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Store-operated Ca2+ entry ( SOCE ) has been associated with two types of channels: CRAC channels that require Orai1 and STIM1 and SOC channels that involve TRPC1 , Orai1 , and STIM1 . While TRPC1 significantly contributes to SOCE and SOC channel activity , abrogation of Orai1 function eliminates SOCE and activation of TRPC1 . The critical role of Orai1 in activation of TRPC1-SOC channels following Ca2+ store depletion has not yet been established . Herein we report that TRPC1 and Orai1 are components of distinct channels . We show that TRPC1/Orai1/STIM1-dependent ISOC , activated in response to Ca2+ store depletion , is composed of TRPC1/STIM1-mediated non-selective cation current and Orai1/STIM1-mediated ICRAC; the latter is detected when TRPC1 function is suppressed by expression of shTRPC1 or a STIM1 mutant that lacks TRPC1 gating , STIM1 ( 684EE685 ) . In addition to gating TRPC1 and Orai1 , STIM1 mediates the recruitment and association of the channels within ER/PM junctional domains , a critical step in TRPC1 activation . Importantly , we show that Ca2+ entry via Orai1 triggers plasma membrane insertion of TRPC1 , which is prevented by blocking SOCE with 1 µM Gd3+ , removal of extracellular Ca2+ , knockdown of Orai1 , or expression of dominant negative mutant Orai1 lacking a functional pore , Orai1-E106Q . In cells expressing another pore mutant of Orai1 , Orai1-E106D , TRPC1 trafficking is supported in Ca2+-containing , but not Ca2+-free , medium . Consistent with this , ICRAC is activated in cells pretreated with thapsigargin in Ca2+-free medium while ISOC is activated in cells pretreated in Ca2+-containing medium . Significantly , TRPC1 function is required for sustained KCa activity and contributes to NFκB activation while Orai1 is sufficient for NFAT activation . Together , these findings reveal an as-yet unidentified function for Orai1 that explains the critical requirement of the channel in the activation of TRPC1 following Ca2+ store depletion . We suggest that coordinated regulation of the surface expression of TRPC1 by Orai1 and gating by STIM1 provides a mechanism for rapidly modulating and maintaining SOCE-generated Ca2+ signals . By recruiting ion channels and other signaling pathways , Orai1 and STIM1 concertedly impact a variety of critical cell functions that are initiated by SOCE .
Store-operated Ca2+ entry ( SOCE ) is activated in response to a reduction of [Ca2+] in the ER . SOCE generates local and global [Ca2+]i signals that regulate a wide variety of cellular functions [1] , [2] . The first store-operated Ca2+ channel to be characterized , the Ca2+ release-activated Ca2+ ( CRAC ) channel , has a high selectivity for Ca2+ versus Na+ and displays a typical inwardly rectifying current-voltage relationship . CRAC channel accounts for the SOCE in lymphocytes and mast cells [3]–[6] and has recently been detected in some other cell types [7]–[9] . Key molecular components of the channel are STIM1 and Orai1 . STIM1 is an ER Ca2+ binding protein that has been established as the primary regulator of SOCE [10]–[12] . In response to store depletion STIM1 oligomerizes and translocates to ER/PM junctional domains where it aggregates into puncta . The site of these aggregates is the location where STIM1 interacts with and activates channels involved in SOCE [13]–[15] . Orai1 is the pore-forming subunit of the CRAC channel [16]–[18] . Following store depletion , Orai1 , which is localized diffusely in the plasma membrane in resting cells , is recruited by STIM1 into the puncta and gated by interaction with a C-terminal region of STIM1 [19] , [20] . While expression of this STIM1-domain induces spontaneous CRAC channel activation in extra ER/PM junctional domains , the site of the STIM1 puncta represents the cellular location where endogenous SOCE is activated by store depletion [21] . Store depletion also leads to activation of relatively non-selective Ca2+-permeable cation channels , usually referred to as SOC channels , that have been associated with SOCE in several other cell types [2] , [22]–[25] . Despite more than a decade of studies , the molecular components of these channels have not yet been established and their function and regulation remain somewhat controversial . TRPC channels have been proposed as molecular components of SOC channels . Data in this regard are strongest for TRPC1 [2] , [26]–[34] although TRPC3 and TRPC4 also appear to contribute to SOCE in some cell types [23] , [25] , [35]–[38] . Numerous studies show that disruption of TRPC1 attenuates SOCE and SOCE-dependent cell function [23] , [26]–[34] . We have previously provided extensive data to demonstrate that TRPC1 is a critical component of SOC channels and SOCE in the human salivary gland cell line , HSG [30] , [39]–[42] . Further , salivary gland acinar cells from TRPC1−/− mice display reduced SOCE and SOC channel activity , which account for loss of sustained KCa activation and , consequently , salivary fluid secretion [29] . However , the role of TRPC1 in SOCE has been questioned based on the lack of function of heterogously expressed channels [43] . Further , some tissues from TRPC1−/− mice do not display any changes in SOCE [44] , [45] . The strongest evidence for the regulation of TRPC1 following store depletion has been provided by data demonstrating that STIM1 interacts with and activates TRPC1-SOC channels in response to Ca2+ store depletion [39] , [42] , [46] . SOC channels are attenuated by knockdown of endogenous STIM1 and spontaneously activated by expression of the STIM1 mutant , D76ASTIM1 [42] , [46] . An important study showed that TRPC1 is gated by electrostatic interaction between STIM1 ( 684KK685 ) and TRPC1 ( 639DD640 ) [47] . An intriguing finding is that STIM1 alone is not sufficient for activation of TRPC1-SOC channels following Ca2+ store depletion . Functional Orai1 is also required since knockdown of Orai1 or expression of functionally deficit Orai1 mutants prevents TRPC1 activation [39] , [48] . We have shown earlier that store depletion leads to the recruitment of a TRPC1/STIM1/Orai1 complex that is associated with the activation of SOCE [39] , [42] . Thus , while STIM1 is the primary protein involved in SOC channel gating , both TRPC1 and Orai1 appear to contribute to SOC channel activity . There has been much debate about the essential role of Orai1 in TRPC1-SOC channel function and more specifically regarding whether TRPC1 and Orai1 contribute to a single SOC channel pore or whether Orai1 is a regulatory subunit of SOC channels . In this study we have assessed the critical role of Orai1 in regulation of TRPC1 function following intracellular Ca2+ store depletion and determined the contributions of TRPC1 and Orai1 to SOCE . We report that TRPC1 and Orai1 constitute two distinct channels that contribute to SOCE in HSG cells . Suppression of TRPC1 function unmasks the underlying CRAC channel function . Further , in response to store depletion , STIM1 mediates association of Orai1 and TRPC1 within ER/PM junctional domains . Ca2+ entry via Orai1/STIM1-CRAC channel triggers plasma membrane insertion of TRPC1 and gating is achieved by interaction with STIM1 ( 684KK685 ) residues . Remarkably , while both Orai1 and TRPC1 contribute to [Ca2+]i increase following store depletion , they impact different cellular functions . Ca2+ entry mediated by TRPC1 is the primary regulator of KCa channel and partially contributes to NFκB activation while Orai1-mediated Ca2+ entry alone is sufficient for maximal NFAT activation and partial NFκB activation . Together these findings reveal the molecular events that determine activation of TRPC1 channels following store depletion . We suggest that local Ca2+ entry mediated by Orai1 determines plasma membrane insertion of TRPC1 while gating by STIM1 controls its activation . Thus , Orai1 and STIM1 not only determine Ca2+ signals generated by CRAC channels but by regulating TRPC1 channel activity rapidly modulate [Ca2+]i and thus significantly impact various cell functions .
Compared to SOCE in control HSG cells ( transfected with vector or scrambled siRNA; black traces in Figure 1 ) , knockdown of endogenous Orai1 , STIM1 , or TRPC1 attenuated thapsigargin ( Tg ) -stimulated Ca2+ influx by >90% , >80% , or >60% , respectively ( Figure 1A ) . These conditions did not significantly affect internal Ca2+ release . Western blots ( Figure S1A ) demonstrate the effectiveness of TRPC1 knockdown in these cells . Ca2+ entry induced by Tg treatment of HSG cells was blocked by 1 µM Gd3+ and 20 µM 2APB ( Figure S1B ) . Further , expression of TRPC1 , TRPC1+STIM1 , Orai1+STIM1 , or TRPC1+STIM1+Orai1 increased Tg-stimulated Ca2+ entry ( Figure S1G ) , which was also blocked by 1 µM Gd3+ and 20 µM 2APB ( Figure S1C–F ) . Together , these data are consistent with our previous studies [42] that Orai1 , STIM1 , and TRPC1 contribute to endogenous SOCE in HSG cells . Additionally , the contributions of TRPC1 , STIM1 , and Orai1 to SOCE were not dependent on the level of stimulation ( Figure S2 ) . The relative decrease in SOCE induced by individual knockdown of the three proteins was similar in cells stimulated with 100 µM carbachol ( CCh , a maximal stimulatory concentration ) or 1 µM CCh ( submaximal stimulatory concentration ) . The contribution of TRPC1 and Orai1 to SOCE in HSG cells was further examined by using whole cell patch clamp technique [2] , [16] , [17] , [40] to record the current generated by intracellular Ca2+ store depletion ( Figure 1B ) . Consistent with our previous findings , Tg stimulation of cells resulted in activation of ISOC in HSG cells that is distinct from the typical ICRAC currents measured in RBL cells and T lymphocytes [40] . We have previously reported [40] that ISOC is a relatively Ca2+-selective cation current with Erev around +20 mV and pCa2+/pNa+ = 40 ( ICRAC displays Erev>+60 mV and Ca2+/Na+ selectivity ≥400 ) . Silencing of Orai1 expression blocked generation of ISOC while knockdown of TRPC1 by shRNA significantly reduced the amplitude of the inward current but induced more pronounced loss of the outward current . Thus the residual current detected in 6/10 shTRPC1 treated cells was more inwardly rectifying , i . e . more like ICRAC ( Figure 1B , blue trace ) . These findings indicate the possibility that ICRAC in HSG cells can be masked by the larger relatively non-selective TRPC1-mediated current that is activated under the same conditions . The extent of TRPC1 knockdown would then determine the detection ICRAC . In the present set of experiments , 40% of the cells displayed ISOC or reduced ISOC . Our present data are somewhat contradictory to our previous finding that the residual current in Tg-stimulated submandibular gland acinar cells from TRPC1−/− mice was a much reduced transient current that was linear and did not display ICRAC-like properties ( i . e . activation by low [2APB] or increase in DVF medium ) [29] . We suggest that other TRPC channels or volume-regulated channels could account for the linear current . While further studies are required to determine the channel ( s ) involved in this residual current , our previous findings strongly demonstrate that TRPC1 contributes to SOCE and is critically required for salivary gland fluid secretion . The two C-terminal residues of STIM1 ( 684KK685 ) mediate gating of TRPC1 via electrostatic interaction with TRPC1 ( 639DD640 ) residues [47] . Consistent with this , expression of a STIM1 mutant that lacks ability to gate TRPC1 , STIM1 ( 684EE685 ) , induced suppression of SOCE in HSG cells while expression of WT-STIM1 resulted in a small increase in function ( Figure 2A ) . Expression of the TRPC1 mutant that cannot be gated by STIM1 , TRPC1 ( 639KK640 ) , induced a similar suppression of endogenous SOCE ( Figure 2A , blue trace ) . Further , TRPC1 was not activated by store depletion when expressed with STIM1 ( 684EE685 ) in HEK293 cells ( Figure S3A ) , but when STIM1 and TRPC1 mutants were expressed together ( i . e . “charge swap” between the proteins ) there was recovery of SOCE ( Figure S3A ) . Importantly , STIM1 ( 684EE685 ) stimulated Orai1 similar to WT-STIM1 ( Figure S3B ) . A key finding of this study , shown in Figure 2B , is that expression of STIM1 ( 684EE685 ) resulted in generation of ICRAC in response to Tg-induced Ca2+ store depletion in >70% of HSG cells displaying currents . Together the data in Figures 1B and 2B suggest that ISOC in HSG cells is composed of a small Orai1-mediated ICRAC and a larger TRPC1-mediated non-selective current ( note that we have not yet measured an isolated TRPC1+STIM1 current ) . To conclusively demonstrate that endogenous Orai1 mediates ICRAC in HSG cells we expressed the STIM1-Orai1-activating region ( SOAR ) [20] . A large increase in basal Ca2+ entry ( Figure 2C ) and spontaneous ICRAC was seen in these cells ( Figure 2D ) . SOAR-induced spontaneous SOCE was abolished by knockdown of endogenous Orai1 but was not affected by knockdown of endogenous TRPC1 ( Figure 2C ) . In contrast , Tg-stimulated Ca2+ entry in SOAR-expressing cells was significantly reduced by knockdown of TRPC1 ( Figure 2E , the residual Ca2+ entry reflects spontaneous Orai1-dependent Ca2+ influx ) . In aggregate , these data provide strong evidence that endogenous Orai1 mediates ICRAC without any contribution from TRPC1 while SOCE and ISOC display significant contribution from TRPC1 . Importantly , the function of TRPC1 requires Orai1 . To identify the mechanism involved in regulation of TRPC1-SOC channels we examined the effect of intracellular Ca2+ store depletion on the surface expression of TRPC1 . In resting cells the surface expression of TRPC1 ( i . e . in the biotinylated fraction ) was relatively low . Tg treatment of cells ( Figure 3A , left panel , total TRPC1 and GAPDH are shown in input ) significantly enhanced ( about 3-fold , Figure 3C ) the insertion of TRPC1 into the plasma membrane . An important finding of this study ( Figure 3A ) is that Tg-stimulated increase in plasma membrane insertion of TRPC1 was dependent on Orai1 . Decreasing Orai1 expression or compromising Orai1 function by expression of the dominant negative mutant Orai1-E106Q ( Figure 3A , middle and right panels , respectively , see Figure 3C for average data ) severely reduced Tg-stimulated surface expression of TRPC1 without significantly affecting the resting level of TRPC1 . To examine whether Ca2+ entry was involved in TRPC1 trafficking , biotinylation of TRPC1 was assessed in cells stimulated with Tg in nominally Ca2+-free medium or in normal Ca2+-containing medium with 1 µM Gd3+ . Both conditions blocked the increase in the surface expression of TRPC1 induced by Tg ( Figure 3B and C ) . These effects on TRPC1 trafficking were not due to loss of TRPC1/STIM1/Orai1 clustering , which was not affected in cells expressing Orai1-E106Q [39] or in the absence of external Ca2+ ( unpublished data ) . The role of Orai1-mediated Ca2+ entry was more directly assessed by using Orai1-E106D , an Orai1 mutant that is permeable to Ca2+ in Ca2+-containing medium , but unlike the wild type channel , it is permeable to Na+ in nominally Ca2+-free medium . Tg treatment of cells expressing this mutant induced surface expression of endogenous TRPC1 in Ca2+-containing medium but not in Ca2+-free medium ( Figure 3D ) . Finally , trafficking of TRPC1 was examined in HSG cells expressing STIM1 ( 684EE685 ) , which display ICRAC in response to Ca2+ store depletion ( see Figure 2B ) . Although TRPC1 activation was suppressed in these cells , trafficking of the channel was not altered ( Figure 3E ) . In aggregate these novel data suggest that Orai1-mediated Ca2+ influx is sufficient for plasma membrane insertion of TRPC1 but not activation; the latter depends on STIM1 . The mechanism involved in the clustering of TRPC1 with STIM1 and Orai1 was assessed by TIRFM . Ca2+ store depletion resulted in co-localization of YFP-TRPC1 and Orai1-CFP into puncta in the sub-plasma membrane region ( Figure 4A , HA-STIM1 was co-expressed in these cells ) . Further , STIM1 co-clustered with both the channels following Tg stimulation of the cells ( Figure 4B ) . As has been reported for Orai1 , Orai1-TRPC1 clustering also required co-expression of STIM1 ( unpublished data ) and was not detected in cells when endogenous STIM1 expression was knocked down ( Figure 4C ) . More significantly , co-IP of endogenous TRPC1 and Orai1 was abolished in cells treated with siSTIM1 ( Figure 4D ) but not in cells expressing STIM1 ( 685EE685 ) ( Figure 4E , F ) . TRPC1 clustering was not dependent on Orai1 since co-clustering of TRPC1 with STIM1 was unaffected by knockdown of Orai1 ( Figure S4 , compare data in A and B ) . Thus , STIM1 determines TRPC1 clustering in the sub-plasma membrane region following Ca2+ store depletion , and Orai1-mediated Ca2+ entry regulates its surface expression . Based on these findings we hypothesize that TRPC1 is present in recycling vesicles that traffic in and out of the plasma membrane region . Following store depletion when STIM1 clusters in ER/PM junctional domains , it interacts with TRPC1 possibly via the ERM domain [46] and increases the retention of TRPC1-containing vesicles . Concurrently , STIM1 also recruits Orai1 into the same regions , thus bringing the two channels in close proximity to each other . Ca2+ entry via Orai1 induces fusion of TRPC1-containing vesicles to the plasma membrane followed by gating of the channel by STIM1 . Further studies are required to elucidate the mechanisms involved in trafficking and plasma membrane insertion of TRPC1 . We next examined whether relatively global or local [Ca2+]i increase regulates plasma membrane insertion of TRPC1 . Figure 5A shows that loading HSG cells with 200 µM BAPTA-AM prior to Tg stimulation ( details given in Methods ) did not suppress trafficking of TRPC1 induced by Tg , although Tg-stimulated global [Ca2+]i increase was completely suppressed ( Figure 5B , compare red trace with black trace , which shows [Ca2+]i increase in cells loaded with low [BAPTA-AM] ) . In addition , Tg-stimulated ISOC was not altered by replacing EGTA in the pipette solution with 10 mM BAPTA ( Figure S5B , C ) , although the latter condition completely suppressed KCa activation in Tg-stimulated cells ( Figure S5C , right panel ) . TRPC4 and TRPC5 are directly activated by elevation of intracellular [Ca2+]i [49] , and a recent study demonstrated that Ca2+ entry mediated via Orai1 or other Ca2+ entry channels , including voltage-dependent channels , can directly enhance TRPC5 activity [50] . To determine whether [Ca2+]i increase directly activates TRPC1 , whole cell current measurement was done with [Ca2+] in the pipette solution clamped to 0 . 1 µM or 1 µM . No current was detected with 0 . 1 µM Ca2+ ( unless Tg was included in the external medium , Figure S5A , black and blue traces ) , 1 µM Ca2+ ( Figure S5A , red trace ) , or up to 5 µM Ca2+ ( unpublished data ) . Note that 1 µM Ca2+ induces >90% activation of TRPC4 and TRPC5 [49] , [50] . These data also rule out possible contribution of other Ca2+-dependent cation channels to SOCE [51] . In aggregate , these data suggest that local Ca2+ entry via Orai1 determines plasma membrane insertion of TRPC1 and that [Ca2+]i elevation due to intracellular Ca2+ release is insufficient for triggering TRPC1 insertion . Further when cells were treated with Tg in a Ca2+ free medium for 5 min , there was no increase in TRPC1 expression in the plasma membrane until Ca2+ was added to the external solution ( Figure 5C , right panel ) . As shown above , when cells were stimulated with Tg in a Ca2+-containing medium ( Figure 5C , left panel ) , TRPC1 insertion in the plasma membrane was enhanced . Surprisingly , subsequent removal of Ca2+ from the external solution ( for 10 min ) did not change the level of TRPC1 in the surface membrane . Functional consequences of these treatments are shown in Figure 5E–F . In this experiment , HSG cells were treated with Tg in Ca2+-free medium prior to whole cell current measurements in DVF medium . Typical inwardly rectifying ICRAC with rapid inactivation was detected in these cells ( Figure 5E ) , consistent with the lack of TRPC1 insertion in the plasma membrane under these conditions . However , when pre-treatment was done in Ca2+-containing medium , ISOC was detected in the DVF medium ( Figure 5F ) . Note that the ISOC in DVF was relatively sustained , again consistent with the stable biotinylation of TRPC1 . In aggregate , the findings presented above suggest that Orai1-mediated Ca2+ entry triggers insertion of TRPC1 in the plasma membrane , followed by activation of the channel by STIM1 . Thus while channel insertion into the plasma membrane appears to depend on local increases in [Ca2+]i , TRPC1 internalization does not strictly depend on a decrease in [Ca2+]i . Further studies will be required to determine the exact molecular mechanisms involved in internalization of TRPC1 . The data presented above demonstrate that two STIM1-gated channels , Orai1 and TRPC1 , are activated in response to internal Ca2+ store depletion in HSG cells . To establish the relative contributions of these channels in SOCE-mediated Ca2+ signaling , we examined three SOCE-activated mechanisms: KCa channel , NFκB , and NFAT . Figure 6A demonstrates that expression of STIM1 ( 684EE685 ) in HSG cells induced a slow , much diminished ( >80% reduction ) , and transiently activated KCa current compared to that in control cells . As shown above ( Figure 2B ) , only CRAC channel activation was seen in cells expressing this STIM1 mutant . Thus , Orai1-mediated Ca2+ entry does not appear to be sufficient for activation of KCa activity following Tg stimulation . Further , NFκB activation ( Figure 6B ) was significantly decreased by the knockdown of TRPC1 expression , and predictably knockdown of Orai1 induced an even greater loss of activity . Significantly , expression of SOAR did not lead to much activation of NFκB . Remarkably , TRPC1 had minimal contribution to the regulation of NFAT since knockdown of Orai1 but not TRPC1 suppressed NFAT activation ( Figure 6C ) . Thus , Orai1-mediated Ca2+ entry is sufficient for regulation of NFAT and for partial stimulation of NFκB , but not for KCa activation . In contrast , TRPC1-mediated Ca2+ entry regulates KCa channel activity and contributes to NFκB signaling , but not NFAT activation . Similar to the findings in HSG cells , KCa activity was severely reduced in acinar cells from submandibular glands of TRPC1−/− mice , which could account for loss of salivary fluid secretion in these animals [29] . While our current findings suggest that Orai1+STIM1 dependent regulation of TRPC1 would be very critical for regulating salivary gland function , functional interaction between these proteins will depend on their precise localization within acinar cells , as is required in HSG cells ( Figure 4B ) . We have previously reported that TRPC1 is localized in the basal and lateral regions of submandibular gland acinar cells [29] , [52] and that TRPC1 and STIM1 co-IP following stimulation of acini by either Tg or CCh [53] . To determine possible physiological relevance of the present findings , we examined the localization of TRPC1 , Orai1 , and STIM1 in submandibular glands excised from resting and pilocarpine-stimulated mice ( tissue was fixed in vivo in mice following pilocarpine injection and after an increase in saliva secretion was detected ) . In the samples from unstimulated mice , endogenous Orai1 was prominantly detected in the apical and lateral regions of submandibular gland acini ( Figure S6A , upper panels , green signal , Orai1 signal shown by white arrows ) , co-localization of Orai1 with the luminal membrane protein AQP5 is also shown ( red signal , right panel ) . STIM1 showed diffused localization within the acinar cells from unstimulated mice ( Figure S6B , red signal , upper panel ) . Consistent with our previous findings , diffuse localization of TRPC1 was detected in the basal and lateral regions ( green signal , upper panels , the same sections were labeled for STIM1 and TRPC1 ) . In samples obtained from stimulated mice , Orai1 and AQP5 localization did not change ( Figure S6A , lower panels ) . However , a dramatic translocation of TRPC1 and STIM1 to the basal and lateral membrane regions was seen with relative decrease in intracellular staining ( Figure S6B , lower panels , see white arrows ) . Thus stimulation induces co-localization of STIM1 , Orai1 , and TRPC1 in the lateral membrane region of cells . While further studies are required to determine whether sufficient Orai1 is present in the basolateral membrane to regulate TRPC1 , our data strongly suggest that regulation of TRPC1 by STIM1 and Orai1 is feasible within the lateral membrane region of salivary gland acinar cells . Our findings are generally consistent with the strong co-localization of Orai1 and STIM1 in the lateral membrane region of stimulated pancreatic acinar cells [54] . STIM1 was also localized in the basal membrane and co-localized with heterologously expressed , but not endogenous , Orai1 , in these cells . This study suggested that localization of Orai1 and STIM1 in the lateral membrane was consistent with the proposed site of Ca2+ entry in exocrine acinar cells [55]–[57] .
The findings described herein address several important and as-yet unresolved questions regarding the molecular components of TRPC1-SOC channel , the mechanism involved in regulation of the channel in response to store depletion , and its contribution to SOCE . We report that the previously described ISOC [39] , [40] , [58] , which is stimulated by store depletion and dependent on TRPC1 , STIM1 , and Orai1 , is a sum of Orai1/STIM1-mediated ICRAC and TRPC1/STIM1-mediated non-selective cation current . Our findings suggest that the latter relatively larger current masks the underlying ICRAC since suppression of TRPC1 function either by shTRPC1 or by expression of the STIM1 ( 684EE685 ) mutant , which does not gate TRPC1 , facilitates detection of ICRAC . Further , SOAR-activated ICRAC required Orai1 but not TRPC1 . Thus Orai1 and TRPC1 are components of two distinct channels . These findings provide strong argument against the possibility that TRPC1 and Orai1 contribute to the same channel pore or that Orai1 is a regulatory , non-conducting , subunit of TRPC channels [59] . We also report that Orai1-mediated Ca2+ entry triggers plasma membrane recruitment of TRPC1 . These data reveal a novel function for Orai1 that can explain its critical requirement in the activation of TRPC1 channels following Ca2+ store depletion . We show that Ca2+ store depletion leads to enhanced surface expression of TRPC1 , which is blocked when Ca2+ is removed from the external medium or SOCE is inhibited by addition of Gd3+ . Knockdown of endogenous Orai1 expression or expression of non-functional Orai1 mutants ( Orai1-E106Q ) also lead to loss of TRPC1 in the plasma membrane . Notably , in cells expressing Orai1-E106D , TRPC1 trafficking is supported in Ca2+-containing medium but not Ca2+-free medium . Together , these findings provide strong evidence that surface expression of TRPC1 is determined by the Ca2+ permeability of Orai1 and that TRPC1 is gated by STIM1 and not directly by [Ca2+]i increase . Presently we cannot conclusively rule out the involvement of possible downstream signaling pathway ( s ) activated by Orai1-mediated Ca2+ entry . The data presented above also reveal important aspects of TRPC1 , Orai1 , and STIM1 clustering that are critical in the regulation of TRPC1 within the same ER/PM junctional domains where Orai1 is regulated by STIM1 . We show that in response to store depletion TRPC1 co-clusters with STIM1 and Orai1 . More importantly while Orai1 is not required for clustering and association of TRPC1-STIM1 , localization of STIM1 in the ER/PM junctional domains is critical for recruitment and association of Orai1 and TRPC1 . Thus far there are no data to show that TRPC1 and Orai1 directly interact with each other , although both channels interact with STIM1 . STIM1 interacts with Orai1 via the SOAR domain , which also leads to gating of the channel . In the case of TRPC1 while the C-terminal 684KK685 residues of STIM1 are involved in gating the channel , the ERM domain [46] could interact with the channel and serve as a scaffold to retain TRPC1 within the ER/PM junctional regions . We suggest that interaction with STIM1 allows the channels to be localized in close proximity to each other , facilitating Orai1-mediated Ca2+ entry to locally regulate plasma membrane insertion of TRPC1 . However , our data show that internalization of TRPC1 is apparently not dependent on [Ca2+]i ( Figure 5C ) . Thus , TRPC1 can remain active provided the Ca2+ stores are depleted and STIM1 is localized in the peripheral domains . Based on our data , we suggest the following sequence of events in the activation of TRPC1: ( i ) Ca2+ store depletion leads to translocation of STIM1 to ER/PM junctional domains and recruitment of Orai1 ( localized within the plasma membrane ) and TRPC1 ( likely localized in intracellular trafficking vesicles ) , ( ii ) Orai1 is activated by STIM1 and Ca2+ entry via Orai1 triggers exocytosis of TRPC1 , and finally ( iii ) STIM1 gates plasma membrane TRPC1 ( depicted in the model shown in Figure 7 ) . We also demonstrate the unique contributions of TRPC1 and Orai1 to SOCE . Remarkably , different cellular functions are regulated when Orai1 alone is activated compared to conditions when both channels are activated . Our data suggest that TRPC1 augments the [Ca2+]i increase resulting from Orai1-mediated Ca2+ entry . Consistent with this , TRPC1-mediated Ca2+ entry is required for KCa function and contributes to NFκB activation , both of which require relatively higher [Ca2+]i , but not for NFAT activation , which can be activated at lower [Ca2+]i ( see Figure 7 ) [29] , [60] . Interestingly , the requirement of TRPC1 for KCa activity is similar to our previous finding that submandibular gland acinar cells from TRPC1−/− mice display loss of sustained KCa activity , which accounts for the decrease in fluid secretion in these glands . We have previously shown that TRPC1 is localized in the basal and lateral regions of acinar cells [29] , [52] and that TRPC1 and STIM1 associate following stimulation of acini [53] . Since Orai1 is critical for TRPC1 function , localization of these proteins in the salivary gland acinar cells is a key determinant for the functional interaction between them . Feasibility for the interaction of the three proteins and regulation of TRPC1 in the gland is demonstrated by our data ( Figure S6 ) , showing that following agonist stimulation Orai1 , TRPC1 , and STIM1 are strongly co-localized in the lateral membrane region of acinar cells while TRPC1 and STIM1 also appear to be colocalized in the basal region . In salivary gland acinar cells agonist stimulation leads to [Ca2+]i elevation , which is first detected in the apical region of the cells and then spreads to basal and lateral regions , irrespective of the level of stimulation [55] , [61] . Although further studies will be required to confirm the presence of Orai1 in the basal membrane region of acini , co-localization of TRPC1 , Orai1 , and STIM1 in the lateral membrane region of stimulated cells supports our suggestion that Orai1 can regulate TRPC1 function in this region and thus modulate SOCE . In conclusion , the data described above reveal novel insight into the molecular components and regulation of TRPC1-SOC channels . Our findings provide strong evidence that TRPC1 and Orai1 constitute distinct SOC and CRAC channels , respectively , both of which are gated by STIM1 in response to store depletion and contribute to SOCE in the same cell . The critical step in the activation of TRPC1 is its insertion into the plasma membrane , which is governed by Orai1-mediated local Ca2+ entry . In addition to gating TRPC1 and Orai1 , STIM1 also mediates the association of the two channels within discrete ER/PM junctional domains , which is the site for SOCE [19] , [21] . The three proteins are also co-localized in the membrane region predicted to be the site of SOCE in acinar cells [56] , [57] , thus highlighting the potential physiological relevance of our findings . Importantly , TRPC1 augments Ca2+ entry mediated by Orai1-CRAC channels and is required for activation of KCa channels and NFκB , but not NFAT , signaling . As has been suggested , the amplitude , frequency of oscillations , or spatial patterning of [Ca2+]i changes determines the regulation of different cell functions [1] , [4] , [51] , [55] , [60] , [62] . Although further studies are required to elucidate exactly how TRPC1 alters the primary [Ca2+]i signals generated by Orai1 , the present data suggest that regulation of TRPC1 trafficking can provide a mechanism for rapidly modulating [Ca2+]i . STIM1 is emerging as a versatile ER Ca2+ sensor that regulates multiple target proteins in response to Ca2+ store depletion . In addition to activation of Orai1 and TRPC channels , STIM1 has been reported to inhibit Cav1 . 2 channels [63] , [64] and activate adenylyl cyclase [65] , both of which depend on Ca2+ store depletion . While regulation of TRPC1 and Cav1 . 2 require association of the channels with Orai1 within ER/PM junctional domains , Orai1 function does not appear to be involved in STIM1-dependent inhibition of Cav1 . 2 . Thus Orai1 and STIM1 by coordinating the regulation of other ion channels and signaling components can modulate [Ca2+]i and critically impact SOCE-mediated Ca2+ signaling and a variety of cellular functions .
HSG cells were cultured in MEM medium , supplemented with 10% heat-inactivated fetal bovine serum , and 1% penicillin/streptomycin . Sequences for the siOrai1 , siSTIM1 , and shTRPC1 targeting to human Orai1 , STIM1 , and TRPC1 , respectively , were similar to previously described sequences [42] . All siRNA duplexes were obtained from Dharmacon . Lipofectamine RNAiMAX ( Invitrogen ) was used for siRNA transfection while Lipofectamine 2000 was used for other plasmids . Cells were typically transfected 24 h after plating and experiments were performed 48 h post-transfection . All other reagents used were of molecular biology grade obtained from Sigma Aldrich unless mentioned otherwise . Fura-2 fluorescence was measured in single HSG cells cultured for 24 h in glass bottom MatTek tissue culture dishes ( MatTek Corp . Ashland , MA ) and transfected as required; experiments were done 48 h post-transfection . Cells were loaded with 5 µM Fura-2 ( Invitrogen ) for 30 min at 37°C . Fluorescence was recorded using a Till Photonics-Polychrome V spectrofluorimeter and MetaFluor imaging software ( Molecular Devices ) . Each fluorescence trace ( 340/380 nm ratio ) represents an average from at least 50–150 cells from >6 individual experiments . Student's t test was used to statistically evaluate the data . Coverslips with HSG cells were transferred to the recording chamber and perfused with Ca2+ containing standard external solution ( Ca2+-SES ) with the following composition ( in mM ) : NaCl , 145; KCl , 5; MgCl2 , 1; CaCl2 , 1; Hepes , 10; glucose , 10; pH 7 . 4 ( NaOH ) . The patch pipette had resistances between 3 and 5 milliohms after filling with the standard intracellular solution that contained the following ( in mM ) : cesium methane sulfonate , 145; NaCl , 8; MgCl2 , 10; Hepes , 10; EGTA , 10; pH 7 . 2 ( CsOH ) . For KCa measurements , pipette solution contained 150 mM KCl , 2 mM MgCl2 , 1 mM Mg-ATP , 5 mM Hepes , 0 . 1 mM EGTA , and pH 7 . 2 , potassium hydroxide . Osmolarity for all the solutions was adjusted with mannose to 300±5 mosM using a vapor pressure Osmometer ( Wescor , Logan , UT ) . All electrophysiological experiments were performed in the tight-seal whole cell configuration at room temperature ( 22–25°C ) using an Axopatch 200B amplifier ( Molecular Devices ) . Development of the current was assessed by measuring the current amplitudes at a potential of −80 mV , taken from high resolution currents in response to voltage ramps ranging from −90 to 90 mV over a period of 100 ms imposed every 2 s ( holding potential was 0 mV ) and digitized at a rate of 1 kHz . Liquid-junction potentials were less than 8 mV and were not corrected . Capacitative currents and series resistance were determined and minimized . For analysis , the current recorded during the first ramp was used for leak subtraction of the subsequent current records . Thapsigargin ( Tg 1 µM ) , dissolved in the bath solution , was used to stimulate the cells . DVF solution contains ( mM ) : NaCl 165; CsCl 5; EDTA 10; HEPES 10; glucose 10; pH 7 . 4 ( NaOH ) . Cells were pretreated with 1 µM Tg for 10 min either in Ca2+ containing or Ca2+ free medium before whole cell configuration was achieved . Cells were switched to DVF 1 min after achieving whole cell configuration in Ca2+ free external medium . Transfected HSG cells were washed with phosphate-buffered saline ( PBS ) and lysed in radioimmunoprecipitation assay ( RIPA ) protein extraction buffer ( 50 mM Tris-HCl , 150 mM NaCl , 0 . 1% sodium dodecyl sulfate ( SDS ) , 0 . 5% sodium deoxycholate , 1% Triton X-100 , 2 mM EDTA , 1 mM dithiothreitol ( DTT ) , pH 7 . 4 ) supplemented with Complete Protease Inhibitor Cocktail tablets ( Roche Diagnostics ) . Where indicated , cells were first stimulated for 5 min with 1 µM Thapsigargin ( Tg ) , lysates was then centrifuged at 12 , 000 x g for 30 min at 4°C , and the supernatant was collected and analyzed by SDS-PAGE and Western blotting ( 50 µg of protein were loaded per lane ) . Protein concentrations in the lysate was adjusted to 2 mg/ml and incubated with 10 µg/ml IP antibody . Immunoprecipitates were released by incubating in SDS-sample buffer and resolved in 4%–12% NuPAGE gels ( Invitrogen ) followed by Western blotting . Anti-STIM1 ( Cell signaling technology , Danvers , MA ) , anti-Orai1 ( Open Biosystems , Huntsville , AL ) , anti-GAPDH ( Abcam Inc , Cambridge , MA ) , and Anti-TRPC1 antibody [42] were used at 1∶1000 , 1∶1000 , 1∶10000 , and 1∶400 dilution , respectively . Cells were transfected with vector or scrambled control as required . For stimulation experiments , cells were pretreated with 1 µM Thapsigargin in the presence ( +Ca2+ ) or absence ( −Ca2+ ) of extracellular calcium , and incubation time was 5 min or otherwise as indicated . The reaction was stopped by adding ice-cold quenching solution . In BAPTA-AM loading experiments , cells were pretreated with 200 µM BAPTA-AM ( Invitrogen ) in SES containing 100 µM extracellular Ca2+ for 30 min at 37°C . Treated cells were then incubated for 20 min with 1 . 5 mg/ml Sulfo-NHS-LC-Biotin ( Pierce ) in 1XPBS ( pH 8 . 0 ) on ice . Following biotin labeling , cells were washed and harvested in RIPA buffer using the same protocol as described above . Biotinylated proteins were pulled down with NeutrAvidin-linked beads ( Pierce ) and detected by Western blotting . Band intensities of surface proteins were obtained using Image J software . NFκB-Luc , NFAT-Luc , and hRLuc-TK were obtained from Promega . HSG cells were transfected with the indicated constructs with either NFκB or NFAT reporter gene , and co-transfection with the Renilla luciferase gene ( hRLuc-TK ) driven by the TK promoter was used to control for cell number and transfection efficiency . Transfected cells were stimulated as described in [60] . Luciferase activity was measured with the Dual-Glo Luciferase Assay System ( Promega ) . For each condition , luciferase activity was measured with four samples taken from duplicate wells with a 96-well automated luminometer ( Turner Biosystems ) . Results are represented as the ratio of firefly to Renilla luciferase activity . An Olympus IX81 motorized inverted microscope ( Olympus ) was used as described previously [42] using 447 , 514 , and 568 nm lasers for excitation of CFP , YFP , and mCherry , respectively , and a TIRF-optimized Olympus Plan APO 60x ( 1 . 45 NA ) oil immersion objective and Lambda 10-3 filter wheel ( Sutter Instruments ) containing 480-band pass ( BP 40 m ) , 525-band pass ( BP 50 m ) , and 605-band pass ( BP 52 m ) filters for emission . Images were collected using a Hamamatsu EM C9100 camera ( Hamamatsu ) and the MetaMorph imaging software ( Molecular Devices ) . MetaMorph was also used to measure the fluorescence intensity before and after stimulation with Tg . Briefly , regions of interest were selected to obtain the values for their fluorescence intensities during a time course experiment . These values were then plotted using the Origin 8 software ( OriginLab ) . Balb/c mice were anesthetized and injected subcutaneously with either saline ( Resting ) or 0 . 5 mg of pilocarpine/kg ( Stimulated ) . After the saliva secretion was observed in stimulated mice , the animals were perfusion fixed with 10% buffered formalin and immediately euthanized . Salivary glands were excised and embedded in paraffin for histologic processing . Slides of paraffin sections were deparaffinized and rehydrated . Sections were unmasked by microwaving samples for 10 min in a microwave pressure cooker ( NordicWare ) in 1 mM EDTA , pH 8 . 0 , containing 0 . 05% Tween 20 . After cooling , sections were blocked either with 0 . 5% BSA in PBS ( for direct conjugates ) or with 10% donkey serum in PBS ( for samples using secondary antibodies ) . After blocking for 30 min at room temperature , primary antibodies were applied and incubated at 4°C overnight . For samples using two or more rabbit host primary antibodies , direct-conjugation with a fluorescent tag using Invitrogen's Zenon labeling kit was used . For antibodies requiring secondary antibody labeling , donkey anti-rabbit Alexa conjugates were used ( Invitrogen ) . A negative control using normal rabbit IgG at the same concentration as specific primaries was included for both methods . After labeling with primary antibodies only , samples were washed extensively and incubated with secondary antibodies for 1 h at room temperature , washed , and mounted with VectaShield mounting medium containing DAPI . Zenon conjugated samples were washed extensively and mounted with cover slips as above . Images were collected by using a Leica Confocal microscope and MetaMorph software ( Molecular Devices , Sunnyvale , CA ) . Data analysis was performed using Origin 8 ( OriginLab ) . Statistical comparisons were made using student's t test . Experimental values are expressed as means ± SD or SEM . Differences in the mean values were considered to be significant at p<0 . 01 . | Store-operated Ca2+ entry is present in all cell types and determines sustained cytosolic [Ca2+] increases that are critical for regulating a wide variety of physiological functions . This Ca2+ entry mechanism is activated in response to depletion of Ca2+ in the endoplasmic reticulum ( ER ) . When ER [Ca2+] is decreased , the Ca2+-sensor protein STIM1 aggregates in the ER membrane and moves to regions in the periphery of the cells where it interacts with and activates two major types of channels that contribute to store-operated Ca2+ entry: CRAC and SOC . While gating of Orai1 by STIM1 is sufficient for CRAC channel activity , both Orai1 and transient receptor potential channel 1 ( TRPC1 ) contribute to SOC channel function . The molecular composition of SOC channels and the critical role of Orai1 in activation of TRPC1 have not yet been established . In this study , we demonstrate that TRPC1 and Orai1 are components of distinct channels , both of which are regulated by STIM1 . Importantly , we show that Orai1-mediated Ca2+ entry triggers plasma membrane insertion of TRPC1 which is then gated by STIM1 . Ca2+ entry via functional TRPC1-STIM1 channels provides additional increase in cytosolic [Ca2+] that is required for regulation of specific cell functions such as KCa activation . Together , our findings elucidate the critical role of Orai1 in TRPC1 channel function . We suggest that the regulation of TRPC1 trafficking provides a mechanism for rapidly modulating cytosolic [Ca2+] following Ca2+ store depletion . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"cell",
"physiology",
"biochemistry",
"ion",
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] | 2011 | Local Ca2+ Entry Via Orai1 Regulates Plasma Membrane Recruitment of TRPC1 and Controls Cytosolic Ca2+ Signals Required for Specific Cell Functions |
Autoimmune thyroid diseases ( AITD ) are common , affecting 2-5% of the general population . Individuals with positive thyroid peroxidase antibodies ( TPOAbs ) have an increased risk of autoimmune hypothyroidism ( Hashimoto's thyroiditis ) , as well as autoimmune hyperthyroidism ( Graves' disease ) . As the possible causative genes of TPOAbs and AITD remain largely unknown , we performed GWAS meta-analyses in 18 , 297 individuals for TPOAb-positivity ( 1769 TPOAb-positives and 16 , 528 TPOAb-negatives ) and in 12 , 353 individuals for TPOAb serum levels , with replication in 8 , 990 individuals . Significant associations ( P<5×10−8 ) were detected at TPO-rs11675434 , ATXN2-rs653178 , and BACH2-rs10944479 for TPOAb-positivity , and at TPO-rs11675434 , MAGI3-rs1230666 , and KALRN-rs2010099 for TPOAb levels . Individual and combined effects ( genetic risk scores ) of these variants on ( subclinical ) hypo- and hyperthyroidism , goiter and thyroid cancer were studied . Individuals with a high genetic risk score had , besides an increased risk of TPOAb-positivity ( OR: 2 . 18 , 95% CI 1 . 68–2 . 81 , P = 8 . 1×10−8 ) , a higher risk of increased thyroid-stimulating hormone levels ( OR: 1 . 51 , 95% CI 1 . 26–1 . 82 , P = 2 . 9×10−6 ) , as well as a decreased risk of goiter ( OR: 0 . 77 , 95% CI 0 . 66–0 . 89 , P = 6 . 5×10−4 ) . The MAGI3 and BACH2 variants were associated with an increased risk of hyperthyroidism , which was replicated in an independent cohort of patients with Graves' disease ( OR: 1 . 37 , 95% CI 1 . 22–1 . 54 , P = 1 . 2×10−7 and OR: 1 . 25 , 95% CI 1 . 12–1 . 39 , P = 6 . 2×10−5 ) . The MAGI3 variant was also associated with an increased risk of hypothyroidism ( OR: 1 . 57 , 95% CI 1 . 18–2 . 10 , P = 1 . 9×10−3 ) . This first GWAS meta-analysis for TPOAbs identified five newly associated loci , three of which were also associated with clinical thyroid disease . With these markers we identified a large subgroup in the general population with a substantially increased risk of TPOAbs . The results provide insight into why individuals with thyroid autoimmunity do or do not eventually develop thyroid disease , and these markers may therefore predict which TPOAb-positives are particularly at risk of developing clinical thyroid dysfunction .
Autoimmune thyroid disease ( AITD ) , including Hashimoto's thyroiditis and Graves' disease , is one of the most common autoimmune diseases , affecting 2–5% of the general population [1] , [2] , [3] . Thyroid dysfunction has been associated with osteoporosis , depression , atrial fibrillation , heart failure , metabolic syndrome , and mortality [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] . High serum antibodies against the enzyme thyroid peroxidase ( TPO ) , which is located in the thyroid and plays a key role in thyroid hormone synthesis , are present in 90% of patients with Hashimoto's thyroiditis [12] , [13] , the most frequent cause of hypothyroidism and goiter . Although TPO antibodies ( TPOAbs ) are a useful clinical marker for the detection of early AITD , it remains controversial if these antibodies play a causative role in the pathogenesis of Hashimoto's thyroiditis [14] , [15] , [16] . Interestingly , TPOAb-positive persons also have an increased risk of developing autoimmune hyperthyroidism ( Graves' disease ) [17] , [18] , which is caused by stimulating antibodies against the thyroid stimulating hormone ( TSH ) receptor [19] . Numerous studies have shown that Graves' hyperthyroidism and Hashimoto's thyroiditis show co-inheritance [17] , [20] , [21] . Finally , thyroid autoimmunity is the most common autoimmune disorder in women of childbearing age , and TPOAb-positive women have an increased risk of developing pregnancy complications such as miscarriage and pre-term delivery [17] , [18] , [22] , [23] , [24] , [25] , [26] . The prevalence of TPOAb-positivity in the general population ranges from 5–24% , but it is currently unknown why these people develop TPOAbs , nor is it known why not all individuals with thyroid autoimmunity develop clinical thyroid disease [27] , [28] . It is estimated that around 70% of the susceptibility to develop thyroid autoantibodies is due to genetic factors [29] . In this context it is remarkable to note that little is known about the genetic factors that determine TPOAb-positivity and the risk of AITD . We therefore performed a genome wide association study ( GWAS ) meta-analysis for TPOAbs in the general population in 18 , 297 individuals from 11 populations . Newly identified genetic variants were studied in relation to subclinical and overt hypo- and hyperthyroidism , goiter , thyroid autoimmunity during pregnancy and thyroid cancer risk .
See Table 1 and Supplementary Figure S1 for TPOAb measurements and Supplementary Table S1 for genotyping procedures . In most autoimmune diseases , both the presence and the level of autoantibodies are relevant for the disease onset [18] , [30] , [31] . Furthermore , different pathophysiological processes may be involved in the initiation and severity of the autoimmune response . We therefore performed a GWAS on TPOAb-positivity ( including 1769 TPOAb-positives and 16 , 528 TPOAb–negatives ) , as well as a GWAS on continuous TPOAb levels ( including 12 , 353 individuals ) in stage 1 . See Supplementary Figures S2 and S3 for QQ ( quantile-quantile ) and Manhattan plots . In stage 2 , we followed-up 20 stage 1 SNPs ( P<5×10−6; 13 TPOAb-positivity and 10 TPOAb level SNPs , with 3 SNPs overlapping ) in 5 populations , including up to 8 , 990 individuals for TPOAb-positivity ( 922 TPOAb-positives and 8068 TPOAb–negatives ) and 8 , 159 individuals for TPOAb level analyses ( see Supplementary Material S1 ) . Results of the combined stage 1 and 2 meta-analyses , including heterogeneity analyses , are shown in Supplementary Tables S2 and S3 . Regional association plots are shown in Supplementary Figures S4 and S5 . In the combined stage 1 and 2 meta-analyses GWAS significant associations ( P<5×10−8 ) were observed near TPO ( Chr 2p25; rs11675434 ) , at ATXN2 ( Chr 12q24 . 1; rs653178 ) , and BACH2 ( Chr 6q15; rs10944479 ) for TPOAb-positivity , and near TPO ( rs11675434 ) , at MAGI3 ( Chr 6q15; rs1230666 ) , and KALRN ( Chr 3q21; rs2010099 ) for TPOAb levels ( Table 2 and Figure 1 ) . The TPOAb level meta-analysis P-values for the 3 GWAS significant TPOAb-positivity loci were: TPO-rs11675434: P = 7 . 4×10−13 , ATXN2-rs653178: P = 1 . 3×10−7 , and BACH2-rs10944479: P = 2 . 0×10−4 . As the 3 GWAS significant loci for TPOAb levels also showed associations with TPOAb-positivity ( TPO-rs11675434: OR , 1 . 21 [95% CI , 1 . 15–1 . 28 ) ] , P = 1 . 5×10−16; MAGI3-rs1230666: OR , 1 . 23 [95% CI , 1 . 14–1 . 33] , P = 1 . 5×10−6; KALRN-rs2010099: OR , 1 . 24 [95% CI , 1 . 12–1 . 37] , P = 7 . 4×10−5 ) , we subsequently studied the ( combined ) effects of these 5 SNPs on clinical thyroid disease . Genetic risk scores were calculated as described in the Supplementary Material . The variance explained by these 5 SNPs was 3 . 1% for TPOAb-positivity and 3 . 2% for TPOAb levels . Subjects with a high genetic risk score had a 2 . 2 times increased risk of TPOAb-positivity compared to subjects with a low genetic risk score ( P = 8 . 1×10−8 ) ( Table 3 ) . Table S4 shows the stage 1 TPOAb-positivity and TPOAb level meta-analyses results for GWAS significant SNPs reported in previous GWAS on thyroid related phenotypes . The associations between the 5 GWAS significant SNPs and the risk of abnormal thyroid function tests are shown in Table 4 . MAGI3- rs1230666 was associated with an increased risk of overt hypothyroidism and increased TSH levels below the Bonferroni threshold ( i . e . , P = 0 . 05/5 = 0 . 01 ) . Borderline significant signals were observed at BACH2- rs10944479 with a higher risk of increased TSH levels as well as overt hyperthyroidism ( P = 0 . 011 and P = 0 . 012 ) , and at the KALRN-rs2010099 SNP with a lower risk of decreased TSH levels ( P = 0 . 010 ) . Furthermore , a higher genetic risk score was associated with a higher risk of increased TSH levels ( Supplementary Table S5 ) . No effects of the genetic risk score on the risk of overt hypothyroidism , hyperthyroidism or decreased TSH levels were observed . Individuals with a high genetic risk score had a 30 . 4% risk of sonographically-proven goiter , compared to 35 . 2% in subjects with a low score ( P = 6 . 5×10−4 ) ( Table 5 ) . None of the individual SNPs was significantly associated with goiter risk . As autoimmunity significantly changes during pregnancy [25] , we additionally studied these effects in an independent pregnant population . Pregnant women with a high genetic risk score had a 2 . 4 times increased risk of TPOAb-positivity compared to women with a low score ( 10 . 3% vs 4 . 8% , P = 0 . 03 ) . These women did not have a higher risk of increased TSH levels . However , a borderline significant signal with a lower risk of increased TSH levels was observed at ATXN2- rs653178 ( OR , 0 . 54 [95% CI , 0 . 34–0 . 87] , P = 0 . 012 ) . Ingenuity Pathway Analyses ( IPA; Ingenuity Systems , Ca , USA ) and GRAIL analyses [32] were performed to identify potential pathways involved in AITD , the results of which are shown in Supplementary Tables S7 and S8 , and Figure S6 . The identified top pathways involved cell death , survival , movement , and OX40 signalling .
This is the first GWAS meta-analysis investigating the genetics of TPOAbs in the normal population in up to 18 , 297 individuals from 11 populations with replication in up to 8 , 990 individuals from 5 populations . We identified 5 GWAS significant loci associated with TPOAb-positivity and/or levels . The most significant hit for both TPOAb-positivity and TPOAb levels was located near the TPO gene itself . TPO is a membrane-bound protein located on the apical membranes of the thyroid follicular cell , catalyzing key reactions in thyroid hormone synthesis [33] . Mutations in TPO have been found in patients with congenital hypothyroidism [34] , [35] . Although TPOAbs are valid clinical biomarkers of AITD , they are generally considered to be secondary to the thyroid damage inflicted by T-cells . The FOXE1 gene has been previously associated with hypothyroidism [36] , [37] and is known to regulate transcription of TPO [38] . In this context it is interesting to note that we did not find any associations of the variant near TPO with hypothyroidism . Most genes that have been associated with AITD ( predominantly Graves' disease ) by candidate gene and GWAS studies so far are located in the HLA class I and II regions , or in genes involved in T-cell ( i . e . , CTLA-4 , PTPN22 ) or other autoimmune responses [28] , [39] . Until now , the TPO gene itself had not been associated with AITD , except in one recent candidate gene analysis in a small cohort ( n = 188 ) without replication [40] . A variant near TPO ( rs11694732 ) , which is in LD with rs11675434 ( r2 = 0 . 97 in HapMap2 ) , has previously been associated with TSH levels by Gudmundsson et al [41] . However , various other GWAS on serum TSH and FT4 levels have not found any significant associations in or near this locus , including a recent similar sized GWAS by Porcu et al [42] . Three of the other four loci identified here are located in or are in linkage disequilibrium ( LD ) with genes previously associated with other autoimmune diseases . Rs1230666 is located in intron 9 of MAGI3 , encoding a protein that modulates activity of AKT/PKB . AKT/PKB is expressed in the thyroid and regulates apoptosis [43] , which seems to play an important role in the development of AITD [44] , [45] . In addition , rs1230666 is in LD with rs2476601 ( r2 = 0 . 70 in HapMap2 ) , a variant causing a R620W substitution in PTPN22 . PTPN22 is a lymphoid-specific intracellular phosphatase involved in the T-cell receptor signaling pathway . Variations in PTPN22 , and specifically R620W , are associated with various autoimmune disorders including type 1 diabetes , rheumatoid arthritis , systemic lupus erythematosus and Graves' disease [46] , [47] , [48] , [49] . The associations of the MAGI3 locus with TPOAb-positivity and Graves' disease may therefore also be explained by linkage with disease-associated variants in PTPN22 [50] . Of note , the association signal at rs2476601 is one order weaker than that of the top variant rs1230666 . The BACH2 locus has been implicated in the susceptibility to several autoimmune diseases , including celiac disease , type 1 diabetes , vitiligo , Crohn's disease , and multiple sclerosis [46] , [51] , [52] , [53] , [54] . A recent candidate gene analysis associated the BACH2 locus with an increased risk of AITD , including Hashimoto's thyroiditis and Graves' disease [55] . However , the associations were not significant when Hashimoto's thyroiditis and Graves' disease were studied separately . BACH2 is specifically expressed in early stages of B-cell differentiation and represses different immunoglobulin genes [56] . Interestingly , BACH2 can bind to the co-repressor SMRT ( silencing mediator of retinoid and thyroid receptor ) , which may suggest a more direct effect on thyroid hormone secretion and action as well . Polymorphisms in ATXN2 have been associated with multiple neurodegenerative diseases , including spinocerebellar ataxia and Parkinson's disease [57] , [58] , [59] . Different epidemiological studies have associated thyroid dysfunction with cerebellar ataxia [60] , [61] . Furthermore , the identified SNP in ATXN2 has been previously associated with renal function , serum urate levels and blood pressure [62] , [63] , [64] . However , this SNP is in high LD with rs3184504 ( r2 = 0 . 873 ) , a variant causing a Trp262Arg substitution of SH2B adaptor protein 3 ( SH2B3 ) . SH2B3 encodes the adaptor protein LNK , a key negative regulator of cytokine signaling playing a critical role in hematopoiesis . This variant is associated with susceptibility to several autoimmune diseases , including celiac disease , type 1 diabetes , vitiligo , and rheumatoid arthritis [46] , [51] , [53] , [65] , suggesting more relevance for TPOAb levels than ATXN2 . This is supported by a recent study which showed that variants in LD with SH2B3 , BACH2 , and PTPN22 are associated with TPOAb levels in patients with type 1 diabetes [66] . Whereas the above four loci are located in genes involved in the immune response or the autoantigen , the KALRN ( Kalirin ) gene encodes a multi-domain guanine nucleotide exchange factor for GTP-binding proteins of the Rho family . The relation of KALRN with levels of TPOAbs is unclear . This gene has recently been found to be associated with megakaryopoiesis and platelet formation [67] , which may suggest a function in the immune system [68] . We furthermore performed pathway analyses on the stage 1 TPOAb-positivity and TPOAb level lead SNPs , and identified the cell death , survival and movement pathway as an important pathway for TPOAbs . This finding is supported by previous studies , which show an important role for apoptosis in the development of AITD [44] , [45] . Another top pathway involved was the OX40 signalling pathway , and it is of interest to note that OX40 is a T-cell activator promoting the survival of CD4+ T-cells at sites of inflammation [69] . Our results have potential clinical relevance for several reasons . Genetic risk scores based on these novel common ( risk allele frequencies: 9–40% ) TPOAb-associated SNPs enabled us to identify a large subgroup in the general population with a two-fold increased risk of TPOAb-positivity ( 10 . 4% vs 5 . 4% ) . These individuals also have a higher risk of increased TSH levels and a lower risk of goiter , suggesting an advanced stage of destruction of the thyroid due to autoimmune processes . Furthermore , pregnant women with high genetic risk scores had a 2 . 4 times increased risk of TPOAb-positivity during pregnancy . In this context it is interesting to note that TPOAb-positive pregnant women have an increased risk of miscarriages and preterm births independent of thyroid function [70] . Associations with thyroid disease were also found on an individual SNP level . The MAGI3 SNP was associated with a substantially increased risk of hypothyroidism , and the BACH2 SNP showed a borderline significant association ( P = 0 . 011 ) with a higher risk of increased TSH levels , which includes subjects with subclinical and overt hypothyroidism . Furthermore , both loci were significantly associated with an increased risk of Graves' hyperthyroidism in an independent population . To predict which patients with first or second degree relatives with documented Hashimoto's or Graves' disease will develop clinical thyroid disease , a clinical algorithm has been developed ( i . e . , the THEA score ) [18] . Future studies should analyze if these genetic markers increase the sensitivity of the THEA score . Graves' hyperthyroidism and Hashimoto's thyroiditis co-segregate in families and subjects with TPOAbs have an increased risk of both diseases [17] , [18] , [20] , [21] , [22] , [26] . The current study provides insight into this phenomenon by showing that specific loci associated with TPOAbs and ( subclinical ) hypothyroidism , i . e . MAGI3 and BACH2 , are also associated with Graves' hyperthyroidism in an independent case-control study . The prevalence of TPOAb-positivity in the general population is high ( 5–24% ) , but it is currently unknown why part of the individuals with thyroid autoimmunity develop clinical thyroid disease whereas others do not [27] , [28] . In this context it is interesting to note that the TPOAb-associated SNPs located in TPO and ATXN2 were not associated with clinical thyroid disease . This suggests that the TPOAbs in these individuals may be of less clinical relevance , providing insight into why TPOAb-positive individuals do or do not eventually develop clinical thyroid disease . Our study has some limitations . The validity of the results is restricted to individuals from populations of European ancestry . Future GWASs in populations from non-European descent will be required to determine to which extent our results can be generalized to other ethnic groups . Secondly , we did not perform conditional analyses to further identify secondary association signals within the identified loci , nor did we perform functional studies for the identified variants . Further research is therefore needed to unravel the exact biological mechanism behind the observed associations . The fact that various TPOAb assays were used across the participating cohorts could lead to bias . We therefore used TPOAb-positivity cut-off values as provided by the respective assay manufacturer , instead of using one fixed cut-off value . This is also of clinical importance as in clinical practice most institutions rely on the TPOAb-positivity cut-off as provided by the assay manufacturer . Furthermore , we did not detect heterogeneity in our results , supporting the fact that results obtained with different assays can be combined across cohorts using the z-score based meta-analysis . Finally , as AITD coincides with other autoimmune diseases , our results could be driven by indirect associations with other autoimmune diseases . However , AITD is the most common autoimmune disease in the general population . We furthermore show that carriage of multiple risk alleles is associated with an increased risk of thyroid dysfunction , which underlines the clinical importance of our findings . In conclusion , this first GWAS for TPOAbs identified five newly associated loci , three of which were also associated with clinical thyroid disease . Furthermore , we show that carriage of multiple risk variants is not only associated with a substantial increased risk of TPOAb-positivity , but also with a higher risk of increased TSH levels ( including subclinical and overt hypothyroidism ) and a lower risk of goiter . These genetic markers not only help to identify large groups in the general population with an increased risk of TPOAb-positivity , but may also predict which TPOAb-positive persons are particularly at risk of developing clinical thyroid disease .
For the TPOAb GWAS stage 1 and 2 analyses , and the hypothyroidism , hyperthyroidism and goiter analyses , individuals were recruited from 16 independent community-based and family studies . For the Graves' disease analyses , cases were recruited from the United Kingdom Graves' disease cohort and controls from the British 1958 Birth Cohort . Thyroid cancer cases and controls were recruited from the Nijmegen and Ohio thyroid cancer cohorts . A detailed description of the original cohorts contributing samples is provided in Table 1 and in the Supplementary Material . All participants provided written informed consent and protocols were approved by the institutional review boards or research ethics committees at the respective institutions , and conducted according to the Declaration of Helsinki . Serum TPOAb levels were determined with a range of assays . TPOAb-positives were defined as subjects with TPOAb levels above the assay-specific TPOAb-positivity cut-off , as defined by the manufacturer ( Table 1 ) . Serum TSH and free thyroxine ( FT4 ) levels were determined using a range of assays ( Table 1 ) . Assay-specific TSH and FT4 reference ranges were used , as provided by the manufacturer ( Table 1 ) . Overt hypothyroidism was defined as a high TSH ( i . e . , a TSH level above the TSH reference range ) and a low FT4 . Increased TSH was defined as a high TSH , including persons with overt hypothyroidism or subclinical hypothyroidism ( i . e . , high TSH with a normal FT4 ) . Overt hyperthyroidism was defined as a low TSH and a high FT4 . Decreased TSH was defined as a low TSH , including persons with subclinical or overt hyperthyroidism . The diagnosis of goiter is described in the Supplementary Material , and the diagnosis of Graves' disease and thyroid cancer in the respective cohorts have been described previously [41] . Samples were genotyped with a range of GWAS genotyping arrays ( Supplementary Table S1 ) . Sample and SNP quality control procedures were undertaken within each study . For each GWAS , over 2 . 5 million SNPs were imputed using CEU samples from Phase 2 of the International HapMap project ( www . hapmap . org ) . Genotyping procedures in the stage 2 , Graves' disease and thyroid cancer populations are described in the Supplementary Material . The heritabilities of TPOAb-positivity and serum TPOAb levels were estimated , as described in the Supplementary Material . In stage 1 , we performed a GWAS on TPOAb-positivity as well as a GWAS on continuous TPOAb levels . Persons taking thyroid medication were excluded . Each SNP was tested for association with TPOAb-positivity using logistic regression analyses , adjusting for age and sex . For cohorts with family structure , we approximated the probability of being affected with a linear mixed model adjusting for age and sex . The produced model was used to predict the expected proportion of “risk” ( effective ) alleles in cases and controls , hence giving the means to estimate odds ratios . Only unrelated individuals were considered for the SardiNIA cohort . For the GWAS of continuous TPOAb levels , samples with a TPOAb level lower than the minimum TPOAb assay detection limit ( Table 1 ) were excluded . TPOAb levels were natural log-transformed , and sex-specific , age adjusted standardized residuals were calculated . Each SNP was tested for association with these TPOAb level residuals using linear regression analyses ( additive model ) , correcting for relatedness in studies with family structure . See Supplementary Table S1 for the software used for these analyses . Before meta-analysis , SNPs with a minor allele frequency ( MAF ) <1% or a low imputation quality were excluded ( Supplementary Material ) , after which the results of each GWAS were combined in a population size weighted z-score based meta-analysis using METAL [71] . Genomic control was applied to individual studies if λ>1 . 0 . In stage 2 , we followed-up stage 1 GWAS significant SNPs , as well as promising SNPs not reaching GWAS significance , in an attempt to reach GWAS significant associations by increasing sample size ( Supplementary Material ) . Results from stage 1 and 2 were combined in a population size weighted z-score based meta-analysis using METAL [71] . A z-score based meta-analysis was used to reduce bias that might be induced by different assays . As this method does not provide betas , and we wanted to provide a rough estimate of the actual effect sizes for convenience , we calculated betas using the fixed effects ( inverse variance based ) meta-analysis method . Heterogeneity was tested , applying bonferroni based P-value thresholds of P = 0 . 004 for the TPOAb-positivity analyses and P = 0 . 005 for the TPOAb level analyses . All studies assessed and , if present , corrected for population stratification using principal-component analysis ( PCA ) and/or multidimensional-scaling ( MDS ) , with the exception of SardiNIA and ValBorbera where the high isolation substantiates a lack of stratification ( Table S1 ) [72] , [73] . Lambda values were all ∼1 , indicating that population stratification was overall properly accounted for ( Table S1 ) . To fully remove residual effects , we applied genomic correction to studies were lambda was >1 . The final meta-analyses reported a lambda of 1 . 01 for both the TPOAb-positivity and the TPOAb level GWAS , thus no genomic correction was applied . The variances explained by the GWAS significant SNPs were calculated . We subsequently studied the individual as well as the combined effects of the GWAS significant SNPs on the risk of clinical thyroid disease , as specified in the Supplementary Material . In short , to study combined effects , a genetic risk score was calculated for every person as the weighted sum of TPOAb risk alleles . The associations between the individual SNPs , genetic risk scores and the risk of abnormal thyroid function tests were studied using logistic regression analyses . Logistic regression analyses were used to study the associations with goiter , Graves' disease and thyroid cancer ( Supplementary Material ) . The results of each study were combined in a population size weighted z-score based meta-analysis using METAL [71] . Various bioinformatic tools were searched for evidence for functional relevance of the GWAS significant SNPs and pathway analyses were performed on the Stage 1 lead SNPs ( see Supplementary Material ) . | Individuals with thyroid peroxidase antibodies ( TPOAbs ) have an increased risk of autoimmune thyroid diseases ( AITD ) , which are common in the general population and associated with increased cardiovascular , metabolic and psychiatric morbidity and mortality . As the causative genes of TPOAbs and AITD remain largely unknown , we performed a genome-wide scan for TPOAbs in 18 , 297 individuals , with replication in 8 , 990 individuals . Significant associations were detected with variants at TPO , ATXN2 , BACH2 , MAGI3 , and KALRN . Individuals carrying multiple risk variants also had a higher risk of increased thyroid-stimulating hormone levels ( including subclinical and overt hypothyroidism ) , and a decreased risk of goiter . The MAGI3 and BACH2 variants were associated with an increased risk of hyperthyroidism , and the MAGI3 variant was also associated with an increased risk of hypothyroidism . This first genome-wide scan for TPOAbs identified five newly associated loci , three of which were also associated with clinical thyroid disease . With these markers we identified a large subgroup in the general population with a substantially increased risk of TPOAbs . These results provide insight into why individuals with thyroid autoimmunity do or do not eventually develop thyroid disease , and these markers may therefore predict which individuals are particularly at risk of developing clinical thyroid dysfunction . | [
"Abstract",
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] | 2014 | Identification of Novel Genetic Loci Associated with Thyroid Peroxidase Antibodies and Clinical Thyroid Disease |
The mouse cytomegaloviral ( MCMV ) protein pM27 represents an indispensable factor for viral fitness in vivo selectively , antagonizing signal transducer and activator of transcription 2 ( STAT2 ) -mediated interferon signal transduction . We wished to explore by which molecular mechanism pM27 accomplishes this effect . We demonstrate that pM27 is essential and sufficient to curtail the protein half-life of STAT2 molecules . Pharmacologic inhibition of the proteasome restored STAT2 amounts , leading to poly-ubiquitin-conjugated STAT2 forms . PM27 was found in complexes with an essential host ubiquitin ligase complex adaptor protein , DNA-damage DNA-binding protein ( DDB ) 1 . Truncation mutants of pM27 showed a strict correlation between DDB1 interaction and their ability to degrade STAT2 . SiRNA-mediated knock-down of DDB1 restored STAT2 in the presence of pM27 and strongly impaired viral replication in interferon conditioned cells , thus phenocopying the growth attenuation of M27-deficient virus . In a constructive process , pM27 recruits DDB1 to exploit ubiquitin ligase complexes catalyzing the obstruction of the STAT2-dependent antiviral state of cells to permit viral replication .
Cytomegaloviruses ( CMVs ) constitute prototypical β-herpesviruses . 50–95% of the global adult population are infected lifelong with human CMV ( HCMV ) . HCMV is a leading cause of disease burden of newborns in western countries due to transplacental transmission of the virus from the mother to the foetus during pregnancy [1] . HCMV infections can also cause life-threatening symptoms in immunocompromised individuals . As a result of an intimately shared evolutionary history with their hosts , CMVs are highly species-specific precluding in vivo analysis of HCMV in small animal models , hampering our understanding of HCMV pathogenesis . Infection of mice with mouse cytomegalovirus ( MCMV ) has been proven to be a suitable model to study CMV pathogenesis [2] . A coordinated response of interferons ( IFNs ) together with T- and NK-cells controls MCMV reactivation from latency in vivo [3] . Consistently , cells with deficiencies in either the IFN induction or the IFN signalling system show increased MCMV susceptibility [4]–[9] underscoring the indispensable role of both type I ( IFN-α/β ) as well as type II ( IFN-γ ) IFN for the control of CMV replication . IFNs directly trigger immune responses by inducing antiviral effector mechanisms and indirectly by activating adaptive immune responses . Thereby , IFNs constitute a constant and selecting pressure for CMV , highlighted by the multitude of viral IFN antagonists [10] . IFNs elicit their antiviral activity by initiating specific transcriptional programs . Upon binding of type I IFNs to the cognate receptor , the Janus kinase ( Jak ) -signal transducer and activator of transcription ( STAT ) signalling cascade is activated . Jak1 and tyrosine kinase 2 initiate a phosphorylation cascade at the IFN receptor chain 2 and 1 , respectively . The Janus kinases phosphorylate STAT1 and STAT2 . Phosphorylated STATs dimerize due to a reciprocal SH2-phospho-Tyr-interaction . The STAT heterodimers , together with the IFN regulatory factor 9 ( IRF-9 ) , constitute the IFN stimulated gene factor 3 ( ISGF3 ) , which translocates to the nucleus , binds to IFN stimulated response elements ( ISRE ) of IFN-inducible genes ( ISGs ) and recruits the transcriptional machinery to express the respective gene . We identified the protein pM27 as MCMV-encoded inhibitor of the Jak-STAT signalling cascade [11] . M27 is an early-late expressed gene essential for reducing STAT2 amounts upon MCMV infection . ΔM27-MCMV replication is attenuated upon IFN treatment in vitro , reproducing the observed attenuation in vivo [11] , [12] . Interestingly , ΔM27-MCMV shows a remarkable growth reduction in IFN-γ-treated cells , revealing the importance of an IFNAR1-independent IFNGR1-initiated activation of STAT2 [11] . ΔM27-MCMV induces increased levels of ISGs [13] but does not induce more IFN-β mRNA [14] , consistent with the notion that MCMV antagonizes IFN-β enhanceosome assembly M27-independently before an ISRE-dependent positive feed-back loop can be initiated [14] . The present study aimed to delineate the molecular mechanism and to identify host factors exploited by pM27 . Here we report that pM27 exploits DNA-damage DNA-binding protein ( DDB ) 1-dependent ubiquitin ( Ub ) -ligase complexes to catalyze ubiquitin-conjugation of STAT2 . Ablation of host DDB1 phenocopied genetic deletion of M27 from the viral genome , demonstrating that viral fitness relies on the availability of a distinct host factor , DDB1 .
Having demonstrated that pM27 is essential and sufficient to decrease STAT2 amounts and that both proteins co-precipitate [11] , we intended to elucidate the mechanism of pM27 . MCMV mutants expressing C-terminal HA-epitope tagged pM27 ( M27-HA-MCMV ) or pM28 ( M28-HA-MCMV ) , the gene product of the M28 gene directly adjacent to M27 in the MCMV genome , were proven to be able to reduce STAT2 , whereas ΔM27-MCMV and UV-inactivated virus did not decrease STAT2 amounts ( Figure S1 ) , indicating suitability of above mentioned mutants for further analysis . A quantitative experimental setup reveals a time-dependent decline of endogenous STAT2 amounts upon infection with wt-MCMV but not upon infection with ΔM27-MCMV until 24 h post infection ( Figure S2 ) . Pre-incubation with IFN-γ significantly increased levels of STAT2 but did not comprise pM27 function ( Figure S3 ) . During the early phase ( 24 h post infection ) of MCMV replication pM27 seems to be the only MCMV-encoded protein significantly reducing STAT2 amounts ( Figure S1 , S2 and S5 ) . Nevertheless , at late times of replication ( ≥48 h post infection ) some STAT2 reduction was observed in ΔM27-MCMV infected cells , raising the possibility that additional MCMV gene products might affect STAT2 ( Figure S3 ) . pM27 operates independent of other viral proteins since pM27-Flag expression from a recombinant vaccinia virus ( VACV ) vector ( M27-Flag-VACV ) , but not wt-VACV or a control VACV , dose-dependently reduced the cellular STAT2 amount in mouse M2-10B4 cells and also in human HeLa ( data not shown ) and human MRC-5 cells ( Figure 1A ) indicating that co-factors of pM27 are evolutionary conserved ( see below ) . To elucidate the molecular mechanism of pM27 , we first constructed an expression construct encompassing the coding sequence of the STAT2 gene devoid of the complete 3′-UTR driven by the constitutive active HCMV major IE promoter . Next , a stably transfected cell line ectopically expressing a C-terminal HA-epitope tagged version of STAT2 complementing STAT2-deficient mouse fibroblasts [15] was generated , designated STAT2-HA , which was permissive for MCMV . The transfectant produced HA-tagged STAT2 at high levels which could be detected either by STAT2- or HA-specific antibodies and became tyr-phosphorylated upon type I IFN treatment , followed by translocation into the nucleus , formation of ISGF3 complexes and induction of IRF-1 expression ( Figure S4 ) , indicating a preserved responsiveness and signalling function of STAT2-HA . M27-HA-MCMV , but not ΔM27-MCMV , reduced the amount of STAT2 in STAT2-HA cells ( Figure S5 ) , indicating that neither the intrinsic STAT2 promoter nor the 3′-UTR are required for the observed reduction , in accordance with a post-transcriptional mechanism of STAT2 depletion . To confirm the reduction of STAT2-HA , STAT2-HA cells were infected with M27-Flag-VACV , resulting in a loss of STAT2-HA in a time- ( Figure S6 ) and dose-dependent manner ( Figure 1B ) , reproducing the data received with endogenous STAT2 . Immunofluorescence staining revealed a decrease of STAT2 amounts upon transfection of M27 expression plasmids ( Figure S7 ) , formally ruling out an intracellular sequestration of STAT2 in detergent resistant compartments . VACV encodes a multitude of IFN antagonists [16] but does not reduce STAT2 amounts ( Figure 1B ) while interfering with STAT2 phosphorylation and activation [17] . The ability of VACV-expressed pM27-Flag to affect STAT2 thus suggested that STAT2 is recognized by pM27 in its unphosphorylated and bona fide monomeric state . To test this hypothesis further , STAT1- , STAT2- , STAT3- and IFNAR1-deficient cells were infected with M27-Flag-VACV and the relative efficiency of pM27 to reduce the amounts of STAT2 was analyzed . M27-Flag-VACV , but not wt-VACV , induced the reduction of STAT2 in all cells ( Figure 1C ) , indicating that pM27 can recognize non-phosphorylated STAT2 molecules , independent of their incorporation into ISGF3 complexes or previously described STAT3∶STAT2 heterodimers [18] . To assess if pM27 affects the pre-existing STAT2 protein pool , STAT2 amounts were compared in presence and absence of pM27 upon administration of the protein synthesis inhibitor cycloheximide ( CHX ) and the transcription inhibitor actinomycin D ( ActD ) . The pM27-dependent STAT2 reduction preceded the reduction upon blockade of de novo protein biosynthesis ( Figure 2A ) . 5 h post MCMV infection STAT2 was hardly detectable whereas combined treatment with CHX and ActD did not significantly affect STAT2 - a finding which is consistent with the previously described long half-life of STAT2 [19] . Next , pulse-chase experiments were performed to compare the STAT2-HA half-life in mock-infected and M27-HA-MCMV-infected cells . Cells were labelled with 35S-L-Met/L-Cys and chased for the indicated time ( Figure 2B ) before the cells were lysed and STAT2-HA protein was precipitated . Upon infection with M27-expressing MCMV the half-life of STAT2-HA was strongly reduced when compared to mock-infected cells , which was not observed upon infection with ΔM27-MCMV either ( Figure 2C ) . pM27 protein longevity lasted more than 9 h ( Figure 2C ) . Altogether , the results demonstrated that STAT2 protein stability becomes strongly down-regulated by pM27 . Interestingly , an additional long-lived ∼125 kDa protein emerged which was co-precipitated with pM27-HA irrespectively of STAT2 presence ( Figure 2C ) . To investigate whether pM27 uses the Ub proteasome pathway , cells were treated with MG132 , an inhibitor of the proteasome . STAT2-HA levels became largely restored and high-molecular weight forms of STAT2 accumulated in the presence of pM27 ( Figure 3A ) . Exploiting the intrinsic host-shut-off mechanism of VACV , thereby blocking STAT2 neo-synthesis , we quantified STAT2-HA amounts upon infection with wt-VACV in comparison to M27-Flag-VACV in presence and absence of MG132 . The STAT2-HA half-life was drastically reduced by pM27 but could be largely restored upon administration of MG132 ( Figure S8 ) . To confirm this phenotype for endogenous STAT2 , NIH3T3 cells were infected with M27-Flag-VACV and treated with MG132 . As shown in Figure S9 , higher molecular weight forms of STAT2 could be detected by a STAT2-specific antibody . When the cells were infected with pM27-encoding VACV only for a short period precluding complete STAT2 degradation , a modification of STAT2-HA was observed in the presence of pM27-Flag and lactacystin , an inhibitor of the proteasome ( Figure 3B ) . The modification was not seen upon expression of a non-functional truncation mutant , pM27 1–487 , in untreated or in DMSO solvent-treated cells . The identity of STAT2 was further confirmed by comparison with STAT2−/− cells ( Figure 3B ) . It has been demonstrated that viral gene expression and genome replication of both CMV and VACV are blocked by inhibitors of the proteasome [20] , [21] . To exclude that STAT2 restoration by proteasome inhibitors occurs indirectly due to reduced pM27-HA expression , CHX was co-administrated with MG132 and lactacystin to terminate protein synthesis . Under this regime pM27 amounts remain unchanged upon proteasome inhibition . Nevertheless , restoration and modification of STAT2 was still evident ( Figure 3C ) , indicating that the proteolytic activity of the proteasome is directly required for pM27-induced STAT2 degradation . To corroborate that the STAT2-modifying moiety is Ub , STAT2-HA cells were infected with M27-Flag-VACV before treatment with MG132 . STAT2-HA was precipitated and analyzed using an Ub-specific antibody . As expected , MG132 treatment stabilized the otherwise degraded STAT2 in the presence of pM27-Flag ( Figure 3D ) . Higher molecular weight forms of STAT2-HA were recognized by an Ub-specific antibody in the presence of pM27 and MG132 . In conclusion , these results indicate that pM27 induces STAT2 ubiquitination targeting the protein for proteasomal degradation . As we did not detect sequences or motifs that are characteristical for Ub-ligases within M27 we surmised that pM27 serves an indirect function to shuttle STAT2 into the Ub-proteasome pathway . To identify potential interaction partners of pM27 , a co-immunoprecipitation ( IP ) strategy was ensued . STAT2-HA cells were infected with M27-HA-MCMV and metabolically labelled . This allowed to follow up the fate of pM27-HA and STAT2-HA simultaneously . By comparing STAT2-HA transfectants with STAT2−/− cells , pM27-HA and STAT2-HA derived co-precipitations could be distinguished . Interestingly , antibodies recognizing pM27-HA specifically co-precipitated a ∼125 kDa protein reproducing the observation made before ( compare Figure 4A with Figure 2C ) . The ∼125 kDa protein was visible after pM27-HA IP but not upon precipitation of pM28-HA ( Figure 4B ) . The co-precipitated protein was also observed in NIH3T3 cells and could be freed by addition of an excess of uncoupled HA-peptides ( Figure S10 ) , confirming that it was recovered via an epitope-specific interaction of HA antibodies . Next , split IP samples were simultaneously analyzed by autoradiography upon metabolic 35S-Met/Cys-labeling and by anti-HA immunoblotting . The co-precipitated ∼125 kDa protein was visible in the autoradiography but remained undetectable in the immunoblot with HA antibodies ( Figure 4B ) indicating that it is not derived from pM27-HA . Upon up-scaling and optimization the co-precipitated protein could be visualized by Coomassie staining of the gels ( Figure 4C ) . The co-precipitating ∼125 kDa protein was also observed upon expression of pM27-Flag by a VACV ( Figure 4D ) confirming its interaction with pM27 . Recovery of the ∼125 kDa protein was achieved in STAT2-HA and in STAT2−/− cells ( Figure 4D ) , ruling out that the protein is STAT2 , a degradation product of STAT2 or that STAT2 is required for its interaction with pM27 . In summary , these experiments identified the ∼125 kDa protein as a novel cellular co-factor of pM27 . Since pM27-Flag co-precipitated further proteins of various sizes ( Figure 4D ) pM27 was assumed to associate with a cellular multi-protein complex . The 125 kDa band was cleaved from a Coomassie-stained gel and analyzed by mass-spectrometry . Five peptides ( YLAIAPPIIK , ALYYLQIHPQELR , VTLGTQPTVLR , IVVFQYSDGK and SVLLLAYKPMEGNFEEIAR ) were found , all belonging to DDB1 , a host 127 kDa protein , concordant with the size of the pM27 co-precipitated material . Two further replications of DDB1-pM27-co-precipitations and subsequent mass-spectrometry analysis reached a peptide coverage rate of 24 . 8% and 30 . 2% of the ∼127 kDa full length protein , respectively , unequivocally defining DDB1 as pM27-interacting protein . DDB1 is an adapter protein for the cellular Cul4A-RocA E3-Ub-ligase complex , previously shown to be an interaction partner for paramyxoviral IFN antagonists targeting STAT molecules for proteasomal degradation [22]–[25] . In cells , DDB1 fulfils a function as component of a multimeric ubiquitin-ligase complex involved in nucleotide excision repair and induces ubiquitination of the licensing factor Cdt1 upon UV irradiation [26] . Next , the pM27-DDB1 association was confirmed by immunoblotting with a DDB1-specific antibody upon pM27 immunoprecipitation ( Figure 5A ) . Moreover , STAT2 was not required for the binding of DDB1 by pM27HA ( Figure 5A ) . As expected , DDB1 was not retrieved upon anti-HA IP from cells infected with wt-MCMV lacking the HA epitope fused to the M27 sequence ( Figure 5B ) . Conversely , DDB1 co-immunoprecipitation was seen with antibodies recognizing pM27-Flag expressed by VACV , irrespective of the presence of STAT2 and in the presence of MG132 , confirming that the interaction occurs independently of the epitope tag and the activity of the proteasome ( Figure 5C ) . The retrieval of pM27-HA-DDB1 complexes was pM27-dose-dependent ( Figure S11 ) and resistant to calf intestine phosphatase ( CIAP ) , the phosphatase inhibitor NaF , the detergent CHAPS and tolerated more than 500 mM NaCl and up to 5 mM EDTA ( data not shown ) , reflecting a strong protein-protein interaction . DDB1 is involved in UV-induced DNA damage responses , and the UV-DDB complex consists of the two separate proteins DDB1-p127 and DDB2-p48 [27] . Hamster cells induce significantly less DNA-binding UV-DDB complexes due to the complete absence of DDB2 [28] . When pM27 was expressed in Chinese hamster ovary ( CHO ) cells , DDB1 was readily retrieved by co-immunoprecipitation of pM27-HA but not pM28-HA ( Figure S12 ) , suggesting that the interaction of the proteins can occur independently of DDB2 . In addition to DDB1 further pM27 co-precipitated proteins were noticed ( Figure 4D ) . Since DDB1 acts as an adapter protein for the Cul4A-RocA complex , we next analysed the co-precipitation of pM27 with the scaffold protein Cul4A which recruits the catalytic RING-finger-containing Ub-ligase RocA . A pM27-Cul4A co-precipitation was weakly visible in mouse cells by immunoprecipitation , presumably due to a poor reactivity of Cul4A antibodies to mouse Cul4A . We therefore expressed pM27 in human cells resulting in a complete STAT2 down-regulation ( Figure 1A ) , reproducing co-precipitation of Cul4A with pM27 and DDB1 ( Figure 5D ) . We concluded that pM27 co-precipitates DDB1 and Cul4A irrespective of the presence of STAT2 or DDB2 . To define the essential domain for the interaction of pM27 with DDB1 a panel of Flag epitope tagged truncation mutants of pM27 expressing VACVs was constructed . As depicted in Figure 6A only the truncation of the first N-terminal 68 amino acids and the last C-terminal 30 amino acids were fully dispensable for the ability of pM27 to induce STAT2 degradation . All functional pM27-Flag mutants able to induce STAT2 degradation invariably co-precipitated DDB1 ( Figure 6B ) , revealing a correlation between STAT2 degradation and their DDB1 binding capacity . Mutants lacking the N- or C-terminal 195 amino acids showed a reduced but still detectable binding to DDB1 without being able to degrade STAT2 ( Figure 6B ) . From the fact that neither the first 195 N-terminal nor the last 195 amino acids were essential for DDB1 precipitation we conclude that the minimal DDB1-co-precipitating sequence lies within aa195–487 of pM27 . To corroborate this finding , we constructed a mutant lacking the N- as well as the C-terminus ( pM27-Flag- ( Δ5-118 ) -651 ) . This mutant was still capable to co-precipitate DDB1 upon transient transfection ( data not shown ) and upon expression from a recombinant VACV ( Figure 6C ) . Notably , within this minimal functional domain DxR motifs and a conserved CxCxxC motif are present ( Figure S13 ) : Binding partners of DDB1 have the consensus motif WDxR , or less frequently YDxR [29] , [30] . PM27 contains a WD dipeptide and four DxR sequences , one of them forming the sequence YDxR ( aa544–aa547 ) . We therefore decided to mutate these motifs . Based on the well-described abrogation of DDB1-binding due to a single mutation ( R273H ) in the DxR motif of DDB2 , found in individuals with xeroderma pigmentosum group E ( [31] ) , we mutated the arginine ( R ) to histidine ( H ) . All four mutant proteins were fully functional in terms of DDB1 co-precipitation ( Figure 6D ) and in terms of STAT2-degradation ( exemplarily shown for R435H –Figure S14 ) indicating functional redundancy of these sites or that pM27 exhibits an unusual DDB1 interaction . SV-5 protein , a paramyxoviral DDB1-binding protein , contains two zinc binding pockets critically required for DDB binding [32] , one of which with the sequence CxCxxC ( aa206–211 ) [33] . Remarkably , a CxCxxC motif is also present in pM27 ( aa274–279 ) raising the question if pM27 is also a Zn2+-binding protein . Intriguingly , the CxCxxC motif is conserved throughout cytomegalovirus evolution in M27 homologs with the exception of HCMV and CCMV ( Figure S13 ) . We therefore mutated individual cysteins to alanine . All three mutant proteins were impaired in their capacity to co-precipitate DDB1 ( Figure 6D ) upon transient transfection into HeLa cells and upon expression by recombinant VACVs ( Figure S14 ) . Consistent with the hypothesis of DDB1 requirement for pM27-mediated STAT2 degradation , the C279A mutant shows a diminished STAT2 degradation potential ( Figure S14 ) . Like MCMV , HCMV induces a down-regulation of STAT2 in infected cells , which is sensitive to inhibitors of the proteasome . This effect occurs independent of pUL27 , the HCMV homolog of pM27 [34] . Consistently , pUL27 expression by VACV neither degraded STAT2-HA nor was sufficient to co-precipitate DDB1 ( Figure 6B and Figure S15 ) . In contrast , pM27 readily co-precipitated DDB1 in human cells ( Figure 5D ) , consistent with the high degree of sequence conservation of DDB1 and the functional competence of pM27 in human cells . From this comparative analysis between HCMV and MCMV we conclude that despite the phenotypical match of STAT2 degradation via the ubiquitin-proteasome pathway the genetic and molecular basis between both viruses is remarkably different . Recently , a floxed DDB1 allele has been cloned and recombined into the DDB1 gene locus in mice . Global Cre-mediated DDB1 excision results in embryonic lethality [35] . Additionally , conditional DDB1 gene knock-down causes a severe growth defect and apoptosis in the chicken DT40 B cell line [36] . This approach prompted us to carefully ablate DDB1 synthesis by siRNA to analyze the functional relevance of DDB1 for the pM27-dependent down-regulation of STAT2 . Transfection of DDB1-specific siRNAs induced a continuous reduction of DDB1 protein amounts ( Figure S16 ) . To exclude that siRNA transfection influences the levels of STAT2 due to type I IFN induction , we performed the experiment in IFNAR1-deficient fibroblasts . As expected , infection with M27-HA-MCMV , but not ΔM27-MCMV , induced STAT2 degradation in cells treated with control siRNA . Conversely , siRNA-mediated knock-down of pM27 restored STAT2 ( Figure 7A , lanes 6 & 12 ) . Likewise , DDB1 ablation fully restored STAT2 amounts 4 h post infection ( lane 4 ) and partially after 24 h ( lane 10 ) . Consistent results were obtained upon pM27 expression from VACV ( data not shown ) . These findings establish that DDB1 is a prerequisite to execute effective STAT2 proteolysis by pM27 . M27-positive MCMVs antagonize the induction of IFN-γ-stimulated , STAT2-containing , ISRE-DNA-binding complexes ( Figure S17 ) . Consistently , replication of the ΔM27-MCMV mutant is characterized by its enormous susceptibility towards IFN-γ in vitro and in vivo [11] . To test whether DDB1 is relevant for this effect , we transfected MEF with DDB1-specific- ( or control- ) siRNAs 48 h prior to infection before the cells were incubated with IFN-γ24 h prior to infection . The MCMV infection was performed with a luciferase expressing mutant , Δm157-MCMV:luciferase in which the coding sequence of m157 has been replaced by the luciferase gene , and cells were harvested 1 , 2 and 3 days post infection . Luciferase activity paralleled the kinetics of MCMV replication . Accordingly , luciferase activity was inhibited upon IFN-γ pretreatment of MEF ( Figure 7B , left panel ) . While DDB1 knock-down precluded viral luciferase expression by the M27-positive Δm157-MCMV:luciferase mutant in IFN-γ preincubated cells , luciferase production was unaffected in cells which were not IFN-treated ( Figure 7B ) . The DDB1 knock-down and reduced viral gene expression was confirmed by western blot analysis of cell lysates using DDB1- and pp89-IE1-specific antibodies ( data not shown ) . The experiment was repeated with wt-MCMV and the progeny virus yield was quantified by standard plaque titration . IFN-γ pre-treatment of cells , which had been treated with DDB1-specific siRNA , strongly impaired MCMV growth ( Figure 7C ) . In clear contrast , the replication of ΔM27-MCMV was highly susceptible to IFN-γ and was not further impaired by ablation of DDB1 by siRNA ( Figure 7C , lower panel ) . Altogether , these data indicate that DDB1 by itself is not required for MCMV replication . However , the virus requires DDB1 to overcome the STAT2-dependent antiviral capacity of IFN-γvia pM27 . The phenocopy of host DDB1 depletion and viral M27-deletion provides complementary evidence for a model in which DDB1 is indispensable for pM27 subversion of the antiviral IFN-γ response .
Recently , genome-wide siRNA-based large-scale screening approaches have been conducted to uncover host factors required for replication of certain viruses including HIV and influenza [37]–[39] , representing new potential targets for antiviral therapy . Despite the fact that relevant factors were successfully identified , these attempts suffer from two common shortcomings . First , the implicit counter-selection against siRNAs which are detrimental for cell survival , i . e . a screening bias against ‘essential’ host proteins . It is tempting to speculate that those ‘essential’ proteins are exactly the host factors many viruses favour as interaction partners due to their evolutionary conservation and the inability of the host to mutate or delete the responsible genes . We feel that our results exemplify the fundamental need to pursue ‘top-down’ approaches to refine biological observation ( e . g . the growth attenuation of ΔM27-MCMV upon conditioning with IFN-γ ) allowing the characterization of underlying molecular mechanisms and finally the identification of ( conditional ) essential host factors . We were surprised to see that viral infection ( presumably due to control over cell cycle progression and apoptosis ) , increased the ability of cells to resist knock-down of DDB1 , raising the apparent question whether it might be reasonable to conduct above mentioned siRNA screens without any previous negative pre-selection . Second , our study documents that distinct host factors are not constitutively essential but become essential under certain conditions defined by the host cell environment , e . g . the IFN-induced antiviral state . It is well possible that only the simulation of conditions which are closer to infected and inflamed organs leads to additional induced essential host factors important for viral replication because they escaped the screening performed under standard cell culture conditions . pM27 has adopted a remarkable substrate specificity to capture its cellular target , monomeric STAT2 [11] . Several findings are fully in accord with the notion that the down-regulation of STAT2 is achieved via the ubiquitin-proteasome pathway: i ) pM27 affected the half-life of STAT2 , ii ) STAT2 reduction was sensitive to proteasome inhibitors , iii ) in the presence of proteasome inhibitors pM27 generated higher molecular weight forms of STAT2 , and iv ) the modification of STAT2 was shown to be conjugated Ub . Given the long protein half-life of STAT2 catalyzing its proteolytic destruction represents a direct and immediate mechanism to shut off its antiviral function . The recognition and binding of STAT2 requires a large and central domain of the pM27 protein as revealed by probing of a set of truncation mutants . Co-IP studies revealed the prominent binding quality of pM27 to a second host protein which was identified to be DDB1 . Forming an adaptor protein of the Cul4A-RocA Ub-ligase complex , the linkage of pM27 with DDB1 generated the hypothesis that pM27 delivers STAT2 to proteasomal destruction via this factor . Two findings support the notion that DDB1 is indeed required for the loss of STAT2 in MCMV-infected cells: i ) truncation mutants of pM27 induced the break-down of STAT2 only when their binding to DDB1 was fully intact; ii ) siRNA-mediated knock-down of DDB-1 protected STAT2 from degradation . Binding partners of DDB1 have the consensus motifs WDxR , or less frequently YDxR [29] , [30] . pM27 contains a WD dipeptide and four DxR sequences , one of them forming the sequence YDxR ( aa544–aa547 ) . Nevertheless , single R>H mutations of the DxR motifs did not impair DDB1 co-precipitation . This might either indicate functional redundancy or that pM27 exhibits an unconventional DDB1-binding mode . Based on experimental data obtained in the fission yeast ( Schizosaccharomyces pombe ) , a so called DDB1-box has been defined to be present in DDB1 binding partners like WDR21 and comprising a RQLG-like motif surrounded by hydrophobic amino acids in positions −7 to −3 and +7 or +9 [40] . PM27 bears two non-identical motifs , which resemble this DDB-box within aa232–256 and aa358–377 , overlapping with the domain that is required for degradation of STAT2 . Future analysis will define further essential amino acids which are critical for DDB1-pM27-complex formation and might delineate the molecular requirements for recruitment and exploitation of DDB1-Cul4A-RocA complexes . The finding that the CxCxxC motif is important for DDB1 co-precipitation suggests that pM27 harbours a coordinative Zn2+ binding pocket . Interestingly , this domain is conserved in different cytomegaloviruses ( Figure S13 ) , raising the apparent question whether the basic function of the pM27 homologs , proteasomal degradation , might also be conserved . At the first glimpse pM27 seems to imitate paramyxoviral SV-5 V-proteins which recruit DDB1 and induce proteasomal degradation of STAT proteins . Neither pM27 nor SV-5 V-protein contain a fully conserved WDxR motif . Besides the CxCxxC motif , pM27 and the SV-5 V-protein are considerably different with regard to structure , function and substrate recognition and they do not share homologous amino acid stretches . V-proteins discriminate between human and mouse STATs and require the presence of both STAT1 and STAT2 to induce the degradation of the other [41] , [42] , whereas pM27 induces the selective degradation of human and mouse STAT2 as a monomer . Several biological observations further imply differences in their molecular functions . pM27 does not affect the induction of type I IFN [14] contrasting with V-proteins [43] , [44] . Stable expression of pM27 was not possible ( M . Trilling , unpublished observation ) but was readily achieved for SV-5 V-protein [45] , suggesting a different mode of interaction with DDB1 which is essential for cell survival [36] . MCMV can arrest the cell cycle of infected fibroblasts both in G1 and in G2 [46] . Since DDB1 is required especially for proliferating cells [35] , an attractive hypothesis would be that MCMV can afford a blockade of DDB1 functions due to its ability to arrest the cell cycle prior to the DDB1-sensitive checkpoint . In line with this hypothesis , DDB1 knock-down did not abrogate MCMV replication in MEF by itself , but became strongly antiviral if cells were pretreated with IFN-γ . Given that DDB1 is expressed ubiquitously in all mouse tissues [35] the conditional exploitation of DDB1 by a proviral protein like pM27 appears to be a perfect strategy which combines the need for a broad cell tropism to establish ‘replication factories’ in a large variety of tissues with the defence against the permanent encounter of omnipresent IFN-γ which is produced in response to the herpesviral life style bringing sustained immune exposure .
MRC-5 ( ATCC CCL-171 ) , M2-10B4 ( ATCC CRL-1972 ) , immortal STAT2−/−- [15] and STAT1−/− mouse fibroblasts [47] , crisis immortalized IFNAR1-deficient ( generated from primary IFNAR1-deficient MEF [11] ) and primary MEF ( prepared as described [48] ) were grown in Dulbecco's modified eagle medium ( D-MEM ) with 10% foetal bovine serum , streptomycin , penicillin and 2 mM glutamine . NIH3T3 cells were grown in 10% newborn calf serum . STAT2-HA cells were generated from STAT2−/− cells [15] . STAT2-HA [11] was subcloned into a pcDNA3 . 1 ( Invitrogen ) -derived pcDNA3 . 1-zeocin expression vector . Cell lines were selected under 200 µg/ml zeocin ( Invitrogen ) . IFN-γ ( #12500-1 ) was purchased from PBL Biomedical Laboratories , New Jersey , USA . Inhibitors of the proteasome ( MG132 and lactacystin ) were purchased from Boston Biochemicals , USA . swt-like MCMV MW97 . 01 , ΔM27-MCMV , M27-HA-MCMV , M27-Flag-VACV and STAT2-HA-VACV have been described [11] . M28-HA-MCMV was generated by amplifying a frt-site flanked kanar-cassette using primers containing M28-homologous sequences prolonged by an HA-epitope encoding sequence ( underlined ) : AZ-M28-HA1: TGCGGGCTCCGTCCGGGATAGCCGAGACCTGCGTGCCCACGCTCGGGTACCCATACGATGTTCCAGATTACGCGTGACCAGTGAATTCGAGCTCGGTAC and AZ-M28-2: AGGCGAGGCGAAACTGGCGGGATAACTGCAAGAGAGGGGAAAAGCGGTCGATCCCAGCCGGACCATGATTACGCCAAGCTCC using pFRT1 as template . The PCR fragment was introduced into the MCMV-BAC by homologous recombination in E . coli . The kanar-cassette was excised from the BAC by FLP-mediated recombination . m157 was deleted accordingly by zeocin selection after replacement of the m157 coding sequence against a zeor-cassette by homologous recombination between the MCMV-BAC and a PCR amplificate generated with the primers: AZ-m157-1-CAGGAGAATCTGAACCCCGATATTTGAGAAAGTGTACCCC GATATTCAGTACCTCTTGAC CCAGTGAATTCGAGCTCGGTAC and AZ-m157-2-AGATCGTGACCATTATCACCAAGATAGTTCCCACCATAATTCCCATCGTCACTAGAGTCGGACCATGATTACGCCAAGCTCC and pFRT-Zeo as template . Afterwards zeor was replaced by the luciferase gene ( derived from pTA-luc [Clontech] ) by homologous recombination between the Δm157-MCMV:zeor BAC and a vector , harbouring a luciferase gene flanked by 800 nts of the MCMV genome , surrounding the m157 coding sequence . BAC-derived MCMV mutants were reconstituted in primary MEFs und correct mutagenesis was confirmed by restriction fragment pattern analysis and PCR ( data not shown ) . Truncation mutagenesis of pM27-Flag-VACV was performed based on the described VACV expression plasmid p7 . 5k131-M27-Flag . The C-terminal sequence of the M27 ORF was amplified with the Az-M27-m1_forw: 5′-CAGAAGATCGGCACGAAGTACC-3′ primer with either the MF-M27-m2_rev: 5′-CGCGCGACTAGTCTCGTTGTCGTCGTCCTCGTAG-3′ or - MF-M27-m4_rev: 5′-CGCGCGACTAGTGGAGCCCGACGAATCCTTGTC-3′ . Amplificates were cleaved by BamHI and SpeI ( underlined , primer intrinsic site ) and cloned into p7 . 5k-M27Fl/SphI vector between an N-terminal fragment of M27 and an in-frame C-terminal Flag-epitope . For N-terminal truncations , M27-intrinsic restriction sites ( ApaI , SacII , PvuI , NcoI , BamHI and MscI ) were used together with a vector intrinsic BglII site . After re-ligation the next ATG in frame served as start codon . The pM27-Flag- ( Δ5-118 ) -651 mutant was constructed by replacing the C-terminal part of the Δ5-118 ‘SacII’ mutant with the truncated C-terminal sequence using an internal BamHI site . VACV mutants were selected with BrdU in tk−143 cells . Site-directed mutagenesis of pM27-Flag was performed using the Quick Change kit ( Stratagene ) according to the instructions of the manufacturer using the following primers and its respective reverse complementary primers: KL-C274A: 5′-catctacgatcaactcGCGtactgtcgcgagtgtc-3′ , KL-C276A: 5′-cgatcaactctgttacGCGcgcgagtgtcggatgc-3′ , KL-C279A: 5′-gttactgtcgcgagGCGcggatgcgccgggg-3′ , KL-R435H: 5′-gcgacgtcgacgccCACatccgcgcgggagc-3′ , KL-R451H: 5′-gtcgcctccgaccccCACcaggacggcatctcg-3′ , KL-R477H: 5′-caccttctcggacgagCACcccgacggctacgagg-3′ and KL-R547H: 5′-gaggatgtacgacgagCACccgctggccggcttc-3′ . Mutations were confirmed by sequencing . UV inactivation of viruses ( MCMV and VACV ) was done by exposing viruses for 25 min to UV light ( 254 nm ) from a light source 10 cm afar . Cells were lysed in RIPA+-buffer ( 50 mM Tris-HCl , 150 mM NaCl , 1% [vol/vol] IGEPAL , 1% Na-Deoxycholate [vol/vol] , 0 . 1% [weight/vol] SDS , 1 mM DTT , 0 . 2 mM phenylmethylsulfonyl fluoride ( PMSF ) , 1 µg/ml leupeptin , 1 µg/ml pepstatin , 50 mM NaF , 0 . 1 mM Na-vanadate with Complete protease inhibitors ( Roche ) pH 7 . 5 ) . Samples were normalized according to Bradford protein staining and equal amounts were subjected to denaturing SDS-PAGE . Gels were blotted on nitrocellulose membranes ( Schleicher and Schuell ) and probed with indicated antibodies . The same membrane was used and consecutively stripped with reblot solution ( Calbiochem ) . The following commercially available antibodies were used: α-β-actin , α-Flag M-2 and α-HA from Sigma-Aldrich; α-IRF-9 , α-STAT1 , α-mSTAT2 , α-STAT3 from Santa Cruz; α-Cul4A ( Acris ) , α-DDB1 ( Bethyl ) , α-pp89-IE1 ( Croma101 , kindly provided by Stipan Jonjić , Rijeka , Croatia ) , α-hSTAT2 ( Upstate ) and α-Ub ( Dako ) . Immunopreciptation was done as described . Briefly , cells were lysed ( lysisbuffer: 0 . 1 mM EDTA; 200 mM NaCl; 10 mM KCl; 10 mM MgCl2; 10% [vol/vol] glycerol; 20 mM HEPES [pH 7 , 4]; 0 . 5% [vol/vol] IGEPAL; 0 . 1 mM PMSF; 1 mM DTT; 0 . 4 mM pepstatin A; 0 . 1 mM Na-vanadate; Complete protease inhibitor ( Roche ) ) . Lysates were spun ( 30 min at 4°C and 16000 g ) and IP antibody was added to the supernatant . Immune complexes were precipitated with Protein-G-Sepharose ( Amersham ) . The pellet was washed by 6–10 consecutive rounds with lysis buffer . For metabolic labelling and pulse-chase experiments cells were starved ( 30 min ) in L-Met-/L-Cys-free media and subsequently pulsed ( 90 min ) with ∼10 MBq/∼106 cells EasyTag Express 35S protein labelling mix ( PerkinElmer ) . After the pulse cells were washed 3 times with chase media ( 10%-FBS D-MEM supplemented with 1 . 5 mg/ml L-Met/L-Cys ) and chased as indicated . Immune complexes were separated by SDS-PAGE . Gels were either stained by silver- or Coomassie-staining or fixed , dried and visualized by autoradiography . 2 . 5–7 . 5 * 104 primary MEF cells were transfected with siRNA using RNAiMax transfection reagent ( Invitrogen ) following manufacturers instructions . The siRNAs were purchased from IBA . The following siRNAs were used for the knockdown: DDB1 ( 5′-[PO4] r ( AACCUGUUGAUUGCCAAAAACTT ) -3′ ) , luc-siRNA ( 5′-[PO4] r ( CUUACGCUGAGUACUUCGATT ) -3′ ) and M27 ( 5′-[PO4] r ( CAAUAAGCCCUUUAAUCAC ) dTdT-3′ ) . | Cytomegaloviruses are strictly species-specific . Mouse cytomegalovirus ( MCMV ) is a prototypical β-herpesvirus , infecting Mus musculus as natural host and is closely related to the human pathogenic cytomegalovirus ( HCMV , HHV-5 ) which both establish lifelong infection . Thus , MCMV infection constitutes an important model for HCMV pathogenesis . Cytomegaloviral evasion from innate immunity has been observed in many respects , but the molecular mechanisms of most viral factors are still elusive . We recently identified the MCMV-encoded protein pM27 to be required for efficient viral replication in the presence of interferons in vitro and to be essential in vivo . We identified STAT2 , a mediator of interferon signalling , as target of pM27 . Here we identify the cellular machinery exploited by pM27 to reduce the STAT2 protein half-life . PM27 was sufficient to induce poly-ubiquitination of STAT2 , tagging it for proteasomal degradation . Since pM27 lacks domains found within ubiquitin-ligases , we conducted a search for cellular co-factors . We found DDB1 , an essential cellular ubiquitin-ligase complex adaptor protein , to associate with pM27 . Ablation of DDB1 increased viral susceptibility towards interferon , phenocopying the attenuation of ΔM27-MCMV . This defines DDB1 as conditional essential host factor of CMV replication . Our findings exemplify how cytomegaloviruses exploit an essential host protein to circumvent innate immunity . | [
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] | 2011 | Identification of DNA-Damage DNA-Binding Protein 1 as a Conditional Essential Factor for Cytomegalovirus Replication in Interferon-γ-Stimulated Cells |
The succession of molecular events leading to eukaryotic translation reinitiation—whereby ribosomes terminate translation of a short open reading frame ( ORF ) , resume scanning , and then translate a second ORF on the same mRNA—is not well understood . Density-regulated reinitiation and release factor ( DENR ) and multiple copies in T-cell lymphoma-1 ( MCTS1 ) are implicated in promoting translation reinitiation both in vitro in translation extracts and in vivo . We present here the crystal structure of MCTS1 bound to a fragment of DENR . Based on this structure , we identify and experimentally validate that DENR residues Glu42 , Tyr43 , and Tyr46 are important for MCTS1 binding and that MCTS1 residue Phe104 is important for tRNA binding . Mutation of these residues reveals that DENR-MCTS1 dimerization and tRNA binding are both necessary for DENR and MCTS1 to promote translation reinitiation in human cells . These findings thereby link individual residues of DENR and MCTS1 to specific molecular functions of the complex . Since DENR–MCTS1 can bind tRNA in the absence of the ribosome , this suggests the DENR–MCTS1 complex could recruit tRNA to the ribosome during reinitiation analogously to the eukaryotic initiation factor 2 ( eIF2 ) complex in cap-dependent translation .
Eukaryotic translation reinitiation is a process that is only recently becoming understood at the mechanistic and functional levels . Unlike prokaryotic ribosomes , eukaryotic ribosomes were thought to engage mainly in a single round of initiation , extension , and termination on an individual mRNA . After recruitment of the ribosomal 40S subunit to the mRNA , often via the 5′ cap , the 40S scans to locate the first appropriate AUG start codon for joining of the 60S subunit and commencement of translation . After translating this open reading frame ( ORF ) and terminating , the 60S subunit dissociates from the mRNA , leaving the 40S subunit bound to the mRNA . In most cases , the 40S subunit then also dissociates from the mRNA , leaving additional downstream ORFs on the mRNA untranslated . However , when the translated ORF is short , as is the case for many upstream open reading frames ( uORFs ) , the 40S can remain bound to the mRNA , resume scanning , and reinitiate translation at a downstream ORF [1] . Indeed , recent ribosome profiling studies have found pervasive translation of uORFs [2–4] , indicating that in many of these cases , translation reinitiation is important for permitting translation of the main ORF on the mRNA . Both standard cap-dependent translation initiation and translation reinitiation consist of a succession of events that are carefully orchestrated to result in initiation of translation . In the case of cap-dependent translation , this has been carefully studied and is known to involve the recruitment of tRNA via the ternary complex to the small ribosomal subunit yielding the 43S preinitiation complex ( PIC ) and the subsequent recruitment of this complex to the mRNA cap via the eukaryotic initiation factor 4F ( eIF4F ) complex . In the case of translation reinitiation , however , the succession of events is not yet clear . In particular , it is not known which factors are responsible for recruiting a new initiator tRNA to the ribosome and how this occurs . Several factors have been implicated in promoting the reinitiation process . One such factor is eukaryotic initiation factor 3 ( eIF3 ) , which remains associated with ribosomes after termination on short uORFs [5] . Recent work has also implicated the proteins density-regulated reinitiation and release factor ( DENR ) , multiple copies in T-cell lymphoma-1 ( MCTS1 ) , and eukaryotic initiation factor 2D ( eIF2D ) in reinitiation [6–10] . MCTS1 was first identified as a gene that is amplified at the genomic level in T-cell leukemias [11] . Subsequent studies found that MCTS1 protein levels are also elevated in T-cell lymphoid cell lines , in non-Hodgkin lymphoma cell lines , and in 85% of primary diffuse large B cell lymphomas [12] . Functional studies , mainly by the Gartenhaus lab , showed that MCTS1 has oncogenic properties . MCTS1 promotes anchorage-independent growth of NIH3T3 and MCF-10A cells [11 , 13] , and its overexpression accelerates the cell cycle , shortening G1 and increasing cyclin D1 and cyclin-dependent kinase 4 ( Cdk4 ) activities [14] . The DENR protein binds MCTS1 [15] and is overexpressed in breast cancer cells [16] . eIF2D is a larger protein that has sequence homology to MCTS1 in its N-terminal half and to DENR in its C-terminal half ( Fig 1A ) , and functional studies suggest it combines the activities of MCTS1 and DENR [6 , 7] . To reinitiate translation , ribosomes need to reacquire an initiator tRNA . eIF2D or the combination of DENR–MCTS1 were found to promote eukaryotic initiation factor 2 ( eIF2 ) -independent and guanosine triphosphate ( GTP ) -independent recruitment of Met-tRNAiMet to 40S complexes if the initiation codon is positioned in the P-site of the 40S , as is the case for hepatitis C virus-like internal ribosome entry sites ( IRESs ) [6 , 7] . Indeed , eIF2D was shown to promote translation reinitiation when added to in vitro–reconstituted systems [8 , 9] . We recently showed that DENR and MCTS1 ( also known as MCT-1 ) promote translation reinitiation on cellular mRNAs in Drosophila and in human cells [10 , 17] . The structure of MCTS1 contains a pseudouridine synthase and archaeosine transglycosylase ( PUA ) domain fold typically present in proteins that bind RNA [26] . DENR contains an SUI1 domain , similar to eukaryotic initiation factor 1 ( eIF1 ) [7] . To gain further insights into the structure and function of the MCTS1–DENR complex , we set out to obtain a high-resolution structure of MCTS1 bound to DENR . Here , we present a 2 . 14 Å–resolution crystal structure of MCTS1 bound to an N-terminal fragment of DENR , which complements two recent low-resolution structures of eIF2D or DENR–MCTS1 bound to the 40S ribosomal subunit [19 , 20] . Our structure identifies specific residues on DENR and on MCTS1 important for heterodimerization of the complex and for tRNA binding , respectively . We find that mutation of these residues leads to strong functional impairments , reducing the ability of the DENR–MCTS1 complex to reinitiate translation in human cells . This shows that DENR–MCTS1 heterodimerization and tRNA binding are both required for this complex to promote translation reinitiation in vivo . Furthermore , we find that the DENR–MCTS1 complex is able to bind tRNA in the absence of the ribosome . This suggests the DENR–MCTS1 complex can first bind tRNA , forming a trimeric complex , and then recruit it to the ribosome during reinitiation , analogously to the eIF2 complex in cap-dependent translation .
MCTS1 and DENR are homologous to the N- and C-terminal domains of eIF2D ( also known as ligatin ) , respectively ( Fig 1A ) , and they bind each other in vivo [15] . To study the binding interface between MCTS1 and DENR , we first aimed to identify the part of DENR responsible for MCTS1 binding . We coexpressed a polyhistidine ( HIS ) -tagged MCTS1 with untagged full-length DENR in E . coli and found that affinity purification of HIS-MCTS1 also pulls down DENR from bacterial cell lysates ( lanes 1–7 , S1A Fig ) . Using this binding assay and a series of DENR truncations , we identified DENR amino acids ( aas ) 24–51 as the minimal region for MCTS1 binding ( Fig 1B and S1A–S1E Fig ) . A previous study reported the crystal structure of MCTS1 containing three mutations ( E137A , K139A , and Q140A , henceforth called MCTS1x ) that were designed to allow crystallization [18] . To determine the structure of DENR–MCTS1 , we tried crystallizing the full-length heterodimeric complex , either with or without these three mutations , but we did not obtain crystals . Instead , we assembled and crystallized the minimal complex consisting of full-length wild-type ( WT ) MCTS1 and residues 24–51 of DENR and determined its crystal structure to 2 . 14-Å resolution ( Fig 1C and 1D ) . Data collection and refinement statistics are summarized in Table 1 . The asymmetric unit contains one DENR–MCTS1 complex , which has been defined to contain one biologically relevant assembly , instead of a complex that satisfies the metal ion coordination ( see next paragraph ) . Although we initially determined the structure by molecular replacement , strong positive density near MCTS1-His58 and the three Cys residues of DENR suggested the presence of a metal ion , most likely zinc . We therefore collected a dataset at the Zn-edge and could determine the structure de novo by means of Zn–single-wavelength anomalous dispersion ( SAD ) . The identity of zinc was confirmed by X-ray fluorescence ( XRF ) analysis ( S1F Fig ) and via a colorimetric assay using 4- ( 2-pyridylazo ) resorcinol ( PAR ) [21 , 22] ( S1G Fig ) . The overall structure of MCTS1 is essentially the same as the previously reported crystallization variant MCTS1x , as indicated by the low root-mean-square deviation ( RMSD ) of 0 . 71 Å for 181 residues ( S1H Fig ) . The DENR N-terminus ( residues 25 to 33 ) is largely extended , lacking secondary structure , while the C-terminal part contains a partial zinc finger ( residues 34 to 44 ) and a short α-helix ( residues 44 to 46 ) , which , according to secondary structure prediction , would extend to residue 60 . We superposed our structure onto MCTS1 in the recently published low-resolution structure of the 40S–DENR–MCTS1 complex [20] . The DENR-peptide from our heterodimer fits well into the unassigned electron density near the MCTS1 PUA domain ( S1I Fig ) . The density for DENR residues 29–31 is not continuous , suggesting some flexibility in binding . In contrast , the density for the zinc finger is very well defined , and additional density is visible at the C-terminus of our construct , in agreement with the predicted α-helix . Notably , no zinc finger was predicted in DENR . The partial Zn2+ coordination site comprises Cys34 , Cys37 , and Cys44 and is completed in crystallo by MCTS1-His58 of a crystallographically related symmetry mate ( Fig 1D ) . We tested whether MCTS1 His58 mediates the binding of MCTS1 to DENR , but this is not the case because HIS-MCTS1[H58A] is still able to pull down DENR ( S2A–S2A’ Fig ) . A mutant form of DENR that cannot bind MCTS1 ( DENR[RAA] described in the Results section “DENR-MCTS1 dimerization is required for activity” below ) is still able to bind Zn2+ ( 61% of WT levels ) , indicating that DENR does not require MCTS1 for Zn2+ coordination . DENR has a fourth cysteine at position 53 that could complete the Zn2+ coordination; however , Cys53 is not present in our crystallization construct . Crystallization trials with a DENR construct including Cys53 ( residues 24–55 ) yielded no crystals . Mutation of Cys53 to alanine , however , had almost no impact on Zn2+ binding ( S1G Fig ) . Together , these data suggest DENR coordinates Zn2+ mainly via three—rather than four—cysteines , as also observed in other proteins [23 , 24] . We previously reported the discovery of a de novo missense mutation of DENR Cys37 in an autism spectrum disorder patient [25] . Our structure now reveals that Cys37 is part of this zinc finger . Interestingly , this single mutation is sufficient to abolish Zn2+ binding by the DENR–MCTS1 complex ( S1G Fig ) . This mutation strongly impairs DENR function [25] , suggesting that zinc conjugation is necessary for proper function of the DENR–MCTS1 complex . These cysteine residues in DENR are not conserved in eIF2D , suggesting the coordination of Zn2+ is specific to the DENR–MCTS1 complex . The N-terminus of DENR ( residues 25–33 ) is unfolded and crawls along the N-terminal domain of unknown function 1947 ( DUF1947 ) of MCTS1 ( Fig 2A ) . The majority of residues involved in DENR binding are conserved—in particular , Leu82 , Leu170 , and Trp175 ( S2B Fig ) , which form a hydrophobic surface . DENR mostly interacts with MCTS1 via the main chain , except DENR-Tyr33 , which hydrogen-bonds with MCTS1-His86 . An extensive network of hydrogen bonds is formed between DENR-Glu42 and the backbones of MCTS1-Gln140 and His141 , between DENR-Tyr43 and MCTS1-Lys139 , between DENR-Tyr43 and the carbonyl of MCTS-His141 , and between DENR-Tyr46 and MCTS1-Lys139 . Although MCTS1-Lys139 is highly conserved , Gln140 and His141 are not conserved at all , which is in agreement with the interaction only requiring the peptide backbone ( S2B Fig ) . Overall , the interaction between MCTS1 and the DENR buries a surface of 733 . 5 Å2 and 834 . 9 Å2 corresponding to 7 . 8% and 33 . 6% of total solvent accessible surface on the MCTS1 and DENR N-terminal region , respectively . To assess the contribution of DENR Glu42 , Tyr43 , and Tyr46 to MCTS1 binding , we generated DENR variants with these three residues mutated either pairwise or all three together ( E42R , Y43A , and Y46A; henceforth DENR[RAA] ) . Although mutating any two of these three residues did not abolish binding between DENR and MCTS1 in E . coli , mutating all three markedly reduced the ability of DENR and MCTS1 to bind each other ( Fig 2B and S2C Fig ) . DENR[RAA] was also unable to bind MCTS1 in HeLa cells , assayed via coimmunoprecipitation assay ( Fig 2C ) . These data identify this interaction surface as the relevant one for DENR–MCTS1 binding and pinpoints Glu42 , Tyr43 , and Tyr46 as important residues for this interaction . We previously used a luciferase reporter bearing a very short upstream open reading frame with a strong initiation context ( stuORF ) as a readout for the ability of DENR–MCTS1 to promote translation reinitiation in vivo ( Fig 2D ) [10 , 17 , 25] . This revealed that both DENR and MCTS1 are necessary for efficient translation reinitiation . Since DENR and MCTS1 bind each other , we next asked whether the ability to form a heterodimer is important for their function . We depleted HeLa cells of endogenous DENR via small interfering RNAs ( siRNAs ) , we reconstituted the cells with either DENR[WT] or DENR[RAA] using constructs that escape siRNA-mediated knockdown , and then we assayed reinitiation activity using the stuORF construct ( Fig 2D–2D'' ) . While performing these reconstitution experiments , we found that DENR[RAA] is less well expressed than DENR[WT] ( S2D Fig ) . Hence , for the reconstitution experiments , we increased the amount of DENR[RAA] expression plasmid compared to the DENR[WT] plasmid so as to have at least as much DENR[RAA] protein as DENR[WT] protein ( Fig 2D'' ) . Nonetheless , DENR[RAA] was significantly impaired in its ability to promote translation reinitiation compared to the WT protein: as previously reported , knockdown of DENR causes stuORF reporter activity to drop compared to a control reporter lacking the stuORF ( second group of bars , Fig 2D' ) . Reexpression of DENR[WT] restores stuORF reporter activity ( third set of bars , Fig 2D' ) . In contrast , DENR[RAA] is impaired in promoting stuORF reporter activity ( fourth set of bars , Fig 2D' ) , indicating that heterodimerization of the DENR–MCTS1 complex is important for its activity in translation reinitiation . MCTS1 contains a PUA domain , which is frequently found in tRNA binding proteins [26] . Indeed , the related protein eIF2D is able to promote Met-tRNAiMet recruitment to the ribosomal 40S subunit [6 , 7] . Furthermore , DENR and MCTS1 are predicted to be in close proximity to tRNA when the structures of DENR and MCTS1 bound to the ribosomal 40S subunit are superposed with the structure of the PIC containing tRNAiMet [20] . To our knowledge , direct binding of DENR–MCTS1 to tRNA , however , has not yet been demonstrated in vitro . To study whether DENR–MCTS1 binds tRNA , we purified recombinant full-length DENR , MCTS1 , or the coexpressed DENR–MCTS1 complex from E . coli and performed gel shift assays with yeast tRNA ( S3A Fig ) . Unlike MCTS1 alone or DENR alone , which showed little or no tRNA binding ability , the DENR–MCTS1 complex was able to significantly retard the mobility of tRNA ( S3A Fig ) . Consistent with DENR and MCTS1 needing to heterodimerize to bind tRNA , the DENR[RAA] mutant , which is not able to bind MCTS1 , also did not shift tRNA ( S3B Fig ) . Interestingly , we observed significantly blunted tRNA binding capacity when MCTS1 and DENR were purified individually from bacteria and then mixed together in vitro ( “DENR+MCTS1 , ” S3A Fig ) , raising the possibility that the complex needs to form in vivo to be functional . The binding of DENR–MCTS1 to tRNA was not strongly influenced by salt concentrations up to 500 mM ( S3C Fig ) . We next asked if the DENR–MCTS1 complex has differential binding affinity for different tRNAs . Binding assays with three different yeast tRNAs revealed that the DENR–MCTS1 complex binds iMet-tRNA and Lys-tRNA more readily than Cys-tRNA and that tRNA binding is not affected by the state of tRNA acylation ( S3D Fig ) . We also assayed binding to in vitro–transcribed human tRNAs ( S3E Fig ) and found that DENR–MCTS1 binds all the tested tRNAs with similar affinity ( S3E’ Fig ) . This is similar to what was described for eIF2D [6] , indicating either that these reinitiation factors are less proficient than eIF2 at discriminating between tRNAs or that additional factors may be involved in vivo to improve tRNA selectivity . To predict residues in the MCTS1 PUA domain contributing to tRNA binding , we superposed MCTS1 on the archeal tRNA-guanine transglycosylase ( TGT ) in complex with a tRNA [27] . The TGT contains a PUA domain , which was used for structural alignment ( Fig 3A ) , leading to the prediction that MCTS1 Phe104 might be involved in tRNA binding . MCTS1 Phe104 resides at a position similar to a phenylalanine in the TGT-PUA domain , which stacks against the last base of the tRNA acceptor arm ( Phe519 , Fig 3A , right panel ) . In contrast , MCTS1 Ala109 is positioned in a location where a bulkier residue would interfere with tRNA binding . To test these predictions , we performed gel shift assays and found that mutating either Phe104 or Ala109 to a bulkier aspartate leads to significantly less tRNA binding by the DENR–MCTS1 complex ( Fig 3B–3B' ) . Importantly , MCTS1 containing either mutation was still able to coimmunoprecipitate endogenous DENR in HeLa cells ( Fig 3C and S4A Fig ) and had similar melting curves to WT MCTS1 ( S4B Fig ) , indicating that MCTS1[F104D] and MCTS1[A109D] are properly folded and that these two sites specifically impair tRNA binding but not DENR binding . Mutating MCTS1 Ala109 to Leu , which conserves hydrophobicity , also blunted tRNA binding ( Fig 3D ) but not DENR binding ( S4A Fig ) . In contrast to the F104D mutation , introducing negative charges at a number of other surface residues such as G100D , R54E , and R74E did not impair tRNA binding ( S5B Fig ) . Together , these data show that the DENR–MCTS1 complex is able to bind tRNA in vitro in the absence of ribosomes . We next tested whether tRNA binding by the DENR–MCTS1 complex is required to promote translation reinitiation in HeLa cells . We used the stuORF reporter assay and a reconstitution setup whereby we knocked down endogenous MCTS1 with siRNAs and then transfected the cells to reexpress either WT or mutant MCTS1 ( Fig 3E ) . Unlike WT MCTS1—which fully restored stuORF reporter activity in MCTS1-knockdown cells—MCTS1[F104D] , MCTS1[A109D] , and MCTS1[A109L] were impaired in their ability to promote reinitiation ( Fig 3E and S5C–S5C’ Fig ) . Unlike the DENR dimerization mutant , MCTS1[F104D] and MCTS1[A109D] were both expressed at equally high levels as WT MCTS1 ( S5C’ Fig ) , excluding this as a possible explanation for their impaired function and showing that tRNA binding does not affect stability of the DENR–MCTS1 complex . Mutating MCTS1 Phe104 to alanine impaired neither tRNA binding ( S5A Fig ) nor reinitiation activity ( Fig 3E ) , indicating that this mutation is milder than the F104D mutation and that tRNA binding and reinitiation activity correlate . Altogether , these data indicate that the ability of the DENR–MCTS1 complex to bind tRNA is critical for its ability to promote translation reinitiation , thereby identifying a molecular function for this complex required for its activity .
Like translation initiation , translation reinitiation is a succession of carefully orchestrated molecular events . Although this succession of events has been extensively studied for canonical cap-dependent initiation , much less is known about the steps of translation reinitiation . Furthermore , we also do not fully understand which molecular functions are required for the various steps of reinitiation . We present here the crystal structure of MCTS1 bound to a fragment of DENR . Based on this structure , we identify and experimentally validate DENR residues Glu42 , Tyr43 , and Tyr46 to be important for MCTS1 binding and MCTS1 residue Phe104 to be important for tRNA binding . By mutating these residues , we find that both DENR–MCTS1 heterodimerization and tRNA binding are important molecular functions for this complex to promote translation reinitiation . This thereby links molecular functions of DENR and MCTS1 to their contribution in the translation reinitiation process . We also find that the DENR–MCTS1 complex is able to bind tRNA in the absence of the ribosome , yielding a trimeric complex in vitro . This activity was not detected in the past , most likely because DENR and MCTS1 need to be coexpressed and copurified to be active ( see next paragraph ) . These results raise the possibility that the DENR–MCTS1 complex first binds tRNA in the cytosol and then recruits it to the ribosome , analogous to recruitment of initiator tRNA to the ribosome by eIF2 during canonical initiation ( Fig 4 ) . One unexpected finding was that if we mixed in vitro DENR and MCTS1 proteins that had each been individually expressed and purified in E . coli , they do form a complex but do not yield a complex capable of binding tRNA ( S3A Fig ) . In contrast , the DENR–MCTS1 complex formed in vivo ( when coexpressed and purified out of E . coli as one complex ) readily binds tRNA as indicated by gel shift assays ( S3A Fig ) . One possibility is that one or both of the proteins may require chaperones for optimal complex formation . This finding is likely useful for future work aiming to reconstitute DENR–MCTS1–dependent translation in vitro . From our structure of MCTS1–DENR ( aa24–51 ) , Phe104 of MCTS1 is predicted to stack against the last base in the acceptor stem of tRNA , thereby “measuring” the length of the tRNA and allowing RNAs that terminate at that position to bind efficiently . This may discriminate between tRNAs and other RNA species binding to this site . To promote translation reinitiation , one might predict preferential binding of the DENR–MCTS1 complex to initiator tRNA . Our gel shift assays , however , indicate the DENR–MCTS1 complex binds similarly to all tRNAs in vitro . A similar lack of ability to strongly discriminate between different tRNAs , or between uncharged and acylated tRNAs , was also observed for the homologous protein eIF2D [6] . This broader tRNA-binding capacity may therefore represent a feature of translation reinitiation that is different from canonical cap-dependent translation initiation , or it may indicate that additional factors play a role in vivo in helping DENR–MCTS1 select the right tRNA . Interestingly , the Kd we measured for tRNA binding ( 1 . 5 μM ) is roughly equal to the intracellular concentration of tRNAs in HeLa cells . Future work will be required to dissect these aspects in more detail . Interestingly , we find that the DENR[RAA] mutant , which has impaired MCTS1 binding , does not express as well in HeLa cells as DENR[WT] . One possibility is that DENR[RAA] has lower protein stability compared to DENR[WT] . This would be in agreement with our previous findings that DENR and MCTS1 are codependent on each other for stability; knocking down either DENR or MCTS1 using multiple different independent siRNAs , either in human cells or in fly cells , causes the other protein to also drop in abundance [10 , 17 , 25] . Hence , DENR and MCTS1 may need to form a complex to be stabilized , which would explain the reduced stability of the DENR[RAA] protein . While drafting this manuscript , a low-resolution crystal structure of DENR and MCTS1 bound to the 40S ribosomal subunit was published [20] , as was a low-resolution EM structure of eIF2D bound to the 40S ribosomal subunit [19] . Although binding of DENR and MCTS1 to the 40S was also shown and described in [19] , the EM data of this complex are not deposited , and therefore a detailed comparison is not possible . Instead , for [19] , we compare our DENR–MCTS1 structure to the structure of the homologous eIF2D , given that DENR and MCTS1 occupy the same binding sites on the ribosome as the corresponding homology regions of eIF2D . Both structures reveal that the suppressors of initiation codon mutations 1 ( SUI1 ) domain of DENR has a similar fold as eIF1 and that DENR binds the ribosome at a similar site as eIF1 . The two studies differ , however , in terms of whether DENR and MCTS1 directly touch each other . While a direct contact between both proteins is observed in the 6-Å crystal structure [20] , in the 10-Å cryo-EM structure they seem not to interact [19] . How does our X-ray structure of the isolated MCTS1–DENR complex relate to these structures ? The N-terminal region of DENR in our heterodimer structure nicely fits to the corresponding density present in the 40S crystal structure [20] ( S1H Fig ) , suggesting that the heterodimer containing the N-terminal region of DENR behaves as a rigid body . Although both the structure of DENR–MCTS1 and of eIF2D bound to the 40S show that these proteins contact tRNA , their conformations and their tRNA contact sites are different . Modeling our DENR–MCTS1 structure onto the eIF2D–40S structure of [19] predicts that MCTS1 Phe104 interacts with tRNA , in agreement with what we observe in our structure and experimentally by gel shift assays . In contrast , the structure of 40S–DENR–MCTS1 [20] , when modeled onto the 48S PIC [28] , does not predict Phe104 to be in contact with tRNA . Furthermore , residues of MCTS1 such as K139 that are predicted from modeling in [20] to bind tRNA are actually involved in DENR binding in our structure . Hence , the structure in [20] may represent a different step in the reinitiation process . In sum , our structure of the DENR–MCTS1 complex extends the current understanding of noncanonical translation initiation by defining two distinct functions of this complex at a molecular level . We identify residues on DENR and MCTS1 important for heterodimerization and for tRNA binding and add to the current model of how the DENR–MCTS1 complex promotes translation reinitiation by discovering that this complex can bind tRNA in the absence of the ribosome . Additional work will be needed to refine this model further .
Sequences for all oligos used for clonings are provided in S1 and S2 Tables . The hDENR ORF was amplified from HeLa cell cDNA using oligos OSS 366/367 and cloned into the NcoI/NotI sites of pET-hisTEV ( pETM10 ) and pET-his vectors . Plasmids for simultaneous expression of MCTS1 and N-terminally HIS-tagged DENR fragments in E . coli were generated by PCR-amplifying DENR fragments from pET-HIS-DENR , using oligos listed in S1 Table and cloning them into the XbaI/NotI sites of the pETDuet dual-expression vector under the control of a T7 promotor . The human MCTS1 ORF was amplified by PCR with oligos OSS 341/373 and cloned into the MfeI/XhoI sites downstream of the DENR ORF and under the control of its own T7 promotor . All plasmids were verified by sequencing . To generate double or triple mutations of DENR on aas E42 , Y43 , and Y46 , primers containing combinations of either double or all three mutations together ( S1 Table ) were used for point mutagenesis in combination with flanking primers OSS 649/367 , and the PCR product was cloned directly into the pETDuet-MCTS1-HIS-FL vector using the XbaI/NotI sites . All plasmids were verified by sequencing . To mutate C-terminally HIS-tagged MCTS1 in the pETDuet vector , upstream and downstream oligos OSS341 and OSS459 were used in combination with primers containing mutations for F104D and A109D ( S1 Table ) to amplify the mutated ORF , which was cloned into the TOPO vector ( Invitrogen ) , sequenced , and then subcloned in frame with C-terminal 6xHIS into the MfeI/XhoI sites of the pETDuet-DENR vector . To express N-terminally tagged HA-DENR[WT] and HA-DENR[E42R , Y43A , Y46A] ( “HA-DENR[RAA]” ) under the control of a CMV promotor in HeLa cells , WT and mutant DENR ORFs were amplified using oligos OSS 072 and OSS 367 from the corresponding bacterial expression plasmids , cloned via EcoRI and NotI sites into a pCDNA-HA expression vector , and sequenced for correctness . For the DENR and MCTS1 reconstitution luciferase experiments in HeLa cells , firefly luciferase and Renilla luciferase reporters were described in [17] . To express DENR and MCTS1 variants that escape siRNA-mediated knockdown , DENR and MCTS1 ORFs containing synonymous mutations previously described in [17] were used as templates for site-directed mutagenesis using oligos OSS684/OSS685 ( for DENR ) or OSS702/OSS703 and OSS706/OSS707 ( for MCTS1 ) . To generate C-terminally Flag-tagged MCTS1 as WT , F104D , and A109D versions , WT and mutant MCTS1 ORFs were amplified with primers OKE084 and OSS 745 ( containing a FLAG ORF , full sequences in S2 Table ) from the bacterial expression vectors , cloned into pRK containing a CMV promoter , and sequenced for correctness . Antihuman DENR and antihuman MCTS1 for immunoblotting were raised in the lab by immunizing guinea pigs with full-length recombinant proteins . Anti tubulin was purchased from Sigma ( T9026 ) . Proteins were expressed using E . coli BL21 ( DE3 ) cells in 2YT media supplemented with either Kanamycin ( 30 μg/ml ) or Ampicillin ( 100 μg/ml ) , depending on the plasmid used . Cells were grown to an OD600 of 0 . 8–1 . 0 at 37°C , then shifted to 18°C . Expression was induced with the addition of 0 . 4 mM IPTG , and cells were grown further overnight , harvested by centrifugation , and the cell pellets either used immediately for lysis and purification or frozen with LN2 and stored at −20°C . All variants of the DENR–MCTS1 complex were purified via an N- or C-terminal His6-tag using NiNTA and SEC . Cells were resuspended in lysis buffer ( 30 mM HEPES , 30 mM Imidazol , 500 mM NaCl ) and lysed with a Microfluidizer ( Microfluidics ) at 0 . 55 MPa . The lysate was cleared by centrifugation for 35 min at 35 , 000 × g and 4°C , and the resulting supernatant was applied to a 2 ml NiNTA column . The column was washed with 25–50 column volumes of lysis buffer and eluted with elution buffer ( lysis buffer plus 400 mM Imidazol ) . The NiNTA-eluate was applied to a Superdex 200 26/60 column , equilibrated with SEC-buffer I ( 10 mM HEPES pH 7 . 5 , 500 mM NaCl ) . Peak fractions containing the DENR–MCTS1 complex were pooled , concentrated to 10–15 mg/ml , and either used directly or shock-frozen with LN2 and stored at −80°C . Attempts to crystallize the full-length DENR–MCTS1 complex as well as various truncations were not successful , and therefore we focused on a minimal complex . The minimal DENR–MCTS1 complex was purified as described in the section “Protein expression and purification” above from bacteria coexpressing untagged full-length WT MCTS1 together with NHis6-tagged DENR aas 24–51 . Crystals were obtained by the sitting drop vapor diffusion method at 18°C in a condition containing 0 . 2 M AmSO4 and 20% PEG3350 . Prior to data collection , crystals were harvested in reservoir solution supplemented with 20% glycerol and flash-cooled with liquid nitrogen . Diffraction data were collected at ESRF beamline ID23-2 and integrated with XDS [29] and further processed with AIMLESS [30] from the CCP4 package [31] . The crystals belong to the space group P4122 . The structure was solved by molecular replacement as implemented in MOLREP [32] . Coordinates of the MCTS1 crystallization variant ( PDB-ID: 3R90 ) were used as a search model . Very strong positive density was observed near His58 . The structure was manually rebuilt in COOT [33] and refined with REFMAC5 [34] and PHENIX [35] . Anomalous density maps were calculated with ANODE [36] , showing a clear peak ( 14 σ ) near His58 , which was initially assigned to Zn2+ . The identity of the Zn2+-ion was confirmed with an XRF spectrum performed at ESRF beamline ID23-1 [37] . XRF data were analyzed with PyMCA [38] . Based on the knowledge that a Zn2+ ion is bound between the two proteins , we collected one dataset on the Zn-edge and were also able to solve the structure de novo by means of Zn-SAD . Data preparation , heavy atom location , and phasing were performed with SHELXC/D/E [39] pipeline navigated with HKL2MAP [40] . The final structure contains one DENR–MCTS1 complex . Data collection and refinement statistics are summarized in Table 1 . Structural comparisons were performed with GESAMT [41] . Determination of conserved residues and projection on the surface was performed with ConSurf [42] . All structural figures were prepared with PyMOL . For coimunoprecipitation experiments , HeLa cells were seeded overnight and transfected the next day with expression constructs using Effectene ( Qiagen ) according to manufacturer’s protocol . After 2 d of expression , cells where harvested and lysed in RIPA buffer ( 50mM Tris , pH 7 , 5 , 150 mM NaCl , 1% Sodiumdesoxycholate , 0 , 1% SDS , 1% Nonidet P-40 ) supplemented with protease inhibitors ( Roche ) and benzonase ( Merck ) . After spinning for 10 min at 10 , 000 g to remove unsolubilized cell debris , prewashed HA-Agarose Affinity Matrix ( Roche 11815016001 , used in Fig 2C ) or anti-Flag Affinity Beads ( Sigma A2220 , used in Fig 3D ) were added to the suspension . After 1 . 5 h of rotation in the cold room , the beads where collected by centrifugation , washed with RIPA buffer 4 times , and subjected to PAGE , followed by immunoblotting . For reconstitution experiments , HeLa cells were reverse transfected with control siRNA ( anti lacZ , Dharmacon #D00-2000-01-20 ) or siRNAs against DENR or MCTS1 ( Dharmacon , sequences provided in S3 Table ) , using RNAi Max ( Thermo Scientific ) . Samples in triplicate in 96-well format for luciferase assays were treated in parallel with western blot samples in 24-well format . After 2 d of knockdown , all samples where transfected with luciferase reporters , with or without rescue constructs , as published in [17] , and incubated for 2 d longer . Plasmid DNA amounts transfected per 24-well were as follows: 0 . 5 μg of each FLuc and Rluc reporter plasmid; and for reconstituting DENR levels , 0 . 1 μg of pCDNA-DENR[WT] or 0 . 26 μg of pCDNA-DENR[RAA] plasmids . The cells for immunoblotting were then lysed in RIPA buffer containing protease inhibitors and benzonase for 5 min at room temperature . Lysates were then clarified by centrifugation for 5 min at 10 , 000 g . Finally , Laemmli buffer was added to 1x final concentration for SDS-PAGE and immunoblotting . For luciferase assays , cells were lysed and analyzed with the Dual-Luciferase Reporter Assay System from Promega ( #E1960 ) . Yeast tRNA mix was purchased from Ambion ( #AM7119 ) . Individual yeast tRNAs , either acylated or nonacylated , were purchased from tRNA Probes , College Station , USA ( iMet tRNA cat# MI-03; iMet tRNA-Met cat# MI-60; Lys tRNA cat# L-03; Lys tRNA-Lys cat# L-60; Cys tRNA cat# C-03; Cys tRNA-Cys cat# C-60 ) . In vitro–transcribed human tRNAs were produced by annealing oligonucleotides containing a 5′ T7 promoter followed by the tRNA sequence . Oligo sequences are provided in S4 Table . The oligos were annealed by heating up to 95°C and slowly equilibrating to room temperature . tRNAs were in vitro–transcribed by combining and incubating annealed oligos ( 1 μM ) , NTPs ( 2 mM ) , Inorganic Phosphatase ( 3 U/100 μL ) , Ribolock ( 100U/100 μL ) , and T7-RNA Polymerase ( 60U/100 μL; all from Thermo Scientific ) for 3 h at 37°C . After in vitro transcription reactions , all RNAs were purified using Trizol ( Thermo Scientific ) , following manufacturer’s instructions . For tRNA electrophoretic mobility shift assays , either single HIS-tagged DENR or MCTS1 was expressed in E . coli Rosetta pLysS cell and purified by His-Trap affinity columns ( GE Healthcare ) , or the two proteins were coexpressed and purified by Ni-NTA Agarose beads ( Qiagen ) . Proteins were eluted in reaction buffer ( 50 mM Tris pH 7 . 5 , 150 mM NaCl , 15 mM Imidazole , Roche Protease inhibitors without EDTA ) containing 300 mM imidazole . After elution , 2 mM DTT was added . Varying amounts of protein were mixed together with RNA and 0 . 5 μl Ribolock ( Thermo Scientific #EO0381 ) in a total volume of 20 μL of reaction buffer to obtain final protein concentrations of 0 . 25 μM to 16 μM in the reaction mix . Final RNA concentrations in the binding reactions were 3 μM for the yeast tRNA mix and otherwise 1 μM . After 30 min of incubation on ice , loading buffer containing DNA/RNA dye GelRed was added to the samples and loaded on a native agarose gel , followed by electrophoresis at 9 volts/cm of gel length for 33 min at 4°C . For calculating Kd’s for tRNA binding , the integrated density of the shifted band was quantified using ImageJ . From this , the percentage of tRNA bound to DENR•MCTS1 ( “theta” ) was calculated for each DENR–MCTS1 concentration . This was used to derive the amount of tRNA•DENR•MCTS1 complex and the amount of free DENR•MCTS1 . The linear regression on a Scatchard was then used to calculate the Kd . This was done for 5 biological replicates . Zn2+ binding was assayed using a protocol adapted from [21 , 22]: DENR–MCTS1 proteins were expressed in E . coli and purified in the absence of DTT as described above in the section “Protein expression and purification . ” After elution in elution buffer ( 20 mM Hepes pH7 . 5 , 150 mM NaCl , 330 mM Imidazole ) , both WT and mutant proteins were diluted in elution buffer to an equal concentration of 1 . 1 mg/ml . Protein solutions were then treated with 10 mM NEM ( N-Ethylmaleimide , Sigma , #E3876 ) for 10 min at 37°C to release the Zn2+ . The samples were then heated for 5 min at 95°C to completely denature the proteins and then centrifuged for 5 min at 20 , 000 g to spin down the precipitates . Zinc was then measured from the supernatant . To measure zinc , 40 μl of 5 mM PAR ( Sigma #323209 ) was combined with 15 μl of ZnCl2 standard solutions ( from 1 μM to 100 μM , also in elution buffer ) or with 15 μl of cleared sample solution . The samples were mixed , incubated at room temperature for 5 min , and absorption at 500 nm was then measured . Melting temperatures of wild-type and mutant DENR–MCTS1 complexes were determined with a Prometheus NT . 48 ( Nanotemper , Germany ) at concentrations of 1 mg/ml . A temperature gradient from 20–80°C with a speed of 1 . 5°C/min was run while tryptophan fluorescence at 330 and 350 nm was recorded . Melting temperatures were determined using the manufacturer-supplied software , which calculates the ratio between fluorescence counts at 350 and 330 nm . The 350/330 ratio is plotted against temperature , and the first derivative is used to determine the melting points . | Usually , eukaryotic ribosomes translate only a single open reading frame ( ORF ) on an mRNA and then dissociate from the mRNA . In some cases , when there is a short upstream open reading frame ( uORF ) that precedes the main ORF , ribosomes can translate the uORF , terminate translation , and then undergo a poorly understood process called “translation reinitiation” whereby they resume scanning for another AUG initiation codon and then translate the main ORF . The molecular functions required for translation reinitiation are not known . We previously showed that two noncanonical initiation factors , density-regulated reinitiation and release factor ( DENR ) and multiple copies in T-cell lymphoma-1 ( MCTS1 ) , are involved in this process . We show here , based on a structure of MCTS1 bound to a fragment of DENR , that in order to successfully promote translation reinitiation , DENR and MCTS1 need to dimerize , and they need to bind tRNA . We thereby identify two molecular functions needed for translation reinitiation . | [
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] | 2018 | DENR–MCTS1 heterodimerization and tRNA recruitment are required for translation reinitiation |
Bacterial cell-surface proteins play integral roles in host-pathogen interactions . These proteins are often architecturally and functionally sophisticated and yet few studies of such proteins involved in host-pathogen interactions have defined the domains or modules required for specific functions . Streptococcus pneumoniae ( pneumococcus ) , an opportunistic pathogen that is a leading cause of community acquired pneumonia , otitis media and bacteremia , is decorated with many complex surface proteins . These include β-galactosidase BgaA , which is specific for terminal galactose residues β-1–4 linked to glucose or N-acetylglucosamine and known to play a role in pneumococcal growth , resistance to opsonophagocytic killing , and adherence . This study defines the domains and modules of BgaA that are required for these distinct contributions to pneumococcal pathogenesis . Inhibitors of β-galactosidase activity reduced pneumococcal growth and increased opsonophagocytic killing in a BgaA dependent manner , indicating these functions require BgaA enzymatic activity . In contrast , inhibitors increased pneumococcal adherence suggesting that BgaA bound a substrate of the enzyme through a distinct module or domain . Extensive biochemical , structural and cell based studies revealed two newly identified non-enzymatic carbohydrate-binding modules ( CBMs ) mediate adherence to the host cell surface displayed lactose or N-acetyllactosamine . This finding is important to pneumococcal biology as it is the first adhesin-carbohydrate receptor pair identified , supporting the widely held belief that initial pneumococcal attachment is to a glycoconjugate . Perhaps more importantly , this is the first demonstration that a CBM within a carbohydrate-active enzyme can mediate adherence to host cells and thus this study identifies a new class of carbohydrate-binding adhesins and extends the paradigm of CBM function . As other bacterial species express surface-associated carbohydrate-active enzymes containing CBMs these findings have broad implications for bacterial adherence . Together , these data illustrate that comprehending the architectural sophistication of surface-attached proteins can increase our understanding of the different mechanisms by which these proteins can contribute to bacterial pathogenesis .
The cell surfaces of bacterial pathogens are complex landscapes of molecules that create an elaborate interface between the host and the bacterium . Integral to this landscape are cell-surface presented proteins that provide a variety of functions from cellular maintenance to communicating with the external environment to interaction with host tissues . A common feature of these proteins , particularly in Gram-positive bacteria , is their very large size and structural sophistication . These architecturally intricate proteins are also often functionally complex and thereby contribute to different aspects of pathogenesis . Carbohydrate-active enzymes ( CAZymes ) , particularly those that break glycosidic bonds joining sugar residues , are frequently found on the surface of bacterial species and are commonly architecturally intricate . By definition these enzymes contain a catalytic domain that confers the ability to break glycosidic bonds; the most common super-family is the glycoside hydrolases ( GH ) , which are further organized into families based on sequence similarity [1] . GHs often contain numerous ancillary modules , the most common of which are the carbohydrate-binding modules ( CBM ) that non-catalytically mediate enzyme-carbohydrate interactions [2] , [3] . The paradigm of CBM function has been that these modules concentrate enzymes onto carbohydrate substrates and , through this local concentration effect , enhance catalytic activity . This , however , has been based largely on non-surface attached enzyme systems . CAZymes have been a focus of study for the opportunistic pathogen Streptococcus pneumoniae ( pneumococcus ) , a leading cause of pneumonia , bacteremia and meningitis . Pneumococci express eight known surface CAZymes that have distinct specificities and that together can modify a wide range of host glycans including N-linked glycans , O-linked glycans and glycosaminoglycans [4]–[6] . S . pneumoniae expresses several exoglycosidases that cleave terminal carbohydrates . Neuraminidase NanA cleaves terminal α-2 , 3 and α-2 , 6 linked sialic acid , while pneumococci express two β-galactosidases BgaA , specific for terminal galactose ( Gal ) β-1 , 4 linked to N-acetylglucosamine ( GlcNAc ) or glucose and BgaC , specific for terminal galactose β-1 , 3 linked to GlcNAc [7]–[9] . N-acetylglucosaminidase StrH , contains two GH20 catalytic modules that both recognize terminal GlcNAc residues that are β-1 , 2 linked to mannose within complex N-linked glycans , but have subtle differences in enzymatic activity [10] , [11] . S . pneumoniae also expresses endoglycosidases EndoD , an Endo-β-N-acetylglucosaminidase , which cleaves the chitobiose core of N-linked glycans and Eng which cleaves the core-1 ( Galβ-1 , 3 N-acetylgalactosamine ) structure of O-linked glycans [12] , [13] . S . pneumoniae also expresses α-glucanase SpuA , which has specificity for α-1 , 6 linkages of glucose in the context of stretches of α-1 , 4 linked glucose , such as those found in glycogen [14] , [15] . Finally , pneumococci express a hyaluronate lyase ( Hyl ) that cleaves the β-1 , 4 linkage of hyaluronic acid by β-elimination [4] , [16] , [17] . Several of these enzymes contribute to the ability of pneumococci to colonize or cause disease in vivo [6] , [18]–[21] . Furthermore , many of these CAZymes have been shown , using in vitro assays , to contribute to specific steps in pathogenesis including growth , avoidance of clearance by the immune system , adherence and biofilm formation [6] , [15] , [19] , [20] , [22]–[31] . Pneumococcal surface-associated glycosidases are multimodular suggesting that they have complex interactions with soluble glyconjugates , mucin layers , and/or the glycocalyx layer that coats mammalian cells . At over 2200 amino acids and with at least 17 modules/domains of 7 different types the β-galactosidase BgaA , is among the largest cell surface attached proteins expressed by S . pneumoniae [7] , [32] ( Figure 1A ) . At present , none of the individual modules of BgaA have been ascribed functions and the functions of similar modules in other proteins , except the predicted catalytic module , remain ambiguous . The gene encoding BgaA is present in all sequenced pneumococcal strains and all strains tested possess β-galactosidase activity [32]–[35] . BgaA is specific for galactose β-1 , 4-linked to glucose or GlcNAc [lactose or N-acetyllactosamine ( LacNAc ) motifs , respectively] found in glycoconjugates . This activity is required for the release of galactose from N-linked glycans and for efficient growth on glycoconjugates having these modifications [5] , [7] , [22] , [36] , [37] . BgaA is also linked to pneumococcal resistance against complement deposition and the resulting phagocytic killing [23] and strongly involved with adherence to epithelial cells [31] . At present , deeper insight into the complex biological roles that BgaA plays is hindered by an absence of studies that relate the complex architecture of this enzyme to its varied functions . In this study , the varied biological functions of BgaA are deconvoluted from the complex architecture of this enzyme . Through detailed structural and functional analyses the molecular basis for the catalytic specificity of BgaA is defined and this activity is demonstrated as critical for the ability of pneumococci to utilize complex N-linked glycans as a carbon source and protect the bacterium from opsonophagocytic killing . Further analyses also revealed the presence of non-catalytic CBMs within the C-terminal region of BgaA that mediate adherence to host cell surface LacNAc and/or lactose . Notably , this is the first demonstration that a CBM within a CAZyme can mediate adherence of a pathogen to host cells , thus extending the paradigm of CBM function . As CBM containing CAZymes are on the surface of many other bacterial species , we hypothesize that BgaA is a member of a novel class of adhesins . Furthermore , we show that these functions can be specifically modulated with small molecule inhibitors or competitors . Together these data highlight that understanding the architectural sophistication of surface-attached proteins can increase our understanding of the different mechanisms by which these proteins can contribute to bacterial pathogenesis and potentially aid in the development of strategies to inhibit these pathogenic mechanisms .
The N-terminal region of BgaA comprising amino acid residues 138–993 has amino acid sequence identity with GH family 2 enzymes . The X-ray crystal structures of a catalytically active fragment of BgaA was determined to 2 . 7 Å resolution ( data not shown ) and an inactive Glu645Gln nucleophile mutant in complex with unhydrolyzed LacNAc to 2 . 2 Å resolution ( Figure 1B and Figure S1A ) . This polypeptide had five distinct domains , four with immunoglobulin ( Ig ) -like folds that are arranged to create a nest in which the central ( α/β ) 8-barrel domain III sits . All other structurally characterized GH2 enzymes with known β-galactosidase activity have a LacZ-type architecture where Ig-like domain V is replaced by a super β-sandwich domain ( Figure S1B ) . The catalytic site of BgaA resides in a pocket located at the center of domain III ( Figure 1B ) and makes a series of direct and water-mediated hydrogen bonds with both residues of the disaccharide while the a-face of the GlcNAc residues lies parallel to Trp685 in a classical carbohydrate ring-aromatic amino acid sidechain interaction ( Figure 1C ) . The LacNAc in this complex does not fully engage the catalytic residues: neither Glu564 , the acid base , nor Gln645 , mutated from the glutamic acid that would normally act as the nucleophile , are appropriately positioned to perform a catalytic function . This “shallow” mode of substrate binding representing an active site loading step is the same as that observed for Escherichia coli LacZ Glu537Gln mutant in complex with lactose ( Figure 1D ) . The positions of the nucleophile and catalytic acid , Glu645 and Glu564 , respectively , in BgaA are conserved with the analogous residues Glu537 and Glu461 of LacZ . Notably , however , there was no evidence of bound metals in the active site of BgaA . Indeed , the side chain of Arg288 occupies the space where a Mg2+ atom is bound in LacZ while Tyr713 and Glu716 fill the region occupied by a Na+ atom . Consistent with this , the activity of our catalytic region construct displayed no sensitivity to the presence or absence of metal ions ( data not shown ) . Given the shallow loading mode of LacNAc binding we also examined the binding of BgaA to the galactoisofagomine ( GIF ) and galactonojirimycin ( GNJ ) , which are known potent galactosidase inhibitors [38] to provide additional insight into sugar recognition . GIF ( Figure 2A ) had a Ki of 25 . 0 ( ±4 . 4 ) nM ( Figure 2B ) , displayed a competitive mode of inhibition ( Figure 2C ) , and isothermal titration calorimetry ( ITC; Figure S2A ) confirmed the tight binding [Kd of 26 . 0 ( ±3 . 2 ) nM] and 1∶1 stoichiometry . GNJ ( Figure 2D ) had a more moderate Ki at 33 . 9 ( ±1 . 6 ) µM ( Figure 2E ) and also displayed a competitive mode of inhibition ( Figure 2F ) . Despite the different chemical structures of the inhibitors they bound with very similar sets of interactions with Glu645 positioned beneath the atom equivalent to C1 at a distance of ∼3 . 5 Å , consistent with the role of this residue as a nucleophile ( Figure 2G ) . GIF binding results in only subtle structural changes compared with LacNAc binding , despite the deeper binding mode of GIF ( Figure 2H ) . These two complexes appear to represent a trajectory that progresses through a substrate-loading mode to a mode where the −1 catalytic subsite is fully engaged . The catalytically non-productive loading mode appears to provide BgaA with its substrate specificity through a pre- ( −1 ) -subsite that recognizes terminal galactose residues and a preceding pre- ( +1 ) -subsite that accommodates the β-1 , 4-linked GlcNAc residue through primary interactions with Trp685 and a series of hydrogen bonds between O6 of this sugar residue and a tailored pocket ( Figure 2H ) . The steric constraints imposed by this architecture legislate against β-1 , 6-linked GlcNAc , with its longer overall length , and β-1 , 3-linked GlcNAc , where the 2-acetamido group would clash with the O6-specific pocket in the active site . Indeed , BgaA has insignificant activity on these sugars . This substrate-loading mode does not , however , suggest a mechanism for discrimination between lactose and LacNAc , where the latter is preferred by a factor of ∼10-fold [37] . It is possible that additional specificity for the 2-acetamido group of the GlcNAc is provided in the transition from the loading mode to fully involving the catalytic site and formation of the Michaelis complex where a deeper binding mode and/or distortion of the substrate might result in the engagement of this chemical group . As previously observed , deletion of bgaA resulted in significantly reduced growth on N-linked glycans decorating glycoproteins [22] ( Figure 3A ) . The addition of 1 µM GIF reduced the growth of TIGR4 on aisalofetuin to approximately that of the bgaA mutant ( Figure 3A ) . The reduction of growth by GIF was dose dependent with an inhibitor concentration ∼75 nM giving half the maximum reduction in growth , which is consistent with the measured Ki and Kd values ( Figure 3B ) . As previously reported , the survival of the bgaA mutant in an opsonophagocytic killing assay was reduced to ∼30% of that of the parental strain ( Figure 3C ) [23] . The addition of 150 nM or 2 . 5 µM GIF to the assays significantly reduced the survival of TIGR4 to ∼60%; GIF had no significant influence on the survival of the bgaA mutant ( Figure 3C ) . It has previously been reported that bgaA mutants in some genetic backgrounds including R6 , but not TIGR4 , were significantly reduced in adherence [31] . Consistent with the published data we observed a significant reduction in adherence of an R6 bgaA mutant to epithelial cells ( Figure 3D ) . GIF did not reduce adherence of the R6 strain and indeed caused a significant increase in adherence in a dose dependent manner ( Figure 3D ) . The concentration of GIF giving an approximately 50% increase in adherence was ∼25 nM , again consistent with the Ki determined for this inhibitor . GIF treatment resulted in a decrease in β-galactosidase activity associated with the bacterium indicating effective inhibition of BgaA catalytic activity ( Figure S2D ) . This observation of increased adherence by inactivation of BgaA β-galactosidase activity was further supported by a similar increase in adherence of a mutant where substitution of the catalytic acid base residue , Glu564 , by a bulky arginine residue to block the −1 subsite destroyed the catalytic activity of the enzyme ( Figure 3E and Figure S2E ) . Thus , BgaA requires neither β-galactosidase activity nor an accessible active site to mediate adherence . Remarkably , the catalytic activity is in fact antagonistic to adherence . Together these results show that the catalytic activity of BgaA is required for nutrient acquisition by this enzyme and protection from the innate immune system . Furthermore , these biological roles can be specifically inhibited by targeting the catalytic activity with an inhibitor . The mechanism by which the catalytic activity of BgaA provides protection from complement-mediated killing is presently unknown; however , it appears to be related to an effect of glycan modification , likely on complement components , that reduces complement deposition . In contrast , the catalytic activity of BgaA appears to inhibit adherence , suggesting that the portion of BgaA that mediates adherence is distinct from the catalytic site and , further , that the receptor may be a substrate for the BgaA catalytic region , and therefore a carbohydrate . To test the hypothesis that the C-terminal region of BgaA mediates adherence , pneumococcal strains expressing either a surface-associated BgaA C-terminal region ( BgaAC ) or a surface-associated BgaA N-terminal enzymatic module ( BgaAN ) were constructed in strains previously used to demonstrate a role for BgaA in adherence , R6 and a low passage clinical isolate C06_18 ( Figure 4A ) . For both strain backgrounds significantly higher adherence of the BgaAC strain as compared to the bgaA mutant to normal human bronchial epithelial ( NHBE ) cells and the pharyngeal cell line Detroit 562 ( D562 ) was observed ( Figures 4B , 4C , S3A and S3Bcbm ) . In contrast , BgaAN strains showed no significant difference in adherence from that of the bgaA mutant . An immunoblot was used to confirm that the N-terminal construct was properly expressed and localized ( Figure S3C ) . Despite appropriate expression and localization , R6BgaAN had reduced β-galactosidase activity ( Figure S3D ) ; however , reduced adherence of R6BgaAN could not be attributed to reduced enzyme activity as catalytically inactive BgaA still facilitates efficient adherence ( Figure 3E ) [31] . The significant increase in adherence previously discerned in the absence of BgaA enzymatic activity was not observed for pneumococci expressing only the C-terminal region of BgaA , while the reason for this is unclear it may be that the large deletion affects surface presentation or stability of the protein . Nevertheless , these data indicate that the C-terminal region of BgaA mediates adherence to receptors on the epithelial cell surface . Furthermore , the observation that the catalytic activity of BgaA is antagonistic to adherence suggests that the receptor for BgaA adherence is the carbohydrate substrate of the catalytic domain . Amino acid sequence similarity searches failed to identify candidate CBMs in the C-terminal portion of BgaA . However , fold prediction using the Phyre2 server [39] , which does not rely on amino acid sequence similarity , distinguished two regions ( XII and XV , Figure 1A ) with a high probability of adopting the β-sandwich fold common to many CBMs found in CAZymes . These two ∼175 amino acid residue modules , which we refer to as CBM71-1 and CBM71-2 , share ∼35% amino acid identity with one another , but have no identity with known CBMs . The two predicted CBMs were recombinantly produced and the polypeptides screened for binding to all commonly occurring monosaccharides by UV difference spectroscopy; only D-galactose gave a signature UV difference spectrum consistent with sugar binding ( Figures S4A and S4B ) . Subsequently , this approach was expanded to the relevant galactose-containing sugars LacNAc , lactose , galactopyranosyl-β-1 , 3-N-acetyl-D-glucosamine ( lacto-N-biose ) , and galactopyranosyl-β-1 , 3-N-acetyl-D-galactosamine [Thomsen-Freidenreich ( TF ) epitope] and binding was only observed to LacNAc and lactose . For both CBMs , the binding to galactose was too weak to quantify . The dissociation constants ( Kds ) determined for CBM71-1 by ITC were 251 ( ±29 ) µM and 368 ( ±52 ) µM for LacNAc and lactose , respectively ( Figures S4C and S4D ) . Similar values of 247 ( ±37 ) µM and 378 ( ±30 ) µM for LacNAc and lactose , respectively , were obtained for CBM71-2 ( Figures S4E and S4F ) . Significantly , the CBMs only bound with significant affinity to sugars that are substrates for the catalytic domain . Though relatively weak , these affinities are consistent with those determined for other CBMs with similar binding specificities [40] . The structure of CBM71-1 solved by X-ray crystallography in complex with LacNAc revealed its β-sandwich fold comprising opposing sheets of 4- and 5-anti-parallel β-strands ( Figure 5A ) . A single bound metal ion was modeled as Ca2+ on the basis of coordination geometry and B-factor analysis . The shallow LacNAc binding site sits at the apex of the β-fold opposite the N- and C-termini ( Figure 5A ) . The structure of CBM71-2 is highly similar to that of CBM71-1 with the most obvious difference being an extended loop adjacent to the binding site ( Figure 5B ) . Though a bound complex of CBM71-2 was not obtained the binding sites of the two CBMs are very well conserved , consistent with the shared specificity of the CBMs and similar binding affinities ( Figure 5C ) . The base of the CBM71-1 active site provides amino acid sidechains that provide specificity for a terminal galacto-configured sugar but prevent accommodation of a 2-acetamido group , providing an explanation for the lack of binding to N-acetylgalactosamine ( Figure 5C ) . Tryptophan 1514 lies directly beneath the glycoside bond and coplanar with the disaccharide thus providing CH-π interactions with both pyranose rings and a higher affinity for β-linked disaccharides than for galactose alone . This binding site architecture accommodates lactose and LacNAc , but would limit the recognition of other sugars terminating in β-linked galactose . Given their carbohydrate binding activity , but lack of amino acid sequence identity between the BgaA CBMs and known CBM families , CBM71-1 and CBM71-2 constitute the founding members of a new CBM family , CBM71 , which is most similar in three-dimensional structure to CBM family 32 ( Figure 5D ) . The ability of these CBMs to mediate adherence to host cells was explored using the free carbohydrates galactose , lactose and LacNAc as well as soluble recombinant CBMs as specific competitors of adherence . The addition of 250 µM CBM71-1 or CBM71-2 significantly reduced adherence of R6 and C06_18 to both NHBE and D562 cells ( Figures 6A , 6B , S5A and S5B ) . The CBM71-1 . 2 tandem construct that comprises both CBMs and the two intervening modules reduced adherence more than either CBM alone; although , this difference was not significant for CO6_18 . Importantly , recombinant CBMs had no significant effect on adherence of a bgaA mutant , demonstrating that the effect of CBMs on adherence was BgaA specific ( Figures 6A , 6B , S5A and S5B ) . Lactose , LacNAc and galactose significantly reduced adherence to NHBE and D562 cells , though , consistent with the low affinity of these CBMs for galactose , this monosaccharide reduced adherence ( Figure S5E and data not shown ) significantly less than the same concentration of disaccharides ( Figure 6C , 6D , S5C and S5D ) . The effect of lactose and LacNAc was BgaA-specific and dose-dependent . Sialidase treated human epithelial cells showed significantly increased adherence to immobilized CBM71-1 and CBM71-2 , as compared to immobilized BSA , indicating that the CBMs within BgaA directly interact with the host cell ( Figure 7A and 7B ) . Furthermore , adherence to CBMs was reduced if epithelial cells were treated with both sialidase and the catalytic domain of BgaA , indicating that the receptor mediating adherence is a substrate of BgaA: terminal β-1 , 4-linked galactose . To ensure that this interaction was relevant in the context of intact bacteria we constructed a strain designed to abrogate CBM binding through point mutations in bgaA that target critical binding residues in the CBMs . W1514 and W1864 in the structures of CBM71-1 and CBM71-2 , respectively , make classical aromatic amino acid side chain – carbohydrate ring interactions , which are typically critical to CBM binding [2] . Thus , these residues were chosen for alanine substitutions . As predicted , the strain expressing the S . pneumoniae mutant carrying the BgaAW1514A , W1864A variant showed dramatically reduced adherence that was not significantly different from the bgaA mutant ( Figure 7C ) . This reduction in adherence was not due to differences in expression , localization or activity of BgaA ( Figure S6A and S6B ) . Together these data strongly support the hypothesis that CBMs in BgaA contribute to pneumococcal adherence by binding to LacNAc and lactose containing cell surface glycoconjugates . Although the majority of β-galactosidases lack the large C-terminal region found within BgaA ( Figure 4A ) , a relatively large number of host-adapted streptococci , including S . gordonii , encode similar β-galactosidases [41] ( Figure S7 ) . In order to test if BgaA orthologs may represent a previously uncharacterized class of bacterial adhesins , we tested adherence of an S . pneumoniae bgaA mutant expressing S . gordonii BgaA ( R6ΔbgaA SgbgaA+ ) at the same locus and under control of the native promoter . Adherence and enzymatic activity of the pneumococcal strain expressing the S . gordonii BgaA was not significantly different from that of the parental strain ( Figure 8 ) . These data indicate that other BgaA orthologs including S . gordonii BgaA have the potential to act as bacterial adhesins .
The catalytic specificity of BgaA is reported to be for LacNAc and lactose , carbohydrate motifs found on a wide variety of glycoconjugates , though the activity on lactose is lower [37] . This catalytic specificity appears to be initially provided by an unusual pre-active or substrate loading complex in the active site that is similar to what has been observed for E . coli LacZ and selects for the β-1 , 4-linkage in these sugars . The overall architecture of the active site , however , which generally accommodates only a disaccharide , suggests that the enzyme would be quite tolerant of sugar residues preceding a LacNAc or lactose motif , consistent with the ability of BgaA to release galactose from a wide variety of glycoconjugates terminating in LacNAc or lactose motifs [42] . Through the use of a S . pneumoniae mutant lacking bgaA the ability to process these sugar motifs has been linked to growth on a glycoconjugate and protection from opsonophagocytosis [5] , [22] , [23] . Here , the use of a tight binding inhibitor that specifically targets the active site of BgaA conclusively links the necessity of having an available catalytic site with these biological outcomes and indicates that glycan processing is responsible for the protective effect of BgaA against opsonophagocytosis . We also identified ancillary CBMs that mediate adherence of the bacterium . Many bacterial species bind host tissues through protein-carbohydrate interactions , which is achieved through a potential myriad of proteins from single , dedicated surface proteins to components of complex flagellar structures [43]–[46] . This is , however , the first demonstration of a CBM mediating adherence of a pathogen to host cells . CBMs typically function to maintain CAZymes in proximity of substrate , thereby enhancing catalytic activity . This may indeed be also be the case with the CBMs in BgaA; however , the overall role in adhering the bacterium to a host cell is a new function for CBMs , not only expanding the repertoire of bacterial adhesins but altering the paradigm of CBM function . It may seem counterintuitive that adherence can be mediated by interactions of CBMs with host glycans that are cleaved by an enzymatic domain within the same protein . However , we propose a dynamic interaction between common host cell surface glycans and multiple copies of a bacterial surface protein . Multiple adhesion events also increase the avidity of the interaction and may provide an explanation of how CBMs with relatively weak affinity for glycans mediate adherence . Though the CBMs in BgaA clearly mediate an interaction with carbohydrate motifs , namely LacNAc and lactose , the exact nature of the glycoconjugate receptor ( s ) remains unknown . LacNAc is very common in the N- and O-linked glycans that decorate glycproteins on the surface of epithelial cells while both LacNAc and lactose are frequent motifs in glycosphingolipids . BgaA is active on both LacNAc and lactose , albeit with approximately 10-fold higher activity on LacNAc [37] , while the CBMs within BgaA show a minor preference for LacNAc , suggesting that LacNAc is the most likely receptor . Given that it has previously been reported that BgaA may bind a non-proteinacious receptor [31] the members of the neolactoceramide subfamily of glycosphingolipids , which contain LacNAc motifs , are possible candidates as glycoconjugate receptors for BgaA . As other bacterial species adept at modifying carbohydrates encode surface-associated CAZymes predicted to contain CBMs [41] , [47]–[49] , we propose that BgaA may be a member of a novel class of bacterial adhesins . This hypothesis is supported by our data demonstrating that bgaA from S . gordonii can complement a S . pneumoniae bgaA mutant . In addition to BgaA , pneumococcal surface-associated glycoside hydrolases NanA , EndoD , Eng and SpuA , contain , or are predicted to contain , CBMs [1] , [12] , [50] , [51] . Two of these enzymes , NanA and Eng , have been demonstrated to contribute to pneumococcal adherence [6] , [26] , [27] . Although NanA acts to reveal a receptor for BgaA-mediated adherence to epithelial cells , enzymatic activity is not required for adherence to endothelial cells [27] . In fact , an N-terminal region including a putative CBM is required for adherence to endothelial cells . The role of Eng in adherence remains to be defined . It is likely that CBM-mediated adherence affects the pathogenesis of multiple bacterial species , but these data are of particular significance to the study of pneumococcal biology . It has long been proposed that initial adherence of pneumococci to host tissue occurs via binding carbohydrates on the epithelial cell surface but the identification of specific adhesin-receptor pairs has been lacking [52] , [53] . This study elucidates the first carbohydrate-mediated pneumococcal adherence mechanism . It was previously reported that mutation of BgaA does not reduce adherence of all pneumococcal strains , but this mechanism of adherence is very likely relevant to pneumococcal pathogenesis as it affects adherence of multiple strains , including low-passage clinical isolates to all human airway epithelial cell lines tested and primary airway epithelial cells [31] . Pneumococci are a very diverse species and variances in the contribution of different adherence mechanisms between strains has previously been reported [54]–[56] . Differential expression of the five CBM containing CAZymes encoded by pneumococci could explain the differential role of BgaA to adherence , especially in light of the published evidence that some of these CAZymes contribute to adherence [6] , [27] . Understanding the specific contributions of different domains/modules of complex proteins to bacterial pathogenesis provides the opportunity to identify inhibitors of these mechanisms . We significantly reduced pneumococcal adherence by the addition of recombinant CBM or free carbohydrate-receptor ( i . e . lactose or LacNAc ) . Additionally , the tight binding β-galactosidase inhibitor GIF inhibited enzymatic activity on the surface of the bacteria to reduce growth on glycoconjugates and resistance to opsonophagocytosis . The demonstrated capacity to modulate the multiple functions of architecturally complex bacterial surface-associated CAZymes with simple molecules may provide a framework for developing approaches to targeting pathogens utilizing such proteins in the host-pathogen interaction .
Bacterial strains and plasmids used in this study are described in Table S1 . S . pneumoniae S . gordonii , and E . coli strains were grown using routine conditions for these organisms and where appropriate media was supplemented with antibiotics . For details see Supporting Information . All cloning was performed using standard molecular biology procedures . Protein production in E . coli was done using pET 28-based expression vectors and purification of the polypeptides using procedures described previously and primers detailed in Table S2 [10] . Protein concentrations were determined by measuring the absorbance at 280 nm and using calculated molar extinction coefficient of 174070 cm−1 . M−1 for GH2 and GH2-E645Q , 29540 cm−1 . M−1 for CBM71-1 , 33920 cm−1 . M−1 for CBM71-2 , and 76320 cm−1 . M−1 for CBM71-1 . 2 , [57] . For details see Supporting Information . All crystallization experiments were performed using sitting-drop vapor diffusion for screening and hanging drop vapor diffusion for optimization , all at 18°C . Diffraction data were collected on cryo-protected crystals at 100 K and data was processed using MOSFLM and SCALA [58] , [59] . All data collection and processing statistics are shown in Table S3 . The structure of CBM71-1 was determined a by single-anomalous dispersion experiment optimized for selenium using the program ShelXC/D/E [60] . All other structures were solved by molecular replacement using standard procedures . For details see Supporting Information . All data collection , processing , and structure refinement statistics are given in Table S3 . All steady state kinetic studies were performed in triplicate at 37°C in a Cary/Varian 300 Bio UV-Visible Spectrophotometer as previously described [10] . The Ki values for GNJ and GIF were determined from plots of the apparent Km/Vmax against inhibitor concentration . Qualitative UV difference scan and ITC were performed using methods already described [10] , [40] , [61] , [62] . All experiments were performed at 25°C in triplicate . For details see Supporting Information . S . pneumoniae TIGR4ΔbgaA strain , was obtained by a PCR ligation technique to replace bgaA with a chloramphenicol cassette [15] , [63] . S . pneumoniae R6 and C06_18 strains expressing the surface attached N-terminal ( BgaAN ) or C-terminal region of BgaA ( BgaAC ) , R6 expressing enzymatically inactive BgaA ( R6BgaAE564R ) , R6 expressing BgaA with point mutants in the CBMs that abrogate carbohydrate binding ( R6BgaAW1514A , W1864A ) , and the S . pneumoniae bgaA mutant expressing S . gordonii BgaA ( R6ΔbgaA SgbgaA+ ) were generated using the Janus cassette selection method using primers described in Table S2 [64] . For details see Supporting Information . The protocol for the growth assays of wild-type and ΔbgaA S . pneumoniae TIGR4 strains on bovine asialofetuin was adapted from Battig et al . [65] and performed as described previously [10] . Neutrophil killing assays were performed essentially as previously described with S . pneumoniae TIGR4 wild type strain with or without inhibitors and ΔbgaA strain in the presence of inhibitors or a vehicle control ( +++ buffer ) [10] , [23] , [66] . For details see Supporting Information . Adherence of S . pneumoniae to monolayer of D562 cells ( ATCC CCL-138 ) and primary NHBE cells ( Lonza ) , grown in 24 well tissue culture plates was determined essentially as previously described [31] , [67] . For details see Supporting Information . Ninety-six well plates coated in a range of concentrations of CBM71-1 , CBM71-2 or BSA ( control ) were blocked with 1% BSA ( w/v ) before addition of D562 cells treated with Clostridium perfringens sialidase ( CpSia ) or sialidase and S . pneumoniae BgaA146-990 ( SpBgaA ) . Following incubation for 1 h at 37°C unbound cells were removed by washing and cells were fixed , stained and counted using an inverted light microscope . The average number of cells bound to BSA coated wells was subtracted from the number of cells attached to CBM coated wells . For details see Supporting Information . Data from opsonophagocytic , adherence assays and cell-binding assays were assessed for statistically significant differences using a two tailed Student's t-test and data points with p value≤0 . 05 were considered significant . Protein Data Bank . Coordinates and structure factors have been deposited with the following accession codes: native BgaA catalytic domain , 4cu6; BgaA catalytic domain in complex with GIF , 4cu7; BgaA catalytic domain in complex with GNJ , 4cu8; BgaA catalytic domain E645Q complex with LacNAc , 4cuc; CBM71-1 Se-met , 4cua; CBM71-1 in complex with LacNAc , 4cub; CBM71-2 , 4cu9 . | The adherence of bacteria to host cells is a critical step in most bacterial infections; yet , mechanisms are poorly understood for many bacteria , including Streptococcus pneumoniae ( pneumococcus ) , a human pathogen of global relevance . The surface of this bacterium is decorated with a landscape of large and structurally sophisticated proteins that mediate contact with the host . Here we show that the sugar-degrading β-galactosidase BgaA , can bind and cleave sugars through separate portions of this protein , which is one of the largest pneumococcal surface proteins and a model for architecturally intricate carbohydrate-active surface proteins . Non-enzymatic carbohydrate-binding modules in BgaA mediate adherence to specific host-cell surface carbohydrates . The identification of the first adhesin-carbohydrate receptor pair in S . pneumoniae provides critical molecular-level support for the long-held hypothesis that pneumococci bind carbohydrates on host cells and extends the paradigm of carbohydrate-binding module function . The enzymatically active portion of BgaA enables the bacterium to grow on host-derived glycans and evade the immune system , aspects of the host-pathogen interaction we show can be modulated by a specific inhibitor of enzymatic activity . Our work advances the concept that large bacterial surface proteins mediate complex host-bacterial interactions through specific functions of the varied regions comprising these proteins . | [
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"glycobiology"
] | 2014 | Unravelling the Multiple Functions of the Architecturally Intricate Streptococcus pneumoniae β-galactosidase, BgaA |
Binocular rivalry and cross-orientation suppression are well-studied forms of competition in visual cortex , but models of these two types of competition are in tension with one another . Binocular rivalry occurs during the presentation of dichoptic grating stimuli , where two orthogonal gratings presented separately to the two eyes evoke strong alternations in perceptual dominance . Cross-orientation suppression occurs during the presentation of plaid stimuli , where the responses to a component grating presented to both eyes is weakened by the presence of a superimposed orthogonal grating . Conventional models of rivalry that rely on strong competition between orientation-selective neurons incorrectly predict rivalry between the components of plaids . Lowering the inhibitory weights in such models reduces rivalry for plaids , but also reduces it for dichoptic gratings . Using an exhaustive grid search , we show that this problem cannot be solved simply by adjusting the parameters of the model . Instead , we propose a robust class of models that rely on ocular opponency neurons , previously proposed as a mechanism for efficient stereo coding , to yield rivalry only for dichoptic gratings , not for plaids . This class of models reconciles models of binocular rivalry with the divisive normalization framework that has been used to explain cross-orientation . Our model makes novel predictions that we confirmed with psychophysical tests .
Binocular rivalry is a visual phenomenon in which perception alternates between incompatible monocular images presented to the two eyes [1]–[4] . For example , when one eye is presented with an oriented grating and the other eye is presented with an orthogonal grating , observers experience alternating periods of dominance in which one grating is visible and the other is invisible or nearly invisible . Computational models have been proposed to characterize the alternating periods of perceptual dominance experienced during rivalry [5]–[14] . These models rely on mutual inhibition between neurons representing the two percepts , so that when one percept is dominant , the other percept is suppressed . To capture alternations between the percepts , rivalry models include endogenous noise , adaptation , or a combination of both . While these models successfully explain the case of dichoptic gratings , in which incompatible gratings are presented separately to the two eyes ( Figure 1 , top ) , the binocular rivalry literature has largely overlooked the case of binocular plaids , in which a pair of orthogonal gratings are presented superimposed to both eyes ( Figure 1 , bottom ) . Because models of binocular rivalry rely on strong competition between neurons tuned to orthogonal orientations , they typically predict rivalry between plaid components that is nearly as strong as the rivalry between dichoptic gratings . This prediction is not borne out by psychophysical evidence or by subjective experience . While plaid components show a type of rivalry known as ‘pattern rivalry’ , it is far weaker than the rivalry experienced with dichoptic gratings [15] , [16] ( Figure 1 ) . Instead of strong rivalry , plaid components undergo much weaker competition caused by “cross-orientation suppression” , in which the neural responses to each of the component gratings is lower than it would have been without superimposing the other component grating [17]–[19] . Cross-orientation suppression is well characterized by the normalization equation , according to which neural responses are normalized ( i . e . , divided ) by a common factor , which includes the summed activity of a large pool of neurons [19] , [20] . The normalization equation was developed to explain a variety of response properties of neurons in primary visual cortex , including cross-orientation suppression , surround suppression , and response saturation at high contrasts . Ocular opponency neurons compute the difference in the signals between the two eyes , and have been identified using both neurophysiology [21]–[23] and psychophysics [24] , [25] . Because of their potential to reduce redundancies between the eyes , they have been proposed as part of a theory of efficient stereo coding [26] , a topic which might appear to be unrelated to rivalry and cross-orientation suppression . In this paper , we explain how it is possible for dichoptic gratings to rival strongly while plaid components rival only weakly , and how to reconcile models of binocular rivalry with the normalization model . We propose a firing rate model that relies on ocular opponency neurons because they uniquely signal when rivalry should occur . For each orientation , opponency neurons receive excitation from one eye , and inhibition from the other eye . For binocular plaids , the opponency neurons in the model are silent because their inhibitory and excitatory inputs cancel . Under these circumstances , conventional normalization causes weak cross-orientation suppression . For dichoptic gratings , the opponency neurons are active , and we propose that they inhibit ( through feedback ) the monocular neurons corresponding to the eye from which they receive inhibition , thus amplifying the competition between the two eyes , resulting in rivalry . To account for the fact that binocular rivalry suppresses all orientations equally [27] , feedback inhibition is directed towards all orientations . Opponency neurons may be the neurobiological analogue of rivalry XOR ( exclusive-OR ) units; These XOR units were proposed 23 years ago to prevent plaid component rivalry in a qualitative model of binocular rivalry [28] , but have been overlooked ever since . To test the theory , we performed a psychophysics experiment that investigated a novel prediction of the model . According to the model , rivalry is predicted to be weaker following adaptation with monocular stimuli than binocular stimuli . This prediction follows from the fact that monocular stimuli evoke strong responses ( and hence adaptation ) in the opponency neurons , whereas binocular stimuli do not . A conventional model without opponency neurons does not make this prediction . Our psychophysical results , along with previously published psychophysical results [29] ( see Discussion ) , supported the prediction of the ocular opponency model .
To understand the relationship between rivalry and cross-orientation suppression , we first implemented a firing rate model of binocular rivalry that incorporated normalization ( Figure 2A ) . The model consisted of two left-eye , monocular neurons selective for orthogonal orientations , two analogous right-eye , monocular neurons , and two binocular summation neurons that received feedforward input from the monocular neurons . While we refer to the units as “neurons” for simplicity , the biological implementation of each unit may be more accurately described as an ensemble of ( e . g . , 50–100 ) neurons with similar response properties [30] . The monocular neurons mutually inhibited one another , and the binocular summation neurons mutually inhibited one another , yielding competition at both the monocular and binocular levels . We included inhibitory connections not only between neurons in different eyes , but also between neurons in the same eye . This arrangement differs from the many conventional models that only allow competition between neurons in different eyes , but similar results supporting the same conclusions were found to hold from the conventional model even when competition was restricted to neurons in different eyes . Mutual inhibition was implemented by the divisive normalization equation [19] , [20] rather than by subtraction , which is typically used in models of rivalry . The normalization equation is not intended to be a mechanistic model of suppression , but instead provides a good description of the computations underlying inhibitory interactions in cortex . Biophysically plausible implementations of this equation have been described elsewhere [19] , [31] . Lowpass-filtered noise was included in the inputs to each neuron ( see Methods for details ) . Unfiltered white noise coupled with neural adaptation would have behaved similarly . Binocular rivalry occurred in response to dichoptic gratings when the mutual inhibition in the model was set to be strong ( by assigning high values to certain weights in the denominator of the normalization equation ) . Rivalry strength was quantified with a winner-take-all ( WTA ) index defined in terms of the responses of the binocular summation neurons ( see Methods for details ) . The index was bounded by 0 and 1 , where 0 indicated that the two binocular summation neurons always had identical responses , and 1 indicated complete rivalry , with only one or the other neuron exhibiting a non-zero response at each time . Although strong inhibition permitted rivalry in the dichoptic grating condition , it had the unintended consequence of generating strong rivalry between plaid components ( Figure 2B–D ) . Rivalry occurred for both monocular plaids ( orthogonal gratings superimposed in one eye ) and for binocular plaids ( orthogonal gratings superimposed in both eyes ) . While the model's plaid component rivalry could be made to be slightly weaker than the dichoptic grating rivalry ( Figure 2B–D ) , actual plaid rivalry in psychophysical experiments is much weaker than the rivalry between dichoptic gratings [15] , [16] . An intuition for this behavior can be obtained by considering situations in which the competition between features is either entirely in the binocular stage or entirely in the monocular stage . If the competition is in the binocular stage , dichoptic gratings can easily be made to rival . However , plaids will provide the same input to the binocular stage as dichoptic gratings , and thus their components will rival as well . Alternatively , if the competition is in the monocular stage , a similar problem emerges . The competition that allows dichoptic gratings to rival will also cause rivalry in the components of plaids . One might think that rivalry could be restricted to dichoptic gratings if competition is made to be only interocular , not intraocular . However , while this arrangement will prevent rivalry for monocular plaids , it will allow rivalry in binocular plaids because each component in each eye will compete with an orthogonal component in the other eye . An exhaustive grid search through plausible weight values and noise amplitudes did not find a single parameterization that produced reasonable responses to plaids and gratings . While there were a few parameterizations that produced stronger rivalry for dichoptic gratings compared to plaids , all of these parameterizations had such high noise amplitudes and weight values that they behaved implausibly during the presentation of monocular gratings , with responses so volatile that they occasionally responded more strongly to a non-presented orthogonal orientation than to the grating that was presented ( see Methods and Figure 3 ) . If these models were correct , human observers viewing a single grating monocularly would occasionally perceive an orthogonal grating instead . We confirmed that a standard model of binocular rivalry [11] , with subtractive instead of divisive inhibition , also failed to solve this problem . This model appropriately exhibited strong rivalry for dichoptic plaids , but it also inappropriately exhibited equally strong rivalry with periods of complete dominance for binocular plaids , and sustained dominance with no alternations for monocular plaids ( Figure 4 ) . Although we did not explicitly test every previous model of binocular rivalry , we infer that they likewise would exhibit the same problem because none of them include a mechanism to modulate the strength of inhibition depending on whether the two component gratings are dichoptic , binocular , or monocular [9] . A robust solution to the plaid problem was obtained by adding ocular opponency neurons to the conventional model . Each opponency neuron computed a difference between the responses of two monocular neurons corresponding to the same orientation but different eyes ( Figure 5A ) . This difference was then halfwave rectified ( setting negative values to zero ) and normalized ( see Methods for details ) . Through feedback , the opponency neurons linearly inhibited ( i . e . , via subtraction ) both monocular neurons corresponding to the eye from which they received inhibition . There were a total of 4 opponency neurons , so that both orientations and both differences ( right-left and left-right ) were included in the model , although only one is shown in Figure 5A . Rivalry was more than three times as strong for dichoptic gratings compared to plaids ( Figure 5B–D ) . As with the conventional model , we again quantified the strength of rivalry with a WTA index defined in terms of the responses of the binocular summation neurons . Unlike in even the best parameterizations of the conventional model , there were periods of near complete dominance for dichoptic gratings but not plaids , without requiring implausible responses to monocular gratings presented alone . The model accomplished this using fewer parameters than the conventional model ( Tables 1 and 2 ) . Ocular opponency neurons solved the plaid problem by responding during conditions in which binocular rivalry might occur . When presented with binocular plaids , the opponency neurons were silent . Under these circumstances , weak cross-orientation suppression occurred because of normalization . In contrast , when dichoptic gratings were presented , the opponency neurons became active and suppressed activity in monocular neurons corresponding to the opposite eye . This suppression amplified the normalization-based competition between the eyes . It was critical that the opponency neurons inhibited monocular neurons they received inhibition from , rather than exciting monocular neurons they received excitation from . While the latter arrangement encouraged rivalry in dichoptic gratings and not in binocular plaids , it created rivalry ( inappropriately ) in monocular plaids , because monocular plaids excited the opponency neurons . In the correct arrangement , monocular plaids excited the opponency neurons , but the inhibitory feedback had a negligible effect , because the unstimulated monocular neurons were already responding only very weakly . The simulation results demonstrated how an opponency model , but not a conventional model , can exhibit both rivalry and cross-orientation suppression under appropriate circumstances . Nevertheless , the simulation results only showed how it is theoretically possible that opponency cells contribute to rivalry; they did not provide evidence that opponency models are necessary for rivalry . We therefore designed an experiment , using adaptation , to test a prediction of the opponency model . Adaptation is a powerful psychophysical tool , because it supports inferences about selectivity [32] , [33] , in this case , selectivity for ocular opponency . Human observers participated in two experimental sessions with different adaptors . During one session , observers adapted to orientation-alternating grating stimuli presented binocularly prior to rivalry ( Figure 6; see Methods ) . According to both the conventional model and the opponency model , these stimuli should activate , and therefore adapt , the monocular neurons and binocular neurons , but not the opponency neurons . During the other session , observers adapted to orientation-alternating stimuli presented monocularly , where one orientation was always presented to the left eye and the orthogonal orientation was always presented to the right eye , with only one or the other orientation presented at a time . According to both models , these stimuli should activate , and therefore adapt , the monocular neurons and the binocular neurons . The critical difference between the two models is that according to the opponency model ( but not the conventional model ) , the monocular adaptors will also adapt the opponency neurons . Following adaptation , observers viewed rival stimuli , with one orientation presented to one eye and the other orientation to the other eye . The rival stimuli were dichoptic gratings , identical in both sessions . Observers reported their percepts with button presses . Intuitively , the opponency model predicts that the monocular adaptation condition should result in weaker rivalry than the binocular adaptation condition , because the monocular condition adapts the opponency neurons that amplify the suppression , resulting in rivalry . The conventional model , which lacks opponency neurons , does not make this prediction . To make the predictions explicit , we first ran simulations on the two models after adaptation to the two conditions . A long-term adaptation variable ( time constant = 80 sec ) was added to both models to capture the slow buildup of adaptation produced by our experimental manipulations ( see Methods ) . This type of adaptation is slower than the adaptation that is sometimes used in other models of binocular rivalry to capture percept alternations . We could not directly measure the WTA index in human participants , so we relied instead on the percentage of “mixed” percepts as a proxy measure , where a high percentage of mixed percepts corresponds to a low WTA index . The conventional model , which lacks opponency neurons , predicts that monocular adaptation will result in a slightly lower percentage of mixed perception during rivalry compared to binocular adaptation ( Figure 7A; see Methods ) . In contrast , the opponency model predicts that monocular adaption will result in a higher percentage of mixed percepts compared to binocular adaptation ( Figure 7B ) . Psychophysical tests on human participants supported the opponency model . We found a higher percentage of mixed perception following monocular adaptation ( M = 25 . 3 , SD = 19 . 1 ) compared to binocular adaptation ( M = 20 . 1 , SD = 18 . 4; paired t ( 29 ) = 2 . 9 , p< . 01; Figure 7C–D ) . These results suggest that opponency neurons contribute to rivalry . Previous research has found that contrast adaptation alone can decrease dominance durations [34] . However , the difference in mixed perception between conditions in our experiment cannot be explained by this mechanism , as overall contrast adaptation would be expected to be higher in the binocular adaptation condition than the monocular adaptation condition . Indeed , the conventional model predicts slightly more mixed perception after binocular adaptation compared to monocular adaptation ( Figure 7A ) , contrary to what we found . The different adaptor conditions did not cause different eye imbalances . We defined eye imbalance as the absolute difference in the fraction of time that the left eye and right eye delivered the dominant percept . The mean absolute eye imbalance was 0 . 15 after monocular adaptation , and 0 . 16 after binocular adaptation . This difference was not significant . ( t ( 29 ) = 0 . 31 , p> . 05 ) . The effect of our adaptation manipulation on mixed perception , while significant , was not very large . Small effects are common in adaptation experiments [35]–[37] , presumably because the neurons are only partially adapted , not completely silenced . Indeed , our own model simulations predict small effects for this reason . Partial adaptation was a particularly important issue in our experiment , because the opponency neurons were adapted not only by the monocular adaptors , but also by the subsequent rival stimuli that were presented in both conditions . Thus , it was expected that the opponency adaptation condition would generate only marginally more opponency adaptation than the binocular adaptation condition .
Conventional models of binocular rivalry rely on strong competition , between neurons tuned to orthogonal orientations , to generate rivalry between dichoptic gratings . Because of this strong competition , conventional models make the incorrect prediction that plaid components , which are also orthogonally oriented , will strongly rival . This problem cannot be solved by adjusting the connection weights between neurons , as demonstrated by an exhaustive parameter search . Lowering the inhibitory weights reduces rivalry for plaids , but also reduces it for dichoptic gratings . Using ocular opponency neurons , we developed a model of binocular rivalry that solves the plaid problem much more effectively than a conventional model , despite using fewer parameters . The new opponency model makes a clear and novel connection between two of the most well-studied forms of competition in visual cortex: binocular rivalry and cross-orientation suppression . The model also makes predictions about functional interactions between monocular neurons , binocular summation neurons , and ocular opponency neurons . Under our interpretation , binocular rivalry and cross-orientation suppression rely on the same neural computations: orientation-selectivity , rectification , and normalization . In binocular rivalry , however , competition is amplified by feedback from ocular opponency neurons . A number of published dynamical systems models have characterized the alternating periods of perceptual dominance for dichoptic gratings , but none has provided simulations showing weak rivalry in plaid components in the same model . One published model proposed separate mechanisms for interocular and intraocular suppression , but it is not clear how this type of model could avoid rivalry in binocular plaids , where both interocular and intraocular mechanisms may contribute to suppression [38] . Another published report showed how rivalry occurred in a strong-inhibition variant of a model , whereas weak suppression occurred in a low-inhibition variant of the model , but it was not explained how the inhibition strengths were controlled depending on whether plaids or dichoptic gratings were viewed [39] . Using opponency neurons , our model demonstrates the appropriate behavior for both types of stimuli with a fixed set of parameters . Opponency neurons may be the neurobiological analogue of rivalry XOR ( exclusive-OR ) units . These XOR units were proposed 23 years ago in a qualitative model of binocular rivalry [28] , but the need for this kind of computation has been overlooked ever since . Neurophysiological studies have identified opponency neurons that algebraically subtract the input between the two eyes [21]–[23] , but these neurons have received little attention , in large part because the functional significance of these cells was unknown . Livingstone and Hubel ( 1984 ) remarked that they were “at a loss to imagine any plausible benefit” for the subtraction operation between eyes . Subsequent theoretical work provided one possible benefit , that opponency neurons may play a critical role in efficient stereo coding [26] . Previous psychophysical studies have provided evidence for ocular opponency , but none have tested the role of opponency in binocular rivalry [24] , [25] . Of most relevance is the observation that the amount of mixed perception increases over the course of binocular rivalry due to long-term adaptation during rivalry , and that this effect is not simply due to contrast adaptation [40] . While this effect has been attributed to adaptation of a generic “rivalry mechanism” , these results are easily explained under our framework . During the presentation of rival stimuli , as opponency neurons become increasingly adapted , they become unable to effectively enforce rivalry . Greater mixed perception following adaption was later replicated both with binocular adaptors and monocular adaptors [29] . This latter experiment was very similar to our own , and thus provides additional support for our model . However , it was also observed that when subjects were deprived of stimulation for an hour after binocular adaptation , subsequent mixed perception was still strong . This suggests that long-term plasticity , and not just adaptation , may have been involved . This form of plasticity is not present in the current version of our model . The authors of that study attributed their results to “anti-Hebbian” learning , in which inhibitory connections are weakened following stimulation by monocular or dichoptic stimuli . In the context of our model , anti-Hebbian learning could be incorporated into the inhibitory connections between opponency neurons and monocular neurons , thus providing a mechanism for long-term plasticity . We do not commit to any normative account for why the visual system would develop the circuitry used in our model , and it remains an open question . No feedback was included in previous models of efficient stereo coding [26] . Instead , gain control was applied to output of the summation and opponency channels to optimize their sensitivities , with stronger gain on the opponency signal . The feedback in our model , which has the effect of increasing the opponency signal more than the summation signal , may be one mechanism by which this gain control is accomplished . Alternatively , some researchers have proposed that binocular rivalry may be a rational form of Bayesian inference , where sampling from the two eyes is used to approximate a posterior distribution over causes [41] . Under this interpretation , the opponency mechanism might be required to allow rivalry only when it is rational ( i . e . under dichoptic conditions ) . We made no attempt to account for all of the known properties of binocular rivalry . Instead , we focused on what has been an under-appreciated shortcoming of binocular rivalry theories . For simplicity , our model uses only a few parameters to address that shortcoming . We believe that some of the remaining properties of binocular rivalry could be accounted for by straightforward extensions to our model . While we included long-term adaptation to simulate our adaptation experiment , we did not include any fast adaptation dynamics , a process that almost certainly plays a role in perceptual alternations during binocular rivalry . Models that incorporate adaptation can account for the gamma distribution of dominance durations and the observation that changing the contrast of one eye primarily affects only the dominance durations of the other eye [42] , although the generality of this observation has been called into question [43] , [44] . There has been considerable debate about whether binocular rivalry occurs primarily between monocular representations ( eye rivalry ) or between binocular and higher level representations ( image rivalry ) [3] . Psychophysical evidence for eye rivalry includes the observations that swapping the images between eyes at the peak of a dominance phase causes an immediate change in perception [28] , [45] , and that target probes presented to the suppressed eye are difficult to detect [46] , [47] . On the other hand , there are two main lines of psychophysical evidence that image rivalry may contribute as well . First , research on interocular grouping has shown that when component patches of two different images are distributed between the eyes , observers often see coherent images [48] . Second , when orthogonal gratings are rapidly swapped between the eyes and accompanied by an even faster flicker , observers report rivalry at a frequency much slower than the swap rate [49] . There is also conflicting physiological evidence over whether rivalry occurs primarily at the monocular level or at later stages [50]–[53] . Our model is hierarchical by design , and thus includes both monocular competition ( contributing to eye rivalry ) and binocular competition ( contributing to image rivalry ) . Our model is agnostic about whether the binocular neurons underlying perceptual judgments reside in V1 or higher cortical areas . To account for the interocular grouping effects [48] , an extension to our model could include top-down modulation of local competition , analogous to computational theories of attention [54] , such that portions of one eye's view and complementary portions of the other eye's view are simultaneously dominant [55] . Our current model can only partially account for the observations from rapid swap experiments [49] , [56] . The binocular summation neurons in the model exhibit slow alternations during high-frequency alternations between stimuli , but the responses of these neurons are weak under these conditions , and do not show all the known frequency-dependent effects of eye swapping [56] . A previously published hierarchical model used relatively strong inhibition at the binocular layer to produce slow and robust alternations during rapid swap stimulation but , like our model , did not attempt to account for the frequency-dependence [11] . In both models , increasing the inhibition in the binocular layer could shift the behavior more to image rivalry during rapid swap stimulation , but would also increase the rivalry between plaid components . Finally , our model makes no attempt to explain ‘rivalry memory’ , although the extensions to our model would be straightforward . In rivalry memory experiments , the rival stimuli are turned off for several seconds immediately after one of them has become dominant . When the stimuli are turned back on , the previously dominant stimulus is typically perceived [57]–[59] . This effect could be explained by including brief , recurrent synaptic facilitation in our model [60] .
Within each subpopulation of neurons ( monocular , binocular summation ) , mutual inhibition was implemented by a dynamical variant of the normalization equation: ( 1 ) where the brackets indicate halfwave-rectification . At steady state , the instantaneous firing rate of neuron was the half-squared drive of the neuron divided by a weighted sum of the half-squared drives of all the other neurons in the normalization pool , plus an additional semi-saturation constant in the denominator . All four monocular neurons were part of a single normalization pool ( Figure 2A ) . Thus , every monocular neuron contributed to the normalization of every other monocular neuron , including itself . For the binocular summation neurons , the pool consisted of both summation neurons . The unnormalized drive for each monocular neuron was determined as follows: ( 2 ) where was the stimulus contrast corresponding to the particular eye and orientation represented by neuron . Lowpass filtered noise was added to each neuron's input , computed by starting with Gaussian white noise and convolving in time with a Gaussian kernel ( = 800 ms ) . The noise was statistically independent for each neuron . The unnormalized drive for each binocular summation neuron depended on excitatory inputs from iso-oriented monocular neurons: ( 3 ) where was the feedforward weight , and and were the activities of the right and left monocular neurons with the appropriate orientation preference . The drive of a right-minus-left ( RL ) opponency neuron was computed as ( 4 ) where and are the activities of the right and left monocular neurons with the appropriate orientation preference . The weights on the feedforward connections were not included in this equation because they were set to 1 . In fact , the opponency model was robust enough that we could discard the weight parameters ( the 's in equations 1 and 3 ) setting them all to 1 ( Table 1 ) . The RL opponency neurons subtracted left monocular activity from right monocular activity for a particular orientation ( Figure 5A , Equation 4 ) . There are a total of 4 opponency neurons , for two orientations and two differences ( RL and LR ) . The two RL opponency neurons formed a normalization pool separate from the normalization pool for the two LR opponency neurons . Because only a single RL opponency neuron is shown in Figure 5A , the pools are not shown either . Through feedback , the opponency neurons linearly inhibited both monocular neurons in the opposite eye . Thus , the drive for a left eye monocular neuron tuned to orientation ‘A’ was: ( 5 ) where and were the firing rates of the two opponency neurons driven by the right eye . The models were numerically approximated with Euler's Method , using a 2 ms time step . During the grid search and the adaptation simulations , we used a 10 ms time step because of the extreme computational time demands . We tested the conventional model and the opponency model on five conditions: dichoptic gratings , monocular plaids , binocular plaids , monocular gratings , and binocular gratings . Grating contrasts were set to 0 . 5 . Matlab code for the conventional model and the opponency model will be available on our website ( http://www . cns . nyu . edu/heegerlab/ ) . To determine the extent of rivalry between components , we defined a winner-take-all ( WTA ) index on the responses of the binocular summation neurons: ( 6 ) where A and B refer to the two orientations , is the time step , and is the simulation duration . The index was bounded by 0 and 1 , where 0 indicated that the two binocular summation neurons always had identical responses , and 1 indicated complete rivalry ( with only one or the other neuron exhibiting a non-zero response at each time ) . Reliable estimates of the WTA index were obtained by averaging over 160 seconds of model time ( Figures 2 and 5 ) . To test whether the conventional model could effectively solve the plaid problem , we performed an exhaustive grid search through plausible parameter values ( Table 2 ) . In total , 390 , 625 parameter combinations were simulated . For each parameter combination , we simulated the model for 40 seconds in model time and considered the model to be a possible candidate for acceptance if it met two initial criteria described below . Because each simulation of each stimulus condition was accompanied by randomly and independently generated noise , a small number of the 390 , 625 parameter combinations met our criteria for model success simply by chance . Therefore , using a longer 400 second simulation duration , we repeated the simulations for the models that passed the criterion the first time . The two initial criteria were: Of the 390 , 625 parameter combinations , only 6 met the initial two criteria both times . However , all 6 of these combinations produced implausible behavior during the presentation of monocular gratings: the responses were so volatile that the model would sometimes show stronger responses in the neurons tuned to the orthogonal orientation that was not presented ( Figure 3 ) . If these models were correct , human observers presented with a single grating monocularly would occasionally perceive an orthogonal grating instead . We rejected all 6 parameter combinations that passed the first 2 criteria because they all produced multiple percepts switches in the second round of simulations . Our grid search sampled weight parameters at intervals of 0 . 4 units and noise amplitudes at intervals of . 02 and 0 . 04 units ( Table 2 ) . We cannot rule out the possibility that a parameter combination overlooked by our sampling rule could produce good behavior in the conventional model . Such a parameter combination , if it were to exist , would have to be very finely tuned . Thirty observers ( 19 females ) participated in the psychophysics experiment . All observers had normal or corrected-to-normal vision . All observers were over the age of 18 and provided written informed consent . The experimental protocol was approved by the University Committee on Activities involving Human Subjects at New York University . Two adaptation conditions were conducted in separate sessions on separate days , and the order of sessions was counterbalanced across observers . To control for potential time-of-day effects , each observer participated in each session at roughly the same time of day . Stimuli were presented on a calibrated CRT display positioned 57 cm from the observer's head . Observers viewed a split screen , through base-out prism glasses , with the left half of the CRT being presented to the left eye and the right half of the screen to the right eye . A black septum blocked contralateral stimuli from reaching the eyes ( i . e . , so that the left half of the screen was not visible to the right eye and vice versa ) . Stimuli were composed of grating patches subtending a diameter of 1 . 2° of visual angle . The contrasts of the gratings were tapered with a raised cosine window ( half cycle = 0 . 3° ) . To facilitate fusion , stimuli were surrounded by a square black border ( side = 2 . 2° ) , and a square patch of 1/f image noise was placed above and below each stimulus ( side = 3°; position = 4 . 5° above and below horizontal ) . Each session consisted of 6 blocks , each divided into two parts: a 100 sec adaptation period in which observers passively viewed a sequence of stimuli , followed by an 80 sec rivalry period ( Figure 6 ) . To further drive the adaptation process prior to rivalry , the last 24 observers viewed two additional 100 sec blocks of the adaptor at the beginning of each session . In the binocular adaptation session , observers adapted to a contrast-reversing ( 15 Hz ) binocular grating . The grating reversed in orientation at . 94 Hz , from 45 degrees clockwise ( CW ) of vertical to 45 degree counterclockwise ( CCW ) of vertical ( Figure 6 ) . The spatial frequency was 6 . 6 cycles/° and the contrast was set to 100% . In the monocular adaptation session , the adaptor gratings were identical to those of the binocular adaptation session , except that one eye was stimulated at a time with the CCW grating presented only to the left eye and the CW grating presented only to the right eye ( Figure 6 ) . After the adaptation period of each block , observers performed a traditional binocular rivalry task for 80 sec in which they viewed static gratings ( i . e . not contrast-reversing or orientation-alternating ) . A CCW-of-vertical grating was shown to the left eye and a CW-of-vertical grating was shown to the right eye . To control for individual differences in contrast sensitivity and eye dominance , the contrast of the stimuli were adjusted for each observer and each eye at the beginning of the first session . While the stimuli on the screen remained fixed throughout each block , observers perceived one of the following at any given moment: ( 1 ) A dominant CW-of-vertical grating; ( 2 ) A dominant CCW-of-vertical grating; ( 3 ) A mixed percept , typically appearing as a plaid . Observers reported their percepts by continuously pressing one of three buttons . To minimize individual differences in response criteria , observers were instructed to report a stimulus as dominant only if it comprised 90% or more of their percept . We computed the prevalence of mixed percepts as the overall percentage of time observers reported mixed percepts ( out of the total time a button was depressed ) . To capture the effects of adaptation , we added a long-term adaptation variable to each neuron according to ( 7 ) where the time constant of long-term adaptation , , was set to 80 sec . This type of adaptation is slower than the adaptation used to capture perceptual alternations in some conventional models of binocular rivalry . The adaptation variable was then multiplied by a scale factor of 0 . 5 and subtracted from the unnormalized drive of each neuron . We simulated 100 blocks of the adaptation experiment using both the conventional model and the opponency model . We presented 100 sec of 100% contrast adaptors that reversed orientation at 0 . 94 Hz , followed by 80 sec of rival stimuli at 50% contrast . For simplicity , we defined the neurons as invariant to spatial phase , so our stimuli did not reverse in contrast , as in the experiment . Since we could only measure a mixed percept percentage in human observers ( rather than a WTA index ) , we computed an analogous measure for the model simulations . For the 80 sec of rivalry in each block , we first computed a percept index according to ( 8 ) The index was bounded by [0 1] , where low values indicated mixed perception and high values indicated dominant perception . Then , to compute the mixed percept fraction , we computed the fraction of time that P ( t ) was lower than a cutoff of 0 . 4 , and then averaged across all 100 simulation blocks for each model . The pattern of results was robust to variation in the cutoff value . | Binocular rivalry is a visual illusion that occurs when the two eyes are presented with incompatible images . Instead of perceiving a mixture of the two images , most people tend to experiences alternations in which they only see one image at a time . Binocular rivalry is more than just an interesting illusion: it reflects actual competition between neurons in the brain , and therefore provides a rare window into neural dynamics . To help us understand these mechanisms , researchers have developed several computational models of binocular rivalry . Yet surprisingly , as we show in this paper , previous computational models of rivalry make an incorrect prediction . They predict that certain types of images ( similar to checkerboards ) will cause strong perceptual alternations even when viewed normally . Since this prediction doesn't hold up , the existing models must not be telling the whole story . In this paper , we develop a new model of binocular rivalry that doesn't make this prediction . The model also makes novel predictions – not made by conventional models – that stand up to experimental test . Our model thus provides a better account of how neurons in the visual system interact with one another . | [
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] | 2013 | A Model of Binocular Rivalry and Cross-orientation Suppression |
Praziquantel at 40 mg/kg in a single dose is the WHO recommended treatment for all forms of schistosomiasis , but 60 mg/kg is also deployed nationally . Four trial sites in the Philippines , Mauritania , Tanzania and Brazil enrolled 856 patients using a common protocol , who were randomised to receive praziquantel 40 mg/kg ( n = 428 ) or 60 mg/kg ( n = 428 ) . While the sites differed for transmission and infection intensities ( highest in Tanzania and lowest in Mauritania ) , no bias or heterogeneity across sites was detected for the main efficacy outcomes . The primary efficacy analysis was the comparison of cure rates on Day 21 in the intent-to-treat population for the pooled data using a logistic model to calculate Odd Ratios allowing for baseline characteristics and study site . Both doses were highly effective: the Day 21 cure rates were 91 . 7% ( 86 . 6%–98% at individual sites ) with 40 mg/kg and 92 . 8% ( 88%–97% ) with 60 mg/kg . Secondary parameters were eggs reduction rates ( ERR ) , change in intensity of infection and reinfection rates at 6 and 12 months . On Day 21 the pooled estimate of the ERR was 91% in both arms . The Hazard Ratio for reinfections was only significant in Brazil , and in favour of 60 mg/kg on the pooled estimate ( 40 mg/kg: 34 . 3% , 60 mg/kg: 23 . 9% , HR = 0 . 78 , 95%CI = [0 . 63;0 . 96] ) . Analysis of safety could not distinguish between disease- and drug-related events . 666 patients ( 78% ) reported 1327 adverse events ( AE ) 4 h post-dosing . The risk of having at least one AE was higher in the 60 than in the 40 mg/kg group ( 83% vs . 73% , p<0 . 001 ) . At 24 h post-dosing , 456 patients ( 54% ) had 918 AEs with no difference between arms . The most frequent AE was abdominal pain at both 4 h and 24 h ( 40% and 24% ) . A higher dose of 60 mg/kg of praziquantel offers no significant efficacy advantage over standard 40 mg/kg for treating intestinal schistosomiasis caused by either S . mansoni or S . japonicum . The results of this study support WHO recommendation and should be used to inform policy decisions in the countries . Controlled-Trials . com ISRCTN29273316 ClinicalTrials . gov NCT00403611
Schistosomiasis is a parasitic infection caused by blood flukes ( flatworms ) of the class Trematoda: Schistosoma haematobium ( causing urinary schistosomiasis ) , S . mansoni , S . japonicum , S . intercalatum and S . mekongi ( causing intestinal schistosomiasis ) . A recent systematic review of evidence estimates that ∼207 million people ( 97% in Africa ) are affected and ∼779 million people are at risk ( 85% in Africa ) in 76 countries ( 46 in Africa ) . With respect to the mid 1990s , there has been an increase of ∼7 . 3% and 10 . 9% in infections and population at risk ( mostly accounted for by Africa ) but a concomitant ( slight ) decrease from 29 . 6% to 26 . 6% of the ratio between people infected and people at risk , primarily as the combined result of socio-economic development and interventions such as sanitation and broader distribution of praziquantel [1] . Traditionally , schistosomiasis has been attributed a low burden of disease as quantified by DALY ( disability adjusted life years ) lost ( 1 , 760 , 000 estimated in 2002 [2] ) . However , the real impact of schistosomiasis infection on people's health and performance is more difficult to quantitate . A systematic review and meta-analysis of disability-related effects of schistosomiasis indicates that the disability weight of schistosomiasis ( 2–15% ) is much greater than the previous estimation of 0 . 5% [3] . Specifically for S . japonicum , a disability rate of 13% has been calculated ( 7–46 times greater than current estimates ) [4] . Today praziquantel is the mainstay of schistosomiasis control . However , while the World Health Organization ( WHO ) currently recommends that it should be used in a single dose of 40 mg/kg for the treatment of both urinary and intestinal schistosomiasis [5] , different doses of praziquantel are being deployed by national control programmes . Studies of the dose-response of praziquantel in urinary and intestinal schistosomiasis are few and incomplete . In particular , no obvious dose-effect was apparent in a Cochrane systematic review and meta-analysis of randomised controlled trials of praziquantel between the doses of 20 to 40 mg/kg for urinary schistosomiasis [6] . A Cochrane systematic review and meta-analysis of treatments for intestinal schistosomiasis is under way . Here , contrary to urinary schistosomiasis , preliminary results ( which were not available when this study was being planned and conducted ) show a dose-response effect between the doses of 20 , 30 and 40 mg/kg with no further gain with doses >40 mg/kg . This study was set up to assess whether using praziquantel at 60 mg/kg for intestinal schistosomiasis ( caused by either S . japonicum or S . mansoni ) offers advantages over 40 mg/kg . Four trial sites in the Philippines , Mauritania , Tanzania and Brazil enrolled patients using a common protocol . The study at each site was powered to show a difference in cure rates between dosage groups . The analytical plan was prospectively designed to allow for reporting the results both for each study site and combined by individual patients' data meta-analysis ( IPD ) .
The studies were conducted in: The prevalences above are from historical data . The actual point prevalence found on screening for this study for each site is reported in the Results section . At baseline , Day 21 , 6 months and 12 months , stool samples were taken on two consecutive days and each tested twice . The mean egg counts are reported . Praziquantel ( Distocide® by Shin-Poong , Korea ) was procured by WHO and administered with food after weighing the participants on a scale by the nearest half tablet according to the randomization schedule . Efficacy . ( i ) Primary: cure rates ( “complete cure” defined as negative stools for Schistosoma eggs ) and egg reduction rates ( ERR , “partial cure” ) with the two regimens on Day 21; ( ii ) Secondary: reinfection rates at 6 months and 12 months after treatment . Safety and tolerability: prevalence and intensity of adverse events ( AEs ) . The choice of Day 21 as the main efficacy outcome is justified by the fact that , as praziquantel is not active on immature schistosomes of less than 24 days , by Day 21 immature S . mansoni worms will not have yet matured into patent egg-producing infections . The evaluation of praziquantel efficacy , using duplicate Kato-Katz slides from two different stool samples , at 3 weeks showed the highest cure and egg-reduction rates [7] . Screening occurred in the village in Brazil and Mauritania and at school in the Philippines and Tanzania . Computer generated randomization list with blocks of 4 in a ratio 1∶1 for each regimen . Sealed and numbered envelopes were kept in a locked cabinet by one responsible person; two different people preparing treatment and evaluating patients; stool specimens read by a technician blinded as to the treatment . The sample size required at each site was computed at 91 patients per treatment group using 60% and 80% as the cure rates of praziquantel 40 mg/kg and 60 mg/kg respectively , with 80% power and 95% confidence . The sample size was adjusted to 109 individuals per treatment group for a total of 218 participants in consideration of the anticipated 20% of participants who might be lost to follow-up . The results were expressed as Odds Ratios ( OR and 95% confidence intervals ( CI ) ) for dichotomous outcomes . Adjusted outcomes ( such as cure rates and hazard ratios [HR] ) were analysed by using an inverse variance methodology . Continuous outcomes were expressed as means and standard deviations . Study bias was examined through the use of a funnel plot of the log-transformed OR/HR [log ( OR ) /Log ( HR ) ] of individual studies against the precision ( 1/SE , standard error ) . Funnel plot asymmetry was further tested by using the Egger's method . For the assessment of heterogeneity , the Cochran's Q and I2 test of heterogeneity were performed to detect non-homogeneity between the estimates of individual studies and by using fixed effects models . The Mantel-Haenszel method was used to analyse cure rates . ORs of adjusted cure rates allowing for age , gender , baseline diarrhoea and nausea were obtained by the generic inverse variance method . An inverse variance method was also used to analyse the difference between groups in the mean of the difference between eggs-per-gram ( epg ) at Day 21 and Day 0 . The Mantel-Haenszel method was used to analyse the reinfection rates at day 180 and day 360 . The HRs of reinfection provided by a Cox proportional hazard model were aggregated by using the generic inverse variance method . While the data were also analysed for each individual study , in this paper we present specifically the results of the prospectively defined individual patient data meta-analysis whereby the analysis was performed by using the same methods as for each study site ( see below ) adjusted for the site . Safety data were gathered through a questionnaire enquiring on the occurrence of adverse events ( AEs ) on Day 0 at 4 hours , Day 1 and Day 21 post-dosing . An AE was defined as any unfavourable and unintended sign ( including an abnormal laboratory finding ) , symptom , or disease temporally associated with the use of a medicinal or investigational product , whether or not related to that product . It is clear that this approach does not allow to distinguish between disease-related and drug-related events , but only allows comparing arms for frequency of events , independent of causality . Recording of the AEs included: ( 1 ) date and time of onset , ( 2 ) duration , ( 3 ) severity , ( 4 ) severity and ( 5 ) relationship to treatment . The report also includes a probable explanation from the investigator as to the cause of the AE . The prevalence and intensity of the following signs and symptoms are assessed: abdominal discomfort , nausea , vomiting , diarrhoea , anorexia , fever ( using an oral thermometer ) , headache , dizziness and allergic reaction . The severity of the signs and symptoms was categorized as “mild” , “moderate” , “severe” and “life-threatening” . The relationship of the signs and symptoms to treatment was categorized as “not related” , “unlikely” , “possible” , “probable” and “most probable” . For the purpose of this study , the cumulative prevalence of AEs is defined as the proportion of those followed-up reporting one or more AEs . The cumulative prevalence of AEs , defined as the presence of at least one AE in a patient , was determined at 4 hours on Day 0 , Day 1 and Day 21 post-treatment . AEs , signs and symptoms were classified according to the WHO Adverse Reaction Terminology dictionary . Patients were explained the scope of the study and signed a written informed consent ( if under 18 years of age , written informed consent from parents/guardians and individual verbal assent ) before inclusion in the study . The studies were conducted according to the Helsinki declaration and were approved by the local ( University of the Philippines Manila - College of Medicine Institutional Review Board , ethics committee of the Institut National de Recherches en Santé Publique in Mauritania , National Medical Research Coordinating Committee in Tanzania , Research Ethics Committee of the Aggeu Magalhaes Research Centre , Oswaldo Cruz Foundation ( Fiocruz ) in Brazil ) as well as the WHO ethics committees . All sites except Tanzania were independently monitored .
While there are only four points to draw the funnel plot , the cure rates are symmetrical and their distribution is narrow - that is , a low risk of bias . The funnel plot of the adjusted Day 21 cure rates is presented as an annex ( Figure S1 ) . Similar results were obtained with the crude rates , the epg , and the hazard ratios of reinfection ( not shown ) . As for the reinfection rates on follow-up , there was no bias on Day 180 but bias was found on Day 360 . No substantial heterogeneity ( defined as I2>50% ) existed in the pooled analysis of the 4 study sites for the primary outcome: I2 for crude cure rates = 8%; adjusted cure rates = 47%; epg differences between Day 21 and Day 0 = 0% , HR of reinfection = 19% , reinfection rates at Day 180 = 23% . Instead , the Higgins I2 for reinfection rates at Day 360 was 76% . ( see reinfection rates analysis below ) The product-limit estimate of the time to reinfection was calculated starting from Day 21 restricted to the patients who were free of parasites throughout Day 360 ( 12 months ) . Table S1 presents mean epg comparisons between groups and over time . The median infection-free survival and reinfection rates for each study site are presented as an annex ( Table S2 ) . The Kaplan-Meier curves for the global estimate are presented in Figure 5 . Reinfection rates were lowest in Mauritania ( 8% and 3% in the 40 and 60 mg/kg arms respectively ) and highest in Tanzania ( 47% and 37% respectively ) . The median infection-free survival was similar between sites ( though this may be an artefact as patients were seen only twice during follow-up: around Day 180 and Day 360 ) . For the comparison between the two doses , the hazard ratio ( HR ) was not significant in all countries except Brazil in favour of 60 mg/kg , and was significant on the pooled estimates ( pooled reinfection rates: 34 . 3% ( 95%CI = [29 . 8; 39 . 3] ) in the 40 mg/kg arm and 23 . 9% ( 95%CI = [20 . 0; 28 . 4] ) in the 60 mg/kg arm; HR = 0 . 78 , 95%CI = [0 . 63;0 . 96] ) In addition to the Kaplan-Meier estimates presented above , we also compared the reinfection rates occurring at 6 and 12 months of follow-up for each individual country as well as for the pooled data ( Figure 6 ) . The pooled OR ( 95%CIs ) were 1 . 70 ( 1 . 18 , 2 . 46 , p = 0 . 0047 ) at 6 months and 1 . 41 ( 1 . 02 , 1 . 95 , p = 0 . 0037 ) at 12 months , both showing a difference in favour of the 60 mg/kg group . The 76% I2 on Day 360 is explained by the OR in Mauritania being in favour of the 40 mg/kg dose . Overall , 40% of the 388 total recrudescences occurred at 6 months . The difference was significant on both occasions in Brazil , and borderline in Tanzania at 6 months and the Philippines at 12 months . Intensity of infection on day 180 raised again in all countries as intensity was at least light in >20–25% of the patients in the Philippines , Brazil and Tanzania . In Mauritania intensity was moderate or heavy in 3% of the patients . The pooled estimate had 22 . 8% for light , 3 . 4% moderate and 1 . 3% for heavy intensity in the 40 mg/kg group and 12 . 6% for light , 3 . 2% moderate and 1 . 6% for heavy intensity in the 60 mg/kg groups ( Table S3 ) . At Day 360 in the Philippines the intensity of infection was more pronounced . While , compared to Day 180 , the proportion of light intensity infections remained stable in all countries except in Tanzania ( where it increased dramatically ) , there were more moderate intensity infection ( 9% vs 13% in the Philippines , 3% vs 6% in Brazil , 2% vs 3% in Mauritania and 8% vs 14% in Tanzania ) . The pooled estimate has 30% vs 23% for light intensity in the 40 mg/kg vs . 60 mg/kg groups , 8% vs . 7% for moderate intensity and 3% vs . 3% for heavy intensity . There was no significant difference between the treatment groups in the intensity classes on either Day 180 or Day 360 .
This study shows that a higher dose of 60 mg/kg of praziquantel offers no significant efficacy advantage over standard 40 mg/kg for treating intestinal schistosomiasis caused by either S . mansoni or S . japonicum when assessed three weeks ( 21 Days ) post-dosing . With 40 mg/kg of praziquantel cure rates are >91% at all sites except Tanzania ( 87–88% ) and egg reduction rates are >89% by Day 21 . During long-term follow-up at 6 and 12 months , reinfection rates were higher in the group receiving 40 mg/kg . Patients treated with 60 mg/kg had a higher risk of adverse events occurring immediately post-dosing ( 4 hours ) , while no difference was seen 24 hours or 21 days later . The results presented are based on the intent-to-treat population and are confirmed by other analyses on per-protocol population as well as various sensitivity analyses . While the sites differed in terms of transmission and infection intensities , there was no bias or heterogeneity across sites for the main efficacy outcomes except reinfection rates at one year of follow-up . Together , these results support the current WHO recommendation of deploying praziquantel at 40 mg/kg . These studies were conducted to respond to the demand for evidence about the correct dose of praziquantel when used by control programmes in endemic countries . The plan was to have a multicentre study with a master protocol whereby each site will follow the same protocol and be powered to show a difference between expected cure rates , and to have results combined prospectively for an individual patient meta-analysis . Prevalence of screening of intestinal schistosomiasis infection ranged 25% ( Brazil ) –57% ( Mauritania ) and the rate of reinfection one year post-treatment ranged from 9% ( Mauritania ) to 44% ( Tanzania ) between the two treatment arms . Of all sites Tanzania had the most intense infections , the lowest cure rates ( though with high egg reduction rates ) , and the highest reinfection rates - all testifying high transmission intensity . Reinfections were more frequent in the 40 than in the 60 mg/kg group at each site , but the difference was significant only for Brazil and the pooled Kaplan-Meier analysis . The difference between the two arms was already significant on aggregate and for Brazil by Day 180 , when 40% of reinfections had occurred . However , there was no difference between the two groups in terms of intensity classes . It is difficult to explain how a single dose of praziquantel could affect the risk of reinfection in the following 12 months . Indeed the difference could be a random effect . However , the Kato-Katz method has been reported to overestimate cure rates [8] ( although here two duplicates were done on consecutive days were used ) and it is hence possible that it failed to identify low egg counts on Day 21; the cases identified would then result from a mixture of undetected failures and true reinfections . Praziquantel treatment of either urinary or intestinal schistosomiasis is known to modify both humoral and cellular responses ( like age does ) . ( see for instance Mutapi et al , 2003 [9] ) . It remains to be investigated whether the higher dose could elicit a greater post-treatment antibody and cytokine shift . The dose-plasma concentration relationship of praziquantel is poorly know . A study conducted in healthy adult volunteers with 5 , 10 , 20 , 50 mg/kg showed with increasing doses ( i ) higher exposure ( exponential increase of Cmax , AUC0–24 ) ; ( ii ) more rapid absorption ( significant reduction of Tmax ) ; ( iii ) slightly reduced t½ [10] . However , no study in the target population ( infected school-age children ) has produced pharmacokinetic and pharmacokinetic/pharmacodynamic information on dose-concentration and dose-effects . Based on data from the above-mentioned study in volunteers the doses of 40 and 60 mg/kg ( neither have data in the above study ) would be expected to produce AUC0–24 ( ng*h/mL ) of 2900 and 4600 or 2100 and 8600 , respectively depending on whether a linear or exponential relationship applies - meaning that the AUC obtained with a dose of 60 mg/kg ( 1 . 5 times higher ) would be 1 . 6 or 4 times higher than with 40 mg/kg respectively . For the 40 mg/kg dose , this is coherent with other studies: for comparison , a dose of 40 mg/kg produced AUC0–24 ranging 2 , 110-4 . 098 ng*h/mL when given in fasting conditions to adult healthy volunteers , but was ∼4 times higher ( 15 , 928 ng*h/mL ) in patients [11] . However , without drug levels this remains in the realm of speculation . In addition , bioavailability ( and thus exposure ) is known to have high inter-individual variability , and to change between an empty stomach and with food ( and the type of food: higher with carbohydrates than fat [12] ) as well as the brand of the product and between health and disease . Finally , there are no data at present to correlate exposure and effects for praziquantel . It is unfortunate that this study could not collect blood samples for drug level determination . In any case , the higher exposure expected with 60 mg/kg did not translate in a sizeable efficacy advantage in this study but may entail a higher risk of toxicity ( at least when given as a single dose ) although adverse events were generally mild and transient . A limitation of this trial is that it was designed with a superiority hypothesis expecting the 60 and 40 mg/kg doses to be 80% and 60% effective at each site . This assumption ( which was based on the recommendations made at the 1991 WHO expert committee [5] ) was disproved by the finding that the cure rates ranged 88–97% ( 92 . 8% on aggregate ) with 60 mg/kg and 87–98% ( 91 . 7% on aggregate ) with 40 mg/kg . A non-inferiority trial design would have been more appropriate . The question is whether with such design the current trial would support the non-inferiority of 40 mg/kg with respect to 60 mg/kg . Assuming the cure rate of the reference treatment ( 60 mg/kg ) to be 93% and accepting a difference ( δ ) of 6% , with a precision ( α ) = 0 . 015 and a power ( 1-ß ) = 90% a study enrolling 428 patients per arm with cure rates of 91 . 7% and 92 . 8% ( difference = 1 . 1 , 95%CI −3 . 63 , +5 . 75 ) would be within the 6% δ margins and thus support the non-inferiority of the 40 vs . 60 mg/kg dose . Another issue is with the safety evaluation . Here , any event occurring after drug intake ( starting 4 hours after administration ) was conservatively reported as an adverse event ( AE ) , and their incidence , type and severity compared between treatment groups . However , the presence and grading before drug administration was not recorded - it is thus not possible to describe the treatment-emergent signs and symptoms ( i . e . those that were not present pre-treatment or worsened with the treatment ) , and to differentiate between the events related to the disease and those that may be caused by the drug . Tanzania reported more events than the other sites - which may be related also to the fact that the Tanzanian patients were more heavily infected than the others . The results of this study , along with those of systematic reviews , should be used to inform policy decisions in the countries [5] . Single dose treatment ensures high population compliance with treatment which would not be possible with spaced doses of 40 mk/kg praziquantel [6] . The Philippines has already changed from 60 to 40 mg/kg after considering the local results [13] . Reliable , up-to-date evidence is needed for policy decisions . As mentioned above , a yet unpublished Cochrane systematic review of treatments of schistosomiasis mansoni found a dose-effect in the cure rates of praziquantel up to 40 mg/kg and no gain beyond this dose . In contrast , no dose-effect was detected in schistosomiasis haematobia [6] . Of note , only 3 of the 10 praziquantel studies of the urinary and 8 the 20 of the mansoni review had been conducted in the past 30 years - all the others were older . One single treatment policy is practical but may not fit all cases . For areas where both intestinal and urinary schistosomiasis coexist the dose of 40 mg/kg is expected to cure both . In an area of Egypt where schistosome parasites were thought to be less responsive to a 40 mg/kg dose of praziquantel , no increase in drug failures was noted following 10 years of drug pressure at this dosage [14] . With the lower dose vigilance is required to ensure that treatment failures and possible resistance can be detected early , particularly for special cases ( such as areas where praziquantel efficacy is reportedly suboptimal ) . There is also a need for information on exposure post-dosing ( drug levels ) in target populations ( esp . school-age children ) and how it correlates with efficacy and safety . | Control of urinary and intestinal schistosomiasis is based on mass administration of praziquantel at the World Health Organization ( WHO ) recommended dose of 40 mg/kg , though some countries use 60 mg/kg . This multi-country randomized clinical trial compared the efficacy ( cure and egg reduction rates three weeks post-treatment ) and safety of these two doses for treating intestinal schistosomiasis in 856 patients in Brazil , Mauritania and Tanzania ( Schistosoma mansoni ) , and The Philippines ( S . japonicum ) . Transmission and infection intensities varied across the sites , but there was no bias or heterogeneity in efficacy outcomes . The two doses are equally effective in curing intestinal schistosomiasis; the higher dose may be less well tolerated , though effects are generally mild and transient . In endemic areas people can be re-infected; one year post-treatment patients on 60 mg/kg had fewer re-infections but this finding is difficult to explain . This study was conducted to respond to the demand for evidence about the dose of praziquantel when deployed in endemic countries . The results , along with those of systematic reviews , support the current WHO recommendation for using praziquantel at 40 mg/kg and should inform policy decisions in countries . The Philippines has already changed from 60 to 40 mg/kg after this study . | [
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] | 2011 | A Multicentre Randomized Controlled Trial of the Efficacy and Safety of Single-Dose Praziquantel at 40 mg/kg vs. 60 mg/kg for Treating Intestinal Schistosomiasis in the Philippines, Mauritania, Tanzania and Brazil |
Parkinson's disease ( PD ) -mimicking drugs and pesticides , and more recently PD-associated gene mutations , have been studied in cell cultures and mammalian models to decipher the molecular basis of PD . Thus far , a dozen of genes have been identified that are responsible for inherited PD . However they only account for about 8% of PD cases , most of the cases likely involving environmental contributions . Environmental manganese ( Mn ) exposure represents an established risk factor for PD occurrence , and both PD and Mn-intoxicated patients display a characteristic extrapyramidal syndrome primarily involving dopaminergic ( DAergic ) neurodegeneration with shared common molecular mechanisms . To better understand the specificity of DAergic neurodegeneration , we studied Mn toxicity in vivo in Caenorhabditis elegans . Combining genetics and biochemical assays , we established that extracellular , and not intracellular , dopamine ( DA ) is responsible for Mn-induced DAergic neurodegeneration and that this process ( 1 ) requires functional DA-reuptake transporter ( DAT-1 ) and ( 2 ) is associated with oxidative stress and lifespan reduction . Overexpression of the anti-oxidant transcription factor , SKN-1 , affords protection against Mn toxicity , while the DA-dependency of Mn toxicity requires the NADPH dual-oxidase BLI-3 . These results suggest that in vivo BLI-3 activity promotes the conversion of extracellular DA into toxic reactive species , which , in turn , can be taken up by DAT-1 in DAergic neurons , thus leading to oxidative stress and cell degeneration .
Mn is the twelfth most prevalent natural element in the Earth's crust [1] and is an essential transition metal required for normal growth , development and cellular homeostasis [2] , [3] . It acts as a cofactor for multiple enzymes ( Mn superoxide dismutase , pyruvate carboxylase , arginase , and glutamine synthase ) [4] , [5] , [6] , [7] , [8] and can substitute for magnesium ( Mg ) in many enzymatic reactions catalyzed by kinases . Although dietary Mn intake by the gastrointestinal tract ( GIT ) and excretion via the bile are tightly regulated [9] , inhalation of toxic concentrations of Mn can lead to nasal and pulmonary inflammation , renal dysfunction and neurodegeneration [7] , [10] , [11] , [12] . A recent study also suggests that high levels of Mn in drinking water ( >300 µg/liter ) are associated with reduced intellectual function in children [13] . Mn mining , steel manufacturing and welding represent occupational exposures linked to increased risk for parkinsonian syndrome [14] , . In addition , Mn is used in other industrial and agricultural applications . Fungicides , such as Maneb or Mancozeb , increase the risk of environmental Mn exposure in agricultural workers [16] . An organic Mn compound , methylcyclopentadienyl Mn tricarbonyl ( MMT ) , used as an octane booster or anti-knock agent in gasoline , has also been shown to cause adverse health effects [17] , [18] , [19] , [20] . Exposure to high levels of Mn in occupational or environmental settings or disease conditions ( hepatic encephalopathy ) [21] is accompanied by Mn accumulation in specific brain regions that are highly sensitive to oxidative injury , namely the substantia nigra ( SN ) , globus pallidus ( GP ) and striatum [22] . Excessive Mn deposition in these regions leads to dopaminergic ( DAergic ) neuronal loss accompanied by an extrapyramidal syndrome referred to as manganism . Manganism patients exhibit rigidity , tremor , dystonic movements and bradykinesia , all of which are also characteristic features of Parkinson's disease ( PD ) [23] , [24] , [25] . Exposure to Mn also represents a risk factor for PD [26] , [27] , [28] . Indeed , the strongest correlation between any type of environmental exposure and PD is noted in Mn-exposed human cohorts [29] , and occupational exposure to Mn for >20 years or combined long-term exposures to Mn and Al ( >30 years ) are associated with an increased occurrence of PD [30] . Parkinsonism in welders ( vs . non-welders ) is clinically distinguishable only by age of onset ( 46 vs . 63 years , respectively ) [28] , and the prevalence of PD is higher among welders as compared with age-standardized individuals in the general population [31] . Appraisal of the literature strongly suggests that in addition to targeting similar brain areas and causing similar clinical syndromes , DAergic neurodegeneration associated with PD or PD-mimicking drugs ( 6-hydroxydopamine/6-OHDA , 1-methyl-4-phenylpyridium/MPP+ , rotenone , paraquat ) and Mn neurotoxicity share multiple common effector mechanisms , namely mitochondrial dysfunction , ATP depletion , aberrant signal transduction , oxidative stress , protein aggregation and the activation of cell death pathways [32] . Damage to DAergic nigral neurons that is induced by MPP+ and rotenone involves oxidative stress [33] . Oxidative damage also plays a significant role in 6-OHDA-induced DAergic neuronal cell death . In cell cultures , hydrogen peroxide ( H2O2 ) , superoxide ions and hydroxyl radicals [34] generated by the non-enzymatic breakdown of 6-OHDA and the direct inhibition of complex-I activity , lead to lipid peroxidation , protein denaturation and a decrease in glutathione ( GSH ) , all hallmark features of post-mortem PD [35] , [36] , [37] . Intrastriatal Mn injections result in the loss of DAergic neurons , a process in which oxidative stress plays a significant role [38] , [39] , [40] , resembling toxicity caused by the mitochondrial poisons , aminooxyacetic acid and MPP+ [41] . Similarly to mitochondrial inhibitors such as MPP+ [42] , Mn increases in vivo synaptic glutamate concentrations , which leads to excitotoxic and oxidative injury [43] and interfers with ATP synthesis [44] . Analogous to MPP+ and 6-OHDA , Mn elevates intracellular H2O2 and related peroxides [45] and reduces tyrosine hydroxylase ( TH ) activity and intracellular antioxidant levels ( GSH , thiols , catalase ) in DAergic neurons [43] , [46] , [47] , [48] . Intracellular Mn2+ inhibits the mitochondrial complex-I , a feature inherent to PD and its experimental models ( MPP+ , 6-OHDA , rotenone , paraquat ) [43] . A link between mitochondrial impairment , oxidative stress and increased α-synuclein aggregation is well documented for Mn and in various models of PD [37] , [43] , [49] , [50] , [51] . Studies have also confirmed that treatment with Mn in a pre-parkinsonian state ( 6-OHDA ) significantly exacerbates neurobehavioral impairment in the rat , not only suggesting that Mn exposure may increase the risk of injury in subpopulations that are in a pre-parkinsonism state , but also pointing to the convergence of signaling pathways that lead to such injury [52] . MPP+ and 6-OHDA exposures as well as wild-type or mutant α-synuclein overexpression cause specific DAergic neurodegeneration in the worm [53] , , a process which involves ATP depletion and oxidative stress [36] , [57] , [58] , analogous to vertebrate models of PD . It was further confirmed that C . elegans orthologues of PD-associated genes play a role in α-synuclein toxicity and DAergic neurodegeneration . Additionally , conserved genetic networks were identified in C . elegans that potentiate or protect against α-synuclein toxicity , such as the torsin pathway [36] , [59] , [60] , [61] , [62] , [63] , [64] . A link between α-synuclein and Mn toxicity was also demonstrated in the worm [59] , [65] . Furthermore , we previously established that Mn uptake and toxicity pathways in C . elegans relate to those described in vertebrates , which involves the NRAMP/DMT family of metal transporters and leads to defects in the developmental and excretory systems [66] . Here we show that at a sub-lethal range of concentrations , acute Mn exposure leads to a specific and dose-dependent neurodegeneration of all C . elegans DAergic neurons , while sparing other neurotransmitter systems , findings that corroborate the specificity of DAergic sensitivity shared with vertebrate models . We investigated the causes of this DAergic specificity , and we demonstrated that endogenous extracellular , but not intracellular DA potentiates Mn toxicity and that Mn-induced neurodegeneration requires the DAergic neuron-specific dopamine re-uptake transporter , DAT-1 . We also found that Mn toxicity in the worm is associated with increased reactive-oxygen species ( ROS ) , lipid peroxidation and lifespan reduction , all of which were dependent on extracellular DA concentrations . Additionally , we observed a relocation of the oxidant-responsive transcription factor , SKN-1 , in ASI nuclei upon Mn exposure , whereas SKN-1 overexpression afforded protection against Mn-induced toxicity . Finally , we identified the NADPH dual-oxidase , BLI-3 , as a key mediator of the DA-dependency of Mn toxicity , suggesting that BLI-3 potentiates the formation of ROS from DA-derived species obtained through the reaction of divalent Mn and extracellular DA .
We first ascertained the suitability of Mn-exposed C . elegans as an in vivo model for manganism and PD and examined whether features inherent to mammalian DAergic neurodegeneration can be convincingly recapitulated in this model . We took advantage of the BY200 strain , which expresses the green fluorescent protein ( GFP ) under the control of the DAergic-specific dopamine re-uptake transporter 1 promoter , dat-1::GFP ( vtIs1 ) ( Figure 1A ) . After acute exposure to Mn , a dose-dependent neurodegeneration was observed in all DAergic neurons , namely the 4 CEP , 2 ADE , 2 PDE and the male specific R5A , R7A and R9A pairs of neurons ( Figure 1A and 1B ) . Typically , the primary defects were observed in neuron extensions , such as CEP mechanosensory processes , resulting in discontinued and punctuated GFP labeling ( Figure 1A , 1B , 1D , arrowheads ) . With an increased dose ( Figure 1D , upper to lower panels ) or longer exposure to Mn ( data not shown ) , these defects were exacerbated , leading to shortening or disappearance of the neuronal extensions ( Figure 1D , lower panels ) and eventually neuronal death as revealed by the shrinkage of the cell body , and ultimately , complete loss of GFP ( data not shown , Figure 2A left panel ) . This effect was specific to DAergic neurons , since it was not observed in GABAergic , cholinergic , glutamatergic ( Figure 1C ) or in other biogenic-amine systems ( data not shown ) . Despite the direct exposure of chemosensory neurons to the Mn-containing solution , DiI staining in these neurons failed to reveal any degeneration ( Figure 1C ) , confirming the specificity of the Mn-induced DAergic neurodegeneration . The selectivity of Mn-induced neurodegeneration to DAergic neurons suggests that some factor ( s ) specific to these neurons sensitize ( s ) them to Mn-induced toxicity . To determine which factor ( s ) may account for this effect , a candidate gene approach was employed , starting with a gene known to be specific to the DAergic neurons , namely , dat-1 . DAT-1 is the C . elegans orthologue of the vertebrate DAT , which is a highly conserved member of a family of transporters involved in neurotransmitter clearance , including the GABA re-uptake transporters , the GATs , the serotonin re-uptake transporter , SERT , and the excitatory amino-acid transporters , the EAATs . DAT is specifically responsible for DA clearance at the synapse , removing excessive extracellular DA into presynaptic DAergic termini [67] , [68] , [69] . Accordingly , chemical inhibition of DAT or DAT loss-of-function leads to high extracellular DA levels [70] , [71] . In C . elegans , the dat-1 ( ok157 ) loss-of-function mutant displays a swimming-induced paralysis ( SWIP ) phenotype , likely due to the hyper-activation of DA-responsive motorneurons exposed to excessive synaptic DA concentrations [71] . C . elegans dat-1 is also required for 6-OHDA DAergic neuron toxicity as noted in dat-1::GFP transgenic worms [56] . Given the specific neurodegeneration of dat-1::GFP-expressing neurons upon 6-OHDA or Mn exposure , we hypothesized that dat-1 is required for Mn-induced DAergic degeneration . Accordingly , dat-1 ( ok157 ) worms expressing dat-1::GFP were exposed to graded Mn doses in parallel with wild-type worms expressing the same dat-1::GFP array , and both strains were scored for DAergic neuron defects . Mn treatment was associated with marked DAergic neurodegeneration in wild-type worms , while dat-1 mutant DAergic neurons were not significantly affected even at the highest doses of Mn ( Figure 2A ) exposure for which fewer than 5% of the worms survived ( Figure 2B ) . Accordingly , dat-1 mutant worms exposed to Mn most likely died from osmoregulation defects before displaying any neurodegeneration in DAergic neurons . These results established that DAergic neurodegeneration in Mn-exposed worms requires a functional DAT-1 transporter . Additional studies showed that the protective effect of dat-1 loss-of-function on DAergic neurodegeneration was not due to an indirect effect , such as an adaptive mechanism resulting in reduced Mn-uptake . First , the lethal dose 50 at 24 h ( LD50 , dose of Mn exposure at which 50% of the animals die from the treatment ) for both transgenic strains , dat-1 ( ok157 ) ; dat-1::GFP and dat-1 ( + ) ; dat-1::GFP , was determined . Notably , the dat-1 ( ok157 ) ; dat-1::GFP strain showed significant hypersensitivity ( p<0 . 001 ) to Mn-induced toxicity with a LD50 = 20 mM , whereas the dat-1 ( + ) ; dat-1::GFP strain exhibited a LD50 = 74 mM ( Figure 2B ) . Next , the non-transgenic dat-1 ( ok157 ) and the N2 wild-type strains were treated with the same range of Mn doses ( 0 . 001 mM to 1 M ) . Both non-transgenic strains were slightly more sensitive than the corresponding dat-1::GFP-expressing strains ( Figure 2C ) . This “protective effect” of the transgene expression was not specific to the dat-1::GFP construct and was systematically observed with any type of transcriptional GFP-expressing construct ( data not shown ) . Transgenic worms may experience higher basal levels of stress associated with increased stress-response protein levels , enabling them to better cope with environmental stress . Nevertheless , dat-1 ( ok157 ) mutants exhibited hypersensitivity to Mn-induced lethality ( p<0 . 001 ) , which was characterized by a LD50 = 9 mM as compared to a LD50 = 47 mM for wild-type worms ( Figure 2C ) . This observation rules out the possibility that dat-1 ( ok157 ) takes up less Mn . Moreover , it shows that upon Mn exposure , DAT-1 loss-of-function is detrimental to worm survival , though protective to DAergic neurons . The importance of a functional DAT-1 in conferring selective neurodegeneration in C . elegans DAergic neurons upon acute Mn exposure reflected upon earlier work with the PD-mimicking drug and DA analogue , 6-OHDA . Indeed , 6-OHDA induced a dose-dependent DAergic neurodegeneration in C . elegans , which was prevented by DAT-1 mutations [56] , [72] . The fact that both Mn and 6-OHDA induce neurotoxicity through DAT-1 raises a question regarding the relationship between Mn and DA analogues upstream of DAT-1 in the DAergic neurodegeneration pathway . It has been hypothesized that Mn , in its divalent or trivalent cationic form , like iron , reacts with biogenic amines , such as DA , to generate ROS [73] , [74] , [75] . It is also known that DA and its derivatives , such as L-DOPA and 6-OHDA , can be toxic to the mammalian DAergic system and can lead to intracellular oxidative stress alone or in combination with metals and ensuing DAergic neurodegeneration [46] , [76] , [77] . Accordingly , we hypothesized that DA and Mn have a synergistic toxic effect in the worm . To test this hypothesis , worms were acutely treated with 10 mM DA prior to Mn exposure . DA pre-treatment led to a significant leftward shift in the Mn dose-response survival curve ( p<0 . 001 ) with a LD50 = 25 mM ( Figure 3A ) , while 10 mM acute DA treatment alone did not affect the worms' survival ( Figure 3B ) . These results establish that exogenously applied DA and Mn can act synergistically in vivo to promote increased toxicity in the worm . In certain mutant backgrounds , such as vertebrate DAT mutants , DA levels were reported to be abnormally elevated due to a lack of DA clearance at the DAergic synapses [71] . Therefore , the effect of DAT-1 loss-of-function on Mn sensitivity may reflect increased endogenous levels of DA ( Figure 4A ) . To test this hypothesis , we measured DA levels in the C . elegans dat-1 ( ok157 ) mutants . We found that dat-1 ( ok157 ) endogenous DA levels were significantly higher when compared with wild-type worms ( Figure 4B ) . To confirm the role of endogenous DA in Mn toxicity , we tested the effect of endogenous DA depletion on Mn sensitivity using the cat-2 ( e1112 ) loss-of-function mutant , in which the tyrosine hydroxylase ( TH ) activity is abolished , resulting in the absence of DA synthesis ( Figure 4A and 4B ) . cat-2 ( e1112 ) mutants exposed to Mn revealed significant hyper-resistance ( p<0 . 001 ) , with a LD50 = 95 mM ( Figure 4C ) . Moreover , the e1112 deletion rescued the dat-1 ( ok157 ) hypersensitivity ( LD50 = 9 mM , Figure 3A ) and led to hyper-resistance to Mn ( p<0 . 001 ) with a LD50 = 83 mM for cat-2 ( e1112 ) ;dat-1 ( ok157 ) worms ( Figure 4C ) . The LD50 difference between cat-2 ( e1112 ) and cat-2 ( e1112 ) ;dat-1 ( ok157 ) worm strains was not statistically significant ( p>0 . 05 ) , suggesting that the cat-2 ( e1112 ) effect takes place upstream of dat-1 ( ok157 ) in the same pathway ( Figure 4A ) . Finally , we supplemented cat-2 ( e1112 ) mutants with 10 mM DA exposure prior Mn treatment , which rendered those mutants hypersensitive ( p<0 . 001 ) to Mn with a LD50 = 34 mM ( Figure 4C ) . Pre-exposure to 10 mM DA of cat-2 ( e1112 ) ;dat-1 ( ok157 ) worms led to a comparable LD50 = 40 mM , not significantly different from cat-2 ( e1112 ) mutants pre-exposed to 10 mM DA , but significantly different from cat-2 ( e1112 ) ;dat-1 ( ok157 ) without DA pre-exposure ( Figure 4C ) . These data further confirm that endogenous DA level regulation plays a key role in Mn toxicity and that DA itself is required for the full extent of Mn toxicity . Next , we determined which step along the DAergic metabolic pathway contributes to the synergistic toxic effect of Mn . To address this issue , endogenous DA levels at the L1 stage were measured in a combination of deletion mutants ( Figure 4A ) . First , we sought to determine if DA release was necessary for the DA-dependent Mn-induced lethality . Accordingly , cat-1 ( e1111 ) mutants in which the vesicular monoamine transporter 2 ( VMAT2 ) is defective , were exposed to Mn ( Figure 4B ) . This mutant is unable to pack DA in secretory vesicles , thus abolishing DA synaptic release , while still producing normal levels of DA , at least at the L1 stage ( Figure 4A ) . Upon exposure to Mn , cat-1 ( e1111 ) mutants were more resistant to Mn-induced lethality than wild-type worms ( p<0 . 001 ) , with a LD50 = 108 mM ( Figure 4D ) , which was statistically insignificant when compared to the cat-2 ( e1112 ) mutant LD50 ( p>0 . 05 ) . These data establish that synaptic DA release is necessary for DA-dependent Mn-induced lethality and that extracellular DA , and not presynaptic DA , mediates this effect . Furthermore , the data imply that blockage of extracellular DA receptor activity ( and the ensuing increase in extracellular DA levels ) should exacerbate Mn sensitivity . C . elegans expresses three DA receptors , namely DOP-1 , DOP-2 and DOP-3 ( Figure 4A ) . A triple knock-out was generated [78] , and as expected , the dop-2 ( vs105 ) ; dop-1 ( vs100 ) dop-3 ( vs106 ) mutant exhibited higher levels of DA ( Figure 4B ) and was hypersensitive ( p<0 . 001 vs . wild-type ) to Mn-induced lethality with a LD50 = 27 mM ( Figure 4D ) . However , this mutant was not as sensitive to Mn as the dat-1 ( ok157 ) mutant , probably because the DA clearance activity of the wild-type DAT-1 limited the extent of the extracellular DA accumulation in the dop-2 ( vs105 ) ; dop-1 ( vs100 ) dop-3 ( vs106 ) triple mutant . These results confirm that extracellular , and not intracellular DA is responsible for the DA-dependent Mn-induced lethality . To further investigate the pathways involved in the DA-dependent Mn toxicity , and especially its long-term effects , we scored the survival rate of worms which survived acute Mn-exposure . For each strain tested , we picked healthy-looking young adults homogeneous in stage at 48 h post-exposure ( for all strains tested these animals were representative of more than 90% of the surviving population ) , and disregarded sick-looking or developmentally delayed worms . Accordingly , all animals selected survived at least 4 days in adulthood , regardless of their genetic background . This bias was deemed necessary to ensure that any significant differences potentially observed between strains could not be attributed to early toxicity events . In accordance with previous reports [79] , low doses of Mn ( 3 mM for 30 min ) led to a minor ( albeit insignificant ) increase in the surviving wild-type lifespan from 14 . 5 days post-treatment ( dpt ) to 15 dpt ( Figure 5A , upper and middle graphs ) . Higher doses of Mn ( 100 mM for 30 min ) led to a decrease in lifespan from 14 . 5 dpt to 13 . 8 dpt ( p<0 . 001 , Figure 5A , bottom graph ) . dat-1 ( ok1057 ) mutants exhibited a significantly shorter lifespan compared with wild-type worms in the absence of Mn treatment ( 13 . 2 dpt , p<0 . 001 ) , which was further reduced ( p<0 . 001 ) by both low- and high-dose acute Mn treatments ( 12 dpt and 11 . 7 dpt , respectively ) . Conversely , the lifespan of short-lived cat-2 ( e1112 ) mutants ( 12 . 3 dpt in absence of Mn , p<0 . 001 ) was significantly increased with increased Mn dosing ( p<0 . 001 , 14 . 6 dpt upon 3 mM and 14 . 8 dpt upon 100 mM acute Mn exposure ) . Moreover , the lifespan of the cat-2 ( e1112 ) mutants was significantly extended as compared to wild-type worms upon 100 mM Mn exposure ( 14 . 8 dpt vs 13 . 8 dpt , p<0 . 001 ) , reaching untreated wild-type worm average lifespan ( 14 . 8 dpt vs 14 . 5 dpt ) . These observations made on worms , all of which survived for at least 5 days post-treatment , mirror the results obtained for lethality rates at 24 h post-treatment , indicating that the DA-dependent Mn toxicity observed upon acute Mn treatment has short- and long-term components , affecting both developmental ( see also [66] ) and aging processes . Toxicity mechanisms implicated in neurodegenerative diseases , such as PD and Alzheimer's disease , involve oxidative stress [32] , [35] , [37] , [80] , [81] , [82] , [83] . In particular , DAergic cell loss in PD patients and experimental PD models invokes excessive ROS production [77] , [84] , [85] , [86] , [87] , [88] . Moreover , oxidizing metals such as copper , Mn and iron in their 2+ or 3+ states are known to be sources of ROS via the Fenton reaction [89] . In C . elegans , pre-treatment with anti-oxidants such as ebselen , affords protection against acutely-induced Mn toxicity ( Avila and Aschner , unpublished data ) . To determine if oxidative stress plays a role in DA-dependent Mn toxicity in C . elegans , a double-pronged approach was undertaken . First , the presence of ROS in response to acute Mn treatment was determined with the fluorescent dye , 2′7′ dichlorodihydrofluorescein diacetate ( H2DCF-DA ) . As shown in Figure 5B , Mn-treated wild-type worms showed a significant ( p<0 . 05 ) increase in fluorescence at sub-lethal Mn doses ( 10 mM ) . Interestingly , dat-1 ( ok157 ) mutants showed a significant increase in fluorescence ( p<0 . 01 ) after 1 mM exposure , whereas cat-2 ( e1112 ) ; dat-1 ( ok157 ) double mutants did not show any significant increase in fluorescence ( p>0 . 05 ) upon exposure up to 30 mM Mn ( Figure 5B ) . To confirm these results , we also used a more quantitative method involving the measurement of lipid peroxidation . Isoprostanes F2 ( F2IP ) and F3 ( F3IP ) are oxidation products of arachidonic acid ( AA ) , which is released from membranes upon oxidative injury [90] , [91] , [92] . A new protocol was developed to extract and measure F2IP and F3IP from C . elegans . Corroborating the H2DCF-DA results ( Figure 5B ) , dat-1 ( ok157 ) mutants exhibited significantly higher levels of F2IP ( Figure 5C ) and F3IP ( data not shown ) upon 1 , 3 and 5 mM Mn exposure ( p<0 . 05 , p<0 . 001 , p<0 . 05 , respectively ) , as compared to wild-type worms , which showed a significant increase in F2IP content upon 5 and 10 mM Mn exposure ( p<0 . 05 and p<0 . 001 , respectively ) ( Figure 5C ) . The double mutant cat-2 ( e1112 ) ;dat-1 ( ok157 ) consistently displayed decreased F2IP levels between 1 and 5 mM Mn , with significantly lower levels at 3 mM ( p<0 . 05 ) , and exhibited significantly higher F2IP levels upon 10 , 20 and 30 mM acute Mn exposure ( p<0 . 05 , p<0 . 01 , p<0 . 001 , respectively ) ( Figure 5C ) . Thus , to attain maximal levels of F2IP ( corresponding to a 2-fold increase ) , higher doses of Mn were required in cat-2 ( e1112 ) ;dat-1 ( ok157 ) mutants compared to wild-type and in wild-type compared to dat-1 ( ok157 ) . Taken together , the H2DCF-DA and isoprostane measurements show that acute Mn treatments induce oxidative stress in C . elegans . In addition , the degree of oxidative stress depends on the extracellular DA content . These experiments strongly suggest that the DA-dependent Mn-induced toxicity involves oxidative stress . An alternative functional way to establish oxidative stress , especially in its earliest stages , is to demonstrate a physiological response to it . Sodium arsenite exposure in C . elegans has previously been shown to induce a strong intestinal expression of the anti-oxidant response gene , skn-1 , the orthologue of the vertebrate gene , Nrf2 [93] . SKN-1 initiates the development of the digestive system and feeding during the earliest embryonic stages , and post-embryonically , is required for a normal lifespan and adequate resistance to stress [94] , [95] , [96] . Consistent with these observations , skn-1 deletions or loss-of-function mutations suppress oxidative stress resistance [97] . Aging in C . elegans is delayed when SKN-1 is transgenically expressed , and a mutant skn-1 form that constitutively localizes to nuclei increases the worm's lifespan [94] , [97] , [98] , [99] . To determine if SKN-1 can afford protection against Mn toxicity and to confirm that oxidative stress plays an important role in Mn-induced toxicity in C . elegans , SKN-1::GFP over-expressing worms were exposed to Mn . These worms exhibited a strong hyper-resistant phenotype to Mn exposure with a LD50 = 114 mM ( Figure 6A ) . This effect was significantly greater in comparison to wild-type ( p<0 . 001 ) or GFP expression alone ( p<0 . 01 ) under various promoters ( cf . dat-1::GFP , Figure 2; data not shown ) despite the aforementioned protective effect of GFP expression , suggesting that SKN-1 directly protects against Mn toxicity . Moreover , a mixed population of two-third heterozygous and one-third homozygous deletion-mutant skn-1 ( ok2315 ) expressing a truncated SKN-1 protein , exhibited increased sensitivity to Mn exposure with a LD50 = 34 mM ( Figure 6A ) , confirming that SKN-1 is required for mediating optimal resistance to Mn exposure . Unexpectedly , no obvious increase in SKN-1::GFP intestinal expression was noted upon Mn exposure , possibly because SKN-1 was already over-expressed or because it is not activated by Mn . However , a notable change in the SKN-1::GFP localization pattern in the ASI neuron was associated with Mn exposure , where SKN-1::GFP relocalized in discrete nuclear puncta , a result distinctly different from the diffuse pattern exhibited in non-Mn exposed worms ( Figure 6B ) . This change was associated with a significantly increased average nuclear density of SKN-1::GFP ( p<0 . 05 , Figure 6C ) but a non-significant increase in ASI expression , as revealed by the integral GFP intensity measurements ( Figure 6D ) . These results support the notion that Mn exposure triggers an increase in ROS levels , thus activating the antioxidant response pathway , at least in ASI neurons . The data also indicate that the nuclear relocation of SKN-1 , rather than its increased expression , is responsible for this protective effect against Mn toxicity . Given that Mn induces DA-dependent oxidative stress , we next sought to identify genetic factors involved in this process . In addition to the mitochondrial electron-transport chain ( ETC ) , plasma membrane NADPH oxidases are major contributors to ROS production in rotenone , paraquat and MPP+-induced toxicity [86] , [100] , [101] , [102] , [103] . The bli-3 gene encodes a C . elegans dual-oxidase orthologue to vertebrates DUOX1 and DUOX2 , which is involved in the di-tyrosine bond formation in the worm cuticle [104] and pathogen-induced ROS production [105] , [106] . Di-tyrosine bonds maintain cuticle integrity , and bli-3 ( e767 ) mutants display a blistered cuticle and a mild dumpy phenotype . This phenotype renders worms more sensitive to toxicants due to increased absorbance of the toxicants through the damaged cuticle [104] . Interestingly , bli-3 ( e767 ) worms displayed hyper-resistance to acute Mn treatments when compared to wild-type worms ( p<0 . 001 ) , with a LD50 = 83 mM ( Figure 7A ) , suggesting that BLI-3 is involved in mediating Mn-toxicity , most likely by potentiating ROS production and oxidative stress . Notably , sub-lethal ( Figure 7B ) DA pre-treatment did not affect bli-3 ( e767 ) worm sensitivity to Mn: LD50 = 84mM ( Figure 7A ) . Moreover , similar to cat-2 ( e1112 ) mutants ( Figure 5B ) , bli-3 ( e767 ) mutants did not show any dose-dependent increase in ROS production from 1 mM to 30 mM Mn exposures ( Figure 7C ) . ANOVA ( comparing data from Figure 5B and Figure 7C ) reveals that bli-3 ( e767 ) mutants show a significant difference in ROS production compared to dat-1 ( ok157 ) worms at 3 mM ( p<0 . 001 ) and compared to wild-type worms at 10 mM ( p<0 . 01 ) , but no difference compared to cat-2 ( e1112 ) worms at any Mn concentration tested . These observations strongly support the notion that BLI-3 is required for the ROS-associated potentiating effect of DA in Mn toxicity . Given the structural analogy between DA and tyrosine , DA or DA-derived species formed upon reaction with Mn may serve as substrates for BLI-3 , facilitating their conversion to highly oxidized species , and in turn , potentiating oxidative stress .
Mn-treated C . elegans recapitulate essential physiological aspects of parkinsonism , namely: the importance of NRAMP/DMT orthologues in the toxicity process [107] , [108] , [109]; the specificity and dose-dependency of the DAergic neurodegeneration [110] , [111]; the involvement of DAT [112] , [113] , [114]; the synergy between DA and Mn [115]; and the associated oxidative stress [116] , [117] , [118] . As many divalent metallic cations are able to oxidize biogenic amines ( amongst which DA and serotonin ) in vitro via the Fenton's reaction , we carried out two control experiments to ascertain the specificity of the relationship between DA and Mn in vivo . Unlike dat-1 mutation , deletion of the serotonin transporter ( mod-5 in C . elegans ) did not affect Mn-induced lethality ( LD50 = 45 mM , Figure S1 ) , and DA-depleted cat-2 ( e1112 ) ;dat-1 ( ok157 ) mutants revealed greater susceptibility to iron-induced ROS production ( p<0 . 05 ) than wild-type worms ( Figure S2 ) , supporting the fact that in vivo , metal-induced DA-dependent toxicity is specific to Mn . An additional novelty resides in the finding of the early and central role played by endogenous extracellular DA and the NADPH dual-oxidase in Mn-induced toxicity ( Figure 4 , Figure 5 , and Figure 7 ) . The centrality of extracellular DA in Mn-induced toxicity was further exemplified by the level of resistance to Mn conferred by mutations in cat-1 ( LD50 = 108 mM ) and cat-2 ( LD50 = 95 mM ) , which approach those conferred by deletion of the Mn transporter , smf-3 ( LD50 = 126 mM ) [66] . Moreover , Mn in its normal toxic range had a strong beneficial effect on the lifespan of DA-depleted cat-2 ( e1112 ) mutants ( Figure 5A ) , further confirming that the DA-dependent component of Mn-induced toxicity accounts for a significant share of the overall toxicity . Importantly , the hypersensitivity to Mn of the DA receptor triple-knockout ( Figure 4D ) , shows that Mn-induced neurodegeneration does not require DA receptors . Combined with the absence of post-synaptic neurodegeneration in dop-1::GFP and dop-3::RFP-expressing worms upon Mn exposure ( data not shown ) and despite the excitotoxic potential of other DA-related compounds , such as L-DOPA and 6-OHDA [119] , the results establish that DA-dependent Mn toxicity does not involve excitotoxicity , in contrary to the glutamate-induced neurodegeneration involved in amyotrophic lateral sclerosis ( ALS ) [120] . Together , these results also provide a novel explanation for the requirement of DAT in the DAergic neurodegeneration in vivo . Rather than being responsible for cytosolic DA accumulation followed by downstream ROS production , which was considered as the reason for the sensitivity of DAergic neurons to PD-mimicking drugs ( MPP+ , 6-OHDA ) [56] , [114] , [121] , [122] , DAT may facilitate the transport of extracellulary oxidized DA-derived species into DAergic neurons . In man and rat , subchronic Mn exposure has been shown to reduce DAT expression levels [123] , [124] . Although it is unclear if it reflected a decline of DAergic neuron activity or a specific down-regulation of DAT , Mn interference with DAT activity should be further investigated . The importance of the NADPH dual-oxidase , BLI-3 , in the DA-dependent Mn toxicity in C . elegans corroborates the up-regulation of its mammalian orthologue in PD patients and MPP+-exposed mice , as well as the protective effect conferred by its loss-of-function against oxidative stress and DAergic neurodegeneration in MPP+-exposed mutant mice [86] . Finally , DA-dependent Mn-induced toxicity has both short and long-term components as revealed by survival rate and oxidative stress measurements at 24 h ( Figure 4 , Figure 5B and 5C ) and by lifespan data ( Figure 5A ) . The protective effect afforded by SKN-1 upregulation upon Mn exposure and its nuclear relocation in ASI neurons ( Figure 6 ) emphasizes the influence of acute Mn treatments on aging . ASI neurons which strongly express SKN-1 , were shown to play a key role in lifespan modulation through the well-studied diet-restriction pathway [125] . Hence , further examination of Mn-induced toxicity and lifespan reduction in ASI neuron-ablated worms , skn-1 ( RNAi ) worms and mutants of the diet-restriction pathway may provide essential insights in the relationship between aging and PD , as well as on the role of specific brain area ( corresponding to the C . elegans ASI neurons ) on aging and PD . Despite the absence of an obvious PD-like behavior in the worm , DAergic neurodegeneration in C . elegans induced either by PD-drug treatment ( 6-OHDA , DA , MPP+ ) or by Mn involves the same key genes and molecular pathways as in vertebrates [32] , [36] , [54] , [59] , [60] , [64] , [65] , [66] , [126] , [127] , [128] , has both short-term and long-term components and therefore represents a powerful model to investigate genetic and environmental causes of PD and manganism . To further demonstrate the relevance of our findings to vertebrate physiology , it would be interesting to test ( 1 ) whether co-treatments of DA and Mn lead to the same synergy in DAergic neurodegeneration in rodents; ( 2 ) whether such synergy is dependent on DAT; and ( 3 ) whether tyrosine hydroxylase ( TH/CAT-2 ) and VMAT inhibition affords neuroprotection . Establishing the contribution of vertebrate NADPH dual-oxidases to the oxidation state of DA in the absence or presence of Mn would also be important . The present study also revealed DA-dependent mechanisms of Mn-induced toxicity by focusing on a whole-organism approach , while acknowledging that further investigation should also focus specifically on DAergic neuron physiology . For instance , determining if upon Mn exposure , cat-2 , cat-1 and cat-2;dat-1 mutants display less or no neurodegeneration and if DA supplementation can reverse this effect , would allow clearer understanding of the role of DAT , and intracellular and extracellular DA in the demise of the DAergic system . It would also be important to develop full-length GFP-tagged protein markers of the DAergic neurons as the dat-1::GFP probe can only reveal relatively advanced stages of neuronal decay , and not more subtle functional changes . This point is exemplified by the observation of SKN-1::GFP relocation in ASI neurons ( Figure 6 ) , which would have been missed if only a transcriptional GFP reporter approach had been used . Finally , exploring the effect of aging and diet-restriction modulated by the ASI neurons and the Nrf2-like transcription factor , SKN-1 , would enhance our understanding of the pathophysiology and the progressive nature of PD . Given the similarities between Mn toxicity in C . elegans and in vertebrates [66] as well as the great conservation of most PD-related genes involved in DAergic neurodegeneration in rodents and humans [32] , [36] , [59] , [60] , [64] , [129] , it is likely that the DA-dependent toxicity revealed herein plays an important role in diseases associated with DAergic neuron loss in mammals . The importance of extracellular DA levels in the toxicity mechanisms described here , if corroborated in vertebrates , could bear important implications for the treatment of DAergic neurodegeneration , as modulating extracellular vs . intracellular DA levels requires different strategies . For instance , L-DOPA is prescribed in PD and manganism patients to treat the tremor and bradykinesia arising from the loss of DAergic activity . If L-DOPA , like DA , led to the excessive generation of oxidized reactive species in vivo , L-DOPA treatment , although compensating for the DA loss , could accelerate or exacerbate the DAergic neurodegeneration over a longer term . If the involvement of dual-oxidases in DA oxidation was confirmed in vertebrates and if L-DOPA was shown to be easily oxidized by dual-oxidases , it would be important to design alternative DA analogues , which maintain high affinity for DA receptors and DAT but are poor substrates for the dual-oxidases . Another strategy to limit the extent of the DAergic neurodegeneration could be the direct inhibition of the dual-oxidases . Finally , the protective effect of SKN-1 overexpression on Mn-induced toxicity ( Figure 6A ) , which mirrors the neuroprotection afforded by astrocytic overexpression of Nrf2 in mice [130] , suggests that promoting Nrf2 activity may be beneficial in limiting Mn-induced toxicity [131] , [132] . Such an approach would be particularly relevant to welding and smelting activities in which workers exposed to metal fumes have an increased risk of developing parkinsonian syndromes [28] , [133] , [134] , [135] , [136] . The current literature provides a conceptual framework for addressing the synergistic mechanism of Mn and DA in DAergic neurodegeneration [76] , [137] , [138] , [139] , [140] , [141] . Accordingly , Mn may enter DAergic neurons via DAT as a complex with DA-derivatives , such as dopaminochrome [138] , [141] , [142] , [143] , [144] , [145] , [146] , [147] . This hypothesis provides explanations both for the specificity of Mn toxicity towards DAergic neurons as well as for the synergistic toxicity of extracellular DA and Mn , while remaining consistent with studies that report a significant Mn uptake by the DAergic neurons [148] , [149] , [150] , [151] . Purification and identification of the DA-derived reactive species ( using HPLC-ED [152] ) are essential for proving these suggestions . Further quantification of those DA-derived species in Mn- , MPP+- , 6-OHDA- and DA-exposed worms or in the rodent brain and cerebrospinal fluid should help identify the key toxic species in DAergic neurodegeneration as well as point out candidate enzymes possibly involved in PD . However , in biochemical approaches , the timing of extraction is critical to robustly detecting and measuring those species . This concern is exemplified by our oxidative stress marker measurements in wild-type and dat-1 ( ok157 ) mutant worms ( Figure 5B and 5C ) . Hence , above the Mn dose reaching the maximum increase in ROS or F2IP levels , higher Mn doses led to lower oxidative stress marker levels suggesting a decrease in oxidative stress , while the lethality rate at 24 h and lifespan results suggest otherwise ( Figure 4 , Figure 5A ) . It is unlikely that oxidative stress decreased past the dose showing the maximum increase in oxidative stress markers . Downstream conversion or degradation of ROS or F2IP associated with a worsening of the condition of the worm may be responsible for this effect . Using genetic backgrounds that stop or delay the oxidative cascade at different steps of the pathway would allow the accumulation of specific ROS and increase their detection . Hence , Mn-resistant C . elegans mutants exposed to medium- to high-dose treatments ( in the 20 to 100 mM range ) could be very helpful in identifying the early steps of the oxidative cascade ( s ) leading to DAergic neurodegeneration and could yield new molecular targets for PD treatment . This study identifies a few of the potential candidates ( bli-3 , SKN-1 overexpressing and maybe cat-1 mutant worms , Figure 4 and Figure 6 ) , but genetic screens to isolate new Mn-resistant mutants would provide powerful means for further investigation . In the course of this study , we came across several indications of the influence of the DA-dependent and Mn-induced toxicity on C . elegans longevity , which is unlikely to be a direct consequence of DAergic neurodegeneration , but rather a concomitant effect ( dat-1 ( ok157 ) mutants did not show DAergic neurodegeneration , whereas their lifespan decreased upon Mn exposure ) . First , the fact that SKN-1 protects against Mn-induced toxicity and modifies its nuclear localization pattern upon Mn exposure in ASI neurons ( Figure 6 ) , provides a genetic link with the Insulin/IGF-1 and caloric restriction pathway known to be involved in the modulation of lifespan in C . elegans [97] . The nuclear relocation of SKN-1 in ASI neurons in a punctuate pattern is suggestive of binding of this transcription factor to specific chromosomal regions , likely corresponding to the loci of its downstream targets . Chromatine immunoprecipitation experiments could be used to identify those loci , while generation of transgenic worms expressing SKN-1::GFP and tagged sequences corresponding to candidate gene ( for instance the superoxide dismutase genes ) regulatory sequences would allow immuno-colocalization of SKN-1 and its targets . Nrf2/SKN-1 is known to be a key regulator of antioxidant response from man to worm [94] , [99] , and its impact on lifespan could as well be due to its ability to reduce oxidative stress naturally occurring with age , without involving caloric restriction . Given the impact of Mn exposure and DA metabolism on oxidative stress ( Figure 5B and 5C ) , SKN-1 overexpression may improve Mn-exposed worm survival merely by compensating for the excessive ROS produced when extracellular DA levels are high and/or when Mn exposure reaches toxicity levels . Interestingly , the survival dose-response curve obtained with a non-null mutation ( ok2315 ) in a mixed population of heterozygous and homozygous animals ( Figure 6A ) showed that even for very low doses of Mn ( 0 . 001 and 0 . 01 mM ) , a noticeable fraction of the worms ( about 15–20% ) died at 24 h post treatment compared to untreated animals , making it difficult to fit the experimental data with a simple sigmoid dose-response curve . As it is a mixed population , it is likely that mostly homozygous mutants died at those lowest doses , suggesting that their LD50 could be in the submillimolar range , making skn-1 ( ok2315 ) worms the most Mn-sensitive mutants tested so far . This requires direct confirmation , but it further supports an essential role for SKN-1 in regulating even slight changes in oxidative stress levels . Second , our lifespan experiments showed that both dat-1 and cat-2 mutants are short-lived in control conditions ( no published literature could be identified reporting on the lifespan of these mutants ) compared to wild-type worms ( Figure 5A ) . According to our ROS and isoprostane measurements ( Figure 5B and 5C ) , dat-1 mutants show increased oxidative stress , which is known to worsen with age; therefore explaining their shorter lifespan . Conversely , cat-2 mutants seemed much less affected by Mn-induced oxidative stress , as Mn treatment did not lead to detectable increase in ROS and only led to a significant increase in isoprostane levels above 10 mM of Mn exposure . Moreover , high Mn exposure was able to rescue the shorter lifespan of cat-2 mutants ( Figure 5A ) . Mn is naturally required for the antioxidant activities of several enzymes , such as catalase and superoxide dismutase , which activities are critical in the aging brain [153] , [154] , [155] . Mn also has bactericidal and fungicidal [156] properties that are exploited in pesticides , such as Maneb . Several hypothesis can be formulated: ( 1 ) cat-2 mutants may take up less Mn than wild-type worms , possibly below optimal levels , ( 2 ) DA depletion in cat-2 mutants could make them feed improperly and somehow normally toxic Mn doses would restore the metabolic balance in those worms , ( 3 ) cat-2 worms may be prone to infections and high Mn exposure would help the worms cope with a weak immunity . The first point could involve DA as a regulator of feeding behavior . This aspect can be tested by measuring Mn levels , the basal slowing response [157] , sharp angle turns [158] and the pharyngeal pumping rate of dat-1 , cat-2 , cat-2;dat-1 and wild-type animals upon Mn exposure . Those tests would also allow functional characterization of the DAergic circuit of Mn-exposed worms . The second hypothesis involves energy depletion as a cause of shortened lifespan , whereas the stress of Mn exposure would restrict energy expenditure by inhibiting the Insulin/IGF-1 pathway , possibly involving SKN-1 activation . In this case , Mn intake may not be different from other worms . Mn level measurements , Nile red and oil red O [159] staining to monitor fat stores , as well as RT-PCR and western-blots to measure daf-2 , akt-1/-2 , sgk-1 , daf-16 and skn-1 expression levels would allow to test this idea . The third hypothesis implies that DA-derived ROS naturally play a role in the worm's immunity and that a high Mn dose compensates for their absence in cat-2 mutants . The reasoning behind this idea is detailed in the next paragraph . This could be easily tested by comparing the resistance to infections of wild-type , dat-1 , cat-2 , cat-2;dat-1 and bli-3 mutants in presence or absence of Mn . Our data show that DA and BLI-3-dependent ROS production is triggered or amplified by Mn , which was shown to be taken up by the NRAMP/DMT orthologues , smf-1 , -2 and -3 [66] . Recently , bli-3 and BLI-3-generated ROS have been implicated in C . elegans defense against bacteria and fungi [105] , [106] . The NRAMP/DMT family of metal transporters has a well-established role in innate immunity in vertebrates [160] , [161] , [162] , [163] . In C . elegans , NRAMP/DMT orthologue deletion mutants were found to be hypersensitive to Staphyloccocus aureus infection , which was rescued by Mn feeding [164] . Involvement of BLI-3-generated ROS , Mn and NRAMP/DMT in the defense against pathogens in C . elegans , as well as in DA-dependent neurodegeneration , raises the question of a link between immunity and DAergic neurodegeneration . The finding that DA and Mn act synergistically in a BLI-3-dependent ROS production pathway suggests that DA may play a direct role in the worm's immunity . If this mechanism is conserved in vertebrates , perhaps as a relic of a primitive immune system , it could bear interesting implications for brain physiology . For instance , DA-derived ROS could help fight infections when the blood-brain barrier ( BBB ) is compromised . On the other hand , they could also injure the brain and more specifically the DAT-expressing DAergic neurons . Infections and inflammation have long been suspected to play a role in the etiology of PD , as various infections have been associated with cases of PD [165] , [166] , [167] , [168] , [169] , [170] , [171] , [172] . The identification of a DA-derived ROS production mechanism implicating dual oxidases may hold some clues for the understanding of those associations . This study confirms the conservation across the animal kingdom of molecular pathways involved in manganism and PD , provides the grounds for further biochemical and genetic investigations using the C . elegans model to tackle the complex issue of environment-gene interactions in age-related DAergic neurodegenerative disorders , and unravels the essential role of extracellular DA oxidation and the NADPH dual-oxidase , BLI-3 , upstream of DAT-1 requirement in the neurodegeneration pathway . The data also point to a genetic link with distinct and more general aging processes , such as the diet-restriction pathway through the involvement of SKN-1 , and with an innate immunity genetic network involving metal-content regulation via the SMF transporters and oxidative defence mechanisms via BLI-3 in C . elegans .
C . elegans strains were handled and maintained at 20°C as previously described [173] . The following strains were used: N2 ( + ) ; BY200 , dat-1::GFP ( vtIs1 ) V; BZ555 , dat-1::GFP ( egIs1 ) ; VH15 , glr-1::GFP ( rhIS4 ) III; LX929 , unc-17::GFP ( vsIs48 ) ; EG1285 , lin-15B ( n765 ) unc-47::GFP ( oxIs12 ) X; DA1240 , adIs1240[lin-15 ( ( + ) eat-4::GFP ) lin-15B ( n765 ) X; LX734 , dop-2 ( vs105 ) V; dop-1 ( vs100 ) dop-3 ( vs106 ) X; LX831 , lin-15B ( n765 ) X; DOP-1::GFP ( vsIs28 ) dop-3::RFP ( vsIs33 ) ; RM2702 , dat-1 ( ok157 ) III; MT9772 , mod-5 ( n3314 ) I; CB1111 , cat-1 ( e1111 ) X; CB1112 , cat-2 ( e1112 ) II; CB767 , bli-3 ( e767 ) I; BY602 , cat-2 ( e1112 ) II; dat-1 ( ok157 ) III; BY645 , dat-1 ( ok157 ) III; dat-1::GFP ( vtIs1 ) V; VC1772 , skn-1 ( ok2315 ) IV;nT1[qIs51] ( IV;V ) . All strains were provided by the Caenorhabditis Genetic Center ( CGC , Minnesota ) , except for the BY602 and BY645 strains , which were generously provided by Randy Blakely ( Vanderbilt University Medical Center , TN , USA ) . Acute ( 30 min ) manganese chloride ( MnCl2 ) treatments were performed on 5 , 000 synchronized L1 per sample , and live worms were scored 24 h later , as previously described [66] . Scores were normalized to percent control ( 0 mM MnCl2 exposure ) . Dopamine ( Sigma Chemical Co . , St Louis , MO ) solutions were prepared in M9 buffer . Acute ( 30 min ) treatments on young L1 worms were first tested from 1 mM to 50 mM DA , guiding us in choosing 10 mM as the working sub-lethal dose . Synchronized L1 were acutely pre-treated with 10 mM DA for 10 min , washed 5 times in 85 mM NaCl solution and subjected to MnCl2 acute treatments . Control worms were pre-treated with M9 . Synchronized L1 worms were collected , washed three times in 85 mM NaCl and distributed in tetraplicates of 200 , 000 L1 . Worms were pelleted and the supernatant was removed . The tubes were then immediately frozen in liquid nitrogen and stored at −80°C . For each tube , the worm pellet was re-suspended in lysis buffer containing EDTA to scavenge free metal ions and was sonicated to disrupt cell membranes . Fifty µL of the lysate was used to perform a BCA assay to measure protein levels . Isoproterenol was added as an internal standard to the remaining 250 µL of the lysate , which was applied to the aluminum membrane to bind DA . Collected DA samples were then processed for High Performance Liquid Chromatography ( HPLC ) . To correct for inter-sample variations in the extraction efficiency , the ratio of DA to the internal standard , isoproternol , was estimated , and the total DA content was calculated relative either to protein levels or to the number of worms . Worms were grown at high densities on 8P-plates . 20 , 000 synchronized L1 per sample were washed off the plates in 85 mM NaCl , collected in 10 mL 85 mM NaCl and acutely treated ( 30 min under gentle agitation ) with MnCl2 . Worms were pelleted and washed three times in 85 mM NaCl and then re-suspended in 85 mM NaCl , 0 . 5% Triton X-100 , 5 mM Tris Buffer pH 6 . 8 , 0 . 5× protease inhibitor cocktail ( Sigma P8340 ) with zirconia beads , up to 1 mL . Samples were then processed with a Mini beadbeater-16 ( Biospec Products , OK , USA ) for 7 cycles of 20 s and kept 1 min in ice-cold water after each cycle . 20 µL of supernatant per sample were kept for measurement of protein levels by the Bradford method . 850 µL were added to 10 mL Folch solution and gently shaken every 5 min for 30 min . 2 mL of 0 . 9% NaCl per tube were added . Tubes were centrifuged at 3 , 000 rpm for 10 min at 4°C , the aqueous layer was discarded , and the organic phase was dried under nitrogen flow at 37°C . The detailed procedures for the purification and derivatization steps were previously described [174] . Synchronized L1 were acutely treated with MnCl2 as described earlier and washed 4 additional times in M9 buffer . 2′7′ dichlorodihydrofluorescein diacetate ( H2DCF-DA ) was added at 1 mM for one hour in the dark . Worms were then washed 4 times in M9 buffer . Worms were frozen and thawed twice and homogenized by sonication and then centrifuged . The supernatants were transferred to a 96-well plate and their fluorescence levels ( excitation: 485 nm; emission: 535 nm ) were detected using a FLEXstation III ( Molecular Devices , Sunnyvale , California ) pre-heated at 37°C . The fluorescence from each well was measured every 20 min for up to 2 h . Here , we report values obtained at 1 h . Fluorescence measurements were normalized to time zero values , and rates of increase in fluorescence ( reflecting ROS levels ) were expressed as percent control . Measurements were repeated 3 times , each condition was performed in triplicate , and the experiment was repeated in three independent worm preparations for each tested strain . Synchronized L1 worms were acutely exposed to MnCl2 concentrations as described earlier . Live and healthy-looking worms ( 60–66 per condition ) were collected on the same day at the late L4 stage and transferred every five days to new OP50-seeded NGM plates . Survival was assessed every two to three days until all worms had died . All tested C . elegans strains were assessed in parallel , and each experiment was performed three times , yielding qualitatively identical results . Plotted curves represent averages of those triplicate independent experiments . For each slide , at least 30 worms were mounted on 4% agarose pads in M9 and anaesthetized with 0 . 2% tricaine/0 . 02% tetramisole in M9 . Fluorescence observations and scoring of neuronal defects were performed with an epifluorescence microscope ( Nikon Eclipse 80i , Nikon Corporation , Tokyo , Japan ) equipped with a Lambda LS Xenon lamp ( Sutter Instrument Company ) and Nikon Plan Fluor 20× dry and Nikon Plan Apo 60× 1 . 3 oil objectives . Confocal images acquired for illustration or GFP intensity measurement purposes were captured through Plan-Neofluar 40× , Plan-Apochromat 63× , or Plan-Neofluar 100× oil objectives with a 1 . 3 , 1 . 4 and 1 . 3 apertures , respectively , on a LSM510 confocal microscope ( Carl Zeiss MicroImaging , Inc . ) scanning every 200 nm for XZ sections . Images were processed with the Zeiss LSM Image Browser 4 . 0 . 0 . 157 software and edited using Photoshop 7 . 0 ( Adobe ) . Microscopes were housed in air-conditioned rooms ( 20–22°C ) . Amphid and phasmid neuron staining was performed following MnCl2 acute treatment by soaking the worms for 2 h in a 10 µg/mL DiI solution prepared with M9 , washing off the dye for 1 h in M9 and then recovering the worms on OP50 coated NGM plates . SKN-1::GFP transgenic worms were acutely treated as previously described , transferred to OP50-1 seeded NGM plates and imaged 1 h post-treatment . Fluorescence measurements of the SKN-1::GFP signal were performed on complete confocal Z-stack maximal projections of L1 C . elegans ASI neurons . Treated and untreated animals were mounted on the same slide and imaged with the same magnification , gain , offset , pinhole and laser power settings . GFP integral intensity and signal density of the maximal projection of the ASI nuclei were measured with the freeware ImageJ ( developed by Wayne Rasband , NIMH , Maryland , USA ) . Dose-response lethality curves , longevity curves and histograms for DA , isoprostane or ROS content measurements were generated with GraphPad Prism ( GraphPad Software Inc . ) . We used a sigmoidal dose-response model with a top constraint at 100% to draw the curves and determine the LD50 or the average lifespan values reported in the graphs . Statistical analysis of significance was carried out by one-way ANOVA for the dose-response curves , longevity curves and dopamine measurements; two-way ANOVA was used to measure isoprostane and ROS content , followed by post-hoc Bonferroni test when the overall p value was less than 0 . 05 . For SKN-1::GFP fluorescence analysis , unpaired two-tailed T-test was used to assess statistical differences in mean values . In all figures , error bars represent SEM; * refers to differences between genotypes; # refers to differences between doses; */# p<0 . 05; **/## p<0 . 01; and ***/### p<0 . 001 . | In Parkinson's disease ( PD ) , motor neurons that produce dopamine degenerate , leading to a characteristic syndrome including tremor , rigidity , and bradykinesia . The mechanisms leading to PD have been under intense investigation , identifying hereditary mutations responsible for about 8% of the cases . However , multiple environmental factors contribute to PD; and , amongst those , manganese ( Mn ) exposure from pesticides , industrial fumes , and gasoline additives has been robustly associated with PD . To gain insights into processes leading to the specific degeneration of dopaminergic neurons , we used a simple animal model , the nematode Caenorhabditis elegans , which , upon Mn exposure , recapitulates key molecular processes known to be involved in PD . Combining biochemistry and genetics , we demonstrate that dopamine secreted by the neurons and not intracellular dopamine is directly involved in the generation of toxic reactive oxygen species . We identify two essential mediators of this dopamine-dependent effect which are an extracellularly active enzyme called dual-oxidase and the dopamine re-uptake transporter . We also reveal that a transcription factor which is strongly expressed in two neurons involved in the regulation of aging is a powerful modulator of the dopamine-dependent toxicity . Our study establishes novel evidence of the link among PD , aging , and oxidative stress within the context of exposure to Mn . | [
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] | 2010 | Extracellular Dopamine Potentiates Mn-Induced Oxidative Stress, Lifespan Reduction, and Dopaminergic Neurodegeneration in a BLI-3–Dependent Manner in Caenorhabditis elegans |
Lung granulomas are the pathologic hallmark of tuberculosis ( TB ) . T cells are a major cellular component of TB lung granulomas and are known to play an important role in containment of Mycobacterium tuberculosis ( Mtb ) infection . We used cynomolgus macaques , a non-human primate model that recapitulates human TB with clinically active disease , latent infection or early infection , to understand functional characteristics and dynamics of T cells in individual granulomas . We sought to correlate T cell cytokine response and bacterial burden of each granuloma , as well as granuloma and systemic responses in individual animals . Our results support that each granuloma within an individual host is independent with respect to total cell numbers , proportion of T cells , pattern of cytokine response , and bacterial burden . The spectrum of these components overlaps greatly amongst animals with different clinical status , indicating that a diversity of granulomas exists within an individual host . On average only about 8% of T cells from granulomas respond with cytokine production after stimulation with Mtb specific antigens , and few “multi-functional” T cells were observed . However , granulomas were found to be “multi-functional” with respect to the combinations of functional T cells that were identified among lesions from individual animals . Although the responses generally overlapped , sterile granulomas had modestly higher frequencies of T cells making IL-17 , TNF and any of T-1 ( IFN-γ , IL-2 , or TNF ) and/or T-17 ( IL-17 ) cytokines than non-sterile granulomas . An inverse correlation was observed between bacterial burden with TNF and T-1/T-17 responses in individual granulomas , and a combinatorial analysis of pair-wise cytokine responses indicated that granulomas with T cells producing both pro- and anti-inflammatory cytokines ( e . g . IL-10 and IL-17 ) were associated with clearance of Mtb . Preliminary evaluation suggests that systemic responses in the blood do not accurately reflect local T cell responses within granulomas .
Mycobacterium tuberculosis ( Mtb ) remains a major threat to global health . The latest World Health Organization analysis of the global burden of tuberculosis ( TB ) estimates 8 . 7 million new cases , 9 . 6–13 . 0 million prevalent cases , and 1 . 4 million deaths per year in 2011 [1] . However , only 5–10% of those infected with Mtb will develop active disease over their lifetime , while the other ∼90% remains asymptomatic ( referred to as “latent” infection ) with a 5–10% chance of reactivation over their lifetime . Thus , it is clear that the human immune response is quite capable of controlling Mtb infection . Mtb infection is characterized by the formation of granulomas , usually in the lungs and lymph nodes [2] . The tuberculous granuloma is an organized structure of immune cells that forms in response to persistent Mtb infection , and consists of macrophages , neutrophils , and lymphocytes [3–6] . Granulomas function both as the niche in which bacilli can grow or persist and an immunological microenvironment in which host cells interact to control and prevent dissemination . The mere presence of granulomas is insufficient to control infection; instead , proper functioning of all granulomas in a host determines the ultimate outcome of infection [4] . T lymphocytes are considered critical to control of initial and persistent Mtb infection , mediating the inflammatory balance suggested in histological and flow cytometry studies [7–12] . Important roles for T cell produced cytokines ( IFN-γ , IL-2 , TNF , IL-17 and IL-10 ) have been demonstrated in animal model studies , with a subset of these cytokines demonstrated to be critical in humans as well [12–22] . However , in humans , responses are generally measured in blood so very little is known about T cell function at the level of the granuloma . To study and understand the functions of T cells in granulomas requires obtaining fresh lung tissue containing granulomas , which is very difficult in humans . Therefore , an animal model that recapitulates human disease and pathology is necessary to understand the role of adaptive immune response in tuberculosis . The standard murine models , although useful for investigating immune responses and pathogenesis , do not establish latent infection and form granulomatous infiltrations rather than organized granulomas [23] . However , non-human primates , primarily macaques , are remarkably similar to humans in terms of infection outcome and presentation as well as pathology , and have the additional advantage of research reagents that permit the analysis of immunological components in controlling the disease . Cynomolgus macaques develop clinically active or latent infection with granulomas that are extremely similar to those in human TB [24–27] . We previously reported that a spectrum of lesions are found in individual animals and among animals with active or latent infection [25] , as has been described for humans [28] . In addition , recent studies from our group support that progressive and healed lesions can coexist within the same animal , with nearly all animals capable of sterilizing at least a subset of individual granulomas . However , animals with active TB present with a subset of lesions that do not control infection which results in dissemination of the disease [29] . Taken together , available data support the hypothesis that the outcome of Mtb infection is determined at a local , not a systemic level . What controls infection at a local level is unknown , but is likely a combination of cellular and cytokine mediators induced by the bacilli together with physiological constraints [2] . The functionality of T cells , i . e . , cytokine responses , and their correlation with bacterial burden in individual granulomas within an animal has not been investigated . Therefore , to understand the nature of individual tuberculosis granulomas within an animal with respect to T cells , cytokine profile and bacterial burden , we evaluated functional characteristics of T cell pro-inflammatory ( IFN-γ , IL-2 , TNF and IL-17 ) and anti-inflammatory or regulatory ( IL-10 ) cytokine responses in cynomolgus macaques infected with Mtb . Our previously published studies on bacterial burden in granulomas and PET/CT and histology showed that a spectrum of granulomas exist within a host [2 , 25 , 29 , 30] , with extensive overlap among animals of different clinical states . Here we extend our evaluation of granulomas to focus on T cell cytokine profiles associated with the ability of individual granulomas to control bacterial burden ( sterilizing and non-sterilizing ) initially without regard to whether the host developed active disease or remained latently infected . Finally , in a preliminary analysis , we compared local T cell cytokine responses from granulomas of each animal to those of the systemic T cell cytokine response to determine how accurately the blood can be used as a “read-out” for local immune responses .
Thirty-four NHP infected with a low dose of Mtb were necropsied primarily as controls for other studies [2 , 30] and were included in this study . Twenty-eight animals were necropsied >17 weeks of Mtb infection ( median 313 , range 124–601 days ) , which included 13 animals with clinically active disease and 15 animals with clinically latent infection [25] . Median time to necropsy post infection was 222 days ( 124–400 days ) for active animals and 367 days ( 284–601 days ) for latent animals . The animals were classified based on clinical signs and microbiologic cultures , as previously described [24 , 25] . In addition , six animals necropsied at approximately 11 weeks post infection ( median 77 . 5 days , 72–85 days ) as controls were also included in this analysis . Granulomas were observed in all animals following Mtb infection with substantial variability in types and numbers of granulomas . As previously reported [2] , in the animals used for this study , the number of culturable Mtb bacilli from individual granulomas covered a large range in individual animals , from 0 ( sterilized ) to ∼106 CFU/granuloma ( S1 Fig . ) . The median CFU/granuloma was highest in animals infected for ∼11 weeks ( log10 3 . 492 , IQR 2 . 646–4 . 461 ) and those with active disease ( log10 2 . 6 , IQR: 1 . 5–3 . 1 ) , compared to those from animals with latent infection ( median log10 1 , IQR: 1–1 . 6 ) ( p<0 . 0001 ) ( Fig . 1 ) . However , the CFU/granuloma overlapped substantially among the clinical groups , with a significant fraction of sterile granulomas even in animals with active TB . These are in line with data previously published from our lab [2] . Since granuloma bacterial burden is variable within individual animals as well as across animals from different clinical classifications , our analysis primarily focused on the evaluation of granuloma T cell profiles based on bacterial burden without regard to clinical classification . This study only focused on individual granulomas as defined by PET/CT imaging obtained prior to necropsy [29] and at necropsy by the pathologist . Thus , complex pathologies as seen in active TB such as coalescing granulomas , consolidations , or TB pneumonia , were excluded . Total cell numbers varied amongst individual granulomas . Overall , the median cell count of all granulomas ( by counting live cells in homogenates using trypan blue exclusion prior to further manipulations ) was 4×105 ( IQR 1 . 8×104–1×106 ) . The total cell count of granulomas from within an individual animal also varied . Non-sterile granulomas had significantly higher cell counts than sterile granulomas ( Fig . 2A ) . Similarly , numbers and frequency of live CD3+ T cells were variable among granulomas within individual animals . Non-sterile granulomas had significantly higher T cell counts ( p = 0 . 0011 ) ( Fig . 2B ) and frequency of live CD3+ T cells ( p = 0 . 0172 ) ( Fig . 2C ) when compared to sterile granulomas . Due to the limited number of viable T cells obtained from many of the granulomas , our analyses of cytokine production were restricted to the CD3+ T cell population . Total cell count , T cell numbers , and bacterial burden correlate with the size of the granuloma ( S1 Table ) . It is generally accepted that Th1-type T cell responses ( characterized by the cytokines IFN-γ , IL-2 , or TNF ) are important in control of Mtb infection [10 , 12 , 18 , 20 , 31 , 32] . Th17 CD4+ T cells ( producing IL-17 ) have also been implicated in control of this infection , particularly in the early phases [7 , 33 , 34] . These pro-inflammatory responses are considered to be necessary for activating macrophages to kill Mtb and organizing cell recruitment to the granuloma . On the other hand , the importance of anti-inflammatory ( regulatory ) responses , such as T cells producing IL-10 , in the granuloma is more controversial [35] . In fact , little data exist regarding the types of T cell responses in individual granulomas in humans or in a model with substantial similarity to humans [36–38] . Thus , we investigated the patterns of pro-inflammatory ( T-1 and T-17: with reference to CD3+ T cells producing Th-1/Th-17 type cytokines ) and anti-inflammatory/regulatory ( IL-10 ) T cell cytokine production in individual granulomas following stimulation with peptides from Mtb-specific RD-1 encoded proteins ESAT-6 and CFP-10 . As with bacterial and T cell numbers , individual granulomas , even within a single animal , displayed highly distinct and variable cytokine profiles . In most animals , the range of T cell cytokine responses was noted to be approximately 1–8% with occasional outlier granulomas that either did not mount any detectable cytokine response or had a higher frequency of T cells making cytokines . For instance , IFN-γ responses were very low or undetectable in all granulomas obtained from 4 animals ( 20612 , 9711 , 15312 and 17211 ) , while in others the range of IFN-γ responses was from <5 to 15% , with granulomas from a subset of animals giving responses over 15% ( Fig . 3 ) . This pattern was observed for all single cytokines tested , with the majority of granulomas having low frequencies of T cells producing T-1 or T-17 cytokines ( S2A–S2E Fig . ) . When all granulomas are analyzed across all animals , the average frequency of T cells making any of the cytokines measured was ∼8% , a surprisingly low frequency given the importance of T cells in control of this infection . As a spectrum of granuloma T cell cytokine profile was noted even within an individual host , we compared T cell responses in granulomas with or without culturable bacteria . The frequency of cytokine producing T cells overlapped considerably across both sterile and non-sterile granulomas . Nevertheless , sterile granulomas had modestly higher TNF ( p = 0 . 0091 ) , IL-17 ( p = 0 . 0344 ) and T-1/T-17 ( CD3+ T cells producing single or combinations of IFN-γ , IL-2 , TNF , or IL-17 cytokines ) ( p = 0 . 0273 ) cytokine producing T cells than non-sterile granulomas . This finding is independent of the clinical classification of animals from which sterile or non-sterile granulomas were obtained ( Fig . 4 ) . In chronic infections , T cell exhaustion can contribute to a reduced ability to produce cytokines [39–41] . To explore this as a possible contributor to the low frequency of T cell responses in granulomas , a subset of granulomas was stimulated with the non-specific stimulators PDBu and ionomycin and cytokine responses were measured . In response to PDBu and ionomycin , higher frequencies of T cells from individual granulomas produced IFN-γ ( median 27 . 25% , IQR 8 . 7%–47 . 40% ) and TNF ( 32 . 25% , 16 . 23%–40 . 85% ) , with modestly higher frequencies of IL-2 ( 5 . 67% , 3 . 5%–12 . 43% ) producing cells and no change in IL-17 ( 3 . 8% , 2 . 3%–6 . 49% ) production . When examining combined cytokine responses ( i . e . ability to produce any of the T-1/T-17 cytokines ) in response to non-specific stimulation , ∼50% of granuloma T cells on average produced cytokines ( S3 Fig . ) . This is much higher than stimulation with ESAT-6/CFP-10 , suggesting that in most granulomas , the T cells are capable of producing cytokines . However , there was a subset of granulomas that still gave very low responses , and these may be granulomas in which the majority of T cells are not capable of responding to stimulation or are exhausted . In a subset of granulomas , we evaluated the expression of exhaustion markers CTLA-4 and PD-1 on T cells . Although overall frequencies of cells positive for these markers were low ( S4 Fig . ) , CTLA-4−/PD-1+ were most common , while CTLA-4+/PD-1− or CTLA-4+/PD-1+ were only seen at very low frequency in granulomas ( S4A Fig . ) . Next , we explored the cytokine production capacity in T cells expressing these markers , following stimulation with Mtb-specific RD-1 antigens ( S4B–S4F Fig . ) . Overall , the frequency of cytokine producing T cells that also expressed CTLA-4 or PD-1 or both was very low , with the highest being 2 . 4% of IL-10 producing T cells co-expressing PD-1 ( S4F Fig . ) . CTLA-4−/PD-1+ T cells generally had slightly higher cytokine responses than those expressing only CTLA-4 or both CTLA-4 and PD1 ( S4B–S4F Fig . ) . Taken together these data suggest that the limited T cell response observed in the T cell of granulomas is not solely due to the exhaustion of T cells . Multifunctional T cells ( those producing several cytokines ) have been suggested to be important in the control of infections , although there are conflicting data on the protective capacity of these cells in tuberculosis [42–46] . Most human TB studies on multifunctional T cells have focused on cells derived from the periphery ( e . g . blood ) , where the frequency of multifunctional T cells is often , although not always , associated with active disease rather than protection [44 , 47 , 48] . We investigated whether cytokine producing T cells in granulomas are multifunctional by assessing the ability to produce 1–5 cytokines . IL-10 is not usually considered to be produced by T cells making other pro-inflammatory cytokines , although in a minor subset of granulomas examined , there was a small population of T cells ( 1 . 2% ) that made both IL-10 and IL-17 . Interestingly , T cells with this phenotype have been associated with control of some bacterial infections rather than with autoimmune disease [49 , 50] . Thus , we excluded IL-10 from the analysis of multifunctional T cells . Surprisingly , T cells producing single cytokines were most frequently observed in the granulomas ( Fig . 5 ) , particularly TNF and IL-17 . There were T cells present that were producing both TNF and IL-2 in a subset of granulomas . However , very few granulomas had T cells that were classically multi-functional ( Fig . 5 ) . There was no significant difference in the multiple cytokine responses between sterile and non-sterile lesions and amongst different clinical groups . To investigate the relationships of functional T cell populations within individual granulomas , we assessed correlations among cytokine responses . The cytokine balance may ultimately determine control of bacterial burden in the granuloma . Overall , there was a significant direct correlation between each of the T-1 pro-inflammatory cytokines ( IFN-γ , IL-2 and TNF ) and among T-1 and T-17 cytokines ( i . e . , in individual granulomas , higher responses of cytokine X were observed with higher responses of cytokine Y ) ( Table 1 ) . Thus , even though there are few T cells that produce multiple cytokines , T cells producing individual T-1 and T-17 cytokines tend to be present together in the same granulomas , suggesting that granulomas are “multi-functional” . Surprisingly , there was also a direct correlation of T-1 and T-17 cells with T cells producing IL-10 in individual granulomas ( Table 1 ) . The correlation between IL-17 and IL-10 producing T cells was significant in both non-sterile and sterile granulomas . In addition , sterile granuloma had a significant direct correlation between IL-10 and IFN-γ and total T cells producing T-1 or T-17 cytokines ( Table 1 ) , suggesting that a balance between pro and anti-inflammatory cytokines are required for reduced pathology and control of bacterial burden . We next explored whether T cell cytokine responses were related to bacterial burden in individual lesions . Overall , a significant inverse correlation was observed between T-1/T-17 and TNF cytokine producing T cells and bacterial burden ( CFU per granuloma ) , i . e . , higher frequencies of cytokine responses were related to lower the bacterial burden ( Fig . 6 , Table 2 ) . This suggests that a pro-inflammatory cytokine environment associates with decreased bacterial burden , most likely by stimulating cells to kill bacilli in the granuloma . When only non-sterile granulomas were analyzed , there was a significant inverse correlation between bacterial burden and IFN-γ and T-1/T-17 cytokine producing T cells , further supporting that higher pro-inflammatory cytokines are associated with control of bacterial burden ( Table 2 ) . To further evaluate the direct relationship between T cell cytokines and granuloma sterilization , we evaluated the effects of pairwise-cytokine combinations and magnitude of response on the frequency of sterilization using Matlab . First , for each granuloma ( N = 133 , only from animals necropsied after 17 weeks post-infection ) , T cell cytokines were binned according to the quartile distributions . Binned data provide the ability to evaluate the combinatorial trends without specific focus on absolute percentages . Briefly , for each pair of cytokines , for example IFN-γ and IL-2 across all granulomas , quartiles were calculated ( Fig . 7A ) . The continuous percentages were transformed into discrete bins representing bin 1 ( quartile 1 , 0–25th percentile ) , bin 2 ( quartile 2 , 25th–50th ) , bin 3 ( quartile 3 , 50th–75th ) , and bin 4 ( quartile 4 , 75th–100 ) of cytokine responses , as described in Fig . 7A . Numbers of granulomas in each bin were counted and summarized in a 4×4 co-occurrence matrix . Frequencies at which each of the pairwise combination occurred in the total number of granulomas were also calculated and plotted ( Fig . 7A & B ) as a density heat map ( Fig . 7B ) ranging from low represented by “dark blue” to high by “dark red” . Even though there were 133 granulomas included in this analysis , not all bins have equal representative number of granulomas , due to the variation in the magnitudes of response . However , the most commonly occurring pair of cytokines was IFN-γ in combination with IL-2 and TNF , and IL-10 with IL-2 , IL-17 and TNF ranging from low to high magnitude . These heat maps confirm the correlation data detailed above , including the high frequency of granulomas with pro- and anti-inflammatory T cells . Next , to evaluate the combinatorial effect of different cytokine combinations and magnitudes on the ability to attain bacterial containment , we calculated the frequency with which sterilization occurred under different cytokine combinations ( Fig . 7C ) . Sterilization frequency matrices were constructed in a similar fashion as described above by calculating the number of times sterilization of a granuloma was observed ( e . g . 0 CFU ) given in a particular combination of cytokines and frequencies in granulomas . For example , there were 5 granulomas binned at bin 3/3 for IL-2 and IFN-γ ( Fig . 7A ) cytokine combinations respectively , of which 4 granulomas were sterile and 1 grew Mtb ( non-sterile ) . Therefore the sterilization frequency for bin 3/3 is 80% , and represented at the respective density ( color ) heat map for 80% ( Fig . 7C . a ) . These resulting density heat maps allowed us to visualize which conditions are associated with the highest frequency of sterilization . This approach revealed interesting features relating to bacterial containment and T cell cytokines . For a large majority of cytokine combinations , there was no apparent direct relationship between cytokine combinations , magnitudes and bacterial control . However , the notable exceptions are the combinations of IL-10 with IL-2 ( Fig . 7C . e ) or IL-17 ( Fig . 7C . h ) or TNF ( Fig . 7C . i ) , which had highest rates of sterilization , with frequencies >70% . Interestingly , T cells producing higher frequencies of IL-17 and IL-10 in the same granuloma give rise to high rates of sterilization , supporting our findings described in Table 1 . A similar trend was also observed for IL-10 and TNF , whereas the more traditional T-1 responses ( e . g . high frequencies of IFN-γ , IL-2 or TNF in the same granuloma ) were not strongly associated with sterilization . These results reinforce that the balance of pro-inflammatory and anti-inflammatory cytokine responses is important for bacterial containment in the granuloma . Our primary goal was to investigate and understand the dynamics of T cell function within the spectrum of Mtb lung granulomas irrespective of the clinical status of the animal . Nonetheless , to address whether clinical state was important in the T cell responses observed , we further analyzed the granuloma T cell profiles based on clinical status ( active disease , latent infection , and 11 weeks post-infection ) . Overall , as with the bacterial burden , there was substantial overlap in cell profiles and T cell cytokine responses of granulomas amongst all clinical classifications . Animals infected for ∼11 weeks presented with granulomas having total cell numbers similar to those of active disease . However , the median total cell numbers were significantly greater in granulomas from monkeys with active disease ( p = 0 . 027 , Dunn’s multiple comparison test ) when compared to those with latent infection ( S5A Fig . ) . Similarly , granulomas from animals with active disease had significantly higher CD3+ T cell counts and frequency of CD3+ T cells when compared to granulomas from animals with latent infection ( S5B–S5C Fig . ) . Granulomas from animals infected for ∼11weeks had the highest T cell counts compared to all other animals ( p<0 . 0001 ) ( S5B–S5C Fig . ) , suggesting that early in infection , the T cell response in granulomas is more robust . Frequencies of cytokine-producing T cells in granulomas also overlapped across monkeys of different clinical classifications ( Fig . 8A–F ) , however , there were some modestly distinguishing features . The frequency of IFN-γ and IL-17 producing T cells was modestly ( but significantly ) higher in granulomas from animals with latent infection , compared to granulomas from active disease animals . Granulomas from animals with latent infection had significantly higher frequencies of T-1/T-17 ( p = 0 . 0012 ) T cells than those from animals with active disease and those infected for ∼11 weeks . The frequencies of IL-10 cytokine producing T cells were significantly lower in granulomas from animals with active disease ( 1 . 5% ) compared to those infected for ∼11 weeks ( 9 . 3% ) or with latent infection ( 5 . 5% ) ( Fig . 8F ) . Nonetheless , the responses in individual granulomas from animals of all infection outcome classifications were variable , with both high and low responding granulomas seen in most animals . We assessed correlation amongst cytokine responses of individual granulomas to understand the relationship of T cell function in different clinical categories . There was a significant negative correlation between IL-2 and IL-10 exclusively ( S2 Table ) in those animals infected for ∼11 weeks . Granulomas from animals with active TB demonstrated a significant positive correlation between T cells producing IFN-γ and those producing IL-10 in addition to T cells producing pro-inflammatory cytokines . However , granulomas from animals with latent infection had a significant direct correlation between multiple pro- and anti-inflammatory cytokines ( S2 Table ) . When we analyzed the association between T cell cytokines and bacterial burden , granulomas that had the highest bacterial burden in individual lesions from animals infected for ∼11 weeks had a negative correlation between IL-10 and bacterial burden , suggesting that IL-10 might also play a role in the early establishment of bacterial control in the granulomas ( S3 Table ) . Studies in humans to understand immune responses during the course of Mtb infection , drug treatment , or vaccine testing rely primarily on analysis of systemic ( blood ) T cell responses . There is a significant knowledge gap as to how systemic responses relate to local responses in the lung , particularly at the granuloma level . This study provided an opportunity to perform preliminary evaluation of the relationship of T cell cytokine responses between the peripheral blood ( systemic compartment ) and granulomas ( local compartment ) . To evaluate the complex dataset ( comparing one measure of blood data to multiple measures of granuloma data from an animal ) , we used a simple mathematical equation to calculate Euclidean distances . We calculated Euclidean distances on datasets for which complete cytokine data were available for blood ( PBMC ) and granulomas ( N = 120 granulomas and 28 animals ) . We calculated the Euclidean distance between the blood of an animal and all lesions from that animal . We then calculated the average distance for each animal . This provides an estimate for “relatedness” in terms of distance between the systemic and local T cell responses ( i . e . , smaller distance means closer or more similar the T cell responses , while larger distance means further away or more dissimilar the T cell responses between blood and granulomas are ) . Surprisingly , we observed a range of distances ranging from 2 to 48 ( Fig . 9 ) . This suggests , that for some animals , systemic T cell responses can reflect and are a reasonably good estimate of the local T cell response . While , for other ( most ) animals , systemic T cell responses are very different from local T cell responses and therefore do not reflect the local T cell responses accurately . The Euclidean distance between blood and granuloma responses was not related to disease state , as animals that are 11 weeks post-infection , active and latent infection were found all along the spectrum of responses . We further explored the factors that could be associated with the variation in the T cell responses between systemic and local compartments . We correlated the average distance between systemic and local response with overall bacterial burden ( CFU score ) , gross pathology ( pathology score ) and total number of lung granulomas of the animal . There was no significant correlation observed with either bacterial burden or the pathology score of the animal . However , there was significant correlation between the average distance and the total number of lung granulomas ( lesions ) of the animal ( Spearman ρ 0 . 4046 , Prob>| ρ| 0 . 0327 ) ( S6 Fig . ) , ( i . e . , the greater the number of lung granulomas , the larger the difference in T cell responses between blood and granuloma ) . This further suggests the existence of spectrum of granulomas within an animal affects the systemic T cell responses . This has important implications for the search for blood biomarkers . Next , we investigated whether the average T cell granuloma cytokine response of an animal correlated with its systemic ( blood ) cytokine response . We performed a multivariate analysis , and used non-parametric Spearman’s ρ for correlations . In animals with active disease and latent infection , overall there was no correlation observed with the exception of a significant direct correlation between local and systemic TNF producing T cells ( S4 Table ) . When analyzed according to the clinical status of the animals , those with active disease had significant direct correlation between local and systemic responses for TNF and IL-17 producing T cells , while there was no correlation observed in animals with latent infection . Similarly , in animals infected for ∼11 weeks , there was a significant negative correlation ( S4 Table ) between blood and average granuloma responses for IL-17 and IL-10 producing T cells . These findings suggest that the systemic T cell responses do not accurately reflect the local ( lung ) T cell responses .
T cells are a major cellular component of tuberculosis lung granulomas and are known to play an important role in containment and progression of Mtb infection . Yet , there are many unanswered questions regarding the functional characteristics of T cells within granulomas , including the relationship between T cell cytokine responses and bacterial burden at the local level , and how T cell systemic responses ( in the blood ) relate to the T cell responses in granulomas . Much of what we know about T cell function in tuberculous lungs comes from mice , which do not develop the full spectrum of granulomas seen in human tuberculosis . Furthermore , functional data assessing T cell activity within individual granulomas in clinically active disease and latent infection are lacking . To address these questions , we used the cynomolgus macaque model of TB , a non-human primate model that recapitulates key hallmarks of human TB [26] . Our primary goal was to investigate and understand the dynamics of T cell function within the spectrum of Mtb lung granulomas . We show here that each granuloma within a single host is independent with respect to total cell numbers , frequency of T cells , the pattern of cytokine profile , and bacterial burden . We observed considerable overlap within these components amongst various clinical states of the animals . This study and a recent publication from our lab [2] support that individual granulomas themselves are unique representations of infection state and cannot be classified as “active” or “latent” . Conventional clinical classifications of active disease and latent infection states are thus more suited for a global or “whole host” classification to reflect overall host status on infection and pathology . These findings are further supported by the radiological and histological studies from our and other laboratories , which demonstrate the highly dynamic and variable nature of granulomas during Mtb infection , establishing that each granuloma is unique even within the same animal [2 , 25 , 27 , 29 , 51–53] . Individual animals have a full spectrum of lesions varying from progressive granulomas with high bacterial burden to healed sterile granulomas , each with varying proportions of functional T cells producing both pro- and anti-inflammatory cytokines . Our current data provide further evidence to support the concept that a spectrum of Mtb infection not only exists amongst animals [52 , 54 , 55] , but also within an individual animal where granulomas are independent of each other with varying magnitudes of bacterial numbers and host responses . T cells secreting IFN-γ , TNF and IL-17 are generally assumed to be necessary for activation of macrophages and initiation of antimicrobial activity [12 , 14 , 56] . Due to the recruitment of activated T cells to the site of disease , T cell responses are also considered to be enriched at the site of disease ( i . e . the granuloma ) compared to the periphery ( blood ) . These concepts are supported by studies from small animal models where whole lung homogenates were studied , and from studies using cells from pleural TB [57] , bronchoalveolar lavage ( BAL ) samples from active TB patients [31 , 45 , 58 , 59] and non-human primates [60] . Our study supports the notion that cells are continuously recruited to the non-sterile granulomas , resulting in increased total cell count , T cell number and increase size of the granuloma , while the sterile granulomas are maintained with the minimum required cell numbers for the continued maintenance . Although our studies support a higher proportion of T cells in granulomas that produce cytokines following stimulation with Mtb-specific peptides in granulomas as compared to blood , a striking finding from the current study is that only a limited proportion of T cells in granulomas were making any of the 5 major cytokines chosen for analysis , irrespective of clinical disease status . The average frequency of T cells producing any of these cytokines was about 8% . There are several possibilities for the unexpected low frequency of T cells observed in the granulomas . It is possible that re-stimulation with a limited number of antigen peptide pools simply does not represent the full T cell recognition of Mtb antigens . We think this is unlikely , since granuloma homogenates likely contains large numbers of Mtb antigens , and our stimulation assays were carried out in granuloma homogenates . Analysis of granulomas that were not restimulated with ESAT-6/CFP-10 peptides showed only slightly lower frequencies of T cell responses , supporting the notion that antigens are present in the homogenate . In addition , stimulation with Mtb-infected dendritic cells , which would present more Mtb antigens , did not yield a higher frequency of T cells expressing cytokines . Separate analysis of T cells from granulomas with multiple antigen peptide pools and analyzed by IFN-γ ELISPOT ( similar to our previous findings [24 , 25] ) gave comparable or even lower frequencies of T cells producing IFN-γ , compared to our ICS analysis . Thus , it is unlikely that the low frequency of responding T cells in granulomas is simply due to stimulation with a limited number of Mtb antigens , or relative insensitivity of the ICS method . There may be inhibition of T cell function due to regulatory T cells or inhibitory cytokines . Preliminary data on a small subset of granulomas suggests that most of IL-10 producing T cells in the granulomas are not Foxp3+Tregs ( S7 Fig . ) , however , larger studies are needed to address the effect of regulatory T cells on other cytokines in granulomas . Another possibility is that Mtb itself might be down-regulating of T cell activity , as suggested in the literature [61 , 62] . Further evaluation of bacterial or host factors on T cell function at the local granuloma level is certainly warranted to address this possibility . T cell exhaustion in granulomas is also another possible explanation for the low frequency of T cell responses . In chronic infections , including TB , T cells can become exhausted or down-regulated [39–41] . However , in most granulomas , non-specific stimulation of T cells resulted in an average of 50% of the cells capable of producing T-1/T-17 cytokines , suggesting that the T cells were not exhausted . Our limited data on T cell exhaustion markers ( CTLA-4 and PD-1 ) did not support an inverse correlation with the cytokine response from granulomas . Nonetheless , this warrants further investigation . In addition to the very limited numbers of cells obtained from individual granulomas , tetramers for use in “Chinese” cynomolgus macaques are not currently available , which currently precludes a more thorough investigation of exhaustion of antigen specific T cells . It remains a distinct possibility that many of the T cells in non-human primate Mtb granulomas are not specific for Mtb antigens as described in mice [39] , but are simply recruited to the granuloma due to inflammatory signals . T cell responses in blood and lung tissues are complex . Recently , Nikitina , et al . , showed that increased proportion of IFN-γ produced by effector T cell within lung tissues and blood is associated with increased lung pathology in humans [63] , while Theron , et al . , showed that there was no correlation in either Mtb-specific and non-specific IFN-γ responses in a high TB burden setting [41] . Current TB literature strongly suggests an association between high bacterial burden , and the poly-functional T cell response in the periphery [12 , 32 , 45 , 48 , 64 , 65] . In contrast , we demonstrated an inverse correlation between bacterial burden and total pro-inflammatory cytokine responses by T cells in granulomas . However , very few T cells in granulomas were poly-functional , even with re-stimulation . Our data support that higher frequencies of overall responding T cells are associated with fewer bacteria within a granuloma . Although most individual T cells appear to primarily produce a single cytokine , the granuloma itself is “poly-functional” since T cells producing T-1 , T-17 or IL-10 cytokines are significantly correlated amongst each other within granulomas , and therefore must co-exist within the same granuloma . In our study , granulomas that had a higher proportion of T cells producing IL-10 in combination with T cells producing pro-inflammatory cytokines IL-2 , TNF or IL-17 were associated with sterilization . Further , our data provide evidence for the co-existence of pro- and anti-inflammatory T cells in granulomas , in both sterile and non-sterile granulomas and in animals with active disease or latent infection . This supports the idea that a balance of inflammatory mediators at the individual granuloma level may contribute to the ability of granulomas to both kill bacteria [2] and limit pathology [10 , 12 , 36–38] . Assessing immune responses as a biomarker in human Mtb infection and in vaccine studies in humans relies heavily on sampling of blood [66–68] . However , there is no clear understanding of the relationship between systemic and local responses . In fact , our data support that the systemic responses do not accurately reflect local responses in granulomas . This relationship might be further complicated due to the existence of a spectrum at both local and systemic levels in macaques and humans . Even though BAL is considered to be a closer approximation of the lung , airway T cell responses differ from granulomas responses [24] , and lung granulomas provide us with exact measures of local responses at the site of bacterial interactions with the host . Thus , caution should be used in interpreting and extrapolating data from peripheral T cell responses in humans , although that does not exclude the potential for biomarkers of risk to be determined in the blood . The major limitation of our study is the paucity of cells from individual granulomas and the size of the multi-parametric flow cytometric panel . Although we started with more than 300 granulomas and obtained usable data from ∼150 granulomas for this manuscript , the unexpectedly high heterogeneity observed in the T cell responses in granulomas suggests that this is relatively small data set and more samples are necessary for further robust analyses and modeling . Therefore , the statistically significant differences in the responses reported on single cytokine responses between sterile and non-sterile responses here are only modest . Due to the low numbers of T cells in individual granulomas , which can be quite small in size [2] , individual CD4+ and CD8+ T cell cytokine responses were not analyzed . Variable number of granulomas from animals could be considered as a potential bias . Even though this is a limitation of the study design , the major focus of this manuscript is the analysis based on individual granulomas irrespective of animals , and therefore it does not affect the outcome of analysis . Another limitation of this study is the lack of evaluation of Th2- type T cell responses at the site of disease . This is largely due to the restricted number of flow cytometry channels that were available for use , and difficulty in detecting IL-4 responses in our preliminary studies . Finally , only a pairwise-combinatorial cytokine effect on sterilization was performed , so that 133 granulomas were included in the analysis . A more complex combinatorial analysis requires larger numbers of granulomas . Clearly there is much more work to be done in this area , and we expect that further work will uncover additional factors that contribute to sterilization of granulomas . In summary , we find that a range of granuloma T cell responses exists within an individual animal . Surprisingly , only limited numbers of T cells produce cytokines at the site of disease , which nonetheless were still able to control bacterial burden given the inverse correlation with the number of recoverable bacteria from the granuloma . Our findings provide further evidence for the importance of the balance between pro- and anti-inflammatory cytokines at the granuloma level for control of bacterial burden . Finally , the systemic responses do not generally reflect the local responses , which have considerable implications in terms of biomarker discovery and interpretation and provides insights into the functioning of T cells within granulomas .
All experimental manipulations , protocols , and care of the animals were approved by the University of Pittsburgh School of Medicine Institutional Animal Care and Use Committee ( IACUC ) . The protocol assurance number for our IACUC is A3187-01 . Our specific protocol approval numbers for this project are 13122689 , 11090030 , 1105870 , 12060181 , and 11110045 . The IACUC adheres to national guidelines established in the Animal Welfare Act ( 7 U . S . C . Sections 2131–2159 ) and the Guide for the Care and Use of Laboratory Animals ( 8th Edition ) as mandated by the U . S . Public Health Service Policy . All macaques used in this study were housed at the University of Pittsburgh in rooms with autonomously controlled temperature , humidity , and lighting . Animals were singly housed in caging at least 2 square meters apart that allowed visual and tactile contact with neighboring conspecifics . The macaques were fed twice daily with biscuits formulated for nonhuman primates , supplemented at least 4 days/week with large pieces of fresh fruits or vegetables . Animals had access to water ad libitem . Because our macaques were singly housed due to the infectious nature of these studies , an enhanced enrichment plan was designed and overseen by our nonhuman primate enrichment specialist . This plan has three components . First , species-specific behaviors are encouraged . All animals have access to toys and other manipulata , some of which will be filled with food treats ( e . g . frozen fruit , peanut butter , etc . ) . These are rotated on a regular basis . Puzzle feeders foraging boards , and cardboard tubes containing small food items also are placed in the cage to stimulate foraging behaviors . Adjustable mirrors accessible to the animals stimulate interaction between animals . Second , routine interaction between humans and macaques are encouraged . These interactions occur daily and consist mainly of small food objects offered as enrichment and adhere to established safety protocols . Animal caretakers are encouraged to interact with the animals ( by talking or with facial expressions ) while performing tasks in the housing area . Routine procedures ( e . g . feeding , cage cleaning , etc ) are done on a strict schedule to allow the animals to acclimate to a routine daily schedule . Third , all macaques are provided with a variety of visual and auditory stimulation . Housing areas contain either radios or TV/video equipment that play cartoons or other formats designed for children for at least 3 hours each day . The videos and radios are rotated between animal rooms so that the same enrichment is not played repetitively for the same group of animals . All animals are checked at least twice daily to assess appetite , attitude , activity level , hydration status , etc . Following M . tuberculosis infection , the animals are monitored closely for evidence of disease ( e . g . , anorexia , weight loss , tachypnea , dyspnea , coughing ) . Physical exams , including weights , are performed on a regular basis . Animals are sedated prior to all veterinary procedures ( e . g . blood draws , etc . ) using ketamine or other approved drugs . Regular PET/CT imaging is conducted on most of our macaques following infection and has proved very useful for monitoring disease progression . Our veterinary technicians monitor animals especially closely for any signs of pain or distress . If any are noted , appropriate supportive care ( e . g . dietary supplementation , rehydration ) and clinical treatments ( analgesics ) are given . Any animal considered to have advanced disease or intractable pain or distress from any cause is sedated with ketamine and then humanely euthanatized using sodium pentobarbital . Cynomolgus macaques ( Macaca fascicularis ) , >4 years of age , ( Valley Biosystems , Sacramento , CA ) were housed within a Biosafety Level 3 ( BSL-3 ) primate facility as previously described [17 , 24 , 25] and as above . Animals were infected with low dose M . tuberculosis ( Erdman strain ) via bronchoscopic instillation of about 25 colony-forming units ( CFUs ) / monkey to the lower lung lobe . Infection was confirmed by tuberculin skin test conversion and/or lymphocyte proliferation assay six weeks post-infection [24] . Serial clinical , microbiologic , immunologic , and radiographic examinations were performed , as previously described . Based on defined clinical criteria , radiographic , and microbiologic assessments during the course of infection monkeys were classified as having latent infection or active disease 6–8 months after infection as described previously [25 , 26 , 29] . In addition , animals that were infected with Mtb and necropsied ≤11 weeks after infection were also included in this study . Necropsy was performed as previously described [17 , 24 , 25 , 29] . Briefly , an 18F-FDG PET-CT scan was performed on every animal 1–3 days prior to necropsy to measure disease progression and identify individual granulomas as described [29] . At necropsy , monkeys were maximally bled and humanely sacrificed using pentobarbital and phenytoin ( Beuthanasia; Schering-Plough , Kenilworth , NJ ) . Individual lesions previously identified by PET-CT and those that were not seen on imaging from lung and mediastinal lymph nodes were obtained for histological analysis , bacterial burden , and immunological studies [29] . A veterinary pathologist described gross pathologic findings . To quantify gross pathologic disease ( disease burden ) , a necropsy score was developed in which points were given for TB disease: number , size , and pattern of granulomas distributed in each lung lobe and mediastinal lymph node and in other organs each lung lobe , lymph node , and visceral organ were included and enumerated , and an overall score was determined as previously described [25] . The size of each granuloma was measured at necropsy and by pre necropsy scan [69] . Representative sections of each tissue were homogenized into single-cell suspensions for immunologic studies , flow cytometric analysis , and bacterial burden , as previously described [17 , 24 , 26 , 65] . 200μl of each granuloma homogenate were plated in serial dilutions onto 7H11 medium , and the CFU of M . tuberculosis growth were enumerated 21 days later to determine the number of bacilli in each granuloma [2 , 25] . As a quantitative measure of overall bacterial burden , a CFU score was derived from the summation of the log-transformed CFU/gram of each sample at the time of necropsy , as previously described [25] . Flow cytometry was performed on a random sampling of granulomas ( 4–12 granulomas per animal ) . Although about 300 granulomas were initially analyzed , a cutoff of total number of T cells by flow cytometry was used to avoid introducing error due to analysis of very small T cell populations . Thus , a total of 149 granulomas from 34 animals were fully analyzed for this study . Single cell suspension of individual lung granulomas was stimulated with peptide pools of Mtb specific antigens ESAT-6 and CFP-10 ( 10μg/ml of every peptide ) in the presence of Brefeldin A ( Golgiplug: BD biosciences ) for 3 . 5 hours at 37°C with 5% CO2 . Positive control included stimulation with phorbol dibutyrate ( PDBu ) and ionomycin and an isotype control were included whenever additional cells were available . The cells were then stained for Viability marker ( Invitrogen ) , surface and intracellular cytokine markers . Flow cytometry for cell surface markers for T cells included CD3 ( clone SP34-2; BD Pharmingen ) , CD4 ( clone L200; BD Horizon ) and CD8 ( clone SK1; BD biosciences ) . In addition , B cell marker CD20 ( clone 2H7; eBioscience ) and macrophage marker CD11b ( clone Mac-1; BD Pharmingen ) were included as the dump channel . Intracellular cytokine staining panel included pro-inflammatory cytokines: T-1 [IFN-γ ( clone B27 ) , IL-2 ( clone MQ1-17H12 ) , TNF ( clone MAB11 ) ] , T-17 [IL-17 ( clone eBio64CAP17 ) and anti-inflammatory ( Regulatory ) IL-10 ( clone JES3-9D7 ) markers . In a subset of granulomas , exhaustion markers PD-1 ( clone EH12 . 2H7 , Biolegend ) and CTLA-4 ( Clone BN13 , BD Pharmigen ) were used to stain a subset of granulomas . Data acquisition was performed using a LSR II ( BD ) and analyzed using FlowJo Software v . 9 . 7 ( Treestar Inc , Ashland , OR ) . Supplementary figure 8 ( S8 Fig . ) describes the gating strategies employed for analysis . Supplementary figure 9 ( S9 Fig . ) provides detailed description of gating strategies in comparison with PBMC for clarity . Cytokine data presented in this manuscript are gated on CD3+ T cells . Heparinized blood was drawn from the animals prior to necropsy ( terminal bleed ) . PBMCs were isolated via Percoll gradient centrifugation as previously described [70] . One million cells were stimulated with each of the antigens and controls , and incubated in similar conditions as described above for 6 hours . Stimulated PBMC were stained using the same panel of markers , acquired and analyzed as described above . D’Agostino & Pearson Omibus normality test was performed , on all data described in this manuscript . Since the data were not normally distributed , nonparametric t test was used when comparing two groups ( Mann-Whitney test ) . Kruskal-Wallis test was used to compare more than two groups with post hoc analysis Dunn’s multiple test comparisons . P values ≤0 . 05 were considered significant . Statistical analysis was performed using GraphPad Prism v6 ( GraphPad Software , San Diego , CA ) . For multivariate analysis , JMP Pro 10 ( SAS ) package was used . Nonparametric Spearman’s ρ was calculated for correlations ( multivariate analysis ) using JMP Pro v10 ( SAS Institute Inc . ) . Frequency of cytokine co-expression and sterilization in granulomas was implemented using Matlab ( Mathworks , Natick , MA ) . Briefly , for each variable of T cell cytokine ( IFN-γ , IL-2 , TNF , IL-17 and IL-10 ) dataset , continuous values for each individual granuloma were binned into one of four categories depending on quartile distribution ( bins 1 , 2 , 3 , or 4 ) ( Fig . 7A ) . We counted the number of times a particular combination of the variables occur and summarized these in a 4×4 co-occurrence matrices spanning all four bins comparing in a pairwise fashion across all the potential combinations . For each of the pairwise combination , the frequency of that occurrence is plotted and summarized in a heat map . Similarly , sterilization frequency matrices were constructed calculating the number of times sterilization occurred in a given combination of bins ( number of sterilizing granulomas with a variable A at level X and variable B at level Y out of the total number of granulomas with variable A at level X and variable B at level Y ) . Each comparison was plotted as a heat map . For this analysis granulomas from animals with established clinical status ( active disease or latent infection ) were used ( N = 113 ) . Euclidean distances were calculated in Microsoft Excel ( for mac 2011 ) , by utilizing the formula ( GIFNγ−BIFNγ ) 2 + ( GIL2−BIL2 ) 2 + ( GTNF−BTNF ) 2 + ( GIL17−BIL17 ) 2 where suffix “G” represents T cell cytokine response from the granuloma ( local ) and “B” represents those from blood ( systemic ) . Datasets for which complete cytokine data were available for individual granulomas ( N = 120 granulomas ) and blood ( PBMC ) ( N = 28 animals ) were used for this analysis . Distances were calculated for each granuloma with the T cell cytokine response of granuloma and blood of that animal . Average distances were then obtained by averaging the granuloma to blood distance for all the granulomas from a particular animal | The characteristic feature of Mycobacterium tuberculosis ( Mtb ) infection is the formation of lesions , which are organized structures of immune cells in the lungs called granulomas , which contain the bacteria . When the granuloma functions effectively , it can kill the bacteria . T cells ( a type of immune cell , also present in granulomas ) are known to play an important role in control of tuberculosis . However , functions of T cells at individual granuloma levels are unknown . Here , we studied the functional characteristics of T cells , which are defined by the production of chemical messengers ( cytokines ) at the granuloma level in a non-human primate model . We compared the relationship between cytokine response and the number of bacteria ( Mtb ) in each granuloma . Each granuloma was found to be unique , suggesting different types exist within an animal . Only a small proportion of T cells produced any cytokine , but different types of cytokines were observed within each granuloma . A balance between different types of cytokine was associated with more killing of bacteria in granulomas . Understanding how to improve the T cell responses to obtain killing of bacteria in the granuloma will be important for vaccine development . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Variability in Tuberculosis Granuloma T Cell Responses Exists, but a Balance of Pro- and Anti-inflammatory Cytokines Is Associated with Sterilization |
Mollicutes is a class of parasitic bacteria that have evolved from a common Firmicutes ancestor mostly by massive genome reduction . With genomes under 1 Mbp in size , most Mollicutes species retain the capacity to replicate and grow autonomously . The major goal of this work was to identify the minimal set of proteins that can sustain ribosome biogenesis and translation of the genetic code in these bacteria . Using the experimentally validated genes from the model bacteria Escherichia coli and Bacillus subtilis as input , genes encoding proteins of the core translation machinery were predicted in 39 distinct Mollicutes species , 33 of which are culturable . The set of 260 input genes encodes proteins involved in ribosome biogenesis , tRNA maturation and aminoacylation , as well as proteins cofactors required for mRNA translation and RNA decay . A core set of 104 of these proteins is found in all species analyzed . Genes encoding proteins involved in post-translational modifications of ribosomal proteins and translation cofactors , post-transcriptional modifications of t+rRNA , in ribosome assembly and RNA degradation are the most frequently lost . As expected , genes coding for aminoacyl-tRNA synthetases , ribosomal proteins and initiation , elongation and termination factors are the most persistent ( i . e . conserved in a majority of genomes ) . Enzymes introducing nucleotides modifications in the anticodon loop of tRNA , in helix 44 of 16S rRNA and in helices 69 and 80 of 23S rRNA , all essential for decoding and facilitating peptidyl transfer , are maintained in all species . Reconstruction of genome evolution in Mollicutes revealed that , beside many gene losses , occasional gains by horizontal gene transfer also occurred . This analysis not only showed that slightly different solutions for preserving a functional , albeit minimal , protein synthetizing machinery have emerged in these successive rounds of reductive evolution but also has broad implications in guiding the reconstruction of a minimal cell by synthetic biology approaches .
Mollicutes constitute a monophyletic class that share a common ancestor with Gram-positive bacteria of low G+C content or Firmicutes but have adopted a parasitic life style ( Figure S1 ) [1] . During their coevolution with their eukaryotic hosts , mollicutes progressively lost the genes coding for cell-wall synthesis enzymes and for enzymes involved in the synthesis of small metabolites , such as amino acids , nucleotides and lipids that were available in the host . As a result , mollicute genomes are much smaller ( 580–1 , 840 Kbp; eg: about 482–2 , 050 CoDing Sequences or CDSs , Table S1 ) than those of model bacteria such as Escherichia coli or Bacillus subtilis ( 4 , 639–4 , 215 Kbp; eg: 4 , 320–4 , 176 CDSs respectively ) . These bacteria have nevertheless retained the full capacity to synthesize DNA , RNA and all the proteins required to sustain a parasitic life-style . In addition most of them are still able to grow in axenic conditions in rich media usually containing 20% serum ( see [2] for review ) ; only the hemoplasmas and the Candidatus phytoplasma species have yet to be cultured in vitro . Mollicutes are therefore considered as the smallest and simplest known bacteria capable of autonomous multiplication [3] , [4] . ‘Simple’ does not mean ‘simplistic’ . One should not underestimate the elaborate solutions that mollicutes have used to solve problems related to their peculiar macromolecular organization and cellular compactness ( discussed in [3] , [5] , [6] and references therein ) . From an evolutionary point of view , mollicutes should be considered as some of the most evolved prokaryotes that still have retained ability to perform the complex reactions that encompass DNA , RNA and protein synthesis , with possibly new tricks and inventions to make the most of their limited genetic capacities [7] , [8] . For these reasons , specific Mollicutes strains have been used as a test bench to improve our understanding of the basic principles of a cell and for reconstructing a microbe that would function with a synthetic minimal genome ( see [3] , [4] , [9] , [10] , [11] for examples ) . Identification of essential proteins is a long-standing problem that is directly linked to the concept of a minimal cell [12] . The approaches used in Mollicutes to identify the set of essential genes have been: i ) comparative genomic analyses to create an overview of the protein content in model mycoplasmas ( notably Mycoplasma genitalium and Mycoplasma pneumoniae ) [5] , [13] , [14] , [15] , ii ) identification of genes that cannot be individually inactivated [16] , [17] , [18] , [19] , iii ) reconstruction of synthetic genomes and transplantation into a recipient cell [10] . Depending on the Mollicutes species considered and the method of analysis , the number of essential genes varies from 256 to 422 . For M . genitalium , 256 were identified by in silico comparative genomics analysis [15] but over 382 were found by saturation transposon mutagenesis experiments [16] , [19] . For Mycoplasma pulmonis and Mycoplasma arthritidis , saturation transposon mutagenesis identified 422 and 417 essential genes respectively [17] , [20] . Messenger-RNA-dependent protein synthesis is one of the most complex cellular processes both in its biogenesis and its function . For a cell with a reduced genome such as M . genitalium , more than 25% of the genome encoding capacity is mobilized to build this complex machinery [2] . The bacterial ribosome is a giant multicomponent complex of several millions of daltons , composed of 3 RNA species ( 5S , 16S and 23S rRNA ) and many structural proteins ( 60–70 ) . Together with other RNAs ( tRNAs , tmRNA and RNA-P ) and a large repertoire of enzymes and protein factors , this protein synthesis machinery allows translation of mRNAs into polypeptides according to precise rules . Comparative analysis of bacterial genomes reveals that the majority of genes coding for the ribosomal proteins , aminoacyl-tRNA synthetases , translation factors and several ribosome biogenesis/maturation enzymes are universal [7] , [21] and essential [22] , [23] , [24] . Genes coding for enzymes involved in rRNA and protein processing , RNA or protein modification , and ribosome maturation RNases appear less important , as deleting these does not lead to severe growth defects , and are the most easily lost genes during genomic erosion in Mollicutes species ( see below ) . As the number of sequenced Mollicutes genomes has significantly increased , most of the phylogenetic sub-groups of this class of bacteria are now covered allowing for the analysis of the erosion of translation from an evolutionary perspective . This analysis defined the minimal set of proteins needed to sustain protein synthesis in various mollicutes . A major goal of this work was to identify the minimal set of proteins that can sustain ribosome biogenesis and translation of the genetic code in Mollicutes that are model organisms of choice for synthetic biology . Also , by careful analysis of the evolutionary pattern of gene losses and a few cases of gene gain in different individual Mollicutes species , light was shed on the progressive adaptation of an ancestral and complex cellular proteome towards a simpler , yet functional alternative one .
The major goal of this work is to identify the minimal set of proteins that can sustain ribosome biogenesis and translation of the genetic code in self replicating bacteria with reduced genomes ( MPSM for Minimal Protein Synthesis Machinery ) . Comparative genomics of 39 Mollicutes species allowed the identification of 104 genes encoding ubiquitous translation proteins designed as the core set herein . The acronyms of these proteins are listed according to their main functions in Figure 4 . The majority of these core proteins are present in both B . subtilis and E . coli , the exceptions are proteins that are found only in Gram-positive bacteria ( indicated in red; Figure 4A ) . In M . genitalium and M . pneumoniae almost all ( except 4 ) of these 104 proteins were experimentally demonstrated to be essential ( Figure S2 ) , attesting their primordial importance for ribosome biogenesis and function in the context of Mycoplasma metabolism . This set of 104 core proteins might not be sufficient for ribosome biogenesis and translation to work . Indeed , extant culturable Mollicutes maintain a set of translation proteins above an apparent lower limit of 138 ( Figure 2 ) . An additional set of essential proteins , not necessarily the same in each species , are obviously required . Among them are the 17 persistent gene products discussed above that are absent only in one ( usually M . suis ) or several non-culturable Mollicutes ( indicated with red asterisks in Figure 4B ) . Eight additional proteins that are notably persistent or can only be replaced by an alternate mechanism have been added in the MPSM . These are: i ) r-protein L9 ( RplI ) absent only in M . penetrans and three non cultivable species , L9 interacts with tRNA in the P site and limits mRNA slippage during translation; ii ) r-protein S21 ( PpsU ) that is essential in the absence of r-protein S1 ( RpsA ) , particularly for translating leaderless mRNAs; iii ) 2′-O-RNA methyltransferase RlmB2 or YqxC predicted to methylate a conserved G residue in the A-loop ( helix 92 ) of the peptidyl-transferase center of 23S rRNA ( counted for one protein ) ; iv ) one of the three paralogous double-stranded endonucleases ( RNases HI , HII , HIII ) as all mollicutes harbour at least one of these enzymes that possibly could have broad specificity; v ) the essential lysidine-tRNA transferase ( TilS ) that can be lost only if compensatory mutations occur in the tRNA recognition domain of IleRS and the anticodon of tRNAIle; finally vi ) the three subunits of the Gln-tRNA amidotransferase complex ( GatA-GatB-GatC ) of the Gln-tRNA amidotransferase complex essential for the formation Glutamine-tRNAGln in Mollicutes lacking the Glutamine-tRNA synthetase GlnRS ( counted for 3 proteins ) . Proteins that were easily lost during Mollicutes evolution were not included as essential elements of an MPSM ( Figure S3A ) . However , some of these proteins may fine-tune ribosome biogenesis , improve efficiency of translation and/or display other side functions , such as coupling of translation with transcription and/or regulating protein expression . Finally , proteins that are absent in all Mollicutes were definitively discarded as elements of the MPSM , the majority of these are also absent in Gram-positive bacteria ( Figure S3B ) . Therefore , in absence of stress conditions that require specific proteins not discussed here , we propose that these 17+8 = 25 proteins , combined with the core of 104 proteins , comprise a theoretical MPSM of 129 proteins . This MPSM corresponds to a set of well characterized homologous proteins in our model bacterial systems and they are encoded by the most persistent genes in the Mollicutes analyzed . However , because some genes are still of unknown function in E . coli , B . subtilis and Mollicutes , we cannot exclude the possibility that a yet unidentified protein involved in the biosynthesis or function of the ribosome might have been missed . Our evaluation of 129 minimal translation associated genes accounts for a large fraction of the total genes identified in mollicutes with reduced genomes ( 26% in the case of M . genitalium and 18% for M . pneumoniae ) . The protein synthesis factory is clearly the dominant and most energy consuming process in small cells such as Mollicutes [14] . The progressive reduction of the size of precursor RNAs ( mainly mRNAs and tRNAs ) by reducing their 3′ and/or 5′-tails is probably also part of the genomic size economization strategy . In Mollicutes , 18% of mRNA in average are leaderless mRNAs ( [123] , thus lacking the classical/canonical Shine-Dalgano ( SD ) sequence required for specific translation initiation on 30S subunit . Similarly precursor tRNAs have shorter 5′-leader sequence and no 3′-tail ( see above ) . However , because of the constraint of maintaining canonical bacterial type of ribonucleoprotein 30S and 50S particles , the length of 16S and 23S rRNAs in Mollicutes is almost identical to those of other bacteria [124] . The best-studied extant Mollicutes with reduced genomes and capable of independent growth are the two phylogenetically related M . genitalium and M . pneumoniae . With a total of about 482 CDS , including 144 CDS for the translation machinery , for a 0 . 580 Mbp genome , M . genitalium is generally considered as the best representative of a minimal free-living cell . A schematic view of the translation machinery in M . genitalium is depicted in Figure 5 , together with the list of all the elements required for ribosome biogenesis and mRNA translation . The 128 proteins classified above as belonging to the MPSM are in bold-black acronyms ( only the putative r-RNA modification enzyme RlmB2/YqxC of the selected 25 additional proteins is missing ) , while the additional 16 proteins present in M . genitalium are in blue italic acronyms ( see also Figure S4 ) . These latter proteins include two DEAD- box helicases , one protein kinase ( PrkC ) and its associated protein phosphatase ( PrpC ) , one r-RNA protein modification ( RimK ) and two chaperones ( GroEL+GroES ) , all classified as proteins of ribosome assembly and protein maturation . In addition are found three ribonucleases of the RNA processing ( RNase M5 , RNase Y and a second nano-RNase ) , three tRNA modification enzymes ( TruA , ThiI and TrmK ) and three translation factors ( DEF , FMT , SpoT/RelA ) . These proteins , especially GroEL/GroES , RNase MV and RimK are lacking in many other Mollicutes ( Figure 1B , Table S3 ) , RimK is even absent in B . subtilis and arose in both M . genitalium and M . pneumoniae probably by lateral gene transfer ( see above ) . In M . genitalium , these proteins may have specific functions such as fine-tuning of RNA processing and ribosome assembly , mRNA translation and its regulation in response to specific physiological demands of the cell . Despite these differences , the translation apparatus in M . genitalium fits well with the MPSM concept developed above and closely resembles the classical scheme of translation in bacteria [125] . The most remarkable features of protein synthesis in M . genitalium and other Mollicutes with minimal genomes are: 1 ) almost all canonical r-proteins are present ( however , as shown in the case of M . pneumoniae [126] not all r-proteins may be present in every ribosome , a certain degree of plasticity in r-protein composition may exist according to specific type of mRNA to be translated ) ; 2 ) the GTP/ATPases involved in 30S/50S/70S assembly are identical in sequence and number to those found in other bacteria with larger genomes , attesting that the assembly process follows a path extremely conserved in bacteria; the frequent lack of DEAD-box helicases probably results from the A/T-rich RNA sequences; 3 ) the DnaK-dependent protein folding/quality control system is ubiquitous . However in only a few Mollicutes , including M . genitalium and M . pneumoniae , GroEL/GroES are present and therefore should not be considered as essential; 4 ) the multiplicity of genes coding for nano-RNases allowing to scavenge for mononucleotide building blocks is of clear advantage for Mollicutes that are devoid of nucleotide biosynthetic pathway; 5 ) among post-translational protein modification enzymes , only the methyltransferase PrmC ( HemK ) that methylates termination factor RF-1 is conserved in Mollicutes; 6 ) a repertoire of 19 aaRSs plus the GatA/GatB/GatC amidotransferase complex allowing to generate Gln-tRNAGln and a minimal set of 28 isoacceptor tRNAs are used to decode all 62 sense codons into 20 canonical aminoacids; 7 ) an extra tRNATrp harboring an anticodon U*CA reads UGA as Trp [55] , the absence of termination factor RF-2 being consistent with this scheme; 8 ) the methionine residue attached to initiator tRNAMet is formylated in M . genitalium but in most mollicutes the formylation/deformylation enzymatic system ( FMT/MAP ) is absent and therefore not essential; 9 ) the majority of post-transcriptional enzymatic modifications in tRNA and rRNA are restricted to a few nucleotides located mostly in the anticodon loop of tRNA , the ribosomal decoding sites ( h18 , h44 and h45 ) of 30S subunit and the peptidyl transferase site ( H90 , H69 ) of 50S subunits; 10 ) the majority of the essential bacterial factors are needed , except the stress rescue and silencing factors TypA , AraFA and RsfA; 11 ) the SpoT/RelA alarmone system is present in M . genitalium and most species of the Pneumoniae sub-group but absent in all species of the Hominis sub-group; 12 ) tmRNA and its associate protein SmpB of the trans-translation system and the ribozyme RNaseP with only one associated protein RnP are preserved; 13 ) because of the use of numerous leaderless mRNAs in Mollicutes , an alternative mechanism of translation initiation exists beside the canonical Shine-Dalgano ( SD ) -depending mRNA initiation , translation initiation of SD-containing mRNA occurs on 30S subunit and is usually mediated by r-protein S1 , while S1 but not S21 become dispensable for translation of leaderless mRNAs on intact 70S ribosome [39]; finally , 14 ) because of their small sizes , a Mollicutes species like M . pneumoniae contains only 140–200 ribosomes per cell volume of 0 . 067 µm3 [11] , while an E . coli cell of about 1 µm3 usually contains several thousands of ribosomes [127] . This study shows that comparative genomics analyses can help define the minimal set of genes required for translation in Mollicutes . Translation genes that have not been lost in any of the species analyzed belong to a translation core that is most certainly needed to sustain protein synthesis . However , loss of a specific protein or enzyme in a given Mollicutes species does not necessarily translate in loss of the corresponding cellular function , as some cellular enzymes or proteins may display overlapping specificities or fulfill closely related , analogous functions . Occasional gene gains are also indicative of the need for compensation for the gene losses or acquiring new functionalities to maintain a reduced , but coherent functional protein synthesis machinery . The corollary of these premices is that different solutions to minimize translation machinery can evolve in different Mollicutes and it is illusory to try to define a universal minimal set of translation proteins that would be common to very distantly related bacteria ( discussed in [28] ) . The class of Mollicutes is particularly suited for defining a minimal translation apparatus . Not only do they include organisms that have eliminated many primordial metabolism genes ( including translation genes ) , while retaining the capability to replicate and translating mRNAs in an axenic medium , but they also appear as some of the most evolved prokaryotes able to sustain complex metabolism with a minimum elements of its cellular chassis ( discussed in: references [3] , [4] , [9] , [10] , [11] ) . Recent studies from independent laboratories have shown that two Mollicutes species ( Mesoplasma florum and Mycoplasma gallisepticum ) exhibit the highest known rate of base-substitutional mutation for any unicellular organism showing these are fast-evolving bacteria [69] , [71] . Although Mollicutes species share a small genome size , our study indicates that there remains room for diversity even in a highly conserved apparatus such as translation . On one side of the spectrum , M . suis probably stands out as the most minimal organism with only 116 proteins dedicated to translation . At this stage , it is not understood how this uncultured organism that lives associated to red blood cells of its mammalian host is able to synthetize proteins with a machinery that appears so deficient . It is tempting to hypothesize that translation in M . suis requires factors from its host , but owing to the lack of general knowledge on hemoplasma biology , it is too speculative to further elaborate . On the other side of the spectrum , A . laidlawii has a much larger repertoire of proteins implicated in translation ( 183 ) than most other Mollicutes species , but still lower proteins than in our model bacteria E . coli ( 228 ) and B . subtilis ( 210 ) . In fact , this species with other Acholeplasmatales also stands apart from other Mollicutes because it has larger metabolic capacities and is ubiquitous , being able to live as a saprophyte in soil , compost or wastewaters [128] . The reconstruction of the evolution of translation-related gene set in Mollicutes ( Figure 3 ) indicated that A . laidlawii is probably the species among the Mollicutes that is the closest to the common ancestor with the Firmicutes . Important aspects of genome downsizing in bacteria concern the accuracy , efficiency and regulation of the minimalist translation process . Recent works at studying aminoacylation of tRNA in vitro demonstrated that several aminoacyl-tRNA synthetases of M . mobile are prone to mistake the amino acid or the tRNA substrate to be charged ( discussed above ) . Such mis-aminoacylations will lead to subsequent incorporation of wrong amino acids into proteins and consequently will reduce the global fitness of the proteome . The possibility that mis-incorporation of amino acids into the nascent polypeptide also occurs because of mis-functioning of the minimalist ribosome cannot be discarded [82] . Elimination of abnormal/misfolded proteins by the usually abundant cellular GroEL/GroES and/or DnaK-dependent chaperone/degradation system acting as promiscuous buffer of genetic variations should not be underestimated ( see for example: [129] ) . As long as the remaining mutant proteins allow cell viability , a low quality of the proteome may even be of some advantage by contributing to the antigenic variation of the mycoplasma exposed to its host's immune response [70] , [130] . The genome-scale analysis of soluble complexes in M . pneumoniae has revealed an unexpected high level of protein interaction leading to an estimate of some 200 molecular machines [11] . The ribosome assembly represents one of the most complex networks of interaction . Interestingly , among the 13 polypeptides for which a function was not yet attributed in this specific network , two of them were predicted in our analysis as DEAD-box RNA helicase ( MPN623 ) and as endonuclease M5 ( RnmV; MPN072 ) ; see Table S3 . In fact , MPN623 was curated as an ATP-dependant RNA helicase in the work of Kuhner et al [11] , which is consistent with our predictions . The small number of proteins of the MPSM in Mollicutes is also reminiscent of the translation machinaries in mitochondria and bacterial endosymbionts [131] . However , in the case of mitochondria , a more massive gene and protein loss occurred , resulting in the loss or transfer to the nuclear host genome of majority of bacterial proteins encoding essential genes , including those related to protein synthesis machinery . Of the original bacterial machinery for translation , only genes coding for the structural RNA ( t/r/mRNAs ) , have been preserved ( only 16 Kbp in mammalian mitochondria ) . All the proteins required for the extant/modern mitochondrial ribosome assembly and translation are nuclear encoded , synthesized on the cytoplasmic ribosomes of the cell host , and subsequently imported into the mitochondria via several transport machineries . Despite this unique mitochondrial organization , translation in mitochondria is essentially bacterial-like . One major difference with Mollicutes , even with M . genitalium described above , is that only a small number of mito-mRNAs ( mono- and di-cistronic ) are translated , all coding for proteins that are part of the membrane reaction centers of the respiratory chain complexes . Consequently , all mito-ribosomes are permanently tethered to the inner membrane and its composition , especially around the polypeptide exit tunnel , is much different from bacterial ribosome . This peculiarity allows a better coordination of the synthesis of the highly hydrophobic mitochondrial proteins and their immediate assembly within the mitochondrial membrane [132] . The possibility exists that , beside the cytoplasmic ribosomes producing mainly soluble cellular proteins , a minor fraction of such specialized membrane-bound ribosomes also exists in Mollicutes , a cellular strategy that certainly allows better efficiency of certain membrane proteins . Another difference is that all mito-mRNAs are leaderless , while in Mollicutes the majority of mRNAs ( 80% in average [123] ) harbor a Shine-Dalgano ( SD ) sequence that determines the translation initiation pathway followed ( Figure 5 ) . Beside these mitochondrial specifications , both organelles and mycoplasmas , uses UGA codon for Trp and the translation factors are essentially the same ( except for the lack of mito IF-1 ) , attesting for a very similar translation mechanism as depicted for M . genitalium in Figure 5 ( reviewed in: [133] , [134] , [135] ) . Bacterial endosymbionts like Wolbachia ( range of genomesize: 958–1 , 482 Kbp ) , and Buchnera ( 422–1 , 502 Kbp ) that infect arthropods and aphids respectively have also evolved in a parasitic life-style by reducing their genome sizes . In some species such as Carsonella ruddii , Candidatus Tremblaya and Nasuia deltocephalinicola , the genomes are even smaller ( 160–112 Kbp ) . These tiny bacteria originated about 200 My ago from independent lineages of diverse bacterial groups . At variance with majority of Mollicutes , they cannot be cultivated as free-living organisms and live in a close symbiosis within the host cell , like an organelle . Beside nutrient exchanges , possible protein exchanges between the endosymbiont , the cell host and often cohabiting additional distinct co-endosymbiont ( s ) remain a matter of debate [136] , [137] . Therefore , insect endosymbionts represent a heterogeneous group of organisms and those with the smallest genomes are not ideal model organisms to identify minimal gene sets for autonomous replication . However , examination of the available information on translation genes from a selected set of endosymbionts [138] , [139] reveals that most persistent translation machinery genes in these minimal organisms correspond to a large part of the MPSM defined in Mollicutes ( see Figure S5 ) . However , from the smallest sets of endosymbiotic proteins it is difficult to build a self-constructing ribosome and successful translation machinery . Evidently in these cases additional proteins from the co-symbiont ( s ) , the host mitochondria or even the host cell would have to complement those translation proteins of the endosymbionts . Owing to the minimal size of their genomes , Mollicutes have been chosen as the starting point in efforts aiming at building a minimal cell using tools from synthetic biology ( for review see [140] ) . The ambitious goal of these studies is not only to decipher all the functions required for sustaining a minimal life but also for building a cell chassis that could be used in biotechnological processes . Following major progress in DNA assembly , genome engineering and transplantation , this goal seems to be within reach . However , building a minimal cell requires an in-depth knowledge of the cell machinery including of the translation apparatus . Our results should contribute to this goal by providing not only one scenario for the MPSM , but rather a series of possible sets based on the analysis of the different Mollicutes sub-groups . This prediction is now open to experimental verification using synthetic biology .
