 10-fold less than seen in WT cells transfected with the LUC reporter construct alone ( Fig . 4 ) . These data thus implicate AGO1 as an essential component of the RNAi pathway of L . braziliensis . We explored the prevalence of RNAi pathways in other Trypanosomatid species by comparative genomics . PCR assays detected AGO1 and/or DCL1 genes in all isolates of the Leishmania subgenus Viannia tested ( L . braziliensis , L . guyanensis , L . panamensis ) but not in Leishmania ( Sauroleishmania ) tarentolae , L . mexicana , L . major or L . donovani ( data not shown ) . Partial genome sequencing of a close non-parasitic ‘outgroup’ , Crithidia fasciculata revealed AGO1 , DCL1 and DCL2 . To confirm the presence or absence of a functional RNAi pathway , we expressed the GFP65-StL RNA in L . tarentolae , L . mexicana , L . panamensis , L . guyanensis and Crithidia fasciculata , and monitored siRNA formation by Northern blotting . Consistent with the observed distribution of RNAi pathway genes , GFP siRNAs were made only in Crithidia , L . guyanensis and L . panamensis ( Fig . 5 , S4 ) . Transfection with the GFP reporters showed strong reductions in GFP expression in L . panamensis , comparable to that seen with L . major in Fig . 1 ( data not shown ) , and we show in a later section that RNAi is active in L . guyanensis using a luciferase reporter The level of GFP expression in Crithidia with the Leishmania vectors used was too low to utilize for quantification of the strength of RNAi by flow cytometry ( data not shown ) . Association of these findings with the trypanosomatid evolutionary tree ( Fig . 6A ) through evolutionary parsimony identified a single point when the RNAi pathway was lost during evolution , located after the divergence of members of the subgenus Viannia from the remaining species complexes ( Fig . 7 ) . Importantly , this corresponds precisely to the point when RNAi genes were lost in evolution , as deduced by comparative genomics and evolutionary parsimony . Inspection of the sequenced Leishmania genomes shows that all RNAi-deficient Leishmania now contain only remnant , highly degenerate pseudogenes ( AGO1 ) or have undergone gene deletion ( as revealed by ‘synteny gaps’ for DCL1 and DCL2 ) for known trypanosomatid RNAi genes . Since species retaining only a partial set of intact RNAi genes have not been reported , from these data we cannot identify which essential RNAi pathway gene was lost first at this distant point in Leishmania evolution . Presumably , once a gene critical for RNAi activity was inactivated , the remaining genes of the pathway become superfluous and fall prey to evolutionary drift , as seen in many other metabolic pathways during evolution . RNAi pathways were probably present in the common eukaryote ancestor [1] , and the evolutionary relationships of the available trypanosomatid RNAi pathway proteins closely resemble those of housekeeping protein-based phylogenies ( shown for AGO1 and DCL1 in Fig . 6 B–D ) . While the L . braziliensis AGO1 gene is not syntenic with that of T . brucei [15] , [32] the congruency of the RNAi gene and ‘housekeeping’ gene phylogenies renders the possibility of lateral gene transfer and/or independent acquisitions unlikely . Thus , RNAi most likely was lost twice independently in trypanosomatids , once in the lineage leading to T . cruzi , and a second time in the lineage leading to Leishmania , subsequent to the divergence of most Leishmania groups from the non-parasitic species Crithidia fasciculata and the Leishmania subgenus Viannia ( Fig . 7 ) . We and others have speculated that one of the forces contributing to the loss of RNAi in eukaryotic microbes may be invasion or loss of RNA viruses [13] , [33] . Significantly , dsRNA viruses termed LRVs are found in many ( but not all ) strains and/or species from the Leishmania subgenus Viannia , including L . braziliensis [34] , [35] . We reasoned that studies of the efficacy of RNAi in extant Leishmania bearing or lack LRVs could provide some insight into their potential roles in evolution . Using specific PCR primers for LRVs we showed that the L . braziliensis strain M2903 used here lacked LRVs , consistent with previous reports [36] , [37] . Unfortunately methods for the introduction and/or cure of LRV from Leishmania are not well developed , precluding tests of isogenic L . braziliensis engineered to harbor the LRV virus . Similarly , just one isogenic virus-free derivative of an LRV-containing Leishmania has been described; L . guyanensis is closely related to L . braziliensis ( Fig . 7 ) , and a virus-free derivative arose fortuitously in the course of other studies [38] . The efficiency of RNAi in these lines was evaluated by introduction of the luciferase RNAi reporter ( LUC-SR ) described earlier , relative to transfectants expressing only LUC . Multiple clonal lines were obtained , and LUC expression was measured in six randomly selected lines ( Fig . 8A ) . Importantly , the level of luciferase expression seen in the lines expressing only LUC were comparable between the closely related Viannia species M2903 L . braziliensis and M4147 L . guyanensis ( Fig . 8A ) . All lines and transfectants were shown to retain or lack the LRV1-4 by RT-PCR tests as expected ( Fig . 8B ) . While the RNAi pathway was active in the LRV+ L . guyanensis M4147 , its efficiency was only about 30-fold ( 3 . 8% LUC-SR vs . LUC ) , compared to the 300-fold reduction seen in the virus free L . braziliensis M2903 ( 0 . 34% LUC-SR/LUC; Fig . 8A ) . The WT LRV+ LgM4147 strain also showed reduced efficiency of RNAi relative to M2903 , in studies using successively transfected GFP reporter and GFP-StL constructs ( data not shown ) . Significantly , the LRV-free line LgM4147/pX63HYG showed a similar 30-fold efficiency of RNAi in these studies ( 3 . 3% LUC SR/LUC ) . These data suggest that the reduced RNAi efficiency seen in L . guyanensis M4147 does not require the continued presence of the virus .
Our studies have established that L . braziliensis possesses a functional RNAi pathway , which enables the down-regulation of a variety of reporter and endogenous genes when assayed at the mRNA or protein levels . RNAi of AGO1 was used to confirm a requirement for the sole argonaute gene AGO1 in this process . As seen in many organisms , strong reductions in mRNA expression were seen , often accompanied by phenotypic changes , albeit of variable strength . As anticipated , it was not possible to introduce stem-loop constructs for essential genes such as α- or β-tubulins . Studies of such genes will require the development of inducible expression systems in Leishmania , which while promising have not yet reached the point of utility attained in trypanosomes . Strong phenotypes were produced by the knockdown of two genes implicated in flagellar motility and paraflagellar rod synthesis ( PFR1 and PFR2 ) , closely approximating the phenotypes seen in gene deletion mutants in L . mexicana [23] . In contrast , at best only weak phenotypes were produced by knockdowns of three LPG biosynthetic genes , in keeping with findings in trypanosomes where it has proven difficult to down-regulate expression of genes implicated in glycoconjugate synthesis far enough to attain phenotypic effects . Overall , the results to date suggest that the range in efficacy of RNAi knockdowns , as judged by various phenotypic criteria , is comparable to that seen in trypanosomes and other organisms , and thus is likely to be similarly useful in the systematic analysis of Leishmania gene function in the future . Given the importance of RNAi pathways in many fundamental aspects of eukaryotic biology , it is unsurprising that it has been lost relatively few times during evolution . While the critical roles of RNAi in metazoan gene regulation would likely select strongly against such attenuation , eukaryotic microbes lacking RNAi have arisen sporadically [1] , [2] . This in turn raises the question of under what circumstances RNAi might occur . We consider three working hypotheses for selective pressures that may act independently or in concert to drive this loss in Leishmania . We proposed previously that viral pressure could act as a selective force for the loss of RNAi in Leishmania evolution [11] , [13] . In one scenario , invasion by LRVs at some point in Leishmania evolution could lead to an attenuation of the RNAi response , as many RNA viruses are prone to attack by cellular RNAi pathways [39] . Attenuation could be achieved through down regulation of the RNAi pathway by the host cell , or through viral genes targeting key RNAi pathway activities . While some RNA viruses encode inhibitors of RNAi , no studies have been undertaken as yet for Leishmania LRVs . The challenge for this model is to explain what forces would prompt cells to favor RNA virus retention over disruptions arising from perturbation or loss of the RNAi pathway . Interestingly , LRV infection has been proposed to be advantageous to Leishmania , possibly by modulating host immune responses in a way beneficial to parasite survival [40] , [41] . In support of this hypothesis , recently we have obtained preliminary in support of the proposal that LRV-containing L . guyanensis show increased survival and pathogenicity ( L-FL , KO , S . Hickerson and SMB , unpublished data; N . Fasel , personal communication ) . Selection for the presence of LRV able to promote parasite survival could thus provide a selective force promoting down-regulation of RNAi activity targeting RNA viruses . While one cannot perform experimental tests in the ancestral Leishmania , one prediction is that in extant species or strains now harboring Leishmania LRVs , attenuation of the RNAi response may occur . Here we compared the efficacy of RNAi seen in the virus-free L . braziliensis M2903 used in the majority of our studies with a closely related species L . guyanensis that bears the cytosolic dsRNA virus LRV1-4 [35] , [36] ( Fig . 8 ) . While the RNAi pathway remained highly active in the LRV-infected L . guyanensis , its activity as assayed with LUC or GFP reporters was attenuated ∼10-fold relative to that seen in virus-free L . braziliensis ( Fig . 8A ) . Although tools for the introduction of LRV are not well-developed , one line of L . guyanensis has been described which was cured of LRV [38] . Notably the efficiency of RNAi in the virus free line was similar to that of the LRV1-4 containing line ( Fig . 8A ) , showing that the attenuated RNAi response did not require the continued presence of virus . This implies that attenuation occurred through a down-regulation of the cellular RNAi pathway occurred in the LRV-bearing L . guyanensis . If a similar process occurred in the evolutionary lineage leading to extant RNAi-deficient Leishmania species , it could in turn have facilitated a later transition to a complete loss of RNAi activity . Future development of methods for more readily introducing and curing LRV infections will permit further tests of these hypotheses , as will the advent of RNAi-deficient lines of Leishmania braziliensis and other Viannia species . However , the data already in hand are consistent with the possibility of a biologically relevant interplay between parasite RNAi pathways and viral infection during evolution , as seen in viral infections of metazoans . A second selective force arises from consideration of the impact of genome plasticity in Leishmania . The ability of mobile elements to produce mutations and genomic rearrangements are well known , and in trypanosomes and other eukaryotes RNAi pathways may help protect against such events [42]–[44] . Importantly , the RNAi-competent L . braziliensis genome contains several classes of mobile elements , including retrotransposons , while RNAi-deficient L . major and L . infantum appear to lack active transposons [15] . While the forces leading to the loss of mobile elements are unknown , their departure could have freed the parasite from the need to maintain activities including RNAi which act to mitigate their effects . Gene amplification is another important form of genomic plasticity in Leishmania , often occurring in the form of extra-chromosomal circular DNAs associated with drug resistance [45] , [46] . In contrast , extra-chromosomal gene amplifications have not been seen in T . brucei , a difference potentially attributable to its active RNAi pathway [11] , [13] since circular amplicons tend to be transcribed from both strands [47] . Consistent with this model , extrachromosomal gene amplifications are uncommon in RNAi-proficient L . braziliensis [48] , and we found that transfections with a variety of circular DNAs were generally unsuccessful , causing us to rely exclusively on integrative constructs in this work . This does not imply that episomal circular DNAs will never arise in RNAi-proficient species; but when found , their transcription will be subject to RNAi effects and/or they will contain cis-acting elements that confer a high degree of strand specificity [49] . These requirements might act to constrain the emergence of episomal elements in RNAi-proficient species . Thus the loss of RNAi could be seen as ‘freeing’ the genome of RNAi-deficient Leishmania from several constraints limiting genome plasticity . In this regards , loss of RNAi may be viewed as ‘mutator’ phenotype , similar to the ‘ARMed’ phenotype described recently in the malaria parasite Plasmodium falciparum or the high mutability phenotypes associated with elevated bacterial virulence in humans [50] , [51] . Lastly , loss of RNAi may have been selected directly through effects on Leishmania virulence during evolution . The RNAi machinery affects gene expression at multiple levels , and its loss could lead to profound changes in parasite biology that could alter parasite virulence . Such direct alterations in gene expression may act in concert with the genomic alterations described above . The Leishmania subgenus Viannia is an early diverging clade within the genus [52] , and these species exhibit a number of distinct features including the nature of the immune response in the mammalian host , the composition of their surface glycocalyx , and their behavior within the sand fly vector [8] , [53] . Any such systematic differences between the RNAi-proficient Viannia subgenus and the RNAi-null Leishmania species groups could potentially reflect changes associated gene expression mediated by the RNAi pathway . Our findings provoke the question of whether the RNAi machinery could be transplanted from L . braziliensis into its close RNAi-deficient relatives . This would be useful given the extensive previous work on species such as L . major and L . donovani , as well as providing a tool for understanding the RNAi machinery . This feat was recently accomplished in Saccharomyces cerevisiae , which required only the introduction of Argonaute and Dicer from the closely related species S . castellii [33] . However , reintroduction of RNAi in L . major or L . donovani may require restoration of a more extensive suite of genes . While only three RNAi genes have been confirmed in trypanosomatids ( an Argonaute and two Dicers ) [9] , [10] , [17] , preliminary data suggest a requirement for at least two additional genes ( E . Ullu and C . Tschudi; unpublished data ) . Importantly , all 5 genes are absent in the genomes available for RNAi-deficient Leishmania species . In other eukaryotes the RNAi machinery includes as many as 9 proteins or more [15] , [31] , [54] . Another obstacle may be the tendency of RNAi-deficient species such as L . major to transcribe the antisense chromosomal strand at low levels [55] , as well as to synthesize antisense transcripts [56] , [57] . This suggests the possibility that introduction of an active RNAi pathway into L . major could be lethal [11] , [58] . Thus re-introduction of RNAi into RNAi-deficient Leishmania species will be a challenging task; nonetheless , efforts to introduce this suite of genes from RNAi proficient L . braziliensis are underway . In summary , we have shown that the RNAi pathway is functional in Leishmania braziliensis . These data provide some optimism for the application of RNAi approaches as a tool for the study of these predominantly asexual organisms , by forward and reverse genetic approaches . While less experimentally developed , L . braziliensis has the potential to emerge as an attractive model , and the advent of RNAi-based tools should provide a further stimulus for this effort . In the long term , delivery of siRNAs targeting essential parasite genes may prove an effective route to chemotherapeutic treatment of RNAi-proficient Leishmania . Lastly , the Leishmania provide an attractive system for testing hypotheses about forces leading to the evolutionary loss of RNAi , including the role of viral pressure , changes in genome plasticity , and virulence . As drug resistance mediated by gene amplification is one manifestation of gene plasticity , these findings have practical implications to parasite chemotherapy .
RNA extraction procedures and Northern analyses were carried out as described [16] . The 5′UTR of L . braziliensis α-tubulin mRNA plus the first 317 nt of the ORF were PCR-amplified from genomic DNA and inserted between the HindIII and XbaI sites of plasmid vector pPD19 . 36 , which contains two opposing T7 RNA Polymerase promoters [59] . The synthesis of dsRNA was according to Ngo et al . [21] . The same DNA was used as a probe in the α-tubulin Northern . PCR products of GFP+ or GFP65 ORFs were used as probes for the GFP Northerns . A portion ( nt 3160 to nt 4482 ) of the L . braziliensis SLACS ( LbrM08−V2 . 0700 ) was PCR-amplified with primers ( LBSLACS1399F: 5′-GCCAGAGAGGTGGTGAGGGTG and LBSLACSORFa-R: 5′-GAGCTCGAGAAAGGTCCACCACCCCGA ) from M2903 genomic DNA and TA cloned to generate a sense radiolabeled RNA probe for Northern analysis of small RNAs . For LPG2 ( LbrM20_V2 . 2700 ) the probe was a PCR fragment ( nt 1 to nt 411 ) amplified with primers SMB3219 and SMB3220 ( Table S1 ) . Leishmania total RNA was isolated using the Trizol reagent ( Invitrogen ) , treated with DNAse and purified using MEGAclear columns ( Ambion ) . Reverse transcription ( RT ) was performed according to the manufacture instructions using Superscript III First-Strand reverse transcriptase ( Invitrogen ) in a 20 µl reaction containing 1µg purified RNA . Controls containing the same amount of RNA but lacking reverse transcriptase or template were used to rule out DNA or other contamination . For test RNAs , primers were designed to amplify ∼100 bp amplicons within the target ORF but outside of the stem-fragment , and tested using L . braziliensis gDNA . PCRs were performed using the SYBR Green ( Applied Biosystems ) and the ABI PRISM 7000 Sequence Detection System instrument ( Applied Biosystems ) . PCR amplifications were performed as follows: 50°C for 2 min and 95°C for 10 sec then followed by 40 cycles of 95°C for 15 sec , 60°C for 1min . The generation of specific PCR products was confirmed by melting curve analysis and agarose gel electrophoresis . Each primer set was individually tested for four StL transfectants ( 2 for StL-F and 2 for StL-R; except 4 for LPG3-StL-F ) . All samples were performed in triplicate . Control samples of H2O were included in each experiment . Amplification of SSU rRNA was used as internal control to normalize the parallel reaction of target amplicons . L . braziliensis M2903 ( MHOM/BR/75/M2903 ) , L . guyanensis M4147 ( MHOM/BR/75/M4147 ) and L . panamensis WR120 ( MHOM/PA/74/WR120 ) were obtained from Diane McMahon-Pratt ( Yale University ) , L . braziliensis strain M2904 from Angela Cruz ( U . Sao Paulo Riberao Preto ) , L . tarentolae strain TarII was obtained from M . Ouellette and B . Papadopoulou ( U . Laval ) , L . mexicana ( MNYZ/BZ/62/M379 ) from David Russell ( Cornell University ) , and Crithidia fasciculata Cf-C1 from Larry Simpson ( UCLA ) . The LRV-bearing strain of L . guyanensis M4147 ( MHOM/BR/75/M4147 ) and a virus free derivative M4147/pX63-HYG [38] were obtained from Jean L . Patterson ( Southwest Foundation for Biomedical Research , San Antonio , Texas ) . The identities of all Viannia strains used were confirmed by partial and/or complete sequencing of the AGO1 or other genes ( not shown ) . Viannia species were grown in freshly prepared Schneider's Insect Medium ( Sigma-Aldrich Cat . No . S9895 ) supplemented with 10% heat-inactivated fetal bovine serum , 2 mM L-glutamine , 500 units penicillin/ml and 50 µg/ ml− streptomycin ( Gibco Cat No . 5070 ) . Other Leishmania and Crithidia were propagated in M199 medium supplemented with 10% heat-inactivated fetal bovine serum , hemin , adenine , biopterin and biotin [60] . For each transfection , 10 ml of log phase L . braziliensis were resuspended in 100 µl human T-cell Nucleofector solution ( Amaxa Cat No . VPA-1002 ) mixed with 5 µl of 4 µg/ µl of α-tubulin dsRNA or control dsRNA and subjected to nucleofection with an Amaxa Nucleofector with program U-033 using the kit's cuvette . The transfection mixture was transferred immediately to 10 ml of complete medium and kept in 28°C for 3 hrs . RNA from 9 ml cells was taken for Northern blot analysis with an α-tubulin hybridization probe . Stable transfections were performed using the high voltage ( 1400V ) procedure described previously [11] . Following electroporation organisms were grown in drug-free media overnight , and then plated on semisolid media [60] to obtain clonal lines . For selections using the SAT marker , parasites were plated on 50–100 µg/ml nourseothricin ( clonNAT , Werner BioAgents , Germany ) , and with the PHLEO marker , parasites were plated on 0 . 2–2 µg/ml phleomycin ( Sigma ) . After colonies emerged ( typically <2 weeks ) they were recovered and grown to stationary phase in 1 ml media , and passaged thereafter in 10 and 0 . 1 µg /ml nourseothricin and phleomycin , respectively . The plating efficiency of untransfected L . braziliensis M2903 ranged from 60–95% and the transfection efficiency from 50–220 colonies / 20 µg DNA . The generation of whole genome shotgun sequence data from Crithidia fasciculata strain Cf-C1 by 454 sequencing technology will be described fully elsewhere . Blast searches using L . braziliensis AGO1 were used to identify homologous sequences , which were then assembled manually into several large contigs . PCR primers were designed to amplify missing gaps , and the 5′ end of the mRNA was obtained by RT-PCR using a forward miniexon primer ( CFSLB 5′-AAGTATCAGTTTCTGTACTTTATTG ) and reverse CfAGO1 specific primer ( SMB2895: 5′-AAGCAGTTCGTCTCCACCGTCACCTG ) . Then a nested PCR was done with CfSLB and CfAGO1 primers ( SMB 2894: 5′- GTGATGCCGCCCTCCTCGCGGTCACG ) . The PCR products were TA cloned and sequenced . The CfAGO1 sequence was deposited in GenBank ( EU714010 ) . We noted a polymorphism in the CfAGO1 sequence , introducing a stop codon yielding a truncated protein terminating after amino acid 198 . The consequences of this polymorphism ( if any ) have not been investigated further . The sequence of the L . guyanensis M4147 AGO1 ORF was determined by direct sequencing of the PCR amplicon obtained with primers B2468 ( 5′-ATGTTGGCGCTAAACGCAGGTTC ) and B2469 ( 5′- CTACAGGTAGTGCATCGTGGGGC ) , and deposited in GenBank ( accession number FJ234150 ) . RT-PCR reactions were performed as described above , with two sets of primers to detect LRV viruses described previously [38] ( set 1 , primers SMB2472/2473 and set 2 , primers SMB3850/3851 ( Table S1 ) . The constructs used in this work are derivatives of pIR1SAT ( B3541 ) [11] or pIR1PHLEO ( B4054 , this work ) , which have two expression sites ( XbaI/SmaI , site a , and BglII , site b ) . High fidelity thermostable polymerases such as recombinant Pfu DNA polymerase ( Stratagene ) were used for PCR , and constructs were confirmed by restriction mapping and sequencing of all relevant regions . Unless otherwise indicated , all constructs were digested with SwaI and the linear SSU-targeting fragment purified for subsequent transfection by electroporation . pIR1PHLEO ( B4054 ) was created by replacing the SAT marker of pIR1SAT with the PHLEO marker ( M . Cunningham , unpublished data ) . pIR1PHLEO-GFP+ ( a ) ( B5793 ) , pIR1PHLEO-GFP65 ( a ) ( B5779 ) and pIR1-GFP65* ( a ) ( B5959 ) were constructed by generating ORF cassettes of the respective genes and inserting into the XbaI ( a ) site . The GFP+ ORF was taken from pXG-GFP+ ( B2799 ) , GFP65 from pXG-GFP65 ( B2355 ) , and GFP65* was obtained by site-specific mutagenesis of pIR1PHLEO-GFP65 ( QuickChange Multi Site-Directed Mutagenesis , Stratagene ) , changing nt 193 from T to A , resulting in a S65T mutation . A luciferase ( LUC ) ORF was amplified using pGL3-basic ( Promega ) as template , with primers adding flanking BglII sites , and a CCACC initiation sequence preceding the initiation codon . The modified LUC ORF was inserted into pGEM-T ( Promega ) yielding pGEM-Luciferase ( B6033 ) ; the LUC ORF was then extracted by BglII digestion and inserted into the BglII site of B5959 to create pIR1PHLEO-GFP65* ( a ) -LUC ( b ) ( B6034 ) . pIR1SAT-GFP65-StL ( b ) ( B4733 ) was described previously [11] . For other StL constructs , we assembled a stem-loop consisting of the target gene sequences in inverted orientation , separated by a PEX11-MYC ( 3 ) loop/stuffer fragment used previously in pIRGFP Stem-Loop ( B4733 ) , and inserted this into either the ‘a’ or ‘b’ expression sites of pIR1SAT . In these constructs the ‘stem’ sequences were organized either in divergent or convergent orientations ( DIV or CONV ) relative to the target gene sequence , and the stuffer fragment similarly could be in a ‘sense’ or ‘antisense’ orientation relative to PEX11 ( F or R ) . The specific target genes and regions studied included LPG1 ( LbrM25_V2 . 0010 , nt 11–592 ) ; LPG2 ( LbrM20_V2 . 2700 , nt 411–1021 ) ; LPG3 ( LbrM29_V2 . 0780 , nt 1657–2236 ) ; HGPRT ( LbrM21_V2 . 0990 , nt 127–626 ) ; α-tubulin ( LbrM13_V2 . 0190 , nt 736–1309 ) ; β-tubulin ( LbrM33_V2 . 0930 , nt 470–1004 ) ; PFR1 ( LbrM31_V2 . 0160 , nt 900–1593 ) ; PFR2 ( LbrM16_V2 . 1480 , nt 951–1644 ) , AGO1 ( LbrM11_V2 . 0360 , nt 247–1070 ) and LUC ( LUC+ from Promega pGL3-Basic , nt 281–788 ) . These steps yielded constructs pIR1SAT-LPG1-StL ( b , DIV , R ) ( B6128 ) , pIR1SAT-LPG1-StL ( b , DIV , F ) ( 6132 ) , pIR1SAT-LPG2-StL ( b , DIV , R ) ( B6137 ) , pIR1SAT-LPG2-StL ( b , DIV , F ) ( B6138 ) , pIR1SAT-LPG3-StL ( b , DIV , F ) ( B6140 ) , pIR1SAT-HGPRT-StL ( b , DIV , F ) ( B6136 ) , pIR1SAT-HGPRT-StL ( b , DIV , R ) ( B6135 ) , pIR1SAT-PFR1-StL ( b , DIV , F ) ( B6294 ) , pIR1SAT-PFR2-StL ( b , DIV , F ) ( B6282 ) , pIR1SAT-αTub-StL ( b , DIV , F ) ( B6283 ) , pIR1SAT-βTub-StL ( b , DIV , F ) ( B6295 ) and pIR1SAT-LUC-StL ( b , CONV , F ) ( B6185 ) , or pIR1SAT-LUC-StL ( b , DIV , F ) ( B6190 ) . A single construct enabling tests of RNAi activity was generated by inserting the LUC ORF into the ‘b’ site and a LUC Stem-Loop into the ‘a’ site of a modified pIR vector ( pIR2SAT-LUC-StL ( a ) -LUC ( b ) ( B6386 ) . This construct is referred to as the ‘LUC RNAi self reporter’ or ‘LUC SR’ . For RNAi studies of AGO1 , an analogous construct was made with a HYG marker ( pIR2HYG-LUC-StL ( a ) -LUC ( b ) , strain B6447 ) . A pIR1SAT-LbrAGO1-StL ( b ) construct was used for RNAi tests ( B6524 ) . Western blots were performed as described elsewhere using anti-GFP ( Abcam Cat No . 6556 , 1∶2500 ) or anti-L . donovani HGPRT antiserum ( 1∶5000; J . Boitz and B . Ullman , Oregon Health Sciences University ) as the primary antibody , and detected using goat anti-rabbit IgG as the secondary antibody ( 1∶10000 , Jackson ImmunoResearch Laboratories , Inc . catalog number 111-035-003 ) . Parasites expressing GFPs were analyzed using a Becton-Dickenson FACS Calibur , using fluoroscein excitation/emission parameters . LPG was purified and quantitated from L . braziliensis lines grown in logarithmic phase ( 4–5×106 cells/ml ) as described [61] . Purified LPG was subjected to western blotting with antisera CA7AE which recognizes the Gal ( β1 , 4 ) Man ( α1-P ) repeat units of the L . braziliensis LPG [62] . 106 logarithmic phase promastigotes were suspended in 200 µl media containing 30 µg/ ml of luciferin ( Biosynth AG ) and added to a 96-well plate ( Black plate , Corning Incorporated , NY , U . S . A . ) . After 10 min incubation , the plate was imaged using a Xenogen IVIS photoimager ( Caliper LifeSciences ) , and luciferase activity quantitated as photons/sec ( p/s ) . Promastigotes were fixed in 2% paraformaldehyde/2 . 5% glutaraldehyde ( Polysciences Inc . , Warrington , PA ) in 100 mM phosphate buffer , pH 7 . 2 for 1 hr at room temperature . Samples were washed in phosphate buffer and postfixed in 1% osmium tetroxide ( Polysciences Inc . , Warrington , PA ) for 1 hr . Samples were then rinsed extensively in water prior to en bloc staining with 1% aqueous uranyl acetate ( Ted Pella Inc . , Redding , CA ) for 1 hr . Following several rinses in water , samples were dehydrated in a graded series of ethanol solutions and embedded in Eponate 12 resin ( Ted Pella Inc . ) . Sections of 95 nm were cut with a Leica Ultracut UCT ultramicrotome ( Leica Microsystems Inc . , Bannockburn , IL ) , stained with uranyl acetate and lead citrate , and viewed on a JEOL 1200 EX transmission electron microscope ( JEOL USA Inc . , Peabody , MA ) . | RNAi interference pathways play fundamental roles in eukaryotes and provide important methods for the analysis of gene function . Occasionally RNAi has been lost , precluding its use as a tool , as well as raising the question of what forces could lead to loss of such a key pathway . Genomic and functional studies previously showed that within trypanosomatids protozoans RNAi was absent in both Leishmania major and Trypanosoma cruzi . The genome of L . braziliensis , a member of the early diverging Leishmania subgenus Viannia , retained key genes required for RNAi such as an Argonaute . We demonstrated that in fact L . braziliensis shows strong RNAi activity with reporter and endogenous genes affecting flagellar function . These data suggest that RNAi may be productively applied for functional genomic studies in L . braziliensis . We mapped the evolutionary point at which RNAi was lost in lineage leading to Leishmania and Crithidia , and establish that RNAi must have been lost at least twice in the trypanosomatids , once on the lineage leading to T . cruzi and independently following the divergence of the Viannia subgenus from other Leishmania species . Lastly , we discuss hypotheses concerning the forces leading to the loss of RNAi in Leishmania evolution , including viral invasion , increased genome plasticity , and altered virulence . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
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] | [
"infectious",
"diseases/protozoal",
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"infectious",
"diseases/neglected",
"tropical",
"diseases",
"infectious",
"diseases",
"genetics",
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"genomics"
] | 2010 | Retention and Loss of RNA Interference Pathways in Trypanosomatid Protozoans |
Tremendous strides have been made in improving patients’ survival from cancer with one glaring exception: brain cancer . Glioblastoma is the most common , aggressive and highly malignant type of primary brain tumor . The average overall survival remains less than 1 year . Notably , cancer patients with obesity and diabetes have worse outcomes and accelerated progression of glioblastoma . The root cause of this accelerated progression has been hypothesized to involve the insulin signaling pathway . However , while the process of invasive glioblastoma progression has been extensively studied macroscopically , it has not yet been well characterized with regards to intracellular insulin signaling . In this study we connect for the first time microscale insulin signaling activity with macroscale glioblastoma growth through the use of computational modeling . Results of the model suggest a novel observation: feedback from IGFBP2 to HIF1α is integral to the sustained growth of glioblastoma . Our study suggests that downstream signaling from IGFI to HIF1α , which has been the target of many insulin signaling drugs in clinical trials , plays a smaller role in overall tumor growth . These predictions strongly suggest redirecting the focus of glioma drug candidates on controlling the feedback between IGFBP2 and HIF1α .
A variety of multiscale modeling methods has been used to describe the growth of solid tumors , including both discrete and continuous approaches . A non-inclusive set of references include [25–29] , along with several recent reviews [30–36] . Previous mathematical models of glioma progression have primarily focused on the growth or migration of cancerous cells from a tumor core [37–41] . Despite the increasing number and sophistication of the models , these studies have not considered insulin signaling . Conversely , computational models of insulin signaling exist [42 , 43] , but have only been applied to other applications , including articular cartilage [44] , ovarian cancer [45] , and human skeletal muscle [46] , and exclude molecules of interest for brain cancer cells [44 , 47] . Thus , we created for the first time , a computational chemical-kinetic model linking the insulin signaling pathway to glioblastoma growth .
We developed a chemical-kinetic model that characterizes the network architecture and dynamics of the insulin signaling pathway—and then links these molecular interactions to cell and tissue level responses . Based on previous literature on the insulin signaling pathway , we constructed a model comprised of 4 differential equations and 1 mass conservation equation which describe interactions between components in the insulin signaling system ( see Fig 1B ) . Our aim was to create the minimal model necessary to capture all the following interactions of key molecules: The growth rate of the glioblastoma tumor , Eq 5 , was determined by regression analysis using the data from both our previous experiments on spheroid growth in vitro using the U87 glioblastoma cell line and LN229 glioblastoma growth in mice [70] . The U87 and LN229 glioblastoma cell lines were used to compare glioblastoma cell lines which were more dependent on insulin signaling ( LN229 ) and less dependent on insulin signaling ( U87 ) [3] . Growth of the glioblastoma is normally measured experimentally by changes in the volume or the diameter of the cancerous spheroid/tumor mass . In the model , glioblastoma growth is a time-varying function , defined as net growth of the glioblastoma spheroid/tumor volume and is assumed to depend on its basal growth and the additional growth that is promoted by HIF1α . A genetic algorithm was used to determine default values for the unknown kinetic rates ( the genetic algorithm was employed in Matlab , and refined using fminsearch ) . The estimated initial conditions and fitted rate constants are shown in Tables 1 and 2 . The model was fitted for three outputs: glioblastoma growth rate; HIF1α vs . O2 levels; and IGFI as a function of IGFBP2 . The glioblastoma growth rates were found for two distinct experiments ( U87 and LN229 ) by fitting the same model and obtaining different initial conditions and growth rates for the two cell lines . Results from fitting the in vitro U87 spheroid growth and literature data of LN229 growth in mice are shown in Fig 2A and 2B , respectively . HIF1α is a function of oxygen levels , and it was fitted using data from Jiang et al . [71] which monitored how the HIF1α levels changed in HeLa cells as a function of O2 . The rate constants were simultaneously fitted using data of IGFI and IGFBP2 levels as a function of each other and time ( see Fig 3A , Slomiany et al . [41] ) . In those experiments , the IGFBP2 concentration was monitored as a function of time under two external concentrations of IGFI ( 0 nM and 100 nM ) . The experiments used the human retinal pigment epithelial ( RPE ) cell line D407; and it is an assumption of the model that the same relationships hold in glioma cells ( these measurements are the only ones we are aware of that measure IGFBP2 as a function of IGFI levels ) . We also estimated that the IGFBP2 response was the same as that of IGFBP3 , which is the IGFBP species available from the in vitro experimental data . Initial conditions were also determined from experiments . The concentration of IGFI under normal conditions was calculated based on the data by Lonn et al [72] . Similarly the mean concentration of IGFBP2 in patients with glioblastoma was calculated from a previous study [73] . Both of the calculations for IGFI and IGFBP2 are shown in the S1 File . Initial concentrations of all molecular factors involved in the system were varied independently between 0 . 1×-10× of the fitted concentrations , and the effect on each compound and overall glioma growth was simulated . Oxygen levels were tested between 2–21% . The sensitivity of glioblastoma growth to changes in kinetic rate constants was determined for kinetic rates of 0 . 1×-10× the fitted values individually . The results from the complete sensitivity analysis can be found in S2 File . Sensitivity analysis was summarized by calculating the sensitivity index ( see below ) at 40 days for the LN229 cell line in Table 1 . The time duration of 40 days was chosen as it matched the duration of studies performed in the in vivo LN229 work from literature . The following equation was used to calculate the sensitivity index to quantify the levels of sensitivity . The sensitivity index was plotted in Fig 4 . The definitions of each variable in the sensitivity index can be found in Table 3 . In addition to varying the rate constants individually , we simultaneously explored the entire parameter space of the rate constants ( varying between 0 . 1×–10× of the fitted values ) using the Latin Hypercube Sampling method [75] . From this sampling , 500 sets of rate constants were simulated in the model for glioma growth over 40 days where the glioblastoma diameter was recorded . Principal component analysis illustrating the resulting glioblastoma diameters as a function of multi-varied kinetics rates is shown in S1–S4 Figs . Additionally , to confirm the kinetic parameters that most significantly influence glioma progression , glioblastoma diameters were correlated to the rate constants by calculating partial correlations ( Fig 5 ) . To simulate the effect of using different drug targeting factors in glioblastoma , we set each rate constant to 0 separately , modeling the effects of removing each interaction , with the exception of the basal production and degradation of HIF1α . The exception is because HIF1α is ubiquitous in cells; targeting HIF1α would not only affect glioblastoma cells but also other cells . Setting the rate constant to 0 simulated the removal of each reaction from the system . The diameter of the glioblastoma for both cell lines U87 and LN229 was then compared to the original pathway before the removal of the reaction . The glioblastoma diameter was simulated over 40 days . Results are shown in Fig 6 . Unknown rate constants were found by fitting existing literature data . Fig 4A shows the model simulations compared to the literature in vitro data , to which the model was fit , that monitored the IGFBP2 concentration as a function of time under two external concentrations of IGFI ( 0 nM and 100 nM ) in the system [74] . For the case with 0 nM external IGFI , the model simulations that best fitted the in vitro data was found to be internal IGFI concentration levels of 92 . 5 nM of IGFI . Fig 3B shows the model simulations compared to literature in vitro data that monitored HIF1α as a function of oxygen [71] .
Results of the sensitivity analysis on the initial model conditions showed that HIF1α and IGFBP2 levels in the insulin signaling system were most sensitive to reduced oxygen ( 2% ) and also elevated IGFItotal levels ( Fig 7 ) . At higher concentrations of IGFItotal , elevated steady state concentrations of IGFI and IGFBP2 were observed . In hypoxic conditions ( 2% oxygen ) , HIF1α and IGFBP2 concentrations were increased initially and reached a steady-state of 7× and 1 . 25× baseline values , respectively . Varying initial conditions in the model showed that the insulin system is highly sensitive to reduced oxygen concentrations and elevated IGFI concentrations compared to the default initial conditions ( control ) . For the remaining initial conditions , the insulin signaling system in glioblastoma was robust over changes in initial HIF1α concentrations and the ( IGFI-IGFBP2 ) complex concentration . In order to analyze the contribution of each rate constant to glioblastoma growth , the sensitivity index was calculated for each rate constant , for LN229 tumor growth ( shown in Table 4 in descending order ) . Results are plotted in Fig 4 , which shows that LN229 glioblastoma growth was most sensitive to the production of HIF1α ( k8 ) production of IGFBP2 ( k1 ) , growth rate due to HIF1α ( k11 ) and promotion of HIF1α by IGFBP2 ( k10 ) . Results of the Latin Hypercube Sampling confirmed these findings . After computing the Partial Correlation Coefficients between rate constants and glioblastoma growth , we found that the production of HIF1α ( k8 ) was the highest correlated rate constant to glioblastoma growth , as shown in Fig 6 . When we removed each reaction independently from the model , the results were striking . When the feedback from IGFBP2 to HIF1α was removed in LN229 cells , the glioblastoma volume over the simulation of 40 days was halved as compared to when the downstream signal from IGFI to HIF1α was removed shown in Fig 6 . Removal of the HIF1α to IGFBP2 connection had minimal effect on the glioblastoma growth . When a similar simulation was conducted for the U87 cell line , there was not a significant change in the glioblastoma volume when either the IGFBP2 to HIF1α or the IGFI to HIF1α connection was removed , see S5 Fig .
We have developed a chemical-kinetic model that predicts glioblastoma growth as a function of insulin signaling . Our model agrees with experimental in vitro data on interactions between IGFI , IGFBP2 and HIF1α . Sensitivity analysis on initial conditions found the insulin signaling pathway to be most sensitive to IGFI concentration and oxygen levels . Current literature data on the relationship between HIF1α and oxygen shows that glioblastoma growth is insensitive to high oxygen levels , but highly sensitive at low oxygen concentrations . This is significant as glioblastoma spheroids are generally under hypoxic conditions . There is maximal HIF1α expression at low oxygen levels [76] . In addition , there are more pronounced changes in HIF1α expression at these low oxygen levels . Small changes in oxygen levels result in large changes in HIF1α levels . As the oxygen levels increase towards 21% , HIF1α levels are exponentially decreased . This relationship explains how glioblastoma tumors have a fairly constant response at higher oxygen levels . However , at low oxygen levels , glioblastoma will have drastically higher HIF1α levels which result in a much different phenotype and growth rate . Drugs have been developed to target the IGFIR pathway by suppressing the IGFI to HIF1α pathway using three main types of compounds: IGFIR targeting antibodies , tyrosine kinase inhibitors for kinase domains of IGFIR , and IGFI ligand neutralizing antibodies [24 , 77–79] . However , these compounds have failed to control glioblastoma growth clinically , and have not made it past phase III clinical trials [24] . Our sensitivity analysis on the rate constants showed that the contribution of basal HIF1α production to LN229 glioblastoma growth is greater than contribution of the IGFI-dependent HIF1α production . This suggests that HIF1α would be a more effective target to reduce glioblastoma growth than targeting the IGFIR molecular interactions by current drugs . In fact , the top four rate constants that glioblastoma growth was most sensitive to when individually perturbed were the production of HIF1α ( k8 ) , production of IGFBP2 ( k1 ) , growth rate due to HIF1α ( k11 ) and promotion of HIF1α by IGFBP2 ( k10 ) . However , since the HIF1α effects are ubiquitous in all cells , alterations in HIF1α and production of IGFBP2 would be difficult to target in cancerous cells only . On the other hand , IGFBP2 overexpression is specific to glioblastoma multiforme compared to gliomas . Thus we focused on the effect of promotion of HIF1α by IGFBP2 ( k10 ) , which had the third highest correlation found by Partial Correlation to glioblastoma growth in Fig 5 . Our results from the growth reduction analysis showed that glioblastoma growth was more sensitive to the removal of feedback from IGFBP2 to HIF1α as compared to the IGFI to HIF1α interaction . There have not been any published drugs that have specifically blocked feedback between IGFBP2 and HIF1α in glioblastoma . Our model predicts that this pathway could result in significantly reduced growth of glioblastoma and should be targeted by the next generation of glioblastoma drugs . This study offers an explanation for the difficulties encountered by current drugs targeting IGFIR to reduce glioblastoma cell growth: a secondary mechanism that upregulates HIF1α . We found that glioblastoma growth was highly sensitive to this new hypothesized interaction , IGFBP2 to HIF1α signaling . While other researchers have highlighted the importance of IGFBP2 in glioblastoma growth [80] , we have been able to suggest a specific mechanism that can be potentially targeted . In our predictions , removing the feedback from IGFBP2 to HIF1α resulted in almost half of the growth in the glioblastoma diameter over 40 days as compared to removing the downstream signal from IGFI to HIF1α . By using two different glioblastoma cell lines in our analysis , we have found that glioblastoma growth through the insulin signaling pathway is tumor specific . When we conducted the glioblastoma growth reduction analyses of the LN229 and U87 cell lines , there was almost no change in growth observed in the U87 cell lines , while the LN229 showed a reduction in the glioblastoma tumors’ growth . Glioblastoma cells lines that rely on the insulin signaling pathway for their aggressive growth phenotype will be more affected by drugs that target the insulin signaling pathway . Conversely , if the glioblastoma cells do not rely on the signaling from insulin for their growth , then targeting the insulin signaling pathway would not be effective in controlling the growth . This explains why when U87 and LN229 were targeted using TAE226 ( IGFIR tyrosine kinase inhibitor ) , a larger amount of apoptosis was observed for the LN229 cell line compared to the U87 cells [3] . Thus , targeting the insulin signaling pathway through the IGFBP2-HIF1α interaction could be effective for those glioblastoma cells dependent on insulin signaling . Compensatory pathways may also influence cancer growth , and the computational results presented here warrant targeted experimental testing focusing on the IGFBP2-HIF1α interaction in the context of other signaling networks . In conclusion , we have been able to achieve a deeper understanding of the interactions between key factors in the insulin signaling pathway through our computational model . The model allowed us to simulate the effects of removing different reactions in the insulin signaling pathway network , to test in silico potential therapeutic targets . These model predictions provide the impetus for future experimental studies exploring the role of IGFBP2-HIF1α interactions . In sum , we have found a possible target in the insulin signaling system that merits exploration as a candidate drug target for glioblastoma patients and other patients with cancers sensitive to the insulin signaling pathway . | Current treatment for glioblastoma patients is limited to nonspecific methods: surgery followed by a combination of radio- and chemotherapy . With these methods , glioma patient survival is less than one year post-diagnosis . Targeting specific protein signaling pathways offers potentially more potent therapies . One promising potential target is the insulin signaling pathway , which is known to contribute to glioblastoma progression . However , drugs targeting this pathway have shown mixed results in clinical trials , and the detailed mechanisms of how the insulin signaling pathway promotes glioblastoma growth remain to be elucidated . Here , we developed a computational model of insulin signaling in glioblastoma in order to study this pathway’s role in tumor progression . Using the model , we systematically test contributions of different insulin signaling protein interactions on glioblastoma growth . Our model highlights a key driver for the growth of glioblastoma: IGFBP2-HIF1α feedback . This interaction provides a target that could open the door for new therapies in glioma and other solid tumors . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Simulation Predicts IGFBP2-HIF1α Interaction Drives Glioblastoma Growth |
Influenza A virus ( IAV ) is an airborne pathogen that causes significant morbidity and mortality each year . Macrophages ( Mϕ ) are the first immune population to encounter IAV virions in the lungs and are required to control infection . In the present study , we explored the mechanism by which cytokine signaling regulates the phenotype and function of Mϕ via epigenetic modification of chromatin . We have found that type I interferon ( IFN-I ) potently upregulates the lysine methyltransferase Setdb2 in murine and human Mϕ , and in turn Setdb2 regulates Mϕ-mediated immunity in response to IAV . The induction of Setdb2 by IFN-I was significantly impaired upon inhibition of the JAK-STAT signaling cascade , and chromatin immunoprecipitation revealed that both STAT1 and interferon regulatory factor 7 bind upstream of the transcription start site to induce expression . The generation of Setdb2LacZ reporter mice revealed that IAV infection results in systemic upregulation of Setdb2 in myeloid cells . In the lungs , alveolar Mϕ expressed the highest level of Setdb2 , with greater than 70% lacZ positive on day 4 post-infection . Silencing Setdb2 activity in Mϕ in vivo enhanced survival in lethal IAV infection . Enhanced host protection correlated with an amplified antiviral response and less obstruction to the airways . By tri-methylating H3K9 , Setdb2 silenced the transcription of Mx1 and Isg15 , antiviral effectors that inhibit IAV replication . Accordingly , a reduced viral load in knockout mice on day 8 post-infection was linked to elevated Isg15 and Mx1 transcript in the lungs . In addition , Setdb2 suppressed the expression of a large number of other genes with proinflammatory or immunomodulatory function . This included Ccl2 , a chemokine that signals through CCR2 to regulate monocyte recruitment to infectious sites . Consistently , knockout mice produced more CCL2 upon IAV infection and this correlated with a 2-fold increase in the number of inflammatory monocytes and alveolar Mϕ in the lungs . Finally , Setdb2 expression by Mϕ suppressed IL-2 , IL-10 , and IFN-γ production by CD4+ T cells in vitro , as well as proliferation in IAV-infected lungs . Collectively , these findings identify Setdb2 as a novel regulator of the immune system in acute respiratory viral infection .
IAV is an airborne pathogen that is responsible for significant mortality in humans [1] . Infection with seasonal strains of IAV is typically limited to the upper respiratory tract and causes mild to moderately severe respiratory disease . In contrast , highly pathogenic strains of IAV can spread to distal airways and alveolar spaces causing pneumonia that can be lethal . Alveolar macrophages ( Mϕ ) are the first immune population to encounter IAV virions in the lungs and are required for host protection [2–5] . Following activation , alveolar Mϕ become highly phagocytic and are a major source of proinflammatory cytokines , including type I interferon ( IFN-I ) [6 , 7] . Viral detection by pattern recognition receptors ( PRRs ) initiates a signaling cascade that activates interferon regulatory factor ( IRF ) 3 and IRF7 , transcription factors involved in the initiation and amplification of the IFN-I response [8 , 9] . IFN-I binds to the IFN-α receptor ( IFNAR ) to induce the transcription of more than 300 IFN-stimulated genes ( ISGs ) with antiviral and immunomodulatory functions [10] . However , the production of IFN-I and other proinflammatory cytokines must be tightly regulated to avoid respiratory failure . Cytokine-induced lung injury , rather than uncontrolled viral replication , is the most common cause of severe morbidity and mortality in individuals exposed to highly pathogenic strains of IAV [11–13] . Several other functions of resident and recruited Mϕ in infection caused by respiratory pathogens have been described . Early production of chemokines by alveolar Mϕ promotes the infiltration of inflammatory cells to the site of infection [14] . Additionally , Mϕ directly initiate adaptive immune responses during infection . It has been shown that alveolar Mϕ rapidly transport antigen to draining lymph nodes in Streptococcus pneumoniae infection [15] . Within the lungs , Mϕ present antigen and activate virus-specific T cells [16] . Expression of the Notch ligand Delta-like 1 by Mϕ regulates the production of the antiviral cytokine IFN-γ by CD4+ and CD8+ T cells in IAV infection [5] . Mϕ further enhance T cell-mediated immunity by undergoing apoptosis , resulting in cross-presentation of antigen to cytotoxic CD8+ T cells by DCs [17 , 18] . Finally , Mϕ play a pivotal role in the resolution of infection and restoration of an anti-inflammatory environment in the lungs . Internalization of residual infected-apoptotic cells and cellular debris by Mϕ inhibits viral dissemination and tissue damage by dampening inflammation and maintaining lung function [19–21] . Epigenetic modifications control gene transcription by altering residues in histone tails of chromatin . It has been shown that specific chromatin-modifying enzymes influence the phenotype and function of Mϕ [22–25] . The SET ( Su ( var ) 3-9 , Enhancer-of-zeste , Trithorax ) -domain superfamily consists of histone-modifying enzymes that transfer a methyl group from S-adenosyl-L-methionine to specific lysine residues in histone tails to either activate or block transcription [26] . Setdb2 ( SET-domain bifurcated 2 ) tri-methylates lysine 9 of histone H3 ( H3K9me3 ) to silence gene expression and was first implicated in the induction of B cell chronic lymphocytic leukemia [27] . Consistent with a recent publication by Schliehe et al . , we demonstrate that cytokine-dependent signal transduction following IFN-I treatment upregulates Setdb2 in myeloid cells in a STAT1- and IRF7-dependent manner . However , despite this overlapping observation , we uncovered a role for Setdb2 in the regulation of the innate and adaptive immune system in primary IAV infection . Generation of mice lacking Setdb2 specifically in Mϕ revealed that Setdb2 controlled the recruitment of inflammatory monocytes to infected lungs and suppressed the expression of a large number of antiviral genes . Setdb2 expression by Mϕ also influenced cytokine production by CD4+ T cells , as well as proliferation of both CD4+ and CD8+ T cells in infected lungs . These results highlight the impact of histone modification in dictating the severity of infection and may represent a potential therapeutic target for controlling pulmonary infection and other diseases associated with IFN-I activity .
Specific cytokines can induce the expression of histone-modifying enzymes , which , in turn , regulate the transcription of target genes in a variety of immune responses [23 , 24] . Since IFN-I is rapidly produced following infection with a number of viral pathogens , we asked if IFN-I induced the expression of histone-modifying enzymes in bone marrow-derived Mϕ ( BM-Mϕ ) . Notably , the lysine methyltransferase Setdb2 was upregulated by more than 700-fold relative to unstimulated BM-Mϕ ( 1 . 0 ± 0 . 17 vs . 781 . 0 ± 108 . 2; p<0 . 001 ) at 24 hours post-stimulation ( Fig 1A ) . IFN-I-dependent induction was specific to Setdb2 , as related histone methyltransferases containing a SET-domain were unaltered following cytokine stimulation ( Fig 1B ) . To determine if the induction of Setdb2 occurred in a dose-dependent manner , BM-Mϕ were stimulated with increasing doses of cytokine . When normalized to unstimulated BM-Mϕ , a direct correlation between the concentration of IFN-I and Setdb2 transcript was observed ( Fig 1C ) . To characterize the kinetics of Setdb2 expression , BM-Mϕ were treated with IFN-I over a time course . Setdb2 transcription peaked at 5 hours post-stimulation , began declining by 24 hours , and returned to baseline levels by 48 hours ( Fig 1D ) . We next examined whether cytokines related to IFN-I could upregulate Setdb2 . BM-Mϕ were treated with IFN-γ ( type II IFN ) or IFN-λ ( type III IFN ) in parallel with IFN-I . While IFN-γ upregulated Setdb2 relative to unstimulated cells ( 16 . 8 ± 1 . 48 vs . 1 . 0 ± 0 . 33; p<0 . 001 ) , it was significantly less potent than IFN-I ( Fig 1E ) . To further characterize Setdb2 expression in vitro and in vivo , we generated Setdb2LacZ reporter mice . To measure the degree of transcription from the Setdb2 promoter , a gene trap vector was used to incorporate the E . coli gene lacZ , which encodes β-galactosidase , into recombinant DNA to generate lacZ fusion transcripts as previously described [28] . BM-Mϕ from reporter mice were treated with a vehicle control or IFN-I and β-galactosidase activity was measured by flow cytometry . At 24 hours post-stimulation , 5% of control cells expressed lacZ . IFN-I treatment increased Setdb2 expression , with more than 20% of BM-Mϕ lacZ positive ( 5 . 17 ± 0 . 48% vs . 22 . 3 ± 1 . 32%; p<0 . 001 ) ( Fig 1F ) . Enhanced lacZ expression following IFN-I treatment correlated with an increase in mean fluorescent intensity ( MFI ) ( 4921 ± 35 . 2 vs . 6464 ± 52 . 3; p<0 . 001 ) ( Fig 1G ) . Since exposure to IAV triggers robust IFN-I production , we next characterized Setdb2 expression in a murine model of infection . On day 4 post-infection , enhanced expression of Ifnb1 correlated with a 11-fold increase in Setdb2 transcript when normalized to uninfected lungs ( 11 . 0 ± 1 . 38 vs . 1 . 0 ± 0 . 43; p<0 . 001 ) ( Fig 1H ) . To confirm these data , Setdb2LacZ reporter mice were inoculated with PBS or IAV . In naïve animals , less than 10% of CD11b+ cells in the lungs were lacZ positive . IAV infection enhanced the percentage of CD11b+ cells expressing lacZ in the lungs ( 7 . 25 ± 0 . 14% vs . 30 . 8 ± 2 . 25%; p<0 . 001 ) ( Fig 1I ) . In addition , Setdb2 was upregulated in the spleen following infection ( 7 . 38 ± 0 . 19% vs . 25 . 9 ± 2 . 37%; p<0 . 001 ) ( Fig 1I ) . Enhanced lacZ expression by CD11b+ cells in IAV infection was accompanied by a significant shift in MFI in the lungs and spleen , respectively ( 2095 ± 41 . 3 vs . 2882 ± 29 . 9; p<0 . 001 , 3576 ± 39 . 1 vs . 5280 ± 122 . 3; p<0 . 001 ) . These results prompted us to characterize SETDB2 expression in human cells . Peripheral blood mononuclear cells ( PBMCs ) were isolated from healthy donors and stimulated with IFN-I . When normalized to unstimulated PBMCs , IFN-I treatment resulted in a 3-fold increase in SETDB2 at 5 and 48 hours post-stimulation ( Fig 1J ) . Since we only observed a slight increase in SETDB2 in PBMCs , CD14+ monocytes were skewed toward a Mϕ phenotype and stimulated with IFN-I . Initially , IFN-I did not induce SETDB2 expression in human Mϕ . However , a slight increase was observed by 24 hours and SETDB2 expression continued to increase through 72 hours post-stimulation ( Fig 1K ) . In contrast to murine BM-Mϕ , IFN-I upregulated SETD2 , SETD5 , and SUV420 in human Mϕ at 72 hours post-stimulation ( Fig 1L ) . Despite being the primary target for IAV , Setdb2 was not induced in murine airway epithelial cells ( AECs ) or normal human bronchial epithelial cells ( NHBEs ) treated with IFN-I ( Fig 1M and 1N ) . IFN-I signals through the JAK-STAT signaling pathway to promote the transcription of ISGs involved in antiviral immunity . This prompted us to explore the signaling pathway regulating Setdb2 transcription . We initially tested if IFN-I-dependent induction of Setdb2 was dependent on the JAK-STAT pathway using the JAK inhibitor tofacitinib [29] . In comparison to control BM-Mϕ , Setdb2 transcript was undetected in both unstimulated and IFN-I-stimulated BM-Mϕ treated with tofacitinib ( Fig 2A ) . Next , we examined Setdb2 expression in Stat1-/- mice since IFN-I signals predominantly through a STAT1-STAT2 heterodimer . Stimulation of wild-type BM-Mϕ with IFN-I resulted in nearly a 1500-fold increase Setdb2 transcription ( 1463 ± 250; p<0 . 001 ) . A deficiency in Stat1 significantly dampened the induction of Setdb2 , with less than a 15-fold induction relative to unstimulated BM-Mϕ ( 14 . 8 ± 4 . 20; p<0 . 001 ) ( Fig 2B ) . In addition , Setdb2 expression was impaired in the lungs of Stat1-/- mice infected with IAV . On day 4 post-infection , a 90% reduction in Setdb2 transcript was observed in CD11b+ cells from Stat1-/- lungs relative to control lungs ( Fig 2C ) . IRF3 and IRF7 are critical transcription factors involved in IFN-I production and the induction of ISGs [30 , 31] . Whereas IRF3 is constitutively expressed at low levels and initiates IFN-I production after viral detection , IRF7 is an ISG that is expressed at high levels in infection and is the master regulator of the IFN-I response . Consistent with published data , stimulation of BM-Mϕ with IFN-I resulted in a dramatic upregulation of Irf7 , but not Irf3 ( Fig 2D and 2E ) . To determine if Setdb2 expression was dependent on either transcription factor , BM-Mϕ were transfected with control , IRF3 , or IRF7 siRNA and stimulated with IFN-I . Silencing of IRF7 , but not IRF3 , resulted in a significant reduction in Setdb2 in comparison to control cells ( 0 . 44 ± 0 . 08 vs . 1 . 0 ± 0 . 09; p<0 . 01 ) . Transcription of Irf3 and Irf7 was diminished by at least 60% and 80% , respectively , when treated with respective siRNA indicating the knockdown was successful ( Fig 2F and 2G ) . Since Setdb2 expression was diminished in BM-Mϕ deficient in Stat1 or treated with IRF7 siRNA , we performed ChIP to determine if these transcription factors regulated expression by binding to the Setbd2 promoter . Using published binding site sequences , we identified STAT1 and IRF7 binding sites upstream of the Setdb2 transcription start site . Prior to cytokine stimulation , STAT1 and IRF7 were absent in the Setdb2 promoter ( Fig 2H and 2I ) . However , a dramatic increase in STAT1 and IRF7 binding was observed in BM-Mϕ stimulated with IFN-I ( Fig 2H and 2I ) . Since IAV infection results in the influx of inflammatory cells to the lungs , we further characterized Setdb2 expression using Setdb2LacZ reporter mice . In naïve lungs , alveolar Mϕ expressed the highest level of Setdb2 , with 30% of the population lacZ positive . Less than 10% of other myeloid and lymphoid cellular populations expressed lacZ in steady-state conditions ( Fig 3A ) . IAV infection resulted in upregulation of Setdb2 in multiple cellular populations . Similar to uninfected lungs , alveolar Mϕ were the predominant population expressing Setdb2 in IAV infection . On day 4 post-infection , the proportion of alveolar Mϕ expressing Setdb2 nearly doubled , with greater than 70% expressing lacZ ( 31 . 3 ± 1 . 74% vs . 74 . 6 ± 2 . 69%; p<0 . 001 ) ( Fig 3A ) . In addition , infection enhanced lacZ expression in other myeloid populations , including inflammatory monocytes , tissue Mϕ , neutrophils , and DCs . In contrast , CD4+ T cells were the only lymphoid population to express more lacZ following infection . Comparable to naïve lungs , less than 10% of NK cells , CD8+ T cells , and B cells were lacZ positive in infected lungs ( Fig 3A ) . IAV-dependent induction of Setdb2 transcription was not limited to the lungs , as lacZ expression was significantly elevated in inflammatory monocytes in the blood ( 6 . 51 ± 1 . 16% vs . 46 . 3 ± 5 . 01%; p<0 . 01 ) and spleen ( 18 . 1 ± 2 . 48% vs . 33 . 9 ± 4 . 19%; p<0 . 01 ) ( Fig 3B and 3C ) . To determine the role of Setdb2 in immunity during respiratory viral infection , we generated mice deficient for Setdb2 in myeloid cells with lysosomes ( monocytes , Mϕ , and granulocytes ) using the Cre-lox system . For validation , Setdb2 transcription was measured in BM-Mϕ from Setdb2ff Lyz2cre- ( control ) and Setdb2ff Lyz2cre+ ( knockout ) mice . Relative to control BM-Mϕ , a significant reduction in transcript was observed in Setdb2-/- BM-Mϕ treated with a vehicle control or IFN-I ( Fig 4A ) . To examine if Setdb2 influenced the outcome of infection , mice were infected with a lethal dose of IAV and survival was monitored for two weeks . While both groups of mice began to succumb to infection on day 7 , survival was enhanced in Setdb2ff Lyz2cre+ mice . Whereas only 20% of control mice were alive by day 9 , greater than 65% of knockout mice survived ( Fig 4B ) . To determine viral load , we quantified the number of copies of IAV proteins in the lungs . While we observed a comparable fold-increase of non-structural protein 1 ( NS1 ) and matrix protein 1 ( M1 ) at day 4 post-infection , Setdb2ff Lyz2cre+ lungs had a reduction in both viral proteins by day 8 post-infection ( Fig 4C ) . Since tissue damage is often the cause of morbidity and mortality in IAV infection , we next examined lung histology in naïve and infected mice . Prior to infection , control and Setdb2ff Lyz2cre+ lung histology was comparable ( Fig 4D ) . Indicative of respiratory viral infection , the airways of control lungs were filled , likely with dead epithelial and inflammatory cells , cellular debris , and virus on day 4 post-infection . In contrast , Setdb2ff Lyz2cre+ lungs had less obstruction to the airways . In addition , we observed dense clusters of lymphoid cells near blood vessels in knockout mice that were absent in wild-type lungs ( Fig 4D ) . IAV sensing by the innate immune system results in robust production of IFN-I and other proinflammatory cytokines and chemokines . Since knockout mice controlled infection better than their wild-type counterparts , we postulated that Setbd2 dictates the severity of infection by regulating the transcription of antiviral genes . To identify potential target genes , we screened antiviral genes in control and Setdb2-/- BM-Mϕ stimulated with IFN-I using a PCR array . In the absence of Setdb2 , the overall gene profile in BM- Mϕ was altered at 24 hours post-stimulation ( Fig 5A; p<0 . 001 ) . A 5 . 8- and 6 . 8-fold increase in Ifna2 and Ifnb1 transcript , respectively , was observed in Setdb2-/- BM-Mϕ . Notably , the CCR2 and CCR5 ligands Ccl2 and Ccl5 , respectively , were upregulated 23- and 29-fold in Setdb2-/- BM-Mϕ ( Figs 5A and 6D ) . Additional chemokines and cytokines , including Cxcl1 , Cxcl2 , Cxcl9 , Cxcl10 , Cxcl11 , Il1b , Il6 , Il10 , Il12a , Il12b , and Il15 were upregulated by 2-fold or more in the absence of Setdb2 . To confirm the cytokine transcription data , we measured the concentration of select cytokines in the supernatants of BM-Mϕ stimulated with IFN-I . Consistently , more IL-1β , IL-6 , IL-10 , IL-12p40 , and G-CSF were detected in the absence of Setdb2 . TNF-α transcript and protein was the only cytokine examined that was reduced in Setdb2-/- BM-Mϕ ( Fig 5B ) . IFN-I induces the expression of ISGs that inhibit viral replication and spreading [32 , 33] . Elevated Ifna2 and Ifnb1 in Setdb2-/- BM-Mϕ correlated with a 22 . 7- and 8 . 5-fold increase in Isg15 and Mx1 transcript , respectively ( Fig 5C ) . Since Setdb2 silences gene expression , we performed ChIP to determine the presence of Setdb2 and H3K9me3 in the promoter region of both genes . In control BM-Mϕ , IFN-I stimulation resulted in a 40- and 300-fold increase in Setdb2 bound to the Isg15 and Mx1 promoters , respectively ( Fig 5D and 5E ) . This correlated with high levels of H3K9me3 in the promoter region of both genes ( Fig 5D and 5E ) . Relative to control BM-Mϕ a significant reduction in Setdb2 and H3K9me3 was observed in Setdb2-/- BM-Mϕ ( Fig 5D and 5E ) . In addition to less Setdb2 and H3K9me3 , enhanced Mx1 expression in Setdb2-/- BM-Mϕ was associated with STAT1 and IRF7 bound to the promoter ( Fig 5F and 5G ) . This was specific to IFN-I , as stimulation with IFN-II did not increase STAT1 or IRF7 binding in the Mx1 promoter ( Fig 5F and 5G ) . To determine if enhanced survival and a reduced viral load was associated with an enhanced antiviral response , RNA was isolated from the lungs on day 4 post-infection . Consistent with BM-Mϕ data , Ifna2 ( 1 . 36 ± 0 . 27 vs . 33 . 1 ± 20 . 8; p<0 . 05 ) and Ifnb1 ( 1 . 47 ± 0 . 36 vs . 488 . 9 ± 241 . 4; p<0 . 05 ) transcript was elevated in the lungs from Setdb2ff Lyz2cre+ mice . This correlated with a 5- and 200-fold increase in Mx1 and Isg15 , respectively . Heightened expression of antiviral effectors was specific to Mx1 and Isg15 , as Rnasel ( Ribonuclease L ) and Pkr ( Protein kinase R ) expression was comparable in control and knockout animals infected with IAV ( Fig 5H ) . An augmented antiviral response in Setdb2-/- BM-Mϕ was associated with higher expression of a variety of upstream genes linked to the JAK-STAT , TBK1-IRF7 , and Iκκ-NF-κB signaling pathways ( Fig 5A ) . This correlated with upregulation of PRRs , as well as downstream signaling molecules that drive the induction of IFN-I and other proinflammatory cytokines and chemokines . This included upregulation of Ddx58 , Ifih1 , Aim2 , Nlpr3 , Tlr3 , Tlr8 , Tlr9 , Mavs , Irf7 , Myd88 , Stat1 and multiple other genes . Moreover , elevated Mx1 mRNA in knockout BM-Mϕ was associated with enhanced recruitment of STAT1 and IRF7 to the gene promoter . Thus , we postulated that Setdb2 indirectly suppresses proinflammatory gene expression by inhibiting the activation and nuclear translocation of transcription factors . Despite a 3 . 4-fold increase in Stat1 mRNA ( Fig 5A ) , no difference in total STAT1 protein based on absorbance at 450-nm was observed in knockout BM-Mϕ stimulated treated a media alone ( 2 . 31 ± 0 . 12 vs . 2 . 39 ± 0 . 09 ) or IFN-I ( 2 . 32 ± 0 . 07 vs . 2 . 30 ± 0 . 11 ) . At 30 minutes post-cytokine treatment , we unable to detect phosphorylated STAT1 in whole-cell extracts in either group . In contrast to STAT1 , a higher concentration of NF-κB p65 protein was detected in knockout BM-Mϕ ( 211 . 4 ± 17 . 64 ng/mL vs . 290 ± 16 . 6 ng/mL; p<0 . 01 ) . However , no correlation between Setdb2 expression and NF-κB p65 activation was observed , as an equal concentration of phosphorylated NF-κB p65 was detected in whole-cell lysates from control and knockout BM-Mϕ treated with a vehicle control ( 16 . 92 ± 0 . 89 ng/mL vs . 18 . 84 ± 3 . 21 ng/mL ) or stimulated with IFN-I ( 21 . 29 ± 1 . 27 ng/mL vs . 24 . 65 ± 1 . 66 ng/mL ) for a half hour . Since several chemokines were upregulated in Setdb2-/- BM-Mϕ , we asked if Setdb2 controls the influx of immune cells to the lungs . Setdb2ff Lyz2cre+ mice had a 2-fold increase in the number of inflammatory monocytes and alveolar Mϕ on day 4 post-infection ( Fig 6A ) . More CCR2+ monocytes in knockout lungs correlated with an increase in Ccl2 transcript and protein , a potent chemotactic mediator of inflammatory monocytes ( Fig 6B and 6C ) . Similar to the lungs , Setdb2-/- BM-Mϕ stimulated with IFN-I transcribed more Ccl2 than control cells ( Fig 6D ) and ChIP analysis revealed Ccl2 is a Setdb2 target gene ( Fig 6E and 6F ) . In respect to control cells , a 10- and 30-fold reduction in Setdb2 and H3K9me3 , respectively , was observed in the Ccl2 promoter in Setdb2-/- BM-Mϕ ( Fig 6E and 6F ) . Transcriptional analysis of IFN-I-stimulated BM-Mϕ identified Setdb2 as a negative regulator of antiviral immunity . This included the suppression of a number of cytokines and chemokines implicated in the migration and subsequent activation of T cells in the lungs following viral infection ( Fig 5A and 5B ) . Although the number of CD4+ and CD8+ T cells in IAV-infected lungs was comparable on day 4 post-infection ( Fig 6A ) , it is possible that this time point occurred prior to the initiation of the adaptive immune system . This is supported by the observation that reduced disease severity in Setdb2ff Lyz2Cre+ mice was not observed until day 8 post-infection . To address this concern , control and Setdb2ff Lyz2Cre+ mice infected with a sublethal dose of IAV were euthanized on days 6 , 8 , and 10 post-infection to determine the proportion and number of T cells in the lungs . In both groups , the proportion of CD4+ T cells represented 10 to 15% of cells in the lungs at all days examined . A very modest , yet significant increase in CD4+ T cells was observed in knockout lungs in comparison to controls ( 9 . 73 ± 0 . 70% vs . 12 . 90 ± 1 . 02%; p<0 . 05 ) . In contrast , the proportion of CD8+ T cells increased over time in IAV-infected lungs . On day 6 post-infection , the proportion of CD8+ T cells were comparable in control and mutant lungs , with CD8+ T cells composing approximately 6% of total cells . Relative to wild-type mice , knockout mice had an increase proportion of CD8+ T cells in the lungs on day 8 ( 13 . 25 ± 1 . 00% vs . 16 . 87 ± 0 . 79%; p<0 . 01 ) and day 10 ( 15 . 92 ± 1 . 44% vs . 21 . 32 ± 1 . 37%; p<0 . 05 ) . Despite only modest changes in the percentage of T cells , the absolute number of T cells was significantly altered in the absence of myeloid Setdb2 . Notably , Setdb2ff Lyz2Cre+ mice had 3 times the number of both CD4+ T cells ( 5 . 81 ± 0 . 50 x 105 vs . 15 . 43 ± 0 . 84 x 105; p<0 . 001 ) and CD8+ T cells ( 3 . 82 ± 0 . 37 x 105 vs . 9 . 69 ± 0 . 59 x 105; p<0 . 001 ) in the lungs on day 6 post-infection . While the number of both T cell populations were similar in control and knockout lungs on day 8 post-infection , a 2-fold increase in CD8+ T cells ( 6 . 54 ± 1 . 05 x 105 vs . 11 . 80 ± 1 . 47 x 105; p<0 . 01 ) was observed on day 10 in mice lacking Setdb2 ( Fig 7A ) . Following homing to the lungs , IAV triggers the activation and subsequent expansion of T cells at the site of infection . Since knockout mice had a greater number of both CD4+ and CD8+ T cells , we asked whether changes in proliferative capability contributed to the altered T cell prolife in IAV-infected lungs . To determine the extent of expansion , IAV-infected mice were injected with EdU ( 5-ethynyl-2'-deoxyuridine ) and euthanized on the indicated days . While proliferation of both CD4+ and CD8+ T cells peaked on day 8 post-infection , the extent of EdU incorporation was significantly higher in knockout lungs . In wild-type lungs , 39 . 55 ± 2 . 06% of CD4+ T cells and 34 . 72 ± 2 . 20% of CD8+ T cells were EdU positive . In the absence of myeloid Setdb2 , EdU incorporation was enhanced by greater than 10% in both CD4+ T cells ( 39 . 55 ± 2 . 06% vs . 51 . 00 ± 2 . 62%; p<0 . 01 ) and CD8+ T cells ( 34 . 72 ± 2 . 20% vs . 48 . 20 ± 1 . 83%; p<0 . 01 ) . In contrast , gating on either CD4+ or CD8+ T cells on days 6 and 10 post-infection revealed no changes in the extent of EdU incorporation between the two groups of animals . However , despite this observation , the absolute number of proliferating T cells was enhanced on two or all three time points depending on the T cell subset . In respect to controls , knockout lungs had approximately 3 x 105 and 1 x 105 more CD4+ EdU+ T cells in the lungs on days 6 and 8 post-infection , respectively . Similarly , Setdb2ff Lyz2Cre+ mice had a greater number of CD8+ EdU+ T cells in the lungs at each time point examined . The greatest difference in cell number was observed on day 8 post-infection ( 2 . 60 ± 0 . 34 x 105 vs . 4 . 44 ± 0 . 47 x 105; p<0 . 01 ) , with nearly 2 x 105 more proliferating CD8+ T cells in IAV-infected lungs . A significant increase in the number of CD8+ EdU+ T cells was also observed in knockout lungs on day 6 ( 0 . 99 ± 0 . 11 x 105 vs . 2 . 32 ± 0 . 66 x 105; p<0 . 05 ) and day 10 ( 2 . 22 ± 0 . 44 x 105 vs . 3 . 95 ± 0 . 52 x 105; p<0 . 05 ) post-infection ( Fig 7B ) . Mϕ , along with DCs and B cells , process and subsequently present viral antigen to CD4+ T cells to stimulate the production of cytokines with antiviral and/or immunomodulatory activity . This prompted us to ask whether Setdb2 expression by Mϕ influences antigen presentation and downstream T cell responses . Since this process is dependent on the expression of MHC class II , as well as several co-stimulatory molecules , we initially examined cell surface markers on IFN-I-stimulated BM-Mϕ by flow cytometry . The level of MHC class II , CD40 , and CD86 expression on the cell surface was comparable in both groups . In contrast , changes in IFNAR and CD80 expression were observed in the absence of Setdb2 . Based on MFI , IFNAR was reduced ( 959 . 0 ± 7 . 0 vs . 679 . 5 ± 10 . 5; p<0 . 001 ) and CD80 was elevated ( 1868 . 0 ± 8 . 0 vs . 2464 . 8 ± 58 . 4; p<0 . 001 ) in Setdb2-/- BM-Mϕ stimulated with IFN-I . Despite similar levels on the cell surface , greater than a 2-fold increase in Cd40 and Cd86 transcript was detected in Setdb2-/- BM-Mϕ stimulated with IFN-I ( Fig 5A ) . To examine antigen-specific T cell responses , CD4+ T cells from naive OT-II mice were cultured alone or at a 5:1 ratio with either wild-type or Setdb2-/- BM-Mϕ stimulated with IFN-I in the presence of ovalbumin peptide . When co-cultured with either control or knockout BM-Mϕ , CD4+ T cells produced more IL-2 , IL-5 , IL-10 , and IL-17 ( Fig 8A ) . While the concentration of IL-5 and IL-17 was comparable between groups , the absence of Setdb2 in BM-Mϕ was associated with enhanced IL-2 and IL-10 production ( Fig 8A ) . In contrast , IFN-γ production was only enhanced in co-cultures containing Setdb2-/- BM-Mϕ ( Fig 8A ) . This correlated with a 4-fold increase in IFN-γ relative to CD4+ T cells cultured alone or with control BM-Mϕ . IL-4 was the only cytokine examined that was unaltered in all experimental conditions ( Fig 8A ) .
This study uncovered a novel role for a histone-modifying enzyme in regulating immunity in acute respiratory viral infection . We have demonstrated IFN-I-dependent induction of Setdb2 in myeloid cell , most notably alveolar Mϕ and inflammatory monocytes , controls the severity of IAV infection by suppressing innate and adaptive immune responses . Robust production of IFN-I is required to control infection with several viral pathogens . IFN-I signaling results in the transcription of ISGs involved in antiviral immunity . We identified IFN-I as a potent inducer of Setdb2 expression in Mϕ . The induction of Setdb2 by IFN-I was dependent on the JAK-STAT pathway , as blocking JAK1 or STAT1 activity diminished expression . IFN-γ and IFN-λ also signal through STAT1; however , these cytokines had little to no effect on Setdb2 expression , suggesting a downstream molecule in the signaling cascade drives IFN-I-dependent expression . In addition to STAT1 , the transcription factor IRF7 regulated Setdb2 expression in in BM-Mϕ . While both STAT1 and IRF7 bound to the Setdb2 promoter , it is unclear if the transcription factors independently regulate transcription or work in concert for optimal expression . It is somewhat unexpected that IFN-λ did not upregulate Setdb2 since IFN-I and IFN-III have overlapping roles in viral infection and despite signaling through different receptors , initiate a similar signaling cascade resulting in ISGF3 activation [34] . The degree of Setdb2 transcription correlated with the concentration of IFN-I , suggesting the extent of expression may be a useful biomarker for determining the severity of infection and autoimmune diseases that result in copious IFN-I production . Alveolar Mϕ are the first line of defense against inhaled pathogens and are required to control IAV infection [2–5] . We found that the majority of alveolar Mϕ in IAV-infected lungs expressed Setdb2 and mice deficient for Setdb2 in Mϕ displayed prolonged survival when challenged with a lethal dose of IAV . One mechanism by which alveolar Mϕ control infection is through the production of IFN-I , which , in turn , induces the expression of antiviral effectors that inhibit viral replication and dissemination . It is well documented that IFN-I-driven induction of Mx1 , a dynamin-like GTPase that blocks viral transcription and replication , is critical for controlling IAV infection [35] . In addition to Mx1 , the ubiquitin-like protein ISG15 has been shown to have an important role in protecting cells from viral pathogens . In IAV infection , ISG15 limits viral replication by binding to the N-terminal RNA-binding domain of NS1 , blocking nuclear import [36] . Setdb2 selectively repressed the expression of Mx1 and Isg15 in BM-Mϕ stimulated with IFN-I and accelerated viral clearance in Setdb2ff Lyz2cre+ lungs correlated with enhanced transcription of both genes . Thus , by regulating the expression of specific ISGs , Setdb2 dictated the severity of infection . Since the selectivity of the identified Setdb2-regulated antiviral genes is not limited to IAV , it is plausible that Setdb2 controls the resolution of infection caused by other viral pathogens . IFN-I upregulated Setdb2 in dose-dependent manner and , in turn , Setdb2 regulated the amplitude of the IFN-I response . We propose three mechanisms by which Setdb2 may repress the expression of IFN-I and downstream ISGs . First , Setdb2 regulated the expression of key transcription factors in the IFN-I signaling cascade , including Stat1 and Irf7 . Second , Setdb2 may control the antiviral response by regulating the expression of PRRs since viral sensing by the innate immune system results in cytokine and chemokine production . Enhanced expression of the RNA helicases Ddx58 and Ifih1 , as well as downstream genes including Tbk1 , Mavs , Myd88 , Irf7 , among others was observed in Setdb2-/- BM-Mϕ This finding suggests Setdb2 suppresses these genes to weaken the IFN-I response . Third , in addition to targeting viral proteins , ISG15 can bind to IFN-associated transcription factors and antiviral effectors to enhance innate immunity [37–39] , suggesting Setdb2-dependent regulation of ISG15 may reduce the stability of components involved in IFN-I production and downstream responses . Although IFN-I is essential for controlling IAV infection , its production must be tightly regulated to prevent tissue damage . Multiple genes involved in the suppression of IFN-I were upregulated in parallel with Ifna2 and Ifnb1 in Setdb2-/- BM-Mϕ . One example is Pin1 ( Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 ) , which negatively regulates the antiviral response by promoting the degradation of IRF3 [40] . The transcription factor Foxo3 ( Forkhead box O3 ) negatively regulates the expression of antiviral genes by forming a regulatory circuit involving IRF7 and IFN-I [41] . The expression profile of antiviral genes in Setdb2-/- BM-Mϕ closely mimicked Foxo3-/- BM-Mϕ . This includes enhanced expression of Ccl5 , Irf7 , Ifnb1 , Ddx58 , Stat1 , as well as additional ISGs suggesting Setdb2 may have a similar role in balancing the beneficial and detrimental consequences of IFN-I . In addition to IFN-I , Setdb2 influenced the expression of proinflammatory cytokines and the immunoregulatory cytokine IL-10 . Of the cytokines examined , TNF-α was the only one repressed in Setdb2-/- BM-Mϕ . This finding is of interest based on reports demonstrating cross-regulation of IFN-I and TNF-α . It is unclear whether Setdb2 promotes TNF-α expression directly or indirectly through other proteins that regulate transcription and/or translation . Since tri-methylation of H3K9 imprints a repressive mark in chromatin , it is unlikely that Setdb2 directly induces Tnf transcription . Indirect regulation of TNF-α may be cytokine-dependent , as reduced TNF-α was associated with overexpression of IFN-I and IL-10 . While it is unclear if IFN-I itself can directly suppress TNF-α expression , IL-10 dampens expression by inhibiting the activation of NF-κB [42] . Another possibility is that Setdb2 influences TNF-α expression by regulating proteins that repress transcription . For example , Twist proteins bind to the Tnf promoter to inhibit transcription and IFN-I suppresses expression by activating the receptor tyrosine kinase Axl upstream of Twist1/2 [43] . Finally , IFNs can suppress TNF-α expression in Mϕ by promoting tristetraprolin-mediated mRNA decay in a STAT1- and p38-dependent manner , suggesting Setdb2 may negatively regulate the expression of tristetraprolin or similar proteins [44] . Although IAV infection results in robust TNF-α production , it is unnecessary for viral clearance . Rather , TNF-α , along with Nos2 , is the major culprit of immunopathology [45 , 46] . Furthermore , imbalanced production of either IFN-I or TNF-α is linked to autoimmunity . Rheumatoid arthritis patients , as well as children with chronic arthritis being treated with TNF-α antagonists can develop lupus-like symptoms due to overexpression of IFN-α and ISGs [47 , 48] . Therefore , targeting Setdb2 in parallel with anti-TNF-α therapy may be beneficial for repressing exaggerated IFN-I activity in autoimmunity . Mortality in individuals exposed to highly pathogenic strains of IAV is often due to lung injury , rather than uncontrolled viral replication . Infection caused by respiratory pathogens that target epithelial cells results in significant cell death and as a consequence , the airways become clogged with dead cells , cellular debris , surfactant material , and virus . Respiratory failure characterized by defective gas exchange and fatal hypoxia is observed in mice lacking alveolar Mϕ [21] , highlighting the importance of sustained Mϕ viability in infection . Consistent with reports demonstrating that the level of morbidity in IAV infection is dependent on the number of resident Mϕ [49 , 50] , a greater number of alveolar Mϕ in Setdb2ff Lyz2cre+ mice correlated with less damage to the airways and prolonged survival . There are several potential mechanisms by which Setdb2 may dictate the number of alveolar Mϕ . First , fewer alveolar Mϕ in control mice may indicate Setdb2 regulates genes that promote cell death . In IAV infection , the majority of resident Mϕ are depleted by one week due to necrosis [51] , indicating Setdb2 expression by alveolar Mϕ may facilitate the necrotic process . Elevated Ccl5 expression may promote Mϕ survival since the CCL5-CCR5 nexus inhibits apoptosis . Accordingly , mortality is increased in Ccl5-/- or Ccr5-/- in IAV infection [19] . Second , Setdb2 may inhibit Mϕ proliferation by suppressing local GM-CSF production [52] . Third , since alveolar Mϕ can arise from blood monocytes [53] , enhanced infiltration in the absence of Setdb2 may re-populate the number of alveolar Mϕ in IAV infection . Furthermore , since the removal of apoptotic cells , cellular debris , and surfactant material limits tissue damage , Setdb2 may control the extent of injury by diminishing the phagocytic capacity of Mϕ . Collectively , Setdb2 expression by Mϕ may control IAV-induced lethality by regulating airway integrity and minimizing tissue damage . Alveolar Mϕ are a major source of chemokines following infection and as a result , promote the infiltration of inflammatory cells to the lungs . The CCR2-CCL2 axis regulates the emigration of monocytes from the bone marrow and subsequent recruitment to infectious sites [54] . An enhanced number of CCR2+ monocytes correlated with more CCL2 in Setdb2ff Lyz2Cre+ lungs and Setdb2 directly regulated Ccl2 transcription in BM-Mϕ However , despite this clear correlation , it does not exclude the possibility that Setdb2-/- cells are highly potent in chemotaxis . In tissue , monocytes differentiate into DCs and exudate Mϕ and these cells are linked to severe disease due to overwhelming proinflammatory cytokine production [45 , 46 , 55 , 56] . In contrast , we found that enhanced monocyte recruitment was associated with prolonged survival . How knockout mice avoided immunopathology needs to be further explored . Although the aforementioned studies implicate monocyte-derived cells as the culprit of IAV-induced tissue damage and death , others have shown that tissue Mϕ can restore lung homeostasis and limit injury by developing an immunoregulatory phenotype that resembles that of alveolar Mϕ [57] . In addition to monocytes , neutrophils cause significant tissue damage in IAV infection . It has been proposed that IFN-I-dependent generation of monocytes attenuates neutrophil infiltration and as a consequence , reduces tissue damage [58] . Consistent with a recent study , the absence of Setdb2 in BM-Mϕ resulted in increased expression of neutrophil chemoattractants [59] . However , neutrophil recruitment was comparable in control and Setdb2ff Lyz2Cre+ lungs . This inconsistency may be due to the model system , as neutrophils are more critical in bacterial infection . Moreover , the mouse strain used may account for the discrepancy . Whereas they used mice deficient for Setdb2 in all cells , our mice specifically lacked Setdb2 in monocytes , Mϕ , and granulocytes . In addition to their central role in innate immune responses , Mϕ are involved in the initiation and maintenance of the adaptive immunity . Resident and recruited Mϕ populations can present antigen to CD4+ T cells and we showed that Setdb2 expression by Mϕ suppresses T cell proliferation and cytokine production . In IAV infection , Th1 cells are characterized by co-production of IFN-γ and IL-10 [60 , 61] and CD4+ T cells cultured with Setdb2-/- BM-Mϕ secreted more of both cytokines . In addition to IFN-γ and IL-10 , CD4+ T cells produced more IL-2 , which likely contributes to enhanced T cell proliferation in infected lungs . Whether Setdb2 expression regulates CD4+ T cell responses through antigen presentation is unclear . Several lines of evidence suggest that Setdb2 may regulate the magnitude of T cell responses indirectly through altered cytokine and chemokine production . The polarization of naïve CD4+ T cells to Th1 cells is dependent on IL-12 and Setdb2-/- BM-Mϕ secreted more IL-12p40 than control cells . Moreover , IFN-I and CCL5 can upregulate the expression of co-stimulatory molecules on antigen presenting cells , as well as regulate the phenotype of CD4+ T cells by promoting cytokine production and proliferation [62–66] . Since transcription of both genes was enhanced in the absence of Setdb2 , heightened IFN-I and CCL5 production may further amplify CD4+ T cell responses . Other lymphoid populations , including CD8+ T cells and B cells , are important for the eradication of virus . Respiratory pathogens can trigger the formation of densely packed clusters of lymphocytes known as inducible bronchus associated lymphoid tissue ( BALT ) . In IAV infection , BALT primes virus-specific T and B cells in the lungs and as a result , accelerates viral clearance [67] . Lung histology revealed the presence of potential lymphoid structures in infected Setdb2ff Lyz2cre+ mice , but not control animals . Future studies are required to determine if the accumulation of lymphoid cells in knockout lungs is in fact BALT and if it facilitates prolonged survival . Understanding why a host would upregulate a histone-modifying enzyme that suppresses antiviral immunity is a challenging question . One possible reason is IAV hijacks host machinery to ‘turn on’ Setdb2 , thereby diminishing IFN-I expression and downstream responses to evade the immune system . By mimicking a sequence in the tail of histone H3 , the viral protein NS1 allows H3N2 to utilize transcriptional regulators to suppress the antiviral response [68] . Additionally , NS1 allows IAV to evade the immune system by preventing apoptosis through PI3K ( phosphatidylinositide 3-kinase ) activation and limiting IFN-β production by inhibiting IRF3 activity [69 , 70] . Recently , it was demonstrated that IAV escapes the IFN-I response by triggering the production of prostaglandin E2 by Mϕ , which suppresses both innate and adaptive immunity allowing the virus to replicate more efficiently [71] . It is also plausible that even if Setdb2 suppresses antiviral immunity , it is not dramatic enough to cause significant damage to the host . IAV-associated death is rarely caused by primary infection alone . Rather , secondary bacterial pneumonia is the leading cause of mortality caused by an infectious agent [72] . While the mechanism responsible for enhanced susceptibility to bacterial superinfection is not fully elucidated , IFN-I sensitizes the host to bacterial pneumonia [73–75] . Since Setdb2 regulates the IFN-I response in viral infection , it is possible the host has evolved to diminish the antiviral response enough to control viral infection , yet prevent secondary complications . While preparing this manuscript , it was shown that Setdb2 regulates the crosstalk between IFN-I and the NF-κB pathway to control the neutrophil response in bacterial superinfection [59] . Together with our findings , these results implicate Setdb2 as a promising therapeutic target in respiratory viral infection and potentially , in secondary complications and autoimmune diseases linked to IFN-I activity .
All animal procedures were approved by the University Committee on the Use and Care of Animals at the University of Michigan ( PRO00004191 ) and done in accordance with the Animal Welfare Act guidelines of the National Institutes of Health . Experiments using human samples were approved by the Institutional Review Board of the University of Michigan ( HUM00075841 ) and conducted in accordance with the principles expressed in the Declaration of Helsinki . Written informed consent was obtained from all adult subjects . C57BL/6 , 129S5 , and Stat1-/- mice were purchased from Taconic ( Germantown , NY ) . B6 . 129P2-Lyz2tm1 ( cre ) Ifo/J ( Lyz2cre mice ) , and B6 . Cg-Tg ( TcraTcrb ) 425Cbn/J ( OT-II ) transgenic mice were purchased from The Jackson Laboratory ( Bar Harbor , ME ) . Setdb2 gene targeted embryonic stem ( ES ) cell clones EPD0164_4-B10 , -E09 , and -E12 were obtained from the trans-NIH Knockout Mouse Project ( KOMP Repository ) . The JM8 . N4 C57BL/6N ES cell clones [76] carried the knockout first tm1a ( KOMP ) Wtsi Setdb2 allele [77] . The ES cells were expanded in cell culture and chromosome counts were performed . Correct targeting of the Setdb2 gene was confirmed by genetic analysis of DNA from the ES cell clones . Germline transmission of the Setdb2 tm1a allele was obtained by breeding ES cell-mouse chimeras produced by the microinjection of C57BL/6/BrdCrHsd-Tyrc albino C57BL/6 blastocysts with the ES cell clones . Chimeras were mated with FLPo recombinase mice to remove the drug selection cassette and produce mice carrying the conditional floxed Setdb2tm1c allele . C57BL/6-Tg ( CAG-Flpo ) 1Afst/Mmucd FLPo recombinase mice [78] were obtained from the Mutant Mouse Resource and Research Centers ( Stock Number: 032247-UCD ) . FLPo mice were backcrossed onto albino C57BL/6 mice so that coat color selection could be used to chimeras to identify germline transmission and maintain an inbred C57BL/6 genetic background . The resulting progeny with a floxed Setdb2 allele were bred with Lyz2cre mice to generate control and mice deficient for Setdb2 in monocytes , Mϕ , and granulocytes [79] . Setdb2ff Lyz2cre and Setdb2LacZ reporter mice were bred in-house and genotyped with custom primers ( S1 Table ) . For infection , the IAV strain A/PR8/34; H1N1 isotype was used ( ATCC ) . Age-matched female mice were inoculated intranasally with 1 x 104 PFU for sublethal infection and 1 x 105 PFU for lethal infection . Murine CD11b microbeads , human CD14 microbeads , and the murine CD4+ T cell Isolation Kit were purchased from Miltenyi Biotec . Magnetic separation yielded 95% purity of each population . PBMCs were isolated from the blood using Ficoll ( GE Healthcare ) . For the generation of BM-Mϕ , bone marrow was differentiated in L929 cell-conditioned media as previously described [80] . For the differentiation of human of Mϕ , CD14+ monocytes were cultured in complete medium supplemented with 50 ng/mL of M-CSF ( R&D systems ) for one week . Adherent cells were washed and harvested with Trypsin/EDTA ( Lonza ) . Murine AECs were isolated from naïve mice as previously described [81] . Briefly , Dispase-digested lungs were depleted of CD16/CD32+ and CD45+ cells using biotinylated antibodies ( BD Biosciences ) and anti-biotin microbeads ( Miltenyi Biotec ) . Non-adherent cells were cultured in fibronectin-coated wells and AECs were harvested after 4 days . NHBEs and the BEGM BulletKit were purchased from Lonza . NHBEs were cultured in 25 cm2 flasks following the manufacturers recommendations . NHBEs were subcultured using Trypsin/EDTA when 80% confluent . Murine IFN-α , IFN-β , IFN-γ , IFN-λ2 , and IFN-λ3 , as well as human IFN-α and IFN-β were purchased from R&D Systems . For IFN-I treatment , cells were given 10 units/mL of IFN-α and IFN-β unless otherwise noted . Cells were treated with 10 ng/mL of IFN-II ( IFN-γ ) or IFN-III ( IFN-λ2 and IFN-λ3 ) . For JAK inhibition , cells were treated with 50 nM tofacitinib ( Cayman Chemical ) at the time of stimulation . IRF3 and IRF7 siRNA were purchased from Santa Cruz Biotechnology . Non-targeting siRNA was purchased from GE Dharmacon . BM-Mϕ were transfected with siRNA using the Amaxa Mouse Mϕ Nucleofactor Kit ( Lonza ) . Transfected cells were cultured for 18 hours before stimulation . CD4+ T cells isolated from the spleens of naïve OT-II mice were cultured alone or with BM-Mϕ at a 5:1 ratio . For cytokine analysis , co-cultures were incubated for 48 hours in the presence of 10 ng/mL of ovalbumin peptide ( Peptides International ) . RNA was extracted using TRIzol Reagent ( Invitrogen ) and cDNA was generated with the iScript cDNA synthesis kit ( Bio-Rad ) . TaqMan primer/probe sets for murine Setdb2 , Ifna2 , Ifnb1 , Irf3 , Irf7 , Ccl2 , Cxcl1 , Cxcl2 , Il10 , Tnf , Il10 , Tnf , Pkr , Rnasel , Isg15 , Mx1 , and human SETDB2 were purchased from Applied Biosystems . NS1 and M1 were detected using custom primers [5] . Gene expression was assessed using an ABI Prism 7500 instrument ( Applied Biosystems ) and normalized to Gapdh or ACTB . The murine/human epigenetic chromatin-modifying enzyme and murine antiviral PCR arrays were purchased from SABiosciences . RNA was DNase-digested using the RNeasy Mini Kit and reverse transcribed with the RT2 First Strand Kit ( Qiagen ) . RT-PCR was performed according to the manufacturer’s instructions and gene expression was normalized to multiple housekeeping genes . The concentration of CCL2 was measured using the mouse CCL2 ELISA Ready-SET-Go ! ( eBioscience ) . All other cytokines were quantified using a Bio-Plex 200 ( Bio-Rad Laboratories ) . A total of 1 x 107 BM-Mϕ were treated with a vehicle control or cytokine for 24 hours and ChIP was performed as previously described [24] . DNA was fragmented by sonication using a Branson Sonifier 450 ( Branson Ultrasonics ) . For immunoprecipitation , the following antibodies were used: IRF7 ( Santa Cruz Biotechnology ) , STAT1 ( Abcam ) , H3K9me3 ( Abcam ) , Setdb2 ( Dr . Yali Dou , University of Michigan ) , and rabbit polyclonal IgG ( Millipore ) . DNA was assessed by RT-PCR using custom primers ( S2 Table ) . For transcription factor analysis , whole-cell lysates were collected from a total of 1 x 107 BM-Mϕ treated with a vehicle control or IFN-I for 30 minutes . The concentration of protein in each sample was adjusted to a concentration of 100 μg/mL in assay buffer . Total and phosphorylated protein was measured using the following kits from abcam: STAT1 ( pY701 ) + total STAT1 ELISA Kit , STAT1 ( pS727 ) + total STAT1 ELISA Kit , and NF-κB p65 ( pS536 ) + total NF-κB p65 SimpleStep ELISA Kit . Lungs were digested in RPMI 1640-based complete medium containing 1 mg/mL of collagenase ( Roche ) and 30 μg/mL DNase I ( Sigma-Aldrich ) . Samples were passed through an 18-gauge needle , filtered , stained with the LIVE/DEAD Fixable Violet Dead Cell Stain Kit ( Life Technologies ) , blocked with anti-CD16/32 , and stained with the indicated antibodies . Antibodies were purchased from eBioscience ( CD11b , CD11c , F4/80 , Ly6C , Ly6G , CD3 , CD8 , NK1 . 1 , and IFNAR1 ) , R&D systems ( CCR2 ) , and BD Biosciences ( MHC-II , CD80 , CD86 , and CD40 ) . To characterize lacZ expression , the FluoReporter LacZ Flow Cytometry Kit was used ( Life Technologies ) . For T cell proliferation , mice were intraperitoneally given 10 mg/mL EdU one day prior to euthanization . Following surface staining , cells were labeled according to the manufacturer’s protocol . Data were acquired on a LSR II ( BD Biosciences ) and analyzed with FlowJo software ( TreeStar ) . Lungs were inflated with 10% formalin and processed using routine histological techniques . Tissue sections were stained with H&E and visualized by light microscopy . For the survival study , the p value was determined by a log-rank survival test . Analysis of the antiviral gene profile in BM-Mϕ was characterized by one-way ANOVA . Differences for remaining experiments were analyzed by Student’s t-test or two-way ANOVA . A p value of ≤0 . 05 was considered significant . | IAV causes seasonal epidemics that result in significant morbidity and mortality annually . Less frequently , novel viral strains emerge and are responsible for much larger outbreaks around the globe . In the last pandemic in 2009 , an estimated 300 , 000 people died from IAV infection or secondary complications . Since the virus rapidly evolves , a new vaccine must be developed each year . Since vaccine effectiveness can be highly variable , identifying other therapeutic targets is appealing for the treatment of severe disease in high-risk individuals such as young children , the elderly , and immunocompromised individuals . In this study , we found that the protein Setdb2 regulates the immune response to IAV via an epigenetic mechanism in Mϕ . Inhibition of Setdb2 activity was beneficial for host protection due to an amplified antiviral response , which correlated with accelerated viral clearance and less damage to the lungs . Therefore , targeting Setdb2 may be a powerful therapeutic strategy for treating severe pulmonary disease caused by IAV and potentially other viral pathogens that trigger robust IFN-I production . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Type I Interferon Induced Epigenetic Regulation of Macrophages Suppresses Innate and Adaptive Immunity in Acute Respiratory Viral Infection |
Acne vulgaris is a chronic inflammatory disorder of the sebaceous follicles . Propionibacterium acnes ( P . acnes ) , a gram-positive anareobic bacterium , plays a critical role in the development of these inflammatory lesions . This study aimed at determining whether reactive oxygen species ( ROS ) are produced by keratinocytes upon P . acnes infection , dissecting the mechanism of this production , and investigating how this phenomenon integrates in the general inflammatory response induced by P . acnes . In our hands , ROS , and especially superoxide anions ( O2•− ) , were rapidly produced by keratinocytes upon stimulation by P . acnes surface proteins . In P . acnes-stimulated keratinocytes , O2•− was produced by NAD ( P ) H oxidase through activation of the scavenger receptor CD36 . O2•− was dismuted by superoxide dismutase to form hydrogen peroxide which was further detoxified into water by the GSH/GPx system . In addition , P . acnes-induced O2•− abrogated P . acnes growth and was involved in keratinocyte lysis through the combination of O2•− with nitric oxide to form peroxynitrites . Finally , retinoic acid derivates , the most efficient anti-acneic drugs , prevent O2•− production , IL-8 release and keratinocyte apoptosis , suggesting the relevance of this pathway in humans .
Acne vulgaris is a chronic inflammatory disorder of the sebaceous follicles . Acne is the most common skin disease , estimated to affect up to 80% of individuals at some point between the ages of 11 and 30 years . Despite its common occurrence , the pathogenesis of acne is not fully understood . Excessive shedding of epithelial cells from the walls of follicles combined with increased amounts of sebum produced by associated sebaceous glands are two important factors that contribute to follicular obstruction . This obstruction leads to the formation of microcomedos , which are believed to precede lesions of acne . These microcomedos may evolve into clinically visible comedos and/or inflammatory lesions . Propionibacterium acnes ( P . acnes ) , a gram-positive anaerobic bacterium part of the normal skin flora , plays a critical role in the development of inflammatory lesions in acne [1] . Various mechanisms can explain the role of P . acnes in skin inflammation . First , it is widely accepted that inflammation may be induced by the immune response of the host to P . acnes . Chemotactic substances released from the bacteria attract polymorphonuclear leukocytes to the site of inflammation . Those cells are activated locally to produce inflammatory cytokines such as TNF-α , IL-1β , and IL-8 [2] . After phagocytosis of the bacteria , the attracted neutrophils are thought to release lysosomal enzymes and produce reactive oxygen species ( ROS ) that can damage the follicular epithelium . Beside the immune response of the host , a direct effect of P . acnes on keratinocytes has also been suspected in the initiation of the inflammatory process . Indeed , P . acnes interacts with toll-like receptors TLR-2 and TLR-4 on keratinocytes [3] . This interaction induces the release of inflammatory cytokines such as IL-1α , IL-1β , IL-8 , GM-CSF , and TNF-α [4] , [5] . Although nothing is known about the interaction between P . acnes or any other bacteria with keratinocytes in terms of reactive oxygen species ( ROS ) production , purified tuberculine has been shown to activate TLR-2 on keratinocytes , leading to the production of ROS during tuberculosis infection [6] . In addition , Vitreoscilla filiformis has been identified to activate MnSOD as an inducible free-radical scavenger in keratinocytes [7] . Furthermore , keratinocytes are known to produce ROS upon exposure to toxic compounds such as inorganic arsenic [8] or to ultraviolet radiations [9] , [10] . Whatever the mechanism implicated in the induction of skin inflammation by P . acnes , ROS are probably involved in that process since the production of hydrogen peroxide ( H2O2 ) is increased in neutrophils from acne patients [11] . Moreover , the decrease in superoxide dismutase ( SOD ) activity in patients with acne lesions [12] is correlated with the severity of acne [13] . ROS are short-lived small molecular structures that are continuously generated at low levels during the course of normal aerobic metabolism . They are also part of the inflammatory process that aims at killing or eliminating invasive microorganisms and/or eliminating damaged tissular structures . Among the large number of ROS that have been described , superoxide anion ( O2•− ) and hydrogen peroxyde ( H2O2 ) play prominent roles . On the other hand , the interaction between O2•− and nitric oxide ( NO ) , leads to the formation of highly reactive peroxynitrites ( ONOO•− ) . ROS interact strongly with a variety of molecules including lipids , proteins , and nucleic acids . Produced in large amounts , ROS can lead to apoptotic or necrotic cell death . To counteract the overproduction of ROS , skin is equipped with antioxidant mechanisms including anti-oxidant enzymes such as superoxide dismutase ( SOD ) that detoxifies O2•− , catalase , and glutathione peroxidase ( GpX ) that uses reduced glutathione ( GSH ) to detoxify H2O2 into water [14] . In this work , we have used an in vitro model to investigate ROS production by keratinocytes upon P . acnes stimulation . We have dissected the control mechanisms of this production , and investigated how they fit into the general inflammatory response induced by P . acnes .
P . acnes increased the production of O2•− , NO and H2O2 by the immortalized keratinocyte cell line HaCaT in a dose-dependent manner ( Figure 1A , B and C ) . At the highest concentration of P . acnes , O2•− , NO and H2O2 levels were increased by 85% ( P<0 . 05 ) , 44 . 5% ( P<0 . 05 ) and 41% ( P<0 . 05 ) , respectively . We then evaluated the kinetics of ROS production ( Figure 2 ) . The production of O2•− , was significantly increased 15 min after P . acnes stimulation ( P<0 . 05 ) . The production reached its peak one hour after the stimulation , then progressively declined ( Figure 2A ) . In contrast , both NO and H2O2 productions increased slowly and reached their highest levels after 24 h of incubation with P . acnes ( Figures 2B and C ) . Since keratinocytes stimulated by P . acnes can produce IL-8 [3] , we next compared the kinetics of ROS production with that of IL-8 production upon P . acnes stimulation ( Figure 2D ) . Significant levels of IL-8 protein appeared 2 h after incubation with P . acnes ( P<0 . 05 ) and increased along with ROS production . Altogether , these results indicate that the production of ROS , and especially of O2•− , is a very early event occurring almost immediately after the stimulation of keratinocytes with P . acnes . The production of O2•− by HaCat keratinocytes was identical whether the cells had been stimulated by an extract of P . acnes surface proteins or by the whole bacteria . O2•− production was measured using DHE , and cell death estimated using YO-PRO-1 were dose-dependent ( Figure 3 ) . Superoxide anions can originate from the mitochondrial complex I or III of the respiratory chain , or from the cytosolic enzymes NAD ( P ) H oxidase or xanthine oxidase . Incubation of P . acnes-stimulated keratinocytes with rotenone and antimycin that inhibit the mitochondrial respiratory chain complexes I and III , respectively , did not significantly alter the production of O2•− ( Figure 4A ) . Incubation of P . acnes-stimulated keratinocytes with DPI ( a NAD ( P ) H oxidase inhibitor ) significantly decreased O2•− production ( P<0 . 03 ) , while incubation with allopurinol ( a xanthine oxidase inhibitor ) had no effect ( Figure 4A ) . To confirm that Nox is the main source of O2•− in keratinocytes stimulated by P . acnes , the level of Nox1 was knocked down using RNA interference . The small interfering RNA ( siRNA ) Nox1A-siRNA was used as described previously [15] . Nox1A-siRNA dramatically decreased the production of O2•− upon stimultion by P . acnes in transfected-keratinocytes , with nearly 100% inhibition after 3 h of stimulation ( Figure 4C ) . Keratinocytes treated with scrambled sequence siRNA produced similar levels of O2•− as non-transfected cells . These results demonstrated that O2•− is mainly produced by NAD ( P ) H oxidase in P . acnes-stimulated keratinocytes . In order to determine the pathways implicated in the detoxification of ROS produced by P . acnes-stimulated keratinocytes , we used specific modulators of the enzymatic systems involved in ROS metabolism . Superoxide anions are converted into hydrogen peroxide by SOD . Inhibiting SOD by the specific inhibitor DDC significantly increased O2•− production by P . acnes-stimulated keratinocytes ( P<0 . 003 ) ( Figure 4A ) . By contrast , incubation of keratinocytes with MnTBAP or CuDIPS , two SOD mimics , significantly decreased O2•− production by P . acnes-stimulated keratinocytes ( P<0 . 05 and P<0 . 04 , respectively ) ( Figure 4A ) . Hydrogen peroxide is converted into H2O by two sets of enzymes , catalase and the GSH/GPx system . The elevation of hydrogen peroxide levels can be caused either by an increase in superoxide dismutation as observed following incubation with MnTBAP ( P<0 . 004 ) or CuDIPS ( P<0 . 02 ) or by a decrease in the detoxification pathways ( Figure 4B ) . Specific inhibition of catalase by aminotriazol ( ATZ ) or addition of exogenous catalase , had no effect on the levels of hydrogen peroxide ( Figure 4B ) . Thus , the catalase pathway is not involved in the control of hydrogen peroxide detoxification in our system . Depleting GSH with BSO , inhibited GPx and significantly increased H2O2 production ( P<0 . 05 ) , while adding exogenous GSH or its precursor NAC , significantly decreased H2O2 levels ( P<0 . 004 and P<0 . 01 , respectively ) ( Figure 4B ) . Those results highlight the role of GSH/GPx in keratinocytes to counteract the overproduction of ROS induced by P . acnes: superoxide anions are dismuted by SOD into hydrogen peroxide , which is further detoxified into H2O through the GSH/GPx pathway . Given the high toxicity of ROS , we were prompted to investigate if the levels of O2•− produced by keratinocytes could impact cellular viability . The apoptosis of keratinocytes induced by P . acnes alone was estimated by YO-PRO-1 ( Figure 5A ) and TUNEL staining ( Figure 5B ) . In order to determine the nature of ROS involved in P . acnes-induced cellular toxicity , we pre-treated keratinocytes wih specific modulators of the enzymes involved in the production of O2•− and H2O2 and measured the death of keratinocytes upon stimulation with P . acnes . Inhibition of superoxide anions by allopurinol , DPI or MnTBAP , significantly decreased P . acnes-induced keratinocyte apoptosis ( P<0 . 005 , P<0 . 005 , P<0 . 001 , respectively ) , whereas DDC and antimycin , two compounds that increase O2•− production , increased cell death ( P<0 . 007 and P<0 . 01 , respectively ) ( Figure 5A ) . CuDIPS , a mimic of the cytosolic superoxide dismutase , increased cell death . This is explained by the cytotoxic properties of this molecule on the cells . No major effect was observed on the rate of cell death with molecules modulating H2O2 production , such as ATZ , BSO , NAC , GSH or catalase . Peroxinitrites result from the combination of O2•− and NO . They are highly reactive metabolites that create nitrosyl residues on proteins and alter their functions . Therefore , the levels of nitrosyl residues not only reflect the intensity of the oxidative attack but are also markers of the cellular damages created by the oxidative burst . As shown by flow cytometry , 3-nitrosotyrosyl residues were dose-dependently increased from with very low concentrations of P . acnes ( Figure 6A ) . This result is in agreement with the observation that the nitric oxide synthase ( NOS ) is activated in keratinocytes stimulated with P . acnes . The expression of iNOS was steady in keratinocytes during a period of time of 24 h as determined by RT-PCR ( Figure 6B ) and by RT-qPCR ( data not shown ) . This data is consistent with those presented in Figure 1 , showing that NO was produced early after P . acnes incubation , making its interaction between O2•− and NO possible . Altogether , those experiments suggested that the toxicity of O2•− produced by keratinocytes stimulated with P . acnes was dependent on the combination with NO and the production of nitrosyl residues . In order to evaluate the role of O2•− in the production of IL-8 by P . acnes-stimulated keratinocytes , we measured the levels of IL-8 produced in presence of the various ROS modulators ( Figure 7 ) . All the molecules that inhibited O2•− production also decreased IL-8 synthesis , but only the decrease induced by DPI reached statistical significance ( P<0 . 03 ) . If all the molecules that increased O2•− levels also increased IL-8 production , only the massive increase caused by DDC reached significance ( P<0 . 04 ) ( Figure 7 ) . Both ATZ and NAC significantly decreased IL-8 production . Since it has previously been shown that ATZ has no effect on H2O2 production while NAC and GSH do ( Figure 4 ) , these results suggest that the effects of ATZ and NAC on IL-8 production are independent of the regulation of H2O2 and are more likely linked to intrinsic properties of those products . Altogether these results suggest that the toxicity of ROS on P . acnes-stimulated keratinocytes is mainly caused by O2•− which also exerts a positive effect on IL-8 production . Since O2•− elicits IL-8 production by keratinocytes stimulated with P . acnes , we investigated which surface proteins could be implicated in the recognition of P . acnes . P . acnes-stimulated keratinocytes were incubated with antibodies to TLR-2 or CD36 , and IL-8 and O2•− productions measured ( Figure 8 ) . The antibody directed to TLR-2 was known as a blocking agent for the production of IL-8 by keratinocytes after stimulation by P . acnes [3] . This was confirmed by the reduction in IL-8 production by 65% ( P = 0 . 01 ) ( Figure 8A ) , whereas no change was observed in O2•− production ( Figure 8B ) . However , when P . acnes-stimulated keratinocytes were incubated in the presence of the antibody to CD36 , the production of O2•− was reduced by 51% ( P = 0 . 03 ) and the production of IL-8 was completely abolished . We first compared the relative sensitivity of HaCaT cells and P . acnes to the toxic effect of O2•− . HaCaT cells and P . acnes were incubated separately with a solution containing O2•− . The growth of P . acnes was dose dependently inhibited by O2•− while the HaCaT cells appear to be more resistant than P . acnes at the same O2•− concentration ( Figure 9A ) . We then tested the hypothesis that the ROS produced by keratinocytes , and particularly O2•− , could be responsible for the inhibition of the growth of P . acnes ( Figure 9B ) . When P . acnes-stimulated keratinocytes were preincubated with MnTBAP , a MnSOD mimic that detoxifies O2•− , or with DPI that inhibits NAD ( P ) H oxydase , the growth of the bacteria was restored . Reciprocally , when keratinocytes where preincubated with DDC , a SOD inhibitor , the bacterial growth was decreased . In order to evaluate the effects of the most common drugs used in the treatment of acne , HaCaT cells were stimulated by P . acnes in the presence of ZnSO4 , doxycycline , nicotinamide , nitroimidazol , retinol , retinoic acid , or isotretinoin ( Figure 10 ) . The production of superoxide anions was reduced by all the drugs tested , at least at the highest concentration ( 0 . 05% ) , except for nicotinamide ( Figure 10A ) . IL-8 production was reduced neither by ZnSO4 at low concentration ( 0 . 01% ) nor by nicotinamide , but all the others drugs tested were effective . This is particularly the case for retinoic acid derivates that completely abolished IL-8 production ( Figure 10B ) . The percentage of cells present in the wells after incubation ranged from 68 to 91% ( Figure S1 ) . All the drugs except ZnSO4 and nicotinamide reduced the apoptosis of keratinocytes stimulated by P . acnes at least at the highest concentration tested ( 0 . 05% ) . This is particularly the case for retinoic acid derivates ( P<0 . 03 in all cases ) and for the antibiotics doxycycline ( P<0 . 02 ) and nitroimidazole ( P<0 . 03 ) ( Figure 10C ) . The rate of cell death in the presence of the various compounds alone ranged from 0 to 26% ( Figure S2 ) . Altogether , these results suggest that the anti-acne drugs are active on the production of O2•− and IL-8 as well as on the decrease in the death rate of keratinocytes .
This report describes the production of ROS by keratinocytes upon bacterial infection by P . acnes . The production of superoxide anions takes place at least one hour prior to that of nitrix oxide and hydrogen peroxide . The same kinetics is observed following UV radiation or arsenite intoxication [8] . Superoxide anions can originate from the cytosolic enzymes NAD ( P ) H oxidase , or xanthine oxidase , or from the complexes I or III of the mitochondrial respiratory chain . The use of DPI , an inhibitor of NAD ( P ) H oxidase and more specifically knocking down Nox1 by small RNA interference clearly shows that , in P . acnes-stimulated keratinocytes , O2•− is produced by NAD ( P ) H oxidase . This data is in line with a recent report showing that NAD ( P ) H oxidase is the major source of UVA-induced ROS in human keratinocytes where mitochondria are rapidly damaged after UVB exposure [16] . However , to date , no link between a specific damage of the mitochondrial respiratory chain and the production of O2•− has been established [15] . Under our experimental conditions , superoxide anions are dismuted by superoxide dismutase to form H2O2 , which is further detoxified into water by the GSH/GPx system and not by the catalase pathway . In contrast , H2O2 generated by UVB applied to keratinocytes is detoxified through both the catalase and the GPx pathways . Usually , catalase finely tunes down H2O2 levels , while the glutathione system ( GPx and reduced glutathione ) is more specialized in buffering acute oxidative stress . This is probably what happens in the case of P . acnes infection . However , the key-element for P . acnes-induced apoptosis of keratinocytes is O2•− and not H2O2 . O2•− can be toxic per se or following its combination with NO to form peroxynitrites ( ONOO•− ) , a phenomenon that requires the activation of inducible nitric oxide synthase ( iNOS ) . We confirm that P . acnes induces the formation of nitrotyrosine residues on proteins , a footprint of in vivo peroxinitrite production [17] . Similarly , keratinocytes exposed to UVB or arsenite produce both O2•− and NO , potentially leading to peroxinitrite formation [8] , [18] . In our model , the production of NO by P . acnes-stimulated keratinocytes is correlated with the steady expression of iNOS , as already observed in keratinocytes [18] . Those data suggest that the cytotoxicity mediated by ROS in our model involves the overproduction of O2•− and also the nitrosylation of amino acid residues on proteins . Keratinocytes are the first line of defense against external aggressions; they participate in the innate immune response by secreting soluble factors with chemotactic activity for leukocytes and neutrophils . Thus , P . acnes triggers the secretion of IL-1α , TNF-α [4] , and the chemokine IL-8 [3] which have been implicated in the inflammatory process of acne . Using activators and inhibitors of the O2•− production , we have been able to modulate the production of IL-8 upon stimulation by P . acnes . Particularly , DPI an inhibitor of the NADPH oxidase , significantly decreases IL-8 production , whereas DDC , a SOD inhibitor that increases O2•− levels , dramatically increases IL-8 production by keratinocytes . The question was then to determine the pathway through which P . acnes stimulates keratinocytes . Several previous observations suggested the implication of the Toll-like receptor ( TLR ) pathway . TLRs can recognize conserved molecular structures at the surface of bacteria . TLR-2 , present at the surface of keratinocytes [19] , [20] , is upregulated in acne lesions [21] and is potentially involved in the recognition of P . acnes during the inflammatory process [22] . Moreover , P . acnes-stimulated TLR-2 induces IL-8 release by keratinocytes [3] , [23] . We have observed a time-lag between the early production of O2•− and the secretion of IL-8 that occurs 2 h later , that probably corresponds to the activation of the TLR-signaling mediated pathway . Therefore , we hypothesized that the molecular mechanism responsible for O2•− production is TLR-independent . Indeed , whereas blocking TLR-2 with a monoclonal antibody decreases the production of IL-8 as described previously [3] , it has no effect on O2•− production . We also tested the role of CD36 , a scavenger molecule expressed on keratinocytes [24] . The generation of ROS by the NAD ( P ) H oxidase-NOX system has already been observed following the activation of scavenger receptors in vitro [25] and in vivo in a murine model of cerebral ischemia [26] . This receptor is a sensor of microbial diacylglycerides that signals via the TLR-2/6 heterodimer . In response to bacterial lipoteichoic acid ( LTA ) and diacylated lipoproteins , CD36 associates with TLR-2/6 [24] , [27] . Although it does not express LTA , P . acnes expresses a closely related amphiphilic antigen , a lipoglycan containing mannosyl , glucosyl , galactosyl residues , and an amino sugar , diaminohexuronic acid [28] , [29] . We observed that , blocking CD36 with a monoclonal anti-CD36 antibody in P . acnes-stimulated keratinocytes , significantly decreases both the level of O2•− and that of IL-8 . In our model , IL-8 secretion is triggered by the binding of P . acnes to TLR-2 and modulated by the generation of superoxide anions resulting from the binding of P . acnes to CD36 . In phagocytic cells , Nox1 oxidizes NADPH on the cytosolic side of the cellular membrane and reduces oxygen across the membrane to generate O2•− which contributes to the killing of P . acnes [30] . On the other hand , in keratinocytes , Nox1 is localized in the nucleus [31] and could release O2•− into the cytoplasm . Therefore , we hypothesized that nuclear Nox1 could generate O2•− which combine with steadily NO to form peroxinitrites . Peroxinitrites activate p38 and ERK in the MAPK pathways , contributing to the tight regulation of IL-8 production by O2•− [32] , [33] ( Figure 11 ) . In addition , O2•− produced by keratinocytes upon stimulation with P . acnes , counteract the growth of the bacteria . Those results highlight a new mechanisms by which keratinocytes participate in the innate immune response to pathogens . Finally , the inhibition of O2•− production , IL-8 release and keratinocyte apoptosis by retinoic acid derivates , the most efficient anti-acneic drugs , demonstrates the relevance of these pathways in vivo . In addition , our data are in agreement with the observations that , retinoic acid can induce MnSOD mRNA in a human neuroblastoma cell line and decrease TPA-induced O2•− production in mouse keratinocytes [34] . In conclusion , keratinocytes are not mere targets of the innate immune response but are directly involved in the defence mechanisms aiming at eliminating pathogens . In response to P . acnes , keratinocytes can produce massive amounts of ROS that , in return , inhibit bacterial growth . Those ROS do not only eliminate the bacteria but also generate inflammation . Thus , we hypothesize that the severity of acne depends on the balance between the ability of the P . acnes strain to induce a potent immune response [3] and the capability of the host to generate and to detoxify the ROS produced [11] , [13] . Therefore , inhibiting this inflammatory reaction using appropriate antioxidant molecules could be considered as a potential treatment of acne .