The phylogenetic tree required for the reconstruction of the ancestral gene sets at the different stages of Mollicutes evolution was generated using concatenated multiple alignments of selected 79 orthologous protein sequences . Proteins encoded by single copy genes present in the genome of all mollicutes were selected . This list is provided in the Figure S1 . Multiple alignments were generated using MUSCLE [141] , concatenatedusing Seaview [142] and curated from unreliable sites with GBlock [143] . The final concatenated alignment contained 10 , 686 sites . The phylogenetic tree was constructed by the Maximum Likelihood method using PhyML [144] available on the web server Phylogeny . fr [145] . The list of mollicutes analyzed with some of their genomic characteristics is given in Table S1 . The whole set of proteins of the of Escherichia coli str . K-12 substr . MG1655 and of Bacillus subtilis subsp . subtilis str . 168 translational apparatus were obtained from the Modomics [146] , Biocyc [147] , SEED [148] , SubtiList [27] databases , and Kyoto Encyclopedia of Genes and Genomes [149] , plus an extensive review of literature ( Table S2 ) . Homology between E . coli and B . subtilis proteins was inferred by sequence similarity using a reciprocal BLAST search approach ( bidirectional best hit ) . All E . coli and B . subtilis proteins were used as queries for BLAST searches in 39 selected genomes from distinct Mollicutes species included in the MolliGen genome database ( [150]; http://www . molligen . org ) ( Table S3 ) . In this database , initial annotated genomes were obtained from GenBank files . These genomes were further curated by expert annotation that resulted in changes in the functional annotation of specific CDSs and in adding CDSs that were missing in the initial Genbank file . This step of data curation was performed in the frame of the present project for all the homologs involved in translation . Multiple genomes from the same species were excluded from our dataset because initial analyses indicated that no intra-species differences are evident in the gene sets encoding proteins involved in a central process such as translation and ribosome biogenesis . They were nevertheless useful for confirming the presence or absence of a given gene or solving some abnormalities due to occasional sequencing errors in the dataset . BLASTp searches were first conducted with an e-value cutoff of e−8 . However , proteins sequences retrieved with an e-value ranging from e−8 to e−3 were maintained in the dataset if a domain related to the considered query was detected using the Conserved Domain search engine [151] . When no hit could be found for a given protein query in one of the Mollicutes genomes , the protein of the closest species identified as a putative hit for this query was used as a query for additional BLASTp and tBLASTn searches . For each query , sequences of the putative Mollicutes homologs were aligned with Clustal W [152] . Subsequent phylogenetic analyses were conducted by using the Neighbour Joining method in Mega5 [153] . Annotation of paralogs was resolved , when possible , by analyzing the microsynteny in MolliGen and the topology of the corresponding phylogenetic trees . The translation-related gene set at ancestral stages of Mollicutes evolution was inferred using probabilistic and parsimony approaches implemented in the COUNT software package [36] . We used the above described phylogenetic tree and a presence/absence matrix describing the occurrence of 210 genes over 39 Mollicutes genomes and one reference genome , B . subtilis . The posterior probabilities were calculated using a birth-and-death model . We maximized the likelihood of the data set using a gain–loss model with a Poisson distribution at the root . Gain rate for B . subtilis was fixed at 0 to avoid false prediction of many gene gains by this species . Several combinations of parameters were tested to maximize the likelihood . The best value was obtained with the edge length , loss and gain rates set at 4 gamma categories . Edge length and loss rate parameters had more impact than gain rate on the final likelihood of the optimized model . Wagner parsimony [37] was also used to infer ancestral gene sets . A gain penalty of 4 was used to minimize predicted gene gain events , in accordance with the massive genome reduction context of Mollicutes evolution . | In all cells , proteins are synthesized from the message encoded by mRNA using complex machineries involving many proteins and RNAs . In this process , named translation , the ribosome plays a central role . The elements involved in both ribosome biogenesis and its function are extremely conserved in all organisms from the simplest bacteria to mammalian cells . Most of the 260 known proteins involved in translation have been identified and studied in the bacteria Escherichia coli and Bacillus subtilis , two common cellular models in biology . However , comparative genomics has shown that the translation protein set can be much smaller . This is true for bacteria belonging to the class Mollicutes that are characterized by reduced genomes and hence considered as models for minimal cells . Using homology inference approach and expert analyses , we identified the translation apparatus proteins for 39 of these organisms . Although striking variations were found from one group of species to another , some Mollicutes species require half as many proteins as E . coli or B . subtilis . This analysis allowed us to determine a set of proteins necessary for translation in Mollicutes and define the translation apparatus that would be required in a cellular chassis mimicking a minimal bacterial cell . | [
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"biology"
] | 2014 | Predicting the Minimal Translation Apparatus: Lessons from the Reductive Evolution of Mollicutes |
Klebsiella pneumoniae is a significant human pathogen , in part due to high rates of multidrug resistance . RamA is an intrinsic regulator in K . pneumoniae established to be important for the bacterial response to antimicrobial challenge; however , little is known about its possible wider regulatory role in this organism during infection . In this work , we demonstrate that RamA is a global transcriptional regulator that significantly perturbs the transcriptional landscape of K . pneumoniae , resulting in altered microbe-drug or microbe-host response . This is largely due to the direct regulation of 68 genes associated with a myriad of cellular functions . Importantly , RamA directly binds and activates the lpxC , lpxL-2 and lpxO genes associated with lipid A biosynthesis , thus resulting in modifications within the lipid A moiety of the lipopolysaccharide . RamA-mediated alterations decrease susceptibility to colistin E , polymyxin B and human cationic antimicrobial peptide LL-37 . Increased RamA levels reduce K . pneumoniae adhesion and uptake into macrophages , which is supported by in vivo infection studies , that demonstrate increased systemic dissemination of ramA overexpressing K . pneumoniae . These data establish that RamA-mediated regulation directly perturbs microbial surface properties , including lipid A biosynthesis , which facilitate evasion from the innate host response . This highlights RamA as a global regulator that confers pathoadaptive phenotypes with implications for our understanding of the pathogenesis of Enterobacter , Salmonella and Citrobacter spp . that express orthologous RamA proteins .
The microbial response to antimicrobial challenge is multifactorial and can be conferred by a combination of extrinsic or intrinsic mechanisms . Those intrinsic mechanisms that confer pleiotropic phenotypes can provide a “stepping stone” to surmounting both the host or drug response . Intrinsic proteins such as the AraC-transcriptional proteins e . g . MarA [1] , SoxS [2] , Rob [3] , RamA [4] and RarA [5] , directly regulate genes linked to microbial permeability barriers which results in reduced susceptibility [6] to multiple antibiotic classes . The perturbation of the permeability barrier is identified as a critical step in the development and emergence of higher levels of resistance [7] . The regulatory proteins , typified by the MarA protein , are unique , as unlike other members of the AraC family , these proteins bind DNA as monomers [8] , interact with RNA polymerase via a process of pre-recruitment [9] and generally confer reduced antimicrobial susceptibility [10] . Microarray analyses has highlighted the wider effects of increased MarA [1] , SoxS [2] , RamA [4 , 11] and RarA [5] levels in modulating gene expression particularly of those genes linked to virulence . This is further supported by studies reporting that either the inhibition or deletion of these regulators [12] can impair the ability of E . coli to colonise and cause infection in vivo [13] . Taken together , it is evident that these AraC proteins can confer bifunctional phenotypes of reduced drug susceptibility and increased virulence , which facilitate pathogen survival . These findings firstly , underscore the relative importance of these factors in microbial survival and secondly , provide a rationale for the development of “Anti-virulence-type” inhibitors against these transcription proteins . The ramA gene which encodes the RamA protein is found in Klebsiella , Enterobacter [14] , Salmonella [15] and Citrobacter spp [16] where the genetic organisation of the ram locus is conserved in most organisms , with the exception of Salmonella enterica serovar Typhimurium ( Fig . 1 ) which lacks romA , a putative metallo-beta-lactamase gene . The levels of both the romA-ramA genes are repressed at the transcriptional level by the TetR-type family regulator RamR , encoded by the ramR gene , which is divergently transcribed from the romA-ramA operon . In both Klebsiella and Salmonella , an increase in ramA expression can be mediated by inactivating mutations [16–18] or ligand mediated interactions [19] with the cognate repressor , RamR which binds to a highly conserved inverted repeat ( atgagtgn6cactcat ) [20] overlapping the promoter region of the romAramA operon ( Fig . 1 ) . Mutations within the ramR gene in K . pneumoniae resulting in ramA overexpression were initially reported as a result of tigecycline exposure [17 , 21] . However , previous work evaluating clinical isolates that pre-date the use of tigecycline demonstrate that ramA overexpressing strains were already present within the nosocomial population of K . pneumoniae , suggesting a broader role for RamA mediated overexpression in antibiotic resistance [16] . Interestingly , studies evaluating the prevalence of ramA-mediated overexpression in clinical isolates of K . pneumoniae and Salmonella spp . indicate that these bacteria are more likely to overexpress ramA than marA or soxS , suggesting that elevated ramA levels may be more relevant to the development of antibiotic resistance in these organisms . Several studies [4 , 11] have addressed the scope of the RamA regulon in Salmonella enterica serovar Typhimurium using microarray profiling . These studies demonstrate that ramA overexpression results in reduced antimicrobial susceptibility due to the differential regulation of acrAB and micF genes , which consequently decrease OmpF levels . One study [4] suggests that genes linked to the Salmonella Pathogenicity Island ( SPI-2 ) are also differentially expressed , leading to the initial observation that RamA may impact on Salmonella-specific virulence attributes . However this link was not corroborated in subsequent in vivo experiments . In K . pneumoniae , the wider impact of RamA-mediated regulation is not known . Despite the apparent similarities in genome structure , the microbial lifestyles of both K . pneumoniae [22] and Salmonella spp . differ . Importantly , the increasing multidrug resistance in Klebsiella spp . demands a thorough understanding of factors within this genus that contribute to the intrinsic microbial ‘resistome’ and survival under selective ( host or drug ) pressure . Therefore to define the broad effects of RamA-mediated expression on microbe-host and microbe-drug phenotypes we carried out transcriptome profiling using directional RNAseq with the wild type strain K . pneumoniae Ecl8 [23] and its isogenic derivatives Ecl8ΔramA and Ecl8ΔramR . Our key findings show the scope of RamA-mediated regulation significantly alters the transcriptional landscape of K . pneumoniae . This occurs by directly modulating the expression of different genes notably those associated with antimicrobial resistance and host-microbe interactions thereby resulting in the emergence of a less antibiotic susceptible and more virulent K . pneumoniae .
The ram locus encodes a sRNA to maintain basal levels of ramA expression . RamR functions as the primary repressor of both romA-ramA expression in K . pneumoniae by binding the palindromic repeats of the IR element which flanks the TSS for romA at position -64T . ramR , itself , has two transcriptional start sites , located at the -83T and -167A positions where expression analyses using GFP fusions suggest that the primary promoter region for ramR transcription is located at the -83T start site ( S1 and S2 Figs . ) . This site is also repressed 5-fold more than the vector only control by ramR in trans indicating that like other TetR-type regulators , RamR expression is autoregulated ( S2 Fig . ) . Previous work in Salmonella has shown that the regulatory RNA , StyR3 , can control expression at the ram locus [24] . Given the expansive role of ramA in gene regulation , we sought to determine whether the K . pneumoniae ortholog of StyR3 , denoted as sRamA5 , would function as co-regulator of ramA expression in K . pneumoniae to promote basal ramA levels . The lack of similarity within the intergenic regions located between the ramR and romA-ramA genes or ramR and ramA genes in K . pneumoniae and Salmonella spp . respectively , excluded the possibility of using sequence analyses to identify the StyR3 ortholog . Direct northern blot analyses of RNA derived from K . pneumoniae strain Ecl8 and its derivatives did not produce a detectable signal for the putative regulatory RNA , sRamA5 . Thus in order to demonstrate the presence of sRamA5 , we cloned the entire intergenic region flanked by the ramR and romA genes and the partial romA open reading frame into the TA cloning vector pGEMTeasy to generate pGEMsRamA5 . Northern blot analyses derived from the expression of sRamA5 encoded on pGEMsRamA5 , using gene specific probes for sRamA5 and romA ORF , demonstrate the presence of sRamA5 ( ~ 60nt ) ( shown in Fig . 2A ) . Notably , the sRamA5 specific probe also detected a further two RNA molecules ( Fig . 2A , arrowed bands 1 and 2 ) . These fragments , detected by both the sRamA5 and romA specific probe , possibly represent primary transcripts initiated from the common start site as determined by 5’ RACE analyses for sRamA5 and romA ( S1 Fig . ) . As expected the romA specific probe did not detect the 60nt sRamA5 molecule ( Fig . 2A ) . Thus we surmise that sRamA5 and romA are co-transcribed into a primary RNA molecule , which undergoes further processing prior to excision proximal to the start of the romA gene , thereby producing sRamA5 . As a classical TetR-family protein , RamR-mediated repression of the romA-ramA locus is likely to be perturbed through ligand-mediated interactions; therefore we hypothesized that to function as a co-regulator of romA-ramA expression RamR would interact with sRamA5 . RNA-EMSA ( S1 Text ) analyses demonstrate that RamR and sRamA5 form a complex , suggesting direct interaction of the RNA ( sRamA5 ) with RamR ( Fig . 2B ) . In order to ascertain whether the interaction of sRamA5 and RamR is attributable to the presence of the highly conserved IR sequence in the ramR-romA inetrgenic region ( ATGAGTGcgtactCACTCAT ) and thus , act as a competitor for RamR-pI binding , we performed EMSA analyses using the pI promoter , sRamA5 and RamR . Our results show a reduction in affinity of RamR to sRamA5 in the presence of excess pI promoter ( Fig . 2B ) . In contrast , competition experiments with excess sRamA5 show no perturbation of the pI+RamR interaction , suggesting that RamR has a higher affinity for the pI promoter compared to sRamA5 ( Fig . 2B ) . Simultaneous qPCR measurements utilizing an LNA probe to assess sRamA5 levels demonstrate firstly , that the transcription levels of sRamA5 and romA are not linked as sRamA5 levels are decreased in contrast to elevated romA levels ( Fig . 2C ) . This suggests that despite being transcribed from the same TSS , sRamA5 and romA are likely subject to different rates of degradation . Secondly , the stability of sRamA5 may be dependent on the presence of a functional RamR . In order to investigate the requirement for a functional RamR in sRamA5 stability , we determined both the romA and sRamA5 levels in Ecl8ΔramR before and after complementation with ramR expressed in trans . As expected , our results show that the level of romA transcription was reduced ( ∼ 30-fold ) in Ecl8ΔramR/pACramR compared to the plasmid only control ( Ecl8ΔramR/pACYC177 ) ( Fig . 2D ) . In contrast , the levels of sRamA5 were found upregulated by ∼ 2 . 8 fold in Ecl8ΔramR/pACramR relative to the plasmid only control ( Ecl8ΔramR/pACYC177 ) . Thus the increase in sRamA5 levels in the presence of a functional ramR supports our hypothesis that sRamA5 is stabilized by RamR . Our data also shows that sRamA5 does compete with pI for RamR binding , although this effect may be abrogated by the higher relative affinity of RamR to the pI promoter ( Fig . 2B ( ii ) ) . Therefore , we surmise that the physiological relevance of RamR-sRamA5 interaction supports the basal level of ramA transcription detected in the wild type K . pneumoniae Ecl8 . To determine the effect of altered RamA levels on the whole transcriptome of K . pneumoniae strain Ecl8 , we quantitatively compared the transcriptomes of the three strains ( Ecl8 , Ecl8ΔramA , Ecl8ΔramR ) using the Kolmogorov-Smirnov ( K-S ) 2-sample test ( S3 Fig . ) as described in the supplementary data [25] . As expected , the distribution curve of Ecl8 and Ecl8ΔramA were more similar to each other compared to that observed for Ecl8ΔramR , suggesting that under normal growth conditions the deletion of ramA is less likely to perturb the transcriptional landscape as opposed to when it is overexpressed . This supports the notion that ramA functions as a pleiotropic regulator of gene expression in K . pneumoniae . In all three strains , the 16S and 23S rRNA genes showed the highest number of mapped reads consistent with the lack of depletion for ribosomal RNA . However , pairwise comparisons of the normalized basemean values associated with these ribosomal regions were not differentially expressed between Ecl8 and Ecl8ΔramR or Ecl8 and Ecl8ΔramA . The lack of differential ribosomal gene expression is contrary to previous observations in Salmonella enterica serovar Typhimurium [4] . Other non-ribosomal genes ( e . g . fusA_1 ( encoding translation elongation factor G ) , atpA ( producing ATP synthase F1 , α subunit ) and aceE ( encoding a pyruvate dehydrogenase ) ) were also found to have significantly high basemean values relative to most other genes within the genome . The increased expression of these genes is perhaps not surprising as atpA is associated with aerobic growth and aceE catalyses the production of precursors to the TCA cycle . Potential regions of antisense transcription were also detected . However , in most cases , these regions appeared as antisense because of in silico errors in annotation or due to transcriptional noise from flanking genes within the chromosome . We did , however , identify antisense transcription , such as with BN373_16241 ( producing an oxidoreductase ) and BN373_02611 , which were differentially expressed due to either elevated RamA levels or loss of the ramA gene ( S4 Fig . ) . Coverage plots analyses indicate that the transcription associated with BN373_02611 may be associated with 3’UTR runoff transcription from the divergently transcribed treBC operon , in contrast to BN373_16241 , which is upregulated when ramA was overexpressed and may be a “true” antisense RNA ( S4 Fig . ) . Genome analyses of K . pneumoniae strain Ecl8 [23] identified 11 unique predicted prophage genes encoding phage structural components ( BN373_03311 , BN373_09871 , BN373_10091 , BN373_14801 , BN373_14811 , BN373_14821 , BN373_14841 , BN373_14921 , BN373_21511 , BN373_37361 , BN373_37371 ) which were not found to be differentially transcribed in the pairwise comparisons tested ( Ecl8 vs Ecl8∆ramA , Ecl8 vs Ecl8∆ramR ( S1 Table ) . However , pairwise comparisons of Ecl8ΔramA and Ecl8ΔramR detected the differential expression of Ecl8-genome specific genes , BN373_33401 , BN373_33411 , which were repressed ( ∼2–3 fold ) in the ramA overexpressing strain Ecl8ΔramR ( S2 Table ) . Of note , no differential gene expression was noted in the 233 plasmid-coding genes in the ramA null mutant or in the ramA overexpressor ( Ecl8ΔramR ) with respect to the wild type ( Ecl8 ) . Transcriptome analyses underscores that perturbations in RamA levels can result in the differential expression of open reading frames , antisense transcripts and Ecl8-specific genes . As the majority of reads were mapped to open reading frames , the main focus of our analyses relates to the differential regulation of genes within K . pneumoniae . The RamA regulon in K . pneumoniae was identified by pairwise comparisons of Ecl8∆ramR versus Ecl8 ( C ) or Ecl8∆ramA ( B ) . The pairwise comparisons of Ecl8 versus Ecl8∆ramA ( A ) ( Fig . 3 ) indicate the cohort of genes ( 13 ) responsive to basal levels of RamA expression; the contrast between Ecl8 versus Ecl8∆ramR ( 35 ) specifies genes that are either affected by RamR or RamA , whereas the comparison between Ecl8∆ramR versus Ecl8∆ramA ( 77 ) identifies genes that largely react to altered RamA levels . As fewer genes are affected due to perturbations in ramR expression as opposed to RamA levels , we surmise that the majority of genes differentially expressed in our pairwise comparison ( B ) are associated with RamA-mediated regulation . Based on this assessment , the probable RamA regulon , Fig . 3 , constitutes a total of 103 genes ( as in genes in categories A , B , AB , BC , CA , ABC ) ( S2 Table ) . Of these , 68 genes were found to be activated and 35 were repressed ( S2 Table ) when levels of RamA is relatively higher . Genes associated with RamA-mediated regulation were initially mapped to the COG ( clusters of orthologous groups ) database to explore their biological function . COG functional classifications of the significantly differentially expressed genes reveal that RamA controls a myriad of cellular and metabolic processes ( COG data presented in S2 Table ) . Generally , altered levels of RamA significantly modulate the expression of genes belonging to the COG functional group C ( energy production and conversion ) . Specifically , when ramA is deleted , genes within the COG ( G ) ( carbohydrate metabolism and transport ) were also found to be differentially regulated . Pairwise comparison between Ecl8ΔramR versus Ecl8 indicates that COG families associated with transcription ( K ) and inorganic ion transport and metabolism ( P ) were also affected . Additionally , when ramA levels are elevated genes associated with cell wall membrane and envelope biogenesis ( M ) , transcription ( K ) and Function UnknowN ( FUN ) categories were most differentially affected . Thus the resulting COG analyses also supports the observation where altered levels of RamA triggers a shift in gene functionality consistent with significant modulations in transcription patterns as predicted by the K-S test ( S3 Fig . ) . A closer analyses of the genes associated with pairwise comparisons of Ecl8ΔramA versus Ecl8ΔramR reveals that firstly , the highest number of genes ( 77 ) are differentially expressed and secondly genes ( yhbW , nfnB , acrAB , ybhT , yrbB-F ) associated with the previously characterized networks for MarA [1] , SoxS [26] or Rob [3] in E . coli or RamA in Salmonella enterica serovar Typhimurium [4 , 11] are also affected . This is consistent with previous observations that demonstrate that these proteins exhibit considerable gene overlap within the regulons [1 , 4 , 11] . Importantly , RamA overexpression results in the modulation of efflux pump genes such as acrAB , oqxAB and yrbB-F , which is consistent with phenotypes linked to multidrug resistance [27] and susceptibility to toxic small molecules , which is associated with alterations in the lipid symmetry of the cell wall [28] . However , the pairwise comparisons for Ecl8 and Ecl8ΔramA also suggest that basal levels of RamA are sufficient to trigger the upregulation of genes such as the trehalose transporter operon treBC and the ribose ABC transporter , rbsACB . Uniquely , genes associated with biofilm formation ( hha-ybaJ encodes a toxin-antitoxin system ) and lipid A biosynthesis BN373_10601 ( encodes lipid A biosynthesis lauroyl acyltransferase , lpxL_2 ) and the related dioxygenase protein encoding gene lpxO ( BN373_36331 ) were also found to be upregulated by RamA . A total of 51 genes were found to be downregulated . As expected , ompF was significantly repressed in the ramA overexpresser ( Ecl8∆ramR ) ( Fig . 3 ) in addition to genes encoding the nitrate reductases ( narGHJI operon and nirD ) , BN373_05601 encoding the LysR-type transcriptional regulator , elongation factor EF2 and the riboflavin synthase encoding gene ribH were also found to be significantly downregulated in the ramA overexpresser ( Ecl8∆ramR ) . Only a subset of those differentially regulated genes was chosen for validation using qPCR . As expected , both the romA and ramA genes were found to show 5 . 25-log2 fold and 14 . 5- log2 fold increase in Ecl8∆ramR respectively compared to Ecl8∆ramA ( S5A Fig . ) . When the activated genes ( with the exception of romA , ramA ) were assessed , increased expression of the following genes was noted ( Fig . 4A ) : tolC ( 4 . 8- log2 fold ) , acrA ( 4 . 6- log2 fold ) , yhbW ( 1 . 8- log2 fold ) , yrbC ( 2 . 8- log2 fold ) , nfnB ( 3 . 3- log2 fold ) , ybjP ( 3 . 95- log2 fold ) , adhP ( 3 . 2- log2 fold ) , BN373_36191 ( encodes putative membrane protein , 2 . 95- log2 fold ) , BN373_39031 ( encodes oxidoreductase , aldo/keto reductase family , 1 . 5- log2 fold ) , lpxO ( 2 . 8- log2 fold ) and lpxL-2 ( 3 . 6- log2 fold ) . As expected , the levels of the ompF ( 4 . 2- log2 fold down ) and BN373_03291 ( encodes conserved hypothetical protein , 1 . 1- log2 fold down ) were also downregulated ( Fig . 4A ) . In order to determine if some of these differentially expressed genes were under the direct or indirect control of RamA , we performed both EMSA and in vitro transcription ( IVT ) using purified recombinant RamA protein . The EMSA results show that RamA directly binds the yrbF , ybhT , yhbW , acrA , nfnB , adhP , lpxO and lpxL-2 promoters ( Fig . 4B ) . Of note , our controls , showed no shift in the presence of the test promoters ( Fig . 4B ) . We then determined whether RamA would directly regulate the different promoters identified . By performing IVT experiments , we initially tested the effects of the RamA protein against the acrAB promoter to ascertain if RamA would function correctly as a transcriptional activator . As expected , the purified recombinant RamA activated the acrAB promoter directly ( Fig . 4C ) thereby confirming the biological activity of the purified RamA protein . Subsequently , we assessed the test promoters identified by the EMSA in our IVT assays . The results show that RamA upregulates yrbF ( 4-fold ) , ybhT ( 3-fold ) , yhbW ( 6 . 9-fold ) , acrA ( 4-fold ) , nfnB ( 10-fold ) , lpxO ( 8-fold ) and lpxL-2 ( ∼3-fold ) ( Fig . 4C ) . Thus purified recombinant RamA alone can directly activate the expression of these promoters in vitro . RamA regulates genes involved in lipid A biosynthesis . Having established that purified RamA directly binds and activates the expression of lpxL-2 and lpxO gene promoters ( Fig . 4B and 4C ) , we sought to determine whether RamA could regulate other genes associated with the lipid A biosynthetic pathway . The lipid A biosynthetic pathway is governed by nine enzymes encoded by lpxA , lpxC , lpxD , lpxB , lpxK , lpxl , lpxM and lpxO genes [29] . Gene expression analyses using qPCR showed that with the exception of lpxC , none of the other lpx genes showed significant differential expression in Ecl8ΔramR in comparison to Ecl8 or Ecl8ΔramA ( Fig . 5A ) . We then chose to assess whether RamA would directly interact with the lpxC and lpxK promoter regions . Subsequent EMSA analyses demonstrate that RamA directly interacts with the lpxC but not the lpxK promoter ( Fig . 5Bi ) and increased lpxC transcription ( 9-fold ) in the presence of purified RamA and RNA polymerase ( Fig . 5Bii ) . Previous work has shown that the control of lipid A biosynthetic genes is mediated by the PhoPQ or PmrAB systems [30] . Further interrogation of the transcriptome data and subsequent qPCR analyses shows that the levels for phoP and pmrA levels remained unchanged in K . pnuemoniae Ecl8 , Ecl8ΔramA and Ecl8ΔramR . Thus the differential modulation of the lpxO , lpxC and lpxL-2 genes is directly linked to increased RamA levels . To ascertain whether RamA-mediated transcriptional activation of lpxC , lpxL-2 and lpxO would actually result in modifications within the lipid A moiety , we performed MALDI TOF mass spectrometry ( S1 Text for details ) . The mass spectrometry analyses confirm alterations in lipid A structure of the ramA overexpresser , Ecl8ΔramR compared to the wild type ( Ecl8 ) , the null mutant ( Ecl8ΔramA ) or the double mutant ( Ecl8ΔramRA ) ( Fig . 5C ) where peaks ( m/z 1840 , 1866 and 2079 ) were found to be elevated . Previous studies in K . pneumoniae [31 , 32] indicate that those peaks correspond to LpxO hydroxylated lipid A species containing a hydroxymyristate group at position 2’ as secondary acyl substitution . Therefore , we surmise that RamA mediated activation of the different lipid A biosynthetic genes leads to alterations within the lipid A moiety in K . pneumoniae . Phenotype microarray analyses . In order to assign phenotypes linked to the differentially regulated genes , Biolog phenotype assays were undertaken for K . pneumoniae Ecl8 and its isogenic derivatives Ecl8ΔramA and Ecl8ΔramR . A comparison of Biolog phenotypic profiles of both Salmonella [11] and K . pneumoniae generally indicates a significant overlap in the susceptibilities to antimicrobial and toxic compounds ( S3 Table ) . As expected , the overexpression of ramA resulted in increased tolerance of Ecl8ΔramR in the presence of antimicrobials such as tetracyclines ( doxycycline , chlortetracycline , minocycline ) , macrolides ( erythromycin , spiramycin , troleandomycin ) , beta-lactams ( 1st , 2nd , 3rd generation cephalosporins , penams ) and ( fluoro ) quinolones ( ciprofloxacin , ofloxacin , nalidixic acid , novobiocin ) , fungicides ( such as chloroxylenol , dodine , domiphen bromide ) and toxic anions ( potassium tellurite , sodium metasilicate ) ( S3 Table , S6 Fig . ) . Notably , comparisons of the Biolog data also indicate that ramA overexpression results in altered polymyxin B susceptibility levels in both K . pneumoniae and Salmonella . Lipid A synthesis in Gram-negative bacteria is controlled at both the transcriptional and translational levels , where alterations in the lipid A profile can result in perturbations in host-microbe interactions as well as reductions in susceptibility to both the polymyxins and the cationic antimicrobial peptides ( cAMPs ) [33] . Accordingly , we tested the strain Ecl8 and its isogenic derivatives Ecl8ΔramA , Ecl8ΔramR against colistin , polymyxin B and the cAMP LL-37 . The relative survival assays for colistin , polymyxin B and LL-37 demonstrated that the ramA overexpressing strain , Ecl8ΔramR strain was significantly ( P < 0 . 05 ) less susceptible to polymyxin B , colistin and LL-37 ( Fig . 6 A , B , C ) compared to the wild type Ecl8 and the null mutant Ecl8ΔramA . The reduction in polymyxin susceptibility , as noted in the survival assays , is also supported by the Biolog data ( S3 Table ) . Taken together these results suggest that RamA-dependent regulation provides an alternative pathway for reduced susceptibility to polymyxins and cAMPs . Macrophage-Klebsiella interaction . To ascertain whether RamA-mediated alterations can have an impact on microbe-macrophage interactions , we examined if Ecl8 and its isogenic derivatives , Ecl8ΔramR , Ecl8ΔramA and Ecl8ΔramRA would exhibit differential interactions in adherence and intracellularization into murine RAW macrophages . In the adhesion and intracellularization assays , the ramA overexpresser , Ecl8ΔramR , was significantly attenuated in its ability ( approximately 50% decrease ) to attach to and internalise into the RAW murine macrophage cells compared to wild type K . pneumoniae Ecl8 , the mutants Ecl8ΔramA and Ecl8ΔramRA ( Figs . 7A , B and C ) . Two possible explanations exist for the reduction in adherence and intracellularization of Ecl8ΔramR; the first , where altered RamA levels confers resistance to phagocytosis and the second , is due to accelerated killing by the macrophage . In order to ascertain whether the reduced intracellularization of Ecl8ΔramR was linked to accelerated killing by macrophages , we determined the levels of extracellular non-phagocytosed bacteria in our experiments and found significantly higher numbers of recovered bacteria for Ecl8ΔramR compared to the wild type Ecl8 , Ecl8ΔramA and Ecl8ΔramRA ( Fig . 7D ) . In previous work [34] , resistance to phagocytosis by K . pneumoniae has been linked to bacterial surface structures which include the capsular polysaccharide ( cps ) . However , ugd gene transcription , representative of the cps cluster [35] , was not found to be altered in Ecl8 , Ecl8ΔramA , Ecl8ΔramR and Ecl8ΔramRA ( S5B Fig . ) , consistent with the RNAseq data . Thus our results underscore that reduced phagocyte adhesion and uptake is linked to RamA-mediated alterations , particularly those associated with lipid A . In order to assign a broader relevance to altered Klebsiella-host interaction , we performed experiments to assess bacterial recovery using the intranasal inoculation method [36] as described previously . Following a 24-hour infection of 5–7 week old C57BL mice , organ homogenates ( spleen and lung ) were plated to determine bacterial counts . At 24 h post infection , bacterial recovery rates for the ramA overexpressor , Ecl8ΔramR were found to be significantly higher compared to the wild type Ecl8 or null mutant Ecl8ΔramA from the lung and spleen ( Fig . 8 ( A ) and 8 ( B ) ) . The intranasal route of infection is expected to result in the primary infection of the lung prior to dissemination to other organs . Our results demonstrate that significantly higher levels of Ecl8ΔramR is recovered from both the lung and spleen highlighting that RamR-dependent RamA overexpression , confers reduced microbial clearance and increased systemic dissemination of K . pneumoniae in an intranasal infection model .
The relevance of the MarA , SoxS , Rob , RamA and RarA regulators in microbial survival is attributed to their control of the antimicrobial resistance phenotype in a wide variety of Gram-negative bacteria [10 , 37 , 38] . Whilst the role of RamA in reduced antibiotic susceptibility is evident from multiple studies [16 , 17 , 37] , its broader role in gene regulation is not known in Klebsiella pneumoniae . Using transcriptome profiling , we demonstrate that RamA-overexpression results in altered K . pneumoniae transcription patterns ( S3 Fig . ) compared to the null mutant or wild type strain thus highlighting its wider role in gene regulation in K . pneumoniae . Our data suggests that RamA functions largely as a transcriptional activator of gene expression , where DNA-binding ( Fig . 4B ) and IVT assays ( Fig . 4C ) demonstrate that this regulation is direct and likely mediated via a mar/ram-box like element [39] located within the promoter region . Whilst our work is the first to demonstrate direct RamA-mediated activation of gene expression , other studies have shown that related proteins such as MarA , SoxS [40] and RarA [5] also exert explicit control of regulon genes . Comparative transcriptome data analyses suggests that RamA-mediated activation is dependent on regulator concentration ( basal versus overexpressed , Fig . 2 ) in addition to the observation that identical RamA levels induce differential levels of promoter activation as supported by our in vitro data ( Fig . 4C ) . The maintenance of basal ramA levels may be necessary for the K . pneumoniae stress response to a variety of agents as has been previously shown when selecting for fluoroquinolone resistant Salmonella [41] or Klebsiella in a ramA-deleted strain . In K . pneumoniae , basal levels of ramA expression is maintained due to titration of the absolute repressory effects of RamR by the RamR-sRamA5 interaction ( S2 Fig . ) . Uniquely for tetracycline family regulators , RamR , directly interacts with the regulatory RNA , sRamA5 , ( Fig . 2B ) which is produced as a cleaved by-product of the primary romA transcript ( Fig . 2B ) . Whilst the sRamA5-RamR interaction , provides basal levels of ramA expression , ramA transcription as observed in the overexpressor , Ecl8ΔramR or clinical strains [16] are linked to loss of function mutations within RamR . Consequently , our data show that the maximal changes in gene expression profiles are observed when ramA is overexpressed as in Ecl8ΔramR ( S3 Fig . ) . In this gene cohort , we demonstrate that RamA impacts on gene transcription linked to operons associated with efflux pumps , biofilm formation and lipid A biosynthesis ( Fig . 3 , S2 Table ) . Whilst it is possible that the differential regulation of these genes is not all directly linked to RamA , we demonstrate that purified RamA directly binds and activates the expression of multiple associated promoters ( Fig . 4C & 4D ) . A comparison of RamA-mediated regulation in Salmonella enterica serovar Typhimurium [4] and K . pneumoniae establishes key similarities in the genes associated with the respective RamA regulons; particularly in the control of genes associated with antimicrobial resistance acrAB and ompF [4 , 11] . Additionally , RamA-dependent direct activation of acrAB is also consistent with phenotypic studies [10 , 16–18] which consistently demonstrate that ramA overexpression is linked to increased elevated efflux via acrAB and decreased outer membrane protein levels ( OmpF ) . Given its role in conferring reduced antimicrobial susceptibility , it is perhaps not surprising that we demonstrate that RamA directly regulates other efflux related operons specifically; the AcrAB linked inner periplasmic protein , YbhT [42] associated with detergent sensitivity , the Yrb operon which encodes an ABC transporter linked to the export of quinolones [27] and also lipid asymmetry [30] . The combined effect of the efflux or influx levels and membrane alterations associated with transport and structural variations likely contributes to the substrate range of compounds impacted by ramA overexpression ( S3 Table ) . However , in the absence of a functional acrAB efflux pump , RamA-overexpression does not confer reduced susceptibility to most antibiotics in K . pneumoniae . This observation is consistent with previous studies for the MarA and RarA [38] proteins . Therefore , it is likely that a functional AcrAB pump is crucial in mediating decreased antimicrobial susceptibility . However , a recent study [43] also suggests that acrAB may play a role in decreased antimicrobial peptide susceptibility and increased virulence in K . pneumoniae . Our findings support this observation and further demonstrate that increased RamA levels can also mediate LPS alterations , which likely contribute towards increased survival to both polymyxins and cationic AMPs ( Fig . 5 , 6 ) . Structurally , LPS is composed of three domains , the serovar dependent O-antigen chain , core oligosaccharide consisting of sugars and lipid A which is a phosphorylated disaccharide decorated with multiple fatty acids which anchor the LPS into the bacterial membrane [29] . The endotoxic lipid A component of LPS constitutes the outermost layer of the outer membrane of Gram-negative bacteria thereby playing a critical role in host-microbe interactions in addition to promoting reduced susceptibility to cAMPs [44] such as polymyxins [30] and host derived factors LL-37 , HBD-1 [30] . Studies have shown that lipid A modifications can result in multiple outcomes such as reduced polymyxin susceptibility [45] in addition to directly facilitating microbial evasion by reduced immune recognition [46] . Our work suggests that the molecular basis for the modified lipid A structure is linked to the differential regulation of the biosynthesis genes e . g . lpxO , lpxL-2 and lpxC identified in this screen . Despite being constitutively produced the regulation of lpxC , lpxL-2 and lpxO , is still subject to either transcriptional or translational control [44 , 46]; generally in response to stress , where , lpxC and lpxL-2 are regulated by the two-component systems , PhoPQ and PmrAB [44] . In contrast , lpxO is not subject to PhoPQ regulation in Salmonella [44 , 46] . In Salmonella Typhimurium , the modulation of LpxO levels results in the remodeling of the outer membrane which reduces the net negative charge whilst simultaneously increasing membrane integrity resulting in increased virulence [47] . A similar phenotype is exhibited by the K . pneumoniae Ecl8ΔramR strain , which has altered LpxO levels ( Fig . 8 ) . Thus we surmise that the altered host-microbe and polymyxin-microbe interactions are in part attributable to the lipid A modifications . Macrophages represent a key innate host defence strategy against microbial infections as phagocytosis of incoming pathogens is a trigger for the inflammatory response . Our data show that ramA overexpression protects against macrophage uptake and internalization ( Fig . 7 ) thus providing a basis for the greater dissemination of the ramA overexpressing strain , Ecl8ΔramR in an in vivo infection model . Taken together , these RamA-linked phenotypes underscore its’ role in Klebsiella virulence and survival in vivo . The molecular basis for phenotypes associated with reduced antimicrobial peptide susceptibility and increased virulence can be attributed to several key loci such as the acrAB pump and lipid A biosynthesis genes , lpxC , lpxL-2 and lpxO . This is supported by studies that demonstrate the involvement of acrAB [48] and lipid A modifications [30 , 44] in host-microbe interactions . However to definitively pinpoint the exact contribution of the lipid A biosynthetic genes or acrAB to phenotypes associated with host-pathogen interactions would require the deletion of genes encoding lpxC [49] , lpxL-2 and lpxO [50] , acrAB individually or in combination with ramA overexpression . We note that previous studies [32 , 50] have shown that strains deleted for these genes , result in avirulent microbes and as such , this phenotype would obscure any RamA-associated effects . Nevertheless , our work is first to demonstrate that firstly , RamA functions as an alternate regulator of certain lipid A biosynthesis genes and secondly , these alterations perturb microbe-host interaction . The significance of our findings lies in the broader implications of RamA-mediated regulation in Enterobacteriaceae . In this work , we describe roles for RamA in both protection against antibiotic challenge but also against the innate host immune response thus resulting in Klebsiellae that are less susceptible to antibiotics and simultaneously more virulent . Notably , our findings highlight that RamA mediated overexpression via both increased acrAB expression and lipid A alterations can result in reduced susceptibility to the last line drugs e . g . tigecycline and polymyxins . This highlights the broader consequences in selecting for ramA overexpression in K . pneumoniae or other members of Enterobacteriaceae . Finally , our study underscores and highlights the importance of intrinsic proteins such as RamA , which regulate survival strategies in K . pneumoniae and likely other Enterobacteriaceae , specifically in priming the microbial population in surviving drug and host immune pressure . This proposes the notion where microbial immune evasive strategies contribute to the development and persistence of antimicrobial resistance .
Bacteria ( Table 1 ) were cultured in Luria-Bertani ( LB ) medium ( 10 g/L tryptone , 5 g/L yeast extract , 10 g/L NaCl ) . Typically , a strain was first grown on an LB plate at 37°C from frozen -80°C stocks . A single colony was picked and inoculated into 5 ml of LB and incubated in a 37°C shaker overnight . A 1 in 100 dilution was made in LB and incubated in a 37°C shaker until the OD600 reached 0 . 6 unless otherwise stated . Antibiotics such as ampicillin ( 100 µg/ml ) and chloramphenicol ( 20 µg/ml ) were added as required . The assay was as described previously by Moranta et al [51] . Briefly , bacteria were grown at 37°C in 5 ml LB medium , harvested ( 5 , 000 × g , 15 min , 5°C ) and washed thrice with phosphate-buffered saline ( PBS ) . A suspension containing approximately 105 CFU/ml was prepared in 10 mM PBS ( pH 6 . 5 ) , 1% tryptone soy broth ( TSB; Oxoid ) , and 100 mM NaCl . Aliquots ( 5 μl ) of this suspension were mixed in tubes with various concentrations of polymyxin B , colistin ( 0 . 064 µg/ml to 0 . 256 µg/ml ) and LL-37 ( 32 µg/ml to 85 . 3 µg/ml ) to a final volume of 30 µl . Following incubation for an hour at 37°C with polymyxin B ( Sigma , UK ) , colistin ( Sigma , UK ) and LL-37 ( Sigma , UK ) the samples were diluted 1:10 with PBS prior to plating ( 100 μl ) on LB agar . Colony counts were determined after overnight incubation , where results are expressed as percentages of the colony count of bacteria that were not exposed to the antibiotics or the antimicrobial peptide . Sensitivity profiles of the different mutants using the phenotypic microarray analyses were determined described in S1 Text . Overnight cultures of strains Ecl8 , Ecl8ΔramA , Ecl8ΔramR were inoculated ( 1/100 dilution ) into LB media and incubated at 37 ºC with vigorous shaking . Cell pellets were harvested at OD600 = 0 . 6 and RNA was extracted using the RNAeasy Extraction Kit ( Qiagen , Hilden , Germany ) , which enriches for RNA molecules larger than 200 nucleotides . No depletion of ribosomal RNA was carried out prior to the synthesis of single-stranded cDNA ( sscDNA ) as previously reported [52] . RNAseq DNA libraries were constructed as previously described [53] . For RNAseq , independent biological samples in triplicate were assessed for each strain . The resulting sscDNA libraries were sequenced in an Illumina HISeq 2000 sequencer . An average of 0 . 715 Gb of sequence data was obtained per sample , in 75 bp paired reads ( Details of RNAseq analyses are outlined in S1 Text ) . The RNAseq read data has been deposited under the ENA data repository and ArrayExpress with the accession numbers ERP001994 and E-ERAD-122 , respectively . RNA for quantitative Real-Time PCR experiments was extracted from K . pneumoniae strains ( Table 1 ) using the TRIzol extraction method [16] . Briefly , cells were grown to mid-log phase ( OD600 = 0 . 6 ) at 37 ºC with shaking and then harvested by centrifugation at 3000g ( PK121R , ALC ) at 4 ºC . The cell pellet was then resuspended in TRIzol reagent ( Invitrogen , Paisley , UK ) and chloroform prior to centrifugation to separate the phases . The upper phase was then precipitated using 3 M sodium acetate , glycogen ( 5 mg/ml ) , and 100% ethanol . Both RNA preparations were washed and resuspended in 50 µl DEPC treated water . RNA was treated with TurboDNase to remove DNA contamination ( Ambion , New York , USA ) . All samples were assessed for RNA integrity and quantification using both the Bioanalyzer 2100 RNA nanochip ( Agilent , UK ) and the ND-1000 ( Nanodrop Technologies ) [4] . Only those samples with integrity level 9 were taken forward for library construction or qPCR analyses . In order to validate the RNAseq data , quantitative Real-Time PCR experiments were undertaken . After the removal of contaminating DNA , cDNA synthesis was generated using the AffinityScript cDNA synthesis kit ( Agilent , UK ) . Gene specific primers ( S4 Table ) were designed using the Primer3 ( http://frodo . wi . mit . edu/ ) software and were tested to produce standard curves with amplification efficiencies ranging from 95–110% . qPCR analyses using the locked nucleic acid probe is detailed in S1 Text . Quantitative Real Time RT-PCR ( RT-PCR ) was performed using the synthesized cDNA with gene specific primers using the Brilliant III Ultra-fast SYBR Green Kit ( Agilent , UK ) in the Agilent Mx3005P . All data were analyzed using Agilent MxPro software , which is based on the efficiency corrected method ( Pfaffl ) of comparative quantification that utilizes the ΔΔCt approach , also taking into account primer efficiency . The relative fold increases in expression levels were determined by firstly normalizing gene expression levels to 16S rDNA and using either Ecl8 or Ecl8∆ramA as calibrators . All comparative analyses were done using the MxPro software ( Agilent , UK ) . DNA fragments that represent the promoter regions of the genes that were differentially regulated in the presence or absence of RamA or RamR were subjected to the electrophoretic gel shift mobility assay ( EMSA ) as described previously [54] . Briefly , DNA templates ranging from 250–150bp upstream of the start site were produced by PCR , and purified by StrataPrep PCR Purification kits ( Agilent UK ) . The purified templates were end-labelled with [γ32P]-ATP by T4 Kinase ( New England Biolabs , USA ) . The unincorporated , labelled ATP was removed using Biospin P6 spin columns ( Biorad , UK ) as per manufacturer’s instructions . Purified RamA was extracted from the recombinant pETramA construct using metal chelation chromatography on superflow nickel / nitrilotriacetate agarose ( Qiagen , Germany ) ( James Hastie , Dundee University ) . His-tagged RamA ( 200 nM ) and labelled DNA ( 2 nM ) were mixed in binding buffer ( 125 mM Tris-HCl , 250 mM KCl , 5 mM DTT 5% glycerol ) and incubated on ice for 15 min prior to electrophoresis at 75 V on a prechilled 7 . 5% native polyacrylamide gel in 1 × TBE buffer . Transcription ( IVT ) experiments were performed as described previously [55] . Briefly 5 × IVT Buffer , 2 nM PCR product of the test and control ( E . coli gnd [56] ) promoters , RNA polymerase , RNAseOUT ( Invitrogen , UK ) was incubated for 15 minutes at 37°C prior to the addition of the transcription mix containing × 5 IVT buffer ( 50 mM Tris-HCl , 0 . 1 mM EDTA , 3 mM magnesium acetate , 0 . 1 mM dithiothreitol , 20 mM sodium chloride , and 250 μg/ml bovine serum albumin at pH 7 . 8 ) , heparin ( 1 . 2 µg/ml ) , NTPS , and α32P-UTP ( Perkin Elmer , UK ) . The reaction was stopped 5 minutes later followed by the addition of Gel loading buffer II ( Ambion , UK ) . The resulting products were electrophoresed on a 7% polyacrylamide / 8 M urea gel . Quantification was determined by densitometric analysis using Fujifilm Multigauge Software where an increase or decrease in transcription levels is after normalization to the endogenous gnd levels and calibration to the no protein control . Murine RAW 264 . 7 macrophage cells ( obtained from ATCC TIB-71 ) were cultured in Dulbecco’s Modified Eagle Medium ( PAA , UK ) supplemented with 10% endotoxin-free foetal bovine serum ( PAA , UK ) and penicillin and streptomycin ( Invitrogen , UK ) in 75-cm2 culture flasks in 5% CO2 for 24 h until subconfluent . Twelve well tissue culture plates were seeded with 5 × 105 cells per well and viability determined using trypan blue exclusion . Bacterial adhesion and internalization experiments were performed as described previously [57 , 58] . For the adhesion assays , RAW cells were washed with PBS and incubated for 2 h at 37°C in 5% CO2 with a suspension of 5 × 107 bacterial cells in DMEM medium alone . After incubation , wells were washed five times with PBS and adherent bacteria were released by addition of 0 . 5% Triton X-100 ( Sigma , UK ) . Bacterial colonies were quantified by plating appropriate dilutions on LB agar plates . In the internalization assays , after the incubation of the RAW cells with the bacterial suspension , wells were washed twice with PBS and then incubated for 2 h with fresh DMEM medium containing gentamicin ( 100 µg/ml ) to eliminate extracellular bacteria . After the incubation , an aliquot of the medium was plated to confirm killing of extracellular bacteria and the gentamicin-containing medium was washed again . RAW cells were lysed and intracellular bacteria were quantified as described above . To estimate levels of extracellular bacteria , the infection of the RAW cells was carried out as described previously for the adhesion assay . After incubation , the media with the non-phagocytosed extracellular bacteria was collected and quantified by plating appropriate dilutions on LB agar plates . All microscopy images were generated as outlined in S1 Text . All mouse experiments were performed under the control of the UK Home Office legislation in accordance with the terms of the Project license ( PPL2700 ) granted for this work under the Animals ( Scientific Procedures ) Act 1986 in addition to receiving formal approval of the document through Queen’s University Belfast Animal Welfare and Ethical Review Body . Overnight bacterial cultures were washed three times in sterile endotoxin free PBS . The bacteria was resuspended to an optical density of 0 . 2 and 20 μl ( ∼ 5 × 107 CFU/animal ) and administered to anaesthetised 5–7 week old weight watched Harlan C57BL6 mice ( n = 5 per group ) using the intranasal inoculation method [36] . In order to ensure maximal delivery of the bacterial inoculation into the lungs the animals were held in a perpendicular position until cessation of laboured breathing . 24 h post inoculation the mice were sacrificed by lethal pentabarbitol injection . Perfused lungs and spleen were harvested and resuspended in 1 ml of sterile PBS . Following mechanical homogenisation dilutions were plated on LB agar plates and incubated at 37°C to establish the CFU/ml . | Bacteria can rapidly evolve under antibiotic pressure to develop resistance , which occurs when target genes mutate , or when resistance-encoding genes are transferred . Alternatively , microbes can simply alter the levels of intrinsic proteins that allow the organism to “buy” time to resist antibiotic pressure . Klebsiella pneumoniae is a pathogen that causes significant blood stream or respiratory infections , but more importantly is a bacterium that is increasingly being reported as multidrug resistant . Our data demonstrate that RamA can trigger changes on the bacterial surface that allow Klebsiella to survive both antibiotic challenge , degradation by host immune peptides and resist phagocytosis . We demonstrate that the molecular basis of increased survival of ramA overexpressing K . pneumoniae , against host-derived factors is associated with RamA-driven alterations of the lipid A moiety of Klebsiella LPS . This modification is likely to be linked to Klebsiella’s ability to resist the host response so that it remains undetected by the immune system . The relevance of our work extends beyond RamA in Klebsiella as other pathogens such as Enterobacter spp and Salmonella spp . also produce this protein . Thus our overarching conclusion is that the intrinsic regulator , RamA perturbs host-microbe and microbe-drug interactions . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [] | 2015 | Elucidation of the RamA Regulon in Klebsiella pneumoniae Reveals a Role in LPS Regulation |
The regulatory architecture of gene expression remains an area of active research . Here , we studied how the interplay of genetic and environmental variation affects gene expression by exposing Drosophila melanogaster strains to four different developmental temperatures . At 18°C we observed almost complete canalization with only very few allelic effects on gene expression . In contrast , at the two temperature extremes , 13°C and 29°C a large number of allelic differences in gene expression were detected due to both cis- and trans-regulatory effects . Allelic differences in gene expression were mainly dominant , but for up to 62% of the genes the dominance swapped between 13 and 29°C . Our results are consistent with stabilizing selection causing buffering of allelic expression variation in non-stressful environments . We propose that decanalization of gene expression in stressful environments is not only central to adaptation , but may also contribute to genetic disorders in human populations .
Canalization , the buffering of a phenotype against environmental or genetic perturbation , has been independently suggested by Schmalhausen [1] and Waddington [2] . Most models of canalization assume that canalization evolves in a response to stabilizing selection ( reviewed in [3 , 4] ) , but using simulations Siegal and Bergman ( 2002 ) found that canalization can also be achieved by regulatory networks even in absence of stabilizing selection [3] . Conversely , genetic or environmental perturbations can result in the loss of canalization ( “decanalization” ) and thus the release of previously hidden or “cryptic” genetic variation . The perhaps best example for such decanalization induced by strong genetic perturbations comes from a series of experiments focusing on impaired function of the chaperone HSP90 [5–7] , but other examples have been described in Drosophila [8–15] . Phenotypes are determined by spatial and temporal patterns of gene expression and environmental and genetic variation has been documented to affect gene expression . How the interplay of genetic and environmental variation affects gene expression can be studied with sequencing based expression profiling ( RNA-Seq ) . Many studies have used expression profiling to study genetically diverged individuals/populations at two environments ( e . g . :[16–20] ) , but the canalization of gene expression across the entire transcriptome still remains understudied . Allele specific gene expression analysis in two inbred lines and their progeny is a popular approach to gain insight into the regulatory architecture of gene expression [21–24] . Here , we expose two D . melanogaster strains to four different temperatures to determine how temperature stress affects gene expression . Specifically , we analyze the canalization of gene expression of two genotypes . Furthermore , we expand the analysis of canalization by dissecting the cis-and trans-effects at different temperatures .
Comparing the F1 individuals from crosses of Samarkand with Oregon R in both directions we tested for the presence of imprinting in our experiments . Consistent with previous results [25–27] , levels of genes expression were very similar between the two genotypes , indicating the absence of imprinting in D . melanogaster . Only two genes , chrU_5299041_5299681 . 0 and CG1275 , showed significant parent-of-origin effect ( FDR = 5 . 6e-19 and 0 . 02 , S4 Fig . ) . While the expression difference of CG1275 was small , chrU_5299041_5299681 . 0 encoding the cytochrome b gene differed more than 5 . 2e4-fold between F1A and F1B . This result indicates a different mitochondrial gene expression rather than imprinting . After excluding both genes , we combined the data from F1 offspring from crosses in both directions . 7 , 189 genes were expressed in parents and offspring and showed allelic differences between the parental strains Oregon R and Samarkand . Comparing the gene expression profiles between the two parental strains we observed a striking difference in gene expression divergence for flies grown at different temperatures ( 13°C , 18°C , 23°C and 29°C ) ( Table 2 ) . At 18°C only 92 genes ( 1 . 2% ) differed significantly in gene expression between Oregon R and Samarkand . In contrast , for flies either kept at 13°C or 23°C the number of differentially expressed genes increased to more than 1 , 000 ( 15% ) . The largest difference between the parental strains was observed at 29°C , where 2 , 581 genes ( 32 . 9% ) were differentially expressed . The same trend of canalization/decanalization was seen when we compared the absolute expression difference between Oregon R and Samarkand strains across all expressed genes , with flies at 18°C exhibiting the lowest absolute log2 fold-difference ( mean = 0 . 47 , Fig . 2 ) , followed by flies at 23°C ( mean = 0 . 50 , Fig . 2 ) . Flies maintained at 13°C ( mean = 0 . 54 , Fig . 2 ) and 29°C ( mean = 0 . 59 , Fig . 2 ) had the highest difference in gene expression ( all pairwise differences were highly significant; Wilcoxon test , FDR<0 . 01 , ) . To eliminate a potential bias in gene expression differences caused by variation in sequencing depth , we down-sampled the data sets to similar read counts by randomly picking paired-end reads without replacement . The final number of reads was determined ranking for each temperature the replicates by the number of reads . For each rank the number of reads was matched to the one with the smallest number of reads . This procedure maximized the number of reads to be included in the matched samples ( S1 Table ) . Importantly , we found the same trends in the down sampled data ( S2 Table ) . Additionally , to rule out that heterogeneous read coverage across the whole gene body could affect our results , we summarized the gene expression differences using only reads aligned to the 500bp at the 3’end . Again , the same trends remain ( S3 Table ) . Since we detected in three libraries ( t29-rep2 OregonR , t23-rep2 Samarkand , and t18-rep3 F1A ) expression of male specific genes we suspected that a small fraction of males were inadvertently included in the flies used for RNA extraction . In order to rule out that any of the conclusions drawn in this manuscript we removed the contaminated samples as well as two other libraries of similar sizes to balance the replicate size in that temperature ( see S1 Table ) . In addition , we used only reads mapping to the 500bp at the 3’ end . The analysis using this reduced data set we confirmed the same patterns we saw for the full data set , albeit less pronounced due to the reduced power ( S4 Table ) . Next , to dissect the regulatory architecture underlying these temperature specific effects , we measured allele specific gene expression by comparing expression of Oregon R and Samarkand alleles in both parents and offspring ( Fig . 3 ) . At 18°C , only a small fraction of genes had significant trans-regulatory ( 50 , 0 . 7% ) and cis-regulatory ( 71 , 1 . 0% ) effects . Flies maintained at 23°C , had approximately 1 , 000 genes with allelic regulatory divergence: 659 ( 9 . 2% ) with trans-regulatory effects and 545 ( 7 . 6% ) with cis-regulatory effects . For the two most extreme temperatures 944 ( 13 . 1% ) genes differed in allelic expression due to trans-acting variants at 13°C and 1 , 798 ( 25 . 0% ) at 29°C . The number of genes affected by cis-regulatory variants was 596 ( 8 . 3% ) at 13°C , and 445 ( 6 . 19% ) at 29°C ( Table 2 ) . Importantly , these results were not affected by different library sizes , since we obtained similar results for a down sampled data set ( S2–S3 Tables ) . Thus , consistent with previous results [28] we found that temperature stress affects both cis- and trans-regulatory variants , with trans-effects being more common . When we compared the effect sizes by correlating cis- and trans-effects across temperatures , we found a striking difference . While cis-effects changed moderately with temperature difference ( mean Spearman’s r = 0 . 86 , Fig . 4a ) , trans-effects were practically uncorrelated at different environmental temperatures ( mean r = 0 . 14 , Fig . 4a , c ) . Previous studies contrasting different yeast strains have identified a similar pattern of pronounced trans-effects under stressful conditions [23 , 29] . In our study cis-regulated genes are also condition-dependent . Among the genes with temperature-specific cis-effects a considerable fraction did not show any cis-effects at any other temperature ( 13°C: 43% , 23°C: 24% , 29°C: 16% , Fig . 4b ) . Until now , we analyzed each temperature separately , hence we complemented this analysis estimating allelic difference , temperature , and their interaction jointly . Consistent with our other analysis this approach identified at least 1200 genes differing in expression level between F0 strains across four temperatures ( S4 Table ) . We also identified 480 genes were regulated by cis-effect across all temperatures while the number of trans-effects was close to 4 , 994 . We found that 22 cis-effects and 4 , 300 trans-effects had significant interactions with temperature . This confirms that both regulatory effects are condition-dependent , and that trans-effects are more pronounced . For about 5 , 000 genes the expression level changed significantly across temperatures in either F0 or F1 datasets . 3 , 336 of them were found in common as temperature response genes in all tests ( a detail gene list can be found in S1 Dataset ) . To test the influence of the large number of trans-effects , we determined the mode of inheritance ( i . e . : degree of dominance ) by comparing the total expression levels of offspring to that of both parents ( S3 Fig . ) . Most genes showed either no difference or were dominant , and only few genes ( <3 . 9% ) were classified as being additive , under-dominant , and over-dominant ( Fig . 5 ) . Again , we noticed a very striking dependence on temperature , with flies at 18°C showing the least differences between parents and offspring . Only 27 genes showed dominance in gene expression . At 23°C , 200 genes ( 2 . 8% ) were Oregon R-dominant , and 553 ( 7 . 7% ) were Samarkand-dominant . At the two extreme temperatures , up to 51 . 7% of the expressed genes showed dominance , but the distribution was not balanced between the two strains ( Table 2 ) . While at 13°C the majority of dominant alleles ( 3 , 287 , 45 . 7% ) had an expression level resembled the Samarkand parent with non-significant or negligible change ( <1 . 25-fold ) , at 29°C the majority ( 2 , 230 , 31% ) resembled the Oregon R parent . Among the genes with dominant gene expression , only a moderate number showed allelic imbalance: 504 ( 14 . 6% of dominant genes ) at 13°C , 19 ( 70 . 4% ) at 18°C , 267 ( 35 . 5% ) at 23°C , and 244 ( 10 . 7% ) 29°C , suggesting that for the majority of genes both parental alleles are subject to the same regulatory input . Among the genes with a dominant expression pattern at either 13°C or 29°C , 1 , 384 genes were dominant in both environments , but their dominance pattern was reverted between the two temperatures ( hereafter we refer to these genes as “dominance-swapped” genes ) . Since among these swapped genes the fraction of trans-regulated ones is very high ( up to 66% ) , we reasoned that they may be regulated by a few transcription factors ( TFs ) . We therefore performed an in silico analysis of enrichment for transcription factor binding sites among these dominance-swapped genes . The binding sites of 13 TFs were significantly overrepresented ( S5 Table ) . We further investigated whether a combination of TFs may be enriched , rather than a single one . Indeed , we found that several combinations of TFs may affect the expression of dominance-swapped genes . The most highly ranked combination of Chro and BEAF-32 may affect the expression of up to 934 genes ( 68% of the dominance swapped genes ) , while the largest combination contained 11 of the 13 TFs with binding site enrichment in the single TF analysis ( S6 Table ) . Eight out of these 13 TFs were present in our data set ( Table 3 ) . Among these , three TFs showed dominance and two of them also exhibited swapped dominance . Three TFs had cis- ( mip120 ) or trans-effects ( Med , BEAF-32 , and mip120 ) . Of particular interest is the transcription factor mip120 , which is affected by cis-regulatory variants at 29°C and at 13°C it is regulated by both trans- and cis-regulatory variants . It is possible that the cis-regulatory variants are responsible for trans-regulatory divergence of multiple downstream TFs , causing the coordinated expression of hundred of genes ( i . e . sensory trans factors sensu [23] ) . To shed more light onto the biology of the dominance-swapped genes , we performed Gene Ontology ( GO ) and pathway analyses and found many genes involved in macromolecule biosynthesis , especially mRNA translation to be overrepresented ( S7 Table ) . Gene ontology categories for cellular components were enriched for mitochondrial ribosome and vitelline membrane . Other molecular functions and bioprocesses had also been identified , annotated as neural signal transmitting , transmembrane transporter activity , and body fluid secretion . On the other hand , genes with cis- and trans-regulatory effects are not enriched for any GO categories or pathways ( after removing genes common with dominance-swapped genes ) .
Analyzing allelic effects on gene expression for flies grown at four different developmental temperatures we found the striking pattern of a large number of trans-effects at stressful temperatures . Similar results were obtained for yeast where stressful culture conditions resulted in large trans-effects [23 , 29] and C . elegans where 59% of the trans-acting genes showed a significant eQTL by environment interaction [28] . The important difference of our study to the previous ones is that we tested multiple temperatures covering stressful and less stressful conditions . Since for flies grown at 18°C the genetic differences between the two strains had almost no influence on the levels of gene expression , our data provide experimental evidence for canalization of gene expression . At stressful conditions , this canalized gene expression is disturbed resulting in many significant differences between the two parental strains as well as significant cis-and trans-effects . Following the widely accepted hypothesis that canalization of traits is the consequence of stabilizing selection ( reviewed in [3 , 4] ) , our data suggest that the most benign temperature for D . melanogaster is 18°C . A similar line of argument has as been applied to the evolution of reaction norms . For fitness related traits , such as egg production , it has been proposed that the maximum egg production should be observed for the optimal temperature . While our experiment suggested an optimal temperature of 18°C , the optimal temperature for ovariole number [30] and fecundity [31] is closer to 23°C than to 18°C . It is thus not clear if the reaction norm of fitness traits could serve as good indicator for optimal temperatures . On the one hand the reaction norms tend to be very similar for flies collected from different environments [30 , 31] , while on the other hand the optimal temperature seems to differ among reaction norms . Finally , thermotactic studies demonstrated that 18°C is the preferred temperature of D . melanogaster [32 , 33] , suggesting that flies prefer temperatures with canalized gene expression . Our study has relied on two old laboratory strains that have been established more than 80 years ago [34] , and since that time they have probably largely been maintained at 18°C to reduce the number of transfers . We can therefore not rule out that canalization might have evolved to match these common laboratory culture conditions . Nevertheless , comparison of various D . melanogaster strains at 25°C identified several hundred differentially expressed genes [35] . Similarly , a study based on the DSPR lines detected 7922 eQTLs at 25°C [36] , demonstrating considerable allelic effects in gene expression . While a proper comparison would require an analysis of gene expression at different temperatures , the substantial number of significant differences in gene expression among genotypes at 25°C suggests that gene expression was also decanalized in these studies . Thus , our observation of strongly canalized gene expression at 18°C may not be specific to the strains used in our study . By uncovering otherwise hidden genetic variation decanalization could facilitate adaptation of populations exposed to a novel , stressful environment . On the other hand , many of the otherwise cryptic variants are expected to be deleterious as recently pointed out by Gibson et al . [37]: environmental and/or genetic stress associated with the recent history of human populations might have resulted in decanalization and a consequence in an increase of complex human genetic disorders . General stressors , such as smoking , have been shown to be a contributing factor to many complex diseases [38] . While it is not proven that decanalized gene expression is causative for these diseases , it is remarkable that for some genetic disorders allelic imbalance in gene expression has been observed [39–42] . Our results considerably strengthen the notion that decanalization might commonly manifest itself as a major allelic imbalance in gene expression . Like most studies on canalization [5 , 43 , 44] , our analysis was based on naturally occurring variants . Since , purifying selection is removing deleterious variation and directional selection increases the frequency of favored variants , it may be possible that natural selection could influence our interpretation of canalization . Assuming that regulatory variants are temperature specific and purifying selection is more effective at 18°C , it may be possible that variation influencing gene regulation at 18°C is purged from the population , while the other variants remain segregating in the population . Alternatively , new mutations occurring during the propagation of the classic laboratory strains may have been purged more efficiently at 18°C since laboratory strains are typically maintained at this temperature , albeit at very small population sizes . The outcome of such scenarios would be indistinguishable from the pattern seen in our study . Consistent with the idea of natural selection mimicking the effect of canalization a recent study failed to verify the previously described effect of the histone variant HTZ1 on mutational robustness in mutation accumulation lines for which the effect of natural selection is minimized [45] . Our current understanding of the regulation of gene expression is not sufficiently advanced to decide if sequence variants are affecting gene expression in a temperature specific manner such that selection could remove variants specific to 18°C but other variants could accumulate in natural populations . Future experiments employing mutation accumulation lines or experimental evolution will be instrumental to distinguish between the two scenarios . The phenomenon of temperature dependent dominance in gene expression seen in our experiments is particularly interesting . A high frequency of genes with dominant modes of inheritance in gene expression has already been documented in other studies in Drosophila [24 , 46] , yeast [47] and A . thaliana [48] . The novelty of our study is , however , that for a large fraction of genes this mode of inheritance depends on temperature: genes with dominance of the Samarkand allele are showing dominance of the Oregon R allele at the other temperature extreme ( Table 2 ) . Our in silico analysis suggested that many genes with such a pattern of swapped dominance are potentially regulated by a few transcription factors , of which two also exhibit a temperature dependent swap in the mode of inheritance ( Table 3 ) . While it is conceivable that these transcription factors are part of a regulatory network of the swapped genes , the question of the regulation of one or a few master regulators deserves special attention . One simple way to achieve dominance in gene expression is loss of function of one of the parental alleles . This model , however , seems not to apply to our data since the genes are expressed in both parental strains . Alternatively , the dominance may be the result of a different tissue representation in the two parental strains . It has been described that ovary sizes/ovariole number differ among D . melanogaster strains [e . g . : 49] . If the F1 does not have an intermediate ovary size but are similar to one of the parents , genes predominantly expressed in the ovaries are expected to show dominance . We tested the hypothesis of differential ovary sizes by comparing the expression of chorion genes and did not find support for this hypothesis since they largely showed no dominance pattern ( S8 Table ) . We also excluded that the swapping dominance is the outcome of a temperature × parental origin interaction ( 688 swapped dominance genes in F1A and 979 in F1B , S9 Table ) . One interesting observation in C . elegans linked dominance in gene expression to nonsense mediated mRNA decay ( NMD ) [50]: in NMD impaired individuals many mutations were highly dominant , while with a functional NMD the same mutations were recessive . While it is conceivable that NMD activity may be affected by temperature , it is not clear how the swapped dominance could be caused . Finally , dominance may be the outcome of some form of allelic crosstalk , either by transvection [51–53] or other mechanisms [54] . How environmental signals are incorporated in such a mechanism , remains an open question .
Flies were reared on standard cornmeal-molasse-yeast-agar medium and maintained at 12 h light/12 h dark conditions . Prior to crossing , the parental strains were subjected to 7 generations of sibling pair matings in order to reduce residual heterozygosity . Virgin females of either strain were used for the following four crosses: O female × O male , and S female × S male ( F0 parents ) ; O female × S male ( hybrid cross F1A ) , and S female × O male ( hybrid cross F1B ) . For each type of crosses , three replicates were set up in parallel , each consisting of approximately 80 crosses of a single female and a single male . After two days of egg laying at 23°C four subsets of 20 vials was transferred at four different temperatures ( 13°C , 18°C , 23°C , and 29°C ) . Virgin females were collected from both parents and F1 flies shortly after eclosion and aged three days before shock-freezing in liquid nitrogen . For each replicate ( out of a total of 48 ) , approximate 30 females were homogenized in peqGOLD TriFast Reagent ( Peqlab , Erlangen , Germany ) using an Ultraturrax T10 ( IKA-Werke , Staufen , Germany ) . Total RNA was extracted , quality-checked on agarose gels , and quantified using the Qubit RNA Assay Kit ( Invitrogen , Carlsbad , CA , USA ) . Paired-end Illumina mRNA libraries were generated from 5μg total RNA . After DNase I treatment ( Qiagen , Hilden , Germany ) , poly ( A ) transcripts were isolated using the NEBNext Poly ( A ) mRNA Magnetic Isolation Module ( New England Biolabs , Ipswich , MA ) . Strand-specific paired-end libraries were prepared using the NEBNext Ultra Directional RNA Library Prep Kit for Illumina and size-selected on AMPureXP beads ( Beckman Coulter , CA , USA ) aiming for fragments between 380–500bp . All libraries were amplified with 12 PCR cycles using index primers from the NEBNext Multiplex Oligos for Illumina Kit ( New England Biolabs , Ipswich , MA ) and sequenced on a HiSeq2000 using a 2×100bp protocol . Our barcoding scheme was such that all 16 samples of a single replicate were included in the same lane ( s ) to minimize lane effects . S1 Fig . illustrates the whole allele specific mapping procedure and statistic analyses applied in this study . We first trimmed all sequence reads using the Mott algorithm implemented in PoPoolation [55] and aligned reads of the two parents separately to the Flybase D . melanogaster 5 . 49 assembly . We identified substitutions in the two parental strains relative to the reference genome using variants occurring at a frequency ≥ 0 . 98 with a read-depth ≥2 . Those variants were used to perform a second round SNP-tolerant read alignment using GSNAP [56 , 57] in order to get the final parental specific SNP datasets with higher confidence . A total number of 177 , 107 and 193 , 699 SNPs ( i . e . : variants relative to the reference genome ) were identified in Oregon R and Samarkand respectively . Amongst those , 182 , 123 SNPs differed between two parents . The read depths of SNPs were high correlated between two parental data sets ( Pearson’s cor . coef = 0 . 94 , p-value <2 . 2e-16 , S2 Fig . ) , suggesting the SNP discovery had suffered marginal bias towards either of parents . We generated two parental specific genomes by modifying the D . melanogaster reference using Oregon R and Samarkand SNP datasets . Reads were assigned to one of the parental genotypes by aligning them simultaneously to both parental genomes . Only unambiguously mapped reads in proper pairs were assigned to either O or S . Reads mapping to both genomes equally well were not included in the analyses . Allelic expression was measured using the ReCOG software tool ( https://code . google . com/p/recog/ ) on each of the two reference genomes separately . We only counted reads that were mapped fully within the gene boundaries . The only exceptions were overlapping genes . Here , the overlapping region was considered ambiguous and not counted . On average 21% of the reads could be assigned to one parental reference genome . Using the RNA-Seq reads from the parents we discovered that 0 . 19% and 0 . 27% of the reads were assigned to the wrong parental genome . To account for these potential mapping errors , we followed a previously suggested strategy [58] and simulated about 18 , 000 , 000 paired-end reads from both parental genomes and assigned them to either of the reference genomes using the same strategy as for the real data . Equal expression levels for both alleles were expected in the simulated data for all genes , given no mapping errors . Therefore , we excluded 15 genes with simulated allelic expression divergence to avoid biased divergence estimation caused by mapping errors . The protocols described above are implemented in the package ALLIM [58] . Finally we filtered for a minimum expression level using the following criteria . A gene is defined as expressed in the F0 flies if in all samples at least one read was mapped and at least one sample had ≥20 counts . In F1 flies the expression counts to both reference genomes were considered jointly and the same cutoffs as for the parental samples was applied . For a gene to be considered expressed in all samples , the criteria for F0 and F1 flies had to be met . Out of 18 , 764 D . melanogaster genes / features annotated in either Flybase r5 . 49 [59] or the developmental transcriptome [60] , we detected the expression of 7 , 853 ( 41 . 8% ) genes in F0 flies , 7 , 208 ( 38 . 4% ) genes in F1 flies , and 7 , 191 ( 38 . 3% ) genes in both F0 and F1 flies . We used the F1 crosses that were carried out in both directions to identify imprinted genes . Read counts of each gene were normalized by total library size and RNA composition of each data set using a trimmed mean of M-values method [61] . For each gene , a generalized linear model ( GLM ) was applied to evaluate the divergence of allelic expression levels between the F1 crosses in two parent-of-origin orders ( F1A and F1B ) , at four temperatures separately: log2 ( OO+S ) F1~ ( F1AF1B ) +ε where ε denotes the error term and the quasi-binominal distribution was used to account for the over-dispersion . P-values were calculated by F-test followed by FDR correction . Two putative imprinted genes were excluded from our further analyses of expression divergence , which resulted in a total number of 7 , 189 genes expressed in F0 and F1 . The parental ( F0 ) data sets were first tested for significance of differential gene expression , and offspring ( F1 ) were tested for differential allelic expression at each temperature separately . We applied TMM normalization on read counts of each gene and performed an empirical Bayesian estimation based on negative-binomial GLM to compute gene-wise dispersions [62 , 63] . The significance of expression divergence was determined by an F-test: AllelicExpression~ ( alleleoregonRalleleSamarkand ) +ε We further compared the strain-specific allele abundance ratio between F0 and F1 data sets: log2 ( OO+S ) ~ ( F0F1 ) +ε Quasi-binominal GLM analysis was performed for each gene and any significant difference between F0 and F1 data set was considered as evidence of trans effects ( T ) . For all statistical analyses applied in F0 , F1 and T , p-values were adjusted by FDR correction [64] with a nominal cutoff of ≤ 5% . Genes were classified into seven categories by comparing the significance levels from all three tests [24 , 47]: Not different: No significant differential expression in F0 or F1 . No significant T . Cis only: Significant differential expression in F0 and F1 . No significant T . Trans only: Significant differential expression in F0 , but not in F1 . Significant T . Cis + trans: Significant differential expression in F0 and F1 . Significant T . Cis- and trans-regulatory effects favor expression of the same allele . Cis × trans: Significant differential expression in F0 and F1 . Significant T . Cis- and trans-regulatory effects favor expression of the different allele . Compensatory: Significant differential expression in F1 , but not in F0 . Significant T . Expression difference caused by cis- and trans-regulatory components had an opposite direction and perfectly compensate each other such that no expression difference in F0 . Ambiguous: Significant in only one of differential expression tests in F0 , F1 or T . Thus , no explicit cis-/trans-effect can be detected . To further confirm our estimates of gene/allelic expression difference , we made a joint estimation using GLM method including allelic difference , temperature , and their interaction: AllelicExpression~ ( alleleoregonRalleleSamarkand ) ×temperature+ε or log2 ( OO+S ) ~ ( F0F1 ) ×temperature+ε We evaluated the divergence of total expression ( i . e . : ignoring allelic information ) between F0 parents and F1 hybrids for each gene and for each temperature , following the previously suggested “mode of inheritance classification” [24 , 47] . The total gene expression level in F1 flies was estimated as the sum of reads mapped to both parental alleles . TMM normalization followed by a negative-binominal GLM analysis was used to evaluate the expression values of F1 flies between either of the two parents ( F0 ) : TotalExpression~ ( F0oregonRF1 ) +ε or TotalExpression~ ( F0samarkandF1 ) +ε Genes that have a parent / offspring expression ratio over 1 . 25-fold and an adjusted p-value ≤5% were considered as diverged between F0 and F1 samples and were classified as additive , Oregon R-dominant , Samarkand-dominant , under-dominant , or over-dominant inheritance , based on the magnitude of the difference between total expression in the F1 and in each parental sample ( S3 Fig . ) . Transcription factor ( TF ) enrichment was tested by comparing the number of target genes for each TF between a test gene-set and all expressed genes . Significance levels were determined by a one tailed hyper-geometric test . We estimated the number of false positives by 1000 random samples , with each sample consisting of the same number of genes as in the test set . The association of multiple transcription factors was investigated by the Limitless-Arity multiple-testing procedure [65] . The significance was calculated using Mann-Whitney U-test and was calibrated by Family-Wise Error Rate . We performed these transcription factor enrichment tests on 149 experimentally verified transcription factors collected from the Drosophila Interactions Database version 2013–07 [66] . The number of regulatory transcription factors in different test sets was used to compare with those in all expressed genes . Gene-set enrichment analysis was carried out with the software FUNC [67] , using all expressed genes as a background gene list . The pathway analysis was performed with the R package “gage” [68] . Genes were mapped to KEGG pathways and pathways enriched with genes of expression divergence were reported . We have deposited all RNA-Seq raw sequencing reads in NCBI Sequence Read Archive with accession numbers SRP041398 ( F0 ) and SRP041395 ( F1 ) . All read-count tables and customized R scripts for statistical analyses have been uploaded to DataDryad . org with accession doi: 10 . 5061/dryad . pk045 . | Gene expression is the most direct link between the genetically encoded information and the phenotype . We analyzed the patterns of gene expression in two different D . melanogaster genotypes and their offspring at four different temperatures to determine if gene expression regulation is modulated by temperature . Interestingly , we find that at the intermediate temperature ( 18°C ) alleles from both genotypes have very similar gene expression , suggesting a strong canalization of gene expression despite substantial genetic differences . More extreme temperatures break this canalization and result in many differently expressed genes , caused mainly by trans-acting factors . Most of the expression differences are non-additive , with a swap in dominance between the two extreme temperatures . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Temperature Stress Mediates Decanalization and Dominance of Gene Expression in Drosophila melanogaster |
Overexpression of the inducible heat shock protein 70 , Hsp72 , has broadly cytoprotective effects and improves outcome following stroke . A full understanding of how Hsp72 protects cells against injury is elusive , though several distinct mechanisms are implicated . One mechanism is its anti-inflammatory effects . We study the effects of Hsp72 overexpression on activation of the transcription factor NF-κB in microglia combining experimentation and mathematical modeling , using TNFα to stimulate a microglial cell line stably overexpressing Hsp72 . We find that Hsp72 overexpression reduces the amount of NF-κB DNA binding activity , activity of the upstream kinase IKK , and amount of IκBα inhibitor phosphorylated following TNFα application . Simulations evaluating several proposed mechanisms suggest that inhibition of IKK activation is an essential component of its regulatory activities . Unexpectedly we find that Hsp72 overexpression reduces the initial amount of the RelA/p65 NF-κB subunit in cells , contributing to the attenuated response . Neither mechanism in isolation , however , is sufficient to attenuate the response , providing evidence that Hsp72 relies upon multiple mechanisms to attenuate NF-κB activation . An additional observation from our study is that the induced expression of IκBα is altered significantly in Hsp72 expressing cells . While the mechanism responsible for this observation is not known , it points to yet another means by which Hsp72 may alter the NF-κB response . This study illustrates the multi-faceted nature of Hsp72 regulation of NF-κB activation in microglia and offers further clues to a novel mechanism by which Hsp72 may protect cells against injury .
Hsp72 is the major cytosolic inducible form of the 70 kDa family of heat shock proteins ( HSP70 ) . Overexpression of Hsp72 is known to protect cells from injury and is positively associated with outcome in models of stroke [1] , [2] , [3] , [4] , [5] . Besides the role it plays as a molecular chaperone , Hsp72 is also an important mediator in intracellular signaling including inflammatory and cell death signaling [6] . One of the important mechanisms by which Hsp72 affects cellular outcomes is its regulation of the proinflammatory transcription factor Nuclear Factor κB ( NF-κB ) [7] . Activation of microglia following stroke with production of numerous signaling and immune modulatory proteins downstream of NF-κB make microglia important potential targets for therapeutic intervention [8] , [9] . NF-κB activation in microglia is attenuated when cells overexpress Hsp72 [6] , [10] , suggesting that Hsp72 attenuation of NF-κB activation may be a key contributor to cytoprotection . NF-κB is a family of dimeric transcription factors that regulate the transcription of hundreds of genes in a coordinated manner in response to an inducing signal . In resting cells NF-κB is found primarily in the cytosol bound to its inhibitor IκB proteins . Upon stimulation by cytokines or other inducers , IκB proteins are targeted for proteasomal degradation by the IκB kinase ( IKK ) . Once IκB is degraded , NF-κB translocates to the nucleus to activate gene expression . Among its target genes are its own inhibitors and other regulatory proteins that form a complex network that tightly regulates the dynamic response and gene transcription [11] . Expression of the IκBα and IκBε inhibitors is strongly induced to provide direct negative feedback of NF-κB [12] . Another early target , A20 , attenuates activation of inhibitor of IκB kinase ( IKK ) and provides an additional layer of negative feedback [13] . Which mechanism or mechanisms Hsp72 uses to regulate NF-κB in microglia is unclear . In protein binding studies from Hsp72-transgenic mice and mixed cultures of glial cells overexpressing Hsp72 , attenuation of NF-κB activation was shown to be dependent on association between Hsp72 and NF-κB and IκBα , but not IKKγ/NEMO [14] . In contrast a study in a different cell type found that Hsp72 associates directly with the IKKγ/NEMO subunit of the IKK complex but not with the IκBα:NF-κB complex [15] . Interactions between Hsp72 and factors further upstream of IKK have also been identified [16] . To add to the confusion others have observed that heat shock can prevent IκBα degradation without affecting its phosphorylation [17] , or that Hsp72 interacts at both the level of IKK activation and with the IκBα:NF-κB complex [18] , [19] . Given the complex nature of NF-κB signaling and the many possible sites of regulation , better understanding how Hsp72 regulates signaling is vital and will contribute to our ability to design therapeutic strategies that target this pathway . Here we examine Hsp72 regulation of NF-κB activation in microglial cells subjected to the inflammatory cytokine tumor necrosis factor-α ( TNFα ) . Using transfected cell lines stably overexpressing Hsp72 we observe that Hsp72 attenuates NF-κB signaling compared to controls . We then utilize a mathematical model to simulate several potential regulatory effects of Hsp72 overexpression on NF-κB activation . These effects are implemented in the model as constant changes to reaction rate parameters that are assumed to be altered constitutively as a result of Hsp72 overexpression . An inconsistency between the model and the data led us to observe a novel effect: in Hsp72 overexpressing cells the p65/RelA subunit of NF-κB is present at lower levels . Using the model we show that reduced p65 is able to account for the observed reduction in total IκBα observed in unstimulated cells but does not fully account for the attenuated IKK activation . This suggests that Hsp72 must act both at the level of inhibiting IKK activation and by altering steady state protein levels of NF-κB . Additional investigation reveals that IκBα mRNA is induced at higher levels in Hsp72 expressing cells following TNFα stimulation .
To study the effects of Hsp72 overexpression in microglia , the mouse microglial cell line BV2 was transfected with a retroviral vector to generate cell populations stably overexpressing the inducible Hsp72 protein in the absence of heat shock . Two cell lines expressing Hsp72 at different levels were isolated . Western blot assays verified that the transfected BV2/Hsp72 cell lines expressed Hsp72 protein under resting conditions , whereas BV2 control cells and LacZ transfection control cells contained no measurable Hsp72 ( Figure 1A ) . Cells were treated with 10 ng/ml TNFα to induce activation of the canonical NF-κB pathway . The time course of NF-κB activity in each cell population was assessed for 150 min following TNFα addition to the medium by measuring DNA binding activity of the p65/RelA NF-κB subunit . Cells expressing Hsp72 at high levels ( Hsp72 ) exhibited similar NF-κB activity in the absence of stimulus , but the amount of NF-κB activation in response to TNFα was reduced to <50% of that seen in control BV2 cells ( Figure 1B ) . Reduced nuclear translocation of p65 in response to TNFα in Hsp72 cells was confirmed using immunostaining ( Figure S1 ) . Kinase activity of the IκB kinase ( IKK ) was also assessed following TNF addition . IKK activation was significantly reduced but not altogether abolished in Hsp72 cells compared to controls ( Figure 1C ) . Cells expressing Hsp72 at lower levels ( Hsp72-L ) exhibited no significant differences in NF-κB or IKK activation compared to controls ( unpublished data ) , suggesting that attenuation of NF-κB signaling requires a sufficient quantity of Hsp72 protein in the cell . Only high Hsp72-expressing cells were used for subsequent analysis . Levels of total IκBα protein and Ser32-phosphorylated IκBα ( p-IκBα ) were additionally measured at several times following TNFα treatment . All cell types showed similar qualitative behavior , with total protein levels significantly decreased 20 min following stimulus before overshooting to higher levels at 60 min and 90 min ( Figure 1D ) . However levels of total IκBα were lower initially and at later time points in Hsp72 cells than in control cells . Phosphorylation of IκBα was also reduced substantially in Hsp72 cells compared to controls ( Figure 1E ) . The reductions in total and phosphorylated IκBα observed in Hsp72 cells were confirmed using Western Blot ( Figure 1F ) . Hsp72 has been demonstrated previously to regulate NF-κB activation in multiple cell types subjected to different stimuli depending on the context . The reported Hsp72 regulatory mechanisms roughly fall into two categories . In the first , Hsp72 interacts with downstream members of the IκBα:NF-κB complex to prevent IκBα proteasomal degradation and subsequent NF-κB nuclear translocation . The second category includes Hsp72 interactions with upstream signaling components to inhibit activation of the IKK complex . In order to examine which of the Hsp72 mechanisms are likely to be present in microglia , we first employed mathematical modeling to assess which possible mechanisms are consistent with the experimental data . Mathematical models are valuable tools for studying biological networks: inconsistencies between experimental observations and simulations of models constructed from known biological interactions can help identify missing essential pieces or guide investigation into previously unknown interactions [20] . A mathematical model describing the dynamic NF-κB response to TNFα in the microglial cell line BV2 was recently developed [21] . This model assumes that basal IKK activity is negligible . While the assays suggest a possible significant level of basal IKK activity ( Figure 1 C ) , this assumption is justified from our other observation that initial concentrations of phosphorylated IκBα are nearly undetectable ( Figure 1 E–F ) coupled with earlier reports in fibroblasts that IKK activity does not contribute to basal turnover of IκBα protein [22] . We therefore adopted this model with slight modifications to analyze proposed Hsp72 regulatory mechanisms computationally . The system of ordinary differential equations ( ODE ) describing the model dynamics is shown in Figure 2 . We used this model to simulate the possible Hsp72 interactions by first making several key assumptions . From our initial experiments , Hsp72 was only observed to have a significant effect on NF-κB signaling when overexpressed at high levels . Because Hsp72 is not a known NF-κB gene target and is only expressed in resting transfected cells under the control of a constitutive promoter , we therefore assume that its concentration remains constant at a sufficiently high level throughout the entire time course considered during our experiments . Accordingly , Hsp72 is not explicitly included in the model as a reacting species; instead we assume throughout the paper that effects of constant Hsp72 overexpression are interpreted in the model as constant changes to specific reaction rates depending on where Hsp72 supposedly interacts . First we considered the effects of Hsp72 on upstream signaling by altering model parameters as summarized in Table 1 and explained below . Hsp72 has been observed in some studies to inhibit IKK activation by directly binding NEMO within the IKK complex [15] , [18] , [23] , or else by impairing IKK activation by interaction with yet further upstream components including TRAF2 [16] . We tested this mode of regulation in computer simulations by reducing the rates responsible for activation of IKK , and alternatively by enhancing inactivation of active IKK ( Figures 3A and S2; Table 1 ) . In particular , it was assumed that these upstream interactions effectively reduce the rate at which IKK is activated ( model species [IKKa ( t ) ] , Figure 2 ) , for which one interpretation in the model is a reduction of the activation rate , ka . Simulations showed that significant reduction of ka dramatically attenuated IKK activation and consequently reduced the amount of NF-κB activated and IκBα degraded ( Figure 3A ) . In addition to these reported mechanisms , several other scenarios by which Hsp72 could affect upstream signaling are hypothetically possible and were explored using the model . Decreasing the rate of recovery of IKK from an inactivated state to a neutral state capable of further activation ( rate kp , Figure 2 ) had minimal effect on reducing peak IKK , and only altered later dynamics of NF-κB ( Figure S2B ) . In contrast , enhancing the rate at which active IKK becomes inactivated reduced peak IKK activation significantly ( Figure S2C and S2D ) . However increasing the rate of auto-inhibition ( rate ki , Figure S2C ) still allowed a high amplitude first peak of NF-κB , potentially suggesting that it is an unlikely candidate for effective attenuation of NF-κB activation . Also of note was that reduction in only the activation rate delayed the peak of IKK activity once IKK activity was sufficiently reduced . This observation , however , may be a consequence of limitations of the IKK model given the lack of detailed experimental observation needed for more rigorous validation and do not necessarily exclude it as a plausible mechanism . Yet other studies of Hsp72 overexpression found that Hsp72 instead stabilizes IκBα protein by directly binding to the NF-κB∶IκBα complex but not to IKK [14] , or possibly by binding intermediate complexes possibly including IKK [18] . Accordingly alternative downstream scenarios were tested with the model by altering the reaction rates for steps involved in the degradation of IκBα and examining the simulated effects . The in silico results indicate that when Hsp72 interaction at this point is assumed to effectively decrease the phosphorylation rate , kc2a , by a factor greater than 10 fold , the amplitude of NF-κB activation is significantly reduced ( Figure 3B ) . Such a mechanism could be the result of Hsp72 physically binding to the IκBα:NF-κB complex to inhibit phosphorylation by the IKK complex [14] . Irrespective of the strength of inhibition , however , IKK activation is initiated in a similar manner as in simulations at the nominal parameters , showing only discrepancies at later time points when NF-κB target gene expression is differentially regulated . Similar results were observed when IκBα phosphorylation was uninhibited but later steps needed for proteasomal degradation were instead assumed to be inhibited ( implemented by reducing rates kua1 , kuc1 , or kupd in the model ) , as suggested elsewhere in the literature [17] , [18] ( Figure S3 ) . While simulations suggest little change in terms of the responses of IKK , NF-κB , and total IκBα between inhibiting the phosphorylation step or steps further downstream , there was a considerably higher proportion of total IκBα found in a phosphorylated or ubiquitinated state when phosphorylation was allowed to occur . Such qualitative behavior observed in simulation would support a claim that such mechanisms are less likely to be the predominant means by which Hsp72 acts downstream . Taken together with the experimental results , model simulations indicate that Hsp72 must act upstream of the NF-κB∶IκB complex in order to inhibit IKK activation following TNFα stimulus unless some unknown mechanism unaccounted for in the structure of the model is present , while no conclusion can yet be drawn regarding downstream regulation . One feature of the data wholly unaccounted for by simulations , however , was the reduction in total IκBα protein observed in Hsp72 cells ( Figure 1C ) . We followed this up by assessing p65 levels . A reduction in total IκBα levels could conceivably be achieved by negative regulation of its basal synthesis by some yet to be determined mechanism . To test whether such a mechanism is likely to occur , we simulated the model assuming that Hsp72 interactions alter several model parameters ( Table 2 ) . First we simulated inhibition of IκBα protein synthesis by decreasing rate c2a of the model ( see Eq . ( 7 ) in Figure 2 ) until basal IκBα was reduced to a level consistent with experiments . Simulations show that such a reduction in IκBα protein synthesis rate causes a precipitous drop in total IκBα concentration following stimulation together with elevated basal and sustained transient NF-κB activation ( Figure 4A ) . Interpreted in the cellular context , the simulations suggest that direct inhibition of IκBα in the network would severely impair the ability of the cell to synthesize adequate de novo IκBα protein to terminate NF-κB activation following stimulus This would imply that such a regulatory mechanism is unlikely to reduce initial IκBα , leading us to examine alternative mechanisms . The basal levels of total IκBα play a key role in maintaining proper levels of NF-κB activation in resting cells [22] . Since no noticeable difference was observed in either NF-κB DNA binding activity or nuclear translocation between BV2 and Hsp72 cells prior to TNFα treatment ( Figures 1B and S1 ) , initial IκBα concentrations would not be expected to change without a corresponding change in total NF-κB concentration . To check whether this is the case , the total levels of p65 NF-κB in resting cells were measured using western blot . The amount of total p65 protein was reduced significantly compared to control BV2 cells ( Figure 4B–4C ) . The model was simulated again , this time assuming that the concentration of total p65 present in the cell was reduced to 70% of the nominal concentration , implemented by reducing the total amount of [IkBaNFkB ( t ) ] provided as the initial condition to the model . Simulations predicted that reduction of the total pool of p65 changes the equilibrium levels of IκBα protein by a similar amount , consistent with the experimental measurements ( Figure 4C ) . These results show that Hsp72 reduces the amount of p65 protein in the cell by some mechanism , and suggest the hypothesis that the amount of total IκBα protein is coordinately reduced to maintain a constant equilibrium level of basal NF-κB activity in unstimulated cells . In light of this result , how much of the observed attenuation of NF-κB activation can be attributed to a reduction in overall protein as opposed to the other potential Hsp72 interactions ? Dynamic simulations of the model with reduced NF-κB protein levels , performed by decreasing [IkBaNFkB ( 0 ) ] in the model to 70% of its nominal concentration , suggest that the effect of this is indeed reduced NF-κB activation in the absence of any further alterations in model parameters due to Hsp72 interactions ( Figure 5A ) . However without regulating any additional points in the system , noticeably more IκBα is degraded at 20 min in simulations than what experimental evidence indicated , and peak amounts of phosphorylated IκBα only were reduced by a smaller amount than what experiments indicate . Furthermore , simulation indicates that the reduction in NF-κB levels will have a negligible effect on the amount of IKK activated , further supporting the earlier claim that at a minimum additional Hsp72 regulation at the level of IKK is required . Indeed , simulations of models lacking either assumed inhibition of IKK activation or reduction in total p65 fail to account for the observations from Hsp72 cells ( Figure S4 ) . Given the unexpected decrease in p65 levels , one potential scenario that could account for the reduced IKK activity is that a similar reduction in the total amount of IKK present in the cell may also result from Hsp72 overexpression . The reduction in IKK simulated here could also be due to interactions of Hsp72 with NEMO that prevent proper IKK complex formation and reduce the pool of IKK able to be activated , similar to what was suggested by Ran et al . [15] . This was probed computationally , simulating the model with reduced p65 as before and additionally reducing basal IKK by modulating [IKKn ( 0 ) ] ( Figure 5B ) . The simulations suggest that under these conditions IKK activation can be reduced while also reducing peak NF-κB activation to a similar level as observed experimentally . When simulating the model this scenario was similar to others when assuming reduced p65 was accompanied by inhibition of IKK activation ( Figure S5 ) , suggesting that similar effects could be achieved using one or a combination of several mechanisms upstream of IKK . In all cases of reduced IKK activity in addition to decreased p65 , however , the amount of IKK inhibition necessary to further attenuate NF-κB activation in simulation was much greater than observed experimentally ( gray ) . Similar results were observed when in addition to reducing p65 , Hsp72 was assumed to inhibit both IKK activation and IκBα phosphorylation ( Figures 5C and S5 ) . With the additional downstream mechanism , the model was able to reduce peak NF-κB signaling sufficiently without requiring near complete inhibition of IKK activity as simulations suggest was necessary for significant reduction in NF-κB activation . Whether the additional downstream mechanism is essential , however , cannot be discerned with the present model . In summary , the results of the computational probe of the possible Hsp72 regulatory scenarios support the hypothesis that two regulatory components are necessary: reduction in total p65 and inhibition of IKK . However due to the limited level of detail in the upstream model , it is not possible to distinguish whether Hsp72 is more likely to inhibit IKK activation by direct binding , to inhibit steps preceding IKK activation , or to reduce the amount of total IKK complex available in the cells . Additional downstream regulation at the level of IκBα phosphorylation appears plausible but not necessarily essential for NF-κB regulation in BV2 cells . The primary function of NF-κB is its role as a transcription factor , directly or indirectly controlling the transcriptional activity of many important genes [24] . The expression of NF-κB target genes differs greatly in terms of their kinetics and can vary significantly depending on cell type [25] . Here we focused on the induction of two early gene targets whose expression is relatively well understood – A20 and IκBα – and which also play a critical role in the feedback regulation of the system . A20 and IκBα are both rapidly induced upon nuclear translocation and are believed to be under similar control [26] , [27] . As such , one expects gene targets under similar control to be influenced similarly by Hsp72 attenuation of NF-κB activation . The model was used to compare the change in induced gene expression under the hypothesis that Hsp72 interacts at multiple points in the network . To simulate the response of a control BV2 cell , the model was simulated with the nominal parameter values and showed that IκBα and A20 transcripts are induced similarly , peaking 30–60 min and declining slightly by 120 min ( Figure 6A ) . When the model is simulated assuming Hsp72 regulation of p65 levels , IKK activation , and IκBα phosphorylation ( implemented assuming [NFkB ( 0 ) ] is reduced to 70% , kiA20 is increased by a factor of 10 , and kc2a is reduced to 1/6 ) , both mRNA expression levels are predicted to decrease significantly , again at similar levels owing to their identical transcriptional dependence on NF-κB and similar transcript stability . To test this experimentally , mRNA transcript levels of both A20 and IκBα were measured in BV2 , LacZ and Hsp72 cells . The relative induction of each was nearly identical in BV2 and LacZ cells and nearly matched the expected early activation profile ( Figure 6B , blue ) . Furthermore in Hsp72 cells A20 expression was significantly decreased , with peak levels decreased by an amount that resembled simulations . Unexpectedly Hsp72 cells showed significantly increased relative expression of IκBα transcripts ( Figure 6B , red ) . The discrepancy between induction of IκBα and A20 mRNA points to a mechanism by which Hsp72 overexpression differentially increases IκBα transcription despite less p65 activity . We conclude from the above observations that Hsp72 overexpression affects mRNA transcript expression by increasing synthesis of IκBα mRNA following TNFα stimulation . This effect stands in contrast to A20 whose transcript levels are induced at lower levels as a consequence of reduced NF-κB activity , as expected from the model . At present the mechanisms underlying this observed effect are not known .
In this work we overexpressed Hsp72 in the microglial cell line BV2 to study its effects on NF-κB activation in response to the inflammatory cytokine TNFα . Our experiments demonstrated that Hsp72 overexpression reduced the amount of IKK kinase activity , the amount of NF-κB DNA binding activity , and the amount of phosphorylated IκBα protein . These results are largely consistent with other studies examining the anti-inflammatory effects of Hsp72 [14] , [15] , [16] , [18] , [28] , [29] . We found that Hsp72 additionally reduced the steady state protein levels of both IκBα and p65 . Computational analysis suggested that reductions in total IκBα were due to a shift in equilibrium induced by reductions in total NF-κB p65 protein rather than direct inhibition of IκBα synthesis itself . Model simulations of the response with the reduced basal protein levels also showed that this contributed in large part to attenuation in NF-κB activation , but that additional mechanisms are likely required . Specifically , modeling suggests that Hsp72 overexpression should act both by attenuating IKK activation rates and by reducing basal protein levels to regulate NF-κB activity in a manner consistent with our experimental observations in microglia . Whether downstream interactions are required could not be inferred from the model . However such a mechanism could permit finer control over inflammatory signaling by Hsp72 or provide a measure of redundancy to ensure more robust regulation . Our analysis therefore provides numerical evidence in support of the hypothesis that simultaneous activity of multiple modes of Hsp72 regulation are active in microglia , and is in agreement with other studies that suggested Hsp72 interacts at multiple points in the signaling pathway [16] , [18] . It is important to keep in mind the limitations of the model when trying to infer conclusions about the biological system from its analysis . Mathematical models are , by necessity , approximate descriptions of the physical system that make a number of assumptions in order to translate physical reality into mathematical expressions . A key limitation of the present model is that it does not explicitly model Hsp72 interactions on the signaling network; rather it is assumed that Hsp72 overexpression effectively manipulates certain rate parameters or initial conditions of the model . Such an approach allowed us to find numerical evidence supporting broad hypotheses about where Hsp72 overexpression is expected to alter network dynamics . However , this assumed effect also limits our ability to examine particular and direct biochemical interactions , the explicit inclusion of which greatly increases the complexity of the model . Currently the kinetic data needed for this is unavailable . It is possible that detailed modeling of the explicit and dynamic interactions by Hsp72 in the future may yield valuable information . Also of note is that in the model used here , the module describing kinetics of IKK activation is a coarse approximation of an intricate and deeply interconnected signaling network . While other studies have chosen to model this module in greater detail than in the model considered here ( see , e . g . [27] ) , all share the critical structural assumption that the only feedback of downstream NF-κB signaling on IKK activation comes through induction of the A20 inhibitor . With only this feedback in place , it is structurally impossible for initial IKK activation to be altered by any Hsp72 interactions that occur downstream of IKK activity . Hence , the conclusion drawn here that Hsp72 regulation upstream of IKK is necessary in microglia is supported by alternative models of IKK activation sharing this hypothesis and is not restricted to the one employed in this study . Our assertion that Hsp72 must inhibit IKK , based on our simulation results manipulating model parameters under the assumption that increased Hsp72 effectively decreases the rate of IKK activation , is not necessarily inconsistent with reports that have found that Hsp72 does not bind with IKK [14] , [29] . In [14] , reduced IκBα phosphorylation and NF-κB activation was observed in both transgenic mice and primary cultures of astrocytes and microglia overexpressing Hsp72 . However , co-immunoprecipitation found that Hsp72 associates with NF-κB and IκB proteins , but not with NEMO/IKKγ . It is possible that while Hsp72 did not interact directly with IKK as suggested , it was still able to inhibit IKK activation by other means . In support of this , despite failing to see association between Hsp72 and IKK , Yoo et al . still observed a reduction in IKK activation in human respiratory epithelial cells [29] . A possible explanation accounting for this is that Hsp72 interacts upstream of IKK , either at the level of TRAF2 to prevent recruitment and activation of IKK , as suggested more recently by Dai et al . [16] , or at the level of TRAF6 in the case of LPS signaling [19] . In this event IKK activation could be attenuated without necessitating direct association with the complex . Determining precisely whether Hsp72 acts directly at the level of IKK or with species involved in its activation further upstream could provide valuable information about potential stimulus-specific regulation . While interactions directly with IKK subunits are likely to inhibit any signaling that requires IKK activation , specific interactions with TRAF2 compared with TRAF6 , for instance , could help tune the inflammatory response when subjected to multiple inputs acting through different receptor channels . While our experiments demonstrate that basal p65 protein in untreated microglia decreases with Hsp72 overexpression ( Figure 3 ) , the mechanism by which Hsp72 overexpression reduces basal p65 is unclear . The promoter region of the RELA gene has three binding sites for SP-1 [30] , pointing to regulation at the level of gene transcription . Regulation of NF-κB by microRNAs may offer another possible explanation . Increasing evidence points to extensive microRNA regulation of NF-κB signaling [31] and cerebral ischemia [32] . Furthermore , many known microRNAs are predicted to target RELA transcripts and other transcripts involved in the NF-κB pathway [33] , [34] . Alternatively , regulation of other NF-κB isoforms as a result of Hsp72 overexpression could theoretically account for reduced total p65 protein . The p65 subunit dimerizes with other NF-κB subunits to form , for example , stable heterodimers with p50 and less stable p65 homodimers [35] . Conceivably reduction in available p50 could sequester less p65 in stable heterodimers and thereby reduce total p65 levels . Interestingly the NFKB1 gene that encodes the precursor to the p50 subunit is itself a known transcriptional target of NF-κB activation in response to TNFα in at least certain cell types [36] , [37] . However , our experiments show no evidence that basal NF-κB DNA binding activity is altered ( Figure 1C ) , making this direct feedback loop an unlikely mechanism to explain reduced steady state p65 . However there are findings that mice deficient in the p50 NF-κB subunit have reduced brain injury following focal cerebral ischemia [38] . The reduction in p65 in microglia is in contrast to a study in respiratory epithelial cells that detected no change in total p65 protein despite high levels of HSP in the cells [29] . These different observations may be explained in part by cell type differences or due to the use of heat shock and sodium arsenite to induce Hsp72 rather than transfection as done here . Understanding whether Hsp72 interaction has any effect on other NF-κB isoforms such as p50 could prove enlightening . Our finding that induction of IκBα mRNA is increased in Hsp72 cells raises the questions what is the mechanism by which this is achieved , and what is its functional effect ? The increase in IκBα mRNA induction is confounded by the other experimental observations from this study showing that total IκBα protein is expressed at reduced levels in the same cells while A20 mRNA expression simultaneously decreases . Model simulations under the assumed mechanisms of Hsp72 regulation predict that induction of A20 mRNA is decreased as expected owing to decreased NF-κB activity , but wrongly predict that IκBα mRNA expression should decrease as it is believed to be under similar transcriptional control by NF-κB . This is suggestive of some Hsp72-dependent means of regulation not yet identified . Evidence exists that heat shock ( though not specifically Hsp72 ) can increase IκBα expression . Heat shock was reported to activate the human IκBα promoter in certain cell types [39] . However the model , simulated assuming that Hsp72 overexpression reduces expression of IκBα by reducing the rate c2a , predicts that induction of IκBα in the absence of other regulation dramatically alters NF-κB signaling in a way completely inconsistent with experiments ( Figure 4A ) . This suggests that such a direct effect is highly unlikely under this assumed regulatory action . A more recent report by Dunsmore et al . also found that heat shock was able to upregulate IκBα expression , but concluded that it did so primarily by means of increasing transcript stability more so than by direct induction of the IκBα gene [40] . The mechanism was found to depend on p38 MAPK kinase , whose activation is also induced in response to TNFα and other inflammatory cytokines [41] . Therefore crosstalk between the two pathways , microRNA regulation , and how these are affected by Hsp72 might be fruitful areas of research . A different possibility to explain differential induction of IκBα mRNA is that an alternative NF-κB subunit compensates for the reduced expression of p65 to induce IκBα rather than A20 . The IκBα promoter is known to exhibit specificity to the different NF-κB subunits [42] . While the p50 and p50/RelB dimers have little effect on its activation and seem unlikely candidates , the c-Rel subunit is still able to activate IκBα transcription , albeit to a lesser degree than p65-containing dimers . Further experiments examining the expression and activation of c-Rel could address this . Analysis of the model combined with experiments gave us ample evidence suggesting that regulation at the level of IKK is required , but did not permit us to pinpoint the exact mechanisms of Hsp72 regulation of IKK . For instance , the profile of IKK activation was predicted to change in terms of amplitude and timing based on the means of inhibition used ( Figure S5 ) . However , caution should be used in ruling out one mechanism over the other until the model is further validated . This is due partially to the large number of simplifying assumptions made in the signaling pathway from ligand-receptor binding to IKK activation . This pathway involves numerous signaling proteins and a rich network of post-transcriptional modifications . Additionally there are a number of regulators , in particular A20 , which can inhibit or activate the response at multiple points [43] . Further characterization of this pathway and the kinetic interactions will help to better identify the steps involved in IKK activation and create models with better descriptive and predictive powers . The mathematical model , while being useful for identifying discrepancies in qualitative behavior between mechanisms , was limited in its ability to reproduce certain features of NF-κB activation and IκBα profiles observed from Hsp72 cells . Structural constraints in the model limit its ability to generate a low amplitude , non-oscillating signal as the population average data suggests is present in Hsp72-expressing cells ( Figure 1B , red ) while also synthesizing and phosphorylating sufficient IκBα at later times . This may be in part due to the failure of population level measurements to accurately describe single cell NF-κB signaling . Single cell studies show that oscillatory behavior in individual cells may be masked by only considering bulk population averages [44] , [45] , [46] , [47] . Keeping this in mind , comparisons between model simulations and experimental data were largely restricted to the initial period of activation during which cells stimulated with a high dose of TNFα tend to respond similarly before losing synchrony at later times [47] . Therefore the main conclusions presented in this paper suggesting that Hsp72 regulation must alter basal p65 protein levels and act to inhibit IKK activation during the first 30 minutes are expected to hold even when taking into account oscillations . An alternative approach using fluorescent reporting to track single cell trajectories of NF-κB activation may prove useful in developing a more accurate model to further study Hsp72 regulation of the network . An aspect that is also important in interpreting results from our in silico analysis is the dependence of the model on rate parameters whose values are unknown . Many of the nominal parameter values were obtained in [21] by finding parameters that minimized the error between the model simulations and population level measurements of NF-κB signaling in BV2 cells . However given the limited amount of data used in identification and the large number of unknown parameters , the identified parameter set is not unique and hence many other sets could provide a similar response . While it is believed to be a general property of biological systems that the response exhibits sloppy parameter sensitivities and therefore leaves parameter values poorly constrained [48] , we repeated the simulations when assuming that all model parameters – both rate constants and initial concentrations – are uncertain to see whether the results change with a different set of parameters . The results indicate some variability in the response depending on the sampling of the parameters , but that the average response when parameters are uniformly distributed in an interval of +/−20% of the fixed values is nearly identical to that using the individual parameter sets ( Figures S6 and S7 ) . Therefore , the results appear to be robust at least in the region of parameter space near the identified parameters . However for all high dimensional models with unknown parameters , including the one used here , the possibility still exists that other parameter sets far from those considered here could be plausible and potentially lead to different results , which must always be kept in mind . This study used a combination of experimental observations and mathematical modeling to provide new insight into how Hsp72 regulates NF-κB activation in microglia ( Figure 7 ) . This study provided experimental evidence supported by model simulations that make a strong argument for the necessity for inhibition of IKK activation in microglia , which was an open question based on prior literature . Furthermore it uncovered a novel mechanism by which Hsp72 overexpression downregulates p65 protein , which has the effect of partially attenuating NF-κB activation and decreasing initial IκBα protein . Our observation that IκBα transcripts are upregulated whereas A20 transcripts are downregulated is still perplexing , and the mechanisms and consequences of this may prove interesting in the future .
BV2 cells , a mouse microglia cell line and kind gift from Dr . K . Andreasson at Stanford University , and Hsp72 and LacZ cell lines made by infecting BV2 cells with retrovirus LXSN-Hsp72 or -LacZ and neomycin selection , were used . Supernatants from Ψ2 packaging cells expressing LXSN/Hsp72 or LXSN/LacZ were used for infection . BV2 cells seeded in 24 well plates at 4×105 cells/well were 60–70% confluent the following day and infected with packaging cell supernatant . Infected cells were stained for Hsp72 . Cells from positive staining wells were subcloned and Western Blot confirmed expression . Subclones were stored in liquid nitrogen . Assays were run from the same clone and Hsp72 expression level was always confirmed by Western Blot before use in experiments . Cell lines were cultured in Dulbecco's Modified Eagle's medium ( DMEM , GIBCO by Life Technologies , Carlsbad , CA ) supplemented with 8% Fetal Bovine Serum ( Hyclone , South Logan , UT ) , Penicillin ( 100 U/ml , GIBCO ) , and Streptomycin ( 100 µg/ml , GIBCO ) . Cells were passaged every four days and used between passages 10–20 . BV2 , BV2-Hsp72 and BV2-LacZ cells were seeded at 4×105 cells per well in six well plates 36 hrs prior to treatment with 10 ng/ml recombinant mouse TNFα ( R&D Systems , Minneapolis , MN ) . Cells were then harvested for protein at the indicated times with Phosphosafe Extraction buffer ( Novagen , Darmstadt , Germany ) supplemented with 0 . 01 volume Protease Inhibitor cocktail ( Sigma , St . Louis , MO ) and 5 mM DTT before use . Protein concentration was measured using the Coomassie Plus assay ( Pierce , Rockford , IL ) . 25 µg total protein/sample was transferred to a pre-chilled Eppendorf tube and brought to 25 µl with complete lysis buffer . Aliquots were stored at −80°C until use for activated NF-κB p65 measurement . Active NF-κB was measured using the Trans AM NFκB p65 Transcription Factor Assay Kit ( Active Motif , Carlsbad , CA , cat#40096 ) according to the manufacturer's instructions on 20 ug total protein/sample . Three cultures were assayed for each group . Standards were prepared from recombinant p65 ( Active Motif ) . Total and phosphorylated IκBα were measured after TNFα treatment using sandwich ELISA kits from Cell Signaling ( Danvers , MA , #7360 for total and #7355 for phospho ) . Cells seeded at 4×105 cells/ml were treated on day 3 with 10 ng/ml TNFα . Cell lysates were prepared and 250 ug total protein , measured by BCA assay , was used for total or phospho-IκBα ( Ser32 ) measurement , according to the kit instructions . Standard curves for phospho-IκBα ( Ser32 ) measurement were made using phospho-IκBα control from Active MOTIF . Total and phospho-IκBα proteins were also run on Westerns to confirm activation . IKK activity was measured by immunoprecipitation of IKK trimers , followed by a kinase assay/ELISA using a modification of the K-LISA IKK Inhibitor Screening Kit ( Calbiochem , Billerica , MA , cat# CBA044 ) . Cells were seeded and treated as above , protein was prepared following the kit instructions , and concentration determined by Coomassie Plus Assay . A total of 500 µg protein/sample was incubated at 4°C for 5 hrs with 5 µg goat anti-IKKγ antibody M18 ( Santa Cruz Biotechnology , Dallas , TX , Cat# SC8256 ) with shaking , followed by overnight incubation with shaking with 50 µl 2× diluted Protein G-Sepharose ( Sigma ) previously washed in complete lysis buffer . Beads were then centrifuged for 5 min at 13 , 000 rpm 4°C , the post-immunoprecipitation supernatant removed , and beads were washed in the 1× kinase assay buffer from the K-LISA kit . Beads were then incubated with shaking in an incubator for 1 h at 30°C in 75 µl 1× kinase assay buffer containing 150 ng GST-IκBα and 1× ATP/MgCl2 mix from the kit . Beads were then centrifuged at 13 , 000 rpm for 5 min at 4°C , and 60 µl of supernatant was transferred to a well of the glutathione coated 96-well plate provided with the K-LISA kit . Two-fold serial dilutions of the recombinant IKKβ provided with the kit were run as standards , but omitting IKK inhibitor . In addition the post-immunoprecipitation supernatant was concentrated 20× and run to demonstrate that all IKK activity was depleted from the supernatant . In all cases this sample showed no IKK activity . The plate was incubated 30 min at 30°C to allow the GST-IκBα to bind , and subsequent processing was done according to the vendor's instructions . Final concentrations measured were normalized to the total amount of protein used in a given experiment . Total RNA was isolated with TRIzol ( Invitrogen , Carlsbad , CA , USA ) and reverse transcription performed using the TaqMan MicroRNA reverse Transcription Kit ( Applied Biosystems , Carlsbad , CA , USA ) on equal amounts of total RNA ( 600 ng ) using 100 mM dNTPs , 75 U reverse transcriptase , 10 U RNase inhibitor , and specific mRNA reverse transcriptase primers ( Applied Biosystems ) at 25°C for 10 min , 37°C for 120 min , and 85°C for 5 min . PCR reactions used the TaqMan MicroRNA Assay Kit ( Applied Biosystems ) at 95°C for 10 min , followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 min . Each reaction contained 0 . 75 µl of the RT reaction product , 5 µl TaqMan 2×Universal PCR Master Mix ( Applied Biosystems ) in a total volume of 10 µl using the 7900HT ( Applied Biosystems ) . Predesigned primer/probes for mRNAs and mouse GAPDH were from Applied Biosystems . The expression of mRNAs was normalized using GAPDH as the internal control . Measurements were normalized to GAPDH ( ΔCt ) and comparisons calculated as the inverse log of ΔΔCT to give the relative fold change for all mRNA levels . The PCR experiments were repeated 3 times , each using separate sets of samples . Cells plated at 4×105/well were harvested 0 , 20 and 40 min after addition of 10 ng/ml TNFα the following day . Cells were washed once with 1× PBS followed by fixation for 20 min in 4% paraformaldehyde then 10 min in 0 . 3% H2O2 for permeabilization . Primary rabbit anti-p65 antibody ( Abcam , Cambridge , MA , Cal# is 16502 ) was applied at 1∶800 dilution , cells were then incubated at 4°C overnight . Alexa Fluor 488 conjugated goat anti-rabbit secondary antibody ( Invitrogen , Cal# is A11008 ) was then applied for 1 hr . Five pictures were taken from each well for analysis . To quantify the images , the fluorescence intensity of the nuclei and the fluorescence intensity of the whole cells were measured , and the proportion of total staining that was nuclear was determined . To assess Hsp72 overexpression equal amounts ( 25 µg ) of protein were separated on a polyacrylamide gel ( Invitrogen ) , and electrotransferred to Immobilon polyvinylidene fluoride membrane ( Millipore Corp . , Billerica , MA ) . Membranes were blocked and incubated overnight with primary antibody against Hsp72 ( 1∶1000 , #SMC100A , StressMarq , Victoria , BC , Canada , cat# SMC100A ) and β-actin ( 1∶1000 LiCOR Bioscience , Lincoln , NE , cat# 926-42210 ) , washed and incubated with 1∶15000 anti-rabbit antibody ( 926-32221 , LiCOR Bioscience ) and anti-mouse antibody ( 926-32220 , LiCOR Bioscience ) . Immunoreactive bands were visualized using the LICOR Odyssey infrared imaging system according to the manufacturer's protocol . Densitometric analysis was performed using ImageJ software ( NIH ) . Band intensities were normalized to β-actin . For p65 protein levels , 35 ug and 70 ug of total protein were assayed for each sample . After separation and transfer , membranes were blocked and incubated overnight with primary antibody against NF-κB p65 ( 1∶200 , Santa Cruz , cat# sc-109 , ) and β-actin ( 1∶5000 , Sigma , cat# A1978 ) , washed and incubated with 1∶15000 anti-rabbit antibody ( 926-32221 , LiCOR Bioscience ) and anti-mouse antibody ( 926-32220 , LiCOR Bioscience ) . Immunoreactive bands were visualized and analyzed as above . For total and phosphor IκBα , protein samples were prepared following the IκBα ELISA kit instructions , Cell Signaling ( #7360 for total and #7355 for phospho ) . 50 ug total protein was used for each sample . After protein separation and membrane transfer , membranes were blocked and incubated overnight with primary antibody against either total IκBα ( 1∶1000 , Cell Signaling , cat# 9242 ) or phospho-IκBα ( Ser32/36 , 1∶500 , Cell Signaling , cat# 9246 ) and β-actin ( 1∶1000 , LiCOR Bioscience , cat# 926-42210 ) , washed and incubated with anti-rabbit antibody ( 1∶15000 , LiCOR Bioscience , cat# 926-32221 ) and anti-mouse antibody ( 1∶15000 , LiCOR Bioscience , cat# 926-32220 ) . Immunoreactive bands were visualized and analyzed as above . Experimental data were analyzed using one-way analysis of variance ( ANOVA ) to detect significant differences in mean values among cell types . Data sets for which a significant effect was determined were further analyzed using the Newman-Keuls multiple comparison post hoc test . For time course data , each time point was considered independently of the others since independent cell populations were harvested . Total p65 levels quantified from western blot were compared using one-sample Student's t-test with the null hypothesis that the test group had mean unity since each group of cells was first normalized by the quantity measured in BV2 control cells . Results were considered significantly different at confidence level P<0 . 05 . Experimental data shown is mean +/− SD . The deterministic ordinary differential model from [21] was modified slightly to permit constitutive degradation of phosphorylated intermediates of IκBα and allow nuclear import of the IκBα:NF-κB complex . Parameters for degradation and synthesis were adjusted to fit rates suggested in [22] to more closely match the additional experimental time courses measured in this study . Numerical simulations were performed using custom code written in Matlab R2010b ( MathWorks , Natick , MA ) . Briefly , conserved protein quantities of total NF-κB and IKK were initialized to assumed concentrations and the system was simulated without stimulus until all remaining species reached equilibrium . Simulations with stimulus began from these equilibrium concentrations at 0 min but with stimulus set to present . Simulations of potential Hsp72 regulation scenarios altered kinetic rates and/or initial concentrations prior to equilibration , and simulations were performed with these modified parameters . All model reactions and initial conditions are provided in the Supplement Tables S1 , S2 . Matlab source code is available for download at http://www . bsse . ethz . ch/ctsb/tools/microglia_nfkb_model . | Inducing heat shock or overexpressing certain heat shock proteins ( HSPs ) is known to protect against brain injury , such as that resulting from stroke . Understanding the mechanisms underlying protection at the cellular and molecular level is a subject of intense research , as such knowledge may prove beneficial in designing future therapies . Regulation of the activation of the key inflammatory transcription factor Nuclear Factor κB ( NF-κB ) is believed to be one critical mechanism . However how its activation is altered by Hsp72 remains unresolved . Here we examine NF-κB signaling in microglia cells overexpressing Hsp72 , combining experimentation and mathematical modeling . We show that Hsp72 affects signaling using at least two essential and distinct mechanisms: attenuation of upstream kinase ( IKK ) activity and reduction of steady state NF-κB protein levels . We provide numerical evidence suggesting that neither mechanism in isolation is sufficient to account for the observed signaling . Furthermore , our observations suggest an intriguing additional level of regulation of gene expression and protein synthesis of the IκBα inhibitor , which opens interesting new avenues of research . These results provide novel insight into the mechanisms by which Hsp72 may regulate inflammation and protect brain cells from injury . | [
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] | 2014 | Overexpression of Heat Shock Protein 72 Attenuates NF-κB Activation Using a Combination of Regulatory Mechanisms in Microglia |
The innate immune system of Anopheles gambiae mosquitoes limits Plasmodium infection through multiple molecular mechanisms . For example , midgut invasion by the parasite triggers an epithelial nitration response that promotes activation of the complement-like system . We found that suppression of the JNK pathway , by silencing either Hep , JNK , Jun or Fos expression , greatly enhanced Plasmodium infection; while overactivating this cascade , by silencing the suppressor Puckered , had the opposite effect . The JNK pathway limits infection via two coordinated responses . It induces the expression of two enzymes ( HPx2 and NOX5 ) that potentiate midgut epithelial nitration in response to Plasmodium infection and regulates expression of two key hemocyte-derived immune effectors ( TEP1 and FBN9 ) . Furthermore , the An . gambiae L3–5 strain that has been genetically selected to be refractory ( R ) to Plasmodium infection exhibits constitutive overexpression of genes from the JNK pathway , as well as midgut and hemocyte effector genes . Silencing experiments confirmed that this cascade mediates , to a large extent , the drastic parasite elimination phenotype characteristic of this mosquito strain . In sum , these studies revealed the JNK pathway as a key regulator of the ability of An . gambiae mosquitoes to limit Plasmodium infection and identified several effector genes mediating these responses .
Malaria is a worldwide disease that is highly endemic in Sub-Saharan Africa and causes over half a million deaths annually . The mosquito Anopheles gambiae is a major vector of Plasmodium falciparum , the parasite responsible for most cases of human malaria in Africa . An . gambiae can mount effective antiplasmodial responses by activating several signaling cascades involved in immune regulation , such as the Imd , Toll , and STAT pathways [1]–[4] . Pathway activation leads to the transcription of effector genes that mediate the antiplasmodial mechanism . The thioester-containing protein 1 ( TEP1 ) and the fibrinogen-related protein 9 ( FBN9 ) are important components of the mosquito complement-like system that are produced by hemocytes and secreted into the mosquito hemolymph; they bind to the ookinete surface and mediate parasite lysis [5] , [6] . Activation of the Imd and Toll pathways decreases ookinete survival as parasites come in contact with the mosquito hemolymph by promoting TEP1-mediated lysis [1] , [3] , [7] . In contrast , the STAT pathway targets a later stage of the parasite , the early oocysts , through a TEP1-independent response [4] . We have recently shown a functional link between midgut epithelial nitration and another mosquito antiplasmodial response that targets the ookinete stage of the parasite , the complement-like system [8] . Ookinete invasion results in extensive damage to the invaded cell [9] and induces a two-step epithelial nitration reaction in which expression of nitric oxide synthase ( NOS ) is followed by the induction of heme peroxidase 2 ( HPX2 ) and nicotinamide adenine dinucleotide phosphate ( NADPH ) oxidase 5 ( NOX5 ) [8] , [10] . The HPX2/NOX5 system potentiates NO toxicity , enhances nitration , and reduces Plasmodium survival . Exposure of ookinetes to these chemical reactions as they traverse the midgut cell modifies them and makes them “visible” to the mosquito complement-like system [8]; however , the immune signaling pathway ( s ) regulating the midgut epithelial response to infection have not been identified . The JNK pathway is a mitogen-activated protein kinase ( MAPK ) pathway that is highly conserved from mammals to insects; however , our understanding of the role of JNK signaling in insect immunity is limited . Several orthologs of genes that mediate JNK signaling in vertebrates have been identified in Drosophila and An . gambiae [11] , [12] . The Jun-N-terminal kinase ( JNK ) is a MAP kinase at the core of this signaling cascade that is activated by a MAPK kinase ( hemipterous , in D . melanogaster ) ( Figure 1A ) [11] , [13]–[17] . JNK phosphorylates the Jun and Fos transcription factors , giving rise to a Jun/Fos dimer ( AP-1 complex ) that activates transcription of target genes ( reviewed in [18] ) . JNK signaling is modulated by puckered ( puc ) , a phosphatase that suppresses signaling by dephosphorylating JNK . Puckered is part of a negative feedback loop , because transcription of puc is regulated by the JNK pathway [16] , [19] , [20] . In Drosophila , JNK signaling has been shown to be involved in a wide range of biological processes including embryonic development , apoptosis , stress response , cell proliferation and differentiation , and immunity [18] . The JNK pathway has a great deal of complexity and is known to receive input from multiple upstream genes , yet to be defined in insects , and from lateral inputs from components of other signaling cascades . For example TAK1 , a kinase that is part of the Imd pathway , can also activate JNK signaling [21]–[23] . It is believed that this complex organization reflects the broad range of responses that are influenced by JNK signaling . Many different stimuli are known to activate the JNK pathway , including microbial elicitors . In particular , the participation of JNK signaling in antibacterial responses has been well documented in Drosophila . Lipopolysaccharide ( LPS ) is a key elicitor of JNK pathway activity in immune-competent cells and flies [13]–[15] , [17] , [22] , [23] , and flies that are deficient in puc ( and therefore have an overactive JNK pathway output ) display increased resistance to Gram− bacteria [19] . In the An . gambiae 4a3B cell line , JNK signaling was weakly activated by H2O2 , while LPS elicited a strong response [11] . The response of JNK signaling to LPS has also been observed in human dendritic cells and splenocytes [24] . We have previously shown that JNK regulates expression of several genes that protect An . gambiae mosquitoes from oxidative damage , such as oxidation resistance 1 ( OXR1 ) , catalase , and glutathione peroxidase [25] . Silencing of these effector genes increased reactive oxygen species ( ROS ) levels and reduced Plasmodium survival . Paradoxically , however , JNK silencing had the opposite effect and enhanced infection , suggesting that—besides the role in ROS balance—JNK may mediate some antiplasmodial response [25] . In this manuscript , we present a detailed functional analysis of several genes that mediate JNK signaling in An . gambiae and identify two key mechanisms by which this cascade mediates antiplasmodial immunity . JNK activation induces expression of HPx2 and NOX5 , the two enzymes that mediate epithelial nitration in response to ookinete invasion [8] . In addition , JNK signaling regulates the basal levels of expression of TEP1 and FBN9 , two effector proteins produced by hemocytes that mediate ookinete lysis [5] , [6] . The participation of JNK signaling in the antiplasmodial responses of the A . gambie L3–5 strain that has been genetically selected to be refractory to Plasmodium infection was also investigated .
Five An . gambiae orthologs of genes known to be part of the JNK pathway signaling cascade in Drosophila have been identified including two kinases , hemipterous ( hep ) and c-Jun N-terminal kinase ( jnk ) ; a phosphatase , puc; and two transcription factors , Jun ( jun ) and Fos ( fos ) ( Figure 1A , ) [11] . These five genes are expressed in the thoraces , abdomens , midguts , hemocytes , and in undeveloped ovaries from sugar-fed mosquitoes ( Figure 1B , Table S1 ) . Jun is expressed at low levels in the head , but the mRNAs of the other genes could not be detected in this tissue ( Figure 1B , Table S1 ) . A notable enrichment of hep transcripts in the thorax and of jnk in the ovary was observed ( Figure 1B , Table S1 ) . The transcriptional response of these five genes to infection with P . berghei ( rodent malaria parasite ) was analyzed in mosquito midguts collected at different times after feeding on either healthy or P . berghei-infected mice . A significant increase in jnk , puc , jun and fos expression in response to infection was observed between 12–48 hours post infection ( hpi ) . In general , the magnitude and kinetics of the inductions were variable between experiments . Jun at 24 and 48 hpi and JNK at 24 hpi had the most consistent inductions that were significant in three independent experiments ( Figure 1C , Table S2 ) . Hep expression changed the least in response to Plasmodium infection ( Figure 1C , Table S2 ) . Only a modest increase was observed in one of the replicates at 12 hpi but , in another , the expression was lower after infection than in the uninfected control . Although activation of the JNK pathway involves a cascade of post-translational phosphorylation events , transcription of JNK pathway members has been reported to increase upon Plasmodium infection in Anopheles and transcriptional activation of JNK at the mRNA and protein level has also been observed in Drosophila midguts in response to bacterial challenge [26]–[28] . JNK protein expression was also induced in the mosquito midgut in response to Plasmodium infection ( Figure S1 ) . This indicates that JNK signaling in infected midguts may be enhanced by increased expression of several components of the cascade . We confirmed that JNK silencing ( Figure S2 ) enhances P . berghei infection ( Figure 1D ) , as previously shown [25] . Furthermore , silencing other genes involved in JNK activation—such as hep , jun , and fos ( Figure S2 ) —also enhanced the intensity of infection , increasing the median number of oocysts by 3 . 8 to 4 . 9 fold , relative to the dsLacZ control ( Figure 1 , D and E , Table S3 ) ( p<0 . 001; Kolmogorov-Smirnov [KS] test ) . As expected , overactivation of this cascade by silencing puc ( Figure S2 ) , a phosphatase that normally suppresses JNK signaling , had the opposite effect and greatly reduced the intensity ( Figure 1D , Table S3 ) ( p<0 . 001; KS test ) and the prevalence of infection from 68% to 41% ( p<0 . 005; chi-squared [χ2] test ) . Co-silencing Jun reversed the antiplasmodial effect of silencing puc ( Figure 1E , Table S3 ) and increased the prevalence of infection from 33% to 84% ( p<0 . 001; χ2 test ) , indicating that Jun is downstream of puc and confirming the functional link between these two genes in An . gambiae . We have recently shown that the HPx2/NOX5 system potentiates NO toxicity and mediates nitration of midgut epithelial cells in response to Plasmodium invasion . The potential participation of JNK signaling in the induction of these two enzymes and epithelial nitration was investigated . A robust increase in HPx2 and NOX5 expression was observed in the dsLacZ-injected control group ( Figure 2A ) in response to Plasmodium infection , as previously shown in uninjected females [8]; however , HPx2 was no longer induced in infected midguts and NOX5 expression was significantly reduced , relative to uninfected controls when JNK was silenced; and expression of both HPx2 and NOX5 is reduced in infected midguts when jun is silenced ( Figure 2A , Table S4 ) . The transcriptional induction of HPx2 in response to infection was more robust when puc was silenced , but NOX5 induction was no longer observed ( Figure 2A , Table S4 ) . In agreement with the overall transcriptional responses , when JNK was silenced , in vivo midgut nitration no longer increased in response to Plasmodium infection , was lower than in the uninfected controls when jun was silenced , while silencing puc had the opposite effect and enhanced the nitration response . ( Figure 2B , Figure S3 , Table S5 ) . Furthermore , co-silencing HPx2 ( Figure 2C , Table S3 ) completely rescued the dramatic antiplasmodial effect of silencing puc alone . Co-silencing HPx2 increased the median number of oocysts/midgut from 0 to 17 ( p<0 . 0001; KS test ) and the prevalence of infection from 20% to 100% ( p<0 . 0001; χ2 test ) . Co-silencing NOX5 and puc ( Figure 2D , Table S3 ) increased the median number of oocysts/midgut from 0 to 10 ( p<0 . 0001; KS test ) to the same level as the dsLacZ control , and the prevalence of infection from 22% to 88% ( p<0 . 0001; χ2 test ) . This indicates that HPx2 and NOX5 are downstream of puc and mediate , to a large extent , the antiplasmodial response triggered by the JNK activation . Jun expression is induced in mosquito hemocytes 24 hpi with P . berghei [27] , suggesting that JNK signaling in these cells could also be an important component of antiplasmodial immunity . TEP1 and FBN9 are proteins constitutively produced by hemocytes that are secreted into the mosquito hemolymph , bind to the surface of P . berghei ookinetes , and mediate parasite lysis [5] , [6] . We investigated the hypothesis that these hemocyte-derived proteins are regulated by the JNK pathway and are important effectors of this signaling cascade . Silencing jun or fos significantly reduced TEP1 by 94% and 69% , respectively ( p<0 . 001 and p<0 . 05; Student's t-test ) and reduced FBN9 expression by 62% and 70% , respectively ( Figure 3 , A and B , Table S6 ) ( p<0 . 01 and p<0 . 001; Student's t-test ) but had no effect on the expression levels of other hemocyte-specific genes such as APL1A or APL1C , and fos silencing actually resulted in a modest increase in LRIM1 expression ( Figure S4 , Table S6 ) . Conversely , silencing puc significantly increased expression of both TEP1 and FBN9 by 1 . 86 and 2 . 6 fold , respectively ( Figure 3 , A and B , Table S6 ) ( p p<0 . 01; Student's t-test ) . Silencing JNK did not affect the total number of circulating hemocytes or the proportions of granulocytes , oenocytoids , or prohemocytes circulating in the mosquito ( Figure S5 ) . We have previously shown that induction of HPx2 and NOX5 mediates epithelial nitration and that the activity of these enzymes promotes both TEP1 binding to the ookinete surface and parasite lysis [8] . Participation of TEP1 and FBN9 as final effectors of the JNK antiplasmodial response was explored by co-silencing these genes with puc . Co-silencing TEP1 increased the median number of oocysts/midgut from 1 to 21 . 5 ( p<0 . 0001; KS test ) and the prevalence of infection from 53% to 88% ( p<0 . 02 , χ2 test ) relative to silencing puc alone ( Figure 3C , Table S3 ) . Co-silencing FBN9 had a similar effect , increasing the median number of oocysts/midgut from 0 to 13 . 5 ( p<0 . 0001; KS test ) and the prevalence of infection from 35% to 84% ( p<0 . 0001 , χ2 test ) ( Figure 3D , Table S3 ) . The An . gambiae refractory ( R ) strain was selected to be refractory to Plasmodium cynomolgi ( simian malaria ) infection but also eliminates most Plasmodium species , including P . berghei [29] . In this mosquito strain , ookinetes develop and invade the midgut , but they are killed and covered with melanin , a black , insoluble pigment [29] . R females are in a chronic state of oxidative stress that is exacerbated by blood feeding [30] , and TEP1 is known to be a critical mediator of P . berghei melanization and killing [5] . We have shown that the JNK pathway regulates expression of two enzymes that mediate midgut epithelial nitration: NOX5 , an oxidase that generates ROS , and the heme peroxidase , HPX2 . Furthermore , exposure of ookinetes to these nitration reactions as they traverse the midgut epithelial cell promotes TEP1 activation [8] . The hypothesis that the refractory phenotype may be mediated , at least in part , by the JNK signaling pathway was investigated . We first compared the basal level of mRNA expression of the genes involved in JNK signaling between the susceptible ( S ) G3 An . gambiae and the R strain . The basal midgut expression of all the genes involved in JNK signaling was higher in the R strain . Midgut jnk expression was dramatically higher ( 4 . 3 fold ) , while the overexpression of hep was less prominent ( 1 . 5 fold ) ( Figure 4A , Table S7 ) . The basal expression level of all genes of the JNK pathway ( hep , jnk , puc , jun , and fos ) was also significantly higher in whole body samples of R females , ranging from 2 . 1 to 4 . 8 fold ( Figure S6 , Table S7 ) . Higher puc expression is indicative of increased JNK activation , because puc expression is transcriptionally regulated by the JNK pathway . In hemocytes , there was no difference in hep , fos and puc expression between the mosquito strains , but jnk and jun levels were also significantly higher in the R strain ( Figure 4A , Table S7 ) . Furthermore , expression of effector genes of the JNK pathway was also higher in the R strain . In the midgut , basal HPx2 and NOX5 expression was 2 . 8 and 3 . 5 fold times higher , respectively ( Figure 4B , Table S8 ) ( p<0 . 01; paired t-test for both ) ; while in hemocytes , TEP1 and FBN9 expression was 3 . 2 and 5 . 9 fold higher in R mosquitoes , respectively ( Figure 4B , Table S9 ) ( p<0 . 01; paired t-test for both ) . The contribution of the JNK pathway to the refractory phenotype was directly tested by reducing JNK expression via gene silencing . JNK silencing had a dramatic effect , increasing the prevalence of infection from 0 to 70% ( Figure 4C ) ( p<0 . 0001; χ2 test ) , and the median number of oocysts from 0 to 6 oocysts/midgut ( Figure 4C , Table S10 ) ( p<0 . 001; KS test ) . The total number of parasites ( live and melanized ) was not significantly different between the dsLacZ control and the JNK-silenced group , indicating that a similar number of ookinetes invaded the midgut and that the difference in infection prevalence was due to ookinete survival once they traversed the midgut . Of the total number of parasites present , 99 . 6% of parasites were melanized in the dsLacZ group; this decreased to 32% when JNK was silenced ( Figure 4C ) ( p<0 . 0001 , χ2 test ) .
The immune response of An . gambiae mosquitoes against Plasmodium parasites is mediated by activation of immune-related signal transduction pathways . We carried out a functional characterization of five An . gambiae orthologs of genes known to mediate JNK signaling in Drosophila . Our studies implicate the JNK pathway as an important mediator of two coordinated steps of the mosquito anti-Plasmodium immune response and as a major determinant of the killing mechanism in a highly refractory strain of An . gambiae . JNK signaling triggers the transcriptional activation of HPX2 and NOX5 , two key enzymatic effectors of midgut epithelial cells , in response to ookinete invasion . Induction of these two enzymes potentiates nitration and limits Plasmodium survival . This was directly confirmed by the observation that midgut nitration is greatly diminished when JNK signaling is disrupted by silencing JNK or jun . Overactivation of the JNK pathway by silencing puc , greatly increased midgut HPx2 expression and nitration in response to Plasmodium infection . Interestingly , puc silencing did not induce higher levels of NOX5 expression . This enzyme generates reactive oxygen species that could be potentially toxic . Our results suggest that in the absence of puc , there might be alternative mechanisms that limit NOX5 expression , probably to prevent deleterious effects on the host . Previous studies in a variety of mammalian cell types have also shown that NOX5 and other NADPH oxidases are regulated by the JNK pathway [31]–[33] and induction of a nitrogen dioxide-producing heme peroxidase has also been shown to be mediated by the JNK pathway [34] . The process of nitration is clearly an essential step in the destruction of malaria parasites , evidenced by the significant increase in parasite survival upon silencing either HPx2 or NOX5 . It is also a critical outcome of JNK activation , as the considerable resistance conferred by puc silencing is reverted by co-silencing either of these two enzymes ( Figure 2 ) . We therefore propose that the JNK pathway is part of an “alarm system” triggered by parasite invasion that activates expression of NOX5 and HPx2 , two enzymes that catalyze nitration reactions , that label ookinetes for destruction as they traverse the mosquito midgut . The dramatic reduction in TEP1 and FBN9 mRNA levels in hemocytes when the JNK pathway was disrupted by silencing jun or fos appears to be specific , because expression of other hemocyte-specific genes involved in the regulation of complement activation ( APL1A , APL1C , and LRIM1 ) was not reduced . We also confirmed that the differences in expression were not due to significant changes in the number or type of hemocytes present in silenced mosquitoes . The co-silencing experiments with puc confirmed that both TEP1 and FBN9 are downstream of JNK . This indicates that the basal level of TEP1 and FBN9 expression in mosquito hemocytes is regulated by the JNK pathway and that both genes are important effectors of the lytic response mediated by this cascade . Previous studies have shown that the R strain is in a chronic state of oxidative stress that is exacerbated when adult females take a blood meal [30] . Genome-wide transcriptional analysis revealed higher expression in the R strain of several immune genes , genes encoded by the mitochondrial genome , and genes involved in oxido/reductive processes or ROS detoxification relative to S females [30] . The R strain also exhibits impaired mitochondrial state-3 respiration and increased rate of electron leak [35] . NOX5 is a member of the NAPDH oxidase family and generates superoxide anion , which is quickly converted into hydrogen peroxide by superoxide dismutase ( reviewed by Bedard and Kraus [36] ) . We found that the genes that mediate signaling ( hep , JNK , jun , fos , and puc ) and key downstream effectors of this pathway in the midgut ( HPx2 and NOX5 ) and hemocytes ( TEP1 and FBN9 ) have increased basal levels of expression . Higher levels of HPx2and NOX5 are expected to accelerate the rate of epithelial nitration , and higher hemolymph levels of TEP1 and FBN9 would promote parasite lysis . The increase in basal expression of NOX5 may be responsible , at least in part , for the higher constitutive levels of systemic ROS that have been observed in the R strain [30] . In An . gambiae , ROS levels have been shown to modulate immunity to both bacteria and Plasmodium [30] , [37] . The dramatic reduction in melanization and the increase in parasite survival when JNK signaling is disrupted in the R strain confirm the key role of this pathway in mosquito antiplasmodial immunity . We have recently shown that some P . falciparum strains , such as NF54 , are able to infect the An . gambiae R strain and that silencing TEP1 did not enhance parasite survival , indicating that the mosquito complement-like system was not activated . In contrast , other parasite strains ( such as 7G8 ) were almost completely eliminated through a TEP1-mediated mechanism [38] . Co-infection experiments with a P . falciparum strain that is melanized and one that survives suggest that survival is genetically determined by a parasite-autonomous mechanism , because the survival ( or lack thereof ) of one strain does not affect the outcome of the other strain in the same mosquito [38] . Together , this indicates that some P . falciparum strains are susceptible to a TEP1-dependent killing mechanism , while others have the capacity to evade it . Given the critical role of TEP1 as an effector of the JNK pathway , it is likely that P . falciparum strains also differ in their ability to avoid—or perhaps may even actively suppress—activation of this signaling cascade . Detailed studies on the participation of the JNK pathway in mosquito antiplasmodial responses to different P . falciparum strains are currently under way and may shed new insights into immune evasion strategies that promote human malaria transmission .
Public Health Service Animal Welfare Assurance #A4149-01 guidelines were followed according to the National Institutes of Health Animal ( NIH ) Office of Animal Care and Use ( OACU ) . These studies were done according to the NIH animal study protocol ( ASP ) approved by the NIH Animal Care and User Committee ( ACUC ) , with approval ID ASP-LMVR5 . An . gambiae G3 and L3–5 mosquitoes were reared at 27°C with 80% humidity on a 12-h light/dark cycle . Cotton balls soaked in 10% sucrose in water were provided as previously described [39] . Approximately 15–20 female mosquitoes 3- to 4 days post-emergence were removed cold anesthetized to immobilize them . Hemocytes from individual mosquitoes were extracted using the method outlined below , Trizol was added and samples were kept on ice . Then the head was severed from the thorax , and the thorax from the abdomen using a clean scalpel . The midgut and undeveloped ovaries were then pulled from the abdomen using fine forceps . Samples from each tissue were pooled together and stored in RNAlater ( Ambion , Austin , Texas , USA ) in a microfuge tube . RNA was extracted , cDNA was generated , and gene expression was quantified using the methods indicated below for “Quantification of gene expression” ( for tissues except hemocytes ) or “Hemocyte collection and counting” ( for hemocytes ) . Fifteen to twenty whole female mosquitoes or dissected tissues were homogenized in RNAlater ( Ambion ) and subject to RNA extraction using RNAeasy ( Qiagen , Los Angeles , California , USA ) kits according to the manufacturer's instructions and first-strand cDNA was synthesized using QuantiTect reverse transcriptase ( Qiagen ) . Gene expression was assessed by SYBR green quantitative real-time PCR ( DyNAmo HS; New England Biolabs , Beverly , Massachusetts , USA ) using the CFX96 system ( Bio-Rad , Hercules , California , USA ) . Each sample was assayed using two technical replicas and 2–3 biological replicates . The amount of cDNA template present in each sample was normalized using the expression An . gambiae ribosomal protein S7 as reference . Fold change values were derived using the 2−ΔΔCt method . The values were adjusted in each experiment by dividing each of the technical replicates in the control and treatment groups by the mean of the control group , thus adjusting the control groups to a value of “1” . The statistical analysis was done using the Student's T-test after log2 transformation of the mean value of each biological replicate for each treatment . Primers used are provided in Table S11; when appropriate , primers were verified against R strain sequences obtained by Solexa transcriptome sequencing of adult S and R females ( Barillas-Mury Lab , unpublished ) . The primers used for TEP1 expression analysis in Figure 3 ( S strain ) are located in a polymorphic region between S and R strains of A . gambiae . For this reason , a different primer set in a conserved region was used for the TEP1 expression data presented in Figure 4 ( comparison between S and R strains ) . P . berghei ( GFP-CON transgenic 259cl2 strain ) parasites from frozen stocks were administered intraperitoneally to donor mice . When the parasitemias of donor mice reached 10–20% , 20–50 µl of infected blood was transferred to naïve mice via intraperitoneal injection . All mice were 3- to 5-week-old BALB/c females . Parasitemia was assessed by light microscopy inspection of Giemsa-stained thin smears obtained by tail snips . At 2–3 days post emergence , female mosquitoes were deprived of sucrose solution for 6–12 h , then allowed to feed on anesthetized mice infected with P . berghei at 3–7% parasitemia and exhibiting 1–3 exflagellation events per field , as previously described [40] . Where indicated , naïve blood-fed control mosquito groups were fed on uninfected mice of the same age . All P . berghei-infected mosquitoes and corresponding control mosquitoes were kept at 21°C and 80% humidity . Unless otherwise indicated , P . berghei infection intensities were quantified 7–9 days post infection ( dpi ) by epifluorescent microscopy inspection of dissected midguts containing GFP-expressing parasites fixed in 4% paraformaldehyde and mounted in Vectashield ( Vector Labs , Burlingame , California , USA ) , enabling manual counting of fluorescent oocysts and/or melanized ookinetes . T7 promoter sequences were introduced at both ends using two different strategies . For LacZ , NOX5 and Tep1 , cDNA fragments were amplified using the primers given in Table S11 and cloned into the pCRII-TOPO vector ( Invitrogen , Carlsbad , California , USA ) following the manufacturer's instructions . T7 promoters were introduced by amplifying the cloned insert using the primers: M13F: 5′-GTAAAACGA CGGCCAGT-3′ and M13R: 5′-CTCGAGTAATACGACTCACTATAGGGCAGGAAA CAGCTATGAC-3′ , which anneal to the vector as previously reported [8] . These PCR products were used as templates for generating dsRNA as described below . For all other genes , the T7 sequences were included in the gene-specific primers and cDNA fragments of about ∼300-bp were generated ( Table S11 ) . For all genes , sense and antisense RNAs were synthesized simoultaneously from templates and purified using the T7 RNAi Megascript kit ( Ambion ) , eluted in water , and concentrated to 3 µg/µl using a Microcon YM-100 filter ( Millipore , Bedford , Massachusetts , USA ) . About 69 nl of this dsRNA preparation was injected into the thorax of cold-anesthetized , 2- to 3-day-old female mosquitoes using a nano-injector ( Nanoject; Drummond Scientific , Broomall , Pennsylvania , USA ) fitted with a glass capillary needle according to previous protocols . dsRNA targeting LacZ was used in each experiment to control for any unspecific effect of wounding and dsRNA exposure . Efficiency of silencing was quantified 2–3 days after dsRNA injection by real-time quantitative RT-PCR with the An . gambiae ribosomal S7 gene as the internal control for normalization . Primers for silencing verification are listed in Table S11 , and silencing efficiencies are displayed in Figure S2 . Midguts were dissected and cleaned of blood meal in cold , sterile PBS supplemented with 1% levamisole ( Sigma-Aldrich , St . Louis , Missouri , USA ) . Pools of 5–10 midguts were transferred to a microfuge tube and homogenized in PBS with protease inhibitor , levamisole , and phosphoStop ( Roche Applied Science , Madison , Wisconsin , USA ) , prepared using NuPAGE buffers and reducing agent ( Invitrogen ) , and run on NuPAGE Bis-Tris 4–12% gels ( Invitrogen ) according to manufacturer's instructions . Proteins were then transferred from gels to membranes using the iBlot system ( Invitrogen ) . Membranes were blocked in TBS with 5% milk +0 . 05% Tween , washed , and incubated in fresh milk solution with primary antibody against JNK ( 1∶2000; Santa Cruz Biotechnology , Santa Cruz , California , USA ) overnight . They were then washed and incubated in fresh milk solution with alkaline phosphatase-conjugated secondary antibody against rabbit ( 1∶5000 ) for 2 h with TBS washings between each step . Membranes were finally rinsed with TBS and incubated for 30 min ( anti-JNK ) with Western Blue substrate ( Promega Corp . , Madison , Wisconsin , USA ) to visualize bands . Assays were performed according to previously established methods [8] . In brief , five midguts were dissected , fixed , and washed with PBS , then triturated and incubated in amino triazole ( 10 mg/ml ) . Pelleted midgut fragments were incubated with 2 mM levamisole , then blocked with PBT and washed . The pellet was subsequently resuspended in 50 µl of PBT , and five replicates of one-midgut equivalents ( 10 µl of the 50-µl suspension ) were incubated overnight with anti-nitrotyrosine primary antibody diluted in PBT ( 1∶3 , 000 ) at 4°C . Samples were washed with PBT and 4 were incubated with a secondary alkaline phosphatase-conjugated antibody ( 1∶5 , 000 ) diluted in PBT , while the remaining sample was reserved as a background signal control . All samples were incubated with ρNPP–ρ-nitrophenylphosphate ( Sigma Aldrich ) and read in a spectrofluorometer plate reader at 405 nm . The relative nitration for each experimental treatment was confirmed in at least two independent experiments . Female mosquitoes were cold anesthetized and injected intrathoracically with a micropipette needle loaded with hemocyte perfusion buffer ( 60% Schneider's insect medium , 30% citrate , 10% FBS ) . After insertion of the needle into the thorax , a small incision was made in the lower abdomen , and buffer was dispensed through the needle and collected 2 µl at a time from the incision using siliconized pipet tips for a total of 10–12 µl . Perfusions were then either collected into a siliconized Eppendorf tube for RNA extraction or applied to a disposable hemocytometer ( InCyto , Seoul , South Korea ) for counting . For RNA extraction , tubes were centrifuged for 30 min at 12 , 000×g to pellet the cells; supernatant was removed , and 500 µl Trizol was added . RNA was isolated according to phenol/chloroform extraction as suggested by Trizol protocol . For counting , cells were visualized under light microscope with 40× objective . Cells contained within the marked grid were separated into three cell types ( granulocyte , oenocytoid , prohemocyte ) and counted accordingly . Population proportions were calculated and total numbers of cells per mosquito were determined by manufacturer's extrapolation . Fold change differences in gene expression across groups were normalized by log transformation . The statistical analysis of differences in gene expression was done using the Student's T-test after log2 transformation of the mean value of each biological replicate from independent experiments . Oocyst distributions were determined not to be normal , and were compared to one another using the Kolmogorov-Smirnov ( KS ) , Mann-Whitney tests and Kruskal-Wallis tests with Dunn's post-test ( see Tables S3 and S10 ) . When the median infection levels of the dsLacZ group of two or more biological replicates were not statistically different using the Mann-Whitney test , the data were merged ( See Tables S3 and S10 ) . Oocyst prevalences were compared using χ2 tests . Differences in nitration levels were compared using the Student's t-test . P-values represented in figures are given in corresponding figure legends and text . All statistical analyses were performed using Prism 5 . 01 software ( GraphPad Software , La Jolla , California , USA ) . | The mosquito Anopheles gambiae is a major vector of human malaria , a disease caused by Plasmodium falciparum parasites that results in more than half a million deaths each year . Several signaling pathways in the mosquito have been shown to mediate the mosquito immune responses to Plasmodium infection . In this manuscript we investigated the participation of the Jun-N-terminal kinase ( JNK ) pathway in mosquito defense responses . We found that JNK signaling is required for mosquito midgut cells to induce expression of two enzymes , HPx2 and NOX5 , that mediate epithelial nitration in response to parasite invasion . These reactions modify the parasites and promote activation of the mosquito complement-like system that results in parasite lysis . The JNK pathway also regulates the basal level of expression of TEP1 and FBN9 , two key components of the complement-like system that are produced by hemocytes and secreted into the mosquito hemolymph . Our studies revealed that JNK signaling plays a key role for mosquitoes to limit Plasmodium infection , making it an important determinant of malaria transmission to humans . | [
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] | 2013 | The JNK Pathway Is a Key Mediator of Anopheles gambiae Antiplasmodial Immunity |
Transcriptional repression of ribosomal components and tRNAs is coordinately regulated in response to a wide variety of environmental stresses . Part of this response involves the convergence of different nutritional and stress signaling pathways on Maf1 , a protein that is essential for repressing transcription by RNA polymerase ( pol ) III in Saccharomyces cerevisiae . Here we identify the functions buffering yeast cells that are unable to down-regulate transcription by RNA pol III . MAF1 genetic interactions identified in screens of non-essential gene-deletions and conditionally expressed essential genes reveal a highly interconnected network of 64 genes involved in ribosome biogenesis , RNA pol II transcription , tRNA modification , ubiquitin-dependent proteolysis and other processes . A survey of non-essential MAF1 synthetic sick/lethal ( SSL ) genes identified six gene-deletions that are defective in transcriptional repression of ribosomal protein ( RP ) genes following rapamycin treatment . This subset of MAF1 SSL genes included MED20 which encodes a head module subunit of the RNA pol II Mediator complex . Genetic interactions between MAF1 and subunits in each structural module of Mediator were investigated to examine the functional relationship between these transcriptional regulators . Gene expression profiling identified a prominent and highly selective role for Med20 in the repression of RP gene transcription under multiple conditions . In addition , attenuated repression of RP genes by rapamycin was observed in a strain deleted for the Mediator tail module subunit Med16 . The data suggest that Mediator and Maf1 function in parallel pathways to negatively regulate RP mRNA and tRNA synthesis .
Nuclear gene transcription in proliferating cells is dedicated primarily to the synthesis of ribosomes and tRNAs . As illustrated by studies in Saccharomyces cerevisiae , the doubling of cell mass with each cell cycle involves the production of ∼200 , 000 ribosomes along with 3–6 million molecules of tRNA and consumes >80% of the nucleotides needed for transcription during this ∼100 minute interval [1]–[3] . This expenditure of metabolic energy is tightly regulated by diverse signaling pathways that sense the quality and quantity of nutrients or environmental stresses [1] , [2] . Under conditions that are unfavorable for cell growth , transcription of rDNA and tRNA genes by RNA pols I and III and RNA pol II transcription of ribosomal protein ( RP ) genes is rapidly and coordinately repressed [1] , [4] . Current evidence suggests that this coordinate response results from the convergence of specific signaling pathways on one or more transcription components in each polymerase system [4]–[9] and references therein . However , substantial gaps in understanding remain concerning the components and structure of these pathways , their targets and mechanisms of action . Studies on RP gene transcription have identified several regulatory factors including Sfp1 , Rap1 , Fhl1 , Ifh1 and Crf1 [5]–[7] , [9] and references therein but it is unclear how these proteins communicate with the general RNA pol II transcription machinery . In contrast to this complexity , a single negative regulatory protein , Maf1 , appears to serve as the conduit through which all repression signals pass in order to affect transcription by RNA pol III [4] , [10] . The Maf1 protein interacts directly with Brf1 , a subunit of the initiation factor TFIIIB , as well as RNA pol III and these interactions inhibit the assembly and function of TFIIIB-DNA complexes in vitro [10] , [11] . The functional importance of these interactions is supported by their conservation from yeast to humans [12] . The essential role of Maf1 in the repression of RNA pol III transcription demonstrates a capacity to integrate responses from multiple nutritional and stress signaling pathways that coordinately regulate ribosome and tRNA synthesis [13] . This property of Maf1 provides unique opportunities to examine the mechanisms of signal integration , the nature of the upstream pathways , their downstream targets and their effects on the transcription machinery . Yeast strains deleted for MAF1 are viable and exhibit wild-type growth rates even though 10–15% of nuclear gene transcription is refractory to repression [2] . Maf1 does not contain any motifs of known function and evidence from a variety of sources suggests that the majority of Maf1 in yeast is not stably associated with other proteins under normal or repressing conditions: Co-immunoprecipitation experiments find only 10–20% of cellular Maf1 associated with RNA pol III and <1% of Maf1 associated with Brf1 [10] , [11] . No other significant interactions have been found by affinity purification and mass spectrometry of protein complexes in yeast or in genome-wide two hybrid screens [14] . Given the limited physical interactions of Maf1 , we initiated a study of its functional relationships using synthetic genetic array ( SGA ) analysis . The local genetic neighborhood around MAF1 is highly interconnected and enriched for components of several protein complexes involved in ribosome biogenesis and RNA pol II transcription . We show that genetic interactions between MAF1 and subunits of the RNA pol II Mediator complex , in particular MED20 , are functionally linked by a common role in repression of tRNA and RP gene transcription , respectively .
A maf1Δ strain was screened in triplicate against an ordered array of ∼4700 viable gene-deletion strains and the relative growth of the double mutants was scored by computer-based image analysis [15] . Random spore analysis was then used to validate candidate genetic interactions . The initial list of MAF1 SSL interactions contained 35 genes ( Figure 1 and Table S1 ) . Subsequently , the analysis was extended to an array of ∼800 strains containing different essential genes under tetracycline ( Tet ) promoter control [16] . Consistent with the ∼five-fold higher interaction density of essential genes in synthetic genetic networks [17] , an additional 29 SSL interactions were validated by random spore analysis from triplicate screens of a maf1Δ query strain against the Tet-promoter array . The entire collection of 64 genes exhibiting synthetic interactions with MAF1 is highly enriched for a small number of functional categories , several of which are logically linked to the function of Maf1 as a transcriptional regulator of RNA pol III genes . Notably , 40% of MAF1 SSL genes ( 26/64 genes , p<7 . 0E-18 ) are involved in ribosome biogenesis or translation ( Table S1 ) . Other functional categories that are represented at significantly higher frequencies than expected by chance include RNA pol II transcription ( 9 genes , p<5 . 0E-4 ) , tRNA modification ( 6 genes , p<4 . 0E-6 ) and ubiquitin-dependent proteolysis ( 5 genes , p<7 . 9E-3 ) . These data suggest important functional relationships between MAF1 and the genes within these categories [18] . To determine the relationships between the genes in the MAF1 genetic interaction network , each SSL gene was queried against the BioGRID database [14] to compile a list of known genetic and physical interactions . These interactions were then superimposed on the set of MAF1 SSL genes and the overlap was displayed graphically using Osprey software ( Figure 2 ) . The resulting interaction network is remarkably coherent; 70% ( 45/64 ) of MAF1 SSL genes are connected by genetic or protein-protein interactions to one or more genes in the network . The majority of these interactions ( 47 gray edges out of 54 total interactions , Figure 2 ) were determined from multiple studies by affinity purification and mass spectrometry [14] and identify components of several well known macromolecular complexes ( the 26S proteasome , the ssu processome , the exosome , pre-ribosomal processing intermediates , the cytoplasmic Lsm complex , the TFIID and SAGA complexes and the RNA pol II Mediator complex ) . The connectivity between these complexes suggests that a relatively small number of biological explanations could account for the ability of MAF1 SSL genes to buffer cells that are unable to down-regulate RNA pol III transcription ( see below and in the Discussion ) . Within the broad functional category of ribosome biogenesis , defects in the synthesis of the large or small ribosomal subunits resulting from impaired rRNA processing , reduced levels of ribosomal proteins or their inefficient assembly yield synthetic phenotypes with MAF1 . Interestingly , some of these genes ( TIF6 and several RPL genes ) have previously been shown to block repression of rDNA and RP gene transcription following interruption of the secretory pathway [19] , [20] . Similarly , the genetic interaction between UTP22 and MAF1 ( Figure 2 ) suggests a functional relationship between the transcription and processing of the large rRNAs and the transcription of RP and RNA pol III genes . These functional associations reflect the role of Utp22 as a subunit of both the ssu processome and the CURI complex [21] , [22] . Based on these results , we hypothesized that other MAF1 SSL genes in the ribosome biogenesis category , along with genes in some of the other functional categories , might play a role in regulating the transcription of ribosomal components . Indeed , a survey of all the non-essential MAF1 SSL genes revealed that rapamycin-mediated transcriptional repression of RP genes was substantially attenuated in RPL20B , MRT4 , KEM1 , BUD20 , LSM1 and MED20 mutant strains ( Figure S1A and S1B ) . Relative to the untreated wild type and mutant controls , northern analysis of the affected strains showed that the levels of RPL3 and RPL28 mRNAs following rapamycin treatment were elevated three to nine fold over wild type ( Figure S1B ) . Along with the elevated levels of RP mRNAs that are seen in cells depleted for Utp22 and Tif6 [20] , [22] , it appears that a subset of MAF1 SSL genes is associated with defects in the repression of RP gene transcription . In light of the preceding observations , we were especially intrigued that Med20 ( Srb2 ) , a non-essential subunit from the head module of the Mediator complex , was among the MAF1 SSL genes exhibiting defects in the repression of RP genes . Given that the role of the head module of Mediator and of Med20 specifically , is not typically associated with transcriptional repression , we confirmed the effect of deleting MED20 on RPL3 and RPL28 mRNA levels by northern analysis of multiple biologically independent samples ( Figure S1C ) . In these experiments , rapamycin-mediated repression in the med20Δ strain was reduced 2 . 6–5 . 0 fold relative to the wild-type strain . This result led us to question why only one subunit of the 25 subunit Mediator complex [23] was identified as having a genetic interaction with MAF1 ( Figure 2 ) . Estimates of the false negative rate in SGA screens [18] and potential differences in the strength of the synthetic phenotype suggested that other Mediator subunit deletion strains might exhibit fitness defects in combination with a deletion of MAF1 . To examine these possibilities , direct random spore tests were performed on an additional nine deletion strains representing Mediator subunits from the other three structural modules of the complex; the middle , tail and Cdk modules . Growth of the haploid meiotic products was conducted at 30°C and at elevated temperatures since we had noted that MAF1 SSL phenotypes were frequently stronger under these conditions . This is illustrated for the med20Δ maf1Δ strain which shows conditional synthetic lethality at or above 35°C ( Figure 3 and Figure S2B ) . While none of the other tested Mediator subunit deletion strains exhibited fitness defects with maf1Δ at 30°C , eight of the nine deletion strains showed reduced viability and/or slow growth at higher temperatures ( Figure 3 and data not shown ) . Notably , deletion of MED16 ( SIN4 ) conferred conditional synthetic lethality at 37°C . Consistent with the fact that loss of MED16 dissociates a set of physically interacting tail module subunits ( including Med2 , Med3 , Med15 ) from the rest of the complex [24] , a similar conditional synthetic phenotype was observed with deletion of MED3 . In summary , these results extend the functional relationship between MAF1 and MED20 inferred from their genetic interaction at 30°C to subunits in every structural module of the Mediator complex . The finding that multiple Mediator subunits interact genetically with MAF1 suggests that Mediator and Maf1 function in parallel pathways . We considered that these buffering pathways might involve the transcriptional response to conditions that repress ribosome and tRNA synthesis since the role of Maf1 in repressing RNA pol III transcription entails the integration of signals that coordinately regulate these processes [4] , [10] , [13] . To examine the function of Med20 under repressing conditions , we conducted microarray experiments in wild-type and med20Δ strains that had been treated ( or not ) with rapamycin to inhibit TOR signaling ( microarray data are available at the National Center for Biotechnology Information GEO database under accession number GSE11397 ) . Messenger RNA representing each of the four conditions ( wild-type , med20Δ , ±rapamycin ) was used to prepare Cy5- and Cy3-labeled cDNAs . Pairs of dye-reversed cDNA samples were then hybridized to spotted arrays of yeast ORFs . The resulting data were filtered to select genes whose expression increased or decreased two-fold or more in any of the four pairwise comparisons ( med20Δ/MED20 , MED20±rapamycin , med20Δ±rapamycin and med20Δ+rapamycin/MED20+rapamycin , Table S2 ) and then subjected to hierarchical clustering ( Figure 4 ) . Several important conclusions emerged from these experiments: ( i ) Deletion of MED20 does not appreciably affect the global pattern of gene expression under normal growth conditions: Only 116 genes were affected beyond the two-fold cutoff in our experiments . Using the same criteria , even fewer genes were affected in a previously reported comparison of unstressed wild-type and med20Δ strains [25] ( see Text S1 ) . An analysis of the combined datasets for shared GO Bioprocess terms indicates that major cellular process such as ribosome biogenesis and assembly , translation , transcription , the organization and biogenesis of the nucleus , membranes and the cytoskeleton , as well as other processes , are largely or entirely unaffected by deletion of MED20 ( Table S3 ) . In particular , the expression of genes involved in the synthesis , processing or function of RNA pol III transcripts is not affected in the med20Δ strain and RNA pol III gene transcription is effectively repressed by rapamycin treatment in the absence of MED20 ( Figure S2A ) . Thus , a function for Med20 in RNA pol III transcription can be discounted as an explanation for its genetic interaction with MAF1 . ( ii ) Rapamycin treatment of the wild-type strain showed a characteristic response with the induction and repression of specific sets of genes representing ∼20% of the genome ( Figure 4 , Text S1 , and Figure S3 ) . As reported in other studies ( [26] and references therein ) , RP genes and genes of the Ribi regulon involved in ribosome biogenesis and related functions were strongly repressed by rapamycin while general amino acid control genes and many other Gcn4-regulated genes were strongly induced ( Text S1 ) . ( iii ) Within the group of rapamycin-responsive genes , deletion of MED20 selectively diminished the level of induction and repression ( Figure 4 ) . For example , the level of activation of a subset of Gcn4-regulated genes was attenuated significantly: Of the 197 genes whose expression after rapamycin treatment was 2–12 fold lower in the med20Δ strain than in the wild-type strain , 74 ( 38% ) were Gcn4 targets ( p = 1E-32 ) . Notably , genes involved in amino acid biosynthesis and related metabolic processes were highly enriched within this group ( 25 genes , GOID 6519 , p = 7 . 34E-19 , Figure 4C ) . These results are consistent with the requirement for Mediator in the activation of specific Gcn4-regulated genes [24] , [27] and extend this requirement to a larger group of Gcn4-target promoters by identifying a critical role for Med20 in their activation following rapamycin treatment . In addition , we found 97 out of 138 RP genes among the 170 genes whose expression following rapamycin treatment was 2 to 6-fold higher in the med20Δ strain than in the treated wild-type strain ( Figure 4B , Table S2 ) . In agreement with our expectations from northern blotting of specific RP mRNAs ( Figure S1 ) , deletion of MED20 compromises the repression of RP genes by rapamycin . The attenuated repression of RP genes in the absence of Med20 is highly specific as repression of genes in the Ribi regulon , which show nearly identical transcriptional responses under many different environmental conditions [5] , [28] , was unaffected: Similar numbers of Ribi genes were down-regulated by rapamycin in both wild-type and med20Δ strains ( 125 and 133 genes , respectively , above the two-fold cutoff ) . Moreover , only six Ribi genes ( statistically equivalent to a random distribution ) were found among the 170 genes exhibiting a two-fold or higher difference in expression when comparing rapamycin-treated med20Δ and wild-type strains . Thus , the data indicate a unique and highly selective requirement for a head module subunit of Mediator in the repression of RP gene transcription by rapamycin . RP genes are coordinately down-regulated under a wide variety of nutrient-limiting and stress conditions [1] , [28] . Virtually all of these conditions also cause Maf1-dependent repression of RNA pol III transcription [4] , [10] , [13] . Given the essential function of Maf1 in conveying the signals from diverse pathways to the RNA pol III transcription machinery , we were interested to know whether Med20 serves a general or condition-specific role in repressing RP gene transcription . Microarray profiles were generated from pairs of fluor-reversed experiments where wild-type and med20Δ strains were treated with tunicamycin , chlorpromazine ( CPZ ) , hydrogen peroxide or mild heat stress ( 29–39°C ) . In addition , expression profiles of the two strains were compared following the diauxic shift from glucose fermentation to respiratory metabolism . All of these conditions repress dramatically the transcription of RP genes [1] , [28] . Clustergram comparisons of 1063 genes whose expression differed two-fold or more in any of the six conditions ( including rapamycin ) , revealed similar profiles for rapamycin , tunicamycin , and CPZ treatments along with post-diauxic cells ( Figure S4 ) . These similarities were especially pronounced for RP genes ( Figure 5 ) , which were highly enriched among the genes exhibiting attenuated repression in the med20Δ strain ( p values ranged from 1 . 85E-9 to 1 . 7E-128 ) . These data suggest an integral role for Med20 in the repression of RP gene transcription under four of the six conditions . In contrast , no significant contribution of Med20 was evident in the down-regulation of RP genes under conditions of oxidative or mild heat stress ( Figure 5 ) . The lack of an effect on RP genes in these experiments is apparently specific since deletion of MED20 clearly affected other responses ( Figure S4 ) . For example , the induction of many heat shock genes was increased in the med20Δ strain following heat stress ( 11 out of 62 genes above the two-fold cutoff , p = 2 . 42E-8 , Table S4 ) . The recruitment of Mediator to heat shock genes and its requirement for gene activation by heat stress is well known [29] , [30] although a role for Med20 in this process has not previously been described . Similarly , the characteristic induction of many oxidative stress and heat shock response genes in hydrogen peroxide-treated cells was also increased substantially in the med20Δ strain ( 17 out of 260 genes , p = 1 . 16E-7 , Table S4 ) . The contribution of Med20 in this response is consistent with previous work demonstrating the importance of Cdk module inactivation for the induction of oxidative stress response genes [31] . Expression profiling of Mediator subunit deletion strains under normal growth conditions has revealed epistatic relationships and a pathway of signal transduction between specific Mediator subunits [25] . This led us to examine the role of subunits in the middle , tail and Cdk modules of Mediator in the repression of RP gene transcription by rapamycin . In contrast to the deletion of MED20 in the head module , deletions of MED31 and CYCC in the middle and Cdk modules , respectively , had no detectable effect on the repression of RP genes at 30°C relative to the wild-type strain ( Figure 5 , Table S5 ) . Repression of RP gene transcription was also examined by northern analysis in a strain deleted for MED13 ( SRB9 ) . This subunit in the repressive Cdk module is a direct target of protein kinase A ( PKA ) and TOR kinase signaling is thought to control ribosome biogenesis in part by antagonizing the Ras/PKA pathway [32] , [33] . However , the wildtype and MED13 deletion strains showed no differences in their response to rapamycin ( data not shown ) . These results are consistent with the genetic interaction data in that synthetic phenotypes between MAF1 and Mediator subunits from the middle and Cdk modules were not apparent at 30°C but were only revealed at 37°C ( Figure 3 ) . Deletion of MED16 ( SIN4 ) in the tail module showed a modest reduction in the extent of repression of RP genes at 30°C ( 1 . 5±0 . 2 fold relative to wild-type for the 121 RP genes yielding signals in the repressed gene set , Figure 5 , Table S5 ) . This effect is consistent with the difference in the strength of the synthetic phenotypes of the med16Δ maf1Δ and the med20Δ maf1Δ strains at 30°C . Considering that these double mutant strains are both synthetically lethal at elevated temperatures ( Figure 3 ) , the findings indicate that Med16 plays a minor role relative to Med20 in rapamycin repression of RP genes under normal growth conditions .
The large ( >1 MDa ) Mediator complex is organized into four structurally distinct modules , the head , middle , tail and Cdk modules , and functions to transduce regulatory information from DNA–bound activators and repressors to the general RNA pol II transcription machinery [23] , [34] , [35] . In addition to its role in regulating transcription , studies with temperature-sensitive head module subunits ( e . g . Med17/Srb4 ) have suggested that Mediator is essential for all transcription in vivo [36] . This is supported by the ability of Mediator to stimulate basal transcription in vitro and by the temperature-sensitivity of this stimulation in extracts of an srb4-138 mutant strain [37] . Recently , the ubiquitous function of Mediator in transcription has been questioned based on chromatin immunoprecipitation ( ChIP ) experiments showing that the association of Mediator and RNA pol II with many actively transcribed genes is not correlated [29] . Indeed , the observation that Mediator associates very poorly with the enhancer regions of RP and glycolytic genes , which together account for 50% or more of RNA pol II transcription in actively growing cells [1] , has suggested that Mediator may not be required for their transcription [29] . Other groups have reported Mediator associations with the coding regions of highly expressed genes [38] , [39] . However , Mediator binding ratios in RP coding regions are also very low ( e . g . an average binding ratio of 1 . 3 was determined from 28 experiments versus 4 . 3 from 13 experiments for RNA pol II , [38] ) . Our examination of the molecular basis for synthetic fitness defects between Maf1 and different Mediator subunits has revealed a prominent role for a non-essential head module subunit , Med20 , in the repression of RP gene transcription under several different conditions . Together with similar observations for a tail module subunit , Med16 , our results bear directly on the issue of Mediator involvement in RP gene transcription . Studies published to date have attributed the head module of Mediator with a largely positive role in transcription [25]; negative regulation by head module subunits under specific nutritional or environmental conditions has not been reported . We find that Med20 functions both positively and negatively on different subsets of genes under a range of environmental conditions ( Figures 4 , 5 and Figure S4 ) . For the induction of Gcn4-regulated genes by rapamycin , the effect of deleting MED20 is consistent with other reports showing reduced recruitment of Mediator by promoter-bound Gcn4 and diminished transcriptional activation of Gcn4-controlled genes when Med20 or subunits of the tail module are deleted [24] , [27] . For RP genes , where the association of Mediator by ChIP is poor , the evidence supporting a direct role for Mediator in repression is based on the specificity of the response and the fact that changes in gene expression in unstressed med20Δ cells are minimal and are unlikely to impact RP gene transcription ( [25] and see below ) . RP and Ribi genes show nearly identical transcription responses to environmental and genetic perturbation [5] , [28] even though the promoters of these genes generally contain different cis-acting elements ( Rap1 and/or Abf1 sites for RP genes , PAC and/or RRPE elements for Ribi genes ) . Despite these differences , both sets of genes are regulated by Sfp1 in response to nutrients and stress conditions including rapamycin [5] . The fact that the Ribi genes are repressed normally by rapamycin in med20Δ strains whereas the repression of RP genes is attenuated indicates that the TOR signaling pathway mediating this response is not impaired and suggests that the differences in repression are likely independent of Sfp1 . Molecular genetic , biochemical and structural studies indicate that deletion of MED20 does not significantly perturb the overall structure of Mediator: The absence of Med20 does not affect the assembly of other head module subunits into a stable complex [40] or the association of the head module with other modules of Mediator [23] , [24] , [41] . These data together with the crystal structure of a Med8-C-Med18-Med20 submodule and EM images suggest that Med20 occupies a peripheral position in the head module and in the complete complex [40] , [41] . In support of the limited structural effects of deleting MED20 , the expression profile of unstressed med20Δ cells shows that only a small number of genes are affected ( Figure 4 , Table S3 , [25] ) . Importantly , the annotated functions of this small group of genes do not reveal changes in transcription or other processes that might indirectly account for the attenuated repression of RP genes . Given the data indicating that Mediator is essential for all RNA pol II transcription [36] , [37] , our findings are consistent with a direct effect of Mediator on RP gene transcription under specific repressing conditions . However , as noted above , Mediator subunits are not efficiently cross-linked to RP genes in ChIP assays [29] , [38] , [39] . We infer from this that the nature of the interactions between Mediator and RP genes is fundamentally different from other genes that exhibit robust Mediator ChIP signals . One possibility is that the function of Mediator on RP genes may require only a transient association . Alternatively , the physical nature of the interaction between Mediator and the nucleoprotein complexes assembled on RP genes may not be compatible with its efficient crosslinking . Focusing on the prominent effect of Med20 ( Figure 4 ) , a third explanation is that this protein functions independently of the Mediator complex in the repression of RP genes . While we cannot exclude this possibility , it does not account for the attenuated repression observed when the tail module subunit Med16 is deleted ( Figure 5 and Table S5 ) . Moreover , the synthetic interactions between MAF1 and Mediator subunits representing each structural module of the complex imply that a function of Mediator , not just Med20 , underlies the functional relationship with Maf1 . As discussed below , a growing body of evidence supports the view that this relationship involves the coordinate regulation of ribosome and tRNA synthesis . Given the role of Maf1 in repressing RNA pol III transcription , an analogous role for Mediator in RP gene transcription is consistent with the typical interpretation of SSL interactions , namely , that the genes function in parallel pathways . Therefore , we suggest that Mediator and Maf1 function at the downstream end of distinct signaling pathways to negatively regulate RP mRNA and tRNA synthesis , respectively . Unlike deletion of MAF1 , which quantitatively blocks repression of RNA pol III transcription [4] , deletion of MED20 only attenuates repression of RP genes . Thus , the signaling pathways that repress RP genes must have multiple targets within the RNA pol II transcription machinery . Besides Mediator , what other transcriptional targets are involved in the repression of RP genes ? Previous work has identified Crf1 as a TOR kinase-regulated corepressor of RP genes [7] . We tested whether deletion of CRF1 , either by itself or in combination with a deletion of MED20 could affect rapamycin-mediated repression of RP genes in the SGA strain background ( S288C ) . Although we generated the crf1Δ strains de novo , northern analysis of multiple RNA samples did not reveal any quantitative differences compared to the controls ( data not shown ) . This result is consistent with findings in the W303 strain background [42] , indicating that the corepressor function of Crf1 at RP genes is strain-specific . Other observations suggest that the TFIID complex may participate in the repression of RP genes . TFIID occupancy of RP genes is high [43] and the transcription of RP genes is strongly TFIID-dependent [44] . This dependence reflects both a core promoter recognition function and a coactivator function of TFIID on these promoters [44] , [45] . Our SGA screens identified synthetic fitness defects between MAF1 and five TAFs , two of which ( TAF8 and TAF11 ) are unique to the TFIID complex [43] . The basis for these genetic interactions may be similar to MED20 . In other words , synthetic growth defects may result , in part , from the inability to repress RNA pol III transcription coupled with attenuated repression of RP gene transcription . This interpretation is consistent with the identification of genetic interactions between MAF1 and genes in the ribosome biogenesis category ( TIF6 and several RPL genes ) , where functional insufficiencies are known to block the repression of rDNA and RP gene transcription following interruption of the secretory pathway [19] , [20] . Another link to transcriptional control of ribosome synthesis is provided by the genetic interaction between UTP22 and MAF1 . UTP22 encodes one of three essential gene products ( the others being Ifh1 and Rrp7 ) that associate with casein kinase II ( CK2 ) to form the CURI complex [22] . This complex is thought to coordinate two parallel pathways necessary for ribosome synthesis , namely , the transcription and processing of pre-rRNA and the transcription of ribosomal protein genes . The presence of CK2 in the complex further strengthens the proposed functional association between MAF1 and ribosome synthesis based on studies of CK2 in the transcriptional response of RNA pols I and III to DNA damage [46] . Finally , we found that nearly one-fifth of the MAF1 SSL genes identified in the non-essential gene-deletion array are associated with defects in the repression of RP gene transcription ( Figure S1 ) . These observations support our hypothesis that the genetic interaction between MAF1 and MED20 is related to the combination of defects in the repression of RNA pol III and RP gene transcription . This interpretation does not exclude the possibility that other changes in the maf1 med20 double mutant strain may contribute to its synthetic phenotype . Given the genetic interactions of MAF1 with subunits of Mediator and the TFIID complex , our identification of a negative regulatory function for Med20 at RP genes suggests a possible relationship with TFIID in this process since the head module of Mediator contains a multipartite TBP-binding site that includes a direct interaction between TBP and Med20 [41] . In addition to genes involved in ribosome biogenesis and transcription , our SGA analysis of MAF1 revealed a significant functional relationship with enzymes involved in tRNA modification ( Figure 2 , Table S1 ) . This group of interactions supports a previous proposal concerning the paradoxical anti-suppressor phenotype of maf1Δ strains . Loss of MAF1 function causes a significant increase in the cellular level of mature tRNA ( from ∼10% to ∼25% of total RNA ) yet the activity of the SUP11-o nonsense suppressor decreases [47] . This anti-suppressor phenotype was suggested to result from incomplete isopentenylation of an adenine base ( A37 , adjacent to the anticodon ) which is important for tRNA decoding efficiency . A recent study of synthetic interactions between certain non-essential tRNA modifying enzymes has highlighted their function in tRNA stability and cell survival [48] . Our findings demonstrate that tRNA modifications become critical in the maf1Δ strain since the additional loss of any one of six tRNA modifying enzymes results in a synthetic growth defect ( Figure 1 ) . We anticipate that an analysis of the genetic interactions between MAF1 and this group of enzymes will provide new insights into their biological function .
Triplicate SGA screens of a maf1Δ query strain ( Y6338 Matα can1Δ::MFA1pr-HIS3 lyp1Δ ura3Δ0 leu2Δ0 his3Δ1 met15Δ0 maf1Δ::natR ) were performed against the non-essential gene-deletion array ( ∼4700 strains ) and against an array of conditionally-expressed essential genes ( ∼800 Tet-promoter strains ) . Each screen was conducted with duplicate copies of the array in a 768 colony per plate format as described previously [15] , [17] , [18] . In Tet-promoter array screens , the haploid double mutant strains were scored for growth on medium with and without doxycycline ( 10 µg/ml ) . Visual inspection and computer-based analysis of digital images was used to identify double mutant strains exhibiting fitness ( growth ) defects [18] relative to a control set of double mutants obtained using strain Y5518 ( Matα mfa1Δ::MFA1pr-HIS3 lyp1Δ ura3Δ0 leu2Δ0 his3Δ1 met15Δ0 can1Δ::natR ) . Candidate synthetic genetic interactions were validated by random spore analysis [15] , [17] at either 30°C or at elevated temperatures ( 35–37°C ) since this enhanced the severity of the synthetic fitness defect in many cases . The enrichment of GO Bioprocess terms in the MAF1 SSL gene set was calculated by hypergeometric distribution with aid of the MIPS Functional Catalogue Database . Random spore-validated MAF1 SSL genes were queried against the BioGRID Database version 2 . 0 . 23 ( released Jan 3 , 2007 ) to compile a list of 4012 interactions involving 1225 genes . Interactions were found for all but two MAF1 SSL genes ( Fyv5 , and YGL007W ) . The set of interactions was superimposed onto the MAF1 SSL gene set using Osprey software and filtered to reveal interactions between nodes in the MAF1 genetic interaction network . Strain BY4741 ( Mata ura3Δ0 leu2Δ0 his3Δ1 met15Δ0 ) and isogenic deletion strains ( xxxΔ:kanR ) were grown in YPD at 30°C to an optical density ( A600 ) of ∼0 . 2 before addition of drugs or drug vehicle , unless otherwise indicated . Treatments with rapamycin ( 0 . 2 µg/ml from a 1 mg/ml stock solution in DMSO , AG Scientific ) and CPZ ( 250 µM from a 500 mM stock solution in water , Sigma ) were for 1 hour [4] . Treatments with hydrogen peroxide ( 0 . 32 mM , Sigma ) and tunicamycin ( 2 . 5 µg/ml from a 5 mg/ml stock in 75% methanol , Sigma ) were for 30 min . and 90 min . respectively [4] , [28] . A transient mild heat shock treatment of cells growing at 29°C was achieved by centrifugation and resuspension in pre-warmed , 39°C medium for 20 min . [28] . To compare cells following the diauxic shift , an early log culture ( OD600 = 0 . 01 ) was grown for 48 hours at 30°C and then harvested . Detailed procedures for culturing cells , RNA preparation , hybridization , image acquisition and data processing for microarrays have been described [49] . Replicates of each sample were performed using a fluor-reversal strategy [50] . Microarray data have been deposited in the Gene Expression Omnibus Database under accession number GSE11397 . | The Maf1 protein is an essential negative regulator of transcription by RNA polymerase III in S . cerevisiae and functions to integrate responses from diverse nutritional and stress signaling pathways that coordinately regulate ribosome and tRNA synthesis . These signaling pathways are not well-defined , and efforts to understand the role of Maf1 in this process have been complicated by a lack of known functional motifs in the protein and by a paucity of direct physical interactions with Maf1 . To understand the biological importance of down-regulating RNA polymerase III transcription and to identify functional relationships with Maf1 , we employed synthetic genetic array ( SGA ) analysis . We show that the genetic neighborhood around Maf1 is highly interconnected and enriched for a small number of functional categories , most of which are logically linked to the function of Maf1 as the regulator of RNA polymerase III transcription . We found that deletions in a subset of MAF1 SSL genes , including subunits of the RNA polymerase II Mediator complex , lead to defects in transcriptional repression of ribosomal protein ( RP ) genes . Since Mediator subunits are not efficiently cross-linked to RP genes in chromatin , our results suggest that Mediator interactions with these highly expressed genes are fundamentally different from many other genes . | [
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] | 2008 | Genetic Interactions of MAF1 Identify a Role for Med20 in Transcriptional Repression of Ribosomal Protein Genes |
Cohesin acetyltransferases ESCO1 and ESCO2 play a vital role in establishing sister chromatid cohesion . How ESCO1 and ESCO2 are controlled in a DNA replication-coupled manner remains unclear in higher eukaryotes . Here we show a critical role of CUL4-RING ligases ( CRL4s ) in cohesion establishment via regulating ESCO2 in human cells . Depletion of CUL4A , CUL4B or DDB1 subunits substantially reduces the normal cohesion efficiency . We also show that MMS22L , a vertebrate ortholog of yeast Mms22 , is one of DDB1 and CUL4-associated factors ( DCAFs ) involved in cohesion . Several lines of evidence show selective interaction of CRL4s with ESCO2 through LxG motif , which is lost in ESCO1 . Depletion of either CRL4s or ESCO2 causes a defect in SMC3 acetylation , which can be rescued by HDAC8 inhibition . More importantly , both CRL4s and PCNA act as mediators for efficiently stabilizing ESCO2 on chromatin and catalyzing SMC3 acetylation . Taken together , we propose an evolutionarily conserved mechanism in which CRL4s and PCNA promote ESCO2-dependent establishment of sister chromatid cohesion .
Faithful genetic inheritance requires precise chromatin replication and separation of sister chromatids into two daughter cells . To ensure accurate chromosome segregation in eukaryotic cells , each pair of sister chromatids must be aligned properly and held together by a cohesin complex from S phase to anaphase [1–6] . The cohesin complex is a four-subunit ring conserved from yeast to human . In human mitotic cells , cohesin is composed of SMC1 , SMC3 , RAD21 ( Scc1/Mcd1 in yeast ) and SA1 or SA2 ( Scc3 in yeast ) [2 , 7–10] . Cohesin is widely believed to have distinct statuses according to its association with chromatin during the cell cycle . In G1 phase , it is loaded loosely onto chromatin ( i . e . , non-cohesive status ) [11] . As cells proceed into S phase , cohesin binds more tightly to hold sister chromatids together ( i . e . cohesive status ) . This transition is called the establishment of sister chromatid cohesion [5 , 12] . Although the structural bases of this transition remain enigmatic , it has been shown to depend on an essential cohesin acetyltransferase , Eco1 in yeast ( Saccharomyces cerevisiae ) [13–15] . Eco1 , whose essential substrate is proved to be Smc3 [13] , triggers the cohesion establishment during S phase through counteracting the opposing activity of Rad61 ( WAPL in human ) [16] . Cohesion is established in a DNA replication-coupled manner [12 , 17 , 18] . To achieve this , the activity of Eco1 is controlled concomitantly with DNA replication through two independent mechanisms . First , Eco1 contains a canonical PIP ( PCNA interaction protein ) box , which mediates its interaction with PCNA , the multivalent-platform of DNA replisome [19] . Second , a member of the cullin-RING E3 ligases ( CRLs ) Rtt101-Mms1 , associates with the replication fork and facilitates Smc3 acetylation via direct association between Eco1 and the substrate receptor component of the ligase , Mms22 [20] . CRLs constitute the largest ubiquitin ligase family in eukaryotes . They are modular assemblies consisting of a Cullin scaffold in complex with an adapter and distinct ligase substrate receptors , giving rise to many combinatorial possibilities . There are three cullins in budding yeast ( Cul1 , 3 and 8 ) and eight in human ( Cul1 , 2 , 3 , 4A , 4B , 5 , 7 and 9 ) [21] . Cul8 , also known as Rtt101 , is unique to budding yeast , yet it shows low sequence similarity with CUL4 . Nevertheless , the Rtt101 adaptor Mms1 is highly homologous to human DDB1 adapter of CUL4 , and Rtt101-Mms1 performs similar functions to CUL4-DDB1 ligases in other species [22] . The human genome encodes two CUL4 paralogs , CUL4A and CUL4B , which share 80% sequence identity aside from CUL4B having an extended N-terminus containing a nuclear localization signal ( NLS ) [23] . Both CUL4A and CUL4B use DDB1 as an adaptor and DCAFs ( DDB1 and CUL4-associated factors ) as substrate receptors to recognize a large number of substrate proteins [24–26] . CRL4s play recognized roles in DNA repair , replication and chromatin modifications through ubiquitylation and/or mediating protein-protein interactions [27 , 28] . Both of the two Eco1 orthologs in mammalian cells , ESCO1 and ESCO2 [29] , have been shown to acetylate SMC3 at two evolutionarily conserved lysine residues ( K105K106 ) [15 , 30 , 31] . Interestingly , ESCO1 acetylates SMC3 through a mechanism distinct from that of ESCO2 [32] . Nevertheless , how the activities of ESCO1 and ESCO2 are controlled to establish replication-coupled sister chromatid cohesion in vertebrates has not been delineated . In this study , we report that CUL4-DDB1 E3 ligases participate in establishing sister chromatid cohesion in human cells . Depletion of CUL4A , CUL4B or DDB1 results in precocious sister chromatid separation in 293T cells . We show that MMS22L ( Mms22-like ) , the human ortholog of yeast Mms22 , the substrate receptor of Rtt101-Mms1 , interacts with DDB1 . Interestingly , ESCO2 , not ESCO1 , co-immunoprecipitates with all CRL4MMS22L subunits . Dosage suppression experiments reveal that CRL4s and ESCO2 are able to compensate each other in SMC3 acetylation and thereby sister chromatid cohesion . Through introducing interaction-defective mutations , we find that ESCO2 acetylates SMC3 dependent on interactions with both CUL4-DDB1 ligases and PCNA . These suggest that CUL4-DDB1 ligases and PCNA contribute together to connect ESCO2-dependent cohesion establishment with the replication process in human .
Recently , we showed that fork-associated Rtt101-Mms1 ubiquitin ligases take part in linking the establishment of sister chromatid cohesion with DNA replication in yeast [20] . We asked whether CUL4-DDB1 , the putative functional homolog of Rtt101-Mms1 in human cells , participate in sister chromatid cohesion as well . To test this , we depleted CUL4A , CUL4B or DDB1 from 293T cells using small interfering RNA ( siRNA ) and measured sister chromatid cohesion . Cultured cells were harvested by trypsinization to enrich for cells in mitosis . Chromosome spreads were stained with Giemsa and the morphology of the mitotic cells was analyzed ( Fig 1A ) . We did not synchronize cells in metaphase with nocodazole since vertebrate cohesins are removed from chromosome arms in prophase so that only centromere cohesion can be monitored [33] . We , however , wished to monitor cohesion not only at centromeres but also at telomeres and chromosome arms , where Rtt101-Mms1 have been shown to be required for cohesion establishment [20] . In our experiments , the term “normal cohesion” is defined as the state in which both centromere and chromosome arms are closely tethered each other ( i , Fig 1A ) , whereas arm open ( ii ) , partially separated but still paired ( also called “railroad” , iii ) , unpaired ( iv ) or completely separated ( v ) chromatids indicate various extents of cohesion impairment . Under our experimental conditions , most chromatids in a single cell display similar morphology . We calculated the cohesion percentages as the proportion of “normal cohesion” cells ( i ) among total mitotic cells , where indicated . Alternatively , the percentage of cells bearing separated centromeres ( iii , iv and v , Fig 1A ) was used as an indicator as severe cohesion deficiency ( S1 Fig ) . Depletion of either CUL4A or CUL4B reduced the normal cohesion from ~80% to ~40% ( Fig 1B and 1C ) . The specificity of RNA interference ( RNAi ) was verified through complementation by over-expressing the respective proteins carrying a FLAG tag . This indicates that CUL4A and CUL4B may play at least partially non-redundant roles in sister chromatid cohesion . Similar results were observed for cells devoid of DDB1 , whereas the cell cycle progression was not significantly affected ( Fig 1D and S1A and S1B Fig ) . These results indicate that CUL4A , CUL4B and DDB1 , like their homologs in yeast , are required for efficient cohesion in human cells . Mms22 is one of the substrate adaptors of Rtt101-Mms1 in yeast . MMS22L , a putative human ortholog of Mms22 , functions together with CUL4-DDB1 in replication-coupled nucleosome assembly [34] . Yet it remains to be proved whether it is a DCAF to date . To test this , we then co-expressed GFP-DDB1 and Flag-MMS22L in 293T cells . Flag-MMS22L was immunoprecipitated by anti-FLAG antibodies from whole cell extracts . As shown in Fig 1E , considerable amounts of DDB1 co-precipitated with Flag-MMS22L , arguing that MMS22L interacts with DDB1 and is likely a new DCAF of CRL4 ligases in human . Interestingly , MMS22L depletion resulted in significant cohesion defects at both chromosome arms and centromeres , reminiscent of the results from depletion of other CRL4 subunits CUL4A , CUL4B or DDB1 ( Fig 1F and S1C Fig ) . Taken together , these data suggest that CRL4MMS22L ligases participate in sister chromatid cohesion in human cells . To answer how CRL4s affect sister chromatid cohesion , we tested whether CRL4 subunits interact with the key cohesin acetyltransferases ESCO1 or ESCO2 . We first examined the cellular distribution of ESCO1 , ESCO2 , CUL4A , CUL4B or DDB1 by immunofluorescence . RFP-labelled ESCO1 or ESCO2 and GFP-tagged CUL4A , CUL4B or DDB1 were introduced into 293T cells . In agreement with previous observations [23 , 35] , CUL4B localized to nucleus , whereas the others distributed throughout the whole cell ( S2 Fig ) , as reported previously by other groups [29 , 36] . We also detected the endogenous proteins by immunofluorescence staining with the corresponding antibodies . DDB1 seemed to be partially co-localize with ESCO2 , but not with ESCO1 ( Fig 2A and 2B ) . Given that CRL4s are ubiquitin ligases , we next compared the endogenous protein levels of ESCO1 and ESCO2 before or after DDB1-depletion . Both ESCO1 and ESCO2 were not significantly affected in the absence of DDB1 , indicating that DDB1 ligases do not function through regulation of the global levels of cohesin acetyltransferases ( Fig 3A ) . Meanwhile , in order to obtain insight into how ESCO2 is regulated , we searched for its interaction partners using affinity purification coupled mass spectrometry ( AP-MS ) . To this end , ESCO2 carrying both His6 and 5FLAG tags was over-expressed in 293T cells and subjected to tandem affinity purification . Interestingly , DDB1 , together with many histone subunits and chaperones ( e . g . , HP1 ) , was repeatedly detected among the co-purified proteins with ESCO2-HF ( Fig 3B ) . We then performed immunoprecipitations to corroborate the interaction by ectopically expressing FLAG tagged subunit of CRL4s in 293T cells . Consistently , ESCO2 clearly co-precipitated with not only DDB1 ( Fig 3C ) but also other CRL4 subunits ( Fig 3D and S3 Fig ) . On the contrary , virtually no ESCO1 was detectable in the precipitates of any CRL4 subunits in all experiments in parallel with ESCO2 ( Fig 3C and 3D and S3 Fig ) . These data indicate that CRL4 ligases might have a preferential association with ESCO2 . We had previously isolated a separation-of-function mutant in yeast , eco1-LG ( L61DG63D ) , which shows a dramatically compromised interaction with Mms22 [20] . Interestingly , the two residues are highly conserved in ESCO2 ( L415G417 ) ( Fig 3E ) , but not in ESCO1 ( S3C Fig ) , which correlates well with their different abilities to interact with CRL4sMMS22L . To validate whether these residues play a conserved role in the ESCO2 interaction , we performed a pull-down experiment . MMS22L was co-purified with GST-tagged ESCO2 WT , but barely with LG ( L415DG417D ) mutant protein ( Fig 3F ) . On the other hand , LG mutant protein exhibited acetyltransferase activity similar to WT in vitro ( Fig 3G ) , demonstrating that L415D and G417D mutations do not affect ESCO2’s catalysis . These results suggest a direct physical interaction between MMS22L and ESCO2 , which is mediated by a conserved LxG motif in ESCO2 . Meanwhile , using a similar approach , we showed that ESCO2 binds directly to PCNA , which is mainly mediated by the PIP box in ESCO2 ( Fig 3E and 3H ) . It is noteworthy that both MMS22L and PCNA were hardly detectable in the ESCO2-IP/MS analysis mentioned in Fig 3B , implying that these interactions are likely weak or transient . Taken together , these data allow us to conclude that both MMS22L and PCNA are able to directly bind ESCO2 through evolutionarily conserved motifs LxG and PIP , respectively . Given the possible interaction between CRL4s and ESCO2 , we asked whether lack of DDB1-MMS22L can be compensated by over-expressing ESCO2 . To test this , we ectopically expressed ESCO2 in a DDB1 ( Fig 4A ) or MMS22L depleted background ( Fig 4B ) . Both mild and severe cohesion defects in either DDB1 or MMS22L-depleted 293T cells were markedly rescued by over-expression of ESCO2 ( Fig 4A and 4B , S4A and S4B Fig , lane 6 ) , indicating a potent functional interaction between DDB1MMS22L and ESCO2 as well as the physical interaction shown in Fig 3 . Over-expression of ESCO2-LG mutant led to a less suppression than that of wild-type ( WT ) ESCO2 ( Fig 4A and 4B , S4A and S4B Fig , compare lane 7 to 6 ) , indicating that the role of ESCO2 is at least partially dependent on its interaction with DDB1MMS22L . In order to further address the contribution of the interaction between ESCO2 and CRL4s in sister chromatid cohesion , we tested the dosage suppression effects in an ESCO2-depleted background . In contrast to that of WT ESCO2 , expression of the interaction-defective mutant ESCO2-LG hardly displayed suppression ( Fig 4C and S4C Fig , compare lane 6 to 5 ) . This result reinforces the argument that the interaction between ESCO2 and CRL4MMS22L is important for the ESCO2’s role in cohesion establishment . Further supporting this , CUL4 , DDB1 and MMS22L were dosage suppressors of ESCO2 knockdown mutant as well ( Fig 4C and S4C Fig , lanes 7–10 ) . These results implicate that DDB1MMS22L might serve as a crucial positive regulator of the cohesion function of ESCO2 . Due to the fact that defects in sister chromatid cohesion often activate the spindle checkpoint and result in the G2/M arrest of the cell cycle , we then examined the proportion of M-phase cells ( i . e . , the mitotic index ) . The mitotic index was very low in untreated 293T cells , but rose to an average of ~12% when ESCO2 was depleted ( Fig 4D , column 2 ) , consistent with the observations from another group [37] . The G2/M arrest induced by ESCO2 knockdown was dramatically alleviated via over-expression of CUL4A or CUL4B ( Fig 4D , columns 6 and 7 ) , DDB1 ( column 8 ) or MMS22L ( column 9 ) . Taken together , these data demonstrate that the interaction between CRL4MMS22L and ESCO2 is important for ESCO2 to function in sister chromatid cohesion and thereby mitotic progression . Apart from interacting with CRL4s , the activity of Eco1 is also linked with replication forks through association with PCNA ( Fig 3H ) [19] . This notion was corroborated because the cohesion defects ( S4D Fig ) and mitotic arrest ( Fig 4D , compare lanes 10 and 11 ) in ESCO2-depleted 293T cells were significantly rescued by over-expression of WT PCNA , but not by an ESCO interaction-defective mutant PCNA-A252V . Together , these data suggest that both CRL4MMS22L and PCNA mediated interactions are critical for the ESCO2-dependent establishment of sister chromatid cohesion . Given that the essential role of Eco1/ESCO lies in catalyzing SMC3 acetylation during cohesion establishment [13 , 38] , we next examined whether the dosage suppression effects observed above are due to facilitating SMC3 acetylation . For this purpose , SMC3 acetylation was measured in 293T cell lysates via immunoblots with an antibody that specifically recognizes SMC3K105ac/K106ac . S5A Fig demonstrates that the antibody recognizes an amount of SMC3ac proportional to the total protein concentrations . ESCO2-depleted cells displayed substantially reduced SMC3 acetylation ( S5B Fig , compare lanes 1 , 2 and 7 ) , which was partially restored through ectopic expression of DDB1 or MMS22L ( S5B Fig , compare lanes 1 , 5 and 6 ) . Over-expression of either CUL4A or CUL4B stimulated SMC3 acetylation to a similar extent ( S5B Fig , lanes 3 and 4 ) . These results suggest that the compensation of cohesion defects in ESCO2-depleted cells by CUL4 , DDB1 , or MMS22L over-expression may be achieved through enhancing SMC3 acetylation . This opens the possibility that CRL4s directly participate in regulating SMC3 acetylation . Depletion of each subunit of CRL4s led to moderately compromised SMC3 acetylation ( Fig 5A and S5C–S5E Fig ) , indicating that CRL4sMMS22L are required for efficient ESCO2-dependent SMC3 acetylation . Meanwhile , the protein levels of both ESCO enzymes were not significantly affected ( Fig 5A , descending panels 3 and 4 ) , suggesting that CUL4-DDB1-MMS22L unlikely regulate the expression and/or protein turnover of ESCO1 and ESCO2 . To further validate the role of CRL4s in SMC3 acetylation , we then set out to determine whether inhibition of HDAC8 is able to restore compromised SMC3 acetylation and cohesion caused by CRL4MMS22L -depletion . Since HDAC8 is the deacetylase of SMC3 [39] , we treated proliferating cells with the HDAC8 inhibitor , PCI-34051 . Both SMC3 acetylation and cohesion efficiency increased markedly in WT and Esco2-depleted cells in the presence of PCI-34051 ( Fig 5B , lanes 1–4 ) , as reported previously [32] . The drug had a similar increase in the levels of SMC3ac and normal cohesion when DDB1 and MMS22L were depleted individually ( lanes 5–8 ) . Nevertheless , Shirahige’s group shows that PCI-34051 is not able to restore the SMC3ac levels caused by compromised PDS5A-PDS5B-ESCO1 branch [32] . This supports that DDB1 and MMS22L function in the ESCO2-catalyzed SMC3 acetylation pathway , which can be reversed by HDAC8 . Consistently , overexpression of the acetylation-mimic SMC3QQ mutant rescues the cohesion defects caused by MMS22L-depletion ( Fig 5C ) . Taken together , these data reinforce the notion that CRL4MMS22L ligases modulate the activity of ESCO2 on SMC3 acetylation , and thus cohesion establishment . Next , we directly tested whether the regulation of CRL4s on the ESCO2 activity depends on their physical interactions . For this purpose , we examined the phenotypes of several ESCO2 alleles defective in either CRL4s-binding ( ESCO2-LG ) or PCNA-binding ( ESCO2-PIP ) shown in Fig 3 . To obtain a catalytic-deficient enzyme , we also introduced the missense mutation W539G in ESCO2 , which occurs frequently in Roberts Syndrome ( RBS ) patients [40] . Indeed , the W539G allele showed a substantial decrease in SMC3 acetylation ( Fig 6A , lane 5 ) , in agreement with its location within the acetyltransferase domain . In addition , ESCO2-LG exhibited a significant decrease in SMC3 acetylation and cohesion efficacy to a similar extent as the catalytic-deficient W539 allele ( Fig 6A , lane 3 ) , indicating that CRL4s-mediated interaction is crucial for ESCO2 operating on SMC3 . Similarly , a PCNA-interaction defective allele , ESCO2-PIP , reduced SMC3 acetylation and cohesion efficacy as well ( lane 2 ) . Interestingly , when we combined both mutations on ESCO2 ( ESCO2-LG-PIP ) , we found a synergistic decline in SMC3 acetylation and cohesion ( Fig 6A , lane 4 ) . These data suggest that the function of ESCO2 is cooperatively regulated through its dual interactions with both CRL4MMS22L and PCNA . Since both CRL4MMS22L and PCNA associate with replication forks , we then analyzed the contribution of DDB1 and PCNA to recruiting/stabilizing ESCO2 on chromatin . Chromatin fractions were prepared from the 293T cells transfected with siRNAs specific to DDB1 or PCNA . Depletion of either DDB1 or PCNA led to moderately reduced amounts of ESCO2 on chromatin ( Fig 6B , lanes 6 and 7 ) , whilst the combinational depletion of DDB1 and PCNA only produced a subtle effect on the total ESCO2 levels ( Fig 6B , WCE , lane 4 ) . However , a clear synergistic loss of ESCO2 on chromatin was observed ( CHR , lane 8 ) . Consistently , the level of acetylated SMC3 largely reduced whereas the total SMC3 protein on chromatin remained virtually unaffected ( Fig 6B , descending panels 4 and 5 ) . Meanwhile , the chromatin-associated ESCO1 level only displayed a mild change ( Fig 6B , lane 8 , panel 3 ) . In good agreement with this , only when DDB1 and PCNA depletions were combined , dramatic cell death was observed by live cell staining ( Fig 6C ) . These data suggest a cooperative mechanism for CRL4s and PCNA to properly target the essential cohesin acetyltransferase ESCO2 on its substrate SMC3 , which contributes to the coupling between the establishment of sister chromatid cohesion and replication fork progression in human cells ( Fig 6D ) .
How sister chromatid cohesion is established in mammals remains largely unclear . Here we have identified an evolutionarily conserved mechanism of CRL4 ubiquitin ligases , together with PCNA , in regulation of DNA replication-coupled cohesion establishment in human cells . The essential step to establish cohesion is SMC3 acetylation by Eco1 in yeast or ESCO in human [13–15] . Precise control of the reaction is required for this essential cellular process . One of the main findings of this study is that human CRL4MMS22L ligases exclusively interact with and preferentially regulate ESCO2 . Despite the fact that both ESCO1 and ESCO2 catalyze acetylation of SMC3 , their temporal regulation is distinct from each other [32 , 37 , 41] . ESCO1 acetylates SMC3 in a Pds5-dependent manner before and after DNA replication [37] , whereas ESCO2 is believed to function during S phase . Our findings provide molecular details of how ESCO2 is controlled in a DNA replication-coupled fashion through dual interaction with CRL4MMS22L and PCNA in human cells . During the revision of this manuscript , Peters’s group reported that ESCO2 is recruited to chromatin via direct association with MCMs , the core of eukaryotic replicative helicase Cdc45-Mcm2-7-GINS [42] . Moreover , Zheng et al found that MCMs also associate with cohesin and its loader , which promotes cohesin loading during S phase [43] . It’s worth noting that the contributions of MCM , PCNA and CRL4s –mediated interactions to ESCO2 regulation are not mutually exclusive , because the defects in one of these interactions only cause partial loss of the essential function of ESCO2 . A very interesting finding in their work is that ESCO2 binds MCMs predominantly in the context of chromatin in spite of the fact that there are considerably excessive amounts of MCMs in nucleoplasm [42] . Even among the abundant chromatin-loaded MCM rings , only a small portion are activated and assembled into replication forks [44 , 45] . How ESCO2 is specifically recognized by the activated MCMs and travels with replication fork has therefore not been addressed yet . But , the interactions of ESCO2 with PCNA and CRL4MMS22L identified previously [19] and in this study , albeit relatively weak or transient , may contribute to the preferential association of ESCO2 with the activated MCMs on replication fork . Intriguingly , in HeLa cells , MMS22L-TONSL bind MCMs as well as replication-coupled H3 . 1-H4 [46–51] . Therefore , it will be of great interest to test the functional interplay among these fork-associated factors in the future . In addition to these interactions , CRL4s have been found involved in multiple replication-coupled chromatid events . For instance , CUL4-DDB1 ( Rtt101-Mms1 ) ubiquitylates histone H3-H4 , which elicits the new histone hand-off from Asf1 to other chaperones for chromatin reassembly in both yeast and human cells [34] . Another CRL4 , CRL4WDR23 , ubiquitylates SLBP to activate histone mRNA processing and expression during DNA replication [52] . Further studies are needed to illustrate the details of crosstalk among these replication-coupled events , for instance , nucleosome assembly and cohesion establishment in human cells . Over-expression of CUL4 , DDB1 and MMS22L has been reported to correlate with lung and esophageal carcinogenesis [53] , implicating them as key genome caretakers . Moreover , mutations in ESCO2 gene cause Roberts Syndromes with a predisposition to cancer [40] . The functional interplay between CUL4-DDB1 and ESCO2 identified here will shed new light on understanding the etiology of these human diseases .
HEK293T cells were cultured in DMEM media supplemented with 10% fetal bovine serum ( FBS , Gibco ) at 37°C with 5% CO2 . For RNAi experiments , cells were transfected with 80 nM siRNAs using Lipofectamine 3000 ( Invitrogen ) for 48 h following the manufacturer’s instructions . Over-expression plasmid or control plasmid for target genes was transferred into the cells when necessary . For HDAC8 inhibition experiments , 6 . 25 μM PCI-34051 ( Selleckchem ) was applied 3 h before harvest . Immunoblotting with specific antibodies was used to confirm the downregulation of the targets . The sequences of siRNA oligos used in this study are listed in Table 1 . All siRNA oligos were synthesized by Sangon Biotech , China . Trizol reagent ( CWBIO ) was used to isolate total RNA according to the manufacturer’s instructions . cDNA was synthesized using reverse transcriptase ( Promega ) . Full length genes studied were inserted to the prk5-Flag , GFP or mCherry vectors [54] . The prk5-Flag vector was kindly provided by Dr . Jun Tang ( China Agricultural University ) and was replaced by GFP or mCherry when necessary . Recombinant GST-tagged ESCO2 or its derivatives were expressed and purified according to the GE healthcare’s instructions . Pull-down and in vitro acetyltransferase assays were performed as described previously [20] . Chromosome spreads were performed as described in [43] , with minor modifications . In brief , cultured cells were harvested by trypsinization and then 75 mM KCl was used as hypotonic treatment . Cells were fixed with methanol and acetic acid ( 3:1 ) three times and then dropped onto the slides . After half an hour , cells were stained with 0 . 05% Giemsa ( Merck ) for 10 min at room temperature . Images were captured using a Leica microscope equipped with a 100×/NA1 . 3 oil objective . The incidence of sister chromatid separation was determined from at least 200 mitotic cells and all experiments were repeated at least three times . In our experimental conditions , almost all chromosomes within a single cell display similar cohesion defects . Total cohesion defects were defined as cells exhibiting precocious separation of both arms and centromeres , while CEN cohesion defects shown in the Supporting Figures were calculated as the percentages of cells bearing separated centromeres among mitotic cells . Cells were washed twice with PBS . To obtain whole cell extracts for immunoblotting , cells were resuspended with RIPA buffer ( 50 mM Tris-HCl , 250 mM NaCl , 1% TritonX-100 , 0 . 25% Sodium deoxycholate , 0 . 05% SDS , 1 mM DTT ) and lysed on ice for 20 min . For immunoprecipitation experiments , cells were resuspended in lysis buffer ( 50 mM Tris-HCl , 150 mM NaCl , 1% NP-40 , 5 mM EDTA , 10% glycerin ) and incubated on ice for 20 min , then sonicated for 30 sec . For each sample , 250 μg total protein was incubated with anti-Flag agarose for 1 . 5 h at 4°C , then washed five times with lysis buffer . All samples were run on sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) and transferred to PVDF membranes . Signals were detected with specific antibodies using eECL Western Blot Kit ( CWBIO ) . ESCO2-HF was purified from whole cell extracts by anti-FLAG M2 ( Sigma ) and Ni2+ affinity gels successively . Nonspecific bound proteins were removed by washing with 0 . 25 μg/μl FLAG peptide . Bound fraction was eluted by 2 μg/μl FLAG peptide and 300 mM imidazole , respectively . An untagged cell line and ESCO1-HF were subjected to the same procedure as controls . The final eluates were analyzed by mass spectrometry analysis ( Q Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer , Thermo Fisher ) . The procedures were repeated three times for both ESCO2-HF and ESCO1-HF to identify the different interactors of ESCO2 and ESCO1 . Cells were grown in cover glasses placed into Nunc 6-well plates . After being fixed with 4% paraformaldehyde , cells were blocked in PBS containing 2% BSA for 30 min prior to incubation with desired antibodies at 4°C overnight . After three washes with PBST , fluorescent secondary antibodies were added for 1 hr at room temperature . Cells were again washed three times with PBST and stained with 1 μg/ml DAPI for 5 min . Images were captured with a laser‑confocal microscope ( DMi8; Leica Microsystems ) . For overexpressing FP-tagged proteins , cells were transfected with plasmids using Lipofectamine 3000 ( Invitrogen ) for 24 h according to the instructions . The slides were viewed as above . Antibodies used in this work were as below: ESCO1 ( Abcam , ab180100 ) , ESCO2 ( Abcam , ab86003 ) , CUL4A ( proteintech , 14851-1-AP ) , CUL4B ( Proteintech , 12916-1-AP ) , DDB1 ( Abcam , ab9194 ) , MMS22L ( Abcam , ab181047 ) , SMC3 ( BETHYL , A300-060A ) , acetylated SMC3 ( Merck , MABE1073 ) , Orc2 ( CST , #4736 ) , Tubulin ( MBL , PM054 ) and PCNA ( Santa Cruz , sc-56 ) . | During the cycle of cell division and proliferation , each chromosome is copied into twin sister chromatids . To make sure a complete set of chromosomes are correctly passed on from generation to generation , the twins must be tethered together by a multi-protein ring called cohesin . ESCO1 and ESCO2 have been known to catalyze the acetylation of a cohesin subunit SMC3 , which triggers the establishment of sister chromatid cohesion . Here , we have shown that CUL4-DDB1 ubiquitin ligases ( CRL4MMS22L ) , in collaboration with PCNA , promote this key reaction . CRL4s selectively bind and stabilize ESCO2 on chromatin through a particular motif , which is lost in its cousin ESCO1 . This explains the functional divergence and division of labor between these two paralogs . Both CRL4MMS22L and PCNA are known components of the moving DNA replication machines . So , our results help us to understand how twin sister chromatids become cohesive concomitantly with chromosome duplication process in human cells . | [
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] | 2019 | Cul4-Ddb1 ubiquitin ligases facilitate DNA replication-coupled sister chromatid cohesion through regulation of cohesin acetyltransferase Esco2 |
Examples of metabolic rhythms have recently emerged from studies of budding yeast . High density microarray analyses have produced a remarkably detailed picture of cycling gene expression that could be clustered according to metabolic functions . We developed a model-based approach for the decomposition of expression to analyze these data and to identify functional modules which , expressed sequentially and periodically , contribute to the complex and intricate mitochondrial architecture . This approach revealed that mitochondrial spatio-temporal modules are expressed during periodic spikes and specific cellular localizations , which cover the entire oscillatory period . For instance , assembly factors ( 32 genes ) and translation regulators ( 47 genes ) are expressed earlier than the components of the amino-acid synthesis pathways ( 31 genes ) . In addition , we could correlate the expression modules identified with particular post-transcriptional properties . Thus , mRNAs of modules expressed “early” are mostly translated in the vicinity of mitochondria under the control of the Puf3p mRNA-binding protein . This last spatio-temporal module concerns mostly mRNAs coding for basic elements of mitochondrial construction: assembly and regulatory factors . Prediction that unknown genes from this module code for important elements of mitochondrial biogenesis is supported by experimental evidence . More generally , these observations underscore the importance of post-transcriptional processes in mitochondrial biogenesis , highlighting close connections between nuclear transcription and cytoplasmic site-specific translation .
Cell construction requires the tight linking of various molecular processes , from nuclear transcription to the site-specific production of proteins . The control of the orchestration of these processes remains poorly understood . In classical experimental conditions , coordinated waves of transcription are difficult to observe because of the metabolic asynchrony of the cells in growing cultures . A yeast system with properties avoiding these difficulties was recently described . In well-defined continuous cultures of Saccharomyces cerevisiae , the oxygen consumption rate oscillates with a constant period [1] , implying that cell-to-cell signaling synchronizes oxidative and reductive functions in the culture . The gene-expression dynamics of the yeast metabolic cycle is therefore a useful model system for studies of the lifecycle of groups of transcripts in eukaryotic cells [2] . Indeed , microarray studies have demonstrated periodicity in the expression of the yeast genome , and consequently the existence of similar temporal expression patterns in functionally connected groups of genes [3] . Genes specifying functions associated with energy appeared to be expressed with exceptionally robust periodicity , consistent with the variations in the amount of dissolved oxygen in the medium of synchronized culture . In pioneering studies [4] , it was shown that yeast mitochondrial morphology oscillates in response to energetic demands driven by the ultradian clock output . In this work , our purpose was to distinguish temporal gene clusters , which may allow describing a biologically relevant scenario of mitochondria biogenesis . Depending on the addressed points and on the quality of the microarray data , several methods such as SVD ( Singular Value decomposition ) , PCA ( Principal Components Analysis ) , self-organizing maps , wavelet multiresolution decomposition and FFT ( Fast Fourier Transform ) have been used to analyze relevant transcript data [5] . We decided to use a model-based approach [6] to decomposition of published expression data for the 626 oscillating nuclear genes encoding mitochondrial proteins . We established a classification of these genes into temporal groups , which cover the 5-hour long metabolic cycle , and present a dynamic and global picture of mitochondrial biogenesis . These temporal groups correlate both with particular functional properties of the corresponding proteins and with specific translational sites in the cell . This global description of mitochondrial transcriptome clusters in temporal phases is consistent with the concept of RNA regulons , according to which post-transcriptional RNA operons may constitute an important element of eukaryotic genome expression [7] , [8] .
Microarray data from the study by Tu et al . [3] were collected from the Gene Expression Omnibus database [9] , under accession number GSE3431 . This dataset comprised the normalized gene expression values , i . e . the median of each array ( all data points ) equal 1 , used by Tu et al . [3] in their pioneering analysis . Tu et al . [3] performed microarray experiments at 25-minute intervals , over three consecutive metabolic cycles ( the length of one cycle is ∼300 minutes ) . For each gene , expression measurements were thus available for 36 successive time points . We considered only those genes for which expression measurements were available and which ( i ) displayed significant periodic patterns , as defined by Tu et al . [3] ( ∼3552 genes with a confidence level greater than 95% ) and ( ii ) were identified as involved in mitochondrial biogenesis , as defined by Saint-Georges et al . [10] ( ∼794 genes ) . The resulting expression matrix comprised data for 626 genes ( the complete list is available in Dataset S1 ) . To cluster genes whose RNA level peaks at the same time points in the yeast metabolic cycle ( YMC ) , we used the ω -values obtained for each gene using EDPM algorithm ( see previous paragraph ) . Pearson correlation coefficients ( r ) were calculated between all W vector pairs , and hierarchical cluster analysis was applied . This classical clustering method can be summarized as follows: ( 1 ) Distances ( d ) between all W vector pairs is calculated using Pearson's correlation analysis ( d = 1−r ) ; ( 2 ) The resulting distance matrix is thoroughly inspected to find the smallest distance; ( 3 ) The corresponding genes are joined together in the tree and form a new cluster; ( 4 ) The distances between the newly formed cluster and the other genes are recalculated; ( 5 ) Steps 2 , 3 and 4 are repeated until all genes and clusters are linked in a final tree . We searched for cis-acting signals in 3′ and 5′UTR sequences , using motifs predicted by the MatrixREDUCE algorithm [11] . For 3′UTR signals , we tested several motifs identified in previous studies [10] , [12] as possible binding sites for mRNA stability regulators in Saccharomyces cerevisiae . For 5′ UTR signals , we examined upstream regions between nucleotide positions −600 and −1 and searched for motifs between 1 and 7 nt long . We assessed whether any of the signals were observed at a frequency greater than that expected by chance , by calculating p-values as described in [13] ( hypergeometric distribution ) . We then search the YEASTRACT database for transcription factors with DNA-binding sites matching the motifs identified with MatrixREDUCE [14] . The EDPM algorithm was implemented in R programming language ( http://cran . r-project . org/ ) and functions were numerically minimized using the quasi-Newton method ( R function available in the BASE package ) . Hierarchical clustering was carried out with the “hclust” function ( also available in R programming language ) , with the “ward” method for gene agglomeration . MatrixREDUCE source code is freely available online from http://bussemaker . bio . columbia . edu/software/MatrixREDUCE/ and was used for analyses of upstream sequences with default parameters ( see the documentation available online for more information ) . All the strains used in this study are isogenic to BY4742 ( MATα; his3Δ1; leu2 Δ0; lys2 Δ0; ura3Δ0 ) from the Euroscarf gene deletion library .
This analysis leads to the prediction that unknown cluster A genes translated in the vicinity of mitochondria in a Puf3p-dependent way ( class I ) are likely to be involved in early steps of mitochondria biogenesis . To test this experimentally , we examined the properties of nine strains carrying deletions of uncharacterized cluster A/class I genes ( Figure 6B and Dataset S3 ) . For each mutant strain , we checked the ability to grow on non fermentable carbon sources and tested the assembly of respiratory complexes III and IV by recording cytochrome spectra ( see Methods ) . Disturbance of early steps of mitochondrial biogenesis —for example replication of mitochondrial DNA , mitochondrial transcription and translation— can affect maintenance of mitochondrial DNA [18] , we also tested whether these mutant strains retained the mitochondrial chromosome by measuring the production of petite cells ( rho− ) . The phenotypes of these deleted strains are presented in Figure 6C . Strikingly , seven out of the nine gene-deleted strains displayed severe respiratory dysfunctions ( poor growth on non-fermentable media ) and/or alterations in their cytochrome spectra . These phenotypes strongly suggest that most of the unknown phase A/class I genes have functions in mitochondrial transcription/translation or assembly of respiratory complexes . This is strongly in favour of the idea that during this short period ( phase A lasts only 25 minutes , Figure 4A ) , there is a surge in the abundance of mRNAs important for mitochondrial biogenesis and that they are translated at particular subcellular localization .
It was recently observed [3] , [19] that yeast cells can be synchronized and exhibit synchronous waves of storing and then burning carbohydrates . Using microarrays , it was shown that many nuclear genes coding for mitochondrial proteins , have their mRNAs which oscillate and peak at a time when highest rate of respiration has passed . It was suggested [19] that cells are either rebuilding or duplicating their mitochondria at this time . We took advantage of these data to better analyze the mitochondria rebuilding program and identified new gene clusters reflecting spatio-temporal groups of gene expression . Our findings are entirely consistent with the notion of RNA regulons [7] , [8] , according to which mRNA-binding proteins ( RBP ) play an important role , coordinating the various post-transcriptional events . We show here that 262 mRNAs coding for important mitochondrial proteins ( assembly factors , ribosomal proteins , translation regulators ) are coordinately and periodically present in increasing amounts early in the mitochondrial cycle ( phase A = 25 minutes ) . In addition , most of these mRNAs are specifically localized in the vicinity of mitochondria under the control of the protein Puf3p . This suggests that during this particular time-window , Puf3p acts in the control of mRNA localization/translation . During the rest of the mitochondrial cycle , Puf3p may function ( possibly in association with other RBPs ) either in mRNA degradation [20] or in the control of bud-directed mitochondrial movement [21] . Following this early phase A , phases B ( 50 minutes ) and C ( 50 minutes ) concern elements of the fundamental mitochondrial machineries ( respiratory chain complexes , TCA cycle , etc . ) . Undoubtedly , this chronology of events should reflect the logic of mitochondria construction . This point can be illustrated with the well-documented assembly process of cytochrome c oxidase ( COX ) [22] , [23] , a fascinating process involving the sequential and ordinate addition of 11 subunits to an initial seed consisting of Cox1p ( Table 1 , “core” and “shield proteins” ) . In addition to the structural subunits , a large number of accessory factors are required to build the holoenzyme . Unexpectedly , we found that all the mRNAs for these accessory factors are relatively abundant early in mitochondrial biogenesis , that is during phase A . Cluster A includes genes whose expression is essential for a preliminary step , consisting of the synthesis of all the elements ( RNA polymerase , ribosomes , translation factors ) required for mitochondrial production of Cox1p; this step is followed by the construction of the core enzyme ( Cox1p+Cox2p+Cox3p ) . We also observed that the mRNAs coding for the 18 assembly factor transcripts involved in COX assembly [22] , [24] are mostly found during phase A ( Table 1 , “assembly factors” ) and , in addition , all but one are translated in the vicinity of mitochondria under the control of Puf3p ( MLR class I , [10] ) . The situation is very different for structural COX proteins ( shield proteins of the complex ) . Except for Cox5A , all the corresponding mRNAs are found in phase B , indicating that the corresponding genes are expressed after those of phase A . Unlike phase A mRNAs , they are all translated on free cytoplasmic polysomes ( MLR class III , [10] ) . This scenario agrees with the previous biochemical description of short intermediates [23]; especially interesting is the observation that Cox5Ap , found here in phase A , was previously identified as the first structural protein added to the S2 complex [23] . The properties of COX assembly described here are common to the other respiratory chain complexes . The mRNAs for assembly factors mostly peak in phase A and they are translated close to mitochondria , under the control of Puf3p; they initiate the formation of respiratory complexes by the successive addition of structural proteins whose mRNAs mostly peak in phase B . This is the first evidence that , at least in the conditions described in [3] , the construction of the respiratory chain is one of the first steps of mitochondrial biogenesis; indeed , all the production machinery ( assembly factors , translation , etc . ) are available in phase A to produce and assemble the protein complexes in phase B . Genes coding for mitochondrial proteins can be classified into two different regulatory systems . This dichotomy is well illustrated in the case of OXPHOS complexes coding genes . The first class corresponds to mRNAs translated to the vicinity of mitochondria , mainly present in phase A and which code , for instance , for assembly factors . Genes of the second class code for structural proteins , and are found mainly in phases B or C during which transcription regulation is the major mechanism . Previous studies suggested that genes coding for assembly factors are not transcriptionally regulated [25] . We confirmed and extended these preliminary observations by showing that genes encoding assembly factors: ( i ) are expressed before genes encoding structural proteins , ( ii ) have a functional Puf3p binding site which controls localization/translation to the vicinity of mitochondria and may thus generate discrete foci on the matrix face of the mitochondrial membrane , and ( iii ) do not contain any evident signals in their 5′UTR , a feature which distinguishes them from the genes encoding structural proteins . The mRNAs for translation and assembly factors are all expressed only during phase A , but mRNAs for structural proteins are found during phases A , B and to a lesser extent C . This is likely to reflect the timing of the building of the various complexes . Thus , for instance , COX assembly requires an intact functional ATPase [22] , which is in agreement with the fact that mRNAs for ATPase structural proteins are mostly found in phase A ( see Dataset S4 ) whereas the COX equivalents are mostly in phase B ( see Dataset S4 ) . Also , unlike genes encoding assembly factors , genes coding for structural proteins of the respiratory chain complexes are mainly controlled transcriptionally . According to the environmental conditions , either Hap4p ( depending on carbon availability [26] , [27] ) or Hap1p ( depending on oxygen concentration [28] ) , regulate the transcription of nuclear genes coding for structural proteins . Binding sites for these two transcription factors are present significantly more frequently than expected from a random distribution in the genes of clusters A , B and C ( Figure 5A ) . In addition , the amounts for both HAP4 mRNA and HAP1 mRNA also oscillate and peak in phases A and C , respectively ( Figure 5C ) . HAP1 mRNA variation is interesting because Hap1p can repress its own transcription and may act either as a repressor or as an activator , depending on oxygen levels [29] . It was observed that fluctuating levels of O2 dissolved in the culture , indicates changing activities of mitochondrial oxygen consumption and cellular redox switching [30] . Thus , Hap1p , which is an oscillating redox sensor , is an excellent candidate to signal the transition between non-respiratory rebuilding and respiratory phases ( Figure 5D ) . Overall , we report a comprehensive picture of the biogenesis of yeast mitochondria and illustrate spatio-temporal differences between groups of nuclear genes . The unexpected finding that transcriptionally or post-transcriptionaly regulated groups of genes are expressed both at different times and translated in different places may be of relevance to mitochondria in other species . Indeed , mammalian β F1-ATPase mRNA is found in the outer membrane and is translated , under the control of 3′UTR signals and RNA-binding proteins [31] , only during cell cycle phase G2/M [32]; this gives credence to the general applicability of our observations . Studies with human cells are currently underway to assess the similarities and differences between yeast and human cells regarding these aspects of mitochondrial biogenesis . | In bacterial and eukaryotic cells , gene expression is regulated at both the transcriptional and translational levels . In eukaryotes these two processes cannot be directly coupled because the nuclear membrane separates the chromosomes from the ribosomes . Although the transcription levels in different cellular conditions have been widely examined , genome-wide post-transcriptional mechanisms are poorly documented and therefore , the connections between the two processes are difficult to explain . In this work , the time-regulated expression of the genes involved in the construction of the mitochondrion , an important organelle present in nearly all the eukaryotic cells , was scrutinized both at transcriptional and post-transcriptional levels . We observed that temporal transcriptional profiles coincide with groups of genes which are translated at specific cellular loci . The description of these relationships is functionally relevant since the genes which are transcribed early in mitochondria cycles are those which are translated to the vicinity of mitochondria . In addition , these early genes code for essential assembling factors or core elements of the protein complexes whereas the peripheral proteins are translated later in the cytoplasm . Also , these observations support the concerted action of important regulatory factors which control either the gene transcription level ( transcription factors ) or the mRNA localization ( mRNA-binding proteins ) . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"computational",
"biology/genomics",
"computational",
"biology/transcriptional",
"regulation"
] | 2009 | Spatio-Temporal Dynamics of Yeast Mitochondrial Biogenesis:
Transcriptional and Post-Transcriptional mRNA Oscillatory Modules |
Escherichia coli cyclic AMP Receptor Protein ( CRP ) undergoes conformational changes with cAMP binding and allosterically promotes CRP to bind specifically to the DNA . In that , the structural and dynamic properties of apo CRP prior to cAMP binding are of interest for the comprehension of the activation mechanism . Here , the dynamics of apo CRP monomer/dimer and holo CRP dimer were studied by Molecular Dynamics ( MD ) simulations and Gaussian Network Model ( GNM ) . The interplay of the inter-domain hinge with the cAMP and DNA binding domains are pre-disposed in the apo state as a conformational switch in the CRP's allosteric communication mechanism . The hinge at L134-D138 displaying intra- and inter-subunit coupled fluctuations with the cAMP and DNA binding domains leads to the emergence of stronger coupled fluctuations between the two domains and describes an on state . The flexible regions at K52-E58 , P154/D155 and I175 maintain the dynamic coupling of the two domains . With a shift in the inter-domain hinge position towards the N terminus , nevertheless , the latter correlations between the domains loosen and become disordered; L134-D138 dynamically interacts only with the cAMP and DNA binding domains of its own subunit , and an off state is assumed . We present a mechanistic view on how the structural dynamic units are hierarchically built for the allosteric functional mechanism; from apo CRP monomer to apo-to-holo CRP dimers .
The Escherichia coli cAMP Receptor Protein ( CRP ) ( also known as Catabolite Activator Protein , CAP ) activates the transcription of more than 150 genes . Upon binding of cAMP ( cyclic AMP ) , the transcriptional activity of CRP is altered resulting in a change in the affinity for its target CRP-dependent promoter region on the DNA . CRP is then able to recruit RNA polymerase ( RNAp ) for the transcription activation to begin . CRP is a 47 kDa homodimer with 209 amino acid residues in each monomer where individual subunits fold into two domains [1] . The N-terminal domain , extending from V1 to N133 , contains the primary cAMP binding site ( anti cAMP ) and mediates subunit-subunit interactions . This domain is formed by α helices A , B , C and eight β strands 1 to 8 . The C-terminal domain extends from V139 to R209 with α helices D , E , F and four β strands 9 to 12 , which has the helix–turn–helix ( HTH ) motif involved in the specific DNA and secondary cAMP binding ( syn cAMP ) . Two domains are connected by the linker or hinge region L134-D138 [2] . The cAMP molecules in the primary cAMP binding sites interact mainly with residues G71 , E72 , R82 , S83 , T127 and S128 of the other subunit [3] . The active state that initiates transcription is accepted to be with two anti cAMPs bound form . The syn cAMP molecules can bind CRP only when the two anti cAMPs are bound . The syn cAMPs interact with the HTH motif ( mainly R180 ) and β strand of the cAMP binding domain ( E58 ) , the DNA , and A135 of the other subunit [4] . Two cAMP molecules ( anti and syn ) bind to each subunit , making a total of four cAMPs per dimer . The binding of four cAMPs to CRP occurs at millimolar concentrations , yet the cAMP concentration is at micromolar levels in vivo [5] . The role of CRP-cAMP4 complex in the transcription mechanism is yet unknown [2] . CRP dimer interacts with DNA by a two-fold symmetric consensus DNA sequence . R180 , E181 and R185 are the key residues of the HTH motif involved in specific protein-DNA interactions [6] , whereas the nonspecific DNA binding residues are R169 , Q170 and S179 [7] with the other DNA binding sites [8] D138 , V139 , T168 , C178 , T182 , G184 , K188 , H199 , and G200 . Next to the HTH motif , residues A156-Q164 ( activating region 1 , AR1 ) are responsible for the transcription activation of lac class I and class II CRP-dependent promoters . The photo-cross-linking experiments [9] indicate that the C-terminal domain of the RNAp α-subunit ( αCTD ) binds one of the CRP subunits contacting AR1 , whereas the other CRP subunit makes contacts with the other parts of the RNA polymerase . Then αCTD interacts with the minor groove adjacent to the DNA site for CRP . Thus , the transcription activation at lac promoter involves both protein-protein and protein-DNA interactions [10]–[13] . In class II promoters , the interaction with RNAp is complemented by two more activating regions of residues H19 , H21 , E96 , and K101 ( AR2 ) and K52–E55 and E58 ( AR3 ) [14] . The X-ray crystal structure of holo CRP and the functional sites are shown in Figure 1A and B , respectively . Within the sequence of events up to the transcription activation , the structural and dynamic characteristics of CRP and the activation mechanism through ligand binding can best be described by the comparison of the apo and holo states . The structures of holo CRP with and without the DNA have been elucidated since its first isolation from E . Coli in 1970 [15] . Several biochemical , genetic and biophysical experiments [2] , [5] , [13] , [14] , [16] along with computer simulations [17]–[19] have been performed to understand the cAMP allosteric switch mechanism from the inactive to active states . The three previous simulations [17]–[19] were based on the crystal structure of holo CRP dimer from which the apo state and the state with a single cAMP were also modeled . There are several recent studies and reviews addressing the allostery in CRP [2] , [14] , [19] . The NMR solution structure of apo CRP [20] and X-ray crystal structures of holo CRP [21] , [22] have contributed significantly to the understanding of allosteric mechanism in play [20] , [23] . The basic mechanism of allosteric control is the transmission of the signal from the cAMP to DNA binding domains upon anti cAMP binding and the interaction of cAMP adenine base atoms with the side chain hydroxyls of residues T127 and S128 . This induces a coil-to-helix transition and the elongation of C-terminus of C-helix by 11 residues . The change in the secondary structure content with a rearrangement of the hinge [14] , [23]–[25] directs the domain movements and triggers the rotation and translation of the DNA binding domain placing the F-helices into the required orientation to enter into the major grooves of the DNA . The shift in the inter-domain hinge was first observed by a structural inspection at the secondary structure level by NMR [24] . The hinge region appears to play a key role by modulating the inter-domain interactions and stabilizing the altered domain movements leading to the transcriptional activation [2] , [14] , [20] . The two states of the coiled-coil and the transition towards the ordered form coupled to the ligand binding functions as a regulatory switch . This ensures the precise allosteric control during the protein's functioning [20] . These studies suggest that the rearrangement of the hinge with the ligand binding is a critically conserved feature that controls the global allosteric transformation in CRP–family structures [14] . Thus , the majority of the CRP* ( cAMP independent mutant of CRP ) mutations in apo CRP are found to achieve proper allosteric transition by modifying the hinge-mediated inter-domain network pre-existing in apo CRP [14] , [26] , [27] . Although many X-ray crystal/NMR structures of different states of CRP are available , further structural information on the naturally occurring functional states of CRP , like CRP-cAMP1 , CRP-cAMP-RNAp , is required for a complete understanding of CRP's allosteric mechanism and the functional implication of each state . Further to the structural understanding of apo and holo states , allostery was also put forward as a dynamic relationship [28] , [29] . Besides a mechanical view [30] , [31] of structural changes , the dynamics is also of interest in the allosteric regulation of protein activity . With the changes in the protein motion , an allosteric communication may evolve [29] . Here , the dynamics of apo CRP monomer/dimer and holo CRP are explored computationally by extensive Molecular Dynamics ( MD ) simulations combined with the Gaussian Network Model ( GNM ) [32] , [33] analysis of MD sampled conformations . The GNM analysis was also performed on apo CRP NMR solution structures and the holo CRP crystal structure . We mainly focus on how the fluctuation dynamics provide the basis for an allosteric communication and describe a conformational switch with its off and on states and how this behavior is evolved from the dynamics of apo CRP monomer to apo-to-holo CRP dimer . The dynamic infrastructure for the coordination between the effector and DNA binding domains is largely observed in apo CRP monomer . This is an example of the pre-existence of functional dynamic states in the smaller subunits of the structure and also the fact that the inter-domain hinges are not simply linkers that connect the two domains but coordinate the global structural motion , where the key dynamic states are pre-encoded [34] .
The root-mean-square deviation ( RMSD ) profiles of the conformations sampled by the MD simulations of apo CRP monomer/dimer and holo CRP dimer are given Figure 2A . Apo CRP monomer and dimer reach similar RMSD values ( ∼5 Å ) in a time window of 150 ns , yet the structural fluctuations are higher for the monomeric state in the first 50 ns . The parallel runs of apo CRP monomer display different profiles as a result of a kink in the C-helix at residues A121-R122 ( Figure S2A ) . The rise of the RMSD in the second run is due to progression of the kink further along the simulation . The main structural change observed during the simulations for apo CRP dimer is the conformational change observed in the C-terminal of C- and F-helices of both subunits , which is more dramatic in apo CRP monomer . In apo CRP NMR structure , the V126-F136 region of C-helix is found to be coiled [20] , whereas we have observed coil-helix transitions in all apo CRP monomer and dimer MD simulations ( See Figure S1A , B for RMSD plots of all CRP MD simulations ) . The latter could be due to unstable and intrinsic conformational preferences of residues in the C terminal of C-helix , unless the Amber force field used over stabilizes α-helices [35] , [36] . The holo CRP dimer , on the other hand , displays lower RMSD values with a conformationally stable C-helix . An average RMSD value of 3 Å is mainly due to the reorientation of the DNA binding domain of subunit A . To this end , the MD simulations suggest that apo CRP monomer is relatively stable , although larger conformational space is accessible in this state . The apo dimer state is more stable and the holo state represents the most stable form . The biochemical studies indicated the role of cAMP binding in modulating the equilibrium between the monomer and dimer forms and this could be important for the different steps of the regulatory mechanism [37] . Figure 2B presents the mean-square fluctuations ( MSF ) ( ) in residue positions for apo CRP unbound monomer/dimer and holo CRP dimer averaged over all MD runs with standard deviation values ( See Figure S1B for MSF profiles as is for all seven runs ) . The differences between average MSF values of different states are given as a sub-plot . The difference-MSF of apo CRP monomer and dimer ( averaged over subunits ) is color-coded on a snapshot of apo CRP monomer in Figure 2C . The MSF profiles display that the loop regions tend to have higher fluctuations , whereas β strands and α helices show more restricted motion as expected . Unbound monomer shows significantly higher fluctuations compared to apo/holo dimers for residues G33 , D53-K57 ( overlapping the RNAp interacting residues K52-E58 region/AR3 ) , dimerization interface of C-helix ( P110-T127 ) , linker/hinge region ( L134-D138 ) , and α helices E and F ( Q170-R180 ) , which overlap with some of the secondary cAMP binding sites ( Q170 , Q174 , G177 , R180 ) and nonspecific ( R169 , Q170 , S179 ) /specific ( R180 , E181 , R185 ) DNA binding sites . Dimerization stabilizes the C-helix at the interface and decreases the mobility of the primary cAMP binding ( T127 and S128 ) and DNA binding residues . Moreover , the binding of the cAMPs further stabilizes the inter-domain hinge and the DNA binding residues . The DNA binding and RNAp interacting sites , as well as the inter-domain hinge region , appear more stabilized with dimerization . CRP as a dimer should be in a more favorable dynamic state for the DNA and RNAp binding as well as for the secondary cAMP binding . Nevertheless , most of the primary cAMP binding sites' dynamics is already defined in the monomeric form . The experimental evidence suggests that the cAMP binds to the dimer [37] and the latter might still be taken as a hint of the dynamics for the order of events in various binding interactions of CRP . For example; the secondary cAMP binding follows the binding of the primary cAMPs [14] . The correlations between residue fluctuations are also of interest here to understand the cooperativity in residues' motion . Figures 3A–3C display the correlations between residue fluctuations based on the first ten essential modes for apo CRP monomer/dimer and holo CRP dimer , respectively . The correlations between residue fluctuations are further evaluated below through the GNM analysis of the MD sampled conformational states of apo CRP monomer/dimer and holo CRP ( Figures S4A–G ) . Apo NMR model and holo crystal structures are also analyzed by GNM ( Figures S5 , Figure 4 ) . The hinge sites are the key mechanical sites for the cooperative motion that we observe in the correlation maps . Although apparent from the MD mean-square fluctuations ( Figure 2B ) , the GNM predictions by the slow mode shapes of MD cluster best members clearly reveals the most global hinge region of CRP as the inter-domain hinge L134-D138 ( Figure S7 ) . The intra-domain hinges/flexible sites K52-E58 of the cAMP binding domain and G173-V176 of the DNA binding domain are evident in apo and holo CRP dimers , which are also observed in apo CRP monomer yet with less stability . On the other hand , the intra-domain flexible site P154-A156 of the DNA binding domain appears in apo CRP monomer and dimer , and becomes less evident in holo CRP dimer ( See Figure S7F for the sites labeled on structure ) . This may imply that this site close to the RNAp binding site may have a plausible role in the cAMP unbound state . The GNM cross-correlation maps of apo NMR structures , holo CRP crystal structure and the conformations from the apo CRP monomer/dimer and holo CRP dimer MD simulations suggest a conformational switch mechanism mediated by the L134-D138 hinge in the allosteric communication of the DNA and cAMP binding domains . The network of cooperative fluctuations describes the off and on states of this switch: In the on state , the L134-D138 hinge displaying stronger fluctuations with the L134-D138 hinge of the neighboring subunit and the K52-E58 and G173-V176 regions in the cAMP and DNA binding domains , respectively , of both subunits , is able to coordinate the movement of the domains and the interactions in between . The coordinated behavior of the two DNA binding domains should provide the DNA binding residues with a more favorable dynamics for the DNA binding . On the other hand , the L134-D138 hinge is no longer a global hinge and only correlated with the DNA binding residues of its own subunit in the off state , when the coordination between the key functional elements of the structure is weakened . The on state and the off state are as well strongly coupled with the conformational preference of the C terminus of C helix . This preference is apparently correlated with the position of K52-E58 with respect to C helix . The highly evolutionary conserved residues of the inter-domain hinge L134 ( 8 ) , A135 ( 9 ) , F136 ( 9 ) , L137 ( 5 ) , D138 ( 9 ) ( ConSurf [40] scores are listed in parentheses; 9 to 1 referring to most conserved to most variable ) should imply their certain conformational preferences and further support the functional importance of this linker . Similarly , L52-E58 , P154-A156 and G173-V176 appear also as highly conserved ( K52 ( 7 ) , D53 ( 7 ) , G56 ( 5 ) , E58 ( 8 ) ; P154 ( 9 ) , D155 ( 7 ) , A156 ( 9 ) and G173 ( 7 ) , Q174 ( 8 ) , I175 ( 7 ) , V176 ( 9 ) ) . The conserved nature of P154-A156 shows the dynamic importance of also this flexible site in the DNA binding domain and its communication with the cAMP binding domain . The overall evolutionary conservation profile of CRP ( See Figure S6 ) demonstrates that the most conserved patches predominantly cover the latter sites . A large amount of mutational studies [14] have focused on the cAMP independent CRP variants ( CRP* ) , often found to be mutated in the proximity of the inter-domain hinge region ( T127/S128 , D138 , T140 , G141 , R142 , A144 ) . Other known mutation sites are D53 , S62 , Y99 , H159 , K52/H159 , and L195 . The major contributions of mutational studies could be achieved by considering the effects on the internal dynamics , in addition to interpretations solely based on the structural data . As noted in the literature , upon binding of cAMP W85 is expelled into solvent and β4–β5 flap ( K52-E58 ) moves towards C helix , resulting in hydrophobic interactions of I51 , K57 , M59 , L61 with F136 [20] . The β4–β5 flap interacts with F136 of the hinge upon cAMP binding and stabilizes the L134-D138 inter-domain hinge . This , in other words , locks the on state with the coupled fluctuations of K52-E58 with the L134-D138 , which in return reassumes its global hinge behavior for the inter-domain and then inter-subunit interactions; i . e . , shifts the hinge region towards C terminus . The latter refers to the two end states of the allosteric transition pathway between apo and holo structures . Nevertheless , the GNM analysis of both NMR model structures and mainly MD sampled conformations shows that the dynamics of conformations may suggest that both states , yet the on state and the off state with respect to both subunits is rare within the time window of simulations . The dynamic infrastructure for the allosteric communication pre-exists in the apo state , yet the fluctuations are not fully organized for a proper communication of the two domains and the two subunits . Binding of the cAMPs organizes the couplings and elicit proper communication . When we look at the dynamics of the unbound monomer , the elements of a plausible allosteric mechanism is still observed: The fluctuations of the position of the L134-D138 hinge to N terminus , the unstable correlated fluctuations of L134-D138 with K52-E58 , and unstable correlated fluctuations between the DNA binding ( P154-A156 and G173-V176 ) and cAMP binding domains . To this end , it is plausible to state the key dynamic elements of the allosteric functional mechanism is hierarchically built up but yet stabilized to the fully functional state with the association of the structural units; the dimerization and cAMP binding . GNM assumes an ensemble of conformations around a given protein structure topology and predicts residue fluctuations . GNM may expand the MD sampled space through the predictions on mainly MD sampled conformations . Clustering is one way to reduce the MD conformational space into a subset of conformational states , where the conformational ensemble could be enlarged from each . The cluster best members could be considered as some energetically favorable conformational states . As alternative to distance based metric such as clustering and PCA , GNM was previously used to characterize different conformational states and dynamics along MD trajectories [41] . We have also used this idea of plotting the frequency distributions of the eigenvalue of the first ( or first few ) GNM eigenvector of a series of MD snapshots to characterize different conformational states visited during the simulations . The frequency distributions were seen to be sensitive to the states of CRP . The frequency values for the cluster best members was distributed homogeneously over the frequency values of all conformations ( data not shown ) , which also shows that the cluster best members could capture possible differences in the topology and dynamics of CRP . The general patterns of the correlation maps are captured by the predicted correlations . Nevertheless , it is observed that , the larger the number of clusters for the MD conformations , the greater the difference between the MD/GNM cross-correlations of MD cluster best members . The differences here observed largest for the monomer ( Figures S2A versus S4A–C ) and the least for the holo structure ( Figures 3C versus S4G ) where the accessible conformational space is increasingly decreased . When there are large conformational changes , given the GNM calculations performed on mainly sampled conformations , the expansion in the conformational space could be larger compared to the relatively constrained cases . Also , for the MD cross correlations , averaging over whole trajectory might hide the dynamic behavior of less dominating conformational states . GNM helps elaborating the dynamics assumed in each of these conformational states . Harmonic motion assumption and non-specificity in the underlying potential function are GNM's limitations together with the dependency on the given structure . On the other hand , PCA-based analyses of MD trajectories provide the dominant motion suggested by the MD time window , but it may change from one sampling window to another [42] , [43] . Although usefulness of PCA analysis on insufficiently sampled MD trajectories may still be enhanced through multiple MD trajectories [44] , the dominant dynamic behavior might have contributions from several conformational states through the trajectory and may not uncover the individual states well . The identified conformational states structurally might look similar , yet the dynamics can be affected with differences in some contacts if involving some key mechanical sites . The cooperative residue fluctuations may allow the propagation of the allosteric signal with the minimal structural changes in the mean conformation [45]–[48] . CRP variants , although having structurally poised DNA interfaces , was seen to bind to DNA with different binding affinities; whereas the S62F CRP mutant , although the DNA binding domain is not reoriented to the active conformation , could show strong DNA binding affinity [28] , [29] , [47] . This suggests that binding may entirely be driven by the conformational entropy change . To this end , the dynamic analysis here demonstrates that the apo state has predisposed dynamics for both on and off state of the allosteric switch mechanism without undergoing the major conformational changes . It has previously been demonstrated that the sequence evolution correlates with structural dynamics [49] , [50] . It is expected that the key residues that mediate cooperative fluctuations could be conserved or assume correlated mutations . These are basically hinge sites that have been also suggested to overlap the regions of maximum frustrations that have a role in the emergence of allosteric interactions [51] . Allosteric functional motion and the cooperative modes are closely related . The robustness of the low frequency cooperative modes should depend on their nearly invariant nature , placing their foundations on the core network of residues responsible for transmitting signals as suggested by [52] . The local perturbations could be coupled to these modes which possibly transmit the signal by inducing conformational and/or dynamic changes encoded in the structure's topology [42] , [53] . Here , as a contribution to the understanding of the allosteric mechanism , we used GNM and MD simulations combined to suggest the dynamic infrastructure of a possible conformational switch mechanism from apo CRP monomer to dimer , then to holo CRP dimer . The key features of the allosteric dynamics are encoded in apo CRP dimer as well as in apo CRP monomer , providing a basis to elicit the transmission of a signal from the cAMP binding site to DNA binding domains . The use of MD sampled conformations along GNM allows having more than one conformational state to be used in the GNM analysis , which could particularly be important for the cases where there are several conformational states accessible with some topological differences . The MD simulations coupled with the GNM analysis has provided a mechanistic view on how the structural units are dynamically built up for a plausible allosteric functional mechanism; from apo CRP monomer to apo-to-holo CRP dimers . The dimerization restricts the conformational states accessible to the structure , so does the cAMP binding , towards a favorable dynamics for the DNA binding . The key dynamic elements; the inter-domain hinge L134-D138 and the K52-E58 , P154-A156 and G173-V176 sites , provide the dynamic infrastructure starting from the monomeric state and the orchestration of which leads to the allosteric communication between the cAMP and DNA binding sites/domains . A switch mechanism appears with the main role of the global hinge L134-D138; the on and off states are evidenced in apo CRP dimer with the precursor dynamics as well observed in the monomeric form .
The MD simulations were performed for the dimer ( subunits A and B ) and the unbound monomer ( subunit A ) of apo CRP for a simulation time of 150 ns each with the initial structure of apo CRP NMR solution structure ( PDB: 2WC2 , model 11 ) [20] , as well as a 150 ns holo CRP dimer run with the initial holo CRP X-ray crystal structure ( PDB: 1G6N ) [22] . Two parallel runs of 75 ns were performed for each apo CRP monomer and dimer with different initial structures ( PDB: 2WC2 , models 2 and 10 ) [20] . The details of the simulated systems are given in Table 1 . The Amber 8 . 0 [54] , [55] and Amber 11 [56] biomolecular simulation programs were used in the MD simulations . The Amber ff03 [57] force field parameter set was used for the proteins/ions . Each system was solvated using TIP3P [58] water molecules in an octahedron periodic box . Histidine residues 17 , 19 , 21 , 31 , 159 and 199 were protonated for the states predicted by H++ server [59]–[61] . Initially , energy minimization was performed using 50 cycles of steepest descent algorithm , followed by the conjugate gradient method . Initial velocities were selected at random from the Maxwell-Boltzmann distribution at a temperature of 10 K , which was gradually increased to 300 K . The Berendsen thermostat and barostat [62] was used for the NPT ensemble ( T = 300 K , P = 1 bar ) with a time step of 2 fs . The SHAKE algorithm [63] was used as the bond constraints for all bonds involving hydrogens to eliminate the high frequency bond vibrations . A cutoff distance of 9 Å was used for the nonbonded interactions . The long-range interactions in electrostatic terms were corrected using the particle mesh Ewald method [64] . The equilibration periods were taken as 5 ns and 50 ns for the dimer and monomer trajectories , respectively . The MD trajectories were analyzed for the structural and dynamic properties , such as the Root-Mean-Square Deviation of MD sampled conformations from the initial structure , Mean-Square Fluctuations of residues and Cross-Correlation of Residue Fluctuations , where all the calculations are based on Cα atoms and performed by ptraj of AMBER toolset 1 . 5 [65] . The first ten essential modes are extracted from the MD trajectories using singular value decomposition of the fluctuation residue matrix [66] . The clustering of conformations was performed to reduce the sampled conformational space for relatively major conformational states . The MD conformations of dimer and unbound monomer CRP ( saved every 4 ps ) were clustered with the k-means method ( kclust script ) implemented in the MMTSB toolset [65] . In clustering , cluster radii of 2 . 0 , 2 . 5 , 2 . 7 , 3 . 0 and 3 . 5 Å was used . Smaller cluster radii values produce more clusters , which are often unmanageable and insignificantly different from each other . The best members ( cluster centroids/the nearest conformation to the centroid ) of clusters with 3 . 5 Å were chosen for the analysis . The cluster populations are reported in Table 2 for the cluster radius of 3 . 5 Å . Cluster best members were further analyzed by using the Gaussian Network Model . The Gaussian Network Model ( GNM ) [32] , [33] is a one dimensional elastic network ( EN ) model . GNM assumes that the residues undergo Gaussian fluctuations about their equilibrium positions , where the residues represented by their backbone alpha carbon atoms ( Cα ) interact if they are within a certain cut-off distance . The interactions between all residue pairs in the network are represented by the Kirchhoff ( or connectivity ) matrix Γ . The correlation between the residue fluctuations of ΔRi and ΔRj is given in ( Eq . 1 ) [33] . ( 1 ) where kB is the Boltzmann constant , T is the absolute temperature in degrees Kelvin and γ is the force constant of the elastic potential function . The correlation matrix in Eq . 1 can also be expressed as the linear superimposition of N-1 eigenmodes as ( 2 ) U is the matrix of eigenvectors uk , where k refers to the kth eigenvector that gives the displacements of the residues along the kth mode . The kth eigenvalue , λk , is proportional to the frequency of the kth mode of motion . The normalized cross-correlation values of residue fluctuations vary in the range [−1 , 1] , referring to the limits of the negatively correlated and positively correlated pairs in their fluctuations , respectively . The N-1 nonzero modes are obtained by the decomposition of the fluctuations of N residues of a structure , where the most cooperative/global modes are the first few modes called the slow modes . The square of these eigenvectors describes the mean square fluctuations of residues from equilibrium positions along the selected modes . The minima of the slow mode shapes describe the hinge regions that coordinate the cooperative motion of the structure in the given mode , which are usually responsible for the correlated movements of the domains [32] , [33] . On the other hand , the hinge regions in the slowest individual modes are the sites where the sense of the residue correlations changes , the global hinge centers are located at the crossover between the positive and negative displacements [67] . Here , the GNM was used in combination with the other MD trajectory analysis approaches . A cut-off radius of 10 Å is used for all GNM calculations . The equilibrium residue fluctuations and cross-correlations of the cluster best members of each trajectory were calculated by the GNM . Additionally , the GNM analysis was also carried out for apo CRP NMR solution structures ( PDB: 2WC2 ) [20] , holo CRP X-ray crystal structures with DNA ( PDB: 1CGP ) [21] and without DNA ( PDB: 1G6N ) [22] , and for the two conformations from a previous MD simulation study of holo CRP ( PDB: 1G6N ) [18] , [22] . The GNM characterizes fluctuations of the molecule near an equilibrium state given by the molecule's Cα atomic coordinates . It thus expands the MD sampled space through the mainly sampled MD conformations , the cluster best members . | Protein dynamics are central in allosteric communication . The cooperative character of atomic motions is key in the propagation of the allosteric signal and in the protein functioning . Here , we explored the dynamics of cAMP Receptor Protein ( CRP ) as an apo unbound monomer/dimer and a holo dimer by molecular dynamics simulations combined with the Gaussian network model analysis . The cAMP binding allosterically promotes CRP to bind specifically to the DNA . To this , our results show how the residue fluctuations provide the basis of an allosteric communication mechanism and describe a conformational switch with its off and on states and how this behavior is evolved from apo CRP monomer to apo-to-holo CRP dimers . The dynamic infrastructure needed for the coordination between the effector and DNA binding domains is largely observed in apo CRP monomer . This is an example of the pre-existence of functional dynamic states in the smaller subunits of the structure and the functional importance of the inter-domain hinges . The dynamics of the holo state pre-exist in the apo state , but disorganized in the way the cAMP and DNA binding sites interact . The cAMPs organize the couplings and elicit proper communication . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"biology",
"computational",
"biology"
] | 2013 | Dynamic Fluctuations Provide the Basis of a Conformational Switch Mechanism in Apo Cyclic AMP Receptor Protein |
We used pulse-labeling with the methionine analogue homopropargylglycine ( HPG ) to investigate spatiotemporal aspects of protein synthesis during herpes simplex virus ( HSV ) infection . In vivo incorporation of HPG enables subsequent selective coupling of fluorochrome-capture reagents to newly synthesised proteins . We demonstrate that HPG labeling had no effect on cell viability , on accumulation of test early or late viral proteins , or on overall virus yields . HPG pulse-labeling followed by SDS-PAGE analysis confirmed incorporation into newly synthesised proteins , while parallel processing by in situ cycloaddition revealed new insight into spatiotemporal aspects of protein localisation during infection . A striking feature was the rapid accumulation of newly synthesised proteins not only in a general nuclear pattern but additionally in newly forming sub-compartments represented by small discrete foci . These newly synthesised protein domains ( NPDs ) were similar in size and morphology to PML domains but were more numerous , and whereas PML domains were progressively disrupted , NPDs were progressively induced and persisted . Immediate-early proteins ICP4 and ICP0 were excluded from NPDs , but using an ICP0 mutant defective in PML disruption , we show a clear spatial relationship between NPDs and PML domains with NPDs frequently forming immediately adjacent and co-joining persisting PML domains . Further analysis of location of the chaperone Hsc70 demonstrated that while NPDs formed early in infection without overt Hsc70 recruitment , later in infection Hsc70 showed pronounced recruitment frequently in a coat-like fashion around NPDs . Moreover , while ICP4 and ICP0 were excluded from NPDs , ICP22 showed selective recruitment . Our data indicate that NPDs represent early recruitment of host and viral de novo translated protein to distinct structural entities which are precursors to the previously described VICE domains involved in protein quality control in the nucleus , and reveal new features from which we propose spatially linked platforms of newly synthesised protein processing after nuclear import .
The manipulation of cellular metabolic processes during virus infection promotes or tempers virus production and determines the outcome of infection not only at the cellular level but also e . g . , acute versus long-term persistence , latency , reactivation and transmission [1] . With regard to infected cell protein metabolism , as well as the regulated de novo synthesis of virus encoded proteins , modulation of the host proteome is necessary for both infection and host cell responses , involving modifications in protein turnover , function and location [2] . Recent advances in global proteomic approaches and mass spectrometry methods have provided broad insight into the synthesis , modification and degradation of viral and host proteins as infection progresses [3–8] . These studies reveal alterations of cellular pathways including for example , the remodeling of glycolytic and metabolic pathways [9] , inflammatory and innate immune response factors [6 , 10] or nucleotide and RNA processing pathways [11] . However , a complete understanding of infected cell protein metabolism requires a parallel approach to spatial aspects of global protein synthesis and transport dynamics and alterations in these processes during different stages of infection . Traditional analysis of proteins at steady-state using antibodies , or fusion of genes to fluorescent proteins for dynamic spatial analysis , provide powerful tools for the investigation of individual proteins [12–14] . However , global spatial analysis requires a different approach . One method to visualise total nascent protein synthesis relies on the incorporation of puromycin , an aminonucleoside antibiotic , either using a fluorescent derivative of puromycin [15] or by the detection of polypeptide-puromycin conjugates using anti-puromycin antibodies [16] . This approach has yielded insight in the spatial analysis of cellular protein synthesis and modulation during bacterial [17] and viral infection [18] . Nevertheless there are disadvantages for spatial analysis of nascent proteins including low signal-noise ratios , qualitative differences with expected patterns [15 , 19 , 20] and importantly that puromycin is a tRNA mimetic that terminates translation , perturbing the system and eliminating the possibility of spatiotemporal analysis of fully translated proteins in e . g . pulse-chase experiments . Advances in organic chemistry , specifically in the area of bioorthogonal ligation reactions [21] has now enabled the development of a new generation of techniques based on the in vivo incorporation of metabolic precursors that contain designed chemical endgroups . This is combined with the use of aqueous-compatible , selective covalent bond-forming reactions , commonly termed “click chemistry” . These reactions link the macromolecular products which incorporate the precursors to capture reagents via a specific paired chemical moiety [22–25] . The most routinely used chemical pairs are the azide- and alkyne moieties together with a copper-catalysed cyclization , in which the azide and alkyne form a covalent triazole ring [24 , 26] . The azide or alkyne groups are well suited to chemical biology since they are small , bear no overall charge , can be introduced to a wide variety of precursors and are extremely well tolerated in vivo [27–29] . Thus for protein synthesis , using the methionine analogues homopropargylglycine ( HPG ) or azidohomoalanine ( AHA ) , it is possible to label newly synthesised proteins and then covalently couple fluorescently labeled capture reagents , for analysis of protein profiles by SDS-PAGE and in-gel fluorescence . At the same time , by fixing cells and then in situ coupling to similar capture agents , it is possible to simultaneously visualise the flux and intercompartmental transport of the “translatome” by microscopy [28 , 29] . Here we provide the first such spatial analysis of bulk newly synthesised proteins during the progression of infection with a large complex DNA virus , herpes simplex virus , providing new insight into protein trafficking . We focus on the induction of novel domains in the nucleus into which newly synthesised proteins are transported and their spatial relationship both with pre-existing PML domains and other domains , termed VICE domains , previously described to have a role in protein quality control in HSV infected cells [30–32] .
Both HPG ( alkyne-bearing ) and AHA ( azide-bearing ) are structural analogues of methionine that contain inert ( bioorthogonal ) chemical groups that enable subsequent covalent ligation to any of a series of capture reagents . They have been shown to be non-toxic , with no effect on global rates of protein synthesis nor protein degradation . They have been evaluated by in-gel fluorescence , by in situ imaging analysis and by mass spectrometry in a variety of cell lines , in primary neuronal cells , in organotypic brain slice cultures , in developing larval zebrafish and in adult mice in vivo [33–39] . In preliminary work we compared HPG and AHA in a series of experiments for sensitivity , efficiency of incorporation and toxicity . With little difference between the two precursors , we pursued further work with HPG as the labeling precursor together with azide-linked capture reagents ( Fig 1A ) . We confirmed HPG incorporation into proteins using qualitative analysis by SDS-page and in-gel fluorescence . Uninfected Vero cells were labeled with 1 mM HPG for 60 min . As controls we either omitted HPG or , in the presence of HPG , included cycloheximide ( CHX , 100 μg/ml ) to block de novo protein synthesis . Labeled cells were lysed , subject to cycloaddition reactions to couple an azide-linked fluorochrome ( IRDye . 800CW ) and proteins then analysed by SDS-PAGE and Coomassie staining for total protein analysis ( Fig 1B , lanes 1–3 ) and the same gel subject to in-gel fluorescence using a LI-COR Odyssey Infrared Imaging System for de novo synthesised proteins ( Fig 1B , lanes 4–6 ) . The results demonstrate efficient labeling of uninfected cell proteins ( lane 6 ) with essentially no background fluorescence detected in control experiments omitting HPG but samples still subject to click chemistry with the azide-fluorochrome ( c . f . lanes 5 and 6 ) . Furthermore when pulse-labeling with HPG in the presence of CHX , incorporation was virtually eliminated ( c . f . , lanes 4 and 6 ) . In parallel we evaluated the effect of the identical HPG-labeling regime on cell viability ( which as with standard 35S-Met labeling included a short methionine depletion phase , see materials and methods ) . We found no difference whether pulsing with HPG or methionine and neither condition had any detectable effect on cell viability ( Fig 1C ) . We next examined HSV infected cell protein synthesis after infection ( MOI 10 ) , pulse-labeling at early times of infection ( 2 hr or 4 hr ) , and additionally fractionating samples into cytoplasmic and nuclear samples prior to the coupling with IRDye . 800CW . Furthermore , in addition to analysis by in-gel fluorescence to detect newly synthesised proteins ( Fig 1D ) , we transferred a duplicate gel to a nitrocellulose blot ( Fig 1F ) . This enables probing with antibodies to candidate proteins e . g . , ICP4 , ICP0 and VP5 ( detected by secondary antibody , red channel ) while bulk newly synthesised proteins were detected by the azide-coupled fluorochrome ( green channel ) . Laser scanning of the blot allows simultaneous analysis of total active protein synthesis in one channel and of specific polypeptide abundance in a separate channel on the same blot . For the in-gel fluorescence , a number of altered species could be observed by 4 hr ( Fig 1D , white arrowheads indicated on lane 3 ) with the fractionation procedure providing effective enrichment of individual species into cytoplasmic or nuclear fractions . Fractionation is exemplified by candidate host species ( HC for cytoplasmic and HN for nuclear ) in the corresponding total protein profile ( panel e ) . We found the in-gel fluorescence of the SDS-PAGE gel itself to yield better resolved bands than fluorescence of the corresponding blots . Nevertheless , it was possible to visualise individual species on the blots , integrated with the newly synthesised population ( panel f ) . For example , ICP4 and ICP0 can be seen by 2 hr , partitioning mainly in the nucleus ( panel f , lane 5 vs 8 ) while VP5 was observed by 4 hr in both cytoplasm and nucleus ( panel e , lane 6 and 9 ) . Generally we found that HPG pulse-labeling and in-gel fluorescence was as good a method , in terms of sensitivity and background , as we have found in past laboratory experience with 35S-methionine labeling and autoradiography [40–43] . In additional control experiments we further evaluated whether HPG pulse-labeling had any effect on the overall accumulation of virus encoded proteins , using ICP8 as a candidate delayed early protein and VP5 as a candidate late protein . Parallel cultures were either untreated ( Fig 2A , lanes 1–3 ) , subject to methionine depletion followed by pulsing with normal methionine containing medium ( Fig 2A , lanes 4–6 ) , or subject to methionine depletion followed by pulsing in medium containing HPG ( Fig 2A , lanes 7–9 ) . Cells were harvested at the various times indicated at the end of the 30 min pulse . Equal cell equivalents were then analysed for total accumulation of ICP8 and VP5 . No significant differences were observed between pulsing with methionine or HPG and neither treatment had any significant effect on protein accumulation compared to the untreated control ( Fig 2A ) . In a variation of this experiment we also evaluated whether pulse-labeling with HPG , especially at early times of infection , had any effect on viral protein accumulation later in infection or on the final yield of infectious virus . Therefore as outlined in Fig 2B , HSV infected cells were subject to methionine depletion and HPG pulse-labeling at either 2 , 4 , 6 , 8 , 16 or 20 hr post infection ( p . i . ) . After each pulse , incubation was continued in the presence of normal methionine-containing medium up to 20 . 5 hr . ( Nb , only the early pulse periods are illustrated on the protocol schematic , Fig 2B ) . Control cells were maintained in normal medium for the duration of the experiment . All cultures were harvested at 20 . 5 hr and analysed for ICP8 and VP5 accumulation ( Fig 2C ) and for the total yield of infectious virus ( Fig 2D ) . The results demonstrate that HPG pulse-labeling had no effect on the later accumulation of test viral proteins and no effect on the overall yield of infectious virus . Taken altogether our results are entirely consistent with previous data [33 , 44] , which demonstrated HPG to be an effective bioorthogonal analogue of methionine , to be non-toxic , and to be incorporated into proteins with no effect on global rates of protein synthesis nor protein degradation [29 , 33] . We validated HPG for analysis of HSV infected cell protein synthesis showing its effective incorporated into proteins in virus-infected cells with no effect on the overall progression of infection , the accumulation of candidate viral proteins nor the total yield of infectious virus . The key advantage in the use of HPG labeling of protein synthesis is the ability to undertake parallel investigation of spatial aspects of newly synthesised proteins and , in the longer term , to selectively purify newly synthesised proteins away from the total proteome based on temporal aspects of their synthesis . We first pulse-labeled uninfected cells with or without HPG ( 30 min ) and processed the monolayers for imaging analysis . As controls , we also pulse-labeled in the absence of HPG or with HPG in the presence of CHX . The results ( Fig 3 ) demonstrate newly synthesised protein localisation in the cell with abundant accumulation in the nucleus and a disseminated lace-like pattern in the cytoplasm . There was virtually no background fluorescence observed in cells in the absence of HPG but subject to click chemistry and azide-fluorochrome detection ( Fig 3 , —HPG ) . Furthermore , in the presence of HPG together with CHX , virtually no incorporation was observed ( Fig 3 , +HPG , +CHX ) , confirming that the spatial distribution represents newly synthesised proteins , with any free HPG contributing little to the signal . These results are also consistent with previous data on the use of HPG for the spatial analysis of newly synthesised proteins in culture demonstrating both rapid import into the nucleus , relatively little free soluble cytoplasmic signal and a distinct web-like pattern in the cytoplasm likely representing association with the endoplasmic reticulum and possibly other cytoplasmic organelles [27 , 34] . We next undertook a spatiotemporal analysis of protein synthesis in HSV infected cells , pulse-labeling for 30 min at several time points up to intermediate times after infection ( 8 hr ) . While there were distinct qualitative differences in the patterns of newly synthesised protein distribution in the cytoplasm ( Fig 4A , small vertical arrowheads , 4 hr and 8 hr panels ) , the main striking feature we observed was the formation very early in infection , within 1–2 hr , of distinct spherical domains containing HPG labeled proteins in infected cell nuclei . These domains which we have termed NPDs ( newly synthesised protein domains ) , are indicated in a selection of nuclei at the different time points ( Fig 4A , diagonal arrows ) by small vertical arrows within the nuclei see also all Figs 5–14 ) . For clarity not all domains or nuclei are labeled . NPDs were quite homogeneous in size and shape , resembling symmetrical spheres , with a consistent initial size range ( mean approximately 0 . 5 μm ) . As infection proceeded , newly synthesised nuclear proteins also localised in small irregular and aggregated clusters and then in diffuse large lobed structures distinctly resembling classical replication compartments , ( Fig 4A , 4–8 hr; Fig 4B ) . NPDs formed early in infection and later were found specifically at the periphery of the large lobed domains . As shown below these large lobed foci , ( indicated in the central cell Fig 4A , 6 hr , small arrowheads at a section of the perimeter ) , colocalise with ICP4 and represent DNA replication compartments [45] . Quantitative analyses of NPD formation in Vero cells are summarised in S1 Fig . Evaluating approximately 100 cells at each time point , over 50% of cells contained NPDs by 2 hr p . i . with virtually 100% positive by 4 hr p . i . ( S1A Fig ) . In terms of NPD numbers/cell , we observed a mean of approximately 13 though some cells could contain over 25–30 ( S1B Fig ) . Average numbers/cell showed no increasing trend as infection progressed , but the average size within a cell increased from approximately 0 . 5 μM diameter at 2 hr p . i . to 1 . 0 μM by 6 hr p . i . ( S1C Fig ) . From quantitative analysis of the relative distribution of HPG-labeled proteins ( see materials and methods ) , 0 . 5–3% of the newly synthesised protein signal in the nucleus was present in NPDs within 1–2 hr after infection , increasing progressively to as much as 10–12% later in infection . The formation of these NPDs was observed independently of cell type and were seen with similar qualitative features after infection of lines including Vero , HaCaT , RPE , HeLa and primary MRC cells ( S2 Fig ) . As shown in Fig 1D and 1F , of virally encoded newly synthesised proteins within 1–2 hr only IE proteins were made to any significant extent and from quantitative scanning analysis of the lane protein profiles of newly synthesised proteins , the overall newly synthesised protein load changed little ( Fig 1D , Mock vs 2 hr ) . It was possible that NPDs represented new IE protein accumulation ( potentially with host proteins ) on infecting genomes localised in the nucleus . Therefore we examined colocalisation of NPDs with ICP4 , previously shown by several laboratories to efficiently localise to nuclear viral genomes [45–47] . In contrast to our expectation , NPDs showed little localisation with ICP4 . In the results ( Fig 4B , 2 hr ) comparing localisation of newly synthesised proteins ( HPG , green channel ) versus steady-state ICP4 ( red channel ) , example NPDs ( diagonal arrows ) are indicated in both channels for ease of cross-reference to ICP4 localisation . The NPDs did not represent sites of ICP4 accumulation ( horizontal arrows ) , nor were the ICP4 foci associated with NPDs . Over the course of this work , including at least ten independent experiments evaluating NPD formation versus ICP4 localisation , enumerating at least 10–20 cells in each experiment , and counting over one hundred NPDs , in no case did we observe selective ICP4 accumulation within an NPD . Rather it was very clear from the results ICP4 was selectively excluded from NPD formation . Moreover later in infection ( Fig 4B , 4 hr ) as ICP4 accumulates in lobed domains which represent DNA replication compartments [45 , 48] , we could see that NPDs did not represent sites of ICP4 accumulation and moreover were excluded from the latter , though frequently specifically abutting the ICP4+ve DNA replication domains . During our analysis we noted that NPDs could frequently be observed by phase microscopy , as small phase dense spherical domains seen exclusively in infected cells . Typical examples showing the spatiotemporal accumulation of HPG-labeled newly synthesised proteins in NPDs are shown in Fig 5 ( HPG-Pulse , 2 , 4 , 6 hr ) . Although exceptionally a small number of phase dense domains could be observed without new protein , for the most almost all NPDs could be observed as spherical phase dense domains . We therefore examined whether such phase dense domains ( which we have labeled as PDs for the purpose of cross-reference ) could be observed during the progression of infection , in normal medium containing methionine in place of HPG . The results ( Fig 5 , Control ) demonstrated that this was the case , with the timing , numbers and relative size of PDs being identical whether cells were pulsed with HPG or not . By definition we cannot analyse newly synthesised proteins in the absence of HPG but we also observed that PDs formed during infection most frequently formed outside and abutting developing replication compartments ( localised here by ICP4 , which is excluded from PDs ) . These data demonstrate not only that PD formation occurs in the natural course of infection and is unrelated to whether or not cells are pulse labeled with HPG , but also allow the conclusion that such domains represent structural sites into which a significant percentage of newly synthesised proteins in HSV infected cells is progressively recruited . ICP4 was specifically excluded from NPDs and we next performed similar analyses attempting to show localisation between NPDs and ICP0 ( Fig 6 ) . Within 1–2 hr , NPDs were observed but showed no clear colocalisation with ICP0 . In this case however , although it was initially difficult to attribute any specific association due to the more abundant ICP0 foci , we noted that a population of ICP0 frequently abutted NPDs ( Fig 6 , 2 hr; white arrows indicate ICP0 foci , in merged image and superimposed on HPG channel ) . As infection progressed ( Fig 6 , 4 hr ) , NPD domains were maintained while nuclear ICP0 gradually reduced as the protein became more prominent in the cytoplasm , consistent with earlier results [49–51] . The possible juxtaposition of at least a part of the NPD population with ICP0 foci led us to investigate the relationship between NPDs and steady-state PML domains ( also called ND10 ) , [52] , where ICP0 accumulates at early times of infection , with resultant progressive PML domain disruption [52–55] . In mock infected cells , classical PML/ND10 domains were observed with no obvious subnuclear accumulation of newly synthesised proteins and no NPD formation ( Fig 7A , Mock ) . Within 1 to 2 hr p . i . , PML domains were progressively disrupted as anticipated [53 , 54 , 56 , 57] . While this made it more difficult to quantify potential colocalisation of the disrupting PML domains with the increasing formation of NPDs , nevertheless we frequently observed an association and a juxtaposition of remaining PML domains with a subset of the NPDs ( Fig 7A , 1 hr , 2 hr; arrows ) . During high multiplicity infections with wild-type HSV , pre-existing PML domains ( i . e . , those present prior to infection ) , are disrupted and progressively disappear . Some PML protein can be mobilised or mislocalised to infecting virus genomes/protein aggregates [58–61] . However in the presence of normal ICP0 , these are small foci/aggregates , discernibly distinct from original PML domains . Furthermore during high multiplicity infections with w/t HSV as performed here , any induced PML-containing aggregates are extremely difficult to discern [58 , 60] . Nevertheless it was possible that at least some of the NPD formation which we observed could be due to their formation adjacent to induced PML -containing aggregates . While possible , this was not the most likely explanation for our results considering the combined results of the following sections . Thus , to investigate NPD formation further we performed a similar analysis using a HSV mutant , HSV-1 ICP0[FXE] , which contains a RING-finger deletion in ICP0 and as a result does not disrupt pre-existing PML domains [46 , 62] . In this case , the progressive formation of the NPDs , including their localisation to the periphery of DNA replication compartments , could now be seen in clear association with PML domains , forming not in a directly colocalised manner , but rather in a juxtaposed manner directly abutting the PML domains ( Fig 7B , 2–4 hr , HPG vs PML ) . High magnification images of several cells illustrating the typical relationship between these two structures are shown in Fig 8 i-iv and quantitative analysis of NPD formation in relation to PML domains in a population of HSV-ICP0[FXE] infected cells is supplied in panels d and e ( S1 Fig ) . For each individual cell examined , total NPDs are indicated by green dots , total PML domains in red dots and NPD-PML associated domains ( termed NPDP ) in orange dots ( S1D Fig ) , summarised in the bar chart ( S1E Fig ) . On average , there were greater numbers of NPD domains in infected cells than there were pre-existing PML domains in uninfected cells with means of 15–16 and 11–12 respectively in this population . In most cells ( Fig 8 , i-iv ) , virtually all the PML domains showed a distinct spatial configuration with respect to the assembling NPDs , which formed directly adjacent and in close association , as if emanating from the PML domains . The same was not true in reverse . As indicated from the comparative numbers , other NPDs frequently formed within cells without an obligatory positional relationship with PML domains ( Fig 8 , i-iv; S1D and S1E Fig ) . To confirm this relationship , we performed a three-way analysis simultaneously visualising newly synthesised proteins ( HPG , green channel ) , PML domains ( red channel ) , and ICP0 ( blue channel ) after infection with HSV-1 ICP0[FXE] . Various combinations of the channels are shown for ease of reference ( Fig 9A , iv and v ) . As anticipated , and as previously shown [54 , 58 , 62] , PML domains were maintained and colocalised with ICP0[FXE] protein ( panels ii , iii and merged panel v ) . Therefore , we anticipated that NPDs would form in frequent juxtaposition to ICP0/PML and this is precisely what we observed . In particular , the HPG/ICP0 image ( panels i , iii and merged panel iv ) illustrates this point . A magnified section of the three colour merged panel v is also shown . Several typical examples of this three-way analysis are further shown in Fig 9B i-iv , illustrating firstly , the broad colocalisation of ICP0[FXE] and PML ( red/blue overlap appearing purple ) and secondly the frequent juxtaposition of NPDs ( green channel ) abutting PML/ICP0 domains . PML domains represent a major site of SUMO-1 accumulation in the cell and are also frequently found in close apposition to bulk ubiquitinated species , especially under conditions where protein degradation is blocked [63–66] . We therefore examined whether NPDs induced early in infection ( 4 hr post infection ) represented sites of increased SUMO or ubiquitin modification . The results ( Fig 10 ) show that while SUMO and ubiquitin colocalised in discrete foci in infected cells ( Fig 10 , horizontal arrows , red v blue channels ) , NPDs were distinct from and showed little accumulation of either SUMO or ubiquitin ( Fig 10 , HPG , green channel , diagonal arrows ) . Since PML colocalises with SUMO , and as shown above , a subset of NPDs localised adjacent to PML domains , it would be anticipated also that certain of the NPDs would localise adjacent to SUMO/ubiquitin foci and this is exactly what was observed ( Fig 10 , HPG v SUMO and HPG/SUMO merge ) . We further investigated whether blocking protein degradation would , a ) induce the formation of NPDs in uninfected cells , or b ) affect their formation in HSV infected cells . We therefore examined the effect of addition of MG132 ( added prior to and during the HPG pulse ) on protein localisation in uninfected and HSV infected cells in relation to PML domains . The results show MG132 treatment produced no qualitative effect on protein localisation in uninfected cell nuclei ( S3A Fig , green channel ) , while at the same time , consistent with previous reports [64] , increased numbers of PML domains were observed ( S3A Fig , red channel ) . In HSV infected cells ( S3B Fig ) , as previously shown [54 , 67 , 68] , MG132 blocked the disruption of PML domains resulting in more numerous domains than those seen in untreated uninfected cells ( S3B Fig , c . f . , PML -/+ MG132 ) . MG132 treatment also resulted in increased numbers of NPDs in infected cells . The spatial and numeric relationships between NPDs and PML domains in infected and MG132 treated cells were complex as described below . Thus , PML localisation could for the most part be categorised into one of two types . Type 1 domains ( S3B Fig , PML channel , vertical arrows ) were generally more homogeneous and spherical , and resembled the domains normally seen in uninfected cells . Type 2 domains ( diagonal arrows ) were generally larger , irregular and more aggregated in appearance . This grouping could be made on the basis of morphology with anti-PML antibody alone but was reinforced by a clear selective association of the type 2 PML domains with a subset NPDs . Thus while type 1 PML domains showed little accumulation of newly synthesised proteins and no obvious association with NPDs , type 2 PML domains showed a clear association , often a completely overlapping colocalisation with NPDs ( S3B Fig , +MG132; diagonal arrows , HPG vs PML ) . The expanded insert ( S3B Fig , +MG132 , centre field ) , illustrates in each channel , adjacent type 1 ( lower domain ) and type 2 PML domains ( upper domain ) , with only the type 2 domain exhibiting distinct colocalisation with a NPD . Possible explanations are proposed in the discussion . Thus Type 1 PML domains could represent original “older” domains , present before treatment with MG132 , which as shown above ( Fig 7 ) , do not accumulation newly synthesised proteins . Type 2 PML domains would then represent those domains induced specifically after proteasome inhibition , with these domains accumulating newly synthesised proteins made during the pulse timeframe after drug treatment . Although the spatial and temporal relationship is complex , taken together with results of the previous section , several points are clear; 1 ) that proteasome inhibition per se was insufficient to induce NPDs in uninfected cells , 2 ) that HSV infection was required and 3 ) that NPDs very frequently accumulated in a precise spatial relationship co-joining pre-existing PML domains . We next used inhibitors to examine what broad phases of events were required in infected cells to induce NPDs . Clearly by definition , inhibition of protein synthesis with CHX completely blocked NPD formation ( Fig 2 ) . Consistent with their formation very early after infection , inhibition of DNA synthesis by acycloguanosine ( S4 Fig , Con vs ACG ) had little effect on the formation of NPDs . Blocking de novo infected-cell transcription with actinomycin D ( Act . D ) prevented the formation of NPDs indicating that virus infection per se did not result in the accumulation of any newly synthesised cellular proteins ( translated in the presence of Act . D ) into NPDs . Interestingly however , inhibition of transcription did result in the accumulation of newly synthesised cellular proteins in a very distinct manner , colocalising around cellular nucleoli ( S4 Fig ) . It is well known that Act . D specifically induces the formation of “nucleolar caps” [69] , representing the relocalisation of nucleolar proteins within a disrupted nucleolus together with the recruitment of novel nucleoplasmic proteins to the caps [70 , 71] . Our results are consistent with these previous data , reflecting the recruitment of newly synthesised proteins ( nucleoplasmic and nucleolar ) to Act . D-induced caps . These structures however are unrelated to NPD formation . Our analysis of protein localisation in the presence of Act . D indicate perhaps not unexpectedly , that virus gene transcription is required for NPD formation . We also tested whether NPDs could be observed in uninfected cells undergoing various stresses or treatments , including proteasome inhibition , heat shock and interferon treatment ( S5A–S5C Fig ) . However while these various conditions produced the expected phenotype , e . g . an increase in SUMO/ubiquitin foci with MG132 treatment [64] , localisation of Hsp70 in the nucleolus during heat shock [72–74] and an increase in PML domains after interferon treatment [75 , 76] , under none of these conditions did we observe formation of the typical NPDs in uninfected cells that we observe in HSV-infected cells ( S5 Fig ) . A number of studies including from our own laboratory , have previously reported on the localisation of individual viral encoded proteins or GFP-fusion proteins to small spherical domains within the nucleus of HSV infected cells [77–80] . In particular we noted the potential similarity between NPDs and previously described virus induced chaperone enriched ( VICE ) domains [31 , 80 , 81] . These domains were reported to form progressively during HSV infection at the periphery of late replication compartments and to recruit certain components of the protein quality control machinery , with the defining feature being the recruitment of Hsc70 and associated co-chaperones [30 , 31 , 82] . However there were certain differences in the characteristics of NPD formation reported here and VICE domain formation characterised in previous reports . For example , we observed efficient NPD formation during infection with an ICP0-defective mutant while VICE domain formation ( i . e . , Hsc70 recruitment ) was significantly suppressed [81] . Furthermore while NPDs did not initially colocalise with abundant Sumo or ubiquitinated species , VICE domains recruit significant levels of ubiquitinated species . Nevertheless given the similarities in relative spatial localisation we investigated the relationship between NPDs and VICE domains using the diagnostic marker Hsc70 . The results ( Fig 11 ) indicated that there was indeed a clear spatial relationship between NPDs and VICE domains but also a distinct temporal separation in their formation . Thus , at very early times after infection NPD formation could be readily observed without any recruitment of Hsc70 ( Fig 11 , 2 hr ) . We noted occasional localisation of Hsc70 with more irregular , aggregated HPG-containing foci ( Fig 11 , 2 hr , small vertical arrows ) . These latter aggregates however were distinct from the defined regular spherical NPDs which showed no enrichment nor any recruitment of Hsc70 ( Fig 11 , 2 hr , large arrowheads ) . By contrast , at later times of infection , Hsc70 could now readily be seen to be recruited to defined NPDs , frequently appearing as a coat , surrounding the outside of the NPDs . Representative images ( Fig 11 , 4 , 6 hr ) show many NPDs by these stages contained significant levels of colocalised Hsc70 , with quantitative analysis demonstrating progressively increasing Hsc70 association with time ( S1 Fig , panel f ) . VICE domains , in addition to recruitment of Hsc70 , have been reported [81] to recruit ubiquitinated proteins as detected by the monoclonal antibody FK2 [83] . However the results of Fig 10 indicated that , at least during early times of formation , NPDs showed no selective recruitment of polyubiquitinated species , although a subset of NPDs were found frequently directly adjacent to ICP0/PML/FK2 positive foci . Considering the temporal distinction between NPD formation and Hsc70 recruitment , we evaluated the temporal recruitment of polyubiquitinated species with results indicating broad similarity to Hsc70 ( Fig 12 ) . Thus , by 2 hr postinfection , numerous NPDs were observed without any co-localisation with polyubiquitinated species ( which were found in variable numbers of discrete independent foci , as well as a diffuse speckled background pattern very similar to uninfected cells ( Fig 12 , 2 hr ) . In the figure , vertical arrows indicate pronounced FK2+ve foci which show no obvious relationship with NPD formation , while diagonal arrowheads show NPD formation without significant recruitment of polyubiquitinated species . The insert shows that a subset of NPDs could be found with co-joining FK2+ve foci consistent with earlier results ( Fig 10 ) . As infection progressed the increasing presence of polyubiquitinated species with NPD domains could be observed , either as more obviously co-joining foci ( Fig 12 , 6 hr , a ) or with more complete co-localisation with FK2 localising in a rim like pattern around the outside of the later NPDs ( Fig 12 , 6 hr , b ) , a pattern very similar to that seen for Hsc70 . Generally it appeared that Hsc70 recruitment to NPDs could be seen more readily earlier than the FK2+ve staining , though this may reflect differences in antibody detection . What was clear that NPDs formed very early without any significant recruitment of either species . To pursue further the association between NPDs and VICE domains , we examined the relative localisation between NPDs and ICP22 . As shown above ( Fig 4 ) ICP4 did not colocalise with NPDs while a population of ICP0 localised within ND10 domains frequently adjacent to NPDs ( Figs 7 and 8 ) . ICP22 on the other hand has been reported to be precisely localised to VICE domains , colocalising with recruited Hsc70 [32] . The results ( Fig 13 ) demonstrated the efficient recruitment of ICP22 to NPDs even at early times after infection prior to Hsc70 recruitment ( 2 hr ) , and as with Hsc70 at later times , frequently coating the perimeter of the NPDs . Both the NPDs and colocalising ICP22 could be observed as phase dense bodies ( Fig 13 , arrowheads ) . We note that the localisation pattern of ICP22 and its recruitment to phase dense bodies was observed whether or not cells were labeled with HPG ( Fig 13 , control ) . Altogether these results reconcile our earlier data and reinforce the conclusion that NPDs form early in infection in the absence of pronounced recruitment of Hsc70 or ubiquitinated species . Based on these results , differences in characteristics of the formation of NPDs and VICE domains can now be explained in the model , as outlined in the discussion , whereby NPDs recruit de novo synthesised proteins into specialised physical subdomains which are precursors to VICE domains and later through qualitative or quantitative alterations progressively recruit Hsc70 and related factors . To examine whether NPD/VICE domain formation included the recruitment of host cell proteins made prior to infection we performed a pulse-chase experiment whereby uninfected cells were pre-labeled with HPG prior to infection ( 30 min pulse ) , the label washed out and cells then infected in the presence of normal methionine . In this type of pulse-chase regime , from the time of infection onwards newly synthesised virus or host proteins would not be labeled , and localisation would track only those proteins synthesised in uninfected cells , prior to infection . In this case , we examined NPD/VICE domain using progression to Hsc70 recruitment as a diagnostic marker . The results indicated that in virtually all Hsc70-containing domains , recruitment of host cell proteins made during the labeling period prior to infection was observed . A typical field is shown in Fig 14 , with pre-labeled protein recruited into foci surrounded by Hsc70 in a coat-like manner . This contrasted with the localisation in uninfected control samples that were pulse-labeled and then chased for a similar period ( Fig 14 , Mock ) . We noted also in infected cells that had not progressed to form Hsc70-containing domains , that the distribution of the previously synthesised cellular proteins could also be observed in more irregular foci and speckles , frequently at the nuclear periphery . These results indicate that the detailed kinetics of recruitment of that cohort of cellular proteins made prior to infection could differ from those cellular proteins made early after infection . Nevertheless it was clear that a significant population of cellular proteins translated just prior to infection were subsequently recruited into NPD/VICE domains as infection progressed .
Our results on spatial analysis in uninfected cells show the rapid accumulation of newly synthesised proteins into the nucleus as well as their distribution into cytoplasmic membranous compartments which , from co-localisation with steady state markers ( S6 Fig ) represent newly synthesised proteins partitioning to ER , Golgi and other organelles . These results are consistent with and expand upon previous analysis of newly synthesised proteins in uninfected mammalian cells [20 , 33 , 44 , 86 , 87] . They also indicate that by varying the duration of HPG pulse-labeling combined with different chase times in normal medium , it should be possible in future work to investigate cytoplasmic protein flux through the protein export and compartmentalisation apparatus and the perturbation of those processes as infection precedes . Here we focus on nuclear protein localisation and the assembly of novel domains in infected cell nuclei which we have termed newly synthesised protein domains ( NPDs ) . NPD formation was observed within 1–2 hr of infection when only the viral IE proteins were expressed to any significant extent and when there was little global change in the overall rate of protein synthesis or load compared to uninfected cells ( see Fig 1D and 1F ) . We initially considered that NPDs may represent sites of protein accumulation , including ICP4 , on infecting virus genomes but this was clearly not the case . Rather we show a distinct spatial association between NPDs , PML domains and virus-induced VICE domains and propose a model for protein processing in the infected cell nucleus which links these observations together ( Fig 15 ) . This model illustrates the outcome of wild-type HSV infection and attempts to unify observations: i ) on the induction of NPDs and parallel disruption of PML domains , ii ) the frequent but not exclusive association between PML domains and NPDs under conditions where PML domains persist ( e . g . , with an ICP0 mutant ) and iii ) on the relationship between NPD formation and VICE domain formation . NPDs are proposed to be absent from uninfected cells and are only induced following infection ( Fig 15A ) . However it is conceivable that NPDs could be present in uninfected cells ( Fig 15B ) but for example , function normally in terms of protein flux , and therefore not be detected by protein accumulation and without prior knowledge of stable structural components would therefore not be detected . PML domains ( Fig 15 , red spheres ) comprise proteins with a more stable structural role , together with proteins in dynamic flux of recruitment and dissociation [75 , 88 , 89] ( Fig 15 , indicated by directional arrows ) Vero . However these domains are detected solely from the point of view of having antibodies ( or targeted chimeric fluorescent proteins ) , to visualise the steady-state localisation of key constituents including e . g . , PML or SP100 . In the pathway for de novo formation following HSV infection ( Fig 15A ) , arrows indicate the recruitment of newly synthesised proteins into the NPDs , likely representing a significant population of newly synthesised nuclear proteins . There is clear selectivity in recruitment to NPDs . PML domains which are generally of similar size and morphology to NPDs , do not detectably accumulate abundant newly synthesised proteins . Analysis of the distribution of HPG containing proteins in NPDs versus the total nuclear population during the pulse period indicates that 1–3% of the labeled nuclear protein could be detected in NPDs within 1–2 hr p . i . Parallel analysis of the distribution of these same proteins by in-gel fluorescence , indicates that the overall newly synthesised protein load in the nucleus changed only marginally , representing at this stage mainly host cell proteins imported into the nucleus and comparatively low overall abundance viral proteins , mainly of the IE class ( Fig 1D , c . f . lanes 1 and 2 or 7 and 8 ) . Neither ICP4 nor ICP0 were recruited into NPDs ( although as shown above ICP0 was frequently in juxtaposition due to its association with PML domains ) , while early in infection ICP22 was selectively recruited . Considering the fraction of the total de novo synthesised nuclear protein recruited , the implication is that the majority of newly synthesised proteins within NPDs initially represents host cell proteins , together with ICP22 and that they progressively recruit additional viral and host proteins ( see below ) . This conclusion is consistent with our pulse-chase analysis with pre-labeled uninfected cells showing recruitment of what must be exclusively newly translated host cell proteins into later NPD/VICE domains . The distinct spatial relationship between PML domains ( under conditions of persistence ) and NPDs warrants speculation on a functional relationship between the two . One possibility is that the recruitment rate of proteins to NPDs is significantly higher than their dissociation rate , resulting in the appearance of these structures ( Fig 15 , HSV , NPD ( a , b ) ) . PML disruption could then be an upstream event , causing e . g . a bottleneck in a spatially and functionally linked processing pathway involving the two types of domains . However , PML disruption is not required for NPD formation ( since the latter are still formed after infection with the ICP0 mutant where PML domains persist ) , and while PML domains were almost always coupled to NPD domains , the same was not true in reverse with NPDs frequently forming without a clear association with persisting PML domains . Indeed the converse , that NPD formation was causally linked and upstream of PML disruption is also plausible , e . g . representing a block in the onward transport of proteins from NPDs to PML domains . In this case again however since NPDs and PML can be observed together after infection with the ICP0 mutant , this proposal would require that NPD formation was linked to but not sufficient for PML disruption . As indicated above one related possibility , still maintaining a link between the two domains , is that NPDs pre-exist in uninfected cells and like PML domains comprise both structural components and sites of dynamic protein trafficking ( Fig 15 , Uninfected B ) . Here the proper functioning and flux of proteins ( signified by light shading ) would mean that they would not accumulate bulk nuclear protein and without any specific antibody with which they are marked , NPDs would not be visualised . There could be trafficking exchange between NPDs and PML domains , whether or not they were in close physical association . If NPDs did pre-exist then our model would propose that rather than de novo assembly , HSV infection would perturb the normal flux through these pre-existing domains , revealing their presence adjacent to PML domains with which they may be already linked . Previous work has demonstrated that HSV infection induces the formation of discrete domains adjacent to and at the periphery of virus replication compartments [81] . These sites were shown to recruit host proteins of the quality control machinery including Hsc70 and contain ubiquitinated proteins and components of the 20S proteasome [31 , 81] while in other work , ICP22 and potentially ICP27 were also shown to be required for Hsc70 relocalisation into such foci [32 , 90] . It has been proposed that these domains , termed VICE domains , ( virus-induced chaperone-enriched domains ) represent sites of nuclear protein quality control that may aid in protein folding or remove aberrantly folded proteins to promote infection [30 , 31] . Indeed drug-mediated inhibition of chaperone function or the presence of a dominant negative Hsc70 mutant have both been reported to reduce HSV replication [80 , 90] . Considering our cumulative data on NPD formation and protein localisation together with previous characterisation of VICE domain formation we can propose a model that reconciles any differences between the two , whereby early in infection de novo synthesised host cell proteins together with in particular , ICP22 and possibly ICP27 [90] are recruited to NPDs which form the precursors to VICE domains . At this stage neither later virus encoded proteins , such as UL6 [81] or VP13/14 [91 , 92] nor host proteins e . g . , Hsc70 are recruited to NPDs . In this model NPDs are still formed efficiently in the absence of functional ICP0 , but because ICP0 is required for normal robust later protein synthesis , in the absence of ICP0 the early formed NPDs would not progress and would not recruit additional proteins including Hsc70 . Since Hsc70 recruitment ( rather than formation of domains containing new synthesised proteins ) is the definition of VICE domain formation , this model therefore adequately reconciles the apparent difference in requirement for NPD and VICE formation . However we find no support for the suggestion that NPDs/VICE domains are formed from components of PML domains [93] and as stated we do not find that NPDs are formed mainly after PML domain disruption , although with Hsc70 recruitment as the hallmark , this does usually occur temporarily later , after PML disruption . Our results provide a linking explanation for the formation of VICE domains and their progressive recruitment of protein quality control machinery . We propose earlier in infection , defined and structurally organised domains ( as for PML domains ) are formed into which significant levels of de novo translated host , -and later viral cellular proteins are recruited . Whether or not these domains pre-exist in uninfected cells will require structural characterisation and protein identification , at least of proteins which would be more stably present perhaps forming some sort of scaffold but our current favoured model is that NPDs form de novo during infection . At least a subset of these NPDs could be present as a spatially linked protein processing platform with PML domains , with proteins dynamically recruited to and trafficking onward from NPDs . It is also possible that NPDs represent a processing or folding bottleneck and their formation ( Fig 15 , HSV NPDs labeled a and b ) , as previously suggested [30 , 31 , 80] , acts as a cyto-protective mechanism to prolong the viability and functioning of infected cell viral protein production and assembly or as an intermediary quality control site from which properly folded and assembled subunits would be released after appropriate monitoring mechanisms , to form functional assemblies e . g . multimeric or multi-subunit complexes ( Fig 15 , NPD , domain labeled c ) . From the combined analysis of our data we believe that a significant fraction of newly translated proteins that are imported into the nucleus , traffic into or through NPDs . Is likely that for individual proteins or assemblies there are differences in the kinetics of trafficking onward from NPDs . Individual species of proteins that have slower kinetics ( or indeed are retained for prolonged periods ) would then more readily register in NPDs by steady-state analysis using antibodies and conventional immunolocalisation . Ultimately a complete characterisation of NPDs will require biochemical , approaches which are tractable since newly synthesised proteins incorporating HPG can be selectively purified away from the total proteome by chemical coupling to other capture reagents , allowing the integration of spatiotemporal data with analysis on biochemical fractionation , enrichment and mass spectrometry of newly synthesised proteins [33] . While such work is beyond the scope of this current analysis which focuses on spatial aspects of bulk protein dynamics early in infection , our results provide novel insight into fundamental processes in protein metabolism and trafficking early after HSV infection and possible unifying explanations for other observations in the field .
African Green Monkey kidney fibroblast ( Vero ) cells , human epithelial keratinocytes ( HaCaT ) , human fetal lung fibroblast ( MRC-5 ) cells and HeLa cells were grown in Dulbecco’s Modified Eagle’s Medium ( DMEM; Gibco ) supplemented with 10% Fetal Bovine Serum ( FBS; Gibco ) , and penicillin/streptomycin ( Gibco ) . RPE-1 , a human telomerase immortalised retinal pigment epithelial cell line , was grown in DMEM/F12 ( Sigma ) supplemented with 200 mM glutamine , 10% FBS and penicillin/streptomycin . The viruses used in this study were HSV-1[17] and ICP0 ring finger-mutated HSV-1 strain FXE ( kindly supplied by Professor R . Everett ) . Infections were routinely performed at a multiplicity of infection ( MOI ) of 10 . Infectious viral titers were determined by plaque assays on Vero cells . For the examination of the effects of HPG pulse-labelling on virus yields ( Fig 2 ) , supernatants were collected either immediately after inoculum removal ( t = 0 ) and an acid wash ( 40 mM citric acid , 10 mM KCl , 135 mM NaCl , pH 3 ) to represent virus input or at 20 . 5 hr p . i . , to represent the final yields . In studies of NPD formation in the presence of various inhibitors , ACG ( Thermo Scientific ) was used at a final concentration of 10 μM for the inhibition of viral DNA synthesis , and Act . D ( Sigma-Aldrich ) was used at 5 μg/ml for the inhibition of transcription . MG132 ( Calbiochem ) at 10 μM was added to uninfected or infected cells for 4 . 5 hr before analysis . For heat shock of uninfected cells , monolayers were transferred from 37°C to a 42°C incubator for 1 hr prior to pulse-labeling ( heat treatment continued during labeling ) and fixed immediately for analysis . Interferon-αA/D ( Sigma-Aldrich ) at 5000 U/ml was added to uninfected cells for 6 hr before pulse-labeling and processing . The following antibodies were used: mouse anti-ICP4 MAb ( Virusys ) ( 1:500 ) ; mouse anti-ICP0 MAb ( Virusys ) ( 1:300 ) ; rabbit anti-ICP0 ( r190 ) serum ( kindly supplied by Prof . R . Everett ) ( 1:500 ) ; rabbit anti-ICP22 serum ( kindly supplied by Prof . S . Rice ) ( 1:400 ) ; mouse anti-PML MAb 5E10 ( a kind gift from Dr . R . van Driel ) ( 1:10 ) ; rabbit anti-SUMO polyclonal antibody ( PAb ) ( Alexis Biochemicals ) ( 1:100 ) ; mouse anti-ubiquitin FK2 MAb ( Affiniti Research ) ( 1:600 ) ; rat anti-Hsc70 MAb ( Abcam ) ( 1:75 ) and mouse anti-HSP70 MAb ( Sigma ) ( 1:200 ) . Goat anti-rabbit Dylight 594 immunoglobulin G ( IgG ) ( 1:500 ) was obtained from Pierce Thermo Scientific . Alexa Fluor 635 Goat anti-mouse IgG and Alexa Fluor 546 Goat anti-mouse IgG were used at a 1:500 dilution and were purchased from Molecular Probes . FluoProbes 547H Donkey anti-rat IgG ( 1:400 ) was obtained from Cheshire Sciences . For immunofluorescence analysis , cells on glass coverslips were fixed at times indicated in 4% paraformaldehyde for 10 min , permeabilised with 0 . 5% Triton X-100 ( Sigma ) for 5 min , and blocked with phosphate-buffered saline ( PBS ) containing 10% goat serum for 30 min at room temperature ( RT ) . Cells were immunolabeled for 1 hr at RT with primary antibodies and 45 min with secondary antibodies , followed by click chemistry reactions as described below . Slides were mounted in ProLong Gold Antifade Mountant ( Molecular Probes ) . Images were acquired with Zeiss Laser Scanning Confocal Microscope using argon lasers at 488 nm , 543 nm and 633 nm with Zeiss LSM 5 software . Each channel was collected separately , with images at 512 x 512 or 1024 x 1024 pixels , with 4x averaging , without or with a zoom factor . Single confocal sections were acquired or multiple z-sections at 0 . 2 μM intervals which were then compiled for maximum projection display . From systematic analysis of various parameters we optimised protocols for HPG incorporation , click chemistry and fluorescence detection as follows . Cells on coverslips were mock-infected or infected with HSV-1 by normal procedures ( MOI 10 ) . At times indicated , medium was removed and replaced with L-methionine-free DMEM ( Sigma-Aldrich ) containing 2% FBS for 45 min to deplete methionine prior to the addition of HPG ( Molecular Probes ) at a final concentration of 0 . 5 mM for a standard labeling time of 0 . 5 hr ( for microscopy imaging ) or 1 mM for 1 hr ( for in-gel fluorescence ) in L-methionine-free DMEM . In control experiments ( e . g . Fig 1C; Fig 2 ) , cells were incubated in either standard media containing methionine ( Con ) or methionine-free media for 45 min prior to standard media containing methionine ( Met ) . In additional controls , cells were incubated with HPG in the absence or presence of 100 μg/ml CHX , added 1 hr before pulse-labeling with HPG ( Fig 1B; Fig 3 ) . For the pre-labeling experiments , methionine depletion and pulse-labeling were performed prior to infection . For pulse-chase analysis , HPG was removed and methionine was reintroduced into the system . When the pulse-labeled cells were to be analysed in parallel for localisation of specific antigens , immunofluorescence with primary and secondary antibodies was carried out as standard ( see above ) . The samples were then subjected to click reaction in a buffer prepared freshly in each case ( premixed for 2 min ) and containing 10 μM Alexa Fluor 488-azide ( Invitrogen ) ; 1 mM CuSO4; 10 mM sodium ascorbate; 10 mM amino-guanidine and 1 mM Tris ( 3-hydroxypropyltriazolylmethyl ) -amine ( TBTA , Sigma-Aldrich ) in PBS pH 7 . 4 . The reaction was then allowed to proceed by incubation for 2 hr at RT in the dark . After removal of the reaction cocktail , cells were washed with PBS and mounted on slides in ProLong Gold Antifade Mountant . Images were acquired as described above . For quantitation of the relative abundance of HPG-containing newly synthesised protein load within NPDs as a fraction of the signal within the nucleus we used the thresholding and object quantification modules of Image Pro Plus software ( Media Cybernetics ) . HPG-labeled cells were co-stained with DAPI to allow outlining of the entire nucleus and then the background subtracted signal in the HPG-green channel quantified . NPDs were manually outlined and tagged as objects and the signal quantitated for each object and for the total cumulative signal in all objects combined . This was then expressed as a percentage of the entire nuclear signal . The numbers of cells containing induced NPDs was expressed as a percentage of the total population as time progressed , evaluating approximately 100 cells of each time point in each of 3 different experiments . Mean and SD are shown . To enumerate the numbers of NPDs in individual nuclei , a total of 50 nuclei per time point were analysed . The size of each NPDs was measured to the nearest 0 . 1 μm using Zeiss LSM 5 Image Overlay and Line Measure functions . A total of 50 NPDs per time point were analysed . To assess association of NPDs and PML domains , individual cells were analysed at 4 hr post infection , enumerating maximum projections of each cell for the total number of NPDs , those NPDs immediately juxtaposed to PML ( NPDP ) , and total number of PML domains . The raw data shown in S1D Fig is represented in a bar graph showing the average number per cell of total NPD , NPDP , and total PML . Similar analyses were performed for NPDs and Hsc70 . Cells were washed with ice-cold PBS and removed from 6-well culture dishes using cell scrapers , and centrifuged for 5 min at 1000 rpm . Cell pellets were resuspended and lysed by incubation with 250 μl of PBS extraction buffer ( 1 mM dithiothreitol ( DTT ) , 1x complete protease inhibitor-EDTA free , 0 . 5 mM phenylmethanesulfonyl fluoride ( PMSF ) , 0 . 1 mM sodium orthovanadate , 0 . 5 mM NaF , and 0 . 5% Nonidet P-40 ) for 10 min on ice . 50 μl of sample was retained as total fraction . Cell suspensions were centrifuged at 3000 rpm for 5 min at 4°C . The supernatant representing the cytosolic fraction ( 200 μl ) was retained and nuclear pellet was washed and resuspended in PBS extraction buffer , centrifuged at 3000 rpm for 5 min at 4°C . The washed nuclear pellet was resuspended in 50 μl of PBS extraction buffer ( nuclear fraction ) . All three fractions were made up to a final concentration of 1% SDS in PBS and sonicated prior to click reactions . The nuclear fraction was loaded at 4-fold more of cell equivalents as compared to the total and cytosolic fraction and consequently equal concentrations of proteins ( 20 μg ) were loaded for each track . Cells were lysed in PBS containing 2% SDS and diluted to 1% SDS before the click reaction . 100 μg of protein samples were subjected to the click reaction as follows . Click reaction buffers were prepared by adding reagents in the following order with vortex-mixing between the addition of each reagent: capture reagent ( IRDye 800CW Azide Infrared Dye from LI-COR , 1 μl , ( stock solution 10 mM in DMSO , final concentration 0 . 1 mM ) , CuSO4 ( 2 μl , stock solution 50 mM in water , final concentration 1 mM ) , Tris- ( 2-Carboxyethyl ) phosphine ( TCEP , 2 μl , stock solution 50 mM in water , final concentration 1 mM ) , TBTA ( 1 μl , stock solution 10 mM in DMSO , final concentration 0 . 1 mM ) . Following the addition of the click mixture ( 6 μl/sample ) , the samples were placed on a rotating mixer for 1 . 5 hr at RT , and the reaction was stopped by addition of EDTA to a final concentration of 10 mM . Subsequently , proteins were precipitated ( chloroform/methanol , 0 . 25:1 , relative to the sample volume ) . The precipitated proteins were pelleted by centrifugation at 14 , 000 rpm for 5 min , washed with methanol and air dried for 10 min . The pellets were then resuspended in 1XSDS sample buffer , boiled for 10 min and 20 μg of proteins were loaded on 12% SDS-PAGE gels . Following electrophoresis , gels were washed with water , fixed in solution containing 40% methanol , 10% acetic acid , 50% water for 5 min and washed with water . In-gel fluorescence detection of translated proteins was performed using a LI-COR Odyssey scanner , and the protein loading was assessed by Coomassie blue staining . Quantitative evaluation of total protein synthesis was assessed by importing scans of the in-gel fluorescence into Quantity One densitometry software ( Bio-rad ) and using the Plot Profile module for individual lane analysis after background subtraction . The total area under the band peaks was used as a measure of ongoing protein synthesis Proteins separated by electrophoresis as above were transferred to nitrocellulose membranes which were blocked with Odyssey blocking solution ( LI-COR ) . After blocking , membranes were incubated overnight at 4°C with primary antibodies: mouse anti-ICP4 MAb ( 1:1500 ) ; mouse anti-ICP0 MAb ( 1:1500 ) ; mouse anti-VP5 MAb ( Virusys ) ( 1:1500 ) ; mouse anti-ICP8 MAb ( Abcam ) ( 1:1500 ) and rabbit anti-ᵞ-tubulin PAb ( Sigma ) ( 1:2000 ) ; diluted in blocking solution . Membranes were washed three times with 0 . 05% Tween in PBS and incubated with goat anti-mouse IgG Dylight 680 ( Pierce Biotechnology ) or goat anti-rabbit IgG Dylight 800 ( Pierce Biotechnology ) in blocking solution for 1 hr at RT in the dark . Visualisation of protein bands was performed using the LI-COR Odyssey Infrared Imaging System . | All viruses reprogram infected cells for the synthesis , modification and targeted localisation of virus-encoded and host proteins . Advances in proteomics and mass spectrometry have provided broad insight into these processes , but these approaches have limited ability to investigate spatial aspects of infected cell protein synthesis and localisation . Here we provide the first report using novel techniques in chemical biology involving labeling newly synthesised proteins with chemically tagged amino acid precursors that enables subsequent biochemical analysis and spatial analysis by microscopy . Using these techniques , we provide new insight into protein metabolism in herpes simplex virus infected cells which is not approachable by standard methods . We report the formation of novel subnuclear domains termed NPDs ( newly synthesised protein domains ) with a spatial link to pre-existing nuclear PML domains and to previously described domains involved in protein quality control . This work provides new insight into metabolic processes early after HSV infection and demonstrates the considerable potential of these techniques to yield fundamental insight into virus infection and virus-host interactions in any system . | [
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] | 2016 | Spatial and Temporal Resolution of Global Protein Synthesis during HSV Infection Using Bioorthogonal Precursors and Click Chemistry |
Snakebite envenoming is a major public health problem throughout the rural tropics . Antivenom is effective in reducing mortality and remains the mainstay of therapy . This study aimed to determine the cost-effectiveness of using effective antivenoms for Snakebite envenoming in Nigeria . Economic analysis was conducted from a public healthcare system perspective . Estimates of model inputs were obtained from the literature . Incremental Cost Effectiveness Ratios ( ICERs ) were quantified as deaths and Disability-Adjusted-Life-Years ( DALY ) averted from antivenom therapy . A decision analytic model was developed and analyzed with the following model base-case parameter estimates: type of snakes causing bites , antivenom effectiveness to prevent death , untreated mortality , risk of Early Adverse Reactions ( EAR ) , mortality risk from EAR , mean age at bite and remaining life expectancy , and disability risk ( amputation ) . End-user costs applied included: costs of diagnosing and monitoring envenoming , antivenom drug cost , supportive care , shipping/freezing antivenom , transportation to-and-from hospital and feeding costs while on admission , management of antivenom EAR and free alternative snakebite care for ineffective antivenom . We calculated a cost/death averted of ( $2330 . 16 ) and cost/DALY averted of $99 . 61 discounted and $56 . 88 undiscounted . Varying antivenom effectiveness through the 95% confidence interval from 55% to 86% yield a cost/DALY averted of $137 . 02 to $86 . 61 respectively . Similarly , varying the prevalence of envenoming caused by carpet viper from 0% to 96% yield a cost/DALY averted of $254 . 18 to $78 . 25 respectively . More effective antivenoms and carpet viper envenoming rather than non-carpet viper envenoming were associated with lower cost/DALY averted . Treatment of snakebite envenoming in Nigeria is cost-effective with a cost/death averted of $2330 . 16 and cost/DALY averted of $99 . 61 discounted , lower than the country's gross domestic product per capita of $1555 ( 2013 ) . Expanding access to effective antivenoms to larger segments of the Nigerian population should be a considered a priority .
Snakebite envenoming is a major public health problem among agricultural communities in the savanna region of West Africa [1]–[3] . A recent global appraisal estimated an incidence of envenomings in West Africa of 8 . 87–93 . 3/100 , 000 persons per year ( PPY ) and a mortality rate of 0 . 504–5 . 9/100 , 000 PPY [4] . Another recent study estimated snakebite incidence of 54/100 , 000 PPY and a mortality of 1 . 35/100 , 000 PPY occurring annually in West Africa [3] . However , aggregate estimates for West Africa do not fully reflect the substantial regional variability in snakebite incidence . For example , estimates from parts of the Benue valley in northeastern Nigeria reported an incidence as high as of 497 per 100 , 000 PPY [5] , nearly 10-fold the regional average . Most commonly , snakebite envenoming in Nigeria results from carpet viper ( Echis ocellatus ) attacks , which accounts for at least 66% of all snakebites . However its range is primarily limited to the savannah regions of Nigeria [1] , [6] , [7] , [8] , [9] . Carpet viper envenoming presents with swelling of the bitten limb and a clotting disorder ( incoagulable blood in the 20 minutes Whole Blood Clotting Test , 20WBCT ) that manifests as local and/or systemic bleeding . Resulting anaemia and shock may ultimately lead to death [1] , [6] , [7] . Non-clotting blood in the 20WBCT is diagnostic of carpet viper envenoming and is used to guide and monitor response to antivenom therapy [1] , [6] , [7] . In Nigeria non-carpet viper envenoming mainly results from African spitting cobra ( Naja nigricollis ) , puff-adder ( Bitis arietans ) , mamba ( Dendroaspis polylepis ) , burrowing asp or stiletto snake ( Atractaspis microlepidota ) , night adder ( Causus maculatus ) and very rarely boomslang ( Dispholidus typus ) . With the exception of boomslang envenoming from them present with negative or normal clotting on 20WBCT , local swelling , necrosis and tissue reaction especially following puff adder , and paralysis particularly from Egyptian or forest cobra and mamba bites [10]–[13] . The mortality rate from non-carpet viper envenoming is generally lower [5] , [10] , [11] , [12] , [14] , [15] , but snakebites may lead to blindness , malignant ulcers , pregnancy loss , physical and psychological impairment , scarring , permanent residual disability and loss of productivity following hospitalization and incapacitation [3] , [11] , [14]–[19] . Clinical response to effective antivenom for carpet viper envenoming is often rapid with restoration of blood coagulability and resolution of spontaneous haemorrhage . Snakebite antivenom is effective in reducing the risk of mortality and remains the mainstay of therapy against carpet viper envenoming [1] , [7] , [20] . Nevertheless , administration of antivenom carries the risk of early adverse reactions ( EAR ) which may , in rare cases , lead to death [21] , [22] . These EAR may require specific treatment and pre-medication given prior to antivenom administration may reduce risk of EAR occurrence [22] , [23] , [24] . Antivenoms are usually liquid formulations that require refrigerated transportation , and they have a shelf life of approximately 3 years [25] , [26] . The average cost per dose is US$124 ( range US$55–$640 ) depending on the manufacturer [27] . In settings where the cost per dose for treatments of other diseases of public health significance could be lower , assessing the health economic value of antivenoms may be of interest to policy makers . However , few economic evaluations have been conducted , and they either concentrated on cost of production of antivenom or were of a preliminary nature on cost per Disability Adjusted Life Year ( DALY ) averted following carpet viper envenoming [28] , [29] . Here , we evaluated cost-effectiveness of using antivenom in Nigeria to manage snakebite envenoming by calculating incremental cost-effectiveness ratios ( ICERs ) of cost per death averted and cost per DALY averted . The analysis was conducted from the perspective of healthcare system to aid policy makers in evaluating whether or not to make antivenoms more widely available .
A decision analytic model ( Fig . 1 ) was developed to estimate health outcomes and costs associated with the availability and use of geographically appropriate and effective antivenoms for snakebite envenoming in Nigeria . The model was restricted to only envenomed victims or about 40% of bites [15] , [29] . Snakebites not leading to morbidity ( e . g . , dry bites ) or bites from non-venomous snakes were excluded . Only 2 . 5% of subjects suffering from envenoming are currently able to access effective antivenoms [27] , so the decision tree assessed the availability of effective antivenoms relative to the current standard of care of no availability in the decision node . As previously mentioned , other harmful snakes contribute to the overall burden from snakebites [10]–[14] . The distinction between carpet viper and other snakebites is made on the basis of the 20WBCT in the treatment arm of the model . Evidence of incoagulable blood would trigger the administration of mono-specific antivenom that neutralizes carpet viper venom only , whereas lack of evidence of incoagulable blood would trigger the administration of a polyspecific antivenom that neutralizes venoms from multiple snakes , including the carpet viper . In the first chance node , the model included EARs associated with antivenom administration , which are more likely to occur with polyspecific rather than the monospecific antivenom [21] , [22] . Symptoms of EAR include vomiting , urticaria , angioedema , itching , bronchospasm , laryngospasm and in severe cases anaphylactic shock developing rapidly within minutes of antivenom administration [21] , and death in about 0 . 9% of cases [21] , [22] . Survivors of snakebite may recover fully or remain with significant disability ( e . g . limb amputation ) that is factored in the model . Treatment outcomes were converted into DALYs on the basis of local life expectancy . No funding was needed for the study . All analyses were conducted using the Tree Age Pro Suite Healthcare 2014 software .
The average per patient cost for the full course of treatment , including complete testing , provision of antivenom , feeding and transportation to hospital , and supportive care was US$214 . 375 . The cost of managing EAR averaged $1 . 875 per patient , resulting in a total average cost of antivenom of $216 . 25 . The average decrease in the risk of mortality was 9 . 2% . Dividing the average antivenom cost by the absolute decrease in the risk of mortality yields a cost/Death averted of $2330 . 16 . The average number of DALYs averted due to antivenom therapy was 40 . 88 , thus , yielding a cost/DALY averted of $56 . 88 undiscounted . While the discounted average number of DALYs averted was 23 . 41 , thus , yielding a cost/DALY averted of $99 . 61 . The model results proved robust to variation of model parameters in one-way sensitivity analyses , see results in Table 1 and Fig . 2 . The projected cost-effectiveness was most sensitive to costs of antivenom , type of snake causing envenoming , costs of caring for envenoming without effective antivenom , efficacy of antivenom in reducing mortality and to natural ( untreated ) mortality following envenoming by carpet viper and non-carpet viper snakes ( Fig . 2 ) . Results were insensitive to prevalence of antivenom related EAR or the cost of caring for EAR ( Fig . 2 ) . Furthermore , results from scenario analysis in which the effectiveness of the polyspecific antivenom against non-carpet viper envenoming is reduced from 75% to 0% yield a cost/life saved of $2709 . 76 and a cost/DALY averted of $116 . 10 . Antivenom cost is also varied from 80% ( $100 ) , 150% ( $187 . 5 ) to 200% ( $250 ) of the base-case price yielding cost/DALY averted of $87 . 69 , $129 . 43 and $159 . 24 respectively . Applying a conservative reduction of 40% on risk of EAR due to adrenaline premedication [23] , [24] yielded a cost/DALY averted of $98 . 67 . Utilizing probability of blindness of about 0 . 01% with a disability weight of 0 . 552 yielded an ICER of $99 . 32/DALY averted while utilizing a probability of Post-traumatic Stress Disorder ( 20% ) with a disability weight of 0 . 105 yielded an ICER of $101 . 44 [14] , [19] , [36] . Similarly , antivenom was assumed to be ineffective on disability in the base-case analysis but applying 75% effectiveness on it yielded $97 . 20/DALY averted .
The results of our study suggest that making antivenoms available to treat snakebite is highly cost-effective in Nigeria . A substantial expected reduction in mortality and DALYs could be achieved at a relatively modest upfront cost , thus , expanding access to antivenom to broader parts of the Nigerian population should be a priority consideration for future investments in healthcare . | Snake bite is a major public health problem throughout rural communities in West Africa and leads to a significant number of deaths and disabilities per year . Even though effective antivenoms exist against the locally prevalent carpet viper and other poisonous snakes , they are generally not available in community settings , possibly because of their high acquisition cost . We evaluated the cost-effectiveness of making antivenom more broadly available in Nigeria by comparing the treatment costs associated with antivenom therapy against their medical benefit in reducing the risk of mortality . We find that the incremental cost effectiveness ratio ( ICER ) associated with making antivenom available in Nigeria was $2 , 330 per death averted and $100 per disability adjusted life year ( DALY ) averted . Both of these suggest that snakebite antivenom is highly cost-effective in Nigeria and they also compare very favorably against other commonly funded health interventions for which similar estimates exist . Since a substantial reduction in mortality and DALYs could be achieved at a relatively modest upfront cost , expanding access to antivenom to broader parts of the population should be a priority consideration for future investments in healthcare . | [
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] | 2015 | Cost-effectiveness of Antivenoms for Snakebite Envenoming in Nigeria |
Specification of the myriad of unique neuronal subtypes found in the nervous system depends upon spatiotemporal cues and terminal selector gene cascades , often acting in sequential combinatorial codes to determine final cell fate . However , a specific neuronal cell subtype can often be generated in different parts of the nervous system and at different stages , indicating that different spatiotemporal cues can converge on the same terminal selectors to thereby generate a similar cell fate . However , the regulatory mechanisms underlying such convergence are poorly understood . The Nplp1 neuropeptide neurons in the Drosophila ventral nerve cord can be subdivided into the thoracic-ventral Tv1 neurons and the dorsal-medial dAp neurons . The activation of Nplp1 in Tv1 and dAp neurons depends upon the same terminal selector cascade: col>ap/eya>dimm>Nplp1 . However , Tv1 and dAp neurons are generated by different neural progenitors ( neuroblasts ) with different spatiotemporal appearance . Here , we find that the same terminal selector cascade is triggered by Kr/pdm>grn in dAp neurons , but by Antp/hth/exd/lbe/cas in Tv1 neurons . Hence , two different spatiotemporal combinations can funnel into a common downstream terminal selector cascade to determine a highly related cell fate .
During nervous system development , vast numbers of different neuronal subtypes are generated , and understanding the process of cell fate specification remains a major challenge . Studies have shown that establishment of distinct neuronal identities requires complex cascades of regulatory information , starting from spatial and temporal selector genes [1] and feeding onward to terminal selector genes [2 , 3] , often acting in combinatorial codes to dictate final and unique cell fate [4–6] . One particularly intriguing regulatory challenge pertains to the generation of highly related neuronal subtypes in different regions of the central nervous system ( CNS ) . Examples are plentiful and include e . g . , various groups of dopaminergic and serotonergic neurons in the mammalian CNS [7 , 8] , as well as neuropeptide-producing neurons in many systems [9 , 10] . The appearance of highly related neurons in different regions and at distinct developmental time-points clearly indicates that different spatial and temporal cues can converge to trigger the same terminal selector code , to thereby trigger a similar final cell fate . However , the underlying mechanisms are unclear . In the developing Drosophila ventral nerve cord ( VNC ) , two distinct sets of neurons selectively express the neuropeptide Nplp1: dAp and Tv1 . Both subtypes express the LIM-homeodomain transcription factor Apterous ( Ap; mammalian Lhx2a/b ) and the transcription co-factor Eyes absent ( Eya; mammalian Eya1-4 ) . dAp neurons constitute a dorsal-medial set of bilateral neurons running the length of the ventral nerve cord , while Tv1 neurons are located ventrolaterally in the three thoracic segments ( Fig 1A and 1B ) . Both dAp and Tv1 project axons ipsilaterally and anteriorly , and join a common Ap fascicle [11 , 12] . While it is possible that other aspects of their cell fate are different , their common neuropeptide expression and axonal projections suggest that dAp and Tv1 can be grouped into a highly related , if not identical , neuronal subtype . A number of regulatory genes and pathways acting in the specification of the Tv1 neurons have been elucidated [6 , 11 , 13–20] . These studies reveal that Tv1 cell fate depends upon a feedforward cascade in which spatial cues , provided by Hox and Hox cofactor input ( Antp , Exd and Hth ) , and temporal cues , provided by the temporal factor Cas , activate a col→ap/eya→dimm terminal selector cascade . This selector cascade ultimately results in the activation of Nplp1 neuropeptide expression . dAp neurons depend upon the same col→ap/eya→dimm terminal selector cascade as Tv1 . However , dAp neurons are not restricted to thoracic segments , but rather are distributed throughout the VNC ( Fig 1A and 1B ) . In addition , they are born at an earlier stage than Tv1 [12] . Furthermore , while Tv1 is generated by NB5-6T , the lineage that generates dAp is unknown [6] . Not surprisingly , the upstream spatial and temporal cues that trigger the terminal selector cascade in the Tv1 neuron do not affect the dAp cells [14 , 17] . Thus , the dAp and Tv1 cells represent a unique scenario for addressing how neurons generated by different neuroblasts ( NBs ) and with different spatial and temporal regulators can activate the identical terminal selector cascade to ultimately dictate a highly related , if not identical , neuronal subtype identity . Here , we identify the NB generating the dAp neurons as NB4-3 and find that these dAp neurons are generated during an earlier time in development than Tv1 , dictated by the temporal factors Kr and Pdm . Hence , the same terminal selector cascade ( col→ap/eya→dimm ) is triggered by distinct spatial and temporal cues in two different neuroblasts . Additionally , we find two crucial and specific factors refining the action of those spatiotemporal selectors: the GATA factor Grain ( Grn ) , acting in dAp neurons , and the Ladybird early factor ( Lbe ) , in Tv1 neurons . Thus , the col→ap/eya→dimm terminal selector cascade is triggered by the Cas/Exd/Hth/Antp/Lbe spatiotemporal code in NB5-6T , but by the Kr/Pdm/Grn code in NB4-3 . These results demonstrate that distinct spatiotemporal combinatorial codes can converge onto a common terminal selector cascade . Because the generation of highly related neurons in different regions of the CNS and at distinct time-points represents a common feature of many animal systems , the regulatory logic outlined here is likely to be widespread .
Previous work demonstrated that the terminal selector cascade composed by col→ap/eya→dimm is critical for the Nplp1 terminal cell fate both in dAp and Tv1 neurons , and can trigger this fate broadly in the CNS when combinatorially misexpressed [6 , 11 , 13–20] . Tv1 neurons have been most extensively studied , and their NB origin is well understood [14 , 17] . They are generated at the end of the NB5-6T lineage , under a Castor ( Cas ) temporal window and through a type 0 division mode ( Fig 1A ) [14 , 21] . dAp neurons arise from a distinct , previously unknown NB lineage . Thus , we began by identifying the progenitor NB that gives rise to dAp neurons , utilizing sets of markers that identify most , if not all , of the 30 NBs generated in each hemisegment [22–26] . Eya expression commences in dAp at St13 , and using Eya together with a number of NB markers , we found that dAp neurons are generated by NB4-3 ( Fig 2A–2I ) . To follow the development of this lineage , we made use of a col enhancer that drives reporter expression selectively in the dAp neuron , as well as in the NB4-3 and parts of the lineage ( Fig 2J and 2K ) . NB4-3 is known to delaminate at St late 11 [25] , and we can observe the lineage using col-GFP from this stage and onward . We mapped the expression of the temporal genes , and as anticipated from the early birth of dAp , evident by Eya expression , we did not find expression of the late temporal factor Cas ( S3A Fig ) . We did not observe expression of the early temporal factor Kr ( Fig 2L–2N ) . Because both hb and Kr mutants affect dAp specification ( see below ) , we envision that Hb and Kr are expressed in the NB4-3 prior to the onset of col-GFP . One of the two “middle” temporal factors Pdm1 ( Nubbin [Nub] , which together with Pdm2 we collectively refer to as Pdm1/2 ) was , however , expressed in several cells in the NB4-3 lineage ( col-GFP cells ) ( Fig 2Q ) . When Col and Eya are turned on , we can identify Nub expression specifically in the early dAp neuron itself at St13 , to subsequently be downregulated at St15 ( Fig 2Q and 2R ) . Using anti-phospho-Ser10 on Histone 3 ( pH3 ) , we were able to monitor cell divisions in the NB4-3 lineage , which revealed that dAp is born by a ganglion mother cell ( GMC ) and is hence generated in a type I proliferation window ( Figs 2K and S3B ) . In order to unambiguously show that dAp comes from a type I lineage , we analyzed the dAp neuron in a sanpodo ( spdo ) mutant background . Corroborating the notion deduced with the pH3 analysis , we observe two dAp neurons in that mutant background ( S3B Fig ) . Therefore , dAp is born in a type I proliferation window . In a screen for regulators and specific markers of dAp cell fate , we identified the GATA factor Grain ( Grn ) as being expressed in the dAp cell ( see below ) , and could hence use a grn-lacZ reporter to map the NB4-3 lineage . We found that grn-lacZ expression is concomitant with Pdm and hence precedes Col , being turned on in the GMC that generates the dAp cell ( Fig 2O ) . We further observed grn-lacZ expression in dAp neurons at all later embryonic stages ( Fig 2P ) . In summary , we map the origin of dAp to NB4-3 and find that it is born in the middle of this lineage . At the stage when NB4-3 generates the GMC that in turn will divide to generate the dAp neuron , it expresses Pdm and Grn ( Fig 2O and 2Q ) . Hence , dAp and Tv1 are lineage-unrelated neurons , generated in different temporal windows , mid versus late , and during two different proliferation modes , type I versus type 0 . Having identified that dAp is born early in NB4-3 , we next tested the expression of Nplp1/Eya markers in mutants for the temporal genes . In the early temporal mutant hb , we observed an apparent duplication of dAp neurons , evident by Col , Eya , and Dimm expression ( Fig 3B and S2 ) . In Kr mutants we found a reduction of Col , Eya , Dimm , and Nplp1 expressing cells ( Fig 3C , 3H and 3L ) . As anticipated from the expression of Pdm in the GMC generating the dAp cells , and in the dAp cells themselves , we also observed loss of dAp neuron markers in pdm mutants ( Df ( 2L ) ED773 , a genomic deletion that removes both nub and Pdm2 ) ( Fig 3D , 3I and 3L ) . Previous studies revealed that Kr regulates Pdm [27 , 28]; to address their relationship with regards to dAp specification , we attempted to cross-rescue Kr mutants with UAS-pdm , driven from the NB driver pros-Gal4 . This experiment revealed a partial rescue , evident by expression of Col and Nplp1 , while Eya was not significantly rescued ( Fig 3J , 3K and 3N and S1 Data ) . As anticipated from previous studies [14] , the late temporal gene cas specifically affected Tv1 and not dAp , while grh did not affect either cell ( Fig 3E , 3F and 3L and S1 Data ) . To determine if dAp neurons undergo cell death in Kr and pdm mutants , we combined these mutants with the cell death mutant Df ( 3R ) H99 , which removes all embryonic cell death [29] . We did not , however , note any rescue of dAp cell in these double mutants ( S7A and S7B Fig ) . We conclude that dAp neurons , which are born in an early temporal window , depend upon the early temporal genes hb , Kr , and pdm for their specification . In contrast , Tv1 neurons , which are born late , depend upon the late temporal gene cas . The distinct NB origin and spatiotemporal generation of dAp and Tv1 demonstrates that two different sets of spatiotemporal inputs can converge upon the same terminal selector cascade ( col→ap/eya→dimm ) , which triggers Nplp1 expression . Although the temporal factors are selectively expressed at different points of the lineage development , they are broadly spatially expressed in most NBs during neurogenesis [27 , 28] . Hence , we predicted the existence of additional upstream spatially-defining regulatory genes acting with the Kr and pdm temporal genes . In order to identify such additional upstream cues , we analysed a number of mutants for changes in Nplp1 expression in dAp but not in Tv1 cells or vice-versa ( see Materials and Methods ) . In the case of the dAp neurons , one mutant identified in this survey was grain ( grn ) , which encodes a GATA transcription factor known to be dynamically expressed in the developing VNC [30] . Our expression mapping of NB4-3 revealed expression of grnlacZ in the NB at StE11 , in the GMC at StL11 , and in early dAp cells from St14 and onward to St16 ( Figs 2O , 2P and S4A ) . Addressing the function of grn , we found that several grn allelic combinations all displayed complete loss of Col , Eya , aplacZ , Dimm , and Nplp1 expression in dAp , but not in Tv1 neurons ( Fig 4A–4G and S1 Data ) . To determine if dAp neurons undergo cell death in grn mutants , we expressed the cell death blocker p35 . This did not , however , result in rescue of dAp cells ( S7C Fig ) . Thus , the grn mutant analysis indicates that grn is an early factor , acting upstream of col , in the dAp specification cascade . Strikingly , grn is not involved in triggering this cascade in the Tv1 neuron ( Figs 4A–4D , S4A and S4B ) . col is a critical determinant of early dAp neuron identity [6] , and we find that grn acts upstream of col . Thus , we next addressed whether all of the grn functions in the dAp specification are mediated by col . To this end , we re-expressed col in grn mutants from Gal4 drivers with different temporal onset: pros-Gal4 at St10 and elav-Gal4 at St12 [17 , 21] . We found robust re-appearance of dAp neurons , showing both the Eya and Nplp1 markers , when we used either the pros-Gal4 or elav-Gal4 drivers ( Fig 5B , 5D and 5G ) . As anticipated from previous studies [6] , expression of UAS-col from either pros-Gal4 or elav-Gal4 also triggered a number of ectopic Eya/Nplp1 cells ( Fig 5A and 5C ) . In a reciprocal experiment we tried to cross-rescue dAp cell fate in col mutants by expressing grn from elav-Gal4 or pros-Gal4 . We did not , however , find any rescue of dAp cell specification in these cross-rescues ( Fig 5E , 5F and 5H; S5 Fig and S1 Data ) . Together , these results suggest that the main , if not the only , role of grn in dAp cells is to trigger the expression of col , setting in motion the cascade of regulatory events that culminate with the dAp specification . Our lineage and expression analyses indicated that grn acts downstream of the Kr and pdm temporal genes , and that its primary role is to trigger col expression . To further test this notion , we drove the expression of grn in Kr and pdm mutants . In both cases , we found partial rescue of the dAp neurons ( Fig 6A , 6B and 6E ) . Next , we expressed col in Kr and pdm mutants and observed rescue of dAp neurons in both experiments ( Fig 6C , 6D and 6F and S1 Data ) . Misexpression of UAS-col again triggered a number of ectopic Eya/Nplp1 cells ( Fig 6C and 6D ) . These findings indicate that dAp cell fate is specified by a Kr/pdm>grn>col cascade , in which the function of Kr/pdm is to activate grn , and the function of grn is to activate col . However , the partial rescue of Kr by grn suggests that Kr may be involved in a feedforward manner to regulate col . Having identified grn as a key spatial regulator upon which the temporal factors act to specify a dAp fate , we attempted to find a counterpart of grn in Tv1 fate specification . Recently we performed a large-scale forward genetic screen looking for genes critical for Tv4/FMRFa cell fate which resulted in the identification of additional genes controlling NB5-6T development [31] . One of the mutants identified in this genetic screen , by its loss of FMRFa-EGFP expression , was mapped to ladybird early ( lbe ) ( mammalian Lbx1/2 ) . This EMS allele , lbe12C005 , has a nonsense mutation at amino acid 29 ( a likely null allele ) [31] , and was placed over deletion Df ( lbl-lbe ) B44 to avoid genetic background problems ( hereafter referred to as lbe mutants ) . In lbe mutants , we observe a complete loss of Eya , Dimm , and Nplp1 ( Fig 7A and 7B ) . Strikingly , we find that lbe does not affect dAp neurons ( Fig 7A , 7B , 7M and 7N ) . In order to further characterize the loss-of-function phenotype of lbe , we analysed the expression of other key regulators acting during Ap cluster specification . In lbe mutants , we observed normal expression of the sub-temporal factor Nab ( Fig 7C and 7D ) [14] . However , we observed complete loss of Col , Eya , Dimm , and Nplp1 expression ( Fig 7A–7D , 7G and 7H ) . The loss of Col expression in lbe mutants prompted us to reciprocally address Lbe expression in col mutants . We observed normal Lbe expression as well as normal Nab expression in col mutants ( Fig 7E and 7F ) . As previously described [6] , col mutants show complete loss of Eya ( Fig 7I and 7J ) . As anticipated , temporal expression analysis revealed that Lbe expression precedes Col expression ( Fig 7K and 7L ) , in line with previous studies showing Lbe expression already at St9 in the NB5-6T [32] . Hence , Lbe is expressed in NB5-6T prior to the onset of any Ap cluster determinants and is critical for the activation of the col→ap/eya→dimm terminal selector cascade . lbe regulates col , but is this the only role that lbe plays , or does it play multiple roles , perhaps acting on targets downstream of col ? To address this , we attempted to cross-rescue lbe using elav-Gal4 driving UAS-col . First , as a control , we rescued lbe mutants with UAS-lbe , and , as anticipated , this resulted in rescue of thoracic lateral Eya/Dimm/Nplp1 cells ( Fig 8A and 8B ) . Next , we attempted to cross-rescue lbe with UAS-col , but did not observe any thoracic lateral Eya/Dimm/Nplp1 cells ( Fig 8C ) . These results indicate that lbe plays roles in addition to activating col , perhaps acting downstream together with col . To test this idea , we misexpressed lbe and col alone , and compared this to the effects of combinatorial misexpression . We noted that each gene alone could trigger ectopic aplacZ/Eya/Dimm/Nplp1 expression . However , their combinatorial action was striking , with vast numbers of ectopic aplacZ/Eya cells ( Fig 8D–8G ) . Interestingly , only a subset of ectopic aplacZ/Eya cells co-expressed Dimm/Nplp1 , which may be explained by the fact that lbe and col are also critical for the Ap cluster Tv2 and Tv3 cell fate: non-neuropeptide expressing interneurons . Finally , we addressed whether lbe is regulated by other Tv1 upstream regulators , and stained for Lbe in cas , hth and Antp mutants . This revealed no effects on Lbe expression in any of these three mutants ( S1A–S1F Fig ) . Reciprocally , we tested lbe mutants for expression of Cas , Hth , and Antp , but did not observe any effects ( S1G–S1J Fig ) . These results demonstrate that lbe acts in parallel to the four other Ap cluster upstream determinants , and acts in a feedforward manner , first activating col and subsequently acting with col to activate Ap/Eya/Dimm/Nplp1 ( Fig 8H ) .
Thoracic Hox input ( Antp ) , along with the Hox co-factors Exd and Hth , is required for Tv1 specification , while dAp does not require segment-specific Hox input , neither from Antp nor from the posterior bithorax Hox genes [17] . The most obvious reason for this difference is that Tv1 displays a thoracic-specific restriction , while dAp is generated throughout the VNC . Along similar lines , Tv1 cells are born late in NB5-6T and depend upon the late temporal selector Cas , while dAp cells are born early-middle and hence depend upon Kr and Pdm . In NB5-6T , Col is triggered by a combinatorial code of spatiotemporal selectors ( cas Antp , lbe , hth , and exd ) that to some extent explain its selective expression . However , Col expression is in itself fairly broad and highly dynamic in the developing VNC [6] , and hence its expression cannot explain the highly restricted expression of Ap/Eya . However , here lbe plays a secondary role , as it acts with col to activate Ap/Eya expression . Hence , the highly selective expression of lbe , in only a few row 5 NBs , and its feedforward action with col combine to refine the action of col . In the case of dAp and NB4-3 , we are likely still missing additional upstream and feedforward regulators . First , although Grn contributes to refine the action of Kr/Pdm , the specific activation of expression of Kr , Pdm , and Grn is still not restricted enough to explain the specific triggering of Col in NB4-3 . Second , as mentioned above , Col itself is also broadly expressed and needs additional factors to refine its role in NB4-3 . Thus , we envision the existence of additional upstream factors in the dAp genetic cascade . Expression analysis in mutant and misexpression backgrounds will most often help to place two regulators , X and Y , in relationship to each other . If X expression is not lost in Y mutants , but Y expression is lost in X mutants , one would propose that X regulates Y . However , to address whether or not the only thing X does is to regulate Y , we employ two other approaches: cross-rescue and combinatorial misexpression . The cross-rescue can show , for example , that dAp cells can be fully rescued in grn mutants by col re-expression , while in contrast , Tv1 cells cannot be rescued in lbe mutants by col re-expression . Regarding combinatorial misexpression , we observe a striking combinatorial misexpression effect of lbe/col when compared to either gene alone . Such cross-rescue and co-misexpression experiments prompts us to postulate a direct linear and non-feedforward regulation of col by grn in dAp cells . In contrast , for Tv1 cells we propose feedforward regulation of lbe on col , and subsequently with col on ap/eya . Such loops , i . e . , X→YX→Z , are denoted coherent feedforward loops , and are common in Escherichia coli and yeast gene regulatory networks [37] . Coherent feedforward loops act as regulatory timing devices and allow for gene X to carry different regulatory output ( or meaning ) at successive developmental time-points . col is a salient example of this; its transient expression in NB5-6T triggers an initial “generic” Ap/Eya interneuron cell fate in the four Ap cluster neurons , while its maintained expression ( specifically in Tv1 ) acts to propagate the terminal selector cascade that ultimately results in the activation of Nplp1 [6 , 14] . Coherent feedforward loops have also been identified during nervous system development in other animals , including Caenorhabditis elegans [38 , 39] . With regards to neuropeptide cell specification , we now find increasingly longer loops; five steps between Kr and Nplp1 in dAp cells , and ranging in developmental time from St10 to late embryonic stage . The presence of coherent feedforward loops has not been extensively tested in vertebrate systems , primarily because cross-rescue and multiple combinatorial misexpression experiments are technically challenging in these systems . But it is tempting to speculate that coherent feedforward loops are extensively utilized by more complex systems , and that the number of regulatory levels in these loops may increase with evolutionary complexity .
lbe12C005 [31] . Df ( lbl-lbe ) B44 , UAS-lbe , and ladybird early fragment K driving lacZ ( referred to as lbe ( K ) -lacZ ) ( provided by C . Jagla ) [32] . lbe ( K ) -EGFP [40] . elav-Gal4 ( provided by A . DiAntonio ) [41] . prospero-Gal4 ( F . Matsuzaki , Kobe , Japan ) . casΔ1 and casΔ3 ( provided by W . Odenwald ) [42] . UAS-nls-myc-EGFP ( referred to as UAS-nmEGFP ) [11] . col1 , col3 [43] and UAS-col ( provided by A . Vincent ) [44] . hkb5953 ( referred to as hkblacZ ) [45] . UAS-ap and apmd544 ( referred to as apGal4 ) [46] . aprK568 ( referred to as aplacZ ) [47] . UAS-grn-HA ( #F001916; provided by FlyORF ) . grhIM [48] . hbP1 , hbFB and Kr1 , KrCD [27] , unpg1912-r37 = unpg-lacZ ( provided by C . Q . Doe ) [23] . Antp12 ( provided by F . Hirth ) [49] . ind-lacZ and en-lacZ ( provided by H . Reichert ) . grn-lacZ , grn7L12 , grnSPJ9 , UAS-grn ( provided by J . Castelli-Gair Hombría ) . col- dAp-GFP was generated by inserting a genomic fragment from the col gene into the vector pEGFP . attB ( provided by K . Basler and J . Bischof ) and generating transgenes by PhiC31 transgenic integration ( BestGene Inc , California , United States ) . From Bloomington Drosophila Stock Center: Antp25 ( BL#3020 ) . Df ( 2L ) ED773 ( removes both nub and Pdm2; BL#7416 ) . mirr-lacZ ( mirrB1-12; BL#30023 ) . elavC155 = elav-Gal4 ( BL#458 ) . elav-Gal4 ( BL#8765 ) . hth5E04 ( BL#4221 ) . Df ( 3R ) Exel6158 ( BL#7637; referred to as hthDf3R ) . Mutants were maintained over GFP- or YFP-marked balancer chromosomes . As wild type , w1118 or OregonR was used . Staging of embryos was performed according to Campos-Ortega and Hartenstein [50] . The following transcription factor mutants were scored for changes in Nplp1 expression , without any apparent effects: escargot ( esg ) , shuttle craft ( stc ) , elbow/No ocelli ( el/noc ) , rotund ( rn ) , eagle ( eg ) , kruppel homolog ( kr h ) , knirps ( kni ) , schnurri ( shn ) , klumpfuss ( klu ) , zfh2 , dachshund ( dac ) , defective proventriculus ( dve ) , seven up ( svp ) , vein ( vn ) , beadex ( bx ) , scribbler ( sbb ) . Primary antibodies were: Guinea pig a-Deadpan ( 1:1 , 000 ) ( provided by J . B . Skeath ) . Rabbit a-ß-Gal ( 1:5 , 000; ICN-Cappel , Aurora , Ohio , US ) . Rabbit a-GFP ( 1:500; Molecular Probes , Eugene , OR , US ) . Guinea pig a-Col ( 1:1 , 000 ) , guinea pig a-Dimm ( 1:1 , 000 ) , chicken a-proNplp1 ( 1:1000 ) , and rabbit a-proFMRFa ( 1:1 , 000 ) . Rat a-Grh ( 1:1 , 000 ) . Rabbit a-Cas ( 1:250 ) ( provided by W . Odenwald ) . Rat mAb a-GsbN ( 1:10 ) ( provided by R . Holmgren ) . Mouse a-Nubbin ( referred to in the figure as Nub [Pdm]; 1:20 ) ( provided by Steve Cohen ) . Mouse mAb a-Dac dac2–3 ( 1:25 ) , mAb a-Antp ( 1:10 ) , mAb a-Pros MR1A ( 1:10 ) , mAb a-Eya 10H6 ( 1:250 ) ( Developmental Studies Hybridoma Bank , Iowa City , Iowa , US ) . Guinea pig anti-Odd ( 1:500 ) ; guinea pig anti-Runt ( both provided by M . Ruiz and D . Kosman ) . Rat a-Msh ( 1:500 ) ( provided by Z . Paroush ) [51] . Zeiss LSM 700 or Zeiss META 510 Confocal microscopes were used for fluorescent images; confocal stacks were merged using LSM software or Adobe Photoshop . Statistic calculations were performed in Graphpad prism software ( v4 . 03 ) . To address statistical significance , Student's t test or nonparametric Mann-Whitney U test or Wilcoxon signed rank test , in the case of non-Gaussian distribution of variables , was used . Images and graphs were compiled in Adobe Illustrator . | A fundamental challenge in developmental neurobiology is to understand how the great diversity of neuronal subtypes is generated during nervous system development . Neuronal subtype cell fate is established in a stepwise manner , starting with spatial and temporal cues that confer distinct identities to neural progenitors and trigger expression of terminal selector genes in the early-born neurons . Terminal selectors are those that determine the final neuronal subtype cell fate . Intriguingly , similar neuronal subtypes can be generated by different progenitors and under the control of different spatiotemporal cues; thus , we wondered how such convergence is achieved . To address this issue , we have decoded the specification of two highly related neuropeptide neurons , which are generated at different locations and time-points in the Drosophila nervous system . We find that two different combinations of spatiotemporal cues , in two different neural progenitors , funnel onto the same terminal selector gene , which in turn activates a shared regulatory cascade , ultimately resulting in the specification of a similar neuronal cell subtype identity . | [
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] | 2016 | Neuronal Cell Fate Specification by the Convergence of Different Spatiotemporal Cues on a Common Terminal Selector Cascade |
This communication reports on the Mycetoma Research Centre of the University of Khartoum , Sudan experience on 6 , 792 patients seen during the period 1991–2014 . The patients were predominately young ( 64% under 30 years old ) males ( 76% ) . The majority ( 68% ) were from the Sudan mycetoma belt and 28% were students . Madurella mycetomatis eumycetoma was the most common type ( 70% ) . In 66% of the patients the duration of the disease was less than five years , and 81% gave a history of sinuses discharging mostly black grains ( 78% ) . History of trauma at the mycetoma site was reported in 20% . Local pain was reported in 27% of the patients , and only 12% had a family history of mycetoma . The study showed that 57% of the patients had previous surgical excisions and recurrence , and only 4% received previous medical treatment for mycetoma . Other concomitant medical diseases were reported in 4% of the patients . The foot ( 76% ) and hand ( 8% ) were the most commonly affected sites . Less frequently affected sites were the leg and knee ( 7% ) , thigh ( 2% ) , buttock ( 2% ) and arm and forearm ( 1% ) . Rare sites included the chest wall , head and neck , back , abdominal wall , perineum , oral cavity , tongue and eye . Multiple sites mycetoma was recorded in 135 ( 2% ) of cases . At presentation , 37% of patients had massive lesions , 79% had sinuses , 8% had local hyper-hydrosis at the mycetoma lesion , 11% had regional lymphadenopathy , while 6% had dilated tortuous veins proximal to the mycetoma lesions . The diagnosis of mycetoma was established by combined imaging techniques and cytological , histopathological , serological tests and grain culture . Patients with actinomycetoma received a combination of antimicrobial agents , while eumycetoma patients received antifungal agents combined with various surgical excisions . Surgical excisions in the form of wide local excision , debridement or amputation were done in 807 patients , and of them 248 patients ( 30 . 7% ) had postoperative recurrence . Different types of amputations were done in 120 patients ( 1 . 7% ) .
Mycetoma is a unique neglected tropical disease . It is a morbid chronic progressive inflammatory condition caused by certain fungi ( eumycetoma ) or bacteria ( actinomycetoma ) [1 , 2 , 3] . It is characterised by devastating distortions , disabilities , high morbidity and it has various negative impacts in term of health and socio-economically on patients , communities and health authorities [4 , 5] . Young adult patients are affected most but no age is immune [6 , 7] . It is more frequently reported in farmers , shepherds and workers of low socio-economic status [8 , 9] . The triad of a painless subcutaneous mass , multiple sinuses and purulent or sero-purulent discharge that contains grains is pathognomonic of mycetoma [10 , 11 , 12] . The mass usually spread to involve the skin and the deep structures , resulting in destruction , deformity and loss of function , and occasionally it can be fatal . The limbs are the most frequently affected sites and that is seen in more than 80% of patients [13] . Mycetoma patients tent to present late with massive disease due to multifactorial factors [14] . The current diagnostic tools for mycetoma are varied and that include imaging , molecular and histopathological techniques , sero-diagnostic tests , as well as the classical grain culture . It is interesting to note , most of these investigations are not available in mycetoma endemic area [15 , 16 , 17 , 18 , 19] . Presently , it is quiet perplexing and challenging to treat patients with mycetoma and the treatment outcome is unsatisfactory . It is characterized by low cure rate and high amputation and high recurrence rates . Currently there is no control or prevention programmes for mycetoma [20 , 21] . In an attempt to bridge the knowledge gap in mycetoma , the Mycetoma Research Centre ( MRC ) was established in 1991 under the auspices of the University of Khartoum and based at Soba University Hospital . It is the only referral centre in the country which aims to provide integrated high quality medical care for mycetoma patients , education and training for medical and health professionals , community development activities and to lead in mycetoma research . In this communication , we report on the MRC 23 years’ experience in managing mycetoma patients .
Statistical analysis was conducted using Stata 12 . Data was summarized as percentages for categorical variables and mean ± standard error of the mean ( SEM ) and median for continuous variables . An ethical clearance was obtained from Soba University Hospital Ethical Committee to conduct the study . As this study is a retrospective one , done by reviewing patients’ notes the Ethical Committee waived the patients’ informed consents . All medical records were anonymized
The study included 6 , 792 patients with confirmed mycetoma; 5 , 150 ( 76% ) of them were males and 1 , 642 ( 24% ) were females . Their age ranged between 3 and 88 years with median of 25 years ( mean 29 ± 0 . 2 standard error ) . Most of them , 4 , 353 ( 64% ) , were less than 30 years old at presentation , 1 , 586 ( 23% ) were under 20 years old and 2 , 435 ( 36% ) were 30 years old or more ( Table 1 ) . In this study 1 , 872 ( 28% ) students were affected . This was followed by farmers 1 , 239 ( 18% ) and manual domestic workers 1 , 239 ( 18% ) . Due to the prolonged illness and disability , 628 ( 9% ) patients were unemployed . Housewives constituted 13% of the patients ( Table 1 ) . The study showed that , 2 , 476 patients ( 37% ) were from Gezira State; 837 patients ( 12% ) were from White Nile State and 747 patients ( 11% ) were from North Kordofan State . There was also a significant number of patients from the Capital , Khartoum State 1 , 037 ( 15% ) ( S1 Fig ) . Darfur States were the least affected area . In this series , thirty three patients were from neighbouring countries of Chad , Ethiopia , Saudi Arabia , Eritrea and some were from Yemen . The duration of the disease at presentation ranged between few months and 60 years with a median duration of 3 years ( mean 6 ± 0 . 1 standard error ) . The majority of the patients 5 , 819 ( 86% ) had mycetoma for 10 years or less; 1 , 691 ( 24% ) had the infection for less than one year . Only 51 ( 1% ) patients had the disease for more than 30 years , ( Table 1 ) . The majority of patients , 5 , 465 ( 81% ) , gave a history of discharge that contained grains ( Table 1 ) and the commonest were black grains ( 78% ) . Eleven percent of the grains were yellow , eight percent were white and two percent were red . In this series , about a quarter of patients 1 , 842 ( 27% ) had painful lesions ( Table 1 ) . Local trauma at the mycetoma site was recalled by 1 , 367 ( 20% ) of the patients , while the majority of patients 4 , 586 ( 68% ) had no recollection of local trauma and 774 ( 11% ) patients were not certain . Different types of trauma were mentioned and that included thorns pricks , stones cuts , trauma in framing and football games and snake bites . Most of the traumas were considered minor . Only 260 ( 4% ) patients had concomitant medical problems that included diabetes ( n = 33 ) , hypertension ( n = 22 ) , tuberculosis ( n = 9 ) , leprosy ( n = 3 ) , renal diseases and renal transplant ( n = 7 ) and others . Family history of mycetoma among the study population was documented in 811 ( 12% ) of patients ( Table 1 ) . Over half of the patients 3 , 847 ( 57% ) had previous surgical excisions performed elsewhere and the number of excisions ranged between one to four excisions , The surgery was performed under general anaesthesia in 1 , 746 ( 50% ) , 1 , 199 ( 34% ) had local anaesthesia and the rest had spinal anaesthesia . Only 194 ( 4% ) of the patients had previous medical treatment for the mycetoma ( Table 1 ) . In this study , the foot was the most common site for mycetoma and it occurred in 5 , 151 ( 75 . 8% ) patients . The right and left foot were affected in 2 , 239 ( 33% ) 2 , 176 ( 32% ) , respectively . Thus both feet were affected to the same extent . The hand was the second most common site for mycetoma with a frequency of 507 ( 7 . 5% ) . The right hand was affected most and it was documented in 307 patients ( 5% ) , the affection of the right hand is significant statistically , ( Table 2 , Figs . 1 , 2 ) . Less frequently affected sites were the leg and knee ( 7% ) , thigh ( 2% ) , buttock ( 2% ) and arm & forearm ( 1% ) . Few patients had mycetoma in the chest wall , head and neck , back , abdominal wall or perineum ( Figs . 3 , 4 ) . In this series , 135 patients ( 2% ) had multiple mycetomata affecting different body sites . These were primary lesions not due to lymphatic spread . The causatives organisms were the same in 90% of them while in 10% , the isolated organisms from the different mycetoma sites were different . The foot was the common site in this group of patients , ( Table 2 ) . The mycetoma lesions were classified according to their size into small lesion ( less than 5 cm in diameter ) , moderate lesion ( 5–10 cm ) and massive lesion ( >10 cm ) . The study showed that 2 , 504 ( 37% ) patients had massive lesion at presentation while 2 , 095 ( 31% ) patients had small lesions . At presentation , sinuses were observed in 5 , 343 ( 79% ) and among these 2 , 828 ( 53% ) were active discharging grains while in 1 , 501 ( 28% ) the sinuses were healed while 1 , 014 ( 19% ) patients had both active and healed sinuses ( Table 3 ) . Local hyper-hydrosis at and around the mycetoma lesion was detected in 514 ( 7 . 9% ) patients . Regional lymphadenopathy was detected in 699 ( 11% ) patients . Dilated tortuous veins proximal to the mycetoma lesions were observed in 422 ( 6% ) patients , ( Table 3 ) . X-Ray examination of the affected sites was performed in 4 , 508 ( 66% ) patients . It was normal in 1 , 235 ( 27% ) . A soft tissue mass was seen in 1 , 709 ( 38% ) , bone destruction was detected in 794 ( 17% ) , a periosteal reaction was found in 180 ( 4% ) and in 590 ( 13% ) a combination of these findings were detected , ( Fig . 5 ) . In the early days of the MRC suspected mycetoma lesions were diagnosed by histopathological examination of surgical biopsies specimens . Recently , we used Fine Needle Aspiration ( FNA ) from lesions to identify the grains and the tissue reaction . If FNA was negative in a suspicious case surgical biopsies and histopathological examination were done . In the present study , 3 , 177 ( 47% ) patients had FNA for cytology . The diagnosis was M . mycetomatis in 2 , 379 ( 75% ) patients , Actinomadura madurae in 316 ( 10% ) patients , Streptomyces somaliensis in 277 patients ( 7% ) Actinomadura pelletieri was uncommon and was found in 39 ( 1% ) of cases . The total number of patients who had surgical biopsies and histopathological examinations was 2 , 557 ( 38% ) patients . Among them , the diagnosis of M . mycetomatis was established in 1 , 714 ( 67% ) patients , Streptomyces somaliensis in 517 ( 20% ) patients , Actinomadura madurae in 140 ( 6% ) patients , Actinomadura pelletieri in 45 ( 2% ) and in eight patients ( 0 . 3% ) other organisms were identified , ( Fig . 6 ) . Ultrasound examination of the mycetoma lesion was performed in 2 , 204 ( 33% ) patients . Eumycetoma was diagnosed in 1 , 599 ( 73% ) patients , actinomycetoma in 299 ( 14% ) while in 291 ( 13% ) no diagnosis was established , ( Fig . 7 ) . MRI was performed in 102 patients , it showed different signs and that included the infiltration of the skin , subcutaneous tissues , muscles and bones with mycetoma grains and inflammatory tissue , obliterating the subcutaneous tissue planes and the dot-in-circle sign which is characteristic of mycetoma , ( Fig . 8 ) . The final diagnosis in this series based on the various diagnostic tools was eumycetoma in 4 , 754 ( 70% ) patients and the common organism was Madurella mycetomatis . Actinomycetoma was diagnosed in 2038 ( 30% ) patients and common causative organisms were Streptomyces somaliensis , Actinomadura madurae , and Actinomadura pelletieri . Using different molecular techniques , three rare causative organisms were identified and these were Streptomyces sudanensis a novel causative agent for actinomycetoma , Pleurostomophoraochracea , another novel agent of human eumycetoma with yellow grains and Madurella fahalii an uncommon eumycetoma agent [23 , 24 , 25] . Patients were treated according to the type of mycetoma . For actinomycetoma a combination of antimicrobial agents was given . In the past , the combination of choice was streptomycin sulphate and dapsone . If there was no response combination of streptomycin and trimethoprim-sulfamethoxazole was given and treatment duration ranged between 6 months and four years with a mean of 18 months . More recently , trimethoprim-sulfamethoxazole at a dose of 8/40 mg/kg/day for 5 weeks and amikacin at 15 mg/kg/day in a divided dose every 12 hours were given for 3 weeks and these drugs were given in the form of cycles . The cycles number ranged between 5 to ten cycles . The renal and audiometric functions are monitored closely during the treatment [20] . For eumycetoma several antifungal agents were used combined with various forms of surgical excisions and the later included wide local excision , debridement and amputation . Oral 400–800 mg/day Ketoconazole was the drug of choice as recommended by Mahgoub & Gumaa in 1984 [26] . However , it was recently banned due to its high toxicity and side effects . Recently , 200–400 mg/day Itraconazole is used as the drug of choice with less toxicity and side effects [27] . The duration of treatment ranged between six months and 3 years . Surgical excisions in the form of wide local excision , debridement or amputation were done in 807 patients and of them 248 patients ( 30 . 7% ) had postoperative recurrence . Different types of amputations were done in 120 patients ( 1 . 7% ) .
The history of mycetoma in the Sudan is long and exciting . Slede and colleagues reported on 1729 hospital patients with mycetoma from different parts of Sudan seen in the period 1951–1952 [28] . From the hospitals records , Abbot in 1956 reported 1231 patients seen over a period of 30 months [29] . In 1964 , Lynch estimated the incidence of mycetoma in the Sudan as 300–400 patients per year [30] . From Central Sudan , Moghraby reported on 817 mycetoma patients seen at Wad Madani Civil Hospital , Central Sudan , in a period of 11 years [31] . Mahgoub from the Mycetoma Clinic at Khartoum North and the Ministry of Health records , reported an incidence of 365 new patients annually seen in the period between 1971–1975 [32] . Following these early reports and for various reasons , there were no reports on the disease in the Sudan . With this background this study was carried out to bridge the knowledge gap in mycetoma in the Sudan . The study reports on the largest number and types of mycetoma and their geographic distribution ever recorded nationally or internationally . The annual incidence of newly diagnosed patients with mycetoma at the MRC is 355 and this is in line with that reported previously by Mahgoub [32] . However , the prevalence of mycetoma in one endemic village in the While Nile State , Central Sudan was recently found to be 14 . 5 /1000 inhabitants [33] . The incidence reported in this communication may be the tip of the iceberg representing only the few patients who were able to reach the MRC seeking medical care . Generally , mycetoma patients tend to present at a late stage with advanced disease as reported in this study . This may be explained by the substantial lack of health education among communities and patients in endemic regions , scarcity of medical and health facilities in rural endemic areas and the patients’ low socio-economic status . To overcome this , vigorous health education programmes need to be introduced in mycetoma endemic areas . Male predominance in mycetoma reported in this series is in accordance with all previously reported series form the Sudan [28–32] . This cannot be explained solely on outdoor activities since in parts of Sudan where mycetoma is endemic , females are also committed to these activities . This is supported by recent field study which showed both sexes were equally affected [33] . However , genetics and hormonal factors may explain this male predominance and further in-depth studies are needed . This study is in agreement with the medical literature which showed that young population is affected most [28–32] . The explanation of this finding is unclear; the early traumatic exposure to the causative organisms in the soil during playing or field activities may offer an explanation . All the reported series showed cultivators , shepherd and field workers are the frequent affected groups [28–32 , 34 , 35] . It is interesting to note that , students were affected most in this study . The explanation of this finding is an enigma . However it is known in endemic areas , students usually help their families during their outdoor activities but further studies should address this issue . As the mycetoma belt passes across Gezira , Sinnar , While Nile and Kordofan States , and they have similar geographical and environmental characteristics , the majority of the study population were from these states and this is in accordance with previous reports [28–32] . It is interesting to note , in this study , a considerable number of the patients were from Khartoum State , which is a non-endemic area . This may be explained by the massive migration of tribes from the endemic areas to Khartoum due to numerous socio-economic reasons . Some of those migrants often go back to visit their homelands and they may get infected and then return to Khartoum . This problem needs be addressed and clarified . Due to the steady increase in international movements and travel some of the patients from the neighbouring countries of Chad , Ethiopia , Yemen , Saudi Arabia and Eritrea were seen and treated at the MRC . Most of the patients had a long standing disease at presentation . This is in line with most of the previously reported series [28–32 , 34 , 35] . This has been attributed to lack of health education , patients’ poor socio-economic status and the absence of medical and health facilities at the endemic regions . To address these short comings , MRC conducted several health campaigns at endemic areas where health education activities were conducted and surgical treatment was performed by mobile surgical unit [33] . The MRC has even established wards and a small laboratory in the White Nile State . The clinical trial of the subcutaneous mass , multiple sinuses and discharge that contained grains reported in this series is typical of mycetoma and it is in line with previous reports [25–29 , 31 , 32] . Although mycetoma can be diagnosed clinically , yet this is not conclusive , hence , accurate and explicit diagnostic tools are required to advise the proper treatment . Typically mycetoma is a painless condition and this is an important cause for the delayed presentation in the majority of patients . In this communication , only 27% of the patients had local pain at presentation . The pain typically presents in bouts due to secondary bacterial infection in the lesion and for various socio-economic and cultural reasons patients tolerate these bouts . It is widely believed that , the practice of walking barefooted and working in the field barehanded predispose to traumatic inoculation of the causative organisms . However , in this study , only 20% of the patients had recalled history of local trauma but they might have had minor unnoticed trauma that facilitated the organisms’ inoculation . However , if the traumatic inoculation theory is true then the incidence of mycetoma should be much higher than it is . However , on the other hand , the presence of thorns within the mycetoma lesions during surgery and fungal hyphae within the thorns may favour the traumatic inoculation theory ( Fig . 9 ) but this may be co-incident finding . Further detailed community based studies are warranted to clarify and offer an explanation . It is interesting to note , that the isolation of Madurella mycetomatis , from the soil was not possible despite detection of its DNA in the soil [36] and also it is known that , the production of mycetoma granulomas by inoculation in experimental animals is difficult [37] all these may favour the theory of the presence of an intermediate host for mycetoma development . In this large series , most of the patients had no concomitant medical conditions that predispose to the infection . Sera from 100 patients with mycetoma were tested for HIV and were negative and clinical evidence of HIV was not encountered in this study . In this series , few patients had tuberculosis and leprosy with mycetoma but it is difficult to ascertain whether they were the predisposing factor for mycetoma or vice versa [38] . Seven patients were on immune-suppressive therapy for renal transplant and it seems that they had re-activation of old mycetoma lesion as all of them had past history of surgically treated disease . Family history of mycetoma among the study population was documented in a minority of patients ( 12% ) . This low incidence has no clear explanation as in endemic areas , all the population are at risk of contracting the infection because they share the same epidemiological risk factors . We cannot rule out that , some individuals may be immune to mycetoma as a result of healed small inoculations or some individuals may be naturally resistant to the infection . Further epidemiological , immunological and genetic studies are needed to explain these observations . Surgical recurrence in mycetoma is a common and serious problem that leads to major morbidity and disability . In this study , most of the patients had past history of recurrent disease with repeated surgical excisions performed elsewhere before presentation to the MRC with massive distortions and mutilation . The explanation of the surgical recurrence is multifactorial . Many patients present late with advanced disease and the excisions were incomplete . Furthermore surgery is often performed by inexperienced junior medical doctors or health assistants in the rural areas under local anaesthesia . It is unlikely that the whole lesion is completely removed under these conditions . Mycetoma commonly spread widely along the fascial planes , lymphatics and blood [39] in the form of hyphae and grains and the latter proved to have numerous protective mechanisms [40] . In this study , the foot and hand were the commonly affected sites which is in agreement with the medical literature [28–32 , 34 , 35] . Rare mycetoma sites in this study included the eye , scrotum , oral cavity , palate and tongue . This has been observed by others [41–44] . Extra pedal mycetoma was encountered in the studied population but less frequent , which is again in agreement with other reports [41–44] . It is interesting to note , in the different sites , eumycetoma was the commonest type which contradicts some other reports which showed most of the extra-pedal mycetoma are actinomycetoma [34 , 35] . This may be explained by the fact that M . mycetomatis is the commonest causative organism in the Sudan The clinical presentation of multiple mycetomata that were not in the same lymphatic drainage line was encountered in this series . Patients had presented with massive advanced multiple lesions which posed a management challenge . The explanation of this phenomenon is unclear; though , double inoculation of the causative organism is the popular theory . Most of the patients presented with long standing disease and massive lesions . However , it is interesting to note , in a recently study in an endemic village in Central Sudan , many patients presented with long standing small lesions and the diagnosis of mycetoma was a surgical surprise [33] . The explanation of this presentation is unclear . However the repeated exposure to the subclinical infection may have stimulated the immune system to localise the infection . In this communication , the disease onset , course and progress were typical and it is characterised by high morbidity and disability in particularly with late advanced disease . In some patients , the disease has progressed wildly and aggressively involving the deep organs such as the urinary bladder , pelvic organs and bones , spinal cord , lung and other structures and some infections resulted in fatal outcome which is uncommon in mycetoma [45–51] . Further immunological and genetic studies are recommended to explain this aggressive behaviour in some patients . The diagnosis of mycetoma in this series was based on the combination of clinical and various laboratory and imaging techniques . Proper diagnosis is essential for advice the appropriate treatment . It is essential to note , most of these diagnostic tools are not available in endemic regions . They are also invasive and expensive to both patients and health authorities . This necessitates the need to have a simple , field friendly and affordable diagnostic tools for mycetoma [19] . In this series , the actinomycetoma was less prevailed than eumycetoma . Most of the causative organisms were diagnosed as Streptomyces somaliensis but recently with the use of PCR , new organism; S . sudaniensis was diagnosed which was previously diagnosed as S . somaliensis by H&E stains [52] . Actinomadura madurae , Actinomadura pelletieri and Nocardia brasiliensis were rare causes of the disease . This is not in accordance with reports from West Africa and Mexico and this may be explained by geographic and environmental factors [34 , 35 , 40 , 43] . The current treatment of mycetoma as seen in this study was suboptimal and in line with previous reports [23 , 53] . Hence there is an urgent need for novel , efficient and cost effective treatment . In conclusion , in this communication we report on the largest mycetoma population ever reported and the findings are almost in line with that reported previously from the Sudan and elsewhere . The findings of this study indicate the need for more efforts to be done for better management of the mycetoma . In view of the difficulty in the diagnosis and treatment of mycetoma [19 , 53] , novel diagnostic tools and treatment are desperately needed to reduce the enormous deformity , disability and the high morbidity encountered in most mycetoma patients . The knowledge gap in mycetoma is substantial [54] and this should be reduced for better disease understanding , management and control . | Many researchers consider the Sudan as the mycetoma homeland . The first report on mycetoma was at the turn of the eighteenth century , and since then many documents on mycetoma have been reported . However , there is no recent report on mycetoma in the country . In 1977 Mahgoub published data on mycetoma in Sudan , but no more data were published until the MRC was established . The present study reports on 6 , 792 patients with mycetoma seen and managed at the MRC in a period of 23 years . This is the largest reported number of cases on the disease at national and international levels . The clinical presentations of the reported patients were in line with the previous reports on mycetoma from the Sudan and elsewhere . Still , many patients presented late with advanced disease and enormous disabilities and deformities . Some of them had a fatal outcome due to several complications . Despite advances in the diagnosis and treatment of this disease , outcome is still unsatisfactory . There is a need for more research to develop effective treatment of mycetoma and field friendly diagnostic techniques . Adequate preventive and control measures to reduce the disease morbidity and mortality are needed . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Mycetoma in the Sudan: An Update from the Mycetoma Research Centre, University of Khartoum, Sudan |
Killed oral cholera vaccines ( OCVs ) are available but not used routinely for cholera control except in Vietnam , which produces its own vaccine . In 2007–2008 , unprecedented cholera outbreaks occurred in the capital , Hanoi , prompting immunization in two districts . In an outbreak investigation , we assessed the effectiveness of killed OCV use after a cholera outbreak began . From 16 to 28 January 2008 , vaccination campaigns with the Vietnamese killed OCV were held in two districts of Hanoi . No cholera cases were detected from 5 February to 4 March 2008 , after which cases were again identified . Beginning 8 April 2008 , residents of four districts of Hanoi admitted to one of five hospitals for acute diarrhea with onset after 5 March 2008 were recruited for a matched , hospital-based , case-control outbreak investigation . Cases were matched by hospital , admission date , district , gender , and age to controls admitted for non-diarrheal conditions . Subjects from the two vaccinated districts were evaluated to determine vaccine effectiveness . 54 case-control pairs from the vaccinated districts were included in the analysis . There were 8 ( 15% ) and 16 ( 30% ) vaccine recipients among cases and controls , respectively . The vaccine was 76% protective against cholera in this setting ( 95% CI 5% to 94% , P = 0 . 042 ) after adjusting for intake of dog meat or raw vegetables and not drinking boiled or bottled water most of the time . This is the first study to explore the effectiveness of the reactive use of killed OCVs during a cholera outbreak . Our findings suggest that killed OCVs may have a role in controlling cholera outbreaks .
Cholera is increasingly being reported , and more countries are now experiencing outbreaks [1] , some lasting for several months . In 2001 , the World Health Organization ( WHO ) recommended the use of oral cholera vaccines ( OCV ) in populations at risk in endemic areas but not reactively once an outbreak has begun [2] . While this recommendation has been updated in March 2010 , to include reactive use of these vaccines [3] , OCVs have only been used for reactive cholera control in 2000 , when a live attenuated OCV ( CVD-103HgR ) was used in an outbreak in Micronesia [4] . The CVD-103HgR was assessed to be effective in this outbreak , although this was an observational study . In contrast , CVD-103HgR conferred no protection in the only randomized controlled efficacy trial of this vaccine [5] , and this vaccine is no longer manufactured . There is one internationally licensed killed oral cholera vaccine , the recombinant B subunit killed OCV ( rBS-WC , Dukoral , Crucell/SBL ) , but it has not been routinely adopted for public health use due to its high cost , limited duration of protection and logistic issues with vaccine administration . A variant of this oral vaccine , containing only killed whole cells ( Vibrio cholerae O1 and O139 ) is manufactured in Vietnam following technology transfer from Swedish scientists . Vietnam is the only country in the world to use an OCV in its public health system for cholera control . Since 1997 , this killed OCV ( ORC-Vax ) has been licensed and produced locally by the Company for Vaccine and Biological Production ( VaBiotech ) in Hanoi . The vaccine was found to confer 66% protection against an El Tor cholera outbreak occurring eight months following vaccination among all individuals aged 1 year and older [6] and 50% protection , three to five years after vaccination [7] . It is safe , inexpensive , and easy to administer [8] . Packaged in five-dose vials , each 1 . 5 ml liquid vaccine dose is drawn and squirted into the mouth by a syringe without a needle . Each dose contained: 5 . 0×1010 formalin-killed V . cholerae Inaba , El Tor strain Phil 6973; 2 . 5×1010 heat-killed V . cholerae Ogawa , classical strain Cairo 50; 2 . 5×1010 formalin-killed V . cholerae Inaba , classical strain 569B; and 5 . 0×1010 formalin-killed V . cholerae O139 strain 4260B . After oral administration , individuals are asked to drink water , but no oral buffer is required . Given in two doses , one to four weeks apart , it may be given to individuals aged one year and older . Since the seventh pandemic reached Vietnam in 1964 , cholera has been reported annually . A review of reported cases to the National Institute of Hygiene and Epidemiology ( NIHE ) from 1991 to 2001 showed that cholera is endemic in the central and southern provinces [9] . Compared with shigellosis and typhoid fever , cholera cases have decreased dramatically in 1997 to 2001 . This decrease in cholera cases has been partly attributed to the extensive use of the killed OCV in Vietnam [10] . From 1997 to 2005 , 9 . 2 million doses of the killed OCV have been used in the Expanded Programme of Immunization ( EPI ) of 20 cholera endemic provinces and metropolitan areas in Vietnam , mostly located in the central and southern areas ( Figure 1 ) . Vaccines are routinely provided in the endemic areas through regular monthly immunization sessions . In the routine EPI setting , depending on the commune , eligible children , aged 2–5 years are gathered for immunization on the same days for cholera vaccination . OCVs are provided 2 to 4 weeks apart . The killed OCV is also used preemptively in mass campaigns whenever an increase in the number of culture-confirmed cases are reported . National diarrheal disease surveillance is performed routinely and culture confirmation of organisms is available at the 61 provincial Centers for Preventive Medicine and in the national and four regional Institutes of Hygiene and Epidemiology . When cholera cases are detected in known endemic areas , mass vaccinations are arranged in designated locations such as schools , commune and district health facilities or government offices in the affected areas . In October 2007 , an increase in acute watery diarrhea cases was reported in Hanoi , caused by genetically altered Vibrio cholerae O1 Ogawa biotype El Tor producing classical biotype cholera toxin . Prior to this outbreak , the strain had never been isolated in Vietnam [11] . From 24 October to 4 December 2007 , nearly 2 , 000 diarrhea cases were reported from Hanoi and neighboring provinces , of which 295 were laboratory confirmed . In response the Ministry of Health of Vietnam mandated the provision of free medical treatment for anyone suffering from acute diarrheal illness . New cholera cases were identified on 24 December 2007 from Hanoi , thus , in the first week of January 2008 , just prior to the Vietnamese Tet New Year , a decision was made to immunize two particularly hard hit districts of Hanoi – Hoang Mai and Thanh Xuan ( combined population of ∼462 , 570 ) . These districts are located close to waterways into which sewage drains . The Vietnam National Institute of Hygiene and Epidemiology ( NIHE ) together with the Ministry of Health launched the mass vaccination campaign on 16–28 January 2008 , providing two doses of the killed oral cholera vaccine , spaced one week apart . Because of the absence of cases detected during the outbreak among children less than 10 years of age , vaccines were only provided to residents aged 10 years and older . Pregnant residents were also not eligible for vaccination . The campaign was announced in newspapers and radio and eligible residents were invited to proceed to commune health centers . Vaccination cards were provided to vaccinees and logbooks containing the names of vaccine recipients were maintained . It was estimated that ∼80% of the estimated 370 , 000 age-eligible individuals received one or more doses of the killed OCV . In addition , educational health campaigns were also conducted to inform the public of the signs of illness and to improve sanitary practices . From 24 December 2007 to 6 February 2008 , 59 diarrhea cases ( 33 culture confirmed V . cholerae O1 ) were identified , all cases coming from Hanoi . No cases were detected until 5 March 2008 , when the number of diarrhea cases increased and V . cholerae O1 Ogawa was again identified as the causative agent . The NIHE requested the International Vaccine Institute ( IVI ) to assist in the outbreak investigation , specifically looking into the role of vaccines for control . This provided a unique opportunity to assess the effectiveness of reactive oral cholera vaccination in a cholera outbreak , as there has been little experience in the use of OCVs in cholera epidemics . Figure 2 shows the clinical cholera cases in Hanoi from 24 October 2007 to 15 July 2008 .
Patient admission logbooks at the five hospitals were reviewed daily to identify patients admitted for diarrhea . Hospital records of identified patients were then reviewed . Patients who met the clinical case definition for cholera were invited to participate . A cholera case was defined , a priori , as being hospitalized for diarrhea with illness onset of 8 April to 20 May 2008 , with diarrhea defined as 3 or more loose , liquid or watery bowel movements in any 24 hour period; were 10 years of age or older and a resident of any of the 4 districts of interest . Cases were identified without knowledge of the vaccination status . One matched control per case was recruited from wards of the same hospital , except for cases admitted to NIID , wherein controls were identified from the trauma and surgical wards of Bach Mai Hospital , an adjacent general hospital . Patient admission logbooks were reviewed to identify controls hospitalized for non-diarrheal conditions . Controls were matched for each case by the date of presentation ( ±5 days ) , age group ( 10–20 years old , 21–40 years old , 40+ years old ) , gender and district of residence . The first control in the logbook that fulfilled the matching characteristics to the case was identified and invited to participate . Controls were chosen by reviewers who were unaware of the vaccination status of the patients . Data were obtained through transcription of clinical records and subject interviews using a standardized questionnaire . Demographic characteristics including occupation , water supply ( tap water , public well ) , behavioral characteristic such as hand washing and sanitation ( toilet with flush , latrine , none ) , as well as exposure factors ( intake of raw vegetables , dog meat , shrimp paste; not drinking boiled or bottled water ) , were collected . Vaccination status including the number and date of dosing was verbally ascertained based on subject recall . When available the reported dosing dates were cross-checked against a vaccination card . In order to evaluate the use of the OCV in this outbreak setting we defined “vaccinated” a priori as receipt of one or two doses of OCV from 16–28 January 2008 without further consideration to dosing interval or interval between vaccination and date of selection into the study . Microbiological culture results , completed by and according to the standard operating procedures of the admitting hospital laboratory , were also obtained during the study when available . To detect 50% vaccine protection , we assumed the following: 40% of controls would be vaccinated; the correlation of vaccine histories among matched cases and controls , phi , was . 05; and with 80% power at P< . 05 ( 2-tailed ) , at least 172 cases and 172 controls were required for the investigation . Characteristics and exposures of hospitalized cases and controls from the vaccinated and unvaccinated districts were compared . To assess the effect of vaccination , we included diarrheal cases and controls hospitalized for non-diarrheal causes from the vaccinated districts . Baseline characteristics were statistically compared using McNemar's test for dichotomous variables and the paired Student t-test for continuous variables . Only complete pairs in which both the case and the control had exposure measurements were included , and the information contained in the incomplete pairs was ignored . The adjusted matched odds ratio ( OR ) and 95% confidence interval ( CI ) for calculation of vaccine effectiveness was determined using multivariate conditional logistic regression [13] . Statistical analysis was planned at the outset , to include all variables with p<0 . 05 in univariate analysis and the primary variable of interest ( vaccination status ) in the multivariable model . Vaccine effectiveness was calculated as: ( 1-matched OR ) ×100 . All p values and 95% confidence intervals , estimated from the point estimates and standard errors for the coefficient for the vaccination variable in the models , were interpreted in a two- tailed manner . Statistical significance was designated as a p value<0 . 05 . All statistical analyses were performed using Stata10 ( StataCorp , College Station , TX ) . The study qualified for exemption from review by the IVI Institutional Review Board and Ethical Review Committee of NIHE as the study was conducted as part of an outbreak investigation establishing risk factors and modifiers . Verbal consent was obtained in lieu of written consent from both cases and controls as the project was conducted as part of an outbreak investigation . Consent was documented in a logbook .
We enrolled 126 matched pairs of cases and controls for the outbreak investigation; one matched pair was excluded when on review the case definition was not met by the case ( Figure 4 ) . After exclusion of this matched pair , among cases , the ages ranged from 17 to 86 years old while the control age range was 15 to 80 years old . Thirty-seven percent of cases had vomiting and 76% had some or severe dehydration on admission . Among those with severe dehydration , only one was vaccinated . Of the 99 cases whose stools were tested , 74 subjects had culture confirmed V . cholerae O1 ( 75% ) . Only one vaccine recipient had culture confirmed cholera . Table 1 shows the causes of hospitalization for the controls . Of the 125 matched pairs , 54 pairs ( 43% ) were residents of districts where the mass vaccination campaign was carried out and were included in this evaluation of vaccine effectiveness . We compared the baseline characteristics of cases and controls from Huang Mai and Thanh Xuan , where the mass vaccination campaigns were carried out , and found no significant differences in demographic and socio-economic characteristics ( Table 2 ) . On comparing the exposure of cases with controls , intake of raw vegetables and not drinking boiled or bottled water were found to be significantly different ( p<0 . 05 ) . Similar results were obtained when comparing all cases and controls in the outbreak investigation , including patients from both the vaccinated and unvaccinated districts ( data not shown ) . Because dog meat is customarily eaten with raw vegetables and 70% of those who ate dog meat also ate raw vegetables , we decided to combine these in the multivariate regression model . Of subjects from the vaccinated districts , 8 of 54 cases ( 15% ) and 16 of 54 controls ( 30% ) were classified as vaccinated , having received at least one dose of the killed OCV from 16–28 January 2008 . Seventy-five percent ( 6/8 ) of vaccinated cases and 63% of vaccinated controls ( 10/16 ) received two doses of killed OCV during the vaccination campaign . The unadjusted vaccine effectiveness ( VE ) was 54% ( 95% CI −31% to 84%; p-value = 0 . 144 ) , however , after adjusting for factors which were found to be significantly associated with being a cholera case at P<0 . 05 in univariate analyses ( intake of dog meat or raw vegetables and not drinking boiled or bottled water most of the time ) ( Table 3 ) , the killed OCV was found to have an effectiveness of 76% ( 95% CI 5% to 94% , p = 0 . 04 ) .
Because there may be inherent differences in health care utilization and knowledge among those who presented for vaccination and those who refused vaccination [14] , [15] , bias may have been introduced in our assessment for vaccine protection , and may have exaggerated our results . The protective effect may have been augmented , as it has been shown that people refusing participation are more likely to engage in high-risk behaviors as compared to vaccines [15] . However , there were no differences in the baseline demographic , socioeconomic and exposure characteristics of vaccinated and non-vaccinated cases and controls . Moreover , there were several factors that may have decreased the true protective effect of the vaccine during this outbreak , namely: ( 1 ) individuals with a recent history of cholera-like diarrhea may not have participated in the campaign and were included in the control group ( 2 ) recipients of a single dose of the vaccine were included in the analysis ( 3 ) vaccinees may have been more likely to use the treatment centers for the care of diarrhea compared to refusers . A comparison of a partially immunized vaccine group to a control group with varying levels of natural immunity would tend to depress apparent vaccine protection against subsequent cholera . Our evaluation was also limited by use of a clinical case definition without culture confirmation , however we used a strict case definition and random cases were culture confirmed . Moreover , inclusion of non-culture confirmed cases , if ever , would have depressed the protection afforded by vaccination as some cases may not be due to V . cholerae . We did not reach the sample size required ( 54 instead of the desired 172 ) because of difficulty enrolling controls during this outbreak , throughout which most hospital beds were occupied by cholera cases . The smaller sample size may explain the unadjusted VE as being not statistically significant . We tried to limit selection bias by enrolling cases and controls without prior knowledge of their vaccination status . Moreover , in order to prevent interviewers from overzealously eliciting vaccination history , several exposure questions were included in the questionnaire . Lastly , our study was initiated more than two months after the campaign , thus we were unable to include cases proximate to vaccination , however since the outbreak was prolonged and recurrent and vaccine effectiveness lasts for three to five years [7] , measurement of the effectiveness of OCV use in this setting was still warranted . To our knowledge , this is the first study that explored the reactive use of a killed OCV in an outbreak . In Hanoi , the outbreak was described as having occurred in three waves , each separated by 14 to 26 day intervals with no recorded cases in between each wave . Vaccination was performed while the second wave was ongoing ( see Figure 3 ) . Since the mass vaccination campaign was performed in the two districts that have been most affected in the previous waves of diarrheal cases , the characteristics of the residents in these districts may have been different from other areas that make them vulnerable to diarrheal outbreaks and may be more amenable to district specific interventions . However , comparison of baseline characteristics and exposures of patients from the vaccinated ( Hoang Mai and Thanh Xuan ) and unvaccinated districts ( Dong Da and Cau Giay ) showed no statistically significant differences ( data not shown ) . In the recently updated WHO recommendations , consideration for both preemptive and reactive use of OCVs is supported after assessment of local infrastructure and epidemiology . A model of a refugee camp based cholera outbreak in Africa compared the cost-effectiveness of several cholera controls strategies , including establishment of treatment centers and reactive vaccination . Based on duration of the hypothetical outbreak and the size of the hypothetical camp , reactive vaccination will only be a cost-effective option if the price of the vaccine falls below $0 . 22 per dose [16] . However , there were several limitations to this analysis [17] and this study did not account for large prolonged outbreaks such as those seen recently in Zimbabwe [18] , [19] , Angola [20] , [21] and Vietnam [11] , which would favor reactive vaccination . Since 1996 , extensive cholera outbreaks of this magnitude had not been reported in Vietnam , especially in areas where the killed OCV is routinely used . Between 5 March and 22 April 2008 , the Vietnamese Ministry of Health reported 2 , 490 cases of severe acute watery diarrhea including 377 that were positive for V . cholerae O1 Ogawa [22] . Twenty provinces in the northern areas were affected in 2007 to 2008 . No deaths were reported during these outbreaks indicating good case management . On the other hand , in Africa , cholera outbreaks are deadly . In Zimbabwe alone from August 2008 to May 2009 , almost 100 , 000 cases have been identified with more than 4 , 000 deaths [18] , 61% of whom did not reach a health facility for treatment [19] . Similarly in Angola , an outbreak from February to June 2006 with 46 , 758 cases and 1 , 893 deaths [20] , [21] were reported with case fatality rates in some provinces tragically reaching up to 30% [20] . Provisions for clean water , adequate sanitation and good case management are necessary for controlling cholera , however , these are unlikely to happen in the near future in most of the developing world where cholera continues to cause significant hardship and misery . New measures need to be taken . Prior to the release of the March 2010 WHO position paper several groups were pressing for a rethink of the WHO stand on vaccine use for outbreaks [23] . The results of our study are consistent with earlier evaluations of the protective effects of OCV [6] . Microbiologic studies have shown that the outbreak was caused by the new strain of El Tor V . cholerae O1 producing classical cholera toxin [11] . This new strain has been increasingly reported in Asia and in parts of Africa [24]–[26] with some indications of increased severity [27] . The killed OCV provided protection against this new strain suggesting that there may be a role for reactive use of the killed OCV in future cholera outbreaks . The Vietnamese killed OCV has now been extensively modified by the IVI to comply with WHO and current Good Manufacturing Practices ( cGMP ) standards . The modified vaccine was recently licensed in Vietnam ( mORC-VAX ) . In order to expand its use internationally and to allow purchase by United Nations agencies , technology transfer of the vaccine production process was made by the IVI to Shantha Biotechnics in India where it is now licensed ( Shanchol® ) . This modified vaccine with higher antigenic content than the previous versions has been found to be safe and protective in India [28] and resulted in comparable vibriocidal immune responses after one or two doses of the vaccine raising the possibility that it may be used as a single dose , which would greatly simplify vaccine delivery in times of outbreaks [29] . Further studies to confirm our findings are necessary; however , these results provide hope that the vaccine will be used not only for endemic cholera control but in times of outbreaks as well , when mortality may be higher [30] . | Simple measures such as adequate sanitation and clean water stops the spread of cholera; however , in areas where these are not available , cholera spreads quickly and may lead to death in a few hours if treatment is not initiated immediately . The use of life-saving rehydration therapy is the mainstay in cholera control , however , the rapidity of the disease and the limited access to appropriate healthcare units in far-flung areas together result in an unacceptable number of deaths . The WHO has recommended the use of oral cholera vaccines as a preventive measure against cholera outbreaks since 2001 , but this was recently updated so that vaccine use may also be considered once a cholera outbreak has begun . The findings from this study suggest that reactive use of killed oral cholera vaccines provides protection against the disease and may be a potential tool in times of outbreaks . Further studies must be conducted to confirm these findings . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"bacterial",
"diseases",
"infectious",
"diseases",
"cholera",
"neglected",
"tropical",
"diseases",
"infectious",
"disease",
"control"
] | 2011 | Use of Oral Cholera Vaccines in an Outbreak in Vietnam: A Case Control Study |
Chikungunya virus ( CHIKV ) belongs to a group of mosquito-borne alphaviruses associated with acute and chronic arthropathy , with peripheral and limb joints most commonly affected . Using a mouse model of CHIKV infection and arthritic disease , we show that CHIKV replication and the ensuing foot arthropathy were dramatically reduced when mice were housed at 30°C , rather than the conventional 22°C . The effect was not associated with a detectable fever , but was dependent on type I interferon responses . Bioinformatics analyses of RNA-Seq data after injection of poly ( I:C ) /jetPEI suggested the unfolded protein response and certain type I interferon responses are promoted when feet are slightly warmer . The ambient temperature thus appears able profoundly to effect anti-viral activity in the periphery , with clear consequences for alphaviral replication and the ensuing arthropathy . These observations may provide an explanation for why alphaviral arthropathies are largely restricted to joints of the limbs and the extremities .
Studying the role of temperature in regulating viral infections and viral pathologies has an eclectic history . The Australian nurse , Elizabeth Kenny ( 1880–1952 ) , pioneered the treatment of polio with application of heat packs to affected limbs [1 , 2] , although the mechanistic basis for this treatment has not been identified [3] . A number of reasons for the increase in respiratory tract infections during the winter months have been proposed , with in vitro experiments in cell lines suggesting a role for improved type I interferon ( IFN ) responses at 37°C compared with lower temperatures [4–6] . A range of other factors have also been implicated including cold stress , respiratory tract vasoconstriction , humidity , transmission , and host behavior [7 , 8] . Fever has also been viewed as potentially beneficial in fighting viral infections [9 , 10] , although effects may be quite marginal [11 , 12] . Mice housed at 30°C have been shown to have increased CD8 T cell activity compared with mice held at 20–26°C , although this study was concerned with anti-cancer responses [13] . Elevated ambient temperatures have been shown to increase expression of type I IFN stimulated genes ( ISGs ) , TNF and IL-1 , and protect Japanese flounders against Hirame rhabdovirus [14] . Olive flounders infected with hemorrhagic septicemia virus if held at 20°C rather than 15°C also showed improved survival , increased virus-induced apoptosis [15] , and increased levels of Toll-like receptor 7 and Interferon Response Factor 7 ( IRF7 ) [16] . In mosquitoes , maintenance at lower temperatures favors replication of chikungunya virus ( CHIKV ) and yellow fever virus by suppressing the efficiency of the insect’s anti-viral ‘RNA-induced silencing complex’ [17] . A number of mosquito-borne viruses in the genus alphavirus ( family Togaviridae ) cause outbreaks of human rheumatic disease around the world; these include CHIKV , the primarily Australian Ross River virus ( RRV ) and Barmah Forest virus , the African o’nyong-nyong virus , the Sindbis group of viruses and the South American Mayaro virus [18] . CHIKV has recently re-emerged to produce the largest documented outbreak of CHIKV disease ever recorded; the outbreak began in 2004 in Africa , spread across Asia to Indonesia , and reached the Americas in 2013 [19–21] . Millions of CHIKV cases have been reported [22] . RRV causes about 4–5000 cases per annum in Australia , and in 1979–80 was responsible for a large epidemic in the South Pacific [23] . Symptomatic infection of adults with these alphaviruses is associated with acute and chronic ( often debilitating ) polyarthralgia and/or polyarthritis , which usually lasts weeks to months , occasionally longer [18] . Alphaviral arthritic disease is generally symmetrical with joints in limbs and at the extremities most commonly affected ( e . g . feet , hands , ankles , wrists , knees , elbows ) [23–26] . Arthritic inflammation arises from viral infection of joint and surrounding tissues with viral RNA and/or viral proteins stimulating innate and adaptive pro-inflammatory immune responses [18 , 27–31] . A number of mouse models for alphavirus infection and disease have been developed [32–38] . The adult wild-type C57BL/6 mouse model of CHIKV infection and disease used herein involves injection of CHIKV s . c . into feet , and recapitulates the viremia and arthropathy seen in humans [32] . RNA-Seq analyses also revealed a good concordance in inflammatory gene induction between the mouse model and CHIKV patients [27] . The latter analyses also illustrated the dominance of the type I IFN response , with ISGs representing ≈50% of CHIKV-induced genes [27] . A key role for the type I IFN response in controlling alphaviral infection is well established [38–40] , with type I interferon receptor deficient ( IFNAR-/- ) mice rapidly succumbing to CHIKV infection [35 , 41] . The transcription factors , Interferon Response Factor 3 ( IRF3 ) and IRF7 are critical for innate protection , with CHIKV-infected IRF3/7-/- ( double knockout ) mice unable to produce detectable type I IFNs , resulting in high viremias , fever , hemorrhagic shock and mortality within a few days ( a disease outcome only occasionally seen in humans ) [41] . Herein we provide evidence that the reason for the largely peripheral alphaviral arthropathy seen in humans is that joints in the limbs and the extremities are a few degrees cooler than the core temperature of ≈37°C , with slightly cooler temperatures associated with suboptimal anti-viral type I IFN responses . Increasing the mouse housing temperature from 22°C to 30°C increased foot temperatures; this led to increased activation of the unfolded protein response ( UPR ) and improved anti-viral type I IFN responses in the feet . CHIKV replication was subsequently reduced and peripheral arthropathy was substantially ameliorated .
Using the adult wild-type mouse model of CHIKV viremia and arthritis described previously [27 , 32 , 36 , 37] , C57BL/6J female mice were inoculated subcutaneously ( s . c . ) into the feet with CHIKV ( Reunion Island isolate ) . Mice were housed at an ambient temperature of 22 ± 1°C ( as per standard animal house temperature ) or 30 ± 1°C five days before viral inoculation and were kept at these temperatures until the end of the experiment . ( Mice housed at the higher temperature did not show any overt changes in appearance or behavior ) . As described previously [32] , high viral loads are reached in the feet within 2 days post inoculation ( Fig 1A ) . Thereafter , the feet of mice housed at 30°C showed 1–2 log lower viral loads than feet of mice housed at 22°C ( Fig 1A ) , with qRT PCR also showing lower CHIKV RNA levels ( S1A Fig ) . In addition , CHIKV RNA levels in feet on day 30 post-infection were ≈20 fold lower in feet of mice kept at 30°C ( Fig 1B ) . In this model for mice housed at 22°C , CHIKV RNA persists in feet for up to 100 days post infection [36] , with persistent viral RNA associated with chronic arthritic disease [28 , 30 , 42 , 43] . Foot swelling , which is a measure of acute arthritis and peaks on day 6/7 in this model [27 , 32 , 36] , was substantially and significantly reduced in mice kept at 30°C ( Fig 1C , arrow ) . H&E staining of foot sections during acute arthritis illustrated the previously described [32] prodigious mononuclear inflammatory infiltrate in feet of CHIKV-infected mice housed at 22°C ( Fig 1D , 22°C ) . In contrast , mice housed at 30°C showed substantially lower numbers of infiltrating cells ( Fig 1D , 30°C ) . Quantification of the histology ( by Aperio pixel count [27] ) confirmed this difference was highly significant ( Fig 1E ) . In addition , immunohistochemistry [44] showed less CHIKV antigen in foot tissues of mice kept at 30°C ( Fig 1F; mock samples are shown in S1B Fig ) ; with quantification demonstrating significance ( Fig 1G ) . Given ( i ) that CHIKV infections are effectively controlled by type I interferon ( IFN ) responses [27 , 38 , 39 , 41] and ( ii ) previous in vitro data suggests lower type I IFN production and activity at lower temperatures [4–6] , these results suggest that for mice housed at 30°C , type I IFN responses in feet are improved , leading to reduced CHIKV replication and a subsequent decrease in viral arthropathy . Mice housed at 30°C also showed a substantially ( ≈2–4 log ) and significantly lower viremia on days 2 and 3 post infection ( Fig 1H ) , with virus tissue titers in lymph nodes , spleen and muscle similarly affected ( Fig 1I ) . These results suggest an increase in systemic antiviral activity ( see below ) . To determine whether type I IFN responses are required for the reductions in viral loads seen for mice kept at 30°C ( Fig 1A , 1B , 1H and 1I ) , IRF3/7-/- were housed at 30°C or 22°C and were infected with CHIKV s . c . in the feet . IRF3/7-/- mice do not generate detectable serum IFNα/β levels following CHIKV infection and usually die within 5–6 days [41] . In contrast to wild-type mice , neither the viremia ( Fig 2A ) , nor the foot swelling ( Fig 2B ) was reduced in IRF3/7-/- mice housed at 30°C relative to IRF3/7-/- mice housed at 22°C . Foot swelling was actually slightly higher at 30°C ( reaching significance on day 2 ) ( Fig 2C ) . In addition , survival of CHIKV-infected IRF3/7-/- mice was also marginally less for mice held at 30°C , although the difference only approached significance ( p = 0 . 071 ) . Thus IRF3/7-/- mice , in contrast to C57BL/6 mice , did not show reduced viral replication and disease when housed at 30°C; if anything disease was slightly increased at this temperature in IRF3/7-/- mice . The above experiments were repeated in IFNAR-/- mice; these mice are unable to respond to IFNα/β due to the lack of a functional IFNα/β receptor and die rapidly after CHIKV infection [35 , 41] . In contrast to wild-type mice ( Fig 1H ) ( but similar to IFNAR-/- mice , Fig 2A ) , there was no difference in viremia between IFNAR-/- mice housed at 22°C and 30°C ( Fig 2D ) . Foot swelling in IFNAR-/- mice was significantly higher at 30°C ( as in IRF3/7-/- mice , Fig 2B ) ; opposite to the reduced swelling seen in C57BL/6 mice housed at 30°C ( Fig 1C ) . The reduced viremia ( Fig 1H ) and foot swelling ( Fig 1C ) seen at 30°C in wild-type mice was thus not recapitulated in either IRF3/7-/- ( Fig 2A and 2B ) or IFNAR-/- mice ( Fig 2D and 2E ) . One should note that CHIKV-induced foot swelling in the latter two type I IFN response-deficient mice is quite distinct from that seen in C57BL/6 mice ( Fig 1D ) , and is largely due to hemorrhage and shock-associated edema [41] . Lower temperatures are known to reduce edema [45] . There was no significant difference in the survival of CHIKV-infected IFNAR-/- mice housed at 22°C or 30°C ( Fig 2F ) . The ability of the higher ambient temperature of 30°C to reduce CHIKV infection and arthritis in wild-type mice ( Fig 1 ) thus requires the presence of key members of the type I IFN pathway . The data so far argues that for wild-type mice infected in the feet , type I IFN responses work better when animals are housed at 30°C . Fig 1H & 1I also suggest increased systemic type I IFN activity at 30°C . Injection of poly ( I:C ) induces very rapid and transient serum type I IFN levels [46] . Furthermore , injection of poly ( I:C ) /jetPEI into feet represents a model of dsRNA-induced arthritis , with jetPEI ( a transfection reagent ) helping to deliver poly ( I:C ) to the cytoplasm , mimicking cytoplasmic viral RNA replication and stimulating cytoplasmic RNA sensors [47] . Mice were housed at 22°C or 30°C and were injected s . c . in the feet with poly ( I:C ) /jetPEI , and IFNα and IFNβ levels were then measured in serum . IFNβ levels at all time points tested , and IFNα levels at 2 hours post injection , were not significantly different . However , at 4 , 6 and 8 hours , IFNα levels were significantly 2–3 fold higher for mice housed at 30°C . IFNβ ( and to some extent IFNα4 ) are generated in the first phase of the type I IFN response , with subsequent signaling via the IFNα/β receptor leading to up-regulation of inter alia IRF7 and the second phase of amplified production of IFNαs [41 , 48 , 49] . Increasing the temperature of feet thus appears to promote the secondary amplification loop , rather than the initial sensing of dsRNA and the production of IFNβ [49] . When CHIKV infection rather than poly ( I:C ) /jetPEI injection was tested , the reverse result was obtained , with serum IFNα levels ( on day 2 ) higher in mice housed at 22°C than in mice housed at 30°C ( S2A Fig ) . However , on day 2 the viremia in mice housed at 22°C was already ≈3 logs higher than in mice housed at 30°C ( S2A Fig ) . Thus the two groups differed not only in terms of housing temperatures , but also vastly different viral loads . This complication is clearly avoided by using poly ( I:C ) /jetPEI rather than CHIKV infection ( see below ) . Nevertheless , Ingenuity Pathway Analysis ( IPA ) of differentially expressed genes ( DEGs ) obtained from RNA-Seq after CHIKV infection also provided some evidence that the secondary type I IFN amplification loop is favored at 30°C ( S2B Fig ) . To further investigate the effects of temperature on IFNα activity , Vero cells which can respond to , but cannot make IFNα/β , were cultured at 37°C , 35°C or 33°C . The cells were then treated with IFNα and infected with CHIKV . At 37°C , 50% protection against CHIKV-induced cytopathic effects ( CPE ) required ≈1 . 5 U/ml of IFNα ( Fig 2H ) . At 35°C , ≈50 U/ml of IFNα were required , and at 33°C no significant protection against CPE was observed even at 100 U/ml ( Fig 2H ) . These data are consistent with Fig 2G and other in vitro data using different mammalian viruses [4–6] and argue that type I IFN receptor signaling and/or downstream anti-viral responses are diminished at the lower temperatures . In the absence of IFNα , CHIKV replication in Vero cells was not significantly affected by these small temperature changes ( Fig 2I ) . Type I IFN protein levels rise rapidly and fall in CHIKV-infected C57BL/6 mice within 4 days [32 , 41] , with this transient cytokine production key to generating protective innate anti-viral responses [27 , 41] . When CHIKV-infected C57BL/6 mice were housed at 22°C and then moved to 30°C at the end of day 4 post-infection ( the end of the viremic period ) , foot swelling was the same as that seen in mice housed at 22°C throughout ( Fig 3A ) . Similarly when C57BL/6 mice were housed at 30°C and then moved to 22°C on day 4 post-infection , the reduction in foot swelling was similar to that of mice housed at 30°C throughout ( Fig 3B ) . The reduced arthritic disease seen at 30°C was therefore due to effects mediated within the first 4 days post infection , the period of significant IFNα/β production and peak viral replication . These experiments ( i ) support the view that the ambient temperature mediates its effects during the time of IFNα/β production , and ( ii ) argue that any warming treatment attempting to reduce arthritic disease [1 , 2] likely needs to be initiated very early in infection . The potential benefits of fever for fighting infections have been suggested in various contexts [9 , 10] . C57BL/6 mice housed at 22°C and infected with CHIKV do not develop a detectable fever [41] , whereas most humans develop a fever after CHIKV infection [50] . To determine whether housing mice at 30°C promotes detectable fever development , estimates of core temperatures were obtained using a pediatric infra-red thermometer gently pressed into the pit of the back leg of a restrained mouse for 10 seconds , with the leg folded over the end of the probe . No significant fever was detected in mice kept at 30°C or 22°C ( Fig 3C ) . In humans , fever coincides with peak viremia [50] , which would be day 1–2 post infection in mice ( Fig 1A ) . As might be expected for a homeotherm , the core temperature measurements were only marginally higher at 30°C ( Fig 3C ) . Temperature readings were also taken from feet by placing the thermometer onto the hairless skin regions of the hind foot , with mice housed at 30°C showing ≈3–4°C higher foot skin temperatures than mice housed at 22°C ( Fig 3C , repeat measures ANOVA p<0 . 001 ) . After day 4 , foot skin temperatures for mice housed at 30°C did show a ≈1°C increase relative to day 0 ( from ≈34°C to ≈35°C ) , presumably as a consequence of the CHIKV-induced arthritic inflammation ( calor ) . Thus for mice housed at 22°C , feet were about ≈6°C cooler than core , whereas for mice housed at 30°C feet were ≈2°C cooler than core . Similar differences in skin temperatures at the trunk and the extremities have been reported in healthy humans placed in rooms kept at 30°C or 22°C [51] . The mice in Fig 1 were inoculated with CHIKV s . c . into the feet , with feet ≈3–4°C cooler in mice housed at 22°C when compared with mice housed at 30°C ( Fig 3C ) . As feet represent a major site of virus replication after s . c . CHIKV inoculation into the feet ( Fig 1A ) [32] , mice were injected with CHIKV via the intra-peritoneal ( i . p . ) route , to ascertain whether the temperature phenotype was dependent on the route of inoculation . Although the viremia showed a hint of reduced viral load at 30°C , no significant differences in viremia ( Fig 4A ) , tissue titers ( Fig 4B ) or feet titers ( Fig 4C ) for mice housed at the two temperatures was seen after i . p . inoculation . This contrasts with s . c . inoculation; viremia ( Fig 1H ) , tissue titers ( Fig 1I ) and feet titers ( Fig 1A ) . ( Foot swelling does not occur after i . p . inoculation of CHIKV [32] ) . Thus the temperature phenotype ( described in Fig 1 ) seen after inoculation of CHIKV into cooler feet ( Fig 3C ) is lost when virus is inoculated via the i . p . route . After i . p . inoculation , initially virus replication likely occurs at core temperatures , which were similar for mice housed at 30°C or 22°C ( Fig 3C ) . In the absence of a temperature difference , type I IFN production would not be different and systemic anti-viral activity remains largely unaffected ( Fig 4A–4C ) . Although viral inoculation via the i . p . route is unlikely to occur naturally , the experiment illustrates that the increased CHIKV replication seen in mice housed at 22°C ( Fig 1 ) requires that initial viral replication occurs in tissues that are slightly cooler than the core temperature . Arbovirus disease models have generally been optimized with respect to route and dose , and the RRV mouse model of arthropathy involves inoculation of virus s . c . into the pectoral area of young mice , with infection showing tropism for tissues in the hind limb [33] . This model might thus be viewed as being an intermediate between the foot s . c . and i . p . routes of inoculation described above . After viral inoculation the viremia in RRV infected mice was ≈0 . 5–1 log lower on days 3–6 for mice housed at 30°C , although this was significant only when a repeat measures ANOVA was applied ( Fig 5A ) . Foot titers started to show slight differences days 3 and 7 , but differences only reached significance on day 10 post RRV infection when titers were ≈2 logs lower in mice housed at 30°C ( Fig 5B ) . The disease severity was significantly lower for mice held at 30°C , with differences in disease scores increasing after day 8 ( Fig 5C ) . The disease severity scoring in this model is primarily derived from assessing hind limb weakness [34] . Thus in a model where viral tropism results in hind limbs becoming a major site of viral replication and disease , a clearly significant reduction in foot titers and disease was again evident for mice housed at 30°C . One might of course now argue that viral tropism in this model is due ( at least in part ) to the lower limb temperatures and the resulting reduction in type I IFN activity [52] . The results so far suggest that for wild-type mice housed at 30°C and infected in the feet , alphaviral replication in limbs and extremities is more effectively controlled by type I IFN responses . As a consequence , viral replication and arthritic disease is significantly reduced . To investigate the role of ambient temperature on type I IFN responses in the periphery , RNA-Seq was undertaken on mRNA from four samples ( i ) feet from naive control mice held at 30°C ( 30C ) , ( ii ) feet from mice held at 30°C treated with poly ( I:C ) /jetPEI for 12 h ( 30T ) , ( iii ) feet from naive control mice held at 22°C ( 22C ) , and ( iv ) feet from mice held at 22°C treated with poly ( I:C ) /jetPEI for 12 h ( 22T ) . As described above , injecting poly ( I:C ) /jetPEI into feet represents a model of dsRNA-induced arthritis [47] , and ensures an equivalent amount of dsRNA is present in the feet of mice housed at the two temperatures . Quality control analyses for the RNA-Seq are shown in S3 Fig . DEGs ( FDR q<0 . 01 and CPM>1 in at least 3 samples ) are shown in S1 Table . Global analyses of DEGs illustrated , for instance , that genes regulated by poly ( I:C ) /jetPEI were qualitatively , if not quantitatively , similar at the two temperatures , with 22C vs 22T and 30C vs 30T showing considerable overlap ( S4 Fig ) . As might be expected ( i ) most of the up-regulated genes were ISGs ( S4 Fig ) and ( ii ) the Molecular and Cellular Functions feature of IPA clearly illustrated that genes associated with cell growth , survival , movement , morphology and development were up-regulated in feet of mice housed at 30°C in the presence or absence of poly ( I:C ) /jetPEI treatment ( S2 Table ) . Gene set enrichment analyses illustrated a highly significant concordance between up and down regulated DEGs after poly ( I:C ) /jetPEI treatment verses up and down regulated DEGs after CHIKV infection ( S5A Fig ) . In addition , at both temperatures , >79% of ISGs up-regulated by poly ( I:C ) /jetPEI were also up-regulated by CHIKV ( S5B Fig ) . These analyses illustrate that poly ( I:C ) /jetPEI injection and CHIKV infection have very similar gene expression signatures . Comparing 30T vs 22T ( using both up and down regulated genes ) and the upstream regulator ( USR ) function of IPA ( direct only ) , a series of USRs were identified . Prominent among those more active at 30°C were USRs associated with the unfolded protein response ( UPR ) , specifically XBP1 , ATF6 , and ATF4 ( Fig 6A ) . mRNA levels of XBP1 ( the most prominent USR ) were also significantly higher at 30°C , although the increase in spliced XBP1 was not significant ( S6A Fig ) . Importantly , two transcription factors central to the type I IFN response to CHIKV , IRF3 and IRF7 [41] , were more active at 30°C , with USRs showing high activation Z scores ( >2 ) and significance for 30T vs 22T ( Fig 6A ) . Other USRs associated with the type I IFN response were also identified; NF-кB ( NFKB1/2 ) , IRF1 , STAT3 , STAT6 , IRF5 and STAT2 ( Fig 6A ) , with most of these also activated during CHIKV infection [27] . β-catenin ( CTNNB1 ) was also recently shown to regulate RIG-I-dependent responses [53] . Only IRF8 and STAT1 [27] showed lower activity at 30°C . TRIM24 , a negative regulator of the type I IFN response showed lower activity at 30°C ( Fig 6A ) . The USR analysis showed no indication of a fever signature , consistent with data in Fig 3C . Multiple USRs associated with promotion of the UPR and type I IFN responses were thus more active in feet of mice housed at 30°C . After poly ( I:C ) /jetPEI treatment the mRNA expression of a number of key genes associated with the type I IFN response were also more up-regulated at 30°C than at 22°C , including IRF7 , Oas2 and Oas3 ( S6B Fig ) . However , mRNA levels for IFNβ were not significantly different for 30T vs 22T ( S6C Fig ) , consistent with the data in Fig 2G . ( There were insufficient reads for the IFNαs to make any comparisons; unique reads mapping to IFNα subtypes are inherently low even after CHIKV infection [27] ) . An identical RNA-Seq and IPA USR analysis to that described above was conducted using feet from naïve untreated mice held at 30°C or 22°C for 5 days ( 30C vs 22C ) ( Fig 6B ) . XBP1 and ATF6 ( UPR-associated USRs ) were again identified with high Activation Z scores . A number of other USRs associated with promotion of the type I IFN response were also more active at 30°C , whereas IRF3 , IRF7 ( and IRF5 ) were not identified ( Fig 6B ) . A recently released program called “Integrated System for Motif Activity Response Analysis” ( ISMARA ) [54] allows for an independent analysis of the RNA-Seq data and provides an activity score for known promoters . The majority of the transcription factors identified as more active at 30°C by the IPA USR ( Fig 6A and 6B ) also showed a trend towards increased activity in the ISMARA ( S7A Fig ) . Importantly , prominent in both the IPA USR ( Fig 6A and 6B ) and the ISMARA outputs ( Fig 6C , S1 Table ) , was XBP1 ( a key player in the UPR ) , with the ISMARA showing a clear increase in XBP1 promoter activity at 30°C after poly ( I:C ) /jetPEI treatment ( Fig 6C , XBP1 30T vs 22T ) . The same trend ( although not significant ) was also seen for 30C vs 22C ( Fig 6C , XBP1 30C vs 22C ) . The spliced form of XBP1 is transcriptionally active [55] . The top scoring site ( by Z score ) in the ISMARA output for 30T vs 22T was ATF3 , with ATF3 showing reduced activity in feet of mice housed at 30°C ( Fig 6C , S1 Table ) . ATF3 was also prominent ( although less significant ) for 30C vs 22C ( Fig 6C , S1 Table ) . ATF3 is well described as a stress-responsive transcription factor [56] , and has been associated with repression of type I ISGs [57] ( and proinflammatory cytokine expression [58 , 59] ) . The reduced ATF3 activity at 30°C ( Fig 6C ) is thus consistent with increased antiviral activity in feet of mice housed at 30°C . As expected for a repressor , the ISMARA showed a negative Expression/Activity correlation for ATF3 ( rho = -0 . 76 , p = 0 . 007 ) , whereas XBP1 and HSF1 showed significant positive correlations ( S7B Fig ) . Further supportive evidence for a role for ATF3 is provided by the observation that a number of ISGs up-regulated in ATF3-/- fibroblasts in vitro [57] were also up-regulated in feet at 30°C after poly ( I:C ) /jetPEI treatment ( RNA-Seq , p<0 . 05 ) ; specifically IRF7 , BST2 , SLFN1 , FPR2 , LY6A , OAS1G , TRIM30D , FCGR4 , SIGLEC1 , PLAC8 , TLR1 , SELL , IFITM6 , FPR2 AND NUPR1 . The ISMARA also highlighted increased heat shock factor 1 ( HSF1 ) activity after poly ( I:C ) /jetPEI treatment for mice held at 30°C ( Fig 6C ) , with a number of heat shock proteins identified as targets of HSF1 ( S7C Fig ) . HSF1 activity is induced during conditions that cause an increase in unfolded or misfolded proteins , primarily in the cytoplasm and nucleus [60] . Such conditions include , but are not limited to heat shock [61] , with HSF1 believed to control transcription of genes dedicated to restoring protein-folding homeostasis [62] .
At room temperature ( 22°C ) limbs and extremities in humans are usually cooler than core temperatures [51] , and alphaviral arthropathies primarily affect joints in limbs and the extremities [23 , 25 , 26]; the marked concordance is illustrated in S8 Fig . The data presented herein argues that a major reason why alphaviral arthropathy primarily affects these joints is that these tissues are often cooler , resulting in reduced type I IFN anti-viral responses , increased alphaviral replication and thus exacerbated arthropathy . A number of other viral arthropathies have similarly been shown to predominantly affect the extremities , including dengue [63] , rubella [64] , parvovirus [65] , and hepatitis B [66] , suggesting this affect is not restricted to alphaviruses . The results suggest that limb temperatures can have profound effects on alphaviral infection and disease . The implications might be that the Elizabeth Kenny limb warming therapy [1 , 2] might find utility for treating alphaviral arthritides . However , such treatment might need to start very early post onset of viremia , before the virus has had a chance to seed and substantially replicate in joint tissues . Applying “heat therapy” 4 days post infection in mice was already too late to change the course of arthritic disease ( Fig 3A ) . In humans , fever ( often accompanied with joint pain ) is usually the first sign of CHIKV infection , by which time viremia has usually already peaked [50] and joints have likely already become infected . Whether warming of peripheral joints at this stage would lead to significant alterations in the course of this often chronic condition [30 , 67] remains unclear . Warming treatment initiated after the acute phase may have little impact , as the virus may no longer be replicating and sensitive to anti-viral type I IFN responses [36 , 68] . The data might argue that alphaviral arthropathies should be overtly less severe in people living in tropical climates; however , establishing such an association would likely be confounded by a number of factors including ( i ) air conditioning [69] , ( ii ) lack of application of validated instruments for defining disease severity and lack of accounting for co-morbidities [70] , ( iii ) different levels of co-morbidities ( often associated with severe disease ) [71] , ( iv ) different age structures with the elderly prone to more severe disease , ( v ) suboptimal diagnosis ( e . g . differentiation from inter alia dengue and rheumatoid arthritis ) [72] , ( vi ) different access to non-steroidal anti-inflammatory drugs and/or paracetamol ( acetaminophen ) , ( vii ) viral genotypes with different pathogenicities and geographical distributions [73] and ( viii ) different mosquito vectors and vector competencies [74 , 75] . We show herein that the augmented type I IFN response for mice housed at 30°C correlated with an increase in UPR-associated transcriptional signatures . That the UPR can augment ( or improve priming of ) the type I IFN response has been reported by several groups in a variety of settings [53 , 76–82] . Network analyses also support the view that the UPR and anti-viral pathways intersect at several nodes ( S9 Fig ) . XBP1 ( dominant in Fig 6A , 6B and 6C ) has also been associated with improved type I IFN response in several settings [83–88] . Some proteins are also reported to be thermolabile even at 37°C [89 , 90] , particularly when other stressors are present [91] , potentially contributing to UPR activation . Unfortunately , measuring the thermostability of cellular proteins in eukaryotic cells in vivo remains challenging [92] , although reducing temperature to increase protein stability is a well understood concept [93] . Exactly how the UPR and/or XBP1 might promote type I IFN responses remains unclear , with XBP1 binding to the IFNβ promoter [87] perhaps unlikely in the current setting ( S6C Fig , Fig 2G ) . A role for GADD34 [94 , 95] is also not supported by the above and GADD34 expression data ( S10 Fig ) . We ( like others [84] ) have also been unable to find a significant correlation between XBP1 splicing [96] and elevated type I IFN responses ( S6A Fig ) , although this might require examination of certain cell types [83 , 86] and/or sampling over a specific time period . The UPR may promote and/or prime type I IFN responses via the IRE1-RIDD-RIG-I pathway [80 , 81] , a pathway for which XBP1 splicing is dispensable [88]; ( RIDD stands for IRE1-dependent decay of mRNA ) . The UPR sensor IRE1α ( as well as splicing XBP1 ) degrades RNA , with the products detected by RIG-I [88] , a RNA sensor that also binds CHIKV RNA [97] . IRE1α may also be ancestrally related to the anti-viral effector RNase L [85 , 98] . However , until a mechanism can be elucidated in the current setting , the UPR and promotion of the type I IFN response remains a correlation , with other mechanisms potentially involved [99 , 100] . The findings presented herein raise a number of questions; for instance , does the reduced body temperature in the elderly [101] , especially those >75 years of age [102] , contribute to the increased CHIKV-associated mortality seen in such patients [103 , 104] ? Is the likelihood of severe CHKV disease increased if the infectious mosquito bite occurs at cooler extremities [105] ? Can CHIKV-induced fever in humans increase the temperature of limbs and extremities in time to increase the local type I IFN responses and ameliorate arthropathy ? That different ambient temperatures can affect anti-viral type I IFN responses in vivo , even in homeotherms , extends in vitro data [4–6] and evidence from poikilotherms [14 , 16] , and may have implications for understanding other human diseases believed to be influenced by environmental temperatures [106–108] .
All mouse work was conducted in accordance with the “Australian code for the care and use of animals for scientific purposes” as defined by the National Health and Medical Research Council of Australia . Mouse work was approved by the QIMR Berghofer Medical Research Institute animal ethics committee ( P1060 A705603M ) and was conducted in a biosafety level-3 facility at the QIMR Berghofer MRI . Mice were euthanized using carbon dioxide . Female C57BL/6J mice ( 6–8 weeks ) were purchased from Animal Resources Center ( Canning Vale , WA , Australia ) . IRF3/7-/- mice were kindly provided by M . S . Diamond ( Washington University School of Medicine , St . Louis , MO ) and were bred in house at QIMR B [41] . IFNAR1-/- mice on a C57BL/6 background [109] were supplied by Dr P Hertzog ( Monash University , VIC , Australia ) and bred in house . Female mice were inoculated with 104 CCID50 of the Reunion Island isolate ( LR2006-OPY1; GenBank KT449801 [36] ) ( i ) i . p . or ( ii ) s . c . into each hind foot as described previously [32 , 36] . Serum viremia , tissue titers and foot swelling were determined as described [32 , 36] . Histology ( H&E ) and immunohistochemistry was undertaken on formalin fixed , decalcified feet using an anti-CHIKV capsid monoclonal antibody as described [44] . Slides were scanned using Aperio AT Turbo ( Aperio , Vista , CA ) and analyzed using Aperio ImageScope software ( v10 ) and the Positive Pixel Count v9 algorithm using default settings . Female 21 day old C57BL/6J mice were inoculated s . c . in the pectoral area with 103 plaque-forming units ( PFU ) of RRV ( T48 strain ) as described [34] . Disease was determined by assessing grip strength and gait , and scored as follows: 0 = no disease; 1 = ruffled fur , 2 = very mild hind limb weakness; 3 = mild hind limb weakness; 4 = moderate hind limb weakness; 5 = severe hind limb weakness/dragging; 6 = complete loss of hind limb function , as described [34] . Temperature measurements were taken using a pediatric infra-red ear thermometer ( Welch Allyn Pro 4000 , Braun Kronberg Type 6021 Hechingen , Germany ) . The thermometer probe was gently pressed into the pit of the back leg of a restrained mouse for 10 seconds ( gently folding and holding the leg over the end of the probe ) before a temperature measurement was taken . This was repeated on the other leg , and the procedure repeated to obtain a mean of four measurements . Temperature readings were also taken from the feet by placing the thermometer probe onto the largely hairless ( walking-pad free ) regions of the hind foot; this was repeated on the other leg , repeated , and a mean of four measurements obtained . Four measurements ( two on the right , two on the left ) were thus taken to produce a mean for each mouse for both leg pits and feet . The mean SD across the data set for quadruplicates was 1 . 04°C . Serum was collected in Microvette 500 Z-gel tubes ( Sarstedt , Germany ) and cytokines measured using IFN alpha/IFN beta 2-Plex Mouse ProcartaPlex Panel ( Thermofisher ) , with beads run on a BD LSR Fortessa 4 and data analyzed using BD FACSDiva ( v8 . 0 . 1 ) and FCAP Array software ( V3 . 01 ) . Vero cells ( ATCC CCL-81 ) were seeded into 96 well plates and cultured for 24 hrs at the indicated temperatures and treated with human IFNα ( Sigma I 2396; a mixture of 10 IFNα species ) for 4 hrs . CHIKV was added ( MOI = 0 . 05 ) and incubation continued at the same temperature . Cytopathic effect ( CPE ) was assessed by crystal violet staining after 3 days . For virus replication Vero cells after seeding at the indicated temperatures in triplicate in 24 well plates were infected with CHIKV for 6 hrs , were washed and then incubated at the same temperatures . Viral titers in the supernatants were assessed at the indicated times after washing . Female C57BL/6J mice ( 6–8 weeks old ) were housed at 30±1°C or 22±1°C for 5 days . Mice were left untreated ( Control ) or feet were injected s . c . ( as for CHIKV infection [32] ) with 0 . 5 μg of poly ( I:C ) mixed with jetPEI transfection reagent ( Polyplus Transfection , NY , USA ) [47] ( Treated ) . Twelve samples were prepared; each contained pooled RNA from 3–4 mouse feet from 3–4 different mice , with equal amounts of RNA from each foot in each pool . The 12 samples represent 3 biological replicates for each of the four conditions; ( i ) feet from control ( C ) mice housed at 30°C ( 30C ) , ( ii ) feet from control ( C ) mice housed at 22°C ( 22C ) , ( iii ) feet from poly ( I:C ) /jetPEI treated ( T ) mice held at 30°C ( 30T ) , ( iv ) feet from poly ( I:C ) /jetPEI treated ( T ) mice held at 22°C ( 22T ) . Sample preparation and RNA-Seq was undertaken essentially as described [27] . Briefly , feet were lacerated and placed in RNAlater ( Life Technologies ) , homogenized in TRIzol ( Invitrogen ) , centrifuged ( 12 , 000 g x 10 min ) and RNA extracted from the supernatants as per manufacturer’s instructions . RNA samples were DNase treated using RNAse-Free DNAse Set ( Qiagen ) and purified using an RNeasy MinElute Kit ( Qiagen ) . Samples were sent to the Australian Genome Research Facility ( Melbourne , Australia ) . cDNA libraries were prepared using a TruSeq RNA Sample Prep Kit ( v2 ) ( Illumina Inc . San Diego , USA ) , which includes isolation of poly-adenylated RNA using oligo-dt beads . cDNA libraries were sequenced ( 100 bp single end reads ) using the Illumina HiSeq 2500 Sequencer ( Illumina Inc . ) . The per base sequence quality was high , with >84% of bases above Q30 for all 12 samples . The primary sequence data was generated using the Illumina bcl2fastq 2 . 18 . 0 . 12 pipeline . After removal of Illumina adaptor/over-represented sequences and cross-species contamination , reads were mapped to the mouse genome ( Mus_musculus . GRCm38 ) using Tophat aligner ( v2 . 0 . 14 ) . The counts of reads mapping to each known gene ( with gencode annotation vM6 as reference ) were summarized at gene level using the featreCounts v1 . 4 . 6-p5 utility of the subread package ( http://subread . sourceforge . net/ ) . The transcripts were assembled with the Stringtie tool v1 . 2 . 4 ( http://ccb . jhu . edu/software/stringtie/ ) utilizing the read alignment and reference annotation based assembly option ( RABT ) . The read counts were used to determine gene expression and identify differentially expressed genes ( DEGs ) using R packages ( R version 3 . 2 . 0 ) ‘edgeR’ ( v3 . 10 . 5 ) and ‘limma’ ( 3 . 24 . 15 ) . ( https://bioconductor . org/packages/release/bioc/html/edgeR . html ) . The default TMM normalization method of edgeR was used to normalize the counts . The GLM model was used to perform differential expression comparison between the groups . Genes that had >1 CPM in at least 3 samples are retained for the further analysis . Differentially gene expression was considered significant if the Benjamini-Hochberg corrected p-value ( i . e . FDR or q value ) was <0 . 01 . DEGs were analyzed by Ingenuity as described [27] and ISMARA [54] by uploading the RNA-Seq fastq files , identifying the replicates ( allowing averaging , n = 3 ) and undertaking pair-wise comparisons ( 30C vs 22C and 30T vs 22T ) . Statistical treatment of mouse data was performed using IBM SPSS Statistics ( version19 ) . The t test was used if the difference in the variances was <4 , skewness was >-2 , and kurtosis was <2; where the data was nonparametric and difference in variances was <4 , the Mann Whitney U test was used , if >4 the Kolmogorov-Smirnov test was used [36] . The repeat measures ANOVA was used for RRV data . | Chikungunya virus ( CHIKV ) and Ross River virus are mosquito-borne alphaviruses that cause epidemics of human arthritic disease that usually last from weeks to months . Arthropathy predominantly manifests in the joints of limbs and the joints at the extremities ( e . g . hand and feet ) . Herein we show a surprisingly large reduction in viral loads and foot arthropathy in mice when animals were housed at 30°C rather than the conventional 22°C , with the feet of the former mice being ≈3–4°C warmer . Using RNA-Seq analyses of mice feet , we illustrate that this small increase in temperature results in a significant increase in both the unfolded protein response and anti-viral type I interferon responses . Taken together these results suggest that the predominantly peripheral alphaviral arthropathy is due to the usually slightly lower temperature of limbs and extremities , which results in less effective type I interferon responses and a subsequent increase in viral loads and ensuing arthritic disease . | [
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] | 2017 | Lower temperatures reduce type I interferon activity and promote alphaviral arthritis |
Chikungunya virus ( CHIKV ) is a re-emerging alphavirus that has caused significant disease in the Indian Ocean region since 2005 . During this outbreak , in addition to fever , rash and arthritis , severe cases of CHIKV infection have been observed in infants . Challenging the notion that the innate immune response in infants is immature or defective , we demonstrate that both human infants and neonatal mice generate a robust type I interferon ( IFN ) response during CHIKV infection that contributes to , but is insufficient for , the complete control of infection . To characterize the mechanism by which type I IFNs control CHIKV infection , we evaluated the role of ISG15 and defined it as a central player in the host response , as neonatal mice lacking ISG15 were profoundly susceptible to CHIKV infection . Surprisingly , UbE1L−/− mice , which lack the ISG15 E1 enzyme and therefore are unable to form ISG15 conjugates , displayed no increase in lethality following CHIKV infection , thus pointing to a non-classical role for ISG15 . No differences in viral loads were observed between wild-type ( WT ) and ISG15−/− mice , however , a dramatic increase in proinflammatory cytokines and chemokines was observed in ISG15−/− mice , suggesting that the innate immune response to CHIKV contributes to their lethality . This study provides new insight into the control of CHIKV infection , and establishes a new model for how ISG15 functions as an immunomodulatory molecule in the blunting of potentially pathologic levels of innate effector molecules during the host response to viral infection .
Chikungunya virus is a member of the genus Alphavirus , which are enveloped positive-strand RNA viruses transmitted by mosquitoes . It was first isolated in Tanzania in 1952 , and reported to cause severe fever as well as myalgia , joint pain and rash within 2–5 days of infection [1] , [2] . Recently , CHIKV reemerged in Eastern Africa and has developed into a major epidemic in the Indian Ocean region . In 2006 , an outbreak on the island of La Réunion resulted in the infection of approximately one-third of the inhabitants [3] , [4] . It has since spread to India and Southeast Asia with estimates of between 1–6 million people having been infected [5] , [6] . Concerns for the globalization of this virus has evolved given the continuation of this epidemic , the high serum viremia seen in infected patients , and mutations in the currently circulating strain of CHIKV that have allowed it to adapt to a more widely distributed mosquito vector [7] . These concerns have been raised by both an increase in the number of foreign travelers contracting CHIKV and returning to both Europe and the United States , and by the possibility of spread by infected individuals , the latter being exemplified by an outbreak in Italy in 2007 and in Southern France in 2010 [5] , [8] , [9] . The typical clinical presentation of adults infected with CHIKV includes fever , rash , arthralgias and severe myalgias . Infected neonates , however , display more severe disease , with symptoms including encephalopathy and cerebral hemorrhage , with a subset of these infants developing permanent disabilities [10] . During the recent epidemic , it was reported for the first time that CHIKV-infected mothers can transmit the virus to their newborns during delivery , with a vertical transmission rate of approximately 50% , and in some instances infection resulted in mortality [10] . This age dependence of disease severity has also been reported for other alphaviruses and can be reproduced in mouse models through the infection of suckling mice . Indeed , it has been shown that neonatal mice succumb to Ross River Virus ( RRV ) [11] , Semliki Forest Virus ( SFV ) [12] , Sindbis Virus ( SINV ) as well as CHIKV [13]–[15] . There are likely several factors that contribute to this increased sensitivity in neonates , including alterations in the neonatal immune response . Regarding immune function in neonates , developmental delays have been described for the adaptive immune responses , however less is known about neonatal innate responses . While still controversial , many reports indicate that neonatal responses are diminished as compared to adults . For example , cord blood cells stimulated with toll-like receptor ( TLR ) ligands produced low levels of TNFα , IL-1β , and IL-12 [16] . Neonatal plasmacytoid dendritic cells ( DCs ) have also been shown to have impaired production of type I IFN in response to CpG stimulation [17] . It has been shown that LPS does not effectively activate IRF-3 dependent responses , including the production of IFNβ [18] . The response to cytokines may also be impaired as there is evidence that STAT-1 recruitment following IFNγ stimulation is less efficient in neonatal than in adult leukocytes [19] . Based on the critical role of type I IFN in the control of CHIKV infection [20] , we considered the possibility that neonates may have a developmental delay in their type I IFN response , possibly contributing to their increased susceptibility to CHIKV infection and viral dissemination . In response to CHIKV infection , the production of type I IFNs is triggered by the engagement of the Rig-I like receptor ( RLR ) pathway [20] , [21] . IFNs then stimulate the induction of hundreds of interferon stimulated genes ( ISGs ) and it is through these ISGs that IFNs mediate their effector function [22] . One of the earliest ISGs induced following IFN stimulation is ISG15 . ISG15 is a 17 kDa protein that contains two ubiquitin-like domains connected by a proline peptide linker [23] . Similar to ubiquitin , ISG15 can form conjugates with an array of intracellular host and viral proteins through the use of an E1 ( UBE1L ) , E2 ( UbcH8 ) , and E3 ( e . g . , Herc5 , EFP , HHARI ) enzymatic cascade [24]–[28] . Conjugation of ISG15 to target proteins has been suggested to cause either a gain or loss of function of the targeted protein , although the consequences of ISG15 conjugate formation are not well understood [29]–[32] . An unconjugated form of ISG15 can also be found in the sera of humans treated with IFN-βser as well as in virally infected mice [33] , [34] . The released form of ISG15 has been suggested to have cytokine like activity [35]–[37] , however its role during an antiviral immune response has not been examined . The rapid induction of ISG15 following IFN stimulation has led to the identification of an antiviral role for ISG15 during infection . ISG15−/− mice display increased susceptibility to SINV , herpes simplex virus-1 ( HSV-1 ) , gamma herpes virus ( γHV68 ) , influenza A and B viruses , and vaccinia virus [34] , [38] . ISG15−/− mice infected with influenza B virus display a 2–3 log increase in lung viral titers as well as elevated cytokine and chemokine levels [34] . The ability of ISG15 to conjugate to target proteins appears to be essential for ISG15's antiviral activity during certain viral infections , as UbE1L−/− mice , which lack the ability to form ISG15 conjugates , phenocopy ISG15−/− mice during both SINV and influenza B virus infection [39]–[41] . Further support for the importance of ISG15 conjugation during viral infection comes from the evolution of viral proteins that directly target ISG15 conjugate formation . Both the NS1 protein of influenza B virus and the E3L protein of vaccinia virus inhibit ISG15 conjugate formation , while OTU-domain containing viral proteins , such as the L protein of Crimean-Congo hemorrhagic fever virus ( CCHFV ) or the Nsp2 protein of Equine Arteritis virus ( EAV ) , and the SARS coronavirus papain-like protease ( SARS-CoV PLpro ) exert both deubiquitinating and deISGylating activity [24] , [38] , [42] , [43] . Therefore , the antiviral activity of ISG15 has been thought to be conjugation dependent . Herein we demonstrate that despite an increased susceptibility of neonates to CHIKV infection , they produce robust levels of type I IFNs . While insufficient to completely control infection , IFNs participate in limiting the infection . We also show that ISG15 is induced during CHIKV infection and plays a critical role in protecting neonatal mice from viral induced lethality . Surprisingly , the mechanism of action by which ISG15 limits infection is independent of UbE1L mediated conjugation , as UbE1L−/− mice displayed no phenotypic differences as compared to WT animals . Furthermore , ISG15 does not directly inhibit viral replication , as suggested by the similar viral loads in WT and ISG15−/− mice . Instead , ISG15 appears to function as an immunomodulatory molecule in this model . These data demonstrate a novel role for ISG15 during viral infection and suggests that prophylactic measures targeting the induction of IFN and ISG15 may help protect neonates during future CHIKV outbreaks .
Based on the increased severity of neonatal disease that has been observed during the recent epidemic of CHIKV , we assessed the inflammatory response of infants during the acute phase of CHIKV infection . Patients were recruited at the time of presentation in the emergency room and sera samples were collected and stored . CHIKV infection was confirmed by RT-PCR; and all patients were negative for anti-CHIKV IgG and IgM , indicating acute infection . We performed multi-analyte testing using Luminex technology , with a focus on inflammatory cytokines and chemokines . The inflammatory signature was compared to uninfected patients presenting to the emergency room for reasons unrelated to acute infection ( e . g . , broken bone ) . The intensity of the immune response in the infant vs . adult cohorts was compared . Non-parametric tests were used for the statistical analysis , and a false discovery rate ( FDR ) correction was applied to all p-values in order to adjust for multiple testing . We detected elevated levels of both IFNα and IFNγ in both groups of patients when compared to their respective control group ( p<0 . 005 ) . Interestingly , both IFNα and IFNγ levels were more elevated in infants in comparison to adult patients ( Figure 1A ) . In addition , the chemokines/cytokines known to be induced by IFNs were highly expressed . These included CCL2 , CCL4 , CXCL9 , CXCL10 , IL-1Rα , IL-12p40/p70; and strikingly these analytes , with the exception of CXCL9 , showed higher plasma concentrations in infants as compared to adult patients ( Figure 1B ) . Despite the clinical presentation including fever ( Table S1 ) , levels of the pyrogenic cytokines IL-1β and TNFα were not significantly elevated in patients when compared to their controls; with only IL-6 being slightly upregulated in adult patient as compared to the control group ( data not shown ) . Also of interest , while we observed marked induction of so-called Th1 cytokines , such as IFNγ and IL-12p40/p70 , there was no clear skewing toward a Th1 response . In fact , we observed high levels of Th2 cytokines as well as Th17 cytokines ( Figure 1C ) . Notably , these interleukins were upregulated as compared to healthy individuals , but were similarly expressed in the infected infant and adult groups . These data suggest that infants , while developing more severe manifestations of CHIKV , do indeed mount a robust acute response to infection . One potential caveat to this conclusion is that the viral load is higher in infants , possibly accounting for greater immune activation ( median viral load in infants = 1 . 6×108 RNA copies; median viral load in adults = 1 . 4×107 RNA copies ) . Within the infant group , viral load negatively correlated with age , corroborating the observation that newborns are more susceptible to CHIKV induced disease ( Figure 2A ) . In adults , there was also an age-dependent trend with elderly harboring higher viral loads ( Figure 2A ) . Again this is consistent with the report that increased age is a risk factor for severe CHIKV disease [44] . Next , we plotted plasma IFNα concentrations as a function of age . These data demonstrate a strong negative correlation in infants , and followed the pattern seen for CHIKV titers ( Figure 2B ) . We found that IFNα levels correlated with viral loads in both group ( Figure 2C ) . To normalize for CHIKV titers , we separated each cohort in two groups ( <global median: low viral load; and > global median: high viral load ) ; in both groups the levels of IFNα were more elevated in infected infants than in infected adults ( Figure 2D ) . Furthermore , we performed univariate linear regression analysis to model the effect of infant status on plasma IFNα concentrations . Infants had higher IFNα concentrations compared to adults ( RGM [95% CI] = 5 . 49 [3 . 16–9 . 53]; p<0 . 001 ) . This effect remained significant after adjustment for viral load ( adjusted RGM [95% CI] = 3 . 44 ( 2 . 07–5 . 70]; p<0 . 001 ) . Thus for a similar viral load , infants produce more IFNα than adults . To represent associations between age , viral load , IFNα and the immune signature , we established a network plot of significant correlations ( Figure 2E ) . These data argue against infants being compromised in their response to CHIKV infection; however , we could not determine whether their IFN response was protective . To address this issue and to examine the mechanism of ISG mediated control of CHIKV , we exploited a recently described neonatal mouse model for studying infection [15] . Previously , we reported an age-dependent susceptibility to CHIKV infection in mice . Infection of 6 day old animals resulted in 100% mortality; 9 day old animals developed paralysis , with approximately 50% of the animals succumbing to infection; while by 12 days of age the mice became refractory to symptoms of severe disease and lethality [15] . To compare our experimental mouse model of CHIKV infection to the response seen in human infants we assessed the IFN and proinflammatory responses in 8–9 day old mice . We first assessed the induction of the IFN response at the local site of infection by monitoring mRNA levels of IFNβ and selected interferon stimulated genes ( ISGs ) in the skin . Mice were inoculated with 2×105 PFU of CHIKV and the injection site was removed between 3–120 hrs post-infection . Increased expression of IFNβ mRNA could be detected as early as 3 hrs post-infection with peak levels being achieved at 16 hrs post-infection ( Figure 3A ) . Similar to IFNβ mRNA induction , IRF7 , Mx1 and ISG15 mRNA levels could also be detected as early as 3 hrs post-infection , with peak levels observed at 16 hrs post-infection ( Figure 3A ) . Thus , at the site of infection , neonatal mice are able to induce IFNβ expression as well as a subset of known ISGs . Of note , IRF7 and Mx1 mRNA expression is indicative of signaling via the type I IFN receptor , suggesting that the production as well as reception of IFNαβ is intact in neonatal mice . We next assessed the systemic inflammatory response in this model . Similar to our findings in human infants infected with CHIKV , we observed a strong induction of IFNα and IFNγ in infected pups ( Figure 3B ) . Plasma concentration of CCL2 , CCL4 , CXCL9 and CXCL10 were also elevated , however IL-12p70 was only modestly induced ( Figure 3C ) . Similar to the human data , a mixed Th1 , Th2 and Th17 cytokine profile was observed with the induction of IL-12 , IL-5 , IL-13 , IL-15 , and IL-17 ( Figure 3C ) . For all analytes , peak levels were seen 16–24 hrs post-infection ( Figure 3 ) . Notably , there were some differences seen between the murine neonate and human infant inflammatory profiles . Most interestingly , the mice displayed increased levels of the pyrogenic cytokines , including IL-1β , IL-6 and TNFα , which were not seen in our studies of human infants ( Figure 3C ) . While this may represent differences in pathogenesis , we believe it is more a reflection of the fact that the mice can be assessed within hours of viral inoculation , while the exact timing of the human infection is unknown . Overall , we find that the similarities between the mouse and human responses support the use of neonatal mice to study the response to CHIKV infection , and indicate that induction of IFNs , as well as the triggering of an ISG response , are both rapid and robust . Next , to confirm that endogenous IFN contributes to the control of CHIKV in neonatal mice , mice lacking subunit 1 of the type I IFN receptor ( IFNAR−/− ) mice were infected with CHIKV at 9 days of age . Consistent with our previous observations in adult mice [15] , neonatal pups lacking IFNAR1 were highly susceptible to CHIKV infection with 100% of the pups dying by day 2 post-infection ( Figure 4A ) . These pups developed a rapid , disseminated infection . Within 1 day of infection , the IFNAR−/− mice displayed viral loads at the injection site that were 100-fold higher than that detected in WT controls . We also observed a striking increase in viral titers in the serum and multiple organs , including the brain , liver , and lung ( Figure 4B ) . These data indicate that endogenous IFN , while insufficient to protect mice , plays an important role in limiting CHIKV infection during disease pathogenesis in neonatal animals . Our results suggest that signaling via the IFNαβ receptor in neonatal mice results in the robust induction of at least a subset of ISGs and interferon-induced serum proteins ( Figure 3 ) , and that the absence of IFNAR1 results in exacerbated infection and rapid death ( Figure 4 ) . One potential explanation is that neonates possess a developmental delay in their IFN-response that makes them susceptible to infection as compared to adult animals . It was therefore important to determine whether prophylactic innate immune responses could protect neonatal animals from CHIKV infection . To evaluate this question , we injected mice with 25 µg of poly I∶C ( pIC ) , a known inducer of type I IFN , and 1 day later mice were challenged with 2×105 PFU of CHIKV . At 7 hrs post treatment with pIC , robust levels of IFNα were detected in the serum of neonatal mice ( Figure 5A ) . Following CHIKV infection , mice were monitored daily for the development of symptoms and followed for lethality . Remarkably , 96% of the pIC treated mice survived , in contrast to only 40% of control mice ( Figure 5B ) . Treatment with pIC also prevented CHIKV induced paralysis , with only 7% of the pIC treated mice developing paralysis during the course of the infection , as compared to 84% in the control group ( Figure 5C ) . Independent experiments were performed using prophylactic treatment with 5000 U IFNβ . At both day 7 and day 11 post-infection , mice receiving recombinant IFNβ displayed less severe disease , as compared to control PBS injected animals ( Figure S1 ) , however , we did not see a statistical difference in survival , possibly due to the short half-life of IFNβ . To confirm that the protection induced by pIC was mediated by IFN , we repeated these experiments in IFNAR−/− mice . As seen in Figure 5D , treatment of IFNAR−/− mice with pIC resulted in no protection from lethality . These results indicate that the prophylactic engagement of the IFN receptor in neonates is able to induce a protective anti-viral response . Interferons mediate their antiviral activity through the induction of ISGs , thus suggesting that investigation of the downstream effector molecules may offer additional insight into how neonates respond to CHIKV infection . The analysis of selected ISGs expressed at the site of infection revealed that ISG15 mRNA was rapidly and strongly induced during CHIKV infection ( Figure 3A ) . ISG15 is a ubiquitin-like molecule that conjugates to both host and viral proteins and has been previously shown to participate in the host response to SINV infection . An evaluation of ISG15 protein expression at the site of infection and within the serum confirmed that ISG15 and ISG15 conjugates were induced during CHIKV infection in WT mice ( Figure 6B , C ) . As we previously described during neonatal SINV infection [45] , the expression of ISG15 during CHIKV infection was also dependent upon intact IFN signaling since IFNAR−/− pups infected with CHIKV did not induce detectable levels of ISG15 ( Figure S2 ) . To test the hypothesis that ISG15 is important in the control of CHIKV , we infected ISG15−/− neonatal mice , comparing them to weight and age-matched WT control animals . Mice were followed daily for signs of illness and survival . As reported above , infection of 9 day old WT neonatal mice resulted in 58% lethality , with deaths occurring between days 10–13 post-infection . In contrast , a dramatic increase in lethality was observed in neonatal mice lacking ISG15 , with greater than 70% of the ISG15−/− mice succumbing to infection within 3 days , and 100% of the mice dying by day 9 post-infection ( Figure 6A ) . It has been previously shown that WT mice become resistant to CHIKV induced lethality between 9 to 12 days of age , while mice lacking IFNAR1 remain susceptible to infection even as adults . Since ISG15 is an IFN-induced protein we next determined if its activity was age dependent . We infected either WT or ISG15−/− mice at 11 or 12 days of age , or we infected adult mice between 6–8 weeks of age . By 11 days of age the WT mice had become largely resistant to CHIKV induced lethality with only 15% of the mice succumbing to infection ( Figure S3A ) . In contrast , we observed 100% lethality in the ISG15−/− mice , although the onset of lethality was delayed , with the majority of the mice dying between 10 and 14 days post-infection . By 12 days of age the ISG15−/− mice still showed clinical signs of disease , with a dramatic decrease in weight gain as compared to the WT controls ( data not shown ) , but by this age only 20% of the ISG15−/− mice succumbed to the infection ( Figure S3B ) . Strikingly , adult ISG15−/− mice , similar to WT controls , displayed no lethality and showed no signs of disease following CHIKV infection ( Figure S3C ) . Therefore , we conclude that ISG15 contributes to the control of CHIKV during neonatal infection , but redundant mechanisms are responsible for the control of CHIKV during adult infection . Since IFNs induce the expression of many ISGs , we next wanted to investigate if ISG15 contributed to the protective anti-viral response established by prophylactic induction of type I IFN . WT or ISG15−/− mice were treated with 10–25 µg of pIC and 1 day later challenged with 2×105 PFU of CHIKV . Not unexpectedly , pretreatment with pIC offered protection to both WT and ISG15−/− mice . After pretreatment with 25 µg of pIC , 14% of the ISG15−/− mice still succumbed to infection as compared to complete protection seen in the WT mice ( Figure 6D ) . Interestingly , only 50% of the ISG15−/− mice were protected after pretreatment with 10 µg pIC , as compared to 85% protection observed in the WT mice ( Figure 6E ) . These data support our observation that ISG15 is induced as part of the IFN response and that it plays an important role during CHIKV infection . We next investigated the mechanism by which ISG15 protects neonatal mice from CHIKV infection . We had previously shown that the conjugation of ISG15 to target proteins is essential for the control of several viral infections [40] , [41] . UbE1L is the only identified E1 for ISG15 , and mice lacking UbE1L express free ISG15 , but fail to form ISG15 conjugates [39] . To establish a role for ISG15 conjugation , 9 day old UbE1L−/− mice were infected with CHIKV . Surprisingly , UbE1L−/− mice displayed no increase in lethality following CHIKV infection , and instead had a lethality curve similar to that observed in WT mice ( Figure 6A ) . Similarly , pIC treated UbE1L−/− mice were protected to the level of WT controls ( Figure 6D , E ) . Western blot analysis on skin/muscle from the site of infection ( Figure 6B ) , as well as serum ( Figure 6C ) , was used to confirm that UbE1L−/− mice generated no ISG15 conjugates during CHIKV infection , whereas WT mice showed robust conjugate formation . Additional organs ( lung and liver ) were also examined and no conjugates were detected in UbE1L−/− mice ( data not shown ) . Thus , we demonstrate that the activity of ISG15 during CHIKV infection is UbE1L independent . In an attempt to provide additional evidence that unconjugated ISG15 was functioning in this model , we generated recombinant double subgenomic CHIK viruses that expressed either WT ISG15 ( CHIK- LRLRGG ) or a mutant , non-conjugatable form of ISG15 ( CHIK- LRLRAA ) ( Figure S4A ) . This strategy was based on our previous report in which recombinant SINV expressing WT ISG15 ( LRLRGG ) rescued the increased lethality observed in the ISG15−/− mice , while mutant ISG15 ( LRLRAA ) failed to compensate for the ISG15 deficiency [34] , [40] . Both CHIK-LRLRGG and CHIK-LRLRAA expressed ISG15 and displayed similar growth kinetics to CHIK-GFP in BHK cells ( Figure S4B , 4C ) . When we infected ISG15−/− mice , however , neither CHIK-LRLRGG nor CHIK-LRLRAA was able to protect ISG15−/− mice ( Figure S4D and data not shown ) . While these results do not allow us to confirm that the activity of ISG15 is independent of conjugation , they do suggest that the mechanism of action of ISG15 during SINV and CHIKV infection are distinct . To further evaluate the mechanism by which ISG15 functions during neonatal CHIKV infection we examined lymphocyte subsets and viral titers in WT , UbE1L−/− and ISG15−/− mice . We detected no significant differences in the lymphocyte subsets of naïve neonatal WT , UbE1L−/− and ISG15−/− mice ( Table S2 ) . Similar results have been observed in both naïve and pIC stimulated adult mice [39] , [45] . These data suggest that differences in starting cell populations do not account for the increase in lethality observed in ISG15−/− mice . We next assessed viral titers on days 1 and 2 post-infection . As expected , UbE1L−/− mice displayed similar viral loads in multiple tissues when compared to WT mice at days 1 and 2 post-infection ( Figure 7A and B ) . Given that the ISG15−/− pups infected at 9 days of age displayed a dramatic increase in lethality , with kinetics similar to that observed in the IFNAR−/− mice , we expected to detect increased viral titers in the ISG15−/− mice . To our surprise , at 1 day post-infection the serum and tissues analyzed from the ISG15−/− mice contained viral titers that were similar to those obtained in both UbE1L−/− and WT mice ( Figure 7A ) . This was in striking contrast to the 2–3 log increase in viral loads detected in the IFNAR−/− mice 1 day post-infection ( Figure 4B ) . By 2 days post-infection , just prior to when the majority of ISG15−/− mice succumb to infection , we still detected similar viral loads between WT , UbE1L−/− and ISG15−/− mice in the analyzed tissues ( Figure 7B ) . Based on these data we suggest that ISG15 may not be playing a direct anti-viral role , but instead may be acting via an unexpected mechanism of regulating host sensitivity to the viral induced immune response . Since our evaluation of human infants and neonatal mice demonstrated that CHIKV infection induces a robust proinflammatory cytokine and chemokine response ( Figures 1 , 3 ) , we investigated the impact of ISG15 deficiency on this host response . IFNβ mRNA levels were induced in the skin of WT , UbE1L−/− and ISG15−/− mice at 24 and 48 hrs post infection with no significant differences noted between the three strains of animals ( Figure 8A ) . Serum from WT , UbE1L−/− and ISG15−/− mice collected at 1 or 2 days post-infection were analyzed for IFNα as well as proinflammatory cytokines and chemokines . Analysis of IFNα serum levels also revealed no significant differences between WT , ISG15−/− , and UbE1L−/− mice at either 24 or 48 hrs post-infection ( Figure 8B ) . Despite similar viral loads and type I IFN induction , the ISG15−/− mice displayed elevated levels of TNFα , IL-1β and IL-6 as compared to both the WT and UbE1L−/− mice ( Figure 8C ) . Interestingly the levels of these three pyrogenic cytokines in the ISG15−/− mice were comparable to what was observed in the IFNAR−/− pups , despite the latter having significantly higher viral loads . ISG15−/− mice also displayed elevated chemokine levels , including CCL2 , CCL3 and CCL5 ( Figure 8C ) . Therefore , although viral titers between ISG15−/− , UbE1L−/− and WT mice are similar , ISG15−/− neonates display an exaggerated proinflammatory cytokine response to CHIKV infection . Together , these data indicate that ISG15 , independent of UbE1L mediated conjugation , is contributing to the control of CHIKV infection by blunting potentially pathologic levels of innate effector molecules .
The ongoing epidemic of Chikungunya virus occurring in the Indian Ocean region has highlighted how little we understand about the pathogenesis of this virus . Epidemiological studies have provided the first documentation of vertical transmission , as well as providing detailed information about the severity of disease and long term sequelae [4] , [46]–[48] . One important finding from these studies concerns the increased susceptibility of neonates and infants to severe forms of Chikungunya disease [10] , [44] , [49] . In this study , we evaluated the response of infants to CHIKV infection using data from both human samples collected during the La Réunion outbreak , as well as taking advantage of a newly described mouse model of infection . Our results show that human infants and murine neonates mount a robust innate immune response to CHIKV infection , which includes the induction of type I IFNs , several cytokines and chemokines , and the induction of at least a subset of IFN induced genes , including ISG15 . We establish a role for ISG15 in the pathogenesis of CHIKV infection with an absolutely essential role in the neonatal response to infection . Moreover , the reported data suggest that ISG15 acts independent of UbE1L mediated conjugation , and rather than exerting a direct anti-viral role , it appears to be implicated in limiting an exaggerated inflammatory response . In general , neonates are more susceptible to microbial and viral infection . This vulnerability has been explained by two principal mechanisms: broader tropism of the infectious agent or a defective host response . Regarding the latter , many argue that neonatal susceptibility to infection is due to a delayed or weaker immune response [16] , [50] . Factors contributing to this include delays in immune system maturation , decreased expression of activation receptors , or distinct regulation of signaling pathways in neonatal vs . adult immune cells . Since the type I IFN response is critical for controlling CHIKV infection [15] , [20] , we hypothesized that neonates may have a defect in their ability to either produce and/or respond to IFN . Instead , we observed that the production of type I IFN was intact in both human infants and mouse neonates . Furthermore , the relative level of IFN produced in infants was higher than the responses observed in adult human patients , even when viral load was normalized between the two patient groups ( Figures 1–2 ) . These data indicate that neonates do not have a defect in their ability to produce type I IFN during CHIKV infection , and may actually be hyper-responsive . Very little has been reported on the signaling through RLRs or other viral sensors in neonates . Most previous work has supported the notion that signaling through TLRs , including TLR4 , are diminished in neonates [51] , [52] . However , one study did find that neonatal mice exhibited increased lethality following LPS treatment , due in part to an exaggerated pro-inflammatory cytokine response as compared to adult mice [53] . Future work is needed to characterize differences that may exist between neonatal and adult RLR signaling . We also demonstrate that neonates can respond to the IFN that is produced . In both human infants and in neonatal mice , several known IFN-induced chemokines and cytokines were upregulated during the course of infection ( Figures 1–3 ) . These results suggest that the ability to respond to IFNs was at least partially intact . These observations were confirmed by demonstrating the greater susceptibility of neonatal mice lacking expression of the type I IFN receptor ( Figure 4 ) ; notably , the kinetics of viral replication and death were massively accelerated as compared to age-matched WT controls . Additionally , we demonstrate that the prophylactic exposure to an IFN-inducing adjuvant protected animals from challenge with lethal doses of CHIKV ( Figure 5 ) . To provide insight into the mechanism by which IFN participates in the host response to CHIKV infection , we evaluated the role of ISG15 , an anti-viral host protein that has previously been shown to be important in the control of several viruses , including SINV [34] . We found that ISG15−/− mice were more susceptible to CHIKV infection , demonstrating a dramatic increase in lethality as compared to WT mice ( Figure 6A ) . Moreover , ISG15 played a critical role in pIC induced protection of mice in a prophylactic setting ( Figure 6D , 6E ) . While OAS has previously been shown to inhibit viral replication when over-expressed in vitro [54] , to our knowledge our data is the first in vivo demonstration of an IFN effector molecule having activity against CHIKV . Most importantly , the results from our current study indicate that ISG15 is regulating CHIKV pathogenesis by a unique mechanism of action . First , we demonstrate that ISG15 regulates CHIKV infection independent of UbE1L mediated conjugation . The protection mediated by ISG15 during pIC prophylaxis also appeared to be UbE1L independent , as UbE1L−/− mice displayed survival curves similar to WT mice ( Figure 6D , 6E ) . These results suggest that the non-classical function of ISG15 is at work both during acute viral infection and in the pIC induced protection seen in our mice . This is in contrast to both influenza virus and SINV infection , where the anti-viral activity of ISG15 is dependent upon ISG15 conjugation and abrogated in UbE1L−/− animals [40] , [41] . To date , UbE1L is the only known E1 for the ISG15 pathway . A second E1 has recently been identified for the ubiquitin pathway [55] , leaving open the possibility that another E1 may exist for ISG15 . However , our current analysis ( Figure 6 ) and previous studies [39] have revealed no conjugation activity in UbE1L−/− cells and mice . Therefore the actions of ISG15 during CHIKV infection are likely to be independent of conjugation , and mediated by free ISG15 . Second , it appears that during CHIKV infection , ISG15 is not functioning as a direct antiviral molecule . In both the influenza and SINV models , the increase in lethality was accompanied by a dramatic increase in viral loads [34] , [38] , [41] . In contrast , during CHIKV infection , ISG15−/− mice did not show an increased CHIKV burden ( Figure 7 ) . Instead , we noted a significant elevation of several proinflammatory cytokines and chemokines in the ISG15 deficient mice ( Figure 8 ) . Therefore , as opposed to having direct antiviral activity , it appears that ISG15 modulates the immune response during CHIKV infection . Finally , in contrast to what was previously reported for control of SINV infection [34] , a recombinant CHIKV expressing ISG15 was unable to rescue neonatal ISG15−/− mice from viral induced lethality ( Figure S4 ) . The inability to rescue the ISG15−/− phenotype may be due to insufficient levels or inappropriate timing of ISG15 expression; or alternatively , may indicate that ISG15 expression is required in an uninfected cell . Since we detect no differences in viral load , and instead observed increased cytokine levels in the ISG15−/− mice , we favor this latter possibility . Further analysis into the precise mechanism by which ISG15 regulates the host response to CHIKV should provide additional insight into this issue . Together , our data suggest a novel mechanism for ISG15 , which is likely to be independent of conjugation and extrinsic to virally infected cells . The precise mechanism by which ISG15 , independent of UbE1L mediated conjugation , contributes to the control of viral infection is currently unclear . The most intriguing difference we have noted to date is the increased cytokine responses in the mice lacking ISG15 ( Figure 8 ) . As noted above , while we cannot formally exclude the possibility of another E1 functioning in this system , it seems most likely that free , unconjugated ISG15 mediates the activity during CHIKV infection . Free ISG15 is found within the cell , but interestingly it may also be secreted by a still undefined mechanism [33] . Previous work has shown that unanchored ISG15 can associate non-covalently with proteins ( i . e . independent of conjugation ) . For example , the NS1 protein of influenza B virus can non-covalently bind ISG15 , thereby inhibiting its interaction with UbE1L and blocking conjugation of target proteins [24] . The over-expression of ISG15 has also been shown to disrupt Nedd4 ligase activity and inhibit Ebola virus VLP release [56] , [57] . Recent research within the ubiquitin field has described a role for unanchored polyubiquitin chains , shown to regulate TRAF6 function , as well as promote RIG-I dimerization and signaling [58] , [59] . It is therefore possible that intracellular , unanchored ISG15 interacts non-covalently with members of an innate immune signaling pathway to regulate cytokine and chemokine production or other host response pathways . Alternatively , released ISG15 could be contributing to the phenotype seen during CHIKV infection . Indeed , the 17 kDa form of ISG15 is released into the serum in both WT and UbE1L−/− mice during CHIKV infection ( Figure 6 ) . Released ISG15 has been reported to function as an immunomodulatory molecule , increasing NK cell proliferation and lytic activity , acting as a neutrophil chemoattractant , and upregulating E-cadherin expression on dendritic cells [35]–[37] . Released ISG15 could function as in immunomodulatory cytokine by signaling through a receptor to regulate the cytokine response or through its ability to function as a chemoattractant . In order to characterize these effects in greater detail , a receptor for ISG15 must be identified . Future studies evaluating these possibilities will be required in order to further define the mechanism by which ISG15 is contributing to the host response to CHIKV . In conclusion , we have demonstrated that neonates are capable of producing type I IFN in response to CHIKV , which serves to limit viral infection though remains insufficient to clear the virus . We have demonstrated a critical , age-dependant role for ISG15 during neonatal infection . We have also characterized the mechanism of ISG15 activity , revealing a novel mechanism for ISG15 , independent of UbE1L mediated conjugation , and functioning as a putative immunomodulator of proinflammatory cytokines . The ability of pIC to protect neonatal mice against CHIKV infection suggests that manipulation of the IFN signaling pathway , and perhaps the induction of ISG15 , may be an appropriate therapeutic target for combating CHIKV infection .
All human studies were approved by the Committee for Clinical Research at the Institut Pasteur , project number RBM 2009-23 , on July 9 , 2009 . Written informed consent was obtained from the study participants or legal guardians . For mouse studies at the Institut Pasteur and at Washington University , the principles of good laboratory animal care were carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and following the International Guiding Principles for Biomedical Research Involving Animals . The protocols were approved by the Animal Studies Committee at Washington University ( #20090287 ) and the Institutional Committees on Animal Welfare of the Institut Pasteur ( OLAW assurance # A5476-01 ) . All efforts were made to minimize suffering . Retrospective study on patients who presented to the emergency or pediatric service of the Groupe Hospitalier Sud Réunion , Saint Pierre , La Réunion , France from January 2006 through May 2006 . Sera samples were collected and stored at −80°C . CHIKV infection was confirmed by RT-PCR . All patients were negative for anti-CHIKV IgG and IgM as assessed by immunocapture Elisa . The control cohort consisted of patients that presented to the orthopedic chirurgy or infant chirurgy service of the Groupe Hospitalier Saint Pierre before the epidemic . These patients were IgG , IgM and RT-PCR negative for CHIKV . Data from adult patients were previously reported [20] , but are shown here for comparison to data from infected neonates . The preparation of CHIKV from clinical samples has been previously described [60] . CHIKV ( 06 . 21 ) strain was isolated during the epidemic in La Réunion and then propagated three time on C6/36 mosquito cells to generate a stock ( 6×107 PFU/mL ) that was used in all experiments . Experiments in mice were carried out at both the Institut Pasteur , Paris , France and at Washington University School of Medicine , St . Louis , Missouri , using the identical CHIKV viral stock described above . Infection of 9 day old pups with 2×105 PFU subcutaneously ( s . c . ) resulted in approximately 50–60% lethality in WT mice at both locations . For experiments carried out at the Institut Pasteur , eight day old C57BL/6 litters were obtained from Charles River laboratories ( France ) . For experiments carried out at Washington University , mice were maintained at Washington University School of Medicine in accordance with all federal and University guidelines . ISG15−/− mice were provided by Dr . Ivan Horak and Dr . Klaus-Peter Knobeloch . UbE1L−/− mice were provided by Dr . Dong-Er Zhang . The generation of both the ISG15−/− and UbE1L−/− mice has been previously described [39] , [45] . C57BL/6 , IFNαβ receptor 1 ( IFNAR−/− ) , UbE1L−/− and ISG15−/− mice , all on the C57BL/6 background , were bred and maintained in our mouse colony . Congenic SNP analysis ( Taconic laboratories ) of UbE1L−/− and ISG15−/− mice confirmed that these mice were fully backcrossed , with 99 . 93% and 99 . 72% identity to C57BL/6 mice , respectively . For neonatal experiments mice were infected between 9 and 12 days of age as indicated . Litters were weight matched at the initiation of the experiments . For adult experiments , mice were infected between 6–8 weeks of age and were age and sex matched within experiments . For neonatal experiments using the clinical isolate of CHIKV , 9 day old pups were infected with 2 . 0×105 PFU CHIKV in 20–30 µL of PBS s . c . into the right flank . Infected mice were followed daily for weight gain , signs of disease , and lethality for 21 days post-infection . Paralysis was scored as an inability or long delay ( >5s ) to return and land on its feet when flipped on its back . For experiments with adult mice , 6–8 week old mice were infected as outlined above and followed for daily weight loss and lethality . For experiments utilizing the recombinant CHIKV clones , 6 day old pups were infected with 3×105 PFU of the indicated recombinant virus diluted in 30 µL of PBS by s . c . injection into the right flank . Viral titers were determined in organs harvested at the indicated days post-infection . Organs were harvested into 1 ml of DMEM without fetal bovine serum and homogenized with 1 . 0 mm diameter zirconia-silica beans at 3 , 200 rpm for 1 minute with a MagnaLyzer prior to plaque assay on BHK cells , protocol modified from [63] . Serial dilutions of organ homogenates in DMEM with 1% FBS was added to BHK cells ( 6×105 cells for 6 well plates ) and incubated for 1 hr at 37°C . An agar overlay was then added to the cells and incubated for 28 hrs at 37°C . Plates were fixed with 1% formaldehyde ( >30 min at room temperature ) , agar plugs were removed and plaques were visualized using a 1% crystal violet solution . Eight day old mice were injected intraperitoneally ( i . p . ) with 10–25 µg of high molecular weight pIC ( Invivogen ) . Twenty-four hours later , mice were challenged with 2×105 PFU CHIKV s . c . Mice were monitored daily for the development of symptoms and followed for lethality as described above . Sera were harvested and conserved at −80°C for analysis . Human cytokines were measured by Luminex ( 25 plex kits , Biosource , Invitrogen ) following manufacturer's instructions . Human CXCL10 was re-titrated by ELISA ( human quantikine ELISA kit , R&D ) . Mouse sera were obtained after coagulation of blood in T-MG tubes ( Terumo ) . Mouse IFNα levels were quantified by ELISA ( PBL biomedical ) and other cytokines were measured using Luminex technology with either the 32 plex from Millipore ( MPXMCYTO-70X ) or by customized 10 plex from Biorad . For determination of patient viral load , total nucleic acid extraction was performed on sera in a MagNa Pure automate using the Total Nucleic Acid Kit ( Roche Diagnostics ) . CHIKV RNA was detected with specific taqman probes using a one step RT-PCR ( Master RNA hybridization probes , Roche ) performed on a Chromo 4 machine ( Biorad ) . The 20 µL reaction mix contained 2 µL of extracted RNA , 7 . 5 µL of LightCycler RNA Master Hyb-Probe , 3 . 25 mmol/L Mn2 , 450 nmol/L CHIKV-forward primer , 150 nmol/L CHIKV-reverse primer , 150 nmol/L CHIKV Probe ( 5 6-carboxyfluorescein-3 TAMRA ) ( TibMol-Biol ) . The thermal cycling consisted of a reverse transcription at 61°C for 20 min followed by 45 cycles at 95°C for 5 s and 60°C for 15 s . The fluorescence was measured at 530 nm . CHIKV load is measured using a synthetic RNA calibrator [64] . CHIKV-rev CCAAATTGTCCGGGTCCTCCT; CHIKV-forw AAGCTCCGCGTCCTTTACCAAG; Probe: Fam-CCATGTCTTCAGCCTGGACACCTTT-TAMRA . For mouse studies , skin tissue was harvested at the site of infection on days 1 and 2 post-infection . Tissue was snap frozen in liquid nitrogen and then homogenized in RLT+ with 0 . 04 M DTT , and RNA was extracted with the Qiasymphony robot ( Qiagen ) with a protease step and a DNase step . The quality and quantity of RNA was evaluated with the Agilent technology , with the RNA integrity number between 8 and 9 . 5 . Reverse transcription was performed with random primers ( Roche ) using Superscript enzyme ( Invivogen ) . cDNA for murine IFN-β and ISG15 were detected using Applied Biosystem Taqman probes ( Mm00439546-s1 , Mm01705338-s1 ) . To analyze the relative fold induction of mRNA , GAPD expression levels were determined in parallel for normalization using the CT method . Nine day old mice were infected with 2×105 PFU CHIKV . Tissue homogenates as well as serum samples were subjected to protein electrophoresis on a 12% Tris gel . The gel was transferred to a polyvinylidene fluoride membrane and probed for ISG15 expression using a rabbit anti-ISG15 polyclonal serum ( 1∶5000 ) as previously described [62] . The membrane was then developed with a horseradish peroxidase ( HRP ) -conjugated goat anti-rabbit antiserum ( Jackson Immunoresearch , West Grove , Pennsylvania ) diluted 1∶200 , 000 . For loading controls , the same blot was re-probed with anti-β-actin mAb ( clone AC-74; Sigma ) and then developed with a HRP conjugated goat anti-mouse antibody ( Jackson Immuno-Research ) . All blots were developed with chemiluminescent reagent ( Millipore ) . Spleens were harvested from naïve nine day old WT , UbE1L−/− and ISG15−/− mice ( 12 mice per strain ) . Lymphocyte subsets were stained using the following cell surface markers: CD3 , CD4 , CD8 , NK1 . 1 , CD19 , F4/80 and Gr1 . Data is represented as percent of the total cell population and Kruskal-Wallis test was used to compare the three genotypes . Human data was analyzed using the OMNIVIZ statistical platform ( BioWisdom , Cambridge , UK ) to perform comparisons among data sets using nonparametric tests ( Mann-Whitney U-test ) and false discovery rate ( FDR ) procedures , a permutation-based method to correct for the increased probability of obtaining a false positives among all significant tests [65] . Additional data was analyzed using the Prism software ( Graphpad software ) . Differences were considered significant if p<0 . 05 . IFNγ: NM_008337 , CCL2: NM_011333 , CCL4: NM_013652 , CXCL9: NM_008599 , CXCL10: NM_021274 , IL12: NM_008352 , IL1Rα: NM_001039701 , IL4: NM_021283 , IL5: NM_010558 , IL13: NM_008355 , IL15: NM_008357 , IL17: NM_010552 , IFNβ: NM_010510 , IRF7: NM_016850 , Mx1: NM_010846 , ISG15: NM_015783 , IL1β: NM_008361 , IL6: NM_031168 , TNFα: NM_013693 , IFNAR1: NM_010508 , UbE1L: NM_023738 . | Type I interferon plays a critical role in the host defense to viral infection . Signaling through the type I IFN receptor allows for the induction of hundreds of interferon stimulated genes ( ISGs ) that generate an antiviral state within host cells . The ubiquitin-like molecule ISG15 has been shown to play an important role during multiple viral infections , including influenza virus infection . To date , the ability of ISG15 to protect against viral infection has been shown to be dependent on its ability to covalently bind ( or conjugate ) to target proteins , including the binding of viral proteins . We investigated the importance of the type I interferon response and ISG15 conjugation in a neonatal model of Chikungunya virus infection , a re-emerging human pathogen in the Indian Ocean region . Remarkably , the role of ISG15 during CHIKV infection appears to be conjugation independent , suggesting a non-classical role for ISG15 during viral infection . Our data also suggests that ISG15 plays an immunoregulatory role , as opposed to having direct antiviral function . Our CHIKV model may provide an opportunity to identify a novel mechanism by which ISG15 contributes to the innate immune response to viral infection . | [
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] | 2011 | ISG15 Is Critical in the Control of Chikungunya Virus Infection Independent of UbE1L Mediated Conjugation |
Retroviruses and retrotransposons are vulnerable to a suicidal pathway known as autointegration , which occurs when the 3′-ends of the reverse transcript are activated by integrase and then attack sites within the viral DNA . Retroelements have diverse strategies for suppressing autointegration , but how HIV-1 protects itself from autointegration is not well-understood . Here we show that knocking down any of the components of the SET complex , an endoplasmic reticulum-associated complex that contains 3 DNases ( the base excision repair endonuclease APE1 , 5′-3′ exonuclease TREX1 , and endonuclease NM23-H1 ) , inhibits HIV-1 and HIV-2/SIV , but not MLV or ASV , infection . Inhibition occurs at a step in the viral life cycle after reverse transcription but before chromosomal integration . Antibodies to SET complex proteins capture HIV-1 DNA in the cytoplasm , suggesting a direct interaction between the SET complex and the HIV preintegration complex . Cloning of HIV integration sites in cells with knocked down SET complex components revealed an increase in autointegration , which was verified using a novel semi-quantitative nested PCR assay to detect autointegrants . When SET complex proteins are knocked down , autointegration increases 2–3–fold and chromosomal integration correspondingly decreases ∼3-fold . Therefore , the SET complex facilitates HIV-1 infection by preventing suicidal autointegration .
Soon after HIV-1 enters a susceptible target cell , the viral genomic RNA is reverse transcribed within the reverse transcription complex ( RTC ) to double-stranded DNA [1] . The RTC matures into the preintegration complex ( PIC ) , which delivers the viral DNA to the nucleus for integration into a chromosome [2] . The PIC may also sequester and protect the viral DNA from cellular DNA-modifying enzymes [3] and from cytoplasmic DNA sensors [4]–[6] that could trigger antiviral innate immunity . Surprisingly little is known about the host proteins that associate with the PIC and assist in HIV-1 integration . Integration can be divided into three steps: ( 1 ) 3′ processing ( integrase ( IN ) -mediated hydrolysis of GT dinucleotides from HIV-1 DNA to produce reactive , recessed CAOH-3′ ends ) ; ( 2 ) DNA strand transfer ( IN-mediated insertion of the cleaved 3′ ends into opposing strands of host chromosomal DNA ) ; and ( 3 ) 5′-end joining ( repair by host enzymes of the gaps between the 5′-ends of viral DNA and the chromosome ) [7] . 3′-processing makes the viral DNA vulnerable to autointegration [8] , [9] in which the reactive CA ends attack sites within the viral DNA . Autointegration is mechanistically analogous to chromosomal integration , but results in nonproductive deletion or inversion circles [9]–[12] . Autointegration is a problem faced not only by retroviruses , but also by mobile genetic elements including bacteriophages and retrotransposons [10] , [13] , [14] . Each element employs a unique mechanism , relying on either self or host factors , to control autointegration . For example , bacteriophage Mu B protein activates DNA strand transfer to favor intermolecular transposition [14] , [15] . In the case of Tn10 , a cellular global regulator , H-NS , acts directly on the PIC to promote intermolecular transposition [16] . The barrier-to-autointegration factor ( BAF ) is a cellular protein that protects Moloney murine leukemia virus ( MLV ) PICs from autointegration and stimulates intermolecular integration in vitro [12] , [17] . Although BAF can also stimulate HIV-1 PIC intermolecular integration activity in vitro , it has not been shown to block HIV-1 autointegration [18]–[20] . 3′-processing can occur soon after the DNA ends are synthesized by reverse transcription in the cytoplasm [21] , suggesting that a cytoplasmic mechanism might be needed to protect HIV-1 from autointegration . We therefore considered host cytosolic DNA-interacting proteins as potential regulators of autointegration . One candidate is the SET complex , an endoplasmic reticulum ( ER ) -associated DNA repair complex that contains three DNases and is mobilized to the nucleus in response to oxidative stress . The SET complex was discovered as a Granzyme A ( GzmA ) target in cells undergoing caspase-independent T cell-mediated death [22] . Two nucleases in the complex , the endonuclease NM23-H1 and the exonuclease TREX1 , are activated by GzmA cleavage of the inhibitor SET protein to cause single-stranded DNA damage [23] , [24] . In addition to the three DNases ( APE1 , NM23-H1 , TREX1 ) and SET ( a histone chaperone of the nucleosome assembly protein family ) , the SET complex contains HMGB2 , a DNA binding protein that preferentially binds to distorted or damaged DNA , and the PP2A inhibitor pp32 [25] . Although individual SET complex components have been implicated in diverse processes ( including DNA repair , histone modification , DNA replication , transcriptional activation , single-stranded DNA degradation , autoimmunity ) , the functions of the intact complex are not well understood [26] . Here we show that the SET complex plays an important role in the early phase of the HIV-1 lifecycle by inhibiting autointegration .
Knockdown of SET and/or NM23-H1 in HeLaCD4 cells reduced HIV-1IIIB infectivity 3 to 4-fold as assessed by p24 levels in culture supernatants ( Figure 1A ) . Although viral replication was impaired , the virions produced from knockdown cells were equally infectious when applied in equivalent amounts ( normalized by p24 level ) to indicator TZM-bl cells that express an HIV LTR-driven luciferase ( Luc ) reporter gene ( Figure S1 ) . This suggested that SET and NM23-H1 act early in the viral life cycle . To focus on early events , cells were infected with an HIV-1-derived single-round reporter virus ( HIV-Luc ) pseudotyped with the vesicular stomatitis virus G ( VSV-G ) envelope glycoprotein [18] , and infection was assessed two days later by Luc activity . Knockdown of SET , NM23-H1 , or both reduced Luc activity to 24% , 19% , and 15% of control levels , respectively ( Figure 1B ) . HIV-Luc activity was restored by expressing RNAi-insensitive SET ( SET-in ) in SET siRNA-treated cells ( Figure 1C ) . SET knockdown and SET-in rescue had similar effects when infections were performed using a range of multiplicities of infection ( MOI ) ( Figure S2 ) . Single-round virus carrying the natural HIV-1 envelope glycoprotein was similarly inhibited by SET and/or NM23-H1 knockdown ( data not shown ) . Because inhibition was independent of the envelope glycoprotein , SET and NM23-H1 likely influenced post-entry steps . SET can influence chromatin accessibility in its role as a histone chaperone and inhibitor of histone acetylation and DNA demethylation , and NM23-H1 enhances the transcription of some genes [26] , [27] . We therefore tested whether knocking down SET and NM23-H1 inhibited transcription from transfected HIV-Luc plasmid DNA and from a chromatinized reporter gene . There was no significant effect on expression from transfected HIV-Luc and only a weak effect ( ∼25% , p<0 . 05 ) on Tat-dependent expression from the chromatinized reporter gene ( Figure 1D and 1E ) . Together these experiments suggest that SET and NM23-H1 act primarily downstream of viral entry and before Tat-dependent transcription . The SET protein is a component of at least two complexes – a ∼150 kDa nuclear complex that contains SET and pp32 and some of their paralogues and the ∼270–420 kDa ER-associated SET complex [22] , [28] . Because NM23-H1 knockdown interfered with HIV-1 , we reasoned that the larger SET complex facilitated infection . In fact , knocking down two other members of this complex , APE and TREX1 , similarly reduced HIV-Luc activity ( Figure 2 ) . To test whether SET complex proteins enhance infection of other retroviruses , the effect of knocking down SET complex proteins on MLV and avian sarcoma virus ( ASV ) was tested using similar Luc reporter systems . Both MLV-Luc and ASV-Luc were largely unaffected when SET complex proteins were knocked down ( Figure 2A and 2B ) . By contrast , the infectivity of two other lentiviruses , SIV-Luc and HIV-2 , was reduced approximately two-fold by SET/NM23-H1 knockdown ( Figure 2C and 2D ) . These results suggest that the SET complex specifically affects lentiviral infection . To pinpoint the block in the viral life cycle , we compared the effect of SET/NM23-H1 knockdown on stage-specific HIV-1 DNA products by quantitative PCR ( qPCR ) [29] , [30] . Late reverse transcription products ( late RT ) measured during the first day of infection were not significantly different in knockdown cells ( Figure 3A ) . In contrast , integrated HIV-1 DNA ( quantified by nested Alu-PCR ) was reduced by 3-fold 24 h post infection ( hpi ) in SET/NM23-H1 knockdown cells ( Figure 3B ) . Two-long terminal repeat ( 2-LTR ) circles were slightly ( about 28% , p>0 . 05 ) increased by the knockdown ( Figure 3C ) . To understand why HIV-1 integration might be impaired , insertion sites were sequenced [29] using DNA isolated 24 hpi from control and SET/NM23-H1 knockdown cells . HIV-1 normally integrates preferentially into transcriptionally active chromatin [31] , [32] . The frequency of integration within transcription units , CpG islands , and promoters was not significantly different in the knockdown cells ( Table 1 ) . Although the DNA for the integration site analysis was isolated from the Hirt pellet , which is enriched for chromosomal DNA , a significant number of clones arose from autointegration . The proportion of autointegrants recovered from SET/NM23-H1 knockdown cells exceeded the control , comprising 259 of 816 ( 32% ) vs 182 of 816 ( 22% ) sequences ( p<0 . 0001 ) ( Table 1 , Figure 4 ) . The autointegration sites in the control and knockdown cells showed the same sequence preference as chromosomal integration , favoring insertion within GG dinucleotides ( Figure S3 ) [29] , [33] . Because there are no assays to quantify HIV-1 autointegration , a nested qPCR autointegration assay ( auto-PCR ) was designed to quantify and clone autointegration events from Hirt supernatant DNA . Three primers ( PBS− ( primer binding site ) , A+ , and B− ) were designed to detect integration of the minus strand U3 CA-3′ end into either strand of viral DNA ( Figures 5A and S4 ) . The first PCR round generates products that contain the upstream LTR and internal viral sequences of variable length depending on the distance between the site of autointegration and primer A+ or B− . The qPCR ( second ) round amplifies LTR sequences of a fixed length from diluted first-round PCR products . To validate the assay , we verified that first-round PCR using only PBS or A+ and B− primers amplified negligible amounts of LTR-containing DNA compared to reactions with all 3 primers . Autointegration is expected to occur shortly after reverse transcription because 3′ processing can happen soon after DNA synthesis [21] . Auto-PCR and late RT DNAs both peaked 10 hpi , while 2-LTR circles and integrated DNA peaked 24 hpi ( Figure 5B ) . These kinetics support the specificity of the auto-PCR assay to detect autointegrants rather than 2-LTR circles . Consistent with the hypothesis that most autointegration events likely arise from the concerted insertion of both U3 and U5 viral DNA ends [34] , the kinetics of U5 end joining closely mirrored those of U3 ( Figure S5 ) . Since autointegration requires IN activity , HeLaCD4 cells were infected with HIV-Luc carrying either wild-type ( WT ) or active site mutant ( D64N/D116N , mt ) IN . As expected , auto-PCR product formation was significantly reduced following mt IN viral infection ( Figure 5D ) . First-round PCR products analyzed by electrophoresis through agarose gels produced a smear migrating at ∼1 kb from WT-infected cells , while mt virus products had no appreciable DNA in this region ( Figure 5E ) . The 1 kb smear is likely the consequence of the 3 min extension time used in the first round PCR ( see Materials and Methods ) . DNA from the regions corresponding to the 1 kb smear were isolated , cloned , and sequenced . 21 of 30 clones from the WT viral infection contained an IN-processed U3 end ( identified by loss of the GT dinucleotide from the unprocessed CAGT strand ) . The processed U3 end joined to an internal viral sequence in 13 cases , whereas the remaining 8 sequences contained only viral LTR sequences ( Table S1 ) . Only 5 of 30 clones from the mt IN virus infection contained any viral sequence , and none contained a processed U3 end ( Table S1 ) . The mt virus likely supported low level auto-PCR product formation ( Figure 5D ) due to background amplification of nonspecific first round PCR products ( Figure 5E , Table 1 ) and/or increased levels of unintegrated DNA that form under these infection conditions [35] . These results demonstrate that the auto-PCR assay predominantly amplifies autointegrated HIV-1 DNA . With the auto-PCR assay validated , we compared autointegration and other stage-specific HIV-1 DNAs in control and SET/NM23-H1 knockdown cells ( Figure 6A ) . Late RT products were comparable 10 hpi as shown in Figure 3A , whereas autointegration assayed at the same time increased 2 . 5 fold in SET/NM23-H1 knockdown cells ( p<0 . 01 ) . Chromosomal integration measured 24 hpi decreased 3-fold ( p<0 . 001 ) , as expected , in SET/NM23-H1 knockdown as compared to control cells ( Figure 6A ) . The corresponding increase in autointegration and decrease in chromosomal integration suggested that the integration defect is due to reduced available substrate because of suicidal autointegration . Because increased autointegration occurred before chromosomal integration , it was unlikely that autointegration was secondary to failed chromosomal integration . To test directly whether autointegration might be an obligate side product of failed integration , we quantified autointegration events in ledgf−/− and ledgf+/+ mouse embryo fibroblasts ( MEF ) infected with HIV-Luc . LEDGF is a nuclear factor , which tethers the PIC to genomic DNA and plays a crucial role in chromosomal integration [29] , [36] , [37] . Although HIV-Luc infection of ledgf−/− cells was barely detectable compared to ledgf+/+ MEF , autointegration did not significantly change in ledgf−/− MEF ( the IN within the PIC is fully active in ledgf−/− cells [29] ) ( Figure 6B ) . Therefore autointegration is not an obligate side effect of decreased chromosomal DNA integration . LEDGF and the SET complex did not coimmunoprecipitate in infected cells and recombinant IN and SET also did not coprecipitate ( data not shown ) . Our results collectively indicate that the SET complex suppresses autointegration rather than augments chromosomal integration . In support of this , individual knockdown of TREX1 or APE1 , two other SET complex components , also significantly increased autointegration ( Figure 6C ) . Just as NM23-H1 knockdown enhanced autointegration , overexpressing NM23-H1 in an NM23-H1 defective human breast cancer cell line ( MDA-MB-435 ) suppressed HIV-1 autointegration by 2-fold ( p<0 . 001 ) but had no effect on reverse transcription ( Figure 6D ) . BAF can augment HIV-1 integration in vitro [20] and in cells [19] , although its overall importance during virus infection is controversial [18] , [19] . To determine whether BAF regulates autointegration , lysates prepared from HIV-Luc-infected control and BAF knockdown cells were assayed by auto-PCR . BAF knockdown was efficient ( 92% protein knockdown ) and reduced Luc activity about 2-fold ( Figure S6A and S6B ) , but had no effect on autointegration ( Figure S6C ) . Although we cannot exclude a role for BAF in regulating autointegration , its knockdown reduced HIV-1 infection ∼2-fold without a concomitant change in auto-PCR product formation . We therefore conclude that suppression of autointegration is unlikely to be the dominant mechanism through which BAF regulates HIV-1 infection . Autointegration product formation peaked in parallel with the late RT product ( Figure 5B ) , suggesting that autointegration occurs in the cytoplasm . In fact , 68% of autointegrants at their peak 10 hpi were in cytoplasmic rather than nuclear lysates ( data not shown ) . We therefore predicted that the SET complex would associate with HIV-1 reverse transcripts in the cytoplasm . The ability of SET complex and control antibodies to capture HIV-1 cDNA from cytoplasmic lysates 6 hpi was analyzed ( Figure 6E ) . IN and matrix ( MA ) antibodies captured 7 . 7% and 4 . 3% of cytoplasmic HIV-1 DNA , respectively , as assessed by qPCR . LEDGF antibody did not pull down a significant amount of HIV-1 DNA , in contrast to a previous report [38] . SET and NM23-H1 antibodies , which immunoprecipitated ∼30–40% of these abundant proteins from input samples ( data not shown ) , pulled down 2 . 4% and 3 . 1% of HIV-1 DNA , respectively , significantly more than rabbit IgG control ( p<0 . 005 and p<0 . 001 , respectively ) . The direct association of SET complex proteins with HIV-1 DNA in the cytoplasm early in infection further supports its role in preventing autointegration . Since our previous studies were performed in HeLaCD4 cells , we wanted to verify the postulated role for the SET complex in more physiologically relevant T cells . We produced Jurkat cells stably knocked down for APE1 by infection with a lentivirus expressing a shRNA targeting APE1 ( sh-APE1 ) . Knocking down APE1 did not alter cell viability or proliferation ( data not shown ) . APE1 knockdown cells infected with HIVIIIB had significantly reduced levels of integrated HIV DNA and viral production compared to cells expressing the sh-CTL control hairpin ( Figure 7A and 7B ) , suggesting that APE1 also facilitates HIV-1 infection in immune system cells . SIV-Luc infection was also strongly inhibited ( ∼10-fold ) in Jurkat cells by APE1 knockdown , but MLV-Luc infection was not significantly inhibited ( p>0 . 05 ) ( Figure 7C ) . These results confirm the lentiviral specificity of the SET complex .
Our results identify the SET complex as a cytoplasmic barrier to autointegration . Knockdown of 4 SET complex proteins increased autointegration and decreased chromosomal integration . Knockdown of individual SET complex components reduced HIV infection between 3 and 10 fold and increased autointegration approximately 2–3 fold . Moreover , the SET complex proteins SET and NM23-H1 associate with HIV-1 DNA in the cytoplasm . Although there are reports that other DNA repair factors either facilitate or inhibit HIV-1 infection , most of these have been postulated to influence 2-LTR circle formation or 5′ gap repair and to act at a later stage of infection in the nucleus [3] , [39]–[43] . A new assay was developed in this study to measure autointegration . Our results verified that HIV autointegration is IN-dependent and that it occurs around the time of reverse transcription . Autointegrants accumulate preferentially in the cytoplasm , suggesting that most autointegration occurs before PIC nuclear import . The auto-PCR assay is semi-quantitative , since the first step involves conventional PCR . Therefore we were unable to use it to quantify the level of autointegration products during HIV infection in comparison to other HIV DNAs , e . g . late RT and 2-LTR circles . We and others [11] , [20] have been unable to detect autointegration products by Southern blot using biochemically fractionated PICs in an in vitro assay . HIV infection is relatively inefficient and only autointegration products arising from opposite-strand joining yield a product of discrete size ( Figure S4 ) . The frequency of opposite- versus same-strand joining is about 1 to 9 ( data not shown ) , possibly contributing to the difficulty in identifying autointegration products by Southern blot . The reduction in HIV replication caused by knocking down SET complex genes is lower than the typical log changes seen by inhibiting key viral enzymes , such as reverse transcriptase or IN . This difference is not unexpected , given the postulated role of the SET complex in preventing a suicidal side pathway rather than as an essential host factor in executing viral replication . Moreover , since knockdown is incomplete , the effect we measured might be dampened by the function of the remaining unknocked down protein . This problem could be exacerbated by the fact that the SET complex is very abundant in cells and not much protein would be expected to interact with a single PIC . The observed increase in autointegration and corresponding decrease in chromosomal integration , measured by the auto-PCR and Alu-PCR assays , respectively , were less than the reduction in viral replication as measured by Luc assay or p24 production . These quantitative differences may be due to different sensitivities of the semi-quantitative PCR-based assays as compared to the Luc and p24 assays . In addition to its role in preventing autointegration , the SET complex might affect other steps in the viral life cycle . For example , SET complex proteins are known to regulate transcription [26] , [27] and a slight , but significant , reduction in transcription from a chromatinized HIV reporter gene was observed in SET/NM23-H1 knockdown cells . SET can act as a histone H2B chaperone to either assemble or disassemble nucleosomes , thereby altering accessibility for transcription , and together with pp32 can regulate histone modifications . Moreover , SET , pp32 and NM23-H1 can enhance transcription from some promoters [26] , [27] . The SET complex contains 3 DNases - the base excision repair ( BER ) apurinic endonuclease APE1 , a DNA nicking endonuclease NM23-H1 , and a 5′-3′ exonuclease TREX1 , which may serve as a BER proofreading endonuclease [44] . Our preliminary data suggest that the presumed BER function of the SET complex , which has not formally been demonstrated , is involved in its role in blocking HIV-1 autointegration ( data not shown ) . Although APE1 nicking has previously been proposed as a threat to HIV-1 cDNA [45] , our results suggest that within the SET complex , APE1 plays a protective role . One of the roles of BER is to repair misincorporated deoxyuridine in DNA that occurs by utilizing dUTP in place of dTTP or by spontaneous deamination of incorporated cytosines , which is enhanced under oxidative conditions . For HIV-1 this represents a particular problem because reverse transcriptase ( RT ) does not effectively distinguish dUTP from dTTP and the dUTP/dTTP ratio is especially high in primary immune cells susceptible to HIV-1 infection [46] . Moreover , the host cytidine deaminase APOBEC3G ( A3G ) can attack the minus strand during reverse transcription in immune cells [47] , [48] and introduces dC-to-dU changes that can be repaired by BER . Although most of our results were obtained in A3G− HeLaCD4 cells , we also showed that the SET complex facilitates HIV replication and integration in A3G+ Jurkat T cells . In addition to its nuclease function , NM23-H1 is a nucleoside diphosphate kinase that catalyzes the exchange of dNDPs for dNTPs and therefore potentially regulates the pool of nucleotides available for reverse transcription and/or repair . HIV autointegration generates defective DNA products including nicked inverted and subgenomic dsDNA circles ( [34] and Figure S4 ) . The presence of these DNAs in the cytoplasm can potentially be recognized by cytosolic DNA sensors and trigger the interferon ( IFN ) -stimulatory DNA ( ISD ) response [5] , [49] . One of the SET complex proteins , TREX1 , has recently been identified as a negative regulator of the ISD response [6] . TREX1 is the major 5′-3′ DNA exonuclease in mammalian cells , and mutations in the human TREX1 gene are associated with Aicardi-Goutieres syndrome ( AGS ) , lupus syndromes and other pro-inflammatory autoimmune diseases [50]–[53] . TREX1 deficient cells accumulate cytoplasmic DNA derived from endogenous retroelements [6] , which can then activate IRF3 to trigger production of type I IFNs leading to autoimmunity . Endogenous retroelements share many features with retroviruses , including cytoplasmic reverse transcription and chromosomal integration . Retroelements can also undergo autointegration [10] . In this study , TREX1 knockdown inhibited HIV infection 10-fold , representing the strongest effect of any of the SET components tested ( Figure 2A ) . By both reducing autointegration and digesting DNA products produced during the autointegration events that do occur , TREX1 may further promote HIV infection by inhibiting the secretion of Type I IFNs , key effectors of antiviral innate immunity . We are intrigued by the possibility that HIV nucleic acids may engage similar cell-intrinsic factors as endogenous retroelements . One example of such a factor is A3G , which was identified through its ability to mutate the genome and inhibit HIV infection [48] , [54] , [55] . APOBEC3 proteins also inhibit Alu and LINE-1 retrotransposition , by potentially sequestering retrotransposon RNAs in high-molecular-weight complexes [54] , [56] . Understanding how the SET complex binds to the HIV PIC and regulates lentiviral autointegration requires further study . Viral DNA in the PIC is accessible to exogenously introduced endonucleases [21] , [57] , [58] , so a direct interaction between SET complex proteins and HIV-1 DNA is plausible . We do not know whether the SET complex remains associated with the PIC during and after nuclear import . Since the SET complex shuttles back and forth to the nucleus [22] , [26] , this remains a distinct possibility . The mechanism used by the SET complex to inhibit lentiviral autointegration may provide insight into how to inhibit viral replication by inducing autointegration . Small molecule drugs that inhibit SET complex function or change its cellular distribution could be explored for antiviral therapy .
Cells were grown in Dulbecco's modified Eagle's medium ( DMEM ) ( Gibco ) supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) at 37°C and 5% CO2 unless specified otherwise . HeLaCD4 and TZM-bl cells were obtained form the NIH AIDS Research and Reference Reagent Program . Jurkat cells , obtained from ATCC , were maintained in RPMI medium supplemented with 10% FBS . MDA-MB-435 cell lines , C100 and H1-117 were a kind gift of Patricia Steeg ( NCI ) [59] . Chicken DF1 cells ( a gift of James Cunningham , Harvard Medical School ) were propagated in DMEM/10% FBS , 100 U/mL penicillin G sodium , and 100 µg/mL streptomycin sulfate . The ledgf+/+ and ledgf−/− MEF used in this study are described as E17 ( +/+ ) and E16 ( −/− ) , respectively , in [60] . HIVIIIB was propagated as described previously [61] . HIV-2 strain MLP-15132 was obtained from the NIH AIDS Research and Reference Reagent Program ( contributed by Lutz Gürtler and Friedrich Deinhardt ) . HIV-2 infectivity was measured by FACS staining with p24-FITC antibody ( BD Biosciences ) 24 hpi . HIV-Luc and MLV-Luc constructs were described previously [18] . SIV-Luc was kindly provided by Nathaniel Landau ( NYU ) . Viral supernatants were produced from transfected 293T cells as described [18] . ASV-Luc ( 24 mL ) was produced from DF1 cells plated at 2×106/10 cm dish the day prior to co-transfecting with 15 µg pRIAS-Luc and 10 µg pHCMV-G [62] using Fugene 6 as recommended by the manufacturer . Virus was harvested over 3 successive days and concentrated approximately 32-fold by ultracentrifugation prior to use . HIV-1 was titered by p24 ELISA , and infections were performed for 6–8 h at an MOI of 1 before replacing viral supernatants with fresh medium . Luc activity was assayed 48 hpi as described [18] . Briefly , cells in 12-well plates were lysed with 250 uL 1× Passive Lysis Buffer ( Promega ) for 15 min at room temperature . Cell lysates were collected as supernatants after a quick spin to pellet cell debris . Luc activity was measured using Luc Assay Reagent ( Promega ) substrate in a Synergy 2 luminometer ( BioTek ) . Protein levels in cell lysates were determined by BCA assay ( Thermo Scientific ) . β-galactosidase activity was measured using Gal Screen ( TROPIX ) . shRNA containing lentiviruses were generated by co-transfecting 293T cells with three plasmids , pLentiLox-shRNA [63] , pHRgagpol and pVSVG ( 4∶4∶2 ratio ) , and viral supernatants were collected 48 h post transfection . Jurkat cell lines expressing shRNAs were generated by infection with VSV-G-pseudotyped lentivirus containing sh-CTL or sh-APE1 and sorting for GFP expressing cells 2 d later . These cells were subsequently challenged with single-round reporter viruses to test the role of APE1 in T cell infection . All Jurkat cell infections were done by spinoculation at 1500 g for 2 h . For experiments that measured stage-specific HIV-1 DNAs , viral supernatants were pretreated with 40 U/mL Turbo DNase ( Ambion ) at 37°C for 1 hr . Cells were infected using DNase-treated viruses , and DNA was isolated using the Hirt method [64] at specified times post infection . SET cDNA was PCR amplified from pET26b-SET [22] using primers containing BamHI and XhoI restriction sites and a FLAG-HA dual-tag on the C-terminal end ( DYKDDDDKQQYPYDVPDYA , FLAG-QQ-HA ) . The resultant fragment was subsequently cloned into pcDNA3 ( Invitrogen ) to generate pcDNA-SET-FLAG-HA for expression in mammalian cells . pcDNA-SET-in-FLAG-HA ( insensitive to SET siRNA ) was constructed based on pcDNA-SET-FLAG-HA with silent mutations introduced using the QuikChange kit ( Statagene ) . Primers were: forward primer: 5′-CCAAccacgacggCGCGGATGAAACGTCTGAGaaagaacagc-3′; reverse primer: 5′-GCTGTTCTTTCTCAGACGTTTCATCCGCGCCGTCGTGGTTGG-3′ . pRIAS-Luc , which encodes for single-round ( replication-incompetent ) ASV carrying the Luc reporter gene ( ASV-Luc ) , was built by amplifying Luc sequences from pNLX . Luc ( R- ) [65] with primers AE675 ( 5′-GGTACTATCGATAAAGCCACCATGGAAG ) / AE3292 ( 5′-CTAGATCGATTACACGGCGATCTTTCC ) , digesting with Cla I , and ligation to Cla I-digested pRIAS [66] . siRNAs were transfected using Oligofectamine ( Invitrogen ) following manufacturer's protocols . Cells transfected twice in two consecutive days were infected on the third day . DNAs were transfected using Lipofectamine 2000 ( Invitrogen ) following manufacturer's protocols . siRNAs were purchased from Dharmacon . Catalog numbers are given for siRNAs pre-designed by Dharmacon and a single siRNA that gave the best knockdown from each set of 4 was labeled as ‘preferred’ and used in this study . An equal molar mix of two TREX1 siRNAs was used to maximize knockdown . All sequences correspond to sense strand sequence of the target gene . sh-CTL and sh-APE1 were cloned into pLenti-LOX3 . 7 using the following oligos: Human ( build 36 . 1 , UCSC hg18 release ) genomic sequence and HIV-1NL4-3 sequence databases were used for integration site sequence analysis , which was done as described [29] . HIV PICs were isolated as described [29] , [58] with slight modifications . Briefly , HeLaCD4 cells grown on 10 cm plates ( 80% confluent ) were infected with DNase-treated HIV-Luc . Each plate provided enough cells for two IP experiments . Cells were washed with cold Buffer K−/− ( 20 mM HEPES , pH 7 . 6 , 150 mM KCl , 5 mM MgCl2 ) twice 6 hpi and lysed by rocking at room temperature for 8 min in 0 . 5 mL Buffer K+/+ ( Buffer K−/− containing 1× Protease inhibitors ( EDTA-free , Roche ) , 0 . 025% digitonin , 1 mM DTT ) per 10 cm plate . Supernatants were obtained following successive centrifugations at 1 , 500× g for 4 min at 4°C and 15 , 000× g for 1 min at 4°C . Resultant cytoplasmic PIC extracts were incubated with specific antibodies that were pre-bound to protein A or G agarose beads overnight . Beads were washed the next morning with 100 mM KCl wash buffer twice ( 20 mM Tris7 . 4 , 0 . 2 mM EDTA , 100 mM KCl , 5 mM ß-mercaptoethanol , 1× protease inhibitors complete , 10% glycerol ) and again with the same buffer containing 300 mM KCl before elution with 2× 100 µL 200 mM glycine ( pH 3 ) . Eluates were neutralized by adding 2 µL of 1 . 5 M Tris-HCl ( pH 8 . 8 ) before phenol/chloroform/iodoacetamide extraction and DNA precipitation . HIV-1 DNA in the IP was quantified using qPCR with late RT primers ( MH531/MH532 ) . Antibodies for IP were: anti-IN ( rabbit , affinity purified ) [68] , anti-MA ( mouse 3H7 ) [69] , anti-SET ( rabbit , affinity purified ) [22] , anti-NM23-H1 ( rabbit , Santa Cruz #sc343 ) and anti-LEDGF/p75 ( mouse , BD Transduction #611714 ) . Antibodies used for immunoblot were anti-Ape1 ( rabbit , this study ) , anti-BAF ( rabbit , a kind gift of Katherine Wilson , John Hopkins University School of Medicine ) [70] . HIV-1 late RT , integrated DNA , and 2-LTR circles were quantified as previously described [29] , [30] . Briefly , mitochondrial DNA , late RT and 2-LTR circles in extrachromosomal DNA fractions were analyzed by qPCR using MIT+/MIT− , MH531/MH532 and AE2948/AE2949 primers , respectively ( sequences below ) . β-globin DNA was similarly measured in chromosomal DNA fractions using β-globin+/β-globin− primers ( sequences below ) . Integrated HIV DNA was also measured in chromosomal fractions , but by Alu-PCR followed by nested qPCR using AE989/AE990 primers ( sequences below ) . Autointegration products were measured using a two-step nested PCR: Step 1 is a semiquantitative PCR using 200 ng extrachromosomal DNA , 1× PCR buffer , 1 . 5 mM MgCl2 , 0 . 2 µM of each primer ( PBS− , NY200/A+ , NY199/B− ) , 0 . 2 mM of each dNTP and 1 . 5 U Platinum Taq polymerase ( Invitrogen ) in a 25 µL reaction volume . PCR program was 94°C/5 min , 24 cycles of 95°C/30 s-60°C/30 s-72°C/3 min , then 72°C/7 min . PCR products from Step 1 were diluted 1∶100 for use in Step 2 . Step 2 was a qPCR assay using AE989/AE990 primers [29] . Autointegration initiated from the downstream U5 end ( Figure S5 ) was measured similarly , except for replacing the PBS− primer with a Luc+ primer ( located at the 3′ end of the Luc gene , adjacent to the right LTR ) . Primer sequences: | When HIV-1 infects a cell , its genomic RNA is copied into DNA . The ends of the viral DNA are then activated by the viral integrase enzyme to enable DNA insertion into a host cell chromosome . However , the activated ends can alternately insert into the virus itself by a process called autointegration , which is a suicidal pathway that aborts the infection . How HIV-1 protects itself from suicidal autointegration is not known . Here we show that a cytoplasmic complex , called the SET complex , which contains three DNA digesting enzymes , binds to HIV-1 and protects it from autointegration . | [
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] | 2009 | The SET Complex Acts as a Barrier to Autointegration of HIV-1 |
Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time . However , in subtropical and tropical regions , influenza epidemics can occur throughout the year , making routine forecast of influenza more challenging . Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics , using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered ( SIR ) model . We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013 , including the 2009 pandemic . The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains ( i . e . , seasonal influenza A ( H1N1 ) , pandemic A ( H1N1 ) , A ( H3N2 ) , and B ) , as well as 19 aggregate epidemics caused by one or more of these influenza strains . Average forecast accuracies were 37% ( for both peak timing and magnitude ) at 1-3 week leads , and 51% ( peak timing ) and 50% ( peak magnitude ) at 0 lead . Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing ( peak magnitude ) increased up to 43% ( 45% ) for H1N1 , 93% ( 89% ) for H3N2 , and 53% ( 68% ) for influenza B at 1-3 week leads . These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions .
Influenza causes a significant public health burden worldwide . Recent studies have shown that reliable forecasts of influenza epidemics can be generated in real time [1–3] . Particularly , operational forecasts of influenza epidemics have been developed for temperate regions such as the continental U . S . [4 , 5] These efforts could be valuable in aiding planning and deployment of intervention measures ( e . g . , health promotion activities and the distribution of vaccines and antivirals ) . However , before operational forecasts can be routinely generated and expanded to other regions of the globe , these forecast systems need to be tested and optimized against epidemics observed in a variety of locales with diverse transmission dynamics . In temperate regions , influenza transmits primarily during winter . This regularity allows the forecast systems to be initiated and optimized prior to the influenza season . For instance , our real-time forecast system for U . S . cities [1 , 4] is initialized at Week 40 each year , the first week that the U . S . Centers for Disease Control and Prevention ( CDC ) begin influenza activity surveillance . The forecast system is then continuously “trained” throughout the following weeks and months as additional observations are received and assimilated to inform the influenza transmission dynamics in that season . Model state variables , e . g . , number of susceptible people and number of infected people , can be inferred through this recursive training process . These model state variables and parameters form the initial conditions of a forecast , which are critical for generating an accurate prediction . In subtropical and tropical regions , however , the seasonal characteristics of influenza are more diverse . Hong Kong is one such area that experiences highly irregular influenza epidemics from year to year [6–8] . Hong Kong is located on the south coast of China , with a humid subtropical climate . It is one of the most densely populated cities , with a population of over seven million people and a population density of 6544 per km2 [9] ( cp . 33 . 7 per km2 in the U . S . [10] ) . In addition , Hong Kong is highly connected with mainland China and other regions around the world , attracting over 50 million visitors per year [11] . This large influx of visitors may increase the importation of influenza cases and further facilitate local transmission . Due to these unique climatic and socioeconomic features , influenza epidemics in Hong Kong can persist year-round in one year , whereas one or more distinct epidemics can occur in another year ( Fig 1 ) . In addition , outbreak intensity , duration , and time from onset to the peak is more variable in Hong Kong than in temperate regions ( S1 Fig ) . This irregularity poses challenges for operational influenza prediction . For instance , initialization of the system at the beginning of the season , as done for temperate regions , would not be possible . As such , it is not clear whether the same forecast system , proven to be valuable for temperate regions with regular epidemics , could be applied to generate forecasts in real time for subtropical and tropical regions . Indeed , our initial attempt to forecast the Hong Kong influenza epidemics using the same system for U . S . cities proved unfruitful ( S2 Fig ) . To overcome these challenges , we developed alternate forecast systems that are more adept at handling the seasonally erratic influenza transmission dynamics of the subtropics and tropics . Here we present these forecast systems and apply them to Hong Kong . The results are promising for forecasting influenza outbreaks in other subtropical and tropical regions , as well as other infectious diseases sharing similar irregular transmission dynamics .
Weekly records of rates of influenza like illness ( ILI ) consultations in the community from the week ending 04 January 1998 to the week ending 15 December 2013 , were reported by a sentinel surveillance network of approximately 50 outpatient clinics [7 , 8 , 12] . The Public Health Laboratory Services Branch in the Centre for Health Protection conducts laboratory testing of specimens provided by the ILI network and local hospitals for surveillance and diagnostic purposes . We obtained data on the weekly number of influenza-positive specimens by type and subtype , and the weekly number of specimens tested [7 , 8 , 12] . From these data streams we calculated the weekly ILI+ rate , a metric more precisely representing influenza infections [1 , 13–15]; specifically , ILI+ was calculated as the ILI rate multiplied by the viral detection rate for each strain individually or all strains combined ( S1 Dataset ) . Forecasts were done separately for these 4 ILI+ time series , i . e . , H1N1 ( combining seasonal and pandemic H1N1 ) , H3N2 , influenza B , and the time series combining all influenza strains . Each forecast system setting was used to simulate and forecast the H1N1 , H3N2 , B , or all strains combined ILI+ time series; each start-to-end forecast run was repeated 100 times to account for random effects from system initialization . The forecast systems using the two filters were then evaluated based on ( 1 ) accuracy predicting the phase or gross activity ( i . e . , epidemic or dormant period ) and ( 2 ) accuracy predicting specific metrics , namely the onset , peak timing , peak magnitude , and duration of individual epidemic . We defined the onset as the first of three consecutive weeks with ILI+ records exceeding a prescribed baseline . The ILI+ baselines were chosen as the 40% quantile of the non-zero ILI+ records for each influenza strain , or the first quartile of the non-zero ILI+ records for all influenza strains combined . Inclusion of only non-zero ILI+ records in this calculation focuses the definition of onset on periods when a strain is circulating; the 40% quantile or 25% quantile of the remaining non-zero records define epidemic periods . Results using alternate baselines , e . g . the 33% quantile , produced similar results; however , some epidemics were not well delineated using this lower threshold ( e . g . , two adjacent epidemics could be classified as one epidemic ) . We defined the ending of an epidemic as the first of two consecutive weeks with ILI+ below the baseline following an onset . The period between an onset and its respective ending was defined as an epidemic; however , only those events with an ILI+ record three times or more above baseline were considered , i . e . intermittent small spikes were excluded . Time periods other than epidemics were defined as dormant periods . The first aforementioned evaluation ( i . e . predicting the gross phase ) was intended to test whether the forecast system can accurately predict upcoming epidemic events while not predicting spurious epidemics during a dormant phase . The forecast was assessed against the entire duration of an event ( i . e . , a dormant period or epidemic period as defined above ) . A phase prediction was deemed accurate if the predicted epidemic trajectory included an epidemic during the predicted period; similarly , it was deemed accurate if there was no predicted epidemic during a dormant period . For example , for a forecast made at Week 10 , within a dormant period lasting from Week 5 to Week 30 , a phase prediction is deemed accurate if the forecast time series over Weeks 5–30 does not include two consecutive weeks exceeding the baseline , but inaccurate if otherwise . The second evaluation was used to assess whether a forecast system can accurately predict the timing of onset , the peak timing ( i . e . , the week with the maximum ILI+ ) , the peak magnitude ( i . e . , the maximum ILI+ ) , and the epidemic duration . All these specific metrics are of potential interest to public health officials for planning influenza-related intervention measures . The accuracy of these metric forecasts was evaluated by comparing the target and the predicted metric . If the week of forecast initiation was within a dormant period and an epidemic was predicted during the following 40 weeks , the predicted epidemic would be evaluated against the metrics of the next observed epidemic ( i . e . , timing of onset , peak , ending , and peak magnitude ) ; if the week of forecast initiation was within an epidemic period , and a second epidemic was predicted during the 40-week forecast , the predicted epidemic would be evaluated against the current epidemic episode . Two evaluation standards were adopted . For the stricter standard , predictions of the week of onset , peak timing , or duration were deemed accurate if they exactly matched observations , and predicted peak magnitude was deemed accurate if it fell within ±20% of the observed ILI+ value . For the looser standard , predictions of epidemic onset or peak within ±1 week of observation , duration within ±2 weeks of observation , and peak magnitude within ±50% of the observation were deemed accurate . To test whether the filter methods outperform a naïve method , we also performed a simple analog forecast predicting the peak timing for the four ILI+ time series . The times from the onset to the peak for each epidemic over the entire study period were compiled from each time series; these historical records formed a database of time-to-peak for each strain or all strains combined ( S1 Fig ) . For each time series , a weekly forecast was generated when ILI+ of the week exceeded the baseline ( same as defined above ) , by randomly drawing a time-to-peak record from the corresponding database . A prediction was deemed accurate if the predicted peak was within ±1 week of observation . One hundred random forecasts were sampled for each week of each time series . Forecast accuracy was tallied over all samples and compared to the filter methods .
With the prescribed baselines and definitions of onset and ending , we identified 14 epidemics of H1N1 ( including 10 epidemics of seasonal H1N1 during 1998–2009 and 4 epidemics of the pandemic H1N1 since 2009 ) , 16 epidemics of H3N2 and 14 epidemics of influenza B during 1998–2013 . For the combined strain time series , for which multiple concurrent epidemics of co-circulating strains could overlap and be counted as one single epidemic , there were 19 influenza epidemics during 1998–2013 . Fig 1 shows these time series along with the onset and ending of each epidemic . The combined system of the SIR model and either of the two filter methods comprises a state-space , or hidden Markov , model that allows estimation of unobserved , or latent , state variables ( e . g . , population susceptibility S ) [28] . Both filters adjust the SIR model variables ( e . g . , numbers of susceptible and infected people ) and parameters ( e . g . , the basic reproductive number R0 and infectious period D ) using observations through a recursive filtering process . For instance , at the beginning of an epidemic , a filter may adjust the susceptibility upwards in light of an increase in incidence . By doing so , it is able to accommodate the dynamics of the system , e . g . , increased population susceptibility when a new strain begins to circulate , despite the fact that the SIR model does not include susceptible replenishment . In effect , the filters partially compensate for model misspecification . Both model-filter methods were able to faithfully recreate each of the ILI+ time series ( S3 Fig ) . In addition , estimates of the model variables and parameters ( S4 , S5 and S6 Figs ) , as recursively updated at each filtering cycle over the course of a simulation , were used to initiate the weekly forecasts of future influenza incidence . The forecast system predictions of gross activity were first evaluated for sensitivity and specificity . This assessment tests whether a particular forecast system could accurately predict upcoming epidemics in time while not predicting spurious epidemics during a dormant phase . Fig 2 shows the sensitivity ( i . e . , true positive rate ) , specificity ( i . e . , true negativity rate ) , precision ( i . e . , positive predictive value ) , and negative predictive value for the two forecast systems . Both forecast systems can accurately detect/predict an ongoing epidemic ( sensitivity>~80% ) and do not falsely predict epidemics during dormant periods ( specificity >~90% ) . Tallied over all weekly forecasts , the PF had slightly higher sensitivity ( 90% vs . 88% ) and specificity ( 95% vs . 94% ) than the EAKF . For both filters , the sensitivity and specificity vary by strain; forecasts for H1N1 and Type B in general had lower sensitivity and specificity ( e . g . , for the EAKF , sensitivity of 83% for H1N1 and 81% for influenza B vs . 93% for H3N2 ) . Supplemental S1–S4 Movies present the forecasts for each of the three strains and all strains combined epidemics at each week . Fig 3 presents prediction accuracy for epidemic onset , duration , peak timing , and peak ILI+ magnitude for both forecast systems . These are tallied for all forecasts—individual strain and all strains—a total of 332 , 400 weekly forecasts ( i . e . , 831 weekly forecasts for each strain × 4 strains × 100 runs ) . Here we focus our analysis on predicted lead weeks ranging from -10 to 10 weeks; a positive lead ( e . g . 2 wk ) indicates the event ( e . g . , the epidemic peak or onset ) is predicted to occur 2 weeks in the future from the time of forecast initiation; a 0 wk lead indicates the event is predicted to occur at the time of forecast initiation; and a negative lead , say -3 wk , indicates the event is predicted to have occurred 3 weeks prior to the forecast initiation . Forecasts with negative lead times may appear counterintuitive; however , accurate prediction that an event has passed is an important capability of a forecast system . In regions experiencing year-round influenza transmission , such as Hong Kong , multimodal epidemics , i . e . epidemics with multiple crests , are common ( Fig 1 ) . A forecast initiated after a smaller crest but preceding the overall peak may mistakenly identify that smaller crest as the peak and predict that the peak has passed , i . e . an inaccurate forecast with negative lead . Conversely , an accurate forecast with negative lead indicates that no spurious future increase in incidence is predicted . Therefore , forecast accuracy at negative leads also represents the ability of the forecast system to predict future epidemic trajectories . Forecast accuracy differs by filter , timing of forecast initiation , and the metric as well as the time series being forecast . Tallied over all forecasts , the PF in general produces more accurate predictions of peak timing ( within ±1 wk of observation ) , peak magnitude ( within ±20% of observation ) , and epidemic duration ( within ±2 wk of observation ) , while the EAKF is more accurate predicting onset timing ( within ±1 wk of observation , Fig 3 ) . However , neither filter was able to predict outbreak onset or duration in advance of these events . Given the great irregularity in epidemic timing in Hong Kong , this outcome is not surprising . Some epidemics last for over a year in Hong Kong ( Fig 1 and S1 Fig ) ; in such instances , even 10 weeks after the outbreak peak , the conclusion of the epidemic remains difficult to predict accurately . Both filters were able to more accurately predict peak timing and peak magnitude by individual strain than for the aggregate time series combining all circulating strains ( Fig 4 ) . This finding suggests that strain specific observations may provide cleaner signals that enable more accurate forecast using the single strain SIR model . Overall , the SIR-PF predictions of peak timing and peak magnitude outperformed those of the SIR-EAKF forecast system ( Fig 3 ) . The performance of the SIR-PF forecast system is promising . Summarized over all weekly forecasts: ( 1 ) for the peak timing predictions ( within ±1 wk of observation ) , accuracy was 37% for peaks 1–3 wk in the future , 51% for a current peak ( 0 wk lead ) , and increased to 73% for peaks 1–2 wk in the past ( 1–2 wk lag ) ; ( 2 ) for the peak magnitude predictions ( within ±20% of the observed peak ILI+ ) , accuracy was 37% for peaks 1–3 wk in the future , 50% for a current peak , and increased to 78% for peaks 1–2 wk in the past ( 1–2 wk lag ) . We also compared with the ILI+ forecasts generated for New York City , a temperate city with population size and density comparable to Hong Kong ( 8 . 4 million vs 7 . 2 million; 10 , 725 people/km2 vs 6 , 544 people/km2 ) , to those of Hong Kong . Over the 2003–2013 period and using epidemic curves aggregated for all circulating strains [20] , peak prediction accuracy for Hong Kong is lower ( Fig 5A ) . This is likely due to the more complex influenza transmission dynamics in Hong Kong , e . g . , longer outbreak duration and multiple peaks in a year ( S1 Fig ) . Indeed , this gap disappeared when forecast accuracy was evaluated by timing relative to the observed peak , as opposed to the predicted lead week ( Fig 5B ) . For those forecasts initiated 3 weeks prior to the observed local peak or thereafter , accuracies for Hong Kong were comparable to or higher than those for New York City ( Fig 5B ) . Moreover , when compared with a simple analog method , both filter methods clearly were more accurate ( Fig 5 ) . Both filters used here adopt an ensemble approach ( see Materials and Methods ) . The ensemble , i . e . , the collection of model replicas , provides an estimated distribution for each model variable and parameter , as well as forecast epidemic trajectories . Previous forecast studies for the U . S . indicate that forecast accuracy increases when the variation within the forecast ensemble decreases [1 , 13 , 19] . This relationship can be used to calibrate forecast certainty and thus segregate more and less accurate forecasts in real time . That is , an expected accuracy for a real-time forecast , similar to the chance of precipitation in a weather forecast , can be derived based on this relationship . In real-time operation , the accuracy of a forecast cannot be verified until the epidemic has concluded; therefore , the expected accuracy , if reliable , provides forecast users , such as public health officials , much richer information . Here we determined whether such a relationship holds for the Hong Kong forecasts . As in our previous study [13] , we found that the ensemble spread can be represented by the percentage of ensemble members predicting the mode ( PEMPM ) . As defined previously [13] , PEMPM is the percentage of the most frequently predicted outcome ( i . e . the mode ) among all predicted outcomes . The PEMPM increases when the agreement among ensemble members increases; it thus provides a measure of the variation within a forecast ensemble . Fig 6 shows this relationship for forecasts of peak timing using the SIR-EAKF and SIR-PF . Forecast accuracy does tend to increase as the PEMPM increases ( i . e . , the ensemble members are more in agreement ) , particularly for forecasts with a predicted peak 1–3 weeks in the future ( i . e . , 1–3 wk lead ) at the forecast initialization or in the past ( i . e . , 0–2 wk lag , 3–5 wk lag , or 6–9 wk lag ) . This relationship is more robust for H3N2 , H1N1 , or all strains combined than for influenza B for forecasts with positive leads ( Fig 6A , 6B and 6D vs . 6C , 1st and 2nd rows , solid lines ) . For the more virulent and dominant H3N2 strain , the forecast accuracy for peak timing at 1–3 wk lead increased steadily up to 93% as the PEMPM increased to 80–90% ( Fig 6B , 1st row ) . In addition , a similar relationship appears between the accuracy of the predicted peak magnitude and the PEMPM of predicted peak timing ( Fig 6 , dashed lines ) . Further , this relationship for peak magnitude forecast accuracy was also clear for influenza B ( Fig 6C , dashed lines ) . For H3N2 , the forecast accuracy for peak magnitude at 1–3 wk lead increased up to 89% as the PEMPM increased to 80–90% ( Fig 6B , 1st row ) . These relationships indicate that the forecast systems are able to accurately predict both peak timing and peak magnitude at least 3 weeks in advance .
In previous work , we developed forecast systems for influenza , which have demonstrated predictive skill when applied to U . S . cities [1 , 13 , 19] . These studies suggest operational forecasts can be achieved and have motivated the generation of real-time influenza forecasts [4] . However , unlike the regular seasonal epidemics in temperate regions such as the U . S . , epidemics in subtropical and tropical regions are highly irregular [29–32] . The Hong Kong influenza incidence time series , for instance , create challenges not seen in seasonal epidemics . Previous studies have developed systems capable of detecting aberrations of flu activity , e . g . , the onset of flu season , in subtropical cities including Hong Kong and Shenzhen [8 , 12 , 32] . Forecasts of other milestones of influenza epidemics ( e . g . , peak timing ) or intensity , however , have not been performed in subtropical and tropical regions , except for the 2009 pandemic [33] . Here we built and tested forecast systems designed to handle the irregular influenza epidemics of the subtropics and tropics . We applied these systems to forecast influenza epidemics in Hong Kong from January 1998 through December 2013 , including the 2009 pandemic . Our findings suggest that to forecast such complex epidemics , the system needs to be sensitive all year round ( Fig 1E , multiple epidemics within a year due to different circulating strains/subtypes ) yet not generate false alarms for individual strains that are not circulating during some years ( e . g . , Fig 1A , H1N1 during 2002–2004 ) . Although both the EAKF and PF have proven capable of forecasting influenza epidemics in U . S . cities , additional methods are needed to generate the forecasts for Hong Kong . Specifically , the space reprobing ( SR ) technique [20] is critical for the SIR-PF system , while adaptive covariance inflation [25 , 26] and re-initialization are critical for the SIR-EAKF system . Using these new algorithms , we are able to forecast non-seasonal epidemics with accuracy near that of U . S . forecasts , despite the more varied epidemic dynamics of Hong Kong . In addition to forecast of aggregate incidence time series for all circulating strains , we have also generated forecasts for individual strains , i . e . , H1N1 ( including both seasonal H1N1 and the 2009 pandemic H1N1 ) , H3N2 , and Type B . Forecasts for the individual strains were in general more accurate than those generated for the aggregate epidemics ( Fig 4 ) . This finding suggests that strain specific surveillance data indeed provide cleaner signals that enable more accurate forecast . Strain-specific operational real-time forecasts are currently being generated for the U . S . [4] . In our previous work , we focused on forecasting peak timing , i . e . , the week with maximum influenza incidence [1 , 13 , 19 , 20] . Here we have expanded the forecast effort to include peak magnitude , onset , and duration . Neither forecast system was able to predict the onset or duration well in advance; however , accuracy predicting outbreak peak magnitude was comparable to that for peak timing . For instance , the SIR-PF system was able to forecast the peak magnitude within 20% of observation with an average accuracy of 37% at 1–3 wk lead and 50% at 0 lead . Further , forecast accuracy increased steadily as ensemble spread decreased: up to 45% for H1N1 , 89% for H3N2 , and 68% for Type B at 1–3 wk leads ( Fig 6 , 1st row ) . This finding suggests that the forecasts provide lead times adequate for the planning of intervention measures . In addition , the forecasts of peak magnitude can be used to inform the scale of response . For instance , the amounts of antivirals and vaccines needed could be assessed based on the predicted peak magnitude . For this study we opted to use a simple SIR model . This model is a gross simplification of actual transmission dynamics in a population . When used in conjunction with the filter , however , the filtering process , through recursive optimization , partially compensates for model misspecification . As our understanding of influenza transmission dynamics in subtropical and tropical regions improves in the future , more mechanistic and detailed models could be used in conjunction with the filters . For instance , epidemic models that account for the cross-immunity due to prior infections from related strains , age-structured models that account for varying transmission dynamics among age groups , or network models that account for spatial connectivity among sub-regions , could be applied . These more complicated model-filter systems could further improve the forecast performance . Future study will also investigate methods to improve forecast accuracy for onset timing and epidemic duration , both of which are important for public health planning . In conclusion , we have developed the first prediction systems able to forecast the course of both inter-pandemic and pandemic influenza epidemics in a subtropical locale . These systems can be applied to currently circulating influenza A subtypes and influenza B , as well as aggregate epidemics due to any combination of these influenza strains . The forecast systems are able to predict the peak timing and peak magnitude at least 3 weeks before the predicted peak , with increased accuracy as the ensemble spread decreases . | Influenza causes high levels of morbidity , mortality , and economic burden . Accurate forecasts of epidemic timing and magnitude would provide public health sectors valuable advance information in support of the planning and deployment of intervention measures . Such forecast systems have been developed for temperate regions with seasonal winter epidemics ( e . g . , U . S . cities ) . In subtropical and tropical regions , however , influenza epidemics can occur throughout the year with varying epidemic intensity; this irregularity makes the generation of accurate forecasts more challenging . For this study we develop alternative forecast systems that are more adept at handling erratic non-seasonal epidemics , using state-of-the-art Bayesian inference methods in conjunction with an epidemiological model . Here we present these forecast systems and apply them to Hong Kong . During 1998–2013 , Hong Kong saw 44 influenza epidemics caused by either the A ( H1N1 ) , A ( H3N2 ) , or B strain , and 19 aggregate epidemics caused by one or more of these influenza strains . The forecast systems are able to forecast both the peak timing and peak magnitude of these epidemics , including the 2009 pandemic . The results suggest that routine forecast of influenza epidemics in other subtropical and tropical regions is possible , as well as forecast of other infectious diseases sharing similar irregular transmission dynamics . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Forecasting Influenza Epidemics in Hong Kong |
The functional structure of all biologically active molecules is dependent on intra- and inter-molecular interactions . This is especially evident for RNA molecules whose functionality , maturation , and regulation require formation of correct secondary structure through encoded base-pairing interactions . Unfortunately , intra- and inter-molecular base-pairing information is lacking for most RNAs . Here , we marry classical nuclease-based structure mapping techniques with high-throughput sequencing technology to interrogate all base-paired RNA in Arabidopsis thaliana and identify ∼200 new small ( sm ) RNA–producing substrates of RNA–DEPENDENT RNA POLYMERASE6 . Our comprehensive analysis of paired RNAs reveals conserved functionality within introns and both 5′ and 3′ untranslated regions ( UTRs ) of mRNAs , as well as a novel population of functional RNAs , many of which are the precursors of smRNAs . Finally , we identify intra-molecular base-pairing interactions to produce a genome-wide collection of RNA secondary structure models . Although our methodology reveals the pairing status of RNA molecules in the absence of cellular proteins , previous studies have demonstrated that structural information obtained for RNAs in solution accurately reflects their structure in ribonucleoprotein complexes . Furthermore , our identification of RNA–DEPENDENT RNA POLYMERASE6 substrates and conserved functional RNA domains within introns and both 5′ and 3′ untranslated regions ( UTRs ) of mRNAs using this approach strongly suggests that RNA molecules are correctly folded into their secondary structure in solution . Overall , our findings highlight the importance of base-paired RNAs in eukaryotes and present an approach that should be widely applicable for the analysis of this key structural feature of RNA .
Recent discoveries reveal that RNAs perform a variety of tasks—ranging from the regulation of gene expression ( e . g . small RNAs ( smRNAs ) , and riboswitches ) to catalytic activities ( e . g . group I self-splicing introns ) —and indicate that this functionality is intimately linked to their three-dimensional structure [1]–[5] . Correct secondary structure is also central to the proper regulation and maturation of RNA molecules [2] , [3] , [6] , [7] . RNAs fold into their three-dimensional structures through specific base-pairing interactions ( double-stranded RNA ( dsRNA ) ) that are encoded within their sequence [2] , [3] , [6] , [7] . These interactions can either be within ( intra-molecular ) or between ( inter-molecular ( heteroduplex ) ) RNA molecules . Although it is clear that secondary structure is abundantly important for the functionality and regulation of RNAs , comprehensive base-pairing interaction data are completely lacking for the majority of these molecules [3] . The recent discovery that RNA silencing pathways play a significant role in gene regulation has brought attention to a vast evolutionarily conserved post-transcriptional regulatory network dependent on self and foreign base-paired RNAs ( dsRNAs ) [8]–[10] . In RNA silencing , production of heteroduplex dsRNA or self-complementary fold-back structures gives rise to smRNAs through the activity of DICER or DICER-LIKE ( DCL ) RNase III-type ribonucleases [9]–[12] . In eukaryotes , smRNAs consist of microRNAs ( miRNAs ) and several classes of endogenous small interfering RNAs ( siRNAs ) , which are differentiated from one another by their distinct biogenesis pathways and the classes of genomic loci from which they arise [8] . These smRNAs are the sequence-specific effectors of RNA silencing , and direct the negative regulation or control of genes , repetitive sequences , viruses , and mobile elements through inter-molecular base-pairing interactions [13] , [14] . Overall , base-paired RNAs are at the core of both the biogenesis and function of all eukaryotic small silencing RNAs , emphasizing the importance of base-paired RNA in regulating gene expression . In plants and several other organisms , there are numerous classes of endogenous and exogenous siRNAs that are processed from long dsRNA molecules synthesized by an RNA-dependent RNA polymerase ( RDR ) [8]–[10] , [15] . The first RDR to be functionally identified as an RNA silencing pathway component in Arabidopsis thaliana , was RDR6 [16] , [17] . RDR6 was initially uncovered due to its ability to utilize aberrant RNAs produced by transgenes as substrates for dsRNA synthesis [16]–[18] . These dsRNA molecules are subsequently converted by DCL4 into siRNAs that silence the transgenes [19]–[23] . More recently , RDR6 has been demonstrated to function in the biogenesis of endogenous smRNA populations [8] , [20] , [24]–[26] . One example is trans-acting siRNAs ( tasiRNAs ) , which are processed from regions of non-coding RNAs known as TRANS-ACTING siRNA ( TAS ) transcripts [20] , [25]–[27] . Biogenesis of tasiRNAs is initiated by siRNA or miRNA-mediated cleavage of the TAS transcript [20] , [25]–[27] . The cleaved TAS transcript is then converted by RDR6 to dsRNA [20] , [25]–[27] , which is subsequently cleaved by DCL4 into phased 21 nucleotide ( nt ) siRNAs [20]–[23] , [28] . Here , we describe a novel , genome-wide , high-throughput sequencing-based method , which we term dsRNA-seq , that can specifically interrogate base-paired ( dsRNA ) RNA molecules , and use this approach to identify and characterize ∼200 novel , smRNA-producing substrates of the dsRNA-synthesizing enzyme RDR6 . Additionally , we find that mRNAs encoding proteins with functions in nucleic acid-based processes have a tendency to be highly structured . Making use of a seven-way comparative genomic approach , we demonstrate that the dsRNA-seq methodology can identify functionally conserved portions of UTRs ( 3′ and 5′ ) , introns , transposable elements , as well as novel , structured RNA molecules throughout the Arabidopsis genome . Finally , we exploit the ability of dsRNA-seq to capture intra-molecular base-pairing interactions to produce mRNA secondary structural models on a genome-wide scale .
To obtain a transcriptome-wide view of base-paired RNA ( dsRNA ) in unopened flower buds of Arabidopsis thaliana Col-0 ecotype ( hereafter referred to as wild-type Col-0 ) , we married classical nuclease-based structure mapping techniques [29] , [30] with high-throughput sequencing technology ( see Figure S1A , and Materials and Methods for details ) . We characterized the dsRNA component of the Arabidopsis transcriptome after one round of ribosomal RNA ( rRNA ) -depletion , and obtained 15 , 499 , 789 raw reads representing 4 , 802 , 974 non-redundant ( NR ) sequences with an average clone-abundance of 3 . 2 ( Accession #: GSE23439 ) . ( The size distributions for this dataset can be seen in Figure S3A . ) As expected , we found that the majority of our dsRNA sequencing reads corresponded to highly structured classes of RNA molecules ( e . g . , rRNA , tRNA , snoRNA , snRNA , etc . ) , smRNA-producing loci ( e . g . , miRNAs ) , and repetitive elements ( e . g . , transposons ) ( Figure 1A ) . We also found a large proportion of dsRNAs that correspond to protein-coding transcripts , which likely represent the self-complementary , base-pairing regions that form the secondary structure of mRNA molecules ( Figure 1A ) . It is noteworthy that dsRNA-seq data mapped to all portions of protein-coding mRNAs , including introns , exons , and both ( 3′ and 5′ ) UTRs . Therefore , the dsRNA-seq methodology can identify base-paired regions within both mature and preprocessed mRNA molecules . ( For this reason , we refer to protein-coding mRNAs within this manuscript as pre-mRNA . ) Overall , our dsRNA-seq approach is robustly biased towards classes of RNA molecules that are highly base-paired in nature , which strongly suggests that this approach is interrogating the desired component of the transcriptome with a stringently estimated false discovery rate ( FDR ) of ≤0 . 067 ( see Text S1 ) . The strand-specific nature of dsRNA-seq affords the opportunity to distinguish between intra-molecular fold-back dsRNAs ( 16 . 6% of total identified dsRNAs; example tRNA in Figure 1C ) and inter-molecular heteroduplex molecules ( 83 . 4% of total identified dsRNAs; example in Figure 1D ) . To determine the strand bias for the different classes of RNAs captured by dsRNA-seq , we interrogated the ratio of sense versus anti-sense sequence reads . As indicated by the Log-odds ( Lods ) values of sense to antisense reads , the majority of RNA classes were strongly enriched for sense-strand reads , especially for the non-coding RNA classes ( rRNA , tRNA , snoRNA , etc . ) ( Figure 1B ) . Specifically , functional RNAs ( tRNA , miRNA , snoRNA , snRNA , and rRNA ) were between 100–1000 fold enriched for the sense compared to the antisense-strand ( Figure 1B ) . Conversely , we identified a strong anti-sense bias in our dsRNA-seq data for transposable element-derived sequences ( Figure 1B ) . This may reflect an amplification of the antisense transposon sequence by an RDR to initiate production of siRNAs and subsequent RNA silencing of these mobile elements . For protein coding regions ( exons ) and 5′ UTRs of mRNAs , there was a significant sense-strand bias ( ∼16-fold ) , which was diluted for introns or 3′ UTRs of these RNA molecules . We suspect that the existence of many overlapping genes and non-coding RNAs ( tRNAs , snRNAs , and snoRNAs ) on the strand opposite to introns or 3′ UTRs is the confounding factor . This hypothesis is consistent with the stronger sense-strand bias in coding regions of mRNAs ( Figure 1B ) , which have an extremely low probability of overlapping with expressed elements on the opposite strand . Additionally , there are numerous instances of 3′ end overlapping transcripts , as well as snRNA , snoRNA , and tRNA loci encoded within the introns and UTRs of protein coding mRNAs throughout the Arabidopsis genome . Taken together , these results suggest that by using dsRNA-seq we have identified the majority of base-paired RNA molecules ( Figure S1B and S1C ) , which encompass a surprisingly large portion of the Arabidopsis genome ( ∼14 . 4% ( 17 . 3 Mb ) ) . As described above , dsRNA-seq captured both intra- and inter-molecular base-pairing interactions ( Figure 1B–1D ) . In fact , we found that regions of tRNAs predicted to form intra-molecularly base-paired stems corresponded to higher levels of dsRNA-seq reads than the unpaired anti-codon loop and the amino acid acceptor stem as expected ( Figure 1C ) . Furthermore , we observed dsRNAs that corresponded to both the Watson and Crick strands of the genome for a known substrate of the intermolecular dsRNA-synthesizing RDR6 ( Figure 1D ) . Taken together , these results suggest that dsRNA-seq can be used to differentiate intra- from inter-molecular base-pairing interactions . An ideal test to both validate and determine the utility of dsRNA-seq is to identify all known and novel substrates of Arabidopsis RDR6 . Accordingly , we sequenced the full complement of base-paired RNA ( using dsRNA-seq ) and smRNA ( using smRNA-seq ) molecules from unopened flower buds of wild-type Col-0 and rdr6-11 mutant ( referred to hereafter as rdr6 ) plants . For wild-type Col-0 , we obtained the dsRNA-seq data described above , as well as 17 , 340 , 638 raw sequence reads representing 8 , 575 , 097 non-redundant smRNA sequences ( the size distributions for this smRNA dataset can be seen in Figure S3B ) . Additionally , we generated a total of 18 , 345 , 980 and 18 , 850 , 891 raw sequence reads representing 9 , 725 , 315 and 9 , 860 , 471 non-redundant dsRNA and smRNA sequences for rdr6 mutant plants , respectively ( the size distributions for these rdr6 datasets can be seen in Figure S3C and S3D , respectively ) . To identify potential RDR6 substrates , we used a sliding-window analysis to select 1 kilobase ( kb ) regions of the genome that produced ≥2-fold more dsRNA in wild-type Col-0 than in rdr6 mutant plants with a p-value <0 . 001 ( see Text S1 ) . Using this approach , we identified 7 , 144 regions where dsRNAs are significantly depleted in rdr6 mutant compared to wild-type Col-0 plants ( Figure 2A , positive Lods-ratio values ) . Within these molecules , we identified 7 of 8 previously characterized TAS transcripts ( Figure 2A , Figure S2A and S2B , blue diamonds ) , while the eighth was represented by a single read in both ( Col-0 and rdr6 ) dsRNA-seq libraries . Additionally , we found that the majority of RDR6-dependent dsRNAs are transposable elements ( mostly MuDRs and Helitrons ) , mRNAs , intergenic RNAs ( mostly centromeric tandem repeats ) , or tRNAs ( Figure 2A and 2B ( green bars ) , and Figure S2B ) . Taken together , these results suggest that RDR6 utilizes specific classes of repetitive elements , numerous categories of functional RNAs ( e . g . tRNAs , snRNAs , snoRNAs , etc . ) , mRNAs , and intergenic transcripts as templates for dsRNA synthesis . Our sliding window approach also identified 7 , 584 dsRNAs that are significantly stabilized in rdr6 mutant compared to wild-type Col-0 plants ( Figure 2A , negative Lods-ratio values ) . The vast majority ( >80% ) of the molecules stabilized in rdr6 mutant plants are TEs ( Figure 2B , yellow bars ) , most of which ( ∼95% ) are pericentrometric Gypsy-like transposons ( Figure 2A and 2B ( yellow bars ) , and S2B ) . We also found a number of these dsRNAs correspond to mRNAs ( ∼15% ) and intergenic transcripts ( ∼4% ) ( Figure 2B , yellow bars ) . Overall , the identification of dsRNA molecules that are stabilized in rdr6 mutant plants suggests a potential model where RDR6 antagonizes the action of other RDRs at some targets , especially at Gypsy-like transposons . The consequence of dsRNA synthesis by RDR6 is often the subsequent formation of siRNAs [19] . Therefore , to identify those RDR6 dsRNA substrates that produce smRNAs , we identified regions that produce ≥2-fold more smRNAs in wild-type Col-0 than in rdr6 mutant plants . These sources of smRNA were then compared with the regions of the genome that produce more dsRNA in wild-type Col-0 than in rdr6 mutant plants , which identified 218 regions that met both criteria ( Figure 2C and Figure 3A–3D; Table S1 ) . These common regions include ∼50% ( 27 total ) of the previously identified smRNA-producing RDR6 substrates , the majority of which were not known to be expressed in Arabidopsis unopened flower buds ( Figure 2C and Figure S2C; Tables S1 and S2 ) [31]–[34] . The other 6 , 926 regions where dsRNAs , but not smRNAs , are significantly depleted in rdr6 mutant compared to wild-type Col-0 plants consist of mostly MuDR and Helitron transposable elements . These results suggest that the double-stranded MuDRs and Helitrons produced by RDR6 may only constitute an insignificant subset of the smRNA-producing population of these transposons . Conversely , RDR6 synthesized MuDR and Helitron dsRNAs may simply not be processed into smRNAs . Our analysis also revealed that the majority of highly confident smRNA-producing RDR6 substrates are mRNAs with a variety of biological functions ( Figure 2D and 2E ) and , surprisingly , tRNAs ( Figure 2D ) . As expected , the identified RDR6 substrates tend to produce 21 nt smRNAs ( Figure 2F ) . It is noteworthy that RDR6-targeted mRNAs mostly encode proteins that function in nucleic acid-based biological functions ( e . g . translation , RNA processing , etc . ) and regulation of gene expression ( Figure 2E ) . Taken together , these results suggest that an RDR6-dependent RNA silencing pathway regulates multiple stages of gene expression through siRNA production in Arabidopsis . The identification of tRNAs as RDR6 substrates is intriguing because it was recently suggested that the mammalian telomerase reverse transcriptase catalytic subunit ( Tert ) functions as a smRNA-producing RDR that can also use tRNAs as substrates [15] . Taken together , these results suggest that plant RDR6 and animal Tert are functional orthologs that can use tRNAs as substrates for production of dsRNA precursors of smRNAs . Therefore , studies of RDR6 may be informative for gaining insight into the function of mammalian RDRs , and vice versa . In order to validate and expand our characterization of new smRNA-producing RDR6 substrates , we turned to a quantitative reverse transcription polymerase chain reaction ( qRT-PCR ) approach . For these loci , RDR6 is required to produce a dsRNA precursor of siRNAs ( see Figure 3A–3D ) . Therefore , if RDR6 is not active ( rdr6 mutant plants ) , then the single-stranded transcripts may be stabilized . To test this hypothesis , we designed qRT-PCR primers to 14 ( four known , 10 novel ) identified smRNA-producing RDR6 substrates . We found that all fourteen tested loci , including the 10 newly identified RDR6 substrates ( e . g . At1g20370 ( Figure 3B ) , the intergenic region just upstream of At2g41490 ( Figure 3C ) , and At3g19890 ( Figure 3D ) ) , had higher transcript levels in rdr6 mutant compared to wild-type Col-0 plants ( Figure 3E ) . These results suggest that most , if not all of the 218 loci we identified using a combination of dsRNA-seq and smRNA-seq methodologies are true smRNA-producing RDR6 substrates; approximately 200 of these loci are novel ( Tables S1 and S2 ) . Most previously identified endogenous RDR6 substrates produce phased 21 nt siRNAs [20]–[23] , [28] . We found that 51 of the RDR6 substrates identified in this study also produce phased smRNAs ( Table S2 and Figure S2D ) . This group includes 22 of the RDR6 substrates that have been previously reported [31]–[34] , as well as the newly identified substrates , At1g20370 ( Figure 3B and 3E ) , the intergenic region just upstream of At2g41490 ( Figure 3C and 3E ) , and At5g02370 ( Figure 3E; Tables S1 and S2 ) . However , we found that >75% of all endogenous smRNA-producing RDR6 substrates ( 167 ) do not produce siRNAs with any recognizable phasing , including the newly identified At3g19890 ( Figure 3D and 3E; Tables S1 and S2 ) . These results suggest that there are multiple mechanisms by which transcripts become susceptible to RDR6-mediated silencing . In summary , our results suggest that the combination of dsRNA-seq and smRNA-seq is a highly sensitive method for identifying transcripts subject to RDR6-dependent silencing , and is likely to be useful for characterizing the substrates of other eukaryotic RDRs - such as mammalian Tert [15] - that have not been demonstrated to produce phased siRNAs . We next identified regions of the Arabidopsis genome that are significantly enriched for base-paired RNA using the dsRNA-seq data for wild-type Col-0 . For this purpose , we used a geometric distribution-based approach to identify unusually long dsRNA molecules ( dsRNA ‘hotspots’ ) based on the average size of dsRNAs computed for each chromosome independently . This analysis revealed 9 , 719 dsRNA ‘hotspots’ of varying lengths scattered along the entire length of all Arabidopsis chromosomes ( Figure 4A and Figure S4A; Tables S3 and S4 ) . In fact , we have identified the vast majority of highly base-paired RNA molecules in the Arabidopsis transcriptome ( Figure S9 ) . For example , the highly repetitive , transposon-rich pericentromeric regions of the Arabidopsis genome were found to be a rich source of dsRNA ( Figure 4A and 4B , and Figure S4A ) . This is not surprising because cis transcriptional silencing of transposons and repetitive elements in the pericentromeric regions of Arabidopsis chromosomes is mediated by RDR2-dependent siRNAs [35]–[38] . These findings not only substantiate that dsRNA-seq interrogates the desired portion of the transcriptome , but also suggest that , as expected , Arabidopsis transposons and repetitive elements are highly enriched in dsRNA on a genome-wide scale . A classification of Arabidopsis dsRNA ‘hotspots’ revealed that transposons and protein-coding mRNAs are the two most highly base-paired classes of RNA molecules ( Figure 4B ) . In fact , we identified 1949 protein-coding mRNAs that contained dsRNA ‘hotspots’ ( Figure 4B ) , so we interrogated over-represented molecular functions for these genes using Gene Ontology ( GO ) analysis . Ribulose-bisphosphate carboxylase was the most significantly over-represented protein in this analysis . However , the most highly over-represented group of genes were those involved in nucleic acid biology ( e . g . , translation , nucleic acid binding , etc . ) ( Figure 4C ) . Interestingly , genes involved in nucleic acid metabolism are also over-represented in dsRNA ‘hotspot’-containing transcripts of Drosophila melanogaster and Caenorhabditis elegans ( Q . Z . and B . D . G . , unpublished data ) . Thus , a propensity to form complex secondary structure ( self base-pairing ) may be a general feature of eukaryotic transcripts that encode proteins involved in processes involving nucleic acids . This may point to a feedback regulatory mechanism that is dependent on an interaction between the proteins encoded by these transcripts and highly structured RNA intermediates . The biogenesis of all functional small silencing RNAs ( e . g . miRNAs and siRNAs ) requires a dsRNA intermediate . Therefore , we determined the propensity of highly base-paired regions ( dsRNA ‘hotspots’ ) to be processed into smRNAs ( Figure 4D ) using corresponding smRNA-seq data ( Figure 2C; see Figure S8 for smRNA data analysis ) . We found that the highly base-paired regions within 9 of 10 interrogated RNA categories were extremely likely to be processed into smRNAs , the exception being pre-mRNA molecules ( Figure 4D ) . Although these results were expected for transposable elements and miRNAs - which are known to be smRNA biogenesis substrates - it was surprising that functional RNAs ( e . g . rRNA , tRNA , snRNA , etc . ) also have a high likelihood of being processed into smRNAs since intramolecular base-pairing interactions are intrinsic to their function . The evidence that highly base-paired regions of RNA molecules are frequently processed into smRNA , suggests that this process may be important for regulating the abundance of functional RNAs in Arabidopsis cells . Our finding that any highly base-paired molecule can be processed into smRNAs , may provide an explanation for the restriction of the miRNA biogenesis machinery to specific sites within the plant nucleus ( dicing bodies ) [39] , [40] . An intriguing hypothesis is that the sequestration of proteins involved in miRNA biogenesis and their MIRNA substrates to dicing bodies provides specificity to miRNA biogenesis , while protecting other structured RNAs ( e . g . rRNA ) from these proteins . Our findings suggest further studies of smRNA sources in eukaryotes will reveal additional siRNA-mediated regulatory pathways , as demonstrated , for example , by the analysis of tRNA-derived RNA fragments ( tRFs ) in human cells [41] . Regulation and maturation of eukaryotic pre-mRNA molecules is intimately linked to the proper formation of secondary structure [2] , [3] , [6] , [7] , which suggests that base-paired regions of these molecules are likely to be functionally conserved . To test this hypothesis , we employed a seven-way comparative genomics approach that determines an average conservation score ( consScore ) for all bases of dsRNA ‘hotspots’ and all other sequences ( ‘flanking regions’ ) within the four structural moieties ( exons , introns , and both UTRs ) of every mRNA . The consScores for dsRNA ‘hotspots’ and ‘flanking regions’ were then compared to determine if base-pairing mediates evolutionary conservation of mRNAs . Using this approach , we found that dsRNA ‘hotspots’ in exons are significantly less evolutionarily conserved than ‘flanking regions’ ( Figure 5A ) , which suggests that intra- and/or intermolecular base-pairing interactions are disfavored in the protein-coding regions of plant mRNAs . Our comparative genomic analysis of pre-mRNA data also demonstrated that dsRNA ‘hotspots’ are significantly more conserved than ‘flanking regions’ in 3′ UTRs ( p = 0 . 0012 ) and introns ( p = 1 . 73e–58 ) ( Figure 5A ) , and that highly base-paired regions within 5′ UTRs ( p = . 072 ) were more evolutionarily conserved than ‘flanking regions’ , but far less significantly than in 3′ UTRs and introns . This analysis suggests the ability to base-pair is functionally important , and has been selected during plant evolution . Just as selection for protein function maintains exonic sequences , base-pairing interactions may be important for conserving functionally important moieties in non-coding regions of mRNAs . These functions may include 1 ) providing appropriate structure for post-transcriptional and/or translational regulation , 2 ) maintaining mRNA stability , 3 ) providing cis-element sites for RNA binding proteins , and/or 4 ) forming the processed precursors of non-coding RNAs . Similar results have been obtained for Drosophila melanogaster and Caenorhabditis elegans ( Q . Z . and B . D . G . , unpublished data ) , suggesting that the ability to base pair is a critical feature of UTRs and introns in both plants and animals . An mRNA secondary structure prediction methodology ( see below ) was used to obtain a folded model of two highly conserved intronic dsRNAs ( see Figure S5A and S5B for alignments ) , and suggested that these regions are almost entirely base-paired , and fold into unique , stable secondary structures ( Figure 5C and 5D ) . Taken together , our results reveal that dsRNA-seq identifies functionally conserved regions of 5′ and 3′ UTRs and introns transcriptome-wide , and thus provides the critical first step towards understanding how such structural moieties affect the maturation and stability of transcripts in eukaryotic organisms . We also noticed that a number of our dsRNA ‘hotspots’ are located in transposons and portions of the genome that do not contain any known genes . Comparative analysis revealed that dsRNA ‘hotspots’ in intergenic regions ( p = 7 . 3e–5 ) and transposons ( p = 9 . 1e–16 ) are significantly more conserved than their flanking regions ( Figure 5B ) . In the case of transposons , this finding was quite surprising because the majority of these repetitive elements are selectively neutral , especially for ancestral repeats ( ARs ) [42] , [43] . However , our findings demonstrate that the highly antisense-prone transposable element dsRNA ‘hotspots’ ( Figure S4C and S4D ) have been undergoing a significant purifying selection compared to their ‘flanking regions’ , suggesting that these portions of TEs are not selectively neutral , but have important functions in plant cells . An intriguing hypothesis is that a class of smRNAs that are integral to initiate and/or maintain the transcriptional silencing of transposable elements are processed from these conserved highly-base paired regions . Overall , these results reveal functionally conserved portions of transposons , as well as novel , structured RNAs that have not been previously identified . We identified a total of 1602 novel transcripts , ∼60% of which are unannotated transposable elements and/or simple repeats ( Figure 6J; Tables S5 and S6 ) . The other >700 transcripts represent newly identified RNAs . To determine the function of these 1602 transcripts we looked for the presence of these sequences in our flower bud smRNA dataset ( see Figure S8 for smRNA analysis ) . 1437 ( 89 . 7% ) of the novel RNAs overlapped regions of the genome that produce significant quantities of smRNAs ( smRNA ‘hotspots’ , Figure S8 ) ( Figure 6 and Figure S6; Tables S5 and S6 ) . Specifically , >98% of the unannotated transposable elements and/or simple repeats and ∼79% of the entirely novel RNAs produced smRNAs , respectively ( Figure 6J ) . Most smRNAs from these transcripts were 24 nt in length ( Figure 6K and 6L ) . In Arabidopsis , this size class is highly correlated with DNA methylation and heterochromatin formation [44] , suggesting that these loci produce 24 nt smRNAs that direct transcriptional silencing . To validate our sequencing data and further interrogate the newly identified transcription units , we characterized several of these RNAs by reverse transcription ( RT ) polymerase chain reaction ( RT-PCR ) in five different Arabidopsis tissues ( leaf blade , leaf petiole , cauline leaves , stem , and unopened flower buds ) . We selected four loci that do ( see Figure 6A and 6C; Figure S6A , S6C , and S6E; Table S5 ) and seven RNAs that do not ( Figure 6B and 6D; Figure S6B , S6D , S6F , S6G , S6K , S6L , and S6M; Table S5 ) produce statistically significant amounts of smRNAs ( 11 total transcripts ) . As expected , all 11 of these RNAs are expressed in flower buds , the tissue used for the initial analysis of base-paired RNAs . Eight of these transcription units are expressed in all five tissues , and three are expressed only in unopened flower buds ( Figure 6E–6I; Figure S6H , S6I , S6J , S6N , S6O , and S6P ) . Two of these latter transcripts are also the source of smRNAs ( Figure 6A and Figure S6A; Table S5 ) . Overall , our findings reveal a large collection of novel , structured RNAs in Arabidopsis flower buds , many of which have evolutionarily conserved functions in land plants ( Figure 5B , intergenic ) . In principle , dsRNA-seq data should reveal the pairing status of all sequences within expressed mRNA molecules ( Figure 1 ) . If this is true , this approach can be used to generate and/or validate secondary structural predictions on a genome-wide scale . To test this hypothesis , we employed a novel methodology that produces structural models using sequence data obtained with a dsRNA-seq approach . For this analysis , we used sequence data obtained from samples that were processed using two rRNA-depletion steps ( 2X Ribominus approach ( see Text S1; Figure S7 ) ) . We used this dataset because - although incredibly similar to the normal dsRNA-seq approach ( see Text S1 ) - it is enriched for sense-strand mRNA sequences ( Figure 7A and 7B , Figure S4D , and Figure S7 ) , increasing the likelihood of generating useful secondary structure models . This mRNA secondary structure analysis revealed base-pairing differences between the structural models produced by the RNAfold program of the Vienna package ( http://www . tbi . univie . ac . at/~ivo/RNA/ ) with and without dsRNA-seq constraints . Many regions that were predicted not to base-pair , but to form large loops and open regions by non-constrained RNAfold were more highly paired when constrained , and vice versa ( see Figure 7C and 7D , http://tesla . pcbi . upenn . edu/annoj_at9/ ) . To test the ability of our structural modeling approach to predict highly base-paired regions , we characterized significantly paired regions of mRNAs ( as determined by our methodology ) ( Figure 7C and 7D , see yellow regions ) by reverse transcription ( RT ) polymerase chain reaction ( RT-PCR ) after digestion with a single-stranded or double-stranded RNase . We expected that the selected mRNA regions would be sufficiently intact for RT-PCR amplification after treatment with the single-stranded , but not the double-stranded RNase . As predicted , the regions of mRNA molecules determined to be highly base-paired were amplified following treatment with the ssRNase ( Figure 7E ) . Conversely , we could not amplify these same regions after treatment with the dsRNase , which implies that they were completely degraded by this enzyme . These results demonstrated that dsRNA-seq reliably identifies base-paired portions of mRNAs . We also found that the models of secondary structure produced using dsRNA-seq data as constraints are predicted to be stable ( Figure 7C , 7D , and 7F–7H , negative G values ) . In total , these results suggest that the constrained secondary structure models are accurate representations of folded RNAs in solution , providing valuable insight into the pairing status of RNA molecules genome-wide . Finally , we used our mRNA secondary structure prediction methodology to produce folded models for the novel intergenic transcripts identified by the RNA-seq approach ( Figure 6 and Figure S6 ) . These structural models indicated that the new RNAs are highly base-paired , and are folded into a diverse array of stable ( negative G values ) secondary structures ( Figure 7F–7H ) . Further evidence that these models are likely to be correct is provided by the observation that we obtained no dsRNA-reads for regions that are predicted to contain large loops by both dsRNA-seq data , as well as the RNAfold program of the Vienna package ( http://www . tbi . univie . ac . at/~ivo/RNA/ ) . We believe that these transcriptome-wide mRNA secondary structure models and corresponding web-based viewer ( http://tesla . pcbi . upenn . edu/annoj_at9/ ) will be useful tools for elucidating the function of RNA folding in regulating gene expression and protein translation . We describe in this report novel methodologies that produce a comprehensive genomic view of intra- and intermolecular base-paired RNAs at unprecedented resolution . We take advantage of the data from these approaches , which capture intra-molecular base-pairing interactions , to generate models of mRNA secondary structure in solution on a genome-wide scale ( Figure 7 ) . Although our methodology reveals the pairing status of RNA molecules in the absence of cellular proteins , previous studies have demonstrated that structural information obtained for RNAs in solution accurately reflects their structure in ribonucleoprotein complexes [3] , [45] . Furthermore , our identification of conserved functional RNA domains using dsRNA-seq strongly suggests that RNA molecules are correctly folded into their secondary structure in solution ( Figure 5 ) . Overall , our results suggest we have produced highly informative models of mRNA secondary structure on a genome-wide scale for Arabidopsis , which can serve as a model for orthologous RNAs from other eukaryotic organisms . As a resource for the larger community we have made available all sequencing data sets to NCBI Gene Expression OmniBus ( GEO ) , and we have displayed them in a powerful and easy-to-use genome browser , Anno-J ( http://tesla . pcbi . upenn . edu/annoj_at9/ ) . Additionally , we have made the models of mRNA secondary structure freely available to the community through a structure viewer that has been incorporated into the dsRNA-seq Anno-J browser . Overall , the methods we have developed , as well as the highly informative sequencing data sets and models of RNA secondary structure that have resulted from this study will contribute positively to future work aimed at illuminating the numerous functions that RNA secondary structure has in regulating eukaryotic gene expression during developmental processes .
Further details on the plant materials , experimental procedures , high-throughput sequencing , processing , mapping , and analysis of Illumina GA sequence reads are provided in Text S1 . Primers used in this study are listed in Table S7 . Briefly , total RNA is subjected to one ( 1X Ribominus ) or two ( 2X Ribominus ) rounds of rRNA depletion as per manufacturer's instructions ( Ribominus , Invitrogen ( Carlsbad , CA ) ) . Next , these rRNA-depleted RNA samples are treated with a single-strand specific ribonuclease as per manufacturer's instructions ( RNase One , Promega ( Madison , WI ) ) . The RNA sample is then used as the substrate for sequencing library construction using the Small RNA Sample Prep v1 . 5 kit ( Illumina , San Diego , CA ) as per manufacturer's instructions . For more detailed methodology see Text S1 and Figure S1A . smRNA-seq and dsRNA-seq libraries were sequenced using the Illumina Genetic Analyzer II as per manufacturer's instructions ( Illumina Inc . , San Diego , CA ) . Sequence information was extracted from the image files with the Illumina ( San Diego , CA . ) base calling software package ( GAPipeline version 1 . 4 ) . Prior to alignment , sequence reads were reduced to a list of only non-redundant ( NR ) sequences . NR sequences for which a 3′ adapter sequence was observed were truncated up to the junction with the adapter sequence , while sequences without recognizable 3′ adapters were also retained and processed independently . The dsRNA-seq and smRNA-seq reads were then aligned to the Arabidopsis genome ( TAIR9 assembly ) . Finally , NR-sequences with their genomic coordinates were combined to form the final dataset . For more detailed methodology see Text S1 . To identify dsRNA ‘hotspots’ in the Arabidopsis genome , we utilized a geometric distribution-based approach . For more detailed methodology see Text S1 . All protein-coding mRNAs overlapping identified dsRNA ‘hotspots’ were subjected to this analysis . Specifically , the GO enrichment analysis was carried out using the GOEAST web-based “Batch-Genes” tool [46] . The plant seven-way comparative genomics analysis was conducted as previously described . ( http://genomewiki . ucsc . edu/index . php/Whole_genome_alignment_howto ) . For more detailed methodology see Text S1 . We generated two computational structures for each annotated transcript . The unconstrained structure was obtained by folding with RNAfold v1 . 8 . 4 from the Vienna package with default parameters . The constrained structure was obtained with RNAfold using default parameters , but with structural constraints as additional input defined by reads from the dsRNA-seq approach . Specifically , any position covered by at least one mapped dsRNA read was constrained as paired ( ‘|’ in the structural constraint input ) ; all other positions were left unconstrained ( ‘ . ’ in the structural constraint input ) . The Anno-J Genome Browser is a REST-based genome annotation visualization program built using Web 2 . 0 technology . Licensing information and documentation are available at http://www . annoj . org . We have developed a structure browser enhancement for Anno-J that enables visualization of the mRNA secondary structure models produced as described above . To do this , each predicted model was rendered as a SVG plot using Vienna ( http://www . tbi . univie . ac . at/~ivo/RNA/ ) RNAplot . Reads and other features of interest such as UTR regions for mRNAs were then added to the SVG file . Read counts were normalized by the length of covered nucleotides ( e . g . number of nucleotides covered by one or more reads ) . Users can visualize the structural model for an annotated transcript by selecting the corresponding genomic interval on Anno-J ( RNA structures track ) or by entering its accession number . | At the heart of RNA functionality , maturation , and regulation is the formation of intricate secondary structures that are dependent on specific nucleotide base-pairing interactions encoded within their sequences . These interactions can either be within ( intra-molecular ) or between ( inter-molecular ( heteroduplex ) ) RNA molecules . Although it is clear that secondary structure is abundantly important for the functionality and regulation of RNAs , comprehensive base-pairing interaction data are completely lacking for the majority of these molecules . To address this , we have developed a new approach for studying the base-pairing interactions of RNA molecules by marrying classical nuclease-based structure mapping techniques with high-throughput sequencing technology . We have used this approach to identify known and novel substrates of the base-paired RNA producing enzyme RNA–DEPENDENT RNA POLYMERASE6 , reveal conserved functionality within introns and both 5′ and 3′ untranslated regions ( UTRs ) of mRNAs , uncover a novel population of functional RNAs , and produce a genome-wide collection of RNA secondary structure models by identifying the base-pairing interactions within each RNA molecule . Our findings demonstrate that our methodology should be widely applicable for the identification and analysis of base-paired RNAs in all biological organisms . | [
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] | 2010 | Genome-Wide Double-Stranded RNA Sequencing Reveals the Functional Significance of Base-Paired RNAs in Arabidopsis |
We have characterized the biological functions of the chromatin remodeling protein Bptf ( Bromodomain PHD-finger Transcription Factor ) , the largest subunit of NURF ( Nucleosome Remodeling Factor ) in a mammal . Bptf mutants manifest growth defects at the post-implantation stage and are reabsorbed by E8 . 5 . Histological analyses of lineage markers show that Bptf−/− embryos implant but fail to establish a functional distal visceral endoderm . Microarray analysis at early stages of differentiation has identified Bptf-dependent gene targets including homeobox transcriptions factors and genes essential for the development of ectoderm , mesoderm , and both definitive and visceral endoderm . Differentiation of Bptf−/− embryonic stem cell lines into embryoid bodies revealed its requirement for development of mesoderm , endoderm , and ectoderm tissue lineages , and uncovered many genes whose activation or repression are Bptf-dependent . We also provide functional and physical links between the Bptf-containing NURF complex and the Smad transcription factors . These results suggest that Bptf may co-regulate some gene targets of this pathway , which is essential for establishment of the visceral endoderm . We conclude that Bptf likely regulates genes and signaling pathways essential for the development of key tissues of the early mouse embryo .
The packaging of eukaryotic DNA into chromatin provides a general mechanism for the modulation of gene activity and DNA metabolism through alterations of chromatin architecture . The structure and composition of chromatin can be altered by a number of distinct pathways , including post-translational modification of histones , ATP-dependent remodeling of nucleosomes , and incorporation of histone variants [1]–[3] . ATP-dependent chromatin remodeling is catalyzed by the large and conserved SWI/SNF super family of multi-subunit chromatin remodeling enzymes that are classified into four major subfamilies ( SWI/SNF , ISWI , CHD , and INO80 ) , and distinguished by the common presence of a SWI2/SNF2-related catalytic ATPase subunit [4] , [5] . The mammalian ISWI chromatin remodeling complexes contain either one of two related ISWI ATPases , Snf2l and Snf2h [6] , [7] . The Snf2l ATPase is contained in two assemblies–NURF ( Nucleosome Remodeling Factor ) , which is dedicated to the regulation of transcription , and the recently reported CERF [8] , [9] . NURF is the founding member of the ISWI family of chromatin remodeling complexes , and was originally characterized in Drosophila [10] . Purified Drosophila NURF catalyzes ATP-dependent nucleosome sliding and promotes transcription from chromatin templates in vitro [9] . As shown by whole genome expression studies of mutants , NURF positively or negatively regulates transcription of several hundred Drosophila genes in vivo , including many genes important for fly development [11] . This is likely accomplished through recruitment of NURF301 , the largest NURF subunit , by gene-specific transcription factors [11]–[13] , and binding of a PHD finger of NURF301 to tri-methylated lysine 4 on histone H3 [14] . Human NURF contains the orthologs of three of four Drosophila NURF components–BPTF ( Bromodomain PHD-finger Transcription Factor ) , the mammalian counterpart of NURF301 , SNF2L ( the ISWI ATPase ) and RbAp46/48 , a WD-40 repeat histone-binding protein found in several chromatin-related protein complexes [15] . Biochemical studies of human NURF have shown that it has similar properties to its Drosophila counterpart [15] . The physiological functions of an increasing number of mammalian chromatin remodeling complexes have been revealed by studies of mouse mutants for the catalytic ATPase . Mutations in Brg1 , Brm , Chd4 , Chd2 , p400 and Etl1 have been shown to be required for proper embryonic development , hematopoiesis or postnatal survival [16]–[22] . A mutant for the Snf2h , one of the two murine ISWI ATPases , revealed severe proliferation defects in the early embryo , resulting in a peri-implantation lethal phenotype [23] . Given the presence of Snf2h in multiple chromatin remodeling complexes , the assignment of biological phenotypes to different enzyme complexes can be problematic [6] , [7] . By analysis of mutations of unique subunits it is possible to identify the biological functions of different Snf2h-containing complexes . This has been accomplished for the Drosophila ISWI complexes . Studies of mutants for Drosophila NURF301 , which is exclusive to the NURF complex [12] , have revealed a late larval-lethal phenotype and mis-expression of homeotic selector genes and genes involved in the response to heat stress , cytokine and steroid hormone signals [11] , [13] . These phenotypes do not overlap with those observed for mutants for Drosophila ACF1 ( a component of the ISWI-containing complexes ACF and CHRAC ) which are impaired in the establishment and/or maintenance of transcriptional silencing in pericentric heterochromatin and in repression by Polycomb-group genes [24] . To elucidate the biological roles of Bptf-containing complexes we have generated embryonic stem cell and mouse mutants for Bptf [15] . Our studies show that Bptf mutant phenotypes begin to manifest just after implantation stage , and mutant embryos are completely reabsorbed by embryonic day ( E ) 8 . 5 . Genetic and molecular analysis in embryonic stem cells and the mouse suggest a role for Bptf in the development of visceral endoderm ( VE ) of the early mammalian embryo . We propose that Bptf is required for the development of the VE , and more importantly the distal visceral endoderm ( DVE ) , in part through regulating cellular proliferation , the expression of homeobox-containing transcription factors and pathways regulated by the Smad transcription factors , a major conduit for cell signaling in development . These findings suggest a model in which the activities of Bptf-containing complexes , likely the NURF remodeling complex , regulate cell proliferation and embryonic development and therefore are essential in the post-implantation embryo .
BPTF , the mammalian ortholog of Drosophila NURF301 , is a large , multi-domain protein that is apparently exclusive to the mammalian NURF complex ( Figure S1A ) [12] , [15] , [25] . An initial characterization of Bptf showed it to be nuclear in the P19 embryonic carcinoma cell line and can exist in at least two electrophoretic variants , which we termed Bptf-H and Bptf-L ( Figure S1B , Figure S1C ) . As previously reported , Bptf is highly expressed in testis , spleen , brain and to a lesser extent in kidney by Western blotting ( Figure S1D ) . Interestingly Bptf is highly expressed during embryonic development , and expression substantially declines upon birth ( Figure S1E ) . The high levels of Bptf expression in the mouse embryo suggest it may have essential functions during embryonic development . To elucidate the biological functions of mammalian Bptf during mammalian embryonic development we generated two mutant mouse lines . One line , designated as BptfXG023 , was derived from an ES cell line carrying an in-frame gene-trap vector insertion between exons 15 and 16 of Bptf ( Figure S2A , Figure S2B , S2C , S2D , S2E , S2F ) ( XG023; http://www . genetrap . org/ ) [26] . We identified the precise junction of the insertion site of the gene trap by DNA sequence analysis of the corresponding PCR products , and confirmed by RT-PCR that trapping of Bptf mRNA into vector sequences leads to loss of RNA splicing between exons 15 and 16 , and reduced expression of Bptf sequences 3′ to the insertion site ( Figure S3A , Figure S3B ) . In addition , we confirmed by 5′-RACE that the insertion resulted in an in-frame fusion between Bptf and β-galactosidase-neomycin phosphotransferase ( β-Geo ) sequences of the gene-trap vector . ( Figure S3C ) . Consistent with previous findings , Northern blotting of adult tissue RNA showed that both wild-type Bptf and the Bptf–β-Geo fusion alleles are specifically expressed at high levels in the testis and at moderate levels in the lung , spleen , and brain ( Figure S3D ) [25] . These identical RNA expression patterns initially suggest that β-galactosidase is a faithful reporter of Bptf expression in adult tissues . These results indicate that the BptfXG023 mutation creates a truncated Bptf–β-Geo fusion carrying the N-terminal 1978 residues of the 2903-residue Bptf open reading frame , which eliminates the conserved glutamine rich region , PHD finger , and bromo-domains . The second line , designated as BptfΔExon2 , was generated by targeting loxP sites flanking exon 2 of Bptf ( Figure S2A , Figure S2G , Figure S2H , Figure S2I , Figure S2J , Figure S2K , Figure S2L , Figure S2M , Figure S2N ) . The expression of the BptfΔExon2 allele was assessed by RT-PCR with the use of primer sets which amplify sequences on the 3′ end of the Bptf transcript . We found that the BptfΔExon2 allele slightly decreases expression of Bptf at the RNA level ( Figure S4A ) . However , amplification and sequencing of the BptfΔExon2 mRNA from exons 1 to 8 shows an out-of-frame mutant mRNA , indicating that BptfΔExon2 behaves as a loss-of-function allele ( Figure S4B , Figure S4C ) . We intercrossed heterozygous mice to determine the biological function of Bptf during mouse development . With 210 mice genotyped for the BptfXG023 allele and 75 mice for the BptfΔExon2 allele , we did not find any surviving homozygous mice at weaning , indicating that Bptf is required for mouse development . An analysis of E6 . 5 to E18 . 5 progeny derived from heterozygous intercrosses confirmed that the homozygous mutant phenotype is embryonic-lethal between E7 . 5 to E8 . 5 , with 100% penetrance ( Table S1 ) . To identify defects in the embryonic development of BptfXG023 and BptfΔExon2 homozygotes , we performed whole mount examinations of mutant embryos . Growth defects increasing in severity from E5 . 5 to E7 . 5 were observed ( Figure 1A ) ( Figure S5A ) . Dissections conducted at E8 . 5 and E9 . 5 were uninformative , because a majority of the embryos had been completely reabsorbed ( data not shown ) . We crossed BptfXG023 and BptfΔExon2 heterozygous mice to generate the trans-heterozygous BptfXG023/BptfΔExon2 embryos . The trans-heterozygote recapitulated the growth defects of the BptfXG023 and BptfΔExon2 homozygotes , indicating that the two mutations are functionally equivalent ( Figure S5B ) . To determine whether the developmental defects originated before implantation , we harvested E3 . 5 blastocysts and performed blastocyst outgrowth assays . We observed normal E3 . 5 homozygous BptfXG023 and BptfΔExon2 blastocysts and normal outgrowths from the blastocysts after tissue culture for 5 days , suggesting that either Bptf is not essential for pre-implantation development or that the maternal Bptf protein or mRNA can mask pre-implantation phenotypes ( Figure S6 ) . The masking of pre-implantation phenotypes by maternal Bptf is possible because it is highly expressed in oocytes [27] , [28] . To further investigate the basis of the early embryonic lethal phenotype , we performed a histological analysis of BptfXG023 mutant embryos at E5 . 5 , E6 . 5 and E7 . 5 . Mid-sagittal sections of E5 . 5 , E6 . 5 and E7 . 5 embryos stained with hematoxylin and eosin ( H&E ) showed a distinct proximal-distal ( P-D ) axis and the development of visceral endoderm but a significant decrease in size of the embryonic and extra-embryonic tissues . This was particularly evident in the embryonic ectoderm at E6 . 5 and E7 . 5 ( Figure 1B ) . By E6 . 5 , mutant embryos showed clear developmental defects . Although there was a clear boundary between extra embryonic and embryonic tissues , the absence of a primitive streak and any discernable mesoderm suggests that the anterior-posterior ( A-P ) axis did not form ( Figure 1B ) . A reduction in cell number can be a consequence of decreased cell proliferation , increased cell death ( apoptosis ) , or a combination of both processes . To ascertain the extent of programmed cell death , we performed TUNEL assays on E5 . 5 , E . 6 . 5 and E7 . 5 homozygotes and found no increased numbers of apoptotic cells when compared to controls ( Figure 1B ) . As a measure of cell proliferation we monitored phosphorylated histone H3 levels in the conceptus by immuno-histochemistry ( Figure 1B ) . E5 . 5 , E6 . 5 and E7 . 5 homozygotes showed ∼40–50% decrease in phosphorylated histone H3 levels indicating that a decrease in cellular proliferation may contribute to the mutant phenotype ( Figure S7 ) . Subsequent to implantation of the mouse blastocyst there is rapid proliferation of the egg cylinder , which consists of three cell types: the more proximal extra-embryonic ectoderm , the more distal embryonic ectoderm or epiblast , and an outer layer of visceral endoderm [29] . The visceral endoderm originates from the primitive endoderm , a layer of cells organized at E4 . 5 , which is composed of cells from the ICM of the E3 . 5 blastocyst expressing Gata6 but not Nanog [30] . At ∼E5 . 5 , a specialized cluster of endoderm cells , the DVE , arises at the distal tip of the embryo . DVE cells migrate toward the prospective anterior , to form the anterior visceral endoderm ( AVE ) . DVE/AVE cells secrete molecules such as cerberus ( Cer1 ) and Lefty1 , antagonists of the Transforming Growth Factor β ( TGFβ ) -related protein Nodal [29] . These antagonists restrict the activity of Nodal to the posterior pole of the embryo at E6 . 0 [31] . The primitive streak forms at E6 . 5 , indicating that gastrulation has begun , and gives rise to the mesoderm and definitive endoderm germ layers [29] . As a first step in the molecular analysis of Bptf in embryonic development we examined the expression of Bptf using in situ RNA hybridization . We observed expression in the inner cell mass ( ICM ) and primitive endoderm at E4 . 5 and all embryonic tissues at E5 . 5 and E6 . 5 . Interestingly we do not observe Bptf expression in the visceral endoderm at E5 . 5 and E6 . 5 ( Figure 2A ) . We also monitored the activity of the β-galactosidase moiety of the Bptf–β-Geo fusion protein in heterozygous mice . Consistent with our in situ analysis a histochemical analysis of whole mounts showed that Bptf–β-Geo is expressed in the embryo proper at E5 . 5 , E6 . 5 and E7 . 5 ( Figure S8A , S8B , S8C , S8D , S8E , S8F , S8G ) . Further analysis of histological sections revealed that Bptf–β-Geo expression at E7 . 5 is primarily confined to the embryonic ectoderm , with reduced levels in mesoderm and no expression in the visceral endoderm ( Figure S8A′ , Figure S8B′ ) . At subsequent stages , from E7 . 5 to E13 . 5 , histochemical analysis of whole mounts showed widespread Bptf–β-Geo expression in the developing embryo ( Figure S9A , Figure S9B , S9C , S9D , S9E , S9F ) . This temporal correlation between the earliest stages of Bptf expression and the stages when the mutant phenotype is revealed , suggests that there could be an essential requirement for Bptf as early as E4 . 5 . Our histological analysis suggests that Bptf mutants are defective in establishing an A-P axis . Apparent defects in A-P axis can be due to defects in the establishment or migration of the DVE [29] . To monitor the development of the DVE and its transition to the AVE , we analyzed the markers Otx2 , Lefty1 , Cer1 , Hesx1 , Hex1 , Gata6 , Nanog and Nodal by in situ hybridization in E4 . 5 to E6 . 5 mutant embryos [32]–[37] . An analysis of pre-implantation embryos suggested that Bptf mutants specify a functional primitive endoderm and ICM . The primitive endoderm of Bptf mutant embryos was found to express the primitive endoderm markers Gata6 , Lefty1 , and Hex1 at comparable levels to wild type controls ( Figure 3B ) . The expression of these markers suggests that Bptf mutants are not defective in the differentiation of the primitive endoderm , the precursor of the visceral endoderm of E5 . 5 and later stage embryos . Consistent with a functional ICM , we observed normal expression of the pluripotency marker Nanog in Bptf mutants compared to controls ( Figure 2B ) . Taken together , these results indicate that Bptf is not required for the specification of the primitive endoderm and the ICM in E4 . 5 embryos . To assess the specification of the VE and DVE , we monitored the expression of Cer1 , Hex1 , Gata6 , and Nodal in E5 . 5 Bptf mutant embryos . We observed that the markers Cer1 , Hex1 are significantly reduced in Bptf mutants relative to the wild type controls ( Figure 2B ) . The absence of expression of these markers indicates that the DVE does not form in the absence of Bptf . Interestingly Gata6 expression , normally expressed only in the VE at E5 . 5 and E6 . 5 , is absent in the VE but present in the epiblast in mutants at E5 . 5 ( Figure 2B ) . This suggests that Bptf could also have roles in specifying the VE as well as the DVE . A key regulator of DVE specification is Nodal . Nodal expression is found as early as E4 . 5 but is not significantly expressed in the epiblast and VE until implantation is well underway at E5 . 0 [36] , [38] . In E5 . 5 Bptf mutant embryos we observe normal expression levels of Nodal in the epiblast and VE as in wild type embryos ( Figure S10A ) . This E5 . 5 pattern of Nodal expression continues into E6 . 5 in Bptf mutant embryos ( Figure S10B ) . To examine the development of the AVE we monitored the expression of Cer1 , Otx2 , Hesx1 , Lefty1 and Hex1 in E6 . 5 Bptf mutant embryos . Otx2 expression is required for the migration of the DVE to establish the AVE . We observed lower expression of Otx2 in the epiblast of Bptf mutant embryos relative to there wild type controls ( Figure S11A ) . As expected we did not observe expression of AVE markers Cer1 , Hesx1 , Lefty1 and Hex1 in Bptf mutant embryos ( Figure S11A ) . Combined with our analysis of E5 . 5 embryos our results strongly suggest that Bptf is required for the speciation of the DVE and the AVE . Since Bptf mutant embryos are unable to form a functional DVE and AVE , we anticipated that they should be defective in specifying the primitive streak and differentiating mesoderm and definitive endoderm . Several critical transcription factors and signaling molecules such as T , Lhx1 , Fgf8 , Gsc , Foxa2 , Nodal , and Cripto ( Tdgf1 ) serve as effective markers for development of the primitive streak in the gastrulating embryo [34] , [39]–[42] . Our analyses revealed that T , Lhx1 , Fgf8 , Gsc , and Foxa2 were undetectable at E6 . 5 , and in the case of T , Fgf8 but not Lhx1 , were delocalized in expression at E7 . 5 ( Figure S11A , Figure S11B ) . The absence of expression of primitive streak markers at E6 . 5 confirms our histological analyses , and further supports the observation that gastrulation and mesoderm formation do not occur in Bptf mutants . Interestingly , the delocalized Nodal and Cripto expression patterns observed in the Bptf mutants at E6 . 5 are highly reminiscent of their expression patterns prior to the establishment of the DVE ( Figure 2B ) ( Figure S11A ) ( Figure S10A , Figure S11B ) [34] , [39] . Taken together , the data suggests that Bptf mutant embryos arrest at a stage prior to DVE formation ( <E5 . 5 ) . Recent models propose that the extra-embryonic ectoderm supports the growth of the embryo and is a source of signals for A-P axis establishment [29] . To address whether the developmental defects of Bptf mutants are caused by defective extra-embryonic ectoderm or by a lack of appropriate growth signals in the epiblast , we monitored the expression of the extra embryonic ectoderm ( Bmp4 , Erbb2 , Fgfr2 ) , trophectoderm ( Mash2 ) , angiogenesis ( Vegf , Flk1 ) and a cell cycle regulator ( JunB ) markers [43]–[47] . We find Bmp4 , Mash2 , Erbb2 , and Fgfr2 to be expressed normally in the mutant embryos at E6 . 5 and E7 . 5 ( Figure S11A , Figure S11B ) . This suggests that the growth defects observed in Bptf embryos are not due to gross defects in the specification the extra-embryonic tissues . We also observe little to no expression of the angiogenesis markers Vegf and Flk1 in the extra-embryonic tissues at E7 . 5 ( Figure S11B ) . However , expression of the cell cycle regulator JunB is increased in mutant embryonic ectoderm when compared to wild-type controls ( Figure S11A ) . JunB is a member of the Ap-1 family of transcription factors which acts as a negative regulator of the cell cycle [48] . This up-regulation of JunB is consistent with our observations that Bptf mutants have reduced cellular proliferation ( Figure 1B ) ( Figure S7 ) . In summary , our analysis of lineage markers by in situ RNA hybridization has revealed an essential role for Bptf in specifying the VE and the DVE of the E5 . 5 post implantation embryo . These defects likely lead to the observed absence of an AVE and primitive streak in E6 . 5 embryos . The absence of these developmental organizers arrests the growth of the embryo prior to gastrulation and is likely a major cause of the early embryonic lethal phenotype of Bptf mutant embryos . To further explore the role of Bptf in cell differentiation we generated Bptf knockout mouse ES cells and examined their development in vitro and in vivo . By gene targeting and transient Cre expression we were able to generate eight independent homozygous BptfΔExon2 knockout ES cell lines with a euploid karyotype ( Figure S12A ) . We observed varying degrees of reduced Bptf transcript levels by Northern blotting in mutant cell lines compared to that of wild type controls ( Figure S12B ) . An analysis of BptfΔExon2 mRNA from exons 1 to 8 in wild type and knockout cell lines shows the message to be out of frame resulting in no observable protein in the knockout cell lines ( Figure S12C , Figure S12D ) ( data not shown ) . To address the possibility the Bptf is essential for cell viability and proliferation we measured the doubling time of knockout Bptf ES and MEF cell lines ( Figure S13 ) . Both the Bptf knockout ES and MEF cell lines were viable but exhibited slightly reduced cellular proliferation ( Figure S14 ) . These results show that Bptf is not required for cell viability and is only marginally required for cellular proliferation . We measured the differentiation potential of the Bptf knockout ES cells in vitro as embryoid bodies and in vivo as teratomas . Bptf wild type and two independent knockout cell lines were subcutaneously injected into NOD/SCID mice and allowed to form teratomas over 8 weeks . Wild type ES cells formed teratomas in 8/11 injected mice . Bptf knockout lines did not form any observable tumors in 10 injections ( data not shown ) . This study demonstrated that Bptf is essential for one or more biological processes including cell viability , proliferation or differentiation in the animal . We next utilized an embryoid body analysis to monitor differentiation in Bptf knockout cell lines to ectoderm , mesoderm and endoderm cell lineages . Analysis of the mutant embryoid bodies showed little evidence of apoptosis by TUNEL , and similar densities of PCNA and phosphorylated histone H3 positive cells ( Figure S15A ) . We did observe that Bptf knockout embryoid bodies were slightly smaller in size and did not form any observable endoderm ( Figure S15B , Figure S15C ) . These results indicate that Bptf is not necessary for cellular survival or proliferation under conditions of embryoid body differentiation but is essential for differentiation of endoderm and possibly other advanced tissue lineages . To investigate the differentiation defects of Bptf knockout embryoid bodies at a molecular level we monitored the transcription of well documented markers of endoderm , mesoderm and ectoderm differentiation ( Figure 3A–3C ) . We observed minimal Bptf dependence for the primitive ectoderm markers FGF5 and Otx2 ( Figure 3A ) . However , we did observe significant defects in Nestin transcription , a marker of neural stem cell progenitors derived from the primitive ectoderm ( Figure 3A ) . These results indicate that primitive ectoderm lineages are less dependent on Bptf than the more differentiated lineages . As anticipated we observed severe defects in the expression of mesoderm and endoderm markers during the differentiation time course . We observed little to no activation of mesoderm markers T , FGF8 , Evx1 , Wnt3 , and Gsc which are significantly activated in wild type cultures by day 5 . ( Figure 3B ) . Similarly , endoderm markers Sox17 , Cer1 , Hnf4a , and Foxa2 show severe expression defects in Bptf mutant embryoid bodies compared to controls ( Figure 3C ) . In contrast to the large differences in expression of mesoderm and endoderm markers we observed less than two fold changes in expression of cell cycle regulators and pluripotency markers with the exception of Cyclin D1 ( Figure S16A , Figure S16B ) . These defects in transcription and differentiation were rescued for two independent knockout lines by retargeting exon 2 to the BptfΔExon2 locus using the targeting vector . Our ability to rescue the expression defects of T , Gsc , Sox17 and Cer suggest that the observed phenotypes are due to Bptf mutation ( Figure S17A , Figure S17B , Figure S17C ) ( data not shown ) . Taken together these results suggest that Bptf is essential for the formation of mesoderm , endoderm and more differentiated ectoderm lineages in the embryoid body . We chose a microarray approach to investigate any differentiation defects of Bptf knockout ES cells in undifferentiated and early differentiation states . Differentiation was induced by LIF withdrawal ( LIF− ) or retinoic acid ( RA ) for three days before harvesting RNA . A comparison of Bptf-dependent genes by Venn diagram and manual clustering identifies six categories; those being affected under only one of the growth conditions ( LIF+ , LIF− or RA Regulation ) , those being affected in all three conditions ( Constitutive Regulation ) , a class of genes which were regulated in the same direction in two of three conditions ( Complex Regulation ) and genes with mixed dependence ( Mixed Regulation ) ( Figure 4A and 4B ) ( Dataset S1 ) . From our microarray analysis we observed large changes in gene expression under conditions of maintained pluripotency ( LIF+ ) and particularly under conditions of differentiation ( LIF− and RA ) ( Figure 4A and 4B ) . As expected many of the essential markers of early embryonic tissue differentiation that are dependent on Bptf in embryos are also Bptf-dependent in ES cells ( Figure 4C ) . These markers include; pluripotency regulators Sox2 , c-Myc , Nanog , visceral endoderm markers Lefty1 , Cer1 , Hex1 , Foxa2 and the primitive streak markers Gsc , Lhx1 , Wnt3 , Fgf8 , T ( Figure 4C ) . These results reinforce the view that Bptf regulates the development of ectoderm , endoderm and mesoderm in both the early embryo and in ES cells . A gene ontology ( GO ) analysis of Bptf-dependent expression datasets revealed an over representation of genes with “transcription factor activity” , genes involved in the biological processes of “development” and “morphogenesis” and the cellular processes of “cell death” and “cell proliferation” ( Figure S18 ) . Notable gene clusters include the consistent activation of genes correlated with “nervous system development” in all datasets , the repression of MHC I and II receptors during LIF−differentiation , the activation of genes correlated with “cytoskeletal components” during RA differentiation ( Figure S18 ) . In addition , we observed a striking over-representation of homeobox transcription factors within the “transcription factor activity” annotation . The homeobox-containing genes were almost exclusively up-regulated in each of the expression categories , and in some cases include almost the entire Hox gene cluster , indicating that Bptf is required for their repression in ES cells ( Figure S19A , Figure S19B ) . From our analysis we also observed that Bptf-dependent gene targets are more likely to be actively regulated genes , repressed in the presence of LIF or RA differentiation and conversely activated under conditions of LIF differentiation ( Figure S20A , S20B , S20C ) . In support with these observations we observed histone modifications in Bptf knockout ES cell lines consistent with a repressed transcriptome under LIF+ growth conditions ( Figure S20D ) . Interestingly some Bptf-dependent genes cluster together in the genome ( Figure S21 ) . A diagnostic defect of Bptf embryos is an inability to form the DVE . In the absence of the DVE , the AVE cannot form preventing the necessary signals for the specification of the primitive streak . To further investigate the functions of NURF in the early embryo we focused on Smad mediated signaling pathways . Smad mediated signaling pathways are essential for the formation of the DVE in the embryo and the induction of mesoderm in differentiating ES cells , two prominent phenotypes of our in vivo and in vitro studies on Bptf [49] , [50] . The most prominent ligand activating the Smad transcription factors in the early embryo is Nodal [51] . Nodal , and the closely related ligand activin , bind to type I and II TGFβ receptors resulting in the phosphorylation of the type I receptor . Phosphorylation of the type I receptor activates a kinase domain which phosphorylates Smads2/3 . The phosphorylation of Smad2 or Smad3 transcription factors promotes interactions with Smad4 and triggers the translocation of the Smad complex into the nucleus [51] . Once in the nucleus , the Smad complex interacts with DNA sequence specific transcription factors to promote the regulation of Smad target genes [52] . Accordingly , we monitored the dependence of Smad-responsive genes on the presence of Bptf in ES cells . ES cells readily responded to activin-A as monitored by the phosphorylation of Smad2 ( Figure S22A , Figure S22B ) . From these experiments we identified a number of Smad-dependent genes which completely or partially require Bptf for full activation . Genes requiring Bptf for full activation include Cer1 , Gsc and T ( Figure 5A ) . Genes which partially require Bptf include FGF8 , Lefty1 and p21 ( Figure 5A ) ( Figure S23 ) . To understand the relationship between Bptf and CBP/p300 , known co-activators of the Smad transcription factors , we knocked down both Bptf and CBP/p300 using siRNA technology and monitored the activation of the Smad responsive genes Lefty1 , FGF8 , Gsc , T , and Cer in ES cells ( Figure S22C ) . As in the BptfΔExon2 knockout ES cell lines we observed that each of these genes are dependent on Bptf for full activation ( Figure 5B ) . While some genes differ in there requirement for Bptf , they are all dependent on CBP/p300 for activation ( Figure 5B ) . Our results demonstrate that a genetic knockout and siRNA mediated knockdown of Bptf result in defects in the activation of Smad responsive genes to varying degrees . Taken together , these results indicate that Bptf , like CBP/p300 , acts as a co-activator of Smad responsive genes in ES cells . We also used the embryonic carcinoma cell line P19 to complement our findings with the Bptf knockout ES cells . We co-transfected P19 cells with DNA plasmids carrying four minimal Smad binding elements ( SBE ) , or three activin response elements ( ARE ) , linked to a core promoter and a luciferase reporter gene . The ARE and SBE elements were previously found to be the minimal Smad-responsive elements [53] , [54] . Under conditions of Bptf knockdown , we observed a significant reduction in luciferase activity from both reporters ( Figure S24A , Figure S24B ) . To simulate Smad signaling in a different way we co-transfected combinations of Smads 2 , 4 with constitutively active TβRI ( ALK5 ) , the type I receptor for the TGFβ signaling pathway ( Figure S24C ) [55] . Using this system we observed efficient reduction in the activation of the SBE regulated luciferase reporter gene with a siRNA to Bptf but not a mock siRNA control ( Figure S24D ) . We repeated the experiments using multiple unique siRNAs and measured the transcription of endogenous TGFβ regulated genes . In these experiments we used three individual Bptf siRNAs which were effective in knocking down protein expression after 2 days of culture ( Figure S24E ) . We then stimulated the P19 cells with TGF-β1 . Like nodal and activin-A , TGF-β1 stimulates the phosphorylation of Smad 2/3 through the dimerization and activation of similar type I and II receptors . We similarly observed a significant reduction of Cer1 and T transcription in Bptf depleted cells upon Smad2/3 activation with TGF-β1 ( Figure S24F ) . We also used the human breast cancer cell line MCF10CA1 in similar assays to determine if BPTF could play a role in Smad signaling in humans [56] . In these experiments we used BPTF siRNAs which were effective in knocking down protein expression in MCF10CA1 cells after 2 days of culture ( Figure S24G ) . We then stimulated the MCF10CA1 cells with TGF-β1 for 1 hour . We observed a significant reduction of PAI-1 but not SMAD7 induction suggesting that , as in the mouse , BPTF could regulate a subset of Smad responsive genes in humans ( Figure S24H ) . We next investigated whether the interaction between the BPTF-containing NURF complex and the Smads is direct or indirect using pull-down assays . Experiments with bacterially expressed GST-Smad2 or GST-Smad 3 showed an interaction to a degree between recombinant NURF complex and each of the Smad transcription factors , but not the GST or GST-βcatenin controls ( Figure 5C ) . Smad transcription factors are composed of functional domains at the N-terminus ( MH1 domain ) and C-terminus ( MH2 domain ) . The N-terminal+linker and C-terminal regions of Smad2 were used in similar pulldown experiments . We observed that recombinant NURF complex interacts specifically with the MH2 domain of Smad2 ( Figure 5C ) . This interaction was also observed for the Bptf and Snf2l components of native NURF complex from crude ES cell nuclear extracts ( Figure 5C ) . We also confirmed the reported interaction of the C-terminal MH2 domain of the R-Smads with the co-regulator CBP ( Figure 5C ) [57] . Hence , our results suggest that NURF , like p300 and CBP maybe recruited to the promoters of TGFβ responsive genes through direct interactions with the Smad transcription factors . Our current model proposes that Bptf-containing complexes like NURF are recruited to the promoter of Smad regulated genes through direct interactions with the Smad transcription factors . To test this model , we initiated chromatin immuno-precipitation ( ChIP ) experiments to detect the Snf2l component of the NURF complex at Lefty1 . As anticipated , we observed significant enrichment of Snf2l at the Neural Plate Specific Enhancer ( NPE ) , a region which contains putative FAST and Smad transcription actor binding sites , of the Lefty1 promoter [58] . This enrichment was dependent on activin-A stimulation and on the presence of Bptf ( Figure 5D ) . Snf2l enrichment correlates with the presence of the activating histone modification H3K4me3 at the 5′ UTR of Lefty1 ( Figure 5D ) . These results are consistent with Bptf recruitment to the promoter of Lefty1 through the Smad transcription factors . In this work we report a post-implantation lethal phenotype for mutations in Bptf , the previously characterized largest subunit of the NURF chromatin remodeling complex [15] . In the early embryo Bptf is expressed by E4 . 5 in the ICM and primitive endoderm . At later stages of development Bptf is expressed in both embryonic and extra-embryonic ectoderm by E5 . 5 . Following gastrulation , Bptf is widely expressed in all germ layers to E13 . 5 . Bptf expression is essential for early development because homozygous Bptf mutant embryos are reabsorbed by E8 . 5 . No overt defects were observed in the mutant E3 . 5 or E4 . 5 embryo or its ability to proliferate in culture , suggesting that Bptf embryos undergo a normal initial specification and proliferation of the ICM , primitive endoderm and trophectoderm . However , mutant embryos exhibit diminished proliferation post-implantation as shown by defects in size of both extra-embryonic and embryonic tissues and decreased phosphorylated histone H3 staining . A histological analysis of mutant embryos at E6 . 5 and E7 . 5 revealed that they develop a VE but do not form a primitive streak or differentiate mesoderm . To investigate the causes of the defect in gastrulation , we monitored the expression of key markers prior to and during gastrulation in the embryo . As anticipated we failed to observe expression of primitive streak markers T , Foxa2 , Gsc , Fgf8 or the posterior localization of Nodal and Cripto expression . Defects in the expression of primitive streak markers and the delocalized expression of Nodal and Cripto are likely due to the absence of the DVE/AVE . Defects in the DVE/AVE were confirmed by observing significantly reduced expression of the markers Cer1 , Hex1 , Lefty1 and Hesx1 in E5 . 5 and E6 . 5 embryos . Defects in Gata6 expression at E5 . 5 suggest that the defects in DVE specification are accompanied by general defects in VE specification . From these studies we conclude that a critical function for Bptf during mammalian development is directly or indirectly to specify the VE and DVE after implantation . To identify Bptf-dependent gene targets we employed a microarray based approach on Bptf knockout ES cells during the early stages of differentiation and an embryoid body model . We discovered a role for Bptf in the regulation of gene clusters essential for development , morphogenesis , nervous system development and cell death and proliferation . Interestingly many transcription factors , primarily the homeobox-containing genes , are dependent on Bptf for their proper repression during undifferentiated and differentiated states . This dependence is interesting as NURF has been shown to be a activator of Hox gene transcription in more differentiated tissues in Drosophila and the mouse [13] , [15] . As expected many markers of ectoderm ( Nestin , Fgf5 ) , mesoderm ( Gsc , Lhx1 , Fgf8 , Tbx6 , Wnt3 ) and endoderm ( Lefty1 , Cer1 , Nodal , Hesx1 ) cell lineages require Bptf for their expression . These gene targets corroborate well with those identified from our in vivo studies on Bptf mutant embryos further supporting the conclusion that Bptf is essential for the development of ectoderm , mesoderm and endoderm . This also suggests that our microarray dataset obtained from ES cells is a reasonable approximation of the expression defects occurring in Bptf mutant embryos in vivo . The functions for Bptf in the early embryo are undoubtedly complex . Our unbiased analysis of gene targets by microarray revealed many potentially Bptf-dependent differentiation pathways . Most relevant to this study include the regulation of the cell cycle and the differentiation of mesoderm and endoderm lineages , specifically the VE and DVE . The underlying cause for these defects could largely be due to the loss of chromatin associated complexes like the NURF chromatin remodeling complex . In the case of NURF the defects could be direct , as a remodeling activity at the promoter of genes necessary for cellular proliferation and differentiation , or indirect through the deregulation of master regulators of development like the homeobox-containing transcription factors . Moreover the underlying mechanism of Bptf action as a co-activator of some genes and a co-repressor of others is unclear . Further studies of nucleosome positioning and chromatin structure in mutants should clarify these possibilities . In addition to system-wide defects with Bptf deletion , there could be specific defects in individual signal transduction pathways within the embryo or in the ability of the embryo to receive growth signals from the extra-embryonic tissues or the surrounding decidua . To identify potential Bptf-dependent signaling pathways we focused on its role in specifying the DVE . The pre-gastrulation embryo uses three well-known signaling pathways , the WNT/β-catenin , FGF/MAPK and Nodal/Smad pathways , to establish A-P asymmetry [29] . The three pathways can be distinguished by different A-P phenotypes . In mutants of FGF/MAPK signaling the epiblast has severe proliferation defects , do not specify primitive endoderm , are quickly reabsorbed and the blastocysts do not outgrow when grown in culture [30] , [44] , [59] , [60] . Mutations in the WNT/β-catenin , but not Nodal/Smad pathways , develop the DVE and in some cases the AVE [50] , [61]–[63] . The ability of the Bptf embryos to form blastocyst outgrowths , specify the primitive endoderm , but not form the DVE is reminiscent of mutants in the Nodal/Smad signaling pathway rather than a defect in FGF/MAPK or WNT/β-catenin signaling ( Table S2 ) . Because Bptf has been associated with the NURF complex , a known regulator of transcription , the data suggests that Bptf is required for the expression of gene targets in the developing VE and DVE . In support of this hypothesis , we showed that Bptf is required for the regulation of endogenous promoters and Smad responsive promoter elements in ES , P19 and MCF10CA1 cells in tissue culture . Smad-dependent gene targets include those essential for cell proliferation ( p21 ) and those essential for DVE function ( Cer1 , Lefty1 ) and primitive streak ( T , Fgf8 , Gsc ) . Moreover , pulldown assays showed that components of the NURF complex have direct interactions with the Smad transcription factors and it is recruited to the promoters of Smad regulated genes under conditions of activation . Taken together , our data suggest that Bptf can directly regulate Smad regulated genes , likely through the functions of the NURF remodeling complex , via recruitment by the Smad transcription factors ( It is also possible that other as yet unidentified Bptf-containing complexes distinct from NURF function in this pathway ) . To address the possibility that the effects on the Nodal/Smad pathway are indirect we monitored the expression of key components of the pathway in embryos . We did not observe significant changes which could explain the observed defects in Smad signaling in the early embryo or during embryoid body differentiation in Bptf mutants ( Figure S25 ) . Consistent with these findings , the ISWI ATPase , a component of Drosophila NURF , ACF and CHRAC has been reported to be important for transmitting Dpp/TGFβ signals to stem cells in the Drosophila ovary [64] . However , we wish to emphasize that the Bptf mutation likely affects many different pathways and biological processes , each of which may contribute to biological phenotypes . The challenge for the near future will be to uncover each of the many functions of Bptf in mammalian chromatin biology .
The RPCI21 mouse PAC library ( MRC Genomic Resource Center , England ) was screened using a random hexamer labeled probe to Bptf exon 2 . Blotting was performed in hybridization bags using 0 . 25 M sodium phosphate pH 7 . 2 , 1 mM EDTA , 7% SDS , at 65°C overnight with rocking . Blots were washed 5 times for 10 min with 0 . 25× SSC , 0 . 1% SDS at 65°C . Eight positive clones were identified using X-ray film as; 367-I21 , 373-D5 , 402-C16 , 402-E15 , 426-P15 , 540-B9 , 625-D23 , 582-P16 . Clone 367-I21 was confirmed by Southern blotting using Eco RI and Sal I digests and Bptf exon2 probe . Genomic sequence for the construction of the targeting vector was retrieved into bluescript SKII ( Stratagene ) and the integration of loxP sites and Neo selectable marker was performed using recombineering technology described previously [65] . The sequence of the targeting vector is available upon request . Linearized vector was electroporated into CJ7 ES cells and 71 individual Neo resistant and HAT resistant clones were isolated according to previously published procedures [66] . Clones were screened for successful Bptf targeting using Eco RI , probe 29–31; Bam HI , probe exon2; and Sca I , probe exon2 . 16 of 71 isolates were found to be correct for a recombination frequency of 22% . To create heterozygous , conditional homozygous , homozygous knockout and rescue Bptf ES cell lines we transiently expressed Cre followed by retargeting . The piCre expression vector was electroporated into clone 7010 and conversion from Bptf/BptfFloxedNeo to Bptf/BptfΔexon2 was first screened for by PCR then confirmed by Southern blotting . Three clones were identified using this strategy , and clone 7004 was used for subsequent targeting . The wild type allele in the Bptf/BptfΔexon2 from clone 7004 was then retargeted using the Bptf exon 2 targeting vector . Individual conditional homozygous clones were first screened for by PCR then confirmed by Southern blotting . Two BptfFloxedNeo/BptfΔexon2 clones were obtained and named H12 and B19 . The piCre expression vector was electroporated into H12 and B19 and conversion from BptfFloxedNeo/BptfΔexon2 to BptfΔexon2/BptfΔexon2 was first screened for by PCR then confirmed by Southern blotting . A total of 9 and 5 homozygous knockout clones were obtained from the H12 and B19 parental lines respectively . The karyotype of the knockout lines were confirmed using Giemsa staining and lines with an anuploid karyotype were discarded . ES cells were maintained on mitomycin C treated primary mouse embryonic fibroblasts ( MEFs ) and ES Cell Growth Media ( 15% FCS ( ES Cell grade , Invitrogen ) , DMEM , essential amino acids , 10 µM mercapto-ethanol , 2 mM glutamine , 100 U/ml LIF ( Chemicon International ) , penicillin and streptomycin ) throughout . Lines were passaged off MEFs onto gelatinized plates for 3 passages prior to any molecular analysis . Rescue lines were made by retargeting one allele of BptfΔexon2 in clones Bptf mutant clones P2-G2 and P2-B9 . Individual Neo resistant and HAT resistant clones were selected on mitomycin-C treated MEF feeder layers in ES Cell Growth Media ( 15% FCS ( ES Cell grade , Invitrogen ) , DMEM , essential amino acids , 10 µM mercapto-ethanol , 2 mM glutamine , 100 U/ml LIF ( Chemicon International ) , penicillin and streptomycin ) throughout . Successful retargeting events were confirmed by Southern analysis as described below . Clone 7010 was used to make chimera mice as described previously [66] . Removal of frt-Neo-frt and deletion of LoxP-exon2-LoxP was accomplished by crossing to mice expressing Tg-CMV-Flp and Tg-CMV-Cre to BptfFloxedNeo to create the BptfFloxed and BptfΔexon2 lines respectively . Individual recombinants were identified by Southern blotting and maintained by backcrossing to C57B6/CRL . Mice used in this study were maintained in a Specific Pathogen Free environment at the National Institutes of Health at Bethesda ( MD , USA ) . Mice were maintained on a 12 hr light/dark cycle given NIH-13 blend lab chow and hypo chlorinated water ad libtium throughout the duration of the study . All experiments and animal maintenance procedures were approved by the Animal Care and Use Committee of the National Cancer Institute under protocol LMCB001 and its modifications . NCI-Frederick is accredited by AAALAC International and follows the Public Health Service Policy for the Care and Use of Laboratory Animals . Animal care was provided in accordance with the procedures outlined in the Guide for Care and Use of Laboratory Animals” ( National Research Council; 1996; National Academy Press; Washington , D . C . ) . BptfXG023 , BptfFloxedNeo , BptfFloxed , and BptfΔexon2 embryos on a B6/129 mixed background have been cryopreserved at the Cryopreservation and Assisted Reproduction Lab , National Cancer Institute , Frederick ( MD , USA ) as BPTFXG023 , BPTFFloxedNEO , BPTFFloxed and BPTFdel-exon2 respectively . These mouse lines are available to the research community by request . Mice were routinely genotyped by Southern blotting restriction digested tail DNA . Tail DNA was prepared using standard procedures [67] . ∼10 µg of DNA was digested with the appropriate restriction enzyme and resolved by 0 . 5% agarose gel electrophoresis for standard electrophoresis or 1 . 0% agarose using FIGE . DNA was transferred to Hybond XL ( Amersham ) using alkaline capillary transfer crosslinked with UV ( Stratalinker ) and probed with random hexamer labeled probes at 65°C using Perfect Hyb Hybridization Buffer ( Sigma ) . Probes used for blotting were PCR amplified from pCH110 ( Genbank # UO2445 ) using primers JL510 and JL511 for Probe B , and from mouse genomic DNA using primers JL299 and JL300 for Probe A; JL60 and JL61 for probe exon2; JL235 and JL236 for probe 29–31; JL293 and JL294 for probe 25–26; JL295 and JL296 for probe 20 . 2–20 . 7 using Taq polymerase ( Invitrogen ) ( see Table S3 for primer sequences ) . Blots were washed twice with 2 . 0× SSC , 0 . 1% SDS and twice with 0 . 5× SSC , 0 . 1% SDS for 10 min @ 65°C for each wash . A Phosphoimager was used to detect the hybridization signal . Embryos were genotyped using a PCR based strategy . Primers used for genotyping the BptfXG023 line were as follows , Primer A ( JL511 ) , Primer B ( JL627 ) , and Primer C ( JL631 ) ( see Table S3 for primer sequences ) . PCR reactions were performed in 25 µl volumes with 20 mM Tris-HCl ( pH 8 . 4 ) , 50 mM KCl , 1 . 5 mM MgCl2 , 0 . 2 mM each dNTP , 0 . 2 µM each primer , 1 Unit Taq Polymerase ( Invitrogen ) . Reactions were heated for 3 min at 94°C , and then cycled for 35 cycles at 30 sec at 94°C , 30 sec at 65°C , and 45 sec at 72°C , followed by one cycle for 2 min at 72°C . PCR products were resolved on 1 . 5% agarose gels . The genotype of embryos was confirmed by the presence of a 250 bp Bptf and/or 500 bp BptfXG023 bands . Two different PCR strategies were used for genotyping the BptfΔexon2 line . The primers used in the first strategy are as follows , Primer A ( JL648 ) , Primer B ( JL649 ) , and Primer C ( JL652 ) ( see Table S3 for primer sequences ) . PCR reactions were performed in 25 µl volumes with 20 mM Tris-HCL ( pH 8 . 4 ) , 50 mM KCl , 1 . 5 mM MgCl2 , 0 . 2 mM each dNTP , 0 . 2 µM each primer , 1 Unit Taq Polymerase ( Invitrogen ) . Reactions were heated for 3 min at 94°C , then cycled for 35 cycles at 30 sec at 94°C , 30 sec at 60°C , and 45 sec at 72°C , followed by one cycle for 2 min at 72°C . PCR products were resolved on 1 . 5% agarose gels . The genotype of embryos was confirmed by the presence of a 250 bp Bptf , 325 bp BptfFloxed and BptfFloxedNeo or 550 bp BptfΔexon2 band . The primers used in the second method are as follows , Primer C ( JL651 ) , Primer E ( JL655 ) , and Primer F ( JL662 ) ( see Table S3 for primer sequences ) . PCR reactions were performed in 25 µl volumes with 20 mM Tris-HCL ( pH 8 . 4 ) , 50 mM KCl , 1 . 5 mM MgCl2 , 0 . 2 mM each dNTP , 0 . 2 µM each primer , 1 Unit Taq Polymerase ( Invitrogen ) . Reactions were heated for 3 min at 94°C , then cycled for 35 cycles at 30 sec at 94°C , 30 sec at 65°C , and 45 sec at 72°C , followed by one cycle for 2 min at 72°C . PCR products were resolved on 1 . 5% agarose gels . The genotype of embryos was confirmed by the presence of a 375 bp Bptf , 475 bp BptfFloxed or 650 bp BptfΔexon2 bands . To confirm BptfXG023 and BptfΔexon2 expression and an out of frame Bptf mRNA in the BptfΔexon2 line we designed primers with homology to the 3′ end and exons 1 through 8 of the Bptf mRNA . Total RNA from wild type and BptfΔexon2 homozygous embryos was converted to cDNA using superscript II according to manufacturer's procedures . Bptf expression was estimated by first normalizing cDNA to equal concentration with GAPDH amplification . Following GAPDH normalization 3′Bptf was amplified . Both GAPDH and Bptf were amplified in the linear range as follows . Reactions were heated for 10 min at 94°C , then cycled for 22 cycles for GAPDH and 30 cycles for Bptf at 20 sec at 94°C , 20 sec at 60°C , and 30 sec at 72°C . PCR products were resolved by native PAGE . The exon 1–8 junction was amplified using primers P3 and JL15 in 25 µl volumes with 20 mM Tris-HCL ( pH 8 . 4 ) , 50 mM KCl , 1 . 5 mM MgCl2 , 0 . 2 mM each dNTP , 0 . 2 µM each primer , 1 Unit Taq Polymerase ( Invitrogen ) ( see Table S3 for primer sequences ) . Reactions were heated for 3 min at 94°C , then cycled for 35 cycles at 30 sec at 94°C , 30 sec at 65°C , and 2 min at 72°C , followed by one cycle for 5 min at 72°C . PCR products were resolved on 1 . 5% agarose gels . PCR fragments were cloned using TOPO TA cloning system ( Invitrogen ) and sequenced with Big Dye V3 . 1 using M13 forward and reverse primers . The exact integration site for the BptfXG023 insertion allele was confirmed by sequencing a PCR product generated by amplifying genomic DNA from a heterozygous mouse tail clipping . The DNA was amplified using primers JL511 and JL515 ( see Table S3 for primer sequences ) . Reactions were heated for 3 min at 94°C , then cycled for 35 cycles at 30 sec at 94°C , 30 sec at 65°C , and 60 sec at 72°C , followed by one cycle for 2 min at 72°C . A ∼1 . 8 Kb PCR product was resolved on 1 . 5% agarose and purified . The product was sequenced using Big Dye v3 . 1 using primer JL511 to obtain BptfXG023 junction sequence . BptfXG023 disruption was confirmed by 5′ RACE according to established procedures from Bptf/BptfXG023 total testis RNA according to procedures established by the Bay Genomics Gene Trap Consortium ( http://baygenomics . ucsf . edu/ ) . Two separate 5′ RACE reactions were sequenced giving identical results . Timed pregnancies used to collect embryos were preformed using standard procedures [67] . Embryonic day 0 . 5 was recorded the morning after an observable plug was found . Portions of later stage E18 . 5 to E10 . 5 embryos were genotyped by Southern blotting and early E9 . 5 to E3 . 5 embryos and outgrowths were genotyped by PCR . Embryos used for β-galactosidase staining , in situ hybridizations and phenotypic analysis were dissected from their deciduas in PBS at room temperature prior to staining/hybridization . Embryos were stained for β-galactosidase activity or used for in situ hybridizations using established protocols [67] ( T . Yamaguchi , personal communication ) . Probes used for in situ RNA hybridizations were labeled with DIG according to standard procedures [67] . Plasmids or PCR products used for probe synthesis were as follows T , Lhx1 , GATA6 , Lefty1 , FGF4 , FGF8 , HesX1 , Hhex , Cripto , BMP4 , Otx2 , Nodal , Cer , Foxa2 , Erb2 , Fgfr2 , Mash1 , JunB , VEGF and Flk1 . In situ signals were detected using BM Purple AP Substrate according to manufactures instructions ( Roche ) . Embryos selected for sectioning were post fixed in 4% PFA overnight at 4°C , dehydrated in alcohol and embedded in paraffin and sectioned after β-galactosidase staining . Sectioned embryos were obtained using standard techniques [67] . Embryos genotyped following β-galactosidase staining or in situ hybridization were prepared post staining by digesting the embryo proper overnight at 55°C in 10 to 20 µl PBS , 0 . 1% tween 20 , 2 mg/ml PCR grade protenase K ( Roche ) . Proteinase K was heat inactivated at 100°C for 15 min and 2 µl was subsequently used for PCR genotyping . Deciduas used for sectioning were dissected from the uterus in PBS , washed once with PBS and immersed in 4% PFA buffered with PBS overnight at 4°C . Deciduas were then dehydrated in an ethanol series and imbedded in paraffin . Paraffin blocks were trimmed and sectioned and sections were stained with hematoxylin and eosin , TUNEL assay and phosphorylated H3 according to standard procedures [67] . Embryos used for RT PCR analysis were dissected in DMEM , 25 mM HEPES pH 7 . 4 , 15% FCS , 2 mM glutamine , penicillin and streptomycin . Embryonic and extra embryonic tissue was removed from the ectoplacental cone and frozen in dry ice . The ectoplacental cone was grown ex vivo in DMEM , 15% FCS , glutamine , penicillin and streptomycin using standard culture conditions . After 4 days growth extra embryonic cells were removed from the outgrowth and subjected to PCR genotyping as described above and qPCR as described below . Outgrowth assays were preformed on E3 . 5 blastocysts . E3 . 5 blastocysts were isolated in 15% FCS , M2 , 10 µM mercapto-ethanol , penicillin and streptomycin using standard procedures . Blastocysts were then transferred to gelatinized plates containing 15% FCS , M16 , 10 µM β-mercaptoethanol , 2 mM glutamine with penicillin and streptomycin . Outgrowths were cultured under standard conditions for 5 days and observed for defects . At the end of the experiment the outgrowths were removed and genotyped by PCR using conditions above . Mouse tissues were extracted from 3 month old male mice sacrificed by CO2 asphixation and immediately frozen in liquid nitrogen . Tissues were then extracted using TriReagent ( Sigma ) according to manufacturers established procedures . RNA precipitate was extensively washed in 80% EtOH , re-suspended in DEPC treated water and stored at −80°C . Protein precipitate was extensively washed with 0 . 3 M guanadininum hydrochloride , 95% ethanol and re-suspended in 8M urea , 1% SDS and stored at −80°C . Protein concentration was determined using the DC Protein Assay ( BioRad ) according to manufacturer's procedures . mRNA from embryos successfully genotyped as WT or Bptf−/− from ectoplacental growth outgrowths was isolated using Quick Prep Micro mRNA Purification Kit ( Amersham ) . RNA samples were denatured , resolved by formaldehyde agarose electrophoresis and blotted to Gene Screen ( Dupont ) by high salt capillary transfer according to standard procedures . Blots were probed with random hexamer labeled DNA probes using Prefect Hyb hybridization solution ( Sigma ) at 65°C according to the manufactures procedures . Probes used were the Probe B and Exon2 probe described above . Blots were washed with four times with 1× SSC for 10 minutes at 65°C . Phosphoimager was used to detect the hybridization signal . Protein samples were diluted in SDS sample buffer and resolved by PAGE . 50 µg total protein was transferred to PVDF ( Biorad ) using 10 mM CAPS , pH 10 . 5 , 15% methanol , 3 . 5 mM DTT transfer solution for 17 hours at 20 mA and 20 V limits . Membranes were blotted with affinity purified anti-Bptf at 1∶10000 dilution [14] , M2 anti Flag monoclonal antibody at 1∶5000 ( Sigma ) , anti Smad2 and Smad2-phos ( Cell Signaling Technologies ) 1∶1000 , anti SNF2H/L ( Abcam ) 1∶5000 , anti-CBP and p300 1∶100 ( Abcam ) overnight at 4°C then anti rabbit or mouse HRP ( Amersham ) at 1∶20000 dilution for 2 hrs at RT in PBS with 5% NFDM and 0 . 1% tween 20 through out . Histone Western blotting was performed essentially as above . Protein samples were extracted from ES cells grown on gelatinized plates in ES cell growth media containing 100 U/ml LIF using TriReagent according to manufacturer's procedures ( Sigma ) . 50 µg of total protein was resolved by 15% SDS PAGE and transferred to PVDF ( Biorad ) using 12 . 5 mM Tris , 96 mM glycine pH 8 . 3 , 20% methanol transfer solution for 1 hour at 200 mA and 25 V limits . Blots were probed with rabbit polyclonal antibodies to H4 ac-K5 , H4 ac-K8 , H4 ac-K12 ( Serotech ) , H4 3me-K20 , H4 acK16 , H3 3me-K9 , H3 3me-K27 ( Upstate ) , g-H2AX , H3 3me-K4 , H3 ac-K14 , H3 ac-K18 ( abcam ) at 1∶2000 dilutions overnight at 4°C then anti rabbit HRP ( Amersham ) at 1∶4000 dilution for 1 hrs at RT in PBS with 5% NFDM and 0 . 1% tween 20 through out . Detection was preformed using Pico signal ECL according to manufacturer's procedures ( Pierce ) . ES cells were maintained on mitomycin C treated MEFs in ESC Growth Media ( 15% FCS ( ES Cell grade , Invitrogen ) , DMEM , essential amino acids , 10 µM mercapto-ethanol , 2 mM glutamine , 100 U/ml LIF ( Chemicon International ) , penicillin and streptomycin ) . Culture techniques used were described previously [66] . Lines were passaged 3 times onto gelatinized plates to remove MEFs prior to experiments . Growth rates of ES cell lines were obtained on gelatinized plates in the absence of feeder layers . Cells were plated @ 1 . 0×105 in 6 well plates . Cell number was determined every day for 4 consecutive days with changes in ESC Growth media occurring every day . Activin-A stimulation of ES cells was performed as follows . ES cell lines were started on gelatinized plates in ESC Growth Media . Cultures were then grown for 2 days in ESC Growth Media without LIF . On the third day , cells were cultured in low serum ESC Growth Media without LIF ( same as ESC Growth Media except 0 . 1% FCS is used instead of 15% FCS ) in the presence or absence of 30 ng/ml activin-A ( R & D Systems ) overnight . Cells were processed using TriReagent according to manufactures standard protocol . p300 , CBP siRNA knockdowns were preformed as follows . ES cells were grown in ES Cell Growth Medium with 100 U/ml LIF on gelatinized plates . Cells were collected using trypsin and transfected using an Amaxa Nucleofector device using solution ES Cell and program A-30 according to manufacturer's procedures . 2 . 0×106 ES cells were transfected with 400 nmoles siRNA duplex ( Darmacon ) or a GFP-Max nucleofection control . Post nucleofection 1 . 0×106 cells were plated to each well of a 6 well gelatinized tissue culture plate . Cells were stimulated with or without Activin-A in low serum ES Cell growth media as described above . Cells at specified time points were lysed using TriReagent reagent ( Sigma ) and RNA and protein were purified according to manufacturer's procedures . MCF10CA1h cells were grown in a 1∶1 mixture of DMEM and Ham's F12 medium ( Gibco ) supplemented with 5% horse serum . Cells were collected using trypsin and transfected using an Amaxa Nucleofector device using solution V and program T-27 according to manufacturer's procedures . 2 . 0×106 cells were transfected with 400 nmoles siRNA duplex ( Darmacon ) or a GFP-Max nucleofection control . Post nucleofection 1 . 0×106 cells were plated to each well of a 6 well tissue culture plate . Cells were grown for 2 days in growth media and then serum deprived in 1% FBS , DMEM , nonessential amino acids , 10 µM β-mercaptoethanol overnight . Following serum shock cells were incubated with or without 5 ng/ml TGF-β1 ( R&D Systems ) for 1 hour . Cells were lysed using TriReagent reagent ( Sigma ) and RNA and protein were purified according to manufacturer's procedures . P19 cells were obtained from the ATCC ( ATCC Number CRL-1825 ) . Cells were grown in 10% FBS , DMEM , 2 mM glutamine , penicillin and streptomycin . Cells were transfected using an Amaxa Nucleofector device using solution V and program C-20 according to manufacturer's procedures . 2 . 0×106 P19 cells were transfected with 400 nmoles siRNA duplex ( Darmacon ) or a GFP-Max nucleofection control . Post nucleofection 3 . 0×105 cells were plated to 12 well plates . Cells were allowed to grow for two days then were serum starved for 18 hours in 0 . 1% FBS , DMEM , 2 mM glutamine , penicillin and streptomycin . Cells were induced with 5 ng/ml TGF-β1 ( R&D Systems ) in low serum growth medium overnight . Cells were lysed using TriReagent reagent ( Sigma ) and RNA and protein were purified according to manufacturer's procedures . Luciferase assays were performed in 12 well format by transfecting 0 . 6 µg pGL3ti- ( SBE ) 4 or pAR3-Luc , 0 . 012 µg pCH110 with or without 0 . 6 µg TβRI or 0 . 6 µg Smad 2-HA and 0 . 006 µg Smad4-HA , 40 . 0 pmoles siRNA duplex per well using Lipofectamine 2000 ( Invitrogen ) . In experiments using TGF-β1 ( R&D Systems ) or Activin-A ( R&D Systems ) cells were allowed to grow for 48 hours in 10% FBS , DMEM , 2 mM glutamine prior to adding TGF-β1 at 5 ng/ml or Activin-A at 30 ng/ml . Cells were induced for 24 hours then lysed with 250 µl siGlow Lysis Buffer ( Promega ) according to manufacturer's procedures . Transfections utilizing the constitutively active receptors were incubated for 48 hours prior to lysis as above . 50 µl or 100 µl of lysate was assayed with 100 µl Bright-Glow Luciferase Assay Reagent ( Promega ) . Transfection efficiency was normalized to β-galactosidase levels by assaying 10 µl lysate to 100 µl Beta-Glo Assay Reagent ( Promega ) . Relative Luciferase units were obtained by dividing the luciferase activity levels by the β-galactosidase levels . Experiments were repeated at least twice and yielded essentially the same results . We used P19 cells to test specificity of TGFβ induction of Bptf protein levels . P19 cells were grown in 10% FBS , DMEM , 2 mM glutamine , penicillin and streptomycin . Ligands were added at the following concentrations; TGFβ1 ( R&D Systems ) at 5 ng/ml , FGF4 ( R&D Systems ) and heparin at 25 ng/ml and 1 ug/ml respectively , BMP4 ( R&D Systems ) at 10 ng/ml and Wnt3a or L cell control conditioned medium at 1∶1 with 10% FBS , DMEM , 2 mM glutamine , penicillin and streptomycin . Cells were incubated overnight and harvested for protein with TriReagent reagent ( Sigma ) according to manufacturers procedures . Bptf Western blotting was performed as described above . Embryoid bodies were cultured as follows . ES cells were treated with trypsin briefly to retain cells in medium sized clumps . Cells were then centrifuged at low speed and resuspended in ESC Growth Medium without LIF and cultured in bacteriological grade petri dishes . ESC Growth Medium without LIF was changed every day for 9 days with minimal disturbance to the embryoid bodies . Samples were taken at specified time points using TriReagent according to manufactures standard protocol or fixed in 10% NBF for histology . Knockout of Bptf in MEFs was accomplished by infecting litter mate wild type and conditional Bptf ( -/Floxed genotype ) MEF lines with an adenovirus expressing CMV-Cre . Infection was performed in 6 well plates at sub-confluence ( 2×105 cells ) with 1 ml growth media containing 45 µl Adenovirus @ 1×1010 PFU/ml . Cells were grown for two days in the presence of virus . Following infection cells were divided into 5 wells of a 6 well plate . Cell numbers were recorded every 24 hours with changes in media occurring every 48 hours . A sample of cells was removed at day 4 for Western analysis of Bptf protein levels . Experiment was repeated with two wild type and conditional knockout MEF lines from littermate embryos . ES cells were maintained on mitomycin C treated MEFs in ESC Growth Media ( 15% FCS ( ES Cell grade , Invitrogen ) , DMEM , essential amino acids , 10 µM mercapto-ethanol , 2 mM glutamine , 100 U/ml LIF ( Chemicon International ) , penicillin and streptomycin ) . Culture techniques used were described previously [66] . Lines were passaged 3 times onto gelatinized plates to remove MEFs prior to experiments . Activin-A stimulation of ES cells was performed as follows . ES cell lines were started on gelatinized plates in ESC Growth Media . Cultures were then grown for 2 days in ESC Growth Media without LIF . On the third day , cells were cultured in low serum ESC Growth Media without LIF ( same as ESC Growth Media except 0 . 1% FCS is used instead of 15% FCS ) in the presence or absence of 30 ng/ml activin-A ( R & D Systems ) overnight . Following activin-A induction cells were washed with PBS and fixed in 2 mM EGS ( Pierce ) in 25% DMSO/75% PBS for 30 min followed by 1% PFA in PBS for 30 min . For histone ChIP , cells were fixed in 1% PFA in PBS for 15 min . Following fixation cells were washed 3X in PBS and removed from the tissue culture dish with a cell scraper . Cell pellets were then frozen at −80°C . The following day the pellets were thawed and processed using the ChIP procedure published by Upstate biologicals . Antibodies used for pulldown were ChIP grade SNF2H/L ( Abcam ) , H3 3me-K4 , H3 3me-K27 ( Upstate ) and pan H3 ( Abcam ) . Quantitation of pulldown was performed by real time PCR using 2× DyNAmo syprogreen qPCR kit ( New England Biolabs ) according to manufacturers procedures . Briefly , reactions were composed of 5 µl 1 . 2 µM forward and reverse primers , 5 µl diluted cDNA template and 10 µl 2× qPCR mix ( see Table S3 for primer sequences ) . Reactions were heated for 10 min at 94°C , then cycled for 40 cycles at 20 sec at 94°C , 20 sec at 60°C , 30 sec at 72°C . After each cycle the sample was heated to 78°C for 10 sec prior to reading sample fluorescence . Pulldowns were quantified as a percentage of input using a dilution series as a standard curve . Histone modification pulldowns are expressed as enrichment relative to histone H3 occupancy under +activin-A conditions . SNF2H/L pulldowns were first normalized to signal at Gapdh and are expressed as the enrichment during +activin-A relative to −activin-A conditions . RT reactions were performed on RNA extracted from ES cells , embryoid bodies or P19 cells using TriReagent according to manufacturer's procedures . 5 µg of total RNA was reverse transcribed with Superscript II using oligo dT priming according to manufactures procedures ( Invitrogen ) . cDNA reactions were diluted 5–10 fold and used in a template for PCR . Real time PCR was performed using 2× DyNAmo syprogreen qPCR kit ( New England Biolabs ) according to manufacturers procedures . Briefly , reactions were composed of 5 µl 1 . 2 µM forward and reverse primers , 5 µl diluted cDNA template and 10 µl 2× qPCR mix ( see Table S3 for primer sequences ) . Reactions were heated for 10 min at 94°C , then cycled for 35 cycles at 20 sec at 94°C , 20 sec at 60°C or 52°C , 30 sec at 72°C . After each cycle the sample was heated to 78°C for 10 sec prior to reading sample fluorescence . Reactions were done in triplicate . ΔΔCt method was used to quantify the relative levels of expression to the Gapdh or β-actin house keeping genes . Expression levels were then normalized to un-induced cells control cells . QPCR was performed on E7 . 5 embryo cDNA as follows . Briefly , reactions were composed of 5 µl 1 . 2 µM forward and reverse primer pair , 5 µl diluted cDNA template and 10 µl 2× qPCR mix ( see Table S3 for primer sequences ) . Reactions were heated for 10 min at 94°C , then cycled at 20 sec at 94°C , 20 sec at 60°C , and 30 sec at 72°C . The number of cycles to maintain linearity was determined using the real time analysis software . PCR reactions within the linear range were resolved using native PAGE . For microarray experiments we used one wildtype and two independent Bptf mutant cell lines in 3 culture conditions ( LIF+ , LIF− , and RA+ ) . Experiments were carried out with 3 biological replications . At the day 3 , Triazole ( 1 ml/well; Invitrogen , USA ) was added to the well and total RNAs were extracted using Phase lock gel columns ( Eppendorf/Brinkman ) according to the manufacturer's protocol . Total RNAs were precipitated with isopropanol , washed with 70% ethanol , and dissolved in DEPC-treated H2O . 2 . 5 g of total RNA samples were labeled with Cy3-CTP using a Low RNA Input Fluorescent Linear Amplification Kit ( Agilent , USA ) . A reference target ( Cy5-CTP-labeled ) was prepared from the Universal Mouse Reference RNA ( Stratagene , USA ) . Labeled targets were purified using an RNeasy Mini Kit ( Qiagen , USA ) according to the Agilent's protocol , quantified by a NanoDrop scanning spectrophotometer ( NanoDrop Technologies , USA ) , and hybridized to the NIA Mouse 44K Microarray v2 . 2 ( whole genome 60-mer oligo; manufactured by Agilent Technologies , #014117 ) [68] . Transcript copy number estimation using a mouse whole-genome oligonucleotide microarray according to the Agilent protocol ( G4140-90030; Agilent 60-mer oligo microarray processing protocol - SSC Wash , v1 . 0 ) . All hybridizations were carried out in the two color protocol by combining one Cy3-CTP-labeled experimental target and Cy5-CTP-labeled reference target . Microarrays were scanned on an Agilent DNA Microarray Scanner , using standard settings , including automatic PMT adjustment . Differential gene expressions in various cell lines in the standard culture condition were analyzed using the NIA Array Analysis software ( http://lgsun . grc . nia . nih . gov/ANOVA/ ) which implements ANOVA statistics with two additional methods to reduce the number of false positives: ( 1 ) small error variances were replaced with the average error variance estimated from 500 genes with similar signal intensity , and ( 2 ) false discovery rates ( FDR<0 . 05 ) were used to select genes with differential expression , instead of p-values [69] . The FDR method accounts for the effect of multiple hypotheses testing . Gene Ontology analysis and clustering was accomplished using DAVID ( http://david . abcc . ncifcrf . gov/ ) and the NIA Mouse Gene Index ( http://lgsun . grc . nia . nih . gov/geneindex/mm8 ) using default settings [70] . In vitro GST pulldown assays were performed in 50 µl volumes in binding buffer ( 25 mM Hepes pH 7 . 4 , 100 mM NaCl , 0 . 5 mM MgCl2 , 0 . 01% NP40 ) , ∼10 µg GST protein fusion bound to a glutathione Sepharose support , 100 ng human NURF complex . Proteins were allowed to bind for 1 hour at 4°C with occasional mixing . The beads were washed with 500 µl binding buffer three times at 4°C and re-suspended in a final volume of 30 µl . NURF and CBP pulldowns from ES Cell extracts were preformed essentially as described above from 500 mM KCl nuclei extractions made as previously described [71] . The extract was diluted 1∶3 with 25 mM Hepes pH 7 . 4 , 0 . 5 mM MgCl2 , 0 . 01% NP40 with protease inhibitors ( Roche ) prior to binding to resin bound GST-Smads . The beads were washed with 500 µl binding buffer three times at 4°C and re-suspended in a final volume of 30 µl . Proteins were eluted from the beads by adding 5 µl 6× SDS sample buffer and incubating at 37°C for 30 min . Proteins were resolved on 4% or 8% polyacrylamide gels and transferred to PVDF ( Biorad ) and prepared for Western blotting as described above . | While the chromatin of eukaryotes provides an efficient means to compact large amounts of DNA into a small nucleus , it renders the DNA relatively inaccessible . ATP-dependent chromatin remodeling complexes mobilize nucleosomes and provide a means to gain access to DNA in chromatin . While the biochemical functions of chromatin remodeling complexes is well-characterized , less is known of their biological functions . In this manuscript , we elucidate the biological functions of Bptf , a subunit of the NURF chromatin remodeling complex . Our studies show that Bptf is required for the establishment of the anterior–posterior axis of the mouse embryo during the earliest stages of development . To understand its functions in tissue differentiation , we generated and characterized Bptf-mutant ES cells . Mutant embryonic stem cells show significant defects in the differentiation of ectoderm , endoderm , and mesoderm . Genome-wide analysis of gene expression defects during differentiation has identified many Bptf-dependent pathways including key regulators of ectoderm , endoderm , and mesoderm differentiation . Moreover , we have identified critical functions for Bptf during the TGFβ/Smad-induced expression of visceral endoderm and mesoderm markers , an important signaling pathway in the early embryo . These results suggest that chromatin remodeling by Bptf regulates key signaling pathways in the early mouse embryo . | [
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] | 2008 | Essential Role of Chromatin Remodeling Protein Bptf in Early Mouse Embryos and Embryonic Stem Cells |
We have identified and characterized a Macrophage Migration Inhibitory Factor ( MIF ) family member in the Lophotrochozoan invertebrate , Biomphalaria glabrata , the snail intermediate host of the human blood fluke Schistosoma mansoni . In mammals , MIF is a widely expressed pleiotropic cytokine with potent pro-inflammatory properties that controls cell functions such as gene expression , proliferation or apoptosis . Here we show that the MIF protein from B . glabrata ( BgMIF ) is expressed in circulating immune defense cells ( hemocytes ) of the snail as well as in the B . glabrata embryonic ( Bge ) cell line that has hemocyte-like features . Recombinant BgMIF ( rBgMIF ) induced cell proliferation and inhibited NO-dependent p53-mediated apoptosis in Bge cells . Moreover , knock-down of BgMIF expression in Bge cells interfered with the in vitro encapsulation of S . mansoni sporocysts . Furthermore , the in vivo knock-down of BgMIF prevented the changes in circulating hemocyte populations that occur in response to an infection by S . mansoni miracidia and led to a significant increase in the parasite burden of the snails . These results provide the first functional evidence that a MIF ortholog is involved in an invertebrate immune response towards a parasitic infection and highlight the importance of cytokines in invertebrate-parasite interactions .
Schistosomiasis , the second most widespread human parasitic disease after malaria [1] , is caused by helminth parasites of the genus Schistosoma and more than 200 million people in 74 countries suffer from the pathological consequences of this disease [2] . Human infection requires contact with freshwater in which infected snails ( the intermediate hosts of schistosomes ) have released cercariae larvae that penetrate human skin . The complex interaction between the intermediate snail host and the parasite and in particular between Schistosoma mansoni and the snail generally used for its laboratory maintenance , Biomphalaria glabrata , is of interest both in terms of transmission dynamics , but increasingly as a model for the study of the innate immune response and its evolution . In order to protect themselves against pathogens , invertebrates use innate immune responses such as wound repair , coagulation , phagocytosis and encapsulation reactions , also used by vertebrates [3] . Major signalling pathways or effector molecules underlying innate immune responses of vertebrates and invertebrates are also shared , as for instance the Toll receptors described for the first time in Drosophila [4] or members of immunoglobulin superfamily such as the FREPs ( Fibrinogen-RElated Proteins ) in B . glabrata [5] . The need for regulation of cellular immunity and the parallels made between vertebrate and invertebrate innate immunity led to an intense search for invertebrate cytokines [6] . Cytokines specific to invertebrates , such as spätzle in Drosophila [4] , astakine in Pacifastacus leniusculus [7] , or CCF in Eisenia foetida [8] have been described , but to date , only very few orthologs of vertebrate cytokines have been incontrovertibly identified in invertebrates [9] , one of which is Macrophage Migration Inhibitory Factor ( MIF ) . MIF was one of the first mammalian cytokines to be discovered and has been described as a pivotal regulator of innate immune and inflammatory responses in mammals [10] . It was first characterized as a factor derived from activated T cells that inhibited random migration of macrophages [11] , [12] . Since the first cloning of a MIF gene [13] many biological activities have been described , including stimulation of cell proliferation through ERK1/ERK2 pathway activation , activation of the response against endotoxin or Gram negative bacteria by upregulation of TLR4 ( the signal-transducing molecule of the LPS receptor complex ) expression and the suppression of p53-mediated growth arrest in macrophages challenged by LPS [10] . In addition MIF possesses intrinsic tautomerase activity ( keto-enol isomerisation of small aromatic substrates such as L-dopachrome methyl ester ) that is dependent on the post-translational cleavage of the initiating methionine to expose an N-terminal proline residue [14] . Interestingly , MIFs have been characterized in a wide variety of parasites , including nematodes and protozoans [15] , [16] but the role of the cytokine has been mainly studied in the context of the host-parasite relationship with the emphasis on the effect of parasite MIF on the host immune response . For instance Ancylostoma MIF has been shown to bind to the human MIF receptor [17] and recombinant Brugia MIF induces the release of cytokines ( IL-8 , TNFα ) from human macrophages [18] . Similarly , Plasmodium MIF is thought to influence the host immune response and the course of anemia during infection [16] . MIFs have also recently been identified in two species of mollusks , disk abalones [19] , but currently , nothing is known about the role of MIF from the invertebrate host during an immune response to a pathogen . Strikingly , an exhaustive search of the S . mansoni genomic sequences ( AB-G , unpublished ) failed to find any MIF signature sequences These in silico findings are consistent with the in vitro work of others describing the absence of MIF homologs in parasitic trematodes [20] . The discovery in B . glabrata of a potential cytokine-like molecule displaying significant sequence similarity to MIF [21] , raised the question of its potential involvement in the regulation of the snail immune response to parasite infection . In this report , we demonstrate that the MIF protein from B . glabrata ( BgMIF ) is expressed in circulating immune defense cells ( hemocytes ) of the snail as well as in the B . glabrata embryonic ( Bge ) cell line that has hemocyte-like features . We show that recombinant BgMIF ( rBgMIF ) possesses the conserved tautomerase enzymatic activity of the MIF family , induces cell proliferation ( correlating with ERK phosphorylation ) and inhibits NO-dependent , p53-mediated apoptosis in Bge cells . Moreover , knock-down of BgMIF in Bge cells inhibits the in vitro encapsulation of S . mansoni sporocysts and this correlates with an inhibition of p38 phosphorylation in these cells . Finally , in whole snails , we demonstrate the involvement of BgMIF in the snail anti-parasitic response towards S . mansoni . Furthermore , the tools developed here pave the way toward a better understanding of the complex interactions between S . mansoni and its molluscan snail host .
Alignment of MIF peptide sequences ( Figure 1A ) shows that BgMIF contains the N-terminal catalytic proline ( Pro2 ) that is exposed by cleavage of the initiating methionine and is essential for tautomerase activity ( see below and [14] ) . With 31% sequence identity to human MIF , BgMIF is less conserved than MIFs from two other mollusks , the bivalve abalones , Haliotis diversicolor sextus ( 39% ) and Haliotis discus discus ( 35% ) . Several invariant active site residues [15] are conserved , including Lys32 and Ile64 . The conserved Val106 residue is substituted by a Cys in BgMIF or by Leu in MIF from Ixodes scapularis , thus maintaining the presence of a hydrophobic residue at this position ( Figure 1A ) . To further investigate the relationship between BgMIF and other MIFs , we performed a phylogenetic analysis ( using two different analyses with similar results: see Methods ) on selected vertebrate and invertebrate proteins ( Figure 1B ) . The phylogeny of selected MIFs proved to be complex with numerous small clades and no strong relationship with taxonomy . Although BgMIF is clearly grouped in the phylogenetic tree with nematode MIF2 sequences [15] , it is not closely related to other mollusk MIFs ( Figure 1B ) . A hallmark of all MIF family members is the enzymatic tautomerase activity; we expressed it as a recombinant protein ( rBgMIF ) in E . coli together with a site-directed mutant ( rBgMIFP2G ) , in which the N-terminal Proline ( Pro2 ) was substituted by Gly . We used rBgMIF and rBgMIFP2G to perform a tautomerase assay with mouse MIF ( rMmMIF ) as a positive control and L-dopachrome methyl ester as a substrate . The results ( Figure 2 ) showed that rBgMIF displayed tautomerase activity comparable to that of the mouse MIF protein and that , as expected; the mutant rBgMIFP2G did not have any detectable activity . Therefore , as in all MIF family members , Pro2 is required for enzymatic activity of BgMIF . In addition we tested the inhibition of the tautomerase activity using the MIF antagonist ISO-1 a specific inhibitor of mammalian MIF [22] . rBgMIF treated with 100 or 200 µM of ISO-1 ( Supplementary data Fig S1 ) was inhibited by more than 95% at both doses . In contrast to most cytokines , MIF is constitutively expressed and stored in intracellular pools . MIF secretion is induced by inflammatory stimuli such as endotoxin ( LPS ) or tumor necrosis factor ( TNF-α ) , as well as by hormones [10] . MIF is expressed by defense cells such as macrophages [23] , monocytes , neutrophils , dendritic cells and other cell types in tissues in contact with the host's natural environment [10] . We examined tissue specific expression of MIF in B . glabrata snails by western blotting of protein extracts from various snail organs using an antiserum raised against two peptides derived from the BgMIF sequence . This antiserum was shown to recognize native BgMIF ( Figure 3A ) . A single band corresponding to BgMIF was found in all tissues tested , including the albumen gland , digestive tract , heart ( hematopoietic organ ) hepatopancreas , and foot ( Figure 3A ) . In order to confirm the presence of BgMIF in B . glabrata hemocytes , we performed both western blotting and immunolocalisation analyses . BgMIF was detected in hemocyte lysates ( Figure 3A ) and immunolocalized in the cytoplasm of hemocytes . BgMIF was found to be more abundant in well spread hemocytes , termed granulocytes , than in unspread hemocytes or hyalinocytes [24] , [25] ( Figure 3B–D ) . ELISA tests performed with anti-BgMIF serum allowed us to detect BgMIF in plasma ( cell-free hemolymph ) and to demonstrate that the amount of BgMIF in plasma progressively decreased during infection by S . mansoni ( 34% of decrease at 48 h post-infection ) ( Supplementary data Figure S2 ) . Bge cells represent the only existing molluscan cell line and display hemocyte-like immune functions [26] . They have previously been described to share with hemocytes a fibroblastic origin and the ability to recognize and phagocyte or encapsulate foreign material including larval trematodes [26] , [27] . In order to assess the pertinence of this cell line as an in vitro system for the analysis of BgMIF activity and function , we first searched for the presence of BgMIF in Bge cells . BgMIF was readily detectable in these cells by western blotting ( Figure 3A ) and immunolocalization showed that , as in hemocytes , BgMIF could be detected in the Bge cell cytoplasm ( Figure 3E–G ) . In order to determine whether Bge cells could also release BgMIF protein upon immune stimulation , as observed in vitro for mammalian macrophages [23] , we cultured Bge cells in the presence of S . mansoni excretory-secretory products ( ESP ) that have been shown to modulate gene expression in these cells [28] . BgMIF secretion was induced by ESP at 30 µg/mL ( protein ) with an apparent maximum at a dose of 120 µg/mL ( Figure 3H ) . We have also carried out a western blot of ESP from sporocysts with the anti-BgMIF antiserum and as expected in view of the absence of MIF signature sequences from the S . mansoni genome , no cross-reactive bands were detected ( data not shown ) . In mammals , MIF stimulates the proliferation of quiescent fibroblasts in an “ERK sustained activation” dependent manner [29] . We determined whether BgMIF promotes cell proliferation by stimulating quiescent Bge cells with rBgMIF for 24 h and measuring BrdU incorporation . Purified rBgMIF stimulated cell proliferation in a dose dependent manner from a concentration of 50 ng/ml , with a maximum incorporation rate at 100 ng/ml ( Figure 4A ) . ERK-MAPK pathway activation is associated with mammalian MIF induced cell proliferation . To investigate whether BgMIF activated ERK , quiescent Bge cells were treated with rBgMIF and the cell lysates examined for ERK phosphorylation by Western blot analysis using phospho-specific anti-ERK antibodies . MIF induced phosphorylation of a B . glabrata ERK homolog in a dose and time-dependent fashion ( Figure 4B ) . ERK phosphorylation was detected as early as 2 h and was sustained for at least 24 h as previously described for ERK in the NIH/3T3 fibroblast cell line ( Figure 4B ) [29] . In addition , U0126 the specific inhibitor of MEK ( mitogen-activated protein kinase/ERK kinase ) , the upstream kinase of ERK [30] , prevented the stimulatory effect of BgMIF on Bge cell proliferation ( Supplementary data Figure S3 ) and ERK phosphorylation ( data not shown ) , further indicating that MIF can induce proliferation via the ERK1/ERK2 pathway . MIF was found to inhibit NO-induced intracellular accumulation of p53 and , therefore , p53-mediated apoptosis in macrophages [31] . To investigate whether BgMIF inhibits apoptosis induced by NO accumulation , Bge cells were treated with the NO donor SNGO and different concentrations of rBgMIF . The proportion of apoptotic cells , labeled by the TUNEL method , was quantified using FACS analysis . As in mammalian macrophages , SNGO induced a significant level of apoptosis in Bge cells that was decreased in a dose-dependent manner by the addition of BgMIF ( Figure 5A ) and except for the lowest concentration of rBgMIF ( 25 ng/ml ) , the decrease in the percentage of apoptotic cells was statistically significant ( Figure 5B ) . In mammalian macrophages , it has been shown that NO treatment is associated with a coordinate increase in the phosphorylation of p53 on Ser15 , and that immunoblotting for phosphorylated p53 is a sensitive way of detecting the influence of MIF on intracellular p53 [31] . Examination of B . glabrata ESTs and genome sequences have allowed us to characterize a p53 ortholog ( GenBank accession number: GU929337 ) . We therefore examined whether inhibition of apoptosis in Bge cells treated with rBgMIF could be related to a decrease in NO-induced p53 accumulation in these cells . Western blot analysis of cell lysates using a phospho-specific ( Ser15 ) anti p53 antibody showed that rBgMIF inhibited p53 phosphorylation in Bge cells and suggested that this mechanism participated in the suppression by rBgMIF of NO-induced apoptosis ( Figure 5C ) . We have demonstrated that Bge cells secrete BgMIF when they are incubated with S . mansoni ESP ( Figure 3H ) . In these experiments , we additionally observed that Bge cells aggregated and changed their form upon ESP induction ( Figure 6A Ctrl+ESP ) like mammalian macrophages induced by LPS [32] , nevertheless this phenotype had not previously been described for B . glabrata cells and was not due to contaminating endotoxin in the ESP preparation ( see Methods ) . In order to determine whether this aggregative behavior was regulated by BgMIF , we used RNAi to knock-down ( KD ) its expression in Bge cells , using dsRNA against BgMIF ( dsMIF ) or dsRNA against luciferase ( dsLuc ) as an unrelated control . The efficiency of BgMIF KD was confirmed by the marked decrease ( 70% ) of BgMIF transcripts observed after a 3 day incubation with dsMIF , as compared to incubation with dsLuc ( Figure 7A ) . When S . mansoni ESP ( 120 µg/mL ) was added to cells treated with dsRNA , aggregation was observed in dsLuc ( Figure 6B dsLuc+ESP ) but not in dsMIF treated cells , which remained well-individualized , with numerous round and unspread refringent cells ( Figure 6B dsMIF+ESP ) , suggesting that BgMIF is involved in the regulation of Bge cell activation induced by parasites . S . mansoni ESP have been shown to stimulate the p38 MAPK signaling pathway in Bge cells [33] manifested by the phosphorylation of Bgp38 . We therefore tested the increase in phosphorylation of Bgp38 in response to the incubation with ESP . We detected a rapid activation of Bgp38 after 15 min in dsLuc treated cells , while in dsMIF treated cells p38 was not activated ( Figure 6C ) . We next determined the effect of BgMIF-induced cell activation using the in vitro model of S . mansoni sporocyst encapsulation by Bge cells [27] , [34] . To Bge cells treated with dsMIF or dsLuc for 72 h , we added 48 h in vitro-transformed sporocysts and followed interaction of Bge cells with sporocysts for a further 72 h . Control ( as well as dsLuc treated ) Bge cells ( Figure 8A ) readily migrated towards and encapsulated the sporocysts as previously observed [34] but dsMIF-treated cells showed a markedly reduced ability to encapsulate the sporocysts ( Figure 8A ) . The proportion of encapsulated sporocysts was indeed significantly reduced ( p<0 . 05 ) in dsMIF treated Bge cells compared to dsLuc treated cells or untreated control cells ( Figure 8B ) . Since BgMIF promotes cellular responses to immune stimulation in vitro , we examined its role in the activation of hemocytes in B . glabrata snails confronting a parasitic infection . We first analyzed the circulating hemocyte population in non-infected versus infected snails , using flow cytometry based mainly on size ( forward scatter-FSC ) and granularity ( side scatter-SSC ) dot plot distribution . In non-infected snails the content of circulating hemocytes was shown to be very heterogeneous and we could discriminate two subpopulations , R1 ( small and medium hemocytes ) and R2 ( large hemocytes ) ( Figure 9A ) . 24 h following infection , the population of circulating hemocytes showed a marked reduction in the R2 subpopulation of large cells or granulocytes ( Figure 9A ) , which together with hyalinocytes , make up the heterogenous cell population present in healthy snails [25] . This decrease in circulating granulocytes is linked to their migration toward the tissues invaded by miracidia [35] , [36] . In order to determine whether these cellular changes were regulated by BgMIF , we performed RNAi KD in whole snails by microinjecting 15 µg dsMIF or dsLuc into the cardiac sinus . BgMIF expression was monitored three days after dsRNA injection . We observed a decrease in BgMIF transcripts and protein in both whole snails and circulating hemocytes ( Figure 7B–7C ) treated with dsMIF , as compared to dsLuc treated animals . Next we infected control and dsMIF or dsLuc-treated snails by S . mansoni miracidia and analyzed the circulating hemocyte content 24 h after infection . Compared to dsLuc controls , dsMIF-treated infected snails exhibited a hemocyte profile similar to that found in non-infected snails ( Figure 9B ) . These results corroborated the data obtained in vitro with Bge cells and further supported a role for BgMIF in the hemocyte response during a parasite infection of B . glabrata . Finally , we tested the effect of BgMIF silencing of the snails on the level of infection observed with S . mansoni miracidia . We observed that dsMIF treated snails have significantly more parasites than dsLuc treated and control , untreated snails ( Figure 10 ) . These results further show that BgMIF is essential for the control of the immune response of snail against parasite infection .
Invertebrate immune systems have now become a major research focus for investigating broader questions such as the diversity of immune responses including those against parasitic and viral infections [37] , [38] , processes involved in the immunity of lophotrochozoan invertebrates , or the question of cytokine-dependent regulatory processes [9] , [39] . Here we investigated the role of MIF , a putative ortholog of the vertebrate cytokine in the immune response of a lophotrochozoan invertebrate , the gastropod Biomphalaria glabrata towards its natural parasite Schistosoma mansoni . The BgMIF protein sequence shares sequence and structural homologies with other members of the MIF family , including residues that are invariant across the whole MIF family ( Asp9 , Pro 56 and Leu88 ) , or involved in the tautomerase enzymatic activity ( Pro2 , Lys32 , Ile64 ) . In addition , analysis of the secondary structure shows that the BgMIF protein is composed of four α-helices and four β-sheets as for other MIF family members . Results obtained with the recombinant BgMIF proteins indicated that BgMIF has a conserved dopachrome tautomerase enzyme activity dependent on the Pro2 residue and the results obtained with ISO-1 , which interacts with the catalytic active site residues of mammalian MIF [22] , show that the catalytic active site of BgMIF is conserved ( even though residues Tyr 95 and Asp 97 in human MIF are respectively Val ( as in the tick I . scapularis , Figure 1A ) and Lys ( as in the abalone MIFs ) in BgMIF ) . The requirement for the enzymatic activity of MIF proteins for their biological activity has not been established and the use of a tautomerase-null MIF gene knock-in mouse model indicated that it was not involved in the growth-regulatory activity of the cytokine [40] . However , it does appear that the catalytic site could be important for pro-inflammatory activity , but the reason for this is still unknown . One possibility is that the catalytic site of MIF affects its binding to the MIF receptor , CD74 , and its activation [41] . It has also been suggested that the tautomerase activity may be a vestige of a role in the invertebrate melanotic encapsulation response against microbial invasion , reflecting an ancestral role of the protein in the innate immune response [42] . In the context of BgMIF the significance of this tautomerase activity remains unknown as is the hypothetical interaction of the catalytic site with an as yet uncharacterized BgMIF receptor . On the other hand , the immunological relevance of this activity does not seem to be related to melanotic encapsulation , since melanization has not been described for B . glabrata . In contrast to most cytokines , MIF is constitutively expressed and stored in intracellular pools and does therefore not require de novo protein synthesis before release into the extracellular milieu . These features provide MIF with the capacity to be released immediately and to act as an effector molecule regulating innate immunity [10] . Macrophages , which represent the first cellular barrier of defense towards pathogen invasion , are an important source of MIF protein , but MIF is also expressed in the tissues that are in contact with the host's natural environment . In this study we showed that BgMIF protein was constitutively present in all the snail tissues tested . In addition we found BgMIF protein in the cytoplasm of hemocytes . It was more abundant in the granulocytic forms that are involved in defence responses including phagocytosis or encapsulation of pathogens , than in the cytoplasm of hyalinocytes . In addition we have demonstrated the presence of BgMIF protein in the plasma of snails and its decrease after infection by S . mansoni . This decrease correlates with the migration of granulocytes towards infected tissues . We further found BgMIF protein in the cytoplasm of Bge cells that share hemocyte characteristics . Bge cells also secreted BgMIF protein in the presence of Schistosoma released products , thus validating the use of these cells in the bio-assays we developed to assess the biological activities of recombinant BgMIF . When they enter B . glabrata snails , S . mansoni miracidia are readily located by the hemocytes and trigger marked cellular and humoral responses involving both migration of hemocytes toward the site of infection and an increase in hemocyte production [43] . In addition , reactive nitrogen and oxygen species ( RNS and ROS ) produced by the hemocytes can damage miracidia/newly-transformed sporocysts [38] , but at the same time they can also induce hemocyte apoptosis . Important biological activities of MIF are likely to play a key role in this immune response , including the activation of MAPK such as ERK1/2 and the inhibition of p53-mediated apoptosis [10] . Although the MAPK pathways have only been partially characterized in mollusks , previous studies on B . glabrata have documented the involvement of ERK1/2 and other MAPK family members in signaling events leading to cellular immune responses in this snail [33] , [44] , [45] , [46] . Using Bge cells as an in vitro model , we demonstrate in this work the induction of cell proliferation by rBgMIF in a dose-dependent manner . This proliferation was correlated with an increase in phosphorylated ERK in Bge cells as early as 2 h that was sustained for at least 24 h . This activity seems directly linked to the proliferation of the defense cells in response to immune stimulation . The inhibition of NO-induced intracellular p53 , which in turn inhibits apoptosis , is a well-documented effect of mammalian MIF that leads to a sustained proinflammatory function in macrophages exposed to LPS [31] . We showed that incubation with rBgMIF significantly reduced the apoptotic response of Bge cells , induced by SNGO as an NO-donor . The inhibition of apoptosis was accompanied by a reduction in the amount of phosphorylated Bgp53 , the p53 ortholog recently identified in B . glabrata snails . These data further support the conservation of essential functions of BgMIF that enable it to regulate the immune response in this invertebrate . BgMIF thus presents conserved activities of the MIF family and in order to address the question of its involvement in the innate immune response we first determined its role in the interaction of Bge cells with the sporocyst larvae of S . mansoni . We have optimized the KD of BgMIF transcripts and showed for the first time that the reduction of protein level was related to a resulting phenotype in vitro . When Bge cells were stimulated with S . mansoni ESP , they acquired an activated phenotype ( aggregation ) that was no longer observed in cells treated with dsMIF . In addition we showed that this aggregative phenotype was correlated with phosphorylation of Bgp38 , an activation known to promote cell adhesion [33] . Using the in vitro co-cultivation system of Bge cells and in vitro-transformed sporocysts [27] , we observed that the KD in BgMIF expression led to fewer Bge cells migrating towards and encapsulating the sporocysts , resulting in a lower percentage of encapsulated sporocysts than observed in control conditions . These results suggest that BgMIF is intimately involved in the response of the snail to parasites . We next performed BgMIF KD in vivo using the microinjection of dsRNA previously described for B . glabrata snails [47] and analyzed its consequences on the hemocyte population of snails exposed to infection by S . mansoni miracidia . After 24 h following infection , the population of circulating hemocytes in non-interfered snails showed a marked reduction in the number of large cells ( granulocytes ) , which , together with hyalinocytes , form the heterogeneous hemocyte population present in healthy snails [25] . This decrease in circulating granulocytes is due to their mobilization by the parasites and their migration towards the invaded tissues [35] , [36] . When we examined the hemocyte populations of infected dsMIF-treated snails , we did not observe such a reduction in the number of granulocytes , suggesting that BgMIF is necessary for hemocyte activation during the in vivo response to the parasite . This change in the behavior of hemocytes was accompanied by a significant increase in the number of sporocysts establishing in dsMIF-treated snails , underlining the importance of MIF in regulating the innate immune response toward the parasite . The absence of hemocyte activation may be due to the lack of a signal generated by BgMIF released under normal conditions . It has been demonstrated that MIF deficient macrophages are hyporesponsive to stimulation by LPS or Gram-negative bacteria stimulation and that this is due to TLR4 downregulation [48] . In dsRNA-treated snails we also showed that the absence of hemocyte migration correlated in these cells with a down regulation of a Toll receptor ortholog , BgToll1 , which we have recently identified ( unpublished data ) . These data suggest that the BgToll1 expression may be regulated by BgMIF and that BgMIF facilitates the activation of hemocytes and their migration towards invaded tissues . Taken together , the results presented here demonstrate the involvement of BgMIF in the innate immunity of B . glabrata . This is the first functional study of a molecule involved in the regulation of the anti-parasite response in B . glabrata , and the tools developed here pave the way towards a better understanding of the complex interactions between medically important helminths and their molluscan snail hosts .
All animal experimentation was conducted following the Nord-Pas de Calais Region and the Pasteur Institute of Lille guidelines for housing and care of laboratory animals and performed in accordance with institutional regulations after review and approval by the Nord-Pas de Calais Region ( Authorization No . A59-35009 ) and Pasteur Institute ( Authorization No . AF/2009 ) Animal Care and Use Committees . Adult ( 6–10 mm in diameter ) B . glabrata snails ( albino strain ) , were raised in pond water and fed ad libitum . A Puerto-Rican strain of S . mansoni was maintained by passage through B . glabrata snails and Mesocricetus auratus . Miracidia were isolated from infected hamster livers and maintained in complete Chernin's balanced salt solution [49] ( CBSS supplemented with 1 mg/ml glucose and 1 mg/ml trehalose ) for 48 h to achieve in vitro transformation into mother sporocysts as described in [50] . Mother sporocysts and/or excretory-secretory products ( ESP ) -containing CBSS were then collected and used . The B . glabrata embryonic ( Bge ) cell line ( ATCC CRL 1494; Rockville , MD ) , was maintained at 26°C under normal atmospheric conditions in complete Bge medium [51] , supplemented with 10% fetal bovine serum ( FBS; Sigma ) , and antibiotics ( 100 U/ml penicillin G; 0 . 05 g/ml streptomycin sulphate , 25 µg/ml amphotericin B , Sigma ) . Total RNA and protein from individual snails was extracted using the TRIZOL reagent ( Invitrogen ) according to the manufacturer's instructions . Total RNA from Bge cells was extracted using the Rneasy Mini kit ( Qiagen ) according to the manufacturer's instructions . For hemocytes the collected hemolymph [52] was divided in two tubes for extraction of protein and for extraction of RNA . For cDNA synthesis , RNA from whole snails ( 1 µg ) and Bge cells ( 0 . 1 µg ) were used for reverse transcription using SuperScript III reverse transcriptase ( Invitrogen ) and the oligo ( dT ) 20 primer . A partial cDNA sequence ( EST GenBank accession number: CK989824[21] ) was used to design specific primers and perform 5′ and 3′ RACE amplification ( SMART RACE cDNA Amplification kit , Clontech ) according to the manufacturer's instructions . The complete BgMIF coding sequence was then amplified using primers containing XhoI and XbaI restriction sites respectively ( see Supplementary data Table S1 for primer sequences ) . The PCR products were digested and cloned into the bacterial expression vector pET303 Ct-His ( Invitrogen ) ( BgMIF construct ) . A BgMIF mutant construct ( P2G pET303 Ct-His ) was generated by site directed mutagenesis using primers ( Supplementary data Table S1 ) encoding glycine instead of proline after the initiating methionine ( BgMIFP2G construct ) . Sequence alignments and analysis were carried out using the DNAStar Lasergene programme package and the BioEdit v7 . 0 . 1 package ( http://www . mbio . ncsu . edu/BioEdit/bioedit . html ) . For phylogenetic analysis , multiple amino acid sequence alignments were performed using MUSCLE [53] or 3DCoffee [54] . The maximum likelihood tree was obtained with PhyML 3 . 0 [55] or MrBayes [56] at LIRMM ( http://www . phylogeny . fr/ ) using the WAG model of substitution with four substitution rate categories and estimated gamma shape parameter and proportion of invariant sites . Branch support values were based on 500 bootstrap replicates with PhyML or 100000 replicates for MrBayes . Recombinant C-terminally His-tagged full-length rBgMIF and rBgMIFP2G fusion proteins were expressed using the pET303 Ct-His vector in E . coli BL21 ( DE3 ) pLys strain . One liter of bacterial culture was grown to an OD600 nm of 0 . 4 and induced by addition of isopropyl β-1-D-thiogalactopyranoside to a final concentration of 0 . 4 mM . After 3 h at 30°C , cells were harvested , lysed and purified with Ni-NTA agarose resin ( Qiagen ) according to the manufacturer's instructions . Briefly , cells were resuspended in lysis buffer ( 50 mM NaH2PO4 , 300 mM NaCl , 10 mM imidazole ) and disrupted by successive freeze-thaw cycles in liquid nitrogen . The soluble protein fraction was mixed with Ni-NTA agarose and incubated under agitation for 1 hour at 4°C . The resin was then washed ( 50 mM NaH2PO4 , 300 mM NaCl , 20 mM imidazole ) and finally tagged proteins were eluted with 50 mM NaH2PO4 , 300 mM NaCl , 200 mM imidazole . The purified protein was dialyzed against endotoxin-free PBS overnight and the content of remaining endotoxin was measured with Limulus Amoebocyte Lysate ( Cambrex ) . Recombinant proteins used in the bioassays contained less than 200 pg endotoxin/mg of protein . Tautomerase activity was measured using a D-dopachrome tautomerase assay as described previously [14] , [22] . Briefly , a fresh solution of D-dopachrome methyl ester was prepared by mixing 4 mM L-3 , 4-dihydroxyphenylalanine methyl ester with 8 mM sodium periodate for 5 min at room temperature that was then placed on ice 20 min before use . Activity was determined at room temperature by adding D-dopachrome methyl ester to a cuvette containing 50 mM rBgMIF , BgMIFP2G or a commercial mammalian MIF ( mouse MIF ) , rMmMIF ( R&D systems ) , in 25 mM potassium phosphate buffer pH 6 . 0 and , 0 . 5 mM EDTA . For the inhibition assays the MIF inhibitor , ( S , R ) -3- ( 4-hydroxyphenyl ) -4 , 5-dihydro-5-isoxazole acetic acid methyl ester ( ISO-1 , Merck ) was dissolved in Me2SO at various concentrations and added to the cuvette with rBgMIF prior to the addition of the dopachrome . The decrease in absorbance at 475 nm was monitored for 5 min using a UV/visible Spectrophotmeter Ultraspec 2100 ( Amersham ) . Antibodies used in this study were as follows: anti-Actin ( Abcam ) , anti-phospo-p53Ser15 , anti-phospho-p44/42 MAPK ( Erk1/2 ) ( Thr202/Tyr204 ) , anti- p44/42 MAPK ( Erk1/2 ) and anti-Phospho-p38 MAPK ( Thr180/Tyr182 ) ( Cell Signaling ) . An anti BgMIF antiserum was produced in a rabbit using the Ac-HKDITEIASEFLQKSQK-amide , Ac-VRVPALFCQFDGHLHGH-amide peptides and the polyclonal sera were purified using a peptide linked resin column ( Proteogenix ) . For western blot analysis cells or snails were lysed in Tris-buffered saline ( 50 mM Tris-Base , 150 mM NaCl , pH 7 . 5 ) containing 1% NP-40 , 0 . 5% deoxycholic acid , 0 . 1% SDS , 2 mM EDTA , and 1 mM PMSF ) , cellular debris was pelleted and the supernatants were adjusted for protein concentration and diluted with reducing SDS-PAGE sample buffer . For BgMIF analyses the total protein extracts were separated by SDS-PAGE in pre-cast 16% tricine gels ( Invitrogen ) and transferred to a PVDF membrane with 0 . 2 µm pore size ( Millipore ) . For actin , ERK , p38 and p53 analyses the total protein extracts were separated by SDS-PAGE Tris-glycine gels and transferred to a PVDF membrane with 0 . 45 µm pore size ( Millipore ) . Western blot analyses were then performed using the SNAP id system ( Millipore ) according to the manufacturer's instructions . For real-time PCR analyses total RNA ( 1 µg of RNA from a 5 snail pool and one tenth of the RNA obtained from 2×105 Bge cells or collected haemocytes ) was reverse transcribed using SuperScript III reverse transcriptase ( Invitrogen ) . For Q-PCR analyses , cDNAs used as templates were amplified using the SYBR Green Master Mix ( Invitrogen ) and the ABI PRISM 7000 sequence detection system ( Applied Biosystems ) . Primers ( Sup . Table 1 ) specific for B . glabrata ribosomal protein S19 ( Genbank accession number CK988928;[21] ) , and BgMIF , were designed by the Primer Express Program ( Applied Biosystems ) and used for amplification in triplicate assays . The organ distribution of BgMIF protein was determined in adult snails . Organs ( albumen gland , hepatopancreas , foot , heart and digestive tract ) were excised , sectioned and homogenized at 4°C with Tris-buffered saline . The cellular debris was pelleted and the supernatants were adjusted for protein concentration , diluted with reducing SDS-PAGE sample buffer and 15 µg of total protein was analyzed by SDS-PAGE and western blotting as described above . For the measurement of BgMIF protein contained in the plasma ( cell free hemolymph ) after infection we used an indirect ELISA protocol . Briefly , the cell hemolymph for five snails was pooled and centrifuged to pellet the cells . Wells of a PVC microtiter plate were coated with 100 µl of hemolymph diluted by half in PBS and plates were incubated for 2 h at room temperature . Then , after the coating solution was removed , plates were washed three times by filling the wells with 200 µL of PBS . The solutions or washes were removed by flicking the plate over a sink . The remaining protein binding sites in the coated wells were blocked by adding 200 µL of blocking buffer ( 5% bovine serum albumin ( BSA ) in PBS ) per well . Plates were covered and incubated for 2 h at room temperature . Plates were then washed twice with PBS , and 100 µL of anti-MIF antibody ( from rabbit , diluted 1/500 , Proteogenix ) diluted in blocking buffer was added to each well . Plates were covered and incubated overnight at 4°C . Subsequently , plates were washed four times with PBS , and 100 µL of conjugated secondary antibody ( antirabbit , diluted 1/5000 , Jackson Immuno Research ) diluted in blocking buffer was added to each well . Then , plates were covered and incubated 2 h at room temperature . After four washes , the horseradish peroxidase ( HRP ) activity was measured using the colorimetric substrat TMB ( 3 , 3–5 , 5 –Tetramethylbenzidine ) blue substrate ( Roche Applied Science ) . A standard with rBgMIF ranging between 0–230 ng/ml was used . The measurements were made twice in triplicate with two different infection experiments using a microplate reader MRXII ( Dynex Technologies ) at 450 nm; the haemolymph was pooled before infection and 6 , 24 and 48 h post infection . For immunolocalisation assays , circulating haemocytes or Bge cells were extracted or cultured as described above and allowed to adhere to glass slides , washed with PBS , fixed in 4% paraformaldehyde for 10 min and permeabilised by a 4 min treatment with Triton X-100 at 0 . 1% . Slides were saturated for 90 min with PBS containing 1% bovine serum albumin ( BSA ) and normal goat serum ( 1/50 ) at room temperature ( RT ) . This blocking step was followed by an overnight incubation with rabbit anti-BgMIF polyclonal serum ( diluted at 1/100 in PBS-BSA 1% ) . After three washes the slides were incubated with goat anti-rabbit Alexa Fluor 488 IgG ( 1/500 in PBS-BSA 1% , Molecular Probes ) for 2 h at room temperature ( RT ) . Slides were then stained with Hoechst 33342 and rhodamine-labeled phalloidin ( 1/1000 in PBS , Sigma ) for 10 min at RT , washed and mounted with Fluoromont G ( Interchim ) . For control slides , anti-BgMIF polyclonal serum was incubated with the peptides used as immunogens for 1 h at RT and the slides were then treated as described above . Samples were analysed by confocal microscopy using a LSM 710 inverted microscope ( Zeiss ) . All the confocal imaging was performed with a LSM710 microscope ( Zeiss ) and a Plan Apochromat objective ( 63×1 . 4 NA oil immersion ) . The associated software ( Zen 2008 ) enabled the adjustment of acquisition parameters . The rhodamine ( red ) signal was excited at 561 nm and emission was collected from 570 to 700 nm . The Alexa488 ( green ) signal , in contrast , was excited at 488 nm and emission was collected from 490 to 530 nm . The nuclear Hoechst dye signal was excited at 405 nm and emission was collected from 410 to 470 nm . Fluorescent signals were collected sequentially , with a 4 lines average , and resulting images are 2048×2048 ( or 1024×1024 ) pixels in size . By setting the photomultiplier tubes and the pinhole size ( 1 AU ) correctly , there was no signal bleed-through . The images were treated with ImageJ ( NIH ) and Photoshop CS3 ( Adobe ) . The protocol used was adapted from [29] . Briefly Bge cells ( 2×104 cells/well ) were cultured until semi-confluent in 96-well plates in complete Bge medium . The cells then were synchronized by culture in 0 . 5% FCS-containing Bge medium overnight . The medium was then replaced by fresh medium ( control condition ) or medium containing different concentrations of rBgMIF . After incubation with rBgMIF the cells were pulsed with 10 µM of BrdU ( Sigma ) for 2 h and the proliferation was measured by ELISA method as described in [57] . In order to test the effect of inhibition of the ERK pathway , cells were treated with the MEK inhibitor U0126 ( Cell Signalling ) at 10 µM or Me2SO ( solvent ) for 30 min prior to the addition of rBgMIF and were then treated as above . For analysis of sustained activation of ERK , cells ( 2×105cells/well ) were cultured and synchronized as described above . Cells were exposed to various concentrations of rBgMIF , for 2 h , 8 h and 24 h , and then lysed as described above . Cell lysates were used for western blot analysis of phosphorylated and total ERK content . The apoptosis assay used was adapted from [31] . Bge cells ( semi-confluent ) were cultured in 6-well plates in complete Bge medium . Cells were pretreated for 12 h with rBgMIF at different concentrations . The NO donor , S-nitrosoglutathione ( SNGO , Sigma ) was then added at 250 µM for 8 h . Apoptosis was measured by the Terminal deoxynucleotidyl transferase mediated dUTP Nick End Labeling ( TUNEL ) assay ( Roche Applied Biosystems ) , following the manufacturer's instructions . Briefly , the cells were fixed in paraformaldehyde 4% for 1 h , washed and permeabilized with sodium citrate 0 , 1%/Triton -X 100 0 . 1% for 2 min on ice . Cells were incubated with “labeling solution” for 1 h at 37° C , washed with PBS and the number of positive cells was visualized on a FACSCalibur flow cytometer ( Becton Dickinson ) and the data were treated with CellQuestPro software ( Becton Dickinson ) . The data are displayed using a logarithmic scale and the results are represented as the percentage of cells undergoing apoptosis . Activation of p53 was investigated by performing a western blot on total protein extracts from Bge cells using the anti-p53ser15P antibody and the anti-actin antibody . PCR products were amplified from the pCR2 . 1 TOPO vector containing the complete BgMIF sequence , purified ( Wizard SV Gel and PCR Clean up system , Promega ) and used as a template for T7 transcription and synthesis of BgMIF dsRNA ( MEGAScript T7 kit , Ambion ) . The firefly ( Photinus pyralis ) luciferase gene dsRNA ( pGL3 vector , Promega ) was used as a control ( see Sup . Table 1 ) . Bge cells ( 2×105cells/well ) were cultured in 12-well plates in complete Bge medium . For the analyses of BgMIF excretion the medium was changed for complete CBSS and the cells were stimulated with different quantities ( 30 , 60 , 120 , 240 and 480 µg/ml of protein content ) of S . mansoni ESP ( prepared as described in [33] ) for 12 h . Cell supernatants were collected , centrifuged for 10 min at 800 g to eliminate non adherent cells , then concentrated 10-fold by membrane filtration with a 10 kDa cut-off ( Centricon , Amicon ) . For the study of Bgp38 activation , the protocol was adapted from [33] . Briefly , cells were exposed to 120 µg/ml of ESP for 5 , 15 and 30 min , and the protein extracts were analysed for the phosphorylated p38 and actin content by western blotting . The content of endotoxin was measured with Limulus Amoebocyte Lysate ( Cambrex ) . ESP used in the bioassays contained less than 17 pg endotoxin/mg of protein . Each dsRNA ( 2 µg ) was transfected into confluent cultures of Bge cells using the FUGENE HD transfection reagent ( Roche Applied Biosystems ) , following the manufacturer's instructions . For the experiments with EPS , the Bge medium was changed to complete CBSS 2 days after the addition of dsRNA . On the third day 120 µg/mL of ESP products were added to the medium and the presence or absence of an aggregation phenotype was determined . For encapsulation experiments , the medium was replaced by fresh medium and S . mansoni mother sporocysts cultured in complete CBSS for 48 hours , were added ( 500 sporocysts/well ) 3 days after the addition of dsRNA . The co-culture was maintained 4 days to allow the observation of an in vitro encapsulation phenotype as described previously [50] . Aggregation and encapsulation phenotypes were observed using an Eclipse TS100 optical microscope ( Nikon ) and the images were acquired with a DS-Fi1digital camera ( Nikon ) and treated with Photoshop CS3 ( Adobe ) . For encapsulation experiments , 250–300 sporocysts were counted per assay and the results were represented as the percentage of sporocysts completely covered in adhering cells . Each dsRNA ( 15 µg in 10 µl of sterile CBSS ) was injected into the cardiac sinus of B . glabrata snails , using a 50 µl Hamilton syringe with a 26s needle ( Hamilton ) . Three days after injection , hemocytes were isolated from three snails per group and the snails were individually frozen in liquid nitrogen for extraction of RNA and soluble protein . Knock down efficiency was checked by real-time PCR and western blot analyses . Snails were infected three days after injection , with 20 S . mansoni miracidia . 24 h post infection the hemolymph of three snails was pooled and diluted by half in complete CBSS containing citrate/EDTA ( 50 mM sodium citrate , 10 mM EDTA , and 25 mM sucrose ) [58] and the composition of the hemocyte population in each condition was assessed by FACS analyses using SSC and FSC parameters in a FACSCalibur flow cytometer ( Becton Dickinson ) . The hemocyte population was analyzed in pools of three snails . In order to determine the number of mother sporocysts ( Sp1 ) present in the head–foot region of infected snails , mollusks were fixed 15 days post-exposure as described [59] , [60] . Briefly , the snails were relaxed in water containing an excess of crystalline menthol for 6 h . The snail shell was removed and the body was fixed in modified Raillet–Henry's solution ( 930 ml distilled water , 6 g NaCl , 50 ml Formol 40% , 20 ml acetic acid ) . The head–foot zone was dissected and visually inspected . In each snail Sp1 were readily observable as translucent white bodies within an opaque yellow tissue background . All data were expressed as mean plus or minus SE . The statistical significance of differences was assessed using the Mann–Whitney U test for nonparametric data or Student's t-test using the program StatView ( Abacus Concepts ) . P values of less than . 05 , 0 . 01 or . 001 were used to indicate statistical significance . Nucleotide sequence data reported in this paper are available in the GenBank database under the accession numbers ACR81564 ( BgMIF ) , GU929337 ( Bgp53 ) . | Schistosoma mansoni , a parasitic blood fluke that causes intestinal schistosomiasis in humans , requires an intermediate host , a freshwater snail of the genus Biomphalaria for its transmission . Infection of the snail triggers marked cellular and humoral immune responses , but the molecular mechanisms of these responses are not well known . We have identified and characterized the involvement of a snail homologue of the cytokine MIF ( Macrophage Migration Inhibitory Factor ) in the snail immune responses to infection by the parasite . By using biochemical and molecular approaches in combination with in vitro culture and in vivo gene knock down , we have demonstrated the role of snail MIF in the regulation of the snail innate immune system . In particular , MIF regulates the proliferation and activation of the hemocytes , the macrophage-like snail defense cells , and the encapsulation response . This shows for the first time that MIF has a conserved cytokine function in an invertebrate and underlines the interest of the schistosome-snail model in the study of innate immunity . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"infectious",
"diseases/helminth",
"infections",
"immunology/innate",
"immunity"
] | 2010 | Involvement of the Cytokine MIF in the Snail Host Immune Response to the Parasite Schistosoma mansoni |
RAD51 recombinase polymerizes at the site of double-strand breaks ( DSBs ) where it performs DSB repair . The loss of RAD51 causes extensive chromosomal breaks , leading to apoptosis . The polymerization of RAD51 is regulated by a number of RAD51 mediators , such as BRCA1 , BRCA2 , RAD52 , SFR1 , SWS1 , and the five RAD51 paralogs , including XRCC3 . We here show that brca2-null mutant cells were able to proliferate , indicating that RAD51 can perform DSB repair in the absence of BRCA2 . We disrupted the BRCA1 , RAD52 , SFR1 , SWS1 , and XRCC3 genes in the brca2-null cells . All the resulting double-mutant cells displayed a phenotype that was very similar to that of the brca2-null cells . We suggest that BRCA2 might thus serve as a platform to recruit various RAD51 mediators at the appropriate position at the DNA–damage site .
Homologous recombination ( HR ) maintains genome integrity by accurately repairing double-strand breaks ( DSBs ) that arise during the mitotic cell cycle or are induced by radiotherapy [1] , [2] . HR also plays an important role in releasing the replication forks that stall at damaged template DNA strands [3] , [4] . Thus , effective HR makes tumor cells tolerant to the chemotherapeutic agents that damage DNA and stall replicative DNA polymerases . Such chemotherapeutic agents include cis-diaminedichloroplatinum ( II ) ( cisplatin ) , camptothecin , and poly ( ADP-ribose ) polymerase ( PARP ) inhibitors , including olaparib ( AstraZeneca ) . Cisplatin is a crosslinking agent that generates intra- and inter-strand crosslinks and thereby stalls replicative DNA polymerases . Camptothecin inhibits the ligation of single-strand breaks ( SSBs ) that are formed during the normal functioning of topoisomerase 1 . Resulting unrepaired SSBs are converted to DSBs upon replication . Similarly , PARP inhibitors interfere with SSB repair [5] . Since HR plays a major role in repairing DNA lesions generated by camptothecin , cisplatin , and PARP inhibitors [6] , measuring HR efficiency in individual malignant tumors may help predict the efficacy of these chemotherapeutic treatment for each tumor [7]–[9] . HR-dependent DSB repair is accomplished by the following step-wise reactions [10] . DSBs are processed by the Mre11/Rad50/Nbs1 complex and the CtIP , Exo1 and DNA2 nucleases to develop 3′ single-strand DNA ( ssDNA ) tails [11]–[17] . RAD51 , an essential recombinase , polymerizes on these ssDNAs , leading to the formation of nucleoprotein filaments . These filaments undergo homology search and subsequent invasion into homologous duplex DNA to form a D-loop structure , where they serve as a primer for DNA synthesis [18] , [19] . After the extended end is displaced from the D-loop , it anneals to its partner-end to complete DSB repair . We know that RAD51 plays a key role in HR in vertebrate cells , as inactivation of RAD51 results in the accumulation of chromosomal breaks in mitotic cells and inhibits the completion of even a single cell cycle [1] , [2] . The polymerization of RAD51 at damage sites is strictly regulated by a number of accessory factors ( hereafter called RAD51 mediators ) , including the five RAD51 paralogs , SWS1 , RAD52 , SFR1 , BRCA1 , BRCA2 , and PALB2 [3] , [20]–[27] . The functional relationships of these RAD51 mediators are poorly understood , because cells deficient in multiple RAD51 mediators have not been established . BRCA2 was originally identified as a tumor suppressor , as germline mutation of the BRCA2 gene results in a high risk of developing breast , ovarian , pancreatic , prostatic , and male breast cancer [3] , [20] , [28] , [29] . BRCA2 is recruited to processed DSBs , and facilitates the assembly of RAD51 at the single-strand tail . The middle of the BRCA2 protein has eight BRC repeats , comprising 26 amino acids . Biochemical studies have revealed that individual BRC repeats prompt the loading of RAD51 on ssDNA [30] , [31] . Since no brca2-null cells have been established , the function of BRCA2 has been postulated from the phenotypic analysis of mice carrying an allele , extending from the N-terminus to the BRC3 motif ( hereafter brca2tr ) that encodes a truncated form of BRCA2 . Cells derived from brca2tr mice and brca2tr DT40 cells are able to proliferate and exhibit increased sensitivity to ionizing radiation , camptothecin , and cisplatin [32] , [33] . It remains unclear whether brca2 null cells display the same phenotype as do rad51 cells , or a milder phenotype . The roles of the RAD51 mediators have been characterized by phenotypic analysis of their mutants . Mammalian brca1-deficient cells show normal focus formation of the RPA ssDNA binding protein but diminished RAD51 focus formation at DSBs , indicating that BRCA1 facilitates the polymerization of RAD51 after the resection of DSBs [15] , [34] . DT40 cells deficient in any one of the five RAD51 paralogs show a very similar phenotype , including compromised RAD51 focus formation and the same degree of DNA damage sensitivity [35] , [36] , suggesting that these five proteins form a functional unit in the promotion of RAD51 polymerization in which each RAD51 paralog is essential for its function . No biochemical studies have yet defined the molecular mechanisms underlying the promotion of RAD51 assembly by BRCA1 , the RAD51 paralogs , or SWS1 . SWS1 is another RAD51 mediator [26] , and sws1-deficient vertebrate cells have not yet been reported . SFR1 was originally identified in fission yeast [25] , and its role in in vitro HR reactions [37] , [38] as well as the phenotypic analysis of sfr1-deficient mice have been recently reported [39] . The biochemical character of full-length human BRCA2 has been recently documented , whereas no biochemical studies have defined its functional interaction with other RAD51 mediators or the molecular mechanisms underlying the promotion of RAD51 assembly by BRCA1 , the RAD51 paralogs , or SWS1 [40]–[42] . In this paper , we addressed the function of Rad51 mediators and their relationship in DNA-damage responses . We generated the single Rad51 mediator mutant cells , including brca2 null , sfr1 and sws1 deficient cells from DT40 chicken cell line [43] . We also disrupted the BRCA1 , one of the RAD51 paralogs ( XRCC3 ) , RAD52 , RAD54 , SWS1 , and SFR1 in brca2 null deficient DT40 cells . The phenotypes of these cells were analyzed to reveal hierarchical relationship of RAD51 , BRCA2 , and the other RAD51 mediators , where RAD51 is able to operate HR without BRCA2 while BRCA2 is required for the functioning of the other RAD51 mediators . Hence , BRCA2 might serve as a platform to recruit various RAD51 mediators at the appropriate position of DNA-damage sites . Our study sheds light on the functional relationship of RAD51 and every known RAD51 mediators for the first time , and thereby significantly contributes to the development of effective anti-cancer therapies .
To analyze SFR1 and SWS1 , we disrupted the SFR1 and SWS1 genes in DT40 cells ( Figure 1A–1D ) . Table 1 summarizes the selection marker genes we used to disrupt genes in this study . The resulting sfr1 and sws1 mutant clones proliferated with nearly normal kinetics ( Figure 2 ) and exhibited an increase in cellular sensitivity to cisplatin . The sfr1 mutant was sensitive also to camptothecin and olaparib ( Figure 3 ) . Both mutants showed a slight but significant decrease in ionizing-radiation-induced RAD51 focus formation ( Figure 4 ) . We conclude that SFR1 and SWS1 indeed work as RAD51 mediators , though their contribution to HR-dependent repair is less significant than that of BRCA1 , BRCA2 , and the RAD51 paralogs . To create brca2-null cells , we generated compound heterozygous mutant cells ( hereafter called BRCA2−/con1 cells ) ( Figure 1E ) . The whole coding sequence was deleted in the minus ( − ) allele of the -/conditional-null allele-1 ( -/con1 ) genotype of the BRCA2−/con1 cells ( Coding sequence deletion allele in Figure 1F ) . We conditionally deleted the con1 allele of the BRCA2−/con1 cells by adding tamoxifen , which activated the chimeric Cre recombinase [28] and thereby eliminated the promoter and coding sequences , including exons 1 and 2 of the con1 BRCA2 allelic gene ( BRCA2 conditional-null allele-1 in Figure 1F ) . At day two of continuous tamoxifen treatment , the vast majority of cells lost the intact BRCA2 gene in the conditional allele , and a substantial fraction of cells began to die . To our surprise , we were able to reproducibly establish clonally expanding cells wherein the conditional BRCA2 allele was deleted ( BRCA2−/− cells , hereafter called brca2-null cells ) from 10 to 20% of the tamoxifen-treated populations . We verified deletion of the BRCA2 allele in the established clones by Southern-blot ( Figure 1G ) and western-blot ( Figure 1H ) analysis . The ability of the brca2-deleted cells to proliferate is in marked contrast to the immediate cell death observed in rad51-deleted cells [1] . The plating efficiency of the brca2-null clones was around 20% , which is significantly lower than that of the wild-type ( 100% ) and brca2tr ( 60% ) cells [33] . One obvious concern with this experiment was that expression of the N-terminal-truncated BRCA2 protein in the Cre-mediated deletion lines could allow for residual function . We therefore created a second version of the conditionally inactivated BRCA2 allele , wherein sequences spanning from the promoter to intron 12 could be eliminated by induction of Cre ( BRCA2 conditional-null allele-2 in Figure S1 ) . We exposed the resulting compound heterozygous mutant cells to tamoxifen and confirmed reproducible establishment of BRCA2-deleted clones ( Text S1 ) . This second brca2 conditional-null allele supported proliferation with generation times very similar to those of the first version of the brca2-null cells ( data not shown ) . The more extensively deleted brca2 clones showed a phenotype indistinguishable from that of the smaller deletion clones , indicating that both deletions confer the null phenotype . We assessed the proliferative properties of brca2-null clones by monitoring their growth curve ( Figure 2A and 2B ) and cell cycle ( Figure 2C ) . Wild-type cells doubled every 8 hours and increased in cell number by 64 times over 48 hours . The brca2-null cells increased by 17 times over 48 hours ( Figure 2A ) , calculated as a 1 . 6-fold increase over 8 hours ( 1 . 66 = 17 ) ( Figure 2B ) . The number of brca1 , brca2tr , and xrcc3 clones [36] increased 1 . 6 , 1 . 9 , and 1 . 8 fold , respectively , over eight hours . The reduced growth kinetics of the brca2-null cells is partly due to apoptosis in a substantial fraction of the cell population , as evidenced by the accumulation of cells in a sub-G1 fraction ( Figure 2C ) . To understand the cause of this cell death , we measured the number of chromosome breaks in mitotic cells . Forty-six chromosomal aberrations were detectable in 100 mitotic brca2-null cells , larger than the number of aberrations observed in any other RAD51-mediator mutants ( Table 2 ) . We therefore conclude that spontaneously arising DSBs resulted in cell death in a fraction of brca2-null cells , thus accounting for the reduced growth kinetics ( Figure 2A and 2B ) . The viability of the brca2-null cells reveals that BRCA2 plays a less important role than does RAD51 in the maintenance of genome integrity [1] . The high number of spontaneously arising chromosomal breaks in the brca2-null cells ( Table 2 ) shows that BRCA2 plays a more fundamental role in genome maintenance than do any of the other RAD51 mediators . We analyzed cellular tolerance to camptothecin , cisplatin , and olaparib by measuring cellular survival at 72 hours ( 7–9 cell cycles ) after continuous exposure to these agents in a liquid medium . We did not use the conventional colony-formation assay for this analysis , because the plating efficiency of the brca2-null cells was only 20% , 5-fold lower than that of wild-type cells . Figure 3A presents an example of cellular sensitivity to camptothecin , a DNA-damaging agent . Subsequent figures illustrate the sensitivity of each mutant , assessed by LC50 values , i . e . , the dose that reduces cell survival to 50% relative to the LC50 value of wild-type cells , which is defined as 100% ( Figure 3B–3E ) . In the cellular-survival analysis , the brca2-null cells showed an increased sensitivity to camptothecin ( Figure 3B ) , cisplatin ( Figure 3C ) , and olaparib ( Figure 3D and 3E ) . Moreover , sensitivity to cisplatin and olaparib was higher with the brca2-null cells than for any of the other HR mutant cells , including the brca1 , rad52 , rad54 , and xrcc3 clones ( Figure 3 ) . We therefore conclude that BRCA2 plays a more important role in HR-dependent repair than do the other RAD51 mediators , including BRCA1 , RAD52 , RAD54 , the RAD51 paralogs , SFR1 , and SWS1 . The less prominent phenotype of the brca2tr cells compared to the brca2-null cells indicates that the BRCA2 BRC3-truncated protein retains significant residual HR function . Although brca1 cells were less sensitive to cisplatin and olaparib than were brca2-null cells , the brca1-null cells exhibited a slightly higher sensitivity to camptothecin than did the brca2-null cells ( Figure 3A ) . The greater contribution of BRCA1 to cellular tolerance to camptothecin might be attributable to the role played by BRCA1 in DNA-damage responses other than HR , such as collaborative action with CtIP to eliminate covalently bound oligo-peptides from DSBs [15] . We next measured the frequency of HR-dependent repair of I-Sce1-mediated DSBs in a recombination substrate , SCneo , inserted into the OVALBUMIN locus [44] , [45] ( Table 3 ) . The frequency of HR-dependent DSB repair was calculated as the number of neomycin-resistant ( neo+ ) colonies relative to the number of plated cells . The frequency of HR in the brca2-null , brca2tr , and brca1 cells was decreased by 1 . 5×104- , 1 . 5×102- , and 4 . 5×103-fold , respectively , compared with wild-type cells . We conclude that the brca2-null cells retain residual HR activity , which may account for their viability even in the complete absence of BRCA2 . Since BRCA2 promotes the loading of RAD51 at damage sites , we measured RAD51 focus formation at 3 hours after ionizing radiation . The number of RAD51 foci was reduced but not eliminated in the brca1 and brca2tr clones , compared with wild-type cells ( Figure 4 ) . These findings are consistent with previous observations [33] , [46] . By contrast , we hardly detected any RAD51 focus formation in the brca2-null cells . In conclusion , the BRCA2 protein plays a key role in the efficient recruitment of RAD51 to DNA-damage sites , but is not essential for every HR reaction . The idea that RAD51 carries out HR even without BRCA2 led us to investigate whether or not other RAD51 mediators substitute for BRCA2 in the promotion of RAD51-dependent HR . To this end , we deleted the BRCA1 , RAD52 , SFR1 , SWS1 , and XRCC3 genes in the conditional brca2-null background , then inactivated the BRCA2 gene by treating the cells with tamoxifen ( Figure 1E ) . We also disrupted the RAD54 gene in the conditional brca2-null background ( Table 1 ) . The RAD54 protein promotes HR after the assembly of RAD51 at DNA-damage sites [47] . To our surprise , we were able to reproducibly establish all resulting double mutants , although a substantial fraction ( ∼30% ) of the brca2-null cells died each cell cycle . The growth kinetics for the brca1/brca2-null , rad52/brca2-null , sfr1/brca2 , sws1/brca2-null , and xrcc3/brca2-null double-mutant clones was similar to those of the brca2-null single mutant ( Figure 2 ) . Taking the very severe phenotype of brca1 cells into account , the viability of the brca1/brca2-null cells was surprising . The cloning efficiency of the brca1/brca2-null cells was slightly higher than that of the brca2-null single-mutant cells ( 30% compared to 20% ) . Accordingly , the number of spontaneous chromosomal aberrations in the brca1/brca2-null cells was consistently slightly lower than that in the brca2-null cells ( Table 2 ) . In summary , although the loss of either BRCA1 or BRCA2 greatly increased the number of spontaneous chromosomal breaks , inactivation of BRCA1 in the brca2-null cells resulted in a slight reduction in the severity of the brca2 phenotype . An early study shows rad52/xrcc3 double-mutant cells are synthetic lethal and exhibit numerous chromosomal breaks [24] , whereas we here found that the brca2/rad52 and brca2/xrcc3 double-mutant cells were viable . Thus , the synthetic lethality might be attributable to BRCA2 mediated formation of toxic HR intermediates , because the brca2/xrcc3 cells exhibit spontaneously arising isochromatid breaks , where two sisters are broken at the same site due to defective completion of HR [48] . To test this hypothesis , we conditionally inactivated the BRCA2 gene in the rad52/xrcc3 cells ( Text S1 ) . We found that the inactivation of the BRCA2 gene indeed rescued the rad52/xrcc3 cells ( Figure S2 ) . This observation indicates that the synthetic lethality of the rad52/xrcc3 cells does not argue against the idea that the functioning of RAD52 and XRCC3 depends on BRCA2 . Likewise , the formation of toxic HR intermediates might explain the apparent discrepancy between the viability of rad52/brca2-null DT40 cells and the mortality caused by shRNA mediated depletion of RAD52 in brca2 deficient mammalian cells [49] , as the latter cells express a residual amount of RAD52 and truncated BRCA2 proteins perhaps leading to the formation of toxic HR intermediates . We next measured the sensitivity of the brca1/brca2-null , rad52/brca2-null , rad54/brca2-null , sfr1/brca2-null , sws1/brca2-null , and xrcc3/brca2-null double-mutant clones to camptothecin , cisplatin , and olaparib ( Figure 3 and Figure 5 ) . Remarkably , inactivation of any gene did not increase cellular sensitivity to the three damaging agents by more than two-fold . This observation indicates that the contribution made by BRCA1 , the RAD51 paralogs , RAD52 , RAD54 , SFR1 , and SWS1 to HR depends mostly on BRCA2 . Interestingly , the loss of BRCA1 , SFR1 , and SWS1 somewhat increased the cellular tolerance of the brca2-null cells to cisplatin . Similarly , the loss of SWS1 increased the cellular tolerance of the brca2-null cells to camptothecin and olaparib . This increased tolerance was not accompanied by the upregulation of RAD51 focus formation ( data not shown ) . We therefore suggest that , in the absence of BRCA2 , SWS1 has a moderately antagonistic effect on HR-dependent repair . By contrast , the loss of RAD52 and XRCC3 significantly increased the cellular sensitivity of the brca2-null cells to olaparib . In summary , BRCA2 is required for all the analyzed RAD51 mediators to function , and the functional relationships between BRCA2 and the other RAD51 mediators in HR-mediated repair differ slightly depending on the type of DNA damage .
Data on Ustilago maydis [50] and Arabidopsis thaliana [51] suggest that BRCA2 might be essential for RAD51 to function in any HR reaction . However , we here report that RAD51 can form HR products even in brca2-null cells , indicating that RAD51 plays a more important role than BRCA2 in HR . This hierarchy between RAD51 and BRCA2 is supported by previous reports of experiments with mice , as rad51 null embryos died earlier ( ∼E6 . 5 ) than did BRCA2 null ( ∼E8 . 5 ) embryos [52] , [53] . The viability of brca2-null DT40 cells is consistent with the clonal expansion of BRCA2-deficient cells derived from mammary epithelial lineage-specific or T cell lineage-specific BRCA2-null-deficient mice [54] , [55] . Adding to these findings , we here show solid evidence that vertebrate RAD51 is capable of functioning in the absence of BRCA2 . The phenotypic analysis of brca1 , brca2-null , and brca1/brca2-null clones , combining with the previous study of rad51-null cells , reveals the functional relationship described as follows . The capability of HR was dramatically diminished when either BRCA1 or BRCA2 was absent , indicating that the collaboration of BRCA2 and BRCA1 is required for efficient HR events . brca2-null cells exhibited more prominent defects in HR than did brca1-null cells , indicating that BRCA2 can function in HR independently of BRCA1 . Moreover , BRCA2′s contribution to the repair of cisplatin-induced interstrand crosslinks is more significant than BRCA1 , which is likely attributable to the fact that BRCA2 , but not BRCA1 , functions in the Fanconi anemia repair pathway [56] . BRCA1 has additional functions other than in HR , such as mediating the damage checkpoint and processing DSBs [15] , [57] . The fact that rad51-null cells have a considerably stronger phenotype than brca2-null cells indicates that RAD51 could still perform HR-dependent repair in brca2-null cells . The phenotypic similarities between the brca2-null and the brca1/brca2-null clones indicate that BRCA1 contributes to HR by collaborating with BRCA2 . Presumably , the two BRCA proteins form a functional unit and collaborate intimately to load RAD51 at damage sites . This idea is supported by the fact that BRCA1 physically associates with BRCA2 through the PALB2 protein [58] . However , this idea is challenged by recent studies that suggest that BRCA1 plays a role in the resection of DSBs [14] , [59] . One possible scenario is that the complex formation of BRCA1 and BRCA2 may allow for close collaboration between the BRCA1-dependent resection of DSBs and the subsequent loading of RAD51 on the resulting 3′ overhang . Such an interaction interface might be shared by the E . coli RecBCD complex , which serves as the DSB resection complex and also interacts directly with RecA following chi site recognition [60] . In summary , the phenotypic analysis of brca1 , brca2-null , and beca1/brca2-null DT40 clones demonstrates that BRCA1 controls RAD51 in HR , mainly through collaboration with BRCA2 . Our study reveals that rad52/brca2-null , sfr1/brca2-null , sws1/brca2-null , and xrcc3/brca2-null clones exhibit a phenotype very similar to that of brca2-null cells ( Figure 5 ) . In a separate study , we conformed phenotypic similarity between brca2-null and palb2/brca2-null clones ( data not shown ) . We therefore suggest that , like BRCA1 , PALB2 , the RAD51 paralogs , RAD52 , SFR1 , and SWS1 are also able to participate in HR , mostly depending on BRCA2 . One possible scenario is that BRCA2 is recruited to DNA-damage sites through PALB2 or by directly interacting with the junction between the duplex DNA and the single-strand sequences [61] . BRCA2 might thus serve as a platform to recruit various RAD51 mediators to the appropriate positions of DNA-damage sites ( Figure 6 ) . DT40 is a unique cell line that offers a panel of DNA-repair-deficient isogenic mutants derived from a stable parental line . DT40 cells have several characteristics that affect cellular responses to anti-cancer agents . First , DT40 appears , for unknown reasons , to possess a significantly higher HR efficiency than any mammalian cell line [43] . The efficient HR in DT40 cells is prominent particularly in HR between diverged homologous sequences such as Immunoglobulin V gene diversification [62] and gene targeting , where the selection marker genes of gene-disruption constructs may interfere with HR as heterologous sequences . Second , like many cancer cells , DT40 lacks the functional p53 , and as a result has no G1/S damage checkpoint [63] . In addition , 70% of the DT40 cell cycle takes place in the S phase . Thus , DNA damage at any phase of the cell cycle may have a direct impact on DNA replication . These characteristics , specific to DT40 , suggest that a defect in DNA repair associated with DNA replication , including HR-mediated DNA repair , may display a more prominent phenotype in DT40 cells than in other cell lines that have a longer G1 phase and/or a normal G1/S checkpoint . Bearing this in mind , DT40 is revealed as a unique and valuable tool and has been used extensively to explore the role of individual HR factors responsible for cancer therapy .
Cells were aquired and cultured as described previously [1] , [43] . All mutants were isolated from single colonies . DNA transfection and selection were performed as described previously [43] , [64] . Details of the cell lines used in this study are shown in Table 1 . To disrupt the SFR1 gene , we generated SFR1-puro and SFR1-bsr disruption constructs by combining two genomic PCR products with the puro- or bsr-selection-marker cassette . Genomic DNA sequences were amplified using the 5′-CCCGGTACTGAGGGGTGCGATTGCTTGCAGG-3′ and 5′-CCCTTAGAGTTGCACTCATTGGCTAAAG-3′ primers for the upstream arm , and the 5′-GGCTCAAACTGGTCAAGATGTACCGATCTAAGG-3′ and 5′-CCACCAGCATCCACTAAAGGGCAAGGAACG-3′ primers for the downstream arm . Amplified PCR products were cloned into pCR2 . 1-TOPO vector ( Invitrogen ) . The 1 . 7 kb fragment of the upstream arm was cloned into the KpnI site of pCR2 . 1 containing the 3 . 0 kb downstream arm . Marker-gene cassettes were inserted at the BamHI site of the resulting plasmid . To generate SFR1−/− cells , SFR1-puro and SFR1-bsr disruption constructs lineralized with NotI were transfected sequentially by electroporation ( Bio-Rad ) . The genomic DNA of the transfectants was digested with both BamHI and EcoRI , and gene-targeting events were confirmed by Southern blot analysis . The probe was prepared from a PCR-amplification of DT40 genomic DNA using primers 5′-GAACAGCACCACGCAATTCA-3′ and 5′-CCTTAGAGTTGCACTCATTGG-3′ . Chicken SFR1 cDNA was isolated by PCR amplification of the primary cDNAs using the 5′-GTTGAGATGGAGGAAGCAGCGTGTGGTAAA-3′ and 5′-CACCACTCAATTCCACTTCAAAGAG-3′ primers . The gene bank accession number of the chicken SFR1 gene is XM-001234167 . To disrupt the SWS1 gene , we generated SWS1 gene-disruption constructs containing the 2 . 6 kb upstream and the 3 . 0 kb downstream genomic fragments . The 2 . 6 kb fragment was PCR-amplified using the 5′-ggggacaactttgtatagaaaagttgTTCTTACGTCACTCCAGAAGAACA-3′ and 5′-ggggactgcttttttgtacaaacttgCCAAGTCTGTGAATCGCAGAAGCA-3′ primers . The 3 . 0 kb fragment was PCR-amplified using the 5′-ggggacagctttcttgtacaaagtggAATTCCAAGCAGTTCCACATCTCT-3′ and 5′-ggggacaactttgtataataaagttgGTATGGCTCCTGTCAGGTTAGAGT-3′ primers . Note that the underlined sequences denote the recognition sequences in the Gateway system ( Invitrogen ) . Using the MultiSite Gateway system with pENTR-lox-his , pENTR-lox-puro and pDEST-DTA-MLS [65] , a floxed his or puro gene was inserted between the upstream and downstream arms on a plasmid carrying a diphtheria toxin A ( DT-A ) gene , thus yielding the two targeting vectors , SWS1-his/loxP and SWS1-puro/loxP . To generate SWS1−/− cells , SWS1-his/loxP and SWS1-puro/loxP gene-disruption constructs linearized with AscI were transfected sequentially into DT40 cells ( Bio-Rad ) . The genomic DNA of the transfectants was digested with both EcoRV and NotI , and gene-targeting events were confirmed by Southern-blot analysis . The probe was prepared by PCR-amplification of chicken genomic DNA using the 5′-GCTCGCAGGAACACAACTCCTT-3′ and 5′-GTACAGGAGTGTTTCTCTGCGG-3′ primers . The gene bank accession numbers for the human and chicken Sws1 genes are XP-058899 and XP-415841 , respectively . RT-PCR of DT40 transcripts was done using the 5′-CGCGTCGACATGGATAGCACCTTACCAGCT-3′ and 5′-CGCGGATCCTCATCCTTCATCCTCTTCCTC-3′ primers . The brca2-null mutant cells were generated as follows ( Figure 1 ) . We inserted conditional brca2 heterozygous cells ( BRCA2+/con1 ) harboring two loxP signals into the other allele upstream of the promoter and downstream of exon 2 . Construction of the BRCA2 conditional-null targeting vector was carried out as described previously [33] . To delete the intact allele of the BRCA2+/con1 cells , we constructed a targeting vector to delete all exons of the BRCA2 gene . The ∼6 . kb and ∼3 . 5 kb fragments at the BRCA2 locus [66] were amplified from DT40 genomic DNA by using the 5′-CCGCTCGAGTTTTGTTAGTTGTGAGATGTG-3′ and 5′-TTATCGGGGCTTTGTCAGCTTTAGCTTCTC-3′ primers and the 5′-CGGAGTTGAATAATGGTACATTTCTGGCAC-3′ and 5′-GTTGAATTTGAAACTGGCTGAACAGAAGAG-3′ primers , respectively . Both fragments were cloned into TOPO-pCRXL cloning vector ( Invitrogen , Carlsbad , California ) to make the topo/6 . 0 kb and topo/3 . 5 kb vectors . The ∼5 . 2 kb NotI fragment from the topo/6 . 0 kb vector was inserted into the NotI site in the multicloning site of the topo/3 . 5 kb vector , resulting in the pUpper/Lower vector . Finally , a loxP-flanked puro-resistance cassette was inserted into the BglII site in the pUpper/Lower vector . The resulting targeting construct was transfected into the BRCA2+/con1 cells followed by selection with puromycin . The genomic DNA of the transfectants was digested with XbaI , and gene-targeting events were confirmed by Southern-blot analysis with a probe that was amplified from genomic DNA using the 5′-ATCCATGTCACTGTTGACATCCTGACTGCC-3′ and 5′-AGATACAAACCCAATGGGAAGCCAGGTGTG-3′ primers . The bands detected by the probe were 8 . 6 kb from the wild-type allele and 5 . 2 kb from the targeted allele . Upon exposure of the BRCA2−/con1 cells to tamoxifen , an estrogen antagonist , nucleotide sequences , including promoter and coding sequences encoding the initiation codon to the 67th amino acid , were excised by a chimeric Cre recombinase fused to the estrogen-receptor ligand-binding domain [24] , leading to the complete disruption of the BRCA2 gene . To disrupt RAD51 mediator genes in BRCA2−/− DT40 cells , we disrupted each gene in the BRCA2−/con1 cells ( Table 1 ) . We exposed the resulting RAD51 mediator−/−/BRCA2−/con1 cells to tamoxifen and isolated the RAD51 mediator−/−/BRCA2−/− cells . Western blotting was performed as previously described [33] . The rabbit polyclonal primary antibody , which recognizes the N-terminal 203 amino acids of chicken BRCA2 , was diluted 1∶100 with blocking buffer . The anti-rabbit IgG HRP conjugated antibody was diluted 1∶5000 with blocking buffer . To measure growth kinetics , cells were counted daily using flow-cytometric analysis , as described previously [7] . To measure cell-cycle distribution , cells ( 5×105/ml ) were labeled for 10 minutes with 20 µmol/L 5-bromo-2′-deoxyuridine ( BrdU ) and subsequently harvested . Harvested cells were fixed and analyzed as previously described [7] . To measure cellular survival , cells ( 1 . 5×103–1 . 5×104 ) were incubated in 1 ml culture medium per well containing various concentrations of the DNA-damaging agents . At 72 hours , the ATP in the cellular lysates was measured to assess the number of live cells . The camptothecin ( TopoGen Inc . , Colombus , OH ) and olaparib ( AstraZeneca ) were diluted with DMSO , and the cisplatin ( Nihonkayaku , Tokyo , Japan ) was diluted with PBS . To measure the sensitivity of the DT40 cell lines to these agents , cells were continuously exposed to various concentrations of the drug and the number of cells was measured at 72 hours . At least three independent experiments were carried out . Sensitivity was calculated by dividing the number of cells treated with the drug by the number of untreated cells [8] . To assess cell numbers after treatment with the genotoxic reagents , we measured the amount of ATP in the whole cell lysate [67] . Cells were harvested at 3 hours after gamma irradiation . Cells were spun onto slides using a Shandon Cytospin 3 centrifuge ( Shandon , Pittsburgh , Pa . ) . Staining and visualization of RAD51 foci were carried out as previously described [34] using rabbit polyclonal antibody , which recognizes human RAD51 , at a dilution of 1∶500 ( Calbiochem , San Diego , CA San Diego , CA ) , and Alexa Fluor 488 goat anti-human IgG antibody at a dilution of 1∶1000 ( Molecular Probes Inc . , Eugene , OR [34] ) . Measurement of chromosomal aberrations was performed as described previously [68] . Measurement of recombination frequencies for I-SceI-induced DSB repair was performed as described previously [34] , [44] . Modified SCneo was inserted into the previously described OVALBUMIN gene construct and targeted into the OVALBUMIN locus in wild-type , xrcc3 , brca1 , brca2 , and brca2tr DT40 clones . For transient transfections , 1×107 cells were suspended in 0 . 5 ml of phosphate-buffered saline , mixed with 30 µg of I-SceI expression vector ( pCBASce ) or pBluescript KS without linearization , and electroporated at 250 V , 960 microfarads . At 24 hours after electroporation , the cells were plated in 96-well plates with or without 2 . 0 mg/ml neomycin analog ( G418 ) . The cells were grown for 7 to 10 days , after which formed colonies were counted . HR frequency was calculated by dividing the number of neomycin-resistant colonies by the number of plated cells . Survival data were log-transformed giving approximate normality . Analysis of covariance ( ANCOVA ) was used to test for differences in the linear dose-response curves between wild-type and a series of mutant cells or brca2-null cells and a series of double-knockout mutant cells . Viability of the DT40 cells was estimated using regressing curves . Regression-curve equations were used to calculate LC50 ( 50% lethal concentration ) values . Relative LC50 values were normalized according to the LC50 value of the parental wild-type cells . | Mutations in BRCA1 and BRCA2 predispose hereditary breast and ovarian cancer . Such mutations sensitize to chemotherapeutic agents , including camptothecin , cisplatin , and poly ( ADP-ribose ) polymerase ( PARP ) inhibitor , since RAD51 mediators including both BRCA proteins promote repair of DNA lesions induced by these drugs . Little is known of the functional relationships among RAD51 , BRCA2 , and other RAD51 mediators , because no brca2-null cells were available . Furthermore , the phenotype of sws1 mutants has not been documented . We here disrupted every known RAD51 mediator and analyzed the phenotype of the resulting mutants in both BRCA2-deficient and -proficient backgrounds . The understanding of the function of individual RAD51 mediators and their functional interactions will contribute to the accurate prediction of anti-cancer therapy efficacy . | [
"Abstract",
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] | [
"genetics",
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] | 2011 | The Epistatic Relationship between BRCA2 and the Other RAD51 Mediators in Homologous Recombination |
Cell-cell recognition is a fundamental process that allows cells to coordinate multicellular behaviors . Some microbes , such as myxobacteria , build multicellular fruiting bodies from free-living cells . However , how bacterial cells recognize each other by contact is poorly understood . Here we show that myxobacteria engage in recognition through interactions between TraA cell surface receptors , which leads to the fusion and exchange of outer membrane ( OM ) components . OM exchange is shown to be selective among 17 environmental isolates , as exchange partners parsed into five major recognition groups . TraA is the determinant of molecular specificity because: ( i ) exchange partners correlated with sequence conservation within its polymorphic PA14-like domain and ( ii ) traA allele replacements predictably changed partner specificity . Swapping traA alleles also reprogrammed social interactions among strains , including the regulation of motility and conferred immunity from inter-strain killing . We suggest that TraA helps guide the transition of single cells into a coherent bacterial community , by a proposed mechanism that is analogous to mitochondrial fusion and fission cycling that mixes contents to establish a homogenous population . In evolutionary terms , traA functions as a rare greenbeard gene that recognizes others that bear the same allele to confer beneficial treatment .
Cell-cell recognition is critical for differentiating friend from foe and for allowing populations of cells to coordinate multicellular functions [1] , [2] . Many eukaryotes simplify aspects of cellular self-recognition by clonal expansion from a single fertilized cell , wherein a privileged environment excludes nonself cells . In contrast , some eukaryotes and bacteria build multicellular structures from heterogeneous free-living cells in the environment . In these cases , coalescing cells are not necessarily siblings or even the same species [1] , [3] , [4] . Therefore , mechanisms involved in cell-cell recognition are required to ensure selective inclusion of cells into cooperative multicellular cohorts . In the case of bacteria , however , little is known about how cells physically recognize one another to coordinate multicellular functions . Myxobacteria represent an attractive model system to understand bacterial cell-cell recognition , because they have complex social behaviors in which cells are recruited from their environment to perform multicellular tasks . For instance , during vegetative growth , myxobacteria can exist as solitary cells or as small groups of cells; upon starvation they transition into large , organized multicellular cohorts that build erect macroscopic fruiting bodies [5] . The ability of myxobacteria to cobble together a coherent population of cells from environments rich in microbial diversity [6] implies that they have a mechanism ( s ) to identify and sort closely related cells from distantly related cells . To date , however , no molecular recognition system has been characterized in myxobacteria . Recently , we discovered a novel social interaction in myxobacteria that suggests a role for cell discrimination . This behavior involves the mutual exchange of outer membrane ( OM ) lipids and proteins between cells [7]–[9] . In contrast , no cytoplasmic or DNA material is exchanged . The output of these interactions includes phenotypic changes to cells and provides a conduit for cell-cell communication [5] . Strikingly , OM exchange involves sharing of large quantities of OM material , i . e . of the components that are transferred are essentially equally divided between interacting cells [7]–[9] . We therefore hypothesized that myxobacteria might have evolved a mechanism to discriminate among candidate partner cells before they commit to the energetically costly behavior of sharing large quantities of cellular material . Insight into the mechanism of OM exchange was made with the identification of the TraA and TraB proteins [7] . With the use of fluorescent reporters , TraAB were shown to be required in both ‘donor’ and ‘recipient’ cells for transfer . Thus , unlike known bacterial secretion or conjugation systems , in which one cell expresses a transport machine to unidirectionally deliver cargo to target cells , the TraAB system instead requires that both cells express the transfer machinery . In other anthropomorphic words , the decision to exchange material is mutually made by interacting cells because both cells must functionally express TraAB . Fluorescently labeled lipids also transfer in a TraAB-dependent manner [7] , and thus the OMs apparently transiently fuse and cargo diffuses or exchanges bidirectionally between cells . TraAB are predicted to reside in the cell envelope , and , because TraAB overexpression results in cells that adhere together in chains , TraA may function as a cell surface adhesin and thus could play a role in cell recognition [7] . The exchange of OM proteins results in phenotypic and behavioral changes in those cells . For instance , certain gliding motility mutants are rescued or complemented extracellularly by protein transfer from a ‘donor’ strain that expresses the corresponding wild-type protein [10] . In the case of tgl mutants , which are defective in assembling their motor , type IV pili , physical contact with a tgl+ cell results in transfer of the Tgl lipoprotein to the mutant ‘recipient’ [9] , [11] , [12] . Once Tgl function is provided , the mutant assembles its type IV pili and the cell therefore can move . Because no DNA is exchanged , the phenotypic rescue of motility is transient , as the Tgl protein is diluted over time by protein turnover and cell divisions . In other examples , OM exchange serves as a conduit for cell-cell signaling [5] . In such cases , one strain can regulate the behavior of another strain with regard to the decision to expand the swarm or to enter fruiting body development . In this work , we sought to address the question of whether myxobacteria use cell recognition to identify partnering cells for OM exchange . By using a panel of environmental isolates , we show that OM exchange is selective and that TraA is a polymorphic receptor that determines specificity .