P . acnes strain 6919 was obtained from the American Type Culture Collection ( Manassas , VA ) and grown under anaerobic conditions in reinforced clostridial liquid and solid medium ( RCM ) ( Difco Laboratories , Detroit , MI ) at 37°C during 5 days in order to reach stationary phase . Typically , 100 ml of RCM were used and bacteria were harvested after centrifugation at 7 , 000 g for 10 min at 4°C . Pellets were pooled and washed in about 30 ml of cold PBS and centrifuged again as described above . Finally , the bacterial pellet was suspended in PBS or DMEM . From this suspension , dilutions of 105 to 108 CFU/ml were prepared , resulting in a multiplicity of infection ( MOI ) of 0 . 05 to 50 bacteria per cell in 0 . 1 ml of inoculum . To obtain total surface protein extract , the bacteria were scraped in the presence of 2 ml of PBS [1 . 5 mM KH2PO4 , 2 . 7 mM Na2HPO4 . 7H2O , 0 . 15 M NaCl ( pH 7 . 4 ) ] from the solid RCM . The bacterial suspension was heated at 60°C for 20 min and the bacteria removed by centrifugation at 16 , 000 g for 20 min at 4°C . The supernatant containing total surface proteins was subjected to ammonium sulfate precipitation at 60% of saturation for 1 h under stirring . The precipitated proteins were recovered after centrifugation at 22 , 000 g for 30 min at 4°C , then resuspended in PBS , and extensively dialyzed against PBS . Protein concentration was determined by the method of Lowry using BSA as standard described by Peterson [35] . The human keratinocyte cell line HaCaT was grown in Dulbecco's modified Eagle's medium-Glutamax-I ( DMEM ) ( Invitrogen , Cergy Pontoise , France ) supplemented with 10% heat-inactivated fetal calf serum ( Invitrogen ) , 20 mM L-glutamine , 1 mM sodium pyruvate , and antibiotic/antimycotic solution ( 10 U/ml Pencillin , 10 µg/ml Streptomycin , 0 . 25 µg/ml Amphoterin ) ( Invitrogen ) at 37°C in humidified atmosphere containing 5% CO2 as described [36] . The cell line was routinely tested to assess the absence of Mycoplasma infection . For stimulation experiments , HaCaT cells were incubated with the P . acnes suspension adjusted at the appropriate concentration in buffer solution for the desired period of time at 37°C in 5% CO2 . HaCaT cells ( 2 . 104/well ) were seeded in 96-well plates ( Corning Costar , Brumath , France ) . After 18 h , cells were washed three times in PBS and incubated with 100 µl per wells of 5 µM DHE ( for determination of O2•− ) or 5 µM H2-DCFDA ( for determination of H2O2 ) or 5 µM DAF2-DA ( for determination of NO ) for 30 min as described previously [37] , [38] , [39] . Fluorescent probes were purchased from Molecular Probes ( Eugene , OR , USA ) . After three washes , cells were incubated with 100 µl of a suspension of P . acnes in PBS ( Abs at 600 nm = 0 . 5 ) and fluorescence intensity was recorded every hour over a period of 5 h . Fluorescence excitation/emission maxima were for DAF2-DA: 495/515 nm , for DHE: 480/610 nm and for H2-DCFDA: 507/525 nm . At the end of the experiment , the number of adherent cells was evaluated by the crystal violet assay as described below . O2•− , NO and of H2O2 were assayed by spectrofluorimetry on a Fusion spectrofluorimeter ( PackardBell , Paris , France ) . Levels of ROS were calculated in each sample as follows: reactive oxygen species rate ( arbitrary units/min/106 cells ) = ( fluorescence intensity [arbitrary units] at T5h – fluorescence intensity [arbitrary units] at To/300 minutes/number of adherent cells as measured by the crystal violet assay , and were expressed as arbitrary unit ( A . U . ) . HaCaT cells ( 2 . 104/well ) were seeded in 96-well plates and incubated for 18 h in complete medium alone or with the following molecules: 2 mM diethyldithiocarbamate ( SOD inhibitor ) , or 400 µM CuDIPS ( Cu/Zn SOD mimic ) , or 100 µM MnTBAP ( MnSOD mimic ) , or 40 µM rotenone ( inhibitor of mitochondrial complex I ) or 40 µM antimycine ( inhibitor of mitochondrial complex III ) , 40 µM diphenyliodonium ( inhibitor of NADPH oxidase ) , or with 40 µM allopurinol ( inhibitor of xanthine oxidase ) . Cells were then washed three times in PBS and incubated with 100 µl per well of 5 µM DHE for 30 min . After three washes , cells were incubated with 100 µl of a suspension of P . acnes ( Abs at 600 nm = 0 . 5 ) and fluorescence intensity was recorded every hour over a period of 5 h as previously described . At the end of the experiment , the number of adherent cells was evaluated by the crystal violet assay . The levels of O2•− were calculated as described above . HaCaT cells ( 2 . 104/well ) were seeded in 96-well plates and incubated for 18 hours in complete medium alone or with the following molecules: 3200 µM reduced glutathione , 800 µM N-acetylcysteine , or 400 µM CuDIPS , or 100 µM MnTBAP , or 100 µM D , L-buthionine-[S , R]-sulfoximine ( inhibitor of glutathione reductase ) , or 400 µM aminotriazol ( inhibitor of catalase ) , or 20 U PEG-catalase ( cell permeable catalase ) . Cells were then washed three times in PBS and incubated with 100 µl per wells of 5 µM H2-DCFDA for 30 minutes . After three washes , cells were incubated with 100 µl of a suspension of P . acnes ( Abs at 600 nm = 0 . 5 ) and fluorescence intensity , was read at a fluorescence excitation wavelength of 507 nm and at an emission wavelength of 525 nm , and was recorded every hour over a period of 5 hours . At the end of the experiment , the number of adherent cells was evaluated by the crystal violet assay . The levels of H2O2 were calculated in each sample as described above . Nox1 silencing was performed as previously described [15] . We used the Nox1-A siRNA primer with the sequence sense 5′-ACAAUAGCCUUGAUUCUCAUGGUAA-3′ , anti-sense 5′-UUACCAUGAGAAUCAAGGCUAUUGU-3′ , located at 750 bp . A scrambled siRNA duplex as negative control was used with the sequence sense 5′-ACACCGAAGUUUCUUGUACGUAUAA-3′ , anti-sense 5′-UUAUACGUACAAGAAACUUCGGUGU-3′ ( MWG Biotech , Les Ulis , France ) . At 24 h before transfection , HaCaT cells were transferred onto 96-well plates at the density of 1 . 104 cells/well and transfected with 10 nM of each siRNA duplex using INTERFERin™ transfection reagent ( Polyplus transfection , Illkirch , France ) for 4 h in serum free DMEM without antibiotics . Then , complete DMEM medium was added and the cells were incubated for 48 h . Western blot using specific antibody against Nox1 ( Santa Cruz Biotechnology Inc . , Santa Cruz , CA ) was used to assess the reduction of Nox1 protein production as previously described [15] . The level of Nox1 using Nox1A-siRNA was decreased by 86% , whereas scrambled siRNA did not affect the Nox1 level ( Figure S3 ) . Cell death was estimated spectrofluorometrically using the fluorescent probe YO-PRO-1 ( Molecular Probes ) on a Fusion spectrofluorimeter ( Packard Bell ) . HaCaT cells ( 2 . 104/well ) were seeded in 96-well plates and incubated for 18 h in complete medium alone or with the following molecules: 2 mM diethyldithiocarbamate , or 40 µM rotenone , or 40 µM antimycine , 40 µM diphenyliodonium , or with 40 µM allopurinol , or 1600 µM reduced glutathione , 3200 µM N-acetylcysteine or 400 µM CuDIPS , or 100 µM MnTBAP , or 800 µM D , L-Buthionine-[S , R]-sulfoximine , or 400 µM aminotriazol , or 20 U PEG-catalase . Cells were then washed three times in PBS and incubated with 100 µl per well of a suspension of P . acnes ( Abs at 600 nm = 0 . 5 ) for 24 h in complete medium . After three washes in PBS , cells were incubated with 10 µM YO-PRO-1 for 30 min . Cell death was measured by reading at an excitation wavelength of 480 nm and an emission wavelength of 525 nm . The level of cell death was estimated in each sample by the fluorescence intensity [arbitrary units] reflecting the disruption of the cell membranes . HaCaT cells incubated or not with P . acnes were fixed in 3 . 7% buffered formaldehyde directly onto the 96-well plate . Cells were then subjected to TUNEL assay using the TACS™ TdT-Fluorescein In situ apoptosis detection kit ( R&D Systems Inc . , Minneapolis , MN ) following the manufacturer's intructions . Briefly , after fixation , cells were permeabilized by Proteinase K and incubated with the reaction mixture containing Terminal deoxynucleotidyl Transferase ( TdT ) and biotinylated-conjugated dNTPs for 1 h at 37°C . After washing , biotinylated nucleotides were detected by incubating cells with a streptavidin-fluorescein conjugate for 20 min at room temperature in the dark . After removing the excess of fluorescein conjugate by washing in 0 . 1% Tween 20 in PBS , labeled DNA was examined under a fluorescence microscope . Crystal violet staining was used to determine the number of adherent cells in 96-well plates . Briefly , after incubation with the test compound , the culture medium was discarded and the cells were incubated with a 0 . 05% crystal violet solution ( Sigma ) for 30 min at room temperature . After washing with PBS , 100% methanol was added , and the absorbance was measured spectrophotometrically at 540 nm on an ELISA multiwell reader . The MTT ( 1- ( 4 , 5-dimethylthiazol-2-yl ) -3 , 5-diphenylformazan ) assay was performed to test cell viability in 96-well plates . The cells were incubated with a 0 . 2% MTT solution in cell culture medium for 4 h at 37°C . The MTT solution was then discarded and DMSO added to solubilize the MTT-formazan cristals produced in living cells . After thorough mixing , the absorbance was measured at 540 nm . HaCaT cells were incubated in presence of two P . acnes concentrations ( Abs at 600 nm = 0 . 2 and 1 . 0 ) for 18 h at 37°C . Cells were washed twice with cold PBS , harvested after trypsinization and fixed with 3 . 5% paraformaldehyde in PBS for 15 min at 4°C . After washing in PBS , cells were permeabilized in 1% NP-40 and incubated with FITC-labelled anti 3-nitrotyrosine monoclonal antibody ( Clone 1A6 , Upstate Cell Signalling Solutions , Lake Placid , NY , USA ) at 6 . 4 µg/ml for 1 h at 4°C . After three washes , cells were pelleted and suspended in 1 ml of PBS , then analyzed by flow cytometry ( FACScalibur , Becton Dickinson , Mountain View , CA ) . Control experiments were perfomed by incubating the cells with a FITC-labelled irrelevant IgG of the same isotype under the same conditions as described above . Human IL-8 protein concentration was measured in the supernatants of HaCaT cells using the Quantikine® human IL-8 immunoassay kit ( R&D Systems Inc . , Mineapolis , MN ) according to the manufacturer's instructions . We used serial dilutions of recombinant human IL-8 for standard curve . The optical density was determined at 450 nm at a wavelength correction of 540 nm . Total RNA was isolated with TRIzol® reagent ( Invitrogen ) according to the manufacturer's instructions and treated with DNAse I ( Roche Molecular Biochemical ) . RNA concentration was determined by reading the absorbance at 260 nm . Complementary DNA ( cDNA ) was generated from 2 µg total RNA using the oligo ( dT ) primer ( MWG Biotech , Les Ulis , France ) and 1 . 6 unit of AMV reverse transcriptase ( Promega , Madison , WI , USA ) and then used as template for standard PCR . Standard amplification was carried out using Taq DNA polymerase ( Invitrogen ) in 25 µl final volume with the cycling conditions set at 94°C for 5 min followed by 35 cycles of 94°C for 1 min , 62°C for 1 min and 72°C for 1 min and ending by an elongation at 72°C for 7 min . Primers amplified a 259 and 113 bp fragment of iNOS and GAPDH cDNA , respectively . Primers used were: iNOS sense 5′-CGGTGCTGTATTTCCTTACGAGGCGAAGAAGG-3′ , iNOS reverse 5′-GGTGCTGTCTGTTAGGAGGTCAAGTAAAGGGC-3′; GAPDH sense 5′-GTGAAGGTCGGAGTCAACG-3′ , GAPDH reverse 5′-TGAGGTCAATGAAGGGGTC-3′ . HaCaT cells were grown on two separate 96-well plates and pre-incubated with neutralizing anti-human TLR-2 mAb TLR-2 . 1 ( 10 µg/ml ) ( eBioscience , San Diego , California ) and anti-human CD36 monoclonal antibody FA6-152 ( Hycult biotechnology b . v ) or isotype-matched control-purified mouse IgG antibodies ( 10 µg/ml ) ( Caltag ) ( 10 µg/ml ) diluted in supplemented DMEM media for IL-8 measurement , and in sterile PBS pH 7 . 4 for O2•− quantitation at 37°C in 5% CO2 atmosphere . After 2 h , cells were incubated for 30 min with 100 µl DHE at 5 µM final concentration . After three washes , cells were incubated with 100 µl of a suspension of P . acnes ( Abs at 600 nm = 1 . 0 ) in PBS and fluorescence intensity was recorded every 30 min over the 3 h time-frame stimulation . After 3 h of incubation , supernatants were collected and used for IL-8 quantitation as described below . A 10 mM O2•− solution was obtained by mixing 16 mM dicylohexano-18-crown-6 with 9 . 8 mM KO2 in DMSO . The solution was allowed to stabilize for 1 h at room temperature with stirring and protected from light before use . The relative sensitivity of HaCaT and of P . acnes was then tested against serial dilution of the O2•− solution . The statistical significance of differences between data from experimental groups was analyzed by paired Student's-test . A level of P≤0 . 05 was accepted as significant . Statistical significance is indicated by * ( P≤0 . 05 ) , ** ( P≤0 . 01 ) , and *** ( P≤0 . 001 ) , respectively . Catalase ( # P04040 ) , CD-36 ( # P16671 ) , ERK ( # P28482 ) , GpX ( # P07203 ) , GM-CSF ( # P32927 ) , IL-1α ( # P01583 ) , IL-1β ( # P01584 ) , IL-8 ( # P10145 ) , iNOS ( # P35228 ) , MnSOD ( # Q7Z7M6 ) , NOX1 ( # Q9Y5S8 ) , p38 ( # Q16539 ) , TLR2 ( # O60603 ) , TLR4 ( # O00206 ) , TLR6 ( # Q9Y2C9 ) , TNF-α ( # P01375 ) . | Acne vulgaris is a chronic inflammatory disorder of the sebaceous follicles . It is the most common skin disease , affecting up to 80% of individuals at some point between the ages of 11 and 30 years . Propionibacterium acnes ( P . acnes ) plays a role in the development of inflammatory acne lesions , but whether it causes inflammation by itself or through indirect mechanisms is not clear yet . Therefore , by exposing epidermal cells to P . acnes in vitro , we tested whether reactive oxygen species ( ROS ) production ( oxidative burst ) was involved in the inflammatory process . We found that one particular ROS , superoxide anion , was generated by epidermal cells following P . acnes stimulation . This phenomenon is associated with the production of a soluble pro inflammatory molecule , IL-8 , and epidermal cell death . The abrogation of P . acnes-induced oxidative burst by the most commonly used and most efficient treatments of acne suggests that superoxide anions produced by epidermal cells are critical in the development of acne inflammatory lesions . | [
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] | [
"immunology/innate",
"immunity",
"dermatology/skin",
"infections",
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] | 2009 | Production of Superoxide Anions by Keratinocytes Initiates P. acnes-Induced Inflammation of the Skin |
We performed a quantitative analysis of the HLA restriction , antigen and epitope specificity of human pathogen specific responses in healthy individuals infected with M . tuberculosis ( Mtb ) , in a South African cohort as a test case . The results estimate the breadth of T cell responses for the first time in the context of an infection and human population setting . We determined the epitope repertoire of eleven representative Mtb antigens and a large panel of previously defined Mtb epitopes . We estimated that our analytic methods detected 50–75% of the total response in a cohort of 63 individuals . As expected , responses were highly heterogeneous , with responses to a total of 125 epitopes detected . The 66 top epitopes provided 80% coverage of the responses identified in our study . Using a panel of 48 HLA class II-transfected antigen-presenting cells , we determined HLA class II restrictions for 278 epitope/donor recognition events ( 36% of the total ) . The majority of epitopes were restricted by multiple HLA alleles , and 380 different epitope/HLA combinations comprised less than 30% of the estimated Mtb-specific response . Our results underline the complexity of human T cell responses at a population level . Efforts to capture and characterize this broad and highly HLA promiscuous Mtb-specific T cell epitope repertoire will require significant peptide multiplexing efforts . We show that a comprehensive “megapool” of Mtb peptides captured a large fraction of the Mtb-specific T cells and can be used to characterize this response .
Antigen-specific CD4 T cell responses are functionally very diverse , and have been classified into several different Th subsets based on their expression of distinct chemokine receptors , secretion of effector cytokines , and different transcriptional programs and differentiation states [1 , 2] . The depth to which these responses can be characterized has increased dramatically in recent years . Novel technologies , such as multiparameter flow cytometry , cytometry by time of flight ( CyTOF ) , and single-cell transcriptomic profiling , which allow simultaneous characterization of many functional and phenotypic markers are revealing an unprecedented degree of complexity in immune responses [3–8] . Human antigen-specific CD4 T cell responses are also highly complex at the level of HLA restriction , antigen and epitope specificity [9–12] . Humans express HLA class II α/β heterodimers encoded by four different β-chain loci , DRB1 , DRB3/4/5 , DQB1 and DPB1 , as well as corresponding α-chain loci DRA1 , DQA1 and DPA1 [13] . All loci , with the exception of the DR α-chain , are extremely polymorphic and more than 1 , 500 alleles have been identified to date [14] . As a result , most individuals are heterozygous at these loci and express up to eight different HLA class II molecules . At the antigen and epitope levels , especially in complex organisms , it is clear that T cell responses are also highly complex , often involving tens of different antigens and hundreds of epitopes [10–12 , 15 , 16] . Patterns of immunodominance in humans are much less narrow than those observed in murine , genetically homogenous model systems . While mechanisms of immunodominance and breadth of T cell responses have been comprehensively analyzed in murine systems and to some degree in humans [17–20] , a quantitative assessment of the complexity of responses at the population level , in the course of natural infections , is lacking . Most immuno-profiling studies have thus targeted individual antigens or a limited set of epitopes under the assumption that these represent the entire pathogen-specific response . It is currently unknown to what degree underestimating the true complexity might impact the outcomes generated by immuno-profiling studies . Tuberculosis ( TB ) is the leading cause of mortality , alongside HIV , in South Africa and worldwide due to a single infectious agent [21] . South Africa has the highest rate of incident TB in the world with almost 1 in every 100 persons developing active TB each year [21] and an estimated 70–80% of the adult population has latent Mtb infection [22] . Several cytokines are involved in T cell responses against Mtb . Individuals with genetic defects in the IL-12 pathway or the IFNγ-receptor have increased susceptibility to mycobacteria [23–25] , providing evidence that IFNγ is necessary for protective immunity against Mtb . Indeed , CD4 T cell responses to Mtb are contained in a CXCR3+CCR6+ Th subset , cells that produce IFNγ , IL-2 and TNFα [10] , and a mutation leading to the loss of the CXCR3+CCR6+ lineage specific transcription factor RORC leads to mycobacteriosis [26] . Increased production of Th2 cytokines have been reported in patients with active TB compared to healthy controls with latent Mtb infection [27 , 28] , while Mtb-specific CD4 T cells have also been shown to express IL-17 in response to PPD , Mtb lysate and more limited studies of defined peptide pools [29–32] . IL-10 has been shown to mediate a decreased ability to clear Mtb infection and the levels of IL-10 is increased in the serum of active TB patients [33 , 34] . Our previous work in a population of healthy Mtb-infected individuals from USA has shown that Mtb epitope-specific CD4 T cells are also very heterogeneous at a cohort level [10] . The breadth of responses was remarkably wide , with an average individual simultaneously recognizing 24 distinct epitopes . However , no antigen was recognized by all individuals , and 82 antigens were required to cover 80% of the T cell response . Here we undertook a comprehensive analysis of the epitope specificity and HLA restriction of human T cell responses associated with Mtb-infection in a healthy cohort of 63 South African individuals from a setting endemic for TB . The results provide evidence of striking heterogeneity in recognition of different Mtb antigens and overall breadth of epitope responses . We identified a set of epitopes that provides good coverage of the Mtb peptide-specific T cell response restricted by an extensive variety of HLA class II molecules in an independent validation cohort .
A total of 63 HIV-negative adults were enrolled into our screening cohort . All , except one , were QFT positive . The QFT-negative donor was previously QFT positive and was therefore a QFT reverter , who may still be infected with Mtb . Demographic characteristics are reported in Table 1 . Traditional approaches to T cell epitope discovery typically rely on methods that detect IFNγ expression . To determine if this Th1-biased approach led to a bias in epitope discovery , we compared identification of T cell responses to peptides by measuring IL-5 ( Th2 cytokine ) , IL-17 ( Th17 cells ) , IL-10 ( regulatory cytokine ) in addition to IFNγ ( Th1 cytokine ) in our ex vivo ELISPOT assays . We anticipated the vast majority of responses to be mediated by CD4 T cells since earlier studies using the exact same methodology found that more than 95% of responses were HLA class II restricted [10 , 35] . We limited these experiments to peptides spanning a set of 11 proteins contained in TB vaccines currently in clinical trials or contained in IGRAs ( termed “TB Vaccine and IGRA antigens”; Table 2 ) . In 12 participants , IFNγ responses accounted for nearly 96% of the recognition events , and for nearly 99% of the total response magnitude ( Table 3 ) . Cells expressing IL-5 or IL-10 were extremely rare and no IL-17-expressing cells were detected , demonstrating that responses to Mtb protein antigens were highly polarized towards IFNγ-producing T cell subsets . For all samples cell viability was above 90% and all cytokines were readily detectable in positive controls , demonstrating that the IFNγ polarization was not a technical artifact . Not a single instance of a donor/peptide pool combination that was positive for IL-5 , IL-17 or IL-10 , but negative for IFNγ , was observed . Thus , screening for IFNγ production correctly identified all positive donor/pool responses . Based on these results , all subsequent experiments and assays on remaining donors were performed with IFNγ ELISPOT assays . The overall goal of our experiments was to broadly evaluate T cell responses in terms of epitope reactivity , immunogenicity , immunodominance , and HLA allele restriction in LTBI donors from the Western Cape region of South Africa , a setting where TB is endemic . We tested all 63 donors for IFNγ reactivity to pools of overlapping peptides spanning the TB Vaccine and IGRA antigens . Positive pools were deconvoluted to identify individual T cell epitopes . Overall , 86% of donors recognized epitopes from at least one antigen , and on average 2 ( range 0–8 ) different antigens per donor were recognized . This is consistent with our previous report that highlights the considerable breadth and inter-individual variability of epitope-specific responses in persons with LTBI [10] . The ex vivo reactivity , expressed in terms of total magnitude of response and response frequency to the different TB Vaccine and IGRA antigens , is shown in Fig 1 . The most frequently recognized proteins were Rv3874 and Rv0288 , which were recognized by more than 40% of the donors . Rv1196 , Rv3875 , and Rv3619c were recognized by more than 20% of the donors , while Rv3620c , Rv2660c , Rv0125 , Rv3804c , Rv1886c , and Rv2608 were recognized in less than 10% of donors . No reactivity was observed to Rv1813c in this cohort . The same hierarchy of reactivity to these antigens was seen when considering the total magnitude of response ( Fig 1 ) . Antigens classified according to the Tuberculist database [36] , into the cell wall and cell processes category were the most immunogenic , followed by the PE/PPE category ( Fig 1 ) . Lower immunogenicity was detected for proteins classified as conserved hypotheticals , involved in intermediary metabolism and respiration , as well as lipid metabolism proteins . Next , we undertook a more detailed analysis to identify individual reactive peptides within the TB Vaccine and IGRA antigens . A total of 64 peptides were recognized by two or more donors . When responses to two consecutive overlapping peptides were positive , the peptide with the highest magnitude and response frequency was chosen as the optimal epitope . We have previously shown that responses to two consecutive overlapping peptides typically map to the same single epitope [35] . Using these criteria , the recognition of 64 peptides defined 37 distinct antigenic regions , ( Fig 2 and S1 Table ) . In the case of Rv0288 , 6 antigenic regions were defined , and for Rv3619c and Rv3620c 4 regions each ( Fig 2A–2C ) . For Rv3874 and Rv3875 , 6 and 5 regions were defined , respectively ( Fig 2D and 2E ) . Interestingly these antigenic regions were almost identical to those previously defined in the cohort of IGRA-positive donors from San Diego [35 , 38] . We only identified one antigenic region each in Rv0125 , Rv1886c , Rv3804c , or Rv2608 ( Fig 2F–2I ) , while 8 antigenic regions were defined in Rv1196 ( Fig 2J ) . Due to their low recognition frequency , no clear antigenic regions were detected in the case of Rv2660c or Rv1813c . The number of responding donors corresponded well to the total magnitude of response within the cohort . When we analyzed peptides that were recognized by a high frequency of responders , none were dominated by a single responding donor ( Fig 2 ) . We also assembled a set of peptides previously described in studies of ex vivo human CD4 T cell epitope responses to Mtb [10 , 35 , 39–41] . A total of 253 epitopes , derived from 94 Mtb antigens , were screened for recognition by IFNγ ELISPOT assay in the 63 South African adult donors . These experiments defined 38 non-redundant epitopes recognized by 2 or more donors in the cohort ( S2 Table ) . Nine epitopes identified amongst the TB Vaccine and IGRA antigens were also found to be reactive in the previously described epitope set . These epitopes were highly immunodominant and accounted for 50% and 54% of the T cell reactivity detected amongst the TB Vaccine and IGRA antigens and previously described epitope set , respectively . This result is consistent with previous estimates that bioinformatic prediction for promiscuous epitopes captures about 50% of the total reactivity [10 , 42] . In conclusion , a set of 66 non-redundant epitopes derived from TB Vaccine and IGRA antigens and previously described epitopes provide comprehensive coverage of the donor cohort investigated . We previously showed in a IGRA-positive donor cohort from the San Diego region that responses were highly heterogeneous [10] . Here we investigated whether this is also true for the present study . A total of 235 peptides were recognized in at least one donor . After removal of redundant epitopes , 125 unique epitopes were recognized by at least 1 donor and the 66 epitopes identified from TB Vaccine and IGRA antigens and previously described epitopes captured 80% of the response ( Fig 3A ) . To investigate whether the IGRA antigens accounted for the majority of the reactivity we divided the 125 unique epitopes into whether they mapped to IGRA or to non-IGRA antigens . Significantly higher reactivity was observed to the other ( non-IGRA ) antigens ( S1A Fig ) . Furthermore , we also stratified the 125 peptides according to whether they were co-expressed by Mtb and BCG or known to be absent in BCG ( according to Behr et al . [43] ) . Significantly higher reactivity was observed to the antigens shared by Mtb/BCG than to the antigens only expressed by Mtb ( S1B Fig ) . Each donor recognized an average of 5 of the 66 epitopes ( range 0 to 19 ) . We further found that 92% of donors recognized at least one epitope and 70% of donors recognized at least three epitopes ( Fig 3B ) . To enable investigation of responses to these epitopes in cohorts where less PBMC numbers were available we pooled the 125 unique epitopes as one peptide pool and the top 66 epitopes as another , see below . To further understand the degree of complexity of the responses in the donor population , we sought to determine the restricting class II HLA molecule ( s ) of each epitope . HLA restrictions can be determined by testing for PBMC responses to each epitope recognized by that donor when the peptide is presented by single-HLA-molecule transfected antigen presenting cell lines that match the HLA type of the donor [44] . Genotypes of each individual donor at the HLA class II DR , DP and DQ loci was determined [45] . A recently described comprehensive panel of 48 HLA transfected cell lines [44] allowed coverage of 63% of the identified HLA class II alleles in the donor cohort ( S3 Table ) . A total of 778 different positive donor/epitope combinations corresponded to 5 , 623 possible HLA/epitope/donor combinations , since each donor expresses up to two alleles at each of the four HLA class II loci . The panel of transfected cell lines allowed HLA restriction to be determined for 3 , 739 ( 66% ) of these combinations . Because PBMCs from the South African donors were limited , not all possible HLA restrictions for each epitope could be tested . In those instances , we focused on determining the restriction of the epitopes associated with the highest response magnitudes . Overall , we were able to define a total of 2 , 916 HLA allele/epitope restrictions , corresponding to 52% of the total and 78% of the epitope/donor combinations for which HLA transfected cell lines were available . Representative data is shown for three epitope/donor combinations in Fig 4 , where a response was detected when the peptide was presented by DRB1*04:04 ( Fig 4A and 4B ) or DQB1*06:02 ( Fig 4C ) transfected cells , but not by any of the other lines transfected with HLA molecules expressed by the donor . Experimentally testing 2 , 916 HLA/epitope/donor combinations allowed us to determine 519 HLA/epitope/donor restrictions ( S4 Table ) . A summary of 37 restrictions identified in 3 or more subjects , corresponding to 19 unique epitopes , is presented in Table 4 . Given the promiscuous nature of HLA class II-restricted responses , we also determined to what extent the recognized epitopes were associated with promiscuous recognition ( i . e . restricted by more than one HLA molecule encoded by different HLA loci , or within the same locus but differing sufficiently—by more than the 2-digit level—to be classified as distinct HLA subtypes ) [46] . Sixty-four % of all epitopes and 72% of epitopes tested in 2 or more donors were associated with promiscuous recognition ( Fig 5 ) . It has been reported that even very dominant epitopes are typically not recognized in 100% of individuals expressing the restricting HLA molecule , suggesting incomplete penetrance . For example , in the case of the well-known HLA class I A*02:01-restricted Influenza MP58-66 or Hepatitis B Virus C18-27 epitopes , only 60–70% of individuals expressing A*02:01 recognized these epitopes in the context of natural infection [47–49] . We defined penetrance for each of the 37 most prominent epitopes restricted by a given HLA as the proportion of individuals who expressed the restricting HLA allele and who responded to the particular epitope . The average penetrance was 64% ( range 30 to 100% ) for all tested epitopes ( Table 4 ) , suggesting incomplete penetrance for most Mtb epitopes and underlying the complexity of responses at the population level in the context of natural infections . Our data allows estimation of the relative contributions of the four different HLA class II loci in terms of restricting the global response . The DRB3/4/5 locus restricted 32% of the total restriction events , followed by DRB1 ( 31% ) , DQB1 ( 27% ) and DPB1 ( 10% ) . These data suggest that focusing on DRB1 and DRB3/4/5 only , a common strategy in studies of CD4 T cell responses , would allow coverage of approximately two thirds of the total HLA class II restricted response . The data further allowed us to assess whether the population frequency of a given HLA allele was associated with the frequency of identified epitopes restricted by that allele ( Table 5 ) . Given that a frequent allele will be present in more of the donors we tested , we assumed that there would be a positive correlation . Indeed , the overall HLA phenotypic frequency strongly correlated ( Spearman r = 0 . 70 , p<0 . 0001 ) with the relative proportion of total restrictions identified for the corresponding alleles ( S2 Fig ) . However , some exceptions were noted . For example , DPB1*01:01 was expressed by 31 . 7% of the donor population , but restricted only 3 . 7% of the identified T cell epitopes ( Table 5 ) . Thus , the correlation between population frequency and recognition frequency is not absolute . We recently reported that sequential lyophilization allows generation and testing of peptide pools encompassing large numbers of epitopes [15 , 50] . As described above , we generated pools of epitopes corresponding either to the total 125 reactive epitopes or to the 66 most dominant epitopes ( S5 Table ) . In addition , we also generated a comprehensive pool ( S5 Table ) , that included 300 epitopes detected in the present and the previously completed genome-wide screening studies [10] . Stimulation of PBMC with these three epitope pools , as well as heat-killed Mtb lysate , was tested in an ICS assay on 34 selected donors . The gating strategy is illustrated in Fig 6A . Frequencies of cytokine-expressing CD4 T cells in response to any of the three individual epitope pools were equivalent to the Mtb lysate ( Fig 6B ) . The results presented above were generated by ELISPOT assays , which detect responses by both CD4 and CD8 T cells . We therefore investigated whether responses were mediated by CD4 and/or CD8 T cells . We compared the reactivity to the three epitope pools and Mtb lysate to a pool of class I and II restricted epitopes derived from Epstein-Barr Virus ( EBV ) and Cytomegalovirus ( CMV ) as control epitopes . The reactivity seen in CD4+ T cells was equivalent irrespective of antigen , whereas significantly higher reactivity was observed to the EBV/CMV epitope pool in CD8+ T cells ( S3 Fig ) . The ICS assay detected some Mtb-specific reactivity in CD8 T cells , suggesting that responses to these epitope pools are mediated by both CD4 and CD8 T cells . Additionally , we investigated the production of IL-4 , IL-10 and IL-17 , as well as CD45RA and CCR7 , to further characterize the functions and phenotypes of the CD4 T cell response to the defined peptide pools ( Fig 7 ) . As described above for ELISPOT assays , we observed no IL-4 , IL-10 or IL-17 expression in response to the peptide pools ( Fig 7 ) . Furthermore , the response was exclusively mediated by memory CD4+ T cells and not naïve ( CD45RA+CCR7+ ) T cells ( Fig 7 ) . The majority of cytokine-positive CD4+ T cells expressed TNFα or IFNγ alone , or co-expressed IFNγ+TNFα+IL-2+ , IFNγ+TNFα+ or TNFα+IL-2+ ( Fig 6C and 6D ) . As expected , all peptide pools elicited IFNγ-production , however , there was heterogeneity amongst responding individual cells and a proportion of CD4+ T cells did not produce IFNγ . Consistent with the finding that the pool of 66 epitopes encompassed most ( 80% ) of the total reactivity , frequencies of cytokine-expressing CD4 T cells detected with this pool were not markedly different to those detected with the larger peptide pools ( Fig 6D ) . Similarly , no differences in cytokine co-expression patterns were observed for the different peptide pools ( Fig 6D ) . Significantly higher proportions of CD4 T cells responding to the peptide pools co-expressed IFNγ , TNFα and IL-2 ( median 48 . 2 , IQR 31 . 5–78 . 2 for single cytokine producers ) , compared to Mtb-lysate reactive cells ( median 63 . 1 , IQR 44 . 7–87 . 6; p<0 . 01; Two-tailed Mann-Whitney ) , which were predominantly single cytokine producers ( Fig 6D ) . Finally , to demonstrate the general applicability of these peptide pools for measuring MTB-specific CD4 T cell responses , we tested them in two independent donor cohorts , encompassing 60 IGRA-positive and 17 IGRA-negative adolescent donors from the same region of South Africa . Response reactivity and total magnitude of responses to the 66 , 125 and 300 peptide pools detected in PBMC from the IGRA-positive adolescent cohort ( Fig 8A ) were generally high . Only very few IGRA-positive donors ( <4% for all three pools ) did not respond at all to the epitope pools , suggesting that these epitope sets provide broad response coverage in donors with LTBI . Furthermore , very few IGRA-negative donors from the same region and population had T cell responses to these peptide pools and those with reactivity typically had low magnitudes of responses ( Fig 8A ) . To investigate potential bias introduced by classifying individuals based on IGRA results we stratified individuals with available TST results from the validation and control groups by their TST status . At an induration cut-off of 5mm , 46 were TST-positive and 8 were TST-negative ( Table 1 ) . As expected , responses detected from the TST-positive donors were generally high ( Fig 8B ) . In contrast , very few TST-negative donors responded to these peptide pools , and those with responses had very low responses ( Fig 8B ) . We also found significantly lower frequencies of IFNγ-expressing CD4 T cells in response to the peptide pools in the IGRA-negative individuals by ICS assay ( S4 Fig ) . Frequencies of IL-2 and TNFα-expressing CD4 T cells also appeared lower in these IGRA-negative individuals compared to IGRA positive individuals . However , these differences were not striking and in some instances not significant ( S4 Fig ) . Our data thus demonstrate that these peptide pools can be used to detect MTB-specific T cell responses by both ELISPOT and ICS assays .
We report the first quantitative analysis on a population level of the complexity of pathogen-specific human HLA class II-restricted CD4 T cell responses . The results are relevant both for our understanding of human responses in a natural infection setting and in the context of immune profiling strategies . Here we estimated the complexity of Mtb-specific human T cell responses . We have defined the epitopes contained in eleven different antigens by synthetizing and testing overlapping peptides spanning the entire sequence of the antigen . We found that previously defined epitopes , based on HLA class II allele binding predictions account for about 50% of the total magnitude of response , thus providing a rough estimate of the success of genome-wide epitope mapping consistent with previously estimates [42] . By combining epitopes from the overlapping peptides spanning the eleven Mtb antigens with the predicted/previously defined epitopes we estimate that about 50–75% of the total magnitude of response can be accounted for in a population sample comprising 63 IGRA-positive donors . Therefore , we propose that additional epitopes that account for the remaining 25% of the total magnitude are yet to be discovered . This study provided a glimpse onto the staggering complexity of population level responses in the context of a natural infection . Using a recently described comprehensive panel of single HLA class II transfected cell lines [44] , we detected 778 different epitope/HLA combinations being recognized in the donor cohort . This is likely to be a gross underestimate of the actual complexity , and does not take into account CD8 T cell responses restricted by HLA class I . The epitopes we studied represent a fraction ( 50–75% ) of the reactivity associated with latent Mtb infection . Further , restrictions could be determined only for 36% of all epitopes/donor combinations due to limitations in the number of cells available , while restrictions could be determined only for about 66% of all HLA/epitopes due to limited HLA-transfected cells available for study . Thus we estimate that the actual complexity might be 4 to 8-fold higher . We hypothesize that similar complexity , defined by highly heterogeneous epitope recognition and HLA class II restriction , is typical for the T cell response in human populations to complex pathogens that comprise multiple antigens . The implications of our findings for studies of immunopathogenesis of Mtb infection , which often focus on responses to ESAT-6/CFP10 and/or PPD , are not definitive . However , the finding that non-IGRA antigens comprise the majority of the human response to peptide antigens from Mtb suggests that future studies of immune responses should include a broader set of antigens than those in the IGRAs . This study also allowed quantitating the relative contributions of DRB1 , DRB3/4/5 , DP and DQ loci to restriction of functional T cell responses . We found that all four HLA class II loci contribute to restrict responses , even though the contribution of DP appeared less prominent . This parallels previous estimates of locus contribution to CD4 T cell responses in allergy and Mtb , which suggest predominant restriction by the DR loci , with lesser , but appreciable contributions by DQ and DP [9 , 10 , 35 , 42 , 51] . Our study provided a very detailed quantitative account of complexity of human T cell responses to Mtb infection in a natural setting . The majority of epitopes were found to be highly promiscuous , as suggested by previous reports published by us and others [10 , 35 , 52–55] . It should be noted that the majority of these previously reported epitopes were predicted for promiscuous HLA binding . Promiscuity can be explained by extensive overlap in the peptide binding repertoires between different HLA alleles [56–58] , and is a significant contributor to expanding complexity of human T cell responses . We further showed that the penetrance of the vast majority of response restricting HLA alleles was highly incomplete . Hence , the fact that a given donor expresses an allele that is known to restrict a response to a given epitope does not predict definite detection of a response . While the molecular basis of this phenomenon is unclear it has been proposed that expression of other HLA alleles , or development of other , more immunodominant responses may be responsible for varying shifts in immunodominance from individual to individual [19] . Our results are also relevant in terms of the different strategies currently being developed for immunoprofiling of immune responses in the context of vaccination or natural infection . Specifically , the dataset generated allowed us to calculate a quantitative estimate of how many different HLA class II tetramers would be required to cover Mtb-specific responses in this exemplary population . In the present study we determined HLA restrictions for 278 of the positive epitope/donor combinations out of a total of 778 ( 36% ) , which accounted for 47% of the total reactivity . Because the same epitope can be restricted by more than one HLA allele in the donor population , a total of 380 HLA/epitope combinations were possible . The best response coverage would thus require construction of 380 unique HLA class II tetramers , reaching 30% coverage of the total magnitude of response ( 63% coverage by HLA transfected cell lines × 47% of total magnitude covered by determined restrictions ) . Thus , if several hundred different HLA class II tetramers were made , these would cover only about 30% of the response and 70% of the response would remain undetected . Practical considerations would further compound this issue . Tetramer production has not been validated for all HLA class II allelic variants , with certain alleles being more technically challenging than others , and not all epitopes readily yield functional tetramers with some epitopes associated with poor yields and others with high non-specific staining . Despite significant advances in tetramer multiplexing and advances in the development of HLA class II tetramers [59–62] , a tetramer based approach may be better reserved to characterize examples of responses restricted by a particular HLA and a specific peptide , and thereby possibly unsuited to characterize responses in a whole population or in a large collection of antigens . A potential issue in characterizing responses using only one or few “prototypic” epitopes is that the available data suggests that the T cell phenotypes associated with different epitopes might be heterogenous and highly complex [63] . In addition , T cells recognizing “decoy” antigens , or T cells crossreactive with other pathogens have been described [64 , 65] , raising the issue that characterization of isolated “representative” epitopes might not be representative of the full breadth of responses . Pooling of many peptides into “megapools” of epitopes , by sequential lyophilization for antigen stimulation assays , such as ICS , may be a more practical approach for response characterization , especially if only small amounts of cells are available . Although our approach was intended for discovery and characterization of CD4 T cell epitopes , we found that CD8 responses can be detected with these peptide pools . This is not unexpected since CD8 T cell epitopes are contained within the same sequences as the CD4 T cell epitopes . Our results thus demonstrate that Mtb megapools encompassing promiscuously recognized peptides can be used effectively to phenotype Mtb-specific T cell responses . We expect these megapools to have wide applicability irrespective of ethnicity and geographical location since previous studies have shown recognition of many of the peptides in diverse human populations and even non-human primates [10 , 66 , 67] . Recent data utilizing a similar approach in the context of dengue virus-specific responses in endemic areas , EBV and CMV-specific , as well as Pertussis-specific T cell responses suggest that this approach will have general applicability ( manuscript in preparation ) . IGRA-negative individuals had no responses or much lower T cell response frequencies to the Mtb megapools than IGRA-positive individuals . This result was not surprising since the IGRA-negative donors were recruited from a setting where infant BCG vaccination is routine , while exposure to environmental mycobacteria is also likely . The low level reactivity of IGRA-negative individuals most likely reflects immunological sensitization by BCG and/or environmental mycobacteria [65] . Interestingly , TST-negative individuals had essentially no response to the Mtb megapools , indicating that the T cell response we can detect with this broad range of antigens reflects mycobacterial sensitization detected by TST . In vitro immunological assays that measure responses to the Mtb megapools may serve as a useful alternative to this test . Further studies will evaluate this in more detail . The results also have direct implications for our understanding of Mtb-specific T cell responses . Our study defined a large set of epitopes with defined HLA restrictions from broad range of Mtb antigens and report the response breadth and population coverage that can be attained with these epitopes . We propose that these well-defined epitopes are a valuable resource for immunological characterization of Mtb-specific T cell responses , either as individual or pooled peptides , or for construction of HLA class II tetramer reagents . Ongoing studies are investigating the chemokine receptor expression of the responding T cells in this cohort , which will be informative in further characterizing the Mtb-specific T cell responses . A recent study underlined that bi-allelic RORC loss-of-function mutations resulted in the absence of IL-17-producing T cells and defective IFNγ production by circulating γδ T cells and CD4+CCR6+CXCR3+ αβ T cells , and was associated with mycobacteriosis [26] . Our parallel measurement of IL-5 , IL-17 , IL-10 and IFNγ-expressing cells did not discover novel Mtb-specific epitope-reactive CD4 T cells that did not produce IFNγ . In fact , as expected for IGRA-positive individuals , the Mtb-specific response was highly polarized towards IFNγ production . Our data also confirmed the previous finding that the CXCR3+CCR6+ Th subset of Mtb-specific CD4 T cells produced IFNγ , IL-2 and/or TNFα , and not IL-17 in response to Mtb epitopes [8] . Our analysis of responses to TB vaccine and IGRA antigens show that different antigens are recognized with markedly different prevalence and magnitude of response . An association between immunogenicity and biological function of the antigenic target was apparent . Antigens in the cell wall and cell processes category were the most immunogenic , followed by PE/PPE proteins . These data are consistent with our previous report [10] , which suggested that certain functional and structural features dictate reactivity of TB antigens in Mtb-infected persons . In terms of implications for vaccine selection , and in agreement with a recent analysis of reactivity to 59 different TB antigens in geographical locations ranging from North , Central and South America , Europe , Africa and Asia [66] , the data demonstrates that certain antigens are associated with poor immunogenicity in the context of natural TB infection . The implications for incorporation of poorly immunogenic proteins into TB vaccines are not clear . It is possible that vaccination-induced T cell responses to such antigens may constitute so-called “unnatural” immunity , which is distinct from that primed by natural Mtb infection . It has been hypothesized that vaccine-induced protection over and above that provided by natural immunity may be achieved by induction of “unnatural” immune responses [68] . On the other hand , it could also be that the “unnatural” antigens are simply not visible to the immune system during infection and are unprotective . These questions will be answered after completion of the ongoing vaccine trials that encompass these antigens . In conclusion , this study provides an in-depth characterization of Mtb antigens and epitopes recognized , and the response-restricting HLA class II molecules , in a cohort of Mtb-infected donors from the TB-endemic Western Cape region of South Africa . The results provide a case study , comparing different strategies to globally characterize immune reactivity against an infectious agent in a human population .
Research conducted for this study was performed in accordance with approvals from the Human Research Ethics Committee of the University of Cape Town . All participants provided written informed consent prior to participation in the study . In the case of adolescents , they provided written informed assent and written informed consent was also provided by a parent or legal guardian . For T cell response screening we recruited 63 healthy adults with latent Mtb infection from the Worcester region of the Western Cape Province of South Africa . LTBI was confirmed by a positive IGRA ( QuantiFERON-TB Gold In-Tube , Cellestis ) ( Table 1 ) , which is the most robust assay for identification of individuals with LTBI in a BCG vaccinated population [69] . We previously reported good agreement between tuberculin skin test ( TST ) and IGRA in this study population , and no significant differences in clinical , epidemiological or immunological attributes between IGRA positive and TST positive persons have been noted [70] . We therefore did not consider TST when defining LTBI . All participants tested negative for HIV infection . All participants provided written informed consent for participation in the study . Venous blood was collected in heparin-containing 450 ml blood bags . For the “validation cohort” we retrieved cryopreserved PBMC from 60 IGRA-positive , healthy adolescents previously enrolled into the adolescent cohort study ( South African Tuberculosis Vaccine Initiative , University of Cape Town ) [71] , also performed in the Worcester region of South Africa ( Table 1 ) . Adolescents provided written informed assent and written informed consent was also provided by a parent or legal guardian . HIV testing of these healthy adolescents was not done . However , since the prevalence of HIV seropositivity in TB cases in the adolescent cohort study was less than 2% [72] , we reasoned that the HIV prevalence amongst healthy adolescents would be negligible . For the negative control cohort we retrieved cryopreserved PBMC from 17 IGRA-negative , healthy adolescents ( Table 1 ) previously enrolled in the adolescent cohort study as described above ( South African Tuberculosis Vaccine Initiative , University of Cape Town ) . Research conducted on all three cohorts above was performed with approvals from the Human Research Ethics Committee of the University of Cape Town . Peptides were synthesized as crude material on a small ( 1 mg ) scale by A and A ( San Diego ) . Peptides representing the vaccine and IGRA antigens ( Rv3874; CFP10 and Rv3875; ESAT-6 ) were 15-mers overlapping by 10 amino acids spanning each entire protein ( Table 2 ) . Previously described epitopes were from the frequently recognized antigens reported by Arlehamn et al . [10] , as well as additional frequently recognized epitopes described in ex vivo experiments and available in the IEDB ( www . iedb . org ) [35 , 39–41 , 49] . S5 Table delineates the peptides in each megapool . Peripheral blood mononuclear cells ( PBMC ) were purified from whole blood either using CPT tubes ( BD ) , for the adolescents , or layered onto Ficoll ( for adults ) using 50ml Leukosep tubes ( Greiner ) by density-gradient centrifugation , according to the manufacturer’s instructions . Cells were cryopreserved in liquid nitrogen suspended in FBS ( company ) containing 10% ( vol/vol ) DMSO . The cryopreserved cells were shipped to La Jolla Institute for Allergy and Immunology for analysis . PBMC were stimulated at 2×105 cells/well in triplicate with peptide pools ( 5 μg/ml/peptide ) , peptides ( 10 μg/ml ) , megapools ( 2 μg/ml/peptide ) , PHA ( 10μg/ml ) or medium containing 0 . 25% DMSO ( percent DMSO in the pools , as a control ) in 96-well plates ( Immobilion-P; Millipore ) coated with anti-cytokine antibody . For single cytokine ELISPOT 5μg/ml anti-IFNγ ( 1-D1K; Mabtech ) was used . For dual ELISPOT ( IFNγ/IL-5 and IL-10/IL-17 ) 10μg/ml each of anti-IFNγ ( 1-D1K ) , anti-IL-5 ( TRFK5 ) , anti-IL-10 ( 9D7 ) , and anti-IL-17A ( MT44 . 6; all from Mabtech ) were used . After 20h incubation at 37°C , wells were washed with PBS/0 . 05% Tween 20 and incubated with biotinylated anti-IFNγ ( 7-B6-1; single cytokine ELISPOT; Mabtech ) for 2h . For dual ELISPOT , anti-IFNγ-HRP ( 7-B6-1-HRP ) , biotinylated anti-IL-5 ( 5A10 ) , anti-IL-10-ALP ( 12G8 ) , and biotinylated anti-IL-17 ( MT504; all from Mabtech ) were used . Spots were developed using Vectastain APC peroxidase ( Vector Laboratories ) and 3-amino-9-ethylcarbazole ( Sigma-Aldrich ) for single ELISPOT . For dual ELISPOT biotinylated antibodies were developed exactly like the single ELISPOT; HRP and ALP conjugated antibodies were developed by alkaline phosphatase complex and then visualized by applying the Vector Blue Alkaline Phosphatase Substrate Kit III ( both from Vector Laboratories ) . Spots were counted by computer-assisted image analysis ( KS-ELISPOT reader; Zeiss ) . Responses were considered positive if the net spot-forming cells ( SFC ) per 106 PBMC were ≥20 , the stimulation index ≥2 , and p≤0 . 05 ( Student’s t-test , mean of triplicate values of the response against relevant pools or peptides vs . the DMSO control ) . All samples had a viability >75% , as determined by trypan blue , and reactivity to PHA >400 SFC/106 cells . The total magnitude of response per participant was defined as the sum of SFCs per 106 PBMC detected against separate epitopes or peptide pools . The total magnitude of response across participants was defined as the sum of responses per individual . Four-digit HLA typing was performed as recently described [45] . Genomic DNA was isolated from PBMC using standard techniques ( REPLI-g; Qiagen ) . Amplicons for HLA class I and class II genes were generated using PCR and locus-specific primers . Amplicons of the correct size were purified using Zymo DNA Clean-up Kit , according to the manufacturer’s instructions . Sequencing libraries were prepared using Nextera XT reagents ( Illumina ) , according to manufacturer’s instructions . The libraries were purified using AMPure XP ( Beckman Coulter ) with a ratio of 0 . 5:1 beads to DNA ( vol/vol ) . The libraries were pooled in equimolar amounts and loaded at 5 . 4pM on one MiSeq flowcell with 1% phiX spiked in ( MiSeq Reagent Kit v3 ) . Paired-end sequencing was performed with 300 cycles in each direction . HLA typing calls were made using HLATyphon ( https://github . com/LJI-Bioinformatics/HLATyphon ) . For DP , DQ , DRB1 and DRB3/4/5 frequencies we considered only the beta chain frequencies , given that the DRA chain is largely monomorphic and that differences in DPA are not thought to significantly influence peptide binding [73] . Single HLA transfected RM3 ( derived from human B lymphocyte cell line Raji ) or DAP . 3 ( L cell fibroblast ) were maintained as previously described [44] ( all cell lines were from La Jolla Institute for Allergy and Immunology ) . The cell lines were harvested and viability ( all >75% ) was determined using Trypan Blue . Each cell line at 2x105 cells/well was pulsed with 10μg/ml individual peptide for 1h at 37°C , followed by four washes in RPMI . PBMC at 2x105/well were stimulated in triplicate with peptide pulsed cell line ( 5x104 cells/well ) , cell line alone ( as a control ) , peptides ( 10μg/ml ) , PHA ( 10μg/ml ) or medium containing 0 . 25% DMSO ( percent DMSO in the peptides , as a control ) in 96-well plates ( Immobilion-P; Millipore ) coated with anti-IFNγ antibody as described above for single cytokine ELISPOT . Criteria for positive responses were as described for ELISPOT assays above . Individual peptides were resuspended in DMSO and equal amounts of each peptide were pooled to construct peptide pools . Each peptide pool was placed in an individual lyophilizing flask ( VirTis ) and lyophilized on VirTis Freezemobile 35 EL for 24 hours . The semisolid product was re-suspended in water , frozen and then lyophilized again . This process was repeated until only solid product remained after lyophilization . The resulting lyophilized peptide pool was re-suspended in DMSO at a higher concentration per peptide ( 0 . 7-2mg/ml per peptide depending on number of peptides in the pool ) than before lyophilization , to minimize DMSO concentrations in the assays . PBMC at 1x106 per condition were stimulated with peptide pools ( 2μg/ml ) , or heat killed H37Rv ( 5x105 CFU/million PBMC ) , together with anti-CD28 ( 1μg/ml ) and anti-CD49d ( 1μg/ml ) for 5h in complete RPMI medium at 37°C with 5% CO2 . After 5h , 2 . 5μg/ml each of BFA and monensin was added for an additional 7h at 37°C . Unstimulated PBMCs were used to assess nonspecific/background cytokine production and PHA stimulation at 5μg/ml was used as a positive control . After a total of 12h , cells were harvested and stained for cell surface antigens CD4 ( anti-CD4-APCEf780 , RPA-T4 , eBioscience ) , CD3 ( anti-CD3-AF700 , UCHT1 , BD Pharmingen ) , CD8 ( anti-CD8-BV650 , RPA-T8 , Biolegend ) , CD14 ( anti-CD14-V500 , M5E2 , BD Pharmingen ) , CD19 ( anti-CD19-V500 , HIB19 , BD Pharmingen ) , and fixable viability dye eFluor 506 ( eBiosciences ) . After washing , cells were fixed using 4% paraformaldehyde and permeabilized using saponin buffer . Cells were stained for IFNγ ( anti-IFNγ-FITC , AS . B3 , eBioscience ) , IL-2 ( anti-IL-2-PECy7 , MQ1-17H12 , eBioscience ) , TNFα ( anti-TNFα-eF450 , MAb11 , eBioscience ) , and IL-22 ( anti-IL-22-PerCPeFluor710 , 22URTI , eBioscience ) in saponin buffer containing 10% FBS . To investigate the production of IFNγ , IL-10 , IL-4 and IL-17 , as well as the memory phenotype , PBMC at 1x106 per condition were stimulated with peptide pools ( 2μg/ml ) , together with anti-CD28 ( 1μg/ml ) and anti-CD49d ( 1μg/ml ) for 2h in complete RPMI medium at 37°C with 5% CO2 . After 2h , 2 . 5μg/ml each of BFA and monensin was added for an additional 4h at 37°C . Unstimulated PBMCs were used to assess nonspecific/background cytokine production and PMA/Ionomycin stimulation at 1μg/ml was used as a positive control . After a total of 6h , cells were harvested and stained for cell surface antigens CD4 ( anti-CD4-APCEf780 , RPA-T4 , eBioscience ) , CD3 ( anti-CD3-AF700 , UCHT1 , BD Pharmingen ) , CD8 ( anti-CD8-V500 , RPA-T8 , BD Pharmingen ) , CD14 ( anti-CD14-V500 , M5E2 , BD Pharmingen ) , CD19 ( anti-CD19-V500 , HIB19 , BD Pharmingen ) , CD45RA ( anti-CD45RA-eFluor 450 , HI100 , eBioscience ) , CCR7 ( anti-CCR7-PerCPCy5 . 5 , G043H7 , BioLegend ) and fixable viability dye eFluor 506 ( eBiosciences ) . After washing , cells were fixed using 4% paraformaldehyde and permeabilized using saponin buffer . Cells were stained for IFNγ ( anti-IFNγ-FITC , AS . B3 , eBioscience ) , IL-4 ( anti-IL-2-BV605 , MP4-25D2 , BioLegend ) , IL-10 ( anti-IL-10-APC , JES3-19F1 , BioLegend ) , and IL-17A ( anti-IL-17A-PECy7 , eBio64DEC17 , eBioscience ) in saponin buffer containing 10% FBS . Samples were acquired on a BD LSR II flow cytometer . Frequencies of CD4+ or CD8+ T cells responding to each peptide pool were quantified by determining the total number of gated CD4+ or CD8+ and cytokine+ cells and background values subtracted ( as determined from the medium alone control ) using FlowJo X Software . Combinations of cytokine producing cells were determined using Boolean gating . | Human pathogen-specific immune responses are tremendously complex and the techniques to study them ever expanding . There is an urgent need for a quantitative analysis and better understanding of pathogen-specific immune responses . Mycobacterium tuberculosis ( Mtb ) is one of the leading causes of mortality due to an infectious agent worldwide . Here , we were able to quantify the Mtb-specific response in healthy individuals with Mtb infection from South Africa . The response is highly diverse and 66 epitopes are required to capture 80% of the total reactivity . Our study also show that the majority of the identified epitopes are restricted by multiple HLA alleles . Thus , technical advances are required to capture and characterize the complete pathogen-specific response . This study demonstrates further that the approach combining identified epitopes into “megapools” allows capturing a large fraction of the total reactivity . This suggests that this technique is generally applicable to the characterization of immunity to other complex pathogens . Together , our data provide for the first time a quantitative analysis of the complex pathogen-specific T cell response and provide a new understanding of human infections in a natural infection setting . | [
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] | 2016 | A Quantitative Analysis of Complexity of Human Pathogen-Specific CD4 T Cell Responses in Healthy M. tuberculosis Infected South Africans |
The host protein TRIM5α inhibits retroviral infection at an early post-penetration stage by targeting the incoming viral capsid . While the detailed mechanism of restriction remains unclear , recent studies have implicated the activity of cellular proteasomes in the restriction of retroviral reverse transcription imposed by TRIM5α . Here , we show that TRIM5α is rapidly degraded upon encounter of a restriction-susceptible retroviral core . Inoculation of TRIM5α-expressing human 293T cells with a saturating level of HIV-1 particles resulted in accelerated degradation of the HIV-1-restrictive rhesus macaque TRIM5α protein but not the nonrestrictive human TRIM5α protein . Exposure of cells to HIV-1 also destabilized the owl monkey restriction factor TRIMCyp; this was prevented by addition of the inhibitor cyclosporin A and was not observed with an HIV-1 virus containing a mutation in the capsid protein that relieves restriction by TRIMCyp IVHIV . Likewise , human TRIM5α was rapidly degraded upon encounter of the restriction-sensitive N-tropic murine leukemia virus ( N-MLV ) but not the unrestricted B-MLV . Pretreatment of cells with proteasome inhibitors prevented the HIV-1-induced loss of both rhesus macaque TRIM5α and TRIMCyp proteins . We also detected degradation of endogenous TRIM5α in rhesus macaque cells following HIV-1 infection . We conclude that engagement of a restriction-sensitive retrovirus core results in TRIM5α degradation by a proteasome-dependent mechanism .
Retroviruses exhibit a restricted host range due to the requirement for specific interactions between viral and host proteins to complete the viral life cycle . Also limiting retroviral tropism are several recently identified intracellular antiviral factors ( [1]–[5] ) ; reviewed in [6]–[10] ) . The prototypical restriction activity , Fv1 , was first detected in the 1970s as differential susceptibility of inbred mice strains to the Friend leukemia virus [11]–[13] . Fv1 blocks infection of murine leukemia viruses ( MLV ) at a stage following fusion but prior to integration [14] , [15] . The block to infection can be overcome at high multiplicities of infection ( m . o . i . ) or by pretreatment of target cells with non-infectious virus like particles ( VLPs ) [11] , [16] . Susceptibility to Fv1 restriction is determined by the sequence of the viral capsid protein ( CA ) [17]–[19] . The gene encoding Fv1 was identified in 1996 by positional cloning [1]; yet the molecular mechanism by which Fv1 inhibits MLV infection remains poorly defined . Recent investigations have identified additional restriction activities present in human and simian cells that govern the tropism of lentiviruses , including human and simian immunodeficiency viruses ( HIV and SIV ) [20]–[25] . Like Fv1 , these restrictions target the incoming viral capsid [23] , [25]–[27] . One factor , TRIM5α , is responsible for post-entry restriction of HIV-1 in many simian cell lines [3] , [28]–[31] . Expression of the rhesus macaque TRIM5α protein ( TRIM5αrh ) in human cells renders them highly restrictive to infection by HIV-1 [3] . Unlike Fv1 , TRIM5α acts at a stage prior to completion of reverse transcription [3] , [20] , [23] , [24] . The human genome encodes a TRIM5α protein ( TRIM5αhu ) that restricts multiple retroviruses including N-tropic MLV ( N-MLV ) , feline immunodeficiency virus ( FIV ) , and equine infectious anemia virus ( EIAV ) but does not efficiently restrict HIV-1 [29] , [30] , [32]–[37] . TRIM5α cDNAs have now been cloned from multiple primate species; these differentially restrict infection by HIV-1 , HIV-2 , and SIV [28] , [31] . Shortly after the identification of TRIM5α , a second HIV-1 restriction factor was identified in owl monkeys [4] , [5] . This protein , TRIMCyp , is the apparent result of a LINE1-mediated retrotransposition event in which the cyclophilin A ( CypA ) mRNA was inserted into the TRIM5 locus resulting in a functional fusion protein [4] . TRIMCyp potently inhibits HIV-1 infection by interacting with an exposed loop on the surface of the CA via the CypA domain . The discovery of TRIMCyp provided a simple explanation for the ability of cyclosporin A ( CsA ) , which inhibits CypA binding to CA , to render owl monkey cells permissive to HIV-1 infection [38] . Mutations in the CypA binding loop that result in a failure to bind CypA also result in a loss of restriction by TRIMCyp [4] , [5] . More recently , novel TRIM5-CypA proteins have also been identified in other primate species [39]–[42] . TRIM5α and TRIMCyp are members of the tripartite motif family of proteins , which encode RING , B-Box , and coiled-coil ( RBCC ) domains [43] . TRIM5α is the longest of the three isoforms ( α , γ , and δ ) generated from the TRIM5 locus by alternative splicing of the primary transcript . While all three TRIM5 isoforms contain identical RBCC domains , the α-transcript also encodes the B30 . 2/SPRY domain required for recognition of the incoming viral capsid and restriction specificity [29] , [30] , [33] , [34] , [36] , [44]–[46] . The coiled-coil domain promotes the multimerization of TRIM5α molecules that is required for efficient restriction [44] , [47] , [48] . While the precise function of the B-Box domain is unclear , deletion of this region results in total loss of restriction potential thus indicating its importance [44] , [49] . The RING domain of TRIM5α is also required for full restriction activity , as mutants that lack this domain or in which proper folding is impaired are severely impaired for restriction and have altered cellular localization [3] , [44] , [49] . Substitution of RING domains from other human TRIM proteins results in changes in both the timing of restriction ( i . e . pre- vs . post-reverse transcription ) and the intracellular localization of the restriction factor [37] , [50]–[52] . RING domains are commonly associated with ubiquitin ligase ( E3 ) activity facilitating specific transfer of ubiquitin from various ubiquitin-conjugating ( E2 ) proteins to substrates ( reviewed in [53] , [54] ) . Polyubiquitylation of proteins commonly targets them for intracellular degradation by proteasomes . TRIM5α can be ubiquitylated in cells [55] , but a role for this modification in TRIM5α stability or restriction has not been established . The δ isoform of TRIM5 , which encodes an identical RING domain to TRIM5α , exhibits E3 activity in vitro and mutation of the RING domain abolishes this activity [56] . The presence of a RING domain on TRIM5α suggested that the restriction factor might function by transferring ubiquitin to a core-associated viral protein , thus targeting it for proteasomal degradation . However , such a modification has not been detected , and the magnitude of restriction imposed by TRIM5α was not altered in cells in which the ubiquitination pathway was disrupted [57] . Nonetheless , recent studies have shown that proteasome inhibitors relieve the TRIM5α-dependent inhibition of reverse transcription , yet a block to HIV-1 nuclear entry remains [58] , [59] . Based on these findings implicating the proteasome in TRIM5α-dependent retroviral restriction , we hypothesized that restriction by TRIM5α leads to proteasomal degradation of a TRIM5α-viral protein complex . Here we show that inoculation of TRIM5α-expressing cells with a restricted retrovirus results in accelerated degradation of TRIM5α itself . Destabilization of TRIM5α was tightly correlated with the ability of the restriction factor to block infection by the incoming virus . Proteasome inhibitors prevented HIV-1-induced degradation of TRIM5αrh when added to cells prior to virus inoculation . These data suggest a functional link between proteasomal degradation of TRIM5α and the ability of TRIM5α to restrict an incoming retrovirus .
We hypothesized that TRIM5α itself might be degraded as a consequence of the post-entry restriction process . To test this , TRIM5αrh-expressing 293T cells were cultured in the presence of cycloheximide to arrest protein synthesis and then challenged with VSV-G-pseudotyped HIV-1 particles . At various times post-infection , cells were harvested for analysis of TRIM5α levels by quantitative immunoblotting . In control cells not exposed to virus , the TRIM5α level declined at a slow rate , eventually leveling off to 55% of the original level after 4 hours ( Figure 1A ) . By contrast , inoculation with HIV-1 induced a more rapid decrease in the TRIM5α level resulting in 85% loss after 4 hours . Analysis of data from 4 experiments indicated that the decay of TRIM5α was significantly faster in the HIV-1-inoculated cultures relative to the control ( Figure 1B ) . The stability of TRIM5α in our cells differs in terms of time as compared to previously published reports using Hela cells [55] . In additional studies we observed a similar destabilizing effect of HIV-1 exposure on TRIM5αrh in HeLa cells ( data not shown ) . Exposure of target cells to saturating levels of virus or VLPs can overcome restriction by TRIM5α . To determine whether the decay of TRIM5αrh was related to saturation of restriction , we inoculated TRIM5αrh–expressing cells with various doses of a GFP-encoding virus in the presence of cycloheximide for a fixed period of time and harvested the cells to quantify TRIM5α levels . To probe the relationship between saturation of restriction and TRIM5α degradation , a portion of the harvested cells were replated and cultured for 48 hours , and the extent of infection determined by flow cytometric analysis of GFP expression . The results showed that the ability to detect degradation of TRIM5αrh was strongly dependent on the dose of virus used ( Figure 1C ) . Furthermore , the TRIM5α level following inoculation was inversely related to the overall extent of infection ( Figure 1D ) . These results indicate that HIV-1-induced degradation of TRIM5α is correlated with saturation of restriction , likely due to a requirement to engage most of the restriction factor to detect the loss of the protein . Human TRIM5α does not efficiently restrict HIV-1 infection . To further probe the link between restriction and TRIM5α destabilization , we analyzed the stability of the human TRIM5α protein following challenge of cells with HIV-1 . As previously shown in Figure 1 , HIV-1 challenge of TRIM5αrh-expressing 293T cells resulted in a more rapid loss of the protein vs . mock-infected cells ( Figure 2A and B ) . TRIM5αhu was intrinsically less stable than TRIM5αrh , as indicated by its more rapid decay in the mock-infected cultures ( Figure 2B and C ) . However , inoculation with HIV-1 did not result in further destabilization of TRIM5αhu , indicating that the HIV-1-induced degradation of TRIM5αrh is not a nonspecific cellular response to the viral challenge . These results suggest that the loss of TRIM5αrh depends on its ability to recognize the HIV-1 core . The owl monkey restriction factor TRIMCyp restricts HIV-1 by binding to an exposed loop on the surface of CA . Restriction can be prevented by addition of CsA or amino acid substitutions in CA that reduce CypA binding . We therefore asked whether TRIMCyp would also be destabilized following encounter of HIV-1 . 293T cells expressing TRIMCyp were treated with cycloheximide and then challenged with VSV-G pseudotyped HIV-1 particles . As a control , parallel cultures were inoculated in the presence of a CsA concentration known to abolish TRIMCyp restriction of HIV-1 . In the control mock-inoculated cells , TRIMCyp was stable in the cells during the six-hour time course ( Figure 3A ) . Challenge with HIV-1 resulted in accelerated loss of TRIMCyp . In the cultures containing CsA , the HIV-1-induced loss of TRIMCyp was markedly reduced ( Figure 3B ) . Next we asked whether the HIV-1-induced degradation of TRIMCyp is correlated with the specificity of restriction . HIV-1 containing the G89V mutation in the CypA binding loop of CA is incapable of binding CypA and is also not restricted by TRIMCyp . However , this viral mutant is susceptible to TRIM5αrh restriction . Parallel cultures of 293T cells expressing either TRIMCyp or TRIM5αrh were treated with cycloheximide and then challenged with equivalent quantities of VSV-G pseudotyped HIV-GFP particles or the G89V CA mutant virus . As seen in Figure 3C , exposure to wild type HIV-1 induced accelerated loss of both TRIMCyp and TRIM5αrh . By contrast , exposure to the G89V mutant particles resulted in loss of TRIM5αrh but not TRIMCyp . These results indicate that exposure of cells to HIV-1 results in destabilization of TRIMCyp by a mechanism requiring recognition of the incoming HIV-1 core by the restriction factor . TRIM5αhu cannot restrict HIV-1 or B-tropic MLV but potently restricts N-MLV . To further test the link between TRIM5α destabilization and retrovirus restriction , we challenged 293T cells stably expressing TRIM5αhu with N- and B-tropic MLV viruses and measured TRIM5α levels following infection . The GFP-transducing N- and B-tropic MLV stocks were first titrated on nonrestrictive CrFK cells ( Figure S2 , detailed in Text S1 ) then normalized to ensure equivalent dosing . Mock-treated cells lost TRIM5αhu at a slow rate ( t1/2∼2 . 5 h; Figure 4A ) . Challenge with B-MLV did not significantly affect the rate of TRIM5αhu decay ( Figure 4A ) . By contrast , cells challenged with an equivalent quantity of N-MLV showed accelerated loss of TRIM5αhu ( t1/2<1 h ) ( Figure 4A and 4B ) . The relative band intensities of the TRIM5α levels for this experiment were calculated and are represented in the graph in Figure 4B . These results , together with the TRIM5α and TRIMCyp data , establish a strong correlation between virus-induced TRIM5α destabilization and the specificity of restriction . TRIM5α proteins from different primates differ in their ability to restrict specific lentiviruses . For example , tamarin monkey TRIM5α ( TRIM5αtam ) restricts SIVmac but not HIV-1 , while spider monkey TRIM5α ( TRIM5αsp ) restricts both viruses . To further test the correlation between virus-induced loss of TRIM5α and antiviral specificity , we stably expressed the TRIM5αtam and TRIM5αsp proteins in 293T cells and challenged them with equivalent titers of VSV-pseudotyped HIV-1 and SIVmac239 GFP reporter viruses ( as determined by titration on permissive CrFK cells ) . The cell lines were found to restrict the respective viruses by at least ten-fold ( data not shown ) . Immunoblot analysis of post-nuclear lysates revealed that TRIM5αrh was specifically destabilized when challenged with HIV-1 but not upon SIVmac challenge ( Figure 5A ) . By contrast , the SIV-restrictive TRIM5αtam was destabilized only in response to SIVmac challenge ( Figure 5A ) . TRIM5αsp , which restricts both viruses , was degraded in response to challenge with either virus ( Figure 5A and B ) . These results further strengthen the correlation between the specificity of retrovirus restriction and virus-induced destabilization of TRIM5α . A major mechanism for cellular protein degradation is via the 26S proteasome . Previous studies have shown that the turnover of TRIM5α is dependent on cellular proteasome activity . Furthermore , inhibition of proteasome activity overcomes the early block to reverse transcription imposed by TRIM5α . We asked whether HIV-1-induced destabilization of TRIM5αrh is dependent on proteasome activity . As previously reported [55] , treatment of cells with the proteasome inhibitor MG132 resulted in an accumulation of TRIM5α protein ( Figure 1 , 0 H . p . i . ) . MG132 also prevented the HIV-1-induced destabilization of TRIM5αrh ( Figure 6A and B ) . Additional studies revealed that epoxomicin , a more specific proteasome inhibitor , also blocked the HIV-1-induced degradation of TRIM5αrh ( data not shown ) . By contrast , infection by HIV-1 in the presence of the S-cathepsin inhibitor E64 did not prevent HIV-1-induced TRIM5αrh degradation ( data not shown ) , suggesting that endosomal proteases are not responsible for TRIM5αrh destabilization . We conclude that the virus-induced degradation of TRIM5α is dependent on cellular proteasome activity . To determine whether HIV-1-induced destabilization of TRIMCyp depends on proteasome activity , we challenged TRIMCyp-expressing 293T cells with either restricted HIV-GFP or unrestricted HIV . G89V-GFP in the presence or absence of MG132 . As shown in Figure 6C , MG132 prevented the HIV-1-induced loss of TRIMCyp . Infection with the unrestricted G89V virus did not alter TRIMCyp stability , while addition of MG132 stabilized the restriction factor . All of the previous experiments studying TRIM5α stability were conducted in transduced 293T cell lines in which TRIM5α was detected by virtue of a hemagglutinin epitope tag . In this setting , it was necessary to treat the cells with cycloheximide to detect virus-induced degradation of the restriction factor , potentially leading to artifacts due to general inhibition of protein synthesis . Virus titration experiments demonstrated markedly greater restriction in the transduced cells vs . rhesus macaque FRhK-4 cell line , indicating that the 293T cells overexpress TRIM5αrh ( our unpublished observations ) . Furthermore , while cycloheximide treatment had only a minor effect on restriction in FRhK-4 cells , the drug markedly reduced restriction in 293T cells ( Figure S4 ) . To probe the physiological relevance of our observations made in 293T cells , we sought a means of detecting endogenous TRIM5α protein in rhesus macaque cells . Using a monoclonal antibody against native TRIM5α for immunoblotting , we detected a band that was consistent in terms of molecular weight with TRIM5αrh that was also absent in cells lacking TRIM5αrh ( data not shown ) . To confirm that the band is TRIM5α , we transfected FRhK-4 cells with either a TRIM5αrh -specific siRNA duplex or a non-targeting control siRNA duplex and quantified the intensity of this band by immunoblotting . As shown in Figure 7A and B , transfection with TRIM5αrh-specific siRNA resulted in a 72% decrease in intensity of the relevant band vs . FRhK-4 cells treated with the non-targeting control . Cells treated with the TRIM5αrh-specific also exhibited a tenfold increase in permissiveness to infection with HIV-1 ( data not shown ) . HIV-1 infection of FRhK-4 cells was not altered by treatment with the non-targeting siRNA control . As expected , treatment with either siRNA duplex did not affect permissiveness to SIV infection ( data not shown ) . These results indicated that the monoclonal antibody is capable of detecting endogenous TRIM5αrh in FRhK-4 cells . They further demonstrated that the transduced 293T cells express a 3 . 3 fold higher level of TRIM5α than FRhK-4 cells ( Figure 7B ) . We next sought to determine if endogenous TRIM5αrh was destabilized by HIV-1 in rhesus macaque cells . FRhK-4 cultures were inoculated with HIV-1 in the presence or absence of cycloheximide and the stability of TRIM5αrh in response to infection was analyzed by immunoblotting . Initial experiments showed no effect of cycloheximide treatment on TRIM5αrh levels in HIV-1-exposed cells ( data not shown ) ; therefore the drug was removed in all subsequent experiments . We observed that TRIM5αrh levels were stable in FRhK-4 cells over the 4 hour period ( Figure 7C and D ) . Infection with HIV-1 resulted in accelerated decay of endogenous TRIM5αrh in rhesus macaque cells without any requirement of inhibition of protein synthesis . We next sought to determine if the loss of TRIM5αrh was specifically due to restriction or was a non-specific effect resulting from viral infection . In the absence of cycloheximide we infected FRhK-4 cells with equivalent titers of HIV-1 or SIVmac239 GFP reporter viruses . As seen in Figure 8A and B , infection with HIV-1 resulted in a potent loss of TRIM5αrh while infection with SIV resulted in only a slight loss of TRIM5αrh as compared to the control cells . We conclude that infection by HIV-1 results in a rapid loss of TRIM5αrh in target cells and that this loss is directly related to the ability of TRIM5αrh to restrict infection by the incoming virus . We sought to determine if inhibition of proteasome function would restore TRIM5αrh stability in rhesus macaque cells . FRhK-4 cells were exposed to HIV-1 in the presence or absence of MG132 for a period of four hours , and the levels of TRIM5αrh were measured by immunoblotting . As can be seen in Figure 8C and D , MG132 stabilized TRIM5αrh in HIV-1-exposed cells . Flow cytometry analysis of GFP signal in a small subset of the infected cells showed no difference in infection levels resulting from inhibition of proteasome function , which is consistent with previously published results . These results indicate that HIV-1-induced destabilization of TRIM5αrh in rhesus macaque cells requires proteasome activity . They further suggest that the results we observed with TRIM5α-transduced 293T cells are unlikely to be an artifact of cycloheximide treatment .
While it is well established that TRIM5α limits the host range of many retroviruses , the precise mechanism of restriction remains undefined . TRIM5α can specifically associate with assemblies of HIV-1 CA-NC protein in vitro , and genetic evidence indicates that TRIM5α and TRIMCyp require an intact or semiintact viral capsid for binding [60] , [61] . However , the detailed molecular consequences of the binding interaction to the viral core remain poorly defined . Two lines of evidence have implicated the ubiquitin-proteasome system in restriction . First , the δ isoform of TRIM5 , which has a RING domain identical to that of TRIM5α , exhibits E3 activity in vitro [56] . Deletion or mutation of the RING domain in TRIM5α results in significant loss of restriction efficacy [44] , [49] . TRIM5α is ubiquitinated in cells , although a role of this modification in retrovirus restriction has not been established [55] . Second , inhibition of proteasome activity alters the stage at which TRIM5α-mediated restriction occurs [58] , [59] . The latter observation led us to hypothesize that the proteasome may participate in restriction by degrading a complex of TRIM5α with one or more incoming viral proteins . To test this , we asked whether exposure of cells to HIV-1 alters the stability of TRIM5αrh . We observed that inoculation with HIV-1 results in an accelerated turnover of the restriction factor . Similar effects were observed in both 293T and HeLa cells ( data not shown ) , suggesting that TRIM5α destabilization is not specific to a unique cell type . HIV-1 challenge resulted in destabilization of TRIM5αrh but not TRIM5αhu . Likewise , TRIM5αhu was destabilized by inoculation of cells with restriction-sensitive N-MLV particles but not by unrestricted B-MLV . Similar results were seen in cells expressing the HIV-1-specific restriction factor TRIMCyp . Treatment of target cells with CsA , which blocks TRIMCyp restriction of HIV-1 , or infection with virus containing mutations that prevent CypA binding [4] , [5] , [38] , did not affect TRIMCyp stability . Specific loss of TRIM5α from cells expressing different primate alleles of the protein also correlated very well with the ability of those alleles to restrict HIV or SIV . The HIV-1-induced destabilization of TRIM5αrh and TRIMCyp was prevented by inhibition of cellular proteasome activity . Destabilization of TRIM5αrh by HIV-1 was also observed in a primate derived cell line without the need of cycloheximide to inhibit protein synthesis . This destabilization was specific for the restricted HIV-1 and was not observed in cells infected with an unrestricted virus . Inhibition of proteasome function restored TRIM5αrh stability in response to infection by HIV-1 in the rhesus macaque cells . We conclude that TRIM5-related restriction factors are targeted for degradation by a proteasome-dependent mechanism following encounter of a restriction-sensitive retroviral core . TRIM5α forms heterogenous structures in cells referred to as cytoplasmic bodies ( CBs ) . While the role of CBs in restriction is unclear , TRIM5α protein in these structures rapidly exchanges with soluble TRIM5α , indicating that the protein is highly dynamic within cells [62] . We observed that most of the cellular TRIM5α can be degraded in response to exposure to a restriction-sensitive retrovirus , which implies that a majority of cellular TRIM5α molecules can engage incoming viral cores . If the CB-associated TRIM5α is inaccessible to incoming virus , our observation that a restricted virus can induce degradation of the majority of the TRIM5α molecules suggests that this protein rapidly redistributes to a compartment accessible to incoming virus . TRIM5α and TRIMCyp are subject to proteasome-dependent turnover under steady-state conditions , yet its rapid turnover is not a prerequisite for restriction activity [55] , [63] . Accordingly , proteasome inhibitors do not overcome restriction ( [57]; Figure S5 ) . Nonetheless , the effect of virus exposure on TRIM5α stability had heretofore not been reported . While alterations of specific individual portions of TRIM5α may alter its intrinsic stability , our results indicate that TRIM5α encounter with a restricted core results in degradation of the restriction factor by a proteasome-dependent mechanism . Retrovirus uncoating is a poorly characterized process , but can be defined as the disassembly of the viral capsid following penetration of the viral core into the target cell cytoplasm . Studies of HIV-1 CA mutants indicate that the stability of the viral capsid is properly balanced for productive uncoating in target cells: mutants with unstable capsids are impaired for viral DNA synthesis , suggesting that premature uncoating is detrimental to reverse transcription [64] . Thus a plausible mechanism for restriction is that binding of TRIM5α to the viral capsid inhibits infection directly by physically triggering premature uncoating in target cells [65] , [66] . In this model , TRIM5α , perhaps with one or more cofactors , promotes the physical decapsidation of the virus core independently of proteolysis . Consistent with this view are studies demonstrating that TRIM5α restriction is associated with decreased recovery of sedimentable CA protein in lysates of acutely-infected cells [65] , [66] . However , these studies fell short of demonstrating that the sedimentable CA protein was associated with intact viral cores . Furthermore , a recent study reported that treatment of cells with proteasome inhibitors prevented TRIM5α-dependent loss of particulate CA protein [67] , indicating the potential involvement of proteasome activity in TRIM5α-induced virus uncoating . Other studies further implicate the activity of the proteasome in TRIM5α-dependent restriction . Inhibition of proteasome activity rescues HIV-1 reverse transcription in TRIM5α-expressing cells , revealing a downstream block to nuclear import mediated by the restriction factor [58] , [59] . Engagement of the viral capsid by TRIM5α may lead to proteasomal degradation of a TRIM5α-CA complex , resulting in functional decapsidation of the viral core and a premature uncoating phenotype . Consistent with this model , TRIM5α restriction has been associated with decreased intracellular accumulation of HIV-1 CA [68] . In addition , a recent study of MLV particle-mediated RNA cellular transfer reported reduced accumulation of viral CA protein in cells in a manner that was correlated with restriction by TRIM5α , and this effect was reversed by proteasome inhibition [69] . Unfortunately , our own efforts to detect an effect of TRIM5α on the stability of the incoming HIV-1 CA have thus far yielded negative results; thus we are reluctant to conclude at this stage that a specific component of the viral core is degraded as a complex with TRIM5α . Another potential mechanism is that proteasomal engagement of TRIM5α bound to the virus core results in physical dissociation of CA from the core followed by its release from TRIM5α , thus leading to destruction of the restriction factor and decapsidation of the core but not necessarily degradation of CA [70] . Genetic evidence from abrogation-of-restriction studies indicates that TRIM5α binding requires an intact or semiintact viral capsid [60] , suggesting that TRIM5α binding to CA is highly dependent on avidity resulting from multivalent interactions with the polymeric viral capsid . It is thus plausible that CA is released from TRIM5α following forced uncoating . This model is attractive in its ability to reconcile most , if not all , of the reported data regarding the mechanism of restriction by TRIM5α . HIV-1 infection in many primate cell lines exhibits biphasic titration curves , and restriction can be abrogated in trans by high concentrations of VLPs , indicating that virus restriction is saturable . While it is generally assumed that the saturation occurs via sequestration of the restriction factor by the incoming virus , our results reveal another potential mechanism . Degradation of TRIM5αrh by HIV-1 was tightly correlated with cellular susceptibility to infection by incoming virus , suggesting that loss of restriction at high virus input may occur via degradation of the restriction factor itself . Consistent with this view , treatment with MG132 resulted in a three-fold decrease in HIV-1 infection of FRhK-4 as well as OMK cells , while infection by unrestricted SIV was inhibited only marginally ( Figure S5 ) . This result , coupled with our observations of proteasome-dependent degradation of TRIM5α proteins in restrictive cells , suggests that depletion of TRIM5α via the proteasome contributes to the saturability of restriction . The potential involvement of ubiquitylation in virus-induced degradation of TRIM5α degradation warrants further study . The autoubiquitylation of TRIM5δ observed in vitro suggests that TRIM5α may be ubiquitylated in trans upon polymerization of the restriction factor on a retroviral capsid . However , we have been unable to detect accumulation of cellular ubiquitylated TRIM5α species following HIV-1 inoculation either in the presence or absence of proteasome inhibitors ( our unpublished observations ) . While many cellular proteins are regulated by ubiquitin-dependent proteolysis , ubiquitin-independent proteasomal degradation is also well documented ( reviewed in [71] ) . Most E3 ligases are not degraded following ubiquitylation of a substrate , yet notable exceptions exist . The E3 enzyme Mdm2 is degraded following its ubiquitylation of its target , p53 [72] , and the stability of several E3 ligases is related to their ubiquitylation status resulting from autoubiquitylation [73]–[75] . It will therefore be of interest to determine whether HIV-1-induced degradation of TRIM5α is dependent on host cell ubiquitylation and the TRIM5α RING domain . The early post-entry stage of infection remains a fundamentally obscure part of the retrovirus life cycle . Our results provide novel evidence for a role for proteasome activity in TRIM5α restriction . Further mechanistic studies of TRIM5α may reveal novel approaches to antiviral therapy and fundamental insights into the molecular details of HIV-1 uncoating .
pLPCX-TRIM5αrh ( rhesus macaque ) , pLPCX-TRIM5αhu ( human ) , pLPCX-TRIM5αsp ( spider monkey ) , and pLPCX-TRIM5αtam ( tamarin monkey ) were generous gifts from Dr . J . Sodroski [3] , [31] . pCIG-N and pCIG-B were generous gifts from J . Stoye [76] . pNL4-3 was obtained from the NIH AIDS Research and Reference Reagent Program and the env gene inactivated as previously described [77] . pHIV-GFP [78] , pSIV-GFP [23] , and pCL-ampho [79] were gifts from D . Gabuzda , P . Bieniasz , and B . Naviaux , respectively . R9-G89V was made by PCR mutagenesis of the wild type HIV-1 provirus R9 utilizing site-specific primers and verified by sequencing . pHIV-G89V-GFP was made by transfer of the BssHII-EcoRI fragment of R9-G89V into the BssHII-EcoRI sites of pHIV-GFP and verified by restriction digest . pHCMV-G was provided by J . Burns [80] . pBABE-eGFP was created by transfer of the BamHI-EcoRI fragment from peGFP ( Clontech ) into the BamHI-EcoRI sites of pBABE-puro [81] . pBABE-rhTRIM5α and pBABE-huTRIM5α were generated by PCR amplification of the rhesus and human TRIM5α sequences from pLPCX-TRIM5αrh and pLPCX-TRIM5αhu using primers TRIM5α-1 ( S ) -Eco 5′- GATCGAATTCAGCTACTATGGCTTCTGGAATCCTG-3′ and pTM1-TRIMHA-R 5′-GTCTCGAGTCAAGCGTAGTCTGGGACG-3′ ( EcoRI and XhoI sites underlined ) . The PCR products were digested and ligated into the EcoRI and SalI sites in pBABE-puro . A TRIMCyp cDNA was generated from oligo dT-primed owl monkey kidney cell cDNA and PCR amplified using the TRIM5α-1 ( S ) -Eco and primer 5′-CTAGCTCGAGTACAGAAGGAATGATCTGG-3′ ( XhoI site underlined ) specific to the 3′-UTR of the human cyclophilin A gene . This amplification results in a Arg to Gly substitution at codon 4 as compared to the original TRIMCyp cDNA . The product was ligated into the EcoRI-XhoI sites of plasmid CMX-PL1 . The Myc-His6 tag was added to TRIMCyp by PCR amplification of CMX-PL1-TRIMCyp with TRIM5α-1 ( S ) -Eco and primer 5′-GTCTCGAGAGAGCTTGGTGAGCACAGAGTCATGG-3′ ( XhoI site underlined ) . This product was then digested with EcoRI and XhoI and ligated into pcDNA 3 . 1/myc-His A ( Invitrogen ) . The TRIMCyp containing the myc- ( His ) 6 epitope tag was then amplified from TRIMCyp-pcDNA3 . 1/myc-His A using TRIM5α-1 ( S ) -Eco and the primer pcDNA3 . 1 HIS-Sal 5′-ACGTCGACTTTCAATGGTGATGGTGATGATGACC-3′ , and the product digested with EcoRI and SalI and ligated into the corresponding sites in pBABE-puro . All constructs were verified via bidirectional DNA sequencing . MG132 and cycloheximide were purchased from Sigma-Aldrich and used at final concentrations of 25 µM and 50 µM , respectively . Cyclosporin A was purchased from CalBiochem used at 2 . 5 µM final concentration . Epoxomicin was purchased from Boston Biochem and used at 10 µM . The cathepsin inhibitor E64 was purchased from Sigma-Aldrich and was used at 40 µM . FRhK-4 cells were purchased from the American Type Culture Collection . Cells were cultured in Dulbecco's modified Eagle's medium containing 10% fetal bovine serum and 1% penicillin/streptomycin . VSV-G-pseudotyped HIV-1NL4 . 3 , HIV-GFP , and SIV-GFP viruses were produced by calcium phosphate transfection of 293T cells with proviral plasmid DNA ( 23 µg ) and pHCMV-G ( 7 µg ) . N- and B-tropic MLV virus stocks were prepared by co-transfection of 23 µg pCIG-N or pCIG-B plasmids with pHCMV-G ( 7 µg ) onto the cell line 293TeGFP . This cell line is a clone generated from 293T cells previously transduced with the retroviral vector pBABE-eGFP and isolated by limiting dilution and selected for high levels of GFP expression . Transfected cells were washed after 24 hours and replenished with fresh media . Supernatants were harvested 48–72 hours after transfection , clarified by passing through 0 . 45 µm filters , and stored in aliquots at −80°C . Retrovirus stocks for transduction of TRIM5α alleles were harvested from 293T cells transfected with the plasmids pCL-ampho ( 10 µg ) , the appropriate TRIM5α vector ( 15 µg ) , and pHCMV-G ( 5 µg ) . Viruses were collected 48 hours after transfection and used to transduce 293T cells . All 293T cell lines expressing TRIM5α proteins were polyclonal cell populations obtained by selection of transduced cells with puromycin . TRIMCyp-expressing cells were obtained by isolation of a single cell clone via limiting dilution . HIV-1 was strongly restricted in these cells , and restriction was prevented by the addition of 5 µg/ml cyclosporin A ( CsA ) . Cells were seeded in 6-well plates at a density of 1 to 1 . 25×106 cells/well and incubated overnight . Prior to infection , cultures were treated for 1 hour in 50 µM cycloheximide to block protein synthesis . In experiments involving proteasome inhibitors , cells were incubated with both cycloheximide and the appropriate inhibitor for 1 hour prior to infection . Viral stocks containing cycloheximide , polybrene ( 5 µg/mL ) , CsA ( 2 . 5 µM ) , and proteasome inhibitors were prewarmed to 37°C prior to addition to cells . After culturing for 1 hr , media from zero hour timepoints was removed and 1 ml of PBS was added . Cells were then detached from the plate by flushing , pelleted , washed in PBS , repelleted , and the pellets frozen at −80°C . Cells that were challenged with virus had media removed and replaced with viral stock and were returned to 37°C . Individual cultures were harvested hourly using same procedure as previously described for the zero hour timepoints . All cell pellets were frozen at −80°C prior to analysis . For experiments utilizing FRhK-4 cells the cells were seeded in 6 well plates at a density of 3×105 cells/well and incubated overnight . Prewarmed viral stocks containing polybrene ( 5 µg/mL ) were added the following day with a well harvested at the time of viral addition serving as the zero hour timepoint . Cells were incubated with the viral stock for the indicated time period then trypsinized , placed in fresh D10 media at a 1∶1 volume , pelleted , washed in 1 mL complete D10 media to inactivate trypsin , repelleted , washed 2 times in 1 mL PBS , then frozen at −80°C . In experiments with FRhK-4 cells involving MG132 , the cells were incubated with inhibitor for one hour prior to viral addition with the zero hour timepoint being an uninfected well harvested after 1 hour pretreatment . 293T and FRhK-4 cells were seeded at a density of 2×105 cells per well in 6-well plates and incubated overnight . 24 hours later , TRIM5αrh-specific siRNA [3] , or a non-targeting control siRNA ( Dharmacon ) , were diluted to a concentration of 3 µM in 1× siRNA buffer then transfected into cells using Dharmafect 1 transfection reagent and OptiMEM I ( Gibco ) according to manufacturers protocol ( Dharmacon ) . Cells were then incubated overnight and retransfected with siRNAs again the following day utilizing the identical protocol . 48 hours after the first siRNA transfection the cells were removed from the 6-well plates and plated onto a 10 cm dish in complete D10 media at a ratio of 1 well to 1 10 cm dish and incubated for either 24 or 48 hours . 24 hours later , one 10 cm dish of either TRIM5αrh-specific siRNA treated cells or non-targeting control treated cells were trypsinized and replated in 24 well plates at a density of 2×105 cells/well then incubated overnight . The following day the remaining two 10 cm dishes of siRNA treated cells were trypsinized , diluted 1∶1 in D10 media , pelleted , washed 1× in D10 media to inactivate trypsin , repelleted , washed 2× in 1 mL PBS per wash , repelleted , then frozen at −80°C . Cells that had been seeded the prior day in the 24 well plates were then infected with dilutions of HIV and SIV-GFP , incubated for 48 hours , then analyzed for GFP expression by flow cytometry . Cell pellets were thawed and lysed in a solution containing 100 mM Tris-HCl ( pH 8 . 0 ) , 100 mM NaCl , and 0 . 5% NP-40 . Nuclei were pelleted via centrifugation at 16 , 000×g for 10 minutes and post-nuclear supernatants were removed . Protein levels were quantified via BCA assay ( Pierce ) . Samples , normalized for total protein , were denatured in SDS and subjected to electrophoresis on 4–20% acrylamide gradient gels ( BioRad ) . Proteins were transferred to nitrocellulose and probed with HA-epitope tag-specific rat monoclonal antibody ( 3F10 , Roche ) and Alexa Fluor 680 conjugated goat anti-rat IgG ( Molecular Probes ) . Cells expressing TRIMCyp were probed with the myc epitope-specific mouse monoclonal antibody ( 9E10 , Invitrogen ) and Alexa Fluor 680-conjugated goat anti-mouse IgG ( Molecular Probes ) . Proteins extracted from FRhK-4 cells were probed the TRIM5α-specific mouse polyclonal antibody ( IMG-5354 , Imgenex ) and Alexa Fluor 680 conjugated goat anti-mouse IgG ( Molecular Probes ) . All immunoblots were probed with β-actin-specific rabbit monoclonal antibody ( A2228 , Sigma ) and IRDye800-conjugated goat anti-rabbit IgG ( Rockland ) . Dilutions of antibodies were 1∶1000 and 1∶5000 for primary and secondary respectively with the exception of IMG-5354 which was used at a dilution of 1∶2000 . Bands were detected by scanning blots with the LI-COR Odyssey Imaging System using both 700 and 800 channels , and integrated intensities were determined using the LI-COR Odyssey band quantitation software with the median top-bottom background subtraction method . The TRIM5α band intensities were then normalized to the signals from the corresponding β-actin bands . All signals were then expressed as a percentage of the initial TRIM5α/actin band intensity ratio . TRIM5αrh ( AY523632 ) ; TRIM5αhu ( AF220025 ) ; TRIMCyp ( AY646198 ) ; TRIM5αtam ( AY740615 ) ; TRIM5αsp ( AY740616 ) . | Recent studies have identified several cellular proteins that restrict infection by a variety of retroviruses . One of these restriction factors , TRIM5α , is partially responsible for the differences in susceptibility of monkeys and humans to SIV and HIV-1 , respectively . TRIM5α inhibits retrovirus infection soon after penetration into the target cell by associating with the viral protein CA , which forms the polymeric capsid shell of the viral core . Although the detailed mechanism of restriction is unknown , TRIM5α is postulated to alter the stability of the viral core , resulting in a failure to complete reverse transcription . The activity of cellular proteasomes , which are responsible for intracellular protein degradation , has also been implicated in TRIM5α-dependent attenuation of retroviral reverse transcription . In this study , we show that cellular TRIM5α is rapidly degraded in cells exposed to a restriction-sensitive retrovirus but not in cells infected with an unrestricted virus . Virus-induced degradation of TRIM5α was dependent on cellular proteasome activity , as inhibition with drugs blocking proteasome function also inhibited degradation of TRIM5α . These results provide additional support for a role of proteasomal degradation in TRIM5α-dependent retrovirus restriction and suggest a novel mechanism by which binding of TRIM5α to the viral capsid prevents infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biochemistry",
"cell",
"biology",
"virology",
"infectious",
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] | 2008 | Proteasomal Degradation of TRIM5α during Retrovirus Restriction |
The role of host movement in the spread of vector-borne diseases of livestock has been little studied . Here we develop a mathematical framework that allows us to disentangle and quantify the roles of vector dispersal and livestock movement in transmission between farms . We apply this framework to outbreaks of bluetongue virus ( BTV ) and Schmallenberg virus ( SBV ) in Great Britain , both of which are spread by Culicoides biting midges and have recently emerged in northern Europe . For BTV we estimate parameters by fitting the model to outbreak data using approximate Bayesian computation , while for SBV we use previously derived estimates . We find that around 90% of transmission of BTV between farms is a result of vector dispersal , while for SBV this proportion is 98% . This difference is a consequence of higher vector competence and shorter duration of viraemia for SBV compared with BTV . For both viruses we estimate that the mean number of secondary infections per infected farm is greater than one for vector dispersal , but below one for livestock movements . Although livestock movements account for a small proportion of transmission and cannot sustain an outbreak on their own , they play an important role in establishing new foci of infection . However , the impact of restricting livestock movements on the spread of both viruses depends critically on assumptions made about the distances over which vector dispersal occurs . If vector dispersal occurs primarily at a local scale ( 99% of transmission occurs <25 km ) , movement restrictions are predicted to be effective at reducing spread , but if dispersal occurs frequently over longer distances ( 99% of transmission occurs <50 km ) they are not .
The role of host movements in the transmission of vector-borne diseases has largely been ignored [1 , 2] . Recently , however , several studies have quantified the importance of human movements for the spread of vector-borne diseases . Mobile phone data were used to infer mobility patterns and , hence , the impact of large-scale movement patterns on the transmission of malaria [3] and dengue [4] . Alternatively , detailed social surveys were used to investigate the importance of house-to-house movements on dengue virus transmission [5 , 6] . Both approaches exemplify a central difficulty in studying the role of human movement in the transmission of vector-borne diseases: they must rely on detailed small-scale studies or proxy measures , because human movements are seldom recorded in detail [2] . By contrast , livestock movements , and those of cattle in particular , are well described in many countries . The role of livestock movements in the spread of infectious diseases has been widely studied for directly-transmitted infections , such as bovine tuberculosis [7–9] or foot-and-mouth disease [10–12] . However , their role in the spread of vector-borne diseases has not been explored in any great detail . In this study we disentangle and quantify the relative importance of livestock movements and vector dispersal in the spread of two viral infections of cattle and sheep , both of which are transmitted by Culicoides biting midges and have recently emerged in northern Europe . In 2006 bluetongue virus ( BTV ) serotype 8 ( BTV-8 ) appeared near Maastricht in the Netherlands and , subsequently , spread to much of northern Europe [13 , 14] . Schmallenberg virus ( SBV ) , a novel orthobunyavirus , was first detected in Germany and the Netherlands in the summer of 2011 [15] and by spring of 2013 had been reported across much of Europe [16] . We develop a model describing the transmission of BTV and SBV within and between farms , which uses separate descriptions for transmission via dispersal of infected vectors and via movement of infected livestock . First , we apply the model to demographic and epidemiological data from the BTV-8 epidemic in Great Britain ( GB ) during 2007 in a Bayesian framework . This approach allows us to link ( unobserved ) infection with reported clinical disease . It also allows us to update previous estimates for epidemiological parameters related to the transmission of BTV within and between farms ( cf . [17 , 18] ) . Next , the model is applied to SBV using previously derived estimates relating to transmission within a farm [19] . Finally , the practical implications of the results are explored by assessing the impact of controlling livestock movements on the spread of the two viruses .
The dynamics of BTV within a farm are described using a stochastic compartmental model that includes two ruminant host species ( cattle and sheep ) and the Culicoides vector [18] . The cattle and sheep populations are assumed to be constant ( Hi ) , except for disease-associated mortality , and are subdivided into the number of susceptible ( i . e . uninfected ) , infected and recovered animals , denoted by X ( i ) , Y ( i ) and Z ( i ) , respectively , where the superscript i indicates cattle ( C ) or sheep ( S ) . To allow for a more general gamma distribution for the duration of viraemia , the infected host population , Y ( i ) , is subdivided into a number of stages , with newly infected hosts entering the first stage and then passing through each successive stage . If the time spent in each stage follows an exponential distribution with mean 1/niri , the total length of time spent in the ni stages follows a gamma distribution , with mean 1/ri and variance 1/niri2 [22] . The vector population ( N ) is subdivided into the number of adult female midges that are susceptible ( i . e . uninfected ) , latent ( i . e . infected , but not infectious ) and infectious , denoted by S , L and I , respectively . To allow for a more general gamma distribution for the extrinsic incubation ( i . e . latent ) period ( EIP ) [23] , the latent class is subdivided into a number of stages in a similar approach to that described above for the duration of host viraemia . Vector mortality occurs at the same rate in all classes and is balanced by the recruitment of susceptible vectors , so that the total vector population ( N ) remains constant . The force of infection for host species i , λi , is given by , λi ( t ) =baϕimiθ ( t ) I ( t ) N , ( 1 ) where b is the probability of transmission from an infected vector to a host , a is the reciprocal of the time interval between blood meals for the vector ( assumed to be equal to the biting rate ) , mi ( = N/Hi ) is the vector-to-host ratio and I/N is the proportion of bites which are from infectious vectors . The proportion of bites on cattle and sheep is given by ϕC=HCHC+σHS , ϕS=1−ϕC , ( 2 ) respectively , where σ is the vector preference for sheep relative to cattle . The seasonal vector activity [24] on day t is given by θ ( t ) ∝exp ( b11sin ( 2πt365 ) +b21cos ( 2πt365 ) +b12sin ( 4πt365 ) +b22cos ( 4πt365 ) ) , ( 3 ) normalised so the maximum value is one . The force of infection for vectors , λV , is λV ( t ) =βaθ ( t ) ( ϕCY ( C ) ( t ) HC+ϕSY ( S ) ( t ) HS ) , ( 4 ) where β is the probability of transmission from an infected host to a vector and Y ( C ) and Y ( S ) are the total number of infected cattle and sheep , respectively . Infection on a farm was related to reported clinical disease by assuming there was a daily probability of a farm with infected cattle or sheep reporting clinical disease , ζC and ζS , respectively , where 0≤ζC , ζS≤1 . Parameters in the model are summarised in S1 Table . The reciprocal of the time interval between blood meals ( a ) , the vector mortality rate ( μ ) and the reciprocal of the mean EIP ( ν ) were assumed to vary with the local temperature ( see S1 Table for details ) . Population sizes in the model take integer values , while transitions between compartments are stochastic processes ( S2 Table ) . The number of transitions of each type during a small time interval δt was drawn from a binomial distribution with population size n and transition probability q ( the appropriate per capita rate multiplied by δt ) ( S2 Table ) . However , binomial random variables are computationally expensive to simulate and an approximating distribution was used wherever possible . If: ( i ) nq ( 1-q ) >25; ( ii ) nq ( 1-q ) >5 and 0 . 1<q<0 . 9; or ( iii ) min ( nq , n ( 1-q ) ) >10 , an approximating normal variate with mean nq and variance nq ( 1-q ) was used , while if q<0 . 1 and nq<10 , an approximating Poisson variate with mean nq was used [25] . To describe the spread of BTV between farms , a stochastic , spatially-explicit model with a daily time-step was used . Transmission between farms was assumed to occur via two routes: movement of infected animals or dispersal of infected vectors . A total of 22 or 23 parameters were estimated by fitting the BTV model to the summary outbreak data for Great Britain in 2007: two or three related to vector dispersal ( depending on the model used; Table 1 ) ; two related to under-ascertainment of infected farms ( ζC and ζS ) ; and 18 related to within-farm transmission ( see S1 Table ) . When applying the model framework to SBV , estimates for parameters related to the transmission of SBV within a farm were derived from an earlier study in which the within-farm component of the model was fitted to sero-prevalence data for Belgium and the Netherlands [19] . Parameters for transmission between farms were assumed to be the same as for BTV . Unlike for BTV , the timing of incursions for SBV in GB has not been investigated in any great detail . Back-calculation from the dates of reported cases of malformed calves and lambs indicates that a plausible date for an incursion is 28 June 2011 [38] , though it is not possible to rule out earlier dates or , indeed , multiple dates of incursion . For simplicity , each incursion was initialised on the 28 June by selecting a single farm at random from a county on the south-east coast of England ( Suffolk , Essex , Kent , East Sussex , West Sussex , Hampshire and Isle of Wight ) , which were the earliest affected regions . The aim here is to compare the importance of transmission routes for SBV with those for BTV , rather than to reconstruct the SBV epidemic in GB . However , the sensitivity of the results for SBV to the timing of incursion was assessed by simulating incursions on five other incursion dates throughout the year ( 1 May , 1 June , 1 July , 1 August and 1 September ) . To explore the impact of movement restrictions on the spread of BTV and SBV we assumed they were applied in a circular zone around known IPs . For BTV IPs were detected on the basis of reported clinical disease , while for SBV they were assumed to be detected when the first newly infected cattle or sheep occurred on the farm ( adult animals show no or very mild clinical signs of disease ) . Farms within a specified radius of an IP became part of the movement restriction zone ( MRZ ) and were allowed to move animals to farms within the MRZ , but not to any farms outside the MRZ . For each radius , one hundred replicates of the model were run for five incursion dates ( 1 May , 1 June , 1 July , 1 August and 1 September ) until 31 December . Each incursion was initialised by selecting a single farm at random from a county on the south-east coast of England ( Suffolk , Essex , Kent , East Sussex or West Sussex ) . The model for transmission between farms via animal movements was parameterised using movement data for 2006 ( i . e . a year in which there were no major outbreaks of disease in cattle or sheep ) . We explored the sensitivity of the model predictions for the effectiveness of movement controls to temperature by using data for two years: 2006 ( a warmer year ) and 2007 ( a cooler year ) .
For the model in which vector dispersal is described as a diffusion process , the predicted number of newly infected holdings each week reaches its peak about seven weeks after the initial incursion ( Fig 1A ) , preceding the peak in newly confirmed clinical farms by one or two weeks ( Fig 1B ) . Moreover , there are substantially more infected farms than confirmed clinical farms , indicating a high level of under-ascertainment . The observed number of newly confirmed clinical farms each week lies close to the posterior mean for most weeks ( Fig 1B ) , while the observed cumulative number of confirmed clinical farms in each county lies in the 95% prediction interval for all counties , except Essex ( Fig 1C ) . In addition , the model predicts spread of BTV into areas where reported clinical farms were not confirmed in only a small proportion ( 2 . 7% ) of simulations and , in each instance , only a single case ( Fig 1C and 1F ) . The observed number of infected farms detected by pre-movement testing or by targeted surveillance lies within the 95% prediction intervals for the model ( Fig 1D ) , while the posterior mean for the within-farm prevalence for cattle herds matches that observed ( Fig 1E ) . The predicted dynamics for the four kernel models are similar to those for the diffusion model ( S1–S4 Figs; cf . Fig 1 ) . However , all four kernel models predict more extensive spatial spread of BTV than the diffusion model . This has the consequence that they are less able to capture the number of infected holdings detected through targeted surveillance around the first two IPs , but are better able to account for the number of confirmed clinical farms in Essex . The kernel models also more frequently ( >10% of simulations for each model ) predict clinical cases in areas in which no cases were reported . In addition , the predictions of the kernel models are more variable than those for the diffusion model . The marginal posterior distributions for the parameters in each model are plotted in S5 and S6 Figs and are summarised in S5 and S6 Tables . Summary statistics for the within-farm parameters are provided only for the model in which vector dispersal was described as a diffusion process ( S6 Table ) . However , the posterior distributions did not differ greatly for most parameters ( S6 Fig ) , except for the probability of transmission from host to vector ( β ) , which was higher for the kernel models compared with the diffusion model ( posterior mean: 0 . 05 vs 0 . 02 ) . All the models predict that transmission of BTV between farms occurs predominantly through dispersal of infected vectors . In simulated outbreaks , the median proportion of farms infected via dispersal of infected vectors was 86–91% , depending on the model for vector dispersal ( Fig 2A ) . This compares with the median proportion of farms infected via movement of cattle and sheep of 5–7% and 3–6% , respectively ( Fig 2A ) . A similar pattern is seen in the number of secondary infections via the three routes , in which most secondary infections are generated by vector dispersal ( Fig 2C ) . In addition , the number of secondary infections per farm by vector dispersal is over-dispersed , with the majority of transmission attributable to a small number of farms ( Fig 2C ) . The mean number of secondary infections for this route was around 1 . 3 for the diffusion model and around 0 . 9 for the kernel models , while the dispersion parameter was around 0 . 05 for all models ( Table 2 ) . By contrast , the mean number of secondary infections per farm arising by the movement of infected cattle or sheep was around 0 . 05 ( Table 2 ) . The distance over which transmission occurred was strongly dependent on the transmission route . Transmission via movement of infected livestock occurred over considerably longer distances than via dispersal of infected vectors and this was independent of the model used for vector dispersal ( Fig 2E ) . When transmission was via movement of infected livestock , the mean distance between source and recipient farms was around 50 km , with 99% of transmission occurring within around 150 km for both cattle and sheep . When transmission was via dispersal of infected vectors , the distances between source and recipient farms depended critically on the model for vector dispersal ( Fig 2E ) . The median distance ( distance within which 99% of transmission occurred ) was 7 . 8 ( 25 . 2 ) km for the diffusion , 30 . 9 ( 49 . 6 ) km for the exponential kernel , 29 . 1 ( 49 . 5 ) km for the Gaussian kernel , 22 . 5 ( 49 . 3 ) km for the fat-tailed kernel and 19 . 9 ( 48 . 6 ) km for the stepped kernel . The characteristics of each transmission route ( frequent , but shorter range for vector dispersal; less frequent , but longer range for cattle and sheep movements ) and the differences between models in vector dispersal distances are demonstrated in maps showing which farm infected which in the simulated outbreaks ( Fig 3 ) . The sensitivity of the importance of the transmission routes to the time of incursion and temperature data was assessed for each model of vector dispersal ( S7 and S8 Figs ) . The proportion of farms infected via each route was not substantially influenced by either the time of incursion or the temperature data used . Both the number of secondary infections per infected farm and the distance over which BTV spread via livestock movements were higher for incursions earlier in the year . Furthermore , the number of secondary infections per infected farm was higher when using the 2006 temperature data ( a warmer year ) compared with 2007 data ( a cooler year ) . The model predicts much larger outbreaks for SBV compared with BTV , in terms of both the number of infected farms and spatial spread ( S9 Fig; cf . Fig 1 ) . Furthermore , the proportion of transmission between farms via dispersal of infected vectors is higher for SBV than for BTV ( Fig 2B ) . In simulated outbreaks the median proportion of farms infected via vector dispersal is 98% and this is independent of the model of vector dispersal . This compares with 1% each for transmission via movement of infected cattle and sheep . This difference in the importance of the transmission routes was reflected in the number of secondary infections per infected farm , which was higher for vector dispersal for SBV than for BTV , but which was lower for cattle and sheep movements ( Fig 2D; cf . Fig 2C ) . For SBV , the mean number of secondary infections per infected farm was around 2 . 0 for the diffusion model and around 1 . 5 for the kernel models , while the dispersion parameter was around 0 . 07 for all models ( Table 2 ) . The mean number of secondary infections via livestock movements was 0 . 01 for both cattle and sheep ( Table 2 ) . The distance over which SBV spread via livestock movements was greater than for BTV ( Fig 2F ) , but this is a consequence of the movement restrictions in place during the BTV outbreak . Finally , the importance of the transmission routes for SBV was not greatly sensitive to the time of incursion ( S10 Fig ) . Movement restrictions ( in this case applied in a circular zone around known IPs ) can potentially reduce the size of a BTV outbreak , but whether or not they are predicted to do so depends critically on assumptions about vector dispersal ( Fig 4 ) . When vector dispersal is described by a diffusion process , movement restrictions reduce the size of an outbreak and , furthermore , there is an optimal radius for the MRZ of approximately 20 km ( Fig 4A ) . When vector dispersal is described by a fat-tailed kernel , there is also some evidence for an impact of movement restrictions on outbreak size , but in this case the optimal MRZ radius is around 35–40 km ( Fig 4D ) . However , when vector dispersal is described by an exponential , Gaussian or stepped kernel , there is no evidence for an impact of movement restrictions ( Fig 4B , 4C and 4E ) . In addition , movement restrictions do not substantially reduce outbreak size if the incursion occurs later in the year ( August or September ) and this conclusion is independent of assumptions about vector dispersal ( Fig 4 ) . Similar results are also obtained if temperature data for 2006 are used ( S11 Fig ) instead of for 2007 ( Fig 4 ) . Whether or not movement restrictions are predicted to reduce the size of an SBV outbreak also depends critically on assumptions about vector dispersal ( S12 Fig ) . Using the model describing vector dispersal as a diffusion process , movement restrictions were predicted to have a substantial impact on outbreak size and to a much greater extent than for BTV ( S12 Fig; cf . Fig 4 ) . By contrast , movement restrictions were predicted to have no impact on outbreak size for any of the kernel models ( S12 Fig ) .