The finding that myxobacteria engage in intimate cellular resource sharing led us to hypothesize that this process may involve a form of self/nonself recognition . To address this possibility , we mixed a double-labeled laboratory strain containing cytoplasmic green fluorescent protein ( GFP ) , which does not transfer , and a transferable OM mCherry lipoprotein reporter ( SSOM-mCherry ) with individual strains from a panel of unlabeled environmental Myxococcus xanthus isolates ( Table S1 ) [8] . The laboratory strain ( a DK1622 derivative ) transferred SSOM-mCherry to only 3 of 15 isolates ( data not shown ) . These results suggested that OM exchange was indeed selective; however , some strains might not be transfer competent . To address this possibility , we developed an analogous transfer assay in which donor cell membranes were stained with a lipophilic red dye ( membrane transfer ) and recipient cells were stained with a cytoplasmically trapped green fluorescent conjugate that cannot transfer ( Fig . 1A ) . This assay again requires TraAB function for lipophilic red fluorescent dye transfer [7] . In this new assay , the 12 isolates that could not transfer with the lab strain were indeed found to be competent for transfer with themselves ( Fig . 1B ) . Thus the failure of the laboratory strain to transfer with certain isolates was due to selectivity , not functional competence . Next , we carried out comprehensive tests for inter-strain transfer among 17 isolates , which included the addition of a closely related M . fulvus species . In total 213 transfer combinations were tested . Interestingly , these experiments found that OM exchange was restricted to particular partners , which are henceforth called recognition groups ( Fig . 1B ) . For example , group A contains four members that all transferred amongst themselves , but not with other isolates . Similarly , group B contains two members that transferred only between themselves . In the cases of the Pali and DK805 isolates , they were highly selective and transferred only to themselves and thus represent single-member groups ( C and E , respectively ) . The remaining strains were classified into a large , loosely defined supergroup designated D . Unlike other groups , transfer among D members was somewhat heterogeneous and was divided into subgroups ( D1 , D2 and D3 ) that exchanged among themselves . Unlike other recognition groups , promiscuous transfer did , however , occur between some subgroup D members . For instance , DK823 and DK852 transferred with all supergroup D members except DK836 . Since our assays are designed to detect only unidirectional transfer between two strains , we carried out reciprocal experiments by reversing the fluorescent labels each strain was stained with , to test for bidirectional transfer . In every case tested , 94 pairs of different strains ( 188 assay combinations ) , the reciprocal transfer experiment gave the identical result ( Fig . 1B ) . For example , DK823 transferred to A96 and A96 transferred to DK823 , while in contrast DK823 did not transfer to DK1622 and DK1622 did not transfer to DK823 ( Fig . 1B ) . These results therefore support the idea that transfer is selective and bidirectional [7] . The domain architecture of TraA consists of a type I signal sequence , a distant PA14-like domain , a cysteine-rich tandem repeat region , and a MYXO-CTERM motif postulated to function in protein sorting to the cell surface [7] . This domain architecture is similar to the FLO adhesin proteins found in yeast [13] and suggests that TraA may function as a cell surface receptor . To investigate whether TraA functions in cell-cell recognition , we sequenced the traA alleles from the environmental isolates and analyzed the sequences for possible polymorphisms . A sequence alignment was generated , and variable amino acid residues were plotted along the length of the protein ( Fig . 2A ) . A hyper-variable region , which contains amino acid polymorphisms and indels , was found to encompass the PA14-like domain ( Figs . 2A and S1 ) . In contrast , the other regions in the protein showed little sequence variation ( Fig . 2A ) . Therefore , the PA14-like domain is polymorphic , suggesting it may play a role in partner selectivity . To test whether the described TraA sequence variations identified in Figure 2A might predict strain specificity , those sequences were compared with the described recognition groups ( Fig . 1B ) . In our initial analysis , a correlation was suggested , because recognition groups A and D1 each contained two members with identical TraA sequences , A66/A88 and A47/A96 , respectively . Additionally , DK823 and DK852 have only two amino acid differences and were D1 partners . We thus constructed a phylogenetic tree based on the variable region that encompasses the PA14-like domain to carry out a more comprehensive analysis . Importantly , this tree shows a strong correlation between traA genetic relatedness and recognition groupings—the recognition groups clustered almost perfectly according to the sequence conservation found in their PA14 domains ( Fig . 2B ) . Based on amino acid substitutions and indels , group A is phylogenetically distant and constitutes an outgroup . Similarly , B , C and E also form distinct recognition groups that correlate to phylogenetic groupings ( Fig . 2B ) . Supergroup D forms a clade that is more heterogeneous in terms of sequences and transfer partner recognition ( Figs . 1B and 2B ) . Although there is some heterogeneity in supergroup D , there is nevertheless nearly perfect agreement between sequence conservation and recognition group partnering . These results indicate that the TraA protein sequence determines specificity among recognition groups . An extension from the above finding suggests that the PA14-like domain could be functionally responsible for recognition . To examine this idea in more detail , we compared the full-length TraA protein sequence from M . fulvus with other M . xanthus sequences , because the former sequence has a divergent C-terminal sequence that encompasses the Cys-rich repeats ( Figs . 2A and S2 ) . The alignment of the M . fulvus sequence to the fellow recognition group D member DK836 and a representative from another recognition group ( DK1622 ) showed striking results ( Fig . S2 ) . Although the C-terminal regions in TraADK1622 and TraADK836 were nearly identical over a >400-amino-acid region , they were not OM exchange partners ( Figs . 2C and S2 ) . In contrast , TraADK836 , which has a divergent C-terminal sequence from TraAM . fulvus but has a similar PA14-like domain , constitute transfer partners ( Figs . 2C and S2 ) . These findings support the idea that the PA14-like domain within TraA serves as the molecular recognition determinant . To address the hypothesis that TraA functions as a cell surface receptor , polyclonal antibodies were raised against the PA14DK1622 domain . Whole-cell western blot analysis identified a single prominent ∼100-kDa band that was absent from a ΔtraA strain ( Fig . 3A ) . The migration of the TraA-specific band was slower than that of the calculated molecular weight of the full-length processed protein ( 71 kDa ) and supports an earlier suggestion that TraA and particularly the MYXO-CTERM domain could be post-translationally modified [7] . Immunofluorescence microscopy was then conducted and found that TraA was detected on live non-permeabilized cells , whereas a ΔtraA strain did not cross-react with the antibodies ( Fig . 3B ) . These results indicate that the PA14-like domain of TraA localizes to the cell surface . The localization of TraA was further found to be enriched at cell poles ( 72% of the time; 321/445 of fluorescent foci counted ) . Large , bright foci were observed on some cells , perhaps suggesting that TraA may form receptor clusters ( Fig . 3B ) . These findings support our earlier claims that transfer involves end-to-end cell contacts and is mediated by the TraA cell surface adhesin [7] , [8] , [14] . To directly test the hypothesis that TraA is the specificity determinant , we replaced the traA allele in a M . xanthus laboratory strain to investigate possible cognate changes in strain recognition . As reported above ( Fig . 1B ) , the wild-type M . fulvus and M . xanthus DK1622 laboratory strains do not transfer OM components ( Fig . 4; top panels ) . Importantly , when an isogenic M . xanthus strain expressed the traAM . fulvus allele , it was able to partner with M . fulvus for efficient transfer ( Fig . 4; bottom panels ) . Similarly , when the traA alleles from strains DK816 , A96 and Pali were used to replace the traA allele in the laboratory strain , we observed a corresponding change in partner transfer specificity ( data not shown ) . In addition , when merodiploid strains were constructed that contained two alleles of traA , transfer occurred between both recognition groups , showing that multiple traA alleles broaden host range recognition and that the alleles are not antagonistic ( data not shown ) . To substantiate the above findings , we used an extracellular complementation ( stimulation ) assay that phenotypically assesses protein transfer . In this assay certain nonmotile mutants ( recipients ) can have their motility defect rescued by the transfer of functional proteins from donor cells that encode the corresponding wild-type protein [7] , [10] , [15] . In these experiments , four isogenic nonmotile and nonstimulatable donor strains were constructed with the indicated traA allele replacements ( Fig . 5 ) . Similarly , four isogenic nonmotile stimulatable recipients ( laboratory strains; ΔcglC Δtgl ) were constructed with the identical set of traA alleles . These eight strains were mixed in all possible combinations between donors and recipients . After 1 day of incubation , phenotypic rescue , as judged by emergent colony flares , had occurred only when donors and recipients expressed the same traA allele; phenotypic rescue did not occur between alleles from different recognition groups ( Fig . 5 ) . Therefore , in isogenic strain backgrounds , TraA interactions are allele specific , and these results are in perfect agreement with the above recognition group designations ( Fig . 1B ) . Myxobacteria are unusual because they exhibit complex and coordinated behaviors that are typically not found in other bacteria . Recently , we discovered that Tra-dependent OM exchange regulates a new form of cell-cell interactions . These interactions were uncovered when genetically distinct strains were mixed and it was found that one strain could regulate the behavior of another strain in terms of motility and development behaviors . In particular , nonmotile strains can prevent swarm expansion and fruiting body formation of motile strains [5] , [7] , suggesting that TraAB-catalyzed OM fusion forms a communication conduit between cells . Because myxobacteria must coordinate their behaviors , any reduction in this ability to coordinate behaviors should result in reduced fitness in those individuals . The nature of the proposed signal ( s ) produced in the nonmotile strain that blocks swarming in the motile strain is unknown , although it clearly is not a diffusible signal [5] . Here we sought to extend those findings to test whether the TraA recognition groups defined above could predict the outcomes for inter-strain swarm regulation . To do this we again tested for traA allele–specific interactions between nonmotile and motile strains . In these experiments six isogenic strains were constructed wherein each strain contained different environmental traA alleles . These strains were mixed at a 1∶1 ratio with eight different environmental isolates that were fully motile ( adventurous and social motility; A+S+ ) and placed on swarm agar surfaces and allowed to swarm for 1 day . As was previously reported [7] , a nonmotile traADK1622 strain blocked swarm expansion of the motile DK1622 strain ( Fig . 6 , top left panel ) . Importantly , DK1622 swarm inhibition was allele specific , as a ΔtraA strain or any of the four other traA allele replacement strains that were not from recognition group A resulted in no swarm inhibition ( Fig . 6 , top row ) . In agreement with Figure 1B findings , the nonmotile traADK1622 strain also specifically blocked swarm expansion of all other group A members ( A66 , A88 and DK801 ) but not of members from other recognition groups ( Fig . 6 , left column ) . Moreover , the engineered traADK816 , traAA96 , traAM . fulvus and traAPali nonmotile strains specifically blocked swarm expansion of their cognate motile strains , i . e . , DK816 , A96 , M . fulvus and Pali , respectively , but not of strains that belonged to different recognition groups ( Fig . 6 ) . These results therefore indicate that traA regulates the decision of the population to swarm in an allele-specific manner . Myxobacteria are highly antagonist toward nonself microbes , and they prey on other bacteria and fungi for food [16] . Myxobacteria also produce growth inhibitory or lytic substances ( bacteriocins ) that act specifically against other myxobacterial strains [17]–[19] . Consistent with these earlier observations , during the course of strain mixing experiments ( Fig . 1B ) , we found evidence that certain isolates killed other strains . For example , when we mixed green and red fluorescently labeled strains , we sometimes observed that one or both of the isolates would contain some cells that lysed during the ∼4-hr incubation . Because TraA facilitates the transfer of many OM components , which may include hundreds of different proteins [7] , [8] , we tested whether OM exchange might regulate antagonistic interactions between Myxococcus strains . Similar to some of the other environmental isolates , M . fulvus was found to kill the laboratory strain ( DK1622 or derivatives ) . This was first observed when red-labeled M . fulvus cells were mixed with a green-labeled M . xanthus strain at a 1∶1 ratio ( Fig . 7A ) . After 6 hr of incubation , green-labeled DK1622 derivative cells were not detected . In contrast , thousands of red M . fulvus cells were easily detected . We further quantified M . xanthus killing by plating cells from such mixtures to determine viable colony forming units ( CFU ) and found that after 1 day of incubation no viable DK1622 derivative cells were found ( >7 log killing; Fig . S3 ) . To test whether killing was influenced by OM exchange , an isogenic traAM . fulvus allele replacement strain that transferred with M . fulvus ( Fig . 4 ) was instead used . Interestingly , heterologous expression of TraAM . fulvus was indeed found to confer protection to M . xanthus ( DK1622 derivative ) from M . fulvus killing , although the protection was not absolute ( Fig . 7A ) . Similarly , when we tested for CFU viability after 1 day of co-incubation , the M . xanthus TraAM . fulvus strain showed a significant increase in survival , i . e . from no detectable survivors for the TraADK1622 strain to >104 CFU for the TraAM . fulvus isogenic strain ( Fig . S3 ) . These results show that heterologous expression of a cognate recognition group traA allele confers protection from killing . In a reciprocal experiment , we sought to test if inactivating OM exchange between natural recognition group members would have an effect on inter-strain killing . For this analysis , we compared killing between M . xanthus recognition group A members DK801 versus DK1622 , in which the latter strain either expressed a cognate traADK1622 allele or contained a ΔtraA allele . Unlike what was found for the M . fulvus/DK1622 strain mixture , DK801/DK1622 mixtures co-existed in a relatively harmonious relationship , as the ratio between the strains remained near one during the time course of the experiment ( Fig . 7B ) . In contrast , when the ΔTraA strain was instead mixed with DK801 , its viability sharply decreased , with approximately 1 , 000-fold fewer cells , as compared with the isogenic TraADK1622 strain ( Fig . 7B ) . Thus inactivation of OM exchange within a natural recognition group can affect inter-strain killing . We hypothesize that OM exchange facilitates the transfer of an immunity factor ( s ) to the susceptible strain , which in turn protects that strain from killing by a bacteriocin or toxin .
Our results indicate that TraA functions as a polymorphic cell surface receptor that mediates cell-cell recognition for OM exchange . The simplest interpretation for how specificity occurs is that TraA binds identical or similar copies of itself on neighboring cells through homophilic interactions ( Fig . 8 ) . In particular it is the distant PA14-like domain within TraA that encodes the predictive features for recognition ( Fig . 2B ) . Therefore our current model differs from our prior model that postulated the distant PA14 lectin-like domain of TraA binds glycans on neighboring cells [7] , [20] . In addition , since traA allele replacements were necessary and sufficient to reprogram partner recognition ( Fig . 5 ) , it suggests that TraB is not a specificity factor . Moreover , since the traBDK1622 allele functioned with four divergent traA alleles ( Fig . 5 ) , it seems that if TraA and TraB physically interact , as we have suggested [7] , they do so between conserved residues within TraA ( Fig . 2A ) . Importantly , the ability of TraA to discriminate between partnering cells supports our original hypothesis that OM exchange , which results in sharing of substantial cellular resources , is a regulated and selective process . Bacterial molecular recognition is an emerging field of study and a number of interesting examples have been described in various levels of detail [21]–[24] . Typically these systems function in adhesion and/or toxin/immunity interactions . However , to our knowledge there is no other example of a bacterial recognition system that is involved in complex cooperative behaviors such as those described here for TraA . In turn OM exchange has broad implications for social interactions among myxobacteria ( Fig . 8 ) . For instance , we propose it provides a communication conduit to coordinate social functions , such as the decision of a population to swarm ( Fig . 6 ) . Importantly , the exchange of hundreds of different proteins [8] also allows cells to transiently repair/replace OM proteins that have been damaged by environmental and genetic insults ( Fig . 5 ) , obtain new protein functions ( Figs . 7 and S3 ) and to strive toward population OM homeostasis by equilibrating component levels . This interpretation has striking similarities to the explanation given for mitochondria dynamics . Here , investigators have proposed that mitochondria undergo rounds of fusion and fission to mix and repair organelle components to establish a coherent and functional population [25] , [26] . One important difference between myxobacteria and mitochondria fusion/fission is that the former represents independent cells , while the latter occurs within the confined space of a single cell . In this context myxobacteria can express different proteins because the cells are not necessarily siblings nor originated from the same micro-environments . Thus myxobacteria OM exchange may provide new functions . In a dramatic display of new function , we found that strain protection from inter-strain killing can be transferred ( Fig . 7 ) . Presumably inter-strain protection occurs by the transfer of immunity factors between cells . Consistent with this idea , bioinformatic analysis has found that Myxococcus genomes encode many toxin/immunity factor pairs [27] . Future studies will need to elucidate the details of how inter-strain killing and TraA-mediated protection works , and such a determination might be challenging , as myxobacteria produce cocktails of anti-microbial agents [16] , [17] , [27] . Unfortunately , our understanding of how myxobacteria—or for that matter most bacteria—actually live and interact in their environments is poor . However , one ecological study did investigate to what extent M . xanthus strains vary within a soil sample [28] . Based on molecular and phenotypic analyses at a centimeter scale resolution , this myxobacterial community was heterogeneous , as the 78 isolates parsed into at least 45 distinctive strains [29] . Five of these strains were used in our study ( A23 , A47 , A66 , A88 and A96 ) . These strains represent two distinct genotypes , as defined by multilocus sequence typing ( MLST ) , and our traA sequence analysis indeed showed that A66/A88 and A47/A96 have identical traA sequences , respectively , as would be expected from the MLST results . In contrast , although A23 belongs to the previously defined A47/A96 genotype , its traA sequence was significantly divergent from that of A47/A96 , and it functionally belongs in a distinctive recognition group ( Figs . 1B and 2B ) . Given that local M . xanthus communities are genetically diverse , the ability of clonal groups to recognize self from nonself would presumably be critical for their social interactions and for the transition into a multicellular fruiting body . Based on our studies , we suggest that TraA represents one molecular mechanism for kin recognition ( Fig . 8 ) . We also predict that myxobacteria have other recognition mechanisms [18] , [29]–[31] . Our results suggest that TraA functions as a molecular determinant for self/nonself recognition . As sibling cells necessarily express identical traA alleles , they would form a kin recognition group . However , as Figure 1B illustrates non-kin cells can also belong to the same group ( Fig . 8 ) . Although not obviously revealed in Figure 1B , in mechanistic terms the relative affinities of TraA receptors within a given recognition group may vary between alleles , such that kin interactions might be preferred . For example , it is possible that receptors with identical sequences may form higher-affinity interactions than those between recognition members with more divergent TraA sequences . In one case , we did observe this: a low level of exchange was observed between M . fulvus and Mxx23 ( Fig . 1B ) . In general , however , our assays likely provide a low-resolution assessment of relative binding affinities , and thus moderate and high-affinity interactions may yield similar outcomes . In contrast , cells in the environment might interact under less favorably conditions than laboratory conditions , where binding affinities may play a stronger discriminatory role . To test the hypothesis that TraA affinities vary within recognition groups , a more quantitative assay will need to be developed . An alternative idea is that promiscuous interactions within recognition groups are functionally important . For example , promiscuous interactions could assist myxobacterial communities to reach the critical number of cooperative cells needed for fruiting body development . This numerical requirement that hundreds of thousands of cells must unite to build a viable fruit is a daunting threshold given the sparse growing conditions associated with microbial life in the soil . Thus the ability of non-kin cells to combine their resources and cell numbers to build a fruit may ease this transition . From our experience , we think the major obstacle in combining inter-strain resources is inter-strain killing ( Fig . 7 ) [18] . As shown here , the formation of functional recognition groups partly alleviates the propensity of myxobacteria strains to kill one another ( Fig . 7 ) . A fundamental question in evolutionary biology is how cooperative social behaviors evolved in the context of seemingly contradictory Darwinian evolution [32] . The ‘greenbeard’ concept , in which a single gene allows individuals to provide preferential treatment toward others , provides a tangible framework for how cooperation could evolve [1] , [33] , [34] . This abstract concept was refined by Haig , who explained it in molecular terms [35] . He proposed that a homophilic cell surface receptor could fulfill the three greenbeard requirements: it is a feature or trait , it allows recognition in others of the same gene product , and it results in cooperative behavior or ‘nepotism’ toward those individuals . The greenbeard concept differs from kin selection in that the helping behavior is directed toward other individuals with the same greenbeard gene , regardless of the genetic relatedness between individuals . Our described properties of traA represent a rare case in which a single gene meets these greenbeard criteria . That is , our evidence suggests that TraA functions as homophilic receptor/adhesin that recognize other cells that bear a genetically related allele , irrespective of kin relationships , to catalyze OM fusion that results in beneficial social outcomes ( Fig . 8 ) . This TraA-dependent form of nepotism allows cell-cell signaling and cellular resource sharing to occur selectively ( Fig . 8 ) . As mentioned , a dramatic display of TraA-dependent greenbeard nepotism is protection from killing . As myxobacteria apparently have many forms of toxin/immunity systems in their genomes [27] , TraA can potentially provide an umbrella protection platform that circumvents toxin ( s ) action specifically to cognate recognition group members . Another implicit requirement of greenbeard genes , which we have shown for TraA , is that their sequences must be polymorphic , which provides a mechanism for selective recognition among individuals . To our knowledge , TraA is the first helping greenbeard ( single ) gene described in bacteria . In yeast and the soil amoeba Dictyostelium discoideum , which similarly transitions from free-living cells into multicellular fruiting bodies , there are other examples of greenbeard genes that police social interactions [36]–[38] . Because OM exchange affects a wide variety of cellular functions , the question arises as to whether there is a single driving benefit that TraA is being selected for in the environment . In a foreshadowing discussion to this work , Haig suggested that myxobacteria use greenbeard recognition in ‘security surveillance’ to identify friend from foe for multicellular development [39] . Haig argued that such a system would prevent exploitation of somatic cells ( terminally differentiated cells that autolyse or form stalk cells ) by germ line cells ( spores ) during fruiting body formation [40] . Whether TraA plays a role in surveillance recognition during development remains to be investigated . We suggest that the TraA greenbeard concept provides clues for the functional and evolutionary transitions from single cell to multicellular life ( Fig . 8 ) . Specifically , TraA confers cell recognition that leads to cell-cell communication and sharing of otherwise private cellular goods . In turn , a cell population can transition from a phenotypically heterogeneous collection of individual cells into a tissue-like state of homeostasis , which refines and promotes cooperative multicellular interactions .
Table S1 lists bacterial strains and plasmids used in this study . Routine cloning was done in DH5α Myxococcus cultures were grown to a Klett reading of ∼100 ( 3×108 colony forming units [CFU]/ml ) at 33°C in CTT medium ( 1% casitone , 1 mM KH2PO4 , 8 mM MgSO4 , 10 mM Tris-HCl , pH 7 . 6 ) in the dark; when necessary , cultures were supplemented with kanamycin ( Km; 50 µg/ml ) or oxytetracycline ( Tc; 15 µg/ml ) . For ½ CTT , casitone was reduced to 0 . 5% . On plates , the agar concentration was 1 . 0 or 1 . 5% . TPM buffer contains 10 mM Tris , 1 mM KH2PO4 and 8 mM MgSO4 ( pH 7 . 6 ) . Escherichia coli cultures were grown at 37°C in LB medium and , when necessary , were supplemented with Km ( 50 µg/ml ) or ampicillin ( Ap; 100 µg/ml ) . GFP and mCherry reporters were used to monitor transfer as described [8] . In addition , as genetic transformation of environmental strains proved difficult , a new method was also developed . Here a red fluorescent DiD lipid dye ( Lipophilic Tracer Sampler Kit; Invitrogen ) vial H ( 5 , 5′-Ph2-DilC18 ( 3 ) ) was used to label Myxococcus OMs . These cells are referred to as donors , as this dye can be transferred via a Tra+-dependent mechanism [7] . Vial H worked best , as it effectively stained cell membranes , was bright under a Texas Red-4040B ( Semrock ) filter set and did not fluoresce under the FITC filter set ( data not shown ) . Log phase cultures were collected by centrifugation and resuspended in TPM to a calculated density of 8×108 CFU/ml . Then 2 µl of dye ( 1 mg/ml in ethanol ) was added to 98 µl of the cell suspension and incubated for 1 hr at 33°C with occasional gentle vortexing . Cells were washed twice with 1 ml TPM and were ready to be mixed with recipients . Recipient strains were labeled in their cytoplasm with Vybrant CFDA SE ( carboxyfluorescein diacetate , succinimidyl ester ) Cell Tracer Kit ( Invitrogen ) . Briefly , concentrated log phase cells ( ∼1 . 5×109 CFU/ml ) were stained for 30 min with CFDA SE dye following the manufacturer's instructions , except TPM was used instead of PBS . Cells were then washed twice with TPM and resuspended in 400 µl ½ CTT and incubated at 33°C for 1 hr with occasional gentle vortexing . During this incubation , cells enzymatically convert the nonfluorescent molecule into a green fluorescent derivative that is trapped in the cytoplasm as reactive products form fluorescent conjugates with intracellular amines ( e . g . , proteins ) ( Invitrogen ) . Live stained recipients were then washed three times in TPM and mixed at a 1∶1 cell ratio with live red donors and pipetted onto ½ CTT 1 . 5% agar plates . After 2–4 hr of incubation at 33°C , cells were collected from the plates and washed twice in TPM . The cells were then mounted on polysine-coated slides for microscopic examination [7] , [8] . Transfer was scored by the ability of green recipient cells to obtain red fluorescence . Three micrographs , phase contrast and red and green fluorescence , were taken for each viewing field , and the red and green fluorescence images were subsequently merged with Image-Pro Plus software ( Media Cybernetics ) . Transfer was scored as positive when the majority ( usually >80% ) of the recipients were red ( yellow/orange in merged images ) . Transfer was scored as negative when ≤1% of the recipients were red . In a few cases , 10–20% of the recipients were positive and thus were scored as ‘±’ for poor transfer . Similar to prior reports , transfer specifically occurred between motile strains and required a biofilm on a hard surface and TraA function [7] , [8] . We note two difficulties with this relatively time-intensive assay . First , mixing wild-type Myxococcus isolates often resulted in severe cell clumping . Second , inter-strain killing also occurred . For these reasons , experiments were repeated two or three times , and the corresponding reciprocal strain transfer was also typically tested to confirm the results . Genomic DNA was purified from cultures with a PureLink Genomic Kit ( Invitrogen ) . The indicated traA alleles were PCR amplified with Taq Master Mix ( New England BioLabs ) , and PCR reactions were gel purified with a QIAquick Gel Extraction Kit ( Qiagen ) and directly sequenced ( Nucleic Acid Exploration Facility at University of Wyoming ) . Primers are listed in Table S2 . The traA allele sequences were deposited in GenBank with the accession numbers JX876748–62 . To construct the traA deletion cassette , regions upstream and downstream of the gene were PCR amplified from the DK1622 chromosome , digested with appropriate restriction enzymes and cloned into pBJ114 to generate pDP28 . This plasmid contains a positive-negative Kmr-galK selection cassette . pDP28 was electroporated into the strain DK8601 ( aglB1 , pilA::Tc ) , and a Kmr transformant was subsequently counter-selected on 1% galactose CTT agar . The ΔtraA allele was confirmed by PCR with primers flanking the deletion site to generate DW1467 . To create a traA allele replacement plasmid , the strong pilA promoter was first amplified with Phusion High-Fidelity DNA Polymerase ( New England BioLabs ) and cloned into pSWU19 at the EcoRI and XbaI restriction sites to make pDP22 . Next , various traA alleles with an engineered ribosomal binding site were similarly PCR amplified from indicated isolates and cloned ( XbaI and HindIII ) downstream of the pilA promoter in pDP22 to generate plasmids pDP23–27 ( Table S1 ) . Verified plasmids were transformed into DW1467 and homologously integrated into the genome by selecting for Kmr . To generate TraA antigen , the domain encoding PA14DK1622 was PCR amplified with Phusion and cloned into pMAL-c2X ( New England BioLabs ) at the EcoRI and PstI sites ( pDP29 ) . All primers are listed in Table S2 . A protease-deficient E . coli strain ( clpX− clpY− lon− ) harboring pDP29 was grown in LB and induced at an OD595 of 0 . 6 with 1 mM IPTG for 4 hr at 37°C . Cells were harvested by centrifugation , lysed with a French press and pulse sonicated . Cell debris was removed by centrifugation ( 20 , 000×g ) , and the resulting supernatant was passed through a 0 . 2-µm PES filter ( Whatman ) to obtain a clear suspension . Soluble material was loaded onto a 5-ml HiTrap column connected to an ÄKTAprime chromatography system ( GE Healthcare Life Sciences ) for purification of maltose binding protein ( MBP ) fusion . Purification was carried out following the manufacturer's instructions . Protein concentration was determined by a Bradford assay ( Thermo Scientific ) . About 10 mg of purified MBP-PA14 protein was sent to a commercial vendor ( Thermo Scientific Pierce Protein Research ) as antigen . Prior to immunization , pre-immune sera from five rabbits were pre-screened by western blot analysis against M . xanthus whole-cell extracts to select two rabbits that exhibited minimal background cross-reactivity . Proteins were separated by 10% SDS-PAGE and transferred to a polyvinylidene difluoride membrane as described [41] . Primary PA14 antibody was used at a 1∶30 , 000 dilution , and a secondary horseradish peroxidase–conjugated goat anti-rabbit antibody was used at a 1∶15 , 000 dilution ( Pierce ) . For immunofluorescence studies , cells were grown to mid-log phase , harvested by centrifugation and washed in TPM . Cells ( 5×108 ) were then resuspended in 1 ml TPM containing 2% BSA . Following 30 min of incubation with gentle shaking at room temperature ( RT ) , primary antibody ( 1∶5 , 000 dilution ) was added and further incubated for 45 min . Cells were then pelleted by centrifugation and washed four times with 1 ml TPM . After the cell pellet was resuspended in 150 µl TPM with 2% BSA , 1 µl of secondary antibody ( 1∶150 dilution; DyLight 488–conjugated donkey anti–rabbit IgG; Jackson ImmunoResearch ) was added and incubated for 45 minutes at RT . After incubation , cells were pelleted and washed four times in TPM . The labeled cells were mounted and examined with a fluorescence microscope equipped with a 100× phase contrast oil objective lens . To determine cell viability after processing for microscopic examination , total cell numbers were counted in a hemocytometer chamber ( Hausser Scientific ) , followed by 10-fold serial dilutions and plating on CTT agar . After 5 days of incubation at 33°C , CFUs were determined . After processing for immunofluorescence imaging , cells were found to be 100% viable . Sequences were aligned with MUSCLE [42] , and model testing was performed using ProtTest [43] . Based on the best ProtTest model with four gamma categories , the tree was constructed by MrBayes3 software [44] . The tree was run for 10 , 000 , 000 generations , and the consensus tree was constructed with a default ( 25% ) burnin phase . Stimulation and swarm inhibition assays were essentially done as described [7] . For the swarm inhibition assay , no CaCl2 was added to ½ CTT agar . For interspecies kill assays , cultures were grown to mid-log phase , harvested by centrifugation and resuspended to a calculated density of 3×109 CFU/ml . Cultures were mixed together ( 50 µl of each ) , and four 25-µl spots were placed on ½ CTT/2 mM CaCl2/1 . 5% agar plates . After 24 hr of incubation at 33°C , spots were harvested in 1 ml of TPM , vortexed for 20 sec and repeat pipetted ( ∼10 times ) . To further break up small clumps , cells were transferred to a sterile 1-ml glass tissue homogenizer and slowly plunged 10 times . Samples were then serially diluted in TPM , and 10-µl spots were placed on CTT and CTT Km agar plates . Plates were inspected daily for about a week to enumerate CFUs . M . fulvus colonies were identified on CTT plates as swarm proficient; the M . xanthus strains were nonmotile and Kmr and were enumerated on CTT Km plates . All experiments were carried out in triplicate , and the resulting values were averaged . In a second approach , interspecies killing was assayed by labeling respective strains with red or green fluorescence markers as described above . Such strains were mixed and pipetted onto ½ CTT agar . At various time points , cells were harvested , and the relative ratios of red and green labeled cells were microscopically determined . | How individual cells recognize each other to cooperate and assemble functional tissues is a fundamental question in biology . Although multicellularity is a trait that is typically associated with eukaryotes , certain groups of bacteria also exhibit complex multicellular behaviors , which are perhaps best exemplified by the myxobacteria . For example , in response to starvation myxobacteria will assemble fruiting bodies , wherein thousands of cells function as a coherent unit in development and cell differentiation . However , how myxobacteria , or for that matter other bacteria , recognize cooperating partnering cells through cell contact-dependent interactions is poorly understood . Here we describe a mechanism where myxobacteria distinguish sibling and cohort cells from other myxobacteria isolates . We show that molecular recognition is mediated by a cell surface receptor called TraA . Cell-cell specificity involves mutual recognition by partnering cells and is mediated by proposed homotypic TraA interactions . The specificity for recognition is determined by variable sequences found within traA alleles . Thus , simply swapping traA alleles between isolates predictably changes partner recognition . TraA-TraA recognition in turn leads to the fusion and exchange of outer membrane ( OM ) components between cells . We suggest that OM exchange allows the cells to communicate and become homogenous with respect to their OM proteome . We further suggest these interactions build a cohesive cell population that functions in multicellular processes . | [
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] | [] | 2013 | Molecular Recognition by a Polymorphic Cell Surface Receptor Governs Cooperative Behaviors in Bacteria |
Membrane-embedded prenyltransferases from the UbiA family catalyze the Mg2+-dependent transfer of a hydrophobic polyprenyl chain onto a variety of acceptor molecules and are involved in the synthesis of molecules that mediate electron transport , including Vitamin K and Coenzyme Q . In humans , missense mutations to the protein UbiA prenyltransferase domain-containing 1 ( UBIAD1 ) are responsible for Schnyder crystalline corneal dystrophy , which is a genetic disease that causes blindness . Mechanistic understanding of this family of enzymes has been hampered by a lack of three-dimensional structures . We have solved structures of a UBIAD1 homolog from Archaeoglobus fulgidus , AfUbiA , in an unliganded form and bound to Mg2+ and two different isoprenyl diphosphates . Functional assays on MenA , a UbiA family member from E . coli , verified the importance of residues involved in Mg2+ and substrate binding . The structural and functional studies led us to propose a mechanism for the prenyl transfer reaction . Disease-causing mutations in UBIAD1 are clustered around the active site in AfUbiA , suggesting the mechanism of catalysis is conserved between the two homologs .
Vitamin K is an essential cofactor required for the posttranslational modification of proteins involved in blood-clotting and normal bone metabolism . One of the major forms of vitamin K in humans , menaquinone-4 , is produced by cleaving the phytyl group from dietary phylloquinone to produce menadione , which is then modified with a polyprenyl group donated from geranylgeranyl diphosphate ( Figure S1 ) . This latter step is catalyzed by the protein UBIAD1 , a member of a family of integral membrane proteins known collectively as UbiA prenyltransferases [1]–[3] . Recently , it has also been proposed that UBIAD1 is responsible for the prenylation of coenzyme Q10 in Golgi membranes [4] . Missense mutations to the UBIAD1 gene are the underlying cause of the genetic disorder Schnyder corneal dystrophy ( SCD ) , which causes accumulation of cholesterol and phospholipids in the cornea of the eye , eventually leading to blindness [5] . Membrane-embedded prenyltransferases belonging to the UbiA family are found in every branch of life , and are involved in the biosynthesis of a highly diverse range of molecules , including respiratory lipoquinones such as ubiquinone and menaquinone [6]–[8] , prenylated hemes and chlorophylls [9] , [10] , archaeal lipids [11] , numerous prenylated plant flavonoids [12] , the antibiotic aurachin [13] , Vitamin E [14] , [15] , and bacterial cell wall precursors [16] . Although the nature of the prenyl acceptor and donor vary considerably , reactions catalyzed by UbiA homologs are Mg2+-dependent and generate pyrophosphate as a leaving group . A representative reaction , catalyzed by the eponymous E . coli protein UbiA , involves the cleavage of the C–O bond in polyprenyl diphosphates of variable length and transfer of the prenyl chain to the ortho position of the phenol 4-hydroxybenzoic acid ( 4HB; Figure 1A ) . UbiA family members are typically predicted to contain eight or nine transmembrane helices and have two characteristic conserved motifs with the consensus sequences NDXXDXXXD and DXXXD ( Figure 1B ) , often referred to as the first and second aspartate-rich motifs , respectively . To understand the structural basis of UbiA function , we set out to elucidate the structure of a member of the UbiA family using X-ray crystallography . The resulting structures of an archaeal homolog reveal locations of Mg2+ and polyprenyl diphosphate binding sites , a possible hydrophobic substrate tunnel allowing the protein to accommodate polyprenyls of variable length , and the location of a cluster of highly conserved residues forming a potential catalytic site .
After screening a large number of bacterial and archaeal UbiA proteins for suitability for crystallization , a homolog from the extremophile Archaeoglobus fulgidus ( AfUbiA ) was chosen for further study based on its stability in detergent ( Figure S2A ) . Crystals of the selenomethionine-substituted protein diffracted to 3 . 2 Å , and the structure was solved by single-wavelength anomalous dispersion ( Table S1 ) . Despite the modest resolution , assignment of the sequence register was greatly facilitated by the high quality of the experimentally phased electron density maps , and by the locations of five selenomethionine residues ( Figure S2B , C ) . The selenomethionine structure was then used as a molecular replacement search model for 2 . 4 and 2 . 5 Å datasets collected on native AfUbiA crystals , grown in lipidic cubic phase ( LCP ) and soaked with either geranyl diphosphate ( GPP ) or dimethylallyl diphosphate ( DMAPP ) prior to freezing ( Table S1 , Figure S2D ) . The final models for the two substrate-bound structures contain four AfUbiA molecules in the asymmetric unit , with one molecule of GPP or DMAPP and two Mg2+ per protein chain . Comparison of the detergent and LCP crystal forms shows that none of the interaction surfaces between neighboring protomers are conserved between the different lattices , and so the protein is likely a monomer in the membrane ( Figure S2E ) . The AfUbiA structure contains a total of nine transmembrane helices , with the N and C termini emerging on opposite sides of the membrane ( Figure 1C ) . Based on the distribution of positive and negative charges in the soluble loops , the N terminus of the protein is probably oriented towards the cytoplasm and the C terminus to the extracellular side [17] . This assignment is also consistent with the experimentally determined orientation of E . coli UbiA [18] . The first eight helices can be grouped into two bundles of four helices each ( Figure 1D ) . The loops connecting the transmembrane helices are short , with the exception of the cytoplasmic loops connecting TM2 and 3 ( L2–3 ) and TM6 and 7 ( L6–7 ) , which are both over 25 and 18 residues in length , respectively , and contain short helical regions . The two conserved aspartate-rich motifs are both positioned on the cytoplasmic side of the protein , between the C-terminal ends of TM2 and TM6 and the L2–3 and L6–7 loops ( Figure 1E ) . Interestingly , the conserved motifs as well as the large cytoplasmic loops that follow them are at equivalent positions in the two four-helix bundles . Closer examination of the two bundles reveals that they are structurally homologous and can be superposed by a twofold pseudosymmetry axis running through the center of the protein perpendicular to the bilayer ( Figure 1D , Figure S3 ) . This raises the possibility that the UbiA fold may have arisen from the duplication of an ancient four-helix , dimeric protein . The crystal structure of another archaeal UbiA homolog from Aeropyrum pernix ( ApUbiA ) was recently reported with a resolution of 3 . 6 Å [19] . The overall fold of AfUbiA is similar to that seen in ApUbiA , but there are differences in the location and coordination of Mg2+ and substrate ( detailed in the section “Differences between the AfUbiA and ApUbiA crystal structures” ) . Both UbiA family members resemble proteins belonging to the isoprenoid synthase superfamily [20] , and in particular , those members that catalyze the synthesis of all-trans polyprenyls by repeated addition of isopentenyl pyrophosphate ( IPP; Figure S1 ) , the trans-IPPSs . These enzymes are also Mg2+-dependent and contain similar aspartate-rich motifs . Although any sequence identity between the soluble and transmembrane families is negligible , comparison of AfUbiA to the trans-IPPS farnesyl diphosphate synthase ( FPPS ) from E . coli ( Figure S4A–B ) [21] reveals that the two four-helix bundles comprising helices 1–8 in AfUbiA and 2–9 in FPPS are superposable . TM9 in AfUbiA and the first helix in the trans-IPPS fold have no equivalent in the other family . Nevertheless , despite their structural similarity , examination of the distribution of charged residues on the two enzymes clearly reveals their distinct identities as soluble and transmembrane proteins ( Figure S4C–D ) . The bound isoprenyl-diphosphates and Mg2+ are located in a large cavity at the interface between the four-helix bundles near the cytoplasmic side , which is partly closed off from the solvent by the L2–3 and L6–7 loops ( Figure 2A ) . Interestingly , in the unliganded structure , a 13-residue long region of the L2–3 loop is disordered , leaving this cavity widely accessible to the solvent , whereas all but four residues become resolved in the GPP-bound structure . In the DMAPP-bound structure , the entire loop is resolved and completely occludes the cavity from the solvent ( Figure S5A , B ) . This difference may be attributed to the lower resolution of the unliganded structure , however , as the substrate in the DMAPP-bound structure is occluded from the cytoplasm , a more likely possibility is that substrate binding induces conformational changes in the L2–3 loop and thus seals off the active site . Near the cytoplasm , the central cavity is broad and is lined with polar and charged residues , including the aspartate-rich motifs and many of the other residues that are most conserved across the UbiA family ( Figure 2B ) . The cavity becomes more hydrophobic and tapers into a narrow tunnel as it extends deeper into the transmembrane region of the protein . Approximately halfway into the bilayer , the tunnel bends sharply and forms a fenestration in the side of the protein that opens into the bilayer ( Figure 2C ) . This tunnel could offer a possible explanation for how UbiA family members utilize prenyl donors of varying lengths , which range from DMAPP ( C5 ) [22] to dodecaprenyl phosphate ( C60 ) [16] . The latter substrate approaches 60 Å in length in a fully extended conformation; in comparison , the membrane-spanning region of AfUbiA is less than 40 Å . The hydrophobic tunnel in AfUbiA could potentially accommodate up to six prenyl units , and even longer polyprenyls could bind to the protein by extending directly into the hydrophobic core of the bilayer . In the GPP-bound structure , the substrate is located in the central cavity with its diphosphate positioned between the two aspartate-rich motifs ( Figure 3A , B ) . Two electron densities are also visible on either side of the diphosphate that likely correspond to Mg2+ . This observation is consistent with data showing that the activity of UbiA family members is Mg2+-dependent , as well as extensive mutagenesis experiments , confirming the importance of the two motifs for activity in E . coli UbiA [23] and other homologs [13] , [24] . The two Mg2+ are coordinated by N68 and D72 in the first aspartate-rich motif and D198 and D202 in the second aspartate-rich motif ( Figure 3A ) . The conserved aspartate D76 is too far away to bind directly to Mg2+ in the first motif , but could interact indirectly by stabilizing a water molecule coordinating the ion . The diphosphate group of GPP is stabilized by Mg2+ in the first motif , which bridges two oxygens with coordination distances of 2 . 3 and 2 . 6 Å . In contrast to the first motif , the Mg2+ bound to the second motif is 3 . 5–4 . 0 Å away from the diphosphate , which is significantly farther than the expected coordination distance of 2 . 0–2 . 3 Å . Additional interactions between the protein and GPP oxygens are provided by the basic residues R22 and K146 . The GPP molecule is slightly kinked after the diphosphate , so that the isoprenyl tail extends along the wall of the cavity close to highly conserved residues on TM2 , 4 , and 5 . In particular , the C–O bond cleaved in the prenyltransfer reaction is positioned near a cluster of conserved polar residues including N68 , Y139 , and S140 ( Figure 3B ) . The geometry of the Mg2+ and substrate binding sites in the DMAPP-bound structure is similar ( Figure S5C , D ) . We used two approaches to verify the interactions between the bound substrate , ions , and protein in our crystal structure . First , to confirm the Mg2+ binding sites , we co-crystallized the protein with Cd2+ ( Table S1 ) . Two strong electron densities consistent with Cd2+ appear coordinated by the aspartate-rich motifs , which align well to the Mg2+ locations in the GPP-bound structure ( Figure S6 ) . Second , we used isothermal titration calorimetry ( ITC ) to measure the Mg2+ dependence of GPP binding to AfUbiA . In the presence of 2 mM MgCl2 , GPP binds to AfUbiA with a KD of 3 . 2±0 . 1 µM ( Figure 3C ) . However , when 1 mM EDTA was added instead of MgCl2 , no GPP binding was observed ( Figure 3D ) . Residues N68 , D72 , D198 , and D202 from the two aspartate-rich motifs , as well as the basic residues R22 and K146 , were then mutated to alanine to test their contribution to Mg2+-dependent GPP binding ( Figure 3E , Figure S7 ) . Mutations to all six residues had pronounced effects on binding of GPP to AfUbiA . Four of the mutations completely abolished GPP binding , whereas the effects of the D198A and D202A mutations were comparatively mild , increasing the KD by 45- and 21-fold , respectively . This is consistent with the observation that the distance between the Mg2+ bound to the second aspartate-rich motif and the GPP diphosphate is outside of the typical coordination distance range . There are notable differences in the shape of the substrate-binding cavity and in the organization of the active sites of the AfUbiA and ApUbiA structures . The central cavity in ApUbiA is smaller than in AfUbiA , largely because ApUbiA lacks the long hydrophobic tunnel and second opening observed in AfUbiA . For the ApUbiA structure , it was proposed that longer prenyl chains may extend out of the protein via its single entrance to the central cavity , which is closer to the membrane interface than in AfUbiA . This mechanism of accommodating long prenyl chains is not likely for the current AfUbiA structure , because although the cytoplasmic opening exists in unliganded AfUbiA , it is completely closed in the DMAPP-bound structure ( Figure S5B ) . In both structures , the bound Mg2+ and the diphosphate moiety are located in the central cavity between the two conserved aspartate-rich motifs; however , interactions between the protein and ligands are different ( Figure 4 ) . Although two bound Mg2+ were modeled in both structures , in the current AfUbiA structure , N68 , D72 , D198 , and D202 directly coordinate the Mg2+ , while in the ApUbiA structure all the corresponding residues are >3 . 4 Å away from Mg2+ . These four residues were demonstrated to be important for Mg2+-dependent GPP binding according to our ITC data ( Figure 3E ) . The differences in Mg2+ location and coordination between the two structures are likely attributable to the significantly lower resolution ( 3 . 6 Å ) of the ApUbiA structure . 4HB was also modeled into the ApUbiA structure , although there is currently no direct biochemical evidence that it can act as a prenyl acceptor for ApUbiA . This proposed 4HB binding site is unlikely to hold the prenyl acceptor in the current AfUbiA structure , as its position clashes with the location of the geranyl moiety . We were also unable to detect binding of 4HB to AfUbiA by ITC . Although two crystal structures are available now for the UbiA family of proteins , both AfUbiA and ApUbiA are from archaeal thermophiles and enzymatic activity has not been demonstrated for either of the proteins . To understand the relevance of the AfUbiA structure to other UbiA family members , we mutated a number of residues on the E . coli MenA homolog ( EcMenA ) that are equivalent to key active site residues in AfUbiA ( Figure 5A , Figure 5B ) . EcMenA catalyzes the transfer of a prenyl chain onto 1 , 4-dihydroxy 2-naphthoic acid ( DHNA ) to produce the menaquinone precursor demethylmenaquinone . Two independent functional assays were used to measure the effects of mutations on EcMenA: an in vivo genetic complementation assay in which growth under anaerobic conditions was measured in an menA− E . coli strain [25] transformed with WT or mutant EcMenA ( Figure 5C ) , and an in vitro assay measuring prenyltransferase activity with purified membranes from E . coli cells overexpressing WT or mutant EcMenA ( Figure 5D , Figure S8 ) . For both assays , mutations to the equivalents of N68 and D72 in the first aspartate-rich motif and D198 and D202 in the second motif resulted in total or near-total loss of function . Mutation of the highly conserved tyrosine ( Y139 in AfUbiA ) near the C–O bond also resulted in loss of function . Functional data for eukaryotic UbiA homologs are currently scarce , but missense mutations to 19 different residues on human UBIAD1 are known to cause SCD [5] , [26]–[35] . Of these , three align to insertions not present on AfUbiA ( Figure 5A ) . As shown in Figure 5E , the remaining 16 mutated residues all map to the region around the putative active site at the cytoplasmic end of the cavity . The residues Y174 and T175 on human UBIAD1 are equivalent to Y139 and S140 , which belong to the cluster of polar residues on TM2 and TM4 likely important for catalysis; the residues A97 and G98 ( F63 and S64 on AfUbiA ) pack into the interface between TM2 and TM4 near this site ( Figure 5F ) . Residues N102 , K181 , D236 , and D240 on UBIAD1 are homologous to N68 , K146 , D198 , and D202 , which form part of the Mg2+/diphosphate binding site ( Figure 3A ) . Residues 112 , 118 , 119 , 121 , and 122 on UBIAD1 align to residues 78 , 90 , 91 , 93 , and 94 on the highly mobile L2–3 loop of AfUbiA , which changes conformation upon substrate binding in AfUbiA . Potential functions for G177 , G186 , and L188 ( P142 , D152 , and I154 on AfUbiA ) are less evident , but all three residues are located in close proximity to the proposed active site . Overall , the above experiments on EcMenA and the mapping of UBIAD1 mutations onto the AfUbiA structure suggest that the fold and location of substrate-binding sites are conserved across the UbiA family , and that the mechanism of the prenyltransfer reaction is conserved as well . Although this mechanism is currently unknown , possible clues may be found by comparison to the soluble trans-IPPS proteins . In addition to sharing a fold with the UbiA homologs , the two protein families also exhibit similarities in the architecture of their active sites . The structure of E . coli FPPS bound to IPP and a thio- analog of the prenyl donor , thioDMAPP , is representative of available structures of trans-IPPS ternary complexes ( Figure S9 ) [21] . Like AfUbiA , members of the trans-IPPS family contain two signature acidic motifs , which both contain the conserved sequence DDXXD [36] and which coordinate Mg2+ atoms that stabilize the diphosphate on the prenyl donor . In trans-IPPS proteins , the reaction is believed to proceed via a three-stage ionization–condensation–elimination mechanism [37] , involving a carbocation intermediate in the allylic site that is stabilized by the liberated diphosphate as well as interactions with nearby polar side chains [21] , [38] . Given the structural and functional similarity between AfUbiA and the trans-IPPS proteins , it is possible that this catalytic mechanism is shared with homologs of the UbiA family ( Figure 6 ) . In the GPP- and DMAPP-bound structures , C-C bond formation would occur near a triad of three polar side chains: N68 from the first aspartate-rich motif , and Y139 and S140 on TM4 ( Figure 3B ) . Interestingly , the tyrosine is the single most highly conserved residue in the UbiA family , present in 97% of the more than 10 , 000 UbiA sequences currently in the Pfam database [39] . Mutation of this residue in both EcMenA ( Figure 5C , D ) and EcUbiA [19] results in a loss of function . The near-universal conservation of the tyrosine residue implies that this site is involved in a function that is shared among all the different branches of the UbiA family , regardless of the nature of the highly variable prenyl acceptor . We therefore propose that this site could be involved in stabilizing the carbocation intermediate on the prenyl donor after cleavage of the pyrophosphate leaving group , possibly by cation–π interactions with Y139 . A similar role for active site tyrosine residues has been proposed for the structurally unrelated aromatic prenyltransferases DMATS and CloQ [40]–[42] .
We have described the structure of an archaeal member of the UbiA family of membrane-embedded prenyltransferases . The substrate-bound structures reveal that the diphosphate group interacts with arginine and lysine side chains as well as Mg2+ coordinated by conserved aspartate-rich motifs . Although short prenyl donors containing only one or two prenyl units were chosen for crystallization due to their higher solubility in water , the structure reveals a long , narrow cavity that opens into the membrane and could allow the protein to bind significantly longer polyprenyl chains . Due in part to its low homology with UbiA family members of known function , we were unable to identify the natural prenyl-accepting substrate of AfUbiA . Nonetheless , the EcMenA functional assays using mutations designed with the AfUbiA structure , as well as the clustering of SCD-causing mutations around conserved residues in the substrate-binding cavity of AfUbiA , suggest that the fold and key aspects of the catalytic mechanism may be conserved between these distantly related homologs and that AfUbiA is a useful structural model for understanding UbiA family prenyltransferases . One curious feature of the substrate-bound structures is that the Mg2+ bound to the second conserved motif is out of range for coordination of the prenyl donor , and yet the functional data for EcMenA indicate that the residues coordinating this Mg2+ are critical for function . One possible explanation is that when the protein is bound to Mg2+ and the isoprenyl diphosphate only , the substrate is coordinated by only one Mg2+ in order to prevent reaction of the isoprenyl diphosphate with water in the absence of the prenyl acceptor , as interactions with one Mg2+ may not be sufficient to induce spontaneous cleavage of the C–O bond . Binding of the prenyl acceptor would then induce a conformational change to bring the Mg2+ bound to the second conserved motif within coordination distance of the diphosphate and exclude water from the cavity . Another possibility has also been proposed based on homology models of E . coli UbiA [23] , that UbiA homologs stabilize the diphosphate via a single Mg2+ bound to only one of the two aspartate-rich motifs and that the other motif activates the phenolic substrate by abstracting a proton . Resolution of the issue will likely require a structure of the full ternary complex . In the structurally related trans-IPPS FPPS , comparisons of the ternary complex and apo-structure show that the enzyme undergoes a conformational change upon binding substrate; in particular , the loops corresponding to L2–3 and L6–7 in UbiA close over the active site to occlude it from the solvent [21] . The unliganded and substrate-bound AfUbiA structures show similar behavior , in that a region of the L2–3 loop undergoes a disordered to ordered transition upon substrate binding . The prenyl acceptor could therefore enter the cavity through an opening formed by fluctuations in the L2–3 loop , which would then be stabilized in the closed conformation . Although short polyprenyl diphosphates like GPP could also enter the substrate-binding cavity via the opening to the cytoplasm , longer polyprenyl diphosphates have poor solubility in water and likely partition into the lipid bilayer , and therefore may bind to the protein laterally from within the membrane . Because it seems implausible that the negatively charged diphosphate group enters the core of the bilayer and threads into the hydrophobic tunnel , the protein may undergo a conformational change to allow the polyprenyl substrate access to the substrate binding cavity . In the crystal structure , one wall of the substrate tunnel is formed by TM9 , whose removal leaves the central cavity completely exposed to the bilayer ( Figure 2A ) . A slight movement of this loosely packed helix could potentially suffice to allow substrates to enter the binding site and allow release of the product as well .
Fifty-two bacterial and archaeal UbiA homologs were cloned and tested for expression [43] . A UbiA gene from Archaeoglobus fulgidus DSM 4304 ( GenBank AAB89594 . 1 ) was identified as the most promising candidate for crystallization trials . The AfUbiA gene was cloned into a modified pET vector ( Novagen ) with an N-terminal polyhistidine tag . For large-scale purification of native AfUbiA , the plasmid was transformed into BL21 ( DE3 ) cells . For expression of the native protein , the transformants were grown in Luria broth supplemented with 100 mg/l Kanamycin at 37°C until OD600 reached 1 . 0 and induced with 0 . 5 mM isopropyl β-D-1-thiogalactopyranoside ( IPTG ) at 20°C for 15 h . For expression of selenomethionine-incorporated proteins , the cells were grown in minimal medium containing 32 . 2 mM K2HPO4 , 11 . 7 mM KH2PO4 , 6 mM ( NH4 ) 2SO4 , 0 . 68 mM Na Citrate , 0 . 17 mM Mg2SO4 , 32 mM glucose , 0 . 008% ( w/v ) alanine , arginine , aspartic acid , asparagine , cysteine , glutamic acid , glycine , histidine , proline , serine , tryptophan , glutamine , tyrosine , 0 . 02% ( w/v ) isoleucine , leucine , lysine , phenylalanine , threonine , valine , 25 mg/l L-selenium-methionine , 32 mg/l thiamine , and 32 mg/l thymine , and induced when OD600 reached 0 . 6 . Cell membranes were solubilized with 40 mM n-decyl-β-D-maltoside ( DM , Anatrace ) , and the His-tagged protein was purified with TALON Metal Affinity Resin ( Clontech Inc . ) . After removal of the N-terminal His-tag with TEV protease , the native protein was subjected to size exclusion chromatography with a Superdex 200 10/300 GL column ( GE Health Sciences ) pre-equilibrated in a buffer of 150 mM NaCl , 20 mM HEPES , pH 7 . 5 , 5 mM β-mercaptoethanol ( βME ) , and 40 mM n-Octyl-β-D-Glucopyranoside ( OG , Affymetrix ) . The protein was concentrated to 10 mg/ml as approximated by ultraviolet absorbance . The selenomethionine-incorporated protein was purified by the same procedure . AfUbiA for the ITC assay was also purified with the same protocol except that 4 mM of DM was used in place of OG in the size-exclusion chromatography buffer . Selenomethionine-incorporated AfUbiA crystals were obtained in mother liquor containing 12 . 5% PEG20000 , 100 mM MES buffer , pH 6 . 7 . To obtain LCP crystals , the purified AfUbiA protein was concentrated to around 35 mg/ml as approximated by ultraviolet absorbance at 280 nm and mixed with monoolein ( 1-oleoyl-rac-glycerol; Sigma Aldrich ) at a 2∶3 ratio ( protein/lipid , w/w ) using the twin-syringe mixing method [44] . The protein/lipid mixture was dispensed manually in 30–50 nl drops onto 96-well glass Laminex plates ( Molecular Dimensions ) and overlaid with 1 . 7 µl precipitant solution per drop . Crystals reached full size within 2 wk at 20°C in 34% ( w/v ) PEG400 , 0 . 1 M Tris-HCl pH 8 . 2 , 0 . 1 M NaCl , and 0 . 1 M MgCl2 . Before harvest , crystals were soaked in 1 mM GPP or 1 mM DMAPP . The LCP crystals were flash frozen in liquid nitrogen without additional cryoprotectant . Crystals of native AfUbiA bound to Cd2+ were obtained in 30% PEG 550 MME , 100 mM MES buffer , pH 6 . 6 , 5 mM MgCl2 , and 100 mM CdCl2 . Before flash-freezing in liquid nitrogen , these crystals were cryoprotected in serial mother liquor solutions containing 5%–25% ( v/v ) glycerol . X-ray data were collected at beamlines X29 at the National Synchrotron Light Source and 24ID-C and 24ID-E at the Advanced Photon Source . A data set collected on a selenomethionine crystal was processed and scaled with a 3 . 2 Å cutoff using HKL2000 [45] . Four selenium sites were located with phenix . hyss [46] , and phases and a partial polyalanine model were obtained with phenix . autosol [47] . The locations of the selenium atoms and clear side chain densities from aromatic side chains in the experimental maps ( Figure S2B , C ) were used to manually assign the sequence register , and the structure was refined through iterative rounds of manual model building and automated reciprocal-space refinement using Coot [48] and phenix . refine . The final refined model has R and Rfree values of 25 . 1% and 28 . 9% , respectively , and contains residues 15–73 and 86–300 of one UbiA monomer and one molecule of OG , which was used to solubilize the protein . The native GPP-bound and DMAPP-bound structures were solved by molecular replacement using the selenomethionine structure as a search model and refined with phenix . refine using strong NCS restraints that were gradually relaxed over the course of refinement . The final structures each contained four molecules of AfUbiA , 8 Mg2+ , and 4 molecules of GPP or DMAPP . The Cd2+-bound structure was solved by a similar protocol . The final Cd2+-bound structure contained two molecules of AfUbiA in the asymmetric unit and eight Cd2+ ions . In the SeMet , GPP- and DMAPP-bound structures , the putative substrate tunnel is partly occupied by a strong , tubular , nonprotein electron density ( ) ; however , the resolutions do not allow a definitive identification of this ligand . The GPP-bound , DMAPP-bound , SeMet , and Cd2+-bound structures have been deposited in the PDB under the accession codes 4TQ3 , 4TQ4 , 4TQ5 , and 4TQ6 , respectively . Chain A in the GPP-bound structure had the highest quality 2Fo-Fc density , as well as the lowest average B-factors of the four protein chains in the asymmetric unit , and was therefore used to generate figures and for distance measurements unless otherwise noted . All structure figures were made using PyMol ( Schrödinger ) . Sequence conservation scores in Figure 2B were calculated with the ConSurf server [49] , using the seed sequences for the UbiA family from Pfam [39] for the multiple sequence alignment . The alignment of AfUbiA , EcMenA , and human UBIAD1 used for Figure 5A was generated by aligning the sequences to the Hidden Markov Model profile for the UbiA family in Pfam . The ITC buffer comprised 20 mM HEPES ( pH 7 . 5 ) , 150 mM NaCl , and 4 mM n-decyl-β-D-maltopyranoside ( DM ) . The chamber contained ITC buffer plus 50 µM AfUbiA and either 2 mM MgCl2 or 1 mM EDTA . The syringe contained ITC buffer plus 0 . 6 mM geranyl pyrophosphate ( GPP ) and either 2 mM MgCl2 or 1 mM EDTA , whichever matches the chamber condition . The buffer-alone control had no AfUbiA in the chamber . For experiments with mutant proteins , 50 µM AfUbiA mutant proteins were in ITC buffer containing 2 mM MgCl2 with either 0 . 6 mM GPP ( for R22A , N68A , D72A , K146A ) or 2 mM GPP ( for D198A and D202A ) in the syringe . Solutions were filtered and centrifuged at 18 , 000× g for 5 min prior to the experiments . All binding measurements were performed using a MicroCal iTC200 System ( GE Healthcare ) at a constant temperature of 25°C . For experiments with apparent binding , thermograms were processed and fit in Origin to a one-site model to obtain n ( stoichiometry ) , K ( association constant ) , and ΔH ( enthalpy ) . The dissociation constant ( KD ) was calculated from KD = 1/K , and ΔS was calculated from ΔG = ΔH−TΔS . All experiments were performed at least three times . The menA-deficient E . coli strain AN67 [25] , which exhibits a grow defect under anaerobic conditions , was obtained from the Coli Genetic Stock Center and transformed with a pET31 plasmid containing WT and mutant EcMenA genes , or an unrelated protein ( the TrkH potassium transporter from Campylobacter jejuni ) as a negative control . The transformants were grown aerobically in Luria broth to an optical density of 1 . 0%±0 . 05% , supplemented with 20% glycerol , flash frozen , and stored at −80°C . The EcMenA genetic rescue experiments were carried out using a protocol adapted from Suvarna et al . [7] . The 10 mL cultures of a glycerol/trimethylamine N-oxide minimal media [50] containing 0 . 5 mM IPTG and 0 . 1 mg/mL ampicillin were inoculated with 10 µl of the glycerol stocks and then incubated in an anaerobic chamber at 37°C . At 24 h postinoculation , OD600 was measured for each culture . Values shown in Figure 5C are averages for three experiments . Purified E . coli membranes were prepared by harvesting 1 l of cells overexpressing WT and mutant EcMenA , grown to 1 OD as described for AfUbiA . The proteins were expressed as SUMO fusion proteins to increase yield . The cell pellets were resuspended in 20 ml lysis buffer ( 20 mM Hepes pH 7 . 5 , 150 mM NaCl , 2 mM βME , 5 mM MgCl2 , 1 mM PMSF , 25 µg/ml DnaseI ) . After breaking the cells by sonication , the cell lysates were centrifuged for 30 min at 3 , 000× g and 4°C . The supernatant was then transferred to clean centrifuge tubes and centrifuged a second time for 60 min at 100 , 000× g and 4°C . The membrane pellet was resuspended in 3 ml 50 mM Tris pH 7 . 5 , flash frozen , and stored at −80°C . Overexpression of WT and mutant SUMO-EcMenA was verified by running 0 . 5 µl of the membrane suspension before and after a 30 min digestion with 1 µg SUMO protease on an SDS-PAGE gel ( Figure S8C ) . In addition to the WT and mutant EcMenA proteins , membrane fractions were prepared in the same manner for cells expressing SUMO-EcUbiA , which does not utilize DHNA as a prenyl acceptor , for the negative control . For the enzymatic assay , 30 µl reaction mixtures were prepared with the following components: 3 µl purified membrane fractions , 2 mM DHNA , 1 mM GPP , 5 mM MgCl2 , 5 mM βME , 5% acetonitrile ( ACN ) , and 50 mM Tris pH 7 . 5 . The reaction mixtures were incubated for 10 min at 37°C , quenched with the addition of 2% formic acid , and extracted with 10 volumes of chloroform . The chloroform was dried under air and the residue resuspended in 60 µl 65% ACN/35% 50 mM Tris pH 7 . 5 in dH2O . The resulting samples were then separated using reverse phase HPLC with a gradient of 65%–75% ACN for the mobile phase . Enzyme activity was quantified as the area of the product peak , normalized by the activity for the WT protein . Values shown in Figure 5D are averages for three experiments . | The biosynthesis of Vitamin K and Coenzyme Q requires the transfer of a long , hydrophobic moiety known as an isoprenyl onto an aromatic acceptor compound . This process is catalyzed by a family of proteins known as the UbiA proteins , which are embedded in the hydrophobic environment of cell membranes . To understand how the prenyltransfer reaction is carried out , we solved the three-dimensional structure of a member of the UbiA family by X-ray crystallography . This structure reveals how magnesium ions and the prenyl substrate are bound within a sealed amphipathic chamber inside the protein and suggests how the reaction intermediate may be stabilized by the protein and protected from the solvent . Functional studies carried out on another member of the UbiA family , as well as comparison to known disease-causing mutations in the human homolog UBIAD1 , demonstrate that the residues involved in this process are conserved across the UbiA family . | [
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] | 2014 | Structure of a Membrane-Embedded Prenyltransferase Homologous to UBIAD1 |
Architecture of phase relationships among neural oscillations is central for their functional significance but has remained theoretically poorly understood . We use phenomenological model of delay-coupled oscillators with increasing degree of topological complexity to identify underlying principles by which the spatio-temporal structure of the brain governs the phase lags between oscillatory activity at distant regions . Phase relations and their regions of stability are derived and numerically confirmed for two oscillators and for networks with randomly distributed or clustered bimodal delays , as a first approximation for the brain structural connectivity . Besides in-phase , clustered delays can induce anti-phase synchronization for certain frequencies , while the sign of the lags is determined by the natural frequencies and by the inhomogeneous network interactions . For in-phase synchronization faster oscillators always phase lead , while stronger connected nodes lag behind the weaker during frequency depression , which consistently arises for in-silico results . If nodes are in anti-phase regime , then a distance π is added to the in-phase trends . The statistics of the phases is calculated from the phase locking values ( PLV ) , as in many empirical studies , and we scrutinize the method’s impact . The choice of surrogates do not affects the mean of the observed phase lags , but higher significance levels that are generated by some surrogates , cause decreased variance and might fail to detect the generally weaker coherence of the interhemispheric links . These links are also affected by the non-stationary and intermittent synchronization , which causes multimodal phase lags that can be misleading if averaged . Taken together , the results describe quantitatively the impact of the spatio-temporal connectivity of the brain to the synchronization patterns between brain regions , and to uncover mechanisms through which the spatio-temporal structure of the brain renders phases to be distributed around 0 and π . Trial registration: South African Clinical Trials Register: http://www . sanctr . gov . za/SAClinicalbrnbspTrials/tabid/169/Default . aspx , then link to respiratory tract then link to tuberculosis , pulmonary; and TASK Applied Sciences Clinical Trials , AP-TB-201-16 ( ALOPEXX ) : https://task . org . za/clinical-trials/ .
Many processes in nature are oscillatory , from heart beats and birds flapping their wings , to firing of neurons [1] and brain rhythms [2] . Oscillators are rarely isolated and they interact when coexisting in the same environment , thus synchronizing by adjusting their rhythms [3] . Synchronization , or consistent phase relationships , of distant regions of the brain has been detected by a variety of measures and may be a key mechanism for the regulation of cortical processing and communication [4 , 5] . Advances of non-invasive structural brain imaging [6 , 7] have made feasible large-scale network modeling approaches using biologically realistic connectivity , defined by the connection topology and delays , the so-called connectome , which is a crucial determinant of the network behavior [8–13] . The Kuramoto model ( KM ) [14] as a paradigm for the emergent group dynamics of coupled oscillatory subsystems [15 , 16] is well suited for assessing how the connectome governs the brain oscillatory dynamics [17–22] , which is then reflected in the phase relationship between brain regions . Studies of networks dynamics predominantly focus on the synchronization properties , while the actual phase relationship between the oscillators is typically ignored , especially in complex networks with delays [16 , 23] . For the tractable case of all-to-all equally coupled phase oscillators in thermodynamic limit , the phases of each oscillator are either constantly shifted from the mean phase , or non-uniformly rotate with a speed dependent on their natural frequencies , while still preserving the overall stationary distribution [14] . For heterogeneous couplings , phases become multimodal [24] and thus imply multimodal phase shifts for stationary synchronization , and for couplings of mixed signs [25] , the oscillators generally split at distance π , but for strong coupling they form a traveling wave . Glassy states with ordered , but uniformly distributed phases for each frequency also appear for distributed parameters [26] , and for structured networks multiple mean fields appear with oscillating , bounded or unbounded phase differences between them [27] . A number of computational studies on brain functional connectivity use neural masses connected with connectome-defined delays and weights . This yields intermittent in- or anti-phase synchronization [8] and a good agreement with experimental studies of phase relationship between local node dynamics and their degree in healthy subjects [28] , and for Alzheimer’s disease [29] . Time-delays have been employed as a necessary condition for modeling anti-phase spatio-temporal patterns in the brain [11 , 30] , and for pair-wise coherence in connectome networks of phase oscillators that reproduce resting state patterns in BOLD fMRI [19] , MEG [31] and EEG [21 , 32 , 33] . Oscillatory processes are particularly sensitive to delays , because shifts in phasing may render excitatory connections to inhibitory , and vice versa . The impact of time-delays on the synchronization , and indirectly to the phase-lags , has been studied for a single delay [34 , 35] or for homogeneously distributed delays [36 , 37] , but so far only for all-to-all connectivities or for simple motifs . Spatially heterogeneous delays are particularly important for number of systems , foremost the brain [10 , 11 , 13] . Depending on their distribution , time-delays impose phase-shifted , in- or anti- phase clusters of oscillators , but their impact for phase-lags in large-scale neural synchronization has not been properly investigated . Stronger connected network nodes have been demonstrated to lag behind the weaker for randomly distributed delays shorter than a quarter period of the oscillators [22] , but this restricts a large portion of the relevant frequencies . In the current study we identify the relationship of the brain topology and its spatio-temporal structure , with the phase lags between the brain regions at any frequency of the brain processes . Analytical insights of synchronization on networks with distributed delays and heterogeneous couplings and frequencies , are applied to in-silico large-scale brain dynamics . Phase lockings and lags are studied in consideration to the limitations of time-series analysis that depend on the regime and levels of coherence . Inhomogeneous interactions due to the connectome are shown to drive the phase relationship , whilst the regimes of synchronization are constrained by the organization of the time-delays . Besides in-phase , these include anti-phase locking that for weak coherence depends on the length of the links , while for strong coupling is prevalent for the nodes of opposite hemispheres .
Numerical integrations utilize a Heun scheme adapted to time delays . The time-step is set as 0 . 01/ ( max ( [max ( K ) , 0 . 05μ , D , 1] ) ) , with noise intensity D and mean of the natural frequencies μ , thus assuring that it is never larger than 0 . 01s and it is accordingly decreased for larger couplings , frequencies or noise . All time series are down-sampled to twice the Nyquist frequency of the fastest oscillator . Complex phase locking values , Eq ( 3 ) , are calculated at sliding windows of length equal to 10 periods of the mean entrainment frequency and with 75% overlap . Qualitatively similar results are obtained for windows lengths between 5 and 10 periods , and for overlapping between 50% and 90% , although longer windows yield systematically lower level for statistical significance [47] . Signals can often be coherent just by chance and statistical testings are necessary to correctly identify the coherence due to the mutual interactions [46 , 47] . The level of significance for PLV is calculated as the 95th percentile of maximum values in 100 surrogate signals using two different procedures , followed by the same processing as for the original time-series . The first surrogates , which yield less strict level of significance are obtained by shuffling the time series of the phases of one of the oscillators . The second , generally stricter level is obtained by two independent uncoupled oscillators with the same frequencies , fixed or time-varying , as the original oscillators , with the same level of noise . The problem with the latter is that in empirical analysis the parameters of the uncoupled oscillators and the noise intensity can be unknown , although the noise intensity could be obtained from the variance of the signal under assumptions of stationarity .
The simplest case of two delay-coupled phase oscillators with constant parameters reads θ ˙ 1 , 2 ( t ) = ω 1 , 2 - K sin [ θ 1 , 2 ( t ) - θ 2 , 1 ( t - τ ) ] . ( 4 ) Steady synchronization occurs when the oscillators start oscillating with a same adjusted frequency Ω , preserving a constant phase shift ϕ1 , 2 = θ1 − θ2 , which becomes ϕ 1 , 2 = arcsin ω 1 - ω 2 2 K cos Ω τ ∈ { ( - π 2 , π 2 ) , if K cos Ω τ > 0 , ( π 2 , 3 π 2 ) , if K cos Ω τ < 0 , ( 5 ) where Ω is described by a transcendental function and the critical coupling reads K c = | ω 2 - ω 1 | / | 2 cos Ω τ | ( 6 ) Oscillators can be locked in- or anti- phase , depending on the sign of K cos Ωτ , so that for the phase shift it holds {ω 2 ≥ ω 1 ⇒ { ϕ ∈ [ 0 , π 2 ) if K cos Ω τ > 0 , ( in-phase ) , ϕ ∈ ( π 2 , π ] if K cos Ω τ < 0 , ( anti-phase ) . ω 2 ≤ ω 1 ⇒ { ϕ ∈ ( - π 2 , 0 ] if K cos Ω τ > 0 , ( in-phase ) , ϕ ∈ [ π , 3 π 2 ) if K cos Ω τ < 0 , ( anti-phase ) . ( 7 ) The model is made more realistic by allowing deterministic variability of the frequencies and the coupling , and additive , independent , Gaussian noise θ ˙ 1 , 2 ( t ) = ω 1 , 2 + ϵ 1 , 2 sin ω ^ 1 , 2 t - ( K + ϵ K sin ω ^ K t ) sin [ θ 1 , 2 ( t ) - θ 2 , 1 ( t - τ ) ] + η 1 , 2 ( t ) . ( 8 ) Here 〈ηi ( t ) 〉 = 0 and 〈η1 ( t ) η2 ( t′ ) 〉 = 2Dδ ( t − t′ ) δ1 , 2 , with 〈 ⋅ 〉 denoting time-average operator , while ω1 , 2 and K are harmonically modulated . In adiabatic limit without noise [48] effective coupling K e f f ( t ) = ( K + ϵ K sin ω ^ K t ) cos Ω τ and frequencies ω e f f 1 , 2 ( t ) = ω 1 , 2 + ϵ 1 , 2 sin ω ^ 1 , 2 t and Δωeff1 , 2 ( t ) = ωeff1 ( t ) − ωeff2 ( t ) , can quantify the synchronization instead of fixed parameters in Eqs ( 5 ) and ( 6 ) , but they give insight into the level of coherence even for stochastic dynamics , and for non-adiabatic response that occurs due to the large inherent time-scale close to incoherence . The stochastic dynamics with constant parameters is shown through the evolution of instantaneous and time-averaged phase lags , and PLVs in Fig 1 . The oscillators in panel ( A ) are identical and the only variability is due to the noise , which causes time-varying cPLV and phases . Nevertheless , phase lags are close to zero during the periods of significant PLV , as seen by the red and magenta lines for a shorter interval in ( a ) , and for the whole time series in ( b ) . Thus , for a sufficient number of time-points the lags for in-phase synchronization will have a mean at 0 , as seen in their histograms and estimated probability density distributions ( PDF ) , ( d , e ) . The mean phase difference in ( B ) is in the interval ( −π/2 , 0 ) , as predicted by Eq ( 7 ) for in-phase locking and different natural frequencies with ω1 < ω2 . The results also indicate that statistical significance has no influence on the mean of the observed lags , but only impacts their variance . Hence , lower significance levels would improve the statistics of short time-series , by increasing the number of significant data points . Non-autonomicity causes intermittent epochs of in- or anti-phase synchronization , Fig 2 . These are still well captured by |2Keff| ≷ |Δωeff| , as predicted by Eq ( 6 ) for fixed parameters , with periods of insignificant coherence , ( c , j ) , corresponding to |2Keff| ≲ |Δωeff| , ( d , k ) . In both examples the coupling is explicitly modulated with ω ^ K , but also implicitly through the NA frequency of synchronization included in cos Ω ( t ) τ , whilst 〈ωeff2〉 is clearly larger than 〈ωeff1〉 for averaging over the times of significant coherence . The latter ensures the phase lags to be in the ranges predicted by Eqs ( 5 ) and ( 7 ) for ω2 ≥ ω1 , although they are wider distributed than for fixed parameters , due to the varying frequency mismatch . The distribution of Δθ is additionally broader due to the noise-induced variability , which gets partially averaged out for ϕ . As in Fig 1 , instantaneous and averaged phases from cPLV have very similar statistics , shown through histograms for significant phase lags in regard to the both levels of significance , calculated at 50 equally spaced bins in the interval [−π , π] , as it is the case in the later figures . This implies that shorter time-windows would affect observed phase shifts only indirectly , through the levels of PLV . Results for time-varying parameters , Eq ( 8 ) , in Fig 2 confirm that the theoretical insights , Eqs ( 5 ) and ( 7 ) , can be also used to describe the statistics for noisy and NA parameters , which resemble the intermittent coherence observed in the real data . The distribution of phase lags depends on the time-delay compared with the frequency of synchronization , and the average ratio of the natural frequencies . The former dictates the regime of synchronization , in- or anti-phase , whereas the latter specifies in which quadrant the mean of the phases will be located . First we derive analytical results for two spatial configurations of time-delays in all-to-all connected oscillators with heterogeneous natural frequencies and coupling strengths . Then we generalize and numerically validate those results for a more biologically plausible scenario with stochastic inhomogeneities . The system Eq 2 cannot be solved for general [Kij , τij] , such as the connectome . Still , based on certain assumptions , we characterize phase relations between different nodes , depending on their location and strength . Firstly we approximate coupling inhomogeneity by the average coupling strength of each oscillator [49] , which also allows for sparse networks . The model Eq 2 is henceforth reduced to θ ˙ i = ω i + K i N ∑ j = 1 N sin [ θ j ( t - τ i j ) - θ i ] , i = 1 … N . ( 9 ) Next , global and local order parameters [13 , 36] , are defined z ( t ) ≡ r ( t ) e i Φ ( t ) = 1 N ∑ j = 1 N e i θ j , ( 10 ) ξ i ( t ) ≡ R i ( t ) e i Ψ i ( t ) = 1 N ∑ j = 1 N e i θ j ( t - τ i j ) . ( 11 ) Here r is the global coherence or the strength of the instantaneous mean field , Ri is the local coherence or the mean field strength felt by each oscillator , whilst Φ and Ψi are the phases of the global and the local mean-fields . Introducing these in Eq 9 , the mean-field character of the model emerges θ ˙ i = ω i + K i Im ( ξ i e - i θ i ) ( 12 ) To facilitate the analysis steady partial synchronization [24] is assumed , as opposed to the so-called standing waves [50] . We build on [13] and we derive analytical results for randomly distributed bimodal-δ delays and delay-imposed symmetrical biclusters . The PDF of the time delays with equal peaks hence reads h ( τ ) = [ δ ( τ - τ 1 ) + δ ( τ - τ 2 ) ] / 2 . ( 13 ) The delays are either spatially homogeneous with the same independent probability for any link , or they are heterogeneously organized so that two identical subpopulations emerge with same internal and external time-delays , Fig 3 . Besides representing distinct phenomenological structures , these topologies are motivated from the connectome . Its simplest decomposition on a left and a right hemispheres identifies the peaks in the delays distribution as intra- and inter- hemispheric links ( see Fig 9 ( d ) –9 ( f ) ) , leading to the clustered organization as a first approximation . However , this division is not strict , and many links are randomly distributed , corresponding to spatial homogeneity . Due to the spatial homogeneity of the random network and of the internal links of ordered subpopulations , Fig 3 , the global order parameter , Eq ( 10 ) , in both cases is z ( t ) = ( z I + z I I ) / 2 . ( 14 ) For the former zI , II = z can represent any proportion of nodes , while for the latter they correspond to the different delay-imposed subpopulations . General analysis for networks with time-delays is practically impossible to this date . Analytic approaches exist only for certain types of complex networks [16] combined with special delay heterogeneities [13 , 36] , but they are still limited to the thermodynamic limit or require averaging . Besides , the connectome typically consists of less than 100 nodes , rarely going above several hundreds , and the state of art large-scale brain-modeling considers personalized connectomes [51] . Therefore , numerical simulations scrutinized by the analytical insights for simpler network topologies are a reasonable direction to proceed with the analysis of the brain networks dynamics . In-silico oscillatory neural activity is explored over connectome based architecture to better understand the phase relation between signals from distant brain areas . A human connectome , Fig 9 , is randomly chosen from a list of 1200 publicly available healthy subjects part of the Human Connectome project [52] . The subject was scanned on a customized 3 T scanner at Washington University and the structural connectivity was constructed using a publicly available pipeline [53] that applies spherical deconvolution method to a probabilistic streamlines tracking algorithm [54] . The obtained connectome consists of few million tracts spatially averaged to connect 68 cortical regions defined according to Desikan-Kiliany atlas [55] . Note however , that different parcellations are also possible , for example by subdividing each of the cortical regions [56] , and these can consist of several thousand nodes [57] , but are not commonly used in simulations because of the computational cost . For each link , weights are numbers of individual tracts , Fig 9 ( a ) , and lengths are their averages , Fig 9 ( b ) . Spatial distribution of the track lengths show that they are bimodaly distributed , Fig 9 ( d ) , with the modes being spatially heterogeneous , and as a first approximation corresponding to the intra- and inter-hemispheric links , Fig 9 ( e ) and 9 ( f ) . This insight suggests that some of the aspects of the large-scale brain dynamics are expected to be explained by the results for fully ordered delays . The propagation velocity is fixed within the realistic range [2 , 58] at 5m/s , and dynamics are analyzed at different frequencies and coherence levels . The latter are additionally constrained by the noise and the global coupling strength that multiplies the normalized weights of the connectome . Since the distribution of natural frequencies across brain regions is generally unknown , equal values with stochastic inhomogeneities are assumed at each node . Moreover , even band-pass filtered recordings of neural activity in most of the cases consist of several overlapping rhythms , which are time-varying and activity-dependent , henceforth equal on average for long recordings . Time delays cause coexistence of multiple stable frequencies of synchronization , larger or smaller than the natural , and can lead to amplitude and oscillation death in more complex systems [59] . However , we observe that unlike for the networks with bi-modally distributed delays , all numerical simulations on the connectome evolve towards a state with lower frequency than the natural , as often reported for different configurations of delay-coupled phase oscillators [60–63] . Pair-wise phase lags . Even though the spatio-temporal structure of the connectome is far more complex than networks with bi-modal δ time-delays , results in Fig 10 still show in- ( A , D ) and anti-phase ( B ) clusterings between the brain hemispheres for realistic frequencies and different levels of synchronization . An intermittent state of in and anti-phase epochs is also often observed , panel ( C ) , as seen by the mean-field parameters shown on the bottom . If the frequency is such that μ〈τext〉 is in the right hemisphere , then the latter regimes occurs for most of the cases with low coherence . High coherence almost exclusively leads to slowing down that pushes Ω〈τext〉 in the first quadrant and therefore in-phase synchronization . Nevertheless such levels of coherence are not expected to occur in a healthy brain , and these regimes are biologically implausible , Fig 10 ( A ) and 10 ( D ) . The good match of the simulated brain dynamics with the theoretical predictions for networks with much simpler structure , is not only limited to the regimes of synchronization . As predicted , in all examples in Fig 10 stronger nodes in each hemisphere generally lag in phase , since Ω < μ . This occurs for in- and anti-phase synchronization , but also during the intermittent regime . The division between the latter two is often fuzzy , since the intervals of anti-phase synchronization rarely last longer than several seconds , before being interrupted with in-phase epochs . Anti-phase regime is assumed when the hemispheric complex order parameters are at a distance larger than π/2 , and in-phase otherwise , allowing comparison with the analytical results . They capture the dynamics fairly well , even for a distance not much larger than π/2 as shown in Fig 10 ( C ) . A better approach would be intermittent intervals to be analyzed separately , since the frequency of synchronization might differ during each interval , and averaging it can lead to wrong values for the phases . Moreover , even if varying frequencies of synchronization are properly detected , averaging of the relative phases over different regimes , Fig 10 ( B ) and 10 ( C ) , makes them to be distributed at distances smaller than π , which might be mistaken for an actual stationary clustering , rather than a mix of 0 and π clusterings . This is shown in Fig 11 , where PLV and the instantaneous and time-averaged phase lags are shown for an intra and an inter hemispheric links . The left panel of Fig 11 shows an intrahemispheric link between in-phase brain regions , and the right depicts an interhemispheric link with epochs of in- and anti-phase locking . Since K26 > K30 the phase difference Δϕ30 , 26 ∈ [0 , π/2 ) and results are very similar for the both significant levels , despite their large difference . The region 41 is stronger connected than the 14 , so it is expected that during in-phase intervals Δϕ14 , 41 ∈ ( −π/2 , 0] , and Δθ14 , 41 ∈ ( π/2 , π] for anti-phase , c . f . with Fig 8 ( n ) and 8 ( o ) . Consequently , distributed peaks appear for the histograms of phase lags in the bottom plots of Fig 11 ( B ) , but their mean is in ( −π/2 , −π] leading to possible wrong conclusion about the synchronization of these nodes . Whole brain phase lags statistics . Whole brain phase statistics are characterized by the mean and the standard deviation of the PLVs , and the correspondent phase-lags for each pair of brain regions . These are shown in Fig 12 , where 1–standard deviation is plotted to keep the colors/coherence consistency across the images . In the upper row , the regions are arranged according to Desikan-Kiliany atlas [55] , with the left hemisphere first , while in the lower row , nodes of each hemisphere are ordered increasingly according to their strength . Strengths of the tracts are reflected in PLV ( first column in Fig 12 ) , where the links with stronger direct connection show higher functional connectivity . The negative bias of the tracking techniques towards interhemispheric connections is also manifested . Consequently fewer external links have significant coherence , especially for the higher surrogates criterion , when only the strongest links survive . Phase lags within hemispheres , especially for stronger links , are around 0 , revealing the in-phase synchronization . Between hemispheres , the phase lags are less informative , due to the intermittent in- and anti-phase synchronization , also seen in Fig 11 ( B ) for the same regime . Still , many inter-hemispheric links have phases around ±π or closer to ±π/2 . The latter is often hallmark of intermittent in and anti-phase regimes , as discussed for the results of Fig 11 ( B ) . The intermittency is also manifested by increased variance of the phases , visible for the inter-hemispheric links . This is much less manifested in the coherence , which stays stable during different regimes , as was also indicated by the hemispheric order parameters in Fig 10 . The large variation of the overall order parameter observed there , is only due to the bursts of anti-phase ordering when it gets close to 0 , whereas the coherence within each hemisphere is quite stable . The impact of the chosen significant coherence , and the difference between instantaneous and averaged phases for the phase statistics of each link is illustrated in Fig 13 for anti-phase regime . Higher significance level causes only slightly larger variance for the phase lags , panel ( A ) , but as seen in Fig 11 , it can substantially reduce the number of accounted links , especially between hemispheres , illustrated through one such a link in panel ( C ) . It is due to the latter mechanism that the overall distribution of the mean lags is less uniform for higher surrogates , panel ( B ) . On contrary , time-averaging stronger decreases the variance for the links , because it diminishes the network and stochastic heterogeneities , but it does not affect the means of the phase lags for particular links , as can be also seen for the link in Fig 13 ( C ) or in Fig 11 ( B ) . The overall statistics for the distinctive phase regimes in the brain are illustrated in Fig 14 . Phase lags and PLVs are depicted for a same subject , for various frequencies and coupling strengths , with noise proportional to the frequency to account for the frequency dependent decrease of the coherence [47] . Time-averaged coherence is shown in the first column , in addition to the mean of the PLVs of each time-window . The former speaks about the overall regime of synchronization , whilst the latter depends on the length of windows compared to the frequency and is more affected by the noise . The mean PLV alone is hence not informative , but needs to be compared with a significance level . Mean phase lags for times of significant coherence are shown in the third and fourth column for two different surrogate procedures . They produce largest difference for anti-phase regime ( second row ) , which requires low coherence that is even smaller between hemispheres due to fewer tracts . For very low synchronization , as shown on the bottom , they are identical and therefore only one is shown , while for high overall coherence ( second and third row ) , higher significance level discards the tails in phase lags’ distribution ( last column ) , which mainly represent links between weaker nodes , thus making the distribution sharper . Possible paths for transition between different regimes of synchronization are also shown in Fig 14 . For low frequencies , in-phase synchronization occurs ( first row ) , which becomes intermittent/anti-phase for increased frequency ( second row ) , at similar level of overall coherence . By increasing the coupling and henceforth the level of coherence , the brain switches to in-phase regime ( third row ) that can again switch to anti-phase by further increasing the frequency , but only at very low overall coherence ( bottom row ) . Also note that the overall low coherence at the bottom row leads to spatially homogeneous values for the mean PLVs , as compared to the cases with much higher coupling and global partial coherence shown in the second and third row . Low coupling renders all links to be around a same level of coherence , without strongly coherent and incoherent links like in the middle rows , and as a result , for every pair of regions exists at least one time-window with statistically significant PLV . Henceforth the absence of links with no significant PLV . Spatial distribution of phase-lags in Fig 14 is in agreement with the theoretical predictions . Besides being 0 centered for strong nodes within the same hemisphere regardless of the synchronization regime , lags around 0 and ±π appear between the hemispheres , resembling in- and anti-phase regimes . In addition , weak regions lead the stronger , and for anti-phase hemispheres π is added . Hence , the inverted distribution of green and blue shades for the intra and inter-hemispheric links in the phase lags matrices , with darker shades corresponding to ±π/4 for internal links , and lighter for external with the values around π ± π/4 . Frequency dependent spatial distribution of phase lags is illustrated in Fig 15 for intra and inter-hemispheric brain subnetworks , for two frequencies and a same global coupling . The subnetworks consist of the 10 strongest brain regions in each hemisphere based on the sum of their outgoing links . Strength of the nodes is reflected in their size , whilst links are color-coded with their phase lags taken from the upper triangle of the matrices . As predicted , strong coherence is observed during in-phase synchronization at f = 6Hz , which together with similar strengths of the nodes , renders almost zero phase lags for all the links , internal and external . During anti-phase regime observed at 20Hz , the links within the hemispheres have lags distributed around 0 , but much wider than before , whilst those between them are distributed around ±π . The coherence decreases for increasing frequencies , and together with the earlier discussed non-stationarity , these cause far higher variability of phases , than during in-phase synchronization . Hence the appearance of dark shades of green and blue for in-phase , and light for anti-phase synchronization .
Phase lags are analyzed when only pair-wise interactions are explicitly considered , and for network connectivity . The former approach is justified if interactions are too weak , and can be represented on average as a stochastic influence to the inherent dynamics at each region . This leads to mean value of the phase differences at 0 or ±π , depending on whether the delay is long enough to change the sign of the interaction . Despite its simplicity that allows analytical tractability , the phenomenological oscillatory model resembles the non-stationary oscillations of the neural activity , which is characterized by transient synchronization . To better understand the underlying organization that regulates the large-scale brain dynamics , and henceforth the phase relationships between network nodes , we analyze synchronization for networks with bimodal delays as a first approximation of the connectome . Theoretical insights are validated numerically for more realistic frequency and couplings heterogeneities , and compared with in-silico brain dynamics , while examining the methodological limitations . Phase lags during these epochs of coherence depend on the delays , which are constant , and on coupling strength and frequency mismatch . The latter can be different across the time-series , but the statistics of the narrow frequency content is generally expected to be similar across the regions . Consequently , the natural frequencies are modeled as stochastic with equal means . Accounting for the network dynamics is a more complex approach that is more realistic , especially when the overall coherence is not insignificant . The brain network model predicts that the distribution of the phase lags will always have a peak at 0 , with an additional peak at ±π appearing for anti-phase synchronization . Of crucial importance here is whether the frequency increases or decreases during synchronization . For lowest frequencies of electro-physiological brain signals , delays cause relative phases within the first quadrant , so the frequency is depressed regardless of the topology of the delays , whilst the phase locking is in-phase . Hence stronger nodes on average phase lag behind the weaker , for any arrangements of the natural frequencies . For higher frequencies , theoretical and numerical results show that for ordered networks both directions of the frequency shift are possible . However , in-silico brain dynamics exclusively shows frequency depression , so for equal stochastic frequencies better connected brain nodes phase-lag behind the weaker , whereas for anti-phase regime the π distance should be also accounted . The frequency depression due to delayed interactions has wider importance than the 1:1 synchronization that is discussed here . Slowing down in an anatomically constrained dynamical system with noise has been shown to induce the whole-brain FC [64] , by utilizing power to phase interactions . The latter are one of the mechanisms for cross-frequency coupling [65] , which besides the well-known beta-theta interactions in the hippocampus [66] , are also shown to occur for cortical signals [67] . The effects of signal mixing and spread due to volume conduction cause artificial synchrony between nearby sources that are alleviated with inverse source reconstruction techniques [68] . Nevertheless , linear mixing of signals from multiple sources can still lead to wrong coherence and phase synchrony estimates and this is commonly eliminated with interaction metrics that detect exclusively lagged interactions [28 , 69] . This comes at the cost of an inability to detect true zero-phase lag interactions , which as we show , may be instead neurophysiologically meaningful and due to the coupling structure , as also suggested by other studies [33] . Including the actual zero-phase lagged interactions would henceforth potentially have an important impact on whole brain data analyses of M/EEG data , as it has been found that indices of Functional Connectivity sensitive to zero lag such as PLV tend to be more reliable within groups and across sessions [70 , 71] . Although the general theoretical findings for the ordered networks still hold for the simulated dynamics over the connectome , the main contributor to their disparity is the complex spatio-temporal structure of the connectome . This is firstly reflected in the distribution of time-delays , which is neither exactly bi-modal δ , nor fully structured or random . Secondly , the weights of the links are not homogeneous for the nodes , as assumed by our approximation , but differ by several orders of magnitude . For the latter , combining other network measures , such as centrality or clustering coefficient [72] , could potentially increase the predictability of the analysis . The remaining open issues of our brain network model are conceptual and are a common concern for most of the studies based on the connectomics . These are questions about the meaning of the weights and utilization of links , but also about the actual propagation velocity along tracts , which is shown to depend on large number of quantities [73] . The level of significant coherence does not impact the overall architecture of phase lags , although when it is lower it mostly increases the variance of the results by flattening the distribution . However , the stricter level of significance can fail to capture phase-locking , especially between the hemispheres where the coherence is lower due to reduced wiring . Increased variance of the phases can also indicate an alteration between different stable states . This often causes intermittent in- and anti- phase synchronization , when several peaks appear in the distribution of pair-wise phase lags . We showed that averaging of these non-stationary dynamics leads to improper description of phase relationships and can be avoided by differentiating of the separate regimes during the analysis . However , identification of time-dependent dynamics is a major challenge in analysis of biological signals [74] . Inherent variability of the frequencies or coupling strengths [48 , 75] is another source of non-stationarity for which we demonstrated that the observed phase-lags depend on the overall statistics of the averaged parameters . Nevertheless , dividing the time-series to different epochs for more precise identification of phase lags for different regimes is also possible in cases when the non-autonomous forcing can be recovered [74] , as well as quantification of the non-autonomicity [76] . Besides the notion of synchronization , functional brain connectivity can be also described by directed information flows [77] , or effective connectivity [78 , 79] . Bayesian frameworks [80 , 81] , although limited to instantaneous interactions , offer another approach for studying the connectivity between neural systems , by inferring coupling functions [82] that are spatially and frequency specific [42 , 43] . | Functional connectivity , and in particular , phase coupling between distant brain regions may be fundamental in regulating neuronal processing and communication . However , phase relationships between the nodes of the brain and how they are confined by its spatio-temporal structure , have been mostly overlooked . We use a model of oscillatory dynamics superimposed on the space-time structure defined by the connectome , and we analyze the possible regimes of synchronization . Limitations of data analysis are also considered and we show that the choice of the significance threshold for coherence does not essentially impact the statistics of the observed phase lags , although it is crucial for the right detection of statistically significant coherence . Analytical insights are obtained for networks with heterogeneous time-delays , based on the empirical data from the connectome , and these are confirmed by numerical simulations , which show in- or anti-phase synchronization depending on the frequency and the distribution of time-delays . Phase lags are shown to result from inhomogeneous network interactions , so that stronger connected nodes generally phase lag behind the weaker . | [
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] | 2018 | Phase-lags in large scale brain synchronization: Methodological considerations and in-silico analysis |
The DOK1 tumor suppressor gene encodes an adapter protein that acts as a negative regulator of several signaling pathways . We have previously reported that DOK1 expression is up-regulated upon cellular stress , via the transcription factor E2F1 , and down-regulated in a variety of human malignancies due to aberrant hypermethylation of its promoter . Here we show that Epstein Barr virus ( EBV ) infection of primary human B-cells leads to the down-regulation of DOK1 gene expression via the viral oncoprotein LMP1 . LMP1 alone induces recruitment to the DOK1 promoter of at least two independent inhibitory complexes , one containing E2F1/pRB/DNMT1 and another containing at least EZH2 . These events result in tri-methylation of histone H3 at lysine 27 ( H3K27me3 ) of the DOK1 promoter and gene expression silencing . We also present evidence that the presence of additional EBV proteins leads to further repression of DOK1 expression with an additional mechanism . Indeed , EBV infection of B-cells induces DNA methylation at the DOK1 promoter region including the E2F1 responsive elements that , in turn , lose the ability to interact with E2F complexes . Treatment of EBV-infected B-cell-lines with the methyl-transferase inhibitor 5-aza-2′-deoxycytidine rescues DOK1 expression . In summary , our data show the deregulation of DOK1 gene expression by EBV and provide novel insights into the regulation of the DOK1 tumor suppressor in viral-related carcinogenesis .
Cellular transformation induced by oncogenic viruses often involves the activation of growth-promoting signaling pathways and the inactivation of tumor suppressor genes . The downstream of tyrosine kinase 1gene ( DOK1 ) has emerged as a newly identified tumor suppressor gene that encodes a multi-domain adapter protein and acts as a negative regulator of signaling pathways involved in several cellular functions . DOK1 inhibits cell proliferation , down regulates MAP kinase activity , and has an opposing role in leukemogenesis and promotes cell spreading , motility , and apoptosis [1] , [2] . Functional studies showed that mice lacking the DOK1 and/or DOK2 genes have a high susceptibility to the development of lung adenocarcinomas [3] and exhibit significant defects in their immune responses and immune cell development , often developing myelo-proliferative and autoimmune diseases , e . g . lupus-like renal disease [4] , [5] . The DOK1 gene locus is located in the human chromosome 2p13 region , which is frequently rearranged in a number of human tumors [6] . Oncogenic tyrosine kinases such as p210BCR-ABL , the causative mutation in chronic myelogenous leukemia ( CML ) , and Src target DOK1 for ubiquitin-mediated proteasomal degradation [7] , therefore promoting cell proliferation . We have reported a frameshift mutation of the DOK1 gene in chronic lymphoid leukemia ( CLL ) resulting in the expression of truncated DOK1 that is exclusively localized in the nucleus and loses its tumor suppressive activities , in contrast with the cytoplasmic wild type protein [8] . We also showed that DOK1 gene expression is repressed in a large proportion of head and neck cancer ( HNC ) , lung cancer and Burkitt's lymphoma [9] , as a result of aberrant hypermethylation of its promoter region . The inactivation of DOK1 through promoter methylation also occurred in liver and gastric cancers [10] , [11] . Thus , DOK1 emerged as a tumor suppressor frequently altered in a variety of human cancers , making it a potential marker and therapeutic target in cancer control . Epstein-Barr virus ( EBV ) is a γ-herpes-virus that is widespread in 90% of human populations . In the majority of individuals , EBV persists as a permanent , asymptomatic infection of the lymphocytes B-lymphocyte pool [12] . EBV occasionally causes infectious mononucleosis in adolescents [13] and is considered a human carcinogenic infectious agent . Indeed , EBV is associated with the development of different types of B-cell lymphoma such as Burkitt's lymphoma ( BL ) , Hodgkin disease , lympho-proliferative disorders in immuno-deficient individuals , and nasopharyngeal carcinoma [14] , [15] , [16] . EBV is also associated with gastric cancer [17] . The oncogenic potential of EBV has been further demonstrated by its ability to immortalize efficiently the primary human B-cells in vitro in lymphoblastoid cell lines ( LCLs ) [18] . LCLs carry the EBV genome in an extra-chromosomal episome state and express nine latent viral proteins: three trans-membrane proteins ( LMP1 , LMP2A and 2B ) and six nuclear antigens ( EBNAs 1 , 2 , 3A , 3B , 3C and LP ) , along with other non-translated RNA products [12] . These viral products enhance the proliferation of quiescent B-cells and maintain the viral genome in its episomal form . However , only EBNA1 , 2 , 3A , 3C , LP , and LMP1 are essential for the transformation of primary B-cells into LCLs [19] . The latent membrane protein 1 ( LMP1 ) is crucial for EBV-induced B-cell immortalization . It is the only EBV latent protein that displays transforming properties in vitro [20] . LMP1 protein is thought to alter cell growth transformation by mimicking the activated forms of tumor necrosis factor receptor ( TNFR ) , CD40 and CD30 receptors [21] , [22] , [23] . Through its long C-terminal cytosolic domain , LMP1 has the ability to induce several signaling pathways , including the MAP kinase ( both ERK/MAPK and p38/MAPK ) , nuclear factor kappa B ( NF-κB ) and c-Jun N-terminal kinase ( JNK ) [24] , [25] , [26] , [27] . The alteration of these signaling pathways by LMP1 is essential for the oncogenicity of EBV . The presence of the EBV genome in several lymphomas , and its ability to induce B-cell immortalization , and alter host-cell expression profiles and epigenome ( i . e . DNA methylation patterns ) strongly support an etiological role for EBV in these cancers . We recently reported that the expression of DOK1 gene is repressed through DNA hypermethylation in BL cell lines , it became of interest to investigate the possible role of EBV in the inhibition of DOK1 expression in infected B-cells . To date , very little is known about the regulation of DOK1 expression by oncogenic viruses . In the present study , we demonstrate a strong association between EBV infection and DOK1 gene silencing via hypermethylation of its promoter in EBV-infected cell lines . We show that EBV infection in B-cells leads to epigenetic repression and CpG methylation of the DOK1 gene and that LMP1 expression inhibits DOK1 promoter activity via the recruitment of inhibitory complexes including E2F1 , pRB , DNMT1 and EZH2 .
Based on our previous results that showed the down-regulation of DOK1 expression in BL cell-lines [9] , we evaluate whether this event was linked to infection with EBV , a key risk factor for this malignancy . Primary human B-cells , isolated from different healthy donors , were infected in independent experiments with recombinant EBV virus expressing the green fluorescent protein ( GFP-EBV ) . The infection efficiency was evaluated by flow cytometry to monitor GFP expression ( data not shown ) . The expression of EBV genes EBNA1 and LMP1 , as well as DOK1 was determined by real-time PCR and western blot at different time points post-infection ( Figure 1A and B ) . EBV infection resulted in a strong reduction of DOK1 mRNA and protein levels , which was evident at 16 hours post-infection ( Figure 1A ) . Similarly , DOK1 mRNA and protein levels were strongly down-regulated by EBV in three cancers B-cell lines ( RPMI , BJAB and Louckes ) infected by EBV , as well as in EBV-immortalized lymphoblastoid cells lines ( LCLs ) ( Figure 1C and D ) . Together , these findings highlight a role for EBV in down-regulating DOK1 gene expression . The EBV oncoprotein LMP1 is essential for EBV-induced B-cell immortalization by altering cellular gene expression via the activation of several signaling pathways [28] . To determine whether LMP1 can affect the expression of DOK1 , we infected the RPMI cells with wild-type GFP-EBV or GFP-EBV lacking the LMP1 gene ( EBVΔLMP1 ) . The infection efficiency was monitored using flow cytometry for GFP expression ( Figure 2A ) . In contrast to wild-type GFP-EBV , EBVΔLMP1 infection in primary B cells and in RPMI cells did not significantly decrease DOK1 mRNA or protein levels ( Figure 2B and C ) . Re-expression of LMP1 in EBVΔLMP1 RPMI cells by retroviral transduction restored the ability of EBV to down-regulate DOK1 expression , while transduction of the same cells with empty retrovirus ( pLXSN ) did not affect DOK1 mRNA or protein levels ( Figure 2D and E ) , highlighting the key role of LMP1 in this event . Accordingly , expression of LMP1 alone in RPMI cells was sufficient to reduce DOK1 mRNA and protein expression ( Figure 2D and E ) , whereas expression of other viral proteins , such as EBNA1 , 2 , 3A , 3B , and 3C , did not lead to down-regulation of DOK1 protein levels ( supplementary Figure S1A–C ) In addition , transient transfection of RPMI with increasing concentrations of LMP1 expressing vector resulted in the decrease of DOK1 expression is a dose dependent manner ( Figure 2F and G ) . Together , these data underline the key role of LMP1 in EBV-mediated DOK1 down-regulation in infected B-cells . We recently showed that the E2F1 transcription factor has a key role in activation of DOK1 transcription [29] . The 500 nucleotide upstream of the start site of the DOK1 promoter contains three E2F1 responsive elements ( RE ) which appear to have a role in transcription activation; in particular the one at position −498/−486 ( ERE1 ) [29] . Transient transfection experiments showed that LMP1 was able to efficiently inhibit the activity of −500/+33 DOK1 promoter cloned in front of the luciferase reporter gene ( Figure 3A ) . The addition of upstream regions ( −1000/−500 or −2000/−500 ) did not modify the pattern of LMP1 inhibition ( Figure 3A ) . In addition , LMP1 was not able to further decrease the activity of DOK1 promoter harboring point mutations in ERE1 ( Figure 3A ) . Together , these results suggest that LMP1 may exert its inhibitory activity targeting the regulatory complexes able to bind ERE1 within the −500/+33 region of the DOK1 promoter . Chromatin immuno-precipitation ( ChIP ) experiments using an anti-E2F1 antibody showed that infection with wild-type GFP-EBV significantly decreases the recruitment of E2F1 to ERE1 in RPMI and two independent LCLs ( Figure 3B ) , while EBVΔLMP1 did not have any impact on this event in RPMI ( Figure 3B ) . Interestingly , LMP1 alone did not prevent the recruitment of E2F1 to the DOK1 promoter in RPMI cells ( Figure 3B ) , although it is able to efficiently down-regulate DOK1 expression ( Figures 2D , 2E , and 3A ) . We next analyzed the chromatin organization within the DOK1 promoter in the same cells by monitoring the tri-methylation of histone H3 at lysine 4 ( H3K4me3 ) or at lysine 27 ( H3K27me3 ) which are events associated with transcriptionally active or inactive chromatin , respectively . According to their ability to repress DOK1 expression , wild-type GFP-EBV or LMP1 alone induced an increase of H3K27me3 and a decrease of H3K4me3 within the DOK1 promoter compared with mock cells ( Figure 3C ) . However , LMP1 was less efficient than the entire virus in promoting these epigenetic changes ( Figure 3C ) . In summary , although LMP1 alone is not able to prevent the recruitment of E2F1 to the DOK1 promoter , it is capable of inducing epigenetic changes and inhibition of DOK1 transcription . Based on these findings , we hypothesized that LMP1 mediates DOK1 down-regulation by altering the composition of the E2F1 complex . To explore this possibility , we performed oligo pull-down experiments using biotinylated DNA probes which contain a region of the DOK1 promoter encompassing the wild-type or mutated ERE1 . Biotinylated DNA probes were incubated with protein extracts from RPMI cells transduced with empty retrovirus or with retrovirus expressing LMP1 . In both extracts and as expected , E2F1 was found associated with the DNA , while only in the presence of LMP1 were three additional cellular proteins , which are usually part of negative regulatory complexes of transcription found associated with the DOK1 promoter fragment: ( i ) the E2F1 inhibitor retinoblastoma ( pRB ) , ( ii ) the DNA methyl-transferase DNMT1 and ( iii ) the polycomb-group ( PcG ) 2 member EZH2 ( Figure 3D ) . Deletion of ERE1 prevented the association of E2F1 in both cellular extracts . In addition , in LMP1-containing extracts , mutation of ERE1 also significantly decreased the pRB and DNMT1 protein levels precipitated with DNA ( Figure 3D ) , suggesting that both proteins are recruited in the same complex as E2F1 . With regard to EZH2 , its binding to the DOK1 promoter was less affected by the ERE1 mutation , indicating that it is recruited by a different complex . Although LMP1 is able to activate the NF-κB pathway , no binding of the p65 transcription factor was found in both cellular extracts ( Figure 3D ) . ChIP Re-ChIP experiments in mock and LMP1-expressing cells confirmed the data obtained in the pull-down assay . Indeed , Re-ChIP showed that a significant proportion of E2F1 complexes recruited to the DOK1 promoter contains pRB and DNMT1 proteins ( 80% and 40% respectively ) , but not EZH2 ( Figure 3E ) , which appears to be associated with an independent complex . Finally , the events occurring at DOK1 promoter were determined at early stages post-infection with EBV . We observed a significant enrichment of pRB , DNMT1 and EZH2 recruitment to DOK1 promoter in primary naive B cells infected with recombinant GFP-EBV virus for 48 hours . Consequently , an increase of H3K27 trimethylation ( ∼5 folds ) and CpG methylation ( ∼10% ) was detected ( Supplementary Figure S3 ) . Thus , early stage of EBV infection mimics the scenario observed in LMP1-expressing cells . In summary , these data show that LMP1 initiates the repression of DOK1 expression by inducing the formation of transcriptional inhibitory complexes . LMP1 has the ability to activate different signaling pathways , such as NF-κB , MAPK p38 , JNK , and MAPK/ERK [28] . To explore the potential role of these pathways in DOK1 down-regulation , RPMI cells infected with recombinant GFP-EBV were treated with different chemical inhibitors specific for these signaling pathways . No change was observed in mock or GFP-EBV cells treated with the chemical inhibitors of the MAPK p38 , JNK , and MAPK/ERK pathways ( SB203580 , S600125 and PD98059 , respectively ) ( data not shown ) . However , DOK1 mRNA and protein levels were found to be considerably increased in GFP-EBV-infected cells treated with a specific inhibitor of NF-κB ( Bay11 ) , but not in mock cells ( Figure 4A and B ) . Similarly , Bay11 treatment of LMP1-expressing cells increased the DOK1 mRNA and protein levels ( Figure 4A and B ) . To further demonstrate the role of NF-κB signaling in EBV-mediated DOK1 down-regulation , we inhibited the NF-κB canonical pathway by expressing a non-degradable deletion mutant of IκBα ( Δ-IκBα ) that lacks the first 36 amino acids at the N-terminus containing the IKK-phosphorylated amino acid . Similarly to Bay 11 , Δ-IκBα expression in GFP-EBV RPMI cells led to an increase of transcript and protein levels of DOK1 ( Figure 4C and D ) . Accordingly , transient transfection experiments using a plasmid containing the DOK1 promoter cloned upstream of the luciferase gene showed that Δ-IκBα antagonized LMP1 in inhibiting the DOK1 promoter ( Supplementary Figure S2 ) . The LMP1 protein has two important C-terminal cytosolic domains named C-terminal activation region 1 ( CTAR-1 ) ( residues 194–232 ) and 2 ( CTAR-2 ) ( residues 351–386 ) . Both the CTAR1 and CTAR2 domains have the ability to activate the NF-κB pathway through their interactions with tumor necrosis factor receptor ( TNFR ) -associated factors ( TRAFs ) [30] , and TNFR-associated death domain protein ( TRADD ) [31] , respectively . In particular , the CTAR2 domain is required for the activation of the canonical NF-κB pathway , while the CTAR1 domain is critical for the stimulation of the non-canonical NF-κB pathway [32] . The LMP1 mutants AxAxA ( mutated CTAR1 ) , 378 stop ( deleted in CTAR2 ) and AxAxA/378 stop ( mutated CTAR1 and deleted CTAR2 ) were expressed in RPMI cells . Both LMP1 378 stop and AxAxA/378 stop mutants failed to down-regulate the DOK1 gene , but not the LMP1 AxAxA mutant , which still retained its ability to suppress DOK1 expression at similar levels of wild-type LMP1 ( Figure 4E and F ) . Therefore , LMP1 down-regulates DOK1 expression through its CTAR2 domain . In addition , we investigated whether the LMP1-mediated NF-κB activation plays a role in the formation of inhibitory complexes and their recruitment to the DOK1 promoter . LMP1-expressing RPMI cells were cultured in the presence of NF-κB inhibitor Bay11 . No significant change in pRB and DMNT1 intracellular levels was observed , whereas EZH2 levels were slightly decreased ( Figure 4H ) . However , oligo pull-down experiments clearly showed that inhibition of the NF-κB signaling affected the binding efficiency of pRB and DNMT1 to DOK1 promoter , while E2F1 and EZH2 continued to be associated with the DNA ( Figure 4H ) . ChIP assay confirmed that inhibition of NF-κB significantly decreased the recruitment of pRB and DNMT1 to the DOK1 promoter ( Figure 4G ) . Together , the data show that activation of the canonical NF-κB pathway by LMP1 is an important event for the down-regulation of DOK1 expression . In our previous study [9] , we reported that DOK1 expression is repressed in 64% of Burkitt's lymphoma cell lines through DNA hypermethylation of its promoter . These findings prompted us to assess whether hypermethylation of the DOK1 promoter could be ascribed to the presence of EBV . Using pyrosequencing and real-time PCR , respectively , DOK1 methylation and expression levels were measured in our experimental model . EBV infection of RPMI cells led to hypermethylation of DOK1 promoter ( Figure 5A ) . This phenomenon was even more evident in LCLs ( Figure 5A ) . EBVΔLMP1 was unable to promote DOK1 promoter methylation , further underlining the importance of the viral oncoprotein in this event . However , in agreement with the fact that LMP1 alone is unable to displace E2F1 from the DOK1 promoter , low DNA methylation was detected in RPMI cells expressing LMP1 alone ( Figure 5A ) . In addition , no further methylation was observed in RPMI cells co-expressing LMP1 with other EBV proteins , i . e . EBNA3A , 3B , or 3C ( data not shown ) , suggesting that a more complex pattern of viral gene expression is required to induce hypermethylation of DOK1 promoter . Treatments with the methyl-transferase inhibitor 5-Aza-2′-deoxycytidine ( 5-Aza ) significantly affected DOK1 promoter methylation in LCLs and RPMI cells infected with EBV ( Figure 5A ) . As expected , 5-Aza treatment rescued the recruitment of E2F1 to the DOK1 promoter in GFP-EBV infected cells , while no change was observed in LMP1-expressing cells ( Figure 5B ) . However , an increase of DOK1 mRNA and protein levels was observed upon exposure to 5-Aza in GFP-EBV-infected cells ( RPMI or LCLs ) as well as in LMP1-expressing RPMI cells ( Figure 5C and D ) . This event correlates with the decrease of H3K27me3 and the increase of H3K4me3 levels ( Figure 5E ) . Together , these data show that EBV induces hypermethylation of the DOK1 promoter . Although expression of LMP1 alone marginally promotes DNA methylation , deletion of its gene in the EBV genome prevents the occurrence of this event . Thus , LMP1 appears to be essential , but not sufficient for hypermethylation of the DOK1 promoter . To understand the biological significance of EBV-induced DOK1-down-regulation , we re-expressed DOK1 in LCLs . We observed that ectopic DOK1 levels decreased LCL proliferation in a dose-dependent manner ( Figure 6A ) . Consistently with these observations , DOK1 induced a significance decrease of cell populations in G0/G1 and G2/M phases ( Figure 6B ) . In addition , high levels of DOK1 led to a significant increase of subG0 population and AnnexinV-positive cells ( Figure 6B and C ) . Together , these data demonstrate the role of DOK1 in inhibiting cell proliferation induced by EBV and promoting both cell growth arrest and apoptosis .
Several studies have demonstrated that the loss of DOK1 function is a key event in human carcinogenesis [1] , [3] , [4] , [9] , [33] . Indeed several mechanisms of DOK1 inactivation have been characterized so far DOK1 expression was found to be silenced by hypermethylation of its promoter in a variety of human cancers , including , head and neck , lung , gastric and liver cancer as well as in Burkitt's lymphoma-derived cell lines [9] , [10] , [11] . In addition , DOK1 was found to be mutated in chronic lymphocytic leukemia ( CLL ) [8] . At the protein level , DOK1 is targeted for proteasome degradation triggered by oncoprotein kinases ( OTKs ) such as p210bcr-abl and oncogenic forms of Src [7] . A recent study has provided evidence that DOK1 inactivation also occurs in virus-induced cancers [10] . Indeed , a correlation between DOK1 aberrant hypermethylation and the presence of hepatitis B virus ( HBV ) has been reported in hepatocellular carcinoma ( HCC ) [10] . Similarly , the expression of DOK1 mRNA was found to be down-regulated in cell lines derived from Burkitt's lymphoma [34] , a pathological condition associated with EBV infection . However , these initial findings do not provide evidence about whether the down-regulation of DOK1 expression is directly induced by the viral proteins or is a consequence of the chromosomal alterations occurring during the carcinogenic processes . In this study , we demonstrate for the first time that EBV is directly involved in the inhibition of DOK1 expression . Our data show that the EBV LMP1 oncoprotein plays a key role in this event . Indeed , an EBV mutant lacking the entire LMP1 gene was unable to inhibit DOK1 transcription , while re-expression of LMP1 in cells infected with the EBVΔLMP1 mutant fully restored the ability of EBV to decrease DOK1 mRNA and protein levels . Expression of LMP1 alone in human cancer B-cells was sufficient to efficiently inhibit DOK1 transcription by promoting the formation of a transcriptional repressor complex containing E2F1 , pRB , and the DNA methyl-transferase DNMT1 . In addition , deletion of the E2F1-binding element ( ERE1 ) strongly affected the binding of three cellular proteins to the DOK1 promoter , and a Re-ChIP assay confirmed that E2F1 is the carrier of pRB and DNMT1 . We also observed that LMP1 promotes the recruitment of the histone-lysine N-methyl-transferase EZH2 independently of E2F1 , leading to an increase in the level of H3K27me3 . In agreement with the recruitment of the two epigenetic enzymes , an increase in H3K27me3 and DNA methylation levels was detected at the DOK1 promoter . It has previously been shown that LMP1 is able to increase the expression and activity of DNA methyl-transferases ( DNMT 1 , 3a , and 3b ) , which could explain the increase of the DOK1 promoter methylation . Interestingly , DNA methylation was strongly enhanced in B-cells infected by the entire virus compared with cells expressing only LMP1 . Thus , it is likely that additional viral products may cooperate with LMP1 in promoting DOK1 silencing via DNA methylation . No down-regulation of DOK1 was observed when EBNA1 , 2 , 3A , 3B , and 3C are expressed in RPMI cells . In addition , none of these viral proteins further stimulate DNA methylation at DOK1 promoter when co-expressed with LMP1 ( data not shown ) . Thus , a more complex pattern of viral gene expression may be involved in the hyper-methylation of DOK1 promoter . Most importantly , we show that in EBV-infected B-cells the DNA methylation extends over a large region of the DOK1 promoter including ERE1 that loses the ability to recruit the active form of E2F1 . Inhibition of DNA methylation significantly increases DOK1 transcription in LMP1-expressing cells as well as EBV-infected cells . In summary , based on our findings , a two-step model can be proposed for EBV in the inhibition of DOK1 expression ( Figure 7 ) . In the first step , LMP1 favors the formation and recruitment of transcriptional repressor complexes containing E2F1/pRB/DNMT1 and EZH2 . These complexes induce epigenetic changes in the DOK1 promoter region , leading to its inhibition . In the second step , LMP1 in collaboration with other EBV proteins leads to further increase of DNA methylation which in turn results in a loss of all transcriptional regulatory complexes and a strong repression of the DOK1 promoter . These data corroborate our previous studies that highlighted the key role of E2F1 and DNA methylation in the regulation of DOK1 expression [29] . Our data also show that the LMP1-induced DOK1 down-regulation is linked to activation of the NF-κB canonical pathway . Indeed , NF-κB activation by LMP1 plays a role in the formation and recruitment of inhibitory complex E2F1/pRB/DNMT1 to the DOK1 promoter . Although we did not observe any recruitment of p65 to the DOK1 promoter , neither by DNA-pull-down assay nor by chromatin immuno-precipitation ( data not shown ) , we cannot exclude the involvement of other NF-κB transcription factors . Until now , several studies reported that DNA methylation patterns were higher in EBV positive tumors compared to the EBV-negative ones and that EBV infection was clearly demonstrated to induce specific methylation epigenotypes that lead to silencing of multiple tumor suppressor genes such as BIM , p16INK4A , p14ARF , p73 , E-cadherin and PTEN in EBV–associated nasopharyngeal and gastric cancers [17] , [35] , [36] , [37] , [38] . While these events are believed to be caused by elevated levels of DNMTs induced by LMP1 and 2 , the mechanisms establishing the methylation patterns themselves are unknown . As DNA methyl-transferases have little specificity in vitro , we propose the notion that LMP1 triggers DOK1 gene repression through the recruitment of DNMT1 to its promoter in a specific manner via E2F1-binding to its response element , and this event might be an early step for EBV-induced DNA methylation . As some of the genes listed above are targets of E2F1 [39] , [40] , it will be interesting to see whether their methylation patterns are also specific to the recruitment of the inhibitory complex E2F1/pRB/DNMT1 . Moreover , EBV appears to have an initiator role of epigenetic alterations and therefore inducing oncogenesis , however , the latency expression patterns of EBV genes differ in different cancers , which make unclear the contribution of the virus to some types . One explanation would be that EBV-induced epigenetic changes , such as EBV-mediated DNA methylation of DOK1 promoter , are stable events and could also persist even after the changes in EBV latent gene expression . As DOK1 gene silencing was found to be related to its promoter hypermethylation in gastric cancer [11] , it will be important to investigate whether these events are associated with the presence of EBV in these cancers and others . In conclusion , the present study sheds light on the association between EBV infection and DOK1 down-regulation in B-cells . It provides novel insights into the regulation of DOK1 in viral-related carcinogenesis , and could define it as a potential cancer biomarker and an attractive target for epigenetic-based therapy .
Cellular and viral genes were expressed using the retroviral vector pLXSN ( Clontech , Palo Alto , CA ) or the expression vector pcDNA-3 ( Invitrogen ) . The pLXSN-LMP-1 and the mutants LMP-1AxAxA , LMP-1 378 stop , and LMP-1AxAxA/378 stop constructs have been previously described [41] . The pGL3 basic luciferase reporter ( Promega ) and pGL3 containing the DOK1 promoter constructs have been described previously [29] , The NF-κB super-repressor Δ-IκBα , which lacks the coding sequence of the first 36 N-terminal amino-acids , was kindly provided by Dr Elliot Kieff ( Harvard Medical School , Boston , Massachusetts , USA ) . The expression plasmids pDEST-myc-EBNA1 , pSG5-EBNA2 , pDEST-myc-EBN3A1 , pDEST-myc-EBNA3B , pDEST-myc-EBNA3C were kindly provided by Dr Evelyne Manet ( ENS , Lyon , France ) . RPMI 8226 cells were kindly provided by Dr Christophe Caux ( Centre Léon Bérard , Lyon , France ) . The EBV-negative immortalized B-cells , BJAB were previously described [42] , and the Louckes cells were kindly provided by Dr Evelyne Manet ( ENS , Lyon , France ) . The primary B-cells were isolated from total blood of healthy donors using negative selection EasySep or RosetteSep ( StemCell Technologies ) . Primary naive B cells and RPMI cells were infected with recombinant GFP-EBV , and GFP-EBVΔLMP-1 as described in [43] , [44] , [45] , RPMI pLXSN-empty or pLXSN-LMP1 cell lines were generated as described previously [41] . Expression of LMP-1 wild-type , LMP-1 AxAxA , LMP1 378 stop , and LMP1 AxAxA/378 stop mutants in RPMI was achieved by transduction with recombinant retroviruses [41] . The EBV-immortalized lymphoblastoid cell lines ( LCLs ) were generated in this study by infecting primary B-cells isolated from different donors with recombinant EBV expressing GFP , as described previously [41] . Primary and immortalized B-cells were cultured in RPMI 1640 medium ( GIBCO , Invitrogen life Technologies , Cergy-Pontoise , France ) supplemented with 10% FBS , 100 U/ml penicillin G , 100 mg/ml streptomycin , 2 mM L-glutamine , and 1 mM sodium pyruvate ( PAA , Pasching , Austria ) . Expression plasmids were transiently transfected in cells using Xtreme gene 9 reagents ( Roche ) according to the manufacturer's protocol . For treatment , cells were incubated in media containing different reagents: with a final concentration of 1 µM of the NF-κB pathway inhibitor Bay11 in dimethyl sulfoxide ( DMSO ) for 6 hours . Inhibition of DNA methylation was performed by incubation for 4 days with 5-aza-2′-deoxycytidine ( 5-aza ) at 1 µM ( Sigma ) dissolved in DMSO . Cells were then harvested for analysis . Total RNA was extracted using TRIzol reagent ( Life Technologies ) . Reverse transcription was performed using the RevertAid H Minus First Strand cDNA synthesis kit ( Fermentas ) according to the manufacturer's protocol . Real-time PCR was performed using the following gene-specific primers: DOK1: Fw ATGGACGGAGCAGTGATGGA , Rev CCCAGGTCTTCCTCCACCTC LMP1: Fw CCCCCTCTCCTCTTCCATAG , Rev GCCAAAGATGAACAGCACAA EBNA1: Fw GGACCCGGCCCACAACCTG , Rev CTCCTGCCCTTCCTCACCCTCATC GAPDH: Fw GAAGGTGAAGGTCGGAGTC , Rev AAGATGGTGATGGGATTTC . Data were analyzed using the ΔΔCT method . The following antibodies were used: anti-DOK1 ( ab8112 , Abcam ) , anti-E2F1 ( KH-95; Santa Cruz Biotechnology ) , anti- β-Actin C4 ( MP Biomedicals ) , anti-LMP1 ( S12 ) , anti- phosphor IκBα ( #9246 , Cell Signaling Technology ) , anti-total IκBα ( #9242 , Cell Signaling Technology ) , mouse IgG , rabbit IgG ( Santa Cruz Biotechnology ) , anti-p65 ( #3034 , Cell Signaling Technology ) , anti-H3K4me3 , and anti-H3K27me3 ( Epigentek ) , anti-EZH2 ( AC22; Cell Signaling Technology ) , anti-pRB ( 4H1 , Cell Signaling Technology ) , anti-DNMT1 ( 60B1220 , Abnova ) , anti-EBNA1 ( 1EB12 , Santa Cruz Biotechnology ) , anti-EBNA2 ( Novocastra ) , anti-EBNA3A ( Exalpha ) , anti-EBNA3C ( ab16128 , Abcam ) . Immunoblotting was performed as described previously [29] . Cells were transfected with 0 . 250 µg of pGL3 or DOK1 promoter constructs along with other experimental plasmids using X-tremeGENE 9 ( Roche Diagnostics ) . The Renilla construct was included for normalization of transfection efficiency . At 48 hours after transfection , cells were harvested and the enzyme activities of firefly and Renilla luciferases were measured using the Dual-Luciferase reporter assay system ( Promega ) . The luminescence signal was quantified using an Optocomp I luminometer ( MGM Instruments ) . Each condition was used in triplicate and replicated in different independent experiments . For each reaction , 106 cells were cross-linked with 1% formaldehyde , harvested and subjected to sonication to shear the chromatin into fragments of 0 . 2 kb , immuno-precipitated with 2 µg of appropriate antibody , and then processed according to the standard protocol for ChIP analysis from Cell Signaling Technology . Low cell ChIP kit ( Diagenode ) was used for primary B cells and infected with EBV for 48 hours . 50 000 cells per reaction were processed according to the manufacturer's protocol . The input and immuno-precipitated DNA from both methods ( standard and low cell ) were then analyzed by real-time PCR using primers flanking the E2F-response element ( −498/−486 ) of the DOK1 promoter: Fw GCCAAAACCGAGGACTTTCG , Rev CATCACTGCTCCGTCCATGG , or primers for GAPDH promoter: Fw GACGGCCGCATCTTCTTGT , Rev CCTGGTGACCAGGCGC . Data were calculated as a percentage of enrichment of input . Following the initial anti-E2F1 ChIP ( performed as above using 107 cells and 10 µg of anti E2F1 KH-95 antibody ) , up to the final wash step with TE buffer , E2F1–chromatin complexes were eluted by the addition of 10 mM dithiothreitol ( DTT ) and incubated for 30 minutes at 37°C . Supernatants were diluted 1∶20 with re-ChIP buffer ( 1% Triton X-100; 20 mM Tris–HCl , pH 8 . 1; 2 mM EDTA; 150 mM NaCl; supplemented with protease inhibitors ) , and immuno-precipitated a second time ( IP 2 ) using 4 µg of antibody against pRB , DNMT1 , and EZH2 . IgG was used as negative control . The Re-ChIP mixtures were incubated overnight at 4°C with rotation . Isolation and purification of associated DNA were carried out as described for the standard ChIP experiment . The binding of each factor was determined by real-time PCR as previously described . Data were calculated as a percentage of enrichment of total input . Cells were lysed by sonication in HKMG buffer ( 10 mM HEPES , pH 7 . 9; 100 mM KCl; 5 mM MgCl2; 10% glycerol; 1 mM dithiothreitol ( DTT ) ; and 0 . 5% NP-40 ) containing protease and phosphatase inhibitors . Cellular debris was removed by centrifugation . Then , 1 mg of total lysate was pre-cleared with 40 µl of streptavidin-agarose beads ( Thermo Scientific ) for 1 hour at 4°C , with rotation , and incubated with 2 µg of biotinylated PCR product oligonucleotides and 20 µg of poly ( dI-dC ) for 16 hours at 4°C , with rotation . Biotin-oligonucleotide-protein complexes were collected with 60 µl of streptavidin-agarose beads for 1 hour at 4°C , with rotation , washed twice with HKMG buffer , separated on SDS-PAGE , and detected by western blotting . The biotinylated double-stranded oligonucleotides were amplified using the same primers as for ChIP with 5′ biotin . Genomic DNA was extracted using the QIAamp DNA minikit ( Qiagen ) and bisulfite converted using the EZ DNA Methylation-Gold kit ( Zymo Research ) . Converted DNA was then subjected to Pyrosequencing ( Qiagen ) as previously described [46] . The primers used to measure the methylation of DOK1 promoter were: Fw GAGGTGGAGGAAGATTTG , Rev BIOTIN-CCACACTCACACACTCAA , and sequencing primer AGTTTTGGGGGTGGT . The percentage of methylation was evaluated as the mean of each CpG analyzed . To determine cell cycle profile , cells were collected 48 hours post-transfection with empty pCDNA3 ( Vector ) or expression vector pCDNA3-Flag-DOK1 , washed twice with PBS 1× , and then cell pellets were re-suspended in 70% ethanol while vortexing , in order to prevent cell clumps . After ethanol fixation ( 30 minutes at 4°C ) the cells were rewashed in PBS 1× and finally re-suspended in PBS 1×+ 100 µg/mL RNAse ( Roche ) + 25 µg/mL of Propidium iodide ( Sigma ) . Apoptotic cells were detected using the PE Annexin V apoptosis detection kit I ( BD Pharmingen ) according to the manufacturer's instructions . Stained cells for cell cycle and for apoptosis were detected using the BD FACSCanto II flow cytometer ( BD Biosciences ) and analyzed using FACSDiva software . Blood samples from healthy donors were provided by the Etablissement Français du Sang ( EFS , Lyon , France ) after being anonymized . All participants signed a written informed consent . | Many oncogenic viruses exhibit cellular transforming properties , often involving oncogenes activation and tumor suppressor genes inactivation . The DOK1 gene is a newly identified tumor suppressor gene with altered expression via hypermethylation of its promoter in a variety of human cancers , including head and neck , lung , gastric and others . In addition , a correlation has been reported between DOK1 aberrant hypermethylation and the presence of oncogenic viruses such as hepatitis B virus ( HBV ) in hepatocellular carcinoma ( HCC ) and Epstein-Barr virus ( EBV ) in Burkitt's lymphoma-derived cell lines . Here we demonstrate for the first time that EBV is directly involved in the inhibition of DOK1 expression in B-cells . We show that EBV leads to epigenetic repression of DOK1 through increased DNA methylation of its promoter and H3K27 tri-methylation . The LMP1 oncoprotein plays a key role in the repression of DOK1 expression . It promotes the formation and the recruitment to the DOK1 promoter of transcriptionally inhibitory complexes composed of E2F1/pRB/DNMT1 and of EZH2 which is part of the polycomb repressive complex 2 . Interestingly , one or more additional EBV protein ( s ) cooperate ( s ) with LMP1 in inducing massive DNA methylation at the DOK1 promoter , leading to the loss of E2F1 complexes recruitment and even stronger repression of DOK1 expression . | [
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] | 2014 | Epstein-Barr Virus Down-Regulates Tumor Suppressor DOK1 Expression |
Zika is one of the most challenging emergent vector-borne diseases , yet its future public health impact remains unclear . Zika was of little public health concern until recent reports of its association with congenital syndromes . By 3 August 2017 ∼217 , 000 Zika cases and ∼3 , 400 cases of associated congenital syndrome were reported in Latin America and the Caribbean . Some modelling exercises suggest that Zika virus infection could become endemic in agreement with recent declarations from the The World Health Organisation . We produced high-resolution spatially-explicit projections of Zika cases , associated congenital syndromes and monetary costs for Latin America and the Caribbean now that the epidemic phase of the disease appears to be over . In contrast to previous studies which have adopted a modelling approach to map Zika potential , we project case numbers using a statistical approach based upon reported dengue case data as a Zika surrogate . Our results indicate that ∼12 . 3 ( 0 . 7–162 . 3 ) million Zika cases could be expected across Latin America and the Caribbean every year , leading to ∼64 . 4 ( 0 . 2–5159 . 3 ) thousand cases of Guillain-Barré syndrome and ∼4 . 7 ( 0 . 0–116 . 3 ) thousand cases of microcephaly . The economic burden of these neurological sequelae are estimated to be USD ∼2 . 3 ( USD 0–159 . 3 ) billion per annum . Zika is likely to have significant public health consequences across Latin America and the Caribbean in years to come . Our projections inform regional and federal health authorities , offering an opportunity to adapt to this public health challenge .
Zika virus ( ZIKV ) is a vector-borne disease that is transmitted among humans through the bite of infectious Aedes mosquitoes . ZIKV is a member of the Flaviviridae family , and genus Flavivirus . The symptoms of ZIKV infection are usually mild and similar to those of other arboviral infections such as dengue including fever , macopapular rash , conjunctivitis , myalgia , and headache [1] . In most infected people the disease is benign . However , in some cases ZIKV infection may result in serious complications such as Guillain–Barré syndrome , microcephaly and maculopathy [2–4] . To the date of this study , there are no vaccines or antiviral therapy readily available for ZIKV infection [5] . However , this could be feasible in the future [6] . In April 2015 , a ZIKV outbreak was reported in Brazil , and subsequently in several Latin American and Caribbean countries . By 3 August 2017 ∼217 , 000 confirmed ZIKV cases , and ∼3 , 400 cases of associated congenital syndrome had been reported to the Pan-American Health Organization [7] . Current research on ZIKV activity has rightly focused upon the disease epidemic stage [8] and its consequences . The next big question is whether Zika will become endemic in Latin America and the Caribbean ( LATAM ) , and what are the potential health and economic burdens . Although it is impossible to ascertain whether ZIKV will become endemic in LATAM , a recent study based on a numerical epidemic model predicts that the virus will eventually become endemic [9] . The lack of vaccines for ZIKV [5] , the environmental suitability of the region [10] , and the endemic status of other arboviruses that share the same vector ( e . g . dengue fever ) also suggest that such an endemic state is plausible . One key aspect for the control of mosquito-borne diseases is vector control . Past experience indicates that aggressive control of Aedes mosquitoes using traditional insecticide-based measures is effective only if implemented in a comprehensive and sustained manner [11] . This may be difficult due to public resistance , lack of expertise , and finance [11 , 12] . A recent meta-review on the effectiveness of Aedes control strategies has found that this type of vector control does not seem to be associated with long-term reductions of mosquito populations [13] . Physical control measures against the vector such as house screens , and the environmental modification or sanitation of larval sites may also be effective [14]; however , these measures may be unavailable to poor residents in crowded urban areas where the impacts of ZIKV are greatest [5] . Recent studies have mapped the potential global scale range of ZIKV based on combinations of environmental , vector abundance , and socioeconomic factors [10 , 15] . One limitation of the method used by these studies is that it maps the environmental suitability which does not necessarily imply that the disease will occur in that area [10] . This issue is critical because experience from similar diseases indicates that such modelling approaches tend to overestimate the geographical areas where disease could occur as they cannot take into account the complex local factors that determine whether potential risk actually translates into disease [16] . An alternative approach is a statistical analysis based on spatially-explicit monthly reports of confirmed ZIKV cases; a difficult task due to the limited time period for which reliable human spatially-explicit case reports are available for LATAM [17–19] . We overcome these limitations by using human dengue case data across LATAM as a surrogate for ZIKV . The advantage of this approach is that it is based upon knowledge on where disease transmission from mosquito to humans occurs in reality . One challenge , however , is that reported disease counts are a fraction of the true incidence ( it has been estimated that for each official dengue report ∼10–27 cases go unreported [20] ) . We argue that this approach is valid because the dengue virus shows remarkable similarities to ZIKV . For example , both viruses have single positive stranded RNA genome encoding three structural proteins ( C , prM/M and E ) , and seven non-structural proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B , and NS5 ) ; are vectored by Aedes mosquitoes; and seem to have similar infectious and viral replication mechanisms [21] . Moreover , phylogenetic analyses have shown that ZIKV is closer to dengue virus than to any other flavivirus [22] . As a consequence of such similarities , there is cross-reaction of antibodies to dengue with ZIKV [23] . Not surprisingly , previous ZIKV modelling studies are largely based upon dengue parameters [8 , 24] . We acknowledge , however , that whilst there is only one ZIKV serotype , there are four different dengue serotypes which do not confer protective immunity against all serotypes [6] . Thus , while Zika could infect an individual only once , dengue could cause disease repeatedly which poses a key difference in the ecology and epidemiology of these two diseases . The aims of our work are three-fold . Focusing upon the post-epidemic period of ZIKV , we first examine the likely incidence of ZIKV in childbearing women across LATAM and the potential number of microcephaly and Guillain-Barré syndrome ( GBS ) cases . Second , we identify areas where ZIKV transmission may be sporadic and hence remains epidemic re-emerging every few years . Finally , we quantify how case numbers are likely to fluctuate in affected areas due to seasonal and meteorological effects such as an El Niño ( ENSO ) event .
Monthly counts of laboratory confirmed dengue cases were obtained from the Mexican [25] , and Brazilian [26] Ministries of Health for the period January 2001 to December 2012 ( 144 months ) . Together , these two countries cover a latitudinal range between 30°N and 30°S , and account for over 60% of the reported dengue cases and ∼53% of the LATAM population . Our dataset consists of nearly 4 million dengue reports ( Brazil 88% , Mexico 12% ) . The Mexican dataset was obtained at the State level ( n = 32 , mean population = 3 . 2 million people ) , whilst the Brazilian dataset was obtained at the municipal county level ( n = 5 , 566 , mean population = 0 . 47 million people ) . Missing counts were imputed for municipalities with less than 20% missing entries using a singular value decomposition-based method [27] , included in the bcv package [28] for R [29] . Areas with over 20% missing counts ( n = 4 , 177 ) were removed from the dataset . Brazilian municipal counties are considerably smaller in area and population than the Mexican States . Such small areas were typically characterized by low counts of cases . We aggregated the Brazilian municipal counties into larger geographical units by dividing the centroid coordinates into 286 latitude-longitude intervals , and merging all counties with centroid coordinates within each latitude-longitude bin together . The merged areas ( n = 286 , mean population = 0 . 45 million people ) were used for analysis . Whilst the presence of four dengue serotypes is an important difference with ZIKV that we acknowledge , it would be impossible to disentangle the dengue epidemiological surveillance data to obtain four different time series , one for each serotype . The ratio of dengue to ZIKV cases is hard to estimate due to the limited period for which ZIKV data are available . Given that the transmission dynamics of ZIKV and dengue are similar when observed in the same setting [30] , for simplicity , we initially assumed that each confirmed dengue report is equivalent to a ZIKV case ( 1:1 ratio ) . To account for uncertainties in this assumption , we also considered scenarios where the ZIKV to dengue ratio varied between 0 . 1:1 and 10:1 . High-resolution gridded datasets of monthly global mean temperature , total precipitation , and potential evapotranspiration ( PET ) data [31] were obtained from the CRU TS3 . 24 Climatic Research Unit climate archives at a 0 . 5 × 0 . 5 degree resolution for land cells only , and for the period January 1991 to December 2015 . Moving averages were computed for the current and previous two months to account for the delayed effects of temperature , precipitation , and PET on incidence [32] . Mean temperature , mean PET , and total precipitation estimates for each administrative unit in the study were calculated using the extract method included in the R [29] raster package [33] . Global gridded total population count estimates were retrieved at a 2 . 5 arc minutes resolution from the Gridded Population of the World project [34] at five year intervals for the period 2000–2010 . For consistency with the meteorological data , demographic data were aggregated at a 0 . 5 × 0 . 5 degree resolution using the Climate Data Operators software [35] . Total population estimates were scaled to agree with the United Nations World Population Prospects yearly population estimates [36] . Monthly estimates for each grid-box were derived using linear interpolation [32 , 37] . The estimated population for each geographical unit included in the study was then calculated using the extract method included in the R [29] raster package [33] . Crude birth rates per country were also retrieved from the United Nations World Population Prospects [36] . The expected number of Zika virus infections E ( Yit ) for area i = 1 , … , I at time t = 1 , … , T was modelled using a generalized additive mixed model ( GAMM ) approach . To account for possible over-dispersion in the data , we fitted Negative Binomial and quasi-maximum likelihood Poisson models . We selected the model specification with the lowest mean absolute error ( MAE ) . The general algebraic definition of both the Negative Binomial and quasi-maximum likelihood Poisson models is given by: l o g ( μ i t ) = η i t ( 1 ) η i t = α + L o g ( ξ i t ) + t ′ + s ′ + ∑ p = 1 P f ( x i t ) + d i ( 2 ) where ηit is a logarithmic link function of the expectation E ( Yit ≡ μit ) , with Yit as the time series of monthly dengue reports . The term α corresponds to the intercept; Log ( ξit ) denotes the logarithm of the population at risk for area i and time t included as an offset to adjust the epidemiological data by population . Here , t′ is a cubic regression spline function of the time variable with 1 degree of freedom ( df ) for every M years of data to control for possible long-term trends . Seasonal trends are modelled using Fourier terms ( s′ ) with N sine/cosine pairs . Long-term and seasonal trends in all variables in the model are controlled for because they may be related to factors other than climate [38] such as changes in reporting or coverage , holidays or seasonal water storage . The term f ( xit ) corresponds to smoothed relationships between the climatic predictors and the crude incidence rate defined by the cubic regression splines . Area-specific random effects ( di ) were included to account for the effects of unknown or unobserved variables in the model such as diagnostic performance variability , immunity , and intervention measures . A time series cross-validation ( TSCV ) algorithm [39] was implemented to select the set of climate predictors producing the lowest prediction error . TSCV was preferred over k-fold or leave-one-out cross-validation algorithms because epidemiological surveillance time series are typically serially correlated [40] violating the assumptions that data are independent and identically distributed . Models were fitted using all climatic predictors ( i . e . mean monthly temperature , mean monthly PET and total monthly precipitation ) in isolation , as well as in all possible combinations . Therefore , we successively fitted all possible models containing one climatic predictor at a time , then two predictors at a time , and so on , until all predictors were included altogether in a single model . We measured the accuracy of each model calculating their MAE . The MAE was selected as the measure for model accuracy because it is a natural and unambiguous measure of average error magnitude [41] . TSCV was implemented dividing the dataset into a training and a test sets . The initial training set comprised 90% of the total number of months ( n = 144 ) . Each time step ( k ) , a further month of data was added to the training set . Thus , at time step k = 1 , the training set comprised observations for month t = 1 , … , 130; at k = 2 it comprised observations for t = 2 , … , 131 , and so on . The test set comprised the first observation for each geographical area immediately after the last observation in the training set . Consequently , at time step k = 1 , the test set contained all area-specific observations for t = 131; at k = 2 , it contained all observations for t = 132 , and so on until the test set contained the observations for month t = n; where n is the total number of months in the dataset . The MAE was calculated at each time step k = 1 , … , K , and for each subset of climatic predictors h = 1 , … , H as in the following matrix: M A E k , h = [ M A E 1 , 1 M A E 1 , 2 ⋯ M A E 1 , H M A E 2 , 1 M A E 2 , 2 ⋯ M A E 2 , H ⋮ ⋮ ⋱ ⋮ M A E K , 1 M A E K , 2 ⋯ M A E K , H ] The MAE for each modelled subset ( henceforth MAEk , h ) was calculated by averaging the subset-specific values ( h ) across all time steps ( k ) . With this process , we aimed to identify the most accurate model or group of models . TSCV was used to identify the specification of long-term and seasonal trends with the lowest MAEk , h . Specifically , we modified the number of df per year ( ranging from 1 df for every two years of data to 1 df for every four years ) for the cubic spline function of time , as well as the number of sine/cosine pairs for the Fourier terms ( ranging from three to six ) . All possible combinations of long-term and seasonal trends were explored . Cross-validated model outputs were used to predict the total number of ZIKV infections for an average month , a typical ENSO month , a strong El Niño ( based on the 1997–1998 and 2015–2016 events ) [42] , and a typical non-El-Niño month across LATAM under the assumption of a 0 . 1:1 , 1:1 and 10:1 ZIKV to dengue ratios . To account for uncertainties in the under-reporting of the health data , we multiplied the predicted number of cases for a given geographical area by a factor of 10 , 18 . 5 or 27 [20] . Model predictions were computed using mean monthly gridded climatic and population data at a 0 . 5 × 0 . 5 degree resolution for each of the aforementioned periods . ENSO events are defined here as periods where the 3-month running mean of the Oceanic Niño Index is greater than 0 . 5°C . The length of an ENSO event was the length indicated by the USA National Weather Service , Climate Prediction Center [43] plus three months to account for potential delayed effects on the local climate . Country-wide totals were retrieved using standard routines within the raster [33] R package . Model estimates of mean monthly cases were downscaled by the proportion of cases occurring in childbearing women ( i . e . 15–44 years of age ) based on the proportion of cases per gender and age reported to the Mexican Ministry of Health over the period 2010–2012 [25] . The number of cases in childbearing women was then used to estimate the potential number of ZIKV-affected pregnancies by multiplying them by the corresponding country-specific crude birth rates [36] . The risk of microcephaly due to ZIKV infection during the first trimester of pregnancy was calculated using the 0% , 50% and 100% percentiles of the distribution of the range of values estimated for the risk of microcephaly due to infection in women aged 15–44 ( i . e . 0 . 88–14 . 4 ) [44] to account for uncertainties on our estimates . Similarly , the potential number of GBS cases in both males and females was estimated using the 0% , 50% and 100% percentiles of the distribution of risk estimates of GBS per 1000 ZIKV infections based on previous research conducted in French Polynesia and LATAM [3 , 45] . The economic impact of the estimated number of cases with neurological sequelae was estimated based on the direct medical cost of each microcephaly and GBS case [46] . Thus , the estimated mean annual number of microcephaly ( X ) and GBS cases ( Y ) for each administrative unit ( i ) was multiplied by the estimated medical cost per case based on previous research [46] . The direct medical cost of each microcephaly case ( δ ) was assumed to be USD 91 , 102 whilst that of each GBS case ( γ ) was assumed in USD 28 , 818 [46] . The total economic impact of the neurological sequelae was estimated as follows: M i c r o c e p h a l y i = X i × δ ( 3 ) G B S i = Y i × γ ( 4 ) T o t a l C o s t i = M i c r o c e p h a l y i + G B S i ( 5 ) Epidemic-prone areas were defined as areas where the month-to-month relative standard deviation ( RSD ) of the model estimates is greater than the mean for a given grid box . The RSD is defined here as the ratio of the standard deviation ( σ ) to the mean ( μ ) . We defined epidemic areas as those where the RSD of the estimated number of cases was larger than one , and highly epidemic areas where this ratio was greater than 1 . 5 [47] .
We fitted 23 different model specifications to test all possible combinations of climatic predictors long-term and seasonal trends whilst assuming a ZIKV-dengue ratio of 1:1 . The TSCV algorithm applied to the dengue-derived ZIKV data ( henceforth ZIKV data ) favoured a Negative Binomial GAMM with a MAEk , h of 105 cases per month that included temperature lagged zero to two months ( T0:2 ) , and PET lagged zero to two months ( PET0:2 ) as climatic covariates . Precipitation lagged zero to two months was not included in the final model . The incorporation of an interaction term between T0:2 and PET0:2 did not increase the predictive ability of the model , and so it was not included in the final model . After performing a sensitivity analysis testing different specifications for the df of long-term and seasonal trends , the long-term trends in the final model were specified with a cubic regression spline with three df , and the seasonality was specified with a Fourier term with three sine and cosine functions of time . The final model explained 79 . 5% of the deviance in the health data . The structure of the final model was then used to compute estimates based on both 0 . 1:1 and 10:1 ZIKV-dengue ratios . S1 Fig compares the observed and predicted temporal trends in the number of cases for each country . We noted that the final model’s predictions ( and their corresponding error estimates ) capture quite closely the temporal variations observed in the observed data with some underestimations in both countries related to major outbreaks that could be related to location-specific non-climatic factors ( e . g . human behaviour and interventions ) not explicitly accounted for in the model [16] . GAMMs are essentially a nonparametric method; therefore , it is difficult to express their results using mathematical equations . Instead , the GAMM-estimated smoothed relationships between ZIKV incidence , T0:2 and PET0:2 are presented in S2 Fig . The solid lines in the figure represent the estimated functional form of the relationship between ZIKV incidence and each predictor . S2A Fig shows an almost null response of ZIKV to T0:2 below 20°C , with rapid increases in ZIKV cases as T0:2 surpasses this threshold . The estimated effect is consistent with the biology of both the vector and ZIKV because rising temperatures shorten the development time and gonotrophic cycle of the vector , and increase its biting rate; also , they reduce the time required for viral development inside the vector all of which results in an increased risk of transmission [42 , 48] . S2B Fig indicates that there is a log-negative relationship between ZIKV incidence and PET0:2 with the risk of infection drastically decreasing between one and three mm per month and remaining low after that threshold . We were unable to identify studies investigating the effects of PET on ZIKV or Aedes mosquitoes . However , previous research using anopheline data [49] has shown similar relationships between PET and vector abundance with low levels being more conducive of vectorial activity than high PET levels , and so increasing the risk of disease transmission . High temperatures and low humidity levels have been found to reduce the oviposition rate and life span of Aedes mosquitoes [50] . We then used the model output to predict the mean monthly number of cases across LATAM for a typical year at a 0 . 5 × 0 . 5 degree resolution . A sensitivity analysis was performed to compute predictions under the assumption of a 0 . 1:1 , 1:1 , and 10:1 ZIKV to dengue ratio to explore the uncertainties in our assumptions of the relationship between the estimated number of ZIKV and dengue virus infections . Based on such sensitivity analysis , we estimate that should ZIKV become endemic ∼12 million ( range: 713 thousand to 162 million ) ZIKV cases could occur across LATAM every year ( Table 1 ) . About 4 million of those cases ( range: 99 thousand to 85 million ) are expected to occur in childbearing women ( 15–44 years of age ) annually . The country-level estimates suggest that Brazil will experience the largest disease burden ( 60% ) ; more than six times the estimated burden for Mexico ( 7% ) or any other LATAM country ( Table 1 ) . Other countries such as Colombia , Mexico , Venezuela , Cuba and Peru are also expected to experience large numbers of ZIKV infection . The risk of ZIKV infection has been estimated to be larger in South America than in any other part of the world [42] . Brazil will experience the largest disease and economic burden particularly in the south-east and north-east where ZIKV infections are currently the highest in the country [1 , 51] . This is not surprising given that Brazil has the largest population in LATAM ( ≈205 million ) , and its climate is conducive for year-round transmission across large urban and rural areas . Other countries with large ZIKV values are Colombia , Mexico , Venezuela and Cuba where high risk of infection has been previously estimated [8 , 19 , 42] . Some differences were observed in the ranking of the countries most affected by ZIKV when we compared the estimated the number of cases and the number of affected pregnancies . One notable feature of these results was Cuba which was fifth in terms of overall infections in childbearing women but ninth in terms of pregnancies due to low crude birth rate [8] . Fig 1 shows the mean annual case predictions and indicates that the highest case estimates correspond to low elevation coastal areas of Brazil , Southern Mexico , the Caribbean , the Pacific coast of Central America , Ecuador , Colombia and Venezuela likely because the abundance of Aedes spp . mosquitoes declines sharply at elevations above 1 , 700 metres above sea level [52] due to the effect of low temperatures on the biology of the virus and the mosquito . Particularly large case numbers are expected in south-eastern and north-eastern Brazil , the Mexican Isthmus , Cuba , Puerto Rico , northern Colombia , and northern Venezuela . The estimated spatial distribution of cases agrees with observations of ZIKV infection in the region [1] , with previous studies estimating the environmental suitability [10 , 19 , 53 , 54] and risk of arboviral infection [8 , 42] , and with records of other arboviral diseases in LATAM [55–59] . Although not explicitly accounted for , urbanisation plays a major role in the occurrence of Aedes-related diseases as it increases its larval habitats [60] . Recent studies indicate that the presence of Aedes mosquitoes and ZIKV incidence is larger in urban than in rural areas [60 , 61] . Our predicted geographical distribution of cases agrees well with such studies as the higher number of cases are predicted to occur in areas where population densities are high . Two distinctive seasonal cycles are observed in the predicted ZIKV cases . Fig 2 shows the estimated seasonal cycles for the six countries with the highest predicted ZIKV burden . In the southern hemisphere the high transmission season is between April and June ( e . g . Brazil , and Peru ) , whilst in the northern hemisphere it peaks between September and November ( see Mexico , Colombia , Venezuela and Cuba ) in agreement with previous studies [62] . A bimodal seasonal cycle is observed in Colombia which may be related to a bimodal annual cycle of precipitation observed in the central and western regions [63] . Although precipitation was not included in the final model , by including PET0:2 , we have accounted for some of its effects on ZIKV incidence . It is reminded that through evapotranspiration , atmospheric moisture returns to land as rainfall [64] . Factors such as intervention measures could also play a role in defining the seasonal trends of ZIKV transmission , yet have not been explicitly accounted for in the model . Our modelling framework also allowed us to investigate spatio-temporal changes in ZIKV occurrence . We estimate that in the Southern hemisphere , ZIKV transmission could extend to ∼35°S between February and May , contracting thereafter to ∼30°S ( see S1 Video in Supplementary material ) . In the Northern hemisphere , transmission remains stable up to ∼20°N most of the year , expanding to ∼30°N between July and November . GBS is an acute immune-mediated muscle weakness that affects the peripheral nervous system leading to paralysis that has been attributed to ZIKV infections [3 , 65] . Assuming a risk of GBS between 0 . 24 and 31 . 78 cases per 1000 ZIKV infections [3 , 45] , and a ZIKV-dengue ratio of 0 . 1:1 , 1:1 , and 10:1 , we estimate ∼64 thousand GBS cases per annum ( range: 0 . 2–51596 thousand ) across the LATAM region . Given that the Asian lineage is related to brain developmental abnormalities [66] , and that it is the lineage present in LATAM , we also estimated the potential number of microcephaly cases . Based on the crude birth rates per country [36] , we estimate that ∼61 ( 3–807 ) thousand pregnancies ( Table 1 ) could be affected by prenatal ZIKV transmission ( i . e . ZIKV infection of the mother at some point during pregnancy ) . Assuming that only first trimester ZIKV infections may cause microcephaly , and a risk of microcephaly due to infection of between 0 . 88% and 14 . 4% [44] , we estimate that ∼5 ( 0–116 ) thousand children could develop microcephaly yearly in LATAM . With an estimated direct medical cost of USD 28 , 818 per GBS and of USD 91 , 102 per microcephaly case per lifetime [46] , the ZIKV-related neurological sequelae would add an economic burden of USD ∼2 . 3 ( USD 0–159 . 3 ) billion each year . The large confidence intervals indicate that the economic impact is largely sensitive to selected zika to dengue ratio . This sensitivity has major implications for surveillance systems and public health preparedness to adequately respond to the presence of neurological sequelae . There are uncertainties in our estimates of neurological risk . First , available data on the risk of GBS and microcephaly due to ZIKV infection are limited , especially in areas where the infection rates are unknown [44] posing problems for the use of country-specific risk factors . Second , the risk of microcephaly has dramatically increased in some locations over the past year [67] suggesting that the risk estimates should be revised relatively often . Third , the introduction of a vaccine and its combination with effective control measures could reduce the risk of infection and hence the risk of neurological sequelae . Under the assumption of endemicity , there are areas that will likely remain epidemic due to intermittent or short transmission seasons . Our model identified the Mexican Plateau , the Andean foothills , and parts of northern Paraguay as highly epidemic ( Fig 3 ) . Some areas with regular transmission also showed a high RSD . Cold regions ( < 20°C ) are marginally permissive for vector development and viral transmission [48] . Populations in these areas are likely to have low herd immunity due to low transmission intensity and viral density [68] increasing their likelihood of succumbing to epidemics . Childbearing women would therefore be more at risk in epidemic-prone areas due to low herd immunity . Areas with regular transmission may also be epidemic-prone due to outbreaks occurring earlier or later than usual with unusual high peaks in seasonal transmission as a consequence [47] . These changes may be related to variability in environmental , socioeconomic or meteorological factors [69] . A typical ENSO event is likely to increase the monthly case load across most of LATAM . Fig 4 shows the areas where increases in transmission are expected during a typical ENSO . Epidemic areas such the Andean foothills in Ecuador and Peru may show increases between 1 . 2 and 2 . 5 times the average case load of a typical non-ENSO period . During a strong ENSO event ( e . g . 1997–1998 or 2015–2016 ) , many more regions are expected to experience large increases ( 1 . 2–2 . 5 times the average case load during a non-ENSO period ) in ZIKV cases including south-eastern Mexico , Honduras , Nicaragua and the western lowlands of South America . Previous studies indicate that ENSO could increase the risk of ZIKV infection due to an amplification effect by providing conducive conditions for transmission [42] particularly during strong events , and so ENSO may be an important driver of inter-annual variation in ZIKV transmission [70] . Our spatially explicit projections of ZIKV risk for LATAM provide useful information for public health preparedness . However , there are several caveats that ought to be mentioned . First , our estimates are based on the occurrence of a different organism ( i . e . dengue virus ) which , despite its remarkable similarities with ZIKV , has important differences [21] that may affect our results . One key difference is that there are four dengue serotypes while there is only one ZIKV serotype ( subdivided into two lineages and three genotypes ) [71] . None of the dengue serotypes confers protective neutralizing antibody responses against all four serotypes [6] . Thus , a single person may succumb to dengue more than once in a lifetime . ZIKV , however , induces a humoural antibody response that seems to confer lifelong immunity against reinfection , although this assumption still needs to be confirmed [72] . The assumption of a lifelong immunity to ZIKV indicates that once individuals ( succumbing to the disease only once in a lifetime ) become immune they also become unavailable for future infections . This situation means that recurring outbreaks would necessarily be related to the remaining susceptible individuals in a population , in addition to newly born hosts . Another important difference between the two viruses is the presence of a sexual transmission mode in ZIKV [73] . Sexual transmission could occur from asymptomatic or symptomatic individuals through genital , oral , or anal intercourse; and from male-male , male-female , and female-male contact [72 , 73] . Not only sexual transmission does not occur in dengue , but also it is not driven by temperature and PET which are the main transmission drivers in our disease model . The extent to which sexual transmission can modify disease occurrence across time and space is unclear and requires further investigation . Second , recent research has shown that the ecological niches of dengue and ZIKV are significantly different , with the niche of ZIKV expanding more than that of dengue [19] . Therefore , the potential distribution of ZIKV could expand a greater geographical area than that predicted by our model [19] . Third , our results do not account for the potential deployment of a vaccine which would significantly reduce the risk of ZIKV infection . Recent studies suggest that two ZIKV vaccine prototypes recently entered a phase-1 human-safety testing [74] and an epitope-focused vaccine for viruses in the so-called ZIKV-dengue super serogroup could be developed soon [22] . However , large-scale efficacy trials and the mass production of such a vaccine may still be years away [75] . Fourth , in the absence of long-term datasets for compareable viruses , we have based our estimates of ZIKV on one of the largest and more spatially diverse dengue datasets ( accounting for over 60% of the reported cases across LATAM ) ; however , local socioeconomic determinants of disease ( e . g . access to protective measures , intervention deployment , urbanisation indices , and international travel data ) in countries not included in our dataset may significantly alter disease occurrence [16] . This issue is important because socioeconomic factors vary at fine scales for political or administrative reasons and so our model could over or under estimate the risk of infection in some regions . This fact highlights the need for spatially explicit , high-resolution , publicly available epidemiological and socioeconomic time series data for LATAM . There are remarkable genomic and epidemiological similarities between dengue virus and ZIKV [21 , 22] . Based on such similarities , we have used a detailed panel of time series of contrywide dengue reports as a surrogate for ZIKV infection to estimate the potential health and economic burden across LATAM under the assumption of endemicity . The geographic distribution of other vector-borne diseases sharing the same vector [53 , 54] , the lack of a vaccine [5] , the absence of effective vector control measures [11] , and the environmental suitability of the region [10] suggest that ZIKV will likely become endemic throughout LATAM in the near future . This hypothesis concurs with a recent study that on the basis of a numerical model predicts that the virus will eventually become endemic [9] . Recent declarations from the WHO also suggest that ZIKV infection will become endemic [76] . We produced to our knowledge , the first high-resolution spatially-explicit projections of future ZIKV cases under the assumption of endemicity . Across LATAM , our projections suggest that ZIKV may impose a health burden of ∼12 ( 1–162 ) million cases per year , ∼69 ( 0–5276 ) thousand of which are likely to have major neurological sequelae . The economic burden imposed across the LATAM region amounts to USD ∼2 ( 0–159 ) billion per year , and this may increase up to ∼2 times in the aftermath of a strong ENSO event particularly in epidemic areas where public health systems are unprepared for major outbreaks . These projections can inform public health preparedness and response , and offer an opportunity to enhance capabilities in LATAM . | In February 2016 the World Health Organisation ( WHO ) declared Zika virus infection in the Americas as a Public Health Emergency of International Concern ( PHEIC ) . By November 2016 , Zika was declared a long-term public health challenge . This change of status implies that Zika is likely to become an endemic problem in the region . Due to the PHEIC status of Zika , most current research has rightly focused on the epidemic stage of the disease; however , it is timely and critical to consider the public health consequences after such epidemic phase . We used one of the largest and most spatially diverse panels of epidemiological surveillance data comprising 12 years of dengue case observations from Brazil and Mexico , and covering an area of over ten million km2 . State-of-the-art statistical models , and high-resolution ( 0 . 5 × 0 . 5 degrees ) climate and demographic data were used to produce spatially-explicit projections of Zika infection for Latin America and the Caribbean . Model projections were then used to estimate the number of cases with neurological sequelae and their economic cost . Our findings indicate that the potential health and economic burden of Zika could be considerably large for the region should it become endemic . The estimated burden of Zika under an endemic state highlights the need for health authorities in the countries at risk to promote preventive and control measures . | [
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] | 2017 | After the epidemic: Zika virus projections for Latin America and the Caribbean |
As Arabidopsis thaliana has colonized a wide range of habitats across the world it is an attractive model for studying the genetic mechanisms underlying environmental adaptation . Here , we used public data from two collections of A . thaliana accessions to associate genetic variability at individual loci with differences in climates at the sampling sites . We use a novel method to screen the genome for plastic alleles that tolerate a broader climate range than the major allele . This approach reduces confounding with population structure and increases power compared to standard genome-wide association methods . Sixteen novel loci were found , including an association between Chromomethylase 2 ( CMT2 ) and temperature seasonality where the genome-wide CHH methylation was different for the group of accessions carrying the plastic allele . Cmt2 mutants were shown to be more tolerant to heat-stress , suggesting genetic regulation of epigenetic modifications as a likely mechanism underlying natural adaptation to variable temperatures , potentially through differential allelic plasticity to temperature-stress .
Arabidopsis thaliana has colonized a wide range of habitats across the world and it is therefore an attractive model for studying the genetic mechanisms underlying environmental adaptation [1] . Several large collections of A . thaliana accessions have either been whole-genome re-sequenced or high-density SNP genotyped [1]–[7] . The included accessions have adapted to a wide range of different climatic conditions and therefore loci involved in climate adaptation will display genotype by climate-at-sampling-site correlations in these populations . Genome-wide association or selective-sweep analyses can therefore potentially identify signals of natural selection involved in environmental adaptation , if those can be disentangled from the effects of other population genetic forces acting to change the allele frequencies . Selective-sweep studies are inherently sensitive to population-structure and , if present , the false-positive rates will be high as the available statistical methods are unable to handle this situation properly . Further experimental validation of inferred sweeps ( e . g . [1] , [8] ) is hence necessary to suggest them as adaptive . In GWAS , kinship correction is now a standard approach to account for population structure that properly controls the false discovery rate . Unfortunately , correcting for genomic kinship often decreases the power to detect individual adaptive loci , which is likely the reason that no genome-wide significant associations to climate conditions were found in earlier GWAS analyses [1] , [8] . Nevertheless , a number of candidate adaptive loci could despite this be identified using extensive experimental validation [1] , [2] , [8] , showing how valuable these populations are as a resource for finding the genomic footprint of climate adaptation . Genome-wide association ( GWA ) datasets based on natural collections of A . thaliana accessions , such as the RegMap collection , are often genetically stratified . This is primarily due to the close relationships between accessions sampled at nearby locations . Furthermore , as the climate measurements used as phenotypes for the accessions are values representative for the sampling locations of the individual accessions , these measurements will be confounded with the general genetic relationship [9] . Unless properly controlled for , this confounding might lead to excessive false-positive signals in the association analysis; this as the differences in allele-frequencies between loci in locations that differ in climate , and at the same time are geographically distant , will create an association between the genotype and the trait . However , this association could also be due to other forces than selection . In traditional GWA analyses , mixed-model based approaches are commonly used to control for population-stratification . The downside of this approach is that it , in practice , will remove many true genetic signals coming from local adaptation due to the inherent confounding between local genotype and adaptive phenotype . Instead , the primary signals from such analyses will be due to effects of alleles that exist in , and have similar effects across , the entire studied population . In general , studies into the contributions of genetic variance-heterogeneity to the phenotypic variability in complex traits is a novel and useful approach with great potential [10] . Here , we have developed and used a new approach that combines a linear mixed model and a variance-heterogeneity test , which addresses these initial concerns and shown that it is possible to infer statistically robust results of genetically regulated phenotypic variability in GWA data from natural populations . This study describes the results from a re-analysis of data from the RegMap collection to find loci contributing to climate adaptation through an alternative mechanism: genetic control of plasticity . Such loci are unlikely to be detected with standard GWAS or selective-sweep analyses as they have a different genomic signature of selection and distribution across climate envelopes . The reason for this difference is that plastic alleles are less likely to be driven to fixation by directional selection , but rather that multiple alleles remain in the population under extended periods of time by balancing selection [11] . To facilitate the detection of such loci , we extend and utilize an approach [12] , [13] that instead of mapping loci by differences in allele-frequencies between local environments , which is highly confounded by population structure , infer adaptive loci using a heterogeneity-of-variance test . This identifies loci where the minor allele is associated with a broader range of climate conditions than the major allele [12] . As such widely distributed alleles will be present across the entire population , they are less confounded with population structure and detectable in our GWAS analysis that utilizes kinship correction to account for population stratification .
A genome-wide association analysis was performed for thirteen climate variables across ∼215 , 000 SNPs in 948 A . thaliana accessions from the RegMap collection , representing the native range of the species [1] , [9] . In total , sixteen genome-wide significant loci were associated with eight climate variables ( Table 1 ) , none of which could be found using standard methods for GWAS analyses [1] , [8] , [14]–[16] . The effects were in general quite large , from 0 . 3 to 0 . 5 residual standard deviations ( Table 1 ) , meaning that the minor allele is associated with a climate that is between 21–35% more variable than that of the major allele . The detailed results from the association analysis for each of these climate variables are reported in S1 Figure–S13 Figure . As expected , there was low confounding between the alleles associated with a broader range of climate conditions and population structure . This is illustrated by the plots showing the distributions of these alleles across the population strata in relation to their geographic origin and the climate envelopes in S14 Figure–S35 Figure . Utilizing the publicly available whole-genome re-sequencing data from the 1001-genomes project [2]–[7] ( http://1001genomes . org ) , we screened the loci with significant associations to the climate variables for candidate functional polymorphisms . Missense , nonsense or frameshift mutations in high linkage disequilibrium ( LD; r2>0 . 8 ) with the leading SNPs were identified in five functional candidate genes associated with eight climate variables ( for details on these see Table 1 ) and 11 less characterized genes ( S1 Table ) . S2 Table provides 76 additional linked loci or genes without candidate mutations in their coding regions . Interestingly , three out of the eight loci with missense mutations affected more than one climate variable , even though these were only marginally correlated . One such potentially pleiotropic adaptive effect for day length and relative humidity in the spring was associated with a locus containing the genes VEL1 and XTH19 ( Table 1 ) . The major allele at this locus was predominant in short-day regions , whereas the alternative allele was more plastic in relation to day-length . XTH19 has been implied as a regulator of shade avoidance [17] , but information about its potential involvement in regulation of photoperiodic length is lacking . VEL1 , is a Plant Homeo Domain ( PHD ) finger protein . PHD finger proteins are known to affect vernalization and flowering of A . thaliana , e . g . by silencing the key flowering locus FLC during vernalization , and is involved in photoperiod-mediated epigenetic regulation of MAF5 [18]–[20] . The finding that VEL1 is associated with day length and relative humidity is thus consistent with the role of previous reports on PHD finger proteins . It also makes this protein an interesting target for future studies into the genetics underlying simultaneous adaptation to day-length and humidity . Another potentially pleiotropic adaptive effect was identified for two more highly correlated traits , minimum temperature and number of consecutive cold days ( Pearson's r2 = 0 . 76 ) . In total , 17 missense mutations were found at this locus . The top candidate gene containing a missense mutation is galactinol synthase 1 ( GolS1 ) . This gene has been reported to be involved in extreme temperature-induced synthesis [21] , [22] , making it an interesting target for further studies regarding the genetics of temperature adaptation . A strong association to temperature seasonality , i . e . the ratio between the standard deviation and the mean of temperature records over a year , was identified near Chromomethylase 2 ( CMT2; Table 1; Fig . 1 ) . Stable areas are generally found near large bodies of water ( e . g . London near the Atlantic 11±5°C; mean ± SD ) and variable areas inland ( e . g . Novosibirsk in Siberia 1±14°C ) . A premature CMT2 stop codon located on chromosome 4 at 10 , 414 , 556 bp ( the 31st base pair of the first exon ) segregated in the RegMap collection , with minor allele frequency of 0 . 05 . This CMT2STOP allele had a genome-wide significant association with temperature seasonality ( P = 1 . 1×10−7 ) and was in strong LD ( r2 = 0 . 82 ) with the leading SNP ( Fig . 1B ) . The geographic distributions of the wild-type ( CMT2WT ) and the alternative ( CMT2STOP ) alleles in the RegMap collection shows that the CMT2WT allele dominates in all major sub-populations sampled from areas with low or intermediate temperature seasonality . The plastic CMT2STOP allele is present , albeit at lower frequency , across all sub-populations in low- and intermediate temperature seasonality areas , and is more common in areas with high temperature seasonality ( Fig . 2A; Fig . 3; S36 Figure ) . Such global distribution across the major population strata indicates that the allele has been around in the Eurasian population sufficiently long to spread across most of the native range and that the allele is not deleterious but rather maintained through balancing selection [11] , perhaps by mediating an improved tolerance to variable temperatures . To confirm that the CMT2STOP association was not due to sampling bias in the RegMap collection , we also scored the CMT2 genotype and collected the geographical origins from 665 accessions that were part of the 1001-genomes project ( http://1001genomes . org ) [2] , [3] , [5]-[7] . In this more geographically diverse set ( Fig . 2A ) , CMT2STOP was more common ( MAF = 0 . 10 ) and had a similar allele distribution across Eurasia as in RegMap ( Figure S36–S37 ) . Two additional mutations were identified on unique haplotypes ( r2 = 0 . 00 ) - one nonsense CMT2STOP2 at 10 , 416 , 213 bp ( MAF = 0 . 02 ) and a frameshift mutation at 10 , 414 , 640 bp ( two accessions ) . Both CMT2STOP and CMT2STOP2 had genotype-phenotype maps implying a plastic response to variable temperature ( Fig . 2B ) and the existence of multiple mutations disrupting CMT2 further suggest lack of CMT2 function as a potentially evolutionary beneficial event [23] . CMT2 is a plant DNA methyltransferase that methylates mainly cytosines in CHH ( H = any base but G ) contexts , predominantly at transposable elements ( TEs ) [24] , [25] . We tested the effect of CMT2STOP on genome-wide DNA methylation using 135 CMT2WT and 16 CMT2STOP accessions , for which high-quality MethylC-sequencing data was publicly available [7] . In earlier studies [24] , [25] , it has been shown that CMT2-mediated CHH methylation primarily affects TE-body methylation . In cmt2 knockouts in a Col-0 genetic background , this results in a near lack of CHH methylation at such sites . Here , we compared the levels of CHH-methylation across TEs between CMT2STOP and CMT2WT accessions . Our analyses revealed that the accessions carrying the CMT2STOP allele on average had a small ( 1% ) average decrease in CHH-methylation across the TE-body compared to the CMT2WT accessions . A more detailed analysis showed that this difference was primarily due to two of 16 CMT2STOP accessions , Kz-9 and Neo-6 , showing a TE-body CHH methylation pattern resembling that of the cmt2 knockouts in the data of [24] . Interestingly , none of the 135 CMT2WT accessions displayed such a decrease in TE-body CHH methylation , and hence there is a significant increase in the frequency of the cmt2 knockout TE-body CHH methylation pattern among the natural CMT2STOP accessions ( P = 0 . 01; Fisher's exact test ) . Our analyses show that the methylation-pattern is more heterogeneous among the natural accessions than within the Col-0 accession , both for the CMT2STOP and CMT2WT accessions ( both P = 0 . 01; Brown-Forsythe heterogeneity of variance test; Fig . 4 ) . There is thus a significant association between the CMT2STOP polymorphism and decreased genome-wide TE-body CHH-methylation levels , and we show that this is apparently due to an increased frequency of the cmt2-mutant methylation phenotype . Further , the results also show a variable contribution of CMT2-independent CHH methylation pathways in the natural accessions . The reason why not all CMT2STOP accessions behave like null alleles is unclear , but the variability amongst in the level of CHH-methylation across the natural accessions suggest that it is possible that CMT2-independent pathways , such as the RNA-dependent DNA-methylation pathway , compensate for the lack of CMT2 due to segregating polymorphisms also at these loci . Alternatively , CMT2STOP alleles may not be null , maybe due to stop codon read-through , which is more common than previously thought [26] . Although our analyses of genome-wide methylation data have established that CMT2STOP allele has a quantitative effect on CHH methylation , further studies are needed to fully explore the link between the CMT2STOP allele , other pathways affecting genome-wide DNA-methylation and their joint contributions to the inferred association to temperature seasonality . To functionally explore whether CMT2 is a likely contributor to the temperature-stress response , we have subjected cmt2 mutants to two types of heat-stress . First , we tested the reaction of Col-0 and the cmt2-5 null mutant ( S45 Figure ) to severe heat-stress ( 24 h at 37°C ) . This treatment was used because it can release transcriptional silencing of some TEs [27] and could thus be a good starting point to evaluate potential stress effects on cmt2 . Under these conditions , the cmt2 mutant had significantly higher survival-rate ( 1 . 6-fold; P = 9 . 1×10−3; Fig . 5A ) than Col-0 . To evaluate whether a similar response could also be observed under less severe , non-lethal stress , we subjected the same genotypes to heat-stress of shorter duration ( 6 h at 37°C ) and measured root growth after stress as a measure of the ability of plants to recover . Also under these conditions , the cmt2 mutant was found to be more tolerant to heat-stress , as its growth was less affected after being stressed ( Fig . 5B; 1 . 9-fold higher in cmt2; P = 0 . 026 , one-sided t-test ) . This striking improvement in tolerance to heat-stress of cmt2 plants suggests CMT2-dependent CHH methylation as an important alleviator of stress responses in A . thaliana and a candidate mechanism for temperature adaptation . To also explore the potential effects of the CMT2STOP allele on other phenotypes measured in collections of natural accessions , we tested for associations between this CMT2 polymorphism and the 107 phenotypes measured as part of a previous study [28] . Three phenotypes were found to be significantly associated with the genotype at this locus ( S39 Figure ) . Associations were found to two phenotypes related to disease presence following inoculation with Pseudomonas viridiflava ( strains PNA3 . 3a and ME3 . 1b; P = 4 . 8×10−3 and P = 1 . 3×10−4 , respectively ) . Scoring of disease was done by eye four days after inoculation in 6 replicates per strain × accession using a scale from 0 ( no visible symptom ) to 10 ( leaves collapse and turn yellow ) with an increment of 1 [28] . The connection between an increased susceptibility ( 0 . 6 and 0 . 7 units for PNA3 . 3a and ME3 . 1b , respectively ) to disease and an increased tolerance to temperature seasonality is not obvious . However , recent work by [29] has shown that widespread dynamic CHH-methylation is important for the response to Pseudomonas syringae infection . In light of this finding , it is therefore not unlikely that these phenotypes are functionally related via an altered CMT2-mediated CHH-methylation in response to abiotic and biotic stress . An association was also found for the level of leaf serration ( increase by 0 . 23 units for the CMT2STOP allele; P = 3 . 3×10−3 ) , determined after growth for 8 weeks at 10°C ( level from 0: entire lamina , to 1 . 5: sharp/jagged serration ) , across 4 plants per accession [28] . Measures of leaf serration were also available at 16 and 22°C , and interestingly there was a significant CMT2 genotype × temperature interaction ( P = 0 . 048 ) . The CMT2STOP accessions have the same level of serration across the three measured temperatures , whereas the level of serration decreases with temperature for the CMT2WT accessions ( S38 Figure ) . Although we are not aware of any earlier results connecting leaf serration to the CMT2 locus or the level of CHH-methylation in the plant , this result further indicates that the effects of the CMT2STOP and the CMT2WT alleles depend on temperature .
A major challenge in attempts to identify individual loci involved in climate adaptation is the strong confounding between geographic location , climate and population structure in the natural A . thaliana population . Earlier genome-wide association analyses in large collections of natural accessions experienced a lack of statistical power when correcting for population-structure [1] , [8] . We used an alternative GWAS approach [12] to test for a variance-heterogeneity , instead of a mean difference , between genotypes . This analysis identifies loci where the minor allele is more plastic ( i . e . exist across a broader climatic range ) than the major allele . As it has low power to detect cases where the minor allele is associated with a lower variance ( here with local environments ) , it will not map private alleles in local environments in a genome-wide analysis [12] , [30] . In contrast , a standard GWAS map loci where the allele-frequencies follow the climatic cline . Although plastic alleles might be less frequent in the genome , they are easier to detect in this data due to their lower confounding with population-structure . This overall increase in power is also apparent when comparing the signals that reach a lower , sub-GWAS significance level ( S40 Figure–S44 Figure ) . Several novel genome-wide significant associations were found to the tested climate variables , and a locus containing VEL1 was associated to both day length and relative humidity in the spring . A thaliana is a facultative photoperiodic flowering plant and hence non-inductive photoperiods will delay , but not abolish , flowering . A genetic control of this phenotypic plasticity is thus potentially an adaptive mechanism . VEL1 regulates the epigenetic silencing of genes in the FLC-pathway in response to vernalization [19] and photoperiod length [20] resulting in an acceleration of flowering under non-inductive photoperiods . Our results suggest that genetically plastic regulation of flowering , via the high-variance VEL1 allele , might be beneficial under short-day conditions where both accelerated and delayed flowering is allowed . In long-daytime areas , accelerated flowering is potentially detrimental hence the wild-type allele has the highest adaptive value . It can be speculated whether this is connected to the fact that day-length follows a latitudinal cline , where early flowering might be detrimental in northern areas where accelerated flowering , when the day-length is short , could lead to excessive exposure to cold temperatures in the early spring and hence a lower fitness . A particularly interesting finding in our vGWAS was the strong association between the CMT2-locus and temperature seasonality . Here the allele associated with higher temperature seasonality ( i . e the plastic allele ) had an altered genome-wide CHH methylation pattern where some accessions displayed a TE-body CHH methylation pattern similar to that of cmt2 mutant plants . Interestingly , a recent study by Dubin et al . [31] in a collection of Swedish A . thaliana accessions report that CHH methylation is temperature sensitive , and that the CMT2-locus is a major trans-acting controller of the observed variation in genome-wide CHH-methylation between the accessions . These findings , together with our experimental work showing that cmt2 mutants were more tolerant to both mild and severe heat-stress , strongly implicate CMT2 as an adaptive locus and clearly illustrate the potential of our method as a useful approach to identify novel associations of functional importance . It is not clear via which mechanism CMT2-dependent CHH methylation might affect plant heat tolerance . Although our results show that the CMT2STOP allele is present across regions with both low and high temperature seasonality , it remains to be shown whether this is due to this allele being generally more adaptable across all environments , or whether the CMT2WT allele is beneficial in environments with stable temperature and the CMT2STOP in high temperature seasonality areas . Regardless , we consider it most likely that the effect will be mediated by TEs in the immediate neighborhood of protein-coding genes . Heterochromatic states at TEs can affect activity of nearby genes and thus potentially plant fitness [32] . Consistent with a repressive role of CMT2 on heat stress responses , CMT2 expression is reduced by several abiotic stresses including heat [33] . Because global depletion of methylation has been shown to enhance resistance to biotic stress [29] , it is possible that DNA-methylation has a broader function in shaping stress responses than currently thought . Our results show that CMT2STOP accessions have more heterogeneous CHH methylation patterns than CMT2WT accessions . The CMT2STOP polymorphism is predicted to lead to a non-functional CMT2 protein , and hence a genome-wide CHH-methylation profile resembling that of a complete cmt2 mutant [24] . Although some of the accessions carrying the CMT2STOP allele displayed this pattern with a lower CHH-methylation inside TE-bodies , most of these accessions did not have any major loss of genome-wide CHH methylation . Such heterogeneity might indicate the presence of compensatory mechanisms and hence that the effects of altered CMT2 function could be dependent on the genetic-background . This is an interesting finding that deserves further investigation , although such work is beyond the scope of the current study . Our interpretation of the available results is that our findings reflect the genetic heterogeneity among the natural accessions studied . In light of the recent report by [25] , who showed a role also of CMT3 in TE-body CHH methylation , it is not unlikely that the regulation of CHH methylation may result from the action and interaction of several genes . We identified several alleles associated with a broader range of climates across the native range of A . thaliana , suggesting that a genetically mediated plastic response might of important for climate adaptation . Using publicly available data from several earlier studies , we were able to show that an allele at the CMT2 locus displays an altered genome-wide CHH-methylation pattern was strongly associated with temperature seasonality . Using additional experiments , we also found that cmt2 mutant plants tolerated heat-stress better than wild-type plants . Together , these findings suggest this genetically determined epigenetic variability as a likely mechanism contributing to a plastic response to the environment that has been of adaptive advantage in natural environments .
Climate phenotypes and genotype data for a subset of the A . thaliana RegMap collection were previously analyzed by [1] . We downloaded data on 13 climate variables and genotypes of 214 , 553 single nucleotide polymorphisms ( SNPs ) for 948 accessions from: http://bergelson . uchicago . edu/regmap-data/climate-genome-scan . The climate variables used in the analyses were: aridity , number of consecutive cold days ( below 4 degrees Celsius ) , number of consecutive frost-free days , day-length in the spring , growing-season length , maximum temperature in the warmest month , minimum temperature in the coldest month , temperature-seasonality , photosynthetically active radiation , precipitation in the wettest month , precipitation in the driest month , precipitation-seasonality , and relative humidity in the spring . More information on these variables is provided by [1] . No squared pairwise Pearson's correlation coefficients between the phenotypes were greater than 0 . 8 ( S7 Figure of [1] ) . We calculated the temperature seasonality for at sampling locations of a selection of 1001-genomes ( http://1001genomes . org ) accessions . Raw climate data was downloaded from http://www . worldclim . org/ , re-formatted and thereafter processed by the raster package in R . The R code for generating this data is provided in S1 Text . The genotype for the CMT2STOP polymorphism was obtained by extracting the corresponding SNP data for the 1001-genomes accessions . The climate data at the geographical origins of the A . thaliana accessions were treated as phenotypic responses . Each climate phenotype vector for all the accessions was normalized via an inverse-Gaussian transformation . The squared normalized measurement of accession is modeled by the following linear mixed model to test for an association with climate adaptability ( i . e . a greater plasticity to the range of the environmental condition ) : where is an intercept , the SNP genotype for accession , the genetic SNP effect , the polygenic effects and the residuals . is coded 0 and 2 for the two homozygotes ( inbred lines ) . The genomic kinship matrix is constructed via the whole-genome generalized ridge regression method HEM ( heteroscedastic effects model ) [13] as , where is a number of individuals by number of SNPs matrix of genotypes standardized by the allele frequencies . is a diagonal matrix with element for the j-th SNP , where is the SNP-BLUP ( SNP Best Linear Unbiased Prediction ) effect estimate for the j-th SNP from a whole-genome ridge regression , and is the hat-value for the j-th SNP . Quantities in can be directly calculated using the bigRR package [13] in R . An example R source code for performing the analysis is provided in S1 Text . The advantage of using the HEM genomic kinship matrix , rather than an ordinary genomic kinship matrix , is that HEM is a significant improvement of the ridge regression ( SNP-BLUP ) in terms of the estimation of genetic effects [13] , [34] . Due to this , the updated genomic kinship matrix better represents the relatedness between accessions and also accounts for the genetic effects of the SNPs on the phenotype . The test statistic for the SNP effect is constructed as the score statistic [35]: implemented in the GenABEL package [36] , where are the centered genotypic values and the centered phenotypic measurements . The statistic has an asymptotic distribution with 1 degree of freedom . Subsequent genomic control ( GC ) [37] of the genome-wide association results was performed under the null hypothesis that no SNP has an effect on the climate phenotype . SNPs with minor allele frequency ( MAF ) less than 0 . 05 were excluded from the analysis . A 5% Bonferroni-corrected significance threshold was applied . As suggested by [30] , the significant SNPs were also analyzed using a Gamma generalized linear model to exclude positive findings that might be due to low allele frequencies of the high-variance SNP . The CMT2STOP genotype was extracted from the publicly available genome-wide genotype data with 107 phenotype measured from [28] . The association between the CMT2STOP genotype and each phenotype was tested by fitting a normal linear mixed model to account for population stratification , where the genomic kinship matrix was calculated by the ibs ( , weight = 'freq' ) procedure in the GenABEL package [36] , and the linear mixed model was fitted using the hglm package [38] . All the loci that showed genome-wide significance in the association study was further characterized using the genome sequences of 728 accessions sequenced as part of the 1001-genomes project ( http://1001genomes . org ) . Mutations within a ±100Kb interval of each leading SNP and that are in LD with the leading SNP ( r2>0 . 8 ) were reported ( S1 Table ) . The consequences of the identified polymorphisms were predicted using the Ensembl variant effect predictor [39] and their putative effects on the resulting protein estimated using the PASE ( Prediction of Amino acid Substitution Effects ) tool [40] . In a previous study , the methylation levels were scored at 43 , 182 , 344 sites across the genome using MethylC-sequencing in 152 natural A . thaliana accessions ( data available at http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE43857 ) [7] . 135 of these accessions carried the CMT2WT and 17 the CMT2STOP alleles . Upon further inspection , the accession Rd-0 was excluded as it did not have sufficient sequence coverage to be used in the analyses . For each accession , across all TEs , moving averages of the CHH methylation level were calculated using a 100 bp sliding window from the borders of the TEs . The same analysis was also performed for four wild-type and four cmt2 knockout accessions ( data available at http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE41302 ) [24] . The results showing the TE-body CHH methylation patterns are visualized in Fig . 4 . A CMT2 T-DNA insertion line ( SAIL_906_G03 , cmt2-5 [24] , [41] ) was ordered from NASC . Seeds of Col-0 wild-type and cmt2-5 was then used for heat stress experiments based on a previously described protocol [27] . This treatment was used because it was shown to interfere with epigenetic gene silencing as evident from transcription of some TE [27] . Seeds were plated on ½ MS medium ( 0 . 8% agar , 1% sucrose ) , stratified for two days at 4°C in the dark and transferred to a growth chamber with 16 h light ( 110 µmol m−2 s−1 , 22°C ) and 8 h dark ( 20°C ) periods . Ten-day-old seedlings were transferred to 4°C for one hour and subsequently placed for 6 h or 24 h at 37 . 5°C in the dark . Plant survival was scored two days after 24 h of heat stress with complete bleaching of shoot apices as lethality criterion ( S46 Figure ) . Experiments were repeated six times , each with ∼30 plants per genotype . Root length was measured immediately before the 6 h heat stress and two days after heat stress . A log-linear regression was conducted to test for the difference in survival rate between Col-0 and cmt2-5 knockout , i . e . where is the number of surviving plants of accession , the corresponding total number of plants , the experiment effect , the accession effect , and an intercept . The model fitting procedure was implemented using the glm ( ) procedure in R , with option family = gaussian ( link = log ) , as response , as offset , and , , as fixed effects . | A central problem when studying adaptation to a new environment is the interplay between genetic variation and phenotypic plasticity . Arabidopsis thaliana has colonized a wide range of habitats across the world and it is therefore an attractive model for studying the genetic mechanisms underlying environmental adaptation . Here , we study two collections of A . thaliana accessions from across Eurasia to identify loci associated with differences in climates at the sampling sites . A new genome-wide association analysis method was developed to detect adaptive loci where the alleles tolerate different climate ranges . Sixteen novel such loci were found including a strong association between Chromomethylase 2 ( CMT2 ) and temperature seasonality . The reference allele dominated in areas with less seasonal variability in temperature , and the alternative allele existed in both stable and variable regions . Our results thus link natural variation in CMT2 and epigenetic changes to temperature adaptation . We showed experimentally that plants with a defective CMT2 gene tolerate heat-stress better than plants with a functional gene . Together this strongly suggests a role for genetic regulation of epigenetic modifications in natural adaptation to temperature and illustrates the importance of re-analyses of existing data using new analytical methods to obtain deeper insights into the underlying biology from available data . | [
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] | 2014 | Natural CMT2 Variation Is Associated With Genome-Wide Methylation Changes and Temperature Seasonality |
Antimicrobial resistance of infectious agents is a growing problem worldwide . To prevent the continuing selection and spread of drug resistance , rational design of antibiotic treatment is needed , and the question of aggressive vs . moderate therapies is currently heatedly debated . Host immunity is an important , but often-overlooked factor in the clearance of drug-resistant infections . In this work , we compare aggressive and moderate antibiotic treatment , accounting for host immunity effects . We use mathematical modelling of within-host infection dynamics to study the interplay between pathogen-dependent host immune responses and antibiotic treatment . We compare classical ( fixed dose and duration ) and adaptive ( coupled to pathogen load ) treatment regimes , exploring systematically infection outcomes such as time to clearance , immunopathology , host immunization , and selection of resistant bacteria . Our analysis and simulations uncover effective treatment strategies that promote synergy between the host immune system and the antimicrobial drug in clearing infection . Both in classical and adaptive treatment , we quantify how treatment timing and the strength of the immune response determine the success of moderate therapies . We explain key parameters and dimensions , where an adaptive regime differs from classical treatment , bringing new insight into the ongoing debate of resistance management . Emphasizing the sensitivity of treatment outcomes to the balance between external antibiotic intervention and endogenous natural defenses , our study calls for more empirical attention to host immunity processes .
Overcoming antimicrobial resistance is currently considered an international medical priority [1 , 2] . The evolution of drug resistance affects our ability to treat new infections as well as carry out hospital procedures that rely on the prophylactic use of antibiotics such as surgeries and organ transplants . Despite extensive research , antimicrobial alternatives to antibiotics , are not yet a practical solution over current therapies ( reviewed in [3] ) . It is thus critical to evaluate different treatment strategies in order to understand how the various parameters involved in the prescription of antibiotics can influence the selection and spread of drug resistance . Optimization of antibiotic treatments to increase the effective life span of drugs , while reducing both the probability of resistance evolution and the adverse effects of treatments , is a key component of hospital antimicrobial stewardship programs [4] , as well as a research priority in evolutionary epidemiology [5–7] . The problem of preventing the emergence of resistance is augmented with the problem of resistance management once it is already present in a population [8] . Often , by the time bacterial infections cause symptoms and treatment is initiated , the within-host bacterial load is large enough to harbour mutants that are resistant to the treating antibiotic [9] . More importantly , in hospital settings , resistant bacteria can already be acquired upon infection , requiring specialized therapeutic regimes [10–12] . Classical wisdom in drug-resistance management recommends that treatments should be as aggressive as possible , using the highest possible dose to ensure that the pathogen load is eliminated , and to prevent de novo evolution of resistance mutations [13] . These aggressive therapies have recently been questioned on the basis that the stronger the treatment applied , the stronger the selection favouring resistant pathogens , in particular in infections harbouring pre-existent resistance . This conventional protocol of hitting hard and hitting fast might be relevant for highly mutable pathogens such as HIV , but in cases where resistant strains are more likely to be acquired in the community such as in TB [8] the advantages of aggressive therapies are less obvious [14] . Alternative strategies could include more moderate treatments , or adaptive regimens where doses and treatment durations closely follow patient health [14–16] . Current empirical and theoretical evidence has examples to support both therapeutic strategies , as well as for a mixed compromise such as high dose and short treatments ( reviewed by Kouyos et al . [15] ) . For instance , experimental studies using rodent malaria parasites in laboratory mice have shown that less aggressive chemotherapeutic regimens substantially reduce the probability of onward transmission of resistance without significant changes in host pathology [16] . In contrast , varying concentrations of vancomycin in vitro[17] and in vivo using a rabbit model [18] has confirmed the advantage of high dose aggressive treatment in controlling the resistant populations of Staphylococcus aureus . This multitude of results indicates that the problem of devising general practices for treatment is far from settled . Conceptual frameworks can help compare aggressive and moderate chemotherapy [15] , but quantitative systematic analyses are also needed . The current challenge is to identify among the diverse potential treatment regimes , those that minimize selection for drug-resistance while not compromising patient health [14] . A general principle advocated to guide rational development of patient treatment guidelines is to impose no more selection than is absolutely necessary . For this , it is important to understand when rules like ‘hit hard and hit early’ should apply [13] , and when more moderate treatment regimes would be more effective . Mathematical models play instrumental role in this endeavour . When focused on population level dynamics they can evaluate and guide antibiotic use regimens for hospitals [11 , 19 , 20] or wider communities [21] , generally in endemic , but also in epidemic scenarios ( e . g . antiviral usage [22 , 23] ) . When modelling pathogen dynamics within host , mathematical approaches can outline the mechanisms of interaction and feedbacks among pathogen types , and quantify how this basic ecology is modulated by one drug [24] or multiple drugs [25] . An important , but often overlooked factor in the process of infection clearance and resistance management is host immunity . A strong immune response can substantially reduce the need for long treatments , as evidenced by some acute infections tending towards shorter drug treatments in hosts with intact immunity [26–28] . The interplay between host immunity and antimicrobial drugs has recently been incorporated into mathematical models of infection [29–31] . Previous work [29] has shown that the presence of an immune response can narrow down the mutant-selection window ( MSW ) , defined as the range of drug concentrations for which the drug is strong enough to remove the sensitive population [32] , but insufficient to remove the partially resistant pathogen population . Along similar lines , Ankomah and Levin , [31] , using an explicit resource-based model for the interaction between pathogen and host immunity , have investigated infection scenarios , separating the effects of pathogen-dependent and pathogen-independent immune responses . Yet , a quantitative understanding of host immunity as a player in optimal treatment of resistant infections remains under-developed . A series of studies have recently addressed the role of timing of antimicrobial use at the population level [22 , 23] . By considering the indirect and direct effects of antimicrobial use , models have found that optimal timing for treatment at the population level is well into the course of an epidemic , where the indirect effects of delays usually result from minimizing the degree of overshoot , i . e . minimizing the number of cases beyond the number that would be needed to reach the epidemic threshold . There are parallels between transmission processes at the population level and pathogen growth dynamics at the within-host level , where timing effects of antimicrobial therapy have also been shown to be important [33] . In this article , we combine these two important concepts to study antimicrobial treatment of drug-resistant infections: i ) we zoom further into host immunity processes , and ii ) we analyze explicitly the role of treatment timing on the success or failure of antibiotic therapies . We consider a dynamic mathematical model that describes the interaction between the host’s immune system , pathogen density , and antimicrobial treatment in mixed infections of drug-sensitive and pre-existing drug-resistant pathogen strains . By analysing a diverse range of therapeutic scenarios , and especially focusing on treatment timing , we uncover critical consequences for infection dynamics and selection of resistance , before , during and after treatment . We also compare in depth through a mechanistic approach , classical and adaptive treatment protocols , applied to the same infection . To facilitate insight into the driving factors of treatment efficacy , we simplify many aspects of host-pathogen interaction , focusing on key features . We examine their interplay with treatment parameters , and their final impact on infection outcomes , such as total immunopathology , time to clearance , pathogen burden , and overall resistance . Our framework formalizes and broadens up the question of what it means for a treatment to be optimal and how such optimality can be achieved in practice .
The within-host model is designed to investigate the interplay between antibiotic treatment regimes and host immune response in acute drug-resistant infections . Our formulation is based on a previous within-host model of infection dynamics [33] , but here we consider two pathogen phenotypes: those sensitive to the drug , Bs , and those partly resistant Br . These are distinguished by their intrinsic growth rates ( r0 and r1 ) and killing rates by antibiotic ( δ0 , δ1 ) . We consider c = r0 − r1 ≥ 0 to be the fitness cost of resistance [34] and a = δ1/δ0 , ( 0 ≤ a ≤ 1 ) to represent the fitness benefit of resistance , i . e . the factor by which antibiotic killing rate is reduced in the resistant sub-population . The action of host immunity , is considered explicitly , in terms of naive antigen-specific precursor cells N , effector cells E , and memory cells M . We thus implicitly consider those infections that may have escaped the first barrier of innate immunity in the host [35] . The pathogen-dependent immune dynamics represents a typical CD8+ T-cell mediated immune response [36 , 37] , but also describes broadly key features of CD4+ cell responses [38 , 39] . These are major players against intracellular bacterial pathogens , such as Listeria monocytogenes[40] , and Legionella pneumophila[41 , 42] , but have also been implicated in Haemophilus influenzae[39 , 43] and protective responses against pneumococcal bacteria [44] . In the interest of generality , we keep the detail of immune responses to a minimal level . Thus , the model is inevitably a simplification of the complex interaction between host immunity , bacteria , and antibiotics [45] . However , the underlying assumptions do capture crucial aspects of the expected immune responses in acute infections . These include induction , activation , proliferation , decay and memory formation , typically studied in greater empirical detail in virus-host interactions [46 , 47] . Several mathematical aspects of our formulation feature in other theoretical models of infection [30 , 31 , 48] . Within-host dynamics for a mixed infection with a drug-sensitive ( Bs ) and pre-existing partially resistant ( Br ) strain are described with the following set of ordinary differential equations: d B s d t = r 0 B s - d B s I - δ 0 B s η ( t ) A m ( 1 ) d B r d t = r 1 B r - d B r I - δ 1 B r η ( t ) A m ( 2 ) d N d t = - σ N B k + B ( 3 ) dEdt= ( 2σN+σE ) Bk+B−hE ( 1−Bk+B ) ( 4 ) dMdt=fEh ( 1−Bk+B ) , ( 5 ) where B ( t ) = Bs ( t ) + Br ( t ) is the total pathogen load at time t , and I ( t ) = N ( t ) + E ( t ) + M ( t ) is the total number of immune cells activated to clear the pathogen . Naive precursor cells ( N ) are stimulated to divide and differentiate into effector cells ( E ) in response to increasing pathogen density . Effector cells proliferate further upon antigen stimulation at rate σ as long as pathogen is still in circulation . As bacteria are cleared , the majority of effector cells undergo apoptosis at rate h per cell , except for a fraction f that differentiate into memory cells ( M ) that persist indefinitely . All three types of immune cells act to kill pathogen , but effector cells represent the dominant arm of the host immune defense , in particular in primary infection , which we focus on . An important model assumption is that the killing rate d by lymphocytes is equal for both pathogen sub-types , regardless of their antimicrobial susceptibility . Another important assumption regards the immunity stimulation function . For immune stimulation by antigen , a monotonically increasing saturating function of pathogen density ( Hill function with coefficient 1 ) is assumed , where the parameter k represents the half-saturation constant for stimulation of lymphocytes to divide and differentiate . We will hereafter refer to this parameter as the host immunity threshold . To reflect the discrete nature of the pathogen , we assume an extinction threshold , when pathogen density of either sub-population falls below a critical level Bext . Since the model is primarily designed to describe acute infection , we do not include a limiting resource for pathogen growth [24] , assuming main control via host immune responses . A detailed description of model parameters is given in Table 1 . Although our simulations are based on a limited set of parameter values , likely to apply to a range of acute infections , the theoretical analysis that we provide alongside simulations enables extrapolation of our results to settings and numerical values departing from the ones considered here . As in another recent study [31] , the exact parameter values used for simulations do not reflect any particular antibiotic-species combination . To model antimicrobial treatment we use an indicator function η ( t ) , which represents the rate of antimicrobial uptake per unit of time . The dose of the drug deployed is denoted by Am . The case when treatment onset ( τ1 ) and duration ( τ2 ) are fixed from the start corresponds to a classical treatment . The case when drug uptake depends on bacterial density within host corresponds to an adaptive regime . For classical treatment , the rate of administration of antimicrobials is: η ( t ) = 1 if τ 1 ≤ t ≤ τ 1 + τ 2 0 if t < τ 1 or t > τ 1 + τ 2 . ( 6 ) In the adaptive regime , treatment onset and duration are influenced by the bacterial dynamics in the infected host . Previous authors have considered tight coupling between adherence to drug and bacterial load [29] . In this study , similar to the study by Ankomah and Levin [31] , we only consider the simplest form of adaptive treatment that uses a threshold for total pathogen load , Ω , above which the patient takes the drug , and below which the patient does not . The rate η ( t ) of antibiotic administration per unit of time , becomes a direct function of pathogen load B and the threshold Ω: η ( t ) = 1 , if B ( t ) ≥ Ω 0 , if B ( t ) < Ω . ( 7 ) Thus , over a given treatment window , the net average amount of drug taken by the host per unit of time in the adaptive case , may be less than the actual administered dose . This alternative model of antimicrobial delivery could mirror a ‘take when feeling bad , stop when feeling good’ approach , requiring necessarily reliable translation between symptoms and pathogen load . Focusing on the net effect of the antibiotic on the bacterial population , which has been shown to be relatively insensitive to changes in the frequency of administration of the drug [31] , we neglect the explicit pharmaco-dynamics of the antibiotic . Thus we model only the average rate of antibiotic-mediated pathogen killing ( represented by the product δ0 Am and δ1 Am , respectively for Bs and Br ) , which simplifies analysis . Both in the classical and adaptive regime , we explore treatment onset at various times over infection , departing from previous studies that typically link treatment initiation to a fixed pathogen load or the peak bacterial density [30 , 31] . Our formulation of treatment delay is inspired by two recent studies [23 , 33] . Its generality enables a deeper understanding of the trade-off induced by antibiotic treatment between reduction in host pathology and immunization , in the new context of resistant infections . Assuming an extinction threshold when pathogen density of each subpopulation within host reaches Bext , we can compute text , the extinction time , or clearance time . Because we simulate treated and untreated infections only up to a finite time horizon T , usually set to 30 days , infection duration is thus defined as: D = m i n ( T , t e x t ) ( 8 ) The total resistance burden over the entire infection is calculated as R t o t = ∫ 0 D B r ( t ) d t . ( 9 ) The total pathogen burden over infection is B t o t = ∫ 0 D B ( t ) d t , and final host immune memory is M ( D ) . We also track the resulting immunopathology [33] , which roughly reflects the cumulative damage to host health due to pathogen killing by cells of the immune response and associated inflammation [57] . For the total immunopathology accumulated up to time t , H ( t ) , following [33] , we define: H ( t ) = ∫ 0 t d B ( s ) I ( s ) d s . As the pathogen population grows and host immunity builds up , the cumulative immunopathology due to immune-mediated killing and inflammation also grows following the infection dynamics . Upon pathogen clearance ( or at the end of a simulation ) the immunopathology accumulated over infection reaches H t o t = ∫ 0 D d B ( s ) I ( s ) d s . ( 10 ) We perform a systematic analysis of these infection outcomes , varying treatment regimes ( classical and adaptive ) and model parameters , such as the fitness cost and benefit of resistance , and host immunity characteristics . We use as reference for comparison summary measures from infections in which no treatment is used . All simulations are performed in Matlab® R2011a .
In the absence of treatment , the infection follows a typical acute dynamics ( Fig 1A ) . Sensitive bacteria grow initially quasi-exponentially , while immune responses are not yet active . Resistant bacteria also increase from their initially low numbers , but relatively more slowly , depending on their fitness cost c = r0 − r1 . Resistant bacteria reach their peak around the same time as the sensitive sub-population , but at a lower density . As sufficient immunity gradually builds up during the bacterial growth phase , bacterial clearance is initiated , primarily through the action of effector cells . Following pathogen decline , effector cells also decline , with a fraction of them differentiating into persistent immune memory cells . High levels of acquired immune memory will act as pre-existing immunity in a secondary infection with the same pathogen and lead to rapid clearance . Mathematical analysis of the model in the absence of antibiotic confirms that stability of the infection-free state requires N* + M* > max ( r0 , r1 ) /d ( see S1 Text , part I ) . Below we derive analytical expressions , to understand how characteristics of the pathogen and of the host , represented by different parameters , interact to determine outcomes of infection . This serves as a starting point to then explore how perturbations like treatment , or variation in parameter values can affect these baseline dynamics . Focusing on the ‘expansion phase’ of immune dynamics , as in [48 , 58] , we can simplify the rates of change in total bacterial density and host immunity by the following sub-system: d B d t ≈ r 0 B - d B I ( 11 ) d I d t ≈ σ I B k + B ( 12 ) where I = N + E + M and B = Bs + Br . By assuming negligible fitness cost of resistance ( r0 ≈ r1 ) , the equations above give somewhat an upper bound on total bacterial growth . Biologically , the relative magnitudes of various parameters satisfy: B0 ≪ k , dI0 ≪ r0 , where B0 = B ( 0 ) is the initial pathogen density , and I0 = I ( 0 ) reflects the precursor frequency , i . e . the number of initial immune cells specific to the pathogen at the time of infection . Dividing the above equations , and integrating , we obtain: log ( B+kB0+k ) =r0σlog ( II0 ) −dσ ( I−I0 ) ( 13 ) This equation gives the relationship between the number of immune cells and parasite density at any given time during the bacterial growth phase . Thus , it allows us to calculate the level of immunity as a function of current pathogen load , and viceversa . Under this approximation , the peak pathogen load , in the absence of treatment , occurs when a critical level of host immunity has been reached , namely when I ≈ I c r i t = r 0 d . ( 14 ) The peak pathogen density at the end of the growth phase in acute infection can be obtained from combining Eqs 13 and 14: Bmax≈ ( B0+k ) ( r0deI0 ) r0/σ ( 15 ) The time it takes for the pathogen load to reach its peak can be approximated considering two phases of growth: i ) the time it takes the pathogen load to reach k , required for half-maximal immune stimulation , and ii ) the time it takes the immune response subsequently to grow from its initial level to the critical level Icrit . This dynamic decomposition focusing on k is an analytically convenient choice , yielding: tpeak=tk+tk→peak≈1r0log ( kB0 ) +1σlog ( IcritI0 ) ( 16 ) Such expressions , taken together , convey how host immunity characteristics , e . g . initial immunity I ( 0 ) [55] , or immune cell recruitment rate σ , affect different infection outcomes . These may vary with host age [59] , or other aspects of immune competence . One can also notice above the importance of the host immunity threshold , k , and maximal pathogen growth rate , r0 , which may vary too across host-pathogen systems . In the absence of the drug , the difference in growth rate between resistant and sensitive bacteria does not significantly affect the dynamics of immune build-up , peak bacterial load , or the cumulative immunopathology over infection . The cost of resistance ( c = r0 − r1 ) only changes the relative frequency of resistance in the total pathogen load . This is because immunity gets equal stimulation from both bacterial types and kills them at the same rate . After the immune ‘expansion phase’ , which leads to pathogen clearance , the ‘contraction’ and ‘memory’ phases of the immune response follow , provided that h > 0 , described in detail by Eqs 3–5 of the full model . Summing those three equations , one can see that total immune response in the system keeps increasing whenever B > k h ( 1 − f ) E σ ( N + E ) . This means some immune stimulation still continues during pathogen decline , thus the peak immune response reached over infection typically exceeds the critical value Icrit required for triggering clearance . Notice that setting h = 0 in the full model would mimic a situation of non-waning immunity ( at least non-waning in the time-scale of interest ) , quantitatively captured by the simple system of Eqs 11 and 12 . Most of the analysis above could thus be useful to understand also such a scenario , where for instance , the final level of immunity Ifinal accumulated after infection could be calculated from Eq 13 , by solving it for B = 0 . In all these theoretical scenarios , infection in principle resolves through action of host immunity , but depending on the severity of parameter values , the total damage to the host can be overwhelming , such that administration of drugs is required . By severity here we mean the clinical relevance or manifestation of Bmax in the absence of treatment ( Eq 15 ) , e . g . how close this peak density would be to a pathogenesis or lethal threshold for the host [58] . This naturally depends on pathogen growth rate and host immune competence . For example , slowly-growing pathogens might never trigger symptoms in their host ( thus may never need antibiotic treatment ) , and eventually will be cleared by the immune system without causing high levels of pathology . Next , we analyze the full model with treatment , where antibiotics interact with host immunity . In treated infections , the presence of a drug-resistant pathogen sub-population becomes relevant in either regime of drug delivery ( see Fig 1B and 1C ) . The effect of the antibiotic can be encapsulated as a reduction in the intrinsic per capita net growth rate of the two bacterial types during the treatment phase ( τ1 ≤ t ≤ τ1 + τ2 ) . The antibiotic reduces pathogen load and immunopathology , relieving the burden on host immunity ( Fig 1B ) . However , its timing , dose , and duration can produce a diverse range of outcomes , as shown in Fig 2 . With very aggressive treatments , resistant bacteria are not selected , and infections get cleared rapidly . In other cases , treatment cessation may result in a second infection peak , or even multiple peaks of bacteria , which may be equal to or even higher than pre-treatment levels , and consist of sensitive or resistant organisms . Treatment consequences vary especially depending on the phase of the infection in which treatment begins , where the growth potential of both strains is modulated by host immunity . To understand critical treatment parameters , we must consider the respective growth rates of bacterial subpopulations within host at the time τ1 when treatment is applied . In the presence of an immune response , the doses needed to halt growth of either subpopulation are decreasing functions of the immunity level I ( τ1 ) upon treatment onset: A m ′ ( I ) = r 0 - d I δ 0 and A m ′′ ( I ) = r 1 - d I a δ 0 , ( 17 ) for the sensitive and resistant strains respectively . In the absence of any immunity , the antibiotic doses that inhibit growth of sensitive and resistant bacteria are given by the maximum values: A m * = r 0 δ 0 and A m * * = r 1 a δ 0 , ( 18 ) where A m * ≤ A m * * , if the cost and benefit of resistance balance in such a way that r0 ≤ r1/a ( the scenario we consider here ) . In general , depending on the level of immunity , thus on the delay for treatment initiation , either bacterial type can decline , persist or grow during treatment , subject to how the actual dose that is deployed , Am , sits in this critical range ( Fig 3 ) . As a consequence , immunity can also decline , persist or grow while antibiotics are applied . If during treatment , the net change in dynamics results in an excessive decline of pathogen-dependent immunity , there is a window of possibility for pathogen relapse after treatment cessation , in case complete clearance has not been achieved with the drug . The sub-population surviving at an advantage at the end of treatment , may be the one to dominate the relapse , provided that such advantage in total numbers is greater than its relative fitness cost in the absence of treatment ( Fig 2A , τ1 = 2 , Am = 4 ) . When such recrudescence is caused by resistant bacteria ( e . g . for τ1 = 2 , Am = 10 in Fig 2A , or Am = 30 in Fig 2B ) , the lower the fitness cost of resistance is , the faster the new peak will be reached after therapy stops . Clearly , the amount of drug interference with normal immune build-up during treatment depends on its dose and duration . Thus , under pathogen density-dependent immunity , if bacteria persist or grow slightly during treatment it may not be so bad , given that such growth helps stimulate more immunity , and reduces the risk of relapse at the end of treatment . By the same argument , removal of antigen stimulus too rapidly during treatment may have adverse effects , because surviving pathogens at the end of therapy could re-grow if immune responses in the meantime have declined to subcritical levels ( assuming waning immunity , h > 0 ) . Selecting a moderate regime to balance between these scenarios is a challenge . While finding an optimal intermediate regime , involving some degree of immune control , is far from trivial , the extreme therapeutic option that does not require immunity at all , is much easier to analyze . Such antibiotic treatment is bound to be of an aggressive type . Consider the total bacterial load at treatment onset B ( τ1 ) . The scenario of drug-only-mediated clearance can be represented as an exponential decay of both bacterial subpopulations during treatment . Notice that resistant bacteria are killed at lowest rate by the drug , so by approximating the total population decline at that lower rate , we explore the worst case scenario for the host . Resistant bacteria are also more likely to suffer a fitness cost ( r1 ≤ r0 ) , thus by approximating total population growth at its highest possible rate , r0 , we are also considering a worst case scenario for the host . In this way , by being conservative in bacterial growth and decline during treatment , we obtain a sufficient criterion for ultimate clearance during classical treatment with dose Am and duration τ2 as B ( τ 1 ) e ( r 0 − a δ 0 A m ) τ 2 ≤ B e x t , which is equivalent to requiring: Am≥1aδ0[ r0−1τ2log ( BextB ( τ1 ) ) ] ( 19 ) Thus , if the dose and duration of classical treatment , in combination satisfy the above inequality , relative to the pathogen density at treatment onset B ( τ1 ) , and pathogen extinction threshold Bext , infection clearance by the end of treatment is guaranteed , without relying on host immunity . As the above expression shows , the earlier treatment begins , thus the lower B ( τ1 ) , the easier it is to meet the criterion with smaller doses and shorter treatment duration ( Fig 4 ) . Generally , the dose Am and duration τ2 , can be traded-off against one another , and still satisfy the clearance criterion for different pathogen loads at classical treatment onset . The caveat is to know whether these effects are possible with antibiotic doses below the toxic threshold for the patient . Notice , that the criterion in Eq 19 , does not depend on the cost of resistance , and also does not exclude that clearance may be achieved with lower doses , because the additional pathogen killing by immunity , accumulated up to and during treatment , is not accounted for by this formula . Here , we explore the interplay between antibiotic treatment and host immunity in the full dose range through numerical simulations of the complete model . In several ‘theoretical experiments’ ( Fig 2 ) , we vary treatment onset τ1 , between 2 and 5 days post-infection , and consider treatment duration between 3 and 15 days , realistic for bacterial infections [56] . Such duration may correspond to the prescribed therapy by a doctor , or may reflect the actual adherence by the patient . Similarly , the delay can reflect the time over a typical infection course when a patient seeks treatment , and this may fluctuate from person to person . Varying the antimicrobial dose , we observe that doses of the drug below A m * ( Eq 18 ) administered somewhat later over infection can be efficient in reducing the bacterial burden without promoting selection for resistance , because they yield the preponderate role in eliminating bacteria to the immune system . As soon as doses go above r 0 − r 1 δ ( 1 − a ) , the fitness differential between sensitive and drug-resistant bacteria is reversed ( e . g . Fig 2A: Am = 4 , τ1 = 2 ) . Small doses , just above A m * , start to interfere with immune build-up , but this interference decreases when treatment onset is delayed ( moving along the delay axis in Fig 2A and 2B ) . Higher intermediate doses of the drug , between A m * and A m * * , instead , promote more selection of resistant bacteria during and after treatment , and ultimately infection clearance is achieved by the delayed action of the immune system ( e . g . Fig 2A: Am = 10 ) . Yet , also here , optimal intermediate delays for initiating treatment , can help reduce host immunopathology and selection of resistance ( S1 Fig ) . In contrast , higher doses of antimicrobial drug , beyond A m * * , are able to induce immediately the decline of both sensitive and resistant populations ( e . g . Fig 2A: Am = 20 , and Fig 2B Am = 40 ) , but at the risk of a resistant relapse if they are not high enough , or applied sufficiently long ( Eq 19 ) . At the extreme case of very aggressive treatment , the host experiences minimal immunopathology from infection , but also does not accumulate any immune memory . As a result of interference by the drug , at certain intermediate doses , relapses in pathogen load can be maintained indefinitely . These arise when immunity at the end of treatment consists approximately only of effector cells I ≈ E , and coincides with r0/d , while pathogen load coincides with B ( t ) = hk/σ ( see S1 Text , part I ) . Since these values are sufficient to yield dE/dt = 0 and dB/dt = 0 , a persistence quasi-steady state is observed with oscillatory dynamics , as reported also in the model by [33] . Such oscillations typically arise in predator-prey systems , making it hard for the immune response to clear the pathogen in the short term . The total pathogen density may consist of sensitive or resistant bacteria , if the dose has been low or sufficiently high respectively . Given enough time however , conversion of effectors into memory cells will gradually build up enough persistent immunity to enable final clearance . When fixing the delay τ1 for treatment onset , we find many dose-duration combinations that select for the same amount of resistance overall , although they lead to varying immunopathology ( S2 Fig ) . On the other hand , many dose-duration combinations lead to the same immunopathology ( clinically neutral range ) [15] , while corresponding to different bacterial burdens and infection duration . Testing for the advantages of increasing treatment duration , we find that these apply only in those combinations of dose and delay where treatment immediately induces net pathogen decline . In dose-delay combinations where treatment allows slight pathogen growth , thus co-stimulation of the immune system , we observe that increasing treatment duration does not significantly improve infection outcomes , as ultimate clearance is driven by host immunity . In cases where antibiotic doses just about prevent growth of B ( t ) , keeping bacterial density at a too low level relative to the immunity threshold k , longer treatments may worsen outcomes: by delaying the relapse bound to occur at the end of therapy , and by increasing resistance selection . When mapping each infection profile to a quantitative assessment of infection summary measures ( see Methods ) , we notice more clearly how treatment dose and delay mediate selection for resistance , infection duration and host immunopathology , as shown in S3 Fig . In Fig 5 instead , we illustrate primarily one dimension of treatment success: resistance selection . Considering several treatment landscapes , we observe that for intermediate doses , there is a selection window for resistant bacteria , namely mutant selection window ( MSW ) , that shifts when treatment onset is delayed . This selection window includes a much narrower range of doses when the susceptibility a of the resistant strain increases , or when treatment duration increases from 7 to 15 days . With diminishing cost of resistance , the resistance selection window moves towards higher doses , as expected . In particular , Fig 5 shows the relative selection of resistance in different infections under treatment , varying the cost and benefit of resistance , and treatment dose , timing and duration . The doses are chosen in each case to represent the relevant relative range interpolating [ 0 . 1 A m * , 2 A m * * ] , thus comprising doses below , within and above the critical inhibitory doses for Bs and Br respectively . The resistance selection window , depicting dose-delay combinations in which Rtot is higher than the resistance burden of an untreated infection is given by the white dashed contour line , and shown separately for clarity also in S4 Fig . In such landscapes , one can seek optimal treatment by imposing that certain targets be met in terms of infection features , taking as reference the corresponding untreated infection . For illustration , we first set a target for treatment to successfully clear infection: quantitatively , to keep infection duration within a factor of 1 . 1 relative to its value in the no-treatment case . The resulting dose-delay combinations are depicted with yellow dots . Then , we set in addition a second target: for treatment to lower immunopathology Htot by at least 2-log orders of magnitude . Those combinations of dose and delay that satisfy both these criteria are depicted by red dots . Among these candidates , we can notice that there are broadly two ways of reaching infection clearance and reducing immunopathology with classical treatment: either with supercritical doses , above the resistance selection window , or with subcritical doses , below the resistance selection window . In the first case , there is little or no involvement of host immunity , whereas in the second case , there is synergistic immune response contribution to pathogen clearance . In the latter moderate treatments , as treatment is delayed , a greater level of host immunity is expected at onset , thus there is less chance for immunity to fall to subcritical levels during treatment . For this reason , with later onsets , we observe that higher doses begin to satisfy our optimality criteria . Although they are not needed for clearance , slightly higher doses , in this moderate range , remove the bacterial killing burden from the immune system , yielding lower pathology . Even when the cost of pre-existent resistance is lower ( Fig 5D and 5G ) , moderate doses , below the corresponding A m * * , applied at moderate delays post-infection , can be effective to rapidly remove the pathogen , limiting the ascent of the resistant sub-population . Regarding the impact of longer classical treatment , 15 days , as opposed to 7 days , we find that in the moderate dose range ( below the selection window ) prolonging the therapy does not add new optimal dose-delay combinations ( Fig 5 ) , and no significant gains in infection outcomes such as resistance selection or time to clearance . In contrast , in the aggressive dose range ( above the MSW ) , longer duration of treatment increases the effectiveness of relatively lower doses: expanding the range of small dose-delay combinations that can be used to clear infection ( shown in S5 Fig ) , as expected from our earlier analysis in Eq 19 . Zooming further into optimal treatments , ( e . g . the low cost resistance scenario of Fig 5G ) , we find that dose-delay combinations satisfying only the duration criterion ( yellow ) yield higher immunization levels for the host at the end of infection , than treatment combinations satisfying both the low duration and low immunopathology criteria ( red ) , shown in Fig 6 . Resistance selection , compared to untreated infection , is also lower in scenarios of low immunopathology . However , in a majority of the other moderate scenarios , shown in the top-right panel in Fig 6 , the resistance selection factor is still below 1 , evidently displaying an improvement compared to no-treatment . Unsurprisingly , when immunity does not wane ( h = 0 ) , clearance of infection by classical treatment becomes easier , ( S6 Fig ) and the range of effective moderate doses below the MSW increases slightly . In this case , the MSW peak is lower than that in the h > 0 case , illustrating reduced potential for pathogen growth within-host , and it is centred at earlier delays , due to a more robust immune response experiencing only minor interference by treatment . Taken together , these results confirm the effectiveness of aggressive therapy , but also uncover the effectiveness of moderate antibiotic doses , in combination with appropriate timing of treatment , for achieving synergistic infection clearance involving host immunity . After considering classical treatment , with fixed onset , duration and dose , we also explore infection dynamics under an adaptive treatment regime ( Fig 1C ) , where drug uptake is related to pathogen density ( B ( t ) ≥ Ω ) . Here we vary the dose Am and the symptom threshold Ω , i . e . the total pathogen load above which the host takes the antimicrobial drug . Our simulations convey that how the symptom threshold Ω compares with the host immunity threshold k impacts strongly duration of treatment and the ensuing dynamics ( Fig 7 ) . Recall that k corresponds to the pathogen density required for half-maximal immune stimulation . We observe that for Ω ≤ k , the drug starts to act too early , namely before or just when host immunity has been triggered to grow at its half-maximal rate , and adaptive treatment does not always clear an infection . In particular , if high doses are applied too early , an adaptive regime leads to chronic maintenance of either resistant or sensitive bacteria , in relapsing mode , peaking at Ω for indefinite time , an effect due to suboptimal immune activation and direct coupling of treatment to pathogen load . If treatment allows for minimal pathogen growth , thus only at low doses , adaptive treatment can yield pathogen clearance . On the other hand , when the symptom threshold exceeds the immunity threshold ( Ω > k ) , effective clearance of infections is more likely to occur ( Fig 8A ) . In these cases the immune system and the drug possibly act in synergy , for as long as needed to initiate and complete bacterial clearance . During such an adaptive treatment , sensitive bacteria can continue to grow slightly above Ω if doses are low , or remain fixed at Ω if doses are higher , but selection of the resistant sub-population is minimal and independent of the dose . Intuitively , for the success of the adaptive strategy , the involvement of the immune system is a pre-requisite . However , how high exactly the ratio Ω/k needs to be , in order to guarantee clearance , must depend on the magnitudes of other immunity parameters , such as σ and h . For example , the higher the rate of immune stimulation , σ , or rate of memory formation , h f the closer to k the adaptive symptom threshold can be ( i . e . the earlier treatment can begin ) . We make this precise by the following arguments . Suppose that when treatment begins , thus when B ( t ) = Ω , most of the bacterial population consists of drug-sensitive bacteria ( i . e . when r0 > r1 ) . Then the dose required to interrupt bacterial growth is roughly A m = r 0 − d I ( t Ω ) δ 0 . Conversely , if r0 ≈ r1 , and the fitness cost of resistance is low , this no-growth dose is determined by the resistant sub-population A m = r 1 − d I ( t Ω ) a δ 0 . By instantaneously modulating rate of drug administration η ( t ) , adaptive treatment manages to effectively scale higher antibiotic doses to these minimal inhibitory values , thereby maintaining pathogen load at Ω . Optimal adaptive treatment , during drug-administration , keeps B ( t ) at the symptom threshold Ω , which is sufficient to provide constant immune stimulation . Under this scenario , using Eqs 4 and 5 , the immune response dynamics during treatment can be approximated as: I ( t ) ≈E ( t ) +M ( t ) =E ( t ) +hf ( 1−ΩΩ+k ) ∫tΩtE ( s ) ds=I ( tΩ ) eZt+hf ( 1−ΩΩ+k ) I ( tΩ ) Z ( eZt−1 ) , ( 20 ) where , for simplicity of notation Z ≡ ( σ + h ) Ω Ω + k - h represents the net exponential rate of change , and I ( tΩ ) is the immunity at treatment onset , i . e . when B ( t ) reaches Ω . This level of immunity at treatment onset can be computed from the approximation in Eq 13 , replacing B by Ω and solving for I . In the derivation of Eq 20 , the contribution of N cells is assumed negligible , and total immune response is given by effector and memory cells only . The condition for the exponential rate of change to be positive ( Z > 0 ) is satisfied if: Ω/k > h/σ . It becomes evident that the rate of growth of the host immune response during treatment depends not only on how Ω compares with k , but also on the lymphocyte recruitment rate σ , rate of decay h , and proportion differentiating into persistent memory cells f . Clearly , the same rate of immune buildup may be achieved by the balanced effects of either higher Ω/k or higher σ ( see S7 Fig ) . Enabling the immune system to catch up during treatment , the adaptive therapy will then last until the critical level of immunity , Icrit , has been reached . Using Eq 20 , the expected duration of successful adaptive treatment can be calculated as: Dur a d a p t i v e = 1 Z log I c r i t I ( t Ω ) Z + h f ( 1 - Ω Ω + k ) Z + h f ( 1 - Ω Ω + k ) , ( 21 ) after which the immune system of the host should be able to finish the ‘job’ of pathogen clearance . From this equation , matching very well with our simulations ( S8 Fig ) , we observe that a stronger immune response guarantees shorter treatment duration , during which the drug is taken only while necessary , to facilitate subsequent action by the immune system . At the end of such an adaptive treatment , the host immune responses have reached the critical level Icrit , required for initiating pathogen decline . Although this is the first necessary step for clearance , it is not sufficient . While pathogen load starts to decline from level Ω , residual immune stimulation continues initially post-treatment , as long as total pathogen load satisfies B > k h ( 1 − f ) E σ ( N + E ) ≈ k h ( 1 − f ) σ , following from Eqs 3–5 . But as B drops to low numbers , the immune response starts to decline as well . If the time it takes for this declining immunity to reach Icrit again , exceeds the time it takes for the declining pathogen load to hit the extinction threshold , then clearance occurs after adaptive treatment ( Fig 8A ) . On the contrary , infection clearance does not occur if immune responses fall to sub-critical levels before pathogen extinction ( Fig 8B ) . In that case , an oscillatory dynamics between pathogen—occasional treatment—and immunity emerges , and infection continues . Mathematically , by analyzing the ‘contraction’ phase of host immunity , these clearance and no-clearance regimes can be approximately distinguished via the following criterion ( see S1 Text , part II ) : log B Ω log B + k Ω + k = σ h ( 1 - f ) + 1 and B ≤ B e x t → clearance B > B e x t → relapse , ( 22 ) which applies to the pathogen load when host immunity hits Icrit during its contraction phase . If the solution to the above equation is below the extinction density Bext , then adaptive treatment administered at symptom threshold Ω will be effective , otherwise oscillatory dynamics will be induced by treatment instead of clearance . The above conditions specify the parameter space where adaptive treatment can provide a sustainable solution for resistant infections under minimal drug pressure , highlighting the important role of the immunity threshold k , as well as other immunity parameters . Thus , not all symptom thresholds Ω above k are effective: only those , which in combination with the rest of immune indicators , and the pathogen extinction threshold , satisfy the necessary and sufficient criteria for clearance , as illustrated in Fig 8C and 8D . Notice that the distinction between clearance and relapse after adaptive treatment , obviously , only applies if there is the possibility of a contraction of the immune response in the time scale of interest , i . e . if h > 0 . In case immunity does not wane ( h = 0 ) , clearance always follows after adaptive therapy , with ever-increasing immunity I > Icrit , and Eqs 20 and 21 still apply . What may vary is the speed of such clearance , ultimately regulated by the pathogen killing efficiency of immune cells , d . To jointly evaluate both protocols on the same infection , we assume τ1 ( classical treatment ) equals the time it takes the total bacterial population to reach the symptom threshold Ω ( adaptive treatment ) in the absence of the antibiotic , so we ensure equivalent treatment onsets . We consider variable symptom threshold Ω = B ( τ1 ) , and variable dose Am . Assuming a fixed duration of 7 days for classical treatment , we compare infection outcomes between the two regimes ( Fig 9 ) , for simulated dynamics up to 30 days . Keeping all other immunity parameters fixed , when the symptom threshold is lower than or equal to the immunity threshold , it is classical treatment with high doses that is more likely to clear infection , by 10 days on average , as shown in Fig 9A ( red circles ) . At such low bacterial densities adaptive treatment generally fails , and induces oscillatory pathogen dynamics , unless the doses are sub-inhibitory , such that bacterial growth continues during treatment , stimulating immunity and eventual clearance . As the delay of treatment onset increases , implying higher symptom thresholds , clearance of the same infection can be obtained by both classical and adaptive regimes ( Fig 9A , blue squares ) . However , while in the classical regime , the clearance doses are generally very high , the effective doses in adaptive therapy are much lower ( Fig 9B ) . Contrary to the fixed protocol of the classical regime , in adaptive treatment , duration and drug uptake are modulated by pathogen load . As immune response grows rapidly during adaptive treatment at symptom thresholds above k , decreasing the growth potential of bacteria within host , the rate of uptake of the drug progressively goes down . This effectively reduces the dosage of therapy to the minimum necessary , thereby limiting resistance selection , and minimizing gradually any interference with immunity . For our illustrative parameter values ( Table 1 ) , as shown in Fig 9C , adaptive treatment achieves infection clearance by day 8 post-infection , averaged over all dose-delay combinations , while classical treatment clears infection later , in 12 days on average . This longer time to clearance results from those cases when low-dose classical therapy disrupts temporarily the natural course of host immune defenses , leading to bacterial relapse post-treatment and delayed immune control . The differences in immunopathology and total bacterial burden from our simulations favour classical treatment by a slight amount , but when considering total resistance burden , the adaptive treatment is remarkably superior , yielding lower values by at least two orders of magnitude , averaged across all dose-delay combinations that we tested . The analogous comparison between the two protocols , for a lower cost of resistance , is shown in S9 Fig , where the greater immune activation obtained from resistant bacteria growing slightly at sub-inhibitory doses , increases the success potential for adaptive treatment , but at the cost of higher pathology . We also find treatment scenarios where infection may be prolonged indefinitely , both within classical and adaptive regimes , a failure feature attributed previously only to adaptive scenarios [31] . As the distance of the symptom threshold from the immunity threshold ( Ω/k ) increases , treatment is initiated at higher and higher pathogen loads , where the preponderate role in bacterial killing can be more readily fulfilled by the immune system . At such high pathogen numbers , classical treatment , by maintaining drug pressure over longer periods of time , relieves to a larger extent the burden on host defenses . Thus , a classical regime is bound to yield lower immunopathology , because it replaces the role of the host’s immune system . Adaptive treatment , instead , does precisely the opposite: it exploits the contribution of the immune system , and while doing so , is able to achieve the same reduction in pathogen load and infection duration . On one hand , this might entail the cost of more pathology , but also brings about the potential benefit of host immunization .
By exploring in detail the treatment parameter space , across classical and adaptive regimes , we uncover the cases where a more prudent use of antibiotics , with lower doses over less time , could be favoured , as a more effective way to reduce the rate of ascent of resistance . Our results thus extend the perspectives of Read et al . [14] , highlighting the novel important role of treatment timing , as advocated recently by other modeling studies [23 , 33] , and bring forward new testable hypotheses . Our model behaviour illustrates four broad infection outcomes: acute infections dominated by either drug-sensitive ( i ) or resistant bacteria ( ii ) , and relapsing infections dominated by drug-sensitive ( iii ) or resistant bacteria ( iv ) , which can reach higher or lower peaks than pre-treatment levels . Relapsing resistant infection , has been observed experimentally in malaria parasites in vivo[16] and in bacterial infections of humans , e . g . urinary tract recurrent infections within 2 weeks of treatment [65] , infections with C . difficile[66] , in clinical treatment of Salmonella bacteraemia [67] and in treatment failure of endocarditis [68] , where relapse was associated to heterogeneous resistance of Staphylococcus aureus pre-treatment . Treatment failures leading to relapses often occur because of a mismatch between first line antimicrobial therapy and the unknown antibiotic susceptibility of the disease-causing agent . On a broader level , infection relapses post-treatment may have parallels with the trade-off between clearance by intervention and immunization , observed also at the epidemiological level , as it has been argued for example in schistosomiasis control [69] , where massive drug administration , reducing antigen exposure , could result in rapid decline of protective immunity in the population and infection rebound if treatment ceased . To successfully treat resistant infection in hosts with an intact immune system , our model suggests that doses can be moderate , especially when the treatment ( symptom ) threshold exceeds the immunity threshold . This results in an optimal delay [33] , after which the critical pathogen density , triggering adaptive immunity , has been surpassed , and the subsequent overshoot in pathogen-growth is minimized through treatment . This is similar to the principles advocated by Tanaka et al . [23] for treatment timing near the epidemic peak at the population level . Our results thus highlight the importance of appropriate timing of moderate therapy and synergistic action of host immunity whenever possible . These theoretical analyses corroborate evidence from experimental and clinical studies of infection [40 , 70 , 71] , and call attention on the empirical values of the host immunity threshold [36] , and other variable immune response determinants [72] as critical modulators of treatment success . In our model , we neglected the process of de novo mutation that can lead to resistance to the current drug . Exploiting the analogy with the Luria-Delbruck process [73] , it is known that the timing at which the first resistant clade arises impacts strongly on the subsequent prevalence of resistance in a growing bacterial population . If the first resistant strain arises early over infection , a large resistance clade ensues within host . In contrast , if the first resistant strain arises later , the resulting clade is expected to be smaller . These effects are amplified in the presence of immune control . A similar scenario at the population level has been explored by Tanaka et al . [23] . In a way , our model considers the worst extreme of this timing spectrum for the host , by assuming that resistance is already pre-existent from the start of infection . Assuming some fitness cost of resistance and a slight advantage in initial numbers of the drug-sensitive bacteria , postponing slightly antibiotic treatment until host immunity is triggered , gives the sensitive sub-population a sufficient head start , limiting the ascent of the resistant competitors . It is possible that during infection , de novo resistance emergence may occur , at any point of the dynamics . Moreover , the probability of de-novo emergence of resistance through a mutation rate per unit of time should apply to each parental strain: the sensitive and resistant bacteria . In fact , it has been shown that already-resistant strains may have higher mutation rates to develop new resistances [74] . However , to describe the subsequent dynamics of these newly emergent strains within host , one would need to make further assumptions about what the cost and fitness benefits of such new mutations may be with regards to the present infection . In this process , one may choose to explicitly simulate emergence , or could track , for example , the overall probability of new ( generic ) resistance emergence over the entire infection period , P e m e r g e n c e = 1 − e − θ ∫ 0 D B ( t ) d t , with θ denoting mutation rate per unit of time , and examine criteria to restrict this quantity . Following [75] , a critical threshold for this probability could be the value 1 − e−1 , above which emergence would be expected almost certainly in a semi-stochastic setting . To constrain such probability of emergence , basically implies a restriction on the total bacterial burden over infection , in relation to the particular value of the mutation rate , and this could be readily investigated with the current framework . In the future , it will be essential to address the process of mutational input to the system in more detail , and analyze the different phenotypes that may emerge , their mutual correlation , and their consequences for infection dynamics . These include drug resistance to the current and other drugs [74] , with the worst scenario being an increase in non-susceptibility ( 1 − a parameter in our model ) , persister phenotypes [76] , compensatory mutations reducing fitness cost [34] , virulence , and interaction with the host immune system . It is likely that some of our findings may not hold in all scenarios , and this remains an open avenue , to be explored on a case-by-case basis . In specific bacterial systems displaying drug resistance , empirically derived distributions of mutation effects [77] can eventually be used to inform realistic model extensions . Another line of exploration remains the process of horizontal gene transfer , where the rates of resistance exchange through mobile elements have been shown to be orders of magnitude higher than those of point mutation [78] . In the interest of generality and clarity , several simplifications were necessary to obtain analytical insight . Focusing on the principal fitness differences between resistant and sensitive bacteria , we did not further consider separate in-host compartments for bacterial growth as in [31] . We also focused on exponentially growing pathogen in the absence of treatment , although previous studies have accounted for logistic growth imposed by resource limitation [24 , 30 , 31] . Logistic growth changes the dynamics of untreated infections in a major qualitative manner , allowing for acute infections , as well as persistent colonization states , typically observed with enteric bacteria [79] . Among other issues , this qualitative change would require a rethinking of the optimality criteria for antibiotic treatment , and of the interplay with host defenses . We believe an understanding of how logistic growth parameters may interact with the host immunity threshold and treatment parameters deserves a deeper study of its own , where systematic analysis of different scenarios can reveal new results . Using a deterministic approach , in line with existing studies on the topic [29 , 30] , we also did not explicitly model demographic stochasticity of bacteria within host , beyond the assumption of an extinction threshold . We decided to focus on understanding the mechanisms of infection dynamics and the selective processes operating during treatment . Naturally , for greater realism and applicability of the model in practical settings , all factors contributing to stochasticity and bacterial growth in different organs and host compartments must be accounted for in the future ( see [80] for a data-driven example on Salmonella infection , and [81] for a recent computational tool developed for TB treatment ) . As previous models have shown , drug concentration heterogeneity in different body compartments may yet be another factor impacting drug resistance evolution within host [82] . Notice that we do not deal with secondary infections in this paper . Immune kinetics over consecutive encounters with the same pathogen is complex , and the precise relationship between memory cells and those of the primary response is the subject of debate [83] , and many theoretical formulations . Stromberg and Antia [33] suggested a quick way to mathematically simulate reinfection , using the same model structure , but assuming that the first immune cell compartment represents pre-existent memory ( instead of naive precursors ) , initialized at the level of memory cells ( Mfinal ) obtained or remaining from the primary response . Upon new pathogen encounter , these cells would then acquire effector function and drive rapid pathogen clearance , boosting adaptive immunity . Another way of extending the current model to accomodate secondary infection and the action of immune memory would be to modify the equations by adding a direct contribution into the E compartment by M cells , as a function of their stimulation by antigen ( see S1 Text , part III ) . In the time scale considered in this paper , such conversion process ( M → E ) is broadly inconsequential for overall clearance dynamics . Our main results , highlighting the treatment-immunity interplay during primary infection , remain robust to this assumption . Yet , when simulating scenarios of secondary vs . primary infection ( S10 Fig ) , the biological importance of pre-existing memory becomes clear , in particular the trade-off between host immunization and pathology reduction induced by antibiotic treatment during primary infection . More detailed conversion processes between effector and memory cell compartments could of course be modelled depending on data availability and the precise question . In future studies , critical attention must be devoted to the optimality targets for treatment . If quantities to be optimized differ across settings , then optimal treatments will vary and may be difficult to compare in a standard manner . We show that different infection features have different sensitivity to treatment parameters . Thus , it is important to inform these control targets in a bottom-up fashion from clinical and medical considerations , but also from the broader epidemiological context of specific pathogens . For example , what is the order of priority for optimizing different health indicators ? How does the symptom threshold , when patients seek specialized help , relate to the host immunity threshold ? What is the upper bound for pathogen load within individual hosts to prevent onward transmission ? What is a tolerable range for immunopathology ? All these questions require integrative approaches at the medical-computational biology-and-immunology interface . Ideally , these should be achieved through the analysis of resistance evolution in vivo , empirical data from clinical outcomes of specific diseases , and integration with theoretical models . Along similar lines , caution must be taken regarding the literal interpretation of the drug doses used in our model , reflecting more what bacteria ‘experience’ . The translation of the modelled antibiotic dose to clinical prescriptions requires pharmacodynamic considerations [84] . A constant level of drug concentration , as the one assumed here , is unrealistic for real infection , except for cases of intravenous administration [85] . Yet , the behaviour described by our model , and the suggested trends , regarding the critical dose range and the susceptibility spectrum of co-infecting bacteria , are expected to be maintained , regardless of the corresponding real drug concentrations . Calibration of the model structure and parameters to real data from in vivo , in vitro , and clinical studies is a natural next step for validation . Matching the in-vitro susceptibility of the pathogen and clinical prescription has been shown to be an important prognostic factor in real clinical settings [17 , 86] . Our findings point to yet another dimension: accounting for the quantitative contribution of host immune responses , whether in immune competent or immunocompromised state . Depending on the type of infection , and host status , the optimal treatment parameters may vary . For some infections , such as Staphylococcus aureus bacteraemia or enterococcal endocarditis , prolonged treatment is recommended to prevent relapse [87] . Conversely , in other situations including otitis in children [88] , the treatment of gonorrhoea [89] , uncomplicated urinary tract infections in women [90] , and uncomplicated cases of community acquired pneumonia [61] , the roles for short courses of antibiotics appear well established . We restricted our analysis to antigen-dependent immunity ( type I ) , as this is likely to be most vulnerable to interference by the antibiotic treatment . Action of antigen-independent immunity ( type II ) could be included in our model as a reduction in net growth rate of bacteria within host ( e . g . via a constant factor , or a function of time [31] ) . Alternatively , to account for the initial trigger by pathogen density , followed by programmed lymphocyte division , model extensions like those in [33 , 37] could be adopted . In principle , programmed immune defenses , when added to the system , should weaken the competition between the drug and the other immune responses that are coupled to pathogen density , facilitating infection clearance , especially in scenarios of antibiotic-driven relapses . When only programmed immune defenses are available instead , then optimal therapies should approach aggressive treatment scenarios ( Eq 19 ) , where the interaction with the drug would occur only in one direction , from immunity to the drug , possibly through a time-dependent net growth rate of the pathogen . As our understanding of pathogen population dynamics within host increases , by incorporating in higher quantitative detail the action of the immune system , a more promising and sustainable line of personalized antimicrobial therapy can be foreseen: the one based on a synergy between antimicrobial drugs and host immunity , whenever possible . In the future it will be important to robustly validate the sensitivity of treatment dynamics to pathogen and host parameters , initial conditions at treatment onset , and concentration of antibiotics . By emphasizing the dependence of therapeutic success on these crucial quantities , across classical and adaptive treatment regimes , our study calls for more empirical attention to host’s natural defenses in fighting drug-resistant infections . | The evolution and spread of antimicrobial resistance is a major global problem , and a cause of substantial human mortality . As the discovery of new antibiotics does not follow the rate at which new resistances develop , a more judicial use of available drugs is needed . Here we develop a mathematical model of within-host infection dynamics that combines the effects of pathogen clearance by the host immune system and by the antibiotics . Computer simulations and mathematical analysis are used to evaluate treatment protocols in order to identify those that can restore patient health and limit the overall pathogen burden and selection of resistance . We focus our study on infections with pre-existing resistance , and explore two main treatment strategies: the classical treatment , characterized by fixed drug dose and treatment duration , and the adaptive treatment that closely follows infection outcomes and patient symptoms . Our results highlight treatment strategies that promote synergy between host immunity and the antimicrobial drug . This can be achieved by moderate treatments that combine appropriate timing , reduced drug dosage , and short treatment durations . Our model is developed for bacterial infections but our framework and findings may apply to other biological scenarios featuring drug resistance . | [
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] | 2016 | Integrating Antimicrobial Therapy with Host Immunity to Fight Drug-Resistant Infections: Classical vs. Adaptive Treatment |
Several components of the mosquito immune system including the RNA interference ( RNAi ) , JAK/STAT , Toll and IMD pathways have previously been implicated in controlling arbovirus infections . In contrast , the role of the phenoloxidase ( PO ) cascade in mosquito antiviral immunity is unknown . Here we show that conditioned medium from the Aedes albopictus-derived U4 . 4 cell line contains a functional PO cascade , which is activated by the bacterium Escherichia coli and the arbovirus Semliki Forest virus ( SFV ) ( Togaviridae; Alphavirus ) . Production of recombinant SFV expressing the PO cascade inhibitor Egf1 . 0 blocked PO activity in U4 . 4 cell- conditioned medium , which resulted in enhanced spread of SFV . Infection of adult female Aedes aegypti by feeding mosquitoes a bloodmeal containing Egf1 . 0-expressing SFV increased virus replication and mosquito mortality . Collectively , these results suggest the PO cascade of mosquitoes plays an important role in immune defence against arboviruses .
The transmission of arboviruses by mosquitoes and other arthropod vectors has considerable adverse impacts on human and animal health . This group of pathogens consists primarily of viruses in the families Flaviviridae , Togaviridae Bunyaviridae , and Reoviridae [1]–[4] . Arboviruses replicate in both vertebrate and arthropod hosts . In mosquitoes , arboviruses must also spread from the midgut , which is the initial site of infection following a bloodmeal to the salivary glands for transmission to another vertebrate host . The genus Alphavirus ( family Togaviridae ) contains several mosquito-vectored arboviruses including models like Sindbis virus ( SINV ) and Semliki Forest virus ( SFV ) [5] , [6] but also the re-emerging human pathogen chikungunya virus ( CHIKV ) [7] . The genetic structure and replication of alphaviruses , which replicate in the cytoplasm , have been analysed in detail [5] , [6] , [8] , [9] . All members of the genus have positive-stranded RNA genomes that are approximately 11–12 kb in size , and have 5′ caps and 3′ poly ( A ) tails ( genetic structure of SFV shown in Fig . 1A ) . All alphaviruses also encode two major polyproteins . The 5′ encoded non-structural polyprotein P1234 is proteolytically cleaved into replicase proteins nsP1–4 while the 3′ encoded structural polyprotein ( translated from a subgenomic mRNA , which is transcribed under control of a subgenomic promoter ) is proteolytically cleaved into the structural proteins that form the capsid and envelope of the virion . The glycosylated envelope proteins play key roles in entry into cells by mediating virus binding to host cell receptor ( s ) and subsequent fusion to endosomes ( though other entry mechanisms may be possible ) while the capsid protein encapsulates the viral genome [10]–[12] . Infection of mosquito cell cultures has also been useful to study arbovirus replication , thus allowing increasingly detailed studies of arbovirus/vector interactions [13] , [14] . The innate immune system of mosquitoes plays an important role in the control of arbovirus infections , and SFV has proven to be a good models to study mosquito antiviral response mechanisms [14] . A key antiviral defence is RNAi ( reviewed in [14]–[17] ) , which also influences arbovirus spread and transmission [18] , [19] . In addition , differential regulation of mosquito immune signalling pathways and other host genes has been described following infection by dengue virus ( DENV ) , West Nile virus ( WNV ) and SINV [20]–[24] . JAK/STAT and Toll signalling pathways both mediate antiviral activity against DENV [23] , [24] . Interestingly , infection of Anopheles gambiae with the alphavirus o'nyong-nyong ( ONNV ) did not result in upregulation of the Toll and JAK/STAT pathways although other genes involved in immunity were upregulated with some displaying antiviral activities [25] . Innate immune signalling can also inhibit SFV replication in mosquito cells [26] , while experiments in Drosophila melanogaster suggest that replication of SINV is inhibited by the IMD pathway [27] . Another conserved component of the insect immune system is the extracellular phenoloxidase ( PO ) cascade , which generates cytotoxic intermediates and the formation of melanin following wounding or infection [28]–[32] . Several factors have been shown to activate the PO cascade including pathogen-associated molecular pattern molecules like bacterial peptidoglycan . Other components of the cascade include multiple clip-domain serine proteases ( cSPs ) whose activation results in processing of the zymogen prophenoloxidase ( PPO or proPO ) to form active PO . PO then catalyses the conversion of mono- and di-phenolic substrates to quinones , which are converted to melanin . A number of studies have shown that deposition of melanin provides defence against bacteria and multicellular parasites , while intermediates like 5 , 6-dihydroxyindole have been shown to be cytotoxic and act against pathogens [29] , [33] , [34] . Studies with the lepidopteran Heliothis virescens ( tobacco budworm ) indicate that haemolymph also contains factors with antiviral activity against Helicoperva zea single capsid nucleopolyhedrovirus ( HzSNPV ) and other viruses including SINV , while bioassays with 5 , 6-dihydroxyindole show that it rapidly inactives Autographa californica multicapsid nucleopolyhedrosis virus ( AcMNPV ) [35]–[38] . Haemolymph melanisation in Lepidoptera also correlates with antiviral activity against Microplitis demolitor bracovirus ( MdBV ) [39] , and Lymantria dispar multicapsid nucleopolyhedrovirus [40] . Whether arboviruses activate the PO cascade in mosquitoes and whether products of the PO cascade exhibit biologically relevant antiviral activity remains unclear , although interestingly RNAi knockdown of PPO I in the mosquito Armigeres subalbatus by a recombinant SINV expressing a dsRNA targeting PPO I resulted in reduced PO activity and higher SINV titres [41] . Previous studies show that Aedes albopictus-derived U4 . 4 cells have a functional antiviral RNAi response and immune signalling pathways [26] , [42] . Here we show that conditioned medium from U4 . 4 cells contains inducible PO activity that is activated by exposure to bacteria and purified SFV particles . Expression of the PO cascade inhibitor Egf1 . 0 from MdBV [39] , [43] by SFV decreased PO activity in U4 . 4 cell conditioned medium and enhanced the spread of virus through cell cultures . Infection of Ae . aegypti mosquitoes with SFV expressing Egf1 . 0 resulted in enhanced viral replication and mosquito mortality . Taken together , our results establish a role for the PO cascade in mosquito immune defence against an arbovirus .
The haemolymph of mosquitoes melanises in response to a variety of stimuli including wounding and infection [28] . Mosquitoes including Ae . aegypti encode multiple PPO genes , with some family members being inducibly expressed in response to microbial infection [44]–[47] . Haemocyte-like cell lines from An . gambiae also express multiple PPO genes [48] , and recent studies identify cSP CLIPB9 as a candidate PAP [49] . Since the U4 . 4 cell line from Ae . albopictus is an important model for studying immune responses against arboviruses [26] , [42] , [50] , we first asked whether conditioned medium from this cell line exhibited an increase in melanisation upon exposure to SFV or the bacterium Escherichia coli which is a well known elicitor of the PO cascade . Using a standard spectrophotometric assay for measuring melanisation activity ( see Materials and Methods ) , our results indicated that PO activity significantly increased in U4 . 4 cell conditioned medium following exposure to each microbe ( p = 0 . 003; E . coli versus control , p = 0 . 004; SFV versus control , p = 0 . 021; E . coli versus SFV , p = 1 . 00 ) ( Fig . 2A ) . Our results also indicated that a 1 h incubation in conditioned medium significantly reduced SFV viability relative to virus incubated in unconditioned medium ( p = 0 . 011 ) ( Fig . 2B ) . Because amphipathic molecules like detergents and alcohol activate insect PPOs [51] , intracellular PO activity is commonly assayed for in PO producing cells like haemocytes by first fixing them in methanol and then incubating in a substrate like dopamine , which PO utilizes to produce melanin . This in turn causes the fixed cell to turn black or darken . In the case of Ae . aegypti and An . gambiae , prior studies establish that one class of haemocytes , oenocytoids , constitutively exhibit intracellular PO activity while a second class , granulocytes , inducibly exhibit intracellular PO activity following immune challenge with bacteria [52] , [53] . To assess whether U4 . 4 cells exhibit intracellular PO activity , we fixed cells in glacial methanol and then incubated them in buffer plus dopamine . Our results showed no intracellular PO activity in the majority of cells but a small fraction of cells ( 0 . 2% ) darkened in manner similar to mosquito haemocytes ( Fig . 2C ) [52] , [53] . We also noted that these melanising cells display a rounded morphology and appear larger than other U4 . 4 cells that do not darken after fixation and incubation with substrate . We thus concluded from these assays that U4 . 4 cell-conditioned medium melanises following exposure to SFV or bacteria , and that a small proportion of U4 . 4 cells also melanise after fixation . We also concluded the increase in melanisation activity that occurs in conditioned medium correlates with a reduction in SFV viability . As previously noted , the PO cascade consists of multiple proteases that terminate with the zymogen PPO [28]–[32] ( Fig . 3A ) . The number of proteolytic steps in the cascade has not been fully characterised in any insect including mosquitoes . However , it is known that infection , wounding , and other challenges trigger activation of upstream serine proteases , which result in processing of proPAPs ( also referred to as pro-PPAEs or pro-PPAFs ) between their clip and protease domains . Activated PAPs then process PPO by cleavage at a conserved arginine-phenylalanine ( R-F ) site in the N-terminal domain of the protein , which results in formation of PO ( Fig . 3B ) . PO catalyses the hydroxylation of monophenols like tyrosine to o-diphenols and the oxidation of o-diphenols to quinones . Quinones thereafter undergo further enzymatic and non-enzymatic reactions that produce cytotoxic intermediates and ultimately melanin . Negative regulation of the PO cascade occurs through endogenous protease inhibitors like serpins , while reducing agents in haemolymph like glutathione ( GSH ) likely inhibit melanisation by reducing PO-generated quinones back to diphenols [54] ( Fig . 3A ) . Several pathogenic organisms have also evolved strategies to suppress the PO cascade of hosts [28] . One of these is the virus MdBV , which produces the protein Egf1 . 0 . Functional characterization of Egf1 . 0 showed that it blocks haemolymph melanisation in diverse insects including mosquitoes through two activities ( Fig . 3A , B ) . First , it competitively inhibits activated PAPs because it contains an R-F reactive site that mimics the cleavage site for PPO [39] . Second , Egf1 . 0 contains another domain that prevents upstream proteases from processing pro-PAPs [43] . Given this background , we asked whether Egf1 . 0 could inhibit the increase in melanisation activity that occurs in U4 . 4 cell-conditioned medium following exposure to SFV or E . coli . To answer this question , we produced two sets of constructs . In the first , we cloned the egf1 . 0 gene from MdBV [39] in forward ( expressing the Egf1 . 0 protein ) and reverse ( negative control not expressing Egf1 . 0 ) orientation into SFV under control of a second subgenomic promoter to produce SFV4 ( 3H ) -FFLuc-Egf1 . 0F and SFV4 ( 3H ) -FFLuc-Egf1 . 0R ( Fig . 1B ) . These viruses also expressed Firefly luciferase ( FFLuc ) , which served as an indicator for viral replication and spread through a U4 . 4 cell culture as previously shown for reporter gene-expressing SFV [50] ( Fig . 1B ) . The second set of SFV constructs expressed Egf1 . 0 in forward or reverse orientation from a second subgenomic promoter plus ZsGreen fluorescent protein inserted into the C-terminal region of nsP3 to produce SFV4 ( 3F ) -ZsGreen-Egf1 . 0F and SFV4 ( 3F ) -ZsGreen-Egf1 . 0R , respectively ( Fig . 1C ) . Next , the properties of SFV-expressed Egf1 . 0 were analysed . We infected U4 . 4 cells with SFV4 ( 3F ) -ZsGreen-Egf1 . 0F and SFV4 ( 3F ) -ZsGreen-Egf1 . 0R at a multiplicity of infection ( MOI ) of 10 . Immunoblot analysis of cell lysates confirmed that each recombinant virus actively replicated as evidenced by detection of the nsP3-ZsGreen protein ( Fig . 4A ) . Using an anti-Egf1 . 0 antibody , we also detected full-length Egf1 . 0 [39] , [43] in the medium and lysates prepared from U4 . 4 cells infected with SFV4 ( 3F ) -ZsGreen-Egf1 . 0F but did not detect this protein in medium or lysates from uninfected cells or cells infected with SFV4 ( 3F ) -ZsGreen-Egf1 . 0R ( Fig . 4A ) . Our Egf1 . 0 antibody also detected several other bands smaller than full-length Egf1 . 0 in samples infected with SFV4 ( 3F ) -ZsGreen-Egf1 . 0F including a 17 . 6 kDa protein that corresponded to the size of the C-terminal Egf1 . 0 fragment that prior studies showed is produced after cleavage by a PAP ( Fig . 4A ) . Expression of Egf1 . 0 by SFV4 ( 3H ) -FFLuc-Egf1 . 0F and absence of Egf1 . 0 expression by SFV4 ( 3H ) -FFLuc-Egf1 . 0R were also verified by immunoblotting ( not shown ) . We then analyzed the functional properties of SFV-expressed Egf1 . 0 in conditioned medium from U4 . 4 cells . Melanisation assays at 48 h post-infection ( p . i . ) showed that conditioned medium from cells infected with SFV4 ( 3H ) -FFLuc-Egf1 . 0F exhibited very low PO activity , which was very similar and not significantly different to conditioned medium from uninfected ( control ) U4 . 4 cells ( p = 1 . 0 ) ( Fig . 4B ) . In contrast , medium from cells infected with SFV4 ( 3H ) -FFLuc-Egf1 . 0R exhibited PO activity levels that were significantly higher than medium from uninfected control cells ( p = 0 . 025 ) ( Fig . 4B ) . Conditioned medium of U4 . 4 cells infected with SFV4 ( 3H ) -FFLuc-Egf1 . 0F also contained significantly less ( 75% ) PO activity than medium from cells infected with control virus SFV4 ( 3H ) -FFLuc-Egf1 . 0R ( p<0 . 001 ) ( Fig . 4B ) . The addition of E . coli to medium from SFV- infected cells had no effect on the PO activity ( p = 0 . 251 ) . As shown in Fig . 4B , the addition of E . coli to medium from SFV4 ( 3H ) -FFLuc-Egf1 . 0F-infected cells did not increase PO activity as would be expected if Egf1 . 0 was inhibiting PAP activity . Addition of E . coli to medium from SFV4 ( 3H ) -FFLuc-Egf1 . 0R-infected cells also did not elevate PO activity beyond the elevated level of activity that already existed . Taken together , these results showed that SFV4 ( 3H ) -FFLuc-Egf1 . 0F produced Egf1 . 0 in U4 . 4 cells , which is secreted into the medium . Given prior evidence that Egf1 . 0 specifically inhibits the PO cascade by disabling PAP function , these data also strongly suggested that U4 . 4 cell-conditioned medium contains a functional PO cascade , which is activated by SFV or gram-negative bacteria , and which is inhibited by SFV-produced Egf1 . 0 . We next asked whether inhibition of PO activity by Egf1 . 0 could enhance virus spread during an infection . We first used our SFV4 ( 3H ) -FFLuc-Egf1 . 0F or SFV4 ( 3H ) -FFLuc-Egf1 . 0R constructs which allowed us to monitor viral replication and spread through a U4 . 4 cell culture by measuring FFluc activity at 24 h and 48 h p . i . , similar to previously described experiments [50] . Infections were carried out at either a high multiplicity of infection ( MOI 10 ) , where most U4 . 4 cells were infected and little or no further spread of virus could occur , or a low MOI ( 0 . 005 ) where only a small fraction of cells were initially infected and SFV could thereafter disseminate through the medium to infect other cells . Overall GLM revealed differences in FFLuc activity as a function of MOI ( 10 or 0 . 005 ) , construct ( SFV4 ( 3H ) -FFLuc-Egf1 . 0F or SFV4 ( 3H ) -FFLuc-Egf1 . 0R ) and sample time ( 24 h or 48 h p . i . ) ( Fig . 5 A&B , p = 0 . 012 ) . As a result the data from the high and low MOI treatments were examined separately . At an MOI of 10 , cells infected with SFV4 ( 3H ) -FFLuc-Egf1 . 0F or SFV4 ( 3H ) -FFLuc-Egf1 . 0R exhibited similar levels of FFluc activity at 24 h or 48 h p . i . ( p = 0 . 74 ) ( Fig . 5A ) . This outcome was fully consistent with most cells being infected and containing actively replicating SFV , while also indicating that Egf1 . 0 had no effect on intracellular replication activity . As expected , rates of replication also dropped to low levels for both recombinant viruses at 48 h p . i . ( p<0 . 001 ) as they each entered the persistent phase of infection [26] ( Fig . 5A ) . In contrast , we observed a very different outcome when cells were infected at a low MOI where FFluc activity differed between cells infected with SFV4 ( 3H ) -FFLuc-Egf1 . 0F or SFV4 ( 3H ) -FFLuc-Egf1 . 0R . At 24 h p . i , there was no difference in FFLuc activity between cells infected with SFV4 ( 3H ) -FFLuc-Egf1 . 0F and SFV4 ( 3H ) -FFLuc-Egf1 . 0R ( p = 0 . 37 ) , but at 48 h p . i . SFV4 ( 3H ) -FFLuc-Egf1 . 0F showed significantly higher spread and replication rates than SFV4 ( 3H ) -FFLuc-Egf1 . 0R ( p = 0 . 004 ) ( Fig . 5A ) . We reasoned that this difference was also most likely linked to the time required for Egf1 . 0 to be expressed and secreted , and infectious SFV to be produced . Repeating these experiments using SFV4 ( 3F ) -ZsGreen-Egf1 . 0F and SFV4 ( 3F ) -ZsGreen-Egf1 . 0R allowed us to visualize virus spread from one cell to another through the green fluorescing foci that form from ZsGreen presence in viral replication complexes ( ZsGreen is inserted into the C-terminal region of nsP3; Fig . 1C ) . At a high MOI of 10 , most U4 . 4 cells contained green foci at 48 h when infected with SFV4 ( 3F ) -ZsGreen-Egf1 . 0F or SFV4 ( 3F ) -ZsGreen-Egf1 . 0R ( Fig . 5B ) . At a low MOI of 0 . 005 , however , more cells exhibited green foci at 48 h p . i . when infected with SFV4 ( 3F ) -ZsGreen-Egf1 . 0F than SFV4 ( 3F ) -ZsGreen-Egf1 . 0R ( Fig . 5B ) . Overall , these data strongly suggested that activation of the PO cascade by SFV reduced virus spread , whereas Egf1 . 0 enhances virus spread by inhibiting the PO cascade . However , these results did not provide any insight into the identity of the effector molecules produced by the PO cascade that reduce SFV viability and spread . To assess whether the anti-SFV effects of PO were due to the formation of reactive intermediates or other products formed by PO , we infected U4 . 4 cells with a low MOI of SFV4 ( 3H ) -FFLuc-Egf1 . 0R ( MOI 0 . 005 ) and added GSH ( 0 . 5 mM ) , which as noted above likely inhibits melanisation by reducing quinones ( see Fig . 3A ) [54] . Our results showed that GSH significantly increased the spread of SFV4-FFLuc-Egf1 . 0R relative to medium without added GSH ( p<0 . 001 ) . As expected though , the addition of GSH did not change the rate of spread of SFV4 ( 3H ) -FFLuc-Egf1 . 0F ( p = 0 . 139 ) ( Fig . 6A ) . Although vertebrates lack a PO cascade , we also tested whether expression of Egf1 . 0 conferred a replicative advantage to SFV in BHK-21 cells . There was no significant difference in the spread of SFV4 ( 3H ) -FFLuc-Egf1 . 0F and SFV4 ( 3H ) -FFLuc-Egf1 . 0R ( p = 0 . 64 ) following low MOI infection ( 0 . 005 ) , indicating that Egf1 . 0 had no effect on dissemination of SFV in this mammalian cell line ( Fig . 6B ) . Immunologically important antiviral pathways in mosquitoes such as RNAi have been previously implicated in promoting mosquito survival after arbovirus infection . Indeed , inhibition of the RNAi pathway through alphavirus-expressed RNAi inhibitors results in rapid death of virus-infected mosquitoes [55] , [56] . To test whether the PO cascade provides an effective antiviral defence in mosquitoes , we extended our experiments to Ae . aegypti , a mosquito species that is generally relevant as an arbovirus vector , and which has also been shown to transmit SFV in the laboratory [57]–[59] . Prior studies also implicate Ae . aegypti alongside Ae . africanus as a natural vector of SFV [60] . Ae . aegypti were fed bloodmeals containing SFV4 ( 3H ) -FFLuc-Egf1 . 0F , SFV4 ( 3H ) -FFLuc-Egf1 . 0R , or no virus ( mock-infection ) . We then monitored mosquito survival ( cohorts of 22–25 mosquitoes ) following infection in three independent experiments to determine survival rates ( Fig . 7A ) . Since no significant differences were detected within treatments in the three experiments ( p>0 . 05 ) , the samples were pooled for further analysis . Overall , mosquito survival differed significantly among treatments ( Kaplan Meier χ2 = 25 . 37; p<0 . 001 ) . Post Hoc multiple comparison tests revealed no significant difference in survival rates between the mock-infected control and mosquitoes infected with SFV4 ( 3H ) -FFLuc-Egf1 . 0R ( p = 0 . 98 ) . In contrast , mosquitoes infected with SFV4 ( 3H ) -FFLuc-Egf1 . 0F exhibited higher mortality than mock-infected mosquitoes ( p<0 . 001 ) or mosquitoes infected with SFV4 ( 3H ) -FFLuc-Egf1 . 0R ( p<0 . 001 ) . In conclusion , inhibition of the PO cascade decreased survival following infection of mosquitoes with SFV . To assess whether the reduced survival of SFV4 ( 3H ) -FFLuc-Egf1 . 0F-infected mosquitoes was associated with enhanced viral replication , mosquitoes ( cohorts of 10 ) were fed bloodmeals containing SFV4 ( 3H ) -FFLuc-Egf1 . 0F or SFV4 ( 3H ) -FFLuc-Egf1 . 0R . Total RNA was then extracted at 3 days post-bloodmeal followed by qPCR analysis to determine SFV genome copy number per individual . This time point was chosen because it just precedes quantifiable differences in mosquito survival , thus avoiding mortality-induced bias . Our results showed that viral genome copy numbers were higher in mosquitoes fed SFV4 ( 3H ) -FFLuc-Egf1 . 0F than in mosquitoes fed SFV4 ( 3H ) -FFLuc-Egf1 . 0R ( Mann-Whitney test , p = 0 . 04 ) ( Fig . 7B ) . Interestingly , infection rates were also higher when mosquitoes were infected with SFV4 ( 3H ) -FFLuc-Egf1 . 0F than SFV4 ( 3H ) -FFLuc-Egf1 . 0R ( Fig . 7B ) . This suggests that Egf1 . 0-mediated inhibition of the PO cascade is also potentially important in establishment of an infection . Higher infection rates have been previously observed with alphaviruses expressing RNAi inhibitors or following silencing of antiviral RNAi genes during mosquito infection [55] , [61] .
Comparative genome analysis of different mosquito species reveals a noticeable expansion of PPO genes relative to other insects . For example , An . gambiae encodes nine PPOs while Ae . aegypti encodes ten . Expansion in the numbers of clip-domain serine proteases and serpins has also occurred [47] . The recent sequencing of the Culex quinquefasciatus genome reveals nine PPOs and thirty-two serpins , compared to originally twenty-three serpins in Ae . aegypti though recent studies and Vectorbase increase this number to twenty-six [45]–[47] , [62] . Compared to other insects including An . gambiae , relatively little is known about regulation of the PO cascade in mosquitoes although recent studies in Ae . aegypti identify some of the processes involved [62] . Interestingly the cSP family also contains proteins with non-catalytic protease domain , so-called clip domain serine protease homologs ( cSPHs ) , and both cSPs and cSPHs ( as co-factors ) are involved in melanisation reactions . In Ae . aegypti and An . gambiae , cSPs and cSPHs are divided into five subfamilies called CLIP A , B , C , D and E [47] . Mainly CLIP B subfamily proteases are known ( or suggested ) to activate PPOs . Melanisation in Ae . aegypti was found to be regulated by protease inhibitor Serpins-1 , -2 and -3 which regulate different cSPs [62] . In that study , two separate pathways leading to PPO cleavage were described; a first pathway linking Serpin-1 to ( CLIP B subfamily members ) Immune melanisation protease ( IMP ) -1 and IMP-2 , and a second pathway linking Serpin-2 to Tissue melanisation protease ( TMP ) and IMP-1 . Depletion of Serpin-2 leads to tissue melanisation and appears to be involved in activation of the Toll pathway , while depletion of Serpin-1 leads to immune responses against the parasite Plasmodium gallinaceum [62] . Other regulators of melanisation in Ae . aegypti such as CLSP2 ( a modular protein consisting of C-type lectin and elastase-like domains ) have been described [63] . Transcription of at least some PPO genes in Ae . aegypti is also regulated by the Toll pathway [44] , thus linking different branches of the immune response . Based on the antiviral activities of insect haemolymph [35] , [36] , we hypothesized that immune reactions induced by PO extend to arboviral infection of mosquitoes . Our experiments collectively indicate that U4 . 4 cell-conditioned medium contains a functional PO cascade . Our detection of a small proportion of U4 . 4 cells that melanise after fixation and incubation with dopamine further suggest these cells are likely source of the PO activity detected in conditioned medium . Notably , these cells morphologically resemble oenocytoids , which also comprise less than 1% of the circulating haemocyte population in mosquitoes like Ae . aegypti and An . gambiae [52] , [53] as well as many other insects , yet are also the primary source of PO in plasma [51] . Ongoing analysis of the U4 . 4 cell transcriptome indicates that PPO orthologs are expressed although at this time it remains unclear whether expression is restricted to the large , rounded cells that stain after incubation with dopamine or is more global . Regardless of these uncertainties , our results strongly indicate that medium conditioned by U4 . 4 cells contains a functional PO cascade that is activated by exposure to SFV or E . coli , and is inhibited by Egf1 . 0 . Prior studies in Lepidoptera show that MdBV also activates the PO cascade [39] while bacterial cell wall components like peptidoglycan are well known activators of the PO cascade in a diversity of insects [28]–[32] . We think it likely that activation of the PO cascade in U4 . 4 cell-conditioned medium by E . coli similarly involves binding of bacterial cell wall components by currently unknown humoral pattern recognition receptors . In contrast , it remains unclear what features of SFV induce a similar increase in PO activity . One possibility is that glycoproteins of the viral envelope function as pathogen-associated molecular patterns . The lectin pathway of vertebrate complement is known to be activated by pattern recognition receptors such as mannose–binding lectin that binds mannose-containing glycoproteins [64] . Several lectins have also been described as candidate pattern recognition receptors in insects [65] . While additional studies will be needed to identify how SFV is being recognised in U4 . 4 cell conditioned medium , our results collectively indicate that activation of the PO cascade and the associated increase in melanisation that occurs reduces the spread of SFV among the U4 . 4 cell population . Reduced survival of Ae . aegypti combined with enhanced virus replication when mosquitoes are infected by SFV expressing Egf1 . 0 also suggests the PO cascade is important in limiting arbovirus spread in mosquitoes . Interestingly , gene expression data obtained following ONNV infection of An . gambiae indirectly suggest that ONNV infection may have led to activation of melanisation pathways in the early stages of infection [25] , which highlights the importance of this study . On the other hand , the effects of PO cascade inhibition on mosquito survival are most apparent at later stages post-bloodmeal compared to experiments with alphaviruses expressing RNAi inhibitors [55] , [56] . This suggests that inhibition of the PO cascade takes more time than disruption of RNAi or that this response is less powerful than RNAi in defence against arboviruses . However these experiments show that viral expression of an inhibitor is a viable strategy for inhibiting insect immune responses . Expression from the subgenomic promoter of recombinant SFV results in high levels of Egf1 . 0 and strong inhibitory activity , which may be difficult to achieve by just silencing a target gene through RNAi . Thus , an important goal for future studies will be to assess how inhibition of the PO cascade affects the spread of SFV in different tissues of mosquitoes as well as how the PO cascade may interact with other immune defence responses including the RNAi pathway . Previous experiments where PPO I was silenced in Ar . subalbatus by expression of PPO I dsRNA using recombinant SINV showed increased titres of SINV [41] . Our results take this observation further by showing that activation of the PO cascade reduces SFV viability in vitro and that Egf1 . 0-mediated inhibition enhances virus replication and spread both in vitro and in vivo . However it is not entirely clear what products generated by the PO cascade are responsible for the antiviral activity against SFV we observe . Given the antiviral properties of 5 , 6-dihydroxyindole against AcMNPV [38] , and the ability of GSH to inhibit anti-SFV activity in conditioned U4 . 4 cell culture medium suggests that the reactive intermediates generated by PO are antiviral . However , it is also possible the PO cascade might reduce arbovirus spread from the initial site of infection through the production of melanin and/or activation of other signaling pathways like Toll or IMD that also have roles in antiviral defence . To distinguish between these possibilities will require studies that directly assess the effects of 5 , 6-dihydroxyindole , melanin , or other compounds on the integrity of SFV virions [38] . Any damage to structural proteins could result in failure to bind receptors and/or enter cells . Questions also remain over the tissue specificity of PO activity . Our in vitro and in vivo data overall suggest products of the PO cascade may be antiviral because they reduce the viability of virions in the haemocoel . However other research describes melanisation reactions in the extracellular space between An . gambiae midgut cells following Plasmodium berghei infection [66] . Thus inhibition of PO activity by Egf1 . 0 could enhance SFV replication and spread in or around midgut tissues . Finally , our study does not directly address the question of whether wild-type SFV can potentially inhibit or evade the PO response . Given though that SFV spread is enhanced by expression of a powerful inhibitor like Egf1 . 0 , we suspect the ability of wild-type SFV to inhibit or evade host-associated PO defence response is likely weak . Alphaviruses are not inhibited by the Toll pathway in insects [26] , [27] , but links between the PO cascade and Toll signalling in Ae . aegypti could , as noted above , play a role in antiviral defence . Infection of Ae . aegypti with DENV-2 results in differential regulation of serpins although it is not possible yet to speculate whether these have a role in controlling PPO activation [24] . It does however suggest that protease-mediated antiviral defences extend to other arbovirus families . Intriguingly , it has been shown that infection of insects with strains of endosymbyotic Wolbachia bacteria , which can inhibit arbovirus infection by yet unknown mechanisms [67] , may upregulate melanisation or genes involved in melanisation [68] , [69] . Thus , our findings also may explain in part the antiviral properties mediated by Wolbachia infection . Future work will determine whether these findings also extend to viruses from other arbovirus families .
Under UK Home Office legislation insects such as mosquitoes are not considered animals . No animals were used in the course of these experiments . Defibrinated sheep blood was obtained from TCS Biosciences ( Buckingham , United Kingdom ) . Ae . albopictus-derived U4 . 4 mosquito cells were grown at 28°C in L-15 medium with 10% fetal calf serum and 10% tryptose phosphate broth . BHK-21 cells were grown in Glasgow minimum essential medium ( GMEM ) with 10% newborn calf serum and 10% tryptose phosphate broth at 37°C in a 5% CO2 atmosphere . Amplification of SFV ( strain SFV4 ) and recombinant clones derived from SFV4 in BHK-21 cells ( grown as described above ) , together with titration of plaque forming units ( PFU ) in BHK-21 cells have been previously described [50] . SFV and derived clones were purified from supernatant as described and resuspended in TNE ( Tris-NaCl-EDTA ) buffer [70] . Viruses were diluted in PBSA ( PBS with 0 . 75% bovine serum albumin ) and added to U4 . 4 cells at room temperature for 1 h followed by washing twice to remove any unbound particles; cells were grown at 28°C following infection . Details of reporter viruses ( Fig . 1 ) can be obtained from the authors . The pCMV-SFV4 backbone for production of SFV4 has been previously described [71] . A second subgenomic promoter was placed behind the SFV4 structural open reading frame for construction of viruses with duplicated subgenomic promoters [72] . This second subgenomic promoter is of the T37/17 type ( consisting of a sequence 37 nucleotides upstream and 17 nucleotides downstream of the original transcription start-site of the SFV subgenomic mRNA ) . The ZsGreen marker was inserted into the C-terminal region of nsP3 via a XhoI site naturally occuring in the genomic sequence ( leading to expression of nsP3 containing ZsGreen ) , while Firefly luciferase ( FFLuc ) was inserted between duplicated nsP2 cleavage sites at the nsP3/4 junction as a cleavable reporter , using strategies previously shown [73] . The full egf1 . 0 coding sequence ( including signal peptide ) derived from MdBV was placed under control of the second subgenomic promoter in sense or antisense ( as negative control ) orientation . Cells on glass slides were fixed in 10% formaldehyde ( Fisher Chemicals ) for 45 min and washed in PBS three times . Cells were treated with TO-PRO 3 ( Invitrogen ) ( 1∶1000 ) in dH2O for 10 min and washed with PBS three times . Slides were mounted using Vectashield mounting medium ( Vector Laboratories ) . Cells and fluorescence were then visualised by confocal microscopy . At 48 h p . i . , U4 . 4 cells infected with SFV ( MOI of 10 ) or control uninfected cells were lysed in Laemmli buffer . Conditioned cell culture medium was concentrated on Millipore Centricon-Plus 70 Centrifugal Filter Units prior to addition of Laemmli buffer . Recombinant Egf1 . 0 produced as previously described [39] served as a positive control . Samples were run on a 4–20% Tris-Gycine PAGEr precast gels ( Lonza ) , and blotted onto Immobilon-P PVDF membranes ( Millipore ) . SFV infection was detected using a rabbit anti-nsP3 antibody ( 1∶20000 ) , while Egf1 . 0 was detected using a rabbit anti-Egf1 . 0 antibody ( 1∶35000 ) [39] , [74] . Primary antibodies were detected using a horseradish peroxidase ( HRP ) -conjugated goat-anti rabbit secondary antibody ( Jackson ImmunoResearch ) ( 1∶45000 ) , followed by visualisation using the ECL Advance Western Blotting Kit ( Amersham ) and a GeneGnome bioimaging system ( Syngene ) . Aedes aegypti ( Liverpool red eye strain optimised for filarial growth ) were kindly provided by R . M . Maizels and Y . Harcus ( Institute of Immunology and Infection Research , University of Edinburgh ) . Mosquitoes were kept at 27°C , in 85% humidity and with a 16 h light: 8 h dark photoperiod . Larvae were fed on a standard yeast diet , while adults were fed on 10% fructose continuously . Female adults were 4 to 5 days old when allowed to feed on defibrinated sheep blood ( TCS Biosciences ) containing 5×107 PFU of virus per ml of blood supplemented with 4 mM ATP . Mosquitoes were starved for 24 h before feeding and the bloodmeal ( at 37°C ) provided by a Hemotek membrane feeder ( Discovery Workshops , Accrington , UK ) for 2 h . Mosquitoes that fed were removed and maintained at standard conditions with fructose . Conditioned cell culture medium from Ae . albopictus-derived U4 . 4 mosquito cells was harvested 48 h post-cell seeding ( 4×106 cells in a 75 cm2 flask ) and centrifuged at 2000 rpm for 5 min in order to eliminate residual cells . Approximately 5 µl of a pelleted E . coli JM109 culture ( New England Biolabs ) or 3 . 5×107 PFU of SFV were added to 1 ml of cell culture medium and incubated for 10 min at room temperature . The mixture was then centrifuged at 3000 rpm for 10 min at 4°C in order to remove debris . Following this , PO activity assays were carried out in 96-well plates with 100 ul of 50 mM Sodium Phosphate buffer ( pH 6 . 5 ) containing 2 mM dopamine added to 20 µl of cell culture medium [75] . PO activity was monitored by measuring absorbance at 490 nm using a plate reader ( Dynatech MR5000 ) over a period of 30 min . It should be noted that this approach predominantly detects dopachrome and/or dopaminechrome rather than melanin itself . One unit of PO activity was defined as ΔA490 = 0 . 001 after 30 minutes , similar to previously described [39] , [76] , [77] . For each experimental condition , PO activities from 10 reactions were determined . Intracellular PO activity was assessed by first fixing U4 . 4 cells in glacial methanol . After rinsing in PBS , fixed cells were then incubated for 1 h in phosphate buffer plus 2 mM dopamine . Following cell lysis in Passive Lysis Buffer ( Promega ) , luciferase activities were determined by using a Dual Luciferase assay kit ( Promega ) on a GloMax 20/20 luminometer . SFV4 genome copy number was quantified as previously described [26] . Briefly , total RNA was isolated from single Ae . aegypti using Trizol ( Invitrogen ) . RNA quality and quantity were assessed with a NanoDrop 1000 spectrophotometer ( Fisher Scientific ) . A total of 0 . 5 µg of total RNA per mosquito was reverse transcribed using Superscript III kit ( Invitrogen ) and oligo-dT primer , and reactions were analysed in triplicate . The reaction mix contained 0 . 8 µM of each primer , FastStart SYBR Green Master x1 ( Roche ) , and 2 µl of template . Tubes were heated to 94°C for 5 min , and then cycled through 94°C for 20 sec , 62°C for 20 sec , and 72°C for 20 sec for 40 cycles on a RotorGene 3000 instrument ( Corbett Research ) . Sequences of the primers were as indicated: 5′ -GCAAGAGGCAAACGAACAGA-3′ ( SFV-nsP3-for ) and 5′ –GGGAAAAGATGAGCAAACCA-3′ ( SFV-nsP3-rev ) . The number of SFV genome copies was calculated using a standard curve generated with the plasmid pSFV1 . Data with 2 groups were analysed using either t-test or Mann Whitney tests , depending on the structure of the data . Data with more than 2 groups was analysed using General Linear Models ( GLM ) . All GLMs were initially performed including all fixed effects and their interactions . Any post hoc tests were adjusted for multiple comparisons using the Bonferroni correction . Survival analysis was performed on cohorts of 22–25 mosquitoes . Differences between survivorship curves were tested using Kaplan-Meier estimator and the log-rank test . Where appropriate , multiple comparisons were performed and the Bonferroni correction was applied . All analyses were conducted using SAS v9 . 1 . 3 ( SAS Institute Inc . , Cary , NC , USA ) . Diagnostics were performed and plots of residuals were examined , confirming the goodness-of-fit of all models . Prior to analysis , it was specified that results with p<0 . 05 would be reported as exhibiting formal statistical significance . | Arboviruses are transmitted to vertebrates by arthropod vectors such as mosquitoes . Infection of mosquitoes with arboviruses activates immune defence responses including the RNA interference pathway . Another component of the insect immune system is the phenoloxidase ( PO ) cascade , which produces melanin that accumulates at wound sites and around invading microorganisms . Some pathogen-associated pattern recognition molecules are known to activate the PO cascade , which results in the proteolytic processing of inactive prophenoloxidase ( PPO ) to PO . PO then catalyses the formation of compounds that ultimately form melanin . Some of these products are also known to have anti-microbial properties but whether activation of the PO cascade provides any defence against arboviruses is unclear . Using the arbovirus , Semliki Forest virus , we show that this virus activates the PO cascade . By using recombinant Semliki Forest virus expressing an inhibitor of the PO cascade , we also demonstrate that this pathway inhibits virus spread in cell culture . Moreover , inhibition of this pathway leads to higher virus genome levels and higher mortality of infected mosquitoes . In conclusion , Semliki Forest virus activates the PO cascade which exhibits antiviral activity and can be added to the list of mosquito anti-viral defence mechanisms . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"mosquitoes",
"immunity",
"vector",
"biology",
"innate",
"immunity",
"virology",
"biology",
"microbiology"
] | 2012 | Phenoloxidase Activity Acts as a Mosquito Innate Immune Response against Infection with Semliki Forest Virus |
ClinicalTrials . gov 2013-0047 .
Protozoan parasites of the genus Leishmania are the causative agents of leishmaniasis , a neglected disease with worldwide distribution . With 350 million people at risk and 300 000 estimated cases , the visceral form of leishmaniasis ( VL ) can be fatal if not treated . The symptoms could vary from fever , weight loss and anorexia to cachexia , hepato/splenomegaly , lymphadenopathy , renal injuries , hemorrhages [1 , 2] . The perpetuation of these parasite populations relies on the subversion of their two hosts: blood-feeding insects and mammals . The establishment of parasites in tissues , including blood , liver , bone marrow , spleen , skin , depends on their capacity to weaken their hosts by parasite-specific and non-specific mechanisms [1 , 3] . The thorough study of the events occurring in the mammalian hosts that are relevant for infectivity and persistence may contribute to a better understanding of parasite dissemination mechanisms and pathogenesis , leading to novel therapeutic strategies . Few in vivo models have been proposed to study VL , with dogs and hamsters being the most reliable [4–6] , since the experimentally infected animals exhibit similar clinicopathological aspects as VL in humans . However , restrictions regarding the number of animals , the prolonged time of infection needed to observe clinical signs and to obtain high parasite loads , the high costs , besides ethical issues , make necessary the search for other suitable alternatives . Regarding the experimental models of VL in mice , the studies depict a premature control of the parasite load in the liver and a delayed parasite burden in the spleen [7–9] , rendering difficult a global long-term evaluation of parasite persistence . All these data indicate a critical need to set up an efficient and functional experimental animal model for VL using virulent parasites . Such model should allow the real-time evaluation of the parasite load and the infectious process in living animals , which must be sensitive enough to assess their pathogenesis and persistence in mammals . We and others have previously demonstrated that the use of imaging is exceptionally helpful for visualizing the dynamic processes that take place in tissues and examining host’s cell-parasite interaction events . So far , different species of bioluminescent or red-fluorescent Leishmania parasites have been constructed and used for in vivo imaging [10–19] . Recently , imaging VL in real time with the golden hamster model has been proposed using bioluminescent and virulent Leishmania donovani [20 , 21] , however , few imaging studies have been proposed to analyze the dynamic of the VL infectious process and parasite virulence [8 , 11 , 22] . Therefore , to overcome the limits of the mouse models of VL , with low number of parasites and low persistence , we used a bioluminescent and fluorescent virulent strain of L . donovani , that would easily allow their follow up both in vivo and in vitro , and which should be able to induce a persistent infection in BALB/c mice . We took advantage of highly performant imaging approaches to follow the parasite load in the liver and in the spleen of infected mice together with the evaluation of cytokine gene expression in these organs . The ability of attenuated bioluminescent/fluorescent L . donovani , generated after serial long-term in vitro culturing , for implantation and persistence in the target organs and whether the persistent parasites are still able to promote infection was also assessed . Our study provides a basis for determining the parasite implantation and dissemination during the disease progression in vivo in a very useful functional model , allowing to assess the role of candidate targets in parasites viability and virulence in order to evaluate their potential putative druggability .
Female golden Syrian hamsters ( Mesocricetus auratus RjHan:AURA ) weighing 60–70 g and six-week-old female BALB/c ByJRj mice were purchased from Janvier Laboratories ( Le Genest-Saint-Isle , France ) , and handled under specific pathogen-free conditions , according to the institutional guidelines of the Central Animal Facility at Pasteur Institute ( Paris , France ) . A fully virulent Leishmania donovani strain ( Ld1S/MHOM/SD/00-strain 1S ) obtained from infected hamster spleen was used in this study . The culture medium used was the M199 supplemented with 25 mM of HEPES pH6 . 9; 2mM of glutamine; 0 . 1 of mM adenosine; 100 μg/mL of penicillin/streptomycin; 10 μg/mL of folic acid; 5 μg/mL of hemin; 1 μg/mL of biopterin; 10% heat-inactivated fetal calf serum ( 29-101-54 , MP Biomedicals , Santa Ana , CA , USA ) ; 7 . 5% of NaHCO3 and 1x RPMI 1640 vitamin mix . Infected hamsters and mice were euthanized , the spleens were collected in gentleMACS M Tubes ( 130-096-335 , Miltenyi Biotec , Bergisch Gladbach , Germany ) containing 5 mL of supplemented M199 medium and homogenized using the gentleMACS dissociator ( Miltenyi Biotec ) . After centrifugation ( 800 rpm for 5 min at 20°C ) , 25 mg of saponin ( S2149 , Sigma-Aldrich , Saint-Quentin Fallavier , France ) was added to the supernatant , re-centrifuged ( 3500 rpm for 10 min at 20°C ) , and washed with M199 medium . The solution was passed several times through a 27G needle connected to a syringe to improve amastigotes release . The number of amastigotes was determined and they were kept at 26°C in supplemented M199 medium ( starting from 1x105 amastigotes/mL ) to promote differentiation into promastigotes and expansion . Short-term cultures of L . donovani promastigotes were obtained from splenic amastigotes . The parasites were expanded until stationary phase cultures ( +2 days after the end of the exponential growth phase ) . Parasites were centrifuged at 2500 rpm for 5 min at 20°C , and less dense parasites , morphologically similar to metacyclic forms [23] were recovered from the supernatant by centrifugation at 4000 rpm for 10 min . Mice were infected with a standardized dose of 5x107 enriched ‘metacyclic’ promastigotes in 150 μL of PBS by intraperitoneal route , and hamsters were infected with a standardized dose of 1x108 enriched ‘metacyclic’ promastigotes in 200 μL of PBS by intracardiac route . We performed a two-step transfection to generate both bioluminescent and fluorescent parasites . For the first transfection , the luciferase coding region was cloned into the Leishmania expression vector pF4X1 . HYG ( Jenabioscience , Jena , Germany ) [11] . A total amount of 5x107 log-phase wild-type promastigotes was mixed with 100 ng of the linearized plasmid and to 99 μL of transfection mix ( human T-cell nucleofector , VPA-1002 , Lonza , Basel Switzerland ) . The mix was added to the AMAXA Nucleofector 2b Device ( Lonza ) and the transfection was performed using the pre-defined X-014 program according to the manufacturer’s instructions . The solution was then plated on a M199 solid medium supplemented with 150 μg/mL of hygromycin and cultivated at 26°C up to 14 days . Once the colonies were identified , they were collected into flasks containing M199 medium and kept at 26°C to promote promastigote expansion until infectious metacyclic promastigotes from stationary phase cultures . One hamster was infected with 1x108 parasites and we followed the infection using in vivo bioluminescence imaging . The weight gain of the hamster was followed up weekly . Three months after infection , the splenic amastigotes ( Ld1S_luci ) were collected and transformed in promastigotes for a novel transfection . The second transfection was performed as described above , but using the Leishmania expression vector pF4X1 . SAT ( Jenabioscience ) containing the E2-crimson coding region , and 50 μg/mL of nourseothricin as selection drug . After transfections , the luciferase and E2-crimson genes were integrated into 18 s rRNA locus of the nuclear DNA of the parasites [11] . One hamster was infected with the transfected parasites , splenic amastigotes were then collected , transformed in promastigotes and expanded as the first passage ( P1 ) of the parasite “Ld1S_luci_E2-crimson” . At different time points following Leishmania inoculation , D-luciferin ( 122799 , PerkinElmer , Walthan , MA , USA ) , the luciferase substrate , was injected intraperitoneally in the mice with a dose of 150 mg/kg; the animals were anaesthetized in a 2 . 5% isoflurane atmosphere ( Aerane , Baxter SA , Maurepas , France ) and placed in the imaging chamber of the IVIS Spectrum ( PerkinElmer ) . 2D-bioluminescence images were captured and total photon emission , expressed in photons/s , was determined in a region of interest ( ROI: liver or spleen ) using the Living Image software ( PerkinElmer ) . The initial concentration of the in vitro cultures was 5x105 parasites/mL . The cultures were counted on a daily-basis to obtain the growth curve and bioluminescence and fluorescence values were recorded . For concomitant acquisition of bioluminescence and fluorescence data , parasites were collected from the culture flasks , washed in PBS and 100 μL containing 1x105 parasites were added to a 96-wells white plate ( 353377 , Corning , Wiesbaden , Germany ) . 60 μg of D-luciferin was then added to each well . After 10 minutes of incubation , the bioluminescence and fluorescence values were consecutively determined in a TECAN luminometer ( Infinity F200 Pro , TECAN , Lyon , France ) , using an integration time of 1000 ms at 26°C for bioluminescence , and excitation of 590 nm and emission of 670 nm for fluorescence . P1 promastigotes were injected in twelve BALB/c mice and the infection was followed using in vivo bioluminescence imaging , as described above , at days 7 , 30 , 60 and 90 post-infection ( p . i . ) . At each time point , three infected mice were euthanized and the liver and spleen were collected for determination of the parasite load by RT-qPCR and for histology . Three uninfected mice were used as control . A total of 50 mice divided in four independent infections were used . For RT-qPCR , livers and spleens were removed , disrupted and lysed in 5 and 3 mL of Trizol ( Invitrogen , Paisley , UK ) , respectively , using gentleMACS M Tubes and the gentleMACS dissociator ( Miltenyi Biotec ) . RNA isolation was performed on the clear upper aqueous layer with the RNeasy Plus Mini kit ( 74134 , Qiagen , Courtaboeuf , France ) according the manufacturer’s instructions . Evaluation of RNA quality was performed by optical density measurement using the NanoDrop spectrophotometer ( Thermo Scientific , Wilmington , DE , USA ) and their integrity were assessed using 2100 Bioanalyzer ( Agilent Technologies , Santa Clara , CA , USA ) , which allowed the calculation of an RNA integrity ( RNAi ) number [24] . Total RNAs were reverse transcribed to first strand cDNA using random hexamers ( 11034731001 , Roche Diagnostics , Meylan , France ) , a set of dNTPs ( 10297–018 , Invitrogen ) and Moloney Murine Leukemia Virus-Reverse Transcriptase ( MMLV-RT , 28025013 , Invitrogen ) . The Leishmania gene target ( ssrRNA ) was selected for quantifying the number of parasites as previously described on murine cDNAs [25] . qPCR was performed in a final volume of 11 μL in 384-well PCR plates using a thermocycler ( 7900HT fast real time PCR system , Applied Biosystems , Villebon-sur-Yvette France ) . Briefly , 2 μL of cDNA ( 20ng ) was added to 9 μL of a master mix containing 5 μl of QuantiTect SYBR Green Kit ( Qiagen ) and 4 μL of nuclease-free water with 1 μM of each primer ( Table 1 ) . The amplification conditions were as follows: 95°C for 15 min , 45 cycles of 95°C for 10 s , 54°C for 25 s and 72°C for 30 s; followed by a melt curve , from 60°C to 95°C . To quantify the parasite load in each organ , serial 10-fold dilutions of parasites ( from 108 to 103 ) were added either to livers or spleens recovered from naïve mice , in order to mimicry possible errors and interferences during parasites RNA extraction from the infected organs . Total RNAs were then extracted and processed for RT-qPCR as described above . A linear regression for each standard curve was determined ( quantification of Leishmania parasites against the relative expression of ssrRNA values ) . To visualize the parasites in the tissues , liver and spleen were collected and embedded in paraffin . Four μm–thick sections were stained with hematoxylin and eosin ( HE ) and Leishmania parasites were immunolabelled on a Bond III immunostainer ( Leica , Germany ) using a rabbit polyclonal antibody raised against L . donovani ( 1:3500 ) . The gene expression of selected cytokines was quantified by RT-qPCR to evaluate host inflammatory mediators’ transcripts in the liver and spleen ( Table 1 ) as described above . For normalization calculations , the pair of control genes ldha ( lactate dehydrogenase A ) /l19 ( ribosomal protein L19 ) and tbp ( TATA box-binding protein ) /rpIIe ( RNA polymerase E ) were selected as the most stable reference genes for the liver and spleen of mice , respectively . We also evaluated the presence of infected cells in the liver and in the spleen at early time points post-infection ( 7 days p . i . ) by flow cytometry . The organs were collected and dissociated in 5 mL of PBS containing 2mM of EDTA and 0 . 5% ( v/v ) of fetal calf serum ( MP Biomedicals ) using gentleMACS M Tubes and the gentleMACS dissociator ( Miltenyi Biotec ) . The cell suspension was centrifuged at 300 × g for 10 min at 4°C and the pellet was resuspended in 2 mL of PBS-EDTA . 200 μL of the cell suspension was placed in a 96-wells plate , centrifuged at 300 × g for 5 min at 4°C . The supernatant was removed and 50 μL of an anti-CD45 antibody ( clone 30-F11 ) ( Becton Dickinson ) was added and incubated for 20 min at 4°C . The cells were washed in PBS and the red blood cells were lysed by washing three times with ACK ( Ammonium-Chloride-Potassium ) Lysing Buffer . The plate was centrifuged and the cells were resuspended in 4% ( w/v ) of paraformaldehyde . Flow cytometry was performed with the flow cytometer Fortessa-X20 ( Becton Dickinson ) and DIVA Flow Cytometry acquisition software and was analyzed with FlowJo software ( Tree Star , Inc ) . Promastigotes freshly derived from splenic amastigotes kept in M199 medium were subjected to a weekly in vitro passage , with the initial concentration of 5x105 parasites/mL . A total of 41 weekly passages was performed . Parasites at passages P5 ( five weeks in vitro ) , P11 ( eleven weeks in vitro ) , P21 ( twenty-one weeks in vitro ) and P41 ( forty-one weeks in vitro ) were selected , and data regarding bioluminescence , fluorescence and doubling time were recorded . Parasites obtained at passages P5 , P11 , P21 and P41 were expanded until stationary phase for promastigotes enrichment as previously described , and injected into BALB/c mice ( seven animals/passage ) . In vivo analyses of the infected mice were performed during 90 days as described above . Amastigotes from the spleen of mice infected with P1 and P21 parasites were collected at day 90 p . i . as described above . Enriched ‘metacyclic’ promastigotes derived from mice P1 ( named mP1 ) and from mice P21 ( named mP21 ) were then injected into seven naïve BALB/c mice , which were followed up during 90 days p . i . . In vivo analyses of the infected mice were performed as described above . The comparison between the groups was performed by Kurskal-Wallis followed by Dunn’s multiple comparison test , or by Mann Whitney test . The area under the curve ( AUC ) was used to compare the infection kinetics . Values of P<0 . 05 were considered statistically significant . Data were expressed as the median and the interquartile range . For qPCR , the variations in the gene-expression were calculated as the n-fold change in expression in the organs of the infected mice compared to the organs of the uninfected ones . The relative expression software tool ( REST-MCS ) was used to determine group wise comparison and statistical analysis of relative expression results in real-time PCR [26] . All animal experiments were conducted in accordance with the project registered under number 2013–0047 and approved by the Institut Pasteur Ethics Committee ( CETEA ) on November 12 , 2014 in accordance to the European legislation/guidelines EU 2010/63 .
The double transfection approach generated bioluminescent and fluorescent Ld1S parasites as virulent as Ld1S wild-type ones . Indeed , the virulence of the transfected parasites was verified at each stage of the process . Hamsters infected with Ld1S_luci presented the same characteristics than hamsters infected with Ld1S wild-type parasites , such as a regular weight gain until 30 days post-infection , with a subsequent progressive weight loss . By measuring the bioluminescent signals in the liver and the spleen of the infected hamster , we noticed a gradual increase of the parasite load in both organs ( Fig 1A ) . With the second transfection , we inserted the E2-crimson gene aiming to generate fluorescent parasites . Even though bioluminescence is more adapted to in vivo analysis , due to its high sensitivity and low background signals , fluorescence is helpful during in vitro and ex vivo experiments [27 , 28] . Since the fluorescent protein is constitutively expressed by the parasites , we could see positive results as soon as the colonies of parasites were identified in the solid medium during the cloning stage after the transfection ( Fig 1B ) . The kinetics of bioluminescence of infected hamsters with Ld1S_luci_E2-crimson remained identical to those infected with Ld1S_luci ( S1 Fig ) , corroborating the maintenance of the parasites’ infectivity . At day 90 p . i . , the time point of amastigotes collection , successive in vivo and ex vivo imaging of the liver and spleen showed concomitant positive bioluminescent and fluorescent signals in these organs ( Fig 1B ) . Ld1S_luci_E2-crimson amastigotes were visualized in infected cells ( Fig 1B ) , and they were collected , purified and differentiated into promastigotes during the first passage in culture , and called P1 . The behavior of the P1 Ld1S_luci_E2-crimson promastigotes was similar to the wild-type promastigotes , with a doubling time of 22 hours and a stationary phase concentration of approximately 1x108 parasites/mL at day 7 . The bioluminescence and fluorescence features of the parasites evidenced stage-specific changes throughout the period in culture ( nine days ) . Bioluminescence values rose sharply during the exponential phase of the growth curve , with a peak at day 4 , and then they decreased sharply as well , with ‘metacyclic’ promastigotes ( day 9 ) presenting the smallest values . On the other hand , variations in fluorescence intensity were less pronounced , with a slight peak at day 3 , and constant values after day 5 ( Fig 1C ) . We analyzed P1 Ld1S_luci_E2-crimson implantation and persistence in the liver and in the spleen by bioluminescent in vivo imaging , RT-qPCR and by flow cytometry ( Fig 2 ) ; the immune response was assessed by cytokine gene expression analysis ( RT-qPCR ) and lesions in these organs were evaluated by histopathology ( Fig 3 ) . In both liver and spleen , the first bioluminescent signals were detected as soon as 7 days p . i . , then rose slightly to reach a peak at day 30 p . i . ( Fig 2A–2C ) . After this , the bioluminescence signal declined slightly up to 90 days pi . To determine the absolute number of Leishmania in the whole organs , parasite transcripts were quantified by RT-qPCR . L . donovani transcript abundance and bioluminescence kinetics recorded from the liver and the spleen had a similar profile , indicating a good correspondence between RT-qPCR and bioluminescence ( Fig 2B and 2C ) . These versatile parasites were sensitive enough to be detected as soon as 7 days p . i . also by flow cytometry ( Fig 2D–2G ) . In infected mice livers , a median value of 0 . 96% of CD45+ cells containing E2-crimson fluorescence ( parasitized cells ) was observed , significantly higher ( P>0 . 0001 ) than the uninfected control ( Fig 2E ) . Similarly , the median value of 0 . 03% of CD45+E2-crimson+ cells were detected in the spleen of infected mice , significantly higher ( P>0 . 0023 ) than the uninfected control ( Fig 2G ) . Finally , in order to attest Ld1S_luci_E2-crimson virulence , we evaluate the immune response in infected mice by determining the gene expression of selected pro- and anti-inflammatory cytokines in the liver and in the spleen , compared to uninfected animals ( Fig 3A and 3B ) . IFN-γ was up-regulated in both organs , throughout the study , from 7 to 90 days p . i . The response in the liver seemed to be more intense than in the spleen , with up-regulation of IL-1β , IL-2 and TNF-α , along with high levels of IL-10 . The spleen , on the other hand , presented a delayed and discreet overexpression of IL-1β and TNF-α . The weight of both organs considerably increased during the infection ( Fig 3C ) , and at histology at 90 days p . i . , the liver presented typical granulomas and parasites ( Fig 3D ) whereas the spleen presented remarkable alterations in its architecture , with important amounts of mononuclear cells infiltration and parasites ( Fig 3E ) . Serial successive in vitro passages promote remarkable changes in promastigotes behavior ( Fig 4A and 4B ) . Regarding their growth , whereas P1 and P5 presented a rather stable doubling time of around 22 hours , the speed of growth increased sharply over the passages , with a doubling time as low as 9 . 8 hours for P41 promastigotes ( Fig 4A ) . Along with the speed of parasite growth measured on 1x105 log-phase promastigotes , both bioluminescence and fluorescence values remarkably increased over the passages ( Fig 4B ) . Additionally , the expression of luciferase and E2-crimson by the parasites was permanent throughout the successive in vitro passages ( Fig 4C ) . The percentage of recovered promastigotes after ‘metacyclic’ enrichment by differential centrifugation decreased through the sequential cultures ( Fig 5A ) . Subsequently , infection of mice with a standardized dose of 5x107 enriched ‘metacyclic’ promastigotes obtained from different in vitro passages ( Fig 5B ) promoted different patterns of infection . The implantation of the parasites in the liver and spleen measured at 7 days p . i . , was remarkably different , both in livers ( Fig 5C ) and spleens ( Fig 5D ) . Mice infected with P1 , P5 and P11 presented no significant differences , nevertheless , the parasite load of P21 and P41 was drastically lower ( P<0 . 0001 ) in both organs . To address the persistence of the parasite in the infected organs , we measured the bioluminescence signals in the liver and in the spleen at days 30 and 90 post-infection ( Fig 5C and 5D ) . At day 30 , the liver of mice infected with P21 and P41 presented lower bioluminescence values compared to P1 ( P<0 . 0001 ) . In the spleen , mice infected with P11 , P21 and P41 presented lower bioluminescence values compared to P1 ( P<0 . 0001 ) . At day 90 , all mice infected with P11 , P21 and P41 presented a reduction in the bioluminescence in both liver ( P = 0 . 0002 ) and spleen ( P = 0 . 0005 ) , with values close to the background signals . P5 , even if not statistically different from P1 , presented lower and quite heterogeneous values , especially in the spleen . The next step was to determine whether amastigotes derived from P21-infected mice at day 90 p . i . were still able to induce an infectious process in naïve mice . To this end , we decided to collect these ‘persistent’ amastigotes of both P1 and P21 infected mice spleens and , after in vitro differentiation , to re-inject new naïve mice with the corresponding mP1 and mP21 parasites , and follow up the parasite load until 90 days p . i . ( Fig 6A ) . Regardless the remarkable difference ( P<0 . 0001 ) between the kinetic curves observed when mice are infected with P1 ( AUC = 9 . 2x108 ) and P21 ( AUC = 4 . 9x107 ) ( Fig 6B ) , we observed that the subsequent infections with parasites derived from mice spleen at 90 days of infection ( ‘persistent’ amastigotes ) , either mP1 or mP21 , presented similar pattern as the original infection with P1 . In the liver , the infection kinetics of mice infected with mP1 ( AUC = 8 . 2x108 ) was similar to the original P1 ( AUC = 7 . 4x108 ) infection ( P = 0 . 5617 ) . On the other hand , even if the parasite load of mice infected with mP21 presented lower bioluminescence values ( AUC = 4 . 5x108 ) , it was similar to P1 ( P = 0 . 2273 ) and superior than the original P21 infection ( AUC = 4 . 0x107 ) ( Fig 6C ) . In the spleen , additionally , both infections with mP1 ( AUC = 5 . 2x108 ) and mP21 ( AUC = 6 . 9x108 ) originated similar infection kinetics , equivalent to P1 ( P = 0 . 1171 and P = 0 . 3193 , respectively ) and superior than P21 ( P = 0 . 0095 and P<0 . 0001 , respectively ) ( Fig 6D ) .
New virulent Leishmania donovani Ld1S parasites expressing two reporter genes have been generated and validated to be used in vitro and in vivo imaging analysis . These parasites permanently express bioluminescence ( firefly luciferase ) and fluorescence ( E2-crimson ) genes , herein named Ld1S_luci_E2-crimson , and they are as virulent as wild-type parasites , as demonstrated in hamsters . These novel parasites were key to the establishment of an innovative non-invasive experimental model of VL , which allows the real-time evaluation of the infectious process and the parasite virulence in living animals , aiming the development of longitudinal studies using the same animals . Indeed , the monitoring of the parasite load in hamsters , considered the most accepted animal model for VL [5 , 6] , when infected with Ld1S_luci_E2-crimson , can be easily performed by bioluminescence , with the animals exhibiting a gradual but simultaneous increasing in the liver and in the spleen , accompanied by the occurrence of clinical signs such as apathy , weight loss and hepatosplenomegaly . The success of the infection can be verified in real time as soon as 15 days p . i . , long before the onset of clinical signs , in a more efficient and less time-consuming manner . By contrast , when employing wild-type parasites , the effectiveness of the infection in living animals is only visible after approximately two months of infection , when the hamsters start to lose weight [5 , 29 , 30] . On the other hand , mice infected with L . donovani do not get sick , nevertheless the proposed BALB/c mouse method proved to be a reliable model to the study of VL [31–33] . The strength of this experimental model was to present an early and intense parasite implantation in the liver and in the spleen , as soon as three days post-infection[34] , followed by a longstanding parasite persistence up to at least three months post-infection . All these in vivo bioluminescence data , validated by RT-qPCR [20 , 25] , show that this infectious process is clearly different from other previous models using mice [8 , 9 , 35] , where liver and spleen usually present distinct infection kinetics and a relative short duration of the infection , with a control of parasite load in the liver between 1 and 2 months p . i . and a delayed and low parasite burden in the spleen . Moreover , mice actually do respond to the infection against Ld1S_luci_E2-crimson , with liver and spleen presenting a typical inflammatory response [8 , 33] , with mixed Th1/Th2 cytokine production , as previously described in experimentally-infected hamsters [5 , 33] , and in a similar way as the natural infection in humans and dogs [36–39] . Altogether , these features render our model exceptional , making possible global studies on parasite virulence and the quick analysis of the early infectious process . By using Ld1S_luci_E2-crimson we are providing a new method to determine the degrees of parasite virulence . As previously shown [40] , long-term in vitro culturing affects the virulence of Leishmania spp , which imposes permanent passages in animals to maintain virulent strains in the lab . Taking advantage of this characteristic , we intentionally produced attenuated L . donovani promastigotes by successive in vitro passages . A previous study conducted by one of us [29] ( ( Barja et al; in press ) clearly established that virulence attenuation of parasites correlated with passage number; only promastigotes of in vitro passage 5 ( P5 ) maintained virulence in hamster similar to host-derived amastigotes . Herein , we demonstrate the loss of virulence both in vitro and in vivo , by analyzing the ability of promastigotes to differentiate into amastigotes , to survive and to persist in the host’s tissues in mice determined at days 7 , 30 and 90 p . i . , respectively . Firstly , in vitro cultures clearly indicated that differences in their metabolism were observed over time , along with the increasing of the in vitro passage number , which included faster doubling time , in accordance with previous studies ( Barja et al . ; in press ) and higher intensity of bioluminescence and fluorescence for the same number of parasites . Secondly , as previously shown in a hamster model [29] , parasite implantation in the liver and spleen of mice was dependent on the number of in vitro passages . Actually , only P1 and P5 Ld1S_luci_E2-crimson promastigotes caused high implantation and long persistent infections in mice . P11 and P21 promastigotes showed a significant decrease of parasitic load in both spleen and liver at day 30 p . i . , suggesting a reduction in the parasite’s capacity to persist in the organs . Interestingly , attenuated parasites ( P41 ) were still able to implant at day 7 p . i . in host tissues , although at low intensity . Altogether , our data seem to indicate that promastigote virulence attenuation , which correlates with passage number is not due just to a deficit in implantation and differentiation into amastigotes , but also to a decrease of their capacity to persist in tissues ( replication defect in amastigotes and/or control by the host immune response ) . One hallmark of this study is the development of a suitable method to reinstate the virulence in a population of attenuated parasites . Despite the virulence deficit after long-term in vitro culturing , small numbers of parasites are still found in the organs of mice infected with these attenuated parasites . Therefore , amastigotes do not totally lose their virulence , since these parasites ( herein named mP21 ) were still able to infect new animals and to persist in their tissues . The control of the parasite load in mice is then probably due to the progressive decrease in the number of fully virulent parasites after several passages in culture ( at the promastigote level ) [40 , 41] , supporting the hypothesis that long-term in vitro cultures could contain a “mixture” of virulent and attenuated parasites [40] , i . e . , a population with an unchanged number of individuals , but with an overall reduced capacity to infect and/or persist in the tissues [42 , 43] . In conclusion , we described herein the generation of innovative virulent Leishmania donovani parasites that are concomitantly bioluminescent and fluorescent ( Ld1S_luci_E2-crimson ) . These parasites are versatile enough to be used in a broad range of in vitro , in vivo or ex vivo techniques , allowing the immediate visualization of parasites or parasitized cells , such as in flow cytometry , fluorescence molecular tomography ( FMT ) , bioluminescence assays [27 , 34 , 44] . Further , the benefits of using mice as a model of VL also include lower costs , easier handling , greater availability of reagents ( such as PCR primers and antibodies ) . Finally , the proposed model using these novel parasites and BALB/c mice allows the non-invasive longitudinal evaluation of the infectious process in real time , as well as the parasite’s virulence and the host’s immune response . Accordingly , the results reported herein open up new perspectives on the study of new therapies and vaccines , since precise and long-lasting follow up studies are then possible , employing day-by-day verifications in a more ethical manner , without the need of frequent and numerous sample collections or euthanasia . | Visceral leishmaniasis is a chronic disease caused by the life-threatening parasite Leishmania donovani . This chronicity character is a major concern in experimental models of visceral leishmaniasis , since infected animals do not develop any visible sign or symptom before one/two months after infection . Consequently , there is an urge to develop a model that allow the early identification of the parasite in the host’s tissues and an easy follow up of the infection . Therefore , we generated genetically-modified virulent parasites that are concomitantly fluorescent and bioluminescent , and using highly performant imaging approaches , we could detect these parasites in tissues of infected hamsters and mice within a week post-infection . With these parasites , we established , then , a mouse model of visceral leishmaniasis that allows prolonged longitudinal studies and enables the investigation of parasite’s survival , using non-invasive in vivo imaging techniques . These features are of extreme importance , particularly for the development of new therapies and vaccines and open up new perspectives on the study of this disease . | [
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] | 2017 | New insights into experimental visceral leishmaniasis: Real-time in vivo imaging of Leishmania donovani virulence |
Epithelial–mesenchymal transition ( EMT ) is a normal cell differentiation event during development and contributes pathologically to carcinoma and fibrosis progression . EMT often associates with increased transforming growth factor-β ( TGF-β ) signaling , and TGF-β drives EMT , in part through Smad-mediated reprogramming of gene expression . TGF-β also activates the Erk MAPK pathway through recruitment and Tyr phosphorylation of the adaptor protein ShcA by the activated TGF-β type I receptor . We found that ShcA protects the epithelial integrity of nontransformed cells against EMT by repressing TGF-β-induced , Smad-mediated gene expression . p52ShcA competed with Smad3 for TGF-β receptor binding , and down-regulation of ShcA expression enhanced autocrine TGF-β/Smad signaling and target gene expression , whereas increased p52ShcA expression resulted in decreased Smad3 binding to the TGF-β receptor , decreased Smad3 activation , and increased Erk MAPK and Akt signaling . Furthermore , p52ShcA sequestered TGF-β receptor complexes to caveolin-associated membrane compartments , and reducing ShcA expression enhanced the receptor localization in clathrin-associated membrane compartments that enable Smad activation . Consequently , silencing ShcA expression induced EMT , with increased cell migration , invasion , and dissemination , and increased stem cell generation and mammosphere formation , dependent upon autocrine TGF-β signaling . These findings position ShcA as a determinant of the epithelial phenotype by repressing TGF-β-induced Smad activation through differential partitioning of receptor complexes at the cell surface .
Shc proteins are intracellular adaptor proteins that relay signals from membrane-associated receptors , including receptor tyrosine ( Tyr ) kinases ( RTKs ) , cytokine receptors and integrins . They interact with phospho-Tyr residues through their N-terminal PTB domain and C-terminal SH2 domain and enable Tyr kinases to phosphorylate Shc on three Tyr residues in a central CH1 domain , thus facilitating activation of the Ras/Erk mitogen-activated protein kinase ( MAPK ) pathway in response to extracellular ligands [1 , 2] . Among the four mammalian Shc proteins , ShcA is widely expressed and generated as three isoforms , p66 , p52 , and p46 , through differential start codon usage and splicing . ShcA is well studied as a signaling mediator of membrane-associated Tyr kinases leading to Erk MAPK activation [1 , 2] , although it also plays a role in activation of PI3K-Akt signaling [2–4] and controls cytoskeletal changes [2 , 5] . Targeted inactivation of ShcA expression does not prevent growth factor-induced Erk MAPK activation but confers an impaired sensitivity to growth factors and an attenuated Erk MAPK activation response [6] . Since nonchordate metazoans lack some or all Tyrs that are phosphorylated [7 , 8] , Shc proteins may also exert functions independent of Tyr phosphorylation . ShcA is additionally controlled by serine ( Ser ) and threonine ( Thr ) phosphorylation , which regulates protein interactions , Shc activities in lipid metabolism , endocytosis and small GTPase regulation , e . g . , following protein kinase C activation [9 , 10] and responses to epidermal growth factor ( EGF ) receptor activation [11] . p52ShcA also plays a role in transforming growth factor-β ( TGF-β ) signaling , which is not initiated by Tyr kinases [12] . TGF-β family proteins control cell differentiation and various functions in metazoans . As secreted dimers , TGF-β and TGF-β-related proteins activate intracellular signaling through a cell surface complex of two type II and two type I receptor kinases . Upon ligand binding , the type II receptors phosphorylate the type I receptors that then activate their signaling effectors , the Smads , through C-terminal phosphorylation on two Sers . Thus , TGF-β induces the type I receptor TβRI to activate Smad2 and/or Smad3 , which then dissociate from the receptor complexes and form trimers of two receptor-activated Smads and one Smad4 . These then cooperate with DNA binding transcription factors and coregulators to activate or repress TGF-β target gene expression [13–15] . In addition to the Smad-mediated changes in transcription , the TGF-β receptors also activate Erk , c-Jun N-terminal kinase ( JNK ) , and p38 MAPK signaling , as well as Rho and PI3K-Akt-TOR signaling , albeit to a lower extent than RTKs [16–18] . Their activation in response to TGF-β may relate to the dual kinase specificity of the TGF-β receptor [12 , 19–21] , which , as is seen with other dual specificity kinases [22] , confers Tyr phosphorylation that is much weaker than Ser/Thr phosphorylation [12 , 21] . TGF-β induces TβRI phosphorylation on Tyr , and TGF-β-induced activation of Erk MAPK signaling results from TGF-β-induced recruitment of p52ShcA to TβRI , enabling TβRI to phosphorylate p52ShcA on Tyr , and more prominently on Ser [12] . At several stages during development , dependent on the microenvironment , epithelial cells repress their differentiation state , resulting in loss of epithelial junction and polarity complexes , and redirect their gene expression and phenotype to transition into motile mesenchymal cells [23 , 24] . This reversible transdifferentiation process is commonly called epithelial–mesenchymal transition ( EMT ) and is potently induced by activation of TGF-β signaling , in cooperation with other signaling mediators [24–27] . Postnatally , EMT is recapitulated in fibrosis [28 , 29] and in cancer progression , where it directs carcinoma cell invasion and correlates with cancer stem cell properties . In both pathological contexts , EMT has been functionally linked with increased TGF-β signaling [25 , 30–32] . In TGF-β-induced EMT , the activated Smads direct the expression of “master” transcription factors , such as Snail , ZEB , and Twist , and then cooperate with these transcription factors to regulate the expression of target genes [24] . While Akt activation is required for completion of TGF-β-induced EMT , with mTOR complex 2 being essential for the cytoskeletal reorganization and motility [33 , 34] , the roles of the MAPK pathways in TGF-β-induced EMT have been less defined , although Erk MAPK signaling has been implicated in EMT responses [26 , 35] . Aiming to better understand the roles of TGF-β signaling in EMT , we down-regulated the expression of ShcA in epithelial cells . These cells underwent EMT spontaneously , thus enhancing their motility and invasion , and the generation of epithelial stem cells , dependent on autocrine TGF-β-induced Smad signaling . Our results show that ShcA protects epithelial cells against EMT through suppression of TGF-β-induced Smad signaling . This inhibition is achieved through direct competition of ShcA with Smad3 for binding to the TβRI , and , consequently , changes in the partitioning of ShcA-interacting TGF-β receptor complexes and receptor complexes that enable TGF-β-induced Smad activation .
To address the role of ShcA in the epithelial phenotype and EMT , we used mouse mammary NMuMG cells and human HaCaT skin keratinocytes as model systems , which like most other cells predominantly express the p52ShcA isoform ( Fig 1A ) . In these cells , which transition into a mesenchymal phenotype in response to TGF-β [34 , 36 , 37] , we down-regulated ShcA expression by transfecting the cells with ShcA-specific small interfering RNAs ( siRNAs ) ( Fig 1A; S1A Fig ) . ShcA mRNA expression was at its best decreased to 15% , as assessed by qRT-PCR , depending on the sequence of the siRNA ( S1B Fig ) . Compared to cells transfected with control siRNA , cells with down-regulated ShcA expression had a less cuboidal epithelial phenotype and a more elongated and spread out phenotype ( Fig 1B ) , suggesting EMT . The EMT phenotype was supported by decreased immunostaining and diffuse mislocalization of E-cadherin at cell contacts , shown by confocal immunofluorescence ( Fig 1C ) . Furthermore , the actin filaments were no longer cortically organized , and the cells showed prominent fibronectin immunostaining that was not apparent in epithelial cells that were transfected with control siRNA ( Fig 1C ) . These changes upon down-regulation of ShcA expression were similar in NMuMG and HaCaT cells ( Fig 1B and 1C ) . That down-regulation of ShcA expression resulted in EMT was also supported by decreased E-cadherin expression and increased fibronectin , N-cadherin and vimentin expression that characterize EMT . Also these changes were apparent in both NMuMG and HaCaT cells , when compared with cells transfected with control siRNA ( Fig 1D and 1E; S1C and S1D Fig ) . The EMT phenotype resulting from decreased ShcA expression could be reversed following reintroduction of p52ShcA expression . HaCaT cells , selected to express an shRNA that targets the ShcA 3’ untranslated region from a lentiviral vector , had decreased ShcA expression ( S1E Fig ) and showed the expected EMT phenotype , as apparent by cell morphology ( S1E Fig , top ) , decreased E-cadherin and increased fibronectin expression ( S1G and S1H Fig ) . Reintroducing p52ShcA expression using a vector that was not targeted by the shRNA ( S1E Fig ) reverted the cells to an epithelial phenotype with increased E-cadherin and decreased fibronectin expression ( S1G and S1H Fig ) . Finally , NMuMG cells can form acini-like structures when allowed to grow three-dimensionally in Matrigel , and inducing EMT in response to TGF-β impairs the integrity of these structures due to cell dispersal [38 , 39] . Whereas NMuMG cells transfected with control siRNA formed such smooth-edged spheres in Matrigel , cells with decreased ShcA expression were unable to do so and showed cell dispersal , similarly to the effect of adding TGF-β ( Fig 1F ) . Based on these observations , we conclude that decreasing ShcA expression confers a transition from an epithelial to a mesenchymal phenotype , and , therefore , that ShcA plays a role in stabilizing the epithelial phenotype . The epithelial to mesenchymal phenotype transition is marked by increased cell motility , which is often the basis for increased invasiveness of cells that have undergone EMT , e . g . , in cancer progression [23 , 24] . We therefore evaluated whether decreased ShcA expression gave rise to cells with enhanced motility and invasion . As shown in Fig 2A , down-regulation of ShcA expression increased cell motility , assessed in a cell monolayer wounding assay . Additionally , the cells showed increased invasion in Transwell assays that score the number of cells that invaded through basement membrane ( Fig 2B ) . To examine the effect of decreased ShcA expression on cell behavior in vivo , we transplanted DiI-labeled cells into the yolk sac of zebrafish embryos ( Fig 2C ) . Recent studies have shown that cancer cell dissemination in such zebrafish xenograft assays correlates with cancer cell behavior in mouse models of metastases [40] . DiI-labeled NMuMG cells with down-regulated ShcA expression ( Fig 2D; S2A Fig ) and control NMuMG cells were injected into the yolk sac of zebrafish embryos ( Fig 2C ) , and the dissemination of the cells was visualized by fluorescence microscopy over 60–84 h . Down-regulation of ShcA expression resulted in increased dissemination from the site of injection , scored at 72 h after injection ( Fig 2E ) , which is consistent with their increased cell motility and invasion ( Fig 2A and 2B ) . Partial or complete loss of epithelial phenotype and EMT can lead to acquisition of stem cell properties in normal and transformed mammary epithelial cells [31 , 41] . Mammary stem cells that have the ability to self-renew and reconstitute mammary glands can form single cell–derived mammospheres when cultured in suspension [42] . Additionally , mouse mammary epithelial or carcinoma stem cells express increased levels of CD49f and moderately increased CD24 levels , when compared to cells lacking self-renewal and gland-reconstituting capacities [43 , 44] . Accordingly , we found that decreasing ShcA expression resulted in increased CD49f expression and a modest increase in CD24 expression ( Fig 2F; S2B Fig ) . Additionally , down-regulation of ShcA expression conferred increased formation of single cell–derived mammospheres ( Fig 2G ) , indicative of the increased number of self-renewing stem cells in the population . These results correlate the EMT phenotype , resulting from decreased ShcA expression , with stem cell properties in culture , and suggest that ShcA expression may control cancer stem cell generation and function . ShcA acts as an adaptor that facilitates Erk MAPK pathway in response to various ligands , as has been best studied in the context of growth factor-induced Tyr kinase receptor signaling [1 , 2] . Cells lacking ShcA expression , as a result of targeted gene inactivation , show attenuated growth factor-induced Erk MAPK activation [6] . Accordingly , down-regulation of ShcA expression in NMuMG cells reduced the level of Erk MAPK activation under our cell culture conditions with serum ( Fig 3A and 3D ) . ShcA down-regulation also exerted a milder decrease in basal Akt activation ( Fig 3B and 3D ) , consistent with the proposed role of Shc in insulin- and growth factor-induced PI3K activation [2 , 3 , 45] . No effect was seen on the basal level of p38 MAPK activation ( Fig 3C and 3D ) . To assess whether the EMT resulting from decreased ShcA expression was due to decreased Erk MAPK pathway activity , we evaluated the effect of U0126 , a MEK1/2 inhibitor that prevents Erk MAPK activation ( Fig 3A ) , on cell morphology . U0126 did not induce the phenotypic changes that are apparent when ShcA expression is down-regulated ( Fig 3E ) . U0126 also did not induce Snail , Twist , fibronectin or vimentin expression , or repress E-cadherin expression , as is seen in response to silencing ShcA expression ( Fig 3F–3J ) . However , inhibition of MEK1/2 activity resulted in a somewhat increased N-cadherin expression ( Fig 3K ) , and slightly repressed Twist expression ( Fig 3G ) . Furthermore , U0126 slightly destabilized the junctional localization of E-cadherin that was revealed by immunofluorescence , with further destabilization upon down-regulation of ShcA expression ( Fig 3L ) . Similarly to NMuMG cells , treatment of HaCaT cells with U0126 did not induce a change in morphology that resembled the effect of down-regulation of ShcA expression ( S3A Fig ) , and did not induce the expression of the Snail-related transcription factor Slug ( S3B Fig ) , which , similarly to Snail in NMuMG , promotes EMT of HaCaT cells [46] . These data suggest that inhibition of the MEK1/2-Erk MAPK pathway does not account for a loss of epithelial morphology , and argue that EMT resulting from ShcA down-regulation is not due to inhibition of the Erk MAPK pathway . Considering the mild decrease in Akt activation following ShcA down-regulation , and the reported roles of Shc in growth factor-induced PI3K activation [2 , 3 , 45] , we also assessed the effects of LY294002 , a direct PI3K inhibitor , on the epithelial morphology . Inhibition of PI3K activity resulted in decreased Akt activation ( Fig 3B ) , without , however , inducing an EMT-like morphology ( Fig 3E ) or Snail or Twist mRNA expression in control epithelial cells , nor did it significantly affect the expression of fibronectin , vimentin or E-cadherin mRNA ( Fig 3F–3J ) . These data suggest that the EMT-like phenotype following down-regulation of ShcA expression does not result from decreased Akt activation . Finally , inhibition of p38 MAPK using SB203580 did not affect the epithelial morphology in control cells , or the EMT phenotype in cells with down-regulated ShcA expression ( Fig 3E ) , and had no major effects on the expression of EMT marker genes ( Fig 3F–3K ) . Similar results were obtained in HaCaT cells ( S3A and S3B Fig ) . To define the mechanism that accounts for the EMT in response to down-regulation of ShcA expression , we treated the cells with SB431542 , a specific inhibitor of the TGF-β/activin type I receptor kinases that prevents TGF-β-induced Smad2/3 activation [47] . As expected , SB431542 blocked the TGF-β-induced activation of Snail mRNA expression ( Fig 4A; S4A Fig ) and the TGF-β-induced transition of NMuMG cells into an elongated cell phenotype ( Fig 4B ) . In untreated NMuMG cells with down-regulated ShcA expression , SB431542 strongly decreased the level of Snail mRNA to a level below the Snail mRNA expression in NMuMG cells with control siRNA ( Fig 4A; S4A Fig ) . A similar inhibition was observed when cells were treated with LY2109761 , another TGF-β/activin type I receptor kinase inhibitor [48] ( S4B Fig ) . Furthermore , these cells reverted to an epithelial phenotype when treated with SB431542 or LY2109761 . This was apparent by visual microscopic examination ( Fig 4B ) , immunofluorescence for E-cadherin at cell contacts and cortical actin ( Fig 4C ) , and immunoblotting , immunofluorescence , and/or mRNA expression of mesenchymal fibronectin , vimentin , and N-cadherin ( Fig 4C; S4C–S4G Fig ) . Treatment of the cells with a neutralizing anti-TGF-β monoclonal antibody also repressed the EMT phenotype , but this repression was less complete when compared with the effects of SB431542 or LY2109761 ( S4B–S4D Fig ) , consistent with an inability to fully block autocrine TGF-β signaling using antibodies . Furthermore , SB431542 inhibited the invasion that resulted from down-regulation of ShcA expression ( Fig 4D ) . As in NMuMG cells , SB431542 also induced HaCaT cells with down-regulated ShcA expression to revert from the mesenchymal into an epithelial phenotype ( Fig 4E–4G ) . Indeed , SB431542 repressed the enhanced expression of the Snail-related transcription factor Slug ( Fig 4E; S4H Fig ) and reverted the cells to an epithelial appearance ( Fig 4F ) with epithelial E-cadherin and actin staining and lack of fibronectin immunostaining ( Fig 4G ) . These observations suggest that autocrine TGF-β signaling , to which all cells in culture are exposed , drives the observed EMT in cells with decreased ShcA expression . By extension , these data also suggest that down-regulation of ShcA expression confers increased sensitivity to autocrine TGF-β signaling . We next evaluated the effect of transfected ShcA siRNA on autocrine and TGF-β-induced activation of Smad2 and Smad3 , the major signaling effector in response to TGF-β . Without adding TGF-β , NMuMG cells showed marginally detectable Smad2 and Smad3 activation , visualized by immunoblotting for C-terminally phosphorylated Smad2 or Smad3 ( Fig 5A ) . Their basal activation was higher when ShcA expression was downregulated ( Fig 5A and 5B ) , and blocked by SB431542 ( Fig 5A ) , reflecting autocrine TGF-β signaling . In addition , the Smad2 and Smad3 activation in response to added TGF-β was also enhanced when ShcA expression was down-regulated ( Fig 5A and 5B ) . Since Smad activation results in nuclear localization of Smad2 and Smad3 , we examined their subcellular localization by immunofluorescence for Smad2/3 ( Fig 5C ) and following separation of the nuclear and cytoplasmic fractions ( S5A and S5B Fig ) . Without adding TGF-β , cells with down-regulated ShcA expression showed a distinct level of Smad2/3 nuclear localization that was much higher than in control cells ( Fig 5C; S5A and S5B Fig ) and was abolished when SB431542 was added to block autocrine TGF-β signaling ( Fig 5C ) . Adding TGF-β induced a robust nuclear translocation of Smad complexes ( Fig 5C ) . These data suggested that decreasing ShcA expression resulted in increased basal and TGF-β-induced Smad signaling . To better appreciate the consequence of the increased Smad3 activation , we examined the transcription from tandem Smad3-binding DNA sequences in luciferase reporter assays ( Fig 5D and 5E ) . Transfection of ShcA siRNA , resulting in decreased ShcA expression , enhanced the basal Smad-mediated transcription in NMuMG and HaCaT cells , and increased the TGF-β-induced luciferase expression ( Fig 5D and 5E; S5C and S5D Fig ) . The enhanced Smad activation in response to autocrine TGF-β signaling , or in response to added TGF-β , predicts that down-regulation of ShcA expression results in increased TGF-β target gene expression . This was indeed the case . Activation of Smad7 or PAI-1 mRNA expression is routinely used to monitor direct TGF-β-induced , Smad-mediated transcription activation . As shown in Fig 5F and 5G , down-regulation of ShcA expression resulted in enhanced Smad7 and PAI-1 mRNA expression , which was prevented by blocking the TGF-β-induced Smad activation using SB431542 . Decreased ShcA expression also enhanced Smad7 and PAI-1 expression in response to added TGF-β . The increased autocrine induction of TGF-β target genes may be at the basis of the spontaneous EMT of NMuMG and HaCaT cells , when ShcA expression is down-regulated . Indeed , Snail mRNA expression in NMuMG cells and Slug mRNA expression in HaCaT cells were higher when ShcA expression was down-regulated , and these increases were prevented in the presence of SB431542 ( Fig 4A and 4E; S4A and S4E Fig ) . Similarly , Twist and ZEB1 mRNA expression were enhanced upon down-regulation of ShcA expression in NMuMG and HaCaT cells , and these increases were repressed by SB431542 ( S5E–S5H Fig ) . With Snail directing the repression of E-cadherin expression in NMuMG cells , ShcA down-regulation resulted in lower E-cadherin mRNA expression , another hallmark of EMT in NMuMG cells , that was prevented in the presence of SB431542 ( Fig 5H; S5I Fig ) . Finally , down-regulation of ShcA expression resulted in enhanced fibronectin , N-cadherin , and vimentin mRNA expression , which was blocked by SB431542 and therefore depended on autocrine TGF-β signaling ( Fig 5I–5K; S5J Fig ) . These data illustrate that decreasing ShcA expression results in enhanced autocrine TGF-β/Smad signaling , and consequently in enhanced TGF-β target gene responses , which drive or contribute to the spontaneous EMT response . We have previously shown that increased TGF-β receptor levels at the cell surface confer increased autocrine TGF-β signaling [49–51] . Inhibition of ectodomain shedding , which enhances TβRI cell surface levels [49] , or high glucose or insulin , which induce a rapid increase in TβRII and TβRI at the cell surface [50 , 51] , both increase autocrine TGF-β signaling and TGF-β responsiveness . We therefore examined the cell surface TGF-β receptor levels using cell surface protein biotinylation in cells with decreased ShcA expression in comparison with control cells . Down-regulation of ShcA expression did not result in increased cell surface levels of TβRI or TβRII ( Fig 6A; S6A and S6B Fig ) , nor did it enhance the TβRI and TβRII mRNA expression ( S6C and S6D Fig ) . We conclude that the increased autocrine TGF-β signaling does not result from increased cell surface levels of TGF-β receptors . Additionally , down-regulation of ShcA expression did not enhance the expression or release of TGF-β1 ( S6E and S6F Fig ) , the major TGF-β made by NMuMG cells in culture , nor did it affect the generation of active TGF-β ( S6F Fig ) that could otherwise have accounted for increased autocrine TGF-β signaling . We next explored whether inhibition of TGF-β-induced Smad activation by ShcA might explain the increased Smad activation when ShcA expression is decreased . Indeed , increasing levels of transfected p52ShcA expression plasmid , and thus of p52ShcA expression , decreased the level of TGF-β-induced Smad3 activation ( Fig 6B; S6G Fig ) . The decrease in Smad3 activation with increased p52ShcA expression correlated with enhanced levels of TGF-β-induced Erk MAPK and Akt activation ( Fig 6C ) . To visualize whether ShcA affected the transient interaction of Smad3 with TβRI that is required for Smad3 activation , we used a mutant of Smad3 with Asp407 replaced by Glu , which has enhanced affinity for TβRI , thus allowing detection of the TGF-β-induced Smad3-TβRI interaction by immunoprecipitation [52] . Increasing ShcA expression resulted in a decreased interaction of Smad3D407E with the TGF-β-activated TβRI ( Fig 6D; S6H Fig ) . Since ShcA can interact with TβRI [12] , this result suggests that Smad3 and ShcA may compete for binding to TβRI . To address directly the nature of the competition , we purified GST-Smad3 and His-tagged p52ShcA from Escherichia coli . Immunopurified Flag-tagged TβRI , obtained from TGF-β-stimulated cells expressing tagged TβRII and TβRI , associated in vitro with glutathione-bound , purified GST-Smad3 , and this interaction was prevented following dephosphorylation of TβRI by lambda phosphatase ( Fig 6E; compare lanes three and seven ) . Purified His-tagged p52ShcA prevented the association of GST-Smad3 with TβRI ( Fig 6E ) . These results suggest that ShcA binding to TβRI interferes with Smad3 binding to TβRI , and that steric incompatibility of ShcA and Smad3 binding may prevent TGF-β-induced Smad3 activation in the direct presence of ShcA . These results further suggest that ShcA expression is an important determinant of TGF-β-induced Smad activation . Since p52ShcA interfered with the association of Smad3 with TβRI and promoted TGF-β-induced Erk MAPK activation , we evaluated whether it affected the compartmentalization of TβRI in clathrin-coated pits , where TGF-β-induced Smad signaling is initiated [53 , 54] . The TβRI receptor associates and coimmunoprecipitates with the β2-adaptin subunit of the AP2 adaptor complex [55] , which mediates clathrin-dependent endocytosis from the plasma membrane [56] . Accordingly , in TGF-β-treated cells transfected to express both TβRII and Flag-tagged TβRI , TβRI coprecipitated with β2-adaptin , and p52ShcA decreased this association ( Fig 7A; S7A Fig ) . Conversely , increasing levels of Smad3 enhanced the association of β2-adaptin with TβRI ( Fig 7B; S7B Fig ) . The TβRI receptor has also been shown to interact with caveolin 1 [57] , a cholesterol-binding protein that is the major component of caveolae [58 , 59] . Increasing ShcA expression enhanced the association of TβRI with caveolin 1 in TGF-β-stimulated cells ( Fig 7C; S7C Fig ) . In contrast , increasing the levels of coexpressed Smad3 decreased the interaction of TβRI with caveolin 1 ( Fig 7D; S7D Fig ) . Finally , decreasing the endogenous level of ShcA expression using siRNA modestly decreased the TGF-β-induced association of endogenous TβRI and caveolin 1 in NMuMG cells while increasing the association of TβRI with β2-adaptin ( Fig 7E; S7E and S7F Fig ) . Conversely , ShcA overexpression increased the interaction of TβRI with caveolin 1 and decreased its interaction with β2-adaptin ( Fig 7E; S7E and S7F Fig ) . Together , these data support the notion that ShcA controls the partitioning of the TβRI receptors between the Smad-activating receptor complexes in clathrin-coated endosomes and caveolar microdomains . A role of ShcA in defining this balance was further supported by sucrose gradient fractionation of lysates of NMuMG cells . In control cells , TβRI cofractionated with caveolin-1 , with only minimal levels of TβRI in the fractions containing clathrin and the early endosomal marker , EEA1 ( Fig 7F ) . TGF-β treatment induced association of some TβRI with the fractions containing clathrin and EEA1 ( Fig 7G ) , as expected since TGF-β induces Smad activation . Decreasing ShcA expression following lentiviral expression of ShcA shRNA resulted , in the absence of TGF-β treatment , in a less confined association of TβRI with caveolin-1 and a broader association of TβRI with the clathrin/EEA1 fractions ( Fig 7H ) . This increased TβRI association with clathrin/EEA1 fractions is consistent with ShcA’s role in sequestering TGF-β receptor complexes in caveolar microdomains , suggested by immunoprecipitation analyses ( Fig 7A–7E ) . It is also consistent with our finding that reduced ShcA expression results in increased autocrine TGF-β-induced Smad activation , which occurs in clathrin/EEA1 endosomes . The TβRI association with clathrin/EEA1 fractions was not much enhanced upon adding TGF-β ( Fig 7I ) .
We previously reported that p52ShcA and p66ShcA can associate with the TβRI receptor and are phosphorylated by its kinase on Ser and Tyr , and that TGF-β-induced recruitment and Tyr phosphorylation of p52ShcA by TβRI enables TGF-β-induced Erk MAPK activation [12] . To address the role of ShcA in TGF-β-induced EMT , we used two epithelial cell lines , NMuMG and HaCaT cells that are commonly used to study EMT . Like most cells in culture , they express predominantly the p52 isoform of ShcA with lower levels of p66ShcA . Down-regulation of ShcA expression repressed the expression of all ShcA isoforms; targeted down-regulation of only p52ShcA cannot be done with the p52ShcA sequence fully comprised in p66ShcA . Our results demonstrate that decreasing ShcA expression enhances Smad signaling and , thus , that ShcA acts to repress Smad activation . This was apparent in the level of Smad3 activation and nuclear import in response to TGF-β and the activation of TGF-β/Smad target gene expression . These findings are consistent with results on the control of TGF-β signaling by a mutant p53 in human prostate carcinoma cell lines , showing that Smad2/3 activation is decreased upon overexpression of ShcA and enhanced when ShcA levels are reduced [60] . The increased Smad3 activation when ShcA expression is down-regulated did not result from an increase in cell surface TGF-β receptors or from generation of active TGF-β by the cells , but from enhanced Smad3 recruitment to the activated TβRI . We found that p52ShcA attenuates TGF-β-induced association of Smad3 with TβRI through direct competition , as shown in cells coexpressing these signaling mediators and using purified proteins . As a result of this interference , ShcA expression , and in particular p52ShcA , acts as a cell-intrinsic determinant in the control of TGF-β-induced Smad activation and gene expression responses . p66ShcA has properties that are distinct from p52ShcA , e . g . , through functional linkage with the oxidative stress response [61 , 62] and was shown to oppose p52ShcA in RTK-induced Erk MAPK activation [63] . Whether p66ShcA also controls TGF-β-induced Smad and Erk MAPK activation , e . g . , through competition with p52ShcA , remains to be seen . TGF-β-induced Smad activation occurs in clathrin-coated pits [53 , 54] , and TGF-β receptors associate with β2-adaptin in clathrin-associated AP2 complexes [55] . Conversely , TGF-β receptors are enriched in lipid-rich caveolae [54 , 64] , and the TβRI receptor can associate with caveolin 1 [57] . Whereas TGF-β receptor degradation in caveolar microdomains dampens TGF-β responsiveness [54] , cholesterol depletion experiments correlate TβRI localization in cholesterol-rich lipid rafts with TGF-β-induced Erk MAPK signaling [64] . Additionally , TGF-β-induced Akt activation requires caveolin [65] . Our results now argue that ShcA controls TGF-β responsiveness by defining the distribution of TGF-β receptor complexes between clathrin-coated pits and cholesterol-rich caveolae ( Fig 8 ) . We show that p52ShcA expression stabilizes the TβRI interaction with caveolin 1 , while decreasing the TβRI association with the AP2 complex , consistent with the decreased TGF-β-induced Smad3 activation , and that depletion of ShcA expression decreases the TGF-β-induced TβRI interaction with caveolin 1 , enabling a higher level of TβRI interaction with AP2 . Membrane fractionation revealed that most TGF-β receptor complexes colocalize with caveolin 1 microdomains in unstimulated cells , and that ShcA down-regulation shifted a fraction of the receptors into clathrin-containing endosomal membranes , enabling increased Smad2/3 activation in response to autocrine TGF-β . We therefore conclude that ShcA controls the compartmentalized distribution of TGF-β receptors between clathrin-coated pits and caveolae ( Fig 8 ) . Consistent with our observation that p52ShcA links TGF-β receptor activation with TGF-β-induced Erk MAPK activation [12] , ShcA appears to balance the TGF-β response through Smad versus Erk MAPK pathway signaling . In RTK signaling or signaling through receptor-associated Tyr kinases , p52ShcA phosphorylation in caveolar membranes facilitates Erk MAPK activation [58 , 66 , 67] . The dual specificity kinase TGF-β receptors allow for a similar mode of Erk MAPK pathway activation in response to TGF-β , albeit to a much lower extent . Our findings also complement a recent observation that Dab2 , an SH2 domain adaptor that interacts with TβRI and clathrin , and is required for TGF-β-induced Smad signaling , helps control the TβRI localization in clathrin endosomes and enhances its clathrin-mediated endocytosis [68] . It is conceivable that p52ShcA and Dab2 may have opposing roles in balancing Smad activation versus non-Smad responses . Previous studies revealed that cells regulate their TGF-β responsiveness by controlling the levels of functional TGF-β receptors at the cell surface . High glucose and insulin enhance TGF-β responsiveness by inducing a rapid mobilization of TβRII and TβRI from intracellular stores to the cell surface [50 , 51] . Conversely , cleavage of cell surface TβRI by the membrane-associated metalloprotease TACE decreases the cell sensitivity to TGF-β [49] . Our findings now present an alternative mechanism of controlling the TGF-β responses , in which ShcA defines the compartmentalization of cell surface TGF-β receptor complexes , balancing Smad-mediated responses against Erk MAPK pathway activation through ShcA . Compartmentalized balancing of TGF-β receptor signaling by ShcA may provide an as-yet-unappreciated level of control with scenarios that impact cell homeostasis and cancer progression , as increasingly valued for the spatial ( de ) regulation of RTK signaling [69] . While much attention is given to the roles of Smads in driving the EMT gene expression program [24] , the MEK1/2-Erk MAPK pathway was shown to be required for TGF-β-induced EMT [35 , 70] , and blocking Akt or mTOR complex 2 activation prevents transition of epithelial cells into the mesenchymal phenotype [34 , 71] . These observations support a model that the Erk MAPK and Akt-mTOR pathways cooperate with Smad signaling in the elaboration of EMT [24] . In the epithelial cells studied , decreasing ShcA expression resulted in attenuated Erk MAPK activation , consistent with the observation that targeted inactivation of ShcA expression attenuates growth factor-induced Erk MAPK activation [6] , and with the presence of growth factors in serum that act through RTKs and thus activate Erk MAPK signaling . Down-regulated ShcA expression also attenuated Akt activation , suggesting a role of ShcA in coupling growth factor signaling to Akt activation , as proposed for some RTKs [2–4] . That increased Smad signaling in the context of attenuated Akt and Erk MAPK pathway activation leads to EMT in response to autocrine TGF-β signaling highlights the role of TGF-β-induced Smad signaling in EMT but does not argue against a requirement for Erk MAPK and Akt . Our finding in nontransformed cells that ShcA protects epithelial cells from transitioning toward a mesenchymal phenotype , by repressing TGF-β/Smad activation , raises the possibility that carcinoma cells control through ShcA the EMT phenotype and , consequently , the invasive and stem cell phenotype and cancer dissemination . ShcA is expressed in many cell types [2] , but little is known about the control of ShcA expression , and most studies do not distinguish p52ShcA from p66ShcA expression , even though p66ShcA and p52ShcA have distinct functions [1 , 2 , 72] . Consistent with its role in mitogenic signaling [2] , ShcA is required for breast cancer development in mice [73] . Immunohistochemistry , however , reveals heterogeneity in ShcA expression among carcinomas with levels that are often lower than those in normal epithelia [74] . Moreover , mammary carcinoma cells that express a mutant ShcA lacking a functional phosphoTyr-binding PTB domain show increased expression of mesenchymal fibronectin and α5β1 integrin [75] , arguing that with impaired ShcA function the carcinoma cells might transition toward a mesenchymal phenotype . Distinct functions of p66SchA , with some antagonizing those of p52SchA [1 , 2 , 60] may explain the complex roles of SchA in controlling epithelial plasticity of carcinomas . Accordingly , analyses of breast cancer cell lines suggest differential regulation of p52ShcA and p66ShcA expression [76] . It is tempting to speculate that carcinoma cells may down-regulate ShcA , or specifically p52ShcA expression , at sites of invasion , where cells undergo EMT . Whether differential p52ShcA and p66ShcA expression correlates with carcinoma cell behavior is an open question . Although in our nontransformed epithelial cells the ShcA expression level defines the sensitivity to EMT through modulation of TGF-β signaling , crosstalk of ShcA with oncogenic signaling may confer a more complex role of ShcA in the epithelial plasticity of cancer cells . Indeed , ShcA cooperates with Neu/ErbB2 signaling in the control of cell motility and invasion in transformed epithelial cells [77] , through effects on focal adhesion turnover [5 , 77] , and increased p52/46ShcA levels enhance migration of prostate carcinoma cells [60] . Additionally , p66ShcA overexpression promotes EMT in ErbB2-driven breast cancer cells , through up-regulated activation of the c-Met receptor by its ligand hepatocyte growth factor ( HGF ) [76] . It is unknown whether in this context p66ShcA antagonizes the role of p52ShcA , as seen in RTK responses [2 , 63 , 72 , 78] . Furthermore , it remains to be seen whether ShcA contributes to epithelial plasticity responses when EMT is driven by increased RTK or Wnt signaling . Finally , consistent with the linkage of EMT with stem cell generation [31 , 32 , 41] , decreased ShcA expression promoted stem cell generation , apparent by marker expression and mammosphere formation . Future studies will reveal whether p52ShcA and p66ShcA expression control cancer stem cell generation and tumor initiation .
NMuMG , HaCaT , and 293T cells were cultured in DMEM with 10% FBS . NMuMG culture medium was supplemented with 10 μg/ml insulin ( Sigma ) for maintenance . Cells were treated with 2 ng/ml TGF-β1 ( HumanZyme ) , 5 μM SB431542 ( Sigma ) or 5 μM LY2109761 ( SelleckChem ) for the indicated times . U0126 ( Calbiochem ) , SB203580 ( Calbiochem ) and LY294002 ( Sigma-Aldrich ) were used at 7 μM , 20 μM , and 2 . 5 μM , respectively . The neutralizing panTGF-β inhibitor monoclonal antibody [79] was used at 200 ng/ml . For immunoprecipitations and/or immunoblotting , we used antibodies to ShcA , EEA1 , caveolin-1 , β2-adaptin , and clathrin heavy chain from BD Biosciences , GAPDH , CD49f , and CD24 ( from Santa Cruz Biotechnology ) , TβRI and TβRII ( Abcam and Santa Cruz Biotechnology ) , phosphoAkt ( Ser473 ) , and Akt , phosphoSmad3 , Smad3 , phosphoErk , Erk , phospho-p38 , p38 , E-cadherin , N-cadherin , vimentin , and fibronectin from Cell Signaling . Anti-Flag M2 ( Sigma ) , anti-HA . 11 ( Covance ) , anti-GFP ( Rockland and Aves labs ) , and anti-Myc 9E10 ( Covance ) were used for immunoprecipitation of tagged proteins . The expression plasmids for C-terminally Flag-tagged TβRI or Myc-tagged TβRII [80] , C-terminally haemagglutinin ( HA ) -tagged p52ShcA [12] and C-terminal Myc-Smad3D407E [52] were described . An expression plasmid encoding His-tagged p52ShcA [81] was a gift from Dr . John Ladbury . The expression plasmids for GFP-tagged β2 adaptin [55] and GFP-tagged caveolin-1 [82] were provided by Dr . Ed Leof ( Mayo Clinic ) and Dr . Martin A . Schwartz ( Yale School of Medicine ) , respectively . Control siRNA and siRNA oligonucleotides targeting mouse or human ShcA [83] were from Qiagen ( Table 1 ) . Lentiviral vectors expressing control shRNA or shRNA targeting human and mouse ShcA were from Sigma-Aldrich ( Table 2 ) . For plasmid transfections , NMuMG , HaCaT , or 293T cells were plated in six-well plates and transfected with Lipofectamine 2000 ( Invitrogen ) or Xtreme HP ( Roche ) . Five hours after transfection , cells were transferred to fresh medium-containing 10% FBS and incubated for 24–48 h . For siRNA transfections , NMuMG or HaCaT cells were plated in six-well plates and transfected with RNAiMax ( Invitrogen ) . Eight to twelve hours after transfection , cells were transferred to fresh medium containing 10% FBS , cultured for another 12 h , followed by a second siRNA transfection and incubation for an additional 48–72 h . NMuMG or HaCaT cells were infected with lentiviral vectors expressing shRNA against mouse or human ShcA or TβRI ( Sigma-Aldrich ) . The lentiviral vector pLKO . 1 was used to generate control cells ( Sigma-Aldrich ) . Following infection , the cells were selected with 1 μg/ml or puromycin ( InvivoGen ) for a week . Stably infected cell populations were generated as described [49] . The expression levels of ShcA or TβRI were assessed by immunoblotting with anti-ShcA or anti-TβRI antibody . Target sequences of shRNAs to silence the expression of human or mouse ShcA are shown in Table 1 . NMuMG cells , infected with lentiviral vector expressing control or ShcA shRNA , were grown to confluence , washed twice with PBS and trypsinized . Cells were seeded in eight chamber culture slides ( BD Biosciences ) coated with growth factor-reduced BD Matrigel matrix ( BD Biosciences ) at a density of 6 , 250 cells/ml/well in DMEM with 2% Matrigel in the absence or presence of 2 ng/ml TGF-β . Cultures were supplemented with fresh medium every 2 or 3 d . Cell morphology was observed after seven days using a phase-contrast microscope ( DMI5000 , Leica Microsystems ) , and pictures were acquired and analyzed using Photoshop CS5 software . Cells plated on chamber slides were fixed with 4% paraformaldehyde ( PFA ) for 20 min , permeabilized with PBS containing 2% PFA and 0 . 2% Triton X-100 ( PBT ) for 15 min and blocked with 2 . 5% BSA for 1 h . The slides were incubated with antibodies to E-cadherin ( BD Biosciences ) , fibronectin ( BD Biosciences ) , or Smad2/3 ( BD Biosciences ) at a 1:200 to 1:500 dilution at 4°C overnight , and then stained for 2 h with secondary antibodies conjugated to Alexa Fluor-488 or -647 ( 1:500 dilution , Invitrogen ) at room temperature . Phalloidin ( Life Technologies ) was used at a 1:500 dilution along with secondary antibodies to stain actin filaments . The slides were mounted with Prolong Gold antifade reagent ( Invitrogen ) with DAPI to visualize nuclei . The cells were viewed with an inverted light microscope ( DMI5000 , Leica Microsystems ) or a laser scanning confocal microscope ( SP5 , Leica Microsystems ) . Cell morphology was evaluated using a phase-contrast microscope ( DMI5000 , Leica Microsystems ) . Images were analyzed using Leica application suite ( Leica Microsystems ) , Axiovision ( Carl Zeiss MicroImaging , Inc . ) , ImageJ , and/or Adobe Photoshop CS5 . To stably down-regulate ShcA expression , HaCaT cells were infected with lentivirus expressing shRNA targeting the 3’UTR of ShcA or the empty lentiviral vector pLKO . 1 , and selected with 3 μg/ml puromycin for one week . Control cells or cells with decreased ShcA expression were then transfected with 0 . 2 μg of p52ShcA plasmid using Lipofectamine 2000 ( Invitrogen ) . p52ShcA expression was evaluated by immunoblot at 24 h after transfection , and the cell morphology was monitored by phase contrast microscopy after 36 h . Immunofluorecence for E-cadherin and fibronectin and quantification of E-cadherin and fibronectin mRNA by qRT-PCR were also done at that time . Confluent cell monolayers in DMEM with 10% FBS were wounded with a 10 μl plastic tip , and migration assays were performed as described [33] , using a Leica DMI 4000B microscope and a Leica DFC 350FX camera , with photographs taken at 0 h and 14 h . Invasion assays , performed as described [33] , utilized cells treated with or without 2 ng/ml TGF-β for 36 h , and 50 , 000 cells added to Matrigel-coated inserts ( BioCoat Matrigel Invasion Chamber; Becton Dickinson ) in DMEM , 0 . 2% FBS . These were then placed in companion plates with DMEM 10% FBS for 24 h . After removal of the cells in the upper chambers , the filters were fixed in methanol for 5 min at –20°C , and mounted using Prolong Gold Antifade reagent with DAPI ( Invitrogen ) . The DAPI-stained cells that transversed the filter were counted using DMI 5000 Leica microscope . Adult zebrafish were maintained in a zebrafish facility with a 14:10 day:night cycle and handled in compliance with an approved institutional protocol . Control NMuMG cells and cells with down-regulated ShcA expression were grown to confluence , washed twice with PBS , trypsinized and labeled with CM-DiI by immersion for 5 min at 37°C , and then transferred to ice for 15 min , as instructed by the manufacturer . Cells were then washed three times with PBS , suspended in PBS , and transferred into a borosilicate needle for injection into anesthetized dechorionated embryos , held on agarose-lined plates . 100–150 DiI-labeled cells were injected in the mid yolk sac region of the zebrafish embryos . After injection , embryos were sorted for fluorescence , and pictures were taken . Subsequently , the xenografted embryos were held at 31–32°C for 3 d prior to imaging of cell dissemination . Larvae were anesthetized with 0 . 003% tricaine ( Sigma ) , and pictures were taken using a Leica M205 microscope . For each condition , the data shown are from at least two experiments with at least fifty embryos per group . Images were analyzed and modified for brightness and contrast using Adobe Photoshop CS5 software . Cells were seeded on ultra-low attachment plates [42] at a density of 200 cells/200 μl/well in MEGM medium ( Lonza ) supplemented with B27 , 10 ng/ml bFGF , 20 ng/ml EGF . After incubating the cells for 4–5 d , the primary mammospheres were counted and visualized using a DMI 5000 Leica microscope . For secondary mammosphere formation assays , mammospheres were collected by centrifugation at 800 g for 5 min , resuspended in 100 μl of 0 . 05% trypsin and incubated at 37°C for 10 min , and further dissociated into single cells by pipetting , as verified by microscopy . Single cell dilutions were then replated on ultralow attachment plates , and colonies were quantified as for the primary mammosphere assays . Cells were lysed in lysis buffer ( 25 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 2 mM EDTA , 0 . 1% EDTA , 0 . 7% Triton X-100 , 10% glycerol ) and protease inhibitor cocktail ( Roche or ThermoScientific ) . For immunoblotting , proteins were quantified using Bio-Rad protein assay ( Bio-Rad Laboratories ) , and 20–80 μg of protein was separated by SDS-PAGE and transferred to 0 . 45 μm PVDF membrane . Membranes were blocked in TBS , 0 . 1% Tween 20 , and 5% BSA or 5% milk in TBS for 1 h to 6 h , followed by overnight incubation with primary antibody diluted at 1:500–1:5 , 000 in blocking solution and 1–2 h incubation with HRP-conjugated secondary antibodies diluted at 1:5 , 000–1:20 , 000 . Immunoreactive protein was detected using ECL ( GE Healthcare and Perkin Elmer ) and BioMax film ( Kodak and Denville ) . For immunoprecipitation , NMuMG , HaCaT , and 293T cells were harvested at 24 or 48 h after transfection and lysed in lysis buffer . Lysates were subjected to immunoprecipitation with anti-Flag M2 , anti-HA , or anti-Myc antibody and protein G-Sepharose 4 fast flow ( GE Healthcare ) . Immune complexes were washed three times with lysis buffer and subjected to immunoblotting with anti-Flag , anti-Myc , or anti-HA antibodies . For immunoprecipitation of proteins at endogenous levels , NMuMG or HaCaT cells were grown to 80% confluence in 100-mm cell culture dishes , serum starved for 4–12 h , treated with 2 ng/ml TGF-β or an inhibitor , washed with cold PBS , and lysed in lysis buffer . The lysates were precleared with rabbit or mouse IgG ( Jackson ImmunoResearch Laboratory ) and protein A/G Sepharose ( GE healthcare ) , followed by immunoprecipitation with anti-TβR1 ( Abcam ) or ShcA ( BD Biosciences ) antibodies , control GFP antibody ( Sigma-Aldrich ) , or IgG from the same species . Immune complexes were precipitated with protein A/G Sepharose ( GE healthcare ) , and separated on SDS-PAGE followed by immunoblotting . NMuMG and HaCaT cells were cultured in 12-well plates and transfected with SBE-binding firefly luciferase reporter plasmid [84] , and a Renilla luciferase reporter under the control of the thymidine kinase promoter ( Promega ) was cotransfected as control . After 24 h transfection , cells were treated with or without 800 pg/ml of TGF-β . The luciferase activities of firefly and renilla were quantified after 6 h of 800 pg/ml TGF-β treatment using the Dual Luciferase Kit ( Promega ) . The firefly luciferase activities were normalized to Renilla luciferase activity . Confluent NMuMG cells in 10 cm dishes were washed with ice-cold PBS and then harvested by scraping in hypotonic buffer ( 10 mM Hepes , 1 . 5 mM MgCl2 , 10 mM KCl , 1 mM PMSF , and 0 . 5 mM DTT with Roche protease inhibitor Mini Complete ) . The cells were lysed using a prechilled Dounce homogenizer ( 10–15 strokes with a tight pestle ) . Cell lysates were subjected to centrifugation at 228 g for 5 min at 4°C to pellet nuclei , and the cytoplasmic supernatant fraction was collected . The nuclear pellet was then resuspended in RIPA buffer ( 25 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 2 mM EDTA , 0 . 1% EDTA , 1 . 0% Triton X-100 , 0 . 1% SDS , and 0 . 25% sodium deoxycholate ) and sonicated with short sonication burst cycles of six times for 10 s . Protein concentrations in the nuclear and cytoplasmic extracts were determined using the Bradford assay . GST-fused Smad3 was generated in E . coli transformed with pGEX-Smad3 [85] and purified with Glutathione Sepharose 4B ( GE Healthcare ) . 293T cells were transfected to express Flag-tagged TβRI and myc-tagged TβRII and treated with 2 ng/ml TGF-β for 30 min . The TβRI was immunopurified using anti-Flag M2 affinity agarose . His-tagged p52ShcA was purified as described . Purified Flag-tagged TβRI was incubated for 20 min with mild shaking at 4°C with GST- Smad3 that was immobilized on glutathione-Sepharose . After three washes , the beads were incubated with His-tagged ShcA for 30 min at 4°C . After adsorption , bound and unbound proteins were separated by SDS-PAGE and analyzed by immunoblotting , as described [52] . Cell surface TGF-β receptors were visualized by cell surface protein biotinylation as described [49] . Briefly , NMuMG cells were grown to confluence , serum starved for 4–6 h , treated with 2 ng/ml TGF-β for 30 min to 1 h , and labeled with Sulpho-NHS-LC Biotin ( Thermo Scientific ) at 4°C for 30 min . Cells were washed with 100 mM glycine and lysed in lysis buffer . Biotinylated cell surface proteins were adsorbed to neutravidin agarose ( Thermo Scientific ) and analyzed by immunoblotting with antibodies to TβRI or TβRII . Active TGF-β was quantified using TMLC reporter cells with an integrated TGF-β/Smad3-responsive luciferease expression unit [86] . NMuMG cells , transfected with control or ShcA siRNA , were grown in serum-free medium for 2 h , conditioned media were collected , concentrated using Pierce protein concentrator ( Thermo scientific ) , and either kept at 4°C or heated at 80°C for 10 min to activate all latent TGF-β . The conditioned media samples were incubated with TMLC reporter cells in 12-well plates at a density of 150 , 000 cells/well for 12–16 h . In parallel , media samples with known concentrations of TGF-β were added to TMLC reporter cells to generate a standard curve . The luciferase activities of the conditioned media were calibrated against the standard curve TMLC reporter cells , allowing us to define the TGF-β concentration in conditioned media samples . NMuMG and HaCaT cells were grown to 80% confluence , serum starved for 2 h , treated with 2 ng/ml TGF-β for 30 min to 1 h , washed with cold PBS , and lysed with 0 . 5 M sodium carbonate buffer pH 11 . 0 . The subcellular fractionation was performed as described [54] . The cells were then homogenized using Dounce homogenizer with 15 tight strokes followed by three 20-s bursts of sonication ( Vibra-Cell ) . The lysates were centrifuged for 800 g to remove debris , adjusted to 4 ml of 40% sucrose in 10 mM HEPES buffer , pH 7 . 5 , and placed at the bottom of ultracentrifuge tubes . Then 30% sucrose was overlaid , followed by 5% sucrose , to generate by centrifugation a discontinuous 5%–40% sucrose gradient . Centrifugation was at 38 , 000 rpm for 16–18 h using a Beckmann Coulter Optima L-90K ultracentrifuge and rotor SW41 . Twelve 1 ml fractions were collected from the top of the tube and analyzed by SDS-PAGE . To quantify ShcA , TβRI , E-cadherin , N-cadherin , fibronectin , Snail , Slug , Smad7 , and PAI-I mRNA expression , NMuMG and HaCaT cells were treated with or without 2 ng/ml TGF-β , and RNA was isolated with RNeasy kit ( Qiagen ) and used as a template for reverse transcriptase . mRNAs were quantified by qRT-PCR with IQ SYBR green Supermix ( Bio-Rad ) and normalized against RPL19 mRNA . The primer sequences are shown in Table 3 . | TGF-β family proteins control cell differentiation and various cell functions . Increased TGF-β signaling , acting through heteromeric receptor complexes , contributes to carcinoma progression and fibrosis . TGF-β drives epithelial–mesenchymal transdifferentiation ( EMT ) , which enables cell migration and invasion . Upon TGF-β binding , “type I” receptors activate , through phosphorylation , Smad2 and Smad3 that control target gene transcription . In EMT , Smad complexes activate the expression of EMT “master” transcription factors and cooperate with these to repress the epithelial phenotype and activate mesenchymal gene expression . TGF-β receptors also activate Erk MAPK signaling , involving association of the adaptor protein ShcA and Tyr phosphorylation of ShcA by type I receptors . We now show that the predominant ShcA isoform , p52ShcA , competes with Smad2/3 for binding to type I TGF-β receptors , thus repressing Smad2/3 activation in response to TGF-β and localizing the receptors to caveolar compartments . Consequently , decreased ShcA expression enhanced TGF-β receptor localization in clathrin compartments and autocrine Smad2/3 signaling , repressed the epithelial phenotype , and promoted EMT . The changes following decreased ShcA expression resulted in increased cell migration and invasion , as well as increased stem cell generation , dependent upon autocrine TGF-β signaling . These findings position ShcA as a determinant of the epithelial phenotype by repressing TGF-β-induced Smad activation through differential partitioning of receptor complexes at the cell surface . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [] | 2015 | ShcA Protects against Epithelial–Mesenchymal Transition through Compartmentalized Inhibition of TGF-β-Induced Smad Activation |
Elimination of blinding trachoma by 2020 can only be achieved if affected areas have effective control programs in place before the target date . Identifying risk factors for active disease that are amenable to intervention is important to successfully design such programs . Previous studies have linked sleeping by a cooking fire to trachoma in children , but not fully explored the mechanism and risks . We propose to determine the risk for active trachoma in children with exposure to cooking fires by severity of trachoma , adjusting for other known risk factors . Complete census of 52 communities in Kongwa , Tanzania , was conducted to collect basic household characteristics and demographic information on each family member . Information on exposure to indoor cooking fires while the mother was cooking and while sleeping for each child was collected . 6656 randomly selected children ages 1-9yrs were invited to a survey where both eyelids were graded for follicular ( TF ) and intense trachoma ( TI ) using the WHO simplified grading scheme . Ocular swab were taken to assess the presence of Chlamydia trachomatis . 5240 ( 79% ) of the invited children participated in the study . Overall prevalence for trachoma was 6·1% . Odds for trachoma and increased severity were higher in children sleeping without ventilation and a cooking fire in their room ( TF OR = 1·81 , 1·00–3·27 and TI OR 4·06 , 1·96–8·42 ) . Children with TF or TI who were exposed were more likely to have infection than children with TF or TI who were not exposed . There was no increased risk with exposure to a cooking fire while the mother was cooking . In addition to known risk factors for trachoma , sleeping by an indoor cooking fire in a room without ventilation was associated with active trachoma and appears to substantially increase the risk of intense inflammation .
Trachoma remains the leading infectious cause of preventable blindness in the world , and a significant public health problem in endemic areas . [1] Elimination of trachoma by 2020 can only be achieved if all affected areas have effective control programs in place at a prudential period before the target date . The World Health Organization ( WHO ) advocates the SAFE strategy to control blinding trachoma ( Surgery , Antibiotics , Facial cleanliness , and Environmental change ) but identifying risk factors for trachoma that are amenable to intervention at family , or community level is important for designing successful control programs . [2] Children are the main reservoir of the disease , so by controlling the risk factors and rate of infection in this population , we can decrease the community pools and risk of transmission , hence a great step in elimination of endemic trachoma . Known risk factors for active trachoma include young age , poor water access , unclean faces , and other household characteristics that are markers of poor socioeconomic status . [3] We had previously also found an association between children sleeping by a cooking fire and increased odds of trachoma; however the assessment of exposure was a simple question . [4] This association makes biological sense as multiple other studies , but conducted in adults , have associated pollution from cooking fires with eye irritation and alteration of ocular immunity . [5–14] However , no detailed study of the risk of trachoma or infection with Chlamydia trachomatis in children , associated with a broader exposure to sleeping rooms and rooms with a cooking fire has been carried out . We propose to determine the risk in children ages 1 to 9 years of active trachoma , both follicular diseases and intense trachoma , with a detailed assessment of exposure to indoor cooking fire .
The research complied with the tenets of the Declaration of Helsinki and all guardians gave written informed consent for study procedures . Research was conducted with approval from the Johns Hopkins institutional review board and the national institute of medical research of Tanzania . This cross-sectional study was conducted in 52 communities in Kongwa district of central Tanzania . These communities underwent mass drug administration for the previous five years , and at the time of the survey were at least one year from having had treatment with azithromycin . The school curriculum stresses face washing as one of its hygiene components but no other hygiene campaign has being carried out . A complete census of households in each community was carried out and a random sample of 128 children from each community between ages 1–9 years was selected for the trachoma survey . A total of 5240 children from 4311 households were surveyed . Details are described below . The census was carried out prior to the survey by a trained census team that collected demographic information for all household members and household information including type of roof of the house , presence of latrine , distance to water source , level of education of the household leader , number of children in the house , age and sex of the children . A specific cooking fire questionnaire was done to collect information about the type , use and location of cooking stoves/fires in and around the house , at different times of the year . The characteristics of the room where each resident child slept was directly observed and recorded . We observed the presence or not of a cooking fire , and observed whether the room had ventilation defined as present if either of the following were present: the room had windows that allowed air in or vents at the top of the walls ( at least 3 inches of space between the roof and the wall where sky could be seen ) . Examination of each everted eyelid was performed by trained trachoma grader using a 2·5X loupe . The trachoma grader was trained by a GTMP certified grader ( HM ) , and had to have a kappa of > . 6 against the trachoma certified grader . Trachoma was assessed in both eyes using the WHO simplified grading scheme , which assesses the presence or absence of follicular trachoma ( TF ) , severe intense trachoma ( TI ) , conjunctival scarring ( TS ) , trichiasis ( TT ) , and corneal opacity ( CO ) . For this study , the relevant signs are TF and TI , signs of active trachoma . [15 , 16] For our analyses , we defined active trachoma as the presence TF or TI , alone or together , and Intense trachoma as TI alone , or with TF in at leat one eye . For quality control purposes photographs of the right upper eyelid of a random 20% sample of children examined ( using a Nikon D-40 camera with a 105mm f/2·8D AF Macro lens ) . These images were used to monitor the consistency of grading . Inter-observer agreement between the grader and the master grader ( SW ) was kappa 0·72 ( 95 CI: 0·62–0·82 ) . All children had ocular swabs taken from the left eye for determination of infection , using strict protocols to avoid field contamination . In each village a 5% sample of children had “air swabs” taken to check for field contamination . Samples were stored at KTP in a refrigerator and shipped within 30 days to the Johns Hopkins International Chlamydia Laboratory to be analyzed using the APTIMA ACT commercial test for C . trachomatis ( Gen-Probe Inc . , San Diego CA ) . Lab personnel were masked to the identified study and “air” swabs . None of the “air” swabs were positive . For sleeping in a room with a cooking fire , we created an exposure index as follows: the lowest exposure was sleeping in a room without a cooking fire and with ventilation , the next lowest was sleeping in a room without a cooking fire but with no ventilation , the higher category was sleeping in a room with a cooking fire but the room had ventilation , and the highest exposure was sleeping in a room with a cooking fire and the room had no ventilation . However , few households were at the high extreme only 3% of children slept in a room with a cooking fire , regardless of ventilation . Therefore , we used three levels to reflect exposure: sleeping in a room with a cooking fire , sleeping in a room with no cooking fire and no ventilation , and sleeping in a room with no cooking fire and with ventilation . Results were adjusted for age . Exposure while sleeping was assessed with logistic regression models to determine the associations between presence of TF and TI and exposure , adjusting for other risk factors . In the multivariate model , a backward elimination procedure was used to construct a parsimonious model that included only factors with significance level ≤ 0·5 . Exposure during the time a mother cooked was also assessed . We used stratified analyses to evaluate the effect of exposure to a cooking fire on risk of infection in children with trachoma . Random effects models including a random intercept for the community were used to account for the correlation of trachoma within residents of the same community . Less than 10% of the children belonged to the same household; in a sensitivity analysis we determined that adjusting for this level of clustering had no effects on our results . All analyses were conducted in SAS ( version 9 . 2 , SAS Institute Inc . , Cary , NC , USA ) .
A total of 6656 children were randomly selected , and 1416 ( 21% ) did not participate primarily because they were absent from the village the day of the exam; 5240 children from 4311 households were examined . Demographic characteristics of the participating children were slightly different than children who were non-participants ( Table 1 ) . Non- participants were more likely to be older , male , and live in houses where the water source was farther and the head of household had no former education . However , the two groups were similar in terms of the characteristics of the room where they slept . Non-participants tended to live in houses where the room with the cooking fire had ventilation . The majority , 99% , of households had an open fire stove with predominately wood or charcoal fuel . The overall prevalence of active trachoma was 6·1% . Three year olds had the highest prevalence ( 9·5% ) . TI was present in all age groups and in 38% of the active trachoma cases , shown in Fig 1A . The overall C . trachomatis infection prevalence for these groups was 3 . 8% and prevalence by age is shown in Fig 1B . A greater odds of active trachoma and severe trachoma was seen in children ages 1–5 years compared to the 6–9 year olds . After adjusting for age , a positive association was seen between active trachoma and severe trachoma and several indicators of socioeconomic status . Living in a household with no latrine , living farther from the water source , and in a house with a mud roof were all associated with active trachoma and severe trachoma ( Table 2 ) . Children whose head of household had no formal education were also significantly more likely to have active and severe trachoma . No increased risk was observed for children who spent time in the room with the cooking fire during cooking . However , we observed a dose response increase in odds of active trachoma , and especially severe trachoma , in children according to the proximity to the cooking fire and the degree of ventilation ( Table 2 ) . Compared to the lowest risk category , children who slept in a room without the cooking fire but no ventilation had an increased odds of trachoma , and children who slept in a room with a cooking fire had a 2·4 fold increased odds of active trachoma and a 5·5 fold increased odds of TI . in children who slept in a room with a cooking fire . After multiple adjustments for age and the other variables , only longer distance to water remained a significant association with trachoma and severe trachoma . Living in a house with a mud roof , or no latrine , was no longer significant ( Table 3 ) . Children who slept in a room with a cooking fire were 1·8 times more likely to have active trachoma and 4·1 times as likely to have severe trachoma as children who slept in a room with ventilation and without a cooking fire . Only the younger children were exposed to the cooking fire during the day while their mothers cooked , resulting in exposure confounded by age . We restricted the analyses to children age 0–5 years and examined the relationship between active and severe trachoma with exposure while cooking , according to the characteristics of the room ( Table 4 ) . Although the prevalence of active trachoma was highest in children who were exposed in a room with no ventilation , the test for trend was not significant . We examined the infection rates in children according to trachoma status and type of sleeping room . We had few infections , but the trend for increasing infection in children with trachoma who slept in unventilated rooms or slept with a cooking fire was observed ( Table 5 ) .
Our study found a strong relationship between cooking fire exposure while sleeping and active trachoma , especially severe trachoma in children . There is good biologic plausibility why trachoma may be higher in children exposed to indoor air pollution ( IAP ) . In addition to increased tearing and irritation , which may result in auto-re-infection , IAP appears to have a direct effect on the immune system . In women who were users of biomass fuels , an increased TH2 response was described with an increase in the Treg cells , CD4+ and CD25+ , a subset that can inhibit effector T cell response . [9] In this case , much of the regulatory activity is exerted by IL-10 and TGFβ , perpetuating the TH2 response and leading to chronic inflammation , and less clearance of infection . [7 , 8] Our data supports this finding , as we noted that infection rates were higher with TF in children who were exposed to a cooking fire , suggesting a prolongation of infection . Another study in children described the effects in T cell immunity from exposure to ambient polycyclic aromatic hydrocarbons , which are compounds produced from combustion of organic matter such as wood or coal . Epigenetic modifications associated with impaired immunity suggesting an increased TH2 response was observed as well . [17] For clearance of acute trachoma , an adequate CD4+ response of the Th1 phenotype appears to be necessary , and the Th1 cytokine gamma interferon assists in infection clearance . So not only might IAP lead to effects that delay clearance of infection , but might also increase risk for trachoma sequelae . Previous studies of exposure to a cooking fire while cooking in women in non-trachoma areas reported an increasing eye irritation , conjunctival damage and tearing of the eyes . [5 , 6 , 18] Similar ocular effects were reported in children exposed to wildfire smoke exposure in California Ellegard and Diaz describe in their studies an increase in tear production or “tears while cooking” ( TWC ) in women and the direct association with indoor air pollution ( IAP ) . The study also found a strong relationship between ocular symptoms in women who cooked inside a room , compared to women who cooked outside , suggesting a role for ventilation . [6] We did not find a statistically significant association of trachoma in children who were in the cooking fire room during cooking , although the greatest prevalence of trachoma in children age five years and under was in those in a cooking room with no ventilation . In addition , the type of stove used also has been shown to play a role in eye symptoms in women . Smoke free stove versus traditional or open fire stoves have been assessed in relationship with the amount of IAP in the house and symptoms that the women experienced . [5 , 6] Women were found to have increased eye symptoms when exposed to open fire stoves especially those who use wood or charcoal as fuel . In our study population 99% of the women cooked using an open fire stove with predominately wood or charcoal , adding to the burden of indoor air pollution . It is possible that the children who sleep in a room with cooking fire or rooms that are not ventilated belong to poorer families with less access to water and general sanitation . We tried to account for these socioeconomic factors in our study and found a strong , independent effect of sleeping in a room with a cooking fire . Interestingly , we found that 3% of the children slept in a room with a cooking fire , compared to the results from the same district in 1986 , where 60% of the children slept in a room with a cooking fire . [4] In 1986 , the estimated prevalence of trachoma in children ages 1–7 years was 60% , and at that time , most of the children slept on animal skins in the room with a cooking fire . Since 1986 , many changes have occurred , with most households now building a separate cooking fire area from the sleeping room , and using beds for sleeping . Trachoma has declined in this district , most markedly in the last eight years in connection with trachoma control measures , to an overall estimate last year of 12% . This change is encouraging , suggesting some environmental and socioeconomic improvement in these communities . A difference between age groups was noted as well , where younger children had more TF and TI than older children . This age difference in clinical signs has previously been described . [19–27] and may reflect decreased exposure to re-infection as children reach school age . Of note , the older aged children did not spend time during the day in the room with the cooking fire , and we had to confine our analyses of exposure during cooking to the younger aged children . There were some significant differences observed between the participating and non-participating children , the latter being older and more likely to be male , and to live in a house farther from a water source . However , the groups were similar in terms of exposure to cooking fire while sleeping . Since those who are older are less likely to have trachoma , but those who live far from water are more likely to have trachoma , the effects of these differences are uncertain . In any case , there was no difference in exposure to cooking fire , suggesting an absence of bias due to differential participation . In addition , these communities are also undergoing mass drug administration . The last MDA was more than one year ago for these villages , but the low rates of trachoma , 6% , likely reflect at least in part the high compliance with this program . We were underpowered to detect a significant difference in infection rates in trachoma in children who slept in a room with a cooking fire and those who did not , although the trend was observed . The fact that Taylor et al in 1986 found a similar risk for sleeping next to a cooking fire in these communities prior to any intervention argues for an effect of exposure on trachoma even when disease rates are low or in the presence of a program with MDA . [4] In conclusion we confirmed that those children who sleep in a room with a cooking fire have an increased risk of trachoma independently from other risk factors for disease . The prevalence of exposure to sleeping next to a cooking fire in this population has declined over time , and programs to encourage continued decline are warranted . Further studies on the impact of exposure to indoor air pollution and the risk of trachoma sequelae should be done . | Trachoma remains the leading preventable cause of infectious blindness in the world . Identifying risk factors for active disease that are amenable to intervention is key to successfully designing effective control programs to eliminate blinding trachoma . Association between cooking fire and eye inflammation makes biological sense , and multiple other studies , but conducted in adults , have been reported . This is the first detailed study of the risk of trachoma or infection with Chlamydia trachomatis in children associated with a broader exposure to cooking fires . We were able to identify an important risk factor: a strong relationship between cooking fire exposure while sleeping and active trachoma that appears to substantially increase the risk of intense inflammation , which may play a role in perpetuating the disease in the community reservoir , the children . Hence a higher risk for transmission and re-emergence in communities seeking elimination . This finding may suggest modifications to current behavioral risks that were not considered before and may significantly impact the progression of the disease . | [
"Abstract",
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"Materials",
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] | [] | 2015 | Exposure to an Indoor Cooking Fire and Risk of Trachoma in Children of Kongwa, Tanzania |
Thosea asigna virus ( TaV ) , an insect virus belonging to the Permutatetraviridae family , has a positive-sense single-stranded RNA ( ssRNA ) genome with two overlapping open reading frames , encoding for the replicase and capsid proteins . The particular TaV replicase includes a structurally unique RNA-dependent RNA polymerase ( RdRP ) with a sequence permutation in the palm sub-domain , where the active site is anchored . This non-canonical arrangement of the RdRP palm is also found in double-stranded RNA viruses of the Birnaviridae family . Both virus families also share a conserved VPg sequence motif at the polymerase N-terminus which in birnaviruses appears to be used to covalently link a fraction of the replicase molecules to the 5’-end of the genomic segments . Birnavirus VPgs are presumed to be used as primers for replication initiation . Here we have solved the crystal structure of the TaV RdRP , the first non-canonical RdRP of a ssRNA virus , in its apo- form and bound to different substrates . The enzyme arranges as a stable dimer maintained by mutual interactions between the active site cleft of one molecule and the flexible N-terminal tail of the symmetrically related RdRP . The latter , partially mimicking the RNA template backbone , is involved in regulating the polymerization activity . As expected from previous sequence-based bioinformatics predictions , the overall architecture of the TaV enzyme shows important resemblances with birnavirus polymerases . In addition , structural comparisons and biochemical analyses reveal unexpected similarities between the TaV RdRP and those of Flaviviruses . In particular , a long loop protruding from the thumb domain towards the central enzyme cavity appears to act as a platform for de novo initiation of RNA replication . Our findings strongly suggest an unexpected evolutionary relationship between the RdRPs encoded by these distant ssRNA virus groups .
RNA viruses strictly depend upon their RNA-dependent polymerases ( RdRPs ) for genome transcription and replication . Detailed structural and functional knowledge of RdRPs using different replication-transcription strategies may provide essential clues for the control of virus propagation . Although RdRPs share limited sequence similarities , their three-dimensional structures and mechanisms of action are closely related . All RdRPs have a closed right hand-like shape encircling seven motifs , A to G , containing highly conserved amino acids that are essential for polymerase function [1 , 2] . The four so-called palm motifs , arranged in the order A , B , C and D , are the most conserved feature of viral RdRPs , with motifs A and C containing the catalytic aspartic acid residues [1] . Exceptions to this design have been reported in members of the Birnaviridae and Permutotetraviridae families , harboring double-stranded ( ds ) and positive ( + ) single-stranded ( ss ) RNA genomes , respectively . In these enzymes , motif C is located upstream of motif A forming a non-canonical C-A-B arrangement with a unique connectivity of the major structural elements of the active site [3 , 4] . Non-canonical palm connectivity has also been described in the RdRP encoded by Grapevine virus Q ( GVQ ) [5] , an alpha-like plant tymovirus . However , bioinformatics analyses suggest that whereas the permuted RdRPs from birna- and permutotetraviruses share a monophyletic origin that of GVQ evolved independently [5] . Besides the permuted connectivity of the palm subdomain , birna- and permutatretravirus replicases also share a conserved N-terminal region , including a VPg sequence motif ( Y/FXXGS/TXXGXXXRL ) that in birnaviruses seems to be used to covalently link a fraction of the replicase molecules to the 5′-end of the genome segments . Birnavirus VPg molecules are likely used as primers for replication [6–8] . Furthermore , it has been hypothesized that the putative VPg signal of permutatetraviruses would be also for RNA synthesis priming [3 , 4] . The X-ray structures of permuted RdRPs from two dsRNA viruses , i . e . infectious bursal disease virus ( IBDV ) and infectious pancreatic necrosis virus ( IPNV ) , belonging to the Birnaviridae family , have been reported . Despite their non-canonical connectivity , the overall architecture of their catalytic sites is akin to those of canonical RdRPs [9–11] . Indeed , the structural similarities of birnavirus RdRPs to their picorna- and calicivirus counterparts conveyed key evidence supporting the existence of an evolutionary link connecting dsRNA birnaviruses and +ssRNA viruses [9] . In addition , a structure-based mutational analysis on the IPNV RdRP revealed that an N-terminal serine residue is required for the formation of covalent RdRP-RNA complexes [11] . Despite the obvious structural and functional interest as well as its critical importance for the understanding of evolutionary relationships between dsRNA and +ssRNA viruses sharing permuted RdRP palms , information about non-canonical +ssRNA RdRPs was missing . In this report , we describe the structural and functional characterization of the RdRP domain of the permutotetravirus Thosea asigna virus ( TaV ) , an insect virus which infects larvae of Setothosea asigna ( Lepidoptera ) , the major defoliating pest of oil and coconut palms in Southeast Asia . Progress in the molecular characterization of TaV , as well as other members of this family , has been hampered by the difficulty in growing them in tissue culture [12] . Our results include the first crystal structures of the TaV enzyme in its apo form ( 2 . 15 Å resolution ) , bound to CTP and to GTP ( 2 . 25 Å and 2 . 3 Å resolution , respectively ) , and to a short ssRNA template in the presence of an incoming ATP ( 3 . 5 Å ) . Surprisingly , the TaV RdRP structures closely resemble those of polymerases encoded by flaviviruses ( a family that includes important human pathogens as dengue [DV] , West Nile [WNV] or hepatitis C [HCV] viruses ) , exhibiting a number of peculiarities typically found in enzymes using de novo replication initiation mechanisms . Of particular importance is the presence of a long loop protruding from the thumb subdomain that is the binding site for the incoming rNTP as evidenced in the structure of the RdRP-ssRNA-ATP ternary complex . Additionally , in vitro polymerization assays show that the TaV enzyme is active in a primer-independent reaction , thus confirming the existence of a functional relationship with flaviviral RdRPs .
The full-length TaV ORF1 ( 140 kDa ) fused to an N-terminal tail containing an hexa-histidine Tag ( TaV rORF1; Fig 1A ) was expressed in Hi5 insect cells infected with a recombinant baculovirus , rBV-TaV ORF1 . After expression , the recombinant protein was rapidly cleaved releasing a 75 kDa protein fragment ( Fig 1B ) [13] . Mass spectrometry ( MALDI-TOF/TOF ) showed that this polypeptide harbors the first 674 residues of the recombinant protein , including the whole RdRP domain ( TaVpol from here on; Fig 1A ) . The final purification step , size exclusion chromatography , showed that TaVpol is a dimer in solution ( Fig 1C ) . This construct was used for both crystallographic analyses and functional characterization of the RdRP activity . In addition , other protein constructs , harboring mutations at the active site motifs C TaVpol ( D351A/D352A ) and B TaVpol ( T443A/T444A ) and at the two putative nucleotidylation sites , TaVpol ( S4A ) and TaVpol ( T157A ) , or deletions at the N- and/or C-terminus of the protein , TaV rORF1 ( Δ27 ) , TaVpol ( Δ27-Δ657 ) and TaVpol ( Δ611–617 ) , were also expressed and purified ( Fig 1A ) . It is important to note that the TaV rORF1 ( Δ27 ) mutant gene resulted in a protein variant that does not undergo a significant proteolytic degradation in insect cells , thus allowing the purification of the whole polypeptide ( Fig 1B ) . Strikingly , this protein appears to be a monomer in solution as determined by analytical size exclusion chromatography ( Fig 1C ) . The truncated version TaVpol ( Δ27-Δ657 ) is also a monomer ( Fig 1C ) . A small amount of purified full-length TaV rORF1 was obtained and used in subsequent activity assays . The in vitro RNA synthesis activities of the full length TaV rORF1 and its RdRP domain TaVpol were first analyzed using a ssRNA template derived from the 3’ untranslated region ( UTR ) of the TaV genome [4] , demonstrating that both constructs are able to synthesize dsRNA from a ssRNA template in the absence of primer , in a reaction dependent of Mg2+ as catalytic ion ( Fig 2 ) . The RdRP activity of TaVpol was also tested in the presence of a short RNA primer ( 8-nts ) complementary to an internal sequence of the TaV 3’-UTR , showing equivalent levels of RNA synthesis ( Fig 2A ) . In addition , the use of ssRNA templates of totally heterologous sequences ( as the 3’-UTR of a nodavirus genome; Fig 2A ) indicates that , at least in vitro , the TaV enzyme does not require specific template sequences or secondary structures for polymerization . RNA polymerization activity was also observed on short RNA templates ( from 6 to 25 nucleotides ) harboring either unrelated or TaV 3’-UTR-derived sequences ( Fig 2B ) . These data illustrate that , although TaVpol is able to carry out de novo RNA synthesis on small non-specific templates , the presence of a guanine at the 3’-end of the template seems to be necessary to initiate the reaction . Like the rest of the well-known polymerases , the RdRP activity of TaVpol is strictly dependent on metal ions as Mg2+ and Mn2+ ( Fig 1C ) . As described before [14–16] , the cofactor Mn+2 strongly enhances RNA synthesis . In contrast to what was observed for the non-canonical IBDV RdRP [17] , only residual activity was observed in presence of 1 mM Co2+ ( Fig 1C ) . Furthermore , the replacement of either Mg2+ or Mn2+ by other divalent cations , i . e . Ca2+ or Zn2+ , exerts a clear inhibitory effect on RNA synthesis ( Fig 1C ) . The polymerization kinetics analysis performed under optimal conditions for this enzyme ( 1 . 3 mM RdRP in 50 mM MES pH 6 , 150 mM NaCl and 5 mM MgCl2 at 35°C; S1 Fig ) shows a sigmoid profile with an initial step of low RNA synthesis and an end state of saturation ( S2 Fig ) . The structure of TaVpol was solved by SAD methods from Lu3+ derivative co-crystals to 3 . 0 Å resolution ( Table 1 ) [13] . Native data was then used to complete and refine the model to a final resolution of 2 . 15 Å ( Table 1 ) . The crystal asymmetric unit comprises a tightly packed polymerase dimer containing 1 , 326 residues: from P10 to K672 of molecule A and from P10 to E674 of molecule B . Monomers A and B are almost identical , with a r . m . s deviation of 0 . 27 Å for the superimposition of all residues . Each monomer consists in a globular RdRP core ( residues 41–648 ) and two terminal arms ( residues 10–40 and 649–674 ) that extend out of the core and are involved in a number of intermolecular interactions that stabilize the dimeric structure ( Fig 3 ) . The RdRP core adopts the classical closed “right-hand” architecture consisting of fingers ( helices α3-α13 and α15-α16; amino acids 41–303 and 375–443 ) , palm ( α14 , β6-β8; 304–374 and α17-α18 , β9-β10; 444–519 ) , and thumb ( α19-α24; 520–649 ) sub-domains , encircling the seven conserved motifs ( A to G ) that are required for substrate recognition and catalysis ( Fig 4A ) . As expected from previous bioinformatics predictions [3 , 4] , structural comparisons using Dali [18] show important similarities between TaVpol and birnavirus polymerases . The highest hits were obtained with the IPNV ( PDB id 2YIB ) and IBDV ( PDB id 2QJ1 ) RdRPs which showed Z scores of 25 . 4 and 21 . 9 and r . m . s deviations of 3 . 1 and 2 . 9 Å for the superimposition of 523 and 524 residues , respectively . Moreover , unexpected and striking resemblances were also observed when the overall TaVpol architecture was compared to those of different members of the Flaviviridae family , with Z scores of 16 . 1 ( Japanese encephalitis virus; PDB id 4K6M ) , 13 . 8 ( DV; PDB id 4V0R ) and 13 . 8 ( HCV; PDB id 2XIZ ) with r . m . s deviations of 3 . 3 , 3 . 3 and 3 . 4 Å for the superimposition of 448 , 444 , and 330 residues , respectively . Similar results were obtained when the individual subdomains were superimposed ( S3 Fig ) . The putative VPg signal ( residues 153–165 ) is located at the index finger ( PV nomenclature [20] ) , covering the α5-α6 connection and the α6 N-terminus ( Fig 4A ) . The structure of this motif appears closely related to its birnavirus counterpart ( Fig 5A ) [9–11] . Upstream this motif , three helices ( α3-α5 ) also contribute to the index finger crossing the palm sub-domain to interact with the thumb and closing the right hand structure ( S4 Fig ) . Finally , α3 is linked by a long loop to the N-terminal helices α2 and α1 that extend outside the polymerase core . Large structural differences are observed in this N-terminal region when birnavirus and permutatetravirus are compared ( Fig 5A ) . The self-nucleotidylation activity of the TaV enzyme was analyzed in vitro using both TaVpol and TaV rORF1 constructs in the presence of the TaV-derived ssRNA template described above . Auto-nucleotidilation of TaVpol has not been detected ( Fig 5B ) . In addition , TaVpol ( T157A ) and TaVpol ( S4A ) mutants , where the predicted nucleotidylation residues [4 , 11] were replaced by alanine , maintain levels of RNA synthesis similar to those detected with the wild type enzyme in in vitro polymerization assays ( S5 Fig ) . Only the full-length TaV rORF1 retains the α-32P GTP radioactive signal ( Fig 5B ) . Although more experiments are required to precisely map the guanylation site , this observation indicates that the TaV ORF1 C-terminus is essential for self-nucleotidylation . The C-A-B permutation of the TaVpol palm , with the GDD motif ( residues 350–352 ) , located at the β6-β7 hairpin , and motif A residues D369 and D374 , lying at the end of strand β8 , is spatially compatible with a canonical organization of the active site ( Fig 4A ) . Similar palm architectures were found in the RdRP structures of birnaviruses [9–11] . The helical thumb of TaVpol is larger than the thumb domains of other ssRNA RdRPs known to initiate replication in a primer-dependent manner as picorna- and calicivirus polymerases [2 , 21] . In addition , the TaVpol thumb possesses a long loop ( λ6; residues 591–625 ) , protruding into the central cavity that is structurally equivalent to the priming loops of flaviviruses and bacteriophage ϕ6 [21–24] ( Fig 4B ) . Structural comparisons show that the λ6 loop , connecting helices α20 and α22 , originates from the same part of the thumb subdomain as for flaviriruses DV and West Nile Virus ( WNV ) but is larger and contains two secondary structural elements in its N-terminus: the short α21- and the one turn 310 η8-helices ( Fig 4A and 4B ) . The position of this element is stabilized by interactions established between different α21 residues which contact the α1 helix at the polymerase N-terminus , and between the tip of the loop ( residues 613–616 ) with residues 301–304 and 317–320 within helix α12 and the loop α12-α13 , respectively . To further investigate the role of the λ6 loop in TaV RdRP activity , we generated a deletion mutant , TaVpol ( Δ611–617 ) , expected to display an open active site , lacking the putative priming platform which supports the rNTP primer during de novo initiation but that , in turn , may favor the accommodation of the newly synthesized dsRNA during elongation . The RNA elongation activity was then tested using the RNA template derived of the TaV 3’-UTR . Analysis of reaction products on denaturing polyacrylamide gels showed an increased activity of the TaVpol ( Δ611–617 ) mutant on this template when compared to the original enzyme ( Fig 4C ) . Comparable increased activities were also observed after similar deletions within the equivalent priming loops of HCV and DV RdRPs [25 , 26] . As the long ssRNA template used in these assays is able to form a fork by base complementarity that can be placed in the RdRP central cavity , the observed elongation products of this mutant would be generated by back-primed RNA synthesis . Supporting this interpretation , the de novo RNA synthesis on short oligonucleotide templates is abolished in the TaVpol ( Δ611–617 ) mutant ( S6 Fig ) . Both polymerase molecules in the asymmetric unit associate in a pseudo two-fold molecular axis . The contact surface between these two molecules , calculated using the PISA program [27] , shows a total area of 6 , 038 Å2 ( ~11% of its total surface ) and predicts a dimer stabilizing energy of ΔGdiss = 46 . 3 kcal/mol . The interface of the interaction involves: ( i ) the N-terminus of one molecule contacting the active site cavity of the second polymerase; and ( ii ) the C-terminal end of one molecule contacting the top fingers of the second one ( Fig 3 ) . Interactions mediated by the polymerase N-terminus involve the visible part of the N-terminal end ( residues 10–14 ) , and helix α1 ( residues 15–29 ) that extends towards the central cavity of the neighboring molecule ( dyad related ) , contacting the finger helix α8 ( residues 205–207 ) and the α8-α9 loop ( 208–225 ) . The intermolecular interactions are mainly main-chain main-chain hydrogen bonds but also include a salt bridge between R37 and D101 ( β3 ) . The first visible residue in the electron density ( P10 ) occupies the base of the template channel , at approximately the expected position of the first templating nucleotide , in close contact with residues 209–211 ( Fig 3B ) . These contacts involve a total of 38 residues , covering a surface of 2 , 725 Å2 with an energy ΔGdiss = 16 . 5 kcal/mol . Equivalent crystals have been obtained after the enzymatic cleavage of the N-terminal hexa-histidine tag . Unfortunately , the resulting structure did not revealed additional information about the positioning of the first nine protein residues . In order to explore the functional role of the polymerase N-terminus in close contact with the template channel of the neighboring enzyme , we designed a TaV rORF1 mutant , TaV rORF1 ( Δ27 ) , lacking the first 27 N-terminal residues ( Fig 1 ) . Surprisingly , TaV rORF1 ( Δ27 ) does not undergo the cleavage into the 75 kDa polypeptide observed in the full-length protein and , in addition , it is organized as a monomer in solution ( Fig 1C ) . Polymerization assays performed with this mutant as well as with the TaVpol ( Δ27-Δ657 ) construct show that the elimination of the first 27 residues that prevents dimer formation also causes a significant increase on RNA synthesis ( Figs 3A , bottom inset , and S7 ) . The TaVpol C-terminus is formed by a long arm ( residues 649–674 ) that extends along the finger helices α8 , α9 and α14 at the external surface of the protein ( Fig 3C ) . The C-terminal-mediated interactions include 43 residues , forming a contact surface of 3 , 313 Å2 , with a ΔGdiss = 17 . 2 kcal/mol . TaVpol dimers were also observed by negative staining transmission electron microscopy ( S8 Fig ) , indicating that the dimer structure , first observed in crystals , is stable in solution and maintained even at very low protein concentrations . TaVpol-ssRNA-rNTP complex co-crystals were obtained after incubation of TaVpol with the oligonucleotide template 5’-CCCAUUCGACUCCUG , ATP , CTP and MnCl2 . This complex crystallized in the space group I222 with one TaVpol dimer in the asymmetric unit . The structure was solved by Molecular Replacement and refined to 3 . 5 Å resolution ( Table 1 ) . Structural comparisons between unbound and ssRNA-rNTP-bound enzymes revealed two significant conformational changes: ( i ) a ∼7˚ rotation of one monomer with respect to the other in the dimer; and ( ii ) a conformational rearrangement of the polymerase N-terminus , resulting in a subtle opening of the central cavity that facilitates template entry ( S9 Fig ) . The structural analysis of this complex revealed the presence of an extra density at the polymerase active site in one of the two molecules of the crystal asymmetric unit ( Molecule B ) . This density , was interpreted as the presence of a bound ATP molecule , with the ATP base tightly stacked to residues Y611 and F613 of loop λ6 and the triphosphate moiety occupying the nucleotide entry tunnel , contacting the basic residues R280 , K278 of motif F and K488 of motif D ( Fig 6A and 6B ) . Unfortunately , the electron density corresponding to the ssRNA template was too weak and discontinuous to allow the building of an accurate model . However , strong peaks were detected along the template channel of the polymerase that would correspond to the phosphate moieties of the oligonucleotide bound in a similar orientation to that of templates derived from the superimposition of available RdRP-ssRNA complexes onto the TaV enzyme ( S10 Fig ) . Furthermore , an additional peak of electron density was also seen in close contact with the motif B residues T443 and T444 , far from the trajectory of the putative template phosphodiester chain ( S10 Fig ) . The X-ray analysis of a second TaVpol-ssRNA complex indicated that this density would correspond to the template base at position 3’ that overshoots its predicted binding site in front of the incoming rNTP , appearing tightly packed to these motif B residues . Unfortunately , only a partial data set could be collected from these crystals ( 53 . 8% completeness to 3 . 1 Å resolution; S10 Fig ) . Motif B contains a number of S/T residues strictly conserved in RdRPs that are involved in template binding and translocation of the newly synthesized dsRNA [28–29] . The TaVpol-RNA complex suggests that these motif B residues might also serve as a binding site for the terminal base of the template in a pre-initiation stage . To assess the role of these conserved residues on RNA synthesis , T443 and T444 were substituted by Ala . The RdRP activity of the mutant enzyme was analyzed in vitro , showing that the T→A replacements at positions 443 and 444 of TaVpol completely abolish RNA synthesis ( S10 Fig ) . TaVpol-GTP and TaVpol-CTP co-crystals were also obtained in presence of MgCl2 and the corresponding structures solved to 2 . 3 Å and 2 . 25 Å resolution , respectively ( Table 1 ) . In both cases , clear electron densities were observed for the triphosphate moieties interacting with electropositive residues at the rNTP tunnel . However , the corresponding nucleoside parts were disordered . In addition , these structures revealed a second nucleotide binding site in a totally unexpected region , a cavity inside the thumb subdomain , at about 30 Å from the active site ( Fig 6C ) . In this position , the nucleoside moieties of the bound NTPs contact residues M556 , T560 and D563 from the α19 helix and to E601 from the λ6 N-terminus , whereas the triphosphate moieties remain partially exposed to the solvent , appearing mostly disordered . It should be noted that the nucleotides are bound next to a strong electropositive region , residues R564 , R545 and R269 , also containing an extra density peak , interpreted as a sulfate molecule derived from the crystallization solution ( Fig 6C ) . Noteworthy , this sulfate is present in all other analyzed TaVpol structures .
The structure of TaVpol confirms the existence of a close relationship between birnaviruses ( dsRNA ) and permutotetraviruses ( ssRNA ) RdRPs , as previously predicted by sequence analyses [3] . In addition , it reveals the presence of unexpected elements ( i . e . the λ6 loop and a terminal arm ) controlling RNA synthesis activity . These elements are present not only in this particular enzyme but also in other RdRPs performing priming independent replication initiation such as those of flaviviruses with which it also shares functional characteristics . Although at this point is not feasible to establish whether our findings reflect convergent or divergent evolution , the striking structural and functional similarities shared by permutotetra- and flavivirus RdRPs , reported here , constitute the first evidence about the existence of an evolutionary relationship connecting the polymerases of these two apparently distant virus groups . Three alternative scenarios for the evolution of polymerases harboring permuted palms can be envisaged: ( i ) a circular permutation giving rise to a non-cannonical RdRP might have taken place in a population of +ssRNA viruses likely belonging to the flavi-like Group II . Thereafter , both canonical and non-canonical polymerases might have coexisted until the advent of the dsRNA birnavirus ancestor; ( ii ) the horizontal transfer of an ancestral permuted RdRP gene between members of two otherwise unrelated +ss and dsRNA virus lineages [44]; and ( iii ) the occurrence of two independent circular permutation events in dsRNA and +ssRNA virus lineages . Although at first these three genetic scenarios seem equally feasible , the remarkable resemblances between capsid proteins of birnaviruses and members of the old Tetraviridae family [45–47] advocate the first alternative . Indeed , this hypothesis might entail the existence of a common birna- , tetra- and flavivirus ancestor RdRP polypeptide .
Expression and purification of TaV rORF1 and TaVpol were previously reported [13] . Briefly , a recombinant baculovirus ( rBV ) , expressing the whole ORF1 polypeptide of Thosea asigna virus ( 1257 residues , 140 kDa; GenBank accession number AF282930 . 1 ) fused to a 6xhistidine tag and containing the TEV protease recognition site ( hTaV ORF1 ) , was generated , according to the Bac-to-Bac protocol ( Invitrogen ) . H5 cells ( Invitrogen ) were infected with the hTaV ORF1 rBV , harvested at 72 h post-infection , washed twice with PBS , resuspended in lysis buffer ( 50 mM Bis Tris pH 6 . 8 , 500 mM NaCl , 0 . 1% Igepal CA-630 ) supplemented with protease inhibitors ( Complete Mini; Roche ) , and maintained on ice for 20 min . After 20 min centrifugation ( 13 , 000xg ) at 4°C , supernatants were collected , and subjected to metal affinity chromatography batch purification using a Co2+ affinity resin ( TALON , Clontech ) . Resin-bound hTaV ORF1 was eluted with elution buffer ( 50 mM Bis Tris pH 6 . 8 , 500 mM NaCl , 500 mM imidazol ) . SDS-PAGE showed that the purified polypeptide was partially cleaved , resulting in a product of ~75 kDa ( Fig 1 ) . The recovered product was analyzed by mass spectrometry ( MALDI-TOT/TOF ) to assess for the integrity of the RdRP domain , included within the first 674 amino acids of the TaV ORF1 . The resulting polypeptide was further purified by size exclusion chromatography on a Superdex 200 HR 10/300 column ( buffer 50 mM MES pH 6 . 0 , 500 mM NaCl , 10% glycerol and 5 mM DTT ) . Finally , the purified TaVpol was pooled and concentrated to 10 mg/ml . In addition to the rORF1 and TaVpol , other protein versions harboring either point mutations TaVpol ( S4A ) , TaVpol ( T157A ) , TaVpol ( D351A/352A ) or TaVpol ( T443A/444A ) or deletions TaV rORF1 ( Δ27 ) TaVpol ( Δ27-Δ657 ) or TaVpol ( Δ611–617 ) , were also generated ( Fig 1 ) , expressed and purified in a similar fashion as for the wild-type protein , lacking only the size exclusion chromatography step . In order to obtain the intact TaV rORF1 protein , 150 μM leupeptine hemisulfate ( Apollo Scientific ) was added both in the Hi5 cell culture and in the lysis buffer during the purification process that was performed in the conditions described above . For biochemical analysis , and once in the crystallization of the apo-form of the enzyme , proteins were treated with TEV protease to eliminate the recombinant tag . ssRNA oligonucleotides of 6- , 12- and 16-nts corresponding to the 3’-end of the TaV 3’-UTR and to heterologous sequences of 13- and 25-nts length were purchased ( Biomers . net ) . The 311-nts ssRNA template , formed by the fusion of a heterologous sequence ( 171-nts ) to the 5’-end of a 140-nts fragment corresponding to the 3’-end region of the TaV 3’-UTR [4] , was produced by in vitro transcription , using as template the pRSET-A/TaV-3’UTR plasmid described below . A fragment corresponding to the last 140-nts of the TaV 3’-UTR flanked by BamHI and EcoRI restriction sites was synthesized in vitro ( GeneScript ) . After restriction with BamHI and EcoRI , the fragment was cloned into the pRSET-A vector previously digested with the same enzymes . The resulting plasmid , pRSET-A/TaV-3’UTR , was linearized by digestion with EcoRI and used as a template for in vitro transcription reactions using a commercial kit ( RiboMax , Promega ) according to the manufacturer specifications . ssRNA was isolated from agarose gels , recovered by electroelution ( International Biotechnologies Inc . ) , precipitated with ammonium acetate/ethanol , and resuspended in DEPC-treated H2O . The unrelated template sequence of 320-nts ssRNA was obtained as described above by fusing the same 171-nts heterologous sequence to the 5’-end of the 149-nts fragment , corresponding to the 3’-end region of RNA1 of the SJNV genome [48] . Polymerase activity assays were performed following a previously described protocol [49] with minor modifications . Briefly , reaction mixtures containing 1 μg of purified TaVpol and TaV rORF1 wild type or variants ( Fig 1 ) , were prepared in 40 μl of transcription buffer ( 50 mM MES pH 6 . 0 , 100 mM NaCl , 5 mM MgCl2 , 10% glycerol , 1 mM DTT , 1 mM rATP , rGTP and rCTP , 0 . 02 mM rUTP , 20 units of RNasin , and 10 μCi [α-32P] rUTP ) , supplemented with 5 μl of ssRNA+ template ( 0 . 2 mg/ml ) . Samples were incubated at 35°C for 1 h , or the time indicated in each experiment , heated to 100°C for 3 min to stop the reaction , and subsequently digested with 0 . 2 mg/ml of Proteinase K for 1 h at 37°C . Reaction products were mixed with loading buffer ( 10 mM Tris-HCl pH 7 . 5 , 15% Ficoll 400 , 50 mM EDTA , 0 . 03% orange G , 0 . 03% bromophenol blue , and 0 . 03% xylene cyanol ) supplemented with 80% formamide , incubated at 60°C during 10 min and subjected to electrophoresis in 7% polyacrylamide TBE ( 90 mM Tris , 64 . 6 mM boric acid , and 2 . 5 mM EDTA , pH 8 . 3 ) gels . Radioactive signals were detected with a Storm gel imaging system ( Molecular Dynamics ) . Results were analyzed and quantified with Image Quant software ( Molecular Dynamics ) . Additionally , filter binding and liquid scintillation counting were used to monitor RdRP activity . Aliquots of the reactions ( 20 μl ) were spotted onto DE-81 filter discs ( Whatman ) . Filters were dried , washed three times with 50 mM K2HPO4 pH 7 . 4 and once with ethanol . After drying , filters were immersed in liquid scintillation fluid , and [α-32P]UTP incorporation measured in counts per minute using a liquid scintillation counter ( Wallac ) . Reaction mixtures containing the TaVpol or the full-length TaV rORF1 supplemented or not with 5 μl of a ssRNA template ( 0 . 2 mg/ml ) were performed for 10 min under optimal conditions ( 50 mM MES pH 6 . 0 , 100 mM NaCl , 5 mM MgCl2 , 10% glycerol , 1 mM DTT , 1 mM ATP , UTP and CTP , 0 . 02 mM GTP , 20 units of RNasin , and 10 μCi [α-32P] GTP at 35°C ) . Reaction products were subjected to 11% SDS-PAGE , and recorded by autoradiography following an 8 h exposure period . Crystals of TaVpol , apo-form and the Lutetium derivative were obtained by the sitting-drop vapor diffusion method as previously described [13] . The RNA oligonucleotide ( sequences 5’-CCCAUUCGACUCCUG ) was used as a template to form the binary complex in a 1:1 . 5 TaVpol:RNA molar ratio with 1 mg/ml TaVpol and incubated at 30°C for 30 min in a buffer containing 50 mM MES pH 6 . 0 , 200 mM NaCl , 10% glycerol and 5 mM DTT . Samples were then concentrated using Centricon 30K tubes ( Millipore ) to a final protein concentration of 10 mg/ml . In order to obtain the ternary complex , the TaVpol-CCCAUUCGACUCCUG complex was also incubated with mixtures of rCTP and rATP , in presence of MnCl2 , to a final concentration of 5 mM . Crystals of the binary and ternary complexes were obtained using the sitting-drop vapor diffusion technique at 20°C , by mixing 0 . 5 μl of complex with 0 . 5 μl of crystallization buffer ( 12% PEG 8K and 750 mM Li2SO4 ) . All crystals were transferred to a cryoprotectant solution containing 20% glycerol in the crystallization buffer , and then were flash frozen in liquid nitrogen . All diffraction data sets were collected at 100 K from single crystals using synchrotron radiation . Native TaV RdRP data were collected up to 2 . 1 Å resolution on ADSC Q4R detector at the ESRF beam line ID14EH4 ( λ = 0 . 98 ) . Lutetium derivative data ( 3 . 0 Å resolution ) were collected on ID23 . 1 beam line at a wavelength corresponding to the lutetium absorption edge ( λ = 1 . 3404 ) [13] . Data from the CTP , GTP and CCCAUUCGACUCCUG/CTP/ATP/Mn2+ complexes were also collected at the ESRF ID14EH4 beamline ( λ = 0 . 98 ) to resolutions of 2 . 25 Å , 2 . 3 Å and 3 . 5 Å , respectively ( Table 1 ) . Diffraction images were indexed and integrated using iMOSFLM [50–52] and XDS programs and scaled , merged and reduced with SCALA from the CCP4 program suite [52] . The structure of the isolated enzyme was determined by a single-wavelength anomalous diffraction ( SAD ) phasing , combined with Molecular Replacement of the partial models obtained as implemented in the Auto-Rickshaw pipeline [53] . A partial model ( containing 629 residues for each of the two molecules in the asymmetric unit ) automatically produced by the program BUCCANEER [54] was then used for phase improvement and model completion using the MRSAD protocol available in Auto-Rickshaw [55] . Manual model rebuilding and sequence assignment , performed with program COOT [56] , was alternated with cycles of automatic refinement by using programs REFMAC5 [57] and PHENIX [58] . Native data was then used to complete and refine the final model of the RdRP apo-form ( Table 1 ) . The structures of the TaVpol complexes were obtained by Molecular Replacement , using the coordinates of the unliganded polymerase as search model , using the program Phaser ( CCP4i ) . Refinement and manual model rebuilding of the different complexes proceeded as for the unliganded crystals . Data refinement statistics are listed in Table 1 . Calculation of contact surfaces and volumes was performed with programs VADAR [59] and MOLE2 . 0 [60] . Illustrations were prepared with Chimera [61] . The atomic coordinates and structure factors have been deposited in the Protein Data Bank , www . pdb . org ( PDB ID codes 4XHA , 4XHI , 5CX6 , 5CYR ) . | RNA dependent RNA polymerases ( RdRPs ) are the catalytic components of the RNA replication and transcription machineries , and thus central players in the life cycle of RNA viruses . The in-depth understanding of both the structure and regulation of viral RdRPs displaying different replication-transcription strategies might provide essential clues for an effective control of virus propagation . The characterization of the first non-canonical RdRP of a positive-stranded RNA virus , the permutotetravirus Thosea asigna virus , has unveiled two essential elements controlling polymerization activity: ( i ) the protein N-terminus that invades the central cleft of the neighboring RdRP molecule , thus stabilizing a dimeric form of the enzyme with partially occluded template binding channels; and ( ii ) a long loop protruding towards the catalytic cavity which harbors the binding site of incoming nucleotides , thus providing a platform for de novo replication initiation . The close structural and functional resemblance between this enzyme and flaviviral RdRPs strongly suggests the existence of an unexpected evolutionary link between these two distant virus groups . | [
"Abstract",
"Introduction",
"Results",
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"and",
"Methods"
] | [] | 2015 | The Structure of the RNA-Dependent RNA Polymerase of a Permutotetravirus Suggests a Link between Primer-Dependent and Primer-Independent Polymerases |
Telomeres are protein–DNA structures found at the ends of linear chromosomes and are crucial for genome integrity . Telomeric DNA length is primarily maintained by the enzyme telomerase . Cells lacking telomerase will undergo senescence when telomeres become critically short . In Saccharomyces cerevisiae , a very small percentage of cells lacking telomerase can remain viable by lengthening telomeres via two distinct homologous recombination pathways . These “survivor” cells are classified as either Type I or Type II , with each class of survivor possessing distinct telomeric DNA structures and genetic requirements . To elucidate the regulatory pathways contributing to survivor generation , we knocked out the telomerase RNA gene TLC1 in 280 telomere-length-maintenance ( TLM ) gene mutants and examined telomere structures in post-senescent survivors . We uncovered new functional roles for 10 genes that affect the emerging ratio of Type I versus Type II survivors and 22 genes that are required for Type II survivor generation . We further verified that Pif1 helicase was required for Type I recombination and that the INO80 chromatin remodeling complex greatly affected the emerging frequency of Type I survivors . Finally , we found the Rad6-mediated ubiquitination pathway and the KEOPS complex were required for Type II recombination . Our data provide an independent line of evidence supporting the idea that these genes play important roles in telomere dynamics .
Telomeres are special DNA-protein structures found at the ends of eukaryotic chromosomes . Telomeres are crucial for genome integrity because they prevent chromosome ends from degradation or fusing with each other [1] . In budding yeast Saccharomyces cerevisiae , telomeric DNA consists of ∼350 base pairs ( bp ) of TG1–3/C1–3 A repeats with a terminal single-stranded TG1–3 tract called a G-overhang [2] . Telomeric DNA can be maintained by either telomerase-mediated elongation or homologous recombination [3]–[5] . Telomerase is a highly specialized reverse transcriptase that adds telomeric DNA sequences to the 3′ G-overhang using its intrinsic RNA template [3] . In Saccharomyces cerevisiae , the core components of telomerase are the catalytic subunit Est2 and its RNA template subunit TLC1 [6] , [7] . In wild-type yeast cells , the telomerase pathway supercedes the recombination pathway as the predominant mechanism of telomeric DNA elongation [8] , [9] . In telomerase-null cells , telomeric DNA is maintained via a recombination pathway termed “alternative lengthening of telomeres” ( ALT ) [10] . Approximately 85% of immortalized human tumor cells use telomerase to maintain telomeres while 15% apply the ALT mechanism to maintain telomeres [11] . In telomerase-null S . cerevisiae mutants , most cells undergo senescence after about 50–100 divisions when telomeres shorten to less than approximately 100 bp [7] , [12] , [13] . Surprisingly , a select few of these senescing cells are able to bypass the short telomere survival crisis through lengthening their telomeres via a Rad52-dependent recombination pathway [14] . These cells are called post-senescence survivors or “survivors” for short [14] . Survivors are categorized into two types: Type I and Type II , which possess different telomeric DNA structures and are defined by their dependence on Rad51 or Rad50 respectively [15] . Type I survivors exhibit highly amplified subtelomeric Y' elements and short terminal telomeric TG tracts . The formation of Type I survivors depends on the canonical homologous recombination proteins Rad51 , Rad54 , Rad55 and Rad57 [14] . On the other hand , Type II survivors have long heterogeneous terminal telomeric TG tracts generated by recombination , and their formation depends on the Mre11-Rad50-Xrs2 ( MRX ) complex and Rad59 [14] . Type II survivors resemble the ALT cells observed in mammals [5] . In S . cerevisiae , about 90% of survivors generated on solid medium are categorized as Type I , while 10% are Type II . Nevertheless , Type II survivors grow at faster rates than Type I survivors , eventually overtaking their counterparts in liquid-grown cultures [14] . In addition to the proteins in the Rad52 epistasis group , which are well-defined in the canonical survivor formation pathways , other genes involved in survivor formation have sporadically been identified . For example , SGS1 , MEC1/TEL1 , MDT1 , DEF1 , CLB2 and SUA5 are required for the generation of Type II survivors , while RIF1 and RIF2 have strong influences toward Type I survivor emerging frequency [16]–[22] . Notably , some of the genes mentioned above appear to contribute to both survivor generation and telomere length regulation . Deletion of RIF1 or RIF2 causes telomere lengthening , while deletion of MRE11 , RAD50 , XRS2 , TEL1 , DEF1 or SUA5 results in telomere shortening [16] , [23]–[25] . These observations suggest that genes involved in telomere recombination pathways and telomere length regulation are in some way linked . So far , there have been 251 telomere length maintenance ( TLM ) genes identified by genome-wide screens [23] , [26] and other studies [16] , [27]–[34] . Furthermore , 29 additional genes previously miss-classified as essential genes in the Saccharomyces genome deletion project have now officially been implicated as TLM genes [24] . In this study we deleted the TLC1 gene encoding the RNA template subunit of telomerase in each of these 280 TLM mutants . We then examined the survivor types that arose and in doing so we were able to identify novel regulators that contribute to telomere recombination . The genes we characterized as telomere recombination regulators may also affect general DNA recombination at other genomic loci .
To search for genes affecting survivor formation , we knocked out the RNA component of telomerase TLC1 in 280 haploid TLM mutants reported to have longer or shorter telomeres than the wild-type strain [23] , [24] , [26] ( Table S1 ) . Knocking out TLC1 in most TLM mutants is typically achieved by transformation of an integrating plasmid but for some strains with extremely short telomeres or severe growth defects , recovering a TLC1 deletion clone using this approach was not possible . For such cases , we mated tlc1Δ mutant ( BY4741 background ) with tlmΔ mutants ( BY4742 background ) to generate heterozygous diploid strains , and then performed tetrad dissection to obtain haploid mutants lacking both TLC1 and TLM genes ( Table S1 ) . After a telomerase-null tlmΔ mutant library was established , each mutant was passaged repeatedly on solid plates to screen for genes that might affect Type I survivor formation . Most of the mutant cells underwent senescence but a small percentage of cells were able to overcome crisis and became survivors [5] . Genomic DNA was extracted from each survival isolate , digested with the XhoI restriction enzyme , and analyzed by Southern blot with a TG probe to determine if the cells were Type I or Type II survivors ( Figure 1A ) ( see Materials and Methods ) . In the first round of screening for genes affecting Type I survivor formation , we passaged two independent senescing colonies from each mutant on solid plates to obtain survivors . Because the emerging frequency of Type I survivors ( ∼90% ) is much higher than that of Type II survivors ( ∼10% ) , most double mutants passaged on a solid plate , like the tlc1Δ single mutant , turned out to be Type I survivors [5] . However , if both of the two colonies picked from a single mutant strain had telomere structures consistent with that of Type II survivors , it was concluded that the gene missing in this Type II strain might contribute to Type I survivor generation and should be analyzed further . For each tlc1Δ tlmΔ mutant selected in this first round of rough screening , eight single colonies were passaged on solid plates in the second round of screening until survivors arose . When more than four colonies became Type II survivors , this TLM gene was subjected to a third round of screening in which fifty colonies of the tlc1Δ tlmΔ mutant were passaged again on solid plates . From these fifty colonies at least forty colonies typically generated survivors that could be examined . The emerging frequency of Type II survivors in each strain was then calculated ( Table 1 ) . Using this screening approach we identified eleven mutants in which the emerging frequencies of Type II survivors was elevated significantly ( Table 1 ) . Among these eleven genes , RIF1 and RIF2 deletion in telomerase-null tlc1Δ mutant generated Type II frequencies of 52 . 2% and 85 . 7% respectively ( Table 1 , the column of “Deleting TLC1 in tlm mutants” ) , percentages which are consistent with a prior study performed by Teng et al . [22] . The other nine genes that affected survivor formation have never before been reported to have such a function . The Type II emerging frequencies in these nine mutants ranged from 45 . 7% to 93 . 6% ( Table 1 , the column of “Deleting TLC1 in tlm mutants” ) and were significantly elevated compared to that of the tlc1Δ cells , which had a Type II emerging frequency of 4% ( Figure 1B ) . In contrast with the eleven genes that affected Type I survivor generation , the PIF1 , helicase gene [35] , [36] , appeared to be essential for Type I survivor generation ( discussed later ) . Very recently , Chang et al . showed that the long telomeres in rif1Δ tlc1Δ and rif2Δ tlc1Δ mutants were preferentially extended by a recombination pathway and senescent cells with long telomeres were more efficient at bypassing senescence via the Type II survivor pathway [37] . These led Chang et al . to propose that rif1Δ tlc1Δ and rif2Δ tlc1Δ mutants affect the ratio of survivor types by altering telomere length at the point of senescence [37] . In order to examine the idea that telomere length affects the type of survivor generated , we generated eleven TLC1/tlc1Δ TLM/tlmΔ diploid strains and performed tetrad dissections to obtain tlc1Δ single and tlc1Δ tlmΔ double mutants ( Table 1 , the column of “Spore from tetrad dissection” ) . Because the ino80Δ tlc1Δ double mutant used in the previous experiments was obtained from tetrad dissection , it was not included in this experiment . Fifty senescing clones of the other ten mutant strains , including tlc1Δ single mutants from each diploid mutant , were streaked on plates until survivors arose . Telomere structures of the survivors generated on plates were examined by Southern blot ( Figure S1 ) . A representative Southern blot result of rpa14Δ tlc1Δ mutant is shown in Figure 1C . The results of these experiments are summarized below and are listed in the column of “Spore from tetrad dissection” in Table 1 . The frequency of Type II survivor formation in the sap30Δ tlc1Δ , rpa14Δtlc1Δ , rrp8Δ tlc1Δ and gup1Δ tlc1Δ double mutants was decreased when compared to that of the corresponding double mutant that had not been through sporogenesis . The frequency of Type II survivor formation in the rpb9Δ tlc1Δ or rps16bΔ tlc1Δ double mutants was increased when compared to that of the corresponding double mutant that had not been through sporogenesis . The frequency of Type II survivor formation in rif1Δ tlc1Δ , rif2Δ tlc1Δ and soh1Δ tlc1Δ double mutants did not change significantly . Recovery of the ies3Δ tlc1Δ double mutant from sporogenesis was not successful . We also examined telomere length around the time of survivor formation and found that similar to the rif1Δ tlc1Δ and rif2Δ tlc1Δ mutants , the critical telomere length in gup1Δ tlc1Δ and ino80Δ tlc1Δ mutants was about 50 bp longer than those in tlc1Δ single mutants from the same crosses ( Figure S2 ) . However , in soh1Δ tlc1Δ and rpb9Δ tlc1Δ mutants , the critical telomere lengths were about 30 bp shorter than those in tlc1Δ mutants from the same crosses ( Figure S2 ) . Additionally , in the rps16bΔ tlc1Δ , sap30Δ tlc1Δ and rrp8Δ tlc1Δ mutants , the critical telomere lengths were slightly longer ( <30 bp ) than those in tlc1Δ mutants from the same crosses ( Figure S2 ) . In the rpa14Δ tlc1Δ mutant , the critical telomere length was similar to that in tlc1Δ mutant from the same cross ( Figure S2 ) . Our data support the idea put forth by Cheng et al . that telomere length affects survivor formation [37] . Our data also show the frequency of Type II emergence in the nine mutants we identified ranged from 44 . 7% to 90 . 5% , which was much higher than the Type II emerging frequencies of less than 10% that were usually observed in tlc1Δ cells ( Table 1 and Figure S1 ) [5] . The INO80 complex is one of the ATP-dependent chromatin remodeling complexes that can move or evict nucleosomes , thereby changing chromatin structure and affecting the accessibility of DNA to other factors [38] . The yeast INO80 complex contains multiple subunits , including five essential and ten ( Ino80 , Ies1 , Ies2 , Ies3 , Ies4 , Ies5 , Ies6 , Taf14 , Arp8 and Nhp10 ) non-essential subunits [38] . A recent study has shown that Ies3 interacts with the telomerase component Est1 [34] . In est1Δ cells , deleting IES3 or ARP8 caused a delay of survivor generation in liquid culturing [34] , suggesting that the INO80 complex affects telomere recombination . In our survivor screening we noted that two subunits in the INO80 complex , Ino80 and Ies3 , significantly affected the generation of Type I survivors ( Table 1 ) . When passaged on solid medium , the ino80Δ tlc1Δ and ies3Δ tlc1Δ mutants produced Type II survivors at frequencies of 70% and 85 . 4% respectively ( Figure 2A and 2B ) , which were significantly elevated in comparison with the 8 . 3% we observed in tlc1Δ cells ( Figure 2B and Figure S3A ) . These results suggested that the INO80 complex may be required for efficient Type I survivor formation . To examine this possibility further we examined the impact of depleting each of the other four non-essential subunits of the INO80 complex on the efficiency of Type I survivor formation in tlc1Δ cells . The Southern blot results revealed that the deletion of each of the non-essential INO80 subunits IES1 , IES4 , IES5 and NHP10 led to the generation of more Type II than Type I survivors ( Figure S3 ) . The frequency of Type II emergence in each of these mutants in tlc1Δ cells was above 60% ( Figure 2B ) , which was much higher than that of the tlc1Δ single mutant . These results indicate that the INO80 complex greatly influences the emerging ratio of Type I vs Type II survivors . PIF1 is a non-essential gene which encodes a 5′ to 3′ DNA and DNA/RNA helicase in S . cerevisiae [36] , [39] . Previous studies have demonstrated that Pif1 can be translated from different start sites and has two forms which are localized to either the mitochondria or the nucleus [35] , [40] . In the mitochondria Pif1 affects recombination of mitochondrial DNA ( mtDNA ) and plays an important role in maintaining mtDNA stability [41]–[43] . In the nucleus , Pif1 inhibits telomere lengthening by removing telomerase from telomeric DNA [35] , [44] and participates in Okazaki fragment maturation [45] , [46] and ribosomal DNA replication [47] . Additionally , Pif1 is able to unwind G-quadruplex structures in vitro [48] , and likely acts on these structures in vivo as well [48] , [49] . In our primary screening the pif1Δ tlc1Δ double mutant had difficulties generating survivors on solid medium , and as a result most clones died out during sequential streaks . The pif1Δ tlc1Δ clones that overcame senescence on solid medium showed a Type II survivor pattern ( Figure 3A and 3B ) , suggesting that Pif1 promotes Type I survivor formation . To further validate the role of Pif1 in Type I survivor generation , we streaked fifty independent pif1Δ tlc1Δ colonies on plates . We noted that forty post-senescence colonies ( 80% ) died during the sequential streaks , indicating that deletion of PIF1 in telomerase-null strains inhibits the creation of post-senescence survivors . The other 10 colonies also underwent senescence , but were able to generate survivors at the 7th streaking . Cells at this stage were harvested , and their telomeres were examined by Southern blot assay ( Figure 3C ) . Only two colonies ( 4% ) , which grew at a normal rate , gave rise to type II survivors ( Figure 3C and 3D ) , indicating that type II survivors can indeed form in the absence of Pif1 . Interestingly , eight colonies ( 16% ) of extremely slow growing survivors showed distinct patterns of telomeric DNA without either long heterogeneous TG tracts or substantial Y' amplification ( Figure 3C and 3D ) , suggesting that a new type of survivor emerged in pif1Δ tlc1Δ post-senescence cells . In these cells the terminal TG tracts seemed to be even shorter than that in Type I survivors but were unexpectedly maintained during subsequent passages . This abnormality of telomeric DNA was also observed by Dewar et al . [50] . Nevertheless our results suggested that Pif1 is required for Type I survivor formation . To confirm this further , since RAD50 and RAD51 are respectively required for Type II and Type I survivor formation we checked whether survivors could form in either a rad50Δ pif1Δ tlc1Δ or a rad51Δ pif1Δ tlc1Δ triple mutant . The isogenic rad50Δ pif1Δ tlc1Δ or rad51Δ pif1Δ tlc1Δ spores were dissected and serially passaged in liquid culture . As expected , two spores of the rad50Δ pif1Δ tlc1Δ triple mutant underwent senescence gradually and virtually died out at the 9th or 11th passage ( Figure 3E ) . A Southern blot analysis showed that tlc1Δ and pif1Δ tlc1Δ mutants displayed Type II survivor telomere structures after eleven passages , whereas rad50Δ pif1Δ tlc1Δ mutant did not ( Figure 3F ) . These results further support our claim that Pif1 is required for Type I survivor generation . For the rad51Δ pif1Δ tlc1Δ triple mutant , three spores behaved differently in liquid culture . One spore could generate survivors , while the other two spores could not ( Figure 3G ) , suggesting that Pif1 might also affect Type II survivor generation . To investigate whether Pif1's helicase activity is required for Type I survivor formation , we constructed the pRS316-pif1-K264A plasmid and transformed it into pif1Δ cells , as the lysine residue of 264 in the ATP-binding domain of Pif1 is essential for Pif1's helicase activity [35] . Fifty senescing colonies of pif1Δ tlc1Δ/pRS316-pif1-K264A strain were randomly selected and passaged to allow survivors to generate . Thirty-seven post-senescence colonies died during the sequential streaks , while thirteen colonies generated survivors . A Southern blot analysis revealed that the telomere structures of these post-senescence survivors were very similar to those of the pif1Δ tlc1Δ mutant survivors ( Figure 3H ) . We therefore concluded that Pif1's helicase activity plays a key role in telomeric DNA recombination . Helicases are nucleic acid-dependent ATP-ases that are capable of unwinding DNA or RNA duplex substrates and play important roles in almost every cellular process including DNA replication and repair , transcription , translation , RNA processing and so on [51] , [52] . In S . cerevisiae , there are 132 open-reading-frames that encode helicase or helicase-like proteins [35] . Thirteen of them have been shown to have DNA helicase activity . We knocked out TLC1 in each of these thirteen DNA helicase gene mutants ( Figure S4 ) and carried out survivor screenings to investigate if these genes affect Type I or Type II survivor generation . In contrast with PIF1 , the other twelve DNA helicase genes and TLC1 double deletion mutants generated Type I survivors on solid medium , indicating that they are not essential for Type I survivor formation ( Figure S4A ) . In liquid medium , sgs1Δ tlc1Δ cells generated Type I survivors , while the other twelve DNA helicase genes and TLC1 double deletion mutants generated Type II survivors after passaging 12 times ( about 200 population doublings ) ( Figure S4B ) . This result is consistent with a previous report which shows Sgs1 helicase is required for Type II survivor formation [53] . We obtained the pif1Δ sgs1Δ tlc1Δ triple mutant dissected from the heterozygous PIF1/pif1Δ SGS1/sgs1Δ TLC1/tlc1Δ diploid mutant . The pif1Δ sgs1Δ tlc1Δ mutant was cultured in liquid medium and no survivors were recovered ( Figure 3I ) . It was therefore concluded that Pif1 and Sgs1 may define the Type I and Type II survivor formation pathways respectively . In order to screen for genes that might affect Type II survivor formation , we grew the 280 telomerase-null tlmΔ mutants serially in liquid medium to generate survivors ( Figure 4A ) [5] . If Type II survivors arise , they eventually out-compete their Type I counterparts in liquid culture because of their aforementioned growth advantage [5] . There were , however , some strains that lacked the genes required for Type II survivor formation , and thus generated only Type I survivors . The viability of these senescing mutants was recorded during passages and survivor cells were harvested at the end of serial culturing . The genomic DNA of the liquid-cultured cells was isolated and subjected to Southern blot with a telomeric TG1–3 probe . Twenty-four tlc1Δ tlmΔ double mutants formed Type I survivors , suggesting these twenty-four genes were required for Type II survivor formation ( Figure 4B and Table 2 ) . To further confirm the Type I phenotypes of these mutants , we used a Y' probe and performed Southern blot hybridization to examine the DNA structure . The results clearly showed significant amplification of Y'-elements , a characteristic typical of Type I survivors ( Figure 4C ) . Among these twenty-four genes , twenty-two had never before been identified for their involvement in Type II survivor formation ( Table 2 ) . The two genes identified in our screening that have been previously reported to maintain such a function include SUA5 and DEF1 [16] , [18] . It is important to note that survivors generated in tlc1Δ yku70Δ or tlc1Δ yku80Δ cells exhibited distinctive telomeric DNA patterns that differed from classical Type I and Type II survivor structures ( Figure 4B and 4C , left panels ) [54] , [55] . Moreover , both tlc1Δ yku70Δ and tlc1Δ yku80Δ cells exhibited more rapid senescence and became survivors as soon as the telomeric DNA from germinating spores could be examined , observations which are consistent with earlier reports [55] , [56] . The results of the yku70 and yku80 mutants were presented in this section with the other mutants which displayed Type I survivors because survivor generation in tlc1Δ yku70Δ and tlc1Δ yku80Δ mutants is more dependent upon RAD51 than RAD50 [54] . As mentioned above , we identified twenty-two genes not previously known to be required for Type II survivor formation ( Table 2 ) . RAD6 remains of particular interest as previous studies have shown that RAD6 plays important roles in recombinational repair [57] . Rad6 is an E2 ubiquitin-conjugating enzyme and it interacts with three E3 ubiquitin ligases ( Bre1 , Rad18 and Ubr1 ) known to be involved in different DNA repair pathways [58] , [59] . Rad6 and Bre1 are responsible for H2B-K123 ubiquitination , which is required for H3-K4 methylation [60] . Rad6 and Rad18 are involved in post-replication repair via their role in ubiquitination of PCNA [61] . Rad6 and Ubr1 have been linked to DNA repair through their function in degradation of cohesin [62] . Our Southern blot analysis showed that rad6Δ tlc1Δ double mutant cells in liquid culture generated only Type I survivors ( Figure 5A ) , suggesting that Rad6 is required for Type II survivor formation . To validate this result , we knocked out RAD51 , which is required for Type I survivor formation , in the rad6Δ tlc1Δ cells . All four clones of the rad6Δ rad51Δ tlc1Δ mutant underwent senescence and were unable to generate survivors ( Figure 5B ) , confirming that Rad6 is required for Type II survivor formation . In order to determine the downstream pathways utilized by Rad6 during Type II survivor generation we constructed the heterozygous diploid strain of TLC1/tlc1Δ RAD18/rad18Δ BRE1/bre1Δ UBR1/ubr1Δ . The isogenic haploid tlc1Δ strains of single- , double- , triple- and quadruple-mutants were derived by sporulation . Three independent colonies of tlc1Δ bre1Δ , tlc1Δ ubr1Δ , tlc1Δ rad18Δ , tlc1Δ bre1Δ ubr1Δ , tlc1Δ bre1Δ rad18Δ , tlc1Δ ubr1Δ rad18Δ , and tlc1Δ rad18Δ bre1Δ ubr1Δ were passaged in liquid medium to allow survivor formation . The analysis of strain viability is shown in Figure 5C and Figure S5 . An aliquot of each liquid-grown survivor was harvested on the second day of recovery , and its telomeric DNA was examined by Southern blot ( Figure 5D ) . The tlc1Δ ubr1Δ , tlc1Δ rad18Δ and tlc1Δ ubr1Δ rad18Δ survivors displayed no obvious amplification of Y'-subtelomeric elements whereas the tlc1Δ bre1Δ , tlc1Δ bre1Δ ubr1Δ and Δtlc1Δ bre1Δ rad18Δ survivors that lacked the BRE1 gene displayed significant Y'-element amplification ( Figure 5D ) . These data suggest Bre1 plays an even more positive regulatory role in Type II survivor generation than Ubr1 and Rad18 . Interestingly , the tlc1Δ rad18Δ bre1Δ ubr1Δ mutant cells only allowed the development of Type I survivors . These results indicate that Rad6 functions through its downstream pathways and most importantly Bre1 to promote Type II survivor formation . In addition to RAD6 , CGI121 and KAE1 were also identified during our liquid-culture screen as contributing to Type II survivor formation ( Table 2 ) . Cgi121 and Kae1 belong to the KEOPS complex , which is evolutionarily conserved from archaea to mammals [63] . In S . cerevisiae , the KEOPS complex consists of five subunits ( Cgi121 , Bud32 , Kae1 , Gon7 and Pcc1 ) and plays multiple roles in transcription , tRNA modification ( t6A ) , chromosome segregation and telomere uncapping-elongation [29] , [64]–[66] . The deletion mutants of BUD32 and GON7 were in our original TLM library but in our initial screening the severe growth defects of the bud32Δ and gon7Δ haploid strain made it impossible for us to knock-out TLC1 . PCC1 was not in the 280 TLM gene list , and therefore was not covered in our initial screening . In order to determine whether Bud32 , Gon7 and Pcc1 were also involved in telomere recombination , we constructed the heterozygous diploid mutants in which one copy of TLC1 and BUD32 , GON7 or PCC1 were deleted . The double mutants of bud32Δ tlc1Δ , gon7Δ tlc1Δ and pcc1Δ tlc1Δ were obtained from tetrad dissection and were serially passaged in liquid medium . All the survivors displayed Type I patterns of Y' amplification ( Figure 6A ) , indicating that Type II recombination could not take place in the absence of Bud32 , Gon7 or Pcc1 . To further confirm the critical role the KEOPS complex plays in telomere recombination , we tested whether survivor formation in cgi121Δ tlc1Δ cells would be affected in the absence of RAD51 or RAD50 . Unfortunately , we could not examine the genetic interaction between RAD51 or RAD50 and the other four KEOPS subunits in telomere recombination because the bud32Δ , kae1Δ , gon7Δ and pcc1Δ mutants all exhibited severe growth defects . Therefore we focused on CGI121 by generating a heterozygous diploid strain in which one copy of TLC1 , CGI121 and RAD51 ( or RAD50 ) was deleted . The isogenic strains of single , double and triple mutants were derived from tetrad dissection and serially cultured in liquid medium . The cgi121Δ tlc1Δ rad51Δ triple mutant died out rapidly , while other tlc1Δ mutants were able to recover robust growth when survivors arose ( Figure 6B ) . Consistently , the cgi121Δ tlc1Δ rad50Δ triple mutant was able to bypass the senescence crisis by generating Type I survivors ( Figure 6C and 6D ) . These results support the conclusion that CGI121 and likely the entire KEOPS complex is required for Type II recombination . Previous studies have shown that Kae1 has ATP-binding activity and Bud32 acts as a protein kinase and the activities of both of these gene products appear to be essential for all the roles played by the KEOPS complex [29] , [63] , [66] , [67] . Based on the previous biochemical and structural analyses of Kae1 and Bud32 [63] , we constructed kae1 ( E213R ) tlc1Δ , bud32 ( K52A ) tlc1Δ and bud32 ( N166A ) tlc1Δ double mutant strains in which the Kae1 ATP-binding site and the Bud32 kinase catalytic sites were mutated . These mutants were unable to generate Type II survivors , but rather exclusively developed Type I survivors when cultured in liquid medium ( Figure 6E and 6F ) . Likewise , the kae1 ( E292R ) and kae1 ( E295K ) mutants , which no longer maintain an interation between Kae1 and Bud32 , also displayed a defect in Type II survivor generation ( Figure 6E ) . These data indicate that both the Kae1-Bud32 interaction and their biochemical activities were indispensable for Type II telomere recombination . We therefore concluded that the whole KEOPS complex was necessary for Type II recombination . Previously , several labs performed genome-wide screens searching for genes that affect DNA repair and/or recombination and dozens of genes were documented [68]–[71] . In this study we have identified ten genes which affect Type I telomere recombination and twenty-two genes which affect Type II telomere recombination . Fifteen of these genes have already been reported to have potential roles in general DNA repair and/or recombination ( Table S2 ) . In order to determine whether the other seventeen genes also play roles in general DNA repair/recombination , we performed three assays used previously [72] , [73] to examine relative levels of inter-chromosomal homologous recombination ( Figure 7A ) and intra-chromosomal homologous recombination in haploid ( Figure 7B ) and diploid strains ( Figure 7C ) . Each assay detected genomic gene conversion events through the recovery of an intact LEU2 marker by the integration of two seperated fragments ( Figure 7A–7C , upper panels ) . Ten of the seventeen mutants we tested exhibited an extremely slow growth phenotype and were not viable for testing using the general recombination assays . The remaining seven mutants ( nam7Δ , ebs1Δ , upf3Δ , nmd2Δ , rps16bΔ , soh1Δ and cgi121Δ ) showed decreased activities in inter- or intra-chromosomal homologous recombination ( Figure 7A–7C ) . Therefore it is likely that these seven genes participate in telomere recombination as well as recombination at other genomic loci . Break-induced-replication ( BIR ) only requires one free DNA end to take place and it has been proposed to be the principal mechanism for telomere recombination and survivor generation [14] . To examine whether the INO80 complex , Pif1 , Rad6 and the KEOPS complex participate in telomere recombination via Rad51-dependent BIR process we used a system developed by Lydeard et al . to measure the BIR efficiencies in ies1Δ , ies3Δ , pif1Δ , rad6Δ and cgi121Δ mutants [74] ( Figure 7D ) . The rad51Δ and rad59Δ mutant strains served as positive controls [14] . Our results showed that similar to the rad51Δ mutant , the pif1Δ mutant displayed little BIR efficiency ( Figure 7E ) . In ies1Δ , ies3Δ and rad6Δ mutants , the BIR efficiencies were greatly decreased as also seen in the rad59Δ mutant ( Figure 7E and 7F ) . In contrast , the BIR efficiency in the cgi121Δ mutant was comparable to that of the wild-type strain ( Figure 7F ) . Taken together , these data indicate that Pif1 is required for Rad51-dependent break-induced-replication , and the INO80 complex and Rad6 , but not the KEOPS complex , contribute to this BIR process .
Unlike most other chromosomal loci , eukaryotic telomeres have unique structures attributed to their repetitive DNA sequence and binding proteins [75] . Linear chromosome ends can be recognized as DNA double-stranded breaks and are thus often subjected to repair by non-homologous-end-joining and homologous recombination . It is possible that telomerase-null senescing cells are able to escape the fate of death as telomeres undergo lengthening and repair via homologous recombination . The distinct DNA makeup of Type I and Type II recombinational telomeres allowed us to carry out a genetic screening to identify genes that affect telomere recombination in telomerase-null cells . Our candidate approach for screening telomere recombination genes had a few shortcomings . In our screening we only covered the 280 known TLM genes , which make up only 5 . 6% of the ∼5 , 000 non-essential genes in S . cerevisiae . It would be ideal to cover all non-essential genes in our screen . However , such a study would be too massive to undertake since the screening procedures included knocking out TLC1 in every strain , two to three-weeks passaging cells until they reach senescence and Southern blot experiments for multiple survivors in each mutant ( see Figure 1A and Figure 4A ) . The candidate approach we chose therefore had a strong bias . As a result , we might have missed potential genes that do not affect telomere length , but play important roles in telomere recombination . Another challenge to our screening approach came from the nature of different growth rates of the various mutants . Although we used heterozygous diploid mutants to generate spores of tlc1Δ tlmΔ double mutants ( Table S1 ) , for quite a few mutants we were not able to distinguish between a defect in a survivor pathway and synthetic lethality ( Table S1 ) . The third issue that we were not able to resolve was to distinguish between hypo-Type I recombination and hyper-Type II recombination . The decrease of Type I survivor frequency seen in the mutants , such as rpa14Δ tlc1Δ ( Figure 1C ) could be caused by either inhibition of Type I recombination or promotion of Type II recombination . In some Type II survivors , the amplified Y'-elements were detected in Southern blot assays ( Figure 1C , Figure S1 ) , suggesting that the increase of Type II survivor frequency in these mutants was a result of enhanced Type II recombination rather than inhibited Type I recombination . This model is supported by the observation that in the nine mutants shown in Figure 1C and Figure S1 , the emerging events of Type I survivors were significantly reduced , but were not entirely blocked . The fourth issue that we had not taken into consideration during our primary screening was the effect of the initial telomere length of each mutant on the recombination pathways . It was recently proposed that longer telomeres , like those observed in rif1Δ and rif2Δ mutants could influence the type of recombination pathway used at the telomere [37] . Additionally , it was shown that the mre11-A470T tlc1Δ mutant promotes telomere recombination and bypass senescence efficiently because the Type I recombination occurs before growth limitation [76] . Therefore , it would have been more appropriate to perform all of our screening steps starting with TLC1/tlc1Δ TLM/tlmΔ diploids to obtain tlc1Δ single and tlc1Δ tlmΔ double mutants following tetrad dissection . The fifth issue with our screening approach was that we assumed the TLM genes only affect Type I or Type II recombination . Surprisingly , the telomere structure in the yku and pif1 mutants might actually be different from that of a typical Type I or Type II survivor ( Figure 3 and Figure 4 ) . Therefore , genes that influence pathway ( s ) of telomere recombination other than that of Type I or Type II might have been overlooked . The sixth issue with our screen was that we only identified ten novel genes affecting Type I survivor formation ( Table 1 ) . This number might be underrepresent the true total because our primary screening was carried out with a relatively stringent criteria and as such we may have overlooked some genes that have minor influences on the frequency Type I survivor emergence . Although our screening approach had some imperfections , we successfully identified thirty-two TLM genes that influence telomere recombination when overcoming senescence . Ten of these TLM genes affected the emerging frequency of Type I survivors while twenty-two were required for Type II survivor generation . A large portion of 280 TLM genes have not previously been characterized for their roles in telomere function other than the length of the telomeres in these deletion strains was altered . The positive results of our screen provide more direct evidence supporting the idea that some of these uncharacterized TLM genes do affect telomeres [23] , [24] , [26] . Additionally , telomere recombination is a means by which cells repair defective telomeres and thus the genes involved in telomeric DNA recombination may also play a role in general DNA recombination/repair . Indeed , the TLM genes that affected either Type I or Type II recombination were also required for general DNA recombination ( Figure 7A–7C ) . The annotated functions of the thirty-two genes that we identified point to several pathways that might contribute to telomere maintenance ( Table 1 and Table 2 ) . Some of the genes are known for functions like “rRNA processing , ” “structural constituent of ribosome , ” and “transport and membrane . ” These gene products seem unlikely to play a direct role in telomere recombination . In contrast , the Pif1 helicase and the KEOPS complex are involved in “telomere capping and maintenance” [29] , [35] and INO80 complex and Rad6 are associated with “chromatin remodeling and modification . ” These genes are likely to play direct roles in telomere recombination . The senescing pif1Δ tlc1Δ cells did not produce Type I survivors on solid medium ( Figure 3C ) and the rad50Δ pif1Δ tlc1Δ triple mutant was not able to generate survivors in liquid medium ( Figure 3E ) . These results indicated that Pif1 was required for Type I survivor generation . Interestingly , not all the rad51Δ pif1Δ tlc1Δ triple mutants were able to generate Type II survivors in liquid medium ( Figure 3G ) . Therefore , we favor a model where Pif1 helicase is required for amplification of Y'-elements to form Type I survivors and promotes TG1–3 recombination to form Type II survivors ( Figure 3 ) . Previous studies have shown that Pif1 takes part in mitochondrial DNA recombination [41]–[43] , however , our data are the first to indicate that Pif1 is also involved in telomeric DNA recombination ( Figure 3 ) . In the survivors of pif1Δ tlc1Δ mutants one group exhibited a severely delayed growth phenotype and had a unique telomere structure that differed from the characteristics of either Type I or Type II ( Figure 3C ) . We speculate that these types of survivors require RAD50 to maintain telomeres since no survivors were recovered in the pif1Δ tlc1Δ rad50Δ triple mutant ( Figure 3E ) . In the future it will be interesting to examine how the short telomeres are maintained in these survivors . Chromatin remodeling complexes have been shown by others to play roles in DNA repair processes via homologous recombination [77] . However , a causal link between chromatin structure alteration and recombination has not yet been well established . We found that in the absence of active chromatin remodeling by the INO80 complex , telomere Type I recombination was unable to efficiently take place ( Figure 2A and 2B ) , suggesting that the alteration of chromatin structure is a pre-requisite to the Type I recombination process at telomeres . Our results could provide an explanation for the previous observation that ies3Δ est1Δ cells generated survivors later than the est1Δ single mutant [34] , as ies3Δ est1Δ cells likely have a lower efficiency of Type I survivor generation than est1Δ cells . SAP30 , which encodes a subunit of histone deacetylase Rpd3 complex , was also identified in our screening . Deletion of SAP30 dramatically reduced the emerging rate of the Type I survivors ( Table 1 ) , suggesting that the Rpd3 histone deacetylase complex may also inhibit Type I recombination . The SWR1 complex is another chromatin remodeling complex that belongs to the INO80 family of remodeling enzymes . SWR1 and INO80 complexes share four common subunits: Rvb1 , Rvb2 , Arp4 and Act1 [38] . It will be intriguing to determine if other chromatin remodeling enzymes like SWR1 or histone modification enzymes play roles in telomere recombination . The KEOPS complex gains its name from “Kinase , Endopeptidase and Other Proteins of small Size” [29] , and is comprised of five small proteins ( Bud32 , Kae1 , Pcc1 , Gon7 and Cgi121 ) which form a stable complex in vitro and in vivo [29] , [63] , [65] , [67] . Bud32 has kinase activity while Kae1 maintains endopeptidase activity [29] . The KEOPS complex or its subunit ( s ) are involved in several biological processes , to which each KEOPS subunit seems to contribute unequally . Pcc1 , Gon7 , Kae1 and Bud32 , for example , are recruited to several genomic loci and affect gene transcription [65] . Kae1 contributes to faithful chromosome segregation [64] while Bud32 , Cgi121 , Gon7 regulate cell polarity in bud-site selection [78] . Additionally , Bud32 , Kae1 and Pcc1 are essential for a universal tRNA modification called threonyl carbamoyl adenosine ( t6A ) , for which Cgi121 is dispensable [66] . Moreover , all the subunits of the KEOPS complex appear to play roles in telomere uncapping and telomere length regulation [29] . Our screen elucidated a novel function of the KEOPS complex in telomere recombination , as deficiency of any subunit of the KEOPS complex led to the failure in generating Type II survivors in the tlc1Δ mutant ( Figure 5A ) . The molecular mechanism by which the KEOPS complex influences telomere recombination remains unclear . A previous study by Downey et al . showed that mutation of the KEOPS complex decreased the amount of single-stranded telomeric DNA in the cdc13-1 mutant [29] . It is possible that the KEOPS complex facilitates the formation of the telomeric 3′-overhang and promotes recombination of TG-tracts . Coincidently , SUA5 , a telomeric single-stranded DNA binding protein , is required for both Type II recombination and t6A modification of tRNA [18] , [79] . It will be interesting to determine whether SUA5 is a downstream target of the KEOPS complex and if it functions in the same pathway in regulating telomere recombination . It is possible that Sua5 is a substrate of the Bud32 kinase . In summary , our screen identified dozens of genes that regulate telomere recombination pathways . Because of the complexity of the recombination process , the molecular mechanisms of telomere recombination remain elusive . Our work not only provides important clues for beginning to understand how telomere recombination is coordinated , but also offers new insights into general DNA repair processes via homologous recombination .
All strains used in this work are summarized in Table S1 and Table S3 . Gene deletions were carried out using standard procedures by genetic cross and homologous recombination . Systematic deletion strains are from EUROSCARF . We constructed CEN plasmids pRS316-PIF1 , pRS313-KAE1 and pRS313-BUD32 by inserting fragments ( from upstream 1000 bp to downstream 500 bp of genes' open reading frame ) into the pRS316 or pRS313 vector . Point mutations were introduced using a site-directed mutagenesis method . A single colony of the indicated yeast strains was inoculated into 5 ml yeast extract-peptone-dextrose ( YPD ) medium and grown at 30°C to saturation ( OD600 ∼2 . 5 to 3 . 0 ) . Then every 24 hours the cell density was measured by spectrometry ( OD600 ) and the cell culture was diluted to the density at OD600 ∼0 . 02 with fresh YPD medium . This procedure was repeated for up to 14 times , unless the cell density is too low for dilution . A single colony of the indicated yeast strains was streaked on YPD plate and grown until emergency of single colonies ( 25 cell divisions ) at 30°C . Individual colonies were restreaked repeatedly at least six times to allow survivors to generate . Genomic DNA was prepared from each strain , digested with XhoI , separated on 1% gel , transferred to Hybond-N+ membrane ( GE Healthcare ) and then probed with TG1–3 telomere-specific probe or Y'-element probe [25] . The CDC15 probe was ∼263 bp sequence of CDC15 gene [50] . Recombination assays for intrachromosomal and interchromosomal recombination in haploid and diploid strains were performed and recombination rates were determined as previously described [72] , [73] . For each mutant , about 2×107 yeast cells were plated on solid selective medium . After growing at 30°C for 2–3 days , about 200 positive colonies would appear on the plate in wild-type haploid strain . Recombination rates were calculated and statistically analyzed by paired two-sample t-test . Break-induced-replication ( BIR ) efficiency was measured in a system developed by Lydeard et al [74] . Semi-quantitative PCR was conducted as previously described [74] . PCR products were quantified in Image Quant Software . | Homologous recombination is a means for an organism or a cell to repair damaged DNA in its genome . Eukaryotic chromosomes have a linear configuration with two ends that are special DNA–protein structures called telomeres . Telomeres can be recognized by the cell as DNA double-strand breaks and subjected to repair by homologous recombination . In the baker's yeast Saccharomyces cerevisiae , cells that lack the enzyme telomerase , which is the primary factor responsible for telomeric DNA elongation , are able to escape senescence and cell death when telomeres undergo repair via homologous recombination . In this study , we have performed genetic screens to identify genes that affect telomeric DNA recombination . By examining the telomere structures in 280 mutants , each of which lacks both a telomere-length-maintenance gene and telomerase RNA gene , we identified 32 genes that were not previously known to be involved in telomere recombination . These genes have functions in a variety of cellular processes , and our work provides new insights into the regulation of telomere recombination in the absence of telomerase . | [
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] | 2013 | Telomerase-Null Survivor Screening Identifies Novel Telomere Recombination Regulators |
The wheat pathogen Stagonospora nodorum produces multiple necrotrophic effectors ( also called host-selective toxins ) that promote disease by interacting with corresponding host sensitivity gene products . SnTox1 was the first necrotrophic effector identified in S . nodorum , and was shown to induce necrosis on wheat lines carrying Snn1 . Here , we report the molecular cloning and validation of SnTox1 as well as the preliminary characterization of the mechanism underlying the SnTox1-Snn1 interaction which leads to susceptibility . SnTox1 was identified using bioinformatics tools and verified by heterologous expression in Pichia pastoris . SnTox1 encodes a 117 amino acid protein with the first 17 amino acids predicted as a signal peptide , and strikingly , the mature protein contains 16 cysteine residues , a common feature for some avirulence effectors . The transformation of SnTox1 into an avirulent S . nodorum isolate was sufficient to make the strain pathogenic . Additionally , the deletion of SnTox1 in virulent isolates rendered the SnTox1 mutated strains avirulent on the Snn1 differential wheat line . SnTox1 was present in 85% of a global collection of S . nodorum isolates . We identified a total of 11 protein isoforms and found evidence for strong diversifying selection operating on SnTox1 . The SnTox1-Snn1 interaction results in an oxidative burst , DNA laddering , and pathogenesis related ( PR ) gene expression , all hallmarks of a defense response . In the absence of light , the development of SnTox1-induced necrosis and disease symptoms were completely blocked . By comparing the infection processes of a GFP-tagged avirulent isolate and the same isolate transformed with SnTox1 , we conclude that SnTox1 may play a critical role during fungal penetration . This research further demonstrates that necrotrophic fungal pathogens utilize small effector proteins to exploit plant resistance pathways for their colonization , which provides important insights into the molecular basis of the wheat-S . nodorum interaction , an emerging model for necrotrophic pathosystems .
Like other parasites , fungal pathogens secrete a battery of molecules known as effectors during the infection process . These effectors can alter plant biological processes resulting in successful colonization [1] , [2] . Conversely , recognition of effectors by the plant innate immune system can initiate a defense response resulting in effector-triggered immunity ( ETI ) [3] , [4] . ETI is characterized by the accumulation of reactive oxygen species ( ROS ) , transcriptional induction of pathogenesis-related ( PR ) genes and production of antimicrobial chemical compounds , eventually leading to rapid and localized plant cell death , known as the hypersensitive response ( HR ) [5] . In ETI , the perception of the fungal effector is mediated by the corresponding plant resistance gene ( R ) which acts in a gene-for-gene manner [6] , [7] . Currently , it is believed that this localized suicide of plant cells induced by ETI halts further growth of the biotrophic fungal pathogen , which requires living plant tissue for survival . Necrotrophic fungal pathogens are known to produce host selective toxins ( HSTs ) , including low molecular weight metabolites and small secreted proteins that function as essential determinants of pathogenicity or virulence [8] , [9] . HSTs can therefore be viewed as effectors of necrotrophic pathogenicity and hence we prefer the term necrotrophic effector ( NE ) [10] , [11] . These effectors play significant roles in determining the outcomes of plant-pathogen interactions by specifically interacting ( directly or indirectly ) with the products of corresponding host genes [12] , [13] . However , in contrast to ETI in the classical gene-for-gene model , the necrosis induced by effectors from necrotrophic fungal pathogens results in disease susceptibility; thus , it can be described as effector-triggered susceptibility ( ETS ) [14] , [15] , a term which was originally used in reference to biotrophic systems [4] . The molecular basis of necrosis-induced ETS involving necrotrophic fungi is still largely unknown , but has in several cases exhibited the hallmarks of programmed cell death ( PCD ) ; DNA laddering , heterochromatin condensation , cell shrinkage , callose deposition and an oxidative burst [9] , [16] , [17] . ToxA , a necrotrophic effector found in both Pyrenophora tritici-repentis and Stagonospora nodorum , causes the loss of plasma membrane integrity and the accumulation of hydrogen peroxide [18] , [19] . Microarray analysis revealed that several wheat genes involved in defense response and signaling pathways were strongly regulated by the ToxA-Tsn1 interaction [20] , [21] . Interestingly , three plant genes involved in susceptibility to necrotrophic effectors ( Pc , the sorghum sensitivity gene corresponding to PC toxin; LOV1 , the Arabidopsis sensitivity gene corresponding to victorin; and Tsn1 , the wheat sensitivity gene corresponding to ToxA ) have been cloned and shown to be resistance-like genes containing both nucleotide binding ( NB ) and leucine-rich repeat ( LRR ) domains [15] , [22] , [23] . This has led to speculation that necrotrophic fungal pathogens may utilize plant resistance signaling pathways to subvert PCD and enable pathogen growth [15] , [24] . Stagonospora nodorum , an ascomycete fungus ( teleomorph: Phaeosphaeria nodorum ) , is the causal agent of wheat Stagonospora nodorum blotch ( SNB ) , a globally distributed and economically important disease [25] . S . nodorum is a typical necrotrophic fungal pathogen [10] , [26] . In recent years , it has been shown that this pathosystem is based largely on interactions involving proteinaceous necrotrophic effectors and corresponding host sensitivity genes that , when occurring together , result in ETS . To date , six interactions have been reported including SnTox1-Snn1 [27] , SnToxA-Tsn1 [28] , [29] , SnTox2-Snn2 [12] , SnTox3-Snn3-B1 [30] , SnTox4-Snn4 [31] , and SnTox3-Snn3-D1 [32] . In addition , several other effector-host gene interactions have been identified ( Friesen and Faris , Oliver and Tan , unpublished data ) . Therefore , the wheat-S . nodorum system is emerging as a model to investigate the molecular mechanisms of necrotrophic pathogenesis [13] . One of our research goals has been to clone necrotrophic effector genes and decipher their molecular and biochemical functions . Of the S . nodorum effector genes , SnToxA and SnTox3 have been cloned with the aid of the S . nodorum genome sequence information [14] , [29] , [33] . The SnToxA gene is essentially identical to the ToxA gene isolated from the wheat tan spot pathogen P . tritici-repentis . Mature ToxA consists of a 13 . 2 kDa protein containing two cysteine residues as well as an RGD-containing vitronectin-like motif that is present in a solvent-exposed loop in the active protein [34]–[38] . The RGD motif has been shown to be essential for internalization and internalization has been shown to be critical for the induction of necrosis [37] , [39] , [40] SnTox3 encodes an approximately 17 . 5 kDa mature protein with six cysteine residues and has no homology to genes in the public databases [14] . Here , we report the molecular cloning and characterization of the SnTox1 gene which encodes the SnTox1 protein , and we show that SnTox1 is specifically recognized by the corresponding wheat sensitivity/susceptibility gene Snn1 . The characterization of the SnTox1-Snn1 interaction provides strong evidence that necrotrophic fungal pathogens use necrotrophic effectors to subvert the host resistance mechanism to cause disease .
Whole genome reference sequences have proven to be powerful for the identification of fungal and oomycete effector genes [1] , [41] . The annotated S . nodorum genome sequence supports a minimum of 10 , 762 nuclear genes with 1 , 782 predicted to encode extracellular proteins [33] . A specific set of criteria was used to prioritize the genes and generate a list of candidates . The criteria ( size less than 30 kDa , predicted to be secreted , expressed in planta , etc , see Materials and Methods ) were based on the characteristics of the previously cloned SnToxA and SnTox3 genes . We focused on the top 100 genes and as expected , SnTox3 and SnToxA were identified among them . PCR analysis was conducted to confirm the absence of genes in the S . nodorum avirulent isolate Sn79-1087 ( data not shown ) . Genes meeting these criteria were expressed in the Pichia pastoris heterologous expression system [14] . This process and the subsequent screening of a set of differential lines ( see Materials and Methods ) led us to identify SNOG_20078 as the SnTox1-encoding gene . Culture filtrates of P . pastoris strain X33 transformed with the coding region of SNOG_20078 cDNA were infiltrated into the leaves of W-7984 , Chinese Spring ( CS ) , CS 1BS-18 and CS ems237 . W-7984 and CS carry the dominant Snn1 allele that confers sensitivity to SnTox1 [27] . CS 1BS-18 and CS ems237 are nearly identical to CS , but harbor mutations at the Snn1 locus , resulting in insensitivity to SnTox1 . Necrosis developed in the SnTox1-sensitive lines W-7984 and CS , but not in CS 1BS-18 and CS ems237 ( Figure 1 ) suggesting that SNOG_20078 was the SnTox1-encoding gene . To map the gene conferring sensitivity , the same culture filtrates were subsequently infiltrated into the entire ITMI mapping population , which segregates for Snn1/snn1 . All lines sensitive to the partially purified native SnTox1 [27] were also sensitive to the culture filtrates of the SNOG_20078 transformed yeast strain . This strongly indicated that SNOG_20078 was the SnTox1-encoding gene and therefore we designated it SnTox1 . SnTox1 is located in supercontig 10 of the assembled SN15 genome sequence ( [33] , Figure 2A ) . Within a ≈7 . 6 kb region , there are three genes upstream ( SNOG_07154-SNOG_7156 ) and one downstream ( SNOG_07153 ) of SnTox1 ( Figure 2A ) . Similarly , there is a short truncated molly-type retrotransposable element ( 183 bp ) sequence following SnTox1 ( http://genome . jgi-psf . org/cgi-bin/browserLoad/ ? db=Stano1&position=scaffold_10 ) . The sequencing of the 5′ and 3′ RACE fragments revealed three exons as well as 5′ and 3′ untranslated regions ( UTRs ) in the full-length transcript of SnTox1 ( Figure 2B ) . Putative TATA and CAAT boxes were identified 114 bp and 570 bp upstream of the ATG start site , respectively ( Figure S1 ) . The SnTox1 protein consists of 117 amino acids with the first 17 amino acids predicted as a signal peptide . Interestingly , 16 of the remaining 100 amino acids are cysteine residues ( Figure 2C ) . No prosequence was predicted using the web-based program ProP 1 . 0 ( http://www . cbs . dtu . dk/services/ProP/ ) and after the cleavage of the predicted signal sequence the mature protein was estimated to be 10 . 33 kDa . To demonstrate that SnTox1 was produced in yeast culture and to verify the size of SnTox1 , we applied western blot analysis to the protein samples prepared from SnTox1 yeast culture filtrates . The antibody for SnTox1 was generated from rabbit immunized with a BSA-conjugated 14 amino acid long SnTox1 peptide ( see Material and Methods ) . A single band was only observed in protein samples prepared from SnTox1 yeast culture filtrates , but not from the control culture filtrates ( yeast strain transformed with an empty vector ) . Furthermore , the western band was visualized between the size standard of 10 and 15 kDa , but much closer to 10 kDa ( Figure S2 ) . The estimated size of SnTox1 obtained from the western blot agreed with the predicted molecular weight of 10 . 33 kDa for the mature protein . A BlastP search of the NCBI non-redundant database with the SnTox1 protein sequence as a query led to the identification of three putative proteins with unknown functions , one from S . nodorum ( SNOG_06487 ) and two from P . tritici-repentis ( PTRT_04748 and PTRT_03544 ) with similarities of 38% , 56% , and 43% , respectively . The conserved amino acids between SnTox1 and these proteins were mostly distributed in the predicted signal sequence and the N-terminal region of the mature protein ( Figure S3 ) . Amino acid alignment with manual adjustment indicated that SnTox1 contained local similarity with cysteine-rich Cladosporium fulvum Avr4-like fungal effectors ( Figure 3A ) from Cercospora beticola , Mycosphaerella fijiensis [42] and two ascomycete human pathogens , Microsporum gypseum and Geomyces pannorum ( this study ) . These conserved motifs were identified within the chitin-binding domain ( ChtBD ) including the C-terminal conserved chitin-binding ( CB ) motif ( Figure 3A ) . Three-dimensional ( 3D ) structure-based sequence alignment suggested that the putative CB motif in SnTox1 was more similar to those of plant-specific ChtBDs ( ChtBD1 , or CBM18 superfamily , pfam00187 ) than to Avr4 proteins , which are related to invertebrate ChtBDs ( ChBD2 , or the CBM14 superfamily , pfam01607 ) [43] ( Figure 3B ) . SnTox1 contained all secondary-structure-related residues including the strictly conserved β-strand-forming “CCS” motif found only in plant-specific ChtBD1 proteins [44] ( Figure 3B ) . In contrast , all Avr4-like proteins lacked the “CCS” motif and had a loosely conserved “QWN” motif at the same positions as that found in the antimicrobial protein tachycitin , a representative ChtBD2 [44] . There were several insertions found between conserved regions in SnTox1 which also lacked the C-terminal extension after the conserved CB motif , suggesting a significant sequence divergence between SnTox1 and Avr4-like proteins . The distribution of SnTox1 in different S . nodorum isolates and related fungal species ( Table S1 and S2 ) was investigated using PCR assays and DNA dot blots . Among the 777 isolates that were sampled from wheat fields around the world , 85% ( 661 ) possess the SnTox1 gene ( Table S1 ) . Dot blot analysis of a subset of a global collection ( Table S2 ) showed that SnTox1 was absent in all S . nodorum isolates collected from wild grasses which are avirulent on wheat ( Figure 4A ) . Additionally , SnTox1 was absent in related fungal species including P . tritici-repentis , P . teres , P . bromi and M . graminicola . To investigate sequence variation in SnTox1 , the gene was PCR-amplified and sequenced from 159 global S . nodorum isolates . We found 12 different nucleotide haplotypes , 11 of which encode different protein isoforms , consistent with strong diversifying selection . The 11 protein isoforms involve amino acid changes at eight positions within SnTox1; however , all cysteine residues remain unchanged across all isoforms ( Figure 2D ) . The nucleotide sequences of all 12 haplotypes have been submitted to GenBank and the accession number for each haplotype is provided at the end of the text . Four codons exhibit significant positive selection using PAML ( Table 1 ) . These findings provide strong evidence that positive diversifying selection , consistent with a co-evolutionary process , has been operating on SnTox1 . To investigate sequence variation of the SnTox1 genomic region in virulent and avirulent isolates , we used PCR to amplify the four genes flanking SnTox1 ( SNOG_07153 , SNOG_07154 , SNOG_07155 , and SNOG_07156 , see Figure 2A for their locations ) . Only SNOG_07154 located directly upstream of SnTox1 could not be amplified from the avirulent isolate Sn79-1087 ( data not shown ) , which suggested that a region containing all or part of SNOG_07154 as well as the entire SnTox1 sequence may be missing in Sn79-1087 . PCR primers were designed within the two genes SNOG_07153 and SNOG_07155 and used to amplify DNA from different virulent isolates as well as Sn79-1087 . The amplified fragment in SN15 was ∼4 . 1 kb as expected but 4 . 5 kb in Sn1501 and 2 . 3 kb in Sn79-1087 ( Figure 4B ) . Sequencing revealed that a 3 . 1 kb region including SnTox1 and the last 85 bp of the 3′ end of SNOG_07154 coding region was replaced by a 1 . 3 kb sequence in Sn79-1087 ( Figure 4C ) . The 1 . 3 kb insertion in Sn79-1087 does not share homology with any other known sequence in the NCBI database . Sequence analysis also revealed that two indels occur in the SnTox1 genomic region with one indel of 400 bp in the upstream , and the other indel of 167 bp at the end of the 3′UTR region ( Figure 4C ) . The avirulent isolate Sn79-1087 does not produce any known S . nodorum necrotrophic effectors , nor does it induce a susceptible response on any of the wheat lines that we have tested . Therefore , a 1 . 1 kb SnTox1 genomic region ( Figure S1 ) containing the native promoter , open reading frame , and the native terminator was cloned into the pDAN vector ( Figure S4A ) and transformed into Sn79-1087 . Southern blot analysis indicated all but one transformant possessed the 1 . 1 kb SnTox1 fragment ( Figure S4B ) . Transformants A1 and A3 , designated Sn79+SnTox1A1 and Sn79+SnTox1A3 , were selected for further analysis . We confirmed that culture filtrates of Sn79-1087 did not cause necrosis nor did spore inoculations cause disease on CS , which contains Snn1 ( Figure 5A ) . However , infiltration of culture filtrates from Sn79+SnTox1A1 and Sn79+SnTox1A3 produced necrosis on the leaves of CS ( Figure 5A ) and inoculation of CS with conidia of Sn79+SnTox1A1 and Sn79+SnTox1A3 produced lesions on the leaves of CS ( Figure 5B ) . The two transformants did not cause disease on CS 1BS-18 or CS ems237 , which lack a functional Snn1 gene ( Figure 5B ) . The virulent isolate Sn2000 was used in the original identification of SnTox1 and Snn1 [27] . Therefore , this isolate was used to conduct gene knock outs of SnTox1 . We exploited a PCR-based split marker method to replace the majority of the SnTox1 gene with the hygromycin resistance gene ( hygR ) . The transformants were verified using Southern blot analysis with a probe amplified from the SnTox1 region that was replaced by hygR ( Figure S4C ) . In two transformants designated Sn2000ΔSnTox1–9 and Sn2000ΔSnTox1–15 , the SnTox1 gene was successfully replaced , and one transformant designated Sn2000ΔSnTox1-ECT was identified as an ectopic insertion due to it being hygromycin resistant but still having an intact and functional SnTox1 gene ( Figure S4D ) . Spores of the three transformed fungal strains along with wild type Sn2000 were inoculated onto the Snn1 differential wheat line W-7984 [27] . The ectopic strain Sn2000ΔSnTox1-ECT induced similar reaction as the wild type including defined tan necrotic lesions with widespread small white flecking , whereas the two knockout strains induced almost no reaction on the leaves ( Figure 6A ) indicating SnTox1 is an important virulence factor for Sn2000 . Sn2000ΔSnTox1–9 and the Sn2000 wild type were also inoculated onto CS . Compared to the wild type , the virulence of Sn2000ΔSnTox1–9 on CS was substantially reduced , but not completely eliminated ( Figure 6B ) , which is due to CS having at least one additional necrotrophic effector sensitivity gene that likely interacts with another effector produced by Sn2000 ( Faris and Friesen , unpublished ) . The wheat ITMI population was used to originally map the QTL associated with disease susceptibility caused by Sn2000 , in which two significant QTL were identified , one on chromosome 1BS corresponding to the Snn1 locus and the other on chromosome 4BL , explaining 48% and 9% of the disease , respectively [45] . We inoculated the three fungal strains: Sn2000ΔSnTox1–9 , Sn2000ΔSnTox1–15 and Sn2000ΔSnTox1-ECT along with wild type Sn2000 onto the ITMI population . For Sn2000 , as expected , we detected two significant QTL with one being at the Snn1 locus and the other being on chromosome 4BL accounting for 50 and 17% of the disease variation , respectively . A very similar result was obtained for Sn2000ΔSnTox1-ECT where the Snn1 QTL and the QTL on chromosome 4BL were detected explaining 50 and 15% of the variation , respectively ( Figure 6C ) . However , in the inoculation with the two SnTox1 knock out strains , the QTL conferred by Snn1 on chromosome 1BS became undetectable showing no association with disease , but the QTL on chromosome 4B was retained and became more significant overall accounting for 40–50% of the disease variation ( Figure 6C ) . The QTL analysis in the ITMI population clearly demonstrated that SnTox1 codes for the SnTox1 protein which plays a significant role in disease by interacting with the host gene Snn1 . SnTox1 had a very similar expression pattern as SnToxA and SnTox3 during infection in a microarray analysis that examined the expression of all fungal genes at 3 , 5 , 7 , and 10 days post inoculation ( DPI ) in the wheat cultivar ‘Amery’ inoculated with SN15 ( Ip-Cho and Oliver unpublished data ) . The analysis showed that the expression of all three genes was highest at 3 DPI ( Figure S5 ) . In this work , SnTox1 expression was examined after inoculation of CS with Sn79+SnTox1A1 , in which no other toxin-sensitivity gene interactions were involved . In the current study , relative expression of SnTox1 to the fungal actin gene was examined at 10 time points ranging from 1 h to 7 d post inoculation using relative-quantitative PCR . Our analysis confirmed that SnTox1 expression was maximized at 3 DPI ( Figure 7A ) . The expression of SnTox1 showed a slow increase between 6 and 12 HPI , increasing to about the same level as the actin gene at 24 HPI and increasing dramatically to 2 . 5 times higher than the actin gene expression at 48 HPI ( Figure 7A ) . Once gene expression peaked at 3DPI , the SnTox1 transcription levels started to drop significantly from 3 to 4 DPI and returned to similar levels as the actin gene between 5 and 6 DPI . The accelerated increase of SnTox1 expression from 24 HPI to 3 DPI indicates that SnTox1 plays an important role in the early stage of infection . The symptom development was examined macroscopically on CS inoculated with Sn79+SnTox1A1 ( Figure 7B ) . Disease symptoms were first visible on leaves at 2 DPI as white flecks and progressed into larger necrotic and chlorotic lesions . Interestingly , tan necrotic lesions start to develop at 3 DPI within the chlorotic areas , which correlates with the maximum expression of SnTox1 ( Figure 7A ) . By 5 DPI , necrotic lesions became evident and the chlorotic areas enlarged ( Figure 7B ) . The overall phenotype of the lesions changed very little from 5 to 7 DPI with only a slight change in size of individual lesions ( Figure 7B ) . The SnTox1 protein contains 16 cysteine residues all of which are predicted to be involved in the formation of disulfide bonds with confidence levels greater than 7 ( 0 to 9 scale , [46] ) ( Figure 8A ) . The prediction software DiANNA [47] was used to identify the most likely connectivity of the cysteine residues as following: 1–11 , 2–5 , 3–6 , 4–13 , 7–9 , 8–16 , 10–12 , and 14–15 ( Figure 8B ) . The stability of SnTox1 was tested by incubation of an SnTox1-containing yeast culture filtrate with different concentrations of dithiothreitol ( DTT ) and different incubation periods . The complete elimination of SnTox1 activity required 4 h in 40 mM DTT ( Figure 8C ) . Additionally , the stability of SnTox1 was tested by directly heating the SnTox1 yeast culture filtrates on a hot plate . Strikingly , the culture filtrates maintained necrotic activity even after boiling for 30 min and did not completely lose activity until after 1 h ( Figure 8D ) . Together , these results show that SnTox1 is a highly stable protein with the ability to resist physical and chemical degradation . The oxidative burst is one of the best-known biochemical responses of plant cells during a resistance response . The oxidative burst can be visualized by 3′–3′ diaminobenzidine ( DAB ) staining for H2O2 production [48] . Chinese Spring ( CS , Snn1 ) wheat leaves were infiltrated with SnTox1 yeast culture filtrate or a control yeast culture filtrate and collected at 48 h post-infiltration . The CS ems237 line ( snn1 ) was included for infiltration and DAB staining as a comparison . Leaves were stained with 1 mg/ml DAB solution followed by clearing of chlorophyll . Dense brown DAB staining was observed on the leaves of CS ( Snn1 ) infiltrated with SnTox1 , but DAB staining did not appear on leaves of CS infiltrated with the control culture filtrates deficient in SnTox1 , nor did DAB staining appear when SnTox1 was infiltrated into leaves of CS ems237 lines ( snn1 ) ( Figure 9A ) , clearly showing that the production of H2O2 is induced only during the SnTox1-Snn1 interaction . A control without DAB staining was also conducted on CS leaves infiltrated with SnTox1 yeast culture . After clearing the leaf , no browning was observed indicating that , in the absence of DAB , the SnTox1 reaction itself was not able to cause brown staining on the leaf ( Figure 9A ) . The production of H2O2 was also detected during the fungal infection . The CS leaves inoculated with Sn79+SnTox1A1 were collected daily from 1 to 7 days post inoculation and stained with DAB followed by the same procedure for leaf clearing . The accumulation of brown staining on the leaf was readily visible from 2 DPI ( Figure 9B ) . The generation of reactive oxygen species ( ROS ) associated with a hypersensitive response in planta often occurs in the chloroplast [49] . Using DAB stained CS leaves from the SnTox1 infiltration , we observed that chloroplasts had the highest intensity of brown color ( Figure 9C ) . Up-regulation of plant defense or signaling pathway genes including pathogenesis-related ( PR ) genes are hallmarks of a resistance response . Using RT-PCR , we examined the transcription level of 28 wheat genes ( Table S3 ) in CS ( Snn1 ) and CS ems237 ( snn1 ) leaves that were collected at different time points from 1 h to 72 h after being infiltrated with SnTox1 culture filtrates as well as control culture filtrates . Three genes including PR-1-A1 , a thaumatin-like protein gene , and a chitinase were found to be significantly up-regulated in CS leaves infiltrated with SnTox1 compared to the control leaf samples infiltrated with culture filtrates deficient in SnTox1 ( Figure 10A ) . In the CS ems237 line which has a mutated snn1 gene , a transcript was undetectable for the PR-1-A1 gene and was at a significantly lower level for the thaumatin and chitinase genes as detected by RT-PCR ( Figure 10A ) . Quantitative PCR ( qPCR ) analysis confirmed the higher expression of the three genes in SnTox1 infiltrated CS leaves compared to the control infiltrated CS leaves . Not only did all three genes show maximum expression at 36 HPI , but each had at least two-fold higher expression in SnTox1-infiltrated samples than the control ( Figure 10B ) . qPCR also showed much lower expression of the three genes in the CS ems237 line infiltrated with either SnTox1 or the control yeast culture filtrates in comparison to CS infiltrated with control culture filtrates ( Figure 10B ) . The reason for this is not clear , but it could be explained by the idea that Snn1 may play a role in sensing other environmental stimuli that can trigger PR gene expression . Programmed cell death ( PCD ) triggered by biotrophic effectors is often evidenced by DNA laddering in plants [16] , [50] . To determine if the necrosis induced by SnTox1 on Snn1 lines was a result of PCD , we isolated DNA from infiltrated CS leaf samples and checked for evidence of DNA laddering . For negative comparisons where no necrosis developed , DNA fragmentation was also examined in CS leaves infiltrated with control culture filtrates ( no SnTox1 ) and CS ems237 ( mutated snn1 ) infiltrated with SnTox1 or control culture filtrates . In the CS leaf samples infiltrated with SnTox1 , DNA laddering was detected as early as 10 h after infiltration and was most evident at 36 h after infiltration ( Figure 11 ) ; however , in the leaf samples from the other three treatments , no DNA laddering was observed at any time point ( Figure 11 ) , indicating that SnTox1-induced necrosis on lines harboring Snn1 is a result of host-controlled PCD . Light has been found to be important in the development of necrosis induced by necrotrophic effectors from P . tritici-repentis and S . nodorum [12] , [37] . Therefore , we investigated whether the development of necrosis induced by SnTox1 as well as the disease development caused by the SnTox1-Snn1 interaction was light dependent . After infiltration with SnTox1 yeast culture filtrates , CS plants were incubated in a growth chamber but covered for 2 days . The plants in the dark did not exhibit a necrotic reaction in the infiltrated area on the leaves , while the plants grown in the same growth chamber without covering showed necrosis ( Figure 12 ) indicating the development of necrosis induced by SnTox1 is light dependent . Interestingly , necrosis did develop on the dark treated plants once they were treated with a 12 h light-dark cycle for 2 additional days . A very similar situation was observed in the inoculation experiment . CS leaves showed no disease symptoms at 3 days post inoculation when plants were kept in the dark and similar to the infiltration experiment , the lesions developed once the dark-treated plants were moved to the light again ( Figure 12 ) . To investigate the role of SnTox1 in disease development , we tagged both the avirulent isolate Sn79-1087 and the pathogenic strain Sn79+SnTox1A1 with GFP and compared their infection processes by fluorescence microscopy in wheat lines CS ( Snn1 ) and the Snn1 mutant , CS ems237 ( snn1 ) . The inoculation of CS with the SnTox1-producing strain Sn79+SnTox1A1 resulted in an infection ( susceptible interaction ) ; however , the other three combinations ( CS inoculated with Sn79-1087 , CS ems237 inoculated with Sn79-1087 , and CS ems237 inoculated with Sn79+SnTox1A1 ) gave no disease ( resistant interaction ) ( Figure 13 ) . Within 24 HPI , there was little difference observed between resistant and susceptible interactions . During this period , conidia germinated , grew short hyphae and began the penetration process . The pathogen was able to initiate penetration in both types of reactions visualized by the formation of the indistinct penetration structure called a hyphopodia [26]; Figure 13 A , B ) and by autofluorescence of the damaged epidermal cell walls ( Figure 13 A , B ) . We observed mainly direct penetration of the leaf surface over both periclinal and anticlinal epidermal cell walls . A strong green autofluorescence was observed beneath the epidermis by 2 DPI in the susceptible interaction , suggesting that the pathogen had successfully penetrated through the epidermal cell layer and started the infection of mesophyll cells ( Figure 13C ) . However , in the resistant interaction , the pathogen grew extensively on the leaf surface and no green autofluorescence was visible ( Figure 13D ) . At 4 DPI , the infection area had enlarged in the susceptible interaction as shown by more mesophyll cells producing a fluorescent signal ( Figure 13E ) . On the leaves of the resistant interactions , most of the fungal mycelium was dead , likely due to scarcity of nutrients , and only a few hyphae continued to grow over the leaf surface with repeated unsuccessful attempts to penetrate ( Figure 13 F ) . The susceptible interaction had induced widespread lesion formation on the leaves by 7 DPI , however , no symptoms were found on the leaves of the resistant interaction ( Figure 13 G , H ) . Examination under the fluorescent microscope of the necrotic lesion formed from the susceptible reaction revealed the extensive growth of fungal mycelium within the lesion ( Figure S6 ) . The fungal infection process was also compared microscopically on Snn1-containing plants that were either grown under a normal light/dark cycle or in complete darkness after inoculation . The pathogen was able to germinate and generate hyphopodia within 24 HPI in both conditions ( data not shown ) . However at 48 HPI , only the plants grown in a normal light/dark cycle showed successful penetration through the epidermal cell layer and the initiation of the infection of mesophyll cells , evidenced by the autofluorescence of the mesophyll cells ( Figure 14A ) . In the plants that were kept in the dark , no autofluorescence was observed in the mesophyll cells and the pathogen still remained on the leaf surface without having successfully penetrated the epidermis ( Figure 14B ) .
The necrotrophic fungal pathogen S . nodorum produces multiple necrotrophic effectors ( host-selective toxins ) that function as virulence factors during the infection process . The cloning of these necrotrophic effector genes is an essential step in the characterization and elucidation of the molecular and biochemical mechanism of fungal pathogenesis in the wheat-S . nodorum pathosystem . Besides the traditional biochemical and genetic tools , new genomic strategies have been recently applied for the identification and cloning of effector genes in a number of fungi and oomycetes as more genome and other sequence data becomes available . A typical procedure would include a process of data mining to identify candidate genes that meet a set of specific criteria followed by gene validation through functional analysis . High throughput functional genomics [1] as well as comparative genomics and association genetics [41] have been successfully used for the identification of pathogen effector genes in fungi and oomycetes . In the current study , we used a set of criteria to mine the S . nodorum genome sequence dataset for the identification of necrotrophic effector genes . This strategy led to the successful identification of SnTox1 from S . nodorum . Through heterologous expression , gene transformation , and gene disruption , we have provided convincing evidence that the candidate gene SNOG20078 ( Gene ID: 5974395 ) is the SnTox1-encoding gene . This research further highlights the value of genome sequence data along with efficient bioinformatics tools in identifying effector genes . We are continuing to use this strategy to identify additional S . nodorum necrotrophic effector genes . SnTox1 was identified using a set of criteria based on the cloned S . nodorum effector genes SnToxA and SnTox3; however , the SnTox1 gene does have some unique features . Unlike many previously identified effector genes including those from Leptosphaeria maculans [51]–[53] , Magnaporthe grisea [41] , [54] , Fusarium oxysporum f . sp . lycopersici [55] , [56] , Blumeria graminis f . sp . hordei [57] , and those from several Phytophthora species [58] , SnTox1 lies in a gene-rich region and was flanked closely by other genes . Except for a short ( ≈300 bp ) sequence predicted to be a truncated molly-type RE , no other obvious RE or AT-rich sequences were identified within the 300 kb genome region surrounding SnTox1 ( http://genome . jgi-psf . org/Stano1/Stano1 . info . html ) showing that not all effector genes are associated with an abundance of repetitive or transposable elements . The occurrence of effector genes in close proximity to one another has also been reported for several fungal and oomycete pathogens [53] , [59]–[62] . This does not appear to be the case for S . nodorum . The three S . nodorum effector genes ( SnToxA , SnTox1 , and SnTox3 ) were located on different supercontigs and have been shown by pulse field gel electrophoresis and Southern analysis to reside on 2 . 35 , 1 . 88 and 1 . 66 Mb chromosomes , respectively , in SN15 ( data not shown ) indicating that these genes are not clustered . Using a worldwide collection of 777 S . nodorum isolates , SnTox1 was found to be present in ∼85% of isolates , a markedly higher frequency than found for SnToxA ( ∼36% ) and SnTox3 ( ∼60% ) [14] , [63] . Like the other NEs , SnTox1 was shown to have a presence/absence polymorphism within individual wheat fields . This type of polymorphism has been reported in other fungal pathosystems , as reviewed in Stergiopoulos and de Wit [64] . The frequency of SnTox1 varied significantly across regional populations . We hypothesize that regional differences in the frequency of SnTox1 reflect regional differences in the frequency of Snn1 . However this correlation was not apparent when tested on a small worldwide collection of wheat . We found that Snn1 is most prevalent in durum wheat lines and much more rare among hexaploid bread wheat lines throughout the world ( data not shown ) . This could indicate that the maintenance of Snn1 in durum wheat is associated with another important trait . Widespread deployment of wheat cultivars lacking Snn1 could cause the frequency of SnTox1 to decrease if there is a fitness cost associated with producing the effector . But the large effective population sizes of S . nodorum [65] make the complete loss of the effector through genetic drift unlikely . Observed diversity at the SnTox1 locus was found to fit a model of diversifying selection significantly better than a neutral model . Positive selection was found for 4 of the SnTox1 codons , consistent with the growing list of prokaryotic and eukaryotic effector candidates that exhibit positive selection [66] . None of the non-synonymous substitutions were found in the signal peptide , the putative chitin-binding domain , the putative Avr4-like domain or any of the cysteine codons . This suggests that the effector's functional domains were preserved , while more flexible amino acid sites were subject to diversifying selection . Possible differences in activity between different protein variants of SnTox1 are currently being tested . Similar to SnToxA and SnTox3 , SnTox1 was shown to play a significant role in disease development . Results presented here on the SnTox1-Snn1 interaction provide further evidence that the necrotrophic wheat-S . nodorum system is largely based on specific host-effector interactions that act in ETS [14] , [15] , which essentially has the opposite outcome of ETI that operates in many biotrophic systems [3] , [4] . One of the most striking features of the SnTox1 protein as a necrotrophic effector is the high cysteine residue content . This feature is often associated with fungal avirulence gene products such as the Avr and ECP effectors from Cladosporium fulvum [64] , SIX ( secreted in xylem ) effectors from Fusarium oxysporum f . sp . lycopersici [55] , and Nip1 from Rhynchosporium secalis [67] . The predicted mature SnTox1 protein has 100 amino acids , 16 of which are cysteine residues , the richest of all effectors that have been identified . The high content of cysteine residues and high stability suggest that SnTox1 may function in the plant apoplastic space which is abundant in plant defense components . We are currently investigating the subcellular location of SnTox1 . Most small cysteine-rich secreted effectors from the tomato fungal pathogen C . fulvum such as Avr2 , Avr4 , Avr9 , and ECP2 are thought to function exclusively in the apoplast to inhibit and protect against plant hydrolytic enzymes [64] . ECP6 , another C . fulvum effector containing LysM chitin binding domains was recently found functioning apoplastically as a scavenger of fungal chitin to prevent it from eliciting PAMP-triggered immunity in planta [68] . Interestingly , we observed that SnTox1 has some similarity to C . fulvum Avr4 within the chitin-binding domain and in the positions of six of the cysteine residues at the C-terminus . However , further tests are needed to determine the binding activity and functional roles of the putative CB domain in SnTox1 . The presence of a potential chitin binding domain provides a point of investigation for an added function for SnTox1 in addition to its interaction with Snn1 . Successful penetration is a prerequisite for a pathogen to establish itself and fulfill its colonization in planta . For S . nodorum , previous studies have observed direct penetration through the junction of epidermal cells [69] or penetration through stomata [70] or both [26] . Based on our observation using GFP-tagging and confocal fluorescent microscopy , the fungus predominantly used direct penetration through the junction of epidermal cells , and stomatal entry was not evident . We have observed that the fungal mycelium grew over guard cells and anchored the penetration point between the junction of the guard cell and the adjacent epidermal cell instead of going through the stomata ( data not shown ) . Although the avirulent isolate belongs to S . nodorum , the preference for direct penetration , which is different from that reported by Solomon et al . [26] , may be due to its adaptation to wild grasses from which it was originally isolated . It was our observation that the fungus could initiate direct penetration by producing a hyphopodia in both the resistant and the susceptible interaction with little difference , which agrees with previous reports [69] indicating that SnTox1 is not required for hyphopodia formation and the initial degradation of the cuticle layer and the cell wall between the junctions of the epidermal cells . Hydrolytic enzymes or other mechanisms may be employed by the fungus to breach the initial physical barrier . Several cell wall-degrading enzymes such as amylase , pectin methyl esterase , polygalacturonases , xylanases , and cellulase have been found to be produced in vitro and during the infection of wheat leaves by S . nodorum [71] . As infection progressed , the pathogen was unable to penetrate through the epidermal cell layer and therefore could not reach the mesophyll cells to establish a successful infection without the SnTox1-Snn1 interaction . This suggests that SnTox1 is significant in the initial penetration process across the epidermal cell layer . Our hypothesis is that SnTox1 interacts with Snn1 to induce cell death in epidermal cells , providing the fungus with nutrients for further invasive growth . In Cochliobolus victoria on oat and Arabidopsis systems , it was also observed that fungal penetration ceases following appressorium development and hyphae remain on the leaf surface in the absence of a compatible interaction , which requires both victorin and its corresponding sensitivity gene [22] . Our speculation was further supported by the fact that the inoculation of an Snn1 line ( CS ) with SnTox1 transformed avirulent isolates induced widespread necrosis - presumably programmed cell death - on leaves . Furthermore , inoculation with the SnTox1-knock out virulent strain lost the ability to cause this necrotic reaction . Additionally , qPCR revealed that SnTox1 expression was induced in planta starting as early as 12 HPI and increased at an accelerated rate from 12 to 24 HPI when the fungus was observed to penetrate . Collectively , this suggests that S . nodorum may use SnTox1 to induce cell death in the epidermal cells , providing a portal to enter the plant and subsequently feeding from dead cells to gain nutrients for further invasive growth . It is well known that plant defenses against pests and pathogens are commonly influenced by environmental conditions , including light . Many studies have demonstrated the requirement of illumination for the interaction of plants with a diversity of bacterial and fungal pathogens as well as the isolated pathogenic elicitors [72] , [73] . The effect of light on the disease development of SNB was first noticed by Baker and Smith [69] who observed that the necrotic reaction and lesion coalescence tended to be suppressed in the absence of light . The necrotrophic effector ToxA , was also shown to induce light-dependent necrosis on Tsn1 lines [37] . Among the S . nodorum necrotrophic effectors published to date , all effectors except SnTox3 have been shown to be light dependent [13] , [32] . Using heterologously expressed SnTox1 and the avirulent isolate carrying the SnTox1 gene , we showed clearly that the necrosis and disease susceptibility induced by SnTox1 on Snn1 lines were completely dependent on light . The requirement of light for resistance to biotrophic disease as well as susceptibility to necrotrophic disease suggests a common host mechanism shared by reactions to the two classes of disease interactions . The molecular mechanism underlying the light dependency of plant pathogen interactions is still poorly understood; however , research on the ToxA-Tsn1 interaction has shown that ToxA is internalized in the plant cell followed by localization to the chloroplast and induction of photosystem alterations ( reviewed in [40] ) , providing a hint for the influence of light on this interaction . Recently , it was demonstrated that Tsn1 is regulated by light and its expression is significantly suppressed in the dark [15] , providing a possible explanation for the light dependency of the ToxA-Tsn1 interaction . SnTox1 is cysteine rich and therefore possibly acts in the apoplastic space . If SnTox1 remains in the apoplastic space , different mechanisms would likely be involved even though both are dependent on light . In Arabidopsis , plants kept in the dark do not accumulate H2O2 in the chloroplasts and show significantly delayed HR cell death after a resistance signaling pathway is activated [49] . This indicates that light is required for H2O2 production in chloroplasts and that this H2O2 production is critical to programmed cell death . The DAB staining in CS ( Snn1 ) leaves infiltrated with SnTox1 was found to be associated with the chloroplast and the CS plant infiltrated with SnTox1 showed no DAB staining if kept in the dark ( data not shown ) , suggesting a similar mechanism underlying SnTox1-induced cell death . Very interestingly , we found that plants kept in the dark developed necrosis and disease symptoms once transitioned to a normal photoperiod . Therefore signal transduction appears to pause rather than stop in the absence of light . This may indicate that the SnTox1 signal is progressing to the chloroplast but this process is interrupted in the absence of light . Biotrophic effectors often function as elicitors of programmed cell death ( PCD ) thereby activating the resistance response in host plants containing the corresponding resistance genes . The host resistance reaction begins with the direct or indirect recognition of the pathogen-produced effector by the resistance gene product , followed by a complicated signaling pathway and a series of biochemical and physiological responses in host plant cells [74] . The host response often includes an oxidative burst , cell wall restructuring , PR-gene expression and antimicrobial compound production culminating in a localized cell death at the infection site . This PCD is known as a hypersensitive response and is typically aimed at halting further colonization by the pathogen [5] . A set of biochemical tests has shown that SnTox1 is able to induce resistance-like host responses and PCD evidenced by the H2O2 production , stronger expression of PR-genes , and DNA laddering in lines carrying Snn1 . It is important to note that SnTox1 physiologically evoked a widespread necrotic flecking on the Snn1 line , which is symptomatically similar to the hypersensitive response in biotrophic disease systems . However , this necrosis spreads into larger lesions resulting in susceptibility ( sporulation ) rather than resistance ( prevention or inhibition of sporulation ) . Together , this indicates that SnTox1 is likely functioning biochemically and physiologically similar to a biotrophic effector ( avirulence factor ) in the presence of Snn1 but with a different end result . A number of other necrotrophic effectors have also been shown to invoke a host resistance response [9] , [17] , [40] . It has generally been thought that necrotrophic plant pathogenic fungi possess simplistic infection mechanisms that rely on lytic and degrading enzymes [11] . In contrast , biotrophic fungal pathogen interactions have been considered more sophisticated due to the formation of special penetration and feeding structures , secretion of effectors to overcome plant PAMP triggered immunity and a constantly changing effector complement to avoid recognition by the plant innate immune system . However , three genes conferring sensitivity to necrotrophic effectors as well as susceptibility to the corresponding necrotrophic fungal pathogens have been cloned , and all possess resistance gene-like features [15] , [22] , [23] . Therefore , it seems that necrotrophic fungal pathogens may subvert plant resistance mechanisms for their own good . Here , we clearly showed that SnTox1 is an important virulence factor for S . nodorum in the presence of Snn1 and that the host response to SnTox1 shows several similarities to a classical resistance response induced by many biotrophic effectors , however , the outcome of the host recognition was susceptibility rather than resistance . SnTox1 is the third effector gene that we have cloned and characterized from S . nodorum , which further strengthens the hypothesis that the wheat-S . nodorum pathosystem is based largely on host-effector interactions . The three effector genes cloned have provided molecular tools to study the mechanisms underlying disease in this system , an emerging model for necrotrophic fungal diseases .
A series of experimental and bioinformatic criteria associated with effectors were evaluated to produce a candidate gene ranking of the predicted genes in the S . nodorum genome . These criteria were based on the known and predicted properties of effectors . Genes matching different criteria were given scores from 1 to 6 . The sum of scores for each gene was ranked and the top 100 genes were considered . The criteria used data from mass-spectrometry analyses of culture filtrates , a genome sequence scan of the strains Sn4 and Sn79-1087 , an in planta microarray experiment and various bioinformatics analyses . The criteria were as follows: predicted to be less than 30 kDa ( 1 point ) , cysteine rich ( >1 standard deviation more cys residues than expected of a protein of that size ) ( 2 points ) , detected by MS in culture filtrates ( 6 points ) , located within 5 kb of repetitive sequences ( 2 points ) , absence of homologues in the NCBI nr database ( 2 points ) , presence of RXLR or RGD motifs ( 2 points ) , predicted to be secreted ( 3 points ) , presence of a modified version of the gene in Sn4 ( 3 points ) , absence of the gene in Sn79-1087 ( 4 points ) , and a gene expression profile similar to ToxA and Tox3 ( 3 points ) . The total RNA of 7 day old mycelium of SN15 grown in Fries media [27] was prepared using the RNeasy plant mini kit ( Qiagen ) and treated with RNase-free DNase I ( Promega ) . First-strand cDNA was synthesized from 2 µg of total RNA using TaqMan Reverse Transcription Reagents ( Applied Biosystems ) . The coding region of SNOG_20078 was amplified from the above cDNA sample using primers 20078CF_EcoRI and 20078CR_ApaI containing the indicated restriction site ( Table S4 ) . The cloning of SNOG_20078 into the corresponding sequencing and expression vectors , yeast transformation , and preparation of culture filtrates from yeast cultures all followed the procedure described by Liu et al . [14] . The pGAPZ A vector containing the SNOG_20078 gene was linearized with AvrII before transformation . Culture filtrates of the yeast culture transformed with the SNOG_20078 coding region were infiltrated into wheat lines including BR34 ( snn1 ) , Grandin ( snn1 ) , BG220 ( snn1 ) , BG223 ( snn1 ) , BG261 ( snn1 ) , W-7984 ( Snn1 ) , Chinese Spring ( Snn1 ) , Opata85 ( snn1 ) , and ND495 ( snn1 ) . Because the culture filtrates caused necrosis on W-7984 and CS , which both possess Snn1 [27] , it was infiltrated onto CS 1BS-18 , CS ems237 , and the ITMI population [27] for verification of SnTox1 based on its interaction with Snn1 . CS 1BS-18 carries a deletion in the distal end of chromosome 1B that harbors the Snn1 locus [27] . CS ems237 is an SnTox1 insensitive mutant derived from CS by EMS ( ethane methyl sulfonate ) mutagenesis ( Faris et al . , unpublished data ) . A 14 amino acid long peptide ( sequence: CKNGKQAAHEAQKQ ) , designated SnTox1:50–63 , was synthesized by GenScript ( Piscataway , NJ ) . The peptide SnTox1:50–63 ( 4 . 7 mg , 0 . 003 mmole ) was first conjugated to bovine serum albumin ( BSA , 20 mg , 0 . 0003 mmole , Sigma-Aldrich , St Louis , MO ) in the presence of 1-ethyl-3- ( 3-dimethylaminopropyl ) -carbodiimide hydrochloride ( EDC , 20 mg , Pierce Biotechnology , Rockford , IL ) in 2 mL of 100 mM 2- ( N-morpholino ) ethanesulfonic acid buffer , pH 6 overnight at 4°C . The protein was separated from EDC through a size-based column ( D-Salt Excellulose , Thermo Scientific , Rockford , IL ) and concentrations were determined by the method of Bradford ( Bio-Rad Laboratories , Inc . Hercules , CA ) using BSA as the calibration standard . Success of the conjugation reaction was assessed on a 13% SDS-PAGE gel . One hundred milligrams of the immunogen were immunized into New Zealand White Rabbits at 3 week intervals for a total of six immunization cycles . The final sera were collected eight days after immunization and were used for western blot analysis . To prepare the SnTox1 protein sample for western blot analysis , 5 mL of culture filtrate from an SnTox1 yeast culture and control yeast culture ( yeast strain transformed with an empty vector ) was precipitated by adding 20 mL of methanol and incubating in a -20 freezer overnight . After centrifuging for 10 min at 13 , 000 rpm on a HERMLE Z 323K centrifuge with a 220 . 80 V02 rotor ( Labnet ) , the pellet was retained , air dried and re-suspended in 500 µL of a 1× sample loading buffer . Protein gels were loaded with 50 µL of the resulting sample solution . SDS-PAGE , protein transferring , and color development followed a routine protocol described in Meinhardt et al . [36] . To ensure the quality of protein sample preps , the same amount of sample solution was also run on a gel and visualized by coomassie blue staining . The same RNA extracted from SN15 was used to amplify the 5′ and 3′ ends of the cDNA of SnTox1 . The 5′ and 3′ RACE were performed using the Smart RACE cDNA amplification kit ( Stratagene , LaJolla , CA ) according to the instructions in the user manual with gene-specific primers 20078CF and 20078CR ( Table S4 ) . The procedure described by Liu et al . [14] was followed for the cloning and sequencing of the amplified 5′ and 3′ RACE fragments . The obtained sequences from 5′ and 3′ RACE fragments were used to assemble the full length cDNA and determine the 5′ and 3′ UTRs based on the SN15 genome sequence . SnTox1 and Avr4 homologs were identified from the NCBI non-redundant ( nr ) protein database ( http://www . ncbi . nlm . nih . gov/BLAST/ ) using BLAST searches . The chitin-binding domains of Avr4 and its homologues were identified using Reverse Position-Specific ( RPS ) -BLAST searches ( www . ncbi . nlm . nih . gov/Structure/cdd/wrpsb . cgi ) . Amino acid alignments were performed using the MegAlign programs from Lasergene 8 . 1 software ( DNASTAR Inc . Madison , WI ) . Three-dimensional ( 3D ) structure-based sequence alignment of the putative chitin-binding motifs identified in SnTox1 with those of ChtBD1 and ChBD2 proteins were performed following the previously published data on related structures [43] , [44] , [75] . SnTox1 presence and absence was screened in 777 S . nodorum isolates from seven geographical regions: Australia , Central Asia , East Asia , Europe , Middle East , North America , South America and South Africa ( Table S1 ) using PCR with primer pair Tox1F_Coding and Tox1R_Coding ( Table S4 ) . A secondary PCR screen using the conserved primer pair Tox1_XF and Tox1_XR ( Table S4 ) was conducted to confirm questionable PCR amplicons . PCR amplification was performed in 20 µl reactions containing 0 . 05 µM of each primer ( supplied by Microsynth ) , 1X Dream Taq Buffer ( Fermentas ) , 0 . 4 µM dNTPs ( Fermentas ) and 0 . 5 units of Dream TaqTM DNA polymerase ( Fermentas ) . The PCR cycle parameters were: 2 min initial denaturation at 96°C followed by 35 cycles of 96°C for 30 s , 58°C for 45 s and 72°C for 1 min . A final 5 min extension was made at 72°C . To demonstrate the wide distribution of SnTox1 , a subset of the global collection ( 79 isolates ) , along with 10 avirulent isolates and several related fungal species including Pti2 ( P . tritici-repentis ) , ND89-19 ( P . teres f . teres ) , Sm15A ( P . bromi ) and S . tr 9715 ( M . graminicola ) ( Table S2 ) were evaluated in a dot blot analysis . For dot blot analysis , the DNA of fungal samples was isolated using a BioSprint 15 instrument ( QIAGEN ) with the corresponding kit ( QIAGEN ) . The DNA samples were blotted onto a nylon membrane using a Bio-Dot microfiltration apparatus ( BIO-RAD ) following the instructions in the user manual . The entire SnTox1 coding region was PCR amplified from the genomic DNA of SN15 and used as a probe for Southern blot analysis . Probe preparation , DNA hybridization , membrane washing and image acquisition followed the protocol described by Faris et al . [76] . The membrane was stripped and hybridized to the S . nodorum actin gene probe to ensure the quality for all the DNA samples . Sequences for the entire coding region were obtained using the primer pair Tox1UTR_F and Tox1UTR_R and the primer pair Tox1Fout and Tox1Rout ( Table S4 ) . In cases of poor amplification primer pair Tox1F_Coding and a new conserved reverse primer , Tox1R_Conserved ( Table S4 ) , were used to confirm observed sequence variation . Sequencing reactions were conducted in 10 µl volume using the BigDye Terminator v3 . 1 Sequencing Standard Kit ( Applied Biosystems ) with both the forward and the reverse primer . The cycling parameters were 96°C for 2 min followed by 55 or 99 cycles of 96°C for 10 s , 50°C for 5 s and 60°C for 4 min . The products were cleaned with the illustra Sephadex G-50 fine DNA Grade column ( GE healthcare ) according to the manufacturer's recommendations and then sequenced with a 3730x/Genetic Analyzer ( Applied Biosystems ) . Alignment of forward and reverse sequences for each isolate was performed in SeqScape software V2 . 5 ( Applied Biosystems , Foster City , CA ) . Translation and identification of protein haplotypes was also performed using this software . Codeml implemented in the software PAML ( http://abacus . gene . ucl . ac . uk/software/paml . html ) was used to test for positive diversifying selection [77] . The program uses four different codon substitution models implemented in a maximum-likelihood framework to test which model , neutral or selection , best fits the data . Each model assumes a different range of values for the estimated value ω ( the ratio of non-synonymous to synonymous nucleotide substitutions ) . Under purifying selection , non-synonymous substitutions are expected to be rare , thus ω will remain below 1 . If non-synonymous mutations offer a selective advantage , they will be fixed at a higher rate than synonymous mutations and ω will be greater than one . We compared the null model M1a ( neutral ) , which assumes two site classes , purifying ( 0<ω0<1 ) or neutral ( ω1 = 1 ) to the alternative model M2a ( selection ) , which adds another class of diversifying sites ( ω2>1 ) . We also compared the more complex null model M7 ( neutral ) that assumes a beta distribution for 0<ω<1 , with the alternative model M8 ( selection ) which also assumes a beta distribution and adds an additional site class with ω2>1 . A likelihood ratio test was used to compare the likelihood estimate scores . The model simultaneously calculates the posterior probability for each codon that belongs to a particular site class ( e . i . ω>1 ) . If the posterior probability for a codon is high and it belongs to the site class with ω>1 , positive selection can be inferred for that codon , known as Bayes Empirical Bayes [78] . Based on the annotated SN15 genome sequence , four genes SNOG_07153-SNOG_07156 were predicted within a ∼7 . 6 kb region containing SnTox1 . Primers were designed ( Table S4 ) to amplify the gene region from start to stop codon for the four genes identified in the avirulent isolate Sn79-1087 . Since all the genes except SNOG_07154 were present in Sn79-1087 , one new primer designed in SNOG_07153 ( 20078g3R , Table S4 ) was used with the SNOG07155 forward primer to amplify the whole region in several virulent isolates as well as Sn79-1087 . The amplified fragments with different sizes were cloned into the pCR-4 TOPO cloning vector ( Invitrogen ) for sequencing . The sequences from different isolates were analyzed manually to identify the variations in the SnTox1 genomic region with the aid of the genome sequence ( http://genome . jgi-psf . org/Stano1/Stano1 . home . html ) . A ≈1 . 1 kb sequence of the SnTox1 genomic region including a putative promoter and terminator was amplified from the Sn2000 isolate using primers 20078gF_XbaI and 20078gR_XbaI , each containing an XbaI restriction site sequence ( Table S4 ) . The amplified fragment was cloned into the pCR-4 TOPO vector ( Invitrogen ) for sequencing to verify the identity and XbaI restriction sites . The SnTox1 gene fragment was then released from pCR-4 TOPO plasmid and cloned into the pDAN vector that carries the cpc-1:hygR ( hygromycin-resistance gene ) cassette . The resulting plasmid , designated pDAN-SnTox1 ( Figure S4 ) containing the 1 . 1 kb genomic region containing SnTox1 and hygR was used to transform Sn79-1087 protoplasts . Plasmid DNA was prepared through the regular alkaline lysis method as described by Sambrook and Russell [79] followed by the purification of the plasmid DNA using precipitation with PEG 8000 [79] . The plasmid DNA was linearized with EcoR V and concentrated to 1 µg/ µl for transformation . The fungal protoplasting and PEG-mediated transformation followed the procedure described by Liu et al . [14] . The regenerated clones were screened by PCR with primers 20078gF_XbaI and 20078gR_XbaI ( Table S4 ) and verified by Southern analysis [76] . The culture filtrate production , and infiltration and fungal inoculation with Sn79-1087 and SnTox1 transformed strains followed the protocol described previously [27] , [45] . The knock out of SnTox1 was performed using a split marker strategy which employed two rounds of PCR to generate replacement fragments as described by Catlett et al . [80] ( Figure S4 ) . In the first round of PCR , the 800 bp of 5′ flanking region and 825 bp of 3′ flanking region of SnTox1 were amplified from Sn2000 using two pairs of primers 20078KOF1 with 20078KOF2 and 20078KOF3 with 20078KOF4 ( Figure S4 , Table S4 ) . Simultaneously , overlapping marker fragments HY and YG of the hygromycin phosphotransferase cassette ( HYG ) were amplified from pDAN with two pairs of primers , M13F with HY and M13R with YG ( Table S4 , Figure S4 ) . All amplified fragments were gel purified and then used in a second round of PCR . Two reactions were set up for the second round of PCR with one to fuse and amplify the SnTox1 5′ flanking region with the HY fragment and the other to fuse and amplify the SnTox1 3′ flanking region with the YG fragment by adding the corresponding first round templates and primers . At least 100 µl of PCR reaction was set up for each reaction in the second round . Standard PCR conditions and Taq polymerase ( NEB BioLabs ) were used for both rounds of amplification except that round 2 used a longer extension time due to the longer template . A small amount of product from the second round of PCR was evaluated on a 1 . 0% agarose gel to ensure a successful fusion and amplification for each fragment . The remaining product was combined and concentrated by routine ethanol precipitation [79] . The pellet was finally re-suspended in 20 µl of TE ( 10 mM Tris and 1 mM EDTA ) for transformation of Sn2000 protoplasts . Fungal protoplasting and transformation followed the procedure described by Liu et al . [14] . The regenerated clones were screened using the PCR primers 20078KOF and 20078KOR ( Table S4 ) which amplifies the partial coding region of SnTox1 that was replaced by the hygromycin-resistance gene cassette . The ectopic transformant and two knock out transformants were verified by Southern blot analysis using the SnTox1 coding region as a probe . Spores of the knock out and ectopic strains as well as wild type Sn2000 were inoculated onto wheat lines W-7984 , and CS for testing the effect of the SnTox1 knock out . The International Triticeae Mapping Initiative ( ITMI ) mapping population was originally used to map the Snn1 gene , which confers sensitivity to SnTox1 , and quantitative trait loci conferring resistance/susceptibility to Sn2000 [27] , [45] . The same 106 recombinant inbred ( RI ) lines of this population were used to evaluate the genetically modified fungal strains including two Sn2000 SnTox1 knock out transformants ( Sn2000ΔSnTox1–9 and Sn2000ΔSnTox1–15 ) , one Sn2000 ectopic transformant ( Sn2000ΔSnTox1-ECT ) , and the wild type Sn2000 as a control . All strains were evaluated with three biological replications by inoculating their conidia onto the ITMI population as previously described [45] . The disease rating was conducted 7 days post inoculation using a 0–5 rating scale as described by Liu et al . [45] . Composite interval mapping with the average of three disease readings was performed as previously described [30] . The web-based program DISULFIND ( http://disulfind . dsi . unifi . it/ ) was used to predict if a particular cysteine residue was involved in the formation of a disulfide bond ( DB_state ) as well as the confidence level of the prediction . The state of each cysteine residue was predicted as either involved ( 1 ) or not involved ( 0 ) in a DB . The scale of confidence of disulfide bonding state prediction ranges from 0 ( low ) to 9 ( high ) [46] . The web-based program DiANNA 1 . 1 ( http://clavius . bc . edu/~clotelab/DiANNA/ ) [47] was used to determine the best connectivity prediction of cysteine residues in SnTox1 . The secondary leaves of CS ( ≈2 week old plants ) were inoculated with a fungal strain modified from the avirulent isolate Sn79-1087 by addition of the SnTox1 gene . The leaf tissues were collected from the inoculated leaves at 1 h , 3 h , 6 h , 12 h , 24 h , 2 d ( day ) , 3 d , 4 d , 5 d , 6 d , and 7 d post inoculation . The RNA was extracted from leaf samples using the RNeasy Plant Mini Kit ( QIAGEN ) and treated with RNase-free DNase I ( Promega ) . RNA sample quantification , cDNA synthesis , and gene transcript abundance analysis were performed as previously described [15] . The gene specific primers SnTox1qPCRF and SnTox1qPCRR ( Table S4 ) designed within the two exons were used for the SnTox1 gene in qPCR . The previously reported primers ActinF and ActinR [14] were used for the S . nodorum actin gene as internal control . Because all cysteine residues were predicted to form disulfide bonds , the protein stability of SnTox1 was investigated by DTT and heat treatment . For DTT treatment , the SnTox1 P . pastoris culture filtrate were treated with DTT ( Fisher Scientific , Pittsburgh , PA ) at final concentrations of 0 , 20 , or 40 mM and incubated at room temperature for 2 h or 4 h . For heat treatment , the P . pastoris culture filtrate was sealed in a 2 ml centrifuge tube and heated for 30 min or 1 h on a hot plate setting at 100°C . All treated culture filtrates were then infiltrated into CS leaves . The fully expanded secondary leaves of CS and CS ems237 were infiltrated with the culture filtrates from yeast transformed with SnTox1 or culture filtrates from yeast transformed with an empty vector ( as control ) . At 24 , 48 and 72 hours post infiltration , leaf samples were collected and leaf segments with an infiltrated area were cut and stained in a freshly made 1 mg/ml 3′–3′ diaminobenzidine ( DAB ) ( Sigma ) solution . The preparation of a DAB staining solution and the staining process followed a procedure described by Thordal-Christensen et al . [48] . The stained leaf tissue was cleared for chlorophyll by placing them on a paper pre-soaked with ethanol/acetic acid solution ( 3∶1 , V/V ) in a petri dish and incubating overnight . The cleared leaves were rinsed and stored in a lactic acid/glycerol/H2O solution ( 1∶1∶1 , v/v/v ) . The fully expanded secondary leaves of CS and CS ems237 were infiltrated with SnTox1 yeast culture filtrates and control culture filtrates . Leaf samples were taken at 1 , 2 , 4 , 8 , 10 , 24 , 36 , 48 , 60 , and 72 hour post infiltration . The DNA was extracted from the collected leaf samples using the CTAB method [17] . The 5 µl of DNA from each sample were separated on a 2% agarose gel . The gel was stained in ethidium bromide solution for 1 hour , destained in water for 1 h and photographed using a Gel LOGIC 100 image system ( Kodak ) . The fully expanded secondary leaves of CS and CS ems237 were infiltrated with SnTox1 yeast culture filtrates and control culture filtrates . Five centimeter segments of infiltrated leaf tissue was collected at 1 , 2 , 4 , 8 , 10 , 24 , 36 , 48 , 60 , and 72 hour post infiltration . Three leaves from different plants were collected as three replications for each time point . Total RNA was extracted from all leaf samples using the RNeasy plant kit ( QIAGEN ) and treated with RNase-free DNaseI ( Promega ) . The RNA quantification and first strand cDNA synthesis were conducted as previously described [15] . Using the cDNA samples , we examined the expression of a total of 28 wheat genes that have been reported or predicted to be involved in the defense response [Lu et al . unpublished , 21] ( Table S3 ) . The RT-PCR and agarose gel electrophorsis were performed using a standard procedure . The same cDNA samples from the three replications were used to conduct the qPCR analysis for three genes: PR-1-A1 , chitinase ( PR-3 ) , and thaumatin-like protein ( PR-5 ) following the description by Faris et al . [15] . The gGFP vector [81] was used to transform the green fluorescence protein gene into two fungal strains that were only different in the production of SnTox1 . One was the avirulent Sn79-1087 that did not produce SnTox1 nor did it cause disease , and the other was an Sn79-1087 SnTox1 transformant ( Sn79+SnTox1A1 ) that expressed SnTox1 and caused disease on Snn1 lines . Since Sn79+SnTox1A1 already carried the hygromycin resistance resulting from the SnTox1 transformation , the plasmid pII99 [82] containing geneticin resistance , was used with gGFP for co-transformation of this fungus . The plasmid DNA preparation , fungal protoplasting , and fungal transformation followed the same methods described above . For all transformations , at least 20 µg of each plasmid DNA linearized with the corresponding restriction enzyme ( gGFP with BglII and pII99 with EcoRV ) was used . The transformants with the strongest GFP signal were selected for both strains under the Nikon Eclipse TE-2000U microscope equipped with a GFP filter and UV light ( Nikon , Japan ) . The two GFP-tagged fungal strains were inoculated onto both genotypes of Snn1 ( CS ) and snn1 ( CS ems237 ) as described in Liu et al . [45] . The inoculated leaves were collected at 1 h , 3 h , 6 h , 12 h , 24 h , 2 d , 4 d , and 7 d post inoculation . The leaves were cut into 5 cm long segments and directly mounted onto glass slides . The specimens were examined immediately using a Zeiss Axioplan 2 Imaging Research Microscope with ApoTome confocal component ( Carl Zeiss Light Microscopy , Germany ) equipped with filter blocks with spectral properties matching those of GFP . The S . nodorum gene SNOG_20078 has been deposited in Genbank with identity numbers of 5974395 for gene ID and XP_001797505 . 1 for protein ID . The nucleotide sequence of 12 different haplotypes of SnTox1 , designated Tox1_H1–H13 , was submitted to GenBank with accession numbers from JN791682 to JN791693 . The other genes and proteins referred to in this paper included Cladosporium fulvum Avr4 protein ( CAA69643 . 1 ) , Mycosphaerella fijiensis Avr4-like protein ( Protein ID: Mycfi1:87167 ) , Cercospora beticola Avr4-like protein ( GU574324 ) , Microsporum gypseum Avr4-like protein ( GeneID:10030079 ) and Geomyces pannorum Avr4-like protein ( DY991214 ) . | In this manuscript we describe the cloning of SnTox1 from Stagonospora nodorum , the gene encoding the first host selective toxin ( SnTox1 ) identified in this fungus . SnTox1 induces necrosis and promotes disease on wheat lines harboring the Snn1 gene . We verified the function of the SnTox1 gene by expressing it in a yeast culture where the resulting culture filtrate induced necrosis but only on wheat lines that carried a functional Snn1 . The SnTox1 gene was also transformed into an avirulent S . nodorum isolate , resulting in an isolate that was virulent on wheat lines harboring Snn1 . SnTox1 was also disrupted in virulent S . nodorum isolates resulting in the elimination of disease on Snn1 differential wheat lines . Additionally , we investigated the host response to SnTox1 and S . nodorum strains producing SnTox1 and discovered that several hallmarks of a resistance response were present during the susceptible reaction , showing that the necrotrophic pathogen S . nodorum is likely using SnTox1 to stimulate a host resistance pathway involving Snn1 to induce disease . | [
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] | 2012 | The Cysteine Rich Necrotrophic Effector SnTox1 Produced by Stagonospora nodorum Triggers Susceptibility of Wheat Lines Harboring Snn1 |
The heavily methylated vertebrate genomes are punctuated by stretches of poorly methylated DNA sequences that usually mark gene regulatory regions . It is known that the methylation state of these regions confers transcriptional control over their associated genes . Given its governance on the transcriptome , cellular functions and identity , genome-wide DNA methylation pattern is tightly regulated and evidently predefined . However , how is the methylation pattern determined in vivo remains enigmatic . Based on in silico and in vitro evidence , recent studies proposed that the regional hypomethylated state is primarily determined by local DNA sequence , e . g . , high CpG density and presence of specific transcription factor binding sites . Nonetheless , the dependency of DNA methylation on nucleotide sequence has not been carefully validated in vertebrates in vivo . Herein , with the use of medaka ( Oryzias latipes ) as a model , the sequence dependency of DNA methylation was intensively tested in vivo . Our statistical modeling confirmed the strong statistical association between nucleotide sequence pattern and methylation state in the medaka genome . However , by manipulating the methylation state of a number of genomic sequences and reintegrating them into medaka embryos , we demonstrated that artificially conferred DNA methylation states were predominantly and robustly maintained in vivo , regardless of their sequences and endogenous states . This feature was also observed in the medaka transgene that had passed across generations . Thus , despite the observed statistical association , nucleotide sequence was unable to autonomously determine its own methylation state in medaka in vivo . Our results apparently argue against the notion of the governance on the DNA methylation by nucleotide sequence , but instead suggest the involvement of other epigenetic factors in defining and maintaining the DNA methylation landscape . Further investigation in other vertebrate models in vivo will be needed for the generalization of our observations made in medaka .
DNA methylation is central to the epigenetic control of transcription in vertebrates and is essential for cell differentiation and embryonic development [1–3] . While the cytosines in cytosine-guanine ( CpG ) dinucleotides are extensively methylated throughout vertebrate genomes , unmethylated CpGs are commonly found clustered at high density inside gene regulatory elements , such as promoters and enhancers . Previous studies have revealed that the methylation state of regulatory regions governs the expression of their associated genes [4 , 5] . Furthermore , aberrant changes in the methylation state can lead to deregulated transcription , resulting in cellular dysfunction , diseases and developmental abnormality [6 , 7] . Given its direct governance on transcription , the methylation landscape needs to be precisely specified and modulated . The DNA methylation pattern is established and maintained through highly dynamic biological processes , in which the methylome undergoes substantial , yet precise , changes . For instance , differentiating cells faithfully acquire specific methylation landscapes that are unique to their committed cell types [8–10] . Remarkably , in human and mice , the DNA methylome is extensively erased [11 , 12] and fully reconstituted during gametogenesis and early embryonic development [13–15] . These facts suggest that the methylation landscape is pre-defined by genetic information . Thus , deciphering how the methylation pattern is encoded is a prerequisite for understanding of differentiation processes and the pathogenesis of various diseases [6 , 16–18] . However , by what means the methylation pattern is defined in vivo remains enigmatic . Researches for the past decade proposed that DNA methylation pattern depends on local sequence context . In particular , in silico analyses asserted that there is the strong statistical association between sequence variants and differential DNA methylation states in vertebrates , from fish [19] to human [20] . A number of recent in vitro studies using cultured cells further demonstrated that high CpG density or the presence of specific DNA sequences that contain transcription-factor binding sites is capable of autonomously determining local hypomethylation in the globally methylated genome [21–24] . These recent in silico and in vitro reports support the notion that DNA methylation pattern is primarily determined by local sequence context [21] . However , the anticipated sequence-dependency of DNA methylation is in contradiction to the pioneer in vitro experiments in early 80’S [25–27] , in which the methylation status of exogenous DNAs ( either artificially CpG-methylated or completely unmethylated ) was found maintained with certain fidelity for many cell generations upon stable genome integration . Given these opposing results , the sequence-dependency of DNA methylome seems less concrete than recently anticipated . Importantly , the above ideas have never been well demonstrated nor rigorously tested in vivo . In this respect , the report by Long et al . [28] provided valuable insights by studying the DNA methylation state of the 42-Mbp fragment of human chromosome 21 in the Tc1 trans-chromosomic mice , as well as the mouse genome loci-containing transgene constructs that were artificially transposed into the zebrafish genome . Their results suggested the existence of sequence-dependent DNA methylation in vivo , but their analyses only focused on non-native sequences ( i . e . examining human genomic sequence in mouse , or mouse genomic sequence in zebrafish ) . Likewise , Li et al . [29] examined the methylation status of a transgene across three generations in rat and found the stable acquisition and inheritance of DNA methylation pattern , but the transgene examined was composed of a mouse promoter and human gene . Thus , it is difficult to draw a general conclusion with these studies on the causal relationship between DNA sequence and methylation in native context in vivo . Herein , we report the first experiments that rigorously tested the governance of DNA methylation state by nucleotide sequence in vivo . The small laboratory fish , medaka ( Oryzias latipes ) , was chosen as an experimental model for their relatively small genome size ( approx . 700 Mbp ) , short generation time ( 2 . 5 to 3 months ) , ease of in vivo genetic manipulation , oviparity , in addition to their capability of producing 10–20 fertilized embryos per pair on daily basis [30 , 31] . Importantly , the medaka has polymorphic inbred lines from two geographically separated subpopulations living in the northern and southern part of Japan ( 2 . 5–3% SNP rate , for review , see [32] ) , and their genomes and methylomes were already decoded [19 , 33 , 34] . Although vertebrates could have variable DNA methylation dynamics , particularly during early embryonic development ( e . g . , the genome-wide methylation erasure immediately after fertilization is highly extensive in human and mice [11 , 12] , but very subtle or virtually absent from sheep [35] , medaka [36] and zebrafish [37] ) , the ultimate zygotic DNA methylation landscape is highly conserved from fish to mammals [37–39] . In addition , since an extensively methylated genome is believed to be prerequisite for the onset of vertebrate evolution [40–42] , the molecular mechanisms and logic underlying the patterning of DNA methylome are likely conserved among vertebrates . Hence , observations made on medaka can potentially shed light on the postulated , yet unproven , link between genomic sequences and DNA methylation in vertebrates . Contrary to expectation , our results suggest that nucleotide sequence , by itself , cannot dictate its own methylation state in vivo , which argues against the prevailing view of DNA methylation in vertebrates .
Statistical association between medaka genomic sequences and local methylation states was modelled using support vector machine ( kmer-SVM [43] ) . Hypomethylated and hypermethylated genomic loci ( a . k . a . hypomethylated domains , “HypoMDs” , and hypermethylated domains , “HyperMDs” , respectively ) at the blastula stage ( Stage 11 according to Iwamatsu [44] ) were identified using the same criteria as described by Nakamura et al . [45] ( see also Fig 1A ) . While HypoMDs and HyperMDs are not readily discernible in terms of length and GC composition ( S1 Fig: panel A & B ) , they bear conspicuous difference in their sequence pattern , allowing robust in silico classification and accurate prediction of the methylation states by the SVMs based solely on nucleotide sequence ( Fig 1B: area under precision-recall curve ≥ 0 . 83 , versus 0 . 08 from the random classifier ) . Consistent with the fact that the median CpG density in HypoMDs is higher than that in HyperMDs ( S1 Fig: panel C ) , sequence pattern enriched in HypoMDs display higher frequency of CpG ( S1 Table: left columns ) . Furthermore , CpG-masking prior to the training of SVM could still result in models with modest classification performance ( Fig 1C: area under precision-recall curve ≥ 0 . 53 ) , suggesting that specific , CpG-free DNA motifs are also differentially enriched in HypoMDs and HyperMDs ( S1 Table: columns on the right ) . All these reinforce the notion that , similar to other vertebrates , there is the strong statistical association between genomic DNA sequences and their methylation states in medaka . To test the dependency between genomic sequences and their methylation state in vivo , we generated transgenic fish that ectopically carry full-length HypoMD or HyperMD , along with their 1 . 5 to 2-kb up- and down-stream sequences . To distinguish the endogenous and the ectopic copies of the assayed sequences , we specifically selected HypoMD and HyperMD that are differentially methylated in two closely related , inbred strains of medaka: HdrR and HNI [32] , i . e . being a HyperMD in HdrR but exists as HypoMD in HNI , or vice versa ( Fig 2 ) . The differential states of these homologous sequences in the two strains were presumably due to minor variation in their nucleotide sequences [19] . These transgenic fish helped reveal not only if the differential methylation state is genuinely due to sequence polymorphisms , but also if genomic sequence at ectopic loci could stably recapitulate its endogenous state over a substantial timeframe and across generations ( i . e . > 6 months , for the collection of F2 transgenic embryos ) . Three transgenic lines were examined , in which DNA sequences from HNI ( either endogenously HyperMD or HypoMD ) were inserted into the host drR strain ( outbred , parental strain of HdrR ) ( see Fig 2 for schematic illustration ) . Host drR and inserted HNI sequences were easily discriminated by SNPs . In concordance with the notion that nucleotide sequence can autonomously determine its own methylation state , the integrated full-length HypoMDs were completely unmethylated in the F2 transgenic blastula embryos ( Fig 2A & 2B: “core” ) . However , on the other hand , the integrated full-length HyperMD ( Fig 2C: “core” ) were also found poorly methylated in the transgenic embryos , which is in stark contrast to its native hypermethylated state . Moreover , while all flanking sequences tested are endogenously hypermethylated in both strains , they were poorly methylated ectopically ( Fig 2A–2C: “flank ( L ) ” and “flank ( R ) ” ) . In fact , substantial de novo methylation was not evident throughout all three integrated sequences , regardless of inside HypoMD , HyperMD , or their flanking regions . Since the transgene constructs were initially propagated in E . coli as bacterial plasmids and were thus completely devoid of CpG methylation prior to transgenesis , these observations suggested that the initial absence of CpG methylation on the transgenes was faithfully maintained regardless of their sequence and respective endogenous methylation states for at least 6 months and across 3 animal generations . This indicates that these assayed genomic sequences ( 1 ) do not carry methylation determination information and/or ( 2 ) randomly integrated into loci ( e . g . , inside or in close proximity to expression cassettes ) that were under strong influence of preexisting epigenetic factors . Given the above unexpected observations , a substantial number of genomic fragments of medaka was interrogated to comprehensively test the general presumption that genomic sequences can genuinely determine their own DNA methylation state in vivo . Medaka genomic DNA was digested and enriched for CpG-containing fragments ( approx . 40–220 bp; extended to approx . 184–364 bp with adapters ) using a library preparation method akin to that was designed for reduced representation bisulfite sequencing ( RRBS ) [46] . The PCR-amplified ( hence , unmethylated ) fragments were labeled ( methylation at the N6 position of adenine in the Dam sites , 5’-GATC-3’ , of the adapters ) , followed by ( or without ) artificial CpG methylation in vitro , then introduced into medaka zygotes at the one-cell stage , and allowed for highly efficient I-SceI-mediated random genome integration ( see Fig 3A for graphical procedures ) . According to Thermes et al . [47] , the integration event was expected to occur at the one-cell stage , i . e . immediately after injection . At the blastula stage ( 2000 to 4000 cells per embryo ) , after the removal of unintegrated fragments by size-selection and DpnI-digestion ( S2 Fig: panel A ) , the methylation state of the integrated fragments was determined via bisulfite PCR and high-throughput sequencing . The assayed integrated fragments encompassed nearly the entire range of GC content and CpG density of HypoMDs and HyperMDs ( S3 Fig vs S1 Fig ) . Approximately equal number of CpGs from HypoMDs and HyperMDs were assayed ( S4 Fig: top vs bottom panels ) . In spite of the strong statistical association between nucleotide sequence and methylation states , the integrated genomic fragments failed to recapitulate their endogenous methylation state at ectopic locations . The methylation rate at endogenous loci and that at ectopically integrated locations showed essentially zero statistical correlation: Spearman’s ρ ≤ 0 . 08 , Kendall’s τ ≤ 0 . 07 ( see also S5 Fig for the biplots ) . Without prior artificial methylation , CpGs on the integrated fragments were almost entirely unmethylated regardless of their endogenous states ( Fig 3B: upper-left vs lower-left panel ) . The lack of sequence dependency was further illustrated by a drastically different ectopic methylation pattern when the genomic fragments were artificially methylated prior to injection and genome integration ( Fig 3B: left panels vs right panels ) . The sharp contrast in the ectopic methylation patterns suggested that nucleotide sequence does not carry adequate information for its own methylation state , or the integrated fragments could escape de novo DNA methylation ( which occurs at some point between 64-cell stage and blastula stage [36] ) and any expected sequence-dependent demethylation in early medaka embryos . The artificially methylated , integrated fragments contained a substantial number of unmethylated CpGs when examined at the blastula stage ( Fig 3B: upper- and lower-right panels ) . These unmethylated CpGs were unlikely due to incomplete artificial methylation prior to injection for the following reasons . The methylase ( CpG DNA methyltransferases M . SssI ) used is known to completely methylate CpGs in all sequence context [48] . This was routinely achievable by our optimized reaction regimen ( see S6 Fig for examples using bacterial genomic DNA and vector library that have higher CpG frequencies per unit weight of DNA than the medaka genome ) . The observed unmethylated CpGs could be caused by demethylation in the injected embryos . However , such demethylation could not be directly inferred as recapitulation of the endogenous methylation state , since there was essentially zero correlation between the endogenous and ectopic states ( Fig 3B: upper-right vs lower-right panel; see also panel B in S5 Fig ) . In addition , the observed loss of premethylated state was unrelated to the endogenous chromatin accessibility ( hence , potential binding of- or recognition by- transcription factors ) , as CpGs originated from heterochromatin and euchromatin were equally susceptible to the loss of methylation ( right panels of S7 Fig; note the peaks at 0% methylation rate in the histograms along Y-axes ) . We also compared the nucleotide sequences ( 10 bp from both up- and down-stream ) encompassing CpGs that were demethylated to those that were maintained as hypermethylated using kmer-SVM with the same parameters as above . However , the resultant SVMs were highly imprecise and insensitive ( S8 Fig: area under precision-recall curve ≤ 0 . 47 , versus 0 . 43 from random classifier ) . Moreover , the overall ectopic methylation states , as well as the demethylation , of the integrated fragments do not correlate with their size or CpG density ( S9 Fig ) . Together , we concluded that the observed demethylated state was not related to intrinsic sequence features of the genomic fragments . Given that the injected genomic fragments were ( 1 ) only partial fragments of HypoMDs or HyperMDs and may lack the presumed sequence features that are required for autonomous determination of methylation state , and ( 2 ) integrated into random genomic positions where they might be influenced by local chromatin state , we speculated that the observed demethylation might be due , at least in part , to the local epigenetic state of the integrated loci ( i . e . position effect; E . g . , integrated into preexisting HypoMDs or somewhere under the influence of trans-acting hypomethylation determining elements , hence rendered hypomethylated ) . Subsequent experiments were thus conducted at pre-specified genomic loci to control for the possible position effect . In order to examine whether full-length HyperMDs and HypoMDs can autonomously determine their own methylation state at an inert genomic location , six unmethylated HyperMDs and eleven pre-methylated HypoMDs were injected into one-cell stage medaka embryos and integrated into the gene desert region presumably devoid of any possible influence of active regulatory elements ( see also Fig 4A ) . The integration was achieved by the highly efficient , PhiC31 integrase-mediated site-specific integration in medaka and was expected to occur at the one-cell stage [49] . Methylation states of the integrated sequences were examined at the blastula stage . Autonomy in methylation state determination by the full-length , integrated sequences would manifest as remethylation of the unmethylated HyperMDs , as well as active or passive loss of the methyl groups on the premethylated HypoMDs , after genome integration ( see also Fig 4B for illustration of the logic of the experiment ) . In concordance with the above experiments , all of the unmethylated , integrated HyperMDs failed to acquire methylation ( Fig 4C ) . Likewise , the pre-methylated , integrated HypoMDs remained hypermethylated ( Fig 4D ) , with very limited number of CpG dinucleotides ( i . e . only 4 out of the 202 CpGs inspected ) having no methylation ( Fig 4D: blue dots on the integrated , ectopic copies of HypoMDs/Loci 1 , 4 , 6 , and 9 ) . Since we were unable to determine the methylation state of these distinct CpGs in the premethylated plasmid library ( as plasmid DNA converts very poorly in bisulfite reaction ) , it is possible that these CpGs were not fully methylated prior to injection . However , as aforementioned , the M . SssI methyltransferase used in the pretreatment has no known sequence specificity . The observed absence of methylation probably reflects highly localized loss of methyl groups on these specific CpGs . Collectively , the above results indicate that the overall , ectopically introduced nucleotide sequences were not perused and the artificially conferred methylation states ( i . e . hypomethylation in the HyperMDs , and hypermethylation in the HypoMDs ) were robustly maintained in vivo . Finally , we edited the methylation state in situ to exclude the risk of artifacts possibly incurred by ectopic genome locations . The methylation state of two HypoMDs were edited in situ via CRISPR-Cas9-triggered homology directed repair ( HDR ) and artificially methylated repair templates ( see Fig 5A for illustration of concept behind the experiment ) . Consistent with the aforementioned observations , in spite of the original hypomethylated state , the loci were rendered largely hypermethylated in the edited blastula embryos ( Fig 5B & 5C ) . Since the observed lack of restoration of native methylation state could be due to the seemingly limited time allowed for the recapitulation ( from injection to sampling , i . e . from 1-cell stage to blastula: approx . 8 hrs , encompassing 11–12 rounds of cell divisions ) , we repeated the editing experiment and extended the endpoints to later developmental stages at 3- ( Stage 31 ) and 7- ( 50% hatched and free-swimming; i . e . Stage 39 ) day-post-fertilization ( i . e . day-post-injection ) . Yet , the edited alleles remained hypermethylated in the mid-/late-stage embryos ( S10 Fig ) . Significant loss of methyl groups could only be observed on two distinct , adjacent CpGs in one of the two edited loci ( S10 Fig , panel B: the 1st and 2nd CpG ) . Taken together , these observations indicate that genomic sequence and its methylation state were not coupled even at the endogenous position .
Although DNA methylation is the best characterized epigenetic signature [50] , the molecular basis and logic of its establishment still remain elusive . Given that CpG dyads are predominantly methylated unless they are clustered at high density [51] , it is generally presumed that hypermethylation is the default state of vertebrate genomes and specific regions ( i . e . gene regulatory elements ) are protected from de novo methylation , rendering them hypomethylated [21 , 24 , 52–55] . Intensive researches for the past decade have demonstrated that the protection on the genomic loci is possibly mediated by nucleosome positioning [56–58] and/or the recruitment of a myriad of proteins [12 , 59–63] which eventually block off local access of DNA methyltransferases or remove methylation on cytosines in vicinity through oxidation and thymine DNA glycosylase ( TDG ) -mediated base excision repair . However , little is known about how are these factors specifically predisposed on the preselected loci . As aforementioned , recent in vitro studies demonstrated that nucleotide sequence features ( especially high CpG density and the presence of certain transcription factor binding sites ) autonomously determined the local hypomethylated state [21–24 , 64 , 65] . However , this preposition has never been rigorously verified in vivo , presumably due to the fact that interrogation of genomic sequences at genome-wide scale requires large number of subject animals , which is prohibitive with classical mammalian models ( e . g . , rodents ) . With the use of medaka as an alternative vertebrate model , our results definitively showed that there is no immediate connection between DNA methylation state and underlying nucleotide sequence in vivo , in spite of their strong statistical association . By manipulating and controlling the methylation state of genomic sequences prior to reintegration into the genome , we demonstrated that the artificially established methylation states were predominantly maintained in medaka in vivo , independent of their nucleotide sequences and native methylation states . This also appears to be true for the transgene that passed across generations . Our results thus argue against not only the recently inferred determining role of DNA sequence on the methylation landscape , but also the longstanding belief that there is a default state ( i . e . hypermethylated ) for the vertebrate genomes . In fact , the postulated strict sequence-dependency seems paradoxical to the concept of epigenetics itself . There are accumulating reports for the last two decades that DNA methylation could be perturbed by transient physiological stress or chemical exposure . More importantly , the perturbed states could be highly persistent and inheritable , while the underlying genomic sequence remains unchanged [66–68] . These observations highlighted that DNA methylation pattern is not directly coupled with the underlying nucleotide sequence in vivo , in spite of what has been recently shown in silico and in vitro . However , our results do not rule out the existence of highly confined , local sequence-dependent DNA methylation . As proposed by Richards [69] , the sequence-dependency of epigenetic signatures may vary with actual sequence-context , i . e . some nucleotide sequences may favor or even fully mandate certain methylation state , while others may be completely independent of DNA methylation . Although the artificially established hypermethylated state of HypoMD sequences examined in this study was mostly maintained after genome integration , we observed spontaneous , complete loss of methyl groups on some CpGs in the eleven pre-methylated HypoMDs , as well as within one of the in situ edited loci . This suggests the presence of local sequence elements that facilitate demethylation on specific CpGs , although their effect was spatially confined . As previously demonstrated in vitro , some DNA motifs , in particular several transcription factor binding sites ( reviewed by Blattler and Farnham [70] ) , are indeed instructive to DNA methylation and may account for the change of methylation state in specific loci upon differentiation [18 , 71] . Importantly , their effect was also demonstrated to be limited to no more than a few tens of base pairs up- and down-stream [22 , 23] . It is thus likely that the restricted governing range ( < 100 bp ) of these DNA sequences is insufficient to account for the span of HypoMDs ( median length > 1 kb ) . The apparent lack of sequence dependency can be explained by the involvement of epigenetic factors in DNA methylation in vivo , as suggested by Kaminsky et al . [20] . Genomic fragments tested in the present study and previous works were all purified prior to reintegration into the genome , hence lacked any associated factor ( s ) that can modulate DNA methylation . Future experiments will need to address the presumed methylation determining factor ( s ) , their deposition onto specific locations of the genome , and their inheritance across cell division and animal generations . The strong link between DNA methylation , nucleosome position and histone modifications [72–74] could provide a hint for further investigation . In summary , with the use of medaka as a vertebrate model , our data presented herein oppose the recent proposition that the genome-wide DNA methylation pattern in vertebrates is primarily and autonomously designated by the underlying genomic sequence in vivo , but instead provide insights into potential involvement of other epigenetic factor ( s ) in defining the DNA methylation landscape . Our results demonstrate that the DNA methylation landscape and genomic sequence are not directly coupled , which underpin the widely-observed plasticity of DNA methylation along differentiation , as well as the transgenerational inheritance of perturbed DNA methylation in vivo . However , it is worth noting that vertebrate species could have variable methylation dynamics of DNA methylation during development and growth , especially during early embryonic stages , although underlying molecular mechanisms are probably conserved . This is true even within the same clade of vertebrate species , such as mammals [35] . Further investigation in other vertebrate models will definitely be needed before generalization of our observations made on medaka .
The culture and handling of medaka and their embryos followed the protocols and guidelines published in "Medaka: Biology , Management , and Experimental Protocols" ( ISBN: 9780813808710 ) . Experiments were conducted with the permission of Life Science Research Ethics and Safety committee of the University of Tokyo ( Permission number: 14–05 ) . Published whole genome bisulfite sequencing reads of medaka blastula embryos [75] were fetched from the Data Bank of Japan ( accession number: SRX149583 ) . Individual reads were trimmed to remove primers , adapters , and low quality basecalls ( Phred score ≤ 3 ) using BBDuk from the BBTools ver . 35 . 85 [76] . Trimmed reads were mapped to the latest ( as of the time of this writing ) medaka genome assembly ver . 2 . 2 . 4 [34 , 77] ( all genome coordinates reported herein refer to this assembly version ) using bwa-meth ver . 0 . 2 . 0 [78] . Methylation rates of the mapped CpG dyads were then extracted using MethylDackel ver . 0 . 2 . 1 [79] with the default quality filters of MAPQ score ≥ 10 and Phred score ≥ 5 . Only those CpG dinucleotides with coverage of ≥ 5× were considered as valid calls [75] and the final mean coverage after filtering was 8× . The same filtering criteria were also applied to all experiments throughout this study , wherever they are applicable . And , unless otherwise specified , the endogenous methylation states of sequences assayed in this study were directly extracted from this mapped , filtered dataset . HypoMDs calling followed the same definition as previously published [19 , 45] . Specifically , any stretch of ten or more hypomethylated ( methylation rate < 40% ) CpGs with no more than four interleaving non-hypomethylated ( methylation rate ≥ 40% ) or undetermined ( unsampled , unmappable or low coverage ) dyads were called as HypoMD . HyperMDs were analogously defined as any stretch of at least ten hypermethylated ( methylation rate > 60% ) CpG dyads containing no more than four interleaving non-hypermethylated ( methylation rate ≤ 60% ) or undetermined CpGs . To elucidate whether HypoMDs and HyperMDs contains distinct sequence features , genomic sequences of all called HypoMDs ( N = 18435 ) and HyperMDs ( N = 231516 ) were subjected to supervised classification using kmer-SVM ( support vector machine with string- , i . e . nucleotide sequences- , based spectrum kernel ) [43] . The default , recommended parameters and k = 6 ( i . e . 6-mer ) were used . Proportionally higher weights were assigned to HypoMDs ( weight = 231516 / 18435 = 12 . 56 ) than HyperMDs ( weight = 1 ) to offset the imbalanced sample sizes . Classification performance was gauged by 10-fold cross-validation and the area under precision-recall curves . Since HypoMDs have a higher average CpG density than HyperMDs ( S1 Fig: panel C ) , CpG density might act as a confounding factor that outweighs and conceals non-CpG-containing sequence features . The impact of CpG density was hence controlled for by masking all CpG dinucleotides ( i . e . from ‘CG’ to ‘NN’ ) and the SVM model was retraining using the same parameters as listed above . Genomic regions that show contrasting methylation state between the HdrR and HNI strains were identified as described by Uno et al . [19] . HNI-specific HyperMD and HypoMDs , along with their 1 . 5 to 2-kb upstream and downstream sequences , were randomly selected and cloned using primers pairs F2-01 through F2-03 ( see S2 Table for the oligo sequences ) . Medaka’s beta-actin promoter and EGFP coding sequences was amplified using primer sets F2-04 and F2-05 , respectively . Amplified fragments were stitched together and cloned into pBlueScript-SK using In-Fushion assembly mix ( Clonetech , Japan ) . The vectors were pre-treated with 5 units of I-SceI meganuclease in 20 μL of 1× I-SceI digestion buffer ( New England Biolabs , USA ) at room temperature for 1 hour and injected into medaka ( drR strain ) embryos at 1-cell stage following standard procedures [30] . Embryos that displayed stable , ubiquitously strong GFP fluorescence were raised and crossed with wild-type drR fish . GFP-positive F1 were inter-crossed to produce F2 generation . GFP-positive F2 embryos at blastula stage , i . e . Stage 11 by Iwamatsu [44] , were sampled for genomic DNA extraction ( see Method 1 in S1 Text ) . The purified genomic DNA was then bisulfite-converted using the MethylEasy Xceed Rapid DNA Bisulphite Modification Kit ( Genetic Signatures , Australia ) following manufacturer’s recommended procedures , except that DNA denaturation was carried out at 42°C for 20 mins . The stably integrated Hypo/HyperMDs and their flanking regions were PCR-amplified using BSP primers designed in MethPrimer [80] ( primer set F2-06 through F2-11 ) and ExTaq polymerase ( Takara Bio , Japan ) under reaction conditions listed in Method 4 in S1 Text . BSP products were TA-cloned using TOPO TA Cloning Kit , Dual Promoter ( Thermo Fisher Scientific , USA ) and Sanger-sequenced ( outsourced to FASMAC Co , Japan ) . Quality check and methylation rate quantification were carried out in QUMA [81] ver . 1 . 1 . 3 with default parameters . To test whether nucleotide sequences can autonomously determine their own methylation state in vivo at genome-wide scale , CpG-rich genomic fragments were captured and injected into medaka zygotes for random reintegration into the genome , then fished out to check for their methylation state . The capturing method was akin to those described for reduced representation bisulfite sequencing ( RRBS ) . In fact , procedures up to the size selection of adaptor-ligated genomic fragments closely followed those optimized for RRBS [46] . The adaptor-ligated fragments were then enriched and amplified by extension PCR , which also introduced ( from 5’ to 3’ , in this order ) I-SceI target sites , bisulfite PCR ( BSP ) primer binding sites ( i . e . for primer F3-01F and F3-01R ) , and the Dam methylation site ( 5’-GATC-3’ ) to the products’ termini . The pool of amplified fragments was then Dam-methylated by incubating with Dam methylase ( New England Biolabs ) to facilitate downstream counter-selection of unintegrated fragments . Dam-methylated fragments were split into two equal halves with one half used directly for injection after purification and the other half subjected to artificial methylation using CpG methytransferase M . SssI ( New England Biolabs ) prior to injection . Detailed procedures are available as supplementary information ( Method 2 & 3 in S1 Text ) . Immediately prior to injection , the fragments ( final concentration: 10 ng/μL ) were pre-treated I-SceI meganuclease as above . Medaka zygotes were injected with Dam-methylated or Dam+CpG-methylated fragments at 1-cell stage . Around 500 embryos were injected with each pools of fragments and were allowed to develop to the blastula stage at 28°C . The embryos were visually inspected under dissecting microscope with dead or malformed embryos discarded . Ultimately , 496 ( 86% ) and 433 ( 92% ) embryos injected with Dam-methylated and Dam+CpG-methylated fragments , respectively , developed normally to the blastula stage , and from which genomic DNA with fragments integrated was extracted ( Method 1 in S1 Text ) . While most of the unintegrated fragments were presumably removed using our optimized DNA extraction method that includes size selection by PEG precipitation , carryover was further minimized by incubating the extracted DNA with 2 μL of FastDigest DpnI ( Thermo Fisher Scientific , USA ) in a 20 μL of 1X NEB Buffer 2 ( New England Biolabs ) for a total of 72 hours at 37°C in an incubator . This was followed by routine phenol-chloroform extraction and isopropanol precipitation . The precipitated DNA was finally re-dissolved in 20 μL of freshly dispensed Milli-Q water ( Merck Millipore , USA ) . Efficient removal of unintegrated fragments was indicated by the parallel use of uninjected , spike-in control . Approximately twice the amount of the injection cocktail was spiked into the lysate of uninjected blastula embryos , which was then processed as described above . Relative quantity of library with or without integration was gauged by real-time PCR ( THUNDERBIRD SYBR qPCR Master Mix , TOYOBO , Japan; in Agilent Stratagene Mx3000P , USA ) using the library-specific primers F3-01F and F3-01R . In parallel , input DNA was also quantitated using primers F3-04F and F3-04R . Amplification plots were imported into qpcR v1 . 4 . 0 [82] , where the relative quantities were determined after sigmoidal modeling ( all adjusted R2 = 1 . 00 ) . The purified genomic DNA was then bisulfite-converted as above . Integrated fragments were enriched via PCR using primers F3-01F and F3-01R . The BSP products were dA-tailed and ligated to Illumina TruSeq adapters , pooled , and sequenced using Illumina MiSeq system . Detailed library preparation procedures are described in Method 4 in S1 Text . Sequencing outputs were minimally trimmed , mapped to genome , and called for methylation rate as aforementioned , except bwa-mem’s “-U” switch was set to its default . In order to relate the methylation state of the integrated fragments to possible binding or recognition by DNA-binding proteins ( e . g . , transcription factors ) , we identified DNase I hypersensitive sites ( DHS ) by remapping the publicly available DNase-seq dataset of drR medaka blastula embryos ( accession number: SRX1032807 [83] ) to the medaka genome assembly v2 . 2 . 4 . Adaptor trimming and alignment was accomplished using BBmap v37 . 36 [76] with default parameters . Aligned reads were filtered for a minimum MAPQ of 20 . MACS v2 . 1 . 1 . 20160309 [84] was subsequently used to called 112987 peaks ( DHS ) with the following switches: “-g 6 . 3e+8 --nomodel --shift -50 --extsize 100 -q 0 . 01” . Vast majority ( > 96% ) of the assayed fragments were originated completely from either inside or outside- , but not spanning across the boundaries- , of DHS ( S3 Table ) . An engineered transgenic line that carries an attP site inside a gene desert on chromosome 18 for PhiC31 integrase-mediated recombination was used for site-specific integration of the full-length , unmethylated HyperMDs ( i . e . PCR-amplified , cloned , and without pretreating with M . SssI ) and pre-methylated HypoMDs ( i . e . PCR-amplified , cloned , and pretreated with M . SssI ) with lengths of 300–400 bp . PhiC31 integrase coding sequence was amplified from pPGK-PhiC31o-bpA ( a gift from Philippe Soriano; Addgene plasmid #13795 ) and attached to SV40 nuclear localization sequence ( NLS ) using primer pair F4-01 and Phusion polymerase ( Thermo Fisher Scientific ) , then blunt-end-cloned using Zero Blunt PCR Cloning Kit ( Thermo Fisher Scientific ) . Cloning direction and proper coding sequence were checked via Sanger sequencing ( by FASMAC Co ) . PhiC31 integrase mRNA was generated from the constructed template via in vitro transcription ( Method 5 in S1 Text ) . Six HyperMDs ( see S1 Dataset ) with flanking BSP primer binding sites ( for F3-01F and F3-01R ) and Dam-sites ( downstream of the BSP primer sites ) were directly synthesized by Thermo Fisher Scientific and Integrated DNA Technologies ( USA ) as double-stranded DNA and cloned into the targeting vector pEx_MCS-attBtagRFPt ( a gift from Joachim Wittbrodt; Addgene plasmid #48876 ) . Eleven HypoMDs were amplified from drR genomic DNA and extended to include BSP primer binding sites and Dam-sites on both ends using primer sets F4-02 through F4-12 , then cloned into the targeting vector pEx_MCS-attBtagRFPt . HyperMD-containing targeting vectors were propagated in dam+ E . coli ( DH5α ) ( Thermo Fisher Scientific ) and pooled in approximately equimolar amount . HypoMD-containing targeting vectors were similarly processed , except that the pooled library was further artificially methylated with CpG methyltransferase M . SssI and purified as aforementioned ( Method 3 in S1 Text ) . Individual plasmid libraries ( final concentration: 10 ng/μL ) was injected with PhiC31 integrase mRNA ( 100 ng/μL ) into >200 embryos of PhiC31 transgenic strain [49] at 1-cell stage . Injected embryos were reared at 28°C to blastula stage , screened for normal development ( > 85% ) , homogenized , and extracted for genomic DNA ( Method 1 in S1 Text ) . The extracted DNA was digested with DpnI to degrade unintegrated vectors , re-purified , bisulfite-converted , subjected to PCR via ExTaq polymerase , TA-cloned , Sanger-sequenced , and quantified for methylation rate as aforementioned . To ensure the injected but unintegrated vectors were efficiently removed , the above injection was also carried out without PhiC31 integrase mRNA . These injected embryos were processed in parallel with those injected with integrase mRNA up to DpnI digestion . The relative abundance of undigested libraries ( both unintegrated and integrated ) was quantified and normalized to amount of input genomic DNA using real-time PCR as described above ( see also S2 Fig: panel B ) . Homology directed repair was triggered by CRISPR-Cas9-induced double-strand breaks . spCas9 mRNA was produced from pMLM3613 ( a gift from Keith Joung; Addgene plasmid #42251 ) via in vitro transcription ( Method 5 in S1 Text ) . The HypoMDs , chr17:6415960–6416269 ( Locus 1 ) and chr21:25260707–25262742 ( Locus 2 ) , were randomly chosen as targets for editing . sgRNAs targeting these regions were designed using CCTop [85] . The six top-ranked guide sequence designs ( sets F5-01 and F5-02 , for the two loci , respectively ) were synthesized ( Thermo Fisher Scientific ) and in vitro transcribed ( Method 5 in S1 Text ) . To construct the repair template , these genomic regions ( with 6 mutations to the targeted spCas9 PAMs , i . e . from ‘NGG’ to ‘NGC’ , in order to protect the template from being cleaved by spCas9 ) along with their up- and down-stream sequences ( 800 bp on both sides ) as homology arms were synthesized ( Integrated DNA Technologies ) , assembled , cloned into pCR-BluntII vector ( Thermo Fisher Scientific ) using NEBuilder HiFi assembly mix ( New England Biolabs ) , and propagated by dam+ E . coli . The repair templates were artificially methylated in vitro using CpG methyltransferase M . SssI and purified as described above . For each of the target regions , sgRNA cocktail , spCas9 mRNA , and artificially Dam+CpG-methylated repair template were co-injected into medaka ( drR strain ) embryos at 1-cell stage at ultra-high concentrations ( 25 ng/μL each , 600 ng/μL and 10 ng/μL , respectively , i . e . 750 ng/μL of RNA and 10 ng/μL DNA in total ) to maximize editing rate . Injected embryos were reared at 28°C for approx . 8 hours to blastula stage , screened for normal development ( > 75% ) and extracted for genomic DNA , which was DpnI-treated to degrade the repair template , re-purified , and bisulfite-converted as aforementioned . The BSP primer pairs ( F5-03 for Locus 1; F5-04 for Locus 2 ) were designed using MethPrimer 2 . 0 and screened for the presence of native Dam-site ( s ) ( 5’-GATC-3’ ) within the target region . The amplification products were gel-purified and directly Sanger-sequenced from both ends . The methylation rate of each CpG was estimated from the sequencing chromatograms as: C ÷ ( C + T ) × 100% , where C and T are the called peak height in the ‘cytosine’ ( i . e . methylated cytosines , after bisulfite PCR ) and ‘thymine’ ( i . e . unmethylated cytosines , which were converted to uracil by bisulfite treatment , then to thymine by PCR ) channels , respectively . The signal intensities were extracted in R 3 . 3 . 3 [86] using the sangerseqR package ( version 1 . 12 . 0 ) [87] . To estimate the editing rate , regions containing the sgRNA target sites was PCR-amplified from unconverted DNA using primer sets F5-05 through F5-09 . Editing rate was gauged by the relative frequency of mutated sgRNA PAMs ( 5’-NGC-3’; on the edited alleles ) versus the native PAMs ( 5’-NGG-3’; i . e . unedited alleles ) from the Sanger sequencing trace using the same approach as described above . Editing efficiency was estimated to be 92 . 04% and 85 . 10% for Locus 1 and 2 , respectively . To collect edited embryos at later developmental stages ( 3 and 7 day-post-fertilization; dpf ) , the above cocktail was diluted 10-fold ( in Milli-Q water; Merck Millipore ) immediately prior to injection to reduce the toxicity ( manifested after gastrulation ) of ultra-high nuclei acid concentration at the expense of efficient editing . DNA extraction and subsequent processing were carried as above . Estimated editing efficiency for Locus 1 = 9 . 56% ( at 3 dpf ) and 7 . 81% ( at 7 dpf ) ; Locus 2 = 27 . 16% ( at 3 dpf ) and 18 . 69% ( at 7 dpf ) . In order to enable comparison across sampling time-points with variable editing rates , the estimated methylation rates were normalized to the editing efficiency ( i . e . “normalized methylation rate” = “methylation rate” ÷ “editing rate” ) . Raw values prior to normalization are available in S1 Dataset . | The genomes of vertebrate animals are naturally and extensively modified by methylation . The DNA methylation is essential to normal functions of cells , hence the whole animal , since it governs gene expression . Defects in the establishment and maintenance of proper methylation pattern are commonly associated with various developmental abnormalities and diseases . How exactly is the normal pattern defined in vertebrate animals is not fully understood , but recent researches with computational analyses and cultured cells suggested that DNA sequence is a primary determinant of the methylation pattern . This study encompasses the first experiments that rigorously test this notion in whole animal ( medaka fish ) . In statistical sense , we observed the very strong correlation between DNA sequence and methylation state . However , by introducing unmethylated and artificially methylated native genomic DNA sequences into the genome , we demonstrated that the artificially conferred methylation states were robustly maintained in the animal , independent of the sequence and native state . Our results thus demonstrate that genome-wide DNA methylation pattern is not autonomously determined by the DNA sequence , which underpins the vital role of DNA methylation pattern as a core epigenetic element . | [
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] | 2017 | Unlinking the methylome pattern from nucleotide sequence, revealed by large-scale in vivo genome engineering and methylome editing in medaka fish |
The subtle effects of DNA-protein recognition are illustrated in the homeodomain fold . This is one of several small DNA binding motifs that , in spite of limited DNA binding specificity , adopts crucial , specific roles when incorporated in a transcription factor . The homeodomain is composed of a 3-helix domain and a mobile N-terminal arm . Helix 3 ( the recognition helix ) interacts with the DNA bases through the major groove , while the N-terminal arm becomes ordered upon binding a specific sequence through the minor groove . Although many structural studies have characterized the DNA binding properties of homeodomains , the factors behind the binding specificity are still difficult to elucidate . A crystal structure of the Pdx1 homeodomain bound to DNA ( PDB 2H1K ) obtained previously in our lab shows two complexes with differences in the conformation of the N-terminal arm , major groove contacts , and backbone contacts , raising new questions about the DNA recognition process by homeodomains . Here , we carry out fully atomistic Molecular Dynamics simulations both in crystal and aqueous environments in order to elucidate the nature of the difference in binding contacts . The crystal simulations reproduce the X-ray experimental structures well . In the absence of crystal packing constraints , the differences between the two complexes increase during the solution simulations . Thus , the conformational differences are not an artifact of crystal packing . In solution , the homeodomain with a disordered N-terminal arm repositions to a partially specific orientation . Both the crystal and aqueous simulations support the existence of different stable binding conformers identified in the original crystallographic data with different degrees of specificity . We propose that protein-protein and protein-DNA interactions favor a subset of the possible conformations . This flexibility in DNA binding may facilitate multiple functions for the same transcription factor .
Specific DNA binding plays a key role in the protein-DNA recognition process necessary for the regulation of gene expression . Binding determinants are complex , including direct amino acid-base contacts , indirect water-mediated contacts , and local geometry of the DNA sequence [1] . Although it is possible to identify certain trends in the recognition process , such as the formation of point contacts between certain base pairs and certain amino acids , at present there are no unequivocal correspondences between bases and amino acids . Complicating matters , many regions of transcription factors are disordered in solution and fold only upon binding to their specific targets [2] . The current study proposes an additional level of complexity , suggesting that the bound state may also consist of an ensemble of stable conformations instead of a single low energy conformation . The homeodomain fold provides an interesting example of the subtle effects of DNA-protein recognition . The homeodomain is one of several small DNA binding motifs with limited DNA binding specificity , yet incorporated in an estimated 235 transcription factors it adopts specific and essential developmental roles [3] , [4] , [5] . The homeodomain is composed of a 3-helix domain and a mobile N-terminal arm . Helix 2 and 3 form a helix-turn-helix type motif that is ordered in solution . Helix 3 , also known as the recognition helix , interacts with the DNA bases through the major groove . The N-terminal arm , on the other hand , becomes ordered upon binding a specific DNA sequence through the minor groove [6] , [7] , [8] . Homeodomain factors participate in a wide range of functions . The current study focuses on Pdx1 ( Pancreatic and duodenal homeobox 1 ) , a ParaHox transcription factor evolutionarily related to the Hox subfamily of homeodomains . Hox homeodomains regulate body plan development from Drosophila to humans [9] , [10] , [11] . Genome-wide binding studies of Hoxa2 and Pdx1 indicate that they may regulate thousands of genes [12] , [13] . Pdx1 regulates differentiation of the duodenum and stomach , and is a master regulator of pancreas development [14] , [15] , [16] , [17] . In the mature pancreas Pdx1 is expressed in beta- and delta-cells that secrete the endocrine hormones insulin and somatostatin , respectively . Mutations in Pdx1 cause a form of familial diabetes , maturity-onset diabetes of the young type 4 ( MODY-4 ) [18] , [19] . How homeodomain factors achieve functional diversity as well as exquisite specificity remains a subject of debate . Many studies have correlated DNA binding affinity of homeodomain factors with in vivo activity [20] , [21] , [22] , [23] , [24] . DNA binding affinity of Pdx1 monomers accounts for differences in transcriptional activity , at least in cell culture [23] . Hox binding sites include the TAAT core consensus sequence , cooperative binding sites with the TALE homeodomain factors , and sites with no recognizable binding motif [12] , [13] . Interactions with the TALE factors PBC and Meis alters DNA binding specificity of the Hox homeodomains [25] , [26] , [27] , [28] . Pdx1 cooperates with Pbx1 and Prep1 on the somatostatin promoter , [29] , [30] , Pbx1 and Mrg1 in pancreatic acinar cells [31] , and the basic-helix-loop-helix factor E47/NeuroD on the insulin promoter [32] , [33] , [34] . Disordered sequences outside of the homeodomain can influence DNA binding specificity suggesting an auto-inhibitory mechanism [35] . Additionally phosphorylation or sumoylation , important for nuclear localization , may affect activity [36] , [37] , [38] . Diversity is also achieved through ‘activity regulation’ , by recruiting different coactivators and corepressors to non-conserved regions outside of the homeodomain [22] , [39] . Many structural studies have characterized the DNA binding properties of homeodomains . All Hox factors contact DNA through similar residues ( Figure 1A ) . Residues Ile 47 , Gln 50 , Asn 51 and Met 54 from the recognition helix insert in the major groove contacting DNA bases directly or through water bridges [40] . Position 50 is particularly important for specificity for some homeodomains , for example Lys 50 in Bicoid [41] , but less so for Hox factors as demonstrated by a Gln 50 to Ala mutation [42] . The conservation of the major groove residues suggests they are insufficient to distinguish binding specificity among Hox factors . The N-terminal arm sequence is less well conserved than the recognition helix , but typically includes positively charged Lys or Arg residues [43] , [44] . The arm sequence contributes to DNA binding specificity as demonstrated by chimeric homeodomains with swapped N-terminal residues [24] , [45] , [46] , [47] . Even so the N-terminal arm is often disordered in crystal structures of homeodomain monomers bound to DNA . Coarse-grained Molecular Dynamics simulations indicate that the disordered N-terminal arm facilitates searching the DNA for binding sites through electrostatic attraction by a sliding mechanism or transferring between DNA strands by a “fly catching” mechanism [44] , [48] , [49] , [50] . Recently a crystal structure of the Pdx1 homeodomain/DNA complex was obtained in our lab with a consensus DNA binding sequence C−1T1A2A3T4G5A6G7 [51] . The structure contained two complexes with differences in the conformation of the N-terminal arm , major groove contacts , and backbone contacts , raising new questions about the DNA recognition process by homeodomains ( Figure 1 B , C ) [51] . At the time we attributed the differences in the two conformations to differences in DNA bending as a result of crystal packing [51] . We proposed an induced fit model in which DNA contacts by residues from helix 3 in the major groove of one conformation stabilized the N-terminal arm in the minor groove . In this work we apply classical Molecular Dynamics ( MD ) with a fully atomistic representation of the complex and solvent to simulate both the crystal and solution behavior of both conformations of the Pdx1 homeodomain/DNA complex [51] . In the last decade MD simulations have become an invaluable tool to complement structural information obtained experimentally [52] , [53] , [54] , [55] . MD simulations of the Pdx1/DNA complexes show that differences in DNA contacts persist between the two conformations even in solution due to distinct positioning of the homeodomain relative to the DNA . Conformation A represents a less specific complex than Conformation B . The simulations suggest that one source of diversity of homeodomain function derives from different bound states with different degrees of DNA specificity . The existence of these “isomeric” bound conformations has not been reported before . We propose that multiple bound isomers are an important feature of the homeodomain/DNA binding processes , adding another layer of complexity to what is known about binding specificity .
Simulations were carried out for: ( i ) the crystal unit cell; ( ii ) aqueous solution; and ( iii ) the DNA and Pdx1 molecules separately . Initial geometries for the simulations were derived from both Pdx1/DNA complexes in the asymmetric unit of the crystal structure ( pdbid 2H1K ) ( www . rcsb . org ) [51] , [56] . Residues missing in the crystal structure ( model A: residues 1–3 , 60–61; model B: residues 58–61 ) were placed in low energy conformations by superimposing short pre-equilibrated peptide fragments onto the experimental structure . For the solution simulations all waters from the crystal structure were removed and replaced with solvent water molecules surrounding the protein/DNA complex . For the crystal simulation the unit cell of the crystal structure was generated from the asymmetric unit by applying the P212121 symmetry operators . Non-crystallographic water molecules were added by sampling them from a box of water equilibrated at constant pressure and temperature conditions and placed “on top” of the unit cell . Specifically , molecules were picked at random from the water box and “copied” into the unit cell provided no sterically forbidden configuration results . The number of the water molecules was varied until the system's density remained unchanged in trial MD runs under normal conditions . The density settled at 1 . 25 g/cm3 with 6901 non-crystallographic waters . Simulations were performed using the AMBER 10 package along with some “in house” codes [57] . The Pdx1/DNA complex was modeled using the ff99SB [58] force field for protein , parmbsc0 [59] for DNA , TIP3P [60] for water molecules and the AMBER stock 1999 version of the Cornell force field [61] for Na+ and Cl− ions . Missing hydrogen atoms were added with the LEaP module of AMBER 10; histidine residues were assumed to be neutral with a proton at the ε2 position . For the solution simulations , at least a 15 Å thick layer of TIP3P water was added around the solute with the LEaP module , and the system was neutralized using either Na+ ( Pdx1/DNA complex ) or Cl− ( protein alone ) ions . The structures were thoroughly equilibrated with the SANDER module of AMBER 10 before the production ( data gathering ) step . During the initial equilibration the heavy DNA and protein atoms were restrained to their initial positions by a harmonic potential . In every case we first performed a few conjugate gradient minimization steps to relax the hydrogens , followed by a 1 nanosecond long constant pressure run at ambient conditions ( T = 298 K , P = 1 atm ) . The last frame of the restrained NPT runs were the starting point of the unrestrained NPT production runs reported in this paper . The production simulations were carried out using the PMEMD module of AMBER 10 . The electrostatic interactions were evaluated by the PME method [62] , [63] using a 9 Å cutoff for the short-range terms . The same cutoff was used for the van der Waals terms with a continuous correction for the long-range terms . The lengths of all bonds that involve hydrogen atoms were fixed via the SHAKE algorithm with the tolerance set to 10−6 Å . Langevin dynamics [64] with collision frequency γ = 1 ps−1 ( ps = picosecond ) was used to maintain the temperature at 298 K with a different random number generator seed set for every run . The Berendsen algorithm [65] with relaxation time τP = 1 ps was used to maintain the pressure at 1 atm . The total time for each simulation was 50 ns with a time step of 2 femtoseconds and coordinates saved for analysis every 10 ps ( 5000 steps ) . The PTRAJ module of AMBER 10 was used for basic analysis ( centering and imaging of the trajectories , computations of RMS deviations , etc . ) , 3DNA ( v . 1 . 5 ) [66] for the calculation of DNA structural parameters , and simple in-house programs for the identification and counting of the intermolecular contacts . The latter were defined as follows [67]: a hydrogen bond was assumed if the distance between the donor hydrogen and the accepter oxygen or nitrogen was 2 . 8 Å or less , and the angle formed by the donor , hydrogen and acceptor atoms exceeded 145°; a hydrophobic contact was defined as a pair of sulfur/carbon atoms separated by less than 4 . 5 Å; a water contact was identified if the oxygen of a water molecule was within 3 Å of a nitrogen or an oxygen atom . A simultaneous water contact from two different macromolecules to the same water molecule is referred to as “water bridge” . Figures of protein structures were generated with Pymol [68] and labeled in Powerpoint ( Microsoft Office ) . The molecular graphics image of the unit cell was produced using the UCSF program Chimera [69] . The figures displaying distances through the simulation are displayed as a running average of 100 ps ( 10 trajectory frames ) .
The two conformations of the Pdx1/DNA complex in the crystal structure contained invariant contacts found in both conformations A and B , and variable contacts specific to each conformation ( Figure 1B ) [51] . Two residues formed direct hydrogen bonds with DNA bases in both conformations: Asn 51 with Ade 3 ( CTAA3T ) in the major groove , and Arg 5 with Thy 1 ( C−1T1AAT ) and Gua −1* ( opposite Cyt −1 ) in the minor groove ( Figure 1A ) . Conformation B was more specific than Conformation A . In Conformation B , Gln 50 formed a water-mediated contact with Gua 5 and Thy 6* ( TAATG5A6 ) , and Asn 51 contacted Ade 2 in addition to Ade 3 . The N-terminal arm was also more ordered in conformation B , with Lys 2 hydrogen bonded with the bases Ade 3 and Thy 2* in the minor groove . The flexibility of the interactions in the two conformations was investigated by MD of the crystallographic unit cell . During the simulation the four copies of model A and model B in the unit cell ( Figure S1 ) showed some variation in mobility and conformation , probably due to fluctuating differences in their local instant environment . This agrees with the crystallographic information where the same fluctuations are likely responsible for the high B-factors . The experimental molecular geometries were well preserved during the simulation ( Figure 2A ) . The instantaneous mass-weighted root-mean-square deviations ( RMSDs ) with respect to the crystal structure were 2 . 5 Å or less . The average structure over all times and over the four replicas of each molecule was computed and its RMSD with respect to each of the crystal conformations was calculated . Note that the RMSD of the average structure is not the same as the average of the instantaneous RMSDs . ( The average structure is of course a better approximation of the experimental structure than the various instantaneous structures ) . The RMSD of the average structures with respect to the original 2H1K coordinates were 0 . 86 Å and 1 . 10 Å for conformations A and B , respectively . We interpret this as validation of the model , the force field and the simulation protocol . In general the differences between conformations A and B were less pronounced after the crystal simulation . Arg 5 is the one residue to hydrogen bond consistently with the same bases in all 8 models in the unit cell , to Thy 1 and Gua −1* through the minor groove , as it does in the crystal structures . The major groove contacts are more variable . The hydrogen bond by Asn 51 with Ade 3 ( CTA2A3T ) is lost consistently in Conformation A , while it is more stable in Conformation B ( Figure S2A , B ) . Both Asn 51 and Gln 50 contact the phosphate backbone of the DNA in the major groove of Conformation A , with Ade 2 and Cyt 7* , respectively ( Figure S2C ) . Only the Gln 50- Cyt 7* contact is accessible to Conformation B . These backbone contacts are characteristic of the partially specific Conformation A after the solution simulation . In the crystal structure five phosphate contacts are unique to Conformation A by residues from helix 2 and 3: Arg 31 , Lys 46 , Gln 50 , Arg 53 and Lys 57 ( Figure 1B ) [51] . During the simulation all of these contacts are also formed in conformation B except Arg 31 and Lys 46 with the phosphate backbone of Ade 8* ( Figure S2D , E ) . Arg 31-Ade 8* is unique to Conformation A in solution too . While Arg 5 is consistently ordered in all homeodomain/DNA complexes , the residues N-terminal of Arg 5 are often disordered [7] , [70] , [71] , [72] . In the crystal structure , these residues are ordered in Conformation B and disordered in Conformation A . During the simulation Lys 2 remains predominantly in the minor groove in Conformation B , hydrogen bonded with Thy 2* O2P ( Figure 3A ) but not Ade 3 . In model B4 residues 1–4 of the N-terminal arm escape from the minor groove after about 20 ns and remain mobile . In Conformation A Lys 2 never enters the minor groove during the simulation . Interestingly , Arg 3 does enter the minor groove to contact Thy 2* for about 20 ns in model A2 , and in model A4 ( at 30–50 ns ) and at the end of the simulation in model A3 . ( Figure 3B ) . Mobility of the N-terminal arm appears to be required for Arg 3 to enter the minor groove since the arm executes large motions in models A2 and A4 , while these motions are restricted in models A1 and A3 by phosphate backbone contacts by Lys 2 or the acetylated N-terminus . From the crystal structure we proposed that ordering of Lys 2 in the minor groove is stabilized by a network of contacts between Arg 43 and His 44 from helix 3 in the major groove and Arg 3 in the minor groove ( Figure 1A ) [51] . These interactions were maintained in conformation B during the crystal simulation: both Arg 3 and Arg 43 hydrogen bond with the phosphate backbone , with Thy 4 and Ade 3 , respectively ( Figure S3 A , B ) . The proximity of the guanidinium groups of Arg 3 and Arg 43 suggest pi-pi stacking . His 44 stabilizes the conformation of Arg 43 ( Figure S3C ) . In model B4 , after the N-terminal arm escapes the minor groove , the Arg 3-Thy 4 O2P and Arg 43-His 44 contacts are broken , consistent with their role in stabilizing the N-terminal arm in the minor groove . In Conformation A the N-terminal 3 residues and the side chain of Arg 43 are disordered in the crystal structure . Arg 43 never associates stably with His 44 ( Figure S3D ) or Ade3 O2P , but forms a stable hydrogen bond ( ∼60% of the time ) with Thy 4 O2P in model A2 and A3 ( Figure S3E ) . In model A2 , the Arg 43-Thy 4 O2P backbone contact correlates with insertion of Arg 3 in the minor groove to contact the base of Thy 2* ( Figure 3B ) . In summary , the simulation reduces somewhat the differences between Conformations A and B found in the crystal structure , particularly in the major groove . Three phosphate contacts are specific to Conformation A: by Asn 51 with Ade 2 , and by Arg 31 and Lys 46 with Ade 8* . The contact by Arg 43 from the major groove with the phosphate backbone correlates with stabilizing the N-terminal arm . In Conformation B the N-terminal arm is mostly ordered with Lys 2 binding in the minor groove . In Conformation A the N-terminal arm is mostly disordered . Arg 3 enters the minor groove in models A2 and A4 , suggesting a second position for the N-terminal arm not present in the crystal structure . We attributed the different contacts between the two conformations in the crystal structure to differences in DNA bending due to crystal packing [51] . The DNA bending of the crystal structure is maintained during the crystal simulation . Simulations of the Pdx1/DNA complex in aqueous solution were initiated from both conformations reported in the 2H1K PDB ( www . rcsb . org ) [56] structure , and trajectories recorded for 50 ns . Mass-weighted RMSDs were calculated relative to the crystal Conformation A or the crystal Conformation B ( Figure 2B ) . After an initial relaxation time , the simulations of both Conformation A and B resembled Conformation A more than B , indicating that Conformation A in the crystal structure , with the less bent DNA , is closer to the solution conformation . The average of the instantaneous RMSD values for complexes A and B relative to the experimental structure in Conformation A were 1 . 69 Å and 1 . 58 Å ( black lines in top and bottom panels of Figure 2B ) , respectively; and relative to Conformation B were 2 . 51 Å and 2 . 08 Å , respectively ( red lines in top and bottom panels Figure 2B ) . The flexibility of the DNA during the simulation of both conformations resulted in an average straight helical axis with large fluctuations in the bending angle ( not shown ) . We were therefore surprised that differences between the conformations persisted throughout the simulation . In conformation A , both Gln 50 and Asn 51 stably contacted the phosphate backbone in the major groove , with Cyt 7* and Ade 2 , respectively ( Figure 4 ) . In contrast Asn 51 in conformation B did not contact the phosphate backbone of Ade 2 ( Figure 4B ) but formed a direct hydrogen bond with Ade 3 N7 ( Figure 5 A , B ) . Periodically Asn 51 OD1 formed a second specific hydrogen bond with Ade 3 N6 ( not shown ) . Gln 50 was too far from the DNA for a direct contact with the DNA bases , but during the simulation two water molecules sometimes ( ∼20% of the time ) bridged between Gln 50 and Asn 51 and the bases of Thy 4 , Gua 5 and Thy 6* . Water 1 ( W1 in Figure 5A ) also bridged between Gln 50 and Asn 51 . These direct DNA contacts indicate that helix 3 continues to form more specific major groove contacts in Conformation B than in A . The contacts by Arg 5 with Gua −1* and Thy 1 through the minor groove are conserved in the trajectories of both conformations ( Figure 6 ) . This remains the only direct hydrogen bond with a DNA base in Conformation A . The N-terminal residues 1–3 are initially ordered in conformation B , and Lys 2 continues to form a hydrogen bond with Thy 2* O2 for 35 ns of the simulation ( Figure S4A ) . After that the N-terminus of Pdx1 moves outside of the minor groove , but Arg 3 and Arg 43 remain in contact with the DNA phosphate backbone , stabilizing the N-terminal arm ( Figure S4 B–D ) . It is therefore plausible that Lys 2 would return to the minor groove in a longer simulation . In contrast to the crystal simulation , Arg 43 contacts Thy 4 O2P instead of Ade 3 O2P when the N-terminal arm is ordered in Conformation B ( Figure S4 B , E , Figure S3A ) . His 44 does not contact Arg 43 during the solution simulation , unlike in the crystal simulation ( Figure S3C ) . In conformation A , Lys 2 begins the simulation outside of the minor groove and does not enter during the simulation . As seen in the crystal simulation , Arg 3 in Conformation A enters the minor groove towards the end of the simulation , but it never settles in a single position , contacting the base of Ade 3 only transiently . The residues that stabilize the N-terminal arm , Arg 3 and Arg 43 , are more mobile in conformation A than conformation B ( Figure S4 B–D ) . Arg 43 contacts Ade 3 only about a third of the trajectory ( Figure S4E ) . All five hydrogen bonds that were specific to Conformation A in the crystal structure were accessible to Conformation B . The Arg 31-Ade 3* contact is favored in Conformation A . The position of Arg 31 is stabilized through a hydrogen bond with Glu 42 in Conformation A ( Figure S4F , Figure 6A , B ) . The contact between Tyr 25 and Thy 6* phosphate is restricted to Conformation B in the solution simulation ( Figure S4G , Figure 6C , D ) . This contact was accessible to both conformations in the crystal structure . Clearly different contacts persist between conformations A and B through the 50 ns of the solution simulation . As mentioned , the DNA is highly flexible during the simulation indicating DNA bending cannot explain the conformational differences . Instead the overall positioning of the homeodomain of Pdx1 relative to the DNA differs for the two conformations , as indicated by the distance between Asn 51 CA and Ade 3 N7 ( Conformation A : 8 . 0±0 . 7 Å; conformation B: 6 . 4±0 . 4 Å ) ( Figure 5C ) and the width of the major groove , measured as the distance between the phosphate of Cyt 7* and Thy 1 ( Conf A 18 . 6±0 . 8 Å , Conf B 20 . 1±1 . 0 Å , defined from the atom centers without subtracting 5 . 8 Å for the phosphorous van der Waals radius ) ( Figure 5D ) . Helix 3 is bound deeper in the major groove in conformation B allowing Asn 51 to contact Ade 3 N7 directly , which may account for the wider major groove ( Figure 4A , 5A ) . The difference in the positioning of the homeodomain between the conformations was not apparent in the crystal structure: the distance between Asn 51 CA and Ade 3 N7 was about 6 . 2 Å for both conformations . The major groove width ( Cyt 7* P – Thy 1 P ) was different: 19 . 4 Å in Conformation A and 20 . 8 Å in Conformation B . During the crystal structure simulation , the distance between helix 3 and Ade 3 varies between 6 and 8 Å in conformation A , and between 6 and 7 Å in conformation B . The constraints of the crystal packing therefore prevented repositioning of the homeodomain .
MD has been applied to DNA/homeodomain complexes previously to study protein-DNA and water mediated contacts [73] , the role of salt bridges [74] , the role of residue 50 [75] , [76] , folding properties of the N-terminal arm [77] , and other studies [67] , [78] , [79] , [80] , [81] . In general these simulations are initiated from a unique structure , assuming that the simulation will explore the relevant conformational space . In the current study we applied MD to investigate two distinct DNA binding conformations of the Pdx1 homeodomain to determine if the differences were the result of crystal packing . Simulations were carried out in the context of a crystal unit cell and in solution . The solution simulations generated two different conformations of the Pdx1/DNA complex depending on the initial conformation derived from the crystal structure . Both conformations were stable during the 50 ns simulation . The current study demonstrates the real possibility of multiple stable conformations that are not accessible during limited simulation times . The AMBER force field ff99SB [57] , [58] used in the simulations reported in this work is considered state-of-the-art , and includes several refinements for DNA simulations [59] , [82] , [83] , [84] . Present computer capabilities allow fully atomistic simulations , minimizing artifacts . The DNA and protein in these simulations are fully solvated with explicit waters; in a relatively large box under periodic boundary conditions ( as opposed to spherical water clusters that may experience various surface potential discontinuities at the cluster-vacuum or cluster-continuum interface [85] , [86] ) ; with a correct treatment of electrostatics [87] , [88] . Crystal simulations at constant pressure and temperature ( NPT ) that reproduce the crystallographic cell and symmetries have traditionally been used to test and tune force fields , as they allow direct comparison with experiments , and will also reproduce packing effects [52] , [53] , [54] , [55] , [89] , [90] , [91] , [92] . After the solution simulation Conformation B bound DNA specifically while Conformation A bound with limited specificity . The unique interactions in the two conformations were due to different positions of the homeodomain in the major groove of the DNA , with helix 3 buried deeper in the major groove in Conformation B than in Conformation A ( Figure 4A and 5A ) . In Conformation B Asn 51 interacts directly with Ade 3 in the major groove , and Lys 2 of the N-terminal arm contacts bases through the minor groove . The proximity of helix 3 to the DNA in Conformation B facilitates ordering bridging water molecules between the protein and DNA , with Gln 50 and Asn 51 ( Figure 5A ) . These bridging water molecules were not observed in the Pdx1 crystal structure , but were observed in the related Antennapedia structure [71] . What determines the position of helix 3 in the major groove ? We previously attributed the presence of two Pdx1/DNA conformations in the crystal structure to the curvature of the DNA in conformation B [51] . The differences between the two conformations identified in the crystal structure diminished during the crystal simulation , despite maintaining the average curvature of the DNA in conformation B . In contrast , differences between the two conformations increased during the solution simulations . A comparison of the Antp homeodomain/DNA complex by NMR and crystallography indicated contacts by Arg 43 with Ade 3 , and movements of Gln 50 and Asn 51 in the NMR structure that could not be explained from the crystal structure [71] . These contacts are consistent with the motions of the Pdx1 homeodomain in the solution simulation . Clearly the crystal lattice constrained the Pdx1/DNA conformation , suggesting caution when interpreting crystal structures of protein/DNA complexes . What properties of the two conformations in the crystal structure directed the solution simulations toward the specific ( starting from Conformation B ) versus less specific ( starting from Conformation A ) complexes ? The average DNA sequence was straight during the solution simulation of both conformations , indicating that DNA bending was not the primary cause . In the crystal structure helix 3 was oriented at slightly different angles relative to the DNA in the two conformations . The specific phosphate contacts formed by Conformation A in the crystal structure are accessible to Conformation B during the solution simulation . Already in the crystal structure the contacts are less specific in Conformation A . The configuration that defines Conformation A includes: Gln 50 contacting the phosphate backbone at base 7* , Asn 51 contacting Ade 3 but not Ade 2 , and the disordered N-terminal residues ( Figure 1B ) . The contacts that define Conformation B include: Asn 51 contacting Ade 2 and Ade 3 in the major grove , Gln 50 making a water mediated contact with DNA bases at positions 5 and 6 , and the ordered N-terminal arm with Lys 2 contacting Thy 2* and Ade 3 . The MD simulations presented here suggest multiple conformations are possible for the N-terminal arm in the minor groove and for the helix-turn-helix domain in the major groove . In both conformations , Arg 5 contacts Gua −1* and Thy 1 through the minor grove ( Figure 6 ) . The most stable ( longest-lived ) configuration for the N-terminal arm of Pdx1 consists of Lys 2 inserted in the minor groove and Arg 3 outside of the minor grove contacting the phosphate backbone and Arg 43 ( Figure S4A–D ) . In Conformation A , Arg 3 inserts in the minor groove and contacts the base Thy 2* for some time in the crystal simulation ( Figure 3B ) . This configuration resembles the configuration in the Scr-Exd DNA complex ( the Drosophila homolog of Hox5-Pbx1 ) with a 14 residue N-terminal extension of Scr , including the YPWM Pbx1 binding motif [25] . In that structure the N-terminal arm was ordered with Arg 3 inserted in the minor groove but contacting the phosphate backbone . The authors suggested that Arg 3 is positioned by a His residue along the N-terminal extension . Therefore while binding of the Pdx1 monomer may favor base contacts by Lys 2 in the minor groove , other protein interactions may favor Arg 3 positioned in the minor groove . The MD simulation also distinguishes two orientations of the helix-turn-helix domain in the major groove . An alternate orientation of the recognition helix was previously characterized for the Mata2 homeodomain bound to a nonspecific DNA sequence [93] . In this structure the homeodomain was rotated with respect to the consensus binding site , altering interactions in the major groove and eliminating contacts by the N-terminal arm in the minor groove . A second paper noted that the Hox homeodomains in the HoxA9-Pbx1 and HoxB1-Pbx1 complexes were oriented differently in the major grove , altering base contacts [94] , [95] . In contrast to these examples , the two conformations of the Pdx1 homeodomain are bound to the same DNA sequences of the consensus-binding site . In the less specific Conformation A of Pdx1 , Arg 5 makes base-specific contacts through the minor groove , like the specific conformation . Many of the same phosphate contacts position helix 3 in the major groove , by Thy 6 , Arg 31 , Arg 53 , Lys 55 , and Lys 57 . But helix 3 of Conformation A is too far from the DNA bases to form direct hydrogen bonds; instead Gln 50 and Asn 51 contact the phosphate backbone ( Figure 4A ) . One interpretation of the partially specific Conformation A is that it represents a DNA binding intermediate in search of the specific DNA binding conformation B . The Pdx1 homeodomain binds nonspecific DNA with just 20-fold lower affinity than the consensus site [23] , [51] . Other homeodomains also bind DNA with low specificity , as noted for Mata2 and Antennapedia [96] , [97] . The stability of the less-specific Conformation A during the MD simulation suggests it might be populated when the Pdx1 monomer binds nonspecific DNA sequences . Pdx1 binds to thousands of DNA sites in vivo , as measured by ChIP-Seq , including sequences distinct from the consensus binding sequence [13] , [98] . In binding a specific DNA sequence , both conformations A and B may be present as two of an ensemble of DNA-bound conformations . In this scenario the DNA sequence and other protein interactions stabilize a subset of this ensemble . The diversity of interactions might explain the myriad of functions accomplished by Pdx1 . In our previous paper we proposed that Arg 43 and Arg 3 bridge between the major and minor grooves to order the N-terminal arm in Conformation B , suggesting some synergistic interactions between the helical and N-terminal domains . Many studies conclude that the N-terminal arm contributes to DNA binding specificity of homeodomains [24] , [45] , [46] , [47] , [99] , [100] , [101] . Synergy between the major and minor groove has been noted for chimeric homeodomains , which generally require mutations in the N-terminal arm and the recognition helix to change specificity between homeodomain factors [24] , [99] . In a survey of all Drosophila homeodomains , specificity determinants for DNA binding originate from both the recognition helix and N-terminal residues [20] , [102] . Like other Hox factors , Pdx1 binds DNA cooperatively with PBC class homeodomains , such as Pbx1 [30] . Extensions of the N-terminal arm to the “YPWM” motif enhance DNA binding specificity of Hox factors , exposing “latent specificity” among the eight Hox paralogs through interactions with Pbx1 [5] , [27] , [103] . But minor groove contacts do not explain all of the sequence preferences observed . For example a comparison of two structures by the Drosophila Hox-Pbx1 heterodimer Scr-Exd bound to different DNA sequences demonstrated conformational changes in the extended N-terminal linker as well as contacts in the major groove [25] . In the context of Pbx1 , the consensus-binding site for the Hox factors is generally not TAAT , necessitating different DNA interactions by the N-terminal arm and recognition helix . The MD simulations reported here suggest that the DNA and protein context may promote “specific binding” by restricting the ensemble of accessible conformations available to the homeodomain on the DNA . Even though longer MD simulations are needed to probe “rare” conformational transitions and to completely characterize the relative stability of the different conformations , the fact that completely independent X-ray studies support the existence of these two conformations lends validity to our conclusions . These can be summarized as follows . Conformation A represents a partially specific DNA bound configuration with a single base contact by Arg 5 in the minor groove . Conformation B represents the specific Pdx1 conformation , forming additional direct and water-mediated contacts with DNA bases by Asn 51 and Gln 50 in the major groove , and by Lys 2 in the minor groove . These conformations differ in the position of helix 3 in the major groove and indicate some of the inherent flexibility of homeodomains in binding DNA . The stability of both conformations suggests they both play a role in the free energy landscape of the complex: either as stable minima or a kinetically trapped intermediate ( Conformation A ) in search of a global minimum ( Conformation B ) . Flexibility in DNA binding of the homeodomain may be important in allowing Pdx1 to fulfill its multiple functional roles , particularly in binding non-consensus DNA sequences or in the presence of DNA binding partners . A source of diversity of homeodomain function may derive from distinct bound states with differing degrees of DNA binding specificity . Further structural and MD studies of Pdx1 to different DNA sequences and in the presence of partner proteins are necessary to characterize DNA binding in the context of authentic enhancers . | All organisms require the capability to control gene expression . In eukaryotes , transcription factors play an important role in gene regulation by recognizing specific DNA control regions associated with each gene . The DNA binding domains of transcription factors belong to evolutionarily conserved families with different protein folds . An example is the homeodomain family . Although this DNA binding domain has been studied for a long time , the properties that determine DNA binding specificity are still not clear . We previously showed in a crystal structure that the homeodomain of a transcription factor Pdx1 ( Pancreatic and duodenal homeobox 1 ) binds DNA in 2 different conformations . In this paper , we used Molecular Dynamics simulations to show that both of these conformations are stable in solution . This is surprising since it is often assumed that proteins recognize DNA by finding a single lowest energy state . This study shows that transcription factors may bind DNA in an ensemble of conformations . This scenario may facilitate their finding the correct binding site among the 3 billion basepairs of DNA in the human genome . It may also provide flexibility in the DNA sequence that homeodomains can recognize to promote gene transcription . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"chemistry",
"biology"
] | 2013 | Binding Polymorphism in the DNA Bound State of the Pdx1 Homeodomain |
Most arthropod-borne viruses ( arboviruses ) are RNA viruses , which are maintained in nature by replication cycles that alternate between arthropod and vertebrate hosts . Arboviruses appear to experience lower rates of evolution than RNA viruses that replicate in a single host . This genetic stability is assumed to result from a fitness trade-off imposed by host alternation , which constrains arbovirus genome evolution . To test this hypothesis , we used Rift Valley fever virus ( RVFV ) , an arbovirus that can be transmitted either directly ( between vertebrates during the manipulation of infected tissues , and between mosquitoes by vertical transmission ) or indirectly ( from one vertebrate to another by mosquito-borne transmission ) . RVFV was serially passaged in BHK21 ( hamster ) or Aag2 ( Aedes aegypti ) cells , or in alternation between the two cell types . After 30 passages , these single host-passaged viruses lost their virulence and induced protective effects against a challenge with a virulent virus . Large deletions in the NSs gene that encodes the virulence factor were detectable from the 15th serial passage onwards in BHK21 cells and from the 10th passage in Aag2 cells . The phosphoprotein NSs is not essential to viral replication allowing clones carrying deletions in NSs to predominate as they replicate slightly more rapidly . No genetic changes were found in viruses that were passaged alternately between arthropod and vertebrate cells . Furthermore , alternating passaged viruses presenting complete NSs gene remained virulent after 30 passages . Our results strongly support the view that alternating replication is necessary to maintain the virulence factor carried by the NSs phosphoprotein .
Most arthropod-borne viruses ( arboviruses ) are RNA viruses , although they use a variety of strategies to ensure their replication and transmission . The feature that best distinguishes RNA genomes from DNA ones is the high mutation rate of the former during replication . Misincorporation errors during replication have been estimated to occur within the range of 10−3–10−5 substitutions per nucleotide and per round of copying [1] . The main factor contributing to such high mutation rates is a lack of proof-reading repair activities that is associated with RNA replicases [2] . Another source of mutations results from the spontaneous deamination of Cytidine residues to Uracil . In DNA genomes , this reaction is repaired by a Uracil-glycosylase , but this cannot function on an RNA template [3] . The resulting complex mixtures of closely related RNA genomes are termed quasispecies [4] , [5] whose existence allow RNA viruses to adapt rapidly to fluctuating environments [6] , [7] . Indeed , mutation rates per nucleotide site of around 10−4 mean that for a 10 kb genome , an average of one mutation is incorporated each time the genome is copied , and it is this , together with short replication times and large population sizes , that ensure the existence of the quasispecies genome pool . However , sequence comparisons reveal that RNA arboviruses are relatively stable in nature , suggesting that the alternating host cycle ( between vertebrate and invertebrate hosts ) constrains viral evolution by a strong conservative sequence selection . This sequence stability may result from the requirements for replication in two separate hosts that present conflicting niches for replication and adaptation [8] . Furthermore , low rates of evolution do not necessarily reflect the adaptive compromise of a virus to the alternating host cycle [9] , but could be principally related to other biological constraints including the need to maintain virulence [10] , [11] . Effectively , more virulent viral strains are generally at a competitive advantage in mixed-strain infections [12] . Virulence can be considered as a consequence of virus efforts to maximize its fitness: the virus must replicate extensively in a host to ensure its transmission to the next host . However , viral replication damages host tissues , leading to host death , which can be considered as seriously deleterious for virus survival [13] , [14] . Alternation may play a significant role in maintaining the genetic stability of arboviruses; setting aside one or several selective filters may lead to accelerate evolution . For such , we used a cell culture system for these studies , as in vitro systems are convenient to investigate the evolution of arboviruses . Contrary to most arboviruses , Rift Valley fever virus ( RVFV ) , a member of the Phlebovirus genus within the Bunyaviridae family can be transmitted through direct contact with body fluids or aborted fetuses . It constitutes an interesting model to study host alternating cycling as cause of genetic stability of arboviruses in nature . RVFV is a tri-segmented negative-stranded RNA virus composed of the L segment that codes for the RNA-dependent RNA polymerase , the M segment that codes for the GN and GC glycoprotein precursor , and the S segment that has an ambisense strategy , coding for the N nucleoprotein and the NSs phosphoprotein [15] . The NSs phosphoprotein plays a key role in RVFV pathogenesis in the mammalian host . A natural isolate defective for the NSs protein ( Clone 13; [16] ) , was found to be avirulent for mice [17] and to induce an interferon response in mammalian cells , in contrast to virulent RVFV strains [18] . Here , we present results showing that genetic material non-essential to viral replication such as the phosphoprotein NSs is rapidly eliminated leading to the loss of virulence .
The Institut Pasteur animal facility has received accreditation from the French Ministry of Agriculture to perform experiments on live mice in appliance of the French and European regulations on care and protection of the Laboratory Animals . This study was approved by the relative IACUC at the Institut Pasteur . We used two cell lines: a mammalian cell line derived from hamster kidney ( BHK21 ) and an insect cell line derived from Aedes aegypti larvae ( Aag2 ) . BHK21 cells defective in IFN-a/b signaling were grown at 37°C with 5% CO2 in Glasgow's minimal essential medium ( G-MEM ) containing 5% fetal bovine serum ( FBS ) , 1000 units/mL penicillin , 1 mg/mL streptomycin , tryptose phosphate broth 1× and HEPES 0 . 01M . Monolayer cultures of Aag2 cells were grown at 28°C in Schneider Drosophila medium supplemented with 10% heat-inactivated FBS , L-Glutamine 0 . 4× , 1000 units/mL penicillin and 1 mg/mL streptomycin . The parental P strain was derived from the ZH548 strain originally isolated in 1977 from a human case in Egypt [19] and passaged three times in Vero cells . The virulence of this strain is related to the phosphoprotein NSs , which is responsible for a general inhibition of cellular RNA synthesis by interacting with the p44 subunit of the TFIIH transcription factor [20] . Furthermore , NSs strongly antagonizes IFN-ß production [17] , [18] , [21] , [22] . The titer of the frozen virus stock was 106 . 8 PFU ( plaque forming unit ) /mL . Moreover , the other virus strains were produced at high titers: Z30Alt at 108 . 3 PFU/mL , Z30B at 107 . 6 PFU/mL and Z30A at 108 . 5 PFU/mL . Viral titers were estimated by serial 10-fold dilutions on Vero cells . In addition , biological clones Z30AC and Z30BC were produced by plaque purification and amplification in Vero cells from the 30th serial passage of parental P strain in Aag2 cells ( Z30A strain ) and BHK21 cells ( Z30B strain ) , respectively . Briefly , six-well plates containing confluent monolayers of Vero cells were infected with serial 10-fold dilutions of virus . Cells were incubated for five days under an overlay consisting of Dulbecco's MEM ( DMEM ) , 2% FBS , antibiotics and 1% agarose at 37°C . The lytic plaques were localized and removed by suction using a pipette . Each agarose plug that contained an individual clone was dissolved overnight at +4°C in DMEM supplemented with 10% FBS before being re-amplified in BHK21 or Aag2 cells , respectively [23] . Both clones were produced at high titers: 108 . 8 PFU/mL for Z30BC and 108 . 7 PFU/mL for Z30AC . The parental P strain virus was subjected to 30 serial passages in BHK21 cells or Aag2 cells , or 30 passages that alternated between BHK21 and Aag2 cells ( i . e . 15 passages in BHK21 cells and 15 passages in Aag2 cells ) , at a multiplicity of infection ( MOI ) of 0 . 1 PFU/cell . Virus was adsorbed for 1 hr onto confluent cell monolayers prepared in plastic flasks of 25 cm2 , at 28°C for Aag2 and at 37°C for BHK21 . After adsorption , the inoculum was removed , cells were washed with medium , 13 mL of maintenance medium ( with 2% FBS ) was added and cells were incubated at the appropriate temperature . Cell supernatants were harvested when titers reached a plateau ( Figure S1 ) : at 48 hr p . i . for BHK21 and 96 hr p . i . for Aag2 cells . At each passage , supernatants were harvested and stored in aliquots at −80°C for titration on Vero cells . Plastic flasks of 75 cm2 containing confluent cell monolayers ( Aag2 or BHK21 ) were infected at a MOI of 0 . 1 PFU/cell as described above . The supernatant from a flask was harvested every 2 hr from 0 to 12 hr p . i . and every 24 hr from 0 to 120 hr p . i . , and titrated on Vero cells by serial 10-fold dilution [24] . Total RNA was extracted from aliquots of supernatants ( 100 µL ) using the Nucleospin RNA II kit ( Macherey-Nagel ) according to the manufacturer's instructions , and RT-PCR targeting the NSs gene was conducted using the Titan One Tube RT-PCR kit ( Roche Applied Science ) following the manufacturer's recommendations . Primers were selected in the NSs gene that lies within the S genome segment . The amplification program was performed as follows: reverse transcription at 50°C for 30 min , an inactivation of RT enzyme step at 95°C for 3 min , followed by 35 cycles of 95°C 30 s , 51°C 30 s , 72°C 1 min , and a final step at 72°C for 5 min . The size of the PCR product was 781 bp . PCR products were excised from the gel and eluted using the QIAquick Gel Extraction Kit ( Qiagen ) as specified by the manufacturer . The recovered DNA was cloned into the Topo TA vector and transformed into Top10 competent cells according to the manufacturer's protocol . Colonies were screened by direct PCR , using insert-specific primers . Plasmid DNA was purified using a QIAprep Spin Miniprep kit ( Qiagen ) , as specified by the manufacturer . Sequencing was carried out using virus-specific primers . In addition , the three segments ( S , M and L ) of the parental P strain and the selected strains ( Z30Alt , Z30B and Z30A ) were completely sequenced . For each segment , primers were designed ( based on the nucleotide sequence of the reference strain ZH548 ) in order to obtain around 700 pb RT-PCR amplicons with an overlap of around 100 pb along the entire segment ( Table S1 ) . Amplicons were obtained using SuperScript One-Step RT-PCR with platinium Taq ( Invitrogen ) following the manufacturer's recommendations . The amplification program was performed as follows: reverse transcription at 50°C for 30 min , an inactivation of RT enzyme step at 94°C for 2 min , followed by 35 cycles of 94°C 15 s , 50°C 30 s , 72°C 1 min 30 , and a final step at 72°C for 10 min . The obtained fragments were purified by ultrafiltration ( Millipore ) . Sequencing reactions were performed using the BigDye Terminator v1 . 1 cycle sequencing kit ( Applied Biosystems ) and purified by ethanol precipitation . Sequence chromatograms from both strands were obtained on automated sequence analyzer ABI3730XL ( Applied Biosystems ) . For sequence analysis , contig assembly was performed using the software BioNumerics version 5 . 1 ( Applied-Maths , Sint-Martens-Latem , Belgium ) . Sequence alignments and computation of substitution tables were also performed using the BioNumerics software . For phylogenetic analysis , maximum-likelihood trees were constructed using MEGA version 4 [25] with the Kimura-2 parameter for corrections of multiple substitutions . Reliability of nodes was assessed by boostrap resampling with 1 , 000 replicates . The pathogenicity of the parental P strain , and the selected strains , Z30Alt , Z30B , Z30A , Z30BC or Z30AC , was assayed in 4- to 5-week-old female Swiss mice ( OF-1; Charles River , France ) by inoculating 104 PFU intraperitoneally into each mouse . The control was inoculated with DMEM supplemented with 10% FBS . Mice surviving at the end of the observation period were bled and their sera tested for IgG by enzyme-linked immunosorbent assay ( ELISA ) [26] . Two experiments were carried out . In the first , each batch of five mice was infected with a different virus strain: P , Z30Alt , Z30B , Z30A , Z30BC or Z30AC . One batch was used as the control . Mice were kept under observation for 21 days post-inoculation or until death occurred . In the second experiment , we aimed to detect the IgG protective capacity induced by selected clones in inoculated mice . Batches of 12 mice were inoculated with the clones Z30BC and Z30AC , and one batch was used as control . At day 14 post-inoculation , one half of surviving mice from each batch was challenged with 104 PFU of the parental P strain and the other half was inoculated with DMEM . Mice were then observed for 21 days after challenge .
To compare the replication of selected RVFV strains ( Z30B , Z30A , Z30BC , Z30AC ) to the parental P strain , replication kinetics were determined in BHK21 cells and in Aag2 cells . When examining replication rates in BHK21 cells ( Figure 2A ) , all strains gave similar patterns of viral growth , with an initial exponential growth phase until 24 hr post-infection ( p . i . ) followed by a plateau . However , the Z30BC clone exhibited higher titers than other virus strains , almost 1 log10 PFU/mL higher from 6 hr p . i . until 72 hr p . i . Furthermore , viral replication was detectable two hours earlier for Z30BC than for the other four strains tested . This strain reached a maximum titer of around 9 log10 PFU/mL from 24 hr p . i . onwards . These results suggest that the Z30BC strain was better adapted to BHK21 cells than other strains were , and that the 30th passage in BHK21 cells ( Z30B ) from which Z30BC was derived encompassed a mixture of different viral clones with variable capacities to replicate in BHK21 cells . When replication rates in Aag2 cells were analyzed ( Figure 2B ) , differences in titers were clear-cut from 24 hr p . i . onwards . Effectively , Z30A and the Z30AC clone derived from this strain replicated to higher titers than the other three strains tested: ∼4 log10 PFU/mL higher at 48 hr p . i . and ∼2 log10 PFU/mL higher at 96 hr p . i . Moreover , replication of Z30A and Z30AC was detectable 24 hr earlier than that of the other strains , suggesting their better adaptation to Aag2 cells . In summary , the two selected clones that resulted from serial passages in either BHK21 or Aag2 cells exhibited an increased replication capacity in the corresponding cell type . We inoculated four-week-old mice intraperitoneally with 104 PFU of one of six viral strains to evaluate their virulence . Strains tested were: P ( the parental strain ) , Z30Alt ( isolated at the 30th alternating passage in BHK21 and Aag2 cells ) , Z30B ( pool harvest at the 30th serial passage in BHK21 cells ) , Z30A ( pool harvest at the 30th serial passage in Aag2 cells ) , Z30BC ( clone selected at the 30th serial passage in BHK21 cells ) and Z30AC ( clone selected at the 30th serial passage in Aag2 cells ) . Mouse survival rates were recorded over 21 days post-inoculation ( Figure 3A ) . The control , inoculated with Dulbecco's MEM ( DMEM ) medium , survived 21 days . All batches of mice inoculated with a given viral strain also survived , except for those inoculated with the parental P strain and the 30th alternating passage strain ( Figure 3A ) . All mice inoculated with P died before day 7 post-inoculation , and 4 of 5 treated with Z30Alt died before day 9 post-inoculation . Thus , the 30th alternating passage strain behaved roughly like the parental P strain after 30 passages . All surviving mice were tested for the presence of IgG against RVFV at day 21 post-inoculation , and showed positive compared to the IgG level in non-infected mice ( Table S2 ) . To test the protective effect of infection by clones Z30BC or Z30AC against infection pool harvest with the parental P strain , further batches of mice were first inoculated with the Z30BC or Z30AC clones and then challenged 14 days later by inoculation with the parental P strain . Mortality rates were scored up to 21 days after challenge ( Figure 3B ) . Before challenge , all mice had survived . The control mice started to die five days after challenge , and no controls survived beyond 9 days after challenge . Batches of mice that received one of the two clones at day 0 survived for 36 days . When mice previously inoculated with the Z30BC clone were challenged with the P strain , all mice survived 21 days after challenge . This suggests protection by the Z30BC clone selected in BHK21 cells . Mice sera were all IgG positive ( Table S3 ) . In batches of mice first inoculated with the clone Z30AC , one among 6 mice died 8 days after challenge with the parental P strain . The five others survived 21 days after challenge . The sera of the surviving mice were IgG positive ( Table S3 ) , suggesting that primary inoculation with the Z30AC clone selected in Aag2 cells could protect mice against a secondary inoculation with the parental P strain . As the phosphoprotein NSs has been shown to be responsible for virulence , we used RT-PCR amplification to monitor the NSs gene upon serial or alternating passages through BHK21 and Aag2 cells . Amplification of the parental P strain NSs gene generated an amplicon of ∼780 bp ( Figure 4A ) . Upon serial passage in BHK21 cells , PCR products from the 10th , 20th and 30th passage exhibited different sized amplicons , with a predominant band at 700–800 bp at the 10th and 20th passages and a band at 500–600 bp at the 30th passage ( Figure 4A ) . When examining the RT-PCR profiles for different serial passages in BHK21 cells in detail , it could be seen that the 500–600 bp band was detectable from the 15th passage , reaching maximum expression from the 25th passage onwards , concomitant with a decrease in the expression of 700–800 bp bands ( Figure 4B ) . The shortening of the NSs gene coincided with the loss of cellular lysis in BHK21 cells , evident by the 30th passage . Upon serial passages in Aag2 cells , a band of the expected size corresponding to the NSs gene was found at the 10th passage , whereas a smaller band of 500–600 bp was detected at the 20th and 30th passage ( Figure 4A ) . This smaller band was actually found as early as the 11th passage , and became predominant by the 21st passage ( Figure 4C ) , since the 700–800 bp band decreased in quantity from the 20th passage . In contrast , when virus was subjected to alternating passages , only a major band at 700–800 bp was found , irrespective of passage number ( Figure 4A ) . To characterize more precisely the molecular events associated with the emergence of viral variants , clones were analyzed by RT-PCR and sequencing of the NSs gene . Virus from the 30th passage in BHK21 or Aag2 cells showed large deletions in NSs , while viruses passaged alternately through the two cell types showed no nucleotide changes , suggesting the maintenance of NSs integrity during alternating cell-type passages . 48 clones isolated from the 30th serial passage in BHK21 presented two deletions: ( i ) a deletion of 259 nucleotides ( nt ) at position 124 that leads to a shift in the NSs open reading frame ( ORF ) , introducing a stop codon at position 437 , and ( ii ) a smaller deletion of 6 nt at position 650 . Thus , the Z30BC clone has a 533 nt NSs gene . 48 clones isolated after 30 passages in Aag2 cells presented two deletions in the NSs gene: ( i ) a deletion of 73 nt at position 374 , inducing a shift in the ORF with the introduction of a stop codon at position 474 , and ( ii ) a deletion of 157 nt at position 536 . The Z30AC clone thus presents a 568 nt NSs gene . Figure 5 summarizes the cartography of the deletions found in the NSs gene of clones Z30BC and Z30AC compared to the parental P strain . In contrast , Z30Alt which was isolated from the 30th alternating passage in BHK21 and Aag2 cells had no deletion in the Nss gene . These results suggest that all clones containing a shorter NSs gene than the parent resulted from a single molecular event in both BHK21 and Aag2 cells; in our experiment , all 48 clones examined from the 30th serial passage presented the same deletions . To further explore the molecular features of the parental P strain and the selected strains ( Z30Alt , Z30B and Z30A ) , the complete sequencing of the three segments S , M and L was achieved . Except for the previously described deletions in the NSs gene for the strains Z30B and Z30A , no other deletions or mutations were observed in the S segment for the parental P strain , the Z30B and the Z30A strains . Only one silent mutation was found in the NSs gene for the Z30Alt strain ( Table S4 ) . Concerning the segments M and L , no deletion event was found for the four strains , and the phylogenetic analysis ( ) confirmed a cluster in the genetic lineage A around the reference strain ZH548 originally isolated in 1977 in Egypt [27] . Nevertheless , the segments M and L presented some mutations , for most non silent , in the three selected strains . As expected , the segment M was the most variable with a total of 11 amino-acid substitutions compared to the segment L with only five amino-acid changes ( Table S4 ) . To note , the segment M of the three selected strains have retained the previously described five in-frame AUG-methionine start codons and the different amino-acids involved in glycosylation [27] .
Results presented in this paper support the hypothesis that an alternating viral cycle comprising of infection of two distinct hosts constrains the evolution of RVFV . The study model used consisted of abolishing the alternate host environment using a cell culture system . Serial passages in a single cell type led to the loss of virulence . Large deletions were observed in the NSs gene , non-essential to viral replication . The rapid emergence of NSs-deleted variants in the course of serial passages is likely to result from a selective advantage in their replication rates . After 30 serial passages in either mammalian BHK21 cells defective in IFN-a/b signaling or mosquito Aag2 cells , single-host adapted viruses from mosquito cells ( Z30A and Z30AC ) replicated better in mosquito cells . These mosquito-cell adapted viruses reached higher titers ( 2 log10 PFU/mL higher ) than non-adapted viruses , and their replication was detectable earlier ( 24 hr earlier ) . Interestingly , full adaptation to mammalian cells needed more passages as shown in the Figure 4B where the deleted variant was not totally predominant at the 30th serial passage in BHK21 cells . Replication patterns of the Z30B strain in mammalian cells were similar to those of the parental P strain and of viruses that had bypassed this host . However , the Z30BC clone isolated from the 30th serial passage in BHK21 cells presented the highest replication rate in mammalian cells ( replication was detectable 2 hr earlier and reached a titer ∼2 log10 PFU higher than for other viruses ) . This result suggested that a higher number of passages would favor adaptation to BHK21 cells which are defective in IFN-a/b signaling . Viral clones from single-cell passages showed a consistent fitness advantage over the parental P strain in the cell type used for their selection: the Z30BC clone exhibited a fitness advantage in BHK21 cells and the Z30AC clone in Aag2 cells . Thus , our clones isolated from single-host adapted passages present fitness gains in their specific host , and no fitness changes in the bypassed host . Similar results have been obtained for members of various arbovirus families using cell culture model systems [28]–[31] or in vivo systems [28] , [30] , [32]–[35] . Surprisingly , when sequencing the NSs gene , that encodes the virulence factor responsible for a general inhibition of cellular RNA synthesis and IFN-ß production , we found that single-host adapted viruses presented large deletions in this gene . Virus passaged in mammalian BHK21 cells defective in IFN-a/b signaling had a deletion of 259 nt introducing a stop-codon in the NSs gene . The resulting protein was shortened to 60 amino-acids instead of 265 . Virus passaged in mosquito Aag2 cells showed a deletion of 73 nt , again with a stop-codon , causing a shortening of the NSs protein to 131 amino-acids . These two distinct deletions in the NSs gene following 30 serial passages in each cell type suggest that the viral genome may function differently depending on whether replication is in mammalian or mosquito cells . Thus , while serial passages of RVFV in a single cell type selected for a virus with a truncated NSs gene specific to that cell type , alternating passages did not allow the emergence of deletions in the NSs gene . Indeed , like the parental P strain , the 30th alternating passage virus Z30Alt did not present any major genetic changes in the NSs gene . The fact that the NSs gene is dispensable in both single host systems suggests that other mutations are involved in the host adaptation process . Thus , the different non-synonymous mutations identified in the segments M and L should be further explored in this context , particularly since they are mostly specialized depending on host . It is likely that these kinds of deletion events take place spontaneously during viral replication and are selected only in the absence of alternation . Further experiments involving independent serial passages would permit to evaluate the frequency at which the phenomenon occurs . Deletions in the NSs gene have been described in a naturally attenuated RVFV ( Clone 13 ) purified from a nonfatal human case in the Central African Republic [16] . This strain has a large internal deletion of 549 nt in the NSs gene ( ∼70% of its length ) . Animals can survive high infectious doses of Clone 13 , up to 106 PFU , without developing any symptoms . Furthermore , Clone 13 has been tested as a vaccine candidate in sheep and cattle that can indeed then elicit a protective response against challenge with a virulent RVFV strain . When the clones we obtained after 30 serial passages in a single cell type were inoculated into mice ( at 104 PFU ) , the animals survived for 21 days and developed protective IgG against challenge with a virulent strain of RVFV . Moreover , most mice inoculated with Z30Alt ( isolated at the 30th alternating passage ) died 2 days later than did those treated with the parental P strain , and one mouse survived virus inoculation . Having said that , it is known that the outcome of infection is mainly determined by a balance between the rate of viral replication and the immune response , which together limit viral spread [36] . This might explain why one of five mice recovered from infection . Nevertheless , the 30th alternating passage virus Z30Alt retained roughly the same level of virulence as the parental P strain owing to the maintenance of NSs integrity . From our results , we have provided new insights as to how biological constraints such as host alternation are necessary to maintain RVFV integrity and virulence . Contrary to most other arboviruses , RVFV could escape from the alternating host replication cycle to evolve more rapidly , like single-host animal RNA viruses . Thus , our single-host adapted RVFV present large deletions in the NSs gene associated with a loss of virulence when inoculated into mice , which develop a long-lasting immunity . In contrast to persistent non-cytolytic replication in insects , arboviruses must replicate to high titers in the mammalian host . This increases the probability of transmission during a blood-meal [37] , [38] . By its transfer from wild animals ( e . g . buffalo [39] ) to livestock , RVFV may intensify direct transmission by contacts with infectious tissues or fluids hosting high viral loads . From our results , viruses selected on mammalian cells may favor attenuation via NSs alteration , leading to the maintenance of avirulent RVFV strains . Surprisingly , mosquito cell-specific selection also leads to large deletions in the NSs gene , with similar phenotypic consequences . In both cases , virulence will only be restored when alternation between both cell types is initiated in conditions that would reconstitute a complete viral genome by reassortments with a virulent RVFV strain . Such in vitro studies should be consolidated with studies using in vivo systems . Indeed , vertebrates are subjected to acute infections , with clearance of the virus being triggered by the immune defense system , whereas insect vectors sustain persistent viral replication and are the site of such genetic changes as reassortment or recombination upon co-infection [40] . Such rearrangements may restore virulence , upon the acquisition of a complete NSs gene in the course of virus replication [41] . Finally , our results suggest that subtle modifications of selective filters can lead to major genetic changes within a viral population . | Arthropod-borne viruses are transmitted among vertebrate hosts by insect vectors . Unusually , Rift Valley fever virus ( RVFV ) can also be transmitted by direct contacts of animals/humans with infectious tissues . What are the molecular mechanisms and evolutionary events leading to adopt one mode of transmission rather than the other ? Viral replication is implied to be different in a vertebrate host and an invertebrate host . The alternating host cycle tends to limit virus evolution by adopting a compromise fitness level for replication in both hosts . To test this hypothesis , we used a cell culture model system to study the evolution of RVFV . We found that freeing RVFV from alternating replication in mammalian and mosquito cells led to large deletions in the NSs gene carrying the virulence factor . Resulting NSs-truncated viruses were able to protect mice from a challenge with a virulent RVFV . Thus , in nature , virulence is likely maintained by continuous alternating passages between vertebrates and insects . Thereby , depending on the mode of transmission adopted , the evolution of RVFV will be of major importance to predict the outcome of outbreaks . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"veterinary",
"diseases",
"veterinary",
"virology",
"veterinary",
"science"
] | 2011 | Host Alternation Is Necessary to Maintain the Genome Stability of Rift Valley Fever Virus |
Annual incidence rates of varicella infection in the general population in France have been rather stable since 1991 when clinical surveillance started . Rates however show a statistically significant increase over time in children aged 0–3 years , and a decline in older individuals . A significant increase in day-care enrolment and structures’ capacity in France was also observed in the last decade . In this work we investigate the potential interplay between an increase of contacts of young children possibly caused by earlier socialization in the community and varicella transmission dynamics . To this aim , we develop an age-structured mathematical model , informed with historical demographic data and contact matrix estimates in the country , accounting for longitudinal linear increase of early childhood contacts . While the reported overall varicella incidence is well reproduced independently of mixing variations , age-specific empirical trends are better captured by accounting for an increase in contacts among pre-school children in the last decades . We found that the varicella data are consistent with a 30% increase in the number of contacts at day-care facilities , which would imply a 50% growth in the contribution of 0-3y old children to overall yearly infections in 1991–2015 . Our findings suggest that an earlier exposure to pathogens due to changes in day-care contact patterns , represents a plausible explanation for the epidemiological patterns observed in France . Obtained results suggest that considering temporal changes in social factors in addition to demographic ones is critical to correctly interpret varicella transmission dynamics .
Varicella is a vaccine-preventable infectious disease caused by exposure to Varicella-Zoster Virus ( VZV ) . The pathogen is antigenically stable so that , in principle , no changes in transmission or immunogenicity caused by mutations of the virus are expected over time [1] . In France , about 90% of the population gets infected with varicella before 8 years of age; most of infections occur in the early childhood and result in relatively mild symptoms [2 , 3] . In this country , vaccination against varicella is not recommended and little used in children . Previous studies have shown that temporal changes in the crude birth rate of a population are key drivers of the dynamics of childhood infectious diseases , such as varicella and measles , by affecting the replenishment of susceptible individuals in the population [4–8] . Since the early 90s , France experienced a roughly constant crude birth rate [9] after a strong demographic transition in the last century characterized by a progressive decline of birth and death rates . This corresponded to stable varicella infection rates at the population level between 1991 and 2015 , as revealed by the French GPs Sentinelles Network for surveillance [10] . However , when looking at the distribution of cases by age , surveillance data highlight that during this period varicella incidence has increased in children aged 0–3 years and decreased in children aged 4–7 years . A similar pattern has been detected in other countries , including Slovenia , the US and England [11–16] , where varicella incidence doubled in children aged 0–4 years between 1983 and 1998 and halved in those aged 5–14 years . This suggests that , beyond changes in fertility and mortality rates , other factors may influence the circulation of childhood infections across the different age segments of the population . One of them is variations in the population mixing patterns driven by socio-demographic changes , affecting school attendance and household structure . In particular , some epidemiological studies have hypothesized that an increase of varicella incidence in young ages may be ascribable to increased social contacts in these age groups , possibly caused by earlier inclusion in nurseries or day-care centers [11–13] . Past modeling efforts , based on a theoretical framework assuming a stationary age distribution of the population , have suggested that a substantial increase of contact rates in preschool children is consistent with the increase in varicella consultations in UK observed between 1970 and 1998 in this age segment [17] . The aim of this work is to assess whether changes in the age-specific varicella incidence observed in France can be the result of an increase of contacts in the early childhood . To this aim , we considered two transmission models with the same demographic and epidemiological structure , but differing in mixing patterns over time . Both models take explicitly into account demographic changes occurred during the last century [9] . In the first model , mixing patterns between individuals of different ages are assumed to remain constant over time and are modelled according to the age-specific contact matrix estimated for France in 2012 [18] . In the second model , we assume a linear increase of contact rates occurring at day-care facilities for children under 3 years of age , starting in the decades before 2012 .
The adopted modelling approach is based on a deterministic age-structured model similar to the one developed by previously published studies to investigate historical dynamics of measles across different countries and varicella in Spain [5–7] . The population , grouped into 1-year age classes ( 0–89+ ) , is initialized in 1850 at the demographic and epidemiological equilibrium . The latter was obtained by running the transmission model with constant crude birth and mortality rates fixed to those observed in 1850 , and by initializing the system with 10 infected individuals in a fully susceptible population . Simulations of varicella dynamics from 1850 to 2015 are obtained by running the model as informed by the yearly variations of birth and age-specific mortality rates provided by the National Institute of Statistics and Economical Studies ( INSEE ) . The demographic model is validated against the age distribution of the population observed in France during the simulated period ( 1850–2015 ) [9] . Realistic mixing patterns by age are modelled using contact matrices estimated for France in 2012 [18] . Age-specific contact matrices are here defined as the average number of unique physical and conversational contacts with individuals of different ages occurring daily , regardless their duration and frequency [18] . The transmission of varicella follows an MSIR model . Briefly , maternal antibodies protect new-borns against varicella infection ( M ) for 2 months on average [19] , after which they become susceptible to varicella infection ( S ) . Susceptible individuals are exposed to a time- and age-dependent force of infection λi ( t ) as follows: λi ( t ) =β∑j=0nCij ( t ) Ij ( t ) Nj ( t ) ( 1 ) where t and i denote time and the individuals’ age , respectively; n = 89 years is the maximum age considered in the model , Ij ( t ) /Nj ( t ) is the fraction of individuals of age j who are infected at time t and Cij ( t ) is the contact matrix at time t , which is defined as the average number of contacts of an individual of age i with individuals of age j; finally , under the social contact hypothesis [20] , β represents an age-independent constant proportionality factor driving the contribution of individuals’ contacts to the transmission of the infection . Once recovered , varicella infected individuals ( I ) acquire life-long immunity against varicella . The generation time of varicella is assumed equal to 3 weeks on average [21] . Epidemiological and demographic transitions occurring within a given year are described by the following set of ordinary differential equations: M˙i ( t ) =δi0b ( t ) N ( t ) −ωMi ( t ) −μi ( t ) Mi ( t ) S˙i ( t ) =ωMi ( t ) −λi ( t ) Si ( t ) −μi ( t ) Si ( t ) ( 2 ) I˙i ( t ) =λi ( t ) Si ( t ) −γIi ( t ) −μi ( t ) Ii ( t ) R˙i ( t ) =γIi ( t ) −μi ( t ) Ri ( t ) where b ( t ) is the yearly crude birth rate , δij is the Dirac delta function , N ( t ) is the population size , ω is the waning rate of maternal antibodies , μi ( t ) is the yearly age-specific mortality rate and γ is the recovery rate from varicella infection . At the end of each year , the age of individuals is incremented by 1 . In this work , we consider two variations of the model described in the previous section , which differ in the assumption made to model mixing patterns during the past . In model M1 , at each time t the average number of contacts of an individual of age i with individuals of age j is computed as: Cij ( t ) =C¯ijS+C¯ijO ( 3 ) Where C¯ijS and C¯ijO are respectively the matrices of contacts within and outside schools estimated for France in 2012 [18] . School contacts of individuals younger than 3 years of age correspond to social interactions occurring at pre-school facilities , such as day-care centers . In this sense , model M1 assumes no changes in mixing patterns of individuals over time . In Model M2 , we account for potential temporal changes in contact patterns by adding a time-dependent scaling factor to the matrix of school contacts . Specifically , we assume that Cij ( t ) =fij ( t ) C¯ijS+C¯ijO where fij ( t ) :={1−α ( 2012−t ) ifi<4orj<41elsewhere ( 4 ) This simple assumption accounts for linear temporal changes of contact rates in children below 3 years of age and aims at illustrating the potential impact of an increased attendance at day-care centers . By keeping track of the contribution of different age segments and different settings to the age specific force of infection , we disentangle the proportion of varicella infections caused by school contacts ( at different school levels , including day care ) and compute the infection matrices representing the proportion of varicella cases generated by contacts of susceptible individuals of age i with infected individuals of age j . Free parameters of the two models were estimated separately through a Markov chain Monte Carlo ( MCMC ) approach applied to the negative binomial likelihood of the yearly age-specific incidence of varicella observed in France over the period 1991–2015 [10] . The two models have the following free parameters in common: the transmission scale factor ( β ) , the varicella reporting rate , which is assumed constant over age and time , and the over-dispersion of the negative binomial distribution . Model M2 has an additional free parameter shaping the changes in school contacts of young children ( α ) . Models’ performances were compared using the Deviance Information Criterion ( DIC ) and the Akaike Information Criterion ( AIC ) . Further details on model formulation and estimation are provided in S1 Text .
The analysis of observed varicella incidence by age group reveals a statistically significant increase of infection rates in children less than 3 years old and a significant decrease in those older than 5 years ( see S1 Text ) . Temporal changes in the infection rates in new-borns , in children of 4 years of age and in total yearly incidence were found to be not statistically significant . According to both measures used to assess model performances , model M2 ( DIC: 6508 . 8; AIC: 6513 . 5 ) is able to better represent the data than model M1 ( DIC: 6513 . 4; AIC: 6523 . 8 ) . Both models considered in our analysis are able to reproduce changes in the overall size and age distribution of the French population as observed during the last century ( details shown in S1 Text ) . Briefly , in agreement with available demographic records , the simulated population dynamics shows that the progressive decrease in the crude birth rate experienced between 1900 and 2015 led to a significant reduction of the fraction of children in the population . Consistently with previous works [5 , 6] and in agreement with observations [10] , estimates from both models suggest that the total incidence of varicella did not significantly change between 1991 and 2015 , when the yearly crude birth rate remained approximately stable ( model M2 , Fig 1A; model M1 shown in S1 Text ) . The estimated varicella reporting rate ranges between 90 . 7% ( 95% CI: 85 . 8%-96 . 0% ) in model M1 and 88 . 6% ( 95% CI: 84 . 1%-93 . 1% ) in model M2 . The simulated varicella dynamics were validated against an independent dataset , represented by the age-specific VZV serological profile observed in France in 2003 [2] . According to our simulations and consistently with data , in 2003 about 30% of 2-year-old children were immune to varicella and this fraction increases up to 87% by age 7 ( model M2 , Fig 1B; model M1 , in S1 Text ) . These results suggest that the overall varicella circulation observed in France during the last decades can be explained in terms of the relatively stable crude birth and mortality rates characterizing this period and does not depend on possible changes in mixing patterns . However , trends in the age-specific varicella incidence estimated with the two models are remarkably different . A detailed analysis on the ability of models M1 and M2 in reproducing the observed dynamics is reported in S1 Text . In particular , model M1 that does not account for changes in mixing patterns over time yields stable infection rates in all age groups between 1991 and 2015 ( see S1 Text ) . This model formulation thus fails to capture the changes in the age distribution of varicella cases observed in the period under study . In contrast , model M2 , by explicitly taking into account possible changes in the rate of contacts of young children ( 0–3 years ) established in day-care structures in the years prior to 2012 , reproduces the observed trends in the age-specific incidence of varicella ( see Fig 2 ) . In particular , model M2 estimates a 12 . 1% increase of varicella incidence in 0–3 years old children over 1991–2015 and a 13% decrease of varicella incidence in older age groups over the same period . The estimated increase in varicella incidence among 0–3 years is the result of a 18% increase in 1-year old children , 11% in 2-year old children , and only 2% in 3-year old children ( Fig 2 , first row ) . The estimated changes of varicella transmission dynamics are ascribable to an increase in the average number of day-care contacts during the last decades . According to our estimates , in 1991 , day-care contacts represented on average the 15 . 5% of the total contacts of children aged 0–3 years , while this fraction increased up to 19 . 2% in 2012 ( details are provided in S1 Text ) . An increase in the proportion of infection transmission due to contacts among children aged 0–3 years from 1992 to 2012 is also detectable ( Fig 3 ) . Specifically , the estimated contribution of contacts among children 0–3 years to the infection transmission increased between 1992 and 2012 from 19 . 4% to 28 . 6% , while that of contacts among children 4–6 years decreased from 24 . 4% to 20 . 1% . The increase of day-care contacts in the early childhood estimated by model M2 has an impact on the relative contribution of different settings to the overall transmission of varicella . According to our results , although the total incidence of varicella slightly decreased between 1991 and 2015 ( from 12 . 1 to 11 cases per 1 , 000 individuals ) , the fraction of cases generated at school facilities of any level ( i . e . day care , pre-primary and primary schools ) raised from about 43 . 1% to 46 . 11% over the same period ( see Fig 4A ) . Such increase is mainly driven by the changing role of day-care centers in varicella transmission , whose contribution to the total varicella cases rises from , on average , 9 . 1% in 1991 to 17 . 6% in 2015 ( Fig 4A ) . In particular , infections among children under 3 years of age caused by day-care contacts raised from 27 . 8% in 1991 to 39 . 1% in 2015 ( Fig 4B and S1 Text ) . On the other hand , the percentage of the total yearly infections generated in pre-primary schools decreased from 27 . 8% to 22 . 8% and the fraction of infections in other settings diminished from 56 . 9% to 53 . 9% ( Fig 4A ) . However , the contribution of different settings to infections occurring among individuals older than 4 years remained substantially unchanged ( see Fig 4C and 4D ) .
Epidemiological data collected in France between 1991 and 2015 show that varicella incidence rates have remained approximately constant following a stabilization of the birth rate in the early 90’s [9 , 10] . This is coherent with our understanding of the role played by dynamics of the crude birth rate in shaping the transmission of childhood infections , such as measles and varicella [4–7 , 22] . Data disaggregated by age group however reveal that infection rates have increased in children younger than 3 years , therefore suggesting an increased circulation of varicella in early childhood . Similar patterns have been detected in other regions and countries , e . g . England , Slovenia and the US [11–16] . A plausible explanation for the observed epidemiological trends may rely on the increase of contacts among pre-school children , possibly caused by growing attendance rates at nurseries and day-care centers [11 , 12 , 23–25] . In this work , we investigated this hypothesis and showed that a progressive increase of mixing among 0–3 year-old children may have led to a 12% increase of varicella incidence in this age group between 1991 and 2015 . Specifically , our results suggest that a 30% growth in the average number of day-care contacts may have increased by 50% the contribution of 0–3 year-old children to the overall number of yearly infections during the considered period . According to model estimates , although in the last decades the fraction of infections generated in schools of any level ( day care included ) remained rather stable around 45% , the fraction of transmission in day-care centers almost doubled during the same period . From a policy-making perspective , our results suggest that the inclusion of children in day-care facilities or nurseries is expected to produce an earlier exposure to pathogens , increasing their risk of contracting infectious diseases . These findings may be useful to interpret results of policies ( e . g . vaccination ) and to design effective and targeted intervention strategies , e . g . scheduling age at vaccination . It is also important to note that a decrease in the age at infection for childhood diseases may be partially masked by rather stable incidence rates in the overall population . The central role of age at entry in the community in the early childhood phase suggested by our analysis is widely supported by previous works . Silhol et al . [26] showed that the median age at varicella infection may be related to the fraction of children attending pre-schools , thus explaining the large variability in the age-specific seroprevalence observed across European countries [27] . Early exposure in day-care facilities was also suggested as a possible driver for the increase in varicella incidence reported in children from 12 months to 2 years in Slovenia in the period 1979–89 [12] . More generally , several studies identified a clear link between changes in mixing patterns due to the school calendar ( e . g . school term vs . school holidays ) and the strong seasonality of varicella dynamics [21 , 28–30] . A modeling study , based on theoretical age-specific contact patterns , suggested that the increase in GP varicella consultations rates observed in the UK between 1970 and 1998 among the youngest age segments of the population was compatible with an increase in early childhood contact rates [17] . Population surveys and modelling approaches have been proposed to investigate human mixing patterns by age , providing static estimates of country-specific contact matrices ( e . g . [18 , 31–35] ) . The characterization of contacts was indeed found to be particularly important to achieve accurate and reliable modelling results and reduce uncertainties on recommendations for vaccination against varicella [36] . However , little is known to what extent mixing patterns by age may change over time , for instance , as a consequence of socio-demographic and legislative changes . Our analysis represents a first step in this direction based on recent realistic estimates of age-specific contact rates in France and a rather simple assumption on how contacts in pre-school children may have changed as a consequence of increased day-care enrolment rates [24] . The model innovates on previous approaches also by explicitly taking into account the potential impact of demographic changes in shaping temporal changes in varicella circulation in the country [5–7] . The hypothesis of an increase in the number of contacts established by 0–3 years old infants at day-care facilities is supported by the increase in both the enrolment rates and the potential capacity of day-care services observed for this age group in France during the last decades [24 , 25] . Nonetheless , our study presents some limitations that call for a deeper understanding of temporal changes in mixing patterns to improve our interpretation of medium to long-term trends in the epidemiology of infectious diseases . First , in our model we assume a linear increase in contacts rates of young children shaped by a unique scaling factor for all age groups . This assumption could be too simplistic to accurately reproduce the considered phenomenon . The inclusion of more flexible functional forms to describe temporal changes in contact rates would possibly improve model accuracy in reproducing varicella incidence over time for some age groups . Future modeling efforts in this direction would certainly benefit from cross-sectional and longitudinal studies showing how social mixing has changed over time . The underestimation of varicella incidence in 3-year-old children with respect to reported data suggests that our model underestimated either the number of contacts or the transmission events in this age class . This may be partially due to mixed enrolment of children in different structures , as this age indicates the transition from day-care centers , potentially available for up to 54% of children ( data for 2014 [25] ) , to pre-primary schools ( ecoles maternelles ) , characterized by 100% attendance since 2000 [24] . Also , under the social contact hypothesis [20 , 37] that is widely adopted for modeling childhood infections [38–40] , here we consider age-specific transmission rates to be proportional to average daily contact rates through a single constant proportionality factor . However , it is possible that considering age-specific proportionality factors and taking into account the duration of social interactions may better describe the VZV transmission across different ages [35 , 41 , 42] . In our model , we did not consider the impact of changes of pre-school and school attendance on the school size . A study in the region of Corsica found that the age of varicella infection decreased as school size increased , likely due to an increased number of contacts per individual [43] . Including this aspect into our model is rather challenging because of the very diversified offer for early childhood services in France ( different types of day-care centers , qualified nannies , qualified nannies at home , etc . ) and the lack of a centralized management and registration [25] . Finally , additional mechanisms such as temporal changes in individual mixing outside schools , e . g . due to changes in household size and composition [44] , may also play a role in the transmission dynamics of varicella . For example , a doubling varicella incidence was reported in Slovenia in the period 1979–1998 in 0 years old infants notwithstanding children less than 10 months are not accepted at day-care facilities [12] . Earlier varicella infections due to infected older siblings may explain the observed trends [43] . Our modelling results suggest that changes in mixing patterns at day-care structures represent one plausible component leading to the increase of incidence estimates over time in all corresponding age classes . The performed analysis focused on the epidemiology of varicella in France over the period 1991–2015 . However , conclusions of this work may apply to other infections , such as measles and pertussis , and to countries that have undergone an increase in the school enrolment of young children , as is the case of England and Slovenia [11 , 12] . Future estimates on how the age-specific VZV immunity profiles have changed over time , based for instance on serological surveys conducted in different years , would help to exclude or quantify the contribution of other competing hypotheses to changes in day-care contact patterns in shaping the observed temporal variations in VZV incidence rates . For instance , while a constant reporting rate over time and across different ages was here assumed , changes in reporting behavior of individuals of different ages might have also occurred between 1991 and 2015 . Previous work , however , showed that extrapolated GP surveillance data were estimated to capture more than 96% of varicella cases in the 90’s in France [45] , suggesting that no considerable improvement in consultation rate is thus possible . | During the last decades , an increasing circulation of varicella in the early childhood has been observed in France . A plausible explanation of this trend may rely on the progressive increase of day-care attendance in the past years , which could have anticipated the exposure of young children to the infection . We propose a retrospective modelling study to assess whether the varicella dynamics in France since 1991 can be explained in terms of increasing day-care contacts of children under 3 years of age . To this aim , we develop a model including demographic changes and variations in age-specific contact rates over time . Our findings suggest that a 30% increase of day-care contacts in early childhood can explain the observed epidemiological trends . Obtained results highlight that temporal changes in contact patterns can significantly affect the transmission of childhood infectious diseases and should therefore be considered when investigating medium and long-term epidemiological patterns . A better understanding of the interplay between changing social behavior and disease transmission can help the interpretation of surveillance data and the design of effective and targeted intervention strategies . | [
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] | 2018 | Modeling the impact of changes in day-care contact patterns on the dynamics of varicella transmission in France between 1991 and 2015 |
While infectious agents have typical host preferences , the noninvasive enteric bacterium Vibrio cholerae is remarkable for its ability to survive in many environments , yet cause diarrheal disease ( cholera ) only in humans . One key V . cholerae virulence factor is its neuraminidase ( VcN ) , which releases host intestinal epithelial sialic acids as a nutrition source and simultaneously remodels intestinal polysialylated gangliosides into monosialoganglioside GM1 . GM1 is the optimal binding target for the B subunit of a second virulence factor , the AB5 cholera toxin ( Ctx ) . This coordinated process delivers the CtxA subunit into host epithelia , triggering fluid loss via cAMP-mediated activation of anion secretion and inhibition of electroneutral NaCl absorption . We hypothesized that human-specific and human-universal evolutionary loss of the sialic acid N-glycolylneuraminic acid ( Neu5Gc ) and the consequent excess of N-acetylneuraminic acid ( Neu5Ac ) contributes to specificity at one or more steps in pathogenesis . Indeed , VcN was less efficient in releasing Neu5Gc than Neu5Ac . We show enhanced binding of Ctx to sections of small intestine and isolated polysialogangliosides from human-like Neu5Gc-deficient Cmah-/- mice compared to wild-type , suggesting that Neu5Gc impeded generation of the GM1 target . Human epithelial cells artificially expressing Neu5Gc were also less susceptible to Ctx binding and CtxA intoxication following VcN treatment . Finally , we found increased fluid secretion into loops of Cmah-/- mouse small intestine injected with Ctx , indicating an additional direct effect on ion transport . Thus , V . cholerae evolved into a human-specific pathogen partly by adapting to the human evolutionary loss of Neu5Gc , optimizing multiple steps in cholera pathogenesis .
Cholera is a life-threatening , human-specific disease caused by the noninvasive enteric bacterium Vibrio cholerae that affects millions of people worldwide [1] . Humans are infected after ingestion of food or water contaminated with the pathogen . Bacteria that survive passage through the acidic milieu of the stomach can colonize and multiply on the surface of the small intestinal epithelium [2] and induce severe watery diarrhea . This main symptom of the disease leads to loss of electrolytes and blood volume depletion , and can be fatal if the individual is not rehydrated rapidly [2] . Cholera diarrhea is triggered after the B subunits of cholera toxin ( Ctx ) bind to the monosialoganglioside , GM1 [3–5] , expressed on the outer leaflet of the apical membrane of intestinal epithelial cells , which facilitates toxin endocytosis and retrograde phagosomal transport to the endoplasmic reticulum ( ER ) [6 , 7] . In the ER , the enzymatically active A subunit of Ctx ( CtxA ) is released from the B subunits and translocated into the cytoplasm , where it activates adenylate cyclase , leading to increased levels of cyclic adenosine monophosphate ( cAMP ) . The rise in cAMP causes intense secretion of chloride ions through the cystic fibrosis transmembrane conductance regulator ( CFTR ) as well as inhibition of electroneutral sodium chloride absorption [8] . These events are followed by passive water flow in response to osmotic gradients , resulting in profuse diarrhea [9] . CtxA further induces epithelial cell barrier disruption by inhibiting exocyst-mediated trafficking of host proteins that make up the intercellular junctions of epithelial cells , a mechanism that may act in parallel with Cl− secretion to drive the pathophysiology of cholera [10] . The pathogenesis of cholera-induced diarrhea is initiated when Ctx binds to GM1 . However , this mono-sialoganglioside is naturally expressed only at low levels in the small intestine compared to di- and tri-sialogangliosides such as GD1a , GD1b , GT1b , etc . [11] . Pioneering studies on the tissue ligand for Ctx demonstrated that V . cholerae uses its uniquely evolved neuraminidase ( VcN ) to hydrolyze α2-3- and α2-8-linked sialic acids from complex gangliosides , thereby generating high concentrations of GM1 and favoring the binding and internalization of Ctx by mammalian cells [4 , 12] . Thus , VcN-mediated cleavage of sialic acids from multisialylated gangliosides ( selectively sparing the one internal α2-3-linked sialic acid that defines GM1 ) acts synergistically with Ctx in the induction of diarrhea . Sialylated glycans ( such as on glycoproteins and glycosphingolipids ) from human cells terminate with only the N-acetylneuraminic acid ( Neu5Ac ) isoform of sialic acid , a pattern that differs from the majority of other mammals , whose glycans can also terminate with N-glycolylneuraminic acid ( Neu5Gc , which differs from Neu5Ac by one additional oxygen atom ) [13] . Humans do not naturally express glycoconjugates containing Neu5Gc because they lack the enzyme CMP-N-acetylneuraminic acid hydroxylase ( CMAH ) due to an Alu-mediated exon deletion in the CMAH gene [14] . Thus , while gangliosides from other adult mammals such as calf , pigs and rabbits can terminate in α2-3- and α2-8-linked Neu5Gc or Neu5Ac [15 , 16] , only α2-3- and α2-8-linked Neu5Ac are found in human gangliosides [17] . Cholera is a human-specific disease . In fact , Robert Koch , one of the founders of modern microbiology , famously wrote in 1884: “…although these experiments were constantly repeated with material from fresh cholera cases , our mice remained healthy . We then made experiments on monkeys , cats , poultry , dogs and various other animals […] but we were never able to arrive at anything in animals similar to the cholera process” [18] . Experimental high dose ( ~1 x 109 organisms ) oral infection of cimetidine-treated 3-day-old infant rabbits with V . cholerae can lead to lethal watery diarrhea [19] . However , to the best of our knowledge no reports of cholera diarrhea in other adult mammals have been available , nor any theories to explain the human specificity for this disease , which remains a long-standing mystery––a rare failure of Koch to fulfill his own “postulates” for defining an infectious disease agent . Previous studies from our group have shown that polymers of α2-8-linked Neu5Gc are much less susceptible to cleavage by mammalian and bacterial neuraminidases compared to polymers containing α2-8-linked Neu5Ac [20] . These findings led us to hypothesize that the difference in the sialic acid composition between the glycans of humans and other mammals influences the ability of VcN to generate epitopes for Ctx binding . This , together with previous studies from our group showing that sialic acid type can modulate host-pathogen interactions and human-specific susceptibility to other infectious diseases such as typhoid fever and malignant malaria [21 , 22] , motivated us to investigate if the loss of Neu5Gc plays a role in human susceptibility to cholera . In this paper , we explore a variety of possible mechanisms and their potential link to the human-specific occurrence of cholera .
V . cholerae neuraminidase ( VcN ) can catalyze the cleavage of α2-3- and α2-8-linked sialic acids from complex gangliosides with multiple sialic acids but it specifically does not cleave the single sialic acid α2-3-linked to an internal galactose ( Gal ) residue present in the GM1 ganglioside [4 , 12] . VcN action on polysialogangliosides is thus critical to generate high concentrations of GM1 and favor the binding and internalization of Ctx by intestinal epithelial cells [4 , 12] . Unrelated prior studies from our group have shown that glycans containing α2-8-linked Neu5Gc are relatively resistant to hydrolysis by other bacterial neuraminidases compared to glycans containing α2-8-linked Neu5Ac [20] . To study possible substrate preferences of VcN for α2-8-linked sialic acids , glycan structures containing terminal α2-8-linked Neu5Ac or Neu5Gc with underlying α2-3-linked Neu5Ac were exposed to increasing amounts of VcN and the resulting glycans were probed with Hsa , a bacterial adhesin that specifically recognizes any exposed α2-3-linked terminal mono-sialic acid [23] . As seen in Fig 1A , de-sialylation of Neu5Ac- or Neu5Gc-terminated sialoglycans with identical underlying glycan structures depended on VcN concentration . When the outermost α2-8-linked sialic acid moiety was removed by VcN , the underlying mono-sialic acid α2-3-linked to lactose became exposed and susceptible to Hsa binding . The Neu5Ac-terminated sialoglycan was more readily cleaved by VcN treatment than its Neu5Gc-terminated counterpart . Thus , VcN prefers Neu5Ac , and glycans containing α2-8-linked Neu5Gc are less susceptible to VcN-mediated hydrolysis ( Fig 1A ) . Interestingly , when higher concentrations of VcN were used , it could further cleave the exposed terminal α2-3-linked Neu5Ac , resulting in a decrease of binding by Hsa , which specifically recognizes terminal α2-3-linked mono-sialic acids ( S1 Fig ) . Since Neu5Gc compromises VcN activity , we next tested if VcN treatment would influence the binding of Ctx to gangliosides isolated from WT and Cmah-/- mice . ELISA assays revealed that treatment of small intestinal monosialogangliosides with increasing concentrations of VcN did not influence the binding of Ctx in samples from either mouse ( Fig 1B ) . This was expected because VcN should not target monosialogangliosides to generate GM1 [24] . However , VcN treatment of di/tri/tetrasialo-gangliosides increased binding of Ctx to gangliosides from Cmah-/- small intestine compared to gangliosides from WT mice ( Fig 1C ) , suggesting that higher levels of GM1 were generated by VcN activity on complex gangliosides in the absence of Neu5Gc . Even mammals that express CMAH in other tissues lack glycans containing Neu5Gc in their brain , a tissue enriched in gangliosides . Correspondingly , although VcN treatment of di/tri/tetrasialo-gangliosides isolated from the brain increased Ctx binding , the effect was comparable between WT and Cmah-/- brain gangliosides ( Fig 1D ) . Moreover , compared to WT controls , Ctx binding to brain gangliosides after VcN treatment was significantly lower in samples from NestinCre+-Cmahtg mice that were genetically modified to overexpress Neu5Gc in the brain [25] ( Fig 1E ) . To extend the findings from the ELISA assay , we compared the binding of Ctx to small intestinal tissue sections from WT and Cmah-/- mice by microscopy . Although there was no significant difference in Ctx binding between WT and Cmah-/- tissues at baseline ( Fig 2A , top panels ) , treatment of frozen sections with VcN enhanced binding of Ctx to Cmah-/- ( Fig 2A , right panels ) but not WT tissues ( Fig 2A , left panels ) , as shown by the quantification of the Ctx-positive ( red ) area ( Fig 2B ) . Together with our previous biochemical observation that absence of Neu5Gc facilitates VcN activity , these results suggest that higher levels of GM1 were generated in the tissue of Cmah-/- mice , thereby promoting Ctx binding to small intestinal gangliosides of Cmah-/- mice . To confirm that absence of Neu5Gc plays a role in the pathophysiologic changes induced by VcN plus Ctx when CMAH is missing , we supplied exogenous Neu5Gc to human colonic cells in culture , which would be metabolically incorporated into cell surface glycoconjugates . We then analyzed VcN activity , Ctx binding and basal and stimulated cAMP levels in the cells that had metabolically incorporated Neu5Gc . As seen in Fig 3 , T84 cells fed with free Neu5Gc ( 5 mM ) successfully incorporate it into cell surface glycans ( Fig 3B , black arrow ) . This result corroborates prior observations that human cells can incorporate Neu5Gc into nearly all major glycans , including gangliosides [17] . Importantly , cells fed with Neu5Ac , used as a control for sialic acid feeding , did not express detectable levels of Neu5Gc ( Fig 3A ) . Using a whole cell ELISA assay [26] , we observed that simply incorporating Neu5Gc in the cell surface did not by itself affect Ctx binding to T84 cells ( Fig 3C ) . However , when cells fed with Neu5Ac were also exposed to VcN , binding of Ctx was significantly increased , whereas VcN had no effect on Ctx binding to cells fed with Neu5Gc ( Fig 3C ) . Further , VcN significantly increased the ability of Ctx to elevate cAMP in cells fed with Neu5Ac- but not in Neu5Gc-fed cells ( Fig 3D ) . Thus , lack of Neu5Gc makes glycans of human cells more susceptible to VcN activity in generating Ctx-binding and subsequent cell intoxication . Since lack of Neu5Gc increased Ctx binding to the small intestinal gangliosides of Cmah-/- mice , and exogenous incorporation of Neu5Gc blocked the ability of VcN to increase Ctx toxicity in human cells , we next studied pathogenic effects of both virulence factors in WT vs . Cmah-/- mice . We hypothesized that WT mice that express Neu5Gc would be less susceptible to VcN-generation of GM1 from α2-8-sialylated gangliosides and thus develop fewer epitopes for Ctx binding . Conversely , Cmah-/- mice should be more susceptible to Ctx-mediated fluid secretion because more GM1 would be readily generated from the α2-8-sialylated gangliosides due to the lack of Neu5Gc . Because adult mice have many compensatory mechanisms to prevent fluid loss , and therefore do not typically develop frank diarrhea following oral infection with V . cholerae ( or many other non-invasive intestinal pathogens ) [27–29] , researchers have used ligated intestinal loops to study Ctx-induced pathogenesis [30 , 31] . Using the same well-established model , we injected Ctx into intestinal loops created in WT and Cmah-/- mice . After 4 h , Ctx induced fluid accumulation in both mice , but fluid levels were significantly higher in the intestinal loops of Cmah-/- mice ( Fig 4A and 4B ) . Corroborating this finding , incubation of jejunal segments with Ctx in Ussing chambers increased ion transport ( most likely reflective of electrogenic chloride secretion ) in Cmah-/- specimens compared to that in WT tissue ( Fig 4C ) . Basal ion transport was also upregulated in tissues from Cmah-/- mice compared to WT , but there was no significant difference in ion secretion induced by forskolin between WT and Cmah-/- tissues ( Fig 4C ) . This latter finding implies that the enhanced ion transport response of Cmah-/- tissues to Ctx is not simply due to differences in responsiveness of the ion transport machinery to intracellular cAMP . VcN addition to the intestinal loops or to the apical reservoir in the Ussing chamber did not affect Ctx-induced fluid ( Fig 4B ) or ion secretion across either WT or Cmah-/- tissues ( Fig 4C ) . However , unlike the effect of VcN treatment in the tissue sections , the apically-applied enzyme would face competitive inhibition by the heavy overlying layer of sialylated intestinal mucins and therefore could not reach the ganglioside structures . In natural human infection the bacterium may deploy other virulence mechanisms to reach the epithelial surface . Despite the lack of an efficient VcN-mediated desialylation process in this model , a significant increase in the fluid secretion from Cmah-/- tissue nevertheless occurred , indicating that these mice are more susceptible to the action of Ctx itself , by an as yet unknown mechanism . In summary , our results show that the absence of glycans containing Neu5Gc makes the intestinal tissue of Cmah-/- mice more susceptible to the pathogenic effects of Ctx . It is well known that changes in the mucin expression profile in murine small intestine can alter tissue permeability [32 , 33] . In addition , Ctx-mediated impairment of endosome recycling disrupts tight junction integrity and thus the intestinal barrier [10] , which might enhance fluid loss . Because the mucins of WT and Cmah-/- mice small intestine differ in their sialic acid composition , it was important to determine if the absence of Neu5Gc influenced intestinal permeability to account for increased Ctx-induced fluid secretion across Cmah-/- small intestine . However , no differences in intestinal FITC-dextran permeability were seen between WT and Cmah-/- mice ( Fig 4D ) . To deliver Ctx and induce diarrhea , V . cholerae must colonize the host small intestine in a process mediated by many different virulence factors [19 , 34–37] . V . cholerae successfully colonizes the human small intestine but is unable to naturally colonize the intestines of other adult mammals studied to date [38] . In addition , Ctx-induced secretion of sialylated mucins plays an important role in V . cholerae colonization of the intestinal tract [39 , 40] . Since mucins from Cmah-/- mice mimic mucins of human tissues by completely lacking Neu5Gc , we analyzed whether the absence of Neu5Gc could influence colonization by V . cholerae . Although we found different levels of bacteria along the small intestine and colon of the infected mice , the bacterial CFU recovered from the small intestinal fractions were similar in both WT and Cmah-/- mice ( S2 Fig ) . These results suggest that the colonization of the small intestine of Cmah-/- mice by V . cholerae is not significantly modulated by the absence of Neu5Gc . We next wanted to investigate V . cholerae growth in minimum media containing either Neu5Ac or Neu5Gc as sole carbon source and without competition from other bacteria . While V . cholerae growth was modestly sensitive to sialic acid differences at high concentrations ( such as 3 mM ) , no differences in bacterial growth were observed at lower concentrations of the two monosaccharides ( S3 Fig ) . Although similar levels of V . cholera were recovered from the gut tissues of both WT and Cmah-/- mice , we asked if increased susceptibility of Cmah-/- small intestine to Ctx-induced fluid secretion ( Fig 4A , 4B and 4C ) would make those animals develop diarrhea after ingesting live V . cholerae . Neither WT nor Cmah-/- mice gavaged with 2 × 108 CFU of V . cholerae showed any signs of diarrhea . Thus , although the lack of Neu5Gc increases susceptibility to Ctx toxicity , the Cmah-/- background is not sufficient in itself to make mice susceptible to frank experimental diarrhea induced by oral infection with V . cholera . These results corroborate previous studies that have shown that adult mice do not develop diarrhea following oral infection with V . cholerae , although the bacteria are able to replicate in the host gut ( S2 Fig ) [41] .
There are multiple potential mechanisms by which the human evolutionary loss of epithelial Neu5Gc could contribute to the human-specific susceptibility to cholera . These include improved survival in gastric fluid ( not studied here ) ; colonization or preferential growth with Neu5Ac or Neu5Gc as carbon source ( no effect seen here ) ; decreased VcN degradation of inhibitory mucin sialic acids ( not studied here ) ; VcN remodeling of higher gangliosides into GM1 ( a major impact seen here ) ; improved delivery of the CtxA subunit into the cytosol ( not studied here ) ; and cAMP production and chloride channel activation ( both shown here to be markedly enhanced ) . While some hypotheses were nullified , we discovered more than one likely contributory mechanism that could underlie the human-specific susceptibility to cholera . We demonstrated that VcN shared its substrate preference with other previously studied mammalian and bacterial sialidases [20] , in being much less able to cleave α2-8-linked Neu5Gc than α2-8-linked Neu5Ac , which also corroborates another recent report [42] . We additionally showed that higher amounts of VcN are required to cleave an inner α2-3-linked sialic acid after removal of the outer α2-8-linked sialic acid , indicating that VcN has a preference for α2-8- over α2-3-linked sialic acids . Previous reports showed that VcN hydrolyses α2-3-sialyl galactoside more efficiently than α2-6-sialyl galactoside [43 , 44] . Taken together , we theorize that V . cholerae neuraminidase evolved to target human gangliosides preferentially , which are terminated only by Neu5Acα2-8-Neu5Acα2-3Gal1- and/or Neu5Acα2-3Galβ1-linked sialic acids . Nonetheless , the presence of α2-8-linked Neu5Gc on gangliosides compromised VcN hydrolysis , which fits our findings concerning the increased generation of GM1 binding sites for Ctx in the small intestine of Cmah-/- mice . Our results also show that VcN treatment affects the binding of Ctx to gangliosides isolated from the small intestine and brain of Cmah-/- and NestinCre+-Cmahtg mice , demonstrating that gangliosides lacking Neu5Gc are better targets for VcN to generate GM1 from a mixture of complex gangliosides . It is worth mentioning that the differences in Ctx binding observed in our experiments should solely correlate with the amount of GM1 generated after VcN action , since Ctx itself binds equally to either Neu5Gc-GM1 and Neu5Ac-GM1 [16] . Human cells induced to express glycans containing Neu5Gc are less susceptible to VcN activity , and therefore permit less generation of the optimal GM1 target for Ctx . The loss of Neu5Gc in humans confers increased susceptibility to VcN , which in turn favors Ctx-induced toxicity in intestinal cells . However , an additional mechanism evidently makes the small intestine of human-like Cmah-/- mice more susceptible to Ctx-induced fluid secretion . Early studies using germ-free mice demonstrated that the mouse gut microbiota is a natural impediment to V . cholerae colonization [45] . Because of this , many studies have used oral treatment with streptomycin as a colon dwelling model to study intestinal infection by this bacterium [46 , 47] . In fact , providing streptomycin in the drinking water did allow the colonization of the gastrointestinal tract of both WT and Cmah-/- mice by V . cholerae . Additional studies have shown that the catabolism of sialic acid initiated by VcN is an important step for intestinal colonization and the pathogenesis of cholera [48] and that V . cholerae can grow in vitro when sialic acid is used as the sole carbon source [49] . Here , we show that V . cholerae colonizes the small intestine of WT and Cmah-/- mice similarly , corroborating previous data that the bacteria can use both Neu5Ac and Neu5Gc as nutrient sources [49] . During natural infection , live bacteria depend critically on their toxin-coregulated pilus ( TCP ) structure to colonize the human small intestine [34–36] . The action of TCP helps the bacteria to get through the mucin barrier [50] and facilitates their direct contact with the epithelial surface where VcN and Ctx act locally to induce diarrhea . We suspect that when purified VcN is injected into the lumen , it heavily engages the highly sialylated mucin layer overlying the epithelial surface and therefore does not reach the relevant ganglioside structures to generate GM1 . This could explain why treatment with VcN did not potentiate the effect of Ctx in tissue segments mounted in Ussing chambers . In keeping with this rationale , VcN treatment did increase Ctx binding to frozen sections of the small intestine , wherein the mucus barrier is bypassed by tissue sectioning . A recent study elegantly showed that human T84 cells have little or no GM1 and that fucosylated and sialylated glycoproteins are instead the main target for Ctx binding to those cells [26] . Follow up studies further demonstrated that Ctx binds to fucosylated Lex-carrying glycoproteins but not to Lex-carrying glycolipids and that human and mouse intestinal cells can be intoxicated by Ctx , even when glycosphingolipid synthesis in inhibited [51] . Although these very interesting findings shed new light in the understanding of additional binding sites for Ctx , which are likely to also be relevant in disease pathogenesis , the authors did not study the impact of VcN in the Ctx binding to glycoproteins . We hypothesize that VcN would hydrolyze α2-3- and α2-6-linked sialic acid from glycoproteins to prevent Ctx binding to such molecules . VcN would also degrade di-tri sialylated gangliosides to generate GM1 and Ctx binding ( See Fig 5 ) . Together with the above arguments , our data suggest that intestinal cells with Neu5Gc-containing gangliosides are less susceptible to the toxicity of Ctx , and that this occurs at least in part due to the differential action of VcN on Neu5Gc compared to Neu5Ac-containing ganglioside targets . Although the data generated using the mouse model neither supported nor ruled out the hypothesis that VcN action enhanced the generation of GM1 target in isolated gangliosides , we found the small intestine of Cmah-/- mice secreted much higher levels of fluid in response to Ctx , suggesting additional mechanisms by which loss of Neu5Gc increases host susceptibility to Ctx . Fluid secretion results from both intense Cl- secretion through CFTR reflected as changes in short circuit current [52] as well as inhibition of NaCl absorption , which cannot be assessed in Ussing chambers . Glycosylation of CFTR is crucial for its function , affecting its cell surface expression , conformation stability and lysosomal degradation [53] . CFTR in Cmah-/- mice will only contain glycans with Neu5Ac , unlike CFTR glycans in WT mice ( and adult mammals other than humans ) that would express both Neu5Gc and Neu5Ac . We speculate that the differences in CFTR glycosylation could influence the increased electrogenic ion transport across Cmah-/- small intestine as observed in Ussing chambers , and also the intense fluid secretion in the Cmah-/- intestinal loops . Enterotoxigenic E . coli ( ETEC ) produce a heat labile enterotoxin ( LT ) , which is ca . 80% identical to Ctx , and acts by a similar mechanism also binding to GM1 [54] . Yet ETEC infects both humans and animals and does not produce a neuraminidase . With ETEC , the difference between strains that infect animals and strains that infect humans is may rely on both enterotoxin and intestinal colonization factors such as CFAI or K88 [54] that confer species specificity in binding of the whole bacterium . At first glance the situation with ETEC would seem to argue against our conclusions that the VcN specificity and differences in intestinal sialic acids accounts for V . cholerae being a human-specific pathogen . However , there is a major difference between the pathogens that needs to be considered . Whereas ETEC LT can only bind to the very small amount of GM1 naturally present in the gut mucosal epithelia , VcN provides extensive trimming of the complex gangliosides , maximizing the production of a much larger amount of GM1 target that , as a striking coincidence , is completely resistant to further VcN cleavage ( See Fig 5 ) . Indeed , this difference can help explain the much greater severity of the diarrhea in terms of volume loss in cholera than in ETEC infections . We do not rule out the possibility that the human specificity of cholera may also include other components such as species-specific intestinal colonization factors . Taken together , our results show that the evolutionary loss of epithelial Neu5Gc is a potential mechanism that contributes , at least in part , to the human-specific susceptibility to cholera , and can explain what appears to be one of the rare instances in which Robert Koch was unable to fulfill his famous postulates [18] for a human infectious disease: isolate the organism from the natural host , grow it in culture and introduce it back into an animal model host to reproduce the disease .
T84 cells ( American Type Culture Collection—CCL-248 ) , a line derived from a lung metastasis of a human colorectal carcinoma , were used as previously described [55] . The active ( AB5 ) form of Ctx , 4 kDa labeled FITC-dextran and 1 , 2-diamino-4 , 5-methylenedioxybenzene ( DMB ) were obtained from Sigma-Aldrich ( St . Louis , MO , USA ) . V . cholerae neuraminidase was purchased from Roche ( Clovis , CA , USA ) . The cAMP ELISA kit was from R&D Systems ( Minneapolis , MN , USA ) . V . cholerae strain N16961 was acquired from ATCC ( Manassas , VA , USA ) . The pairs of glycans [56] were printed on glass slides . Resulting slides were blocked with ethanolamine , washed , dried , and then fitted into a hybridization cassette [21 , 23] . The slides were then blocked with ovalbumin ( 1% w/v ) in PBS ( pH 7 . 4 ) for 1 h at RT , with gentle shaking . Subsequently , various amounts of VcN were applied to the glass slides . After incubating for 2 h , the slides were extensively washed to remove the neuraminidase . A GST-tagged bacterial adhesin probe , Hsa , which specifically recognizes mono sialic acid α2-3-linked to underlying glycan structures [23] was applied and incubated for 2 h at RT with gentle shaking , followed by anti-GST antibodies ( 1:2000 dilution in DPBS ) and Alexa Fluor 555-conjugated anti-rabbit IgG ( 1:10 . 000 in DPBS ) incubation . Resulting slides were washed and dried . Finally , the slides were scanned with a Genepix 4000B scanner ( Molecular Devices Corp . , Union City , CA ) . Data were analyzed with the Genepix Pro 7 . 0 analysis software . Cmah−/− mice , generated as previously described [57] , were bred onto a congenic C57BL/6 background and maintained in the University of California , San Diego vivarium according to Institutional Animal Care and Use Committee ( IACUC ) guidelines . Cmah−/− and WT mice were raised in the same vivarium room , in the same cage rack and fed with the same chow and water source to avoid variations in the gut microbiome . Both male and female mice from 8–14 weeks of age were used for all experiments . All studies were approved by the UC San Diego Institutional Animal Care and Use Committee ( IACUC ) under the protocol number S01227 . All studies complied with federal regulations regarding the care and use of laboratory animals: Public law 99–158 , the Health Research Extension Act , and Public Law 99–108 , the Animal Welfare Act which is regulated by USDA , APHIS , CFR , Title 9 . Parts 1 , 2 , and 3 . Mice were euthanized in the humane CO2 chamber according to our animal protocol approval and the small intestine of WT and Cmah-/- mice and brain tissues from WT , Cmah-/- and NestinCre+-Cmahtg [25] were harvested . Brains were bisected in the sagittal plane and the small intestine mucosa was exposed using a blunted pair of scissors . The mucosa was then harvested using a cell scraper . Approximately 4 g of small intestinal mucosa and 0 . 5 g of brain tissue were homogenized in 4 volumes of ice-cold water using a polytron homogenizer and a total lipid extraction from both tissues was performed as previously described [58] . Briefly , gangliosides from both tissue preparations were purified from neutral glycolipids by running the total lipid phase through a DEAE-Sephadex ( Bio-rad ) column as previously described [59] . Next , the fractions containing monosialogangliosides were eluted from the column using 0 . 01 M ammonium acetate and the di/tri/tetrasialo-gangliosides were eluted with 0 . 5M ammonium acetate . Gangliosides in each fraction were quantified using DMB derivatization and HPLC analysis as previously described [17] and the concentration of sialic acid was used to normalize for the ganglioside concentration . 96 well plates ( Cat: 9018 –Corning , USA ) were coated with 100 μL of 0 . 2 nM of gangliosides purified from mouse brain and small intestine and left to dry overnight at RT . On the next day , the wells were washed 3 times with PBS and incubated with increasing amounts of VcN diluted in PBS for 45 min . The wells were washed 3 times with PBS and blocked with PBS containing 5% bovine serum albumin ( BSA ) for 2 h at RT . The blocking solution was aspirated from the wells and 100 μL of 40 μg/mL biotin-conjugated Ctx B subunit were added for 45 min . The wells were washed 3 times with PBS and 100μL of 1:5000 dilution HRP-streptavidin was added for 45 min . The wells were washed 3 times and 140 μL of O-phenylenediamine dihydrochloride ( OPD ) solution was added for approximately 10 min until the reaction was stopped with 40 μL of 4M H2SO4 solution and read at 490 nm . Samples of jejunum from freshly harvested WT or Cmah-/- mouse small intestine , were flash frozen in optimal cutting temperature ( OCT ) and dry ice/isopentane slurry . Frozen sections were prepared at 5 μm and briefly air-dried , followed by blocking of endogenous biotin and then overlaid with 10 μg/mL of biotin-conjugated B subunit of Ctx either alone , or together with either 10 or 50 mU/mL of VcN , both diluted in PBS containing Ca2+ and Mg2+ , for 45 min at RT . Following washes in PBS , the slides were incubated for 45 min with a 1:500 dilution of Cy3-conjugated streptavidin and then the nuclei were counter stained with Hoecsht . Digital images were captured using a Keyence BZ9000 using 10x and 40x magnification . Frozen sections from the small intestines of 4 WT and 4 Cmah-/- mice were used for the fluorescence overlay assay using the following number of sections for each condition: Ctx ( WT n = 5; Cmah-/- n = 4 ) ; VcN 10 mU + Ctx ( WT and Cmah-/- n = 5 ) ; VcN 50 mU + Ctx ( WT and Cmah-/- n = 4 ) . The percentage area per picture showing binding with Ctx was calculated using the formula ( % of total fluorescent area—% Hoechst fluorescent area = % of Ctx positive area ) using ImageJ 1 . 50i software . The observer who captured the pictures was blinded for the experimental groups . To study the effect of VcN and Ctx on fluid secretion , mouse small intestinal loops were studied as previously described [10 , 30 , 31] . Briefly , WT and Cmah-/- mice were anesthetized with a single intraperitoneal injection of 100 μL of a ketamine ( 70 mg/kg ) + xylazine ( 10 mg/kg ) solution in PBS . A midline laparotomy was performed and one ligated intestinal loop ( ≈ 3 cm ) was formed in the jejunum of each animal using 5–0 USP 12 mm sutures . 100 μL of PBS containing 10 μg/mL of the active form of Ctx ( AB5 Ctx ) plus or minus 10mU of VcN were injected into the loop . The ligated loops were returned to the abdominal cavity , and the mice were kept anesthetized for 4 h with the body temperature kept at 37°C using heating pads . The animals were then euthanized in a CO2 chamber and the intestinal loops were excised and weighed to determine fluid accumulation , which was expressed as mg per cm of loop length . An electrophysiological analysis of electrogenic ion transport across mouse intestinal segments was performed using an Ussing chamber system ( Physiologic Instruments Inc . , San Diego , CA , USA ) as previously described [60] . Segments of jejunum from WT or Cmah-/- mice were mounted in Ussing chambers ( window area: 0 . 1 cm2 ) and both apical and basolateral hemichambers were filled with Ringer’s bicarbonate buffer containing ( in mM ) : 140 NaCl , 25 NaHCO3 , 2 . 4 KH2PO4 , 0 . 8 K2HPO4 , 1 . 2 CaCl2 , 0 . 8 MgCl2 ( pH 7 . 4 ) . 10 mM mannitol or 10 mM glucose were added to the apical and basolateral hemichamber buffers , respectively . The solution was continuously bubbled with 5% CO2/95% O2 and maintained at 37°C . Tissues were voltage-clamped to zero potential difference by the application of short-circuit current ( Isc ) , and a baseline was established as previously described [60] . Ctx ( 10 μg/mL ) and/or VcN ( 10 mU/mL ) were then added to the apical hemichambers and changes in Isc ( ΔIsc ) , reflective of electrogenic chloride secretion , were measured at 4 h after treatment . Tissues from both mouse strains were also treated with forskolin ( 20 μM ) and the ΔIsc was calculated at the time of peak response . To compare intestinal permeability in WT and Cmah-/- mice , both strains were orally gavaged with 44 mg of 4 kDa FITC-dextran per 100 g of body weight . The flux of FITC-dextran from the intestine to the bloodstream was determined 4 h after gavage by spectrophotofluorometry with 485 nm excitation and 528 nm emission wavelengths as previously described [61 , 62] . T84 cells were maintained in 1:1 DMEM-F12 medium supplemented with 5% newborn calf serum and 1% penicillin/streptomycin solution at 37°C and 5% CO2 . To study the ability of these cells to incorporate Neu5Gc , 5 mM free Neu5Gc ( INALCO ) was added to the medium for three days using the same culture conditions . The incorporation of sialic acid into cell surface glycans was confirmed by DMB-HPLC analysis as previously described [17] . Briefly , the cells were detached with PBS containing 0 . 5 mM EDTA and submitted to hypotonic lysis in 0 . 1X diluted in water with three cycles of freezing ( -70°C ) and thawing ( 37°C ) . The resulting lysate was centrifuged at 25 , 000 RPM for 15 min and the pellets containing the cell membrane fractions were subjected to acid hydrolysis with 0 . 1 M H2SO4 at 80°C for 1 h to release sialic acid bound to cell membranes . After hydrolysis , the samples were centrifuged at 25 , 000 RPM for 15 min and the supernatant was filtered through an Amicon 10K column and derivatized in DMB reagent for 150 min at 50°C . The incorporation of Neu5Gc was analyzed by HPLC as previously described [17] . To investigate if Neu5Gc-containing glycans would influence Ctx binding , 25 , 000 T84 cells per well were seeded in 96-well ( Cat: 353072 –Falcon , USA ) plates and exposed to 5 mM free Neu5Ac or Neu5Gc for three days as described above . On the third day , the binding of Ctx to T84 cells was performed as previously described [26] with some modifications . Cells were washed 2 times in RT PBS and further incubated with 10mU of VcN in PBS pH 6 for 30 min at RT . The wells in the same plate that were not treated with VcN were kept in PBS at pH 6 in parallel . After VcN treatment , the wells were washed twice in cold PBS and incubated with 5 μg/mL of biotinylated Ctx B-subunit ( CTB ) in PBS ( containing 1 mM CaCl2 , 1 mM MgCl2 , 0 . 2% ( w/v ) BSA , and 5 mM glucose ) for 20 min on ice . Unbound biotin-CTB was removed by washing 3 times in cold PBS and the cells were fixed with 4% paraformaldehyde for 10 min on ice and 20 min at RT . After 3 washes with PBS , cells were blocked with 1% BSA/PBS for 45 min and incubated with HRP-streptavidin ( Roche ) at 1:10 . 000 dilution in PBS . HRP activity was measured by adding ortho-phenylenediamine ( OPD ) substrate and reading at 490 nm in a microplate reader ( Molecular Devices ) . All values were corrected by light absorbance at 650 nm and normalized to total cell protein content ( bicinchoninic acid assay , Pierce BCA protein assay kit , Pierce ) . To study whether Neu5Gc influences the activity of VcN and Ctx on intracellular levels of cAMP , 1 × 105 T84 cells per well were seeded in 12-well plates ( Cat: 353043—Corning Inc , USA ) and treated for 3 days with 5 mM free Neu5Gc as described previously [63] . Cells fed with 5mM Neu5Ac were used as controls . After 3 days , fresh medium with no sialic acid was added . Cells were then treated with Ctx ( 10 μg/mL ) in the presence or absence of VcN ( 10 mU/mL ) for 6 h at 37°C and 5% CO2 . The cells were washed twice with ice-cold PBS and the lysate was used to measure cAMP according to the manufacturer’s instructions . In parallel , the percentage of cell surface Neu5Gc vs . Neu5Ac was measured by DMB-HPLC on the third day after feeding as described elsewhere [64] . For intestinal tissue colonization , V . cholerae El Tor serovar O:1 strain N16961 was grown overnight at 37°C from a -80°C frozen glycerol stock in Luria-Bertani broth ( LB ) medium . The next day , the overnight culture was centrifuged at 4000 rpm for 5 min and the pellet resuspended in 300 mM sodium bicarbonate ( NaHCO3 ) buffered solution , pH 9 . 0 . As per the streptomycin-treated model of cholera infection [47] , mice were treated with 5 mg/mL of streptomycin ( Sigma ) in their drinking water one day before infection . The concentration of streptomycin was then lowered to 0 . 25 mg/ml for the duration of the experiment . Each animal received 1×108 CFU in 100 μL of the above NaHCO3 buffer via oral gavage . At each time point , animals were humanely euthanized in CO2 . The small intestine and colon were excised from the mice and the small intestine was divided into duodenum , jejunum , and ileum . Intestinal sections were opened lengthwise and stool was gently removed by washing in sterile PBS . Intestinal tissue was then cut into small segments and homogenized . Cholera CFU were enumerated by serial dilution and plating on Thiosulfate-citrate-bile salts-sucrose agar ( TCBS ) media . For in vitro growth curves , V . cholerae El Tor serovar O:1 strain N16961 was grown overnight as per oral infection . The overnight bacteria culture was diluted the next day at 1:20 into fresh LB medium and grown to early log phase at 37°C , corresponding to an optical density at 600 nm ( OD600 nm ) of 0 . 4 . One mL of bacterial suspension was then spun down at 13 , 200 rpm for 5 min , washed and resuspended in M9 minimal media comprised of Na2HPO4 ( 6 g/L ) , KH2PO4 ( 3 g/L ) , NaCl ( 0 . 5 g/L ) , NH4Cl ( 1 g/L ) , MgSO4 ( 2 mM ) , and CaCl2 ( 0 . 1 mM ) . Bacteria ( 10 μL ) were then added to 1 mL of M9 minimal media containing 0 . 2% ( w/v ) glucose , as well as M9 minimal media with 0 . 2% glucose plus Neu5Ac or Neu5Gc ( 3 mM , 1 . 5 mM , 0 . 75 mM , or 0 . 375 mM ) . Each inoculated media ( 200 μL ) was added , in quadruplicate , to a Honeycomb plate and covered with a lid . Bacteria were then grown at 37°C for 24 h on a Bioscreen C ( Bioscreen Inc ) . Growth was monitored by OD600 nm readings at 30 minute intervals , and the growth curve was analyzed using GraphPad Prism version 7 . Statistical analyses for two-group comparisons were performed using an unpaired Student’s t test . Analyses for multiple group comparisons were done using one-way ANOVA and Bonferroni post-test . Both analyses were performed using GraphPad Prism version 7 . The data are represented as means ± standard deviation ( SD ) and P values of <0 . 05 were considered to represent statistically significant differences . | Ingestion of food or water contaminated with the bacterium Vibrio cholerae can cause fatal diarrheal disease ( cholera ) in humans , but not in other mammals exposed to the bacterium . This unusual pathogen uses a specialized enzyme V . cholerae neuraminidase ( VcN ) to cleave sugars called sialic acids and thereby remodel host intestinal glycans , generating high levels of ganglioside GM1 , the optimal ligand for the second secreted molecule ( cholera toxin , Ctx ) that causes diarrhea . Humans lost the ability to produce a kind of sialic acid called Neu5Gc during our evolution and the glycans of our small intestine are different from most other mammals . We hypothesized that V . cholerae evolved into a human-specific pathogen by adapting its virulence factor to optimally target human intestinal glycans . We found that VcN remodeling of gangliosides into the Ctx ligand was slowed by the presence of Neu5Gc . In keeping with this finding , incorporation of Neu5Gc into human cells impedes facilitation of Ctx intoxication by VcN . Finally , human-like Neu5Gc–deficient Cmah-/- mouse intestine was more susceptible to Ctx-induced intoxication , implicating a second , as yet undefined , mechanism dependent on the Neu5Gc loss in humans . Thus , the unique glycan composition of human cells and human-like Neu5Gc-deficient mice influences many steps of cholera pathogenesis . This could help explain , at least in part , Robert Koch’s failure to find any nonhuman animal model susceptible to diarrhea caused by V . cholerae . | [
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] | 2018 | Human evolutionary loss of epithelial Neu5Gc expression and species-specific susceptibility to cholera |
Dynamic modification of histone proteins plays a key role in regulating gene expression . However , histones themselves can also be dynamic , which potentially affects the stability of histone modifications . To determine the molecular mechanisms of histone turnover , we developed a parallel screening method for epigenetic regulators by analyzing chromatin states on DNA barcodes . Histone turnover was quantified by employing a genetic pulse-chase technique called RITE , which was combined with chromatin immunoprecipitation and high-throughput sequencing . In this screen , the NuB4/HAT-B complex , containing the conserved type B histone acetyltransferase Hat1 , was found to promote histone turnover . Unexpectedly , the three members of this complex could be functionally separated from each other as well as from the known interacting factor and histone chaperone Asf1 . Thus , systematic and direct interrogation of chromatin structure on DNA barcodes can lead to the discovery of genes and pathways involved in chromatin modification and dynamics .
The epigenetic landscape in the cell is dynamic and shaped by histone modifying and demodifying enzymes . In addition , histones themselves can also be dynamic; they can be moved along the DNA through the action of ATP-dependent nucleosome remodeling enzymes or can be evicted and replaced by new histones . Many histone modifying and remodeling enzymes have been identified and several factors have been found to be involved in changing nucleosome occupancy during gene activation and repression [1]–[3] . Recent studies indicate that histones can also be replaced by replication-independent mechanisms that do not involve obvious changes in nucleosome occupancy [3]–[9] . The replacement of existing chromatin-bound histones by newly synthesized histones most likely affects the stability of chromatin marks and thereby epigenetic mechanisms of gene regulation . Histone replacement or turnover requires assembly and disassembly of nucleosomes , processes that most likely involve the action of histone chaperones . Chaperones are acidic proteins that bind the highly basic soluble histone proteins and thereby prevent non-specific interactions of histones with other proteins and DNA [10]–[12] . The HAT-B complex is one of the factors that binds newly synthesized histones H3 and H4 in the cytoplasm [13] . This evolutionary conserved complex , composed of the chaperone Hat2 and the acetyltransferase Hat1 ( also known as Kat1 ) , acetylates newly synthesized soluble histone H4 on lysine 12 ( H4K12 ) and lysine 5 ( H4K5 ) [14]–[17] . Hat1 specifically acts on soluble histones because it is inactive towards chromatin-bound nucleosomal histones [13] . Hat1 is the founding ( and still only known ) member of the family of type B HATs , which are cytoplasmic and specific for free histones [13] , [14] . Whether the HAT-B complex or its acetyltransferase activity towards the H4 tail has a role in subsequent steps of histone trafficking or chromatin assembly is not well understood [14] . Cells lacking the HAT-B complex show no growth defect , indicating that acetylation of newly synthesized histones by Hat1 is not essential for replication-dependent histone deposition [14] . In addition , the acetylation marks introduced by HAT-B are removed upon deposition of new histones in chromatin [14] . However , several studies have indicated connections between Hat1 and chromatin [15] , [18]–[24] . In addition , recent biochemical studies suggest that HAT-B guides newly synthesized histones from the cytoplasm to the nucleus , where it binds to the histone chaperone Hif1 to form the NuB4 complex and hand over the histones to other chaperones such as Asf1 [25] , [26] . Asf1 is involved in the stimulation of H3K56 acetylation on soluble histones prior to their deposition [11] , [12] . By binding to the chromatin assembly factor complex ( CAF1 ) and chaperone Rtt106 , Asf1 can subsequently deliver histones for deposition at the replication fork [27]–[30] . In addition , Asf1 can bind to the HIR complex and thereby deliver histones for replication-independent histone deposition [11] , [12] , [27]–[29] , [31] , [32] . How chaperones affect histone assembly and disassembly is still largely unknown but recent studies are starting to reveal some of the underlying mechanisms [30] , [33]–[36] . We recently developed Recombination-Induced Tag Exchange ( RITE ) as an assay to measure histone turnover under physiological conditions [7] . RITE is a genetic pulse-chase method in which replacement of old by new histones can be examined by immunoblots or chromatin immunoprecipitation ( ChIP ) . To unravel the significance of the high rate of histone turnover that we and others observed in yeast [4]–[9] , [37] , the underlying mechanisms will need to be identified . However , identification of genes involved in histone turnover is not straightforward . Screening for mutants that affect epigenetic processes is usually carried out using indirect read-outs such as activity of reporter genes or developmental phenotypes . Mutants that affect histone post-translational modifications have also been identified by global proteome analysis [38] . However , it is not clear whether and how histone turnover affects gene expression , reporter genes , or developmental phenotypes . As a consequence , no indirect reporter assays are available to screen for histone turnover genes by mutant hunts . The alternative , direct assessment of chromatin changes in mutant clones is typically laborious ( involving ChIP-sequencing or ChIP-on-chip ) and is usually not suitable for genetic screening . To speed up the discovery of histone turnover pathways , we directly interrogated chromatin structure using RITE combined with methods that have been developed for parallel analysis of fitness phenotypes in yeast [39] , [40] . Using this strategy we identified mutants that either positively or negatively affected histone turnover and we provide the first in vivo evidence for a function of the NuB4 complex in histone exchange .
The collection of gene-deletion mutants in Saccharomyces cerevisiae enables the systematic analysis of gene function . A pair of unique DNA barcodes ( UpTag and DownTag ) is present in each yeast deletion strain , flanking a common selectable marker gene used to knock out the respective genes ( Figure 1 ) . Molecular counting of the barcodes by DNA microarrays or digital counting by next-generation sequencing allows parallel analysis of the relative abundance of yeast clones in pooled cultures [40] , [41] . The fitness of each yeast deletion mutant can be inferred from the changes in the relative abundance of the barcodes after exposure to the condition of interest . Using these same principles , we reasoned that in a pool of yeast deletion mutants the relative abundance of each barcode in a ChIP experiment might report on the abundance of a particular chromatin mark in that region in each mutant . Here we refer to the identification of epigenetic regulators by a barcode-ChIP-Seq approach as Epi-ID ( Figure 1 ) . To explore the possibilities of Epi-ID and to search for genes involved in histone turnover we used the genetic pulse-chase method RITE to allow the detection of old and new histone H3 proteins in yeast [7] ( Figure 1 ) . Briefly , following deletion of one histone H3 gene copy , the sole remaining H3 gene was tagged with an HA tag flanked by LoxP sites , and a downstream orphan T7 tag . Initially all H3 proteins are tagged with an HA tag . Upon induction of a hormone-dependent Cre recombinase by the addition of estradiol , the HA tag in the genome is replaced by the T7 tag and from then on all newly synthesized H3 will be T7 tagged . Histone turnover results in replacement of H3-HA by H3-T7 , which can be detected and quantified by immunoblot and ChIP ( Figure 2 ) . We note that histone turnover measurements obtained using RITE correlate well with measurements obtained using the previously used inducible pGAL-system to ectopically overexpress a tagged copy of histone H3 [42] . One of the advantages of RITE is that the tagged histone gene is expressed from its endogenous promoter , and old and newly synthesized histone H3 can be simultaneously detected and followed under any ( physiological ) condition of interest , independent of changes in nutrients to induce ectopic promoters [7] , [43] . We introduced the RITE elements into 92 clones of the yeast deletion collection using Synthetic Genetic Array ( SGA ) analysis [44] ( Figure 1 ) . The deletions in this library represented genes known or suspected to be involved in epigenetic processes and a set of non-chromatin genes ( Table S1 ) . The clones of this new library of RITE deletion mutants were first grown separately in liquid cultures , then pooled , and subsequently arrested by starvation ( Figure 3A and Figure S1 ) . Recombination was induced to switch the epitope tags and chromatin samples were taken before and one and three days after induction of the tag switch . We previously found that yeast cells arrested by starvation ( which we here refer to as G0 ) undergo replication-independent turnover of chromatin-bound histones [7] . In addition , we found a substantial amount of new bulk histone synthesis during three days of starvation by immunoblot analysis and ChIP ( Figure 2C-2D and Figure S2 ) . Arresting cells by starvation allows for efficient switching of the epitope tags by the induced Cre recombinase . Moreover , replication-dependent histone deposition and cell-cycle or growth rate differences between different mutants are eliminated . To measure histone turnover ChIP was performed on old ( H3-HA ) and new ( H3-T7 ) histone H3 . The barcode regions in the bound DNA were amplified using common primer sequences and adapters to allow parallel sequencing on the Illumina platform . Four base pair index tags were introduced in each sample to allow multiplex analysis ( Figure S1 ) . After digital barcode counting ( see Materials and Methods ) the relative ratio of new/old H3 was calculated as a value for replication-independent histone turnover in the pool of gene deletion mutants for each UpTag and DownTag barcode and for each of two time points after induction of the tag-switch ( Figure 1 , Figure 3A ) . We performed three analyses to test the validity of the concept of Epi-ID . First , we verified that the independent measurements of the two time points ( day 1 and 3 ) showed similar trends ( Figure 3B–3C ) . Second , we compared UpTags with DownTags ( U and D ) . The overall correlation between UpTag and DownTag barcodes suggests that position effects are not a major confounder in this assay ( Figure 3D–3E; but also see Discussion ) . The few clones that did not correlate well between different time points or between UpTag and DownTag barcodes were eliminated from further analysis ( see below ) . Third , the barcodes of the SIR3 and SIR4 deletion mutants ( which do not mate and cannot be used for genetic crosses such as SGA ) , were integrated in the genome of strains constitutively expressing only H3–HA or only H3-T7 . These clones were combined with the RITE library pool as internal negative and positive controls , respectively . The two control strains could be separated from each other at all three time points , both at the UpTag and DownTag barcodes . They also provided an indication of the dynamic range of the turnover measurements in this assay . For further analysis , clones for which severe growth defects were observed after the tag switch ( and after release of the arrest by re-feeding ) were excluded to eliminate mutants in which the new H3-T7 tagged histone may not be fully functional or causes tag-specific rather than true turnover effects ( see Materials and Methods ) . Only those clones were included that showed low variation between the two time points and between UpTag and DownTag . The two control strains are shown as a reference ( Figure 3F ) . Of the resulting set of deletion mutants that passed the selection criteria , two clones with the lowest and two clones with the highest turnover signal were selected to examine whether the mutants affected turnover at loci independent of the barcode sequences . Each clone was grown individually and arrested by starvation . After induction of the epitope tag switch histone turnover was examined by ChIP-qPCR at four independent loci unrelated to the barcoded region analyzed in the parallel screen ( promoter regions of IMD1 , ADH2 , HHT2 , and ADH1 ) ( Figure 3G ) . The changes in histone turnover at these four loci was similar to the changes measured at the barcodes , confirming that the chromatin changes of the barcodes reflected overall changes in the genome ( Figure 3G ) . Nhp10 and Gis1 were found to be negative regulators of histone turnover . Hat1 positively regulated histone turnover . For every turnover experiment , the efficiency of the tag switch ( percent of cells that had undergone a Cre-mediated recombination event ) was determined ( Table S2 ) . By a colony plating assay we noticed that cells lacking HAP2 showed very poor Cre-mediated recombination , which was most likely the cause of the low ratio of new/old H3 in this clone ( Figure S3 ) . This clone was excluded from further analysis . Given the high conservation of Hat1 and its known activity towards new histones , we focused our further studies on Hat1 . The histone acetyltransferase Hat1 together with the histone chaperone Hat2 forms the evolutionary conserved HAT-B complex that acetylates soluble histones . The functional consequences of Hat1's activity are not well understood . Hat1 plays a role in gene silencing [19] and DNA repair [14] , suggesting that it affects chromatin structure . However , many of these phenotypes require additional mutations in the N-terminal tail of histone H3 and how chromatin is affected by Hat1 is not known . Our findings provide direct evidence that the Hat1 protein is important for efficient histone turnover in vivo ( Figure 3F-3G ) . We first examined the role of Hat1's enzymatic activity . A strain containing a catalytically compromised ( but not completely inactive ) Hat1 protein ( HAT1-E255Q ) [19] showed a decrease in histone turnover similar to a hat1Δ strain ( Figure 4A ) , suggesting that the acetyltransferase activity is important for efficient histone turnover . Hat1's primary known targets are lysines 5 and 12 of histone H4 ( H4K5 and H4K12 ) . Mutating the target lysines to arginine ( H4K5 , 12R ) did not substantially affect histone H3 turnover , whereas alanine or glutamine mutants ( H4K5 , 12A and H4K5 , 12Q ) showed enhanced turnover of histone H3 at most loci tested ( Figure 4B and Figure S4 ) . Arginine and lysine both contain a long hydrophobic side chain and a positive charge . Therefore , arginine might mimic the constitutively unacetylated ( positively charged ) state of lysine . Our results suggest that loss of the positive charge of H4K5 , 12 by acetylation is not sufficient to explain the role of Hat1 in histone turnover . However , changing the positively charged residues to neutral amino acids enhanced turnover . These results suggest that H4K5/K12 play a role in histone turnover but that loss of acetylation of these sites is not sufficient to cause a histone turnover defect . Several lines of evidence suggest that H4K5 and H4K12 have evolutionary conserved roles in replication-dependent chromatin assembly [13] , [26] , [45]–[49] or nuclear import of histone H4 [50] , [51] . However , in yeast , mutation of these lysines does not lead to growth defects and no changes in global chromatin organization have been observed [51]–[53] . To investigate the role of H4K5 , 12 in replicating cells we performed the tag switch in starved cells , released the switched population into fresh media , and then measured histone turnover in cells arrested in G2/M after one round of replication ( monitored by FACS analysis ) . Cells expressing H4K5 , 12Q showed increased turnover at two of the three promoter regions analyzed , whereas cells expressing H4K5 , 12R showed decreased histone turnover ( Figure 4C ) . In contrast , in two long coding sequences , the two H4K5 , 12 mutants affected turnover in a similar manner . In both H4K5 , 12 mutants turnover at the 3′ end was reduced relative to turnover at 5′ regions ( Figure 4C ) . This is consistent with results we obtained previously with the H4K5 , 12R mutant in replicating cells and may indicate a role of these residues in 3′ to 5′ retrograde movement of old histones by passage of RNA Polymerase II [54] . Hat1 in yeast and other organisms was initially identified as a cytoplasmic histone acetyltransferase [13] , [14] . More recently , Hat1 was also found to be ( predominantly ) localized in the nucleus [14] , [19] , [26] , [49] , [55] . To investigate whether the role of Hat1 in histone turnover is mediated by a cytoplasmic or nuclear activity , we next examined the consequences of fusion of Hat1 to a nuclear export signal ( Hat1-NES ) , which excludes Hat1 from the nucleus [19] . The NES fusion resulted in a modest decrease of histone turnover ( Figure 5A ) , indicating that the cytoplasmic activity of Hat1 is not sufficient for Hat1's function in histone turnover and that at least part of Hat1's effect on histone turnover is mediated by a nuclear activity . To further investigate whether Hat1's role in histone turnover is indeed linked to its nuclear location , we analyzed the nuclear binding partners of Hat1 . In the nucleus the members of the HAT-B complex , Hat1 and Hat2 , interact with Hif1 ( Hat1 Interacting Factor-1 ) and form the nuclear NuB4 complex [49] , [55] . Hif1 belongs to the evolutionary conserved family of SHNi-TPR family of histone chaperones , which also includes Hs_NASP , Xl_N1/N2 and Sp_Sim3 [26] , [56] . To examine the role of the NuB4 complex in histone turnover , we deleted Hif1 and compared this to independent deletions of Hat1 and Hat2 . In this strain background , deletion of Hat1 by homologous recombination ( which was confirmed by standard PCR analysis and by microarray analysis [e . g . see Figure S9] ) did not affect histone turnover as much as in the mutant derived from the genetic cross with the yeast deletion collection or the catalytic mutant . The cause of this difference is unknown , but may involve differences in the genetic strain backgrounds . . However , cells lacking Hat2 or Hif1 showed reduced histone turnover , supporting the idea that the nuclear NuB4 complex plays a role in histone turnover ( Figure 5B ) . To genetically test whether Hif1 and Hat-B affect turnover by means of a common pathway or protein complex ( NuB4 ) , we generated double mutant strains for epistasis analysis . Previous studies have shown that Hat2 is a central component of the NuB4 complex; deletion of HAT2 disrupts the nuclear localization of Hat1 and interactions between Hif1 , Hat1 , and histones [19] , [49] , [55] . Unexpectedly , deleting either HAT1 or HAT2 in combination with HIF1 resulted in a more severe decrease in histone turnover than in either one of the single mutants , suggesting that Hif1 and Hat1/Hat2 act at least in part by independent mechanisms ( Figure 5C ) . Histone turnover is strongly correlated with and induced by transcription by RNA Polymerase II [4]–[7] ( and Figure S2 ) . To investigate whether the observed G0 histone turnover defects in mutants of the NuB4 complex were caused by transcription defects we performed expression profiling and measured RNA Polymerase II occupancy . In mutant cells arrested in G0 , no significant changes were found in the expression of the target genes analyzed in the turnover experiments when compared to wild-type cells ( Figure 6A–6B ) . In addition , no significant changes ( fold change >1 . 7 , p<0 . 01 ) were found in the expression of the single H3 and H4 genes , with the exception of the H4K5 , 12 mutants , which showed a slight upregulation of the histone H3 gene . Thus , in G0 cells , reduced histone H3 turnover was not caused by reduced expression of ( new ) histones or by reduced expression of the loci at which histone turnover was measured . To compare transcriptional changes in the NuB4 mutants to other mutants , we also performed microarray analyses of NuB4 mutants made in the genetic background of the yeast deletion collection . These mutants were grown under standard mid-log conditions [57] , [58] . We note that under these conditions the members of the NuB4 complex play the same role in histone turnover as in G0 ( Figure S5 ) . In general , no significant transcriptional changes were found in any of the NuB4 mutants compared to WT ( fold change >1 . 7 , p<0 . 01 ) in mid-log cultures . However , when examined in more detail , the expression profiles of mutants that contain a deletion of HIF1 and to a lesser extent HAT2 , showed upregulation of the genes encoding histone H3 and H4 in mid-log cultures ( Figure 6C ) . Regulation of histone gene expression seems to be a common property of nucleosome assembly factors [27] , [59] , providing further support for a link between the NuB4 complex and histone turnover . Biochemical studies suggest that the NuB4 complex interacts with Asf1 , which led to the suggestion that NuB4 might hand over newly synthesized histones to Asf1 for subsequent transfer to nucleosome assembly factors [25] , [26] , [60] . However , the histone genes clearly respond differently to deletion of ASF1 than to deletion of genes encoding members of the NuB4 complex [27] , [59] ( Figure 6C ) , suggesting a more complex relationship . Unfortunately , we could not test the genetic relationship between Asf1 and Hat1 because deletion of Asf1 in the strain background used for the RITE assay is lethal , similar to what has been reported previously [61] . To investigate the connection between Hat1 and Asf1 by alternative means , we used RITE as a genetic pulse-chase tool to examine the nature of the histone molecules bound to each protein . Rather than indirectly inferring the origin of the histones ( new or chromatin derived ) from the pattern of post-translational modifications , the epitope tag-switch pulse-chase allows for a direct distinction between resident and newly synthesized histones . In cells that had recently undergone a tag switch on H3 and therefore contained a mix of new and old histone H3 , affinity purified Hat1 bound both new and old histones with a preference for new histones ( Figure 7A–7B and Figure S6 ) . Asf1 also bound both new and old histones but without a preference for new histones . ( Figure 7A–7B and Figure S6 ) . The binding of Hat1 and Asf1 to a different subset of the pool of soluble histones suggests that they affect different steps of chromatin assembly and disassembly .
Using RITE as a biochemical-genetic pulse-chase tool , we previously observed rapid exchange of histone H3 in chromatin in yeast cells outside S-phase [7] . Similar results have been reported using an inducible pGAL-system to overexpress an ectopic tagged histone H3 copy [4]–[6] , [37] , [62] , [63] . By using RITE , in contrast to the pGAL system , the tagged old and new histone H3 species are expressed by the endogenous H3 promoter from the endogenous chromosomal location [43] . Therefore , the high levels of histone exchange observed with RITE were not caused by misregulation of histone H3 expression . Indeed , qRT-PCR and microarray analyses showed that RITE strains containing tagged H3 express very similar H3 mRNA levels as wild-type cells containing untagged H3 at different phases of the cell cycle [7] ( Figure S7 ) . Interestingly , although histone mRNAs are cell cycle regulated and peak in S-phase when the demand for new histones is highest [64] , [65] , histone H3 mRNA expression is still relatively high outside S-phase , providing an explanation for the abundant synthesis of new histones outside S-phase [7] . To investigate the biological function of histone turnover and its consequences for chromatin structure and function , we developed the Epi-ID barcode screen for chromatin regulators and combined it with RITE . In this screen we found that Hat1 and subsequently also the other members of the NuB4 complex positively regulate histone turnover . To our knowledge , our data provide the first evidence that a Type B histone acetyltransferase complex regulates histone assembly in vivo . Hat1 was the first histone acetyltransferase identified [13] , [66] . It is part of a multi-subunit complex that interacts with histone chaperones and acetylates free histones but is inactive towards nucleosomal histones [14] , [26] . The biological significance of these biochemical activities of the Hat1 complexes remained elusive [14] although in genetic tests Hat1 was found to play a role in gene silencing and DNA damage response [14] . However , manifestation of these phenotypes required additional mutations in the N-terminal tail of histone H3 and whether these chromatin-related phenotypes are related to histone deposition defects remained unknown . The known and conserved substrates of HAT-B/NuB4 are lysines 5 and 12 of histone H4 [14] . Mutation of these residues has revealed functions in histone H4 nuclear import and chromatin assembly [48] , [50] , [51] . However , H4K5 , 12 mutants generally show no major growth phenotypes or global changes in chromatin organization [24] , [48] , [52] , [53] . Here we found a positive effect of H4K5 , 12A and H4K5 , 12Q mutants on histone turnover in promoters , suggesting that NuB4 may exert its turnover function via H4K5/K12 acetylation . However , H4K5 , 12R , mimicking the hypo-acetylated state of these lysines , did not cause a turnover defect ( Figure 4 ) . One possible explanation of these results is that NuB4 has additional substrates that contribute to its role in histone turnover [67] . We do not know whether other substrate lysines on histones or perhaps non-histone proteins are also involved and play roles redundant with the acetylated histone H4 tail . The nuclear function for HAT-B in histone turnover ( Figure 5 ) indicates that HAT-B's role in histone metabolism may be more complex than previously anticipated and extends beyond the acetylation of newly synthesized histones . This is in line with observations that Hat1 can be recruited to chromatin at origins of replication and DNA double strand breaks [20] , [21] and with the role of members of the NuB4 complex in depositing histones following repair of a DNA double strand break [18] . Unexpectedly , our studies revealed that Hat1 and Hat2 act in parallel with Hif1 , and that Hat1 and Asf1 bind a different subset of the soluble histone pool . In previous studies Hat1/Hat2 , Hif1 , and Asf1 have been shown to bind to each other [26] , which led to the suggestion that Asf1 acts downstream of Hat1/Hat2/Hif1 and passes on new histones acetylated on H4K5/K12 ( and H3K56 ) to chromatin assembly factors CAF-I , HIR , and Rtt106 [11] , [12] . Our results suggest that Hat1/Hat2 , Hif1 and Asf1 act , at least in part , via distinct pathways of chromatin assembly and/or disassembly ( Figure 6C and Figure 7 ) . The equal binding of Asf1 to new and old histones suggests that Asf1 may be involved in depositing as well as escorting histones evicted from chromatin ( Figure 7C ) , which is in concordance with the finding that H3K56 acetylation ( mediated by Rtt109/Asf1 ) is a mark of new histones , yet is important for histone eviction and nucleosome destabilization [11] , [33] . Indeed , histone chaperones may not exclusively function in chromatin assembly [68] . For example Nap1 , which can escort H3/H4 and H2A/H2B and assemble histone octamers into nucleosomes , but may orchestrate this by promoting nucleosome disassembly [33] . Another example is CAF1 , which is involved in replication-coupled assembly of new histones into chromatin , yet histone H3 bound to this complex ( or to Rtt106 or Asf1 ) contains methylated H3K79 [30] , which is a mark of chromatin-bound histones [69] , [70] . What are the functional consequences of altering histone turnover ? Histone turnover might affect several aspects of the epigenome , such as nucleosome occupancy , DNA accessibility , or dynamics of histone modifications . No changes in growth or cell cycle progression were observed for single , double , or triple hat1Δ , hat2Δ , hif1Δ mutants ( Figure S8 ) and no significant transcriptional changes were observed ( see Figure 6 and Material and Methods ) . Apparently , slowing down turnover of histone H3 by loss of the NuB4 complex has no profound consequences under these conditions . Deletion of HAT2 or HIF1 resulted in a moderate increase in expression of the genes encoding histone H3 and H4 in mid-log cultures ( Figure 6C ) . We expect that this may be a response to the histone turnover defects caused by deletion of Hat2 and Hif1 , since deletion of Hat1 , which overall has a lower impact on histone turnover , did not affect histone gene expression . It is possible that the phenotypes of the hat1Δ strain are relatively weak because of compensation of Hat1's activity by other HATs , such as Gcn5 , which acetylates newly synthesized histone H3 [71] . In our microarray analyses , we did not observe significant upregulation of mRNA levels of other ( putative ) HATs in G0 cultures ( Figure S9 ) . Overall , our results indicate that loss of NuB4 function alone has no major consequences for global chromatin organization . What is the function of histone turnover ? Histone turnover leads to turnover of histone modifications and can thereby affect the pattern as well as dynamics of the epigenome . When a chromatin state is controlled by two opposing activities ( e . g . modification and demodification by turnover ) this could lead to a more rapid establishment a new equilibrium after perturbation of the epigenome , such as during DNA replication or after exposure to stress ( e . g . see [72] ) . Based on models proposed for histone acetylation one could also envision that dynamic turnover ( cycles of modification and demodification ) rather than the steady state may be relevant for chromatin function [73] . Alternatively , histone turnover could counteract the accumulation of histone modifications that are less susceptible to demodification . For example , methylation of histone H3K79 , which accumulates in a non-processive manner on aging histones [72] is enriched in genomic regions that show low histone turnover and retain old histone H3 molecules , suggesting that histone inheritance and dynamics help shape the epigenome [54] . The identification of additional mutants in future screens will help to further deconstruct the pathways of histone turnover and to discover their biological significance . In the Epi-ID screen we also identified Gis1 and Nhp10 as negative regulators of histone turnover . Gis1 is a zinc-finger transcription factor involved in regulation of stress genes [74] and contains a Jumonji domain , which has been associated with histone demethylase activity [75] . Gis1 has also been reported to bind to several factors involved in DNA metabolism [76] . It will be interesting to test whether any of these Gis1-binding proteins or its putative demethylase activity is involved in this novel function of Gis1 . Nhp10 is a non-essential subunit of the essential INO80 chromatin remodeling complex that can move or mobilize nucleosomes . Two recent studies suggest a role for INO80 in redeposition of histones during induced transcription [77] , [78] . That Nhp10 slows down histone turnover provides further support for the idea that the INO80 complex can help to preserve the chromatin architecture during transcription . In an Epi-ID screen using 1536 chromosome biology mutants in which the old and new tags on histone H3 were swapped ( old-T7 and new-HA ) , NHP10 and GIS1 mutants also showed more histone turnover ( data not shown ) , indicating that the phenotypes observed were not caused by tag-specific effects and that Epi-ID can be scaled up . The application of Epi-ID is not restricted to histone turnover . In fact , in screens for other epigenetic marks such as histone modifications or nucleosome occupancy Epi-ID , can be applied without the elaborate genetic crosses and genetic switches that are required for screens based on the RITE pulse chase assay . Future applications in yeast may benefit from other barcoded mutant collections that are being developed [79]–[81] . Although our study suggests that position effects of the barcoded marker are not major confounders in Epi-ID and can be ( at least in part ) excluded by comparing UpTag with DownTag barcodes , DNA barcodes at a common genetic locus separated from the gene deletion would be preferable for epigenetic screens . The recently developed Yeast Barcoders Library represents such a collection in which barcoded markers are integrated at the common HO locus thereby providing opportunities to further expand and improve the application of Epi-ID in yeast [82] . Finally , the basic principles of this approach should also be applicable to barcoded mutant libraries in other organisms , such as barcoded episomes , or transposon or virus insertion libraries .
Yeast strains used in this study are listed in Table S3 . Yeast media were described previously [7] . The pilot set of mutants ( see Table S1 ) was manually made from the MATa haploid gene knockout library ( Open Biosystems ) . H3-RITE ( strain NKI4114 ) was crossed in duplicate with 92 mutants by Synthetic Genetic Array analysis [44] with the following modifications . After mating , diploids were selected and kept on Hygromycin , G418 and CloNat triple selection on rich media for one night . After 13 days on sporulation media a series of selections followed to select for the proper MAT haploids: twice on haploid MATa selection ( YC-His+Can+SAEC ) , twice on triple resistance selection ( YC-His+Can+SAEC+MSG+ Hygromycin , G418 and CloNat ) , and then twice on YC-His-Leu to select for H3-RITE strains in which the second , untagged , copy of H3 was deleted by insertion of LEU2 . NKI2178 and NKI4179 are derivatives of BY4733 . Plasmid pTW087 , which was used to make strain NKI2178 , was made by inserting a 6xHis tag behind the HA tag into pFvL118 [7] by PCR mutagenesis . Plasmid pTW088 , which was used to make strain NKI4197 , was made by replacing the HA tag in pTW081 [7] by a HA-6xHIS tag generated by PCR amplification from pTW087 . NKI4128 was derived from a cross between Y7092 and NKI4004 [7] , [44] . NKI8013 and NKI4140 were derived from NKI4179 and NKI4128 after elimination of the first tag and HphMX marker by induction of recombination . BAR1 was deleted using pMPY-ZAP . NKI2176 was derived from BY4733 using reagents described previously [7] . NKI2215 and NKI2216 were derived from NKI2176 by targeting the RITE cassettes from pFvL118 and pTW081 to the HHT2 locus [7] . NKI2300 and NKI2301 were derived from NKI2215 and NKI2216 , respectively , after elimination of the first tag and HphMX marker by induction of recombination . The tag switch assay was performed as described previously [7] with a few adjustments . Briefly , all strains were grown in 600 µl YPD containing Hygromycin ( 200 µg/ml , Invitrogen ) in 96-well format for three nights at 30°C . Cells were then pooled in 50 ml of saturated media without Hygromycin containing 1 µM β-estradiol ( E-8875 , Sigma-Aldrich ) . Approximately 1x109 cells were fixed with 1% formaldehyde for 15 minutes before addition of β-estradiol ( t = 0 ) , after 16 hours ( t = 1 ) and after 3 additional days ( t = 3 ) for chromatin immunoprecipitation . In the follow-up analysis of candidate turnover mutants , we identified several possible confounders in our specific turnover screen . In certain mutants low turnover measurements were caused by lack of the Cre-recombinase mediated tag-switch . These mutants were excluded from the follow-up studies . Some mutants showed severe loss of viability after the tag-switch and when released into fresh media . These clones were also excluded from the follow-up studies to avoid clones in which the new H3-T7 tagged histone may not be fully functional and possibly causes tag-specific rather than a physiological turnover effects . ChIP was performed as described previously [7] . One tenth of each sample was taken as input . After DNA isolation all samples were amplified using different SeqiXU1 primers in combination with P7U2 for the amplification of the UpTag and SeqiXD1 primers with P7D2 for amplification of the DownTag ( primers are listed in Table S4 ) . PCR amplification was conducted in 50 µl reactions using Phusion DNA polymerase ( Finnzymes ) with the following conditions: 10 cycles of 98°C/15 s , 56°C/15 s , 72°C/20 s; 20 cycles of 98°C/15 s , 72°C/15 s , 72°C/20 s . The amplicons of different conditions were pooled per tag , size separated on a 2% gel and the correct sized amplicons were excised and extracted using a Qiagen gel purification column . In a subsequent PCR reaction equal amounts of DNA of the UpTag and DownTag were amplified with primers P5seq and either P7U2 or P7D2 to attach the adapter fragments necessary for cluster formation and sequencing on the Illumina genome analyzer . PCR amplification was conducted in 50 µl reactions using Phusion® DNA polymerase with the following conditions: 10 cycles of 98°C/15 s , 56°C/15 s , 72°C/20 s; 20 cycles of 98°C/15 s , 72°C/25 s . The indexed barcode libraries were analyzed on an Illumina GAII genome analyzer and processed as described below . The indexed barcode libraries were analyzed on an Illumina GAII . Sequence reads were expected to have the following composition: 4 bp index ( i ) , 18 bp common UpTag ( U1 ) or 17 bp common DownTag ( D1 ) primer sequence , up to 20 bp unique UpTag or DownTag barcode sequence . A database of expected sequence reads was generated by combining the barcode sequences originally designed ( http://www-sequence . stanford . edu/group/yeast_deletion_project/deletions3 . html ) with corrected sequences based on re-sequencing of the barcodes of the yeast diploid heterozygous deletion collection [40] , [83] . Multiplex indexed barcodes were identified at position 1 to 6 allowing no mismatches . Barcodes were identified starting at position 22 , 21 , or 23 , respectively , initially allowing no mismatches over a length of 11 nt . Unidentified reads were further analyzed in a second round by FASTA using the optimal alignment of gene tags . FASTA alignments were only considered with a minimal alignment length of 10 bases and a minimal identity of 90% . Only alignments that start within 2 bases from position 22 were allowed and alignments were not allowed to stop more than 5 bases from the end of the barcode . A set of unused barcodes [39] , [41] was used to verify that allowing mismatches did not lead to a high false discovery rate and to determine cut-offs for P-values ( see below ) . Out of a total number of 7446311 reads , 6249225 could be assigned to an indexed barcode amplicon . The mapped sequence reads were binned in UpTag and DownTag barcode fractions , further binned in sample fractions using the 4 bp indexes , and then the relative abundance of each barcode within each specific bin was determined using reads per million counts for each bin . Based on the behavior of the unused barcodes , to avoid false positive assignments clones with outlying up / down ratio counts ( P-value <0 . 01 ) in any of the indexed samples were excluded from further analysis . Histone turnover was determined by calculating the ratio of T7 ChIP over HA ChIP for t = 1 and t = 3 days ( t = 1 , t = 3 ) and for the UpTag and DownTag barcodes . Only clones with a low variation between these four samples ( SD <0 . 17; and thereby only clones for which both the UpTag and DownTag barcode were identified ) were included for further analysis . Cut offs for variation were set such that all false positive identifications of the unused barcode set were excluded . Of the 92 clones in the screen , 53 were included in the final dataset . Drop-outs were caused by the genetic crossing or by the stringent selection criteria . Strains were grown individually to saturation in 50 ml of YPD; ChIP was performed only on samples after three days of saturation . ChIP DNA was quantified in real-time PCR using the SYBR Green PCR Master Mix ( Applied Biosystems ) and the ABI PRISM 7500 as described previously . An input sample was used to make a standard curve , which was then used to calculate the IP samples , all performed in the 7500 fast system software . As a measurement for turnover , the amount DNA of the T7-IP was divided over the HA-IP . The antibodies used for ChIP and immunoblots are HA ( 12CA5 ) , T7 ( A190-117A , Bethyl or 69522-3 , Novagen ) , H3 C-terminus [7] , RITE-spacer+LoxP [7] , RNA PolII/Rpb1 ( 8WG16 ) . Primers used for qPCR are listed in Table S5 . The equivalent of 1x109 cells was washed with cold TBS , resuspended in 1ml cold TBS with a protease inhibitor cocktail . All steps were performed cold at 4°C unless otherwise stated . Cells were briefly spun and the pellet was frozen at −80°C . The pellet was dissolved in 400 µl lysis buffer ( 25 mM Hepes pH 7 . 9 , 50 mM NaCl , 0 . 1% NP-40 , 1 mM EDTA , 10% glycerol ) containing a protease inhibitor cocktail . Cells were lysed by the addition of 400 µl glass beads and vortexing for 15 min on a multivortex . The total lysate was spun at maximum speed for 5 min , the soluble fraction was transferred to a new tube and 1 ml of lysis buffer was added . The lysate was then spun for 5 min 14K , transferred to a new tube , then spun for 15 min 14K and again transferred to a new tube . Of this fraction 50 µl was used as input , the rest was incubated with 30 µl IgG beads ( Invitrogen ) for 2 hrs . The beads were washed three times with cold lysis buffer for 5 min and once with TEV buffer ( 50 mM Tris pH 8 , 0 . 5 mM EDTA , 50 mM NaCl , and 1 mM DTT ) . The beads were resuspended in 100 µl TEV buffer to which 175 µg recombinant TEV protease is added and kept overnight . The soluble fraction contains the immunoprecipitated fraction and was analyzed by quantitative immunoblotting . Lysates were separated on a 16% polyacrylamide gel and blotted onto 0 . 45 µm nitrocellulose membrane . Membranes were blocked with 2% Nutrilon ( Nutricia ) in PBS . Primary antibody incubations were performed overnight in Tris-buffered saline-Tween with 2% Nutrilon , anti-HA ( mouse 12CA5 ) , anti-T7 ( Novagen , 1∶1000 ) or a polyclonal antibody obtained against the LoxP peptide ( 1∶2500 ) [7] . Secondary antibody incubations were performed for 45 minutes using LI-COR Odyssey IRDye 800CW ( 1∶12 . 000 ) . Immunoblots were subsequently scanned on a LI-COR Odyssey IR Imager ( Biosciences ) using the 800 channel . Signal intensities were determined using Odyssey LI-COR software version 3 . 0 . To monitor cell cycle progression and cell cycle arrests the DNA content of the cells was measured by flow cytometry as described previously [7] , using SYTOX Green and a 530/30 filter ( Becton-Dickinson ) . Analysis was performed using FCSexpress2 . Each mutant strain was profiled four times from two independently inoculated cultures and harvested in early mid-log phase in synthetic complete medium with 2% glucose or harvested in starvation conditions in rich media as described above for the turnover experiments . Sets of mutants were grown alongside corresponding WT cultures and processed in parallel . Dual-channel 70-mer oligonucleotide arrays were employed with a common reference WT RNA . All steps after RNA isolation were automated using robotic liquid handlers . These procedures were first optimized for accuracy ( correct FC ) and precision ( reproducible result ) , using spiked-in RNA for calibration [84] . After quality control , normalization , and dye-bias correction [85] , statistical analysis for mid-log cultures was performed for each mutant versus the collection of 200 WT cultures as described by Lenstra et al [57] . The reported FC is an average of the four replicate mutant profiles versus the average of all WTs . HAT1 , HAT2 , and HIF1 single , double , and triple mutants in the BY4742 background were not different from wild type ( less than three genes changed p<0 . 01 , FC >1 . 7 after removal of WT variable genes ) . Mutants in G0 were compared to replicates of the corresponding wild-type RITE strain . Due to variability under conditions of starvation [86] we did not perform genome-wide statistical analyses of expression changes in G0 cultures . Microarray data have been deposited in ArrayExpress under accession numbers E-TABM-1175 ( mutants ) and E-TABM-773/E-TABM-984 ( 200 WT replicates ) , as well as in GEO under accession number GSE30168 . | Packaging of eukaryotic genomes by the histone proteins influences many processes that use the DNA , such as transcription , repair , and replication . One well-known mechanism of regulation of histone function is the covalent modification of histone proteins . Replacement of modified histones by new histones has recently emerged as an additional layer of regulation ( hereafter referred to as histone turnover ) . Although histone replacement can affect substantial parts of eukaryotic genomes , the mechanisms that control histone exchange are largely unknown . Here , we report a screening method for epigenetic regulators that we applied to search for histone exchange factors . The screening method is based on our finding that global chromatin changes in mutant cells can be inferred from chromatin states on short DNA barcodes . By analyzing the chromatin status of DNA barcodes of many yeast mutants in parallel , we identified positive and negative regulators of histone exchange . In particular , we find that the HAT-B complex promotes histone turnover . HAT-B is known to acetylate the tails of newly synthesized histones , but its role in chromatin assembly has been unclear . Hif1 , the nuclear binding partner of HAT-B in the NuB4 complex , also promotes histone exchange but by non-overlapping mechanisms . These results provide a new perspective on pathways of histone exchange . | [
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] | 2011 | A Barcode Screen for Epigenetic Regulators Reveals a Role for the NuB4/HAT-B Histone Acetyltransferase Complex in Histone Turnover |
Cooperativity is one of the most important properties of molecular interactions in biological systems . It is the ability to influence ligand binding at one site of a macromolecule by previous ligand binding at another site of the same molecule . As a consequence , the affinity of the macromolecule for the ligand is either decreased ( negative cooperativity ) or increased ( positive cooperativity ) . Over the last 100 years , O2 binding to hemoglobin has served as the paradigm for cooperative ligand binding and allosteric modulation , and four practical models were developed to quantitatively describe the mechanism: the Hill , the Adair-Klotz , the Monod-Wyman-Changeux , and the Koshland-Némethy-Filmer models . The predictions of these models apply under static conditions when the binding reactions are at equilibrium . However , in a physiological setting , e . g . , inside a cell , the timing and dynamics of the binding events are essential . Hence , it is necessary to determine the dynamic properties of cooperative binding to fully understand the physiological implications of cooperativity . To date , the Monod-Wyman-Changeux model was applied to determine the kinetics of cooperative binding to biologically active molecules . In this model , cooperativity is established by postulating two allosteric isoforms with different binding properties . However , these studies were limited to special cases , where transition rates between allosteric isoforms are much slower than the binding rates or where binding and unbinding rates could be measured independently . For all other cases , the complex mathematical description precludes straightforward interpretations . Here , we report on calculating for the first time the fast dynamics of a cooperative binding process , the binding of Ca2+ to calretinin . Calretinin is a Ca2+-binding protein with four cooperative binding sites and one independent binding site . The Ca2+ binding to calretinin was assessed by measuring the decay of free Ca2+ using a fast fluorescent Ca2+ indicator following rapid ( <50-μs rise time ) Ca2+ concentration jumps induced by uncaging Ca2+ from DM-nitrophen . To unravel the kinetics of cooperative binding , we devised several approaches based on known cooperative binding models , resulting in a novel and relatively simple model . This model revealed unexpected and highly specific nonlinear properties of cellular Ca2+ regulation by calretinin . The association rate of Ca2+ with calretinin speeds up as the free Ca2+ concentration increases from cytoplasmic resting conditions ( ∼100 nM ) to approximately 1 μM . As a consequence , the Ca2+ buffering speed of calretinin highly depends on the prevailing Ca2+ concentration prior to a perturbation . In addition to providing a novel mode of action of cellular Ca2+ buffering , our model extends the analysis of cooperativity beyond the static steady-state condition , providing a powerful tool for the investigation of the dynamics and functional significance of cooperative binding in general .