Initial modelling studies for BTV-8 in northern Europe used kernel- or wave-based approaches to explore spread , implicitly incorporating all modes of transmission in a single description [18 , 35 , 39] . Subsequently , models were developed which separate animal and vector movements [40–42] , but these were not fitted to outbreak data nor did they quantify the relative importance of the two transmission routes . Here we have developed a model framework that allows us to disentangle and quantify the roles played by livestock movements and vector dispersal in the transmission of two Culicoides-borne viruses . Our results show that dispersal of infected vectors accounts for the majority ( around 90% ) of spread of BTV between farms and an even higher proportion ( 98% ) of spread of SBV between farms ( Fig 2A and 2B ) . We are able to attribute spread to each route because of the detailed , independent data available to describe spread via movement of infected livestock . If this were not the case , it would be more challenging to estimate the relative contribution of each route , because a decrease in transmission due to one route could be compensated for by an increase in transmission due to another . One previous study has quantified the role of vector dispersal in the spread of BTV-8 in northwest Europe in 2006 [31] . The authors could explain infection onset for 94% of reported BTV-infected farms based on wind and midge flight activity . As they did not consider livestock movements , this represents an upper bound on the proportion of farms infected via vector dispersal , but is consistent with our estimate for the BTV-8 outbreak in GB in 2007 ( Fig 2A ) . Similar methods were subsequently applied to quantify the role of vector dispersal in the spread of SBV in northwest Europe in 2011 [32] . In contrast with BTV , the authors could explain infection onset for only 70% of reported SBV-infected farms based on midge flight activity , which is markedly lower than our estimate of 98% ( Fig 2B ) . This discrepancy is likely to be a consequence of under-ascertainment of SBV-infected farms . This results in a greater distance between infected farms , making it more difficult for the vector-only approach to explain SBV transmission [32] . The number of secondary infections arising through each route also emphasises the major role played by vector dispersal in the transmission of BTV and SBV between farms compared with livestock movements ( Fig 2C and 2D ) . In particular , the mean number of secondary infections via both cattle and sheep movements was estimated to be substantially below one for both viruses ( Table 2 ) , indicating that these routes alone cannot sustain transmission ( cf . [41] ) . By contrast , the mean number of secondary infections via vector dispersal was above one for both BTV and SBV , indicating transmission can be sustained by this route . However , the number of secondary infections is over-dispersed , so that a small proportion of farms account for the majority of transmission: an example of the 80/20 rule [43] . In the model , it is the larger farms which account for most of the transmission via dispersal of infected vectors . This is primarily a consequence of our assumption that the number of vectors is proportional to the number of livestock on a farm ( though the constant of proportionality does vary amongst farms ) . Few studies have investigated the relationship between vector abundance and host numbers . One recent study found that Culicoides abundance was higher at trap locations with a high density of cattle in the locality [24] . Another study suggested that catches in light traps increase linearly with sheep numbers , at least for small host numbers [44] . Although these results do not allow robust generalizations , the findings are compatible with the assumption of a constant vector-to-host ratio . In addition , the common alternative assumption is that the number of vectors is independent of host numbers , but this results in the conclusion that outbreaks are more likely on smaller farms because they will have higher vector-to-host ratios . Both the proportion of transmission and the number of secondary infections via dispersal of infected vectors are independent of the model used for vector dispersal ( Fig 2A–2D ) . This is not the case , however , for the distances between source and recipient farms , which differ markedly amongst the models ( Fig 2E and 2F ) . The distances inferred in the present study of BTV-8 in GB using a diffusion model ( median: 7 . 8 km; Fig 2E ) are similar to those estimated for BTV-8 in northern Europe based on wind and midge flight activity ( median: ~5 km; see [31] , their Fig 3 ) . These contrast with the considerably larger distances inferred using the kernel models ( median for all models: ≥20 km; Fig 2E ) . Comparing the different models suggests that , while the diffusion model is able to capture the general pattern of local-scale spread , it is not able to capture the relatively infrequent longer-range dispersal events ( see , e . g . [30] ) . By contrast , the kernel models can predict longer-range jumps , but at the expense of missing the detail of local-scale spread . However , given the under-ascertainment of infected holdings and the spatial resolution of the data , it will be difficult to infer a more robust model for vector dispersal from the 2007 GB outbreak . Moreover , the challenges associated with studying Culicoides biting midges in the field [45–47] make it difficult to estimate dispersal patterns empirically , especially over longer distances . The lower proportion of transmission attributed to movement of infected livestock for SBV compared with BTV ( 2% vs 10%; Fig 2A and 2B ) can be accounted for by two key differences between the viruses . First , vector competence ( i . e . the probability of transmission from host to vector ) is much higher for SBV ( 0 . 14; [19] ) than for BTV ( 0 . 02; S6 Table ) . This means there is a higher prevalence of infectious vectors for SBV compared with BTV , increasing the importance of vector dispersal for SBV ( see Eqs ( 5 ) and ( 6 ) ) . Second , the mean duration of viraemia is much longer for BTV ( 21 days; S6 Table ) than for SBV ( 3–4 days; [19] ) . Consequently , there is a lower probability of moving an animal while it is infected for SBV than for BTV , reducing the importance of livestock movements for SBV . Host movements may only account for a small proportion of transmission , but our results reinforce the important role that they play in the transmission of vector-borne diseases , particularly through the introduction of infection to new areas [3 , 4 , 48 , 49] . In the case of BTV and SBV , host ( i . e . livestock ) movements spread the virus over much longer distances than would be expected by vector dispersal ( Fig 2E and 2F ) and facilitate the establishment of new outbreaks away from existing foci ( Fig 3 ) . While seldom practical for human movements , it is feasible to restrict livestock movements as part of disease control measures . The predicted impact of movement restrictions for both BTV and SBV depends critically on the model used for vector dispersal . In particular , there is a significant reduction in outbreak size ( i . e . cumulative number of farms infected ) only for the diffusion model ( Fig 4; S11 and S12 Figs ) . Furthermore , the magnitude of the reduction resulting from movement restrictions is predicted to be much lower than alternative control measures , in particular , vaccination [50 , 51] . The difference in predicted effectiveness of movement restrictions amongst models reflects the distances over which dispersal of infected vectors occurs in each one ( Fig 2E and 2F ) . When dispersal is primarily local , as is the case with the diffusion model , movement restrictions are effective because the virus is unlikely to escape the MRZ through vector dispersal into an area in which movements are allowed and , hence , can spread over longer distances . When vector dispersal occurs more frequently over longer distances , as is the case with the exponential and Gaussian kernels , infection is likely to escape any MRZ through vector dispersal alone . As a result , movement restrictions are predicted to be ineffective in this case . When fitting the BTV model to outbreak data we have used summary or aggregated epidemiological measures , rather than more detailed data on location and timing of infected farms . For many outbreaks , including the 2007 BTV outbreak in GB , summary statistics are the only data available , which makes ABC a natural framework in which to implement epidemiological models [37 , 52] . Moreover , ABC methods facilitate integrating data from different surveillance sources ( in the case of BTV , reported clinical farms , pre-movement testing and targeted surveillance ) , which helps overcome the limitations associated with individual sources ( e . g . under-ascertainment of reported cases ) . This does , however , require a model relating disease occurrence and reporting to the underlying pattern of infection , which is not always straight-forward . Here , we have used a simple model ( a fixed daily probability of reporting ) , which captures this acceptably for most areas in each of the models . Where there are discrepancies ( e . g . for Essex in the diffusion model; Fig 1C ) , this could reflect differences in reporting behaviour between regions or changes in the probability of reporting over time . This is difficult to explore in detail , however , given the limited numbers of cases . In this study we have demonstrated that both vector dispersal and host movements play important roles in transmission of vector-borne diseases of livestock , though for different reasons . Vector dispersal is the principal mode of spread between farms , while livestock movement is the principal means of introducing infection to new areas . However , the relative importance of the routes differs between viruses , even when they share the same vector species . This has practical implications for disease control and , in particular , movement restrictions , so that generic measures may not be effective . | Diseases which are transmitted by the bites of insects can be spread to new locations through the movement of both infected insects and infected hosts . The importance of these routes has implications for disease control , because we can often restrict host movement , and so potentially reduce spread , but cannot easily restrict insect movements . Despite this , the importance of host movements has been little studied . Here we develop a mathematical model which allows us to disentangle and quantify transmission by insect dispersal and by host movement . We apply the model to two diseases of cattle and sheep transmitted by biting midges that have emerged in northern Europe in the past decade , bluetongue virus ( BTV ) and Schmallenberg virus ( SBV ) . For both viruses , we show insect movements account for a majority of spread between farms . Although they cannot sustain an epidemic on their own , animal movements play an important role in introducing disease to new areas . | [
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] | 2017 | Quantifying the roles of host movement and vector dispersal in the transmission of vector-borne diseases of livestock |
The dynamics of the cellular proportion of mutant mtDNA molecules is crucial for mitochondrial diseases . Cellular populations of mitochondria are under homeostatic control , but the details of the control mechanisms involved remain elusive . Here , we use stochastic modelling to derive general results for the impact of cellular control on mtDNA populations , the cost to the cell of different mtDNA states , and the optimisation of therapeutic control of mtDNA populations . This formalism yields a wealth of biological results , including that an increasing mtDNA variance can increase the energetic cost of maintaining a tissue , that intermediate levels of heteroplasmy can be more detrimental than homoplasmy even for a dysfunctional mutant , that heteroplasmy distribution ( not mean alone ) is crucial for the success of gene therapies , and that long-term rather than short intense gene therapies are more likely to beneficially impact mtDNA populations .
Most human cells contain 100-10 , 000 copies of mitochondrial DNA ( mtDNA ) which are situated inside the mitochondria . The proteins encoded by mtDNA are crucial for mitochondrial functionality , and mutations in mtDNA can cause devastating diseases [1–6] . Heteroplasmy , the proportion of mutant mtDNA molecules in a cell , typically has to pass a certain threshold ( ∼ 60-95% ) before any biochemical defects can be observed [7–14] . The existence of thresholds at which mutant loads begin to have an effect has profound implications for our understanding of disease onset , drawing attention to the variance dynamics of the mutant fraction in cellular populations . As this variance increases more cells can be above threshold , and thus show pathology , even if average mutant load is unchanged . Mitochondrial biogenesis and maintenance require cellular resources , and mitochondria are key sources of ATP and play other important metabolic roles . The particular ‘effective cost’ that cellular control of mitochondria acts to minimise remains poorly understood: for example , both decreases [15] and increases [15 , 16] in wildtype copy numbers have been observed for different mutations as the mutant load increases . Some studies suggest that mtDNA density is controlled [17–19] , others that total mtDNA mass [20 , 21] , or mtDNA transcription rate [22] is controlled . Understanding mtDNA population dynamics inside cells , and how these populations react to clinical interventions , is crucial in understanding diseases [23 , 24] . However , experimental tracking of mtDNA populations over time is challenging , necessitating predictive mathematical modelling to provide a quantitative understanding . In parallel with efforts to elucidate cell physiological control , protein engineering methods to artificially control mtDNA heteroplasmy are making fast progress . Two recently developed methods for cleaving DNA at specific sites involve zinc finger nucleases ( ZFNs ) and transcription activator-like effector nucleases ( TALENs ) [25–31] , which have been re-engineered to specifically cleave mutant mtDNA [32–36] . MitoTALENs have been successfully used to reduce mutant loads in cells containing disease-related mutations , but elimination of the target mutant mtDNA was not complete [32 , 37] . Similarly , treating cells multiple times with mtZFNs led to near-complete elimination of mutant mtDNAs [35 , 36] . Quantitative theory for these therapeutic technologies has not yet been developed , leaving open questions about how these tools can be optimally deployed . In this paper , we develop theory from bottom-up bioenergetic principles which allows us to study the effects of distinct cellular mtDNA control strategies , to analyse the bioenergetic cost of different mtDNA states , and to combine mtDNA control and energy-based cost to identify optimal control strategies for the cell . Finally , we construct a model for therapeutic mtDNA control using recent experimental data [36] and highlight challenges linked to heteroplasmy variance .
We employ a linear form of mtDNA feedback control and assume each mtDNA molecule replicates and degrades according to Poisson processes with rates λ and μ , respectively . Because control of biogenesis or autophagy yield similar behaviours [38] , we assume that the degradation rate μ is constant and that feedback control is manifest through the replication rate λ ( w , m ) , where w and m denote the number of mutant and wildtype mtDNA molecules in the cell . To connect with experiments , we use μ ≈ 0 . 07 day−1 corresponding to a half-life of about 10 days [39] . We only model post-mitotic cells , though our analysis can be extended to include cell divisions . Specifically , we use a birth rate of the form: λ ( w , m ) = μ + c 1 ( w o p t - ( w + δ m ) ) ( 1 ) where c1 > 0 , wopt > 0 and δ are constants , with wopt denoting the steady state value towards which the effective population , here defined as w + δm , is controlled . The magnitude of c1 determines how tightly the population is controlled . We use the term ‘mitochondrial sensing’ to describe how the cell might sense the mitochondrial population that is present . ‘Mutant sensing’ then refers to how strongly mutants are sensed relatively to wildtypes , which is encoded in the parameter δ . When steady state is reached ( i . e . w + δm = wopt ) , replication and degradation rates are equal . In the absence of mutants , the resulting wildtype steady state is assumed to be optimal . We note that assuming the existence of wopt does not imply a control based on copy number . Other quantities related to mitochondria may be controlled instead , such as total mitochondrial mass or ATP production , their desired values being reached at an effective population size of wopt . Thus , we define ‘mitochondrial sensing’ to refer to a wide range of mechanisms available to the cell to infer properties of its mitochondrial population , which can then be used to decide on a control action . The deterministic dynamics resulting from this control are described in Eq ( 4 ) . We do not include the possibility of de novo mutations but our approach can straightforwardly describe the subsequent behaviour if new mutations arise . Our linear model shares features with the ‘relaxed replication model’ [40 , 41] ( Eq ( 5 ) ) , though is written in a simpler form . The relaxed replication model has been used in a variety of other models [42 , 43] and has obtained experimental support [15] . We will first investigate properties of more general control strategies , after which we return to our linear control and discuss parameterisations that optimise the energy status of the cell . Finally , we use the linear control to fit recent experimental data involving treatment of heteroplasmic cells with mtZFNs . Next , to find general quantitative principles underlying mitochondrial energy budgets , we build a cost function that assigns a cost to any given mtDNA state ( w , m ) and allows a general quantitative investigation of the tradeoffs in maintaining cellular mtDNA populations . The ‘true’ energy budget of a cell with a given mitochondrial population is highly complex , involving many different metabolic processes in which mitochondria are involved [44–46] . We provide a simpler description , focussing on ATP production as a central mitochondrial function , and removing kinetic details in favour of a coarse-grained representation , to provide qualitative rather than quantitative results .
In this work , we have built a quantitative theory bridging stochastic optimal control , costs of mtDNA populations , and gene therapies . Our results contribute to a growing body of evidence [63–66] that the variance of mtDNA populations has important physiological and therapeutic implications independently of mean heteroplasmy , and underline that stochastic theory is required to understand this biologically and medically important quantity . Key findings of our model ( Table 1 ) include ( I ) the identification of tradeoffs in the control of one or the other mtDNA species; ( II ) the observation that increasing mtDNA variance can lead to increased energetic costs over time and ageing even when means and demands are preserved; ( III ) intermediate heteroplasmy states can be more expensive than states homoplasmic in either mutant or wildtype; ( IV ) mutant sensing can be required to avoid an exponentially increasing cost; ( V ) sensing of cellular energetic status can be more effective than other targets like mitochondrial mass; ( VI ) reduction of mutant mtDNA alone is not always the optimal control strategy; ( VII ) high heteroplasmy variance challenges gene therapy treatments; and ( VIII ) weak , long gene therapy trajectories are more effective than short , intense ones . Our findings hold qualitatively under the range of conditions we discuss above . The aim of our manuscript is not to make detailed quantitative predictions and conclusions based on complex models , nor do we intend to imply that our models are the only possible models one could construct . Rather , we aim to provide general biologically plausible models to gain qualitative insights and to comment on large-scale behaviours . To this end , our cost function , used to illustrate some of our results , is phenomenological and contains several parameters . Most of these are biologically interpretable , meaning their values can be obtained or estimated from the literature . The main elements in our cost function are quite general: terms involving supply , demand , and resource . To test the qualitative shape of our cost function , one could sort cells based on mitochondrial copy number and heteroplasmy to obtain samples at different points in ( w , m ) space . Measurements of e . g . cell proliferation , ROS or apoptosis rates allow for the evaluation of an effective cost at each of these points . By measuring the relative consumption rates of NADH and succinate , as well as the amount of ATP produced per glucose consumed , in identical cells exposed to different energy demands , the saturating output model may be probed . If the parameter δ is low , i . e . mutants are sensed less , mutant copy numbers at high heteroplasmies will be higher than wildtype copy numbers at low heteroplasmies . Experimentally , it has been observed that heteroplasmic cells can have total mtDNA copy number values that are 5-17-fold higher compared to cells homoplasmic in wildtype [67–70] . The cell has somehow allowed these mutants to expand , which may mean that they are less tightly controlled; controls based on total energy output or mtDNA mass ( which can result in δ < 1 ) may lead to such behaviours . A control on mtDNA mass could explain why deletion mutants are often seen to expand [71 , 72] and would also predict normal copy number levels in cells harbouring mtDNA point mutations . Recently , it was found that samples with mtDNA indels had very high mtDNA copy number levels , but single nucleotide variants did not [73] . We showed that heteroplasmy distributions in cell populations can provide important information about the possibility of successfully treating these cells with endonucleases . A tissue may be harder to treat if its high mean heteroplasmy level is caused by a small percentage of dysfunctional cells . Experimental values of mean homogenate heteroplasmy in heart tissue of patients with the 3243A>G mutation are roughly around 0 . 8 ( though ranges can be large [74–77] ) and muscle tissue often shows mosaic structures , with deficient patches of cells adjacent to healthy cells . These examples show that it may be that , at least in some cases , high mean levels are indeed caused by a relatively low percentage of cells , meaning that there are still challenges ahead for efficiently treating these tissues . One of the features of our cost function is that resource limitations play an important role in shaping the cost landscape . There are indications that cellular levels of NAD ( a coenzyme involved in oxidative phosphorylation ) are limiting , and that a sufficient supply of NAD to mitochondria becomes critical [78–81] . An increase of intracellular NAD can lead to an increase in oxygen consumption and ATP production [81] indicating that resource limitation may , at least in some cases , be a genuine constraint . Adding various kinds of resources can significantly change mitochondrial basal respiration rate [82–84] . Like any other model , our models have a defined range of applicability . A key baseline assumption was using identical replication and degradation rates for mutants and wildtypes . Various possibilities of distinct rates have been offered in the literature , including faster mutant replication rates [22 , 68 , 85–88] , lower mutant degradation rates [89] , and higher mutant degradation rates [90 , 91] . Including such differences , and other features such as de novo mutations , degradation control , and cell divisions [38 , 64 , 92 , 93] , constitute natural extensions to our theory .
Wildtype and mutant mtDNA copy numbers are considered to have birth rate λ ( w , m ) = μ + c1 ( wopt − ( w + δm ) ) and death rate μ , leading to the following evolution equations: d w d t = w ( λ ( w , m ) - μ ) d m d t = m ( λ ( w , m ) - μ ) ( 4 ) The corresponding stochastic system , required to e . g . describe fixation , does not have an explicit solution due to nonlinearities . The deterministic steady state solution of Eq ( 4 ) is given by ( wss + δmss ) = wopt and represents a straight line in ( w , m ) -space ( S1A Fig ) , whose slope depends on the value of δ . Stochastic dynamics will fluctuate around the steady state line , causing heteroplasmy to change over time until fixation of either species occurs . This means that , over long times , a cell will reach either h = 0 or h = 1 ( in the absence of mutations ) . When mutations do occur , a cell will always reach a state with h = 1 ( though many different mutant species may be present ) . The relaxed replication model assumes a constant death rate μ and a birth rate of the form λ ( w , m ) = μ w + m ( α R [ w o p t - ( w + η m ) ] + w + η m ) ( 5 ) with αR > 1 and η constants [40 , 41] . We have renamed the parameters of the original model for convenience . Note that both αR and η influence the mutant contribution to λ ( w , m ) ( rather than the single parameter δ in our linear model ) . Let the cost per unit time of state ( w , m ) be denoted by C , and the cost corresponding to the steady state ( wss , mss ) by C ¯ . Even if steady state copy numbers are constant over time ( i . e . the mean values of w and m are always equal to wss and mss ) the mean cost per unit time is generally not equal to C ¯ . By performing a Taylor expansion , the mean cost per unit time can be written as follows: E [ C ] ( t ) ≈ C ¯+ 1 2 ( var ( w ( t ) ) ∂ 2 C ∂ w 2 + var ( m ( t ) ) ∂ 2 C ∂ m 2 + 2 cov ( w ( t ) , m ( t ) ) ∂ 2 C ∂ w ∂ m ) ( 6 ) where E[C] ( t ) is the expected cost per unit time given that the trajectory starts in state ( wss , mss ) , and all partial derivatives are evaluated at steady state . These findings imply the following: suppose all cells in a population of cells are initialised in a state with minimum cost ( corresponding to some specific number of mutant and wildtype mtDNA molecules ) . At some later time , the mtDNA populations in the different cells will have drifted apart and even if mean copy numbers ( averaged over all cells ) of w and m are identical to their initial values , the increase in variance between cells means that the overall mean cost ( averaged over all cells ) is higher than it was initially . We assume that the net energy supply per unit time in a state ( w , m ) , called S ( w , m ) , involves the following four terms: ( i ) the energy output per unit time ( si ) produced by the mitochondria; ( ii ) a maintenance cost per unit time ( ρ1 ) to maintain the mitochondria , as their presence imposes some energetic cost ( e . g . mRNA and protein synthesis ) ; ( iii ) a building cost ( ρ2 ) for the biogenesis of new mitochondria; and ( iv ) a degradation cost ( ρ3 ) to degrade mitochondria . We will assume that every mtDNA molecule is associated to a particular amount of mitochondrial volume which we refer to as a ‘mitochondrion’ ( section 4 in S1 File ) . At any time , mitochondria experience a certain energy demand and to meet this demand they need to have a certain resource consumption rate ri ( where i = w , m refers to wildtype or mutant ) . Here we use the term ‘resource’ as an amalgamation of the substrates used for the oxidation system . We need to specify the relationship between the power supply ( s ) and the rate of resources consumed ( ri ) by mitochondria . We use two different models s ( ri ) which are discussed further in section 3 in S1 File s ( r w ) = ϕ ( r w - β ) s ( r w ) = 2 s m a x 1 + e - k r w - 1 . 1 s m a x ( 7 ) where ϕ , β , k and smax are constants respectively describing the mitochondrial efficiency , a basal proton leak-like term , the saturation rate of the efficiency , and the maximum power supply ( section 4 in S1 File ) . We assume that pathological mutants can have a deficient electron transport chain ( which may support a smaller flux leading to a lower resource consumption rate for mutants and therefore a lower ATP production rate ) and a lower energy production efficiency , leading to the following mutant energy output: ϵ2s ( ϵ1rw ) . Here , ϵ1 , ϵ2 ∈ [0 , 1] describe the mutant resource uptake rate and the mutant energy production efficiency relative to that of a wildtype , respectively . In the main text we set ϵ2 = 1; other values of ϵ2 are discussed in section 4 . 7 in S1 File . The mitochondrial maintenance cost is denoted by ρ1 and corresponds to the energetic cost required to maintain the mitochondrion that contains the mtDNA . This energetic costs involves factors like the synthesis and degradation of mitochondrial proteins and enzymes . We assume the maintenance cost is the same for wildtype and mutant mitochondria ( though for some mutations this is quite possibly not the case ) . The net energy supply per unit time , S ( w , m ) , then follows as Eq 3 . To determine the value of rw for a given state ( w , m ) , we first check whether the demand D ( which we assume is a constant ) can be satisfied by supply S ( w , m ) . If it can , we set Eq ( 3 ) equal to D and solve for rw , i . e . we assume that if possible , the mitochondria will exactly satisfy demand . It may , however , not be possible to satisfy demand , which can be because of two reasons: i ) there are not enough mitochondria present to produce enough energy , or ii ) the resource supply rate , R ( a constant ) , is not enough to meet demand . In the former case , we set rw = rmax ( a specified maximum resource consumption rate per mitochondrion ) : the mitochondria work as hard as possible to keep their energy output closest to demand . In the latter case , we assume that the total available resource supply is shared equally between the mitochondria: r w = R w + ϵ 1 m . Further details of the cost function are given in sections 3–5 in S1 File . The parameters used in our cost function are summarised in S2 Table and motivated in section 4 in S1 File . Despite our model being simple , most parameters are biologically interpretable . Experimentally , cells are transfected with two mtZFN monomers: one which binds selectively to mutant mtDNAs , and one that binds mutants and wildtypes with equal strength [62] . We simplify this picture by assuming an ‘effective’ mtZFN pool and use [ZFN] to denote its concentration . The increase in mtDNA degradation rate caused by the mtZFNs is then assumed to be proportional to [ZFN] . Nucleases are imported into the cell and then degrade over time , meaning that their concentration in the cell ( and in the mitochondria ) may be approximated by an immigration-death model: d [ Z F N ] ( t ) d t = I ( t ) - μ z [ Z F N ] ( t ) ( 8 ) where I ( t ) and μZ are the immigration and death rates of the effective mtZFN pool , respectively . In recent experiments [36] , nucleases are expressed for short times meaning that the immigration rate will increase sharply at the start of the treatment after which it decreases over time: we chose to model I ( t ) as an exponentially decaying function , I ( t ) = I0e−bt , where I0 denotes the initial rate directly after the treatment is initiated and b is a constant describing the duration of the treatment . The mtZFN concentration now becomes [ Z F N ] ( t ) = I 0 μ z - b ( e - b t - e - μ z t ) ( 9 ) which is shown for various parameter values in S8A Fig . The data we use to fit our models concerns heteroplasmy and total copy number measurements over four rounds of treatment , each treatment consisting of mtZFN transfection followed by a 28-day recovery period . During this recovery period , total copy numbers recover their initial values due to cellular feedback control . The increase in mtDNA death rate due to the presence of the mtZFNs , μZFN , is given by μ Z F N ( 28 · i < t < 28 · ( i + 1 ) ) = μ + ∑ j = 0 i [ Z F N ] ( t - 28 · j ) ( 10 ) where i = 0 , 1 , 2 , 3 indicates the treatment round . This equation is simply stating that new mtZFNs are added every 28 days . Death rates for m and w are now assumed to be μ ( t ) w = μ + ξ · μ Z F N ( t ) μ ( t ) m = μ + μ Z F N ( t ) ( 11 ) where μ denotes the baseline degradation rate and ξ represents treatment selectivity ( e . g . when ξ = 0 there is no off-target cleavage ) . To fit our nuclease model to recently obtained experimental data [36] , we use Eq ( 4 ) with μ replaced by μ ( t ) w or μ ( t ) m and λ ( w , m ) given by Eq ( 1 ) : d w d t = w [ c 1 ( w o p t - ( w + δ m ) ) - ξ · μ Z F N ( t ) ] d m d t = m [ c 1 ( w o p t - ( w + δ m ) ) - μ Z F N ( t ) ] ( 12 ) Total mtDNA copy numbers in pre-treatment 80% heteroplasmy cells were measured using quantitative PCR ( section 6 . 4 in S1 File ) and were found to be 889 ± 214 ( S . E . , n = 3 ) . We therefore assume an initial total copy number of 900 , meaning w and m were initialized at 0 . 2 ⋅ 900 = 180 and 0 . 8 ⋅ 900 = 720 , respectively . These evolution equations incorporate cellular feedback control as well as the nuclease treatment which occurs in cycles of 28 days . The mtZFN degradation rate was assumed to be μz = ln ( 2 ) day−1 , corresponding to a half-life of 1 day . This is in accord with the experimental observation that almost no mtZFN was present 4 days post-transfection ( with a half-life of 1 day , only 6% of initial copy numbers remain after 4 days ) . MCMC inference was performed using the Python package Pymc3 , a package designed for Bayesian statistical modelling and probabilistic machine learning [94] . A Gaussian error model was assumed , i . e . the observed heteroplasmy y i ( h ) and total copy number y i ( T ) data are given by y i ( h ) = y ^ i ( h ) + N ( 0 , σ h 2 ) y i ( T ) = y ^ i ( T ) + N ( 0 , σ T 2 ) ( 13 ) where y ^ i ( h ) and y ^ i ( T ) denote our predicted heteroplasmy and copy number values obtained by numerically solving Eq ( 12 ) , and we allow for different noise variances for h and T ( in general , different experimental errors are expected as different methods are used to measure h and T ) . A metropolis sampler is used for parameter estimation . Maximum a posteriori ( MAP ) values were found to be ( I 0 , b , c 1 , ξ , δ , σ h 2 , σ T 2 ) M A P ≈ ( 122 . 82 , 46 . 68 , 1 . 90 × 10 - 4 , 0 . 72 , 1 . 26 , 0 . 061 , 0 . 10 ) . Due to a degeneracy in our mtZFN dynamics model ( section 6 . 5 in S1 File ) the MAP values of I0 and b are not necessarily unique at large b ( details in section 6 . 5 in S1 File ) . We explore the ability of our model to account for additional data from Ref . [36] ( Fig 5C and 5D ) which was not included in our inference . Using the MAP values for parameters I0 , b , c1 , δ , σ h 2 and σ T 2 ( based on the data shown in Fig 5A and 5B ) , the maximum likelihood estimate of ξ is obtained based on the additional data , using a Gaussian error model similar to Eq ( 13 ) . This maximum likelihood value is ξ ≈ 0 . 15 . | Mitochondria , best known for their role in energy production , are crucial to the survival of most of our cells . To respond to energetic demands and mitigate against mutational damage , cells control the mitochondrial populations within them . However , the character of these control mechanisms remains open . As experimental elucidation of these mechanisms is challenging , theoretical approaches can help us understand the general principles of cellular control of mitochondria in physiology and disease . Here , we use stochastic modelling to compare control strategies by studying their impact on the dynamics of mitochondrial DNA ( mtDNA ) populations as well as their energetic burden to the cell . We identify optimal strategies for the cell to control against mtDNA damage and preserve energy production and use this theory to explore the action of recently developed mitochondrial gene therapies , which reduce the fraction of mutant mtDNA molecules inside cells . We show how treatment efficiency may depend on pre-treatment distributions of mutant and wildtype mtDNA molecules: treatments are less effective for tissues consisting of cells with highly varying mutant levels , and long-term , rather than short intense , gene therapies should be favoured . | [
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"organelles"
] | 2019 | Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations |
Melioidosis is a life threatening infectious disease caused by the gram-negative bacillus Burkholderia pseudomallei predominantly found in southeast Asia and northern Australia . Studying the host transcription profiles in response to infection is crucial for understanding disease pathogenesis and correlates of disease severity , which may help improve therapeutic intervention and survival . The aim of this study was to analyze gene expression levels of human host factors in melioidosis patients and establish useful correlation with disease biomarkers , compared to healthy individuals and patients with sepsis caused by other pathogens . The study population consisted of 30 melioidosis cases , 10 healthy controls and 10 sepsis cases caused by other pathogens . Total RNA was extracted from peripheral blood mononuclear cells ( PBMC’s ) of study subjects . Gene expression profiles of 25 gene targets including 19 immune response genes and 6 epigenetic factors were analyzed by real time quantitative polymerase chain reaction ( RT-qPCR ) . Inflammatory response genes; TLR4 , late onset inflammatory mediator HMGB1 , genes associated with antigen presentation; MICB , PSMB2 , PSMB8 , PSME2 , epigenetic regulators; DNMT3B , HDAC1 , HDAC2 were significantly down regulated , whereas the anti-inflammatory gene; IL4 was up regulated in melioidosis patients compared to sepsis cases caused by other pathogens . Septicaemic melioidosis cases showed significant down regulation of IL8 compared to sepsis cases caused by other pathogens . HMGB1 , MICB , PSMB8 , PSMB2 , PSME2 , HDAC1 , HDAC2 and DNMT3B showed consistent down regulation of gene expression in melioidosis patients compared to other sepsis infection , irrespective of comorbidities such as diabetes , duration of clinical symptoms and antibiotic treatment . Specific immune response genes and epigenetic regulators are differentially expressed among melioidosis patients and patients with sepsis caused by other pathogens . Therefore , these genes may serve as biomarkers for disease diagnosis to distinguish melioidosis from cases of sepsis due to other infections and therapeutic intervention for melioidosis .
Melioidosis , an emerging infectious disease of public health importance in many tropical countries , is caused by the gram negative bacterium Burkholderia pseudomallei and is commonly reported in southeast Asia and northern Australia [1] . B . pseudomallei is an intracellular facultative pathogen , which is widely distributed in muddy soils such as rice paddy fields and pooled surface water in endemic regions [2] . Skin inoculation is considered as the main route of infection . However , evidence also suggests that inhalation of aerosolized bacteria during extreme weather events such as cyclones , heavy rainfall , storms and ingestion of bacteria via contaminated water are important routes of infection [2 , 3] . The disease is strongly associated with comorbidities such diabetes mellitus , chronic kidney disease , thalassemia , immunosuppression and excessive alcohol intake [1 , 4 , 5] . Melioidosis has a wide spectrum of severity ranging from acute to chronic forms of illness , with common clinical presentations being pneumonia , septicaemia , abscesses and skin lesions [1 , 5] . There are no available vaccines for disease prevention . Melioidosis is challenging to treat as B . pseudomallei is intrinsically resistant to many antibiotics and mortality in endemic regions is high ( 40–50% ) [2] . Recurrence due to reactivation of latent infection is also common [2] . However , standard guidelines for therapy have proven effective in reducing mortality and preventing recurrences [6 , 7] , with early diagnosis playing a crucial role in successful treatment . Therefore , development of new early diagnostic tools and therapeutic strategies is imperative [2] . Studying the host immune responses to infection is crucial for understanding disease pathogenesis , susceptibility to severe disease and immune correlates of protection . Gene expression profiling of important host factors from peripheral blood , which constitutes an accessible source of circulating immune cells , provides key insights into host responses and defense mechanisms against infection[8] . Several studies have shown an association between expression levels of cytokines and disease progression and severity [9–13] . Further evaluation of gene expression at the mRNA level can help us to establish useful disease biomarkers and immune correlates of B . pseudomallei infection . Toll-like receptors ( TLR ) play a major role in host defense , as they detect host invasion by pathogens and initiate immune responses that form the crucial link between innate and adaptive immune responses , thus influencing disease progression[14 , 15] . Several studies have suggested that TLR’s play a significant role in host susceptibility to melioidosis [13 , 16–18] . A broader study to determine TLR receptor expression and signaling pathways could provide insights for understanding disease susceptibility and progression following B . pseudomallei infection . Major histocompatibility complexes ( MHC ) and genes associated with antigen presentation pathways play an important role in host defense mechanisms against intracellular pathogens . Gene expression profiling studies on melioidosis also suggest that proteasomes associated with antigen presentation pathways have an important role in disease progression and severity[8] . Epigenetic modifications , such as DNA methylation and histone modifications are regulatory mechanisms which have a considerable impact on gene transcription and , thereby , pathophysiological processes leading to altered risk for disease [19] . Several studies have shown that the enzymes responsible for these epigenetic modifications are dysregulated in several common diseases , playing an influential role in initiation and progression of the disease by modulating host immune responses , inflammation and intracellular host defenses [19–25] . Studying these epigenetic regulators which have already been implicated in host response to infection by pathogens like bacteria and viruses , can facilitate better understanding of the molecular basis of disease pathogenesis and susceptibility to severe melioidosis[24] . Diagnosing melioidosis based on clinical symptoms from a broad range of infection and septicaemic conditions can be challenging , thus requires tedious , time consuming laboratory diagnostic tests for disease confirmation . Such laboratory facilities may not be available in rural hospital settings and rapid diagnostic point of care tests would be very useful in early diagnosis of infection , provided diagnostic biomarkers for melioidosis are identified . Therefore , studies comparing host immune responses during melioidosis and septicaemic conditions caused by other pathogens , could potentially identify differentially expressed immune markers to serve as diagnostic markers and in monitoring antibiotic treatment regime during therapeutic intervention . In this study we aimed to analyze the gene expression profiles of important cytokines , TLR’s , genes associated with antigen presentation pathways and cell mediated immunity and epigenetic factors in melioidosis patients , patients with sepsis infection caused by other pathogens and healthy individuals in order to establish correlations with disease biomarkers . Our study on human host mRNA expression profile is the first reported study in a Sri Lankan melioidosis patient cohort and has identified significant differential expression of key immune response genes and epigenetic regulators during melioidosis infection .
Nationwide active surveillance for melioidosis was established in multiple state and private hospitals throughout Sri Lanka , with ethics approval from the Ethics Review Committee , Faculty of Medicine , University of Colombo , Sri Lanka and Office of Human Use and Ethics ( OHU&E ) of U . S . Army Medical Research Institute of Infectious Diseases ( USAMRIID ) ; and U . S . Army Medical Research and Material Command- Office of Research Protection- Human Research Protection Office ( USAMRMC-ORP-HRPO ) . Patients fitting the clinical case definition of melioidosis i . e . febrile illness for more than 5 days , pneumonia , septic arthritis , skin lesions , septicemia , lung , soft tissue or deep abscess were recruited for initial screening for melioidosis with informed consent . Blood , pus and other patient specimens were collected for bacterial cultures and serum samples were collected for the indirect haemagglutination ( IHA ) antibody test . Any positive bacterial cultures were further screened and confirmed as B . pseudomallei by PCR . All samples for the study were collected between September 2014 and December 2015 . Patients who were culture positive for B . pseudomallei and / or had high antibody ( >640 ) titers by the IHA test were recruited for our study with written informed consent and classified as cases of melioidosis . Culture positive samples were considered as confirmed cases of melioidosis . Samples with an antibody titre of >640 by IHA testing were considered as probable cases of melioidosis . We also recruited healthy individuals and patients fitting the clinical definition of severe sepsis/septic shock ( as per the 2012 International Guidelines for Management of Severe Sepsis and Septic shock ) who were negative for B . pseudomallei , as controls for our gene expression profiling study[26] . Primary isolation was done at the admitting hospital and relied on conventional culture techniques for blood , sputum , pus and other specimens . Bacterial isolates that were oxidase positive , gentamicin-resistant and gram-negative bacilli were forwarded to the reference laboratory in Colombo where they were sub-cultured to establish pure growth and maintained at −70°C in 15% brain heart infusion ( BHI ) glycerol for subsequent definitive tests . Bacteria were resuscitated by subculture onto 5% blood agar and incubated for 24 h at 37°C to give single colony growth for all subsequent tests . A single colony of B . pseudomallei grown on blood agar from patients sample was re-suspended in ultrapure water . The suspension was heated at 95°C for 10 min and centrifuged at 13500 x g to pellet the cell debris . The supernatant was used as the template for all subsequent PCR assays . Real time PCR assay was done for gene targets of the lpxO , YLF and BTFC gene clusters using the primers and methods described in Merritt et al , 2006 and Tuanyok et al , 2007 respectively [27 , 28] . Antibody testing against B . pseudomallei antigen was performed using an in-house method adapted from Alexander et al . 1970 . Antigen was prepared from a heat killed culture supernatant of a Sri Lankan B . pseudomallei strain BPs7 . A 1/80 diluted antigen prepared was used to sensitize sheep erythrocytes . Serum samples were heat inactivated at 56°C for 30 minutes and tested by serial dilution from 1/10 to 1/10 , 240 with sensitized sheep erythrocytes and the highest dilution at which hemagglutination occurred was recorded as end point titer [29] . 10 ml of whole blood was collected from patients/volunteers after informed consent , of which 7ml were collected into B . D vacutainer mononuclear cell preparation tubes ( catalog no:362761 ) for lymphocyte purification and 3ml were collected into BD vacutainer EDTA tube for plasma collection . The lymphocytes were purified using the Ficoll fractionation method as per manufacturer’s instructions and lysed with RLT buffer ( Qiagen RNeasy mini kit-catalog no: 74104 ) , homogenized and stored at -80°C for total RNA extraction . Total RNA was extracted from the stored cell lysate samples using the Qiagen RNeasy mini kit ( catalog no:74104 ) as per manufacturer protocol . 30μl of eluted RNA was stored at -80°C until further use . RNA extracted from 2 million PBMC’s was used for cDNA synthesis as the standard for all samples analyzed by RT-qPCR . cDNA synthesis for a 25μl reaction was carried out with 2μl of Qiagen genomic DNA wipeout buffer ( catalog no:205311 ) , according to manufacturer’s recommendations and incubated for 10 minutes at 42°C . 0 . 5μl each of Promega random primers ( catalog C1181 ) and Promega oligo dT 15 primers ( catalog no: C1101 ) was added to the reaction and incubated at 70°C for 5 minutes . A separate 10 μl reaction mixture containing 5μl Promega M-MLV 5X Reverse transcriptase buffer- , 1 . 25μl 10mM Promega dNTP’s - , 0 . 5μl Promega RNasin Plus RNase inhibitor- , 2 . 25μl nuclease free water- and 1μl Promega M-MLV reverse transcriptase- was prepared and added to the earlier reaction mixture ( DNA wipeout reaction ) and incubated at 37°C for 1 hour , followed by 95°C for 3 minutes . The synthesized cDNA samples were stored at -20°C until further use . The primers for the gene expression analysis study ( Table 1 , S2 Table ) were verified by real time PCR followed by agarose gel visualization for correct amplicon sizes . PCR efficiency calculation by standard curve efficiency calculations was performed in triplicates for each primer pair to ascertain PCR efficiency . Real Time qPCR was performed in a 25μl reaction mixture containing 12 . 5μl of RT2 SYBR green ROX qPCR master mix ( catalog no: 330520 ) , 1μl each of 10uM forward and reverse primers , 9 . 5μl of nuclease free water and 1μl of cDNA template , on the Qiagen Q5 plex thermal cycler . The PCR conditions were 94°C-2 min of initial denaturation followed by 35 cycles of 94°C-30 sec , 63°C-1 min , 72°C-1 min . 30 melioidosis cases ( identified as confirmed or probable cases ) , 10 healthy negative controls and 10 sepsis negative controls ( negative for B . pseudomallei ) were analyzed by RT-qPCR . Housekeeping genes GAPDH and 18S rRNA , negative controls with no template , genomic DNA control and 25 target genes were run in duplicates for all samples analyzed . Efficiency corrected relative expression was calculated according to Pfaffl , 2001 [30] . 18S rRNA housekeeping gene was used for normalizing the data for analysis , as it was stably expressed across experimental and control samples . Efficiency corrected delta delta Ct values / relative expression are presented as log values to the base 2 . The statistical analysis was performed using SAS PROC MIXED , version 9 . 4 with statistical significance of relative expression ratios obtained by paired T tests . Values of P<0 . 05 were considered statistically significant .
mRNA expression levels of IL18 , CCL5 and IL12 in PBMC’s were low in all the samples . IL8 , IL15 and HMGB1 were significantly up regulated in other sepsis cases compared to healthy controls ( Table 2 , Fig 1 ) . IL6 , IL8 , IFNγ , TNFα , IL1β and IL15 did not show any statistically significant differential gene expression in melioidosis samples compared to sepsis cases . IL4 showed significant up regulation in melioidosis cases compared to other sepsis cases ( Table 2 , Fig 1 ) while HMGB1 , an inflammatory mediator was consistently down regulated in melioidosis cases compared to other sepsis cases , irrespective of all other factors like comorbidities , duration of fever/clinical symptoms and antibiotic treatment ( Tables 2–5 , Figs 1–3 ) . Septicaemic melioidosis patients showed a similar expression pattern as above , in addition to IL8 being down regulated compared to other sepsis cases ( Table 3 , Fig 1 ) . IL8 down regulation was also seen in early acute melioidosis cases with less than 15 days of fever/clinical symptoms and antibiotic treatment . ( Table 3 , Fig 2 ) . Melioidosis patients with regular alcohol consumption habits also expressed significant down regulation of IL8 compared to other sepsis cases ( Table 5 , Fig 3 ) . TLR2 and TLR4 were significantly up regulated in sepsis caused by other pathogens compared to healthy controls . Melioidosis patients showed significantly down regulated expression of TLR4 while both TLR2 and TLR4 was down regulated in septicaemic melioidosis cases compared to sepsis caused by other pathogens ( Table 2 , Fig 1 ) . MICB , PSMB2 , PSMB8 and PSME2 were significantly up regulated in other sepsis cases compared to healthy controls while these target genes showed no significant differential expression in patients suffering from melioidosis compared to healthy controls . Therefore , the expression of these genes can be considered as down regulated in the melioidosis cohort including septicaemic melioidosis cases compared to other sepsis cases ( Tables 2 and 3 , Fig 1 ) . This differential expression between melioidosis cases compared to sepsis controls was seen consistently , irrespective of other factors like duration of fever/clinical symptoms , antibiotic treatment and associated comorbidities ( Tables 2–5 , Figs 1–3 ) . DNMT3B , HDAC1 and HDAC2 were significantly up regulated in other sepsis cases compared to healthy controls ( Table 2 , Fig 1 ) . These epigenetic markers were significantly down regulated in melioidosis including septicaemic melioidosis cases compared to other sepsis cases ( Tables 2 and 3 , Fig 1 ) . Our results also showed a consistent differential expression pattern of these epigenetic factors irrespective of other factors like duration of fever/clinical symptoms , antibiotic treatment and associated comorbidities ( risk factors ) ( Tables 2–5 , Figs 1–3 ) . Melioidosis patients with a regular alcohol consumption habit also expressed significant down regulation of DNMT3A , apart from a similar differential expression of other markers compared to other sepsis cases ( Table 5 , Fig 3 ) .
Our gene expression analysis of melioidosis patient samples did not reveal any statistically significant differential gene expression pattern in comparison with healthy controls ( Table 2 , Fig 1 ) . The cytokine cascade events following B . pseudomallei infection has been studied in several animal models and show an up regulated mRNA expression of inflammatory cytokines such as IL6 , IL12 , IL10 , IFNγ , TNFα and IL1β within 72 hours of infection[10 , 11 , 31 , 32] . Studies have also shown an elevated level of expression of IL8 , IL6 , IL12 , IL18 , IL15 , and IFNγ in plasma of melioidosis patients[12 , 33] . Our findings are contrary to a published study showing an increased mRNA expression of inflammatory response genes such as IL6 , IL15 , IL10 , IL4 , IFNγ , TNFα and IL1β in melioidosis patients compared to healthy controls [9] . Majority of our samples in the melioidosis cohort , consisted of patients with greater than 10 days of fever/clinical symptoms and antibiotic treatment , in comparison to other studies which have sampled melioidosis patients in early acute phase , within 3 days of antibiotics treatment . This could have been the main reason for the contradicting result . We did not have adequate number of samples in early acute phase of melioidosis ( n = 4 ) to see a statistically significant differential expression compared to healthy controls . Melioidosis patients display differential expression of several immune response genes compared to cases of sepsis resulting from other infections , indicating that the differential expression of these genes can be used as diagnostic marker for melioidosis . IL4 was up regulated in the melioidosis patients , including the diabetic cohort , compared to other sepsis cases ( Tables 2 and 5 , Figs 1 and 3 ) . IL4 plays a major role in B-cell activation and T-cell proliferation , thus acting as a key regulator of humoral and adaptive immunity . Its role as an anti-inflammatory cytokine participating in decreasing the production of Th1 cells and pro-inflammatory cytokines is suggestive of down regulation of inflammatory responses in melioidosis . Up regulated IL4 expression has been reported in melioidosis patients and acute melioidosis models [9 , 13] . Thus , further investigations are currently being carried out on gene expression of IL4 and closely related anti-inflammatory cytokine IL13 in melioidosis patients . IL8 was significantly down regulated in septicaemic melioidosis patients when compared to other sepsis cases , suggesting that it could be a marker of disease severity ( Table 3 , Fig 1 ) . IL8 down regulation in early acute melioidosis cases ( less than 15 days of fever/clinical symptoms and antibiotic treatment ) was also seen compared to other sepsis cases ( Table 3 , Fig 2 ) . A study using a human lung epithelial cell line showed that IL8 production upon B . pseudomallei infection was lower than cells infected with other gram negative bacteria which is in agreement with our findings [34] . Studies have also shown that B . pseudomallei can activate NF-κB and induce IL8 production without involving TLRs[35] . Increased level of plasma IL8 , IL6 concentration being associated with mortality have also been reported[33] . Type 2 diabetes ( T2D ) has been reported to be a significant comorbidity associated to melioidosis , particularly septicaemic cases [36] . A study by Morris et al , showed significantly elevated levels of IL8 in plasma of diabetic cohorts compared to non-diabetics 3 . 5 hours after B . pseudomallei stimulation , suggesting a dysregulated immune response in T2D as underlying factor for susceptibility to melioidosis[37] . Thus IL8 , a key mediator of innate immunity associated with inflammation , being down-regulated in early acute stages of melioidosis and in septicaemic cases needs to be investigated further with a larger sample pool , to get a better understanding of IL8’s role in disease severity . HMGB1 , classified as a late onset mediator of sepsis which may function as a pro-inflammatory and anti-bacterial factor [38 , 39] , was consistently down regulated in melioidosis patients irrespective of other confounding factors like comorbidities ( risk factors ) and duration of clinical symptoms and treatment , compared to other sepsis infection cases ( Tables 2–5 , Figs 1–3 ) . Our findings also reveal a significantly up regulated expression of HMGB1 in other sepsis cases compared to healthy controls . HMGBI has been reported to show high levels of expression in plasma of melioidosis patients , particularly septicaemic melioidosis cases compared to other sepsis infections and has been associated with clinical severity and mortality[39] . Some studies have also shown that HMGB1 , could induce Th2 type response , leading to increased production of anti-inflammatory cytokines like IL4 , IL5 etc , while lowering the Th1 type response [40 , 41] . HMGB1 , plays a key role as an immune modulating factor and its potential role in cell mediated immune dysfunctions ought to be investigated further . TLR2 and TLR4 did not exhibit a differential expression in melioidosis patients compared to healthy controls which is contrary to published studies showing up regulated expression[9 , 16] . This contradiction , we believe is also due to variation in duration of clinical symptoms and antibiotic treatment in the studies . However , TLR4 is significantly up regulated in the sepsis cohort , compared to healthy controls ( Table 2 , Fig 1 ) . As such bothTLR2 and TLR4 are down regulated in the septicaemic melioidosis cases compared to other sepsis infections ( Table 2 , Fig 1 ) , suggesting poor pathogen recognition . Studies have indicated that LPS of B . pseudomallei signals via TLR2 as opposed to TLR4 being the main receptor for other gram-negative bacteria and TLR-mediated dysregulation of immune response plays a major role in disease pathogenesis [13 , 16 , 18] . HMGB1 shows a TLR4 dependent activity , with CD14 playing a key role in activation of TLR4 dependent signaling by HMGB1 has also been reported [42 , 43] . These correspond with our findings of a low level of expression of HMGB1 , which plays a major role in activation of TLR4 mediated immune responses , hence down regulation of inflammatory and defense responses upon B . pseudomallei infection . MICB , PSMB2 , PSMB8 and PSME2 showed consistently low level of expression in melioidosis cohort irrespective of other factors like comorbidities ( risk factors ) , duration of clinical symptoms and antibiotic treatment , compared to other sepsis controls ( Tables 2–5 , Figs 1–3 ) . Low level of expression of these immune response genes which play a major role in antigen presentation and thereby cell mediated immunity , suggests altered defense responses during melioidosis or diminished proteasomal activity at the time of sample collection during later stages of infection . Our findings are contradicting with a similar study that showed differential gene expression pattern of proteasomes and other genes involved in MHC class I & II antigen presentation pathway , where genes PSMB2 , PSME2 , PSMB8 , PSMA5 and HLADMB were over expressed in meliodosis patients compared to other sepsis cases [8] . Variations in duration of clinical symptoms , administration of antibiotics at the time of sampling and associated comorbidities could have played a role in this contradicting result , while taking into consideration that the Pankla et al , 2009 study included samples mostly collected within 48 hours of hospitalization . It is also to be noted that the results of this 2009 study has not been independently verified . There have been extensive studies on the role of epigenetic factors and their association with several communicable and non-communicable diseases [19–21 , 25] . DNA methylation , histone deacetylation and methylation are epigenetic modifications associated with a repressed chromatin state , which contributes to repression of gene transcription[44] . Studies have shown that DNMT3B , exhibits an inverse correlation between gene expression levels and DNA methylation levels[19] . A study by Bonsch et al , 2006 showed that genomic DNA hypermethylation was associated with lower mRNA levels of DNMT3B in patients with alcohol dependence [45] . Study by Zong et al 2015 , showed that reduction of HDAC activity and expression , was associated with disease severity in smokers with chronic obstructive pulmonary disease ( COPD ) [20] . Epigenetic modifications are heavily influenced by practices such as smoking , alcohol dependence[19 , 20] , diseases such as diabetes , cardiovascular and kidney diseases [22 , 23 , 25 , 46] which in turn could play a major role in disease pathogenesis and susceptibility . A recent study on epigenetic changes in human host DNA following B . pseudomallei infection , revealed significant changes in DNA methylation in the vincity of genes involved in inflammatory responses , intracellular signaling , apoptosis and pathogen induced signaling , suggesting that DNA methylation changes could be altering gene transcription thus affecting key immune pathways [47] . High throughput gene expression analysis in melioidosis have not revealed strong association of epigenetic factors with B . pseudomallei infection [8] . However , extensive study in this area is required . While this study on whole-genome transcriptional profiles of septicaemic melioidosis and sepsis caused by other infections has revealed a transcriptional signature [8] , those findings were never validated in an independent study . Therefore , we found it necessary to follow up on those studies , as well as investigate a unique set of target genes involved in epigenetic regulation to evaluate whether the expression of the epigenetics markers were deferentially regulated in melioidosis compared to sepsis caused by other infections . While those genes were not found to be deferentially expressed in whole-genome studies [8] , given the relapse and reactivation of infection in susceptible groups , it was of interest to further investigate the transcription profiles of this set of genes involved in epigenetic modifications . Our study investigated the differential expression of several epigenetic regulators in melioidosis . DNMT3B , responsible for DNA methylation , HDAC1 and HDAC2 , responsible for histone deacetylation were significantly down regulated in melioidosis patients compared to other sepsis cases irrespective of confounding factors like duration of fever/clinical symptoms , antibiotic treatment and associated comorbidities ( Tables 2–5 , Figs 1–3 ) . Thus , correlation between mRNA levels of DNMT’s and levels of methylation ought to be analyzed further . Our comparison of melioidosis patients with regular alcohol consumption habit ( n = 8 ) and sepsis controls ( n = 10 ) showed a similar expression pattern to the entire melioidosis cohort ( n = 30 ) , in addition to down regulated expression of DNMT3A and IL8 ( Table 5 , Fig 3 ) . We could observe a significantly lower level of expression of the epigenetic factors in melioidosis cases compared to sepsis controls , while assessing alcohol consumption as a risk factor , indicating its confounding effect . From our studies we find that epigenetic factors HDAC1 , HDAC2 and DNMT3B show consistent differential expression compared to other sepsis cases , suggesting a role in disease susceptibility and pathogenesis . However , further studies such as DNA methylation arrays with a larger sample pool and further analysis of confounding comorbidities , is required to fully understand the role of epigenetic mechanisms in relation to pathogenesis of melioidosis . The main limitation of our study was that the melioidosis patient samples were collected well after start of antibiotic treatment which may affect immunocompetant cells , which in turn affects the immune response genes investigated . Studies have shown that antibiotics like meropenem exert an immunomodulatory effect , affecting the production of some cytokines in PBMC’s [48] . This may have been the main reason , as to why we could not see any significant differential expression of some key inflammatory response cytokines such as IFNγ , especially between the melioidosis and healthy cohorts , while similar studies have sampled within 3 days of antibiotic treatment . Duration of clinical symptoms ranged from >10 days to >90 days and duration of antibiotics treatment ranged from 1 day to >30 days at the time of blood collection for all the melioidosis samples . Since our sample collection was nationwide , duration between patient identification/ disease confirmation and sampling was substantial due to logistical issues . Thus , due to wide range of duration of infection and limited number of samples with ≤15 days of fever ( n = 4 ) we could not evaluate statistically significant differential expression of inflammatory genes during the early stages of infection . Melioidosis is severely under-reported and under-diagnosed in Sri Lanka and most patients who get hospitalized and diagnosed of the disease , are already in a later stage of infection . This makes it very difficult to carryout investigations with patient samples within an early stage of infection and or anti-microbial therapy . While its important to gather information on expression levels of host factors during early acute phase of infection , it is also imperative to have some understanding on expression levels of important host genes during later stages of infection like in our study , which may be useful to monitor antibiotic treatment regimes . As we see diabetes as a major comorbidity in our experimental cohort , we analyzed our data to see if there was any significant differential expression between diabetic melioidosis cases and non-diabetic melioidosis cases . The gene expression between these two groups were comparable and we could not find any statistically significant differential expression due to diabetes ( S3 Table ) . Therefore , our results show a consistent differential expression of HMGB1 , MICB , PSMB8 , PSMB2 , PSME2 , HDAC1 , HDAC2 and DNMT3B when compared to other sepsis cases , irrespective of comorbidities ( risk factors ) , duration of fever/clinical symptoms and antibiotic treatment , primarily due to melioidosis infection . We also note that gene expression analyses of blood leukocytes only provide insight in immune pathways regulated at mRNA level in circulating cells , hence we look to expand our future study on a proteomic level as well . We also take note of the limitation of IHA test used in this study for diagnosis of melioidosis . Though the sensitivity and specificity of IHA test is limited , it has been used worldwide as a laboratory method for melioidosis diagnosis . The bacterial loads of patient samples are not always high for culture positive results , with general antibiotic treatment playing an intervening role . Hence IHA test results were included , as they serve as a useful reference for melioidosis diagnosis . Our findings did not show significant differential expression among the immune response genes and the epigenetic modifiers in melioidosis cases and the healthy controls . However , our study indicates differential expression in inflammatory responses , defense responses and epigenetic factors during melioidosis compared to other cases of sepsis , thus differential gene expression among the genes under investigation that can distinguish melioidosis cases from other sepsis infections . These findings suggest that the differentially expressed genes during melioidosis should be validated during different stages of infection for their potential as markers of disease diagnosis and for therapeutic intervention . Thus , our future studies shall be aimed at studying gene expression profiles in early and late acute phases of melioidosis and also looking into susceptible groups for further study on disease severity . Studies would also be broadened into the four areas showing differential gene expression pattern- key cytokines , antigen presentation pathways , toll-like receptor signaling pathways and epigenetic chromatin modifying enzymes . Further investigations to confirm these findings in a larger cohort is needed , which may validate the potential of these differentially expressed genes to serve as disease biomarkers that could pave the way for novel diagnostic and therapeutic approaches for melioidosis intervention . | Melioidosis is a life threatening infectious disease caused by a soil-associated gram-negative bacterium , B . pseudomallei . Melioidosis is endemic in southeast Asia and northern Australia; however , the global distribution of B . pseudomallei and the disease burden of melioidosis is still poorly understood . Melioidosis is severely under-reported in several tropical countries in which it is probably endemic and warrants a public health response . A recent research article predicts the global melioidosis burden to be 165 million cases with a predicted 73 million cases from the high risk zone of south Asia . Melioidosis is difficult to treat as B . pseudomallei is resistant to many antibiotics and requires a long course of treatment . Mortality rate remains high in endemic areas with reoccurrence being common . Therefore , it is imperative to diagnose the disease at an early stage and provide vital clinical care to reduce the mortality rate . With limitations in treatment and lack of a vaccine , it is crucial to study the immune response mechanisms to this infection to get a better understanding of disease pathogenesis and susceptibility . Therefore , this study aimed to analyze the gene expression levels of important immune response genes and epigenetic modifiers to establish useful correlation for diagnostic and therapeutic purposes . | [
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] | 2017 | Host gene expression analysis in Sri Lankan melioidosis patients |
Dynamins are large superfamily GTPase proteins that are involved in various cellular processes including budding of transport vesicles , division of organelles , cytokinesis , and pathogen resistance . Here , we characterized several dynamin-related proteins from the rice blast fungus Magnaporthe oryzae and found that MoDnm1 is required for normal functions , including vegetative growth , conidiogenesis , and full pathogenicity . In addition , we found that MoDnm1 co-localizes with peroxisomes and mitochondria , which is consistent with the conserved role of dynamin proteins . Importantly , MoDnm1-dependent peroxisomal and mitochondrial fission involves functions of mitochondrial fission protein MoFis1 and WD-40 repeat protein MoMdv1 . These two proteins display similar cellular functions and subcellular localizations as MoDnm1 , and are also required for full pathogenicity . Further studies showed that MoDnm1 , MoFis1 and MoMdv1 are in complex to regulate not only peroxisomal and mitochondrial fission , pexophagy and mitophagy progression , but also appressorium function and host penetration . In summary , our studies provide new insights into how MoDnm1 interacts with its partner proteins to mediate peroxisomal and mitochondrial functions and how such regulatory events may link to differentiation and pathogenicity in the rice blast fungus .
Dynamins are large GTPase superfamily proteins that are involved in scission ( cleavage of the vesicle from the parent membrane ) of nascent vesicles from parent membranes in eukaryotic cells [1] . Dynamins interact directly with the lipid bilayer at the necks of clathrin-coated pits to sever and release coated vesicles [2–5] . Dynamins contain five domains , including GTPase domain , middle domain , PH domain , GTPase effector domain ( GED ) , and proline rich domain ( PRD ) , while the dynamin-related proteins ( DRPs ) lack one or more of these domains or have additional domains . Dynamins and DRPs participate in a wide variety of cellular processes , including budding mitochondrial fission ( mammalian Dlp1 and Saccharomyces cerevisiae Dnm1 ) and fusion ( mammalian OPA1 , S . cerevisiae Mgm1 and Schizosaccharomyces pombe Msp1 ) , vacuolar fission ( S . cerevisiae Vps1 ) , interferon-induced anti-viral protection ( fish Mx proteins ) , plant cell cytokinesis and membrane fission ( Arabidopsis thaliana DRP proteins ) , as well as pathogen resistance [1 , 6] . Peroxisomes are ubiquitous organelles that participate in a variety of important catabolic and anabolic processes , including reduction of hydrogen peroxide and lipid metabolism [7] . Peroxisomal oxidation , biogenesis and matrix protein importing are also important in cellular growth and differentiation [8–12] . Among two groups of peroxisomal proteins that have a pronounced influence on peroxisome size and abundance , DRPs are required for the scission of peroxisomal membranes [13] , while Pex11-type peroxisome proliferators are involved in the proliferation of peroxisomes [14–16] . In S . cerevisiae , peroxisomal division depends on Fis1 and WD40 domain-containing adaptor proteins Mdv1 and Caf4 that recruit Dnm1 to the peroxisomal membrane [17 , 18] . The DRP involvement in peroxisomal fission has also been found in plants . In A . thaliana DRP3A mutants , peroxisomes are elongated and reduced in number with aberrant mitochondria in contrast to the wild type plant [19] . Mitochondria are ubiquitous subcellular organelles essential to cellular physiology , energy supplies , amino acid biogenesis , certain metabolites , and programmed cell death . Mitochondria are dynamic organelles undergoing constant fusion and fission during cell division [20] . The equilibrium between fission and fusion is controlled by the activity of conserved molecular machines driven by self-assembling GTPases and DRPs [21] . Present evidence indicates that dynamin Dnm1 is the master regulator of mitochondrial division . Dnm1 self-assembles and exists at steady state in punctate structures in association with the outer mitochondrial membrane , often at points of membrane constriction and fission . In the budding yeast , four subunits of mitochondrial fission complex are identified as Dnm1 , Fis1 , Mdv1 , and Caf4 [22–24] . Dnm1 forms atypical helical filaments that first encircle , then constrict the membrane-anchored protein Fis1 [25–28] . Dnm1 , mammalian Dlp1 , and Caenorhabditis elegans Drp1 are also known to mediate mitochondrial fission [26 , 29] . Interestingly , Fis1 is required for the proper assembly and activation of the fission-mediating complex for mitochondrial division [22 , 30] . Fis1 is proposed to be anchored in the outer mitochondrial membrane with its N-terminal region exposed to the cytosol and a short C-terminal tail protruding into the mitochondrial inter-membrane space [22] . In mammals , Fis1 recruits Dlp1 to peroxisomes and mitochondria with the assistance of adaptor protein Mdv1 or Caf4 [31] . Peroxisome-related virulence has been reported in pathogenic fungi . For example , the peroxisome-related proteins Pex11A and Pex19 are required for full virulence in M . oryzae [14 , 28] . A recent study showed that a peroxisome-related protein , Pef1 , mediated peroxisomal fission during appressorium formation is important for infection of the rice blast fungus [32] . Mitochondria regulate virulence in Heterobasidion annosum [33] and the loss of mitochondrial function in Candida glabrata and C . albicans results in a defect in virulence [34–36] . The changes in mitochondrial morphology toward more tubular-structured organelles play a positive role in virulence in Cryptococcus gattii [37] . Another study showed that reduced virulence associated with dysfunctional mitochondria is probably due to reduced fitness , metabolic changes , and sensitivity to oxidative stress caused by defective respiration [38] . Autophagy is a common and evolutionarily conserved process where cytosol and organelles can be degraded and recycled in all eukaryote cells [39–41] . Autophagy not only recycles intracellular components to compensate for nutrient deprivation but also selectively eliminates organelles to regulate their number and maintain quality control [42] . It was first identified by electron microscopy and considered as nonselective for its cytosolic cargos [43] . Later analyses identified different types of selective autophagy , including glycogen autophagy [44] , mitophagy [45] and pexophagy [46] . A set of evolutionarily conserved autophagy-related genes ( ATG genes ) were initially identified in yeast [47 , 48] . The scaffold protein Atg11 and the pexophagy receptor Atg36 interact with both Dnm1 and Vps1 , which occurs in mitochondria and peroxisomes [49] . In M . oryzae , a total of 22 ATG genes were identified , with MoATG8 and MoATG24 being implemented in autophagy induction and glycogen autophagy during conidiogenesis , respectively [50] . Although relationships and functions among Dnm1 , Mdv1 and Fis1 were explored in S . cerevisiae and mammals , they were less understood in filamentous and pathogenic fungi , including M . oryzae . Recent studies have showed that PEX proteins such as Pex11A and Pex19 are required for peroxisomal proliferation and virulence in M . oryzae [14 , 28] . Here , we described MoDnm1 function in peroxisomal and mitochondrial fission and in the appressorium-mediate infection of the rice blast fungus . We also described that MoDnm1-mediated peroxisomal and mitochondrial fission involves conserved functions from mitochondrial fission protein MoFis1 and WD40 domain-containing adaptor protein MoMdv1 .
Since dynamin proteins are highly conserved , we searched the available genomes of M . oryzae ( http://www . broadinstitute . org/annotation/genome/magnaporthe_comparative/MultiHome . html ) by BLAST using the S . cerevisiae dynamin Dnm1 protein [1] as the reference [51] and identified the MGG_06361 genetic loci encoding the Dnm1 homolog MoDnm1 . Since previous studies indicated that DRPs are distinct from other GTPases by having a large GTPase domain ( ~300 amino acids ) , a middle domain , and a GED domain [1] , we identified three DRP candidates: MoVps1 ( MGG_09517 ) , MoDnm2 ( MGG_02114 ) , and MoDnm3 ( MGG_02648 ) . Sequence analysis revealed that MoDnm1 is also homologous to Gaeumannomyces graminis GgDnm1 , MoVps1 shares a high sequence conservation with Rattus norvegicus Dyn1 and Homo sapiens Drp1 , MoDnm2 and MoDnm3 are exclusive to M . oryzae ( Fig 1A ) , and all four M . oryzae proteins contain large GTPase and GED domains ( Fig 1B ) . These results suggest that dynamin family members of M . oryzae are conserved with many other organisms , with Dnm1 being the most conserved dynamin protein . To examine functions of dynamin proteins in M . oryzae , we generated deletion mutant specific to each dynamin-related gene . For reasons unknown , a ΔMovps1 ( MGG_09517 ) mutant could not be obtained despite screening of more than three thousand transformants . This may indicate that MoVps1 plays an essential role in M . oryzae . For the other three dynamin-related genes ( MGG_06361 , MGG_02114 and MGG_02648 ) , deletion mutants were generated ( S1A and S1B Fig ) and examined for phenotypic changes in vegetative growth and conidiation . The ΔModnm1 mutant displayed significantly attenuated growth in either the complete medium ( CM ) or minimal medium ( MM ) , and showed marked reduction in conidiation in comparison to the wild type strain ( Guy11 ) . No phenotypic changes were found in the ΔModnm2 and ΔModnm3 mutants ( S1 Table ) . These results indicate that MoDnm1 , but not MoDnm2 and MoDnm3 , is involved in vegetative growth and conidiation . To examine the role of dynamins in fungal pathogenicity , we inoculated conidial suspensions of the wild type , ΔModnm1 , ΔModnm2 , ΔModnm3 mutants and complemented strains on the susceptible rice cultivar CO-39 . The ΔModnm1 mutant produced small , restricted lesions , in contrast to ΔModnm2 and ΔModnm3 mutants that were as virulent as the wild type at 7 days post-inoculation ( dpi ) . The similar result was also obtained on the detached barley cultivar Four-arris leaves ( Fig 2A ) . Disease lesions on rice leaves were also quantified by a ‘lesion-type’ scoring assay [52] . The ΔModnm1 mutant yielded more restricted small dark brown spot lesions ( type 1 ) and less type 2 to 5 lesions , in comparison to the wild type and complemented strains ( Fig 2B ) . To further elaborate these observations , we examined penetration and invasive hyphal ( IH ) growth in rice sheath cells . The wild type strain showed at least a rate of 90% successful appressorium penetration events , with more than 80% of type 3 ( extend but limit in one cell ) and 4 ( extend to surrounding cells ) infectious hyphae at 36 hours post-inoculation ( hpi ) . In contrast , only 32% of ΔModnm1 appressoria had successful penetration with less than 18% of penetration sites showing type 3 and 4 invasive growth ( Fig 2C and 2D ) . Moreover , ΔModnm1 mutant invasive hyphae were limited within one cell at 48 hpi , whereas wild type invasive hyphae extended to surrounding cells ( Fig 2E ) . All these results indicate that MoDnm1 plays an important role in invasive hyphal growth and host colonization . In the budding yeast and mammals , Dnm1 functions in peroxisomal and mitochondrial fission through a complex with Fis1 and Mdv1 proteins [24 , 26 , 29–31 , 53] . To test whether MoDnm1 functions similarly , we identified M . oryzae MGG_06075 ( MoFis1 ) and MGG_01711 ( MoMdv1 ) as yeast Fis1 and Mdv1 homologs , respectively , and tested their interactions through yeast two-hybrid screen . MoDnm1 and MoMdv1 , and MoMdv1 and MoFis1 were respectively co-transformed into the yeast host , and selected on SD-Leu-Trp-His-Ade medium containing 1 mM X-gal and 5 mM 3-AT ( 3-amino-1 , 2 , 4-triazole ) . MoMdv1 was found to interact with both MoDnm1 and MoFis1 ( Fig 3A ) . These interactions were further validated by the protein pull down assay in which GST-Dnm1 and GST-Fis1 were bound to His6-Mdv1 ( Fig 3B ) . Interestingly , a direct interaction cannot be established between MoDnm1 and MoFis1 as GST-Dnm1 could not bound to His6-Fis1 , suggesting that MoDnm1 does not directly couple to MoFis1 and MoMdv1 functions as an adaptor linking MoDnm1 with MoFis1 . To examine functions of MoFis1 and MoMdv1 , we also generated respective mutant strains ( S1C Fig ) . Similar to the ΔModnm1 mutant , ΔMofis1 and ΔMomdv1 mutants were significantly attenuated in growth and conidiation , with normal conidium germination and appressorium formation , when compared with the wild type and complemented strains ( Table 1 ) . These results indicate that MoFis1 and MoMdv1 share similar functions with MoDnm1 in the regulation of vegetative growth and conidial formation . We also assessed virulence of the ΔMomdv1 and ΔMofis1 mutant on rice and found that both caused smaller and less lesions than the wild type strain ( Fig 4A and 4B ) . To verify this finding , we also performed penetration and invasive hyphal growth assays in rice sheath and found that less than 11% and 14% of appressorium penetration was successful in ΔMofis1 and ΔMomdv1 mutants , respectively . Moreover , less than 5% of penetration sites exhibited type 3 and 4 lesions by both mutants ( Fig 4C and 4D ) . Limited infectious hyphae of ΔMofis1 and ΔMomdv1 mutants were observed after 48 h inoculation , which is similar to the ΔModnm1 mutant ( Fig 4E ) . Taken together , all these results indicate that MoMdv1 and MoFis1 play important roles in virulence by affecting host penetration and invasive hyphal growth . Appressorium-mediated penetration requires high internal turgor pressure to generate sufficient mechanical force to breach the rice leaf cuticle [10 , 54 , 55] . As appressorial formation appears to be normal in the ΔModnm1 , ΔMofis1 , and ΔMomdv1 mutants , we examined whether the defect in turgor generation resulted in the reduction in pathogenicity . In incipient cytorrhysis ( cell collapse ) assay using the 1–4 molar concentration of glycerol solution , appressoria of the ΔModnm1 , ΔMofis1 and ΔMomdv1 mutants showed higher collapse ratios in comparison to that of the wild type ( S2 Fig ) . These results suggest that the reduced pathogenicity of the ΔModnm1 , ΔMofis1 , and ΔMomdv1 mutants are due to the aberrant development of functional appressoria . In M . oryzae , effective transfer of glycogen and triacylglycerol is required for appressorial maturation and appressorium-mediated host penetration [10 , 55–57] . We therefore examined the cellular distribution of glycogen and lipid bodies during appressorium development . Upon iodine-staining abundant glycogen was seen in conidia , germ tubes , and appressoria . We found that mobilization of glycogen was retarded in the ΔModnm1 mutant with glycogen depletion in conidia until 12 h , in comparison to 6–8 h in the wild type ( S3A Fig ) . Next , we investigated the distribution of lipid bodies by Nile red staining and found that there was no difference on intracellular lipid storage between the ΔModnm1 mutant and the wild type ( S3B Fig ) . Additionally , no difference was found in the distribution of glycogen and lipid during appressorium morphogenesis between the ΔMofis1 and ΔMomdv1 mutants , and the wild type strain ( S3C and S3D Fig ) . These results suggest that the defects of these mutants in appressorium turgor pressure are not attributed to the mobilization of glycogen and lipid droplets mobilization . Endocytosis is the process through which cells internalize portions of their plasma membrane along with extracellular material and it is fundamentally important in cell function . The dynamin-linked GTPase function has a critical role in membrane remodeling and endocytic membrane fission events [3] . To investigate whether the loss of MoDnm1 results in defects in endocytosis and intracellular transport , we observed endocytosis by staining the hyphae with FM4-64 . We found that FM4-64 was internalized within 1 min after exposure in wild type , but a delay of approximate 7 min in the ΔModnm1 mutant ( Fig 5 ) . Like MoDnm1 , MoFis1 and MoMdv1 were also found to be required for endocytosis , as agreed by the similar delay in FM4-64 internalization . Following exposure , the dye was not detected until up to 20 min in the ΔMomdv1 mutant and 8 min in the ΔMofis1 mutant ( Fig 5 ) . To further characterize MoDnm1 functions , we examined the subcellular localization of MoDnm1 . MoDnm1 was fused with a GFP tag at the N-terminus ( GFP-Dnm1 ) and expressed in the ΔModnm1 mutant . Punctate green fluorescence was observed in both vegetative hyphae and conidia . To test if GFP-Dnm1 is located in organelles , an RFP-PTS1 ( PTS1 encodes peroxisomal targeting signal 1 ) was introduced into the same strain . Red and green fluorescence was detected and overlapped ( Fig 6A ) , suggesting that MoDnm1 is localized to the peroxisomes . In addition , hyphae and conidia of the ΔModnm1 mutant transformed with GFP-Dnm1 were incubated with a far red-fluorescent dye ( MitoTracker , abs/em ~644/665 nm ) that stains mitochondria in live cells . As shown in Fig 6B , GFP fluorescence was observed as distinct spots partially co-localized with the mitochondrial structures , in line with Dnm1p localizations reported in other species [26] . These results indicate that MoDnm1 is localized to both peroxisomes and mitochondria . Since the mammalian Dlp1 and S . cerevisiae Dnm1 proteins mediate peroxisomal and mitochondrial fission in complex with Fis1 and Mdv1 proteins [24 , 26 , 29–31 , 53] , we also tested the localizations of MoFis1 and MoMdv1 using the same method . Fluorescence signal of GFP and RFP was detected and overlapped , indicating that both MoFis1 and MoMdv1 are located to the peroxisomes . Moreover , studies of mitochondria localization of these two proteins revealed that both MoFis1 and MoMdv1 are partially co-localized with mitochondria ( Fig 6C–6F ) , consistent with an earlier report [58] . Taken together , the observation of colocalization supports that MoDnm1 , MoFis1 , and MoMdv1 function as a complex . To examine functions of MoDnm1 , MoMdv1 , and MoFis1 in peroxisomal fission , we expressed RFP-PTS1 in the wild type , ΔModnm1 , ΔMofis1 , ΔMomdv1 mutants , and the complemented strains Microscopic observations showed the presence of independent punctate pattern of RFP-PTS1 in hyphae of all strains , but the numbers of peroxisomes were markedly reduced in the ΔModnm1 , ΔMofis1 and ΔMomdv1 mutants ( Fig 7A ) . In addition , all three deletion mutants showed a higher percentage of peroxisomes with plaque and tubular morphology in conidia in comparison to those of the wild type and the complemented strains ( Fig 7B and 7C ) . Furthermore , we observed the morphology of peroxisomes in appressoria and found more tubular peroxisomes and less globular peroxisomes ( Fig 7D ) . Transmission electron microscopy ( TEM ) was finally used to compare the impaired fission on peroxisome organization in the mutants . Peroxisomes in the ΔModnm1 , ΔMofis1 and ΔMomdv1 mutants appeared dramatically enlarged compared to the wild type ( Fig 8A ) . The decreased peroxisome numbers and altered peroxisomal morphology suggest that MoDnm1 , MoMdv1 and MoFis1 are key regulators of peroxisomal proliferation . In addition , we expressed the mitochondria localization fusion protein MoIlv2-GFP in all strains to determine mitochondrial fission conditions . Green fluorescence that indicates mitochondria was observed as an elongated tubule in hyphae , conidia and appressoria of the deletion mutants , but as punctate patterns in different stages of the wild type and the complemented strains ( Fig 9A–9C ) . Electron microscopic comparison revealed that mitochondria in the ΔModnm1 , ΔMofis1 , and ΔMomdv1 mutants all appeared dramatically enlarged and elongated ( Fig 8B ) . Taken together , these results suggest that MoDnm1 mediates peroxisomal and mitochondrial fission in complex with MoMdv1 and MoFis1 . To reveal the underlying role of dynamins and the fission complex in pathogenicity , we further studied the role of peroxisomal and mitochondrial fission in appressorial functions . We treated the appressoria of the wild type strain with the peroxisomal fission inhibitor , GW9662 [59] and mitochondrial fission inhibitor Midvi-1 [60] , respectively . Fewer and enlarged peroxisomes were observed in appressoria after incubation with GW9662 for 24 h , and fewer mitochondrial punctate patterns were observed after incubation with Midvi-1 for 24 h ( Fig 10A ) . In addition , the pathogenicity was significantly reduced after incubation with either GW9662 or Midvi-1 for 24 h ( Fig 10B ) . All these results are consistent with that both peroxisomal and mitochondrial fission are required for infection . To explore whether the attenuated virulence in ΔModnm1 , ΔMomdv1 , and ΔMofis1 mutants is due to defects in peroxisomal and mitochondrial fission , appressoria from the mutants and wild type strain were treated with oleate and glycerol to induce peroxisomal and mitochondrial fission , respectively [32 , 61–63] . Globular peroxisomes and mitochondria were markedly increased in deletion mutants as well as in the wild type after treated with 0 . 1% V/V oleate or 1% V/V glycerol for 10 h , respectively ( S5A–S5D Fig ) . In addition , the post treatment conidia of deletion mutants and the wild type were drop-inoculated to barley . The wild type treated with both inducers showed at least a 30% increase of type 4 infectious hyphae at 20 hpi . The ΔModnm1 , ΔMomdv1 , and ΔMofis1 mutants treated with oleate showed about 19 , 37 and 45% increase of type 4 infectious hyphae at 26 hpi , and treated with glycerol showed 16 , 27 and 42% increase of type 4 infectious hyphae , respectively ( S5E and S5F Fig ) . The increased virulence by inducing peroxisomal and mitochondrial fission suggests that the loss of appressorial function in ΔModnm1 , ΔMomdv1 , and ΔMofis1 mutants is partially due to impaired peroxisomal and mitochondrial fission . To validate that the peroxisomal proliferation is important for infection , we deleted the PEX11A gene [14] in the ΔModnm1 , ΔMofis1 , and ΔMomdv1 mutants and characterized these mutants ( S4A–S4C Fig ) . They all showed similar growth rates to the ΔModnm1 , ΔMofis1 , and ΔMomdv1 mutants , respectively ( S4D Fig ) . The double mutants showed a significant reduction in virulence compared to the single mutant lines in the assays using rice and detached barley leaves for close examination ( Fig 11A ) . These results demonstrate that all three double deletion mutants have more severe defects in virulence than that in the single deletion mutants . In addition , elongated conidia were observed in the ΔMopex11A/ΔMofis1 and ΔMopex11A/ΔModnm1 mutants ( S4E Fig ) . Furthermore , to observe whether peroxisomal proliferation was inhibited in these double deletion mutants , RFP-PTS1 was expressed in these strains . Peroxisomes showed plaque-like morphology in all three double deletion mutants similar to that in ΔMopex11A mutant ( Fig 11B ) . These results indicate that MoPex11A , MoDnm1 , MoMdv1 , and MoFis1 all have functions in peroxisomal proliferation but in different ways . Mitochondria are major compartments for the TCA cycle to supply energy while peroxisomes are required for fatty acid degradation and the glyoxylate cycle [64] . To investigate whether abnormal mitochondrial fission would result in lack of energy for penetration and extending , exogenous ATP was added in conidia suspensions of the deletion mutants before infection . However , this did not rescue the virulence of the ΔModnm1 , ΔMomdv1 or ΔMofis1 mutants ( S6A Fig ) . HPLC assays also showed that these three deletion mutants contain similar concentration of ATP when compared with the Guy11 strains in mycelia ( S6B Fig ) . Therefore , these results suggest that the Dnm1-mediated alteration of mitochondria morphology might have no effect on ATP production . Pexophagy and mitophagy are cellular process to selectively remove peroxisomes and mitochondria through autophagy [42 , 65] , and peroxisomal and mitochondrial fission are required for pexophagy and mitophagy [49 , 66] . Since peroxisomal and mitochondrial fission were severely inhibited in ΔModnm1 , ΔMofis1 , and ΔMomdv1 mutants , we determined whether these three proteins are required for selective autophagy , mitophagy and pexophagy . Pexophagy was monitored based on the amount of stable Pex14 protein , a peroxisomal integral membrane protein by immunoblot analysis . When pexophagy is induced , peroxisomes , along with Pex14 , are delivered into the vacuole for degradation . As a result , fewer stable Pex14 was detectable in the ΔModnm1 , ΔMofis1 , and ΔMomdv1 mutants than that in the wild type ( Fig 12A ) . Similarly , mitophagy was monitored by assessing the total amount of stable porin , a mitochondrial marker protein by immunoblot analysis [61] , and few stable porin was detected in the ΔModnm1 , ΔMofis1 , and ΔMomdv1 mutants than that in the wild type ( Fig 12B ) . Overall , these results suggest that MoDnm1 , MoFis1 , and MoMdv1 are important for mitophagy and pexophagy . We next determined whether MoDnm1 , MoMdv1 , and MoFis1 are involved in nonspecific autophagy . GFP-Atg8 was expressed in the wild type , ΔModnm1 , ΔMofis1 , and ΔMomdv1 strains to monitor nonspecific autophagy . When autophagy is induced , autophagic bodies , along with GFP-Atg8 , are delivered into the vacuole for degradation . In the ΔModnm1 , ΔMofis1 , and ΔMomdv1 mutants , a vacuolar GFP signal was detected after starvation in MM-N for 2 or 5 h , with a reduced number of vacuole ( 2 h: 38 , 17 and 42% , 5 h: 71 , 49 and 74% , respectively ) containing autophagic bodies , compared to that of the wild type strain ( 2 h: 80% , 5 h: 98% ) ( Fig 13A–13D ) . To further confirm this , autophagic bodies were observed using Transmission electron microscopy after culturing in MM-N with 2 mM PMSF ( Phenylmethanesulfonyl fluoride ) for 4 h [63] . All these three mutants accumulated less autophagic bodies in the lumen of vacuoles within hyphae than that of the wild type ( Fig 13E ) . Autophagy was monitored based on the amount of free GFP by immunoblot . In the wild type , a considerable amount of free GFP was detected after 5 h nitrogen starvation . The amount of free GFP was reduced in ΔModnm1 and ΔMomdv1 mutants , and dramatically reduced in ΔMofis1 mutant . The extent of autophagy was estimated by calculating the amount of free GFP compared with the total amount of intact GFP-Atg8 and free GFP . Consistent with the microscopic observations , the ΔModnm1 , ΔMofis1 , and ΔMomdv1 mutants showed the delayed nonspecific autophagy and the higher stabilized levels of GFP-Atg8 at the same time points compared with the wild type ( Fig 13F ) . Thus , we conclude that MoDnm1 , MoMdv1 , and MoFis1 are all involved in nonspecific autophagy of M . oryzae . Dynamins are large GTPases with conserved GTPase domain , middle domain and the GED domain . The GTPase domain contains the GTP-binding motifs ( G1–G4 ) that are required for guanine-nucleotide binding and hydrolysis . Only one GTP molecule can be bound to each GTPase domain , but the sequences that contribute to the interactions are spread over the domain [1] . The key residues of MoDnm1 are shown in S7A Fig . The G1 motif ( in the so-called P-loop ) coordinates the phosphates , whereas the threonine in the G2 motif is involved in catalysis . The glycine in the G3 motif forms a hydrogen bond with the γ-phosphate of GTP and the G4 motif is involved in base and ribose coordination [1] . In place of the PH domain , an InsB motif was identified in S . cerevisiae Dnm1 . The amino acid sequence of this motif is strictly conserved among fungi and amino acid substitutions in this InsB helix inhibit the recruitment of Dnm1 to mitochondria and block fission [67] . The key residues of InsB are shown in S7B and S7C Fig . In this study , we generated seven point mutation mutants MoDNM1K43A , MoDNM1T64G , MoDNM1G157V , MoDNM1D226A , MoDNM1F627A , MoDNM1F631A , MoDNM1F632A , and two main function domain deletion mutants MoDNM1ΔDYN and MoDNM1ΔGED ( S7D Fig ) . Compared with the wild type and the complement strains ( MoDNM1 ) , the growth rates of all these mutants were significantly reduced ( S8A Fig ) . On the other hand , the InsB motif point mutation mutants and GED deletion mutants showed restored virulence similar to that of ΔModnm1 mutant and the complement strain ( MoDNM1 ) ( Fig 14A ) . Similar to ΔModnm1 conidia suspensions , very few and small lesions developed at 7 dpi with conidia suspensions of G1 , G2 , G3 , G4 motifs point mutation mutants and the DYNc domain deletion mutants ( Fig 14A ) . Quantification of the M . oryzae biomass in rice using a ‘relative fungal growth’ assay by qRT-PCR showed a similar result . The InsB motif point mutation mutants and GED deletion mutants showed a virulence complementation up to 70% of the wild type ( S8B Fig ) , while other mutants showed similar virulence as the ΔModnm1 mutant . These results indicate that G1 to G4 box have critical role in M . oryzae growth and pathogenicity , and the InsB motif has partial functions on growth and pathogenicity . In addition , we observed peroxisomal morphology in all these point mutation mutants . In contrast to the abundant globular peroxisomes in the wild type , the morphology of peroxisomes in point mutation mutants changed to plaque or tubular ( Fig 14B ) . The number of peroxisomes was significantly reduced in all point mutation mutants , and G1 , G2 , G3 , G4 motifs point mutation mutants and GED deletion mutants showed more severe inhibition of peroxisome fission . These results suggest that G1 to G4 boxes have critical role on peroxisomal fission and the InsB motif has partial functions on peroxisomal fission . As the short C-terminal tail of hFis1 is both necessary and sufficient for its targeting to peroxisomes , whereas the N-terminal region is required for organelle fission [31] , we examined the role of MoFis1 N-terminal region in fission in M . oryzae . We expressed three N-terminally truncated MoFis1 constructs MoFis1Δ1–29 ( Δ1–29 ) , MoFis1Δ1–57 ( Δ1–57 ) , and MoFis1Δ1–88 ( Δ1–88 ) in ΔMofis1 with RFP-PTS1 cells . In contrast to the wild type , aggregation of peroxisomes was observed with the RFP-tagged constructs in these three strains ( S9C Fig ) . Furthermore , the N-terminal region of MoFis1 was essential for its growth and pathogenicity , which is different from the C-terminal tail ( S9A and S9B Fig ) . In addition , we examined the influence of C-terminal modifications of MoFis1 on peroxisomal targeting and morphology . Two C-terminal truncated MoFis1 constructs lacking the eight and twenty-nine amino acids , GFP-MoFis1Δ148–155 ( Δ148–155 ) and GFP-MoFis1Δ127–155 ( Δ127–155 ) were co-expressed with RFP-PTS1 , respectively . The truncated protein showed an aggregated distribution with GFP signals gathered into big plaque , but peroxisomal proliferation was normal in these transfected cells ( S9D Fig ) . Furthermore , we found the short C-terminal tail of MoFis1 was dispensable for vegetative growth and pathogenicity ( S9A and S9B Fig ) . These results suggest that the intact C-terminal structure is required for proper peroxisomal distribution , whereas the N-terminal region is required for vegetative growth , peroxisomal fission and pathogenicity . As MoMdv1 interacts with both MoDnm1 and MoFis1 , we tested whether MoMdv1 mediates the distribution of MoDnm1 and MoFis1 by comparing the localization pattern of MoDnm1 and MoFis1 in the ΔMomdv1 mutant and the complement strains . Interestingly , in the ΔMomdv1 mutant , the pattern of GFP-Dnm1 and GFP-Fis1 remained punctate . However , fewer GFP-Dnm1 and GFP-Fis1 punctate structures were observed and these punctate structures failed to localize to peroxisome in comparison to the complement strains ( Fig 15A and 15B ) . In the ΔModnm1 and ΔMofis1 mutants , the distribution and quantity of Mdv1-containing structures were similar to that in the ΔMomdv1 complement strains ( Fig 15C ) . We also tested the localization of MoDnm1 in the ΔMofis1 mutant and MoFis1 in the ΔModnm1 mutant , respectively . In the ΔModnm1 mutant , the pattern of GFP-Fis1 was changed to the string shape , but remained localized to peroxisome ( Fig 15B ) . The quantity of GFP-Dnm1 punctate structures was decreased , but remained localized to peroxisome in ΔMofis1 mutant ( Fig 15A ) . These observations indicate that the localization of MoDnm1 and MoFis1 to peroxisome requires MoMdv1 , and MoMdv1 possesses a Dnm1- or Fis1-independent peroxisome targeting signal .
Membrane transport between compartments in eukaryotic cells requires proteins that mediate the membrane budding and fission events . Classical dynamins and dynamin-related proteins are the essential vesicle-scission molecules [1] . Both are involved in a wide variety of cellular processes including severing endocytic vesicles from the plasma membrane , mitochondrial fission and fusion ( Dnm1 ) , vacuolar fission ( Vps1 ) , plant cell plate formation , plant cell cytokinesis and membrane fission , and pathogen resistance [1 , 6] . In this study , we characterized the functions of main dynamin proteins in M . oryzae . While previous studies focused more on the functions of dynamins on binding and division of lipid membranes , our studies focused not only on the conserved functions in membrane fission but also on growth and differentiation . Our studies also attempt to establish a novel link between dynamin function on membrane fission and fungal virulence . In addition to the characterization of the most conserved MoDnm1 protein , we also tackled additional dynamin related proteins and found that they often function in complex . In the budding yeast S . cerevisiae , four subunits of mitochondrial and peroxisomal fission complex have been identified as Dnm1 , Fis1 , Mdv1 , and Caf4 [22–24] . We also characterized MoFis1 and MoMdv1 and demonstrated that MoMdv1 functions as an adaptor linking MoDnm1 to MoFis1 . Further study on the relationship of MoDnm1 , MoMdv1 , and MoFis1 revealed that MoDnm1 and MoFis1 failed to localize to peroxisome in the ΔMomdv1 mutant , and the distribution of MoFis1 was also changed dramatically in ΔModnm1 mutant . These data are consistent with our hypothesis that MoDnm1 acts as a regulatory molecule through indirect recruiting MoFis1 by an adapter MoMdv1 . In this study , we observed that the ΔModnm1 , ΔMomdv1 , and ΔMofis1 mutants exhibited dramatically decreased virulence , which may result from multiple defects of the mutants . First , the vegetative growth rate of the mutant is significantly reduced . This might be a major reason for reduced virulence since the mutant grew significantly slower than the parental strain on different mediums . Second , all these mutants showed a marked decrease in conidiation . This may not be surprising since asexual spores ( conidia ) are often the major source of primary inoculum and dissemination in phytopathogenic fungi . Third , the ΔModnm1 , ΔMomdv1 , and ΔMofis1 mutants have defect in appressorium turgor that leads to the decline of invasion . Following this observation , we further point out that the defect of glycogen degradation was not the reason for lower appressorium turgor in ΔModnm1 , ΔMomdv1 , and ΔMofis1 mutants , but rather the fact that peroxisomal and mitochondrial morphology was altered in ΔModnm1 , ΔMomdv1 , and ΔMofis1 mutants at different developmental stages . This is consistent with recent studies that showed Pex11A , Pex19 and MoPef1 ( Mdv1 ) are required for peroxisomal proliferation and virulence of M . oryzae [14 , 28 , 32] . One of the most important functions of the Modnm1 , MoMdv1 and MoFis1 complex is the inhibition of peroxisomal and mitochondrial fission . Lack either one of the complex leads to defects in the formation of small , punctiform peroxisomes and fragmented mitochondria . The studies with Mdivi-1 ( mitochondrial division inhibitor ) , a small molecule , selective inhibitor of DLP1 , revealed that inhibition of DLP1 exerts protective effects in heart and cerebral is chemia-reperfusion models and provides neuroprotection in Parkinson models [68] . Our studies with inhibitor Mdivi-1 and inducer oleate demonstrated that the mitochondrial fission in appressorium plays an important role in pathogenicity in M . oryzae . Similarly , experiments on peroxisomes with peroxisomal fission inhibitor GW9661 and inducer glycerol indicate that peroxisomal fission in appressorium has an important role in pathogenicity . Deletion of MoPEX11A leads to attenuated peroxisomal fission and virulence in the rice blast fungus [14] . Here , peroxisomal proliferation is inhibited by deleting MoPEX11A in the ΔModnm1 , ΔMomdv1 , or ΔMofis1 mutant . The double deletion mutants showed more severe reduction in virulence than single deletion mutants . This observation suggests that the MoDnm1 , MoMdv1 , and MoFis1 complex mediates peroxisomal fission to affect virulence in this fungus . In addition to the significantly reduction of peroxisomes and mitochondria , our studies provided evidence that pexophagy , mitophagy and autophagy are delayed in ΔModnm1 , ΔMomdv1 , and ΔMofis1 mutants . These results are consistent with the studies in yeast that peroxisomal and mitochondrial fission is important for the degradation [49 , 66] . Studies in mammalian cells provide another evidence that normal endocytosis is essential for efficient autophagic flux . The membranes and proteins involved in autophagy initiation and autophagosome precursor formation are internalized by endocytosis [69] . As endocytosis is delayed in ΔModnm1 , ΔMomdv1 , and ΔMofis1 , we speculate that the delayed endocytosis could affect the autophagic process . Autophagy is a cellular process involved in various developmental processes , including conidiation and pathogenicity , by assisting carbohydrate metabolism to meet the energy requirements during cellular differentiation [70–72] . Mitochondria are essential for programmed cell death and mitophagy plays an important role in the foot cells during conidiation in M . oryzae [20 , 61] . These results suggest that MoDnm1 , in complex with MoMdv1 and MoFis1 , is located to peroxisomes and mitochondria to mediate their fission , which underlies processes to regulate conidiation and pathogenicity . In summary , we presented a working model of MoDnm1 in association with MoFis1 and MoMdv1 in M . oryzae ( Fig 16 ) . MoDnm1 , MoFis1 , MoMdv1 function as a complex , and MoDnm1 recruits MoFis1 to peroxisome and mitochondria through the adaptor protein MoMdv1 . MoDnm1 , MoFis1 , and MoMdv1 play important roles in asexual development , appressorium function , infectious growth , and pathogenicity . This complex mediates peroxisomal and mitochondrial fission , which may also mediate pexophagy and mitophagy to regulate conidiation and pathogenicity . Ours findings are largely consistent with the functions of Dnm1 and dynamin proteins in S . cerevisiae and other model organism [49 , 66] . However , it is not clear how peroxisomal and mitochondrial fission are required for growth or conidiation in M . oryzae , thus , further analysis of these proteins will shed lights on the understanding of not only developmental processes but also pathogenesis mechanisms involved in rice blast fungus .
The M . oryzae Guy11 strain was used as wild type ( WT ) for transformation in this study . For vegetative growth , small agar blocks were cut from the edge of 7-day-old cultures and placed onto fresh media ( CM , MM , OM and SDC ) , followed by incubation in the dark at 28°C . The radial growth was measured after incubation for 7 days [73] . Liquid CM medium was used to prepare mycelia for DNA and protein extraction . For conidiation , strain blocks were maintained on SDC ( 100 g of straw , 40 g of corn powder , 15 g of agar in 1 l of distilled water ) agar media [73] at 28°C for 7 days in the dark , followed by constant illumination with mycelia removed for 3 days . Phylogenetic tree of dynamin-related proteins and MoDnm1 homologues from several other species were drawn by the divergence distance method using the CLUSTAL_W and MEGA 5 . 1 programs . Neighbour-joining tree with 1000 bootstrap replicates of phylogenetic relationships . Species names and GenBank accession numbers are as follows: XP_003717217 . 1 ( M . oryzae MoDnm1 ) ; XP_003712225 . 1 ( M . oryzae MoVps1 ) ; XP_003708884 . 1 ( M . oryzae MoDnm2 ) ; XP_003721138 . 1 ( M . oryzae MoDnm3 ) ; XP_009217281 . 1 ( Gaeumannomyces graminis GgDnm1 ) ; NP_013100 . 1 ( S . cerevisiae Dnm1p ) ; NP_542420 . 1 ( R . norvegicus Dyn1 ) ; XP_011516636 . 1 ( H . sapiens Drp1 ) . The gene deletion mutants were generated using the standard one-step gene replacement strategy . First , two 1 . 0 kb of sequences flanking the targeted genes were PCR amplified with primer pairs ( S2 Table ) . Then , the resulting PCR products of MoDNM1 , MoDNM2 , MoDNM3 , MoFIS1 , and MoMDV1 were digested with restriction endonucleases and ligated with the HPH cassette released from pCX62 . Finally , the completed inserts were sequenced . The 3 . 4 kb fragments , which contain the flanking sequences and hygromycin resistance cassette , were amplified and transformed into protoplasts of Guy11 . Putative mutants were screened by PCR and confirmed by Southern blot analysis . The complement fragments , which contain the entire MoDNM1 , MoFIS1 , MoMDV1 , MoDNM1K43A , MoDNM1T64G , MoDNM1G157V , MoDNM1D226A , MoDNM1F627A , MoDNM1F631A , MoDNM1F632A , MoDNM1ΔDYN and MoDNM1ΔGED genes with their native promoter regions , were amplified by PCR ( Phanta Super-Fidelity DNA Polymerase , Vazyme Biotech Co . , Nanjing , China ) with primers ( S2 Table ) and inserted into pYF11 ( bleomycin resistance ) to complement the respective mutant strains . For appressorium formation , conidia harvested from 10-day-old cultures were filtered through two-layers of miracloth and washed with double-distilled water ( ddH2O ) for three times . Droplets ( 20 μl ) of conidial suspension ( 5 x 104 spores/ml ) were placed on cover glass ( Fisher-brand , UK ) and incubated at 28°C . The appressorium turgor was measured using an incipient cytorrhysis ( cell collapse ) assay with a 1 . 0 to 4 . 0 M glycerol solution . The water surrounding the appressoria was removed carefully and then replaced with an equal volume ( 20 μl ) of glycerol ( 1 . 0 to 4 . 0 M ) . Appressoria were observed through direct microscopic examination and percentages were obtained from at least 100 conidia per replicate at 24 h in at least three experiments . Virulence assays were performed as described [74] . Conidia were harvested from 10-day-old SDC agar cultures , filtered through one-layer Miracloth and resuspended by 0 . 2% ( w/v ) gelatin solution to a concentration of 5 x 104 spores/ml . For detached barley assay , leaves from 7-day-old barley ( H . vulgare cv . Four-arris ) seedlings were drop-inoculated with three droplets ( 20 μl ) of conidial suspension . Photographs were taken 5 days after incubation alternating light and dark at 25°C . For rice seedling spraying assay , two-week-old seedlings of rice ( O . sativa cv . CO39 ) were sprayed with 5 ml of conidial suspension of each treatment . Inoculated plants were kept in a growth chamber at 25°C with 90% humidity and in the dark for the first 24 h , followed by a 12 h/12 h light/dark cycle . Lesion formation was daily checked and photographed 7 days after inoculation [75] . For ‘relative fungal growth’ assay , total DNA was extracted from 1 . 5 g disease leaves and test by qRT-PCR ( HiS cript II Reverse Transcriptase , Vazyme Biotech Co . , Nanjing , China ) with 28S/Rubq1 primers ( S2 Table ) [76] . For microscopic observation of penetration and invasive hyphae expansion , conidial suspension ( 1 x 105 spores/ml ) was inoculated in the inner leaf sheath . After incubation for 30 h or 48 h at 28°C with 90% humidity , the inner leaf sheath cuticle cells were observed under Zeiss Axio Observer A1 inverted microscope . The glycogen metabolism in the germinating conidia and appressoria of strains were visualized by staining these tissues with glycogen staining solution containing 60 mg/ml KI and 10 mg/ml I2 [77] . Once the samples become yellowish-brown , the glycogen deposits can be visualized in bright field optics with Zeiss Axio Observer A1 inverted microscope . The lipid droplets in the germinating conidia and appressoria of strains were visualized by staining these tissues with a Nile red solution consisting of 50 mM Tris/maleate buffer ( pH 7 . 5 ) and 2 . 5 mg/ml Nile red Oxazone ( 9-diethylamino-5H-benzo-a-phenoxazine-5-one , Sigma ) [10 , 55 , 78] . After 3 min incubation , the lipid droplets in the conidia and appressoria began to fluoresce and were observed under Zeiss Axio Observer A1 inverted microscope To examine endocytosis , strains were grown on a thin layer of CM agar on the microscope slides . After 40 h incubation at 28°C , the hyphae were stained with N- ( 3-triethylammoniumpropyl ) -4- ( p-diethylamino-phenyl-hexatrienyl ) pyridinium dibromide ( FM4-64 ) ( Molecular Probes Inc . , Eugene , OR , USA ) following several times washing by ddH2O [79] . Photographs were taken under confocal fluorescence microscope ( Zeiss LSM710 , 63x oil ) . To investigate the cellular localization of MoDnm1 , MoMdv1 , and MoFis1 , three genes fused with a GFP tag in the N-terminus and a fluorescent marker appended with a type I peroxisomal targeting signal ( RFP-PTS1 ) were co-transformed into three deletion mutants , respectively . Green and red fluorescence were observed in both vegetative hyphae grown in fluid complete medium ( CM ) for 24 h and conidia harvested from 10-day-old SDC medium plates under confocal fluorescence microscope ( Zeiss LSM710 , 63x oil ) . In addition , three GFP fusion proteins , including GFP-Dnm1 , GFP-Mdv1 , and GFP-Fis1 were expressed in three deletion mutants , respectively . Hyphae of GFP signal strains were incubated with 100 nM MitoTracker Red CMXRos ( Invitrogen , Cat . M7512 ) for 2 min at room temperature . Green and red fluorescence were observed in both vegetative hyphae grown in fluid complete medium ( CM ) for 24 h and conidia which harvested from 10-day-old SDC medium plates under confocal fluorescence microscope ( Zeiss LSM710 , 63x oil ) . To investigate peroxisomal fission , RFP-PTS1 was transformed to Guy11 and deletion mutants . For mitochondrial fission , the mitochondria localization fusion protein MoIlv2-GFP was expressed in Guy11 and deletion mutants . Red or green fluorescence was observed in vegetative hyphae grown in fluid complete medium ( CM ) for 24 h , conidia which harvested from 10-day-old SDC medium plates and appressoria incubated on cover glass ( Fisher-brand , UK ) for 8 h at 28°C . Vegetative hyphae of indicated strains were cultured in fluid complete medium ( CM ) for 30 h and conidia were harvested from 10-day-old SDC medium plates . For Transmission electron microscopy observation , hyphae and conidia were fixed with 2 . 5% glutaraldehyde in phosphate buffer ( pH 7 . 0 ) for more than 4 h , washed three times in the phosphate buffer , fixed with 1% OsO4 in phosphate buffer ( pH 7 . 0 ) for 1 h and washed three times in the phosphate buffer . Then , the specimen was firstly dehydrated by a graded series of ethanol ( 30 , 50 , 70 , 80 , 90 , 95 and 100% ) for about 15 to 20 minutes at each step , transferred to absolute acetone for 20 minutes . Later , the specimen was placed in 1:1 mixture of absolute acetone and the final Spurr resin mixture for 1 h at room temperature; then transferred to 1:3 mixture of absolute acetone and the final resin mixture for 3 h and to final Spurr resin mixture for overnight . Specimen was placed in capsules contained embedding medium and heated at 70˚C for 9 h . The specimen sections were stained by uranyl acetate and alkaline lead citrate for 15 min respectively and observed under transmission electron microscopy ( Hitachi H-7650 ) . To inhibit peroxisomal fission , 10 mM PPARγ ( nuclear receptor peroxisome proliferator activated receptor ) inhibitor GW9662 ( MedChem Express , HY-16578 , USA ) was added in conidial suspension . Similarly , 10 mM mitochondrial fission inhibitor Midvi-1 ( MedChem Express , HY-15886 , USA ) was added in conidial suspension [59] . Conidia were incubated on cover glass ( Fisher-brand , UK ) at 28°C for 8 h and observed under confocal fluorescence microscope ( Leica TCS SP8 , 100x oil ) . To induce peroxisomal fission , 0 . 1% V/V oleate was added in conidial suspension . Similarly , 1% V/V glycerol was added to conidial suspension to induce mitochondrial fission . Conidia were incubated on cover glass ( Fisher-brand , UK ) at 28°C for 8 h and observed under confocal fluorescence microscope ( Leica TCS SP8 , 100x oil ) . For microscopic observation of penetration and infectious hyphae expansion , conidial suspensions ( 1 x 105 spores/ml ) were inoculated on the back of detached barley . After incubation for 20 , 24 or 26 h under humid conditions at 28°C , the barley back cuticle cells were observed under Axio Observer A1 Zeiss inverted microscope . For mitophagy assays , the strains were grown in complete medium for two days , followed by 30 h growth in basal medium with 1 . 5% v/v glycerol . Mycelia were then starved by culturing in minimal medium lacking nitrogen for 12 h and total protein were resolved by 10% SDS-PAGE followed by Western blotting with anti-Porin antibody ( mouse; 1:2 , 000; Invitrogen , 459500 ) [61 , 72] . For pexophagy assays , strains were grown in CM for two days , followed by 20 h growth in basal medium with 1% v/v oleate . Mycelia were then subjected to nitrogen starvation for 12 h and total protein were resolved by 10% SDS-PAGE followed by Western blotting with anti-Pex14 antibody ( rabbit; 1:2 , 000; Agrisera , AS08372 ) [61] . For autophagy assays , the GFP-Atg8 expressing strains were grown in complete medium ( CM ) for 30 h , then washed and subjected to nitrogen starvation ( cultured in MM-N for 2 or 5 h ) to induce nonselective autophagy . Mycelia induced for 2 or 5 h was microscopy observation and biochemical assays for GFP-Atg8 cleavage . Mycelia induced for 5 h were analyzed using Transmission electron microscopy . Immunobloting for GFP-Atg8 cleavage was done with anti-GFP ( mouse; 1:5000; Abmart ) and anti-β-Actin antibodies ( mouse; 1:5000; zoonbio , ABM-0001 ) [80] . The amount of free GFP and GFP-Atg8 were conducted by densitometric analysis ( Image-pro plus , Media Cybernetics Inc . , Shanghai , China ) . The bait constructs were generated by cloning MoDNM1 and MoFIS1 full-length cDNAs into pGBKT7 , respectively . The cDNAs of MoFIS1 and MoMDV1 were cloned into pGADT7 as the prey constructs ( see primers in S2 Table ) . The resulting prey and bait constructs were confirmed by sequencing analysis and transformed in pairs into yeast strain AH109 as the description of BD library construction & screening kit ( Clontech , USA ) . The Trp+ and Leu+ transformants were isolated and assayed for growth on SD-Trp-Leu-His-Ade medium and the expression of LacZ reporter gene following the instructions provided by Clontech . Yeast stains for positive and negative controls were provide by the BD library construction & screening kit . For protein production in Escherichia coli , the full length of MoDNM1 and MoFIS1 was inserted into the pGEX4T-2 vector and the full length of MoDNM1 and MoFIS1 was inserted into the pET32a vector . The resultant plasmid DNA was transformed into Rosetta 2 ( DE3 ) cells ( Novagen , Madison , WI ) . Protein expression was induced for 8 h at 25°C after the addition of isopropy-β-D-thiogalactoside ( IPTG ) to a final concentration of 0 . 1 mM . Cells were collected by centrifugation , washed , and stored at -70°C . To extract proteins , cells were suspended in lysis buffer containing 0 . 5 mM EDTA , 1% Triton , 20 mM Tris-HCl , 0 . 15 M NaCl , 1 mM DTT and protease inhibitors ( 1 mM PMSF ) and ultrasonic wall-breaking under frequency: 100–200 w 2 min , pause 4 s run 2 s . Samples were centrifuged at 13 , 000 rpm for 15 min at 4°C and the supernatants were collected . For protein purification , supernatants were mixed with glutathione-Sepharose resin ( Amersham Pharmacia , Piscataway , NJ ) for 2 h , centrifuged at 500 rpm for 2 min at 4°C , washed with Tris-NaCl buffer ( 1 mM PMSF , 1% Triton , 50 mM Tris-HCl , 100 mM NaCl ) and the proteins were eluted with 15 mM glutathione . Proteins were also verified by SDS-PAGE analysis and Western blotting with the anti-GST antibody ( Abmart ) . For binding , 500 μl of the GST- proteins extract were added to glutathione-Sepharose resin washed with Tris-NaCl buffer . The proteins His- supernatants were added to resin and incubated overnight with gentle rotation at 4°C , precipitated , and washed three times with Tris-NaCl buffer . SDS-containing gel loading buffer ( 100 μl ) was added to the resin before a brief boiling . Samples ( 4 μl ) were analyzed by SDS-PAGE electrophoresis and Western blotting using anti-His and anti-GST antibodies . GST protein was used as a control . | Dynamin superfamily members are involved in budding of transport vesicles and division of organelles in eukaryotic cells . To further understand how dynamins function in phytopathogenic fungi , we characterized several dynamin-related proteins from the rice blast fungus M . oryzae . In addition to revealing major conserved dynamin functions , we described how MoDnm1 interacts with mitochondrial fission protein MoFis1 and WD repeat adaptor protein MoMdv1 to mediate peroxisomal and mitochondrial fission , pexophagy and mitophagy . Importantly , we provided evidence to demonstrate that MoDnm1- , MoFis1- and MoMdv1-dependent peroxisomal and mitochondrial functions are linked to differentiation and pathogenicity of the rice blast fungus . | [
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] | 2016 | MoDnm1 Dynamin Mediating Peroxisomal and Mitochondrial Fission in Complex with MoFis1 and MoMdv1 Is Important for Development of Functional Appressorium in Magnaporthe oryzae |
Transposable elements ( TEs ) are exceptional contributors to eukaryotic genome diversity . Their ubiquitous presence impacts the genomes of nearly all species and mediates genome evolution by causing mutations and chromosomal rearrangements and by modulating gene expression . We performed an exhaustive analysis of the TE content in 18 fungal genomes , including strains of the same species and species of the same genera . Our results depicted a scenario of exceptional variability , with species having 0 . 02 to 29 . 8% of their genome consisting of transposable elements . A detailed analysis performed on two strains of Pleurotus ostreatus uncovered a genome that is populated mainly by Class I elements , especially LTR-retrotransposons amplified in recent bursts from 0 to 2 million years ( My ) ago . The preferential accumulation of TEs in clusters led to the presence of genomic regions that lacked intra- and inter-specific conservation . In addition , we investigated the effect of TE insertions on the expression of their nearby upstream and downstream genes . Our results showed that an important number of genes under TE influence are significantly repressed , with stronger repression when genes are localized within transposon clusters . Our transcriptional analysis performed in four additional fungal models revealed that this TE-mediated silencing was present only in species with active cytosine methylation machinery . We hypothesize that this phenomenon is related to epigenetic defense mechanisms that are aimed to suppress TE expression and control their proliferation .
Transposable elements ( TEs ) are mobile genetic units that colonize prokaryotic and eukaryotic genomes and generate intra- and inter-specific variability . Despite the ubiquity of TEs in the eukaryotic domain , the genome fraction occupied by these elements is highly diverse , accounting for approximately 3% in yeast genomes [1] , up to 50% in mammalian genomes [2] , and more than 80% in some plants , including wheat or maize [3 , 4] . The expansion of these elements is mediated by transposition events that can lead to their own duplication . TEs are classified into two classes based on transposition mechanisms . Class I elements transpose via RNA intermediates and include five orders ( LTR , DIRS , PLE , LINE , and SINE ) that are differentiated based on their structure and transposition system [5 , 6] . Class II encompasses elements that transpose directly from DNA to DNA . This class is divided into two subclasses . One includes the TIR and Crypton orders , and the other contains Helitrons and Mavericks [5] . The majority of transposable elements generate target site duplications at their insertion sites ( TSD ) , which are formed as part of the insertion process . Exceptions include Helitrons [7] and the recently discovered Spy elements [8] . In addition , TE families are formed by both autonomous ( coding for the proteins necessary for its transposition ) and non-autonomous elements that rely on compatible transposases/retrotransposases for their mobilization . Transposable elements can be considered selfish elements that parasitize their host genomes , and eukaryotes have developed defense mechanisms for preventing their expansion . Three mechanisms of TE silencing have been described in fungi: i ) repeat-induced point mutations ( RIP ) [9] , ii ) transposon methylation [10 , 11] , and iii ) RNA-mediated gene silencing ( quelling and meiotic silencing ) [12 , 13] . Repeat-induced point mutations were originally described in Neurospora crassa and have been more recently studied in a broad range of filamentous fungi [14–16] . Transposon DNA methylation has been increasingly studied in the last few years , and recent genome-wide methylation analyses confirm the importance of this epigenetic mechanism in the control of TE proliferation in fungi [11 , 17 , 18] . Quelling and meiotic silencing occur through the detection of aberrant RNAs , which trigger RNAi pathway genes to silence . Meiotic silencing occurs when chromosomal regions are unpaired during meiosis , such as when a TE is present in one parent but not in the other . Previous studies have shown that meiotic silencing targets unpaired transposable elements [19] . Although TEs were originally considered “junk DNA” , we know today that the activity of these elements has strong consequences for genome architecture and that they are key drivers in rapid shifts in eukaryotic genome size [6 , 20] . Due to their repetitive nature , TEs promote chromosomal rearrangements through homologous recombination and alternative transposition [21] . TE activity can also shape genome function in multiple ways . Transposition events can lead to insertional mutations [22] , which can modify or disrupt gene expression , as well as generate new proteins by exon shuffling and TE domestication [23 , 24] . In addition , TEs are powerful sources of regulatory sequences [25] that can be spread across the genome , rewiring pre-established networks or even creating new ones [26] . Transposable elements are associated with several classes of small RNAs that regulate the expression of multiple genes at the post-transcriptional level [27] . These reasons , among others , have transformed the originally underestimated importance of TEs into a new , exciting subject of study . This is especially relevant in fungi because international sequencing efforts are rapidly increasing the availability of genome sequences of divergent species with different lifestyles [28 , 29] . Fungal genomes are generally smaller than those of plants and animals , which greatly facilitates their assembly and annotation . However , the accurate annotation and quantification of transposable elements in a genome are not simple tasks , especially in draft assemblies with many scaffolds . Factors such the divergence between TE copies ( due to mutations and rearrangements ) or the occurrence of nested elements complicate the annotation process and necessitate the use of different algorithms to achieve reliable results [30 , 31] . With the rapid generation of fungal genomes , TE annotation has typically been performed using different strategies , thus limiting the ability to draw robust conclusions about the differences in TE family expansion in different species when copy differences can be ascribed to either methodological differences or biological variation . Recent comprehensive analyses of fungal TEs have described an exceptional variability in the repeat content [15 , 28 , 29] , in which amplification events tend to be more related to the fungal lifestyle than to phylogenetic proximity [15 , 32] . LTR-retrotransposons are usually the most abundant mobile elements in fungal genomes , especially those that belong to the Gypsy and Copia superfamilies . In contrast , DNA elements generally constitute a smaller fraction of the fungal repeats , although in some species such as Fusarium oxysporum , they have undergone important amplifications in lineage-specific genomic regions [33] . In this study , we used a multi-approach pipeline for TE annotation in a collection of fungal genomes of varying phylogenetic distances and a detailed analysis of TEs in two strains of P . ostreatus . This species is a white rot basidiomycete fungus that grows on tree stumps in its natural environment . Its life cycle alternates between monokaryotic ( haploid ) and dikaryotic ( dihaploid ) mycelial phases . When two compatible monokaryotic hyphae fuse , a dikaryotic mycelium forms that is able to perform karyogamy , which occurs at the end of the life cycle , immediately before the onset of meiosis . Our results depict a P . ostreatus TE landscape dominated by Class I elements that tend to aggregate in non-homologous clusters . These clusters have profound impacts on the genome architecture at intra and inter-specific levels . In addition , we show that TE insertions modulate the global transcriptome of P . ostreatus and other fungi .
The two monokaryotic strains of P . ostreatus used in this study were sequenced by the Joint Genome Institute ( JGI ) . PC15 was sequenced with the Sanger whole-genome shotgun approach [34] , and PC9 was sequenced using Sanger whole genome shotgun and 454 paired end sequencing reads . PC15 genome assembly version 2 . 0 ( 34 . 3 Mb ) was subjected to targeted genome improvement which led to a complete assembly of 12 scaffolds with a very low gap content ( 1 gap of 91 base pairs in the whole assembly ) that matched the corresponding P . ostreatus chromosomes ( eleven nuclear plus one mitochondrial chromosome ) [35] . In contrast , PC9 assembly v1 . 0 ( 35 . 6 Mb ) contains 572 scaffolds and a total of 476 gaps that cover 9 . 72% of the whole assembly . Two monokaryotic strains of the basidiomycete P . ostreatus ( PC9 and PC15 ) [34 , 35] were used as a model to analyze differences in the occurrence and expansion of transposable element families . We identified and classified 80 TE families based on structural features and homology to previously described elements ( Table 1 ) . These families accounted for 6 . 2 and 2 . 5% of the total genome size in PC15 and PC9 genomes , respectively . In addition , we found 144 repeat-like consensus sequences that could not be reliably classified and occupied 3 . 6 and 2 . 3% of PC15 and PC9 assemblies , respectively . These elements are referred to hereafter as ‘unknown’ ( S1 Table ) , and were not used in downstream analyses . Our integrated pipeline combined de novo predictions of LTRharvest [36] and RepeatModeler ( http://www . repeatmasker . org ) , which were run on the two P . ostreatus genomes and merged to obtain a final TE library . This library was used then by RepeatMasker ( http://www . repeatmasker . org ) to detect and mask TE copies in each genome assembly . Our results showed that the merging strategy clearly outperformed the four independent approaches in terms of the number of detected families ( Fig 1A ) . In fact , none of the TE families could be simultaneously detected by all four approaches , and very few were detected by three . In addition , up to 38 families ( 48% of the total ) were detected by only one of the four methods . The distribution of family sizes showed that 9 of the 80 families accounted for the N50 repeat fraction in PC15 ( 50% of the total TE sequences ) , whereas 15 families accounted for the N50 repeat fraction in PC9 ( Fig 1B ) . The P . ostreatus repetitive element landscape was clearly dominated by Class I transposons , which accounted for 93% of the total TE content in PC15 and 89% in PC9 . LTR-retrotransposons were the most abundant TE order , and were responsible for the main differences in TE content between PC15 and PC9 . In fact , the four largest Gypsy families ( Gypsy_1 , Gypsy_2 , Gypsy_3 and Gypsy_4 ) accounted for 2 . 2% of the PC15 genome size , but only 0 . 3% in the case of PC9 . In addition , these families displayed 80 full-length copies in the former , whereas only fragments and two full-length copies were found in the latter ( Table 1 ) . A similar situation occurred with the most prominent Copia families ( Copia_1 and Copia_2 ) . Despite the important differences found between PC15 and PC9 in the number of full-length copies and the amount of LTR-retrotransposon masked sequences , the total number of detected TE fragments was closer ( 1 , 051 in PC15 vs 873 in PC9 ) . The same was true with the amount of solo-LTRs ( 609 in PC15 vs 585 in PC9 ) . Non-LTR retrotransposons ( L1 elements ) were found in similar abundance in PC9 and PC15 , although at lower copy numbers than LTR-retrotransposons . The repertoire of Class II elements found in the genomes was dominated by the previously described Helitron families HELPO1 and HELPO2 [37] . In addition , we identified a family of Tc1-mariner transposons ( TIR_1 ) showing putative autonomous elements as well as non-autonomous truncated copies . Autonomous elements of the latter family were present in both genomes , encoding a transposase carrying DDE3 endonuclease ( pfam13358 ) and Tc3 transposase ( cl09264 ) domains . Additionally , TIR_1 elements show terminal inverted repeats of 214 nt and generate a 2bp target site duplication ( TA ) upon insertion . Full TE annotations in PC15 and PC9 assemblies are deposited in the Supplementary Information ( S1 and S2 Datasets , respectively ) . Our screening of TE sequences in P . ostreatus genome assemblies uncovered that some of the most important LTR-retrotransposon families of PC15 were under-represented in PC9 ( Table 1 ) . We hypothesized that our estimation of TE content in PC9 could be underestimated in comparison to PC15 due to its lower assembling quality . In order to know whether this TE families were present in the genome but couldn’t be properly assembled , we analyzed the TE content of PC9 clean 454 sequencing reads ( read length of 80 to 626 nt , median length of 364 nt ) . Datasets of 1 . 58x and 1 . 76x genome coverages were randomly sampled from two sequenced libraries , and repeat-masked using our curated TE library to provide an unbiased estimation of TE content . The analysis yielded an average TE content of 4 . 98% , being the amount of sequence masked by each TE family highly correlated between the two datasets ( R2 = 0 . 98 , S3 Dataset ) . In addition , the results showed that Gypsy_1 , Gypsy_2 and Gypsy_3 LTR-retrotransposon families were the most abundant in PC9 genome , similarly to that found in the fully assembled PC15 strain . The density of TEs in P . ostreatus was highly variable among the twelve chromosomes and regionally within each chromosome ( Fig 2 ) . TEs were not randomly distributed over the genome ( Mann-Whitney-Wilcoxon p = 2 . 2e-16 ) , and overlapped frequently with annotated genes ( 502 in PC15 and 339 in PC9 , hereafter referred as “TE-associated genes” ) . The results of a hypergeometric test performed on the fully assembled PC15 strain revealed that 58% of the TEs were arranged in retrotransposon-rich clusters showing poor sequence conservation between the two genomes . A total of 2 , 108 genes out of 12 , 330 were present in these repeat-rich regions . Of these genes , 70 were annotated as lignocellulose-degrading enzymes such CAZymes , manganese and versatile peroxidases , although their presence in TE clusters was not over-represented in comparison to the whole genome ( Fisher p value = 0 . 52 ) . At an inter-specific level , the impact of TE insertions was even more striking , as the conservation of these transposon-enriched regions drops dramatically compared with other basidiomycetes ( S1 Fig ) . A whole genome alignment between PC15 and PC9 was performed to detect in silico polymorphic TE insertions . The alignment of every TE locus was extracted and parsed to detect the allelic state ( genotype ) based on the alignability of such regions . We used the same pipeline to analyze the allelic state of 11 , 630 protein-coding genes . While only 7 . 7% of the protein coding genes were heterozygous alleles , up to 50% of TE insertions were polymorphic . Bioinformatics predictions were validated by PCR in a subset of eight polymorphic insertions ( Fig 3 ) . The insertion ages of all intact LTR-retrotransposons ( carrying both Long Terminal Repeats , n = 189 ) were estimated based on the nucleotide divergence of LTRs using the approach described in [38] and the fungal substitution rate of 1 . 05 × 10−9 nucleotides per site per year [39 , 40] . Our results showed that 33% of the LTR-retrotransposon insertions occurred during a recent amplification burst ( 0 My ) , and up to 64% were amplified during the last 5 My ( Fig 4 ) . The oldest PC15 LTR-retrotransposon insertion clocked 41 My ago , while the oldest element in PC9 clocked 12 My ago . The phylogenetic reconstruction of the LTR-retrotransposon families revealed that some of the most prominent and recently amplified Gypsy families ( Gypsy_1 , Gypsy_2 , Gypsy_5 and Gypsy_6 ) were phylogenetically close ( S2 Fig ) . We obtained the average expression of every TE family normalized per family size using RNA-seq ( Fig 5 ) . Among the main TE groups , LINE was the most abundantly expressed in both strains , followed by Helitrons ( especially the HELPO1 family ) in PC15 and Gypsy retrotransposons in PC9 . At the family level , 60% were expressed in PC15 and 59% in PC9 , while at the copy level only 14% and 17% showed transcription , respectively . In addition , 16 out of the 80 families were transcriptionally silent in both strains . Notably , the three strain-specific families in P . ostreatus ( Copia_17 , DIRS_4 and Gypsy_53 , present only in PC9 ) were transcriptionally active . To investigate the impact of TEs on the functional genome of P . ostreatus , we explored the effect of TEs on the expression of the surrounding genes . The closest TE insertion to each gene was identified in the three following scenarios ( TE-associated genes were excluded from the analysis ) : i ) a TE was present in a 1kb window upstream of the gene start codon , ii ) a TE was present in a 1 kb window downstream of the gene end , and iii ) a TE was present in both upstream and downstream regions in a window of 1 kb ( gene “captured” between two TEs ) . This window size was selected based on the small intergenic distance of P . ostreatus ( 1 . 14 Kb ) . When we analyzed the gene expression distribution in every scenario , significant differences were uncovered between controls and genes under TE influence ( Fig 6A and 6B ) . In particular , a strong repression was found for genes captured between two TEs ( scenario III ) , while a discontinuous repression was found when the TE was present upstream or downstream of the gene body ( scenarios I and II ) . In the latter case , distribution shapes indicate that approximately half of the genes were repressed and the other half remained unaltered . To investigate whether this silencing effect could be influenced by the TE distribution along the chromosomes , we split the analysis of the PC15 strain in two additional scenarios: i ) the gene under TE influence was located inside a significant TE cluster ( Fig 6C ) and ii ) the gene under TE influence was located outside a significant TE cluster ( isolated TE ) ( Fig 6D ) . The results showed that the impact of TEs on gene expression was more intense when insertions occurred inside TE clusters . Additionally , significant differences were found between the distribution of gene expression of genes inside clusters that were not under the influence of TEs ( control plot , Fig 6C ) and that of the genes in the same condition but outside TE clusters ( control plot , Fig 6D , p = 1 . 22e-8 ) . To corroborate the hypothesis of TE-mediated gene repression we studied the transcription of orthologous genes displaying polymorphic insertions ( always in a window size of 1 Kb ) , where a TE was present in PC15 and absent in PC9 and vice versa . Tables 2 and 3 show 21 genes that were inactive under TE influence and active in the orthologous , TE-free allele . Gypsy LTR-retrotransposons were the main TEs involved in the repression with only two exceptions , which involved the Copia_5 ( LTR-retrotransposon ) and HELPO1 ( Helitron ) families . The inactivated genes displayed a broad range of functions . Additional orthologous pairs showing strong repression in the allele under TE influence ( 5 fold ) are shown in S2 Table . Our pipeline for the identification , classification and annotation of transposable elements was performed in eighteen Ascomycetes and Basidiomycetes genomes ( Fig 7 ) . The results demonstrated great variability in TE content at the phylum , genus and species levels ( Fig 7 , S3 Table ) . Elements belonging to 20 different TE superfamilies ( 11 of Class I and 9 of Class II ) were identified and classified into the main groups shown in Fig 7 . The genome percentage occupied by these TE families showed a positive correlation with genome size ( R2 = 0 . 38 ) . Within the genera analyzed , Serpula showed a surprisingly high TE content in proportion to its genome size , especially due to LTR-retrotransposon expansions in the Gypsy and Copia superfamilies . In fact , when excluding the two Serpula genomes from the analysis , the correlation between TE content and genome size in the remaining species was much higher ( R2 = 0 . 71 ) . The Ascomycete species analyzed had a ratio of Class I / Class II elements ranging from 0 . 78 to 4 . 23 and a low content of repetitive sequences , with the exception of the plant pathogen F . oxysporum . Interestingly , this species showed a 15-fold enrichment of transposable elements compared with F . graminearum as a result of important expansions of Class II elements ( Tc1-mariner and hAT families ) . The variability in the TE content in the analyzed Basidiomycetes ranged from species practically free of TE repeats , such as in the Pseudozyma genera ( 0 . 02% of the genome ) , to species with almost one third of their genome masked by the TE library , such as Serpula lacrymans or Puccinia graminis . TE expansions seemed to be constrained in basidiomycete yeasts such Pseudozyma or Mixia compared to the rest of the basidiomycetes analyzed . LTR-retrotransposons in the Gypsy and Copia superfamilies families were the main elements responsible for differences in TE content , with the Class I / Class II ratio much higher in basidiomycetes than in ascomycetes ( 9 . 3 in average ) . In fact , these two superfamilies were detected in all species analyzed in this study . When we studied the differential TE amplifications at the genus/species level , we found six pairs that displayed similar content ( Botrytis , Cryptococcus , Phanerochaete , Serpula , Pleurotus and Pseudozyma ) and two pairs ( Fusarium and Puccinia ) that showed important differences between counterparts . The effect of TE insertions in nearby genes was analyzed in four additional fungal models: Laccaria bicolor , Fusarium graminearum , Botrytis cinerea B05 . 10 and Saccharomyces cerevisiae S288C . These species were chosen based on the public availability of genomic ( full genome sequence ) and transcriptomic ( RNA-seq ) data . In addition , L . bicolor and S . cerevisiae were chosen based on their opposite methylation patterns ( evidence of methylation vs absence of methylation , respectively [11] ) . The analysis uncovered two clear profiles . First , L . bicolor and F . graminearum showed a pattern of TE-mediated repression similar to P . ostreatus , in which an important number of genes carrying TE insertions within a 1 kb upstream/downstream window were repressed ( Fig 8 ) . Second , B . cinerea and S . cerevisiae genes under TE influence did not show any alteration in expression , with distributions identical to the control ( p > 0 . 05 , Fig 8 ) During the process of TE classification using BLASTX against Repbase peptide database we noticed high similarity between the P . ostreatus TIR_1 family and the previously described Mariner2_PPa [41] ( 71% nucleotide identity over 71% of the sequence ) , a Tc1-mariner element identified in the moss Physcomitrella patens . According to the nucleotide divergence estimated by K2P distance and the fungal nucleotide substitution rate , TIR_1 and Mariner2_PPA diverged 517 My ago , despite mosses and fungi diverged about 1 , 600 My ago [42] . To investigate if horizontal transfers could have played a role in the distribution of fungal and other eukaryotic Tc1-mariners , we reconstructed the phylogeny of their encoded transposases ( Fig 9 ) . Our dataset included fungal , animal , plant and bacterial Tc1-mariner transposases , which were obtained based on best BLAST hits against NCBI and JGI reference proteins databases . The topology of the gene tree shows clear incompatibilities with the phylogenetic relationships of the species analyzed , which might be explained by horizontal transfers of Tc1-mariners . Specifically , basidiomycete and animal transposases were placed in a single clade with very high support , separated from ascomycete transposases . Other phylogenetic incongruences were the presence of the moss Physcomitrella patens and the mucoral Rhizopus oryzae in the basidiomycete clade , as well as the endosymbiont bacteria Wolbachia present in the animal clade .
Fungal TE content is highly diverse , even within species that are phylogenetically close [28] . However , studies analyzing the intra-specific variability in TE content have been infrequent . According to our results , transposable elements accounted for a small to moderate amount of the genome size in the two P . ostreatus strains analyzed ( 6 . 2% in PC15 and 2 . 5–4 . 9% in PC9 ) . Although the number of TEs detected varies according to the pipeline used , the TE content in P . ostreatus fell within the range reported for most fungal genomes ( from 0 to 25% ) [15 , 28 , 43 , 44 , 45] , with the exception of some plant pathogens and ectomycorrhizal species that have undergone massive TE amplifications [32 , 44] . Despite all TE groups are generally more abundant in PC15 than in PC9 , major differences between the strains were observed in LTR-retrotransposons . Most of the LTR-retrotransposon families under-represented in PC9 were actually present in the genome , but could not be assembled into the main scaffolds due to its length and repetitive nature . Assembling transposable elements is technically challenging because identical TE copies require sequencing reads exceeding the TE length to be resolved [46] . This is especially relevant in P . ostreatus , as we show that most of its LTR-retrotransposons underwent a recent amplification burst , thus sharing high nucleotide similarity . The presence of TE sequences in the unassembled reads is common in plants and animals [47 , 48] . In fungi , a recent study performed on several Amanita species identified many TEs that could not be found in the assembled regions , especially Gypsy elements [32] . In addition to the difficulty in assembling TE repeats , their structural complexity , which is caused by internal rearrangements , mutations , nested elements and DNA fragment acquisition events , complicated their identification using generic annotation tools . Our multi-way approach used for TE detection greatly improved the discovery of repeats , as revealed by the number of detected families in our combined TE library ( Fig 1A ) . Using this approach was of particular importance for TE detection in PC9 , because families that could not be detected by de novo searches in the assembly due to its high gap content could be found in PC15 and thus were present in the TE library . P . ostreatus repeat content is enriched in Class I transposons , especially in the Gypsy and Copia superfamilies . LTR-retrotransposons are divided into five superfamilies , but these two are the most abundant in the fungal kingdom [28 , 49] . The replicative transposition mechanism of autonomous LTR-retrotransposons makes them efficient genome colonizers because the copy number increases with every transposition event . Autonomous LTR-retrotransposons contain gag and pol genes flanked by long terminal repeats , and they differ from retroviruses in that they do not have infection capacity [50] . The difference between the Gypsy and Copia superfamilies lies in the order of the internal protease , integrase , reverse transcriptase and RNAse H domains present in the pol gene . We also found retrotransposons of the DIRS superfamily , which contains a gag , pol and tyrosine recombinase ORFs flanked by terminal repeats . This group of TEs is less abundant than other retrotransposons , and it exhibited patchy distribution in the fungal phylogeny [51] . One necessary condition for an active TE family is the presence in the genome of autonomous elements encoding the structural features and protein domains necessary for their own transposition . In this sense , the Gypsy architecture seems to be the most successful , as shown by the number of families and number of full-length copies per family . A second condition for TE transposition is that autonomous elements must be transcribed . We showed that although most genomic regions containing TEs are silenced , about 60% of the TE families showed at least one transcriptionally active copy . Interestingly , Class I transposons show high transcriptional levels , which are essential because they are propagated through RNA intermediates that can be translated into proteins necessary for replication or can act as replication templates . In parallel to the successful amplification of LTR-retrotransposons in P . ostreatus , the presence of solo-LTRs suggests the occurrence of homologous recombination between LTRs leading to retrotransposons elimination . Class II DNA transposons are less abundant than Class I RNA elements and are represented by the Helitron and Tc1-mariner superfamilies . In a previous work , we reported the presence and structure of the two Helitron families in P . ostreatus [37] . Helitrons were discovered by bioinformatics approaches in Arabidopsis thaliana and Caenorhabditis elegans more than a decade ago [7] . Nevertheless , the experimental demonstration of their transposition was not described until very recently [52] . Their rolling-circle transposition mechanism and their ability to capture and amplify gene fragments make them interesting subjects of study . Helitrons are present in all eukaryotic kingdoms [53] , although they show patchy distribution in some phylogenetic clades , such as mammals . In plants , they play an important role in genome evolution , introducing functional diversity by creating new genes and isoforms [54] . In this study , we showed that Helitrons are the most abundant DNA transposons in the P . ostreatus genome and are the second superfamily in transcriptional activity . Our results add a piece of evidence to the fact that this superfamily is actively populating the P . ostreatus genome . Interestingly , within the 19 described superfamilies of cut and paste DNA transposons , only Tc1-mariner is present in P . ostreatus . According to our results , this superfamily would be the most efficient fungal cut and paste transposon , as it is the most represented in the species analyzed . Nevertheless , most of the copies present in P . ostreatus are truncated , and the putative autonomous elements encoding transposases are not expressed in the condition tested . Our phylogenetic reconstruction of TIR_1-like Tc1-mariner transposases shows important discordances with organismal phylogenies , suggesting that horizontal transfer has shaped the distribution of these Class II transposons within the eukaryotic kingdom . Specifically , the presence of animal , plant , bacterial , mucoral and basidiomycete transposases in a monophyletic group separated from ascomycetes supports the hypothesis that multiple horizontal transfers occurred after the divergence of basidiomycetes and ascomycetes , event that took place about 1200 My ago [42] . It is known that transposable elements are horizontally transferred in eukaryotes at a higher frequency than regular genes [55] , and this ability allows them to persist in the course of evolution escaping from vertical extinction [56] . Our data suggests that horizontal gene transfer has played an important role in the dynamics of eukaryotic Tc1-mariners . Nevertheless , the diversity of TE copies , their repetitive nature and the limitations of the taxonomic sampling make difficult to reconstruct the full evolutionary history of TIR_1-like Tc1-mariner transposases . Most fungal species have streamlined , compact genomes . Owing to international efforts and advances in genome sequencing over the last decade , there is genomic information for nearly 500 fungal species covering most of the fungal phylogenetic diversity , with more being produced ( http://1000 . fungalgenomes . org ) . The assembled genome sizes in fungi range from about 2 to 190 Mb , while flow cytometry estimations have uncovered genome sizes of up to 893 Mb in the Pucciniomycotina subphylum [57] ( Gymnosporangium confusum ) . The available data demonstrate the impressive variability in fungal genome size , and our results suggest that an important part of this variability could be explained by differential expansions of TEs that seem to be related to the fungal lifestyle . Our results confirm that obligate biotrophs such P . graminis and P . striiformis are highly enriched in TEs [45] . By contrast , the ( not obligate ) biotroph M . osmundae is practically free of TEs , similarly to other basidiomycete yeasts such the P . hubeiensis and P . antarctica . Previous studies have shown that TE-driven expansions have played important roles in the genomes of filamentous plant pathogens [58] . An example of the impact of TEs in host adaptation and pathogen aggressiveness is the Leptosphaeria genus [59] . According to [58] , faster adaptation occurs because genes encoding proteins for host interactions are frequently polymorphic and reside within repeat-rich regions of the genome . Due to the presence of P . ostreatus lignin degrading enzymes within TE clusters , is tempting to hypothesize that TEs could play an important role in the evolution of wood decayers . Transposable elements are undoubtedly an important source of genetic variation in fungi . As previously found in other fungal species [43] , P . ostreatus TEs are preferentially arranged in non-homologous genomic regions that display low conservation at both the intraspecific and interspecific levels . These genomic blocks are hotspots for LTR-retrotransposon accumulation , which could target these regions due to specific chromatin structures adopted by pre-existing elements [60] . The compatible monokaryotic strains PC9 and PC15 can mate to form a dikaryon , the nuclei of which coexist in the same cell [35] . Thus , the unpaired long blocks of repetitive DNA are unlikely to undergo crossover and are likely inherited as supergenes after meiosis . We show that the transcription of these TE-rich regions tended to be strongly repressed ( Figs 2 and 6 ) and we hypothesize that genes with essential functions might eventually be captured and silenced during the formation of these TE clusters , leading to a looseness of fit by the monokaryotic genotypes carrying these genomic regions . Selection against these TE blocks would lead to the loss of these alleles in the course of evolution . On the other hand , the higher plasticity of these repeat regions might create novel opportunities for diversification and adaptation . In addition to the permanent genomic modifications that TEs can promote , we showed that both isolated and clustered TE insertions modulate the expression of surrounding genes . In addition to the disruption-mediated changes originated by TE insertions into promoter regions , there are additional mechanisms by which TEs can alter the expression of surrounding genes . TEs often carry cis-regulatory elements that can be spread over the genome [26] . Similarly , LTR-retrotransposons and solo-LTRs contain promoters that can activate the expression of dormant genes [60] . Additionally , transcripts from full-length TEs can read through into a neighbor gene , producing spurious transcripts that can be subjected to transcriptional and post-transcriptional control [61] . Finally , TEs can be targeted for heterochromatin formation , thus potentially silencing the transcription of the adjacent gene [26] . Several studies have shown that Arabidopsis genes close to TEs had lower expression than the average genome-wide expression [62 , 63] . Similarly , a recent study showed that the insertion of SINE retrotransposons close to human and mouse gene promoters led to transcriptional silencing mediated by the acquisition of DNA methylation [64] . The few studies available on the subject in fungi indicate that methylation targets transposon sequences selectively , leading to TE transcriptional silencing [11 , 17 , 18] . Although methylation within fungal genes tends to be low , studies in the plant pathogen Magnaporthe oryzae showed that genes that were methylated in upstream or downstream regions resulted in lower transcription than un-methylated genes [17] . We hypothesize that the transcriptional repression of genes surrounded by TE insertions could be related to the epigenetic status of the given TE . In fact , the discontinuous repression found in P . ostreatus genes under TE influence ( gene repressed vs non-repressed ) fits with the putative methylated vs non-methylated status of the involved TEs . Although we lack experimental evidence of methylation in PC15 or PC9 , the presence in both strains of transcriptionally active homologs of the Dim-2 DMTase ( S3 Fig ) responsible for cytosine methylation in fungi [65] suggests that the methylation machinery is active in P . ostreatus . In addition to P . ostreatus , we used the same transcriptional analysis pipeline in two species with well-known methylation profiles [11]: S . cerevisiae ( methylation-free ) and L . bicolor ( TE regions highly methylated ) . The expression distribution of S . cerevisiae genes under TE influence was identical to the control ( p < 0 . 05 ) , while the distribution in L . bicolor showed a severe bias towards low expressed genes . Additional analyses performed in other species uncovered that the ascomycetes F . graminearum and B . cinerea showed different expression patterns for genes under TE influence . Whereas B . cinerea genes remained unaltered , the expression in F . graminearum genes was lower than the control . Bisulfite sequencing of Gibberella zeae ( anamorph: F . graminearum ) showed that this species has low cytosine methylation levels , although it displays related mechanisms of TE silencing , such as RIP and meiotic silencing [66] . Regarding B . cinerea , the unique reference found on the subject showed that no or very little methylation occurred in this species , according to HpaII/MspI restriction patterns [67] . In summary , we show that transposable element dynamics differentially impact fungal genome-wide transcription patterns , likely as a result of the epigenetic machinery evolved to control TE proliferation .
Eighteen Ascomycetes and Basidiomycetes species were selected in this study as sample sets of closely related species for genomes comparisons . Publicly available genomic assemblies were downloaded from the Joint Genome Institute’s fungal genome portal MycoCosm [68] ( http://jgi . doe . gov/fungi ) , the Broad Institute ( https://www . broadinstitute . org/ ) and FungiDB [69] . The genome sequences of the P . ostreatus monokaryotic strains PC15 v2 . 0 [34] and PC9 v1 . 0 , which were obtained by de-dikaryotization of the dikaryotic strain N001 [35] , were used as models for building the pipelines described in this paper . De novo identification of repetitive sequences in the genome assemblies was performed by running the RECON [70] and RepeatScout [71] programs ( integrated into the RepeatModeler pipeline ) . LTRharvest [36] was used to improve the detection of full length LTR-retrotransposons . LTRharvest results were filtered to avoid false positives as follows: elements were de-duplicated and used as queries for BLASTN searches ( cutoff E-value = 10−15 ) against the genome assembly and for BLASTX ( cutoff E-value = 10−5 ) against the Repbase peptide database [54] . Only sequences longer than 400 bp with more than five copies or yielding a significant hit to a described LTR-retrotransposon were kept for further analysis . The outputs of the above programs were merged and clustered at 80% similarity using USEARCH [72] to create species-specific ( i . e . , P . ostreatus PC15 and PC9 ) or genus-specific ( i . e . , F . oxisporum and F . graminearum ) TE libraries . Each consensus sequences library was classified using BLASTX against the Repbase peptide database , and the final libraries were used as input for RepeatMasker ( http://www . repeatmasker . org ) . Consensus sequences without similarity to any Repbase entry were labeled as ‘unknown’ . The RepeatMasker output was parsed using the One_code_to_find_them_all script [73] to reconstruct TE fragments into full-length copies and estimate the fraction of the genome occupied by each TE family . To identify solo-LTRs , the left terminal repeat of every autonomous copy was extracted , and a BLASTN against each assembly was performed . The flanking sequences of every hit ( 5 , 000 bp , cutoff E-value = 10−15 ) were extracted and screened for retrotransposon internal sequences . Solo-LTRs were defined as those hits lacking internal retrotransposon sequences at the flanking sites . To determine whether TEs were non-randomly distributed , the distribution of inter-TE distances was compared ( Mann-Whitney-Wilcoxon text ) with that of the inter-element distances of a randomly generated subset of 1 , 196 elements . In addition , TEs and gene model annotations were merged and used as reference for a hypergeometric test to test for the presence of regions enriched in TEs . The analysis was performed using REEF [74] with a Q-value of 0 . 05 ( FDR 5% ) , a window width of 100 kb with a shift of 10 kb and a minimum number of 10 features in clusters . The P . ostreatus PC15 and PC9 genome assemblies were aligned using the Mercator and MAVID pipeline [75] , using the fully assembled PC15 genome as a reference . Gene model positions and TE hits of the PC15 strain were used to extract individual alignments and to check the homozygous vs . heterozygous nature of the insertions . A locus was considered homozygous if the alignment spanned at least 80% of the whole locus length , and heterozygous when the PC9 allele was absent . Long Terminal Repeats of every intact , full-length element were extracted and aligned . Kimura 2-Parameter distance was obtained using a Python script and transformed to My using the approach described in [39] and the fungal substitution rate of 1 . 05 × 10−9 nucleotides per site per year [40] . Mycelia were harvested , frozen and ground in a sterile mortar in the presence of liquid nitrogen . DNA was extracted using a Fungal DNA Mini Kit ( Omega Bio-Tek , Norcross , GA , USA ) . Sample concentrations were measured using a Qubit 2 . 0 Fluorometer ( Life Technologies , Madrid , Spain ) , and purity was measured using a NanoDrop 2000 ( Thermo-Scientific , Wilmington , DE , USA ) . PCR reactions were performed according to Sambrook et al [76] using primers designed after the TE flanking sequences ( S1 Text , Supplementary Information ) . Total RNA was extracted from 200 mg of deep frozen tissue using Fungal RNA E . Z . N . A Kit ( Omega Bio-Tek , Norcross , GA , USA ) , and its integrity was estimated by denaturing electrophoresis on 1% ( w/v ) agarose gels . Nucleic acid concentrations were measured using a Nanodrop 2000 ( Thermo Scientific , Wilmington , DE , USA ) , and the purity of the total RNA was estimated by the 260/280 nm absorbance ratio . Messenger RNA was purified using a MicroPoly ( A ) Purist kit ( Ambion , USA ) . Transcriptome libraries were generated and sequenced by Sistemas Genomicos S . L . ( Valencia , Spain ) on a SOLiD platform , following the manufacturers’ recommendations ( Life Technologies , CA , USA ) . P . ostreatus RNA-seq datasets corresponding to PC15 and PC9 strains ( 8 . 4 and 9 . 7 million reads in PC15 and PC9 , respectively ) cultured in SMY medium and harvested during the exponential growth phase , were used to analyze the transcription of genes and TEs . The quality of the SOLiD RNA-seq reads was verified using FastQC ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) , and they were mapped to their corresponding PC15 v2 . 0 or PC9 v1 . 0 assemblies using TopHat [77] , restricting the multihits option to 1 . HTseq-count [78] was used to determine the number of reads mapping to every feature . SAMtools [79] , BEDTools [80] and custom Python scripts were used to manipulate the data , to calculate RPKMs and to obtain genome coverages . Public RNA-seq data from other species were downloaded from the NCBI SRA database and were analyzed using the same pipeline ( accessions SRR1257938 Saccharomyces cerevisiae S288C [81] , SRR1284049 Botrytis cinerea B05 . 10 [82] , SRR1592424 F . graminearum [83] and SRR1165053 Laccaria bicolor [84] ) . For analyzing the expression of TE families , reads were mapped to the extracted transposon sequences using Bowtie [85] and allowing multi-mapping . RSEM software was used to calculate TE expression because its algorithm is especially designed to handle multi-mapped reads [86] . Afterwards , the FPKMs of each family were normalized to the number of elements . Gene and TE annotations were intersected to obtain TE-associated genes ( genes overlapping with any TE ) and non-TE genes ( genes not overlapping with any TE ) . Afterwards , the closest TE upstream and downstream to each non-TE gene was obtained at a maximum distance of 1 kb . The resulting genes were organized in three groups: i ) genes with an upstream TE , ii ) genes with a downstream TE and iii ) genes with both upstream and downstream TEs . Control groups were obtained by subtracting target genes ( three previous scenarios ) to all the non-TE genes . The predicted proteomes of all species were downloaded from the Mycocosm database ( http://genome . jgi . doe . gov/programs/fungi/index . jsf ) . After all-by-all BLASTP , proteins were clustered with MCL [87] using an inflation value of 2 . Clusters containing single copy genes of each genome were retrieved ( allowing two missing taxa per cluster ) and proteins were aligned with MAFFT [88] . The alignments were concatenated after discarding poorly aligned positions with Gblocks [89] . Maximum-likelihood phylogeny was constructed using RaxML [90] under PROTGAMMAWAGF substitution model and 100 rapid bootstraps . Using the P . ostreatus JGI browser we identified the internal transposase gene of a full length element of TIR_1 family . This protein was used as query for BLASTP searches ( cutoff = E-5 ) against NCBI RefSeq protein database ( independent searches were carried out against animal , plant and bacterial databases ) . The best five animal , plant and bacterial hits were retrieved when possible ( i . e . only one hit was obtained using plant database ) . The same search was performed in the JGI database to retrieve the best five basidiomycete hits , and the best five non-basidiomycete hits . Proteins were aligned with MUSCLE [91] , and the alignments were trimmed using trimAl [92] with the default parameters . An approximate maximum likelihood tree was constructed using FastTree [93] and edited with Figtree ( http://tree . bio . ed . ac . uk/software/figtree/ ) . Transposases from P . patens , Wolbachia and Rhizopus oryzae were further analyzed to exclude the possibility of being a result of database contamination: Using TBLASTN against NCBI Whole-genome shotgun contigs or JGI genomic scaffolds , we identified their genomic position and verified that they were assembled in long scaffolds and surrounded by other host genes . Raw sequencing data was deposited in GEO database under the accession number GSE81586 . | Transposable elements ( TEs ) are enigmatic genetic units that have played important roles in the evolution of eukaryotic genomes . Since their discovery in the 1950s , they have gained increasing attention and are known today as active genome modelers in multiple species . Although these elements have been widely studied in plants , much less is known about their occurrence and impact on the fungal kingdom . Using a diverse set of basidiomycete and ascomycete fungi , we quantified and characterized a huge diversity of DNA and RNA transposable elements , and we identified species that had 0 . 02 to 29 . 8% of their genomes occupied by transposable elements . In addition , using our basidiomycete model Pleurotus ostreatus , we demonstrated how TE insertions produced detrimental effects on the expression of upstream and downstream genes , which were downregulated compared with the control groups . This silencing mechanism was present in the basidiomycetes tested but exhibited a patchy distribution in ascomycetes , and might be related to specific genome defense mechanisms that control transposon proliferation . This finding reveals the broader impact of transposable elements in fungi . In addition to their importance as long-term evolutionary forces , they play major roles in the more dynamic transcriptome regulation of certain species . | [
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] | 2016 | Transposable Elements versus the Fungal Genome: Impact on Whole-Genome Architecture and Transcriptional Profiles |
Pseudomonas syringae pv . tomato DC3000 ( PtoDC3000 ) is an extracellular model plant pathogen , yet its potential to produce secreted effectors that manipulate the apoplast has been under investigated . Here we identified 131 candidate small , secreted , non-annotated proteins from the PtoDC3000 genome , most of which are common to Pseudomonas species and potentially expressed during apoplastic colonization . We produced 43 of these proteins through a custom-made gateway-compatible expression system for extracellular bacterial proteins , and screened them for their ability to inhibit the secreted immune protease C14 of tomato using competitive activity-based protein profiling . This screen revealed C14-inhibiting protein-1 ( Cip1 ) , which contains motifs of the chagasin-like protease inhibitors . Cip1 mutants are less virulent on tomato , demonstrating the importance of this effector in apoplastic immunity . Cip1 also inhibits immune protease Pip1 , which is known to suppress PtoDC3000 infection , but has a lower affinity for its close homolog Rcr3 , explaining why this protein is not recognized in tomato plants carrying the Cf-2 resistance gene , which uses Rcr3 as a co-receptor to detect pathogen-derived protease inhibitors . Thus , this approach uncovered a protease inhibitor of P . syringae , indicating that also P . syringae secretes effectors that selectively target apoplastic host proteases of tomato , similar to tomato pathogenic fungi , oomycetes and nematodes .
Pseudomonas syringae is an important model system for plant-pathogen interactions . Different pathovars of this Gram-negative bacterium can cause disease on a broad variety of plants . Most intensively studied is pathovar tomato DC3000 ( PtoDC3000 ) , which causes bacterial speck disease on tomato and Arabidopsis [1 , 2] . This pathogen can enter the extracellular space ( apoplast ) of leaves through stomata and colonizes the apoplast , causing black specks , hence the name bacterial speck disease [1 , 2] . P . syringae manipulates its host using effectors , which are secreted metabolites or proteins that manipulate the host cell . Most intensively studied are the type-III ( T3 ) effectors that are injected into host cells through the T3 secretion system ( T3SS ) [3 , 4] . These T3 effectors are collectively required but individually not essential to cause disease [5] . Filamentous tomato pathogens secrete dozens of apoplastic effectors with different functions , often contributing to pathogen virulence . The fungal tomato pathogen Cladosporium fulvum , for example , secretes Avr4 to prevent degradation of chitin in the fungal cell walls by secreted host chitinases [6] . C . fulvum also secretes Ecp6 to sequester chitin fragments and prevent their detection [7] , and Avr2 to inhibit secreted host proteases [8] . Likewise , the oomycete tomato pathogen Phytophthora infestans secretes Epi and EpiC proteins inhibiting secreted host serine and cysteine proteases , respectively [9 , 10] . In other pathosystems , apoplastic effectors include P . sojae Gip1 , which inhibits a secreted glycosidase of its host , soybean [11] and Ustilago maydis Pep1 , which blocks the apoplastic peroxidase of its host , maize [12] . Hence , secreted effectors are commonly used to manipulate the host apoplast . Importantly , all of these apoplastic effector proteins are small and often share no or low homology with annotated proteins . The production of apoplastic effectors by filamentous pathogens suggests that also bacterial pathogens may employ apoplastic effectors to inhibit harmful enzymes in the apoplast . Here , we mined the genome of the model pathogen PtoDC3000 for genes encoding potential apoplastic effectors and found that many of these putative effectors are common to Pseudomonas species and expressed during apoplast colonization . We expressed over 40 of these non-annotated putative effectors as soluble proteins and screened them using competitive activity-based protein profiling ( ABPP , [13] ) for the inhibition of the C14 immune protease of tomato . Our results revealed that one of these proteins can inhibit immune proteases of tomato and contributes to virulence . This study investigates a repertoire of new putative effector proteins and describes the targets of the first apoplastic effector for this important model plant pathogen .
To identify non-annotated small secreted proteins of PtoDC3000 , we analyzed the 5616 predicted proteins encoded by the PtoDC3000 genome ( [14] , S1 Table ) . First , we ranked the 5616 proteins on the length of the proteins , resulting in a histogram that visualizes that the majority of the PtoDC3000 proteins are 150–400 aa in length ( Fig 1A ) . From this list we selected 2420 proteins with a length of 50–260 amino acids , which corresponds to protein sizes of 5–25 kDa . Most of the published apoplastic effectors fall in this size region . Second , we analyzed these 2420 proteins for the presence of a Gram-negative bacterial signal peptide using SignalP [15] . SignalP predicts signal peptides using two algorithms: Hidden Markov ( HM ) and Neural Network ( NN ) . Because the HM and NN algorithms predict signal peptides independently , we plotted each of the 2420 proteins against their scores in a dot plot ( Fig 1B ) . To ensure that we select proteins that are likely to have a functional signal peptide , we selected the proteins that have an additive score of HM+NN>1 . 1 . A total of 234 proteins were selected this way ( Fig 1B ) . Third , the HM and NN algorithms also produce a significance score ( HM ( 0–2 ) and NN ( 0–5 ) ) , which we used for further selection . By selecting proteins with a sum of both significance scores being 5 or higher , we selected 200 proteins having the highest confidence for secretion ( Fig 1C ) . Fourth , we investigated the 200 selected proteins for their annotation in the Pseudomonas genome database . Of the 200 small putative proteins , 69 are annotated , e . g . as components of secretion or motility systems ( S1 Table ) . This group also includes chaperones , prolyl isomerases , various transporters , and a superoxide dismutase , carbonic anhydrase , and sorbitol dehydrogenase . None of these proteins are annotated as hydrolase inhibitor . The 131 remaining small proteins have previously only been annotated as ‘hypothetical proteins’ or ‘lipoproteins’ , which means that they carry a lipobox after the signal peptide ( Fig 1D ) . To investigate if these 131 non-annotated proteins can be genuine proteins , we counted the number of cysteine residues in each mature protein , after omitting the signal peptide . The rationale being that secreted proteins frequently have disulphide bridges to increase their stability , and secreted effectors should therefore possess an even number of cysteine residues . Our analysis revealed that 47% of the 131 mature proteins have indeed an even number of cysteines , whereas only 11% have an odd number of cysteines ( Fig 1E ) . The other 42% do not contain cysteines in the putative mature protein domain . These data suggest that our 131 non-annotated proteins represent a genuine set of secreted proteins and that a large portion of these putative proteins is equipped with putative disulphide bridges to provide stability in the apoplast . Next , we investigated how common these putative proteins are amongst Pseudomonas species . We selected 24 other Pseudomonas strains of which genome sequences are publicly available . This collection included three other plant pathogenic P . syringae strains: pathovars syringae B728a ( PsyB728a ) , phaseolicola 1448A ( Pph1448A ) and tabaci 11528 ( Pta11528 ) , which are pathogenic on snap bean , soybean , and tobacco , respectively [14 , 16–17] . The genome collection also included genomes of the rice and rapeseed epiphytes ( P . fulva and P . brassicacea , respectively ) , twelve human epiphytes and opportunistic pathogens ( P . mendocina , P . stutzeri , and P . aeruginosa ) and eight soil bacteria ( P . fluorescence , P . entomophila and P . putida ) . BLAST searches of the 131 selected putative secreted small non-annotated proteins against these 24 Pseudomonas genomes revealed that most putative proteins have clear homologs in other Pseudomonas species ( Fig 2 and S1 Fig ) . Interestingly , these homologies cluster in five groups . Group-1 consists of six putative proteins that are unique to PtoDC3000 . Group-2 consists of 25 putative proteins that have close homologs only in other plant pathogenic bacteria . Group-3 consists of 26 putative proteins common with plant pathogenic bacteria and soil bacteria . Group-4 consists of nine putative proteins shared with opportunistic human pathogens , whilst the largest group ( Group-5 ) consists of 65 putative proteins that are common to all Pseudomonas species . The high degree of conservation amongst Pseudomonas species suggests that these 131 putative proteins are genuine , bona fide proteins and are not incidentally generated by an occasional misannotation in the PtoDC3000 database . Of these , we randomly selected 43 putative proteins having relatively high SP confidence scores and we produced and purified these putative proteins for further studies ( bottom of Fig 2 ) . To investigate if the genes encoding the selected 43 proteins are also expressed during infection , we mined gene expression databases for P . syringae infections . Infection with PsyB728a has been investigated for bacterial gene expression during epiphytic and apoplastic colonization of bean [18] . For the 38 of the 43 putative selected small non-annotated proteins , the ortholog was identified in the PsyB728a genome ( Fig 3 ) . For relative comparison , we also extracted the expression levels of the 25 type-III effectors of PsyB728a [19] from the same gene expression database [18] . The majority ( 21/38 ) of the selected genes encoding putative small , secreted non-annotated proteins are expressed with higher transcript levels than the average transcript levels of type-III effectors ( Fig 3 ) . It is therefore likely that many of the 43 selected genes are expressed during infection . To also investigate if these proteins accumulate in the apoplast , we performed proteomic analysis of apoplastic fluids extracted from PtoDC3000-infected plants . This approach is challenging because small proteins may not produce two or more unique peptides for robust identification . Nevertheless , we could detect eight of the 43 selected proteins ( dark gray bars and asterisks in Fig 3 ) . Interestingly , these eight correspond to the genes with the highest transcript levels , often being higher than the highest expression level of type-III effectors genes , indicating that gene expression levels of the remaining genes are probably too low to detect the gene products by proteomics . In conclusion , the transcript levels and detection by proteomics indicates that several selected proteins are expressed by PtoDC3000 during apoplast colonization , and present in the apoplast . To produce the selected proteins heterologously , we took advantage of the commercially available pFLAG-ATS expression system in Escherichia coli ( Sigma-Aldrich ) . This expression system secretes N-terminally FLAG-tagged proteins into the growth medium using an N-terminal OmpA signal peptide to facilitate secretion . The growth medium of E . coli cultures has relatively low protein content and is easy to collect as supernatant after centrifugation , making this expression system ideal to produce small secreted bacterial proteins . Because of the large number of proteins , we decided to use the Gateway cloning strategy and add an N-terminal His tag to simplify the purification . We therefore generated a derivative of pFLAG-ATS that carries an extra fragment encoding a His-tag and the ccdB suicide gene located between the two attR1 and attR2 recombination sites ( pTSGATE1 , Fig 4A and S1 File ) . This construct was used for the expression and purification of 43 soluble proteins in a single step to a scale of over 100 μg per protein . Detection of the purified proteins on coomassie gels and anti-FLAG western blots confirmed the purity and molecular weight of these proteins ( Fig 4B and S2 Fig ) . Various tomato pathogens secrete apoplastic effectors that target papain-like cysteine proteases ( PLCPs ) that are secreted by the host during the immune response [8 , 10 , 20] . We therefore hypothesized that also P . syringae might employ this strategy and secrete protease inhibitors during infection . We first tested if any of the putative effectors would inhibit the secreted immune protease C14 of tomato . C14 is targeted by both EpiC1 and EpiC2B proteins of P . infestans [21] and is inhibited by Avr2 of C . fulvum [22] . C14 is also targeted by the RxLR-type effector AvrBlb2 of P . infestans , which blocks C14 secretion [23] . Furthermore , C14 knock-down dramatically increases P . infestans susceptibility [21] , whilst C14 overexpression increases resistance [23] . Because C14 is a ‘hub’ for multiple effectors and because it plays a role in immunity , we decided to test if any of the 43 purified putative apoplastic effectors of PtoDC3000 would inhibit C14 . We screened the 43 proteins for their ability to block the activity of the mature C14 protease using competitive ABPP . Competitive ABPP is based on a preincubation of a protease with a putative inhibitor , followed by labeling of the non-inhibited proteases with an activity-based probe that reacts with the active site of the enzyme . Competitive ABPP has been routinely used to uncover the targets of Avr2 , Epic1 , Epic2B , Gr-Vap-1 , CC9 , and Pit2 [8 , 10 , 18–20 , 24–29] . To facilitate quantification and medium-throughput screening , we used MV201 , a fluorescent probe based on the papain inhibitor E-64 [30] . The C14 protease was transiently expressed by agroinfiltration of Nicotiana benthamiana [24] . Leaf extracts were labeled with MV201 , separated on protein gels and fluorescently labeled C14 was detected by in-gel fluorescence scanning as a strong 30 kDa signal , representing the soluble , mature isoform of C14 ( mC14 [21 , 24] ) . This signal is absent in agroinfiltrated tissues that do not express C14 and in extracts pre-incubated with an excess of E-64 ( S3 Fig ) . C14-containing extracts were diluted such that a robust fluorescent mC14 signal could still be detected upon MV201 labeling . Preincubation of the diluted mC14 with 1μg ( 50 μM ) EpiC1 and EpiC2B blocked subsequent labeling by MV201 ( Fig 5A ) , showing that mC14 inhibition by EpiCs can be detected using this competitive ABPP approach . To demonstrate that we could also detect interactions with weak inhibitors , we preincubated mC14 with 1 μg ( 33 μM ) Avr2 , a weak inhibitor of mC14 [21–22] . Importantly , Avr2 also prevents labeling of mC14 under these conditions ( Fig 5A ) , indicating that we use conditions that allow us to detect even weak inhibitors of mC14 . We next screened the 43 proteins by preincubating 1μg ( 20–85 μM ) of each protein with the mC14-containing leaf extract , followed by MV201 labeling . To select C14 inhibitors , signals were quantified and plotted for each PSPTO protein ( Fig 5B ) . To exclude false positives , signals that were more than 0 . 5 times the signal of the no-inhibitor control were considered non-significant . Interestingly , this screen revealed one PtoDC3000 protein , PSPTO4211 , that blocked labeling of mC14 by MV201 ( Fig 5B ) . Hence , we named this protein C14-inhibiting protein ( Cip1 ) . To determine the importance of cip1 for the virulence of PtoDC3000 , we generated two independent knock-out mutants of PtoDC3000 through homologous recombination ( Δcip1a and Δcip1b ) and generated complemented strains by transforming the Δcip1 mutants with wild-type Cip1 using Tn7 transposition [31] . When infiltrated into tomato leaves , the Δcip1 mutants grow significantly less when compared to wild-type PtoDC3000 ( Fig 6A and S4 Fig ) . By contrast , both Δcip1 mutant strains grow indistinguishable from wild type PtoDC3000 in liquid cultures ( in vitro , S5 Fig ) . Importantly , the in planta growth defect is complemented in the Δcip1 +Tn7:cip1 strain ( Fig 6A ) . The differential bacterial growth of the strains correlates with the severity of the disease symptoms: Δcip1 strains cause less bacterial spot symptoms than the wild-type or the complemented strains ( Fig 6B ) . This experiment demonstrates that cip1 encodes an important virulence factor for PtoDC3000 on tomato . The phenotype of the Δcip1 mutant suggests that the cip1 gene is expressed during infection . To confirm cip1 expression , we performed semi quantitative RT-PCR on RNA extracted from wild-type and a Δcip1 mutant PtoDC3000 grown in minimal medium ( which mimics infection conditions ) and isolated from the apoplast of infected plants , two days after infection . RT-PCR with cip1-specific primers amplified a gene product that is absent in the Δcip1 mutant and if no reverse transcriptase was added ( Fig 6C ) , demonstrating that cip1 is expressed when bacteria are grown in minimal media or during infection . By contrast to cip1 itself ( PSPTO4211 ) , the expression of cip1-flanking genes PSPTO4210 and PSPTO4212 is similar in the Δcip1 mutant when compared to wild-type bacteria ( Fig 6C ) , indicating that the flanking genes are unaffected in the Δcip1 mutant . Cip1 has a predicted signal peptide for secretion of 21 amino acids . To investigate if Cip1 protein can also be detected in the apoplast during infection , we raised a Cip1-specific antibody against the Cip1 protein and performed western blot analysis of apoplastic fluids isolated from tomato plants infected with wild-type and Δcip1 mutant bacteria . Unfortunately , the affinity of the Cip1 antibody is not high enough to detect Cip1 in apoplastic fluids . We therefore also tested the supernatant of a centrifuged culture of wild-type and Δcip1 mutant bacteria grown in minimal medium . Western blot analysis of these samples displayed a signal of the expected molecular weight in both the WT strain and the ΔhopQ-1 mutant control that was absent in both tested Δcip1 mutants ( Fig 6D ) . These data demonstrate that Cip1 protein is detected in the medium of PtoDC3000 cultures and suggest that Cip1 occurs in the apoplast during infection . We have performed several experiments to also investigate the suppression of apoplastic PLCPs during infection but failed to detect a consistent suppression using MV201 labeling on apoplastic fluids isolated from infected and non-infected plants ( S6 Fig ) . We believe this is caused by the relatively low amount of Cip1 produced locally during infection when compared to the active PLCPs that are present in apoplastic fluids isolated from whole leaves . Analysis of the Cip1 protein sequence using PFAM revealed that this protein contains a chagasin motif , NPTTG . Chagasins are cysteine protease inhibitors initially described for the human protozoan parasite Trypanosoma cruzi , the causal agent of Chagas disease [32] . In T . cruzi , chagasin is an intracellular protein that controls the activity of cruzipain , an endogenous cysteine protease during the development of this parasite . Similar roles in regulation of endogenous proteases have been described for chagasin-like proteins from other human protozoan parasites [33–36] . Alignment of Cip1 protein sequence with homologs from other Pseudomonas species and chagasins of three human protozoan parasites shows that the homology of Cip1 is high to the homolog in other P . syringae strains ( >90% identity ) and moderately high ( ca . 50% identity ) to homologs from other Pseudomonas species , but Cip1 has less than 26% identity with the well-characterized chagasins from protozoans ( Fig 7A ) . Nevertheless , in addition to the conserved NPTTG motif , two additional Chagasin motifs ( GxGG and RPW ) are conserved amongst these proteins . Importantly , the alignment also reveals that all Pseudomonas chagasins carry a putative signal peptide for secretion , whereas the protozoan chagasins do not ( Fig 7A ) . To confirm that Cip1 can inhibit papain-like proteases , we performed classical protease inhibition assays on purified papain using the chromogenic substrate BAPNA . Lineweaver-Burk plot analysis revealed that Cip1 is a classical , competitive inhibitor of papain with a Ki of 3 . 98 nM ( Fig 7B ) . To confirm that Cip1 is a chagasin-like protein , we mutated the conserved NPTTG motif by deleting the two conserved threonines ( ΔT ) or by substituting them into two alanines ( AA ) . These mutations were previously shown not to affect the chagasin structure but significantly reduce the affinity of chagasin for papain [37] . The wild-type and mutant Cip1 proteins are soluble proteins and were purified using the N-terminal His-tag ( Fig 7C ) . When compared to Cip1 ( WT ) , the Cip1 ( AA ) substitution mutant has significantly less inhibitory activity , whereas the Cip1 ( ΔT ) deletion mutant is even less able to inhibit papain ( Fig 7D ) . This relative activity is consistent with the mutants described for chagasin [37] . To examine if this reduced activity is also displayed on C14 , we preincubated apoplastic fluids of plants transiently overexpressing C14 with ( mutant ) Cip1 and then added MV201 to label the non-inhibited proteases . Papain was included as a control to compare with the traditional protease activity assay . Suppression of labeling shows that the Cip1 ( ΔT ) deletion mutant has lost most of its inhibitory activity towards both papain and C14 whereas the Cip1 ( AA ) substitution mutant is still able to partially suppress labeling of papain and C14 ( Fig 7E ) . These data are consistent to the traditional substrate conversion assay ( Fig 7D ) and the previously described chagasin mutants [37] . In conclusion , the NPTTG chagasin motif is also important for Cip1 inhibitory activity to the same extend as chagasins , consistent with Cip1 being a chagasin-like protease inhibitor . We next tested if , in addition to mature C14 , also the intermediate isoform of C14 can be inhibited by Cip1 . Intermediate C14 ( iC14 ) migrates at 35 kDa and differs from the 30 kDa mature C14 ( mC14 ) by carrying a C-terminal granulin-like domain [21] . Since iC14 tends to precipitate , the activity of this isoform can be monitored in extracts that have not been centrifuged . Preincubation of non-centrifuged extracts containing iC14 with Cip1 also blocks labeling of iC14 ( Fig 8A ) , indicating that Cip1 inhibits both isoforms of C14 . Other tomato papain-like proteases , such as Rcr3 and Pip1 are often targets of the same set of pathogen-derived inhibitors [20 , 21 , 24] . We therefore tested if Cip1 also inhibits these proteases . Both Pip1 and Rcr3 were produced by agroinfiltration in N . benthamiana and isolated from agroinfiltrated plants in apoplastic fluids . Labeling of diluted apoplastic fluids with MV201 causes the characteristic 25 and 23 kDa signals for Pip1 and Rcr3 , respectively ( Fig 8A ) . Preincubation with 7 . 8 μM Cip1 blocks Pip1 labeling and suppresses Rcr3 labeling , respectively ( Fig 8A ) , indicating that Cip1 also inhibits Pip1 and Rcr3 . All our assays are performed at apoplastic pH ( pH 5–5 . 5 ) , which is important since some inhibitors ( e . g . Avr2 , [8] ) only inhibit at acidic and not at neutral pH . By contrast , inhibition of mC14 , Pip1 and Rcr3 by Cip1 occurs at both apoplastic and neutral pH ( S7 Fig ) . The consistently weaker suppression of Rcr3 labeling , however , suggests that Cip1 is a weak inhibitor of Rcr3 when compared to Pip1 and C14 . To further investigate the relative strength of inhibition , apoplastic fluids of plants transiently overexpressing Rcr3 , C14 and Pip1 were pre-incubated with a dilution series of Cip1 and then labeled with MV201 . The fluorescence intensity was measured from protein gels and plotted against the Cip1 concentration . This inhibitor dilution experiment revealed that Cip1 is a strong inhibitor of both C14 and Pip1 , and a weak inhibitor of Rcr3 , requiring at least 200-fold more Cip1 to reach the same suppression level for Rcr3 when compared to C14 and Pip1 ( Fig 7B ) . We also tested an additional 34 natural Rcr3 variants [26] but we were unable to identify a natural variant of Rcr3 with increased sensitivity for Cip1 inhibition ( S8 Fig ) . When leaves of tomato plants carrying Rcr3 and the Cf-2 resistance gene were injected with Avr2 and Gr-Vap-1 , a hypersensitive cell death was triggered by their ability to interact with Rcr3 [20 , 38] . To test if Cip1 is also recognized in Money Maker Cf2 ( MM-Cf2 ) tomato plants , which carry Rcr3 and Cf-2 , we injected Cip1 at protein concentrations up to 1 μM . However , neither hypersensitive cell death nor chlorotic responses were observed , even after prolonged incubation times ( Fig 7C ) . By contrast , 100 nM Avr2 is sufficient to trigger hypersensitive cell death in MM-Cf2 tomato plants , but not MM-Cf0 tomato plants , which lack the Cf-2 resistance gene ( Fig 7C ) . This indicates that Cip1 is not recognized by MM-Cf2 tomato plants . We also tested if the presence of Rcr3 had an effect on bacterial growth of PtoDC3000 . We therefore performed bacterial growth assays on MM-Cf2 plants carrying the wild-type Rcr3 gene ( MM-Cf-2/Rcr3 plants ) or the rcr3-3 null mutant gene ( MM-Cf-2/rcr3-3 plants , [39] ) . These bacterial growth assays showed that PtoDC3000 grows equally well on MM-Cf2/Rcr3 as well as MM-Cf2/rcr3-3 tomato plants ( Fig 7D ) , consistent with the absence of Cip1 recognition by the Cf-2/Rcr3 system .
Here we describe a diverse set of small secreted putative proteins encoded by the P . syringae genome that could act as effectors that manipulate the apoplast . We developed and employed an efficient expression system to produce these proteins heterologously and we used competitive ABPP to discover a novel chagasin-like protein that inhibits the activity of apoplastic immune proteases of tomato and contributes to virulence of PtoDC3000 . By choosing stringent selection criteria to select putative small non-annotated secreted proteins , we are relatively confident that these proteins are genuine proteins . First , the vast majority of these proteins are also predicted from genomes of other Pseudomonas species . Second , the observed high ratio of paired versus unpaired cysteines is not a random distribution and suggests that these proteins carry disulphide bridges , which would stabilize them upon secretion . Third , most genes are probably expressed during apoplast colonization at a higher level than T3 effectors and eight proteins with the highest expression levels were detected in the apoplast by mass spectrometry . Fourth , we were able to produce a large number of these putative proteins as soluble proteins , indicating that they were properly folded also upon heterologous expression . The ability to produce these secreted proteins opens several research avenues to elucidate their functions . Although several of these proteins can have enzymatic or structural functions , we suspect , based on the small size , that many of these proteins are enzyme inhibitors that manipulate secreted host enzymes , such as glycosidases , proteases , peroxidases and lipases . For example , Gip1 from Phytophthora sojae , Avr2 from Cladosporium fulvum , Epi1 from Phytophthora infestans and Pep1 from Ustilago maydis , are secreted , pathogen-derived inhibitors that target host endoglucanases , cysteine proteases , subtilases or peroxidases , respectively [8–9 , 11–12] . Some of the apoplastic effectors might interact with the components of the host or pathogen cell wall or membranes; retrieve metabolites or ions; or sequester elicitors to prevent perception . For example , C . fulvum secretes Avr4 which binds to the fungal cell wall to protect it against chitinases , and C . fulvum Ecp6 , which sequesters chitin fragments to prevent their perception by the plant [6–7] . In addition to testing for inhibitors of apoplastic hydrolases , our FLAG-His-tagged proteins can be used for pull-down assays to identify interacting molecules , and to produce pure protein for crystallographic studies to elucidate their structures . We employed a new approach to screen putative apoplastic effector proteins for novel functions by using competitive ABPP . Competitive ABPP is a powerful method to identify inhibitors as this assay can be performed in medium through-put and without the need to purify the enzyme and/or know their substrates . Cravatt and co-workers used library-to-library competitive ABPP screens to identify selective inhibitors for dozens of mammalian serine hydrolases [40] . Here , we used competitive ABPP to identify natural inhibitors secreted by a pathogen . Competitive ABPP assays are relatively quick and robust , and the throughput will increase further using broad range probes that label multiple enzymes simultaneously . The introduction of new activity-based probes for other apoplastic enzymes can now be used to identify PtoDC3000 apoplastic proteins that inhibit subtilases , lipases , acyltransferases and glycosidases [41–42] . Our competitive ABPP screen revealed that PtoDC3000 secretes the chagasin-like Cip1 that can inhibit tomato immune proteases . Chagasins have been mostly described for protozoan parasites . Protozoan chagasins are produced without signal peptide and control the activity of endogenous papain-like cysteine proteases that play essential roles during infection [32–36] . In recent years , however , these protozoan chagasins were found to be released during later stages of the infection and inhibit extracellular proteases of the host [43] . A role for bacterial chagasins has not yet been elucidated [44] . An argument supporting the hypothesis that Pseudomonas chagasins have extracellular targets lies in the fact that chagasins so far exclusively target C1A proteases , but that , even though all Pseudomonas species carry a chagasin ortholog , most Pseudomonas genomes do not encode C1A proteases [43–45] . Like Cip1 , the chagasin ortholog of other pathogenic Pseudomonas bacteria may also act by suppressing secreted host proteases . Likewise , chagasins produced by soil Pseudomonas may act in controlling extracellular proteases that might be produced by the microbiome . The role of chagasins in other Pseudomonas species remains to be investigated . Importantly , Cip1 contributes to virulence on tomato because cip1 mutants show significantly reduced bacterial growth on this host and this phenotype can be complemented by transformation with wild type Cip1 . This is consistent with a role for secreted immune proteases being harmful to PtoDC3000 because transgenic tomato lines with reduced Pip1 protein and activity levels are significantly more susceptible for PtoDC3000 infection [46] . Thus , Cip1 provides protection against secreted host proteases that would otherwise suppress pathogen growth . We found that Cip1 inhibits Rcr3 less efficiently when compared to Pip1 and C14 . Pip1 and Rcr3 are close homologs and it is therefore remarkable that Cip1 is able to distinguish between these proteases . Previously described pathogen-derived inhibitors Avr2 , Gr-Vap-1 , Epic1 and Epic2B , do not show such a distinct discrimination between Rcr3 and Pip1 , even though they showed distinct affinities for the less related C14 [20–25] . The ability to distinguish between Pip1 and Rcr3 could be a strategy of PtoDC3000 to prevent recognition by Rcr3 while suppressing the dominant proteolytic activities of Pip1 and C14 in the apoplast during infection [25] . This strategy is different when compared to that used by Phytophthora infestans , which uses ‘stealthy’ EpiC inhibitors , which do inhibit Rcr3 , but evade recognition [25] . The molecular basis for the evolution of this evasive chagasin-like effector that can distinguish between Rcr3 and Pip1 is yet another exiting topic for future studies .
The predicted protein sequences of the PtoDC3000 genome and their annotations [14] were obtained from http://www . pseudomonas-syringae . org/ . SignalP3 . 0 ( http://www . cbs . dtu . dk/services/SignalP-3 . 0/ ) was used for signal peptide prediction [15] and both the scores for Hidden Markov and Neural Network ( sum >1 . 1 ) , as well as their significance scores ( sum >4 ) , were used to select secreted proteins . The annotation in the protein database was used to remove annotated proteins . The number of cysteines in each of these candidate proteins was counted after the removal of predicted signal peptide . The list of 234 candidate effectors was blasted against a database of predicted proteins from 25 different Pseudomonas strains ( 144 , 359 sequences total ) using Blastp 2 . 2 . 26 and an initial e-value cut-off of . 001 ( correlating for most candidates to roughly 35% similarity ) . The blast results were parsed and a matrix was generated in which each candidate protein was assigned a vector of 25 values reflecting the percentage similarity between the candidate and the best matching protein from each of the strains . When no hit was found with e < . 001 , the percentage similarity was set to -1 . Both the rows ( candidates ) and columns ( strains ) were hierarchically clustered using Cluster 3 . 0 [47] using a Euclidean distance metric and centroid linkage and the resulting trees were visualized using java TreeView ( http://jtreeview . sourceforge . net ) . The closest PsyB728a-homologs of the 43 putative small secreted , nonannotated proteins of PtoDC3000 proteins were identified in the PsyB728a genome . The respective expression levels of these genes during colonization of PsyB728a in the apoplast were extracted from the microarray data ( dataset GSE42544 from the GEO database at NCBI ) , published as supplemental data of [18] . Also the expression levels of 25 type-III ( T3 ) effectors of PsyB728a were extracted . Nicotiana benthamiana and tomato ( Solanum lycopersicum Money-Maker ) were grown in a climate chamber under a 14h light ( 22°C ) and 10h dark ( 18°C ) cycle . N . benthamiana plants were grown until four to six week old and used for Agrobacterium-mediated transient protein expression ( agroinfiltration ) . To detect the selected PtoDC3000 proteins during infection , we inoculated Nicotiana benthamiana with the ΔhopQ1-1 mutant of PtoDC3000 [48] , which is fully pathogenic on this host . This pathosystem causes strong infections and sufficient apoplastic proteomes for proteomic analysis . Plants were hand-infiltrated with 108 bacteria/mL and two days after inoculation the apoplastic fluids were isolated by vacuum infiltration and centrifugation . Apoplastic fluids were concentrated by methanol/chloroform precipitation , digested with trypsin and analyzed by LC-MS/MS , against the annotated PtoDC3000 proteome . Cloning primers of candidate proteins ( S2 Table ) were designed in three steps . The 5’ ends of cloning fragments were defined by SignalP predictions to remove the predicted signal peptide from the candidate amino acid sequence . The 3’ ends of cloning fragments were selected from the predicted amino acid sequences to include stop-codons at the end of the sequence . From the defined positions , 20 to 22 bases of nucleotide sequences of the Pseudomonas gene were selected and fused behind Gateway adaptor sequences 5’-gggacaagtttgtacaaaaaagcaggcttgatg-3’ ( forward ) and 5’ggggaccactttgtacaagaaagctgggta-3’ ( reverse ) . To produce a Gateway-compatible pFLAG vector carrying an additional His-tag , a PCR fragment of a Gateway cassette encoding an N-terminal 6x His-tag was generated using primers 5’-atgcctcgagcaccatcaccatcaccataagcttacaagtttgtacaaaaaagctgaacg-3’ and 5’-atgctctagataccactttgtacaagaaagctgaacg-3’ using a destination vector as template , and cloned into pFLAG-ATS ( Sigma-Aldrich ) using XhoI and XbaI restriction enzymes , resulting in pTSGATE1 ( S1 File ) . Genomic DNA isolated from PtoDC3000 was used as PCR template . Pfu-Ultra-II polymerase ( Stratagene ) was used for PCR reactions according to the guidelines of the manufacturer . PCR fragments were cloned into pEntry201 vector ( Invitrogen ) by BP reactions according to the guidelines of the manufacturer . Cloned fragments were verified by sequencing . The LR reaction was performed to transfer inserts from pEntry201 into pTSGATE1 . A list of names for pENTRY clones and pTSGATE clones is provided in S1 Table . Candidate expression vectors were transformed in E . coli for protein expression , either in Rossetta or BL21 strains . pTSGATE1 was tested for protein expression by cloning and expressing Avr2 , EpiC1 and EpiC2B [21] . Protein expression was induced with 1 mM IPTG and proteins were purified on Ni-NTA affinity resin ( Qiagen ) according to the instructions of the manufacturer . Protein levels and purity was verified by protein gel electrophoresis followed by coomassie staining or western blotting using anti-FLAG antibody ( Sigma ) and an HRP-conjugated secondary anti-mouse antibody ( Pierce ) . Signals were generated by chemiluminescence using the ECL Super Signal West ( Thermo ) and visualized on X-ray films ( Kodak ) . Purified proteins were dialysed with protein storage buffer ( 50 mM NaCl , 10 mM NaH2PO4 ( pH 8 ) and 20% glycerol ) and further concentrated using Vivaspin spin columns ( 3 kDa MW cut-off , Sartorius ) . Protein quantity was measured using RCDC protein assay ( Bio-Rad ) . Proteins were stored in aliquots at -80°C until used for the inhibition screen . Cip1 was recloned in pFLAG-ATS using primers 5- atgcaagcttcatcaccatcaccatcacgactacgacattcctactacggaaaacctatacttccagggccaaacgcccaagaacatcgtttcg-3’ and 5’-gcatgaattctcagttcaccgtgattgcgcactcgaaggtctg-3’ using HindIII and EcoRI restriction sites , resulting in pSK8 encoding WT Cip1 with a N-terminal , TEV-cleavable FLAG-His purification tag . Mutant Cip1 protein were subsequently generated by site-directed mutagenesis of pSK8 using primers 5’-agcaacccgggctttcgctggctgacccag-3’ and 5’-cgaaagcccgggttgctgggcagcgtgagg-3’ for the deletion mutant ( ΔT ) and 5’-acccggctgccggctttcgctggctgaccc-3’ and 5’-aagccgggcagccgggttgctgggcagcgtg-3’ for the substitution mutant ( AA ) . ( Mutant ) Cip1 proteins were produced and purified as described above . An antibody was raised against the Cip1 protein in rabbit by Eurogentec . The secondary anti-rabbit antibody was from sheep and conjugated to horse radish peroxidise ( HRP ) . An overnight-grown culture of PtoDC3000 grown in Minimal Medium was centrifuged and the supernatant was used for western analysis . Agrobacterium-mediated transient expression of C14 ( pTP41 ) , Rcr3 ( pTP36 ) and Pip1 ( pTP43 ) , was carried out as described previously [24] . Agrobacterium was grown overnight in Luria-Bertani ( LB ) medium containing 25 μg/mL Rifampicin and 10 μg/mL Kanamycin . The bacterial cultures were centrifuged at 4000g for 10 min and the obtained bacterial pellet was re-suspended in 10 mM MgCl2 , 10 mM MES ( pH 5 ) and 1 mM acetosyringone and diluted to OD600 = 2 . The same procedure was applied to Agrobacterium carrying the p19 silencing inhibitor on a binary vector [49] . Agrobacterium cultures carrying protease genes were mixed with cultures carrying p19 to a 1:1 ratio . After 1-2h incubation in the dark , the cultures were injected into leaves of four-to-six-week-old N . benthamiana plants using a needleless syringe . The plant material was harvested at three days after injection . In case of C14 expressing leaves , leaves were frozen in liquid nitrogen , ground to frozen leaf powder and stored at -80°C . Protein was extracted from frozen leaf material in 1 mM DTT and used for ABPP assays . In case of Rcr3 and Pip1 , apoplastic fluid was isolated as described previously [24] . Ice cold water was vacuum-infiltrated into the detached leaves transiently expressing Rcr3 or Pip1 . Surface-dried leaves were placed into apoplastic fluid collection tubes and centrifuged for 10 min at 2000g . Collected apoplastic fluids were transferred to microtubes , flash-frozen in liquid nitrogen and stored at -80°C . Protein concentrations were measured using the RCDC protein assay ( Bio-Rad ) . The level of active protease was evaluated by ABPP on a dilution series . ABPP of cysteine proteases was performed as described in [30] with small modifications . A single 30 μL standard reaction contained 100 mM NaAc ( pH 6 . 2 ) , 2 mM DTT , 1 μM MV201 and total extracts or apoplastic fluids from agroinfiltrated plants over expressing various proteases . For each single inhibition assay , approximately 1 μg protein purified from E . coli was incubated with the plant proteome for 30 min before adding MV201 . Controls contained 50 μM E-64 ( positive control ) or the same volume of protein storage buffer ( negative control ) . After adding the probe , the reaction mixture was incubated in the dark for 1h at room temperature . The reaction was terminated by adding SDS-containing sample buffer and either immediately heated at 95°C or stored at -20°C until heat denaturation . The reaction mixtures were loaded onto large 50-well , 12% SDS polyacrylamide gels and separated by electrophoresis . Fluorescent signals were detected using the Typhoon FLA9000 scanner . Signal intensities were quantified using ImageJ ( http://imagej . nih . gov ) . Genes with the following accession codes were aligned: PtoDC3000 , gi28871353 ( Pseudomonas syringae pv . tomato DC3000 ) ; Pph1448A , YP_276078 . 1 ( Pseudomonas syringae pv . phaseolicola 1448A ) ; Pta11528 , gi331010052 ( Pseudomonas syringae pv . tabaci 11528 ) ; PsyB728a , gi66047172 ( Pseudomonas syringae pv . syringae B728a ) ; PpW619 , gi170720265 ( Pseudomonas putida W619 ) ; Pf0-1 , gi77460801 ( Pseudomonas fluorescens Pf0-1 ) ; PmNK-01 , gi330502174 ( Pseudomonas mendocina NK-01 ) ; PaPAO1 , gi553899616 ( Pseudomonas aeruginosa PAO1-VE13 ) ; LmICP , gi28625248 ( Leishmania mexicana ) ; Chagasin , gi14250894 ( Trypanosoma cruzi ) ; EhICP2 , gi122082030 ( Entamoeba histolytica ) ; and EhICP1 , gi68056711 ( Entamoeba histolytica ) . Apoplastic fluids were isolated at 2 days upon infiltration of leaves of 4-week-old N . benthamiana with PtoDC3000 ( WT/Δcip1 ) at OD = 0 . 0002 . The RNA was isolated from the bacterial pellet and bacteria grown in minimal medium containing mannitol and glutamate using the RNeasy mini kit ( QIAGEN ) according to the manufacturers protocol with an in solution DNase digest ( QIAGEN ) and cDNA was generated with random hexamer primers ( Invitrogen ) in the absence or presence of SuperscriptII reverse transcriptase ( RT ) following the manufacturers protocol ( Thermo Fisher ) . PCR was performed with the primers below using Phusion polymerase ( NEB ) according to the manufacturer’s protocol using the program 3’ 98°C; 32 cycles of 10 sec 98°C; 20 sec 66°C; 10 sec 72°C; then 5’ 72°C . PCR products were separated on a 1 . 5% agarose gel , stained with ethidium bromide and detected under UV . The used primers are for PSPTO4033 ( recA ) : 5’-cggcaagggtatctacctca-3’ and 5’-ctttgcagatttccgggta-3’; PSPTO4210 ( Lon ) : 5’-gcctggacctctccaaagtc-3’ and 5’-cacttccatccggtccaaca-3’; PSPTO4211 ( cip1 ) : 5’-atgccccctgttcgttttct-3’ and 5’-gaccatctccttgctctcgg-3’; and PSPTO4212 ( methyltransferase ) : 5’-agcgatctggaaattgccca-3’ and 5’-cgttggcggtgttcttcaag-3’ . Two different types of PtoDC3000 Δcip1 mutants were made . UNL231 ( Δcip1a ) contains a polar mutation in cip1 . It was made by PCR amplifying 2 . 0 kb upstream and downstream of cip1 using primer set 5’-agtcggtacccgtgcgcatccgcacctggctc-3’ ( which contains a KpnI site ) and 5’-agtcctcgagggcaagttccggttttgcgagacg-3’ ( which contains a XhoI site ) and primer set 5’-agtcggatccctgtttgcgcgcggcttgtccg-3’ ( which contains a BamHI site ) and 5’- agtctctagagaggtgtcgctgttcatcgatgc-3’ ( which contains a XbaI site ) , respectively . These PCR products were cloned using the indicated restriction enzyme sites in the same orientation on either side of a Spr/Smr omega fragment from pHP45Ω [50] resulting in pLN3217 . The insert from this construct was cloned into the suicide vector pRK415 [51] using KpnI and XbaI restriction enzymes resulting in pLN3272 . pLN3272 was transformed into PtoDC3000 by electroporation . DC3000 ( pLN3272 ) transformants were grown in liquid KB medium containing spectinomycin for five consecutive days before the culture was plated onto KB plates containing spectinomycin . Homologous recombination was selected for by screening for the spectinomycin marker linked to the mutation and loss of the tetracycline marker carried on pRK415 . The PtoDC3000 cip1 polar mutant ( UNL231 ) was confirmed by PCR to show that cip1 gene was replaced by the omega fragment . The second cip1 mutant UNL232 ( Δcip1b ) , which contains a non-polar mutation in cip1 was generated using a similar strategy . The only difference was that the 2 . 0 kb fragments upstream and downstream of cip1 were cloned on either side in the same orientation of an nptII gene in pCPP2988 [52] resulting in pLN3218 . This nptII gene confers resistance to kanamycin but lacks transcriptional terminators . The insert containing the cip1 mutation was cloned into pRK415 resulting in pLN3273 and this construct was electroporated into PtoDC3000 . Homologous recombination of the non-polar cip1 mutation was done as described above except selection was for the kanamycin marker linked to the non-polar cip1 mutation . UNL232 was confirmed with PCR . The wild type cip1 gene under its native expression was reintroduced into UNL231 and UNL232 using a Tn7 transposon strategy described by [31] with some modifications . Briefly , DNA regions containing the cip1 promoter and coding region were amplified with primer set 5’-caccgaattcctgccggattacctcaaaga-3’ and 5’-cataagctttcaagcgtaatctggaacatcgtatgggtagttcaccgtgattgcgcact-3’ . The reverse primer contains nucleotides that encode a hemagglutinin ( HA ) tag . The resulting PCR fragment was cloned into pENTR/D-TOPO ( Invitrogen , Carlsbad , CA ) and then recombined into pUC18::Tn7-GATEWAY destination vector using LR Clonase according to the manufacturer’s instructions resulting in construct pLN6048 . Construct pLN6048 was integrated into UNL231 and UNL232 by electroporation using rifampin ( 100 μg/ml ) and gentamicin ( 1 μg/ml ) , the latter selected for strains that contained the Tn7 cassette . Primer set 5’-attagcttacgacgctacaccc-3’ and 5’-ttgaaaagagcctgccgagca-3’ was used to identify strains that contained Tn7::cip1-HA . Inoculation and bacterial growth assays using PtoDC3000 were performed as previously described [53] . Briefly , for cip1 complementation experiments 4-week-old tomato ( S . lycopersicum cv . Moneymaker ) plants were blunt syringe-inoculated with 105 bacteria /mL and leaf disks were taken from surface-sterilized leaves at 4 h post inoculation ( 0 dpi ) , and at 3 days post inoculation . Three 1 cm2 leaf disks were combined and ground in 10 mM MgCl2 and a 1-fold dilution series was plated out on selection media . For other infection assays 4-week old tomato plants were spray-inoculated with 108 bacteria /mL as described previously [54] . Leaf disks were taken from surface-sterilized leaves at 4 h post inoculation ( 0 dpi ) , and at various days post inoculation . This was repeated four times per genotype per time point per assay . Leaves of N . benthamiana plants were untreated or infiltrated with water ( Mock , M ) , or with Agrobacterium tumefaciens ( OD = 0 . 5 ) carrying the P19 silencing inhibitor alone ( P19 ) , or mixed with Agrobacterium carrying C14 ( C14 ) . Two days later ( 2dpi ) , PtoDC3000 ( ΔhopQ1-1 ) was infiltrated at OD = 0 . 001 , or water was used as mock control . Apoplastic fluids were isolated two days later ( 4dpi ) and preincubated with and without 100 μM E-64 for 30 minutes and then labeled with 2 μM MV201 for 5 hours . Proteins were separated on 14% SDS-PAGE and the gel was scanned for fluorescence ( 532nm excitation , 580BP filter , 600PMT ) and stained by Sypro Ruby . Wild-type and both Δcip1 mutants of PtoDC3000 were inoculated at OD = 0 . 05 in LB medium without antibiotics at 28°C and bacterial growth was measured every 30 minutes for 12 hours at OD600 . The inhibitory activity of Cip1 against papain ( Sigma-Aldrich ) was determined by assaying the proteolytic activity of 30 μl of 1 mg/ml papain in Tris-HCl buffer , pH 6 . 8 in the presence of 1 mM glutathione , using 1 . 5 mM Nα-Benzoyl-L-arginine 4-nitroanilide hydrochloride ( BAPNA , Sigma-Aldrich ) as the substrate in the presence or absence of Cip1 . The kinetic parameters for substrate hydrolysis were determined by measuring the initial rate of enzymatic activity . The inhibition constant Ki was determined with the Lineweaver-Burk equation . The Ki value was also calculated from the double reciprocal equation by fitting the data into the computer software Origin 6 . 1 . For the Lineweaver-Burk analysis , 1 μM papain was incubated with and without 3 . 98 nM and 7 . 46 nM inhibitor and assayed at increasing concentration of BAPNA ( 0 . 1–5 mM ) at 37°C for 30 min . The reciprocals of substrate hydrolysis ( 1/V ) for inhibitor concentration were plotted against the reciprocal of the substrate concentration , and the Ki was determined by fitting the resulting data . For comparison of Cip1 with mutant Cip1 , 4 . 1 μM papain was incubated with 0 . 56 μM ( mutant ) Cip1 inhibitor or chicken cystatin and 0 . 2 mM BAPNA in a 100 μl volume . Substrate conversion was monitored by increased fluorescence at 410 nm over time using an Infinite M200 Tecan microtiter plate reader . 5 μg/ml papain ( Sigma-Aldrich ) or 10-fold diluted apoplastic fluids from N . benthamiana leaves overexpressing C14 ( see above ) , were preincubated in a buffer containing 25 mM NaAc pH 5 . 0 and 2 mM DTT with 100μM E-64 or ( mutant ) Cip1 for 30 minutes , and then labeled with 1 μM MV201 for 1 hour . The labeling reaction was stopped by adding SDS loading buffer and boiling for 5 minutes . Rcr3 ( Solyc02g076980 ) , Pip1 ( Solyc02g077040 ) , C14 ( Solyc12g088670 ) , and Cip1 ( PSPTO4211 ) | The extracellular space in the leaf ( the apoplast ) is colonized by a diversity of microbes that will have to deal with host-secreted hydrolytic enzymes , many of which accumulate during defence responses . We hypothesize that in addition to fungal and oomycete pathogens , the bacterial model plant pathogen Pseudomonas syringae also protects itself in the apoplast by secreting inhibitors targeting these apoplastic hydrolases . The genome of P . syringe harbours over 131 genes encoding putative small , non-annotated secreted proteins that have not been characterized previously . Here , we produced and purified 43 of these small proteins and tested them for their ability to inhibit the secreted immune protease C14 of tomato . We discovered a C14 protease inhibitor , coined Cip1 , which carries chagasin-like motifs and contributes to virulence . Cip1 also effectively inhibits Pip1 , another immune protease of tomato , known to suppress P . syringae infection . Interestingly , Cip1 has a lower affinity for the immune protease Rcr3 , explaining why this protein , and PtoDC3000 producing Cip1 , is not recognized in tomato plants carrying the Cf-2 resistance gene , which uses Rcr3 as a co-receptor to detect pathogen invasion . | [
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] | 2016 | Screen of Non-annotated Small Secreted Proteins of Pseudomonas syringae Reveals a Virulence Factor That Inhibits Tomato Immune Proteases |
An attenuated line of Leishmania infantum ( L . infantum H-line ) has been established by culturing promastigotes in vitro under gentamicin pressure . A vaccine trial was conducted using 103 naive dogs from a leishmaniosis non-endemic area ( 55 vaccinated and 48 unvaccinated ) brought into an endemic area of southeast Iran . No local and/or general indications of disease were observed in the vaccinated dogs immediately after vaccination . The efficacy of the vaccine was evaluated after 24 months ( 4 sandfly transmission seasons ) by serological , parasitological analyses and clinical examination . In western blot analysis of antibodies to L . infantum antigens , sera from 10 out of 31 ( 32 . 2% ) unvaccinated dogs , but none of the sera from vaccinated dogs which were seropositive at >100 , recognized the 21 kDa antigen of L . infantum wild-type ( WT ) . Nine out of 31 ( 29% ) unvaccinated dogs , but none of vaccinated dogs , were positive for the presence of Leishmania DNA . One out of 46 ( 2 . 2% ) vaccinated dogs and 9 out of 31 ( 29% ) unvaccinated dogs developed clinical signs of disease . These results suggest that gentamicin-attenuated L . infantum induced a significant and strong protective effect against canine visceral leishmaniosis in the endemic area .
Leishmania infantum ( L . infantum ) is a causative agent of visceral leishmaniasis ( VL ) , which is a severe and frequently lethal protozoan disease of humans and dogs . Canine visceral leishmaniosis ( CVL ) is widely distributed in large areas of Europe , South America , the Middle-East , Central Asia , China , and Africa , particularly in the countries of the Mediterranean Basin [1] , [2] . In Iran , at least seven endemic foci in dogs have been identified including the Baft district in the southeast of the country where there is a high seroprevalence in domestic dogs [3] . Dogs are the principal reservoir of L . infantum and can be an important threat to public health . Control of the disease in dogs has been shown to reduce the human incidence [4] , [5] . Although there have been a number of vaccine trials , there is currently no effective and completely safe vaccine against any form of leishmaniasis . A successful vaccine against Leishmania is most likely to be either an attenuated line or a subunit vaccine based on antigens with demonstrable protective function [6] , [7] . Subunit and attenuated vaccines can be highly effective and induces protection against pathogen [8] , [9] , [10] . We previously reported that a cultured attenuated line of L . infantum , identified as L . infantum H-line , was selected by culturing promastigotes in vitro under pressure of gentamicin [11] . Gentamicin , which has frequently been added to cultures of Leishmania to prevent bacterial contamination [12] , [13] , is an aminoglycoside that interacts with RNA in prokaryotic cells [14] . The precise mechanism of bactericidal activity of aminoglycosides is not fully understood , but some hypotheses include disruption of ribosomal activity by breaking up polysomes , misreading of mRNA during protein synthesis and production of abnormal or nonfunctional proteins . Comparative proteomics profiling of the attenuated line identified key changes in parasite thiol-redox metabolism [15] . Thiol-redox metabolism is crucial for Leishmania which is exposed to an oxidative burst when they encounter their mammalian macrophage host cell [16] . L . infantum H-line is more susceptible to oxidative stress , and thus a change in thiol-redox metabolism in this line may explain its loss of virulence [15] . L . infantum H-line invaded but was unable to survive within bone marrow derived macrophages of BALB/c mice in vitro [11] . Moreover , the attenuated line failed to spread to , and within , the visceral organs of BALB/c mice and dogs over a 12 week observation period [17] . Immunohistochemical investigation showed no parasites in the popliteal lymph node ( PLN ) of immunized dogs whereas there were parasites in the PLN of 60% of dogs infected with L . infantum WT [18] . No clinical signs and histopathological abnormalities were found in the dogs immunized with the attenuated line of parasite over 2 years post-immunization [17] , [19] . Dogs immunized with the attenuated line parasites elicited a Th1 response and were protected against experimental CVL [19] . We previously reported that Western blot analysis of antibodies to the 21 kDa antigens of L . infantum H-line and WT might be a useful technique for distinguishing between dogs vaccinated with L . infantum H-line and dogs naturally infected with L . infantum WT in epidemiologic studies [20] . In the present study , for the first time , we show the impact of L . infantum H-line vaccine against natural infection in dogs in a highly endemic area of Iran over a 24 month follow-up .
Promastigotes of L . infantum JPCM5 ( MCAN/ES/98/LIM-877 ) , were cultivated in complete haemoflagellate minimal essential medium ( HOMEM ) ( GIBCO ) supplemented with 10% ( vol/vol ) heat-inactivated fetal calf serum ( HI-FCS ) ( Labtech International ) . L . infantum H-line was generated in the same medium supplemented with 10% ( vol/vol ) HI-FCS and gentamicin at 20 µg/ml ( Sigma ) [11] . Stationary phase promastigotes of the attenuated line were harvested after 48 subpassages and a suspension at a concentration of 5×108 cells/ml in PBS was prepared . The field study was conducted in 3 villages , Dehsard , Khosrowabad and Dehsarar of Baft County ( 56 . 2147°E , 28 . 2727°N ) , Kerman Province , in the southeast of Iran ( Fig . 1a ) . The area has a desert climate and the total annual rainfall is 309 mm with a minimum of 3 mm in July and maximum of 120 . 9 mm in April . The minimum mean monthly relative humidity is 26% ( June ) and the maximum is 56% ( January ) . Initially , 77 household dogs were examined for clinical signs of the disease and tested for the presence of specific anti-Leishmania antibody by an immunofluorescence assay ( IFA ) . A vaccine trial was conducted on 103 dogs ( 55 vaccinated and 48 unvaccinated ) . The protocol for vaccination of the dogs had been reviewed and approved by the Medical Ethics and Animal and Use Care Committee of the Kerman Medical University ( study protocol number KA/89/15 ) , in accordance with the Guide for the Care and Use of Laboratory Animals Eighth Edition . The animals were kept under typical local conditions of food and housing and sampled with the owners' consent . All dogs with clinical signs of disease were sacrificed to avoid unnecessary suffering . On the basis of the rate of seropositivity detected in the 77 dogs tested ( see above ) , and to anticipate a number of dogs being lost during the follow-up period of 24 months , a total 103 dogs were used in this study . At follow-up prior of starting , we expected 60% seropositive in the unvaccinated group and 20% seropositive in vaccinated group , with a confidence level of 0 . 95 and power of 0 . 9 , and ratio of 1 . 29 sample size ( i . e . n2/n1 equivalent to 45/35 ) . This estimated a sample of 31 in unvaccinated group and 40 in vaccinated group . Ten unvaccinated dogs and 13 vaccinated dogs were considered to lose at follow-up prior in this study . One hundred and three healthy male German shepherd cross dogs from non-endemic areas ( Kerman city 225 Km northwest of Dehsard ) ( Fig . 1b ) between 6–18 months old were used . All of the animals were negative for presence of leishmanial DNA and serum specific anti-Leishmania IgG antibody . The dogs had previously been vaccinated against canine parvovirus and rabies and were also treated with the anthelmintic drugs praziquantel and pyrantel . The weight and age were recorded for each dog and they were randomly divided into 2 groups ( 55 dogs in vaccinated group and 48 dogs in unvaccinated group ) . The dogs in the vaccinated group were injected subcutaneously ( s . c ) with 100 µl of the suspension of stationary phase promastigotes in the foreleg of the animals . The unvaccinated dogs were injected subcutaneously with 100 µl of PBS also in the foreleg . The dogs were transferred into the endemic area over a period of 1 . 5 months before June 2010 and re-homed at households within the endemic area . Information about the risk of the procedures was given to persons who became owners of dogs . We included vaccinated and control dogs in each house whenever possible , in order to match their degree of exposure to natural infection . The dogs were followed up over 24 months , from June 2010 to cover four sandfly seasons , which occur in June and September in the endemic areas of the southeast of Iran [21] . The efficacy of the vaccine was evaluated by clinical examination and serological and parasitological analyses . Active disease surveillance measures were implemented in each of the study villages . A trained worker was located in the Health House in the village , and together with our team had responsibility for disease monitoring . The follow-up was performed at 3 , 6 , 9 , 12 , 18 , 20 and 24 months after starting trial . The peripheral blood samples were taken for complete blood cells ( CBC ) count and biochemical parameters including serum total protein , serum albumin and serum globulin . The clinical signs of disease were classified according to a modified version of leishvet guidelines as described previously [22] . Briefly , Stage I , Mild disease , animals exhibiting peripheral lymphadenomegaly or papular dermatitis , creatinine <1 . 4 mg/dl , non-proteinuric , negative to low levels of antibody . Stage II , Moderate disease , animals , which apart from the signs listed in stage I , may exhibit diffuse or symmetrical cutaneous alterations ( exfoliative dermatitis/onychogryphosis , ulcerations ( planum nasale , footpads , bony prominences , mucocutaneous junctions ) , anorexia , weight loss , fever , and epistaxis , mild non-regenerative anemia , hyperglobulinemia , hypoalbuminemia , normal renal profile , creatinine <1 . 4 mg/dl , low to high levels of antibody . Stage III , Severe disease , animals which apart from the signs listed in stages I and II , may exhibit signs originating from immune-complex lesions such as vasculitis arthritis and glomerulonephritis , chronic kidney disease , creatinine 1 . 4–2 mg/dl , medium to high levels of antibody . Stage IV , Very severe disease , animals which , apart from the signs listed in stages I , II and III , may exhibit signs pulmonary thromboembolism , or nephrotic syndrome and end stage renal disease ( creatinine >5 mg/dl ) with medium to high levels of antibody . Twenty six out of 103 ( 25 . 2% ) dogs left or died from a disease unrelated to leishmaniosis over the 24 month period follow-up . All unvaccinated dogs were sacrificed by intravenous injection of thiopental sodium 33% ( 5 ml/kg ) [23] at the end of the study . Five ml of peripheral blood were taken from the foreleg vein of each dog in EDTA for isolating parasite DNA for PCR test , and 2 ml for separation of sera for IFA and Western blotting test . The samples were stored at −20°C . Specific anti-Leishmania total IgG antibody was measured by IFA as described previously [24] . Briefly , slides were coated with washed promastigotes of L . infantum WT and air dried . Slides were incubated with twofold dilutions of the test samples in humid moist conditions for 60 min at 37°C . Excess antibody was washed off the slides and bound antibody was detected using fluorescein isothiocyanate ( FIT ) conjugated sheep anti-dog IgG ( Sigma ) , diluted at 1∶400 in PBS-0 . 01% Evans blue . Specific anti-Leishmania IgG1 and IgG2a antibodies were measured by ELISA as described previously [19] . Briefly , serum was prepared from the clotted blood of dogs at 20-month post follow-up and stored at −20°C . Each well of flat-bottom microtitre plates was coated with 1 µg of soluble Leishmania antigen in 0 . 1 M carbonate buffer pH 9 . 6 and incubated at 4°C overnight . Following 3 washes in PBS ( pH 7 . 4 ) containing 0 . 05% Tween-20 , the plates were blocked with 200 ml of blocking buffer ( 1% BSA in PBS ) , and incubated at 37°C for 1 h . After 3 washes , 100 µl of serially diluted serum sample ( 1∶100 starting dilution in PBS/BSA 1% ) was added to the wells and incubated for 2 h at 37°C . Bound antibodies were detected by 50 µl/well goat anti-dog IgG1 conjugated to HRP at 1∶500 dilution and for detection of IgG2 , 50 µl/well sheep anti-dog IgG2 conjugated to HRP at 1∶5000 dilution ( Bethyl Laboratories , Montgomery , TX , USA ) . The plates were incubated at 37°C for 1 h and subsequently washed 6 times . One-hundred µl of TMB substrate were added to each well . The reaction was stopped after 15 min incubation at room temperature using H2SO4 ( 0 . 5 M ) ( 50 µl ) . Absorbances were measured at 405 nm on an ELISA reader . The Western blot technique was applied as described previously [25] . Briefly , stationary phase promastigotes of L infantum H-line or wild-type parasite ( 1×107 cells per lane ) were washed with ice-cold PBS three times , and disrupted by sonication . An equal volume of sample buffer [0 . 1 M Tris ( Merck ) , 12% sodium dodecyl sulfate ( Merck ) , 10% glycerol ( Merck ) , 5% β-mercaptoethanol ( Merck ) , 0 . 1% bromophenol blue , pH 8 . 0] was mixed and the solution denatured at 95°C for 5 min . Promastigote lysates were fractioned individually on a 12% SDS-PAGE gel and subsequently transferred onto a nitrocellulose membrane ( Sigma-Aldrich ) . The blots were individually incubated with 1∶50 diluted sera in PBS containing 3% skimmed milk at room temperature for 18 h . The blots were incubated with 1∶10000 diluted goat anti-dog IgG-heavy and light chains antibody horseradish peroxidase ( HRP ) conjugated ( Bethyl Lab . Inc ) in PBS containing 5% skimmed milk at room for 2 h . The blots were washed as above , incubated with ECL Plus chemiluminescent substrate ( GE Healthcare ) , and exposed to X-ray film . The possible presence of leishmanial DNA was assayed in peripheral blood and PLN necropsy . DNA was extracted ( Promega , Columbus , OH , USA ) , according to the manufacturer's instructions and stored at −20°C until use . PCR amplification was carried out in 50 µl reaction volumes using 0 . 5 pmol of the kinetoplastid-specific primers K13A ( 5′-GTGGGGGAGGGGCGTTCT-3′ ) and K13B ( 5′-ATTTTACACCAACCCCCAGTT-3′ ) [26] . The amplification products were analysed by 1 . 5% agarose gel and visualized under UV light . A positive control containing genomic DNA of Leishmania-infected dog and negative control without template DNA were included . VL cases were monitored from the records of each of the pediatric wards of Afzelipour Medical Centre at Kerman University of Medical Sciences . As VL cases might occasionally be referred to other hospitals , further information on VL cases were obtained from Centre for Disease Control and Prevention in Kerman Medical University . Statistical analyses were performed with statistical package EpiTool ( available at http://epitools . ausvet . com . au ) to determine the number of animals in the vaccine trial . For quantitative data , Student's t-test was used to calculate differences the levels of IgG between two groups . Comparison between the levels of IgG1 and IgG2 antibodies was performed using Paired sample t-test . Chi-square test was used to examine the relationship between the number of dogs which IFA titers of IgG antibody were at >1∶100 between 2 groups . Fisher's exact test was used to calculate difference leishmanial DNA between 2 groups . Data are expressed as the mean ± standard deviation mean ( SDM ) for each group . Differences were considered significant when P<0 . 05 .
Seventy seven household dogs , living in the endemic areas , were examined for clinical signs of the disease and tested for presence of specific anti-Leishmania IgG antibody . Specific anti-Leishmania antibody by IFA was found in 31 out of 77 ( 40 . 2% ) of the householder dogs ( ≥1∶100 ) . Sixty two out of 77 ( 80 . 5% ) animals were asymptomatic . The efficacy of the vaccine was evaluated after 4 sandfly transmission cycles by clinical examination , and serological and parasitological analyses . A vaccine trial was conducted on 103 dogs ( 55 vaccinated and 48 unvaccinated ) . No local indications including swelling , and pain at the injection site and no general indications of disease including anorexia , apathy , vomiting and diarrhoea were observed after the vaccine administration . Twenty three dogs ( 9 vaccinated and 14 unvaccinated ) ( 22 . 3% ) left the study after a change in residence or disappearance . Three unvaccinated dogs ( 2 . 9 ) , died . Two of these dogs had to be put down because of accidental injury and one died from a disease unrelated to leishmaniosis . All vaccinated dogs gave positive titers of specific anti-Leishmania antibodies whereas , all but 2 unvaccinated dogs were seronegative over the 3 month follow-up ( Fig . 2 ) . Fluctuations of the mean levels of antibody were observed in the sera of vaccinated dogs over the 20 month follow-up ( Fig . 2 ) but did not rise . In contrast , the mean levels of antibody increased in the sera of unvaccinated dogs over the same period ( Fig . 2 ) . There was a significant difference between mean levels of antibody in the sera of vaccinated and unvaccinated dogs ( P<0 . 001 ) . The cut-off for which animals were considered seropositive was established to be a positive IFA results at serum dilutions of >1∶100 . As shown in Table 1 , twelve out of 31 ( 38 . 7% ) unvaccinated dogs and 2 out of 46 ( 4 . 3% ) vaccinated dogs were seropositive at >1∶100 . The rest of dogs , 19 out of 31 ( 61 . 2% ) unvaccinated dogs and 44 out of 46 ( 95 . 7% ) vaccinated dogs were seropositive ≤1 . 100 over the 24 month follow-up . The number of unvaccinated dogs which were seropositive at >1∶100 was significantly higher than vaccinated dogs ( P<0 . 0005 ) . Specific anti-Leishmania IgG1 and IgG2 antibodies were present in the sera of dogs vaccinated with L . infantum H-line , predominantly of the IgG2 subclass . In the sera of vaccinated dogs , the level of IgG1 was significant lower than the level of IgG2 ( P<0 . 001 ) whereas , the level of IgG1 was significantly higher than the level of IgG2 in the sera of unvaccinated dogs ( P<0 . 05 ) . Two sera from vaccinated dogs which were seropositive at >1∶100 , dogs V20 and V33 , recognized the 21 kDa antigen of L . infantum H-line but not of L . infantum WT ( Fig . 3 ) . Ten out of 31 ( 32 . 2% ) unvaccinated dogs which were seropositive at >1∶100 recognized the 21 kDa antigen of L . infantum WT , but not of L . infantum H-line ( Table 1 ) . As shown in Table 1 , sera from 2 unvaccinated dogs , C2 and C61 were seropositive at >1∶100 but did not recognize the 21 kDa antigen of both L . infantum H-line and L . infantum WT . Sera from all dogs in both groups which were seropositive at ≤1∶100 did not recognize any antigens of L . infantum H-line or wild-type parasites . The presence of leishmanial DNA and clinical signs of disease in vaccinated and unvaccinated dogs after 4 sandfly seasons are summarized in Table 1 . No leishmanial DNA was found in the vaccinated dogs . In contrast , 9 out of 31 ( 29% ) unvaccinated dogs were positive for the presence of leishmanial DNA over the 24 month period follow-up . As shown in Table 1 , eight of 12 ( 66 . 7% ) unvaccinated dogs with high levels of antibody ( >1∶100 ) became PCR positive . The number of unvaccinated dogs that were PCR positive was significantly higher than that in the vaccinated dogs ( P<0 . 002 ) . All but 1 vaccinated dogs , [ ( 2 . 2% ) , remained free of clinical abnormalities over the 24 months period of observation . Among the unvaccinated dogs , 9 out of 31 [ ( 29% ) , two dogs , C8 and C1 in stages II and III of clinical signs of disease , respectively] , presented one or more clinical signs of disease ( Table 1 ) . No VL cases were referred to the Afzalipour Medical Centre or recorded in the Centre for Disease Control and Prevention from the area of study more than 3 years since June 2010 .
This is the first study to demonstrate efficacy of an attenuated L . infantum vaccine against natural CVL in dogs in a highly endemic area . The progression of leishmaniosis in dogs is associated with humoral response and depression of cellular immunity [27] . We previously reported that L . infantum H-line induced a CD4+Th1 response which was characterized by the production of relatively higher levels of IFN-γ and lower levels of IL-10 compared with those in the dogs infected with wild-type parasite [18] , [19] . In contrast to L . infantum WT , the attenuated parasite was unable to multiply and survive in the visceral organs of immunized dogs [17] and remained localized in the skin at the site where the promastigotes were injected . It has been reported that promastigotes of L . infantum WT develop to amastigote forms in infected macrophages at the site of inoculation and the infection may spread , resulting in a systemic form [1] . Dissemination of the parasite in the visceral organs of symptomatic dogs is the result of the development of a non-protective Th2 response [28] . Subcutaneous vaccination with the attenuated line in the foreleg of the dogs , an area covered with hair , will prevent or significantly reduce the likelihood of uptake of attenuated line parasites by sandflies , which tend to feed only on areas of exposed skin . This observation alleviates concerns about the possibility of reversion to virulence by the attenuated line during passage through sandflies . We reported subclasses of IgG in vaccinated dogs and correlated higher IgG2 with protection provided by L . infantum H-line [19] . In the present study , we found the level of IgG1 was significant lower than the level of IgG2 in the sera of vaccinated dogs . It has been reported that specific anti Lieshmania IgG1 in dogs is associated with the development of disease , whereas IgG2 antibody is associated with asymptomatic infection [29] . The present study was carried out in 3 villages , in the district of Dehsard , Baft County in the southeast of Iran highly endemic for CVL [3] , [30] , [31] . In a preliminary study , we found that 40 . 2% of the household dogs were seropositive for L . infantum ( >1∶100 ) . However , the prevalence of CVL in domestic dogs in this area might be higher than 40 . 2% . It has been reported that 37% of seronegative asymptomatic dogs from an endemic area were positive by the PCR with skin tissue [32] . It is recognized that introducing 103 dogs into an area could disturb the ecological dynamics between dogs , parasite and vectors . Householders whose dogs were seronegative did not allow their dogs to be used in the study . Thus dogs from a non-endemic area were brought in for the trial and whenever possible each household included unvaccinated and vaccinate dogs in order to equalize their degree of exposure to the risk of natural infection . The seroposivity of unvaccinated dogs was higher than that of household dogs , living in the area . It has been reported that some dog breeds such as the German shepherd are more susceptible to development of CVL [33] , [34] . In the unvaccinated group 12 out of 31 dogs ( 38 . 7% ) dogs were highly seropositive ( >1∶100 ) which 35 . 5% of them developed signs of CVL over 24 months of period of monitoring . The specific anti-Leishmania IgG antibody was raised in the sera of the dogs vaccinated with L . infantum H-line [17] , [19] . We found that except 2 unvaccinated dogs , all sera from the vaccinated dogs and unvaccinated dogs which were seropositive at >1∶100 recognized the 21 kDa antigens of L . infantum H-line or WT . It is in agreement with another study that band of 21 kDa has the highest immuno-reactivity and the most often recognized in the case of CVL [35] . In the present study we found sera from vaccinated dogs recognized the 21 kDa antigen of L . infantum H-line whereas , sera from unvaccinated dogs , which were natural infected with L . infantum WT recognized the 21 kDa antigens of L . infantum WT ( Table 1 ) . This observation is in agreement with our previous study that Western blot analysis of antibodies to the 21 kDa antigens of L . infantum H-line and WT is very useful method for distinguishing between dogs vaccinated with L . infantum H-line and dogs experimentally infected with L . infantum WT [20] . We found that sera from 2 unvaccinated dogs , C2 and C61 , which were seropositive at >1∶100 did not recognize any antigens of L . infantum H-line or L . infantum WT ( Table 1 ) . It has been reported that the sensitivity and specificity of Western blotting is greater than IFA for diagnosis of CVL in dogs [25] . IFA cross-reaction antibody between L . infantum and other diseases such as Ehrlichias canis ( E . canis ) and Babesia canis and also some kind of clinical signs of disease might be possible [36] , [37] , [38] . Moreover , E . canis infection might induce immunosuppression [39] and therefore the immune system is not able to develop the protective immunity induced by the attenuated line and the vaccinated animal is not immune to natural challenge . This observation may be useful to explain the sign of disease in 2 vaccinated dogs , V20 and V33 , over 24 months period monitoring and suggests in the future , we need to check for the presence of E . canis during vaccination with the attenuated L . infantum . A number of vaccines such as FLM , LiESAp-MDP have shown a degree of effectiveness against experimental CVL in dogs [40] , [41] . Our study is the first vaccine trial in dogs that might show an impact of a vaccine and in reducing the occurrence of VL in the local human population . It has been reported that human seropositive ( >1∶800 ) in this area was 1 . 55% and approximately half of 108 registered cases were from Baft [3] . No VL cases were recorded from the area of study 3 years since these data were collected . Impact of this vaccine on human population should be confirmed in the further studies . The results presented clearly demonstrated that a gentamicin-attenuated line L . infantum vaccine induced a significant and strong protective effect against CVL in dog and holds considerable promise for vaccination of dogs against CVL in the field . | A 24 month vaccine trial was conducted using 103 leishmania free dogs in an area of southeast Iran endemic for visceral leishmaniosis . The dogs were vaccinated with gentamicin-attenuated line of Leishmania infantum . No local and/or general indications of disease were observed in the vaccinated dogs immediately after vaccination with an attenuated line of Leishmania infantum . Nine out of 31 ( 29% ) unvaccinated dogs , but none of those vaccinated , were positive for presence of Leishmania DNA by PCR . In western blots , sera from 10 out of 31 ( 32 . 2% ) unvaccinated dogs , but none of the sera from vaccinated dogs , recognized the 21 kDa antigen of Leishmania infantum wild-type . One out of 46 ( 2 . 2% ) vaccinated dogs and 9 out of 31 ( 29% ) unvaccinated dogs developed clinical signs of disease . The attenuated Leishmania infantum induced a significant and strong protective effect against Leishmania infantum infection in the field . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"biology",
"and",
"life",
"sciences",
"veterinary",
"science"
] | 2014 | Gentamicin-Attenuated Leishmania infantum Vaccine: Protection of Dogs against Canine Visceral Leishmaniosis in Endemic Area of Southeast of Iran |
Cystic echinococcosis ( CE ) is an important health problem in many areas of the world including the Mediterranean region . However , the real CE epidemiological situation is not well established . In fact , it is possible that CE is a re-emerging disease due to the weakness of current control programs . We performed a retrospective observational study of inpatients diagnosed with CE from January 2000 to December 2012 in the Western Spain Public Health-Care System . During the study period , 5510 cases of CE were diagnosed and 3161 ( 57 . 4% ) of the cases were males . The age mean and standard deviation were 67 . 8 ± 16 . 98 years old , respectively , and 634 patients ( 11 . 5% ) were younger than 45 years old . A total of 1568 patients ( 28 . 5% ) had CE as the primary diagnosis , and it was most frequently described in patients <45 years old . Futhermore , a secondary diagnosis of CE was usually found in patients >70 year old associated with other causes of comorbidity . The period incidence rate was 17 cases per 105 person-years and was significantly higher when compared to the incidence declared through the Notifiable Disease System ( 1 . 88 cases per 105 person-years; p<0 . 001 ) . CE in western Spain is an underestimated parasitic disease . It has an active transmission , with an occurrence in pediatric cases , but has decreased in the recent years . The systematic search of Hospital Discharge Records of the National Health System Register ( HDR ) may be a more accurate method than other methods for the estimation of the incidence of CE in endemic areas .
Human echinococcosis is a zoonotic infection caused by cestodes of the genus Echinococcus sp . Four species infect humans: cystic echinococcosis ( CE ) is caused by Echinococcus granulosus , alveolar echinococcosis ( AE ) is caused by E . multilocularis , and polycystic forms are caused by either E . vogeli or E . oligarthrus; however , they are less frequently associated with human infection . CE is considered a neglected disease whose clinical manifestations range from asymptomatic infection to severe disease [1–3] . Although CE is considered an eradicable parasite , CE remains a considerable health problem in endemic regions with substantial economic losses for agricultural sectors and public health systems [4] . CE occurs worldwide; however , this disorder is endemic in central Asia , northern and eastern Africa , Australia , South America and the Mediterranean basin [5–7] . The transmission rate of E . granulosus in Spain remains high , and it is considered a highly endemic area inside the European region [8] . The central , northeastern and western regions of Spain are the most important endemic regions , such as Castilla-Leon , where extensive or semi-extensive farming of livestock ( mostly sheep ) is common [8 , 9] . Since the mid-1980s , several prevention and control campaigns have been implemented to reduce E . granulosus infection in Spain[10] . The epidemiological methods used in the evaluation of human hydatidosis were based , mainly , on notifiable cases system and detection of cases from Hospital Discharge Records ( HDR ) [9 , 10] . In a recent study , in a province of western Spain , we studied the evolution of incidence during fifteen years using both methods , and we detected an important under-notification of CE cases [11] . Thus , the aim of this study was to compare these epidemiological methods for evaluation of CE in a region of western Spain and to determine the evolution of the incidence over thirteen years .
The annual/period incidence rate of CE was calculated by dividing the number of new cases of disease observed in the defined time period ( 1 year or 13 years , respectively ) by the total free periods of disease-person time during the observation period defined in the study , multiplied by 100 , 000 and expressed as “cases per 105 person-years” . As it is not possible to accurately measure disease free periods , the total figure of person-time at risk can be estimated approximately and satisfactory when the size of the population is stable , multiplying the average population size studied by the duration of the observation period . Thus , the denominators were obtained from population counts for each year at the municipality level of the National Institute of Statistics ( INE; http://www . ine . es/ ) . The results were expressed as percentages ( with corresponding 95% confidence interval , 95% CI , for a proportion ) for categorical variables and as the mean and standard deviation ( SD ) for continuous variables . A chi-square test was used to compare the association between categorical variables , such as clinical and demographics variables , and the measured outcome was expressed as the odds ratio ( OR ) together with the 95% CI for OR . Continuous variables were compared with Student’s t-test or the Mann-Whitney for two groups , depending on their normal or non-normal distribution . Additionally , we applied the corresponding regression models for multivariate analysis . We considered a statistically significant difference from chance at a p-value <0 . 05 . All of the data were analyzed with SPSS 21 ( Statistical Package for the Social Sciences ) . This study was approved by the Ethics Committee of Complejo Asistencial Universitario de Salamanca ( CAUSA ) . Due to it is an epidemiological study , the written consent was not obtained and it was specifically waived by the approving IRB . All data analyzed were anonymized .
Between January 2000 and December 2012 , 5510 patients with CE were registered with HDR in the 14 hospitals . The main demographic data of the participants are shown in Table 1 . Fifty-one diagnosed patients ( 0 . 9% ) were children or adolescents ( 0–19 years ) , 583 patients ( 10 . 6% ) were between 20–44 years old , 1791 patients ( 32 . 5% ) were between 45–69 years old and 3085 patients ( 56 . 0% ) were ≥70 years old ( Fig 1 ) . Collectively , the young had a higher probability of being male OR = 1 . 2 ( 95% CI , 1 . 055–1 . 483; p = 0 . 010 ) . The period incidence rate was 17 cases per 105 person-years ( 5510 cases ) , which was significantly higher than the data reported by the “Notifiable Disease System” ( 17 cases per 105 person-years versus 1 . 88 cases per 105 person-years , ( p<0 . 001 ) ) as shown in Fig 2 . A progressive decrease in the incidence of CE was detected , from 19 . 6 cases per 105 person-year in the 2000 to as low as 12 . 3 cases per 105 person-year in the 2010 , although this incidence has increased in the last two years ( Fig 2 ) . According to these data , a decrease in the diagnosis of new cases in individuals <45 years old was found from 2007–12 ( 382 versus 208 cases; OR = 1 . 49; 95% CI , 1 . 254–1 . 793; p<0 . 001 ) , with a more pronounced decline in the pediatric population ( 34 versus 11 cases; OR = 2 . 42; 95% CI , 1 . 227–4 . 803; p = 0 . 008 ) . Regarding the origin areas , 2 , 873 ( 52 . 1% ) patients were residents in rural areas , whereas 2 , 637 ( 47 . 9% ) cases came from urban areas , and the incidence of CE in rural areas was twice as much as that in urban areas ( 24 . 6 cases per 105 person-years versus 12 . 2 cases per 105 person-years , p<0 . 001 ) . A logistic regression model revealed significant differences in relation to gender ( p<0 . 001 ) and age ( p = 0 . 003 ) , with more frequent rural origin among men ( OR = 1 . 36; 95%CI , 1 . 22–1 . 52 ) and those individuals older than 70 years ( OR = 1 . 17; 95% CI , 1 . 05–1 . 31 ) . The most frequent location of CE was the liver with 4 , 364 patients ( 79 . 1% ) . We further classified the patient’s diagnosis of CE according to ICD-9 as shown in Table 2 . CE was the primary diagnosis and the main cause of hospitalization in 1568 ( 28 . 5% ) patients , and CE was a secondary diagnosis in 3 , 942 ( 71 . 5% ) of the cases . Patients younger than 45 years of age had a more frequent primary diagnosis of CE than did patients older than 45 years o age ( 72 . 2% versus 22 . 8%; OR = 8 . 8; 95% CI , 7 . 329–10 . 637 , p<0 . 001 ) . Ninety-five percent ( 5231 ) of the patients had at least one chronic disease . The average number of diseases per patient with CE was 5 . 87 [interval range: 1–9] . The most common chronic diseases were cancer ( 1561 , 28 . 3% ) , heart failure ( 461 , 8 . 3% ) , atrial fibrillation ( 562 , 10 . 2% ) , cerebrovascular disease ( 317 , 5 . 7% ) , chronic obstructive pulmonary disease ( 704 , 12 . 8% ) , diabetes mellitus ( 770 , 14% ) , and chronic kidney failure ( 180 , 3 . 3% ) .
CE is a worldwide zoonotic infection that affects human and animal health , and it is the cause of significant economic loss for the agricultural sectors and public health systems in the endemic area [8] . Recent studies have shown that CE is a re-emerging disease in several countries and regions , even in places where the prevalence was previously low[5 , 6 , 11] . It has been demonstrated that control campaigns based on health education , control , elimination of the slaughter of sheep at home , a change in risk behaviors , such as elimination of stray dogs , the reduction of parasite biomass in the definitive hosts ( by administering praziquantel ) and the removal of animal corpses , may decrease the incidence and prevalence of infection by CE [10 , 12–15] . The reduction of these programs due to the lack of economic resources may have catastrophic consequences , leading to severe disease , considerable economic loss , and a definite public health problem of increasing concern [6] . Thereby , the WHO is working toward the validation of effective cystic echinococcosis control strategies by 2018 . Historically , CE in Spain is one of the most important existing anthropozoonoses , and western Spain is a region with a highly endemic occurrence due to extensive or semi-extensive farming of livestock and the E . granulosus cycle and its continuation over many years [9] . To the best of our knowledge , the autochthonous transmission in Spain is only by E . granulosus ( never by E . multilocularis nor other species ) , therefore , the reported cases of E . multilocularis is probably due to misclassification or less likely to imported cases originating from an endemic country . Unfortunately , given the characteristics of the study these results can not be assessed . Our group , using HDR detected a number of local cases that were not previously identified due to a lack of notification [11] . In this work , we also compared these two epidemiological methods in a wide area with almost 2 . 5 million inhabitants to determine the incidence of CE during 2000 to 2012 . Thus , according our previous work , we used HDR and we detect a higher incidence of CE than that detected by the “Notifiable Disease System” . A low percentage of surgical cases detected in other studies ( <70% ) supports the fact that HDR is at the moment the most accurate method in the evaluation of health campaigns regarding echinococcosis . Methods based on serological or ultrasonographic screening have been used to study the prevalence of CE in different areas [16–18] , but these methods are more expensive and cannot be used in large populations over multiple years to establish the epidemiological evolution of echinococcosis in humans . The initiative European formally named FP7 project HERACLES ( Human cystic Echinococcosis Research in Central and Eastern Societies ) , was born in 2013 [19 , 20] . One of the most important objectives was create the European Registry of Cystic Echinococcosis ( ERCE ) . However , in this moment , the participation of groups that diagnose and treat to patients is not assured . In this sense , until the results of this registry are published , we think the HDR system may be the best method for surveilling CE in our area . Thus , in our work , we showed that in the study period , the incidence of CE in this region had a slow reduction . According to these data , we found a decrease in the diagnosis of new cases younger than 45 years old , with a decline of almost half the number of cases between 2007–2012 compared to 2000–2005 . This decrease is still higher in the pediatric population with a reduction to one-third the number of cases . These results show that campaigns of public health , based on the elimination of stray dogs and especially the removal of animal corpses ( implemented after the crisis of bovine spongiform encephalopathy ) , may decrease the incidence of infection in a wide endemic area and help control CE [10] . However , our data support that the economic burden of CE in Spain was clearly underestimated; Benner et al . estimated the economic losses due to CE in Spain in 2005 at 148 . 9 million euros[12 , 21] , and the diagnosed cases of CE were nearly triple in the same period in our region . Despite the wide distribution of cases in our region , we found a higher cumulative incidence in rural than in urban areas and this pattern of CE infection has also been documented in previous studies [22] . Most patients with CE were living in rural areas with a wide geographic distribution . This heterogeneity on the geographic distribution of CE has also been reported in numerous countries; therefore , it is difficult to identify risk factors for this disease in our province , region and country [23] . Additionally , we detected that the disease incidence is very similar in both sexes , suggesting that the occupational component of the risk is less relevant than other risk factors attributable to environmental conditions [22] . This result supports that health educational strategies must be intensified , especially in rural areas . Regarding the diagnosis of CE , we found that the primary diagnoses of CE were performed in young patients , while the secondary accidental diagnosis was most frequently found in the elderly population and usually associated with other causes of comorbidity . Despite being traditionally considered as a “benign” pathology , CE is an important cause of morbi-mortality in patients older than 65 years [1] . Thereby , the diagnosis of CE in the elderly population is usually understimated . Therefore , an expectant management of the disease can be dangerous , and it must be only employed in select patients . Additionally , we detected that the patients that were primarily admitted for CE are approximately third of the cases , with the remainder being a secondary diagnosis with the patient admitted for some other reason . This means that that nearly two third of the CE cases was an incidental finding . This is indicative that a large numbers of patients with echinococcosis who remain undiagnosed , and is a further evidence that the disease is under reported . The main limitation of our work was the initial selection bias . The present study only considers the cases admitted to public hospital care; cases of private clinics and primary care were not included in this study . Therefore , we can assume that the actual incidence of human hydatidosis is even higher than the incidence estimated in this study . One aspect to assess is the immigration impact on these results , which can not be unavailable by the HDR . Data from our center show that immigration has limited impact , with figures around 3% ( A . Romero-Alegria , M . Belhassen-Garcia , Supporting Information ) . It can be concluded that the systematic search of HDR may be a more accurate method than other methods , based on the notification of cases in the estimation of the incidence of CE in endemic areas . The incidence of CE in our region is still high; however , in this period of study , a slow decrease was observed . The sharp decline of incidence in pediatric population highlights the importance of long-term control of CE . | The incidence of CE in our region is still high; however , in this period of study , a slow decrease was observed . The sharp decline of incidence in pediatric population highlights the importance of long-term control of CE . The systematic search of HDR may be a more accurate method than other methods in the estimation of the incidence of CE in endemic areas . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Surveillance of Human Echinococcosis in Castilla-Leon (Spain) between 2000-2012 |
Single-stranded DNA binding proteins ( SSBs ) regulate multiple DNA transactions , including replication , transcription , and repair . We recently identified SSB1 as a novel protein critical for the initiation of ATM signaling and DNA double-strand break repair by homologous recombination . Here we report that germline Ssb1−/− embryos die at birth from respiratory failure due to severe rib cage malformation and impaired alveolar development , coupled with additional skeletal defects . Unexpectedly , Ssb1−/− fibroblasts did not exhibit defects in Atm signaling or γ-H2ax focus kinetics in response to ionizing radiation ( IR ) , and B-cell specific deletion of Ssb1 did not affect class-switch recombination in vitro . However , conditional deletion of Ssb1 in adult mice led to increased cancer susceptibility with broad tumour spectrum , impaired male fertility with testicular degeneration , and increased radiosensitivity and IR–induced chromosome breaks in vivo . Collectively , these results demonstrate essential roles of Ssb1 in embryogenesis , spermatogenesis , and genome stability in vivo .