In all eukaryotic cells , Ca2+ signals play a crucial role in the regulation of many cellular processes , including gene expression , cytoskeleton dynamics , cell cycle , cell death , neurotransmission , and signal transduction . To achieve its role as messenger , the intracellular Ca2+ concentration ( [Ca2+] ) is very tightly regulated in time , space , and magnitude . The spatiotemporal characteristics of short-lived and often highly localized changes in intracellular [Ca2+] result from a complex interplay between Ca2+ influx/extrusion systems , mobile/stationary Ca2+-binding proteins ( CaBPs ) , and intracellular sequestering mechanisms . Understanding the kinetics of cellular Ca2+ transients and its influence on Ca2+-regulated processes requires a precise knowledge of the Ca2+ sensitivities and binding properties of all the components involved , including the binding dynamics to buffering and signaling CaBPs . However , uncertainties in current models studying intracellular Ca2+ signaling arise mostly from the lack of accurate data on the binding properties of specific molecules involved in Ca2+ handling , considerably limiting the value of such modeling [1] . An important step towards the goal of precisely describing intracellular Ca2+ transients was the study by Nagerl et al . [2] , in which the relevant parameters ( affinities and on- and off-rates of Ca2+ binding ) for the CaBP calbindin D-28k ( CB ) were determined in vitro by flash photolysis of caged Ca2+ . Cooperative binding of Ca2+ , known to play a significant role in multisite CaBPs such as calmodulin [3] and calretinin ( CR ) [4] , has never been directly determined in rapid kinetic experiments , but only inferred from steady-state conditions using Hill [5] and Adair-Klotz models [6 , 7] . Cooperativity first evidenced by oxygen binding to hemoglobin [8] is considered one of the most imperative functional properties of molecular interactions in biological systems , even considered to be the great secret of life , second only to the structure of DNA [9] . Cooperativity is the ability to influence ligand binding at a site of a macromolecule by previous ligand binding to another site of the same macromolecule . Many proteins show increased ( positive cooperativity ) or decreased ( negative cooperativity ) affinity for a ligand after binding of a first ligand . Over the last 100 years , hemoglobin has been a paradigm for cooperative ligand binding and allostery . Oxygen binding to hemoglobin resulted in four commonly used descriptions for cooperativity ( for review see [10] ) : the Hill [11] , the Adair-Klotz [6 , 7] , the Monod-Wyman-Changeux ( MWC ) [12] , and the Koshlan-Némethy-Filmer ( KNF ) [13] models . Yet all these models describe cooperativity only when the binding reactions are at equilibrium . Since temporal aspects of most ligand binding processes are essential for correct physiological functioning , it is imperative to consider the kinetics of cooperative binding . To date , studies determining the kinetics of cooperative binding to biologically active molecules have been carried out using the MWC model , in which cooperativity is established by assuming two allosteric isoforms with different binding properties . These studies were limited to special cases where transition rates between allosteric isoforms are much slower than the binding rates [14 , 15] or where binding and unbinding rates could be measured independently [16] . For all other cases , the mathematical description becomes too complex for simple interpretations [10 , 17] . The Hill equation is perhaps the oldest and most widely used description for the relative amount of binding by a cooperative molecule , and the cooperative binding is described with two constants: the dissociation constant ( Kd ) reports on the concentration of ligand at which the cooperative molecule is half occupied and the Hill number ( nH ) describes the steepness of the binding curve at the value of Kd , denoting a simple quantification of cooperativity . Although not representing a mathematically correct description of cooperative binding at equilibrium—a fact that is stated in the original work [11]—the Hill equation has proven to be extremely useful , as it describes occupancy as a function of ligand concentration with merely two constants that are easy to interpret intuitively . With this in mind , we wanted to resolve the kinetics of Ca2+ binding to CR and to find an intuitively “accessible” quantitative description of the binding kinetics . CR belongs to the superfamily of EF-hand Ca2+-binding proteins . This superfamily is named after the common Ca2+ binding structure—the EF-hand—first described as the C-terminal E-helix–loop-F-helix Ca2+ binding site in parvalbumin [18] . Most members have an even number of EF-hand domains organized in pairs [19] , representing a structurally conserved architectural unit . CR has six EF-hand domains [4 , 20–24] , which can be subdivided into two independent domains: one with the cooperative pair of binding sites I and II , and another with binding sites III–VI [23] . Sites III–VI can be further subdivided into one cooperative pair , sites III and IV and sites V and VI [4] . Of the latter pair , only site V binds Ca2+ , whereas site VI is “inactive” [4] . Thus , CR has two pairs of cooperative binding sites ( I–II and III–IV ) and one independent binding site ( V ) for Ca2+ . We tried several approaches to describe the kinetics of these five binding sites based on the published models , and discovered a new and simplified kinetic model that quantitatively resolves the kinetics of cooperative binding . This new model also revealed unexpected and highly specific nonlinear properties of cellular Ca2+ regulation by CR .
We determined the kinetics of Ca2+ binding to CR by Ca2+ uncaging using DM-nitrophen ( DMn ) and measuring the changes in [Ca2+] with the fluorescent Ca2+ indicator dye Oregon Green BAPTA-5N ( OGB-5N ) as previously described [2 , 25] . Changes in the OGB-5N fluorescence were observed immediately after photolysis of DMn in solutions containing various concentrations of CR ( Figure 1A ) . A rapid rise in [Ca2+] ensued from a resting concentration of approximately 2 . 4 μM to 11 μM . At this initial [Ca2+] , approximately 99 . 5% of the DMn is in the Ca2+-bound form , ensuring that ( 1 ) virtually every uncaged DMn molecule will release Ca2+ , and ( 2 ) the amount of free DMn capable of rebinding uncaged Ca2+ ions [25 , 26] is considerably limited . This is evidenced by the negligible drop in [Ca2+] in the absence of CR , leading to an almost step-wise increase in [Ca2+] ( Figure 1A ) . The presence of CR ( 31 and 62 μM ) resulted in a [CR]-dependent drop in [Ca2+] . Figure 1B depicts a simplified reaction scheme of the experiment . To determine the association and dissociation rates of Ca2+ binding to CR , all these different reactions were incorporated into a mathematical model ( see below ) . The aim was to find a mathematical description for the Ca2+-binding properties of CR that best fits the experimental fluorescence traces generated under various conditions . As a starting point to determine CR's kinetics of Ca2+ binding , we relied on steady-state Ca2+-binding properties determined previously . With the same human recombinant CR , selected Hummel-Dryer experiments yielded a Hill coefficient of 1 . 3 for the four binding sites , with a Kd of 1 . 5 μM [4] . However , by flow dialysis , the steady-state binding of Ca2+ to human CR could be described with the following macroscopic constants ( K1 through K5 ) 2 . 2 × 105 M−1 , 3 . 2 × 105 M−1 , 4 . 7 × 105 M−1 , 8 . 0 × 105 M−1 , and 2 . 0 × 104 M−1 [4] . The resulting binding curve derived from these values could be accurately fitted with two Hill equations; one equation described four cooperative binding sites with a Kd of 2 . 5 μM and a Hill coefficient of 2 . 4 , and the other one described a single independent site with a Kd of 53 μM . In agreement with this data , equilibrium dialysis experiments with chick CR revealed a Hill coefficient of 1 . 9 [22] . Even Hill coefficient values of up to 3 . 7 for Ca2+-induced tryptophan ( Trp ) fluorescence changes in rat CR have been reported [21] . However , these conformational changes measured by Trp fluorescence do probably not linearly relate to Ca2+ binding . Thus , the absolute values should be interpreted with caution , but nonetheless , cooperativity of Ca2+ binding also occurs in rat CR . Based on these Hill coefficients of 1 . 3 , 1 . 9 , and 2 . 4 , we conjectured that CR has four binding sites for Ca2+ with positive cooperativity , with a Hill coefficient of approximately 2 and one independent binding site for Ca2+ . In accordance with these and other earlier findings on the structure and physiology of CR ( see Introduction ) , we modeled the protein as possessing two pairs of cooperative binding sites ( BIBII ) and ( BIIIBIV ) , and one independent binding site BV . Because the properties of the two cooperative pairs in CR were considered indistinguishable in the steady-state study [4] , thus indicating that the cooperative binding sites are fairly similar , we assumed the properties of both cooperative pairs to be identical: Such an assumption is most useful for reducing the number of variables , thus increasing the reliability of fitting procedures by constraining the model . The most straightforward approach to determine the kinetics of a system would consist of fitting the [Ca2+] decay with a set of exponential functions . However , rebinding of Ca2+ to free DMn affects the decay kinetics . In addition , the changing properties of the binding sites that underlie cooperativity are expected to cause a shift in the kinetic properties of binding during the decay phase . As a consequence , the relative contribution of multiple decay time constants is continuously shifting . Although fitting the [Ca2+] decay with exponentials might result in time constants for a given trace , this does not allow accurate deduction of the Ca2+-binding kinetics of CR . A mathematical model simultaneously describing all processes taking place in the recording chamber , including a “total” description of the cooperative and noncooperative binding of Ca2+ to CR is expected to yield more reliable information on the kinetic properties of CR . To model the cooperativity , we started out by including an allosteric influence between the binding sites of the pairs . This was achieved by setting two states ( R and T ) for a particular binding site , with each having its own set of rate constants . A binding site is in the “tensed” state ( T ) , with a low affinity for Ca2+ , when no Ca2+ is bound to the other site in the pair , whereas a binding site is in the “relaxed” state ( R ) , with a high affinity for Ca2+ , when the other site already has a Ca2+ ion bound . We assumed that binding of Ca2+ to one site always leads to a rapid transition T→R in the other site and that an unbinding of Ca2+ from one site always leads to a rapid transition R→T in the other site . This allowed us to incorporate the transition rates between states R and T in the binding and unbinding rate constants , further simplifying the model ( Figure 2A ) . CR was thus modeled as if consisting of two independent proteins described by the following binding reactions: This cooperative part of the model can be easily related ( see Discussion ) to the Adair model [6] , which provides the most general description of equilibria in terms of stochiometric binding . For the independent site of CR , we used a standard equilibrium equation: where kon ( R or T ) and koff ( R or T ) are the association and dissociation rate constants for the individual cooperative binding sites depending on their Ca2+-binding status , and kon ( V ) and koff ( V ) are the rate constant for the independent site . The total concentrations of the different “virtual” parts are: Despite the simplifying assumptions concerning the cooperative sites , the model that allows a fitting routine to proceed is fairly complex , because it has a considerable number of degrees of freedom . Thus , a procedure was developed that significantly constrains the fit to minimize the variance of the fit results . Simultaneously fitting combined sets of uncaging data ( Figure 3; see Materials and Methods ) obtained under different experimental conditions sufficiently constrained the model to yield consistent results . We performed a number of individual uncaging experiments generated at one of seven initial conditions A–G ( Table in Figure 3 ) . These conditions varied in the initial free [Ca2+] ( hence , total [Ca2+] ) , total [CR] , total [DMn] , as well as on the lot number of OGB-5N , with each lot having slightly different properties ( see Materials and Methods ) . Under each condition ( A–G ) , we performed 12 to 25 uncaging experiments , each one with a different flash energy of the UV laser leading to different amounts of uncaged Ca2+ . In total , 123 traces were obtained , covering a wide rage of uncaging energies and , subsequently , a wide range of increases in [Ca2+] ( see gray areas in Figure 3; for all 123 individual traces , see Figure S1 ) . We set out to find a satisfactory model that would be able to fit all curves obtained under conditions A–G and all tested uncaging intensities . The obtained results from the modeling should be able to describe all experimental curves with a unique set of parameters describing the kinetic properties of CR . To confine the fits , 38 sets of 14 pseudo-randomly picked traces consisting of two traces from each initial condition A–G were generated . The 38 sets were chosen randomly , with the precondition that every trace of a specific starting condition A–G was represented equally . To create 38 sets , each individual measurement was picked at least three and at most eight times . On average , each trace was picked 4 . 3 times ( for details , see Figure S2 ) . Each set of traces was fitted with the model , and the fitted parameters describing the properties of CR were constrained to be identical for all individual traces within one set . The only variable parameter between traces was the amount of uncaging that was fitted individually for each trace . An example of a dataset of 14 traces ( two sets of data points [•] for each condition [A–G] ) and the fitted traces ( red or blue lines , see Figures S1–S3 for additional details ) are shown in Figure 3 . Fit results for this dataset are depicted in Figures 4 and 5 ( yellow symbols ) together with the results of the fits on the other 37 sets . The new model was programmed as to fit the Kd ( V ) and kon ( V ) for the independent site . However , to aid the choice of starting values , the cooperative part of the model was set up such that kon ( R ) , kon ( T ) , the apparent Kd ( Kd ( app ) ) for the pairs , and the Hill number ( nH ) could be fitted . This was achieved by adding a calculation step that determined Kd ( R ) and Kd ( T ) from the latter two parameters ( see Protocol S1 ) : Previously determined steady-state parameters ( apparent ) Kd's and nH of CR [4 , 20–22] served as starting points in the modeling and helped to further constrain the model . Various combinations of kon starting values between 105 and 108 M−1s−1 were tested , but this did not significantly influence the outcome of the fit , indicative of the “robustness” of the modeling procedure . Occasionally a particular set out of the 38 yielded an atypical fit with values significantly deviating from the general population of results . If this was the case , we followed up with two approaches . First , we tested whether any of the other 37 sets of fluorescence traces could also be fitted with these deviating values , which in almost all cases yielded unsatisfactory fits . Second , we tested whether the deviating set of traces could also be fitted with the more homogeneous values of the general population of sets by choosing starting parameters closer to these values . In this case , the deviating set could always be fitted with values comparable to the homogeneous constants . It should be noted that the critical parameters ( Kd ( V ) , kon ( V ) , kon ( R ) , kon ( T ) , Kd ( app ) , and nH ) were never constrained or fixed to a certain value . The atypical fit results were probably caused by local minima in the error function of the fit routine . Such local minima are expected , based on the fairly large number of degrees of freedom where the parameters are not completely independent . Deviations in one parameter can be partially compensated by “shifting” other parameters . Initially , we used a model that did not include cooperativity and found that most of the 38 individual sets could be fit reasonably well . For a given individual dataset , the quality of the fits were similar between a model with a Hill coefficient of either 1 or 1 . 9 . But when comparing the fit results of all 38 sets , most of the fitted parameters showed strong deviations ( up to five orders of magnitude , depending on how the model was exactly defined ) when using nH = 1 . The high variability of the binding parameters found when assuming nH = 1 is shown in Figure S3 . This indicated that there is no unique solution to describe CR's Ca2+-binding properties without cooperativity , in line with previous steady-state findings of nH values between 1 . 3 and 2 . 4 [4 , 22] . Thus , only when including cooperativity and starting the modeling procedure with previously determined steady-state parameters for CR [4 , 20–22] did we find a congruent set of values for the fitted parameters for all 38 sets of 14 traces . An accurate model describing CR's Ca2+-binding dynamics should be able to fit all the experimental traces obtained under any condition . This should be the case at lower resting [Ca2+] , when Ca2+-free binding sites of any affinity in any state are abundant ( Figure 3A–3D and 3G ) , but also at higher [Ca2+] , when mostly the lower affinity independent site V is available ( Figure 3E and 3F ) . Furthermore , the model is also able to closely describe the [Ca2+] signals after a relatively large uncaging , when [Ca2+] is so high that the buffering by CR is relatively small ( see upper trace in Figure 3F ) . The goodness of fit of the fit procedure can be appreciated by the averaged error for the 38 fits ( Figure 3H , black bars ) , which shows systematic deviations ( if any ) . Errors were found to be extremely small for the first 20 ms after the flash , and at time points greater than 20 ms , the averaged fits show a small systematic undershoot of the experimental data , yet never exceeding 1 . 5% of the actual amplitude ( Figure 3H , black bars ) . The larger errors towards the end of the traces are likely due to the small amplitudes of the signals at these time points , which increase the relative error when there is a constant absolute deviation . But even the largest deviations of the 38 fits ( the striped bars showing the largest deviation in either direction ) never exceeded 5% of the measured amplitude . The average absolute error ( not allowing for positive and negative errors to cancel each other ) was maximally 2 . 1% and again only found towards the end of the traces . Thus , the fit procedure , applying our model , allowed accurate quantifying of the kinetic properties of CR . All 38 results for the fitted values were plotted as a log-normal cumulative probability distribution because they have a log-normal distribution ( Figures 4 and 5 ) , except for the nH value , which was normally distributed ( Figure 5E ) . These results were then fitted with a normal distribution to determine average and standard deviation . The results of these fits are shown in Table 1 . To summarize , we conclude that CR can be described with one independent binding site with a Kd of 36 μM , a kon of 7 . 3×106 M−1s−1 , and a koff of 240 s−1 together with two identical cooperative pairs of binding sites with an initial ( T state ) Kd of 28 μM with a kon of 1 . 8×106 M−1s−1 and a koff of 53 s−1 that will dramatically change to a ( R state ) Kd of 68 nM with a kon of 3 . 1 × 108 M−1s−1 and a koff of 20 s−1 once the cooperative partner site has already bound Ca2+ . The Kd ( app ) for the cooperative sites is 1 . 4 μM with an nH of 1 . 9 . To compare the results obtained with our new “simple” model , we subjected our experimental data to a previously described cooperative model , the MWC model . There , the protein as a whole can switch between two states: one state in which all the binding sites are in the T state , and another one in which all the binding sites are in the R state ( Figure 2B ) . The equilibrium between the T and R states is described with the equilibrium constant L , which is also dependent on the number of Ca2+ ions bound ( see Figure 2B ) . It can be shown [12 , 27] that: where k+ and k− are rates of the transition between the two states R and T . The indices ( 0 and i in Equations 8 and 9 , respectively ) indicate the number of Ca2+ ions bound . K is often indiscriminately used for both association and dissociation equilibrium constants; here , we denote K as association equilibrium constants , whereas we use Kd for dissociation equilibrium constants . Furthermore , the pair of cooperative binding sites can transition from R to T and back , independently of the number of sites that are occupied , thus all transitions defined by L0 , L1 , and L2 are possible . However , for steady-state purposes , one transition ( L0 ) suffices , as described in the original MWC model [12] . Although steady-state properties are independent of the transitions allowed , the kinetic properties will highly depend on the number of allowed transitions . We chose to allow all possible transitions because it was used in earlier kinetic fits with this model [14 , 16] . We also attempted to fit the data with a MWC model in which only the L0 transition was possible; however , the resulting fits showed large deviations ( >20% ) and generated traces with significant deviations from the experimental data . From Equation 9 , we can derive the information that while KT < < KR and L0 > > 1 , the equilibrium between the T and R states is shifted towards the lower affinity T state when no or little Ca2+ is bound , whereas it shifts towards the higher affinity R state when plenty of Ca2+ is bound [27] , which causes the cooperative effect . The identical 38 sets of 14 experimental traces as used above were fitted with the MWC model . Here also , both cooperative sets were considered to be identical and the cooperative part of the model was set up such that kon ( R ) , kon ( T ) , the apparent Kd ( Kd ( app ) ) for the pairs , and the Hill number ( nH ) , could be fitted . This was achieved by adding a calculation step that determined Kd ( R ) and Kd ( T ) from the latter two parameters ( see Protocol S1 ) : As discussed above , L is dependent on the number of Ca2+ ions bound to CR . To establish this dependence , we changed the forward and backward rate constants between the R and T states equally: We started with the same values for the ( apparent ) Kd's and nH as above with various combinations of kon starting values between 105 and 108 M−1s−1 . As with the first model , occasionally a set of traces was fitted with values that deviated significantly from the general population , but again we found that only with one general set of constants , all 38 sets could be accurately fitted; the details are reported in Table 2 . For comparison , we depicted the fit and the fit errors obtained with the MWC model , using the identical set of traces used for the new model ( compare Figure 3A–3G , blue vs . red traces; errors for MWC fit are not shown , but are comparable to the fits with the other model ) , and observed that the MWC model can fit the data with a similar accuracy as our new model . The average results from all traces were obtained as described for the new model ( Figures 4 and 5 ) . For the independent site ( Figure 4A and 4B ) , results based on the MWC model ( blue symbols ) were essentially identical to the ones found with the new model ( red symbols , compare also Tables 1 and 2 for the independent site V ) . Also , the properties of the apparent Kd and the nH of the cooperative sites were similar between the two models ( Figure 5A , compare green and pink circles , and Figure 5E , compare blue and red symbols ) . This is a first indication that both models quantitatively describe the same process . Obviously , the detailed descriptions for the cooperative sites , applying either the new or the MWC model , deviate from one another , based on the differently modeled processes as described in Figure 2 . Both models accurately fitted the data in a quantitative manner; based on the quality of the fits , they were indistinguishable , and technically can both be used to quantify the kinetic properties of Ca2+ binding to CR . In particular , results for the independent site ( V ) are virtually identical ( Figure 4 ) . For the cooperative sites , both models describe binding sites that have a similar steady-state/affinity profile ( Figure 5A; apparent Kd and Figure 5E; nH ) . Our uncaging experiments were performed over a fairly narrow range of resting [Ca2+] ( 2 . 0–5 . 3 μM ) , dictated by the constraints that most ( >99% ) of the DMn should be in the Ca2+-bound form to obtain valuable data . At lower resting [Ca2+] , the unbound DMn , present at higher concentrations than the free [CR] , would rapidly rebind most of the released Ca2+ [25] , making CR's relative contribution to the [Ca2+] decay small and difficult to distinguish . Because buffering kinetics depend on both the on-rates and the concentration of free buffer , the overall Ca2+ binding speed to DMn would be much higher than that to CR , thus masking Ca2+ binding to CR . With much less Ca2+-bound DMn , the changes in [Ca2+] would be quite small and difficult to detect . Such technical constraints do not allow performing the experiments over the whole “physiological” range of [Ca2+] , e . g . , from 10 nM to 100 μM . Thus , we could not exclude that the two models describe systems with different kinetic properties outside the boundaries of our experiments . This possibility was tested by examining the behavior of each model as a filtering system for Ca2+ signals regulated by resting levels of [Ca2+] . The filtering properties of both models were determined over a range of conditions covering the whole physiological range that CR is expected to encounter . CR ( 500 μM ) was subjected to a wide frequency range ( 0 . 3 Hz to 10 kHz ) of small ( 1 nM ) sinusoidal perturbations of the [Ca2+] at a wide range of starting [Ca2+] ( 1 nM to 100 μM ) . The resulting Ca2+ “waves” were close to sinusoidal . We used their amplitude as output of the filter to determine the transfer function of CR ( Figure 6 ) . The attenuation of the sine wave is plotted as a function of frequency and resting [Ca2+] . Both models “filter” Ca2+ signals in a very similar way; the signals are less attenuated as the frequency gets higher ( CR acts as a high-pass filter ) or when CR becomes fully occupied as the starting [Ca2+] gets higher . Remarkably , both models show a similar strong increase in attenuation at the lower frequencies , when the Ca2+ concentration gets close to the apparent Kd value of the cooperative sites , i . e . , approximately 1 . 5 μM . The similarity of the transfer function of CR using either model indicates that they quantify the kinetics of Ca2+ binding by CR in a similar way ( but via different mechanisms ) over the whole physiological range of conditions . Both our new model , which is closely linked to the Adair-Klotz model [6 , 7] , and the MWC model can be used equally well to quantify the Ca2+-binding kinetics of CR . However , we consider the new model to facilitate the “intuitive” understanding of how the kinetic properties of the cooperative sites relate to the binding kinetics at the level of the whole protein or at the macroscopic level . The Adair-Klotz model is the most general description of equilibria in terms of stochiometric binding . It describes the steady-state equilibrium using the constants ( K1 , K2… . Kn ) for the successive binding ( or macroscopic ) steps , but not as the affinity constants of the individual ( or microscopic ) binding sites: where for which in equilibrium , the fractional occupation ( ν ) of a protein P is described by the Adair-Klotz equation: Usually , rate constants are not denoted in the macroscopic equilibrium equation ( Equation 15 ) ; instead , only the K values are denoted , which is sufficient for steady-state descriptions . The equilibrium constants for the new model ( KT and KR ) and the MWC model ( KT , KR , and L0 ) can often rather easily be translated into macroscopic K values [10] ( also see Protocol S1 ) . Therefore , it is fairly simple to relate any of the steady-state constants of cooperative models ( new , MWC , and KNF ) to the more generally used Adair-Klotz equation . In addition to the calculation of the steady-state equilibrium ( Equation 17 ) , the macroscopic Adair-Klotz model compiles the binding of multiple binding sites into an intuitively easy-to-understand sequential binding model . It would even be more insightful if one could also obtain the rate constants for each macroscopic step . Unfortunately , the macroscopic rate constants are generally extremely hard to define when cooperative mechanisms are involved . For example , the macroscopic kon ( 1 ) for the MCW model depends on the relative amounts of totally unoccupied molecules in the R and T states ( see Figure 2B ) . At steady state , this equilibrium is fairly straightforward , as this is simply defined by L0 . However , when the balance is disturbed by a sudden change in Ca2+ concentration , it will disturb the equilibrium between unoccupied molecules in the R and T states . This equilibrium will settle over some time according to k+ and k− . During this time , the relative amount of binding sites in states R and T is dynamically changing , making the macroscopic kon ( 1 ) itself dynamic . With most cooperative models , the macroscopic rate constants will be dynamic because they are dependent on most perturbations . This makes the rate constants very difficult to interpret ( and to calculate ) . However , for the new model of cooperativity , the macroscopic rate constants are easily defined and are truly constant . For instance , for two cooperative sites as described in this paper: and Through these simple relationships and according to our data , CR can be quantitatively described as a mixture of two “virtual” CaBPs . The cooperative part can be described as: and the independent part as: where As described above , at a starting [Ca2+] around the apparent Kd for the cooperative binding sites , CR will more effectively buffer perturbations at lower frequencies ( Figure 6 ) . Thus , the Ca2+-buffering kinetics of CR clearly depends on the starting [Ca2+] that determines the distribution between states T and R of the cooperative binding sites . While more cooperative sites get occupied , more Ca2+ binding will take place through the second faster binding step as described in Equation 20 . To better understand the cooperative nature of Ca2+ binding by CR , we simulated with the new model a 1 μM step in [Ca2+] from a resting [Ca2+] of 10 nM in the presence of 100 μM CR . In comparison , we also simulated the widely used synthetic Ca2+ buffers BAPTA ( Kd = 160 nM , kon = 2 × 108 M−1s−1 , Maxchelator software version 10/02 , see Materials and Methods ) and EGTA ( Kd = 70 nM , kon = 1 × 107 M−1s−1 [2] ) . Under these conditions , the [Ca2+] decay kinetics mediated by 100 μM CR could be faithfully reproduced by either 7 . 8 μM BAPTA or 153 μM EGTA ( Figure 7A ) . Now , to compare the binding kinetics of CR to the two synthetic chelators without cooperative binding , we kept all parameters constant , i . e . , 1 μM steps in [Ca2+] , buffer concentrations of CR , BAPTA , and EGTA , and only varied the resting [Ca2+] ( 10 nM to 10 μM ) from which the [Ca2+] step was induced . As the resting [Ca2+] increases , the [Ca2+] decay kinetics in the presence of either one of the noncooperative chelators BAPTA and EGTA is slowed down ( Figure 7A–7G ) . This slowing results from the fact that at higher initial [Ca2+] , more BAPTA or EGTA molecules will be in the Ca2+-bound form , and thus , less Ca2+-free buffer is available . In contrast , as the initial [Ca2+] increases from 10 nM to approximately 1 . 1 μM , CR speeds up the decay in [Ca2+] and buffers Ca2+ faster until approximately 1 . 1 μM resting [Ca2+] , above which CR behaves “similarly” to EGTA or BAPTA . More simulations have indicated that the exact breaking point between this novel behavior and classic behavior is dependent on the CR concentration and the Ca2+ step size; however , the breaking point is always close to 1 . 1 μM for CR . Evidently neither EGTA nor BAPTA are able to mimic the properties of CR over the whole physiological range of [Ca2+] . Yet for a given resting [Ca2+] and a defined step increase in [Ca2+] , a BAPTA or EGTA concentration can be found that will closely mimic the effect of CR on the [Ca2+] decay kinetics . But with only a slight change in the initial [Ca2+] or the step size , this particular concentration of the synthetic chelator will not accurately reflect the action of CR . As an example , the concentrations of BAPTA or EGTA needed to mimic Ca2+ binding by CR for the simulations ( Figure 7A–7G ) are shown in Figure 7H . Since CR has five binding sites , 100 μM CR is , in terms of Ca2+-binding sites , equivalent to 500 μM of either EGTA or BAPTA . For the example shown , at initial [Ca2+] smaller than 0 . 3 μM , the concentration of EGTA needed to mimic 100 μM CR is in that same order of magnitude ( ∼150–1 , 400 μM ) . The amount of BAPTA necessary to mimic CR is on the order of 500 μM when the initial [Ca2+] is approximately 1–10 μM ( ∼150–800 μM , respectively ) ( Figure 7H ) . Thus , one can conclude that CR behaves EGTA-like around physiological resting [Ca2+] ( 20–100 nM ) typically seen in neurons and more BAPTA-like at the higher [Ca2+] observed during bouts of activity .
The kinetic properties of Ca2+ binding to CR were quantified using two different models featuring cooperativity between Ca2+-binding sites . The quantification of ligand binding to a protein with multiple binding sites is inherently difficult . First of all , when assuming that all binding sites are never truly identical , then for each binding site , at least two constants need to be determined . When these binding sites cannot be studied individually , the number of variables to be simultaneously determined quickly increases in a mathematical description of the protein . Consequently , degrees of freedom in the model will increase , which will decrease the accuracy and enhance the variability of the fitting . These difficulties are further exacerbated by cooperative binding , in which properties of the binding sites dynamically change , resulting in even more variables to determine . Although it is unlikely that any two binding sites are truly identical , a general way to decrease the number of variables is to assume the different binding sites to be identical in binding and allosteric behavior [12 , 13] . Even with such simplifications , the number of variables remains fairly large . To overcome this problem , we performed our experiments at a variety of initial conditions and at a variety of uncaging energies . In this way , we created a large set of data that varied by known parameters . We used a bootstrap method to create several datasets to be fitted simultaneously . The fit routine was set up in such a way that the models were required to consistently describe the properties of CR simultaneously over the whole range of variations in experimental conditions within one set , as well as over the whole range of uncaging energies . This technique resulted in ( log ) -normally distributed results so that erroneous fit results could be easily identified among the fits to any individual set . Initial studies on CR have revealed the protein to contain cooperative Ca2+ binding sites as evidenced by Hill coefficients of 1 . 3 or 2 . 4 [4] and 1 . 9 [22] for human and chick CR , respectively . Thus , this CaBP appeared to be well suited to serve as a model to quantitatively describe its cooperative Ca2+-binding properties . Even if such a description would fail to unravel the “exact” physical interpretation of Ca2+ binding to CR , the obtained data were expected to serve as a powerful tool to understand the role of CR in shaping intracellular Ca2+ dynamics in neurons . To quantify the cooperative binding to CR , two models were tested: a newly developed one and the MWC model . The latter was chosen because it has been used earlier to determine cooperative binding to biologically active molecules [14 , 16] . These studies were limited to special cases where transition rates between allosteric isoforms are much slower than the binding rates [14 , 15] or where binding and unbinding rates could be measured independently [16] . Here , we showed that it is feasible to determine the rate constants of CR using a MWC model outside of these constraints . In addition , we developed a different model closely related to the Adair-Klotz model [6 , 7] , in which we did not account for the transition rate between the two possible states for a cooperative binding site . Both models yielded a similar quantitative description of CR's cooperative properties , but as long as crystal structures of CR in different states of Ca2+ occupancy remain unknown , the exact physical interpretations of Ca2+ binding to CR will not be available . Within one molecule , cooperativity can only be established by sets of at least two binding sites that change their properties based on the occupancy by Ca2+ . In both models used here , four cooperative binding sites can occur in either the T state with low affinity for Ca2+ or R state with high affinity for Ca2+ . The dualistic nature of the binding sites causes the classical cooperativity as seen in steady-state binding studies in which the four cooperative sites in CR can be described with a Kd of 2 . 5 μM and a Hill coefficient of approximately 2 [4 , 22] . The steady state properties for the cooperative sites of either the new model ( Figure 5A and Table 1 , Kd ( app ) = 1 . 4 μM; and Figure 5E and Table 1 , nH = 1 . 9 ) or the MWC model ( Figure 5A and Table 2 , Kd ( app ) = 1 . 5 μM; and Figure 5E and Table 2 , nH = 1 . 9 ) are in close agreement with the steady-state values for CR . Also for the independent site , the Kd's found with the new model ( Figure 4A and Table 1 , 36 μM ) or MWC model ( Figure 4A and Table 2 , 41 μM ) are in agreement with the earlier determined value ( 53 μM ) by steady-state measurement [4] . The mechanism to create positive cooperativity with an initial low-affinity binding step as expressed in Equation 20 for the new model can be quite confusing . Since the first Ca2+-binding step is with a binding site in the low-affinity T state , it may appear that this will be limiting for the whole process , so that overall binding will only occur at higher [Ca2+] . However , it should be considered that even at lower [Ca2+] ( e . g . , at 100 nM inside a cell ) , at equilibrium . some of the sites in the T state will bind Ca2+ , yielding some cooperative pairs with one Ca2+ bound , which then rapidly leads to some fully occupied cooperative pairs: Since the second step in this process is governed by a high-affinity site , a considerable number of the cooperative pairs will be fully occupied . The combination of these reaction steps gives rise to the intermediate apparent Kd ( Kd ( app ) ) ( also see Equation 6 and , in Protocol S1 , Equation S76 ) . Although high-affinity R sites ( short for “a site in the R state” ) only become available after Ca2+ binding to CR's low-affinity T sites , they still play a determining role for the steady-state equilibrium . In dynamic situations , the slower binding of Ca2+ to a T site has to precede binding of a second Ca2+ to a faster R site , the former step apparently being rate limiting . However , at a given initial [Ca2+] , CR molecules are present in different states ( metal-free , T state , R state , and completely Ca2+-bound ) according to the parameters described in Tables 1 and 2; one example is described in more detail here . Assuming a 10 μM step in [Ca2+] from a resting [Ca2+] of 100 nM in the presence of 100 μM CR , the initial ratio for [unoccupied T sites]/[unoccupied R sites] is 395 μM/1 . 4 μM ( calculated with Equations S34–S36 in Protocol S1 ) . Of the 10 μM increase in Ca2+ , almost all will be buffered by CR: 5 . 6 μM will bind to T sites , 3 . 9 μM will bind to R sites , and 0 . 35 μM to the independent site V . Evidently , the initially available R sites ( 1 . 4 μM ) will not be sufficient , and most of the bindings to the R site will be time-limited by initial binding to T sites . However , at a resting [Ca2+] of 1 μM , the initial ratio of unoccupied T/R sites is 250 μM/9 μM . In this case , 5 μM Ca2+ will bind to T sites , 4 . 8 μM will bind to R sites , and 0 . 15 μM to site V . Thus , enough sites ( 9 μM ) in the fast R state will be available and allow for fast buffering , not necessitating the slower stepwise change from the T to the R state . Model simulations of these experiments indicate that the surplus of sites in the R state will , within approximately 1 ms , bind up to 0 . 7 μM Ca2+ more , which at a later time shifts to a CR molecule in the T state . By acting as a temporary substitute , the free sites in the R state can even “speed up” the eventual buffering of binding to sites in the T state . Albeit the initial amount of free sites in the R state is relatively small , it will significantly contribute to the buffering speed at initial [Ca2+] around Kd ( app ) . Also , the low-affinity independent site , which will hardly play a role in the steady-state buffering , initially binds up to 1 . 8 μM Ca2+ which is later “transferred” to sites in the T state and will contribute to the overall buffering speed . However , this contribution is virtually identical when starting from either 100 nM or 1 μM Ca2+ . To circumvent the difficulties of “real” CaBPs encountered in electrophysiological experiments ( e . g . , washout , unknown concentrations , and kinetic properties ) , they are often substituted by the artificial Ca2+ buffers BAPTA and EGTA . Since BAPTA and EGTA have significantly different on-rates ( 2 × 108 M−1s−1 and 1 × 107 M−1s−1 , respectively ) , it is generally assumed that a process influenced by BAPTA , but not by EGTA , must have Ca2+-binding on-rates comparable to ones of BAPTA . So far , two studies [28 , 29] have inferred the kinetics of Ca2+ binding by CR from comparing it to BAPTA and EGTA . It was thus surmised that CR must have one or several binding sites with fast on-rate ( s ) [28] . The conclusion was drawn from the finding that addition of BAPTA , but not EGTA , to CR-deficient cells could rescue the CR deficiency . However , such generalizations are certainly error-prone , because both EGTA and BAPTA have Kd values much lower than CR . This will considerably affect the results , since the speed of buffering is also dependent on the concentration of free sites and not only on the rate constants . Under resting conditions inside cells ( ∼100 nM [Ca2+] ) , at least 40% of either EGTA or BAPTA are occupied by Ca2+ ions and do not add to the buffering speed . Our findings show that if BAPTA ( or EGTA ) can replicate a cellular buffering process under certain experimental conditions defined by , e . g . , initial resting [Ca2+] , step size , and geometry of Ca2+ influx , it does necessarily mean that the cellular buffering is “comparable to BAPTA or EGTA” and therefore “fast” or “slow , ” respectively . The exact intracellular distribution of CR is not well understood [30]; although principally considered as a cytosolic protein , a fraction of CR molecules could be anchored to specific sites [31–33] , leading to higher local concentrations , possibly at Ca2+ hotspots , as suggested for calmodulin around the L-type Ca2+ channel [34] . Such local accumulation could lead to local high buffering speeds , especially at hotspots , where the higher local Ca2+ concentrations would drive CR into a faster mode . Consequently , if CR is concentrated at certain subcellular compartments , the concentration of freely diffusing CR molecules will be lower . And if this freely moving CR is only confronted with smaller Ca2+ signals , this will result in slower CR Ca2+-buffering kinetics . Slower properties of CR at lower [Ca2+] together with its suggested mobility ( a diffusion coefficient of approximately 20 μm2s−1 , assuming a similar mobility as the closely related CaBP CB [29 , 35] ) , supports the notion that part of CR will slowly bind and release Ca2+ and thus act like a “slow” buffer . In contrast , the faster Ca2+ buffering of CR at Ca2+ hotspots or when present in the “fast” mode , i . e . , with one of the cooperative sites in the Ca2+-bound form , may explain earlier findings that BAPTA could functionally rescue CR deficiency [28 , 36] . According to our findings , “simple” Ca2+ chelators ( EGTA and BAPTA ) can never fully replicate certain functional aspects of CaBPs , because the complexity of Ca2+ binding to “real” CaBPs such as CR cannot be mimicked by small synthetic Ca2+ buffers lacking cooperativity . The steady-state aspect of cooperative binding has been reported and analyzed in detail for Ca2+ sensors such as calmodulin ( for a review , see [3] ) . Cooperativity was also reported for CB [37 , 38] , a protein with sensor and buffer functions [35 , 38] , but the quantitative aspects of cooperativity have not yet been investigated in detailed steady-state binding studies . Nevertheless , cooperative binding has not been modeled in a CaBP to examine its effect on cellular Ca2+ transients . Our results on CR pave the way to more realistically model intracellular Ca2+ dynamics , thus leading to a better understanding of the spatial and temporal actions of Ca2+ within a cell . The importance of correctly determining the physiological actions of CaBPs was recently shown in Xenopus oocytes . The effect of parvalbumin ( PV ) , a CaBP with two Ca2+ binding sites and minimal cooperativity [39] , could be closely mimicked by the synthetic slow Ca2+-buffer EGTA [29] . Yet , the effect of CR on IP3-mediated Ca2+ release was significantly different from that of the fast Ca2+-buffer BAPTA , in particular at low [IP3] , when Ca2+ elevations were small . Under these conditions , CR caused a leftward shift in the concentration-response relationship as observed with the slow buffer PV . Under the same conditions , CR produced localized Ca2+ transients or “puffs , ” a phenomenon never observed in the presence of BAPTA [28] . Our findings strongly support the hypothesis [29] that the kinetic properties of individual CaBPs are finely tuned to specific cellular functions and may explain the need for a large number of CaBPs ( more than 240 EF-hand–containing proteins ) detected in the human genome [40] . Our novel approach determining the cooperative kinetics of Ca2+ binding to CaBPs will lead to a better understanding of their highly specialized roles in cellular Ca2+ signaling . In general , the method should also be applicable to any CaBP or to other multisite cooperative binding processes . This is expected to yield a more detailed understanding how CaPBs shape the spatiotemporal aspects of Ca2+ signaling . Our findings may also help to reconcile some reported discrepancies concerning CR's function and putative effects on biological processes . The new model devised in the present study extends the analysis of cooperativity beyond the static steady-state condition , providing a powerful tool for the investigation of the dynamics and functional significance of cooperative binding in general .
All experiments were performed in solutions containing 120 mM KCl , 40 mM HEPES ( pH set at 7 . 30 ) , 100 μM Oregon Green BAPTA-5N ( OGB-5N; Molecular Probes ) , a varying amount ( 4–17 mM ) of DM-nitrophen ( DMn , ( 4 , 5-dimethoxi-2-nitrophenyl ) -1 , 2-diaminoethane-N , N , N′ , N′-tetrasodium salt; Calbiochem ) and CaCl2 . In selected experiments , the solution also contained calretinin ( CR ) . Because DMn is extremely sensitive to light and might uncage spontaneously , solutions were freshly prepared before every experiment . The light source in the experimentation room was equipped with a yellow filter ( 500 nm long-pass ) to avoid unwanted photolysis . The concentration of fresh stock solutions of DMn ( 15 or 30 mM ) was determined photometrically at 350 nm ( Hewlett-Packard 8453 ) using an extinction coefficient of 4 . 33 × 103 M−1cm−1 for DMn [26] . For these measurements , small samples of the final experimental solutions were diluted to yield [DMn] of approximately 50 μM in a solution containing 4 mM EGTA to remove any free Ca2+ . Most stock solutions contained approximately 80%–90% of the expected DMn concentration based on the manufacturer's specifications . The basis of the discrepancies between the expected and measured DMn concentrations is difficult to determine and might result from impurities; it is conceivable that some of the compounds decayed due to inadvertent illumination . To accurately account for the amounts of Ca2+ cage , the actual [DMn] in our experimental solutions were calculated , and the corrected values were used in our analyses . Experiments were carried out at room temperature ( ∼25 °C ) . All chemicals were obtained from Sigma-Aldrich , unless otherwise mentioned . Values are expressed as mean ± the standard error of the mean . The initial free [Ca2+] of each solution was titrated to be between around 2 μM or 8 μM ( indicated in Results ) , using custom-made Ca2+-selective electrodes [25] . As opposed to earlier experiments [25] in which the Kd of the Ca2+ buffer in standard solutions was regulated by changing the pH [41] , here we made KCl-based pCa ( −log[Ca2+] ) standard solutions with an ionic strength of 120 mM and pH around 7 . 3 ( see Protocol S1 , Table 1 ) using Maxchelator software version 10/02 ( http://www . stanford . edu/~cpatton/maxc . html ) . These standard solutions were used for calibrations; the standard pCa 6 solution was used as a reference solution in the Ca2+ electrodes . To obtain the experimental solutions , Ca2+-free solutions containing DMn , OGB-5N , and CR ( in the case of control solution without CR ) were titrated with aliquots ( 1–3 μl ) of the same solution containing 1–100 mM CaCl2 . After thoroughly mixing every added aliquot , the [Ca2+] was measured with the Ca2+-sensitive electrodes . This procedure was repeated until the sought concentration was reached . All [Ca2+] were verified in the uncaging experiments by comparing baseline fluorescence ( Frest ) with the maximal possible fluorescence ( Fmax ) for that solution . Fmax was determined by repetitive uncaging of Ca2+ in some of the samples until fluorescence did not increase any further ( so that [Ca2+] ≫Kd ( OGB-5N ) ) . The [Ca2+] was calculated using the following equilibrium formula: To measure the dynamics of Ca2+ binding to CR with high temporal resolution , we used UV-flash photolysis of DMn . We used a setup that was described earlier [2 , 25 , 42 , 43] . Briefly , it consisted of a small chamber ( 20 μl ) mounted on an inverted microscope equipped for epifluorescence ( IM35; Carl Zeiss ) with a 505-nm dichroic mirror and 510 LP emission filter ( Chroma Technology ) . In the chamber , the polished end of a silica multimode optical fiber ( Ø 800 μm; Thorlabs ) was mounted to deliver 20-ns flashes of UV light ( 347 nm ) from a frequency-doubled ruby laser ( Lumonics ) to photolyze DMn . To excite the OGB-5N molecules , an argon laser ( 488 nm , 1 W; model 95; Lexel ) was focused through the epifluorescence illumination port of the microscope with a 20× objective ( Fluo20; Nikon ) , forming a small illumination spot directly in front of the optical fiber . The relatively small spot size of the excitation light ( 1–10 μm ) compared to the large area of UV illumination ( cone with minimal of Ø 800 μm ) ensured minimal diffusion effects during the time span ( 200 ms ) of [Ca2+] changes . The [Ca2+] transients changed substantially only when the illumination spot was moved towards the outer edge of the optical fiber , i . e . , to the edge where uncaging took place . This indicated that diffusion artifacts between areas of UV illumination ( uncaging ) and no UV illumination ( no uncaging ) on the timescale of our measurements occur only very close to the edge of the UV-illuminated area and not in the area where data were collected . The fluorescence of OGB-5N was measured with a photodiode ( PIN-HR008; UDT Sensors ) in the focal plane of the microscope . The small diameter of the photodiode ( 200 μm ) minimized errors caused by diffusion in the z-axis . Despite using appropriate optical filters and excitation spectrum of OGB-5N peaks at 494 nm , the high-energy UV flashes still induced brief , but large , optical transients that saturated the detection system . To avoid these artifacts , a patch clamp amplifier ( Axopatch 200A; Axon Instruments ) with an integrating headstage was used to measure the currents generated by the photodiode . The feedback capacitor of the headstage was short-circuited ( reset ) , and its readout was blanked exactly at the time of the UV flash so that there was no signal measured at the instant of the flash . The analog signal was low-pass filtered with the eight-pole low-pass Bessel filter of the amplifier at 10 kHz , digitized at 50 kHz ( PCIO-MIO-16XE-10; National Instruments ) , and sampled on a PC with a custom-made program ( EVAN ) written in LabView ( National Instruments ) . A pulse generator ( 4000 PG; Neuro-Data Instruments ) was used to trigger the sampling , UV laser , headstage reset and blanking , and the shutter for the OGB-5N excitation light . For a typical experiment , an approximately10-μl droplet of DMn solution was placed in the recording chamber . During each flash , only 0 . 05% to 1 . 5% of the DMn in the spot directly in front of the optical fiber was uncaged ( predicted by our model; see Results ) . However , to avoid significant changes in baseline conditions due to excessive uncaging of DMn or evaporation , no more than three flash-evoked transients were acquired from each droplet . Measurements in the same droplet were performed at least 1 min apart to ensure that all the components in the droplet returned to steady-state baseline conditions . The data were stored for offline analysis of the fluorescence transients with a computer model ( see below ) . We used the low-affinity dye OGB-5N because of its fast kinetics of Ca2+ binding and unbinding needed for tracking the expected rapid changes in [Ca2+] . The properties of the dye were determined as previously described [25] . For one batch of the dye ( lot# 34B1–2; Molecular Probes ) , we found a Kd of 29 . 3 μM , a koff of 7 . 52 × 103 s−1 , a kon of 2 . 6 × 108 M−1s−1 , and an Fmin/Fmax ratio of 10 . 8 . For another used batch ( lot# 15C1–2 ) , we measured a Kd of 36 . 1 μM , a koff of 8 . 67 × 103 s−1 , a kon of 2 . 4 × 108 M−1s−1 , and an Fmin/Fmax ratio of 40 . 0 . These values were used in the mathematical model ( see below ) to describe the properties of the two batches of OGB-5N used in the various experiments . We have no explanation for the variability between these two batches other than the fact that specific contaminations might occur in different batches from the supplier ( Molecular Probes , personal communication ) . For each group of experiments , we determined the properties of DMn independently by uncaging experiments with no protein present , as described before [25] . These properties of DMn were then set for that specific experiment to compensate for possible differences between DMn batches . The observed properties of DMn were comparable to ones previously found ( see Table 1 in Protocol S1 ) [25] . Human recombinant CR was expressed in Escherichia coli and purified with a series of chromatographic steps as described before [4 , 44] . The purity of the isolated protein was estimated to be greater than 98% as judged from bands on SDS polyacrylamide gels ( unpublished data ) . The initial protein concentration was determined by absorption measurements at 280 nm and using a molar extinction coefficient ε280nm of 26 , 860 . Small aliquots of the protein ( 100–500 μg ) in 10 mM ( NH4 ) HCO3 , 0 . 1 mM CaCl were lyophilized and then reconstituted directly in the solutions used for the uncaging experiments . To accurately determine the protein concentrations of all solutions used for the uncaging experiments , 10–15-μl samples were removed , stored at −20 °C , and simultaneously measured at the end of the series . The protein concentration was measured using a detergent-compatible assay based on a folin-phenol reagent ( Bio-Rad ) and using bovine serum albumin ( BSA ) as standard . All samples were measured in duplicates . Initial tests with solutions containing DMn and OGB-5N revealed that the colorimetric effect of these compounds was negligible at the concentrations present in the experimental solutions . The accuracy of the concentration measurements was validated by one round of fitting , in which the CR concentrations were fitted by the model , while the kinetic rates were allowed to deviate maximally 10% from their expected value . These fits confirmed the results of the protein assay . All data were analyzed using MS Excel ( Microsoft ) and Berkeley Madonna 8 . 0 ( University of California Berkeley ) . To determine the kinetic parameters ( association and dissociation rates ) from the fluorescence recordings , we used a mathematical model build in the ordinary differential equation solver Berkeley Madonna 8 . 0 that incorporates all of the reactions in the uncaging solution ( Figure 1 ) . The DMn uncaging and OGB-5N signaling part of this model was used earlier to determine the exact properties of DMn [25] . This model was expanded with a part to simulate the binding of Ca2+ to CR ( see Figure 1B , and for a complete description of the models , see Protocol S1 ) . The model fit the simulations to the fluorescence recordings by iterating the parameters with the fourth-order Runge-Kutta method . The output of the model is: where F ( t ) is the fluorescence acquired over time ( t ) with t = 0 at the time of the flash , and Frest is the resting fluorescence averaged over 50 ms before flash delivery . To more accurately fit the fast-rising phase while avoiding bias from late slow-decaying phase of the fluorescence transients , data points were omitted exponentially towards the end of every trace the fitting routine . | The binding of a ligand to a protein is one of the most important steps in determining the function of these two interactive biological partners . In many cases , successive binding steps occur at multiple sites such that binding at one site influences ligand binding at other sites . This concept is called cooperative binding , and constitutes one of the most fundamental properties of biological interactions . The functional consequences of cooperativity can be accurately resolved when reactions are at equilibrium , but mathematical complexity has prevented insights into the dynamics of the interactions . We studied the protein calretinin , which binds Ca2+ in a cooperative manner and plays an important role in shaping Ca2+ signals in various cells . We used two models , a widely tested one and a novel , mathematically simplified one , to resolve the dynamics of a cooperative binding process . The cooperative nature of Ca2+ binding to calretinin results in accelerated binding as calretinin binds more Ca2+ . This behavior constitutes an important new insight into the regulation of intracellular Ca2+ that cannot be matched by noncooperative artificial Ca2+ buffers . Our simple mathematical model can be used as a tool in determining the kinetics of other biologically important molecular interactions . | [
"Abstract",
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] | [
"biochemistry",
"biophysics",
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] | 2007 | Resolving the Fast Kinetics of Cooperative Binding: Ca2+ Buffering by Calretinin |
Computational biology is replete with high-dimensional ( high-D ) discrete prediction and inference problems , including sequence alignment , RNA structure prediction , phylogenetic inference , motif finding , prediction of pathways , and model selection problems in statistical genetics . Even though prediction and inference in these settings are uncertain , little attention has been focused on the development of global measures of uncertainty . Regardless of the procedure employed to produce a prediction , when a procedure delivers a single answer , that answer is a point estimate selected from the solution ensemble , the set of all possible solutions . For high-D discrete space , these ensembles are immense , and thus there is considerable uncertainty . We recommend the use of Bayesian credibility limits to describe this uncertainty , where a ( 1−α ) % , 0≤α≤1 , credibility limit is the minimum Hamming distance radius of a hyper-sphere containing ( 1−α ) % of the posterior distribution . Because sequence alignment is arguably the most extensively used procedure in computational biology , we employ it here to make these general concepts more concrete . The maximum similarity estimator ( i . e . , the alignment that maximizes the likelihood ) and the centroid estimator ( i . e . , the alignment that minimizes the mean Hamming distance from the posterior weighted ensemble of alignments ) are used to demonstrate the application of Bayesian credibility limits to alignment estimators . Application of Bayesian credibility limits to the alignment of 20 human/rodent orthologous sequence pairs and 125 orthologous sequence pairs from six Shewanella species shows that credibility limits of the alignments of promoter sequences of these species vary widely , and that centroid alignments dependably have tighter credibility limits than traditional maximum similarity alignments .
The study of genomics , and much of computational molecular biology , is about the inference or prediction of discrete , high-dimensional ( high-D ) unobserved variables , based on observed data . For example , in RNA secondary structure prediction , the challenge is to select a specific set of base pairs from a combinatorially large collection , as a prediction of the secondary structure of an RNA polymer , given its sequence . Similarly , in pathway inference , the challenge is to select a set of graph edges to connect genes or their products ( nodes ) from a combinatorially large collection of possible edge sets , based on gene expression or other data . Model selection problems for studying diseases stemming from mutlifactorial inheritance are becoming increasing common in the post-genome era . In these studies , the ultimate goal is to identify the combinations of genes responsible for inheritance components of disease etiology based on genetic and/or other post-genome data . In motif finding , the challenge is to select a single member of a large ensemble of possible combinations of motif sites in a set of sequences . Procedures that select the single best scoring solution , such as maximum similarity , maximum likelihood , maximum a-posteriori ( MAP ) , or minimum free energy , dominate nearly all of these problems . Sequence alignment is a typical example and is arguably the most important high-D discrete prediction problem for biology . Because it is the cornerstone capability used by a multitude of computational biology applications , we employ sequence alignment to make these general concepts concrete . Sequence alignment methods commonly focus on identifying the highest scoring alignment between two sequences , and assessing the statistical significance of this alignment [1]–[7] . Thus , alignment algorithms , heuristic [5] , [8]–[10] ( http://www . ncbi . nlm . nih . gov/BLAST/ ) and optimization [11] ( http://fasta . bioch . virginia . edu/fasta_www2/ ) alike , typically report the selected alignment , and a statistical score that assesses how likely an alignment with a score as good or better could have emerged by chance , under a specified null distribution ( commonly an E-value ) . While methods that assign the significance of alignments under a null distribution have been well studied , assessments of the uncertainty of a proposed alignment , defining the confidence in this alignment and assessing its overall reliability , have received considerably less attention . Regardless of the alignment procedure employed , when a single alignment is chosen for the comparison of two ( or more ) sequences , it is a point estimate ( or estimating alignment ) selected from a large ensemble of all possible alignments . For example , two sequences of length m and n have possible local alignments , where k represents the number of matches in the alignment [11] , [12] . This number grows rapidly with the length of the sequences being aligned; for example , two small sequences of only length 20 generate over 1029 possible local alignments . The question addressed here is: How , based on the available data , should we articulate the overall uncertainty of a selected estimating alignment ( how well does it represent the large ensemble of possible solutions ) , and thus assess the reliability of this alignment ? The traditional approach to address the reliability of a single alignment is to evaluate the optimal alignment in the context of a set of near-optimal alignments . Near-optimal or suboptimal alignment analysis involves evaluating residue alignment consistency over the set of defined near-optimal alignments [12]–[17] . Specifically , the reliability of an alignment position ( i , j ) is assessed by comparing the score of the optimal alignment to the score of this alignment under the constraint that positions i and j do not align [14] , [15] . More advanced methods have been proposed that determine reliability measures between residues aligned to both residues and gaps [17] . An alternative to computing near-optimal alignments , involving a single model that assigns probabilities to a specific residue pair , such as a pair Hidden Markov Model [7] , [18] , [19] , can be derived and used to assess the reliability of individual aligned pairs . With this in mind , these near-optimal alignment and model-based methods have offered significant improvements in reliability for tasks such as structural alignment . However , these methods are focused on delineating the reliability/uncertainty of the individual components of an estimated alignment , not the reliability of an estimated alignment in the context of the entire alignment space . There are methods to assess the accuracy of an alignment in the prediction of a ground-truth standard such as an alignment based on crystal structures [7] , [18] , [20]–[22] . But our focus here is on assessment of the reliability of an alignment based on its own characteristics , rather than the assessment of its accuracy in predicting an established reference . Toward this end , we describe a procedure for global assessment of the degree to which the members of the ensemble may depart from a selected estimate . The introduction of probabilistic alignment methods [23]–[26] established the notion of sequence alignment as an inference procedure . For example , optimization-based alignment routines often search for the single alignment that is most probable among all those in the entire space of alignments . It is not surprising , given the immense size of the alignment space , that the most probable alignments , and thus all individual alignments , often have very small probabilities . This finding raises three questions: We suggest the following answers to these questions: To address these questions and test our proposed answers , we employ a Bayesian probabilistic approach . In the Methods section , we review some concepts on probabilistic alignments and distance measures , and then consider the distribution of the distances of the alignments in an ensemble from a proposed estimating alignment , including the quantiles and expected value of this distribution . We use the quantiles to identify credibility limits . The identification of credibility limits begs the question: What procedures can be developed to identify alignments with tight credibility limits ? In an effort to achieve this goal , we employ statistical decision theory to find an estimation procedure that identifies the estimates with the minimum average distance from the posterior weighted ensemble; that is , the centroid . Centroid estimators , which were recently described by Carvalho and Lawrence [27] , look promising to yield tight credibility limits because they minimize an average Hamming distance . Furthermore , we show that since popular procedures that select an estimate because it scores better than any other single solution ( e . g . , maximum likelihood , maximum similarity , maximum a-posteriori Viterbi solutions ) are optimal under a zero/one-loss function , there is no principled reason to expect them to have tight credibility limits and , thus , to have high credibility . Below we compare the credibility limits for centroid alignments to those for maximum similarity alignments .
A probabilistic alignment model from which samples can be drawn can be described as follows . An alignment describes a set of aligned residues and associated insertion and deletion events . For a pair of sequences , and , let A be a matrix that characterizes an alignment whose ( i , j ) -entry is defined as:Without loss of generality , let I≤J . Because a residue cannot align with more than one other residue , two constraints must be satisfied , and . In addition , the alignment co-linearity constraint requires that Ai , j+Ak , l≤1 , i≤k , l≤j . Let Θ be a matrix of residue pair similarities , such as one of the BLOSUM [29] or PAM [30] scoring matrices , and let Λ = ( λo , λe ) be the probability of opening and extending a gap , respectively . Most sequence alignment methods optimize an objective function that can be described , based on a probabilistic model , as a log-likelihood [31] , [32] . In traditional ( frequentist ) statistics , only the observed data , here R ( 1 ) and R ( 2 ) , are seen as random variables , and the remaining terms are deterministic variables with perhaps unknown values . In maximum likelihood estimation , the values of these unknowns , which maximize the likelihood , are the maximum likelihood estimates . Typically , the user must set specific parameter values for the scoring matrix Θ0 and gap probabilities Λ0 to find the most probable alignment A* over all possible alignments: ( 1 ) This alignment is guaranteed to be the alignment that has the largest probability over all possible alignments , and with appropriate re-parameterization , it can also be shown to be the maximum similarity ( MS ) alignment [19] . To capture the entire alignment space in a probabilistic manner , the problem of alignment can be formulated as a Bayesian inference problem [19] , [23] , [26] . The Bayesian Algorithm for Local Sequence Alignment ( BALSA ) [24] describes such a probability model , the full joint distribution of all alignments , as the product of the likelihood and priors:Recursion can be employed to marginalize ( i . e . , sum out ) over all possible alignments to obtain the marginal probability of the data in the two sequences , given only the defined scoring matrix , Θ0 , and gap penalties , Λ0:The required sums are completed in an analogous manner to the Smith-Waterman recursion by essentially replacing the maximum function with a summation . The alignment parameters Θ and Λ can also be defined as random variables and marginalized over using Markov chain Monte Carlo ( MCMC ) sampling methods . In this application , to mirror common alignment practice , a specific scoring matrix ( PAM 110 ) and gap-penalty parameters ( gap opening = −14 and gap extension = −2 ) were selected as generic parameters used by sequence alignment algorithms . Now the probability of any single alignment can be computed as a posterior probability using the following Bayes formula: ( 2 ) Equation 2 is a ratio of the likelihood of the data and the alignment A* to the sum of these joint likelihoods over all alignments . It approaches a value of 1 when a single alignment dominates all others . Given that the number of possible alignments for even small biopolymer sequences is immense , it is not feasible to calculate the probability of all alignments in a brute force manner . However , we can almost always use the recursive relationships that are fundamental to dynamic programming ( DP ) to draw guaranteed representative samples from the solution ensemble [19] . Because of the power of the recursions , such sampling procedures require no burn-in period to ensure that the samples are drawn from the equilibrium distribution , and these samples are independent of one another . Briefly , these algorithms use modified versions of the two fundamental steps of DP: the forward and back-trace recursions . In DP , the forward recursion finds the optimal value of the objective function ( e . g . , the best total alignment score ) by using optimal solutions of subproblems to recursively build up to the best total score . In the sampling algorithm , we instead use an analogous recursion to build up to the sum over the entire ensemble of solutions . This sum finds the normalizing constant that assures that probabilities sum to one . In the back-trace step , instead of finding the solution that yields the optimal value of the objective function , we use an analogous recursion to sample solutions in proportion to their posterior probabilities . An important unappreciated fact is that for large ensembles , the accuracy of estimates based on a sample depends on the sample size only , and not on the size of the population [23] . Thus , a representative sample ( i . e . , a sample drawn in proportion to the probabilities of the unknowns ) of even modest size , say 1000 , can yield accurate estimates of unknowns , even if this sample is drawn from an ensemble of immense size . As we illustrate below , representative samples can be used to estimate credibility limits and define an ensemble centroid ( EC ) solution . In this section , we describe procedures for finding credibility limits and mean distances for the sequence alignment problem . We begin by examining the distribution function of the distances of the ensemble members from a proposed estimate . Basic to this perspective are two concepts: 1 ) given the available data , the solution space is inherently uncertain; and 2 ) a proposed estimate is a point estimate ( i . e . , a single member of the ensemble ) that is intended to represent the entire ensemble [33] . A simple measure of the difference between two members of a discrete ensemble ( e . g . , two possible alignments of a pair of sequences ) is the Hamming distance . For two alignments , A ( k ) and A ( m ) , of a pair of sequences , R ( 1 ) and R ( 2 ) , of length I and J , the Hamming distance is simply the number of aligned positions that differ between A ( k ) and A ( m ) , D ( A ( k ) , A ( m ) ) . For alignments , this distance is simply the sum of the differences in two binary matrices of size ( I×J ) . When ensemble members are binary objects , the Hamming distances are also equal to distances on other scales [34]: ( 3 ) Using the metric in Equation 3 , the distance between any proposed estimating alignment and the ensemble of alignments can be computed regardless of how one selects the estimating alignment . In this report , we compare the results of using two different estimating alignments: AM , the MS alignment , and AC , the EC alignment . Specifically , let Di = D ( Ai , Ax ) be the distance of the ith member , Ai , of the ensemble from a proposed estimating alignment , Ax , where X is a categorical variable indicating the estimator ( X∈[M , C] ) . We then rank the ensemble members by their distances from Ax , and let be the order statistics of these distances ( i . e . , the distances of the ensemble members from the estimating alignment ) with the indices permuted to reflect their order in the distance ranking [35] . The distribution function of the distances is: ( 4 ) where d ( 1−α ) is the ( 1−α ) th quantile . Now the credibility limit at ( 1−α ) is d ( 1−α ) . While higher-order DP recursions can be used to obtain these limits , they can also be quite reasonably estimated from a representative sample of even modest size by the following algorithm [35]: The expected value of Di is ( 5 ) where An , i , j is 1 if i aligns with j in the nth member of the sample , and zero otherwise; qi , j is the marginal probability that An , i , j = 0; and pi , j is the marginal probability that An , i , j = 1 . The required marginal probabilities can be estimated based on a sample , or when DP is available , they can be obtained using the forward- and back-trace algorithm described by Durbin et al . [19] . Hamming distances will , in general , be dependent on the lengths of the ensemble members . For example , in alignment , longer sequences will tend to return larger distances simply because the alignment matrix is larger . Thus , normalization is in order . For this normalization , we employ a normalization factor that uses maximum realized alignment lengths . Specifically , when calculating a credibility limit , the length of the estimating alignment ( LE ) is known , and the maximum length of an alignment in the ensemble is the length of the shorter of the two sequences ( I ) . Thus , the maximum Hamming distances between an estimating alignment and the longest member of the ensemble is ( LE+I ) . However , in our studies , we found that using this sum as a normalizing factor was misleading for cases in which the posterior space of alignments tended to be dominated by shorter local alignments . For example , the local alignments of the randomly shuffled sequences described in the Results section ( see Figure 1 ) were dominated by short alignments . As a result , using ( LE+I ) as the normalizing constant in this case produced normalized distances that were not close to one , even when there were no base pairs in common between a sampled alignment and the estimating alignment . To adjust these differences , we used the length of the longest sampled alignment , LS , as the second term in our normalizing sum , and the normalizing distance between the estimating alignment Ax and the ith alignment in the sample is where S indicates the set of sampled alignments . Using this normalization factor yields normalizing distances with values between zero and one . A perfect match would yield an ND score of zero , and in the case where the longest sampled alignment has no base pairings in common with the estimating alignment , the ND score would be one . We define the credibility of the alignment at ( 1−α ) to be ND ( 1−α ) . Maximum similarity alignments , and the associated Viterbi alignments , have been the dominant alignment procedures for decades . In these procedures , an alignment output is typically the single alignment that has the maximum probability over all possible alignments . However , having the largest probability does not indicate that it represents the alignment space described by the billions ( or more ) possible alignments , except in the unusual event that this single alignment alone has high probability . In fact , the most probable alignment , the MS alignment , often has very small probability . For example , in this study , the probabilities of the MS alignments ranged from 10−37 to 10−249 for the alignments of the human/rodent pairs of gene and promoter sequences . Because it is the most probable alignment for a pair of sequences , all other alignments for that pair can be no more probable than the MS alignment . Thus , from a Bayesian prospective , any individual alignment represents the data only weakly at best . As Carvalho and Lawrence [27] point out , procedures that identify the single , highest scoring alignment are optimal under a zero/one loss function . Accordingly , after the highest scoring alignments have been identified , all other alignments have a penalty of one ( i . e . , are all equally unimportant ) ; thus , if no single alignment has a high probability mass then the expected loss will be large . As a result , with zero/one loss there is no reason for the optimal alignment to be positioned near any other member of the ensemble of alignments , therefore failing to garner support from any other member of the ensemble . In contrast , centroid alignments garner information from the complete ensemble of alignments , because these alignments minimize the expected Hamming distance from the complete posterior weighted ensemble of alignments . Centroid alignments correspond directly to the reliable alignments of Miyazawa with a cut off 0 . 5 [26] . Reliable alignments are further described by Durbin et al . [19] and are elaborated on by Holmes and Durbin [34] . Furthermore , because these alignments minimize the average Hamming distance , we expect that they may yield tighter credibility limits than MS alignments . The alignment that is the centroid of the entire ensemble of alignments is called the EC alignment . These alignments meet the exclusive pairing and colinearity constraints of the alignment problem , but they do not necessarily meet the common requirement that a gap in one sequence cannot be followed by a gap in the other sequence . We compare the credibility limits of MS alignments and EC alignments below .
The 20 orthologous genes for human/rodent are specifically up-regulated in human skeletal muscle tissue , and their upstream sequences have been used in previous studies to locate cis-regulatory modules [36] . The coding regions of the 20 human/rodent orthologous gene pairs were evaluated , as were the 20 sequence pairs that represent up to 3 kb of sequence upstream of the orthologous gene pairs . All sequence pairs were masked using RepeatMasker ( http://www . repeatmasker . org/ ) . For the local alignments of the 20 gene pairs and the 20 intergenic regions , we examined the credibility limits associated with two estimating alignments: the MS , and the EC . Specifically , we examined the 95% quantiles of the normalized distances ( ND ) , computed based on the distances between these estimating alignments from the 1000 sampled alignments from the posterior alignment distribution . Figure 1 shows a scatter plot of the MS 95% credibility limits ( MS ND95 ) versus the EC 95% credibility limits ( EC ND95 ) for the local alignments of the genes and the intergenic regions . For contrast , the genes were randomly shuffled , and 95% credibility limits were defined for these non-related sequence pair alignments . First , notice that the credibility limits for the gene sequence alignments are small , and the difference between the EC and MS is negligible . These genes are so highly conserved that the majority of the posterior distribution falls along a small set of paths with high probability , thus creating high correlation between the EC and MS . Alternatively , when the gene sequences are shuffled , the hyper-sphere surrounding 95% of the posterior distribution is very large because the probability of aligning any two residues is essentially random . This results in extremely large credibility limits with high deviation in the distance of the ensemble from the EC and MS . The intergenic regions are less conserved than the genes and , thus , are intermediate between these two extremes . Notice that the credibility limits are often surprisingly large , with normalized distances over 50% for 18 of the 20 MS alignments , and for 17 of the 20 EC alignments . This indicates that we have confidence in less than half the predicted aligned base pairs . As the plot shows , there is considerable variation in the credibility limits over the 20 examples when either the EC or MS limit is used . The credibility limits for the EC range from 29% of maximal to nearly 91% , while the MS limits range from 37% to almost 100% of maximal . This result highlights the need to report credibility limits for every sequence pair . We also see that for all but one of the sequence pairs , the MS credibility limits are greater than those for the EC . Furthermore , for 11 of the 20 upstream sequence pairs , the MS credibility limits were more than 600 base pairs larger than EC credibility limits . Thus while the differences in Figure 1 look modest , the MS credibility limits are often hundreds of base pairs larger than those of the EC estimators . Taken together , the differences between the 20 MS normalized distances and 20 EC normalized distances in Figure 1 are significantly different ( i . e . , p<0 . 001 , Wilcoxon Signed Rank test [37] ) . To offer further insight , we chose four alignments from the 20 to examine in more detail ( Table 1 ) ; the results for all 20 pairs are in Table S1 . In Figure 2 , we show histograms of the distance of the 1000 sampled alignments from the two estimating alignments ( MS , EC ) ; in addition , the 95% quantile ( ND95 ) for the EC and MS are shown as bars , and the values are given in Table 1 . As Figure 1 indicates , pair ( A ) has the tightest credibility limits of all the promoter sequences . These tighter limits are a reflection of the fact that the ensemble of alignments is relatively close to the estimators; the 95th percentile alignment differs from the EC estimator by 270 of a possible 1556 base pairs that could potentially differ ( ND95 = 0 . 29 ) , while the MS is about 20% larger with an ND95 = 0 . 37 . Of the 20 promoter sequence pairs , there are 11 in which the two credibility limits are markedly different ( i . e . , by more than 0 . 05 ) . Figure 2D is another illustration of the characteristics of these 11 pairs for which the MS credibility limits are substantially larger than those of the EC , although for pair ( D ) the distance distributions have very little overlap , as well as large credibility limits . Figure 2C is representative of the remaining nine pairs , in which the posterior surface is quite flat , and the two credibility limits differ by less than 0 . 05 . For the sequence pair shown in Figure 2C , the credibility limits for both estimators are large . Because the EC alignment is the nearest alignment to the mean [34] , the large size of this limit for the EC alignment indicates that the alignments in the posterior distribution are widely dispersed over the ensemble . Also notice that in ( B ) and ( C ) , the two distributions overlap substantially and have high ND95 values; for example , the alignment in Figure 2B shows a ND95 = 0 . 72 for the EC , and ND95 = 0 . 77 for the MS alignment . Because the centroid estimator is the closest feasible alignment to the mean , for this sequence pair the mean and the mode are close , as is typical of symmetric distributions [27] . We also examined the credibility limits for the MS and EC estimators for local alignments of orthologous pairs of intergenic regions ( up to 500 bp upstream of orthologous genes ) from six species of Shewanella for which full genome sequence data are available: 1 ) S . denitrificans OS217 ( DENI ) , 2 ) S . loihica PV-4 ( SPV4 ) , 3 ) S . oneidensis MR-1 ( SONE ) , 4 ) S . putrefaciens CN-32 ( CN32 ) , 5 ) Shewanella sp . MR-4 ( SMR4 ) , and 6 ) Shewanella sp . MR-7 ( SMR7 ) . We chose SMR4 as our base species , aligning orthologous sequences from each of the other five to the region from SMR4 . Starting with SMR4 , the species in order of increasing evolutionary distance are SMR4>SMR7>SONE>CN32>SPV4∼DENI . As before , we examined the 95% quantiles of the normalized distances , computed based on the distances between the estimating alignments and the sampled ensemble of alignments drawn from the posterior alignment distribution . Figure 3 shows a scatter plot of the MS ND95 versus the EC ND95 values for each of 24 randomly selected orthologous regions , for the pairwise comparison of SMR4 to each of the five species at varying evolutionary distances ( 120 total comparisons ) . The two species SMR4 and SMR7 are very closely related , having been isolated from samples taken at different depths ( 5 m and 60 m , respectively ) from a single location ( latitude and longitude ) in the Black Sea [38] . Thus , it is not surprising that even the intergenic regions are highly conserved and that the EC and MS exhibit tight credibility limits . Among the comparisons to species at increasing evolutionary distance , we observe increasing credibility limits . In fact , for many of the SMR4-DENI sequence pairs , the credibility limits are no better than expected for randomly shuffled sequence . While , on average , the credibility limits of a pair of species increase with increasing evolutionary distance , the figure also shows that the credibility limits of the alignments for a given pair of species vary greatly . For example , even though the credibility limits of most SMR4-DENI pairs are large ( >0 . 8 ) , there are sequence pairs from these two species that have credibility limits <0 . 3 . The fact that there is wide variability in credibility limits for all of these pairs of species , except SMR4-SMR7 , highlights the importance of assessing the reliability ( credibility limits ) of nearly all alignments . For example , there is a pair of SMR4-CN32 sequences whose alignment is very reliable ( EC ND95 and MS ND95<0 . 05 ) , but there are also three pairs whose alignments cannot be trusted ( EC ND95 and MS ND95>0 . 6 ) , and the remainder are scattered over the full range in between . We further evaluated the findings shown in Figure 3 in the context of a single gene's orthologous upstream sequences . Often in evaluating promoter sequences across species it is unknown a priori which sequences it would be most beneficial to align . The tight credibility limits shown in Figure 4A and 4B indicate that when evaluating the promoter region of SMR4_0576 , we would have confidence in the alignments with the orthologous region from SONE and CN32 ( also with SRM7 , data not shown ) . This is not the case for the orthologous regions from SPV4 and DENI . The high ND95 values for the EC and MS alignments indicate that alignment of SPV4 or DENI sequences would not contribute to a meaningful evaluation of the SMR4_0576 promoter region . Unfortunately , not all alignments of promoter regions from SMR4 with the promoter sequences of orthologous genes in SONE and CN32 are reliable . For example , as Figure 5 shows , the posterior distribution of the alignments of the SMR4_ 1557 promoter region with its CN32 ortholog is substantially more widespread and variable than the posterior distribution of alignments for the promoter region of SMR4_0576 with its orthologous region in CN32 . These findings of large differences in the reliability of alignments within species pairs have had a substantial practical impact on our studies of phylogenetic motif finding using these Shewanella species . Specifically , alignment of orthologous promoters can substantially increase the power of motif finding , if the alignments can be trusted . However , the findings shown in Figure 5 indicate that reliance on a single genome-wide measure of species distances is very frequently insufficient to assure that alignments of promoters from species pairs can be trusted . Thus , we are using credibility limits on a gene-by-gene and species-by-species basis to make decisions about which alignments can be trusted . The use of heat maps or other means to visually illustrate confidence in the individual alignment of individual pairs of bases must accommodate a different feature for centroid alignments . Specifically , EC alignments have a feature not present in standard alignments , in that they allow stretches of sequence in the middle of an alignment to remain unaligned in a manner analogous to those regions at the ends of local alignments . That is , a residue in one sequence that cannot be reliably aligned with any single residue in the other sequence is excluded from the centroid alignment . Aligning any such residues to any bases in the other sequence would only increase the average distance of the centroid alignment from the posterior distribution of alignments . In addition , with probabilistic alignment , we return marginal probabilities of all residue pairs . Therefore , to display all the features of this alignment , we employ 1 ) a traditional dash to represent gaps , 2 ) a dot to represent residues that cannot be reliably aligned and are thus ignored in the alignment , and 3 ) a gradient color scheme ( i . e . , a heat map ) to show the base pair alignment probabilities , where red indicates high probability for that residue pair , green indicates probabilities nearing 50% , and the ignored region is grayed out to further differentiate those residues for which the variability in alignments is too great to permit marginal pair probabilities of 0 . 5 or greater . Figure 6 gives an example of the heat map alignment display for a human/rodent intergenic sequence pair ( the region upstream of the MYL2 gene ) . The red-to-green coloring of aligned regions allows quick distinction of areas of alignment of high versus low confidence .
Our findings of 1 ) high variability in the credibility limits in the alignments of promoter sequences of 20 human/rodent sequence pairs and 2 ) similar high variability among 4 of the 5 pairs of Shewanella species highlight the need for assessing the overall reliability of sequence alignments . Without such limits , there is little to distinguish alignments that vary greatly from one another in their reliability . Furthermore , our findings indicate that centroid estimators have promising potential to improve sequence alignment . For example , for over half of the human/rodent non-coding sequence pairs ( each of ∼3000 bases ) in our sample , the EC and MS alignments differ by more than 600 base pairs , and similar relative differences are observed in Shewanella alignments . While we report here on the credibility of nucleotide sequence alignments , they are equally applicable and valuable for protein sequence alignments . In some discrete high-D inference problems , the posterior ensemble of solutions may not only be asymmetric , but also it may be multimodal , as has been reported for RNA secondary structures [39] . Since , in such a case no single point estimate can reasonably represent the posterior ensemble , class-specific estimates , with one for each distinct class , will be required . In these cases , samples associated with each class can be used to find credibility limits for the class estimates , and the overall credibility limits around these class-specific estimates can be identified based on distances to the nearest class estimate . As mentioned above , the probabilistic model used is a Smith-Waterman recursive DP algorithm whose Viterbi alignment corresponded exactly to the MS alignment reported here . Thus , differences in credibility limits reported here are solely the result of the differences in the estimation procedures . In addition , the alignment that minimizes expected Hamming distance loss and also follows the requirement concerning adjacent gaps in the two sequences are available using a DP algorithm [19] , [34] . However this alignment can only increase the average Hamming distance above that of the centroid . While we believe this evidence supports reconsideration of the maximum scoring alignment paradigm , stronger evidence for reconsideration has been in the literature for over a decade . In 1995 , Miyazawa [26] was the first to report what we now call centroid alignments [27] . In addition to his very insightful development of reliable alignments , he showed that these alignments are superior , using x-ray crystal structures of proteins as ground truth . Figure 7 ( reproduced from Miyazawa's work [26] , with permission of the author and Oxford Journals ) shows that structural predictions based on reliable ( centroid ) alignments quite consistently produce lower root mean squared deviations than those based on maximum similarity alignments . Thus , from a practical biological prospective , there is already clear evidence in the literature that centroid alignments can be applied with advantage in the prediction of protein structures . We also note that the time complexity of algorithms for obtaining centroid alignments and credibility limits is not different from those of more traditional optimization based methods . When recursions can be employed to obtain optimal solutions via DP , analogous recursions are frequently available for associated probabilistic models , and stochastic back-trace procedures can be employed to draw samples from the posterior ensemble of solutions [19] . In general , the time complexity for drawing these samples will be the same as that of the associated DP algorithm , and is set by the forward step of these algorithms . For example , in local sequence alignment , the most computationally intensive step is the forward-recursive step . For two sequences of length n and m , the time complexity is O ( n*m ) for both the optimization and Bayesian algorithms . Running times to obtain credibility limits in a recursive setting will generally be longer than times required to obtain optimal estimates because a back-trace step must be executed only once to obtain the optimal , while it must be employed multiple times to draw samples . However , this sampling will not generally greatly increase overall running times , because back-trace recursions are usually of a lower time complexity than their forward steps . For example , for local alignments the time complexity of the back-trace recursions is only O ( min ( n , m ) ) . For problems not open to recursive solutions , MCMC algorithms are commonly employed , using procedures like simulated annealing . Credibility limits and centroids also can be obtained using MCMC sampling with run times that may be less than those for optimizations [27] . Some caveats are appropriate . In settings in which uncertainty is low , such as shown for the alignments of coding regions of human/rodent sequence pairs in Figure 1 and the promoter sequence pairs of very closely related species like Shewanella sp . MR-4 and MR-7 in Figure 3 , credibility limits will likely be tight and not vary greatly among examples . Nevertheless , it would be reassuring to document this low variability by reporting credibility limits . While we have given principled arguments supporting our belief that centroid solutions should dependably have tighter credibility limits than optimization estimators , this advantage cannot be guaranteed . However , this trend was observed in both the human/rodent pairs and the Shewanella pairs . In our on-going work with Shewanella , we have found 1329 orthologous genes that were present in all six species and computed the 95% credibility limits for both the MS and EC , for all the promoters from SMR4 aligned with the orthologous sequences from each of the remaining 5 strains . The EC ND95 credibility limits were smaller than the MS ND95 limits in 6078 ( 91 . 55% ) of these 6645 sequence pairs ( i . e . , p<1e-100 , Wilcoxon Signed Rank test [37] ) . In our comparison of centroid alignments to MS alignments , we focused on the alignment of individual pairs of sequences . However , we did not address how these two estimators would compare if we had available multiple pairs of sequences all drawn from a model with a single common “true” alignment . In the context of sequence alignment , such a situation would not be observed in nature because we know of no families of biological sequence pairs for which one can be confident that sequence pairs within this family all follow the same “true” alignment . For example , even for sequence pairs drawn from orthologous regions from clearly related species , alignments are likely to differ . This same absence of replicates , all of which are sampled from the same “true” value of the unknown , is expected for many , but not necessarily all , high-D discrete biological inference problems . Even when obtaining a large number of such biological replicates is possible in principle , such as a large number of biological replicates in a microarray study , obtaining them in practice is often prohibitively expensive . However , with advances in technology , this limitation may be overcome . When a substantial number of such replicate observations are available , the asymptotic properties of maximum likelihood estimates , such as consistency and asymptotic unbiasedness , can be brought to bare . In such cases , as sample size increases , the MS estimator will approach the true value , and the bias will tend toward zero . This reduction in bias might well counter-balance the higher variability ( high credibility limits ) reported here for individual sequence pairs . The findings reported in this paper are for pairwise alignments . When multiple alignments are employed , we expect credibility limits to narrow because of the increased size of the data sets; however , we caution that the alignment space grows rapidly with increasing sequences in an alignment . Therefore , these limits may or may not shrink as quickly as expected . Furthermore , it is important to keep in mind that the credibility limits reported here are sampling estimates of true 95% quantiles , but with samples of 1000 the error bars on these estimates are 95%±1 . 35% . All the estimates in this work are based on a local probabilistic alignment model . While local alignment is the most common procedure , other probabilistic alignment procedures , or local alignments with other parameter settings [25] , [26] , may give varying results . As is common practice , all alignments here are given for a fixed set of parameters . Alignment parameters also can be estimated from the data; perhaps with such an approach , credibility limits could be smaller and more consistent , although this may not be the case because uncertainty of the parameter estimates would be introduced into the procedure . Beyond the usual interest in putting error limits on point estimates , our findings of substantial variability in credibility limits of alignments argues for wider adoption of these limits , so that the degree of error is delineated prior to the subsequent use of the alignments . From a practical prospective , when credibility alignments are tight , those using these alignments in subsequent procedures can be confident in the input alignments and know the limited degree to which input alignment may vary . The absence of such limits may well lead to a false sense of confidence in subsequent findings , especially when credibility limits are wide , and/or seriously limit an investigator's ability to determine the source of difficulties or inconsistencies in subsequent procedures that depend on these unreliable alignments . In practice , knowing early in a study that alignments required for subsequent results are unreliable ( i . e . , have high credibility limits ) might well lead an investigator to reconsider his/her plans . For example , in studies of phylogenetic tree reconstruction when it is known that input alignments are reliable , investigators' conclusions about phylogenetic relationships will be bolstered; whereas , prior knowledge that input alignments are unreliable will motivate serious investigators to revise their study design or , after the fact , permit reviewers to raise legitimate questions about the studies conclusions . While the results presented here concern only sequence alignment , the procedures described are generally applicable to point estimates for high-D discrete spaces; this includes many major inference problems in computational biology , such as pathway prediction in systems biology , the prediction of phylogenetic trees , the reconstruction of ancestral states , the delineation of alternate splice forms , and prediction of RNA secondary structures . For any of these problems , the algorithm given in the Methods section “Credibility limits and means distance” can be employed to obtain ND95 values for any proposed estimate given a procedure for drawing samples from the posterior distribution . We caution that while the Hamming distance will be appropriate in many of these areas , it may not be as appropriate in some of these settings . Regardless of the distance measure used , the proposed procedure will return credibility limits for an estimator when a representative sample can be obtained . We believe the use of confidence or credibility limits is long overdue throughout the full spectrum of discrete high-D inference problems encountered in computational biology . These limits have a number of valuable uses , including gauging the degree by which solutions might depart from their estimated value , appraising the overall credibility of a prediction , and comparing the performance of alternative estimators in cases where a “gold standard” is not available . | Sequence alignment is the cornerstone capability used by a multitude of computational biology applications , such as phylogeny reconstruction and identification of common regulatory mechanisms . Sequence alignment methods typically seek a high-scoring alignment between a pair of sequences , and assign a statistical significance to this single alignment . However , because a single alignment of two ( or more ) sequences is a point estimate , it may not be representative of the entire set ( ensemble ) of possible alignments of those sequences; thus , there may be considerable uncertainty associated with any one alignment among an immense ensemble of possibilities . To address the uncertainty of a proposed alignment , we used a Bayesian probabilistic approach to assess an alignment's reliability in the context of the entire ensemble of possible alignments . Our approach performs a global assessment of the degree to which the members of the ensemble depart from a selected alignment , thereby determining a credibility limit . In an evaluation of the popular maximum similarity alignment and the centroid alignment ( i . e . , the alignment that is in the center of the posterior distribution of alignments ) , we find that the centroid yields tighter credibility limits ( on average ) than the maximum similarity alignment . Beyond the usual interest in putting error limits on point estimates , our findings of substantial variability in credibility limits of alignments argue for wider adoption of these limits , so the degree of error is delineated prior to the subsequent use of the alignments . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"molecular",
"biology/bioinformatics",
"mathematics/statistics",
"computational",
"biology/sequence",
"motif",
"analysis"
] | 2008 | Measuring Global Credibility with Application to Local Sequence Alignment |
Molecular determinants and mechanisms of arthropod-borne flavivirus transmission to the vertebrate host are poorly understood . In this study , we show for the first time that a cell line from medically important arthropods , such as ticks , secretes extracellular vesicles ( EVs ) including exosomes that mediate transmission of flavivirus RNA and proteins to the human cells . Our study shows that tick-borne Langat virus ( LGTV ) , a model pathogen closely related to tick-borne encephalitis virus ( TBEV ) , profusely uses arthropod exosomes for transmission of viral RNA and proteins to the human- skin keratinocytes and blood endothelial cells . Cryo-electron microscopy showed the presence of purified arthropod/neuronal exosomes with the size range of 30 to 200 nm in diameter . Both positive and negative strands of LGTV RNA and viral envelope-protein were detected inside exosomes derived from arthropod , murine and human cells . Detection of Nonstructural 1 ( NS1 ) protein in arthropod and neuronal exosomes further suggested that exosomes contain viral proteins . Viral RNA and proteins in exosomes derived from tick and mammalian cells were secured , highly infectious and replicative in all tested evaluations . Treatment with GW4869 , a selective inhibitor that blocks exosome release affected LGTV loads in both arthropod and mammalian cell-derived exosomes . Transwell-migration assays showed that exosomes derived from infected-brain-microvascular endothelial cells ( that constitute the blood-brain barrier ) facilitated LGTV RNA and protein transmission , crossing of the barriers and infection of neuronal cells . Neuronal infection showed abundant loads of both tick-borne LGTV and mosquito-borne West Nile virus RNA in exosomes . Our data also suggest that exosome-mediated LGTV viral transmission is clathrin-dependent . Collectively , our results suggest that flaviviruses uses arthropod-derived exosomes as a novel means for viral RNA and protein transmission from the vector , and the vertebrate exosomes for dissemination within the host that may subsequently allow neuroinvasion and neuropathogenesis .
Exosomes are small membranous extracellular microvesicles ( 30 to 250 nm in diameter ) of endocytic origin formed in late endosomal compartments ( as multivesicular bodies; MVBs ) of several different cell types [1–5] . Initially , exosomes were considered as garbage bins to discard the unwanted cellular or molecular components or membranous proteins from reticulocytes [6–9] . Other studies have suggested that exosomes are mere cell debris or apoptotic blebs and signs of cell death [10–12] . Recently , the role of exosomes has been highlighted in important medical research on cancer and autoimmune diseases and they are now recognized as novel therapeutic targets for neurological disorders such as Parkinson’s disease [11 , 13–16] . Over the past 10 years , exosomes have been given potential biological significance by identifying a variety of their specific roles [3 , 5 , 11 , 17–20] . Exosomes derived from several different cells have been shown to function as signaling related vesicles , transporting cell-specific collections of several proteins , lipids and nucleic acids such as DNA , RNA and microRNA [12 , 20–28] . Exosomes are released into circulation after the fusion with the plasma membrane and these vesicles serve as mediators of molecular transmission [3 , 10 , 18 , 29] . Cell-derived exosomes have been shown to be important modes of intercellular communication and as transmitters of information over longer distances for e . g . , between different tissues or multiple organs [2 , 15 , 27 , 30 , 31] . Studies have also shown that exosomes are vehicles of transmission for a variety of microorganisms and that some pathogens uses exosomes to manipulate their environments [10 , 15 , 32–34] . As an example , malaria parasites , Plasmodium falciparum , uses exosomes for communication between infected red blood cells [35] . Hepatitis C virus ( HCV ) , an enveloped RNA virus , associates with exosomes isolated from cell culture supernatants and from infected patients [36 , 37] . Recent findings of HCV transmission through hepatic exosomes establish infection provides new insight into hepatitis drug discovery [38 , 39] . Exosomes also function in the transfer of immuno-stimulatory viral RNA from HCV-infected cells to co-cultured plasmacytoid dendritic cells [32] . In addition , exosomes facilitate receptor-independent transmission of replication-competent HCV viral RNA that was found to be in complex with Ago2-miR122-HSP90 in HCV-infected individuals or infected hepatocytes [36] . Interestingly , exosomes have been shown to play dual roles in transmitting Hepatitis A virus ( HAV ) and HCV , thereby evading antibody-mediated immune responses [40] . It has been demonstrated that Toll-like receptor 3 ( TLR-3 ) activated macrophages release exosomes containing anti-HCV micro ( miRNA ) -29 family members that suggest a novel antiviral mechanism against HCV infections [41] . Herpes Simplex-1 virus and Epstein-Barr virus also use exosomes for transmission [42 , 43] . Several studies have suggested exosomes as important players in HIV-1 pathogenesis [33 , 34 , 44] . HIV Nef protein secreted in exosomes has been shown to trigger apoptosis in CD4+ T cells and the Gag p17 coding RNA is also targeted to the exosomes [45 , 46] . HIV-infected cell-derived exosomes have been shown to contain the TAR ( Trans-Activation Response Element ) miRNA that facilitates production of pro-inflammatory cytokines [47 , 48] . Moreover , a recent but very highlighting study showed that exosomes from uninfected cells activates the transcription of latent HIV-1 [49] . Ixodes ticks transmit several viruses belonging to the family Flaviviridae such as tick-borne encephalitis virus ( TBEV ) , Powassan virus ( POWV ) and Langat virus ( LGTV ) [50–53] . LGTV is considered as a model biosafety level 2 ( BSL2 ) pathogen to study pathogenesis of TBEV , due to its significant genome homology with the later . Transmission modes of these arthropod-borne flaviviruses ( with positive sense single-stranded RNA ) are poorly understood [37 , 54] . Our study shows for the first time that exosomes facilitate transmission of flavivirus RNA and proteins from arthropod to human cells . We have demonstrated that cells from the medically important vector tick , Ixodes scapularis , secretes exosomes that mediate transmission of tick-borne LGTV RNA and proteins from arthropod to human . Our study shows the presence of abundant amounts of LGTV RNA and proteins in exosomes isolated from arthropod and neuronal cells . We also found that LGTV-infected tick cell-derived exosomes were capable of transmigrating and infecting naïve human skin keratinocytes ( the initial barrier lining the human cells that comes in contact during bites from infected ticks ) and human vascular endothelial cells ( that comes in contact during arthropod blood feeding ) . Our data show that vertebrate exosomes mediate transmission of tick-borne LGTV RNA and proteins from infected-brain microvascular endothelial cells ( a component of the blood-brain barrier; BBB ) to neuronal cells . In addition , we have demonstrated that exosomes containing tick-borne LGTV and mosquito-borne West Nile virus ( WNV ) facilitate transmission of viral RNA and proteins from one neuronal cell to others suggesting their novel role in neuropathogenesis . Dihydrochloride hydrate , GW4869 ( a selective inhibitor for neutral sphingomyelinase; N-SMase , an enzyme that regulates production and release of exosomes ) , reduced LGTV loads in exosomes and inhibited the transmission of LGTV RNA and proteins in both arthropod and vertebrate host cells . Overall , our study suggests that exosomes are not only the mediators for transmission of arthropod-borne flavivirus RNA and proteins from arthropod to the vertebrate host , but also facilitate dissemination of these infectious RNA and proteins within the vertebrate host , including crossing of BBB cells and allowing neuroinvasion and neuropathogenesis in the Central Nervous System ( CNS ) .
Despite the significance of ticks as important medical vectors , we know little about the transmission modes of tick-borne viruses and other tick-borne pathogens to the vertebrate host . We first analyzed whether tick cells secrete extracellular vesicles ( EVs ) and exosomes and if tick-borne flaviviruses use those exosomes as modes of pathogen transmission . LGTV , a flavivirus closely related to TBEV , readily infected Ixodes scapularis ISE6 tick cells , with increased viremia at 72 h post-infection ( p . i . ) ( S1A Fig ) , similar to the viral infection kinetics observed in Vero cells ( S1B Fig ) . We selected 72 h p . i . as the time point for the isolation of exosomes from tick cells due to the higher viral loads . First , we isolated exosomes by density gradient centrifugation technique using OptiPrep ( DG-Exo-iso ) as described in [55] . This isolation method used in our settings with a floor ultracentrifuge unit is shown as a schematic representation in ( S1C Fig ) . Exosomes were also independently isolated by differential ultracentrifugation with slight modifications and longer spin times for 155 minutes ( S2 Fig ) [22 , 29 , 32 , 56 , 57] . We also isolated arthropod-derived exosomes using commercially available exosome isolation reagent following manufacturer's instructions ( Invitrogen/ThermoScientific ) . Notably , all preparations contained 30 to 200 nm vesicles and these techniques have been used extensively in several studies . Cryo- Electron Microscopy ( cryo-EM ) performed on tick cell-derived exosomal fractions showed the presence of purified arthropod exosomes with the size range of 30 to 200 nm in diameter ( Fig 1A ) , similar to exosomes isolated from mammalian cells [1–3] . Exosomes isolated from arthropod cells showed a heterogenous population of vesicles in the cryo-EM analysis . In order to understand such heterogeneity in exosome populations , we did quantitative analysis using images collected from both uninfected and LGTV-infected tick cell-derived exosomes . We noted that majority of the exosomes were of sizes between 50–100 nm in both uninfected and infected groups ( Fig 1B and 1C ) . However , exosomes of other sizes 100–150 and 150–200 nm were evenly distributed in infected group when compared to the uninfected group ( Fig 1B and 1C ) . Fewer vesicles from sizes of 200–250 nm were slightly more in uninfected ( 10 . 1% ) in comparison to the infected group ( 6 . 5% ) . The large exosomes were very few and were from 0–1 . 5% in both the uninfected and infected groups ( Fig 1B and 1C ) . Counting of exosomes per image showed higher number of exosomes in LGTV-infected ( n = 14 ) in comparison to the uninfected ( n = 27 ) group ( Fig 1D ) . This data suggested that LGTV-infection ( 72 h p . i . ) might enhance the production and/or release of exosomes . The OptiPrep ( DG-Exo-iso ) method yielded purified exosomes in six different fractions . Immunoblotting analysis ( with highly cross-reactive 4G2 monoclonal antibody that recognizes the viral Envelope ( E ) - protein ) of these fractions ( 20 μl ) showed presence of LGTV E-protein in all six fractions but enriched amounts of E-protein were present in fractions four and five in comparison to the other fractions ( Fig 1E ) . These results correlated with the size analysis data ( Fig 1B , 1C and 1D ) . Enhanced detection of LGTV-E protein in fractions four may correspond to the 50–100 nm ( fraction 4 ) size exosomes that are highly populated ( Fig 1E ) . As expected , we did not detected E-protein in the fractions from uninfected control . Total cell lysates ( 20 μg ) from uninfected and LGTV-infected groups were used as internal controls to compare the amounts of E-protein detected in the 20 μl of different fractions used ( Fig 1E ) . The PonceauS images showing the protein profile serve as control ( Fig 1E ) . Quantitative Real-Time PCR ( QRT-PCR ) analysis revealed presence of LGTV total mRNA in exosomes isolated from infected tick cells ( Fig 1F ) . The copy numbers of viral RNA in exosomes derived from LGTV-infected ( 72 h p . i . ) tick cells is shown in ( S3A Fig ) . In addition , we also determined the presence of both positive and negative sense LGTV RNA strands in tick cell-derived exosomes ( Fig 1G ) . LGTV mRNA was also evident in exosomes from tick cells cultured and infected in exosome-free FBS medium ( with no cross-contaminating bovine exosomes present in regular commercial FBS ) , further suggesting the presence of viral RNA in tick cell-derived exosomes ( S3B Fig ) . Presence of LGTV E-protein in tick cell-derived exosomes was further recognized by SDS-PAGE followed by immunoblotting with 4G2 antibody ( Fig 1H ) . Higher E-protein loads were detected ( at ~50kDa ) in total cell lysates in comparison to exosomal preparations ( Fig 1H ) . Immunoblotting with monoclonal anti-Langat virus NS1 ( Clone 6E11 ) antibody ( obtained from BEI resources ) also showed the presence of NS1 in both tick cell-derived exosomes and total cell lysates ( Fig 1H ) . Although , higher NS1 protein loads were evident in total lysates , but the presence of NS1 in tick-cell derived exosomes ( Fig 1H ) further confirmed that these arthropod exosomes contain LGTV proteins . Remarkably , we also detected the presence of tick HSP70 ( heat-shock cognate protein 70 , a specific exosomal marker in mammalian cells ) in exosomal lysates ( Fig 1H ) . No differences were noted in HSP70 loads between uninfected and infected exosomal lysates ( Fig 1H ) . Presumably due to low amount in cell lysates , no HSP70 was detected in the tested condition ( Fig 1H ) . Total protein lysates prepared from same batch of uninfected or LGTV-infected tick cell-derived exosomes or from whole tick cells served as loading control for all immunoblots ( Fig 1H ) . It was noted that some of the bands in the total protein profile gel were enhanced in LGTV-infected tick exosome lysates in comparison to the uninfected controls ( Fig 1H ) . Furthermore , native-PAGE followed by immunoblotting with 4G2 antibody , showed enhanced levels of LGTV E-protein ( at <250kDa; in native state ) in exosomes treated ( 30 min , RT ) with Triton-X-100 ( a detergent that lyses the exosomal lipid bilayer membranes ) in comparison to the exosomes treated for three rounds of freeze-thaw cycle ( 1 h , at -80°C ) or the untreated exosomes held at 4°C ( Fig 1I ) . Total protein lysates prepared from uninfected or LGTV-infected tick cell-derived exosomes with similar treatments served as controls in this immunoblotting analysis ( Fig 1I ) . Detection of LGTV E-protein inside exosomes ( but not outside in the PBS suspensions ) was further analyzed by E-protein-4G2-antibody-beads binding assay as described in methods . No significant ( P>0 . 05 ) differences in viral loads were observed in LGTV-infected ( 72 h p . i . ) exosome samples that were either untreated or treated with 4G2 antibody ( that binds to LGTV E-protein ) or relevant isotype control antibody ( Fig 1J ) . Similar results were obtained with LGTV-infected exosomal preparations derived from GW4869 inhibitor treated tick cells collected at 72 h p . i . , ( Fig 1J ) . Native-PAGE and the beads assay clearly suggest that exosomes contain viral RNA and proteins inside exosomes . To further evaluate if viral E-protein is indeed ( totally ) inside the exosomes , we performed the protease-resistance assay with Proteinase K that generally digest proteins in all biological samples . We found that treatment with Proteinase K ( 0 . 5 μg/μl or 50 μg/ml , 15 min at 37 ºC ) at typical and suggested working concentrations ( 50–100 μg/ml ) digested all proteins ( S3C Fig ) . We detected E-protein in untreated infected samples but not in treated infected samples . Uninfected samples either treated or untreated served as internal controls ( S3C Fig ) . The Ponceau S stained blot showed no proteins in infected or uninfected proteinase K-treated samples ( S3C Fig ) . During isolation of tick exosomes , pellet fraction ( containing exosomes ) and supernatant fraction ( generated after pelleting exosomes and before PBS wash; See S2 Fig ) was tested in plaque assays to determine infectivity and replication of viral RNA and titers as described in methods . Plaque assays performed with the tick cell-derived exosome pellet fractions yielded plaques at dilutions of 1:10 and 1:100 that were too numerous to count , and around 20–22 plaques at a dilution of 1:1000 ( Fig 1K and S3D and S3E Fig ) . No plaques were detected in plates where Vero cells were treated with the supernatant fractions at any tested dilution ( Fig 1K and S3D and S3E Fig ) . Plaque assays indicated the presence of infectious viral RNA or proteins in LGTV-infected exosomes that resulted in high loads of LGTV in Vero cells ( 6 . 6 x 104 pfu/ml ) and increased formation of viral plaques . Plaque assays further confirmed that tick cell-derived exosomes contain LGTV RNA and proteins capable of replication and forming viable plaques that are highly infectious to mammalian cells ( Fig 1K and S3D and S3E Fig ) . No detection of viral plaques in the supernatant fractions suggests presence of abundant amounts of LGTV RNA and proteins in exosomes ( Fig 1K and S3D and S3E Fig ) . Overall , these results suggest that majority of the LGTV RNA and proteins exit tick cells via exosomes and that exosomes could mediate transmission of these and possibly the other closely related viruses such as TBEV and POWV . As tick-borne viruses ( including TBEV , LGTV and Powassan virus ) are transmitted by an infected tick bite to the vertebrate hosts , we tested whether exosomes isolated from LGTV-infected tick cells are infectious to human cells . In an infection kinetics assay , LGTV readily infected human keratinocytes ( HaCaT cells ) at all tested time points ( 24 , 48 and 72 h p . i . ) and there were no changes in viral loads at different times p . i . ( S3F Fig ) . Infection of HaCaT cells with exosome fraction prepared from LGTV-infected tick cells ( 72 h p . i . ) showed significantly ( P<0 . 05 ) increased levels of viral loads at 72 h p . i . in comparison to HaCaT cells treated with supernatant fractions prepared from 72 h post-infected-tick cells ( Fig 1L ) . Tick cell-derived exosomes containing LGTV grown in the presence of exosome-free FBS medium were also found to be infectious to HaCaT cells ( S3G Fig ) . However , LGTV was not detectable in HaCaT cells ( grown in exosome-free FBS medium ) treated with the supernatant fractions ( S3G Fig ) . Our data also showed that LGTV ( laboratory viral stocks , prepared from Vero cells ) was capable of infecting human vascular endothelial ( HUVEC ) cells with no differences in viral loads at 24 h p . i . in comparison to later tested time points ( 48 and 72 h p . i . ) ( S3H Fig ) . HUVEC cells treated with exosomes-containing-LGTV showed significantly ( P<0 . 05 ) increased viral loads at 48 h p . i . in comparison to the cells treated with supernatant fractions , suggesting that LGTV RNA is enriched in exosomes ( S3I Fig ) . We then performed transwell assays ( as described in the methods ) to test whether tick exosomes mediate transmission of LGTV from infected tick cells ( plated in upper inserts ) to uninfected/naïve human keratinocytes ( seeded into the lower well ) . We found that tick cells treated with infected tick-cell-derived exosomes ( that were isolated from independent batch of LGTV-infected tick cells ) readily transmitted infectious exosomes to uninfected HaCaT cells ( Fig 1M ) . However , upon incubations with tick cell-derived exosomes collected from GW4869 ( 5 μM ) treated cells , significantly ( P<0 . 05 ) reduced transmission of viral RNA to HaCaT cells was noted ( Fig 1M ) . Infection of arthropod cells with laboratory virus stocks with known titers ( MOI 1 ) served as control in this assay ( Fig 1M ) . Taken together , these results suggest that LGTV infectious RNA and proteins are transmitted to human cells via arthropod exosomes . Upon transmission to the vertebrate host , arthropod-borne neurotropic encephalitis viruses are known to first replicate in the blood and peripheral tissues ( spleen and liver ) , cross the BBB and invade the CNS [58 , 59] . We used mouse brain-microvascular endothelial cells ( bEnd . 3 cells; that constitutes the BBB ) to test whether LGTV infectious RNA and viral proteins are transmitted to neuronal cells via bEnd . 3 cell-derived exosomes . LGTV readily infected and replicated in bEnd . 3 cells at all tested time points ( 48 and 72 h p . i . ) ( S4A Fig ) . In addition , we found that the viral loads in brain endothelial cells were not significantly different over the infection period as revealed by the viral loads at much later time points ( 96 and 120 h p . i . ) ( S4B Fig ) . QRT-PCR analysis revealed significantly ( P<0 . 05 ) increased viral burden and copy numbers in exosomes isolated from bEnd . 3 cells at 24 h p . i . in comparison to the other tested time points ( 48 , 72 , 96 and 120 h p . i . ) ( Fig 2A and S4C Fig ) . We also detected higher loads of LGTV positive and negative sense RNA strands at 24 and 48 h p . i . , in comparison to the other tested time points ( 72 , 96 and 120 h p . i . ) ( Fig 2B ) . LGTV infected and replicated in neuronal cells ( mouse N2a cells ) in a time-dependent manner with peak level of infection at 72 h p . i . ( S4D Fig ) . N2a cells were then infected with bEnd . 3 cell-derived exosomes collected at different time points ( 24 and 48 h p . i . ) . LGTV RNA and proteins containing exosomes from bEnd . 3 cells were found to be infectious to N2a cells with peak level of infection observed with exosomes isolated from endothelial cells at 48 h ( p . i . ) ( Fig 2C ) . N2a cells treated with supernatant fractions ( collected at the indicated time points ) derived from endothelial cells resulted in significantly ( P<0 . 05 ) lower viral loads in comparison to the treatments with exosome fractions isolated from the bEnd . 3 cells ( Fig 2C ) . Transwell assays performed with exosomes isolated from LGTV-infected-brain endothelial cells showed transmission of viral RNA and proteins from bEnd . 3 cells ( plated in upper inserts ) to uninfected/naïve N2a cells seeded in the lower well ( Fig 2D ) . Presence of exosome inhibitor significantly reduced transmission of LGTV infectious RNA from bEnd . 3 cell-derived exosomes to N2a cells ( Fig 2D ) . Infection of bEnd . 3 cells with laboratory virus stocks with known titers ( 6 MOI ) showed transmission of LGTV to N2a cells ( by crossing the membrane barriers in transwell plates ) and served as control in this assay ( Fig 2D ) . These results suggest that exosomes derived from brain-endothelial cells are perhaps the mediators for BBB permeability ( crossing of infectious exosomes from infected-endothelial cells lining the BBB and transmission to the neuronal cells ) that may facilitate neuroinvasion of tick-borne LGTV and possibly TBEV and POWV . Upon entry in to the brain , tick-borne neuroinvasive viruses ( such as TBEV ) infects neuronal cells [60] . To test whether transmission of these viruses within the brain from one neuronal cell to another is mediated by exosomes; we first infected N2a cells with LGTV ( S4C Fig ) . Cryo-EM showed the presence of purified exosome preparations from neuronal cell-derived exosomal fractions with the size range of 30 to 200 nm in diameter ( Fig 3A ) , similar to exosomes isolated from tick cells . Also , we isolated exosomes by precipitation using the commercially available kit isolation reagent following the manufacturer’s protocol ( S5A Fig ) . Cryo-EM images ( generated using this method ) showed the presence of purified exosome preparations from neuronal cell-derived exosomal fractions with the similar size range of 30 to 200 nm in diameter ( S5B Fig ) . Like arthropod exosomes , neuronal cell-derived exosomes also showed a heterogenous population of vesicles . In a very similar way , we did quantitative analysis using cryo-EM images collected from both uninfected and LGTV-infected N2a cell-derived exosomes . Majority of these exosomes were also of sizes between 50–100 nm in both uninfected and infected groups ( Fig 3B and 3C ) . Smaller exosomes of sizes 0–50 nm were of slight higher percentages in infected exosomes when compared to the uninfected group ( Fig 3B and 3C ) . Fewer vesicles from sizes of 150–200 ( 9–11% ) or 200–250 ( 6 . 3% ) were found in both infected and uninfected groups . Less than 1% of larger vesicles ( 250–350 nm sizes ) were found in infected group ( Fig 3B and 3C ) . Counting of exosomes per image showed higher number of exosomes in LGTV-infected ( n = 13 ) in comparison to the uninfected ( n = 9 ) group ( Fig 3D ) . This data suggested that LGTV-infection ( 72 h p . i . ) might enhance the production of exosomes . The OptiPrep density gradient exosome separation ( that separates exosomes from viruses and large microvesicles ) yielded purified exosomes at six different fractions . Immunoblotting analysis ( using 4G2 antibody ) of these fractions ( 20 μl ) showed presence of LGTV E-protein in all fractions but enriched amounts of E-protein were present in fractions four and five in comparison to the other fractions ( Fig 3E ) . We did not detect E-protein in the fractions from uninfected control . The cell lysates ( 20 μg ) from uninfected and infected groups were used as controls to compare the amounts of E-protein detected in the different fractions volume ( Fig 3E ) . Immunoblotting with anti-HSP70 antibody detected enriched amounts of HSP70 ( exosomal marker ) in fourth fraction of both uninfected and infected samples ( Fig 3E ) . HSP70 levels were also detected in three and five of infected fractions but not in uninfected fractions ( Fig 3E ) . In addition to the HSP70 , we also analyzed the CD9 ( a protein enriched in the mammalian cell-derived exosomes and recognized as exosomal marker ) levels in uninfected and infected fractions . CD9 was detected in all six of the uninfected fractions in an increasing manner , with higher levels in fractions four and five ( Fig 3E ) . However , CD9 was detected in 2–5 of infected fractions with higher-level detection in fractions three and four ( Fig 3E ) . It was interesting to note that LGTV E-protein was enhanced in similar exosomal fractions ( fractions 3–5 ) that had enhanced loads of both HSP70 and CD9 , suggesting that infectious exosomes in fraction four have higher levels of exosomal markers . OptiPrep DG-isolation of exosomes using 0 . 1 μm filter ( culture supernatants were filtered before concentration and processing for gradient steps ) detected E-protein also in the fraction 4 , suggesting that these infectious exosomes have sizes of 50–100 nm ( Fig 3E ) . This data also correlated with the quantitative analysis from cryo-EM images . In order to address , where the intact LGTV particles may run on the parallel gradients , we performed OptiPrep DG-isolation on the laboratory stocks of LGTV ( prepared in Vero cells , collected at 7–14 days post-infection and stored at -80°C ) . We noted a differential pattern in E-protein loads when density gradients were performed on LGTV-infected exosomal fractions from N2a cells ( Fig 3E ) or on LGTV laboratory stocks containing viruses ( S5C Fig ) . An enhanced E-protein signal was detected in fraction 6 ( indicating presence of virions in this fraction ) and not in fraction 5 . Detection of E-protein in fractions 4 , 3 and 2 from the laboratory virus stock suggested the presence of infectious exosomes containing viral E-protein ( S5C Fig ) . This data indicated that the viral stocks are not just the virions but are perhaps mixtures of infectious exosomes containing viral E protein . Upon LGTV infection of N2a cells , exosomes were collected at different time points ( 24 , 48 , 72 h p . i . ) and analyzed for viral loads . QRT-PCR analysis revealed an increased total viral RNA load and copy numbers at 72 h p . i . in comparison to the other tested time points ( 24 and 48 h p . i . ) ( Fig 3F and 3G ) . Both positive- and negative-sense RNA was detected at higher levels in the exosomes isolated from N2a cells at 72 h p . i . in comparison to the other tested time points ( Fig 3H ) . Exosomes collected from the kit reagent also yielded similar results with increased LGTV loads in exosomes ( S5D Fig ) . Next , we addressed the possibility that viral RNA could be binding to the outside of the exosomes and may be transmitted to the recipient cells . In order to test this possibility , we treated freshly prepared LGTV-infected ( 72 h p . i . ) - N2a cell-derived exosomes with RNase A ( 5 μg/ml , for 15 min , at 37°C ) . We did not find any differences in LGTV loads from infected treated or untreated groups ( Fig 3I ) . The uninfected group treated with RNase A was kept as internal control ( Fig 3I ) . In addition , we treated freshly derived exosomes isolated from LGTV-infected N2a cells , with Triton-X-100 ( 0 . 1%; for 45 min , at RT ) , followed by treatments with RNaseA ( 5 μg/ml , for 15 min , at 37°C ) . QRT-PCR analysis showed that exosomes treated with both Triton-X-100 and RNaseA has lower LGTV loads in comparison to exosomes not treated with RNaseA ( S5F Fig ) . Immunoblotting analysis further suggested the presence of LGTV E-protein in the exosomes isolated from N2a cells ( Fig 3J ) . The E-protein loads were one-or two-fold higher in total lysates in comparison to the exosomal lysates derived from N2a cells ( Fig 3J ) . Reduced molecular mass of LGTV E protein was found in exosomes derived from N2a cells in comparison to the total lysates ( Fig 3J ) , suggesting a possible de-glycosylation of the viral E protein in neuronal cell-derived exosomes . We found similar de-glycosylation of the viral E protein in immunoblots performed on laboratory virus stocks ( S5E Fig ) . A high level of CD9 was detected in the LGTV-infected N2a cell-derived exosomes in comparison to low levels in the uninfected control and the total cell lysates prepared from LGTV-infected or uninfected N2a whole cells ( Fig 3J ) . Total protein lysates used in the immunoblot analysis served as loading control ( Fig 3J ) . Enhanced levels of LGTV- E protein in neuronal exosomes treated with Triton-X-100 ( 0 . 03%; for 30 min , RT ) in comparison to the exosomes treated after freeze-thaw cycle ( thrice frozen and thawed at -80°C ) or untreated exosomes held at 4°C was detected by native-PAGE followed by immunoblotting with 4G2 antibody ( Fig 3K ) . We noticed that E-protein was detected at higher molecular mass ( <250kDa ) in neuronal exosomes when samples were processed for native-PAGE analysis under non-reducing and non-denaturation conditions . Detection of NS1 protein in independent samples at the similar molecular mass suggests presence of other LGTV proteins or polyprotein in exosomes ( Fig 3K ) . Exosomes derived from uninfected N2a cells served as control ( Fig 3K ) . Total protein lysates prepared from uninfected or LGTV-infected neuronal cell-derived exosomes after freeze-thaw or Triton-X-100 treatments or untreated samples served as loading control ( Fig 3K ) . ELISA corroborate results of the native-PAGE , where higher loads of LGTV E-protein were detected when exosomes were treated with 0 . 1% of Triton-X-100 in comparison to untreated exosomal fractions ( Fig 3L ) . Lower level of E-protein in LGTV-infected untreated neuronal exosomes was considered as background signal due to non-specific antibody binding ( Fig 3L ) . Furthermore , we analyzed the presence of E-protein inside neuronal exosomes by a 4G2-antibody-coated bead-binding assay as described in methods ( Fig 3M ) . No significant ( P>0 . 05 ) differences in viral loads were observed in LGTV-infected ( 72 h p . i . ) neuronal exosome samples that were untreated/treated with either 4G2 antibody or isotype control antibody ( Fig 3M ) . GW4869 inhibitor treated exosomes from LGTV-infected neuronal cells collected at 72 h p . i . , followed by treatments with either 4G2 or isotype control also showed no significant ( P>0 . 05 ) differences in viral load in comparison to untreated samples ( Fig 3M ) . However , a significant decrease in LGTV loads were observed in the inhibitor treated group in comparison to no-inhibitor treated group ( Fig 3M ) . In addition , we found that exosomes treated with Proteinase K ( 100 μg/μl , 15 min at 37°C ) may be digested all proteins on the surface , thereby , lysing the vesicles and allowing degradation of the exosomal luminal proteins ( S5G Fig ) . We detected E-protein in infected- untreated samples but not in treated samples . Untreated , uninfected samples serve as internal controls ( S5G Fig ) . The Ponceau S stained blot showed no proteins upon Proteinase K treatment ( S5G Fig ) . Plaque assays further confirmed that exosomes isolated from LGTV-infected N2a cells contain infectious viral RNA , with a significantly higher number of plaques , evident upon infection with exosome fractions in comparison to the infection with supernatant fractions ( Fig 4A and S6A and S6B Fig ) . Furthermore , infectious exosomes containing LGTV RNA and proteins prepared from N2a cells at different time points ( 24 , 48 , 72 h p . i . ) were capable of re-infecting naïve N2a cells ( Fig 4B ) . Significantly higher level of viral burden was evident in N2a cells freshly infected with LGTV-containing exosome fractions prepared from 48 or 72 h ( p . i . ) in comparison to the infection with exosome fractions prepared from 24 h p . i . ( Fig 4B ) . Re-infection with supernatant fractions showed undetectable levels of LGTV ( Fig 4B ) . Similar levels of viral re-infection kinetics were observed upon incubations with LGTV-infected N2a cell-derived exosomes isolated using commercially available isolation reagent that were used to infect naïve/fresh N2a cells ( S6C Fig ) . To find , if mosquito-borne flaviviruses such as WNV viral RNA is also present in exosomes , mouse N2a cells were infected with WNV . Viral infection kinetics showed that WNV readily infected N2a cells with increased viremia at 72 h p . i . ( Fig 4C ) . Also , exosomes derived from WNV-infected N2a cells showed a peak in viral burden at 72 h p . i ( Fig 4D ) , suggesting that WNV RNA is also present in exosomes . We treated ( 4 h ) N2a cells with 5 μg of 4G2 monoclonal antibody , followed by infection with exosomes from LGTV-infected ( 72 h p . i . ) N2a cells to analyze if treatment with 4G2 antibody affects viral transmission . No differences were found in antibody treated or untreated groups ( Fig 4E ) . Next , we determined if exosome mediated viral transmission is receptor-dependent and requires clathrin-mediated endocytosis . We treated N2a cells with clathrin specific inhibitor ( Pitstop-2; 30 μM and 15 min ) , and infected these clathrin-inhibitor treated cells with infectious ( LGTV; 72 h p . i . ) exosomes derived from N2a cells . We noted significant ( P<0 . 05 ) reduction in LGTV loads ( 72 h p . i . ) in Pitstop-2 treated cells in comparison to the DMSO ( vehicle ) treated controls ( Fig 4F ) . These results suggest that exosome-mediated LGTV transmission to naïve cells is receptor-dependent endocytosis that requires clathrin . Presence of exosome-inhibitor at different concentrations ( 1 , 5 and 10 μM ) significantly ( P<0 . 05 ) reduced LGTV loads in exosomes ( from N2a cells ) in comparison to DMSO-treated controls ( Fig 5A ) . In addition , exosomes prepared from inhibitor-treated ( 1 μM ) N2a cells were significantly reduced in re-infecting naïve N2a cells in comparison to DMSO-treated control group ( Fig 5B ) . Furthermore , we found that exosomes isolated from N2a cells pre-treated with 5 μM inhibitor before LGTV infection had significantly lower viral loads in comparison to exosomes isolated from cells post-treated with inhibitor after infection ( Fig 5C ) . However , viral loads in exosomes were significantly reduced in N2a cells irrespective of pre- or post- inhibitor treatment in comparison to the infection performed with LGTV from laboratory viral stocks with known titers ( 5 MOI ) ( Fig 5C ) . Plaque assays performed with LGTV-infected N2a cell-derived exosomes isolated from DMSO-treated group yielded significantly ( P<0 . 05 ) increased number of plaques in comparison to the number of plaques with exosomes isolated from inhibitor-treated group ( Fig 5D and 5E ) . Also , plaque assays performed with exosome fractions from N2a cells revealed the viral titers for both N2a-DMSO control group ( 8 x 103 pfu/ml ) and N2a 1 μM-inhibitor treated group ( 2 . 3 x 103 pfu/ml ) . We also determined the effects of GW4869 inhibitor on LGTV viral particles from laboratory virus stocks . Immunoblotting with 4G2 antibody showed no differences in 5 or 10 μM treated ( 4 h ) groups , in comparison to the DMSO control ( Fig 5F ) . This data suggested that GW4869 has no effect on viral particles . To analyze whether exosomes are the mediators of viral transmission from one neuronal cell to other in an in vivo model , LGTV infections were performed on primary neuronal cultures of murine cortical neurons ( isolated from embryonic day E16 brains , as described in methods ) . Infection of cortical neurons with LGTV ( MOI 4 ) showed time dependent kinetics of LGTV infection with increased viral burden at 72–96 h p . i . ( Fig 6A ) . QRT-PCR analysis revealed significantly ( P<0 . 05 ) increased LGTV total loads and copy numbers in exosomes isolated from murine cortical neurons at 72 h ( p . i . ) when compared to exosomes isolated from other tested time points ( 24 and 48 h p . i . ) ( Fig 6B and 6C ) . We also detected higher loads of LGTV positive- and negative- sense RNA strands , suggesting presence of both viral genomes in the exosomes derived from infected-cortical neurons ( Fig 6D ) . Immunoblotting showed abundant LGTV E-protein amounts ( 2–3 folds ) in exosomes isolated from cortical neurons in comparison to the loads found in total cell lysates ( Fig 6E ) . Similar to N2a cells , possibly de-glycosylated LGTV E protein ( with low molecular mass ) was detected in exosomes isolated from cortical neurons in comparison to the total cell lysates ( with high molecular mass ) prepared from cortical neurons ( Fig 6E ) . Elevated levels of CD9 ( exosomal enriched marker ) were found in the exosomes derived from LGTV-infected cortical neuronal cells and in total cell lysates in comparison to their respective uninfected controls ( Fig 6E ) . In addition , levels of CD9 were dramatically elevated in exosomes from LGTV-infected cortical neuronal cells in comparison to the levels in total cell lysates ( Fig 6E ) , supporting that LGTV infection may regulate the enrichment of CD9 in neuronal exosomes . Also , exosomes derived from LGTV-infected cortical neurons showed higher amounts of CD9 , when compared to the N2a cell-derived exosomes containing LGTV ( Figs 6E and 3J ) . Total protein profiles served as loading control in the immunoblotting analysis ( Fig 6E ) . Immunoblotting showed presence of NS1 in both exosome fractions and in total cell lysates suggesting that exosomes from cortical neuronal cells contain LGTV proteins ( Fig 6F ) . Plaque assays confirmed that exosomes isolated from cortical neurons carry infectious and replicative viral RNA , since significantly increased number of plaques were evident upon infection with exosome fractions ( in different dilutions; 1:10 , 1:100 , 1:1000 ) in comparison to the infection with supernatant fractions ( Fig 6G and S7A and S7B Fig ) . Similar observations were previously noted for N2a cells , suggesting that LGTV is enriched in neuronal exosomes . Additionally , exosome fractions prepared from LGTV-infected cortical neurons at different time points ( 24 , 48 , 72 h p . i . ) were capable of re-infecting naïve primary cultures of cortical neurons ( Fig 6H ) . A significant higher viral burden was evident in the cortical neurons infected with exosome fractions ( prepared from 24 , 48 , 72 h p . i . ) in comparison to the infection with the supernatant fractions prepared from respective time points ( Fig 6H ) . These data suggest that exosomes derived from LGTV-infected neuronal cells are potential mediators for spreading infection to other neurons . Furthermore , presence of exosome-inhibitor at concentrations of 10 or 20 μM significantly ( P<0 . 05 ) reduced viral infection in cortical neurons in comparison to DMSO-treated controls ( Fig 7A ) . However , no differences in the viral burden of cortical neurons were noted upon treatment with 1 μM exosome inhibitor in comparison to DMSO-treated control ( Fig 7A ) . This data suggested that neuronal cells in in vivo might produce higher number of exosomes that could not be inhibited with less concentration ( 1 μM ) of inhibitor . Exosomes isolated from 10 μM-treated cortical neurons showed significantly ( P<0 . 05 ) reduced re-infection of naïve cortical neurons in comparison to the infections performed with exosomes isolated from DMSO-treated control group ( Fig 7B ) . Plaque assays confirmed that LGTV-containing exosomes isolated from DMSO-treated neurons contained viable and increased LGTV loads in comparison to exosomes isolated from 10 μM exosome inhibitor-treated group ( Fig 7C and 7D ) . Collectively , these results suggest that LGTV and perhaps TBEV , uses exosomes as novel modes of transmission from one neuronal cell to the other .
Exosomes contribute to the transmission of intracellular information from one cell to another and from one tissue to the other [2 , 30 , 61] . Several biological implications and medical applications have been associated with the exosomes as potential mediators of communication between cells and tissues [3 , 20 , 62 , 63] . For the first time our study shows that exosomes are novel mediators for transmission of arthropod-borne flaviviruses that infect a wide variety of vertebrate hosts including humans . Our discovery that tick cells secrete exosomes and that these exosomes are the carriers of tick-borne LGTV ( Fig 1 ) suggest that other tick-borne flaviviruses such as TBEV and POWV might also use this novel mode of transmission from arthropods . Cryo-EM data showed that arthropod or neuronal cell-derived exosomes are of variable sizes and were in the ranges of 30–250 nm ( Figs 1A and 3A ) . Exosomes isolated from both arthropod and neuronal cells had majority of the exosome sizes between 50–100 nm and fewer vesicles from sizes of 200–250 nm in both uninfected and infected groups ( Figs 1B , 1C , 3B and 3C ) , suggesting purity in isolation methods . Increased number of exosomes in LGTV-infected in comparison to the uninfected groups ( Figs 1D and 3D ) , suggested higher production and release of exosomes . Our immediate future avenue determines the loads and activity of the neutral sphingomyelinase in LGTV-infected arthropod and neuronal cells . To make virus preparations for structural studies , concentrated supernatants or titers with 109 to 1012 PFU/ml and centrifugal forces of 200 , 000g are used [64] , that are not similar in exosomal preparation methods . However , in order to minimize the viruses and large protein aggregates that co-sediments during ultracentrifugation , we adopted the buoyant density of exosomes for purification purposes . Continuous or discontinuous sucrose density gradient centrifugation has been used extensively to purify exosomes . However , this method does not allow separation of exosomes from viruses and macro vesicles or large microvesicles with comparable sedimentation velocities [55] . Substituting sucrose with iodoxanol ( OptiPrep ) in the velocity gradients using 5–40% density gradients has been shown to overcome the limitations and result in purified exosomal preparations [55] . Detection of tick HSP70 in exosomal fractions ( Fig 1H ) , suggested it to be a novel arthropod marker that may be present in exosomes from saliva and facilitate tick feeding on vertebrate host . Our recent study reported that arthropod HSP70 may aid in the host fibrinogenolysis at the tick bite site [65] . Detection of CD9 in all uninfected fractions and enrichment in fractions four and five suggested these fractions to be exosomes . The observed shift in enrichment of CD9 in LGTV-infected fraction three and four and no detection in fractions one and six suggested presence and enrichment of other proteins or cargo ( including viral E-protein in fraction four ) in those fractions . Our findings showing the enrichment of both arthropod and neuronal E-protein in exosomal fractions four and five confirmed the presence of viral E-protein in exosomes ( Figs 1E and 3E ) . We hypothesize that due to space limitation and tightly regulated cargo sorting mechanisms , exosomes are certainly filled with viral RNA and proteins that are trafficked to extracellular space and later recycled back through fusion with plasma membranes . If virions or entire viral particle are perhaps exported through exosomes , we could anticipate enclosure ( or packaging ) of few LGTV viruses of 40–60 nm size in ~150–200 nm diameter of arthropod/neuronal cell-derived exosomes . We did not detect any viral particles or fully assembled virions inside of the exosomes , in several of our preparations processed for cryo-electron microscopy . However , we do not exclude the possibility of viral particles presence in the exosomes . Based on our findings , we believe that if viral RNA ( both positive and negative strands ) and proteins are loaded into exosomes , they can be exported and subsequently transmitted to the neighboring cells and distant tissues for pathogenesis in short times . The matured virions containing positive sense RNA strand exit cells through membrane budding . On the other hand , the replicative viral RNA genome will have a negative RNA strand and are cytosolic [17 , 54 , 66–68] . Detection of both positive and negative-sense RNA strands in tick/neuronal cell-derived exosomes suggest that exosomes facilitate transmission of both negative and positive-strand RNA genomes . The higher loads of negative-strand RNA in the exosomes derived from neuronal cells implied that LGTV negative strand RNA may simply get trafficked during endocytosis/uptake by these cells . The negative-strand of RNA generally exists as dsRNA with positive-strand . Thus , it seems that dsRNA may be present inside exosomes rather than single-stranded positive or negative strand of LGTV . In addition , entry of more viral RNA and proteins inside cells via receptor-mediated endocytosis may simply force the replicative viral RNA to exit the host cell and seek other neighboring cells through exosome-mediated transmission . Our finding that exosome mediated viral transmission is dependent on clathrin ( Fig 4F ) further suggest an important role for exosomes as viral RNA and protein transporters . Up-regulation of some proteins in LGTV-infected tick exosomal lysates in comparison to the uninfected controls suggests the importance of these proteins in facilitating the transmission of tick-borne flaviviruses from tick cell-derived exosomes ( Fig 1H ) . Our current efforts are focused in identification and characterization of these important cargo proteins on arthropod exosomes that could be candidates for the development of novel transmission-blocking vaccine ( s ) [69] . The presence of LGTV RNA ( as determined by RNase A treatment studies ) and E- protein inside exosomes but not outside in suspensions of exosomal fractions , suggest that exosomes not only securely carry the viral RNA ( both positive and negative strands ) , but also transport the essential viral E-protein into host endosomal membranes for release of viral content inside cells . Our finding that exosome mediated viral transmission is clathrin-dependent suggests a possible receptor-mediated endocytosis uptake of infectious exosomes into naïve cells . Our transwell assays with tick cell-derived exosomes and human keratinocytes ( Fig 1M ) suggest that tick spit/secreted saliva ( that could contain exosomes loaded with LGTV viral RNA and proteins ) could facilitate transmission of this virus from the bite site to the vertebrate host skin cells . No differences in time course of LGTV infection in human keratinocytes suggested that these cells may not keep persistent infection , but may transmit viruses to dendritic/other migratory immune cells in the skin at their earliest . We also assume that keratinocytes are probably highly immune tolerant and may maintain viral infections to lower peaks . Abundance of LGTV infectious RNA and proteins in exosomes also suggests that exosomes may readily facilitate the dissemination of these viral factors within the tick body ( for example , from midgut , upon entry , and through hemolymph to salivary glands during transmission ) or transmission through saliva to the vertebrate host upon infected arthropod bite or blood feeding . Infection of vascular endothelial cells with tick cell-derived exosomes containing LGTV infectious RNA and proteins suggests that upon tick blood feeding , arthropod exosomes facilitate infection of the blood endothelium in vertebrate host . It is noteworthy that GW4869 inhibitor significantly ( P<0 . 05 ) lowered LGTV in exosomes derived from tick , bEnd . 3 , N2a and neuronal cells ( Figs 1M , 2D , 5A and 7A ) . These data suggest a common pathway shared in the production and release of exosomes in both arthropod and vertebrates . Overall , these studies revealed a novel mode of flavivirus transmission from the arthropod vector to the vertebrate host via arthropod exosomes that could be envisioned as transmission-blocking strategies . Most of the flaviviruses can infect and replicate in the vertebrate brain microvascular endothelial cells that line and guard the BBB . Infected endothelial cells allow these flaviviruses to enter and cause neuroinvasion of the CNS [70–73] . We hypothesize that initial entry of few infected exosomes derived from endothelial cells , lining the BBB may lead to virus transmission into the CNS . Infection of neuronal cells and secretion of abundant loads of infectious exosomes by neuronal cells may promote the breaching of the BBB , thereby allowing entry of higher peripheral viral loads , in addition to trafficking of immune cells from the periphery . Based on our results ( Fig 2 ) , we assume that initial batch of infected-brain microvascular endothelial ( bEnd . 3 ) cell-derived exosomes containing higher loads of infectious LGTV RNA and proteins may enter into the CNS at an early time point ( 24 h p . i . of endothelial cells ) . The higher viral loads in brain endothelial cell-derived exosomes from early time points ( 24 h p . i . ) in comparison to lower loads in exosomes at later time points further suggest earlier transmission of viral RNA through exosomes that infects neighboring neuronal cells . We also noted that bEnd . 3 cells ( in infection kinetics assays ) were more resistant to LGTV infection with no severe cytopathological effects when compared to neuronal cells . We hypothesize that the brain endothelial cells may not support the higher rate of viral replication or persistent infection for longer times . This could result in higher packaging of LGTV viral RNA and perhaps proteins in bEnd . 3 cell-derived exosomes that would lead to dissemination of flaviviruses to neuronal cells at earlier times . The transwell assay data ( Fig 2D ) mimic in vivo scenario , where exosomes derived from infected-bEnd . 3 cells might transmigrate through astrocyte foot layer and infect neurons in the CNS . This data could be directly related to the in vivo situation that proposes virus transmission from infected-brain microvascular endothelial cells ( lining the BBB ) to the interior of the CNS . Taken together , these studies imply that infected-brain endothelial cells may not entertain flavivirus replication for longer times and hence transmit these viral RNA and proteins to their neuronal counterparts at the earliest and via exosomes . Our study also suggest that exosomes derived from neuronal cells likely able to mediate transmission of tick-borne flavivirus RNA and proteins from one neuronal cell to the other in the CNS . Higher loads of E-protein ( 2–4 folds more ) in exosomes derived from murine cortical neurons in comparison to the in vitro cultures of N2a cell-derived exosomes suggest higher packaging of viral RNA and proteins in cortical neurons ( Fig 6E ) . We believe that the observed lower mass for LGTV E-protein in both N2a cells and cortical neurons is due to possible de-glycosylation of the E- protein . The de-glycosylated E-protein in laboratory viral stocks suggested mixture of virions with exosomes in those frozen supernatants . However , this effect was not evident in arthropod cell-derived exosomes , suggesting that E-protein in arthropod exosomes may not undergo protein modification . Glycosylated form of LGTV E- protein in arthropod cells is maintained possibly to facilitate exosome fusion and viral infection of host cells immediately upon host seeking and tick blood feeding on vertebrate host . It has been also observed that in mosquitoes , WNV E-protein is heavily glycosylated and is required for pathogen transmission to the vertebrate host [74] . We assume that presence of de-glycosylated form of viral E-protein in neuronal and other mammalian cell-derived exosomes may allow viral E-protein to maintain its stability in these small vesicles during transmission from one cell to other . Alternatively , we also hypothesize that higher packaging of viral E-protein in vertebrate neuronal exosomes may be feasible only if E-protein exist in de-glycosylated form with less molecular mass in comparison to the glycosylated form . Also , arthropod and vertebrate host may require different conformations of E-protein that may aid when contents from exosomes are delivered to the host cytosol . The de-glycosylation of E protein in neuronal exosomes may also facilitate higher infectious ability to form matured virions in new hostile environment . Higher loads of CD9 in exosomes derived from neurons suggest that cortical neuronal cells might have greater production of exosomes in comparison to the in vitro cultured N2a cell line ( Figs 3J and 6E ) . It is reasonable to consider that neurons have complex ways of cell communication such as synaptic transmission and neurotransmitter release that might require greater production of exosomes in the CNS . Low total protein content observed in the N2a and cortical neuronal exosomes compared to the protein content in the whole cells also suggest that only few essential proteins are imported as cargo in LGTV-infected neuronal cell-derived exosomes . Total protein profiles in N2a cells showed absence of some exosomal proteins upon LGTV infection , implying that these proteins may affect or inhibit viral proteins in N2a cell-derived exosomes . Our future studies in identifying these reduced exosomal proteins upon LGTV infection would assist in identifying novel therapeutic targets against transmission . The detection of E-protein at higher molecular mass ( <250kDa ) in native-PAGE gels , suggested that exosomes might contain higher order structures of E-protein as oligomers . The presence of NS1 in the same samples at similar molecular mass further indicated that exosomal fractions might contain polyprotein ( Fig 3K ) . Presence of highly infectious LGTV RNA and proteins in exosomes from neuronal cells suggests that these cells upon infection , mediate dissemination in the CNS . Also , detection of WNV in neuronal cell-derived exosomes , further suggest exosomes as novel transmission modes for both tick- and mosquito-borne flaviviruses in neuronal cells . We assume that exosomes may maintain viability of these viral RNA and proteins that may favor persistent pathogenesis . GW4869 ( dihydrochloride hydrate ) is a cell permeable but selective inhibitor for neutral sphingomyelinase ( an important enzyme required for the exosome production and release ) . Effect of this inhibitor on both arthropod and mammalian cells used in this study suggests , presence of neutral sphingomyelinase in these cells . Treatment with GW4869 affected LGTV- replication , loads and transmigration from one cell type to other suggesting that LGTV or other tick-borne flaviviruses may use neutral sphingomyelinase or its related pathway ( s ) for packaging into exosomes . Future studies would unravel the role of neutral sphingomyelinase on packaging of LGTV and other flaviviruses in arthropod or mammalian exosomes . In N2a cells , 1 μM of inhibitor was sufficient to inhibit the loads of LGTV ( as revealed by infection , reinfection and plaque formation ) in contrast to higher doses ( 10 and 20 μM ) of GW4869 that was required for inhibition of viral loads in primary cortical neuronal cells ( Fig 7A ) . Higher sensitivity of N2a cells to GW4869 could be explained by the possibility of low number of exosomes or less neutral sphingomyelinase in these cells in comparison to cortical neurons . The effects of GW4869 on LGTV infection in N2a cells implied that inhibition of exosomes either before or after infection would affect LGTV loads and transmission ( Fig 5C ) . Our data suggested that inhibition of exosomes reduced LGTV loads in both arthropod and mammalian cells and that infection with tick-borne flaviviruses was affected when exosome production and release was hampered with GW4869 treatment . No effects of GW4869 on laboratory viral stocks suggested that it is specific for blocking release of exosomes and has no direct effect on viral particles . It would be interesting to determine whether GW4869 or other novel exosome inhibitor ( s ) could serve as potential therapeutic approaches for treating flaviviral infections . The proposed model ( Fig 8 ) summarizes the role of exosomes in transmission of tick-borne flavivirus RNA and proteins from the arthropod vector to human cells and dissemination of these infectious exosomes within the vertebrate host . Taken together , our study suggests that exosomes play following important roles: 1 ) In the transmission of tick/mosquito-borne flaviviruses from infected arthropod vector to the vertebrate host cells , 2 ) In the infection of the human skin keratinocytes and vascular endothelial cells during tick bite/blood feeding , 3 ) In mediating the infection of brain microvascular endothelial cells ( lining the BBB ) and crossing these infectious exosomes to allow neuroinvasion and 4 ) In the infection of neuronal cells resulting in high production of exosomes containing infectious viral RNA and proteins , necessary for the dissemination and infection of naïve neuronal cells in the CNS that leads to neuropathogenesis and severe neuronal loss .
Ixodes scapularis ISE6 tick cell line was obtained from Dr . Ulrike Munderloh , University of Minnesota . ISE6 cells were grown as per the culture methods provided by Dr . Munderloh [75] . Human keratinocytes ( HaCaT cells ) or Human Umbilical Vein Endothelial Cells ( HUVEC ) were obtained from Drs . Loree Heller and John Catravas laboratories , respectively . Vero ( African Green Monkey kidney ) , mouse brain endothelial ( bEnd . 3 cells ) and mouse neuroblastoma Neuro-2a or N2a cells were purchased from ATCC and were grown according to Company guidelines . Briefly , HaCaT , Vero , bEnd . 3 and N2a cells were grown in complete DMEM medium containing 5–10% heat-inactivated FBS ( Invitrogen/ ThermoScientific ) . HUVEC cells were grown in human lung MVEC medium ( M199 medium containing 150 mg ECGF- bovine brain extract and 20% FBS ) kindly provided by Dr . Catravas laboratory . To determine infection kinetics , 1 x 105 cells were seeded in a 12-well plate , infected with various multiplication of infections ( MOI 1; tick cells ) , ( MOI 6: Vero , HaCaT , HUVEC , bEnd . 3 and N2a cells ) of LGTV . Wild type LGTV ( LGT-TP21 ) strain used in this study was obtained from Dr . Alexander G . Pletnev , NIAID , NIH . Cells were collected at different time points ( 24 , 48 , and 72 or 96 and 120 h post infection , p . i . ) and processed for RNA or protein extractions . Details for infection studies corresponding to the data shown in different figures is mentioned in their respective Figure legends . Briefly , for infection experiments ( or re-infection studies ) with exosome fractions containing infectious LGTV RNA and proteins , we infected 1 x 105 HaCaT/HUVEC cells or N2a cells with 20 μl ( from 150 μl ) of tick ( 6 . 6 x 104 pfu/ml ) or bEnd . 3/N2a cells ( 3 . 5 x 103 pfu/ml ) derived exosomal fractions , respectively . We used same ratio of supernatant fractions ( collected from the step before PBS wash during exosome isolation ) from tick or bEnd . 3/N2a cells . Titers were determined after plaque countings and calculations . For studies with exosomes and exosome-free supernatant fractions , infected cells ( infected with exosomes or supernatant fractions collected at different time points ) were either collected at 24 or 48 or 72 h p . i . and processed for RNA extractions . For infection of mouse N2a cells with WNV , we used CT2741 wild-type strain ( MOI 5 ) and analyzed cells for WNV loads in cells and exosomes at different time points ( 24 , 48 and 72 h p . i . ) . Exosomes were vitrified as previously described [76 , 77] on carbon holey film grids ( R2x2 Quantifoil; Micro Tools GmbH , Jena , Germany; or C-flat , Protochips , Raleigh , North Carolina ) . Briefly , purified concentrated suspensions of exosomes in PBS were applied to the holey films in a volume of ca . 3 μl , blotted with filter paper , and plunged into liquid ethane cooled in a liquid nitrogen bath . We used computerized Vitrobot plunger ( FEI , Hillsboro , OR ) for freezing . Frozen grids were stored under liquid Nitrogen and transferred to a cryo-specimen holder ( 70 deg . 626 , Gatan , Inc . , Pleasanton , CA , or 2550 cryo-tomography holder , E . A . Fischione Instruments , Inc . , Export , PA ) under liquid Nitrogen before loading into a JEOL 2200FS , or a JEOL 2100 electron microscopes ( JEOL Ltd . , 3-1-2 Musashino , Akishima , Tokyo 196–8558 , Japan ) . JEOL 2200FS was equipped with in-column energy filter ( omega type ) and a field emission gun ( FEG ) ; JEOL 2100 had a LaB6 filament , both were operating at 200 keV . Grids were maintained at near-liquid Nitrogen temperature ( -172–-180°C ) during imaging . Preliminary screening and imaging of exosomes was done using a 4k x 4k Gatan US4000 CCD camera ( Gatan , Inc . , Pleasanton , CA ) , and final imaging was done at indicated 40 , 000x magnification with a 5k x 4k Direct Electron Detector camera ( DE-20 , Direct Electron , Inc . , San Diego , CA ) using a low-dose imaging procedure . An in-column omega electron energy filter was used during imaging with a zero-loss electron energy peak selected with a 20 eV slit . Images were acquired with a ca . 20 electrons/Å2 dose; the pixel size corresponded to 1 . 5 Å on the specimen scale . We used a 2 . 0–2 . 3 μm defocus range for imaging . Overall , individual exosome images were acquired from two-three independent batches of exosomes from tick and N2a cells . For quantitation of exosomes size ranges , we manually analyzed the sizes using scale bar from cryo-EM images and counted exosomes per image in each group . Three independent estimations and countings were performed without any bias . Percentages ( for size determination ) were calculated based on the total number of exosomes in each size range . In addition , total number of exosomes/cryo-EM images were counted and analyzed . Tick cells ( 1 . 2 x 107 cells cultured in 12 of Nunc tubes; ThermoScientific ) or N2a neuronal cells ( 8 x 107 cells cultured in 8 different T75 flask; Greiner ) were infected with either 1 MOI ( tick cells; six of each tube ) or 5 MOI ( five of each flask with N2a cells ) of LGTV . Remaining tubes ( 6 ) or flasks ( 4 ) were maintained as uninfected controls . The detailed protocol is shown as S1C Fig . Supernatants ( 20-50ml ) from uninfected/infected cells of respective cell type were collected and centrifuged at 4°C ( 480g for 10 min followed by 2000g for 10min to remove cell debris and dead cells ) . Cell culture supernatants were either first filtered ( using 0 . 1 μm filtering devices; VACUCAP filter for conical tubes; Pall Laboratory/VWR ) and concentrated to 2–2 . 5ml using the Corning Spin-X UF concentrators or centrifugal filter device with a 5 k nominal molecular weight limit ( NMWL ) . The concentrated culture medium were processed for OptiPrep ( DG-Exos ) isolation as described [55] . In case of OptiPrep ( DG-iso ) on laboratory virus stocks ( 7 . 4 x 106 pfu/ml ) , we added concentrated stocks of 1 . 5 ml supernatants directly on the gradient cushion . Briefly , discontinuous gradient of 40% ( w/v ) , 20% ( w/v ) , 10% ( w/v ) and 5% ( w/v ) solutions of iodixanol was prepared from the stock solution of OptiPrep 60% ( w/v ) of aqueous iodixanol ( Axis-Shield PoC , Norway ) with 0 . 25M Sucrose/10mM Tris , pH 7 . 5 . We used the polycarbonate bottles with cap ( Beckman Coulter ) and maximum volume capacity of 26 . 3 ml to load the discontinuous gradient of iodixanol ( 4ml each of 40% ( w/v ) , 20% ( w/v ) , 10% ( w/v ) and 3 ml of 5% ( w/v ) from bottom to top ) . The cell culture supernatants ( 2–2 . 5ml ) was overlaid onto the top of the gradient , and centrifuged at 100 , 000g for 18 h at 4°C . Six individual uninfected or infected fractions of ~3ml were collected ( from top to bottom ) manually ( with increasing density ) and diluted with 5ml of sterile PBS . Fractions were centrifuged at 100 , 000g for 3 h at 4°C , and followed by one more wash with 5ml of PBS and resuspended in 80 μl PBS . DG-Exos were stored in -80°C and used for analysis . Exosomes were isolated and purified by either DG-Exo gradient method as described before or differential ultracentrifugation method as described by [29] . Details for exosome isolation procedure and modifications ( used in this study ) are also schematically shown and discussed in S1 and S2 Figs and in corresponding figure legends . Briefly , cells were seeded for exosomal- RNA ( 5 x 106 tick cells; 1 x 105 of either bEnd3 . 1 or N2a or murine cortical neurons ) or protein ( 1 x 106 tick cells , 2 x 106 N2a cells or 2 x 107 cortical neurons ) extractions in either 12/6-well or 10cm2 plates in complete L15 , DMEM or Neurobasal medium with FBS for overnight , respectively . Next day , cells were changed to respective medium containing bovine exosome-free FBS ( Systems Biosciences Inc; SBI ) . Tick cells plated in commercially available exosome-free FBS medium showed severe loss of cells but infectious loads were not affected . After 4–6 hour of medium replacement , cells were infected with LGTV ( tick cells MOI 1; bEnd3 . 1 and N2a cells MOI 6; and cortical neurons MOI 4 ) . Tick cells were susceptible to 2 or 3 MOI of infection and showed massive death , hence we used 1 MOI dose for tick cell infection studies . Cell culture supernatants were spun at 300 x g , for 10 min , cell pellet was discarded and the supernatant was spun again at 2000 x g for 10min . The pellet containing dead cells was discarded and the supernatant was spun again at 10 , 000 x g for 30 min to remove cell debris . Increased centrifugation times and rotor types is shown to improve exosome yield and purity [57] and , hence we used these modifications for isolation of exosomes from tick cells . Either supernatants were spun at 100 , 000 x g , for 70 min ( for bEnd . 3 , N2a and cortical neurons ) or for 155 min ( for ISE6 tick cells ) . Supernatants collected after this spin step served as supernatant fractions and were used as controls in our study ( indicated with * in S2 Fig ) . For plaque assays performed in this study 600 , 60 and 6 μl and for infection studies 400 μl of supernatant fractions were used for all except HUVEC cells ( 300 μl ) . The pellets containing exosomes and any contaminants were washed one- time with ice-cold PBS and spun again at 100 , 000 x g , for either 70 min ( for bEnd . 3 , N2a , cortical neuronal cells ) or 155 min ( for tick cells ) , respectively . Resulting exosomes pellet is referred as exosome fractions in this study . Freshly prepared exosome pellets were collected in PBS ( and stored frozen at -80°C for re-infection studies performed on uninfected cells or for plaque assays or other tested evaluations ) or resuspended in RNA lysis buffer for total RNA extractions , or in modified RIPA buffer ( G-Biosciences , BioExpress ) for total protein extractions . We also isolated exosomes from N2a cells using the total exosome isolation reagent and extracted total RNA and proteins using total exosome RNA and Protein Isolation kit ( Invitrogen/ThermoScientific ) as per the manufacturer’s instruction . Total RNA from ISE6 tick cells , HaCaT , Vero , HUVEC , bEnd . 3 , N2a cells or murine cortical neurons infected with various MOI of LGTV or WNV or uninfected controls were extracted using Aurum Total RNA Mini kit ( BioRad ) following manufacturer’s instruction . Using BioRad iScript cDNA synthesis kit , 1 μg RNA was converted to cDNA and the generated cDNA was used as template for the amplification and determination of the viral burden . For determination of positive- or negative-sense strands of LGTV , we used the iTaq Universal SYBR Green One-Step kit ( BioRad ) and followed manufacturer’s instructions . For detection of positive- and negative-sense strands of LGTV RNA , we used published forward and reverse primers for Langat prM-E [78] . For WNV detection , published primers for E gene were used [73] . To normalize the amount of templates , either tick or mouse or human beta actin amplicons were quantified with published primers [73 , 79] . Equal amounts of tick/mouse/human cDNA samples were used in parallel for beta actin and Langat prM-E . The ratio of Langat prM-E gene copy/beta actin gene copy was used as an index to determine the rate of infection in each analyzed sample . QRT-PCR was performed using iQ-SYBR Green Supermix ( BioRad , USA ) . Standard curves were prepared using 10-fold serial dilutions starting from standard 1 to 6 of known quantities of actin or Langat prM-E gene fragments and QRT-PCR reactions were performed as described [72 , 73 , 79] . To determine the copy number of viral RNA in exosomes , we used the LGTV RNA values with standards and converted to copy numbers using the formula: Number of copies ( molecules ) = ( amount of amplicon ) ng x 1023 molecules per mole/ ( length of dsDNA amplicon * 660g per mole ) † *1 x 109 ng per g . Alternatively , we also used the online calculator to convert to copy numbers ( http://scienceprimer . com/copy-number-calculator-for-realtime-pcr ) . For RNase A treatment , we isolated fresh exosomes from either uninfected or LGTV-infected N2a cells ( 2 x 107 ) , distributed the infected exosomes as treated ( 5 μg/ml RNase , 37°C for 15 min ) or untreated groups . Exosomes were also treated with Triton X-100 ( 0 . 1% , for 45 min at RT ) and then followed by treatment with RNaseA as before . N2a cells ( 2 x 105 ) were infected ( 72 h p . i . ) with these treated or untreated LGTV-infected exosomal samples were processed for RNA extractions and QRT-PCR . Untreated exosomal samples from uninfected group served as internal controls . Western blotting was performed as described [72 , 73] . For DG-Exos samples , equal volume ( 20 μl ) of each fraction from 1–6 or 20 μg of total protein lysates from uninfected and infected cells or 10 μl of each fraction from virus stock samples were loaded onto 12% SDS-PAGE , followed by immunoblotting and labeling with highly cross-reactive 4G2 monoclonal antibody to detect LGTV E-protein or exosomal specific markers such as HSP70 ( rabbit polyclonal; Cell Signaling Technologies , Inc ) or CD9 ( mouse monoclonal; Invitrogen/ThermoScientific ) and respective secondary antibodies ( Santa Cruz Biotechnologies , Inc ) . For immunoblotting using cell lysate and exosome lysates , briefly , 5 x 106 ISE6 tick cells , or 2 x 106 N2a cells or 2 x 107 cortical neurons were seeded in 10 cm2 plates and allowed to settle/adhere for overnight . Next day , we changed the media on N2a cells and neurons to DMEM or Neurobasal medium , respectively containing bovine exosome free FBS ( Systems BioSciences , Inc; SBI ) . ISE6 cells were retained with complete L-15 media containing 5% regular FBS ( to avoid massive cell death and loss observed when processed for exosome isolation using commercially available exosome-free FBS; SBI ) . After 4–6 hours of media replacement , cells were infected with LGTV ( tick cells , MOI 1; N2a cells , MOI 6 and cortical neurons , MOI 4 ) . After 72 h ( tick cells ) or 24 , 48 , 72 h p . i . ( N2a cells and neurons ) , cell culture supernatants were collected and processed for exosome isolation by ultracentrifugation ( See S2 Fig ) . The exosome fractions collected after PBS wash and the adherent cells collected from same plates ( washed twice with 1 x PBS ) , were resuspended in modified RIPA buffer . Total protein amounts were estimated using BCA kit ( Pierce/ThermoScientific ) . We loaded 25 μg ( tick cells ) or 30–35 μg ( N2a and cortical neurons ) of total cell lysates or total exosomal proteins and separated them on either 12% ( Laboratory casted ) or precasted 4–20% SDS-PAGE gradient stain-free gels ( NuSep; BioExpress ) . Followed by gel electrophoresis , blots were blocked in buffers and probed with either highly cross-reactive 4G2 ( obtained from Dr . Michel Ledizet , L2 Diagnostics; under non-reducing conditions ) or CD9 ( Invitrogen/ThermoScientific; under non-reducing conditions ) or monoclonal anti-Langat virus NS1 ( Clone 6E11; BEI Resources ) antibodies , followed by mouse monoclonal HRP-conjugated secondary antibodies ( Santa Cruz Technologies , Inc ) . Total protein profiles ( images obtained from stain free gels after running or imaged from Coomassie stained gels ) serve as loading controls . For protease-resistance assay using proteinase K ( that generally digest proteins in biological samples ) , we used typical working concentrations of 50–100 μg/ml ( for tick cells; 50 μg/ml ) or much above the concentrations ( for N2a cells; 100 μg/μl ) . Briefly , we isolated fresh exosomes ( ultracentrifugation methods ) from tick ( 2 x 106 ) or N2a cells ( 2 x 107 ) , and treated with Proteinase K for 15 min at 37°C . Samples were then heat-inactivated at 60°C for 10 min and loaded on SDS-PAGE gels and processed for immunoblotting with 4G2 antibody followed by relevant secondary antibody . Antibody binding was detected with WesternBright ECL kit ( Advansta , BioExpress ) . Blots were imaged using Chemidoc MP imaging system and processed using Image Lab software from the manufacturer ( BioRad ) . For the native-PAGE analysis , we seeded ISE6 tick cells ( 2 x 106 ) in regular L15 medium for overnight and infected with LGTV ( MOI 1 ) . For N2a cells ( 5 x 106 ) , we plated them in regular complete DMEM medium and allowed them to adhere for overnight , cells were then replaced with exosome-free FBS medium . After 4 h of media change , N2a cells were infected with LGTV ( 6 MOI ) . Post 72 h of infection , tick/N2a cell culture supernatants were processed for isolation of exosomes . Exosomes collected from uninfected or LGTV-infected tick/N2a cells were resuspended in PBS and distributed into three groups ( from the same preparations ) , that were either held as untreated group on ice , treated with Triton-X-100 ( 0 . 03%; 30 min , RT ) , or processed for three cycles of freezing at -80°C ( for each freezing cycle samples were incubated for 1 h ) . After treatment and processing , protein lysates were prepared in a non-reducing and non-denaturating sample buffer ( 62 . 5 mM Tris-HCL , pH 6 . 8 , 25% Glycerol and 1% Bromophenol blue ) , that maintained the proteins secondary structure and native charge density . Gels were pre-run for 60 min in gel running buffer ( 25 mM Tris and 192 mM Glycine ) . Uninfected or LGTV-infected exosomal preparations with different treatment or untreated samples were separated on 12% native-PAGE gels . Gels were transferred on to nitrocellulose membranes followed by immunoblotting using 4G2 or NS1 monoclonal antibodies followed by mouse monoclonal HRP-conjugated secondary antibodies ( Santa Cruz Technologies , Inc ) . Total protein profiles ( Coomassie blue stained gel ) serve as loading controls . Antibody binding was detected with WesternBright ECL kit ( Advansta , BioExpress ) . Blots were scanned using Chemidoc MP imaging system and instructions from the manufacturer ( BioRad ) . We collected N2a cell-derived exosomes from 5 x 106 uninfected or LGTV-infected ( MOI 6; 72 h p . i . ) cells and resuspended in PBS ( 250 μl/sample ) . Exosomal fractions were grouped as untreated or treated with 0 . 1% of Triton-X-100 for 30 min . Nunc grade ELISA plates were coated with 50 μl of untreated or Triton-X-100 treated- uninfected or infected samples for overnight and incubated at 4°C . Samples were incubated with 4G2 antibody for 1 h , followed by HRP-conjugated mouse monoclonal secondary antibody for another 1 h as described [72] . We used SureBlue TMB Microwell Peroxidase substrate and Stop solution ( KPL ) and followed manufacturer’s instructions . After stopping the reactions with TMB Stop solution , optical density was measured from triplicate samples at an absorbance of 450nm using a Multimode infinite M200 Pro Microplate reader ( Tecan ) . LGTV-infected tick or N2a cell-derived exosomes ( from 72 h p . i . ) were freshly isolated from 2 x 106 tick cells ( infected with MOI 1 ) or 5 x 105 N2a cells ( infected with 6 MOI ) . We also isolated exosomes from GW4869 inhibitor ( 5 μM ) treated tick or N2a cells . For inhibitor treatment , cells were seeded in plates for overnight , changed to exosome-free FBS medium ( in case of N2a cells ) and after 4 h , treated with exosome release GW4869 inhibitor for 4h , followed by infection with LGTV for 72 h p . i . The exosomes collected from untreated or inhibitor treated cells were resuspended in PBS and grouped into three categories for both inhibitor treated or untreated samples as; untreated , treated with 4G2 antibody ( that recognizes LGTV E-protein ) or relevant isotype control antibody ( R & D Systems ) groups . Exosomal fractions were incubated for 1 h ( RT ) with respective antibodies followed by incubation ( 4°C ) with protein A/G agarose beads ( Pierce/ThermoScientific ) for another 30 min . The antibody-beads complexes were spun ( 13k rpm ) at 4°C for 30 min and supernatants were collected and lysed in RNA lysis buffer , processed for RNA extractions , followed by cDNA synthesis and QRT-PCR to detect LGTV loads . Assays were performed to analyze the trans-migration of infectious exosomes from infected cells ( seeded in inserts; upper chamber ) to uninfected cells seeded in 12-well plates ( lower chamber ) . Sterile , polycarbonate tissue culture-treated transwell inserts ( 12mm insert size ) with 0 . 4 μm microporous membrane pore size were used in our assays ( Corning ) . We plated , 1 x 105 ISE6 tick or bEnd . 3 cells in inserts ( upper chamber ) and 1 x 105 HaCaT or N2a cells were seeded in 12-well plates ( lower chamber ) . Inserts with tick or bEnd . 3 cells were first kept in a separate 12-well plates containing 0 . 5 ml ( in order to keep microporous membranes moist/wet ) of L-15 ( tick cells ) or DMEM complete medium ( bEnd . 3 cells ) , respectively . Inhibitor-treated group in transwell assays was treated with 5 μM of GW4869 inhibitor , and at 24 h post treatment , tick cells or bEnd . 3 cells were either infected with exosomes containing LGTV ( 25 μl of the exosome fraction collected from infected tick or bEnd . 3 cells ) or with LGTV from laboratory viral stocks ( MOI 1 for tick cells or MOI 6 for bEnd . 3 cells ) prepared from infected Vero cell culture supernatants . Four hours post-infection , inserts with tick or bEnd . 3 cells ( with change of new media ) were moved to 12-well plates containing HaCaT or N2a cells , respectively . Exosomes containing viral RNA and proteins produced from tick or bEnd . 3 cells were allowed to transmigrate and infect HaCaT or N2a cells ( that were kept uninfected ) . After , 48 h post incubation with inserts ( containing either infected tick or bEnd . 3 cells in inserts or upper chambers ) , HaCaT or N2a cells from lower chamber were washed with ice-cold PBS ( 3x ) and collected for RNA extractions , cDNA synthesis and QRT-PCR to determine viral loads from cells . Plaque assays were performed as described [72] . To determine infectious and replicative viruses after incubation with exosome and exosome-free supernatant fractions , we seeded Vero cells in 6-well plates at densities of 1 x 106 cells per well , allowed them to adhere and grow as monolayers to reach 65–85% confluency ( for ~24 h ) . Exosome fractions containing unknown PFU ( plaque forming units ) of LGTV viral genomes were collected from tick cells ( 5 x 106 cells ) or N2a cells or murine cortical neurons ( 1 x 105 cells ) and resuspended in 250 μl of PBS , 30 μl of this suspension ( exosome fraction ) was used for plaque assays . Exosome free supernatants ( 600 μl ) that corresponds to the same ratio of exosome fractions were used as controls . Serial dilutions ( 1:10 , 1:100 and 1:1000 ) of the exosomes or supernatant fractions were prepared in duplicate ( shown are the representative plate images from two-three independent experiments ) . Monolayers of Vero cells were infected with exosomes or supernatant fractions or with exosomes from 1 μM or 10 μM ( LGTV-infected N2a cells or mouse cortical neurons ) of GW4869 inhibitor-treated or DMSO-treated controls . Four hours post infection , medium was removed and warm 2% Seaplaque agarose ( Lonza ) overlay with complete DMEM media ( 1:1 ratio ) containing antibiotic and antimycotics solution ( 1% each; Sigma ) was added . Plates were incubated for 6–7 days , at 37 °C , 5% CO2 . After incubation period , plaques were stained with 0 . 03% of Neutral Red ( Sigma ) for 4 h , and the stain was removed to either count plaques on the same day or otherwise plates were incubated ( inverted and covered in foil ) for overnight , and then plaques were counted next day to determine the viral titers from LGTV-infected exosomal fractions from tick/neuronal cells . Gestation period ( day13 ) wild-type female C57BL/6 ( Charles River Laboratories ) mice were purchased and allowed to reacclimatize . All animal experiments were done in accordance with the University Animal Care and Use committee regulations . Primary cortical neurons were isolated from embryonic day-16 ( E16 ) brains [72 , 73] . Murine cortical neurons ( 1 x 105 ) were seeded in a 12-well plate coated with poly-L-Lysine and cultures were established in neurobasal complete medium with FBS . After 24h of plating , half of the medium was replaced with FBS-free neurobasal media , to slow growth of glial cells . For infection kinetics , cortical neurons were infected with LGTV ( MOI 4 ) ( after 48 h of post-seeding ) , neurons were collected at different time points ( 24 , 48 , 72 and 96 h p . i . ) and processed for RNA extractions . For infection with neuronal cell-derived exosomes or supernatant fractions , 1 x 105 murine cortical neurons were infected with 20 μl of neuronal exosomes ( 2 . 2 x 103 pfu/ml ) or 400 μl of exosome free supernatant fractions ( collected from the step before PBS wash during exosome isolation , See S2 Fig ) . Cells were harvested at 48 h p . i . and processed for RNA extraction . Protein extractions were collected from uninfected or LGTV-infected cortical neurons ( seeded at 1 x 107 cells ) or from exosomes isolated from these cell culture supernatants . For exosome inhibition studies , we used GW4869 a cell permeable , selective inhibitor for Neutral Sphingomyelinase ( N-SMase ) ( Santa Cruz Biotechnologies , Inc ) and DMSO as controls . Cells did not show any toxicity at tested doses . For both transwell assays , inhibitor-treated group was treated with 5 μM exosome inhibitor . N2a cells or murine cortical neurons were seeded at 1 x 105 cells in a 12-well plate . Next day , before treatment with inhibitor , cells were replaced with bovine exosome free-FBS ( Systems BioSciences , Inc . ) containing DMEM ( N2a cells ) or neurobasal medium ( neurons ) . Cells were treated with either 1 , 5 or 10 μM ( N2a cells ) or with 1 , 10 or 20 μM ( neurons ) of inhibitor for 4 h , followed by infection with LGTV ( N2a cells , MOI 6; cortical neurons MOI 4 ) . Plaque assays were performed with 30 μl of exosome fractions derived from N2a cells or cortical neurons to determine the unknown titers for both DMSO control or inhibitor treated groups , respectively . N2a cells were either pre- or post- treated with inhibitor , where cells were first treated with inhibitor ( 5 μM ) for 4 h followed by infection for 72 h or vice versa , respectively . Supernatants collected from uninfected controls and cells infected with LGTV ( laboratory virus stocks with known titers ) ( 48 h p . i . ) were processed for exosome isolation . Purified exosomes were resuspended in PBS and processed for either RNA extraction or used for infection of new cells to determine re-infection kinetics or used to determine viral titers by plaque assays . For GW4869 treatment on laboratory virus stocks , we treated the viral supernatants ( collected from Vero cells ) with known titers ( 7 . 4 x 106 pfu/ml ) . We used 30 μl of the virus stocks and treated with either DMSO or inhibitor ( 5 and 10 μM for 4 h at 37°C ) followed by immunoblotting with 4G2 antibody . For 4G2 functional blocking antibody studies , we plated N2a cells ( 2 x 105 ) , treated with 5 μg of antibody for 4 h and infected cells with freshly isolated exosomes from LGTV-infected ( 72 h p . i . ) N2a cells . N2a cells were infected through infectious exosomes for 72 h p . i . and collected for RNA extractions and QRT-PCR analysis . Untreated samples serve as control . For Pitstop-2 inhibitor treatment , N2a cells ( 2 x 105 ) were treated with 30 μM Pitstop-2 ( dissolved in DMSO ) for 15 min followed by infection through freshly isolated exosomes from LGTV-infected ( 72 h p . i . ) N2a cells . Cells were collected for RNA extractions after 72 h p . i . and further processed for QRT-PCR . DMSO treated cells served as controls . Statistical difference observed in data sets was analyzed using GraphPad Prism6 software and Microsoft Excel . The non-paired , two-tail Student t test was performed ( for data to compare two means ) for the entire analysis . Error bars represent mean ( +SD ) values , P values of <0 . 05 were considered significant in all analysis . Statistical test and P values are indicated for significance . All animal work in this study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institute of Health . The approved protocol from the Institutional Animal Care and Use Committee ( Animal Welfare Assurance Number: A3172-01 ) was used in this study ( permit number: 16–017 ) . | In this study we have demonstrated that cells from the medically important vector tick , secretes exosomes that mediate transmission of tick-borne Langat ( LGTV ) viruses from arthropod to human and other vertebrate host cells . This study not only provides evidence that suggest tick-borne pathogens use arthropod-derived exosomes for transmission from vector to mammalian cells but also use exosomes for dissemination within the vertebrate host . | [
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] | 2018 | Exosomes serve as novel modes of tick-borne flavivirus transmission from arthropod to human cells and facilitates dissemination of viral RNA and proteins to the vertebrate neuronal cells |
Characterization of Human Endogenous Retrovirus ( HERV ) expression within the transcriptomic landscape using RNA-seq is complicated by uncertainty in fragment assignment because of sequence similarity . We present Telescope , a computational software tool that provides accurate estimation of transposable element expression ( retrotranscriptome ) resolved to specific genomic locations . Telescope directly addresses uncertainty in fragment assignment by reassigning ambiguously mapped fragments to the most probable source transcript as determined within a Bayesian statistical model . We demonstrate the utility of our approach through single locus analysis of HERV expression in 13 ENCODE cell types . When examined at this resolution , we find that the magnitude and breadth of the retrotranscriptome can be vastly different among cell types . Furthermore , our approach is robust to differences in sequencing technology , and demonstrates that the retrotranscriptome has potential to be used for cell type identification . We compared our tool with other approaches for quantifying TE expression , and found that Telescope has the greatest resolution , as it estimates expression at specific TE insertions rather than at the TE subfamily level . Telescope performs highly accurate quantification of the retrotranscriptomic landscape in RNA-seq experiments , revealing a differential complexity in the transposable element biology of complex systems not previously observed . Telescope is available at https://github . com/mlbendall/telescope .
Transposable elements ( TEs ) represent the largest class of biochemically functional DNA elements in mammalian genomes[1 , 2] comprising nearly 50% of the human genome . As many of these transcriptionally active elements originated as retroelements , we refer to the set of RNA molecules transcribed from these elements in a population of cells as the retrotranscriptome . The contribution of the retrotranscriptome to the total transcriptome , cell-type specific expression patterns , and the role of retroelement transcripts in disease remain poorly understood[3] . Although most TEs are hypothesized to be transcriptionally silent ( due to accumulated mutations ) , recent studies have found many elements to be actively expressed and involved in key cellular processes . For example , aberrant expression of LINE-1 ( L1 ) elements , the most expansive group of TEs , has been implicated in the pathogenesis of cancer[4–7] , while human endogenous retroviruses ( HERVs ) are reported to be involved in human embryonic stem cell differentiation[8 , 9] and in the pathogenesis of amyotrophic lateral sclerosis[10] . We , and others , have shown that HIV-1 infection increases HERV transcription[11–15] . These lines of evidence therefore indicate that TEs have important roles in the regulation of human health and disease . The ability to observe and quantify TE expression , especially the specific genomic locations of active elements , is crucial for understanding the molecular basis underlying a wide range of conditions and diseases[16] . Traditional techniques for interrogating the TE transcriptome include quantitative PCR[17 , 18] and RNA expression microarrays[19–23] . However , these techniques are unable to discover elements not specifically targeted by the assay , and may fail to detect rare , previously unknown , or weakly expressed transcripts . High-throughput RNA sequencing ( RNA-seq ) promises to overcome many of these shortcomings , enabling highly sensitive detection of transcripts across a wide dynamic range . Mathematical and computational approaches for transcriptome quantification using RNA-seq are well established[24 , 25] ( reviewed by Garber et al . [26] ) and provide researchers with reproducible analytical pipelines[27 , 28] . Such approaches are highly effective at quantifying transcripts when sequenced fragments can be uniquely aligned to the reference genome , since the original genomic template for each transcript can be unambiguously identified[29 , 30] . In contrast , sequencing fragments that originate from repetitive sequences often have high scoring alignments to many genomic locations , leading to uncertainty in fragment mapping and the derived transcript counts . Issues arising from these “multimapping” or “ambiguous” fragments are well known and are often addressed by masking repetitive sequences or otherwise discarding ambiguous fragments[31–33] . The disadvantage of ignoring repeats is that interesting biological phenomena , including those involving TEs , are missed[31] . Several approaches have been proposed that account for read mapping uncertainty using statistical models . The most common approach , described by Li et al . [34 , 35] , involves modeling read assignments using a mixture model , with expression levels as mixture weights and fragment assignments as latent variables; model parameters are then estimated using an expectation-maximization algorithm . Several variations on this model have been proposed , such as modeling read counts instead of individual reads ( MMSEQ[36] ) or using Markov chain Monte Carlo ( MCMC ) to sample model parameters ( BitSeq[37] ) . A few approaches deviate from the mixture model approach; notably , MMR instead evaluates alignments by minimizing a loss function[38] . To our knowledge , none of these packages have been adapted specifically for quantifying TE expression . A growing field of study is now interested in using high-throughput sequencing to characterize the retrotranscriptome[8 , 9 , 39–41] . Instead of considering repetitive sequences as a source of noise that interferes with gene expression analysis , the TEs themselves are the features of interest . Three general approaches are used to deal with challenges of aligning short sequencing reads to repetitive elements . i ) “Family-level” approaches combine read counts across multiple instances of a TE subfamily , since fragments mapping to multiple genomic locations can often be uniquely assigned to a single repeat subfamily . This approach provides valuable information about which TE subfamilies may be differentially regulated , but lacks the resolution needed to identify specific expressed elements . ii ) “Heuristic” approaches simplify the problem of multi-mapped fragments by examining alignments and using filtering criteria to resolve ambiguity . Examples of heuristic approaches include discarding ambiguous reads ( unique counts ) , randomly assigning ambiguous reads to one of its best scoring alignments ( best counts ) , or dividing counts among possible alignments ( fractional counts ) . Finally , iii ) “statistical” approaches implement a statistical model that estimates the most probable assignment of fragments given underlying assumptions about the generating process and the observed data . Several existing software packages have implemented these approaches specifically for TE quantification . RepEnrich[42 , 43] maps reads to “pseudogenomes” composed of multiple loci belonging to the same subfamily , then uses a fractional counts heuristic to resolve any remaining ambiguous fragments . TEtranscripts[44] and SalmonTE[45] are both statistical approaches that use mixture models estimated by expectation-maximization . The main difference between these approaches is that TEtranscripts begins with genome alignment , while SalmonTE adapts the Salmon[46] approach of quasi-alignment to transcriptome sequences . Like MMSEQ , SalmonTE also uses equivalence classes to reduce the effort needed for parameter optimization . By default , all three TE quantification approaches summarize estimates by subfamily . Here , we introduce Telescope , a tool which provides accurate estimation of TE expression resolved to specific genomic locations . Our approach directly addresses uncertainty in fragment assignment by reassigning ambiguously mapped fragments to the most probable source transcript as determined within a Bayesian statistical model . We implement our approach using a descriptive statistical model of the RNA-seq process and use an iterative algorithm to optimize model parameters . We use Telescope to investigate the expression of HERVs in cell types from the ENCODE consortium .
Resolution of transposable element ( including those of human endogenous retroviruses , HERVs ) expression from RNA-seq data sets has been complicated by the many similarities of these repetitive elements . Telescope is a computational pipeline program that solves the problem of ambiguously aligned fragments by assigning each sequenced fragment to its most likely transcript of origin . We assume that the number of fragments generated by a transcript is proportional to the amount of transcript present in the sample; thus , the most likely source template for a randomly selected fragment is a function of its alignment uncertainty and the relative transcript abundances . Telescope describes this relationship using a Bayesian mixture model where the estimated parameters include the relative transcript abundances and the latent variables define the possible source templates for each fragment[47] . The first step in this approach is to independently align each fragment to the reference genome; the alignment method should search for multiple valid alignments for each fragment and report all alignments that meet or exceed a minimum score threshold ( Fig 1A ) . Next , alignments are tested for overlap with known TE transcripts; transcript assignments for each fragment are weighted by the score of the corresponding alignment ( Fig 1B and 1C ) . In our test cases , we typically find that less than 50% of the fragments aligning to TEs can be uniquely assigned to a single genomic location and many fragments have more than 20 possible originating transcripts . Telescope uses a Bayesian mixture model to represent transcript proportions and unobserved source templates and estimates model parameters using an expectation-maximization algorithm . In the expectation step ( E-step ) , the expected value of the source template for each fragment is calculated under current estimates of transcript abundance ( Fig 1D ) . The maximization step ( M-step ) finds maximum a posteriori estimates of the transcript abundance dependent on the expected values from the E-step ( Fig 1E ) . These steps are repeated until parameter estimates converge ( Fig 1D and 1E ) . Telescope reports the proportion of fragments generated by each transcript and the expected transcript of origin for each fragment ( Fig 1F ) . The final counts estimated by Telescope correspond to actual observations of sequenced fragments and are suitable for normalization and differential analysis by a variety of methods . The software also provides an updated alignment with final fragment assignments that can be examined using common genome visualization tools . Telescope is available at https://github . com/mlbendall/telescope . The core statistical model implemented in Telescope is based on the read reassignment model described by Francis et al . [47] and is similar to existing models for resolving mapping uncertainty[34 , 35 , 44 , 45] . Three main differences distinguish our model from existing models . First , our model includes a reassignment parameter , theta , that is absent in other models . This parameter effectively penalizes ambiguous alignments and may be important in cases where many highly similar transcripts are present . Second , our model includes an additional mixture component for fragments that map outside of the known transcriptome , accounting for missing transcripts in the annotation . Finally , our model does not use equivalence classes; reassignment occurs at the fragment level . To demonstrate that our algorithm can truly resolve repetitive element expression to precise genomic locations , we generated sequencing fragments from a single genomic locus in silico and used Telescope to resolve alignment ambiguity and quantify expression . The locus selected was HML2_1q22 ( HERV-K102 ) , an HML-2 provirus that is highly similar to several other HML-2 loci[48] and should thus generate many ambiguously mapping fragments . All of the simulated fragments aligned to multiple genomic locations , and most of these ( 68 . 4% ) had multiple distinct alignments sharing the same “best” alignment score ( S1 Fig ) . Fragments mapped to 71 different HERV proviruses , including 58 HML-2 loci . After using our model to identify the most probable source locus for each fragment , we found that all fragments could be confidently assigned to HML2_1q22 with greater than 99% posterior probability ( S1 Fig ) . This is possible because our model effectively reweights ambiguous alignments by borrowing strength from nearby alignments that are unique or high-scoring . In this case , there were no uniquely aligned fragments within HML2_1q22 , but many fragments had best-scoring alignments to this locus . This result demonstrates that our approach can accurately reassign ambiguously mapping fragments and thus enables accurate expression quantification at single-locus resolution . To investigate HERV expression in a robust way across a diverse platform of cell types we relied on publicly available RNA-seq data . The ENCODE data project is an invaluable source of genomic data from disparate sources and provides the opportunity to mine the transposable element expression in a setting of maximum genomic information . We profiled 13 human cell types , including common lines designated by the ENCODE consortium , as well as primary cell types , and applied our approach to determine HERV expression across the spectrum of human cell types , including normal or transformed , and contrasting cell lines with primary cells ( Table 1 , S1 Table ) . Over 2 . 7 billion sequenced fragments aligned to human reference hg38 with between 23 . 6% and 46 . 1% of the fragments in each sample aligning ambiguously to multiple genomic locations . Telescope intersected the aligned fragments with a set of 14 , 968 manually curated HERV loci belonging to 60 families ( see methods ) and identified over 27 million fragments that appear to originate from HERV proviruses . Most ( 80 . 1% ) of these fragments aligned to multiple genomic locations; we used Telescope to reassign ambiguous fragments to the most likely transcript of origin and estimate expression at specific HERV loci . We developed genome-wide maps of HERV expression for 8 of the analyzed cell types that had replicates ( Table 1 , S1 Table ) , and used CIRCOS[49] to visualize the data ( Fig 2 ) . The outer track is a bar chart showing the number of HERV loci in 10 Mbp windows , with the red part of the bar representing the number of loci that are expressed in one or more cell types . The 8 inner rings show the expression levels ( log2 counts per million ( CPM ) ) of 1365 HERV loci that were expressed at least one of the cell types examined . Moving from the outer ring to the inner ring are replicates for each of the 8 cell types with replicates: H1-hESC , GM12878 , K562 , HeLa-S3 , HepG2 , HUVEC , MCF-7 , and NHEK . We found 1365 HERV loci that were expressed in at least one of the cell types ( CPM > 0 . 5 ) . Not all HERVs were expressed in all cell types , some were widely expressed in all cells , whereas others were only expressed in one or more cell type ( Fig 2 ) . There is also a spectrum of differential HERV expression , with some HERVs having significantly higher expression than others . Visual inspection of HERV expression maps suggest that there are certain regions of the genome that have minimal HERV expression , while other regions appear dense in HERV expression ( Fig 2 ) . The genomic context of HERV expression can also be inspected more closely in areas of interest , i . e . chromosome 19 ( S2 Fig ) and chromosome 6 ( S3 Fig ) . To ascertain global , subfamily and locus level specific HERV expression , we assessed the number of HERVs expressed in each cell type . All cell types expressed HERVs; the number of expressed loci ranged from 216 ( in MCF-7 ) , to 533 ( H1-hESC ) ( Fig 3A ) . The number and proportion of cell type specific locations ( expressed in only one cell ) differed among cell types . Nearly half ( 46 . 3% ) of locations expressed in H1-hESC were not expressed in any other cell type , while 89 . 3% of locations expressed in MCF-7 were also present in other cell types ( Fig 3A ) . This suggests that regulatory networks are shared among some cell types but not others . We next examined the relative contribution of HERV families to overall HERV transcription and found that different cell types could be characterized by enrichment for different HERV families . For example , HERVH accounted for 91 . 8% of the transcriptomic output in H1-hESC cells , while HERVE was dominant in K562 cells ( 24 . 4% ) ( Fig 4A ) . Other families , such as HERVL , were evenly distributed across cell types , both in number of expressed locations and in expression levels ( Fig 4B ) . Resolving the most highly expressed locations in each cell type at a locus specific level shows that the distribution of expression varies among cell types . ( Fig 3C ) . For example , HepG2 is characterized by high expression from a single locus , while H1-hESC has many locations that are activated . Previous work has suggested that estimates of HERV expression are highly sensitive to sequencing technology used , and differences due to sequencing technology can obscure biological differences due to cell type[40] . Since aligning shorter fragments ( i . e . single-end reads ) tends to produce more ambiguously mapping fragments compared to longer fragments , we hypothesized that Telescope ( which resolves ambiguity ) would create HERV expression profiles that are robust to differences in sequencing technology . Hierarchical clustering of all 30 polyA RNA-seq HERV profiles shows that replicates from the same cell type cluster most closely with other samples from the same cell type , regardless of the sequencing technology used ( Fig 5A ) . Clusters for all cell types had significant support using multiscale bootstrap resampling ( approximately unbiased ( AU ) > 95% ) . Principal component analysis ( PCA ) also indicates that cell type , not sequencing technology , is associated with the strongest differences among expression profiles . The first principal component , accounting for 44% of the total variance in the data , separates H1-hESC samples from all other samples ( Fig 5B ) . The second and third components further separate the samples into the other 12 cell types , and capture 13% and 10% of the total variance , respectively . Interestingly , the second component separates blood-derived cell types ( K562 , GM12878 , CD20+ and CD14+ ) from the other cell types , suggesting that cells derived from the same tissue may share similarities in HERV expression profiles . We further explored differences among cell types using differential expression ( DE ) analysis . Pairwise contrasts between cell types were performed to determine the number of significant DE loci ( FDR < 0 . 1 , abs ( LFC ) > 1 . 0 ) ( Fig 5C ) . As found in the unsupervised analysis , HERV expression in H1-hESC was drastically different from other cell types , with between 578 and 1127 significantly DE loci . Finally , we asked whether other existing approaches for TE quantification would be sufficient to identify cell type specific signal in the data or whether these approaches would be sensitive to other variables . We analyzed the ENCODE datasets using default parameters for five other approaches , including best counts , unique counts , TEtranscripts , RepEnrich , and SalmonTE . Hierarchical clustering of the resulting expression profiles reveal that cell types clusters are only recovered using unique counts and Telescope ( S3 Fig ) , though unique counts tended to have less support for clusters . In contrast , clustering with the other four approaches did not recover all cell type clusters; 7 out of 8 cell types clustered together when using best counts expression profiles , 5 cell types were recovered with TEtranscripts and RepEnrich , and only 1 cell type cluster was recovered with SalmonTE profiles ( S4 Fig ) . Interestingly , clustering of the SalmonTE expression profiles revealed 5 samples that did not cluster with their respective cell types , but instead clustered with other single-end datasets ( S4 Fig ) . In order to examine the sensitivity and biases of computational approaches for quantifying TE expression , we designed simulation experiments with known expression values . Earlier studies have suggested that the HERV-K ( HML-2 ) subfamily ( hereafter referred to as HML-2 ) is expressed in human tissue and may be relevant to human health[8 , 10 , 50 , 51] . Furthermore , its relatively few subfamily members ( ~90 distinct genomic loci[48] ) and high nucleotide identity make HML-2 a good model for studying TE expression . Here , we report on the performance of each method to detect locus-specific expression of HML-2 by simulating RNA-seq fragments with sequencing error . We simulated 25 independent RNA-seq datasets ( see methods ) and analyzed each using 7 TE quantification approaches: 1 ) unique counts , 2 ) best counts , 3 ) RepEnrich , 4 ) TEtranscripts , 5 ) RSEM , 6 ) SalmonTE , and 7 ) Telescope . To ensure equal comparisons , all approaches use the same annotation ( S1 File ) , and modifications to the annotation were made to allow locus-specific quantification ( instead of family-level quantification ) for RepEnrich , TEtranscripts , and SalmonTE . For all simulations , we plotted the final counts estimated by each approach compared to the expected read count ( Fig 6A–6G ) . We calculated the precision and recall across all loci and simulations ( Fig 6H ) and represented the overall accuracy of the approach using the F1 score ( Fig 6I ) . Five out of seven approaches were highly sensitive , with true positive rates above 95% in most simulations . The two exceptions were RepEnrich and unique counts , which both tend to discard many more reads than expected ( “Unassigned” , Fig 6A and 6C ) . The unique counts approach consistently underestimated expression levels with ~40% of all estimates ( 96 out of 250 ) missing at least 50% of the true expression ( Fig 6A ) . One striking example of this underestimation was for HML2_8p21e; this locus did not generate any fragment that could be uniquely mapped , thus was never detected by this approach . Performance of the other five approaches differed primarily in the type and magnitude of misclassification errors . False positives occur when reads are incorrectly assigned to annotated loci that are not expressed , resulting in incorrect detection of unexpressed HERV loci . Best counts had a high false positive rate; on average , 12 . 1% of fragments were incorrectly assigned to unexpressed loci resulting in false detection of unexpressed loci in all simulations ( “Other” , Fig 6B ) . Similarly , the average proportion of reads assigned to unexpressed HERVs is greater than 5% for TEtranscripts , RSEM , and SalmonTE ( “Other” , Fig 6D–6F ) but is less than 0 . 1% for Telescope ( “Other” , Fig 6G ) . On the other hand , false negatives occur when reads originating from non-TE regions are assigned to TEs . Since we expect non-TE reads to be unassigned , the number of false negatives can be measured by the difference between the expected number of non-TE reads and the final number of unassigned reads ( “Unassigned” , Fig 6 ) . Best counts and Telescope both tend to correctly discard non-TE reads ( “Unassigned” , Fig 6B and 6G ) , while TEtranscripts , RSEM , and SalmonTE tend to incorrectly assign these reads to annotated TEs ( “Unassigned” , Fig 6D–6F ) . We suspect that the model implemented in TEtranscripts attempts to assign all fragments to annotated transcripts , as there is no category for unannotated regions in their model . For RSEM and SalmonTE , this error may be due to the restricted sequence space used to classify the reads . As these methods are mapping to the transcriptome , the true originating sequence is absent from the index and fragments are forced to map to similar , yet incorrect , sequences . This error could be avoided by developing more complete TE annotations or including additional loci that share sequence similarity with TEs of interest . Of all methods considered here , Telescope had the highest rate of precision and recall from all other counting methods tested ( Fig 6H and 6I ) . In contrast to the best counts approach ( Fig 6I ) , Telescope assigned only 20 fragments to genomic annotations that were not expressed , while 6061 fragments were assigned incorrectly by best counts . The overall accuracy of Telescope estimates from true expression levels , as measured by F1-score , was the highest of all approaches ( Fig 6I ) . These simulation results demonstrate that Telescope resolves ambiguously aligned fragments and produces unbiased estimates of TE expression that are robust to sequencing error .
Transposable elements represent a major biochemically active group of transcripts that are increasingly recognized as important regulators in complex biological systems and disease . However , difficulties in identifying and quantifying these elements has led to TEs being largely ignored in the literature . Here we present Telescope , a novel software package that can be used to mine new or existing RNA-seq datasets to accurately quantify the expression of TEs . The key advantage of our approach is the capability to localize TE expression to an exact chromosomal location . Based on our analysis of 13 ENCODE cell types , we have identified 1365 individual HERV loci that are expressed in one or more cell types and generated genomic maps that showing cell type specific HERV expression profiles . The ability to quantify expression at specific loci demonstrates that regulation of HERV expression occurs at the locus level ( in addition to subfamily-level regulation ) , as different expression patterns are observed for loci within the same subfamily . For example , our results confirm previous studies identifying HERVH upregulation in embryonic stem cells [9 , 39 , 52] and add to this finding by identifying the precise location of HERVH insertions that produce the most transcripts . This high level of resolution for TE expression enables further investigation into the local genomic context , epigenetic regulation , and coding potential of expressed loci . An earlier study investigating HERV expression using the same datasets found strong differences in estimated HERV expression profiles depending on the sequencing technology used ( paired or single end ) [40] . Using Telescope , we did not find this same bias; instead , replicates of the same cell type clustered together , while most variance in the data was among cell types . Four of the other TE quantification approaches tested did not appear biased with respect to sequencing technology , while one ( SalmonTE ) appeared to separate single-end from paired-end samples . We suspect that this is a result of SalmonTEs pseudoalignment approach , as more ambiguous assignments can occur if pairing information is not considered . Other types of bias , such as fragment bias , have been identified in RNA-seq data[53] and may influence expression estimates in Telescope and other programs . We expect future versions of our software to implement corrections for these biases . Our simulations show that Telescope is highly sensitive and has low type I and II error rates . Unique counts , a heuristic that is commonly chosen for its unambiguous assignments , was shown to discard much of the data and underestimate true TE expression . Best counts , which is commonly used for convenience , also performed poorly and spuriously identified transcripts that were not expressed . Several software packages , including RepEnrich , TEtranscripts , and SalmonTE , also aim to quantify TE expression , but use a family-level approach that quantifies TE subfamilies instead of individual loci . Our simulations used modified inputs for these approaches that allowed us to compare them to Telescope . Based on our simulation results , we find that our approach achieves high sensitivity while minimizing spurious detections , while all other approaches tend to identify TEs that are not expressed . We conclude that Telescope offers superior accuracy for TE quantification and is the only available software packages that quantifies TE expression at single-locus resolution . Telescope will have widespread utility in other settings . Studies on TE expression have become prominent in studies of embryonic stem cell development[8][9] , neural cell plasticity[54 , 55] , oncogenesis[4–7 , 56 , 57] , psychiatric and neurological disorders[58–60] and autoimmune diseases[61 , 62] . As the breadth of knowledge on TEs expands , expression profiling of TEs using Telescope will allow scientists to discover unique and collective TE transcripts involved in the biology of complex systems .
Telescope implements a generative model of RNA-seq relating the probability of observing a sequenced fragment to the proportions of fragments originating from each transcript . Formally , let F = [f1 , f2 , … , fN] be the set of N observed sequencing fragments . We assume these fragments originate from K annotated transcripts in the transcriptome T = [t0 , t1 , … , tK] . In practice , annotations fail to identify all possible transcripts that generate fragments , thus we include an additional category , t0 , for fragments that cannot be assigned to annotated transcripts . Let G = [G1 , G2 , … , GN] represent the true generating transcripts for F , where Gi∈T and Gi = tj if fi originates from tj . Since the process of generating F from T cannot be directly observed , the true generating transcripts G are considered to be “missing” data . The objective of our model is to estimate the proportions of T by learning the generating transcripts of F . The alignment stage identifies one or more possible alignments for each fragment , along with corresponding alignment scores . Telescope uses the alignment score generated by the aligner and reported in the AS tag[63] . This is typically calculated by adding scores and penalties for each position in the alignment; a higher alignment score indicates a better alignment . Let qi = [qi0 , qi1 , … , qiK] be the set of mapping qualities for fragment fi , where qij = Pr ( fi|Gi = tj ) represents the conditional probability of observing fi assuming it was generated from tj; we calculate this by scaling the raw alignment score by the maximum alignment score observed for the data . We write the likelihood of observing uniquely aligned fragment fu as a function of the conditional probabilities qu and the relative expression of each transcript for all possible generating transcripts Gu Pr ( fu|π , qu ) =∑j=0Kπjquj where π = [π0 , π1 , … , πK] represents the fraction of observed fragments originating from each transcript . Note that quj = 0 for all transcripts that are not aligned by fu . For non-unique fragments , we introduce an additional parameter in the above likelihood to reweight each ambiguous alignment among the set of possible alignments . The probability of observing ambiguous fragment fa is given by Pr ( fa|π , θ , qa ) =∑j=0Kπjθjqaj where θ = [θ0 , θ1 , … , θK] is a reassignment parameter representing the fraction of non-unique reads generated by each transcript . Using these probabilities of observing ambiguous and unique fragments , we formulate a mixture model describing the likelihood of the data given parameters π and θ . The K mixture weights in the model are given by π , the proportion of all fragments originating from each transcript . To account for uncertainty in the initial fragment assignments , let xi = [xi0 , xi1 , … , xiK] be a set of partial assignment ( or membership ) weights for fragment fi , where ∑j=0Kxij=1 and xij = 0 if fi does not align to tj . We assume that xi is distributed according to a multinomial distribution with success probability π . Intuitively , xij represents our confidence that fi was generated by transcript tj . In order to simplify our notation , we introduce an indicator variable y = [y1 , y2 , … , yN] where yi = 1 if fi is ambiguously aligned and yi = 0 otherwise . The complete data likelihood is L ( π , θ|x , q , y ) ∝∏i=1N∏j=0K[πjθjyiqij]xij Telescope iteratively optimizes the likelihood function using an expectation-maximization algorithm[64] . First , the parameters π and θ are initialized by assigning equal weight to all transcripts . In the expectation step , we compute the expected values of xi under current estimates of the model parameters . The expectation is given by the posterior probability of xi: E[xij]=πjθjyiqij∑k=0Kπkθkyiqik In the M-step we calculate the maximum a posteriori ( MAP ) estimates for π and θ πj^=∑i=1NE[xij]+ajM+∑k=0Kakandθj^=∑i=1NE[xij]yi+bj∑i=1Nyi+∑k=0Kbk Where M=∑j=0K∑i=1NE[xij] and aj and bj are prior information for transcript tj . Intuitively , these priors are equivalent to adding unique or ambiguous fragments to tj . As currently implemented , the user may provide a prior value for either parameter that is distributed equally among all transcripts . We have found that providing an informative prior for the bj ( —theta_prior ) is recommended given the repeat content of the human genome , since large values for this parameter prevents convergence to boundary values . Convergence of EM algorithms to local maxima has been shown by Wu[65] , and is achieved when the absolute change in parameter estimates is less than a user defined level , typically ϵ<0 . 001 . A Telescope analysis requires an annotation that defines the transcriptional unit of each TE to be quantified . For HERV proviruses , the prototypical transcriptional unit contains an internal protein-coding region flanked by LTR regulatory regions . Existing annotations , such as those identified by RepeatMasker[33] ( using the RepBase database[32] ) or Dfam[66] identify sequence regions belonging to TE families but do not seek to annotate transcriptional units . Both databases represent the internal region and corresponding LTRs using separate models , and the regions identified are sometimes discontinuous . Thus , a HERV transcriptional unit is likely to appear as a collection of nearby annotations from the same HERV subfamily . Transcriptional units for HERV proviruses were defined by combining RepeatMasker annotations belonging to the same HERV subfamily that are located in adjacent or nearby genomic regions . Briefly , repeat families belonging to the same HERV subfamily ( internal region plus flanking LTRs ) were identified using the RepBase database[32] . RepeatMasker annotations for each repeat subfamily were downloaded using the UCSC table browser[67] and converted to GTF format , merging nearby annotations from the same repeat subfamily . Next , LTRs found flanking internal regions were identified and grouped using BEDtools[68] . HERV transcriptional units containing internal regions were assembled using custom python scripts . Each putative locus was categorized according to provirus organization; loci that did not conform to expected HERV organization or conflicted with other loci were visually inspected using IGV[69] and manually curated . As validation , we compared our annotations to the HERV-K ( HML-2 ) annotations published by Subramanian et al . [48]; the two annotations were concordant . Final annotations were output as GTF ( S1 File ) ; all annotations , scripts , and supporting documentation are available at https://github . com/mlbendall/telescope_annotation_db . We identified 30 ENCODE datasets with available whole-cell bulk RNA-seq data from tier 1 and 2 common cell types ( S1 Table ) . Sequence data was obtained from SRA and extracted using the parallel-fastq-dump package ( https://github . com/rvalieris/parallel-fastq-dump ) . Adapter trimming , quality trimming , and filtering were performed using Flexbar[70] ( version 3 . 0 . 3 ) . For Telescope analysis , the trimmed and filtered reads from each run were aligned to human reference genome hg38 using bowtie2[71] . Alignment options were specified to perform a sensitive local alignment search ( —very-sensitive-local ) with up to 100 alignments reported for each fragment pair ( -k 100 ) . The minimum alignment score threshold was chosen so that fragments with approximately 95% or greater sequence identity would be reported ( —score-min L , 0 , 1 . 6 ) . Sequence alignment map ( SAM/BAM ) files from different runs corresponding to the same sample were concatenated to obtain sample-level BAM files . An annotation of HERV locations in hg38 ( S1 File ) and the BAM file for each sample were provided as inputs for Telescope . Telescope options included up to 200 iterations of the expectation-maximization algorithm ( —max_iter 200 ) and an informative prior on theta ( —theta_prior 200000 ) . The “final counts” column in the Telescope report are used as HERV expression data in subsequent analysis . ENCODE datasets were also analyzed using five other approaches . Unique and best counts approaches use the same alignment and annotation as above and are included as part of the Telescope output . RepEnrich , TEtranscripts , and SalmonTE were all run according to author instructions , with author-provided annotations and default parameters . Library size for each sample is considered to be the total number of fragments that map to the reference genome . Counts per million ( CPM ) were calculated by dividing the raw count by the library size and multiplying by 1 million . A CPM cutoff of 0 . 5 was used to identify expressed loci; since the smallest sample considered has more than 20 million fragments , expressed loci are represented by at least 10 observations . Raw counts output by Telescope were used for differential expression analysis . Size factors for normalization were calculated by dividing the library sizes by their geometric mean . Normalization , dispersion estimation , and generalized linear model fitting was performed using DESeq2[72]; the model was specified with cell type as the only covariate . Contrasts were extracted for each pair of cell types; HERVs with an adjusted p-value < 0 . 1 and log2FoldChange > 1 . 0 were considered to be differentially expressed . Read counts for clustering were transformed using a variance stabilizing transformation in DESeq2[72] . Hierarchical clustering with multiscale bootstrap resampling was performed on transformed counts using correlation distance and UPGMA clustering implemented in pvclust[73] . Uncertainty in hierarchical cluster analysis was assessed by calculating two p-values for each cluster that range from 0 to 1 , with 1 indicating strong support for the cluster . The bootstrap probability ( BP ) is calculated by normal bootstrap resampling and approximately unbiased ( AU ) probability is computed by multiscale bootstrap resampling[74] . For the simulation study , we simulated 25 independent RNA-seq datasets with 2100 paired-end fragments each . For each dataset , we randomly selected 13 loci to be expressed , including 10 HML-2 proviruses and three “non-TE” loci . HML-2 proviruses were selected from 92 HML-2 loci present in our annotation; non-TE loci were selected from a set of 968 unannotated genomic regions that share sequence similarity with the HML-2 subfamily ( S2 File ) . Non-TE loci are included to examine the type II error rate of the approaches; assigning non-TE fragments to HML-2 loci is considered a false negative . Expression levels for the 10 HML-2 loci in each dataset were randomly chosen , ranging from 30 to 300 fragments per locus . Each of the three non-TE loci were expressed at 150 fragments each . Using this expression pattern , we simulated sequencing fragments with the Bioconductor package for RNA-seq simulation , Polyester[75] . All simulations used the parameters of read length: 75 bp; average fragment size: 250; fragment size standard deviation: 25; and an Illumina error model with an error rate of 5e-3 . Each simulation dataset was analyzed using 7 TE quantification approaches: 1 ) unique counts , 2 ) best counts , 3 ) RepEnrich , 4 ) TEtranscripts , 5 ) RSEM , 6 ) SalmonTE , and 7 ) Telescope . To ensure a fair comparison among approaches , the same annotation ( S1 File ) was used as input for all approaches . Note that the HML-2 loci used for simulation are contained in this annotation , but the non-TE loci are absent . For RepEnrich , TEtranscripts , and SalmonTE , the locus identifier was used in place of the family name in order to generate locus-specific estimates . Aside from these changes , each program was run as suggested by the authors . Unique counts was implemented by aligning reads with bowtie2 , allowing for multi-mapped reads ( -k 100—very-sensitive-local—score-min L , 0 , 1 . 6 ) and filtering reads with multiple alignments . The same bowtie2 parameters were used for best counts without specifying -k ( —very-sensitive-local—score-min L , 0 , 1 . 6 ) . The five software packages include final read counts as part of the output . Read counts for the unique counts and best counts approaches were obtained using htseq-count[76] . After mapping and counting the reads for each annotated HERV , reads can be divided in two categories , depending their origin , HML-2 reads or non-TE reads . Those reads can then be correctly or incorrectly mapped , depending of the outcome of the counting method , leading to 4 different categories: a ) reads assigned to HML-2 correctly ( True Positive ) b ) reads assigned to HML-2 incorrectly ( False Positive ) c ) reads not assigned correctly ( True Negative ) d ) reads not assigned incorrectly ( False Negative ) . All classifications were made based on counts and not fragment assignments , as several approaches do not provide final fragment assignments . The classifications were used for recall and precision calculations . Telescope is implemented in Python , is available as an open-source program under the MIT license , and has been developed and tested on Linux and MacOS . The software package and test data can be found at https://github . com/mlbendall/telescope . We recommend installing Telescope and its dependencies using the bioconda package manager[77] . A complete snakemake[78] pipeline for reproducing the ENCODE analysis is available from https://github . com/mlbendall/TelescopeEncode . Scripts for reproducing the simulations are available from https://github . com/LIniguez/Telescope_simulations . A tutorial for running the single-locus analysis is available from https://github . com/mlbendall/telescope_demo . | Almost half of the human genome is composed of Transposable elements ( TEs ) , but their contribution to the transcriptome , their cell-type specific expression patterns , and their role in disease remains poorly understood . Recent studies have found many elements to be actively expressed and involved in key cellular processes . For example , human endogenous retroviruses ( HERVs ) are reported to be involved in human embryonic stem cell differentiation . Discovering which exact HERVs are differentially expressed in RNA-seq data would be a major advance in understanding such processes . However , because HERVs have a high level of sequence similarity it is hard to identify which exact HERV is differentially expressed . To solve this problem , we developed a computer program which addressed uncertainty in fragment assignment by reassigning ambiguously mapped fragments to the most probable source transcript as determined within a Bayesian statistical model . We call this program , “Telescope” . We then used Telescope to identify HERV expression in 13 well-studied cell types from the ENCODE consortium and found that different cell types could be characterized by enrichment for different HERV families , and for locus specific expression . We also showed that Telescope performed better than other methods currently used to determine TE expression . The use of this computational tool to examine new and existing RNA-seq data sets may lead to new understanding of the roles of TEs in health and disease . | [
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] | 2019 | Telescope: Characterization of the retrotranscriptome by accurate estimation of transposable element expression |
Selective sweeps are at the core of adaptive evolution . We study how the shape of coalescent trees is affected by recent selective sweeps . To do so we define a coarse-grained measure of tree topology . This measure has appealing analytical properties , its distribution is derived from a uniform , and it is easy to estimate from experimental data . We show how it can be cast into a test for recent selective sweeps using microsatellite markers and present an application to an experimental data set from Plasmodium falciparum .
Consider the coalescent tree for a sample of size . It is a binary tree without left-right orientation , with ordered internal nodes and branch lengths representing a measure of time . All leaves are aligned on the bottom line , representing the present . We use the term tree topology when talking about the branching pattern and tree shape when talking about topology and branch lengths . We remark that topology and shape can be conceptually distinguished , but in practice estimating topology relies on polymorphism patterns . Since these depend on branch lengths , i . e . on shape , topology can usually not be estimated independently . We call the size of a tree the number of leaves and the length of a tree the combined length of all branches . The height is the time interval between present and root , indicated by in Figure 1 . Let the label of the root be . The leaves can be grouped into two disjoint sets , and , the ‘left-‘ and ‘right-descendants’ of the root . Let be the smaller of the two sets and . Hence , . Let be the ‘right’ child of , i . e . the root of the subtree with leaf set . The descendants of can again be grouped into two disjoint subsets , and , the left- and right-descendants of . Again , without loss of generality , let and denote . Hence , . Proceed in this way to define subsets , , and so on . For any tree there are such pairs where , with depending on the topology of the tree . The set constitutes a – not necessarily unique – top-down sequence of maximal subtrees .
Consider a coalescent tree of size under the neutral model with constant population size , where is assumed to be large . Root imbalance is measured by the random variable . The distribution of is ‘almost’-uniform [18] , [19] on . More precisely , ( 1 ) where . , . denotes here the Kronecker symbol . The expectation isThe variance isand the standard deviationprovided is sufficiently large . The compound random variables , , have support which depends on , . More precisely , the distribution of , given , , is almost-uniform on with ( 2 ) where ( ) is a random variable which is bounded below by and above by . The moments are somewhat more complicated . For instance , Continuing this way , evaluating sums iteratively and using the above approximation , one derives ( 3 ) Similary , one can obtain the second moments and combine these to ( 4 ) Define now the normalized random variables . Since is a constant , we have for andTo calculate the moments of , , we replace by . Simulations suggest that this is acceptable , as long as is not too small . Figure 2 shows this fact for . Here we focus on for , where is small and is large ( , , say ) . Since , we obtain ( 5 ) Similarly , ( 6 ) andIt is very convenient to work with the normalized random variables instead of . Their support is bounded by and for all and they are well approximated by independent continuous uniforms on the unit interval . This considerably facilitates the handling of sums and products of . For instance , the joint distribution of is then approximated by the continuous uniform product with distribution function ( 7 ) expectationand varianceThe coefficient of variation , , isAs is well known , the normalized sum of continuous uniforms converges in distribution to a normal random variable rather quickly . In fact , we have for the standardized sum ( 8 ) In practice , already yields a distribution which is reasonably close to a normal ( see Suppl . Figure S1 ) . A positively selected allele sweeping through a population leads to a drastic reduction of tree height due to its short fixation time ( see Figure 1C ) . The fixation time depends on the selection coefficient and population size . In units of , , where [23] . This is much smaller than the neutral average fixation time . The reduced fixation time leads to a severe reduction of genetic variability . Furthermore , external branches of the tree are elongated relative to internal branches , yielding a star-like phylogeny of an approximate length of . Replacing the neutral tree length in eq ( 9 ) by this figure , we obtain the following estimate for the correlation half-life ( 10 ) For the parameters used in Figure 3 , we have bp , which agrees well with the simulation result . In contrast to tree height and length , tree topology at the selected site does not necessarily differ from a neutral tree; only when moving away from the sweep site , and with recombination , topology may drastically change . In fact , given a shallow tree , recombination leads with high probability to an increase of tree height and to unbalanced trees [15] . Thus , recombination events next to the selected site tend to increase tree height ( see sketch in Figure 1B and C ) and to create a bias in favour of unbalanced trees , i . e . trees with small ( Figure 4A ) . The expected proximal distance from the selected site of such a recombination event can be estimated as ( 11 ) where , is the per site recombination rate , and is the length of a star-like phylogeny; the factor accounts for the fact that it is more likely to recombine with an ancestral chromosome ( thereby increasing tree height ) as long as these are more abundant than the derived chromosomes carrying the selected allele . Roughly , this is the case during the first half of the fixation time . Assuming instead of the star phylogeny a random tree topology of average length at the selected site , one obtains the larger ( call it distal ) estimate ( 12 ) where . Unbalanced trees tend to have strongly elongated root branches and harbor an over-abundance of high frequency derived SNP alleles [6] , [16] . With microsatellites it is usually not possible to determine the ancestral and derived states of an allele , because they mutate at a high rate and possibly undergo back-mutation . However , under the symmetric single step mutation model , the expected distance between a pair of alleles ( in terms of motif copy numbers ) behaves as the distance in a one-dimensional symmetric random walk and therefore increases at a rate proportional to the square root of the scaled mutation rate ( see Methods ) . Thus , alleles which are separated by long root branches tend to form two distinct allele clusters . Tree topology is ususally not directly observable and has to be estimated from data . We focus on estimating , , from microsatellite data . Given a sample of microsatellite alleles with tandem repeat counts , , we use UPGMA [24] to construct a hierarchical cluster diagram . If subtree topology within a particular cluster node should not be uniquely re-solvable , for instance if alleles are identical , we randomly assign the alleles of the subtree under consideration to two clusters with equal probability . This gives preference to clusters of balanced size in case of insufficient resolution . We then use the inferred tree topology to estimate of the true tree . This procedure is conservative for the test statistics described below , since it gives preference to large values when the true value is small ( Figure 4 , column A ) . For a cluster pair , , define the distance as ( 13 ) We find that UPGMA clustering gives good estimates of when clusters are clearly separated from each other , i . e . when . Let be the indicator variable for this event . Then , we have for the median ( Figure 4 , column B ) . Without requiring the estimate is more biased . In part , this is due to the conservative UPGMA strategy mentioned above . However , estimation of is very accurate when root branches are strongly elongated , i . e . under conditions of selective sweeps or certain bottlenecks ( Figure 4 , bottom ) . We now turn to an application of the above results and explain how a new class of microsatellite based tests of the neutral evolution hypothesis can be defined . Consider a sample of alleles at a microsatellite marker and record their motif repeat numbers . Applying UPGMA clustering to the alleles , we obtain estimates , as described above . These are transformed to . Then , we determine the following test statistics ( 14 ) ( 15 ) ( 16 ) Thus , the test variable in eq ( 14 ) is the estimate of given in eq ( 8 ) . Similarly , and are the estimates of the product and of . We now test the null hypothesis for a critical value . For a given level we obtain the critical value for from the standard normal distribution and for from the uniform product distribution in eq ( 7 ) ( Table 1 ) . For we use the critical value of the normalized version of eq ( 1 ) . Generally , these critical values are conservative , since tends to over-estimate , when small ( Figure 4 ) . In particular , statistic is very conservative due to the additional condition on the distance . The true critical values for level would be larger than those shown in Table 1 . Emergence of drug resistance in malaria parasites is among the best documented examples for recent selective sweeps . We re-analyzed microsatellite markers surrounding a well studied drug resistance locus of malaria parasites [29] ( Figure 7 ) . The signature of recent positive selection is consistently detected by all tests on two markers somewhat downstream of the drug resistance locus pfmdr1 ( marker l– and l– in the notation of [29]; Table 5 ) . Highest significance is reported by test ( -value close to ) . reports a -value of and reports -values slightly above . In addition , reports locus l– ( located upstream of pfmdr1 ) to be significant at . This locus is also detected by ( ) . Other four loci are reported only by ( l– ( ) , l– ( ) , l– ( ) , l– ( ) and l– ( ) ) . Discrepancies in the test results are due to their different sensitivities to various parameters . The simple and compound tests have different power profiles with power peaks at different positions from the selected site ( Figure 6 ) . Plasmodium in South-East Asia is most likely expanding and sub-structured; however , there is only limited knowledge about the details . As shown above , is quite sensitive to biased sampling from different sub-populations . Some of the significant results of may be inflated due to sub-structure . There is also some disagreement between tests and regarding significance , although both test imbalance at tree nodes , and . In fact , the cases reported by the two tests may still differ in their details . Comparing the three components , and with respect to their maximum and minimum , we find that the cases reported as significant by have a and a up to . In contrast , for , the maximum is close to while the minimum tends to be less than ( Figure S4 ) . Thus , test is more restrictive in the sense that all components , and have to be small to yield a significant result . is more permissive and accepts that one of the three components may be large . All tests agree on significance of two markers close to a site which was previously shown to have experienced a selective sweep . They also agree all on strongly increased -values in the immediate vicinity of the selected site ( l– , l– ) . Together , these results confirm the accuracy and practical utility of our tests .
The binary coalescent has a number of well-studied combinatoric and analytic properties [1] , [30] , [31] . Here we only concentrate on tree topology and use a classic result of Tajima [19] to define a simple measure , , of tree balance . It is the minimum of the left and right subtree sizes under internal node . Its normalized version is approximately uniform on the unit interval and the summation over internal nodes , , is close to normal . Another summary statistic of tree balance is Colless' index [32] . It also depends on the sizes of left- and right subtrees of the internal nodes , but its distribution is more complicated . has received attention in the biological literature before [33] and , more recently , in theoretical studies , for instance by Blum&Janson [34] . A problem with Colless' index is that it is difficult to estimate if the true tree structure is unknown . But , limiting attention to the tree structure close to the root , we show that the balance measure can be estimated , for instance , from microsatellite allele data by a clustering method . We found that a version of UPGMA clustering gives most reliable results . Coalescent trees for linked loci are not independent . However , correlation dissipates with recombinational distance . In fact , under neutral conditions only about ten recombination events are sufficient to reduce correlation in tree topology by 50% . Thus , estimating tree imbalance at multiple microsatellites can be performed independently for each marker , if they are sufficienty distant from each other . Conversely , with a very small number of recombination events , is not drastically altered on average [15] . Thus , when working with SNPs , one may afford to consider haplotype blocks containing a few more recombination events than segragting sites and still be able to reconstruct a reliable gene genealogy . This possibility will be explored in more detail elsewhere . Microsatellites have been used before as markers for selective sweeps . Schlötterer et al . [35] have proposed the lnRH statistic to detect traces of selection and Wiehe et al . [28] have shown that a multi-locus vesion of lnRH for linked markers can yield high power while keeping false positive rates low . However , a severe practical problem with the lnRH statistic is that it requires data from two populations , and for each of them two additional and independent sets of neutral markers for standardization . There are a few methods to detect deviations from the standard neutral model based on single microsatellite locus data from one population . For instance , the test by Cornuet and Luikart [36] , which compares observed and expected heterozygosity , is designed to detect population bottlenecks . A test by Schlötterer et al . [37] uses the number of alleles at a microsatellite locus and determines whether an excess of the number of alleles is due to positive selection ( SKD test ) . However , as the authors pointed out , the test depends critically on a reliable locus-specific estimate of the scaled mutation rate . We have compared SKD and the test proposed here with respect to power and false positive rates . While the SKD-test is generally more powerful , especially at larger distances from the selected site ( Table 4 and Suppl . Tables S1 , S5 ) , it has higher false positive rates than the tests proposed here , in particular when compared to ( Suppl . Table S6 ) , and for non-standard mutation models ( Suppl . Tables S13 , S14 ) . Note also that under population sub-structure SKD yields up to times more false positives than our tests ( Suppl . Tables S9 to S12 ) . It should be emphasized that it is the topology of the underlying genealogical tree , not the genetic variation , which constitutes the basis for the test statistics proposed here . The two steps , estimating topology , and performing the test are two distinct tasks . The quality of the tests hinges on the quality of the re-constructed genealogy . With a perfectly re-constructed genealogy the false positive rates are completely independent from any evolutionary mechanisms which do not affect the average topology , such as historic changes of population size . However , simulations show that power would still remain under 100% in this case . The robustness of topology based tests with respect to demographic changes has been shown before by Li [16] for a similar test which uses SNP data to reconstruct . But Li's test can only be performed if an additional non-topological criterion is satisfied and thus can only test a subset of trees with . The tests and defined here rely only on topological properties of the genelaogy and we argue that multi-allelic markers , such as microsatellites , help estimating the true genealogy and improving test results . Although our analyses and simulations are based on the binary Kingman [1] coalescent , we expect that the new test statistics should be robust also under more general coalescent models , for instance when multiple mergers during the selective sweep phase are allowed [38] . Despite a shift to high throughput sequencing technologies in the last decade , microsatellite typing continues to be a cost-efficient and fast alternative to survey population variability in many experimental studies . This is in particular true for projects directed towards parasite typing , e . g . of Plasmodium , and projects with non-standard model organisms , e . g . social insects [39] , [40] , but also for many biomedical studies .
We simulated population samples under neutral and hitchhiking models with modified versions of the procedures described by Kim and Stephan [41] and Li and Stephan [42] and of ms [43] , termed msmicro . In the modified versions we incorporated evolution of microsatellite loci under the symmetric , single step and multi-step mutation models . Microsatellite mutations are modeled as changes to the number of motif repeats , where only numbers but not particular sequence motifs are recorded . Output data comprise coalescent trees in Newick format and the state of microsatellite alleles for each of sequences . With msmicro also multiple linked microsatellites can be modeled . Coalescent simulations were run under different evolutionary conditions: neutral with constant population size ( ) , neutral with bottleneck ( bottleneck severity , time since bottleneck ) , population size expansion ( growth rate ) , neutral two-island model with migration , and hard selective sweeps ( selection and , time since fixation of sweep allele ) . Realizations of the ‘true’ random variables , were extracted from the simulation results . Estimation of was performed by UPGMA hierarchical clustering . If a cluster node could not be uniquely resolved then we gave preference to a bi-partite partition in which the left and right subtrees were of equal or similar size . This was accomplished by randomly assigning alleles to two clusters with equal probability . To estimate we also explored a simple clustering method which works in the following way: we first sorted alleles by size; then we divided the sorted list into two halfs . The separator was placed between those two alleles which had maximal distance ( in terms of microsatellite repeat units ) from each other . If this was not unique , the separator was placed between those two alleles that resulted in two sets of most similar size . While this clustering method is very effective in estimating , it is less accurate than UPGMA clustering for , . The single step symmetric mutation model behaves as a one-dimensional symmetric random walk of step size one . The theory of random walks ( e . g . [44] ) tells that the average distance between the origin of the walk and the current position scales with the square root of the number of steps . More precisely , The variance is linear in . Here , steps are represented by mutational events occuring at rate . Thus , and , where is Euler's constant . The empirical distance between two clusters and can be calculated as | It is one of the major interests in population genetics to contrast the properties and consequences of neutral and non-neutral modes of evolution . As is well-known , positive Darwinian selection and genetic hitchhiking drastically change the profile of genetic diversity compared to neutral expectations . The present-day observable genetic diversity in a sample of DNA sequences depends on events in their evolutionary history , and in particular on the shape of the underlying genealogical tree . In this paper we study how the shape of coalescent trees is affected by the presence of positively selected mutations . We define a measure of tree topology and study its properties under scenarios of neutrality and positive selection . We show that this measure can reliably be estimated from experimental data , and define an easy-to-compute statistical test of the neutral evolution hypothesis . We apply this test to data from a population of the malaria parasite Plasmodium falciparum and confirm the signature of recent positive selection in the vicinity of a drug resistance locus . | [
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] | 2013 | Coalescent Tree Imbalance and a Simple Test for Selective Sweeps Based on Microsatellite Variation |
Mice expressing a Cre recombinase from the lysozyme M-encoding locus ( Lyz2 ) have been widely used to dissect gene function in macrophages and neutrophils . Here , we show that while naïve resident tissue macrophages from IL-4Rαflox/deltaLysMCre mice almost completely lose IL-4Rα function , a large fraction of macrophages elicited by sterile inflammatory stimuli , Schistosoma mansoni eggs , or S . mansoni infection , fail to excise Il4rα . These F4/80hiCD11bhi macrophages , in contrast to resident tissue macrophages , express lower levels of Lyz2 explaining why this population resists LysMCre-mediated deletion . We show that in response to IL-4 and IL-13 , Lyz2loIL-4Rα+ macrophages differentiate into an arginase 1-expressing alternatively-activated macrophage ( AAM ) population , which slows the development of lethal fibrosis in schistosomiasis . In contrast , we identified Lyz2hiIL-4Rα+ macrophages as the key subset of AAMs mediating the downmodulation of granulomatous inflammation in chronic schistosomiasis . Our observations reveal a limitation on using a LysMCre mouse model to study gene function in inflammatory settings , but we utilize this limitation as a means to demonstrate that distinct populations of alternatively activated macrophages control inflammation and fibrosis in chronic schistosomiasis .
Tissue macrophages exhibit substantial plasticity and can quickly change their function in response to different stimuli found in the local milieu [1] , and distinct subsets with characteristic functional activities have been described . Alternatively activated macrophages ( AAMs ) , also called M2 or M ( IL-4 ) [2] , are induced in response to the type-2 cytokines IL-4 and IL-13 [3] , exhibit potent immunoregulatory activity , and have been linked with mechanisms controlling wound healing and fibrosis [4] . In addition to expressing mediators that directly regulate wound repair pathways such as arginase 1 ( Arg1 ) , resistin-like molecule alpha ( Relm-α ) , transforming growth factor beta-1 ( TGF-β1 ) , vascular endothelial growth factor ( VEGF ) , and insulin-like growth factor-1 ( IGF-1 ) [5] , AAMs also suppress pro-inflammatory Th1 , Th17 , and classically activated macrophage ( CAMs ) responses that contribute to tissue injury [6] . To prevent alternative activation , Herbert and colleagues generated macrophage/neutrophil-specific IL-4Rα-deficient mice ( IL-4Rαflox/ΔLysMCre ) by expressing Cre recombinase in the regulatory region of the lysozyme M gene expressed in macrophages and neutrophils . They showed AAMs are required to suppress pathogenic Th1/CAM responses during infection with the helminth parasite Schistosoma mansoni [7] . However , AAMs had no significant impact on the development of the Th2 response or fibrosis . In contrast to IL-4Rαflox/ΔLysMCre mice , mice with a macrophage/neutrophil-specific deletion of Arg1 ( Arg1flox/ΔLysMCre ) , an enzyme involved in the conversion of L-arginine into L-ornithine and urea , developed enhanced type-2 effector responses following S . mansoni infection without acute type-1 cytokine-driven hepatotoxicity or endotoxemia [8] . Pesce et al . observed Arg1flox/ΔLysMCre mice developed stronger CD4+ Th2 cell responses , larger eosinophil-rich granulomas , more severe liver fibrosis , and failed to down-regulate the type-2 inflammatory response when chronically infected , suggesting that Arg1+ macrophages critically suppress granulomatous inflammation , fibrosis , and mortality [9] . Similar but even more dramatic findings were observed with Arg1flox/floxTie2Cre mice , which delete Arg1 in all macrophage populations [8] . The ability of IL-4Rαflox/ΔLysMCre mice to control fibrosis during S . mansoni infection was completely unexpected , since Arg1 expression in macrophages was thought to be highly dependent on IL-4Rα signaling [7] . Because it was concluded that IL-4Rα-expressing AAMs suppress lethal type-1-associated inflammation during acute schistosomiasis , while arginase 1-expressing AAMs are dispensable during the acute stage , we theorized that an IL-4Rα-dependent but arginase 1-independent mechanism was responsible for the early protective activity exhibited by AAMs . To identify this mechanism , we set out to compare pathology , fibrosis , and the macrophage phenotype of IL-4Rαflox/ΔLysMCre mice and Arg1flox/ΔLysMCre mice following S . mansoni infection . We began by systematically studying IL-4Rαflox/ΔLysMCre mice during acute and chronic infection , and unexpectedly , we identified a subset of IL-4Rα-expressing macrophages that are resistant to LysMCre-mediated gene deletion , exhibited an Arg1+ AAM phenotype , and regulated type-2 cytokine-dependent fibrosis . In contrast , we identified mature Lyz2hi tissue macrophages that are susceptible to LysMCre-mediated gene deletion as the critical population of AAMs mediating the downmodulation of granuloma formation in chronic schistosomiasis . So while these data suggest that the LysMCre deleter mouse is only useful for studying gene function in mature tissue macrophages , we were able to demonstrate that distinct populations of Lyz2hi and Lyz2lo AAMs collaborate to control inflammation and fibrosis in schistosomiasis , respectively . In chronic inflammatory diseases where the recruitment of immature monocytes/macrophages , differentiation to mature tissue macrophages , and acquisition of the alternatively activated phenotype is a dynamic and ongoing process , LysMCre can be used to distinguish the contribution of genes expressed in Lyz2hi and Lyz2lo macrophages .
The previous study by Herbert et al . found nearly 100% of IL-4Rαflox/ΔLysMCre mice succumbed to infection by 8 weeks post-infection [7] . Those experiments were conducted with 75–100 S . mansoni cercariae , a relatively high dose of parasites . To further elucidate the role of AAMs during a chronic S . mansoni infection and to explore the role of IL-4Rα-expressing macrophages in the initiation and regulation of fibrosis , we infected IL-4Rαflox/ΔLysMCre mice and IL-4Rαflox/Δ littermate control mice with 35 cercariae , a dose that in wild-type mice leads to substantial disease and liver fibrosis but low mortality through the chronic phase of infection [10] . We hypothesized that lighter infections would enable us to quantify fibrosis , characterize the immune response , and phenotype the macrophage response in the granulomatous liver at both acute ( 9 weeks post-infection ) and chronic ( 16 weeks post-infection ) time-points . We observed 30–40% of the infected littermate control group ( IL-4Rαflox/Δ ) died through week 16 of infection ( Fig . 1A ) . Surprisingly , we observed equal mortality in the IL-4Rαflox/ΔLysMCre group , suggesting that AAMs might be less important to survival in schistosomiasis than previously thought . The majority of the deaths occurred during the acute phase of the infection when the host immune response peaks and before additional protective mechanisms like IL-10 or the IL-13 decoy receptor ( IL-13Rα2 ) are fully activated [11] . After week 10 of infection , few deaths were observed in either group . In the prior study , IL-4Rαflox/ΔLysMCre mice infected with ≥75 cercariae developed hepatotoxicity and gut pathology leading to endotoxemia and death . The authors also observed a stronger type-1 immune response in the IL-4Rαflox/ΔLysMCre mice , defined by increased IFN-γ production , which they hypothesized was contributing to the rapid death of the mice . Intriguingly , in our studies with IL-4Rαflox/ΔLysMCre mice infected with fewer cercariae , we observed no increase in IFN-γ ( Fig . 1B ) or hepatotoxicity at either acute or chronic time points ( Fig . 1C ) . Importantly , we included wild-type IL-4Rαflox/flox mice and IL-4RαΔ/Δ mice in the survival study to demonstrate universal deletion of IL-4Rα on one chromosome is the explanation for the enhanced mortality of both flox/Δ cohorts ( Fig . 1A ) . As expected , mice with two copies deleted ( Δ/Δ ) , showed the most susceptibility . We confirmed the infectious burdens were not different between the groups ( Fig . S1 ) . Because this finding was unexpected , we also tested a higher infectious dose . While an increase from 35 to 100 cercariae accelerated and significantly increased mortality in both groups , there was still no significant difference in mortality between the IL-4Rαflox/ΔLysMCre mice and IL-4Rαflox/Δ littermate controls ( Fig . S2 ) . Intestinal lesions and hepatotoxicity were reported as the pathological features of high dose schistosomiasis in IL-4Rαflox/ΔLysMCre mice [7] . Consequently , we examined the liver and intestine at weeks 9 and 16 post-infection to determine whether similar pathological changes were occurring following infection with 35 cercariae . Strikingly , even after 16 weeks of infection , an experienced pathologist failed to detect any increase in intestinal damage in the IL-4Rαflox/ΔLysMCre group when compared with littermate control mice ( Fig . 2A ) , which likely explains the equivalent survival . Liver sections were stained with Giemsa to quantify the granulomatous inflammatory response ( Fig . 2B ) and with picrosirius red to evaluate the accumulation of liver collagen at acute and chronic time points ( Fig . 2C ) . Granulomas appeared normally organized in IL-4Rαflox/ΔLysMCre mice with an equivalent proportion of eosinophils , but granuloma size increased significantly compared to littermate controls at both 9 and 16 weeks post-infection ( Fig . 2B and 2D ) . Like Herbert et al . , we found the exacerbated granulomatous inflammation led to only subtle increases in fibrosis , however , and did not lead to statistically significant increases in chronic fibrosis , determined qualitatively by picrosirius red staining ( Fig . 2C ) and quantitatively by hydroxyproline assay ( Fig . 2E ) . These observations suggest that while AAMs limit granulomatous inflammation at both acute and chronic time points , additional regulatory mechanisms limit the progression of fibrosis . This interpretation was surprising because uncontrolled granulomatous inflammation in the liver has been hypothesized to contribute to the development of fibrosis in infected mice and humans [12] , [13] . These data were also difficult to interpret because Arg1-expressing AAMs are critical to the suppression of fibrosis in infected mice [8] , and their numbers should have been greatly diminished in the IL-4Rαflox/ΔLysMCre mice according to Herbert et al . and others who have demonstrated Arg1 expression in macrophages is highly dependent on IL-4Rα signaling [7] , [14] , [15] . Recently , we showed that multiple mechanisms collaborate to slow the progression of fibrosis during chronic schistosome infection [10] . These included IL-13Rα2 , a high-affinity decoy receptor for IL-13 [16] , [17] , IL-12p40 , a key driver of Th1 and Th17 responses [18] , and IL-10 , a potent immunosuppressive cytokine [11] , [19] . To determine whether the induction of any of these important immunoregulatory mechanisms was altered in the IL-4Rαflox/ΔLysMCre mice , we analyzed their expression in the granulomatous livers of acutely and chronically infected mice . Levels of IL-13Rα2 in the serum , whether circulating free or bound to IL-13 , were indistinguishable between infected IL-4Rαflox/Δ control and IL-4Rαflox/ΔLysMCre mice ( Fig . 3A ) . Likewise , IL-12p40 and IL-10 mRNA were expressed at similar levels in the livers of IL-4Rαflox/Δ littermate controls and IL-4Rαflox/ΔLysMCre mice at 9 and 16 weeks post-infection ( Fig . 3B ) . Expression of both IL-4 and IL-13 by CD4+ T cells , the principal stimuli driving fibrosis in this system [20] , [21] , increased identically at 9 weeks post-infection and remained at equivalent levels through week 16 ( Fig . 3C ) . Consistent with the leukocyte responses , we observed no significant increases in IL-4 or IL-13 gene expression in the livers ( Fig . 4 ) or intestines ( not shown ) of IL-4Rαflox/ΔLysMCre mice when compared with expression in IL-4Rαflox/Δ littermate controls 9 and 16 weeks post-infection . These observations suggested a much less critical role for IL-4Rα-expressing AAMs during the chronic response to low dose S . mansoni infections . Surprisingly , however , after further analysis of gene expression in the liver , we found the IL-4Rαflox/ΔLysMCre mice displayed no reduction in the expression of multiple genes that characterize the AAM phenotype [22] , including Chi3l3 ( encoding Ym1 ) , Retnla ( Relm-α ) , and Arg1 ( Fig . 4 ) . Together , the similarities in pathology , survival , and gene expression indicated that in our experiments with IL-4Rαflox/ΔLysMCre mice , AAM development was not substantially impaired or at least not to the degree previously suggested [7] , [23] . We hypothesized that a subset of myeloid cells in IL-4Rαflox/ΔLysMCre mice resisted LysMCre-mediated gene deletion , remained IL-4Rα-positive , and developed into AAM-like cells with immunoregulatory activity . To test for functional expression of IL-4Rα in different leukocyte populations , we isolated peritoneal cells from naïve controls and IL-4Rαflox/ΔLysMCre mice , stimulated them with IL-4 , and measured STAT6 phosphorylation . We used flow cytometry to analyze peritoneal lymphocytes and macrophages separately ( Fig . 5A ) . Lymphocytes ( Fig . 5B ) and macrophages ( Fig . 5C ) harvested from wild-type BALB/c and naïve IL-4Rαflox/Δ littermate controls phosphorylated STAT6 to the same degree . Lymphocytes from IL-4Rαflox/ΔLysMCre mice also exhibited normal STAT6 phosphorylation in response to IL-4 ( Fig . 5B ) . In contrast , macrophages from naïve LysMCre-expressing mice displayed no STAT6 phosphorylation ( Fig . 5C ) , confirming the ablation of IL-4Rα signaling in resident peritoneal macrophages . Inflammatory immune responses recruit , expand , and replace diverse populations of myeloid cells , and while there is strong evidence that resident tissue macrophages can also expand by proliferating [24] , [25] , resident cells may become rapidly and greatly outnumbered by monocyte-derived differentiating cells [26] . Therefore , we next examined whether IL-4Rαflox/ΔLysMCre macrophages elicited in response to a sterile inflammatory stimulus are as defective as the resident tissue macrophage population in their response to IL-4 . We injected IL-4Rαflox/ΔLysMCre and littermate control mice intraperitoneally ( i . p . ) with thioglycollate ( a stimulus recently shown to elicit only bone-marrow derived inflammatory cells and not to expand tissue resident cells [27] ) , harvested peritoneal cells 4 days later , and repeated our IL-4-induced phospho-STAT6 assay ( Fig . 5D ) . Lymphocytes again displayed similar STAT6 phosphorylation in response to IL-4 ( Fig . 5E ) . But critically , over a quarter of the thioglycollate-elicited F4/80hiCD11bhi macrophages from IL-4Rαflox/ΔLysMCre mice were still able to respond to IL-4 as shown by phosphorylated STAT6 ( Fig . 5F ) . Quantification and statistics are shown in Fig . S3 . To confirm that this phenomenon is a reflection of genomic rearrangement of the Il4rα locus , we isolated DNA from F4/80hiCD11bhi macrophages FACS sorted from naïve and thioglycollate-treated IL-4Rαflox/ΔLysMCre mice alongside IL-4Rαflox/flox , IL-4Rαflox/Δ , and IL-4RαΔ/Δ controls . As expected , in macrophages sorted from naïve IL-4Rαflox/ΔLysMCre mice , the wild-type ( WT ) Il4rα allele was present minimally compared to the knockout ( KO ) allele ( Fig . 5G ) . In contrast , the WT Il4rα allele was markedly more abundant in macrophages taken from thioglycollate-treated IL-4Rαflox/ΔLysMCre mice . Together , these data consistently demonstrate that amongst inflammatory cells in IL-4Rαflox/ΔLysMCre mice , a subset of F4/80hiCD11bhi macrophages fails to delete the floxed Il4rα gene as expected and therefore remains capable of undergoing IL-4Rα-mediated alternative activation . We hypothesized that the discrepancy in Il4rα excision is explained by differential expression of Lyz2 ( encoding lysozyme M ) by the naïve and thioglycollate-elicited peritoneal macrophage populations . We sorted CD11bhiF4/80hi macrophages from both peritoneal environments and measured Lyz2 expression . The magnitude of Lyz2 expression is lower in IL-4Rαflox/ΔLysMCre mice than corresponding Cre-negative controls because IL-4Rαflox/ΔLysMCre mice transcribe Cre rather than lysozyme M at one locus [28] , but in support of the hypothesis , naive macrophages expressed significantly more Lyz2 than thioglycollate-elicited macrophages in both IL-4Rαflox/Δ and IL-4Rαflox/ΔLysMCre mice ( Fig . 5H ) . We next examined whether type-2 response-inducing schistosome eggs also generate a subset of inflammatory macrophages that resists LysMCre-mediated gene deletion . For these studies , we directly compared four distinct populations of peritoneal macrophages: resident macrophages ( naïve ) , S . mansoni egg-elicited macrophages 4 days following i . p . egg injection ( 1o ) , S . mansoni egg-elicited macrophages 18 days following i . p . egg injection ( 1o-rested ) , and macrophages from mice injected i . p . with eggs twice over 14 days and then harvested 4 days after the second challenge ( 1o-rechallenged ) . F4/80hiCD11bhi peritoneal macrophages were sorted from each group ( representative flow plots and cytospins in Fig . 6A–B; images for each condition are shown in Fig . S4 ) , and Il4rα and Lyz2 mRNA expression was quantified by qPCR . Resident peritoneal macrophages isolated from naïve IL-4Rαflox/ΔLysMCre mice had indeed ablated Il4rα expression ( Fig . 6C ) . In the resident population , IL-4Rα mRNA expression decreased to less than 15% of littermate levels , explaining the absence of IL-4-induced STAT6 phosphorylation , and concurring with the initially reported efficiency of LysMCre-mediated excision [28] . Strikingly , the peritoneal macrophages isolated from IL-4Rαflox/ΔLysMCre mice 4 days after egg challenge ( 1o ) expressed Il4rα at a level near 50% of littermates . If the macrophages were isolated on day 18 rather than on day 4 after egg challenge ( 1o-rested ) , more than 50% of the F4/80hiCD11bhi macrophages had deleted Il4rα , suggesting that maturation in residence or proliferation of resident cells are factors influencing Cre-mediated excision of Il4rα . In marked contrast , IL-4Rαflox/ΔLysMCre peritoneal macrophages purified 4 days after the second dose of S . mansoni eggs ( 1o-rechallenged ) showed no reduction in Il4rα expression compared with littermate controls ( Fig . 6C ) , suggesting that recently recruited and differentiated F4/80hiCD11bhi macrophages had yet to undergo LysMCre-mediated excision . Compared side-by-side , these data suggest that new F4/80hiCD11bhi macrophages ( through recruitment or proliferation ) were most resistant to LysMCre-mediated gene deletion . As observed with naïve and thioglycollate-elicited macrophages , we hypothesized that differences in expression of Lyz2 in resident , rested , and recently recruited macrophages explain the pattern of IL-4Rα expression observed in macrophages isolated from the Cre-expressing mice . As expected , resident naive peritoneal macrophages expressed the most Lyz2 ( Fig . 6C , right panel ) . In littermate IL-4Rαflox/Δ mice , Lyz2 expression by macrophages was between 50–75% as high in the 1o and the 1o-rested populations and less than 25% as high in the 1o-rechallenged cells ( Fig . 6C , right panel ) . The greater than 75% reduction in Lyz2 expression observed in the 1o-rechallenged F4/80hiCD11bhi macrophages likely explains why the greatest fraction of these cells are resistant to LysMCre-mediated deletion and remain IL-4Rα positive . Finally , to confirm that inflammatory macrophages in the IL-4Rαflox/ΔLysMCre mice are capable of becoming alternatively activated in response to schistosome eggs in vivo , we isolated F4/80hiCD11bhi macrophages from the naïve , 1o-rested , 1o-rechallenged groups and analyzed the gene expression of several well-documented markers of alternative macrophage activation including Mrc1 ( mouse mannose receptor , C type 1 ) , Chi3l3 , Retnla , and Arg1 . As expected , there was no evidence of alternative activation in the macrophages isolated from naive mice unexposed to schistosome eggs ( Fig . 6D and 6E ) . However , consistent with Il4rα expression ( Fig . 6C ) , F4/80hiCD11bhi macrophages isolated from littermate control and IL-4Rαflox/ΔLysMCre 1o-rechallenged groups displayed marked and equivalent increases in Mrc1 , Chi3l3 , Retnla , and Arg1 mRNA expression ( Fig . 6D ) . They also displayed similar cell surface expression of the mannose receptor ( Fig . 6E ) and nearly identical arginase activity ( Fig . 6F ) . In contrast , if the egg-elicited macrophages were left two weeks to rest in vivo , Lyz2 mRNA expression was higher ( Fig . 6C ) , and the macrophages isolated from the IL-4Rαflox/ΔLysMCre mice expressed lower levels of schistosome egg-induced Mrc1 , Chi3l3 , Retnla , and Arg1 mRNA than littermate controls ( Fig . 6D ) . Together , these data demonstrate that a substantial population of Arg1-expressing AAMs was preserved in egg-challenged IL-4Rαflox/ΔLysMCre mice , likely explaining why Arg1flox/floxTie2Cre and IL-4Rαflox/ΔLysMCre mice display distinct fibrosis phenotypes following acute and chronic infection with S . mansoni [7] , [8] . Lastly , we sorted myeloid cells from the livers of infected IL-4Rαflox/ΔLysMCre mice and IL-4Rαflox/Δ littermate controls to confirm that there are macrophages resistant to Il4rα excision during active infection in the liver and to discern whether Lyz2 expression was responsible for this . As expected [27] , myeloid cells isolated from the infected liver were more heterogeneous than peritoneal macrophages , hence we sorted them as CD45+ SiglecF- CD11b+ Ly6G- F4/80+ CD64+ and then separated them based on Ly6C expression with the aim of gaining insight to their identity as recruited or resident cells ( Fig . S5 ) . Corroborating observations with peritoneal macrophages exposed to S . mansoni eggs , we found that macrophages isolated from infected IL-4Rαflox/ΔLysMCre livers excise Il4rα less efficiently than the 83–98% deletion efficiency ascribed to mature macrophages when IL-4Rαflox/ΔLysMCre mice were originally characterized [28] ( Fig . 7 ) . While Il4rα is expressed by all sorted macrophage populations from IL-4Rαflox/ΔLysMCre livers to at least 50% of levels in littermate controls , the Ly6C- cells in particular manifest almost no deficit . Each sorted population also largely maintains alternative activation marker expression . Arg1 is upregulated several hundred-fold by each sorted population from the infected liver compared to naïve controls although its expression is significantly less in Ly6Cint cells from cre-positive mice . Compared to naïve controls , Relma expression is greatly upregulated in all sorted populations and at comparable levels in both the groups . Similar results were obtained for Chi3l3 and Mrc1 ( Fig . S6 ) . Furthermore , we observed that the higher the Lyz2 expression in the sorted macrophage populations , the greater the magnitude of excision of Il4rα .
The LysMCre knock-in mouse has a Cre recombinase gene under control of endogenous lysozyme 2 ( Lyz2 ) promoter/enhancer elements and has been used extensively in Cre-lox studies of the myeloid lineage ( monocytes , mature macrophages , and granulocytes ) for over a decade [28] . In a notable earlier study , conditional IL-4Rα-deficient ( IL-4Rαflox/flox ) mice were crossed to IL-4RαΔ/ΔLysMCre mice to generate animals with a selective IL-4Rα deletion in macrophages and neutrophils , with the goal of preventing the alternative activation of macrophages [7] . Herbert and colleagues found IL-4Rαflox/ΔLysMCre mice were highly susceptible to acute S . mansoni infection ( 100% mortality by 8 weeks post-infection ) because they developed sepsis and severe hepatic and intestinal histopathology . This acute mortality was also associated with increased IFN-γ production and NOS-2 activity , suggesting that AAMs are critically involved in the suppression of highly pathogenic type-1 immune responses during infection with S . mansoni [29] . We initiated our studies to directly compare the role of IL-4Rα-deficient and Arg1-deficient macrophages in the pathogenesis of fibrosis [8] , but we began to question the merits of this strategy when we discovered IL-4Rαflox/ΔLysMCre mice were not displaying the striking susceptibility to S . mansoni infection originally reported by Herbert and colleagues . At a lower dose of 35 cercariae , no difference in mortality occurred between IL-4Rαflox/ΔLysMCre mice and Cre-negative littermates through week 16 , and while a larger dose of infectious cercariae accelerated death in both groups , again no difference emerged . It remains difficult to fully explain the difference between the two studies , however the additional controls included in ours , suggest that the global rather than cell-specific deletion of IL-4Rα is the major determinant regulating acute mortality during S . mansoni infection . These unexpected results led us to reexamine the role of IL-4Rα-expressing AAMs in the pathogenesis of schistosomiasis . We first attempted to verify that the mice were indeed deficient in AAMs by isolating peritoneal macrophages from naïve IL-4Rαflox/ΔLysMCre mice and their Cre-negative littermates and stimulating ex vivo with IL-4 . As expected , STAT6 phosphorylation was entirely defective in IL-4-stimulated macrophages but not in lymphocytes isolated from the IL-4Rαflox/ΔLysMCre mice , confirming myeloid cell-specific deletion of IL-4Rα . However , the livers of infected IL-4Rαflox/ΔLysMCre mice showed little to no reduction in the expression of genes associated with alternative activation [3] , suggesting that alternative activation was not significantly impaired in vivo during infection . Most notably , Arg1 mRNA was not reduced , yet Arg1 expression in macrophages is predominantly driven by an IL-4Rα/STAT6-dependent mechanism in schistosomiasis [30] . Arg1 activity was of particular interest because prior studies showed that Arg1-expressing macrophages play a critical host protective role in schistosomiasis by suppressing the pro-inflammatory activity of IL-12/IL-23 during the acute phase and by slowing the progression of IL-13-driven fibrosis in the chronic phase of schistosomiasis [8] , [31] . Therefore , we hypothesized that the maintenance of a substantial population of Arg1-expressing AAMs during infection keeps fibrosis and disease progression from being significantly altered in IL-4Rαflox/ΔLysMCre mice , even when chronically infected with S . mansoni . In the original description of S . mansoni-infected IL-4Rαflox/ΔLysMCre mice , Herbert and colleagues showed that F4/80+ macrophages isolated from the mesenteric lymph nodes of infected mice did not express IL-4Rα , and that peritoneal macrophages from uninfected mice did not respond to IL-4 and IL-13 [7] . Consequently , Arg1 activity was markedly decreased in those macrophages when cultured and stimulated in vitro . The behavior of inflammatory macrophages , which dominate most chronic inflammatory diseases remained unknown , however . To begin dissecting the behavior of inflammatory monocytes , we used thioglycollate , a stimulus recently shown to elicit bone marrow-derived inflammatory monocytes but results in nearly undetectable proliferation of tissue resident cells [27] . We compared thioglycollate-elicited peritoneal macrophages with resident peritoneal macrophages . Under this sterile inflammatory condition , over a quarter of the peritoneal macrophage population in IL-4Rαflox/ΔLysMCre mice resisted LysMCre-mediated gene deletion , expressed IL-4Rα , and subsequently developed an Arg1+ alternatively activated phenotype when stimulated ex vivo with IL-4 or IL-13 . Importantly , when we conducted similar studies eliciting type-2 inflammation by injecting S . mansoni eggs , an even greater fraction of the F4/80hiCD11bhi macrophage population resisted LysMCre-mediated excision of Il4rα . Indeed , if we rechallenged mice with eggs , nearly 100% of the peritoneal macrophages expressed Il4rα . As shown recently by Jenkins et al . , the inflammatory cells could result from proliferation as well as recruitment from monocyte precursors [24] , [25] . Mechanistically , we found the peritoneal macrophages in this inflammatory environment also expressed very low levels of Lyz2 , likely explaining the resistance to LysMCre-mediated excision of Il4rα . In addition to driving proliferation of IL-4Rα+ cells , the high levels of IL-4 present following egg exposure can also suppress Lyz2 , maintaining IL-4Rα expression in LysMCre+ cells [27] . Following rechallenge , the peritoneal macrophages also exhibited an alternatively activated phenotype , with IL-4Rαflox/ΔLysMCre mice and Cre-negative littermates expressing comparably high levels of Arg1 . However , when the egg-elicited macrophages were given two weeks to mature in vivo , a much larger percentage of the isolated macrophages expressed Lyz2 and deleted Il4rα . Nevertheless , even 18 days after S . mansoni egg challenge , nearly 40% of the peritoneal macrophages in IL-4Rαflox/ΔLysMCre mice retained IL-4Rαcould respond to IL-4 , and exhibited a functional AAM phenotype . Macrophages isolated from the livers of infected IL-4Rαflox/ΔLysMCre mice displayed a similar failure to fully delete the IL-4Rαwhen Lyz2 expression is lowest , and their AAM phenotype was also preserved . Sorting on Ly6C expression allowed us to distinguish liver macrophage populations with varying levels of Lyz2 expression , but we were surprised to find the Ly6C- macrophage subset to express the lowest Lyz2 and be most resistant to Il4rα excision . Although Ly6C may be a satisfactory marker for circulating/recently recruited inflammatory monocytes , its regulation during chronic inflammation likely results in more variable expression in the tissue . Together , our observations of the peritoneum and the infected liver demonstrate that while LysMCre mice are useful for studying gene function in mature tissue macrophages that have expressed Lyz2 , they are less effective in chronic disease settings where the resident tissue population is eclipsed by the constant accumulation of immature Lyz2lo macrophages , which in the case of S . mansoni-infected IL-4Rαflox/ΔLysMCre mice , remain IL-4Rα+ and quickly develop into functional AAMs in response to IL-4 and IL-13 found in the local milieu . IL-4Rαflox/ΔLysMCre mice did develop larger granulomas than IL-4Rαflox/Δ littermates at both acute and chronic time points , suggesting the mature Lyz2hi population is the critical subset of AAMs mediating the downmodulation of granulomatous inflammation at the chronic stage of infection . However , these mice did not manifest increased liver fibrosis , portal hypertension , bleeding , or mortality than the littermate controls . Together , these findings were surprising since the progression of fibrosis in schistosomiasis has been linked with the severity of the egg-induced granulomatous response and the ability to downregulate granuloma formation in the chronic phase of the disease [10] , [32] , [33] . Nevertheless , in studies of S . mansoni-infected Arg1flox/floxTie2Cre mice , where Arg1 is deleted from all macrophage populations , fibrosis was substantially increased , suggesting that Arg1 activity in macrophages is critical to the regulation of fibrosis . Thus , we conclude that the preservation of Arg1 activity in more imfmature Lyz2lo F4/80hi CD11bhi macrophages from egg-exposed or infected IL-4Rαflox/ΔLysMCre mice explains why IL-4Rαflox/ΔLysMCre mice , in contrast to Arg1flox/floxTie2Cre mice , did not develop a significantly augmented fibrotic response at any time point [8] , [31] ( Summary diagram , Fig . 8 ) . This also likely explains the greater increase in granulomatous inflammation and fibrosis observed in acutely infected Arg1flox/floxTie2Cre versus Arg1flox/ΔLysMCre reported previously [8] . Our study demonstrates that a substantial subset of macrophages induced in response to a sterile stimulus or pathogen exposure resists LysMCre-mediated genomic excision . We believe this finding is important because numerous studies have employed the LysMCre mouse to dissect gene function in macrophages . In some diseases , including gastrointestinal nematode infection and allergic airway disease [7] , [34] , [35] the reported results could be due to a failure to delete the gene of interest in a sufficient proportion of more immature macrophages arising from proliferation or recruitment from monocyte precursors . We found that while mature “resident” tissue macrophages successfully delete the gene of interest , newly differentiating macrophages in inflammatory environments transcribe insufficient Lyz2 to efficiently accomplish the Cre-mediated deletion . Our discovery suggests a new experimental approach to distinguish the function of resident tissue and fully mature macrophages from more immature Lyz2-negative cells , an emerging topic for research in many infections and inflammatory diseases . Accordingly , our findings complement a recent study showing tissue macrophages and AAMs derived from monocytes are phenotypically distinct [27] . Our findings also demonstrate how quickly this immature Lyz2lo macrophage population can become alternatively activated with high expression of Arg1 , which we have shown critically controls the pathogenesis of fibrosis in schistosomiasis [8] . This conclusion is consistent with another recent study that found inflammatory monocytes recruited to the skin quickly adopt a suppressive AAM-like phenotype in response to IL-4 [36] . Collectively , we conclude that it is a Lyz2lo IL-4Rα+ Arg1+ population of F4/80hiCD11bhi macrophages that is critically involved in the suppression of fibrosis in chronic schistosomiasis , while the Lyz2hi IL-4Rα+ population of mature resident macrophages controls the magnitude of the egg-induced inflammatory response at both acute and chronic time points post-infection .
The National Institute of Allergy and Infectious Diseases Division of Intramural Research Animal Care and Use Program , as part of the National Institutes of Health Intramural Research Program , approved all of the experimental procedures ( protocol LPD 16E ) . The Program complies with all applicable provisions of the Animal Welfare Act ( http://www . aphis . usda . gov/animal_welfare/downloads/awa/awa . pdf ) and other federal statutes and regulations relating to animals . IL-4Rαflox/Δ LysMWT/Cre mice backcrossed on a BALB/c background were kindly provided by Dr . Fred Finkelman ( U . Cincinnati , Ohio ) and Dr . Frank Brombacher ( University of Cape Town; Cape Town , South Africa ) [7] . IL-4Rαflox/flox females were crossed with IL-4RαΔ/Δ LysMWT/Cre males to generate IL-4Rαflox/Δ LysMWT/Cre mice ( called IL-4Rαflox/ΔLysMCre in this paper ) and Cre-negative IL-4Rαflox/Δ littermates . All cells in both the Cre-positive and Cre-negative mice maintain the Il4rα gene on one allele . This breeding scheme prevents embryonic deletion of IL-4Rα by Cre-expressing females . BALB/c and IL-4RαΔ/Δ mice were obtained from Taconic Farms Inc ( Derwood , MD ) . All animals were housed under specific pathogen-free conditions at the National Institutes of Health in an American Association for the Accreditation of Laboratory Animal Care-approved facility . Mice were infected percutaneously via the tail with 35 or 100 cercariae , as indicated , with a Puerto Rican strain of Schistosoma mansoni ( NMRI ) obtained from infected Biomphalaria glabrata snails ( Biomedical Research Institute; Rockville , MD ) . Mice were perfused at the time of euthanasia to determine worm and tissue egg burdens as described previously [37] . Serum was analyzed for liver enzyme quantification at the National Institutes of Health Clinical Center using a Vista Analyzer ( Siemens; Deerfield , IL ) . IL-13Rα2 serum levels were determined by ELISA as previously described [38] . Liver tissue was fixed in Bouin-Hollande solution , embedded in paraffin for sectioning , and stained ( Histopath of America; Clinton , MD ) with Wright's Giemsa for analysis of inflammation or picrosirius red for fibrosis analysis . A blinded pathologist measured the size of approximately 30 granulomas in Giemsa-stained sections of each sample . Swiss rolls of small intestine were fixed as above and stained with hematoxylin and eosin for blinded scoring of inflammation . Hydroxyproline was measured as a surrogate for collagen content . A known weight of liver tissue was hydrolyzed in 6 N HCl at 110°C for 18 h and then neutralized in 10 N NaOH before colorization . A standard curve comprised of dilutions of 1 mM hydroxyproline ( Sigma-Aldrich; St . Louis , MO ) was used for quantification [39] . About 200 mg of granulomatous liver was ground into a single-cell suspension through a 100-µm nylon mesh . Leukocytes were separated on a 40% Percoll ( Sigma-Aldrich ) gradient ( 2000 rpm for 15 min ) and treated for 2 min with 1 ml ACK ( ammonium chloride–potassium bicarbonate ) lysis buffer to lyse erythrocytes . After 3 hours of stimulation with phorbol 12-myristate 13-acetate ( PMA 10 ng/ml ) , ionomycin ( 1 µg/ml ) , and Brefeldin A ( BFA , 10 µg/ml ) , leukocytes were fixed and permeabilized for 30 minutes ( Cytofix/Cytoperm buffer; BD Biosciences; San Diego , CA ) and then stained for 30 minutes with antibodies for CD4 ( eBioscience; San Diego , CA ) , IFN-γ ( eBioscience ) , IL-4 ( eBioscience ) , and IL-13 ( eBioscience ) diluted in the Permwash buffer ( BD Biosciences ) . Expression of CD4 and the intracellular cytokines was analyzed with a BD FACSCanto II flow cytometer and FlowJo v . 7 . 6 software ( Tree Star; Ashland , OR ) . Whole naïve or granulomatous livers were chopped into fine pieces with a razor blade and digested in 100 units/ml collagenase ( Sigma ) for 1 hr at 37oC with rocking . The tissue was then ground into a single-cell suspension through a 100-µm nylon mesh . Hepatocytes were pelleted out with a 50 g spin for 5 min for cleaner density separation . Leukocytes were separated on a 40% Percoll ( Sigma-Aldrich ) gradient ( 2000 rpm for 15 min ) and treated for 2 min with 1 ml ACK ( ammonium chloride–potassium bicarbonate ) lysis buffer to lyse erythrocytes . Leukocytes were stained for 30 minutes with antibodies for CD16/32 ( BDBiosciences ) , CD45 ( Biolegend; San Diego , CA ) , CD11b ( Biolegend ) , Siglec F ( BDBiosciences ) , Ly6G ( BDBiosciences ) , F4/80 ( Biolegend ) , CD64 ( Biolegend ) , and Ly6C ( Biolegend ) diluted in FACS buffer . CD45+ SiglecF- CD11b+ Ly6G- F4/80+ CD64+ cells were sorted with at least 90% purity from amongst the stained cells using a FACS Aria ( BDBiosciences ) . Liver tissue was homogenized in TRIzol Reagent ( Life Technologies; Grand Island , NY ) using Precellys 24 ( Bertin Technologies; Montigny-le-Bretonneux , France ) . Total RNA was extracted from the homogenate by addition of chloroform followed by the recommendations of the MagMax-96 Total RNA Isolation Kit ( Life Technologies ) . Total RNA was isolated from peritoneal cells with an RNeasy kit ( Qiagen ) . RNA from all cell types was then reverse transcribed using SuperScript II Reverse Transcriptase ( Life Technologies ) . Real-time RT-PCR was performed on an ABI Prism 7900HT Sequence Detection System ( Applied Biosystems ) . Quantities of mRNA expressed by a particular gene were determined using Power SYBR Green PCR Master Mix ( Applied Biosystems ) , normalized to ribosomal protein , large , P2 ( RPLP2 ) mRNA levels in each sample , and then articulated as a relative increase or decrease compared with mRNA levels expressed by the same gene in uninfected controls . Primers were designed using Primer Express software ( version 2 . 0; Applied Biosystems ) . Forward and reverse primer sequences are listed in Table S1 . Peritoneal cells were collected by washing the peritoneal cavity with PBS containing 5 mM EDTA . The cells were stained for 30 minutes with anti-mouse antibodies for F4/80 ( Biolegend ) , CD11b ( Biolegend ) , and CD16/32 ( BD ) diluted in the same buffer . F4/80hiCD11bhi cells were sorted with at least 90% purity from amongst the stained cells using a FACS Aria ( BDBiosciences ) . Mice were intraperitoneally ( i . p . ) injected with 2 ml 3% thioglycollate ( BD; Franklin Lakes , NJ ) to elicit peritoneal macrophage recruitment or left untreated , as indicated . 4 days later , peritoneal cells from both groups were harvested as described above , and equal numbers of cells per mouse were resuspended with 20 ng/ml recombinant murine IL-4 ( Peprotech ) in complete RMPI or complete RMPI alone . The resuspended cells were placed in a 37°C water bath for 30 minutes with periodic agitation . Next , cells were fixed with 1 . 5% paraformaldehyde , washed , and permeabilized with cold methanol overnight at −20°C . Permeabilized cells were washed twice with PBS containing 0 . 1% bovine serum albumin and stained with anti-mouse STAT6 ( BD ) , F4/80 ( Biolegend ) , CD11b ( eBioscience ) , Gr1 ( BD Pharmingen ) , and CD16/32 ( BD ) for 1 hour on a shaker at room temperature . Phosphorylation of STAT6 in F4/80hiCD11bhiGr1– macrophages and F4/80l°CD11bloGr1– lymphocytes was measured using a BD FACSCanto II flow cytometer and FlowJo v . 7 . 6 software ( Tree Star; Ashland , OR ) . To extract DNA , equal numbers of FACS sorted peritoneal cells were resuspended in 25 mM NaOH , incubated at 95°C for 15 minutes , and neutralized with 40 mM Tris-HCl . DNA was amplified with GoTaq DNA Polymerase ( Promega ) with the following primers: Il4rα wild-type: F - 5′-GTACAGCGCACATTGTTTTT-3′ , R - 5′-CTCGGCGCACTGACCCATCT-3′; Il4rα knockout: F - 5′-GGCTGCCCTGGAATAACC-3′ , R - 5′-CCTTTGAGAACTGCGGGCT-3′ . Gel was imaged and band intensity quantified with BioSpectrum set up with VisionWorksLS software ( UVP; Upland , CA ) . 5000 live Schistosoma mansoni eggs ( obtained from the same source as the cercariae described above ) were injected i . p . to prime some mice on day 0 while others were left untreated . On day 14 , some of the primed mice were challenged i . p . with 5000 live eggs , and some of the primed mice were left unchallenged . On day 18 , peritoneal cells were harvested from each group of mice ( naïve , primed/rested , primed/rechallenged ) . Equal numbers of unsorted peritoneal cells were stained with anti-mouse antibodies against F4/80 ( Biolegend ) , CD11b ( eBioscience ) , CD16/32 ( BD ) , and CD206 ( mouse mannose receptor , C type 1 ) ( Biolegend ) or with a rat IgG2a isotype control . CD206 fluorescence on F4/80hiCD11bhi macrophages was measured using a BD FACSCanto II flow cytometer and FlowJo v . 7 . 6 software ( Tree Star; Ashland , OR ) . F4/80hiCD11bhi peritoneal cells were also sorted as described above . Some sorted cells were spun for 5 mins with a Shandon Cytospin 3 centrifuge ( Thermo Scientific; Waltham , MA ) onto a slide before being fixed with methanol and stained with Diff-Quik ( Boehringer ) . Aliquots of 5×105 sorted cells were resuspended in lysis buffer and arginase activity was measured as previously described [39] . All data were analyzed with Prism ( Version 5; GraphPad ) . Data sets were compared with a two-tailed t-test , and differences were considered significant if P values were less than 0 . 05 . Rplp2: NM_026020 , Il4: NM_021283 , Il13: NM_008355 , Il13rα2: NM_008356 , Il10: NM_010548 , Chi3l3: NM_009892 , Retnla: NM_020509 , Mrc1: NM_008625 , Arg1: NM_007482 , Col6α: NM_009933 , Timp1: NM_001044384 , Mmp12: NM_008605 , Il4rα: NM_001008700 , Lyz2: NM_017372 , Ifnγ: NM_008337 , Il12p40: NM_008352 | Chronic injury and inflammation lead to irreversible fibrosis in a range of diseases and infections . Macrophages alternatively activated by the immune system are capable of regulating inflammation and fibrosis , but our understanding of the source and function of these cells is incomplete . Mice genetically engineered to specifically prevent macrophages from becoming alternatively activated have been used to study the cells' role following infection with the parasite , Schistosoma mansoni . To our surprise , we found these mice prevent alternative activation only in macrophages that have had time to mature and some , perhaps more nascent , macrophages can become alternatively activated following exposure to S . mansoni eggs . We detected lower expression of Lyz2 gene in these cells , leading to less expression of the enzyme excising the receptor gene necessary for alternative activation . Following S . mansoni infection , the livers of these mice have similar levels of fibrosis but significantly more inflammation compared to controls . We conclude that during schistosomiasis , distinct populations of alternatively activated macrophages control inflammation and fibrosis: macrophages expressing low levels of Lyz2 express Arg1 and thus are sufficient to control fibrosis , while more mature Lyz2-expressing macrophages are required for downmodulation of egg-induced inflammation in chronic schistosomiasis . | [
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] | 2014 | Incomplete Deletion of IL-4Rα by LysMCre Reveals Distinct Subsets of M2 Macrophages Controlling Inflammation and Fibrosis in Chronic Schistosomiasis |
Human papillomaviruses ( HPV ) have genotype-specific disease associations , with high-risk alpha types causing at least 5% of all human cancers . Despite these conspicuous differences , our data show that high- and low- risk HPV types use similar approaches for genome maintenance and persistence . During the maintenance phase , viral episomes and the host cell genome are replicated synchronously , and for both the high- and low-risk HPV types , the E1 viral helicase is non-essential . During virus genome amplification , replication switches from an E1-independent to an E1-dependent mode , which can uncouple viral DNA replication from that of the host cell . It appears that the viral E2 protein , but not E6 and E7 , is required for the synchronous maintenance-replication of both the high and the low-risk HPV types . Interestingly , the ability of the high-risk E6 protein to mediate the proteosomal degradation of p53 and to inhibit keratinocyte differentiation , was also seen with low-risk HPV E6 , but in this case was regulated by cell density and the level of viral gene expression . This allows low-risk E6 to support genome amplification , while limiting the extent of E6-mediated cell proliferation during synchronous genome maintenance . Both high and low-risk E7s could facilitate cell cycle re-entry in differentiating cells and support E1-dependent replication . Despite the well-established differences in the viral pathogenesis and cancer risk , it appears that low- and high-risk HPV types use fundamentally similar molecular strategies to maintain their genomes , albeit with important differences in their regulatory control . Our results provide new insights into the regulation of high and low-risk HPV genome replication and persistence in the epithelial basal and parabasal cells layers . Understanding the minimum requirement for viral genome persistence will facilitate the development of therapeutic strategies for clearance .
Papillomaviruses ( PV ) are small double-stranded DNA viruses which infect stratified epithelia , with the completion of their life cycle being intimately linked to the terminal differentiation program of keratinocytes . One of the most distinctive characteristics of papillomaviruses is their genotype-specific host-restriction , and the preference shown by different papillomavirus types for distinct anatomical sites[1–3] . Human papillomaviruses ( HPVs ) , which consist of more than 200 HPV types , are subdivided into 5 genera , and within the alpha genus , HPVs are further divided into low- and high-risk types based on the cancer risk associated with their infection [4 , 5] . HPVs express a relatively small number of early and late viral genes during their life cycle , which are tightly regulated according to the differentiation stage of the infected keratinocytes . The viral genes may be categorized as ‘core genes’ or ‘accessory genes’ . All known papillomaviruses have evolved a group of core genes that were present early during papillomavirus speciation , which share similarities in sequence and protein function . The core genes play primary roles in viral genome replication and amplification ( E1 and E2 ) as well as virion assembly ( L1 and L2 ) [6–10] . In contrast , the accessory genes ( E6 , E7 and E5 ) , which are often referred to as viral oncogenes , have evolved in each papillomavirus type as it adapted to different epithelial niches [11–13] . The sequence and function of these genes are similar yet divergent between types compared to the core genes . In general , accessory genes are involved in modifying the cellular environment in order to complete the viral life cycle at the site of infection . Accessory genes play a variety of important functions at different stages of the viral life cycle , such as the maintenance of infection in the basal layer and the amplification of viral genomes in the suprabasal layers . As such , the function of these accessory genes as well as their regulation may largely attribute to virus diversity in tropism and pathogenicity [5 , 6] . In fact , the deregulation of the accessory genes of high-risk HPV can mediate the progression to invasive cancer [4 , 14] . The high-risk HPV-mediated carcinogenic process has drawn most of the research effort in the past decades , leaving open important questions about the comparative analysis of the evolutionary adaptation of different HPV types in relation to their specific epithelial sites of infection . Among the best characterized functions of high-risk HPVs there are the E6-mediated degradation of p53 and PDZ domain-containing proteins and the activation of hTERT ( reviewed in [12] ) . High-risk HPV E7 is known to interact and destabilise pRB family proteins , promoting cell cycle re-entry in the parabasal layers of the epithelium and above ( reviewed in [11] ) . Furthermore , both E6 and E7 expressed by high-risk HPV types have multiple additional functions , such as the inhibition of differentiation , apoptosis and host-immune response . In many cases , low-risk HPV E6 and E7 are considered as a ‘de-potentiated’ version of their high-risk counterparts . These HPV types are maintained and propagated in the general population without having the carcinogenic properties of high-risk HPVs , indicating that the induction of cancer is a collateral effect of the life cycle strategy of high-risk HPVs rather than an adaptation . In this study , we have performed a comparative analysis of how ‘core’ and ‘accessory’ proteins cooperate in promoting the replication of both low- and high-risk viral episomes . We have used HPV11 and 16 as the key prototypes of low- and high-risk alpha HPV types respectively , and used the immortal and isogenic NIKS keratinocyte cell line as a cell model to draw direct comparisons of the life cycle strategies used by the two viruses . We have identified a two-phase HPV replication mode common to both low- and high-risk alpha papillomaviruses that is regulated by cell density . Our data shows that viral replication switches from requiring E2 and being E1-independent while genomes are maintained , to being E1-dependent as keratinocytes commit to differentiation at high cell density , a situation that also requires the function of E6 and E7 . Curiously , the low-risk E6 protein was necessary in order to sustain low-risk HPV genome copy number as cell confluence was reached , and that like high-risk E6 , this was dependent on E6-mediated p53 proteasomal degradation . Our results suggest that for both high and low-risk HPVs , that E1 is non-essential for synchronous HPV genome replication during maintenance , and that the low-risk E6 protein has a p53-degradation function that is regulated at cell confluence , and which like that high-risk HPV types , can inhibit commitment to differentiation and stimulate cell cycle entry .
293T ( ATCC ) were maintained in Dulbecco’s Modified Eagle’s Medium ( DMEM , SIGMA ) supplemented with 10% fetal calf serum ( FCS , HyClone ) and 1% penicillin and streptomycin . NIKS ( a gift from Paul Lambert , McArdle Laboratory for Cancer Research , University of Wisconsin ) , a HPV-negative spontaneously immortalised human keratinocyte cell line , was maintained at sub-confluence on γ-Irradiated J2 3T3 feeder cells ( a gift from Paul Lambert ) in F medium with all supplements as previously described [15] . Bromodeoxyuridine ( BrdU ) was used at a final concentration of 30 μg/ml for 18 hours . The pSPW12 plasmid was a kind gift from Prof . Margaret Stanley ( University of Cambridge ) containing the HPV16 genome . The pBT-1 clone of HPV11 ( Hershey ) plasmid was a kind gift from Prof . Neil Christensen ( The Pennsylvania State University ) . HPV16E6SAT , HPV16ΔPBM , HPV16 E1 defective mutant were described previously [16 , 17] . All mutant genomes were constructed using a KOD -Plus- Mutagenesis Kit ( TOYOBO ) , prior to DNA sequencing to ensure that no additional base changes were present . The mutants that were constructed in this study comprise the HPV16 E1 helicase defective mutant ( K383 to A ) , HPV16 E6 defective mutant ( D9 to STOP ) , HPV16 E7 defective mutant ( L13 to STOP ) , HPV16 E2 defective mutant ( G50 to stop ) , HPV11E1 defective mutant ( D46 to STOP and M47 to STOP ) , HPV11 E1 helicase defective mutant ( K384 to A ) , HPV11 E6 defective mutant ( E2 to STOP ) , HPV11 E7 defective mutant ( L15 to STOP ) and the HPV11 E2 defective mutant ( L79 to STOP ) . The primer sequences used for mutagenesis are available upon request . In order to construct HPV11 and HPV16 reporter genomes , two restriction enzyme recognition sites , BglII and XhoI , were generated in pSPW12 ( HPV16 ) , pBT-1 ( HPV11 ) and pSELECT–zeo-GFPBsr ( InvivoGen ) using the KOD -Plus- Mutagenesis system ( Fig 2A , primer sequences available upon request ) . All mutants were sequenced to ensure that no additional base changes were present . The mutated pSPW12-BglII/XhoI , pBT-1-BglII/XhoI and pSELECT–zeo-GFPBsr-BglII/XhoI were excised from the BglI and XhoI sites . These two flagments , the pSPW12- BglII/XhoI plasmid or pBT-1-BglI-XhoI and GFPBsr-BglI/XhoI , were ligated using Ligation high Ver . 2 ( TOYOBO ) . Plasmids containing HPV16 and HPV11 genomes ( wild type or mutant ) were digested with BamHI to release the whole viral genome . The linearized HPV11 or HPV16 genomes were then re-circularized and purified as described previously , before being co-transfected with a plasmid encoding blastocidin into NIKS cells [18] . 2x105 cells were seeded in each well of a 6-well plate the day before transfection with F-media incomplete ( no EGF ) . The cells were transfected with 1600 ng of re-circularized HPV DNA , and 400 ng of pcDNA6 encoding a blasticidin resistance gene ( Invitrogen ) , using FuGENE HD ( Promega ) . The next day the cells were seeded onto a 75 cm2 flask over blastcidin-resistant feeders with F-media incomplete , then NIKS cells were selected with 4 μg/ml blasticidin S with F-media complete ( with 10 ng/ml of EGF ) for 4 days and cultured extra 2–3 in the absence of Blastcidin and designated passage one ( P1 ) . All experiments were carried out in triplicate using NIKS cell lines containing HPV genomes which were generated at least two independent transfections . 3 . 3x105 NIKS cells containing HPV genomes were seeded in to a 25cm2 flask with the same number of feeder cells in F-media complete . Cells were collected for analysis at day 1 , 2 , 3 , 4 and 7 . All experiments were done using NIKS cells containing > than 10 copy per cell of each HPV genome at passage 2 post-transfection , . The production and infection of recombinant retroviruses were accomplished as previously described [19] . Construction of retrovirus vectors LXSN-HPV16E6 , HPV16E6SAT , HPV16E6ΔPDZ , HPV16E7 , HPV16E6E7 , HPV11E6 , HPV11E7 , HPVE6E7 were described previously [20] . Retrovirus vectors of LXSN-HPV11E6 , HPV11E7 , and HPVE6E7 were constructed by cloning ORF of HPV11 E6 and/or E7 into LXSN using Gateway Recombination cloning technology ( Thermo Fisher Scientific ) following the manufacturer’s instruction ( primer sequences available upon request ) . LXSN-11E6W133R was constructed using KOD -Plus- Mutagenesis Kit ( primer sequences available upon request ) and sequenced to ensure that no additional base changes was present . The E6AP-specific shRNA constructs pCL-SI-MSCVpuro-H1R-E6APRi4 was described previously [21] . To generate NIKS cells expressing E6 and/or E7 , the cells were seeded 1 day before and inoculated with at MOI of 5 in the presence of 4 μg/ml of Polybrene ( Santa Cruz ) followed by Geneticin ( Thermo Fisher Scientific ) selection ( 400 μg/ml ) for 4 days . For the delivery of siRNAs , 3 . 3x105 of cells were seeded on 25cm2 flasks and transfected 12nM of siRNA using HiPerfect Transfection Reagent ( Qiagen ) at days 0 and 4 . Non-targeting siRNA ( MISSION siRNA Universal Negative Control ( Sigma ) ) was used as a negative control and ON-TARGET plus Human TP53 ( Dharmacon ) was used as a siRNA to p53 . Total DNA from NIKS for qPCR was purified using a QIAamp DNA Mini Kit ( Qiagen ) , according to the manufacturer's instructions . All samples were digested with DpnI to remove any residual input DNA prior to analysis . The RNA from NIKS for RT-qPCR was purified by using an RNeasy Mini Kit ( Qiagen ) , and cDNA was synthesised with SuperScript III Reverse Transcriptase ( Thermo Fisher scientific ) using 100 μM random hexamer primers , according to the manufacturer's instructions . The HPV genome and GAPDH were measured by a ViiA 7 Real-Time PCR System ( Life Technologies ) using Power SYBR Green/ROX master mix ( Thermo Fisher scientific ) with 15 min denaturation at 95°C , followed by 45 cycles of 95°C for 15s and 60°C for 60s . The PCR primers for qPCR were as follows . The HPV11 forward primer was 5’-ACATTAGATCCGTGGACAGTACAATC-3’ or 5’-TCGTCCAGCCTAGACATTGAG-3’ , HPV11 reverse primer was 5’-TTCCTTCTTTGGTGCTTGTTGTAA-3’ or 5’- TCCAATCGTATGCATTTCCA-3’ , HPV16 forward primer was 5’-TGTTTCAGGACCCACAGGAGC-3’ , HPV16 reverse primer was 5’- CGCAGTAACTGTTGCTTGCAG-3’ , GAPDH forward primer was 5’-CCTCCCGCTTCGCTCTCT-3’ , GAPDH reverse primer was 5’-CTGGCGACGCAAAAGAAGA-3’ . To quantitate HPV DNA levels in HPV genome-containing cell populations and cell lines , HPV copy number per cell was expressed relative to GAPDH copy number . The human genome blast using designed primers and probe for human GAPDH indicated that there are 4 copies , comprising 2 copies of GAPDH and 2 pseudogene copies of GAPDH per NIKS cell . This was confirmed by determination of GAPDH copy number against known NIKS cell number . The copy number of virus genomes per cell was found to vary at passage 2 between experiments , with HPV11 WT and mutant genomes typically present at around 100 copies per cell . The viral genome copy number per cell at passage 2 , day 1 was set at 100% , and subsequent changes in copy number were normalized to this . The quantification of viral gene expression was carried out as described previously [18] . 2 . 4x104 cells were seeded in each well of a 4-well culture slide ( Falcon ) and cultured for 3 and 7 days . The cells were washed in PBS and fixed in 4% paraformaldehyde ( PFA ) in PBS for 30 min . at room temperature . The cells were permeabilised in PBS with 0 . 1% Triton X-100 ( Promega ) for 30 min . , then washed in PBS . The cells were blocked in 10% normal goat serum ( Cell Signaling Technology ) in PBS for 1 hour . The antibodies used were anti-Keratin10 antibody ( dilution 1:200 , Thermo Fisher Scientific ) , anti-p53 ( DO-1 ) antibody ( dilution 1:300 , Santa Cruz ) , anti-MCM antibody ( dilution 1:100 , Abcam ) an anti-mouse Alexa 594-conjugated antibody ( dilution 1:150 , Thermo Fisher Scientific ) . For p53 and MCM , the signal was amplified using a Tyramide Signal Amplification Kit ( Perkin-Elmer ) , according to the manufacturer's instructions . Finally , the cells were mounted in mounting medium ( Agar Scientific ) for visualization . The number of cells with positive signal were quantified using ilastik software . Proteins were extracted from cells using RIPA buffer and quantified using the BCA protein assay kit ( Pierce ) , before being separated on 4–12% gradient polyacrylamide-SDS-Tris-Tricine denaturing gel ( Invitrogen ) and transferred onto PVDF membranes ( Bio-Rad ) . After transfer , membranes were blocked for 1 hour at room temperature in 1% milk in PBS-T ( PBS , 0 . 1% tween20 ) . Blots were then incubated overnight at 4°C with the appropriate primary antibody diluted in 1% milk PBS-T . Primary antibodies used were anti-HPV16E6 ( 2E-3F8 , Euromedex ) , anti-HPV16E7 ( NM2 , Santa Cruz ) , anti-p21 ( EA10 , Abcam ) , anti-p53 ( DO-1 , Santa Cruz ) , anti-GAPDH ( Millipore ) , followed by the appropriate HRP-conjugated secondary antibody ( GE Healthcare ) , and detection using ECL , or ECL plus kits ( GE Healthcare ) or by the appropriate IRDye 800CW fluorescent secondary antibody ( Licor ) followed by detection using an Odissey imaging system ( Licor ) . Sub-confluent NIKS were collected and fixed for 10 min . in 4% PFA in PBS at a concentration of 1x106 cells/ml at room temperature . Immediately before sorting , the cells were passed through a 40μm cell strainer ( BD Biosciences ) . The cells were sorted on a Dako Cytomation MoFlo MLS high-speed cell sorter . DNAs were extracted from the sorted cells as described above . 5x104 cells were seeded in each well of a 12-well and transfected Cignal p53 Pathway Reporter Assay Kit ( Qiagen ) at the same day , according to the manufacturer's instructions . The cells were cultured for 3 or 7 days and collected . The activities of firefly and Renilla luciferases were measured by a FLUOstar Omega Microplate Reader ( BMG LABTECH ) using Dual-Luciferase Reporter Assay System ( Promega ) , according to the manufacturer's instructions . TDIG-labelled probes comprising the entire HPV11 or HPV16 genome were prepared , and Southern blot analyses were carried out using DIG High Prime DNA Labelling and Detection Starter Kit II ( Roche ) following the protocol provided by the manufacturer . Briefly , digested DNA was separated on a 0 . 7% agarose gel , soaked in 0 . 25 M HCl for 15 min , and alkaline transferred onto nylon membranes ( Boehringer Mannheim ) . The membranes were prehybridized in Hybrisol I ( Millipore ) for 1 h at 42°C . A DIG-labelled probe was applied for hybridization , and the hybridized DNA was visualized using the detection kit . DNA was mixed with cesium chloride ( CsCl ) , and the mixture was adjusted to a volume of 4 . 5 ml , and a the density of 1 . 753 g/ml ( i . e . corresponding to a refractive index of 1 . 404 ) . The DNA-CsCl solution was transferred to Beckman ultracentrifuge tubes , and samples were centrifuged at 30 , 000 rpm at 22°C for more than 48 h in a SW55 rotor . After centrifugation , the tube was inserted into a gradient collector , a hole was punctured at the bottom of the tube , and fractions of 5 drops each were collected in Eppendorf tubes ( up to 50 fractions ) . The DNA concentration of each fraction was measured using a spectrophotometer , and the refractive index measured using a refractometer , after which the fractions were slot blotted onto a positively charged nylon membrane . The wells of the slot blotter were washed with denaturation buffer ( 0 . 5 M NaOH , 0 . 5 M NaCl ) . The membrane was then air dried and UV cross-linked . The HPV genomes were detected using DIG-labelled probes ( see Southern blot analyses above ) .
In order to compare the specific requirements for HPV16 and HPV11 genome replication in ‘infected’ basal-like keratinocytes we used NIKS cells , which are an isogenic immortal keratinocyte cell line previously shown to recapitulate the full epidermal differentiation program in vitro and to support the full HPV life cycle [22–24] . In order to establish NIKS cells harboring low- and high-risk HPV genomes , HPV11 and HPV16 genomes were transfected into NIKS cells along with a plasmid encoding a blasticidin resistance gene . Following drug-selection with blasticidin S , cells containing the HPV genomes were expanded and maintained at sub-confluent conditions for 2 to 8 passages . Total DNA was extracted at each passage and treated with DpnI to remove the input genomes , and the levels of replicated viral genome were monitored by qPCR using GAPDH as a reference gene to calculate the HPV genome copy number per cell . As previously indicated [25–27] , the HPV11 genome copy number rapidly declined upon passage to typically reach less than 0 . 2 copies/cell by passage 8 ( Fig 1A ) . In contrast , HPV16 genomes , and indeed the genomes of other high-risk HPV types [23] , persisted at a uniform copy number throughout the experiment . Although the actual HPV genome number varied between experiments , and was different for each HPV type , the trend was similar between passages . In order to overcome these tissue culture issues , and to carry out our experiments in a more controlled manner , we decided to monitor the viral genome copy number variations within the same passage ( passage 2 ) , growing cells from sub-confluent to post-confluent conditions in a 7 day time-frame . Previous studies have indicated that epidermal keratinocytes cultured in vitro switch between two interconvertible growth modes depending on the local cell density: an expanding growth mode , characterized by and excess of proliferating cells , and a balanced growth mode in which proliferation is counterbalanced by differentiation [28] . Similarly , as can be seen in Fig 1B ( upper panels ) , NIKS cells grown in monolayer switch from a proliferative and undifferentiated ( expanding ) growth mode at sub-confluence ( Day 3 ) , in which they express the cell cycle progression marker MCM7 in the absence of the early keratinocyte differentiation marker keratin 10 ( K10 ) , to a differentiated and post-mitotic ( balanced ) growth mode at post-confluence ( Day 7 ) . Interestingly , when low passage NIKS harboring HPV11 and HPV 16 ( passage 2 post-transfection/selection ) were analyzed in a similar way , NIKS/HPV11 showed a very similar growth pattern to that of parental NIKS Fig 1B middle panels , Fig 1C and 1D ) , indicating that low-risk HPV fails to modulate keratinocyte homeostasis . In contrast however , while HPV16 did not significantly affect the growth mode of NIKS at sub-confluence , it conferred a potent proliferative capacity to post-confluent cells , which was accompanied by a loss of keratinocyte differentiation marker ( Fig 1B lower panels , Fig 1C and 1D ) . This indicates that HPV16 is able to overcome the normal contact inhibition signals promoting the maintenance of an expanding growth mode at high cell densities . We then went back to analysing the HPV genome copy number in low passage ( passage 2 ) HPV11/16 transfected NIKS , and found that both HPV11 and HPV16 have the same ability to maintain their genomes in sub-confluent keratinocytes ( Fig 1D; days 1–4 ) . At post-confluence ( Day 7 ) , however , HPV16 genome copy number undergoes an approximately 3-fold increase relative to day 4 , while HPV11 genome copy number declined significantly ( Fig 1E ) . When taken together , these data indicate that maintenance-replication of both low- and high-risk HPV types can be similarly efficient , but that HPV11 is unable to overcome keratinocyte differentiation at higher cell density , which compromises its replication ability . In all cases , viral genomes were episomal , as experiments were carried out shortly after transfecting circularized virus genome ( confirmed by Southern blotting at passage 2 ( [18] , S1 Fig ) ) . As predicted from this , transcript mapping studies revealed a characteristic ‘episomal’ pattern of viral gene expression in both HPV16 and HPV11-NIKS , with the majority of transcripts spanning E6 and E7 , and terminating downstream of the E5 ORF[18] . As NIKS cells underwent 2 or 3 doublings during the first 4 days of our experiments , our data suggest that HPV11 does not differ from HPV16 in its overall replication competence , and can replicate along with cellular DNA in proliferating keratinocytes . Importantly , our results also suggest a similar ability of both genomes to partition accurately during cell division . To investigate this further , a hEF1-HTLV promoter/GFP-Blastocidin reporter cassette was inserted into the late gene region of HPV11 and HPV16 ( Fig 2A ) in order to directly visualize cells containing HPV genomes following transfection , an approach similar to the that used previously [29 , 30] . The GFP-Blastocidin fusion gene also allowed the use of drug-selection to enrich for cells harbouring recombinant viral episomes . This is particularly important for low-risk HPV types such as HPV11 , which does not obviously modify the cell phenotype , and has a tendency to decline in copy number over passage ( Fig 1A and 1B ) . To examine whether the GFP intensity correlated with HPV copy number , NIKS cell populations harboring either the HPV11 or HPV16 reporter genomes ( i . e . NIKS/HPV11GFPbsr or NIKS/HPV16GFPbsr , respectively ) , were separated by fluorescent activated cell sorting ( FACS ) , into 4 groups according to the intensity of the GFP signal . Total DNA was subsequently extracted from each group , and HPV genome copy number was quantified by qPCR . In both the NIKS/HPV11GFPbsr and NIKS/HPV16GFPbsr populations , the intensity of the GFP signal correlated well with HPV genome copy number ( Fig 2B ) . The NIKS/HPV11GFPbsr and NIKS/HPV16GFPbsr populations were then seeded along with parental NIKS at a ratio of 1:50 . Colonies were allowed to expand in the dish for 4 days without drug selection , before being fixed and examined by fluorescent microscopy . Single GFP-positive cells were readily apparent immediately after plating , with larger GFP-positive colonies developing by day 4 . Although there was great diversity in the GFP signal , suggesting great diversity in viral copy number , in individual colonies , individual cells within the same colony always showed uniform levels of fluorescence , irrespective of whether they contained HPV16 or HPV11 genomes ( Fig 2C and 2D ) . In fact , when normalized to the mean GFP intensity of each colony , the variation in GFP intensity amongst individual cells in both NIKS/HPV11GFPbsr and NIKS/HPV16GFPbsr colonies was broadly similar ( Fig 2E ) . When taken together , these results suggest that the HPV11 genome is partitioned with similar dynamics to that of HPV16 in proliferating keratinocytes . Following papillomavirus infection of basal keratinocytes , the productive phase of the virus life cycle is triggered by host keratinocyte differentiation , with viral genome replication and amplification rising gradually as cells enter the suprabasal layers , followed eventually by the expression of viral capsid proteins and virion assembly in the uppermost layers of the epithelium [31 , 32] . The fact that HPV11 is unable to maintain or amplify its genome during the commitment to differentiation in our experimental setting ( Fig 1B and 1E ) , indicates that the modulation of keratinocyte differentiation is crucial for genome maintenance , and that differentiation-commitment can negatively influence the HPV replication machinery . During productive infection , the viral DNA helicase E1 is essential for genome amplification in the mid epithelial layers [7] , whereas the importance of E1 for maintenance-replication in basal keratinocytes is still controversial [17 , 33] . This data suggests that HPV might follow a ‘two-phase’ replication mode with respect to E1-dependency [34] . To establish the contribution of E1 to HPV16/11 maintenance-replication , as well as for HPV16 genome amplification in differentiating keratinocytes , E1 deficient mutants were generated in the context of the full length HPV11 and HPV16 genomes ( HPV16E1def & HPV11E1def ) , and stable NIKS populations were established as described above . At the time of seeding for the experiment , ( passage 2 post-transfection/selection ) viral genomes were predominantly episomal as determined by Southern blotting ( S1 Fig ) . The viral genome copy number was monitored to see its variations within the same passage in the same way as shown in Fig 1E ) . As can be seen in Fig 3A , both HPV11E1def and HPV16E1def genomes were maintained at similar levels as the WT genomes until day 4 during the ‘maintenance-replication’ phase , but declined dramatically post-confluence ( Day 7 ) . These data support the hypothesis that HPV16 at least , can switch its replication mode from E1-independent to E1-dependent replication as cell density increases , and that HPV16 does not simply hamper transit to a balanced growth mode at high cell density and prolong the expanding growth mode as well as the maintenance replication of virus genome . In addition , these results show that both HPV11 and HPV16 can replicate without the E1 viral helicase in expanding NIKS cells , and that the inability of low-risk HPV to overcome differentiation signals triggered by high cell densities has dramatic consequences on their replication capacities . To support these conclusions , a second set of E1 defective mutants were made in the context of the HPV16 and 11 genomes , this time to disrupt the integrity of the conserved E1 helicase domain that is essential for E1’s replicative function ( HPV11 E1K484A and HPV16 E1K483A ) [35] . The results obtained with these mutants were indistinguishable from those seen with HPV11E1def and HPV16E1def mutants ( Fig 3B ) . To determine the two modes of HPV DNA replication in different cell density , we investigated how many times HPV DNAs replicate in a single S phase by labelling cells with BrdU [26 , 27 , 34] . As Hoffman et al . reported , DNAs that have replicated once will incorporate BrdU on a single DNA strand ( heavy-light , HL ) . Those that have replicated more than once will incorporate BrdU on both strands ( heavy-heavy , HH ) , and those that didn’t replicate will have no BrdU in their DNA ( light-ligh , LL ) . These three DNA species were separated on a cesium chloride gradient according to their different densities , with LL DNA at 1 . 709 g/ml ( Refractive Index ( RI ) = 1 . 400 ) , and HL DNA at 1 . 753 g/ml ( RI = 1 . 404 ) and HH DNA at 1 . 795 g/ml ( RI = 1 . 708 ) ( Fig 4A and 4B ) . Both HPV16WT and HPV11WT genomes were replicated once in a single S-phase , with the emergence of HL viral DNA , but not HH viral DNA at day 3 during the E1-independent maintenance replication phase ( Fig 4B ) . In contrast , the HPV16 WT genome was replicated more than once at day 7 , during the E1-dependent replication phase , with the emergence of HH viral DNA at day 7 ( Fig 4B ) . These results suggest that the mode of HPV genome replication switches from a synchronous mode ( i . e . once in a single S-phase ) , to amplification mode ( i . e . multiple times in a single S-phase ) in a cell-density related manner . During the establishment of infection , it is thought that one single viral particle may efficiently establish the full viral intracellular life cycle [36] , suggesting that the papillomavirus DNA copy number is amplified to a certain level ( 50 to 400 ) per cell immediately after infection . Prior to P1 , at 3 days post transfection , both HPV16WT and HPV11WT genomes were replicated once in a single S-phase , with the emergence of HL viral DNA but not HH , with only a small proportion of total HPV DNA contributing to replication ( Fig 4C ) . At this point the majority of the viral DNA extracted from the NIKS cells is DpnI sensitive ( 91 . 2% ( 1 . 6 standard deviation ) ) . Interestingly , both the HPV11 and 16 E2 defective genomes were not successfully replicated , with no HL and HH viral DNA produced ( Fig 4C ) . These results suggest that in our experiments during the establishment phase , HPV genomes are replicated once per S-phase in an E2-dependent manner , after the introduction of multiple copies of viral DNA by transfection . We suspect that the initial amplification phase that occurs after infection by a single virus , and which is thought to be E1-depenent , is bypassed as a result of the introduction of a high copy number of episomal viral DNA by transfection . Although E1 is dispensable for the maintenance of either HPV11 or 16 genomes in dividing NIKS cells , it is clearly important for the HPV16 genome copy number elevation in post-confluent keratinocytes , with E1 disruption leading to HPV16 genome loss as cells transition to post-confluence . The different abilities of HPV11 and HPV16 to replicate their genomes in keratinocytes at high cell density prompted us to investigate the interplay between the viral replicative machinery and the HPV accessory proteins E6 and E7 . Recent studies indicate that HPV16 E6 has a predominant role , over E7 , in skewing basal keratinocyte cell fate towards proliferation by inhibiting differentiation in a p53- and Notch-dependent manner [20 , 37] . In order to assess the potential roles of E6 and E7 functions in creating a replication-competent environment for the virus , we tested whether high-risk HPV E6/E7 proteins might complement the replicative deficiency of HPV11 at high cell densities . To do this , we established NIKS cells stably expressing HPV16 E6 and/or E7 by retroviral transduction , and the expression of E6 and E7 were confirmed by western blot analysis ( S2A Fig ) . The HPV11 genome was transfected into these cell lines and NIKS transduced with empty vector ( LXSN ) as a control , and , following drug selection , cells were seeded at passage 2 or 3 post-transfection for the evaluation of the viral genome copy number . Strikingly , the expression of HPV16 E6 and/or E7 allowed replication of the wild-type HPV11 genome post-confluence ( Fig 5A left panel ) , which was associated with an inhibition of differentiation and p53 expression when HPV16 E6 was expressed either alone or in combination with E7 , as well as a potent increase in the cell-cycle activity post-confluence ( Fig 5B and 5C ) . It is interesting to note however , that unlike E6 , HPV16 E7 exerts its function by uncoupling cell-cycle entry from differentiation as previously reported [38] . In order to confirm that the expression of the viral helicase E1 was indeed necessary to mediate post-confluence HPV11 genome maintenance in the presence of HPV16 E6 and E7 , we transfected the E1-defective HPV11 genome in NIKS expressing HPV16 E6 and/or E7 and the viral genome copy number was monitored across 7 days . The results of this assay are shown in Fig 5A ( right panel ) , and as can be seen , the lack of a functional E1 gene product impaired the ability of HPV16 accessory proteins to rescue HPV11 genome replication post-confluence . Taken together , these data indicate that there is an interplay between the replication and accessory proteins of HPV , and that disruption of this interplay affects genome maintenance as cells transit towards a balanced growth mode and E1-dependent replication is triggered at high cell density . Our data suggest that maintenance replication in dividing basal cells prior to this may be E1-independent . Previous studies indicate that the ability of HPV16 E6 to target p53 and PDZ domain-containing proteins for degradation is required for the successful completion of the viral life cycle [16 , 39–41] . Our result showing that 16E6 is able to facilitate HPV11 replication post-confluence , prompted us to investigate what functions of the 16E6 protein are essential to mediate this effect . To do this , we established two NIKS cell lines by retroviral transduction , to express either the HPV16 E6 SAT mutant ( NIKS/LXSN-16E6SAT ) , which is unable to bind and target p53 for degradation , or the PDZ-binding defective mutant ( NIKS/LXSN-16E6ΔPBM ) , which lacks the C-terminal PDZ-binding motif ( PMB ) responsible for the association with PDZ proteins . The wild-type HPV11 genome was transfected into these cell lines , and genome replication was assessed in sub- and post-confluent cells . As expected , neither of the 16E6 mutants affected HPV11 genome maintenance sub-confluence ( Fig 6A ) , with the loss of p53-degredation capability alone , affecting E6’s ability to support viral genome replication post-confluence ( Fig 6A ) . In order to further examine the importance of p53-loss on genome maintenance as cells become confluent , the expression of p53 was transiently ablated by RNA interference ( siRNA ) in NIKS cells harboring wild-type HPV11 genomes , and the effects on genome maintenance and replication are shown in Fig 6B . As can be seen , the levels of p53 were efficiently reduced both in sub- and post-confluent cells ( Fig 6B lower panel ) , and consistent with this , a higher level of maintenance replication was seen in sub-confluent cells from days 2 to 4 ( Fig 6B upper panel ) . Intriguingly , the ablation of p53 had its most dramatic effects post-confluence , where cells transfected with the control non-targeting ( NT ) siRNA showed the most dramatic decline in HPV11 copy number ( Fig 6B upper panel ) . These results suggest that p53 binding , but not the association with PDZ domain-containing proteins , is necessary and sufficient to allow HPV genome maintenance in confluent keratinocytes . In order to confirm that a similar role for E6 during HPV16 replication , we engineered the wild-type HPV16 genome to express either an E6 protein defective in its PDZ-binding activity ( E6ΔPBM ) , or the E6SAT mutant that is unable to interact and degrade p53 . Mutant genomes were transfected into NIKS , and after selection , low passage NIKS transfectants were seeded and viral genome replication assessed . As can be seen in Fig 4C , both HPV16E6ΔPBM and HPV16E6SAT mutant genomes were replicated until day 4 , but the copy number of HPV16E6SAT mutant genome dramatically declined post confluence at day 7 ( Fig 6C , right ) . In contrast , the HPV16E6ΔPDZ mutant genome was amplified as efficiently as the wild-type genome ( Fig 6C , left ) . These results suggest that E6-mediated p53 inactivation contributes to genome maintenance as cells reach confluence , and point perhaps to a more important role in this than for E7 . Previous studies have shown E6 and E7 , but not E4 and E5 , to be required for the establishment and maintenance of HPV genomes in keratinocytes in a tissue culture model [18 , 25 , 42–44] . Our results suggest that E1 , and some of 16E6 functions ( degradation of p53 and PDZ-proteins ) are not required for replication in growing keratinocytes . To address this further , E6 and E7 deficient mutants in the context of HPV11/16 genomes , were transfected into these cell lines , and the viral genome copy number monitored over 7 days . As can be seen in S3A and S3B Fig , both E6 and E7 defective genomes of HPV11 and 16 were established and maintained at levels similar to those of the WT genomes until day 4 during the ‘maintenance-replication’ phase , but declined dramatically post-confluence ( Day 7 ) . This data supports the idea that E6 and E7 , the viral accessory genes , are required only for E1-dependent replication but not for E1-independent replication . Although most of the HPV11 and HPV16 mutant genomes replicated successfully in sub-confluent NIKS cells ( with the exception of the E2 knock out ) , we have not been able to establish long term cultures of NIKS cell lines harboring any of these HPV genomes , which is consistent with our earlier studies on cell passage , as well as with previous reports [16 , 40] . As HPV16SAT and HPV16E1def genome copy numbers also declined during passage , we conclude that E1 and the inhibition of p53 are required for long-term tissue culture maintenance of HPV16 genomes ( S4A Fig ) . Interestingly , the ability of 16E6 to target PDZ proteins , as well as the presence of E7 , was also required for the genome maintenance during cell passage in this system ( S4A Fig ) [16] . Previous reports suggest that the stress induced by cell passaging triggers keratinocyte differentiation via activation of the Notch pathway [45] , indicating an overlapping mechanism of viral genome loss during passaging and at high cell densities , where E1-dependent replication could be triggered by the induction of keratinocyte differentiation . The fact that HPV11 WT genomes were not maintained over passage also in the presence of HPV16E6 and E7 ( S4B Fig ) , suggests that the requirements for copy number maintenance during passage are more stringent than those that affect cells at and around the point of confluence . Our focus here is on the latter , given that this is the more physiologically relevant situation . Previous studies have shown that HPV11 can inhibit p53 transactivation activity in an E6AP-independent manner through association with the p300 acetyl-transferase/transcriptional co-activator [46 , 47] , but have not yet revealed a p53 degradation capability comparable to that of HPV16 . To understand HPV maintenance regulation further , viral transcripts from NIKS/HPV11 and NIKS/HPV16 cells grown to sub-confluence ( Day 3 ) or post-confluence ( Day 7 ) , were examined following cDNA synthesis and qPCR . Interestingly , the level of the early promoter activity of HPV11 ( p90 ) was very low when compared with HPV16 ( p97 ) especially at day 7 ( typically around 200 and 10500 copies per 1000 GAPDH mRNA copies respectively across repeat experiments ) . The expression level of transgenes from LXSN retrovirus vector is between 6000 and 12000 copies per 1000 GAPDH , which is broadly equivalent to the transcriptional activity of HPV16 early promoter . This result suggests that NIKS cells grown in monolayer may not support the expression of HPV11 E6 and E7 at levels high enough to allow viral genome replication post-confluence . To evaluate whether HPV11 E6 and E7 could have a similar effect to their HPV16 counterparts in mediating the replication of HPV11 genome post-confluence , we established NIKS cells stably expressing HPV11 E6 ( NIKS/LXSN-11E6 ) and/or HPV11 E7 ( NIKS/LXSN-11E6E7 and NIKS/LXSN-11E7 ) by retroviral transduction . After confirming the expression of HPV11 E6 and E7 in these cell lines by RT-PCR ( S2B Fig ) , control ( LXSN ) and HPV11 E6 and/or E7-expressing NIKS were further transfected with the HPV11 genome , and the levels of viral replication were assessed by qPCR as described above . The HPV11 genome was again well maintained until day 4 in control NIKS , as well as in the presence of 11E6 and/or 11E7 during this ‘maintenance replication’ phase ( Fig 7A left panel ) . As seen with HPV16 E6 and E7 however , the HPV 11 E6 and E7 proteins could also facilitate the replication , albeit not the amplification , of the HPV11 genome post-confluence . Furthermore , the lack of a functional HPV11 E1 gene impaired this post confluence maintenance ability , which appears to be mediated by a switch to E1-dependent replication ( Fig 7A left panel ) . These results suggest that HPV11 E6 and E7 are similar to the HPV16 proteins in supporting HPV11 genome replication in post-confluence cells providing that they are present at sufficiently high levels . Having shown that HPV11 E6 and E7 are functionally equivalent to the high-risk HPV accessory proteins in supporting viral genome maintenance , we were interested in understanding how HPV11 E6 and E7 may achieve this . To do this , freshly transfected low passage control NIKS/LXSN harboring HPV11 genomes , and NIKS/HPV11 expressing HPV11 E6 and/or E7 were seeded , and the levels of MCM7 , p53 and keratin 10 were assessed by immunofluorescence at sub- and post-confluence , similar to the experiments carried out using the HPV16 E6 and E7 proteins earlier ( Fig 5 ) . As shown in Fig 7B and 7C , and consistent with previous reports [48] , at sub-confluence HPV11 E6 and E7 failed to significantly increase the levels of cell-cycle entry of NIKS or to promote the degradation p53 . Surprisingly however , in post-confluent cells , HPV11 E6 and E7 showed very similar effects to those seen earlier with HPV16 E6 and E7 ( Fig 7B and Fig 5 ) ; with HPV11 E6 leading to a loss of p53 , an increase in the number of cycling cells , and an inhibition of commitment to differentiation at high cell densities . As mentioned previously , the level of the early promoter activity of HPV11 ( p90 ) was not sufficient to show these phenotypes in this experimental model ( Figs 5 and 7B and S5 Fig ) . Like HPV16 E7 , HPV11 E7 was found to uncouple differentiation from proliferation ( Fig 7B and 7C ) . To our knowledge , this is the first demonstration that both HPV11 and HPV16 E6 can similarly reduce p53 levels in the cell , albeit for 11E6 , in a cell density-dependent manner . As a result of this , we were interested to establish whether the HPV11 E6-mediated loss of p53 in post-confluent cells occurs through a mechanism similar to that used by HPV16 E6 . First we confirmed the immunofluorescence data by monitoring the levels of p53 in the presence of HPV11 E6 and/or E7 at sub- and post-confluence by Western blotting analysis , using HPV16 E6 as a positive control . As can be seen in Fig 8A , the expression of HPV16 E6 in NIKS cells led to a potent degradation of p53 at both time points , which also resulted in an inhibition of expression of the p53 target gene p21 . Conversely , HPV11 E6 , when expressed either alone or in combination with HPV11 E7 , led to a reduction of p53 and p21 levels , but in this case , p53 loss was restricted to the post-confluent cell populations . As expected , HPV11 E7 had no influence on p53 levels in the rich culture medium conditions used at either day 3 or 7 . The cell density-dependent inhibition of p53 transcriptional activity that is suggested from the p21 western blots described above , was further confirmed using a reporter gene assay , in which luciferase was expressed from a plasmid-containing tandem repeats of p53 responsive DNA elements ( Fig 8B ) . In support of this , HPV11 E6 strongly affected p53 transcriptional activity only at high cell densities , whereas HPV16 E6 did so irrespective of the cell confluence status . Since HPV16 degrades p53 in an E6AP- and proteasome-dependent manner [49] , we tested whether HPV11 E6 could also use this mechanism of p53 degradation in post-confluent cells . To do this , control ( LXSN ) NIKS cells or NIKS expressing HPV11 E6 alone or in combination with E7 were grown post-confluence and treated with the proteasome inhibitor MG-132 for an additional 3 hours prior cells were harvested and the levels of p53 monitored by Western blotting analysis . As can be seen in Fig 8C , our proteasome inhibition had no effect on p53 levels in control cells . In post-confluent NIKS expressing HPV11 E6 and E6/E7 the levels of p53 were strongly reduced however , with proteasomal inhibition leading to a potent up-regulation of the p53 . Interestingly the 11E6 mutant ( W133R ) , which cannot bind to E6AP [25 , 50] , still reduced p53 level at post-confluence ( S6 Fig ) , suggesting that 11E6 mediates p53 degradation in an E6AP-independent manner . This conclusion is supported by a complimentary experiment showing that 11E6 is also able to reduce p53 abundance in the presence of shRNA against E6AP at post-confluence ( S6 Fig ) . As expected from these results , HPV11 E6 conferred a potent proliferative capacity , especially on post-confluent cells , which was similar to what was seen with HPV16 E6 ( Fig 8D ) . From this we conclude that HPV11 E6 is able to exploit the proteasomal pathway in order to target p53 for degradation , and suggest that this is regulated in the epithelial basal layer to support virus genome maintenance in response to increasing cell density and the eventual need to differentiate and enter the virus productive cycle .
The study of papillomaviruses has for the most part , focused on the association of high-risk Alpha papillomavirus types with anogenital and oropharyngeal cancers , and as a result of this , our knowledge of these types is more complete than our understanding of the low-risk Alpha papillomaviruses . The functional differences between low- and high-risk gene products , especially those encoded by the E6 and E7 accessory genes , provides a molecular explanation for the cancer-causing properties of the high-risk HPV types ( see [6 , 11 , 39] ) . The low-risk types have however been as evolutionarily successful as the high-risk HPVs , and with respect to the molecular evolution of alpha HPVs and niche adaptation , their life cycle strategies centre around a basic set of molecular functions that are required by all alpha HPVs to complete their productive life cycle , with high-risk types having evolved additional functions which confer their oncogenic properties . The similarities , as well as differences between high- and low-risk papillomaviruses , particularly in the context of the virus life cycle , have not yet been elucidated in any detail . In this study , we have compared the replication capabilities of the two most representative members of the high-risk ( HPV16 ) and low-risk ( HPV11 ) alpha papillomavirus groups in a common keratinocyte background , and have analyzed the fluctuation in genome copy number over sequential passage , as well as day by day within a single passage . In contrast to HPV16 , HPV11 was poorly maintained over passage , which is in agreement with previous reports [25 , 51] . To our surprise however , both the HPV16 and HPV11 genomes replicated similarly in proliferating sub-confluent keratinocytes within the same passage , with the tendency towards keratinocyte differentiation as cell density increases , representing a crucial regulatory point that HPVs must control if their genomes are to be maintained long term . This modulation reflects a requirement for HPVs to subtly disturb normal epithelial homeostasis to favour retention of the infected cell in the basal layer [20] . At the point where keratinocytes transit to the balanced growth mode as cell density increases , the HPV replication mode appeared to switch from being an E1-independent/E2-dependent synchronised mode to an E1-dependent unsynchronised mode . At the same time , keratinocyte differentiation was suppressed by E6 , with cell cycle re-entry ( for genome amplification ) being mediated by E6 and E7 ( Figs 3C and 5B ) . In contrast to HPV16 , where viral gene expression and the phenotypic consequences for the cell can be understood , for HPV11 only low levels of viral gene expression were apparent , which may cause low level modulation of p53 level ( Fig 6B , right panel ) , but was not sufficient to show inhibition of differentiation and promotion of cell cycle at post-confluence ( Figs 5B and 7B ) . When the HPV11 E6 and E7 proteins were expressed exogenously from a retroviral vector however , they had very similar effects on the cell as seen with 16 E6 and E7 , with E6 promoting the proteasome-mediated degradation of p53 and inhibiting keratinocyte differentiation , which is thought to take place in the basal and/or parabasal layers , and E7 uncoupling cell-cycle entry from differentiation , which is thought to take place in the parabasal and upper layers . Of some importance however , was our observation that the HPV11 E6 protein was able to promote the degradation of p53 in a cell density-dependent manner via E6AP independent manner , which suggests that the mechanism of p53 regulation by low-risk HPV types is more subtle than that of the high-risk types . It is interesting to note however , that in both HPV11 and HPV16 , E1 function as well as p53 inhibition were dispensable for viral genome replication up until the point that cells were triggered towards differentiation at confluence . These two phases in the virus replication cycle are intimately linked to the biology of the differentiating keratinocytes infected by the virus . The expanding and the balanced growth modes observed in keratinocytes grown in monolayer have been shown to broadly recapitulate the growth dynamics in epithelial tissues in vivo [28] . During the expanding growth mode in sub-confluent cells , which is suggestive of a wound-healing environment in the epithelium [28 , 52] , both HPV16 and HPV11 genomes are replicated and maintained in a E6/E7- and E1-independent manner . In contrast , when a balanced growth mode was triggered , which resembles the differentiation dynamics regulating stratified epithelial homeostasis [28 , 53] ) , the HPV replication mode switched towards E1-dependency and required the functions of the accessory genes , E6 and E7 . It is reasonable to speculate that a balanced growth mode is followed in at least a proportion of basal keratinocytes , since the commitment to differentiation has been shown to occur already in the basal layer [20 , 54] . This suggests that an E1-independent replication mode might be used by HPVs to promote the long-term genome maintenance in the basal layer of the epithelium during productive infection , as well during subclinical or latent infections that may be subject to immune control . These are important considerations when evaluating possible targets for therapy , with E1 inhibitors having a role primarily as viral genome amplification occurs during epithelial differentiation , rather than ubiquitously at all stages of the virus life cycle . Interestingly , a well-studied two-phase replication strategy is also used by Epstein-Barr virus ( EBV ) during its latent and lytic phases . In this case , the EBV genome is replicated once per S phase in the latent phase of infection [55 , 56] , and at this point EBV expresses only a few viral genes and employs the host-encoded replication licensing proteins , MCMs and ORC , for replication , a process which is facilitated by the viral protein EBNA-1 [57] . Interestingly HPV E2 and EBNA-1 shares structural and functional similarities , even though the sequence are not conserved [58–61] . Recently , the interaction between HPV31 E2 and ORC2 has been reported , although its contribution to viral genome replication is unclear [62] . Our data indicate that HPV may also have a latent replication phase , in which the viral genome is replicated once per S phase mediated by E2 ( Fig 4 ) . It is currently unknown whether and how MCMs and ORCs are involved in HPV DNA replication , but we speculate that the E1-independent/E2-dependent maintenance replication employs the cellular replication machinery to support viral genome maintenance under S phase control . In addition , E1- and E2-independent cis-replicating elements may also reside outside the LCR and possibly in the late region ( L2-L1 open reading frames [ORFs] ) of the HPV16 genome [63] . Our studies also suggest functional similarities in the way that the HPV E6 and E7 proteins contribute to the phenotype of the infected basal cell , by modifying the cellular environment to facilitate genome replication . E6 and E7 impact on a range of biological events , including cell survival , transcription/translation , host cell differentiation , growth factor dependence , DNA damage responses , and cell cycle progression [11 , 12] . In this study , we directly compared the early stages of the high- and low-risk HPV life cycles in a common keratinocyte background . It is clear from this that E6 plays a critical role in early differentiating keratinocytes , as the HPV16 E6SAT mutant genome , which has intact E7 but is incompetent for p53 degradation , failed to support E1-dependent replication in differentiating cells . E7 undoubtably has an important role during the later stages of differentiation , although this was not modelled as part of this study . Our data also link HPV genome replication to the inhibition of Notch signalling [20] . The E6 proteins of both high and low-risk alpha papillomaviruses have evolved to stimulate the degradation of p53 ( Fig 6A ) , albeit with different mechanisms ( especially in E6AP-dependency ) , dynamics and effects expected on Notch signalling ( see also [20 , 37] ) . Interestingly , E6 proteins expressed by cutaneous papillomaviruses belonging to the Beta , Delta and Mu genera associate with MAML1 , a Notch transcriptional co-activator , in order to repress Notch signal transduction [64–66] . Indeed , we suspect that use of these different regulatory pathways may reflect the different roles of p53 in regulating differentiation according to epithelial sites , and the adaptation of HPVs to different epithelial niches where the role of p53 is more or less important . In fact , p53 levels in the basal and parabasal layers of normal ectocervical epithelium are high when compared to normal dermal epithelium [20] , which suggests that this epithelial site is in fact normally regulated by p53 , and that p53 may have a fundamental role in normal homeostatic control at this site [20] . E6 is also expected to play a role in countering p53-dependent growth arrest and/or apoptosis of course , especially during E1-dependent genome amplification , where DNA damage sensors ATR or ATM DDR [67 , 68] are triggered . When taken together , our data suggest that HPV 11 and HPV 16 , which are both Alphapapillomavirus , but which are categorized as low or high risk , make use of similar strategies for viral gene replication . During maintenance replication , which occurs in cycling cells in the absence of differentiation signals , the requirement for viral factors appears minimal , with E1-independent/E2-dependent replication occurring in the absence of a requirement for E6-mediated p53 degradation . Once cells reach confluence , they commit to differentiation as a result of cellular contact inhibition . In this environment , the HPV E6/E7 accessory genes modulate the cellular environment to facilitate E1-dependent replication by inhibiting differentiation as a result of E6-mediated p53 degradation , which is a characteristic of both the high and low-risk Alpha HPV E6 proteins . A clear role for E7 in driving cell cycle re-entry for productive genome replication is apparent across different HPV types . A role in evasion of the innate immune response and in regulating virus tropism are other important functions that we expect to be mediated primarily by E6 and E7 . | Human papillomavirus research has been prioritised towards understanding the progression of high-risk HPV infections to malignant cancer , rather than the regulation of the HPV productive cycle . Low-risk HPV types are often considered as a ‘less effective’ version of their high-risk counterparts . To date , they have been inadequately investigated and have generally served as reference viruses that lack oncogenic HPV functions . Here we carried out a comparative analysis using the most prominent high-risk ( HPV16 ) and low-risk ( HPV11 ) HPV papillomaviruses , focusing on viral genome replication and maintenance in the epithelial basal layer . During the maintenance phase , the E1 viral helicase of both HPV types are dispensable . Neither E6 nor E7 proteins are required for the maintenance replication . Once cells become confluent , replication switches from an E1-independent to an E1-dependent mode and requires p53 degradation by E6 . Interestingly , the characteristic ability of high-risk E6 to degrade p53 and inhibit keratinocyte differentiation was also seen with low-risk HPV E6 , but in this case was regulated by cell density and the extent of viral gene expression . Despite the differences in the pathogenicity of low- and high-risk HPV types , they appear to follow similar molecular strategies to complete viral life cycle . | [
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] | 2019 | Roles for E1-independent replication and E6-mediated p53 degradation during low-risk and high-risk human papillomavirus genome maintenance |
Lifespan is influenced by a large number of conserved proteins and gene-regulatory pathways . Here , we introduce a strategy for systematically finding such longevity factors in Saccharomyces cerevisiae and scoring the genetic interactions ( epistasis ) among these factors . Specifically , we developed an automated competition-based assay for chronological lifespan , defined as stationary-phase survival of yeast populations , and used it to phenotype over 5 , 600 single- or double-gene knockouts at unprecedented quantitative resolution . We found that 14% of the viable yeast mutant strains were affected in their stationary-phase survival; the extent of true-positive chronological lifespan factors was estimated by accounting for the effects of culture aeration and adaptive regrowth . We show that lifespan extension by dietary restriction depends on the Swr1 histone-exchange complex and that a functional link between autophagy and the lipid-homeostasis factor Arv1 has an impact on cellular lifespan . Importantly , we describe the first genetic interaction network based on aging phenotypes , which successfully recapitulated the core-autophagy machinery and confirmed a role of the human tumor suppressor PTEN homologue in yeast lifespan and phosphatidylinositol phosphate metabolism . Our quantitative analysis of longevity factors and their genetic interactions provides insights into the gene-network interactions of aging cells .
Lifespan is a complex trait with clear genetic underpinnings [1] . Mutations in the insulin/IGF-1 receptor gene daf-2 increase the lifespan of Caenorhabditis elegans [2] and Drosophila melanogaster [3] , while mice lacking the insulin receptor in adipose tissue live longer as well [4] . Studies on the budding yeast Saccharomyces cerevisiae have also been pivotal to discover conserved longevity mechanisms [5]–[7] . Aging of dividing cells has been modeled in yeast by its replicative lifespan , defined as the number of daughter cells produced before death [8] . In addition , chronological lifespan ( CLS ) , the duration of yeast survival in stationary phase , provides a model of aging of non-dividing quiescent cells [9] , [10] . Evidence to date indicates that both forms of yeast aging are modulated by the Tor/Sch9 [11] , [12] and Ras/cAMP/PKA [13] pathways . Likewise , the downstream activity of proteins with roles in autophagy [14] , redox balance [15] , [16] , and mitochondrial function [17] , [18] influence lifespan from yeast to mammals . Moreover , dietary restriction delays both replicative and chronological aging in yeast; this non-genetic intervention has been linked to the down-regulation of Tor and Sch9 in response to nutrients [12] , [19] . Current phenotyping strategies allow the characterization of yeast CLS in a high-throughput manner . Survival as a function of time in stationary phase has been estimated by monitoring the outgrowth of either individual-strain cultures in parallel [19] , [20] or large pools of gene-deletion strains coupled to microarray [21] , [22] or deep-sequencing analysis [23] . Such large-scale approaches have recently opened the door to the first genome-wide screens for lifespan factors [19] , [21]–[23] . With these catalogs in hand , the next great challenge is to describe how different genetic factors are integrated to determine lifespan in yeast . Quantitative genetic interaction ( GI ) analysis has great potential to shed light on the genetic architecture of the lifespan phenotype and the mechanisms that underlie the aging process . By depicting the ways in which individual mutations affect each other's phenotypic consequences , GIs inform whether genes act in common or independent pathways [24] . A number of breakthrough studies based on the large-scale GI analysis of cell-proliferation phenotypes ( mainly colony growth ) have provided important insights into the genetic organization of the living cell [25]–[31] . Multi-conditional and multi-phenotype GI maps have further helped to elucidate cellular pathways and responses to stimuli [32] , [33] and , notably , GI networks retain conserved features and overall levels of pathway crosstalk across large evolutionary distances [34] . Yet , systematic double-mutant analyses of lifespan phenotypes and their ensuing GI networks are still missing . In this study , we sought to investigate in a quantitative manner the genes and gene-network wiring that underlies yeast CLS . By using a novel profiling tool based on the competitive survival of yeast populations on stationary phase , we generated a high-resolution CLS catalogue of 3 , 878 single-knockout mutations and constructed the first systematic lifespan GI map derived from over 1 , 800 double-knockout phenotypes . In doing so , we revealed novel genetic aging factors and shed light on functional associations within and between lifespan-determining pathways .
To gain deeper knowledge on which genes shape the lifespan phenotype and—more importantly—on how these genes work together , we developed a sensitive parallel technique for quantifying the CLS of yeast single- or double-gene deletions ( Figure 1 ) . We used fluorescent-protein labeling to track the apparent death rates of mutant and wild-type ( WT ) reference populations in stationary phase . Specifically , each mixed population suspended in stationary phase was automatically sampled at regular age intervals by inoculating to fresh medium , where competition assays were used to estimate the abundance of viable mutants relative to the WT at the time of sampling . An apparent relative lifespan , L , of each mutant strain was defined from the change in relative viability as a function of time in stationary phase . Importantly , we accounted for differences in growth rates during outgrowth ( see model in Materials and Methods ) . By using fully-prototrophic strains in all screens , we avoided the strong effects of amino acid auxotrophy on stationary-phase survival [23] , [35] . In addition , we buffered the aging medium to identify the lifespan effects of genes and their interactions independently of acetic acid toxicity [36] . We evaluated the performance of our CLS method in several different ways . First , we estimated the technical and biological replicability of our L measurements . We observed that the results of two independent large-scale ( 720 deletion strains ) experiments were highly correlated ( Pearson's r = 0 . 88; Figure S1 ) . Dye-swap experiments in which 235 deletion strains were independently tagged with both RFP and CFP fluorescent proteins also showed a good correlation ( Pearson's r = 0 . 79; Figure S1 ) . In addition , we compared results of our competition-assay with an alternative high-throughput approach that is based on outgrowing individually-cultured strains at different age intervals [20] ( Figure S2 ) . Competition-based monitoring of CLS resulted in considerably less variability in the measured phenotypes than experiments in which strains were individually aged and outgrown , underscoring the effectiveness of our experimental approach in terms of precision . Given that strain-strain interactions could obscure the results of a CLS screen based on competitive survival , we also evaluated the effects of using different reference strains . The relative lifespans of a set of 24 knockouts were mostly consistent across experiments in which different combinations of strains were used ( p<0 . 05 , Spearman's correlation ) , indicating that the survival ranks were typically insensitive to the choice of reference strain ( Figure S3 ) . Taken together , these results indicate that our strategy possesses accuracy and robustness that enable the large-scale characterization of CLS at high resolution . To screen the genome for lifespan-determining genes and pathways , we used our CLS method to characterize 3 , 878 nonessential-gene knockouts from the yeast deletion collection [37] ( data set in Table S1 ) . The distribution of lifespan effects peaked near neutrality ( L = 1 ) , with a long tail of short-lived knockouts and a smaller but substantial proportion of long-lived strains ( Figure 2A ) . Given that our high-throughput screen was performed at low aeration and not under the conventional aeration conditions ( see Materials and Methods ) , we characterized a subset of mutants at high aeration and confirmed that there was a general agreement in their long- and short-lived behaviors regardless of aeration ( Figure S4 ) . In genetic studies , mutations that increase a maximal lifespan are more likely to be informative about aging processes than mutations that reduce the life span [38] , as such deleterious phenotypes can arise from a variety of reasons not necessarily related to aging . Indeed , we observed that short-lived knockouts are more likely to be affected in their rate of exponential growth than strains with wild-type CLS ( Figure S5 ) . Still , we note that at least 64% of the short-lived knockouts identified in our genome-wide screen were specific for CLS , that is , their deletion affected stationary-phase survival but not the rate of growth ( Figure S5 ) . Furthermore , the overlap of mutants affected in both stationary-phase survival and exponential-growth rate did not increase substantially when a subset of mutant strains were grown non-fermentatively under ethanol-glycerol medium ( Figure S6 ) , which indicates that respiratory integrity and its overlap with stationary-phase survival is largely taken into account by describing the mutant's fermento-respiratory growth capacity under standard glucose medium . We found that an important fraction of the non-essential yeast genome mediates CLS . Overall , 6 . 8% of single-gene knockouts had a long-lived phenotype , while 7 . 2% lived significantly less than the wild type without showing major growth defects ( 5% FDR; see Figure 2a inset , genes in Table S2 ) . To quantitatively describe the cellular processes and protein complexes that underlie CLS in yeast , we also identified functional groups of single deletions with a skewed distribution of lifespan effects ( Figure 2B ) . Mutant strains with compromised mitochondrial function , de novo purine biosynthesis , vacuole function , and autophagy typically resulted in reduced lifespan phenotypes . This confirmed the main findings of recent genome-wide screens for yeast CLS factors [21]–[23] . We also note that genes involved in the regulation of replicative cell aging were enriched in CLS effects . An inspection of the list of short- and long-lived strains ( Table S2 ) indicated that we were also able to confirm a number of previously-reported CLS genes . For instance , inactivation of the Leu3 transcription factor involved in amino acid biosynthesis increased CLS , as expected [39] . Lifespan extension was also observed for ald6Δ [40] . In another example , depletion of the apoptotic proteins Fis1 and Aim14 extended yeast CLS , in consonance with studies showing that chronological aging induces apoptosis in yeast [41] , [42] . This was also the case of the mitochondrial ubiquinone oxidoreductase: disruption of Ndi1 decreases ROS production and elongates the CLS of yeast [43] . Moreover , strains impaired in Msn2 , Glc8 , and Glc3 had reduced lifespan , which was in agreement to studies showing that decreased Tor activity activates stress-response transcription factors and genes for glycogen accumulation [19] . Importantly , our screen allowed us to uncover biological processes and protein complexes that had not been previously associated to the CLS phenotype . Noteworthy , depletion of a number of chromatin-modification proteins resulted in extended lifespan ( Figure 2B ) . Such novel chronological aging factors included the Swr1-complex proteins Arp6 , Swc3 , and Swr1 . The SAGA-complex subunits Chd1 and Sgf11 were also , to our knowledge , previously unrecognized pro-aging proteins , which add to proteins of the SAGA-like complex as a histone-modifying factors underlying yeast lifespan [44] . On the other hand , disruption of the Rpd3L histone-deacetylation complex subunits Cti6 , Dep1 , Rxt2 , Rxt3 , and Ume1 resulted in short lifespan , without altering the cell's growth capacity . Likewise , the Sdc1 , Spp1 , and Swd1 subunits of the Set1/COMPASS histone-methylation complex were novel yeast CLS factors ( see Table S2 ) . We also identified a number of novel lifespan phenotypes associated to genes with roles on sterol and sphingolipid metabolism and homeostasis ( Figure 2B; Table S2 ) . Depletion of the ergosterol-biosynthetic genes Erg5 , Erg6 , and Hmg1 extended CLS , while disruption of the fatty-acid elongase Sur1—involved in sphingolipid biosynthesis—reduced CLS without affecting growth . The ARV1-encoded protein , which mediates sterol and shingolipid homeostasis [45]–[48] , was also a novel lifespan factor identified in our genome-wide CLS screen . To further validate the results of our competition-based genome-wide screen , we assayed 14 of the longest-lived and seven of the shortest-lived knockout strains using the standard CLS method with non-buffered medium [49] ( see Materials and Methods ) . Knockout strains Δrps4b , Δrps24a , Δrps10b , Δrps18b , Δleu3 , Δrcy1 , and Δald6 were confirmed as long-lived ( Figure 3A–G ) , while Δlsc1 , Δglk1 , Δlsc2 , Δstf1 , and Δubp14 were confirmed as short-lived ( Figure 3H–L ) . The CLS of strains deleted for ADE5 , 7 , AQR1 , CYB5 , ERG6 , NEW1 , RRP8 , SPP1 , YAK1 , and YPL062W was not distinguishable from the WT when retested ( data not shown ) . Overall , seven out of 14 long-lived strains and five out of seven short-lived knockouts were confirmed using the standard CLS method . Subpopulations in aging stationary phase cultures may undergo adaptive regrowth , a phenomenon whereby non-dividing cells acquire mutations that allow them to re-enter the cell cycle and take advantage of the nutrients released by dead cells [50] . If the rate of adaptive regrowth of a given mutant strain was different than the wild-type , such mutant would register an artificially high or low abundance ratio as a function of age . We therefore estimated the extent at which adaptive regrowth gave rise to false-positive lifespan factors in our screen ( Figure S7 ) . To do so , we focused on nearly all mutant strains scored as short- or long-lived ( 505 out of 511 knockouts; see Table S2 ) and monitored their chronological aging in water to prevent the occurrence of adaptive regrowth [49] . We observed that the phenotypes from these two aging conditions were consistent with each other; the distributions of lifespan effects in water were markedly different between mutants scored as short-lived or long-lived in buffered medium ( p<10−79 , Wilcoxon rank sum test ) . In other words , strains that aged fast or slow in buffered medium typically did so in water as well . For instance , the CLS of only one of the longest-lived and one of the shortest-lived strains in buffered medium were not significantly different from the WT CLS in water ( see Table 1 ) . Overall , we were able to confirm 61% short-lived and 48% long-lived phenotypes , which sets such minimal fractions of true-positive lifespan factors in our original genome-wide screen in terms of accounting for adaptive regrowth . Swr1 is a multisubunit chromatin-remodeling complex that contains the SWR1-encoded Swi2/Snf2-related ATPase and is required for the incorporation of the histone variant H2A . Z into chromatin [51] . To further investigate the mechanisms by which activity of the Swr1 complex promotes chronological aging , we quantified the lifespan effects of Swr1 mutants under glutamine ( nitrogen-rich ) or GABA ( nitrogen-poor ) conditions ( Figure 4 ) . By doing so , we asked whether lifespan extension by dietary restriction depends on the reduced activity of this novel aging factor . Interestingly , the Swr1-complex gene knockouts lived relatively longer than the WT in glutamine , but not under nitrogen-limited condition , with the exception of arp1Δ which had a significant effect in both GABA and glutamine ( Figure 4 ) . As a control , we analyzed the effects Tor1 and Rim15 , which are known to be key players of the pathway that extends longevity by nutrient limitation [13] . As expected , the tor1Δ and rim15Δ strains showed , respectively , increased and decreased relative longevity preferentially under nitrogen-rich conditions . These results suggest that lifespan extension by dietary restriction depends , at least in part , on the action of the Swr1 histone-exchange complex . To gain deeper knowledge on the lifespan role of Arv1 , a protein involved in lipid homeostasis , we asked whether this novel CLS factor modulates yeast lifespan together with or independently from autophagy , which is a conserved modulator of lifespan in yeast and multicellular eukaryotes [14] , [19] , [52] and is intimately linked to lipid metabolism [53] . We generated a collection of double knockouts combining arv1Δ to 26 deletions of core-autophagy or CVT-pathway genes . Strikingly , the relative lifespan of many double knockouts was significantly increased when compared to the expected from the independent combination of the deleterious lifespan phenotypes of each of the corresponding single knockouts ( Figure 5A ) . This result suggested that the anti-aging effect of autophagy depends to some extent on the action of Arv1 . To confirm the observed genetic association of Arv1 with the autophagy machinery , we monitored different reporters of autophagic activity in wild-type and Arv1-disrupted cells . We found that arv1Δ cells accumulate less Atg8—a marker of autophagic bodies—in the vacuole , partially resembling the molecular phenotype of atg1Δ cells in which autophagy activity is completely blocked ( Figure 5B ) . In addition , we examined the degradation of Pgk1-GFP , a low-turnover rate cytosolic fusion protein that is rapidly degraded by non-selective autophagy upon nitrogen starvation [54] . Also in agreement with the genetic interactions scored between autophagy genes and ARV1 , we found that the autophagy-directed appearance of processed GFP protein was clearly diminished in arv1Δ cells ( Figure 5C ) . Taken together , these results showed that autophagic activity was compromised in arv1Δ deletion strains and confirmed a novel connection of Arv1 with the autophagy pathway , as anticipated by means of genetic- interaction analysis . The combination of high throughput and sensitivity of our phenotyping approach not only provides an accurate catalogue of genes and pathways underlying CLS , but also lends itself for the systematic construction of lifespan-based quantitative genetic interaction ( GI ) maps . By describing the ways in which mutations affect each other's phenotypes , GI analyses in yeast have been very successful in elucidating genetic pathways [27]–[31] , [55] . Since cell-proliferation phenotypes are typically used to generate such maps , and given that the genes that promote growth are not necessarily important for survival , it was expected that CLS-based GI data would uncover novel pathways , with particular relevance to aging . Indeed , we noted that 64% of the genetic factors identified in our genome-wide screen were specific for stationary-phase survival , that is , their deletion affected CLS but not the rate of exponential colony growth ( Figure S5 ) . For the systematic study of lifespan-epistatic interactions we focused on the autophagy pathway , which drives the degradation and recycling of unnecessary or dysfunctional cellular components [56]–[58] . While autophagy has consistently been shown to modulate CLS , the way in which autophagy genes interact phenotypically with one another and with genes of related processes has not been systematically analyzed . We surveyed interactions for almost all core-autophagy genes , along with several signaling and accessory factors ( 39 genes and all possible combinations ) . The single knockout of most genes examined had at least a small but significant deleterious effect on CLS , and the measured CLS phenotypes of two marker-swap experiments showed a good correlation ( Figure S8 ) . Importantly , to verify the CLS specificity of the genes tested , we confirmed that exponential growth capacity of their single knockouts remained largely unaffected ( Figure S9 ) . For each gene pair , lifespan epistatic interactions were defined as significant deviations from a null multiplicative expectation based on the CLS phenotype of the two single knockouts . Neutrality was defined when the lifespan phenotypes of the single knockouts combined multiplicatively , suggesting that the two genes act independently from one another ( Figure 6A , left panel ) . Departing from this neutral expectation , GIs were classified as either negative ( aggravating ) or positive ( alleviating ) [59]: Negative epistasis was scored when the double mutant exhibited a significantly shorter CLS phenotype than one would expect from the CLS of the two single knockouts ( Figure 6A , center panel ) . Such aggravating phenotype is expected from the deletion of pairs of genes that act in lifespan pathways that partially compensate for each other's loss . Conversely , in a positive epistatic interaction , the double mutant exhibited an extended CLS relative to the neutral expectation ( Figure 6A , right panel ) . In general , this phenotypic response takes place when both deleted genes are part of the same linear lifespan-determining pathway or protein complex . Overall , we scored a significant GI for 201 out of 721 gene pairs tested ( 3 . 2% negative and 24 . 7% positive , 95% CI , see data in Table S3 ) . In agreement with the common assumption that most gene pairs do not interact , we found that the lifespan-epistasis spectrum peaked close to neutrality , though skewed to positive epistasis ( gray histogram in Figure 6B ) . Many of the positive interactions in our data set corresponded to gene pairs within the core-autophagy machinery ( purple histogram outline in Figure 6B ) . For this set , we scored 55 . 8% significant positive interactions and no negatively interacting gene pair . The distribution of epistatic effects remained largely unaffected when interactions were defined from an additive rather than multiplicative neutral expectation model ( Figure S10 ) . We also observed that the distribution of epistasis among gene pairs that code for proteins that interact physically had a higher median ε value than that of gene pairs whose protein products do not interact ( Figure 6C ) . In summary , the observed prevalence of positive epistasis within our data set was consistent with the fact that many autophagy proteins operate in concert as a non-essential functional unit that promotes CLS . Based on the correlation of the quantitative epistatic-interaction pattern derived from CLS phenotypes of each gene , we constructed a high-density GI map for autophagy ( Figure 7A ) . Using purely phenotypic information , this unsupervised map recapitulated known pathways and complexes and shed light on novel gene associations . With few exceptions ( Atg5 , Atg12 , and Vps30 ) , all core autophagy proteins were grouped together ( core-ATG module , Figure 7A ) . Proteins with a major role in the CVT pathway ( Atg19 , Atg23 , and Atg27 ) were clustered together with other autophagy-accessory proteins and apart from the core-ATG module ( CVT module , Figure 7A ) . We also used gene-clustering information along with gene-interaction significance to generate a lifespan-epistasis network ( Figure 7B ) . We observed a high enrichment of positive epistatic interactions among genes within the core-ATG module , again consistent with the fact that core-autophagy proteins act as a single functional unit ( p<10−4 , Fisher's exact test ) . Furthermore , directionality was assigned to positive epistatic interactions when one of the genes had a larger single-knockout effect than its interaction partner [30] . For instance , we scored positive interactions targeted to ATG18; single-deletion of this gene had a smaller quantitative effect on CLS when compared to the typical core-autophagy gene . This observation was consistent with the fact that Atg18 is only required for reaching full autophagic activity and most autophagy proteins localize to the pre-autophagosome in the absence of Atg18 , but not in the absence of other core-autophagy proteins [60] . Our lifespan-epistasis map also recapitulated a group of proteins related to phosphatidylinositol phosphate metabolism and regulation ( Figure 7B ) , including Atg21 , Tax4 , Snx4 , Hsv2 , and Tep1 . The association of Tep1 within the PIP module provided genetic evidence for its putative role as a phosphoinositide phosphatase . In cancer cells , the TEP1 homologue PTEN controls autophagy by downregulating the PI3KclassI/PKB pathway [61] . The syntaxin-like t-SNARE Tlg2 protein was also part of this PIP module . Recent evidence suggests that vesicle-tethering proteins and some SNARE proteins are required for autophagosome biogenesis and normal anterograde transport of Atg9 [62] . This result provided further genetic evidence for the role of Tlg2 as a SNARE protein which is essential for autophagy and suggested that this functional association is needed for normal lifespan . Three signaling factors ( Pho80 , Snf1 , and Reg1 ) were grouped together in our lifespan-epistasis map ( Figure 7A , B ) . For PHO80 , positive epistatic interactions were all directed from this signaling gene to autophagy genes , in agreement with the regulatory role of Pho80-Pho85 upstream of autophagy [63] and suggesting that this cyclin regulates other downstream pathways that are important for yeast CLS . As opposed to PHO80 , SNF1 interacted negatively with the core-ATG and PIP modules , which may reflect that Snf1 independently promotes autophagy and inositol biosynthesis [64] , [65] . Taken together , these results show that quantitative lifespan-epistasis maps represent a powerful systematic strategy to establish functional associations within and between aging pathways .
We have used a novel functional genomics platform to generate a high-resolution compendium of genes that affect yeast stationary-phase survival and to construct the first systematic GI map based on lifespan phenotypes . Our genome-wide screen indicated that a substantial fraction of the non-essential yeast genome ( 14% ) regulates stationary-phase survival independently of general growth defects . Among novel CLS factors detected in our screen were different proteins of the Swr1 complex , which is required for the ATP-dependent recruitment of histone variant Htz1—the yeast homologue of mammalian H2A . Z—and promotes expression near silent heterochromatin [51] . It is known that chromatin structure and dynamics change during the aging process [66] . To further explore the specific role of the Swr1 complex as a regulator of CLS in yeast , we characterized a number of Swr1-complex knockouts under different nitrogen conditions . In doing so , we provided evidence suggesting that lifespan extension by dietary restriction depends on the activity of the Swr1 complex . We also showed that Arv1—an endoplasmic reticulum protein conserved from yeast to mammals—and autophagy promote chronological longevity in concert . Such genetic association of ARV1 was confirmed by monitoring different autophagy reporters in Arv1-depleted cells . This result adds to a growing body of evidence suggesting that autophagy is intimately connected to lipid metabolism: While autophagy regulates lipid metabolism [53] , sphingolipids and ceramides play important roles in autophagy itself , both at the level of regulation and as membrane constituents of autophagic vesicles [67] , [68] . In the absence of Arv1 , yeast cells up-regulate the unfolded protein response [69] and display a variety of lipid-related phenotypes that include aberrant sterol and sphingolipid metabolism and loss of plasma-membrane asymmetry [45]–[48] . In humans , Arv1 is required for normal cholesterol and bile acid homeostasis [70] . In regard to our finding that autophagy activity depends on Arv1 , we speculate that this protein could be specifically required for proper targeting of Trs85 to the pre-autophagosomal structure , as it has been recently shown that Arv1 is involved in the targeting of tail-anchored proteins to the endoplasmic reticulum [48] . Alternatively , the general lipid-homeostasis role of Arv1 could be essential for the correct activity of the autophagy machinery . In any case , our results indicate that the functional link between Arv1 and autophagy has an impact on cellular lifespan . Our systematic lifespan-epistasis network successfully recapitulated functional associations of the autophagic machinery and related biological processes . While most core-autophagy genes clustered together as expected , few exceptions included ATG5 and ATG12 , which were grouped with genes of selective autophagy and of the CVT pathway . A specific role of ATG5 and ATG12 has been reported for mitophagy and lifespan regulation in human umbilical vein endothelial cells: Mitochondrial damage induces upregulation of these genes together with LC3B/ATG8 , which in turn increases the replicative cellular lifespan [71] . Furthermore , our epistasis network established that Tep1 and Tlg2 work in concert with the phosphatidylinositol-phosphate metabolism pathway to modulate lifespan . While it has been shown that Tep1 is not a replicative lifespan factor in yeast [72] , our data indicated that Tep1 activity promotes CLS , in line with PTEN-overexpressing mice with increased longevity [73] . The CLS phenotype of yeast Tep1 and its association to other phosphatidylinositol proteins provide a simple model system to elucidate the molecular mechanisms underlying the role of its mammalian homologue PTEN in embryonic survival and tumor suppression [74] . Compared to previous genome-wide screens in which 6% [19] or 31% [21] of longevity phenotypes were confirmed when retested , our approach performed modestly better by correctly predicting 50% ( seven out of 14 ) knockout strains with extended lifespan . There are a number of possible reasons why not all of the mutants retested with the standard CLS assay were successfully confirmed . Our relative lifespan measurements are based on a simple deterministic model that assumes constant exponential death and outgrowth rates as the population ages . For instance , cell heterogeneity of stationary-phase cultures [75] was not possibly taken into account . Moreover , results of our control screens suggest that differences in aeration or the effects of adaptive regrowth explain part of the false positives that were obtained . Importantly , once a set of mutants have been confirmed , our approach can be readily used to leverage the chronological aging paradigm to generate aging networks , as we have shown for autophagy . Our pure phenotypic characterizations proved powerful for establishing functional relationships among such genes that had remained hidden to large-scale genetic analysis . Despite the fact that our epistasis data set is limited in its scale and is a priori enriched for genes that share functional relationships , we anticipate that the strategy herein described will open the door to describe the spectrum of lifespan-epistatic interactions in a comprehensive and unbiased manner . With a growing number of lifespan-related genes , a systems view of aging is much needed to grant a deeper mechanistic understanding of cellular longevity [7] , [76] . Our quantitative analysis of longevity factors and their genetic interactions in yeast is an important step to this end .
Fluorescent SGA starter strains YEG01-CFP and YEG01-RFP ( MATα PDC1-XFP-CaURA3MX4 can1Δ::STE2pr-SpHIS5 lyp1Δ his3Δ1 ura3Δ0 LEU2 ) were generated by direct PCR-based tagging of the S288C-derivative SGA starter strain Y8205 [77] with the pBS10 Cerulean-hphMX4 ( CFP ) or pBS35 mCherry-hphMX4 ( RFP ) cassettes ( The Yeast Resource Center ) at the PDC1 locus and subsequent direct replacement of the hphMX4 cassette with CaURA3MX4; strong constitutive expression of fluorescent proteins during exponential growth was achieved by carboxyl-terminal fusion to the Pdc1 protein . Deletion strains were from the yeast deletion collection ( Open Biosystems ) in the S288C-derivative BY4741 background ( MATa xxxΔ::kanMX4 his3Δ1 ura3Δ0 leu2Δ0 met15Δ0 ) . The GFP-ATG8 fusion was generated by PCR-based tagging with the natMX4-CUP1pr-yeGFP cassette from pYM-N4; the PGK1-GFP fusion was obtained from the GFP-tagged yeast strain collection ( Invitrogen ) . Genes of interest were knocked-out from GFP fusion strains by direct gene replacement with the kanMX4 cassette . The following growth media were used: ( 1 ) Aging medium: synthetic complete ( SC ) medium with 2% glucose , 0 . 2% amino acid supplement mix as defined ( Cold Spring Harbor Laboratory Manual 2005 ) , and buffered to pH 6 . 0 with 17 . 9 mM citric acid and 64 . 2 mM dibasic sodium phosphate . When noted , 25 mM glutamine or 25 mM gamma-aminobutyric acid ( GABA ) instead of ammonium sulfate and 0 . 07% of amino acid supplemented mix were used . ( 2 ) Low-fluorescence medium ( YNB-lf ) [78] . ( 3 ) Nitrogen-starvation medium ( SD-N ) : 0 . 17% yeast nitrogen base without amino acids and ammonium sulfate and with 2% glucose . ( 4 ) Yeast-extract peptone with 2% dextrose ( YPD ) or 2% ethanol , 2% glycerol ( YPEG ) medium . Collections of prototrophic yeast strains were generated by synthetic genetic array ( SGA ) methodology [77] . Colony arrays were transferred manually with a 384-head pin tool ( V&P Scientific , VP384F ) ; antibiotic concentrations used for selection were 200 µg/ml G418 ( Invitrogen ) and 100 µg/ml clonNAT ( Werner BioAgents ) . To generate the collection of RFP-tagged strains used for the genome-wide CLS screen , the YEG01-RFP starter strain was mated to an array of 4 , 844 viable single-deletion strains , followed by diploid selection , sporulation , and three rounds of haploid selection ( HIS+ for MATa mating type , URA+ for fluorescence marker , and G418+ for knockout selection ) . The resulting prototrophic strains were MATa PDC1-RFP-CaURA3MX4 can1Δ::STE2pr-SpHIS5 lyp1Δ ura3Δ0 his3Δ1 LEU2 MET15 xxxΔ::kanMX4 . For the double-knockout collection of autophagy-related genes , 41 query strains were generated by PCR-based gene replacement of the YEG01-RFP background using the natMX4 module . This query collection was mated to an array of 44 deletion strains . Double-knockout haploid strains were selected as described above , plus clonNAT+ for the second knockout . The query collection included two neutral-insertion hoΔ controls , while four his3Δ controls were present in the array collection . All combinations of a knockout strain with a neutral insertion ( up to six replicates ) define the “single knockouts” , while the combination of two neutral markers define the “WT strain” . Likewise , the CFP-labeled reference strain was obtained from the YEG01-CFP starter strain by the same procedure of combining two neutral markers . All query and array strains were PCR-verified for correct marker insertion and disruption of the WT allele ( data not shown ) . To obtain ARV1 double knockouts , arv1Δ::natMX4 and hoΔ neutral-insertion query strains ( YEG01-RFP background ) were mated in triplicate to an array of 29 autophagy-gene deletion strains , including the arv1Δ::kanMX4 and the his3Δ::kanMX4 neutral-insertion . Double knockouts were selected as described above . Saturated cultures of RFP-labeled mutant ( single or double knockout ) or CFP-labeled reference strains grown in SC medium were mixed in a 1∶1 ratio to a final volume of 150 µl in 96-well plates ( Corning 3585 ) and pinned with a manual replicator ( V&P Scientific , VP 407 ) onto 700 µl of fresh aging medium in deep-well plates ( Nunc 260251 ) with disposable plastic covers . All subsequent steps were carried out in an automated robotic station ( Tecan Freedom EVO200 ) that integrates a plate carrousel ( Liconic STX110 ) , a high-performance multilabel plate reader ( Tecan Infinite M1000 ) , a 96-channel pipetting head , an orbital plate shaker , and a robotic manipulator arm , all contained in an isolated environmental room . Mixed stationary-phase cultures were maintained at constant temperature ( 30°C ) and relative humidity ( 70% ) without shaking; cultures were fully resuspended every 24 h by automated pipetting . Five days after inoculation ( age zero , as previously defined [50] ) and every 2–3 days for up to 23 days from this point , stationary-phase cultures were transferred to the liquid-handling deck for outgrowth: 5 µl aliquots were inoculated into 150 µl of fresh low-fluorescence medium in 96-well plates . Such outgrowth cultures were kept at 30°C without shaking and monitored by the following procedure: Every 3 h ( 15 h , five data points ) , outgrowth cultures were vigorously shaken and transferred to the plate reader to collect data for both fluorescence channels ( CFPraw: Ex 433 nm/5 nm and Em 475 nm/5 nm; RFPraw: Ex 587 nm/5 nm and Em 610 nm/5 nm ) and optical density at 600 m ( OD600 ) . Background fluorescence signal as a function of OD600 was routinely collected from RFP-only ( CFPbg ) and CFP-only ( RFPbg ) control cultures . Fluorescent signal in the outgrowth culture was defined as: RFP = RFPraw–RFPbg and CFP = CFPraw–CFPbg . The log of the ratio of RFP to CFP signal ( lnR/C ) was obtained and a single value for each outgrowth culture at age T was calculated by interpolating lnR/C to a fixed point in time , t* ( see Model below ) . For all experiments , t* was fixed at 10 h . Importantly , results were insensitive to the choice of t* for outgrowth data collected during late exponential growth ( 8–12 h post inoculation , data not shown ) . An apparent survival coefficient , s , and its standard error , σs , were obtained from the slope of the linear fit of lnR/Ct* to T ( robust regression , Matlab ) . From an empirically determined dynamic range ( Figure S11 ) , we defined conservative low and up lnR/Ct* thresholds of −3 . 5 and 2 . 5 , respectively . Only fits with at least three data points were considered to obtain a value of s . Hence , mutant strains with low fluorescence signal or strains that grew too slow in co-culture ( less than 5% of the tagged deletion collection ) were excluded from our analyses . All screens included a large number of WT samples scattered through the strain arrays ( typically >300 wells ) ; these were used to account both for survival differences between WT and CFP-labeled reference strain and potential batch effects . By definition , the survival coefficient of the RFP-labeled WT strain , , equals zero . Hence , data was normalized by subtracting the median value of all WT control samples , , to all raw s values in each batch ( see example in Figure S12 ) . The mutant's apparent relative lifespan was expressed as the decrease in mutant population relative to WT , that is , . In our experimental setup , outgrowth from stationary phase is used to estimate the relative numbers of viable cells across an experiment . In practice , this quantity is obtained after several hours of inoculation into fresh medium . In what follows , we show that measuring the ratios of two exponentially-growing populations at a fixed time-point in hours , t* , across different ages of the culture in stationary phase , T , decouples potential differences in growth rates from differences in the quantity of interest , that is , the rates of death . In other words , the decrease in relative abundance of a slow-growing strain will remain constant and will thus not affect the estimation of a change in relative viability ( slope ) , provided that such relative abundance is measured in each outgrowth iteration at the same time after inoculation onto fresh medium . Let Nx and Nwt be the sizes of two populations , a mutant ( x ) and a wild-type reference ( wt ) , growing exponentially in co-culture at rates gx and gwt:Therefore , the relative number of viable cells from an outgrowth curve inoculated at age T ( measured in days ) in stationary phase and measured at a fixed time point after inoculation ( t* , measured in hours ) , is given by: ( 1 ) What we get is our measure of interest—the relative number of viable cells at age T—plus a constant term that depends on a potential difference in growth rates and which can be ignored . This convenient decoupling would not be possible if the measurement was taken , for instance , at a fixed optical density of the culture . We note that , experimentally , population sizes were large ( >106 ) and all data was collected during exponential growth ( see above ) . To model aging populations , we consider the simplest model in which mutant ( x ) and reference ( wt ) populations in co-culture die at constant exponential rates [79] , rx and rwt , resulting in population sizes Nx and Nwt that decay exponentially as a function of age , T:To measure the relative number of viable cells of each population , we take the logarithm of the population ratios as a function of age and obtain the equation for a line , with slope equal to the difference in the death rates of the two populations: ( 2 ) where s is the apparent survival coefficient and is the quantity that is obtained experimentally by estimating the number of viable cells at different ages of the culture ( Equation 1 ) . Since the death rate of the mutant , rx , is positive , the death rate of the reference strain , rwt , sets an upper bound for the value of s at s≤rwt . We note that , experimentally , only a small number ( 16 out of 3 , 878 ) of CLS measurements exceeded this theoretical upper bound , presumably due to the higher error in the estimate of extreme positive s values ( λ = NaN; Table S1 ) . Where needed , we estimated an absolute apparent lifespan value for the mutant from its relative s value by combining Equation 2 with the relationship between the death rate and the half life of a population , , and obtaining an estimate of the mutant's half life:where λwt is the measured half life of the RFP-labeled WT strain . A total of 4 , 340 mutant strains from the yeast deletion collection were successfully tagged; 4 , 050 reached OD600 and RFP signal above an arbitrary threshold . This collection was rearranged in 47 96-well plates which included 414 WT-RFP strains as controls and 48 additional wells with fresh medium or individual cultures of each WT-RFP or CFP-reference to account for background signal . The RFP-labeled deletion collection was screened against a WT CFP-reference strain with a half-life of 21 . 7 days . After data acquisition and analysis , a relative survival coefficient ( corrected s ) was successfully calculated for 3 , 878 strains ( data in Table S1 ) . Significantly short- and long-lived strains were scored by assigning a Z-score ( Z ) to each knockout obtained from its L value and the standard deviation of 414 L measurements for the WT ( normally distributed , p = 0 . 48 , x2 test ) . Two-tailed p-values were obtained from Z to estimate the false discovery rate ( FDR ) for multiple hypothesis testing; significant phenotypes were assigned using a q<0 . 05 cutoff . The median relative lifespan ( ) of genes from each Gene Ontology ( GO ) term was compared to the median relative lifespan of the entire data set ( ) . GO terms with an value which was significantly different from that of the genome are shown in Figure 2B ( p<0 . 05 , Wilcoxon rank sum test ) . GO annotations were downloaded from the Yeast Genome Database ( April 2013 ) ; only those GO terms with at least five and no more than 60 genes and genes with a successful L measurement and relative growth at G>0 . 9 were considered for analysis . The final collection of 1 , 845 double-gene knockouts ( 41 query strains X 45 array strains ) included up to six single-knockout replicates per gene of interest and two marker-swap replicates per double knockout ( see correlation in Figure S8 ) . Such independently-measured replicate values of L±σ were combined to obtain an error-weighted average relative lifespan , , with standard error . For every double deletion of genes X and Y , epistasis was defined from a multiplicative neutral expectation as: , where Lx , Ly , and Lxy are the relative lifespan of the Δx and Δy single knockouts and the Δxy double knockout , respectively . Epistasis error was estimated as . Negative and positive interactions were defined at the 95% confidence interval . The use of an additive neutral expectation results in a bias to positive epistasis ( Figure S10 ) , as has been suggested for epistasis derived from cell growth based phenotypes [59] . We therefore use the conservative multiplicative model in all our epistasis analyses . Genes sharing similar epistatic-interaction patterns were clustered hierarchically ( complete linkage ) based on the correlation of raw ε values ( Spearman's rank correlation was used to account for strong outliers at both sides of the distribution of ε ) . Significant interactions and hierarchical clusters were used to generate an epistasis network , which was visualized with Cytoscape 2 . 8 [80] . For validation experiments using the standard CLS method [49] , overnight cultures in non-buffered SC ( starting from three isolated colonies ) were diluted to 0 . 1 OD units into 25 mL fresh non-buffered SC medium and incubated at 30°C with shaking at 220 rpm in 125 mL flasks covered with aluminum foil . Viability was measured by plating aging cells onto YPD-agar plates and monitoring colony-forming units starting from day 4 after inoculation , which was considered to be the initial survival ( 100% ) . Yeast cells expressing the CUP1pr-GFP-ATG8 construct were grown to mid-log phase ( OD600≅1 ) in SC medium . Expression of the fusion protein was induced with 10 mM CuSO4 for 2 h , followed by FM4-64 labeling at a final concentration of 10 µg/ml for 20 min . Labeled cells were incubated 90 min in YPD medium to allow for dye uptake and then starved for 7 hours in SD-N medium to induce autophagy . Images were taken using a fluorescence microscope ( Leica DM600 B ) with a digital camera ( Leica DFC420 C ) . The autophagic degradation of Pgk1 was monitored by immunoblotting of the Pgk1-GFP protein fusion as described [54] . Cells were grown overnight at 30°C to stationary phase ( OD600>4 ) in SC medium . 20 OD600 units of cells were harvested and starved for nitrogen in SD-N medium . At each time point after starvation , 2 OD600 units of cells were lysed with 1 . 85M NaOH , 7 . 5% β-mercaptoethanol ) . Protein extracts were separated on SDS-polyacrylamide gels , transferred to a PVDF membrane , and probed with anti-GFP ( Roche Applied Science 11814460001 ) and secondary antibody ( Goat Anti-Mouse IgG coupled to HRP , 31430 Thermo Scientific ) ; reactive band were detected by chemiluminescence ( GE Healthcare Life Sciences , RPN2209 ) . | The budding yeast Saccharomyces cerevisiae has emerged as an important model for the genetic analysis of aging , and insights gained about this process in yeast cells enhance our understanding of aging in other organisms , including humans . Even in yeast , our knowledge of the number and identity of the genes that determine lifespan is limited and we are still lacking a general picture of how different genetic aging factors work together . Here , using an innovative sensitive technique , we have characterized the stationary-phase survival of yeast single- and double-gene knockout mutants to screen for genes that control chronological aging and to score the genetic interactions among these genes . Our results showed that an important fraction ( 14% ) of the genome contributes to the regulation of lifespan , including genes and pathways that had not been previously associated to this phenotype . We also constructed a genetic interaction map which recapitulated lifespan-determining pathways and highlighted genetic associations between the autophagy machinery and the phosphatidylinositol-phosphate and lipid-homeostasis pathways . Our study provides not only an accurate catalogue of yeast aging genes , but also a picture of the gene-network wiring of aging cells . | [
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] | 2014 | High-Resolution Profiling of Stationary-Phase Survival Reveals Yeast Longevity Factors and Their Genetic Interactions |
The mosquito midgut microbiota has been shown to influence vector competence for multiple human pathogens . The microbiota is highly variable in the field , and the sources of this variability are not well understood , which limits our ability to understand or predict its effects on pathogen transmission . In this work , we report significant variation in female adult midgut bacterial load between strains of A . aegypti which vary in their susceptibility to dengue virus . Composition of the midgut microbiome was similar overall between the strains , with 81–92% of reads coming from the same five bacterial families , though we did detect differences in the presence of some bacterial families including Flavobacteriaceae and Entobacteriaceae . We conducted transcriptomic analysis on the two mosquito strains that showed the greatest difference in bacterial load , and found that they differ in transcript abundance of many genes implicated in amino acid metabolism , in particular the branched chain amino acid degradation pathway . We then silenced this pathway by targeting multiple genes using RNA interference , which resulted in strain-specific bacterial proliferation , thereby eliminating the difference in midgut bacterial load between the strains . This suggests that the branched chain amino acid ( BCAA ) degradation pathway controls midgut bacterial load , though the mechanism underlying this remains unclear . Overall , our results indicate that amino acid metabolism can act to influence the midgut microbiota . Moreover , they suggest that genetic or physiological variation in BCAA degradation pathway activity may in part explain midgut microbiota variation in the field .
Aedes aegypti mosquitoes are a primary vector for multiple arboviruses that infect humans including dengue virus , chikungunya virus , yellow fever virus and Zika virus . Dengue alone inflicts a staggering disease burden , with approximately 3 . 9 billion people globally at risk of contracting the virus [1] and an estimated prevalence of 390 million cases per year [2] . Because of their central role in pathogen transmission , understanding vector competence of Aedes mosquitoes is critically relevant to developing new methods of reducing this disease burden and improving public health . One issue complicating our ability to understand and/or predict vector competence is the fact that it varies substantially in natural populations [3] . This variation has been attributed in part to both genetic heterogeneity as well as environmental factors such as temperature [4–7] . One additional factor that has the potential to influence variation in vector competence is the mosquito midgut microbiota [8] . Previous work has shown that reducing the number of bacteria in the mosquito midgut through antibiotic ingestion results in increased dengue virus titers in A . aegypti [9] . Additionally , experimental introduction of multiple bacterial species into the mosquito midgut results in decreased dengue titers [10 , 11] , while at least one species of bacteria , Serratia odorifera , has been shown to increase dengue and chikungunya susceptibility when present in the midgut [12 , 13] . These findings demonstrate how the load and species composition of the midgut microbiota can influence vector competence . They also suggest that variability in either factor could contribute to differences in vector competence and pathogen transmission in natural populations [8] , and thereby emphasize the importance of understanding the nature and causes of midgut microbiota variability . In natural mosquito populations , the composition of the midgut and whole-body microbiota has been repeatedly reported to vary between sampling locations [e . g . 14–16] . This spatial variation is hypothesized to be largely attributable to differences in the local microbial fauna [15 , 17] , given that both larvae and adults ingest bacteria from the environment . Larvae ingest bacteria from the breeding water , which can be transstadially transmitted to the adult stage [17 , 18] . Adults have been shown to ingest bacteria from larval breeding water [19] and are also thought to acquire microbes from flower nectar , which contains bacteria [20] . Others have shown , however , that individuals of the same species collected from the same field site or from the same laboratory population over time also display substantial variation in midgut microbial load and composition [21–23] , suggesting that microhabitat , physiology and life history could also affect midgut microbiota variation . Additionally , it has been reported that microbiota composition can vary between mosquito species [21 , 24] , and that composition and total bacterial load varies between strains of the same species , [25] , suggesting that genetic variation may also influence midgut bacterial colonization and proliferation . Given the substantial variation observed in the midgut microbiota , a thorough understanding of how the microbiota influences vector competence and vectorial capacity requires improved knowledge of the basic biology of the microbiota itself . Current research suggests that factors such as metamorphosis , feeding ( both on sucrose and blood ) , and immune system signaling all have the capacity to influence the bacterial species composition and total number of bacteria in the mosquito midgut . During metamorphosis , midgut bacterial loads are dramatically reduced [26] , though there is evidence that some bacteria persist through the pupal stage into adulthood via transstadial transmission [17 , 26] . Sugar feeding alters the composition of the midgut microbiota , resulting in a decrease in microbial diversity [23] . Blood feeding causes dramatic proliferation of bacteria in the midgut [27–29] coupled with substantial decreases in population diversity [23] , possibly because certain bacterial species are better able to utilize the blood meal as a nutrient source . The increase in bacterial load after blood feeding has been shown in Anopheles gambiae to be facilitated by a peroxidase/dual oxidase system in the midgut which promotes formation of a crosslinked layer of proteins in the midgut lumen immediately after blood feeding [28] . This layer prevents the midgut epithelium from coming into contact with microbes and therefore prevents their detection and elimination by the immune system [28] . In addition to this physical barrier , bacterial proliferation after a blood meal has been shown in A . aegypti to be promoted by heme in the blood , which serves to decrease anti-bacterial reactive oxygen species ( ROS ) in the midgut , allowing bacteria to proliferate [27] . The mosquito’s immune system , specifically the IMD pathway , also influences bacterial load in the midgut . Silencing the IMD upstream receptor PGRP-LC as well as the downstream transcription factor Rel-2 leads to increased midgut bacterial loads [22 , 30] . Additionally , activating the pathway through overexpression of Rel-2 or silencing the negative regulator Caudal leads to decreased midgut bacterial loads [31 , 32] . Collectively , this work has successfully identified multiple factors that act to regulate the midgut microbiota . It remains unclear , however , whether any of these pathways influence the aforementioned intra-species variability in the midgut microbiota . Here we focused on determining factors that may contribute to differences in midgut bacterial load between adult females from multiple strains of the same mosquito species , A . aegypti . We first investigated whether mosquitoes from five strains of A . aegypti [Rockefeller ( Rock ) , Singapore ( Sing ) , Orlando , Waco and Bangkok] reared in highly similar bacterial environments as larvae varied in midgut microbial load and/or composition as adults . We found significant variation in midgut microbial load between these strains . We also found that midgut bacterial community composition was largely the same between strains , though there were some notable differences . We then proceeded to investigate potential mechanisms underlying the variation in midgut bacterial load between two strains that were maximally disparate . Using whole genome transcriptome analysis , we compared midgut mRNA abundance between the two strains and found that many genes involved in metabolism were differentially regulated . One pathway that was highly represented among differentially expressed genes was the valine-leucine-isoleucine degradation pathway . Silencing of multiple genes in this pathway each resulted in a significant increase in bacterial load in one mosquito strain , and a consequent elimination of the difference in bacterial load between the strains . Taken together , these data suggest that differences in midgut bacterial load may be influenced by variation in amino acid metabolism in the midgut .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . Mice were used only for mosquito rearing as a blood source , according to the approved protocol . The protocol was approved by the Animal Care and Use Committee of the Johns Hopkins University ( Permit Number: M006H300 ) . Strains of Aedes aegypti that were used in the current work are described in detail in [33] . In brief , the Rockefeller ( Rock ) strain was isolated from the Caribbean in the 1930s [34] , the Orlando strain was isolated from Orlando , Florida USA prior to 1940 [35] , the Waco strain was isolated from Waco , Texas USA in ~1987 [36] , the Bangkok strain was isolated from Bangkok , Thailand in 2011 [33] and the Singapore ( Sing ) strain was isolated from Singapore , Singapore in 2010 [33] . All strains were maintained at 27°C and 80% humidity with a 14:10 hour light:dark cycle . As larvae , they were provided larval food ( liver powder , tropical fish flake food , and rabbit food pellets mixed in a 2:1:1 ratio ) ad libitum and upon eclosion were then transferred to sterile cages and provided with 10% sucrose ad libitum . For all experiments , mosquitoes were maintained at 27°C and 80% humidity with a 14:10 hour light:dark cycle . Unless otherwise stated , eggs were hatched in sterile water in a vacuum and then moved to larval rearing pans containing 1L sterile water and provided larval food ad libitum . At 3 days post hatching , larvae from all strains were removed to separate sterile beakers , and as much breeding water as possible was removed from the beaker . An equal volume of rearing water from each strain was then pooled by passing through a coarse filter ( Whatman #1 filter paper ) into a common beaker . 200ml of this pooled water plus 1L sterile water was added to fresh trays and larvae were returned to these pans and fresh food was added . This was repeated without filtering on day 5 post hatching . On day 6–7 post hatching , water was mixed again between strains and portioned into pupal cups . Pupae plus some L4 larvae from each strain were added to these cups containing the pooled water and allowed to eclose in separate cages over a period of 2 days . Adults were provided 3% sterile sucrose ad libitum . 3–6 days post eclosion , females from each strain were provided a blood meal via artificial membrane feeding system . Females were starved overnight to encourage feeding . The blood meal contained 40% human defibrinated red blood cells , 10% human heat-inactivated serum , 40% 1X Phosphate Buffered Saline ( PBS ) and 10% 10mM ATP to facilitate feeding . In experiments where bacteria were introduced via blood meal , the 1X PBS was replaced with bacterial liquid culture resuspended in 1X PBS . Females were allowed to feed for 30–60 minutes and were then cold-anaesthetized and those that had not fed were removed . Non-blood fed controls were also cold-anaesthetized at this time to control for effects of cold shock . All females were then returned to the incubator to recover and given 3% sterile sucrose ad libitum . Females to be dissected were cold-anaesthetized and surface sterilized in 70% ethanol for 1 minute followed by 2 washes in 1X PBS . Midguts were dissected on a sterile glass slide on a cold block using sterile forceps that were cleaned between each dissection with 70% EtOH . Unless samples were to be pooled , each individual mosquito was dissected in a separate pool of 1X PBS . If bacteria were to be cultured or DNA extracted from the midgut ( s ) , samples were transferred to a 1 . 5 mL microcentrifuge tube containing 1X PBS . If RNA and DNA were to be extracted from the sample or pool of samples , midgut ( s ) were moved to a 1 . 5 mL microcentrifuge tube containing TRIZOL reagent and kept on ice until freezing at -80C . Midguts were homogenized in 1X PBS using a sterile pestle . For the initial experiment using all five strains , the homogenate was then diluted 1:100 in additional sterile 1X PBS and 80 μL of the undiluted and diluted homogenates were then spread on LB agar plates . For Singapore blood fed individuals in this experiment , bacteria were too numerous for many samples to be accurately counted . We therefore assigned the maximum value we obtained from countable samples in that treatment to uncountable plates as a conservative under-estimate of total bacterial number . For the subsequent time course experiment using only Rock and Sing strains , the homogenate was diluted 1:100 and 1:104 and 50 μL of the undiluted and diluted homogenates were then spread on LB agar plates . For larval water , an aliquot of breeding water was taken from rearing pans at the 4th larval instar and pupal stage and serially diluted in sterile LB . 80μL of each dilution was plated on LB agar . For all experiments , plates were incubated at room temperature for 48 hours . Colony forming units ( CFUs ) were counted by hand when there was a high diversity of colony types and by a colony counter ( aCOLyte ) when fewer colony types were detected . Bacterial colony types were first characterized for each experiment based on margin , form , elevation , color and translucency . Each colony was then re-isolated on LB agar and single colonies were used to generate liquid cultures . Sequencing was generally performed using single bacterial colonies as a template . In cases where direct colony PCR was unsuccessful , gDNA was extracted from liquid culture and used as template . In rare cases , we were unable to re-isolate colonies that grew and in those cases sequencing was performed using the original colony as template . The 16S rDNA gene was sequenced for each colony type using the 27F and 1492R universal 16S primers ( S1 Table ) . We then used Sequence Match through the Ribosomal Database Project ( http://rdp . cme . msu . edu/ , [37] ) to identify sequences to genus level , considering a match to be at or above 97% similarity . Though we classified to genus level , we present these data at family level to allow for comparison with 16S high-throughput sequencing data . Genus level data can be found in S1 File . All families but one ( Enterobacteriaceae ) were represented by a single genus , therefore diversity at the family level closely mirrors that at the genus level . To extract DNA and RNA from the same midgut or pool of midguts , we homogenized midgut ( s ) in TRIzol reagent ( Invitrogen ) using a sterile pestle and followed the manufacturer’s instructions . RNA was resuspended in 30uL DEPC H2O and DNA was resuspended in 30uL UV-treated DEPC H2O . To extract only DNA , we performed a standard phenol:chloroform extraction [38] , with the addition of an initial step where lysozyme was added at a final concentration of 20mg/ml and the samples were incubated at 37°C for 1 hour . gDNA was removed from RNA samples using the TURBO DNA-free Kit according to the manufacturer’s instructions . 500ng RNA was primed with Oligo ( dT ) and M-MLV Reverse Transcriptase ( Promega ) was used to synthesize cDNA according to the manufacturer’s instructions . Rockefeller and Singapore mosquitoes were reared in parallel and provided 3% sucrose + antibiotics [penicillin ( 100U/ml ) , streptomycin ( 100ug/ml ) , and gentamycin ( 75ug/ml ) ] upon eclosion . Because adults were to be treated with antibiotics , larval water was not mixed during rearing . Five to seven days post eclosion , females from each strain were randomly allocated to one of three treatments: ( 1 ) maintenance on 3% sucrose , ( 2 ) sterile blood meal or ( 3 ) blood meal containing bacterial cocktail . Blood meals contained 40% human defibrinated red blood cells , 10% human heat-inactivated serum , 10% 10mM ATP and 40% 1X PBS ( for the sterile blood meal ) or 40% bacterial cocktail ( for the septic blood meal ) . To create the bacterial cocktail , seven bacterial species were grown overnight in liquid bacterial culture and each culture was washed 2X in 1X PBS and then standardized to an optical density of 1 . 0 ( ±0 . 1 ) at 600nm . An equal volume of each bacterial culture was then combined to create a cocktail . The bacterial species used are detailed in S2 Table . Approximately 12 hours after blood feeding , 15 midguts from all six strain/treatment combinations were dissected in sterile 1X PBS and transferred immediately to TRIzol reagent . Samples were stored at -80°C until RNA extraction , which was performed according to the manufacturer’s instructions . Genomic DNA was removed from all samples using TURBO DNA-free ( Invitrogen ) according to the manufacturer’s instructions and all samples were analyzed on the Agilent 2100 Bioanalyzer ( Agilent Technologies Inc . , Santa Clara , CA ) to verify the integrity of the RNA . All samples were labeled using the Two-Color Low Input Quick Amp Labeling Kit ( Agilent Technologies ) according to the Two-Color Microarray-Based Gene Expression Analysis Protocol ( Agilent Technologies ) . 200ng of RNA from each sample was used as input for the labeling reaction , and experimental samples were labeled with Cy5 fluorescent dye while reference samples were labeled with Cy3 . Labeled cRNA was purified using RNeasy columns ( Qiagen ) . All samples were co-hybridized to the same reference sample , which was formed by pooling an equal amount of RNA from all samples collected in the experiment . All six samples from the same biological replicate were hybridized to six arrays on the same slide , and a different slide was used for each replicate , resulting in a complete block design . Hybridization was performed according to Agilent’s Two-Color Microarray-Based Gene Expression Analysis Protocol . Feature extraction was performed using an Agilent Scanner and Agilent Feature Extraction Software . Differential expression analysis was performed using the package limma in R [39] . Background correction was performed for all arrays using the normexp method [40] . Signals were normalized first within array using loess within-array normalization [41] and then between arrays using quantile normalization . Replicate probes for each gene were then averaged and differential expression for each gene was determined . The following contrasts were performed: Rock sucrose vs . Rock blood fed; Sing sucrose vs . Sing blood fed; Rock blood fed . vs . Rock bacteria fed; Sing blood fed vs . Sing bacteria fed . Two additional contrasts were performed to determine whether any genes responded to blood feeding or bacterial feeding significantly differently between strains ( e . g . = ( Rock sucrose–Rock bl . f . ) – ( Sing sucrose–Sing bl . f . ) ) . P-values were adjusted for multiple comparisons using the Benjamini-Hochberg method . Gene lists from each pairwise comparison were further refined to include genes only affected by treatment in a single strain ( i . e . strain-specific differential expression ) . Genes showing strain-specific differential expression were then used for Gene Ontology and KEGG analyses . Gene Ontology analysis was performed for biological process GO terms using the GOstats package in R [42] and p-values of enriched terms were adjusted using the Benjamini-Hochberg method . Redundancy in lists of significant GO terms was reduced using the online tool REVIGO [43] . KEGG pathway analysis was performed using DAVID [44 , 45] . Sample prep and sequencing: For 16S rDNA high-throughput sequencing , we characterized a subset of samples used in the single time point bacterial load analysis . Upon midgut collection and homogenization , all remaining homogenate was preserved at -80°C in TRIzol ( Invitrogen ) and DNA was extracted using TRIzol according to the manufacturer’s instructions . We then pooled an equal volume of DNA from all samples of Rockefeller-sucrose , Rockefeller-blood fed , Singapore-sucrose and Singapore-blood fed treatments from a single experimental replicate ( Rock sucrose , n = 8; Sing sucrose , n = 8; Rock blood fed , n = 15; Sing blood fed , n = 10 ) . 16S rRNA gene sequencing was performed using Illumina MiSeq as previously described [46] . Briefly , the universal primers 319F and 806R were used to PCR amplify the V3V4 16S hypervariable regions [47] in 96-well microtiter plates using procedures previously published [46 , 48 , 49] . Negative controls without a template were processed for each primer pair . The presence of amplicons was confirmed using gel electrophoresis , after which the SequalPrep Normalization Plate kit ( Life Technologies ) was used for clean-up and normalization ( 25 ng of 16S PCR amplicon pooled for each sample ) before sequencing . 16S rRNA reads were initially screened for low quality bases and short read lengths [46] . Paired-end read pairs were then assembled using PANDAseq [50] and the resulting consensus sequences were de-multiplexed ( i . e . assigned to their original sample ) , trimmed of artificial barcodes , 5’ primer and 3’ primers and assessed for chimeras using UCHIME in de novo mode [51] implemented in QIIME [52] ( S1 Data and S2 Data ) . OTU picking was performed in QIIME using a 97% similarity cutoff and taxonomic assignments were made using the GreenGenes 16S sequence database ( version 13 . 8 ) [52 , 53] . Weighted UniFrac values , Nonmetric Multidimensional Scaling ( NMDS ) ordination and accompanying heatmap were generated in R using the package Phyloseq ( www . r-project . org ) . Weighted UniFrac is a distance metric incorporating both the phylogenetic distance between the OTUs found in each sample as well as the difference in abundance of each OTU between samples [54] . We chose to use this metric rather than the qualitative unweighted UniFrac ( which does not incorporate abundance data [55] ) because we were particularly interested in identifying differences in composition that could account for the differences we detected in bacterial load . We normalized the weighted UniFrac values , such that a value of 0 indicates identical bacterial composition between samples and a value of 1 indicates no overlap . Annotation information for genes selected for RNAi-based silencing assays was verified by BLASTN using the NCBI non-redundant nucleotide collection as the search set . For additional validation , we performed a reverse BLASTN , using the human ortholog of each gene to BLAST the Aedes aegypti genome . In each case , the top hit was our gene of interest further confirming the annotation of these genes . PCR products to be used for dsRNA synthesis were generated using the T7 primers in S1 Table . PCR products were run on a 1% agarose gel and gel-purified if more than one product was present . Cleaned-up PCR products were then Sanger sequenced to verify the PCR product sequence matched that of the target gene . dsRNA was synthesized using the HiScribe T7 Quick High Yield RNA Synthesis Kit ( New England Biolabs ) according to the manufacturer’s instructions . dsRNA was run on a 1% agarose gel after synthesis and purification to verify that a single dsRNA product of the appropriate size was produced . Three to five days post eclosion , Rockefeller and Singapore females were cold-anaesthetized and injected in the thorax with 200ng dsRNA targeting a single gene . Injections were performed using a Nanoject II Auto-Nanoliter Injector ( Drummond Scientific ) . dsRNA targeting the eGFP gene was injected into a separate group from each strain to serve as an injection control . After injection , females were returned to the incubator to recover and provided 3% sucrose ad libitum . Midguts were dissected from all treatments and strains approximately 48 hours after injection . Midguts were stored in TRIzol at -80°C until RNA/DNA extraction . Dengue virus propagation , mosquito infection , and plaque assays were conducted as described previously [10 , 56] . In brief , to propagate the virus , C6/36 cells were grown in MEM medium ( Gibco , USA ) supplemented with 10% heat-inactivated FBS , 1% non-essential amino acid solution , 1% L-glutamine , and 1% penicillin-streptomycin to 80% confluency , infected with dengue virus serotype 2 , and the infected cells were then incubated for five days at 32°C and 5% CO2 . Virus was harvested from cell culture supernatant as well as from lysed cells subjected to three freeze-thaw cycles and stored at -80°C until used for infection . Rockefeller strain females were injected with dsRNA targeting AAEL003125 , AALE004137 , AAEL006928 , or eGFP as described above . Approximately 48 hours after dsRNA injection , females were provided a blood meal containing virus stock mixed 1:1 with commercial human blood and supplemented with 10% heat inactivated serum and 1% 100mM ATP . Midguts were dissected as described above from blood fed adult females after seven days and stored at -80°C in 150ul DMEM media until used for viral titration . DMEM was supplemented with 10% heat-inactivated FBS , 1% L-glutamine , 1% penicillin-streptomycin , and 5μg/ml Plasmocin . Titration was performed via plaque assay in BHK-21 cells; cells were fixed five days after infection with each sample and plaques were visualized by staining fixed cells with 1% crystal violet . Quantitative real-time PCR was performed using the StepOnePlus Real-Time PCR System ( Applied Biosystems ) and SYBR Green PCR Master Mix ( ThermoFisher Scientific ) . To test silencing by RNAi , cDNA synthesized from RNA was used as template; to quantify bacterial 16S levels , gDNA was used as template . All reactions were performed in duplicate . Primers used for qPCR can be found in S1 Table . Rockefeller and Singapore strain female midguts were dissected at 5–7 days post-eclosion and total valine , leucine , and isoleucine levels were assayed via Amino Acid Analysis by the Molecular Structure Facility in the Proteomics Core , University of California , Davis . Twenty-five midguts were collected from each strain and pooled in 50μL 1X PBS , and the entire experiment was repeated three times . All samples were homogenized using a sterile pestle and hydrolyzed using 6N HCl and 1% phenol for 24 hours at 110°C under a vacuum . Samples were dissolved in sodium diluent ( Pickering ) supplemented with 40nmol/mL NorLeucine as an internal standard and analyzed using an L-8800 Hitachi amino acid analyzer . This system utilizes an ion-exchange column ( Transgenomic ) for separation followed by a secondary reaction with ninhydrin for amino acid detection . Standardization corrections and calculations to determine amino acid molar fraction were performed by the UC Davis Molecular Structure Facility . Zero-inflated data analysis was performed using the hurdle function in the pscl package in R as recommended by Zuur et al . [57] . This is a two-step analysis , the first being a regression on count data from all samples with nonzero values ( here on referred to as “count data” ) . In our case , this was a negative binomial regression due to overdispersion of the count data . The second step is a binomial regression with the binary response variable representing presence or absence of bacteria in the gut ( here on referred to as “presence/absence data” ) . For the single time point bacterial load analysis , we started with a model that included strain , feeding status and a two-way interaction between these variables . For the multiple time point bacterial load analysis , we started with a model that included strain , feeding status and time post blood feeding , as well as a three-way interaction between these factors and two-way interactions between each pairwise combination of factors . We then performed stepwise backwards selection on each model to determine the best model as well as the significance of relevant terms . ANOVA , Tukey’s HSD , and Dunnett’s tests were performed in R . Raw data for all figures can be found in S1 File .
While a role for environment has been implicated in shaping the mosquito midgut microbiota [14 , 15 , 17] , it remains unclear whether it also is influenced by mosquito genotype[25] . To test for a role of mosquito genotype , we investigated whether adult females from multiple strains of A . aegypti vary in their midgut microbial load when reared under identical conditions . We reared five strains of A . aegypti [33] in parallel in the laboratory , mixing the rearing water three times between the strains during development to ensure exposure to highly similar rearing water microbiota . We analyzed the LB-cultivable bacterial composition and load of the larval rearing water at the 4th larval instar and pupal stage and found both to be nearly identical between the strains ( S1 Fig; L4 bacterial load , p = 0 . 1991; pupal bacterial load , p = 0 . 9928 ) . We either maintained adult females on 3% sterile sucrose or provided a sterile blood meal , and at forty-eight hours post blood feeding we assayed total LB-cultivable midgut bacterial load ( Fig 1 ) . We used a culture-dependent method for this experiment because it allowed us to assay only live bacteria . We found that total adult female midgut bacterial load differed significantly among the A . aegypti strains ( Fig 1 ) . We analyzed these data using a zero-inflated data analysis because multiple samples harbored zero LB-cultivable microbes . For count data ( i . e . samples with at least one CFU ) , the effect of strain was highly significant ( p = 6 . 19 × 10−14 , S3 Table ) , suggesting that when LB cultivable bacteria are present , the number of bacteria varies significantly between strains . For presence/absence data ( i . e . zero vs . at least one CFU ) , we detected a significant interaction between strain and feeding status ( p = 0 . 0174 , S3 Table ) . This suggests that the proportion of individuals lacking LB-cultivable bacteria differs between the strains in a manner that is feeding-status dependent . This is illustrated in Fig 1 and S4 Table , where the differences between strains in the proportion of “zero” values are much more pronounced after blood feeding . These observed differences in bacterial load between strains are unlikely to be due to variability in the pre-adult rearing environment , since the strains were exposed to highly similar bacterial communities during development ( S1 Fig ) . In summary , our results indicate that female midgut bacterial load differs significantly among strains of A . aegypti , both before and after blood feeding . In order to test whether the differences in female midgut microbial load between the two most disparate strains , Rock and Sing , persists over time , we repeated the same experiment as in the single time point bacterial load analysis shown in Fig 1 for these two strains , but analyzed the midgut microbiota at 24 , 48 and 72 hours post blood feeding ( Fig 2 ) . Sugar fed controls were collected in parallel at the same time points . We again conducted a zero-inflated data analysis , testing for three factors: strain , feeding status and time post blood feeding . We tested for a three-way interaction between these factors as well as two-way interactions between each pairwise combination of factors ( S5 Table ) . For individuals with at least one CFU , we found that bacterial load differed significantly between strains ( p = 0 . 0237 ) and we also detected a significant interaction between feeding status and time post blood feeding ( p = 0 . 0060 ) , meaning that the effect of blood feeding varied over time ( Fig 2 , S5 Table ) . While bacterial loads increased in both strains at 24 hours , there was little effect of blood feeding on bacterial load by 48 hours . This is consistent with findings by others showing that most bacterial proliferation after a blood meal occurs in the first 24 hours post feeding [27 , 28] . We note that , despite an increase in bacterial load at 24 hours after blood feeding , Rock strain bacterial load levels are consistently lower than Sing strain levels and rapidly drop off after this time point , returning to near zero levels by 48 hours , similar to what was observed in our single time point bacterial load analysis ( Fig 1 ) . For presence/absence data , we detected a significant interaction between strain and feeding status ( p = 0 . 0390 ) , meaning that the proportion of individuals in each strain with zero CFUs depended on feeding status ( Fig 2 , S5 Table ) . A larger proportion of Rock strain sugar fed females had undetectable microbiota than Sing strain sugar fed females , while for blood fed females the difference between strains in the number of individuals with undetectable midgut bacteria is less extreme ( Fig 2 ) . Despite this , Rockefeller strain had a higher number of females with undetectable midgut microbiota than Sing strain females at all time points and for both feeding statuses ( Fig 2 ) . Taken together , these data show that Rock strain females have consistently lower bacterial loads than Sing strain females . Though they do display an increase in midgut bacterial loads after blood feeding , this increase quickly drops off over time and the LB-cultivable number of bacteria returns to near zero . This is in stark contrast to Sing strain females , who maintain very high midgut microbial loads over time . While our live bacteria enumeration assays clearly show lower bacterial loads in the midgut of Rock strain females , culture dependent methods exclude detection of many microbes . With this in mind , we also assayed total bacterial load in sucrose and blood fed Rock and Sing strain female midguts at 24 hours post blood feeding using qPCR targeting the bacterial16S rRNA gene ( Fig 3 ) . Consistent with our findings using the live bacteria enumeration method , our qPCR-based assays also showed that in both sucrose and blood fed treatments , Rock strain females had lower relative 16S DNA copies compared to Sing strain females ( sucrose fed , pstrain = 0 . 018; blood fed , pstrain = 0 . 011 ) . Using both culture–dependent and–independent methods , we have shown significant differences in female midgut bacterial load between strains of A . aegypti . The strains used in these experiments were collected from different parts of the globe [33] , and had been maintained at identical laboratory conditions for at least two years prior to the time of this experiment . The fact that we observe significantly different bacterial loads between the strains when they were reared in a controlled laboratory environment suggests that they harbor genetic variation for factors that influence midgut microbial load . This suggests that , in addition to environment [15 , 17] , mosquito genotype also has the potential to influence the midgut microbiota , which could in turn influence vector competence [9] . Importantly , Sing strain females have been reported to be significantly less susceptible to dengue virus serotype 4 infection as measured by viral titer and infection prevalence , and to dengue virus serotype 2 as measured by infection prevalence ( though not viral titer , [33] ) . It is therefore possible that higher bacterial load levels in the Sing strain midgut contribute to this difference in vector competence . However , further work is necessary to address the influence of mosquito genotype on the midgut microbiota in the field and any consequent implications for vector competence . The differences we detected in bacterial load between the mosquito strains could potentially be attributed to: ( 1 ) a universal effect on bacterial load , in which the bacterial population as a whole proliferates more in some mosquito strains than in others , ( 2 ) a species-specific effect on bacterial load , in which only certain bacterial species differentially proliferate , or ( 3 ) an effect on composition of the midgut , in which different bacterial species colonize certain mosquito genotypes . In the third scenario , differences in bacterial load would manifest if some species of bacteria inherently proliferate more readily in the mosquito midgut . To explore these different possibilities and to better understand the nature of the observed bacterial load variation , we next analyzed the midgut bacterial composition of females from Rock and Sing strains by assaying live bacterial colonies as well as high-throughput sequencing of the 16S rRNA gene . We determined the composition of the midgut bacterial community of each strain using the same samples as in the single time point bacterial load analysis ( Fig 1 ) . To assess composition of the LB-cultivable ( live ) bacteria in Rock and Sing strain midguts , we first characterized and counted distinct bacterial colony types cultured from individual mosquito midguts using standard microbiological techniques . We then determined the bacterial species of each colony type based on 16S rRNA gene sequence analysis . Colony counts for each bacterial family cultured from Rock and Sing strain females were combined from all individual mosquito samples within each treatment to determine an overall proportion of each bacterial family in each treatment group ( Fig 4A ) ( Composition data for the remaining strains presented in the single time point bacterial load analysis ( Fig 1 ) can be found in S2 Fig ) . The data reveal that the vast majority of LB-cultivable bacteria in Sing strain female midguts from both sucrose and blood fed groups belonged to the Flavobacteriaceae family , and all bacteria from this family were identified as Elizabethkingia meningoseptica . Elizabethkingia are commonly found in mosquito midguts , and have been detected both in laboratory and field mosquitoes [29 , 58–60] . Interestingly , E . meningoseptica was absent from Rock strain samples , suggesting that differential prevalence of this bacteria is primarily responsible for the dramatic difference we observed in LB-cultivable midgut microbial loads ( Fig 1 ) . Approximately 20% of Rock individuals in the multiple time point bacterial load experiment ( Fig 2 ) harbored E . meningoseptica . We assessed bacterial loads of this species over time in both Rock and Sing strain females and found that bacterial loads of this species decreased over time in Rock strain females but remained quite high in Sing strain females ( S3 Fig ) . At 24 hours post blood feeding , the number of E . meningoseptica in midguts of both strains did not significantly differ ( S3 Fig , p = 0 . 5127 ) . By 48 hours , we detected a trend toward a difference between the strains ( S3 Fig , p = 0 . 06856 ) which became more pronounced and highly significant by 72 hours post blood feeding ( S3 Fig , p = 0 . 0001 ) . This suggests that even when E . meningoseptica is present , Rock strain midguts are less amenable to its maintenance over time . Together , these data suggest that genetic differences between these strains may influence colonization of the midgut by at least one species of bacteria , E . meningoseptica , as well as persistence of these bacteria over time . To assess the composition of the midgut microbiome of Rock and Sing strain females at a higher resolution , we employed high-throughput sequencing of the V3-V4 hypervariable regions of the bacterial 16S rRNA gene on the same samples used in the single time point bacterial load experiment ( Fig 4B ) . As expected , this culture independent method revealed much higher diversity in the midgut microbiota of all strain/treatment combinations compared to our culture-dependent method , with sequencing detecting 15 bacterial families and culturing detecting only 8 ( Fig 4 ) . Consistent with the culture-dependent data , Flavobacteriaceae was only detected in Sing strain females , but at very low levels ( <1% total reads ) in the sucrose-fed mosquitoes while representing 17% of the total reads in blood fed females ( Fig 4B , S1 File ) . The sequencing-based assays also revealed that the midgut bacterial composition was much more similar between the strains than was originally suggested by culture-dependent assays . Weighted UniFrac analysis revealed little differentiation between most samples , with values ranging from 0 . 2–0 . 3 ( Table 1 ) . The only exceptions were comparisons between Rock sucrose and both blood fed treatments , where UniFrac values were higher ( Table 1 , Rock sucrose vs . Rock blood = 0 . 406 , Rock sucrose vs . Sing blood = 0 . 448 ) , suggesting that sucrose fed Rock females have a midgut microbiome that is comparatively more dissimilar from either blood fed sample than any other pairwise comparison . The Rock strain sucrose fed females showed a higher proportion of Oxalobacteraceae compared to other treatments , coupled with a near absence of Entobacteriaceae ( Fig 4B ) . Nonmetric Multidimensional Scaling ( NMDS ) ordination performed using Bray-Curtis dissimilarity values validated these results; Rock sucrose fed was most dissimilar from Sing blood fed followed by Rock blood fed , and most similar to Sing sucrose fed ( S4 Fig ) . This pattern is likely attributable to the general absence of Entobacteriaceae in Rock strain sucrose fed females as well as the high abundance of Flavobacteriaceae in Sing strain blood fed females ( S4 Fig ) . Overall , the culture-independent data suggest that the composition of the midgut microbiome is quite similar between strains and especially within feeding treatments . This is particularly true for Rock and Sing strain blood fed samples , which shared the highest degree of similarity of all the samples ( UniFrac = 0 . 207 ) . High similarity in composition suggests that differences in midgut bacterial load obtained using culture independent qPCR-based studies ( Fig 3 ) are less likely to be due to differences in species-specific bacterial proliferation , especially among blood fed females , and more likely to be due to general proliferation of the total midgut microbiome . We cannot rule out the possibility that cumulative minor differences in composition could contribute towards bacterial load differences between the strains ( e . g . that of Flavobacteriaceae ) , but given the overall similarity in composition between the strains , we consider it more likely that Rock strain female midguts may be generally less amenable to bacterial proliferation or survival in a manner that broadly affects bacterial load of many bacterial species . In order to begin to understand why Rock and Sing strain females differ so substantially in midgut bacterial load , we performed a microarray-based whole-genome transcriptome analysis to study changes in mRNA transcript abundance in midguts of the strains after multiple feeding treatments ( Fig 5 ) . We reared both strains in parallel and upon eclosion treated adult females with antibiotics to significantly reduce their native midgut microbiota [61] . We reasoned that without this step , differences in transcript abundance could arise simply as a response to standing variation in midgut microbial load . By treating with antibiotics , we aimed to maximize our detection of gene expression differences that are causal of , rather than simply a response to , midgut bacterial load variation . We prepared samples from midguts of females of each strain that had been either maintained on 3% sucrose , given a sterile blood meal 12 hours earlier , or given a blood meal containing a cocktail of commonly isolated midgut bacteria12 hours earlier ( S2 Table ) . We then hybridized all samples against a common reference sample , and made pairwise comparisons between treatments ( Fig 5A ) . To assess differences in the way that each strain responded to treatment , we queried for genes that changed significantly in response to treatment in only one strain ( Fig 5B ) . In response to blood feeding , transcript abundance of 279 genes changed significantly in Rock strain female midguts but not in Sing strain females ( Rock-specific changes ) , while 422 genes changed significantly in Sing strain females but not in Rock strain females ( Sing-specific changes ) . Rock strain-specific transcript abundance changes included many putative metabolism-related genes that were up-regulated in response to blood feeding ( Fig 5B ) . Biological process Gene Ontology ( GO ) analysis on this same gene set also showed many GO terms related to metabolism were significantly enriched ( Table 2 ) . In particular , the GO term “carboxylic acid catabolic process” ( GO0046395 ) was the most significantly enriched . Carboxylic acids are those molecules that contain a carboxyl group , including acetic acid , fatty acids and amino acids . We performed a KEGG pathway analysis on this Rock strain-specific gene set and found that the KEGG pathway “valine , leucine and isoleucine degradation” ( aag00280 ) was significantly enriched ( Table 3 ) . Upon further inspection , we found that 25% of the genes in this pathway ( 7 of 28 ) showed significantly increased transcript abundance in Rock but not in Sing strain female midguts . Additionally , multiple KEGG pathways involved in fatty acid metabolism were also enriched ( Table 3 ) . Previous work has shown that upregulation of genes involved in amino acid catabolism and fatty acid metabolism occurs after a blood meal in A . aegypti [62] . This occurs in order to utilize proteins and lipids present in the blood , which are necessary for egg production [63] . Differences in expression of these genes between the Rock and Sing strains suggest that they may differ in their absorption or utilization of lipids and proteins from the blood meal . In particular , expression of genes controlling branched-chain amino acid ( valine , leucine and isoleucine ) degradation differs between the strains ( Table 3 ) . The branched-chain amino acids ( BCAAs ) are essential amino acids for mosquitoes , meaning they cannot synthesize them and must therefore obtain them from their diet [64 , 65] . In addition to serving as building blocks for protein production , BCAAs are degraded in the cell in order to produce acetyl-CoA and succinyl-CoA for use in cellular respiration [66] . Furthermore , BCAAs and their degradation products–branched chain α-keto acids ( BCKAs ) –are signaling molecules which activate the major nutrient signaling pathway Target of rapamycin ( TOR ) [67 , 68] . TOR signaling regulates many basic cellular processes in response to nutrient availability , including cellular growth , autophagy , and mRNA translation [69 , 70] . In mosquitoes , BCAA activation of TOR promotes expression of the yolk protein precursor gene vitellogenin [71 , 72] , and in Drosophila , TOR has been shown to regulate expression of multiple antimicrobial peptide genes , which are the main effector molecules of the humoral immune response [73] . In examining Sing strain-specific changes in gene expression , we found many genes belonging to the replication , transcription and translation functional group to be down-regulated in response to blood feeding ( Fig 5B ) . GO analysis revealed that the term “translation” ( GO0006412 ) was significantly enriched in this gene set ( Table 2 ) , and KEGG pathway analysis showed gene transcripts relating to the “ribosome” ( aag03010 ) pathway to be enriched ( Table 3 ) . A decrease in expression of translation-related genes in response to blood feeding has been reported in A . aegypti previously , and it is thought that this is part of a generalized stress response of the midgut [62 , 74] . The fact that this phenomenon is so much more pronounced in Sing strain compared to Rock strain females suggests that Sing females may perhaps respond more effectively to cellular stress after a blood meal . In response to bacterial ingestion , transcript abundance of 50 genes was altered significantly in Rock but not in Sing strain females while transcripts of 69 genes were altered significantly in Sing but not in Rock strain females ( Fig 5B ) . Rock strain-specific transcript abundance changes again reflected the potential influence of metabolic processes , including carboxylic acid metabolism ( Table 2 ) . Sing strain-specific transcript abundance changes also implicated metabolic processes , specifically asparagine metabolism ( Tables 2 and 3 ) . In addition to genes involved in metabolism , we also found that many immune-related genes showed differential mRNA abundance between the strains ( Fig 5B ) . In Rock strain females , eight immunity genes responded to bacterial ingestion in a Rock strain-specific manner . These included five serine proteases and a serine protease inhibitor ( S1 File ) . Serine proteases play important roles in regulating multiple components of immune signaling , including the melanization cascade and the Toll pathway , both of which produce anti-microbial effector molecules [75 , 76] . The Toll pathway is also an important component of the mosquito immune defense against dengue virus [9] . Serine proteases also regulate digestion , however [77] , and the specific function of the Rock strain differentially regulated enzymes is not clear . Differential immune system signaling could affect bacterial proliferation through production of anti-microbial peptides or other anti-microbial molecules such as the toxic quinones produced during melanin synthesis . Digestion regulation could also potentially influence midgut bacterial load by affecting nutrient availability in the midgut . In Sing females , eight immunity-related genes responded to bacterial ingestion in a Sing strain-specific manner . These included the immune gene cactus as well as two genes coding for fibrinogen and fibronectin-domain containing proteins ( S1 File ) . Cactus is a negative regulator of the Toll immune pathway [9 , 78 , 79] , which plays an important role in mosquito immune defense against dengue virus [9] and also acts to inhibit the complement-like pathway , which protects against both parasites and bacteria in mosquitoes [80 , 81] . Fibrinogen-domain containing proteins serve as pattern recognition receptors and are important for anti-bacterial and anti-parasitic immunity in mosquitoes [82] . Our genome-wide transcriptome analyses strongly suggested that metabolic signaling differs between Rock and Sing strain females . We therefore decided to investigate whether relevant metabolic pathways play a causal role in controlling midgut microbial load . We chose to focus on the BCAA degradation pathway , because it was indicated as being enriched by both GO analysis and KEGG pathway analysis . Additionally , multiple genes in this pathway are differentially expressed between the strains , suggesting a robust and concerted difference in pathway activity . In order to functionally test the role of the BCAA degradation pathway on midgut bacterial load , we silenced multiple genes from this pathway in both strains and measured relative levels of the bacterial 16S rRNA gene using qPCR . Silencing efficiency for the three pathway genes ( dihydrolipoamide dehydrogenase , an acyl-CoA dehydrogenase , and isovaleryl-CoA dehydrogenase ) was successful in both strains ( S5 Fig ) , and resulted in an increase in midgut bacterial load in Rock strain females thereby eliminating the difference in bacterial load between the two strains ( Fig 6 ) . These data suggest that strain-specific differences in regulation of the BCAA degradation pathway may in part explain the differences in bacterial load , though the mechanism by which this occurs remains unclear . One potential hypothesis is that BCAA degradation activity in the mosquito gut influences BCAA levels , which have a consequent effect on bacterial load . BCAAs can be used as an energy source for bacteria [83] , and Rock strain midguts may have lower availability of BCAAs leading to slower relative bacterial proliferation . Differences in BCAA levels between the strains could also affect midgut bacterial load through BCAA-mediated cellular signaling . As mentioned above , BCAAs and their primary degradation products BCKAs activate the TOR pathway . TOR pathway activation has been shown to cause reduced expression of the antimicrobial peptide ( AMP ) genes Diptericin and Metchnikowen in D . melanogaster [73] , and lower AMP gene expression has been shown to correlate with higher midgut bacterial loads in Anopheles gambiae mosquitoes [22 , 32] . Differences in transcript levels of BCAA degradation genes , either between strains or as a result of gene silencing , could therefore potentially influence gut microbial load via altered AMP production . To begin to explore these hypotheses , we tested whether BCAA levels in midgut tissue differ between Rockefeller and Singapore strain females . We dissected midguts from Rock and Sing strain females 5–7 days post eclosion and subjected the tissue to amino acid analysis to determine the levels of valine , leucine , and isoleucine ( S6 Fig ) . We found no difference in the molar fraction of any of these amino acids between the Rock and Sing strain midguts ( S6 Fig ) , suggesting the strains do not differ in availability of BCAAs in the midgut . We also tested the role of BCAA availability in the mosquito midgut by feeding BCAAs to Rock and Sing females in a sugar meal . We predicted that BCAA ingestion would recapitulate the results in Fig 6 , causing bacterial proliferation in the Rock midgut . We found no effect of BCAA ingestion on midgut bacterial load in either strain ( S7 Fig ) . We note that our amino acid analysis does not distinguish between amino acids in gut bacteria versus those derived from mosquito tissue specifically , which could potentially obscure or overwhelm a difference between the strains . It is also possible that the microbiota may only be influenced by BCAA levels above a specific threshold , or that a specific amino acid ratio is required for bacterial proliferation which we failed to replicate with this experiment . Furthermore , sugar meal-mediated ingestion of BCAAs may not have been spatially distributed in the gut in a manner required for influencing the microbiota . Elucidating the mechanism by which BCAA degradation signaling influences gut bacterial load will require further investigation . In addition to investigating the potential mechanisms by which BCAA signaling may influence gut bacterial load , we also tested whether this pathway has the potential to influence vector competence to dengue virus . We injected adult Rockefeller strain females with dsRNA targeting one of the BCAA degradation pathway genes described above ( AAEL006928 , AAEL004137 , AAEL003125 ) or eGFP as a control . Two days post-injection , we provided females with a blood meal containing infectious dengue virus and we dissected midguts from individual blood fed females seven days after dengue infection . We then assayed dengue viral titers in each sample by plaque assay . We found that mean viral titer did not significantly differ between females of each treatment , and overall prevalence of infection was also similar ( S8 Fig ) . These results suggest that the bacterial proliferation caused by silencing genes in the BCAA degradation pathway ( Fig 6 ) is not sufficient to affect vector competence to dengue virus in the laboratory . While higher bacterial load in the midgut has been associated with lower dengue viral titer [9] , additional studies have indicated that not all midgut bacteria have the same effect on vector competence . Rather , some species of bacteria caused reduced viral titers [10 , 11] , while some have no effect [10] and some increase viral titers [13] . It is possible that the bacteria proliferating in the midgut of Rockefeller females in this experiment are not protective against dengue virus and that the influence of BCAA degradation pathway signaling on vector competence varies depending on the bacterial composition of the midgut microbiota . In this work , we investigated molecular processes that influence variation in female adult midgut bacterial load among A . aegypti strains . Using culture–dependent and–independent methods , we found significant variation in gut bacterial load between mosquito strains . Transcriptomic analysis revealed that two of these strains , Rockefeller and Singapore , also differed in transcript abundance of many genes related to branched chain amino acid metabolism . Silencing three genes in the branched chain amino acid degradation pathway resulted in a total elimination of the midgut bacterial load difference between the strains , suggesting that BCAA degradation may in part act to influence variation in midgut bacterial load . Additional experiments suggest that availability of BCAAs in the midgut does not differ between the strains nor does increased BCAA availability influence bacterial proliferation . Silencing BCAA degradation pathway genes did not influence susceptibility to dengue virus , and the implications for BCAA signaling on vector competence for dengue remain unclear . More broadly , these results suggest that genetic or physiological variation in amino acid metabolism may have important implications for the midgut microbiota of disease vector mosquitoes . | The mosquito midgut microbiota plays an important role in mosquito susceptibility to human pathogens and therefore is an important component of mosquito disease transmission . The microbiota can be highly variable , however , and the sources of this variation are not well understood . In this work , we aimed to improve our understanding of the ways in which the mosquito can influence its microbiota . To do this , we utilized strains of Aedes aegypti that vary in their midgut microbial load . We compared the transcriptomes of two strains with highly disparate bacterial loads , and found that genes involved in amino acid metabolism ( specifically branched chain amino acid degradation ) were differentially regulated between the strains . We found that when we silenced these genes using an RNA interference approach , bacterial loads increased in a strain-specific manner , confirming a role for these genes in controlling proliferation of the midgut microbiota . Overall , these results suggest that differences in amino acid metabolism or catabolism in the mosquito could have important implications for the mosquito microbiota . | [
"Abstract",
"Introduction",
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] | 2017 | Amino acid metabolic signaling influences Aedes aegypti midgut microbiome variability |
Sexual reproduction is essential for the life cycle of most angiosperms . However , pseudovivipary is an important reproductive strategy in some grasses . In this mode of reproduction , asexual propagules are produced in place of sexual reproductive structures . However , the molecular mechanism of pseudovivipary still remains a mystery . In this work , we found three naturally occurring mutants in rice , namely , phoenix ( pho ) , degenerative palea ( dep ) , and abnormal floral organs ( afo ) . Genetic analysis of them indicated that the stable pseudovivipary mutant pho was a double mutant containing both a Mendelian mutation in DEP and a non-Mendelian mutation in AFO . Further map-based cloning and microarray analysis revealed that dep mutant was caused by a genetic alteration in OsMADS15 while afo was caused by an epigenetic mutation in OsMADS1 . Thus , OsMADS1 and OsMADS15 are both required to ensure sexual reproduction in rice and mutations of them lead to the switch of reproductive habit from sexual to asexual in rice . For the first time , our results reveal two regulators for sexual and asexual reproduction modes in flowering plants . In addition , our findings also make it possible to manipulate the reproductive strategy of plants , at least in rice .
Flowering is an important process essential for sexual reproduction , seed development and fruit production . Although flowering is composed of a series of typically irreversible sequential events , reversion from floral to vegetative growth is frequently observed in nature . Reversions can be divided into two categories: inflorescence reversion , in which vegetative growth is resumed after or intercalated within inflorescence development , and flower reversion , in which vegetative growth is resumed in an individual flower [1] , [2] . Reversion , which can serve a function in the life history strategy ( perenniality ) or reproductive habit ( pseudovivipary ) , is essential for the life cycle of some plant species [1] , [2] . Vivipary in flowering plants is defined as the precocious and continuous growth of the offspring while still attached to the parent plant [3] , [4] . Vivipary can be divided into two distinct types: true vivipary and pseudovivipary [3] . True vivipary is a sexual reproduction process in which seeds germinate before they detach from maternal plant . On the other hand , pseudovivipary is a specific asexual reproductive strategy in which bulbils or plantlets replace sexual reproductive structures [3] , [5] . Pseudovivipary has been widely recorded in monocots , in particular grasses that grow in extreme environments [1] , [3] , [5]–[11] . Characteristics of the environments which favour pseudovivipary include climate changes , high precipitation and humidity , drought , fungal infection , high altitudes and latitudes , late-thawing habitats , or arid/semi-arid areas [1] , [3] , [5] . Several authors have argued that pseudovivipary has evolved in response to a short growing season , enabling plants to rapidly complete the cycle of offspring production , germination and establishment during the brief periods favourable to growth and reproduction [3] . In developmental terms pseudovivipary occurs in two principal ways . The first way to proliferate , as in Festuca ovina , Poa alpina and Poa bulbosa , is through the transformation of the spikelet axis into the leafy shoot . The second way is to form the first leaf of the plantlet by lemma elongation , as is the case in Deschampsia caespitose and Poa robusta [1] , [11] . In some cases , such as Deschampsia alpine and Phleum pratense , both modes of propagule development have been found in a single plant [11] , indicating that the molecular difference between the two types of pseudovivipary might be rather small . Pseudovivipary has fascinated biologists , as elucidation of its mechanism could lead to an understanding of flower evolution and sexual reproduction; hence , it might provide an opportunity to manipulate a plant's reproductive strategy . As pseudovivipary is always closely associated with various environmental factors , the molecular basis of pseudovivipary is still unknown . Here we report mutations of two MADS-box transcription factors that are essential for sexual reproduction and mutations of which lead to stable pseudovivipary in rice .
In this study , a naturally occurring mutant showing inflorescence reversion was found in the offspring of an Oryza sativa spp . indica var . Zhongxian 3037 . Instead of normal floral organs , this mutant generated new plantlets ( Figure 1A and 1B ) . The plantlets , like normal juvenile plants , generated roots , produced tillers and showed normal vegetative growth when explanted in paddy fields ( Figure S1A and S1B ) . In the subsequent life cycle , plants again displayed inflorescence reversion . Thus , this mutant could be considered to be a complete pseudovivipary mutant in which the reproductive mode has completely changed from sexual to asexual . In fact , this mutant has accomplished six life cycles via this asexual reproductive method . This type of mutation has not been reported before in rice . We named the mutant phoenix ( pho ) to reflect its stable “never die and reborn anew” phenotype . Two additional mutants were also found in this segregating population . The first mutant was named degenerative palea ( dep ) , and was characterized by shrunken paleas . Paleas in severe dep florets degenerated to glume-like organs that were prone to splitting . The lemmas and glumes in dep florets were slightly elongated ( Figure 1D ) . The second mutant abnormal floral organs ( afo ) displayed a phenotype opposite to dep , with abnormalities primarily in lemma and the inner three whorls ( Figure 1E ) . In order to examine the genetic basis of the three mutations , seeds of the 28 individual plants showing the normal phenotype from the above population were planted into lines by parent plants . We found that those genotypes self-segregated into two categories . The first category only produced afo and wild phenotype plants , while the second category produced dep , afo , and pho , as well as wild phenotype plants . As the segregation ratios in both categories seemed unclear , seeds of the wild phenotype plants from each category were planted in individual lines for two more generations . Subsequently , all plants in the final generation were counted and analyzed ( summarized in Figure 1F ) . In the first category lines , 35 . 34% of plants displayed the afo phenotype , while 64 . 66% of plants exhibited the wild phenotype ( n = 232 ) . As the segregation did not follow Mendelian patterns ( 3∶1 ratio , χ2 ( 1 ) = 13 . 24 , P<0 . 01 ) , we proposed that afo might be a non-Mendelian mutant . In the second category lines , 28 . 44% plants showed the afo phenotype , 18 . 35% plants showed the dep phenotype and 7 . 34% plants showed the pho phenotype ( n = 218 ) . We observed that pho only appeared in the line where afo and dep mutants coexisted . In addition , when we put the wild phenotype plants and afo mutants into one group and dep and pho into another group , the segregation ratio would fit a 3∶1 ratio ( 162∶56 , χ2 ( 1 ) = 0 . 06 , P>0 . 50 ) , indicating that dep might be a Mendelian mutant . Therefore , we further hypothesized that pho might be a double mutant containing both a Mendelian mutation in DEP and a non-Mendelian mutation in AFO . To understand the molecular mechanism of pseudovivipary in pho , we began by isolating the DEP gene through map-based cloning . The dep mutants from the second category line were crossed to O . sativa spp japonica var . Zhonghua11 to generate a mapping population . In the F2 population , 71 of 302 plants showed the dep phenotype ( 3∶1 ratio , χ2 ( 1 ) = 0 . 36 , P>0 . 50 ) , confirming that the phenotype of the dep mutant is controlled by a single recessive gene . 2 , 292 F2 and F3 plants showing the dep phenotype were used to map DEP to a 50-kbp region on the short arm of chromosome 7 . All genes within this region were amplified and sequenced . A single nucleotide G to C substitution at position 94 in coding region was found in the first exon of the OsMADS15 in the dep mutant . This substitution results in a change from a MADS-box conserved alanine residue to proline ( Figure 2A and Figure S5 ) . The same nucleotide mutation was also found in all the pho mutants analyzed ( n = 20 ) , further implying that the mutation of OsMADS15 might be partly responsible for the pho phenotype . To confirm that the loss of function of OsMADS15 is responsible for dep , we utilized an RNA interference approach to down-regulate OsMADS15 . Forty transgenic plants expressing an inverted repeat of 317 bases of OsMADS15 were generated in Nipponbare . Among them , 35 plants also displayed the dep degenerative palea phenotype ( Figure S1C and S1D ) . Therefore , we concluded that the phenotype of the dep mutant is indeed caused by mutation in OsMADS15 . We found five OsMADS15 transcripts with differing sequences in GeneBank . To identify the WT DEP sequence , we performed RT-PCR and found that our cDNA sequence was identical to GB accession AB003325 . This cDNA was used for subsequent analysis . MADS-box proteins are transcription factors , so we conducted experiments to evaluate whether amino acid substitution impaired the transcriptional activation function of OsMADS15 in the dep mutant . OsMADS15 from both WT and dep were fused with GFP protein and transiently expressed in onion epidermal cells as well as rice protoplast cells . The OsMADS15 GFP signal was localized in the nucleus , whereas the dep mutant caused redistribution of OsMADS15 GFP to the cytosol ( Figure 2B and Figure S2 ) . Previous study has revealed that the KC region of OsMADS15 ( Amino acids of AF058698 ) does not show any transcriptional activation function [12] . However , a single amino acid substitution , from leucine to histidine mutation , has occurred at position 117 of the amino acids of AF058698 . In our study , we found that the OsMADS15 protein itself exhibited transcriptional activator activity . Furthermore , when the MADS domain of OsMADS15 was eliminated , the residual IKC region of OsMADS15 also displayed transcriptional activator activity . However , the mutated protein in dep lost its transcriptional activator activity completely , though the amino acid mutation only occurred in the MADS domain ( Figure 2C ) . Taken together , it is very likely that the mutated OsMADS15 protein has lost its transcriptional activation function in dep . From the above genetic analysis , it was deduced that pho and afo were non-Mendelian mutants , so we proposed that they might be epigenetic mutants . Epigenetic mutations are often marked by a reduction or elimination of an associated transcript . Microarray experiments were carried out to investigate whether there were any variations in transcript accumulation between pho and WT young panicles ( Table 1 ) . These experiments showed that the transcript levels of multiple genes were altered . Of those altered genes , OsMADS1 ( also known as LEAFY HULL STERILE1 , LHS1 [13] ) , was the most significantly altered transcript , with a 2 , 208-fold reduced expression in pho relative to WT . Real-time PCR was further performed using WT , dep , afo and pho panicle transcripts to confirm this result and to examine whether the afo mutant also showed a reduced expression of OsMADS1 transcripts . As expected , the expression of OsMADS1 was hardly detectable in afo as well as pho ( Figure 2D ) . Additionally , no mutations were detected in the 12 , 879-bp genomic sequence of the OsMADS1 locus , including the eight exons , seven introns , 2 , 507-bp upstream sequence and 1 , 870-bp downstream sequence . We hypothesized that the afo mutant might be caused by an epigenetic modification of OsMADS1 . Interestingly , recent studies in hexaploid wheat ( Triticum aestivum ) revealed that WLHS1-B , one of the homologs of OsMADS1 , was silenced by cytosine methylation [14] . To test if this was also the case in rice , we used bisulfate sequencing of exon 1 and the 5′ upstream regions of OsMADS1 in afo to characterize their methylation status . Compared with the WT plants , the promoter region of OsMADS1 in afo was more heavily methylated ( from 31 . 43% to 62 . 86% ) , which might contribute to the silencing of OsMADS1 ( Figure 2E and 2F ) . To ascertain whether pho was a dep/afo double mutant , We crossed dep with naked seed rice ( nsr ) , a mutant of the OsMADS1 gene [15] , to generate dep/nsr double mutants . In the F2 and F3 population , all the dep/nsr double mutants analyzed ( n = 35 ) showed a similar pseudovivipary phenotype to that of the pho mutants ( Figure S3 ) . This double mutant has accomplished three life cycles via asexual reproductive method . So , this result confirmed that pho was a double mutant of Osmads1 and Osmads15 . The spikelet development of each of the three mutants was further analyzed to explore functions of the two MADS-box genes during spikelet development . Previous studies have characterized OsMADS1 as a SEPALLATA ( SEP ) -like gene and performed multiple investigations in rice . However , the function of OsMADS1 is still not fully elucidated [13] , [15]–[20] . The afo mutant shared many similarities with those severely affected Osmads1 ( lhs1 ) mutants and OsMADS1RNAi plants ( Figure 1E ) : all spikelets were sterile; lemmas were more severely affected than paleas; palea marginal tissues ( PMTs ) were absent while palea main structures ( PMSs ) were only slightly effected; lodicules were converted into glume-like organs; and ectopic florets that are indicative of partial reversion had frequently arisen from the parent florets [13] , [15] , [19] , [20] . In summary , the phenotype of afo mutant suggests that OsMADS1 is required for the specification of lemma , PMTs and the three inner whorls [13] , [15] , [19] , [20] . Its pleiotropic defects indicate that OsMADS1 is essential for flower meristem ( FM ) determinacy [13] , [15] , [19]–[22] . Phylogenetic analyses have characterized OsMADS15 as an APETALA1 ( AP1 ) /FRUITFUL ( FUL ) -like gene ( Figure S4 and Figure S5 ) [21]–[23] . In addition , previous study has shown that OsMADS15 ( RAP1A ) RNA was expressed in the incipient floral primordium and later mainly accumulated in empty glumes , lemma , palea and lodicules [23] . However , the function of OsMADS15 is still unclear [21] , [22] . The effects of OsMADS15 on cell specifications of all spikelet whorls were histologically examined . In a severely affected dep spikelet , the transformed palea was actually only composed of two PMTs while the PMS was completely lost ( Figure 3A and 3B ) . This implied that the identity of palea was lost in the dep spikelet with the severe phenotype . The lemma in the dep spikelet was also slightly affected , but its identity was still maintained ( Figure 3A and 3B , and Figure S6 ) . The glumes of dep spikelets contained many more bundles than the WT glumes , suggesting a possible partial reversion of glumes to leaf-like organs . No obvious difference was found in the inner three whorls , hinting that they are not affected by the mutation of OsMADS15 . Thus , OsMADS15 is required for the specification of PMS and empty glumes , those floral organs are just opposite to the affecting whorls of OsMADS1 . dep showed a stable degenerative palea phenotype when grown in paddy fields with a normal climate . Unexpectedly , however , we found that , under a continuous rain for several days during its heading stage , roots occasionally emerged from the base of dep rachillas ( Figure 3C ) . Only one root was formed in each spikelet and it merely located at the lemma side ( n = 22 ) . These roots would soon degenerate if the spikelets were dried . Interestingly , if the continuous rain occurred after the heading stage , the inner floral organs or developing seeds of dep always got mildewed because of the lack of protection by paleas , but emergence of new shoots was occasionally visible in dep spikelets ( Figure 3C and Figure S7A , S7B , and S7C ) . In contrast to the emerged roots that were only formed on the lemma side , these emerged shoots only appeared between paleas and upper empty glumes on the other side ( n = 24 ) . Moreover , prophylls were found on these shoots , indicating that these emerged shoots are actually tillers . These tillers also generated roots , produced new tillers and showed normal vegetative growth when replanted in fields ( Figure S7D and S7E ) . So , dep can also be considered to be an unstable pseudovivipary mutant that was closely associated with environmental factors . In the dep mutant , most floral organs develop normally , demonstrating that OsMADS15 might only play a minor role in the FM determinacy . However , the occasional emergence of roots and tillers in dep implies that the shoot apical meristem ( SAM ) identity is restored and begins to grow under a suitable environment ( continuous rain ) , so OsMADS15 might also participate in inhibiting SAM formation in incipient floral primordium . However , pseudovivipary has not been observed in DEP RNAi plants that grow in paddy fields; it is probably that the residual transcripts in RNAi plants are sufficient to inhibit SAM formation in incipient floral primordium . Alternatively , pseudovivipary , which is mainly observed in natural plants , might be a dep allele–specific phenomenon . Finally , the primordium development of pho mutant was also analyzed . In WT , two empty glumes , lemma and palea were arranged in alternate phyllotaxis while stamens and carpel were not ( Figure 3D and 3E ) . In contrast , in the pho mutant , no stamen or carpel was observed and all lateral organs were arranged in alternate phyllotaxis ( Figure 3F–3H ) . As those lateral organs finally grew into true leaves but not simple leaf-like organs , it is obvious that FM at least partially transformed into functional SAM although some following floral genes still expressed at this stage ( Table 1 ) .
Morphological studies in other grasses have revealed that pseudovivipary occurs either by proliferation of the spikelet axis or by transformation of the lemma [1] , [11] . In most cases , pseudovivipary is achieved by the transformation of the spikelet axis . The grass spikelet is a structure consisting of two glumes subtending one or more small florets . The rice spikelet is generally considered to have three florets , which are subtended by two tiny glumes ( rudimentary glumes ) [21] , [24] . The uppermost floret is fertile while the two lower florets are reduced and sterile . The two empty glumes ( or sterile lemmas ) are considered to be reduced lemmas of two lower florets [21] , [24] . So , theoretically , rice spikelet axis is located between the palea and upper empty glume ( Figure 4 ) . In this study , new shoots in the dep mutant are merely found between paleas and upper empty glumes . Thus , we conclude that pseudovivipary in the dep mutant is also achieved by the transformation of the spikelet axis . Poa alopecurus and Poa fuegiana , which are non-pseudoviviparous and pseudoviviparous species , respectively , can also be recognized as the same species because of the close affinities between them [11] . The characters of Poa fuegiana have been well described [11] . A detailed comparison of rice dep plant with Poa fuegiana shows that there are many similarities between the two pseudoviviparous plants: the palea is reduced or rudimentary; the lemma is elongated; new shoots are only formed on the palea side; both are not stable pseudoviviparous plants; and pseudovivipary mainly happens under high rainfall conditions . Considering so many similarities , it is very likely that the occurrence of pseudovivipary in Poa fuegiana and rice dep mutant might share the same mechanism . However , the validity of this speculation remains to be verified by molecular investigations on Poa fuegiana . The pho mutant should be classified into the second type of pseudoviviparous plant since the lemma in pho undergoes elongation to form the first leaf of the propagule . However , pho , which differs from those environment-dependent pseudoviviparous grasses , shows stable pseudovivipary phenotype and is not associated with environmental factors . Till now , to our knowledge , no similar stable pseudoviviparous plant has been reported in nature . If similar stable pseudoviviparous plants are found in nature , they are very likely to be recognized as new species , because of the extreme difference in morphology and reproductive method . Early studies have showed that both OsMADS1 and OsMADS15 are expressed in the incipient floral primordium [16]–[18] , [23] . Furthermore , OsMADS1 interacts with OsMADS15 in yeast two-hybrid experiments [12] . The defects of their mutants indicate that OsMADS1 might work cooperatively with OsMADS15 to determine FM , but their individual roles are divergent: OsMADS1 mainly works in promoting the determinacy of FM while OsMADS15 mainly functions in inhibiting the formation of SAM in incipient floral primordium . Consistent with those indications , the mutations of both OsMADS1 and OsMADS15 in pho result in a stable inflorescence reversion . In addition , OsMADS1 is required for the specification of lemma , PMTs and three inner whorls . On the contrary , OsMADS15 is required for the specification of PMS and empty glumes . So , it is also probably that all floral organs in the double mutant , pho , lost their modifications and transformed into their basal state , namely , leaves . It has been shown that both transcripts of OsMADS1 and OsMADS15 are eventually accumulated in lemma and palea , suggesting that OsMADS1 and OsMADS15 might also be involved in the development of lemma and palea [17] , [23] . In severely affected Osmads1 spikelets , both lemma and palea are affected , but the lemma is affected to a greater extent , suggesting that OsMADS1 might function as a lemma identity gene [19] , [22] . Additionally , PMTs are lost in Osmads1 spikelets , indicating that OsMADS1 is also essential for the specification of PMTs . In contrast , in severely affected Osmads15 spikelets , both lemma and palea are affected , but the palea is affected to a greater extent and PMS is completely lost , implying that OsMADS15 might be mainly involved in the specification of PMS . Collectively , both OsMADS1 and OsMADS15 might control the differentiation of lemma and palea , but their different roles might contribute to the asymmetric development of the first whorl of rice spikelets . OsMADS1 and OsMADS15 are characterized as SEP-like gene and AP1/FUL-like gene , respectively [12] , [13] , [15]–[23] . AP1 , FUL and SEP1/2/3/4 genes in dicot model plant Arabidopsis are also involved in floral meristem identity determination [25]–[28] . In addition , previous studies in Arabidopsis have transformed floral organs into leaf-like organs [26] , [29] , [30] . However , transformation of flowers into true plantlets that is indicative of pseudovivipary has not been found in Arabidopsis , but has been reported in many grasses in nature [1] . The difference might be caused by the distinction of floral development between grasses and dicot plants , as well as the diversification of those floral genes during evolution [16] , [21] , [31] . More than 200 years ago , Goethe proposed that the floral organs are modified leaves . This belief is supported by the observation that triple mutants lacking the ABC genes in Arabidopsis have a conversion of all floral organs into leaf-like organs [29] , [30] . In this study , we revealed that mutations in OsMADS1 and OsMADS15 lead to the transformation of all rice flowers into plantlets that can produce true leaves , thereby further confirming Goethe's hypothesis . The complete transformation of flowers into juvenile plantlets in rice , as well as similar transformations in other grasses , leads us to hypothesize that in grasses a flower may be a modified juvenile plantlet meant for reproduction . It is widely accepted that sexual reproduction evolves from asexual reproduction , so we speculate that pho might be an atavistic mutant , and plants with similar phenotype might play an important role in the evolution of reproductive strategy from asexual to sexual . The dep mutant , which can produce both flowers and plantlets , is more similar to most natural pseudoviviparous plants than the pho mutant . Thus , its analogous plants might play an intermediate role in this evolution , because such environment-dependent pseudoviviparous plant has the ability not only to reproduce via sexual way under favourable conditions , but also to reproduce via asexual way when the harsh conditions affect its sexual reproduction . In conclusion , we have shown that dep is a genetic mutant in OsMADS15 while afo is an epigenetic mutant in OsMADS1 , and their combination led to stable pseudovivipary . These findings suggest that the two MADS-box genes might play important roles in plant adaptation to various reproductive strategies .
All plant materials were grown in individual lines in paddy fields to monitor climate-change triggered pseudovivipary . In summer , all materials were planted in Beijing and Yangzhou , while , in winter , all materials were grown in Hainan Island in South China . The primers used in this study are listed in Table S1 . To fine map DEP , STS markers ( P1–P8 ) were developed based on sequence differences between indica variety 9311 and japonica variety Nipponbare according to the data published in http://www . ncbi . nlm . nih . gov . A 317-bp fragment of OsMADS15 was amplified by PCR with their specific primers; this fragment was cloned into the pGEM-T vector ( Promega ) and sequentially cloned into the BamHI/SalI and BglII/XhoI sites of the pUCRNAi vector . Subsequently , the stem-loop fragment was cloned into the pCAMBIA2300-Actin vector . The resulting RNAi construct was transformed into an A . tumefaciens strain and used for further rice transformation . The amplified coding region of OsMADS15 of both wild-type and dep was fused with green fluorescent protein ( GFP ) and cloned into the HindIII/BamHI sites of the vector pJIT163 . Those plasmids were bombarded into onion epidermal cells using a PDS-1000/He particle gun ( Bio-Rad ) . The expression constructs were also transfected into rice Nipponbare protoplasts . Twenty hours after transfection , protein expression was observed and images were captured with a Zeiss LSM 510 Meta confocal laser scanning microscope . We carried out the transcriptional activation assay using a MATCHMAKER LexA Two-Hybrid system ( Clontech ) . Different length sequences were amplified and fused in frame to the pLexA to construct pOsMADS15 , pOsMADS15-dep , pOsMADS15△C180-267 and pOsMADS15△N1-66 . All constructs were used to transform the recipient strain EGY48 with p8op-lacZ . Transformants were selected on Ura/His depleted plates at 30°C for 3 days . The activation ability was assayed on Gal/Raf ( Ura−/His− ) /X-gal to test the activation of the LacZ reporter gene for 3 days . In order to generate gene expression profiles of WT and the pho mutant , we conducted 57K Affymetrix rice whole genome array . The total RNA of rice panicle ( 5–8 cm ) samples was isolated using TRizol reagent ( Invitrogen ) and purified using Qiagen RNeasy columns ( Qiagen ) . All the processes for cDNA and cRNA synthesis , cRNA fragmentation , hybridization , staining , and further scanning , were conducted according to the GeneChip Standard Protocol ( Eukaryotic Target Preparation , Affymetrix ) . 5 ug of total RNA was used for making biotin-labeled cRNA targets . 10 ug of cRNA was hybridized for 16 h at 45°C on GeneChip Rice Genome Array . GeneChips were washed and stained in the Affymetrix Fluidics Station 450 . GeneChip were scanned using the Affymetrix GeneChip Scanner . The information about GeneChip Rice Genome Array ( MAS 5 . 0 ) could be accessed from Affymetrix website: http://www . affymetrix . com/products_services/arrays/specific/rice . affx . GCOS software ( Affymetrix GeneChip Operating Software ) was used for data collection and normalization . The overall intensity of all probe sets of each array was scaled to 500 to guaranty that hybridization intensity of all arrays was equivalent , each probe set was assigned with present “P” , absent “A” and marginal “M” and p-value from algorithm in GCOS . The microarray data has been deposited in the Gene Expression Omnibus ( GEO ) of NCBI under accession GSE17194 . All MADS-box proteins were retrieved by BLAST searches using the conserved M- , I- , K-domain regions ( 174 amino acids ) of OsMADS15 protein ( http://www . ncbi . nlm . nih . gov ) . Protein sequences were aligned using the CLUSTALX 1 . 83 [32] . The phylogenetic tree was constructed using the Molecular Evolution and Genetic Analysis ( MEGA ) package version 3 . 1 [33] . For SEM , samples were fixed overnight at room temperature with 2 . 5% glutaraldehyde in a 0 . 1 M phosphate buffer ( pH 7 . 4 ) and dehydrated through an ethanol series . Then the samples were replaced by isoamyl acetate , critical point dried , sputter coated with gold , and observed with a scanning electron microscope . For histology , samples were fixed in FAA ( 5% formaldehyde , 5% glacial acetic acid and 63% ethanol ) overnight at 4°C , dehydrated in a graded ethanol series , embedded in Technovit 7100 resin ( Hereaus Kulzer ) and polymerized at room temperature . Transverse sections were performed using an Ultratome III ultramicrotome ( LKB ) , stained with 0 . 25% toluidine blue ( Chroma Gesellshaft Shaud ) and photographed using an Olympus BX61 microscope . Total RNA was extracted from rice young panicles ( 5–8 cm ) using TRIZOL reagent ( Invitrogen ) as described by the supplier . 3 µg RNA was reverse-transcribed with Oligo-dT ( 18 ) primer using the superscript II RNaseH reverse transcriptase ( Invitrogen ) . For quantitative real-time RT-PCR , first strand cDNAs were used as templates in real-time PCR reactions using the SYBR Green PCR Master Mix ( Applied Biosystems ) according to the manufacturer's instructions . The amplification of the target genes were analyzed using the ABI Prism 7000 Sequence Detection System and Software ( PE Applied Biosystems ) . Ubiquitin was used as a control to normalize all data . Five micrograms genomic DNA isolated from panicles ( 5–8 cm ) was digested with EcoRI and PstI . After centrifugation , pellets were dissolved in 50 µL of water , heated at 95°C for 15 min , and quenched on ice . Fifty microliters of NaOH ( 3 M ) was added and incubated at 37°C for 30 min , followed by the addition of 565 µL bisulfite solution to the denatured DNA . Samples were treated at 55°C for 20 h . After being purified using a Wizard DNA clean-up system ( Promega ) , 50 µL bisulfite-treated DNA was added with 5 µL NaOH ( 3 M ) and incubated at 37°C for 15 min . The Bisulfite-treated DNA was precipitated with ammonium acetate and ethanol , and the pellets were dissolved in 50 µL of water . PCR analysis was performed at 50°C using four primer sets ( BSP1-4 ) . PCR products were cloned into PMD18-T vectors . Ten clones of each product were sequenced to determine the methylation ratio . Cytosine methylation was only found in the BSP1 region . | Sexual reproduction is essential for the life cycle of most flowering plants . However , pseudovivipary , in which floral organs are replaced by bulbils or plantlets , provides an asexual means for many grasses to reproduce in extreme environments . Although the molecular mechanism of pseudovivipary is still unknown , the high-frequency occurrence of pseudovivipary in extreme environments indicates that only a few key regulators are responsible for the switch of reproductive habit . Here , by analyzing three naturally occurring mutants in rice , we show that mutations in DEP and AFO lead to the transformation of rice flowers/spikelets into juvenile plantlets and subsequently the switch of reproductive strategy from sexual to asexual , suggesting that DEP and AFO might work cooperatively to regulate reproductive habit in rice . Thus , we reveal a critical mechanism of the switch of reproductive habit in plants . In addition , our results also make it possible to manipulate the reproductive habit of plants , at least in rice . | [
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] | 2010 | DEP and AFO Regulate Reproductive Habit in Rice |
Human menopause is an unsolved evolutionary puzzle , and relationships among the factors that produced it remain understood poorly . Classic theory , involving a one-sex ( female ) model of human demography , suggests that genes imparting deleterious effects on post-reproductive survival will accumulate . Thus , a ‘death barrier’ should emerge beyond the maximum age for female reproduction . Under this scenario , few women would experience menopause ( decreased fertility with continued survival ) because few would survive much longer than they reproduced . However , no death barrier is observed in human populations . Subsequent theoretical research has shown that two-sex models , including male fertility at older ages , avoid the death barrier . Here we use a stochastic , two-sex computational model implemented by computer simulation to show how male mating preference for younger females could lead to the accumulation of mutations deleterious to female fertility and thus produce a menopausal period . Our model requires neither the initial assumption of a decline in older female fertility nor the effects of inclusive fitness through which older , non-reproducing women assist in the reproductive efforts of younger women . Our model helps to explain why such effects , observed in many societies , may be insufficient factors in elucidating the origin of menopause .
Evolutionary theories of life history predict that selection should operate against living beyond the age of reproduction [1]–[3] . Postmenopausal survival is , however , a characteristic that almost is uniquely human [4] , reported otherwise only in whales [5] , [6] or captive chimpanzees [7]; data for other nonhuman primates [e . g . ] , [ 8 , 9] or animals [e . g . , 10] are equivocal and controversial . Human life histories are exceptional in having extended dependence by juveniles and support of reproduction by older , less fertile females ( i . e . , through rearing ) . Thus , a decline of reproduction in older women constitutes an evolutionary puzzle . Solutions may be grouped into two general explanatory categories [7] , [11]–[49] ( specific explanations are summarized in Table 1 ) : 1 ) trade-offs between prolonged life span and reproduction; 2 ) fitness benefits for older , non-reproductive women through increasing the reproductive success of their offspring . Explanations in the first category are based on unique aspects of human life history , such as intelligence , social organization , and cultural transfer that allowed the evolution of longevity [45]–[51] . Female menopause ( decreased fertility with continued survival ) follows as a consequence of being the ( assumed ) ancestral state or as a trade-off favoring longevity over reproduction in women . For explanations in the second category , menopause has been considered as an adaptation that drives extended longevity beyond the decline in female fertility . Williams [3] recognized that reproduction could be extended to include individuals who promote the transmission of their own genes and that this inclusive reproduction could explain human menopause . Hawkes et al . [52] proposed that grandmothers could increase their inclusive fitness sufficiently to counteract their loss of reproduction and termed such mechanisms “grandmother effects” , although they have been recognized as being applicable to mothers as well [26] . Research in support of this hypothesis , theoretical and empirical , has focused on the possible kindred advantages of post-reproductive life and if such contributions are sufficient to explain the maintenance of menopause [2] , [40] , [53]–[58] , as they would have to overcome the twofold cost for a grandmother to raise each grandchild rather than one of her own . Hamilton [1] provided the fundamental theoretical observation that genes imparting deleterious effects on post-reproductive survival will accumulate , yet human females experience menopause . More recently , researchers have recognized that Hamilton based this death barrier paradox on an inappropriate model of human demography . In his one-sex model , female [demographics] were treated as the predominant factor because they bear offspring . Male demographics were treated independently but , in fact , may differ from female demographics . Pollack [59] described a two-sex model in which a “mating preference matrix” , [Mij] , connected male and female life histories . The mating preference matrix represented the propensity for a male of age i to form a bond with a female of age j , which might have yielded offspring if both were fertile . Such two-sex models are mathematically much more complex than are one-sex models because of the inherent non-linearity introduced by the mating preference matrix . Tuljapurkar et al . [60] showed that a two-sex model could explain life expectancy beyond the age of reproduction . In their model , older fertile males provided selection against age-dependent , mortality-causing mutations . Thus , their accumulation was prevented , and the survival of males and females was prolonged even though female fertility declined . To further test the consequence of mating preference on the evolution of menopause , we modeled the effect of mutations having delayed age of onset , using stochastic , computer simulation of a population with constant size , without pre-existing diminished fertility in females , and involving mutations that affected fertility as well as mortality . Discrete age classes and time intervals were taken to correspond to five-year periods . Intrinsic fertility and survival probabilities were fixed ( Table S1 ) . Initially , only mortality-causing mutations were introduced at a constant rate into individuals in the population . Our approach differs from that adopted by Charlesworth [61] , [62] , who developed mathematical models to formalize the mutation accumulation hypothesis [63] that , together with the antagonistic pleiotropy hypothesis [3] , [64] , may be used to show how senescence can evolve by the accumulation of deleterious alleles through mutation-selection balance at frequencies that increase with their age of onset; such mutations enhance reproductive performance early in life but diminish survival late in life through physiological trade-offs . We considered mutations that affected independently mortality and fertility , and we established mutation-selection balance equilibria that were effected by mate choice and affected fertility . We implemented mutant alleles at any among five autosomal loci that imparted nonpleiotropic , deleterious effects ( i . e . , mutations that affected fertility did not affect survival and vice versa ) . These five loci affected survival probability for individuals in age classes beyond a particular age of onset . Individual survival thus was determined by the number and kind of mortality-causing mutations that had accumulated in the population in which a male or female were a member and , ultimately , had been inherited ( details are provided in the section “Model” and tables in the Supporting Information ) . Eventually , simulations produced mutation-selection balance ( or alternatively , fixation ) in which the introduction of new mutations was balanced by the elimination , through stochastic deaths , of existing mutations . Deaths were compensated by births from pseudo-randomly chosen mating pairs . Mating pairs were formed according to an age-dependent mating preference matrix . Pairs could produce offspring only if both male and female parents were fertile , as determined from age-dependent probabilities in fertility tables . Model AP involved an age indifferent preference in the formation of mating pairs ( ‘All Pairs’ , matrix MijAP , Table S2 ) , whereas model YP ( ‘Young Pairs’ , matrix MijYP , Table S3 ) involved preferences between only younger males and younger females ( models used indicated by bars in Fig . 1 ) .
Our results corroborate those obtained by Tuljapurkar et al . [60] , by showing that an age indifferent preference ( MijAP ) combined with extended male fertility prevents an early decline in female survival ( Fig . 1A ) . Purifying selection ( provided by male fertility ) against late age of onset , mortality-causing mutations prevented them from accumulating in females , and mutation-selection balance prevented fixation ( compare AP vs . YP in Table 2A ) . Plausible male and female fertility and survivorship curves were produced in equilibriums at which a menopausal decline in female fertility was established prior to decline in survival , even when a model involving preference among young pairs was used ( Fig . 1B ) . But this model for the evolution of menopause is not entirely satisfactory . Whereas the accumulation of age-dependent mutations that were deleterious to survival was delayed , the ancestral state already was characterized by early female loss of fertility ( menopause ) . How did this arise ? Some evidence suggests that ancestral human populations were characterized by intrinsic female menopause [55] . Human evolution , particularly through social change following brain modification , decreased extrinsic causes of mortality , and such modifications would account for survival beyond the ( assumed ) fixed decline in female fertility . But why did not natural selection act to extend female fertility in parallel with extended life span ? And why was male fertility extended concomitantly with life span ? Lahdenperä et al . [65] suggested that positive selection to increase male life span allowed reproduction throughout . This implies that female life span did not increase , because female fertility was fixed . Research foci should shift from why women live past reproductive age to why older women experience a reproductive decline . As an alternative model for the origin and evolution of menopause , we simulated the introduction of sex-specific , infertility-causing mutations into a population for which prolonged fertility was the ancestral state for both sexes ( Table S4 ) . In this model , an early decline in female fertility was driven by a shift in male mating preference . Male behavior changed from age indifferent preference ( model AP ) to preference for younger females ( model YF , Table S3 ) . When an age indifferent preference matrix ( MijAP , Table S3 ) was used , purifying selection operated against sex-specific , infertility-causing mutations ( Table S5 ) , as well as sex-indifferent , mortality-causing mutations ( Table S6 ) . This is indicated by the equilibrium frequencies of infertility-causing and mortality-causing mutations that were obtained ( Table 2 B1 ) . Only for the locus having the oldest age of expression did infertility-causing mutations accumulate to appreciable frequencies . As infertility-causing mutations generally did not accumulate , fertility and survival remained high into old age ( Fig . 2A ) . There was no menopause . However , with a matrix involving preference for younger females ( MijYF , Table S3 ) , female-specific mutations with a late age of onset became nearly neutral and accumulated in the population , reaching fixation in most simulation replicates . Because male mating was retained into old age , mortality in both sexes , as well as male fertility , behaved in the same way they did with the age indifferent preference model . However , female-specific infertility-causing mutations accumulated ( Table 2 , compare model AP vs . YF ) . As a result of these mutations , female fertility declined before female survival , and female menopause arose ( Fig . 2B ) . Male menopause never arose because male-specific infertility-causing mutations were subjected to purifying selection and did not accumulate ( Table 2 , B2 , model YF ) . We emphasize that the role of the sexes would be reversed in this model if the matrix were to encode female preference for younger males ( rather than male preference for younger females ) and that the current role of the sexes may be interpreted equivalently as older females being out-competed completely by younger females , their dominant behavior driving the decline in female fertility . One difficulty with this model is the effect of sex-indifferent , mortality-causing mutations . These produce a sex-specific decline in survival that parallels mating preferences . This parallel decline could be avoided if mutations affecting male mortality were more frequent than were mutations affecting female mortality . The difference between age indifferent preference ( model AP ) and preference for younger females ( model YF ) in producing a menopausal effect was robust across population sizes and mutation rates . We next explored the effect of shifting from a matrix encoding age indifferent preference ( MAP; producing mutation-selection balance ) to a matrix encoding preference for younger females ( MYF; relaxation of selection leading to fixation ) , this time involving a single locus with age of onset in class 6 ( equivalent to 30–34 years ) . Larger mutation rates produced more-rapid approaches to fixation when selection was reduced ( Fig . 3 , compare μ = 0 . 005 vs . 0 . 001 for N = 1000 , e = 0 . 025 ) . During the mutation-selection balance period ( model AP ) , the equilibrium frequency was higher for higher mutation rates . Increasing the selection strength reduced the equilibrium frequency of mutations ( Fig . 3 , compare e = 0 . 025 vs . 0 . 075 for N = 1000 , μ = 0 . 005 ) but imparted little effect on the approach to fixation during relaxed selection ( YF ) . Decreasing the population size imparted little effect ( Fig . 3 , compare N = 1000 and 250 for μ = 0 . 001 , e = 0 . 025 ) . Thus , although the parameters in our model , such as age of onset , mutation rate , selection strength , and population size did affect the shift from mutation-selection balance ( model AP ) to relaxed selection ( model YF ) , the shift consistently tended to produce a menopausal period .
Two assumptions in our model are critical to the generation of menopause . The first is a shift of male preference toward younger females . The second is the existence of female-specific mutations with detrimental effects on fertility in older women ( such detrimental effects in reality may manifest physiologically as increasing Follicle Stimulating Hormone and Leutinizing Hormone levels [66] and in oocyte depletion and ultimate loss and ovulation cessation and menstruation termination [9] ) . Human male mating preference for younger females could be viewed as an aspect of male driven sexual selection [67] , [68] . Our results demonstrate the importance of considering mating preference in population demography . Under certain conditions , mate choice can be a predominant factor in mutation accumulation and the evolution of senescence . We also have shown that it is not necessary to introduce corollary effects of inclusive fitness or grandmother effects to explain the persistence of less fertile females . That older males and females may provide reproductive benefits to others may be intuitively obvious , but these are not necessary to explain the evolutionary origin of menopause .
The computational model is encoded to simulate a population containing N males and N females evolving over time . Each individual ( at time t ) is assigned to an age class ( 1 to 18 ) . Each individual is associated with a diploid genotype encoded at a number of loci . Mutations at a specific locus may affect either fertility or survival . Mutations may act in a sex-indifferent ( i . e . , either sex ) or sex-specific ( i . e . , male or female ) manner defined by an age of onset ( for the locus ) and with a fertility table . Initially , individuals are assigned pseudo-randomly to age classes , without mutations . Then , a ‘burn-in’ period allows the age distribution to reach an approximate steady state . Each time interval ( representing a 5-year period ) consists of determining a survival probability for each individual according to an intrinsic survival table , modified by the number and kind of mortality-affecting mutations carried . A pseudo-random number is used with the computed survival probability to determine if each individual survives and is assigned to the next age class or dies . All individuals in age class 18 die . Deaths are replaced in the population by new births assigned to the first age class . Births are simulated by selecting a potential father pseudo-randomly from the surviving male population and a potential mother pseudo-randomly from the surviving female population . The appropriate ‘mating preference matrix’ [Mij] is applied to potential parents ( based on their age classes i and j ) to determine the probability of pair formation . Parent male and female fertility probabilities are determined using intrinsic fertility tables , modified by the number and kind of mutations carried . A pseudo-random number is used to determine if a birth occurs . If not , the birth simulation process is repeated until potential parents become actual parents . Parents transmit their alleles to offspring according to autosomal Mendelian genetic rules; thus , if a parent is homozygous , then that parent transmits one mutation; if heterozygous , then the mutant allele is transmitted with 50% probability . At this time , the introduction of new mutations into each newborn is simulated using a pseudo-random number and the appropriate mutation rate . Simulations were run on a Power Macintosh G5 personal computer ( running OS X 10 . 5 . 8 ) , using the C programming language ( with the Apple GCC compiler ) . Computational model code is accessible online in the Supporting Information ( Text S1 ) . | The origin and evolution of menopause is understood poorly and explanations remain contentious . Virtually ignored among explanations is the effect that mate choice can exert on an evolving population . We designed and used a computational model and computer simulation to show that male mating preference for younger females in humans could have led to the accumulation of mutations deleterious to female fertility and thereby produced menopause . Our model demonstrates for the first time that neither an assumption of pre-existing diminished fertility in older women nor a requirement of benefits derived from older , non-reproducing women assisting younger women in rearing children is necessary to explain the origin of menopause . | [
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] | 2013 | Mate Choice and the Origin of Menopause |
Malaria and schistosomiasis are major parasitic diseases causing morbidity and mortality in the tropics . Epidemiological surveys have revealed coinfection rates of up to 30% among children in Sub-Saharan Africa . To investigate the impact of coinfection of these two parasites on disease epidemiology and pathology , we carried out coinfection studies using Plasmodium yoelii and Schistosoma mansoni in mice . Malaria parasite growth in the liver following sporozoite inoculation is significantly inhibited in mice infected with S . mansoni , so that when low numbers of sporozoites are inoculated , there is a large reduction in the percentage of mice that go on to develop blood stage malaria . Furthermore , gametocyte infectivity is much reduced in mice with S . mansoni infections . These results have profound implications for understanding the interactions between Plasmodium and Schistosoma species , and have implications for the control of malaria in schistosome endemic areas .
Malaria and schistosomiasis are two of the most important parasitic diseases in the tropics , and together constitute a severe burden to public health and to the economic development of endemic countries . Malaria is estimated to cause 429 , 000 deaths per year , 70% of those occurring in children aged under five years old [1] . The WHO has estimated that schistosomiasis causes about 200 , 000 deaths every year in sub-Saharan Africa and 218 million people were required to undergo preventive chemotherapy against the helminth globally in 2015 . The ranges of Plasmodium and Schistosoma overlap in much of the tropical world , leading to the potential for a great many coinfections of the two parasitic species . It has , for example , been estimated that there may be a greater than 30% coinfection rate among children in Sub-Saharan Africa [2] . Given the importance of such coinfections , interactions between Plasmodium and Schistosoma have been extensively studied both in nature , and using animal models in the laboratory . Epidemiological studies on coinfections have often produced contrasting results: some reports contend that Schistosoma infection can increase susceptibility to Plasmodium falciparum [3–5] , whilst others document a protective effect on P . falciparum incidence [6–9] . Differences in study design , genetic background of host populations and other environmental factors presumably contribute to these conflicting results . Most laboratory-based animal studies have shown an exacerbation of malaria parasitaemia in Schistosoma infected mice [10–13] whilst others have revealed a protective effect of Schistosoma infection against experimental cerebral malaria and associated mortality [14–17] . In experimental S . mansoni infections , it is known that eggs deposited in the liver induce a strong Th2 type immune response [18] . Previous work has suggested that the exacerbation of malaria parasitaemia and protection against experimental cerebral malaria were mediated by a polarized Th2 immune environment which down-modulates inflammatory responses [10 , 15] . The interactions between Schistosoma and Plasmodium are mainly mediated via host immune responses [19 , 20] . Previous animal studies have investigated inter-species interactions using experimental infection with Schistosoma cercariae and Plasmodium-parasitized erythrocytes . As both parasites infect the liver at specific stages in their life cycle , we have focused on the immune reactions against those parasites in the liver . The major immunopathology in schistosomiasis is induced by egg-derived antigens in the liver , and hepatic immune cells develop immunity not only against pre-erythrocytic stages but also the blood stages of malaria parasites [21–24] . We therefore investigated whether an ongoing infection with S . mansoni could affect the rodent malaria parasite Plasmodium yoelii in the livers of mice challenged with sporozoites ( SPZ ) . We also evaluated whether S . mansoni infection affects malaria parasite gametocyte infectivity to mosquitoes , as it has been shown that the infectivity of malaria gametocytes decreases during the early stage of malaria infection due to host serum-mediated immunity [25–27] .
All mouse experiments were approved by the Institutional Animal Research Committee of Nagasaki University ( No . 1506181240 ) and performed according to Japanese law for the Humane Treatment and Management of Animals ( Law No . 105 dated 19 October 1973 modified on 2 June 2006 ) . Six week-old female BALB/cCrSlc ( hereafter referred to as BALB/c ) and C57BL/6NCrSlc ( hereafter referred to as B6 ) mice were purchased from Japan SLC , Inc . ( Shizuoka , Japan ) . Six week-old female CBA/J mice were purchased from Charles River Laboratories Japan , Inc . ( Kanagawa , Japan ) . IFN-γ-deficient ( IFN-γ-/- ) mice and IL-4-deficient ( IL-4-/- ) mice were produced at RIKEN Yokohama Institute , Yokohama , Japan . All mice were maintained in the animal facilities of Nagasaki University with environmentally controlled , specific pathogen free conditions . Experiments were conducted with BALB/c mice unless otherwise specified . The rodent malaria parasites Plasmodium yoelii yoelii 17x1 . 1pp [28] ( hereafter referred to as P . yoelii ) and Plasmodium berghei ANKA ( hereafter referred to as P . berghei ) were used throughout these experiments . A Puerto Rican strain of Schistosoma mansoni was maintained in the animal facilities of Nagasaki University by passage through Biomphalaria glabrata snails and ICR mice or Meriones unguiculatus ( Mongolian jirds ) . In the coinfection model , experimental mice were percutaneously infected with 50 cercariae . In the intraportal infusion model , eggs were collected from the liver harvested from ICR mice after 7 weeks infection with 250 cercariae and stored at -30°C until use . The mice were anesthetized and inoculated with 3 , 000 eggs in a total volume of 100 μL PBS via the portal vein . Infections with P . yoelii and P . berghei were performed by i . v . inoculation with SPZ collected from Anopheles stephensi mosquitoes , as previously described [29] . 50 , 500 , or 1 , 500 SPZ in a total volume of 100 μl PBS were inoculated for assessment of malaria parasitaemia or malaria parasite liver burden . Malaria challenges were performed 10 weeks after S . mansoni-cercariae infection in the coinfection model and 3 weeks after S . mansoni egg inoculation in the intraportal infusion model . In transmission experiments , mice were intravenously infected with one million P . yoelii-parasitized erythrocytes . On day 3 , 4 , and 5 after P . yoelii infection , six-day-post emergence female A . stephensi mosquitoes were allowed to feed on individual mice for 30 minutes and reared in the insectary at 24°C with 80% humidity for seven days . Midguts were harvested from the mosquitoes and oocysts counted by light microscopy . Ten microliters of blood were collected from the tale-vein of mice at each time point . Livers were harvested 42 hours after malaria challenge infection . DNA was extracted from blood or livers using the EZ1 BioRobot ( Qiagen N . V . , Hilden , Germany ) following the manufacturer’s instructions . Real-time quantitative PCR ( qPCR ) was performed on DNA samples using the Applied Biosystems 7500 Real Time PCR system ( Thermo Fisher Scientific , Inc . , Massachusetts , USA ) . A master mix of the following reaction components was prepared: 4 . 75 μL water , 6 . 25 μL Power SYBR Green Master Mix ( Qiagen N . V . , Hilden , Germany ) , 0 . 25 μL forward primer ( 10 μM ) , and 0 . 25 μL reverse primer ( 10 μM ) . 5 μL DNA samples were added as PCR template . P . yoelii or P . berghei 18s gene was amplified using the primers 18s F1 5’ GGAACGATGTGTGTCTAACACAAGGA 3’ and 18s R1 CGCGTGCAGCCTAGTATATCTAAGGACA 3’ ( Table 1 ) . Copy numbers of the parasite 18s gene were quantified with reference to a standard curve calculated from known numbers of plasmids containing the same gene sequence , as previously described [30] . The copy number of parasite 18s gene in each sample was standardized by the simultaneous quantification of the mouse glyceraldehyde 3-phosphate dehydrogenase ( G3PDH ) gene . The mouse G3PDH gene was amplified using the primers MmG3PDHF1 5’ CATCTGAGGGCCCACTGAAG 3’ and MmG3PDHR1 5’ TGCTGTTGAAGTCGCAGGAG 3’ ( Table 1 ) . Thin blood films from the tail-vein were prepared daily from day 2 to 8 after SPZ inoculation , and stained with Giemsa’s solution . The numbers of infected and non-infected erythrocytes ware counted per 10 microscopic fields to calculate parasitaemia and gametocytaemia . Hepatic nonparenchymal cells were isolated after Schistosoma inoculation as follows . Livers taken from each experimental mouse were homogenized in 5 mL of culture medium ( RPMI-1640 supplemented with 10% FCS and 1% penicillin/streptomycin ) using gentleMACS Dissociator ( Miltenyi Biotec , Bergisch Gladbach , Germany ) , filtered through a mesh and suspended in HBSS . To remove liver parenchymal cells , the cells were resuspended in 33% Percoll ( GE Healthcare UK Ltd . , Buckinghamshire , England ) containing 2 . 5mL of 5000U/5mL Heparin ( Mochida Pharmaceutical Co . , Ltd . , Tokyo , Japan ) and were centrifuged at 900 g for 20 min at room temperature . The pellet was resuspended in RBC lysis buffer then washed in HBSS , and resuspended in the culture medium . The following mAbs were used for flow cytometric analysis using BD FACSVerse ( Nippon Becton Dickinson Company , Ltd . , Tokyo , Japan ) : PE anti-mouse CD3e ( 145-2C11 ) , and PE anti-mouse T-bet ( eBio4B10 ) ( Affymetrix , Inc . , California , U . S . ) , PE-Cy7 anti-mouse CD 4 ( GK1 . 5 ) , Percp-Cy5 . 5 anti-mouseCD3e ( 145-2C11 ) , PE-Cy7 anti-mouse F4/80 ( BM8 ) , APC anti-mouse Gata3 ( 16E10A23 ) , APC anti-mouse TCRgd ( GL3 ) , PE anti-mouse Ly6G ( Gr1 ) ( RB6-8C5 ) , FITC anti-mouse CD11b ( M1/70 ) , and Biotin anti-mouse F4/80 ( BM8 ) ( BioLegend , California , U . S . ) , FITC anti-mouse CD49b ( DX5 ) , and BV421-streptavidin ( Becton , Dickinson and Company , New Jersey , U . S . ) . Fixation , permeabilization , and staining of the target transcription factors ( T-bet and GATA-3 ) were conducted with Foxp3/Transcription Factor Staining Buffer Set ( Affymetrix Japan K . K . , Tokyo , Japan ) according to the manufacturer’s instructions . Data analyses were performed using Microsoft Excel 2010 ( Microsoft Corporation , Washington , USA ) and GraphPad Prism6 ( GraphPad Software , Inc . , California , USA ) . Significance between control and treatment groups was determined with Student’s t-tests . Survival and protection rates were statistically examined using the log-rank test . P values less than 0 . 05 were considered significant .
To investigate the impact of chronic S . mansoni infection on the growth of malaria parasites in the liver , BALB/c mice were infected with 50 cercariae subcutaneously 10 weeks prior to i . v . inoculation of 1 , 500 P . yoelii sporozoites ( SPZ ) . The number of copies of the malaria parasite 18s gene present in the liver of mice was measured by qPCR 42 h after SPZ challenge . Parasitaemia was monitored daily from day 2 to 8 post SPZ challenge . Malaria parasite liver burden was significantly reduced to one-tenth or less in S . mansoni-infected mice compared with non-S . mansoni infected controls ( Fig 1A ) . This reduction in the numbers of malaria parasites in the liver resulted in a delay in the onset of parasitaemia in the S . mansoni-infected group . All mice in both groups developed blood stage malaria parasite infection , and the peak parasitaemia was not affected by S . mansoni infection ( Fig 1B ) . There was no difference in mortality between S . mansoni-infected and non-infected mice ( Fig 1C ) . The genetic background of inbred mouse strains can determine the course of experimentally induced malaria parasite infections [31 , 32] . We examined the malaria parasite liver burden in three different mouse strains; BALB/c , B6 , and CBA/J in coinfections with S . mansoni . Female BALB/c , B6 , and CBA/J mice were challenged with two different Plasmodium species: P . yoelii and P . berghei in order to investigate whether the reduction of malaria parasite liver burden occurs across mouse and parasite species . Both B6 and CBA/J mice showed significant reduction in P . yoelii liver burden in S . mansoni-infected mice , consistent with the results observed in BALB/c mice ( Fig 2A ) . The liver-stage growth of P . berghei was also reduced in S . mansoni-infected mice in all mouse strains examined ( Fig 2B ) . There are several potential mechanisms for the reduction in malaria parasite burden at 42 hours post SPZ inoculation in S . mansoni infected mice . It is possible , for example , that antibodies raised against schistosomes may offer protection against sporozoites through cross-reactivity [33] . It is also possible that liver fibrosis caused by schistosomiasis may physically impede the invasion of hepatocytes by sporozoites . Alternatively , there may be suppression of P . yoelii hepatocytic stages in the liver mediated by an altered immune environment in the liver caused by the presence of S . mansoni eggs in the organ . In order to investigate at which point S . mansoni-mediated reduction of malaria parasite in the liver occurs , we measured the number of malaria parasites in both the blood and the liver at time points throughout the 48 h growth of parasites in the liver . Ten microliters of blood were sampled from mouse tail-veins at 10 , 30 , 60 , and 120 min , and livers were harvested at 2 , 12 , 24 , and 42 h after i . v . inoculation of 1 , 500 SPZ . We found that malaria parasite load in the blood was not significantly different in infected compared with non-infected mice at any time point following SPZ inoculation ( Fig 3A ) . In contrast , the number of malaria parasites in the liver were significantly reduced in S . mansoni infected mice from the first 2 h of development in the liver , with the proliferation rate from 2 to 24 h of liver stage parasites also suppressed in these mice ( Fig 3B ) . As eggs are the principle cause of immunopathology in schistosomiasis [34] , we assessed whether direct inoculation of S . mansoni eggs to the portal vein of mice could also reduce liver-stage malaria parasite growth . Female B6 mice were inoculated with 3 , 000 or 10 , 000 frozen S . mansoni-eggs in a total volume of 100 μL PBS into the portal vein . Control mice were inoculated with 100 μL PBS . Three weeks after egg inoculation , each group was challenged with 1 , 500 SPZ of P . yoelii . Liver-stage malaria parasite burden was significantly reduced in both groups inoculated with S . mansoni-eggs ( Fig 4A ) . S . mansoni infection causes severe liver damage associated with eosinophilic granulomas , collagen deposition , and fibrosis due to immunologic reactions to Schistosoma eggs trapped in the tissues . However , livers harvested from the intraportal infusion group did not show conspicuous damage macroscopically . Correspondingly , livers inoculated with Schistosoma eggs developed less and smaller granulomas compared with those harvested from the S . mansoni-cercariae infection group ( Fig 4B and 4C ) . Schistosoma infection is known to induce Th2-biased immune responses in mice . However , egg inoculation induced the infiltration of various immune cells into the liver with little pathology . Interferon-mediated innate immune responses are important modulators of malaria parasite growth in the liver [35] . We therefore focused not only on CD4+ T-bet+ cells and CD4+ GATA-3+ cells but also NK , NKT , and γδT cells which have previously been implicated as important sources of interferon-gamma ( IFN-γ ) in the liver during Plasmodium infection . The representative FACS plots are shown in the supplementary graph ( S1 Fig ) . As shown in Fig 4D and 4E , the upregulation of the immune reaction induced by S . mansoni eggs in B6 mice was similar to that induced by S . mansoni-cercariae infection in BALB/c mice . Both CD4+ T-bet+ cells and CD4+ GATA-3+ cells significantly increased following intraportal inoculation of S . mansoni-eggs as well as after S . mansoni-cercariae infection . In both cases , the increase of CD4+ GATA-3+ cells was the largest among immune cells . The numbers of NK cells significantly increased only during the early phase , one week after intraportal inoculation of S . mansoni-eggs or 8 weeks after S . mansoni-cercariae infection . The numbers of NKT cells also significantly increased in both cases and this increase was greater in the group inoculated with S . mansoni-eggs . There was no significant increase in the numbers of γδT cells in either group . B6 mice infected with S . mansoni-cercariae showed a similar pattern of upregulation of immune cells ( S2 Fig ) . We hypothesised that the reduction of liver-stage malaria parasite burden is dependent on the immune environment such as IFN-γ production from host immune cells accumulated and activated by the presence S . mansoni-eggs in the liver . IFN-γ is a key cytokine for the control of malaria parasites and is induced in the liver during S . mansoni infection . Interleukin-4 ( IL-4 ) is a major mediator for the induction of the immune response against S . mansoni . Therefore , we examined the impact of IFN-γ and IL-4 on liver-stage malaria parasite burden using IFN-γ-deficient ( IFN-γ-/- ) and IL-4-deficient ( IL-4-/- ) mice . IFN-γ-/- and IL-4-/- mice along with wild-type B6 ( B6 WT ) mice were inoculated 3 , 000 S . mansoni-eggs 3 weeks prior to SPZ challenge . Liver-stage malaria parasite burden was measured 42 h after i . v . challenge with 1 , 500 P . yoelii SPZ . Consistent with the result shown in Fig 4A , malaria liver burden was significantly reduced in the intraportal infusion group; however , this reduction was abrogated in IFN-γ-/- and IL-4-/- mice ( Fig 5 ) . Through challenge with 1 , 500 SPZ of P . yoelii , we have demonstrated the impact of S . mansoni infection on liver-stage malaria parasite burden; this number of sporozoites , however , would not be expected to be inoculated naturally during a mosquito bite . In order to mimic the numbers inoculated by a mosquito bite , we reduced the challenge dose from 1 , 500 to 50 SPZ and examined the outcome of Plasmodium infection . BALB/c mice ( N = 16 ) infected with 50 S . mansoni-cercariae 10 weeks previously along with non-infected controls were challenged with 50 P . yoelii SPZ . Seven out of 16 S . mansoni-coinfected mice did not develop malaria parasitaemia , in contrast to all non-S . mansoni infected control mice developing blood-stage malaria on day 5 after SPZ challenge ( Fig 6A ) . When blood stage malaria parasite infection became patent in coinfected mice , it increased rapidly and matched the peak parasitaemia of non-S . mansoni infected controls ( Fig 6B ) . We measured the influence of S . mansoni on the infectivity of malaria parasites to mosquitoes . We allowed Anopheles stephensi mosquitoes to feed on mice infected with S . mansoni + P . yoelii or P . yoelii alone on days 3 and 4 post-infection , when P . yoelii is at its most infectious to mosquitoes . A minimum of eight mosquitoes were allowed to feed on each of 5 mice per group on each day , and the resulting infectivity measured by assessing the proportion of mosquitoes that were infected with oocysts eight days post-feeding , and through quantifying the numbers of oocysts per infected mosquito per group on each day of feeding . Prior to mosquito feeding , the parasitaemia and number of gametocytes circulating in the peripheral blood of mice was calculated via microscopy of thin blood films following i . v . inoculation of P . yoelii-parasitized erythrocytes . The number of gametocytes circulating in S . mansoni-infected mice was significantly higher than that of mice without S . mansoni infection on days 3 and 4 ( Fig 7B ) . Despite this , the proportion of mosquitoes infected with oocysts eight days post blood feed was significantly higher for the S . mansoni uninfected mic , compared to mice infected with S . mansoni ( Fig 7C ) . Furthermore , oocyst numbers were significantly reduced in mosquitoes that had fed on mice infected with S . mansoni , compared to uninfected controls ( Fig 7D ) .
We have examined the impact of schistosome infection on malaria pathology and transmission capacity in a mouse model . Previous animal studies on the interactions between Plasmodium and Schistosoma have focused exclusively on the blood stage pathology of the malaria parasite infection , and have consistently shown that parasitaemia is enhanced in the presence of the helminth [10–13] . We have investigated the effect of helminth-malaria parasite coinfection on the growth of rodent malaria parasites in the liver , and show that S . mansoni-coinfection significantly reduces Plasmodium parasite numbers in the liver following sporozoite inoculation . S . mansoni-coinfection with malaria parasites resulted in a large reduction in the percentage of mice developing blood stage malaria parasite infection when mice were challenged with numbers of SPZ similar to those inoculated during natural inoculation through the bites of infected mosquitoes [36–39] . These results might suggest a possible explanation for the often contradictory results observed in epidemiological studies . Some prospective cohort studies in human populations have suggested a protective effect of Schistosoma infection on Plasmodium infection: Lyke et al . concluded that children infected with Schistosoma haematobium showed increased time to first clinical malaria infection and fewer malaria episodes over the follow-up period compared to children without S . haematobium infection [7] . Doumbo et al . also showed that people coinfected with S . haematobium showed significant delays in time-to-first malaria episode[9] . Hürlimann et al . demonstrated that the Plasmodium parasitaemia incidence rate of children infected with S . mansoni increased after treatment with praziquantel; in contrast , children infected with hookworms had reduced Plasmodium parasitaemia rates following albendazole treatment [40] . Our results , which show that S . mansoni infection reduces the malaria parasite burden in the liver and the number of hosts developed blood stage infection following SPZ inoculation , may account for some of the observed protective effects of Schistosoma infection against malaria . The intraportal inoculation of S . mansoni eggs reduced malaria parasite burden in the liver to the same extent as inoculation of cercariae . As shown in Fig 4 , intraportal inoculation of S . mansoni-eggs induced an increase in the numbers of various lymphocytes , including both Th1 and Th2 cells , NK , NKT , and γδT cells in the liver without apparent fibrotic or granulomatous liver damage . This suggests that the suppression of the growth of malaria parasites in the liver is not mediated by physiological or morphological changes of the liver environment provoked by S . mansoni-eggs , but rather by changes in the hepatic immune microenvironment . Previous animal studies have shown that interferon-mediated innate immune responses are important modulators of malaria parasite growth in the liver [41]; Miller et al . have shown that IFN-γ production from NKT cells ( but not NK cells or T cells ) is critical in reducing malaria parasite burden in the liver [35] . We initially hypothesised that malaria parasite liver burden would increase in S . mansoni-coinfected mice as S . mansoni infection has been shown to induce robust Th2 type immune responses with a corresponding downregulation of Th1 responses [42] . However , contrary to our expectations , malaria parasite liver burden was significantly reduced in S . mansoni-coinfected mice . Both IFN-γ and IL-4 are required for this effect , and both the inoculation of S . mansoni eggs to the hepatic portal vein , and the infection of mice with cercariae were shown to increase innate immune cells and both Th1 and Th2 cells in the liver ( S2 Fig ) . Taken together , this suggest that possible sources of IFN-γ and IL-4 include not only innate immune cells such as NKT cells but also helper T cells accumulated and activated by S . mansoni infection in the liver [43] . IFN-γ-/- and IL-4-/- mice had lower numbers of malaria parasites in the liver after SPZ challenge compared with B6 WT mice even without S . mansoni infection . The experiment was repeated twice with similar results . These results are in agreement with previous studies with IL-4-/- mice although the mechanisms are not fully understood . IL-4-/- mice are known to be more resistant to sporozoite infection than wild-type mice owing to increased NK cell numbers and expression of inducible nitric oxide synthase in the liver [44] . Since the reduction of malaria parasite burden in the liver in co-infected mice was reversed in IFN-γ-/- and IL-4-/- mice , we assume that the mechanism of protection is both IFN-γ and IL-4 dependent . To elucidate the mechanisms underlying this phenomenon , further research into the interactions between Plasmodium and Schistosoma are required . We demonstrated that the infectivity of Plasmodium gametocytes to Anopheles mosquitoes dropped abruptly from a peak on day 3 to zero on day 4 in control mice not infected with S . mansoni . This phenomenon is known in animal models as malaria infection crisis and has previously been reported to occur in numerous Plasmodium species [45] . The mechanisms behind this loss of infectivity have not been fully explained , but previous animal studies suggest that cytokines such as tumor necrosis factor ( TNF ) , IFN-γ , interleukin-6 ( IL-6 ) , and nitric oxide may inhibit the development of gametocytes [25–27] . The infectivity of P . yoelii gametocytes in S . mansoni infected mice was significantly reduced on day 3 post-inoculation which is the peak of infectivity in non-S . mansoni infected mice . Observing higher levels of IFN-γ in the sera of S . mansoni infected mice on day 3 and day 4 ( S3 Fig ) , we assume that pre-existing S . mansoni infection induces activation of the host immune environment and this leads to an accelerated infection crisis . Although the mechanisms remain to be elucidated , this experimental transmission model suggests that S . mansoni infection may reduce malaria transmissibility from mosquitoes to mice . In conclusion , the presence of S . mansoni infection dramatically reduced the number of rodent malaria parasites in the liver . This reduction leads to the inhibition of the development of blood stage malaria parasite infection following inoculation of biologically relevant numbers of sporozoites . We also demonstrate that P . yoelii gametocyte infectivity to mosquitoes is significantly reduced in the presence of S . mansoni in co-infected mice . These results imply that S . mansoni infection can reduce malaria transmission both from mosquitoes to mice and from mice to mosquitoes , and may explain some of the protective effects of Schistosoma infection against malaria described in previous epidemiological studies . | Malaria and schistosomiasis are parasitic infectious diseases that cause severe morbidity and mortality in the tropics . Chronic schistosomiasis causes malnutrition and impaired intellectual development to children while malaria can cause fatal acute infections . Since coinfection of these two parasites is common in the tropics , many studies of both epidemiology and coinfection in animal models have been performed in order to reveal interactions between them . Previous animal studies on the interactions between Plasmodium and Schistosoma parasites have focused on the blood stage pathology of the malaria infection , and have consistently shown that parasitaemia can be enhanced in the presence of the helminth . In contrast , we focused on liver immunopathology in mice during coinfection between with Schistosoma and Plasmodium . We show that S . mansoni infection inhibits Plasmodium parasite growth in the liver resulting in a large reduction in the percentage of mice that go on to develop blood stage malaria following inoculation of low numbers of sporozoites . We also demonstrate that gametocyte infectivity is much reduced in mice with S . mansoni infections . Our results imply that S . mansoni infection can reduce malaria transmission both from mosquitoes to mice , and from mice to mosquitoes . | [
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] | 2018 | Schistosoma mansoni infection suppresses the growth of Plasmodium yoelii parasites in the liver and reduces gametocyte infectivity to mosquitoes |
Bubonic plague ( a fatal , flea-transmitted disease ) remains an international public health concern . Although our understanding of the pathogenesis of bubonic plague has improved significantly over the last few decades , researchers have still not been able to define the complete set of Y . pestis genes needed for disease or to characterize the mechanisms that enable infection . Here , we generated a library of Y . pestis mutants , each lacking one or more of the genes previously identified as being up-regulated in vivo . We then screened the library for attenuated virulence in rodent models of bubonic plague . Importantly , we tested mutants both individually and using a novel , “per-pool” screening method that we have developed . Our data showed that in addition to genes involved in physiological adaption and resistance to the stress generated by the host , several previously uncharacterized genes are required for virulence . One of these genes ( ympt1 . 66c , which encodes a putative helicase ) has been acquired by horizontal gene transfer . Deletion of ympt1 . 66c reduced Y . pestis' ability to spread to the lymph nodes draining the dermal inoculation site – probably because loss of this gene decreased the bacteria's ability to survive inside macrophages . Our results suggest that ( i ) intracellular survival during the early stage of infection is important for plague and ( ii ) horizontal gene transfer was crucial in the acquisition of this ability .
Plague ( caused by the bacterium Yersinia pestis ) is one of the diseases that has most greatly affected the course of history [1] , [2] and is still an international public health concern . The WHO has even categorized this disease as re-emergent in some parts of the world . This problem is heightened by the emergence of multidrug-resistant strains and the potential use of plague as a bioweapon [3] , [4] . Plague is commonly acquired through the bite of an infected flea , when Y . pestis is regurgitated into the dermis during the flea blood meal [5] . It is thought that Y . pestis is then taken up by macrophages , within which it replicates and produces antiphagocytic factors [6] . Next , bacteria released from dying macrophages spread into the draining lymph node , where they actively replicate , circumvent phagocytosis by polymorphonuclear neutrophils ( PMNs ) and confront a number of toxic effectors released by degranulating or lysing PMNs [7] , [8] . These events lead to a hemorrhagic , edematous , swollen and painful lymph node ( the bubo ) , which is the defining clinical feature in the bubonic form of plague [9] , [10] . Overwhelmed , the lymph node allows the bacteria to disseminate into the blood stream; this produces a fulminant , systemic infection and fatal septicemia . In a very low proportion of infections , septicemia causes pneumonia , which in turn enables Y . pestis to be transmitted from person to person via contaminated aerosols . However , we lack important data on the above-mentioned mechanisms . For instance , genes dedicated to the intracellular step have not been identified . Furthermore , recent work suggests that transit through the flea vector enhances Y . pestis' ability to resist phagocytosis by the macrophage [11] . Very few pathways for resisting innate immune effectors have been elucidated [12] . Lastly , the metabolic pathways that enable Y . pestis to replicate during infection are incompletely understood . Regurgitation of Y . pestis into the skin does not always cause bubonic plague and bubonic plague does not always progress consistently to septicemic plague . This indicates that the skin and the lymph node are the two decisive arenas in which Y . pestis must deploy its arsenal to produce a successful infection . Better knowledge of this arsenal will undoubtedly improve our understanding of the pathogenesis of plague and should help us to develop better countermeasures against this disease . To this end , we previously determined the transcriptome of Y . pestis in the most relevant context of infection: the rat bubo [7] . Although the study results were valuable , they were not predictive of the requirements for virulence and merely provided clues about putative biological roles of upregulated genes in vivo that will have to be confirmed using traditional experimental approaches . We therefore pursued our efforts by devising a means of screening a mutant Y . pestis library in which genes previously identified as being up-regulated in vivo had been deleted . This method enabled us to draw up a hierarchical table ranking genes in order of their importance in disease production . The results of further investigations suggest that horizontal gene acquisition had a key role in establishing Y . pestis' ability to successfully initiate infection after the dermal fleabite .
Animals were housed at the Institut Pasteur de Lille animal facility ( accredited by the French authorities for the performance of experiments on live rodents ( #B59-350009 ) and which complies with French and European regulations on the care and protection of laboratory animals ( EC Directive 86/609 and the French Act #2001-486 , issued on June 6 , 2001 ) ) . At the time some experiments were performed , regulations did not require ethical approval . However , all animal experiments had been authorized by the French veterinary authorities ( #59-350218 ) and were performed in compliance with the terms of NIH Animal Welfare Assurance #A5476-01 ( issued on July 2 , 2007 ) . Since 2013 , animal experiments were approved by the Nord-Pas-de-Calais Ethics board committee ( #CEEA 23012 ) . Human serum samples were obtained after the provision of written , informed consent by the donors . The use of human samples has been declared and approved by the MESR and CPP boards ( #DC-2013-1765 ) . The bacterial strains and plasmids used in this study are presented in Table S1 and Text S1 . Mutant Y . pestis strains were generated using allelic exchange methods ( based on lambda Red recombinase or the pCVD442 suicide vector [13] , [14] ) and with the primer sets and plasmids described in Tables S1 and S2 . The coding sequence of interest ( apart from the first and last 50 base pairs ) was replaced by a kanamycin , zeocin or trimethoprim resistance gene that had been amplified from the suicide vectors listed in Table S1 . Each mutant's genotype was confirmed by PCR assays using primer sets that were complementary to the target flanking sequence not involved in the allelic exchange ( Table S2 ) . Mutant strains were complemented with the recombinant pCRII plasmid ( Life technologies , Saint Aubin , France ) containing a wild-type ( WT ) copy of the gene of interest ( including the latter's putative promoter sequence ) . Virulence attenuation was evaluated in either pools of mutants or individual mutants . Wild-type and mutant strains were cultured in Luria broth ( LB ) at 21°C , quantified ( via the optical density at 600 nm ) , diluted to the desired cell density in sterile phosphate-buffered saline ( PBS ) and mixed with equal colony-forming unit ( CFU ) counts of other mutants if required . Fifty microliters of the bacterial suspension were intradermally inoculated ( as previously described [9] ) into 8-week-old female Brown-Norway rats ( Janvier , France ) or OF-1 female mice ( Charles River , France ) . For individual screening , rats and mice were inoculated with 20 and 10 CFUs respectively . For per-pool screening , rats were injected with a total of 100 CFUs ( 20 CFUs of each of five mutants ) . Groups of eight and ten animals were respectively used for individual and per-pool screening experiments . For per-pool screening , animals were euthanized when signs of terminal plague appeared . The inguinal lymph nodes proximal to the inoculation site and the spleens were immediately collected after euthanasia and then triturated . Serial dilutions of the triturated organs were plated on blood agar containing the antibiotics of interest . The CFUs were counted after a 48-hour incubation at 28°C . The virulence attenuation of Y . pestis was measured by calculating the average relative competitive index ( ARCI ) , as follows: where is the CFU count of the tested mutant , is the count for the most abundant mutant ( i . e . the strain that acts like a WT strain in the animal of interest ) , is the animal of interest , is the number of animals used in the experiment , and . Groups of five 8-week-old female Brown-Norway rats ( inoculated as above ) were used to study the time course of lymph node , blood and spleen colonization by bacteria . Lastly , in order to study the role of PMNs cells during infection , groups of 8-week-old OF-1 mice were inoculated intraperitoneally with 100 µg of anti-GR1+ ( RB6-8C5 ) antibody ( as described above ) on the day before the intradermal challenge [8] . Y . pestis grown overnight in LB at 21°C were centrifuged , washed and adjusted to 2×107 bacteria/mL in PBS . Twenty microliters of the bacterial suspension were added to 180 µl of fresh human serum in the wells of a 96-well plate . After different incubation times at 37°C , serial dilutions of the serum were plated on blood agar and the CFUs were subsequently quantified . Macrophages were cultured in high-glucose ( 4 . 5 g/L ) Dulbecco's Modified Eagle Medium ( DMEM ) supplemented with 5% heat-inactivated fetal bovine serum . All incubations were performed at 37°C in a 5% CO2 atmosphere . In the intracellular survival assay , cells were infected as previously described [15] . The day before interaction with Y . pestis , 1 . 4×105 RAW macrophages in the incubation medium were allowed to seed in the wells of a 24-well plate . The macrophages were washed three times in incubation medium prior to addition of Y . pestis that had been grown overnight at 21°C in LB , washed and suspended in DMEM . The multiplicity of infection was 10 . Immediately after mixture of the bacterial suspension and the macrophages , the plates were centrifuged ( at 18 g for 5 minutes ) and then incubated . After 30 minutes , the cells were washed three times . Fresh medium supplemented with 8 µg/mL gentamicin was then added . One hour later , the wells were washed twice and fresh medium containing 2 µg/mL of gentamicin was added . At various time points after contact , macrophages were lysed by incubation in 500 µl of cold water for 10 minutes on ice . The lysate was plated on blood agar and the number of CFUs was counted . A mutant was considered to be virulence-affected when the survival curves for animals infected with the mutant and the wild-type strain were found to be significantly different ( p<0 . 05 ) in a Gehan-Breslow-Wilcoxon test . Two-way analysis of variance was also used to compare data obtained from in vitro experiments; a p<0 . 05 was considered significant .
We previously reported that 526 Y . pestis genes ( other than previously characterized virulence factors ) were up-regulated more than two-fold in the rat bubo , relative to Y . pestis cultures grown at 21 or 37°C [7] . Seventy-nine of these are highly likely to encode factors important for plague pathogenesis because they are ( i ) up-regulated in both the rat bubo and the mouse lung or ( ii ) thought to be involved in the bacterium's defensive response against nitric oxide , oxidative stress and iron stress generated by the immune system during bubonic plague in rat [7] , [16] . We therefore generated a library of Y . pestis mutants in which one or more of these 79 genes had been deleted and then tested the individual mutant's virulence in mouse or rat models of bubonic plague . In order to reduce the number of mutants and the number of animals to be sacrificed , we deleted blocks of neighboring genes ( whenever possible and regardless of their genetic organization and relationship ) . Hence , a total of 33 mutants ( rather than 79 ) were produced . The mutant lacking the functional pyruvate dehydrogenase AceEF [17] was the only one to show a serious growth defect on LB agar . As expected , this mutant did not kill any mice 15 days after inoculation with a dose ( ∼20 CFUs ) that usually kills all the animals ( Table S3 ) - indicating that the mutant was virulence-attenuated . Screening of the library for virulence revealed five attenuated mutants ( Table S3 ) . The strains lacking the homoserine O-succinyltransferase gene metA , the ribonucleoside reductase operon nrdHIEF or the uncharacterized gene ypo0426 required significantly more time to cause fatal plague ( median survival ≥5 days vs 4 days for the mutants and the wild-type strain respectively ) . The Δypo0988 , ΔptsG and ΔznuABC ypo2062 mutants ( respectively lacking the genes for an uncharacterized protein , the glucose transport PtsG and both the Zinc ( Zn ) transporter ZnuABC and the putative zinc murein DD-endopeptidase YebA [18] ) caused plague but at a lower incidence ( survival rate >60% vs ≤12 . 5 for the mutants and the wild-type respectively ) . Further virulence assays showed that deletion of yebA ( but not znu ) was responsible for the loss of virulence in the ΔznuABC ypo2062 mutant ( Figure 1 ) . Next , we sought to establish which of the remaining 447 up-regulated genes were required for plague progression in rats . To this end , we used the approach described above to build a library of 137 mutants and designed a per-pool screening method for application in the most relevant model of infection . Hence , rats were intradermally inoculated with about the dose of Y . pestis ( the input pool ) that is regurgitated by the rat flea Xenopsylla cheopis ( i . e 100 CFUs ) [19] . In order to reduce the selection of false positives and false negatives , each mutant in the pool was given at the lowest dose that consistently killed all the rats in experiments with the WT ( 20 CFUs ) . In other words , each pool comprised five mutants . A wild-type strain was not included in each pool because ( i ) we postulated that at least one mutant in the pool would have a wild-type like phenotype ( i . e . colonizing and killing the animals as efficiently as the wild type ) and ( ii ) we expected a bottleneck restricting bacterial dissemination from the skin to the other tissues [20] . To overcome bias due to bottlenecking , we were therefore obliged to evaluate each pool in several animals ( see below ) . To identify mutants within a pool , we provided each one with a specific antibiotic resistance marker ( kanamycin , trimethoprim or zeocin , denoted respectively as K , T and Z ) . The mutants were generated using a virulent strain bearing ( or not ) a zeocin resistance gene at the only neutral att Tn7 chromosomal site ( Figure S1 ) . Hence , five groups of tagged mutants were produced ( K , T , Z , ZK , and ZT ) . Each mutant was counted in the lymph nodes and the spleen ( the output pool ) , in order to ( i ) identify genes that might be necessary during early or late stages of infection and ( ii ) reduce the likelihood of missing false negatives ( i . e . attenuated mutants that might artificially appear to be virulent as a result of transcomplementation of the mutant's defects by other strains in the pool or the production of an abnormal environment that facilitates the growth of an otherwise virulence-attenuated mutant . We expected rescued mutants to be more outcompeted in the spleen than in the lymph node . Hence , we did not expect a virulence-attenuated mutant to be fully rescued in the final “bacterial read” in the spleen - even when it may have been transcomplemented in the pool . Lastly , the bacterial loads for each mutant were determined in animals displaying terminal clinical signs of plague; at this stage of the disease , ( i ) the bacterial load recovered from an organ ( >107 CFU ) is high enough to ensure reproducible recovery of each mutant , and ( ii ) all the mutants would have experienced similar in vivo growth conditions because the organs from terminally ill animals described in the literature ( and certainly from our experience ) were all histologically similar [9] , [21] . Using this strategy , a total of 24 pools comprising five mutants and one pool comprising four mutants ( i . e . 124 mutants in total ) was tested . Due to technical issues , the remaining 13 mutants had to be individually evaluated for virulence . One of the 13 had a longer incubation period in animals and was found to lack the prophage ypo1089-ypo1091 locus ( Table S3 ) . Terminal signs of plague occurred 2 to 5 days after inoculation of the pools . The mean time to sacrifice was 2 . 6±0 . 4 days ( Figure S2 ) . At the time of sacrifice , the lymph nodes and the spleens from all rats were heavily colonized , with a median bacterial count of >108 CFU ( Figure S3 ) . These data suggested that each pool behaved collectively like a WT strain and that at least one mutant within each pool was fully virulent because i ) the time course of infection of animals infected with either ∼20 or ∼100 wild-type CFU did not differ significantly ( data not shown ) and ii ) organs from animals showing terminal signs of plague reportedly contain more than 107 CFU [9] , [22] , [23] . Analysis of the CFU counts in the output pools revealed that there was a bottleneck that affected bacterial dissemination from the skin to the other tissues . The proportions of clones recovered from the lymph nodes and the spleens did not appear to follow a particular trend; a clone that completely dominated colonization in one animal could be totally outcompeted in another ( Figure S4 ) . This variable “dominant effect” hampers the identification of genes needed for microbial pathogenesis [24] , [25] . However , we considered that the “dominant effect” observed in individual animals would be balanced by an analysis of the group of animals ( notably by averaging the data obtained from the different replicates ) . Hence , we determined the relative virulence of each mutant for a specific organ by calculating an average relative competitive index ( ARCI ) for each mutant by using the equation described in the Methods section . Following inspection of the data , we divided the mutants into three virulence classes with either a high to moderate likelihood of being virulence-attenuated ( ARCI≤10 ) , a moderate to low likelihood ( 10<ARCI<20 ) or no likelihood ( ARCI≥20 ) ( Table S4 ) . The first two categories comprised about 40% of the mutants ( 48 out of 124 ) ( Table 1 ) . Fifteen of the 48 mutants lacked metabolic genes , 5 lacked stress response genes , 2 lacked fimbriae/adhesin genes and 26 lacked uncharacterized genes . Interestingly , some mutants were more outcompeted in the lymph node than in the spleen ( or vice-versa ) , suggesting that specific factors are required to colonize each organ . To confirm the validity of our hierarchical ranking , the individual virulence of a number of mutants from each of the three different ARCI classes ( 11 , 5 and 4 mutants , respectively ) was evaluated in the rat . Eight mutants ( variously lacking Pap fimbriae , Ail or uncharacterized proteins ) were found to be significantly less virulent ( Table S4 ) and all had an ARCI<20 . Furthermore , 65% and 20% of the attenuated mutants were from the “high ARCI” and “low ARCI” categories , respectively; this suggests the existence of a correlation between the ARCI category and loss of virulence . The attenuation of the mutants described above might conceivably result from a secondary mutation generated during the construction process . To test this hypothesis , we compared the virulence of the wild-type strain and several mutants ( Δypo0988 , Δypo2062 , Δypo0656 , Δypo2560-61 , Δypo3369 and Δypo3991 ) harboring ( or not ) a wild-type copy of the gene of interest on a plasmid ( Figure S5 ) . In accordance with the results of the above-described infection experiments , all mutants were significantly affected in virulence ( p<0 . 05 in a Gehan-Breslow-Wilcoxon test ) . The virulence of the complemented mutants Δypo0988 , Δypo2062 and Δypo0656 was fully restored . In contrast , the virulence of the complemented mutants Δypo2560-61 , Δypo3369 and Δypo3391 was not restored - indicating that these mutants carry a secondary mutation or that the methodology used for complementation was inadequate . Iron , manganese and zinc play a crucial role in many biological processes - including bacterial virulence . Hence , it is not surprising that a marked number of metal acquisition systems are up-regulated by Y . pestis in vivo [7] , [16] . Notably , Y . pestis overexpresses 12 of the 15 proven or potential iron transport systems ( Table S5 and Text S1 ) . It has previously been shown that the absence of the Yersiniabactin ( Ybt ) siderophore-dependent system or the Yfe ATP-binding cassette ( ABC ) iron transporter ( but not that of the heme transporter Hmu , the hemin storage system Hms or the ABC iron transporters Yiu and Yfu ) attenuates the virulence of Y . pestis in a mouse model of bubonic plague [26] , [27] , [28] , [29] , [30] . Our present data ( obtained in a different Y . pestis strain and a different rodent species and strain ) confirmed that Hmu , Hms , Yiu and Yfu are not required for bubonic plague ( Table S5 ) . Furthermore , we found that three siderophore-based systems , two ABC ion transporters and two iron permeases were not necessary for disease production , despite upregulation of the corresponding genes in vivo; this is presumably because some of these transport systems are not functional in Y . pestis due to frameshift mutations or gene disruption by an insertion sequence [31] . In vivo , Y . pestis also overexpresses the ZnuABC zinc transporter and the MntH manganese transporter ( which are also the predominant zinc and manganese importers in vitro ) . However , mutants lacking Znu and MntH were found to be fully virulent ( Figure 1 and Table S5 ) , which agrees with recent findings in a different Y . pestis strain and a different animal model [32] , [33] . Inspection of the expression patterns for genes involved in metabolic pathways has suggested that carbohydrates constitute Y . pestis' main carbon and energy source during successful colonization of the mammalian host [7] . The results of our virulence tests indicated that the uptake of glucose , gluconate and , to a lesser extent , maltose ( but not that of fructose , mannose or fructuronate ) was important for plague production ( Figure 2 and Table S3 and S4 ) . Glucose is known to be metabolized via glycolysis; however , the latter did not appear to be essential for plague production because deletion of the first two enzymes in this pathway ( Pgi and PfkA ) did not affect Y . pestis' virulence . Although the presence of a complete glycolysis pathway was not essential for host colonization , its terminal part appeared to be essential because a gpmA mutant was completely outcompeted in vivo . The end product of glycolysis ( pyruvate ) is an important source of acetyl CoA , some of which enters the tricarboxylic acid ( TCA ) cycle via reaction with citrate synthase . However , this step ( and perhaps the TCA cycle as a whole ) was not essential for virulence . Thus , Y . pestis relies on carbohydrate metabolism to produce disease but carbohydrates such as glucose are not necessarily directed towards the expected metabolic pathways . During infection , Y . pestis is in close contact with PMNs that produce inducible nitric oxide synthase . The bacterium uses the flavoglobin Hmp to detoxify reactive nitrogen species ( RNS ) released by the PMNs [7] . Of the Y . pestis genes that are up-regulated in the rat bubo and for which homologs in other bacterial species are involved in the response to RNS , aceE that we found to be necessary for virulence was also found to be required for optimal bacterial growth in vitro ( Table S6 and Text S1 ) . Therefore , one can hypothesize that aceE is essential for virulence but is not necessarily required to counter the host PMNs' production of nitric oxide . Of the 8 other loci thought to protect Y . pestis against RNS in vivo , only nrdHIEF ( the ribonucleotide reductase operon ) appeared to contribute moderately to virulence; deletion of this operon was associated with a slightly longer time to disease onset ( Table S6 ) . The absence of induction in the rat bubo of ( i ) the OxyR oxidative stress regulator regulon and ( ii ) the genes known to protect bacteria against peroxynitrite ( i . e . the product of the reaction between hydrogen peroxide and nitric oxide ) suggested that Y . pestis is not exposed to reactive oxygen species ( ROS ) in the mammalian host [7] . However , a limited number of genes involved in bacterial resistance against ROS are known to be activated in the rat bubo , and they may encode the major detoxifying proteins . Furthermore , several genes involved in the detoxification of ROS ( which are downregulated or at least not upregulated in the rat bubo ) were shown to be upregulated in Y . pestis replicating inside a macrophage-cell line in vitro [34]; macrophage infection is thought to be important in the initial stages of infection [6] . Of the genes known to protect bacteria against oxidative stress , Y . pestis overexpresses mntH , fumC , yfiD ( grcA ) and ibpA in vivo . As mentioned above , MntH is required for manganese import ( which in turn is involved in protection against ROS ) , whereas FumC and GrcA rescue enzyme reactions when FumA and PflB are damaged by ROS [35] , [36] , [37] . IbpA protects several metabolic enzymes against oxidation [38] . Deletion of the ibpA gene was associated with attenuation of virulence because the mutant was outcompeted in per-pool infection . However , IbpA is also involved in resistance to other forms of stress , such as high temperatures [38] , [39] . In contrast , deletion of fumC , yfiD or mntH did not affect the virulence of Y . pestis ( Table S6 ) . Thus , our data provide further evidence for the hypothesis whereby ROS production may not be a significant host defense factor in plague . Some of the Y . pestis genes overexpressed in the rat bubo and required for virulence in the present study have been acquired horizontally . These genes include the P4-like prophage genes ( ypo1089 to ypo1091 ) and the ypmt1 . 66c gene ( which encodes a putative DNA binding protein and is located on the pMT1 plasmid ) . A Δypmt1 . 66c mutant was outcompeted in a per-pool assay ( Table S4 ) . Furthermore , the absence of the ypmt1 . 66c gene decreased the incidence of plague in both rats and mice ( Figure 3 ) . The virulence of the mutant expressing a WT copy of ypmt1 . 66c under the control of its own promoter from a high copy number plasmid ( pCR2 ) was almost identical to that of the wild-type strain . The complemented mutant killed 100% of animals but caused fatal plague with a slight lag ( a median survival time of 4 days , vs . 3 . 5 for the WT ) . Even though this slight difference in virulence was statistically significant ( P = 0 . 038 ) , this result indicated that loss of virulence was certainly due to deletion of ypmt1 . 66c . Interestingly , animals which survived inoculation with the Δypmt1 . 66c mutant had enlarged , purulent lymph nodes but not severe splenomegaly - suggesting that the mutant was impaired in its ability to initiate skin colonization and/or to escape from the draining lymph node . Consistently , the times to colonization of the lymph node , blood and spleen were significantly longer in rats infected with the mutant than in rats infected with the WT strain ( Figure 4 ) . Taken as a whole , our data indicate that a mutant's virulence is attenuated because the host manages to contain the infection during the initial skin colonization step , for which intracellular replication within macrophages is thought to be important [6] . In accordance with this model , the Δypmt1 . 66c mutant was more susceptible to macrophages than the WT and the complemented mutant ( Figure 5A ) . The mutant's failure to initiate infection at the fleabite site could also result from its inability to use available nutrients or to survive contact with PMNs once released from lysing macrophages . The mutant showed a slight but statistically significant growth defect in serum ( used here as model of nutrient acquisition in the skin ) , when compared with the WT and the complemented strain ( Figure 5B ) . Furthermore , the mutant's respective levels of virulence in neutropenic and immunocompetent mice did not differ significantly ( Figure 3C ) ; this indicates that YPMT1 . 66c is involved in a biological process situated upstream of interaction with PMNs . Taken as a whole , our data suggest that YPMT1 . 66c's role in survival within macrophages is important in the production of plague . Approximately 40% of the mutants found to be virulence-attenuated in individual tests and classified in the high-to-low likelihood class for virulence attenuation ( i . e . ARCI≤10 and 10<ARCI<20 ) are known to encode proteins related to metabolic pathways . The remaining 60% of mutants lack factors considered to be involved in stress resistance ( e . g . IbpA or RseC ) , bacterial attachment or serum resistance ( e . g . Ail ) . Indeed , most of the remaining mutants are involved in hypothetical or uncharacterized processes . To determine whether the selected mutants were impaired for bacterial growth in vivo rather than for resistance to the host immune system , we measured their ability to grow in fresh serum ( Figure 6 ) . However , the mutants with a 10<ARCI<20 were not evaluated because we expected few strains in this ARCI category to be truly virulence-affected ( see discussion ) and therefore expected even fewer mutants to be serum-sensitive . Furthermore , the ΔaceEF mutant ( identified in an individual virulence test ) was not evaluated because its serious growth defect in LB led us to expect a growth defect in serum . Lastly , the ARCI≤10 mutants lacking genes that encode fimbriae or genes found to be non-essential for plague in individual tests were not evaluated because ( i ) we did not expect fimbriae to be involved in serum resistance and ( ii ) mutants that were found to be attenuated in per-pool screening but not in individual tests were considered to be false positives . Hence , a total of 23 mutants were evaluated for serum resistance . Ten of them differed significantly from the wild-type strain . In particular , the ΔrseC , Δypo0337 and ΔgpmA mutants were sensitive to serum ( presumably because of the bactericidal effect of the complement ) , and the ΔibpA , Δypo3369 , Δypo0988 , Δamn , Δypo0617-0618 , Δypo2586-2587 and Δypo0426 mutants had significant a growth defect ( either because they confer a metabolic advantage or effective complement tolerance ) .
We leveraged our previously collected in vivo gene expression profiling data on Y . pestis [7] to generate and screen a non-polar tagged deletion mutant library composed of bacteria lacking genes known to be upregulated in vivo . This approach constituted the first integrated attempt to identify the molecular mechanisms of the pathogenesis of plague in the most relevant biological model of infection - the mammalian host . Our approach could be transposed to other pathogens and notably those for which a dissemination bottleneck has been described [24] , [25] . Our data provided further insights into the pathogenesis of plague by identifying new genes important for plague pathogenesis ( including several previously uncharacterized sequences ) . The fact that these genes are shared with several other pathogens suggests that they belong to a core set of bacterial pathogenesis genes . Confirmation of these virulence results ( by individually testing the virulence of mutants selected from the pools and by performing complementation experiments ) and further characterization of these new identified genes will be necessary . Future studies should therefore improve our understanding of pathogenesis and , in particular , yield putative antibiotic targets for several diseases . | In order to understand and combat infectious diseases , it is essential to characterize the full set of genes required by pathogenic bacteria to overcome the many immunological and physiological challenges encountered during infection . Here , we used a genome-scale approach to identify genes required by the bacterium Yersinia pestis in the production of bubonic plague ( a fatal , flea-borne zoonosis ) . Our results suggest that when colonizing the mammalian host , the bacterium ( i ) relies on carbohydrates as its carbon source , ( ii ) shifts to anaerobic respiration or fermentation and ( iii ) experiences and resists several ( but not all ) types of stress generated by the host's innate immune system . Strikingly , only a small set of genes ( including horizontally acquired and uncharacterized sequences ) are required for these infectious processes . Further investigations of the ypmt1 , 66c gene provided evidence to suggest that accretion of genetic material via horizontal transfer has played a key role in Yersinia pestis' ability to successfully initiate infection after the dermal fleabite . Lastly , we believe that ( i ) application of our approach to other pathogens and ( ii ) additional studies of selected Yersinia pestis genes important for plague pathogenesis ( some of which are shared with other pathogens ) will provide a better understanding of bacterial pathogenesis in general . | [
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] | 2014 | New Insights into How Yersinia pestis Adapts to Its Mammalian Host during Bubonic Plague |
Negative-sense RNA viruses assemble large ribonucleoprotein ( RNP ) complexes that direct replication and transcription of the viral genome . Influenza virus RNPs contain the polymerase , genomic RNA and multiple copies of nucleoprotein ( NP ) . During RNP assembly , monomeric NP oligomerizes along the length of the genomic RNA . Regulated assembly of the RNP is essential for virus replication , but how NP is maintained as a monomer that subsequently oligomerizes to form RNPs is poorly understood . Here we elucidate a mechanism whereby NP phosphorylation regulates oligomerization . We identified new evolutionarily conserved phosphorylation sites on NP and demonstrated that phosphorylation of NP decreased formation of higher-order complexes . Two phosphorylation sites were located on opposite sides of the NP:NP interface . In both influenza A and B virus , mutating or mimicking phosphorylation at these residues blocked homotypic interactions and drove NP towards a monomeric form . Highlighting the central role of this process during infection , these mutations impaired RNP formation , polymerase activity and virus replication . Thus , dynamic phosphorylation of NP regulates RNP assembly and modulates progression through the viral life cycle .
Influenza viruses are major human respiratory pathogens that cause isolated seasonal outbreaks as well as the sporadic emergence of severe pandemics [1] . Influenza A virus ( Orthomyxoviridae ) is a segmented , negative-sense RNA virus . Like all other negative-sense RNA viruses , the influenza virus genome associates with the viral RNA-dependent RNA polymerase and multiple copies of the viral nucleoproteins ( NP ) to form ribonucleoprotein complexes ( RNP ) [2] . Within the RNP , the hetero-trimeric polymerase ( composed of subunits PB1 , PB2 and PA ) catalyzes both transcription of viral messages and replication of the viral genome using an RNA template that is encapsidated by oligomeric NP [2 , 3] . The regulated oligomerization of NP and assembly of the RNP is absolutely essential for successful infection , yet how influenza virus controls the formation of these multi-subunit complexes is largely unknown . Upon infection , viral RNPs are released in to the host cell cytoplasm and actively transported to the nucleus [4] . In the nucleus , incoming RNPs are transcribed by their resident polymerase through a cap-snatching mechanism utilizing short host-derived 7mG-capped RNAs to prime synthesis of influenza mRNAs [5] . Synthesis of new proteins from the resultant mRNAs enables replication of the viral genome . Replication is primer independent and proposed to be performed in trans by a “free” RNA polymerase that copies the vRNP template to create positive-sense complementary-RNA ( cRNA ) [6] . cRNA synthesis is accompanied by concomitant encapsidation by NP to form cRNP complexes [7] . These cRNPs then direct the synthesis of new viral RNAs ( vRNAs ) and vRNPs that may be transcribed , template further replication , or be packaged into progeny virions . cRNPs and vRNPs assume double helical structures composed of repeating NP subunits coating the genomic RNA [8–12] . Both genomic termini are located at the same end of the RNP where they are bound by the viral polymerase . This structural organization can be attributed to extensive intermolecular contacts between individual molecules of NP and between NP and genomic RNA . NP oligomerization occurs via a small “tail loop” ( aa402-428 ) that projects away from the body of the protein and inserts into the binding groove of the interacting protomer [13 , 14] . Multiple interactions between the tail loop and binding groove , notably a critical salt bridge between R416 of the tail loop with the E339 of the groove , contribute to self-association of NP and its ability to support RNP formation [13–18] . Additional contacts outside of the tail loop:binding groove interface are also important for the formation of higher-order structures; NP makes secondary intersubunit contacts thought to be important for helical strand formation , binds RNA via a patch of conserved basic residues located opposite the tail loop , and associates with the polymerase via conserved residues in a surface loop [9 , 14 , 19 , 20] . Current models suggest that an NP monomer is initially recruited to form RNPs through direct interaction with the viral polymerase and binds to the nascent 5’ end of the viral genome [7] . This nucleates RNP assembly which is followed by NP:NP homo-oligomerization [8 , 9 , 13 , 21–23] . The NP tail loop likely undergoes a conformational change during oligomerization , where it extends away from the body of the protein and fully exposes the RNA-binding surface [24] . RNA binding stabilizes the replication intermediates , and RNA binding and oligomerization are likely cooperative [24–26] . Incoming NP molecules unidirectionally extend the oligomer by inserting their tail loop into the binding groove of a pre-existing complex [14 , 24 , 25 , 27] . When expressed alone , influenza NP self-assembles into oligomers and binds cellular RNA in a sequence-independent fashion [13 , 14 , 20 , 24 , 28] . Yet , RNP assembly during infection requires a fraction of NP to be maintained in an RNA-free monomeric form prior to its assembly into bona fide viral RNP complexes [24 , 29] . Small molecules that disrupt this balance , by either inhibiting or forcing premature oligomerization , prevent RNP assembly and inhibit virus replication [16 , 30–32] . Thus , in infected cells a regulated process must maintain a pool of NP monomers and simultaneously allow for the ordered assembly of new RNPs . Non-segmented RNA viruses solve this problem by encoding the phosphoprotein P . P maintains nucleoprotein in an RNA-free monomeric form and chaperones its assembly into the RNP [33–36] . Segmented RNA viruses like influenza lack any analogous viral proteins . It is currently unknown how influenza virus maintains a pool of monomeric , RNA-free NP that then dynamically changes to a high-order oligomeric state and encapsidates genomic RNA in the RNP . NP is phosphorylated during infection , with patterns changing throughout the infectious cycle and dependent on both the viral strain and the host cell [37–40] . Phosphorylation occurs primarily on serine residues , several of which have been recently mapped in a phosphoproteomic survey of influenza virus proteins [24 , 37 , 39–41] . It was proposed over 35 years ago that phosphorylation may regulate its function [39] , but the mechanism ( s ) remained largely unknown . Here we show that phosphorylation of NP inhibits oligomerization in cells and define the molecular mechanisms by which this modification impairs RNP formation . We identified three serine residues at the NP:NP interface that regulate oligomerization and play a critical during viral replication . We show that two of these residues are phosphorylated during infection . One of our newly identified phosphorylation sites , NP S407 , resides in the tail loop , whereas the previously identified phosphorylation site S165 is at the entrance to the binding groove . Thus we demonstrate that phosphorylation on either side of the binding interface blocks NP:NP interactions . An additional residue at the binding groove entrance , S486 , contributes to regulated assembly by preventing hyper-oligomerization . Mutation of these evolutionarily conserved residues or introduction of phosphomimetics distorts the monomer:oligomer balance in cells and severely impairs polymerase activity and virus replication . We show that a similar regulatory mechanism controls influenza B virus NP , suggesting a common strategy used throughout influenza virus genera . Our findings show that the regulated conversion of NP between mono- and oligomeric states is important for RNP formation , gene expression and viral replication , and support a model by which dynamic phosphorylation of NP regulates the viral replication machinery by controlling NP oligomerization .
The mechanisms controlling NP oligomerization and RNP formation are poorly understood , although it has been recently suggested that post-translation modifications may be key regulators of this process [24 , 41 , 42] . We sought to examine whether phosphorylation of NP can modulate its oligomerization state in cells . Using the prototypical influenza strain A/WSN/33 as a model , the oligomerization state of NP was assessed by expressing protein in human 293T cells and separating the lysate by size exclusion chromatography . Wild-type ( WT ) NP formed a wide distribution of different oligomeric species with only a very minor portion eluting in the monomeric fractions ( Fig 1A ) . In contrast , the previously characterized oligomerization-defective mutant NP R416A shifted dramatically to lower molecular weight species and eluted with a large portion of the protein in the monomer fractions . These data argue that the large majority of NP in cells spontaneously oligomerizes and that only a very minor fraction exists as a monomer . These findings agree with previous observations [17] and establish a robust system to analyze NP oligomerization in cells . Phorbol 12-myristate 13-acetate ( PMA ) stimulates the phosphorylation of NP , presumably by activating its target proteins , protein kinase C ( PKC ) family members , and their downstream effectors [37] . To determine the effect of NP phosphorylation upon its oligomerization , cells expressing NP were treated with PMA and the phosphatase inhibitor okadaic acid prior to lysis and fractionation . PMA treatment significantly shifted the distribution of NP towards the monomeric fractions , suggesting phosphorylation inhibits oligomerization . Moreover , a distinct species of NP migrating more slowly in the gel was detected in the PMA-treated samples , suggestive of a hyperphosphorylated form . Treatment of the cell extracts with phosphatase completely eliminated the slower migrating species ( Fig 1B ) , confirming that PMA stimulation induces NP hyperphosphorylation resulting in slower migration . The hyperphosphorylated species of NP was enriched in the lower molecular weight complexes ( Fig 1A ) , further confirming that phosphorylation negatively regulates NP oligomerization and shifts NP towards a monomeric state . Phospho-labeling has shown that most NP phosphorylation occurs on serine residues [37] . We therefore exploited an unbiased approach to identify serines that are important for polymerase function . Using the NP structure [13 , 14] , prior phospho-peptide analyses [43] , and sequence conservation as a guide , we identified and mutated to alanine 20 surface-exposed serines that could potentially be phosphorylated ( Fig 2A ) . Mutated serines were located in all three major structural domains—the head , body , and tail loop—and included residues identified by phosphoproteomics ( i . e . S9 , S402/403 , S457 and S473 ) . We also mutated the previously identified phosphorylation site at position 3 [43] , which in the A/WSN/33 strain is a threonine . High-throughput polymerase activity assays were performed in human cells expressing NP , the viral polymerase proteins PB1 , PB2 and PA , and a vRNA-like reporter . To ensure sensitivity to minor changes in function , NP was expressed at limiting concentrations . Most NP mutants displayed activity within 2-fold of WT . Many of these mutations removed previously identified phosphorylation sites , indicating that not all phosphorylation sites in NP are essential for viral gene expression ( Fig 2A and [41 , 43 , 44] ) . Strikingly , mutation at S407 and S486 drastically reduced polymerase function supporting less than 10% of the activity of WT NP . To determine if any of the NP mutants selectively block replication versus transcription , we repeated these assays using a cRNA-like reporter that requires at least one round of replication before it can serve as a template for mRNA production ( Fig 2B ) . The NP S407A and S486A mutants continued to demonstrate severe defects in polymerase activity . However , no additional defects in NP function were revealed as all of the other mutants retained activity within 2-fold of WT . Immunofluorescence also showed that mutation at S407 or S486 did not alter the dynamic subcellular localization of NP ( S1 Fig ) . Similar to WT , both NP S407A and S486A localize to the nucleus at early time points and redistribute to the cytoplasm at later time points . These data identify serine residues 407 and 486 in NP as important for supporting polymerase function . Our findings motivated a focused analysis of NP phosphorylation to determine if S407 and/or S486 are post-translationally modified . NP was purified from infected cells and subject to high resolution mass spectrometry ( S2A Fig and S1–S5 Tables ) . From this work we identified two new phosphorylation sites in the NP tail loop at S407 ( Fig 2C ) and S413 ( Fig 2D ) . Two additional phosphorylation sites , S402 , S403 were also identified in this experiment ( S2B and S2C Fig ) . Phosphorylation had previously been partially localized to the tandem S402/S403 in the WSN strain [37 , 41] , but here we uniquely identify both of these residues as phosphorylation sites . These data show that tail loop is subject to multiple phosphorylation events . Interestingly , the NP S402A/S403A mutant exhibited only minor changes in polymerase activity ( Fig 2B ) , suggesting that phosphorylation at this position is not essential for NP activity . As expected , phosphorylation was also identified at NP S165 ( S2D Fig ) , a phosphorylation site that has been described previously and is important for polymerase activity [24 , 41] . Despite identifying peptides containing residue S486 , phosphorylation was not detected at this position . To determine if these phosphorylations at the NP:NP interface are important for polymerase activity , we created phosphomimetic mutants and tested their functionality in polymerase activity assays where NP was expressed in excess , to best mimic conditions during infections . NP S407D and S413D mimic tail loop phosphorylations , while S165D mimics phosphorylation in the binding groove where the tail loop from the neighboring protomer interacts . Polymerase activity was severely impaired by the tail loop mutant NP S407D and was indistinguishable from background levels obtained in the absence of NP ( Fig 3A ) . This was an additional ~10-fold decrease in activity compared to NP S407A . Similarly , the binding groove mutant S165D dramatically reduced polymerase activity . By contrast , NP S413D exhibited modest defects reducing polymerase activity by only ~50% , indicating that S413 is not an essential sites , and this position was not analyzed further . Confirming earlier results , even with higher levels of NP the S486A mutant at the entrance to the binding groove supported polymerase activity at only ~20% of the WT level . Western blotting confirmed comparable expression of WT and mutant NPs ( Fig 3A ) . Primer extension assays were performed to directly monitor production of viral RNAs in the presence of the NP mutants identified in our screen . Influenza polymerase in the presence of WT NP produced large amounts of viral mRNA in addition to low levels of the replication intermediate cRNA ( Figs 3B and S3 ) . vRNA was exogenously expressed and present in all conditions , increasing slightly in the presence of WT NP compared to conditions where the polymerase was absent . The NP mutants S407A , S486A , and S407D demonstrated decreased or undetectable levels of mRNA , cRNA , and vRNA when compared to WT NP . Compared to WT NP , the viral polymerase produced only 20–30% the amount of mRNA and background levels of cRNA in the presence of NP S407A or S486A ( S3 Fig ) . In agreement with our polymerase activity assays , NP S407D exhibited the strongest defect with activity completely ablated . These mutants disrupted both gene transcription and genome replication , suggesting a defect in the early stages of RNP assembly . Recombinant influenza virus was generated to test the impact of mutations at S486 and the phosphorylation sites S165 and S407 in the biologically relevant context of a viral infection . Multicycle replication assays were performed by infecting cells with virus encoding WT or mutant NP . Virus encoding NP S165A or S486A replicated to 10-fold lower levels than WT . NP S407A was the most severely attenuated , demonstrating a ~1000-fold decrease in viral titers compared to WT from 24–72 hours post-infection ( hpi ) . Our results differ from those reported with A/Victoria/3/1975 strain , where NP S407A was functional in polymerase activity assays , although this was not tested with a recombinant virus [45] . NP S407A has also been suggested to possess a temperature sensitive phenotype [46] . Whether these different properties attributed to NP S407 arise from differences in experimental systems and viral strains , or possibly represent the presence of a redundant regulatory mechanism remains to be determined . The strength of the replication defect in our system for S407A and S486A mutants paralleled results from the polymerase activity assay . Despite multiple attempts , we were unable to rescue viruses encoding NP S407D , even when complementing with WT NP in trans , indicating an extreme defect caused by mimicking constitutive phosphorylation at this site . To further examine the function of S486 , we tested the hypothetical scenario of S486 phosphorylation by assessing replication of the phosphomimetic NP S486D . Although phosphorylation was not detected at NP S486 ( Fig 2 and [41] ) , this residue is located at the entrance to the binding groove opposite the phosphorylation site S165 and if it were phosphorylated it might impact engagement of the tail loop from incoming NP . The NP S486D mutation resulted in an intermediate phenotype , reducing replication an additional ~10-fold when compared to NP S486A ( and ~100-fold when compared to WT ) , but was not as defective as NP S407A . Thus , our high-throughput polymerase activity assay identified conserved serine residues in NP that are important for high-level virus replication , including at least one novel phosphorylation site . Our data demonstrate that NP S165 , S407 , and S486 are important for viral replication ( Fig 3 ) . These residues are located at the NP:NP interface where the tail loop of one protomer inserts into the binding groove of the neighboring molecule , and S165 is known to be phosphorylated and important for oligomerization [13 , 14 , 41 , 47 , 48] . Combined with our data that NP S407 is phosphorylated ( Fig 2C ) and that phosphorylated NP favors a monomeric state ( Fig 1 ) , this immediately suggested that phosphorylation may interfere with oligomerization . Intersubunit interactions are dominated by hydrogen bonds and a critical salt bridge between R416 in the tail loop and E339 in the binding groove ( Fig 4A ) [13 , 15] . Phosphorylation and/or mutation to alanine , which removes hydrogen bonding potential , is likely to significantly alter the local binding environment . The structure of NP suggests that S407 in the tail loop has the potential to participate in multiple hydrogen bonds with the binding groove , including with S165 . Moreover , S486 is located at the entrance to the binding groove , opposite S165 . In addition to disrupting hydrogen bonds important for oligomerization , the structures of NP suggest that a phosphate could not be accommodated at either S165 or S407 in the oligomer [13 , 14] . To test these possibilities , we analyzed the oligomerization of recombinant , RNA-free protein . After extended incubation in solution to permit oligomerization to reach equilibrium [20] , proteins were analyzed by size exclusion chromatography ( Fig 4B ) . WT NP elutes as a mixture of monomeric and multimeric species , whereas the S165D mutation created an exclusively monomeric peak , as previously reported [20 , 48] . The WT NP oligomer elutes as a broad peak , therefore to determine the exact oligomerization state individual fractions were analyzed by transmission electron microscopy . The recombinant NP oligomers are composed of different ring shaped molecules , ranging from trimers to hexamers ( Figs 4B and S4 ) . No such structures were observed in the monomer fraction . NP mutants S165A and S407A eluted exclusively as monomers ( Fig 4B ) , demonstrating that in the absence of RNA these serines are essential for oligomerization of recombinant NP , likely through H-bonding interactions between the tail loop and binding grove . The phosphomimetic NP S407D was also monomeric , suggesting that phosphorylation at this position may negatively regulate NP:NP assembly . By contrast , NP S486A showed the opposite effect , shifting the entire population to the oligomeric state . Transmission electron micrographs of the peak fraction showed that the S486A mutant forms ring-shaped structures ranging from trimer to hexamers , identical to WT NP ( Figs 4B and S4 ) . No monomeric peak was detected , suggesting that S486 is important in balancing the equilibrium of NP:NP interactions . NP S486 is located at the entrance to the binding groove opposite the known phosphorylation site S165 , and NP S165D disrupts NP oligomerization . We asked whether a hypothetical phosphorylation at S486 on the other side of the binding groove might also disrupt oligomerization . Indeed , whereas NP S486A shifted the equilibrium completely to the oligomeric state , the phosphomimetic NP S486D was almost completely monomeric ( Fig 4B ) . NP undergoes a number of post-translational modifications and interacts with several cellular factors , some of which have been proposed to modulate NP function[41 , 49–51] . It is therefore important to assess NP oligomerization and its potential regulation by phosphorylation in eukaryotic cells . WT or mutant NPs were expressed in 293T cells and their self-association was assessed by size-exclusion chromatography after rigorous RNase treatment . Again , WT NP showed a characteristic distribution of different oligomeric forms and only a minor population of monomers , while the oligomerization mutant NP R416A eluted as a lower molecular weight species close to the expected position of a monomer ( Fig 5 ) . Paralleling results with recombinant protein , the mutants S407A and S407D drove NP towards a monomeric state , with the phosphomimetic NP S407D producing the most pronounced shift in oligomerization of all the mutants examined ( Fig 5 ) . Similarly , the NP mutants S165A and S165D also assumed a larger proportion of lower molecular weight complexes and the phosphomimetic mutant exhibited a more pronounced phenotype . NP S165A purified from cells possessed notably more multimers than the bacterially expressed RNA-free protein , in agreement with the high degree of oligomerization observed for NP S165A purified from insect cells [47] . Mutations at NP S486 induced an intermediate effect . As seen in vitro , NP S486A from cells eluted largely as an oligomer , although the distribution is more compact than the wild-type protein . Introducing NP S486D restored a more pronounced monomer population , but did not fully recapitulate the oligomerization defect of the recombinant protein . Our cell-based results thus reinforce those obtained with recombinant proteins . Together , these data identify new residues that make critical inter-subunit contacts during NP oligomerization and provide evidence that phosphorylation at the NP:NP interface directly regulates self-association . We and others have shown that NP mutants with altered oligomerization profiles reduced polymerase activity and viral replication ( Figs 2–5 and [16–18] ) . Each of these events is dependent upon successful formation of viral RNPs . To specifically investigate whether changes in NP self-association perturbs viral RNP formation in the presence of the viral polymerase and genomic RNA , NP mutants were used in an RNP reconstitution assay . In this assay viral RNPs were reconstituted in human cells by expressing the viral polymerase ( PB2-HA , PB1 and PA ) , WT or mutant NP , and a vRNA-like template . The efficiency of RNP formation was determined by immunoprecipitating the viral polymerase via PB2-HA to isolate RNPs and detecting co-precipitated NP by western blotting . WT NP co-purified with the viral polymerase indicating efficient RNP formation ( Fig 6A ) . As a control , the NP mutant E339A , which disrupts the critical inter-NP salt bridge [16] , severely impaired RNP formation . Mutation of the phosphorylation sites NP S407 and S165 or introduction of phosphomimetics caused significant decreases in RNP formation , despite expression levels similar to WT . NP S486A showed a similar reduction in RNP formation , whereas S486D showed intermediate levels of RNP formation . Given that NP and free PB2 can interact directly and may result in co-precipitation independent of polymerase trimerization and RNP formation [52] , we repeated these experiments isolating RNPs via PA-FLAG immunoprecipitation . PA and NP do not interact directly , therefore co-precipitation can only occur via interactions with the trimeric polymerase ( Fig 6B ) . These experiments yielded identical results , where all of the NP mutants were severely impaired for RNP formation except for S486D that displayed an intermediate phenotype . These data analyzing RNP formation agree with the polymerase activity assays performed earlier that showed a significant loss of function for these mutants ( Figs 2 and 3A ) . Notably , NP mutants that were primarily monomeric ( e . g . S165D , S407D ) or that were exclusively oligomeric ( e . g . S486A ) exhibited similar defects in RNP assembly . To confirm that this defect in RNP formation is a result of the abnormal NP oligomerization and not due to any defects in NP-polymerase interactions , the binary interactions between NP and PB2 or PB1 were tested for all of the mutant proteins ( Fig 6C and 6D ) . Lysates containing NP and either PB2 or PB1 were subject to NP immunoprecipitation and co-purification of the interacting partner was detected by western blot . To eliminate any non-specific complex formation containing cellular RNA , lysates were treated with high amounts of RNase A before immunoprecipitation . WT and mutant NP precipitated similar amounts of PB2 ( Fig 6C ) and PB1 ( Fig 6D ) , suggesting that mutations at the NP:NP interface do not interfere with binding to the polymerase . Moreover , all of the NP mutants displayed proper subcellular localization , present in the nucleus early after expression and exported to the cytoplasm at later time points ( S1 Fig ) . These data highlight the essential role that regulated NP oligomerization plays in RNP formation and raise the possibility that a balanced equilibrium between monomeric and oligomeric forms is crucial for RNP assembly and function . Based on our results , we hypothesized that phosphorylation at the NP:NP interface interferes with oligomerization through at least two possible mechanisms: 1 ) by modifying NP S407 , thereby making the tail loop unsuitable for insertion into the existing oligomer , and 2 ) by modifying S165 and masking the binding groove to preclude accepting the tail loop from an incoming NP molecule . In both cases , phosphorylation would dynamically control NP:NP interactions and negatively regulate oligomerization and RNP formation . As NP can both be incorporated into a growing oligomer via its tail loop and subsequently accept a tail loop from the next incoming molecule , it is challenging to differentiate the exact oligomerization defect of our NP mutants using full-length protein . Therefore , we exploited a tail loop-binding groove interaction assay [16] . Binding assays were performed with a tail loop deletion mutant of NP ( NPΔTL ) , which retains a functional binding groove but cannot self-associate due to the absence of the loop , and a GFP-tail loop fusion , which possesses only the tail loop ( aa402-428 ) . NPΔTL was co-precipitated by the GFP-tail loop fusion , but not with GFP alone , demonstrating a specific binary interaction between these two domains ( Fig 7 ) . NPΔTL was mutated to determine the impact of phosphorylation on binding groove functionality . The binding groove was completely defective upon introduction of the phosphomimetic residues S165D or S486D ( Fig 7A ) . The GFP-tail loop protein was also investigated . The tail loop mutant S407A and the phosphomimetic S407D tail loop both failed to interact with NPΔTL ( Fig 7B ) . All of the mutants were expressed equivalently to WT . These results suggest that phosphorylation at either side of the NP:NP interface blocks insertion of the tail loop into the binding groove and prevents NP oligomerization ( Fig 7C ) . Furthermore , they reinforce our earlier findings demonstrating oligomerization defects for these mutants in the context of full-length proteins ( Figs 4 and 5 ) and suggest that modifications at this interface alone can regulate NP oligomerization , and the downstream processes of RNP formation , gene expression and replication , and ultimately virion production . Multiple structures of NP have been determined from diverse orthomyxoviruses , including influenza A virus [13 , 14] , influenza B virus [53] , and even infectious salmon anemia virus [54] . While NP from different orthomyxoviruses displays limited sequence identity , the structures of each revealed a similar global architecture . Alignment of the tail loops from all of the influenza virus NP structures shows the structure of this region is completely conserved ( Fig 8A ) . Furthermore , critical phosphorylation sites and salt bridge residues are retained at the same positions ( Figs 8A and S5 ) : the serine residue at the entrance to the binding grove in influenza A virus NP ( S165 ) is present in influenza B virus NP ( S226 ) , and the critical features of the tail loop from influenza A NP ( S407 and the salt-bridge residue R416 ) are also shared by influenza B NP ( S463 and R472 , respectively ) . In both influenza A and B NP structures , the serine residues in the tail loop and binding groove are apposed at the NP:NP interface ( Fig 8B ) . Phosphoproteomics of influenza B virus identified a phospho-peptide from the tail loop containing S463 [41] . We therefore used the tail loop-binding groove interaction assay to test whether oligomerization of influenza B NP ( B/Brisbane/60/2008 ) is also regulated by phosphorylation . The tail loop of B NP was sufficient to mediate interactions with the binding groove , whereas mutation of the salt-bridge residue R472A disrupted binding ( Fig 8C ) . Phosphomimetics in either the tail loop ( S463D ) or the binding groove ( S226D ) completely ablated binding . Recombinant influenza B viruses encoding either wild type of phosphomimetic NP were used to assess the impact of these mutations on virus replication . Introduction of the phosphomimetic S463D into the tail loop of influenza B NP attenuated multicycle virus replication by at least 100-fold with respect to the wild type ( Fig 8D ) . Despite multiple attempts , we were unable to rescue virus encoding NP S226D , suggesting severe defects in function for this mutant . Thus , phosphorylation at the NP:NP interface controls homotypic binding and replication of influenza B virus , and phospho-regulation is a conserved mechanism modulating NP oligomerization for both influenza A and B viruses , and possibly other genera of Orthomyxoviridae .
The influenza virus RNP directs gene expression and genome replication . During assembly of the RNP , NP dynamically changes from the RNA-free monomeric state to the high-order oligomeric state that encapsidates genomic RNA in the RNP [20 , 48] . NP oligomerizes by inserting a tail loop from one NP protomer into the binding groove of the neighboring molecule [13 , 14 , 54] . This ordered assembly is essential for virus replication , as mutations or small molecules that dysregulate oligomerization impair RNP assembly and block virus replication [16 , 30–32] . Here we demonstrate that phosphorylation of NP regulates RNP assembly . We identify key residues in NP important for assembly , including a new phosphorylation site , and define the molecular mechanism by which phosphorylation regulates self-association of both influenza virus A and B NP . Mimicking phosphorylation of residues at the NP:NP interface , either at the entrance to the binding groove or in the tail loop , inhibits oligomerization by specifically blocking insertion of the tail loop into the binding groove . Disrupting these residues severely impaired polymerase activity and virus replication . These data support a general model for Orthomyxoviridae where the dynamic phosphorylation of NP by host proteins plays a critical role in RNP assembly , and by extension genome replication and successful completion of the virus life cycle ( Fig 9 ) . NP exists in cells as a mixture of monomers and oligomers of varying sizes [55 , 56] . Our data confirm that the formation of high-order oligomers occurs spontaneously and does not require other viral proteins or genomic RNA ( Fig 5 ) . Mutational analysis performed here identified serine residues that influence the transition of influenza A NP between these two populations ( i . e . S165 , S407 and S486 ) . Unmodified serines at positions 165 and 407 are required for NP to form higher-order oligomers ( Figs 4B and 5 ) . Mutation of these residues to alanine prevents oligomerization , due to the loss of important hydrogen bonds these serines make . By contrast , mutation to alanine of S486 , which flanks the entrance to the binding groove , resulted in hyper-oligomerization ( Figs 4B and 5 ) . The extreme C-terminus of NP , including S486 and F479 [18] , appears to inhibit oligomerization , possibly by reducing binding affinity between protomers or by helping to establish the conformation assumed by the monomeric form [24] . Thus , NP has residues that are required for direct contacts in the homotypic interactions as well as residues that control the assembly process , and single mutations can shift NP to largely monomeric or oligomeric states . Mutants that disturb the equilibrium distribution of NP impair RNP assembly and viral replication ( Fig 3C ) . Thus , both the assembly process and its regulation are critical for successful RNP formation and virus replication . Stimulation of kinase activity in cells shifted NP towards a monomer , with phosphorylated NP further enriched in the lower molecular weight fractions . Phosphorylation was detected at influenza A NP S165 and S407 and phosphomimetics at these positions all inhibited oligomerization ( Figs 2D and 4B ) . Similarly , a peptide containing S463 in influenza B NP is phosphorylated [41] and phosphomimetics at this position also inhibit binding ( Fig 8C ) In this scenario , phosphorylation actively blocks protein:protein interactions as phosphorylated residues cannot be accommodated at the interface due to steric clashes . However , phospho-regulation is not absolute as phosphorylated NP was detected in purified RNPs and virions , including phospho-S165 for influenza A NP and phospho-peptides containing S463 for influenza B NP [41] . It is possible the oligomerization is not continuous along the entire length of the RNP [20] , allowing phospho-NP to be incorporated at breaks in the NP chain . Additional factors have also been suggested to impact NP oligomerization , including RNA-binding [26] , interactions with host proteins [50] , secondary NP:NP interaction sites [20 , 53] and conformational rearrangements [24] . Thus , phosphorylation is a major regulator of NP oligomerization and may work in tandem with these other processes to tightly control RNP assembly and function . Our results show that the mechanisms of phospho-regulation are conserved for influenza A and B viruses . We used influenza A and B NP structures to create homology models and structure based alignments for influenza C NP and for NP from the provisionally classified influenza D genera ( S5 Fig ) . These models position phosphorylatable residues in the binding groove and the tail loop at the crucial phosphorylation sites we identified ( T169 and S418 for influenza C , and T161 and S416 for influenza D , respectively ) , as well as the salt-bridge pair at the tail loop-binding grove interface ( E354 and R427 for influenza C , and E352 and R425 for influenza D ) . These interaction sites are well conserved both within and between influenza virus genera , but whether each site is absolutely essential or part of a partially redundant control pathway remains to be determined . It is therefore likely that the regulatory mechanism uncovered here for influenza A and B virus , wherein phosphorylation of NP at the inter-molecular interface blocks oligomerization , is likely shared amongst all influenza virus genera . The results presented here show that shifting the balance between monomer and oligomer , in either direction , impairs RNP function and reduced the replication of influenza virus . We propose a working model for the regulated assembly of the RNP ( Fig 9 ) . A portion of newly synthesized NP is phosphorylated to establish a pool of monomeric , RNA-free NP . Phospho-NP cannot be incorporated into growing RNPs , and might even compete for the polymerase to prevent premature RNP formation . At later times during infection , when genomic RNA synthesis and RNP formation dominates , NP is located to sites of assembly in a non-phosphorylated form . The non-phosphorylated form is then assembled into RNPs aided by the presence of nascent genomic RNA . The reversible nature of phosphorylation establishes a protected pool of monomeric NP that can rapidly transition to become substrate for RNP assembly , consistent with the changing patterns of NP phosphorylation that occur throughout the viral life cycle[37] . Influenza virus encodes neither a kinase nor a phosphatase , therefore it will be important to identify the cellular factors regulating NP phosphorylation as their manipulation might have broad antiviral activity across influenza A , B and C viruses while simultaneously reducing the emergence of resistant viruses by targeting host proteins .
All genes were derived from the influenza A ( A/WSN/33 ) or influenza B ( B/Brisbane/60/2008 ) viruses . pET28a-NΔ7NP was constructed for bacterial expression of protein with a C-terminal His tag and a seven amino acid deletion on the N-terminus , as described [13] . pCDNA3 . 2-NP-V5 was constructed for eukaryotic expression . Mutations were introduced into the NP gene using the QuickChange mutagenesis kit ( Agilent Technologies ) and confirmed by sequencing . Polymerase proteins were expressed in cells from the plasmids pCMV-PB2-HA ( encoding a C-terminal HA tag ) , pCDNA3-PA , pCDNA-PA-FLAG and pCDNA3-PB1 [57] . vNA-luc and cNA-luc reporter plasmids encode the firefly luciferase gene flanked by UTRs from the NA gene in the minus or positive sense , respectively [58] . The rescue vectors pTMΔRNP , pBD-PB2 , pBD-PB1 , pBD-PA and pBD-NP were used to generate recombinant influenza A virus and were based on the influenza reverse genetics system [59 , 60] . Recombinant influenza B virus was generated in a similar fashion . Mutations were introduced into pBD-NP by inverse PCR and confirmed by sequencing . GFP:tail loop fusions were generated by inserting coding sequence corresponding to amino acids 402–428 ( influenza A NP ) or 459–486 ( influenza B NP ) downstream of GFP in the plasmid pEGFP-C1 ( Clontech ) . Additional sequences were incorporated to encode a cysteine at each end of the tail loop and a four-glycine linker between GFP and the tail loop . Antibodies used include: anti-HA clone 3F10 ( Roche ) , anti-V5 ( R961-25 , Invitrogen ) , anti-GFP ( B-2 , Santa Cruz Biotech ) , anti-NP ( H16-L10-4R5 ) [61] , anti-FLAG M2 ( Sigma ) and anti-influenza virus RNP ( BEI Resources NR-3133 ) . Wild type or mutant NPs were expressed in E . coli strain Rosetta 2 ( DE3 ) ( Novagen ) and purified using Ni-NTA affinity ( Qiagen ) . Purified proteins were treated with RNaseA and further purified through a HiTrap Heparin HP column ( GE Healthcare ) . Proteins were concentrated to equivalent levels and oligomerization of NP was allowed to reach equilibrium by incubating purified protein at 4°C for 96 hours in buffer containing 50mM Tris , pH7 . 5 , 200mM NaCl and 1mM TCEP . The oligomeric state of NP was subsequently analyzed by size exclusion chromatography through a Superose-6 column calibrated with size standards . For electron microscopy , peak fractions were immediately absorbed on a carbon-coated Cu-grid and stained with a freshly prepared 0 . 5% Uranyl acetate solution . Images were taken using a Tecnai T12 electron microscope operating at 120 kV with a magnification of 56 , 000 . To determine the oligomerization state in cells , NP-expressing 293T cells were lysed in 50 mM Tris-HCl , 100 mM KCl , 5 mM MgCl2 and 0 . 5% NP40 containing protease and phosphatase inhibitor cocktails . Where indicated , cells were stimulated with 2 . 4 μM phorbol 12-myristate 13-acetate ( PMA ) and 100nM okadaic acid for 2 h before lysis . Total cell extract was clarified by centrifugation , treated with 50 μg/ml of RNaseA for 2 hours at room temperature , and fractionated through a Superose-6 column pre-equilibrated in lysis buffer . Fractions were probed by western blotting . 293T cells were transfected in triplicate with plasmids encoding PA , PB1 , PB2-HA , NP and vNA- or cNA-luciferase reporters . Polymerase activity was measured using the luciferase assay system ( Promega ) and NP expression was confirmed by western blotting . Primer extensions were performed as described [57] . 293T cells expressing NP and other interacting partners were lysed in radio-immunoprecipitation assay ( RIPA ) buffer ( 50 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 2 mM EDTA , 1% NP-40 , 0 . 5% deoxycholate , 0 . 1% SDS ) supplemented with 5mg/ml of BSA and clarified by centrifugation . Lysates were incubated with appropriate antibodies and immunocomplexes were captured on Protein A Dynabeads ( Invitrogen ) . Beads were subsequently washed with RIPA buffer containing 500 mM NaCl and finally in RIPA buffer without BSA . Immunoprecipitates were analyzed by western blotting . Recombinant virus was produced as described by transfecting a co-cultures of 293T and MDBK cells with rescue vectors [62 , 63] . Media was replaced 24 hrs later with virus growth media ( DMEM , 0 . 2% bovine serum albumin ( BSA ) , 25 mM HEPES buffer , and 1 μg/ml TPCK trypsin ) . Virus was subsequently amplified in MDBK or MDCK cells and titered by plaque assay on MDCK cells using a 1 . 2% Avicel overlay ( RC581; FMC Biopolymer ) [64] . Multicycle replication assays were performed in triplicate by infection of MDCK cells and viral titers were determined at the indicated time points by plaque assay . MDCK cells were infected with WSN ( MOI = 5 ) and samples were collected 2 , 4 , 6 and 8 hpi . Lysates were prepared in RIPA buffer supplemented with 2 mg/ml BSA and protease and phosphatase inhibitors , pooled , and subject to NP immunoprecipitation as described above . Samples were washed extensively in 10mM Tris pH 7 . 5 , 100mM NaCl and 1mM EDTA and eluted with 8M urea . Purity was confirmed by SDS-PAGE of a small sample of eluted protein and the identity of NP was validated by western blot . The pooled protein sample was reduced with 5 mM dithiothreitol for 30 minutes at 55°C , alkylated with 15 mM iodoacetamide in the dark at ambient temperature for 45 minutes , and quenched by addition of 5 mM dithiothreitol[65] . The protein sample was diluted 1:1 with 50 mM Tris and 5 mM CaCl2 and digested with 9 μg tryspin ( Promega ) overnight at room temperature . Resultant peptides were desalted using a tC18 Sep-Pak cartridge ( Waters ) and enriched for phosphorylation by immobilized metal affinity chromatography ( IMAC ) using Ni-NTA magnetic agarose beads ( Qiagen ) [66] . Both non-phosphorylated and phosphorylated peptide samples were resuspended in 14 μL of 0 . 2% formic acid and analyzed by mass spectrometry ( MS ) . An 80 minute nano-liquid chromatography ( nLC ) gradient was used to introduce peptides to an Oribtrap Elite mass spectrometer ( Thermo Scientific ) . Preliminary MS experiments used data dependent acquisition ( DDA ) to discover IMAC-enriched peptides which were present in the sample , using either collisonally activated dissociation ( CAD ) or higher-energy collisonial dissociation ( HCD ) to fragment eluting peptides[67] . Spectra obtained from these DDA experiments were searched against a concatenated target-decoy database containing the protein sequences of Canis familiaris and Influenza A ( Uniprot ) using Sequest within the Proteome Discoverer software package ( Thermo Fisher ) . For all samples , cysteine carbamidomethylation and methionine oxidation were searched as fixed and variable amino acid modifications , respectively , and phosphorylation of serine , threonine , and tyrosine residues were searched as variable modifications . Precursor mass tolerance was defined as 40 ppm and fragment ion tolerance was set to 0 . 30 Da ( ion trap MS/MS ) and 0 . 02 Da ( FT MS/MS ) [68] . Search results were filtered to 1% false discovery rate ( FDR ) using precursor mass error . PhosphoRS[69] was used to localize phosphorylation to amino acid residues using a fragment mass tolerance of 0 . 02 Da , automatically considering neutral loss peaks for HCD and considering a maximum of 200 maximum position isoforms per phosphopeptide . Using the untargeted DDA MS approach , a singly phosphorylated peptide corresponding to site S165 was identified from the IMAC enriched sample , mapping to the sequence 163-MCpSLMQGSTLPR-174 . Additionally , several peptide-spectral matches mapping to a singly phosphorylated peptide , 401-ASSGQISIQPTFSVQR-416 , were identified from the untargeted MS experiments . Follow-up , targeted MS runs were used to isolate only the ASSGQISIQPTFSVQR peptide m/z values corresponding to the phosphorylated peptide . Using targeted CAD or HCD , four distinct phosphoisoforms of the peptide were observed , with phosphorylations localized to the S402 , S403 , S407 , and S413 residues . Data are presented as the mean +/- standard deviation ( n≥3 ) . For polymerase activity assays , data were normalized to WT and error was propagated throughout to yield normalized standard deviation . | Replication and transcription by negative-sense RNA viruses occurs in large macromolecular complexes . These complexes contain the viral polymerase , genomic RNA , and multiple copies of nucleoprotein that bind RNA and oligomerize to coat the genome . For influenza virus , nucleoprotein ( NP ) non-specifically binds nucleic acids and spontaneously oligomerizes . It is essential that a portion of NP be maintained as a monomer so that it can selectively oligomerize into replication complexes . Despite the fact that this process must be tightly regulated during the viral life cycle , how this regulation is achieved is largely unknown . Here we show that phosphorylation of NP negatively regulates assembly of the influenza virus replication machinery . We identified two phosphorylation sites on opposite sides of the NP:NP interface and showed that phosphorylation at either site blocks homotypic interactions , distorting the monomer:oligomer balance of NP in cells and severely impairing virus replication . Our findings show that the phospho-regulated conversion of NP between mono- and oligomeric states is important for RNP formation , gene expression and viral replication . Moreover , we showed that these critical phosphorylation sites play the same role in influenza B virus and are likely present in influenza C and D viruses , suggesting our results are broadly applicable across viral strains and genera and reveal a global regulatory strategy for Orthomyxoviridae . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Phosphorylation at the Homotypic Interface Regulates Nucleoprotein Oligomerization and Assembly of the Influenza Virus Replication Machinery |
S6 kinases ( S6Ks ) act to integrate nutrient and insulin signaling pathways and , as such , function as positive effectors in cell growth and organismal development . However , they also have been shown to play a key role in limiting insulin signaling and in mediating the autophagic response . To identify novel regulators of S6K signaling , we have used a Drosophila-based , sensitized , gain-of-function genetic screen . Unexpectedly , one of the strongest enhancers to emerge from this screen was the nuclear receptor ( NR ) , Drosophila hormone receptor 3 ( DHR3 ) , a critical constituent in the coordination of Drosophila metamorphosis . Here we demonstrate that DHR3 , through dS6K , also acts to regulate cell-autonomous growth . Moreover , we show that the ligand-binding domain ( LBD ) of DHR3 is essential for mediating this response . Consistent with these findings , we have identified an endogenous DHR3 isoform that lacks the DBD . These results provide the first molecular link between the dS6K pathway , critical in controlling nutrient-dependent growth , and that of DHR3 , a major mediator of ecdysone signaling , which , acting together , coordinate metamorphosis .
During development , cell growth arrests when organs reach their appropriate size [1] such that differentiation acts to impede further growth . However , the growth-regulating module can be reactivated in specific cell types to maintain homeostasis in the adult . Moreover , pathological settings , such as cancer and obesity , can lead to aberrant activation of cell growth in a differentiated setting [2] . Despite this understanding , we have little knowledge of the molecular links that act to integrate differentiation programs with those that control growth . In identifying the underlying molecular mechanisms that regulate cell growth and differentiation in mammals , Drosophila genetics has proved a powerful tool . This is because many of the molecular components are evolutionarily conserved , as are the regulatory pathways in which they function [3] . In cell growth , such studies have been critical in revealing the central role of the Target of Rapamycin ( TOR ) as an effector of an insulin- and nutrient-signaling network that acts to maintain cell , tissue , and organismal homeostasis [4] . The value of Drosophila genetics in such studies was initially demonstrated in the identification of the genes responsible for Tuberous Sclerosis Complex , dTsc1 ( hamartin ) , and dTsc2 ( tuberin ) , as negative effectors of dTOR signaling [5]–[7] and subsequently the identification of their target , the small GTPase Ras homologue enriched in brain ( dRheb ) [8]–[10] , a direct effector of TOR signaling [11] . In Drosophila , both the insulin-related peptides ( Dilps ) , acting through the insulin receptor [12] , and nutrients [13] , such as amino acids acting through their cognate transporters [14] , integrate at the level of dTOR to control cell growth [15] , [16] . A key downstream effector of insulin- and nutrient-mediated dTOR-dependent growth is the Drosophila ribosomal protein S6 kinase ( dS6K ) [13] , [17] . Although loss of dS6K largely results in late larval lethality , the few escapers that survive to adulthood are severely delayed in development and exhibit pronounced defects in cell size , with no effect on cell number [18] . Moreover , such mutants express elevated levels of Protein Kinase B ( PKB ) activity [17] , which is mediated through a dS6K-negative feedback loop [15] . Although many of the effects of loss of dS6K appear to be controlled in a cell-autonomous manner [17]–[19] , it is known that loss of the dS6K orthologue , S6K1 , has humoral effects in the mouse [20] . Consistent with these findings , depletion of the amino acid transporter slimfast within the fat body ( FB ) reduces dS6K activity and causes a global growth defect similar to that seen in loss-of-dS6K mutants and nutritionally deprived Drosophila [14] . Following embryogenesis , Drosophila larvae , which are specialized in feeding and growth , increase their mass approximately 200 fold [21] . During this phase , endoreplicative tissues assume specific physiological functions , whereas the imaginal discs grow and proliferate [22] . At the termination of larval development , overall growth and feeding ceases . However , with the onset of metamorphosis , most of the endoreplicative organs are degraded , whereas the imaginal discs grow and differentiate into adult structures [23] . Metamorphosis is initiated by a peak in production of the steroid hormone ecdysone , which induces the activation of a cascade of nuclear receptors ( NRs ) [24] and the ensuing program of tissue remodeling . During metamorphosis , the degradation of the endoreplicative tissues , including the salivary gland and the midgut , is initiated by autophagy [25] , a cellular process in which portions of cytoplasm are sequestered within double-membrane vesicles known as autophagosomes before delivery to lysosomes for degradation and recycling of cellular components [26] . Interestingly , although the dTOR signaling pathway acts as a negative effector of autophagy , there is evidence that dS6K promotes rather than suppresses this response [27] , revealing a mutual dependency between these two pathways . Moreover , treatment with rapamycin , the inhibitor of dTOR/dS6K activation , blocks the production of ecdysone [28] , which is mediated by prothoracicotropic hormone ( PTTH ) [29] . Although a connection between the signaling pathways induced by ecdysone and those induced by nutrients has not yet been formally established , earlier studies indicated that ecdysone antagonizes insulin and dTOR signaling [30]–[33] . However , recent findings demonstrate that during metamorphosis ecdysone also induces the fat body to produce Dilp6 , which mediates the growth and proliferation of mitotic cells of the imaginal discs during the remodeling of tissues [23] , [34] . In search of novel effectors of dS6K signaling , we have taken advantage of a sensitized phenotype , such that ectopic expression of dS6K within the developing dorsal wing compartment causes the wing to bend down [18] . This phenotype is characterized by an increase in the size of the dorsal wing blade , attributable to an increase in cell size , which is mediated by the level of dS6K activity [17] , [19] . Using this sensitized phenotype in a genome-wide genetic screen , we have identified a number of potential effectors in dS6K signaling . Unexpectedly , one of the strongest amongst these was the NR DHR3 , a critical signaling component in the coordination of Drosophila metamorphosis . Moreover , we show that the ligand-binding domain of DHR3 is essential in modulating dS6K-regulated cell growth , which led us to the identification of a novel isoform of DHR3 , devoid of the DNA-binding domain .
Imaginal discs are subdivided into compartments , with each constituting an individual growth unit that differentiates into an adult structure during metamorphosis [22] . In this context , ectopic expression of dS6K within the developing dorsal wing compartment , using the apterous-Gal4 ( ap-Gal4 ) driver , induces a moderate overgrowth in this unit [18] and a bending-down of the adult wing ( Figure 1A and 1B ) . Consistent with PDK1 being the mammalian S6K1 activation-loop kinase [35] , [36] , we have previously demonstrated an enhanced bending-down of the adult wing by co-expression of the Drosophila PDK1 ( dPDK1 ) , whereas expression of dPDK1 alone had no effect on this phenotype [17] . Likewise , the expression of particular phosphorylation-site mutants of dS6K that would be predicted to increase or decrease the activation state of dS6K , enhances or suppresses this phenotype , respectively [19] . These findings demonstrate that the bent-down wing phenotype varies according to dS6K activation status , and prompted us to use this sensitized phenotype in a gain-of-function genetic screen to identify novel modulators of dS6K activity . We also found that ectopic expression of an active form of the mammalian S6 Kinase 1 , S6K1dE/D3E [37] , induced a bent-down wing phenotype equivalent to that induced by dS6K ( compare Figure 1B and 1C ) . Like dS6K , co-expression of S6K1dE/D3E and dPDK1 led to an enhancement of the bent-down wing phenotype , but not to the extent observed with dS6K ( data not shown ) . We reasoned that this differential phenotype may represent dPDK1 specificity for dS6K , a bona fide substrate , unlike S6K1dE/D3E [17] , [38] , and have utilized this differential effect to increase the selectivity of the gain-of-function screen ( see below ) . In the screen , approximately five thousand Enhancer-Promoter ( EP ) bi-directional insertions were co-induced with dS6K in the developing dorsal wing compartment [39] and then scored for either enhancement or suppression of the dS6K-dependent bent-down wing phenotype . Approximately 1000 of the EP lines either acting as suppressors ( ∼90% ) or enhancers ( ∼10% ) , were further analyzed for their effects on the bent-down wing phenotype when induced alone or with the ap-Gal4 driver . In this way , nonspecific effectors , which alone could induce the bent-wing phenotype , were eliminated ( data not shown ) , allowing us to narrow down the potential candidates to 220 lines . These were then tested in the tertiary screen in combination with either dS6K or the active S6K1dE/D3E , such that 19 suppressor and 76 enhancer lines were retained ( Table S1 ) , 71 of which were localized by reverse PCR mapping . We focused on the 76 enhancer lines , which were largely confirmed through the three screening steps . Of these , 19 were not considered because they interfered with wing development ( Table S1 ) , including perturbation of vein formation , compartment adhesion ( Blister phenotype in Table S1 ) , or the bending down of the wing along the anterior-posterior axis ( Figure S1 ) . Among the 57 enhancer lines selected through this process , the candidate list was further narrowed to the strongest 21 enhancers ( Table S1 ) , of which 18 induced a more severe phenotype with dS6K than with S6K1dE/D3E . That the 18 strong enhancers represented authentic dS6K interacters was supported by the finding that 9 of the enhancers were localized as independent insertions in the dPDK1 locus ( Table S1 ) . Genomic mapping of the additional strong enhancers led to the identification of 5 novel loci . Of these , we focused our attention on EP lines EP12 . 218 and EP23 . 014 ( Figure S2 ) , which were inserted in the DHR3 locus , coding for a nuclear receptor ( CG33183 ) , and are collectively referred to as DHR3-EP . Induction of either of these EP lines alone , using the ap-Gal4 driver , was without visible effect on wing development ( Figure 1D and data not shown ) ; whereas , in combination with dS6K or S6K1dE/D3E both EP lines induced a strong enhancement of the bent-down wing phenotype ( compare Figure 1B and 1C with Figure 1E and 1F , respectively , and data not shown ) . As the EP element employed in the screen contained two UAS promoters to direct transcription in opposite directions [39] , the DHR3-EP could , theoretically , induce transcription of either DHR3 or the histidine-decarboxylase gene ( CG3454 ) . As the UAS promoter driving the latter gene was flanked by loxp sequences , induction of Cre recombinase was used to excise this promoter in the EP23 . 014 line . The resulting unidirectional EP line retained the ability to enhance the dS6K-induced bent-down wing phenotype ( Figure 1J ) arguing that the EPs mediate their effects through DHR3 . Using this same approach , we determined that the four additional loci most likely regulate the expression of rab40 ( CG1900 ) , involved in vesicle trafficking; peste , encoding a scavenger protein ( CG7228 ) ; orb2 , a Cytoplasmic-Polyadenylation-Element-Binding ( CPEB ) protein ( CG5735 ) ; and hephaestus , a Polypyrimidine-Track-Binding ( PTB ) protein ( CG31000 ) ( Table S1 ) . As both DHR3-EP elements are inserted within the first intron of the DHR3 gene ( see below ) , it is most likely that the enhanced bent-down wing phenotype results from either inhibiting or increasing the expression of DHR3 . We therefore generated inducible UAS-RNA-interference lines ( DHR3-RNAi ) to specifically reduce DHR3 expression . Induction of the DHR3-RNAi alone by ap-Gal4 caused a bending up of the wing ( Figure 1G ) , indicating that normal growth of the dorsal wing blade is restricted when DHR3 expression is suppressed . Further supporting this observation , when co-induced in the dorsal wing compartment with either dS6K alone or in combination with DHR3-EP , the DHR3-RNAi completely suppressed the bent-down wing phenotype in both settings ( Figure 1H and 1K ) . These findings indicate that the positive genetic interaction observed with dS6K is due to increased expression of the DHR3 gene product . Although we found that co-expression of DHR3-EP and S6K1dE/D3E enhanced the bent-down wing phenotype ( Figure 1C and 1F ) , this was to a lesser degree than when co-expressed with dS6K ( compare Figure 1E and 1F ) , suggesting that DHR3 , like dPDK1 , acts specifically on dS6K signaling . Consistent with this interpretation , DHR3-RNAi suppressed the bent-down wing phenotype induced by dS6K more strongly than that of S6K1dE/D3E ( compare Figure 1H and 1I ) . Taken together , these differential effects indicate that S6K1dE/D3E is less sensitive than dS6K to relative changes in the dosage of DHR3 and favor a specific role for DHR3 in dS6K-dependent growth . Recently , it has been suggested that the nutrient-effector arm of the TOR signaling pathway may have been integrated with that of the insulin-PI3K pathway following the rise of multicellular organisms [40] . Although it is clear that the nutrient and insulin pathways are also integrated in Drosophila , it is less clear where the point of integration resides [15] , [17] . In part , this lack of clarity resides in the finding that depletion dTsc1/2 , but not dPTEN , leads to dS6K activation , and that the overgrowth phenotype caused by loss of dTsc1/2 , but not of dPTEN , is abolished by loss of dS6K [17] . Consistent with these findings , when either tumor suppressor is ectopically expressed in the developing eye , they suppress growth of this compartment , with co-expression of dS6K counteracting only the effects of dTsc1/2 , but not of dPTEN ( data not shown ) . This difference allows us to test whether DHR3-EP is acting exclusively on the dTsc1/2 growth response . As stated above , ectopic expression of either dTsc1/2 or dPTEN suppressed the growth of the developing eye ( compare Figure 2A–2C ) . In contrast , ectopic expression of DHR3-EP had no apparent impact on eye development ( compare Figure 2A and 2D ) , similar to what was observed in the wing ( Figure 1D ) . However , ectopic expression of DHR3-EP , combined with either dTsc1/2 or dPTEN , largely counteracted the growth-suppressive effects due to dTsc1/2 , but not of dPTEN ( compare Figure 2E and 2B , and Figure 2F with 2C ) . These results support the notion that DHR3 acts to promote dS6K signaling . DHR3 is known to play a central role in coordinating metamorphosis [41] , [42]; however , when DHR3-RNAi was expressed in the dorsal wing compartment it led to a decrease in the size of the dorsal wing blade , causing the wing to bend upwards , with no obvious detrimental effect on the differentiation of the wing ( Figure 1G ) . In agreement with this finding , DHR3-EP suppressed the growth defect induced by overexpression of dTsc1/2 in the eye , without altering differentiation of this organ ( Figure 2E ) . These findings were unexpected as they suggest that DHR3 is not only involved in fate decisions associated with differentiation , but that it may also play an integrative role in controlling cell growth . Both EP elements were inserted within the large first intron of DHR3 , and failed to complement previously reported DHR3 mutants ( Figure 3A and Figure S2; Table S2 ) . However , in contrast to these previously described DHR3 mutants , which are lethal during early development , homozygous and trans-heterozygous DHR3-EP insertions are semilethal ( data not shown ) , indicating that they represent hypomorphic DHR3 mutants . The few larvae that underwent metamorphosis were delayed ( data not shown ) and exhibited a significant reduction in body weight ( Figure S3 ) . The adult escapers emerged with an approximate 2-day delay , and displayed female sterility . A reduction in body weight and developmental delay have been reported for a number of other mutants that affect growth , further supporting a role for DHR3 in controlling this process [18] , [43] . Consistent with this observation , we also found that ubiquitous suppression of DHR3 by RNAi provoked larval death , but also provoked a significant developmental delay ( data not shown ) . Taken together , these findings imply that DHR3 has a distinct function in controlling cell growth , potentially through dS6K . DHR3 is a NR that classically comprises an amino-terminal DNA-binding-domain ( DBD ) and a carboxyl-terminal ligand-binding domain ( LBD ) , separated by a linker domain [44] . The FlyBase Consortium [45] first reported two potential transcripts for DHR3 termed RA and RB ( “R” stands for RNA , whereas “P” denotes the corresponding protein ) ( Figure 3A ) , though , more recently , two additional transcripts , RC and RD , have been listed . All reported DHR3 polypeptides are translated from AUGs located at specific alternative upstream first exons ( Figure 3A and data not shown ) . To identify the DHR3 gene product responsible for the genetic interaction with dS6K , RACE ( rapid amplification of cDNA ends ) -PCR has been performed using wild-type and DHR3-EP larvae ubiquitously induced by a daughterless-Gal4 driver ( da-Gal4 ) . The transcript identified for the latter was a splice variant extending from the EP to the DHR3 second exon , and lacking an AUG initiator codon upstream of the sequences encoding the DBD ( R-EP in Figure 3A and Figure S2 ) . In addition to previously described mRNAs , RACE-PCR experiments using wild-type larvae revealed a novel DHR3 transcript lacking a first alternative exon ( RS , where S stands for short , Figure 3A and Figure S2 ) . This transcript would be predicted to encode a DHR3-PS protein that is devoid of the DBD , as the most proximal AUG is located beyond the DBD-coding sequence ( Figure 3A and Figure S2 ) . The functional existence of a DHR3 isotype lacking the DBD is supported by the chimeric EP/DHR3 transcripts . To determine which DHR3 isotype was responsible for the genetic interaction with dS6K , three UAS constructs were generated , two of which corresponded to the RA and RB transcripts described above . The third UAS construct , DHR3-RS , lacked an upstream translational initiator codon , but retained an AUG , to potentially allow translation of the PS variant ( Figure 3A and Figure S2 ) . When induced by the ap-Gal4 driver , both RA and RB led to lethality ( data not shown ) . This lethality was most likely due to expression in organs other than the wing , as the apterous promoter is known to be active in several tissues , including some embryonic neurons [46] . Conversely , induction of DHR3-RS with the ap-Gal4 driver was not lethal and phenocopied the enhancement of the dS6K wing phenotype observed with DHR3-R-EP ( compare Figure 3C and 3E with Figure 1B and 1E , respectively ) . Moreover , co-induction of DHR3-RNAi suppressed this phenotype ( compare Figure 3E and 3G ) , as it did when co-expressed with dS6K and the DHR3-R-EP ( Figure 1K ) . Interestingly , induction of DHR3-RS alone was sufficient to induce a bent-down wing ( compare Figure 3B and 3D ) , and this phenotype was largely reverted by co-induction of DHR3-RNAi ( data not shown ) . Hence , expression of the DHR3 gene product lacking its DBD alone is sufficient to induce growth of imaginal discs and can further cooperate with dS6K in this process . The ability of ap-Gal4-driven DHR3-RS alone to induce the bent-down wing phenotype ( Figure 3D ) , as compared with DHR3-EP ( Figure 1D ) , could be explained by higher expression levels of DHR3-PS ( see below ) . Combined with data in Figure 1 , these data also suggest that DHR3-RS–driven growth relies on dS6K . To test this possibility , we induced ap-Gal4-driven DHR3-RS in the dS6Kl-1 null-mutant , of which a small number survive to adulthood [18] . In this genetic background , a clear suppression of the bent-down wing phenotype was observed ( compare Figure 3F and 3D ) , indicating that overgrowth induced by DHR3-RS is dependent on the presence of dS6K . The genetic interactions between DHR3 and dS6K raised the possibility that DHR3 might control either dS6K levels or activity . To discriminate between these two possibilities , ubiquitous expression of DHR3-RNAi was induced by a da-Gal4 or actin-Gal4 driver , and both the level and the activity of dS6K were monitored in larval extracts . With either driver , RNAi-induced DHR3 suppression led to a strong reduction in dS6K activity , as measured by histone 2B ( H2B ) phosphorylation ( Figure 3H ) or dTORC1-dependent phosphorylation of dS6K1 T398 [47] ( Figure S4 ) . Under these conditions there was no effect on dS6K protein levels ( Figure 3H and Figure S4 ) . Importantly , RNAi-induced DHR3 suppression also suppressed dTORC1-dependent phosphorylation of d4E-BP T37/T46 ( Figure S4 ) , the inhibitor of the translation initiation factor d4E [48] . The results indicate that DHR3 is required during larval development to maintain full dS6K activity , potentially acting through dTORC1 . To determine whether the endogenous isoform DHR3-PS , lacking the DBD , is expressed in vivo , a rabbit antiserum to DHR3 was produced using peptides that correspond to sequences downstream of the first AUG following the DBD coding sequence ( Figure S2 ) . Expression of the UAS–DHR3-RS ( Figure 3A ) , was induced in the posterior wing-disc compartment using the engrailed-Gal4 ( en-Gal4 ) driver . This line also harbored a UAS-GFP , activated by the en-Gal4 driver leading to the production of GFP , which allowed for double immunostaining . The results of this experiment revealed co-localization of GFP and DHR3-PS expressions ( Figure 4A and 4B ) . Likewise , when induced by the ap-Gal4 driver , both the DHR3-RS and the DHR3-EP lines exhibited increased immunostaining within the dorsal wing-disc compartment , which was much stronger for DHR3-RS than for DHR3-EP ( compare Figure 4C and 4D ) . Because DHR3-RS , but not DHR3-EP , provoked the bent-down wing phenotype when induced alone by ap-Gal4 ( compare Figure 1D with Figure 3D ) , these results are consistent with the ability of DHR3-RS to induce growth in a dosage-dependent manner . To determine whether we could also detect endogenous DHR3 , flip-out clones directing DHR3-RNAi expression were generated , and a UAS-GFP was used to positively label these clones [49] . The staining observed in prepupal discs was strongly reduced in flip-out clones ( Figure 4E and 4F ) , with remnant staining most likely reflecting incomplete depletion of DHR3 expression . Clones displaying a decrease in specific staining could be detected in all imaginal discs from prepupae ( data not shown ) , indicating that DHR3 is widely represented at this stage of development . In addition , weak staining could be detected in both the imaginal discs and the fat body from mid-third-instar larvae ( data not shown ) , suggesting the presence of low levels of DHR3 at this stage . Thus , endogenous DHR3 is detectable in prepupae , but also likely present at low levels in larval tissues . To analyze the distinct DHR3 polypeptides by western blotting , expression of the UAS–cDNAs , DHR3-RA , RB , RS , or R-EP ( Figure 3A ) , were induced by a one-hour heat-shock treatment using the heat-shock-Gal4 driver ( HS-Gal4 ) . Because larvae expressing the DBD-containing DHR3 isotypes died within a day following heat shock , larval extracts were prepared four hours after heat shock and analyzed by western blotting . Larvae expressing the DBD-containing DHR3 variants ( RA and RB ) displayed distinct protein patterns . DHR3-RA produced a single protein that migrated at the expected molecular weight for PA ( Figure 4G , lane RA ) . Similarly , DHR3-RB produced a band migrating at a molecular weight very similar to that of PA , which most likely represented PB ( Figure 4G , lane RB ) . Unexpectedly , DHR3-RB also produced a second polypeptide migrating at a significantly smaller molecular weight ( Figure 4G lane RB ) . Consistent with this latter polypeptide representing the DHR3 variant lacking the DBD , the DHR3-RS and the two DHR3-EP lines ( Figure 4G , lanes RS , E1 , and E2 ) produced a protein that migrated at the same position as the smaller polypeptide produced by DHR3-RB ( Figure 4G , lane RB ) . According to the immunostaining ( Figure 4C and 4D ) , the DHR3-RS line expressed significantly more protein than the two DHR3-EP lines ( compare Figure 4G , lanes RS , E1 , and E2 ) . As DHR3 has been reported to be highly expressed at the onset of metamorphosis in response to ecdysone signaling [50] , we monitored its expression pattern by western blot analysis in third-instar larvae and during pupariation . Neither the long nor the short forms of DHR3 could be observed in late third-instar larvae , but both were clearly detectable in prepupae ( Figure 4H ) . That these two bands represent DHR3 was shown by their reduced expression levels in prepupae expressing the DHR3-RNAi using a da-Gal4 driver ( Figure 4H ) . The smaller protein was most likely produced from the DHR3-RB transcript or , alternatively , from the RS messenger species devoid of an upstream AUG ( Figure 3A ) . These data are consistent with the surge of DHR3 expression during pupariation . To gain further insight into the protein domain of DHR3 required for the dS6K-dependent growth function , an EMS revertant screen was performed . DHR3-EP males were fed EMS and crossed to females bearing ap-Gal4–induced dS6K . Approximately 50 , 000 offspring were screened to establish 8 lines that had clearly lost the ability to cooperate with dS6K in producing the bent-down wing phenotype ( compare Figure 5A and 5B ) . After remobilization of the EP-element , only two lines displayed homozygous lethality and did not complement previously described DHR3 mutants ( Table S2 ) . These two lines contained stop codons at positions 243 and 284 of the DHR3-PA reading frame , respectively , and are referred to as DHR3K243X and DHR3W284X ( Figure 5C and Figure S2 ) . Remobilization of the EP element may provoke imprecise excisions , creating putative deficiencies within the DHR3 locus . Hence , several lines for each DHR3 mutation were generated from independent remobilization events . Eight and ten independent lines for DHR3K243X and DHR3W284X , respectively , were used to further investigate the function of the DHR3 LBD . All were homozygous lethal , failed to complement one another , and neither complemented the previously described DHR3G60S and DHR3R107G mutants [51] that affect the DBD ( Table S2 ) . Almost all of these mutant combinations died as embryos indicating that the LBD is required for the transcriptional function of DHR3 . However , it was possible to identify a few DHR3K243X/DHR3W284X mutants that survived to the second larval instar . These larvae were then used to perform kinase assays for dS6K . Consistent with the results of assays using DHR3-RNAi extracts ( Figure 3H ) , a significant drop in dS6K activity , but not expression , was observed in larval extracts prepared from trans-heterozygous DHR3K243X/DHR3W284X mutants ( Figure 5D and 5E ) . To examine the LBD mutants with respect to cell-autonomous growth , both lines devoid of the EP-element were fused to an FRT , and using the flipase recombinase , analyzed in specific tissues of the adult [52] . We first investigated the FRT-associated mutations in the eye disc of heterozygous DHR3 mutant flies by using the eyeless promoter to drive flipase during eye development [53] . As the FRT chromosome arm carrying a wild-type DHR3 copy also contained a homozygous cell-lethal Minute mutation ( M ( 2 ) 53 ) , the recombined sister cells , which were wild type for DHR3 , were eliminated during development . This led to adult eyes that were largely made up of homozygous DHR3 mutant cells . With either the DHR3K243X or DHR3W284X mutation , a significant reduction in eye size was observed ( Figure 5F and 5G , and data not shown ) , demonstrating that DHR3 controls growth in a compartment-autonomous manner . The flipase recombinase was also induced by heat shock , and adult homozygous DHR3 mutant clones were followed by their yellow marker . At the scutellum ( posterior part of the dorsal thorax ) , DHR3 mutant yellow bristles were easily distinguishable from their neighbors and were significantly reduced in size ( Figure 5H ) . Thus , mutations in the DHR3 ligand-binding domain appear to have significant effects on growth , independent of differentiation . To evaluate the growth defects due to DHR3 LBD mutation , statistical analyses were performed on the size of eyes and ommatidia as well as bristle length . Homozygous DHR3-mutant eyes were generated in a trans-heterozygous M ( 2 ) 53/DHR3− mutant background , which produces variation in the body size of adult flies ( data not shown ) . Therefore , the areas of the homozygous eyes were normalized to the areas of the corresponding heterozygous thoraces . As compared to control recombined eyes , the homozygous DHR3K243X and DHR3W284X mutant eyes exhibited a significant reduction in surface area ( Figure 6A ) . The surface area of ommatidia from scanning electron micrographs of flies of equivalent size was also determined . Notably , the reduction in ommatidia area ( Figure 6B ) was not as strong as for the surface of the entire eye , indicating that the number of ommatidia was also affected . To precisely measure the effect on cell growth , bristle length was analyzed at the edge of the wing margin , as the shaft of each bristle corresponds to a single cell . Comparison of homozygous clonal bristles to the neighboring control bristles ( Figure 6C–6E ) revealed that the length of homozygous yellow-marked bristles was unaffected ( Figure 6C and 6F ) , indicating that , in this setting , the yellow marker is appropriate to monitor cell-autonomous growth . In contrast , there was a significant reduction in the size of both DHR3K243X and DHR3W284X homozygous mutant bristles , as compared to the neighboring control bristles ( Figure 6D–6F ) indicating that the LBD of DHR3 is required to sustain cell-autonomous growth . The DHR3 homozygous mutant bristles were affected also in their orientation , as compared with the surrounding bristles ( Figure 6D and 6E ) . Misorientation was also observed for the ommatidia-associated bristles in homozygous DHR3-LBD mutant eyes ( insets in Figure 5F and 5G ) , potentially reflecting one of the pleiotropic functions of DHR3 . Taken together , our findings demonstrate that , in addition to a role in coordinating the onset of metamorphosis , DHR3 also acts in a cell-autonomous manner to control cell growth .
By using Drosophila genetics and a gain-of-function strategy , we identified the NR , DHR3 , as an enhancer of a dS6K-regulated growth phenotype . This effect can be mediated by an isoform of DHR3 lacking the DBD . Moreover , using a revertant screening strategy , we have generated LBD-specific DHR3 mutants and demonstrated that the LBD of DHR3 is necessary to maintain normal growth and dS6K activity . In contrast to the role DHR3 plays in transcriptional regulation affecting the onset of metamorphosis [41] , [42] , our studies indicate that it also plays a role in regulating cell-autonomous growth . These effects are most likely mediated through dS6K , as the ability of ectopically expressed DHR3-RS to drive growth in the dorsal wing blade is blunted in Drosophila deficient for dS6K . Consistent with these findings , we have previously demonstrated that dS6K also controls cell growth in a cell-autonomous manner [18] . However , the effect on cell size is more pronounced in dS6K mutants [18] than in the DHR3-mutant clones described here . This may reflect the fact that dS6K activity is blunted , but not abolished , in DHR3 LBD-mutant larvae . Compatible with this hypothesis , we previously found that in a dS6K P-element–induced mutant ( P{PZ}S6K[07084] ) we could not detect dS6K protein ( unpublished results ) ; however , this mutation induced a much less severe phenotype as compared with the dS6Kl-1 null mutation [18] . In homozygous DHR3 mutant eyes both the size and the number of ommatidia were decreased , whereas in dS6K mutant flies the size reduction of the eye was only due to a decrease in ommatidia size but not number [18] . This difference might be attributed to the experimental settings . In the current study , DHR3 mutant eyes were generated by mitotic recombination in a heterozygous Minute background , whose developmental delay is less than two days . In contrast , the size and number of ommatidia in dS6K mutant eyes were measured in homozygous mutant flies that exhibit a five-day delay at eclosion . The longer time for the latter to emerge as adults allows additional cell divisions to proceed , leading to a higher number of ommatidia [54] . Previous studies demonstrated that DHR3 participates in a hierarchal regulatory circuit in response to ecdysone signaling [41] , [55] , but also acts in a negative feedback loop to repress ecdysone receptor-mediated signaling [42] . Prothoracic gland production of ecdysone is mediated by the brain neuropeptide prothoracicotropic hormone ( PTTH ) [56] . Recent studies in Drosophila have shown that genetic ablation of PTTH-producing neurons induces a delay in larval development and results in larger adult flies as a direct consequence of reduced levels of ecdysone [29] . Interestingly , in the tobacco hornworm , Manduca sexta , PTTH-induced ecdysone production is paralleled by the phosphorylation of the Manduca orthologue of Drosophila ribosomal protein S6 [28] . Moreover , this process is sensitive to rapamycin [28] and we have observed a burst of dS6K activity at early pupation ( unpublished data ) . As the body size of the adult fly appears to be determined by growth regulators , including dS6K , as well as by hormones that control the timing of developmental windows , such as PTTH , our results suggest that the DHR3/dS6K regulatory module acts to integrate these two processes . The studies presented here support the existence of a novel DHR3 polypeptide devoid of a DBD , DHR3-PS . Nonetheless , although DHR3-PS is sufficient to potentiate a dS6K-dependent growth phenotype , we can not exclude that the other DBD-containing DHR3 isoforms also contribute to dS6K activation . In general , DHR3 , like other NRs , is a transcription factor composed of four elements: a modulator domain , the DBD , the hinge region , and the LBD [57] . The DBD of NRs typically consists of two zinc fingers , with the first being critical for conferring DNA-binding specificity [58] . Like DHR3-PS , NRs lacking a DBD have been previously reported . Notably , in Drosophila , the NR E75B , a DHR3 partner , lacks one of the 2 zinc fingers that is required to form a functional DBD [59] . However , E75B , through its ability to interact with DHR3 , modulates DHR3 transcriptional activity in a gas-responsive manner [60] . Like the putative DHR3-PS , the NR short heterodimer partner ( SHP ) in mammals is also devoid of DBD , but , as with E75B , it interacts with other NRs to modulate their transcriptional activity [61] . It is unlikely that DHR3-PS behaves as a dominant-interfering effector of full-length DHR3 as ectopic DHR3-PS expression induces growth , whereas DHR3-RNAi inhibits growth . However , DHR3 also heterodimerizes with two NRs: E75 and the ecdysone receptor [41] , [42] . Thus , in the case of E75 , ectopically expressed DHR3-PS may act to decrease the levels of free E75 , leaving full-length DHR3 free to increase the transcription of target genes . In contrast , DHR3-PS binding to the ecdysone receptor could counteract the negative growth regulation mediated by ecdysone signaling [31] . However , it should be noted that the negative effects of ecdysone are humoral [33] and mediated by dFOXO-inactivation within the fat body [31] , whereas , as we have shown here , DHR3 regulates growth in a cell-autonomous manner . Moreover , dFOXO subcellular distribution was not altered in DHR3 mutant clones in third instar wing imaginal discs ( data not shown ) , indicating that the DHR3 cell-autonomous effect on cell growth is not mediated by the PKB/dFOXO signaling . In contrast to acting as a dominant-interfering isoform , the results presented here also suggest that DHR3 activates dS6K through a non-genomic mechanism , an effect of NRs that does not require the DBD function . Such a model is supported by NR responses whose kinetics are too rapid to be explained by de novo transcription and translation of a gene product [62] . Indeed , nongenomic effects typically occur within minutes following addition of the cognate ligand and are resistant to transcriptional inhibitors . In the case of DHR3 , it is experimentally difficult to address this question as the ligand for DHR3 is unknown and we are scoring for a genetic endpoint resulting from events induced much earlier in larval development . It has been demonstrated that vitamin D3 [63] , [64] and all-trans-retinoic acid [65] both induce activation of S6K1 within minutes of administration to cells . Moreover , in the case of vitamin D3 , it was shown that these effects were mediated through protein phosphatases PP1 and PP2A in a vitamin D3 receptor ( VDR ) -dependent manner . VDR appears to directly interact with the catalytic subunits of PPI and PP2A , and vitamin D3 acts to disrupt this interaction and enhance an interaction between VDR and S6K1 , stabilizing S6K1 in its phosphorylated active state [63] , [64] . However , depleting DHR3 levels by RNA interference blunts both dS6K T398 and d4E-BP T37/T46 phosphorylation , suggesting that DHR3 acts upstream or at the level of dTORC1 . Identification of potential partners for DHR3-PS may be useful in determining , at the molecular level , the mechanism by which DHR3 controls cell growth and dS6K activity . The data further support the notion that a ligand exists for DHR3 , and that the ligand is required for many of the pleiotropic activities of DHR3 . Those NRs that bind steroid hormones are , in general , high-affinity receptors , whereas the low-affinity NRs bind ligands that are present in high concentration , such as dietary nutrients [66] . The observation that an NR , generated by fusing the DHR3 LBD with the DBD of Gal4 , is transcriptionally active in a number of specific embryonic and larval tissues suggests that such a ligand is widely present [67] . Given the role of dTOR/dS6K as a nutritional effector [14] , it is interesting to note that the chimeric DHR3/Gal4 NR is active in organs that provide basal nutrients , in particular , in a group of cells of the larval midgut , which are essential for the transfer of nutrients to the hemolymph [67] . Importantly , the mammalian orthologues to DHR3 and its partner E75 are retinoid-related orphan receptor ( ROR ) α and Rev-erb ( NR1D ) α , respectively [68] . As in Drosophila , the NR1D subgroup functions as dominant transcriptional silencers by inhibiting transactivation mediated by RORα [68] . Interestingly , it was recently reported that RORα-deficient mice , like S6K1-deficient mice [69] , exhibit reduced fat-pad mass , smaller adipocytes , and resistance to diet-induced obesity [70] . Moreover , in solving the X-ray structure of the RORα LBD , it was revealed that cholesterol was bound in the ligand-binding pocket [71] . While the Drosophila NR , DHR96 , has recently been shown to bind cholesterol thereby modulating cholesterol homeostasis [72] , this does not exclude the possibility that DHR3 could also bind cholesterol . However , the predicted models of the structure of DHR3 indicate that the size of the ligand-binding pocket is smaller than those of either RORα or RORβ [73] . Given the role of the mTOR/S6K1 nutrient-responsive pathway in mammals [74] , it raises the possibility that DHR3 is a low-affinity receptor for an abundant nutrient ligand . Identification of this specific ligand constitutes the next issue to investigate .
The following fly strains were used: dS6Kl1 and UAS-dS6K; ap-Gal4 [18]; UAS-Tsc1/2 [7]; UAS-PTEN [75]; pumpless-Gal4 [14]; DHR3G60S and DHR3R107G [51]; eyeless-Gal4 [76]; Cre-lox ( a generous gift from K . Basler ) ; actin5c>CD2>Gal4 , UAS-GFP [77]; and da-Gal4 , actin-Gal4 , engrailed-Gal4 , UAS-GFP , FRT-42D , M ( 2 ) 53 , and FRT-42D , P ( y+ ) 44B ( Bloomington stock center ) . Because y+ and w+ markers were used , all the experiments were performed in a y , w genetic background . In the screen , lines with about 5000 independent EPy+ insertions [39] were mated to ap-Gal4>UAS-dS6K virgin females and offspring were scored for modulation of the bent-down wing phenotype . Approximately 900 suppressor and 100 enhancer lines were further analyzed for their effects on wing development when mated to ap-Gal4 virgin females . In a third step , 90 enhancer and 130 suppressor lines were retained and mated to either ap-Gal4>UAS-dS6K or ap-Gal4>UAS-S6K1dE/D3E virgin females , to test their differential effect on dS6K versus S6K1dE/D3E . For the EMS revertant screen , about 500 DHR3-R-EP males were starved overnight and then transferred on wet paper containing a 25 mM EMS solution in 10 mg/ml sucrose . After one day , these males were mated to approximately 1500 ap-Gal4>UAS-dS6K virgin females . Flies were then transferred every day for egg laying . An estimated 150 , 000 F1 flies were obtained; as both parental lines were balanced over a CyO chromosome , about 50 , 000 flies were screened for the reversion of the bent-down-wing phenotype . Localization of the EP insertions was performed as described [39] . To generate UAS-DHR3-RNAi , a PCR fragment spanning the DHR3 reading frame from Leu114 to Lys265 was cloned as described [78] . Congruent results were obtained by repeating the experiments with 2 other distinct UAS-DHR3-RNAi strains provided by H . Tricoire and the National Institute of Genetics ( http://www . nig . ac . jp/ ) . For RACE-PCR , polyA+ cDNAs were obtained by using the RNeasy kit and Oligotex mRNA purification ( both from Qiagen ) and then amplified with the SMART RACE cDNA Amplification Kit ( Clontech ) . 5′ RACE to obtain endogenous cDNAs and the chimeric DHR3-EP cDNAs followed a 2-step process: first , using a DHRS-RR–specific primer ( catggtctgctgtggcgtcacggaggc ) and universal primer mix , and then by nested PCR using a combination of nested universal primer mix/DHRS-RR–specific primer ( cggttgcgattaacacggtccaccac ) . UAS-S6K1dE/D3E and DHR3 cDNAs were cloned in the pUAST vector and injected as previously described [18] . The RA-cDNA was kindly provided by Carl Thummel; the RB- cDNA was obtained from DGRC; the RS transcript was artificially generated by truncation of the RA-cDNA lacking the AUG initiator codon upstream of the DBD coding sequences . To identify EMS point mutations , DHR3 coding sequences were PCR amplified from the genomic DNA of revertant flies . Fragments were then sequenced and searched for double picks , as compared with wild-type genomic DNA . Identified point mutations were confirmed by independent repetition of the entire procedure . Larval tissues were dissected , stained as previously described [18] , and then observed on a Leica Sp2 confocal microscope . For SEM , flies were fixed by successive baths of increasingly concentrated ethanol solution , up to 90% , and directly observed on an S-3000N HITACHI scanning-electron microscope . To measure eye area , eye-flp;FRT-42D , M ( 2 ) 53 females were mated to FRT-42D , P ( y+ ) 44B control males , and to FRT-DHR3K243X and FRT-DHR3W284X mutant males . Photographs of offspring female flies were used to measure the area of homozygous eyes and heterozygous thoraces , as described [18] . To circumvent potential individual variation , the eye size of each individual was normalized to its corresponding thorax . The ommatidia size was measured from SEM pictures of 6 flies of identical size for each genotype . Protein extracts were prepared and western blotting was performed as previously described [13] . To select prepupae , wandering larvae of the corresponding phenotype were collected and transferred to a new tube . After 8 hours , newly formed prepupae and late third-instar larvae were collected to make protein extracts . The in vitro dS6K kinase activity assays were performed on second-instar larval extracts , essentially as described [13] using histone H2B as the substrate [79] . The antiserum to DHR3 was produced commercially by Eurogentec . The peptides 144QMRAQSDAAPDSSYYD159 and 209SADYVDSTTYEPRSTI224 were used to immunize rabbits . The specific anti-peptide antibodies were then affinity purified as previously described [80] . | In biological systems , the execution of morphogenic programs requires coordinated integration of the essential processes of growth , proliferation , and differentiation . Signaling networks embedded within these processes include the insulin and nutrient pathways required for cell growth and the steroid hormone-regulated pathways that control discrete developmental steps . Although these pathways are known to be integrated and coordinated , the molecular bridges that link them remain to be identified . Taking advantage of Drosophila , we performed a genetic screen for novel regulators of the dS6K , which previously has been identified as a key effector of cell growth downstream of insulin and nutrient signaling . Unexpectedly , we identified the nuclear receptor DHR3 , a key regulator of morphogenesis , as a potent modulator of dS6K–mediated cell growth . Nuclear receptors typically comprise a DNA–binding domain and a regulatory ligand-binding domain . Here we show that a DHR3 isoform , devoid of the DNA–binding domain is sufficient to potentiate dS6K–mediated cell growth through its ligand-binding domain . We further demonstrate that , like dS6K , DHR3 regulates cell-autonomous growth . These data provide a unique molecular link between steroid-regulated development and nutrient-dependent growth . | [
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] | [
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] | 2010 | The Nuclear Receptor DHR3 Modulates dS6 Kinase–Dependent Growth in Drosophila |
In eukaryotic cells , surface expression of most type I transmembrane proteins requires translation and simultaneous insertion of the precursor protein into the endoplasmic reticulum ( ER ) membrane for subsequent routing to the cell surface . This co-translational translocation pathway is initiated when a hydrophobic N-terminal signal peptide ( SP ) on the nascent protein emerges from the ribosome , binds the cytosolic signal recognition particle ( SRP ) , and targets the ribosome-nascent chain complex to the Sec61 translocon , a universally conserved protein-conducting channel in the ER-membrane . Despite their common function in Sec61 targeting and ER translocation , SPs have diverse but unique primary sequences . Thus , drugs that recognise SPs could be exploited to inhibit translocation of specific proteins into the ER . Here , through flow cytometric analysis the small-molecule macrocycle cyclotriazadisulfonamide ( CADA ) is identified as a highly selective human CD4 ( hCD4 ) down-modulator . We show that CADA inhibits CD4 biogenesis and that this is due to its ability to inhibit co-translational translocation of CD4 into the lumen of the ER , both in cells as in a cell-free in vitro translation/translocation system . The activity of CADA maps to the cleavable N-terminal SP of hCD4 . Moreover , through surface plasmon resonance analysis we were able to show direct binding of CADA to the SP of hCD4 and identify this SP as the target of our drug . Furthermore , CADA locks the SP in the translocon during a post-targeting step , possibly in a folded state , and prevents the translocation of the associated protein into the ER lumen . Instead , the precursor protein is routed to the cytosol for degradation . These findings demonstrate that a synthetic , cell-permeable small-molecule can be developed as a SP-binding drug to selectively inhibit protein translocation and to reversibly regulate the expression of specific target proteins .
CD4 is a type I integral membrane glycoprotein that is expressed on the surface of thymocytes , T-helper lymphocytes , and cells of the macrophage/monocyte lineage [1] . It plays a central role in immune responses but also represents an obligatory component of the cellular receptor complex for HIV [2] , [3] . Several reports demonstrate that down-modulation of surface CD4 protects cells from HIV infection [4]–[8] . In addition , natural CD4 down-modulation by memory CD4+ T cells in vivo protects African green monkeys from developing AIDS after infection with simian immunodeficiency virus ( SIV ) , while maintaining the immunological functions normally attributed to CD4+ T cells [9] . Reduction in surface CD4 can be elicited by several factors that interfere with its translation or intracellular trafficking ( reviewed in [10] ) . Phorbol esters are known to induce CD4 endocytosis through serine phosphorylation of the cytoplasmic tail of CD4 [11] . The concerted action of the three HIV-1 proteins Nef , Env , and Vpu results in a complete removal of CD4 from the surface of HIV infected cells through ( i ) enhanced routing of CD4 to the endoplasmic reticulum ( ER ) degradation pathway [12] , [13] and ( ii ) activated endocytosis and lysosomal degradation [14] , [15] . Surface expression of type I transmembrane ( TM ) proteins , such as CD4 receptors , requires translation of precursor proteins and their insertion into the ER membrane for subsequent routing to the cell surface . This co-translational translocation pathway begins when a hydrophobic N-terminal signal peptide ( SP ) on the nascent protein emerges from the ribosome and is recognized by the signal recognition particle ( SRP ) . This complex of ribosome , nascent chain , and SRP is then targeted to the ER membrane via the interaction between SRP and its membrane receptor . Subsequently , the ribosome tightly binds to the Sec61 complex in the ER-membrane , a protein-conducting channel composed of the membrane proteins Sec61α , Sec61β , and Sec61γ . Finally , the ribosome continues the translation and the elongating polypeptide chain moves directly from the ribosome exit tunnel into the associated membrane channel . When the TM domain within the nascent polypeptide chain reaches the Sec61 complex , the channel opens laterally and the membrane anchor is released into the lipid bilayer ( reviewed in [16] , [17] ) . Simultaneously with the translocation of the polypeptide chain , cleavage of the signal sequence occurs at the luminal side of the ER together with other possible modifications such as N-glycosylation and proper folding of the polypeptide . A screen for anti-HIV drugs led to the identification of CADA , a cyclotriazadisulfonamide with broad spectrum antiviral activity against laboratory strains and clinical isolates of HIV-1 , as well as HIV-2 and SIV [18] , [19] . The anti-HIV activity of CADA and its analogues correlated with their ability to down-modulate cell surface CD4 expression [8] . In the present study , we focused on the mechanism of action and molecular target of CADA . We demonstrate that CADA inhibits CD4 biogenesis during the early translational steps . More specifically , we show that ( i ) CADA selectively binds to the SP of human CD4 ( hCD4 ) , ( ii ) CADA prevents the growing CD4 polypeptide from entering the lumen of the ER , ( iii ) the SP of hCD4 is first inserted into the translocon channel with its N-terminus facing the lumen of the ER ( Nlum/Ccyt ) before an obligate inversion into a Ncyt/Clum topology takes place , and ( iv ) CADA locks the SP of hCD4 in an intermediate position during inversion and prevents further translocation of the polypeptide chain into the ER lumen .
To evaluate the effect of CADA on CD4 expression , different cells were treated with the compound under various conditions ( Figures 1 and S1 ) . CADA induced a dose-dependent down-modulation of hCD4 regardless of the cell background in which it was expressed , i . e . , in primary T-cells and T-cell lines that express the receptor naturally , as well as in transfected cells stably expressing CD4 ( Figure 1B–1D ) . This down-modulating effect appeared to be reversible ( Figure 1G ) , and selective for CD4: in a set of 14 different surface receptors CADA inhibited only CD4 ( Figure 1E and 1F ) . The sensitivity of CD4 for CADA was species-specific: the compound did not affect the expression of mouse CD4 , whereas primary T-cells from macaques responded to the compound in a similar way to human T-cells ( Figure 1D ) . Thus , CADA selectively and reversibly down-modulates CD4 of primate origin in a dose-dependent manner . To elucidate how CADA interferes with CD4 protein expression , we analyzed the impact of CADA on the life cycle of CD4 . A kinetic study with CADA and the phorbol ester PMA , a drug known to induce rapid endocytosis and degradation of surface CD4 [11] , suggested an effect of CADA on CD4 translation or transport to the cell surface . As shown in Figure 1H , the appearance of newly synthesized CD4 molecules at the surface was prevented by CADA . To test directly whether CADA interferes with the de novo synthesis of CD4 we performed pulse-chase experiments followed by immunoprecipitation for CD4 . CADA profoundly inhibited CD4 synthesis in CHO . CD4+ cells as indicated by the absence of this protein band in CADA-treated samples ( Figure 2A and 2B ) . Although CADA strongly affected CD4 , analysis of the total cellular protein extract revealed no general inhibition of protein synthesis ( Figure 2A , lanes 3 and 4 ) . We further analyzed this CD4-specificity of CADA by examining protein synthesis in different cellular compartments . Both cytosolic and membrane fractions were investigated in CD4-negative and CD4-positive CHO cells . The expression of cytosolic proteins was not altered by CADA-treatment , both in the absence or presence of CD4 ( Figure 2C and 2D ) . In contrast , addition of the translation elongation inhibitor cycloheximide ( CHX ) resulted in an almost complete protein shut-down . Interestingly , CADA seemed not to affect other membrane proteins in the CD4-negative cells . Also , a similar membrane protein expression pattern was observed in DMSO and CADA-treated CD4+ CHO cells , except for one protein band migrating around 80 kDa . As this protein band could not be detected in the CD4-negative cells , we concluded that the affected protein was most likely CD4-YFP , an 80 kDa fusion protein that is stably and highly expressed in these CHO cells . The significant reduction in synthesis of membrane-associated proteins in CADA-treated CD4+ CHO cells ( Figure 2D ) can be ascribed to the complete block of CD4-YFP production . To isolate the glycosylated surface proteins from the membrane fraction that contains proteins not only from the surface membrane but also from different intracellular organelles and export pathways , we used Concanavalin A ( ConA ) agarose beads . Again , CD4-YFP was strongly inhibited by CADA , whereas the expression of other glycosylated membrane proteins was not affected ( Figure 2E ) . In addition , CADA did not inhibit the secretion of proteins into the culture medium ( Figure 2E ) . Similar observations were made in other CADA-treated cells , such as U87 and SupT1 ( Figure S2 ) . Finally , down-modulation of CD4 occurred post-transcriptionally , because CD4 messenger RNA levels were not altered by the compound ( Figure S1F ) . From these data , we concluded that CADA has a high selectivity for the protein synthesis of hCD4 . Next , we established which domain of CD4 is required for drug sensitivity by investigating C-terminal deletion mutants ( Figure 3 ) . Deletion of the cytosolic tail of CD4 , which is involved in signal transduction and endocytosis of the receptor [11] , [20] , did not affect its sensitivity to CADA ( Figure 3A , mutant hCD4-426 ) . Exchanging the extracellular D3 , D4 , and transmembrane ( TM ) domains of CD4 with a related type I TM protein such as the alpha chain of CD8 , expression of which is not affected by CADA ( Figure 1F ) , failed to prevent CADA-induced down-modulation ( Figure 3A , mutant hCD4-CD8 ) . Furthermore , human/mouse chimaeric fusion constructs excluded a potential role for the immunoglobulin-like domains D1 and D2 of hCD4 in CADA-sensitivity . In agreement with primary murine T-cells ( Figure 1D ) , wild-type mouse CD4 ( mCD4 WT ) did not respond to the down-modulating activity of CADA , whereas hCD4 containing either mouse D1 or mouse D2 did ( Figure 3B ) . These data narrowed down the CADA-sensitive region of hCD4 to the N-terminal SP and the first seven N-terminal residues of the mature protein , as this is the only remaining fragment of hCD4 common to all CADA-susceptible constructs ( Figures 3 and S1E ) . Previous experiments demonstrated an inhibitory effect of CADA on the expression of membrane-anchored proteins that contained the SP of hCD4 . However , removal of the TM domain of CD4 did not diminish the sensitivity of the protein to CADA ( Figure 4B ) , showing that CADA-susceptibility is not membrane anchor-dependent . Therefore , in our further study we also included smaller secreted proteins ( Figure 4A ) . In general , precursors of type I TM proteins ( e . g . , CD4 ) contain an amino-terminal SP that is involved in the early steps of biogenesis such as targeting of the nascent polypeptide to the ER membrane for co-translational translocation [21] , [22] . To determine how CADA interferes with CD4 synthesis , translation and translocation of CD4 were analyzed in vitro using cell-free rabbit reticulocyte lysate with or without pancreatic rough microsomes ( RMs ) . In the absence of RMs , translation of full length and truncated hCD4 was unaffected by CADA ( Figure 4C , lanes 1 and 2 , open arrowhead ) . However , in the presence of RM , translocation of hCD4 into the RM lumen was markedly inhibited by CADA as indicated by a reduction in glycosylated WT hCD4 ( Figure 4C , lanes 4 and 7 , star ) and SP-cleaved truncated hCD4 ( Figure 4C , lanes 4 and 7 , solid arrowhead ) that were resistant to degradation by added proteases ( Figure 4C , lanes 5 and 8 ) . In agreement with the in cellulo results of Figure 4B , the in vitro translocation of full length ( hCD4 WT ) and truncated secreted ( hCD4-201 ) CD4 was dose-dependently inhibited by CADA ( Figure 4D ) . In contrast , the translocation of two control molecules , mCD4-186 and bovine pre-prolactin ( pPL ) , was not affected by CADA at any concentration tested ( Figure 4D and 4E ) . We next prepared two chimaeric constructs ( Figure 4A ) in which the mature domain of pPL was fused either directly to the SP of hCD4 ( [hCD4]-pPL ) or to the seventh residue of mature hCD4 ( [hCD4]- ( 7 ) -pPL ) . Translocation of [hCD4]-pPL was profoundly inhibited by CADA ( Figure 4D and 4E ) . While this mutant was slightly less sensitive to the compound than WT CD4 , this confirms that sensitivity to CADA is determined primarily by the hCD4 SP . However , the presence of seven additional amino acids of the N-terminal CD4 mature domain enhanced CADA sensitivity , resulting in dose-dependent translocation inhibitory levels similar to WT CD4 ( Figure 4E , construct [hCD4]- ( 7 ) -pPL ) . Furthermore , fusing these N-terminal 32 residues of hCD4 to a non-SP containing yellow fluorescent protein ( YFP ) resulted in translocation of a non-CD4 related cytosolic protein into the RM lumen and full susceptibility to CADA ( Figure S3A–S3C ) . These data show that inhibition of protein translocation by CADA is specific to the SP and the first seven N-terminal residues of mature hCD4 . To investigate if CADA inhibits CD4 translocation into the ER in vivo , we performed experiments to rescue cytosolic CD4 in HEK293T cells by inhibiting proteosomal degradation with MG132 . In order to detect all precursor forms of CD4 , we generated an hCD4 construct that contained the simian virus 5 ( V5 ) epitope at the N-terminal end of the mature protein ( outlined in Figure S3E ) . Only when the cells were treated with the combination of CADA and the proteasome inhibitor MG132 , a higher molecular form of CD4 could be detected that corresponded to the precursor form of the V5-tagged CD4 ( Figure 4F ) . Accordingly , for [hCD4]-YFP proteasome inhibition with MG132 also rescued the precursor form in CADA-treated samples ( Figure S3D ) . These in vivo results indicate that CADA diverts CD4 synthesis towards cytosolic proteosomal degradation . We then looked for direct interaction between CADA and the hCD4 SP . Chemically synthesized SPs of hCD4 and mCD4 were captured on a streptavidin sensor chip and examined by surface plasmon resonance ( SPR ) . Selective and dose-dependent binding of CADA to hCD4 SP was observed , and almost no binding to mCD4 SP ( Figures 5A and S4A ) . Also , CADA did not show interaction with the SP of bovine pPL ( Figure S4C ) . As a control , SRP interaction with the SPs was evaluated and revealed equal binding profiles of SRP to human and mouse SP , thus excluding non-functionality of mSP or insufficient peptide-coupling to the chip ( Figures 5B and S4B ) . In addition , the lack of hCD4 SP binding by the structural analog MFS105 ( Figure 5C ) , a CADA-derivative with no CD4 receptor down-modulating activity ( Figure S4E ) [8] , further strengthens the selectivity of CADA for hCD4 SP . SPs display a general structure consisting of three regions: a ( mostly ) positively charged N-terminus ( N-region ) , a central hydrophobic alpha helical region ( H-region ) , and a more polar C-terminal part ( C-region ) that includes the SP cleavage site [23] . On the basis of the unresponsiveness of mouse CD4 to CADA ( Figures 1D , 3B , 4D , and 4E ) , we generated human/mouse chimaeric constructs with exchanged SP subregions in order to analyze the contribution of each subregion of hCD4 SP to CADA-susceptibility ( Figure 5D ) . Exchanging the N-region of the SP ( construct [mhh]-hCD4 ) slightly decreased the sensitivity of CD4 to CADA , as analyzed by flow cytometry ( Figure 5E ) . A stronger reduction in susceptibility was observed when the C-region of mSP and the first seven N-terminal residues of mature mCD4 were inserted in hCD4 ( Figure 5E , construct [hhm]-hCD4 ) . Swapping the hydrophobic H-region had a major negative impact on the sensitivity to CADA ( Figure 5E , construct [hmh]-hCD4 ) . Reversibly , insertion of the hydrophobic alpha helix of hCD4 SP into CADA-insensitive SPs , such as those of murine CD4 or bovine pPL , significantly enhanced their responsiveness to CADA ( Figure S5C and S5D ) . In addition , the different inhibitory levels of CADA on surface expression of the human-murine chimaeras could be linked to different degrees in ER translocation inhibition ( Figure S5A and S5B ) . Although a more than 10-fold reduction in CADA-activity was noted in the in vitro assay as compared to the in cellulo data ( Figure 5E versus S5B ) , the chimaeras responded to CADA in the same relative order . These data suggest contributions from all three hCD4 SP subregions to CADA-sensitivity , but show a crucial role for the hydrophobic H-region . To identify the step at which CADA interferes with the translocation of hCD4 , we analyzed the targeting and translocation of [CD4]-pPL nascent chains . Transcripts lacking a stop codon were truncated at sequential sites in the mature domain ( Figure 6A ) and translated in vitro . Via an intact peptidyl-tRNA bond the nascent chains remain attached to the ribosomes and are synchronized at a defined length . Addition of pancreatic RMs will result in docking of the different ribosome-nascent chain complexes ( RNCs ) to the ER membranes , giving translocation intermediates that represent static snapshots of the movement of the SP within the translocon channel . Subsequently , the nascent chains are released from membrane-bound ribosomes by puromycin , allowing for a controlled translocation into the ER lumen . Nascent chains of 80 residues ( 80-mers ) were translated in the absence of RM ( Figure 6B , left panel , first lane ) . These chains are bound to the ribosome as peptidyl-tRNAs with about 30 residues buried inside the ribosome . Protease treatment of these RNCs will degrade the N-terminal part of the peptide that is exposed to the cytosolic compartment ( about 50 residues ) , and will generate a faster migrating protein band on the gel ( Figure 6B , lane 6 ) . Addition of RM to the nascent chains will allow for targeting of the RNCs to the membrane and insertion of the SP into the translocon channel . If well-targeted , nascent chains will be shielded from exogenous protease because of a tight interaction between the ribosome and the translocon after transfer from SRP [24] , [25] , and appear as intact RNCs after proteinase K ( PK ) treatment . Equal protease-protected peptidyl-tRNA bands were observed in control and CADA samples , ruling out an inhibitory activity of CADA on targeting and transfer of the nascent chains to the translocon ( Figures 6B , lanes 7 and 8 , arrow ) . Addition of puromycin to the targeted chains induced the release of the nascent chains from the ribosome , with subsequent translocation of the peptides into the PK-protected RM lumen and cleavage of the SP ( Figure 6B , lanes 4 and 9 , solid arrowhead ) . However , in the presence of CADA , very few cleaved species were observed ( Figure 6B , left panel , lane 5 , solid arrowhead ) , indicating a profound inhibition by CADA on the co-translational translocation of pPL species that contained the SP of hCD4 but not of those with the SP of WT pPL ( Figure 6B , left and right panel , respectively ) . This inhibition was also dose-dependent ( Figure 6C ) . Translocation inhibition by CADA was observed at all chain lengths , but was most effective on nascent chains up to 80 residues ( Figure S7 ) . Remarkably , we could delay the administration of CADA to the membranes until targeting was completed . A similar inhibitory effect on the translocation of 80-mers was recorded when the compound was applied to the RM before targeting or to the RNC/RM mixture 15 minutes after initiation of targeting ( Figure 6D ) . Taken together , these results indicate that CADA acts at a step after the targeting and transfer of the nascent chains to the translocon , but before the growing peptide chain has reached the luminal side of the membrane . Although N-terminal signal sequences are generally considered to insert in a tail-first , hairpin-looped topology [26] , [27] , hydrophobic sequences can also enter the translocon in a head-first configuration or reorient within the translocon channel [28]–[32] . To determine if CADA might have a role in changing the topology of the SP during translocation , we introduced a diagnostic N-glycosylation site at the N-terminus of the SP ( Figure 7A ) . Head-first N-terminal translocation of the SP will give rise to glycosylated species , whereas tail-first C-terminal orientation will result in SP-cleaved forms . The hCD4 SP was extended with 17 residues based on a construct used in other studies [33] , [34] , and either contained or lacked ( control ) an N-linked-glycosylation site ( Figure 7A ) . Full length proteins with the extended hCD4 SP translocated well , irrespective of the presence of the glycosylation site , and responded to CADA in a dose-dependent way ( Figure S8A ) . Through analysis of nascent chains we investigated if and when head-first translocation occurred , and determined the stepwise movement of the extended SP within the translocon ( Figure 7B ) . At shorter truncations ( i . e . , 17+58-m ) , a substantial fraction of the nascent chains were glycosylated , indicating that targeting may begin with the N-terminus of the SP in the ribosome/translocon complex facing the RM lumen . At this chain length , the binding of the RNC to the translocon was already highly stable as evidenced by its high-salt resistance ( Figure S8E ) . Elongation of the mature part of the polypeptide with four residues ( 17+62-m ) resulted in a profound increase in glycosylated RNCs ( Figure 7B , second lane of third panel , star ) . At longer truncations , the glycosylation efficiency decreased gradually and was almost undetectable for the 17+88-mer . Interestingly , administration of CADA to the RNCs had very little effect on the N-terminal glycosylation of the shorter chains ( 17+58-m ) and the high-salt resistant binding of the RNC to the translocon ( Figure 7B , third lane of second panel , and Figure S8E ) . However , the inhibitory activity of CADA increased significantly when the C-terminus of the polypeptide was extended , and resulted in non-detectable glycosylation levels for chains with a minimum length of ( 17+71 ) residues ( Figure 7B ) . Notably , administration of CADA to the 17+71-mers resulted in a loss of the high-salt resistant binding of the RNC to the translocon ( Figure S8E ) , suggesting that with the compound the positioning of the nascent chain in the channel was altered so that the peptide tether could allow the ribosome to dissociate from the channel . Analysis of the puromycin-treated samples revealed a similar N-terminal glycosylation pattern for the released chains as for the intact RNCs ( Figure 7C and 7D ) . Again , the inhibitory activity of CADA on N-terminal glycosylation gradually increased with growing chain length for chains up to ( 17+71 ) residues ( Figure 7D ) . Breaking the peptidyl-tRNA bond with puromycin also allowed for translocation of the chains into the RM lumen with subsequent SP cleavage . SP-cleaved species were hardly detectable for the shorter chains , irrespective of the presence of a glycosylation site ( Figure 7C and 7E , blue line ) . However , translocated SP-cleaved chains that were PK-protected first appeared for the 17+80-mers , i . e . , a chain length at which a profound decrease in N-terminal glycosylation was noted ( Figure 7C–7E ) . This suggests that at this specific chain length the SP is mainly positioned in the hairpin-looped topology with the C-terminus facing the RM lumen . In accordance with the data from the WT SP-containing chains ( Figure S7B ) , maximum C-terminal translocation and SP-cleavage were noted for the 17+80-mers , thus when the distance from the SP cleavage site to the ribosome peptidyltransferase centre ( PTC ) had reached a chain length of about 55 amino acids . Further extension of the polypeptide chain diminished the C-terminal translocation efficiency that was consistently blocked by CADA ( Figure 7E ) . Notably , for the 17+80-mers CADA strongly inhibited both the ( weak ) N-terminal glycosylation and the C-terminal translocation and SP-cleavage with equal efficiency , in a dose-dependent manner ( Figure S8C and S8D ) . These results together show that the nascent chains need a minimum length in order for CADA to exert its inhibitory effect on glycosylation , whereas at all lengths where SP-cleavage can be observed translocation with SP-cleavage is inhibited . Thus , on one hand , CADA hinders vertical positioning of the polypeptide with the N-terminus faced to the ER lumen for efficient glycosylation and , on the other hand , disturbs the completion of SP inversion for a hairpin-looped structure that can be SP-cleaved ( Figure 7F ) . This suggests that in the presence of CADA the SP is held in a folded conformation in the translocon channel , so that the polypeptide is prevented from reaching the luminal side of the ER .
In this study we have characterized CADA , a small-molecule HIV entry inhibitor as , to our knowledge , the first SP-binding drug that selectively inhibits hCD4 protein translocation into the ER in a SP-dependent way . CD4 is a type I membrane protein expressed on the surface of a subset of immune cells [1] . Sorting of this protein to the plasma membrane requires that the CD4 pre-protein contains a cleavable SP , an obligatory component for protein targeting to the ER through co-translational translocation . This synthetic pathway begins when a hydrophobic N-terminal SP emerges from the ribosome , is recognized by the SRP , and targets the whole RNC to the ER membrane so that the emerging polypeptide is inserted into the Sec61 translocon channel [16] , [17] . Simultaneously with the translocation of the polypeptide chain , cleavage of the SP occurs at the luminal side of the ER . Here , we revealed that targeting of hCD4 to the ER is initiated with a head-first N-terminal insertion ( Nlum/Ccyt ) of the SP in the translocon , before it inverts into an obligate Ncyt/Clum topology necessary for SP cleavage during C-terminal translocation . Thus , initial insertion of hCD4 SP in the channel occurs differently from the more widely accepted hairpin positioning ( Ncyt/Clum ) of cleavable N-terminal signals [26] , [27] , [35] . Head-first topology of non-cleavable N-terminal sequences has been proposed by the Spiess group for signal-anchor ( SA ) sequences that anchor the polypeptide in the bilayer [29]–[31] , [36] . Recently , a detailed study with an N-terminal type II SA sequence confirmed the head-first insertion of the SA into Sec61α before inversion from type I to type II topology takes place [32] . It is plausible that cleavable N-terminal SPs insert and invert in a similar way as type II SA sequences . Moreover , for a relatively short nascent polypeptide that is C-terminally attached to the ribosome PTC , the first possible way for the N-terminal SP to interact with the translocon is most likely a head-first ( Nlum/Ccyt ) orientation . Initial positioning of signal sequences may also be directed by interaction with cytosolic chaperones other than SRP , and translocon-associated proteins . Recently , Sec62 has been found to mediate the orientation of SA proteins depending on the hydrophobicity of the SA sequence [37] . It would be interesting to explore if there are other type I membrane proteins with initial Nlum/Ccyt insertion and to investigate if the factors that determine this early positioning of cleavable SP are different from those reported for N-terminal SA sequences . Reorientation of SA sequences is driven by flanking charges according to the positive-inside rule [38] , [39] and inhibited by increased hydrophobicity of the SA sequence [40] . For hCD4 the two adjacent lysine residues immediately down-stream of the SP cleavage site might overrule the more dispersed positive charge of the N-domain ( two arginine residues R3 and R8 ) and initially orient the SP according to this positive-inside rule with its C-terminus to the ( negatively charged ) cytosolic side of the membrane . In line with the canonical SA derived from the first TM segment of aquaporin 4 [32] , hCD4 SP initiates ER targeting at a nascent chain length of 58 residues , when the size of the polypeptide between the SP C-terminus and the PTC is about 33 residues . Inversion of hCD4 SP and translocation of its C-terminal segment with SP-cleavage were observed upon lengthening the polypeptide chain ( at its C-terminus ) with 22 amino acids , a stretch theoretically long enough to span the bilayer membrane and thus to allow for a hairpin looped positioning of the SP . For our study we extended the N-terminal hydrophilic domain of hCD4 SP with 17 residues as this was the minimum length required to obtain optimal glycosylation of the N-terminus ( Figure S9A–S9C ) , because of the distance between the active site of the oligosaccharyl transferase complex and the inner bilayer of the ER membrane [41] . Extension of the N-domain of SA sequences with more than 20 residues can change their signal insertion behaviour , but in favour of C-terminal insertion [36] . Our extended hCD4 SP did not shift to a preferentially C-terminal translocation with SP-cleavage ( Figure S9D ) , suggesting that the extended hCD4 SP preserved the same insertion behaviour as WT SP . Our data indicated equal targeting efficiencies for control and CADA-treated nascent chains , making interference of CADA with SRP-binding less likely . Furthermore , the equal mRNA levels that were detected in CADA-treated and control cells indicate that the compound does not induce an mRNA degradation response as has been observed with defective signal sequence recognition by SRP [42] . Also , early insertion of the SP into the translocon channel was not altered by our drug , ruling out inhibition of channel gating by CADA . The post-targeting translocation inhibition by CADA is probably related to the obligate inversion of hCD4 SP inside the translocon . One can expect that the reorientation of the hCD4 SP from an Nlum/Ccyt into an Ncyt/Clum topology requires a high degree of flexibility of the SP and the translocation channel to undertake such a gymnastics , which may be compromised after CADA-binding . Based on the CADA-sensitive chimaeras that contained the hydrophobic alpha-helix of hCD4 SP , we could attribute a crucial role to the core hydrophobic domain of the SP and hypothesize compound binding to this region . In fact , minor changes in this alpha-helix , such as removing the helix terminator Pro20 , could already reduce CADA-dependency , but introducing this proline residue at the C-terminal part of the murine ortholog was not sufficient to render sensitivity to the drug ( see Figure S6 ) . However , not only the central hydrophobic α-helical H-region of hCD4 SP , but also its C-terminus together with the first residues of the mature protein was shown to be important for full CADA-sensitivity . Interaction of CADA with both regions might re-position the SP in the translocon and profoundly reduce flexibility or alter the balance between hydrophobic and electrostatic interactions of the SP with the translocon and the lipid environment [43]–[45] . Also , post-targeted folding of a zinc-finger modified pPL has recently been shown to inhibit co-translational translocation into ER microsomes , indicating that folding events within the ribosome-Sec61 translocon complex ( RTC ) can occur and induce a mechanical block within the RTC that diverts the nascent chain into the cytosol for degradation [46] . Accordingly , in the presence of CADA the non-translocated CD4 precursor forms are routed to the cytosol for proteasomal degradation , which also indicates that translocation and not translation of CD4 is inhibited by the compound . In our experiments , C-terminal translocation of nascent chains was determined by SP-cleavage . Inhibition of the signal peptidase activity could also be interpreted as an inhibition of translocation , however , CADA did not interfere with the enzymatic activity of RM-extracted signal peptidase ( Figure S9E ) . Furthermore , both inhibition of N-glycosylation and SP-cleavage were observed with the N-terminally extended 80-mers , making it very unlikely that CADA would inhibit both luminal enzymes that act at distinct parts of the SP . Therefore , we propose that in the presence of CADA the SP is held in a folded conformation in the translocon ( presumably at the lateral gate ) , so that the polypeptide is prevented from moving through the channel . The focus of this work was to unravel the mechanism through which CADA selectively regulates CD4 expression . We show that CADA selectively inhibits the biogenesis of CD4 in cells without affecting other membrane or secretory proteins , and that this is due to its ability to inhibit specifically the co-translational translocation of CD4 into the lumen of the ER . The activity of CADA maps to the cleavable N-terminal SP of hCD4 . Moreover , through SPR analysis we were able to show direct binding of CADA to the SP of hCD4 and identify this SP as the main target of our drug . In view of the very high selectivity of CADA for hCD4 , it is most plausible to hypothesize a unique SP as its target . However , we cannot completely exclude a secondary interaction of CADA with another target of the translocation machinery . This could be in line with the suggested bimolecular binding model for CADA based on data from unsymmetrical CADA analogs and the 3-D quantitative structure-activity relationship ( 3D-QSAR ) study [47] , [48] . If CADA interacts with Sec61 ( or closely related factor ) in a more unspecific and non-discriminating manner , the final decision for a substrate to become translocation defective would then depend on the degree of reduced SP-flexibility by CADA binding , suggesting that CADA-dependency would mostly rely on SP-recognition and binding . Hydrophobic signal sequences , such as SPs , perform several functions in the biogenesis of secretory and membrane-bound proteins [26] . Although these hydrophobic sequences may represent interesting targets for drug design in order to regulate the expression level of proteins , CADA is the first small-molecule drug , to our knowledge , known to selectively bind to a SP and inhibit translocation of a specific protein in a SP-dependent way . There is precedence for the cyclic heptadepsipeptide HUN-7293 and its derivatives CAM741 and cotransin to down-modulate vascular cell adhesion molecule 1 ( VCAM1 ) in a signal-sequence-discriminatory way [49] , [50] . However , these compounds were reported to bind to the Sec61 translocon channel and to inhibit the expression of a subset of secretory and membrane proteins , including VCAM1 [34] , [51]–[53] . In contrast , the expression levels of a range of cell surface receptors including VCAM1 ( Figure 1F ) and CD4 from a different ( non-primate ) species ( Figure 1D ) , were not decreased by CADA , which is explained by the interaction of the drug with a SP of a specific cell surface receptor instead of a common Sec61 channel . Recently , the cyclic dodecadepsipeptide valinomycin has been described to selectively destabilize the SP of hamster prion protein [54]; however , this might be related to the down-regulation of the luminal chaperone protein BiP involved in protein translocation , explaining the general apoptosis-inducing effect of this drug [55] . The toxic , small-molecule eeyarestatin 1 ( ESI ) has been reported to inhibit co-translational translocation of a wide-range of receptors , but this compound prevents the transfer of the nascent polypeptide chain from the membrane-bound SRP delivery complex to the ER translocon , thus acting more upstream of CADA and the cyclodepsipeptides and less substrate-selective [56] . Mycolactone , the virulence factor of Mycobacterium ulcerans has recently been described to block protein translocation into the ER , and thus , to prevent secretion of innate cytokines and expression of important immune-related membrane receptors [57] . Also , apratoxin A , a natural product from a marine cyanobacterium prevents co-translational translocation of a wide-spectrum of cellular proteins , which may also explain its cytotoxic nature [58] . Cellular cytotoxicity is a major concern when using small synthetic drugs; however , cellular viability was not compromised by CADA and long-term ( ∼1 year ) CADA-treatment of T-cells was achieved , with preservation of CD4 re-expression following workout . In conclusion , our findings demonstrate that with cell-permeable small synthetic compounds selective SP-binding and subsequent translocation inhibition of the associated protein is feasible . This opens a new field , not only for CD4 related immunomodulation and antiviral intervention , but also for a wide range of cell surface proteins for which this principle of selective SP-targeted translocation inhibition should be applicable .
CADA . HCl and MFS105 . HCl were synthesized as described previously [47] . The compounds were dissolved in DMSO and stored at room temperature in the dark . The pcDNA3 expression vector ( Invitrogen ) encoding WT hCD4 was kindly provided by O . Schwartz ( Institut Pasteur , Paris , France ) . The A2 . 01/hCD4 . 426 and A2 . 01/hCD4-CD8 cell clones were from C . Devaux ( Montpellier , France ) and have been described elsewhere [59] . The pReceiver-M16 vector encoding mouse CD4-YFP was purchased from Imagenes and the human-mouse chimera constructs [60] were kindly provided by A . Trkola ( Zurich , Switzerland ) . Bovine pPL in the pGEM4 vector has been described previously ( plasmid pGEMBP1 [61] ) . Human and mouse CD4 were cloned into the pGEM4 vector for in vitro translation experiments . Fusion constructs were generated by PCR and cloned into the pGEM4 vector . The human/mouse chimaeric constructs were generated by incorporating the mouse sequence into the coding human sequence using PCR overlap extension and site-directed mutagenesis ( Stratagene ) . The same strategy was used for the murine/hCD4 and pPL/CD4 chimaeras . For the V5-tagged CD4 construct [hCD4]-V5sCD4 , the simian virus 5 ( V5 ) epitope ( GKPIPNPLLGLD ) was inserted in the pcDNA3 . 1-hCD4D1D2 plasmid , which contains the coding sequence of the two N-terminal domains of the hCD4 protein . The V5 sequence was incorporated at the N-terminus of mature CD4 between residue 32 and 33 as schematically presented in Figure S3E . Note that the first two residues of V5 ( residue G and K ) were already present in CD4 at positions 31 and 32 . The CD4+ T-cell lines MT-4 and SupT1 were cultured in RPMI-1640 medium , and Hela , U87 , HEK293T , and CHO cells were cultured in DMEM , supplemented with 10% FCS and penicillin/streptomycin . Human PBMCs were isolated by density gradient centrifugation as described previously [18] . For the monkey PBMCs , blood was collected from cynomolgus macaques ( Macaca fascicularis ) as described elsewhere [62] . Murine PBMCs were isolated from blood collected from Balbc mice . Stable transfections of U87 cells were performed with FuGENE 6 Transfection Reagent ( Roche Diagnostics ) , and transient transfections of HEK293T cells were performed with PolyJet in vitro transfection reagent ( Tebu-Bio ) , in accordance with the manufacturer's instructions . Western blot analysis of cell lysates was performed in accordance with standard protocols . Mouse monoclonal antibodies anti-hCD4 ( clone SK3 , BD Biosciences ) , anti-clathrin heavy chain ( clone 23 , BD Biosciences ) , anti-THE SV5-tag mAb ( Genscript ) , anti-actin ( Abcam ab3280 ) , and a HRP-conjugated goat anti-mouse antibody ( Dako ) were used for detection . For the cytosolic CD4 precursor rescue experiments , HEK293T cells were transfected with a mixture of 500 ng pcDNA3 . 1-V5-hCD4D1D2 plasmid DNA and 2 µl lipofectamine 2 , 000 ( Invitrogen ) . Six hours after transfection , growth medium was removed and replaced with fresh medium containing 10 or 2 µM CADA and/or 200 nM MG132 ( Sigma ) . After a 22 hour incubation in the presence of the compounds , cells were washed with ice-cold PBS and lysed in ice-cold lysis buffer ( 25 mM Tris , 150 mM NaCl , 1 mM EDTA , 1% NP-40 , 5% glycerol , pH 7 . 4 ) , supplemented with 0 . 4 µM PMSF ( Fluka ) and Complete protease inhibitor cocktail ( Roche ) . Flow cytometric analysis of surface receptor expression was performed as described previously [18] . All FITC- , PE- , PerCp- , or APC-labelled mAbs were from BD Biosciences . Data were acquired with a FACSCalibur flow cytometer ( BD Biosciences ) and the CellQuest software ( BD Biosciences ) . Data were analyzed with the FLOWJO software ( Tree Star ) . Down-modulation of CD4 was evaluated by the decrease in fluorescence intensity on CADA-treated cells relative to matched , untreated cells stained for CD4 . To calculate the efficiency of CADA on CD4 down-modulation , the median fluorescence intensity ( MFI ) for CD4 labelling for each sample was expressed as a percentage of the MFI of control cells ( after subtracting the MFI of the unstained control cells ) . Pulse-labelling of CD4 was performed on CD4+ . CHO cells preincubated for 45 min at 37°C with CADA ( 16 µM ) in medium lacking methionine , cysteine , and serum . After being pulsed with 0 . 75 mCi ml−1 [35S]methionine/cysteine for 30 min , cells were chased by replacing the radiolabel with pre-warmed medium containing 1 mM cysteine and 1 mM methionine . At the end of the chase this medium was replaced by ice-cold DMEM . Cells were kept on ice and lysed in NP-40 buffer ( 2% NP-40 , 20 mM Tris [pH 7 . 8] , 150 mM NaCl , 2 mM MgCl2 ) , and cell lysates ( separated from nuclei and debris ) were immunoprecipitated for CD4 as described [63] . Proteins were analyzed by SDS-PAGE and autoradiography . Cells were pretreated with CADA ( 5 µM ) or DMSO for 1 h before starvation in Met/Cys free medium with CADA , DMSO , or 50 µg/ml CHX ( Sigma ) . Cells were pulsed with 0 . 022 mCi ml−1 [35S]methionine/cysteine EasyTag Protein Labeling mix ( Perkin Elmer ) for 30 min , washed twice with PBS , and incubated in fresh medium without serum for 90 min . After collection of the supernatant , cells were washed in ice-cold PBS and then either lysed in ice-cold lysis buffer ( 25 mM Tris , 150 mM NaCl , 1 mM EDTA , 1% NP-40 , 5% glycerol , pH 7 . 4 ) supplemented with 0 . 4 µM PMSF and protease inhibitor cocktail , or separated into cytosolic and membrane fractions . To collect the cytosolic proteins , cells were permeabilized with digitonin buffer ( 20 mM Tris , 150 mM NaCl , 2 mM MgCl2 , 0 . 03% digitonin , pH 7 . 8 , supplemented with 0 . 4 µM PMSF and protease inhibitor cocktail ) . Permeabilized cells were then washed in digitonin buffer and further lysed in ice-cold lysis buffer ( 25 mM Tris , 150 mM NaCl , 1 mM EDTA , 1% NP-40 , 5% glycerol , pH 7 . 4 , supplemented with 0 . 4 µM PMSF and protease inhibitor cocktail ) . To isolate glycosylated proteins , total cell lysates or digitonin-resistant membrane fractions were further incubated with Concanavalin A ( Vector Laboratories ) agarose beads overnight at 4°C by gentle rotation . Full length cDNAs and truncated CD4-pPL nascent chains generated by PCR were transcribed in vitro using T7 RNA polymerase , and translated in rabbit reticulocyte lysate ( Promega ) in the presence of [35S]methionine ( Perkin Elmer ) . Translations were performed at 25°C for 45 min ( full length ) or for 25 min ( truncated ) in the presence or absence of mammalian pancreatic microsomes ( RMs ) . Digestion with Proteinase K ( Roche ) was performed on ice for 30 min and stopped by the addition of phenylmethylsulfonyl fluoride ( PMSF; Thermo Fisher Scientific ) . Release of the nascent chains ( NCs ) from the targeted ribosome was induced by treatment with 2 mM puromycin for 10 min at 25°C . NCs were isolated by sedimentation at 4°C and pellets were dissolved in SDS sample buffer for analysis by SDS-PAGE . For the quantitative analysis of translocation experiments we used a Cyclone Plus phosphorimager ( Perkin Elmer ) with accompanying software . For the time-of-addition experiments , [hCD4]- ( 7 ) -pPL nascent chains of 80 residues were synthesized in rabbit reticulocyte lysate by translating the mRNA for 20 min at 25°C . Translation was done in the absence of RM , and the translation mixture was left untreated ( C , control ) or was treated with CADA ( 15 µM ) from the beginning of the translation reaction ( R , ribosomes ) . In the mean-time , some RMs were pretreated with CADA ( 15 µM ) . After 20 min of translation , the untreated control sample was split in three aliquots . Next , one aliquot of control sample and the CADA-sample were given untreated RMs . The CADA-pretreated RMs were administered to the second control sample ( M , microsomes ) . The third control sample received untreated RMs . All samples were then incubated for 10 min on ice and 5 min at 25°C . Finally , CADA was administered to the third control sample ( P , post-targeting ) , and all samples were further incubated for 5 min at 25°C , before treatment with puromycin . NCs were isolated by sedimentation at 4°C and pellets were dissolved in SDS sample buffer for analysis by SDS-PAGE . Chemically synthesized SPs ( PEPperPRINT ) were captured on a streptavidin-coupled C1 sensor chip ( GE Healthcare ) . The amino acid sequence of the SP was: MNRGVPFRHLLLVLQLALLPAATQGKKVVLGKK-PEG11-K-biotin for hCD4 , MCRAISLRRLLLLLLQLSQLLAVTQGKTLVLGKE-PEG11-K-biotin for mCD4 , and MDSKGSSQKGSRLLLLLVVSNLLLCQGVVSTPVCPNGP-PEG11-K-biotin for pPL . The chip density was between 84 and 215 resonance units ( RUs ) . A reference flow cell was used as a control for non-specific binding and refractive index changes . All interaction studies were performed at 25°C on a Biacore T200 instrument ( GE Healthcare ) . The compound CADA was diluted in HBS-P ( 10 mM HEPES , 150 mM NaCl , and 0 . 05% surfactant P20 [pH 7 . 4] ) supplemented with 5% DMSO . Signal recognition particle ( tRNA Probes ) was included as a positive control . Samples were injected for 2 min at a flow rate of 30 µl/min and the dissociation was followed for 2 min . All data are presented as means with standard deviations ( SD ) , unless otherwise stated . Two-tailed Student's t test was used to determine statistical significance as calculated with GraphPad software . For the flow cytometric data , statistical analysis was done on the background-corrected MFI values between compound treated and control ( without compound ) samples . | All cells are highly crowded with proteins that , once synthesized , have to reach their proper subcellular location in order to maintain the cellular homeostasis . Approximately 30% of the proteome needs to be sorted from the cytosol and inserted into , or transported through , biological membranes . For proteins sorted via the secretory pathway , an important step is the translocation into a cellular compartment called the endoplasmic reticulum ( ER ) . The cell uses an elegant way to discriminate proteins that need to be translocated into the ER from those that have to reside in the cytosol by scanning for the presence of an N-terminal ER-entry tag . Although these tags , called signal peptides , have a common structure , they each contain a unique hydrophobic peptide sequence . In this work , we describe how a small chemical drug , CADA , can bind to one specific signal peptide present in the human CD4 pre-protein . We show that by influencing the signal peptide orientation in the translocation channel located in the ER membrane , CADA prevents CD4 translocation into the ER lumen . As a consequence , the CD4 protein is not properly synthesized and routed to the cell surface , resulting in a clear reduction in the amount of surface CD4 , a membrane protein found on immune cells , and implicated in HIV-infection and other diseases . We believe that other drugs can be designed to selectively regulate , in a similar way , ER translocation of specific target proteins . | [
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] | 2014 | Signal Peptide-Binding Drug as a Selective Inhibitor of Co-Translational Protein Translocation |
The surface HIV-1 exterior envelope glycoprotein , gp120 , binds to CD4 on the target cell surface to induce the co-receptor binding site on gp120 as the initial step in the entry process . The binding site is comprised of a highly conserved region on the gp120 core , as well as elements of the third variable region ( V3 ) . Antibodies against the co-receptor binding site are abundantly elicited during natural infection of humans , but the mechanism of elicitation has remained undefined . In this study , we investigate the requirements for elicitation of co-receptor binding site antibodies by inoculating rabbits , monkeys and human-CD4 transgenic ( huCD4 ) rabbits with envelope glycoprotein ( Env ) trimers possessing high affinity for primate CD4 . A cross-species comparison of the antibody responses showed that similar HIV-1 neutralization breadth was elicited by Env trimers in monkeys relative to wild-type ( WT ) rabbits . In contrast , antibodies against the co-receptor site on gp120 were elicited only in monkeys and huCD4 rabbits , but not in the WT rabbits . This was supported by the detection of high-titer co-receptor antibodies in all sera from a set derived from human volunteers inoculated with recombinant gp120 . These findings strongly suggest that complexes between Env and ( high-affinity ) primate CD4 formed in vivo are responsible for the elicitation of the co-receptor-site-directed antibodies . They also imply that the naïve B cell receptor repertoire does not recognize the gp120 co-receptor site in the absence of CD4 and illustrate that conformational stabilization , imparted by primary receptor interaction , can alter the immunogenicity of a type 1 viral membrane protein .
The human immunodeficiency virus ( HIV-1 ) exterior envelope glycoprotein , gp120 , and the transmembrane glycoprotein , gp41 , are non-covalently associated to comprise the trimeric , functional viral spike . These glycoproteins mediate entry and represent the sole virally encoded targets for neutralizing antibodies ( nAbs ) on the surface of the virus . The HIV-1 envelope glycoproteins , and those from related immunodeficiency viruses , are somewhat unusual in that they mediate target-to-membrane fusion by receptor-triggered conformational changes rather than by low pH-mediated fusion events typified by the influenza virus type 1 viral membrane protein , hemagglutinin ( HA ) [1] . The interaction of gp120 with the primary receptor , CD4 , induces formation or exposure of a bridging sheet mini-domain that is , along with elements of the gp120 third variable region ( V3 ) , involved with binding to the co-receptor , CCR5 [2] , [3] , [4] . As was previously shown , antibodies against this induced co-receptor binding site are abundantly generated during natural HIV infection [5] and may be in part elicited due to the unique ability of gp120 to undergo receptor-induced conformations required for the sequential entry process . The co-receptor site antibodies are termed CD4-induced ( CD4i ) because following CD4 binding to gp120 ( which functionally induces the co-receptor binding ) , these antibodies bind with enhanced affinity to gp120 . The prototype for the co-receptor-directed , CD4i antibodies is 17b . However , it is less well appreciated that several full-length gp120 proteins actually are recognized by CD4i antibodies like 17b with high affinity ( or avidity ) even in the absence of the primary receptor [6] . The co-receptor-directed antibodies do not generally neutralize most circulating isolates [7] . However , these antibodies have attracted considerable interest due to the remarkable post-translational sulfation of a subset of these antibodies that mimics the functionally important sulfation of the CCR5 co-receptor N-terminus and their selective VH gene usage [8] , [9] . Viral evasion of the CD4i antibodies likely occurs due to the in vivo selection for viruses that occlude or do not form this highly conserved region until the virus interacts with the primary receptor , CD4 [7] , [10] . Once formed , the conserved site interacts with the largely invariant HIV co-receptor , CCR5 . In contrast to the ability of affinity-matured CD4i antibodies , which can recognize the co-receptor site in the absence of CD4 with high functional affinity , the requirements for the naïve B cell receptor to recognize the same site is not presently understood and may differ from that of a mature CD4i antibody . Therefore , one aim of this study was to determine if previously described soluble envelope glycoprotein trimeric immunogens [11] might elicit CD4i antibodies in primates that possess a CD4 that is capable of a high-affinity interaction with the viral spike . As an immunogen , monomeric gp120 does not elicit broadly nAbs [12] and has failed as a vaccine in a large clinical trial [13] . Therefore , much of the field has moved toward the design of soluble trimeric Env immunogens that more closely mimic the functional spike [11] , [14] , [15] , [16] , [17] , [18] . The gp140 trimers which we have studied are derived from a neutralization resistant primary isolate , YU2 , and are stabilized by heterologous trimerization domains ( foldon ) and somewhat improve the elicitation of neutralizing antibodies when inoculated into small animals possessing CD4 molecules that do not interact with gp120 [19] , [20] . However , these stabilized trimeric immunogens have not been extensively tested in primates , which possess CD4 molecules capable of high affinity interaction with HIV-1 Env . Here , we demonstrate directly that the elicitation of CD4i antibodies by Env trimers is dependent upon the in vivo presence of high-affinity CD4 found in primates , but not present in the wild-type ( WT ) rabbits . We definitively demonstrate that the presence of endogenous primate CD4 is sufficient to generate CD4i antibodies following inoculation of these same trimers into rabbits rendered transgenic for human CD4 . Consistent with these data , we also show the presence of co-receptor-directed antibodies in sera from a subset of patients who participated in the non-efficacious VaxGen phase III clinical trial using monomeric gp120 as a candidate vaccine . Our findings provide clear evidence that binding of a type-I viral membrane protein to its primary receptor can lead to its in vivo altered immunogenicity . It also illustrates that , in contrast to antibodies that have the undergone affinity maturation , the naïve B cell receptor repertoire does not recognize the co-receptor binding site with sufficient affinity to elicit antibodies against this region in the absence of primate CD4 .
The highly glycosylated gp140-F trimers derived from the primary isolate YU2; previously referred to as YU2gp140 ( - ) /FT [11] were purified from the supernatant of transiently transfected mammalian cells by lentil lectin affinity chromatography followed by chelation chromatography . In most cases , size exclusion chromatography was used to isolate the predominant trimeric peak fraction ( Fig . S1 ) . To confirm binding of the trimers to sCD4 independent of avidity effects , a solution-based binding assay was developed . To begin , 1 to 137 nM of the gp140-F molecules were co-incubated with 2 , 4 or 9 nM soluble human 4-domain CD4 ( sCD4 ) in solution . Next , non-Env-bound sCD4 was captured by RPA-T4 and detected in an ELISA format to evaluate the relative binding ( Fig . 1A ) . The gp140-F trimers bind to sCD4 in a concentration dependent manner with half-maximal binding at approximately 7 , 13 and 26 nM respectively . To confirm the specificity of the binding , we introduced a mutation at position 368 of gp140 such that 368 Asp was changed to Arg . This mutation ( 368D/R ) was previously shown to specifically reduce or abrogate CD4 binding of monomeric gp120 [21] and as expected in this soluble CD4 reporter assay , the gp140 368D/R trimers did not bind sCD4 at any concentration tested . Since in vivo , abundant cell-surface CD4 is a potential source for high-affinity binding of Env , we sought to confirm that the YU2 trimers could bind to CD4-positive cells derived from non-human primates before initiating immunogenicity studies . We co-incubated primate peripheral blood mononuclear cells ( PBMCs ) with 2 µg/ml , 10 µg/ml or 20 µg/ml gp140-F trimers and stained the cells for CD3 , CD4 , CD8 and a marker for dead cells . Trimer binding to cell-surface-expressed CD4 was detected with the V3-directed antibody 447-52D on live , CD3+/CD4+/CD8− cells by flow cytometry ( Fig . 1B; for staining and gating strategy , see Fig . S2 ) . Similar to the results obtained in the CD4 solution assay , the gp140-F trimers bound to the CD4+ T cells in a concentration- dependent manner . Trimer binding to the CD4+ cells could be fully abrogated by pre-incubation of 20 µg/ml of the gp140-F molecules with an excess of sCD4 . Further , no binding could be detected after incubation of the PBMCs with the gp140 368D/R CD4 binding-defective trimers . Together , the data confirmed that the YU2 gp140-F trimers used in this study bind both soluble , and importantly , cell-surface CD4 in a dose-dependent and specific manner . To confirm that the highly purified trimers used in this study were competent for recognition by 17b , as well as competent for induction of the CD4i epitope by CD4 , we performed both ELISA-based and surface plasmon resonance ( SPR ) binding assays . First , we incubated 3 . 2 ng/ml to 10 µg/ml of the YU2 gp140-F trimers in solution , without or with an excess of sCD4 , after which gp140-F was captured by 17b on a plate ( Fig . 2A ) . Consistent with previous data with monomeric YU2 gp120 [6] , [22] , [23] , the trimers were well recognized by 17b in the absence of CD4 . However , under the conditions of this assay , the relative binding increases approximately 2 to 5-fold in the presence of 1 ug/ml or 20 ug/ml sCD4 , confirming that sCD4 induces better exposure , by formation or stabilization , of the CD4i site on the gp120 moieties present in the soluble trimeric context . We next determined the recognition of the trimers by the protypic CD4i antibody 17b by Biacore SPR in two formats ( Fig 3 ) . In the first format , the gp140-F trimers were flowed over 17b immobilized on the surface of chip as the analyte . Because the trimers are oligomeric , this binding analysis detects avidity rather than strict affinity . However , this would be the case if the trimers were in solution and recognized by the bivalent BCR in multi-valent array on the surface of a B cell or if the trimers were displayed on the surface of a CD4+ lymphocyte . By this means , we determined that the avidity of the trimers for 17b was nanomolar to subnanomolar regardless if the trimers were in complex or not with CD4 ( see Fig 3A ) . To better approximate the actual affinity of interaction , the 17b antibody was flowed over the gp140-F trimers immobilized on the chip and the binding was analyzed by bivalent curve fitting . This analysis also confirmed that recognition of the trimers by 17b in the absence of CD4 was a high-affinity interaction in the low nanomolar range ( Fig 3B ) . To assess if in vivo interaction of primate CD4 with HIV-1 Env is a requirement for elicitation of CD4i antibodies , we utilized the previously published observations that WT rabbit CD4 is unable to bind HIV-1 Env [24] . We immunized cynomolgus macaques and rabbits four times with the YU2 gp140-F trimers formulated in the GSK Adjuvant System AS01B and confirmed that the ELISA titers saturated by three inoculations and were roughly equivalent ( data not shown ) . For neutralization , we first analyzed the serum samples from individual animals for their ability to inhibit viral entry against a panel of selected HIV-1 isolates . The rationale for this analysis was to assess the over-all neutralization capacity of responses elicited in monkeys versus the rabbits against HIV-1 to make a comparison of other responses more meaningful ( i . e . CD4i-directed HIV-2 neutralizing antibodies , see below ) . The sera were analyzed at a 1 to 5 dilution against a panel of nine HIV-1 Env pseudotyped viruses in a standardized neutralization assay using TZM-bl target cells [25] , [26] ( Fig . 4A ) . Sera derived from animals of both species potently neutralized the three sensitive viruses , the lab-adapted HxBc2 ( clade B ) , SF162 ( clade B ) and MW . 965 ( clade C ) with values between 90 and 100% . Overall , the potency and breadth of neutralization for this panel of viruses were very similar between the monkey and rabbit sera . Subtle cross-species differences in neutralization were observed , but these were not statistically significant . For example , sera from the immunized rabbits tended to display more consistent animal-to-animal neutralization capacity against the homologous YU2 strain . In contrast , the sera derived from the monkeys displayed a trend of greater potency against BaL and the tier 2 isolate SS1196 ( clade B ) . Sera derived from both species of animals inoculated with the YU2 trimers sporadically neutralized the DJ293 isolate ( clade A ) , but poorly neutralized JRFL ( clade B ) , as well as TRO1 . 1 ( clade B; not shown , done only with monkey sera ) , demarking the limits of the neutralization activity elicited by the current immunogen design . Perhaps the subtle differences observed in the neutralization potency against some of the isolates between sera derived from the monkeys versus the rabbits is due to slight differentials in the elicited antibody repertoires , however , in general , the data highlights the overall similarities in the elicited neutralization capacity . To detect if there is a species-difference in specific antibody elicitation against the co-receptor site of gp140-F trimers , we analyzed the sera in the same assay format as above but against virus pseudotyped with Env from an HIV-2 isolate , 7312 ( containing a V434M amino acid change ) . While this virus is relatively insensitive to antibodies raised against HIV-1 Env it becomes highly sensitive to anti-HIV-1 CD4i-antibodies in the presence of sub-inhibitory concentrations of sCD4 , facilitating the specific detection of such antibodies [5] . Consistent with data from HIV-1 infected individuals [5] , [27] and gp140-inoculated humans ( GMS , unpublished observations ) , CD4i-antibodies detected by this assay were abundant in sera from all five monkeys ( ID50 titers: 55 , 91 , 268 , 479 and 2618; Fig 4B ) . We could detect low-level cross-neutralization of HIV-27312/V434M in sera from four of the five monkeys , consistent with what had been observed previously from some HIV-1 infected humans [5] . Following these results , we analyzed three cynomolgus macaques that had been inoculated with the YU2 gp140-F trimers in Ribi adjuvant 2 times and at a similar dose , and detected CD4i antibodies in these animals with ID 50 values of 21 , 27 and 198 ( Fig S3 ) . The lower levels of CD4i antibodies relative to those elicited by trimers formulated in AS01B , correlated with similarly lower potency of neutralization against the HIV-1 isolate , MN ( Fig S3 ) . We also detected CD4i antibodies in 5 out of 5 cynomolgus monkeys primed with Semliki Forest virus ( SFV ) particles expressing the YU2 gp140-F trimers and boosted with trimeric protein with ID50 values of 57 , 1619 , 44 and 335 and in 3 out of 3 baboons immunized with YU2 gp140 molecules rendered trimeric with a modified GCN4 motif [16] in Ribi adjuvant ( data not shown ) . Taken together , these data clearly demonstrate that elicitation of CD4i antibodies by Env trimers in non-human primates is a highly reproducible and commonly elicited response and can occur at low levels when Env is expressed from a viral vector ( SFV; not shown ) . In stark contrast , CD4i-antibodies could not be detected in the serum from any of the WT rabbits ( Fig 4B ) , suggesting that the elicitation of CD4i-antibodies is dependent on the in-vivo presence of CD4 with affinity for the YU2 trimers in the non-human primates . Alternatively , it might be that rabbits lack B cell receptors ( BCR ) in their naïve repertoire with the ability to recognize the HIV-1 co-receptor binding site while the monkeys possess such a capacity . While the HIV-2 assay detects antibodies specific for the co-receptor binding site , we wanted to confirm these results by performing an ELISA based assay where serum from immunized animals were tested for their ability to compete with a biotinylated 17b antibody for binding to gp120 . It is known that antibodies not directly directed against the co-receptor site are capable of competing with 17b for binding to gp120 [28] . To minimize such indirect effects , we analyzed the ability of sera to compete for biotinylated 17b binding to an HXBc2 gp120 core protein capable of binding 17b with high affinity ( Dey et al , manuscript in preparation ) . In this assay format serum samples from monkeys were able to inhibit 17b binding at an approximately 10-fold higher dilution than that of serum samples from the rabbits ( Fig 4C ) . The weak , but detectable , level of antibodies capable of competing with 17b in serum from rabbit serum may be due to antibodies recognizing the co-receptor binding elements in the pre-CD4 induced state or other antibodies that can inhibit 17b binding , such as CD4 binding site antibodies [28] . Direct interaction of the trimers with primate CD4 might be expected to expose as well V3 , the other element of gp120 involved in co-receptor interaction [4] . To address this issue , we performed a binding assay to determine the proportion of V3-specific antibodies compared to antibodies against intact gp120 in sera derived from the 3 types of test animals . We observed a slight 1 . 6-fold average higher proportion of antibodies against V3 in serum samples from primates than in WT rabbits that was not statistically significant ( Fig S3 ) . The huCD4 rabbits , although possessing lower gp120 and V3 binding titers compared to WT animals , also displayed a slightly greater proportion of V3-specific antibodies . The lack of a significant increase in V3-directed antibodies may be due to maximal exposure of V3 on gp140 as we could see no enhancement of binding of a V3-specific antibody to the trimers following addition of CD4 ( not shown ) consistent with a previous study [28] . To address if the presence of primate CD4 was required for the elicitation of CD4i antibodies from the gp140-F trimeric immunogens , we used rabbits previously engineered to be transgenic for human CD4 ( huCD4 ) [24] . These rabbits were generated from the New Zealand White ( NZW ) background , and are relatively similar in their genetic background to the out-bred NZW WT rabbits used for the initial immunogenicty analysis above . The huCD4 transgenic rabbits allowed us to perform controlled immunogenicity experiments to determine if the in vivo presence of primate CD4 allows for BCR recognition of the YU2 gp140-F co-receptor site and subsequent elicitation of CD4-induced antibodies in rabbits . Before initiating the immunogenicity experiment , we confirmed that the huCD4 transgenic animals , ranging from 2 to 5 years of age , still expressed human CD4 on their PBMCs as follows . Incubation of 20 µg/ml gp140-F trimers with PBMCs from WT and huCD4 rabbits and analysis by flow cytometry using species-specific cellular makers ( for staining and gating strategy , see Fig . S5 ) confirmed that only PBMCs from the transgenic animals can bind the gp140-F , and that binding occurred only on cells co-expressing human and rabbit CD4 ( rCD4; Fig . 5A ) . These results are consistent with the initial design of the transgenic rabbits to restrict expression of huCD4 only to cells also co-expressing rCD4 by use of a cell-type-specific promoter [24] . Five huCD4 rabbits were inoculated with the YU2 gp140-F trimers formulated in AS01B adjuvant by an identical regimen as the WT rabbits and monitored for the appearance of CD4-induced antibodies by the in vitro HIV-2 assay . CD4-induced antibodies could be detected in the sera from four out of five huCD4 rabbits after three immunizations with gp140-F trimers ( Fig 5B ) . The ID50 titers detected were 84 , 127 , 190 and 4507 , which is comparable with the levels detected in serum samples derived from immunized monkeys . These data demonstrate that rabbits have the capacity to induce antibodies against the HIV-1 co-receptor site , but that the in vivo presence of primate CD4 is required for the elicitation of these antibodies . This most likely occurs by a direct interaction with primate CD4 and induction of the co-receptor binding site , consistent with a recent study that detects CD4i antibodies following inoculation of monkeys with a CD4-gp120 fusion protein [29] . We also monitored for elicitation of neutralizing antibodies against HIV-1 pseudotyped virus in serum samples of these huCD4 rabbits and observed that the titers were not as consistent and potent for as for the WT rabbits ( data not shown ) . These results might be due to the relatively advanced age of the huCD4 rabbits or as a consequence of unanticipated immune-related effects of the huCD4 transgene . However , as a slightly diminished immune response in these animals would only bias the results against the elicitation of CD4i antibodies , this remains a stringent model to assess the dependence upon primate CD4 for elicitation of these antibodies . The elicitation of CD4i antibodies in monkeys and huCD4 rabbits after immunization with YU2 trimers suggests that the co-receptor site of gp120 is accessible for BCR recognition: likely on the surface of CD4+ PBMCs . Therefore , we investigated if the prototypic , co-receptor-site-directed mAb , 17b , could bind gp140-F trimers once they were bound cell-surface CD4 . We sought to confirm that there was adequate accessibility of the CD4-induced co-receptor binding site on the trimers , once they were removed from the context of the virus . In the viral spike context , the induced co-receptor site is apparently not accessible to most CD4-induced antibodies due to steric constraints . To approximate the in vivo scenario in which trimers formulated in adjuvant would likely drain to proximal lymph nodes to encounter abundant CD4+ cells , we incubated cynomolgus macaques PBMCs with 20 µg/ml of the gp140-F trimers and detected CD4-specific binding to live CD3+/CD4+/CD8− cells by the V3-directed antibody 447-52D or 17b using flow cytometry ( Fig . 6A ) . Binding of the gp140-F trimers could be detected with both antibodies . Recognition of the trimers by 447-52D verifies that the YU2 gp140-F molecules bind to the CD4+ cells , while cell-surface recognition of the trimers by 17b confirms that the co-receptor site is accessible after trimer binding to membrane-bound CD4 . Similar results were obtained when the cell-surface binding assay was repeated using human PBMC targets as shown in Fig 4B . Monomeric gp120 bound to the human CD4+ cells was used as a control and displayed the 17b epitope at levels higher than that of the gp140-F trimers ( Fig 6B ) . Sera from humans immunized with gp120 possess CD4i antibodies . Following the observation that the gp120 monomers bound to cell-surface CD4 displayed the CD4i epitope , we obtained serum samples from the VaxGen Inc phase III clinical trial now licensed to the Global Solutions for Infectious Diseases . Twenty randomly selected sera from trial volunteers that had been inoculated four times with recombinant gp120 ( MN/GNE8 mixture ) formulated with alum were assessed for gp120 binding antibodies by ELISA . All sera exhibited detectable binding titers to the unmatched YU2 gp120 ranging from 5000 to 100 , 000 endpoint titers ( not shown ) . We assessed the ability of the sera to inhibit the entry of MN and , in the presence of CD4 , the HIV-2 virus 7312 . As shown in Table 1 , all sera neutralized not only the homologous virus , MN , but displayed detectable , relatively high-titer , cross-neutralizing , co-receptor-directed antibodies against the CD4-triggered HIV-2 isolate .
In this study , we demonstrate that the elicitation of co-receptor site directed antibodies by the YU2 gp140-F trimers requires the presence of primate CD4 . We show that the relatively homogenous , soluble , stable YU2 trimers bind to human CD4 with high affinity in a solution-based assay that , by design , should be independent of oligomeric influences on functional affinity by avidity-dependent interactions . Analysis of the interaction between the prototypic co-receptor antibody , 17b , and the trimers demonstrated that high-affinity and high-avidity binding is detectable even in the absence of CD4 . Binding of the trimers to primate cell-surface CD4 , but not to cell-surface rabbit CD4 was also shown . WT rabbits and monkeys inoculated with the CD4-binding YU2 Env trimers formulated in the same adjuvant system elicited an overall similar pattern of HIV-1 in vitro neutralization against the viruses tested . However , cross neutralization of HIV-2 in presence of sCD4 , an assay that is diagnostic for the detection of CD4i antibodies , was observed initially in sera derived from monkeys inoculated with the YU2 trimers but not in WT rabbits . Taken together , these data strongly suggest that Env-CD4 complexes generated in vivo upon inoculation are the source for elicitation of the CD4-induced antibodies following vaccination . This observation was confirmed by the inoculation of Env trimers into rabbits transgenic for human CD4 and the detection of CD4i antibodies in the sera of these animals , in contrast to WT rabbits , revealing conclusively the mechanism of their generation ( see schematic Fig 7 ) . Consistent with this observation , high levels of co-receptor-directed , HIV-2 ( +CD4 ) cross neutralizing antibodies were detected in 20 of 20 human serum samples from the VaxGen phase III clinical trial using monomeric gp120 [13] , suggesting that during natural infection shed , soluble gp120 can elicit these antibodies [30] . The induction of co-receptor site directed antibodies in non-human primates and humans is consistent with previous reports that detected the presence of CD4i , co-receptor directed antibodies in gp140-immunized humans ( GMS , unpublished observations ) , as well as in naturally infected humans [5] , [31] and following SHIV challenge of naïve non-human primates [29] . However , the mechanistic basis for the elicitation of CD4i antibodies was not previously addressed in a direct manner . In this study , we present a controlled experiment , which demonstrates that the elicitation of the co-receptor binding site antibodies by Env alone requires the presence of , and likely direct interaction with , primate CD4 . This requirement has not previously been defined , despite numerous Env trimer immunogenicity experiments performed to date in both monkeys and non-primate species [17] , [18] , [20] , [32] , [33] . This is in part , because the HIV-2-based neutralization assay diagnostic for CD4i antibodies was a relatively recent development and is more definitive for neutralizing capacity directed at the co-receptor binding site then are binding assays employed by us and others previously [23] , [34] , [35] . Elicitation of CD4i antibodies in primates by Env trimers also nicely illustrates another potential mechanism of immune escape by HIV-1 . Not only does Env binding to CD4 obscure a conserved surface that , if it was highly immunogenic , might elicit antibodies capable neutralizing a broad array of isolates ( essentially antibodies mimicking the soluble primary receptor ) , but the binding event induces a second conserved and apparently immunogenic region: the co-receptor binding site . The CD4i antibodies directed against this region are not generally able to neutralize primary isolates in vitro [7] . This is likely due to a commonly elicited selection pressure that renders the co-receptor binding inaccessible to most antibodies of this type [10] . The inability of the CD4i antibodies to control HIV-1 infection is supported by data from a recent study where elicitation of CD4i antibodies can be detected prior to the detection of autologous virus neutralization capacity in sera derived from HIV-1 infected patients [27] , as well as the data here , which indicates that they were elicited in the phase III Vaxgen clinical trial where no protection was observed . However , a recent study in non-human primates suggests that the presence of CD4i antibodies ( as determined in vitro by the same HIV-2 detection assay as used here ) is associated with more rapid viral clearance following SHIV162P3 challenge [29] , illuminating that this is an area worthy of further investigation . In the present study , the most likely in vivo source for presentation of the CD4i region to the humoral immune system is by direct interaction of the trimers with cell-surface CD4 displayed on CD4-expressing T cells or other CD4-positive cells of the hematopoetic lineage . It is also possible that low levels of CD4 are shed from CD4+ cells into interstitial spaces and soluble complexes are formed . Detection of low levels of sCD4 has been previously reported in humans [36] , [37] , although in the assay used here we could not detect soluble CD4 in the sera of animals examined . It is also possible that trimer binding to cell-surface CD4 induces shedding of complexes , but we could find no such evidence for soluble Env-CD4 complexes in the sera . The implications of inducing the co-receptor binding site has been discussed extensively at the level of entry , but less so at the level of antibody elicitation . That the CD4i antibodies are not elicited by trimers in the absence of CD4 , even though the gp140-F molecules are well recognized by 17b , and that CD4 induction of the epitope in the trimer context is not a requirement for 17b binding , may seem to be a bit of a paradox . However , we interpret these data to indicate that the conformational fixation imparted by CD4 binding to gp120 is a critical requirement for the naïve , germline B cell repertoire to efficiently recognize the site as opposed to the affinity-matured 17b antibody , which can likely induce the fit of its epitope in the absence of CD4 ( see Schematic Fig 7 ) . This is more likely to be an affinity limitation of the non-affinity matured BCR repertoire , although it is possible that it is somehow related to binding limitations of Ig molecules on the surface of B cells and epitope accessibility issues . Another possible interpretation of the data is that elements of the pre-CD4 co-receptor binding site are immunogenic , but do not elicit antibodies that cross react efficiently with the fully formed site induced by CD4 . However , the 17b blocking assay , using a form of the gp120 core with the potential to be recognized by any antibodies to the co-receptor site indicated that the pre-CD4 state of the trimers did not elicit many antibodies directed toward this region compared to those elicited in the presence of primate CD4 ( Fig 4C ) . Also , we cannot rule out that array of gp140 or gp120 on the surface of CD4+ cells might also enhance the elicitation of CD4i antibodies in a manner independent of conformational fixation . However , the study by DeVico et al [29] clearly demonstrates that covalent gp120-CD4 complexes incapable of binding cell-surface CD4 efficiently elicit CD4i antibodies , so CD4-dependent cell-surface array cannot be the only explanation for their elicitation in the presences of primate CD4 . The implications of the data presented here are also an important consideration for vaccine candidates designed to elicit neutralizing antibodies against the conserved gp120 CD4 binding site . The Env CD4bs likely remains fully accessible in animals without human or primate CD4 , however the elicitation of the CD4i antibodies in animals with primate CD4 indicates that this is likely not the case in species harboring CD4 molecules with a high affinity to Env . These results suggest that a fraction of the population of a CD4-binding-competent immunogen will interact with primate CD4 and thereby occlude the CD4 binding region on this protein subset . It is possible that the subtle differences detected in the neutralization profile between WT rabbits and monkeys occur as a result of such an interaction , partially altering the spectrum of antibodies that are elicited . However , the fractional component of the inoculate which binds to CD4 as yet remains to be determined , and may not be absolute as the overall HIV-1 neutralization profile elicited by the trimers used in this study was similar between the rabbits and the non-human primates . It was also previously shown that HIV Env-CD4 interaction resulted in altered CD4+ T cell function in vitro [38] and it was suggested that elimination of Env interaction with CD4 in the context of vaccination might be beneficial to better elicit functional T cell help and more potent neutralizing antibody responses . From that study and the data presented here it will be interesting to assess if Env variants that do not bind CD4 , but still retain the ability to bind CD4 binding site antibodies might make better immunogens than do unmodified YU2 gp140-F proteins . Alternatively , redirecting the immunogen more efficiently to B cell and antigen presenting cells might also overcome any potential detrimental effects of Env-based immunogens interacting with primate CD4 . Follow up immunogen trimer design , characterization and immunogenicity studies are warranted to clarify these issues further in the near future .
Proteins were produced by transient transfection of adherent 293F cells or 293Freestyle suspension cells . The highly glycosylated and His-tag containing YU2gp140-F trimers were captured and purified from the serum-free media in a three-step process . First , the protein was captured via glycans over with lentil-lectin affinity chromatography ( GE Healthcare , Uppsala , Sweden ) . After extensive washing with PBS the protein was eluted and captured in the second step via the His-tag by nickel-chelation chromatography . ( GE Healthcare ) then washed and eluted with a 300 mM Imidazole containing PBS buffer . In some cases the YU2gp140-F trimers were separated from lower molecular weight forms by the third step of gel filtration chromatography using a superdex200 26/60 prep grade column by the ÄKTA Fast protein liquid chromatography system ( GE Healthcare ) . In contrast , the YU2gp120 and HXBc2 gp120 core proteins were purified by capturing the molecules on an IgG17b affinity column . After extensive washing with PBS , the proteins were eluted from the column with 100 mM glycine/Tris HCl/150 mM NaCl . pH 2 . 8 and immediately neutralized with Tris base , pH 8 . 5 . Env protein was co-incubated at concentrations of 0 . 4 to 46 nM in PBS with 2 , 4 or 9 nM sCD4 at room temperature for 1 h , allowing for CD4-Env trimer complexes to form . Non-Env bound sCD4 was captured on a plate pre-adsorbed with 200 ng/well of the anti-CD4 antibody RPA-T4 ( Ebioscience , San Diego , CA ) . RPA-T4 binds to domain 1 of CD4 and competes with HIV-1 gp120 for binding . RPA-T4 bound sCD4 was probed with a biotin-conjugated , non-competitive anti-CD4 antibody , OKT-4 ( Ebioscience ) . Horseradish peroxidase ( HRP ) conjugated streptavidin ( Sigma ) followed by the colorimetric peroxide enzyme immunoassay substrate ( 3 , 3′ , 5 , 5′-tetramethylbenzidine; Bio-Rad ) was added to induce a colorimetric change and the reaction was stopped by adding 1 M H2SO4 . OD was read at 450 nm . High-protein-binding MaxiSorp plates ( Nunc ) plates were coated with 200 ng/well of mAb 17b in 100 µl of PBS at 4°C overnight after which the wells were blocked for 2 h at room temperature ( RT ) with PBS-2% fat-free milk . The gp140-F trimers , at concentrations between 3 . 2 ng/ml to 10 µg/ml , were pre-incubated with 20 µg/ml sCD4 for 1 h at RT and then added to the wells . The wells were then probed for 17b bound gp140-F trimer with rabbit anti-gp140-F polyclonal sera . Addition of HRP conjugated anti-rabbit-Ig ( Fc region ) ( Jackson Laboratories , Bar Harbor , MN ) followed by the colorimetric peroxide enzyme immunoassay substrate ( 3 , 3′ , 5 , 5′-tetramethylbenzidine; Bio-Rad ) was used to induce a colorimetric change and the reaction was stopped by adding 1 M H2SO4 . OD was read at 450 nm . The 17b binding competition assay was performed by coating the ELISA plate with 200 ng/well of lectin from Galanthus nivalis ( Sigma ) , followed by 200 ng/well of HXBc2 core protein . After blocking with 2% fat-free milk , serum was incubated for 45 minutes at dilutions between 25 and 6400 in a total volume of 100 ul after which 25 ul of biotin conjugated 17b antibody was added to a final dilution of 2500 and incubated for an additional 45 minutes . The plate was probed with HRP conjugated streptavidin and developed as above . To determine the kinetic constants of YU2gp140-F interaction with 17b IgG , we performed binding analysis were in two different formats on a Biacore3000 surface plasmon resonance spectrometer . In one format ( Fig 3A and B ) , YU2gp140-F ( without or with pre-binding to 20-fold molar excess of D1D2 sCD4 for 1 h ) was passed over the 17b IgG surface . Because of the potential oligomeric interaction of the trimers with 17b IgG presented on the surface of the chip , this analysis likely measured avidity rather than simple affinity . However , since the curves approximated single binding kinetics and we did not know how many monomeric subunits within the trimers were capable of 17b interaction , curve fitting was done assuming a 1∶1 interaction . In the reverse format ( Fig . 3C ) , the 17b IgG was passed over YU2gp140-F surface and functional ( or apparent ) affinity was calculated using the bivalent analyte program ( Biacore ) that derived affinity from the potential bivalent interaction of the 17b with trimers immobilized on the chip surface . To prepare binding surfaces , ligands ( 7 µg/ml in 10 mM NaOAc , pH 5 . 5 buffer ) were immobilized on CM5 chip by the amine coupling method following manufacturer's protocol . One flow cell receiving only NaOAc buffer was used as reference control for correction of background binding . For binding experiments , analytes were serially diluted at concentrations ranging from 4 . 6 nM to 600 nM in the HEPES-EP reaction buffer . To determine the rate of association , each analyte was allowed to flow over the activated surfaces at a rate of 30 µl/min for 3 minutes . Dissociation was determined by washing off bound analyte for the next 5 min . Likely due to avidity of the oligomeric analytes , especially in Fig 3A , the rate of dissociation was difficult to determine and likely represents and over estimate of the actual 1∶1 binding kinetics . The surface was regenerated by removing any unbound analyte with two injections ( 60 sec each ) of 10 mM Glycine , pH 3 . 0 . All procedures were performed at RT . Monkey , human and rabbit PBMCs were analyzed by flow cytometry using a modified LSR I system ( BD Biosciences ) . Data analysis was performed using FlowJo software ( Tree Star , San Carlos , CA ) . Staining and gating strategies to detect YU2 trimer binding to live , CD3+/CD4+/CD8− cells from primates is described in Fig . S2 . Staining and gating strategy for trimer binding to rabbit cells is described in Fig . S4 . The mAb 447-52D was a kind gift from Susan Zolla-Pazner ( New York University School of Medicine ) . Five female cynomolgus macaques ( Macaca fascicularis ) of Chinese origin , 5–6 years old , were housed in the Astrid Fagraeus laboratory at the Swedish Institute for Infectious Disease Control . Housing and care procedures were in compliance with the provisions and general guidelines of the Swedish Animal Welfare Agency . The animals were housed in pairs in 4 m3 cages , enriched to give them possibility to express their physiological and behavioural needs . They were habituated to the housing conditions for more than 6 weeks before the start of the experiment , and subjected to positive reinforcement training in order to reduce the stress associated with experimental procedures . All immunizations and blood sampling were performed under sedation with ketamine 10 mg/kg intramuscularly ( i . m . ) . ( Ketaminol 100 mg/ml , Intervet , Sweden ) The macaques were weighed and examined for swelling of lymphnodes and spleen at each immunization or sampling occasion . Before entering the study , all monkeys were confirmed negative for simian immunodeficiency virus ( SIV ) , simian T-cell lymphotropic virus and simian retrovirus type D . Female New Zealand White Rabbits and male huCD4 New Zealand White transgenic rabbits were housed at BioCon , Inc ( Rockville , MD ) or at an animal facility at the National Institutes of Health according to current regulations . Cynomolgus macaques were injected once with 200 ug followed by three injections with 100 µg YU2gp140-F trimer . Rabbits were injected four times with 50 ug YU2 trimer . All proteins were formulated in the GSK-AS01B adjuvant system ( GlaxoSmithKline , Rixensart , Belgium ) prior to injection unless otherwise stated and all injections were administered i . m . at an interval of one month . Sera were collected before the first injection as well as two weeks after each injection . All procedures were approved by the Local Ethical Committee on Animal Experiments . Analysis for HIV-1 and HIV-2 neutralization in serum samples were performed as previously described [25] , [26] . Briefly , Env pseudoviruses were prepared by co-transfecting 293T cells with an Env expression plasmid containing a full gp160 env gene and an env-deficient HIV-1 backbone vector ( pSG3 Env ) . For screening , a single dilution of sera or plasma was used and the percent neutralization was calculated compared to controls with no sera added . To determine the dilution of the sera that resulted in a 50% reduction in RLU against selected viruses , serial dilution assays were performed and the neutralization dose-response curves were fit by non-linear regression using a 4-paremeter hill slope equation programmed into JMP statistical software ( JMP 5 . 1 , SAS Institute Inc . , Cary , NC ) . The results are reported as the serum neutralization ID50 , which is the reciprocal value of the serum dilution resulting in a 50% reduction in viral entry . Dana Gabuzda ( Dana Farber Cancer Institute ) provided the Env plasmid for YU2 . Env plasmids for SF162 and JRFL were provided by Leonidas Stamatatos ( Seattle Biomedical Research Institute ) and James Binley ( Torrey Pines Institute ) , respectively . The Clade A Env-pseudovirus DJ263 . 8 was cloned from the original PBMC derived virus provided by Francine McCutchan and Vicky Polonis ( U . S . Military HIV Research Program ) . Env plasmids BaL . 01 was recently described by our laboratory [26] and the Env used to generate the pseudovirus SS1196 . 1 was previously described [39] . The HIV-2 Env-pseudovirus 7312 containing the V343M mutation have previously been described [5] . The remaining functional Env plasmids were obtained from the NIH ARRRP . Twenty randomly chosen serum samples were obtained via an MTA with the Global Solutions for Infectious Diseases . These sera were derived from volunteers from the VaxGen Inc phase III clinical trial . At the time of sampling ( month 12 . 5 ) the participants had received four injections ( month 0 , 1 , 6 and 12 ) with the AIDSVAX B/B vaccine containing 300 ug each of recombinant HIV-1MN or HIV-1GNE8 derived gp120 in Alum adjuvant [13] . | A major goal of HIV-1 vaccine research is to design novel candidates capable of neutralizing the vast array of viruses circulating in the human population . One approach is to base the vaccine upon the HIV-1 outer surface envelope glycoproteins to generate antibodies . However , during persistent infection in humans , the HIV-1 envelope glycoproteins have evolved structural features that limit the elicitation of broadly neutralizing antibodies . These immune “decoys” divert the antibody response resulting in virus subpopulations that can escape the host response . A potential means by which the virus elicits these decoy responses comes as a by-product of the entry process . Binding of the HIV-1 envelope glycoproteins to the primary receptor , human CD4 , induces the formation of a second co-receptor binding site on the envelope glycoproteins , which then binds to another protein required for viral entry . Antibodies to the co-receptor binding site are generally ineffective at neutralizing HIV-1 patient isolates . Here , we demonstrate the mechanism by which antibodies to the HIV-1 co-receptor binding site are elicited in animals and humans injected with HIV-1 envelope glycoproteins and describe the implications of their formation regarding natural HIV-1 infection and vaccine design . | [
"Abstract",
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] | [
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] | 2008 | B Cell Recognition of the Conserved HIV-1 Co-Receptor Binding Site Is Altered by Endogenous Primate CD4 |
In Latin America , Bothrops snakes account for most snake bites in humans , and the recommended treatment is administration of multispecific Bothrops antivenom ( SAB – soro antibotrópico ) . However , Bothrops snakes are very diverse with regard to their venom composition , which raises the issue of which venoms should be used as immunizing antigens for the production of pan-specific Bothrops antivenoms . In this study , we simultaneously compared the composition and reactivity with SAB of venoms collected from six species of snakes , distributed in pairs from three distinct phylogenetic clades: Bothrops , Bothropoides and Rhinocerophis . We also evaluated the neutralization of Bothrops atrox venom , which is the species responsible for most snake bites in the Amazon region , but not included in the immunization antigen mixture used to produce SAB . Using mass spectrometric and chromatographic approaches , we observed a lack of similarity in protein composition between the venoms from closely related snakes and a high similarity between the venoms of phylogenetically more distant snakes , suggesting little connection between taxonomic position and venom composition . P-III snake venom metalloproteinases ( SVMPs ) are the most antigenic toxins in the venoms of snakes from the Bothrops complex , whereas class P-I SVMPs , snake venom serine proteinases and phospholipases A2 reacted with antibodies in lower levels . Low molecular size toxins , such as disintegrins and bradykinin-potentiating peptides , were poorly antigenic . Toxins from the same protein family showed antigenic cross-reactivity among venoms from different species; SAB was efficient in neutralizing the B . atrox venom major toxins . Thus , we suggest that it is possible to obtain pan-specific effective antivenoms for Bothrops envenomations through immunization with venoms from only a few species of snakes , if these venoms contain protein classes that are representative of all species to which the antivenom is targeted .
Envenomation by snakebites , which is incorporated by the World Health Organization ( WHO ) in its list of neglected tropical diseases , constitutes an important worldwide public health concern , particularly in the rural areas of tropical countries as Africa , Asia and Latin America , affecting mostly agricultural workers and children [1] . The estimated number of global envenoming events exceed 400 , 000 , with more than 20 , 000 fatalities [2] . In Brazil , the incidence is above 25 , 000 accidents/year , and the incidence in the northern region was 52 . 6 accidents/100 , 000 inhabitants in 2008 [3] . Most of the Brazilian accidents with species notification are due to vipers of the genera Bothrops ( 83 . 8% ) , Crotalus ( 8 . 5% ) and Lachesis ( 3 . 4% ) , with only 3 . 4% of accidents related to the Elapidae snakes of the genus Micrurus [3] . Antivenoms raised in horses are the recommended treatment in Brazil . Based on early reports [4] , it was accepted that the efficacy of a specific antivenom covers bites by those snake groups with venom represented in the pool of antigens used for horse immunization for the production of that specific antivenom . Recently , the knowledge of venom toxins has increased considerably , especially due to the characterization of detailed composition of venom proteomes based on mass spectrometry . In 2007 , the concept of ‘venomics’ was introduced by Calvete et al . [5] and the method was important to describe the venom composition from a great number of snake species , as revised recently [6] , [7] . Then , it was possible to characterize the families of venom toxins represented in the venoms of different species of snakes [6] , [7] . The implications of venomics in the rational necessary for the development of antivenoms was further supported by the ‘antivenomics’ [8] , [9] , that allowed the identification of venom proteins bearing epitopes recognized by one antivenom and the toxins not covered by the immune response of the hyperimmunized animal . The importance of venomics and antivenomics was readily incorporated in antivenom development , indicating the possibility of a rational design of pan-specific antivenoms combining distinct protein families in immunization pools [10]–[12] . The venom composition of many species of Bothrops complex is already known by venomics [13]–[27] or indirectly by transcriptomics [28]–[32] . From these studies , it has become clear that a limited number of protein families compose the venoms of Bothrops snakes , with snake venom metalloproteinases ( SVMPs ) , snake venom serine proteinases ( SVSPs ) and phospholipases A2 ( PLA2s ) being the most abundant and most frequently correlated with the clinical symptoms of envenoming . SVSPs are generally thrombin-like enzymes that are involved in the coagulation disturbances observed in most patients [33] . PLA2s are involved in local effects and the myotoxicity observed in bites with some species [34] . SVMPs are multifunctional enzymes involved in the local and systemic symptoms of bites , such as the induction of local hemorrhage , inflammatory reaction , activation of coagulation factors and inhibition of platelet aggregation [35] . The variability in venom composition is notable and can be correlated with phylogeny [36] , [37] , age [38] , [39] , sex [40] , geographical distribution [13] , [40] , [41] and diet [42]–[44] of the snake . However , venom variability is mostly related to the expression level of each group of toxin rather than to the presence or absence of major families of venom proteins . Moreover , within the same protein family , variability in the toxic properties may also occur when distinct functional motifs are introduced in structurally related toxins , increasing the diversity of targets that can be affected by venom toxins [45] , [46] . Thus , the relevance of variability in venom composition should also be reflected in the reactivity with antivenom and its efficacy . This problem particularly affects Bothrops snakes , which are diverse in their morphological and ecological traits and are distributed in different habitats throughout Latin America [47] . Due to the great diversity of Bothrops snakes , the systematics and phylogenetic relationships of this group are not completely resolved , and the distinction of snakes in different genera is often suggested . Based on morphology and mtDNA sequences , a broad classification of the Bothrops complex by Wüster et al . recognized Bothrops and Bothrocophias as independent genera [48]; furthermore , Castoe and co-authors [49] have proposed the classification of Bothrops , Bothrocophias and also Bothriopsis as independent genera . More recently , the Bothrops genus was further divided into three independent genera by Fenwick et al . [50]: Bothropoides , Rhinocerophis and Bothrops , representing the groups of “jararaca/neuwiedi” , “alternatus” and “jararacussu/atrox” snakes , respectively , previously recognized by Wüster et al . [48] . This classification was further questioned by Carrasco and collaborators [47] , and the maintenance of Bothrocophias as an independent genus and synonymizing Bothriopsis , Bothropoides and Rhinocerophis within the Bothrops genus was suggested . However , according to the emerging methodology of DNA sequencing for cladistic analyses , it is reasonable to expect that further revisions of Bothrops systematics will be offered in the near future . Following the classification of Fenwick and coworkers [50] , several species of Bothropoides , Rhinocerophis and Bothrops groups are involved in snakebite envenomings , contributing to the high number of reported incidents in Brazil [3] . Antibothropic antivenoms are used in the treatment of these patients and are produced in Brazil by horse immunization with the venoms of five species of these snakes: Bothropoides jararaca , Bothropoides neuwiedi , Rhinocerophis alternatus , Bothrops moojeni and Bothrops jararacussu . In spite of venomics evidences showing the venom composition of several species , there are still concerns about the efficacy of Bothrops antivenoms in the treatment of envenomings inflicted by species whose venom is not used for animal immunization . These objections include mostly the accidents by Bothrops atrox , which is the snake responsible for the majority of snake bites in the Amazon , whose venom is not included in the immunization mixture . Most of these concerns arise because , in previous studies , the venoms were independently analyzed and , also , by the lack of comparative neutralization assays in the few papers showing antivenomics data for Brazilian Bothrops [16] , [19] , [26] . Thus , the complexity of the Bothrops group and the relevance of these species from a public health viewpoint justify the need for a multifaceted study comparing the venoms of the most relevant species and their reactivity with antivenoms in the light of recent proteomics studies . In this study , we used a shotgun approach that allowed a simultaneous comparison of the composition of venoms collected from six species of snakes from the Bothrops complex , distributed in pairs from three distinct genera [50] . Fractionated venom components were tested for reactivity with the widely-used antivenom ( SAB ) . The efficacy of the antivenom was then assessed for the neutralization of relevant symptoms of experimental envenomings by ( a ) B . jararaca , which accounts for 50% of venom composition in the immunization pool and is prevalent in the southeastern Brazil , and ( b ) B . atrox , which is not present in the immunization pool although representing a common cause of snakebite in the Amazon . The venom analysis showed that phylogenetic classification per se is not directly linked to venom composition . Furthermore , the antivenoms reacted equally with the toxins from the same protein family , regardless of snake phylogeny or the presence of the venom in the immunization pool used for antivenom production , highlighting new priorities when considering the selection of venoms to be used in the production of antivenoms .
The venoms of Bothropoides jararaca , Bothropoides neuwiedi ( B . n . pauloensis , B . n . matogrossensis , B . n . marmoratus , B . n . neuwiedi and B . n . diporus subspecies ) , Rhinocerophis alternatus , Rhinocerophis cotiara , Bothrops jararacussu and Bothrops atrox were obtained from adult snakes of both sexes kept in captivity at the Laboratório de Herpetologia , Instituto Butantan , Brazil . The venoms from more than 10 specimens of each species were pooled , freeze-dried and stored at −20°C until use . Venoms from snakes kept under captivity represented as close as possible the same pools of venoms used for antivenom production and were used for proteomics and immunoreactivity assays . For experiments involving the neutralization of B . atrox venom toxic activities , we used venoms from wild B . atrox snakes collected at the Amazonian Floresta Nacional ( FLONA ) do Tapajós , Pará , Brazil , under SISBio license 32098-1 , aiming to get venom samples as close as possible to the ones responsible for human accidents . Eight snakes were collected in pitfalls or by active search ( five males and three females , with sizes ranging from 82 to 110 cm ) . The snakes were extracted in the herpetarium of Faculdades Integradas do Tapajós , Santarém , Pará , Brazil , and the venom from each snake was individually lyophilized and stored frozen until use , for which a pool was generated with equal proportions of venom from each snake . The chromatographic profile of the pool of venoms from snakes collected at Floresta Nacional do Tapajós was similar to that described below for the B . atrox venom pooled from snakes kept under captivity ( data not shown ) . The antibothropic serum ( SAB ) was produced at the Instituto Butantan , São Paulo , Brazil in horses immunized with a mixture of the following venoms: B . jararaca ( 50% ) , B . neuwiedi ( 12 . 5% ) , R . alternatus ( 12 . 5% ) , B . moojeni ( 12 . 5% ) and B . jararacussu ( 12 . 5% ) . The final preparation consists of soluble IgG F ( ab′ ) 2 fragments: 1 mL neutralizes the lethality of 5 mg standard B . jararaca venom ( according to the manufacturer ) . Anti-jararhagin monoclonal antibodies ( MAJar-3 ) were produced in hybridomas previously selected and maintained in our laboratory , as previously described [51] . The MAJar-3 antibodies are IgG1 isotypes and recognize conformational epitopes located on the jararhagin disintegrin-like domain . MAJar-3 neutralizes jararhagin collagen binding and hemorrhagic activity and cross-reacts with hemorrhagins from venoms of different species of viper snakes [52] . Fifty micrograms of each venom were subjected to trypsin digestion , as previously described [53] . The tryptic digests were desalted with in-lab-generated columns packed with Poros R2 resin ( Life Technologies , USA ) . Each of the 12 venom digests generated ( 6 venoms in duplicate ) were analyzed in triplicate by nanoLC-MS/MS . The separation was performed on a 75 µm×30 cm column packed with a 5-µm , 200 A Magic C-18 AQ matrix ( Michrom Bioresources , USA ) . The eluted peptides were directly injected into an LTQ/Orbitrap XL mass spectrometer ( Thermo , USA ) for analysis . The MS1 spectra were acquired using the orbitrap analyzer ( 300 to 1 , 700 m/z ) at a 60 , 000 resolution ( for m/z 445 . 1200 ) . For each spectrum , the 10 most intense ions were subjected to CID fragmentation , followed by MS2 acquisition on a linear trap analyzer . The tandem mass spectra were extracted by RAW Xtractor ( version 1 . 9 . 9 . 2 ) [54] . All of the MS/MS samples were analyzed using ProLuCID ( version 1 . 3 . 1 ) [55] . ProLuCID was set up to search a database ( forward + reverse decoy ) that was built from the protein entries contained in the NCBI non-redundant database from April 29 , 2012 that satisfied the following search terms criteria: “serpentes OR snakes OR snake OR venom OR venoms OR bothrops OR bothriopsis OR bothrocophias OR rhinocerophis OR bothropoides” . The database was comprised of 87 , 384 entries ( 43 , 692 “forward” and 43 , 692 “reverse decoy” ) . The ProLuCID search was performed with a fragment ion mass tolerance of 600 ppm and a parent ion tolerance of 70 ppm . Cysteine carbamidomethylation was specified as a fixed modification . Scaffold version 4 . 0 . 4 ( Proteome Software Inc . , USA ) was used to validate the MS/MS-based peptide and protein identifications . The peptide identifications were accepted if they could be established at greater than 99 . 0% probability by the Peptide Prophet algorithm [56] , with Scaffold delta-mass correction , and the protein identifications were accepted if they could be established at greater than 99 . 0% probability and contained at least 2 identified peptides . The protein probabilities were assigned by the Protein Prophet algorithm [57] . The acceptable false discovery rates , at the peptide and protein levels , were less than or equal to 1% . The venoms were fractionated by reverse-phase high-performance liquid chromatography ( HPLC ) according to previously described reports [16] , with some modifications . Samples of 5 mg of crude lyophilized venom were dissolved in 250 µL 0 . 1% trifluoroacetic acid ( TFA ) , and the insoluble material was removed by centrifugation at 18 , 400×g for 10 min at room temperature . The proteins in the soluble material were applied to a Vydac C-18 column ( 4 . 6×250 mm , 10-µm particle size ) coupled to an Agilent 1100 HPLC system . The column was eluted at 1 mL/min with a gradient of 0 . 1% TFA in water ( solution A ) and 0 . 1% TFA in acetonitrile ( solution B ) ( 5% B for 10 min , followed by 5–15% B over 20 min , 15–45% B over 120 min , 45–70% B over 20 min and 70–100% B over 10 min ) . The separations were monitored at 214 nm , and the peaks were collected manually and dried in a Speed-Vac ( Savant ) . The fractions were resuspended in PBS , and the protein concentration was estimated by OD at 280 nm in a NanoVue plus spectrophotometer ( GE Healthcare ) . The venoms were classified according to their toxin composition by hierarchical clustering of observations constructed using nearest neighbor linkage method ( minimum Euclidean distance between items in different clusters ) , considering initially each observation as an individual cluster . The degrees of similarity between observations were expressed in terms of a cluster tree ( dendrogram ) . We performed also a Principal Component Analysis ( PCA ) in order to understand the key toxins responsible for the venom clustering . The principal components 1 ( PC1 ) and 2 ( PC2 ) , which were responsible for explaining more than 70% of the total variability , were calculated using the covariance matrix . The toxin composition loadings and venom scores were expressed in terms of loading and score plots . These procedures were performed in Minitab 16 software . The variables used for clustering and PCA were the relative concentrations of each toxin family , accessed by shotgun mass spectrometry . The mean of each protein family spectral counts was normalized by the total venom counting [1 , 891 ( B . atrox ) ; 1 , 727 ( B . jararacussu ) ; 2 , 719 ( B . jararaca ) ; 2 , 287 ( B . neuwiedi ) ; 1 , 252 ( R . alternatus ) and 1 , 767 ( R . cotiara ) ] , distributed within the identified protein families: SVMP-I , -II and –III ( snake venom metalloproteinase - classes P-I , P-II and P-III ) ; PLA2 ( phospholipase A2 ) ; SVSP ( snake venom serine proteinase ) ; CLEC ( C-type lectin ) ; CLECL ( C-type lectin-like ) ; LAAO ( L-amino acid oxidase ) ; NGF ( nerve growth factor ) ; HYALU ( hyaluronidase ) ; VEGF ( vascular endothelial growth factor ) ; CRISP ( cysteine-rich secretory protein ) ; PDIEST ( phosphodiesterase 1 ) ; ECTONT ( ecto-5′-nucleotidase ) ; PLB ( phospholipase B ) ; GLUTCYC ( glutaminyl cyclase ) and ACTIN ( actin ) . The venoms were also analyzed by the relative mAU of the highest peaks collected in C-18 reverse-phase chromatography in the elution time intervals of 56–57 , 57–58 , 58–60 , 67–71 , 108–112 , 113–116 , 121–123 , 124–127 , 128–129 , 130–132 , 134–136 , 136–138 , 139–140 , 140–150 , 150–152 , 153–155 , 157–159 , 160–162 , 163–164 , 164–166 , 166–168 , 169–170 , and 171–172 minutes . The mAU values of the peaks were normalized in % by the mAU of the highest peak eluted in the chromatography , taken as 100% . Samples containing 100 µL whole venom ( 10 µg/mL ) or isolated fractions ( 1 µg/mL ) , in carbonate buffer ( pH 9 . 6 ) , were used to coat maxisorb microplates ( Nunc ) . To determine the antibody titers , plates coated with whole venom were incubated with serial dilutions of SAB ( from 1∶10 , 000 ) , followed by incubation with anti-horse IgG labeled with peroxidase ( 1∶2 , 000 ) . For assessing the antigenicity of the fractions , the plates were incubated with a fixed dilution of SAB ( 1∶1 , 000 ) or MAJar-3 ( 1∶50 ) , followed by incubation with anti-horse IgG ( 1∶1 , 000 ) or anti-mouse IgG ( 1∶1 , 000 ) labeled with peroxidase . The reactions were developed with ortho-phenylenediamine/H2O2 as the enzyme substrate , and the products were detected at 490 nm . The reactions were performed in duplicates in three independent experiments . The results of antivenom titration are expressed as mean ± sd of the six OD values . The results of fraction antigenicity were calculated as mean of the six OD values after normalization using as 100% the maximal OD value obtained in each of the independent experiments [ ( Fraction OD/maximal OD of the test ) ×100] . Samples of crude venom ( 10 µg ) were subjected to 12 . 5% sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) under non-reducing conditions . After SDS-PAGE , the separated proteins were transferred to nitrocellulose membranes , which were then immersed in a blocking solution ( 5% non-fat milk in Tris-saline ) . The membranes were incubated with SAB ( 1∶1 , 000 ) as the primary antibody and then with peroxidase-labeled goat anti-horse IgG ( 1∶1 , 000 ) . The reactive bands were detected by incubation with 4-chloro-α-naphthol and H2O2 . The results shown represent three independent experiments . For accessing the neutralization of the lethal and hemorrhagic venom activities , Swiss mice bred and maintained at the Instituto Butantan ( Brazil ) animal house were used as an animal model . For the neutralization of hemorrhagic activity , doses of 10 µg B . jararaca or B . atrox venom were incubated with SAB at ratios of 1 , 2 or 4 times the SAB volume required to neutralize 10 µg of reference venom , according to the manufacturer . The mixtures were incubated at 37°C for 30 min , and a 50-µL aliquot of each mixture was injected intradermically in the dorsa of a group of 5 mice . The control groups included mice injected with PBS or with venom incubated with PBS . At three hours after the injection , the mice were sacrificed by CO2 inhalation; the skin of the dorsa was removed , and the hemorrhagic spots were measured ( longest diameter multiplied by the diameter perpendicular to it ) . The results represent the values obtained for 5 different mice and are expressed as the % neutralization using as 100% activity the value obtained after an injection with venom incubated with PBS . For the neutralization of lethal activity , the LD50 values of B . jararaca and B . atrox venoms were estimated according to previous studies [58] to avoid unnecessary animal sacrifice . In all experiments , 3 LD50 doses of B . jararaca ( 105 µg ) or B . atrox ( 225 µg ) venom were incubated with SAB at ratios of 1 , 2 and 4 times the potency reference value ( 1 mL/5 mg venom ) . The mixtures were incubated at 37°C for 30 min , and 500-µL aliquots were injected intraperitoneally in groups of 5 mice . Control groups included mice injected with PBS or with venom incubated with PBS . Lethality was recorded over a period of 48 hours . The results shown represent the values obtained in 3 independent experiments and are expressed as the % neutralization considering the number of dead/live mice after 48 hours . The neutralization of the coagulant activity was determined as previously described [59] , with some modifications . Samples containing 2 minimum coagulant doses of B . jararaca ( 71 . 3 µg/mL ) or B . atrox ( 21 . 7 µg/mL ) venom were incubated with several dilutions of SAB for 30 min at 37°C . Each mixture was added to 100 µL bovine plasma , and the clotting times were recorded using a model ST4 mechanical coagulometer ( Diagnostica Stago ) . Neutralization was expressed as the effective dose ( ED ) , defined as the antivenom/venom ratio at which the clotting time was increased threefold when compared to the clotting time of plasma incubated with venom alone . All experiments involving mice were approved by the Ethical Committee for Animal Research of the Instituto Butantan ( CEUAIB ) , São Paulo , Brazil , ( application approval number 752/10 ) , who certified its agreement with the Ethical Principles in Animal Research adopted bt the Brazilian College of Animal Experimentation ( COBEA ) .
To evaluate the relationship between venom composition and phylogenetic position of the species , we analyzed the proteome of the venoms from the six selected species using shotgun nanoESI-LTQ/Orbitrap . The distribution of the protein families in selected venoms was calculated according to the normalized total spectral counts . As shown in Figure 1 , the data analysis revealed 15 different protein groups in different proportions: SVMP-I , -II and –III ( snake venom metalloproteinase - classes P-I , P-II and P-III ) ; PLA2 ( phospholipase A2 ) ; SVSP ( snake venom serine proteinase ) ; CLEC ( C-type lectin ) ; CLECL ( C-type lectin-like ) ; LAAO ( L-amino acid oxidase ) ; NGF ( nerve growth factor ) ; HYALU ( hyaluronidase ) ; VEGF ( vascular endothelial growth factor ) ; CRISP ( cysteine-rich secretory protein ) ; PDIEST ( phosphodiesterase 1 ) ; ECTONT ( ecto-5′-nucleotidase ) ; PLB ( phospholipase B ) ; GLUTCYC ( glutaminyl cyclase ) and ACTIN ( actin ) . The SVMPs were the most abundant toxins in all of the venoms , particularly in the B . atrox , R . alternatus , R . cotiara and B . jararaca venoms , in which class P-III was notably the predominant toxin . PLA2s predominated in the B . jararacussu venom and was found in significant amounts in the B . neuwiedi venom . A significant contribution of C-type lectin-like proteins was also detected in the B . jararaca , R . alternatus and B . atrox venoms , whereas the SVSPs and LAAOs were almost equally distributed in all of the venoms . One interesting fact was the significant contribution of C-type ( true ) lectins in the B . jararacussu ( 8 . 8% ) and B . neuwiedi ( 3 . 5% ) venoms , in parallel with its absence ( <1% ) in the other venoms ( Figure 1 ) . Comparing these data with previous venomics studies [16] , [18]–[20] , [23] , the major venom protein families as SVMPs , PLA2s and SVSPs were detected in our study in equivalent proportions . However , shotgun nanoESI-LTQ/Orbitrap allowed the detection in all venoms tested of some proteins not yet described as PDIEST , ECTONT , PLB and GLUTCYC . Also , NGF , detected here in all venoms , and HYALU , present in B . atrox , B . jararaca , R . alternatus and R . cotiara venoms , were previously detected in transcriptomes of B . jararacussu and Bothropoides pauloensis , respectively [19] , [30] , but not in their venomes . Five spectra identified as actin were detected in R . cotiara venom shotgun and due to the high sensitivity of the method , may derive from a minor contamination of the venom with venom gland cells . The most striking difference was the presence of significant amounts of LAAO , CLECL and CLEC spectra detected in our samples , compared to the previous venomics studies . Proteomics by shotgun nanoESI-LTQ/Orbitrap is based on a whole venom digestion by trypsin and the peptide mixture is then fractionated and analyzed in a high sensitive detection system . This approach may bias peptides with higher ionizable efficiency , but all protein families will be represented in the original mixture at the same proportions as they are present on venoms and the bias due to ionization efficiency will be the same for similar peptides present on venoms from different species . Thus , this method is appropriate for comparative studies , allowing the simultaneous analysis of different venoms , under exactly the same conditions . On the other hand , the traditional venomics [5] includes one step in which proteins are quantified and selected after SDS-PAGE separation , according to their staining by Coomassie blue . After trypsinization of selected bands , peptide detection and protein identification will also depend on peptide ionizable efficiency . It is well known that proteins present in venom mixtures in low proportions are hardly detectable by SDS-PAGE as some other venom proteins may be weakly stained . These proteins would be neglected in total protein detection and also when calculating their proportional participation in venom composition . The differences in protein separation methods and sensitivity of detection systems could explain the higher participation of some protein families described in our study when compared to the traditional venomics . The venoms were also compared according to the elution profile from reverse-phase C-18 columns . To compare our findings with the previous data from B . atrox , B . cotiara and B . neuwiedi venomics studies [16] , [19] , [20] , C-18 reverse-phase chromatography protocols using similar columns , buffer systems and elution conditions were used to fractionate the venoms . Figure 2 shows the chromatographic profile of the venoms from the six species selected for this study . As expected , the venoms presented comparable chromatographic profiles to those reported in the referenced studies . According to these previous studies , the major protein families were eluted as follows: disintegrins at approximately 50–60 min [19] , [20]; basic PLA2s at approximately 110–120 min [19]; P-I SVMPs , some D-49 PLA2s and SVSPs between 120 and 160 min [18]–[20] and P-III SVMPs predominating after 160 min [18]–[20] . Using these data as references , P-III SVMPs appeared to be the most abundant antigens in the chromatograms of the B . atrox , R . alternatus , R . cotiara , B . jararaca and B . neuwiedi venoms , whereas several different peaks in the region corresponding to P-I SVMPs and SVSPs were detected . These observations are consistent with our venomic analysis results shown in Figure 1 and with previous proteomic studies in which P-III SVMPs comprised more than 50% of B . atrox venom [16] , [18] , approximately 50% of R . alternatus venom [23] , approximately 70% of R . cotiara venom [20] and approximately 25 . 9% of B . neuwiedi venom [19] . SVMPs were also reported to comprise 53 . 1% of B . jararaca venom gland toxin transcripts [31] . The B . jararacussu venom was the most distinct venom in this group , showing a predominant peak in the PLA2 region and a low abundance of SVMPs , which is consistent with the literature showing a high expression of PLA2 in B . jararacussu venom glands and representing 35% of the total transcripts , followed by only 16% SVMPs and 2% SVSPs [30] . The marked difference in B . jararacussu venom compared to the other Bothrops species was previously reported [60] , and a K-49 myotoxin yield of 25% from the crude venom was purified and considered to be the predominant antigen of the B . jararacussu venom [61] . According to the independent parameters used to compare the venoms , in Bothrops , the B . jararacussu profile was very different from that of B . atrox , showing a higher content of phospholipase A2 and a smaller amount of the class P-III metalloproteinase ( SVMP ) group , as detected either by proteomics or by the elution profile of the native proteins . Within the Bothropoides genus , major differences were observed by proteomics , such as the higher content of CLECL and P-III SVMP in B . jararaca and PI and PII SVMPs , PLA2 and CLEC in B . neuwiedi . The venoms were more similar within the Rhinocerophis genus , particularly when comparing the elution profile of the native proteins , though a higher contribution of CLECL was found in R . alternatus , and higher contents of L-amino acid oxidase and serine proteinase were detected in the R . cotiara venom using the proteomics approach . However , the distribution of B . atrox venom components was very similar to that of R . alternatus by both methods . Furthermore , the pattern observed for B . neuwiedi was closer to that of B . jararacussu venom due to the presence of higher levels of PLA2 and CLEC ( Figures 1 and 2 ) . Thus , apparently , venom composition was not related to the phylogenetic position of the snakes . In order to statistically demonstrate these differences , the normalized values of the venom composition obtained by the total spectrum counts of each protein family , and the mAU values of the major peaks eluted in different volumes during the C-18 chromatography , were used as variables to cluster the venoms of snake species . A Principal Component Analysis ( PCA ) was also carried out in order to understand the key toxins responsible for the venom clustering . The resulting dendrograms and loading and score plots of the PCA are shown in Figures 3 and 4 , respectively . Clustering according to the C-18 elution profile shows a strong similarity between R . alternatus and B . jararaca venoms . B . atrox and R . cotiara venoms also show similar elution profile , but different than R . alternatus and B . jararaca venoms , forming , therefore , two different clusters . On the other hand , B . neuwiedi and B . jararacussu venoms reveal lower similarity with the two former clusters , with B . jararacussu having the most distinct features ( Figure 3 ) . In the PCA , shown in Figure 4A , components with most prominent loadings that contributed to venom clusterization are the fractions eluted after 160 min with the highest negative values of PC1 ( Fraction 164–166: PC1 = −0 . 365 , PC2 = 0 . 209; Fraction 166–168: PC1 = −0 . 175 , PC2 = −0 . 128; Fraction 169–170: PC1 = −0 . 461 , PC2 = 0 . 481; Fraction 171–172: PC1 = −0 . 311 , PC2 = −0 . 783 ) . These fractions were characterized mostly as class P-III SVMPs in other studies [18]–[20] and reacted with MAJar-3 monoclonal antibodies in this study ( see below ) . Fractions with the highest PC1 positive values were eluted between 108–112 min ( PC1 = 0 . 630 , PC2 = 0 . 029 ) , recognized as PLA2s in previous studies [19] , and fractions between 130–132 min ( PC1 = 0 . 330 , PC2 = −0 . 018 ) , characterized as class P-I SVMP in the venom of adult B . atrox from El Paují ( Orinoquia , Venezuela ) that underwent ontogenetic variation [16] . With respect to proteomic data , B . atrox and R . alternatus venoms were the most closely related , and distances to this group increased gradually for R . cotiara , B . jararaca , B . neuwiedi and B . jararacussu venoms . The clustering of B . atrox and R . alternatus venoms is related to high values of CLECL and P-III SVMPs , which are the proteins with most prominent loadings ( CLECL: PC1 = −0 . 431 , PC2 = 0 . 789 , P-III SVMPs: PC1 = −0 . 592 , PC2 = −0 . 472 ) , and low values of PLA2 and CLEC , also with significant loadings ( PLA2: PC1 = 0 . 245 , CLEC : PC1 = 0 . 245 ) . R . cotiara venom shows similar pattern with respect to P-III SVMP , PLA2 and CLEC , but low values of CLECL and high values of LAAO ( PC1 = 0 . 037 , PC2 = −0 . 339 ) . On the other hand , B . jararaca venom reveals low values of LAAO and large values of CLECL . B . neuwiedi and B . jararacussu venoms present an opposite pattern , with high values of PLA2 and CLEC and low values of PIII-SVMP ( Figure 4 B ) . The dendrograms and PCAs obtained using the distinct sets of variables do not coincide , as they were based in distinct parameters . The number of total spectral counts of a given protein is not necessarily related to its mAU 214; moreover , chromatographic fractions represent mixtures of protein families treated as independent variables in the cluster corresponding to the proteomic data . In spite of these differences , both sets of variables indicate that the distribution of venoms is not related to the phylogenetic position of the snakes . It is important to note that a more comprehensive study using venoms from a larger number of species , quantitative assays for isolated components and also complete sequences of venom proteins would be essential to a definitive support of the lack of connection referred to above . Nevertheless , our data are supported by the literature . Taken together , the clusterization and PCA analysis indicate a polarization among the venoms . According to significant PC1 loadings , B . atrox , R . alternatus , R . cotiara and B . jararaca venoms are clearly opposite to B . jararacussu venom , the former group with prominent negative PC1 values of class P-III SVMPs , while B . jararacussu venom shows a polarization towards the presence of PLA2s and class P-I SVMPs . The same toxin polarization has been indicated to venoms from snakes that conserved the paedomorphic characteristics in their venoms ( first group ) and venoms of snakes whose venom underwent ontogenetic variation ( in our study , B . jararacussu venom ) [13] , [16] , [18] , [38] , [39] . Interestingly , B . neuwiedi venom was grouped closer to B . jararacussu in the cluster analysis , but showed smaller negative PC1 scores , in opposition to B . jararacussu venom . According to the distances , B . neuwiedi venom apparently conserved the paedomorphic phenotype , but may be suffering a transition to the ontogenetic changes observed in B . jararacussu or B . atrox from Colombia . Correlations between phylogeny and venom composition have been appointed in the literature [36] , [37] . Nevertheless , differences in composition of venoms from snakes belonging to the same genera are also present in the literature [62]–[64] . In a recent study , Gibbs et al . [65] found no evidence for significant phylogenetic signal in venom variation of Sistrurus spp , suggesting that diet variation may play a more important role in molding the venom composition . A remarkable variation in venom composition and toxicity was reported for rattlesnakes from Crotalus viridis/oreganus complex [66] and Crotalus durissus and Crotalus simius in Central and South American species [67] . In the latter , differences were related to the conservation of the newborn characteristics of Central American rattlesnake , C . simus , in the South American species and sub-species of C . durissus , a typical example of paedomorphism [67] . These examples are also found in snakes of the Bothrops complex . Tashima et al . [20] reported significant differences in venom composition between two species closely related , R . cotiara and R . fonsecai . A paedomorphic characteristic was also conserved along the dispersion of B . atrox from Central America to the Brazilian Amazon [16] , including in the population used in this study . The conservation of the paedomorphic characteristics in B . atrox accounted for the concentration of class P-III SVMPs , which greatly contributes to the overall toxicity of Bothrops venoms [35] . Paedomorphic characteristics were not conserved in B . jararacussu venom , which has predominance of enzymatically inactive myotoxic PLA2s [60] and therefore , presents lower toxicity compared to B . atrox venom . The difference in composition and toxicity of B . atrox and B . jararacussu venoms argues in favor that the gain in toxicity was favorable in B . atrox due to its smaller size . According to this hypothesis , paedomorphic characteristic would not be essential to B . jararacussu snake that is very large and capable of inoculating large amount of venoms in mammalian preys . Our next approach was to evaluate the reactivity of the whole venoms and their isolated fractions with antivenoms . Figure 5 shows the titration curves of the antibothropic serum ( SAB ) in ELISA plates coated with equal amounts of each venom . The SAB antibody titers were the same , regardless of the antigen used , and they corresponded to a dilution of 640 , 000 . The only differences among the venoms were the values obtained for the 10 , 000 and 20 , 000 dilutions of SAB against the B . jararacussu venom , which were significantly lower than comparing with other venoms . These dilutions reflect the zone at which the antigen concentration is the limiting factor , and differences in antibody binding may reflect the lower amount of reactive antigens in B . jararacussu venom , highlighting the antigenic relevance of P-III SVMPs . Indeed , the region correspondent to bands of approximately 50 kDa , which is the approximate molecular mass of P-III SVMPs , were less intense in the B . jararacussu venom electrophoresis than others ( Figure 6A ) . SAB preferentially recognized bands of approximately 50 kDa by western blotting ( Figure 6B ) , confirming the higher immunogenicity of SVMPs class P-III . Bands between 20 and 30 kDa , with masses corresponding to SVSPs and P-I SVMPs , were also recognized by SAB ( Figure 6B ) . The SAB reactivity with each fraction from reverse-phase chromatography was also assessed and compared to the reactivity of a monoclonal antibody , MAJar-3 , which recognizes the disintegrin domain of P-III SVMPs [51] . In Figure 7 , we demonstrate the strong reactivity of the monoclonal antibody with the fractions eluted after 160 minutes ( in all chromatograms ) , confirming that these fractions correspond to P-III SVMPs . The same fractions were the most SAB-reactive antigens in all venoms , regardless of whether these venoms were included in the immunization pool used to prepare the SAB antivenom . Even for the B . jararacussu venom , with a low abundance of SVMPs , the fractions eluted after 160 minutes were the most reactive . Intermediate levels of reactivity were detected with the fractions eluted between 120 and 160 minutes , with very limited reactivity for some , particularly the venoms of B . atrox and B . alternatus , suggesting a lower antigenicity of P-I SVMPs and SVSPs in relation to the SAB antivenom . Interestingly , three small peaks collected from the R . cotiara venom at approximately 140 minutes were strongly reactive with SAB and also with MAJar-3 , suggesting the presence of P-III SVMPs in this venom , with distinct structural features and elution profiles . Despite the inclusion of B . jararacussu and B . neuwiedi venoms in the immunization pool , the reactivity of SAB with their fractions ( showing PLA2 elution characteristics ) from 100 to 110 minutes was moderate . The fractions eluted prior to 100 minutes in all of the chromatograms were poorly recognized by SAB . In other publications , fractions that eluted before 100 min under similar chromatographic conditions corresponded to disintegrins [19] , [20] , vasoactive peptides [19] or DC fragments of SVMPs [20] . Interestingly , despite the different methods used in this study , our results are comparable to those of Núñez et al . [18] and Calvete et al . [16] , who showed the complete immunoprecipitation of PIII-SVMPs , to a minor extent of SVSPs and DC-fragments , and limited immunoreactivity towards PLA2 molecules and PI-SVMPs by antivenomics of B . atrox venom with commercial antivenoms . Using antivenomics of B . asper venom and commercial antivenoms , Gutiérrez et al . [9] also showed complete immunodepletion of P-III SVMPs and partial depletion of PLA2s , some serine proteinases , and P-I SVMPs . Correa-Neto et al . [26] approached the same issue by immunomics where the western blots of 2D-gel electrophoresed venoms revealed that antiserum against B . jararacussu venom showed higher reactivity to SVMPs and weaker reactivity towards SVSPs and PLA2s , and anti-jararaca serum preferentially recognized SVMPs and SVSPs among other antigens . Both of these sera failed to recognize low-molecular weight proteins [26] . Comparing the different methods , antivenomics is the best choice for a detailed study , since identifications of non-depleted proteins will show exactly the antigens that are partially immunodepleted or non-reactive with the antivenom . However , the method used here has the advantage to allow simultaneous tests of different venoms , at exactly the same conditions , and gives comparable results to antivenomics , thus is appropriate for comparative studies . Important conclusions arise from these results . It becomes clear that P-III SVMPs are the predominant antigens in the venom of snakes from the Bothrops complex . Moreover , at least among the Bothrops , SVMPs are cross-reactive antigens that are equally recognized in venoms , regardless of their inclusion in the immunization pool . This is a good indication for antivenom efficacy , as P-III SVMPs are also abundant in most of these venoms and are related to the important symptoms of local and systemic envenomings , such as hemorrhage , the activation of coagulation factors , the inhibition of platelet aggregation and the activation of several factors that lead to local symptoms [35] . Interestingly , P-III and P-I SVMPs share similar catalytic domains and catalytic properties [68] , which are involved in most of the toxic activities of SVMPs . Therefore , it is very intriguing that P-I SVMPs are less recognized by the antivenoms than are P-III SVMPs and raises some concerns about the neutralization efficacy of those activities related to the catalytic domain of these molecules . This observation suggests different interpretations: the most immunogenic epitopes of SVMPs may be located within the disintegrin-like or cysteine-rich domains; or catalytic domains of P-III SVMPs are more immunogenic than catalytic domains of P-I SVMPs . For instance , high hemorrhagic activity and the inhibition of platelet aggregation are typical for P-III SVMPs and depend upon disintegrin-like/cysteine-rich domains [69] , [70] , yet P-I SVMPs are able to induce local reactions [71] and activate coagulation factors [72] , which are important effects of snake bites . SVSPs and PLA2s are important toxins involved in the coagulopathy and local effects , respectively , of patients bitten by snakes of the Bothrops complex . Thus , the limited reactivity of SAB with these fractions should be addressed . Most SVSPs are thrombin-like enzymes involved in the blood coagulation disturbances induced by venom [33] , and this symptom is easily controlled in patients after antivenom administration [73] , suggesting that the presence of anti-SVSP antibodies in SAB is appropriate to neutralize the activity . However , PLA2s are generally myotoxic or pro-inflammatory [34] , and these symptoms are not well neutralized by antivenoms . In the case of SVSPs , it appears that the low levels of antibodies present in SAB are sufficient to neutralize the systemic effects of SVSPs after intravenous administration . In contrast , this does not appear to be the case for the neutralization of the local effects of envenomings induced by PLA2s or P-I SVMPs . This lack of efficacy could most likely be dependent upon antivenom biodisponibility at the site of the lesion rather than on the potency of an antivenom against the myotoxic or dermonecrotic components of the venom [74] or the antibody titer against the toxins inducing the local effects . Another important point observed in this study was the limited reactivity of antivenom with disintegrins and the DC fragments of SVMPs , which are recognized as inhibitors of platelet aggregation [69] , [75] , and its reactivity with vasoactive peptides . Although they are not presently considered major toxins correlated with the symptoms of envenomings , the additive or synergistic role of these small toxins in snake bite disorders cannot be ruled out . These low molecular mass peptides are known to be weakly immunogenic; however , in antivenomics studies , at least DC fragments and disintegrins were depleted from B . atrox [18] and B . asper [9] venoms by commercial antivenoms . Nevertheless , the presence of antibodies against such classes of low molecular size toxins in antivenoms should be regarded with more attention . The next step of this study was to evaluate the SAB neutralization efficacy of the lethality , hemorrhagic and coagulant activities of B . atrox venom in comparison to B . jararaca venom . For these experiments , we used venoms from snakes collected in a region where many accidents are reported . The accepted potency of SAB efficacy , calculated as the volume necessary to neutralize the lethality of standard B . jararaca venom , is 1 mL antivenom/5 mg venom . This value was used as a reference to design the neutralization protocols used in this study , whereby this proportion was sufficient to protect more than 50% of mice from the challenge with 3 LD50 doses of B . jararaca venom ( 105 µg ) . However , neutralization of the 3 LD50 doses of B . atrox venom ( 225 µg ) was achieved only when the proportion of 2 mL antivenom/5 mg venom was used ( Figure 8 ) . Most of the standard protocols to assess antivenom potency use a fixed LD50 value to challenge experimental mice . Therefore , this is also the reference assay used to compare the antivenom efficacy against different venoms . However , it is important to consider that LD50 values are variable among venoms and reflect the toxic activity of each toxin and their synergistic effect to induce death . Additionally , in most tests , the mice are challenged with pre-incubated mixtures of venoms and antivenoms , and , in these reactions , toxins are neutralized or cleared from the solution on a molar concentration basis rather than according to the neutralization of activity . This fact may explain why several previous studies reported that some venoms with higher LD50 values , such as B . atrox and B . jararacussu , are neutralized with higher concentrations of commercial antivenoms . Similar findings were observed in our study regarding the neutralization of the coagulant activity of B . atrox and B . jararaca venoms . In this case , the B . atrox venom was more coagulant ( minimal coagulant concentration in plasma: 10 . 8 µg/mL ) than the B . jararaca venom ( minimal coagulant concentration in plasma: 35 . 6 µg/mL ) , and higher concentrations of B . jararaca venom were used in the assays . The SAB neutralized the coagulating activity of both venoms; in this case , however , lower amounts of antivenoms were needed to neutralize the B . atrox activity ( ED = 627 µL antivenom/mg venom ) , whereas B . jararaca venom neutralization required a higher antivenom concentration ( ED = 1400 µL antivenom/mg venom ) , as shown in Figure 8 . The hemorrhagic activity was comparable between the venoms , and the ratio of 1 mL antivenom/5 mg venom neutralized more than 50% of the hemorrhage induced by both venoms ( Figure 8 ) . Taken together , these data suggest that SAB is efficient in neutralizing the most important effects of B . atrox venom despite the phylogenetic distance of the snake and the fact that the venom is not included in the immunization pool used to produce SAB . There are previously published papers in the literature suggesting the need to include B . atrox venom for horse immunization [76]–[78] . However , our data showing the opposite are supported by a previous study in which SAB immunodepleted the venom proteins from B . atrox populations exhibiting the paedomorphic venom phenotype , the same pattern found in specimens collected in Pará State , Brazil [16] . Moreover , our present data are supported by other pre-clinical assessments showing neutralization of the toxic activities of venoms not included in immunization protocols [79] , [80] and by a clinical trial for the treatment of snake bite patients clinically classified as mild and moderate in Pará State ( Brazil ) demonstrated that the efficacy of a conventional antivenom ( SAB ) was comparable to the efficacy of an experimental antivenom prepared through horse immunization with B . atrox venom [81] . Recently , the understanding of venom composition by venomics [5] , [6] and tests of the efficacy of antivenoms by antivenomics [8] , [9] , [11] have been extremely important approaches in order to achieve efficient antivenoms [82]–[84] . In this work , we approached this issue by a multifaceted comparative study of venoms from six species of snakes of distinct phylogenetic clades of Bothrops complex . Important differences were observed in venom composition of the snakes from Bothrops complex , mainly for B . jararacussu venom . However , these differences showed no apparent relationship with the phylogeny of the snakes . In this regard , although the taxonomy of this group is still under revision , the toxins present in the venoms are similar , in agreement with previous molecular data showing that the ancestral genes encoding Bothrops major toxin families were already present before the differentiation of the Bothrops species [85] , [86] . As a result , the antivenom reacted similarly with toxins from the same protein family , as SVMPs , SVSPs or PLA2s , regardless of the snake phylogeny or the presence of the venom in the immunization pool used for antivenom production . Thus , we confirm previous data of antivenomics and suggest that it is possible to obtain pan-specific and efficient antivenoms to Bothrops snakes through immunization with venoms from a few species of snakes , if immunogenicity and antigenicity of the distinct protein classes of toxins are considered . | Snakebite envenomation is a serious health issue in Latin America , particularly in the Amazon , where antivenom administration may be delayed due to logistic constraints . Bothrops snakes are involved in most of the snakebite-related accidents in Brazil . This work reports a comparative study of the toxin composition and antigenicity of the Bothrops venoms used to prepare the commercial antivenom and its effectiveness against the venom from Bothrops atrox , a prevalent Amazon species that is not included in the pool . Our data show a lack of connection between Bothrops taxonomic identity and venom composition . We also show that different toxins display distinct reactivity with the tested antivenom . However , the antivenom reacted similarly with each class of toxin present in the venoms of the different snakes studied . Important evidence was the neutralization of the major toxic effects of B . atrox venom , not included in the mixture of antigens used to produce the antivenom . Based on the observed antigenicity of the distinct protein classes of toxins , we suggest that it is possible to obtain pan-specific and efficient Bothrops antivenoms via immunization with venoms from a few species of snakes that are representative of the protein composition of a large number of targeted species . | [
"Abstract",
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"Materials",
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"Methods",
"Results",
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"Discussion"
] | [] | 2013 | Comparison of Phylogeny, Venom Composition and Neutralization by Antivenom in Diverse Species of Bothrops Complex |
Parameters predicting the evolution of leptospirosis would be useful for clinicians , as well as to better understand severe leptospirosis , but are scarce and rarely validated . Because severe leptospirosis includes septic shock , similarities with predictors evidenced for sepsis and septic shock were studied in a hamster model . Using an LD50 model of leptospirosis in hamsters , we first determined that 3 days post-infection was a time-point that allowed studying the regulation of immune gene expression and represented the onset of the clinical signs of the disease . In the absence of tools to assess serum concentrations of immune effectors in hamsters , we determined mRNA levels of various immune genes , especially cytokines , together with leptospiraemia at this particular time-point . We found differential expression of both pro- and anti-inflammatory mediators , with significantly higher expression levels of tumor necrosis factor α , interleukin 1α , cyclo-oxygenase 2 and interleukin 10 genes in nonsurvivors compared to survivors . Higher leptospiraemia was also observed in nonsurvivors . Lastly , we demonstrated the relevance of these results by comparing their respective expression levels using a LD100 model or an isogenic high-passage nonvirulent variant . Up-regulated gene expression of both pro- and anti-inflammatory immune effectors in hamsters with fatal outcome in an LD50 model of leptospirosis , together with a higher Leptospira burden , suggest that these gene expression levels could be predictors of adverse outcome in leptospirosis .
Leptospirosis is the most widespread zoonosis occurring worldwide with possible fatal outcomes [1] . Though most often an endemic disease , epidemics have been associated with particular meteorological events [2]–[4] or clusters of cases related to occupations or leisure activities [5]–[8] . It is notably highly prevalent in tropical areas , but some of the clusters of cases have been reported in temperate countries [7] , [8] . Its clinical presentation is highly variable and is often initially suggestive of influenza , malaria or dengue fever , making the differential diagnosis more hazardous in tropical countries , during dengue or influenza epidemics , or in areas of high malaria incidence [9] . However , because of a relatively high fatality rate in leptospirosis , increased medical care must be provided to some of the patients suffering leptospirosis . Validated prognostic factors to help forecast the evolution of a leptospirosis are scarce . Yet , they would be valuable for clinicians to decide whether their patients should only be treated with antibiotics , kept at the hospital in a standard unit or directed to an intensive care unit . Few data have been published addressing this question; furthermore , contrasting observations were obtained [10] , [11] . Severe leptospirosis manifestations include acute renal failure , caused by acute interstitial nephritis and pulmonary haemorrhage . Spirochete invasion and toxicity of outer membrane components cause robust inflammatory host responses [12] leading to clinical manifestations reflecting a sepsis syndrome . This latter condition has been characterized as a dysregulation of the inflammatory response , including a massive release of pro-inflammatory cytokines that induces multiple organ dysfunctions . Concomitantly , compensatory mechanisms , mostly regulatory cytokine-mediated ( although having protective effects to prevent overwhelming inflammation ) may become deleterious and have been associated with an immune paralysis and poor outcome [13] , [14] . Cytokines are potent , pleiotropic , non-antigen-binding polypeptides secreted by cells of the immune system and are responsible for cell activation , differentiation and proliferation after they act on their target cells via specific receptors primarily through autocrine and paracrine stimulation . Their expression levels are largely studied in the context of septic shock and severe sepsis both for their possible prognosis value [13] , [15]–[17] , as well as for a better understanding of sepsis physio-pathology [18]–[20] . Because of high similarities in clinical presentation of septic shock and severe leptospirosis , we hypothesized that strong similarities in immune gene expression between severe sepsis and severe leptospirosis could help predict the evolution of leptospirosis towards multiple organ failure or recovery . Tumor Necrosis Factor α ( TNFα ) is a cytokine involved in early systemic inflammation that stimulates the acute phase reaction , in synergy with interleukin -1 ( IL-1 ) and interleukin-6 ( IL-6 ) [21] . Elevated plasma concentrations of TNFα have been associated with poor prognosis in sepsis , but also in patients with leptospirosis [10] . IL-6 , one of the most important pro-inflammatory mediators of the acute phase response to pathogens , has been suggested to be a downstream mediator of TNFα and IL-1 . However , it also regulates anti-inflammatory effectors by controlling the level of pro-inflammatory cytokines . Many studies were conducted to evaluate the value of circulating IL-6 concentrations as indicators of clinical outcome in patients with severe sepsis , correlating high levels with fatal outcomes . Interferon-γ ( IFN-γ ) is a pluripotent pro-inflammatory cytokine [22] . Its production was shown as dependent on IL-12p40 in human blood stimulated by L . interrogans [23] notably inhibiting Th2 cell activity . Cox-2 , one of the two forms of cyclooxygenase ( COX ) , is highly induced and rapidly produced in macrophages and endothelial cells in response to proinflammatory cytokines and may be responsible for the oedema and vasodilatation associated with inflammation . It is recognized that inflammatory mediators such as COX-2 but also nitric oxide , a derived product of inducible nitric-oxide synthase ( iNOS ) , are responsible for the symptoms of many inflammatory diseases [24] , [25] . Increased level of nitric oxide have notably been evidenced in the sera of patients with severe leptospirosis [26] . Anti-inflammatory effectors play an important role to counter-regulate the effects of pro-inflammatory cytokines . Interleukin-10 ( IL-10 ) is classically described as an anti-inflammatory cytokine with pleiotropic effects in immunoregulation and inflammation by down-regulating the expression of Th1 cytokines [27] . An early imbalance of IL-10 in sepsis was shown to be associated with death despite TNFα production . [17] . Transforming Growth Factor β ( TGF-β ) is believed to be important in the regulation of the immune system by regulatory T cells; it notably acts by blocking the activation of lymphocyte- and monocyte-derived phagocytes and by controlling iNOS expression [28] . Together with IL-10 , TGFβ is considered as contributing to the immunosuppression observed in septic shock [13] , [14] . The hamster is considered as the most valuable animal model for human leptospirosis [29] , [30] . This animal model is notably used to maintain virulence of Leptospira strains or isolates . It was also used in studies aiming at better deciphering the virulence and pathogenesis mechanisms or the host immune response to leptospirosis or to vaccine candidates [30]–[37] . Using this animal model , our group [38] notably demonstrated in vivo the expression of Th1 cytokines during acute leptospirosis . The aim of our study was , using a LD50 model of leptospirosis in hamsters , to evaluate gene expression levels in individual animals . Additionally , the Leptospira burden in blood was also assessed because it was shown to have a prognostic value [39] . The immune gene expression levels and Leptospira burdens were compared according to the spontaneous outcome of the leptospirosis . Differential expression levels were observed that related to the outcome of the infection .