Appropriate and timely repair of damaged DNA is critical for maintaining genomic integrity and tumour suppression [1] , [2] . DNA double-strand breaks ( DSBs ) are the most cytotoxic genomic lesions , and can arise from exogenous genotoxic insult , stalled replication forks , or during physiological processes such as meiosis and B and T cell maturation . Organisms have evolved two main pathways for DSB repair: non-homologous end joining ( NHEJ ) and homologous recombination ( HR ) . In the initial step of HR , DSBs are resected to generate 3′ single-stranded DNA ( ssDNA ) tails . The ssDNA intermediates are protected from further degradation by ssDNA-binding proteins ( SSBs ) . The SSB family of proteins are conserved in all three kingdoms of life [3] and are characterised structurally by their oligonucleotide-binding ( OB ) folds that bind ssDNA . SSB proteins can be subdivided into two sub-groups . First , simple SSBs , typified by the Escherichia coli ( E . coli ) SSB , contain a single OB-fold . The second sub-group includes the higher ordered replication protein A ( RPA ) , which contains multiple OB-folds and is conserved in yeast and higher eukaryotes [3] . Human RPA is a heterotrimeric polypeptide , widely believed to be a central component of both DNA replication and DNA repair pathways [4] , [5] , [6] . Recently , we identified two novel SSB proteins , named SSB1 ( also known as OBFC2B , NABP2 or SSOS-B1 ) and SSB2 ( also known as OBFC2A , NABP1 or SOSS-B2 ) [7] , which are conserved in vertebrates but not in lower eukaryotes . These SSBs are more closely related to the bacterial and archaeal SSB sub-group than to RPA [3] . Both SSBs encode a conserved single OB-fold followed by a divergent spacer domain and a conserved C-terminal motif , suggesting functional overlap between these proteins . The spacer region is the only significant difference between human SSB1 and SSB2 . Our functional characterization of SSB1 revealed that it is stabilised following exposure of cells to ionizing radiation ( IR ) forming distinct foci at DSB sites [7] . Depletion of SSB1 compromises the DNA damage checkpoints and HR , resulting in an increased sensitivity to IR . Further studies showed that human SSB1 and SSB2 exist in two separate sub-complexes that also contain IntS3 and C9orf80 ( also known as SSBIP1/MISE ) [8] , [9] , [10] , [11] . Similar to depletion of SSB1 , silencing of INTS3 and C9orf80 results in defects in ATM signalling and HR as well as hypersensitivity to IR [8] , [9] , [10] . Here , we describe the generation of Ssb1 knockout mice to define the physiological role of Ssb1 . We report that germline deficiency for Ssb1 causes perinatal lethality due to aberrant rib-cage formation and improper lung differentiation . Furthermore , conditional knockout of Ssb1 in adult mice leads to reduced fertility in male mice , increased sensitivity to γ-irradiation and a predisposition to tumorigenesis . Taken together , our data demonstrate that Ssb1 is essential for embryogenesis and the maintenance of genomic stability in vivo .
The murine Ssb1 gene is located on chromosome 10 and spans 7 exons . We engineered a “floxed” Ssb1 allele with unidirectional loxP sites flanking its major protein coding exons 3–6 , including the OB-fold domain critical for its DNA binding activity ( Figure S1A ) . Correct targeting was confirmed by Southern blot ( Figure S1B ) and genotyping PCR ( Figure S1C ) . Evaluation of the growth of Ssb1 heterozygous mice ( Ssb1+/− ) relative to wild-type littermates ( Ssb1+/+ ) revealed no apparent physiological abnormalities in Ssb1+/− mice monitored for up to 2 years . To generate mice with targeted deletion of Ssb1 , we intercrossed Ssb1+/− breeding pairs , with the expectation that approximately 25 percent of the offspring would be of an Ssb1−/− genotype . Interestingly , no viable Ssb1−/− mice were detected amongst more than one hundred offspring from these intercrosses genotyped at 12 days post-partum ( Table I ) . These results suggested that Ssb1 deletion might result in lethality during embryogenesis . In order to define the time point of embryonic lethality caused by Ssb1 ablation , we collected embryos from Ssb1+/− intercrosses at different gestational days , assessed by the presence of a vaginal plug at E0 . 5 . Ssb1−/− embryos were recovered at near-Mendelian ratios at E13 . 5 and E18 . 5 ( Table I ) , but were significantly growth retarded in terms of both body weight and length at the latter time-point , when compared to wild-type and heterozygous littermates ( Figure 1A , 1B; Figure S2A , S2B ) . Ssb1+/+ and Ssb1+/− embryos were morphologically indistinguishable , in terms of both body size and body length . Ssb1−/− embryos also displayed craniofacial abnormalities , including a recessed mandible ( lower jaw ) and misshapen snout ( Figure 1A , arrowheads; Figure 2C , 2D , Figure S2C ) . Furthermore , there was a defect in the outgrowth of both fore- and hindlimbs , as well as hindlimb-specific oligodactyly ( missing digits ) ( Figure 1A , arrows ) . However , these embryos appeared otherwise grossly normal , suggesting that Ssb1 ablation may cause lethality during the perinatal period . To further investigate the cause of Ssb1−/− lethality , we performed caesarian recovery of embryos at E18 . 5 or at the time of birth ( P0 ) , and stimulated breathing by clearing the facial orifices and gentle stroking of the snout . In the litters examined , all Ssb1+/+ and Ssb1+/− pups established rhythmic breathing , a healthy pink skin color and movement within minutes . However , Ssb1−/− pups rapidly became asphyxic and typically died between 10∼30 min post caesarian excision , despite taking short , sporadic gasping breaths , suggesting that they could not breathe and oxygenate their blood properly ( Figure 1A , Figure S2A ) . Haematoxylin and eosin ( H&E ) staining on these embryos suggested that atelectasis was the primary cause of respiratory failure ( Figure 1C ) . These results suggest that Ssb1−/− embryos survive the entire course of development in utero but die at the perinatal stage . To further investigate the abnormalities we observed in the craniofacial region and hindlimb of Ssb1−/− embryos , we next sought to determine if their skeletal architecture was altered by performing whole-mount cartilage and mineralized bone staining with alcian blue and alizarin red . Strikingly , we observed a number of defects in formation of both the axial and appendicular skeleton . Most notably , the ribcage of Ssb1−/− embryos was poorly formed , small in size , and exhibited an almost complete lack of ossification when compared to control littermates ( Ssb1+/+ , Ssb1+/− ) ( Figure 2A ) . This defect led to the appearance of “floating ribs” , with no evidence of ossification in all but the four most anterior rib pairs . In addition , the ribcage of Ssb1−/− embryos was misshapen , with a lack of curvature in the anterior ribs , and horizontally orientated rib-sternum attachments ( Figure 2A , Figure 2B , arrow ) . The more posterior “floating” ribs in these embryos were also rudimentary and abnormally shaped , contributing to a general decrease in size of the rib-cage ( Figure 2A , Figure 2B ) . The lack of structural support from the misshapen and poorly developed rib-cage in Ssb1−/− embryos would have significantly contributed to the respiratory distress evident in these embryos at birth , and resulted in rapid atelectasis and perinatal death . Examination of the skull of E18 . 5 embryos revealed normal formation of major bone structures , including the parietal ( pr ) , intraparietal ( ip ) , frontal ( fr ) and supraoccipital ( so ) bones . We noted a modest elongation of the premaxillary bone ( pmx ) , consistent with the pointed snout seen in these embryos , and a shortened mandible ( micrognathia ) , which was set at a wider angle than in control embryos ( Ssb1+/+ , Ssb1+/− ) ( Figure 2C , 2D ) . The tympanic ring ( tr ) , which supports the eardrum , was also poorly formed in Ssb1−/− embryos ( Figure 2C ) . Furthermore , we observed evidence of a variably penetrant cleft palate ( n = 2 of 5 embryos ) , which was evident even between Ssb1−/− mice of the same litter ( Figure S2D–S2F; arrows , arrowheads ) . Together , these data suggest a spectrum of craniofacial abnormalities in Ssb1−/− embryos . The limb skeleton of Ssb1−/− E18 . 5 embryos showed a significant decrease in the length of all long bones , including humerus , radius , ulna , femur and tibia , as well as the scapula ( Figure 2E–2G ) , indicating a limb outgrowth defect ( ***P<0 . 001 , Figure 2G ) . Overall , this phenotype was more pronounced in the hindlimbs , where we observed varying degrees of abnormalities in these structures , including absent fibulas ( Figure 2F ) . Finally , although the forelimbs of Ssb1−/− embryos were properly patterned ( albeit smaller in size ) , hindlimbs displayed aberrant bone mineralization and severe defects in patterning along the anterior-posterior axis , which always manifested as oligodactyly ( Figure 2H ) . Interestingly , this phenotype was variable in penetrance , with between two to a maximum of four digits present , and we often observed variation of patterning defects between the left and right hindlimb autopods within the one embryo . Taken together , these data indicate that Ssb1 is necessary for skeletogenesis and hindlimb digit specification in the embryo , and that it is of particular importance for the later steps of chondrogenesis involving bone ossification . These data highlight a novel and unexpected role for Ssb1 during embryogenesis . To determine if other causative factors may have contributed to the perinatal lethality in Ssb1−/− embryos , we next performed histological analysis of sagittal sections from E18 . 5 embryos . We observed grossly normal morphology for major organs including the brain , heart , thymus , intestine , and liver ( Figure S3 ) . However , consistent with the respiratory distress phenotype , we observed immature lung morphology in these sections ( Figure S3 ) . To more closely examine this , we dissected lungs from E18 . 5 Ssb1+/+ , Ssb1+/− and Ssb1−/− embryos ( Figure 3A ) and confirmed complete deletion of the Ssb1 protein by western blot ( Figure 3B ) . Interestingly , we also noted an increase in the protein level of Ssb2 in Ssb1−/− lungs ( Figure 3B ) , similar to what has been observed based on siRNA depletion in human cells [8] , [9] , [10] . A comparison of the gross morphology of the lungs revealed that the lungs of Ssb1−/− embryos were consistently smaller than their Ssb1+/+ and Ssb1+/− counterparts when measured in terms of lobe length and width ( Figure 3A , data not shown ) , although this was in proportion to the overall growth retardation in these embryos . In addition , lungs of Ssb1−/− embryos were correctly lobulated , with four right lobes and a single left lobe flanking the heart , suggesting that early lung development patterning in these embryos is intact ( Figure 3A ) . However , H&E analysis on coronal sections of these lungs revealed aberrant late-stage lung development , with reduced alveolar lumens and thickened , hypercellular alveolar walls in Ssb1−/− lungs when compared to control ( Ssb1+/− and Ssb1+/− ) littermates ( Figure 3C–3E ***P<0 . 001 ) . During lung development , regression of the mesenchyme occurs from approximately E15 . 5 onwards by apoptosis to form the air-blood barrier , necessary for efficient respiration . To determine if the higher cell density in Ssb1−/− lungs results from either a decrease in apoptosis during development or increased proliferation , we performed immunohistological staining on E14 . 5 and E18 . 5 lung sections for ApopTag and Ki67 , respectively . However , no differences in the levels of Ki67 or ApopTag were observed at these developmental stages ( Figure S4 ) . Perinatal death due to respiratory failure can be caused by impaired differentiation of the proximal and/or distal airway epithelium . To determine if proximal airway epithelium was properly differentiated , we examined levels of Cc10 ( also known as Scgb1a1/Ccsp ) , a marker for secretory Clara cells , as well as Foxj1 ( also known as Hfh-4 ) , a marker of ciliated epithelial cells in the proximal epithelium by quantitative real-time PCR ( qPCR ) in Ssb1 control ( Ssb1+/+ , Ssb1+/− , n = 4 ) and Ssb1−/− ( n = 4 ) lung tissue at E18 . 5 . In addition , we also examined transcript levels of Cd31 , a marker of endothelial cells . These analyses revealed no significant differences in the mRNA levels of these markers , suggesting that both proximal airway differentiation and blood vessel formation of Ssb1−/− lungs are intact ( Figure S5A ) . Furthermore , immunohistological staining of smooth muscle actin revealed normal bronchi and bronchioli development in these embryos ( Figure S5B ) . Next , we examined differentiation of the distal saccules which contain alveolar epithelial type I and type II cells ( AECs ) , responsible for gas exchange and the maintenance of surface tension through surfactant protein secretion , respectively . To determine if Ssb1−/− embryos exhibited defective differentiation in either of these cell types , we performed qPCR on E18 . 5 control and Ssb1−/− embryos to assess the transcript levels of Aqp5 and Pdpn , as markers of type I AECs , as well as the surfactant protein transcripts Sftpa , Sftpb , Sftpc and Sftpd , as markers of type II AECs . Although Sftpa and Sftpd were unaffected by Ssb1 ablation , we observed a −2 . 4 fold change in Sftpb expression , suggesting aberrant type II AEC differentiation ( ***P<0 . 001 , Figure 3F ) . Notably , deletion of Sftpb in the mouse has been shown to result in severe neonatal respiratory distress syndrome , and is the only surfactant protein that is indispensable for neonatal survival [12] , [13] , [14] . In addition to the decrease in Sftpb levels , we also observed a smaller ( −1 . 25 fold ) change in Pdpn , a type I AEC marker ( **P<0 . 01 ) , as well as small , but statistically non-significant decrease in Aqp5 , another type I AEC marker ( Figure 3F ) . As type II cells are thought to trans-differentiate to type I cells , this may be a secondary effect of improper type II AEC differentiation [15] , [16] . Interestingly , we also observed a 1 . 3 fold increase in Sftpc mRNA ( *P = 0 . 01 , Figure 3F ) . As pro-SPC is expressed from E11 . 5 to E17 . 5 in lung epithelial progenitor cells , the relative increase in this transcript may simply represent developmental immaturity of Ssb1−/− lungs [16] . This is in accordance with blinded assessment by an independent pathologist , who observed an increase of immature type II AECs in the lungs of Ssb1−/− P0 embryos . These data indicate that Ssb1 is necessary for proper lung differentiation in the late stages of embryogenesis . Taken together , our results point to an important and novel role of Ssb1 in skeletal and lung differentiation . Mouse embryonic fibroblasts ( MEFs ) from Ssb1+/+ and Ssb1−/− E13 . 5 embryos were isolated to investigate the role of Ssb1 in DSB repair and signaling in the mouse . Early passage Ssb1+/+ and Ssb1−/− MEFs exhibited similar cell-cycle profiles , but Ssb1−/− MEFs had a slightly diminished proliferative capacity and more rapidly reached the plateau phase when compared with Ssb1+/+ MEFs ( Figure S6A , S6B ) . As we and others had previously described a role of SSB1 in the activation of ATM signaling in response to IR based on siRNA depletion in human cells [7] , [9] , [10] , we assessed activation of this pathway in MEFs . Although we observed stabilization of Ssb1 in response to IR , interestingly , no attenuation of Atm activation was detected when we assessed autophosphorylation of Atm on serine1987 ( serine1981 in human ) or phosphorylation of its downstream activation target p53 on serine18 ( serine15 in human ) ( Figure S6C ) . Similar to what we observed in Ssb1−/− lungs , Ssb2 protein levels were upregulated in Ssb1−/− MEFs . These results suggest that deletion of Ssb1 does not abrogate Atm activation in MEFs , and may highlight potential redundancy between Ssb1 and Ssb2 in these cells . To determine if the response to ionizing radiation was intact in Ssb1−/− cells , we also assessed the dynamics of γ-H2ax foci formation in Ssb1+/+ and Ssb1−/− MEFs by immunofluorescence . These studies revealed no significant differences in the baseline level of γ-H2ax foci induction nor in the clearance of IR-induced γ-H2ax foci ( Figure S6D , S6E ) , indicating that these cells did not exhibit higher levels of endogenous DNA damage and/or defective repair of IR-induced DSBs . Next , we sought to utilise an in vivo model of DSB repair to interrogate if Ssb1 is necessary for DSB repair in the mouse . Class switch recombination ( CSR ) involves a programmed Ig heavy gene rearrangement in B-lymphocytes that requires repair of physiological DSBs generated as a result of activation-induced deaminase ( AID ) catalysed DNA base damage . In B-lymphocytes , the initial secreted antibodies contain heavy chains of the IgM class ( or IgD formed via alternative splicing ) . Upon stimulation of these B-lymphocytes by antigen , the original IgM class heavy chain gene undergoes CSR to encode heavy chains of IgG , IgE , or IgA classes [17] . Several proteins involved in DSB repair including ATM , H2AX and 53BP1 have been suggested to have a role in CSR , to different extents , probably due to their roles in synapsis and/or DNA repair [17] . To assess whether loss of Ssb1 affects CSR , we generated B cell specific conditional Cd19-Cre+: Ssb1−/− mice . Western blotting of whole cell extracts showed loss of Ssb1 protein in B cells from Cd19-Cre+: Ssb1−/− mice ( Figure 4A ) and upregulation of Ssb2 protein levels ( Figure 4B ) , similar to what we observed in Ssb1−/− lungs and MEFs ( Figure 3B , Figure S6C ) . Mice lacking Ssb1 in the B lineage produced normal numbers of mature IgM+ lymphocytes in the bone marrow and had spleens of normal size and cellularity . Upon in vitro stimulation of B cells isolated from spleens with anti-CD40 antibody plus IL-4 over 3 days , the extent of IgM to IgG1 switching and cell viability in wild-type and Ssb1-deficient B cells was also comparable ( Figure 4C , 4D ) . No difference was found in the percentage and total numbers of direct or microhomology-mediated joins in switch region junctions from IL4 plus anti-CD40 stimulated primary Ssb1−/− B cells and wild-type B cells ( Figure 4E ) . These results suggest that Ssb1 is dispensable for DSB repair by class-switch recombination . Given the perinatal lethality we observed in constitutive Ssb1−/− mice , we next employed a conditional approach to ubiquitously ablate Ssb1 postnatally using a tamoxifen-inducible Cre system by interbreeding Ssb1fl/fl mice with the Rosa26-CreERT2 strain ( Figure S7 ) [18] . Efficiency of Ssb1 deletion in adult mice ( 4 weeks old ) following a series of tamoxifen injections was confirmed by both PCR for genomic recombination , and western blot analysis for protein depletion in various tissues ( Figure S8A–S8C ) . The floxed Ssb1 allele was efficiently deleted in bone marrow ( BM ) , thymus , spleen , testes and small intestine , partially deleted in lung , kidney , liver and heart , but not in the brain ( Figure S8A ) . Dramatically decreased Ssb1 protein levels were confirmed in multiple tissue samples from tamoxifen induced Rosa26-CreERT2: Ssb1−/− mice , with undetectable levels of Ssb1 protein in splenocytes and thymocytes as early as 10 days after the final tamoxifen induction ( Figure S8B ) . Interestingly , we observed a dramatic up-regulation of Ssb2 in response to Ssb1 ablation in the bone marrow and spleen , but not in the testes and thymus of Rosa26-CreERT2: Ssb1−/− mice ( Figure S8C ) . Monitoring of Rosa26-CreERT2: Ssb1−/− mice and control Rosa26-CreERT2: Ssb1+/− mice revealed no significant differences in body weight over a period of up to 90 weeks ( Figure S9 ) . In addition , histological analysis of all major organs , including the brain , thymus , lung , heart , liver , kidney and small intestine revealed no gross abnormalities . The abrogation of many DNA repair factors , ( such as Atm [19] , H2ax [20] , Mdc1 [21] and Mcph1/Brit1 [22] ) has been shown to result in impaired fertility , due to important roles of these proteins in DSB repair during meiosis . To determine the impact of Ssb1 deficiency on fertility , we examined ovaries and testes of Rosa26-CreERT2: Ssb1−/− mice six weeks after induction with tamoxifen . Whereas Rosa26-CreERT2: Ssb1−/− ovaries were morphologically normal in females , the testes from Rosa26-CreERT2: Ssb1−/− males were reduced in size ( Figure 5A ) , in terms of both absolute weight ( n = 8 , ***P<0 . 001 , Figure 5B ) and gonado-somatic index ( GSI ) [23] , an indicator of gonad weight as a proportion of total body mass ( n = 8 , ***P<0 . 001 , Figure 5C ) , when compared to their Rosa26-CreERT2: Ssb1+/− littermates . Histological examination of testes from 3-month-old Rosa26-CreERT2: Ssb1−/− male mice showed bilateral testicular degeneration with a spectrum of alterations in spermatogenesis . Testicular tubules showed degenerate , sometimes vacuolated , or necrotic spermatogenic cells , the latter with pyknotic nuclei and hypereosinophilic cytoplasm , or apoptotic body formation . Multinucleated giant cells were also frequently present in the lumen , either derived from spermatocytes with arrested development or the coalescence of spermatids ( Figure 5D , left panel ) . Increased apoptosis at approximately the same stage , equivalent to stage IV of the normal seminiferous epithelial cycle has been reported in a number of mutants defective for meiotic recombination and/or meiosis-specific chromosome structures [24] . We performed ApopTag staining to determine the rate of spermatocyte apoptosis in testes from Rosa26-CreERT2: Ssb1+/− and Rosa26-CreERT2: Ssb1−/− littermates . As expected , the spermatocytes of Rosa26-CreERT2: Ssb1−/− testes exhibited increased ApopTag staining , compared to Rosa26-CreERT2: Ssb1+/− spermatocytes that were uniformly immunonegative for apoptosis ( Figure 5D , right panel ) . As newly formed spermatozoa are released for passage into the epididymis for further maturation , we examined epididymides from Rosa26-CreERT2: Ssb1−/− mice for developing germ cells that were prematurely sloughed from the seminiferous epithelium and passed into the epididymis . The presence of round germ cells within the lumen of the epididymis ( Figure 5E ) suggests that , in addition to apoptosis , a significant number of germ cells were being lost via premature sloughing from the supporting Sertoli cells . Taken together , these results reveal a spectrum of testicular degenerations in the Rosa26-CreERT2: Ssb1−/− mice . To further characterize the consequences of Ssb1 ablation on fertility , we interbred induced Rosa26-CreERT2: Ssb1−/− mice with wild-type mice . Consistent with the normal physiological appearance of their ovaries , induced female Rosa26-CreERT2: Ssb1−/− mice at ten weeks of age were found to be fertile . In contrast , only 4 out of 6 pairings of male Rosa26-CreERT2: Ssb1−/− mice with wild-type females led to successful pregnancies . In addition , in the 4 successful breeding pairs , we observed significantly smaller litter sizes ( *P<0 . 05 , Figure 5F ) and much longer litter intervals ( 63 days vs . 27 days , ***P<0 . 001 , Figure 5G ) for Rosa26-CreERT2: Ssb1−/− breeders compared to Rosa26-CreERT2: Ssb1+/− control males . Histological analysis of testes sections revealed a dramatically decreased number of elongated spermatids in the infertile compared to the fertile Ssb1-deleters . Thus , post-natal Ssb1 deletion leads to a spectrum of partial to complete male fertility defects , demonstrating the importance of this protein for spermatogenesis . To assess if conditional deletion of Ssb1 in mice causes a DNA damage response defect in vivo , we challenged Rosa26-CreERT2: Ssb1+/+ , Rosa26-CreERT2: Ssb1+/− and Rosa26-CreERT2: Ssb1−/− mice with 8 Gy of total body irradiation ( TBI ) at 4 weeks post tamoxifen-induction and monitored them for up to 30 days post-IR ( Figure 6A ) . Although we observed comparable progressive weight loss in all 3 groups within the first few days of radiation exposure , death events started to occur in the group of irradiated Rosa26-CreERT2: Ssb1−/− mice by the 10th day . By day 19 , 92% ( 11 out of 12 ) of Rosa26-CreERT2: Ssb1−/− mice had died . In contrast , in Rosa26-CreERT2: Ssb1+/+ and Rosa26-CreERT2: Ssb1+/− groups , the first death event occurred on the 13th day and more than 50% of mice survived for at least 30 days after irradiation ( Figure 6B ) . Thus , in vivo radiation sensitivity was significantly increased in Rosa26-CreERT2: Ssb1−/− mice compared to Rosa26-CreERT2: Ssb1+/+ or Rosa26-CreERT2: Ssb1+/− controls based on Kaplan-Meier survival analysis ( **P<0 . 01 ) ( Figure 6B ) . As injury of the small intestine or bone marrow are the most common causes of death in irradiated mice , we examined these tissues to establish the cause of death in induced Rosa26-CreERT2: Ssb1−/− mice . At 24 h and 3 days post TBI , the histology of the small intestine was comparable across induced Rosa26-CreERT2: Ssb1+/+ , Rosa26-CreERT2: Ssb1+/− and Rosa26-CreERT2: Ssb1−/− mice , as assessed by Haematoxylin and eosin , Ki67 and ApopTag immunohistochemical staining ( Figure S10A and data not shown ) . However , at 5 days post TBI , we observed some pathological abnormalities in Rosa26-CreERT2: Ssb1−/− mice , including distended crypt lumina lined by attenuated enterocytes and containing desquamated necrotic cellular debris as well as a small increase of cells near deep crypts with apoptotic bodies ( Figure 6C ) . Further , we performed complete blood count ( CBC ) analysis on peripheral blood of these mice to assess hematologic radiation toxicity , but no significant difference between the groups was observed ( Figure S10B ) . To assess whether Ssb1 deficiency affects radiosensitivity in other tissues , we also isolated and exposed thymocytes to various doses of IR ( 1–6 Gy ) . We observed increased radiosensitivity in Ssb1−/− thymocytes as assessed by Annexin V and 7-AAD staining ( Figure S11 ) . Taken together , these data indicate that depletion of Ssb1 leads to increased radiosensitivity in vivo and in thymocytes in vitro . To further investigate the increased radiation sensitivity of conditional Ssb1 null mice , we cytologically examined bone marrow metaphase spreads from Rosa26-CreERT2: Ssb1+/+ , Rosa26-CreERT2: Ssb1+/− and Rosa26-CreERT2: Ssb1−/− mice at 24 h after 2 and 6 Gy of TBI to assess chromosomal abnormalities . We observed a significant increase in chromosomal breakage , fragmentation and fusion in Rosa26-CreERT2: Ssb1−/− bone marrow metaphases upon irradiation , as assessed by fluorescence in situ hybridization ( FISH ) analysis ( Figure 7 ) . These results provide in vivo evidence that Ssb1 functions to maintain genomic stability . To assess whether conditional Ssb1 deletion would lead to increased cancer susceptibility , we monitored tumour development in age- and gender-matched long-term survival cohorts of Rosa26-CreERT2: Ssb1+/− ( n = 35 ) and Rosa26-CreERT2: Ssb1−/− ( n = 35 ) mice . No significant difference in body weight was found between Rosa26-CreERT2: Ssb1+/− and Rosa26-CreERT2: Ssb1−/− mice over the 86 week observation period post-Ssb1 deletion ( Figure S9 ) . However , during this period , 11 out of 35 ( 31 . 4% ) Rosa26-CreERT2: Ssb1−/− mice developed tumours , in contrast to only 2 out of the 35 ( 5 . 7% ) Ssb1+/− mice , revealing a statistically significant difference ( **P<0 . 01 ) in tumour-free Kaplan-Meier survival analysis ( Figure 8A ) . No tumours were observed in a Cre-negative control group ( Ssb1fl/fl mice , n = 10 ) treated with an identical tamoxifen dose or in a vehicle ( olive oil: ethanol at 19∶1 ratio ) treated Rosa26-CreERT2: Ssb1+/− control group ( n = 5 ) . In the 11 Rosa26-CreERT2: Ssb1−/− mice that developed tumours , we observed a broad tumour spectrum ( Figure 8B ) including splenic and metastatic B lymphomas , T cell lymphoma in thymus ( Figure 8C ) , hepatocellular carcinoma , ( HCC , Figure 8D ) and B or T lymphoblastic leukemia ( Figure S12 ) . We also observed p53 stabilization , which is most likely an indication of the presence of mutated p53 , in a high proportion of tumours ( 9 of 11 Ssb1−/− tumours and 2 of 2 Ssb1+/− tumours ) when compared with adjacent normal tissue from the same mice ( Figure S13 and Figure S14 ) . Moreover , in the two tumours observed in Rosa26-CreERT2: Ssb1+/− mice , the Ssb1 protein was undetectable by immunohistochemical staining , indicating possible loss of heterozygosity ( LOH ) of the other Ssb1 allele in these tumors ( Figure S14 ) . Taken together , these data indicate that Ssb1 prevents tumor formation in vivo .
Previous studies using siRNA depletion in human cells have reported a role for SSB1 in the proper co-ordination of DNA repair in response to IR [7] , [8] , [9] , [10] . By disrupting the major protein coding exons of Ssb1 in mice , including the OB-fold domain , we have created mouse models to study the physiological function of Ssb1 in vivo , and describe a wide spectrum of phenotypes upon Ssb1 deletion during embryogenesis and in adult and aged mice . Major unexpected findings include novel roles of Ssb1 in the regulation of lung and skeletal development , as constitutive germline ablation of Ssb1 resulted in immature alveolar differentiation and multiple skeletal defects encompassing the ribs , craniofacial skeleton , and limbs . Interestingly , a handful of other DNA repair factors have been linked to roles in skeletogenesis: patients with Rothmund-Thompson and Rapadilino syndrome , who have mutations in the DNA helicase RECQ4 , exhibit some skeletal defects in the limb [25]; patients with mutations in the repair-associated proteins Ctbp-interacting protein ( CtIP/RBBP8 ) , Centrosomal Protein 152 ( CEP152 ) , microcephalin1 ( MCPH1 ) , or Ataxia-Telangiectasia Related ( ATR ) exhibit dwarfism and a characteristic “bird-shaped” face with micrognathia , which is similar to the craniofacial phenotype we observe in Ssb1−/− mice [26] , [27] , [28] , [29] . Similarly , patients with Nijmegen Breakage Syndrome ( NBS ) , who have mutations in the MRN complex protein NBS-1 , also have similar craniofacial abnormalities [30] . Previously , we demonstrated an interaction between SSB1 and NBS1 , which , in in vitro studies , was abrogated by NBS-1 mutations observed in patients [31]; therefore it is tempting to speculate that SSB1 may be involved in some of the craniofacial phenotypes of this disorder . However , the broad spectrum of skeletal phenotypes in Ssb1-deficient mice is more pronounced than those reported for any of these human syndromes . This , together with the absence of obvious defects in signalling and repair of IR-induced DNA damage in both MEFs and absence of CSR defects in B cell-specific Ssb1−/− mice , may suggest additional functions of Ssb1 during embryogenesis that are outside of DNA repair . Skeletal patterning is a complex process , and involves the spatial and temporal co-ordination of a number of developmental signalling pathways , including Hedgehog ( in particular Indian Hedgehog [Ihh] and Sonic Hedgehog [Shh] ) , Bone Morphogenic Protein ( BMP ) and the Transforming Growth Factor Beta ( TGF-β ) family , Fibroblast Growth Factor ( FGF ) and Wnt signalling [32] , [33] . Not surprisingly , a plethora of proteins have been implicated in skeletogenesis . During vertebrate skeletal development , mesenchymal condensations ( known as somites ) differentiate into the sclerotome and dermomyotome [34] , [35] . Whilst the sclerotome differentiates into chondrocytes , which form the ribcage and axial skeleton , the dermomyotome further differentiates into the skin ( dermatome ) and muscle ( myotome ) . Correct outgrowth and differentiation has been shown to be dependent on signalling from each of these compartments [33] , [34] , [35] . Interestingly , the rib-cage phenotype we observe in Ssb1−/− skeletons bears striking similarity to that of targeted disruption of the myotome regulator Myf5 [36] , [37] . In Myf5-deficient mice , a similar lack of ossification in the ribcage and “floating-rib” phenotype is observed , with a partial or complete lack of ossification of the dorsal region of the ribcage , combined with micrognathia [36] , [37] . Myf5−/− mice also die perinatally , but do not show the same degree of hindlimb defects that we observe in Ssb1−/− mice . Intriguingly , Myf5 is one of the genes hypothesized to have a causal role in cerebro-costo-mandibular syndrome , a rare multiple congenital anomaly syndrome characterized by absent ossification of the posterior rib-cage and micrognathia [38] , [39] . Strikingly , cerebro-costo-mandibular syndrome patients also usually exhibit lung hypoplasia , due to improper development of the lung inside a poorly formed rib-cage , and have a poor prognosis for survival [40] . In addition , this disorder has also been associated with hearing defects , variable palate clefting , and sometimes mental retardation [38] , [39] , [40] . Although limb-patterning defects have not been described for this disorder , given the striking similarity in other phenotypes , Ssb1 may prove an interesting new candidate gene for this disorder . Bone development can occur through two major processes , endochondral ossification , where a cartilage precursor template is laid down prior to bone formation , or intramembranous ossification , where mesenchymal cells condense and directly transition to form bone [41] , [42] . Whilst endochondral ossification is the process responsible for skeletal formation in the majority of the axial and appendicular skeleton , intramembranous ossification is restricted to parts of the skull , including the cranial vault , and maxillo-mandibular bones [41] , [42] . The skeletal outgrowth and patterning defects observed in Ssb1−/− mice suggest that Ssb1 is important for endochondral ossification . During the preparation of this manuscript , another report of the critical role of Ssb1 in skeletogenesis was published , where the authors had used a similar genetic targeting approach to delete Ssb1 in the mouse [43] . Interestingly , the authors described an almost identical skeletal phenotype to this report , with a similar lack of ossification of the rib-cage , micrognathia , timpanic ring malformation and variably-penetrant oligodactyly . In addition , they also reported clefting of the palate , which we observed in two cases but not in others . However , although both mouse models were generated in C57BL/6 mice , craniofacial phenotypes can be heavily affected by sometimes-subtle strain differences [44] . Intriguingly , the role of Ssb1 in skeletogenesis was attributed to p53-dependent apoptosis at E12 . 5 throughout the somites and limb , and a partial rescue of these phenotypes was observed upon crossing to a p53−/− background . In the case of combined Ssb1 and p53 ablation , however , although the hindlimb digit patterning and ribcage structure was substantially rescued , a distinctive lack of ossification was still evident , particularly in the dorsal extremities of the ribs abutting the vertebrae [43] . This suggests that the Ssb1−/− phenotype cannot be solely attributed to apoptosis , and that some steps in the later stages of endochondral ossification are dependent on Ssb1 . Interestingly , the authors did not observe differences in canonical chondrogenic and osteogenic markers by microarray analysis on E18 . 5 sternum chondrocytes and calvarial osteoblasts [43] . However , the late time point of analysis and tissue origin of these cell lines may have affected the outcome of this study . Indeed , calvarial osteoblasts form through intramembranous , not endochondral ossification [41] , and sternum development was not as severely affected as the rest of the rib-cage in Ssb1−/− embryos . It will therefore be of great interest to more rigorously investigate the role of Ssb1 in bone development , and to determine the precise mechanisms that lead to bone-specific apoptosis observed in these mice . While the development defects in germline Ssb1 knockout mice were surprising , effects of inducible ablation of Ssb1 in adult mice revealed phenotypes more relevant to the proposed role of Ssb1 in maintaining genomic stability , as we observed defects in spermatogenesis , increased radiation sensitivity , increased genomic instability as well as an increased tumour incidence in induced Ssb1−/− mice . Spermatogenesis in the mouse commences postnatally at day 7 and by day 35 post-natal mature sperm can be found within the seminiferous tubules . One round of spermatogenesis takes approximately 28 days and it is a continuous process within the testes . The major phases of spermatogenesis are mitosis , meiosis , and post-meiotic germ cell maturation , which last 11 , 10 and 14 days , respectively [45] . We commenced induction of Ssb1 deletion at the age of 28 days , which is at the late stage of meiosis during the first wave of spermatogenesis . We observed a variable degree of testicular degeneration and defective spermatogenesis , which led to smaller sized testes and reduced fertility in conditional Ssb1−/− adult male mice . The increased number of apoptotic spermatocytes in testes and premature sloughing of germ cells into the epididymis may be the cause of reduced fertility . The observation of a degree of phenotypic variation between conditional Ssb1−/− mice suggests that the severity of the fertility defects was dependent on the degree of testicular degeneration , which may be correlated with the variation in the residual amount of Ssb1 protein between different mice after Cre-recombination . Further investigation of the function of Ssb1 in spermatogenesis is beyond the scope of this first report , but it would be of great interest to study testicular defects in testis-specific Ssb1-deleted mice . Aside from meiotic chromosome rearrangement , physiological programmed DSBs are also generated during Class Switch Recombination ( CSR ) in mature antigen-stimulated B lymphocytes [17] . CSR involves programmed DNA rearrangements within the Ig heavy chain locus of B-lymphocytes to switch from IgM to other Ig isotypes [17] . Splenic B cells with Ssb1−/− specific deletion showed similar ex vivo induced switching from IgM to IgG1 . This lack of a CSR defect in B-cell specific Ssb1 knockout mice was unexpected , and may be due to functional compensation by Ssb2 as we observed dramatically up-regulated Ssb2 protein levels in bone marrow and spleen from Rosa26-CreERT2: Ssb1−/− mice and B cells from Cd19-Cre+: Ssb1−/− mice . However , we suspect the potential compensation of Ssb2 might not be sufficient to compensate for all lost Ssb1 functions in the long term or in tissues such as testes , where Ssb2 is already abundantly expressed . To investigate this aspect , a double inducible knockout mouse model of Ssb1 and Ssb2 is under investigation in our group , which will provide insight into how the Ssbs are functionally related in DNA repair . A major role of DDR proteins , particularly crucial HR proteins , in mammalian cells is to maintain genomic integrity [46] . Not surprisingly , the impairment of this process increases cancer risk [19] , [20] , [21] , [22] , [47] , [48] , [49] , [50] . Interestingly , the increased radiation sensitivity and chromosomal instability in total body irradiated conditional Ssb1−/− mice demonstrate the importance of Ssb1 in the maintenance of genomic integrity . Further confirming the role of Ssb1 in genomic stability was the increased incidence of spontaneous tumor formation in aged conditional Ssb1−/− mice compared with their heterozygous littermates , revealing a potential tumor suppressor function of Ssb1 in vivo . Notably , we were unable to observe a defect in γ-H2ax induction or clearance , nor in Atm signaling in response to ionizing radiation in isolated Ssb1−/− MEFs; however , this does not rule out a potential role of Ssb1 in these processes in a context- or tissue-specific manner . In conclusion , our results highlight a novel , and non-redundant role of Ssb1 in embryonic development , which may be due to a function independent of its previously described role in DNA repair . Furthermore , our conditional deletion studies of Ssb1 in adult mice highlight the importance of Ssb1 in maintaining some aspects of genome stability and may represent tissue-specific and context-dependent roles of this protein in the adult mouse .
To target the mouse Ssb1 allele , a targeting construct was engineered with unidirectional lox-P sites flanking exons 3–6 of mouse Ssb1 , which encompasses the DNA-binding OB-fold domain of the protein . A neomycin resistant cassette ( PGK-NEO ) , necessary for gene targeting in mouse ES cells , was flanked by FRT recombination sites and situated within the lox-P flanked region ( Figure S1A ) . Genomic targeting of the construct was performed in C57BL/6J ES cells using standard homologous recombination and blastocyst manipulation techniques . Gene targeting was confirmed by Southern blot using 5′ and 3′ probes situated outside the targeting vector , in addition to an internal neo probe following restriction digest of genomic DNA using HindIII , SacI or ScaI restriction enzymes . Generation of Ssb1 floxed/neo ( flneo ) mice was a contracted service performed by Ozgene Pty Ltd ( Perth , Australia ) . Ssb1 floxed ( fl ) mice were generated by first crossing Ssb1 targeted mice against FLPe recombinase transgenic mice to remove the neomycin cassette , and subsequently backcrossed onto a C57BL/6J strain to remove the FLP transgene . To generate constitutive germline deletion of Ssb1 , Ssb1fl/fl mice were crossed against CMV-Cre ( TgN ( CMV-cre ) 1Cgn ) transgenic mice that have been described previously [51] . Offspring containing the Ssb1 null ( − ) allele were backcrossed to the C57BL/6J strain to segregate the Ssb1 null allele and Cre transgene . Ssb1+/− heterozygous mice were intercrossed to generate Ssb1−/− animals . Ssb1+/+ and Ssb1+/− embryos were indistinguishable at the phenotypic level and were used interchangeably for some experiments as explicitly stated in the text . To generate conditional and ubiquitous Ssb1−/− mice , Ssb1fl/fl mice were crossed against Rosa26-CreERT2 transgenic mice ( Figure S7 ) [18] , [52] . Double transgenic progeny carrying both the floxed and Cre transgenes ( Rosa26-CreERT2: Ssb1fl/+ ) were subsequently crossed to the Ssb1fl/fl mouse line to generate experimental animals ( Rosa26-CreERT2: Ssb1fl/+ and Rosa26-CreERT2: Ssb1fl/fl ) . Induction of Ssb1 knockout was performed by intraperitoneal ( IP ) injection of 1 mg tamoxifen/mouse for 5 consecutive days into 4 week-old experimental animals . Cre-mediated excision was verified in a number of tissues by both genotyping PCR and western-blot ( Figure S8 ) . To determine if Ssb1 plays a role in class switch recombination ( CSR ) , we crossed Ssb1fl/fl mice with Cd19-Cre transgenic mice to conditionally delete Ssb1 in B cells [53] . All experimental animals were maintained on a C57BL/6J strain , and were housed at 25°C with a 12 h light/12 h dark cycle . All experiments were performed in accordance with the Queensland Institute of Medical Research animal ethics guidelines . Genotyping was performed using genomic DNA extracted from tails . The sequences of PCR primers for genotyping Rosa26-CreERT2 mice are: 5′-TGTGGACAGAGGAGCCATAAC-3′ ( forward primer ) and 5′-CATCACTCGTTGCATCGACC-3′ ( reverse primer ) . As expected , PCR amplification of the 356-bp Rosa26-CreERT2-specific product reliably identified transgenic mice . Assessment of the Ssb1 gene before and after Cre recombination was performed by PCR designed to detect if the floxed-sequence had been deleted via Cre/loxP recombination . Two different reverse PCR primers were used , together with a common forward primer , result in 482 , 360 and 118-bp PCR products , specific for Ssb1 floxed , wild-type , and null alleles , respectively ( Figure 2A ) . The sequences of the common forward primer for Ssb1 wild type , floxed and null allele is: 5′-GCTTTGCTTCTGTTCCTTTACCT-3′ . The reverse primer for Ssb1 wide-type and floxed alleles is 5′-ACAACCTTTGAACACTGAAGC-3′and for the Ssb1 null allele is 5′-GAAATGGATTCCGAGCTCAA-3′ . Alcian Blue and Alizarin Red whole-mount skeletal preparations were performed as described previously [54] on E18 . 5 embryos . Skeletal Preparations were imaged on a Nikon SMZ45 dissecting microscope equipped with a Nikon 5MP colour camera . For protein extraction , tissue samples were homogenized in RIPA lysis buffer ( 50 mM Tris-HCl pH 7 . 4 , 150 mM NaCl , 1% NP40 , 0 . 25% Na-deoxycholate , 1 mM PMSF , 1× Roche complete mini protease inhibitor cocktail , 1× Pierce phosphatase inhibitor cocktail ) . Western blotting was performed as described previously [3] with the following antibodies: sheep anti-SSB1 ( 1∶1000 ) , rabbit anti-SSB2 ( 1∶250 ) , mouse anti-β-actin ( Sigma , 1∶10 , 000 ) , mouse anti-phosphorylated-ATM serine1981 ( Cell Signaling , 1∶1000 ) and rabbit anti phosphorylated-p53 serine 15 ( Cell Signaling , 1∶1000 ) . Detection of the signals with the chemiluminiscence reagent ( Super Signal West Pico , Pierce ) was carried out using the LAS-4000 imaging system ( Fujifilm Life Science ) . Images of Haematoxylin and eosin stained lung sections from Ssb1 control ( Ssb1+/+ , Ssb1+/− , n = 3 ) and Ssb1−/− ( n = 4 ) were analysed using Image J software ( rsbweb . nih . gov/ij/ ) on four representative images for each lung , with care taken not to include areas with conducting airway . Briefly , images were converted to greyscale and thresholded equally across images from control and Ssb1−/− lungs to highlight alveolar septa . The average area occupied by septa and airspace was calculated and subjected to statistical analysis . The right lobes of Ssb1+/+ , Ssb1+/− and Ssb1−/− lungs were homogenized and RNA extracted using the RNeasy mini kit ( Qiagen ) , followed by DNAse I ( New England Biolabs ) digestion to remove genomic DNA contamination . 2 µg of RNA was used for first-strand cDNA synthesis using random primers ( Life Technologies ) and Superscript III reverse transcriptase ( Life Technologies ) . qRT-PCR was performed using Light Cycler 480 Sybr green I mastermix ( Roche Applied Science ) on a Light Cycler 480 Real-time PCR system ( Roche Applied Science ) . Primer sequences for Cc10 , Foxj1 , Cd31 , Pdpn , Sftpa , Sftpb , Sftpc and Sftpd have been described previously [55] , [56] . Aqp5 and β-Actin primer sequences were as follows: Aqp5 , 5′-CTGCGGTGGTCATGAATC-3′ ( forward ) and 5′-CTACCCAGAAGACCCAGTGA-3′ ( reverse ) ; β-Actin , 5′-GGCTGTATTCCCCTCCATCG-3′ ( forward ) and 5′-CCAGTTGGTAACAATGCCATGT-3′ ( reverse ) . Negative controls with no template and no reverse transcriptase were also included and used in qRT-PCR reactions to ensure no contaminating genomic DNA was present . Mouse embryonic fibroblasts ( MEFs ) were isolated from E13 . 5 embryos from Ssb1+/− intercrosses as described previously [57] . At least three independent embryos per condition were used for analysis . For 3T3 fibroblast growth assays , Ssb1+/+ and Ssb1−/− cell lines were seeded at passage 2 at a concentration of 0 . 5×106 cells/10 cm dish . Cells were trypsinised , counted and re-seeded every 3 days at the same concentration to monitor relative changes in growth at each passage . Cells were plated on glass coverslips and used at approximately 70 percent confluency . Immunofluorescence with the γ-H2AX antibody ( Millipore ) was performed as described previously [7] . For γ-H2ax foci quantitation , 50 cells for each MEF cell line ( n = 2 Ssb1+/+ , 3 Ssb1−/− ) were scored for those containing >10 foci/cell at the indicated timepoints following 2 Gy of gamma-irradiation , across two independent experiments . The testes from Rosa26-CreERT2: Ssb1 mice were dissected out and weighed with an analytical balance ( Mettler AT261 ) . The gonado-somatic index was determined according to the formula: Gonado-Somatic Index ( GSI ) = ( Gonad weight/total body weight ) X 100 , where gonad weight = ( weight of the right testis+ weight of the left testis ) /2 [23] . Splenic B cells were stimulated for IgH CSR to IgG1 using anti-CD40 antibodies plus IL-4 and analyzed by flow cytometry as described previously [58] . Total body irradiation ( TBI ) was performed using a 137Cs source at 108 cGy/min . Mice were placed in plexiglass cages and irradiated in groups of five simultaneously with the indicated doses . Metaphases were prepared directly from bone marrow cells in demicolcine-treated mice for FISH analysis . Five weeks after tamoxifen induction , nine-week-old Rosa26-CreERT2: Ssb1+/+ , Rosa26-CreERT2: Ssb1+/− and Rosa26-CreERT2: Ssb1−/− mice were given 2 or 6 Gy of TBI and kept for 24 h before bone marrow collection . Demicolcine ( Sigma , 250 µl of a 200 µg/ml solution ) was administered by intraperitoneal injection into each mouse 1 h prior to bone marrow collection . Bone marrow was flushed from each femur and tibia with pre-warmed potassium chloride solution ( 0 . 06 M ) . Fluorescence in situ hybridization ( FISH ) analysis was performed on metaphases using a biotinylated centromere-specific minor satellite probe . Three mice were analyzed for each genotype per condition and thirty metaphases were analyzed per case for chromosome breaks . Within each spread , the number of chromosomal fragments and fusions ( identified by the presence of more than one centromere signals ) was determined . Lymphocyte surface makers were measured in peripheral blood samples by flow-cytometric analysis . Following lysis with 0 . 145 M ammonium chloride to remove red blood cells , the remaining lymphocytes were washed and incubated with APC conjugated anti-Cd3 , PerCP-conjugated anti-Cd8 , FITC-conjugated anti-Cd4 , and PE-conjugated anti-Cd19 ( BD Pharmingen ) , at 4°C for 30 minutes . Cells were washed , resuspended in PBS , and acquired on a FACS Canto II . Data were analyzed with Flowjo software ( Ashland , OR , USA ) . Tissues were collected and fixed in 10% buffered formalin fixative or 4% Paraformaldehyde , embedded in paraffin blocks , and 5-µm-thick sections were stained with Haematoxylin and eosin for histological examination . Slides were coded and examined in a blinded fashion by an independent veterinary pathologist . Immunohistochemistry staining was performed following standard procedures . Apoptosis was assessed using the ApopTag peroxidase in situ apoptosis detection kit ( Chemicon International ) , according to the manufacturer's instructions . Stained slides were scanned on Aperio ScanScope XT Slide Scanner and the images were analyzed with Image Scope software . Data were analyzed with GraphPad Prism software . The student's t-test was used for the statistical analysis of embryo weight and length , long bone comparison , qPCR , lung airspace analysis , testis weight , GSI , litter interval , litter size , chromosome breaks and blood cell counting data . Survival curves were plotted using Kaplan-Meier estimates and compared by log-rank ( Mantel-Cox ) analysis . P values less than 0 . 05 were considered statistically significant . | Single-stranded DNA binding proteins ( SSBs ) play a variety of roles in the cell , regulating transcription , replication , and DNA repair . We recently identified and described a novel SSB , designated SSB1 , which was shown to be critical for DNA repair in the cell . In this study we have used a mouse model in which the Ssb1 gene is deleted to further investigate its physiological function . Here , we show that deletion of Ssb1 causes death at birth due to severe respiratory failure , which is caused by an improperly formed rib cage and immature lung development . In addition , we observed multiple additional skeletal defects in Ssb1 deleted mice , indicating that Ssb1 is necessary for proper development of the embryonic skeleton . Furthermore , Ssb1 deletion in the adult mouse caused fertility defects in male mice and led to the development of a variety of tumours . Together , these studies demonstrate a novel and critical role of Ssb1 in embryonic development , in fertility , and in the protection from tumour formation . | [
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] | 2013 | Essential Developmental, Genomic Stability, and Tumour Suppressor Functions of the Mouse Orthologue of hSSB1/NABP2 |
The live attenuated yellow fever ( YF ) vaccine has an excellent record of efficacy and one dose provides long-lasting immunity , which in many cases may last a lifetime . Vaccination stimulates strong innate and adaptive immune responses , and neutralizing antibodies are considered to be the major effectors that correlate with protection from disease . Similar to other flaviviruses , such antibodies are primarily induced by the viral envelope protein E , which consists of three distinct domains ( DI , II , and III ) and is presented at the surface of mature flavivirions in an icosahedral arrangement . In general , the dominance and individual variation of antibodies to different domains of viral surface proteins and their impact on neutralizing activity are aspects of humoral immunity that are not well understood . To gain insight into these phenomena , we established a platform of immunoassays using recombinant proteins and protein domains that allowed us to dissect and quantify fine specificities of the polyclonal antibody response after YF vaccination in a panel of 51 vaccinees as well as determine their contribution to virus neutralization by serum depletion analyses . Our data revealed a high degree of individual variation in antibody specificities present in post-vaccination sera and differences in the contribution of different antibody subsets to virus neutralization . Irrespective of individual variation , a substantial proportion of neutralizing activity appeared to be due to antibodies directed to complex quaternary epitopes displayed on the virion surface only but not on monomeric E . On the other hand , DIII-specific antibodies ( presumed to have the highest neutralizing activity ) as well as broadly flavivirus cross-reactive antibodies were absent or present at very low titers . These data provide new information on the fine specificity as well as variability of antibody responses after YF vaccination that are consistent with a strong influence of individual-specific factors on immunodominance in humoral immune responses .
The live-attenuated yellow fever ( YF ) vaccine based on the 17D virus strain is considered to be one of the most successful vaccines ever produced [1] , [2] . Since its development in the 1930s by Max Theiler , several hundred million doses have been administered and its effectiveness in protecting from disease has been reported to be at least 90% [1] . Recent studies , including systems biology approaches [3] , analyzing innate , cellular and humoral immune responses after YF vaccination indicate that all arms of the immune system are activated , leading to a polyfunctional response that is most likely essential for the long-lasting immunity induced by this vaccine [3] , [4] , [5] . Despite the broad immunological stimulation , there is strong evidence that humoral immunity mediated by virus-neutralizing antibodies is the primary effector mechanism of protection [1] . Such antibodies may persist for more than 45 years and apparently protect against all naturally occurring genotypes of YF virus [1] . YF virus is the prototypic and name-giving member of the genus flavivirus , family flaviviridae [6] . It is closely related to other mosquito-borne and tick-borne human pathogens , the most important of which are dengue , Japanese encephalitis , West Nile and tick-borne encephalitis viruses [6] . Structural details of flaviviruses have been elucidated for dengue , tick-borne encephalitis ( TBE ) , West Nile ( WN ) , and Japanese encephalitis viruses using X-ray crystallography and cryo-electron microscopy [7] , [8] , but no such data are yet available for YF virus . However , based on the similarity of the structures determined for different flaviviruses and their close molecular biological and even antigenic relationships , it is justifiable to postulate that the structural organization of YF virus particles as well as its constituting proteins follows the same principles that are typical of flaviviruses in general ( Figure 1 ) . Immature virions ( Figure 1A , left panel ) are assembled in the ER and contain three structural proteins , designated as C ( capsid ) , prM ( precursor of membrane ) and E ( envelope ) . These particles contain 60 trimeric spikes of prM-E heterodimers and are secreted through the exocytotic pathway of the cell [9] . In the acidic environment of the trans-Golgi network , prM is cleaved by furin [10] and during this process the E protein is completely rearranged to form a herringbone-like lattice of 90 E homodimers at the surface of mature virions [11] , [12] ( Figure 1A , right panel and Figure 1B ) . Because of its functions in virus entry ( cell attachment and viral fusion in the endosome; [7] , [13] ) , the E protein is the major inducer and target of virus neutralizing antibodies . Neutralizing activity ( albeit low ) has also been reported for antibodies against prM [14] , [15] , which is consistent with the fact that the maturation cleavage of prM is not always complete and that partially mature ( but already infectious ) particles play a role in flavivirus infections [16] . The overall structure of E is highly conserved among all flaviviruses and consists of three characteristically folded domains ( DI , DII , and DIII ) , which are schematically depicted in Figure 1 . Studies with other flaviviruses and monoclonal antibodies have shown that antibodies to each of the domains can lead to virus neutralization , although those directed to DIII appeared to have the greatest specific neutralizing activity [17] . The tip of DII contains a highly conserved sequence - the fusion peptide ( FP ) loop - that is essential in membrane fusion and contributes to the induction of broadly flavivirus cross-reactive antibodies with no or relatively low neutralizing activity [18] , [19] , [20] . Factors controlling the dominance of antibody responses to different sites on protein antigens are ill-defined but can strongly influence the breadth and functional activity of polyclonal immune sera [21] , [22] . Studies on the molecular antigenic structure of flaviviruses have shown that E protein domain-specific , domain overlapping , subunit overlapping , and complex quaternary epitopes can be targets of neutralizing antibody responses , both in animals and humans ( reviewed in [8] ) . Very little information , however , exists with respect to the relative proportions of antibody subsets and individual variations in polyclonal sera after infection or vaccination . The mechanism of B cell stimulation cannot distinguish between sites involved in virus neutralization and ‘decoy’ sites that induce only ‘junk antibodies’ [23] , [24] , [25] , and such variations can have a strong impact on the functional activity of the humoral immune response . Thus , the primary objective of this study was to dissect the polyclonal antibody response after YF vaccination in a large panel of vaccinees and to obtain information on the extent of individual variation as well as its possible consequences for virus neutralization . For this purpose , we made use of the modular organization of the flavivirus E protein and established a platform of immunoassays on the basis of recombinant E protein and its domain substructures ( DIII and DI+II ) ( Figure 1C ) . We provide evidence that the antibody response to YF vaccination is subject to strong individual variation with respect to the relative proportions of antibodies produced to the domains of E as well as to prM . Importantly , this vaccine appears to induce very little flavivirus cross-reactive antibodies that have been previously shown to dominate immune responses to other flavivirus infections [15] , [26] , [27] , [28] . Antibody depletion experiments strongly suggest that a substantial proportion of the neutralizing antibody response is directed to complex antigenic sites that are not represented by the isolated E protein , but rather are dependent on oligomeric interactions between these proteins at the virion surface .
In this study , we analyzed serum samples from volunteers who had been vaccinated with the live YF vaccine in Switzerland and Germany between 0 . 5 and 42 years prior to sample collection . Since another flavivirus ( TBE virus ) is endemic in both countries and many people have been vaccinated against this antigenically related virus , we made sure that none of YF vaccinees enrolled in the study had a history of TBE vaccination by interrogation and - as an additional control – by TBE neutralization tests ( NT ) . There was only one serum with a positive TBE NT titer for which we were not able to identify his/her TBE vaccination history and which was excluded from our analyses . The final cohort consisted of 51 individuals which tested positive in YF-NT ( titer ≥20 ) and negative in TBE-NT ( titer <10 ) and had no history of TBE vaccination . The basic characteristics of this cohort with respect to age at vaccination , age at sample collection and time elapsed since vaccination are summarized in Table 1 and displayed in Figure 2A–C . These parameters were not homogeneously distributed , reflecting the age-related travel behavior of YF vaccinees of central Europe . As shown in Figure 2D , a significant negative correlation was found between NT50 titers and the interval of time since vaccination . No statistically significant influence , however , was found with respect to the age at vaccination by comparing the <30 and >50 years old vaccinees that had received the vaccine within 3 years before sample collection ( Figure 2E ) . For dissecting the specificities of antibody populations present in the 51 post-vaccination sera and the contribution of such antibody subsets to virus neutralization , we produced the following set of C-terminally strep- or His-tagged recombinant YF proteins as shown in Figure 1C: 1 . a C-terminally truncated soluble monomeric form of YF E ( YF sE-strep ) ; 2 . domains DI+II of YF E ( YF DI+II-strep ) ; 3 . domain III of YF E as a fusion protein with thioredoxin ( YF DIII TR-His ) ; and 4 . a C-terminally truncated soluble form of YF prM ( YF prM-strep ) . To analyze the presence of flavivirus cross-reactive antibodies , we used sE proteins from TBE virus ( TBE sE-strep ) and WN virus ( WN sE ) . Evidence for proper folding was obtained in a number of control experiments , which are described in detail in the supporting Information ( Figure S1 , Text S1 and S2 ) . Using these recombinant proteins , we established ELISAs that were all highly sensitive and specific ( see Supplemental materials , Figure S2AB , Text S1 and S2 ) and – with the use of internal standards - allowed us to quantify antibodies directed to E and its substructures as well as to prM in relation to those measured in virion ELISAs and virus neutralization assays ( Figure S3 , Text S1 and S2 ) . The results obtained in these assays with the YF post-vaccination sera from the 51 individuals are shown in Figure 3 . All of the sera were positive in the virion ELISA and neutralization assay ( with complete neutralization at low serum dilutions in all instances ) , compared to 82% positives in the sE and DI+II ELISAs , respectively . Remarkably , only 14% low positives were observed in the ELISAs using YF DIII or sE of distantly related flaviviruses ( WN and TBE viruses ) , although the corresponding assays had sensitivities similar to those of the YF sE and DI+II ELISAs ( see Figure S2A for YF DIII ELISA and Figure S2BC for WN and TBE ELISAs ) . Antibodies to prM were somewhat intermediate , with 57% of the post-vaccination sera being positive . Statistical analyses showed highly significant positive correlations ( p<0 . 0001 ) between all of the units/titers measured in the different antibody assays ( Table 2 ) . Independent of the differences observed with respect to antibody titers ( at least in part due to the different time windows between vaccination and blood sampling; Figure 2D ) , our primary interest in this study was directed at individual-specific variations in the composition of antibody specificities and their proportions in post-vaccination sera as well as their possible influence on virus neutralization . To identify such variations , we plotted the reactivities in the virion ELISA ( Figure 4A ) against those of the sE , DI+II and prM ELISAs ( Figure 4B–D ) as well as NTs ( Figure 4E ) . DIII-reactivities were not included because they were mostly negative . This analysis revealed substantial deviations from the order of reactivities in the virion ELISA , providing evidence for different antibody compositions of individual sera that also affected their functional activities in virus neutralization . To quantify the extent of this variation , we calculated the ratios of antibody units obtained in sE , DI+II , and prM-ELISAs as well as NT titers relative to virion ELISA units for each individual serum sample . These ratios are shown in Figure 5 , which reveals a substantial degree of variation , especially in the case of the NT/virion ELISA ratios . In the subunit ELISAs , sera yielding negative results were omitted from these calculations and the real extent of variation is most likely higher than displayed in the figure . In other viral systems , the possibility of a selective decline of certain antibody specificities has been reported . Since the time between vaccination and sample collection varied substantially between individuals and differences in the decline of antibody subsets cannot be excluded a priori , we assessed the correlation between the ratios shown in Figure 5 and the time elapsed since vaccination . The Pearson correlation coefficients between these parameters , however , were 0 . 07 to −0 . 17 ( p values≥0 . 3 ) , showing that there was no significant change in antibody composition of post-vaccination sera over time . To obtain information on the contribution of different antibody populations to virus neutralization in individual sera , we conducted depletion analyses by removing distinct antibody subsets with the recombinant YF antigens sE , DI+II , DIII , and prM bound to magnetic beads . The complete removal of antibodies was controlled by performing an ELISA with the antigen used for depletion ( Figure 6 , left panels ) , and then the residual reactivity of the depleted sera was analyzed in the virion ELISA ( Figure 6 , middle panels ) and NT ( Figure 6 , right panels ) compared to sera mock-depleted with magnetic beads only as the 100% control . A set of 8 serum samples was selected for these analyses , which all had relatively high NT titers and differed in their ratios of reactivities with the recombinant antigens . As an example , the original data of the depletion analyses for vaccinee 2 are provided in Figure S4 which shows that the curves before and after depletion were almost parallel . The characteristics of all 8 vaccinees ( marked with asterisks in Figure 4 ) are summarized in Table 3 and the results of depletion analyses are presented in Figure 6 . Depletion with DIII ( Figure 6 , panels C ) did not result in any significant reduction of the virion ELISA- and NT-reactivities , consistent with the low or negative titers measured in DIII ELISA ( compare Figure 3 ) . In contrast , substantial proportions of antibodies were removed by sE and DI+II from many ( but not all ) serum samples , ranging from 25 to 59% of the virion ELISA reactivity and 0 to 79% of the NT-activity ( Figure 6 , panels A and B ) . In some instances , there was a good correlation between the depletion measured in the virion ELISA and NT , and in other cases substantial deviations were observed , which are specifically addressed below . The depletion assays also confirmed the induction of prM-specific antibodies by YF vaccination ( ranging from 12 to 37% of the total virion ELISA reactivity ) but their contribution to virus neutralization ( ranging from 0 to 26% ) was not statistically significant ( Figure 6D , right panel; p>0 . 05 ) . Overall , this analysis confirmed a high degree of individual variation in the composition of post-vaccination sera with respect to virion-specific and neutralizing antibodies . Because of their distinctive patterns , the results obtained with the sera of vaccinees 1 and 33 on the one hand and vaccinees 5 and 7 on the other are especially noteworthy . In the case of serum from vaccine 33 , almost half of the virion reactivity was removed by depletion with sE ( as well as DI+II ) ; however , in contrast to other sera this did not result in any significant reduction in neutralizing activity . Since DIII-specific and prM-specific antibodies also did not contribute to neutralization in this case , we conclude that antibodies to subunit-overlapping ( i . e . , dimer-specific ) and/or herringbone-specific quaternary epitopes ( but not antibodies to the monomeric sE ) were primarily responsible for the neutralizing activity of this serum . The opposite was seen with serum from vaccinee 1 . Similar to serum from vaccinee 33 , approximately 50% of the virion reactivity was removed by sE and DI+II , but in contrast , the neutralization effect was substantial and accounted for more than 75% of the total neutralizing activity ( Figure 6A , right panel ) . The similarity and excellent correlation of reactivities observed in sE and DI+II ELISAs ( Figure 4 and Table 2 ) was also confirmed when sE- and DI+II-depleted sera were analyzed in the virion ELISA ( Figure 6A and B , middle panels ) . However , a significant discrepancy was observed for NT of sera from vaccinees 5 and 7 . In both cases , approximately 65% of the total neutralizing activity was depleted by sE , whereas DI+II had no measurable effect . Since there was no evidence for DIII antibodies in these sera ( Figure 6C ) , it is likely that antibodies to epitopes at the junction between DI and DIII ( present in sE but not in DI+II ) were responsible for these results . From these data , we conclude that individual vaccinees not only differ dramatically with respect to the composition of their antibody subsets directed to different parts of viral surface proteins , but also with respect to the contribution of such subsets to virus neutralization .
The primary goal of this study was to gain insight into individual variations in fine specificities of the antibodies and their possible impact on virus neutralization after YF vaccination . This was accomplished by quantifying subsets of antibodies directed to distinct domains of the viral envelope protein E and determining their contribution to virus neutralization . Our data provide evidence for extensive differences in the specificities and relative proportions of antibody populations induced by YF vaccination in different individuals . This conclusion is primarily based on the observation that the ratios of reactivities in ELISAs with the monomeric E , DI+II and prM relative to the virion ELISA reactivities varied substantially ( compare Figure 5 ) . Substantial variation was found in the ratio between virion ELISA reactivities and neutralization titers , suggesting a strong influence of antibody subset composition on the functional activity of individual sera . Further confirmation of the observed heterogeneities was obtained by the quantitative analysis of antibodies in sera depleted with recombinant antigens . Specifically , depletion with the monomeric sE resulted in a strongly diverging pattern of neutralizing activity removed ( ranging from 0 to 79% ) which in several instances did not match the reactivity pattern of the virion ELISA ( compare the panels in Figure 6 A ) , and similar differences were also observed in depletions with DI+II . Our data not only demonstrate extensive differences in the fine specificities of antibody subsets in post-vaccination sera , but also that these heterogeneities can strongly affect functional activities such as virus neutralization . It is likely that these findings are related to cooperative and/or competitive interactions between antibody populations directed to the same target antigen but displaying different fine specificities , avidities and concentrations . Such effects have been described in studies with monoclonal antibodies [29] , [30] , [31] and were proposed for explaining variations in the efficiency of polyclonal sera to neutralize influenza virus [32] and HIV [31] . In the case of YF and other flaviviruses , the phenomenon of virus breathing has to be considered as an additional layer of complexity , because it allows binding of antibodies to epitopes that are seemingly inaccessible in a static model of virion structure but become exposed through dynamic motions of the virion shell [33] , [34] , [35] , [36] , [37] , [38] . Antibodies to such sites can thus contribute to virus neutralization and increase the potential individual variation . Studies with dengue viruses as well as other flaviviruses have shown that genotype- or strain-variations affecting individual epitopes and/or the degree of virus maturation can have a profound effect on the results of neutralization assays [14] , [39] , [40] , [41] , [42] . Since the infecting strain is rarely known in studies with post-infection sera , neutralization results generated with a specific laboratory strain may be biased by such phenomena . In this context , our study had the advantage of using antigens in the ELISAs as well as the virus in neutralization assays that were identical to the 17D virus strain used for immunization . The variations observed can therefore be considered true variations in fine specificities of antibodies for the same immunogen in different individuals , which are not influenced by possible strain-specific effects . One important finding of our study is that a varying proportion of neutralizing antibodies induced by YF vaccination in individuals is apparently directed to complex epitopes found at the virion surface only , but not on an isolated form of the monomeric E protein . In all instances , only part of the total neutralizing activity could be removed by sE depletion , suggesting that the residual activity is due to antibodies specific for E dimer-dependent or even more complex dimer-overlapping epitopes that are generated by the herringbone-like quaternary arrangement of E dimers at the virion surface ( Figure 1B ) . Indeed , an epitope comprising residue 71 in DII and 155 in DI of the opposing E monomer was identified as a dimer-specific epitope by the use of human monoclonal antibody fragments derived by repertoire cloning from YF patients [43] . E-dimer- and herringbone-dependent epitopes have also been characterized for other flaviviruses using monoclonal antibodies [44] , [45] , [46] , [47] and - in the case of post-infection dengue sera - the majority of antibodies seemed to be directed against such complex epitopes [48] . In some YF post-vaccination sera , substantially different patterns of neutralizing activity were also observed after depletion with DI+II compared to sE ( Figure 6AB , right panels ) . Since antibodies to DIII did not contribute to the depletion results in these cases , it can be assumed that the discrepancies observed were due to antibodies directed to epitopes at the junction between DI and DIII . In contrast to the high frequencies and titers of antibodies against DI+II , only a very low proportion of the post-vaccination sera had antibodies against DIII ( only 14% were positive ) and the titers of positive sera were low compared to those against sE and DI+II , although the assays had comparable sensitivities ( Figure S2A ) . Furthermore , depletion analyses did not reveal any contribution of DIII antibodies to virus neutralization in a set of 8 selected serum samples ( Figure 6 ) . DIII-specific antibodies had been shown to dominate humoral immune responses in the mouse [27] , [49] and to exhibit a higher specific neutralizing activity than antibodies to other sites in E [17] . As revealed in several recent studies , however , DIII-specific antibodies appear to play only a minor role in human antibody responses to flaviviruses ( reviewed in [8] , [50] ) . Our results are consistent with data on human immune responses to dengue [26] , [51] , [52] as well as West Nile [27] , [45] virus infections , which demonstrate that DIII-responses formed only a very small proportion of the total antibody response in these species and that most of the neutralizing activity was due to antibodies directed to other sites in E [51] , [52] . Similar to the situation with DIII-specific antibodies , we also found only very low frequencies and titers of broadly flavivirus cross-reactive antibodies . Such antibodies were shown to recognize epitopes around the highly conserved FP loop ( Figure 1 ) and usually do not contribute significantly to virus neutralization [18] , [19] , [20] . Our data are consistent with previous reports on highly type-specific antibody responses after primary YF vaccination measured in hemagglutination inhibition assays and also in ELISA [53] , [54] but differ from the antibody response to dengue virus infections . In the latter case , FP-specific antibodies were shown to make up a large proportion of the total antibody response [26] , [28] . The reasons for these discrepancies are presently unclear but may be related to differences in the formation and/or structure of partially immature particles during virus replication in dengue-infected individuals and YF17D vaccinees . The degree of virus maturation ( i . e . cleavage of prM ) has been shown to affect FP exposure by the demonstration of enhanced accessibility to antibodies [55] and thus can potentially have a profound effect on post-infection antibody profiles . In contrast to the low DIII- and FP-specific responses , a substantial proportion ( 57% ) of the YF vaccinees in our study had antibodies to prM . Studies on prM responses after flavivirus infections are still limited but Western blot analyses have detected prM antibodies in dengue and JE post-infection sera [56] , and a surprisingly high proportion of human monoclonal antibodies generated from dengue infected individuals were found to be prM-specific and able to promote antibody-dependent enhancement of infection [14] , [15] , [57] . In our depletion analysis , we found a moderate ( but not statistically significant ) contribution of prM antibodies to virus neutralization ( Figure 6D , right panel ) , which is consistent with the poor neutralizing activity of dengue virus prM antibodies [14] . It is likely that these neutralization data are strongly influenced by the maturation state of the virus used in the assays [14] , [42] and therefore further studies using artificial viruses with defined prM-content will be necessary to compare the extent of prM-responses after different flavivirus infections as well as assess their role in virus neutralization , infection enhancement and protection . In conclusion , our study demonstrates a high degree of individual variability in the fine specificities of antibody responses to YF vaccination which affects virus neutralization . It is currently unclear , whether and to what extent such variations can impact the protective efficacy of YF vaccination and are related to rare but existing vaccination failures [2] . Our data are consistent with the assumption that the phenomenon of antibody immunodominance is strongly influenced by individual factors that control the selection of high-affinity B cell clones for antibody production , in addition to possible structural factors intrinsic to the antigen . Such individual variations have to be considered in structure-based vaccine designs that attempt to target the immune response to the most potent protective antigenic sites [58] .
Serum samples were collected from individuals 0 . 5 to 42 years after YF vaccination at the Institute of Virology , University of Duisburg-Essen , Germany , the Institute of Medical Virology , University Clinic of Frankfurt , Germany , and the Division of Infectious Diseases and Hospital Epidemiology , University Hospital of Zuerich , Switzerland . In total , 51 serum samples with no records of other flavivirus infections or vaccinations were sent to the Department of Virology , Medical University of Vienna , Austria for diagnostic analyses and were used anonymously in this study . The studies were approved by the ethics committees of the University of Duisburg-Essen , Germany , the University Clinic of Frankfurt , Germany , the University Hospital of Zuerich , Switzerland , and the Medical University of Vienna , Austria . A suckling mouse brain suspension of the YF virus 17D-204 vaccine strain was used as an inoculum for propagating the virus in Vero cells using Dulbecco's Modified Eagle Medium ( DMEM ) supplemented with 0 . 1% bovine serum albumin ( BSA ) . Cell supernatants were harvested 72 h post-infection , clarified by centrifugation at 10 , 000 g ( 30 min; 4°C ) , concentrated by ultracentrifugation at 150 , 000 g ( 1 h; 4°C ) and purified by rate zonal sucrose gradient ultracentrifugation as described for TBE virus [59] . The virus peak was identified by hemagglutination assays using goose red blood cells at pH 6 . 4 as described by Clarke and Casals [60] . The sE ( aa 1–397 ) , DI-II ( aa 1–294 ) and prM ( aa 1–129 ) proteins of YF virus 17D ( GenBank accession number X03700 ) , as well as sE ( aa 1–400 ) of TBE virus strain Neudörfl ( GenBank accession number U27495 ) were produced in Drosophila Schneider 2 ( S2 ) cells using the pT389 vector ( provided by Thomas Krey and Felix Rey , Institut Pasteur , France ) , which encodes the Drosophila export signal sequence BiP , an enterokinase cleavage site and a double strep-tag . All of these proteins were produced in soluble form by C-terminal truncations that removed their membrane anchors . In the case of the YF prM protein , the furin cleavage site was mutated to obtain unprocessed prM , as previously described [61] . WN sE ( aa 1–400 ) was produced in Drosophila Schneider 2 ( S2 ) cells using the pMTBip/V5-His vector ( Life Technologies ) , with a stop-codon introduced after the sE-sequence to yield the expression of the protein without a His-tag as previously described [49] . Transfection of S2 cells was carried out with CaCl2 according to the manufacturer's protocol ( Invitrogen ) and blasticidin resistance was used for the selection of stably transfected cells . Recombinant protein expression was induced by CuSO4 , and the supernatants were harvested 7 d post-induction . Strep-tagged recombinant proteins were purified using Strep-Tactin columns ( IBA ) , - according to the manufacturer's protocol , and the untagged WN sE protein was purified by immunochromatography using the flavivirus cross-reactive antibody 4G2 ( ATCC ) as previously described [49] . DIII of YF virus 17D ( aa 295–391 in E ) was expressed in E . coli BL-21 cells as a fusion protein with thioredoxin and a C-terminal His tag using the pET 32a Xa/LIC vector ( Novagen ) . In this vector , the internal His-tag was removed from the expression cassette by site-directed mutagenesis ( Life Technologies ) , leaving only the C-terminal His-tag [49] . The recombinant fusion protein was purified from clarified bacterial cell lysates by Ni2+ affinity chromatography ( GE Healthcare Life Sciences ) following the manufacturer's protocol . Schematics of the YF recombinant proteins are shown in Figure 1 . ELISAs for analyzing YF virus-specific antibodies were performed essentially as previously described [18] . Briefly , microtiter plates were coated overnight at 4°C with pre-determined optimized dilutions of purified recombinant antigens or virus in carbonate buffer ( pH 9 . 6 ) . Plates were blocked with phosphate-buffered saline ( PBS ) pH 7 . 4 containing 2% lamb serum for 20 min at 37°C . Threefold serial dilutions ( starting at 1∶100 ) of human sera were then added for 1 h at 37°C . Biotin-labeled goat anti-human IgG ( Pierce ) together with Streptavidin–Peroxidase ( Sigma ) was used for detection . Sera were analyzed in at least three independent experiments and specific IgG was quantified using human YF post-vaccination sera - arbitrarily defined to contain 1000 IgG Units – as internal standards ( see Supporting information ) . Four dilutions of each sample were analyzed and data points within the linear range of the standard curves were used for determining IgG units . The cut-off was determined in each test by including flavivirus-negative human sera and set at the mean plus three standard deviations . Neutralization assays were carried out in baby hamster kidney cells ( ATCC BHK-21 ) using two-fold serial dilutions of sera ( in triplicates ) - starting at a dilution of 1∶20 – and the YF vaccine virus propagated in suckling mouse brain . The serum samples were incubated with 20–40 TCID50 virus for 1 h at 37°C before the addition of cells , which were then incubated for three additional days . After removal of cell supernatants , the cells were fixed with 4% paraformaldehyde for 20 min at room temperature , and treated with a Tris-buffer ( 50 mM Tris , 150 mM NaCl , pH 7 . 6 ) containing 3% nonfat dry milk , 0 . 5% Triton X-100 , and 0 . 05% Tween 20 for 30 min at 37°C . The YF virus-specific monoclonal antibody 2D12 ( ATCC ) was then added and the fixed cells were incubated for 1 . 5 h at 37°C . Bound antibodies were detected with alkaline phosphatase-labeled anti-mouse IgG ( Sigma ) and SigmaFast pNNp ( Sigma ) as a substrate . The enzymatic reaction was stopped with 1 . 5 N NaOH after 30 min and the absorbance was measured at 405 nm . Titers were determined after curve fitting with a four-parameter logistic regression ( GraphPad Prism 5; GraphPad Software Inc . ) using 50% of the absorbance in the absence of antibody as a cut-off ( NT50 ) . Titers ≥20 were considered positive . Antibody depletion was essentially performed as previously described [49] using the “Dynabeads His-Tag Isolation&Pulldown kit” ( Life Technologies ) for binding proteins containing a His-tag and Strep-Tactin magnetic beads ( Qiagen ) for binding proteins containing a strep-tag . Thirty micrograms of recombinant proteins were incubated with 100 µl paramagnetic beads and divided into three aliquots . After pelleting by magnetic force , the beads were resuspended in a buffer according to the manufacturer's instructions and incubated for 1 h at 37°C with a 1∶5 dilution of serum . The beads were pelleted by magnetic force and the depleted serum was collected . To achieve quantitative depletion , this procedure was performed three times . Absence of non-specific binding of the antibodies to the beads was controlled by incubating sera with unloaded beads . Statistical analyses were conducted using GraphPad Prism 5 ( GraphPad Software Inc . ) . Logarithmic transformations of data were performed to obtain approximate normal distribution of IgG arbitrary units and NT titers . Two-tailed t-tests were used to compare the transformed data and Pearson correlation tests were used to determine correlation coefficients . P values<0 . 05 were considered statistically significant . Supporting information includes four figures ( Figures S1 , S2 , S3 , S4 ) and a description of these figures ( Text S1 and S2 ) . | The live-attenuated yellow fever vaccine has been administered to more than 600 million people worldwide and is considered to be one of the most successful viral vaccines ever produced . Following injection , the apathogenic vaccine virus replicates in the vaccinee and induces antibodies that mediate virus neutralization and subsequent protection from disease . In principle , many different antibodies are induced by viral antigens , but it is becoming increasingly clear that only a subset of them is capable of inactivating the virus , and some antibody populations appear to dominate the immune response . However , to date there has been very little information on individual-specific variations of immunodominance and how such variations can affect the functionality of antibody responses . In our study , we addressed these issues and analyzed the fine specificities of antibodies induced by YF vaccination as well as the contribution of different antibody subsets to virus neutralization in 51 vaccinees . We demonstrate an extensive degree of individual variation with respect to immunodominance of antibody populations and their contribution to virus neutralization . Such variations can have an impact on vaccine-mediated protection , and thus insight into this phenomenon can provide leads for novel strategies in modern vaccine design . | [
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] | 2013 | Dissection of Antibody Specificities Induced by Yellow Fever Vaccination |
Failure to detect a disease agent or vector where it actually occurs constitutes a serious drawback in epidemiology . In the pervasive situation where no sampling technique is perfect , the explicit analytical treatment of detection failure becomes a key step in the estimation of epidemiological parameters . We illustrate this approach with a study of Attalea palm tree infestation by Rhodnius spp . ( Triatominae ) , the most important vectors of Chagas disease ( CD ) in northern South America . The probability of detecting triatomines in infested palms is estimated by repeatedly sampling each palm . This knowledge is used to derive an unbiased estimate of the biologically relevant probability of palm infestation . We combine maximum-likelihood analysis and information-theoretic model selection to test the relationships between environmental covariates and infestation of 298 Amazonian palm trees over three spatial scales: region within Amazonia , landscape , and individual palm . Palm infestation estimates are high ( 40–60% ) across regions , and well above the observed infestation rate ( 24% ) . Detection probability is higher ( ∼0 . 55 on average ) in the richest-soil region than elsewhere ( ∼0 . 08 ) . Infestation estimates are similar in forest and rural areas , but lower in urban landscapes . Finally , individual palm covariates ( accumulated organic matter and stem height ) explain most of infestation rate variation . Individual palm attributes appear as key drivers of infestation , suggesting that CD surveillance must incorporate local-scale knowledge and that peridomestic palm tree management might help lower transmission risk . Vector populations are probably denser in rich-soil sub-regions , where CD prevalence tends to be higher; this suggests a target for research on broad-scale risk mapping . Landscape-scale effects indicate that palm triatomine populations can endure deforestation in rural areas , but become rarer in heavily disturbed urban settings . Our methodological approach has wide application in infectious disease research; by improving eco-epidemiological parameter estimation , it can also significantly strengthen vector surveillance-control strategies .
Chagas disease is caused by Trypanosoma cruzi ( Kinetoplastida: Trypanosomatidae ) , a parasitic protozoan transmitted through the feces of infected blood-sucking hemipterans ( Reduviidae: Triatominae ) [1] , [2] . Human infection is endemic throughout Latin America , where it causes loses of more than 650 , 000 disability-adjusted life years annually [3] . From 1990 , burden figures have declined by about 80% [3] , [4] , reflecting the success of Chagas disease control programs over vast geographical areas [5] . However , the burden of Chagas disease in the Latin American-Caribbean region is still consistently larger than the combined burden of malaria , leprosy , the leishmaniases , lymphatic filariasis , onchocerciasis , schistosomiasis , viral hepatitides B and C , dengue , and the major intestinal nematode infections [6] , [7] . Because most transmission is mediated by household-infesting insect vectors , and because no effective treatment or vaccine are available for large-scale use , the elimination of domestic triatomines was defined as one major goal of control programs , together with systematic serological screening of blood donors [8] , [9] . The widespread occurrence of native triatomine species that reinvade insecticide-treated households is a major difficulty for the consolidation of Chagas disease control [9]–[12] . Except for a few key vector species ( e . g . , [13] ) , the ecological dynamics of reinfestation are still poorly understood , and it is expected that research on sylvatic triatomine populations will help confront the challenge of residual , low-intensity disease transmission mediated by sylvatic vectors . The situation in the Amazon , where enzootic T . cruzi transmission cycles involve a great diversity of vectors and reservoir hosts ( e . g . , [14] , [15] ) , suitably illustrates these concerns . Adventitious adult triatomines maintain continuous , low-intensity transmission in rural ( and some urban ) settings; as a result , human infection is hypoendemic in the region , with about 100 , 000 to 300 , 000 people chronically carrying T . cruzi [16] , [17] . Sylvatic triatomines are also involved in localized disease outbreaks related to oral T . cruzi transmission via contaminated foodstuffs [14] , [16] , and account for the relatively high infection prevalence ( 4–5% ) reported among extractivist forest workers such as piaçava palm fiber collectors [15] , [16] . The vast majority of these transmission events are mediated by triatomines of the genus Rhodnius , which are primarily associated with palm trees [18]–[20] . The widespread occurrence of palm tree-living Rhodnius populations in Amazonia , together with epidemiological evidence suggesting their active role in disease transmission , underscores the importance of obtaining reliable estimates of palm tree infestation rates by these vectors . Such estimates are currently unavailable , and this substantially hinders our understanding of Chagas disease transmission dynamics in the Amazon . Palms of the genus Attalea ( Arecoideae ) play a major role as breeding and foraging habitats of sylvatic Rhodnius populations in Amazonia and other Neotropical regions ( e . g . , [18]–[23] ) . The strong Attalea-Rhodnius association led to the proposal that the presence of Attalea palms can be used as an ‘ecological indicator’ of areas where enzootic T . cruzi transmission cycles probably occur [23] . Later studies showed that the probabilities of palm infestation by triatomines can differ among sites , landscapes , and palms with varying structural traits [20] , [21] . We moved beyond these preliminary proposals , based on limited datasets and crude analytical approaches , and asked under what sets of circumstances is the potential of palms to harbor bug colonies realized; in other words: are all Attalea equally likely to be occupied by Rhodnius bugs ? If not , what are the likely causes of variation ? In a region as vast as Amazonia , knowledge of the environmental determinants of palm infestation by triatomines may represent a key tool to optimize resource allocation for epidemiological surveillance . Should resources be aimed at intervention in one particular region , in one particular type of landscape , or on certain particular types of palms – regardless of the region and landscape where they are found ? Answers to these questions may prove crucial to enhance disease prevention programs [20] , [21] . The estimation of palm infestation by triatomines is limited by the inescapable reality of field sampling: the target organisms may be present at a site yet go undetected during the survey . There are two standard solutions to this pervasive problem . One is to develop improved sampling techniques that bring detection close to perfection . The other is to incorporate detection failure explicitly in the analyses; estimates of infestation can thus be derived that statistically compensate for false absences . Near-perfect sampling techniques are expensive and labor-intensive – clearly a problematic option for a vast study area . In this paper , we apply models developed by wildlife biologists to estimate site-occupancy probabilities when detection of the target organism is imperfect [24] , [25] . We define palm infestation as site ( i . e . , palm ) occupancy , the probability that a palm is occupied by at least one Rhodnius spp . Our approach leads to strong inferences on Attalea palm occupancy rates by Rhodnius spp . and allows for the comparison of models relating palm occupancy to environmental covariates at three different scales: region , landscape , and individual palm . We aimed at ( i ) describing palm infestation patterns and the way they vary at different spatial scales; ( ii ) identifying the most likely causes of such variation; and ( iii ) incorporating this information into predictive models of palm occupancy that can be useful in the context of disease risk mitigation . More generally , we illustrate a methodological approach that yields reliable estimates of eco-epidemiological parameters out of imperfect data .
Our sample of 298 Attalea palms spanned four regions ( totalling 19 localities ) in two countries ( Fig . 1 ) . The westernmost region was Napo , a white-water river system close to the Ecuadorian Andes . ( All model covariates are named in bold typeface on their first appearance in the Methods section . ) Moving to the east , we sampled three regions in the Brazilian Amazon: the lower right bank of the black-water Negro river , the left bank of the white-water Amazon river east of Manaus , and the forested part of the northern Branco river basin , an intermediate clear/white-water system . These survey sites spanned areas between ∼120×60 km ( Napo ) and ∼30×20 km ( Negro ) , and were located , respectively , within each of the following moist forest ecoregions [26]: Napo , Japurá/Solimões-Negro , Uatumã-Trombetas , and Guyanan Highlands/Piedmont . From field observations and available literature [27] , [28] , we ranked our survey regions in decreasing order of soil fertility as Napo , Amazon , Negro , and Branco . Thus , or sampling is representative of four ecologically distinct sub-regions influenced by the three main Amazonian hydrological systems – white- , black- , and clear-water . Within each region , we surveyed Attalea palms in three landscape classes: forest , rural , and urban . At each site , a sample of non-adjacent palms was selected haphazardly for the survey . Urban palms where sampled in plots within the street framework of cities , towns , or villages . Rural palms were surrounded by farming land , orchards , or pasture on previously forested sites . Forest palms were located in forested sites , most often medium to large fragments of mature secondary forest . These three landscape classes were easily distinguished in the field , and palms sampled in each of them were at least 50–100 m from the nearest patch of landscape in another class . Our sample included palms of three species ( A . maripa , A . speciosa , and A . butyracea ) ; their known distribution is shown in Fig . 1 . All three species are large , solitary palms with large inflorescences/infructescences and in which old leaf bases remain adhered to the stem after leaf abscission . Palm identification followed Henderson et al . [29] . Individual palm trees vary considerably with regard to the amounts of epiphytic vegetation and dead organic material ( dead fronds , husks , flowers , fruits , fibers , and dead epiphytes ) that accumulate on their crowns and stems . We used a pre-established score system [21] to measure the approximate amount of live epiphytic plants and decomposing organic material present on each palm . These epiphyte and organic matter values were first recorded in the field and , for about 85% of palms , cross-checked by another team member by examination of individual palm photographs; we then derived a mean ‘organic score’ value for each palm – ranging from 0 to 4 points , with higher values denoting ‘dirtier’ palms . We measured palm stem height as the linear distance between the ground and the lowest base of a green leaf . Finally , we preliminarily assessed the effects of slash-and-burn farming practices , which are commonplace across the Brazilian Amazon , on palm infestation . We defined two coarse categories to distinguish palms standing on plots that had a fire less than about two years before our survey from palms on plots that were not burnt over a similar period . Fire information was obtained from landowners and complemented by recording fire scars on palms and nearby trees and the presence and size of fire-adapted pioneer trees in each survey plot . We sampled each individual palm with a combination of mouse-baited adhesive traps [30] , [31] and manual bug searches [32] ( Fig . 2 ) . Traps were set in the afternoon and checked the following morning , after approximately 15 hours of operation . We placed traps among organic debris or epiphytes in the palm crown , around the upper end of the stem , or directly in the angle between palm fronds . Most palms ( 234 , or 78 . 5% ) were sampled with four traps , with a minimum of one trap in eight palms and a maximum of nine in one palm . The total trapping effort was 1 , 098 trap-nights . Manual searches were performed on the organic matter of the palm crown after trap removal . We searched either directly in the palm crown or by collecting organic material in a 50-liter plastic bag and later checking bag contents on a white canvas . Both sampling techniques were used in 255 palms ( 85 . 6% ) , only manual searches in nine , and only traps in 34 . Each individual trap or manual search was treated as a sampling event yielding a binary result of either “1” for bug detection or “0” for no bugs detected . Thus , a typical palm tree was sampled five times – four traps and one manual search . Each detection history is represented by a row of “1”s and “0”s . For instance , “1100-----0” represents a palm with two positive traps , two negative traps , and a negative manual search ( the last “0” ) ; the five dashes indicate that only four traps , up to a maximum of nine , were operated in this particular palm . The raw dataset is provided as Supporting Information ( Dataset S1 ) . We combine two different but interconnected procedures: parameter estimation and model selection . All our models have a biological process component that expresses the probability that a palm is occupied by bugs ( ψ ) , and a sampling process component that expresses the probability that we detect bugs in a palm where they actually occur ( p ) . This hierarchical approach makes it possible to estimate the probability that animals are present in places where they are not seen , accommodating an explicit treatment of imperfect detection [24] , [25] , [33] , [34] . We fit models using the software PRESENCE [35] , which provides maximum-likelihood estimates of parameters and their standard errors ( SE ) in user-defined models that can contain covariates of occupancy and/or detection . Before performing the analyses , we built a set of 23 models ( below ) each expressing an a priori hypothesis of palm occupancy and bug detection . Model selection followed the Akaike Information Criterion ( AIC ) , which combines information and maximum-likelihood theories to find models with the best compromise between model fit and complexity [36] . We use model selection as a tool for hypothesis testing: each model represents one hypothesis , and hypotheses represented by models with lower AIC values are better supported by the data . We treat palms as independent sites with regard to occupancy by bugs of the genus Rhodnius; to ensure independence , several sites were surveyed within each locality , and neighboring palms were rarely sampled . Live-bait traps and manual searches are treated as replicate sampling events with an average probability of detecting bugs , conditioned on palm occupancy . Field observations and exploratory analyses motivated us to compare the performance of manual searches and traps in detecting bugs; furthermore , we observed relatively high numbers of triatomines per palm in the Napo region , suggesting that bug presence might be easier to detect in Napo palms than elsewhere . Accordingly , we modeled detection always as an additive logistic function of two binary covariates: sampling technique and region , with the latter specifying only whether sampling took place in Napo or elsewhere . Since we aimed at understanding which spatial scale contributes most to explaining observed variation in palm occupancy , we built models that include different palm , landscape , and regional covariates of occupancy . Our a priori set of 23 models includes six regional models , four landscape models , six local ( palm ) models , six models with different combinations of covariates from different scales , and one null model without covariates of occupancy . Some of the combined models include interactions between covariates at different scales . In particular , considering the more fertile soils of the Napo region , we model an interaction between Napo and the rural landscape , as well as between Napo and the forest landscape . These models represent hypotheses stating that the relationship between landscape and occupancy differs between Napo and the remaining regions . For ease of presentation , we will report modeling results grouped by spatial scale , concluding with a comparison of the best models across scales .
We first estimated detection probability with a simple model that has no covariates of palm occupancy . We designate this model with the notation ‘ψ ( . ) , p ( manual+Napo ) ’ , where the ‘ . ’ denotes no covariates on the occupancy part of the model and ‘manual’ and ‘Napo’ designate the technique and regional covariates of detection , respectively . Under this null model of no predictable variation in palm occupancy rates , the probability of detecting bugs where they actually occur ranges from 0 . 05 ( SE = 0 . 01 ) with traps in the Brazilian Amazon to 0 . 82 ( SE = 0 . 05 ) with manual searches in Napo , Ecuador . Both covariates increase detection probabilities; the Napo effect estimate is 3 . 01 ( SE = 0 . 3 ) . Had we not taken detection failure into account , we would report a proportion of 0 . 24 palms occupied by bugs – the number of palms where we detected bugs divided by the total number of palms sampled , which when expressed as a percentage is the classical ‘infestation index’ [9] ( Table 1 ) . When we consider that the probability of detection may be less than one , our null model estimate of occupancy is 0 . 59 ( CI95% 0 . 42–0 . 75 ) . We found little evidence of regional variation in occupancy , as shown by the small differences in AIC values between the null model and models with regional covariates ( Table 2 ) . When we constrain models to only one regional covariate , the region that contributes most to explaining the data is Napo . All the models that estimate occupancy in the Napo region separately from other regions set that value at 0 . 68 ( CI95% 0 . 50–0 . 83 ) , almost twice the average occupancy estimated for Brazilian regions ( 0 . 37; CI95% 0 . 22–0 . 54 ) . The second model in Table 2 includes regional covariates for the two hypothetical extremes of occupancy , Napo and Branco . Despite our prior expectation , based on published soil richness information , this model does not explain the data any better than the single-covariate Napo model . Thus , even if the Napo region appears to have higher palm occupancy rates , the data do not provide strong evidence of variation in occupancy across regions , and in particular among regions within Brazil . Estimated palm occupancy is highest in rural and lowest in urban settings , without striking differences between estimates for different landscapes ( Table 3 ) . The models with interaction terms ( Napo*forest and Napo*rural ) do not explain the data particularly better than models without those terms . Among models with only one landscape covariate , the best model estimates a negative effect of urban landscapes on occupancy and lumps rural and forest areas into one landscape class . Estimated palm infestation rates are 0 . 33 ( CI95% 0 . 15–0 . 57 ) for urban and 0 . 63 ( CI95% 0 . 45–0 . 78 ) for forest/rural landscapes . Despite these broad patterns , there is no strong evidence of landscape-level effects: AIC values vary within less than 10 units for all models , and there is overlap of 95% CIs for estimates of occupancy in different landscapes . All the models that include the ‘organic score’ palm attribute perform substantially better than the null model ( Table 4 ) . We modeled the effects of organic score , height , and recent fire separately and in two additive combinations ( all effects and the combination of height and organic score ) after preliminary analyses suggested that recent fire was the least important of the three covariates . AIC variation across models indicates that height and organic score are indeed most useful to explain the data . A model with all covariates does not rank any better than the model with height and organic score alone . When the three covariates are modeled separately , organic score ranks better than height , which , in turn , ranks better than fire . The strength of these relationships between infestation and individual palm traits is at odds with expectations under random bug migration among palms within a given site , indicating that the assumption of palm independence with regard to occupancy holds . Tables 2 and 3 show how regional and landscape models fall within less than 10 AIC units of the null model , suggesting that they do not improve our ability to explain the data when compared with a model lacking occupancy covariates . Conversely , Tables 4 and 5 show strong support for local-scale models that use palm attributes as covariates of occupancy . Models that include regional and/or landscape covariates jointly with palm attributes also perform substantially better than the null model . However , these multi-scale models do not explain the data any better than a simple local model of occupancy as a function of organic score and palm height – the first model of Tables 4 and 5 , where both effects are positive and significantly larger than zero ( 1 . 41 , SE = 0 . 41; and 0 . 43 , SE = 0 . 13 , respectively ) . Figure 3 shows occupancy estimates according to this best-performing model . Short and ‘clean’ Attalea palms have the lowest probability of infestation , whereas tall palms ( ∼10 m ) with plenty of accumulated organic debris are predicted to be almost certainly infested . According to these point estimates of occupancy by Rhodnius spp . , a ‘clean’ palm would have , at most , a 0 . 3 probability of infestation; this probability would rise to over 0 . 5 in a palm with an organic score close to 4 . Parameter estimates for the best-ranking models are provided as Supporting Information ( Table S1 ) .
This paper highlights the importance of accounting for imperfect detection in the study of vector ecology; in addition , our assessment of the explanatory power of regional , landscape , and local environmental covariates aimed at identifying those that hold more promise for improving vector surveillance and control strategies [49] , [50] . Our results are relatively discouraging with regard to broad-scale risk mapping; the use of soil richness datasets seems attractive , but prior validation studies are necessary . On the other hand , local-scale covariates are overwhelmingly more useful than regional or landscape features in explaining variations in palm occupancy . This suggests that the assessment of potential disease risk situations will require detailed knowledge of local , site-specific conditions . The participation of decentralized vector control teams linked to local malaria control services [16] , [37] may therefore be key to the advancement of Chagas disease prevention in Amazonia . Our results also suggest that peridomestic palm tree management could lower palm infestation rates and , therefore , might help reduce transmission risk [21] . Model-predicted effects of removing organic debris from palms range from halving to reducing palm infestation probability by more than 70% ( Fig . 3 ) . This result indicates correlation , not necessarily causation , but provides a clear-cut working hypothesis that can be put to test in the context of environmental management research . Imperfect detection of the target organism is a real and pervasive problem both in wildlife management and in epidemiology . Wildlife biologists often use sampling strategies ( e . g . , [51] ) and analytical tools [52] , [53] that yield unbiased parameter estimates under imperfect detection . Latent class analysis and capture-recapture approaches are used to formally account for detection failure in epidemiological studies; they allow estimation of prevalence or incidence rates when a diagnostic gold standard is unavailable or undercount of disease events is likely ( e . g . , [54]–[58] ) . Even if the contribution of these and similar approaches is growing , we still find that many epidemiological and most vector ecology studies simply overlook the problem of imperfect detection . Here we show how replicate sampling of vector ecotopes with a practical , yet imperfect field methodology can be used to ( i ) derive unbiased statistical estimates of eco-epidemiological parameters and ( ii ) test hypotheses about the effects of environmental covariates on such parameters . As long as model assumptions ( e . g . , population closure or independent detection histories ) hold reasonably and study design is adequate , this strategy can help enhance research on vectors , pathogens , and hosts ( see Box 1 ) . For instance , replicate malaria blood smears could be used to measure between-slide variation in Plasmodium spp . detection . The same reasoning applies to vector surveillance schemes with replicate sampling , e . g . , of Aedes aegypti [59] , or when pathogen diagnosis involves serial testing , e . g . , for intestinal parasites [60] . The generality of our methodological proposal is particularly compelling in the case of vector-borne zoonotic diseases , which are those more likely to become emerging public health threats [61] , but the formal treatment of imperfect detection can significantly strengthen other areas of eco-epidemiological research .
A . Paucar , C . Carpio , R . Perry , and technicians of Fiocruz and the Ecuadorian and Brazilian vector control services participated in fieldwork . We thank T . V . Barrett ( INPA , Brazil ) , C . J . Schofield ( LSHTM and ECLAT , UK ) , F . Noireau ( IRD , Bolivia ) , and S . L . B . Luz ( ILMD-Fiocruz , Brazil ) for helpful discussion and suggestions . The Brazilian Instituto Nacional de Colonização e Reforma Agrária provided logistic support for several field trips . This paper is contribution number 9 of the Research Program on Infectious Disease Ecology in the Amazon ( RP-IDEA ) of the Instituto Leônidas e Maria Deane . | Blood-sucking bugs of the genus Rhodnius are major vectors of Chagas disease . Control and surveillance of Chagas disease transmission critically depend on ascertaining whether households and nearby ecotopes ( such as palm trees ) are infested by these vectors . However , no bug detection technique works perfectly . Because more sensitive methods are more costly , vector searches face a trade-off between technical prowess and sample size . We compromise by using relatively inexpensive sampling techniques that can be applied multiple times to a large number of palms . With these replicated results , we estimate the probability of failing to detect bugs in a palm that is actually infested . We incorporate this information into our analyses to derive an unbiased estimate of palm infestation , and find it to be about 50% – twice the observed proportion of infested palms . We are then able to model the effects of regional , landscape , and local environmental variables on palm infestation . Individual palm attributes contribute overwhelmingly more than landscape or regional covariates to explaining infestation , suggesting that palm tree management can help mitigate risk locally . Our results illustrate how explicitly accounting for vector , pathogen , or host detection failures can substantially improve epidemiological parameter estimation when perfect detection techniques are unavailable . | [
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] | 2010 | Modeling Disease Vector Occurrence when Detection Is Imperfect: Infestation of Amazonian Palm Trees by Triatomine Bugs at Three Spatial Scales |
Ubiquitin-dependent processes control much of cellular physiology . We show that expression of a highly active , Epstein-Barr virus-derived deubiquitylating enzyme ( EBV-DUB ) blocks proteasomal degradation of cytosolic and ER-derived proteins by preemptive removal of ubiquitin from proteasome substrates , a treatment less toxic than the use of proteasome inhibitors . Recognition of misfolded proteins in the ER lumen , their dislocation to the cytosol , and degradation are usually tightly coupled but can be uncoupled by the EBV-DUB: a misfolded glycoprotein that originates in the ER accumulates in association with cytosolic chaperones as a deglycosylated intermediate . Our data underscore the necessity of a DUB activity for completion of the dislocation reaction and provide a new means of inhibition of proteasomal proteolysis with reduced cytotoxicity .
Protein quality control and ubiquitin-dependent degradation are essential for cellular homeostasis and survival [1] . The ubiquitin-proteasome-system ( UPS ) is responsible for the turnover of most cytosolic proteins . Likewise , secreted and membrane proteins that do not fold properly or fail to associate with their requisite partners in the ER are delivered to the cytosol and then destroyed by the UPS [2] . To facilitate this reaction , one or several dedicated receptors recognize misfolded ER-luminal proteins , which are then recruited to the dislocation machinery and rendered accessible to the cytosolic ubiquitylation apparatus . For both cytosolic and ER-derived substrates , attachment of polyubiquitin ( poly-Ub ) chains by an enzymatic E1-E2-E3 cascade is the signal for proteasomal degradation [3] . Poly-Ub chains serve as a recognition signal also for protein dislocation from the ER [4] . When an ER-derived misfolded protein gains access to the cytosol , the attachment of a poly-Ub chain recruits the cytosolic ATPase p97/VCP/CDC48 ( Swiss-Prot ID: P55072 ) and its associated co-factors Ufd1-Npl4 [5]–[7] , believed to provide the force required for extraction of substrate from the ER . It is not known whether these Ub-chains are utilized as a handle to exert a mechanical force or whether they target the dislocated protein directly to the proteasome [5] , [6] , [8] . The 19S lid of the 26S proteasome and p97/VCP/CDC48 both occur in association with ubiquitin ligase and deubiquitylating activities [9] , [10] . Ubiquitylation is a dynamic process , tightly controlled by a collection of associated ubiquitin-processing factors , both at the level of the proteasome and at the level of p97 [9] , [10] . Ubiquitylation and its reverse reaction , catalyzed by deubiquitylating enzymes ( DUBs ) , are crucial for p97-mediated dislocation and for proteasome function [3] , [5] . Impairment of p97-associated DUB activity can block substrate dislocation [11] , [12] . The removal of poly-Ub chains by DUBs associated with the proteasomal lid precedes the threading of unfolded proteins through a narrow pore into the proteolytic chamber of the core 20S proteasome [1] , [13] , [14] . The removal of Ub prior to degradation also recycles this essential modifier and replenishes the cellular pool of free Ub . It follows that DUB activity can have distinct outcomes for proteasomal turnover of proteins: some DUBs facilitate degradation , whereas others may stabilize proteins destined for degradation . Removal of glycoproteins from the ER involves multiple distinct enzymatic reactions: ubiquitylation , deubiquitylation , deglycosylation , and ATP-dependent dislocation [2] . How are the opposing activities of ubiquitylation and debuiquitylation coupled in the course of extraction from the ER and delivery to the proteasome ? The activity of DUBs is no less carefully controlled than that of the ligases that carry out ubiquitylation . The catalytic domains of DUBs , both cellular and viral , are flanked by often sizable segments that likely mediate such control [12] , [15] , [16] . We reasoned that the expression of a highly active DUB protease domain , excised from its normal context , might preemptively remove Ub from substrates targeted for degradation and stabilize them . We chose the protease domain of the Epstein-Barr Virus ( EBV ) large tegument protein ( BPLF1 , Swiss-Prot ID: P03186 ) for that purpose . We use this EBV-DUB to cause an enzymatic blockade of the UPS and show that its expression uncouples dislocation from degradation . Our data demonstrate that protein dislocation from the ER requires a DUB activity upstream of p97-mediated extraction from the ER . Furthermore , a side-by-side comparison of different experimental strategies that impede degradation of a misfolded ER-luminal substrate enabled us to identify this substrate's interactors at distinct stages en route to degradation . This allows us to propose a timeline for the discrete steps involved . We further identify the ER-luminal machinery for disulfide bridge formation as a putative target of eeyarestatin-I , a small-molecule inhibitor of dislocation . The cytosolic co-chaperone recruiter BAT3 ( Swiss-Prot ID: P46379 ) surfaces as a specific interactor of an ER-derived—and now cytosolic—substrate when the UPS is blocked by the EBV-DUB . Our data suggest a previously unanticipated function of cytosolic chaperones , namely to cope with ER-derived misfolded proteins that arrive in the cytosol . The consequences of EBV-DUB expression are less toxic than those caused by pharmacological proteasome inhibitors and might find wide application in cell biology .
We aimed to shift the balance of ubiquitylation towards deubiquitylation through enforced expression of a highly active DUB . To this end we employed the protease domain ( aa1-270 ) of the EBV BPLF1 gene ( Figure 1A ) [16] . The isolated protease domain ( EBV-DUB ) hydrolyzed K48 Ub-linkages more readily ( >10-fold ) than preparations of the cellular DUB YOD1 ( Swiss-Prot ID: Q5VVQ6 ) ( Figure 1B ) . The EBV-DUB was an excellent substrate for the activity-based probe HA-Ub-VME [17] , but not when the putative catalytic cysteine was substituted to alanine ( C61A ) or when Ub-binding was abolished through blocking the catalytic cleft of the EBV-DUB by an I173W mutation ( Figure 1A , C ) . BPFL1 is active towards both K48- and K63-linked di-Ub , as well as NEDD8 [18] , but not against linear di-Ub despite its topological similarity to K63-linked Ub ( Figure 1D ) [19] . The cellular function of BPLF1's DUB activity remains largely unknown [20] . Our experiments do not address the hydrolysis of poly-Ub chains of other linkage types , including K11 , or chains of mixed topologies , all of which could contribute to proteasomal targeting to different degrees [21] . K63 linkages have been linked primarily to endocytosis and other non-proteasomal events [22] but could contribute to protein homeostasis as well [23] , [24] . As a control for all subsequent experiments , we employed the I173W mutant unable to bind and hydrolyze Ub-chains ( Figure 1A , C , E ) . Expression of a FLAG tagged variant of the wild-type EBV-DUB in 293T cells resulted in a substantial downward shift of polyubiquitylation in HA-ubiquitin expressing cells ( Figure 2A ) . In contrast , the expression of a cellular , less active DUB ( YOD1 WT ) failed to do so . Consistent with previous observations , the catalytically inactive mutant ( YOD1 C160S ) caused accumulation of polyubiquitylated proteins , presumably due to stalled dislocation [11] . The efficiency with which the viral DUB eliminated polyubiquitylated conjugates in living cells is even more apparent when the activity of the proteasome is blocked by prior exposure of cells to ZL3VS: polyubiquitylated proteins now accumulated in control cells but were largely absent from EBV-DUB WT cells when examined at similar sensitivity of detection ( Figure 2A ) . To corroborate our findings , we repeated our experiments in the absence of co-transfected HA-Ub . Immunoblots using antibodies directed against ubiquitin or specific for Lys48-linked ubiquitin revealed diminished polyubiquitylation in EBV-DUB WT cells , but not in control cells ( Figure S1 ) . A strong reduction in polyubiquitylation should affect protein turnover globally . Therefore we analyzed the effect of EBV-DUB expression on steady-state levels of two short-lived , cytosolic GFP variants: the ubiquitin-fusion degradation ( UFD ) substrate Ub-G76V-GFP and the N-end rule substrate Ub-R-GFP ( Figure 2B ) [25] . Both proteins are unstable and their detection improves upon inhibition of the UPS . Ub-R-GFP is processed by cellular ubiquitin hydrolases , which results in exposure of arginine as the N-terminal destabilizing residue [26] . Ub-G76V-GFP cannot be processed by cellular hydrolases , but the fusion with ubiquitin itself serves as a degradation signal [27] . Co-expression of Ub-G76V-GFP and EBV-DUB WT gave rise to a population with high GFP fluorescence , best illustrated by the 5 . 3-fold higher median fluorescence intensity of GFP-positive cells ( MFI ) as compared to control cells and the 1 . 8-fold increased MFI compared to cells treated with the proteasome inhibitor ZL3VS ( Figure 2B ) . Similarly , Ub-R-GFP and EBV-DUB co-expressing cells exhibited a 4 . 4-fold higher MFI compared to control cells and a 1 . 6-fold higher MFI compared to ZL3VS-treated cells , apparent also when titrating ZL3VS ( Figure S2 ) . As expected , the relative fraction of GFP-positive cells increased when protein degradation was impaired ( Figure 2B ) . ZL3VS is an efficient inhibitor of the chymotryptic and the peptidyl-glutamyl peptide hydrolyzing activities of the proteasome and impairs its tryptic activity by ∼50% [25] , [28] . Taking this into account , our flow cytometric data on two well-established , short-lived proteins suggest a near complete blockade of the UPS [25] , [29] . Both the N-end rule and UFD pathway are affected , likely at an ubiquitinyl-dependent step in commitment of the substrate to the proteasome . Ubiquitylation and protein turnover are central to many cellular processes . Overexpression of a highly active DUB or proteasomal inhibition by small molecules might affect the physiology of a cell in many ways . Pharmacological inhibition of the proteasome impairs de novo protein synthesis [30] , [31] . In our hands , treatment of cells with ZL3VS for 40 min resulted in ∼40% reduced incorporation of radioactivity in a 10 min pulse labeling experiment when compared to control and EBV-DUB expressing cells ( Figure 2C ) . Thus , de novo protein synthesis was impaired upon pharmacological inhibition but remained unperturbed in EBV-DUB cells . Prolonged treatment ( 20 h ) of cells with proteasome inhibitors caused growth arrest and changes in cell morphology , consistent with the known ability of proteasome inhibitors to induce apoptosis and cell cycle arrest [25] , [32] . EBV-DUB cells appeared normal at times of cultivation where ZL3VS treated cells were morphologically aberrant ( >26 h ) ( Figure 2D ) , but after much longer cultivation they , too , succumbed , presumably because the continued operation of the UPS is essential for cell survival . Does expression of the viral DUB affect protein degradation directly ? To allow a direct comparison of an ER-derived and cytosolic substrate , we employed RI332 , a C-terminally truncated form of ribophorin-I that is commonly used as a model to study dislocation and degradation of ER-luminal proteins [11] , [33] , [34] . When devoid of its N-terminal signal sequence ( ΔSS-RI332 ) the protein fails to enter the ER , cannot be glycosylated , and remains cytosolic , but is otherwise identical to RI332 ( see schematic representation in Figure 3E ) . The cytosolic UPS substrate ΔSS-RI332 is turned over in both control and EBV-DUB I173W cells ( t1/2 = 20 min ) , but its degradation was blocked by expression of EBV-DUB WT ( Figure 3A ) . This confirms our flow cytometric data on Ub-R-GFP and Ub-G76V-GFP ( Figure 2B ) and demonstrates an efficient blockade of the UPS imposed by EBV-DUB WT . An arrest of the UPS should also affect the degradation of ER-derived proteins . When equipped with its natural signal sequence , RI332 is translocated into the ER and glycosylated but rapidly destroyed in control and EBV-DUB I173W cells ( t1/2 = 44 min ) ( Figure 3B ) [33] . The banding pattern observed in these experiments requires explanation . The lower band at the 0 min chase time point corresponds to ER-luminal , non-glycosylated RI332 ( RI332 no CHO ) with its signal sequence removed , while the upper band is the glycosylated form of RI332 ( RI332 +CHO ) [11] . Removal of the N-linked glycan by cytosolic N-glycanase converts asparagine at the site of glycan attachment to aspartate ( N275D ) [35] . As a consequence , deglycosylated RI332 ( RI332-CHO ) shows altered electrophoretic mobility . This form was readily apparent in EBV-DUB WT cells after a 90 min chase and beyond ( Figures 3B , S3 ) and can arise only as a consequence of glycan removal from previously glycosylated RI332 . Such deglycosylated intermediates are normally rapidly degraded by the proteasome and therefore escape detection , unless the activity of the proteasome is compromised [33] . Co-expression of the EBV-DUB stabilized RI332 , but did not do so completely ( Figure 3B ) . Since dislocation and proteolysis are at least to some extent coupled processes [33] , [36] , we reasoned that some Ub-chains present on ER-derived dislocation substrates might not be accessible to the EBV-DUB . We therefore targeted the viral DUB domain to p97 , the “motor of dislocation , ” by equipping it with the UBX domain of YOD1 to copy the strategy employed by this cellular DUB . This chimeric protein ( UBX-EBV WT ) associated with p97 ( Figure S4 ) and blocked degradation of the ER-derived RI332 substrate completely ( Figure 3B , UBX-fusion ) . The degradation of two unrelated membrane proteins—the polytopic transmembrane protein insig-1 and the glycosylated α-chain of the T-cell receptor ( TCRα ) with one transmembrane helix ( Figure 3E ) —were likewise affected . Expression of UBX-EBV WT halted the turnover of myc-tagged insig-1 , an ER-localized transmembrane protein that regulates cholesterol synthesis ( Figure 3C ) [8] . Of note , insig-1-myc is not a glycoprotein but has two alternative start codons yielding two translation products with distinct electrophoretic mobilities [37] . Also the degradation TCRα , an unstable protein when expressed in the absence of other T-cell receptor subunits , was blocked upon co-expression of UBX-EBV WT ( Figure 3D ) . In summary , the EBV-DUB arrests turnover of cytosolic and ER-derived proteins . In all cases , targeting of the viral DUB to p97 improved the stabilization of ER-derived substrates , but its activity towards cytosolic substrates of the UPS persisted ( Figures 3 , S5 ) . The occurrence of the deglycosylated RI332 intermediate that accumulated in UBX-EBV WT and in EBV WT cells ( RI332 –CHO; Figure 3B ) was informative . Since N-glycanase is confined to the cytosol [35] , [38] , this observation immediately suggested that the single N-linked glycan of RI332 gained cytosolic exposure and therefore that dislocation must have occurred , entirely or in part . However , when turnover of RI332 was inhibited by expression of YOD1 C160S , the deglycosylated form RI332 was not observed , even after long chase periods ( Figure 4A ) , indicating its confinement to the ER . The appearance of deglycosylated intermediates was not specific for the ER-luminal RI332 but was readily observable as well for TCRα when co-expressed with UBX-EBV WT ( Figure 3D ) . For reasons that remain to be determined , we consistently observe greater recovery of label for TCRα at later chase points . It is possible that detergent extraction of newly synthesized TCRα is somehow less efficient than material that has left the site of membrane insertion . We do not observe a similar discrepancy for the other substrates analyzed , insig-1 and RI332 . We confirmed cytosolic accessibility of the deglycosylated RI332 intermediate by a proteinase K protection experiment in mechanically disrupted cells ( Figure 4B ) . In the absence of detergent , only deglycosylated RI332 ( RI332 – CHO ) was accessible to protease . The glycosylated , ER-luminal form ( RI332 + CHO ) was not affected by the protease under identical conditions and serves as an internal control for membrane integrity ( Figure 4B ) . Proteinase K sensitivity of deglycosylated RI332 implied that a substantial portion of RI332 , if not RI332 in its entirety , was exposed to the cytosol in UBX-EBV WT expressing cells . To further corroborate this result , we performed a fractionation experiment in which we made use of the pore-forming toxin Perfringlolysin O ( PFO ) [39] . UBX-EBV WT cells were pulse labeled and the radiolabeled , deglycosylated intermediate of RI332 was enriched during a 90 min chase period ( Figure 4C ) . After selective permeabilization of the plasmamembrane by PFO , the cytosolic fraction was separated from cellular remnants by centrifugation , in the presence or absence of added high salt to ensure release of peripherally membrane-associated materials . Only for UBX-EBV WT cells did we see release of the deglycosylated RI332 ( RI332 – CHO ) into the supernatant fraction ( S ) , even more pronounced in the presence of high salt ( S' ) . However , glycosylated RI332 ( RI332 + CHO ) was retained in the pellet fraction under all conditions , consistent with an ER-luminal localization . PFO permeabilization did not damage intracellular compartments , as verified by complete retention of the ER-resident chaperone PDI ( Figure 4C ) in the particulate fraction . We conclude that the deglycosylated form of RI332 was indeed dislocated from the ER and arrived in the cytosol . Combined , our observations show that expression of UBX-EBV WT uncouples dislocation and degradation of RI332 . We wondered if the deglycosylated intermediate of RI332 once cytosolic would remain associated with the ER or whether it might travel to a different location . Immunofluorescence microscopy showed that RI332 localized to the ER in UBX-EBV WT expressing cells , with no evidence of obvious aggregation ( Figure 4D ) . In light of the fractionation data , this suggests that upon dislocation , a sizable fraction of deglycosylated RI332 remains loosely associated with the cytosolic face of the ER-membrane . We blocked dislocation from the ER by the expression of YOD1 C160S , which causes stabilization of ER resident , glycosylated RI332 [11] . We previously proposed that p97-mediated dislocation is stalled under these conditions , because Ub removal is required to allow threading of the dislocation substrate through p97's central pore [11] . If this interpretation is correct , then it should be possible to reverse this block by expression of a DUB capable of attacking the hypothetical stalled intermediate and to overcome the YOD1 C160S-imposed block . Indeed , co-expression of comparable levels of UBX-EBV WT and YOD1 C160S ( Figure 4E ) resulted in the accumulation of the deglycosylated intermediate of RI332 indicative of dislocation ( Figure 4F ) . Co-expression of the inactive mutant UBX-EBV I173W failed to do so , thus excluding simple competition of the UBX-fusion protein with other p97-interactors as the explanation . Consistently , even the non-targeted EBV-DUB without fused UBX-domain could relieve the blockade of dislocation imposed by YOD1 C160S ( Figure 4F ) . We conclude that a DUB-catalyzed reaction is essential for protein dislocation from the ER . Because ubiquitylation by HRD1-SEL1L is required for the initial engagement of the cytosolic dislocation apparatus [2] , [34] , [40] , premature removal of ubiquitin might also inhibit the earliest steps in this pathway , if EBV-DUB has access to these ubiquitylated intermediates . The rate of dislocation as determined by the disappearance of glycosylated RI332 ( Figure 3B; RI332 + CHO ) was not affected compared to control cells in EBV-WT expressing cells , but was lower in cells expressing the p97-targeted variant ( Figure 3B , UBX-fusion; Figure S3 ) . Thus , the non-targeted form of the EBV-DUB interfered exclusively with the degradation of already dislocated RI332 , while the ER-targeted variant stabilized RI332 at the initiation of dislocation and through prevention of delivery to the proteasome by preemptive removal of poly-Ub chains from the substrate . Nevertheless , both variants , p97-targeted or not , caused accumulation of deglycosylated , dislocated RI332 in the cytosol . What keeps the breakdown intermediate ( s ) of RI332 from aggregation ? To address this question and to gain a more global perspective on the natural history of a misfolded protein , we staged the different steps in degradation through identification of proteins that interact with RI332 and its various dislocation intermediates . Through interference with dislocation and degradation by different means , we generated discrete intermediates in the breakdown pathway of RI332 as explained in the preceding sections . Using affinity tagged RI332 as a bait , we retrieved interacting proteins from UBX-EBV WT , YOD1 C160S , or p97 QQ-expressing ( an ATPase-deficient form of p97 ) cells [6] , and from cells exposed to the proteasome inhibitor ZL3VS or eeyarestatin I , an inhibitor of dislocation and possibly membrane insertion [29] , [41] , [42] . As controls , we performed immunoprecipitations from cells that either did not express RI332 or that co-expressed RI332 with YOD1 ΔZnf C160S , YOD1 WT , or UBX-EBV I173W , none of which significantly perturb the dislocation/degradation process [11] . We performed a total of nine independent large-scale immunopurifications and analyzed each by LC/MS/MS . We identified 836 candidate interactors and enumerated the number of peptides that originated from each one of them . We sought to identify interactions enriched upon inhibition of dislocation/degradation to gain insight into their spatial and temporal occurrence . We therefore normalized our dataset as follows . Each candidate protein was represented by multiple peptide fragments in different experimental conditions , and the maximum number of peptides ( MNOP ) for a given candidate was based on the condition that yielded the highest peptide count . All interactions between RI332 and its candidate interactors ( retrieved from independent immunoprecipitation experiments ) were expressed as a percentage of the MNOP . Such a normalized interaction matrix should facilitate the identification of groups of proteins that responded similarly if certain discrete steps of the dislocation/degradation pathways are perturbed . After application of a stringent set of rules ( inclusion requirement based on a threshold number of peptides and absence from normal serum controls; see Text S1 for details ) a total of 33 candidate interactors remained ( Table S1 ) . The candidates were arranged in three groups via k-means clustering and are depicted in a heat map ( Figure 5 ) . The heat map is a graphical representation of the normalized interaction matrix and visualizes the conditions under which a particular interactor co-precipitated with RI332 . Group 1 comprised those interactors of RI332 that were specifically retrieved from eeyarestatin-I treated cells ( Figure 5 ) . Consistent with an ER-luminal accumulation of RI332 , we observe the ER-luminal disulfide shuffling and/or PDI-domain containing proteins ( ERdj5 , ERp72 , ERp57 , ERp5 , PDI , and Calreticulin ) in association with RI332 ( see Table S1 for Swiss-Prot IDs of candidate interactors ) [42] , [43] . When a protein is terminally misfolded , a family of substrate recognition molecules targets the substrate to the dislocation machinery . A snapshot of this type of intermediate was provided by co-expression of YOD1 C160S or mutant p97 ( QQ ) . Specifically enriched interaction partners of RI332 under these conditions clustered in group 2 and include glycan-binding and modifying proteins ( OS9 , UGGT2 ) , general ER-luminal substrate recruiting factors and chaperones ( SEL1L , Endoplasmin/GRP94 , DNAJC3/P58IPK ) , p97 , and the cytosolic UBR5 , implicated in ubiquitylation according to the N-end rule [6] , [44]–[46] . Our unbiased proteomic approach supports our earlier proposal that YOD1 C160S blocks the dislocation reaction itself [11] . RI332 is recognized as misassembled by OS9 , SEL1L , and GRP94 , associates with p97 , but cannot be extracted in YOD1 C160S and p97 QQ cells [11] , [34] , [40] , [45] . Interactors that clustered in group 2 comprised both ER-luminal and cytosolic components of the ER-quality control machinery and UPR signaling . After successful dislocation , misfolded proteins are targeted to the cytosolic proteasome . Group 3 comprised those proteins that were co-immunoprecipitated with RI332 from UBX-EBV WT or ZL3VS treated cells . Our mass spectrometry data suggested that the deglycosylated intermediate of RI332 associates with cytosolic chaperones , namely the co-chaperone recruiter BAT3 and the TRiC complex/CCT . The TRiC complex is important for transient stabilization of nascent polypeptide chains prior to their translocation into the ER [47] , [48] , and BAT3 was recently implicated in integration of tail-anchored proteins into the ER membrane [49] . Not surprisingly , we can demonstrate an interaction of RI332 with the proteasome when its activity was blocked by the action of ZL3VS . The identified cytosolic interactors of RI332 fully support our biochemical characterization and suggest that dislocation and degradation are uncoupled in UBX-EBV WT expressing cells . As corroboration of the mass spectrometry experiments , we verified interactors of RI332 ( Figure 6A ) by different means . After immunoprecipitation of RI332 from cells transfected/treated as in the large-scale pulldown experiments , we confirmed an interaction of RI332 with p97 when the ATPase activity of p97 was blocked by mutation ( p97 QQ ) . We likewise confirmed the association of SEL1L and OS9 with RI332 when co-expression of either YOD1 C160S or p97 QQ blocked its dislocation from the ER . We established the association of RI332 with BAT3 , which required the co-expression of UBX-EBV WT . These data hint at the existence of a chaperone-mediated buffer to sequester dislocated proteins from the canonical degradation pathway . The mass spectrometry experiment predicted a strikingly enriched association of PDI/PDIA1 with RI332 , when cells were treated with eeyarestatin-I . Indeed , we could verify an interaction of these proteins when co-expressed with either YOD1 C160S or p97 QQ or when cells were treated with eeyarestatin-I ( Figure 6B ) . More surprisingly , eeyarestatin-I induced the formation of SDS- and β-mercaptoethanol-resistant adducts of PDI . The enrichment of these adducts relative to monomeric PDI in immunoprecipitates of RI332 was indicative for adduct formation between RI332 and PDI upon eeyarestatin-I treatment . Similar observations were also made for PDIA3/ERp57 ( unpublished data ) .
The expression of a highly active viral DUB markedly shifts the cellular balance towards deubiquitylation ( Figures 2A , S1 ) . Similar to pharmacological inhibition of the proteasome , accelerated or premature Ub removal from substrates should affect their ubiquitin-dependent degradation . Indeed , overexpression of the EBV-DUB blocks the degradation of several model substrates , but without the immediate cytotoxic effects that are commonly observed upon treatment of cells with pharmacological proteasome inhibitors ( Figure 2C , D ) . In view of Ub's role in cell cycle control , through K11-linked Ub-chain assembly by the ubiquitin ligase APC/C [50] , the ultimate demise of cells with an arrested UPS is of course hardly surprising . The greater cytotoxicity of pharmacological inhibition could perhaps be related to ubiquitin-independent functions of the proteasome or reflect the critical importance of free Ub in the cell [3] , [51] . Interference with the UPS by blocking proteolysis with small molecules or by enzymatic interference with proteasomal targeting are two fundamentally different approaches . Small molecule proteasome inhibitors cause an accumulation of polyubiquitylated proteins and deplete cells of free Ub [52] . Cell viability critically depends on a pool of free Ub and its depletion kills cells [51] . Shifting the cellular balance towards deubiquitylation , as achieved by the EBV-DUB , does not result in accumulation of polyubiquitylated proteins ( Figure 2A ) and represents a novel means of inhibiting the UPS . Unlike the EBV-DUB , pharmacological inhibition of the proteasome reduces de novo protein synthesis even after relatively short times of exposure ( Figure 2C ) [30] , [31] . Translation , protein folding , secretion , and dislocation are interdependent processes and the ability to block proteasomal protein degradation without immediately affecting translation provides an additional benefit . We showed previously that YOD1 C160S causes complete retention of RI332 in the ER , a substrate otherwise extracted and targeted for degradation ( Figure 7A , B ) [11] . This retention can be reversed at least in part by expression of the p97-targeted EBV-DUB ( Figures 4F , 7C ) . Combined , these results demonstrate a need for removal of Ub to achieve dislocation . Ramping down the p97-associated DUB activity blocks dislocation but can be rescued by an active DUB . This immediately suggests that DUBs can have opposing functions for the degradation of ER-derived proteins . Some DUBs might impair dislocation by reversing ubiquitylation; others might facilitate dislocation and subsequent degradation . Indeed , TCRα-GFP is stabilized by knockdown of USP13 but destabilized by knockdown of Ataxin-3 [12] . Moreover , the observed uncoupling of dislocation and degradation by the EBV-DUB suggests that a persistently ubiquitylated state is not essential for the physical extraction of substrate from the ER . Misfolded proteins are escorted to the proteasome by ubiquitin binding proteins [53] , [54] . It is therefore no surprise that the removal of Ub-chains by the EBV-DUB abrogates proteasomal turnover of these proteins . By analogy , p97-mediated extraction would be arrested by the EBV-DUB to a similar extent if persistent substrate modification with Ub-chains were required to exert a mechanical force for such extraction . Indeed , protein dislocation is slowed down , but clearly the reaction continues in cells that express EBV-DUB ( Figures 4A , S3 ) . Together with the evident requirement of a DUB-catalyzed reaction upstream of p97 ( Figure 4E ) , these findings suggest that Ub-chains are not required to exert a mechanical force on the dislocation substrate [7] but may instead be required only as recognition signal [5] . Our data are consistent with the following model ( Figure 7A ) . Dislocation substrates are targeted to a dislocon that connects with an Ub ligase activity , as exemplified by the HRD1-SEL1L complex [2] , [34] , [45] . Consistent with its requirement for dislocation , substrate ubiquitylation recruits p97 and the Ub-recognizing co-factors Ufd1 and Npl4 to initiate dislocation [6] , [7] , [11] , [12] , [55] . Once the dislocation machinery is recruited , one or several p97-associated DUBs remove the initial Ub tag to enable ATP-dependent threading through the central pore of p97 [7] , [11] , [12] , [55] . Unless p97 is coupled directly to the proteasome , for which there is no firm experimental support at present [56] , a second round of ubiquitylation would be required to target the unfolded protein to the proteasome , again necessitating removal of the Ub-chain prior to its insertion into the proteolytic chamber [14] . There is of course also a structural resemblance between the 6-fold symmetrical p97 complex , present in association with Ub-recognizing and -processing factors , and the similarly equipped proteasomal cap complex [53] . The first round of ubiquitylation would be responsible for the engagement and proper assembly of the p97-Ufd1-Npl4 dislocation complex and may involve K11-linked ubiquitin chains [21] , [57] . The second round would then target the dislocated , now cytosolic protein to the proteasome like any other p97-dependent , cytosolic substrate of the UPS [12] , [29] , [57] . Ubiquitylation-dependent events occur both upstream and downstream of p97 [53] , [54] . Two consecutive rounds of ubiquitylation are conceptually similar to the use of multiple independent ubiquitylation sites on many standard proteasomal substrates: modification of more than a single site on a given substrate requires sequential engagement by a given ligase , or the involvement of more than one ligase . The p97-targeted EBV-DUB interferes with degradation of ER-derived substrates at two distinct steps ( Figure 7C ) . First , it interferes with proper initiation of dislocation by premature removal of ubiquitin . Second , it blocks substrate degradation by removal of the poly-Ub chains that would otherwise have mediated delivery of the misfolded protein to the proteasome . In the absence of a fused UBX-domain , the EBV-DUB affects to a lesser extent the initial stages of dislocation but still strongly inhibits proteasomal proteolysis of cytosolic substrates and ER-derived substrates ( Figures 2B , 3A , B ) . The EBV-DUB can even facilitate the dislocation reaction when dislocation is otherwise stalled by expression of YOD1 C160S ( Figures 4F , 7B ) . Our data establish the necessity of a DUB-catalyzed reaction upstream of p97-mediated protein extraction from the ER . These observations are most consistent with a model of two consecutive rounds of ubiquitylation and deubiquitylation . Using different tools to block protein dislocation and degradation , we identified interacting partners of a misfolded , ER-derived glycoprotein at different stations on its road to destruction . Starting in the ER , eeyarestatin-I was first identified as an inhibitor of dislocation [42] . However , there is no consensus on the identity of its molecular targets or its exact mode of action [29] , [41] , [58] . Eeyarestatin-I contains two halogenated benzene rings ( Figure S6 ) . Bromobenzene , a hepatotoxic compound , is metabolized to reactive metabolites ( e . g . bromobenzene-3 , 4-oxide ) and forms covalent adducts with cellular proteins , including PDIA1 , PDIA6 , and PDIA3 [59] , [60] . Also other halogenated aromatic derivatives covalently modify proteins in a reaction mechanism similar to that involving bromobenzene [61] . If the two halogenated benzene rings of eeyarestatin-I would react similarly , formation of covalently cross-linked adducts of PDI might ensue , consistent with our observations that eeyarestatin-I induces SDS-resistant adducts of PDI ( Figure 6B ) . We therefore suggest the possibility that the two halogenated benzene rings of eeyarestatin-I might promote crosslinking of proteins in the ER lumen and that its targets include the machinery for disulfide shuffling . If the machinery for disulfide shuffling in the ER is indeed a molecular target of eeyarestatin-I , then both import into and export from the ER are likely to be affected , explaining the seemingly divergent observations reported for eeyarestatin-I's mechanism of action [29] , [41] , [42] . By staging the process of dislocation and degradation we identified several novel and intriguing candidate interactors for the RI332 dislocation substrate , including proteins important in the maturation of extracellular matrix components ( PLOD1 , CHPF2 , EXT2 ) . Whether the machineries for heparan sulfate synthesis , chondroitin sulfate synthesis , and collagen polymerization merely undergo rapid turnover and are processed via p97 or whether they are otherwise involved in the dislocation reaction remains to be established . We validated BAT3 as a cytosolic interactor of the deglycosylated intermediate of RI332 . This result implies not only that RI332 is dislocated from the ER but also that it remains associated with chaperones , possibly to prevent aggregation . This type of interaction may be an example of how cells cope with dislocated proteins that escape degradation . Could it be that the machineries for translocation and dislocation share certain co-factors ? Intriguingly , BAT3 has recently been implicated in proteasomal degradation of newly synthesized defective polypeptides [62] . This might suggest that cytosolic quality control machineries handle defective , ribosome-derived nascent chains similarly to how they deal with defective polypeptides that originate from the ER . The specifically enriched association of the TRiC/CCT with RI332 in UBX-EBV WT expressing cells ( Figure 5 ) further supports this interpretation: Cytosolic chaperone complexes implicated in the stabilization and quality control of folding intermediates prior to translocation in the ER [47] , [48] also interact with an ER-derived , dislocated protein . The approach developed here—expression of the EBV-DUB—stabilizes a range of proteins that are normally degraded in an Ub-dependent manner . Although capable of blocking Ub-dependent protein degradation globally and efficiently , the EBV-DUB is less toxic to cells than pharmacological proteasome inhibitors , providing an extended window of observation . We show that dislocation and degradation of ER-derived misfolded substrates can be uncoupled by the expression of the viral DUB domain and so allows the unprecedented visualization of a deglycosylated dislocation intermediate in the absence of pharmacological proteasome inhibitors . We identify the necessity of a DUB-catalyzed reaction for protein dislocation from the ER and place this activity upstream of p97 . The determination of the sets of interacting partners of a misfolded , ER-derived glycoprotein at different stations on its road to destruction helped us characterize the order of events during dislocation and degradation . The expression of this highly active DUB has provided new mechanistic insights into protein quality control . Given its low toxicity and the possibility of achieving cell-type or tissue-specific expression in vivo , the EBV-DUB and variants derived from it may prove to be an attractive alternative to the use of small molecule inhibitors of the proteasome .
Antibodies against the HA-epitope were purchased from Roche ( 3F10 ) ; anti-Flag , anti-Ubiquitin antibodies were purchased from Sigma-Aldrich; and Lys48-specific anti-Ubiquitin antibodies ( anti-K48-Ub , clone Apu2 ) were purchased form Millipore . Anti-p97 and anti-BAT3 antibodies were purchased from Fitzgerald Industries and Abcam , respectively . Polyclonal anti-OS9 and anti-SEL1L antibodies were described previously [45] , [63] . A . E . Johnson ( Texas A&M University , TX ) provided a plasmid encoding perfringolysin O . Polyclomal anti-PDI serum ( rabbit ) was generated with bacterially expressed human PDI . 293T cells were cultured and transfected as previously described [63] . The deletion constructs and mutants of YOD1 have been described elsewhere [11] . All p97-targetting constructs were cloned into the pcDNA3 . 1 ( + ) vector system ( Invitrogen ) with a Kozak sequence ( GCCACC ) inserted directly upstream to the Start-Codon , and encoded for a N-terminal Flag-tag ( DYKDDDK ) followed by the UBX domain of YOD1 ( aa1–131 ) . For the UBX-GFP construct , these aa1–131 of YOD1 were followed by a LEGS linker sequence and the aa2–239 of enhanced GFP ( EGFP ) . The UBX-EBV-DUB fusion construct comprised the aa1–128 of YOD1 , a GGGS linker sequence and the DUB domain of the EBV large tegument protein BPLF1 ( aa1–270 ) . The construct coding catalytically impaired p97 ( p97 QQ ) was described earlier [11] . Site directed mutagenesis of the EBV-DUB was performed with the QuikChange II mutagenesis kit ( Stratagene ) . The predicted catalytic cysteine residue at position 61 of the original EBV protein BPLF1 was mutated to alanine ( C61A ) , threonine ( C61T ) , serine ( C61S ) , or lysine ( C61K ) . The isoleucine at position 173 and alanine at position 178 were mutated to tryptophane ( I173W ) and to arginine ( A178R ) , respectively . Maria Masucci provided the Ub-R-GFP and the Ub-G76V-GFP construct . Untagged RI332 was a generous gift from N . Erwin Ivessa . The Plasmid pCMV-INSIG-1-Myc was obtained from the American Type Culture Collection ( ATCC number 88099 ) . HA-RI332 was cloned into pcDNA3 . 1 ( + ) via HindIII and XbaI restriction sites . The HA-epitope ( YPYDVPDYA ) and a GSLE linker sequence were inserted between aa27 and aa28 of the signal sequence . Pulse chase experiments were performed as previously described [38] . Prior to pulse labeling , the cells were starved for 30 min in methionine/cysteine-free DMEM at 37°C . Cells were then labeled for 10 min at 37°C with 250 µCi of [35S]methionine/cysteine ( PerkinElmer ) . De novo protein synthesis was quantitated in a pulse labeling experiment . Where indicated , 50 µM ZL3VS was applied to the cells during the starvation , pulse labeling , and chase period . Incorporated radioactivity was quantified after SDS-mediated cell lysis and TCA precipitation . Transient transfection , cell lysis , immunoprecipitations and transfections , SDS-PAGE , and fluorography were performed as described earlier [34] . All quantifications were performed on a phosphoimager . For the protease protection assay cells were homogenized by passing through a 23 x g needle in hypotonic buffer ( 20 mM Hepes pH 7 . 5 , 5 mM KCl , 5 mM MgCl2 , 1 mM DTT , and a protease protection cocktail ( Roche ) ) . Proteinase K was added to a final concentration of 100 µg/ml in the presence and absence of 0 . 5% NP40 . After 20 min on ice , the proteinase K was inactivated by inclusion of PMSF ( 5 mM ) and samples were analyzed by SDS-PAGE . For selective permeabilization of the plasmamembrane , cell 293T cells were trypsinized harvested and washed with PBS . Perfringolysin O was added to a final concentration of 0 . 5 µM at 0°C followed by an incubation of the cells at 37°C for 15 min to induce pore-formation . Where indicated , the mixture was adjusted to 0 . 5 M NaCl . Centrifugation ( 5 min , 9000 x g ) separated cytosolic proteins in the supernatant from cellular remnants in the pellet . The pellet was washed with ice-cold PBS and lysed in PBS/1% SDS to solubilize membrane proteins and release organelle contents . Structural modeling is described in Text S1 . Data analysis and clustering are described in the Supporting Information section . Cells were grown on coverslips , fixed in 4% paraformaldehyde , quenched with 20 mM glycerine , 50 mM NH4Cl , and permeabilized in 0 . 1% Triton X-100 at room temperature . Fixed and permeabilized cells were blocked in 4% BSA and incubated either with anti-HA antibodies ( 3F10 , rat monoclonal , Roche ) or anti-PDI ( Abcam ) antibodies as described [45] . Images were acquired by using a spinning disk confocal microscope , a Nikon 60× magnification , and a 1× numerical aperture oil lens . Cells were harvested by trypsinization 10 h after addition of proteasome inhibitors and 20 h after transient transfection . Cells were washed and incubated for 30 min at 4°C with a LIFE/DEAD cell viability stain ( Invitrogen ) . Subsequently , cells were washed and fixed with 2% paraformaldehyde . Fluorescence intensity of GFP was measured with LSR I flow cytometer ( BD Biosciences ) . Data were collected with CellQuest ( BD Biosciences ) and analyzed with FlowJo ( Tree Star ) . Cells were transiently transfected with EBV-DUB WT or empty vector . After 6 h , pharmacological proteasome inhibitors ( 10 µM MG132 or 10 µM ZL3VS ) were applied to the cells . Photographs were taken 20 h post-treatment . | Constant turnover of proteins is part of normal cellular physiology . Newly synthesized proteins that fail to fold are recognized by dedicated receptors and tagged for immediate degradation . The tag usually consists of a chain of a small protein , ubiquitin , and is recognized by the proteasome . We introduce a new tool to interfere with proteasomal degradation: a ubiquitin-specific protease domain ( EBV-DUB ) derived from Epstein-Barr Virus . This EBV-DUB , when expressed in mammalian cells , preemptively removes ubiquitin chains ( deubiquitylation ) and so frustrates substrate recognition and engagement by the proteasome . The natural history of misfolded , secretory proteins is poorly understood because of the strict coupling of recognition , tagging for degradation , and proteolysis . The EBV-DUB uncouples these processes and stabilizes short-lived intermediates , an activity that helped us to address the question of how such misfolded proteins are extracted from the relevant cellular compartments . Our data are consistent with the idea that unfolded substrates targeted for degradation are threaded through a narrow pore of the chaperone protein known as p97 . In order to pass through the pore , the protein must first have any already attached ubiquitin chains removed; a second cycle of ubiquitylation is then required to allow engagement of the proteasome . Entry of substrate into the proteolytic chamber again requires removal of ubiquitin . We thus propose two rounds of ubiquitin attachment and removal in the course of the extraction and degradation of misfolded proteins . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biochemistry",
"cell",
"biology"
] | 2011 | Enzymatic Blockade of the Ubiquitin-Proteasome Pathway |
Regulated nuclear entry of clock proteins is a conserved feature of eukaryotic circadian clocks and serves to separate the phase of mRNA activation from mRNA repression in the molecular feedback loop . In Drosophila , nuclear entry of the clock proteins , PERIOD ( PER ) and TIMELESS ( TIM ) , is tightly controlled , and impairments of this process produce profound behavioral phenotypes . We report here that nuclear entry of PER-TIM in clock cells , and consequently behavioral rhythms , require a specific member of a classic nuclear import pathway , Importin α1 ( IMPα1 ) . In addition to IMPα1 , rhythmic behavior and nuclear expression of PER-TIM require a specific nuclear pore protein , Nup153 , and Ran-GTPase . IMPα1 can also drive rapid and efficient nuclear expression of TIM and PER in cultured cells , although the effect on PER is mediated by TIM . Mapping of interaction domains between IMPα1 and TIM/PER suggests that TIM is the primary cargo for the importin machinery . This is supported by attenuated interaction of IMPα1 with TIM carrying a mutation previously shown to prevent nuclear entry of TIM and PER . TIM is detected at the nuclear envelope , and computational modeling suggests that it contains HEAT-ARM repeats typically found in karyopherins , consistent with its role as a co-transporter for PER . These findings suggest that although PER is the major timekeeper of the clock , TIM is the primary target of nuclear import mechanisms . Thus , the circadian clock uses specific components of the importin pathway with a novel twist in that TIM serves a karyopherin-like role for PER .
The mechanisms that generate a circadian ( ~24 h ) clock within organisms have been a subject of investigation for many years . In Drosophila , the core clock mechanism consists of a negative feedback loop comprised of the period ( per ) and timeless ( tim ) genes [1] . Transcription of the per and tim genes is initiated during mid-day by the transcription factors CLOCK ( CLK ) and CYCLE ( CYC ) , while the two proteins , PER and TIM , accumulate in the early night and translocate into the nucleus several hours later , to suppress the activity of CLK and CYC . Degradation of TIM in the early morning promotes progressive phosphorylation and ultimately degradation of PER , which releases the repression activity in the nucleus and allows re-initiation of per and tim transcription . Delayed nuclear entry of the PER-TIM proteins , relative to their first appearance , is essential to generating the clock as it separates the phase of mRNA synthesis from transcriptional repression [2] . Also , precise timing of nuclear entry appears to be critical for generating a clock that maintains accurate period [3–5] . In fact , the timing of nuclear entry and duration of PER and TIM localization in the nucleus are likely among the most critical determinants of circadian period . For the reasons noted above , the mechanisms underlying the nuclear entry of PER and TIM have been of great interest . The two proteins are believed to regulate each other's nuclear entry , but this has been challenged in many studies conducted both in vitro and in vivo [6–9] . Analysis of this issue in flies has been complicated by the fact that PER is unstable in the absence of TIM [10 , 11] making it difficult to assess its localization . In addition , although TIM is stable , and cytoplasmic in per-null mutants , it actually shuttles in and out of the nucleus , suggesting that PER is required for its nuclear retention rather than its localization [12] . Overall , the mechanisms that drive nuclear entry of PER-TIM remain unclear although this event is clearly very important in the clock mechanism . The nuclear transport of proteins is fundamental for regulating cell biogenesis , physiological homeostasis , development and disease [13] . Studies have established a classical nuclear import pathway in which a macromolecule/cargo ( greater than 40kDa ) containing a nuclear localization signal ( NLS ) binds to importin α- IMPα [14 , 15] , which in turn binds to importin β- IMPβ , and this ternary complex targets the nuclear pore complex ( NPC ) proteins to facilitate nuclear translocation of the cargo [14] . After translocation into the nucleus , the binding of nuclear Ran-GTP to IMPβ dissociates the trimeric complex ( cargo-IMPα-IMPβ ) and the free IMPα and β are recycled back to the cytoplasm by the exportin CAS and Ran respectively [16 , 17] . This classical pathway is sometimes modified; for instance , nuclear import can be mediated by IMPβ proteins alone ( one or more at the same time ) [18 , 19] , IMPα has also been shown to carry the cargo protein into the nucleus independent of IMPβ [20] , and there are also examples of nuclear translocation through the NPC independent of the involvement of either alpha or beta importins [21] . The Drosophila genome encodes a full complement of importins , including at least four importin α homologs ( importin α1 , α2 , α3 , and α4 ) , as well a Ran-GTPase ortholog [13 , 14] . Despite what we know about the nuclear import process , none of the classical import factors has been examined for its role in the nuclear entry of PER or TIM . As both PER and TIM qualify as macromolecules ( > 40kDa ) and each contains at least one NLS that is functional in cells [6 , 22 , 23] , we speculated that the importin system would be involved in their nuclear localization . We report here that the nuclear localization of PER and TIM in clock cells , and thereby behavioral rhythms in Drosophila , depend upon a specific alpha-importin: IMPα1 . We also demonstrate roles for other components of the nuclear import machinery—Ran and a nuclear pore protein , NUP153—in the localization of PER-TIM . Based upon structure-function analysis of the IMPα1-TIM interactions , we suggest that TIM is the primary cargo of the importin system , and it acts in a karyopherin-like capacity to co-transport PER . We also identify the molecular basis of a previously identified tim mutant as an impaired interaction between TIM and IMPα1 .
To determine if any member of the importin α family is required for the nuclear entry of PER/TIM , we used the UAS/GAL4 system to knock down the expression of each importin α via RNAi in clock cells . We obtained UAS-importin α RNAi lines from the Vienna-based Drosophila stock center ( VDRC ) or the Bloomington stock center , and crossed each one to Pdf-GAL4 and tim-UAS-GAL4 ( TUG ) to achieve knockdown in central clock neurons , the ventral lateral neurons ( LNvs ) , and all clock cells , respectively . Dicer was also coexpressed to increase the efficacy of RNAi . We assayed the locomotor rhythms of these flies under continuous dark conditions ( DD ) and found that knockdown of importin α1 in PDF-positive LNvs resulted in weak rhythms , as determined through fast fourier transform ( FFT ) analysis ( Table 1 ) . Use of stronger drivers ( TUG and tim-GAL4 ) to express importin α1 RNAi in all clock cells rendered all flies arrhythmic in DD ( Table 1 and Fig . 1A ) . We used two independent RNAi lines for importin α1 , one from VDRC ( α1 RNAi-1 ) and the other from the Bloomington stock center ( α1 RNAi-2 ) . α1 RNAi-1 expressed by TUG produced arrhythmic behavior , which was accompanied by lower mRNA levels of importin α1 ( ~ 64% ) . TUG driven α1 RNAi-2 lines did not show a behavioral phenotype , which was consistent with lack of an effect on importin α1 mRNA ( S1 Fig . ) . We found that Pdf-GAL4- and TUG-driven importin α2 RNAi also produced arrhythmia or weak rhythmicity , although only after 4–5 days in DD ( Fig . 1A ) . In addition , knockdown of importin α3 by TUG in clock cells resulted in arrhythmia in 50% of the flies after 5 days in DD , and knockdown of importin α3 with a stronger tim-GAL4 driver induced lethality ( Table 1 ) . Overall , the behavioral phenotype produced by knocking down these other two importins was weaker than that seen for importin α1 , even though mRNA levels of the relevant importin were reduced in the flies tested ( S1 Fig . ) . Knockdown of importin α4 knockdown did not produce any behavioral phenotype . To determine whether the arrhythmic behavioral phenotype of importin α1 knockdown flies was caused by blocked nuclear entry of PER and TIM , we examined PER/TIM protein localization in adult LNvs . In control flies , PER and TIM were nuclear at zeitgeber time 0 ( ZT0 ) ( ZT0 = lights on and ZT12 = lights off ) , as has been reported in several previous studies [24–26] . However , both proteins were cytoplasmic at ZT0 in TUG driven α1 RNAi-1 flies ( Fig . 1B ) . We did not observe any anatomical defects in the PDF+ cell bodies or in the PDF projections of LNvs in these flies , suggesting that the observed behavioral and molecular phenotype is not due to gross developmental defects ( S2 Fig . ) . As mentioned above , flies in which expression of importin α2 or α3 was reduced in adult LNvs or in all clock neurons were also arrhythmic 4–5 days after transferring to DD . To investigate whether importins α2 or α3 also play a role in the nuclear entry of clock proteins , we immunostained whole-mount brains of α2 and α3 knockdown flies using a PER antibody at ZT1 . In both fly lines , PER was expressed in the nucleus at ZT1 ( S2 Fig . ) , indicating that neither importin α2 nor α3 is required for the nuclear translocation of PER in LNvs . However , the distal layer of the optic medulla , including the PDF projections of large LNvs into the medulla , was impaired in these flies , suggesting that importin α2 and α3 contribute to the development of the LNvs . Thus , the arrhythmic phenotype observed after a few days in DD in flies with reduced importin α2 and α3 may derive from developmental problems . We infer that only importin α1 affects the nuclear entry of PER and TIM in central clock cells . Given the limitations with RNAi technology , such as nonspecific and off-target effects [27] , we sought to verify the results of RNAi knockdown using a complete loss-of-function allele of importin α1- Df ( 3L ) α1S1 . This allele consists of a deletion , which removes the entire importin α1 gene and several other genes , and is homozygous viable [28] . Consistent with the phenotype obtained by knocking down importin α1 , Df ( 3L ) α1S1 flies were arrhythmic in DD . These flies also showed reduced rhythmicity in the presence of light:dark cycles , with ~60% of flies scored as arrhythmic ( Table 2 and Fig . 2A ) . As the deletion in Df ( 3L ) α1S1 flies includes multiple genes besides importin α1 , we sought to determine whether the mutant phenotype derived exclusively from the loss of importin α1 . We tagged a cDNA encoding wild type importin α1 with an HA epitope and cloned it under control of a UAS sequence and then expressed in the Df ( 3L ) α1S1 background using the GAL4 system . In DD , expression of importin α1 in clock neurons using TUG restored rhythmicity in 84% of Df ( 3L ) α1S1 flies ( Table 2 and Fig . 2B ) . We were also able to rescue rhythmicity in 70% of mutant flies using the Pdf-GAL4 driver , suggesting that expression of importin α1 is predominantly required in LNvs for free running locomotor rhythms ( Table 2 ) . These results demonstrate that importin α1 is essential for maintaining rhythms in DD and LD . To investigate whether nuclear translocation of PER/TIM was affected in Df ( 3L ) α1S1 mutant flies , we performed immunostaining . At ZT0 , PER and TIM were completely cytoplasmic in mutant flies while they were nuclear in wild-type flies , recapitulating the result obtained with importin α1 RNAi flies ( TUG > importin α1 RNAi-1 ) ( Fig . 2C ) . Over a 24 hour cycle , as expected , the localization and expression of PER was rhythmic in wild type flies , such that it was nuclear at ZT0 and ZT6 ( with lower levels at ZT6 than ZT0 ) , cytoplasmic at ZT18 and practically undetectable at ZT12 ( S3A Fig . ) . In mutant flies , PER was cytoplasmic whenever detected , but expression was undetectable at ZT12 , indicating that its levels are regulated in the cytoplasm . Under DD conditions , in wild type flies , the pattern of localization and expression of PER were similar to those in LD . However , in mutants PER was cytoplasmic at all times and levels did not cycle ( S3B Fig . ) . Likewise , the TIM expression pattern in wild-type flies ( nuclear at ZT0 , expressed at very low levels at ZT6 and 12 and cytoplasmic at ZT18 ) was similar in LD and DD except that expression was slightly higher at CT12 than ZT12 ( S3C/D Fig . ) . In mutant flies , TIM was cytoplasmic at ZT0 and ZT18 and undetectable at ZT6 and ZT12 , presumably due to its degradation by light . However , in DD TIM was detected in the cytoplasm at all times ( S3D Fig . ) . These data are also supported by studies of temporal patterns of PER and TIM protein accumulation in fly heads as discussed below . The deletion mutant of importin α1 , Df ( 3L ) α1S1 , confirmed that importin α1 is important for mediating the nuclear entry of clock proteins , PER and TIM , and thus maintaining behavioral rhythms . We also determined if IMPα1 produces an over-expression phenotype in impα1 heterozygotes ( Df ( 3L ) α1S1/+ ) , which show wild type behavior , or in period-altering per mutants , perL and perS . The perL mutation , which increases circadian period , was of particular interest as it is associated with delayed nuclear entry of PER and TIM . However , IMPα1 expression did not affect rhythms of “wild type” flies ( S4A Fig . ) or the periodicity of per mutants ( S4B/C Fig . ) . These data suggest that while IMPα1 is required for nuclear expression of PER and TIM , it is typically not a limiting factor in flies . From the late night to early morning , nuclear PER and TIM inhibit their own transcription , resulting in low levels of both mRNAs . As nuclear TIM is degraded in response to light at daybreak and then PER is hyperphosphorylated and also degraded , repression activity is released and levels of per and tim mRNA rise [1 , 2] . We examined whether blocked nuclear entry of PER and TIM in Df ( 3L ) α1S1 mutant flies affects cycling of their mRNAs . We expected that RNA levels of per and tim in mutant flies would not cycle and levels would be higher than those of wild-type flies at all times over the course of a day due to reduced repression . Consistent with our expectation , mRNA levels did not cycle . However , we noticed higher levels of per and tim mRNA , relative to wild type , only during the normal trough of mRNA expression , which corresponds to nuclear expression of TIM and PER ( ZT21-ZT1 ) ( Fig . 3A ) . At other times , RNA levels were lower in the mutant . It is possible that release of transcriptional repression in wild type flies leads to more efficient gene expression than constitutive transcription , which could account for this effect . We also tested whether mRNA levels of importin α1 are expressed cyclically . Quantitative PCR ( qPCR ) revealed that importin α1 transcript levels do not cycle . We then assayed the expression of PER and TIM proteins in whole heads of Df ( 3L ) α1S1 mutant flies . Western blots of fly head extracts from different time points indicated 24 hour oscillations of PER and TIM protein levels in LD . However , the amplitude of the oscillation was blunted relative to wild type ( Fig . 3B ) . As reported previously [25] , hyperphosphorylated forms of PER , detected as low mobility forms on the gel , are observed predominantly at times of nuclear expression e . g . at ZT1 . These are the forms that are subsequently targeted for degradation . PER in mutant flies was less phosphorylated at ZT1 and more stable at ZT7 than in wild-type ( Fig . 3B ) . In DD , it appeared that PER stability , but not its phosphorylation , still cycled in mutant flies , albeit with an aberrant and variable phase ( Fig . 3C ) . Thus , the stability of PER may fluctuate in the cytoplasm . It is also possible that the translocation of PER is regulated in an importin α1-independent manner in some cells that contribute to the signal on whole head western blots . Based upon the lack of cyclic phosphorylation , however , we favor the explanation that PER remains in the cytoplasm . As for PER , TIM cycling was dampened in importin α1 mutants . TIM levels were reduced at daybreak in mutant flies , suggesting that light can degrade cytoplasmic TIM; indeed , the weak cycling seen for PER could be driven by light-induced cycling of TIM , as TIM stabilizes PER [10 , 11 , 29] . However , some TIM remained in mutant flies at ZT1 while TIM in wild type flies was completely gone ( Fig . 3B and S5A Fig . ) . It is possible that cytoplasmic TIM is less sensitive to light-driven degradation . On the other hand , as TIM degradation around dawn is also regulated by circadian clock mechanisms , which persist in constant darkness [30] , we speculate that clock-dependent degradation of TIM requires nuclear expression . Consistent with this idea , TIM cycling was completely abolished in DD ( Fig . 3C and S5B Fig . ) . Given the importance of IMPα1 in the nuclear translocation of PER/TIM in clock cells , we sought to determine if its effect on the subcellular localization of clock proteins could be recapitulated in S2 cells . Both per and tim were tagged with CFP and YFP respectively , and their expression was induced by heat-shock as previously described [9 , 25] . Previous studies have shown that either PER or TIM alone is cytoplasmic when transfected into S2 cells , but each shows more nuclear expression when the other protein is co-expressed [12 , 22 , 25] . However , even under these conditions , neither protein becomes completely nuclear in the S2 cells we use [25] . As these cells likely express very low levels of IMPα1 , we wondered if it was the missing component , and so added IMPα1 in these experiments . The addition of IMPα1 had no significant effect on PER localization by itself , but it increased uniform ( nuclear and cytoplasmic ) expression of TIM up to almost 50% ( Fig . 4A/B ) . As previously reported [22 , 25] , co-expression of PER and TIM also increased nuclear and uniform expression of both proteins ( 40–50% ) . Interestingly , co-transfection of all three proteins ( PER , TIM and IMPα1 ) resulted in strong and specific nuclear localization of PER/TIM in ~70% of cells , with most other cells showing uniform expression ( Fig . 4A/B ) . These results suggested that IMPα1 primarily facilitates nuclear entry of TIM in S2 cells . Previous studies have found a delay in the nuclear translocation of PER/TIM following induction of their expression in S2 cells . Thus , they remain in the cytoplasm for 5–6 hours and then transfer into the nucleus [9 , 22] . This delay is reminiscent of the delay in nuclear entry observed in clock cells , a delay that is thought to be critical for clock function [24] . To investigate whether IMPα1 might be a rate-limiting factor that causes the delay in nuclear transport of PER/TIM in S2 cells , we followed subcellular localization every 2hr after heat-shock induction of both proteins . When PER and TIM were expressed together without IMPα1 , they were mainly cytoplasmic 2hr after induction and showed increased uniform and nuclear expression 4hr after induction . However , increased nuclear and uniform expression of PER and TIM was detected even 2hr after induction when IMPα1 was co-expressed with PER/TIM ( Fig . 4C ) . Thus , the delay in S2 cells may typically result from low expression levels of endogenous IMPα1 . As S2 cells vary from batch to batch , we also examined a different sub-culture that shows higher nuclear expression ( ~85% ) of co-expressed PER and TIM [9] . Interestingly , these cells express detectable levels of IMPα1 ( S6A Fig . ) , which may account for the increased nuclear expression . To determine if this was the case , we down-regulated IMPα1 via RNAi , and found that it confined the expression of PER and TIM to the cytoplasm; however , it did not affect nuclear expression of dCLK in S2 cells ( S6A/B Fig . ) . Together these data support the idea that IMPα1 is specifically required for the nuclear translocation of PER/TIM in S2 cells . Importins typically drive nuclear transport of cargoes in concert with a number of other proteins , including nuclear envelope proteins ( nucleoporins ) . Interestingly , we have frequently noticed nuclear envelope association of TIM in LNvs of adult flies at ZT20 ( S7A Fig . ) . This expression of TIM at the nuclear rim was also observed for TIM transfected in S2 cells ( Fig . 4B and S7B Fig . ) . To investigate whether nucleoporins ( NUPs ) are part of the circadian mechanism , we knocked several NUPs ( NUP214 , 88 , 153 , 154 and Megator ) down in all clock cells using TUG with dicer and assayed rest:activity behavior . None of these showed significant change in circadian behavior compared to control flies . However , knockdown of NUP153 resulted in lethality . To circumvent the lethality , we restricted the expression of nup153 RNAi to adult clock neurons by coupling the TUG driver with tublin-GAL80ts . We found that these flies also died 3–4 days after being moved to 29°C . However , they were arrhythmic before they died ( S7C Fig . ) . Use of the weaker Pdf-GAL4 driver ( along with dicer ) to downregulate NUP153 expression dampened rhythmicity of flies without causing any lethality ( Fig . 5A and S1 Table ) . As with the importins , we assayed expression of PER in the PDF+ cells of flies that had NUP153 knocked down . Knockdown of NUP153 disrupted the s-LNvs , and so we assayed PER in l-LNvs and found that it was cytoplasmic at ZT1 , when PER is nuclear in control flies ( Fig . 5B ) . These data indicate that while important for development , NUP153 is also required for the nuclear entry of clock proteins . Given the nuclear envelope localization of TIM , sought to determine if NUP153 binds TIM . Co-immunoprecipitation assays in S2 cells revealed a specific interaction between V5-tagged TIM and endogenous NUP153 , which we confirmed through GST-pull down assays . A GST-NUP153FG fusion protein , in which phenylalanine glycine ( FG ) repeats of NUP153 were fused to GST and expressed in E . coli , interacted with TIM-V5 expressed in S2 cells ( Fig . 5C ) . Another key regulator of importin-mediated nuclear transport is the small GTPase Ran , which is predominately GDP-bound in the cytoplasm and GTP-bound in the nucleus . In the nucleus Ran-GTP dissociates the IMPα/IMPβ/cargo complex by binding to IMPβ [17 , 31] . To examine the role of Ran in flies , we expressed a dominant negative form of Ran ( RanDN ) in adult PDF+ neurons using the inducible PdfGeneSwitch ( Pdf-GS ) driver . RU486-induced expression of RanDN caused arrhythmia under DD conditions ( Fig . 5D and S1 Table ) . To investigate whether this behavioral phenotype was related to altered nuclear translocation of clock proteins , we determined the localization of PER in Pdf-GS > UAS-RanDN flies at ZT1 on the 3rd day after RU486 treatment . In these flies PDF expression and the dorsal projection from sLNvs were severely impaired . Also , PER was difficult to detect , presumably due to low expression levels . To minimize these effects of Ran , we assayed the localization of PER at ZT1 on the 1st day after RU486 treatment , as morphological effects were not visible at this time . PER was cytoplasmic at ZT1 in RanDN flies whereas it was nuclear in control flies ( Fig . 5F ) , suggesting that Ran is needed for nuclear translocation of clock proteins . Interestingly , we found that TIM expressed in S2 cells also interacts with GST-Ran and this interaction is enhanced when Ran is in the GTP-bound form ( Fig . 5E ) . Our cell culture data indicated that TIM was required for the nuclear localization of PER by IMPα . This could reflect co-transport of two cargoes , or perhaps even an IMPβ like function for TIM . To further test this idea , we sought to determine whether TIM interacts with IMPα1 in a manner that would be predicted for an IMPβ . IMPα proteins bind IMPβ through the Importin-β-binding ( IBB ) domain and this interaction modulates the binding of cargo to IMPα [32] . Specifically , the IBB domain folds over and interacts with the NLS-binding site on IMPα and so it competes with cargo for binding to this site [32] . Cargo binding is facilitated when the IBB domain binds IMPβ and releases the NLS-binding site [33 , 34] . We hypothesized that if TIM functioned as an IMPβ it would not bind to an IMPα1 that lacked the IBB domain . Contrary to our expectations , V5-tagged wild-type TIM showed strong binding to VSV-tagged IMPα1∆IBB whereas it did not bind to wild-type IMPα1 ( Fig . 6A ) . Previous work has shown that the binding affinity of IMPα for cargo increases when the IBB domain is mutated [35] . This result suggests that TIM is cargo for IMPα1 rather than an IMPβ- like molecule . The idea that TIM is classical cargo for importins is also supported by the finding that mutation of the TIM NLS lengthens the circadian period in flies ( ~30hr ) , significantly delays nuclear accumulation of both TIM and wild-type PER in S2 cells , and affects molecular oscillations of PER and TIM in the same manner as the importin α1 mutation [23] . Also , previous work indicated that the nuclear entry of mCRY2 , a mammalian clock component that acts as a PER partner , is mediated by the importin α/β system through a bipartite NLS [36] . To determine if the interaction between TIM and IMPα1∆IBB was dependent on the TIM NLS , we mutagenized basic residues of the NLS to alanines . TIM carrying this mutated NLS did not bind IMPα1∆IBB ( Fig . 6A ) , suggesting , as expected for a typical cargo protein , that the NLS mediates the interaction with IMPα1 . In addition , we found that mutation of the TIM NLS prevented nuclear expression of TIM and PER co-expressed in S2 cells with IMPα1 ( S8A Fig . ) . We also assayed interactions between PER and IMPα1 by immunoprecipitating PER with an anti-HA antibody ( against PER ) and assaying the pellets for expression of IMPα1 . Wild type IMPα1 was not pulled down with PER regardless of the presence of TIM . IMPα1∆IBB was pulled down by PER , but only when TIM was co-expressed , suggesting that TIM serves as a bridge between PER and IMPα1∆IBB ( Fig . 6B ) . Based upon sequence analysis , PER has at least two monopartite NLSs: one near the N-terminus ( aa 73–77 ) and the other in the middle of the protein ( aa 788–791 ) . However , mutations in these NLSs have little to no effect on the nuclear localization of PER [6] . Chang et al . identified a functional bipartite NLS near the C-terminus between amino acids 813–840 . However , mutations in this NLS did not affect the nuclear entry of PER and TIM co-expressed with IMPα1 ( S8A Fig . ) . Thus , TIM is likely the primary target of IMPα1 and its nuclear entry is regulated by the canonical nuclear entry mechanism described above; however , at the same time TIM acts as a carrier for PER . To assay the interaction between IMPα1 and PER/TIM in flies , we immunoprecipitated HA-tagged IMPα1 from head extracts of flies expressing IMPα1-HA under control of the TUG driver . TIM was pulled down in the early night as well as the late night ( S8B Fig . ) . PER was only detected over background in the late night , but we note that PER levels are also substantially higher at this time . Although the TIM structure has not been experimentally determined yet , it was previously proposed to belong to an ARM/HEAT protein superfamily [37] . ARM ( for Armadillo ) and HEAT ( for Huntingtin , Elongation factor 3 , regulatory subunit A of Protein phosphatase 2A , and Target of rapamycin ) repeats are structural units of two ( HEAT ) or three ( ARM ) α-helices which form one turn of a superhelix [38] . Intriguingly , karyopherins ( both importins and exportins ) and even some nuclear pore complex proteins belong to the ARM/HEAT family . Using diverse up-to-date homology modeling programs , we also observed that TIM is weakly homologous to proteins in the ARM/HEAT superfamily or more specifically to karyopherins ( S8C Fig . ) . In addition , TIM shuttles in and out of the nucleus , as do importin molecules [12] . Taken together , these data ( requirement of TIM for PER nuclear entry , TIM expression at the nuclear rim , TIM binding with NUP153 and Ran , and homology modeling of TIM ) support the idea that TIM serves as a karyopherin-like protein for PER . We sought to determine if the mechanism uncovered here contributes to the cytoplasmic expression of TIM and PER caused by the mutant timPL allele . This proline 115 to leucine mutation leads to behavioral and molecular phenotypes similar to those observed in IMPα1 mutant flies . In addition , mutation of a nearby threonine residue at 113 ( TIMTA ) recapitulates the cytoplasmic expression of TIMPL in S2 cells and the behavioral/molecular phenotypes of timPL mutant flies [25] . We assayed interactions of each of these proteins with IMPα1∆IBB . In transfected S2 cells , we observed interactions between wild-type TIM and IMPα1∆IBB , but neither mutant TIM , TIMPL or TIMTA , bound IMPα1∆IBB ( Fig . 6C and S9 Fig . ) . Lack of an interaction with IMPα1 likely accounts for the cytoplasmic localization , and thereby arrhythmic behavior , of the timPL mutant . These data suggest that the interaction with nuclear transport machinery is dependent on the phosphorylation state of TIM . Phosphorylation likely drives a conformational change in TIM , exposing the NLS for interaction with the cargo-binding site on IMPα1 . Together these data indicate that TIM is primary cargo for IMPα1 and it is transported through a classical nuclear entry mechanism , but at the same time , it co-transports PER and thus acts as a karyopherin-like molecule .
The mechanisms underlying the nuclear translocation of PER and TIM have been debated ever since the tim mutation was identified in 1994 [39] . At the time , cytoplasmic localization of a PER-β galactosidase fusion protein in tim01 mutants and nuclear expression of this fusion protein in wild type was taken as evidence that TIM was required for nuclear expression of PER [29] . Efforts to examine endogenous PER were unsuccessful as PER was found to be very unstable in tim01 mutants [10 , 11] . Subsequent cell culture studies indicated that PER and TIM are required for each other's nuclear expression in S2 cells [22] , but this result was also questioned by reports indicating that high expression of PER alone represses transcription [6 , 7 , 12] , and hence enters the nucleus . In addition , studies in Drosophila suggested that PER enters the nucleus before TIM [24] , and that PER is nuclear in tim01 mutants when the destabilizing kinase , casein kinase 1ε ( known as double-time ( DBT ) in Drosophila ) , is also removed [8] . However , we found that when PER is stabilized in tim01 mutants through mutation of a critical destabilizing phosphorylation site ( S47 ) , it is cytoplasmic [25 , 40] . Moreover , we identified a new mutation in tim , timPL , that allows stable expression of TIM and PER , but prevents their nuclear localization , indicating that TIM is required for nuclear expression of PER [25] . Also , mutating the putative NLS in TIM delays the nuclear entry of TIM and PER [23] . Together , these recent findings supported an important role for TIM in the nuclear expression of PER . Here we identified the mechanisms by which TIM and PER are transported to the nucleus ( Fig . 7 ) . We show that TIM is primary and classical cargo for the importin pathway , and it may act in a karyopherin-like capacity to transport PER . We also find that deficits in the interaction with this pathway underlie the phenotype of the timPL mutant . While some of our data suggested the possibility that TIM serves as an IMPβ for nuclear entry of PER/TIM ( Fig . 5 ) , additional tests show that TIM is the primary clock cargo of a specific importin α: IMPα1 . In addition , an IMPβ is likely required , indicated by the finding that the IBB domain is required for IMPα1-driven nuclear localization of PER-TIM ( S8A Fig . ) ( note that cargo binding is actually better without the IBB domain ) . Although we detected interactions of TIM with NUP153 and Ran proteins using immunoprecipitation ( IP ) assays , binding of TIM to these proteins may not necessarily be direct . The source of TIM for the immunoprecipitation experiments was transfected protein from S2 cells , which also express many endogenous proteins . Thus , even though we expressed NUP153/RAN in E . coli , we cannot exclude the possibility that other proteins from S2 cells bridged the interaction . As noted above , both NUP153 knockdown flies and RanDN flies showed morphological defects in clock cells as well as low expression of PER , suggesting that NUP153 and RAN also regulate clock proteins other than PER/TIM . These other proteins could well be CLK and CYC , as loss of these yields morphological defects similar to those seen with reduced NUP153 and mutant RAN [41] . Thus , we believe that only IMPα1 is specific for the nuclear entry of PER/TIM , and other components we tested ( nup153 and Ran ) have a more general role in clock cells . We show here that the TIMPL mutant and the TIMTA mutant are defective in their interaction with IMPα1 , which most likely accounts for the cytoplasmic localization of these proteins . Given our data showing that the interaction between TIM and IMPα1 is dependent on the TIM NLS , these mutations probably affect the conformation of TIM and block access to the NLS . We hypothesize that phosphorylation at threonine 113 , which is also affected by the PL mutation , is required to expose the NLS and allow TIM binding to IMP α1 . This suggests that phosphorylation is important for nuclear entry by regulating the interaction of TIM with the nuclear import machinery , thus providing a specific new function for phosphorylation in circadian clock function . Our cell culture experiments showed that IMPα1 had no effect on PER localization ( PER + IMPα1 ) but dramatically increased nuclear expression of PER/TIM when TIM was added ( PER + TIM + IMPα1 ) , supporting the idea that TIM is required for PER nuclear entry . Also , IMPα1 increased nuclear expression of TIM in the absence of PER ( TIM + IMPα1 ) , although more nuclear localization of TIM was detected when PER was co-expressed . These results are consistent with the idea that ( 1 ) TIM is needed for the nuclear translocation of PER [22 , 25] and ( 2 ) PER retains TIM in the nucleus [12] . Once PER is transported , TIM may not immediately be retained in the nucleus , which would explain the slightly earlier nuclear expression of PER [24] . The requirement of TIM for the nuclear entry of PER is a surprising new twist in the classical nuclear import pathway- the cargo molecule ( PER ) needs an adapter molecule ( TIM ) to bind to the classic adapter molecule ( IMPα1 ) . We suspect that this unusual modification of the classic import pathway has to do with the need to tightly regulate the nuclear translocation of PER in order to generate a clock . As mentioned earlier , TIM shuttles in and out of the nucleus , regardless of whether PER is present [12] . While the export is leptomycin-dependent , and therefore likely mediated by exportins , the nuclear import is probably dependent on the pathway we report here . The mutant TIMPL protein is cytoplasmic even in the presence of leptomycin [25] , suggesting that lack of an interaction with IMPα1 prevents its shuttling . As noted above , TIM is only retained in the nucleus when PER is nuclear . So is the timing of nuclear expression of TIM-PER , in the middle of the night , controlled only by PER ? We do not believe this is the case based upon the phenotype of a new dbt mutation ( dbtEY02910 ) we recently characterized [42] . In this mutant , PER can be detected in the nucleus at all times , and yet TIM still shows temporal nuclear expression in the presence of light:dark cycles [42] . We suggest that normally TIM , and perhaps PER , is modified in the middle of the night , and subsequently PER is recruited into the TIM-IMPα1 complex and PER and TIM are retained in the nucleus . Prior to this , PER is likely anchored in the cytoplasm by DBT , and even when it is nuclear ( in the dbt mutant ) it cannot retain TIM . The modifications probably include phosphorylation , given the data implicating specific kinases [8 , 30 , 43 , 44] and phosphatases [45] in the timing of nuclear entry . In the absence of DBT , PER may be recruited into the TIM-IMP complex at all times , so it is constantly nuclear , while TIM continues to shuttle until a specific time . Interestingly , PER is even nuclear in the tim01; dbtEY02910 double mutant flies at ZT21 [42] . Thus , in the absence of DBT and TIM , PER may be transported into the nucleus by non-regulated nuclear import pathways . At this point , we do not know why TIM is retained in the nucleus given that PER appears to be the major component required for negative feedback [3] . Nevertheless , TIM must have an important function in the nucleus as its nuclear expression is tightly regulated . Loss of circadian cycling of TIM in IMPα1 mutants suggests that clock-controlled degradation of TIM occurs only in the nucleus ( note that light-driven cycling can occur in the nucleus or cytoplasm ) . As maximal phosphorylation of PER as well as maximal repression by it occur only after TIM is degraded at daybreak , TIM may serve to delay maximal feedback . Such delays are thought to be essential for maintaining a clock per se , and also for the circadian timing of it . Thus , TIM may be required to transport PER to the nucleus and to delay feedback by it . Consistent with this idea , the interaction between PER and CLK , which is thought to be critical for transcriptional repression , is TIM-dependent [46] . In addition , the functions of TIM in PER stability [10 , 47] and in entrainment to light are well-known [48 , 49] . Together these and other findings are providing a mechanistic , step-by-step account of how a clock might be generated .
RNAi stocks were obtained from VDRC and Bloomington stock centers . Df ( 3L ) α1S1 , importin α1 null mutant , flies were kindly provided by Dr . R . Fleming [28] . RanDN flies were kindly provided by Dr . W . Odenwald [50] . The UAS-importin α1-HA construct was generated by inserting the coding region of importin α1 tagged with HA epitope into the pUAST-attB vector . Transgenic fly lines carrying this construct were generated by the site-specific PhiC31 Integration System ( Rainbow Transgenics ) using the attP on the 2nd chromosome [51] . These transgenic flies were outcrossed 5–6 times into an isogenic w1118 ( iso31 ) strains . For the behavioral assay , male flies were entrained to 12 h light/dark cycles at 25°C for a least 3 days . Locomotor activity rhythms were measured for 7 to 12 days in DD or LD as previously described [52] . 3–5 day old flies were collected at indicated Zeitgeber times ( ZT ) on the 4th day of LD entrainment or on the indicated day of LD after drug ( RU486 or EtOH ) treatment . Fly heads of each genotype were dissected open , and brains were immediately fixed with 4% paraformaldehyde ( in 1× PBS ) for 15–20 minutes , followed by dissection in 1× PBST ( 0 . 3% Triton X-100 in PBS ) at room temperature . After a 30min-wash with 1x PBST at room temperature , brains were blocked with 5% normal donkey serum ( NDS ) for 20 minutes , and then incubated overnight at 4°C with primary antibody: rat anti-PER ( UPR34 , 1:1000 ) , anti-TIM ( UPR42 , 1:500 ) , and mouse anti-PDF ( C7 , Developmental Studies Hybridoma Bank , 1:500 ) . After a 30-min wash in 1× PBST at room temperature , brains were incubated with secondary antibodies ( Jackson ImmunoResearch Laboratories , 1:500 ) for two hours in NDS at room temperature , followed by an extensive 30min-wash . Samples were imaged using a Leica TCS SP5 confocal microscope . Eight to ten brains were examined for each time point . Fly heads were collected on dry ice at indicated time points in LD . Total RNA was isolated using the Trizol isolation system ( Life Technologies ) . After DNase treatment , cDNAs were synthesized by using a high-capacity cDNA Reverse Transcription kit ( Applied Biosystems ) . RT-PCR was performed on an ABI prism 7100 using a SYBR Green kit ( Applied Biosystems ) . The sequences of primers used for real-time PCR are as follows: per ( fwd: 5'- CCAGATTCCCGAACGTCCGT-3'; rev: 5'- GCAGGAGTGGTGACCGAGTG-3' ) , tim ( fwd: 5'-CCAATGGACAAAAAGGAGCTTAGA-3'; rev: 5'-GTAACCCTTG AGGAGGAAATCCAC-3' ) , importin α1 ( fwd: 5'-CCAATGATAAAATCCAGGCT GTAA-3'; rev: 5'-GGCTAATGCAGGT CAAAGCGTTGT-3' ) , importin α2 ( fwd: 5'-GGCACAGATCAACAGACTGACG-3'; rev: 5'- TGCTTCTGGTTACCTGCTGT GA-3' ) , importin α3 ( fwd: 5'- ACCTTGATCAAGGAGGGCG TCATT-3'; rev: 5'- TTCCTCAATGCAGTTGGCCACCGC-3' ) , importin α4 ( fwd: 5'- CAAAATTC GAGCCGACGCCGCAGA-3'; rev: 5'- AATATACCCTTTTCGCAGACTTCA-3' ) , and actin ( fwd: 5'-GCGCGGTTACTCTTTCACCA-3'; rev: 5'-ATGTCACG GACGATTTCACG-3' ) . At the indicated time points in LD or DD , eight to ten fly heads were collected on dry ice and homogenized in EB1 lysis buffer ( 1X CompleteEDTA-free Protease Inhibitor ( Roche ) , 20mM HEPES pH 7 . 5 , 100mM KCl , 5% glycerol , 2 . 5mM EDTA , 5mM DTT , 0 . 1% Triton X-100 , 25mM NaF , 0 . 5mM PMSF ) [53] . After 30-minute incubation on ice , fly head extracts were spun down , boiled and resolved on 4–12% Tris-Glycine gels ( Novex; Life Technologies ) . Gels were transferred to nitrocellulose membrane and probed with the following antibodies: guinea pig anti-PER ( PA1141 , 1:1000 ) , rat anti-TIM ( UPR42 , 1:1000 ) , and mouse anti-Hsp70 ( sigma , 1:5000 ) . Western blot assays were repeated two or three times with similar results . The PCR-amplified-coding region of importin α1-VSV was cloned into a pIZ/V5-His vector ( Invitrogen ) . pCaspeR-hs-per-CFP and pCaspeR-hs-tim-YFP were used as previously described [9] , and also with advice of Taichi Hara ( former Sehgal lab member ) . S2 cells were cultured in a standard Schneider medium and transfected with these constructs as indicated using an Effectene kit ( Qiagen ) according to manufacturer's protocol . For RNA interference of importin α1 in S2 cells , the first 400 bp and last 400 bp of a full-length Drosophila importin α1 cDNA was PCR amplified using standard PCR techniques and primers encoding a T7 transcription promoter sequence in both directions . In vitro transcription followed by DNAse I digestion was used to generate double stranded RNA ( Ambion ) , as per manufacturer's protocols . Medium from cells transfected in a 6-well plate was replaced with 1 ml Schneider's medium lacking FBS a day after transfection and 1 ug of each RNA preparation was added to each well . After 1 hour of shaking incubation at room temperature , 1 ml of Schneider's medium with 20% FBS was added to the cells to bring the FBS levels to 10% . After 48 h , cells were treated with heat shock ( 37°C ) for 30 minutes and then fixed 8 h or at indicated times after heat shock induction . S2 cells were scored as previously described [25] . At least 100 cells were scored for each condition in two or three independent blind tests . For immunoprecipitation assays in S2 cells , cDNAs encoding per and tim were cloned into the expression vectors pAc-HA and pIZ/V5-His , respectively . NLS mutants for each construct ( pAc-per_mNLS-HA and pIZ-tim_mNLS-V5 ) were generated by site-directed mutagenesis with the Quick Change mutagenesis kit ( Stratagene ) . The cDNAs of importin α1 lacking the IBB domain ( importin α1∆IBB ) and wild-type were amplified and tagged with a VSV epitope using PCR and cloned into a pIZ/V5-His vector . Both pIZ-timPL-V5 and pIZ-timTA-V5 were generated as previously described [25] . S2 cells were transfected with various combinations as indicated . After 60 h , cells were collected and lysed in lysis buffer ( 1X CompleteEDTA-free Protease Inhibitor ( Roche ) , 50 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 0 . 5% Triton X-100 ) . Extracts were incubated with anti-V5 or anti-VSV antibodies at 4°C for 4 h and followed by 2 h-incubation with Protein G or A Dynabeads ( Invitrogen ) . After washing , bound proteins were eluted and subjected to western blot analysis with the following antibodies: rabbit anti-NUP153 antibody ( a gift from Dr . M . Capelson [54] , 1:1000 ) , mouse anti-V5 ( Invitrogen , 1:1000 ) , anti-HA ( Sigma , 1:1000 ) and anti-VSV ( Sigma , 1:1000 ) . To perform the immunoprecipitation from fly head extracts , fly heads were homogenized in EB1 lysis buffer . Extracts were incubated overnight with anti-HA beads ( sigma ) at 4°C and washed three times with lysis buffer . Proteins were eluted and subjected to western blot analysis . For the GST pull-down assay , the FG repeat of Nup153 or Ran ( for GDP-bound ) or Ran Q69L ( for GTP-bound ) was cloned into a pGEX-4T-1 vector ( GE Healthcare ) . These GST-fusion proteins were expressed in E . coli and purified using glutathione-Sepharose 4B beads ( GE Healthcare ) . GST-Ran proteins were loaded with GDP or GTP γS using the Ran activation assay kit from New East Biosciences . GST alone ( control ) or GST-fusion proteins on beads were incubated with TIM-V5 expressed in S2 cells for 4 h at 4°C . After washing , bound TIM-V5 was detected by using a mouse anti-V5 antibody ( Invitrogen ) . Secondary structure prediction programs detected two alpha-helical regions in TIM . These two regions were threaded with different homology modeling programs: PHYRE [55] , Robetta [56] , ITasser [57] , pGenThreader [58] , HHPred [59] and Raptor X [60] . | In Drosophila , circadian rhythms are driven by a negative feedback loop that includes the key regulators , period ( per ) and timeless ( tim ) . To generate this feedback loop , PER and TIM proteins first accumulate in the cytoplasm and then translocate to the nucleus where PER represses transcription . Thus , the nuclear import of PER-TIM proteins is a critical step to separate the phases of activation and repression of mRNA synthesis . In this study , we discovered that a member of the nuclear import machinery , importin α1 is an essential component of this feedback loop . Flies lacking importin α1 ( IMPα1 ) display arrhythmic behavior and cytoplasmic expression of both PER and TIM at all times . In cultured S2 cells , IMPα1 expression directly facilitates nuclear import of TIM , but the effect on PER appears to be indirect . TIM expression is detected at the nuclear envelope and it interacts with other components of the nuclear transport machinery , which we show are also required for nuclear expression of TIM-PER and for behavioral rhythms . Our results thus suggest that TIM functions to link PER to the nuclear import machinery through IMPα1 . Altogether , this study provides the mechanistic basis of a crucial step in the circadian clock mechanism . | [
"Abstract",
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] | [] | 2015 | Drosophila TIM Binds Importin α1, and Acts as an Adapter to Transport PER to the Nucleus |
Function diversification in large protein families is a major mechanism driving expansion of cellular networks , providing organisms with new metabolic capabilities and thus adding to their evolutionary success . However , our understanding of the evolutionary mechanisms of functional diversity in such families is very limited , which , among many other reasons , is due to the lack of functionally well-characterized sets of proteins . Here , using the FGGY carbohydrate kinase family as an example , we built a confidently annotated reference set ( CARS ) of proteins by propagating experimentally verified functional assignments to a limited number of homologous proteins that are supported by their genomic and functional contexts . Then , we analyzed , on both the phylogenetic and the molecular levels , the evolution of different functional specificities in this family . The results show that the different functions ( substrate specificities ) encoded by FGGY kinases have emerged only once in the evolutionary history following an apparently simple divergent evolutionary model . At the same time , on the molecular level , one isofunctional group ( L-ribulokinase , AraB ) evolved at least two independent solutions that employed distinct specificity-determining residues for the recognition of a same substrate ( L-ribulose ) . Our analysis provides a detailed model of the evolution of the FGGY kinase family . It also shows that only combined molecular and phylogenetic approaches can help reconstruct a full picture of functional diversifications in such diverse families .
The large and functionally heterogeneous protein families that we see today result from long evolutionary processes with multiple duplications , gene losses , lateral gene transfers , and speciation events . The gene duplications usually leads to functional diversification within the family , for example , through the emergence of new catalytic mechanisms while preserving a common catalytic step as in the enolase superfamily [1] , [2] . Even more common is the diversification of substrate preferences with the overall conservation of a catalytic mechanism [3] as in various amidohydrolases [4] and kinases [5] . It is generally agreed that new functional specificities emerge as a result of gene duplication and subsequent specialization , while they usually remain unchanged during speciation events [6] . In phylogenetic terms , functions tend to differ between paralogs and be conserved between orthologs , but the complex evolutionary history of most protein families , which includes also gene losses and lateral gene transfers , limits the application of purely phylogenetic approaches in interpreting function divergence . At the same time , other mechanisms , including convergent evolution of the same functions , are also possible . Among plausible evolutionary scenarios , a simple divergent model assumes the emergence of distinct functional specificities following duplication . In this scenario the same function is never invented twice , although it might become a subject of multiple gene losses and horizontal transfer events leading to mosaic phylogenetic distribution . Mixed models include instances of convergent evolution in which the same functional specificity is reinvented in distinct groups of species through lineage-specific expansions and specialization events . For example , the latter model was inferred for the evolution of some receptors in the innate immune system [7] . An extreme case of convergent evolution of essentially identical functions from non-homologous solutions is well documented in literature ( for a recent review , see [8] ) . It is tempting to speculate that the same functional specificity would more readily reemerge ( be reinvented ) within the same family than between non-homologous families . Yet , whether such a phenomenon is indeed characteristic of functionally heterogeneous protein families remains an open question . Two major constraints that limit our ability to effectively address this question are the insufficient knowledge of the actual functions within such families and the limited accuracy of their evolutionary models . Indeed , experimental data about functional specificities are typically available for only a handful of representative proteins , and the homology-based annotation , available for other members of the family , is often imprecise ( general class annotation such as carbohydrate kinase ) or simply incorrect ( misannotation ) [9] . Likewise , the existing methods of evolutionary reconstruction based on sequence information alone often fail to disambiguate divergent branches on phylogenetic trees [10] . In this study we attempted to overcome both limitations by applying a combination of several complementary bioinformatic techniques . We tested this approach on a large protein family of FGGY carbohydrate kinases , which displays extensive variations in functional specificity: proteins in this family carry out ATP-dependent phosphorylation on one out of at least nine distinct sugar substrates ( see Table 1 and the right panel of Figure 1A ) . The choice of the FGGY family for this analysis was supported by several considerations . The broad cross-genome distribution of this family is illustrated by the identification of over 4 , 000 members in the NCBI Non-Redundant sequence database . A remarkable functional diversification of this family ( mentioned above ) was also emphasized in a recent review [8] . Three-dimensional structures of at least 44 members of this family have been solved , providing a solid overview of the structural divergence in this family . Known substrates of FGGY kinases include several distinct sugars ranging from trioses to heptoses . This family also contains a divergent subfamily functioning in quorum sensing , which phosphorylates AI-2 , a bacterial signaling molecule derived from 4 , 5-dihydroxy-2 , 3-pentanedione ( DPD ) [11] , [12] . The functional plasticity of FGGY kinases plays an important role in evolutionary diversity and adaptability of bacterial carbohydrate utilization machinery . Many bacterial genomes contain several representatives of the FGGY family with distinct specificities involved in catabolic pathways of different carbohydrates . For example , the E . coli and B . subtilis genomes each contain six FGGY kinases . Biological functions and biochemical substrate preferences of individual representatives of each specificity type were experimentally characterized , mostly for model species . For instance , in a recent study , substrate specificities of five FGGY kinases from the hyperthermophilic bacterium Thermotoga maritima were predicted and experimentally characterized ( Rodionova et al . , unpublished ) as an extension of our genome-scale reconstruction of a T . maritima metabolic network [13] . The metabolic network are used here to illustrate the diversity of FGGY kinases in the genomic and functional context of carbohydrate metabolism ( Figures 1B , 1C ) . Notably , the divergent nature of three out of five T . maritima FGGY kinases would not have allowed a confident homology-based assignment of their substrate specificity ( biological function ) . Indeed , in most public databases their annotations were typically limited by a general class function ( e . g . , “sugar kinase of FGGY family” ) . Likewise , incomplete and often incorrect functional assignments are widespread in public archives for thousands of FGGY kinases that are present in genomes beyond a handful of model organisms and their close relatives . This consideration was an additional motivation for the present analysis , which takes advantage of the well-defined biochemistry of carbohydrate utilization pathways and their known tendency to form conserved operons and regulons ( Figures 1B , 1C ) to efficiently use genomic and functional context as an important additional evidence for the functional assignment of associated enzymes . The application of such an approach ( as recently illustrated [14] ) in combination with literature information for the accurate functional classification of FGGY kinases was a key factor in building an extensive reference dataset , which enabled an evolutionary analysis reported in this study .
The comparative structural analysis of FGGY kinases reveals a number of highly conserved topological elements . All described members of this enzyme family are composed of two homologous actin-like ATPase domains . The two domains are named FGGY_N and FGGY_C , respectively ( Figure 1A , left panel ) , using nomenclatures in the Pfam database [15] , [16] . A catalytic cleft is formed by the interface between these two domains , where the sugar substrate and ATP co-substrate bind . Extensive structural and functional studies have been carried out on many members of the FGGY family , among them the glycerol kinase ( EC:2 . 7 . 1 . 30 , GlpK ) and rhamnulokinase ( EC:2 . 7 . 1 . 5 , RhaB ) from E . coli ( Table 1 ) . Analysis of experimentally determined three-dimensional structures have shown that the sugar substrate binds deeply within the catalytic cleft , forming interactions mainly with the N-terminus domain , whereas ATP binds near the opening , contacting both N- and C-terminus domains ( Figure 1A , left panel ) . The binding of the sugar substrate drives a conformational change in which the two domains close to prevent solvent from entering the catalytic cleft [17] , [18] , [19] . Although some proteins appear to contain a single FGGY_N or FGGY_C domain , our analysis included only those conforming to the canonical two-domain architecture . We have developed an FGGY kinase reference set starting from 31 representative enzymes with experimentally assigned substrate preferences and biological functions ( including those verified by us using biochemical experiments; see Table 1 ) . Although we focused our analyses on bacterial species , 3 out of the 31 representative enzymes are from eukaryotes , including 1 glycerol kinase ( GlpK ) from Trypanosoma brucei and 2 xylulose kinases ( XylB ) from Pichia stipitis and Candida sp . Xu316 , respectively . We kept them in our dataset just to show that our analysis could potentially be extended to eukaryotic proteins . An expansion of this reference set to 446 proteins from a collection of fully sequenced bacterial genomes was based on two simultaneous requirements: ( i ) no less than 30% sequence identity to one of the reference proteins and ( ii ) conserved genomic ( operons and regulons ) and functional ( pathways and subsystems ) context [20] , [21] . The latter type of requirement is rarely used in protein family analysis , as it is not easily amenable to automation . The use of SEED subsystems [22] allowed us to streamline identification of genomic neighbors of candidate FGGY kinases , as well as the presence or absence of certain “signature enzymes” of catabolic pathways , helping support or refute a considered functional assignment ( see Supplemental Table S2 for the identified genomic and functional context with relevant functions ) . Although the 30% sequence identity threshold is lower than typically used for homology-based functional assignments , in our analysis it was strongly supported by the observed consistency with genomic and functional context . In further analysis we refer to this set of 446 FGGY kinases as a confidently annotated reference set ( CARS ) . It is important to emphasize that our approach focused on the biological function of CARS enzymes , using their participation in specific biological pathways ( Figure 1 ) as a key evidence for the functional assignment . In general , the relationship between biological function and biochemical substrate preferences of enzymes may be quite complex . However , our computational analysis and some experimental data argue in favor of rather good overall agreement between these characteristics in the FGGY kinase family ( see below ) . The confidently annotated reference set ( CARS ) of FGGY proteins included 9 distinct substrate specificities forming 25 clusters based on 30% sequence similarity clustering ( Table 1 ) . Some of the functions , such as glycerol kinase ( GlpK ) , xylulose kinase ( XylB ) , and L-ribulokinase ( AraB ) , span multiple clusters . To identify cases of possible convergent evolution of substrate specificities , we built a phylogenetic tree of all the CARS proteins ( Figure 2 ) . The leaves of the tree represent individual proteins in the reference set ( Supplemental Table S1 ) , and their leading branches were colored according to the functions of the leaves . As depicted in Figure 2 , most proteins form tight clusters with uniform function . This favors the divergent model , suggesting a common ancestor for all enzymes of the same substrate specificity . For example , the largest group in our dataset , the glycerol kinase ( GlpK , colored blue in Figure 2 ) group , although coming from five different sequence clusters , forms a single large branch in the phylogenetic tree , pointing to its ancestral nature and its single origin . Some outliers , however , have been observed for a few other FGGY kinase functions . The L-ribulokinase ( AraB , colored dark cyan in Figure 2 ) group appears to split into distinct branches interspersed by D-ribulokinase ( RbtK ) and gluconokinase ( GntK ) branches . This observation , in principle , opens a possibility of a mixed model with elements of convergent or parallel evolution of substrate specificities in some sub-branches . To explore such ambiguous cases and elucidate the molecular-level evolutionary history of the FGGY family , we used an additional approach aimed at identifying signature amino acid residue positions that are responsible for the recognition of a specific substrate . Ideally , these so-called specificity-determining ( or signature ) positions ( SDPs ) should indicate columns in the multiple sequence alignment of a family that are conserved within an isofunctional subgroup of proteins while different between subgroups with distinct specificities . Many tools have been developed for the prediction of SDPs , using a variety of techniques that take into account the sequence , structural , and phylogenetic information of a protein family [23] , [24] , [25] , [26] , [27] , [28] . The SDPpred algorithm [25] , which we adopted for the purpose of our analysis , is based solely on the statistical analysis of a multiple sequence alignment . It determines a ranking of alignment columns based on the assumption that proteins in the same group use a similar molecular mechanism , i . e . , conserved amino acid residues , to carry out specific functions . In our analysis we combined the predicted ranking of SDPs with protein structural information to define signature residues ( see Materials and Methods for details ) . Since some functional subgroups in our dataset span multiple low-identity sequence-based clusters , we first applied SDPpred to a collection of the largest clusters from each specific group and then mapped the predicted ranking of SDPs into all other clusters through a “master” multiple sequence alignment to allow the comparison of molecular mechanisms among different clusters within an isofunctional group ( Supplemental Figure S1 ) . Of the three-dimensional structures in the Protein Data Bank [29] , 44 structures representing 17 distinct proteins were mapped to the FGGY family based on database search using Hidden Markov Models provided by Pfam database version 24 . 0 [30] . As a result , five SDPs were selected , from which signature residues were defined for each CARS protein , and a signature that reflects the amino acid distribution in the five SDPs was determined for each compact branch on the phylogenetic tree . Below , we will show detailed analyses of these positions and their amino acid distributions . The five selected SDPs , while distant from each other in sequence , are located in the vicinity of the active site holding approximately the same positions in three-dimensional structures of representative FGGY kinases from distinct isofunctional groups ( Figure 3 ) . Side chains of the signature residues in these positions tend to point toward the center of substrate-binding sites , forming interactions with the substrates . For example , among the five signature residues of glycerol kinase ( GlpK ) , four ( Arg83 , Glu84 , Tyr135 , and Asp245 ) form hydrogen bonds with the hydroxyl groups of glycerol and one ( Phe270 ) is in van der Waals contact with the carbon backbone of glycerol [15] . An additional validation of the predicted signature positions was provided by the observation that the SDP-derived signature sequences ( a concatenated sequence of signature residues ) would allow functional classification of FGGY kinases , splitting the entire FGGY protein space into tightly clustered and largely mono-functional groups . This is illustrated by the protein similarity networks ( PSNs ) reconstructed based on the sequence signatures ( Figure 4 ) . Furthermore , a comparison of the PSN built on sequence signatures with the PSN built on the entire sequences , under various thresholds of sequence alignment E-values ( Supplemental Text S3 ) , showed that the functional information encoded in the entire sequences is preserved in the five signature positions , adding to the functional relevance of signature residues . These observations strongly argue that the residues in the identified SDPs play important roles in substrate recognition and the determination of functional specificities . The amino acid distributions on SDPs form a signature of each specific isofunctional group ( and their subgroups ) and are shown in Figure 2 as consensus logos made with the Weblogo [31] software . In addition , the SDP signatures can also be represented as position weight matrices , and they were compared using a hierarchical clustering approach based on the similarity of signature pairs ( Supplemental Text S2 ) . The SDP signature of glycerol kinases ( GlpKs ) , in addition to their tight branching in the phylogenetic tree , confirmed that they all came from the same origin . Although members of the GlpK group have largely variable global sequences forming five distinct clusters of less than 30% sequence identity , among which only one cluster was selected for sequence-based SDP prediction and others had their SDPs mapped from a “master” multiple sequence alignment of all CARS proteins , their signature residues are extremely well conserved and identical among different clusters . In the middle of the spectrum , the xylulose kinase ( XylB ) group was divided into three branches in the phylogenetic tree: XylB-I contains four clusters and is the most dominant form of the xylulose kinases , while XylB-II , which contains three clusters , was divided into prokaryotic branches ( XylB-II , Prk ) and eukaryotic branches ( XylB-II , Euk ) . The signature motifs of all three clusters are very similar ( Supplemental Figure S3 ) , with the prokaryotic branches of XylB-II having the most variable amino acid distributions acting as a transition between XylB-I and the eukaryotic branches of XylB-II . This is also reflected in the relative positions of XylB-I and the two XylB-II branches in the phylogenetic tree . In a more extreme case , the L-ribulokinase ( AraB ) group has the least conserved signature residues . This group was divided into four branches—AraB-I , -II , -III , and -IV—on the phylogenetic tree . Indeed , they all have distinct signature motifs , of which three ( AraB-I , -II , and -IV ) are more similar than the fourth ( AraB-III ) ( Supplemental Figure S3 ) . Although the signature of AraB-III is more distant from other L-ribulokinases ( AraBs ) , this branch is closer to AraB-I and -II on the phylogenetic tree , whereas AraB-IV , whose signature is closer to AraB-I and -II ( Supplemental Figure S3 ) , is more distant from other L-ribulokinases ( AraBs ) on the tree . This observation favors the simple divergent model in the evolution of L-ribulokinases ( AraBs ) , in which a common ancestor of all L-ribulokinases ( AraBs ) diverged within the same isofunctional group , followed by the emergence of at least two distinct biochemical mechanisms for the implementation of the AraB function . The D-ribulokinase ( RbtK ) and gluconokinase ( GntK ) branches , which independently emerged from AraB-II and AraB-III , carry signatures that are similar to these two subgroups , respectively , indicating that they adopted the different signature residues and evolved from the two subgroups of L-ribulokinase ( AraB ) . An additional case of interest was the L-fuculokinase ( FucK ) and rhamnulokinase ( RhaB ) groups . They carry very similar signatures ( Supplemental Figure S3 ) and form a poorly resolved single cluster on the phylogenetic tree ( shown as green and orange in Figure 2 ) , suggesting these two functions not only are evolutionarily related , but also have employed similar biochemical mechanisms . In addition to assisting the evolutionary analysis , identification of SDPs helped in accurate propagation of functional assignments . Thus , we used a combination of context-based and signature-based analyses ( see Materials and Methods for details ) to expand annotations of all 9 specificities over the entire set of 191 completely sequenced bacterial genomes comprising the original CARS . As a result , functional assignments were made for 785 additional proteins from these complete genomes based on the consensus between signature residues and the genomic context of newly annotated FGGY kinases . This expansion allowed us to analyze the taxonomic distribution of FGGY kinase functions , illustrated here by a projection over a species tree ( a subtree derived from [32] , see Materials and Methods for more details ) ( Figure 5 ) . Comparative analyses of the protein and species trees ( as in Figures 2 and 5 ) are commonly used for detailed evolutionary reconstructions of functionally heterogeneous protein families [33] , [34] , [35] . The obtained results showed that glycerol kinase ( GlpK ) is the most dominant isofunctional group that exists in nearly all species . As another extreme , D-ribulokinase ( RbtK ) and erythritol kinase ( EryA ) appear exclusively in Alphaproteobacteria , whereas L-xylulose kinase ( LyxK ) exists only in a few groups of Gammaproteobacteria . The taxonomic distributions of the various subgroups of L-ribulokinase ( AraB-I , -II , -III , -IV ) and xylulose kinases ( XylB-I , -II ) were indicated with roman numerals on the species tree . In the case of AraB , while the most abundant group , AraB-I , is spread broadly among various phyla , AraB-III and AraB-IV are contained within the two lineages of Firmicutes and Thermotogales , respectively . Similarly , XylB-I is the dominant case , whereas XylB-II is confined only in Actinobacteridae . Notably , the FGGY kinase family experienced several instances of lineage-specific expansion , for example , in Pasteurellales and Enterobacteriales , leading to an extensive repertoire of six FGGY kinases including a “newborn” L-xylulose kinase ( LyxK ) function . Although the analysis of lateral gene transfer events was beyond the scope of this study , it apparently played a substantial role ( together with massive lineage-specific gene losses ) in shaping up the observed mosaic distribution of several FGGY kinase functions . For example , the AraB-III subgroup was shared by species out of the Firmicutes lineage ( Actinobacteridae and Pasteurellales ) , while the AraB-II subgroup appeared in some Firmicutes .
A majority of proteins in the protein universe belong to a relatively small number of large protein families [6] . In evolution , large protein families have expanded through duplications and subsequent specializations within single genomes , leading to the emergence of new ( but usually similar ) functions within the same family . However , little is known about how different functions emerge in protein families and how the emergence of such functions happens on the molecular level . Here , in the example of the FGGY carbohydrate kinase family , we assessed contributions of divergent and convergent evolution of functions in the history of this family . Such analyses could not have been done even a few years ago because of the lack of experimental data , three-dimensional structural information , and sufficiently large sets of functionally assigned protein sequences from a wide variety of organisms . In order to reconstruct the evolutionary history of FGGY kinases , we built a reference set of proteins with accurate annotations of biological functions ( CARS ) based on experimental data and context-based functional predictions . We then expanded it by combining predictions based on genomic and functional context analysis with predictions based on signature residues . This allowed us to study the evolution of substrate specificity in the FGGY kinase family on both phylogenetic and molecular levels , combining a protein phylogenetic tree , a species tree , and signature residues . To determine specificity-determining positions ( SDPs ) , we used a modified integrative approach combining the sequence-based algorithms [25] , [26] , [36] with the structure-based determination of functionally important residues [15] using three-dimensional structures of representative FGGY kinases co-crystallized with their natural substrates . The consensus of both helped eliminate false-positive predictions and narrowed our search to positions that are essential for molecular binding rather than for general catalysis ( Supplemental Figure S2 ) . The identified SDPs were highly useful in the evolutionary analysis of FGGY kinases and provided consistent functional assignments for both the CARS proteins ( Figure 4B ) and the new proteins in a number of complete genomes . They also gave remarkable insights into the structural basis and biochemical mechanism of specific substrate recognition in the FGGY family . The molecular signatures of SDPs , however , have certain limitations in discriminating some functional groups . For example , the signatures of the L-fuculokinase ( FucK ) and rhamnulokinase ( RhaB ) groups are very similar to each other ( Supplemental Figure S3 ) , and these two functional groups form a poorly resolved single branch in the phylogenetic tree ( Figure 2 ) . In this case it is tempting to speculate that the observed similarity at their overall sequence and signature levels reflects the chemical similarity of the stereoisomeric substrates , L-fuculose and L-rhamnulose , but at the same time we should keep in mind that this does not hold true for all stereoisomers . For instance , the similarity between SDP signatures of D- and L-xylulose kinases , or D- and L-ribulokinases , is less obvious . The construction of protein phylogenetic trees is a common approach for the evolutionary study of protein families . In the case of the FGGY family , phylogenetic analyses revealed a single origin for eight out of nine studied isofunctional groups . However , different L-ribulokinase ( AraB ) branches were seen on the phylogenetic tree , suggesting two possible evolutionary scenarios: ( i ) these branches represent decedents of distinct ancestor proteins or ( ii ) these branches represent an early divergence of a common ancestor . The former would support the mixed model , in which the same function was “invented” independently more than once . The latter scenario would support a simple divergent model . A likely solution of the AraB conundrum was obtained by combining the location of different branches on the phylogenetic tree with the molecular-level analysis of signature residues . The signatures of AraB-III and GntK contain successive methionine and histidine residues at the first two positions , which also appeared in the XylB-I cluster , suggesting a divergent evolution event that created the GntK function from AraB-III . The same is true for RbtK , which emerged from the AraB-II branch following the divergence of their molecular binding sites . Therefore , the latter evolutionary scenario ( the simple divergent model ) is favored , and it is quite likely that the D-ribulokinase ( RbtK ) , gluconokinase ( GntK ) , and L-ribulokinase ( AraB ) functions all emerged from a common ancestor but followed distinct molecular-level solutions . The evolution of multiple molecular solutions for the L-ribulokinase function reflected the plasticity of enzyme active sites as described by Todd et al . [37] . The combined analyses of a protein phylogenetic tree and a species tree allowed a complete evolutionary reconstruction of the FGGY kinase family ( Figure 6 ) . The glycerol kinase ( GlpK ) , L-ribulokinase ( AraB ) , and xylulose kinase ( XylB ) groups are the most dominant isofunctional groups that exist in the majority of bacterial species . As inferred by both the protein and the species trees , they are probably the ancestral forms of FGGY kinases in bacteria . While GlpK remained unchanged in its functional specificity and molecular mechanism , AraB and XylB diverged to form distinct biochemical mechanisms within the same function or to form new functional groups . In the case of AraB , at least two distinct solutions have emerged for the recognition of L-ribulose substrate with the splitting of Thermotogales and Firmicutes species . Additionally , an ancestral AraB apparently gave rise to two distinct functions , gluconokinase ( GntK ) and D-ribulokinase ( RbtK ) . Four distinct groups of diverse specificities appear to have evolved within the XylB branch , and among them is a relatively recent divergence of rhamnulokinase and L-fucolokinase functions . The GlpK group stayed remarkably unchanged during evolution , exhibiting a high level of conservation of signature residues compared to other isofunctional groups . This observation may be rationalized by considering at least two types of constraints . First of all , GlpK plays a unique role , which extends beyond carbohydrate catabolism and links to the lipid metabolism , in the central metabolic network of all bacterial species ( Figure 1B ) . This is consistent with the ancestral origin and broad conservation of GlpK across the species tree ( Figure 5 ) . Second , glycerol is the smallest ( three-carbon ) in the entire panel of substrates of FGGY kinases ( Figure 1A , right panel ) , which may provide another set of constraints on active site variations while preserving optimal affinity and specificity . The functional assignments in our dataset were based on the reconstruction of genomic and functional context and reflect the biological functions of proteins . The signature residues , on the other hand , should in principle reflect the biochemical specificities of proteins regardless of their biological context . The fact that we can extract compact biochemical signatures from the isofunctional groups annotated through biological-context analyses suggests that there is good agreement and relatively little promiscuity between biological function and biochemical specificity . In fact , experimental data available so far suggest a typically narrow specificity of FGGY kinases to the preferred physiologically relevant substrate . Our experiments on all five T . maritima FGGY kinases showed non-overlapping substrate specificity profiles , in each tested case showing over tenfold preference for the respective physiological substrate ( Rodionova et al . , unpublished ) . Therefore , members of the FGGY family are characterized by their specific functions that are mediated by a small number of specificity-determining residues . A notable exception is the rhamnulokinase ( RhaB ) and L-fucolokinase ( FucK ) functions . Our computational analyses on the protein phylogenetic tree and signature residues suggested that they might have mixed specificities to both substrates ( L-rhamnulose and L-fucolose ) , and biochemical experiments also confirmed that an E . coli rhamnulokinase has a spectrum of potential substrates that includes L-rhamnulose , L-fuculose , and L-xylulose [38] , [39] . This biochemical promiscuity can also be explained by our evolutionary model , in which the functions of L-fuculokinase ( FucK ) , L-xylulose kinase ( LyxK ) , and erythritol kinase ( EryA ) emerged relatively recently from rhamnulokinase ( RhaB ) . Finally , our study provides a workflow that can be efficiently used for the functional and evolutionary analysis of large and functionally heterogeneous protein families . Based on our experience with the specific protein family of FGGY kinases , we believe that this approach can be generally adapted for the analyses of other protein families . Specifically , this workflow can be useful in building an initial set of high-quality annotations to allow the application of other high-throughput approaches for the identification and analyses of isofunctional subfamilies [33] , [40] , [41] .
The confidently annotated reference set ( CARS ) of proteins was created through expanding a literature-based reference set of 31 proteins ( Table 1 ) based on sequence similarity and comparative genomic approaches . Specifically , we required that the annotated proteins should have at least 30% sequence similarity to a reference protein and that the annotation should be supported by the genomic and functional context [20] . The 30% sequence clustering was achieved using the UCLUST method implemented in the USEARCH package [42] . We used the SEED annotation and analysis tool [22] to collect information about the genomic and functional context of proteins . A subsystem was built for the nine reported functions of FGGY kinases and their adjacent functions in the respective metabolic pathways , and then all the complete genomes in the RAST server [43] were mapped to the subsystem to check for the functional annotation of FGGY kinases and their neighbors in the genomes . The functional assignment of a specific protein is confirmed and included into CARS only when it has genomic neighbors that perform relevant functions in a metabolic pathway . The signature residues in our analyses were identified based on two criteria . First , the residues should be significantly conserved within a subgroup of proteins of identical substrate specificity , and they should be distinct among different subgroups . Second , the residues should locate within a certain range of a co-crystallized substrate in the three-dimensional structure . The first criterion was achieved using the SDPpred server [25] , which requires as input a multiple sequence alignment ( MSA ) and a grouping of proteins based on their specificity . In this case , the MSA of all CARS proteins was built with the MUSCLE program [44] , and we used a modified procedure to better accommodate the algorithm of SDPpred ( Supplemental Text S1 ) . The result of SDPpred is a list of rankings for each individual alignment position , and the ranking indicates the significance of the position in distinguishing different isofunctional groups . The second criterion was achieved by calculating the average distance from a residue position to the substrate . The residues were mapped from structures to the MSA so that an average distance could be calculated for each alignment position . The average distances of the residues to the substrates were plotted against the SDPpred ranking of residue positions to show the overall trend of these two parameters ( Supplemental Figure S2 ) . Five positions were selected from the MSA using the following threshold: the ranking should be better than the global minimum of a Bernoulli estimator , and the average distance to co-crystallized , functionally relevant ligands in the structured proteins should be no more than 4 Å . The false-positive predictions using the sequence-based SDPpred algorithm alone were indicated with filled black dots , and those using structure-based information alone were shown in a dashed square in the upper left of Figure S2 . The consensus of both helped to eliminate these false-positive predictions . The protein similarity networks ( PSNs ) were built using the method described in [45] , and the same approach was used to build PSNs for both signatures and entire sequences . First , alignments were obtained for each pair of sequences using the “blast2seq” program from the NCBI toolkit [46] with a parameter of word-size equal to 1 . The program returns an E-value for each pairwise alignment , indicating its significance . Second , a threshold is chosen for the selection of sequence pairs that are significantly similar ( with an E-value better than the cutoff value ) . Finally , a network is built based on the pairwise E-values and the selected threshold . Each node in the network indicates a protein in CARS , and each edge in the network indicates that the pair of nodes linked by this edge has an alignment with an E-value more significant than the selected threshold . The network was visualized using Cytoscape software version 2 . 7 [47] , and the nodes in the network were arranged using the yFiles organic layout method . Position weight matrices ( PWMs ) were built for the annotated protein set on each 30% sequence cluster . The matrices each have 20 rows and 5 columns , indicating the distribution of 20 amino acids on the 5 signature positions . Pairs of signatures ( in the form of PWMs ) were compared based on the correlation coefficients calculated with the corr2 function in MATLAB . Based on the correlation coefficients , signatures of different protein clusters were grouped using hierarchical clustering implemented in the hclust function in the R software package . More details on how to evaluate the similarity of PWMs and how to perform hierarchical clustering of signatures are in the Supplemental Text S2 . A protein phylogenetic tree ( Figure 2 ) was built on the CARS proteins with the FastTree program [48] using default parameters . The root ( marked as a black circle ) of the protein tree was determined using the endonuclease subunit of excinuclease ABC ( UvrC ) as an out-group . The bootstrapping values on the tree were computed with the consensus of 100 random trees using the “Fast Tree-Comparison Tools” provided with the FastTree program ( scaled so that 100 is the maximum ) , and the random trees were calculated with the “-n” option of the FastTree program [48] over a list of bootstrapped sequences generated from the original sequence alignment using the SEQBOOT program in the PHYLIP package . The species tree was extracted from a published tree of bacterial species built on a concatenated alignment of a number of marker genes [32] . When extracting the species tree , we only considered species whose genomes were completely sequenced , and we only used this tree to determine location of a species , but not specific strains . In Figure 5 , branches were collapsed to show an overview of the more general taxonomic level , and this general taxonomic annotation was shown as leaves of the tree . A complete expansion of all species and their functional predictions in the species tree is listed in Supplemental Table S3 . The functional distribution view in Figure 5 was prepared with the support of the iTol interface [49] . The width of the colored bars is proportional to the ratio between the number of species that contain a protein of given function and the total number of species examined in a taxonomic group . In order to remove biases given by false-negative annotations in the species tree analyses , we expanded the training set ( only for the species tree analysis ) through full genomic surveys of all species in the annotated set . All the predicted FGGY functions ( Supplemental Table S3 ) were based on genomic and functional context prediction , as well as the identity of signature residues . Functional assignments were made when consistencies were reached between the two criteria and were added to the functional spectrum views in Figure 5 . | The protein universe is under constant expansion and is reshaping through multiple duplication , gene losses , lateral gene transfers , and speciation events . Large and functionally heterogeneous protein families that evolve through these processes contain conserved motifs and structural scaffolds , yet their individual members often perform diverse functions . For this reason , the exact functional annotation for their individual members is difficult without detailed analysis of the family . In our study , we performed such a detailed analysis of a particularly heterogeneous FGGY kinase family through the integration of several computational approaches . The combination of phylogenetic and molecular approaches allowed us to precisely assign function to hundreds of proteins , thus reconstructing carbohydrate utilization pathways in almost 200 bacterial species . This analysis also showed that different molecular mechanisms could evolve within a group of isofunctional proteins . Moreover , based on our experience with this specific protein family of FGGY kinases , we believe that our approach can be generally adapted for the analyses of other protein families and that the accumulation of evolutionary models for various families would lead to a better understanding of the protein universe . | [
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] | 2011 | The FGGY Carbohydrate Kinase Family: Insights into the Evolution of Functional Specificities |
Hyperostosis Cranialis Interna ( HCI ) is a rare bone disorder characterized by progressive intracranial bone overgrowth at the skull . Here we identified by whole-exome sequencing a dominant mutation ( L441R ) in SLC39A14 ( ZIP14 ) . We show that L441R ZIP14 is no longer trafficked towards the plasma membrane and excessively accumulates intracellular zinc , resulting in hyper-activation of cAMP-CREB and NFAT signaling . Conditional knock-in mice overexpressing L438R Zip14 in osteoblasts have a severe skeletal phenotype marked by a drastic increase in cortical thickness due to an enhanced endosteal bone formation , resembling the underlying pathology in HCI patients . Remarkably , L438R Zip14 also generates an osteoporotic trabecular bone phenotype . The effects of osteoblastic overexpression of L438R Zip14 therefore mimic the disparate actions of estrogen on cortical and trabecular bone through osteoblasts . Collectively , we reveal ZIP14 as a novel regulator of bone homeostasis , and that manipulating ZIP14 might be a therapeutic strategy for bone diseases .
Hyperostosis Cranialis Interna ( HCI , OMIM 144755 ) is a rare bone disorder characterized by endosteal hyperostosis and osteosclerosis of the calvaria and the skull base . This results in the entrapment and dysfunction of cranial nerves I , II , V , VII and VIII , causing disturbances in smell , vision , sensation in the face , facial expression , hearing and balance , respectively [1 , 2] . In addition , increased ocular and intracranial pressure can occur , leading to frequent headaches . Remarkably , there is no indication that the remainder of the skeleton is affected in HCI patients . The first radiological abnormalities are often seen in the first decade , whereas the first symptoms occur late in the first decade or in adulthood and slow progression of the disease can be seen until the age of 40 [1 , 2] . Untimely death may occur in severely affected cases , due to decreased intracranial volume [2 , 3] . HCI was originally described by Manni et al . in three related families with common progenitors from the Netherlands with currently 13 affected family members over four generations [1] . This family is still the only family known with HCI . As a monogenic skeletal disorder , HCI has an autosomal dominant inheritance pattern . The genetic cause of HCI has been investigated previously by performing a whole-genome scan and linkage analysis in this family , where we assigned the locus for HCI to chromosome 8p21 [4] . The aim of this study was to further look for the disease-causing gene and get insights in the mechanism underlying HCI . Therefore , whole-exome sequencing was performed , which resulted in the identification of a missense mutation ( p . L441R ) in the SLC39A14 ( ZIP14 ) gene , encoding a zinc transporter . In vitro studies were performed to investigate the subcellular localization and p . L441R ZIP14 . Furthermore , two conditional knock-in mouse models were generated , overexpressing p . L438R Zip14 in osteoblasts and osteoclasts . Thorough skeletal phenotyping of these mice was performed to unravel cell-specific effects of p . L438R Zip14 in vivo . Finally , to learn more about the pathogenesis of this disorder , histology of a HCI skull biopsy specimen was performed and luciferase reporter assays were done to look for aberrations in signaling pathways caused by p . L441R ZIP14 .
Whole-exome sequencing ( WES ) was performed on one affected individual from the family with HCI . The average coverage throughout the whole exome was 66x . After filtering variants for their absence in dbSNP and excluding non-coding and synonymous variants , we focused on the variants present in the linkage region on chromosome 8 ( chr8: 21 , 593 , 210–28 , 256 , 787 ) after which only two variants remained ( Fig 1A ) . Both variants , one in SCARA3 with a 5x coverage and one in SLC39A14 with a 66x coverage , were checked with Sanger sequencing . The variant in the SCARA3 gene appeared to be a false positive , since we could not confirm it in the patient . The other variant is a heterozygous c . 1322T>G substitution in the solute carrier family 39 member 14 ( SLC39A14 or ZIP14 ) gene ( Fig 1B ) . This variant co-segregates with the disease in the complete family and was not found in 100 control individuals with the same ethnic background and is not present in sequence databases , including dbSNP , 1000 Genomes Project and ExAc databases . Eighteen exons from the linkage region remained partially or completely uncovered by WES and were all checked with Sanger sequencing , but no additional pathogenic variants were identified . Our results therefore indicate that the c . 1322T>G variant found in ZIP14 is the only coding variant in the 8p21 region previously linked to HCI , confirming its disease causality . The human SLC39A14 gene has four protein coding isoforms according to the National Center for Biotechnology Information ( NCBI ) , all consisting of nine exons . The heterozygous c . 1322T>G substitution in exon 8 of ZIP14 affects all isoforms of the gene and results in a p . L441R substitution ( Fig 1C ) , altering a highly conserved amino acid . Accordingly , this missense mutation has a Combined Annotation Dependent Depletion ( CADD ) score of 29 . 4 , indicating it belongs to the top 0 . 11% most deleterious substitutions that can occur in the human genome [5] . As a zinc ( Zn ) transporter , ZIP14 has six or eight transmembrane domains , depending on the literature or prediction program used ( TMHMM , MEMSAT , PRED-TMR , HMMTOP ) [6 , 7] . Nevertheless , the p . L441R mutation is always located at the end of the second-to-last transmembrane domain of ZIP14 . All transmembrane prediction programs predict the variant to cause one or more shifts in a preceding , the affected or the following transmembrane domain , due to the replacement of a hydrophobic leucine by a hydrophilic arginine . To evaluate the subcellular localization of mutant ( L441R ) ZIP14 , HEK293T cells were transfected with wildtype ( WT ) , L441R or truncated ( W22X ) ZIP14-GFP constructs and visualized with confocal microscopy ( Fig 2 ) . WT ZIP14 is located on the plasma membrane and in the cytoplasm , as previously reported [8–12] . In contrast herewith , L441R ZIP14 is not present on the plasma membrane , but appears to be trapped in the cytoplasm . Further investigation of the cytoplasmic localization of L441R ZIP14 with markers for the Golgi apparatus and for early and late endosomes demonstrated no difference in the intracellular localization of WT and L441R ZIP14 ( S1 Fig ) . A heterozygous model ( WT/L441R ZIP14 ) clearly shows increased expression in the cytoplasm ( compared to WT ) and some co-localization on the plasma membrane . W22X ZIP14 shows expression in the cytoplasm as well as in the nucleus . Moreover , there is a difference in cytoplasmic distribution of the different ZIP14 forms , i . e . both WT and L441R ZIP14 appear to be clustered in similar vesicular-shaped structures , whereas W22X ZIP14 is uniformly spread across the cytoplasm ( Fig 2 ) . 65Zn uptake and Zn accumulation studies were performed to assess the basic functionality of p . L441R ZIP14 as a transporter of Zn ( and other metals ) ( Fig 1D and 1E ) . 65Zn uptake experiments revealed that overexpressing WT ZIP14 significantly ( p<0 . 001 ) increases 65Zn uptake by 4-fold when compared to cells transfected with empty vector . On the contrary , L441R and W22X ZIP14 showed no sign of 65Zn uptake from the extracellular space into the cell ( Fig 1D ) . This was no surprise , as L441R and W22X ZIP14 were no longer detected on the plasma membrane of the cells . FluoZin3-AM measures the accumulation of labile Zn in the cell . Results show that there is a significant ( p<0 . 05 ) increase in Zn accumulation in cells overexpressing WT ZIP14 . Overexpression of L441R ZIP14 also results in a significant ( p<0 . 001 ) increase in intracellular Zn accumulation , which is greater than for WT ZIP14 , indicating that labile Zn is trapped in cells with L441R ZIP14 ( Fig 1E ) . Altogether , L441R ZIP14 no longer reaches the plasma membrane , but still resides on the same cytoplasmic structures as WT ZIP14 from where it causes an entrapment of labile Zn . ZIP14 was reported to be expressed in many tissues with increased expression in the liver , pancreas , thyroid gland , heart and intestine , and a low expression in the brain [6] . Information on the expression of ZIP14 in skeletal cell types ( osteoblasts , osteoclasts , osteocytes ) has not been reported in the literature . We therefore performed immunohistochemistry on sections of giant cell tumor and osteoblastoma tissue , bone tumors known to be enriched with osteoclast-like giant cells and osteoblasts , respectively . Here , expression of ZIP14 was detected in osteoblasts of osteoblastoma tissue and giant cells from giant cell tumor tissue ( Fig 3A ) . ZIP14 was not expressed in osteocytes of osteoblastoma or giant cell tumor tissue . Moreover , quantitative real-time PCR ( qPCR ) was performed on KS483 cells , murine mesenchymal stem cells , to assess expression level of murine Zip14 ( mZip14 ) during the different phases of osteoblast differentiation to a mature mineralizing osteoblast . Our results , depicted in Fig 3B , indicate that expression of mZip14 is stable during proliferation ( first week ) and maturation ( second week ) of osteoblast differentiation and rises during the mineralization phase ( day 18–21 ) . Lastly , Zip14 expression was checked with qPCR in murine osteoclasts derived from calvaria and long bones . Here we also detected expression of mZip14 in both osteoclastic cell populations , but in osteoclasts derived from the calvaria we found an average 2-fold greater expression of mZip14 in osteoclasts ( Fig 3C ) . A skull and first cervical vertebra biopsy specimen were obtained from a patient with HCI as well as a skull biopsy from a control during a neurosurgical intervention . All fragments were embedded in paraffin , sectioned and stained with H&E to examine the micro-structure of the internal cortex ( interna ) , diploë and external cortex ( externa ) of the skull ( Fig 4A ) . First , in the control sample we did not find significant microscopic differences between the interna and externa ( Fig 4C ) , but in the patient samples the interna is severely affected . The number of Haversian channels and osteocytes is significantly lower in the patient interna , compared to the externa and the cortex of the cervical vertebra of the patient ( Fig 4C ) . When we compare the externa of the patient with that of the control , the number of osteocytes was significantly lower ( p = 0 . 0054 ) in the patient ( Fig 4C ) , although osteocyte distribution is comparable ( Fig 4A ) . Comparing the patient and controle internae , however , demonstrates that the patient interna is wider and characterized by a great and dense amount of well-organized bone , suggesting an increased bone formation or decreased bone resorption . Moreover , the number of Haversian channels ( p = 0 . 0075 ) and the number of osteocytes ( p = 0 . 0042 ) are significantly lower in the patient interna , compared to interna of the control . Remarkably , the osteocytes in the patient interna appear grouped around the Haversian channels . Some osteocyte lacunae , especially further away from the Haversian channels , appear empty , suggesting osteocyte apoptosis . This was not seen in the patient externa or vertebral tissue or in the skull of the control . Zip14-/- mice were previously generated at the University of Florida , USA [13] . These Zip14-/- mice show dwarfism and general osteoporosis of the appendicular skeleton and vertebral column , with a decrease in trabecular bone volume , but normal cortical bone [14] . As no information was available on the calvarial phenotype of these mice , we performed μCT analysis on calvaria of Zip14+/+ and Zip14-/- mice but found no significant differences in calvarial thickness ( Calv . Th ) or porosity ( Calv . Po ) ( Fig 5A ) . An in vivo model to study the effect of ZIP14L441R was generated by creating a floxed mutant Zip14 ( Zip14flox ) mouse model to express Zip14L438R ubiquitously ( Sox2-Cre ) or in specific cell types , i . e . osteoblasts ( Runx2-Cre ) and osteoclasts ( CtsK-Cre ) . Breeding Zip14flox/flox mice with Sox2-Cre mice demonstrated that ubiquitous expression of mutant Zip14 results in perinatal lethality . We therefore focused on mice with conditional expression of Zip14L438R . In total , 6-month old Zip14fl/- controls ( n = 6 ) , Zip14fl/-; Runx2-Cre ( osteoblast-specific knock-ins , Zip14L438R Ob-KI , n = 6 ) and Zip14fl/-; CtsK-Cre ( osteoclast-specific knock-ins , Zip14L438R Oc-KI , n = 6 ) were collected for skeletal phenotyping . No gender-specific differences were observed , so the results presented in this article are solely these from the skeletal analysis of male mice . Skeletal phenotyping results of 6-month old female Zip14fl/- controls ( n = 3 ) , Zip14fl/-; Runx2-Cre ( n = 3 ) and Zip14fl/-; CtsK-Cre ( n = 3 ) can be found in S2–S4 Figs . μCT analysis of the calvaria and femora was performed to unravel structural differences of Zip14L438R Ob-KI mice versus Zip14fl/- controls . Although calvarial porosity appears lower in these mice there were no significant differences in calvarial parameters ( Fig 5B ) . In contrast herewith , μCT analysis of the femora showed a severe skeletal phenotype versus controls ( Fig 6 ) . Compared to Zip14fl/- controls , the Zip14L438R Ob-KI mice had a significant increased cortical thickness ( Ct . Th , p = 6 . 0E-6 ) with a decreased cortical porosity ( Ct . Po , p = 0 . 0014 ) and a significantly smaller midshaft diameter ( Ms . D , p = 4 . 1E-6 ) ( Fig 6A and 6B ) . Furthermore , Zip14L438R Ob-KI mice have a significantly decreased trabecular bone volume ( BV/TV , p = 0 . 0071 ) , number ( Tb . N , p = 0 . 033 ) and connecting density ( Conn . D , p = 0 . 018 ) with an increased trabecular separation ( Tb . S , p = 0 . 035 ) ( Fig 6C ) . X-ray radiographs of the whole skeletons indicated a fracture with callus in the tibiae of two Zip14L438R Ob-KI mice ( arrow , Fig 7A ) . Moreover , as seen in the μCT analysis of the femora , X-rays also revealed severe narrowing of the femoral midshaft in these mice ( arrowhead , Fig 7A ) . Assessment of the biomechanical properties of the femora with three-point bending tests indicated that they bear significant higher stress levels ( p = 5 . 0E-4 ) but work-to-fracture was 42% percent lower ( p = 0 . 0086 ) than of femora of controls , probably due to the observed changes in cortical thickness and midshaft diameter . The elastic modulus ( p = 0 . 013 ) and work to reach ultimate stress levels ( p = 0 . 021 ) were also significantly lower in femora of these mice , suggesting more elastic femora ( Fig 8B ) . Consequently , qBEI analysis indicated a significantly reduced cortical mineralization ( Ct . CaMean , p = 0 . 026 ) , contributing to this increased flexibility . This clearly illustrates that expression of Zip14L438R in osteoblasts results in more fragile and more flexible femora in vivo . Undecalcified sections of lumbar vertebral bodies and tibiae were stained with Von Kossa/Van Gieson staining , as depicted in Fig 7C . Quantification of parameters of structural histomorphometry confirmed the trabecular phenotype observed with μCT analysis ( S5 Fig ) . Sections stained with toluidine blue were analyzed to further investigate the skeletal phenotype on a cellular level . In Zip14L438R Ob-KI mice we observed no significant differences in osteoblast-covered surface ( OB . S/BS , p = 0 . 50 ) or number ( OB . N/B . Pm , p = 0 . 63 ) . Surprisingly , the osteoclast-covered surface ( OC . S/BS , p = 0 . 043 ) and number ( N . OC/B . Pm , p = 0 . 0012 ) were significantly increased , compared to Zip14fl/- controls ( Fig 7D ) . Double calcein labelling allowed us to investigate the ( endosteal and periosteal ) cortical and trabecular mineralizing surface ( MS/BS ) , bone formation rate ( BFR/BS ) and mineral apposition rate ( MAR ) by fluorescence microscopy . Compared to Zip14fl/- controls , Zip14L438R Ob-KI mice had an increase in endosteal MS/BS ( p = 0 . 012 ) and even more in BFR/BS ( p = 0 . 0012 ) ( Fig 8A ) , whereas there were no significant differences in periosteal ( S6 Fig ) or trabecular bone formation parameters ( Fig 8B ) . Serum was collected prior to euthanasia of the animals for measurement of procollagen I C-terminal propeptide ( PICP ) and C-terminal telopeptide ( CTX Crosslaps ) as serum markers for bone formation and resorption , respectively . Zip14L438R Ob-KI mice had similar levels of PICP and CTX , compared to Zip14fl/- mice ( Fig 8C ) . Serum levels of OPG and RANKL were both slightly higher ( not significant ) in these mice , resulting in a similar RANKL/OPG ratio as controls ( Fig 8D ) . Finally , primary osteoblasts derived from the long bones and calvariae of Zip14fl/- controls and Zip14L438R Ob-KI mice were isolated and subsequently cultured for 21 days . During this period , RNA was isolated at day 0 , day 14 and day 21 of differentiation for qRT-PCR analysis . In calvarial osteoblasts , there was no difference in the expression of osteoblast markers ( Runx2 , Col1a , Ibsp , Bglap ) or inflammatory cytokines ( Il-6 , Tnf ) between controls and Zip14L438R Ob-KI mice ( Fig 9 ) . In osteoblasts derived from the long bones of Zip14L438R Ob-KI mice , however , we found a significant higher expression of Il-6 ( day 0 ) and Tnf ( day 14 ) ; compared to Zip14fl/- controls . Bglap expression was , on the other hand , significantly lower in these osteoblasts at day 0 ( Fig 9 ) . As these expression data and the skeletal phenotype were very different in calvaria and long bones of Zip14L438R Ob-KI mice , we additionally verified Zip14L438R overexpression in calvarial and long bone osteoblasts . Nevertheless , by amplifying and sequencing the region surrounding the c . 1535 T>G ( p . L438R ) mutation in Zip14 , Zip14L438R overexpression was confirmed in cDNA from calvarial and long bone osteoblasts of Zip14L438R Ob-KI mice ( S8 Fig ) . μCT analysis of Zip14L438R Oc-KI mice demonstrated a significantly decreased cortical porosity ( p = 0 . 016 ) compared to Zip14fl/- controls , whereas trabecular bone was unaffected ( Fig 6 ) . Histological analysis of undecalcified Von Kossa/Van Gieson stained spine and tibia sections confirmed trabecular bone mass to be unaffected in these mice ( Fig 7C , S2 Fig ) . Three-point bending tests indicated that biomechanical properties of the femora of these mice were similar to that of Zip14fl/- controls ( Fig 7B ) . Furthermore , toluidine blue stained sections of the tibiae showed a significant decrease in osteoclast-covered bone surface ( p = 0 . 024 ) , whereas osteoclast number ( p = 0 . 22 ) and osteoblast-covered surface ( p = 0 . 22 ) and number ( p = 0 . 50 ) were unaltered ( Fig 7D ) . Regarding dynamic histomorphometry , Zip14L438R Oc-KI mice presented with a significant increase in endosteal mineralizing surface ( p = 0 . 039 ) , whereas trabecular MS/BS ( p = 0 . 0086 ) and BFR/BS ( p = 0 . 020 ) were decreased ( Fig 8 ) . Finally , serum PICP levels of osteoclast knock-in mice were slightly increased , but did not reach significance , whereas CTX was at the same level as controls ( Fig 8C ) . The RANKL/OPG ratio of osteoclast knock-in mice was somewhat lower , due to a slight decrease in RANKL and increase in OPG . Again , this did not reach significance ( Fig 8D ) . Zip14 was previously linked to cAMP-CREB signaling [15] . To evaluate the effect of WT and L441R ZIP14 on the cAMP-CREB signaling activity , a luciferase reporter assay with a cAMP-responsive luciferase construct was applied . Here , overexpression of WT ZIP14 in HEK293T caused a decrease in cAMP-CREB signaling , whereas overexpression of L441R ZIP14 resulted in a significant ( p = 0 . 004 ) 5-fold increase in activity ( Fig 10A ) . Next to cAMP-CREB signaling , ZIP14 has been associated with immune response and inflammation in the literature . We therefore checked both NF-κB and NFAT signaling activity , due to their importance in bone cells and their association with inflammatory processes . No significant difference in NF-κB signaling was observed between WT and L441R ZIP14 , but NFAT signaling by L441R ZIP14 was significantly increased ( p = 0 . 031 ) compared to WT ZIP14 in HEK293T cells ( Fig 10 ) . All luciferase reporter assays were also performed in Saos-2 cells , i . e . osteoblast-like cells , with similar results ( Fig 10B ) .
Hyperostosis Cranialis Interna ( HCI , OMIM 144755 ) was described in a Dutch family as a bone disorder that solely affects the calvaria and skull base through intracranial hyperostosis and osteosclerosis [1 , 2] . We performed a whole genome linkage analysis in the past and mapped the disorder to a region on chromosome 8 ( 8p21 ) [4] . In this study we additionally performed WES on one HCI patient which led to the identification of a heterozygous c . 1322T>G ( p . L441R ) substitution in the SLC39A14 gene that co-segregates with the disorder . SLC39A14 encodes a Zn transporter that belongs to the SLC39A or Zrt- , Irt-related protein ( ZIP ) family and is therefore often referred to as ZIP14 . ZIP transporters invariably function by replenishing cytosolic Zn from the extracellular space and the lumen of intracellular compartments ( influx ) [16] . ZIP14 has previously been localized to the plasma membrane and in the cytosol , in early and late endosomes [8–12] . From here , ZIP14 mainly mobilizes Zn , but transport of other divalent cations ( iron , manganese , cadmium ) into the cytosol is also described [17 , 18] . We demonstrate that ZIP14L441R is still localized in the early and late endosomes , but loses its presence on the plasma membrane , implying trafficking defects of ZIP14L441R in vitro . It is subsequently possible that ZIP14L441R is retained in the endosomes . Of note , patients with HCI have a heterozygous p . L441R substitution , indicating that fifty percent of ZIP14 is wildtype and reaches the plasma membrane ( and early/late endosomes ) , whereas the other fifty percent will reasonably be trapped onto the endosomes . Consistent with the changes in localization , ZIP14L441R was not able to transport Zn from the extracellular space into the cell . Accumulation of labile Zn in the cell , however , was increased by ZIP14L441R , indicating an aberrant cellular Zn homeostasis . It is essential to note that the cellular localization of labile Zn excess is currently unknown and depends on transport capacity of ZIP14L441R . This is highly relevant as Zn generally plays a vital role in cells as it is estimated that about 10% of the human genome encodes proteins with Zn-binding sites . More than half of those are thought to be transcription factors and enzymes , distributed across the different cellular compartments . Local alterations in Zn homeostasis can therefore have significant effects on the functionality of corresponding Zn-dependent proteins and of cells , which could thus be the basis of the pathogenesis of HCI [16 , 19] . Similarly , mutations in SLC39A4 ( ZIP4 ) and SLC39A13 ( ZIP13 ) have been linked to Zn deficiency and/or accumulation in specific cellular compartments resulting in acrodermatitis enteropathica and spondylocheiro dysplastic Ehlers-Danlos syndrome , respectively [20–23] . Next to aberrations in Zn homeostasis , it is important to note that mutations in ZIP14 can affect manganese ( Mn ) , cadmium and iron homeostasis as well . Recently , homozygous missense , nonsense and frameshift mutations in ZIP14 were identified in patients with childhood-onset parkinsonism-dystonia , due to defects in Mn homeostasis [12] . These mutations were all part of transmembrane domains that are not predicted to form a pore ( according to MemSatSVM ) , where our mutation is part of . The subcellular localization of all ZIP14 mutants in the study by Tuschl et al . were similar to that of wildtype ZIP14 , Mn uptake was reduced and specifically accumulated in the brain of a mutant zebrafish model [12] . For our study , we focused on Zn as it is more relevant in skeletal homeostasis [16 , 19 , 24] . Zn is described to have a stimulatory role on osteoblastic bone formation and mineralization and an inhibitory effect on osteoclastic bone resorption [24 , 25] and we demonstrated expression of ZIP14 in osteoclasts and osteoblasts . Effects of ZIP14L441R on skeletal homeostasis were therefore investigated in conditional knock-in mice with expression of Zip14L438R in osteoblasts or osteoclasts . First , femoral length ( growth ) was similar for all mice ( S8 Fig ) . This is relevant since Zn deficiency is generally associated with growth retardation ( and other symptoms ) [16 , 19] and Zip14-/- mice exhibit such phenotype marked by growth retardation and dwarfism [15] . As the role of Zip14 in growth was however attributed to its effects on the hypertrophy of chondrocytes , this could explain the normal growth in our osteoblast or osteoclast knock-in mice . Nevertheless , skeletal growth or height is not affected in patients with HCI as well . Since patients with HCI carry a heterozygous p . L441R mutation and Zip14+/- mice are phenotypically normal [15] , it could be that the wildtype allele fulfills a compensatory role and that growth defects in Zip14-/- mice are due to a general state of Zn deficiency . Moreover , it was documented that ZIP14 has roles in adipose tissue and glucose utilization that can influence growth of Zip14-/- mice as well [11 , 26] . Knowing the long bones were affected by Zip14L438R in our conditional knock-in mice , we were surprised to see no calvarial phenotype as this is truly opposite of what we see in HCI patients . One aspect to be discussed here is the difference in expression of human ZIP14L441R and murine Zip14L438R . In HCI patients , endogenous ZIP14 is expressed in its own spatiotemporal manner , whereas Zip14L438R expression is driven by the Runx2 and Cathepsin K promoter in our conditional knock-in mice . Nevertheless , Cre expression was reported in long bones and calvariae of both Cre-models used in this study [27 , 28] and overexpression of Zip14L438R was confirmed in calvarial and long bone osteoblasts derived from Zip14L438R Ob-KI mice ( S8 Fig ) . Still , we analyzed the calvarial phenotype of Zip14-/- and Zip14+/+ mice and found that loss of endogenous Zip14 did not affect the calvariae , even though the appendicular skeleton and vertebral column were osteoporotic . This suggests that aberrations in Zn homeostasis by Zip14 do not seem to affect calvariae of mice , even though the rest of the skeleton is affected . Whether this is due to a specific protective mechanism present in murine calvariae but not in humans , remains to be determined . In contrast to the calvariae , the appendicular skeleton and vertebral column were affected by knock-in of Zip14L438R in osteoblasts and osteoclasts . Generally , knock-in of Zip14L438R in osteoblasts resulted in a severe skeletal phenotype , whereas the skeletal phenotype in osteoclast knock-in mice was milder . Based on these findings , we conclude that osteoblasts are the primary cells through which mutant ZIP14 exerts its effects on bone homeostasis . Nevertheless , a remarkable finding was that both conditional knock-in models had an increased ( endo ) cortical bone formation rate . Additionally , osteoblast knock-in mice had an increased cortical thickness , where excessive endosteal bone formation even led to narrowing of the bone marrow cavity . Similarly , a study investigating the metabolic activity in the calvariae of HCI patients with 18F-fluoride PET/CT depicted the highest rates of 18F-fluoride uptake in the hyperostotic regions and more specifically at the endosteal side of the diploe ( towards the bone marrow ) [29] . Bone overgrowth of the inner calvarial cortex of HCI patients is thus also the result of an increased endosteal bone formation . Therefore , even though the location of the skeletal defect is different , i . e . in the appendicular skeleton and vertebral column versus the calvaria , the ( endo ) cortical phenotype and the underlying cause of this are strikingly similar in Zip14L438R osteoblast knock-in mice and HCI patients . To further elucidate the in vivo effects of Zip14L438R through osteoblasts , we focused on the fact that Zip14L438R has disparate effects on cortical and trabecular bone in Zip14L438R Ob-KI mice . These mice had an increased cortical thickness and narrowed bone marrow cavity along with a decreased trabecular bone volume . According to the literature , only few hormones and pathways have similar effects on the skeleton and these are parathyroid hormone ( PTH ) /parathyroid-related protein ( PTHrP ) and estrogen . Of note , Zip14 was previously associated with PTH1R-cAMP-CREB signaling in Zip14-/- mice [15] . Pth-/- mice and mice with osteoblast/osteocyte-specific Gsα deficiency ( BGsKO ) , bearing in mind that PTH mediates its effects through Gsα signaling , have an increased cortical bone mass , decreased bone marrow cavity and a decreased trabecular bone mass in both models [30 , 31] . Albeit more severe , this phenotype has the same differential effects on bone as seen in our Zip14L438R Ob-KI mice . A contrasting skeletal phenotype is also seen in mice with PTH/PTHrP receptor overexpression in the osteoblastic lineage [32] . This suggests that the skeletal phenotype of Zip14L438R Ob-KI mice resembles that of deficient or restrained PTH-signaling in osteoblasts . Despite the fact that estrogen was not previously associated with Zip14 , it exerts opposing actions on bone compared to PTH in osteoblasts and studies show that Zn has actions similar to estrogen on osteoblasts and osteoclasts [25 , 33] . Estrogen is generally known to restrain periosteal and stimulate endosteal bone formation during bone modeling and remodeling through osteoblast progenitors [33 , 34] . Consequently , postmenopausal sex-steroid deficiency has been associated with an enlargement of the marrow cavity , thinning of the cortex and slight increase in midshaft diameter [35] . Zip14L438R Ob-KI mice , on the contrary , have a smaller midshaft diameter , due to a restricted periosteal bone formation , along with a thicker cortex and narrowed bone marrow cavity , resulting from a stimulated endosteal bone formation . Moreover , estrogen has protective effects on the resorption of both trabecular and cortical bone , but these are exerted by disparate cell types , i . e . by direct effects on osteoclasts and indirect effects on osteoblasts , respectively [33] . A possible explanation for the trabecular phenotype of Zip14L438R Ob-KI mice is that by sole osteoblastic expression of Zip14L438R , there is no protective ( estrogen-mimicking ) effect on the resorption of trabecular bone . Another important hint for a role of estrogen-like signaling by mutant ZIP14 was found in clinical reports on the disease progression of HCI patients . Female patients exhibit sudden aggravation of HCI symptoms during pregnancy , like abrupt loss of smell or hearing , of which they sometimes recovered after pregnancy . Furthermore , female patients are often more severely , albeit not significant , affected by HCI [2] . As mentioned in the introduction , radiological abnormalities associated with HCI are often seen in the first decade of life and a slow progression of the disease can be seen until the age of 40 [2 , 3] . Altogether , these stages in life share critical changes in estrogen levels , i . e . estrogen gain associated with puberty and pregnancy and estrogen loss associated with aging-related sex-steroid deficiency . We therefore hypothesize that an increased estrogen production is comparable to the estrogen-mimicking effects of Zip14L438R , resulting in aggravation of symptoms in ( female ) HCI patients . Finally , we aimed at identifying possible downstream mechanisms or second messengers through which ZIP14 mediates its effects by osteoblasts . Zip14 was previously shown to play an important role in G-protein coupled receptor ( GPCR ) -mediated signaling by importing Zn into the cytosol and maintaining basal cAMP levels [15] . We detected a 5-fold increase in cAMP levels in Saos-2 cells transfected with ZIP14L441R . Cyclic AMP is a well-known second messenger for several hormones , like PTH/PTHrP [15 , 31 , 32] . However , Zip14L438R expression in osteoblasts did not result in a PTH-mimicking skeletal phenotype in vivo , not to say that it led to a PTH-contrasting phenotype . In the literature , the G-protein-coupled estrogen receptor ( GPER ) is documented to act predominantly intracellularly and stimulate cAMP production , calcium mobilization and c-Src . GPER is described to play a role in the reproductive system , nervous system and neuroendocrinology , immune system , cardiovascular system , pancreatic function and glucose metabolism and bone growth and chondrocyte metabolism [36] . Remarkably , Zip14-/- mice are characterized by impaired gluconeogenesis , hyperinsulinemic/diabetic pancreatic islets , chronic inflammation state , osteopenia and growth retardation [14 , 15] . Next , since Zip14-/- mice have a proinflammatory phenotype with increased systemic interleukin-6 ( Il-6 ) levels that are coincident with a decrease in BMD [14] , we also investigated NFAT signaling activity by ZIP14L441R . We demonstrated a doubled NFAT signaling activity in Saos-2 cells by ZIP14L441R . NFAT signaling in osteoblasts has been linked to the production of chemoattractants ( TNF-α , IL-6 ) to attract osteoclast progenitors and hence increase osteoclast numbers , as seen in Zip14L438R Ob-KI mice ( with normal RANKL/OPG ratio ) . qRT-PCR analysis indeed confirmed a significant higher expression of Il-6 and Tnf in osteoblasts derived from the long bones of Zip14L438R Ob-KI mice , compared to long bone control osteoblasts . This difference in expression was not detected in calvarial osteoblasts , where no skeletal phenotype is present . We therefore believe that NFAT signaling and the production of inflammatory cytokines by Zip14L438R in osteoblasts is also essential in the development of the skeletal pathology . Finally , GPER activation is also linked to increased intracellular calcium mobilization , which is known to bind activators of NFAT [36] . Our overall hypothesis therefore is that mutant Zip14 increases intracellular Zn levels , GPER signaling and cAMP-CREB and NFAT activity from the intracellular organelle where it resides , with estrogen-mimicking effects on osteoblasts . Although we are convinced that we identified ZIP14 as disease causing gene for HCI and a putative underlying pathological mechanism , a major unresolved question is the exclusive skull phenotype of these patients . Here , ZIP14 , along with numerous other Zn transporters and Zn-dependent proteins , define a local and spatiotemporal micro-environment and , for some reason , only that of the internal cortex of HCI patients calvariae results in severe bone overgrowth . Whether this is due to a specific deficit in the skeletal cells of the calvariae or fortunate differences in the expression pattern of compensatory mechanisms in the rest of the skeleton , remains to be determined in the future by performing RNA sequencing and a proteomic analysis , for example .
The family with HCI originates from The Netherlands and has been described in detail previously [1 , 2 , 4] . Peripheral blood was collected from 24 family members and five non-related partners . Genomic DNA was isolated from these blood samples using standard procedures . Exome sequencing was performed on a female patient using the NimbleGen SeqCap EZ Human Exome V2 enrichment panel on the HiSeq2000 ( Illumina Inc . ) . Data analysis was performed with DNA Nexus ( DNAnexus Inc . ; dnanexus . com ) . Variants were filtered for their absence in dbSNP and non-coding and synonymous variants were excluded . As published previously , we already defined a linkage region on chromosome 8 ( chr8: 21 , 593 , 210–28 , 256 , 787 ) . Variants present in this specific region were selected for further investigation . Possible variants were confirmed with Sanger sequencing on other family members . Non-covered exons were amplified by GoTaq DNA polymerase-mediated PCR ( Promega ) with primers covering the exons and the intron-exon boundaries . Sequencing was carried out with the ABI 310 Genetic Analyser ( Thermo Fisher Scientific ) , using an ABI Prism BigDye terminator cycle sequencing kit , version 1 . 1 ( Thermo Fisher Scientific ) . Wildtype ( WT ) human full length ZIP14 cDNA ( NM_001128431 . 2 ) cloned in a pCMV6-XL6 vector was obtained from OriGene Technologies and the mutation ( c . 1322T>G , p . L441R ZIP14 ) was introduced using the QuickChange Site-Directed Mutagenesis Kit ( Agilent Technologies ) . Similarly , a construct generating a truncated form of ZIP14 was created ( p . W22X ZIP14 ) . This construct is used as a negative control for transfection experiments . Green fluorescent protein ( GFP ) fusion proteins for WT , mutant and truncated hZIP14 were generated using the above described expression constructs as template . A PCR amplification was performed to disrupt the termination codon and create the correct restriction sites . Then , the complete region of interest was subcloned in a pEGFP-N1 vector ( Clontech Laboratories ) . As a control , all cloned products were sequenced with Sanger sequencing . HEK293T cells were grown in DMEM medium with 10% FBS supplemented with 100 U/mL penicillin and 100 U/mL streptomycin ( Life Technologies ) . Twenty-four hours prior to transfection , cells were plated at a density of 1 x 105 cells/mL in 35mm glass bottom dishes coated with poly-D-lysine ( MatTek Corporation ) . HEK293T cells were transfected with WT , L441R or W22X ZIP14-GFP constructs using Fugene 6 ( Promega ) in a 3:1 ratio ( Fugene 6:DNA ) . As the mutation in HCI patients is dominant , a heterozygous model was created by co-transfecting WT and L441R ZIP14-GFP . Forty-eight hours after transfection , cells were fixed with methanol , washed with PBS ( Thermo Fisher Scientific ) , incubated with UltraCruz Blocking Reagent ( sc-516214 , Santa Cruz Biotechnology ) for 30 minutes and washed PBS . Specific staining of the Golgi apparatus and early and late endosomes was obtained by first using monoclonal IgG1 antibodies targeting golgin-97 ( sc-59820 , Santa Cruz Biotechnology , 1:300 dilution ) , EEA1 ( sc-137130 , Santa Cruz Biotechnology , 1:100 dilution ) and Rab7 ( sc-376362 , Santa Cruz Biotechnology , 1:200 dilution ) , respectively . Then , after washing with PBS , a mouse IgG kappa binding protein ( m-IgGκ BP ) conjugated to CruzFluor 555 ( sc-516177 , Santa Cruz Biotechnology , 1:100 dilution ) was used to provide a specific fluorescent signal . Fluorescent staining of the plasma membrane was performed by incubating the fixed HEK293T cells with 1μg/mL tetramethylrhodamine conjugate of wheat germ agglutinin ( Thermo Fisher Scientific ) for 10 minutes and washed with PBS . Vectashield antifade mounting medium with 4' , 6-diamidino-2-phenylindole ( DAPI; Vector Laboratories ) was used to preserve fluorescence and to stain the nucleus . High resolution images were obtained using an Eclipse Ti-E inverted microscope ( Nikon ) attached to a dual spinning disk confocal system ( UltraVIEW VoX; PerkinElmer ) equipped with 405 , 488 and 561nm diode lasers for excitation of blue , green and red fluorophores , respectively . Images were acquired and processed using Volocity 6 . 0 . 1 software ( PerkinElmer ) . Uptake of 65Zn and accumulation of Zn2+ with FluoZin3-AM in HEK293T cells were performed as described before [10 , 37 , 38] . In short , for 65Zn-uptake , HEK293T cells were plated at a density of 5 x 105 cells/mL and transiently transfected with the WT , L441R or W22X ZIP14 expression vector , using the Effectene Transfection Reagent ( Qiagen ) . An empty vector was used as a transfection control . Forty-eight hours after transfection , cells were washed with HBSS ( pH 7 . 0 , Thermo Fisher Scientific ) and incubated at 37°C in serum-free DMEM containing 65Zn ( GE Healthcare ) and 4μM ZnCl2 for 15 minutes . Cells were washed three times with wash buffer ( 0 . 9% NaCl , 10mM EDTA , 10mM HEPES ) and then solubilized with 0 . 2% SDS and 0 . 2M NaOH for 1 hour . Uptake of 65Zn was measured with a γ-ray spectrometer . Total protein concentrations were measured with the Pierce BCA protein assay kit ( Thermo Fisher Scientific ) and used as a normalizer . For Zn2+ accumulation , transfected HEK293T cells were incubated with 5μM FluoZin3-AM ( Thermo Fisher Scientific ) in serum-free DMEM for 30 minutes at 37°C . Cells were then stimulated with 40μM ZnCl2 after which fluorescence was measured at 494/516nm excitation/emission[37] . From the Tumorbank of the Antwerp University Hospital ( Belgium ) , tissue of a giant cell tumor of bone and an osteoblastoma were obtained . Tissue specimens were fixed in 4% formaldehyde and paraffin embedded on a routine basis . Five μm-thick sections were subjected to heat-induced antigen retrieval by incubation in 10mM citrate buffer ( pH 6 . 0 ) for 20 minutes at 97°C . Subsequently , endogenous peroxidase activity was quenched by incubating the slides in peroxidase blocking buffer ( DAKO ) for 10 minutes . Incubation with primary anti-human ZIP14 antibody ( PA5-21077 , Thermo Fisher Scientific , 1:200 dilution ) was performed at room temperature for 1 hour . Bound antibody was detected with the Envision FLEX+ detection kit ( DAKO ) using 3 , 3’-diaminobenzidine chromogen solution ( DAKO ) . A negative control , using a rabbit IgG isotype control ( 10500C , Thermo Fisher Scientific , 11 . 2ng/μL ) was included in each staining run and did not show positive expression in osteoblasts or giant cells ( S7 Fig ) . Sections were counterstained with haematoxylin , dehydrated and mounted . KS483 cells , murine pre-osteoblast cells with mesenchymal characteristics , were used to examine the expression of murine Zip14 ( mZip14 ) during the differentiation to mature and mineralizing osteoblasts . KS483 cells were grown in α-MEM with GlutaMAX ( Thermo Fisher Scientific ) and 10% FBS ( Lonza ) supplemented with penicillin-streptomycin ( Thermo Fisher Scientific ) . Cells were plated at a density of 2 x 104 cells/mL in a 24-well plate and incubated at 37°C in humidified air containing 5% CO2 . RNA was extracted at day 4 , 7 , 11 , 14 , 18 , 21 , 24 and 28 with the ReliaPrep RNA Cell Miniprep System ( Promega ) and reverse transcribed with an oligo-dT primer and Superscript II Reverse Transcriptase ( Thermo Fisher Scientific ) . Quantitative real-time PCR ( qPCR ) analysis was performed on all samples with qPCR Core kit for SYBR Green I , No Rox ( Eurogentec ) . For each sample , mZip14 expression was analyzed and normalized to b2m , rpl13a and ubc expression . Stability of reference genes was verified using geNorm ( Biogazelle ) and efficiency of all primer pairs was checked with the qbase+ software ( Biogazelle ) . Expression of target and reference genes was quantified using qbase+ software . To assess expression of mZip14 in osteoclasts , bone marrow cells from calvaria and long bones were isolated from mice as previously described [39] . Osteoclasts were cultured on plastic or bovine cortical bone slices with supplementation of M-CSF or M-CSF with RANKL . RNA from cultured bone marrow cells was isolated using the RNeasy Mini Kit ( Qiagen ) and reversed transcribed to cDNA for qPCR . Samples were normalized for the expression of b2m [39] . All primer sequences are available upon request . An occipital skull bone biopsy was taken during neurosurgical intervention from a 29-year old female patient with HCI , after receipt of informed consent by the patient . The biopsy specimen was fixed in 4% paraformaldehyde , decalcified and embedded in paraffin . Sections were stained by standard hematoxylin-eosin staining procedures . As a control sample , an occipital skull bone biopsy was taken during neurosurgical intervention from a 37-year old female with a posterior fossa meningioma , after receipt of informed consent . Peripheral blood was collected for the isolation of genomic DNA and genetic screening of ZIP14 with Sanger sequencing . The biopsy specimen was fixed , decalcified , embedded and stained according to the same procedures as described above . Quantification of the number of Haversian channels and osteocytes was performed on three microscopic images of the patient and control externae/internae of the skull and of the patient vertebral cortex . Heterozygous Zip14 knockout ( Zip14+/− ) mice of the C57BL/6 strain were obtained from the Mutant Mouse Research Resource Consortium at the University of California , Davis via a contract . A breeding colony was established at the University of Florida , generating homozygous ( Zip14+/+ ) WT and homozygous Zip14 knockout ( Zip14−/− ) mice [13 , 26] . Zip14-/- ( n = 7 ) and Zip14+/+ mice ( n = 6 ) were fixed in 10% formalin and stored in 70% EtOH . μCT scans of the calvaria were generated with the SkyScan1076 system ( Bruker microCT ) . Images were reconstructed with NRecon software and data were analyzed with Dataviewer and CTAn ( Bruker microCT ) . Cortical thickness and porosity were measured at the calvariae . Nomenclature , symbols and units used are those recommended by the Nomenclature Committee of the American Society of Bone and Mineral Density[40] . The mutated leucine at amino acid position 441 in ZIP14 of HCI patients is highly conserved in mice and corresponds to mL438 in both isoforms of mZip14 ( NP_001128624 . 1; NP_659057 . 2 ) . As no difference in function between both isoforms was reported , wildtype full length mZip14 cDNA corresponding to NP_001128624 . 1 cloned in a pCMV6-Entry vector was obtained from OriGene Technologies ( MC216777 ) . The mutation resulting in the p . L438R substitution was inserted using the QuickChange Site-Directed Mutagenesis kit ( Agilent Technologies ) . This construct was sent to genOway ( France ) to create a mouse model with Zip14L438R through targeted insertion within the ROSA26 locus via homologous recombination in embryonic stem cells . A loxP-flanked transcriptional STOP cassette is incorporated between Zip14L438R and a CAG promoter to allow the expression of Zip14L438R to be dependent upon the Cre recombinase ( S8 Fig ) . For breeding , Sox2-Cre mice , Runx2-Cre mice and CtsK-Cre mice were kindly provided by Vincent Timmerman and Delphine Bouhy [41] ( University of Antwerp ) , Jan Tuckermann[28] ( Universität Ulm ) and Rachel Davey [27] ( University of Canberra ) , respectively . Mice homozygous for the floxed mutant Zip14 allele ( Zip14flox/flox ) were crossed with the different Cre mice . Offspring was weaned after 3 weeks and marked by ear clipping . DNA , isolated from the tail tip , was used for genotyping of the ROSA26 locus by performing two PCRs ( S8 Fig ) . The Expand Long Template PCR System ( Roche ) and dNTP solution mix ( Bio-Rad Laboratories ) are used for both genotyping PCRs . Fragments were separated on a 2% agarose gel simultaneously running a GeneRuler 100bp Plus DNA Ladder and GeneRuler 1kb DNA Ladder ( Thermo Fisher Scientific ) . In offspring from breedings with Runx2-Cre and CtsK-Cre mice , a third PCR is performed to check the corresponding Cre-allele . Here , standard GoTaq DNA polymerase-mediated PCR reactions ( Promega ) were performed . Skeletal phenotyping was performed at the age of 6 months , corresponding to the age of 30 years in humans at which the HCI phenotype is prominent[42] . Since no gender-specific differences were found , only the data from male mice are presented in this manuscript . All mice were given two injections of 30 mg/kg calcein at 9 and 2 days before death to assess dynamic histomorphometric indices . At least six mice per group were subjected to histomorphometry and serum analysis to obtain sufficient results to perform statistical analyses . All mice were maintained on a twelve-hour light-dark cycle , with a regular unrestricted diet available ad libitum . Dissected skeletons were fixed in 3 . 7% PBS-buffered formaldehyde for 18 hours at 4°C and stored in 80% ethanol . All mice were analyzed by contact X-ray and μCT scanning . For the latter , a μCT 40 desktop cone-beam μCT ( Scanco Medical ) was used and reconstructed slices were examined using the Scanco MicroCT software suite . To assess biomechanical stability of the femora , three-point bending assays and a quantitative backscattered electron imaging ( qBEI ) analysis were performed as described[43–46] . The lumbar vertebral bodies ( L1-L4 ) and one tibia were dehydrated in ascending alcohol concentrations and embedded in methylmethacrylate as previously described[46] . Parameters of structural and cellular histomorphometry were quantified on Von Kossa/Van Gieson and toluidine blue stained sections , respectively , of 4μm thickness . Analysis of bone volume , trabecular number , trabecular spacing , trabecular thickness , and the determination of osteoblast and osteoclast numbers and surface were carried out according to standardized protocols using the OsteoMeasure histomorphometry system ( OsteoMetrics ) . Dynamic histomorphometry was performed on unstained 12μm sections of the vertebral bodies and tibia as previously described [46] . Primary osteoblasts were isolated from calvaria and long bones ( tibiae ) of Zip14flox/- and Zip14flox/-; Runx2-Cre mice as described previously [47] . In brief , cleaned calvariae and long bones were cut into small pieces and incubated with 2 mg/ml collagenase II ( Sigma ) solution for 2 h at 37°C in a shaking water bath . Then , the bone fragments were washed and cultured in α-MEM containing 10% FCS , 100 U/ml penicillin , 100 μg/ml streptomycin , and 250 ng/ml amphotericin B in 25 cm2 culture flasks . After confluence , we removed the bone fragments , the confluent layers were trypsinized and the cells were replated in 24-well plates for 21 days . RNA of primary osteoblasts was isolated at day 0 , day 14 and day 21 of differentiation using the RNeasy Mini Kit ( Qiagen ) and reverse transcribed to cDNA using the First Strand cDNA synthesis kit ( Thermo-Fischer Scientific ) for qPCR . qPCR reactions were performed in a 15 μl volume containing 2 ng cDNA , 7 . 5 μl SYBR Greener qPCR supermix ( Invitrogen ) and 300 nM of each primer [47] . Samples were normalized for the expression of Hprt . Moreover , cDNA samples from day 0 calvarial and long bone osteoblasts were used for the amplification and sequencing of the region surrounding the c . 1535 T>G ( p . L438R ) mutation in Zip14 . Amplification was performed using a GoTaq2 polymerase-mediated PCR ( Promega Corporation ) and verified by agarose gel electrophoresis . Hereafter , primers and unincorporated dNTPs were removed using exonuclease I ( New England Biolabs ) and calf intestine alkaline phosphatase ( CIAP , Roche Applied Science ) . Sequencing was carried out directly on purified fragments with the ABI 310 Genetic Analyzer ( Applied Biosystems ) , using an ABI Prism BigDye terminator cycle sequencing ready reaction kit , version 1 . 1 ( Applied Biosystems ) . The BigDye XTerminator purification kit was used as purification method for DNA sequencing with the purpose of removing unincorporated BigDye terminators . ELISA was used to determine serum concentrations of procollagen I C-terminal propeptide ( PICP; SEA570Mu , USCN ) , C-terminal telopeptide ( RatLaps ( CTX-I ) EIA , AC-06F1 , Immunodiagnostic Systems ) , osteoprotegerin ( OPG; MOP00 , R&D Systems ) and receptor activator of nuclear factor kappa-B ligand ( RANKL; MTR00 , R&D Systems ) . HEK293T and Saos-2 cells were grown in DMEM ( Thermo Fisher Scientific ) supplemented with FBS ( 10% v/v ) . Twenty-four hours prior to transfection , cells were plated at 0 . 3 x 105 cells/well in 96-well plates . Cells were transiently transfected with pRL-tK ( 2 , 5ng ) and pCRE-Luc , NF-kB-Luc or pGL4 . 30 ( NFAT-Luc , Promega ) ( 25ng ) along with 20ng of empty pcDNA3 . 1 vector , WT , L441R or W22X ZIP14 expression constructs using Fugene 6 ( HEK293T cells ) or ViaFect ( Saos-2 cells ) ( Promega ) . Each transfection was carried out in triplicate and repeated independently in three separate experiments . Forty-eight hours after transfection , cells were lysed and firefly and renilla luciferase activity were measured on a Glomax Multi+ Luminometer ( Turner Designs ) using the dual luciferase reporter assay system ( Promega ) . Finally , the ratio of the firefly and renilla luciferase measurement was calculated . All data are presented as mean values ± SD and analyzed by a one-way ANOVA or a two-tailed Student’s t-test . Both statistical tests were provided by the SPSS v22 . 0 software ( SPSS Inc ) . Statistical analysis of the mouse phenotyping data was performed by comparing the results of osteoblast knock-in mice and osteoclast knock-in mice with those of heterozygous Zip14flox animals . Here , a value of p<0 . 05 ( * ) and p<0 . 025 ( ** ) were considered statistically significant and significant after Bonferroni correction , respectively . All HCI patients gave written informed consent , and the study was approved by the Committee of Medical Ethics of the University of Antwerp , according to the Declaration of Helsinki ( EC UA 12/3/29 ) . The skull biopsy specimen from an individual with a posterior fossa meningioma was obtained after receipt of informed consent and this study was approved by the Committee for Medical Ethics of the Antwerp University Hospital ( EC UZA 16/14/166 ) . All animal experiments were conducted according to the National Institutes of Health Guide for the Care and Use of Laboratory Animals and approved by the Committee of Medical Ethics of the University of Antwerp ( ED 2012–01 ) . | Osteoporosis is a skeletal disorder affecting hundreds of millions of people , and is characterized by a low bone mineral density ( BMD ) and increased susceptibility to fracture . Genetic factors are the greatest determinants of BMD , but only a small fraction of these have been identified through genome-wide association studies . Studying rare , monogenic skeletal disorders is therefore an interesting strategy to identify genes with a putative large effect on BMD . Hyperostosis Cranialis Interna ( HCI ) is a rare monogenic disorder resulting in bone overgrowth exclusively at the skull , for which the underlying genetic cause was previously mapped to a region on chromosome 8 . Our study demonstrates that HCI results from a mutation in SLC39A14 ( ZIP14 ) , resulting in trafficking defects of ZIP14 and an aberrant cellular zinc homeostasis . Conditional mouse models demonstrate primary actions of Zip14 through osteoblasts , resulting in a HCI-like phenotype in the long bones and reveal estrogen-mimicking and PTH-opposing effects of Zip14 on bone homeostasis . This study designates ZIP14 as a novel regulator of BMD , and that manipulating ZIP14 might be a therapeutic strategy for complex bone diseases , like osteoporosis . | [
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] | 2018 | Conditional mouse models support the role of SLC39A14 (ZIP14) in Hyperostosis Cranialis Interna and in bone homeostasis |
In areas where schistosomiasis control programs have been implemented , morbidity and prevalence have been greatly reduced . However , to sustain these reductions and move towards interruption of transmission , new tools for disease surveillance are needed . Genomic methods have the potential to help trace the sources of new infections , and allow us to monitor drug resistance . Large-scale genotyping efforts for schistosome species have been hindered by cost , limited numbers of established target loci , and the small amount of DNA obtained from miracidia , the life stage most readily acquired from humans . Here , we present a method using next generation sequencing to provide high-resolution genomic data from S . japonicum for population-based studies . We applied whole genome amplification followed by double digest restriction site associated DNA sequencing ( ddRADseq ) to individual S . japonicum miracidia preserved on Whatman FTA cards . We found that we could effectively and consistently survey hundreds of thousands of variants from 10 , 000 to 30 , 000 loci from archived miracidia as old as six years . An analysis of variation from eight miracidia obtained from three hosts in two villages in Sichuan showed clear population structuring by village and host even within this limited sample . This high-resolution sequencing approach yields three orders of magnitude more information than microsatellite genotyping methods that have been employed over the last decade , creating the potential to answer detailed questions about the sources of human infections and to monitor drug resistance . Costs per sample range from $50-$200 , depending on the amount of sequence information desired , and we expect these costs can be reduced further given continued reductions in sequencing costs , improvement of protocols , and parallelization . This approach provides new promise for using modern genome-scale sampling to S . japonicum surveillance , and could be applied to other schistosome species and other parasitic helminthes .
In China , schistosomiasis has been reduced from approximately 12 million cases in the 1950s to approximately 100 , 000 cases in 2014 , and public health officials are attempting to interrupt transmission of schistosomiasis nationwide [1–3] . The success of such programs has important implications for public health , as there are approximately 200 million people infected worldwide , and health impacts include anemia , impaired growth and cognitive development , and , in the case of S . haematobium , cancer [4–6] . We have studied schistosomiasis in Sichuan Province , China for the past decade , documenting the reemergence and persistence of infections in some areas despite aggressive control efforts [7–9] . These remaining pockets of schistosomiasis highlight gaps in our ability to prevent new infections and ultimately interrupt transmission . Advances in genomic technology offer new opportunities to examine the sources of schistosomiasis infections . Combining new sequencing technologies with computational genomics , it is possible to evaluate parasite relatedness over small temporal and spatial scales , and to infer infection pathways . Such methods have been used to infer detailed transmission trees and dispersal pathways in viral outbreaks [10 , 11] . Application of these methods to schistosomiasis is complicated by the inaccessibility of adult worm pairs , the cost of sequencing , and the limited DNA available from miracidia , the most readily available life stage and progeny of adult worm pairs . Over the past decade , methods for genotyping several dozen microsatellite loci ( repeating sequences of 2 to 6 nucleotide base pairs ) from a single miracidia have been developed for the three schistosomes of major human health importance [12–14] . Microsatellite genotyping has been used to answer questions about population structure of schistosomes over landscapes [15–18] and host species [16 , 19] , as well as to evaluate changes in parasite diversity following chemotherapy [20 , 21] . However , the power to resolve detailed questions about the source and relatedness of schistosomes is limited by the number of loci tested; multi-locus genotypes based on a limited number of microsatellite loci may be the same in siblings and cousins by chance alone , and therefore indistinguishable from clonal individuals [22] . Genetic stratification of populations may also not be discernable . This makes it challenging to evaluate recent inbreeding and population bottlenecks that may be important to understanding parasite transmission in areas approaching elimination . Genotyping methods that take advantage of next generation sequencing , such as double digest restriction site-associated DNA sequencing ( ddRADseq ) [23] , offer a complementary approach to microsatellite methods [24] , and can detect orders of magnitude more loci at costs that continue to decline . In ddRADseq , tens of thousands of loci are selected for sequencing by digestion with two restriction enzymes followed by enrichment for fragments in a desired range of lengths . Because the restriction sites are generally conserved , the same set of fragments tends to be obtained across individuals , allowing identification of variation at tens of thousands of orthologous loci . The large number of loci obtained , and the fact that each locus is sequenced at considerable depth across the entire locus make it possible to identify tens of thousands of variants that describe geographic differences in genetic variation at a fine scale . An additional advantage of ddRADseq is the ease of marker development relative to microsatellites , as the latter is based on a highly curated and specific set of loci that require locus-specific primer development and PCR amplification . Furthermore , the cost of high-resolution sequencing is declining rapidly as technology evolves , and may be appropriate for large-scale epidemiological studies in the near future [25] . Finally , the availability of reference genomes of all three major schistosome species [26–29] makes evaluation of the recovery of loci in the targeted range possible , allowing the technique to be fine-tuned for the reproducible recovery of a specific subset of loci in these species . We have initiated a collaborative project that leverages advances in next-generation sequencing to better understand the dynamics of schistosomiasis infections in areas where infection is approaching elimination . Here we report the development of a ddRADseq technique that we applied to archived S . japonicum miracidia preserved on Whatman FTA cards . We demonstrate the utility of this approach in capturing a large amount of genetic variation information and show how this information might be used to reveal population structure at fine spatial resolution in this population . Our long-term goal is to use the method described here to evaluate pathways of schistosomiasis infection in pockets of residual transmission . We thus focused our efforts on developing a method that is efficient , reproducible and practical for field-collected samples–able to accommodate the limited DNA available in field collected samples and appropriate for samples that have been transported and archived without need of cold-chain storage for multiple years .
Restriction enzyme double digestions were performed in silico on the S . japonicum reference genome [27] downloaded from schistodb . net ( SJC_S000000—SJC_S024939 , 25 , 048 contigs in total ) [30] to determine expected fragment locations and size distributions following digestion . We used a Perl script written in-house to determine the location of restriction enzyme cut sites and the size distribution of expected double-digested fragments ( fragments containing one end cut by one restriction enzyme and another end cut by a second restriction enzyme ) . Fragments that contained transposable elements were masked out because these highly duplicated elements tend to share high sequence similarity , and distinguishing orthologous from paralogous fragments among highly duplicated sequences is problematic . The remaining fragments were used in comparison to experimental results to assess what proportion of fragments were recovered . S . japonicum adult worm DNA was obtained from the Schistosomiasis Resource Center via BEI Resources , NIAID , NIH ( pooled genomic DNA from adult male and female Schistosoma japonicum , Chinese Strain , NR-36066 ) . According to BEI , the worms used as founders in this standard stock population were originally collected in Anhui Province , China in 1928 and augmented with a second Anhui isolate in 1977 . Archived field-collected samples of S . japonicum miracidia were collected from infected humans in Sichuan Province , China in 2010 as described elsewhere [9 , 31] . Briefly , participants were tested for infection using the miracidia hatching test and miracidia were collected from positive hatching tests . Miracidia were collected from the top of the hatching test flask , isolated using a hematocrit tube or Pasteur pipette drawn to a narrow bore with a flame , washed three times with autoclaved deionized water and placed on a Whatman FTA indicating card ( GE ) for long-term storage . Discolored spots appear on the card where the sample is dropped . After drying , cards were stored in a desiccator at room temperature . We selected 15 miracidia obtained from three humans in two villages located approximately 15 km apart for the work described here . The names and exact locations of these villages within Sichuan Province are not provided to maintain anonymity and promote candid reporting . Miracidia contain on the order of 1–2 ng of DNA to start with , and field-collected specimens on Whatman cards may degrade over time , making direct application of ddRADseq on such samples problematic . Instead , whole genome amplification was applied to single miracidia on Whatman cards using isothermal ( or multiple ) strand displacement amplification [32] . This amplification strategy was chosen because previous studies have shown that whole genome amplification ( using GenomiPhi; GE Healthcare ) is capable of amplifying DNA for use in ddRADseq without detectable bias or introduction of mutations [33 , 34] . Individual miracidia were extracted from Whatman cards using a Whatman Harris 2mm micro-core punch ( Whatman WB100029 ) . Following excision , punches underwent five consecutive 5-minute washes . The first three washes consisted of 200 μL FTA purification reagent , and the final two washes consisted of 200 μL TE buffer . After the final wash , punches were left to dry for at least 1 hour at room temperature . Miracidia DNA was amplified directly from the punch using GenomiPhi V3 whole genome amplification kits ( GE Healthcare Biosciences 25660124 ) following the manufacturer’s recommended protocol for amplification , with minor adjustments made to accommodate amplification from a 2 mm disk . Specifically , for miracidia , dried disks were transferred to an amplification tube containing 20 μL of 1x denaturation buffer . Tubes were incubated at 95°C for 3 minutes and then immediately placed on ice . Liquid from the tube was then added to individual amplification pellets provided in the kit , and allowed to dissolve the pellet for 10 minutes on ice . After gentle mixing , the liquid was transferred back to its original tube with the 2mm disk still present , and each amplification tube was then subjected to 90 minutes of amplification at 30°C , followed by enzymatic heat kill at 65°C for 10 minutes , and ending with a hold at 4°C . Adult worm DNA or whole-genome-amplified miracidium DNA was digested with two restriction enzymes , PstI-HF ( New England Biolabs ( NEB ) R3140 ) , a 6-cutter , and Sau3AI ( NEB R0169 ) , a 4-cutter , for eight hours at 37°C . Following digestions , DNA was purified via solid phase reversible immobilization ( SPRI ) using Axygen AxyPrep paramagnetic beads . The adult worm DNA was divided into eight replicate samples at this point in the process . A universal adaptor corresponding to the Sau3AI cut site and another adaptor corresponding to the PstI-HF cut site were then ligated to digested and purified DNA fragments . Adaptors contained unique molecular identifiers ( UMIs; eight consecutive Ns prior to the ligation site that allow for PCR clone filtering ) , and a sample-specific 5 bp barcode on the adaptor corresponding to the PstI-HF cut site ( see S1 Table for adaptor sequences ) . Samples were combined in equimolar ratios , cleaned via SPRI , and fragments between 300 and 600 bp ( sizes reflect fragment size before adaptor ligation ) were collected using a Pippin Prep 1 . 5% agarose gel ( Pippin CDF1510 ) . Size-selected fragments were PCR amplified with primers that add unique sequencing indexes that are required to multiplex multiple sample libraries per sequencing lane ( see S1 Table for PCR primer sequences ) . PCR primers are designed to amplify only double-digested fragments , effectively reducing the number of off-target fragments in the size collection range to an extremely small percentage of clones . Following PCR , libraries were cleaned via SPRI and tested for size recovery . They were then pooled and prepared for sequencing by combining libraries in an equimolar ratio . DNA libraries were sequenced on an Illumina HiSEQ platform using 125bp single-end reads ( miracidia ) , or on an Illumina MiSEQ using 75 bp paired-end reads ( worm DNA ) . Following sequencing , PCR clones were filtered out ( based on UMI sequences ) and reads were de-multiplexed into individual samples using the program Stacks [35]; 54 . 8% of all reads were filtered as PCR clones before de-multiplexing . Reads were mapped to the S . japonicum reference genome using bowtie2 [36] . Recovery of fragments was assessed in comparison to in silico digested fragments ( see above ) using a combination of custom Perl scripts and the intersect command from bedtools [37] . Fragments containing a substantial amount of sequence from repetitive elements and low-copy duplicates that were not eliminated in our in silico screening process would have been un-mappable; empirically , they amounted to about 15% of the reference fragments . Variant discovery and filtering were performed using the Genome Analysis Toolkit ( GATK ) [38–40] Haplotype Caller and other utilities of GATK . Different filters were applied to single nucleotide polymorphisms ( SNPs ) and indels . For SNPs , variants with a quality depth score less than two , mapping quality less than 22 , or mapping quality rank sum score less than -20 were filtered out . For indels , only those with quality depth scores less than 2 were filtered out . We used PALfinder [41] , custom Perl scripts , and bedtools [37] to identify microsatellite loci in the raw sequencing reads and VCF files . A microsatellite locus was considered to be entirely located within a read if both its beginning and end were 10 or more bp away from the ends of where the read aligned to the reference genome . We also applied more stringent filtering of recovered ddRADseq loci and SNPs to test our ability to use miracidia-derived data to make precision inferences about population structure and genetic variation . This filtering was aimed at ensuring that there were a sufficient number of high-quality reads for each locus to confidently discriminate heterozygous and homozygous calls at polymorphic sites . For these analyses , the forward read ( i . e . , read 1 ) for each parsed individual was first quality trimmed using the program Trimmomatic v . 0 . 33 [42] with the settings LEADING:10 , TRAILING:10 , SLIDINGWINDOW:4:15 , and MINLEN:36 . Quality-trimmed reads were mapped to the S . japonicum genome [27] using the MEM algorithm of BWA v . 0 . 7 . 15-r1140 [43] , with shorter split hits marked as secondary ( -M flag engaged ) . The radcap software package [44] , which incorporates SAMtools v . 1 . 3 [45] , Picard Tools v . 1 . 106 ( http://broadinstitute . github . io/picard ) , and GATK [38–40] , was used to perform the following: merge mapping files , realign around indels , call variants using the Unified Genotyper ( both SNPs and indels ) , and filter SNPs around indels and by quality ( genotype calls with a read depth < 5 and a quality score < 20 were removed ) . Only SNPs were used for subsequent analyses . A custom script was used to remove SNPs called against the genome that were either monomorphic in our samples or non-biallelic , and to code individual genotypes as missing data if the genotype quality score was below 20 or the individual read depth fell below 10x . Lastly , variants were filtered out using VCFtools v . 0 . 1 . 15 [46] to control the number of samples missing data at each locus ( i . e . , samples that did not have sufficient data mapped at a locus to pass the more stringent quality filters ) . This resulted in three datasets: one with all loci that were not missing data in any of the eight samples ( one adult worm sample and eight miracidia samples ) ; one that excluded the adult worm sample and included all loci that were not missing data in any of the miracidia; and one that excluded the adult worm sample and allowing missing data from two of the eight ( 25% ) miracidia . This last sample was aimed at discovering loci that might have incomplete representation but sufficient sample representation to be of utility in some cases . Custom Perl scripts were used to calculate the proportion of heterozygous ( or polymorphic ) loci among variable loci for each sample in the three data sets . RAxML 8 . 0 [47] was used to infer a maximum likelihood phylogenetic tree detailing relationships based on SNP variation in the nine-sample dataset . For RAxML analyses , we applied an ascertainment bias correction because our SNP collection contained no invariant sites; we otherwise used the default program settings , and specified 1 , 000 bootstrap replicates following the ML search . While we did not pursue it here , we note that using the full set of ddRADseq loci instead of only variable sites can produce even more accurate tree estimates , which is relevant for extending the utility of this type of data [48] . Principle components analysis ( PCA ) using the R package SNPRelate [49] was applied to the nine-sample dataset with and without the adult worm data . A custom Perl script was used to calculate pairwise genotype sharing among samples . Briefly , genotypes at every locus were compared between samples and determined to be either 100% identical , 50% similar , or 0% similar , and the mean similarity was calculated . Variation in similarity was calculated from 1 , 000 permutations , sampling variants at random with replacement . Variant datasets , microsatellite information , and custom scripts are deposited in the Dryad repository: http://dx . doi . org/10 . 5061/dryad . 8091q [50] . Sequences have been deposited in the NCBI Sequence Read Archive and can be accessed through BioProject ID PRJNA349754 .
Based on comparisons of in silico digestions of the complete genome sequence of S . japonicum [27] using different potential pairs of restriction enzymes , we chose the combination of PstI-HF and Sau3AI for empirical ddRADseq library construction and sequencing . This pair of restriction enzymes was predicted to produce 17 , 131 double-digested fragments in the 300–600 bp range that would map to unique regions in the genome ( S1 Fig ) . For reference , these fragments comprise 7 . 29 Mbp of the 397 Mbp in the S . japonicum genome [27] . To benchmark the ddRADseq approach on high-quality DNA , we first tested it on Chinese Strain adult worm DNA from Anhui Province ( Fig 1 ) . We ran eight replicates post-digestion ( pre-ligation ) , using different barcode and index combinations to test that each adaptor worked and that we could recover the same loci across experiments . The size-selected and amplified libraries contained fragments mostly in the 450–700 bp range ( including 143 bp adaptors and amplification primers ) , indicating that our size targeting and purification procedure was largely successful , but with some loss of longer fragments ( Fig 2A ) . The pooled replicate libraries were sequenced to obtain a total of over 38 million paired-end reads that were then mapped to the reference genome . Recovery of in silico expected loci across a range of sequencing depths was generally at most 85% , indicating that about 15% of the expected loci either did not doubly digest or were un-mappable due to repetitive elements or low-copy duplicates not eliminated by the in silico screening process . Recovery in the 300–500 bp range was excellent , with about 70% of loci ( out of 85% maximum ) sequenced at least 20x in each replicate ( Fig 3A and S2 Fig ) . Fragment lengths longer than 500 bp are not well represented , probably due to biases in amplification and recovery , but this is compensated by a reasonable recovery rate for sequences in the 100–300 bp range . To test the ddRADseq method on field-collected samples , we performed whole genome amplification on miracidia samples from three human individuals that had been archived on Whatman cards in 2010 . Amplification was successful from 13 out of 15 miracidia , with an average production of approximately 6 μg of DNA . Eight of the 13 were subjected to multiplexed ddRADseq library preparation and pooled; approximately two thirds of the DNA in the eight sequenced miracidia libraries was in the target size range , while the remaining third was in the 150–450 bp range ( including adaptors and primers , Fig 2B ) . This excess of short off-target DNA is common for genome amplification , partly because more PCR amplification cycles are required to obtain sufficient DNA for quantification and sequencing . The pooled miracidia libraries were sequenced to obtain a total of over 280 million 125 bp single-end reads . Nearly all reads were generated from RAD double-digested fragments; 28 . 48 million reads ( 98 . 5% ) that map to unique regions in the S . japonicum reference genome begin with the expected restriction site sequence . Recovery of fragments in the 300–500 bp range at 20x coverage was comparable to the worm replicates ( about 70% out of a maximum of 85% ) for most samples , but as expected based on the library length distribution , more recovered sequences mapped to shorter fragments than in the case of the un-amplified worm ( Fig 3B and S3 Fig ) . This means that more loci provided good sequence and variation data than were enriched for using size selection . For example , in one of the sequenced miracidia ( Fig 4 and Table 1 ) there were 10 , 899 loci covered at 20x or more in the 300–600 bp range , and 26 , 794 < = 600 bp; at 10x or more coverage , it had 32 , 804 loci < = 600 bp . Similar results were obtained from other miracidia ( S4 Fig and S2 and S3 Tables ) . Though not the primary motivation of our study , the large sample of S . japonicum loci enabled us to identify many microsatellite loci that may be of use in future population genetics studies . We located 33 , 286 paired end reads with microsatellite loci in the adult worm data . Forward and reverse PCR primers were designed for these loci , and after filtering out duplicated sequences and primers , 1 , 609 unique primer sets remained ( S4 Table ) ; we were unable to assess indel variation at these loci because the microsatellite regions generally overlapped with the unsequenced regions between the paired ends . To evaluate the utility of ddRADseq for direct microsatellite genotyping and microsatellite/SNP comparative analysis , we determined that 11 , 208 microsatellite loci were sequenced at any depth in at least half of the eight miracidia samples . 7 , 595 of these microsatellite loci were wholly contained within the sequence read , indicating their potential for microsatellite genotyping , and 1 , 100 of them were variable in the microsatellite region among the eight sequenced samples . Well over half of the variable loci ( 652 , or 59% ) passed low stringency variant filtering ( see Methods ) in all eight miracidia samples , and 123 of these had indel variants in the microsatellite loci . Although this amount of variability is low for microsatellites in general , it is perhaps reasonable given the bias towards short loci caused by the requirement that they be fully contained within the 125 bp sequencing read . Even so , this represents an approximately seven-fold increase in indel information from the 17 microsatellite loci used in previous S . japonicum PCR studies [12 , 13] . There were 25 , 721 SNP variants that passed stringent quality and coverage filters for all nine samples . The average proportion of heterozygous ( or polymorphic , for the adult worm sample ) SNPs among these samples was 0 . 29 ( s . d . 0 . 03 ) , which is over 438 times as many variable loci as would maximally be obtained from previous PCR-based microsatellite studies in S . japonicum . It was somewhat surprising to us that the proportion of polymorphic loci in the adult worm sample is 0 . 33 because it comes from a strain that has been maintained for nearly 90 years , with the addition of only a single outside isolate nearly 40 years ago . The high amount of variation is somewhat assisted by the fact that it comes from a pooled sample and the fact that with only nine samples we are mostly considering high frequency SNPs , but it is also a testament to sound strain maintenance practices that must have avoided severe population bottlenecks . The genome-amplified miracidia samples appear to contain many more off-target short loci than the unamplified adult worm data , and the filtering requirement that all variant loci be genotyped in all samples excludes from analysis many variants found in everything but the adult worm . We therefore created a dataset of SNP variants that passed stringent quality and coverage filters for just the eight miracidia samples; this dataset contained 67 , 525 variants , about 2 . 5 times the number that were also shared with the adult worm sample . We were also interested to identify loci that might commonly but not always provide data; we therefore created a third dataset including loci that were missing quality data in up to two of the eight miracidia , and obtained 102 , 877 variants , or about four times the number of variants than shared with all nine samples including the adult worm sample . There was more variation in this sample , with an average heterozygosity of 0 . 37 ( s . d . = 0 . 04 ) , which is over 2 , 000 times as many variable loci as could be obtained from previous microsatellite PCR studies in S . japonicum . Although this study was not specifically designed to test population structure because of the limited population sampling included here , we conducted principle components analysis [51 , 52] on the nine-sample shared SNP dataset to obtain a preliminary estimate of how much this massive increase in genetic variation information might enable the discrimination of population structure in future studies . The first principle component was almost entirely devoted to separating the Anhui adult worm stock sample from the Sichuan miracidia samples , with the second principle component mostly separating the miracidia depending on which person they came from ( Fig 5A ) . We interpret this result with caution because the adult worm sample and miracidia samples were prepared differently ( see Methods ) , and the adult worm sample from a stock strain is unlikely to reflect current Anhui isolates; however , it is clear that genetic differences between the adult worm and miracidia samples are much greater than differences among miracidia . To test if we could obtain additional resolution from the larger dataset excluding the adult worm sample , we ran an additional PCA with the dataset excluding the adult worm sample and found that the first principle component cleanly separated the miracidia obtained from different people and from different villages . The second principle component tended to separate different samples within people , although the miracidia within Person 1 were still not well differentiated ( Fig 5B ) . In summary , although the limited sample size precludes a more in-depth analysis of population structure , the amount of highly informative genetic variation obtained does enable the detection of clear genetic differences between miracidia samples . To complement the PCA analyses , we inferred a phylogenetic tree using the stringent set of SNPs in all nine samples ( Fig 5C ) . There was nearly 100% bootstrap support for all but one clade on the tree ( which had 80% bootstrap support ) , with the first major split separating the miracidia ( all from Sichuan Province , Fig 1 ) and the adult worm ( from Anhui Province stock , Fig 1 ) . While we interpret this result with caution due to the Anhui sample representing a long-standing and previously supplemented stock sample , it is notable that our result is consistent with previous results indicating genetic separation between provinces [53] . The miracidia from each village formed strongly supported clades , as did the miracidia from human 1 within village A . The four samples from a single human host from village B exhibited considerably more structure and longer branch lengths compared to the three samples from a single human host from village A . These results generally mirror and support those from the PCA analyses , and suggest that even with this limited sample , the large amount of variation information is sufficient to clearly identify patterns that distinguish S . japonicum from different provinces , villages , and people . Because the size of the sample is so small , we could not get accurate estimates of allele frequencies , and thus could not make estimates of relatedness definitive enough to qualify as results; however , we include the implications of preliminary estimates of relatedness in the discussion below .
We have shown here that S . japonicum miracidia samples archived for many years at room temperature on Whatman cards can be used to provide large amounts of valuable individual differentiation and population structure information for relatively low cost . We were able to consistently and economically obtain sequence information from tens of thousands of loci , yielding approximately 100 , 000 SNPs genotyped from multiple miracidia samples , even with stringent filtering criteria . We showed that although ddRADseq locus recovery varies among samples ( see Figs 3 , S2 , 4 and S4 ) , reproducible and in-depth recovery of the majority of genomic fragments from a selected size range is possible , even between libraries prepared from different source DNA types , and with different sequencing protocols . It is this reproducible recovery of a limited number of loci across samples without the need for extensive protocol optimization that gives ddRADseq much of its power . In addition to surveying genome-wide SNPs , we also identified approximately 11 , 000 microsatellite loci , about 10% of which had quantifiable variation among the eight miracidia sequenced and 123 of which contained indel variation . Furthermore , we designed PCR primers for 1 , 609 new microsatellite loci that could be used alone or in conjunction with high-throughput sequencing experiments—this resource provides a considerable increase from the 17 microsatellite loci used previously to genotype S . japonicum samples [31] . Importantly , this study demonstrates the ability to obtain many orders of magnitude more data than was previously possible , even from archived miracidia samples collected six years in the past . This drastically expands the ability to evaluate parasite population dynamics through time and space . A useful aspect of the ddRADseq method is that it is flexible enough to handle different sample types while remaining relatively easy to scale the amount of data collected per sample to balance cost and accuracy requirements . This can be done by changing the restriction enzymes and/or by changing size-selection of fragments to include greater or fewer loci to target per sample . Such alterations of the protocol , coupled with adjustments to the numbers of samples pooled per sequencing run , allow the approach to be readily re-scaled to address particular questions . For example , by combining an extended set of the adaptor barcodes and PCR-added indices utilized in this study , and size-selecting a smaller set of loci , the approach could readily be scaled to include over 100 samples per Illumina HiSEQ sequencing lane . Furthermore , such high multiplexing of samples , coupled with modern SNP calling approaches that incorporate uncertainty in SNPs ( genotype uncertainty methods; [54] ) , allow for a high degree of parallelization and economy without sacrificing accuracy in SNP calling . Using such approaches one can obtain useful data even from regions or samples with less-than-ideal sequencing coverage . In our laboratory , costs per sample were in the $50-$200 range , depending on the amount of sequence information produced per sample . In addition to parallelization , we expect that sequencing costs will decrease and that protocols can be refined or improved , further reducing costs . Nevertheless , our results demonstrate that it is possible to affordably obtain large amounts of variation using ddRADseq without excessive protocol optimization . This technique therefore promises to be both a powerful and a cost-effective tool in the arsenal against neglected tropical diseases . Variant filtering strategies for studies with different purposes may vary substantially from the variant filtering we performed here , depending on the goals and tolerances of a particular analysis . We applied both low and high stringency filters to allow the possibility of interpreting all variants with a probabilistic approach and making use of as much of the data as possible , as well as to provide for more traditional ( non-probabilistic ) direct estimates of relatedness and genetic variation . The flexibility of ddRADseq readily enables adjustments to be made to the amount of sequencing per sample as appropriate for the questions being addressed . For example , high confidence variant calling applications , such as association studies , may require higher coverage , and thus need relatively more sequencing . Analysis of gross population structure or sibling detection , in contrast , may require less coverage depth per locus , and may produce better results if sequencing efforts are focused on more multiplexing and larger sample sizes . The ability to reliably sequence large numbers of loci from numerous archived miracidia at economic costs enables the use of this method to answer a number of epidemiological questions relevant to the control of S . japonicum using field-collected samples . With increased sampling within and among human individuals , for example , it may be possible to answer questions such as: 1 ) How many genetically distinct adult worm mating pairs are active in an individual ? 2 ) Is there evidence that an individual who is repeatedly infected is harboring the same adult worm pair , or are they repeatedly infected with new worms ? 3 ) Do infections in geographically-clustered individuals appear to come from a single source , and how are such individuals geographically distributed ? 4 ) Do infections in a single village tend to come from a restricted local source , or are they acquired from a larger region ? 5 ) What proportion of human schistosomiasis burden can be attributed to non-human mammalian reservoirs ? Finally , although we could not obtain accurate estimates of allele frequencies due to the small and non-random sample size in this pilot study , it is of interest to discuss preliminary estimates of relatedness obtained from this data to demonstrate the potential to apply this type of data in a broader epidemiologically-relevant context with far greater sampling . We made such preliminary estimates by calculating genotype sharing between individual miracidia , which can be estimated with low variance from this data ( S5 Table ) . The amount of genotype sharing between miracidia sampled in different villages is 0 . 75–0 . 79 , and there is one miracidia pair within each village that is also in this range . For the sake of discussion , we assume , despite the small sample size and proximity of the villages , that this is the range of sharing we might expect among unrelated individuals or distant cousins within a Province . At the other extreme , the miracidia from Person 1 share a substantially higher proportion of variants than other pairs ( 0 . 87–0 . 90 , S5 Table ) ; this is consistent with the idea that they are all siblings , progeny of the same adult worm pair or progeny of clones of the same pair , and is consistent with the PCA and phylogenetic results . Sibling relationships are a reasonably likely outcome if this person was infected by a single mating worm pair at the time of sampling , although the amount of sharing is slightly high if the parents were unrelated , indicating that the parent worms may have been cousins . It is also possible that the higher genotype sharing in one pair ( miracidia 1 and 3 , sharing = 0 . 90 ) indicates that they are full siblings , while the other two pairs ( miracidia 1 and 2 , and 2 and 3 , sharing = 0 . 87 ) are half siblings with related half-parents . We plan to further evaluate this hypothesis with better population sampling and allele frequency estimates , but it is of epidemiological interest because it potentially indicates a mixture of clones and non-clones among the parental worm pairs . The genotype sharing for two of three miracidia pairs from different people in the same village ( Person 1 and Person 2 from Village 1 , comparing miracidium 4 versus miracidia 1 and 3 ) is compatible with the idea that their miracidia show 2nd or 3rd degree ( close cousin ) relatedness , as do three of the four miracidia from Person 3 . The relatedness of these pairs are in the range of 0 . 80–0 . 83 , somewhat above the average 0 . 77 in presumed unrelated individuals , but substantially below the average 0 . 88 in the presumed siblings from Person 1 . This suggests either considerable local geographic population structure or a higher probability of cousin relationships within villages . If these preliminary results are supported by more in-depth study , they may indicate that sources of infection are village specific . If widespread evidence of 2nd or 3rd degree relatedness among and within people in the same village is confirmed , this would suggest that infections in villages may be the progeny ( offspring or grand-offspring ) of an extremely limited number of adult worm pairs ( which could live in a human or non-human reservoir ) . A more in-depth analysis using complete haplotype sets between worm pairs would likely be fruitful , though we note that this analysis is not yet feasible due to the fragmented nature of the reference genome and small population sample size in this study . Given its prevalence and the serious health risks it poses , and in light of the considerable efforts to attain regional elimination of S . japonicum in China , it is essential to develop practical genomic tools that are capable of resolving complex questions of schistosome transmission to assist complete elimination in China and extend such success to other countries and schistosome species . The ddRADseq-based genotyping method applied here is appropriate for field collected samples: it is able to accommodate the limited DNA available in schistosome miracidia , and appropriate for samples that have been stored for multiple years in a format that does not require refrigeration , allowing easy transport from the field to the laboratory . This method should enable detailed determination of population structure that can be used to accurately identify sources of infections and reinfections , creating the potential to track and target human or mammalian source reservoirs . Finally , we expect that the ddRADseq variation information can be used to identify genetic adaptation events in these parasitic worms , and thus enable early detection and eradication of strains that may evolve resistance to the critical anthelmintic drug Praziquantel . Given the unique role of this drug in schistosome control in Asia as well as worldwide , such detection may prove invaluable to prevent a great deal of future human suffering . | Schistosomiasis is a devastating tropical disease that affects more than 200 million people worldwide . Over the past several decades , transmission control strategies implemented in China have reduced the prevalence and morbidity of Schistosoma japonicum in many areas . Infections still persist , however , and it is therefore of great interest to determine the sources of recurring infections . Genetic analysis is a promising means to achieve this . Towards this aim , we conducted a pilot study to assess the feasibility of using high-throughput sequencing to assess the geographic distribution of schistosome genetic variants . Because DNA yields from miracidia , the most easily accessible life stage , are insufficient for high throughput sequencing , we first employed whole genome amplification to obtain sufficient quantities of DNA . We then employed a technique that reproducibly sequences the same fraction of a genome across numerous samples . We successfully sequenced 6-year old S . japonicum samples from Sichuan Province , China , easily and economically identifying tens of thousands of variable loci , a sufficient number to discriminate fine-scale population structure . Further population sampling will help answer important questions concerning the persistence of infections , the sources of new infections , and whether parasite populations have undergone incipient evolution of drug resistance . | [
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] | 2017 | Whole Genome Amplification and Reduced-Representation Genome Sequencing of Schistosoma japonicum Miracidia |
Listeriolysin-O ( LLO ) plays a crucial role during infection by Listeria monocytogenes . It enables escape of bacteria from phagocytic vacuole , which is the basis for its spread to other cells and tissues . It is not clear how LLO acts at phagosomal membranes to allow bacterial escape . The mechanism of action of LLO remains poorly understood , probably due to unavailability of suitable experimental tools that could monitor LLO membrane disruptive activity in real time . Here , we used high-speed atomic force microscopy ( HS-AFM ) featuring high spatio-temporal resolution on model membranes and optical microscopy on giant unilamellar vesicles ( GUVs ) to investigate LLO activity . We analyze the assembly kinetics of toxin oligomers , the prepore-to-pore transition dynamics and the membrane disruption in real time . We reveal that LLO toxin efficiency and mode of action as a membrane-disrupting agent varies strongly depending on the membrane cholesterol concentration and the environmental pH . We discovered that LLO is able to form arc pores as well as damage lipid membranes as a lineactant , and this leads to large-scale membrane defects . These results altogether provide a mechanistic basis of how large-scale membrane disruption leads to release of Listeria from the phagocytic vacuole in the cellular context .
Listeriolysin-O ( LLO ) is Listeria monocytogenes powerful molecular weapon in host cell invasion , which is the first step of the disease listeriosis [1] . Following accidental ingestion of Listeria-contaminated food , healthy humans suffer from gastroenteritis , while immunocompromised individuals are affected in the nervous system and can suffer severe damage . Listeria infection is treated by antibiotics , but as the development of novel antibiotics is a serious bottleneck , an improved understanding of LLO action may provide novel angles of attack to fight against this disease . LLO is a soluble protein of 56kDa molecular weight that belongs to the cholesterol-dependent cytolysins ( CDCs ) protein family . CDCs are characterized by the requirement of cholesterol for their pore forming activity and by the formation of largest known transmembrane pores that can exceed 40nm in diameter [2 , 3] . LLO effectively binds to lipid membranes that contain high concentrations of cholesterol [4] . Subsequently , LLO monomers oligomerize to form assemblies and then undergo a major conformation change that allows them to penetrate the membrane and form pores . LLO is different from other CDCs in that it shows pH-dependent stability , its membrane binding is diminished and its structural integrity weakened at pH of 7 . 4 and higher and at temperatures above 30°C [5–7] . This allows LLO to act optimally at the lower pH within the phagosomes of the infected cells , where Listeria is engulfed after cell entry . Membrane insertion of LLO oligomers and permeabilization of the Listeria-containing vacuole enables escape of Listeria from the phagosome into the infected cells and spread to other tissues [1 , 2 , 6 , 8–11] . Bacterial escape to the cytosol is accompanied by uncoupling of the pH gradient between the primary phagosome and the cytosol . It was shown that this is caused by LLO-mediated membrane permeabilization that occurs soon after the entry of bacteria into the cell [11 , 12] . This delays maturation of vacuoles , prevents further acidification and allows replication of bacteria [11 , 13] . Larger membrane lesions of the phagocytic vacuole finally evolve and allow escape of bacteria to the cytosol of the cell and further spreading to neighboring cells [11] . The mechanisms of phagocytic membrane disruption by LLO are , however , unknown and understanding of LLO lipid membrane damaging activity would crucially improve understanding of this most important step in the Listeria pathogenicity mechanism . Cholesterol-dependence , endosomolytic pore-formation , and pH-dependence of LLO have been revealed in the last decades and recent works reported structural details of its monomers and oligomeric complexes [1 , 14–17] . Studies using conventional atomic force microscopy ( AFM ) and electron microscopy ( EM ) depicted CDCs on model membranes [14 , 16 , 18–20] , yet , the slow image acquisition speed ( of one to several minutes ) of conventional AFM and sample fixation in EM prohibited a detailed understanding of the dynamic action of LLO . Here , we used high-speed atomic force microscopy ( HS-AFM ) [21 , 22] , a unique tool for studying the structure and dynamics of membrane processes [23 , 24] , and acquired HS-AFM measurements at high spatio-temporal resolution during the entire LLO action cycle from monomer assembly on the membrane surface , LLO oligomerisation , prepore-to-pore transition and finally to the processive membrane lysis by assembly of many LLO oligomers and monomers acting at formed or existing lipid membrane defects . This unique observation was independently confirmed by experiments employing giant unilamellar vesilces ( GUVs ) that show reduction of number of large GUVs in the presence of LLO and membrane permeabilization to compounds much larger than the diameter of previously assumed pore architecture . A comprehensive description of LLO-induced membrane damage at different membrane cholesterol concentrations and at different environmental pH values provides a mechanistic basis for understanding Listeria escape from phagolysosomes .
LLO was prepared as described in Podobnik et al [16] . The protein was aliquoted and was stored in 20mM MES , pH 5 . 6 , 100mM NaCl at a concentration of 17μM . Lysenin was prepared as described in Munguira et al . [35] All lipids in this study were purchased from Avanti Polar Lipids , and used without further purification: Cholesterol from ovine wool specified as 98% pure and 1 , 2-dioleoyl-sn-glycero-3-ethylphosphocholine ( DOPC ) specified as 99% pure . Briefly , lyophilized lipids were dissolved in organic solution chlorophorm:methanol 3:1 vol:vol to give a final concentration of 3mM . An aliquot was poured in a glass vial and evaporated to dryness with clean nitrogen flow . The resulting lipid film was kept under reduced pressure overnight to ensure the absence of organic solvent traces . Then , the lipid film was hydrated with Milli-Q water to give a final lipid concentration of 500μM , subjecting the vials to 5 cycles of agitation of 1 min , and heating ∼70°C , well above the transition temperature of the lipid mixtures studied herein . The obtained multilamellar vesicles were sonicated for 40 minutes in order to obtain LUVs . After preparation , LUVs suspensions were stored at ∼4°C and used during maximal 10 days . During all the preparation processes , samples were protected from light to avoid unspecific oxidation . Supported Lipid Bilayers ( SLBs ) were prepared by fusion of Large Unilamellar Vesicles ( LUVs ) on the mica support , adapted from [25] . To form the SLBs , 2μL of LUVs were deposited on 1 . 5mm2 freshly cleaved mica surface , which was glued with epoxy to the quartz sample stage . After 30–40 minutes incubation in a humid chamber , sample was gently rinsed with milli-Q water and never let dry . High-speed atomic force microscopy movies were acquired using 8 μm-long cantilever with nominal spring constant k = 0 . 15N/m and resonance frequency f = 0 . 6MHz in solution . Both the cantilever and the rinsed mica surface with incubated bilayer were placed into a 120μL imaging buffer chamber . HS-AFM was operated in oscillating mode . Small oscillation free and set point amplitude of about 1nm and 0 . 9nm , respectively , were used , to achieve minimum tip-sample interaction . LLO water soluble monomers were added to a final concentration of 500nM after identification of the membrane patches on the mica surface . HS-AFM measurements were performed at room temperature . Buffer of 20mM MES , pH5 . 6 , 100mM NaCl , 5mM MgCl2 was used for structural observation of LLO by HS-AFM while dynamic characterizations were carried out in 20mM MES , pH5 . 6 , 100mM NaCl , 1mM EDTA; as we found that the presence of divalents significantly slowed or inhibited LLO action probably due to stabilisation of the lipid bilayer . HS-AFM image and data processing were performed using ImageJ software with a dedicated Plugins developed for HS-AFM [26] . All further analysis , i . e . histogram distributions were analyzed in Matlab and Origin . Giant Unilmellar Vesicles ( GUVs ) were prepared by the electroformation method . Lipid stock solutions of DOPC/Cholesterol 4:1 mol:mol , DOPC/Cholesterol 9:1 mol:mol , DOPC and POPC/sphingomyelin 1:1 mol:mol ( for imaging lysenin activity ) were prepared in chloroform . Rhodamine DHPE was added as fluorescent probe with the final concentration of 0 . 5mol% . 20μL of lipid stock solution was placed on the conductive ITO slide and dried under reduced pressure for 30 minutes . The sucrose solution ( 290mM sucrose in 1mM MES , pH5 . 6 , for preparation of DOPC/Cholesterol GUVs and 290mM sucrose , 1mM HEPES , pH7 . 4 for preparation of POPC/sphingomyelin GUVs ) was added to the dry lipid film in the center of the O-ring and covered with another conductive ITO slide . Electroformation was carried out inside Nanion vesicle prep pro , where AC current with an amplitude of 3V and a frequency of 5Hz was applied across the ITO slides for 3 hours ( for preparation of DOPC/Cholesterol GUVs ) and for preparation of POPC/Sphingomyelin GUVs an amplitude decreased from 3V to 1 , 6V and a frequency decreased from 5Hz to 1Hz in 5 hours . GUVs were sedimented with the glucose solution ( 290mM glucose in 1mM MES , pH5 . 6 for preparation of DOPC/Cholesterol GUVs and 290 mM glucose , 1mM HEPES , pH7 . 4 for preparation of POPC/ sphingomyelin GUVs ) . The buffer was then exchanged by gentle pipetting with 20mM MES , pH5 . 6 , 150mM NaCl for preparation of DOPC/Cholesterol GUVs and with 20mM HEPES , 150mM NaCl , pH7 . 4 for preparation of POPC/ sphingomyelin GUVs ) . All solutions used for electroformation , sedimentation and analysis with proteins were isoosmolar , the solution osmolarity was adjusted using an osmometer . Sedimented GUVs were used immediately . They were stored at ∼4°C and never used after 4 days . During the preparation process and storage the samples were protected from light . The GUV suspension was mixed with LLO , dissolved in buffer ( 20mM MES , pH 5 . 6 , 150mM NaCl ) to final LLO concentrations of 1 , 10 , 100 and 500nM . The buffer was used instead of LLO solution in the negative control sample . Samples were incubated for 30 minutes at room temperature and then analyzed by flow cytometry . Flow cytometric data acquisition and analysis were performed by the PARTEC CyFlow flow cytometer with a 488nm laser and equipped with FloMax software . The presence of particles was determined by forward and side scatter ( FSC/SSC ) parameters , set at logarithmic gain . Minimum threshold of 70 was set at the FSC parameter to limit the measurement of the smallest vesicles and micelles . At least 15000 events were recorded for each sample analysis . Size-calibrated fluorescent beads of 1μm , 3 . 1μm , and 10μm size were used to determine the appropriate size of vesicles in the sample . Flow Jo software was used for the analysis of the results . For the LLO activity experiments , GUVs suspension was mixed with buffer ( 20mM MES , pH5 . 6 , 150mM NaCl ) and fluorescent dextrans ( FDs ) and incubated at room temperature . In parallel , for the experiments with lysenin , GUVs suspension ( POPC: Sphingomyelin 1:1 mol:mol ) was mixed with buffer ( 20mM HEPES , pH7 . 4 , 150mM NaCl ) and fluorescent dextrans ( FDs ) and incubated at room temperature . The dextrans were passed across a gel filtration column to determine homogeneity . Fractions were collected and analyzed for permeability of GUVs and size with dynamic light scattering . Buffer alone was used instead of LLO or lysenin solution for negative control . The final concentrations were 500nM for LLO or lysenin and 1mg/ml for FDs of 4 , 20 , 70 , 150 , 2000kDa in size . Images were recorded on a Leica TCS SP5 laser-scanning microscope with a 40 × oil-immersion objective ( numerical aperture = 1 . 25 ) . FDs were excited at 488nm and fluorescence emission detected from 497 to 534nm . Rhodamine in the GUV membranes was excited at 543nm and fluorescence emission was detected from 573 to 604nm .
LLO was added to 500nM final concentration onto the bilayers ( DOPC:Cholesterol 4:1 mol:mol , at pH5 . 6 ) . In typical HS-AFM image frames , the darkest image areas correspond to the membrane surface and the arc-shaped brighter structures are the protruding LLO complexes ( Fig 1a ) . From such images the protrusion height from the membrane of the oligomeric protein complexes could be analyzed . The highest protruding clusters displayed heights of 11 . 1±0 . 5nm ( Peak±FWHM ) ( Fig 1b , blue ) , very similar to the height of water-soluble LLO [14] , and were therefore assigned to the prepore state . Later , arc-shaped complexes protruded less , only 7 . 3±0 . 2nm ( Peak±FWHM ) , from the membrane surface , and were consequently assigned to the pore state ( Fig 1b , red ) , as the height difference suggested the vertical collapse of CDCs accompanying membrane insertion [16 , 19] . These results are in good agreement with previous studies characterizing the soluble and membrane structure of LLO with various techniques [6 , 14 , 16] . Recently the soluble state atomic-level-structure of LLO has been solved [14] . However , little is known about the detailed structure of the membrane-embedded protein complex , and studies diverge on the apparently simple question whether the LLO oligomers formed rings or incomplete arc-shaped pore structures [1 , 2 , 14–16 , 27–29] . A recent structural model , based on X-ray crystallography and electron microscopy proposed a ring with about 50nm diameter composed of 36 LLO monomers [14] . Our HS-AFM data permits to obtain detailed insights into the characteristics of LLO pore assemblies in a native-like environment ( Fig 1a ) . Interestingly , we never observed ring-shaped LLO assemblies . In contrast , LLO formed arcs independent on the protein density . Arcs displayed rather well-conserved structural characteristics with an average arc-length of 51±6nm ( Peak+FWHM ) ( Fig 1c , blue ) , and an arc curvature radius of 31±3nm ( Peak±FWHM ) ( Fig 1c , red ) . Individual LLO monomers in these arcs had dimensions of 2 . 5±0 . 2nm ( Peak±FWHM ) times 7 . 0±0 . 3nm ( Peak±FWHM ) , along the arc direction and across the assembly , respectively ( Fig 1d ) , meaning that the average arc is constituted of about 20 monomers . This implies that the final assemblies , if they would form a ring , would have a diameter of between 60 and 65nm , larger than earlier estimates [14] . However , we propose that the LLO assembly should not be regarded in that way , as we have evidence that membrane-asssociated LLO exist mainly in the arcs . We show ( i ) that > 95% of all assemblies are arc-shaped with rather constant dimensions , ( ii ) that arc radius analysis indicates a much larger effective diameter than what was proposed rendering the formation of a closed circular assembly difficult , ( iii ) strong evidence for the spatio-temporal separation of assembly and membrane insertion ( see below ) hence hampering further assembly growth once the arc transforms into the pore-state . Compared to conventional AFM , HS-AFM provides the possibility to assess dynamics at time-ranges of biological relevance allowing LLO action to be analyzed in real-time ( S2 , S3 , S4 and S5 Videos ) . HS-AFM directly revealed the assembly of LLO into arc-shaped oligomers on the membrane surface ( Fig 2a ) . We reason that the membrane disturbance of a monomer penetrating into one membrane leaflet is energetically costly and that bringing several units together minimizes this energy cost , driving oligomerization . This process occurred on time scales as short as 10s at 500nM LLO concentration . Once arcs have reached maturity , i . e . assembling about 20 monomers in an arc of about 50nm length , assembly stalled and further length analysis over 60s revealed length fluctuations of a few nanometers , the size of a single subunit and within the measurement error ( Fig 2b ) . The formation of complete circular ring-shaped complexes from arc-shaped lysteriolysin was never observed . Closest to this , annealing of neighboring arcs was regularly observed . In this case , arc-shaped oligomers were interlocked to exhibit a rather ellipsoidal surface contour that did not further evolve as a function of time ( Fig 2c ) . Membrane insertion has only been observed when rather advanced arc assemblies have been formed , indication that the prepore-to-pore transition needs an advanced state of oligomerization and might therefore be cooperative ( Fig 2d ) . Quantitatively , single molecule analysis showed varying residence lifetime of prepore complexes on the membrane of up to minutes , followed by insertion that is completed within seconds . This more rapid membrane insertion compared to the residence lifetime of prepores is further evidence for a cooperative conformational change within the units of the prepore arcs for membrane insertion ( Fig 2e ) . Further oligomerisation of already membrane embedded arc-shaped oligomers with other existing LLO arcs in the insertion state was never observed . These results show that the oligomerisation and membrane insertion actions of LLO are spatiotemporally uncoupled . Once LLO is in the membrane , the dynamic destruction of the bilayer could directly be observed: membrane defects occur inside the arc and grow by lateral propagation of the toxins in the membrane ( Fig 2f ) . It is notable that the perimeter edged by LLO remaining in the prepore state does not propagate in membrane disruption ( Fig 2f , S5 Video ) While the LLO membrane destructive action in dependence of cholesterol has been described in detail [1 , 4 , 15 , 30 , 31] , and recent AFM analysis described the LLO assembly structures at high resolution [17] , the morphology and dynamics of LLO action on cholesterol containing membranes remains unknown . The versatility of HS-AFM to dynamically image bilayers of various compositions and under varying buffer conditions allows structural description of the mode of action of LLO in detail . Notably , the influences of cholesterol and pH on LLO action have been studied , both essential factors during the cell infection process . As earlier reported , LLO activity is optimized at slightly acidic pH [5 , 6] . We therefore carried out measurements to analyze how varying concentrations of membrane cholesterol influences LLO action in a buffer at pH5 . 6 , containing 20mM MES , 100mM NaCl and 1mM EDTA . These measurements were performed by adding LLO to a final concentration of 500nM onto DOPC/Cholesterol model membranes with varying cholesterol content . Our results demonstrate a novel dynamic view of LLO membrane activity . Notably , two types of membrane destruction , either from the inside of the membrane , following membrane insertion , or from membrane borders could be distinguished . In the first case , the pre-pore to pore transition is indispensible , while in the second case lineactivity alone is sufficient ( S6 , S7 , S8 , S9 Videos ) . We conceptualize the lineactant activity as the 2D ( two-dimensional ) analogue to surfactant activity in 3D . A lineactant is a line-tension modifying agent that ‘solubilizes’ the 2D membrane . At 0mol% cholesterol , S6 Video , the membrane is basically resistant to LLO ( Fig 3a , 1st row ) . As a function of incubation time with 500nM LLO , no membrane damage is detected , not even after more than 15 minutes . LLO adsorbes next to the bilayer on the mica and is unable to adhere or damage the membrane border . At 10mol% cholesterol , S7 Video , the membrane becomes senistive to LLO ( Fig 3a , 2nd row ) . However , under these conditions LLO was never observed to form arc-shaped assemblies on the membrane and/or insert the membrane , yet LLO could act from the membrane edges , ‘solubilizing’ the membrane from the sides as a lineactant . This shows that 10mol% cholesterol are not enough for oligomerisation and insertion , but is a neccessity for membrane disruption . After about 20 minutes , only small membrane fragments remained . At 20mol% cholesterol , S8 Video , LLO oligomerization , arc-formation and prepore-to-pore transition were observed ( Fig 3a , 3rd row ) . LLO penetrated the membrane rapidly under these conditions . In addition we noted that LLO subsequently disrupted the membrane both from within the membrane , where the initial arc-complexes served as nucleation points for further membrane disruption , and from membrane edges . At 40mol% cholesterol , S9 Video , LLO activity is further accelerated ( Fig 3a , 4th row ) . Arcs form rapidly everywhere on the membrane , insert and lyse the bilayer; within about 5 minutes of LLO action the entire membrane was destroyed . Beyond morphological aspects of the membrane disruption dynamics , the HS-AFM movies allowed numerical analysis of the membrane disruption velocity ( Fig 3b ) . The membrane area was computationaly analyzed in each image frame and the disruption process plotted as a function of time . The average velocity could be calculated as roughly 0nm2/s , ∼300nm2/s , ∼600nm2/s , and ∼1200nm2/s membrane disruption velocity for 0 , 10 , 20 and 40 mol% membrane cholesterol content , respectively , at constant 500nM LLO concentration . Although membrane disruption is not linear , because the circumference-area-ratio changes as a function of time-course of LLO-action ( and this is particularly important for the conditions in which LLO acts mainly from the membrane edges ) and the experiment is limited to relatively small observation areas , a rough linear correlation between LLO membrane disruption efficiency and membrane cholesterol-content emerges ( Fig 3b ) . Membrane disruption only occurred from borders or defect edges that were decorated with LLO arcs . These arcs appeared to remain of constant size during dynamic large-scale membrane defect generation ( Fig 3c , S9 Video ) . Based on the HS-AFM observations , it cannot be determined whether the retracting lipid material during the disruption process was solubized by the protein and released into solution or moved out of the membrane defect . LLO was shown to act efficiently in very different environments such as the phagolysosomal membrane , where the pH is low , as well as at the plasma membrane level , where it is exposed to the physiological pH [4 , 10–12 , 20] . To learn more about the pH-dependence of LLO membrane disruption , we designed our next measurements under three typical pH conditions , i . e . acidic pH5 . 6 , neutral pH7 . 6 , and alkaline pH9 . 6 , on a DOPC:Cholesterol 4:1 mol:mol membrane , knowing that this cholesterol content is typically close to physiological and allows the toxin to act efficiently [32] ( S10 and S11 Videos ) . As reported above , at pH5 . 6 LLO is highly efficient ( Fig 4a , 1st row ) : The entire functional path is supported under such conditions , notably , LLO undergoes prepore-to-pore transition creating novel membrane defects and then disrupts the membrane at about ∼600nm2/s ( Fig 4b ) . At pH7 . 6 , S10 Video , membrane disruption could also be observed , but only from membrane edges and no well-defined prepore oligomers were observed on the membrane ( Fig 4a , 2nd row ) . Nevertheless , this experiment demonstrated that the inactivation of LLO at neutral pH was not complete , despite that neutral pH hampered the formation of oligomeric complexes and insertion , the membrane ‘solubilisation’ process still occurred . Importantly , membrane disruptions occurred at the same velocity as at acidic pH , ∼600nm2/s ( Fig 4b ) . In contrast , at alkaline pH9 . 6 , S11 Video , LLO was inactive and no membrane disruption ( ∼0nm2/s ) could be detected . Fast LLO diffusion along the membrane surface was detected , but no membrane attachment , oligomerisation or penetration could be observed , despite the presence of cholesterol in the membrane ( Fig 4a , 3rd row ) . This finding indicates that LLO at alkaline pH does neither engage into protein-lipid or protein-protein interactions , probably due to unfavorable charges exposed on the protein surface , in agreement with earlier work [4] . The HS-AFM experiments have revealed that LLO can induce large-scale membrane destruction . In order to prove that these observations on supported lipid bilayers were representative for LLO action , GUVs were formed in order to independently confirm LLO membrane destructive activity . GUVs were prepared by electroformation from DOPC/Cholesterol 4:1 mol:mol lipid mixture ( lipid composition used in the HS-AFM experiments ) and imaged at pH5 . 5 . GUVs size distributions were analyzed by flow cytometry in the presence of various concentrations of LLO . Flow cytometry revealed that presence of LLO caused drastic reduction in the number of GUVs that were larger than 3μm in diameter ( Fig 5a and 5b ) . At 500nM LLO concentration ( concentration used in HS-AFM experiments ) only few GUVs remained in the mixture ( Fig 5b ) . Confocal microscopy confirmed these results and showed a drastic reduction in the number of GUVs in the presence of LLO , in addition to GUV permeabilization ( Fig 6a–6c ) . We have performed several additional control experiments . The population of large GUVs composed of DOPC alone is not decreased in the presence of LLO , in agreement with the known LLO inability to associate with the membranes devoid of cholesterol ( Fig 5c and 5d ) . As another control we have used pore forming toxin lysenin , which belongs to the aerolysin-like pore forming toxin family [36] . Lysenin forms stable β-barrel pores formed by 9 subunits with a pore diameter of approximately 3 . 5nm , which are significantly smaller than pores of CDCs [35 , 37] . Lysenin does not show lineactant activity on lipid membranes as imaged by HS-AFM [35 , 38] . In agreement with these literature data , the population of large vesicles does not decrease in number upon addition of comparable lysenin concentration ranges ( Fig 5e and 5f ) . Furthermore , HS-AFM results showed that in the presence of cholesterol LLO arc formation is rapid and that arcs form pores ( Fig 2 ) . Most importantly , HS-AFM evidenced a novel LLO activity mechanism where membrane inserted LLO has high lineactant efficiency and successively destroys membrane on large-scale . Functional pore formation was analyzed in the GUVs system by monitoring uptake of fluorescent dextrans of various sizes . We have added fluorescent dextrans of various sizes , FDX , where X denotes the size of the dextran in kDa , to the exterior volume of the GUVs in the presence of LLO and checked for fluorescence equilibration at different time points . Dextrans of 4kDa and 20kDa dextran , with estimated diameter of 2 . 8nm and 6 . 6nm , respectively , equilibrated readily at 5 min ( Fig 6c , 1st and 2nd panels ) , while larger dextrans needed more time for equilibration . Particularly interesting is the 150kDa dextran , which is approximately 17nm in diameter , which poorly equilibrate initially , but almost fully after 60 minutes ( Fig 6c , 5th panel ) . This dextran is too large to equilibrate through arc pores . Finally , the huge FD2000 showed some membrane permeability after 60 minutes ( Fig 6c , 6th panel ) . We have also used DOPC membranes with 10 mol% of cholesterol . We show that 70 kDa dextran could only enter these GUVs at much later times ( 60 min ) , but not at 5 min as in the presence of 20 mol% cholesterol or more ( Fig 6c , 4th panel , data in red ) . This is in agreement with HS-AFM data where at this cholesterol concentrations LLO disrupt membranes primarily by lineactant activity but only rarely pores are formed ( Fig 3a ) . In order to check whether the observed effects are specific to LLO , we have used lysenin , as a negative control . As expected lysenin pores allowed passage of FD4 across the membranes , but not other dextrans , such as FD10 or FD70 ( Fig 6d and 6e ) [36 , 37] . This experiment with a toxin of fixed pore size illustrates that LLO indeed presents a second functional mechanism that is qualitatively different from other toxins , namely in its capacity of creating large-scale membrane damages as a function of time . These experiments on GUVs altogether confirm the HS-AFM imaging on supported membranes . While the kinetics in these experiments are somewhat slower compared to HS-AFM , the data further supports the model in which small arc pores initially formed are nucleators for large-scale membrane lesions by LLO .
Here we present a first dynamic analysis of LLO membrane activity at high-spatio-temporal resolution . A DOPC/Cholesterol model membrane system was used at various cholesterol content bathing in buffers of various pH . These experimental conditions combined with the capacity of dynamic imaging , allowed us to acquire a detailed understanding of the molecular action of LLO . There are four key statements that characterize the function of LLO ( Fig 7 ) . First , LLO associates to the membrane: preconditions to membrane association are mildly acidic pH and the presence of at least 20mol% membrane cholesterol . Second , LLO oligomerises: preconditions for oligomerisation are also mildly acidic pH and at least 20mol% membrane cholesterol . In contrast to preconceptions and models [14 , 16] , LLO does not oligomerize in full circles in the used membrane lipid composition ( DOPC/cholesterol ) . LLO forms arc-shaped assemblies of about 50nm in length and a curvature radius of about 30nm comprising about 20 subunits . Third , the prepore-to-pore transition leads to membrane insertion: this process is rapid , and needs slightly acidic pH , as reported before [1 , 4–6 , 10 , 16] . Its efficiency is favored by cholesterol-content , maybe because cholesterol intercalates between lipids and hence diminishes lipid-lipid interactions . In this context it is notable that some CDCs , e . g . ILY , bind protein receptors , but still need cholesterol for insertion [33] . The prepore-to-pore transition seems to comprise a cooperative aspect , as insertion of monomers or small oligomers were not observed , and as the insertion of arcs was faster than the residence lifetime of the same arcs before the first subunits inserted . Importantly , the oligomerisation process and the membrane insertion are uncoupled , as oligomerisation in well defined structures takes only place in the prepore state . Assemblies inserted into the membrane are sufficient to create membrane defects allowing further membrane disruption . Fourth , membrane disruption takes place: The ‘solubilization’ process of the membrane is roughly linear with membrane cholesterol content ( a further indication that cholesterol intercalation between lipids favros LLO action ) and scaled at 500nM LLO concentration with about ( ∼300nm2/s ) at 10mol% cholesterol . This membrane lysis can start either from a pore formed by LLO , or from a membrane edge , i . e . membrane defect , in the experimental system . It is important to note that the ‘solubilization’ process works already at 10mol% cholesterol and is pH-independent , separating completely the membrane disruption process from the membrane insertion process that is pH-dependent . We propose that these findings have importance in a physiological context of listeriolysis . In contrast to other CDCs , LLO is not only designed to make holes into the membrane to destroy ion or nutrient gradients . One of the major tasks of LLO is to allow escape of a large bacteria from the intracellular phagocytic vacuole . For this , membrane disruption ( ‘solubilization’ ) is needed [11–13] . We quantitatively reveal that LLO membrane disruption involves pore formation into a continuous membrane , and disrupting the membrane from the borders , i . e . continuing to disrupt membrane from pre-existing damages ( Fig 7 ) . While the first necessitates slightly acidic pH , the second is maintained at physiological pH . This is the first experimental distinction of these different modes of action and allows understanding that pH sensitivity is crucial at the very early stages of LLO action . At an advanced stage , when the pH in the phagocytic vacuole has leveled with the cytoplasm through ion- and proton-gradient dissipation through small LLO pores , the second mechanism is sufficent for further membrane disruption and Listeria release . The membrane ‘solubilizing’ action is preserved and well efficient at neutral pH , and therefore , it allows complete disruption of the vacuolar membrane . This second step is likely enhanced with Listeria phospholipases that were shown to act in concertion with LLO . Phospholipases hydrolytic activity on phagosomal lipid membrane may further expose hydrophobic parts of the membrane , where LLO could act upon [34 , 39] . The membrane ‘solubilisation’ proceeds through activity of LLO at the membrane exposed sites in the pore formed by inserted arcs and is mechanistically a lineactant process , as LLO were permanently imaged at the processive borders progressing in membrane destruction . Altogether , experiments presented here provide a solid basis for an understanding of the dynamics of membrane damage induced by LLO in listeriolysis . | Listeriolysin-O ( LLO ) plays a crucial role in Listeria monocytogenes infection by allowing bacteria to escape from intracellular phagosomes and cells via an unknown molecular mechanism . We used high-speed atomic force microscopy ( HS-AFM ) supported with giant unilamellar vesicles imaging ( GUVs ) to characterize the interaction and dynamics of LLO with the lipid membranes at the nano-and micro-scale . We show that LLO efficiency and mode of action as a membrane-disrupting agent is strongly dependent on membrane cholesterol content and environmental pH . LLO is able to form arc pores and damage membranes as a lineactant , which is crucial for the processive membrane disruption . The latter mechanism , a previously uncharacterized mode of action for this toxin , is strongly cholesterol dependent and may provide a novel angle of attack against listeriosis . | [
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"organisms"
] | 2016 | Listeriolysin O Membrane Damaging Activity Involves Arc Formation and Lineaction -- Implication for Listeria monocytogenes Escape from Phagocytic Vacuole |
The e-liquids used in electronic cigarettes ( E-cigs ) consist of propylene glycol ( PG ) , vegetable glycerin ( VG ) , nicotine , and chemical additives for flavoring . There are currently over 7 , 700 e-liquid flavors available , and while some have been tested for toxicity in the laboratory , most have not . Here , we developed a 3-phase , 384-well , plate-based , high-throughput screening ( HTS ) assay to rapidly triage and validate the toxicity of multiple e-liquids . Our data demonstrated that the PG/VG vehicle adversely affected cell viability and that a large number of e-liquids were more toxic than PG/VG . We also performed gas chromatography–mass spectrometry ( GC-MS ) analysis on all tested e-liquids . Subsequent nonmetric multidimensional scaling ( NMDS ) analysis revealed that e-liquids are an extremely heterogeneous group . Furthermore , these data indicated that ( i ) the more chemicals contained in an e-liquid , the more toxic it was likely to be and ( ii ) the presence of vanillin was associated with higher toxicity values . Further analysis of common constituents by electron ionization revealed that the concentration of cinnamaldehyde and vanillin , but not triacetin , correlated with toxicity . We have also developed a publicly available searchable website ( www . eliquidinfo . org ) . Given the large numbers of available e-liquids , this website will serve as a resource to facilitate dissemination of this information . Our data suggest that an HTS approach to evaluate the toxicity of multiple e-liquids is feasible . Such an approach may serve as a roadmap to enable bodies such as the Food and Drug Administration ( FDA ) to better regulate e-liquid composition .
Electronic cigarettes ( E-cigs ) , also known as electronic nicotine delivery systems ( ENDS ) , are devices that deliver nicotine to the lung without combustion in a process known as “vaping” [1] . They differ from traditional cigarettes in that they do not contain tobacco , and—instead—they produce an aerosol by drawing and heating a liquid vehicle ( e-liquid ) over a battery-powered coil . This aerosol is inhaled and deposited in the lungs so that nicotine can be absorbed into the bloodstream and translocate to the brain [2] . E-cigs were introduced as a potentially safer alternative to tobacco smoking because they do not contain the toxic byproducts of tobacco combustion , including tar-phase chemicals [3 , 4] . However , vaped e-liquids also undergo pyrolysis and generate oxidative species , which may lead to the formation of additional toxic components ( i . e . , formaldehyde and carbonyls ) that are similar to those seen in cigarettes [5 , 6] . In addition , while e-liquids do not contain tobacco , they may contain nicotine derived from tobacco and therefore may contain certain tobacco-related components such as nitrosamines [7] . However , despite these observations , little is known about the toxicity potential of most e-liquids . Since their inception , E-cig design has progressed rapidly . The first-generation E-cigs , dubbed “cigalikes , ” were prefilled disposable devices that were designed to look like traditional cigarettes . In contrast , second- and third-generation E-cigs have interchangeable parts including an aerosol generator , a heating element ( coil ) , a refillable tank , and much more powerful rechargeable batteries [8 , 9] . These devices have broken from the traditional design in favor of handheld tanks that have an increased and even customizable ability to deliver aerosolized nicotine ( along with other aerosolized constituents ) [10] . Moreover , second- and/or third-generation E-cigs produce a higher concentration of plasma nicotine metabolites ( cotinine and trans-3’-hydroxycotinine ) than the first-generation cigalikes that is now comparable to plasma cotinine levels seen in regular smokers [11 , 12] . The e-liquid vehicle used in E-cigs is composed of propylene glycol ( PG ) and vegetable glycerin ( VG ) at varying ratios . There are currently over 7 , 700 e-liquid flavors on the market from over 1 , 200 different vendors in the United States , and the number continues to increase [10] . E-liquids come in many different flavors , colors , nicotine concentrations ( 0–36 mg/mL ) and PG/VG ratios ( e . g . , 80:20 , 70:30 , 55:45 , and 40:60 ) . Despite their ubiquity , manufacturing standards for e-liquids do not currently exist , and they can differ in composition from vendor to vendor [13] . The sheer diversity and variability have made it difficult to comprehensively study e-liquids , and to date , very little to no research has been conducted to assess the safety of most available e-liquids . Many of the chemical constituents in e-liquids , including PG and VG , are on the Food and Drug Administration ( FDA ) ’s Generally Recognized As Safe ( GRAS ) list . However , most GRAS studies on flavors were performed following oral ingestion in rats [14 , 15] , and many GRAS chemicals have not been tested for safety after inhalation [16–18] . Indeed , the toxicity profile for inhalation is markedly different from the oral route . As a case in point , diacetyl , which is used as butter-flavored chemical , is on the GRAS list but causes bronchiolitis obliterans when inhaled [19 , 20] . Emerging studies have shown that e-liquids have measurable biological effects on cells , including altering Ca2+ signaling , cell growth , viability , and inflammation . However , the research that has been conducted thus far has looked at only a small proportion of the available e-liquids , leaving the effects of many e-liquid flavors unknown [21–24] . Given the growing number of untested , commercially available e-liquids , new paradigms need to be introduced to rapidly screen these e-liquids using in vitro assays to better inform both the policy makers ( i . e . , the legislature/FDA ) as well as the public . Here , we introduce a high-throughput screening ( HTS ) assay designed to assess growth characteristics , viability , and chemical composition of e-liquids . The overall goal of this work was to screen neat e-liquids and identify potential flavors and/or chemical constituents that were more toxic than PG/VG and would warrant additional , more detailed attention . Therefore , as a proof of concept , we screened 148 e-liquid flavors to determine their relative toxicity and chemical composition . We then validated these results in multiple cell types and after exposure to E-cig aerosols .
We initially designed 2 screens to assess cellular toxicity . The first method consisted of quantifying cell surface area by thresholding automatically acquired bright-field images over time as an indicator of cell growth . Using this approach , we assessed the effects of 148 e-liquids and a PG/VG control ( added at 1% and 10% , respectively ) to human embryonic kidney 293 ( HEK293T ) cells cultured in 384-well plates . Cells were plated at a density of 5 , 000 per well and placed in an imaging plate reader for 8 h at 37 °C , 5% CO2 . After addition of the vehicle control ( 100% media ) , cells exhibited normal , log-phase growth over 12 to 32 h and showed duplication of cell surface area , consistent with healthy cell growth ( Fig 1A and 1B; S1 Data ) . Addition of 10% 55:45 PG/VG in media significantly attenuated cell growth , which served as a negative control in subsequent studies . Fig 1A ( S1 Data ) depicts representative images from cells exposed to different e-liquids ( Popcorn , 88% Δ growth; Candy Corn , 86% Δ growth; Banana Pudding , 18% Δ growth; Chocolate Fudge , 14% Δ growth ) , as well as phosphate-buffered saline ( PBS ) and PG/VG controls . We classified the complete growth curves for these e-liquids as normal , reduced , no growth , and toxic ( Fig 1B; S1 Data ) . The second approach used to assess toxicity of e-liquids was to fluorescently measure the number of live cells using calcein-AM ( Fig 1C; S1 Data ) . Using this approach , we detected significant attenuation of viability ( i . e . , decreases in calcein fluorescence ) after 24 h ( see Fig 1D for representative examples and Fig 1E and 1F for summary data; S1 Data ) . We then performed hierarchical clustering on all e-liquids tested , taking into account both Δ growth and live-cell fluorescence ( Fig 1E; S1 Data ) . Using complete agglomerative hierarchical clustering , e-liquids could be separated into 3 relevant categories: ( i ) red , e-liquids that showed low Δ growth and low live-cell fluorescence; ( ii ) yellow , e-liquids that showed moderate Δ growth and low live-cell fluorescence; and ( iii ) green , e-liquids that showed higher Δ growth and high live-cell fluorescence . Because we could discern distinct trends based on the clustering methods , we then compared e-liquids according to their growth rates and viability , and we found that the fluorescence assay—which meets these criteria—was more sensitive than the cell growth density ( Fig 1F; S1 Data ) . The coefficient of variation for this method , which indicates the variation of a standard measurement throughout a 384-well plate , was below 15% ( ≤20% is considered satisfactory ) . In addition , the signal-to-background ratio was 3 . 47 , indicating a significant degree of separation between them . Finally , we calculated the Z’ score to quantify the suitability of this assay for use in high-throughput screens and found it to be 0 . 84 . An assay with a Z’ score between 0 . 5 and 1 . 0 is considered an excellent assay because the separation between the positive and negative controls , relative to the variability , is significant [25] . Because PG/VG is an integral component of all commercially available e-liquids and appeared to induce toxicity ( Fig 1C and 1D; S1 Data ) , we then studied its effects on cell toxicity alone by performing dose–response curves for 55:45 PG/VG . Because live-cell fluorescence was more sensitive than cell growth ( Fig 1F; S1 Data ) , we extended this assay and simultaneously measured calcein and propidium iodide as markers or live and dead cells , respectively , as described for tobacco exposure [26] . Here , we used dimethyl sulfoxide ( DMSO ) as a known toxic control [27] and PBS as a nontoxic control . Serial dilutions in DMSO resulted in a decrease in cell viability with an LC50 ( i . e . , the concentration at which a given agent was lethal to 50% of the cells ) of 6 . 0 ± 0 . 4% . In contrast , serial dilutions of the media with PBS did not affect cell viability and therefore could not be fitted with the equation parameters required to calculate LC50 . PG/VG caused dose-dependent decreases in cell growth with an LC50 of 2 . 2 ± 0 . 2% ( Fig 2A and 2B; S2 Data ) . We then measured cell viability using the calcein/propidium iodide assay ( Fig 2C; S2 Data ) . PG/VG exerted a similar toxicity as DMSO ( LC50 = 5 . 5 ± 0 . 4%; p = 0 . 68 , Fig 2C; S2 Data ) . To test whether higher levels of PG/VG affected cell viability by reducing media O2 levels , we measured the partial pressure of O2 ( PO2 ) in the media after overnight addition of 30% PG/VG using solid-state O2 electrodes . pO2 was 20 ± 1 . 1% ( n = 3 ) in control media and 18 ± 0 . 4% ( n = 4 ) after addition of 30% PG/VG , suggesting that the observed changes in cell growth and/or viability were not due to reduced O2 levels . We then generated full , 16-point dose–response curves for the e-liquids using the fluorescent viability assay ( Fig 3A–3E; S3 Data ) . All data are shown in S1 Table . E-liquid flavors were sorted by LC50 values to show the range of responses ( Fig 3F; S3 Data ) . The LC50 ( % volume/volume ) ranged from 0 . 14 to 6 . 00 , and its distribution is shown in Fig 3G; S3 Data . A further summary of this data is available in an online database ( www . eliquidinfo . org ) . After having tested all e-liquids using HEK293T cells , we retested a subset of e-liquids in cell lines that are less suitable to HTS but more germane to the respiratory tract , namely the human adenocarcinomic alveolar basal epithelial ( hA549 ) cell line , an immortalized cell line derived from human alveolar epithelia and primary human airway smooth muscle cells ( hASMC ) , isolated from human large airways ( see Materials and methods for details ) [28 , 29] . We triaged the testing by choosing every 14th e-liquid from Fig 3F ( S3 Data ) and generated full-dose responses for cell viability ( Fig 4; S4 Data ) . The tested e-liquids showed a slight left curve shift for hA549 cells , indicating that these e-liquids were more toxic in these cells than in HEK293T and hASMC cells ( Fig 4A–4C; S4 Data ) . Importantly , these e-liquids maintained the same relative toxicity in all cell lines and the LC50 for Banana Pudding < Key Lime Pie < Popcorn < Blueberry Tobacco ( Fig 4D; S4 Data ) , suggesting that the use of HEK293T cells is valid . We have previously shown that vaped e-liquids exert similar toxicity as neat e-liquids [30] . However , during the course of vaping , e-liquids are heated to approximately 300 °C before inhalation , which may induce chemical transformations that could alter their toxicity [5] . While it is not currently possible to vape HEK293T cells under HTS conditions , we performed additional validation steps—due to the importance of this issue—by comparing the relative toxicity of e-liquids after vaping versus direct liquid addition . We selected a range of e-liquids that had high , medium , and low toxicity , as well as air and PG/VG controls . HEK293T cells and primary human macrophages were vaped in 96-well plates using a 3D printed manifold as described [30 , 31] . According to our published work , 10 × 4 sec , 70 ml puffs ( see Materials and methods ) of e-liquid elicit significant effects on cell viability under these conditions [30] . We vaped HEK293T cells ( Fig 5A; S5 Data ) and primary alveolar macrophages ( Fig 5B; S5 Data ) using this approach . We also vaped well-differentiated human bronchial epithelial cells ( HBECs ) cultured at the air–liquid interface using an automated vaping system that allowed for selective exposure of HBEC mucosal surfaces to the e-liquid aerosol . Due to the larger chamber size for this system , we exposed HBECs to 70 puffs using the same puff parameters described above to achieve comparable exposures as per the HEK293T cells and macrophages . For all cell types , our data demonstrated that e-liquid vaping caused a significant decrease in viability relative to the controls that varied according to the individual e-liquids ( Fig 5A–5C; S5 Data ) . In agreement with our previous study [30] , we did not find that vaping e-liquids changed their relative toxicity . To see if vaping correlated with direct e-liquid addition , we then plotted LC50 values obtained from HEK293T cells using neat e-liquids ( see S1 Table ) against the vaped viability ( i . e . , calcein/propidium iodide ratios ) for the 3 different cell types ( Fig 5D–5F; S5 Data ) . Using this approach , we observed a linear correlation for HEK293T cells ( Fig 5D; R2 = 0 . 65; S5 Data ) and HBECs ( Fig 5F; R2 = 0 . 74; S5 Data ) . In contrast , vape-exposed macrophages correlated poorly to the HEK293T LC50 ( Fig 5E; R2 = 0 . 06; S5 Data ) . In order to better understand how chemical composition contributed to e-liquid toxicity , we used gas chromatography–mass spectrometry ( GC-MS ) to identify e-liquid constituents . Chromatograms obtained using this approach were compared to the National Institute of Standards and Technology ( NIST ) 2014 mass spectral database for compound identification . Fig 6A shows a representative chromatogram of the “Dulce de Leche” e-liquid . As expected , PG , VG , and nicotine are clearly present at high concentrations . More than 10 other constituents were identified in this e-liquid , including vanillin , ethyl vanillin , and piperonal . Fig 6B depicts 10 representative chromatograms of additional e-liquids . We also performed electron ionization to obtain mass spectra to quantify select constituents present in representative e-liquids , and an example spectrum for vanillin is shown in Fig 6C . We compared LC50 values and chemical composition to identify key chemical constituents that might drive toxicity . We first compared LC50 values with the presence/absence of constituents to determine whether any particular chemicals and/or diversity drive the toxicity . We performed nonmetric multidimensional scaling ( NMDS ) on the presence/absence matrix of chemical constituents in each liquid under binary Euclidean distances , which resulted in a stress value of 0 . 1367 , indicating a weak correlation . A qualitative separation of high and low LC50 values was observed that was generally supported via a k-modes clustering ( k = 2 ) . Here , a Welch two-sample t test showed significant differences ( p < 0 . 0005 ) between the cluster LC50 means ( 1 . 55 and 2 . 66 , respectively; Fig 7A; S6 Data ) . When comparing the presence/absence data of each chemical between the 2 clusters , vanillin and ethyl-vanillin—among other chemicals—were present in significantly ( Bonferroni corrected; p < 0 . 0004 ) higher abundance ( 98% and 100% , respectively ) in the lower LC50 cluster than in the higher cluster ( 10% and 17% , respectively ) . In addition , we saw a trend of negative correlation between the number of chemicals in individual e-liquids versus their toxicity ( Pearson correlation = −0 . 48 , R2 = 0 . 16 ) ( Fig 7B; S6 Data ) . Because presence or absence of chemical constituents showed some correlation with cell toxicity , we next investigated whether the actual concentration of chemical constituents ( not presence or absence ) could predict toxicity . As a proof of concept , vanillin and cinnamaldehyde concentrations were measured in several e-liquids via electron ionization mass spectra because these are flavoring agents regularly used in the food industry . We also measured triacetin as a nontoxic control . We compared the concentration of each constituent and its LC50 values . In the case of vanillin , toxicity ( i . e . , LC50 ) was proportional ( R2 = 0 . 62 ) to the actual vanillin concentration in the e-liquids tested ( Fig 8A; S7 Data ) . Similarly , cinnamaldehyde toxicity was also proportional ( R2 = 0 . 75 ) to the measured concentrations of cinnamaldehyde ( Fig 8B; S7 Data ) . On the contrary , triacetin did not correlate to toxicity ( R2 = 0 . 048; Fig 8C; S7 Data ) .
Fluorescent-based HTS techniques have been used for decades for drug discovery screens , toxicity , and for genetic screens , and their adoption by the scientific community has increased following the development of better fluorescent dyes and better instrumentation both in the pharmaceutical industry and academia [32 , 33] . Despite the different endpoints , most HTS have common phases , including the initial phase ( sometimes called the primary screen ) , which sets to capture as much preliminary data as possible—including false positives , which are subsequently confirmed or refuted . This key step allows for the inclusion of all possible prospective “hits” and minimization of lead loss . This is followed by the main ( or secondary ) screen in which extensive data are collected . Finally , validating ( i . e . , tertiary ) screens are performed to confirm the main dataset . In our study , we performed the initial screen on 148 e-liquids to determine the optimum duration of the screen , find appropriate positive and negative controls , and to determine the most sensitive endpoint ( Figs 1 and 2; S1 and S2 Data ) . Our colleagues have found that physiologic endpoints tend to be more sensitive than gross toxicological ones [34] . However , we chose assays that could be applied to most cells types , were readily available , and relatively easy to measure . We found that the viability assay , which meets these criteria , was more sensitive than the cell growth density ( Fig 1F; S1 Data ) . That is , more e-liquids showed some alteration using the viability assay than the cell growth assay ( 126 e-liquids showed a decrease in viability , while 91 e-liquids showed a decrease in growth ) . We used HEK293T cells for this screen because they are a cell line that is amenable to HTS and one of the favorites in the screening field [35 , 36] . We also validated our findings using pulmonary cell lines . Importantly , we demonstrated that the toxicity seen in HEK293T cells was reproduced in hASMC and hA549 cells and the e-liquids showed similar rank order of toxicity in all cell lines ( Fig 4; S4 Data ) . Similarly , using a 96-well–plate approach , we previously demonstrated that cultured human airway epithelial 3 ( CALU3 ) cells showed differences in viability to 13 e-liquids [30] . In this study , we used the 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyl tetrazolium bromide ( MTT ) assay that measures dye uptake by live cells . However , we found that the MTT assay , while sensitive , was less amenable to HTS because it required more wash steps , which can cause cells to detach and required more preparation time . However , this assay may be useful as a validation step . It is unlikely that PG/VG will ever reach 10% or 30% in the lung lumen during normal vaping . However , we wanted to perform a full-dose response in order to fully understand the upper toxic limit of PG/VG . Accordingly , we can now say that it does not induce cell death below 1% . Furthermore , we have previously measured the PO2 in physiologic solutions ( i . e . , airway surface liquid ) containing mucus gels that have exceeded 20% solids [1] . Despite a large amount of mucus and an extremely high viscous component , PO2 was normal , suggesting that similar levels of PG/VG will not alter PO2 levels . Other groups have reported cytotoxicity of e-liquids , but their screening capacity was 40 e-liquids or less [24 , 37 , 38] . Separately , studies have reported chemical composition analysis of e-liquids , but again , the total number was low [6 , 39] . Overall , we are the first group to have designed and implemented a robust , high-throughput technique that allows for the parallel screening of hundreds of e-liquids . Several researchers have found evidence of chemical transformation ( i . e . , pyrolysis ) after vaping [5 , 6] . However , in agreement with our previous study [30] , we did not find that vaping e-liquids changed their relative toxicity , with the exception of one flavor ( Hot Cinnamon Candy; Fig 5A and 5D; S5 Data ) , suggesting that this phenomenon may be flavor dependent . In our experience , vaping is more variable and less amenable to HTS . However , we vaped HEK293T cells , primary HBECs , and primary human macrophages . We found that the toxicity that occurred after vaping HEK293T cells and HBECs compared very well to the neat e-liquid addition ( Fig 5D and 5F; S5 Data ) . Because the HBECs were well differentiated and exposed only apically under air–liquid interface conditions , this is perhaps the most realistic of conditions ( Fig 5B and 5F; S5 Data ) . Similarly , after neat e-liquid exposure , hASM relative toxicities also correlated well with the HEK293T data ( Fig 4; S4 Data ) . Surprisingly , the LC50 did not correlate at all with the vaping of macrophages ( Fig 5E; S5 Data ) . Therefore , while we do not appear to observe a general increase in e-liquid toxicity after vaping as compared to liquid addition , the HTS data may not be representative of all pulmonary cell types . The actual deposition fraction of vaped e-liquids into the lungs remains to be determined . However , the predicted deposition of E-cig aerosol in the lungs is approximately 25% [40] . Therefore , if 1 ml of e-liquid is aerosolized and inhaled—and assuming a total airway surface liquid volume in the lung of approximately 3 ml—this would lead to 0 . 25 ml being deposited , suggesting a dilution factor of 1:12 , or approximately 8% . Given that e-liquids have a LC50 of approximately 6% or less , this would suggest that e-liquids may reach biologically relevant levels in the lung . Indeed , it has recently been reported that vaping significantly alters the secreted human airway proteome , suggesting that this may be the case [41] . We are still understanding the relative toxicity of e-liquid constituents and their implications for airway exposure . PG is a common chemical used to produce polyester and as deicer/antifreeze , as well as being a base constituent in e-liquids . Intravenous PG can cause acute renal and central nervous system ( CNS ) toxicity [42] , and PG inhalation causes renal and liver toxicity [43] . PG has previously been shown to inhibit renal glucose transport and corneal Na+/K+ATPase activity [44 , 45] . Beyond PG , VG , and nicotine , we previously found no chemical similarity in 13 e-liquid flavors [30] . Therefore , given their heterogeneous nature , the overall goal of this project was to screen a greater number of neat e-liquids to identify flavors and/or chemical constituents that are more toxic and would direct additional studies . We found a number of highly toxic e-liquids that should be prioritized for study ( Fig 1; S1 Table; S1 Data ) . Furthermore , in addition to identifying vanillin as potentially highly toxic , we also identified—using this screen—3 e-liquids that contain diacetyl ( 2 , 3-butanedione ) , which causes bronchiolitis obliterans , and 5 e-liquids that contain 2 , 3-butanedione monooxime , which is a chemical diphosphatase that blocks ATP-sensitive K+ channels [46–51] . Interestingly , many flavors , e . g . , benzaldehyde ( almond ) and cinnamaldehyde ( cinnamon ) , are aldehydes that can form protein adducts [52] . Benzaldehyde was only detected in 4 e-liquids . Cinnamaldehyde was found in 8 e-liquids ( including Cinnamon Roll , Hot Cinnamon Candies , and Root Beer ) and has previously been shown to impair phagocytosis in macrophages [34] . Sherwood and Boitano [24] recently exposed airway epithelia to 7 chemical flavors and concluded that vanillin and a chocolate flavor ( 2 , 5-dimethypyrazine ) had the biggest effect on their cells . We found that vanillin was present in 63 out of 148 e-liquids . However , 2 , 5-dimethypyrazine was not detected in any of our e-liquids . Vanillin activates transient receptor potential cation channel subfamily V member ( TRPV ) channels . TRPV channels are expressed in neurons and serve as nonselective cation channels that can increase cytoplasmic Ca2+ levels in epithelia [53] . Of note , prolonged increases in cytosolic Ca2+ can alter cell division rates and are indicative of apoptosis [54] . Using an NMDS approach , we found only a weak correlation between the presence of flavorings and toxicity ( i . e . , LC50 ) . Of the e-liquids shown in Fig 7A and 7B ( S6 Data ) , all had a constant ratio of 55% PG to 45% VG , indicating that PG/VG did not influence the change in toxicity . Furthermore , most flavors analyzed in Fig 7A ( S6 Data ) had 12 mg/ml nicotine , suggesting that changes in nicotine levels also did not account for the variability . However , of the 148 e-liquids tested , we found approximately 123 different chemicals . Therefore , we may have to further expand our HTS dataset in order to obtain a better correlation between the presence/absence of e-liquid constituents and toxicity , assuming that if we expand the dataset from 148 to 500 or 1 , 000 e-liquids , the number of chemical constituents eventually levels off and does not increase proportionally . We also determined the concentrations of cinnamaldehyde , vanillin , and triacetin against known concentrations of these compounds ( see Fig 6C ) . Using this approach , we found a positive correlation between vanillin ( 0 . 62 ) and cinnamaldehyde ( 0 . 75 ) but not triacetin ( 0 . 048 ) , with concentration/toxicity ( Fig 8; S7 Data ) , suggesting that other chemicals present in these e-liquids may also have influence . Therefore , while we now have a better appreciation of the range of toxicities of different e-liquids , further work will be required to fully understand which additional components influence toxicity . A limitation of the proposed studies is that the chemical of interest will need to be available for purchase or be synthesizable and purifiable in order to be quantified by GC-MS or similar techniques . To further categorize and evaluate these data , we have developed a database ( www . eliquidinfo . org ) . This website is publicly available , and in its current form , it is most likely to be useful to academic and government researchers . Through this portal , one can browse LC50 values , search for different chemicals , and determine which e-liquids contain them . Given the diversity of e-liquid toxicity and composition , we have found this website extremely useful in choosing new e-liquids for future studies . For example , we now select some less toxic , intermediate , and more toxic e-liquids when starting new investigations rather than just study one e-liquid . As we complete additional rounds of HTS , we hope to grow this database to make it more applicable . However , this website may also serve to inform the general public as to the relative toxicity and heterogeneity of e-liquids . Given that the rise in vaping popularity has vastly outstripped our knowledge of its potential health benefits versus potential adverse effects , such HTS approaches will allow us to rapidly screen the approximately 7 , 700 different e-liquids that are on the market [10] . Whether or not PG/VG and nicotine are less harmful than inhaled tobacco is highly contentious [55] . However , HTS approaches for both e-liquids and their chemical constituents still have an important role in helping to shape future legislation for e-liquids and vaping . This is becoming all the more important , especially as researchers from the tobacco industry are now making claims that vaping represents a reduced risk of exposure compared to tobacco smoking [55 , 56] . Therefore , it is vital that academic and government laboratories independently test as many different classes of these e-liquids as possible using multiple approaches and use evidence-based research as the guide for regulation . For example , when low-tar cigarettes were introduced and producers claimed they were a safer alternative to traditional cigarettes , these claims were later refuted . Given the claims of some groups , including tobacco companies and the public perception that e-liquids are safer than tobacco products , such an approach to study e-liquid toxicity may serve as a roadmap to enable bodies such as the US FDA to properly regulate e-liquid manufacturing and sale .
Flavored e-liquids were purchased from The Vapor Girl ( www . thevaporgirl . com ) , NJOY ( https://www . njoy . com ) , and E-TONIC ( https://www . hookah-shisha . com ) . The e-liquids contained a variety of nicotine concentrations , ranging from 0 to 12 mg/mL , and a PG to VG ratio of 55:45 . Therefore , a 55/45 PG/VG vehicle control was made in our laboratory using chemicals purchased from Sigma-Aldrich ( St . Louis , MO ) . For more information about the e-liquids , see S1 Table . PG ( FG grade ) , VG ( FG grade ) , and DMSO ( ACS grade ) were purchased from Sigma-Aldrich . Calcein-AM and propidium iodide were purchased from Thermo-Fisher ( Waltham , MA ) . A modified Ringers solution ( 101 mM NaCl , 12 mM NaHCO3 , 24 mM HEPES , 1 . 2 mM MgCl2 , 1 . 2 mM CaCl2∙2 H2O , 5 . 2 mM KCl , and 10 mM D- ( + ) -Glucose ) was made with chemicals purchased from Sigma-Aldrich ( all ACS grade ) . Cell culture reagents were obtained from Gibco ( Waltham , MA ) . HEK239T cells were incubated at 37 °C with 5% CO2 and cultured in DMEM supplemented with 10% FBS and 1X penicillin/streptomycin . hA549 cells were incubated at 37 °C with 5% CO2 and cultured in RMPI 1640 supplemented with 10% FBS and 1X penicillin/streptomycin . Primary HBECs and hASMCs were harvested by enzymatic digestion of human bronchial tissue obtained from donor lungs using protocols approved by the University of North Carolina at Chapel Hill Committee on the Protection of the Rights of Human Subjects . HBECs were plated on 6 . 5 mm Transwell T-col culture inserts ( Coning , NY ) and cultured at the air–liquid interface in UNC air–liquid interface media for 28 days before use as previously described [57] . hASMCs were cultured in 384-well plates , incubated at 37 °C with 5% CO2 , and cultured in DMEM-α supplemented with 10% FBS and 1X penicillin/streptomycin using passages 3–6 [29] . Bronchoalveolar lavage fluid was obtained from healthy human subjects under a protocol approved by the University of North Carolina at Chapel Hill Committee on the Protection of the Rights of Human Subjects ( #91–0679 ) . All patients included in this study gave their written informed consent . Airway macrophage ( AM ) isolation was performed as previously described [58] . In brief , the cell pellet was resuspended in macrophage medium ( RPMI 1640 , 10% FBS , 100 U/ml penicillin , 100 μg/ml streptomycin ) . Following a 3-h adherence at 37 °C , 5% CO2 , supernatants were removed , and adherent cells were washed 5 times with PBS . Cell preparations typically consisted of >98% AMs . Freshly isolated AMs were seeded onto 96-well plates at a concentration of 10 , 000 AMs per well and cultured in macrophage medium for the duration of the experiment . HEK293T cells were plated on poly-L-lysine–coated 384-well plates from Corning ( Corning , NY ) at a density of 5 , 000 cells per well at t = 0 and incubated at 37 °C , 5% CO2 for 4 h to allow cells to adhere . Cells were imaged with a Cytation5 imaging plate reader ( BioTek , Winooski , VT ) using the bright-field feature to establish baseline surface area . After 4 h , cells were treated with e-liquids at a concentration of 1% ( n = 4 ) and returned to the Cytation5 , and images were acquired every 2 h for 24 h . Controls included PBS ( negative ) , vehicle ( 10% 55:45 PG/VG , positive ) , and media ( baseline ) . At t = 30–32 h , media were replaced with a modified Ringers solution containing calcein-AM ( 3 μM ) and propidium iodide ( 3 μM ) and incubated for 30 min to measure cell viability . The ratio of the fluorescence intensity of calcein and propidium iodide was normalized to media controls . Gen5 2 . 09 software ( Biotek ) was used to acquire bright-field images , and ImageJ ( NIMH , Bethesda , MD ) was used to calculate covered surface area to assess cell growth . HEK293T cells were plated on poly-L-lysine–coated 384-well plates ( Corning , NY ) at a density of 5 , 000 cells per well at t = 0 and incubated for 4 h to allow cells to adhere . At that time , cells were treated with various concentrations of e-liquid diluted in media for 24 h , including PBS ( negative ) , vehicle ( PG/VG , positive ) , and media controls . At t = 28–30 h , media were replaced with a modified Ringers solution containing calcein-AM ( 3 μM ) and propidium iodide ( 3 μM ) as a live/dead cell stain and incubated for 30 min . Cells were then imaged with a Cytation5 imaging plate reader ( BioTek ) . The ratio of the fluorescence intensity of calcein and propidium iodide was normalized to media controls and plotted as 8- or 16-point dose–response curves . A nonlinear 4-parameter regression was conducted , and the LC50 value was determined for each e-liquid using GraphPad Prism6 ( La Jolla , CA ) . Cells were plated on poly-L-lysine–coated 384-well plates at a density of 5 , 000 cells per well for A549 and 1 , 000 cells per well for hASMC cells at t = 0 and incubated for 4 to 6 h to allow cells to adhere . At that time , cells were treated with various concentrations of e-liquid diluted in media for 22 to 24 h , including PBS ( negative ) , vehicle ( PG/VG , positive ) , and media controls . At t = 28–30 h , media were replaced with a modified Ringers solution containing calcein-AM ( 3 μM ) and propidium iodide ( 3 μM ) as a live/dead cell stain and incubated for 30 min . Cells were then imaged with a Cytation5 imaging plate reader ( BioTek ) . The ratio of the fluorescence intensity of calcein and propidium iodide was normalized to media controls and plotted as 8- or 16-point dose–response curves; each dose was run in triplicate ( n = 3 ) on 3 independent occasions ( N = 3 ) . A nonlinear 4-parameter regression was conducted , and the LC50 value was determined for each e-liquid ( GraphPad Prism6 ) . E-cig aerosols were generated using a Sigelei FuChai 200 W device with a Crown stainless steel subtank and a 0 . 25 Ω SUS316 dual coil from Uwell ( City of Industry , CA ) . Aerosols were generated by activating the E-cig device and drawing into a 100 mL syringe from the mouthpiece of the subtank . Based on existing E-cig topography [59–61] , we generated 70 mL puffs drawn over 4 s and dispensed with a flow rate of 0 . 84 L/min at 100 W , unless otherwise stated . To directly vape into 96-well plates , we used a 3D printed manifold as previously described [31] . These manifolds were used to simultaneously vape 6 wells per plate . Cells were exposed to 10 puffs of vaped e-liquid as indicated above . We have shown that e-liquids are autofluorescent , and using autofluorescence as an indicator of deposition , we previously found that our vaping approach in 96-well plates resulted in an even deposition of e-liquid vapor that was highly reproducible [31] . HBECs were incubated apically for 20 min with PBS to remove excess mucus 24 h before exposure . On the day of exposure , cultures were loaded into the exposure block of a VC10 smoking robot ( Vitrocell , Germany ) with each culture insert exposed to E-cig vapor from a Sigelei Fuchai 200 W third-generation device set to 100 W , using Uwell Crown tanks with 0 . 25 Ω dual coils . The device was activated by a pneumatic actuator integrated into the system and connected directly to the syringe pump of the VC10 before a triangle curve puff was applied over 4 s for a volume of 70 mL and exhausted over 8 s with a period of 30 s . The vapor flowed into the 24 wells of the exposure block , with each well being fed directly by a “trumpet” allowing the vapor access to each HBEC mucosal surface . Serosally , the inserts were in contact with ALI media and were maintained at 37 °C throughout the exposure period . Cells were exposed to 70 puffs using the puff parameters described above . After the exposure , the cells were replaced in 24-well plates and returned to the 37 °C , 5% CO2 incubator for 24 h . The lines of the VC10 were also exposed to filtered compressed air to flush the majority of the vapor condensate from the lines , pump cylinder , and exposure apparatus before the next exposure with a new e-liquid , and the entire system was cleaned after each vaping session . HEK293T cells were plated on poly-L-lysine–coated 96-well plates ( Corning , NY ) at a density of 30 , 000 cells per well at t = 0 and incubated for 4 to 6 h to allow cells to adhere . AMs were plated as described above . Cells were exposed to 10 puffs of vaped e-liquids using a 4 s , 70 ml puff and incubated for 22 to 24 h . Media were then replaced with a modified Ringers solution containing calcein-AM ( 3 μM ) and propidium iodide ( 3 μM ) and incubated for 30 min to measure viability stain . Cells were then imaged using a Cytation5 imaging plate reader ( BioTek ) . The normalized ratio of the average fluorescence intensity of calcein and propidium iodide was reported . The PO2 was measured using a modification of our previous method [62] . In brief , the voltage output from a solid-state O2 electrode ( STDO11 ) from Ohaus ( Parsippany , NJ ) was read using a pH/voltage meter ( Thermo-Fisher , Waltham , MA ) operating in voltage mode . The O2 electrode was calibrated using media with atmospheric O2 ( i . e . , approximately 21% O2 ) , and media bubbled for 2 h with 100% N2 ( i . e . , 0% O2 ) . Cell culture media ± 30% PG/VG were added to HEK293T cells for 24 h , and O2 levels were read immediately after calibration . Qualitative e-liquid analysis was performed on a Bruker EVOQ 456 gas chromatograph-triple quadrupole mass spectrometer ( Billerica , MA ) using an Agilent DB-5MS capillary column ( 30 m , 0 . 25 mm ID , 0 . 25 μM film ) and helium carrier gas ( 99 . 999% purity; Santa Clara , CA ) . Injections ( 1 μL ) were performed using a Bruker CP-8400 autosampler with an injector temperature of 270 °C . The GC oven was programmed with a 12 . 5 min temperature gradient ( 60–250 °C ) , and the transfer line and EI source were held at 250 °C . Samples were prepared by diluting 50 μL of e-liquid in 1 mL of methanol ( optima grade ) and vortexing for 30 s . Full-scan mass spectra were acquired from m/z 40–500 . Compound identification was performed using the NIST 2014 mass spectral database ( Gaithersburg , MD ) and AMDIS chromatography software . For the quantitative process , flavor concentrations were determined by standard addition . E-liquids were diluted in methanol ( optima grade ) and quantitative standards . A full list of e-liquid dilutions and standard concentrations is given in S2 Table . Selected ion monitoring ( SIM ) mass spectra were acquired for each of the quantified flavors . SIM parameters are given in S3 Table . Peak areas of quantitative ions were integrated for quantification of each of the flavors . Qualitative ions were used for confirmation of compound identity . All experiments were performed on a minimum of 3 separate occasions ( N = 3 ) . All data are shown as mean ± standard error , such that “n” refers to the number of plates or donors as appropriate . For 384-well–plate experiments , each dose was performed in triplicate per plate . All statistic and curve plotting were performed using Prism 6 ( GraphPad , La Jolla , CA ) . An ordination technique , NMDS was applied in R version 3 . 3 . 3 [63] using the package “vegan” [64] to matrices containing e-liquids and their binary ( presence/absence ) chemical composition . The same data table was clustered using k-modes ( k = 2 ) using the package “klaR” [65] , and chemicals within each cluster were compared using a Welch two-sample t test , in which resultant p-values were adjusted using Bonferroni correction . | The e-liquids used in electronic cigarettes ( E-cigs ) typically consist of a mixture of propylene glycol ( PG ) , vegetable glycerin ( VG ) , and nicotine , as well as numerous chemical additives that are used for flavoring . There are currently over 7 , 700 different flavored e-liquids that are commercially available , but there is very limited information regarding either their chemical composition or toxicity . In this work , we developed a high-throughput screening ( HTS ) assay to rapidly triage and validate the toxicity of multiple e-liquids in parallel . Our data indicated that e-liquids are extremely heterogeneous , so we also performed gas chromatography–mass spectrometry ( GC-MS ) of all e-liquids to evaluate their composition/toxicity relationship . We found that the presence of either vanillin or cinnamaldehyde in e-liquids was associated with higher toxicity values . In addition , our data demonstrated that the PG/VG vehicle by itself was toxic at higher doses . We have also developed a publicly available and searchable website ( www . eliquidinfo . org ) that contains these chemical composition and toxicity data . Given the large numbers of available e-liquids , this website will serve as a resource to disseminate this information . Our HTS approach may serve as a roadmap to enable bodies such as the United States Food and Drug Administration ( FDA ) to better regulate e-liquid safety . | [
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] | 2018 | Evaluation of e-liquid toxicity using an open-source high-throughput screening assay |
Ribosome biogenesis is a ubiquitous and essential process in cells . Defects in ribosome biogenesis and function result in a group of human disorders , collectively known as ribosomopathies . In this study , we describe a zebrafish mutant with a loss-of-function mutation in nol9 , a gene that encodes a non-ribosomal protein involved in rRNA processing . nol9sa1022/sa1022 mutants have a defect in 28S rRNA processing . The nol9sa1022/sa1022 larvae display hypoplastic pancreas , liver and intestine and have decreased numbers of hematopoietic stem and progenitor cells ( HSPCs ) , as well as definitive erythrocytes and lymphocytes . In addition , ultrastructural analysis revealed signs of pathological processes occurring in endothelial cells of the caudal vein , emphasizing the complexity of the phenotype observed in nol9sa1022/sa1022 larvae . We further show that both the pancreatic and hematopoietic deficiencies in nol9sa1022/sa1022 embryos were due to impaired cell proliferation of respective progenitor cells . Interestingly , genetic loss of Tp53 rescued the HSPCs but not the pancreatic defects . In contrast , activation of mRNA translation via the mTOR pathway by L-Leucine treatment did not revert the erythroid or pancreatic defects . Together , we present the nol9sa1022/sa1022 mutant , a novel zebrafish ribosomopathy model , which recapitulates key human disease characteristics . The use of this genetically tractable model will enhance our understanding of the tissue-specific mechanisms following impaired ribosome biogenesis in the context of an intact vertebrate .
Ribosome biogenesis is a highly conserved and remarkably complex process that is essential for cell growth and proliferation . It utilizes 60% of total cellular transcription in a growing yeast cell , with 2 , 000 ribosomes synthesized every minute [1] . It requires the coordinated action of all three RNA polymerases ( RNAP I , II , III ) and the synthesis of 4 ribosomal RNAs ( rRNAs ) , 82 core ribosomal proteins ( RPs ) , more than 200 non-ribosomal proteins and approximately 70 small nucleolar RNAs ( snoRNAs ) [2] . While complete loss of expression of genes encoding ribosomal components has not been described in humans , haploinsufficiency or partial loss of protein expression of some ribosome biogenesis proteins has been shown to be a common basis of the group of disorders known collectively as “ribosomopathies” [3] . Although all ribosomopathies involve ribosomal dysfunction , they show different modes of inheritance and the clinical presentations may vary even among patients carrying the same mutation . Clinical features of the ribosomopathies include defects in growth and development , craniofacial and skeletal defects , hematological abnormalities with increased propensity to the development of malignancies such as acute myeloid leukemia ( AML ) and myelodysplastic syndrome ( MDS ) [3] . Ribosomopathies that present with hematological defects include Diamond Blackfan anemia ( DBA ) , 5q- syndrome , Shwachman-Diamond syndrome ( SDS ) and T-cell acute lymphoblastic leukemia ( T-ALL ) . Additional examples of ribosomopathies include Treacher Collins syndrome ( TCS ) , isolated congenital asplenia ( ICAS ) , aplasia cutis congenita ( ACC ) , Bowen-Conradi syndrome ( BCS ) , North American Indian Childhood cirrhosis ( NAIC ) and alopecia , neurological defects and endocrinopathy ( ANE ) syndrome [4] . One of the most intriguing aspects of ribosomopathies is that , despite the fact that they all share a common defect in the same biological process there is a high degree of cell and tissue-specific pathology [5] . Evidence from a number of model systems suggested activation of Tp53 [6–16] and mTOR [17 , 18] pathways following ribosome dysfunction . However , modulation of these pathways had variable success in reverting tissue specific phenotypes in different models of ribosomopathies , again emphasizing the complexity of these disorders . This lack of basic insight into the molecular and biochemical defects that underlie ribosomopathies is reflected by a lack of effective therapeutic strategies , often resulting in poor outcomes for patients suffering from these disorders [3] . Current therapies include steroids and chronic transfusions for DBA patients and pancreatic enzyme replacement , granulocyte colony-stimulating factor , antibiotics and transfusion support for SDS patients . The only definitive treatment for the hematopoietic defects in DBA and SDS is bone marrow transplantation [19 , 20] . However , graft failure , graft versus host disease and infection contribute to the substantial morbidity and mortality associated with these treatments [19 , 21] . Therefore , there is a need to develop novel , targeted , therapeutic strategies based on understanding the pathophysiology of these disorders; which is why ongoing work to generate optimal in vivo models is essential for further progress in the field . Here we report the identification and analysis of a zebrafish mutant with a loss-of-function mutation in the nol9 gene . The human NOL9 protein is a polynucleotide 5’-kinase involved in ribosome biogenesis [22] . It is a non-ribosomal protein that is required for cleavage of the ITS2 region from a pre-rRNA to generate 5 . 8S and 28S rRNAs and for the synthesis of the 60S ribosomal subunit [22] . Similarly , the NOL9 homolog in S . pombe , Grc3 , is required for pre-rRNA processing , particularly ITS2 processing [23] . In this study , we demonstrate that nol9sa1022/sa1022 mutants have a defect in pre-rRNA processing within ITS2 . As in the majority of congenital syndromes in human , zebrafish nol9sa1022/sa1022 larvae had multiple organ failures including intestine , liver , pancreas and impaired definitive erythropoiesis and lymphopoiesis . These phenotypic features of nol9sa1022/sa1022 mutants are reminiscent of the clinical symptoms of Shwachman-Diamond syndrome , an autosomal recessive ribosomopathy [24–27] that is characterized by exocrine pancreatic insufficiency , hematological abnormalities including neutropenia , anemia and thrombocytopenia and skeletal defects . Our detailed characterization of nol9sa1022/sa1022 larvae further shows that both pancreatic and erythroid defects are due to impaired proliferation as well as differentiation of respective progenitor cell types . Additionally , we show that impaired erythropoiesis , but not pancreatic development , is rescued in double knock out nol9sa1022/ sa1022/tp53zdf1/zdf1 larvae .
The nol9sa1022/sa1022 mutant was first identified in the Zebrafish Mutation Project , and it carries a nonsense mutation in codon 195 of the gene ( Fig 1A ) [28 , 29] . nol9sa1022/sa1022 embryos developed normally during the first 72 hpf with no obvious morphological differences when compared to heterozygous ( nol9+/sa1022 ) or wild-type ( wt ) siblings . However , at 96- and 120 hpf , the nol9sa1022/sa1022 larvae lacked intestinal folds , had a smaller liver and pancreas and exhibited impaired yolk absorption compared to wt siblings ( Figs 1B and 1C and S1A–S1C ) . Additionally , over 90% of the nol9sa1022/sa1022 fish failed to inflate their swim bladder . The digestive organ phenotype was completely penetrant and the nol9sa1022/sa1022 larvae died by 10 days post fertilization ( dpf ) . Some common features of ribosomopathies include craniofacial and hematological defects [30] . At 120 hpf , the nol9sa1022/sa1022 larvae displayed normal jaw and branchial arch structures indicating that nol9 is not required for normal skeletal development ( S1D Fig ) . We also examined erythropoiesis in our model . Zebrafish erythropoiesis , similar to mammals , occurs in two sequential waves: primitive and definitive . While primitive erythropoiesis was intact at all the time points examined ( 48- , 96- and 120 hpf ) as assessed by o-Dianisidine staining ( S2A–S2D Fig ) , nol9sa1022/sa1022 larvae displayed a dramatic decrease in the number of hbae1-positive definitive erythrocytes in the caudal hematopoietic tissue ( CHT ) at 96- ( S2E and S2F Fig ) and 120 hpf ( Fig 1D and 1E ) . These results strongly suggest that nol9 is critical for the production of definitive but not primitive erythrocytes . Although ribosomopathies are characterized by mutations in components of a fundamental process in all cells , the clinical manifestations vary and display tissue specificity . One of the hypotheses that has been proposed to explain this tissue specificity is the distinct expression pattern of genes involved in ribosomal biogenesis [31] . We therefore assessed the expression of nol9 during early zebrafish development using whole-mount in situ hybridization ( WISH ) ( Fig 2A ) . nol9 is ubiquitously expressed at 4- and 12 hpf but its expression is not apparent at 48- hpf ( Fig 2A ) . At 72 hpf , the expression of nol9 is evident only in the branchial arches . At 96 hpf , nol9 expression is restricted to the branchial arches and pancreas . Finally , at 120 hpf , strong expression of nol9 is present in the branchial arches , liver , pancreas , as well as the CHT , which is consistent with the observed digestive organ and hematological defects of nol9sa1022/sa1022 larvae . Mature 5 . 8S , 18S and 28S rRNAs are produced from the 45S pre-rRNA transcript by a series of enzymatic cleavage steps ( Fig 2B ) . Human NOL9 protein ( like its homolog Grc3 in S . pombe [23] ) is a polynucleotide 5’-kinase that is required for the efficient processing of the 32S precursor into 5 . 8S and 28S rRNAs in HeLa cells [22] . To investigate the role of Nol9 in zebrafish , we performed Northern blot analysis ( Fig 2B–2D ) using probes designed to hybridize to the external ( 5’ETS ) and internal transcribed spacer ( ITS1 and ITS2 ) regions of zebrafish 45S pre-rRNA . These probes detect the full-length rRNA precursor and all intermediate species , but not the mature rRNAs . We detected accumulation of the full-length precursor ‘a’ and of the intermediates ‘b’ and ‘c’ in nol9sa1022/sa1022 mutants compared to wt siblings at 120 hpf ( Fig 2B and 2C ) . Based on the relative signal intensities of the intermediate bands , we conclude that the block in rRNA processing in nol9sa1022/sa1022 mutants occurs at the level of the intermediate ‘c’ , which corresponds to the 27SB precursor in yeast and 32S in human [22 , 23] . Therefore , the function of Nol9 in ITS2 processing is conserved across yeast , zebrafish and human cells [22 , 23] . We hypothesize that the presence of intermediate ‘b’ reflects an alternate cleavage pathway [32] . Methylene blue staining of ribosomal RNAs after Northern transfer was used to confirm equal loading of samples ( Fig 2D ) . Furthermore , E-bioanalyzer analysis revealed a decrease in mature 28S rRNA in nol9sa1022/sa1022 larvae compared with nol9+/+nol9+/sa1022 siblings at 120 hpf , while mature 18S rRNA was unaffected ( S3A and S3B Fig ) . During pancreas development , contiguous areas of the gut bud sequentially . The first posterodorsal bud develops into the endocrine principal islet whereas the second anteroventral bud gives rise to exocrine tissue , pancreatic ducts and late-forming endocrine cells [33 , 34] . To dissect the role of nol9 in pancreatic cell lineage development , we generated nol9sa1022/sa1022 zebrafish in a Tg ( ins:mCherry ) jh2/Tg ( ptf1a:EGFP ) jh1 background [35 , 36] . This allowed us to visualize ins:mCherryjh2-expressing endocrine β-cells in conjunction with ptf1a:EGFPjh1-expressing exocrine pancreatic cells in nol9sa1022/sa1022 embryos and their wt siblings . In both wt and nol9sa1022/sa1022 larvae , β-cells localized appropriately and maintained the normal islet volume from 48- to 120 hpf ( Figs 3A and S4A ) . Furthermore , the volume of somatostatin-producing δ-cells and glucagon-producing α-cells was comparable between nol9sa1022/sa1022 mutants and wt siblings at 96 hpf ( S4B Fig ) . Thus , our data strongly suggest that the development of the endocrine pancreatic islet in nol9sa1022/sa1022 larvae is normal . In contrast , the ptf1a-positive exocrine pancreas appeared smaller in nol9sa1022/sa1022 larvae compared to wt siblings after 72 hpf ( Figs 3A and S4A and S4C ) . This phenotype was recapitulated by microinjection of one-cell stage zebrafish embryos with the translation blocking morpholino oligonucleotide targeted to nol9 mRNA ( S5 Fig ) . Transmission electron microscopy ( TEM ) analysis revealed that exocrine pancreatic cells in nol9sa1022/sa1022 larvae lacked rough endoplasmic reticulum ( RER ) and had fewer and smaller zymogen granules compared to wt siblings ( Fig 3B ) . This was in line with the observed decrease in overall protein synthesis in nol9sa1022/sa1022 mutants compared to wt siblings at 120 hpf , as assessed by puromycin incorporation assay ( S6 Fig ) . In addition , the mitochondria in exocrine pancreatic cells appeared enlarged and abnormal in nol9sa1022/sa1022 mutants compared to wt siblings ( Fig 3B ) . However , carboxypeptidase-a ( α-Cpa ) , an exocrine pancreatic enzyme [37] , was detected by immunohistochemistry in both nol9sa1022/sa1022 mutants and wt siblings at 120 hpf ( Fig 3C ) . This suggests that even though the morphology of acinar cells is severely affected in nol9sa1022/sa1022 mutants , the differentiation of exocrine pancreatic cells into acinar cells is normal . The pancreatic ducts and secondary islets are also derived from the anteroventral bud , therefore we wished to examine if their formation was affected by the loss-of-function mutation in nol9 . Immunohistochemistry against α-cytokeratin , a marker of pancreatic ductular epithelia [38] , revealed that nol9sa1022/sa1022 larvae failed to form pancreatic ducts ( Fig 3D ) thus mirroring the defective morphogenesis of ptf1a-positive progenitors . Finally , we investigated secondary islet formation in nol9sa1022/sa1022 larvae . The secondary islets arise from Notch-responsive progenitor cells that reside in the main pancreatic duct [39 , 40] . Since the number of ins:mCherryjh2-expressing β-cells of the secondary islets is scarce in wt larvae at 120 hpf , we utilized Notch inhibition as a way to accelerate differentiation of the endocrine cells of the secondary islets [39] . We incubated nol9sa1022/sa1022 embryos and their wt siblings in either 100 μM of Notch inhibitor N-[N- ( 3 , 5-difluorophenacetyl ) -L-alanyl]-S-phenylglycine t-butyl ester ( DAPT ) or dimethyl sulfoxide ( DMSO ) , as a control , from 72 to 120 hpf . The DAPT treatment resulted in a comparable increase in the percentage of both nol9sa1022/sa1022 larvae and wt siblings displaying ins:mCherryjh2-expressing secondary islets compared to DMSO treated embryos ( S7A and S7B Fig ) . Our observations suggest that the relative number of secondary islet progenitor cells is retained even in the absence of Nol9 . Overall we conclude that the defects arising from the loss-of-function nol9 mutation affect only cells of the exocrine pancreas , namely the acinar and pancreatic duct cells . The reduced size of the exocrine pancreas in nol9sa1022/sa1022 larvae at 96 hpf could be attributed to increased apoptosis and/or impaired cell proliferation . The terminal deoxynucleotidyl transferase dUTP nick end labelling ( TUNEL ) assay revealed apoptotic cells in different areas of the zebrafish , including the tail , but none in the pancreas of either nol9sa1022/sa1022 or their wt siblings at 96 hpf ( S7C and S7D Fig ) . These data indicate that apoptosis does not contribute to the exocrine pancreatic defect in nol9sa1022/sa1022 mutants . In order to examine cell proliferation in the pancreas of nol9sa1022/sa1022 larvae , we measured 5-bromo-2’-deoxyuridine ( BrdU ) incorporation in 96 hpf larvae from a Tg ( ptf1a:EGFP ) /nol9+/sa1022 x Tg ( ptf1a:EGFP ) /nol9+/sa1022 cross . We detected fewer ptf1a-expressing cells in S phase after normalising for pancreatic volume in nol9sa1022/sa1022 mutants compared to siblings control ( S8A and S8B Fig ) . Thus , the impaired expansion of the pancreas in nol9sa1022/sa1022 mutants most likely results from decreased cell proliferation of pancreatic progenitor cells . Hematological defects in ribosomopathies often include , but are not limited to , anemia . In Shwachman-Diamond Syndrome , the most common hematological symptom is neutropenia , followed by anemia and thrombocytopenia [41] . To dissect hematological defects caused by the loss-of-function mutation in nol9 , we evaluated blood lineage differentiation in nol9sa1022/sa1022 larvae and their heterozygous and wt siblings . Interestingly , while nol9 was essential for definitive erythropoiesis ( Fig 1C ) , all larvae from a Tg ( cd41:EGFP ) /nol9+/sa1022 x nol9+/sa1022 cross had comparable numbers of cd41high thrombocytes at 96 hpf ( S9A and S9B Fig ) . Since thrombocytes are the ontogenetically closest lineage to definitive erythrocytes , these data suggest that the thrombocyte-erythroid progenitors are not affected in nol9sa1022/sa1022 fish . Similarly , we did not observe a significant difference in the number of neutrophils between nol9sa1022/sa1022 larvae and their wt siblings at 72 hpf , as assessed by Sudan Black staining ( S9C and S9D Fig ) . In contrast , in situ hybridization using rag1 riboprobe revealed that the number of lymphocytes in nol9sa1022/sa1022 larvae was markedly decreased compared to heterozygous and wt siblings at 96 hpf ( Fig 4A and 4B ) . However , nol9sa1022/sa1022 mutants and wt siblings showed comparable levels of expression of the thymic epithelial marker foxn1 [42] , as assessed by in situ hybridization at 96 hpf ( Fig 4C ) . These data suggest that in nol9sa1022/sa1022 mutants the development of the thymus is unaffected compared to wt siblings and that the observed decrease in the number of lymphocytes is not secondary to a defect of the thymic niche . To further investigate the mechanism behind defects in hematopoiesis in nol9sa1022/sa1022 embryos , we assessed the temporal expression of one of the early markers of hematopoietic stem cells ( HSCs ) , the transcriptional activator c-myb [43] . In situ hybridization using c-myb riboprobe revealed that the specification of HSCs in the aorta–gonad–mesonephros ( AGM ) was normal in nol9sa1022/sa1022 embryos at 36 hpf ( Fig 4D ) . However , the number of hematopoietic stem and progenitor cells ( HSPCs ) was markedly decreased in the CHT at 72 hpf ( Fig 4D ) . These data were further corroborated by flow cytometric analysis of c-myb+ cells in wt and nol9sa1022/sa1022 larvae at 96 hpf . We detected a significant decrease in the number of EGFP+ cells sorted from the dissected CHT region of Tg ( c-myb:EGFP ) /nol9sa1022/sa1022 compared to Tg ( c-myb:EGFP ) /nol9+/+ larvae ( S10A–S10C Fig ) . Quantification of HSPCs using the Tg ( cd41:EGFP ) line [44] confirmed a decrease in the number of cd41low ( GFPdim ) HSPCs in the CHT of nol9sa1022/sa1022 mutants compared to wt siblings at 96 hpf ( Fig 5A and 5B ) . Similar to our earlier observations in the pancreas , the reduced number of c-myb+ cells was not due to increased apoptosis in the CHT region of nol9sa1022/sa1022 larvae ( S11 Fig ) . Instead , 5-bromo-2'-deoxyuridine ( BrdU ) incorporation assay revealed that the proliferation of HSPCs was significantly decreased in nol9sa1022/sa1022 embryos at 48 hpf ( Fig 5C–5E ) . This led us to conclude that the decrease in the number of HSPCs in nol9sa1022/sa1022 larvae at 72 hpf is due to a defect in HSPC proliferation and not due to increased apoptosis . Although the gross morphology of the CHT and vessels appeared normal in nol9sa1022/sa1022 mutants , we wanted to examine it further at the ultrastructural level . To this end we used transmission electron microscopy ( TEM ) to assess the cellular architecture of the CHT ( Fig 6 ) . At 120 hpf , CHT is confined within the space limited dorsally by the caudal artery , ventrally by the caudal vein , and laterally by myotomes ( Fig 6A ) . In wt fish the CHT contained a number of rounded cells supported by deposits of extracellular matrix ( ECM ) ( Fig 6D ) . In contrast , in nol9sa1022/sa1022 larvae the CHT area was largely depleted of cells and ECM and instead filled by electron-loose , possibly necrotic , cell projections full of debris ( Fig 6E ) . As a result , the CHT area was markedly decreased in nol9sa1022/sa1022 mutants compared to wt siblings ( Fig 6B and 6C ) . Interestingly , we also observed signs of degeneration of endothelial cells in the caudal vein of nol9sa1022/sa1022 mutants . Endothelial cells seemed “swollen” and contained a high number of , what appeared to be , lipofuscin granules ( Fig 6F and 6G ) . The accumulation of lipofuscin granules is often associated with damage to mitochondria , membrane and lysosomes and is implicated in many degenerative diseases [45] . Thus , our TEM analysis further underlines the complexity of the phenotype observed in nol9sa1022/sa1022 larvae and suggests much broader changes in the CHT niche , including ECM and endothelial cells . Sufficient protein synthesis is essential for cell growth and proliferation and one of the pathways implicated in the regulation of these processes is the Target of Rapamycin ( TOR ) pathway . The TOR pathway controls ribosomal protein gene transcription and rRNA synthesis and processing [46 , 47] . To assess whether the defective protein synthesis in nol9sa1022/sa1022 larvae activates the TOR pathway , we assessed the phosphorylation of the mTOR target 4E-binding protein 1 ( p4EBP1 ) in nol9sa1022/sa1022 and wt larvae . We found no difference in the levels of phosphorylated p4EBP1 in nol9sa1022/sa1022 larvae compared to nol9+/+ and nol9+/sa1022 siblings at 120 hpf using Western blot analysis ( S12A Fig ) . In addition , we tested the effect of L-Leucine on exocrine pancreas and hematopoietic phenotypes in nol9sa1022/sa1022 mutants . L-Leucine was previously reported to improve the defects resulting from faulty ribosome biogenesis by activating mRNA translation via the mTOR pathway [17 , 48] . In nol9sa1022/sa1022 mutants , however , the mean volume of the ptf1a-expressing region remained significantly smaller when compared to nol9+/+ or nol9sa1022/+ larvae regardless of the treatment of embryos with L-Leucine or its inactive enantiomer D-Leucine ( S12B and S12C Fig ) . Similarly , in situ hybridization against hbae1 revealed that nol9sa1022/sa1022 larvae had significantly lower expression of hbae1 than their nol9+/+ or nol9sa1022/+ siblings at 120 hpf , irrespective of whether they were treated with L-Leucine , D-Leucine or L-Alanine ( S12D Fig ) . Therefore , we concluded that both the exocrine pancreas and hematopoietic phenotypes observed in nol9sa1022/sa1022 mutants are independent of the mTOR pathway . We do not , however , rule out the possibility that defects in translation of specific mRNAs may occur independently of mTOR and may directly contribute to the observed phenotype . Studies have shown that Tp53-dependent mechanisms contribute to the phenotypes resulting from defective ribosome biogenesis [6–8 , 11–13 , 15 , 16 , 49–53] . Publicly available mRNA expression data generated by the Zebrafish Mutation Project showed that on a whole-organism level , tp53 mRNA is 4 . 3-fold more abundant in nol9sa1022/sa1022 mutants than in their wt siblings at 120 hpf ( adjusted p-value = 6 . 49 x 10−79 ) [54] . In order to investigate whether the exocrine pancreas phenotype of nol9sa1022/sa1022 larvae is dependent on Tp53 signalling , we outcrossed the Tg ( ins:mCherry ) jh2/Tg ( ptf1a:EGFP ) jh1/nol9+/sa1022 line to the tp53+/zdf1 line [55] . In tp53/zdf1/zdf1 mutant fish , the M214K missense mutation interferes with Tp53 activity by disrupting its DNA-binding domain . We found that at 120 hpf , the mean volume of the ptf1a-expressing region of nol9sa1022/sa1022/tp53+/+ larvae was not significantly different from that of nol9sa1022/sa1022/tp53zdf1/zdf1 larvae although both were significantly smaller than the mean volume of the ptf1a-expressing region of nol9+/+/tp53+/+ and nol9+/+/tp53zdf1/zdf1 larvae ( S13A and S13B Fig ) . These data suggest that the exocrine pancreas phenotype of nol9sa1022/sa1022 is independent of Tp53 signalling . In order to assess whether the hematopoietic defects observed in nol9sa1022/sa1022 mutants are Tp53-dependent , we in-crossed nol9+/sa1022/tp53zdf1/+ fish and used their progeny for in situ hybridization against c-myb and hbae1 at 96 hpf ( Fig 7A–7D ) . Statistical analysis revealed that in the tp53zdf1/zdf1 background , the level of c-myb signal in nol9sa1022/sa1022 larvae reverted to wt ( nol9+/+;p53+/+ ) levels ( Fig 7A and 7B ) . In contrast , although nol9sa1022/sa1022/tp53zdf1/zdf1 larvae had a significant increase in the hbae1 in situ signal compared to tp53+/zdf1 and tp53+/+ siblings , the hbae1 signal was still over three-times lower than in wt ( nol9+/+/p53+/+ ) siblings ( Fig 7C and 7D ) . Therefore , we conclude that in nol9sa1022/sa1022 mutants the defect in the proliferation of HSPCs , but not in the differentiation of definitive erythrocytes , is Tp53-dependent . To further investigate the involvement of Tp53 in the development of defects in different tissues of nol9sa1022/sa1022 mutants , we assessed the levels of tp53 mRNA in nol9sa1022/sa1022 mutants and wt siblings by whole-mount in situ hybridization ( Fig 8 ) . At 48 hpf , tp53 was expressed at high levels in the CHT of nol9sa1022/sa1022 mutants and wt siblings alike ( Fig 8A ) . However , at 72 hpf the expression of tp53 remained high in the CHT of nol9sa1022/sa1022 mutants but was down-regulated in wt siblings ( Fig 8B ) . At this stage , tp53 was also significantly up-regulated in the liver and intestine of nol9sa1022/sa1022 mutants , but tp53 expression was not detected in the pancreas of nol9sa1022/sa1022 mutants nor wt siblings ( Fig 8C and 8D ) . In conclusion , expression of tp53 was detected in the CHT but not in the pancreas prior to the development of the phenotype in nol9sa1022/sa1022 mutants , i . e . 48 hpf for the CHT and 72 hpf for the pancreas .
In this study , we have characterized the phenotype of the loss-of-function nol9sa1022/sa1022 zebrafish mutant . We have shown that in zebrafish , similar to human , Nol9 is involved in 28S rRNA processing . In addition , nol9sa1022/sa1022 larvae had impaired development of the intestine , liver , pancreas and definitive erythrocytes and lymphocytes , thus recapitulating several common features of ribosomopathies in human . Although ribosomopathies have a wide spectrum of clinical manifestations , many are characterized by hypoproliferative phenotypes . Zebrafish harboring a loss-of-function nol9sa1022 mutation displayed an early hypoproliferative defect restricted to progenitor cells of the digestive and hematopoietic systems . Both ptf1a-positive progenitor cells of the exocrine pancreas and cmyb-positive HSPCs had significantly lower rates of proliferation in nol9sa1022/sa1022 larvae compared to their wt siblings . This tissue specific phenotype can be partially explained by the distinct expression pattern of nol9 . Indeed , we observed that nol9 was most highly expressed in branchial arches , liver , pancreas , as well as caudal hematopoietic tissue at 120 hpf . Alternatively , Nol9 may have tissue-specific interacting partners and extra-ribosomal functions in the digestive organs and in HSPCs [56 , 57] . Furthermore , it has been proposed that the spectrum of translated mRNAs might change with the overall reduction of fully functional cytoplasmic ribosomes or specific ribosomal biogenesis proteins , suggesting that ribosome composition may vary between distinct cell types [56–58] . Another explanation for the tissue specific effect of nol9 depletion stems from the high proliferative rate of the affected tissues . The reason for this is twofold . Firstly , it is reasonable to speculate , that highly proliferative cell populations , such as pancreatic cells and HSPCs , deplete maternally-derived Nol9 protein earlier than other cell populations [58] . And secondly , defects in ribosomal biogenesis affect primarily highly proliferative cells due to their high demand for ribosome production . This is particularly germane to HSCs which are shown to be very sensitive to changes in the level of protein synthesis [59] . Impaired development of the pancreas , liver and intestine has been a recurrent pathological feature in many zebrafish models of ribosomopathies , including nil per os ( npo ) [60] , digestive expansion factor ( def ) [8] , titania ( tti ) [61] and nucleolar protein with MIF4G domain 1 ( nom1 ) [62] . In contrast , the hematopoietic defects have been studied to a lesser extent; the reported phenotypes are usually limited to primitive erythropoiesis or specification of HSCs as in rpl11 , rps19 , rps7 , rps29 and nop10 mutants [16 , 18 , 53 , 63 , 64] . In our nol9sa1022/sa1022 model , primitive erythropoiesis , as well as specification of HSCs , was normal . However , the nol9sa1022/sa1022 larvae had decreased numbers of definitive erythroid and lymphoid cells , whereas the number of thrombocytes remained within the normal range at 96 hpf . Thus , to the best of our knowledge , the nol9sa1022/sa1022 mutant is the only zebrafish model of ribosomopathy in which the later stages of definitive hematopoiesis have been described . Although blood defects were initially restricted to specific lineages , the whole hematopoietic tissue appeared degenerated at 120 hpf . The CHT of the nol9sa1022/sa1022 larvae was largely depleted of cells and ECM and filled with a number of cell projections that were reminiscent of cells undergoing necrosis . Interestingly , we also detected signs of pathological processes in endothelial cells of the caudal vein . The endothelial cells appeared swollen and filled with lipofuscin granules , featuring structural changes not previously reported in other zebrafish models of ribosomopathy . Therefore , the phenotype of the nol9sa1022/sa1022 mutants should be viewed as a progressive degenerative process , which culminates with the death of larvae by 10 dpf . It is hypothesized that impaired ribosomal biogenesis promotes excess free ribosomal proteins in the nucleus , resulting in nucleolar stress . In response to this stress , free ribosomal proteins ( including RPL5 , RPL11 , RPL23 , RPS7 and RPL26 ) bind and inactivate MDM2 , which results in Tp53 stabilization and Tp53-dependent cell cycle arrest [65–71] . However , Tp53-independent processes involving cell cycle arrest and apoptosis due to ribosome biogenesis defects have been also described [72–75] . As in nom1 , tti , pes , rpl3 , rpl23a and rpl6 mutants , the defect in pancreatic development in nol9sa1022/sa1022 mutants was Tp53-independent [61 , 62 , 76 , 77] . In rpl11 mutants , defects in specification of HSCs as well as primitive erythropoiesis were Tp53-dependent [16] . Similarly , mice with heterozygous mutations in ribosomal genes Rps20 and Rps19 had a tp53-mediated decrease in the number of erythrocytes [78] . Interestingly , in our nol9sa1022/sa1022/tp53zdf1/zdf1 double mutant model , the number of HSPCs in the CHT was restored to wt levels , whereas the number of definitive erythrocytes was only partially rescued . This suggests that in nol9sa1022/sa1022 mutants there are two independent hematopoietic defects—one affecting HSPC proliferation and the other affecting definitive erythrocyte differentiation . Interestingly , we observed high expression of tp53 in the CHT of wt and nol9sa1022/sa1022 embryos at 48 hpf , which suggests that Tp53 may play a developmental role in HSPCs before 72 hpf , which could account for the different responses of HSPCs , definitive erythrocytes and exocrine pancreas to the loss of tp53 in nol9sa1022/sa1022 mutants . It has been speculated that disruption in translational machinery in erythroid precursors leads to anemia in many ribosomopathies [3] . For example , altered translation of the key erythroid transcription factor GATA-1 has also been reported in DBA [79] . The late stage erythroid cells exhibit rapid cell division and extensive translational demand for globin synthesis . Therefore , anemia may be attributed to the hypersensitivity of these cells to the reduced level of mRNA translation . Treatment of embryos with amino acid L-Leucine , a potent activator of the mTOR pathway and mRNA translation , has been shown to alleviate anemia in rps14 and rps19 morpholino knock-down zebrafish embryos [17 , 80] . In nol9sa1022/sa1022 mutants , L-Leucine treatment did not rescue the erythroid or pancreatic phenotype suggesting that these are independent of mTOR . The phenotypic features of the nol9sa1022/sa1022 mutants are highly reminiscent of the tissue specific clinical features of Shwachman-Diamond syndrome ( SDS ) that is associated with deficiency of the SBDS ( Shwachman-Bodian-Diamond Syndrome ) protein [24] . SBDS plays a role in the same process as NOL9 , although at a later stage: it is required for late cytoplasmic maturation of 60S ribosomal subunits and translational activation of ribosomes [25–27] . In zebrafish , sbds and nol9 are both highly expressed in the pancreas at 120 hpf [77 , 81] . In addition , MO knock-down of sbds leads to a Tp53-independent defect in the development of the exocrine , but not endocrine pancreas [77] , in line with the phenotype observed in nol9sa1022/sa1022 mutants . The involvement of SBDS and NOL9 in 60S ribosomal subunit biogenesis , coupled with the remarkable similarity of the phenotypes resulting from depletion of both genes in zebrafish ( some of which mimic clinical manifestations of SDS ) strongly supports the view that nol9sa1022/sa1022 mutant zebrafish are a highly relevant , valuable ribosomopathy model system . Our study describes the zebrafish nol9sa1022/sa1022 mutant , a novel vertebrate model of ribosomopathy , which recapitulates key human disease characteristics . Further characterisation of the nol9sa1022/sa1022 mutant will foster insights into the molecular pathology of ribosomopathies and provide fundamental new insights into how ribosome dysfunction leads to tissue-specific pathologies . Ultimately , the use of good genetically tractable models , such as the zebrafish nol9sa1022/sa1022 mutant , will have significant impact not only on our understanding of the range of human ribosomopathies but will also accelerate the development of targeted therapies .
The maintenance , embryo collection and staging of the wild type ( Tubingen Long Fin ) and transgenic and mutant zebrafish lines ( Tg ( cd41:EGFP ) , Tg ( c-myb:EGFP ) , Tg ( ptf1a:EGFP ) , Tg ( ins:mCherry ) , tp53zdf1 , nol9sa1022 ) were performed in accordance with EU regulations on laboratory animals , as previously described [82 , 83] . Melanization of embryos was prevented by incubating embryos with 0 . 002% phenylthiourea ( PTU , Sigma Aldrich ) from 24 hpf . DNA isolation and genotyping using allele-specific probes were performed as previously described [84] . Whole-mount in situ hybridization was performed with gene-specific probes against c-myb , rag1 , hbae1 , prox1 , fabp2 , foxn1 and tp53 as previously described [85] . Primers used for probe synthesis against nol9 were: 5’-GACAATGAAAGTACACAAGGTTC-3’ ( forward ) and 5’-TAATACGACTCACTATAGGGTAACACTGCACGGTTCTTGG-3’ ( reverse ) . The riboprobe was synthesized by in vitro transcription using T7 polymerase , with the PCR product used as the template . Quantification of prox1 WISH signal was performed by measuring the area of stained tissue in ImageJ . Quantification of hbae1 and c-myb WISH signal in the CHT region was performed in an objective manner by counting the number of pixels with color within a prescribed distance from a given color in each TIFF image . The particular values used were ( 100 , 100 , 120 ) color in the RGB space with tolerance of 16% of maximum possible intensity for hbae1 and 10% for c-myb . Calculations were carried out by ImageMagick raster image processing software suite controlled by a Bash script that automated processing and collecting of data . Images were cropped prior to the procedure in order to include the whole CHT region and to prevent accidental misclassification of pixels without signal . Masks consisting of classified pixels were visually inspected for accuracy . In order to quantify the level of tp53 WISH signal in the CHT at 72 hpf , two observers independently classified the larvae into four groups according to the strength of the signal in a blinded manner . The difference between the signal in nol9sa1022/sa1022 larvae and wt siblings , assessed with Mann-Whitney U test , was significant ( p<0 . 05 ) in each case . Whole-mount immunohistochemistry was performed as described before [86] , with incubation in 0 . 1% collagenase ( Sigma ) in PBS-Tween for 30 min for the digestion step . For immunohistochemistry against glucagon and somatostatin , larvae were deyolked before incubation in blocking buffer . Primary antibodies used were anti-cytokeratin ( Santa Cruz Biotechnology , 1:50 ) , anti-glucagon ( Sigma , 1:1 , 000 ) , anti-somatostatin ( Dako , 1:200 ) and anti-carboxypeptidase-a ( Sigma , 1:100 ) . The larvae were mounted with Vectashield Mounting Media ( Vector Laboratories ) . Images of live larvae , as well as larvae treated with WISH and histochemical stains , were taken using either a Leica DFC 450 CCD camera attached to a Leica LM80 or MZ16 FA dissecting microscope and Leica Application Suite software ( Leica Microsystems , Germany ) , or an AxioCam ICc1 camera attached to a Leica AxioZoom . V16 microscope with ZEN software ( Carl Zeiss , Germany ) . Confocal images were captured using a Leica TCS SP5 confocal microscope with Leica LAS AF software ( Leica Microsystems ) , using a 20x or 40x lens . The volume of the ptf1a-positive exocrine pancreas in Tg ( ptf1a:EGFP ) larvae was measured from a z-stack of confocal pictures , using ImageJ 1 . 48v programme , with a 10 μm slice interval using the Measure Stacks plugin in the ImageJ64 software ( National Institutes of Health ( NIH ) , http://imagej . nih . gov/ij/ ) . Embryos at either 48- or 72 hpf were incubated in 10mM BrdU ( Sigma Aldrich ) for 20 min on ice , washed in egg water and incubated at 28°C for 3 h ( 48 hpf ) or 5h ( 72 hpf ) . The embryos were then used for immunohistochemistry with anti-GFP primary antibody ( Invitrogen , 1:200 ) and anti-rabbit secondary antibody conjugated to Alexa Fluor 488 ( Invitrogen , 1:200 ) , followed by fixation with 4% paraformaldehyde at room temperature for 15 min . The embryos were then cut and their heads were used for genotyping . Embryos of the same genotype were pooled together and subjected to washes with water followed by treatment with 2N HCl for 1h at room temperature . The embryos were then washed consecutively with PBS-Tween and 1M Tris-HCl pH 9 . 5 and subjected to immunohistochemistry against BrdU with rat anti-BrdU antibody ( Abcam , 1:200 ) and anti-rat antibody conjugated to Cy3 ( Millipore , 1:400 ) . Fluorescence was visualized using confocal microscopy . For assessment of apoptosis in the pancreas , the TUNEL assay was performed on 96 hpf larvae using In situ Cell Death Detection Kit , TMR Red ( Roche ) . Fixed larvae were digested in 0 . 1% collagenase ( Sigma ) before staining . The larvae were counterstained with DAPI and imaged using confocal microscopy . For assessment of apoptosis in the CHT of 96 hpf larvae , In situ Cell Death Detection Kit , AP ( Roche ) was used . After incubation with TUNEL reaction mixture , larvae were heated to 80°C for 10 min . After blocking in a solution of 5% heat inactivated Fetal Bovine Serum in PBS-Tween , the larvae were incubated overnight with anti-fluorescein Fab fragments conjugated to alkaline phosphatase ( Roche , 1:2 , 000 ) and stained with NBT/BCIP solution ( Roche ) , according to the manufacturer’s protocol . The progeny from a Tg ( c-myb:EGFP/nol9+/sa1022 x nol9+/sa1022 cross at 96 hpf was screened for the presence of EGFP and subjected to immunohistochemical staining with anti-GFP primary antibody ( Invitrogen , 1:200 ) and anti-rabbit secondary antibody conjugated with Alexa Fluor 488 ( Invitrogen , 1:200 ) . Afterwards , each larva was dissected: the CHT was stored and the rest of the body was used for genotyping for the nol9sa1022 allele . CHTs from 8 larvae of the same genotype were pooled , incubated for 30 min with 10 mM DTT ( Life Technologies ) in Danieau’s solution , followed by incubation with 50 μg/ml liberase ( Roche ) in PBS for 3 h at 37°C . Single-cell suspension was prepared by passing the solution through a 40 μm mesh cell strainer ( Becton Dickinson ) . Flow cytometry was performed on a BD LSR-Fortessa analyzer ( Becton Dickinson ) . Non-transgenic siblings subjected to immunohistochemical staining were used as a negative control . Western blotting was performed using the NuPAGE SDS-PAGE gel system ( Novex ) , according to the manufacturer’s protocol . The following antibodies were used at the dilutions indicated: anti-phospho-4EBP1 ( Cell Signalling , 1:200 ) and anti-β-actin ( Sigma Aldrich , 1:1 , 000 ) . Secondary antibodies conjugated with horseradish peroxidase ( HRP ) were used at 1:50 , 000 dilution . The signal was developed with a SuperSignal West Femto Substrate kit ( Thermo Scienctific ) and visualized using an ImageQuant LAS 4000 instrument ( GE Healthcare Life Sciences ) . For TEM , 120 hpf larvae from a cross of Tg ( ptf1a:EGFP ) ;nol9+/sa1022 fish were genotyped and fixed in 2% glutaraldehyde and 2% formaldehyde in 0 . 05 M cacodylate buffer at pH 7 . 4 for 6 hours at 4°C . They were then processed for infiltration with Quetol epoxy resin as described previously [87] . Images were taken on an FEI Tecnai G2 operated at 120Kv using an AMT XR60B digital camera running Deben software . Transverse sections through the caudal hematopoietic tissue ( CHT ) , as well as longitudinal sections through the pancreas were obtained . Alcian blue staining was performed on 120 hpf larvae as described previously [88] . o-Dianisidine staining for globin was performed as previously described [89] . The o-Dianisidine signal was quantified using an analogous method to the one used for the quantification of c-myb and hbae1 WISH signal . Sudan Black B staining for visualization of neutrophil granules was performed as previously described [90] . N-[N- ( 3 , 5-Difluorophenacetyl ) -L-alanyl]-S-phenylglycine t- butyl ester ( DAPT , StressMarq ) was used to inhibit Notch signaling . Larvae were incubated in 100 μM DAPT in egg water containing PTU from 72 hpf until 120 hpf . For studies involving the mTor pathway , embryos from a cross of nol9+/sa1022 fish were incubated in 100 mM solution of L-Leucine , D-Leucine or L-Alanine ( Sigma ) in egg water with addition of PTU from 24 hpf until 120 hpf . Total RNA from phenotypic and non-phenotypic larvae at 120 hpf was isolated using phenol:chloroform extraction . 2 μg of total RNA per lane was subjected to electrophoresis on a 0 . 8% agarose gel ( with the addition of 7% formaldehyde ) in 1xMOPS/1% formaldehyde buffer . RNA was then blotted to a Hybond-Nylon membrane ( Amersham ) and subsequently visualized using methylene blue staining . The detection of rRNA precursors was performed using the High Prime DNA Labeling and Detection Starter Kit II ( Roche ) kit , according to manufacturer’s protocol . The DNA probes targeted 5’ETS , ITS1 , and ITS2 rRNA [61] . The signal was detected using an X-ray film ( Kodak ) . Total RNA extracted from 120 hpf nol9sa1022/sa1022 larvae and their wt siblings was analyzed on an Agilent Bioanalyser 2100 according to the manufacturer’s instructions . Global protein synthesis was assessed using puromycin incorporation [91] . An equal number of nol9sa1022/sa1022 and wt 120 hpf larvae were suspended in blocking solution ( 5% FBS / PBS ) , with the addition of 20 μg/ml puromycin ( Sigma ) and 100 μg/ml cycloheximide ( Santa Cruz Biotechnology ) , where applicable . Larvae were dissociated by passing through 100 μm mesh and the cell suspension was incubated at room temperature for 10 min , followed by centrifugation at 2 , 000 rpm for 3 min . The pellets were re-suspended in blocking solution ( with the addition of 100 μg/ml cycloheximide , where applicable ) and incubated at 28°C for 40 min . After centrifugation , cell pellets were used for Western blot analysis using anti-puromycin antibody ( 1:150 , kind gift from Yusuke Sekine ) and anti-β-actin antibody ( Sigma Aldrich , 1:1 , 000 ) for loading control . Signal strength was quantified by densitometry measurements using ImageJ . For β-actin , the single band was quantified . For puromycin , an area of gel with the weakest background ( the signal in the no puromycin control ) was chosen . For each lane , the full signal within the chosen area was measured and the value for the background was subtracted . The puromycin signal value was then divided by the β-actin value for the same sample . Morpholinos ( Gene-Tools ) were diluted in distilled water with 0 . 25% phenol red ( Sigma ) and injected into 1- to 2-cell stage embryos at 4 ng . The MO sequences were: nol9 ATG MO: 5’- ACCTTGTGTACTTTCATTGTCATCC-3’ , Std Ctrl MO 5’-CCTCTTACCTCAGTTACAATTTATA-3’ . Statistical analyses were performed in Microsoft Excel and Statplus ( AnalystSoft ) . | The production of ribosomes , the protein-synthesizing machines , is fundamental in all cells . It is a very complex process that requires the coordinated actions of ribosomal and non-ribosomal proteins . Impairment of ribosome formation and function leads to a class of disorders known as “ribosomopathies” . Here , we describe the identification and characterization of a zebrafish mutant in nol9 , a gene encoding a non-ribosomal protein involved in ribosome biogenesis . The nol9sa1022/sa1022 mutants show defects in the exocrine pancreas and erythrocytes due to impaired cell proliferation . These phenotypic features of nol9sa1022/sa1022 mutants are reminiscent of the clinical symptoms of Shwachman-Diamond syndrome , a ribosomopathy characterized by exocrine pancreatic insufficiency and hematopoietic defects . Interestingly , we found that hematopoiesis but not pancreas morphogenesis in nol9sa1022/sa1022 larvae is Tp53-dependent , highlighting that the consequences of impaired ribosome biogenesis differ between tissues within the same organism . This study provides novel insight into the function of the ribosome biogenesis protein Nol9 in zebrafish development and presents a novel model that will help to decipher the tissue-specific mechanisms of ribosomopathies . | [
"Abstract",
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"Methods"
] | [] | 2015 | The Ribosome Biogenesis Protein Nol9 Is Essential for Definitive Hematopoiesis and Pancreas Morphogenesis in Zebrafish |
Reef coral cover is in rapid decline worldwide , in part due to bleaching ( expulsion of photosynthetic symbionts ) and outbreaks of infectious disease . One important factor associated with bleaching and in disease transmission is a shift in the composition of the microbial community in the mucus layer surrounding the coral: the resident microbial community—which is critical to the healthy functioning of the coral holobiont—is replaced by pathogenic microbes , often species of Vibrio . In this paper we develop computational models for microbial community dynamics in the mucus layer in order to understand how the surface microbial community responds to changes in environmental conditions , and under what circumstances it becomes vulnerable to overgrowth by pathogens . Some of our model's assumptions and parameter values are based on Vibrio spp . as a model system for other established and emerging coral pathogens . We find that the pattern of interactions in the surface microbial community facilitates the existence of alternate stable states , one dominated by antibiotic-producing beneficial microbes and the other pathogen-dominated . A shift to pathogen dominance under transient stressful conditions , such as a brief warming spell , may persist long after environmental conditions have returned to normal . This prediction is consistent with experimental findings that antibiotic properties of Acropora palmata mucus did not return to normal long after temperatures had fallen . Long-term loss of antibiotic activity eliminates a critical component in coral defense against disease , giving pathogens an extended opportunity to infect and spread within the host , elevating the risk of coral bleaching , disease , and mortality .
It appears that antibiotic activity and the competition between beneficial and potentially pathogenic microbes such as Vibrio spp . are key to understanding community dynamics within the SMC . In this paper , we develop models for how these interactions affect the outcome of competition within the SMC and either limit or promote overgrowth by pathogenic microbes . We assume that interactions can be simplified to a few key players , each representing some set of microbial organisms or substances within the mucus layer . Some parameter values and model assumptions are based on taking Vibrios as a model system for coral pathogens , because less is known about other pathogens of current concern . Yet because many characteristics of the SMC are still uncertain , we explore general properties of the model by varying parameters , rather than attempting to closely simulate any specific coral-pathogen interaction . We thereby identify the parameters and processes that are predicted by the model to have the most significant impacts on SMC dynamics and on the potential for pathogen outbreaks . We describe first a model that assumes a spatially homogeneous ( “well-mixed” ) mucus layer . This model does not depict the physical processes of mucus production by the coral or endosymbionts or the loss of mucus by sloughing . Furthermore , gradients in chemical concentrations and in microbial abundances within the mucus layer may have a considerable effect on the qualitative dynamics of the microbial community ( as has been observed in other models of interacting microbial populations [39]–[41] and experimentally [42] ) . We therefore develop a model that includes the spatial gradient in nutrient and microbe concentrations from coral surface to the surrounding seawater , mucus production by the coral , and ablation of mucus into the surrounding seawater . By contrasting this model with the well-mixed model , we examine the role of spatial gradients in SMC dynamics and in defense against pathogen invasion .
Our well-mixed model and its underlying mechanistic assumptions are presented in detail in the Materials and Methods section . To gain insight into the dynamics of our well-mixed model , we consider a generalized well-mixed model whose long-term properties can be determined by a simple nullcline analysis ( nullclines are defined below ) . Two important aspects of the general model that simplify the analysis are: ( 1 ) Microbial populations are measured in units of the growth-limiting substrate provided by the host and its endosymbionts . For example , if we posit that the limiting factor is carbon , then the units for microbial abundance are grams carbon ( rather than total biomass , total biovolume , or number of individuals ) . The model is then nondimensionalized by choosing units such that the total amount of limiting factor in microbes , antibiotics , and the mucus is 1 . ( 2 ) We assume for now that there is no microbial inoculation from external sources ( we later return to the situation in which external inoculation occurs and show that it has no important effects ) . Because microbes are far less abundant in seawater than in the SMC , inoculation is a small perturbation whose only effect is to prevent complete extinction of either pathogens and beneficials ( this is explained in more detail below ) . The general well-mixed model is ( 1 ) where p is the abundance of pathogenic microbes and b is the abundance of antibiotic-producing beneficial microbes plus antibiotics , all measured in substrate units as noted above . The amount of free limiting substrate available for microbial uptake and reproduction is then s = 1 − p − b , due to the rescaling such that the total amount of limiting substrate equals 1 . Bacterial growth-rate functions ƒp and ƒb are increasing functions of substrate s , and ƒp is a strictly decreasing function of b . We assume ( as is true asymptotically in our specific mechanistic model ) that antibiotic concentration is a constant fraction of b . We assume growth rates ƒp and ƒb are both positive when substrate is at its maximum possible value ( s = 1 ) , because otherwise the populations die out . We also assume that ƒp and ƒb are both negative when s = 0 , so neither population can persist in the absence of nutrients . We can see how model ( 1 ) behaves by plotting its nullclines in the ( b , p ) plane . The b nullcline is the line where is the solution of , i . e . , the line ( 2 ) which has constant slope of −1 . The p nullcline is the curve ( 3 ) where is the solution of . Because antibiotics are harmful to the pathogens , is an increasing function of b . Therefore the p nullcline has a negative slope that is always below −1 . Consequently , there are only three possible qualitative behaviors ( Figure 1 ) . If one nullcline lies completely above the other , then all initial conditions with beneficials and pathogenic microbes both present lead to competitive exclusion of the population with the lower nullcline , exactly like the Lotka-Volterra model ( Figure 1A , 1C ) . If the nullclines cross , their intersection is a saddle ( locally unstable ) , so there is competitive exclusion again but with the identity of the winner depending on initial conditions ( sometimes called contingent exclusion ) . Both of the single-species equilibria ( on the coordinate axes ) are then locally stable ( Figure 1B ) . Without the antibiotic-mediated interactions , we would have pure resource competition for a single limiting substrate . The p and b nullclines would then be parallel lines , and the pathogen is the superior competitor if the p nullcline is above the b nullcline ( as in Figure 1A ) . Adding antibiotic effects , if they are sufficiently strong , will make the p nullcline decrease more quickly as b increases , giving the situation shown in Figure 1B . Control of potentially dominant pathogens through antibiotic activity is thus the “recipe” for contingent exclusion . A numerically dominant pathogen population can prevent regrowth of beneficials by pure resource competition alone , keeping free substrate too scarce for beneficials to increase . A numerically dominant beneficial population can prevent pathogen regrowth by a combination of resource competition and antibiotic production: by maintaining high ambient antibiotic concentration as well as by consuming substrate . Finally , we can return to the biologically realistic situation in which there is some inoculation of microbes from the surrounding seawater . The empirical observation that microbes are orders of magnitude less abundant in seawater than in mucus implies that external inputs are a small perturbation . Consequently , their only qualitative effect is to prevent complete extinction when interactions within the mucus layer would lead to competitive exclusion . That is , stable equilibria on the axes as replaced by stable equilibria near the axes; unstable equilibria on the axes are eliminated while interior unstable equilibria move to slightly different locations . Figure 1D shows an example of how small rates of immigration change the nullclines and how the equilibria are affected . We now explain how the properties of the well-mixed model can lead to a sudden and persistent “takeover” of the SMC by pathogens following a brief period of conditions stressful to the host and to beneficial microbes . To illustrate the process , we consider thermal stress , which has been implicated most consistently as the environmental driver linked to pathogen outbreaks . External inoculation of microbes is ignored to simplify the presentation , but readers should keep in mind how inoculation would modify the dynamics ( as in Figure 1D ) . For a healthy coral we can assume that during colder months that are unfavorable to the pathogens , the beneficial bacteria are able to outcompete and exclude the pathogens ( Figure 2A ) —only the pathogen-exclusion equilibrium is stable . During warmer months ( Figure 2B ) higher temperatures may give the pathogens a higher intrinsic growth rate than the beneficials , but pathogen growth is kept in check by the effect of antibiotics , so the pathogen-exclusion equilibrium remains locally stable . However , a thermal anomaly ( transient , unusually high temperature ) causes the loss of antibiotic activity ( Figure 2C ) and eliminates the coexistence equilibrium , so the system jumps to being pathogen-dominated . Figure 3 illustrates the corresponding microbial population dynamics during a warm anomaly scenario based on sea surface temperatures at Glover's Reef , Belize ( Figure 3A ) . Under normal seasonal variation in temperature , beneficial microbes are dominant year-round ( Figure 3B ) . Regardless of how a simulation is initiated , during winter the beneficials become dominant and they remain dominant through the summer , while pathogens persist at very low levels due to inoculation from the water column . But even a brief thermal anomaly that eliminates antibiotic activity for 14 d ( Figure 3B ) allows the pathogens to become dominant and remain so for approximately 3 mo , until temperatures drop low enough that the pathogen-dominant equilibrium becomes unstable . The well-mixed model therefore provides a mechanistic explanation for the empirically observed sudden switches to pathogen dominance following a change in conditions , and moreover the model predicts that such sudden switches will occur even if the change in conditions is gradual . Another key prediction is that the return from pathogen-dominance to beneficials-dominance when conditions improve will also be sudden , but it will occur under different conditions: pathogens may remain dominant even after environmental conditions return to those where beneficials were initially dominant , while beneficials may not recover dominance until the environment becomes considerably more favorable for them . Below , we present experimental results supporting the prediction that beneficial populations may not recover even long after the environmental conditions leading to pathogen takeover have abated . The fundamental question addressed by the spatial model is whether spatial variability allows for a broader range of qualitative outcomes than the well-mixed spatially homogeneous model . In particular , spatial variability might allow stable coexistence of pathogenic and beneficial microbes , for example if pathogens segregate away from beneficials and so avoid the effects of antibiotics produced by the beneficials . The well-mixed model's prediction of potentially abrupt changes in community composition in response to gradual changes in environmental conditions might then prove to be an artifact of neglecting spatial variability . We therefore expand the model to include spatial variability within the mucus layer along the gradient from host surface to seawater . The model and its underlying assumptions are presented in Materials and Methods , while details of numerical analysis and simulation methods are in the Supporting Text . Numerical study of the spatial model shows that the results from the nullcline analysis of the nonspatial model ( Figure 1 ) continue to hold . Specifically , the spatial model behaves like a two-dimensional system of differential equations , even though it has an infinite-dimensional state space . This occurs because , apart from a brief transient period , the entire spatial distribution of all of the state variables is predictable from the total abundances of beneficial and pathogenic microbes . Figure 4 shows an example . Two model simulation runs were initialized by choosing two different shapes for the spatial distributions of the beneficial and pathogen populations at time t = 0 ( Figure 4A and 4B ) , and then finding ( using numerical optimization ) total population sizes at t = 0 such that the total beneficial and pathogen populations at time t = 12 would be , for example , 4 and 5 , respectively ( to within 0 . 001 or smaller ) . The outcome ( Figure 4C and 4D ) is that the two runs are nearly identical in all respects at t = 12 , not just in their microbial population totals . Beneficials and pathogens are aggregated near the host surface ( x = 0 ) where substrate is provided , and where the substrate concentration is sufficient for reproduction to occur . The ( lower ) microbe abundances further from the host surface are mostly the result of populations being carried along by the mucus . In technical terms , the fact that the two runs have become nearly identical in all respects at t = 12 shows that the model has converged quickly onto a two-dimensional inertial manifold [43] . On the inertial manifold , the total abundances of the microbial populations are sufficient information to determine the complete state of the system: there is only one way ( on the manifold ) to have total B = 4 and total P = 5 , and both runs reached that state at t = 12 . Convergence onto the inertial manifold at time t = 12 does not mean that the system has reached equilibrium . As time goes on ( Figure 4E ) the system state continues to move within the inertial manifold , the pathogens continuing to increase and eventually excluding the beneficials , with both runs following the same path . Convergence of model solutions onto an inertial manifold means that the long-term outcome of the beneficial-pathogen interaction is completely determined by the long-term dynamics on the manifold . For any value ( B ( t ) , P ( t ) ) of the total microbe populations , there is a unique corresponding system state on the inertial manifold and corresponding instantaneous rates of total population growth dB/dt and dP/dt . This correspondence defines a two-dimensional dynamical system for the total beneficial and pathogen populations ( that is , dB/dt and dP/dt are both functions of just B and P ) , and its behavior can be determined by plotting the nullclines ( using the methods described in Text S3 ) . Figure 5 shows nullclines for the slower “baseline” parameters listed in Table 1 . These confirm that the spatial model is in the bistable situation of Figure 1 ( B ) , indicating that a healthy population of beneficial microbes can keep pathogens from increasing , but beneficials would be at a competitive disadvantage in a community dominated by pathogenic microbes . Given the large uncertainties in our parameter estimates , we cannot regard this property as a prediction about nature . The important feature of Figure 5 is that , as in the well-mixed model , the pathogen nullcline is steeper than the beneficials nullcline , which is the property that precludes robust stable coexistence of beneficials and pathogens with both types abundant ( versus low-level persistence of a weaker competitor in the presence of a dominant , due to low-level inoculation from the water column ) . Consequently , the spatial model preserves the key qualitative prediction of the well-mixed model: if temporary extreme conditions allow the community to become dominated by pathogenic microbes , the pathogen takeover may persist even after conditions return to normal and may not terminate until conditions occur that are highly unfavorable to the pathogens , such as winter temperatures . We performed a local sensitivity analysis to determine the relative impact of each parameter on system dynamics . Due to the high uncertainty of parameter estimates , parameters were varied up to ±50% from their default values ( Table 1 ) using Latin Hypercube sampling ( see Appendices D and E for additional information about our sources for parameter values and the methods used to carry out the sensitivity analysis ) . We carried out sensitivity analysis under three different scenarios: baseline ( the parameter values listed in Table 1 ) , heat stress , and high antibiotic conditions . Baseline parameters correspond to the situation in Figures 1B and 2B , where temperatures are warm enough that pathogens have the higher intrinsic growth rate , but can be held at low levels by beneficials through antibiotic production and resource competition . For the heat stress scenario , beneficials growth rate was reduced , pathogen growth rates increased , and the production of antibiotics was decreased . For the high antibiotic scenario , the antibiotic production rate was increased and the efficacy of the antibiotics against the pathogens increased . We also considered both the “slower growth” and “faster growth” values for the microbe growth rate parameters rB , rp . Parameter values for these scenarios are listed in Table 2 . Overall , the results of the sensitivity analysis ( Figure 6 ) indicate that the most important parameters are either ( 1 ) the advection and diffusion coefficients , which control the balance between active movement towards favorable conditions and mortality through mucus ablation , or ( 2 ) the maximum growth rates ( rB , rp ) , which are important for the direct competitive interactions between the microbial populations . The importance of advection coefficients ( ηB and ηP ) reflects our assumption that microbes retain just enough active movement capability to avoid high mortality through mucus ablation , so they are near “tipping point” where a small loss of movement ability has large consequences . Movement parameters were generally less important in the faster growth scenarios ( Figure 6B , D , F ) where competitive interactions are stronger . Changes in antibiotic production ( α ) and efficacy ( λ ) have the most effect on pathogen success in the faster growth baseline ( 6B ) and heat stress scenarios ( 6D and 6C ) , because the rate of antibiotic production correlates with the beneficials' population growth rate . The most surprising outcome is that the parameters controlling the antibiotic production rate ( α ) , degradation rate ( μA ) , and bacteriostatic efficacy ( λ ) are never among the most significant parameters , even though the beneficials' ability to produce antibiotics is essential for their persistence . This suggests that ( if our parameter ranges are realistic ) the role of antibiotics in normal conditions is to tip the balance in a competition between near-equals . To explore this idea further , we modified the baseline/faster growth scenario by holding the ratios rB/rp and kB/kP constant ( i . e . , for each Latin Hypercube sample parameter vector , we perturbed rB and kB and then set the values of rP and kP so that the values of rB/rP and kB/kP were the same in the default and perturbed parameter vectors ) . As expected , with these constraints ( Figure 6G ) the importance of growth rate variation ( indicated by rB in the axis label ) is greatly reduced relative to that shown in Figure 6B , and the importance of antibiotic-related parameters is greatly increased . The fact that higher overall growth rates are detrimental to the pathogen also reflects the impact of antibiotics , because of the proportionality between beneficials' growth rate and antibiotic production rate in the model .
Analysis of the well-mixed model shows that under competition for a single limiting substrate , control of pathogen via antibiotic activity is the key to the contingent exclusion of pathogens by the beneficial bacteria . However , under the empirically supported assumption of reduced antibacterial production during heat stress , the model predicts a rapid switch from dominance by beneficial microbes to dominance by pathogens during thermal anomalies . In addition , dominance by beneficials is not restored when temperatures return to the normal conditions under which the beneficials were previously dominant . Instead , conditions must become unusually unfavorable to pathogens before a switch back to dominance by beneficial bacterial can occur . This prediction is consistent with observational findings that antibiotic activity did not return to measurable levels in Acropora palmata coral even after recovery and temperature reduction ( Table 3 ) . Antibiotic efficacy of coral mucus was assayed by challenging various environmentally relevant sources of microbes against coral mucus . Assays were conducted in April and September of 2005 , before ( 24°C ) and during ( 30°C ) one of the highest sea surface temperature increases on record ( see Materials and Methods and [30] ) . Antibiotic activity of mucus sampled from unbleached , apparently healthy areas of A . palmata tissue was greatly reduced or eliminated during the September 2005 bleaching event ( Table 3 ) . By April 2006 ( 24°C ) most corals on the sampled reef had recovered zooxanthellae , and the Acropora were normal in color and not bleached . However , the antibiotic activity of mucus sampled from A . palmata in April 2006 remained at levels too low for the assay to detect ( Table 3 ) . Simulations of the spatial model show that the key qualitative result from the analysis of well-mixed model ( contingent exclusion ) holds in the heterogeneous case as well . However , because of the level of uncertainty in our parameter assumptions and estimations , we performed sensitivity analyses to better quantify the effects of parameter variation under normal warm-season conditions ( Figure 6A–B ) , heat stress conditions ( Figure 6C–D ) , and finally conditions of high antibiotic production ( Figure 6E–F ) . Under normal conditions , the ability to move towards substrate-rich fresh mucus is critical to the success of a slowly reproducing pathogen , as shown in the relative importance of the advection coefficients ηP and ηB . For a more rapidly growing pathogen under these conditions ( Figure 6B ) , the ability to compete under substrate-limited conditions becomes more important . Under conditions causing heat stress ( Figure 6C–D ) , the pathogen uniformly has the upper hand and its success hinges primarily on its potential population growth rate , rP . Results for high antibiotic production are similar to baseline conditions , except antibiotic production favors the beneficial bacteria across the entire range of parameters considered . Thus as indicated by sensitivity analysis , the critical parameters overall are those which govern movement ( microbial advection and diffusion coefficients and mucus ablation rates ) and maximum microbial growth . Finally , a numerical nullcline analysis of the heterogeneous model suggests that it retains the hysteresis observed in the well-mixed model: once temporary extreme heat allows pathogens to overgrow , their dominance will persist until conditions become highly unfavorable for pathogenic persistence . Our models have omitted for clarity and simplicity some biological processes important to microbial community dynamics in order to focus on the dynamics between resident and invading bacteria on the coral surface . Our focus on bacterial interactions is motivated by observed correlations between bleaching , coral disease , and a shift in the bacterial community on the SMC of some corals [5] , [30] , [31] , [34] . We thus simplify the interactions in the entire coral holobiont , as interactions between coral zooxanthellae and surface bacteria , marine viruses [42]–[44] , and fungi all play a role in coral homeostasis , to see what insight may be gained by focusing on the interactions between resident bacteria and invading pathogens . Though our results address just one important interaction , they nonetheless yield insight into the mechanism behind disease transmission ( successful pathogen invasion ) in corals . Our model suggests that higher motility might be very beneficial for microbe populations; if so , temperature-dependent changes in mucus physical properties could be an additional mechanism underlying changes in SMC composition . Indeed , Vega-Thurber discovered that in stressed corals , motility genes associated with Vibrio species were dramatically upregulated [14] . Thus , an additional weapon pathogens may have is enhanced motility during pathogenesis , and this might be addressed in a future model . Future questions include: Why does antibiotic production decline as sea temperatures increase ? Are the beneficial bacteria being succeeded by a more temperature-tolerant ( and virulent ) bacterial type , as suggested by Rosenberg and Ben-Haim [28] , who discovered that a Vibrio species becomes more virulent and invades at high temperatures; and if so , why ? Future models could address the cost of antibiotic production by beneficial bacteria ( Does antibiotic production become costly as temperature rises ? ) , incorporate variability in antibiotic conversion efficiency , and allow production of defensive antibiotics by the pathogens themselves , as suggested by Ritchie [30] , who showed that visitor microbes , like the Vibrio inoculated into the SMC at the mucus-seawater interface , also produce antibiotics . The present models focus primarily on the loss of defenses within the SMC , without considering the defenses of the host coral itself once an infection has penetrated through the mucus layer . An earlier article focused on cellular immune responses of soft corals to an established fungal infection [44] , but did not address how vulnerability to infection is modulated by processes in the SMC . Future models should integrate the process of infection with host immune responses , to address how the onset of pathogenic overgrowth and host cellular response interact to determine the outcome of infection . Fully three-dimensional modeling of the SMC is also important to understand the lateral spread of infections across a colony . But the simplifying assumption of spatially homogeneous conditions ( which is typical in theoretical models of spatial population dynamics ) is not safe in this case . In our one-dimensional model , the physical processes of mucus creation and loss , and the gradients imposed by the mucus layer's environment , proved to be crucial for all of the model's qualitative predictions . Similarly , realistic modeling of colony modular structure and of spatial variation in multiple limiting factors ( where we have assumed single-substrate limitation ) may be crucial for modeling the lateral spread or containment of pathogens . We have presented models that yield insights into a current crisis in our oceans: the decline of coral cover due to increased vulnerability to disease in a warming climate . Our models show that the physical structure and the nature of the biotic interactions in the SMC facilitate the existence of alternate stable states , one dominated by beneficial microbes and the other dominated by pathogens . This provides a mechanistic explanation for the empirically observed sudden switches to pathogen dominance following heat stress . The models also predict that sudden switches will occur even if the temperature increase is gradual , and that the switch to pathogen dominance will persist long after thermal stress has ceased , so that a short-term heating event may give pathogens an extended opportunity to establish and spread . These predictions are robust consequences of an interaction between beneficial and pathogenic microbes mediated by beneficials' production of antibiotic substances , rather than depending on any fine details of our models . An important practical implication of our findings is that preventing a shift to pathogen dominance ( e . g . , through amelioration of stressors increasing disease susceptibility such as poor water quality [45] , [46] or stimulation of coral immune responses [33] ) may be much easier than reversing a shift to pathogen dominance .
Experimental work originally described in Ritchie et al . 2006 [30] . Mucus samples were taken from three apparently healthy A . palmata colonies in April of 2005 ( mean water temperature of 24°C , sustained at 22–25°C for 2 mo prior to sampling ) , September of 2005 ( mean water temperature of 30°C , sustained at 28–30°C for 2 mo prior to sampling ) , and April of 2006 ( mean water temperature of 25°C , sustained at 23–26°C for 2 mo prior to sampling ) . Inhibition assays were carried out by mimicking the coral surface microlayer on growth media by plating 400 microliters of pooled , undiluted coral mucus followed by UV irradiation to kill native mucus-associated microbes . Control media plates were UV irradiated for 10 min to control for UV alteration of media . Environmental sources of potentially pathogenic microbes were diluted and plated onto both mucus treated , and untreated , plates . Potential sources of invasive microbes included Serratia marscecens isolate PDL100 that has been implicated in white pox disease of A . palmata; Florida Keys canal water collected at sampling intervals; dust from Mali , Africa ( collected by V . Garrison ) ; and water column samples collected from the proximity of sampled A . palmata colonies during each sampling period . Environmental samples containing viable microbes were serially diluted and plated onto glycerol artificial seawater agar ( GASWA ) control plates and GASWA + mucus plates . Mucus was tested against all Florida Keys canal water isolates on Luria broth ( LB ) agar to address resistance to potential water quality contaminants . All other sources were tested on GASWA to address growth inhibition of marine bacteria or microbes implicated as potentially viable in the marine environment . Colony counts were recorded for each experiment and the number of colony forming units ( CFUs ) per milliliter was estimated . To frame our studies of the SMC , we have developed a simple model for a spatially homogeneous ( “well mixed” ) mucus layer focusing on the key community members . We explain each state variable and model equation below . Spatial structure within the mucus layer is potentially important and inevitably present because of the essential differences between host tissue at the base of the layer that is providing mucus and nutrients and the seawater environment into which mucus and nutrients are lost . We therefore generalize our well-mixed model by allowing spatial variability in the SMC along the gradient from host to water column . In this section we describe the model in some technical detail; readers who wish can omit this section on first reading . We consider a one-dimensional spatial gradient on the interval 0≤x≤1 , where the coral surface is at x = 0 and the mucus layer meets the water column at x = 1 . Because mucus is provided at the coral surface and lost by ablation into the water column , we conceptualize the mucus layer as a “conveyor belt” moving from coral to seawater at some constant velocity δ . The conveyor belt motion carries along microbes and substrate away from the coral surface ( i . e . , in the positive x-direction ) , but this is counteracted ( in part ) by diffusion and by active chemotactic motion of microbes . Because the relative concentrations of substrate , microbes , and antibiotics can vary from one place to another , we cannot reduce the model from four to two state variables . Thus the model tracks the S , P , B , and A as functions of space x and time t . We assume that all particles inside the mucus remain inside the mucus [47] and that no particles diffuse into the water column or through the coral surface . Particles may leave the SMC via mucus sloughing . At any fixed location x within the mucus layer ( 0<x<1 ) , the local interactions are described by the well-mixed model ( 4–5 ) , but without the supply terms IS and IP because these are “active” only at the boundaries . Added to the local interactions are transport terms representing diffusion and advection ( directed motion ) . For the substrate and antibiotics , the transport terms are random Fickian ( concentration independent ) diffusion and the “conveyer belt” motion at rate , so we have: ( 9 ) where DS , DA are the diffusion coefficients for S and A , respectively , and μA is the antibiotic degradation rate . The microbes also have diffusion and “conveyor belt” transport terms , and in addition we assume that they are positively attracted to increases in substrate concentration and ( for the pathogen ) decreases in antibiotic concentration . To represent this mathematically , we posit that the chemotactic velocity component is linearly proportional to the gradient in reproductive rate W , where W is given in our model by ( 10 ) The microbial dynamics are then ( 11 ) where the chemotaxis coefficients ηB , ηP determine how strongly the microbes respond to gradients in substrate and antibiotic concentration . Flagella are energetically expensive , are often dropped during pathogenesis [48] , and are a target of antibody responses , so we assume that microbial motility will be limited , and not much more than the minimum needed to avoid being “swept out to sea” at x = 1 through mucus ablation ( because substrate is supplied at the coral surface , attraction to substrate automatically favors motion away from x = 1 ) . The differential equations ( 9 ) and ( 11 ) apply for x between 0 and 1 , so to complete the model we need to specify what happens at the mucus layer boundaries . Here we give a brief description; see Text S2 for full details and a description of how we numerically solved the spatial model . At the water column boundary x = 1 , we expect a fairly sharp transition . This can be represented most simply by assuming that anything that reaches the end of the “conveyer belt” falls off it instantly , so the boundary at x = 1 is effectively coupled to a void from which nothing returns . We therefore impose the “absorbing” boundary conditions: ( 12 ) To allow some immigration from the water column we could set B ( 1 , t ) ≡ B1 , P ( 1 , t ) ≡ P1 with B1 , P1<<1 . For simplicity we use ( 12 ) but recognize that immigration would prevent complete extinction of either beneficial or pathogenic bacteria , as discussed in the main text . Substrate is supplied at the coral surface , which means in our “conveyor belt” model that new mucus has a high substrate concentration determined by the host . The boundary condition for substrate at x = 0 is therefore S ( 0 , t ) ≡ S0>0 . Antibiotic is neither supplied nor absorbed at the coral surface , so the appropriate boundary condition is that there be zero flux across the boundary . The same is true for the microbial populations , but a simple no-flux condition would lead to microbes piling up at the coral surface to get the most possible substrate . This is not observed , perhaps because there is increased viscosity in newly released mucus that would inhibit mobility and keep the microbes from reaching the coral surface . Schneider and Doetsch [49] observed the effect of viscosity on motility under experimental conditions , finding that motility decreased at high and low viscosities and was maximized at intermediate viscosity . Therefore , following [44] we made the boundary at x = 0 inaccessible to the microbes by having the diffusion and advection coefficients decrease smoothly to zero near the coral surface . | An important correlate in bleaching and disease in reef-building corals is a shift in the makeup of the microbial community in the mucus layer surrounding the coral . Resident microbes critical to the healthy functioning of the coral organism are outcompeted by pathogenic microbes , often species of the Vibrio bacteria , and usually in the context of environmental disruptions such as ‘heat waves’ during the warm summer months . In this study we introduce mathematical models for microbial community dynamics in the mucus layer to explore how the surface microbial community responds to changes in environmental conditions , under what circumstances it is vulnerable to pathogen overgrowth , and whether it can recover . Consistent with observations that antibiotic properties in coral mucus did not return to healthy , normal levels for many months after temperatures had fallen , we discover that the shift to pathogen dominance under transient stressful conditions may persist long after environmental conditions return to normal . | [
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] | 2010 | How Microbial Community Composition Regulates Coral Disease Development |
Many animals exploit several niches sequentially during their life cycles , a fitness referred to as ontogenetic niche shift ( ONS ) . To successfully accomplish ONS , transition between development stages is often coupled with changes in one or more primitive , instinctive behaviors . Yet , the underlining molecular mechanisms remain elusive . We show here that Leptinotarsa decemlineata larvae finish their ONS at the wandering stage by leaving the plant and pupating in soil . At middle wandering phase , larvae also switch their phototactic behavior , from photophilic at foraging period to photophobic . We find that enhancement of juvenile hormone ( JH ) signal delays the phototactic switch , and vise verse . Moreover , RNA interference ( RNAi ) -aided knockdown of LdPTTH ( prothoracicotropic hormone gene ) or LdTorso ( PTTH receptor gene ) impairs avoidance response to light , a phenotype nonrescuable by 20-hydroxyecdysone . Consequently , the RNAi beetles pupate at the soil surface or in shallow layer of soil , with most of them failing to construct pupation chambers . Furthermore , a combination of depletion of LdPTTH/LdTorso and disturbance of JH signal causes no additive effects on light avoidance response and pupation site selection . Finally , we establish that TrpA1 ( transient receptor potential ( TRP ) cation channel ) is necessary for light avoidance behavior , acting downstream of PTTH . We conclude that JH/PTTH cascade concomitantly regulates metamorphosis and the phototaxis switch , to drive ONS of the wandering beetles from plant into soil to start the immobile pupal stage .
Movements to stage-dependent resources , i . e . , ontogenetic niche shifts ( hereafter ONS ) , occur in nearly 80% of animal taxa . The shifts enable animals to exploit several niches sequentially during their life cycles to meet stage-dependent nutritional requirements , to overcome stage-specific physiological limitations , and to reduce intraspecific competition between juveniles and adults . ONS is thus widely accepted as an evolutionary adaptation [1–4] . To successfully finish ONS , transition between development stages is often accompanied with changes in one or more primitive , instinctive behaviors , allowing inexperienced novices to obtain novel abilities to detect new environmental cues [5] . To date , however , the underlining mechanisms driving these behavior switches are still largely unexplored . Insects are a suitable animal group to explore the molecular modes of these instinctive behavioral switches . Throughout developmental excursion , most insects ( Holometabolans ) undergo four discrete periods ( complete metamorphosis ) , characterized by the presence of a pupal stage between a feeding larva and a reproducing adult [6 , 7] . Sessile pupae are vulnerable to potentially harmful factors such as desiccation , predation , parasitism and pathogen infection . These latent mortal dangers drive a lot of Holometabolans shifting into less risky habitats for pupation [8–12] . For example , pupation in soil and other relatively inaccessible sites for predators and parasitoids ( concealed placement ) has been documented in almost all Holometabolan orders [8 , 12–15] . Insect final instar larval stage is divided into two sub-stages , the foraging and the wandering phases . While foraging final instar larvae display generally similar behaviors like previous instar animals , a wandering larva typically undergoes an ONS by leaving the food source and moving to a proper pupation site [16] . Obviously , positive phototaxis directs most foraging insect herbivores to reach plant top for tender plant tissues such as shoots , young leaves , buds and flowers that are more nutritious and less defended [17] , whereas negative phototaxis facilitates the wandering larvae to reach pupation refuge in the dark , such as soil [5] . Accordingly , it can be reasonably hypothesized that the change from foraging to wandering stages should be coupled with a switch for phototactic behavior from photophilic to photophobic in most herbivorous Holometabolans . For a soil-pupated insect species , a wandering larva usually shows a sequence of three primary behavioral components before pupation: a ) leaving the food , crawling to the ground and searching for a suitable location , b ) mining into soil , and c ) building a pupation chamber in soil [18–20] . Ecdysteroids ( the major active component is 20-hydroxyecdysone , 20E ) , the products in a pair of prothoracic glands ( PGs ) , activate the wandering behavior [18 , 21–23] . Up to now , however , the molecular mechanism elicits the phototaxis switch in insect herbivores remains elusive . In Drosophila melanogaster , larval phototaxis and behavioral responses have been described [5 , 24–27] . Unfortunately , photophobic is age-independent in the larvae [5 , 24] . At the foraging stage , a Drosophila larva feeds inside rotting fruits; it is strongly repelled by light and seeks for dark or less light-exposed surroundings [5 , 25–27] . Two pairs of neurons called NP394 ( each pair in a hemisphere of central brain ) are required to maintain light avoidance in the foraging phase . Modulating activity of NP394 neurons affects larval light preference [28] . The NP394 neurons turn out to be prothoracicotropic hormone ( PTTH ) -expressing cells [5] . These two pairs of PTTH-producing neurons release PTTH to concomitantly promote steroidogenesis and light avoidance during wandering stage of the final instar larvae [5] . On one hand , PTTH , through its receptor Torso , activates a canonical mitogen activated protein kinase ( MAPK ) pathway to trigger ecdysteroidogenesis by PGs to regulate metamorphosis [29–31] . On the other hand , PTTH/Torso complex acts on two light sensors , the Bolwig’s organ ( a group of 12 photoreceptors in the larval eye ) and the peripheral class IV dendritic arborization neurons , to reinforce light avoidance [5] . The young larvae of the Colorado potato beetle Leptinotarsa decemlineata , a notorious insect defoliator of potatoes , reveal a tendency to rest and feed on the upper surfaces of leaves during foraging stage [32] . At the late stage of the final ( fourth ) instar , conversely , the wandering larvae bury themselves into soil , where , after several days , they metamorphose into pupae [33 , 34] . Moreover , by RNA interference ( RNAi ) , we have identified major components of the hormonal network that regulates larval metamorphosis in L . decemlineata [33–41] . This offers a great opportunity to explore the molecular mode driving the phototaxis switch in an insect herbivore . The first aim of the current study was to determine whether Leptinotarsa larvae changed their light preference from photophilic to photophobic during wandering stage . We then uncovered that juvenile hormone ( JH ) /PTTH cascade concomitantly regulated metamorphosis and the phototaxis switch . Finally , we provided clear evidence that PTTH-induced light avoidance drove ONS from plant to pupation refuge in an insect herbivore .
We first observed phototaxis of Leptinotarsa beetles on the natural potato field . The females unselectively deposited their egg masses on upper and lower leaf surfaces ( for a total of 100 egg masses 48 and 52 respectively on upper and lower surfaces , P>0 . 05 for χ2 test ) at the inner part of the potato canopy ( S1A and S1B Fig ) . Aggregated hatchlings consumed foliage near the egg mass from which they hatched ( S1C Fig ) . All the second- , third- and foraging fourth-instar larvae were found to feed and rest on the upper surfaces of the potato leaves ( S1D–S1F Fig ) . These larvae molted under sunny lighting condition ( S1F Fig ) . During the wandering larval stage from approximately 4 . 1 to 7 . 0 days after ecdysis to the fourth instar ( S1G–S1I Fig ) , the animals left the potatoes and crawled to the ground , walked along the ground ( 4 . 5–5 . 5 days post ecdysis ) , dug into soil , constructed their pupation chambers and pupated therein ( 5 . 6–7 . 0 days ) ( S1G–S1I Fig ) . When given a choice in the laboratory , second-instar , third-instar and foraging fourth-instar larvae prefer light-exposed over shaded areas ( Fig 1 ) . For wandering Leptinotarsa larvae , however , light avoidance index significantly increased at 4–5 days post ecdysis , and reached nearly 1 . 0 at 5 . 5 days ( Fig 1 ) . These basic findings demonstrate that an obvious phototactic switch occurs at the middle wandering period . In the present paper , we intended to knock down target gene to study molecular modes underlining phototaxis switch using dietary dsRNA treatments . Although our previous results revealed that ingestion of dsRNA can silence target genes in various tissues including neurons , PGs and the corpora allata that producing JH [33–41] , we have not compared the RNAi efficacy of dsRNA ingestion with that of dsRNA injection . Here we determined time-effect and concentration-effect curves of two genes , a nutrient amino acid transporter gene LdNAT1 that is mainly expressed in gut [42] and a transient receptor potential cation channel gene LdTrp that is highly transcribed in eyes [43] . Our results showed that injected and fed dsRNAs could reduce approximately 90% of LdNAT1 and LdTrp mRNAs 24 and 36 hours after treatment ( S2A and S2B Fig ) . Moreover , RNAi efficacies are dose-dependent , no matter the dsRNAs are introduced by ingestion ( S2C and S2D Fig ) or injection ( S2E and S2F Fig ) . In L . decemlineata , transition from foraging to wandering phases is associated with drastic level fluctuations of three larval hormones: 20E , insulin-like peptide ( ILP ) and JH [33 , 34 , 36 , 40 , 44] . Are these hormone signaling cascades involved in the regulation of phototaxis switch ? In the final larval instar of L . decemlineata , a small 20E rise appears at 4 days after ecdysis [42] . Here , we found that dietary supplement with 20E to generate a premature 20E peak at the foraging stage ( Fig 2A ) , or knockdown of an ecdysteroidogenesis gene ( Shade , SHD ) [38] to remove this 20E rise ( Fig 2B and S3A Fig ) , did not affect the phototaxis switch , neither did silencing of a 20E receptor gene EcR ( Fig 2C and S3B Fig ) or a 20E cascade gene HR3 ( Fig 2D and S3C Fig ) to eliminate 20E signal . Conversely , wandering behavior occurred at 4 . 5–4 . 7 days after ecdysis ( S1 Table ) , ingestion of 20E accelerated the onset of wandering behavior , whereas interruption of 20E signal retarded the onset ( S1 Table ) . This piece of clear evidence demonstrates that different signal cascades respectively regulate the light preference and the onset of wandering behavior . Cessation of feeding decreased the contents of several nutrients in the larval hemolymph and thereby reduced ILP level during wandering stage [36] . Here we found that premature insulin deficiency brought about by knockdown of ILP2 ( S3D Fig ) had little effect on light avoidance ( Fig 2E ) , but delayed the onset of wandering ( S1 Table ) . At the wandering stage , JH titer obviously decreased [41] , and the expression of JH degradation genes were activated [39] . Here we found that ingestion of JH ( Fig 2F ) , or knockdown of an allatostatin gene ( allatostatin C , AS-C ) [41] or either of two JH degradation genes ( JH epoxide hydrolase , JHEH1; JH diol kinase , JHDK ) [35 , 39] to delay JH decrease significantly reduced light avoidance ( Fig 2G and S3E–S3G Fig ) and postponed the occurrence of wandering ( S1 Table ) ; whereas knockdown of a JH biosynthesis gene ( JH acid methyl transferase , JHAMT ) [34] and a JH receptor gene ( methoprene-tolerant , Met ) ( to decrease the accumulated proteins ) to prematurely reduce JH signal enhanced light avoidance ( Fig 2H and S3H and S3I Fig ) and accelerated the onset of wandering ( S1 Table ) . It is clear that JH signal concomitantly inhibits the premature switch of light preference and the early onset of wandering . Providing elimination of JH is a prerequisite for successful PTTH release and signal transduction [45] , we determined the expression levels of two PTTH signaling genes ( LdPTTH and LdTorso ) in the larvae whose JH signal had been disturbed . As expected , the mRNA levels of the two genes were reduced in the larval specimens whose JH signal had been enhanced ( S4A–S4D and S5A–S5D Figs ) , and were increased in the larval samples in which JH signal had been repressed ( S4E , S4F , S5E and S5F Figs ) . It is suggested that PTTH promotes larval light avoidance in L . decemlineata . We next knocked down PTTH gene ( Fig 3A ) , and verified the knockdown by determination of the mRNA level of an ecdysteroidogenesis gene ( LdPHM ) and 20E titer in the resultant larval specimens ( Fig 3B ) . The pupation rate was decreased and the development time was lengthened in LdPTTH RNAi ( and PTTH depleted ) larvae; dietary supplement with 20E can rescue the two phenotypes ( Fig 3C and S1 Table ) . Moreover , silencing of PTTH reduced avoidance response to light ( Fig 3D ) , rate of larvae that had buried in soil per day ( RLB ) ( Fig 3E ) and accumulated RLB ( Fig 3F ) . Approximately 20% of wandering larvae excavated only a slight depression at the soil surface and pupated ( Fig 3G and 3H . Please note , photo in Fig 3G was collected from a separate experiment ) . Furthermore , the remaining around 80% LdPTTH RNAi larvae pupated at shallow layer of soil ( Fig 3I ) ; most of them did not construct pupation chambers ( Fig 3J ) . Dietary supplement with 20E could not alleviate the reduced light avoidance response and the negative influences on pupation in LdPTTH depleted larvae ( Fig 3D–3J ) . We repeated the bioassay by knockdown of another PTTH pathway gene , LdTorso [46] , and obtained similar results ( S5 Fig and S1 Table . Please note , photo in S5M Fig was collected from a separate experiment ) . We then examined the relative mRNA levels of a JH biosynthesis gene LdJHAMT and two JH signaling pathway genes ( LdMet and LdKr-h1 ) , and found that the levels of these genes varied little in LdPTTH or LdTorso RNAi larvae when measured 1 and 2 days post ecdysis to fourth-instar larvae ( S4G and S5Q–S5S Figs ) . We further investigated the combination effects of PTTH and JH signaling pathways on phototaxis switch by depletion of LdPTTH and disturbance of JH signal ( Fig 4 and S6 Fig , S1 Table ) . We found no additive effect on avoidance response to light ( Fig 4A–4D ) , accumulated rate of larvae in soil ( Fig 4E–4H ) and rate of pupae on the soil ( Fig 4I–4L ) by knockdown of LdPTTH and enhancement of JH signal ( an addition of JH , or RNAi of LdAS-C ) , or silencing of LdPTTH and reduction of JH titer ( RNAi of LdJHAMT ) or JH signal ( silencing of LdMet ) . Knockdown of LdTorso and disturbance of JH pathway mimicked the combination effects on the larval phototaxis switch ( S7 Fig ) . These results show that JH and PTTH act on the same pathway to promote light avoidance . Our previous results showed that four PTTH/Torso cascade genes were highly or moderately expressed in the brain [46] , suggesting that PTTH may act on neuronal cells to control light avoidance . Consistent with the suggestion , we found that the relative mRNA levels of LdTrpA1 that encodes transient receptor potential ( TRP ) cation channel in Drosophila [47] were significantly decreased in LdPTTH and LdTorso depleted larvae . In contrast , the levels of LdRh5 ( the opsin gene involved in light avoidance behavior in Drosophila ) [28] and LdGr28b ( a gustatory receptor family gene that plays an opsin-like role in class IV da neurons in Drosophila ) [48] were not affected ( S8 Fig ) . It appears that PTTH/Torso exerts its action downstream of the photoreceptors , and upstream of TrpA1 channel activation . Accordingly , we observed depletion of LdTrpA1 reduced light avoidance response , and the accumulated number of larvae in soil , and increased the rate of pupae on the soil . Moreover , knockdown of both LdTrpA1 and LdPTTH , or LdTrpA1 and LdTorso showed no additive effects ( Fig 5 and S9 Fig ) ( we dietarily supplemented 20E in all treatment to relieve the effect of knockdown on pupation and development time ) . Conversely , knockdown of LdTrp did not affect light avoidance response ( S10 Fig ) . This provides strong evidence that PTTH/Torso signaling cascade activates a step in photo transduction between the photoreceptor molecule and the TRP channel .
PTTH is synthesized in two pairs of dorsolateral neurosecretory cells in the brain and transported to the corpora allata ( CA ) , an endocrine organ that produces JH , by axons running through the contralateral hemisphere of the brain . PTTH is secreted into the hemolymph from arborized axon endings in the CA . The release of PTTH is negatively controlled by JH during the early stages of the last larval instar [29–31] . Similarly , our recent results revealed that JH signal plays an inhibitive role on PTTH production and release in Leptinotarsa larvae [45] . In this survey , we provide another three compelling pieces of evidence to support the conclusion . Firstly , we found reduced transcription levels of the two PTTH signaling genes were correlated with enhanced JH signal , and vise verse ( S4 and S5 Figs ) . Secondly , JH signal concomitantly inhibited the premature switch of light preference and the early onset of wandering ( Fig 2 , S2 Table ) , two behaviors can be elicited by PTTH signaling [5 , 18 , 21] . Lastly , no additive effects on light avoidance response and pupation were observed by simultaneous inhibition of PTTH pathway and disturbance of JH signal ( Fig 4 ) . Insects such as L . decemlineata , Manduca sexta , D . melanogaster and Trichoplusia ni , must reach critical weight before larval-pupal transition [45 , 49–51] . When premature metamorphosis occurs below this weight , individuals tend to burden disproportionately high costs [49–52] . Therefore , it can be reasonably proposed that the presence of JH prevents premature PTTH release , and allows insect to obtain species-specific body size , before the onset of wandering and the switch of light preference . In L . decemlineata , the final-instar larvae obtained their maximum fresh weights at approximately 84 hours after ecdysis [45] . At this time the JH should be completely removed , just as that in M . sexta [49] . The elimination of JH allows the brain to release PTTH . Therefore , the release of PTTH occurs at approximately 84 hours after ecdysis , i . e . , around 12 hours before the small 20E rise at day 4 [44] . In this study , we found the wandering behavior occurred on 4 . 5–4 . 7 days ( S1 Table ) . The latency between appearance of 20E and the onset of wandering is approximately 12–15 hours in Leptinotarsa , similar to the dormancy time in Manduca [22 , 23] . Accordingly , it can be estimated that the latency time from the release of PTTH to the occurrence of wandering is around a day . Moreover , we found here that the phototaxis switch had finished on day 5 . 5–6 . 0 post ecdysis ( Fig 1 ) . The latency time from the release of PTTH to the phototaxis switch is around two day . The same dormancy time was noted from LdPTTH or LdTorso RNAi larvae , the enzymatic removal of dsRNA caused the restoration of the mRNA levels of LdPTTH or LdTorso 5–6 days after initiation of dsRNA ingestion ( S2 Fig ) and the re-activation of transcription of PTTH and Torso finally occurred hereafter; high level of mRNAs were tested at 7 days after ecdysis ( Fig 3 and S5 Fig ) . This means that functional PTTH/Torso proteins are produced before 7 days after ecdysis . Therefore , the PTTH and Torso RNAi larvae are drove to burrow into soil around 2 days after functional PTTH/Torso proteins are produced , with peaked RLB values at about 8 days ( Fig 3 and S5 Fig ) . In contrast , PTTH in Drosophila was secreted into the hemolymph and reached two light sensors , the Bolwig’s organ and the peripheral class IV dendritic arborization neurons involved in light avoidance . Inactivation of PTTH-expressing neurons affected light avoidance with 8 to 10 hours delay [5] . In this study , we transferred L . decemlineata final-instar larvae to soil at 4 days post ecdysis ( the small 20E rise occurs within this day [44] ) . During the period from the appearance of wandering behavior to the occurrence of phototaxis switch , the Leptinotarsa wandering larvae kept crawling at the soil surface; they did not burrow into soil ( Fig 3 and S5 Fig ) until the phototaxis switch had finished ( Fig 1 ) . Therefore , PTTH-induced phototaxis switch drives Leptinotarsa larvae to burrow into soil . From an ecological point of view , it seems an important evolutionary fitness for wandering Leptinotarsa larvae to crawl at the soil surface for an average of 24 hours before the switch of phototaxis . During the 24 hours’ crawling period , the larvae walk a long distance away from damaged plants to complete ONS before pupation . Since herbivore-induced plant volatiles emitted by damaged plants [53 , 54] attract natural enemies , maximizing distance from damaged plants prior to pupating increases the likelihood of survival . Therefore , respective regulation of the onset of wandering and the switch of phototaxis by two distinct PTTH signal branches may be a molecular evolutionary approach for insect herbivores to shift into less risky habitats for pupation . Conversely , a portion of Drosophila larvae begin wandering at 108 hours after egg laying ( AEL ) , and almost all the larvae enter wandering stage at 120 hours AEL . Comparably , some larvae start to pupariate at 108 hours AEL and almost all the larvae finish pupariation at 120 hours AEL [5] . This finding demonstrates that Drosophila larvae immediately form puparium even at the very beginning of the wandering period when they find an appropriate pupariation site . Consistent with the finding , the interval between PTTH release and the reinforce of light avoidance was 8 to 10 hours , while the latency time of 20E release and the occurrence of wandering is approximately 4–6 hours [55] . Considering the latency between PTTH release and ecdysone release in PGs , it is obvious that two PTTH-induced signal branches simultaneously trigger wandering behavior and reinforce light avoidance in Drosophila larvae . Therefore , other cues rather than phototaxis switch decide where wandering Drosophila larvae pupariate . Actually , it is believed that hydrotaxis ( seeking for not moisture environment ) drives wandering Drosophila larvae to leave the food source and find a suitable pupation site [56] . It is well known that ecdysteroidogenesis in PGs are redundantly regulated by several tropic signals , such as PTTH , ILP , target of rapamycin , transforming growth factor-β/Activin and nitric oxide signals [57] . In agreement with this accepted notion , our results revealed that the wandering behavior was still activated in PTTH and Torso RNAi larvae , with a retardation of about a day ( S1 Table ) . However , the reduced PTTH signal cannot trigger the light avoidance behavior to drive LdPTTH and LdTorso RNAi larvae to mine into soil; the beetles keep crawling for a longer period of time compared with controls ( Fig 3 and S5 Fig ) . Due to extended crawling period , PTTH and Torso RNAi beetles have less time to tunnel into soil and build their pupation chambers before the big 20E peak that elicits pupation [42] . As a result , we found in this survey that PTTH or Torso RNAi larvae pupated at the soil surface or at shallow layer of soil , with unfinished pupation chambers . Similarly , Manduca larvae can construct their pupation cells when they are placed in the observation chambers during the first 20 hours of wandering . At 30 hours , larvae begin to lose their ability to complete the pupation cell . By 35–40 hours , the larvae dig only a slight depression at the soil surface before pupation [58] . Accordingly , we argue that relative constant time interval between the onset of wandering and the switch of phototaxis is crucial for an insect herbivore to accomplish ONS during the final instar to correct pupation site , a trait potentially beneficial for ecological selection . If the interval is too short , the wandering larvae have no time to choose less risky habitats for pupation . If it is too long , the wandering insects have no time to tunnel into soil and construct chambers before pupation . JH/PTTH signaling is thus at the core of a hormonal network that coordinates developmental progression and appropriate phototactic behavior to maximize insect fitness . We propose a model summarizing these findings ( Fig 5E ) . Although the conclusions are drawn using feeding-based RNAi knockdown and pharmacological application of hormones in whole beetle , many important genes involved in this survey such as PTTH , Torso , most ecdysteroidogenesis genes , AS-C and JHAMT , are only expressed in specific neurons or endocrine organs . Knockdown of these genes in whole animal only affects these neurons and endocrine organs . As a result , our findings are comparable to those from Drosophila [5 , 28] , even though many manipulations are done at cellular levels in the fly . It seems that PTTH-droved ( reinforced in Drosophila ) light avoidance is a conserved trait to facilitate the wandering larvae to find suitable pupation sites . As a result , RNAi of the related genes makes the juveniles to be exposed to latent mortal dangers [8–12] , and is a potential dsRNA-based method to control the agricultural pest .
The L . decemlineata beetles were kept in an insectary according to a previously described method [59] , with potato foliage at the vegetative growth or young tuber stages in order to assure sufficient nutrition . At this feeding protocol , the larvae progressed through the first , second , third , and fourth instars at an approximate period of 2 , 2 , 2 and 4 days , respectively . In a 15-hectare potato field located at Urumqi city ( 43 . 82 N , 87 . 61 E ) in the Xinjiang Uygur autonomous region of China , 100 egg masses were selected randomly and marked along a diagonal line in June 15 , 2017 . The development was observed and recorded at an interval of 4 hours ( at night , the larvae were observed under red light ) until all the larvae left the plants . The same method as previously reported [5 , 27 , 28] was used to test light avoidance of the larvae , with slight modifications . To synchronize the developing stage , newly-ecdysed larvae ( the second through fourth instar larvae ) were collected at an interval of 4 hours , and determined light avoidance at specific developing stage and different treatment , according to the experimental schedule ( see Fig legend for detail ) . Five larvae were subjected to 20 and 30-min phototaxis assay in a Petri dish ( 9 cm diameter and 1 . 5 cm height , half of which is covered with black paper ) which was illuminated from above using a white LED light at 500 lux ( a continuous range of radiated wavelengths from 400 to 700 nm , peak at 470 nm ) . The larvae were placed on the center spot along the junction line between light and dark , and the larvae on which half was recorded after 20 and 30 min at a constant temperature of 25°C . Ten larvae were set as a repeat , the assay repeated six times , a total of 60 larvae were determined for each instar . A steady state was reached after 20 min and we did not find any difference in the results after 20 or 30 min . The following formula for Light Avoidance Index was used: LightAvoidanceIndex= ( numberoflarvaeindark–numberoflarvaeinlight ) / ( totalnumberoflarvae ) . For bacterially expressed dsRNA , specific primers used to clone dsRNAs were listed in S2 Table . These dsRNAs were from the fragments of genes lined in Data Accessibility . They were individually expressed using Escherichia coli HT115 ( DE3 ) competent cells lacking RNase III following the established method [60] . Individual colonies were inoculated , and grown until cultures reached an OD600 value of 1 . 0 . The colonies were then induced to express dsRNA by addition of isopropyl β-D-1-thiogalactopyranoside to a final concentration of 0 . 1 mM . The expressed dsRNA was extracted and confirmed by electrophoresis on 1% agarose gel . Bacteria cells were centrifuged at 5000 ×g for 10 min , and resuspended in an equal original culture volume of 0 . 05 M phosphate buffered saline ( PBS , pH 7 . 4 ) . The bacterial solutions ( at a dsRNA concentration of about 0 . 5 μg/ml ) were used for experiment . For lab-synthesized dsRNA , LdNAT1 and LdTrp fragments were amplified by PCR using specific primers conjugated with the T7 RNA polymerase promoter ( primers listed in S2 Table ) . The dsRNA originated from each of the above-mentioned sequences was synthesized using the MEGAscript T7 High Yield Transcription Kit ( Ambion , Austin , USA ) according to the manufacturer's instructions . Subsequently , the synthesized dsRNA was determined by agarose gel electrophoresis and the Nanodrop 1000 spectrophotometer and kept at -70 °C until use . The same method as previously reported [60] was used to individually introduce bacterially expressed dsRNAs listed in S1 Table into larvae . Potato leaves were immersed with a bacterial suspension containing a dsRNA for 5 s , removed , and dried for 2 h under airflow on filter paper . The PBS- and dsegfp ( enhanced green fluorescent protein ) -dipped leaves were used as controls . Five treated leaves were then placed in Petri dishes ( 9 cm diameter and 1 . 5 cm height ) . For knockdown of LdSHD , LdEcR and LdHR3 , the newly-ecdysed fourth-instar larvae were used . For other dsRNA feeding bioassays , the newly-ecdysed third-instar larvae were used . The larvae were starved for at least 4 h prior to the experiment . Then , ten larvae were transferred to each dish as a repeat . The larvae were allowed to feed treated foliage for 3 days ( replaced with freshly treated ones each day ) , and were transferred to untreated foliage if necessary . For lab-synthesized dsRNA , it was injected ventrally between two segments of the abdomen of the newly-ecdysed forth-instar larvae with 6 μl of dsRNA . For testing time-effect curve , 50 ng of dsNAT1 or dsTrp was injected into hemolymph . For measuring concentration-effect curve , each dsRNA was injected at a dose of 100 . 0 , 25 . 0 , 6 . 3 and 1 . 3 ng . 20-Hydroxyecdysone ( 20E ) ( Sigma-Aldrich , USA ) and juvenile hormone ( JH ) ( Sigma-Aldrich , USA ) were respectively dissolved in distilled water with added surfactant ( Tween 20 , 1 g/L ) to obtain two solutions at the concentration of 10 ng/mL . Potato leaves were dipped with 20E or JH solution . 20E supplemented leaves were provided at day 3 fourth-instar larvae , whereas JH supplemented leaves were offered at newly-ecdysed fourth-instar stage . The larvae were allowed to feed the foliage for a day . Total RNA was extracted from treated and control larvae . Each sample contained 5–10 individuals and repeated three times . The RNA was extracted using SV Total RNA Isolation System Kit ( Promega ) . Purified RNA was subjected to DNase I to remove any residual DNA according to the manufacturer’s instructions . Quantitative mRNA measurements were performed by qRT-PCR in technical triplicate , using 4 internal control genes ( LdRP4 , LdRP18 , LdARF1 and LdARF4 , see S1 Table ) according to our published results [59] . An RT negative control ( without reverse transcriptase ) and a non-template negative control were included for each primer set to confirm the absence of genomic DNA and to check for primer-dimer or contamination in the reactions , respectively . According to a previously described method [61] , the generation of specific PCR products was confirmed by gel electrophoresis . The primer pair for each gene was tested with a 10-fold logarithmic dilution of a cDNA mixture to generate a linear standard curve ( crossing point [CP] plotted vs . log of template concentration ) , which was used to calculate the primer pair efficiency . All primer pairs amplified a single PCR product with the expected sizes , showed a slope less than -3 . 0 , and exhibited efficiency values ranging from 2 . 0 to 2 . 1 . Data were analyzed by the 2-ΔΔCT method , using the geometric mean of the four internal control genes for normalization . 20E was extracted according to a ultrasonic-assisted extraction method [40] , and its titer ( ng per g body weight ) was analyzed by an LC tandem mass spectrometry-mass spectrometry ( LC-MS/MS ) system using a protocol the same as described [62] . Hemolymph was collected and JH was extracted following the methods described previously [60] . LC-MS was used to quantify JH titers ( ng per ml hemolymph ) [63] . The data were given as means ± SE , and were analyzed by analyses of variance ( ANOVAs ) followed by the Tukey-Kramer test , using SPSS for Windows ( SPSS , Chicago , IL , USA ) , or t test . Light preference index , light avoidance index ( LAI ) , rate of larvae that had buried in soil per day ( RLB ) , accumulated RBP ( ARLB ) , rate of pupae on soil ( RPS ) , and rate of pupae that had constructed pupation chambers ( RPC ) were subjected to arc-sine transformation before ANOVAs . | Many animals occupy distinct niches and utilize diverse resources at different development stages in order to meet stage-dependent requirements and overcome stage-specific limitations . This fitness is referred to as ontogenetic niche shift ( ONS ) . During the preparation for ONS , animals often change one or more primitive , instinctive behaviors . Holometabolous insects , with four discrete developmental periods usually in different niches , are a suitable animal group to explore the molecular modes of these behavioral switches . Here we find that Leptinotarsa decemlineata larvae , an insect defoliator of potatoes , switch their phototactic behavior , from photophilic at feeding period to photophobic during the larval-pupal transition ( wandering stage ) . This phototactic switch facilitates the wandering larvae to accomplish the ONS from potato plants to their pupation sites below ground . We show that JH/PTTH cascade controls the phototaxis switch , through a step in photo transduction between the photoreceptor molecule and the transient receptor potential cation channel . | [
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] | 2019 | Hormonal signaling cascades required for phototaxis switch in wandering Leptinotarsa decemlineata larvae |
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