The virulent Leptospira interrogans serovar Icterohaemorrhagiae strain Verdun , was obtained from the Reference Collection of the Institut Pasteur in Paris , France . Virulence was maintained by passages in Syrian golden hamsters ( Mesocricetus auratus ) and was regularly tested by lethal injection of 2×108 leptospires intraperitoneally . An avirulent variant corresponding to an isogenic clone of this strain was derived from the virulent strain by in vitro high-passage . Leptospires were cultivated in liquid EMJH ( Ellinghausen McCullough Johnson and Harris ) medium at 30°C under aerobic conditions [40] . The bacterial cell density of the cultures was assessed in a Petroff-Hausser counting chamber . Specific pathogen-free animals which parents were initially purchased from Charles River Laboratories ( Charles River Wiga GmbH , Sulzfeld , Germany ) were bred at the Institut Pasteur of New Caledonia . All in vivo studies were carried out using five- to six-week-old outbred golden hamsters handled in individual cages . During preliminary experiments , we infected hamsters by intraperitoneal injection of various doses of live virulent Leptospira ranging from 2×107 to 2×108 per hamster . Hamsters were checked four times a day to evaluate the appearance of clinical signs , deep unconsciousness or recovery . Deeply unconscious animals that did not react to a tactile stimulus were considered dead and euthanized . Additional preliminary experiments included the determination of the time course of gene expression by sampling three individual infected hamsters at 0 , 4 , 8 , 14 hrs then day 1 ( D1 ) , D2 , D3 , and D4 post-infection to determine the most relevant time point allowing to evaluate the expression level of as many relevant genes as possible for future experiments . Each LD50 experimental set was composed of six- to eighteen animals intraperitoneally infected with 108 leptospires of a virulent culture in EMJH medium , and three or four negative controls injected with sterile EMJH medium . The study was made up of three independent experiments . Whole blood ( 400 µl ) was collected on PAXgene blood RNA tubes ( PreAnalytiX , Qiagen , Australia ) by cardiac puncture under non lethal gas anaesthesia [29] on D3 after infection . Clinical symptoms and/or death were monitored four times daily for 21 days . Surviving animals at D21 were considered as spontaneously recovering . Negative controls and surviving animals were euthanized at D21 . In order to evaluate the effect of the infective dose on the gene expression patterns , we used two other experimental infection models . We first injected five hamsters with a LD100 of the same virulent Leptospira , with 2×108 leptospires injected per hamster using the same intraperitoneal route . Secondly , we injected hamsters with a similarly high dose ( 2×108 leptospires per hamster ) of the high-passage isogenic Leptospira variant , known not to induce any mortality . Control animals were similar to the LD50 experiments and were injected with an equal volume of sterile EMJH . Protocols for animal experiments were prepared and conducted according to the guidelines of the Animal Care and Use Committees of the Institut Pasteur and followed European Recommendation 2007/526/EC that provides “guidelines for the accommodation and care of animals used for experimental and other scientific purposes” . The protocol was validated before the start of the experiments by a scientific committee and an animal care committee of the Institute Pasteur in New Caledonia . Total RNA was isolated from whole blood not later than 24 hours post-collection using the PAXgene Blood RNA system ( PreAnalytiX ) according to manufacturer's instructions , then immediately frozen at −80°C until use . DNase-treated RNAs were used to synthesize cDNA with the Transcriptor First Strand cDNA Synthesis Kit using random hexamers as specified by the manufacturer ( Roche Applied Science ) . To minimize variation in the reverse transcription reaction , all RNA samples from a single experimental setup were reverse transcribed simultaneously and in duplicate . The sequences of all primers used in this study are listed in table 1 . They were designed with the LightCycler Primer Probe Design Software 2 . 0 ( Roche Applied Science ) , selected according to intron spanning and GC% , and synthesized by Proligo Singapore Pte Ltd ( Biopolis way , Singapore ) . External standard curves either for household or effector genes consisted of serial dilutions of specific purified DNA ranging from 107 to 1 copies as described previously [38] . The copy number of each standard was calculated by standard methods using the Avogadro constant and the size of the amplified target as described [41] . Each standard curve was validated using established criteria ( specific melting temperature , size of the PCR product , a mean error ≤0 . 03 and a slope near −3 . 3 ) . PCR amplifications and analysis were achieved using a LightCycler 2 . 0 instrument ( Roche Applied Science ) with software version 4 . 05 . All reactions were performed in duplicates with the LightCycler FastStart DNA Master SYBR Green I kit ( Roche Applied Science ) in a final 20 µl volume with 4 mM MgCl2 , 0 . 5 µM of each primer and 2 µL cDNA or 2 µL DNA standard dilution . Cycle conditions were optimized for each target , either immune mediator or β-actin . Amplification conditions consisted of an initial pre-incubation at 95° for 10 min ( polymerase activation ) followed by amplification of the target cDNA for 45 cycles ( 95°C for 8 s , 60°C for 5 s and a variable extension time at 72°C ) . Extension periods varied for each PCR depending on the length of the expected amplicon ( ∼1 s/25 bp ) as shown in table 1 . Leptospiraemia was also determined after cDNA amplification with a PCR specific of a 331 bp sequence of the L . interrogans rrs ( 16S ) gene [42] using a LightCycler 480 II instrument with software version 1 . 5 . 0 . Amplification reactions were performed in duplicates with the LightCycler 480 SYBR Green I Master kit in a final 10 µL volume with 0 . 5 µM of each primer , 1 µL cDNA as follow: a 10 min enzyme activation at 95°C then 50 amplification cycles , each made of 8 s at 95°C , 5 s at 62°C and 12 s at 72°C . A negative control with PCR-grade water instead of cDNA was included in each run . With either instrument , melting peaks were automatically plotted by the software and used to assess the specificity of the amplified product . Absolute quantification of each target was done using the comparative cycle threshold ( CT ) method: the concentration of a given target mRNA in any unknown sample was calculated by comparing its CT with the corresponding standard curve . Relative expression was calculated as the ratio of the target mRNA copy number to β-actin mRNA copy number . This ratio was then normalized using the same ratio calculated in uninfected controls ( used as calibrators ) . This expression of the results allows directly providing an n-fold change ratio in gene expression of the experimental animals compared to their control counterparts . In this study , β−actin was used as the household reference gene since former work demonstrated that no significant difference was observed using either HPRT or β−actin [38] . The outcomes were defined as the spontaneous outcome of the infection and were either death ( “nonsurvivors” ) or spontaneous recovery ( “survivors” ) . The results of three independent LD50 experiments were pooled and compared according to the outcome using Student's t test and Kruskal-Wallis test on Stata SE/8 . 0 for Windows ( Stata Corporation , Texas , USA ) . The overall survival curve for all LD50 experiments was also plotted with 95% confidence intervals using Stata SE/8 . 0 .
Preliminary experiments demonstrated that a dose of 108 live virulent leptospires per hamster injected by the intra-peritoneal route led to ca . 50% mortality , a dose therefore used for our LD50 experiments . The first signs of illness ( prostration and anorexia ) were observed at day 3 post-infection . This dose was confirmed in further experiments as being a relevant and reproducible model of LD50 ( figure 1A ) . The relative normalized gene expression levels at various time points are summarized in figure 1B . After a rapid and intense rise to its maximum , TNFα expression rapidly decreased before to slowly and regularly increase again up to D3 . A similar pattern was observed for IL-1 and IL-6 with much a higher amplitude of regulation . After no significant modulation during the first 14 hrs post-infection , IL-10 and COX-2 were expressed at a maximum level around D3 after a steady increase notably for IL-10 . IFNγ expression levels were poorly modulated along this time-course . However its maximum expression level became relatively stable around D3 . Reproducible results were obtained for all these effectors with RNA extracted from 400 µL whole blood , except IL-2 and IL-4 , because of their low mRNA copy numbers . Additionally , the peak of IL-12p40 gene expression was observed very early at 4 hours post-infection . Therefore these 3 latter effectors ( IL-2 , IL-4 and IL12p40 ) were not analysed in further studies . Taken together , these results led to determine D3 as a relevant time point for future studies , a consensus when most of the relevant effectors could be efficiently monitored and the appearance of the first clinical signs before any mortality occurs . In the LD100 experiment , all 5 infected hamsters died at D5 . As expected , all hamsters infected with a similarly high dose ( 2×108 per hamster ) of the high-passage isogenic variant survived until D21 and were euthanized . In total , 36 infected hamsters were included from our three independent LD50 infection challenges . Twenty two of them died at 6 . 1 ( range 4 . 04–6 . 92 ) days post-infection ( nonsurvivors ) , whereas 14 were considered as spontaneously recovering being alive at D21 ( survivors ) . Gene expression levels evidenced that the pro-inflammatory cytokines IL-1α and TNFα but also the enzyme Cyclooxygenase-2 and the cytokine IL-10 were expressed at significantly higher levels ( p<0 . 01 , see table 2 ) in nonsurvivors when compared to survivors ( Figure 2A ) . Considering the basic criterion that a 2-fold change in transcript abundance represents differential expression [43] , [44] , the gene expression levels in survivors were not significantly different from controls ( i . e . relative normalized gene expression levels in the range 0 . 5–2 , see table 2 ) . As expected , the live Leptospira burdens , as evaluated by the ratio of Leptospira 16S rRNA to hamster β-actin , were nil in controls and were also significantly ( p<0 . 01 , see table 2 ) lower in spontaneously recovering survivors ( figure 2B ) . Contrastingly , the expression of the cytokines IFNγ and TGFβ appeared poorly modulated , their expression levels in infected animals being not significantly different from that in control animals ( ratio not different from 1 ) . Additionally , no difference in expression levels was observed between survivors and nonsurvivors after the Leptospira LD50 challenge ( table 2 and figure 3 ) . Though IL-6 is notably induced as a response to the LD50 infectious challenge ( i . e . ratio significantly higher than 1 . 0 ) , similar levels ( p>0 . 1 , see table 2 ) were observed in hamsters whatever the outcome of the LD50 infection . However and interestingly , two out of our three independent experiments , higher IL-6 expression levels were observed in nonsurvivors in two out of our 3 independent experiments and a very high expression level in a few survivors accounted for the similar average expression ( see figure 3 insert ) . Hamsters infected with a high ( LD100 ) dose of virulent Leptospira displayed a gene expression pattern very similar to the one observed in nonsurvivors after the LD50 infection challenge . They displayed a similarly increased gene expression of IL-1α , TNFα and Cox-2 and a very similar Leptospira burden ( figure 2 ) . Their mean IL10 gene expression level was higher than in nonsurvivors after the LD50 challenge but this difference was not significant , due to high inter-individual variability . IFNγ and TGFβ expression levels were very poorly modulated , again not significantly different from uninfected controls ( figure 3 ) . Similar to IL-10 , IL-6 gene expression was largely increased compared to animals infected with a lower dose but a high inter-individual variability was also noted . In hamsters infected with a similarly high dose of the high-passage non-virulent Leptospira variant , the gene expression pattern was similar to the one displayed by survivors of the LD50 challenge . Surprisingly , a leptospiraemia was still observed at D3 in most of the animals , though no clinical sign was noted and no mortality occurred .
We first developed a LD50 model of leptospirosis in hamsters . Used together with a non-lethal blood sampling technique , it allowed the acquisition of individual gene expression patterns during the course of acute leptospirosis . Using this model , we demonstrated that the expression of some immune genes in blood , together with the Leptospira burden in blood of infected animals could be correlated with the outcome of the infection . The hamster is recognized as a good animal model for severe human leptospirosis [29] . Using the virulent Leptospira interrogans Icterohaemorragiae strain Verdun [45] , [46] , we determined the dose of 108 live leptospires injected via the intra-peritoneal route as leading to ca . 50% mortality . This challenge technique proved to be reproducible in 5 to 6-week old Syrian hamsters and was used for our study . When infected this way , the first clinical signs in hamsters held in individual cages ( anorexia , then prostration and ruffled fur ) were observed from 3 days post-infection on . This 3-day post-infection time point was also shown , in another experimental hamster model of leptospirosis , to be the time point for the first detection of Leptospira mRNA in target organs and a relevant time point for immune gene expression studies [47] . The use of a non-lethal sampling technique together with the follow up of individual hamsters allowed relating the gene expression levels observed with the individual outcome . We additionally conducted two experimental infection experiments for comparison purpose , one using a high dose of the virulent strain ( 2×108 live leptospires via the intra-peritoneal route ) leading to 100% mortality and a similarly high dose of an isogenic avirulent variant causing no mortality . During these preliminary experiments , we determined that a 400 µL blood collection under gas anesthesia at this time point would not be too deleterious to the animals and was sufficient to allow the extraction of mRNA in adequate quantity and quality for gene expression studies . Based on our previous knowledge [38] and with additional preliminary experiments , we determined that TNFα , IL-1α , IL-6 , IL-10 , IFNγ , TGFβ and Cox2 gene expression levels could successfully be quantified using this experimental procedure . These targets were chosen for their relevance in studying our hypothesis of similarities between severe leptospirosis and septic shock . Only those evidencing a significant number of mRNA copy numbers at this time point , therefore allowing accurate determination of the gene expression levels , were studied . The Leptospira burden was reported to be of prognostic value in human leptospirosis [39] . In our study , it was evaluated by q-RT-PCR targeting the 16S-rRNA allowing to evaluating the burden of mostly live leptospires , bacterial rRNAs being very short-lived when cells have reduced activity or die [48] , [49] . Using a ratio of Leptospira 16S rRNA copy number to host β-actin mRNA copy number allowed a comparison between individuals . As expected and observed in humans [39] , significantly higher Leptospira burdens were noticed in nonsurvivors . Interestingly , a leptospiraemia was still observed at D3 in animals infected with a high dose of the avirulent Leptospira variant , suggesting that this high-passage variant , though not lethal , has retained some degree of pathogenicity . This also suggests that leptospiraemia might be strain- and virulence-dependent , possibly jeopardizing its use as a tool for prognosis , when the virulence of the infecting strain is not known . The immune response to an infection is nowadays considered as precisely modulated rather than simply induced . Cytokines expression levels are largely studied in the context of septic shock and severe sepsis both for their possible prognostic value [13] , [15]–[17] and as a way to improve our understanding of host-pathogen interactions . Actually , sepsis is now recognized as associated with an exacerbated production of both pro- and anti-inflammatory cytokines and the prognostic value of some of these is widely recognized [20] . Using reverse transcription-real-time PCR , the transcripts can be quantified directly in a biological sample , providing information about the in vivo immune response mechanisms of the individual . Studying the immune response is only possible at the transcriptional level in our animal model , due to the lack of tools for assessing serum conentrations of immune efectors . however , it is also probably more sensitive to evaluate the fine-tuning of the immune response because the amount of circulating cytokines only represents a minor part of the total amount of cytokines produced [50] . Our results in the LD50 model evidenced differential gene expression according to the outcome . TNFα , IL-1α , Cox-2 were expressed at significantly higher levels in nonsurvivors than in spontaneously recovering hamsters . Using the other two infection models , the results demonstrated similar gene expression levels in animals challenged with a LD100 and in nonsurvivors after a LD50 challenge on one hand , and on the other hand in survivors after a LD50 and animals infected with a high dose of the avirulent variant , both actually surviving . These similarities using different doses and strains confirm the validity of our results . Our IL-1 RT-PCR targets IL1α , one of the two main active forms in the IL-1 family . IL-1β is most often considered as the prototypic IL-1 effector because it is released in the bloodstream , whereas Il-1α mostly remains cytosolic with an autocrine activity or is bound to the cell surface . Though IL-1β was more frequently considered as an indicator of Il-1 activity , IL-1α was shown to have an action very similar to the action of the more largely studied IL-1β . Furthermore , it was shown that its gene expression is quite similarly regulated [51]–[53] . TNFα and IL-1 are prototypic pro-inflammatory mediators that have been reported to have a prognosis value in sepsis [16] , [54] , even if their clinical relevance was also questioned [55] . Interestingly , TNFα was also reported to have a similar prognosis value in leptospirosis [10] , though these results were not confirmed in other studies [11] . Cox-2 is a highly inducible enzyme involved in the early phase of the inflammatory response . Notably induced by IL-1 and TNFα through the NFκB pathway , its induction can be considered as an end-result of the initial pro-inflammatory response . Interestingly , a significant induction of Cox-2 was observed only in nonsurvivors whatever the infective dose . This further suggests the probable contribution of a sepsis-like mechanism in severe leptospirosis . IL-10 is expressed at higher levels in nonsurvivors compared to the survivors in the LD50 model . These results are in agreement with several studies showing an exacerbated production of anti-inflammatory cytokines resulting in aggravation of a systemic disease and adverse outcome in febrile patients [14] , [17] . The decreased production of Th1 cytokines in many cellular types was reported as an IL-10-induced adaptative immune response , by interfering on antigen-presenting cells and T cells , possibly via inhibition of NFκB nuclear translocation [14] , [56] . This immunoregulatory role of IL-10 was clearly established after an anergy was observed in stimulated T cells in the presence of IL-10 . Moreover , Il-10 production in innate immune response to a stimulus promotes the expansion of regulatory T cells , amplifying the anti-inflammatory effect of IL-10 [13] , [14] , [56] . However , the results observed with the other two infection models suggest a possible effect of the infective dose on IL-10 expression levels , higher levels being noted in animals infected with a high Leptospira dose ( either virulent or not ) when compared to their respective counterparts with the same outcome in the LD50 model . The IL-10/TNFα ratio has been proposed as a prognosis indicator in sepsis [17] , [54] and in leptospirosis [57] . A high IL-10/TNFα ratio was reported as correlated with poor outcome in septic patients , contrary to the opposite results obtained in one limited study reported in leptospirosis [57] . This ratio was generally regarded as reflecting a persistent secretion of IL-10 at later time points , when concomitant down-regulation of TNFα occurs . From our experiments , this ratio , at least at the transcriptional level , does not appear relevant , both cytokines being induced in animals with fatal outcome , at least on the basis of our D3 time-point . In our LD50 experiments , IL-6 , often reported as the best marker of the severity of infectious ( or even non-infectious ) stress in humans and to have a prognosis value in sepsis [20] , was not differentially expressed according to the outcome . However , our results merely reflect a very high inter-individual variability in IL-6 expression . Contrastingly , IL-6 expression in hamsters infected with ( and dying from ) a LD100 of Leptospira actually showed a highly increased expression , though again with a very high variability . Similarly , high fluctuations of bioactive IL-6 levels were reported in serum from septic patients [58] . This high variability would also be limiting the value of IL-6 as a predictor in leptospirosis . In our model , IFNγ and TGFβ had no prognostic value and were similarly expressed in infected hamsters whatever the dose or the outcome . Interestingly , the gene expression of IFNγ and TGFβ appeared not being significantly regulated based on the common 2-fold variation criterion [43] , [44] . Because IFN-γ gene expression is antigen-presenting cells dependent , we could hypothesize that high Il-10 levels limited its expression , though it could have been beneficial for an optimal defense against infection . However , some cytokines have been shown not to be regulated at a transcriptional level and post-transcriptional regulation also plays a major role in cytokine cascades even after a transcriptional regulation has occurred . Unfortunately in the hamster , our animal model , no tool is available to evaluate the serum concentrations of the effectors studied . On one hand , gene expression techniques allow a rapid and highly sensitive study of immune gene transcriptional regulation , notably because the circulating part of cytokines is considered as being the “tip of the iceberg” [50] . On the other hand , some immune mediators of the septic shock are known to be poorly regulated at the transcriptional level and can therefore not be studied in our model . As an example , High-mobility group box ( HMGB ) -1 is primarily known as a nuclear DNA-binding protein with a transcription regulatory activity , but can also be excreted by stimulated macrophages , then displaying a cytokine activity , notably inducing the release of TNFα and IL-6 [59] . It has been proposed as a prototypic late mediator of inflammation in severe sepsis [60] , its delayed and prolonged release in established sepsis rising an increasing interest as a prognostic indicator or a therapeutic target in late-phase inflammation processes . Its usefulness as a prognostic indicator or as a therapeutic target remains unexplored in leptospirosis . Though obtained in an animal model with Leptospira strains of known and relatively low virulence , these encouraging results prompted us to initiate a clinical study aiming at investigating the prognostic value of these effectors in patients with confirmed leptospirosis . Cytokines will similarly be studied at the gene expression level , but also by measuring their serum concentration levels , a technique much easier transmissible to health centers . This study is currently underway . | Leptospirosis is a widespread bacterial infection that is transmitted by soil or water contaminated by the urine of infected animals , or directly from these animals . It has highly diverse clinical presentations , making its differential diagnosis difficult . Though most cases are minor and self-resolving , there are also severe forms that include a sepsis pattern and multiple organ failure , and have possible fatal outcomes . Predictors of disease evolution and outcome are scarce , yet they would be very valuable to clinicians as well as to better decipher disease pathogenesis . In this study , we used a hamster model of leptospirosis to evaluate if immune genes were differentially expressed between individuals and if their expression levels could help forecast the outcome of the disease . We found that hamsters that later died from leptospirosis had significantly higher expression levels of both pro- and anti-inflammatory mediators compared to survivors . These results suggest that expression levels of these immune effectors might be helpful predictors of outcome in leptospirosis and that septic shock contributes to fatal leptospirosis . | [
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] | 2010 | Differential Cytokine Gene Expression According to Outcome in a Hamster Model of Leptospirosis |
A female’s reproductive state influences her perception of odors and tastes along with her changed behavioral state and physiological needs . The mechanism that modulates chemosensory processing , however , remains largely elusive . Using Drosophila , we have identified a behavioral , neuronal , and genetic mechanism that adapts the senses of smell and taste , the major modalities for food quality perception , to the physiological needs of a gravid female . Pungent smelling polyamines , such as putrescine and spermidine , are essential for cell proliferation , reproduction , and embryonic development in all animals . A polyamine-rich diet increases reproductive success in many species , including flies . Using a combination of behavioral analysis and in vivo physiology , we show that polyamine attraction is modulated in gravid females through a G-protein coupled receptor , the sex peptide receptor ( SPR ) , and its neuropeptide ligands , MIPs ( myoinhibitory peptides ) , which act directly in the polyamine-detecting olfactory and taste neurons . This modulation is triggered by an increase of SPR expression in chemosensory neurons , which is sufficient to convert virgin to mated female olfactory choice behavior . Together , our data show that neuropeptide-mediated modulation of peripheral chemosensory neurons increases a gravid female’s preference for important nutrients , thereby ensuring optimal conditions for her growing progeny .
The behavior of females in most animal species changes significantly as a consequence of mating . Those changes are interpreted from an evolutionary standpoint as the female’s preparation to maximize the fitness of her offspring . In general , they entail a qualitative and quantitative change in her diet , as well as the search for an optimal site where her progeny will develop . In humans , the eating behavior and perception of tastes and odors of a pregnant woman are modulated in concert with altered physiology and the specific needs of the embryo [1–3] . While several neuromodulatory molecules such as noradrenaline are found in the vertebrate olfactory and gustatory systems , little is known about how reproductive state and pregnancy shape a female’s odor and taste preferences [4 , 5] . Very recent work in the mouse showed that olfactory sensory neurons ( OSNs ) are modulated during the estrus cycle [6] . Progesterone receptor expressed in OSNs decreases the sensitivity of pheromone-detecting OSNs and thereby reduces the non-sexually receptive female’s interest in male pheromones . The mechanisms of how mating , pregnancy , and lactation shape the response of the female olfactory and gustatory systems remain poorly understood . The neuronal underpinnings of mating and its consequences on female behaviors have arguably been best characterized in the fruit fly Drosophila melanogaster [7 , 8] . Shortly after copulation , female flies engage in a series of post-mating behaviors contrasting with those of virgins: their sexual receptivity decreases , and they feed to accumulate essential resources needed for the production of eggs [9–12]; finally , they lay their eggs . This suite of behaviors results from a post-mating trigger located in the female’s reproductive tract [12] . Sensory neurons extending their dendrites directly into the oviduct are activated by a component of the male’s ejaculate , the sex peptide ( SP ) [13 , 14] . Sex peptide receptor ( SPR ) expressed by these sensory neurons triggers the post-mating switch [15] . Mated females mutant for SPR produce and lay fewer eggs while maintaining a high sexual receptivity [13–15] . In addition to SP , male ejaculate contains more than 200 proteins , which are transferred along with SP into the female . These have been implicated in conformational changes of the uterus , induction of ovulation , and sperm storage [7 , 16–18] . Additional SPR ligands have been identified that are not required for the canonical post-mating switch , opening the possibility that this receptor is involved in the neuromodulation of other processes [19–22] . These alternative ligands , the myoinhibitory peptides ( MIPs ) /allatostatin-Bs , unlike SP , have been found outside of drosophilids , in many other insect species such as the silkmoth ( Bombyx mori ) , several mosquito species , and the red flour beetle ( Tribolium castaneum ) [19] . They are expressed in the brain of flies and mosquitoes , including in the centers of olfactory and gustatory sensory neuron projections , the antennal lobe ( AL ) , and the subesophageal zone ( SEZ ) , respectively [19 , 23 , 24] . Although these high-affinity SPR ligands have recently been implicated in the control of sleep in Drosophila males and females [25] , nothing thus far suggests a function in reproductive behaviors [19] . To identify optimal food and oviposition sites , female flies rely strongly on their sense of smell and taste [26–29] . D . melanogaster females prefer to oviposit in decaying fruit and use byproducts of fermentation such as ethanol and acetic acid to choose oviposition sites [29 , 30] . Their receptivity to these byproducts is enhanced by their internal state [29 , 31] . It was shown , for instance , that the presence of an egg about to be laid results in increased attraction to acetic acid [31] . Yet the mechanisms linking reproductive state to the modulation of chemosensory processing remain unknown . We have examined the causative mechanisms that integrate reproductive state into preference behavior and chemosensory processing . We have focused on the perception of another class of byproducts of fermenting fruits , polyamines . Polyamines such as putrescine , spermine , and spermidine are important nutrients that are associated with reproductive success across animal species [32] . A diet high in polyamines indeed increases the number of offspring of a fly couple , and female flies prefer to lay their eggs on polyamine-rich food [33] . Importantly , we have previously characterized the chemosensory mechanisms flies use to find and evaluate polyamine-rich food sources and oviposition sites . In brief , volatile polyamines are detected by OSNs on the fly’s antenna , co-expressing two ionotropic receptors ( IRs ) , IR41a and IR76b [33 , 34] . Interestingly , the taste of polyamines is also detected by IR76b in labellar gustatory receptor neurons ( GRNs ) [33] . This beneficial role of polyamines has a well-characterized biological basis: polyamines are essential for basic cellular processes such as cell growth and proliferation , and are of specific importance during reproduction [35] . They enhance the quality of sperm and egg and are critical during embryogenesis and postnatal development [32 , 36] . While the organism can generate polyamines , a significant part is taken in with the diet [37 , 38] . Moreover , endogenous synthesis of polyamines declines with ageing and can be compensated for through a polyamine-rich diet [32] . Therefore , these compounds represent a sensory cue as well as an essential component of the diet of a gravid female fly . Here , we show that the olfactory and gustatory perception of polyamines is modulated by the female’s reproductive state and guides her choice behavior accordingly . This sensory and behavioral modulation depends on SPR and its conserved ligands , the MIPs that act directly on the chemosensory neurons themselves . Together , our results suggest that mating-state-dependent neuropeptidergic modulation of chemosensory neurons matches the female fly’s decision-making to her physiological needs .
Males and female flies are strongly attracted to polyamines [33] . The perception of sensory stimuli , however , can be modulated and depends on behavioral context [39] . Given that polyamine-rich foods increase the number of progeny [33] , we wondered whether mating state influences the perception of these important molecules . To test this , we compared olfactory and oviposition behaviors of mated to virgin female flies . In an olfactory choice assay , the T-maze , mated females showed a strong attraction to volatile polyamines , which requires their sense of smell , as we have shown in the companion paper and as previously suggested by Silbering et al . [33 , 34] . Virgin flies displayed a significantly altered preference for the polyamines putrescine and cadaverine compared to mated flies ( Fig 1A ) . While mated females preferred relatively high concentrations of polyamines typically present in fermenting fruit ( 1 mM or 10 ppm , [36 , 37] ) , virgin females showed strong attraction to only the lowest levels and increasing avoidance of higher levels of these odors ( Fig 1B ) . We next analyzed whether virgin flies would make different egg-laying choices compared to mated flies . Mated females taste polyamines with taste sensilla on their labellum and use this information during egg-laying decisions [33] . Although egg-laying substrates containing just polyamines are avoided as egg-laying substrates because of their bitter taste , polyamine-rich sugary substrates such as decaying fruit are strongly preferred over fresh fruit [33] . To assay the egg-laying preferences , we used a simple oviposition assay consisting of a plate with a plain agarose substrate ( 1% ) that was on one-half of the egg-laying plate supplemented with the polyamines , putrescine or cadaverine ( 1 mM , Fig 1C , see Materials and Methods ) . Consistent with our dissection of polyamine perception [33] , mated flies displayed a strong preference and laid the majority of their eggs on plain agarose ( Fig 1C ) . By contrast , virgin females , albeit laying very few ( and unfertilized ) eggs , distributed their eggs equally between polyamine and control sides ( Figs 1C and S1A ) . Therefore , we concluded that , while mated females actively develop a choice behavior , virgin females are indifferent to polyamines as an egg-laying substrate . Taken together , odor as well as taste perception of polyamines strongly depends on the female fly’s mating state . We have shown that a polyamine-rich diet increases the number of offspring of a fly couple [33] . These data could potentially indicate that needs arising through egg production and laying , and not exclusively or primarily through mating , drive a female to seek polyamines . We therefore first tested whether polyamine choice behavior correlated with the female’s egg-laying activity and time after mating . This appeared to be the case , because mated females that had ceased to lay eggs at 14 d after mating returned to their pre-mating preference behavior and made choices that resembled the choices of virgin flies ( Fig 1D ) . This return to virgin behavior could be due to the time elapsed after mating or to a reduction in egg-laying . To dissect the relative contribution of egg-laying activity and mating , we analyzed the preference behavior of mated ovoD1 mutant females [40] . These females are sterile due to an atrophy of the ovaries . Mated ovoD1 mutant females showed the same preference to polyamines in the T-maze compared to control mated females ( Fig 1E ) . From these data , it appears that mating itself provides a key signal that changes the female’s perception and stimulates her to seek polyamines . While previous research has shown that mating state and egg-laying activity influence the choice behavior of female flies when selecting food or oviposition substrates [9 , 29 , 31] , how mating state modulates neural sensitivity and processing of sensory information remains not understood . Having defined the gustatory and olfactory receptors and sensory neurons for the detection of polyamines [33] , we sought to identify the mechanism that modulates this detection and processing in a mating state-dependent manner . SPR and SP are required for the classical post-mating switch ( see Introduction ) and changes in feeding behavior [9 , 10 , 41] . To test the role of SPR in mating-state-dependent polyamine choice behavior , we initially examined the olfactory preference and oviposition behavior of SPR mutant females ( Df ( 1 ) Exel6234 ) [15] . Mated SPR mutant females showed a significantly reduced preference behavior in the T-maze ( odor ) as well as in oviposition assays ( taste ) compared to that of mated control females ( Fig 1F and 1G ) . Importantly , SPR mutant males maintained the same level of attraction as wildtype control males , possibly representing the constant need of polyamines such as spermine and spermidine for sperm production ( Fig 1H ) . These results indicated that the SPR pathway is part of the mechanism that controls mating-induced changes in the perception of the smell and taste of polyamines . Increasing evidence in different model organisms indicates that chemosensory neurons themselves are potent targets for neuromodulation [6 , 42–44] . Although SPR is required in specific internal sensory neurons in the female reproductive tract for the canonical post-mating switch , its rather broad expression in the nervous system , including chemosensory organs and their projection zones in the brain [15 , 45] , prompted us to ask whether SPR signaling was acting directly in peripheral chemosensory neurons . Previous work successfully employed RNA interference ( RNAi ) directed against SPR to identify the set of sensory neurons in the female reproductive tract sufficient to trigger two important post-mating behaviors: increased egg-laying and rejection of males [13 , 14] . We induced RNAi against SPR ( UAS-SPRi ) specifically in olfactory and gustatory neurons that sense polyamines , using the driver IR76b-Gal4 [33] . Importantly , this driver was not expressed in the internal sensory neurons that require SPR to induce the mating switch ( S2A and S2C Fig ) . Mated females of the genotype IR76b-Gal4;UAS-SPRi showed a significantly reduced attraction to polyamine odor in the T-maze assay as compared to controls ( Fig 2A ) . Remarkably , this reduction was similar to the reduction seen in SPR mutants ( see Fig 1 ) . Importantly , SPR RNAi did not reduce the attraction of virgin females further , showing that the regulation by SPR is indeed mating-state-dependent ( Fig 2B ) . Similarly , expression of SPR RNAi in IR76b neurons fully abolished the taste-dependent egg-laying preference behavior of mated females ( Figs 2C and S1C ) . We then refined the experiment with another , significantly more specific Gal4 driver , IR41a-Gal4 , targeting only the small number of olfactory neurons sensing polyamine odor ( IR41a-Gal4;UAS-SPRi ) . We observed a similar reduction in attraction to polyamine odor in the T-maze compared to knockdown with IR76b-Gal4 in mated females ( Fig 2D ) . By contrast , egg-laying preference was comparable to control mated females ( Figs 2E and S1D ) . This result was consistent with the absence of IR41a-Gal4 expression in IR76b gustatory neurons [33] . These data were consistent with the hypothesis that SPR in chemosensory neurons is necessary to modulate the attraction of females to the smell and taste of polyamines after mating . Given the central role of SPR in the classical post-mating switch , we asked whether SPR in chemosensory neurons was not only necessary but also sufficient to modulate their sensitivity . To this end , we re-expressed SPR in SPR mutant females in all IR76b neurons ( IR76b-Gal4 , polyamine taste and olfaction ) , in bitter taste neurons ( GR66a-Gal4 ) , or just in the olfactory subset of IR76b-expressing neurons that express IR41a ( IR41a-Gal4 ) and assayed olfactory behavior ( T-maze ) and taste-dependent oviposition behavior . We found that re-expression of SPR in IR76b neurons fully rescued the SPR mutant phenotype of mated females in olfaction as well as in oviposition behavior ( Fig 2F and 2G ) . Expression of SPR in GR66a bitter neurons , by contrast , had no effect on the SPR mutant phenotype in either of the two choice behaviors ( Fig 2F and 2G ) . Re-expression of SPR selectively in IR41a OSNs did not rescue oviposition behavior of SPR mutant females , consistent with the fact that the egg-laying choice is mediated by taste neurons ( Fig 2G ) . It did , however , rescue the olfactory attraction of SPR mutant females to polyamine odor in the T-maze ( Fig 2F ) . This suggests that SPR plays a cell-autonomous role in a specific set of peripheral chemosensory neurons independent of its function in the cells in the female reproductive system . Altogether , based on these data , we propose that SPR regulates choice behavior in a mating-state-dependent manner directly in chemosensory neurons , providing a mechanistic link between mating state and the neurons that process odors and taste . SPR signaling in chemosensory neurons appears to be required for the change in choice behavior after mating . This genetic mechanism could influence neuronal physiology at several levels of olfactory and taste processing starting at the peripheral level . We have previously shown that IR76b taste neurons on the labellum are of particular importance for egg-laying choices on polyamine substrates [33] . Loss of IR76b completely abolishes the egg-laying preference of a mated female [33] . To test whether mating modulates the sensitivity of gustatory neurons , we examined the activity of IR76b chemosensory neurons by recording their Ca2+ responses to polyamines at the level of their axon terminals in the SEZ of the central brain ( Fig 3A ) . Because mating induces short-term ( <24 h ) and long-term ( ~1 wk ) effects [46 , 47] , we performed these experiments at two different time points: at 1–6 h or at 1 wk post-mating ( Fig 3B–3F ) . We measured Ca2+ increases by recording GCaMP6f signals in IR76b axon terminals in the SEZ ( IR76b-Gal4;UAS-GCaMP6f ) , which we divided based on the innervation pattern of IR76b neuron subsets into two broader innervation zones , region of interest ( ROI ) 1 and ROI 2 ( Fig 3A ) . At 1–6 h post-mating , labellar IR76b neurons projecting to ROI 1 , the primary response area for polyamines [33] , responded significantly more strongly to a putrescine taste solution in mated females than in virgin females ( Fig 3C and 3D ) . Interestingly , this was not the case for ROI 2 , which responded significantly only to higher concentrations of putrescine ( 10–100 mM ) . IR76b neurons projecting to this region of the SEZ of virgin and mated females showed a similar response ( Fig 3E ) . Interestingly , at the later time point ( 1 wk post-mating ) , the difference observed for axons projecting to ROI 1 was no longer significant . Hence , we conclude that mating transiently increases the sensitivity of polyamine-detecting IR76b labellar taste neurons after mating . Is this shift of sensitivity in the GRNs mediated by SPR signaling directly in chemosensory neurons as the behavioral data suggests ? To answer this , we recorded GCaMP signals from polyamine-sensitive taste neurons of mated females , in which we triggered RNAi against SPR . Knock-down of SPR in IR76b GRNs ( IR76b-Gal4 , UAS-GCaMP5;UAS-SPRi ) of mated females led to a significant decrease in the presynaptic calcium increase of these neurons in response to polyamine taste compared to the response of mated controls ( Fig 3G–3I ) . Notably , SPR knockdown had no effect on the response of IR76b neurons projecting to the ROI 2 region of the SEZ . These neurons responded like control neurons ( Fig 3H and 3I ) , suggesting that SPR modulation only occurred in neurons that were affected by the mating state . These results provide a mechanistic explanation for behavioral change occurring in the oviposition choice behavior of females upon mating , and they are consistent with our model that SPR in GRNs directly modulates sensory neuron sensitivity and thereby regulates choice behavior . Olfactory preference behavior appears to undergo a similar shift as gustatory preference behavior after mating . We therefore carried out a set of experiments in the olfactory system similar to those described above . Axons of OSNs project centrally to the AL , the functional equivalent of the vertebrate olfactory bulb . This first-order olfactory information is further processed by local interneurons and then transferred by projection neurons ( PNs ) to higher brain centers ( Fig 4A ) [48] . Recent studies have shown that hunger enhances olfactory sensitivity to food odor by increasing presynaptic responses of OSNs via the OSN-resident short neuropeptide F ( sNPF ) and its receptor , sNPFR [42 , 43] . Metabolic state thereby regulates the efficacy of the synapse between OSN and PN similarly to what we have observed for mating state and GRNs . To test whether OSNs are modulated in a similar manner as GRNs , we imaged calcium increases of IR41a axon terminals at the level of the AL ( IR41a-Gal4;UAS-GCaMP6f ) ( Fig 4B ) . Surprisingly , we observed that mating significantly decreased the response of these neurons to behaviorally relevant concentrations of polyamines ( Fig 4C–4E ) . As in the gustatory system , this decrease was strongly significant at 1–6 h and remained only a trend at 1 wk post-mating ( Fig 4C ) . In contrast to GRNs , however , mating transiently suppresses the sensitivity of OSNs . How does this result explain the behavioral shift toward higher polyamine levels after mating ? Virgins show highest attraction to very low levels of polyamines and reduced attraction or enhanced aversion at levels preferred by mated females ( Fig 1B ) . By contrast , mated females show the highest attraction to relatively high amounts of polyamine , which roughly corresponds to decaying fruit ( 10 ppm/1 mM; Fig 1B ) . It was previously shown that different odor concentrations can have differential behavioral effects and can even recruit different PNs downstream of the same OSNs [49 , 50] . Such a mechanism could also explain the change of behavior to polyamines , whereby a reduction of olfactory sensitivity may change higher olfactory processing and consequently shift the mated female’s preference to increased levels of beneficial polyamines for egg-laying . Again , we asked whether this change in sensitivity was mediated by SPR signaling in OSNs themselves , as the behavioral data would suggest . As in the gustatory system , this appeared to be the case for the olfactory system , as knockdown of SPR in IR41a OSNs resulted in a significant change in presynaptic calcium responses of these neurons ( Fig 4F–4H ) . As predicted from the comparison of mated and virgin OSN responses to putrescine , we observed a greater increase of GCaMP fluorescence in OSN axon terminals of mated females with SPR knockdown ( IR41a-Gal4 , UAS-GCaMP5;UAS-SPRi ) compared to mated genetic controls ( Fig 4H ) . Together , we interpret these data to mean that SPR in chemosensory neurons regulates the sensitivity of OSNs and GRNs to polyamines directly at the level of these chemosensory neurons . This change in sensitivity follows two different neural mechanisms , i . e . , increased calcium responses of GRN and decreased responses of OSN axon terminals . This , in turn , appears to alter the mated female’s perception and adjusts her choice behavior to polyamines . We showed that polyamine perception changes upon mating and that this change is mediated by SPR signaling in chemosensory neurons . How SPR signaling is triggered in chemosensory neurons , however , remains unclear . The best-characterized SPR ligand is SP itself . A role for SP in feeding behavior was demonstrated previously . For instance , SP provided by the male stimulates feeding in mated females , and SP mutant male-mated females do not show this increase [10] . Furthermore , the mated female’s feeding preference for yeast and salt depends on SP provided by the male during mating [9 , 41] . Here , SP activates the canonical SPR pathway through ppk-positive SPR neurons in the female’s oviduct , which leads to a change in feeding preference . Whether and how mating and/or SP alter the sensitivity of taste neurons to yeast or salt or their higher-order chemosensory processing is not known . Furthermore , in the present context , if SP were to act directly on the chemosensory neurons , some SP would have to be transferred from its point of delivery , the female reproductive tract , to SPR in chemosensory neurons on the head . To test the requirement of SP in the sensitivity to polyamines , we crossed males that were mutant for the SP gene ( SP0 ) , and thus lacking SP from their semen , to wild-type virgin females [51] . We compared the behavior of these females to that of females mated to wild-type males . Interestingly , the attraction of females mated to SP0 males to polyamine odor in the T-maze was not significantly different from females mated to wild type males ( Fig 5A ) . This suggested that SP was not the key to mating-state-dependent olfactory sensitivity modulation . Furthermore , it also indicated that changes in feeding behavior as reported by Carvalho et al . [10] are not necessary for the observed olfactory modulation . We also analyzed the contribution of SP to oviposition preference . SP0 mated females appeared to show the same lack of preference as virgin flies and laid their very few eggs on either side of the assay ( Fig 5B ) . Nevertheless , the olfactory preference data as well as the site of action of SP indicated that another additional ligand was involved in mating-state-dependent chemosensory changes in females . Moreover , this result was in agreement with our data showing that re-expression of SPR in gustatory or olfactory neurons was sufficient to modulate their responses to polyamines . We therefore asked whether MIPs could be the functional ligands of SPR at the level of the chemosensory neuron central projections and could mediate the modulation of polyamine behavior . The expression of MIPs in the vicinity of IR41a axon terminals in the AL and in the vicinity of IR76b axons and axon terminals in the SEZ ( Fig 5C ) is consistent with their possible requirement in the chemosensory neurons themselves . We employed four different , independent RNAi-triggering transgenic lines to knockdown the expression of MIPs in IR76b-positive sensory neurons and tested fly behavior in the T-maze ( olfaction ) and oviposition ( taste ) assays . RNAi-mediated suppression of MIP expression in chemosensory neurons ( IR76b-Gal4;UAS-MIPi ) reduced the expression of MIP in chemosensory processing centers , but not in the rest of the brain as compared to controls or knockdown with a pan-neural driver ( S5A Fig ) . Importantly , this manipulation ( IR76b-Gal4;UAS-MIPi ) also significantly lowered the attraction of mated females to polyamines in the T-maze as compared to genetic controls ( Fig 5D and 5E ) . Notably , although MIP expression appears highly similar between males and females ( S4 Fig ) [52] , this reduced olfactory attraction was only observed in females , but not in male flies ( Fig 5D and 5E ) . These data mirror the lack of olfactory phenotype in the SPR mutant male ( see Fig 1G ) and further supported our model of a gender-specific role for SPR signaling . Furthermore , similar to what was observed upon SPR knockdown ( see Fig 2C ) , virgin female attraction to polyamines was not further decreased when MIPs were down-regulated by RNAi , showing that the effect of MIP was mating-state-dependent ( Fig 5F ) . Finally , a similar analysis in the context of oviposition behavior showed that knockdown of MIPs in IR76b neurons ( IR76b-Gal4;UAS-MIPi ) had the same effect on female oviposition behavior as knockdown of SPR ( Fig 5G ) . Female flies laid their eggs in equal numbers on polyamine-rich and control substrates ( S5B Fig ) . These data describe a role for MIPs in female reproductive behavior and indicate that they regulate polyamine-mediated chemosensory behavior presumably as ligands for SPR . Furthermore , similar to sNPF and its receptor [43] , MIPs and SPR appear to be required directly in gustatory and olfactory neurons . In contrast to sNPF and sNPFR , SPR and MIPs are only required in the female . Mating appears to induce a change in SPR signaling not only in the female reproductive tract as previously shown [15] , but also in her chemosensory neurons . In the female reproductive tract , SP is only available upon mating . How is this change brought about in peripheral neurons or in regions of the central brain such as the AL or SEZ ? The most straightforward mechanism would be an alteration in the expression of MIP or SPR upon mating such that there is more functional SPR or available MIP in the mated female compared to the virgin . Notably , MIP expression cycles with the circadian rhythm of the fly , in line with the role of SPR and MIP in maintaining a sleep-like state in flies [25] . As the authors did not observe any change in MIP mRNA levels , a post-transcriptional regulatory mechanism could be involved [25] . To test whether SPR and MIP expression was being modulated , we used quantitative PCR to compare mRNA levels of SPR and MIP before and after mating ( Fig 6A ) . To this end , we dissected antennae and brains of virgins and mated females at 1–6 h after mating and compared the expression of MIP and SPR to a control mRNA not expected to change upon mating ( see Materials and Methods ) . We found that SPR expression increases about 10-fold upon mating in the antenna but to a lesser extend in the brain ( ~3-fold , Fig 6A ) . By contrast , MIP expression after mating remained more similar to the expression before mating in both antenna and brain ( Fig 6A ) . These data are consistent with our hypothesis that SPR expression is selectively increased in chemosensory neurons upon mating and modulates female preference behavior . Furthermore , it strengthens the conclusion reached by genetic experiments that SPR signaling is required in chemosensory neurons . While we were not able to challenge or confirm this result by using antibody staining against SPR , both a previously published antibody [15] and another antibody that we produced ourselves showed similar stainings in wild-type and SPR mutant brains ( S6A–S6C Fig ) , we sought to quantify MIP protein expression at the level of the OSN terminals in the AL . This was especially important because MIP expression was previously suggested to be regulated at the level of the protein and not at the level of the mRNA [25] . Antibody staining against MIPs reveals central neurons as well as axon tracts of peripheral neurons projecting into the brain ( Figs 5C and S4 ) . In the SEZ , passing neuronal tracts of central neurons dominate ( S4 Fig ) and unfortunately mask the MIP-stained axons projecting from peripheral taste organs , including the proboscis ( Figs 5C and S4; see arrowheads ) . This situation prevented us from quantifying MIP expression selectively in GRNs . In the olfactory system , nevertheless , MIP expression was defined and appeared to stem only from OSNs and from local interneurons . Although MIP protein expression analysis did not show any gross differences between mated and virgin females ( S4 Fig ) , using more detailed image quantification we observed a significant increase of MIP expression in the AL in mated compared to virgin females ( Figs 6B and S7A ) . While this increase appears small , it is statistically significant . These results suggest that mating leads to a marked increase of SPR in chemosensory organs . This increase in SPR expression , accompanied by a small increase in MIP expression , might be the trigger for the mating-state-dependent modulation of polyamine taste and smell neurons . Of note , hunger modulates levels of the receptor sNPFR but not the expression of the neuropeptide itself [43] . Based on these results , we tested the effect of overexpression of SPR or MIP in chemosensory neurons in virgin females . We overexpressed SPR and MIP under the control of the IR76b enhancer ( IR76b-Gal4 ) in all IR76b neurons ( taste and olfaction ) as well as only in OSNs under the control of the IR41a enhancer ( IR41a-Gal4 ) in virgin females and tested their preference for polyamines . These manipulations had no effect on the number of eggs that virgin females laid , and egg numbers remained very low and similar to control virgins ( S7B Fig ) . In contrast to the unchanged egg-laying activity , virgin females overexpressing SPR in chemosensory neurons showed a strongly increased attraction to polyamine odor ( Fig 6C ) . This was true regardless of whether SPR was overexpressed under the control of IR76b-Gal4 or selectively in OSNs using IR41a-Gal4 ( Fig 6C ) . We observed similar results in a reminiscent experiment , in which we overexpressed MIP instead of SPR . Also in this case , virgin females with increased levels of MIP in their chemosensory neurons showed a significantly increased preference for high polyamine levels compared to control virgins ( Fig 6D ) . We also tested oviposition behavior . Given the low numbers of eggs , however , these data were less revealing and very variable , as small changes in egg-placing preference lead to large changes in preference index . In spite of these limitations , no clear preference was observable in virgins overexpressing SPR or MIP and control virgins ( Fig 6E and 6F ) . Our data would predict that the observed change in choice behavior upon overexpression of SPR is triggered by sensory neuron modulation . To analyze this , we used in vivo calcium imaging as described above . Indeed , we found that in virgins , overexpression of SPR selectively in IR41a OSNs significantly reduced the presynaptic response of IR41a neurons to polyamines compared to controls ( Fig 6G–6I ) . This result was the exact opposite of the effect seen when SPR expression was knocked down using RNAi in IR41a OSNs ( see Fig 3 ) and correlated well with the observed behavior of virgins overexpressing SPR . In conclusion , expression analysis in conjunction with behavioral and imaging analysis leads us to propose that mating induces primarily an increase of SPR expression in chemosensory neurons . Boosted levels of SPR activated by mildly increased levels of MIPs modulate chemosensory neuron output in response to polyamines and thereby increase female preference for higher concentrations of polyamines . Thus , SPR/MIP signaling in chemosensory neurons seems not only necessary and sufficient but , as these data indicate , an instructive signal adjusting choice behavior to reproductive state .
Reproductive behaviors such as male courtship and female egg-laying strongly depend on the mating state [8 , 9 , 29 , 31 , 53] . While previous work has suggested that mating modulates odor- or taste-driven choice behavior of Drosophila females [9 , 29 , 31 , 41 , 54] , how mating changes the processing of odors and tastes remained elusive . We show here that a female-specific neuropeptidergic mechanism acts in peripheral chemosensory neurons to enhance female preference for essential nutrients . Our data suggests that this modulation is autocrine and involves the GPCR SPR and its conserved MIP ligands . Notably , MIPs are expressed in chemosensory cells in the apical organs of a distant organism , the annelid ( Platynereis ) larvae , in which they trigger settlement behavior via an SPR-dependent signaling cascade [22] . Importantly , as SP and not MIP induces the SPR-dependent canonical post-mating switch [15 , 19] , our findings report the first gender and mating-state-dependent role of these peptides [25] . Whether this regulation is also responsible for previously reported changes in preference behavior upon mating [9 , 29 , 31 , 41] remains to be seen , but we anticipate that this type of regulation is not only specific to polyamines . On the other hand , mating-dependent changes for salt preference—salt preference is also dependent on IR76b receptor but in another GRN type—might undergo a different type of regulation , as RNAi-mediated knockdown of SPR in salt receptor neurons had no effect on salt feeding [41] . Instead , the change in salt preference is mediated by the canonical SP/SPR pathway and primarily reflects the fact that mating has taken place . The mechanism of how salt detection and/or processing are modulated is not known . In contrast to salt preference and polyamine preference , acetic acid preference is strongly modulated by egg-laying activity and not just mating [31] . The extent to which changes in salt or acetic acid preference are similar to the modulation of behavior to polyamine that we describe here can currently not be tested , because the olfactory neurons that mediate acetic acid preference have not been determined [31] . While SPR regulates the neuronal output of both olfactory and gustatory neurons , our behavioral and our physiological data surprisingly revealed that it does so through two opposite neuronal mechanisms . SPR signaling increases the presynaptic response of GRNs and decreases it in OSNs . Behaviorally , these two types of modulation produce the same effect: they enhance the female’s attraction to polyamine and tune it to levels typical for decaying or fermenting fruit . How these two effects are regulated by the same receptor and ligand pair remains open . GPCRs can recruit and activate different G-proteins . SPR was previously shown to recruit the inhibitory Gαi/o-type , thereby down-regulating cAMP levels in the cell [19 , 55] . In the female reproductive tract , SP inhibits SPR-expressing internal sensory neurons and thereby promotes several post-mating behaviors [15] . This type of inhibitory G-protein signaling could also explain our data in the olfactory system . Here , mating decreases the presynaptic activity of polyamine-detecting OSNs , and conversely , RNAi knockdown of SPR increases their responses strongly . This decrease in neuronal output also shifts the behavioral preference from low to high polyamine levels . While the relationship between behavior and GRN activity is much more straightforward in the gustatory system ( increased neuronal response , increased preference behavior ) , it implies that another G-protein might be activated downstream of SPR . G-protein Gαi/s increases cAMP levels and Gαq enhances phospholipase C ( PLC ) and calcium signaling [56] . In addition , Gβγ subunits regulate ion channels and other signaling effectors , including PLC [56] . Future work will address the exact mechanisms of this bi-directional modulation through SPR signaling . Nonetheless , it is interesting to speculate that different cells , including sensory neurons , could be modulated differentially by the same molecules depending on cell-specific states and the availability of signaling partners . While our data provides a neuronal and molecular mechanism of how chemosensory processing itself is affected by mating , it remains unclear how mating regulates MIP/SPR signaling in chemosensory neurons . Our data indicates that SPR levels strongly increase in chemosensory organs upon mating . In addition , MIP levels appear to be mildly increased by mating . This suggests that mating regulates primarily the expression of the GPCR resembling the modulation of sNPFR expression during hunger states . On the other hand , MIP overexpression also induced mated-like preference behavior in virgin flies , suggesting a somewhat more complex situation . For instance , it is possible that overexpression of MIP induces the expression of SPR . Alternatively , active MIP levels might also be regulated at the level of secretion or posttranslational processing , and overexpression might override this form of regulation . In the case of hunger , sNPFR levels are increased through a reduction of insulin signaling [43] . SP could be viewed as the possible equivalent of insulin for mating state . Females mated to SP mutant males , however , do not show a significant change in olfactory perception of polyamines . It is yet important to note that male sperm contains roughly 200 different proteins , some of which might be involved in mediating the change in MIPs/SPR signaling upon mating [7] . In the mosquito , which does not possess SP , the steroid hormone 20E serves as the post-mating switch [57] . Interestingly , mating or treatment with 20E induces in particular the expression of the enzymes required for the synthesis of polyamines in the female spermatheca , a tissue involved in sperm storage and egg-laying [57] . Whether such a mechanism also exists in Drosophila is not known . In addition to mating and signals transferred by mating , it is possible that egg-laying activity contributes to the regulation of MIPs/SPR signaling in chemosensory neurons through a mechanism that involves previously identified mechanosensory neurons of the female’s reproductive tract; such neurons may sense the presence of an egg to be laid [31] . Indeed , females that cease to lay eggs return to polyamine preferences as found before mating . On the other hand , SP mutant male-mated females and ovoD1 sterile females still show enhanced attraction to polyamine odor , although they lay very few or no eggs . Conversely , knockdown of SPR in IR41a neurons reduced polyamine odor attraction but had a marginal effect on the number of eggs laid . We observed , nevertheless , somewhat reduced numbers of eggs laid upon inactivation of IR76b neurons . At this point , we can only speculate about possible reasons . Although IR76b receptor is not expressed in ppk-positive internal SPR neurons , we do find expression of IR76b-Gal4 in neurons innervating the rectum and possibly gut ( data not shown ) . Hence , there might be an IR76b-mediated interplay between metabolism and nutrient uptake that influences egg-laying . However , females mated to SP-mutant males do not display an increase in feeding [10] , indicating that preference for polyamines does not depend on the metabolic cost of egg-laying . This conclusion is strengthened by the data obtained with mated ovoD1 sterile females , who show similar attraction to polyamines as compared to mated controls . Due to very few or no eggs laid by SP mutant male-mated females and ovoD1 females , respectively , we cannot , however , fully exclude a contribution of egg-laying activity to taste-dependent oviposition choice behavior . A further argument against an important role of egg-laying activity in our paradigm comes from the observation that the sensory modulation of OSNs and GRNs occurs rapidly after mating and is maintained only for a few hours . Similarly , SPR expression increases within the same time window shortly after mating . Egg-laying , however , continues for several days after this . In addition , overexpression of SPR was sufficient to switch virgin OSN calcium responses and female behavioral preferences to that of mated females without increasing the number of eggs laid . All in all , these data are more consistent with the hypothesis that mating and not egg-laying activity per se is the primary inducer of sensory modulation leading to the behavioral changes of females . It remains that the exact signal triggered by mating that regulates odor and taste preference for polyamines through the here-identified mechanism needs to still be determined . Furthermore , the role of metabolic need and polyamine metabolism is not completely clear . This is similar to the situation found for increased salt preference after mating . While more salt is beneficial for egg-laying , sterile females still increase their preference for salt upon mating [41] . Regardless , in the case of polyamines , it is tempting to speculate that exogenous ( by feeding ) and endogenous ( by enzymatic activity or expression ) polyamine sources are regulated by reproductive state and together contribute to reach optimal levels for reproduction in the organism . Our results bear some similarities to recent work on the modulation of OSN sensitivity in hunger states [43] . sNPF/sNPFR signaling modulates the activity of OSNs in the hungry fly . MIPs/SPR might play a very similar role in the mated female . Overexpression of sNPFR in OSNs of fed flies was sufficient to trigger enhanced food search behavior [43] . Likewise , an increase in SPR signaling in taste or smell neurons converts virgin to mated female preference behavior . Therefore , different internal states appear to recruit similar mechanisms to tune fly behavior to internal state . Furthermore , such modulation of first order sensory neurons appears not only be conserved within a species , but also for regulation of reproductive state-dependent behavior across species . For instance , a recent study in female mice showed that progesterone-receptor signaling in OSNs modulates sensitivity and behavior to male pheromones according to the estrus cycle [6] . Also in this case , sensory modulation accounts in full for the switch in preference behavior . What is the biological significance of integrating internal state at the level of the sensory neuron ? First , silencing of neurons in a state-dependent manner shields the brain from processing unnecessary information . As sensory information may not work as an on/off switch , it is possible that an early shift in neural pathway activation might reduce costly inhibitory activity to counteract activation once the sensory signal has been propagated . Second , another interesting possibility is that peripheral modulation might help to translate transient changes in internal state into longer-lasting behavioral changes that manifest in higher brain centers . This might be especially important in the case of female reproductive behaviors such as mate choice or caring for pups or babies . In contrast to hunger , which increases with time of starvation , the effect of mating decays slowly over time as the sperm stored in the female’s spermatheca is used up [58] . We have shown that the effect of mating on chemosensory neurons mediated by MIPs/SPR signaling is strong within the first 6 h after mating and remains a trend at 1 wk post-mating . However , it triggers a long-lasting behavioral switch , which is observed for over a week . Therefore , this transient modulation and altered sensitivity to polyamines could be encoded more permanently in the brain when the animal encounters the stimulus , for instance , in the context of an excellent place to lay her eggs . Because polyamine preference continues to be high for as long as stored sperm can fertilize the eggs , we speculate that this change in preference might be maintained by a memory mechanism in higher centers of chemosensory processing . Thus , short-term sensory enhancement not only increases perceived stimulus intensity , it may also help to associate a key sensation to a given reward or punishment . These chemosensory associations are of critical importance in parent–infant bonding in mammals , including humans , which form instantly after birth and last for months , years , or a lifetime [59] .
Drosophila melanogaster stocks were raised on conventional cornmeal-agar medium at 25°C temperature and 60% humidity and a 12 h light:12 h dark cycle . Following fly lines were used to obtain experimental groups of flies in the different experiments: The majority of the lines were obtained from Bloomington ( http://flystocks . bio . indiana . edu/ ) or the Vienna Drosophila Resource Center ( VDRC ) stock center ( http://stockcenter . vdrc . at ) except where indicated otherwise . Adult fly brains were dissected , fixed , and stained as described previously [60] . Briefly , brains were dissected in cold PBS , fixed with paraformaldehyde ( 2% , overnight at 4°C or for 2 h at RT ) , washed in PBS , 0 . 1% Triton X-100 , 10% donkey serum and stained overnight at 4° C or for 2 h at RT with the primary and after washes in PBS , 0 . 1% Triton X-100 with the secondary antibody using the same conditions . For SPR staining , a procedure previously published was followed [15] . All microscopic observations were made at an Olympus FV-1000 confocal microscope or at a Leica MZ205 epifluorescence stereomicroscope . Images were processed using ImageJ and Photoshop . The following antibodies were used: chicken anti-GFP ( molecular probes , 1:100 ) , rabbit anti-Dsred ( Clontech , Living colors DsRed polyclonal AB , 1:200 ) , rat anti-N-cadherin ( anti-N-cad DN-Ex #8 , Developmental Studies Hybridoma Bank , 1:100 ) , mouse anti-Dlarge ( 4F3-anti-discs large-c Developmental Studies Hybridoma Bank , 1:50 ) , mouse anti-MIP ( gift of C . Wegener , 1:50 ) , rabbit anti-SPR ( [15] , gift of Y . -J . Kim , 1:10 ) , rabbit anti-SPR ( generated by H . Ammer , Ludwig Maximilians University Munich , Germany against the same peptide as used in [15] ) . Secondary antibodies used were: anti-chicken Alexa 488 ( molecular probes , 1:250 ) and anti-rabbit Alexa 549 ( molecular probes , 1:250 ) , respectively . MIP expression was analyzed using antibody staining with the aforementioned MIP antibody . All brains were processed at the same time using the same conditions . Images were taken at an Olympus FV-1000 confocal microscope at the exact same settings . Seven single confocal sections were selected over the entire volume of the antennal lobe without knowledge of the mating state . ROIs were drawn around the AL in each section , and image quantification was carried out blindly using FIJI ImageJ software . All MIP quantifications were normalized to the intensity of anti-Ncad staining of the same ROI of the same section . Statistical analysis ( t test ) and data illustration were carried out using Excel and GraphPad Prism software . For calcium imaging experiments , GCaMP6f ( or for technical reasons GCaMP5 in experiments with SPR RNAi knockdown ) were expressed under the control of IR41a-Gal4 or IR76b-Gal4 . In vivo preparations of flies were prepared according to a method previously described [60] . In vivo preparations were imaged using a Leica DM6000FS fluorescent microscope equipped with a 40x water immersion objective and a Leica DFC360 FX fluorescent camera . All images were acquired with the Leica LAS AF E6000 image acquisition suit . Images were acquired for 20 s at a rate of 20 frames per second with 4 x 4 binning mode . During all measurements the exposure time was kept constant at 20 ms . For all experiments with odor stimulation , the stimulus was applied 5 s after the start of each measurement . A continuous and humidified airstream ( 2000 ml/min ) was delivered to the fly throughout the experiment via an 8 mm diameter glass tube positioned 10 mm away from the preparation . A custom-made odor delivery system ( Smartec , Martinsried ) , consisting of mass flow controllers ( MFC ) and solenoid valves , was used for delivering a continuous airstream and stimuli in all experiments . In all experiments , stimuli were delivered for 500 ms , and during stimulations the continuous flow was maintained at 2 , 000 ml/min . For putrescine stimulations , 1 ml of a precise concentration was filled in the odor delivery cup and the collected airspace odor was injected into the main airstream to give 0 ppm , 6 ppm , 8 ppm , and 10 ppm final concentrations for 500 ms without changing airstream strength . To measure the fluorescent intensity change , the region of interest was delineated by hand and the resulting time trace was used for further analysis . To calculate the normalized change in the relative fluorescence intensity , we used the following formula: ∆F/F = 100 ( Fn-F0 ) /F0 , where Fn is the nth frame after stimulation and F0 is the averaged basal fluorescence of 15 frames before stimulation . The peak fluorescence intensity change is calculated as the mean of normalized trace over a 2 s time window during the stimulation period . The pseudo-colored images were generated in MATLAB using a custom written program . All analysis and statistical tests were done using Excel and GraphPad6 Prism software , respectively . Imaging with taste stimuli was performed in a similar setup as described above , with some modifications . The flies expressing GCaMP-fluorescence under IR76b-Gal4 were prepared according to a method previously described [61] . The proboscis of the fly was pulled out by suction and fixed by gluing to prevent it from going back into the head capsule . For taste stimulation , taste stimuli were diluted in distilled water and delivered by a custom-built syringe delivery system to the proboscis . Distilled water ( control ) , 1 mM , 10 mM , and 100 mM putrescine were applied , respectively . Application of the stimulus was monitored by a stereomicroscope . A drop of taste was delivered to touch the labellum . The stimulus was applied for 1 s after the start of each measurement . All analysis and statistical tests were done using Excel and GraphPad6 Prism software as described above . Individual virgin female flies were mated with single males , observed , and separated after copulation . Two hundred of these mated females were kept for 4–6 h after mating following the same protocol as used for imaging . Antenna and brains of mated and virgin female flies of the same age were collected for RNA extraction . This procedure was repeated for three genetic replicates of 200 virgin and 200 mated flies . RNA was extracted using an RNA easy minikit ( Qiagen ) and used as a template for reverse transcription by superscript III reverse transcriptase ( Invitrogen ) . Quantitative PCR was conducted using the following target gene primers: SPR ( SPR-fwd: atgcacatcgtcagtagcct , SPR-rev: cagccgaccgaggaatatct ) and MIP ( MIP-fwd: ggacaatccgcacagcag , MIP-rev: ctgaacttgttccagccctg ) . H2A . Z , a histone variant ( H2A . Z-fwd: tcgcatccatcgtcatctca , H2A . Z-rev: ctcggcggtcaggtattcc ) , was used as an internal control . All qPCR experiments were performed using the Applied Biosystems 7500 Fast Real-Time PCR system ( Applied Biosystems ) . All amplifications were done using SYBR Green PCR Master Mix ( Applied Biosystems ) . The thermal cycling conditions included an initial denaturation step at 95°C for 20 s , followed by 40 cycles at 95°C for 3 s and 60°C for 30 s . Melting curve analysis of every qPCR was conducted after each cycle . CT ( cycle threshold ) values were used for analysis . The ∆∆CT was calculated as previously described [62] by subtracting the control ∆CT value of H2A . Z from the individual ∆CT values of SPR and MIP for normalization ( CT mated–CT virgin ) , respectively . The inverse logarithm was calculated to receive the expression fold change . The numerical data used in all main and supplementary figures are included in S1 Data . | Food choices often correlate with nutritional needs or physiological states of an animal . For instance , during pregnancy , women frequently report that their food preferences change—sometimes dramatically . In part , this change in preference is brought about by a change in the perception of smells and tastes . Research has shown that female insects also change their food and egg-laying site preferences depending on their reproductive state . However , the mechanisms that trigger these changes are not understood in either mammals or insects . We have unraveled a mechanism that changes a mated female’s perception of odors and tastes and thereby adapts her choices to her reproductive state . Using the model fly Drosophila melanogaster , we show that mating increases females’ interest in sources of specific beneficial nutrients: polyamines such as spermine and putrescine . Polyamine levels in the body are maintained by diet , microorganisms in the gut , and own synthesis . Increased levels are required during pregnancy and reproduction . Indeed , mated females were more attracted to the taste and smell of polyamines than virgins were . We found that this behavioral modulation is regulated through a secreted peptide and its receptor , whose expression rises markedly in sensory organs upon mating . This signal appears to change the intensity of how polyamine taste or smell information reaches the brain and ultimately elicits a choice . Given that odor and taste processing in mammals and insects are similar , our findings in flies can lead to a better understanding of how dynamic physiological states affect our perception of the environment and lead us to adapt our choices of food and other relevant decisions . | [
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] | 2016 | Neuropeptides Modulate Female Chemosensory Processing upon Mating in Drosophila |
The term heterochromatin has been long considered synonymous with gene silencing , but it is now clear that the presence of transcribed genes embedded in pericentromeric heterochromatin is a conserved feature in the evolution of eukaryotic genomes . Several studies have addressed the epigenetic changes that enable the expression of genes in pericentric heterochromatin , yet little is known about the evolutionary processes through which this has occurred . By combining genome annotation analysis and high-resolution cytology , we have identified and mapped 53 orthologs of D . melanogaster heterochromatic genes in the genomes of two evolutionarily distant species , D . pseudoobscura and D . virilis . Our results show that the orthologs of the D . melanogaster heterochromatic genes are clustered at three main genomic regions in D . virilis and D . pseudoobscura . In D . virilis , the clusters lie in the middle of euchromatin , while those in D . pseudoobscura are located in the proximal portion of the chromosome arms . Some orthologs map to the corresponding Muller C element in D . pseudoobscura and D . virilis , while others localize on the Muller B element , suggesting that chromosomal rearrangements that have been instrumental in the fusion of two separate elements involved the progenitors of genes currently located in D . melanogaster heterochromatin . These results demonstrate an evolutionary repositioning of gene clusters from ancestral locations in euchromatin to the pericentromeric heterochromatin of descendent D . melanogaster chromosomes . Remarkably , in both D . virilis and D . pseudoobscura the gene clusters show a conserved association with the HP1a protein , one of the most highly evolutionarily conserved epigenetic marks . In light of these results , we suggest a new scenario whereby ancestral HP1-like proteins ( and possibly other epigenetic marks ) may have contributed to the evolutionary repositioning of gene clusters into heterochromatin .
The organization of eukaryotic genomes into euchromatin and heterochromatin represents one of the most important and still unsolved aspects of genome evolution . Heterochromatin was originally identified from the early observation that specific regions of interphase nuclei possess distinctive staining properties [1]; it remains an elusive component of the eukaryotic genome . Our understanding of its nature and properties has progressively expanded through decades of intensive research , initially in cytological and classical genetic studies and later by sophisticated molecular and in silico techniques . Key steps have been the identification of two types of heterochromatin , constitutive and facultative [2]; the discovery of the unique genetic properties of heterochromatin [3 , 4 , 5 , 6 , 7]; the mapping of satellite DNAs , transposable elements and other repeated sequences in the heterochromatin [8 , 9 , 10 , 11 , 12]; the recent advances in whole genome sequencing [13 , 14 , 15]; and the systematic analysis of heterochromatin-binding proteins [16 , 17] , and of histone modifications in heterochromatin [18 , 19] . However , our knowledge of heterochromatin is far from being exhaustive or satisfactory . For example , dozens of essential genes and hundreds of putative genes have been identified in the heterochromatin of D . melanogaster [12 , 13 , 14 , 15] , but their existence is quite paradoxical [6 , 7] . In fact , a signature feature of heterochromatin is its ability to silence euchromatic genes that are brought within a heterochromatic environment following a chromosome rearrangement or a transposition event , a well-known phenomenon called position effect variegation ( PEV ) which provides an important model for studying the mechanisms regulating gene repression by chromatin modifications [20 , 21 , 22 , 23 , 24] . Yet , the single-copy genes embedded in the heterochromatin of D . melanogaster are bound by specific proteins [25 , 26 , 27] , show a pattern of modified histones [18] and their proper expression depends on their heterochromatic location [28 , 29] , despite the fact that they are transcribed from promoter regions sharing basic similarities with those of euchromatic genes [30 , 31] . To our knowledge , the evolutionary history of the D . melanogaster heterochromatic genes has only been addressed in two studies , that have investigated the chromosomal location in D . pseudoobscura and D . virilis of: i ) a small cluster of genes , including the light gene , located in the heterochromatin of chromosome 2 [30] , and ii ) the two adjacent RPL15 and Dbp80 genes located in the heterochromatin of chromosome 3 [31] . In both cases , the counterparts of D . melanogaster genes were shown to map in euchromatic regions in D . pseudoobscura and in D . virilis , suggesting a repositioning of these genes during genome evolution in the Drosophilidae lineage . Comparative studies of genes located on the dot chromosomes of D . melanogaster and D . virilis , both of which show heterochromatic properties , have shown that most of the genes maintain an overall synteny and only rare instances of “wanderer” genes ( present in a euchromatic chromosome arm in one species and on the dot chromosome in the other ) were found [32] . However , although the D . melanogaster dot chromosome 4 is considered heterochromatic , it shares only certain properties with the pericentric regions of the large autosomes , and genes located there are likely to be under different gene expression constraints [33] . To investigate the evolutionary dynamics underlying the emergence of heterochromatic genes in D . melanogaster , we identified and mapped their orthologs in D . pseudoobscura and D . virilis genomes . We took advantage of the extensive whole genome sequencing data currently available for at least 12 Drosophilidae species and of recent comparative studies confirming the overall conservation of chromosomal synteny in Drosophilidae [34 , 35 , 36] . In particular , we focused our analyses on 53 single-copy genes mapped to the heterochromatin of chromosome 2 of D . melanogaster ( Fig 1 ) . This genomic region corresponds to about 18 . 3 Mb and has been extensively characterized in the last decades at genetic , cytological and molecular levels for the presence of essential and putative genes and other genetic loci [37 , 38 , 39 , 40 , 41] . Our comparative studies show that the orthologs of chromosome 2 heterochromatic genes of D . melanogaster tend to be clustered at specific regions of separate chromosomal elements of D . pseudoobscura and D . virilis . These results suggest that the repositioning of genes to pericentric heterochromatin and their interspersion with numerous heterochromatic sequences , as seen in D . melanogaster , may have originated concomitantly with the rearrangements leading to the centric fusion of two separate chromosomal elements during the evolution of the Drosophilidae karyotype [35 , 42 , 43 , 44] . We also have data suggesting that ancestral HP1-like proteins may have contributed to the evolutionary repositioning of gene clusters to pericentromeric heterochromatin .
The phylogenetic relationships among the syntenic chromosomes as defined by Muller in D . melanogaster , D . pseudoobscura , its sibling species D . persimilis and D . virilis [45] are shown in Fig 2 . These species share a common ancestor dated around 40–50 million years ago , their genomic assembly is almost complete , and thus they are very useful for comparative genomic studies . Since D . pseudoobscura and D . persimilis genomes show a very low level of divergence [46] , D . persimilis was included in this study as a useful source of information to compensate for the absence or partial lack of data from D . pseudoobscura . The cytogenetic location of D . melanogaster heterochromatic genes studied in this paper is shown in the mitotic heterochromatin map of chromosome 2 ( Fig 1 , Table 1 ) [3] . The most distal mitotic bands , h35 ( 2L , left arm ) and h46 ( 2R , right arm ) , are the only heterochromatic regions that can be resolved on Bridges’ polytene map and contain the transition zone between heterochromatin and euchromatin [39 , 40 , 47] . The euchromatin-heterochromatin cytogenomic borders , as defined by Hoskins et al [13 , 14] and by Riddle et al [19] , fall within these regions , and all the genes reported in Fig 1 should be considered embedded in the pericentromeric heterochromatin of chromosome 2 , with the exception of CG1298 and CG11066 that are at 40 Kb outside the 2Rh cytogenomic border . The genomic coordinates of regions containing the examined genes are: from CG3262 to CG40042 ( 2Lh ) : 22 , 239 , 906 . . 23 , 313 , 578; from CG45781 to CG11066 ( 2Rh ) : 432 , 219 . . 5 , 691 , 723 . Heterochromatic genes , as well as numerous euchromatic genes , have been annotated as having orthologs in several Drosophila species [36 , 48] . A database , Ortho DB , is easily accessible and represents an important source of information [49] . However , at present a large number of orthologs of D . melanogaster heterochromatic genes have been only partially annotated or not yet identified in other Drosophila species . Using tBLASTn ( see Materials and Methods ) , we were able to retrieve ortholog candidates of 9 D . melanogaster heterochromatic genes in D . pseudoobscura , D . persimilis and D . virilis . Some were already annotated , while others were not . All 9 retrieved genes appear to be true orthologs based on the following features: 1 ) strong sequence similarities in coding regions and exon-intron structure among the analyzed species; 2 ) high level of identity of deduced proteins; 3 ) no evidence of related duplicated sequences within the genomes found by FISH or BLAST analyses . The identified D . melanogaster heterochromatic gene orthologs with their genomic coordinates are listed in Table 2 and structural comparisons between species are shown in S1 Fig . Schaeffer et al . [35] produced an integrated physical and cytogenetic map of 11 species of the Drosophila genus , anchoring the genome assembly scaffolds to the polytene chromosome map . We first analyzed in detail the genomic coordinates of the assembled scaffolds containing specific marker genes [Tables S21 and S24 in ref . 35] , to retrieve the polytene chromosome maps of a group of D . melanogaster heterochromatic gene orthologs in both D . pseudoobscura and D . virilis ( Table 1 ) . To confirm these data and to extend mapping to a larger number of orthologs , we performed fluorescent in situ hybridization ( FISH ) on polytene chromosomes . For each gene , we cloned PCR fragments to be used as specific probes ( see Mat and Met ) . Examples of FISH mapping are shown in Fig 3 and the results are summarized in Table 1 . It is clear that the D . melanogaster heterochromatic orthologs tend to be clustered at specific regions of the D . pseudoobscura and D . virilis genomes ( Table 2 and Fig 4 ) . In D . virilis three main clusters were found at polytene regions 47C , 42F-43A and 55D , that we will call Dvir_47C , Dvir_42F-43A and Dvir_55D , respectively . Dvir_47C and Dvir_42F-43A are on chromosome 4 ( Muller B element ) , while Dvir_55D is on chromosome 5 ( Muller C element ) . The Dvir_47C harbors 16 orthologs of D . melanogaster heterochromatin genes located in 2Lh , together with at least 3 other orthologs from 2Rh . Dvir_42F-43A contains 18 orthologs from 2Rh , while Dvir_55D harbors 11 orthologs from 2Rh ( mainly localized in region h46 of the mitotic map ) . In D . pseudoobscura three main clusters were also found at polytene regions 63A , 83A and 82AB , that we will call Dpse_63A , Dpse_83A and Dpse_82AB . The Dpse_83A and Dpse_82AB main clusters on the Muller B element share at least 15 and 11 orthologs with the Dvir_47C and Dvir_42F-43A clusters on Muller B , respectively . The Dpse_63A Muller C shares 9 orthologs with the Dvir_55D main cluster and 2 with the Dvir_53D minor site on the Muller C element . In addition , 12 D . melanogaster heterochromatic gene orthologs were not found in the above mentioned clusters and map to different locations in both the D . virilis ( CG40285 at Dvir_42C; CG40129 at Dvir_59F; CG17678 at Dvir_49A; CG40218 and CG11066 at Dvir_53D ) and D . pseudoobscura ( CG17493 at Dpse_96B; CG41265 at Dpse_94A; CG17684 at Dpse_93D; CG42596 at Dpse_93A; CG17715 , CG18140 and CG40191 at Dpse_89B ) . With the exception of the CG17715 , CG18140 and CG40191 orthologs , found together at Dpse_89B , and CG40218 and CG11066 found together at Dvir_53D , the other 7 orthologs represent evidence of individual gene movements . Our analysis of D . melanogaster heterochromatic gene orthologs in D . virilis and D . pseudoobscura shows that i ) a cluster of orthologs retained its location on Muller B and Muller C elements in the three species analyzed; ii ) a group of 14 D . melanogaster Muller C heterochromatic genes were originally located in the Muller B element ( Table 1 and Fig 4 ) of D . virilis and D . pseudoobscura To explore in more detail the gene content and syntenic relationships between the clusters identified by FISH , we compared annotated genes of D . pseudoobscura with their orthologs in D . virilis and D . melanogaster , looking at the relative direction of transcription . The genes we examined are shown in Tables 3 , 4 and 5 , where they are listed using the species-specific designation . Each gene was also colored coded on the basis of its position in the genome of D . melanogaster . The results of the analysis are depicted in Fig 5 . As shown in Fig 5A and Table 3 , 194 Kb of Dpse_63A harbors 17 genes; 13 are colinear with those located at Dvir_55D , 2 with those located at Dvir_53D , while 2 genes ( numbered 15 and 16 in Table 3 ) are not present at either Dvir location . Together , 15 genes maintain the same transcription direction in both Dpse_63A and Dvir_55D-53D . This suggests that Dpse_63A results from a recombination event that occurred between Dvir_55D and Dvir_53D in an ancestral chromosome like that in D . virilis . Notably , the same gene arrangement expected to be produced by the reciprocal event of such a hypothetical recombination is found at Dpse_78B of D . pseudoobscura . This arrangement in turn matches perfectly ( and hence is syntenic ) with region Dmel_47C1-3 of D . melanogaster ( Fig 5A , Table 4 ) . Furthermore , 12 out 15 genes in these two syntenic blocks of the Muller C elements of D . pseudoobscura and D . virilis are found scattered on the Muller C element in D . melanogaster heterochromatin ( Table 3 and Fig 4 ) . We compared the gene content of the Dpse_83A block of the Muller B element with that of the Dvir_47C syntenic block of the Muller B element ( Fig 5B and Table 5 ) . Within the 123 Kb region analyzed of Dpse_83A , 23 genes are found that are colinear with genes located within the 112 Kb of Dvir_47C , sharing same transcription direction . Among those genes , 13 are orthologs of D . melanogaster 2Lh genes ( Muller B ) , whereas 5 are orthologs of D . melanogaster 2Rh genes ( Muller C ) ; two remaining genes are orthologs of still unmapped D . melanogaster heterochromatin genes ( CG12423 and CG17878 ) . Thus , 20 out of 23 genes in Dvir_47C and Dpse_83A syntenic blocks are embedded in pericentric heterochromatin in D . melanogaster . As shown in Table 1 , among the D . melanogaster 2Rh gene orthologs found , 11 map to Dpse_82AB and 18 map to Dvir_42F-43A . A detailed comparative analysis of Dvir_42F-43A and Dpse_82AB cannot be performed , because Dpse_82AB is not assembled in a continuous scaffold and the orthologs are either not annotated or found in different scaffolds , most of which are unmapped ( Table 6 ) . Thus , we used the assembled D . virilis genome sequence to further analyze 400 Kb of the Dvir_42F-43A region . 50 genes ( from CG42595 to CG5277 ) are located in this region , ( Dvir:scaffold_12963:13 , 968 , 303 . . 14 , 371 , 342; Table 6 ) . As previously shown ( Table 1 ) , 18 genes of Dvir_42F-43A ( Muller B ) were confirmed to be orthologous to genes of D . melanogaster 2Rh ( Muller C ) . Our FISH mapping has also shown that 11 of these genes map to Dpse_82AB ( Muller B ) , while 5 of them were previously not annotated ( Table 1 ) . It is therefore conceivable that Dvir_42F-43A is syntenic with Dpse_82AB , both regions sharing clustered D . melanogaster 2Rh gene orthologs . The analysis of the Dvir_42F-43A region also revealed an intriguing result . Adjacent to the cluster of Dmel_2Rh genes orthologs , we found another gene cluster which contains orthologs of genes located in polytene chromosome region 31 ( Muller B ) of D . melanogaster ( Table 6 ) . Interestingly , in D . pseudoobscura most of the orthologs are found in unmapped scaffold , that usually contains genomic regions derived from heterochromatic regions [34 , 36] . The Dpse_63A , Dpse_83A and Dpse_82AB blocks are proximal to chromocentric heterochromatin . Interestingly , hybridization signals produced by most gene probes mapping to Dpse_63A , Dpse_83A and Dpse_82AB tend to exhibit a dispersed and grainy morphology ( Fig 3 ) , compared to the sharp euchromatic signals . This morphology is distinctive of sequences derived from partially polytenized heterochromatin found in the so-called β-heterochromatin of the chromocenter [50 , 51] . Other peculiar characteristics of pericentromeric heterochromatin in D . melanogaster are the enrichment of repeated sequences and the increased size of gene introns , compared to euchromatin [52] . We then asked whether the repositioning of gene clusters in regions located proximal to the chromocenter in D . pseudoobscura ( Fig 4 ) was accompanied by an increase repeated sequences and/or in intron size . To this aim , we investigated the repeat content using RepBase analysis ( see Materials and Methods ) . The results are shown in S2 Fig . The block Dpse_63A contains 21 . 4% repeats , while only 0 . 2% is found in the syntenic block Dvir_55D and 6 . 0% in Dvir_53D . The block Dpse_83A contains 9 . 0% repeats , while 1 . 7% was observed in the syntenic Dvir_47C . A precise repeat content of block Dpse_82AB cannot be estimated because this region is found fragmented in several unmapped scaffolds ( see Table 6 ) which are likely to correspond to repeat-rich heterochromatin regions [34 , 36] . The repeat content of the inferred syntenic block Dvir_42F-43A was estimated to be low , around 3 . 0% . Thus , it appears that the repositioning of gene clusters from D . virilis to D . pseudoobscura pericentric regions has occurred concurrently with an increase of repeats , which mainly correspond to transposable element-homologous sequences ( S2 Fig ) . The intron size estimates of assuredly homologous introns found in the coding regions of genes present in the syntenic blocks are reported in S2 Table . Intronless genes and genes with unknown intron size have been not considered; genes with partially sequenced introns have been included in the analysis , which may give rise to understimated intron size . Genes from the Dpse_78B/Dvir_53D-55D euchromatic gene cluster have been used as control . This analysis revealed that intron size of genes in the syntenic clusters Dpse_63A/Dvir_55D and Dpse_83A/Dvir_47C remains stable , with a mean value ranging from 427 bp at Dpse_63A to 597 bp at Dvir_55D , and from 362 bp at Dpse_83A to 418 bp at Dvir_47C . An increase in intron size was observed in genes of the proximal syntenic blocks Dpse_82AB/Dvir_42F-43A; in this case the mean value of Dpse_82AB cluster is about 2 fold that of Dvir_42F-43A cluster and is mainly due to the intron increase of 2 orthologs ( CG12559 and CG15848 ) among the 10 genes present in the clusters . A consistent intron size increase is indeed observed in the D . melanogaster orthologs of the three above mentioned clusters . The mean values being 10 fold higher for orthologs of Dpse_63A cluster and 7–8 fold higher for those of Dpse_83A and Dpse_82AB . Taken together , it appears that the repositioning of the analysed gene clusters from D . virilis to D . pseudoobscura has occurred concurrently with an accumulation of repeats , while the increase in intron size has been limited . By contrast , a significant increase in intron size of D . melanogaster heterochromatic genes is apparent . The genomic organization of region Dvir_42F-43A in D . virilis opens an interesting issue . Although the polytene region 31 lies in the euchromatin of D . melanogaster , it shows some heterochromatic features; it has a poorly visible banding pattern with a “gooseneck” chromosomal morphology and shows abundant association with the heterochromatin protein HP1a , an evolutionarily conserved epigenetic mark very abundant in heterochromatin [53 , 54 , 55] . To test whether this association was evolutionarily conserved , we performed immunofluorescence ( IF ) experiments on D . virilis and D . pseudoobscura polytene chromosomes using anti-HP1a antibodies . The results clearly show that HP1a is present at Dvir_42F-43A ( Fig 6 ) and remarkably also at Dvir_47C and Dvir_55D , where the other gene clusters are located . Similarly , in D . pseudoobscura HP1a is present at polytene regions Dpse_63A , Dpse_82AB and Dpse_83A where it colocalizes with the gene clusters ( Fig 6 ) . To further investigate the association of HP1a with the gene clusters , we performed sequential IF/FISH experiments where IF with anti-HP1a antibodies was followed by FISH using specific probes . We focused our attention on the Dvir_42F-43A cluster , using PCR probes of the GJ22088 , GJ17515 , GJ21982 and GJ21862 genes which are orthologous to the CG42595 , CG17665 , CG41265 and CG42596 heterochromatic genes of D . melanogaster , respectively ( Table 6 ) . As shown in Fig 7A , the FISH signals of CG41265 ( GJ21982 ) and CG42596 ( GJ21862 ) clearly colocalize with the strong HP1a signal found at division Dvir_42F . In particular , CG41265 ( GJ21982 ) and CG42596 ( GJ21862 ) map to the proximal and distal end of the HP1 region , respectively . The FISH signals of CG42595 ( GJ22088 ) and CG17665 ( GJ17515 ) , on the other hand , map to Dvir_43A and therefore are linked to the HP1a signal , but not included in it . Interestingly , in D . pseudoobscura the CG42595 ortholog is included in the large HP1 signal mapped to Dpse_83A , while the CG42596 ortholog maps to Dpse_93A , a region located in the middle of euchromatin , which is also strongly stained by the anti-HP1 antibody ( Fig 7B ) . Together , these results reveal an unexpected , evolutionarily conserved association between HP1a and the clusters of heterochromatic gene orthologs , even though not all genes in the cluster are included in the region decorated by the anti-HP1a antibody .
In the Muller C element of D . pseudoobscura we found a cluster of 13 orthologs of D . melanogaster 2R heterochromatin , located at ± 200 Kb DNA from the proximal region Dpse_63A . In the Muller C element of D . virilis , 10 out of these 13 orthologs map to the euchromatic regions Dvir_55D , 2 map to Dvir_53D , while one ( CG30438 ) was not detected in these regions . Our analysis suggests that the arrangement of genes within Dpse_63A arose by recombination between Dvir_55D and Dvir_53D in an ancestral Muller C element like that of D . virilis ( Fig 5A ) . Evidence for this recombination event relies on the proximity of GJ20406 and GJ22228 at Dpse_63A and reciprocally by the proximity of GJ20124 to GJ21920 found at Dpse_78B ( Tables 3 and 4 ) . The junction GJ20406-GJ22228 is present in the obscura group species , in D . ananassae and in D . willistoni , but not in the melanogaster subgroup species ( D . erecta , D . yakuba , D . sechellia , D . simulans , D . melanogaster ) , whereas the junction GJ20124-GJ21920 , representing the reciprocal event , is found in all the species analyzed except D . willistoni . Moreover , the GJ20406-GJ22228 association is almost proximal to the pericentromeric heterochromatin as seen in D . pseudoobscura , while the GJ20124-GJ21920 association maps to the euchromatin in all the analyzed species . Taken together , these observations suggest that: i ) the hypothetical recombination event occurred in the ancestor of the lineage that led to the subgenera Drosophila and Sophophora and ii ) the two reciprocal events have had different evolutionary fates . Indeed , in the melanogaster subgroup species , gene order was lost in the pericentromeric region , yet was maintained in euchromatic regions . Furthermore , when we compare the gene order in the syntenic blocks Dpse_63A/Dvir_55D-53D with that found in the D . melanogaster 2R heterochromatin ( Table 3 ) , a scrambled arrangement of the 13 orthologous heterochromatin genes is apparent , suggesting that recurrent internal rearrangements occurred during chromosome evolution of this pericentric region . Dpse_83A and Dpse_82AB adjacent regions are located proximally to the chromocenter in the D . pseudoobscura Muller B element and harbor at least 29 orthologs of D . melanogaster heterochromatic genes . Dpse_83A contains 13 orthologs of Dmel_2Lh and at least 5 orthologs of Dmel_2Rh ( Fig 5B , Table 5 ) , while the Dpse_82AB carries 11 orthologs of Dmel_2Rh ( Table 1 ) . Our data indicate that Dpse_83A and Dpse_82AB are syntenic with Dvir_47C and Dvir_42F-43A of the Muller B chromosome ( Tables 5 and 6 ) . The novelty of this observation is that during chromosome evolution , further rearrangement must have occurred to separate the two contiguous regions Dpse_82AB and Dpse_83A , giving rise to the relocation of a group of genes from these two Muller B regions to a pericentromeric Muller C region in the descendent lineage , that is the D . melanogaster 2R heterochromatin ( Fig 4 ) . The mechanistic details responsible for this event are unknown , because extensive sequence data are not available for the Dpse_82AB-Dpse_83A regions in the D . pseudoobscura genome assembly . Some observations can , however , help to reconstruct the molecular events which occurred at the time of the fusion of two separated Muller B and Muller C arms into a single chromosome . The first observation is the finding of duplicated sequences ( CG3057 ) at Dpse_83A ( Fig 5B and Table 5 ) . Although these duplications cannot be associated with specific breakpoints , they might have arisen by staggered single-strand breaks , as postulated in the isochromatid model by Ranz et al . [64] . The second observation is the scrambled arrangement of the D . melanogaster heterochromatic genes compared to the gene order seen in the syntenic blocks Dpse_83A/Dvir_47C ( Table 5 ) and in Dvir_42F-43A ( Table 6 ) , which would suggest numerous internal rearrangements during chromosome evolution . In addition , these rearrangements may have contributed to the expansion of DNA sequences , since the location of Dmel_2Rh genes spans megabases of DNA ( from mitotic bands h41 to h46 ) . Notably , in Dvir_42F-43A the cluster of Dmel_2Rh gene orthologs is flanked by a cluster of D . melanogaster gene orthologs mapping to polytene region 31 , in the middle of euchromatin ( Table 6 ) , a location known to be enriched in HP1a protein . We indeed found that the HP1a protein is associated with the gene cluster at Dvir_42F-43A ( Fig 6 ) and also with the other clusters of both D . virilis and D . pseudoobscura ( Fig 7 ) . If such association represents an ancestral condition , then the D . virilis clusters may represent proto-heterochromatic sites whose repositioning and expansion led to the origin of pericentric regions of D . melanogaster chromosome 2 . Together , our results are consistent with a scenario where the D . melanogaster heterochromatin genes of chromosome 2 arose through an evolutionary repositioning from a euchromatic location ( D . virilis ) to heterochromatin , passing through an intermediate location in regions associated with distal heterochromatin ( D . pseudoobscura ) . A similar trend was described by a study on the evolution of the heterochromatic gene cluster containing the light gene [30] . Recently , we have reported that the heterochromatic Yeti gene underwent a similar repositioning [65] . Here we showed that Yeti was part of a euchromatic gene cluster that was relocated by recombination to the heterochromatin of chromosome 2 ( Fig 5A ) . Previous studies have investigated the epigenetic states that enable the expression of heterochromatic genes and render their promoters heterochromatin-dependent [18 , 33] , yet little is known about the evolutionary process ( es ) through which this has occurred . The results of the present study indicate that , in the Drosophilidae evolution , movement of genes from euchromatin to heterochromatin preferentially occurred by evolutionary repositioning of gene clusters which show close association with the HP1a protein ( Figs 6 and 7 ) . The molecular mechanisms responsible for repositioning of the large clusters are presently unknown , because of our poor knowledge of the breakpoints involved in the rearrangements and arrangements of genes flanking the regions under analysis . However , comparative studies carried out in Drosophila species have shown that during chromosomal evolution , transposable elements and other repetitive sequences have been implicated in the generation of rearrangements [34 , 35 , 40 , 42 , 64] . The association of D . virilis and D . pseudoobscura gene clusters with HP1a is indeed intriguing . Spellman and Rubin [66] have suggested that block conservation is needed to regulate a coordinated expression of the genes present in a block . There is evidence showing that HP1a , among its versatile functions as an element of the chromatin architecture and in gene silencing , also has a stimulating effect on the expression of both heterochromatic genes and region 31 genes [67 , 68 , 69] . Although not all genes within the clusters cytologically map to the HP1a-enriched portion ( Fig 7 ) , it is unlikely that such association arose by chance . It is worth noting that the association with HP1a is conserved even by genes that have moved away from the cluster ( see the example of the Dmel_CG42596 ortholog found at Dpse_93A ( Fig 7B ) . In addition , while the ortholog of Dmel_CG42595 lies at the very proximity of the HP1a signal at Dvir_43A , but is not included in it , in D . pseudoobscura it has become included in the HP1 signal at Dpse_83A . Could we interpret this association with HP1a ( and possibly with other epigenetic regulators ) in an evolutionary perspective ? If so , might the presence of HP1 in the ancestral domains have contributed to the success of gene repositioning into pericentric heterochromatin ? Although the results of the present work cannot provide a final answer to these questions , some speculations about possible scenarios are possible . We could envisage an evolutionary scenario where ancestral HP1-like association may have favored the expression of relocated gene clusters . It is well known that euchromatic genes moved into pericentric heterochromatin are subjected to position effect variegation ( PEV ) [21] . However , heterochromatic genes of D . melanogaster such as light and rolled are bound by HP1a which positively contributes to their expression [25 , 26 , 70] . We can speculate that genes in ancestral "proto-heterochromatic" clusters were already used to being active in an HP1a environment , and were hence less likely to exhibit deleterious position effects when moved to a pericentric region . Thus , at least in certain instances , the ancestral association with HP1 would have acted as an "epigenetic shield" , protecting the relocated gene clusters against silencig effect , and favoring their expression in pericentromeric regions . In addition , ancestral HP1-like may have contributed to the formation of rearrangements that eventually led to the gene cluster repositioning . For instance , HP1 may have mediated long-distance interactions between different HP1-chromatin domains ( i . e . , located in euchromatin and in pericentric heterochromatin ) . If DNA breaks occurred in physically close domains , then rearrangements may eventually be generated following end-joining repair events , thus promoting the repositioning of HP1-associated chromatin domains . Although further experiments will be required to investigate the evolutionary significance of the association of HP1 with the D . virilis and D . pseudoobscura gene clusters , our work provides previously unanticipated results that will contribute to the understanding of the molecular evolution of genes embedded in pericentric heterochromatin .
The following species were used for the cytological preparations: D . pseudoobscura ( 14011–0121 . 94 ) , and D . virilis ( 15010–1051 . 00 ) , from Tucson Drosophila Stock Center , University of Arizona , USA . Polytene chromosomes were prepared according to Pardue [71] . Gene names reflect the species-specific nomenclature , or use the D . mel gene symbol . Species-specific probes were generated by PCR on genomic DNAs . Opposite primers were selected on the basis of published sequenced genomes and chosen to avoid the inclusion of intronic sequences . The list and the size of the probes generated are in S1 Table . Primers obtained from the D . persimilis sequenced genome were used to amplify the orthologs CG12559 , CG12547 , CG42595 , and CG41265 with D . persimilis ( 14011–0111 . 01 ) and/or D . pseudoobscura DNAs . Amplified DNA fragments were eluted from agarose gels and cloned in pGEM-T vector ( Promega ) . In every case the plasmid probes were verified by DNA sequencing . Fluorescence in situ hybridization ( FISH ) was performed according to Pimpinelli et al . [72] . Squashed preparations of polytene chromosomes from salivary glands dissected from third instar larvae of D . virilis were denatured and hybridized with Cy3-dCTP or FluorX-dCTP ( GE Healthcare ) labeled probes . CG41265 and CG17665 were simultaneously localized using mixed probes . Polytene chromosomes were stained with DAPI , 4’ , 6’-diamidine-2’-phenylindole-dihydrochloride . Chromosome preparations were analyzed using a computer controlled Nikon E1000 epifluorescence microscope equipped with a cooled CCD camera ( Coolsnap ) . Digital images were obtained using an Olympus epifluorescence microscope equipped with a cooled CCD camera . Gray scale images , obtained by separately recording Cy3 , FluorX and DAPI fluorescence with specific filters , were pseudo colored and merged for the final image using Adobe Photoshop . Labelled sites were identified on the basis of published polytene maps [35] . Polytene chromosomes of D . virilis and D . pseudoobscura were HP1 immunostained according to James et al . [53] using the C1A9 anti-HP1 antibody . In brief , salivary glands were rapidly dissected in Cohen and Gotchell medium G containing 0 . 5% Nonidet P-40 and incubated in 2% formaldehyde fixative solution for 25 min . The preparations were incubated with monoclonal anti-HP1 C1A9 antibody ( 1:50 ) overnight at 4°C in a humid chamber . The slides were washed in TBS/0 . 05% Tween 20 three times for 5 min and incubated with secondary antibody 1:40 dilution of FluoroLink Cy2-labeled goat anti–mouse ( Amersham Biosciences ) for 1 h at room temperature in a humid chamber . Finally , the slides were washed three times in TBST at 4°C , stained with 4 , 6-diamidino-2-phenylindole ( DAPI ) at 0 . 01 μg/ml , and mounted in antifading medium . Genomic Databases on the Flybase website ( http://flybase . org; version FB2014_06 ) were searched by TBLASTN using amino acids from individual translated exons of D . melanogaster heterochromatic genes . Only those genes lacking a clear orthologous member in the OrthoDB database were searched . To retrieve orthologs we did a tblastn search using the amino acids of single or of two contiguous exons of D . melanogaster over the genomes of D . virilis and D . pseudoobscura ( or D . persimilis when the query produced partial information in D . pseudoobscura ) . When the results showed high scores ( E-value < e-80 ) and most importantly , all the subject results were in the same plus or minus frame in adjacent sequences , we then assembled a complete coding region that was compared with the D . melanogaster gene structure . Splice junctions were analysed by visual inspection comparing exons in the pairwise alignment of two DNA sequences . By these criteria we were able to reconstruct a bona fide structure of the gene . Multiple sequence alignments were performed with ClustalW procedure available at EMBL-EBI ( http://www . ebi . ac . uk ) or with the multialin interface at http://multialin . toulouse . inra . fr . Assembly of exon-intron was obtained by manual inspection of high-scoring alignments obtained using the Blast tools with translated ORF of D . melanogaster proteins and supported by EST annotated sequences . The changes proposed to the orthologous heterochromatin genes for the bestfit alignments are reported in S3 Table . To estimate the repeat content within syntenic blocks , DNA sequences were retrieved from Flybase and repeats were identified using Censor [73] , implemented at Repbase ( www . girinst . org/censor/index . php ) and the Arthropoda dataset . The masked sequences correspond to transposable elements and no satellite/simple repeats were masked in the final output . The masked sequences were filtered out retaining only alignments longer than 100 bp and with similarity greater than 80% , roughly corresponding to a score greater than 800 . These values were used as threshold in our analysis which allowed elimination of spurious matches that might inflate the repeat content and prevented as much as possible false positive cross-species matches . Bed files containing the masked positions were uploaded and visualized as custom tracks in Gbrowse ( www . flybase . org ) . | This study concerns the evolutionary dynamics underlying the emergence of heterochromatic single-copy genes in D . melanogaster heterochromatin . By combining genome annotation analysis and high-resolution cytology , we have performed a comparative mapping of the orthologs of 53 single-copy genes of D . melanogaster heterochromatin , in the genomes of D . pseudoobscura and D . virilis evolutionarily distant species . The results of our work are consistent with a scenario where the D . melanogaster heterochromatin genes arose through an evolutionary repositioning from a euchromatic location ( D . virilis ) to heterochromatin , passing through an intermediate location in regions associated with distal heterochromatin ( D . pseudoobscura ) . The previously unanticipated observation , that in both D . virilis and D . pseudoobscura the gene clusters show a striking association with the HP1a protein , is remarkable in that it implies that the HP1 epigenetic mark may have contributed to the the success of repositioning of genes to pericentromeric heterochromatin by protecting them against silencing effects . | [
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] | 2016 | Comparative Genomic Analyses Provide New Insights into the Evolutionary Dynamics of Heterochromatin in Drosophila |
Buruli Ulcer ( BU ) is one of the most neglected debilitating tropical diseases caused by Mycobacterium ulcerans , which causes considerable morbidity and disability . Building on earlier findings that community-based interventions could enhance case detection and reduce treatment dropout and defaulter rates , we established an active surveillance-response system in an endemic sub-district in the Ga West municipality of Ghana to enhance early case detection , diagnosis and treatment to reduce or eliminate severe ulcers and its related disabilities . We established surveillance response system , implemented in collaboration with the sub-district disease control officers , selected clinical staff and trained community-based volunteers . The active community-based surveillance- response system was implemented for 12 months . Also , pre and post intervention surveys were conducted to document any change in perceptions on BU in the study population over the period . The baseline and endline surveys were conducted in August 2016 and August 2017 respectively . On average , each person was seen 11 times in 12 months . In all 75 skin lesions were detected during surveillance rounds , out of which 17 were suspected to be BU and 12 out of the 17 were confirmed as BU using Polymerase chain reaction ( PCR ) . Out of the 12 , five , three and four were categories I , II and III lesions respectively . Physical examination was done on 94% of the people seen during the surveillance rounds . Knowledge on BU has also increased in the communities at the end of the study . The findings from this study have demonstrated that it is possible to establish surveillance-response system for BU and by extension , other neglected tropical diseases to enhance control and elimination efforts through the use of community-based volunteers .
Buruli ulcer ( BU ) is one of the most neglected debilitating tropical diseases caused by Mycobacterium ulcerans . Though , BU-related fatality is very rare , it causes considerable morbidity and disability [1–3] , which lead to stigma and anguish among infected individuals and affected families . The disease occurs mainly in remote areas , deprived of basic social infrastructure like health care facilities , potable water and accessible roads [1] . Most endemic countries lack efficient reporting systems , making the assessment of the disease burden unclear [2] . Buruli ulcer has recently received some attention focusing mainly on diagnosis , transmission and clinical management [4–6] . However , there is no active community-based surveillance-response system to generate the essential data necessary to capture and treat cases early enough to prevent complications that contribute so much to morbidity and disability , and cost of treatment to both the health system and affected families . In order to institute an effective public health response to the problem of BU , a strong surveillance system is needed to systematically collect , analyse and interpret data on the disease [7–9] . The epidemiology of BU in endemic countries is not entirely known , due to several factors including the focal distribution of cases , late reporting of cases and lack of health facilities among others in endemic countries of Africa [10] . In Ghana , the first passive surveillance system reported about 1 , 200 BU cases between 1993 and 1998 and more than 9 , 000 BU cases were also reported between 2004 and 2014 [9 , 11] . A nation-wide active case search that was conducted in 1999 found BU in all the 10 administrative regions of Ghana with an overall prevalence of 20 . 7 per 100 , 000 of the population [9] . Currently , BU control in Ghana is mainly through early case detection [9 , 12] and clinical diagnosis at peripheral health facilities designated by the National Buruli Ulcer Control Program ( NBUCP ) followed by laboratory confirmation and subsequent antimycobacterial therapy . The mode of transmission of the pathogen is still elusive and therefore control relies mainly on case detection and treatment with Streptomycin and Rifampicin for eight weeks , followed by surgery to speed healing and correct deformities [11] . The success of this treatment modality depends very much on detecting suspected cases early for diagnosis and treatment at health facilities [12–14] . However due to socio-cultural and economic factors most cases are detected very late with large wounds with massive cell destruction by the cytopathic toxin , mycolactone [11 , 15] . It is known that BU patients in West Africa do not typically seek care from health facilities or do so late with severe ulcers coupled with disabilities leading to under reporting of the disease burden at health facilities [5 , 14–17] . However , it has been reported that community-based interventions could enhance case detection and reduced treatment dropout and defaulter rates [17 , 18] . Huge resources are being invested to develop new diagnostic tools for BU and to improve on its clinical management . However the discovery of new diagnostic tools and improved clinical management techniques could only be useful when suspected patients seek health care for their conditions , especially at the early stages of the disease , before debilitating complications set in . When identified early , BU can be treated with a high degree of success with rifampicin and streptomycin . This antibiotic combination treatment is noted to have a positive impact on treatment outcomes as it has the potential to cure small lesions and limit surgery for larger lesions [19–21] . The overall aim of this study was to test the feasibility of training periphery health workers and community-based volunteers to implement community-based active surveillance–response system for early BU case detection , diagnosis and treatment on outpatient basis in an endemic sub-district in Ghana .
The study was reviewed and approved by the Institutional Review Board ( IRB ) of the Noguchi Memorial Institute for Medical Research ( NMIMR ) with Federal Wide Assurance Registration FWA 00001824 . The protocol was assigned certified protocol number ( CPN ) 075/14-15 . Written informed consent was taken from all participants ( Informed consent from all adults aged 18 years and above , Parental consent for all children under 18 years old and Child assent from all children aged between 12 and 17 years . ) This was an epidemiologic study designed to test and evaluate a community surveillance-response system in the study area for 12 months . It was a longitudinal study with baseline and endline surveys to compare pre- and post- implementation perceptions among the population . The Ga West Municipality with Amasaman as its capital has an estimated population size of about 215 , 824 with a growth rate of 3 . 4 percent [22] . The major economic activity in the municipality is farming , employing about 70% of the people . Other economic activities include fishing , stone quarrying and petty trading . The district has the highest number of reported BU cases in the Greater Accra Region and is the fifth most BU endemic in the country in 2002 . About 1000 cases of BU are reported in Ghana yearly , giving a nationwide prevalence of 20 . 7/100 , 000 , in 1998 . However , the district level prevalence of the Ga South district , where this study took place , was 87 . 7/100 , 000 population [23 , 24] . The District Hospital at Amasaman has a specialized unit for BU treatment but due to poor road networks and socio-cultural factors , most cases report at the health facility late . The Amasaman hospital continues to be the main health services provider in the district with a few health posts , private clinics , and family planning and maternity homes doted within the municipality . Health care to the rural communities is mostly provided by the Ghana Health Service through monthly outreach services . Among the top five common diseases prevalent in the District are malaria , skin diseases , diarrhoea , HIV/AIDS ( Human immunodeficiency virus/ Acquired immunodeficiency syndrome ) and BU . Ten communities namely; Kojo-Ashong , Onyansana , Otuaplam , Yahoman , Okushibiade , Adeyman , Kramo , Domsampaman , Kwashikuma , Odumtia/Akwakyere were identified to participate in the study . These communities were selected with the support of the district health management team and the National BU control programme . The Ga West municipality was selected because it has not only the highest number of reported BU cases in the Greater Accra Region and the fifth most BU endemic in the country but also continue to report the worst forms of the disease , category three ulcers in Ghana . The 10 communities selected were identified by the municipal health directorate as the most endemic communities in the district . Census was conducted in each of the selected communities to register everybody , which then formed the target populations for the establishment of the community-based surveillance -response system . The current BU control in Ghana is based on the WHO recommended first line treatment for BU using oral rifampicin ( 10 mg/kg ) plus intramuscular streptomycin injection ( 15 mg/kg ) , both given daily for 8 weeks under supervision coupled with passive surveillance . Case finding is based on passive surveillance coupled with occasional case search in communities with no on-going active surveillance in endemic communities for early case identification , though early treatment is being promoted . At baseline , with an estimated population of 4 , 000 in the 10 selected communities and assuming that 50% of the people will be willing to participate in the interviews and 5% confidence limits and the design effect of 1 . 5 , we arrived at a sample size of 570 ( 57/cluster ) However , we sampled 60 respondents from each of the 10 communities ( 600 participants ) . At the endline survey , a known population size of 3255 was used for the calculation giving a sample size of 52/cluster , thus a total of 520 respondents in the 10 communities , however 526 respondents were interviewed . Participants were randomly selected from the adult population , using a community register , proportionally to represent both sexes . Data entry and analyses were done using EpiInfo version 7 . Findings were presented in descriptive statistics , especially frequencies and percentages . We compare proportions ( percentages ) from baseline and endline to determine any difference between the two data points . A difference of less than 0 . 05 ( P<0 . 05 ) was considered statistically significant .
A total population of 3070 in 837 households in 10 communities was registered during the census . However , by the end of the 12 month , the population has increased by 185 ( 6 . 0% ) to 3255 . Beside the natural population growth , we believe that the increased in population was due mainly to people moving to settle in the area , which is not far from Accra the capital city of Ghana . Majority of the study participants were females ( 52 . 8% ) . At the end of 12 months , there were 36084 surveillance person contacts made with physical bodily examination done on 33835 ( 93 . 8% ) . The average surveillance contact per person was 11 . 1 ( 92 . 3% ) out of the maximum number of 12 expected ( Table 1 ) . Bodily examination was not done on 2249 ( 2249/36084 x 100 = 6 . 0% ) , mainly because participant were; not at home or travelled ( 70 . 7% ) , moved out of the community permanently ( 19 . 4% ) , refused to be examined 136 ( 6 . 0% ) and others like infants or sick persons made up of only 3 . 9% . During the 12 months follow up period , a total of 75 skin lesions were encountered , out of which 17 ( 22 . 7% ) were suspected to be BU by the volunteers . Out of the 17 suspected cases , 12 ( 70 . 6% ) were confirmed as BU using PCR , making the clinical judgment of the volunteers very good . Out of the 12 confirmed cases , five , three and four were categories I , II and III respectively ( Table 2 ) . Thus , two out of three cases were picked from the community early and these could be easily managed at the peripheral health facilities at a lower cost to both the individuals and the health system . All but one ( three out of four ) of the category III lesions were invited by their relatives who were participating in the study to migrate into the study area in 2017 so that they could benefit from the project activities , especially facilitating laboratory diagnosis and treatment at the nearest health facility . The remaining category III case was hidden in the community for a long time . She reportedly went to the clinic once , many years ago , but had decided against biomedical treatment , and instead resorted to herbal or traditional treatments at home , however , through the surveillance response system , she was rediscovered , diagnosed and helped to receive treatment on an outpatient basis , and her wound has healed . The left leg ( 75 . 0% ) was the dominant site of confirmed BU lesion ( 9 out of 12 ) with one each on the right leg , left and right arms respectively . Majority ( 62 . 5% at baseline and 64 . 5% at endline ) of the respondents had primary level education . As expected , majority ( 71 . 3% at baseline and 61 . 6% at endline ) of the respondents belong to the Ga speaking ethnic group . The Christian religion was the most professed religion reported among respondents; 81 . 2% at baseline and 84 . 6 at endline ( Table 3 ) . In line with the dominant ethnic groups in the area , majority of the respondents ( 72 . 8 at baseline and 66 . 2% at endline ) referred to the disease in the Ga/Agangbe local language as Odonti hela/ Aboagbonyo . Others include Detsifudor/detsifubi in Ewe language ( 12 . 4% at baseline and 14 . 3% at endline ) and Kisi kuro/Asawa kuro in Akan language ( 2 . 5% at baseline and 3 . 2% at endline ) . Also , 11 . 2% at baseline and 15 . 0% at endline referred to it as Buruli . It is worth reporting that all the dominant local names could be translated literally to mean cotton wool wound or bad wound . Majority of the respondents ( 89 . 8% at baseline and 92 . 6% at endline ) said they know about Buruli ulcer as a disease that affects people in the community and have local names for it . The dominant local names reported—adonti hela ( Ga ) , detsifu dor ( Ewe ) and asawa kro ( Twi ) , could be translated literally to mean cotton wool wound and this could be linked to the cotton wool-like necrotic tissues that are usually found around the edges of BU wounds . The BU related knowledge was acquired from varied sources . The most commonly reported source of knowledge was to know someone with the infection , 71 . 2% at baseline and 53 . 6% at endline ( Table 4 ) . Majority ( 69 . 8% at baseline and 73 . 4% at endline ) of the respondents said they could recognize BU at an early stage before it becomes a wound/sore or ulcerated . Among those who said they could recognize early BU , Nodule ( 87 . 1% at baseline and 96 . 9% at endline ) was the most commonly reported sign . This was followed by Itching ( 10 . 3% at baseline and 9 . 8% at endline ) . Others were; Papule ( 1 . 9% at baseline and 1 . 3% at endline ) , Plaque ( 1 . 7% at baseline and 2 . 9% at endline ) and Oedema ( 1 . 2% at baseline and 1 . 0% at endline ) . Various treatment options were reported; chiefly among them was Hospital/clinic treatment ( 78 . 3% at baseline and 72 . 4% at endline ) . Others were; Traditionalists/Herbalists ( 34 . 4 at baseline and 22 . 2% at endline ) , Home prepared herbal medicine ( 17 . 5% at baseline and 5 . 7% at endline , self-medication with biomedicine ( 3 . 0% at baseline and 1 . 7% at endline with faith healers reported by two people only at baseline . Majority ( 86 . 2% at baseline and 74 . 1% at endline ) mentioned that BU can be prevented . Prominent prevention methods mentioned included; Cleaning the Environment ( 30 . 6% at baseline and 50 . 8% at end-line ) , Avoid bathing in the river ( 32 . 5% at baseline and 36 . 9% at end-line ) and Regular use of biomedicine; 38 . 3% at baseline and 27 . 7% ( Table 5 ) . At baseline , majority ( 62 . 8% ) of the respondents said they were not aware of any active BU control activity in their communities . However , this had changed at the endline , where majority ( 55 . 1% ) reported spontaneously that they were aware of a control activity going on in the study communities . When respondents were asked to explain the control activities in their own words , the dominant statement is represented in the following narrative “I know someone who moves from house to house to examine people for the disease and then refer those with the disease to the clinic for treatment . ” This statement indicates that people were aware of the surveillance-response system that was implemented in these communities . As expected , malaria was reported by the majority ( 88 . 5% at baseline and 86 . 88% at endline ) as the single most common disease in all the study communities . This was not surprising as the area is noted for high malaria prevalence in the Greater Accra region , where malaria is generally low due to its urban nature . Though , only few respondents mentioned Buruli ulcer as a common disease ( 6 . 8% at baseline and 3 . 8 at end-line ) , it is important to know that it was considered as an important health problem in the study area . Other health problems mentioned at both baseline and endline were waist pains , stomachache , headache , eye problem and hypertension . All respondents at endline testified that volunteers come to their homes on monthly basis . However , 92 . 01% reported that the volunteers did talk to them about Buruli ulcer whenever they visited . Also , 91 . 82% of the respondents mentioned that they were examined by the volunteers whenever they visited . Majority ( 91 . 0% ) of the respondents said that they were happy with the work of the volunteers . Interestingly , 92 . 6% of the respondents indicated that they would like the visit to continue with the majority ( 67 . 6% ) opting for once in a month visit , just as was done during the project implementation .
This study has demonstrated that it was possible to establish surveillance response system to conduct physical examination on participants on monthly basis . In the context of BU , this may enhance early case detection , diagnosis and treatment . The surveillance-response system implemented was not only able to pick cases at early stages to eliminate or , at least , reduce severe and debilitating ulcers associated with late reporting at health facilities , which causes morbidity and disabilities , but also rediscovered an old ulcerated case that was hidden from the view of the health system . This confirmed the report that patients are not only reporting late at health facilities but many more maybe hidden in the communities [16] and this may require active community-based surveillance to discover and support their diagnosis and treatment . The surveillance system was able to pick all categories of BU lesions based on WHO ( 2008 ) categorization; category I—a single lesion <5 cm in diameter; category II—a single lesion between 5 and 15 cm in diameter; category III—a single lesion >15 cm in diameter , multiple lesions , lesion ( s ) at critical sites ( eye , breast , genitalia ) and osteomyelitis [23] . Proper community engagements and training of community-based volunteers , nominated by community members themselves to implement the surveillance-responses system has contributed to the acceptance and participation of the people in the project activities , where on average , each person received at least 11 surveillance contacts with physical examination done on over 90% of them . It was possible to achieve this because efforts were made to follow all community protocols and encouraged the people to see the project as their own . It was reported from a malaria study that , community participation is vital for the success of community based interventions and to achieve this may require full engagement of community members in the process from the start of the project , making them to claim ownership of it by observing existing community protocols and respecting established hierarchy of power within the study community [25] . With reduction in BU cases in endemic communities , other skin conditions must be integrated into BU surveillance-response system that is established to make it more cost effective . In our study , the other skin conditions were referred to the health facilities for management and most of them were treated without any laboratory diagnosis . Though the patients were treated , it will have been better if they were taken through diagnosis to know exactly what conditions were being treated . This may be useful to determine the kinds of lesions circulating in the study communities , to aid the design of any control and prevention strategies . The socio-demographic characteristics of the study area has remained virtually the same as was reported in earlier studies [12 , 15 , 17 , 18 , 26] , where the Ga ethnic groups were dominant with majority of them identifying with the Christian religion with primary level education . The knowledge of the disease was very high among respondents , gained mostly through the experiences of knowing someone with the infections . It was interesting to note that the socio-demographic characteristics did not affect the level of BU knowledge in the study area , implying that BU-related knowledge is evenly spread among the population . It was encouraging to know that majority of respondents could recognized early suspected lesions and this must be promoted to encourage early reporting at health facilities to promote early diagnosis and treatment as this may help reduce the disease burden on the individual , the affected family and the health system , since the current treatment is very effective when the infection is treated early [21] . Community-based volunteers have been used in Ghana in varied ways to support community-based public health service delivery . However , in the context of BU control , they have been involved in passive case finding in their communities and the added value for using them in this study was their involvement in active case finding through surveillance home visits , which makes it difficult for them to miss any case in the community . The volunteers were motivated with a token , which is about $20 every month to cover their communication and any other incidental cost that they might have incurred during surveillance rounds . In addition , bicycles were given to them to aid mobility during monthly rounds and this has been attested to by respondents as very helpful . The motivation was not solely in the token given to them but also in the fact that they saw themselves as contributing to bringing good health to their people and it was encouraging to hear some respondents asking for the continuation of the project , so that they could be visited at home monthly . This is an indication that with good community entry , respect for local authorities and rapports , it is possible to conduct community-based surveillance with over 90% bodily examinations in BU endemic communities . Since efforts in determining the transmission rout remains elusive , we could take advantage of the acceptance of the community-based surveillance-response system to not only pick cases early but also discover old but hidden ulcers for diagnosis and treatment . A major limitation of the study was that , it was designed only to confirm BU lesions but not to test for any other cutaneous NTDs and this must be addressed in subsequent studies , given the current downward trend of BU cases in endemic communities including our study area . Secondly , the study period of 12 months was rather short for a longitudinal study of this nature . The study was setup to determine the feasibility of training periphery health workers and community-based volunteers to implement a community-based active surveillance–response system for early buruli ulcer case detection , diagnosis and treatment at outpatient clinics . At the end of the study , it can be concluded that , it is feasible to train periphery health workers and community-based volunteers to carry out community-based surveillance-response system for early BU case detection , diagnosis and treatment . Given the high number of non-BU skin lesions detected during the 12 month surveillance period , it is recommended that any BU surveillance response system must be an integrated one to aid the detection , diagnosis and treatment of other skin conditions . This will make such an intervention more cost effective as the number of BU cases continues to decline in most endemic communities in Ghana after the introduction of antibiotics treatment . | The study revealed that it is feasible to train periphery health workers and community-based volunteers to implement a community-based active surveillance–response system for early buruli ulcer ( BU ) case detection , diagnosis and treatment at outpatient clinics . At the end of 12 months follow-up , there were 36084 surveillance person contacts made with physical bodily examination done on 93 . 77% of them . The average surveillance contact per person was 11 . 3 ( 91 . 7% ) . We believe that this was achieved largely because of the use of community-based volunteers for the surveillance visits . Given the high number of non-BU skin lesions detected during the surveillance period , it is recommended that any BU surveillance-response system must be an integrated one to aid the detection , diagnosis and treatment of other skin conditions to make it more cost effective , this has become even more imperative because the number of BU cases have been declining in most endemic communities in Ghana , since the introduction of antibiotics treatment . | [
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Prions are a group of proteins that can adopt a spectrum of metastable conformations in vivo . These alternative states change protein function and are self-replicating and transmissible , creating protein-based elements of inheritance and infectivity . Prion conformational flexibility is encoded in the amino acid composition and sequence of the protein , which dictate its ability not only to form an ordered aggregate known as amyloid but also to maintain and transmit this structure in vivo . But , while we can effectively predict amyloid propensity in vitro , the mechanism by which sequence elements promote prion propagation in vivo remains unclear . In yeast , propagation of the [PSI+] prion , the amyloid form of the Sup35 protein , has been linked to an oligopeptide repeat region of the protein . Here , we demonstrate that this region is composed of separable functional elements , the repeats themselves and a repeat proximal region , which are both required for efficient prion propagation . Changes in the numbers of these elements do not alter the physical properties of Sup35 amyloid , but their presence promotes amyloid fragmentation , and therefore maintenance , by molecular chaperones . Rather than acting redundantly , our observations suggest that these sequence elements make complementary contributions to prion propagation , with the repeat proximal region promoting chaperone binding to and the repeats promoting chaperone processing of Sup35 amyloid .
The ability of some proteins to adopt alternative conformations expands the functional range of the proteome by allowing individual proteins to access multiple conformers . For a unique and expanding class of proteins , these conformational transitions are extensive and lead to the assembly of the protein into ordered , linear , β-sheet-rich aggregates , known as amyloid [1] . The amyloid conformation is self-perpetuating , allowing the activity of the alternative conformer to dominate , and in the case of a subset of amyloids known as prions , to become transmissible [2] . These alternative conformational states are often associated with new phenotypes , arising from either the loss of normal functions or the gain of new functions for the proteins [2] . Indeed , amyloids and prions contribute both to normal cellular homeostasis , regulating gene expression , immunity , memory , organelle biogenesis , and ultrastructure [2–6] , and to the disruption of this balance through diseases , including neurodegeneration , Type II diabetes , and familial hypertension [1] . But , these biological consequences only arise because the amyloid structure , once it appears , can persist in vivo . One component of this persistence , is the ability of the linear amyloid fibers to template the refolding of normal conformers of the same protein into the alternative state through interactions at the ends of the aggregates [1] . This propensity to undergo self-templated conformational replication is an inherent property of each protein , dictated by the composition and sequence of its amino acids , typically in one region of the protein that is predicted to be intrinsically disordered [7] . These regions tend to be rich in glutamine and/or asparagine ( Q/N ) , which promote aggregation via their propensity to form hydrogen-bonding networks [8–10] , and depleted for prolines and charged amino acids , which are thought to act as amyloid breakers [11] . As such , the spacing of these latter residues , when present , is an important predictor of amyloidogenicity [11 , 12] . Beyond the formation of the amyloid structure , the amplification and persistence ( i . e . propagation ) of these complexes in vivo is also absolutely essential to realize their physiological consequences . In contrast to amyloid formation , the molecular basis of amyloid propagation is far less well understood . For example , a computational study aimed at identifying prion proteins in the yeast proteome based on sequence characteristics effectively predicted proteins capable of self-assembling into SDS-resistant aggregates , a hallmark of the amyloid state . However , only a small subset of these aggregation-prone proteins could support inheritance of an amyloid reporter-based phenotype [12] , indicating our limited ability to predict the persistence of these complexes in vivo . Nonetheless , some insight into the protein characteristics that support amyloid propagation has been gleaned from extensive analysis of the yeast prion protein Sup35 , a translation release factor that can access a heritable amyloid state known as [PSI+] in vivo [2] . The Sup35 protein contains a bipartite N-terminal prion-determining domain ( PrD ) composed of a Q/N-rich region ( amino acids 1–41 ) followed by five and a half imperfect oligopeptide repeats ( amino acids 42–97 ) with the consensus sequence PQGGYGGYN [13] . The Q/N-rich region is required for and confers specificity to the aggregation [14 , 15] , but this region alone is not sufficient to induce or sustain [PSI+] in vivo [16 , 17] . Instead , these activities depend on the oligopeptide repeat region [16–25] . The addition of the first repeat to the Sup35 Q/N-rich region allows the protein to join existing amyloid aggregates composed of full-length Sup35 in vivo , but five repeats , in addition to the Q/N-rich region , are required for prion propagation in the absence of full-length Sup35 [16 , 17] . Consistent with these observations , the addition of the Sup35 oligopeptide repeats converted an aggregation-prone polyglutamine track ( Q62 ) to a heritable prion [16] , demonstrating that the essential function of the oligopeptide repeats in prion propagation is transferrable . The number of Sup35 oligopeptide repeats has also been implicated in the stability of the non-prion [psi-] state in vivo . Deletion of four repeats leads to a decrease in the spontaneous frequency of [PSI+] appearance , while the introduction of two additional repeats leads to an increase in the spontaneous frequency of [PSI+] appearance in vivo , with both changes contributing a two-order-of-magnitude effect [21] . Both the repeat deleted and expanded proteins retain the ability to form amyloid in vitro , albeit at different rates , suggesting that the contribution of the repeats to prion propagation in vivo cannot be explained solely by aggregation propensity [21] . The mammalian prion protein , PrP , also contains five oligopeptide repeats , four with the sequence PHGGGWGQ and a fifth with the sequence PQGGGTWGQ [26 , 27] . In humans , expansion of the repeats , ranging from a single to twelve additional copies , is associated with neurodegenerative disease [28 , 29] . Paralleling the observations of [PSI+] appearance in yeast , the age of onset of neurodegenerative disease in humans correlates inversely with the number of PrP repeats [30–32] . Intriguingly , PrP repeats can substitute for the essential function of Sup35 oligopeptide repeats in the maintenance of [PSI+] in yeast , indicating functional overlap [17 , 33 , 34] . Despite this functional significance and evolutionary conservation [35] , the exact mechanism by which Sup35 oligopeptide repeats , and by extension PrP repeats , promote prion propagation in vivo is poorly understood . To date , three models , which are not mutually exclusive , have been proposed . First , repeats may promote the conversion of soluble protein to the amyloid state , an idea that is consistent with the increased accumulation of soluble Sup35 upon repeat deletion [36] . Second , repeats may stabilize Sup35-Sup35 interactions outside of the conversion interaction , which dictates the amyloid core [17] . Consistent with this model , repeat sequences have been shown to mediate Sup35-Sup35 interactions and to be partially protected from solvent exchange in amyloid fibers in vitro [37–39] . Finally , repeats may facilitate chaperone interactions by serving as direct binding sites or by altering the conformation of the amyloid to allow chaperone access [16] , a prediction that is supported by the observation that changes in aggregate size , an attribute linked to chaperone processing [40] , inversely correlate with repeat number [36] . Distinguishing among these models has been challenging . Previous studies have relied on steady-state observations , but prion propagation in yeast is a multistep process that requires prion protein synthesis , its conversion to the amyloid state via interaction with existing amyloid , the fragmentation of growing amyloid aggregates by molecular chaperones , and the transmission of these complexes to daughter cells [2] . Here , we address the specific mechanistic contributions of the Sup35 oligopeptide repeats to prion propagation in vivo . We find that this region is composed of two functional elements , the oligopeptide repeats themselves and a downstream region , which we refer to as the repeat proximal region ( RPR ) . Without significantly altering the kinetic stability of the amyloid state , the presence of the oligopeptide repeats and the RPR primarily promote chaperone-mediated fragmentation of Sup35 aggregates . But , these elements likely act through distinct mechanisms , with the RPR promoting chaperone binding , and the oligopeptide repeats promoting chaperone processing .
Wildtype Sup35 contains five and a half oligopeptide repeats [41 , 42] . But , previous analyses of their contribution to prion propagation in vivo used deletion constructs that also removed the RPR , a downstream asparagine-rich region ( amino acids 98–111 , RGNYKNFNYNNNLQ ) . Thus , the potential contributions of each element to prion propagation have not been disentangled . To separately assess the contribution of the Sup35 oligopeptide repeat region to [PSI+] propagation in vivo , we constructed a series of yeast strains expressing repeat sequence variants ( RVs ) , composed of full-length Sup35 containing a different number of repeats ( Fig 1A ) . This collection includes repeat deletion strains ( R1-X ) , which contain the N terminal X repeats , from 2 to 5 , and repeat expansion strains ( R2E1 and R2E2 ) , which contain one or two extra copies of the second repeat for a total of 6 . 5 or 7 . 5 repeats , respectively . The RVs replaced the wildtype copy of Sup35 at the endogenous locus and were expressed from the Sup35 promoter to wildtype levels ( S1A Fig ) . However , using this same configuration , the repeat expansion variants ( R2E1 and R2E2 ) were expressed at a much lower level . Thus , we integrated a second copy of the repeat expansion proteins expressed from the SUP35 ( R2E1 ) or MFA1 ( R2E2 ) promoters at another locus in these strains to raise their expression to wildtype levels ( S1A Fig ) . Because the extent to which this region of the protein is buried in the fiber interface varies based on the conformation of the protein in the amyloid [37] , we have restricted our analysis to the [PSI+]Strong variant of Sup35 . To assess the prion phenotypes of our RV strains , we took advantage of the premature termination codon ( PTC ) in the ADE1 gene in our yeast strain background ( the ade1-14 allele ) , which provides a sensitive reporter of Sup35 translation release factor activity in vivo [43] . Soluble non-prion Sup35 promotes efficient translation termination at the PTC in [psi-] strains , while aggregated Sup35 in [PSI+] strains is functionally compromised , allowing read through of the PTC . These differences in Ade1 expression cause [psi-] strains to form red colonies on rich medium and block their growth on minimal medium lacking adenine , while [PSI+] strains form white colonies on rich medium and can grow on minimal medium lacking adenine [44] . Changes in [PSI+] propagation efficiency , which lead to the accumulation of increased soluble and decreased aggregated Sup35 , result in the formation of pink colonies on rich medium and partial growth on medium lacking adenine [45] . The phenotypes of strains containing four , five , or the wildtype number of repeats were nearly indistinguishable on rich medium ( Fig 1B ) , but the strain expressing R1-4 lost the [PSI+] prion at a frequency of ~3% , while [PSI+] was fully stable in the other strains . When the number of repeats was reduced , the colonies grew slightly worse on medium lacking adenine compared to the wildtype strain ( Fig 1B ) . In contrast , when the number of repeats was expanded , the colonies appeared more white on rich medium and grew slightly better on medium lacking adenine , indicating more efficient stop codon read-through ( Fig 1B ) . Repeat deletion strains containing only 2 or 3 repeats ( R1-2 and R1-3 , respectively ) were unable to support [PSI+] ( S1B and S1C Fig ) , indicating that at least four repeats are required for prion maintenance . Our observations are in conflict with previous studies , in which [PSI+] maintenance was reported to require the presence of at least five repeats [16 , 17] , suggesting the RPR may play a role in prion propagation in vivo . Indeed , deletion of the RPR in our wildtype construct modestly reduced the strength of the [PSI+] phenotype , producing colonies that were more pink on rich medium and that grew at a reduced rate on medium lacking adenine ( Fig 1B ) . This same trend was also apparent when the RPR was removed from the repeat deletion strains ( Fig 1B ) . Notably , while a construct containing only 4 repeats was unable to maintain [PSI+] in the absence of the RPR , as previously reported [16 , 17] , it regained the ability to do so in the presence of the RPR ( Fig 1B and S1D Fig ) . Thus , removal of the RPR enhances the prion propagation defect caused by deletion of Sup35 repeats . The defect in prion propagation induced by deletion of the repeats or the RPR suggests a change in some aspect of prion aggregate dynamics in vivo: conversion of soluble Sup35 to the amyloid form and/or amyloid fragmentation by the molecular chaperones Hsp104 , Hsp70 ( Ssa1 ) , and Hsp40 ( Sis1 ) [2] . To identify the defects specific to each sequence variant , we first characterized a subset of strains , which represented each type of alteration: R1-5 ( repeat deletion ) , R2E1 ( repeat expansion ) , and ΔRPR ( RPR deletion ) . For the repeat deletions and expansions , we first chose the most conservative changes to minimize toxicity , as doubling times increased with more severe changes in the number of these sequence elements ( Table 1 ) . Using these strains , we first directly monitored the conversion of soluble Sup35 to the amyloid state using a fluorescent read-through assay , which reports on the defect in Sup35 translation release activity upon aggregation of the protein [46 , 47] . In this assay , [psi-] strains , expressing one of the sequence variants and the read-through reporter GST ( UGA ) YFP-NLS , were mated to a wildtype [PSI+] strain . While YFP is not initially expressed in the reporter strain due to the absence of [PSI+] , the conversion of the soluble Sup35 protein in the [psi-] strain to the amyloid state upon mating to the [PSI+] partner leads to the accumulation of nuclear fluorescence in the zygote ( Fig 2A ) . As expected , in zygotes formed by crossing the R1-5 , ΔRPR , R2E1 or wildtype [psi-] strains to a wildtype [psi-] strain , nuclear fluorescence was low and nearly identical ( Fig 2B , lanes 1–4 ) , indicating that the Sup35 sequence variants in these crosses remain soluble and functional . In contrast , when the R1-5 , ΔRPR or wildtype [psi-] strains were crossed to a wildtype [PSI+] strain , nuclear fluorescence was higher than seen in the cross to a wildtype [psi-] strain but was still similar among the three crosses ( Fig 2B , lanes 5–7 ) , indicating that the Sup35 sequence variants containing a repeat or RPR deletion are not significantly impaired in their ability to convert to the amyloid state . However , the R2E1 [psi-] X wildtype [PSI+] cross produced nuclear fluorescence intensity that was significantly increased relative to the wildtype zygote ( Fig 2B , compare lanes 5 and 8 ) , suggesting , at face value , that the repeat expansion may more readily convert to the amyloid form , an observation consistent with the idea that the repeats promote conversion [36] . Because differences in amyloid fragmentation by molecular chaperones affect the accumulation of templates and thereby the conversion efficiency of soluble protein [48] , we hypothesized that the difference in nuclear fluorescence intensity observed in the R2E1 heterozygous zygote was a downstream effect of an increase in fragmentation efficiency ( i . e . more templates support more conversion ) . To test this hypothesis , we repeated the wildtype and R2E1 [psi-] crosses to a wildtype [PSI+] strain in the presence of guanidine HCl ( GdnHCl ) , which inhibits Hsp104 activity , blocks fragmentation , and reduces the number of amyloid templates [49 , 50] . In the presence of GdnHCl , the nuclear fluorescence intensity was similar in zygotes produced by crossing either a wildtype or R2E1 [psi-] strain with a wildtype [PSI+] strain ( Fig 2C ) . Because the increased nuclear fluorescence intensity of the R2E1 strain was abolished upon Hsp104 inhibition , we conclude that the R2E1 protein converts to the aggregated state with similar efficiency to wildtype Sup35 . While the striking and Hsp104-dependent increase in nuclear fluorescence observed in the R2E1 [psi-] X wildtype [PSI+] cross did not obviously correspond to conversion propensity , it did suggest that the R2E1 amyloid was fragmented at an increased efficiency relative to the wildtype and RV deletion amyloids ( Fig 2B and 2C ) . If true , we would expect the R2E1 protein to accumulate more and smaller aggregates at steady-state , and this increase in template number would in turn decrease the level of soluble Sup35 at steady-state . Indeed , by semi-denaturing detergent agarose gel electrophoresis ( SDD-AGE ) [51] , the R2E1 protein accumulated in smaller SDS-resistant aggregates than wildtype Sup35 ( Fig 3A ) , and this change correlated with a decrease in SDS-sensitive ( i . e . soluble ) R2E1 protein in comparison with wildtype Sup35 ( Fig 3B ) . This decrease in soluble protein in the R2E1 strain should also induce a more severe translation termination defect , a prediction consistent with its colony-based phenotypes: whiter on rich medium and more robust growth on medium lacking adenine ( Fig 1B ) . Surprisingly , we could not detect an increase in heritable R2E1 aggregates ( known as propagons ) using a genetic assay based on the transfer of existing aggregates to daughter cells in the absence of fragmentation [52] ( Fig 3C ) . We reasoned that this observation could be explained by the fact that the shift in the size distribution of R2E1 aggregates was slight relative to wildtype Sup35 ( Fig 3A ) and therefore beyond the sensitivity of this genetic-based assay . But , similar analyses of the R2E2 strain demonstrated a more severe shift in size to smaller SDS-resistant aggregates by SDD-AGE ( Fig 3A ) , a further decrease in SDS-sensitive protein ( Fig 3B ) and a corresponding increase in propagons ( Fig 3C ) . Thus , expansion of the number of repeats in the Sup35 protein likely promotes amyloid fragmentation . Consistent with this idea , deletion of repeats 4 and 5 in the context of R2E2 , which returns the total number of repeats to 5 . 5 , shifts the steady-state size of Sup35 aggregates to a near wildtype distribution ( S1A and S1E Fig ) , suggesting that repeat number rather than identity is the dominant force in amyloid fragmentation . If true , deletion of repeats below the wildtype number should further decrease amyloid fragmentation . To assess this prediction , we performed similar analyses on the R1-5 strain , which removed one repeat and decreased the efficiency of [PSI+] propagation in vivo ( Fig 1 ) . Surprisingly , the steady-state size distribution of aggregates ( Fig 3A ) , the level of soluble protein ( Fig 3B ) , and the number of propagons ( Fig 3C ) were indistinguishable between the R1-5 and wildtype strains . Given our observations of progressive effects in the repeat expansion strains , we next analyzed the size distribution of aggregates in the R1-4 strain , which removed one additional repeat from R1-5 ( Fig 1A ) . Rather than observing a simple shift in aggregates to a larger size distribution , we observed a broadening of entire size distribution when R1-4 aggregates were analyzed by SDD-AGE ( Fig 3A ) . In contrast to the wildtype strain , [PSI+] propagated by the R1-4 protein induces a strong extension of the strain doubling time ( Table 1 ) , suggesting it is a toxic state that could indirectly alter amyloid dynamics through changes in protein homeostasis ( proteostasis ) [53] . To circumvent this possibility , we expressed , in the R1-4 strain , the domain of Sup35 that functions in translation termination ( amino acids 254–685; Sup35-C ) , which had been previously demonstrated to relieve the toxicity induced by overexpression of the Sup35 prion domain ( amino acids 1–253; Sup35-NM ) [54] . Expression of Sup35-C eliminated the toxicity of [PSI+] propagated by the R1-4 protein ( Table 1 ) , and the R1-4 protein accumulated in larger aggregates than those of the wildtype protein in a [PSI+] strain that also expressed Sup35-C ( Fig 3D ) . This observation is consistent with the idea that deletion of repeats decreases the efficiency of fragmentation in vivo . We next assessed the effects of deletion of the RPR on fragmentation , given its synergistic effects on [PSI+] propagation with repeat deletions ( Fig 1B ) . In the context of a wildtype number of repeats , deletion of the RPR slightly shifted the aggregate size distribution to larger complexes ( Fig 3E ) . This change in size did not significantly alter the number of propagons ( Fig 3C ) , perhaps again reflecting the limited sensitivity of the assay , but it did increase the accumulation of soluble ΔRPR protein in comparison with wildtype ( Fig 3B ) . These observations , in combination with the direct demonstration that the ΔRPR protein converts to the aggregated state with an efficiency that is not significantly different from wildtype ( Fig 2B ) , suggest that RPR deletion reduces the number of amyloid templates and therefore the efficiency of fragmentation . Because deletion of the RPR exacerbated the effects of repeat deletion on [PSI+] propagation ( Fig 1B ) , we reasoned that the ΔRPR protein might sensitize our assays to more directly reveal a fragmentation defect for R1-5 amyloid . Deletion of the RPR in the R1-5 protein did not shift the distribution of aggregates to larger sizes beyond its effect in the wildtype protein ( Fig 3E ) . But , it did increase the soluble protein ( Fig 3B , compare lanes 5 and 6 ) and decrease the number of propagons ( Fig 3C , compare lanes 5 and 6 ) in comparison with deletion of the RPR alone . Thus , removing a single half repeat is sufficient to reduce amyloid fragmentation efficiency in the ΔRPR strain . To directly assess the rate of amyloid fragmentation in vivo , we turned to a propagon amplification assay . In this assay , strains are grown in rich medium containing GdnHCl , which reversibly inhibits Hsp104 [50] , to reduce Sup35 aggregate number to the point just before [psi-] cells appear in the population . Strains are then allowed to recover in rich medium in the absence of GdnHCl , where the re-amplification of existing aggregates can occur upon Hsp104 reactivation [55] . The rate of this re-amplification had been previously linked to the product of the conversion and fragmentation rates [55] , but since our sequence variants have similar conversion efficiencies ( Fig 2B and 2C ) , this amplification rate provides a proxy for relative fragmentation rate . Following release from GdnHCl , individual cells were isolated at the indicated time points , and the number of propagons per cell was determined . Importantly , the number of propagons in each strain was similar prior to GdnHCl treatment ( Fig 3C ) , and GdnHCl treatment similarly reduced propagon number in all strains ( Fig 4A–4C , 0 time point ) . The R1-5 protein subtly , but reproducibly , recovers its propagon levels more slowly than wildtype ( Fig 4A ) . For the ΔRPR and R2E1 strains , propagon amplification is strongly and significantly different than wildtype , with the ΔRPR strain having a reduced and the R2E1 strain supporting an enhanced rate of recovery relative to the wildtype strain ( Fig 4B and 4C ) . These activities are consistent with our observations at the colony ( Fig 1B ) , protein ( Figs 2B , 3A and 3B ) , and propagon ( Fig 3C ) levels . While it is formally possible that these mutants differ from wildtype in their conversion efficiencies to a degree that is undetectable by our fluorescence-based assay ( Fig 2 ) , the predicted changes in their efficiencies based on the shifts in steady-state size of Sup35 aggregates are opposite of what would be predicted based on their rates of aggregate amplification . For example , an increase in aggregate size ( i . e . ΔRPR Fig 3E ) should correspond to an increase in conversion efficiency [40] and in turn an increase in amplification rate [55] , predictions that are not born out by our observations ( Fig 4 ) . Thus the combination of observations can be most parsimoniously reconciled with a model in which the dominant contributions of both the repeats and the RPR are to promote amyloid fragmentation in vivo . To assess the impact of these sequence elements on fragmentation rates using another method , we developed a mathematical model of aggregate amplification , capable of determining the basic reproductive number of aggregates ( R0 ) , which is the number of additional aggregates produced by each aggregate in its lifetime , using a different experimental input parameter: the steady-state level of soluble Sup35 ( S1 Table ) . R0 is proportional to the fragmentation rate ( γ ) , when the rates of synthesis ( α ) and dilution ( μ ) and minimum aggregate size ( n0 ) are held constant ( see Supplementary Materials ) . The repeat expansion ( R2E2 ) had a higher R0 and the RPR deletion had a lower R0 than wildtype Sup35 ( Fig 4D and S2 Table ) , consistent with a direct effect on rate of fragmentation or an inverse effect on the minimum aggregate size , respectively . While we cannot rule out a change in n0 for these mutants relative to wildtype , we also cannot explain the wholesale shifts in the steady-state size distributions of the Sup35 aggregates for these mutants ( Fig 3A ) without a change in fragmentation [40] . As was the case for the propagon amplification assay ( Fig 4A ) , the wildtype and R1-5 strains could not be distinguished by this method ( Fig 4D and S2 Table ) . However , the R0 value for R1-5ΔRPR was smaller than that of ΔRPR alone ( Fig 4D and S2 Table ) , suggesting , that deletion of the half repeat also reduced the rate of amyloid fragmentation in vivo . Fragmentation is catalyzed by the molecular chaperone Hsp104 [48 , 50 , 56–58] , and Sup35 sequence elements impacting fragmentation efficiency would therefore act through this catalyst . As such , their deletion or expansion would be predicted to have distinct genetic interactions with an Hsp104 mutant ( Y662F ) having a reduced efficiency of substrate processing [59] . Specifically , Sup35 sequence changes that inhibit fragmentation should enhance the Hsp104Y662F defect , while those that promote fragmentation should suppress the Hsp104Y662F defect . As previously reported , Hsp104Y662F reduces the efficiency of [PSI+] propagation by wildtype Sup35 [60] , leading to the formation of pinker colonies on rich medium , a reduced growth rate on medium lacking adenine ( Fig 5A ) , and a shift in aggregate distribution to larger size by SDD-AGE ( Fig 5B ) . This Hsp104Y662F defect was suppressed by the R2E1 protein , which has a higher fragmentation rate in vivo ( Fig 4C and S2 Table ) , both at the colony level ( Fig 5A ) and by aggregate size ( Fig 5B ) . In contrast , the Hsp104Y662F defect was enhanced by deletion of prion domain sequence elements . Both the R1-5 and ΔRPR strains form colonies that are more pink on rich medium and that are unable to grow on medium lacking adenine in the presence of Hsp104Y662F ( Fig 5A ) , and aggregates of these proteins are shifted to larger size distributions in the presence of Hsp104Y662F ( Fig 5B ) . The latter effect is especially dramatic for the RPR deletion , where Hsp104Y662F nearly eliminates Sup35 amyloid ( Fig 5B ) . Taken together , our data indicate that the repeat and RPR regions function in the Sup35 protein to promote amyloid fragmentation . Having identified fragmentation as the dominant step of prion propagation that is impacted by the repeats and the RPR , we next sought to determine the mechanism by which these sequence elements act . Because expression of the more extensive repeat expansion and deletion constructs was toxic ( Table 1 ) , we first considered the possibility that the sequence variants might induce a stress response , resulting in altered chaperone levels , substrate load and amyloid fragmentation [61–63] . However , the expression levels of Hsp104 and its co-chaperones Ssa1 ( Hsp70 ) and Sis1 ( Hsp40 ) in our RV and ΔRPR strains were similar to wildtype ( Fig 6A ) . Therefore , changes in the levels of the chaperones that catalyze amyloid fragmentation are unlikely to explain the differences in fragmentation efficiency in these strains . We next considered the possibility that the repeats and the RPR may promote fragmentation by altering the kinetic stability of Sup35 amyloid , as has been observed for the Sup35 G58D mutant [46 , 64] . Indeed , combined repeat and RPR truncations assemble into amyloid with higher kinetic stability when overexpressed [65] . However , the [PSI+] prion variant used in that study was distinct from the [PSI+] strong strain used in these studies [36 , 65] , and Sup35 sequence variants are known to confer conformation-specific effects on amyloid [64 , 66] . To determine the kinetic stability of Sup35 amyloid in our strains , we assessed their solubility in 2% SDS over a range of temperatures by their ability to enter an SDS-polyacrylamide gel [67] . As the temperature was increased from 65°C to 80°C , the amount of SDS-soluble Sup35 increased , as expected [67 , 68] , due to amyloid disassembly ( Fig 6B ) . But across this temperature range , the amount of SDS-soluble R1-5 , ΔRPR and R2E1 protein was similar to that of wildtype Sup35 ( Fig 6B ) . Thus , the changes in protein sequence did not significantly alter the kinetic stability of amyloid or , by extension , their conformation . Because the relative chaperone levels and kinetic stability of Sup35 amyloid are not significantly altered in the repeat or RPR sequence variant strains , we next considered the possibility that these sequence elements might promote fragmentation by affecting the efficiency with which Sup35 amyloid is recognized or processed by molecular chaperones . We expressed HA-tagged Sup35 prion domain ( NM-HA ) from the Sup35 promoter from a single-copy integrated construct , using repeat and RPR variants of this fragment to match the full-length protein expressed in each strain . Importantly , the NM-HA protein was expressed at similar levels in all strains ( S1A Fig ) . For the R1-5 and the R2E1 strains , the size of SDS-resistant aggregates was similar in the presence ( S2B Fig ) and absence ( Fig 3A ) of NM-HA . However , for the ΔRPR strain , expression of NM-HA eliminated the shift in SDS-resistant aggregate size ( S2B Fig ) that we observed in its absence ( Fig 3E ) , suggesting that expressing the prion domain as a separate fragment perturbs amyloid dynamics in vivo . However , expression of the ΔRPR variant as an HA-tagged full-length Sup35 ( NM-HA-C ) protein ( S2C Fig ) recapitulated the shift in the size distribution of SDS-resistant aggregates to larger complexes by SDD-AGE ( Fig 3E and S2D Fig ) . Using quantitative co-immunocapture of the HA-tagged Sup35 sequence variants , similar levels of Ssa1 and Sis1 were bound to the R1-5 , R2E1 , and wildtype proteins ( Fig 7A and S3A Fig ) . However , Hsp104 binding was altered by changes in repeat number . When repeats were deleted ( R1-5 ) , slightly but significantly less Hsp104 was bound , but when repeats were expanded ( R2E1 ) the amount of bound Hsp104 increased ( Fig 7A and S3A Fig ) . For the ΔRPR protein , binding to all three chaperones , Hsp104 , Ssa1 and Sis1 , was reduced by ~20% in comparison with wildtype Sup35 using the full-length HA-tagged protein ( Fig 7B and S3B Fig ) . Thus , chaperone binding correlates directly with fragmentation efficiency for the Sup35 sequence variants . While our binding studies revealed that changes in both the repeats and the RPR alter the interaction of Sup35 with chaperones , their targets were distinct , with the number of repeats directly correlating only with Hsp104 binding and deletion of the RPR lowering the binding of all three chaperones ( Fig 7A and 7B ) . Sup35 amyloid fragmentation is believed to be initiated by the binding of these complexes to Sis1 and Ssa1 and their subsequent transfer to Hsp104 [69–71] . Thus , one interpretation of these observations is that the RPR promotes initial chaperone binding , but that once the amyloid is transferred to Hsp104 , the repeats impact the efficiency with which Hsp104 processes this substrate , with lower processing corresponding to lower binding at steady-state . If the primary function of the RPR is to promote initial chaperone binding , why would this contribution become unnecessary in the context of the NM protein ? In comparison with full-length Sup35 , the truncated prion domain completely lacks a stable , folded domain . As such , it could be recognized more easily by the chaperone machinery , making the contribution of the RPR less important . Indeed , more Ssa1 , Sis1 and Hsp104 bound to ΔRPR NM-HA ( Fig 7C and S3A Fig ) than to ΔRPR NM-HA-C ( Fig 7B and S3C Fig ) . Thus , increasing chaperone binding by removing the functional C-terminal domain can compensate for the ΔRPR prion propagation defect . In light of this observation , we reasoned that we could distinguish between binding and processing events by inserting the Sup35 prion domain fragments into another Hsp104 substrate . If the Sup35 fragment impacted substrate processing efficiency , it should similarly affect Hsp104 action on this substrate . However , if the Sup35 fragment promoted chaperone binding , it may not affect Hsp104 action because the bona fide substrate already effectively recruits chaperones in the absence of this fusion , in the same way that deletion of the RPR had no effect in the context of the isolated prion domain ( Fig 7C and S2B and S3A Figs ) . To test this idea , we modified an existing microscopy-based assay , in which a folding sensor composed of firefly luciferase fused to GFP is expressed in yeast cells , induced to misfold and aggregate by heat shock , and allowed to disaggregate and refold during recovery at normal temperature in an Hsp104-dependent manner [72 , 73] . Other members of AAA+ ATPase chaperone family , to which Hsp104 belongs , have been shown to process substrates from either terminus or from internal sites [74] . To ensure that effects would be visible regardless of the direction of processing , we included Renilla luciferase , which misfolds and aggregates upon heat shock and is reactivated by Hsp104 ( S4A Fig ) , in the reporter . In this system , the N domain of Sup35 ( amino acids 1–123 ) and its sequence variants are inserted between the two luciferase proteins ( Fig 8A ) . Importantly , both the levels of reporter protein and activity in the absence of heat shock were identical for all of the variants studied , indicating similar efficiencies of protein maturation and stability ( S4B and S4C Fig ) . Using a microfluidics chamber , we monitored the relative rate of recovery of reporters containing the R1-4 , ΔRPR , R2E2 and wildtype Sup35 N domains or lacking a Sup35 N insertion at 30°C in the presence of cycloheximide following a sublethal heat shock at 40°C . For each strain , the reporter protein coalesced into foci upon heat shock and resolved in an Hsp104-dependent manner ( Fig 8B and S4D and S5A Figs ) . The addition of the Sup35N fragment reduced the rate at which foci were resolved ( S5B Fig ) but did not decrease the amount of bound Hsp104 ( S5C Fig ) , suggesting that Hsp104 binding was not limiting for substrate resolution . Consistent with this idea , the RPR , which promotes initial chaperone binding to Sup35 but becomes unnecessary in the context of a misfolded protein capable of independently recruiting chaperones ( Figs 3E , 7B and 7C , S2D Fig ) , can be removed from the reporter without altering the rate of its resolution in comparison with the reporter containing the intact N region ( Fig 8B ) . In contrast , deletion of the repeats ( R1-4 ) , which shifts the amyloid size to larger complexes consistent with a processing defect ( Fig 3D ) , resolved foci more slowly than wildtype ( Fig 8B ) , and expansion of the repeats ( R2E2 ) , which shifts the amyloid size to smaller complexes consistent with enhanced processing ( Fig 3A ) , resolved foci faster than wildtype ( Fig 8B ) . Thus , our observations , together , are consistent with the idea that the RPR promotes chaperone binding and that the repeats promote substrate processing .
While the oligopeptide repeat region of Sup35 had been previously implicated in “prion maintenance” [16] , the precise mechanism by which it contributed to this process was not understood . Through detailed analyses , we have separated the oligopeptide repeat region into two elements: the repeats themselves and a repeat proximal region . We uncovered no significant contribution of either region to Sup35 conversion efficiency , aggregate kinetic stability , or chaperone levels . While we cannot rule out minor changes in these attributes outside the limit of detection for our assays , our studies suggest that the dominant role of these regions is to promote Sup35 amyloid fragmentation by molecular chaperones . This event has been previously shown to be essential for prion propagation in vivo , necessary to create sufficient templates to direct the conversion of soluble Sup35 to the amyloid state and to be transmitted to daughter cells upon division [40 , 48] . Importantly , the two sequence elements appear to mediate functional interaction of Sup35 amyloid with the chaperone machinery , through two separate activities . The repeat proximal region appears to mediate the binding of chaperones to Sup35 amyloid , while the repeats themselves appear to promote efficient chaperone processing of these aggregates . The RPR had been previously identified as a functional element in various in vitro studies of Sup35 amyloid . This asparagine-rich region is predicted to be amyloidogenic on its own [75] , and it is partially protected from solvent exchange and labeling in Sup35 amyloid [37 , 38] . In addition , residues in the RPR have been shown to be in close proximity in neighboring molecules in the Sup35 amyloid [38 , 76] and to be able to capture soluble Sup35 in vitro [39] . Finally , the RPR contains a predicted binding site for Hsp70 [77] and binds to Hsp104 in vitro [78 , 79] . Nevertheless , deletion of the RPR alone had a mild phenotypic effect on [PSI+]-dependent stop codon read-through in vivo and was not further characterized [17] . But , the synergistic effect of the RPR and the repeats on fragmentation efficiency and their inadvertent linkage in previous studies , led this element to be overlooked in favor of the repeats . Our studies now indicate that deletion of the RPR has a stronger effect on Sup35 amyloid dynamics than deletion of a single repeat ( Figs 1B , 3A , 3B , 4A and 4B ) and that both elements contribute separate activities to the efficiency of amyloid fragmentation and thereby prion propagation in vivo . Previous studies have suggested that the amino acid composition of the Sup35 oligopeptide repeats , rather than their primary sequence per se , was the dominant contributor to prion propagation [80] . In these studies , Sup35 mutants were generated by scrambling the sequence of the repeat domain but leaving the amino acid composition intact . The fact that this sequence can be scrambled and still support [PSI+] propagation , albeit to varying efficiencies [80 , 81] , is inconsistent with the region functioning as a primary binding site for molecular chaperones [16] . However , the potential role that we have uncovered for the repeats in substrate processing can be explained as a primary sequence independent event . Hsp104 functions as a hexamer containing a central pore , through which substrates are threaded and unfolded . Within this pore , conformational changes in flexible aromatic residues provide the power stroke to drive substrate processing [82] . It is tempting to speculate that the low sequence complexity of the repeats affects substrate threading , and thereby amyloid fragmentation , by providing few architectural elements with which Hsp104 can interact . In this scenario , Hsp104 exerts an unfolding force as it processes Sup35 , but it would disengage once it reached the repeats . Because Sup35 remains aggregated , Hsp104 would iteratively attempt to resolve these complexes , providing additional force that would ultimately lead to fragmentation . But , this outcome is also likely to be promoted by the inherent folding rate of the oligopeptide repeat region . Indeed , the processing of substrates by AAA+ ATPases in other systems has been shown to be a competition between substrate unfolding by the enzyme and its ability to refold in between each power stroke , with fast refolding requiring additional rounds of engagement [83] . Consistent with this idea , R2E2 amyloid fibers , which have a higher fragmentation rate in vivo , are able to quickly refold following mechanical unfolding in vitro , whereas repeat deletion ( RΔ2–5 ) fibers cannot [84] , differences which are likely to promote additional rounds of engagement with Hsp104 for the former but not the latter substrate . The idea that the oligopeptide repeats promote substrate processing is also consistent with the existence of stop-transfer sequences in the substrates of other AAA+ ATPases: ClpXP and the proteasome . For example , polyQ sequences have been demonstrated to decrease the processivity of the proteasome in a length dependent manner [85–87] . In addition , the transcription factors NF-κB in higher eukaryotes and cubitus interruptus ( Ci ) in Drosophila both exist as full-length inactive precursors in the cytosol . In response to the appropriate signals , these precursors are then partially degraded by the proteasome to produce active truncated products , which then translocate to the nucleus and activate target gene transcription [88 , 89] . The partial degradation of these proteins is dependent on a stretch of simple sequence . When the proteasome reaches the simple sequence , the lack of architectural features reduces the amount of force it can exert to thread the substrate . This reduced force , combined with the nearby tightly folded domain , results in stalling of the proteasome and the release of a partially degraded product [90 , 91] . A similar mechanism has been proposed to explain the activation of the yeast transcription factors Spt23 and Mga2 as well as the immune evasion of the EBNA1 protein of the Epstein-Barr virus [92 , 93] . As an alternative scenario , specific amino acid residue ( s ) within the repeats may directly promote fragmentation efficiency . For example , the repeats contain tyrosine residues , which may promote interaction with the aromatic residues in the Hsp104 pore . Consistent with this idea , addition of tyrosine residues to a polyQ protein , reduces the number of Q residues required for the formation of SDS-resistant amyloids in vivo [94] . Importantly , this addition of tyrosines is also associated with a shift in the steady-state size distribution of these amyloids to smaller complexes , suggesting that the tyrosines facilitate fragmentation and thereby accumulation of amyloid [94] . In this model , the requirement for a minimum number of repeats would be interpreted as a threshold of tyrosine residues necessary to promote efficient fragmentation . Consistent with this idea , replacement of the tyrosines in repeats 3 , 4 and 5 with non-aromatic residues leads to [PSI+] loss in vivo , although progressive effects and the mechanism by which this loss occurs were not assessed [95] . Interestingly , the number of repeats is generally conserved across a variety of amyloids . In all yeast species in which the Sup35 homologue has been shown to be capable of forming a prion in the S . cerevisiae cytoplasm , the Sup35 protein contains between five and six repeats [17 , 96 , 97] . Similarly , PrP contains five copies of an octarepeat [26 , 27] , and bacterial functional amyloids maintain similar numbers of repeated elements , with CsgA containing 5 copies of a hexapeptide repeat and FapC containing three copies of a repeat [98 , 99] . One possible explanation for this similarity of repeat number across diverse species and proteins is evolutionary optimization to allow maintenance of both the amyloid and non-amyloid states . Our studies suggest that fragmentation efficiency is the mechanism underlying the contributions of repeated elements to these transitions . While a mammalian AAA+ ATPase responsible for PrP amyloid fragmentation in vivo has not been identified , mathematical models suggest that the kinetics of disease progression can only be explained with a fragmentation event [100 , 101] . The ability of PrP repeats to functionally substitute for those of Sup35 in [PSI+] maintenance suggests that they could play a similar role in mammals . Recent studies have identified potential candidates for this function , including the AAA+ ATPase RuvbL [102] or the combination of Hsp70 , DNAJB1 , and Hsp110 [103] , although their contributions to mammalian prion propagation have not yet been addressed . Together , our studies suggest specific roles for amino acid sequence and composition biases in the propagation of a prion in vivo . Beyond conferring amyloid propensity , these characteristics can mediate essential functional yet mechanistically distinct interactions with the cellular chaperone machinery to promote the appearance and maintenance of alternative , self-replicating conformers in vivo . Their evolutionary conservation suggests a selection for these functions [27 , 35] , and their impact on the phenotypic consequences of amyloid suggests that they may represent unique therapeutic targets .
All plasmids used in this study are listed in S3 Table; all oligos used in this study are listed in S4 Table . All plasmids generated by PCR were confirmed by sequencing . All strains used in this study are listed in S5 Table and are strong [PSI+] derivatives of 74-D694 [104] . All strains were grown in rich medium supplemented with 3mM adenine ( YPAD ) , unless otherwise specified . Cultures were grown in a shaking incubator at 30°C and maintained at an OD600 of less than 0 . 5 for at least 10 doublings to ensure exponential growth . SDS-PAGE and quantitative immunoblotting were performed as previously described [106] . Semi-Denaturing Detergent Agarose Electrophoresis ( SDD-AGE ) was performed as previously described [51] . The number of propagons per cell was determined by an in vivo colony-based dilution assay , as previously described [52] . For propagon amplification experiments , cultures were first grown in YPAD + 3mM GdnHCl for 12 hours . Then , cells were pelleted , resuspended in YPAD to an OD600 of 0 . 1 , and grown at 30°C . Propagon counts were then performed at the indicated timepoints . Cultures were grown in SD+2 . 5mM adenine overnight , collected by centrifugation and incubated in medium conditioned by cells of the opposite mating type for one hour . Equal OD600 equivalents of each mating partner were then mixed and incubated on solid SD + 2 . 5mM adenine and allowed to mate for 4 hours at 30°C ( where indicated , mating took place on solid SD + 2 . 5mM adenine + 3mM GdnHCl ) . Cells were then resuspended in SD + 2 . 5mM adenine and transferred to microscope slides for imaging . Imaging was performed in complete minimal medium supplemented with 2 . 5mM adenine and 2% glucose . Static images were obtained on a Zeiss Axio Imager M2 fluorescent light microscope with a 100x objective . Microfluidics were performed on a Zeiss Axio Observer Z1 using a CellAsics microfluidics plate with temperature controls and media flow of 2 psi on a Y0C4 yeast perfusion plate ( channel size 3 . 5–5μm ) . Fluorescence intensity was analyzed using the Zen software package ( Zeiss , Germany ) . For NM-HA and NM-HA-C immunocapture , native lysates were prepared as described [61] , and immunocapture was performed using anti-HA magnetic beads or anti-Myc magnetic beads ( Thermo Scientific Pierce ) . Co-captured proteins were resolved by SDS-PAGE and analyzed by western blotting for Sup35 , HA ( Roche ) , Hsp104 ( Abcam ) , Ssa1 ( gift from E . Craig ) , and Sis1 ( gift from M . Tuite ) . The amount of Sup35 and NM-HA or NM-HA-C captured was adjusted to reflect only the amount of Sup35 proteins present in aggregates in each strain , as determined by incubating lysates at 53°C and 100°C in the presence of SDS and resolving the protein on an SDS-PAGE gel . The percentage of protein in aggregates was then calculated as the fraction of Sup35 that did not enter the gel at 53°C . The amount of each chaperone that was co-captured was then compared to the amount of captured aggregated Sup35 . For Hsp104 binding to luciferase reporters , cells were incubated at 37°C for 30 minutes followed by 40°C for 35 minutes , with the addition of cycloheximide to 100μg/mL for the last 10 minutes . Cell lysates were prepared , and immunocapture was performed as described [61] , except 600mM NaCl was used in lysis and wash buffers . Co-captured proteins were separated by SDS-PAGE and analyzed by western blotting for Firefly luciferase ( Sigma ) and Hsp104 ( Abcam ) . Cultures were grown to an OD600 of 0 . 1 at 30°C , then incubated at 37°C for 30 minutes to induce chaperone expression . Cultures were then incubated at 40°C for 25 minutes , followed by addition of cycloheximide to 100μg/mL , and returned to 40°C for 10 minutes , followed by recovery at 30°C . Cells were imaged at the indicated timepoints . | Protein misfolding and assembly into ordered aggregates known as amyloid has emerged as a novel mechanism for regulation of protein function . In the case of prion proteins , the resulting amyloid is transmissible , creating protein-based elements of infectivity and inheritance . These unusual properties are linked to the amino acid composition and sequence of the protein , which confer both conformational flexibility and persistence in vivo , the latter of which occurs through mechanisms that are currently poorly understood . Here , we address this open question by studying a region of the yeast prion Sup35 that has been genetically linked to persistence . We find that this region is composed of two separable elements that are both required for efficient persistence of the amyloid . These elements do not contribute to amyloid stability . Rather , they promote distinct aspects of its functional interactions with molecular chaperones , which are required for efficient conformational self-replication and transmission . | [
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] | 2016 | Distinct Prion Domain Sequences Ensure Efficient Amyloid Propagation by Promoting Chaperone Binding or Processing In Vivo |
During vertebrate early embryogenesis , the ventral development is directed by the ventral-to-dorsal activity gradient of the bone morphogenetic protein ( BMP ) signaling . As secreted ligands , the extracellular traffic of BMP has been extensively studied . However , it remains poorly understood that how BMP ligands are secreted from BMP-producing cells . In this work , we show the dominant role of Marcksb controlling the secretory process of Bmp2b via interaction with Hsp70 in vivo . We firstly carefully characterized the role of Marcksb in promoting BMP signaling during dorsoventral axis formation through knockdown approach . We then showed that Marcksb cell autonomously regulates the trafficking of Bmp2b from producing cell to the extracellular space and both the total and the extracellular Bmp2b was decreased in Marcksb-deficient embryos . However , neither the zygotic mutant of marcksb ( Zmarcksb ) nor the maternal zygotic mutant of marcksb ( MZmarcksb ) showed any defects of dorsalization . In contrast , the MZmarcksb embryos even showed increased BMP signaling activity as measured by expression of BMP targets , phosphorylated Smad1/5/9 levels and imaging of Bmp2b , suggesting that a phenomenon of “genetic over-compensation” arose . Finally , we revealed that the over-compensation effects of BMP signaling in MZmarcksb was achieved through a sequential up-regulation of MARCKS-family members Marcksa , Marcksl1a and Marcksl1b , and MARCKS-interacting protein Hsp70 . 3 . We concluded that the Marcksb modulates BMP signaling through regulating the secretory pathway of Bmp2b .
Early vertebrate development involves the formation and patterning of body plan , such as dorsoventral axis formation and anteroposterior axis formation . Bone morphogenetic protein ( BMP ) signaling gradient is critical for the specification of ventral and posterior cell fate [1] . Like other morphogens , the formation of BMP signaling gradient depends on several factors , including the graded transcription and secretion of BMP ligands , the extracellular transport of BMP ligands and the interaction between BMP ligands and their antagonists [2] . In zebrafish , the secreted ligands Bmp2b and Bmp7a act as heterodimers and bind to their receptors type I and type II to transduce signal and to phosphorylate the regulatory Smads ( Smads 1 , 5 , and 9 ) , which in turn regulate BMP target genes with Smad4 in the nuclei [3 , 4] . As secreted ligands , the extracellular traffic of BMP homolog Dpp has been extensively studied in Drosophila . The long-range distribution of Dpp is mainly dependent on restricted extracellular diffusion [5] , which process is regulated by glypican members of heparin sulfate proteoglycans [6] . In zebrafish , it was reported that BMP gradient is mainly determined by the graded expression of BMP ligands [7] . The secretion of several morphogens , such as WNTs , FGF-2 and Hedgehog has been studied in different animal models [8–11] . Recent study implies that the release of Dpp is regulated by inwardly rectifying potassium channel and calcium transients [12] . However , it remains poorly understood how the secretory pathway , including the intracellular trafficking and the secretion to extracellular space , of BMP ligands is regulated . The myristoylated alanine-rich C-kinase substrate ( MARCKS ) is a ubiquitous substrate for protein kinase C ( PKC ) . Two conserved domains within the MARCKS proteins are known to be critical for their functions: the N-terminal myristoylated domain helps anchoring MARCKS to the plasma membrane; and the phosphorylation site domain ( PSD ) domain serves as the site for MARCKS binding to actin filaments and calcium/calmodulin [13–17] . A notable function of MARCKS is to regulate the secretion of different substances including airway mucin [18 , 19] . The well-studied regulated mucin secretion process via MARCKS involves its PKC and calcium/calmodulin dependent phosphorylation , high binding affinity with F-actin and membrane phosphoinositides , and interaction with intracellular molecular chaperons [20–23] . The MARCKS family proteins have also been reported to play various roles in gastrulation movements in Xenopus [24] and zebrafish [25] , and the morphogenesis of neural tube in mouse [26] and chick [27] . However , the potential roles of MARCKS in morphogen secretion and embryonic patterning has never been studied and reported . In this study , we unveiled a role of a MARCKS family member–Marcksb in dorsoventral patterning by regulating the BMP signaling activity through interacting with Heat-shock protein 70 ( Hsp70 ) to control the secretion of BMP ligands . Interestingly , unlike the marcksb knockdown embryos showing dorsalization , the maternal-zygotic mutants of marcksb ( MZmarcksb ) showed mild ventralization , suggesting that genetic over-compensation arises in the MZmarcksb embryos . We further proved that the transcription of other MARCKS family members were strongly activated during oogenesis of MZmarcksb females , and Hsp70 . 3 –the MARCKS interaction protein was up-regulated at shield stage in MZmarcksb embryos , suggesting a sequential compensation of different genetic factors .
We previously identified zebrafish marcksb which is important for gastrulation movements [25] . To further understand the role of MARCKS family genes in early embryonic development , we examined the expression patterns of all the four members of MARCKS family–marcksa , marcksb , marcksl1a and marcksl1b during early embryogenesis . Among these four genes , marcksb is the only one showing maternal expression and is the most highly expressed one at the time of zygotic genome activation ( S1 Fig ) . We then injected the morpholino ( MO ) blocking the translation of marcksb into zebrafish embryos and evaluated their phenotypes . The MO-injected embryos ( morphants ) showed spindle-like shape at bud stage ( Fig 1A ) and 77 . 9% showed dorsalization at 1 day post-fertilization ( dpf ) ( Fig 1A and 1B ) . The defect of dorsalization in marcksb morphants was rescued by the injection of morpholino-insensitive marcksb mRNA ( Fig 1A and 1B ) . Whole-mount in situ hybridization ( WISH ) analysis further confirmed the dorsalization defects in marcksb morphants , as revealed by the ventral expansion of otx2 expression ( labeling neural ectoderm ) ( Fig 1C ) and chordin expression ( labeling dorsal organizer ) ( Fig 1D ) . Accordingly , the expression level and region of ventral markers foxi1 ( labeling non-neural ectoderm ) ( Fig 1E ) and eve1 ( labeling ventral margin ) ( Fig 1F ) were strongly reduced . To understand whether inhibition of marcksb could affect the development of ventral tissues , we performed a tail organizer graft assay as described previously ( Fig 1G ) [28] . We transplanted the wildtype ventral margin cells to the animal pole of wildtype host , and as expected , 5 out of 27 host embryos had extra tails ( Fig 1H ) . In contrast , when the ventral margin cells of marcksb morphants were grafted to wildtype embryos , they failed to induce any extra tail structures ( Fig 1I ) . Taken together , our data indicate that marcksb is required for the specification of ventral cell fate in zebrafish . As zygotic BMP signaling plays a pivotal role in specifying the ventral cell fate , we next examined the BMP signaling activity in marcksb-depleted embryos . WISH showed that the expression of two direct transcriptional targets of BMP signaling—szl and ved were decreased in marcksb morphants compared to wildtype embryos ( Fig 2A and 2B ) . We then performed immunofluorescence to measure the nuclei-enriched phosphorylation level of Smad1/5/9 ( p-Smad1/5/9 ) . The data showed that the relative intensity of p-Smad1/5/9 was lower in marcksb morphants than that in wildtype embryos ( Fig 2C and 2D ) . Moreover , knockdown of marcksb could restore the ventralization phenotype in bmp2b-overexpressed embryos ( 5 pg of bmp2b mRNA per embryo ) ( Fig 2E ) . Accordingly , the injection of bmp2b caused robust expression expansion of szl and ved at dorsal region , and this dorsal expansion could be inhibited by knockdown of marcksb ( Fig 2F and 2G ) . Altogether , our data indicate that marcksb knockdown leads to attenuation of BMP signaling and marcksb is required for the normal activation of BMP signaling . We then conducted ectopic overexpression experiments of marcksb . Since marcksb was strongly maternally expressed ( S1 Fig ) , injection of moderate dosage of marcksb mRNA ( 200pg per embryo ) did not result in any visible effects , whereas injection of extremely high dosage of marcksb mRNA ( 1000 pg per embryo ) led to ventralization ( Fig 3A and 3B ) . To test whether phosphorylation of Marcksb is required for the activation of BMP signaling , two mutated forms of Marcksb , the HA-tagged S4D-Marcksb ( phosphomimetic type ) and S4N-Marcksb-HA ( non-phosphorylatable type ) were generated according to previous study [29] , and their mRNA were injected into one blastomere at 16-cell stage ( Fig 3C-a , b ) . The wildtype Marcksb-HA mainly localized at the cell membrane ( Fig 3C-c ) . In accordance with the notion that phosphorylation of Marcksb leads its translocation from the cell membrane to the cytoplasm [19] , S4D-Marcksb mainly located inside cytoplasm ( Fig 3C-d ) and the S4N-Marcksb mainly co-localized with membrane-labeled EGFP ( Fig 3C-e ) . We then examined szl expression in the embryos overexpressed with mutated marcksb . When compared with wildtype embryos , marcksb overexpressed embryos showed mildly increased expression of szl ( Fig 3D-b ) , while both S4D-marcksb and S4N-marcksb overexpressed embryos showed decreased expression of szl ( Fig 3D-c , d and 3E ) . These data suggest that both types of mutated Marcksb caused a dominant negative effect on regulating the BMP signaling activity . To establish a sensitive way to examine the effects of marcksb-overexpression , we overexpressed marcksb in chd_MO injected embryos ( chd morphants ) in which BMP signaling was slightly enhanced . As expected , all the chd morphants showed moderate ventralization ( Fig 3F and 3G ) . Strikingly , injection of moderate dosage of marcksb mRNA resulted in severe ventralization in chd morphants , although injection of the same dosage of marcksb mRNA did not result in any visible phenotype in wildtype embryos ( Fig 3F and 3G ) . This phenomenon was further proved by WISH analysis of szl in those embryos at shield stage ( Fig 3H-a , b and 3I ) . Strikingly , the elevated BMP signaling activity in chd morphants was dramatically inhibited by overexpression of both types of mutated Marcksb ( Fig 3H-a , c , d and 3I ) . Thus , our data suggest that the phosphorylation and de-phosphorylation switch of Marcksb is tightly related to the activation of BMP signaling . Next , we asked whether Marcksb regulates the BMP signaling through the BMP secretory pathway . We first constructed tagged Bmp2b by insertion of mCherry or Myc tag right after the pro-domain of ligand protein according to previous study [30] . The overexpression of both myc-bmp2b and mcherry-bmp2b caused similar ventralization defect ( Fig 4A a-c ) . To further confirm that fusion of mCherry to the N-terminal of Bmp2b does not interfere its in vivo function , we used the mCherry-bmp2b to rescue the mutant of bmp2b ( bmp2bta72a/ta72a ) . We did individual genotyping for embryos of bmp2bta72a/ta72a and mcherry-bmp2b injected bmp2bta72a/ta72a . We found that all the bmp2bta72a/ta72a were dorsalized ( Fig 4-d ) , while injection of mcherry-bmp2b mRNA could either rescue the dorsalization of bmp2bta72a/ta72a or cause ventralization ( Fig 4A e-f ) . These data demonstrate that the insertion of myc- or mCherry-tag dose not interfere the biological function of Bmp2b . To test whether Myc-Bmp2b or mCherry-Bmp2b can be properly cleaved and secreted , we transfected the plasmids containing either myc-bmp2b or mcherry-bmp2b and collected the cells and growth medium for immuno-analysis . For Myc-bmp2b , we found that the precursor ( 49 KD ) was enriched in the cell lysis while the matured Myc-bmp2b ( 15 KD ) in the medium ( Fig 4B ) . For mCherry-Bmp2b , we transfected the cultured cells with plasmid containing mCherry alone as a control . Similarly , the precursor of mCherry-Bmp2b ( 74 KD ) was mainly observed in the cell lysis while the matured mCherry-Bmp2b ( 41 KD ) in the medium ( Fig 4C ) . These data demonstrate that the insertion of Myc or mCherry does not interfere the proper cleavage and secretion of Bmp2b . To investigate whether Marcksb regulates the secretion of Bmp2b , we then performed mosaic injection assay ( Fig 4D-a ) . In the mcherry-bmp2b overexpressed embryos , the mCherry-Bmp2b could be detected outside the overexpressed-cells ( Fig 4D-b and 4E ) . Strikingly , in the marcksb morphants , the level of mCherry-Bmp2b outside their producing cells was significantly decreased ( Fig 4D-c and 4E ) . To further confirm that the above extracellular signal was from mature Bmp2b-mCherry , we detected the embryonic and extracellular mCherry-Bmp2b using immunoblotting . We revealed that there was mainly the precursor of mCherry-Bmp2b in the embryonic cells of wildtype or marcksb morphants and there were only properly cleaved matured mCherry-Bmp2b fusion proteins in the extracellular space , and the extracellular mCherry-Bmp2b was less in the marcksb morphants than that in wildtype embryos ( Fig 4F ) . Moreover , it appeared that the total cleaved mature mCherry-Bmp2b of marcksb morphants was less and the precursor of mCherry-Bmp2b was more than that from wildtype embryos ( Fig 4F ) . Thus , based on the above data , we conclude that marcksb is likely required for the intracellular trafficking and/or secretion of Bmp2b in which the cleavage of the Bmp2b precursor may be involved . To investigate whether Marcksb regulated the secretion of Bmp2b in a cell-autonomous manner , we performed a transplantation assay . When the transplanted embryos developed to shield stage , we sorted the transplanted embryos of wildtype-to-wildtype into three groups according to the location of labeled descendants—ventral , lateral and dorsal ( Fig 4G-a-d ) . Although the locations of labeled cells were different in those three groups , we did not observe any difference on the extracellular level of Bmp2b suggesting a similar capability of Bmp2b secretion from ventral to dorsal regions ( Fig 4G-e-g ) . Subsequently , we transplanted the myc-bmp2b-overexpressed cells into the marcksb morphant host and found that they were capable of secreting Bmp2b in marcksb morphants as the myc-Bmp2b could be detected abundantly outside of the producing cells ( 62% , n = 21 , Fig 4G-h ) . In contrast , the extracellular level of Bmp2b was significantly less in embryos with the marcksb-depleted cells transplanted to the wildtype host ( 100% , n = 21 , Fig 4G-i ) . These data indicate that marcksb cell-autonomously regulates secretory pathway of Bmp2b . To further unveil the role of marcksb on BMP signaling and dorsoventral patterning , we generated marcksb mutant by CRISPR/Cas9 mediated knockout ( Fig 5A ) . After screening and verification by sequencing , we obtained two types of mutations–marcksbihb199/ihb199 ( https://zfin . org/ZDB-ALT-180302-14 ) and marcksbihb200/ihb200 ( https://zfin . org/ZDB-ALT-180302-15 ) , both of which were predicted to shift their opening reading frames . There were no differences between these two alleles in phenotype analysis in subsequent studies . Therefore , we only presented the results of marcksbihb199/ihb199 in the following part . To our surprise , the homozygous zygotic mutants ( Zmarcksb ) did not show any early patterning defects and they could be raised up to adulthood , and we further generated maternal-zygotic mutant ( MZmarcksb ) . WISH analysis showed that the expression of marcksb was dramatically decreased from 2-cell stage to shield stage in MZmarcksb , indicating that both maternal deposition and zygotic expression of marcksb were severely reduced in MZmarcksb ( Fig 5B ) . This might be due to the failure of ribosome binding to mutated marcksb mRNA in the MZmarcksb embryos [31] . Surprisingly , MZmarcksb did not show any visible dorsoventral defects . However , we observed that the distance between the leading edges of enveloping layer ( EVL ) and deep cell layer ( DCL ) was enlarged in MZmarcksb during epiboly ( Fig 5C ) . At bud stage , some MZmarcksb embryos showed a yolk bulge phenotype . A yolk droplet could be squeezed out of the body in some of the MZmarcksb embryos ( Fig 5D arrow ) . These results indicated that MZmarcksb does not have dorsoventral defects but has moderate epiboly defects probably due to mild disorder of F-actin assembly [32] . As MZmarcksb did not show any dorsoventral defects as marcksb morphants , we speculated that there was genetic compensation occurring in the MZmarcksb [33 , 34] . To challenge the hypothesis , we first injected the marcksb_MO into the MZmarcksb . The injected MZmarcksb showed to be marcksb_MO resistant and has no obvious dorsalization defect ( Fig 6A ) . WISH analysis also showed that the expression of BMP targets—szl and ved ( Fig 6B ) and dorsal and ventral ectoderm markers—otx2 ( S2A and S2B Fig ) and foxi1 ( S2E and S2F Fig ) did not show any obvious difference between the MZmarcksb embryos with or without marcksb_MO injection . All these indicate that MZmarcksb is a null mutant of marcksb which does not respond to marcksb_MO and the marcksb morphant phenotype in wildtype embryos is a specific effect . However , when we carefully compared the expression of szl and ved in wildtype and MZmarcksb embryos , we found a slight increase of expression levels of szl and ved in the MZmarcksb embryos , suggesting an elevation of BMP signaling activity in MZmarcksb . To further confirm this finding , we detected and compared the nuclear localization of P-Smad1/5/9 in MZmarcksb and wildtype embryos at shield stage . Consistent with the WISH results , the intensity of P-Smad1/5/9 was significantly increased in MZmarcksb ( Fig 6C and 6D ) . Additionally , we performed the live imaging of Bmp2b by mosaic injection of mcherry-bmp2b mRNA . We found higher amount of mCherry-Bmp2b outside their producing cells in MZmarcksb in comparison with that in wildtype embryos ( Fig 6E and 6F ) . Finally , we found that the chd_MO injection only led to moderate ventralization phenotype ( V1-V2 ) in wildtype embryos , but it resulted in very severe ventralization ( V3-V4 ) in MZmarcksb embryos ( Fig 6G ) . Consistently , WISH analysis showed that knockdown of chd caused more robust increase of szl and ved expression in MZmarcksb than those in the wildtype embryos ( Fig 6H and 6I ) . Taken together , these data strongly suggest that genetic compensation occurred in the MZmarcksb embryos , and moreover , the BMP signaling activity was even “over-compensated” . To better understand the compensation network in MZmarcksb , we carried out RNA-Seq analysis of the MZmarcksb mutant at shield stage ( S1 Dataset ) . Consistent with WISH analysis of marcksb ( Fig 5B ) , RNA-Seq data showed that the expression level of marcksb was significantly reduced in MZmarcksb ( Table 1 ) . We also found that bmp7a was up-regulated in MZmarcksb , which is consistent to our observation that BMP signaling activity was slightly enhanced in MZmarcksb embryos ( Table 1 ) , as bmp7a is a transcriptional target of the BMP signaling [35 , 36] . To dig out the main compensation factors , we searched for the list of differentially expressed genes ( S1 Dataset ) and found that hsp70 . 3 was the second most up-regulated gene after hsp90aa1 . 2 on the list of up-regulated genes in MZmarcksb . We then performed RT-qPCR analysis of all the MARCKS genes and the hsp70 . 3 in MZmarcksb , maternal mutant of marcksb ( Mmarcksb ) , marcksb morphants and wildtype embryos . Interestingly , in MZmarcksb , all the other MARCKS members , marcksa , marcksl1a and marcksl1b were all significantly up-regulated at 1-cell stage ( Fig 7A ) , but not at shield stage ( Fig 7B ) . Moreover , hsp70 . 3 was up-regulated in MZmarcksb at shield stage but not 1-cell stage ( Fig 7A and 7B ) . These data suggest a phenomenon of sequential genetic response by MARCKS family members and hsp70 . 3 to maternal-zygotic loss of marcksb from oogenesis to early embryogenesis . Interestingly , this phenomenon could also be seen in the Mmarcksb embryos , suggesting that the genetic responses is independent of zygotic activation of marcksb in Mmarcksb ( Fig 7A and 7B ) . Unlike the upregulation of hsp70 . 3 in MZmarcksb or Mmarcksb embryos , the expression of hsp70 . 3 was significantly decreased in marcksb morphants . To further confirm whether this genetic compensation persists even after wildtype zygotic gene activation of marcksb , we knocked down marcksb in Mmarcksb embryos and found that those embryos did not show any dorsalization defect ( S2C , S2D , S2G , S2H and S2I–S2L Fig ) . Together , these results suggest that the MARCKS family members and hsp70 . 3 were up-regulated sequentially from oogenesis to early embryogenesis to response to the genetic loss of marcksb , and these genetic responses appear to be independent of zygotic activation of marcksb . We then asked whether the other three MARCKS family members or hsp70 . 3 could compensate the function of marcksb in the absence of functional Marcksb , we injected marcksa , marcksl1a , marcksl1b or hsp70 . 3 mRNAs individually into marcksb morphants and we found that all of them could partially rescue the dorsalization defect of marcksb morphants ( Fig 7C ) . These results suggest that the other MARCKS family members have the potential to replace the role of the Marcksb in the MZmarcksb mutant and hsp70 . 3 may have some genetic interaction with MARCKS family genes in regulating BMP signaling activity . As it was reported previously that MARCKS interacts with HSP70 to regulate mucin secretion in human airway epithelial cells [37] , We performed in vitro co-IP analysis to test whether the zebrafish MARCKS also bind to Hsp70 . 3 . The Hsp70 . 3 had the highest binding affinity to Marcksb and moderate binding affinity to Marcksl11a and Marcksl1b . However , the binding affinity between Hsp70 . 3 and Marcksa is rather weak ( Fig 7D ) , which is in consistent to the relatively low rescue efficiency of marcksa-overexpression in marcksb morphants ( Fig 7C ) . To further address whether hsp70 . 3 , marcksa , marcksl1a and marcksl1b over-compensate the BMP signaling activity in MZmarcksb embryos , we performed loss-of-function analysis of those genes in MZmarcksb . We found that the expression of szl was severely decreased in MZmarcksb injected with moderate dosage of hsp70_MO ( previously published morpholinos against all three variant splicing isoforms [38] ) or a combination of morpholinos against marcksa , marcksl1a and marcksl1b ( previously published morpholinos , for abbreviation , a_l1a_l1b_MOs [39 , 40] ) ( Fig 7E and 7F ) , while the same dosage of hsp70_MO or a_l1a_l1b_MOs only led to slightly decreased szl expression in wildtype embryos ( S3 Fig ) . To further verify the compensatory role of Hsp70 . 3 and other MARCKS members in MZmarcksb , we performed the experiments with CRISPR/Cas9 knockout method using the gRNAs against hsp70 , marcksa , marcksl1a and marcksl1b . All the gRNAs were validated by sequencing of the target sites ( S4 Fig ) . We found that the expressions of szl and ved were severely decreased in MZmarcksb embryos injected with either hsp70_gRNA or a mixer of MARCKS gRNAs ( S5 Fig ) , which was similar to the observations from their MOs mediated knockdown in MZmarcksb . All these data revealed that hsp70 . 3 , marcksa , marcksl1a and marcksl1b over-compensated the BMP signaling activity in MZmarcksb embryos . We then performed BMP imaging in MZmarcksb using mCherry-fused Bmp2b as reporter . Although we previously observed a higher level of extracellular Bmp2b in the MZmarcksb than that in the wildtype embryos ( Fig 6E and 6F ) , knockdown of either hsp70 or a combination of marcksa , marcksl1a and marcksl1b remarkably reduced the secreted Bmp2b level in MZmarcksb ( Fig 7G and 7H ) . These lines of evidence demonstrated that the genetic over-compensation was due to the cooperation between the other members of MARCKS family and the molecular chaperone–Hsp70 . 3 , which might even lead to mildly enhanced Bmp2b secretion level and BMP signaling activity in MZmarcksb embryos . To investigate whether Marcksb interacted with Hsp70 . 3 to regulate the secretory pathway of Bmp2b in wildtype embryos , we performed a series of genetic interaction experiments . Firstly , we knocked down hsp70 by injection of full dosage of hsp70_MO . We found that knockdown of hsp70 led to inhibition of BMP signaling activity shown by decreased expression of szl and ved , which could be partially rescued by morpholino-resistant mRNA injection ( S6 Fig ) . In the embryos co-injected with sub-dosage of marcksb_MO and hsp70_MO , a series of criteria were performed for careful evaluation: spindle shape of morphological defect was visible at early-somite stage ( Fig 8A ) ; the expression of BMP targets szl and ved was dramatically decreased ( Fig 8B and 8C ) ; the expression of neuronal dorsal marker otx2 was expanded to the ventral region ( Fig 8D ) ; the expression of epidermal marker foxi1 was decreased ( Fig 8E ) . By contrast , in the embryos injected with either marcksb_MO or hsp70_MO alone did not show such defects ( Fig 8A–8E ) . The Bmp2b live imaging was performed by transplantation of wildtype cells or cells injected with sub-dosage of hsp70_MO or marcksb_MO either alone or together into wildtype host . To our expectation , there were very few signals of the mCherry-Bmp2b outside the source cells in the hsp70 and marcksb double morphants , unlike that the mCherry-Bmp2b could be efficiently secreted from the source cells in the wildtype , or the embryos injected with sub-dosages of hsp70_MO or marcksb_MO ( Fig 8F ) . All these data demonstrate that Marcksb interacts with Hsp70 . 3 to regulate the secretory process of Bmp2b in wildtype embryos , and BMP signaling activity is over-compensated in MZmarcksb embryos likely by mildly enhanced secretory pathway involving MARCKS family members and Hsp70 . 3 ( Fig 9 ) .
MARCKS is known to be involved in regulating secretion of many proteins in various cell types . The role of MARCKS in mucin secretion in the airway has been intensively studied [19 , 22 , 37 , 41–46] . The translocation of MARCKS from the cell membrane to the cytoplasm upon phosphorylation by PKCδ is the initial step allowing MARCKS binding to the mucin granules [44] , and this binding requires the interaction among translocated MARCKS , Hsp70 and Cysteine string protein ( CSP ) [22 , 37] . After dephosphorylated by protein phosphatase I and 2A , MARCKS mediates the mucin granules binding to the myosin V and move along the cytoskeleton to the cell membrane [20] . In our study , the interaction between MARCKS and Hsp70 and the Phosphorylation of Marcksb both affect the extracellular level of Bmp2b , which indicates that MARCKS acts similarly to its role in mucin secretion in the intracellular trafficking and the secretion of Bmp2b . The maturation of TGF-β superfamily ligands , such as BMPs , requires endoproteolytic cleavage of the prodomain from BMPs precursors ( ProBMPs ) which coincides with the intracellular trafficking process [47 , 48] . Our data show that Bmp2b is properly cleaved before it being secreted to the extracellular space , as only properly cleaved Bmp2b is detected in the extracellular medium . The extracellular level of Bmp2b is much lower in marcksb-deficient embryo , indicating that marcksb is required for the secretory pathway of BMP ligands . Besides , we also noticed that the proBmp2b level was slightly increased and the cleaved Bmp2b level was slightly decreased in the embryonic lysis of marcksb-deficient embryo when compared with wildtype embryo . In consideration of the key role of MARCKS in intracellular trafficking system , we propose that proBmp2b would not traffic properly to the place where it is cleaved without the help of MARCKS . Moreover , the defective cleavage of proBMPs may further interfere dimerization , folding , and secretion of the active ligands [49 , 50] . Therefore , it is possible that marcksb and hsp70 are required for one or several steps in the whole secretory pathway of BMPs , which mainly includes the intracellular trafficking along with endoproteolytic cleavage and the secretion to extracellular space . Embryonic gastrulation includes dynamic events of cell migration and cell fate determination , both of which some molecules are involved in . One example is that the ventral to dorsal BMP signaling gradient transducing through Alk8 and Smad5 can create loose cell-cell adhesiveness at ventral region and allow ventral cells migrating dorsally [51] . This effect of BMP signal is different from its classical role in ventral cell fate determination and possibly is achieved by transcriptional activation of gene regulating cadherin function [51] . Our study provides another example on how one molecule could act on both morphogenesis and cell fate determination . It is widely accepted that MARCKS is required for gastrulation movements , which might be related to its binding with phosphoinositides [52] and F-actin [24] . Although the previous MARCKS knockdown studies in Xenopus and zebrafish mainly focused on its function on gastrulation movements [24 , 25] , they could not exclude the possibility that MARCKS family members also participate in embryonic patterning before or during gastrulation . In the present study , we also observed epiboly defects in both marcksb morphants and marcksb mutants , which is consistent to its classical role in regulation of cell migration . For the first time , however , we revealed that zebrafish marcksb is also required for dorsoventral patterning , and the function is achieved by interacting with Hsp70 . 3 to regulate the secretory process of BMPs , a type of morphogen crucial for ventral cell fate specification . Therefore , our study provides new insights into how a classical factor involved in cell migration also acts on cell fate determination . In this study , we faced the genetic compensation responding to gene knockout which was reported recently [33] . Interestingly , the transcription of other MARCKS family members were activated during oogenesis in MZmarcksb females , probably driven by non-sense mRNA decay mechanism [53 , 54] , and Hsp70 . 3 –the MARCKS interaction protein was up-regulated at shield stage which was presumably driven by zygotic activation in MZmarcksb embryos , suggesting a sequential compensation of different genetic factors via different mechanisms . Knockdown of either hsp70 . 3 or a combination of marcksa , marcksl1a and marcksl1b can efficiently block the activity of BMP signaling and reduce the extracellular level of Bmp proteins in the MZmarcksb embryos , which indicates that both Hsp70 and other MARCKS proteins collaborate closely to respond to the genetic loss of marcksb . In our case , the genetic compensation raised both from genes with sequence homology , and from genes within the same functional network , which support the recently proposed working model [33] . Interestingly , MZmarcksb showed a higher level of secreted Bmp2b ( Fig 6E and 6F ) and was sensitive to the knockdown of Bmp2b antagonist Chordin ( Fig 6G–6I ) , suggesting that the genetic compensation could even lead to elevated output of the overall products and mild enhancement of certain biological process . This phenomenon has never been demonstrated in previous studies . In addition , the detection of maternal expression of other MARCKS family members in MZmarcksb suggests that they may have switched from zygotic genes to maternal genes in the genetic adaption process during oogenesis .
The experiments involving zebrafish followed the Zebrafish Usage Guidelines of the China Zebrafish Resource Center ( CZRC ) and were performed under the approval of the Institutional Animal Care and Use Committee of the Institute of Hydrobiology , Chinese Academy of Sciences under protocol number IHB2014-006 . Embryos were obtained from the natural mating of zebrafish of the AB genetic background ( from the China Zebrafish Resource Center , Wuhan , China; Web: http://zfish . cn ) and maintained , raised , and staged as previously described [55] . For overexpression of proteins , short peptides tags , mCherry or EGFP was inserted in frame after amino acid 295 of Bmp2b according to a previous study [4 , 30] . The tagged Bmp2b were inserted into the pCS2+ vector for mRNA synthesis . The constructs of S4N-marcksb and S4D-marcksb were generated by PCR of the construct of marcksb-HA with mutation on the primer pairs . The primer pair for S4N-marcksb were F: AACGGTTTCAACTTTAAGAAGAACGCCAAAAAAG and R: CAGCTTGAACGGCTTCTTAAAGTTGAATCG . The primer pair for S4D-marcksb were F: GACGGTTTCGACTTTAAGAAGGACGCCAAAAAAGAAG and R: CAGCTTGAACGGCTTCTTAAAGTCGAATCGCTTTTTG ( mutated bases in the primer pairs were underlined ) . Capped mRNA was synthesized using the mMessage mMachine Kit ( Ambion ) . The previously validated morpholino antisense oligonucleotides ( MOs ) targeting the following genes were used: marcksa [39] , marcksb [25 , 39] , chordin [56] , hsp70 . 3 [38] , marcksl1a [40] , marcksl1b [40] . mRNA and MOs were injected into the yolk at the one-cell stage or into one-cell at 32- to 64-cell stage for mosaic injection . Doses for RNAs and MOs were indicated in the text or figures . The mutants of marcksb were generated using CRISPR/Cas9 mediated mutagenesis . The gRNA target for marcksb was designed by CRISPRscan [57] . Capped mRNA of zebrafish codon optimized Cas9 [58] and gRNAs of marcksb were synthesized by in vitro transcription using the mMESSAGE mMACHINE kit ( Ambion ) . 500pg Cas9 mRNA and 50pg gRNAs were co-injected at one-cell stage for each embryo . The gRNA target sequence is as follows: 5’-GGAGCACAAATCTCCAAAAACGG-3’ ( the PAM sequence is underlined ) . The target region was amplified using specific primers of marcksb ( fwd: 5’-GCGTTGTATCTCGCATCTCAT-3’ and rev 5’-CACACCCCCTCATAACATCA-3’ ) . The PCR products were subject to Sanger sequencing for direct evaluation of the targeting efficiency and identification of mutation [59] . The gRNA targets for hsp70 ( hsp70 . 1 , hsp70 . 2 and hsp70 . 3 ) , marcksa , marcksl1a and marcksl1b were designed by CRISPRscan [57] . The gRNA target sequences for the above genes were as follows: hsp70: 5’-CCTTTAATCCTGAAGAGATTTCC-3’ marcksa: 5’-GGCACCGCACCAGCAGAGGATGG-3’; marcksl1a: 5’-GGAGAAGCAGTGGCAGCGGACGG-3’; marcksl1b: 5’-GGATCCCAGGCATCAAAGGGAGG-3’ ( the PAM sequence is underlined ) . 500pg Cas9 mRNA and 50pg gRNAs were co-injected at one-cell stage for each embryo . Digoxigenin-labeled antisense RNA probes were synthesized by in vitro transcription . Whole-mount in situ hybridization ( WISH ) was performed as described [3 , 60] . For tail organizer transplantation assay , donor embryos were either injected with egfp mRNA or a combination of egfp mRNA and marcksb_MO at 1-cell stage . Donor embryos were them raised till the shield stage . Approximately 30 donor cells from ventral margin were transplanted to the animal pole of wildtype host embryos of sphere or dome stage as described [28] . Embryos were raised till 1 dpf for evaluation . The Bmp2b secretion assay was performed either by mosaic injection or transplantation . For mosaic injection , 50 pg memGFP mRNA and 50 pg mCherry-bmp2b mRNA with or without 1 ng marcksb_MO were injected into one blastoderm cell of a 16-cell to 32-cell stage embryo . The injected embryos were raised till shield stage for confocal imaging . For transplantation method , 50pg myc-bmp2b mRNA and 150 pg memGFP mRNA with or without 6 ng marcksb_MO were injected into the wildtype fertilized egg . Approximately 30 donor cells at the dome to sphere stage were randomly transplanted into wildtype or marcksb-morphant host embryos at the equivalent stage . The correspondent donors and hosts were indicated ( Fig 4G-a ) . Transplanted embryos were screened at shield stage for position identification of donor cells . Embryos were fixed at 60%-epiboly for immunofluorescence staining . Immunofluorescence was performed as described [61] . Generally , embryos were fixed in 4% Paraformaldehyde for overnight at 4 oC . Embryos were permeabilized by serial treatments with distilled water for 5 minutes at room temperature , cold acetone for 5 minutes at -20 oC , distilled water for 5 minutes at room temperature . For immunofluorescence of P-Smad1/5/9 , all the steps before adding secondary antibody should be performed under 4 oC . Anti-Phospho-Smad1/5/9 ( D5B10 ) Rabbit mAb ( CST ) was used at dilution 1:500 . Anti-Myc ( Santa Cruz ) was used at dilution 1:500 . Anti-rabbit Alexa Fluor 568 were used as secondary antibody ( Molecular probes ) at dilution 1:500 . Embryos were counterstained with DAPI ( 5mg/ml in stock , 1:5000 diluted with PBS for working solution ) for 1 hour . After immunofluorescence , the embryos were kept in 50% glycerol-50%PBS with 1mg/ml anti-fade reagent phenylenediamine ( Sigma ) avoid from light at 4 oC . Confocal images were acquired using a laser-scanning confocal inverted microscope ( SP8 , Leica ) with a LD C-Apo 40×/NA 1 . 1 water objective . Z-stacks were generated from images taken at 0 . 5 μm intervals , using the following settings ( 2048x2048 pixel , 400MHz ) . For detection of p-Smad1/5/9 signal , confocal images were acquired by the same scope using Lan-Apo 20x/NA 0 . 75 objective at zoom 0 . 75 . Z-stacks were generated from images taken at 3 μm intervals , using the following settings ( 1024x1024 pixel , 400MHz ) . Embryos of shield to 60% epiboly stage were mounted in 0 . 5% low-melting agarose and positioned with animal pole to the bottom . The Fiji software was used to quantify the average fluorescent intensity of P-Smad and the secreted Bmp2b protein [62] . For quantifying the P-Smad1/5/9 intensity , all the embryos were firstly orientated as dorsal region to the right . 8-bit image of each channel was transformed into 32-bit image . The threshold was made using default method and the background was set to NaN . A rectangle selection tool was used to select an area covering the whole embryo . The selected area was added to the ROI manager . The intensity from ventral to dorsal region of both P-Smad1/5/9 and DAPI were measure by plot profile function of Fiji . The data were then exported into Microsoft Excel and calculated . The ratio of P-Smad to DAPI from ventral region to dorsal region was plotted using GraphPad Prism 7 . For quantifying the secreted mCherry-Bmp2b or myc-Bmp2b , the 8-bit image was first transformed into 32-bit . The threshold was made using default method and the background was set to NaN . A polygon selection tool was used to select an area covering the outside of Bmp2b-source cell . A total selected area ( Areatotal ) and the area ( Areathreshold ) limited to the threshold were measured . The secreted Bmp2b ( Bmp2bsecreted ) was calculated by the formula: Bmp2bsecreted = Areathreshold / Areatotal . The data was plotted using GraphPad Prism 7 as scatterplots with median for small sample size studies [63] . Co-immunoprecipitation experiments were performed as described previously [64] . For immunoprecipitation assays , the cDNAs of marcksa , marcksb , marcksl1a and marcksl1b were cloned into pCS2+MTC ( C-terminal multiple myc tag ) and hsp70 . 3 was cloned into pCGN-HAM ( N-terminal multiple HA tag ) vectors . HEK293T cells were transiently transfected with the indicated constructs of interest using VigoFect ( Vigorous Biotechnology , China ) at dosage of 10 μg plasmid for cells covering about 70% surface of culture bottle ( Nest , 100mm cell culture Dish ) . For immunoblotting of intracellular and extracellular Bmp2b in cultured 293T cells , 10 μg of endotoxin-free pCS2-myc-bmp2b or pCS2-mcherry-bmp2b were transfected for cells covering 70% surface of culture bottle . After 8 hours , we replaced the growth medium ( DMEM ( high glucose , Biological Industries , 01-052-1ACS ) with 10% FBS ( Biological Industries , 04-001-1A ) ) with serum-free high-glucose DMEM and cultured cells for another 12 hours . One bottle of cells and growth medium were collected separately . Cells was lysed with 500μl RIPA buffer ( 50 mM Tris at pH 7 . 4 , 150 mM NaCl , 1% NP-40 , 0 . 5% deoxycholate , 1 mM NaF , 1 mM EDTA and protease inhibitors ) at 4 oC . The protein concentration was measured by Enhanced BCA Protein Assay Kit ( Beyotime Biotechnology , P0010 ) . About 50 μg protein was loaded to a lane for immunoblotting . The growth medium was centrifuged at 300g for several minutes to precipitate the cells and the supernatant was collected and concentrated by Centrifugal ultrafiltration tube ( Amicon Ultra UFC9001096 and UFC5010BK ) . For immunoblotting of embryonic and extracellular Bmp2b in vivo , zebrafish embryos were either injected with 10 pg mCherry-bmp2b mRNA per embryo or co-injected with 10 pg mCherry-bmp2b mRNA and 6 ng marcksb_MO per embryo . Each of 300 embryos at shield stage were harvested and dissociated by pipetting in 350 μL calcium-free Ringer’s solution . The cells were collected by centrifugation at 300 g for several minutes . The cells were then lysed with RIPA , vortexed vigorously , added with 5xSDS loading buffer ( Beyotime Biotechnology , P0015 ) , incubated for 10 minutes at 95 oC and used for immunoblotting . 5 embryos were loaded for each lane . 300 μL supernatant was incubated with mouse anti-mCherry antibody ( Abclonal , AE002 ) embedded Protein G beads ( Life , Dynabeads protein G , 10003D ) ( 10 μL antibody for 50 μL beads ) overnight at 4 oC . After washing 3 times with PBS with 0 . 02% Tween-20 , the beads were added with RIPA and 5xSDS loading buffer , incubated for 10 minutes at 95 oC and used for immunoblotting . For immunoblotting , anti-Myc ( Santa Cruz Biotechnology , 1:2000 ) , anti-HA ( Sigma-Aldrich , 1:5000 ) , anti-mCherry ( Abclonal , AE002 ) antibodies were used . Two hundred Embryos of either wildtype or MZmarcksb at shield stage were divided into two groups as replicates . The RNA was extracted using Trizol according to the manufacturer’s manual . Then the RNA was purified using RNA purification kit ( Tiangen , China ) . The RNA samples were quantified and integrity was assessed by the Agilent 2100 Bioanalyser . The RNA integrity Numbers ( RIN ) of all RNA samples were >8 . 0 . The RNA libraries were prepared using the Illumina TruSeq RNA sample preparation kit v2 . The amount of input RNA is 1 μg . The average final library size is 309 bp . Sequencing was performed on Illumina Miseq with read length of 150 bp paired-end ( PE ) at the Analysis and Testing Center of Institute of Hydrobiology , Chinese Academy of Sciences . Clean data were mapped to zebrafish reference genome GRCz10 Ensembl release 87 using HISAT2 with default parameters [65] . Cuffquant and Cuffnorm from Cufflinks software package were used to calculate the normalized gene expression level [66 , 67] . The differential expression analysis was performed using DEseq2 [68] . The original RNA-seq data has been deposited to the BioProject with accession number PRJNA432757 ( https://www . ncbi . nlm . nih . gov/bioproject/PRJNA432757 ) . Wildtype , MZmarcksb and Mmarcksb embryos at 1-cell stage and shield stage , and marcksb morphants at shield stage were collected for RNA extraction and reverse-transcription with about 60~70 embryos per sample and at least biological triplicate . The BioRad CFX Connect Real-Time System was used for transcript quantification . Samples were tested in technical triplicate for each gene , and resultant Cq values were averaged . Primer efficiencies and gene expression levels were calculated according to the previous study [69] . eef1a was selected as reference gene . Data were processed using 2-ΔΔCq method . All RT-qPCR gene-specific primers are listed in S1 Table . | Bone morphogenetic proteins ( BMPs ) are extracellular proteins which belong to the transforming growth factor-β ( TGF-β ) superfamily . BMP signaling is essential for embryonic development , organogenesis , and tissue regeneration and homeostasis , and tightly linked to various diseases and tumorigenesis . However , as secreted proteins , how BMPs are transported and secreted from BMP-producing cells remains poorly understood . In this study , we showed that Marcksb interacts with a molecular chaperon–Hsp70 . 3 to mediate the secretory pathway of BMP ligands during early development of zebrafish . Moreover , we discovered a novel phenomenon of “genetic over-compensation” in the genetic knock-out mutants of marcksb . To our knowledge , this is the first report that reveals the molecules and their related trafficking system mediating the secretion of BMPs . Considering the wide distribution of BMP and MARCKS within the human body , our work may shed light on the studies of BMPs secretion in organogenesis and adult tissue homeostasis . The finding of MARCKS in controlling BMP secretion may provide potential therapeutic targets for modulating the activity of BMP signaling and thus will be of interest to clinical research . | [
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] | 2019 | Marcksb plays a key role in the secretory pathway of zebrafish Bmp2b |
Infection with Leishmania parasites causes mainly cutaneous lesions at the site of the sand fly bite . Inflammatory metastatic forms have been reported with Leishmania species such as L . braziliensis , guyanensis and aethiopica . Little is known about the factors underlying such exacerbated clinical presentations . Leishmania RNA virus ( LRV ) is mainly found within South American Leishmania braziliensis and guyanensis . In a mouse model of L . guyanensis infection , its presence is responsible for an hyper-inflammatory response driven by the recognition of the viral dsRNA genome by the host Toll-like Receptor 3 leading to an exacerbation of the disease . In one instance , LRV was reported outside of South America , namely in the L . major ASKH strain from Turkmenistan , suggesting that LRV appeared before the divergence of Leishmania subgenera . LRV presence inside Leishmania parasites could be one of the factors implicated in disease severity , providing rationale for LRV screening in L . aethiopica . A new LRV member was identified in four L . aethiopica strains ( LRV-Lae ) . Three LRV-Lae genomes were sequenced and compared to L . guyanensis LRV1 and L . major LRV2 . LRV-Lae more closely resembled LRV2 . Despite their similar genomic organization , a notable difference was observed in the region where the capsid protein and viral polymerase open reading frames overlap , with a unique −1 situation in LRV-Lae . In vitro infection of murine macrophages showed that LRV-Lae induced a TLR3-dependent inflammatory response as previously observed for LRV1 . In this study , we report the presence of an immunogenic dsRNA virus in L . aethiopica human isolates . This is the first observation of LRV in Africa , and together with the unique description of LRV2 in Turkmenistan , it confirmed that LRV was present before the divergence of the L . ( Leishmania ) and ( Viannia ) subgenera . The potential implication of LRV-Lae on disease severity due to L . aethiopica infections is discussed .
In the highlands of Ethiopia , patients infected with L . aethiopica mostly develop localized cutaneous lesions , which can self heal . There is no accurate national figure for the overall burden of cutaneous leishmaniasis ( CL ) in Ethiopia , although some estimates suggest annual incidence to range from 20 to 50 thousands [1] . The prevalence varies from location to location , mostly being sporadic or endemic . In some cases , CL tends to persist and to metastasize to other parts of the body causing mucosal leishmaniasis ( ML ) or diffuse cutaneous leishmaniasis ( DCL ) , two different clinical presentations . DCL starts with a cutaneous lesion that metastasizes to other cutaneous sites . Patients are anergic in response to parasite antigens and poor responders to treatment . ML patients also begin with a single lesion , but in this case infection metastasizes to mucosal tissue causing chronic inflammation and facial disfiguring lesions . The mechanisms underlying the ability of L . aethiopica infections to cause such pathologies are not known . The parasite genetic variability does not account for the variability in clinical presentations [2] . In South America , leishmaniases are major health problems represented by a spectrum of pathological manifestations leading to clinical forms ranging from cutaneous leishmaniasis ( CL ) , visceral leishmaniasis ( VL ) , mucosal ( ML ) or disseminated leishmaniasis ( DL ) depending on the infecting species [1] . ML and DL are caused primarily by infections with species of the Leishmania subgenus Viannia ( e . g . Leishmania braziliensis , L . panamensis or L . guyanensis ) . One of the key questions concerning ML and DL is the basis by which some isolates are associated with metastasis in human infections . It is likely that these outcomes reflect polygenetic factors of both the human host and parasite [3]–[13] . This includes for example parasite resistance to oxidative stress [3] , and a variety of host genetic susceptibilities ( reviewed in [12] ) related to TGF-beta signalling [6] , infiltration of phagocytes [7] , [11] or IL-6 production [8] . In addition , the risk of ML is increased in HIV co-infection [4] and was also found to correlate with the age , gender , nutritional status of the patients as well as duration of the disease [14] . Recently , we reported that , in L . guyanensis mice infection , inflammation and severity of the infection was associated with high burden of a Leishmania RNA virus ( LRV ) infecting the parasites , suggesting that LRV might also contribute to disease severity in metastasizing leishmanisis [15] . Although first described more than two decades ago [16] , [17] , it was only very recently reported that high burden of LRV , a member of the Totiviridae family , is present in parasites metastatic in hamsters and isolated from secondary lesions of ML patients [15] , [18]–[20] . Thus far , these viruses have been described only in L . ( Viannia ) braziliensis and L . ( V . ) guyanensis , with the exception of one L . major strain from Turkmenistan ( Lmj ASKH ) [21] , [22] . On the basis of sequence divergence and organization of the open reading frames , the L . ( Viannia ) and L . major viruses have been named LRV1 and LRV2 respectively . The viral particles are composed of a capsid protein ( CP ) , an RNA-dependent RNA polymerase ( RdRp ) and a 5 . 3 kb double stranded RNA ( dsRNA ) genome . In most Totiviridae , such as in yeast , RdRp is not expressed as a single polypeptide , but as a fusion protein with CP [23] . Although the presence of a fusion protein was not directly proved for LRV in vivo , in vitro translation experiments conducted on LRV1 from Lg M4147 shows that the overlapping region of the two ORFs promotes translational frameshifting [24] . Another hypothesis is that an individual RdRp protein , as observed using a specific antibody , is produced after clevage of the potential fusion protein [25] . As demonstrated in the Totiviridae from Helminthosporium , RdRp could also be produced as a single protein after a termination/reinitiation mechanism [26] . In the L . guyanensis infection model , the dsRNA genome is recognized by the host endosomal Toll-like receptor 3 ( TLR3 ) to induce pro-inflammatory cytokines and chemokines [15] . These TLR3-mediated immune responses render mice more susceptible to infection , and the animals develop an increased footpad swelling and parasitemia . Thus , LRV1 in L . guyanensis parasites subverts the host immune response to Leishmania and promotes parasite persistence . In this study , we hypothesized that , because of the severity of the clinical presentations observed in L . aethiopica infections , a Leishmania RNA virus ( LRV ) could be present in some L . aethiopica strains . We showed that a virus related to L . major LRV2 was widespread and can evoke cytokine responses similar to those seen previously with LRV1 from L . ( Viannia ) [15] . This sets the stage for future studies looking at the role of LRV2 in the severity and nature of human leishmaniasis .
Two L . guyanensis clones , designated here as Lg M4147 LRV1+ and Lg M4147 LRV1− , and which were previously shown to be highly- and non-infected by LRV respectively ( designated as LRVhigh and LRVneg in [27] ) , were used as reference parasites . Eight strains of L . aethiopica parasites were freshly isolated from infected patients who contracted leishmaniasis in Ethiopia ( Table 1 , fresh isolates ) . In addition , three L . aethiopica lines from Leishmania species reference centers were also used ( Table 1 , cryobank lines ) , and kindly provided by Charles Jaffe and Lee Schnur ( Jerusalem , Israel ) . All parasite strains were cultivated as promastigotes at 26°C in Schneider's insect medium ( Sigma ) supplemented with fetal bovine serum , Hepes , penicillin/streptomycin , biopterin and hemin as described before [28] . Viral dsRNA genome was detected using the J2 monoclonal mouse antibody ( English & Scientific Consulting ) as described before [28] . Briefly , approximately 5×105 stationary phase promastigotes ( 2 µg of total proteins by BCA quantification ) were directly spotted on a nitrocellulose membrane for dot blot analysis . After blocking with milk , the membrane was incubated with the J2 antibody ( 1∶1000 ) that was finally recognized by an anti-mouse IgG antibody coupled to peroxidase ( Promega ) . For immunofluorescence microscopy ( IFM ) , stationary phase promastigotes were fixed in formaldehyde before being attached to poly-lysine coated slides . After permeabilization and blocking steps , slides were incubated with the J2 antibody ( 1∶800 ) , which was then visualized using a goat anti-mouse IgG coupled to AlexaFluor 488 ( 1∶600 , Invitrogen ) . Total nucleic acids from stationary phase promastigotes were obtained either by standard Trizol ( Invitrogen ) protocol or after lysis in sarkosyl followed by proteinase K and ssRNase incubation as described before [28] . After phenol-chloroform extraction and precipitation of total nucleic acids ( containing genomic parasitic DNA and LRV dsRNA ) , parasite DNA was eliminated by a RQ-DNase treatment ( Promega or Invitrogen ) and LRV dsRNA was visualized on 0 . 8% agarose gel by staining with ethidium bromide , and purified from the gel for further cDNA preparation ( below ) . To decrease intensity of ethidium bromide stained rRNA , and thus better visualize LRV dsRNA from the L . aethiopica L494 strain , total cellular RNA ( after Trizol extraction ) was treated with two volumes of FFS buffer ( 7 . 5M formaldehyde , 20% formamide , 0 . 2M sodium chloride , 30% glycerol and 0 . 02% bromphenol blue ) at 37°C for 15 min . The LRV-Lae L494 sequence was obtained by combination of total small RNA sequencing and specific-primer sequencing from cDNA . A library of small RNAs ( <42 nt ) was generated from total RNA ( purified with Trizol , described above ) using the method described by Atayde and co-workers [29] . Recent genome sequence data of the Lae L147 line reveals an absence of genes required for RNAi , consistent with the evolutionary position of L . aethiopica within the Leishmania clade shown previously to lack RNAi [27] , and thus the small RNAs represent primarily degradation products ( unpublished data ) , as seen previously in similar studies in Leishmania tarentolae which also lacks RNAi [27] , [30] . The library was sequenced using Illumina HiSeq2000 technology , yielding 35 . 9 million reads . The 5′ and 3′ adapters were trimmed from the data and then mapped to the sequence of the Lae L494 PCR products described above and/or the L . major LRV2 . From this analysis , three large contigs were obtained , and the remaining regions of the LRV2 were obtained by PCR amplification across the gaps and sequencing . This strategy allowed us to get the complete 5193 bp LRV-Lae L494 genome sequence ( GenBank accession number: KF757256 ) . After purification of LRV genomic dsRNA from the infected L . aethiopica 303 and 327 strains ( see previous section ) , it was quantified and used for cDNA synthesis as described before [28] . Different overlapping PCR fragments were progressively amplified from the viral cDNA and further sequenced ( by Fasteris , Switzerland ) to finally obtain most of the viral genomic sequence ( 5048 bp ) , including the complete open reading frames encoding the capsid protein ( CP ) and the RdRp ( GenBank accession numbers: KF256264 and KF256265 ) . PCR was performed as previously described [28] with an annealing temperature adapted to each set of oligonucleotides that were used ( generally about 2°C below the lowest melting temperature of both primers used ) . All primer sets ( Microsynth , Switzerland ) that were used for LRV sequencing , detection and cDNA are listed in Table S1 . Bone marrow derived macrophages ( BMM ) were obtained from C57BL/6 and TLR3 knock-out mice , and infected by Leishmania promastigotes as described before [15] . Culture supernatants were collected 24 h post-infection and analyzed for IL-6 and TNF-α cytokine production . For this purpose , ELISA kits were purchased from eBioscience and read on a Biotek Synergy HT spectrophotometer . Cytokine production was quantified relatively to purified mouse IL-6 and TNF-α standards . The number of parasites per macrophage was counted after fixation with formalhehyde followed by DAPI staining ( as described in [28] ) . Four different pictures from each experiment were used for counting ( at least 90 macrophages ) .
We surveyed eight strains of L . aethiopica freshly isolated from infected patients who contracted leishmaniasis in Ethiopia , six exhibiting typical CL and two with typical DCL pathologies ( Table 1 , fresh isolates ) . LRV presence was first assessed using a dot blot technique based on a monoclonal antibody ( J2 ) , which recognizes specifically dsRNA irrespective of the nucleic acid sequence [31] , [32] . Here , LRV can be easily detected in minute quantities of whole parasites or from lesion biopsies ( e . g . less than 100 parasites or 100 ng RNA extract in highly infected strains ) [28] . Briefly , the eight freshly isolated L . aethiopica ( Lae ) parasites were cultured and then spotted on a nitrocellulose membrane followed by an immunoblotting assay using the J2 antibody . As positive and negative controls , we used two clonal lines shown previously to bear LRV1 ( LgM4147 LRV1+ ) or selected for loss of LRV1 ( LgM4147 LRV1− ) [27] , [28] . Out of the eight L . aethiopica fresh isolates , four strains showed a detectable level of dsRNA , while all others were found negative ( Table 1 ) . The three strains that showed the strongest dsRNA reactivity ( Lae 303 , 316 and 327 ) as well as one negative strain ( Lae 372 ) were selected for further analysis . Figure 1A shows the dsRNA detection by dot blot using the J2 antibody of these four L . aethiopica strains , in comparison to Lg M4147 reference clones . We concluded that Lae 303 and 327 had a higher level of dsRNA than Lae 316 ( although weaker than the Lg positive control ) , while it was undetectable in Lae 372 similarly to the Lg negative control . In order to demonstrate that the dsRNA detected by dot blot in these three L . aethiopica strains was accompanied by the presence of a dsRNA viral genome , nucleic acids were extracted and treated with DNase and ssRNase , allowing the visualization of viral dsRNA on agarose gels . As expected in the case of Leishmania RNA virus ( LRV ) infections , dsRNA molecules of approximately 5 . 3 kb were identified in LRV-positive Lae strains , only in those lines positive in anti-dsRNA tests ( Figure 1B ) . As previously observed for other infected L . guyanensis and L . braziliensis strains [28] , the LRV genome sometimes appears as a doublet in non-denaturing high-resolution gels such as that presented here . Considering the homogeneity between the viral sequences that we obtained so far ( including the ones presented below for L . aethiopica ) , the doublet is unlikely to represent a mixed population of LRV . Instead , we propose that this migration pattern is due to differences in their secondary structures . Currently , it remains an open question , which deserves further analysis . The Lae 303 and 327 parasites harboring high dsRNA levels , as well as the LRV-negative Lae 372 and the Lg controls , were subjected to immunofluorescence microscopy to study LRV localization . As shown in Figure 2 , dsRNA was detected as clusters throughout most of the cytosol in Lae 303 ( as well as in Lae 327 , data not shown ) , similar to what was seen previously with the Lg M4147 LRV1+ control [28] . In agreement with the prior results , dsRNA reactivity was weaker in Lae relative to Lg M4147 LRV1+ ( Figure 1A ) , and no reactivity was visible in the LRV-negative Lae 372 promastigotes . Although never described in this Leishmania species neither on the African continent before , LRV was strikingly present in half of the recently isolated parasite strains tested . We therefore wondered if it was a particularity of this sampling , the region where samples were isolated or a general phenomenon . To this purpose , we tested already described L . aethiopica lines from reference centers ( Table 1 , cryobank lines ) . Three strains , isolated more than twenty years ago , were screened for LRV presence by PCR using primers specific for regions conserved across known LRV1 and LRV2 genomes ( see Methods and Table S1 ) ; one of these is a WHO reference line ( Lae L147 ) . A specific fragment was amplified from total cDNA from one of these strains , namely Lae L494 ( Figure 3A and Table 1 ) , and correspondingly , this strain alone exhibited a 5 . 3 kb dsRNA LRV genome ( Figure 3B ) . Thus the presence of LRVs was not uncommon in L . aethiopica . We determined the dsRNA sequence of the virus infecting L . aethiopica L494 by a combination of random small and specific primer-based RNA ( cDNA ) sequencing ( see Methods and Table S1 ) . This yielded a complete 5193 nucleotide genome ( GenBank accession number: KF757256 ) , designated LRV2-Lae L494 by the revised taxonomy proposed for LRVs as discussed below . This primary sequence was then used to design primers for sequencing most of the dsRNA genome from cDNA of two additional LRV-Lae isolated from Lae 303 and 327 ( 5048 bp ) , including the complete open reading frames for the capsid protein ( CP ) and the viral polymerase ( RdRp ) ( GenBank accession numbers: KF256264 and KF256265 ) ( Figure 4A ) . The LRV-Lae nucleotide sequences were then aligned to those of the three complete LRV genomes in GenBank: LRV1-Lg CUMC1 ( formerly LRV1-1 ) , LRV1-Lg M4147 ( formerly LRV1-4 ) and LRV2-Lmj ASKH ( formerly LRV2-1 ) ( GenBank accession numbers NC002063 , NC003601 and NC002064 respectively ) . The overall nucleotide sequence identity amongst the three L . aethiopica LRVs ranged from 77 to 85% , while it was 68% with L . major LRV2 and 52–58% with the two L . guyanensis LRV1s ( Table 2 , genome column ) . Thus L . aethiopica LRVs were most closely related to L . major LRV2 at the overall nucleotide and , even more at the amino acid level as discussed below . This close relationship of LRV-Lae with LRV2-Lmj was clearly illustrated by a phylogenetic analysis ( Figure 4B ) . Significantly , it mirrored the evolutionary relationship of Lae and Lmj parasites [33] , consistent with the prevailing view that most members of the Totiviridae are thought to be inherited vertically and that Leishmania RNA viruses show relationships similar to their host [22] . For these and reasons evident below , we have assigned the L . aethiopica LRVs as new members of the LRV2 species of Leishmania RNA virus ( LRV2-Lae ) . Conceptual translation of the LRV2-Lae genomes revealed the presence of two long and overlapping open reading frames ( ORFs ) coding for a capsid protein ( CP ) and an RNA-dependent RNA polymerase ( RdRp ) similarly to previously described LRVs ( Figure 4A ) . The position of these ORFs in the viral genome and the size of the encoded proteins are strikingly similar to what was observed in LRV2-Lmj , if we admit that LRV capsid starts at an internal AUG ( position 341 , Figure 4A ) and not at an upstream in-frame AUG , which is present in two of the LRV2-Lae but not in LRV2-Lmj ( position 248 ) . Analysis of LRV2-Lae303 sequence further supported our hypothesis that the AUG at nucleotide 248 is unlikely to be used , since it is followed by an in-frame stop codon upstream of the AUG at nucleotide 341 ( GenBank KF256264 ) . Therefore , in the discussion below , we took this shorter predicted protein as the LRV2-Lae CP . Similarity between the LRV2 genomes also applied to an additional short ORF located upstream of the CP gene , that potentially encoded a 39 amino acid peptide highly conserved in both LRV2-Lae and LRV2-Lmj ( 85% identity ) . In contrast , the upstream short ORFs that were described in LRV1 genomes were not conserved ( Figure 4A ) . Whether such ORFs are translated into protein is still unknown . The amino acid sequences of the CP and RdRp from the three LRV2-Lae were then compared to the three available LRVs . As expected , and even more strikingly than the genome analysis , both LRV proteins from the L . aethiopica strains were clearly more homologous to their counterpart in L . major ( sharing 80% and 60–61% identical residues for CP and RdRp respectively ) , than to the South American LRV1s ( with only 36–41% of the residues being conserved for both proteins ) ( Table 2 , CP and RdRp columns ) . These genome analysis also revealed that RdRp showed more diversity than CP , even between closely related strains such as the three LRV2-Lae , with the exception of certain highly conserved central domains ( Figures S1 and S2 ) . These include six regions that were previously reported to be conserved among various Totiviridae , three of them having been directly shown as critical for polymerase activity from the S . cerevisiae L-A totivirus [34]–[36] ( Figure S3 ) . From the CP and RdRp alignments , phylogenetic trees were constructed , again clearly dividing the LRVs into two separated groups , the New and Old World species accordingly to their parasite hosts ( Figure 4C–D ) . In LRV1-Lg M4147 and LRV1-Lg CUMC1 , the open reading frames ( ORFs ) for CP and RdRp overlap over 71 nucleotides with a −1 frameshift ( Figure 5 ) . A similar organization was seen in LRV1s isolated from other Viannia subgenus species ( manuscript in preparation ) . In contrast , LRV2-Lmj CP and RdRp are encoded by non-overlapping ORFs that are in-frame [21] . LRV2-Lae showed a third pattern , where the reading frames overlaped by 46 nucleotides , but now with a +1 frameshift ( Figure 5 ) . Potentially , LRVs are particularly diverse in the mechanisms used despite their close relationships . If RdRp is produced as a fusion protein with CP ( as in yeast [23] ) , this would occur through a non-conserved mechanism of either translational frameshifting ( +1 for LRV1 or −1 for LRV2-Lae ) , or via ribosomal hopping as in the case of in-frame ORFs of LRV2-Lmj . However it is important to note that no evidence has been provided yet establishing the existence of LRV CP-RdRp fusion proteins in vivo ( unpublished data and [21] , [24] , [25] ) . Alternatively , RdRp could be produced as a single protein , as has been observed in Helminthosporium virus , which is undertaken by a termination/reinitiation mechanism [26] . Previously , we showed that LRV1-bearing L . guyanensis induced a TLR3-dependent hyperinflammatory response in in vitro infected macrophages , characterized by elevated expression of a suite of cytokines [15] . We performed similar studies here , focusing on two representative important inflammatory cytokines , IL-6 and TNF-α , by measuring their release into supernatants of Lae-infected macrophages . Similar to L . guyanensis , the infection with the two L . aethiopica strains harboring the highest levels of LRV ( Lae 303 and 327 ) yielded significantly elevated levels of both cytokines . This was dependent on TLR3 signalling , as shown by infection of macrophages from TLR3 knock-out mice ( Figure 6 ) . Only background levels of IL-6 and TNF-α were seen with infections by the LRV-negative Lae 372 parasites , as it was observed with the Lg M4147 LRV1-negative control clone and non-infected macrophages . Interestingly , the Lae 316 strain , that had a low LRV load , behaved identically to a LRV-negative strain , suggesting that a minimum amount of virus was required to drive the TLR3-dependent production of IL-6 and TNF-α . The differences in cytokine levels observed with the four Lae strains were not due to differences in parasite uptake or survival , since a similar number of amastigotes per macrophage were quantified 24 hours post-infection with all Lae strains ( Figure S4 ) . IL-6 and TNF-α production upon Lae 303 and 327 infection was less than that observed for L . guyanensis , which might be attributable to the lower LRV load in the L . aethiopica strains ( Figures 1A and 2 ) and/or because of a higher parasite survival rate in the case of L . aethiopica . This hypothesis is supported by the observation that L . aethiopica survive significantly better than L . guyanensis parasites after macrophage infection ( Figure S4 ) . Since the activation of TLR3 by the viral dsRNA requires parasite killing and release of the virus in the phagolysosome , the different survival rates ( in addition to the lower LRV load ) might therefore explain the lower cytokine production observed with L . aethiopica LRV-infected strains in comparison to L . guyanensis . In vivo infection experiments were also conducted in mice using these L . aethiopica parasites . In accordance to previous reports on such species [37]–[39] , no significant footpad swelling , weight loss or any other sign of infection was measurable with any strain ( data not shown ) . No swelling of the nose , even faint as was described in [38] , was observed . This lack of clinical disease prevented the determination of the parasite load . Therefore , it was not possible to test if the presence of LRV in L . aethiopica and the consecutive inflammatory cytokine production had any effect in mice infection as it is observed with L . guyanensis parasites [15] .
Using several approaches to detect Leishmania RNA virus ( LRV ) in whole parasite isolates [28] , we showed that some strains freshly isolated from human patients infected with L . aethiopica contained a dsRNA virus ( designated LRV-Lae ) . A complete LRV-Lae genome was obtained for one isolate , and more than 97% of it , including the CP-RdRp regions , for two others . These data allowed us to definitively classify the Lae dsRNA virus as relatives of the Leishmaniaviruses LRV1 and LRV2 within the viral family Totiviridae , on the basis of nucleotide comparisons across the LRV genomes , the organization of the ORFs , and amino acid comparisons of the CP and RdRp proteins ( Table 2 , Figures 4 , S1 and S2 ) . These comparisons further showed that the L . aethiopica LRVs were much more closely related to L . major LRV2 than to the Viannia LRV1s , and were therefore designated as LRV2-Lae . In combination with the prior report of LRV2 in L . major , these data confirmed the presence of LRV outside of South America and the likelihood that it was present prior to the divergence of the two L . ( Viannia ) and L . ( Leishmania ) subgenera . Despite being close to LRV2-Lmj , L . aethiopica viruses showed a striking difference in the region surrounding the CP and RdRp open reading frames . Similar to LRV1s and unlike LRV2 , the reading frames from LRV2-Lae overlaped , but with a −1 frameshift rather than the +1 seen in all LRV1s . In contrast , the LRV2-Lmj CP and RdRp ORFs are non overlapping and in frame ( Figure 5 ) . Viruses are known to exhibit many forms of translational frameshifting and/or hopping [40] . Amongst Totiviridae , S . cerevisiae L-A virus and Trichomonasvirus TvV use frameshifting [41] , [42] while others such as Helminthosporium virus HvV use stop/restart mechanisms [26] . LRVs are potentially diverse in the mechanisms used despite their relatively close relationships . Whether RdRp is produced as a fusion protein with CP by ribosomal frameshift/hopping or as a separate protein is still an open question that will be addressed in future studies . Importantly , LRV2-Lae mirrored our previous findings with LRV1-Lg , and further supported the hypothesis that LRV dsRNA was a major innate immunogen as measured by the TLR3-dependent production of two key pro-inflammatory cytokines following infection of macrophages in vitro ( Figure 6 ) . In addition , there was likely a correlation between the viral load and the inflammatory response . It also showed for the first time that these cytokine productions were not restricted to L . guyanensis LRV1 but were indeed probably a general feature of LRV–infected strains . Remarkably , LRV2-Lae was found in nearly half ( 5/11 ) of the L . aethiopica strains tested , in both ‘recent’ and archival strains ( Table 1 ) . This suggests that LRV is frequently found in Leishmania strains from Ethiopia , although most of the patients develop cutaneous lesions that often self-heal . As a general and primary consequence , it is unlikely that the presence of LRV2-Lae would by itself be sufficient to explain ML and DCL complications . Other aggravating factors may obviously combine to lead to such pathologies , as previously suggested with L . ( Viannia ) species ( described in the introduction [3]–[14] ) . Unfortunately , our collection did not include any ML patients , therefore no conclusion could be drawn for this clinical presentation . Three samples from DCL patients were included , none of which bore LRV ( or any other virus detectable by dsRNA antibody or eletrophoretic profile ) . While the numbers were small and as yet inconclusive , they did not point to a strong relationship between a ‘digital’ classification of human disease pathology ( CL/DCL ) and LRV2-Lae presence . A similar conclusion was reached in studies of LRV1 in South America recently [43] . This may in part reflect the difficulty of assessing human disease , as potentially CL patients may have progress to more severe disease at the time of diagnosis , parasite isolation and/or treatment . It would be of special interest to follow the CL cases from which LRV-positive parasites were isolated , and establish if the risk of ML complication is increased by the virus presence in such patients . Similarly , given the spectral nature of leishmaniasis , the range of disease severity is unlikely to be fully captured by ‘digitization’ into CL/ML . In general , a large survey of geographically and comprehensively clinically catalogued patients ( CL/DCL/DL/ML ) infected with LRV1 and LRV2-bearing parasites is needed to fully assess the contribution of these viruses to human pathology . Unfortunately , animal models for L . aethiopica are not well developed and thus do not allow tests of the relationship between LRV and in vivo disease as was possible with L . guyanensis [15] . There , the data pointed strongly to a causal association of LRVs with disease severity and metastasis . Thus , another priority in the future is to explore and develop better models to facilitate testing of the pathogenic consequences of LRV in leishmaniasis caused by L . aethiopica . | Leishmania RNA virus ( LRV ) has been detected in Leishmania ( Viannia ) braziliensis and guyanensis species , parasites causing not only cutaneous but also mucosal and disseminated leishmaniases . In a mouse model , the viral dsRNA genome within L . guyanensis parasites is recognized by host Toll-like receptor 3 ( TLR3 ) and induces pro-inflammatory cytokines and chemokines , typically IL-6 and TNF-α , which are hallmarks of human mucosal leishmaniasis . Metastatisic complications such as mucosal and diffuse cutaneous leishmaniasis have also been described in other parts of the world , e . g . in Ethiopia . We detected LRV within L . aethiopica human isolates . Sequencing of three L . aethiopica LRVs ( LRV-Lae ) genomes confirmed that LRV-Lae belongs to the same Totiviridae family of LRVs found in South American species ( LRV1 ) and present in a single L . major isolate from Turkmenistan ( LRV2 ) . LRV-Lae genomic organization is similar but not identical to the other LRVs , with a unique −1 frameshift situation in the overlapping region of the capsid protein/polymerase genes . Finally and similarly to L . guyanensis LRV1 , LRV-Lae induced a TLR3-dependent inflammatory response in infected macrophages . The presence of LRV and its detection could be a crucial step towards better understanding the pathology spectrum of L . aethiopica infections . | [
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] | 2014 | Leishmania aethiopica Field Isolates Bearing an Endosymbiontic dsRNA Virus Induce Pro-inflammatory Cytokine Response |
Since divergence ∼50 Ma ago from their terrestrial ancestors , cetaceans underwent a series of adaptations such as a ∼10–20 fold increase in myoglobin ( Mb ) concentration in skeletal muscle , critical for increasing oxygen storage capacity and prolonging dive time . Whereas the O2-binding affinity of Mbs is not significantly different among mammals ( with typical oxygenation constants of ∼0 . 8–1 . 2 µM−1 ) , folding stabilities of cetacean Mbs are ∼2–4 kcal/mol higher than for terrestrial Mbs . Using ancestral sequence reconstruction , maximum likelihood and Bayesian tests to describe the evolution of cetacean Mbs , and experimentally calibrated computation of stability effects of mutations , we observe accelerated evolution in cetaceans and identify seven positively selected sites in Mb . Overall , these sites contribute to Mb stabilization with a conditional probability of 0 . 8 . We observe a correlation between Mb folding stability and protein abundance , suggesting that a selection pressure for stability acts proportionally to higher expression . We also identify a major divergence event leading to the common ancestor of whales , during which major stabilization occurred . Most of the positively selected sites that occur later act against other destabilizing mutations to maintain stability across the clade , except for the shallow divers , where late stability relaxation occurs , probably due to the shorter aerobic dive limits of these species . The three main positively selected sites 66 , 5 , and 35 undergo changes that favor hydrophobic folding , structural integrity , and intra-helical hydrogen bonds .
Upon adapting to the aquatic environment , marine mammals acquired features that improved their diving skills such as increased blood volume and hematocrit , efficient modes of locomotion ( stroke-and-glide swimming ) [1] , [2] and ∼10–20 times higher myoglobin ( Mb ) concentration ( CMb ) in the skeletal muscles contributing substantially to total body oxygen stores and aerobic dive limits [3] , [4] . Using an integrated Krogh model of the muscle cell , models of convective oxygen transport and aerobic dive limit ( ADL ) , and thermodynamics of O2-binding , we recently showed that wild-type ( WT ) Mb is more efficient than mutants under severely hypoxic conditions , whereas low-affinity mutants are in fact better transporters at intermediate oxygen pressure [5] . Moreover , while many sites do not affect O2-binding , conserved WT Mb traits are critical for prolonging the ADL of the animals: As the extreme example , mutating the distal His-64 residue can reduce the ADL by up to 14 minutes under routine dive conditions , and CMb almost linearly extends the ADL ceteris paribus , explaining the extreme increase in CMb occurring in the cetaceans [6] . Despite the intense research into the structure , function and physiological role of Mb [5]–[9] , the evolution of Mb is not well understood [7] . Several studies have suggested that Mb is under a selection pressure for its function and structural integrity [7] , [10]–[11] . Based on amino acid chemical properties and comparative studies of known Mb sequences , some form of selection has been suggested in the evolution of mammalian Mb to favor retention of the conformational structure [10] . Moreover , it has been shown that variable sites in cetacean Mbs are fewer in number but more prone to change than primate Mbs suggesting a probable shift in the function of Mb in cetaceans [11] . However , it is still unclear what drives Mb evolution , as are the specific sites potentially under positive selection and the changes in phenotype they might introduce . Mb is a relatively conserved protein in all mammals [12] . In a sequence alignment of Sperm whale , Pig , Bovine , Dog , Sheep , Horse and Human Mb , 107 out of 153 residues , including those essential for O2 binding , are identical ( See Text S1 ) . Also , Mb oxygen affinity is nearly the same ( KO2≈0 . 8–1 . 2 µM−1 ) for mammalian species . This observation is probably due to the “reversible binding” requirement of molecular O2 to Mb [13] at a given oxygen pressure , PO2 , which strongly constrains oxygen binding thermodynamics across mammalian cells [5] . Despite similar KO2 , another protein phenotype , the folding stability ( i . e . the free energy of folding the protein , ΔGfolding = Gfolded−Gunfolded ) , is systematically higher in marine mammals compared to their terrestrial counterparts [14] . In a study of mammalian apoMbs , sperm whale apoMb was found to be ∼2 . 5 kcal/mol more stable than horse apoMb [15] . The stability difference can reach up to ∼4 . 5 kcal/mol when goose-beaked whale is compared to pig [16] . In this work , using current Bayesian methods to detect selection and a physical force field to compute the stability of single-point mutations , we first identify specific residues under positive selection in the cetacean clade and find that the evolution rate is substantially higher in cetacean Mbs compared to terrestrials . Second , we find that mutations in positively selected sites overall contribute to maintaining stability . Third , using ancestral state reconstruction , we demonstrate that most stabilization occurred during the divergence of cetaceans from the terrestrials . Furthermore , we observe a correlation between Mb folding stability and its abundance across species , further confirming that Mb stabilization is selected for in proportion to protein abundance . Thus , the higher Mb abundance required by speciation of cetacean seem to be accompanied by a larger selection pressure to preserve stability , possibly to reduce the copy number of misfolded Mb in the cell , which is a suggested universal selection pressure for highly expressed proteins [17] .
The available mammalian Mb sequences were divided into two datasets: 33 nucleotide sequences of mammalian Mbs were used to construct a phylogenetic tree used for evolutionary analysis with codon models ( Figure 1A ) . To infer ancestral states with highest possible accuracy , a larger tree was also constructed from the substantially larger number ( 82 ) of available amino acid sequences of mammalian Mbs ( Figure 1B ) . For both phylogenies , Zebra finch was the outgroup , cetaceans were divided into two major suborders , Mysticeti ( minke whale and sei whale ) and Odontoceti ( sperm whales , beaked whales , dolphins , and porpoises ) , and all the branching patterns followed the known mammalian organism tree with order-specific patterns in primates , rodents , carnivore , cetardiodactylans , and cetaceans [18]–[26] . The accession numbers of all sequences used in this work , as well as full sequences of relevant ancestors are shown in Text S1 . The sequence of ancestral cetacean Mb was inferred from the available mammalian Mb sequences within all orders using the consensus mammalian species tree . Mb sequences from rodents and primates have minor effects on the most probable inferred ancestral sequence of cetacean Mb ( see Text S1 for details ) . To test for positive selection , we used codon-based models of nucleotide substitutions to estimate the rate of nonsynonymous to synonymous mutations , dN/dS , across different sites and branches of the mammalian phylogeny [27] . Also , all mutations were studied using the FoldX force field [28]–[30] to investigate whether the sites under selection in some way contribute to the stability phenotype of the Mbs ( See Methods section for details ) . Table 1 presents a comparison of the nested M0 ( i . e . one dN/dS for all lineages ) and FR ( i . e . one dN/dS for each branch ) models for both terrestrial and marine mammals . In the cetacean clade , the likelihood ratio test ( LRT ) gives a non-significant result of relatively similar ω ratios across the species . We also constrained ω to be the same in the whole cetacean clade ( ω1 ) and different for the rest of the mammals ( ω0 ) . LRT is significant when it is compared with the one-ratio test with P-value<10−16 . For ∼26% of sites in Mb , ω1 = 0 . 43 and ω0 = 0 . 19 , testifying to a significantly higher evolution rate in cetaceans . As a further support , a higher rate of evolution was also observed in the whole-gene dN/dS comparison of cetaceans ( Table 2 ) and primates ( Table 3 ) . The null hypothesis of two sets of dN/dS in primate and cetacean Mbs being similar is strongly rejected with the P-value of ∼1 . 33×10−16 using the two-sample t-test . The higher rate of evolution in the cetacean clade could suggest accelerated evolution driven by positive selection of specific sites . To test this , we compared three site pair-models as M1–M2 , M7–M8 and M8fix-M8 to identify sites under positive selection , as presented in Table 1 ( see Methods section for details ) . From Table 1 , the most stringent test ( M8 vs . M8fix ) indicated that seven sites ( 5 , 22 , 35 , 51 , 66 , 121 , and 129 ) are under positive selection with overall probabilities greater than 0 . 5 using the Bayes empirical Bayes ( BEB ) test [31] . Residue 21 was also detected to have a substantially high dN/dS , but its rate was not significantly greater than 1 and thus this residue was not detected by the BEB test . All eight sites are shown in Figure 2 with their posterior BEB probabilities using the M8 model , and with a mapping of sites onto the structure of sperm-whale Mb [32] . Table 1 also shows the results of a branch-site test of positive selection , model A , compared with M1a and the null model-A . Evolution rate ( i . e . ω ) was left to vary ( model A ) or fixed to 1 ( null-model A ) on the foreground tree with the marked branch leading to cetaceans ( Figure 1A ) . The LRT was in this case not significant when model A was compared with its null model , but significant compared to model M1a . To track the mutational pathways across different lineages of cetaceans , we constructed ancestral sequences as shown in Figure 3 . Ancestral states were inferred using the large species tree in Figure 1B constructed from 82 Mb amino acid sequences , applying the Dayhoff substitution matrix allowing for among-site-rate-variation as explained in the Methods section . Overall probability of inference was 1 except in the sites 1 , 13 and 28 where it is 0 . 5–0 . 9 . In all of these sites , the alternative preferred amino acid is the initial mutated amino acid . Overall , our results did not encounter the problem of combinatorial ancestral characters that typically lead to non-unique reconstruction of ancestral sequences [33] . Using the FoldX algorithm , we computed the ΔΔG associated with the mutations in each branch of phylogeny as is shown in Figure 3 . The overall stabilization or destabilization of each branch is depicted in red or blue , and the branch height is proportional to the absolute computed ΔΔG value of that specific branch . The overall stability increases in seven branches distributed from −0 . 3 to −5 . 1 kcal/mol . Upon divergence of cetaceans from the rest of mammals , the most substantial increase of ∼5 . 1 kcal/mol was gained by mutations G15A , E27D , V28I , V101I , K118R , and G129A . From Table 1 , the total ω is not significantly greater than 1 , but this may be an unrealistically strict criterion for a small , highly constrained protein such as Mb , as evolutionary rate is strongly correlated to protein size due to the fraction of near-neutral sites increasing with size . Instead , LRT is significant when the branch-site test for positive selection ( model A ) is compared with the nearly neutral model ( M1a ) , which indicates a higher ω in this first branch leading to cetaceans . In addition to positive selection under a new selection pressure ( to be explained later , selection for a higher CMb proportional to ADL , and additionally for folding stability ) , this might also be caused by relaxation of constraints ( loss of selection pressure ) [34] . Since the O2-binding affinity of Mb is nearly the same in all mammalian species ( KO2 at 298 K and pH 7 of ∼0 . 8–1 . 2 µM−1 ) , we conclude that the higher ω along this ancestral branch is consistent with positive selection under another arising selection pressure . As presented in Table 1 , selection is further supported by the identified amino acid sites in the BEB test having high probabilities along this specific branch , and by the massive increase in the stability phenotype of ∼5 kcal/mol occurring during this branching . Altogether , these results suggest that the common ancestor of whales already possessed the new stability phenotype that will later be shown to imply that this ancestor was most likely a deep-diver , although our terminal nodes contain both terrestrial , shallow- , and deep-diving mammals . After this early divergence that presumably established the majority of the new Mb stability , throughout the cetacean lineages , folding stability is seen to be maintained by fixation of several stabilizing mutations . From Figure 3A , the key mutations preserving this tendency are G5A , V13I , V21I , V21L , E27D , G35S , S35H , N66V , N66H , N66I , G74A , D83E , K118R , G121S , and G129A mutations . Eight of these mutations occur in the five sites 5 , 35 , 66 , 121 , and 129 which were detected by to be under positive selection . Thus , the insight from pure sequence-based maximum likelihood methods , amino acid substitution probabilities , and changes in biophysical stability as detected by structure-based approaches converge to the same interpretation of positive selection to obtain and maintain a higher Mb stability for the whales . As a further support for the link , G5A , G35S , and G129A mutations have been observed in more stable Mbs in comparative studies [14] . Figure 3B shows dN/dS values for the variable sites in the cetacean clade versus the inferred ΔΔG of the mutations . Four of the positively selected residues ( i . e . residues 5 , 35 , 66 , and 121 ) show an effect on folding stability >0 . 5 kcal/mol , with 5 and 66 being most significant , both towards stabilization ( ∼0 . 7 and ∼1 . 0 kcal/mol ) . Although the G129A mutation , which is fixated in the first branch leading to cetaceans ( see Figure 3 ) , is stabilizing ( i . e . ΔΔG = −0 . 69 kcal/mol ) , it undergoes three inversions from Ala to Gly in the branches leading to sperm whales , beaked whales and the suborder of Delphinidae , which makes it net destabilizing when summing over occurrences , although this is less significant and could reflect a partial relaxation of stability selection . Insignificant destabilization is also observed in the residues 22 and 51 which will be discussed later . Figure 3B and 3C show an interesting feature of the evolutionary dynamics of protein stability . As was recently shown by relating protein stability ( i . e . ΔG ) and evolution rate ( i . e . dN/dS ) , proteins may evolve to a stability regime having a detailed balance between stabilizing and destabilizing mutations [35] . Without the stability effects of sites detected to be under positive selection , mutations are distributed nearly symmetrically in the ΔΔG vs . dN/dS scatter plot with an average mutation having ΔΔG = 0 . 1 kcal/mol . The average ΔΔG of an arising mutation in Mb is estimated to be ∼1 . 2 kcal/mol [36] . Together , these values suggest a balance between stabilizing and destabilizing mutations in the late branches of the cetacean clade . Positive selection however shifts this balance by fixating stabilizing mutations such as G5A , G35S , S35H , N66V , N66H , N66I , G121S and G129A in the cetacean Mbs , providing a further stabilization of −1 . 7 kcal/mol for the whole clade and −4 . 4 kcal/mol when the branches leading to harbor porpoise and common minke whale are removed . These animals have ΔG similar to that of terrestrials both from experimental mutagenesis and stability measurements and from the FoldX computations . Also , they are shallow divers , consistent with their reduced CMb ( i . e . reduced need for a long ADL [6] ) , which might suggest that they are under less selection for stability ( vide infra ) . Thus , after divergence towards the common deep-diving ancestor , positive selection still acted to maintain and purify Mb stability except in the mentioned case of apparent phenotype relaxation . The role of positive selection is also reflected in the probability of stabilization ( i . e . ΔΔG<0 kcal/mol ) conditional of positive selection , pr ( ΔΔG<0 | ω>1 ) , using the Bayes rule [37] , being ∼0 . 80 ( see Text S1 for details ) . Moreover , the average ΔΔG of positively selected residues is significantly less than that of non-positively selected residues with P-values of 0 . 0382 and 0 . 0456 using the two-sample t-test assuming unequal and equal variances in the two datasets , respectively . Among the seven positively selected sites , four sites display a mutation from Gly to Ala ( 1 , 5 , 121 , and 129 ) . Gly is known as a strong helix breaker and thus its replacement with Ala will strengthen the helix specifically in soluble proteins [38] . As is shown in Figure 4A and 4B , the G5A mutation is preferred in both Ziphidae ( beaked whales ) and Mysticeti ( baleen whales ) suborders of phylogeny . In position 66 , a hydrophobic amino acid is stabilizing , confirmed by experimental measurements and most likely due to the hydrophobic effect ( i . e . this mutation destabilizes the solvent exposed site in the unfolded protein relative to the folded protein ) . From Figure 4C , both Ser and His in position 35 can make a hydrogen bond to Arg31 . The G35S/G35H mutations are selected in the two more stable physeter species ( pygmy sperm whale and dwarf sperm whale ) as is shown in Figure 4D . In position 51 which is a surface residue , a Thr to Ser mutation is preferred in two branches leading to beaked whales and to the more stable sperm whales . Both Thr and Ser have similar chemical properties and may form a hydrogen bond with αNH of residue 54 [14] . So far we have shown that the systematic increase in folding stabilities of cetacean Mbs , partly known from experimental data and further elaborated by the FoldX calculations , is caused by positive selection in this clade of mammalian phylogeny . It is thus important to investigate the biological origin of the selection pressure driving this stabilization . Olson et al . has made the rationale for this increased stability as due to the sustained anaerobic and acidic conditions in the skeletal muscle of marine mammals [14] , [16] . Since whales and seals experience prolonged dives , their Mbs have been suggested to be under selective pressure for increased resistance to unfolding during acidosis [14] , [16] . This hypothesis is in contrast with several observations . First , marine mammals generally stay under aerobic metabolism due to the high cost of recovery after switch to anaerobic conditions [39] . The longest dives recorded for large whales such as blue and fin whales are much shorter than predicted the dive limits under aerobic conditions ( ADL ) [40] . In similar studies of sperm whales and seals , almost all the dives were found to not greatly exceed ADL [41] , [42] . Second , the pH-fall in muscle and blood of seals after the long dives is reported to be less than one unit from its physiological value ( ∼7 . 5 ) which is too small to initiate unfolding [43] . These observations show that a switch to anaerobic metabolism and sustained acidosis in the muscle is less relevant for the diving patterns of marine mammals as observed in the wild [42] . As seen in Figure 5 , upon divergence of marine mammals , a ∼10–20 fold increase in Mb concentration ( CMb ) is experimentally observed , which has been shown to be critical for O2 storage and diving capacity [6] . Moreover , the stability of Mb is also increased: For Pig , Horse , Sheep , Human , Bovine and Dog , ΔG of apoMb has been reported to be −4 . 4 , −4 . 8 , −4 . 9 , −5 . 7 , −5 . 8 and −6 . 3 kcal/mol [14] , increasing to −5 . 1 , −7 . 4 , −7 . 5 , −7 . 8 , −8 . 4 and −8 . 7 in Dwarf sperm whale ( K . simus ) , Pygmy sperm whale ( K . breviceps ) , Sperm whale ( P . catadon ) , Goose beak whale ( Z . cavirostris ) , Dolphin ( Delphinus delphis ) , and Minke whale ( B . acutorostrata ) . The stability of holoMb is ∼2 . 7 kcal/mol higher than that of apoMb and this difference is assumed to be a constant , since residues in the heme pocket are conserved across all cetaceans [12] , [14] . The average stability of holoMb is thus ∼−7 to −8 kcal/mol for terrestrial mammals and ∼−10 to −11 kcal/mol for cetaceans . More importantly , as shown in Figure 5 , stability is highly correlated with the species-specific CMb with a correlation coefficient ρ = 0 . 88 at the significance level <0 . 01 . This correlation cannot be explained by adaptation to acidic conditions , because acidic robustness would not depend on protein abundance . The ΔG−CMb correlation is sensitive to various factors: First , CMb varies somewhat among different muscle types in mammals . Swimming muscles in dolphins contains ∼82–86% of total Mb but constitute ∼75–80% of total muscle mass , compared to non-swimming muscles [44] . In humans , it is generally known that slow oxidative type I muscles contain more Mb than fast twitch type II muscles [45] . Second , Mb concentration is also age-dependent . Several studies of marine mammals suggest that skeletal muscle of pups have approximately 30% less Mb compared to adults [46] , [47] . Despite these individual and tissue-wise variations in Mb expression , CMb for marine mammals is still generally ∼10 fold higher than for terrestrial mammals [48] . The correlation between protein folding stability and its expression level in the cell was recently proposed to be a consequence of protein misfolding prevention [17] . This hypothesis could explain the universal , strong anti-correlation between protein expression level and evolution rate ( ER ) in proteins , known as ER anti-correlation , i . e . highly expressed proteins are under stronger selection for stability to reduce the copy number of misfolded proteins [49] . While there may be many other explanations for the ER anti-correlation ( i . e . the fitness impact , and hence conservation , of a protein would be proportional to its abundance regardless of the property selected for ) , the observation of a correlation between protein folding stability in Mb , as one of the most highly expressed mammalian proteins , and its abundance level in different organisms is the first , specific indication that stability as a protein phenotype may be the main property under selection in a real mammalian protein . We propose that selection against unfolded protein is the cause of both the observed increased evolution rate ( Table 1 and 2/3 ) and the higher stability of the cetacean Mbs . The increased evolution rate of cetacean Mbs with higher expression level seems at first to be in contrast with the average tendency of highly abundant proteins to evolve slowly [50] , [51] . The explanation for this is most likely that highly expressed proteins that evolve slowly are normally close to equilibrium at their fitness optimum and under stronger selection for conserving stabilizing traits , whereas in the present specific evolutionary history , the increased evolutionary rate results from a divergence event where the higher abundance is established together with enhanced stability . This is fully consistent with our observed CMb-stability correlation using available experimental data , with the dive depths of the respective animals , and with the observation of highest evolutionary rate during the first branching event where stability ( and presumably CMb ) increased the most . The present results thus also demonstrate how the evolution rate , dN/dS , of a single protein depends on a biophysical property such as in this case stability . Upon divergence to a new niche ( deep-diving ) , the rate increased due to positive selection of new stabilizing mutations , but it is very conceivable that once the optimal stability has been obtained , fixation of new traits will also occur in cetacean Mbs , at least in so far as speciation is complete , which would reduce the rate of evolution as is partly seen in the latter part of the cetacean clade vs . the earlier part . Thus , our results are consistent with the general abundance-evolutionary rate anticorrelation but also suggest that the relation breaks down when highly expressed proteins undergo positive selection towards establishing new traits , leading to a speciation event of both higher evolutionary rate and higher abundance . In this interpretation , upon the divergence of cetaceans from their terrestrial counterparts , the speciation towards deep divers quickly led to selection for higher CMb , which for deep divers is almost proportional to ADL and by inference , fitness [6] . This early speciation led to an increased selection pressure acting to increase Mb stability in order to minimize the burden of misfolded Mbs within the cell . With a typical 10-fold increase in CMb , an unchanged stability would increase the burden of unfolded Mb by 10-fold in cetaceans , but an average stability increase of ∼2 kcal/mol would change the folding equilibrium constant to keep the total copy number of unfolded Mb almost constant across lineages , implying that the burden would be checked in this way . Among the significantly stabilizing mutations , 5 , 35 , and 66 were detected to be under positive selection with high posterior probabilities ( p ( ω>1 ) ∼0 . 80–0 . 95 ) . The remaining detected sites under positive selection were not significantly affecting stability as seen in Figure 3B . However , they might affect the protein in various other ways that also relate to the increased need for Mb and the adaptation of Mb-enriched deep-divers such as increased signalling requirements or structure preservation beyond thermodynamic stability , e . g . kinetic denucleation/unfolding prevention . Notably , sites 22 and 51 are predicted to be destabilizing by FoldX in an agreement with previous comparative mutagenesis experiments [16] . Since both these surface residues are substituted for Ser , they may be involved in post translational modifications such as phosphorylation , although a physiological role phosphorylation is unknown [52] . In fact , both residues 22 and 51 are predicted to be phosphorylation sites in whale Mbs using the NetPhos 2 . 0 server ( available at http://www . cbs . dtu . dk/services/NetPhos/ ) with high scores of 0 . 82 and 0 . 97 , respectively ( See Text S1 ) . Moreover , residue 117 is also detected here as a phosphorylation site as proposed relevant for Beluga whale ( Delphinapterus leucas ) Mb [52] . This observation is consistent with previous studies in enzymes that gain-of-function mutations are on average destabilizing [53] , but overall , positive selection still contributes to stability despite these marginally destabilizing sites . This work suggests that in an important real case of protein evolution , folding stability could be selected for in response to speciation in a new habitat: Our results suggest that the evolution of cetacean Mbs concurred with a divergence of one phenotype – stability – while oxygenation properties remained similar . Folding stability increased significantly ( ∼5 . 1 kcal/mol ) due to the fixation of G15A , E27D , V28I , V101I , K118R , and G129A mutations . We have explained how and why increased Mb stability correlates with increased protein abundance during this evolutionary event , which probably involved substantial competition and speciation as niches were established in the diving regime . The early , substantial increase in folding stability was accompanied by a significantly higher dN/dS in the first branch leading to cetaceans as judged from the comparison between the nearly neutral model ( M1a ) and the branch-site model of positive selection on this specific branch . This initial gain of folding stability was then later maintained through the fixation of G5A , V13I , V21L , V21I , V28I , G35S , S35H , N66V , N66I , G74A , V101I , K118R , G121A , and G129A mutations which compensate the deleterious effects of various destabilizing mutations possibly having marginally beneficial fitness effects relating to e . g . regulation . The full picture of these other functionalities would be a relevant focus area in future work . Later in the clade , we have observed relaxation of the selection for stability . Notably , the common minke whale ( Balaenopetra acutorostrata ) and harbor porpoise ( Phocoenoides phocoena ) display ΔG and CMb similar to terrestrial mammals with −8 . 4 and −7 . 8 kcal/mol and 0 . 37 and 0 . 40 gram per 100 g muscle , respectively . Given the linear effect of CMb on ADL and by inference the action radius and fitness of the marine mammals [3] , [6] , This observation might be explained by the reduced oxygen consumption demands of both species during diving: Common minke whale is the smallest of the baleen whales with short dive times of ∼5–10 minutes [54] compared to sperm whales with an average dive time of ∼45 min [55] . Porpoises are also shallow divers ( <50 m ) with dive times less than two minutes [56] . Therefore , the selective pressure towards more ( and more stable ) Mb seems to be relaxed in these species if our mechanism is correct , explaining why shallow divers such as porpoises have reverted to less stable Mb . However , across the species , other factors , notably body mass reducing metabolic rate of the animal , also contribute to the total ADL [57] , and future data on dive capacities vs . Mb stability would help to clarify the validity of the inferred mechanism . While evolution is often interpreted as selection for new protein functionality [58] , the evolution of cetacean Mbs described in this paper provides the first real example of protein stability being selected for as a consequence of protein abundance , using as control the terrestrials that have 10-fold less Mb . The mechanism by which evolution still acts on the cetacean Mbs , in addition to conservation of the heme pocket due to the reversible binding requirement [13] , appears to be one of reducing the animal's burden of the more unfolded Mb copies in the muscle cells by increasing the selection for stability of the highly expressed protein . We suggest that this is the main explanation for the observed accelerated evolution in the cetacean clade .
The mammalian species tree was analyzed with the MEGA5 package [59] to select the best nucleotide/protein model with the lowest BIC scores , which was the Tamura-Nei92 and Dayhoff model allowing among-site-rate-variation ( ASRV ) sampled from a discrete gamma distribution with four categories ( See Text S1 for details ) [60]–[62] . To infer the ancestral sequences of the cetacean clade , branch lengths were first estimated using the Dayhoff model with ASRV , and the Bayesian posterior probabilities were calculated for each possible ancestral state for each node [63] . To explore the ancestral sequences inferred , we then used the maximum likelihood method [64] instead of the maximum parsimony ( MP ) approach due to the limitations of MP in dealing with branch lengths and possible uncertainties in the phylogeny [65] . New Mbs of any member of Ancodonta such as Hippos ( Hippopotamus ) , Camelidae and more species from Cetardiodactyla order such as Alpaca ( Vicugna vicugna ) could possibly resolve better the branch leading to cetaceans and thus provide a finer tree for investigating the episodic nature of dN/dS with respect to protein stability . The pair-wise comparisons of Mb sequences of cetaceans and primates shown in Table 1 were estimated by the Maximum likelihood approach with codon models in CODEML program implemented in the PAML suite [66] . The equilibrium codon frequencies were estimated from the products of the average observed nucleotide frequencies in the three codon positions ( F3X4 model ) . To detect adaptive evolution , three codon-based models of nucleotide substitutions for the data [67] with the maximum likelihood inference were employed , first via “branch models” that allow the ω ratio ( i . e . dN/dS ) to vary among branches in the phylogeny [68]; M0 ( one ω ratio for all lineages ) and FR ( one ω ratio for each branch ) , and second , via “site models” that allow the ω ratio to vary among codon sites within the sequence [69] . We used five different models referred to as M1 ( nearly neutral ) , M2 ( positive selection ) , M7 ( beta ) , M8 ( beta and ω ) , and M8fix ( M8 with ω fixed at 1 ) [31] . The tree branch lengths were first estimated with the M0 model and were used in the more advanced codon models . We also used the site-models by estimating the branch lengths rather than taking their ML estimated values from the M0 model . With both approaches , the same sites were detected to be under positive selection with significant results in LRTs ( see Table S3 in Text S1 for details ) . Positive selection in the specified residues was also robust to the use of gene tree instead of the organism tree ( see Table S4 in Text S1 for details ) . Synonymous estimates in both marine and terrestrial mammals were less than 1 . 5 with the exception of one branch having ω = 1 . 56 , and could thus be considered reliable . We ran the CODEML program several times with different initial values to prevent local optima in the Bayesian identification . To compare the fit of nested models , classified as null and alternative models , the Likelihood Ratio Tests ( LRT ) was used [70] . Within a LRT test , twice the log-likelihood difference between two nested models has a chi-square distribution with a number of degrees of freedom equal to the free-parameter differences [71] . Different nested pairs of models were compared using the LRT such as branch models M0 versus FR , and Site models M1 versus M2 , M7 versus M8 , and M8fix versus M8 . In cases where the LRT was significant , the Bayes empirical Bayes ( BEB ) method implemented for models M2 and M8 was employed to calculate the posterior probabilities for codon classes . A third class of LRT tests known as “branch-site” model that allow the ω ratio to vary among both sites and lineages [34] was also employed to infer positively selected sites in the ancestral branch leading to cetaceans . This branch-site test of positive selection was only used on the first branch leading to cetaceans to test the importance of this branching event in the overall divergence of cetaceans from terrestrials ( shown with a black circle in Figure 1A ) . Any further statistical inference in the cetacean clade by detecting branches with high dN/dS values based on the free-ratio model should be corrected by the multiple-hypothesis corrections [72] . The initial 3D-structures used for calculating the stability of single point mutations were taken from the PDB structures of sperm whale Mb at 1 . 6 Å [32] and 1 . 4 Å resolution [73] . These structures were subject to the standard protocol of FoldX [28] . We validated the FoldX predicted ΔΔG values for both PDB structures against a set of experimentally reported Mb mutants . We then finally used the repaired PDB structure at 1 . 4 Å [73] which gave the strongest correlation between calculated and experimental ΔΔGs , for computing stabilities within the phylogeny . Individual mutations in the cetacean clade ( Figure 3A ) were built using “Build Model” command , and ΔΔG values were extracted from the FoldX output files . For both the validation set and mutations in Figure 3A , we repeated each mutation five times and took the average ΔΔG to reduce internal uncertainties of FoldX in estimating the stability effects of mutations , as recently recommended [74] ( see Text S1 for details ) . | In this work , we identify positive selection in cetacean myoglobins and an early , significant divergence event . While O2-binding is nearly unchanged , positive selection acts to introduce and later maintain stability . Stability correlates with abundance across the species , supporting that selection for increased stability concurred with the known 10–20 fold increase in myoglobin abundance of cetaceans relative to terrestrial mammals , which itself resulted from speciation towards longer dive lengths of the animals . We suggest that this selection acted to keep constant the otherwise increasing number of unfolded Mb . Altogether , this work for the first time links protein phenotype ( stability and abundance ) in a specific , real protein to organism-level evolution and fitness of mammals . | [
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] | 2013 | Positively Selected Sites in Cetacean Myoglobins Contribute to Protein Stability |
One major consequence of economic development in South-East Asia has been a rapid expansion of rubber plantations , in which outbreaks of dengue and malaria have occurred . Here we explored the difference in risk of exposure to potential dengue , Japanese encephalitis ( JE ) , and malaria vectors between rubber workers and those engaged in traditional forest activities in northern Laos PDR . Adult mosquitoes were collected for nine months in secondary forests , mature and immature rubber plantations , and villages . Human behavior data were collected using rapid participatory rural appraisals and surveys . Exposure risk was assessed by combining vector and human behavior and calculating the basic reproduction number ( R0 ) in different typologies . Compared to those that stayed in the village , the risk of dengue vector exposure was higher for those that visited the secondary forests during the day ( odds ratio ( OR ) 36 . 0 ) , for those living and working in rubber plantations ( OR 16 . 2 ) and for those that tapped rubber ( OR 3 . 2 ) . Exposure to JE vectors was also higher in the forest ( OR 1 . 4 ) and , similar when working ( OR 1 . 0 ) and living in the plantations ( OR 0 . 8 ) . Exposure to malaria vectors was greater in the forest ( OR 1 . 3 ) , similar when working in the plantations ( OR 0 . 9 ) and lower when living in the plantations ( OR 0 . 6 ) . R0 for dengue was >2 . 8 for all habitats surveyed , except villages where R0≤0 . 06 . The main malaria vector in all habitats was Anopheles maculatus s . l . in the rainy season and An . minimus s . l . in the dry season . The highest risk of exposure to vector mosquitoes occurred when people visit natural forests . However , since rubber workers spend long periods in the rubber plantations , their risk of exposure is increased greatly compared to those who temporarily enter natural forests or remain in the village . This study highlights the necessity of broadening mosquito control to include rubber plantations .
Today we have entered the Anthropocene epoch , in recognition of the major impact human beings have on the environment [1] . Many of the changes in land use and climate are also likely to increase the risk of vector-borne diseases [2–6] . One of the largest environmental changes in South-East Asia ( SEA ) has been the rapid expansion of rubber plantations . Natural rubber , obtained as latex from the rubber tree Hevea brasiliensis ( Willd . ex A Juss . ) , provides 42% of the global rubber [7 , 8] . In 2010 rubber plantations covered 9 . 2 million ha in SEA [9] , supplying more than 90% of the global demand for natural rubber [10] . Stimulated by the high profitability of this crop , the area cultivated for mature rubber in Lao PDR increased rapidly from 900 ha in 2010 to 147 , 500 ha in 2015 [11] . This is likely to increase to 342 , 400 ha of mature rubber plantations in the next decade , employing over 100 , 000 people [11] . Although rubber cultivation is decreasing with the slowdown in the Chinese economy , an estimated four and a half to six million workers will be needed to tap the mature rubber trees in the region in the next decade [12] . Outbreaks of mosquito-borne diseases such as malaria , dengue , and chikungunya have been reported in rubber plantations [13–16] . It has been suggested that rubber workers in SEA are at increased risk of malaria , as plantation workers tap latex at night when malaria vectors are active [13] . With the high number of migrant workers in the rubber plantations , there is fear that these plantations may aid the spread and increase the incidence of mosquito-borne diseases in the region . Surprisingly little work has been done to assess the importance of rubber plantations as a nidus for the transmission of mosquito-borne diseases in SEA . In this study we investigated the risk of exposure to dengue , Japanese encephalitis ( JE ) , and malaria vectors in relation to different patterns of behavior or typologies commonly represented in this part of northern Lao PDR , in order to understand which behaviors put people most at risk from mosquito-borne diseases .
The adult mosquito sampling and behavioral studies were conducted in Thinkeo ( 19°41’02 . 13”N 102°07’05 . 49”E ) , Silalek ( 19°37’02 . 80”N 102°03’05 . 70”E ) , and Houayhoy ( 19°33’03 . 22”N 101°59’42 . 42”E ) in Xieng-Ngeun and Nane district , Luang Prabang province . In each study site four common habitats were selected: secondary forests , mature rubber plantations , immature rubber plantations , and villages . The secondary forests were young forests consisting of young small trees with a high density of undergrowth . The mature rubber plantations were those where >70% of the trees were tapped for latex and the immature rubber plantations consisted of rubber trees less than five years old which have not been tapped for latex . The rural villages were small linear settlements of about 150 to 200 bamboo and cement houses . The risk of mosquito-borne disease is highest during the rainy season from May to October . Dengue and JE cases are relatively common , but according to data from Xieng Ngeun and Nane district health centers , malaria has not been locally-transmitted in our study area , with one to five malaria cases imported into the districts every year . Routine entomological measurements were made monthly for nine months from July to November 2013 and in February , March , May and July 2014 . A detailed description of the mosquito species collected in the different habitats during the adult mosquito sampling is described in [17] . A total of 78 human subjects gave written informed consent to participate and collect mosquitoes using the human-baited double bed net ( HDN ) trap [18] . This trap consists of a person on a bamboo bed ( 30 cm high x 230 cm long x 100 cm wide ) covered by two untreated bed nets ( small: 97 cm high x 200 cm long x 100 cm wide , mesh size 1 . 5 mm; large 100 cm high x 250 cm long x 150 cm wide , mesh size 1 . 5 mm ) . The internal net protects the occupant from mosquito bites , whilst the outer large net is raised off the ground and traps mosquitoes coming to feed . Mosquitoes were collected outdoors from between the nets at hourly intervals during the day and night . A total of 36 HDN traps were used i . e . three HDN traps in each of the four different habitats . Mosquitoes were morphologically identified to species or species complex using stereo-microscopes and mosquito identification keys of Thailand [19] . Daily and monthly activities of the rubber workers and villagers were described qualitatively in the three study sites in November 2013 using rapid participatory rural appraisals ( PRA ) [20] . All villagers and rubber workers from the study area were invited to participate in the discussions with a local translator present to facilitate the meeting . Participants were asked to complete timetables together , in which they recorded the intensity , from one to five , monthly and hourly according to their experience ( one: very low , five: very high ) for: rainfall , temperature , mosquito numbers , villagers feeling unwell and travel , including visits to secondary forest , latex tapping , collecting latex and rice production . A further survey was carried out in June 2015 , at the beginning of the rainy season , to collect information on the daily activities of the local population in the past 24 hours . The frequency of visits to the rubber plantations and the methods used to protect themselves from mosquito bites when outdoors was recorded . The study was conducted by a medical doctor fluent in the Lao language . For realistic representation of the different villages , 54 people per village were surveyed ( power ω = 0 . 8 , α = 0 . 05 and size effect of 0 . 5 ) [21] . Both studies were anonymous with no sensitive information collected . Mosquito survival was assessed in all habitats in Thinkeo during the rainy season in July and August 2015 . Two HDN traps were deployed in each habitat from 17 . 00–6 . 00 h . All Anopheles species previously identified as putative malaria vectors [22–29] and Aedes albopictus ( Skuse ) , previously identified as a putative dengue vector in Lao PDR [30 , 31] , were dissected to determine parity [32] . The basic reproduction number ( R0 ) for dengue and malaria was calculated in each habitat during both the rainy season ( May-September ) and dry season ( October-April ) . R0 is calculated based on the Ross-Macdonald model and is an estimate of the number of new infections derived from one infective case in a habitat before the patient dies or is cured [33–35] . Values greater than one suggest that the pathogen would persist in an area if introduced , and values less than one indicate that the pathogen would become extinct . R0 for dengue was calculated for Ae . albopictus , the only dengue vector in our study area , based on the following formulae ( 1 ) [36] , using parameters in Table 1 . The R0 for malaria was calculated for both Plasmodium falciparum and Plasmodium vivax malaria infections . We calculated the R0 for both parasites , since although 73% of all malaria infections in Lao PDR are due to P . falciparum [44] , the last malaria outbreak recorded close to our study area was caused by P . vivax . The R0 was calculated for the primary malaria vectors Anopheles maculatus s . l . , An . minimus s . l . , and An . dirus s . l . , using the following formula ( 2 ) [45 , 46] , with the description of parameters in Table 2 . The hourly mosquito sampling results were averaged for the nine months collection period to describe the daily activity of dengue , JE , and malaria vectors in the different habitats . The three PRA’s were summarized by taking the mean intensity of activities from the three appraisals . The study results were described as percentages . The exposure risk to the dengue vector Ae . albopictus , JE vector Culex vishnui s . l . , and malaria vectors was assessed using several behavioral typologies . The daily activities of villagers and rubber workers were associated with vector mosquito exposure risk , using the entomological and human behavioral data . The basic reproductive numbers were calculated as described earlier and compared for the different habitats . This study was approved by the Lao ethics committee ( approval number 017/NECHR issued 21-04-2013 ) and the School of Biological and Biomedical Sciences Ethics Committee , Durham University ( issued 25-07-2013 ) .
During the adult mosquito sampling 24 , 927 females were collected . Of these 8 , 585 were Aedes , with 6 , 302 Ae . albopictus . The greatest numbers of Ae . albopictus were collected in the secondary forests with similar numbers in rubber plantation habitats ( Fig 1 ) . Aedes albopictus were active throughout the day , from 06 . 00 to 18 . 00 h . A total of 5 , 022 Culex were collected , of which 3 , 562 were Cx . vishnui s . l . Culex vishnui s . l . showed peak activity in the evening from 18 . 00 h to 20 . 00 h for all habitats ( Fig 1 ) . A total of 1 , 341 Anopheles mosquito species were collected , of which 661 were putative malaria vectors , including An . maculatus s . l . ( n = 294 ) , An . barbirostris s . l . ( n = 170 ) , An . minimus s . l . ( n = 151 samples ) , and An . dirus s . l . ( n = 46 ) . Malaria vectors were collected in low numbers throughout the day and night . In the secondary forests An . barbirostris s . l . was mostly collected during the day and An . maculatus s . l . during the evening ( Fig 1 ) . In all the other habitats malaria vectors were generally collected between 18 . 00 to 21 . 00 h . The An . dirus s . l . mosquito samples collected in the different habitats showed similar behavior . About 67% of total An . dirus s . l . were collected between 18 . 00 and 22 . 00 h ( 30/46 ) , with the remaining samples collected between 01 . 00 and 05 . 00 h . All data has been deposited in the Dryad repository http://dx . doi . org/10 . 5061/dryad . 8nf05 [57] . Between 15 to 19 villagers , 16 to 60 years old , participated in a single two hour long PRA at each of the three study sites . During the rainy season ( May to November ) considerable time was spent cultivating rice , the staple food . Secondary forests were also visited during the rains , most frequently during daylight hours ( 05 . 00 h to 17 . 00 h; S1 Table ) , to collect food , wood , and other commodities . Occasionally the forests were visited at night to hunt small animals , like rodents and muntjacs . Rubber tapping also occurred in the rainy season with the trees tapped at night , between 02 . 00 h and 07 . 00 h , when latex flow is highest . Generally latex is collected from the latex collection cups in the morning from 07 . 00 h to 10 . 00 h . From 17 . 00 to 07 . 00 h usually most villagers were in the village to cook , clean , and sleep . Young children ( < 14 years ) , villagers who did not have to work and elderly villagers ( > 60 years ) stayed in the village throughout the day . From December to February , when there was no farming , some villagers travelled to other parts of Lao PDR and abroad to find work ( S2 Table ) . A total of 162 participants were surveyed to identify their movement in the last 24 hrs , of which 8 . 6% ( 14/162 ) were rubber workers . Usually villagers 14 to 55 years old leave the village during the day from 07 . 00 h to 17 . 00 h; with 40% ( 65/162 ) working on the farm , 10% ( 17/162 ) going to high school , 5% ( 8/162 ) working in rubber plantations , 3% ( 5/162 ) going to the forest and 3% ( 4/162 ) visiting Luang Prabang , the provincial capital . The remaining 39% ( 63/162 ) stayed in the village . More than 91% ( 147/162 ) of villagers and rubber workers stayed in the village at night the day before the study was conducted . They generally slept from 20 . 00 h to 05 . 00 h . The remaining 6% ( 10/16 ) slept in the farms and 3% ( 5/162 ) worked in the rubber plantations . One person spent the whole night in the secondary forest . About 77% ( 114/148 ) of the non-rubber workers visited the rubber plantations at least once every month ( range in age from one to 96 years ) to help with maintenance of the plantation area ( cutting undergrowth and clearing fallen trees ) , to collect fire wood , and to collect food such as mushrooms , insects , and edible plants . More than 90% ( 148/162 ) of participants had insecticide-treated bed nets in their houses . Furthermore , a total of 34% ( 55/162 ) of respondents used methods to protect themselves against mosquitoes when outdoors , with 60% ( 33/55 ) using mosquito coils and 35% ( 19/55 ) using the repellent N , N-Diethyl-meta-toluamide ( DEET ) . About 7% ( 4/55 ) of participants said they wore long sleeves to protect against mosquito bites and 2% ( 1/55 ) mentioned the use of lemongrass . We identified four distinct behavioral typologies: ( 1 ) villagers that visit the forest during the day , ( 2 ) villagers that work in the rubber plantations , ( 3 ) migrant workers that live and work in the rubber plantations , and ( 4 ) villagers that stay in the village .
We assessed how human behavior changes the risk of exposure to mosquito-borne diseases in rural parts of northern Lao PDR . This study shows that the greatest risk is associated with visiting secondary forest during the day; increasing the risk of dengue , JE , and malaria . Working in the rubber plantations also increases the risk of dengue , which is exacerbated when workers both live and work in the plantations . However , staying in the rubber plantations did not increase risk of exposure to JE vectors and decreased risk of exposure to malaria vectors . Our estimates of R0 show that the risk of dengue outbreaks in secondary forests , mature rubber plantations , and immature rubber plantations is extremely high , largely because of the high survival of the vector , Ae . albopictus . The villages are relative sanctuaries with values of R0 considerably less than 1 , indicating that the transmission of dengue would not be maintained . The R0 estimates also showed that the risk of malaria outbreaks in all investigated habitats is very high , with the most important malaria vector in the rainy season being An . maculatus s . l . and in the dry season , An . minimus s . l . Dengue is a sylvatic disease that has been spread from the forest to rural and urban areas by the highly adaptable vector Ae . albopictus , that has readily colonized a variety of different rural habitats [58 , 59] . In this study , we found a substantial risk of Ae . albopictus exposure and consequently risk of dengue infection in the natural forests and rubber plantations , compared with the villages . According to the behavioral analysis , both the natural and man-made forests are regularly visited by villagers . It therefore seems likely that the forest and plantation habitats are where dengue transmission occurs . As dengue is endemic in our study area and malaria is not , rubber workers should be encouraged to live in the villages instead of plantations . This is especially important for migrant rubber plantation workers , as presence in the village increases knowledge on diseases and lowers the threshold to get treatment . Worryingly , dengue vector control in the country is presently focused in the villages where the risk of transmission is low . There is therefore a clear need to broaden the control efforts to protect people when entering the surrounding forest and rubber plantations . In future studies , the presence of dengue infections in Ae . albopictus needs to be molecularly confirmed . Villagers that visit the secondary forests during the day are exposed to a higher number of JE vectors than when staying in the village . Japanese encephalitis infection risk is dependent on the presence of water birds , the reservoir hosts , and pigs , the amplifying host . Although there are pigs in the forests , there are considerably more water birds and pigs within and close to the villages , increasing the risk of JE infections in the villages . It is therefore important to take the local dynamics of the disease pathogens into account . Rubber workers that live in the villages are exposed to similar numbers of malaria vectors as the villagers staying at home , with the risk of malaria exposure dropping when workers both live and work in the rubber plantations . This is contrary to earlier suggestions from Thailand that rubber tapping activity increases exposure to malaria vectors [13] . Working in the rubber plantations at night from 02 . 00 to 10 . 00 h is not a risky behavior for malaria vector exposure in this study area , due to the early evening host seeking behavior of the malaria vectors An . maculatus s . l . and An . minimus s . l . However , the high R0 of malaria identified for all habitats does imply that if a malaria-infected person moves into the rubber plantations , the potential for a large number of new infections would arise , transmitted by An . maculatus s . l . and An . minimus s . l . We identified two ways in which malaria transmission could occur in the study area . Firstly , we found that local villagers often migrate to find temporary work in other areas of SEA . These migrant workers could be infected by malaria parasites in other regions and carry the parasites back to their own village . Secondly , many of the rubber plantations workers that live in the plantations are migrant workers that only live in the plantations during the rainy season . These migrant workers could introduce malaria parasites from other areas in SEA to the rubber plantation areas . In this study we have shown that the rubber plantations are visited regularly by the local population , indicating that the pathogens established in the rubber plantations could easily spread to the villages . Although malaria is currently not endemic in the study area , if malaria parasites are introduced , all necessary factors are present for an outbreak , and the establishment of malaria . Monitoring the malaria disease presence is thus essential in both the local population and migrant workers . Future entomological studies in the area should focus on the dissection of putative malaria vectors for the identification of sporozoites and oocysts , and focus on the molecular identification of malaria parasites , including the possible presence of Plasmodium knowlesi . Mathematical models simplify the complexity of natural systems . The R0 calculations in this paper are no exception . Our models do not consider the dynamics of the larval stages of the mosquitoes , spatial heterogeneity , interrupted feeding of Ae . albopictus , the vertical and sexual transmission of dengue viruses , nor the immune status of the population . The high basic reproduction numbers found in this study reflects the extraordinarily high mosquito survival rates calculated in this study , often exceeding 90% . Including human behavioral patterns is important for appropriate recommendations on disease control [60] . There is a lack of suitable methods to measure human behavior , especially on an individual scale , with limits to the predictability of human mobility [61–63] . There are a number of techniques commonly used to capture human movement , such as GPS tracking systems [64 , 65] , cellular phones [66] and photo voice [67] . In this study we used a combination of PRA’s and surveys to collect human behavior data , which is novel for vector-borne disease studies . The PRA’s and surveys do not result in detailed quantitative information . Both methods are sensitive to memory decay , social desirability , and other biases . Yet the two methods combined allowed us to describe broad patterns of human behavior and relate risk of vector-borne infections to villagers and rubber workers behavior . Identifying risky behaviors should help explain the heterogeneous pattern of vector-borne diseases , and result in more targeted disease control [61 , 68–72] . Currently mosquito control in Lao PDR focusses on the distribution of long-lasting insecticidal nets ( LLINs ) , indoor residual spraying ( IRS ) and larval source management ( LSM ) in the villages . The current control strategies are insufficient to control vector-borne diseases , with dengue and malaria outbreaks still occurring regularly . This study has highlighted the importance of secondary forest and rubber plantations in the mosquito-control strategies , specifically for the control of dengue . As in our study area , dengue is an important endemic disease and malaria is not , rubber workers could be encouraged to live in the villages , where dengue vector exposure is lower . Mosquito-control in rubber plantations should focus on the rubber worker houses inside the plantations and on outdoor control . For control in rubber plantation houses , similar methods can be used as in the villages; such as using LLINs , spatial repellents , and screening of houses [12] . For outdoor control , both personal protection and LSM is necessary . Personal protection methods should include motivating rubber workers to wear long-sleeved clothing and closed shoes when in the plantation . Additionally , insecticide-treated clothing , insecticide emanators , and portable insecticide coils could be used for personal protection [12] . However , these personal protection methods need to be further investigated to identify if vector-borne disease cases can be prevented . Rubber plantations provide a plethora of potential breeding sites including latex-collection cups [73–75] . Larval control in rubber plantations can therefore be achieved by draining the latex collection cups by turning them upside down . In forested areas , mosquito control is more challenging than the rubber plantation areas . Particularly larval control is difficult to implement in the natural forests due to the vastness and diversity of breeding sites , and the high biodiversity of other insects present . Emphasis should therefore be on personal protection methods , which are similar to the rubber workers . Additionally , insecticide treated hammocks could be used when staying in the forests overnight [76–78] .
This study demonstrates that entering secondary forest or rubber plantations represents a higher risk of dengue vector exposure than staying in the villages , where current vector control is focused . As rubber workers spend a substantial amount of time in the plantations , this increases their risk of dengue vector exposure compared to villagers who irregularly visit the natural forests or remain in the village . Rubber workers could be encouraged to live in the villages instead of the rubber plantations . Additionally , JE and malaria vector risk increases when visiting the forests during the day , but does not increase when working and living in the rubber plantations . This study highlights the importance of understanding human behavior in order to identify risky behaviors . Specifically , it demonstrates the necessity of broadening current vector control activities to include rubber plantations . | Rapid economic development in South-East Asia has resulted in a high demand for rubber , leading to the felling of natural forest and the expansion of rubber plantations . Hundred-thousands of people work in these man-made forests throughout the region , with some studies showing a higher risk of vector-borne diseases for rubber workers compared to typical village populations . In this study we assessed the risk of exposure to vector mosquitoes in relation to different typologies of human behavior . Whilst the highest risk of vector-borne diseases is in natural forest , those living and working in the rubber plantations are at higher risk of dengue and lower risk of malaria vector exposure than villagers that stay in the village . As dengue is endemic in our study area and malaria is not , rubber workers should be encouraged to live in the villages instead of plantations . Furthermore , vector-borne disease control in Lao PDR should broaden from its current focus on villages to include outdoor protection in both rubber plantations and forests , using larval control and personal protection methods . | [
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] | 2017 | Risk of exposure to potential vector mosquitoes for rural workers in Northern Lao PDR |
The organ of Corti in the cochlea is a two-cell layered epithelium: one cell layer of mechanosensory hair cells that align into one row of inner and three rows of outer hair cells interdigitated with one cell layer of underlying supporting cells along the entire length of the cochlear spiral . These two types of epithelial cells are derived from common precursors in the four- to five-cell layered primordium and acquire functionally important shapes during terminal differentiation through the thinning process and convergent extension . Here , we have examined the role of Six1 in the establishment of the auditory sensory epithelium . Our data show that prior to terminal differentiation of the precursor cells , deletion of Six1 leads to formation of only a few hair cells and defective patterning of the sensory epithelium . Previous studies have suggested that downregulation of Sox2 expression in differentiating hair cells must occur after Atoh1 mRNA activation in order to allow Atoh1 protein accumulation due to antagonistic effects between Atoh1 and Sox2 . Our analysis indicates that downregulation of Sox2 in the differentiating hair cells depends on Six1 activity . Furthermore , we found that Six1 is required for the maintenance of Fgf8 expression and dynamic distribution of N-cadherin and E-cadherin in the organ of Corti during differentiation . Together , our analyses uncover essential roles of Six1 in hair cell differentiation and formation of the organ of Corti in the mammalian cochlea .
In response to a variety of signals , the prosensory progenitors in the floor of the mammalian cochlear duct enter terminal mitosis and then differentiate into a mosaic of mechanosensory hair cells ( one row of inner and three rows of outer hair cells ) interdigitated with several subtypes of nonsensory supporting cells , including inner border , inner phalangeal , inner and outer pillar and three rows of Deiters’ cells aligned in a medial-to-lateral direction . Failure to correctly produce or maintain these epithelial cells in the organ of Corti causes deafness . Understanding how hair cell morphogenesis is regulated has significant clinical implications , as hair cells are susceptible to damage from a variety of insults and are unable to regenerate . The cochlea develops from the ventral portion of the otocyst , which elongates and begins to coil at E12 to reach a full 1 . 5 turns of the cochlear duct by E17 . 5 [1] . The prosensory progenitor cells proliferate to expand , and after reaching a defined number , exit the cell cycle from apex toward base between E12 . 5 to E14 . 5 to form a four- to five-cell layered non-proliferating precursor domain–the primordial organ of Corti , which is marked by expression of p27Kip1 [2 , 3] . Soon after their cell cycle exit , the precursors initiate cell-type specific terminal differentiation near the base toward apex from E14 . 5 and undergo unidirectional cellular intercalation movement called convergent extension to form the two layers of epithelial cells , a lumenal layer of hair cells and a basal layer of supporting cells [3–5] . The Sox family transcription factor Sox2 is known to specify the precursor cells [6] . As distinct cell types undergo their specific differentiation in the precursor primordium , Sox2 shows a differential pattern of expression that is highly maintained in supporting cells through adulthood but downregulated in hair cells , which are induced by the basic helix-loop-helix ( bHLH ) transcription factor Atoh1 [7] . Current in vitro experimental evidence suggests that Atoh1 and Sox2 may have a mutually antagonistic relationship , in which Sox2 expression represses Atoh1-induced hair cell formation and expression of Atoh1 in hair cells leads to downregulation of Sox2 [8–11] . However , whether Atoh1 directly antagonizes Sox2 activity in vivo and how Sox2 is downregulated in the differentiating hair cells remain unclear . Moreover , despite extensive research on identifying factors that are important for hair cell morphogenesis , how these individual factors interact to generate different types of epithelial cells with distinct shapes and functions in the organ of Corti is still poorly understood . It is even more unclear how these interactions are precisely regulated to induce robust epithelial morphogenesis of the cochlea . We have recently shown in the cochlear explant that Six1 of the homeodomain protein Six/So ( Sine oculis ) family interacts with Eya1 of the phosphatase-transcriptional coactivator Eya ( Eyes absent ) family to form a key transcriptional complex to activate Atoh1 expression to induce a hair cell fate by interacting with Sox2 [11] . Six1 is expressed in the otic placodal ectoderm as early as E8 . 75 and its expression becomes restricted to the ventral region of the otocyst where inner ear sensory organs form [12 , 13] . The importance of Six1 in inner ear development has been demonstrated by loss-of-function studies in mice and humans [12–15] . Mutations in the human SIX1 gene cause sensorineural hearing loss [15] , and the inner ear defects include either no or an undercoiled cochlea and absence or truncation of vestibular organs [16] . Loss of Six1 in mice leads to an early arrest of inner ear development at the otocyst stage [12 , 13] . More recently , a mouse model ( Catweasel ) carrying a novel point mutation ( Cwe ) within the Six1 homeodomain ( p . E121G ) has been identified through a large ENU mutagenesis screen and the Cwe/Cwe homozygous animals have severely truncated cochlea and semicircular canals [14] . Although the levels of Six1 expression have not been measured in Cwe/Cwe animals , the nature of the inner ear defects associated with this mutation indicates that it is a hypomorphic allele of Six1 . During later stages of inner ear morphogenesis , strong Six1 expression is maintained in the differentiating hair cells [12 , 13] . However , despite the absolute necessity of Six1 for inner ear development , it remains unknown how Six1 acts to drive sensory hair cell formation and the patterning of the organ of Corti . In the present study , we used tamoxifen-inducible Cre mice to conditionally delete Six1 after cochlea duct outgrowth to specifically investigate its potential role in auditory sensory epithelium development . Our data provides novel evidence to support a model in which Six1 serves as a critical factor for hair cell fate induction , differentiation and formation of the auditory sensory epithelium .
To specifically investigate the role of Six1 during the specification of sensory epithelial primordium in the developing cochlea , we used an inducible system to temporarily delete Six1 after cochlear duct outgrowth by crossing the conditional Six1flox mice [17] with Eya1CreER [18] or Sox2CreER [19] and administering tamoxifen from E11 . 5 to E12 . 5 before the sensory precursor cells exit cell cycle . Lineage tracing using R26RLacZ reporter confirmed that one dose of tamoxifen administration at E11 . 5 induced Eya1CreER-lineage traced cells in the GER and all cells in the organ of Corti at P0 ( S1A Fig ) [20] . Similarly , previous studies have shown that tamoxifen treatment at E11 . 5 and E12 . 5 induced Sox2CreER-lineage traced cells in the GER and all cells in the organ of Corti as well as those in the vestibular organs [21] . Next , we confirmed whether tamoxifen administration at E11 . 5–12 . 5 specifically deletes Six1 function in only hair cell precursors or also supporting cell precursors using Eya1CreER or Sox2CreER . Immunostaining with anti-Six1 revealed that Six1 is widely expressed in the cochlear epithelium at E12 . 5–13 . 5 ( S1B Fig ) , but Six1 CKO ( Eya1CreER;Six1fl/fl or Sox2CreER;Six1fl/fl ) showed a significant reduction in the levels of Six1 within the sensory region 1–2 days after tamoxifen treatment ( S1B Fig ) . At E17 . 5 , Six1 antibody appeared to label not only hair cells but also supporting cells and the flanking GER/LER cells ( S1C Fig ) , Six1 expression was lost in the hair cells in Six1 CKO cochlea using either Eya1CreER or Sox2CreER as a deletor ( S1D and S1E Fig ) . In addition , Six1 expression levels in some supporting cells adjacent to the GER also appeared to be reduced ( S1D and S1E Fig ) . However , Six1 signal was strongly maintained in GER cells , some supporting cells and LER cells in the mutant , suggesting that the expression of Six1 in those precursor cells is activated before removal of Six1 and maintained even after tamoxifen induction . Based on these data , we conclude that both Eya1CreER and Sox2 CreER are able to specifically delete Six1 function in the hair cell precursors within the organ of Corti . To assess the effect of loss of Six1 function between E11 . 5–12 . 5 on the establishment of the prosensory epithelial domain in the cochlea , we harvested inner ears from embryos at E14 . 5 and found that Six1Cko/Cko ( Eya1CreER;Six1fl/fl or Sox2CreER;Six1fl/fl ) inner ears were noticeably smaller in size compared to wild-type , Eya1CreER or Sox2CreER littermate controls ( n = 6 embryos for each genotype; S2A Fig ) . We performed co-immunostaining of E14 . 5 cochleae with anti-Sox2 antibody to label all prosensory progenitors and p27Kip1 to mark postmitotic precursors in the nascent organ of Corti ( Fig 1 ) . In wild-type embryos , the cochlea had already reached more than one turn and most Sox2+ progenitors had exited the cell cycle to become p27Kip1+ along the entire cochlear duct ( Fig 1A ) . In Eya1CreER or Sox2CreER littermates , the cochlea development appeared indistinguishable from that in wild-type controls at this stage ( n = 6 embryos , Fig 1B ) . However , in Six1 CKO littermates , shortening of the cochlear duct was evident at E14 . 5 ( Fig 1C and 1D ) , and its length was comparable to that of the E12 . 5 control embryos [1] . While the Sox2+ domain expanded medially in Six1 CKO samples along the length of the cochlear duct , most of Sox2+ cells still underwent cell-cycle exit to form the p27Kip1+ non-proliferating domain , which almost reached the base ( Fig 1C ) . However , some Six1 CKO embryos had fewer p27Kip1+ cells in the basal end ( n = 5 out of 11 embryos , arrows in Fig 1D , compare to 1A-C ) . This suggests that not all prosensory progenitors in the base completed their cell-cycle exit to become non-proliferating precursors in the CKO . Immunostaining for Sox2/p27Kip1 on sections showed medially widened Sox2+ domain in the CKO ( Fig 1E–1G ) . Statistical analysis of the width , height and area of Sox2+ and p27Kip1+ domain ( n = 2 cochleae and 15 sections per cochleae ) confirmed that the Sox2+ domain within the CKO cochlear epithelium is widened compared to that in littermate controls ( Fig 1H ) . To confirm that p27Kip1-negative Sox2+ progenitors in the base of the CKO cochlea are indeed proliferative progenitors , we injected the mitotic tracer 5-ethynyl-2’-deoxyurindine ( EdU ) at E14 . 5 and harvested inner ears at E17 . 5 . Co-immunostaining for EdU and Sox2 and quantitative cell counting confirmed that there were more EdU-incorporated hair cells and supporting cells , majority of which was located in the base , in the CKO mutant than in control littermates ( Fig 2A and 2C ) . More EdU-incorporated p27Kip1+ cells within the sensory epithelium were also observed in the base of Six1 CKO cochlea ( S2C and S2D Fig ) , while the Six1 CKO inner ears were also smaller in size compared to littermate controls ( S2B Fig ) . Together , these data suggest that there is a slight delay in the sensory epithelium development in the Six1 CKO . Next , we asked whether defective cochlear elongation between E12 . 5 and E14 . 5 prior to terminal differentiation of the precursor cells in Six1 CKO mutant might be , at least in part , due to defective cell proliferation by co-injecting EdU together with tamoxifen at E11 . 5 . Immunostaining and quantitative analysis indicated that EdU-incorporated Sox2+ cells were reduced to ~37% of those in the littermate controls at E14 . 5 ( Fig 2B and 2C ) . TUNEL assay revealed that the number of apoptotic cells was only mildly increased in the CKO at E12 . 5–14 . 5 compared to the number in littermate controls ( S3 Fig ) . Together , these data indicate that Six1 activity is necessary for normal cell proliferation of the epithelial progenitors and cochlear growth . Sox2 specifies prosensory progenitors and is expressed in all progenitor cells at the early stages , but later during differentiation its expression is downregulated in hair cells and becomes restricted to the supporting cells in the organ of Corti [6 , 8] . By E17 . 5 , Sox2 levels in hair cells located in the basal and medial cochlear regions are normally downregulated in comparison to its high expression levels in the supporting cells ( Fig 2A ) . However , we noticed that high levels of Sox2 are maintained in all cells within the Six1 CKO organ of Corti ( arrows , Fig 2A ) . This led us to speculate that Six1 may regulate the spatiotemporal pattern of Sox2 expression in the organ of Corti during differentiation . To rule out the possibility that the high levels of Sox2 in the cells within the lumenal layer in the CKO organ of Corti is due to developmental delay and confirm that those high Sox2+ cells are indeed hair cells , we harvested cochlea 1–2 days later at E18 . 5-P0 after tamoxifen administration at E11 . 5–12 . 5 and performed immunostaining for Sox2 and Myo7a , a marker specific for differentiating hair cells . While strong Sox2 expression is maintained in supporting cells through adulthood , relatively low Sox2 activity is detectable in GER ( greater epithelial ridge ) cells flanking the inner hair cells at E18 . 5 ( Fig 3A and 3B ) . In Six1 CKO , the cochlear duct not only appeared wider and thicker with discontinuation of Sox2+ domain in the base compared to that in wild-type controls ( arrows , Fig 3A; n = 6 ) , but also was shortened to 0 . 75- to 1-turn . Along with cochlear elongation between E14 . 5 to E18 . 5 , hair cell differentiation occurs near the mid-base and reaches the basal end and apex in a medial-to-lateral gradient to form one row of inner and three rows of outer hair cells along the entire length of the cochlea by E18 . 5 as marked by Myo7a ( Fig 3B ) . The hair cells are interdigitated by distinct subtypes of specialized supporting cells: one row of inner border cells and one row of inner phalangeal cells surrounding the inner hair cells , two rows of pillar cells ( one row of inner and one row of outer pillar cells ) lining the space between the inner and outer hair cells–the tunnel of Corti–and three rows of Deiters’ cells associated with the outer hair cells . In Six1 CKO cochlea , Myo7a+ hair cells were indeed present but they appeared irregularly with only one cell toward the base and more than four cells toward the apex . However , high levels of Sox2 expression were still maintained in Myo7a+ hair cells , as in the supporting cells that also appeared to be aligned irregularly . Furthermore , lower Sox2 activity appeared to expand medially into GER cells in the mutant ( arrows , Fig 3B ) . The observation of high levels of Sox2 expression in Six1 CKO hair cells was a surprising finding because it has been argued that Atoh1 is involved in the downregulation of Sox2 . To test this further , we examined the dependence of Sox2 levels on Atoh1 in hair cells by deleting Atoh1 from E14 . 5–15 . 5 using Eya1CreER . In situ hybridization of cochlea at E17 . 5 confirmed deletion of Atoh1 in the differentiating hair cells ( Fig 3C ) . However , immunostaining for Sox2 , Six1 and Pou4f3 revealed no detectable changes in Atoh1 CKO ( Fig 3D and 3E ) , which was consistent with previous observations detected by western blot and in situ hybridization for these genes [22] . Thus , deletion of Atoh1 in differentiating hair cells does not lead to upregulation of Sox2 . Based on these data , we conclude that Six1 activity is crucial for downregulation of Sox2 in the differentiating hair cells during cochlear development . As inner hair cells differentiate prior to outer hair cells , we next sought to characterize whether the Myo7a+ hair cells observed in Six1 CKO cochlea are inner hair cells , outer hair cells or both . Whole-mount immunostaining of cochlea at E18 . 5 revealed one row of inner and three rows of outer hair cells along the entire length of the cochlea in wild-type control ( Fig 4A and 4A” ) . In the Six1 CKO , while the length of cochlea was shortened to ~0 . 75- to 1-turn , Myo7a+ cells extended to the apical end but were missing in the base ( n = 6; Fig 4B ) . This suggests that hair cell differentiation toward the basal end fails to occur . Higher magnification analysis showed that hair cells that had formed in Six1 CKO had abnormal morphology and irregular alignment with an uneven numbers of hair cells on the mediolateral axis , ranging from one to multiple cells ( Fig 4B” ) . As seen on sections , the organ of Corti consists of two layers of epithelial cells , a lumenal layer of hair cells and a basal layer of supporting cells ( Fig 4A’ ) flanked by nonsensory epithelial cells in the GER/LER ( greater/lesser epithelial ridge ) . In contrast , Six1 CKO organ of Corti is retained as a four- to five-cell layered epithelium ( Fig 4B’ ) , which is almost comparable to the non-proliferating precursor domain in E14 . 5 control embryos . This clearly indicates a defect during terminal differentiation of the p27Kip1+ precursor cells in the mutant . Analysis of vestibular sensory organs showed largely reduced utricular and saccular macula with fewer hair cells and no hair cells in crista ampullaris in all three semicircular canals ( S4 Fig ) . Interestingly , all Myo7a+ cells in Six1 CKO cochlea are positive for Calretinin ( Fig 4D–4F ) , a marker specific for inner hair cells ( Fig 4C ) , suggesting that the hair cells developed in the CKO cochlea treated with tamoxifen between E11 . 5–12 . 5 might be inner hair cells . Quantitative counting revealed that the total number of hair cells in Six1 CKO cochlea ( Myo7a+Calretinin+ cells/total Myo7a+ cells = 370±24/370±24 per cochlea; n = 3 ) decreased to ~53% of the total number of inner hair cells in wild-type cochlea ( Calretinin+Myo7a+ cells/total Myo7a+ cells = 671±32/2784±148 per cochlea; n = 3 ) . Thus , the absence of outer hair cell formation in Six1 CKO is not likely due to a conversion of outer hair cells to inner hair cells , but rather caused by a failure of activation of the outer hair cell differentiation program . To further confirm our observation , we immunostained for the calcium-binding protein S100A , which labels inner hair cells , inner phalangeal cells and Deiters’ cells [23 , 24] ( Fig 5A ) . In Six1 CKO cochlea , S100A labeled not only all hair cells but also all supporting cells . This further suggests that the remaining hair cells in the CKO might be inner hair cells . In situ hybridization with Fgf8 riboprobe , a marker specific for inner hair cells [25] , revealed strong Fgf8 expression in the inner hair cells in E16 . 5 cochlea ( Fig 5B ) . However , Fgf8 expression was decreased in Six1 CKO littermates and appeared in the remaining hair cells at E16 . 5 ( Fig 5B ) . Quantitative real-time RT-PCR ( qRT-PCR ) confirmed a large reduction of Fgf8 expression in the mutant at E15 . 5 and E17 . 5 ( Fig 5D ) . Together , these results suggest that Six1 may regulate the maintenance of Fgf8 expression in the inner hair cells . In situ hybridization confirmed that Atoh1 mRNA is expressed in the hair cells of Six1 CKO cochlea at E15 . 5 , at which Atoh1 expression has not yet reached its apex in both controls and Six1 CKO littermates ( Fig 5C ) . However , its expression levels appeared to be lower in the CKO than in the control littermates ( Fig 5C ) , and this reduction was confirmed by qRT-PCR ( Fig 5D ) . Thus , while Atoh1 expression is induced in the postmitotic precursors , which only differentiate into inner hair cells in Six1 CKO treated with tamoxifen between E11 . 5–12 . 5 , Six1 may play a role in controlling the maintenance or upregulation of Atoh1 in hair cells during differentiation . As outer hair cells differentiate more than one day after the onset of inner hair cell differentiation , we next sought to further clarify our observation by administering tamoxifen more than one day later between E12 . 75-E13 . 5 or E13 . 5–14 . 5 to examine whether outer hair cells also form in the mutant . Analysis of E18 . 5 cochleae treated with tamoxifen between E12 . 75–13 . 5 showed that the Six1 CKO cochlea reached a full 1 . 5 turns , and that four rows of hair cells formed in the basal turn , but revealed a pattern of severity that parallels the normal process of hair cell differentiation , with outer rows more affected than inner rows in the medial turn , and no outer hair cells in the apex ( n = 6 , Fig 6A ) . A similar observation was obtained in P0 cochleae treated with tamoxifen between E13 . 5–14 . 5 ( n = 6 , Fig 6B ) . All hair cells formed in Six1 CKO cochlea also displayed abnormal morphology and maintained high levels of Sox2 ( Fig 6B ) . These data provide additional evidence that Six1 activity is necessary for hair cell fate specification and downregulation of Sox2 in the differentiating hair cells . We failed to observe S100A-negative pillar cells on all sections from the Six1 CKO cochleae ( n = 3 , Fig 5A ) , suggesting that the pillar cells are not formed in the mutant . We therefore further investigated whether loss of Six1 also results in loss of supporting cell subtypes , using specific marker gene analysis . Despite irregular shape , all Sox2+ supporting cells underlying the hair cells in Six1 CKO cochlea were positive for p27Kip1 at E18 . 5 ( n = 5 , Fig 7B and 7C ) , whose expression is normally restricted to all supporting cells , including Hensen’s cells flanking the outermost outer hair cells ( Fig 7A ) . Similar to Sox2 , p27Kip1 expression also showed medial expansion to the flanking GER cells ( arrows , Fig 7B and 7C , n = 3 ) . However , those cells expressed neither hair cell markers nor other supporting cell markers . For example , Prox1 , which is expressed in pillar cells and Deiters’ cells ( Fig 7D ) and thought to act downstream of Sox2 [8] , was found to be expressed in supporting cells underlying the hair cells but not in adjacent GER cells in Six1 CKO cochlea at E18 . 5 ( n = 4 , Fig 7E and 7F ) . However , the Prox1+ cells in the CKO failed to align into a characteristic one-cell layer , and there were five or more in the basal to middle region ( Fig 7E ) and up to ten Prox1+ cells underlying the two- to three-cell layered hair cells toward the apex ( Fig 7F ) . Inner border and inner phalangeal cells labeled by Glutamate-aspartate transporter ( GLAST ) ( Fig 7G ) were detectable in the medial region of the organ of Corti in Six1 CKO ( n = 4 ) . However , in contrast to their apical process that only surrounds the inner hair cells in the wild-type littermates ( Fig 7G ) , these GLAST+ cells in the mutant appeared to make apical process that surrounded all hair cells ( arrow , Fig 7H ) . As Prox1 is expressed in both inner and outer pillar cells , we next used inner pillar cell specific marker p75NTR to further clarify the presence of inner pillar cells in the CKO . Indeed , inner pillar cells labeled by p75NTR were present but showed changes in cell shape and cell contacts on the lumenal surface . During cochlear elongation , rearranging epithelial cells shrink junctions that are oriented perpendicular to the axis of extension and subsequently resolve such shrinkage to restore more isodiametric shapes . In control animals at E17 . 5 , inner pillar cells in the basal region are aligned in a single row with stable cellular contacts ( long junction ) ( Fig 8A ) . Dynamic remodeling of cellular contacts was seen toward the less differentiated apical region ( arrow in Fig 8A ) . In Six1 CKO , the inner pillar cells showed changes in morphology and there were three or more inner pillar cells in contact ( arrows , Fig 8B , n = 5 ) and cellular contact shrinkage ( arrowhead , Fig 8B ) throughout the entire cochlea , indicating a clear defect in cellular rearrangement in the absence of Six1 . Further examination also uncovered morphological alteration in hair cells in Six1 CKO . During differentiation , hair cells form actin-rich V-shaped stereociliary bundle with graded heights that are all individually aligned and point in the same direction toward the lateral side of the organ of Corti . This polarization process is initiated by the migration of the centrally positioned kinocilium to the periphery from ~E16 . 5 [26] . At P0 , the uniform orientation of hair cells and their interdigitation with nonsensory supporting cells on the lumenal surface with asymmetric and lateral distribution of kinocilium as outlined by F-actin and acetylated tubulin staining respectively were evident throughout the entire organ of Corti in wild-type controls ( n = 6 , Fig 8C ) . However , in absence of Six1 function , we found that individual hair cell orientation was severely affected ( n = 6 , Fig 8C ) . Collectively , our results demonstrate that absence of Six1 not only limits the normal extension of the cochlear duct , but also results in significant defects in cell shape within the plane of cochlear sensory epithelial sheet during terminal differentiation . As selective cell adhesion , mediated by cadherins , plays a pivotal role in regulating the shape and topology of the cells in tissue morphogenesis [27] , we therefore tested whether loss of Six1 leads to changes in adhesion by comparing the distributions of cadherins in controls and Six1 CKO littermates . At E15 . 5 , the organ of Corti has differentiated in the base and middle , and consists of one row of inner and three rows of outer hair cells interdigitated with nonsensory supporting cells as outlined by phalloidin staining ( Fig 8D ) . N-cadherin is expressed in the cochlea from E14 . 0 [28] and its distribution is restricted to cells medial to the outer hair cell from E15 . 5 ( Fig 8D ) . In Six1 CKO , the general integrity of the organ of Corti is not maintained and as expected , due to the expanded ( more than one-cell ) layer of hair cells ( Fig 4B’ and 4B” ) , the organization between hair cells and their interdigitated nonsensory supporting cells is apparently disrupted , similar to that observed at P0 ( Fig 8C ) . In Six1 CKO , N-cadherin expression was observed in the medial region , but its expression expanded to the lateral region of the organ of Corti ( n = 5 , Fig 8D ) . In contrast , E-cadherin is normally restricted to the region of outer hair cells and the region lateral to it from E16 . 0 ( Fig 8E ) , and its onset of membrane localization coincides with the stabilization of cell junctions in the region lateral to the inner pillar cells . However , the levels of E-cadherin at the cell membrane were reduced in Six1 CKO cochlea at E16 . 0 ( n = 4 , Fig 8E ) . By E18 . 5 , relatively lower levels of E-cadherin at the cell membrane were widely detectable in all cells within the sensory epithelium as well as in the GER cells in the CKO ( n = 4 , Fig 8F ) . These results indicate that loss of Six1 function alters the patterns of N- and E-cadherin distribution in the cochlea and the structure of the organ of Corti .
The role of Six1 in inner ear development has been previously investigated [12–14] . However , due to lack of inner ear formation beyond the otocyst stage in Six1-null mice , the molecular modules carried out by Six1 in the auditory sensory epithelium remain unknown . In cochlear explant , we have shown that Six1 forms a complex with Eya1 and Sox2 to activate Atoh1 expression to induce hair cell fate in the GER cells [11] . Here , we for the first time investigated the in vivo requirement of Six1 in auditory sensory epithelium specification and differentiation . Our analyses indicate that Six1 is crucial not only for proper fate specification but also for proper patterning of the precursor cells in the auditory sensory epithelium , which are necessary steps for the formation of the organ of Corti in the cochlea . Previous studies have shown that , in the otocyst , Six1 promotes both proliferation and survival of the otic epithelial cells [12 , 13] . However , Six1 activity does not appear to be crucial to cell survival in the developing cochlea as no significant difference in apoptosis was observed in Six1 CKO . Reduced proliferation in Six1 CKO cochlear epithelium suggests that Six1 plays a critical role in maintaining the prosensory progenitor cells at proliferative state in order to expand to a certain number , which explains shortened cochlea and a reduced number of hair cells observed in Six1 CKO . How does Six1 act to regulate cell proliferation ? We found that Six1 forms a complex with Eya1 , N-Myc and c-Myc proteins in E12 . 5–13 . 5 cochlea [11 , 29] . Myc proteins are known to be important for cell proliferation and growth , making it plausible to speculate that Six1 works together with its cofactors such as Eya1 and Myc proteins to regulate cell proliferation and growth . Defects in cell division and growth before terminal mitosis are likely to lead to shortened cochlea occurring in the Six1 CKO mutant . Our observation of the absence of outer hair cell formation in the Six1 CKO mutant provides the first in vivo evidence supporting a model in which Six1 serves as a key factor for hair cell fate specification ( Fig 9 ) . In cochlear explants , we found that Six1 forms a complex with Eya1 and Sox2 to synergistically activate Atoh1 to induce hair cell fate [11] . Given that Eya1 is a transcriptional coactivator interacting with DNA-binding proteins Six1 and Sox2 to transactivate Atoh1 expression , and the latter ( Sox2 ) is necessary for Atoh1 activation in vivo [6 , 11 , 30] , we have previously proposed a model in which Eya1 bridges Sox2 and Six1 to undergo protein-interaction-dependent and binding sequence-dependent conformational changes to form a compact and active complex capable of transcriptional activation of Atoh1 [11] . Based on this model , if all three genes are necessary for Atoh1 activation in vivo , deletion of any one of the three genes would lead to failure to induce Atoh1 expression and hair cell fate specification . Deletion of Eya1 in the differentiating hair cells using Atoh1-CreER at E13 . 5–14 . 0 fails to activate Atoh1 and results in the absence of both inner and outer hair cell differentiation in the apex [11] , demonstrating the necessity of Eya1 activity for Atoh1 activation in vivo , which is induced from ~E13 . 0 and becomes detectable at ~E13 . 5 [4 , 31] . So how do we explain the presence of Atoh1 expression in the Six1 CKO mutant cochlea ? There are two probable explanations . First , as it takes at least ~6 hours for tamoxifen to induce CreER in the nucleus , it is possible that weak Atoh1 expression might have already been induced in some precursors before complete removal of Six1 by tamoxifen administration from E11 . 5–12 . 5 . Such weak Atoh1 expression might have been sufficient to induce hair cell differentiation , which would also explain why outer hair cells failed to form and why the number of inner hair cells in the Six1 CKO decreased to ~53% of that of the wild-type control . Second , there may be functional redundancy with other members of the Six gene family . While Six4 mice are normal [32] , Six4 is coexpressed with Six1 in otic progenitors in the otocyst [13] and in the cranial placodes , and is known to play redundant roles with Six1 in cranial placode development as well as in several other organ systems [33–39] . Thus , it is possible that Six4 activity in the otic epithelial precursors may participate in complex formation with Eya1 and Sox2 to activate Atoh1 in the prosensory precursors . In support of this idea , we found that coexpression of Eya1 and Six4 in cochlear explants can also induce hair cell fate as efficiently as the combination of Eya1 and Six1 [11] . Besides Six4 , weak Six2 expression is detected in the cochlear epithelium [40] , where Six5 activity may also exist as mutations in human SIX5 [41] , SIX1 [15] or EYA1 [42 , 43] cause Branchio-Oto-Renal or Branchio-Oto syndrome . Thus , Six4 , Six2 or Six5 in the prosensory progenitor cells may participate in complex formation with Eya1 and Sox2 for initial Atoh1 activation , but this is clearly insufficient to replace Six1 in upregulating Atoh1 in the differentiating hair cells either due to the absence of their expression or low activity in the differentiating hair cells . While the relative ratio of Sox2 , Eya1 , Six1 and Atoh1 in the precursor cells may be important in specifying hair cells , our data clearly demonstrate that Six1 activity is necessary for specifying hair cell fate in vivo . The pattern of the outer hair cell defect induced by tamoxifen at later stages between ~E13 . 0–14 . 5 provides additional support for Six1 as a key factor in specifying the gradient pattern of hair cell differentiation longitudinally and laterally in the cochlea . In the normal organ of Corti , hair cell differentiation begins in the mid-base and extends not only laterally , but also towards both the basal end and the apex of the cochlea [4] . The longitudinal pattern of hair cell differentiation is also disrupted in the Six1 CKO , which had no hair cell formation in the basal end ( Fig 4B ) . Thus , Six1 is likely to play a key role in establishing the hair cell developmental program within the auditory sensory epithelial sheet . While detailed characterization of the Six1-regulatory network controlling hair cell development is an ongoing project in the laboratory , our observation of downregulation of Fgf8 in the Six1 CKO suggests that Six1 may regulate Fgf8 signaling pathway during differentiation . A key piece of supporting evidence for this has been recently obtained from our ChIP-seq analysis for whole-genome mapping of Six1 in the cochlea , which identified a Six1 peak near the Fgf8 gene . Characterization of this Six1-bound peak sequence in transgenic mice by cloning upstream of the Hsp68 minimal promoter driven-LacZ reporter cassette flanked by the H19 insulators has indicated that this enhancer fragment is a cochlear hair-cell specific enhancer ( J . Li , E . Loh , J . Xu , T . Zhang , L . Shen and P-X . Xu , manuscript in preparation ) . While detailed transgenic and mutational analyses of the Six1-binding site within this enhancer of Fgf8 in vivo are still currently underway , our data suggest that Fgf8 is a direct in vivo target of Six1 in the cochlea . In the organ of Corti , Fgf8 is expressed in the inner hair cells and previous studies have suggested that Fgf8 may act through its receptor Fgfr3 to regulate pillar cell development [25 , 44 , 45] . In Six1 CKO , inner pillar cells marked by p75 are present ( Fig 8A and 8B ) , but pillar cells that are negative for S100A appear to be missing ( Fig 5A ) . Since the inner pillar cell lies next to the inner hair cell , we speculate that outer pillar cell is more sensitive to the Fgf8 levels , and a decrease in the effective range of Fgf8 signaling due to reduced Fgf8 levels in the inner hair cells in Six1 CKO mutant might result in the lack of outer pillar cell formation . In addition to Fgf8 , Six1-bound regions associated with Fgfr1 have also been identified by Six1 ChIP-seq analysis ( J . Li , E . Loh , J . Xu , T . Zhang , L . Shen and P-X . Xu , manuscript in preparation ) . Given the similarity of the phenotype between the Six1 CKO and the Fgfr1 mutants , which range from missing the outermost row of outer hair cells in mildly hypomorphic Fgfr1 mutant to only residual numbers of inner hair cells in the most severe mutants , Six1 may also regulate Fgfr1 signaling in the organ of Corti . One intriguing finding of this study is the high level of Sox2 expression in the Six1 CKO hair cells . While Sox2 is expressed in type II hair cells in the adult mouse utricle [46] , it is absent from auditory hair cells . Sox2 is known to play a direct role in establishing the prosensory domain within the cochlea , but Sox2 alone is unable to induce Atoh1 expression [8] , as it interacts with Eya1 and Six1 to regulate the initiation of Atoh1 expression [11] . However , following the onset of Atoh1 expression in the hair cell precursors , Sox2 levels become downregulated in differentiating hair cells , and an antagonistic interaction between Sox2 and Atoh1 was suggested to play a role in this downregulation [8] . In Six1 CKO cochlea , although the expression level of Atoh1 appears to be lower than normal , it is sufficient to promote subsequent hair cell differentiation to generate Myo7a+ hair cells even in the presence of high levels of Sox2 . Thus , the capacity of endogenous Atoh1 to direct the hair cell differentiation program does not depend on its ability to downregulate Sox2 . In agreement with this view , forced expression of Atoh1 in cochlear epithelium in young mice is able to induce Sox2+ cells to become ectopic Myo7a+ hair cells in the GER [47] , but Sox2 levels appear to drop in Myo7a+ ectopic hair cells as their differentiation advances . Although detailed Six1 expression in postnatal cochlea has not been studied , the GER cells in young animals soon after birth are likely to retain some levels of Six1 , which is widely expressed in the cochlear epithelium at birth ( Fig 3 ) . Our data show that in the absence of Six1 , Sox2 does not appear to inhibit Atoh1 function in the differentiating hair cells . In the presence of Six1 , deletion of Atoh1 in hair cells at later stages does not lead to upregulation of Sox2 . Therefore , the antagonistic effect between Sox2 and Atoh1 is likely to be indirect and mediated through other factors , which may vary among different types of hair cells in the inner ear . This would explain why there are Sox2+ type II hair cells in the utricle and why a subset of Atoh1-induced high Sox2+ ectopic hair cells exist in the cochlea [47] . While future studies are necessary to determine how many factors are involved in Sox2 downregulation in the auditory hair cells and how Six1 works together with them to repress Sox2 expression , our finding of high levels of Sox2 in Atoh1+ hair cells in Six1 CKO cochlea uncovers a previously unknown function of Six1 in regulating the spatiotemporal pattern of Sox2 during the differentiation of the organ of Corti . Our data show that Six1 is essential not only for hair cell fate induction , but also for proper patterning of the postmitotic precursor cells in the sensory epithelium . The precursor cells undergo rearrangements through both mediolateral and radial intercalation to achieve extension and establish the mosaic structure between hair cells and supporting cells [4 , 48] . These processes require adhesion changes that allow cells to move and maintain adhesion , and cadherins are known to control differential adhesive properties of cells during morphogenesis [49] . In the cochlea , the adhesion junction proteins E-cadherin and N-cadherin at the cell membrane mark a sharp boundary between the inner and outer hair cells and a direct involvement of these proteins in convergent extension in the cochlea has been shown recently [28] . In Six1 CKO cochlea , the sharp border formed by the expression of N-cadherin or E-cadherin is disrupted and their expression is expanded to all cells in the organ of Corti . Such alterations in cadherin distribution are likely to lead to cellular morphological alterations . These data together support a role for Six1 in establishing the mosaic structure between hair cells and supporting cells in the organ of Corti . Lastly , it is worth mentioning that Six1 gene dosage may have differential effect on the development between inner and outer hair cells , as the hypomorphic Cwe/+ heterozygous mice have extra inner hair cells in the apex of the cochlea , while their outer hair cells appear unaffected [14] . Similar phenotype also occurs in Eya1+/- [50] and Eya1CreER mice ( Fig 6A ) . Thus , it is possible that Six1 may be required at a certain level at specific time points in cochlear development to regulate a different set of genes between inner and outer hair cell precursors that are particularly sensitive to the level of Six1 . Six1 may mediate organ of Corti formation through Fgf signaling , including Fgf8 signaling . While allelic series of Six1 will provide insight into its dosage effect on Fgf signaling and inner ear development , whole-genome mapping of Six1-DNA interactions and identification of its direct targets at different developmental stages will be necessary toward understanding how sensory progenitor cells use Six1 to create precise patterns of gene expression and cell differentiation to shape and generate a functional organ of Corti for hearing in the mammalian inner ear .
All animal protocols were approved by Animal Care and Use Committee of the Icahn School of Medicine at Mount Sinai ( protocol #06–0807 ) . Six1Flox [17] , Eya1CreERT2 [11] , Sox2CreERT2 [19] , and Atoh1Flox ( JAX # 008681 ) mice were maintained on a 129/Sv and C57BL/6J mixed background at the Icahn School of Medicine at Mount Sinai Animal Facility . Mice were bred using timed mating , and noon on the day of vaginal plug detection was considered as E0 . 5 . For induction of the CreERT2 protein , tamoxifen ( Sigma , T5648 ) was dissolved in corn oil ( Sigma , C8267 ) and administrated ( 1 . 5 mg/10 g body weight ) by oral gavage . Observed variations among Six1 mutants is likely due to pre-existing developmental variation between embryos when tamoxifen was given . Histological examination , whole-mount and section immunostaining and ISH were carried out according to standard procedures . Briefly , inner ears were fixed in 4% paraformaldehyde ( PFA ) for 1 hr at 4°C , dehydrated , and embedded in wax . Paraffin sections were generated at 6 μm . For ISH , tissues were fixed overnight . We used five embryos for each genotype at each stage for each probe and the result was consistent in each embryo . Primary antibodies: anti-Sox2 ( PA1-094 , Thermo Fisher ) , -Myo7A ( 25–6790 , Proteus and 138-1-s , DSHB ) , -Six1 ( HPA001893 , Sigma ) , -Atoh1 ( Math1-s , DSHB ) , -p27kip1 ( 554069 , BD Pharmingen ) , -Calretinin ( MA5-14540 , Thermo Fisher ) , -p75NTR ( #07–476 , EMD Millipore ) , -N-cadherin ( 610921 , BD Bioscience ) , -E-cadherin ( U3254 , Sigma ) , -S100A ( ab11428 , Abcam ) , -GLAST ( ab416 , Abcam ) , -Pou4f3 ( sc-81980 , Santa Cruz ) , -Prox1 ( AB5475 , Millipore ) , -Acetylated tubulin ( T7451 , Sigma ) , -Cy3- , Cy2- , Cy5- and FITC-conjugated secondary antibodies were used . Alexa Fluor 488 or 350-conjugated phalloidin ( A12379 and A22281 , Life technologies ) were used for actin staining . Hoechst 3342 was used for nuclear staining . The EdU assay was performed using a kit ( catalog no . C10640 , Life Technologies ) following the manufacturer’s instructions . EdU was co-injected with tamoxifen at 9 am of E11 . 5 and embryos were harvested at noon of E14 . 5 . EdU was also injected at noon of E14 . 5 embryos following tamoxifen treatment at 9 am of E11 . 5 and embryos were harvested at noon of E17 . 5 . The TUNEL assay was performed using the Apop Tag kit for in situ apoptosis fluorescein detection ( catalog no . NC9815837 , Millipore ) following the manufacturer’s instructions . Whole inner ears collected from E15 . 5 or E17 . 5 embryos were divided into two parts with forceps and the cochlear parts , which also contained the spiral ganglion , were used for total RNA extraction using Trizol Reagents ( Invitrogen ) . Total RNAs were treated with RNase-Free DNase Set ( QIAGEN ) and then used for reverse transcription using a SuperScript IV Reverse Transcriptase ( Thermo Fisher Scientific ) for first-Strand cDNA Synthesis . Gene specific primers and SYBR Green Master Mix ( Applied Biosystems ) were used for PCR amplification using the Applied Biosystems StepOnePlus Real-Time PCR Systems . Expression levels of each transcript were normalized using β-actin as an internal control . Each set of experiments was repeated three times , and the DDCT relative quantification method was used to evaluate quantitative variation . Two-tailed Student's t test was used for statistical analysis . Primers used are as follows: for Atoh1 , forward primer-5’-GCTTATCCCCTTCGTTGAACT-3’ and reverse primer-5’-TGCTATCCAGGAGGGACAGTTCTG-3’; for Fgf8 , forward primer-5’-ACGACATTCCACGAGCCGCGTC-3’ and reverse primer-5’-GAAGGGTCGGTCCTCGTGTCCCT-3’; and for β-actin , forward primer 5’-AACGGCTCCGGCATGTGCAAAG-3’ and reverse primer 5’-ACACGCAGCTCATTGTAGAAG-3’ . EdU-incorporated Sox2+ prosensory progenitor cells in the floor of the cochlear epithelium were counted in basal , medial and apical turn of the entire cochlea . Values represent average number of EdU+Sox2+ cells ( ±standard deviations ) per section ( 6 μm ) or per cochlea . Width and height of the Sox2+ or p27kip1+ domain at E14 . 5 were measured on sections ( height at 6 μm/section ) for spatial calibration using Image J software ( NIH ) . 15 sections per cochlea and 2 cochleae for each sample were measured . Two-tailed Student's t test was used for statistical analysis . | Auditory sensory hair cells and surrounding supporting cells are derived from common prosensory progenitors , which undergo rearrangements through intercalation to achieve extension and establish the mosaic structure between hair and supporting cells . Hair cells are susceptible to damage from a variety of insults and are unable to regenerate . Through temporal deletion of Six1 in the developing cochlea , we found that Six1 activity is crucial for proper hair cell fate specification and for the regulation and maintenance of the spatiotemporal pattern of Sox2 , Fgf8 and E- and N-cadherins during differentiation . Our data uncover novel roles of Six1 in hair cell differentiation during the formation of the organ of Corti . | [
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"neurons"
] | 2017 | Six1 is essential for differentiation and patterning of the mammalian auditory sensory epithelium |
The spikes on virus surfaces bind receptors on host cells to propagate infection . High spike densities ( SDs ) can promote infection , but spikes are also targets of antibody-mediated immune responses . Thus , diverse evolutionary pressures can influence virus SDs . HIV’s SD is about two orders of magnitude lower than that of other viruses , a surprising feature of unknown origin . By modeling antibody evolution through affinity maturation , we find that an intermediate SD maximizes the affinity of generated antibodies . We argue that this leads most viruses to evolve high SDs . T helper cells , which are depleted during early HIV infection , play a key role in antibody evolution . We find that T helper cell depletion results in high affinity antibodies when SD is high , but not if SD is low . This special feature of HIV infection may have led to the evolution of a low SD to avoid potent immune responses early in infection .
Viruses gain entry into their host cells by attaching to specific receptors on the host surface . The proteins that mediate entry comprise the viral spike . Since the host receptor does not mutate rapidly , spike proteins , while often being highly mutable , have conserved regions that bind to elements on the host receptor . For example , the HIV spike protein gp120 contains relatively conserved residues that bind to the CD4 co-receptor on T helper cells . In influenza , the spike is composed of a HA glycoprotein , that attaches to sialyl-oligosaccharide , which is a sugar found in many cell surface proteins [1] . From the standpoint of mediating cell entry and thus propagating infection , it is evolutionarily beneficial to exhibit a high concentration of spikes on the virus surface , thus increasing the probability of attaching to host cell receptors [2] . But , parts of the proteins that comprise the viral spikes are also the targets ( epitopes ) of antibodies produced by the humoral immune response . A lower spike density ( SD ) would hinder antibodies from binding to the same epitope on two spikes on the viral surface simultaneously with its two arms , thus taking advantage of cooperativity of binding by the two arms ( avidity ) [3] . Thus , there is also an evolutionary driving force for viruses to evolve a low SD . But , evasion of potent immune responses may not always favor a low SD . For example , in influenza , hypervariable features at the head of the spike have high immunogenicity . A high SD protects the more conserved domains near the stem from being targeted by antibodies [4] . In HIV , the conserved regions are partially protected from the action of antibodies by a shield of glycans or by their membrane-proximal location ( a high SD would presumably better shield the latter epitopes ) . Furthermore , many immunogenic epitopes that do not include any conserved residues are also present on the HIV spike . Available data indicate that most viruses express a very high number of spikes on their surface . For example , Influenza has around 450 spikes or a SD of 1 spike per 100 nm2 [5] , HCV has 250 spikes or SD of 1 . 73 per 100 nm2 [6] . Table 1 summarizes much of the available information on SDs of common viruses , and this data shows that HIV is an extreme outlier , exhibiting between 7 and 14 spikes on its surface , resulting in a very low SD of 0 . 01 spikes per 100 nm2 , which is 50–100 times lower than that for other viruses [7] . So , it appears that most viruses evolved high SDs ( presumably enhancing infectivity and possibly distracting the immune system from targeting more conserved epitopes ) , while HIV has not . If HIV has evolved a low SD to avoid potent antibody responses , why have other viruses not employed the same strategy ? If HIV spike proteins were significantly more vulnerable to antibody responses compared to other viruses , this could explain why HIV has evolved a significantly low SD to lower the avidity of antibody binding to epitopes on the spike proteins . But , no evidence exists suggesting that this is true . Another possibility would be that HIV as a retrovirus , has some fundamental architecture constraints on the maximum number of spikes it can display . However , MLV ( another retrovirus ) has a high surface density [8] with at least a hundred spikes on its surface [9] . Shedding of the spike ( gp120 ) as an immune decoy could be another reason for the low spike number . However , high spike density viruses such as Ebola also shed their glycoprotein spikes [10] . Hence , this mechanism is not sufficient to explain HIV’s uniqueness of low spike density . Thus , an obvious long-standing question remains unanswered: why has HIV evolved to exhibit a significantly lower SD compared to other viruses ? Upon infection with pathogens , high affinity antibodies develop by a Darwinian evolutionary process called affinity maturation ( AM ) . We inquired if the biology of HIV may influence the effects of SD on AM in a way that is not characteristic of other viruses , and whether this is the underlying reason for a low SD being favored by HIV . To explore this possibility , we developed a coarse-grained computational model of the dynamics of AM . Results of our calculations show that antibody affinity to the epitopes on the viral spike is a function of its SD for all cases . In particular , highest affinity antibodies are produced for an intermediate SD . To the best of our knowledge , this effect of SD on the resulting Ab affinity has not been reported before . Importantly , we find that the decline in antibody affinity when SD exceeds the optimal value is very gradual , while the affinity declines sharply for SDs below the optimum density . These results suggest that a high SD ( beyond the optimum defined above ) allows viruses to exhibit high infectivity and evade potent responses directed toward mutationally vulnerable epitopes if they are located at the stem of the spike , while also reducing the affinity of antibodies directed toward the spike . Note , however , that this still allows the immune system to generate reasonably high affinity antibodies as the decline in affinity for SDs higher than the optimum is gradual . Why is HIV different ? Our calculations suggest that the answer lies in a key feature of HIV infection . HIV principally infects T helper cells ( CD4 T cells ) . We find that if T helper cells become extraordinarily limiting during affinity maturation , as is the case immediately following HIV infection , then high spike densities will elicit even higher affinity antibodies—a bad outcome for the virus . We find that this is circumvented if the SD is low . Therefore , our results suggest that a key benefit of a lower SD for HIV is an avoidance of high affinity antibody responses that would otherwise be produced if the SD was high when T helper cell availability becomes much more limiting than usual . However , the tradeoff for having low SD is reduced infectivity [11] , a long noted feature of this virus . The virus’s low infectivity has not prevented HIV transmission from reaching epidemic proportions; perhaps , because of its high replication rate in infected hosts .
Antibodies develop in domains within secondary lymphoid organs called germinal centers ( GCs ) , which appear shortly after infection [12] . B cells with a moderate threshold affinity for the antigen ( Ag ) are activated and seed GCs . These B cells then undergo an evolutionary process of mutation and selection that results in B cells with higher affinity receptors as time ensues [13] . The AID gene introduces mutations into the B cell receptor ( BCR ) at a high rate in GCs . The mutated B cells undergo selection against Ag , which is displayed on Follicular dendritic cells ( FDC ) on immune complexes ( IC ) [14] ( Fig 1a ) . The B cells attempt to capture Ag by forming transient synapses with the FDCs [15] . Captured Ag is processed and presented on their surface as peptide-MHC class II complexes . The B cells then compete with each other to bind to T follicular help cells ( TfhCs ) via interactions between these peptide-MHC complexes and the T cell receptor on the surface of TfhCs . Successful binding results in a survival/proliferation signal . B cells that display more peptide-MHC complexes have an advantage in this competition[16] . The majority of positively selected B cells undergo further rounds of mutation and selection [17–19] . Some of the positively selected B cells differentiate into antibody-secreting plasma cells and memory cells . As time progresses , antibodies with increasing affinity for the Ag are generated . Affinity maturation has been studied extensively using theoretical models over the last decades , mostly using population dynamics approaches [19–24] and more recently , using detailed simulation of the dynamics of the immune cells invovled in the GCR [25] . To explore how B cell selection and the generation of high affinity Abs depend on Ag concentration and presentation , we constructed a simplified model of Ag capture from the FDCs , and a coarse-grained model of B cell selection in a GC . Thus , our model generalizes previous modeling approaches to include the interaction of the BCR with Ag ( spike ) . We are interested in ICs presenting a virus , or a virus fragment ( Fig 1a ) . The Ags of interest are the spike proteins distributed on the virus surface . Assuming that the spikes are distributed on the surface of the virus of radius R , with average density nAg , it contains a total of MAg = 4πR2nAg spikes ( Fig 1b ) . A virion particle with a radius of 120nm ( Table 1 ) with spike density of 0 . 35 spikes/100nm2 , has about MAg = 160 spikes on its surface . During the Ag capture process , BCRs scan for Ag over a region of the synapse and encounter an Ag molecule with probability p . We assume that the number NAg of Ag molecules in the scanning area is distributed randomly , according to the Binomial distribution ( NAg ~ B ( MAg , p ) ) . Consistent with data showing that B cell protrusions that extended toward FDCs retract if the associated BCRs do not find Ag [26] , we assume that the BCR has a characteristic time to find the Ag ( see Methods ) . If it does not bind to Ag during that time , no Ag is captured ( see Fig 1ci and 1cii ) . Once one of the arms of the BCR binds to Ag , an Ag molecule may be pulled away by force[27] . At this stage , there is a tug-of-war over the Ag between the BCR and the IC ( see Fig 1ciii ) . Characteristically , when the binding energy between the BCR and the Ag is much larger than the binding energy between the Ag and the virus/Fc receptor/FDC membrane ( see Fig 1 ) , the Ag will be captured by the BCR . We denote the potential interaction energy required to extract the Ag by EAg−mem , and the interaction energy of the BCR/Ab and Ag by EAg−Ab . A successful Ag capture event occurs when the bond between the Ag and the membrane ruptures before the bond between the BCR and the Ag ( see Methods ) . When the off-rate of the BCR arm from the Ag is smaller or of the order of the effective on-rate ( qNAg ) , and if one arm is attached initially ( see Fig 1cii ) , the second BCR arm can bind to another Ag molecule if one is available . Thus , a pulling attempt can have three possible outcomes , with zero , one or two Ag molecules captured ( see Fig 1d ) , depending on the binding affinity and the number of available and accessible Ag molecules NAg . In the first days following infection or immunization , M different clones ( unique BCRs ) of activated B cells proliferate with little competition , creating a pool of cells on which AM operates[28] . Few or no mutations are introduced to the BCR sequence at this early stage . We use a simple birth/death process to describe this initial growth stage . B cells then start to mutate , and whether or not a B cell is subsequently positively selected depends on its ability to internalize Ag and compete with other B cells for survival signals by interacting with TfhCs . TfhCs have an important role in regulating the duration of the cell cycle in B cells during AM [17 , 18] . Following a TfhC signal , B cells divide ( and mutate ) multiple ( 4–6 ) times before going back for another round of selection [29–31] . Most theoretical models enforce selection by eliminating cells with low affinity BCR [23 , 24] . It has been shown [17] that TfhCs control the rate at which B cells go through the cell cycle . B cells that receive strong proliferation signal from the TfhCs divide multiple times in the dark zone before going back to the light zone . We model this behavior by using a birth-rate for B cells which is proportional to the amount of captured Ag . The proliferation rate of B cells is a function of the number of captured Ag molecules , further modulated through competition for TfhCs . Indeed , it was shown [32] , that TfhCs preferentially interact with B cells that have captured more Ag , presumably giving them a stronger proliferation signal . To mimic this competition we assume the birthrate of B cell i to be: βi=β0C+AiC+〈A〉 , ( 1 ) where 〈A〉 is the average amount of Ag consumed by all B cells that captured at least 1 Ag ( B cells that do not capture Ag have zero birth rate ) ; β0 is the basal birthrate , and C is a measure of the availability of TfhC help . When C>>〈A〉 , all B cells get the same amount of metabolic boost and divide at the same rate β0 . When C<<〈A〉 TfhC help is limiting and the cell birthrate is proportional to the amount of captured Ag . The GC has a limited capacity and grows during this competitive phase according to a stochastic logistic growth process . Thus , the selection process depends on the number of B cells in the system through the logistic death rate ( Methods Eq ( 6 ) ) . This logistic term accounts for the increased competition between B cells to receive a survival signal from TfhCs when the number of the latter is limiting . TfhCs are essential for GC maturation of B cells , and without them , AM of B cell cannot occur [33] . We assume here that the number of TfhCs is not so small as to stop the formation of the GC . Thus , at the limit where C goes to zero , our model does not corresponds to the absence of TfhCs in the GC . However , we do use the reduction of C to model increased B cells competition in a GC when the number of TfhCs is gradually reduced , as occurs during the first stages of HIV infection . For these reasons , we do not consider here the limit of C→0 . In a more complete proliferation model C should depend on the total number of B cell in the GC and thus change with time . However , the qualitative behavior of our model would not change if the parameter C would have such explicit dependency on the GC size as upon HIV infection we expect Tfh levels to be smaller at all times than upon infection with other pathogens . During AM , B cells mutate the BCR encoding gene using the AID enzyme . This results in changing cytosine to tyrosine on one DNA strand [34] . We modeled the effect of mutation as a change in the on-rate q or the interaction energy EAg−Ab upon cell division ( see Methods ) . We convert the interaction energy to the off-rate by r0=e−βEAg−Ab , and the affinity of the BCR is calculated as ω = q/r0 . Our simplified model assumes that affinity improvements and reductions ( through q or EAg−Ab ) are equally likely following mutation and that all mutations change affinity . These choices are certainly not realistic , but making advantageous mutations rarer than deleterious ones , or making some of the mutations silent or lethal ( as in reality ) do not change the qualitative results . To explore the role of SD on AM , we calculated the affinity of the most dominant clone at the end of the GC reaction ( GCR ) , which is day 16 of the competitive phase . The affinity of this clone will play an important role in the resulting humoral immune response . An important observation is that the affinity of the dominant clone varies non-monotonically with the SD ( nAg ) . Fig 2a demonstrates this finding for a TfhC level that was limiting across the range of nAg ( SD ) spanning 0 . 01 to 0 . 36 . There is also a pronounced asymmetry in the variation of affinity with nAg . As nAg increases from very small values , there is a sharp rise in affinity , while its drop for larger nAg is gradual . The number of B cells in the GC is not reduced when SD is high . Since B cells have to capture at least 1 Ag molecule in order to attempt proliferation , the average proliferation rate increases with SD . These qualitative results are robust to variations in the other parameters in the model ( S1 Fig ) , and for mutations resulting in changes in q ( S2 Fig ) or EAg−Ab ( Fig 2a ) . ( The variation of affinity with SD is less pronounced when mutations change q for technical reasons described in S2 Fig . ) In the GC , memory and plasma B cells are produced throughout the GCR [12] . As a proxy for the affinities of memory and plasma B cells produced during the maturation process , we randomly selected 10% of the B cells at each intermediate time point . The resulting average , displayed in ( S3 Fig ) still exhibits a non-monotonous dependence on SD similar to that observed in Fig 2a . The mechanistic reason underlying these results relates to the amount of antigen that is internalized by B cells as nAg changes ( Fig 2b ) . Upon increasing nAg from a small value , the amount of Ag internalized by B cells grows significantly as it becomes more probable to bind one or two Ag molecules ( see Fig 2c ) . However , once the SD is sufficiently high , most BCRs bind/internalize on average the same amount of Ag irrespectively of their affinity ( if it is above a threshold value ) , and it is difficult to distinguish B cells based on the affinity of their receptor for the antigen . Competition between B cells to be positively selected is not severe in the latter regime , limiting the driving force to increase affinity by further mutations . Thus , the affinity of the antibodies produced begins to decline beyond an optimal SD . The gradual drop-off in affinity as nAg becomes too high , compared to the sharp decline when nAg becomes too low , is because the number of internalized antigens rises very sharply as nAg increases from a low value , but then slowly saturates to its maximum for sufficiently high SDs as depicted in Fig 2b . In the limit of high nAg , there is a finite probability for the BCR to capture 1 or 2 Ag mols . At this limit , the probability to extract one Ag mol is given by r/ ( r + λ ) and the probability to extract two Ag mols is λ/ ( r + λ ) , where λ is the off-rate of an Ag mol from the membrane when one arm of the BCR is bound and r is the rupture rate of the BCR from an Ag mol when one arm of the BCR is bound . Our model suggests that clonal diversity also varies with SD . When SD is low , antigen is scarce and the selection forces will be fiercer . That could result in a more rapid loss of diversity . We estimate diversity by computing the fraction of the GC comprised of the dominant clone ( S2b Fig , S4a Fig ) , which we denote by fd . For very low SD a few dominant clones comprise most of the GC’s B cells at the end of the GCR ( large fd ) . This is because for low SD , selection during AM is dominated by the large competitive advantage , compared to most B cells , of a few B cells that internalize more antigen due to stochastic fluctuations . As the SD increases , a greater number of B cells can internalize antigen successfully and the GCR produces a more diverse clonal population . The fraction of dominant clones has a large variability across different GC realizations . Indeed , it has been shown [31] that while some GCs appear to have been taken over by a single clone at day 16 , others show high clonal diversity . This is the direct result of stochastic selection forces at play in the GC [35] . A similar behavior is observed in our GC model ( see S4 Fig ) . Here , the source of stochasticity is the random Ag capture process , as well as the stochastic proliferation and death of B cells . While the fraction of the dominant clone mostly decreases with increasing SD , the higher affinity of the BCR at intermediate density contributes to a small increase in fd . Thus , fd is not monotonically decreasing with SD . Indeed , the improved selection at an intermediate density leads to greater loss of diversity in this range , which appears as a small bump in fd ( approximately between nAg = [0 . 1 , 0 . 18] , see inset S4 Fig ) . This effect is more pronounced when the overall diversity loss is slower as a result of a smaller death rate ( see S1 Fig ) . To understand how cell selection in AM is related to Ag density , we estimated the probability Pweaker aff selection that , in any given round of mutation and selection , a B cell with a lower affinity for the antigen gets selected and expands in favor of another B cell that has a higher affinity . A B cell with a higher affinity receptor is likely to internalize more antigen , be more successful at obtaining a survival signal from T helper cells and proliferate more . However , because Ag capture is probabilistic , and the birthrate relates to the amount of captured Ag , there is a chance that a B cell with lower affinity will be selected in favor of one that expresses a higher affinity receptor . To empirically estimate pweaker aff selection , we sampled B cell pairs from the affinity distribution computed in our simulations ( see S5 Fig ) . We detail how the probability is estimated in the methods section . Interestingly , we find that the probability changes with time , and depends on Ag density ( Fig 2d ) . Importantly , pweaker aff selection is minimal at intermediate Ag density , at a value similar to the maximum for the BCR affinities . Thus , very high or low SDs lead to poor differentiation between higher and lower affinity B cells . Because the B cells with a higher affinity are most likely to be chosen to proliferate at an intermediate SD , selection is strongest for such SD , and thus highest affinity antibodies evolve . To further elucidate the origin of the maximum in antibody affinity at an intermediate SD , we studied the time evolution of the mean affinity of the B cell population . We aimed to find the dependency of the optimal density on the rates of different processes in our model , rather than attempt to recapitulate our simulation results . Assuming that the time τ to find the first Ag molecule before retracting the B cell protrusion is very long and that each B cell has only a single BCR , we find a mean-field equation ( see SI ) for the evolution of the mean on-rate q¯ τ0dq¯dt=cov ( q , A ) 〈A〉+C1 , ( 2 ) where 〈A〉 is the average amount of captured Ag by the B cell population ( here 〈A〉 is between 0 and 2 ) , cov ( q , A ) is the covariance between the number of captured Ag molecules A and q , C1 is the parameter that corresponds to TfhC availability parameter and τ0 ∝ ( β0−μ0 ) -1 is the time scale of the process . We recall Fisher’s fundamental theorem of natural selection stating that “The rate of increase in fitness of any organism at any time is equal to its genetic variance in fitness at that time” [36] . Eq ( 2 ) is a generalization of Fisher’s fundamental theorem and is also reminiscent of Price’s equation [37] if we consider q to be a trait of the population . We further find that the variance σ2 of the on-rate distribution evolves as τ0dσ2dt=cov ( ( q−q¯ ) 2 , A ) 〈A〉+C1+2D . ( 3 ) Solving Eqs ( 2 ) and ( 3 ) numerically ( S6 Fig ) , we find that q¯ exhibits the same non-monotonic behavior as in our simulation results S2a Fig . When TfhC level is scarce ( small C1 ) , the affinity increases faster due to the increased competition between B cells ( S6 Fig ) . The position of the optimal density changes with time ( S6 Fig ) towards smaller Ag densities . For a given on-rate distribution ( fixed mean and variance ) , the rate of increase in affinity is maximal at density n* given by n*= ( r+λ ) ( k+ξ ) q¯ξλ+C1 ( r+λ ) ( 1+C1 ) r+ ( 2+C1 ) λ , ( 4 ) where λ and ξ are the off-rates of the Ag from the IC when one or two arms of the BCR are bound , respectively . r and k are the BCR arm rupture rate from the Ag when one or two arms of the BCR are bound to Ag , respectively ( see SI ) . When the interaction energy of the Ab and the Ag is equal to that of the Ag with the IC ( EAg−Ab = EAg−mem ) , the optimal density is n*=4λ0q¯eβxbF1+2C13+2C1 , ( 5 ) where λ0 is the disassociation rate of the Ag from the IC . Thus , at the optimal selection density , the characteristic effective on-rate ( n*q¯ ) is equal to λ0 . At this density , B cells spilt into different populations , each capturing a different amount of Ag . Interestingly , for very large values of q , the number of captured Ag molecules A is independent of q as the covariance between them goes to zero ( see SI ) . In this limit , it is impossible to differentiate between B cells affinities and the mean affinity stops increasing , reaching an asymptotic value . Similarly , the variance of the distribution reaches an asymptotic value . Finally , we speculated whether a scenario where the BCR has one arm ( see S7 Fig ) would still show the non-monotonic dependence on Ag density . We estimated the time evolution of q¯ for a hypothetical BCR with one arm ( see SI and S7 Fig ) . Interestingly , when the time τ to find the first Ag molecule is of the same order as the effective on-rate ( basal on-rate multiplied by the density ) , the mean affinity has a maximum at intermediate densities ( see S7 Fig ) . However , τ is likely much larger than the on-rate , in which case the affinity is monotonically decreasing as a function of the Ag density ( see S7 Fig ) . We conclude that the optimal SD observed in our simulations is a direct result of the cooperativity between the two arms of the BCR because the analytical model shows that an optimal SD emerges only when the BCR has two arms . The cooperativity allows a second arm to search and bind an Ag molecule while the first is bound to another Ag molecule on the surface of the IC . During AM , Ag is depleted from IC as it is being consumed by the B cells . As shown in the previous sections , AM depends on the ability of the GCR to differentiate bewteen B cells of different affinities . Optimal selection is achieved when only B cells with the highest affinity are likely to capture 2 Ag molecules ( see Eq ( 5 ) ) . Depletion of Ag from FDCs during AM will result in fewer immune complexes encountered by the BCRs . It will not change the local SD on the virus . Rather , it will reduce the first encounter probability of Ag molecules by a BCR ( See Eq ( 7 ) ) . To study how the competative forces may be modulated in time , we studied a hypothetical scenario where SD exponentially decays during AM ( nAg ( t ) =nAg ( 0 ) e−t/TAg−decay ) . Interestingly , when the SD decays , selection improves when the initial SD is high ( S8 Fig ) . As a result , the affinity at day 16 of the GCR is higher compared to the fixed SD scenario ( Fig 2a ) . At high SD , as the affinity of the B cell population improves , decreasing SD allows the GCR to remain at close to the optimal differention point . However , decreasing Ag density harms the development of GCs for which the initial SD is low . Since B cell have to capture at least one Ag mol to proliferate , most GCs do not succeed in reaching day 16 ( S8 Fig ) in this limit . HIV dominantly infects CD4 T cells and significantly reduces their number immediately following infection during a time when the first antibody responses are developing in the host [38] . We therefore explored the effects of making TfhC activity even more limiting ( than what is usual ) . To do so , we changed the parameter C ( Eq ( 1 ) ) , which represents the availability of TfhCs during AM . Again , we use the affinity of the dominant clone at the end of the GCR as a metric of the affinity of antibodies generated . Previously ( Fig 2a ) we found that for high SDs , antibody affinity was lower than optimal . This was because , for sufficiently high SD , most BCRs internalized one or two antigens and so competition between B cells was restricted . But , when the level of TfhC ( C ) is even more limiting , even for high SDs , small differences between B cells with regard to the amount of internalized antigens are amplified by the intense competition between B cells for interacting with , and receiving a survival signal from TfhCs ( Fig 3a , S9 Fig ) . Thus , if the SD is high , more potent antibodies are generated as TfhC levels decline . However , when the SD is low , decreasing C does not alter the affinity of the resulting antibodies significantly . Consistent with this result , the probability of selecting the lower affinity B cell decreases as C decreases ( see Fig 3b ) . This is because selection is a stronger force when there are fewer TfhCs . In agreement with our result in Fig 3a , this enhancement in selection forces is more pronounced at intermediate and high SDs compared to low SDs . Thus , as TfhC levels are smaller , high affinity antibodies can be generated more readily for high SDs . Thus , perhaps , HIV , which is the only virus that principally targets T helper cells and depletes their numbers , evolved a low SD to prevent strong antibody responses from developing during the early stages of infection when T helper cell levels rapidly decline . ( Note that Human T cell Leukemia viruses also infect T cells , but they do not result in a sharp decline in T helper cell numbers [39] . Their effect on the number and functioning of TfhCs is still unclear [40] ) .
Proteins that comprise the spikes on the surface of viruses bind to receptors on host cells to propagate infection . A high SD increases the probability of encounters with the host cell’s receptor , thus enhancing infectivity [41] . At the same time , the viral spike is the target of immune attack by antibodies [42] and a high SD may make the virus more susceptible to neutralization . For example , HIV’s low SD can inhibit effective neutralization [3] . The average inter-spike distance for HIV is about 23nm , while the average distance between the arms of the Ab is 15nm . Thus , the two Ab arms are unlikely to bind simultaneously to two Ag molecules , which would decrease avidity [3] . In some viruses like influenza , however , hypervariable features near the head of the spike have high immunogenicity , and a high SD can serve to protect the virus from antibodies that could target more conserved epitopes in the stem of the virus [4] . Thus , there appear to be evolutionary forces that favor both a high and low virus SD . Yet , most viruses have very high SD , and HIV appears to be unique in that its SD is about two orders of magnitude lower ( see Table 1 ) . In order to shed light on the evolutionary forces that may have led to the evolution of a low SD for HIV , we studied a simplified computational model of AM . We found that the affinity of Abs produced by GC reactions is maximal for an intermediate SD ( Fig 2a ) . For very low SD , most B cells internalize a single antigen molecule by binding via a single arm of the BCR or internalize no antigen at all . The occasional B cell that internalizes Ag by chance quickly evolves to become the dominant clone during AM . Thus , for very low SD , fluctuations prevent the system from being in the strong selection regime , thus resulting in lower affinity antibodies . For a high SD , BCRs on B cells are very likely to internalize one or two Ags , and so soon after AM ensues , most B cells internalize quite a bit of antigen . Therefore , there is reduced competition between B cells for obtaining a survival signal from TfhCs , and thus a low driving force to evolve higher affinity BCRs . An intermediate SD results in strong selection forces , and the highest affinity antibodies . The basis for the optimal affinity at intermediate SD being related to the ability to differentiate between B cells with different affinities during the GC reaction is made clear by another aspect of our results . We show that the non-monotonic dependence of antibody affinity with SD is directly related to the binding cooperativity between the two arms of the BCR . Indeed , for a hypothetical single arm BCR , the affinity of the resulting Abs monotonically decreases with SD ( S7 Fig ) . For normal BCRs with two arms , at the optimal density , the second arm serves to split the B cell population into those who manage to capture the additional Ag molecule while the first arm is still bound , and those who do not . Thus , resulting in strong selection for the B cells that internalize two antigens per BCR . We thus hypothesize that having two arms may be beneficial for optimal B cell selection in the GC . Obviously , the two arms allow Abs to bind Ag with high avidity . However , the two arms also allow for a more precise differentiation between B cells . B cells usually capture Ag using BCR clusters [26] that can function as a “multiple arms” BCR for the purpose of differentiation . However , perhaps it is most beneficial for the BCR to have two arms ( in the context of GC selection ) when Ag density is very low . The probability that a lower affinity B cell is stochastically selected to proliferate in favor of a higher affinity B cell is minimal for the intermediate densities . In other words , the ability of the GC reaction to produce highest affinity Abs at an intermediate SD is because this is the regime where selection effects are strongest . So , viruses could have evolved either a low or high SD to reduce the efficacy of antibodies directed against them . The results in Fig 4 suggest that evolving a low SD would be especially advantageous from this perspective . Furthermore , such a strategy reduces the avidity of antibodies , further inhibiting neutralization [43] . However , most viruses have evolved a high SD ( Table 1 ) . Our results suggest that this may be because the evolutionary driving forces of increasing infectivity and shielding conserved epitopes by maintaining a high packing density may be dominant . A very high SD ( past the optimal density for AM ) can lower antibody affinity somewhat ( Fig 4 ) while favoring these two selection forces . Why has HIV evolved SD that is roughly two orders of magnitude smaller than that observed for most viruses ? HIV is unique in that it principally infects T helper cells and reduces their numbers significantly during the early stages of infection . We suggest that T helper cell depletion during HIV infection results in increased competition between B cells during AM . When T helper cells are limiting to the normal extent , once the SD is higher than the optimal value , selection forces are weaker . But , if T helper cells are significantly depleted , selection forces remain strong at high SDs , resulting in high affinity antibodies . As Fig 3b shows , depletion of TfhCs results in a lower probability of low-affinity B cells stochastically proliferating in favor of higher affinity B cells . However , our results show that , for low SDs , Ab affinity hardly increases upon depletion of TfhCs . This may be the reason that HIV , which is the only known virus that principally infects T helper cells and sharply depletes their numbers during early stages of infection , is the rare virus that has evolved a low SD . The low SD may aid HIV to avoid effective antibody responses in the early stages of infection in a way that would not be possible if it had a high SD . We note also that a low SD can decrease the ability to form diverse clonal lineages during the GC reaction , thus inhibiting AM from deeply exploring the antigenic space upon infection [44] . Because it is a chronic infection and replicates fast , the reduced infectivity of a low SD may be alleviated . The Measles virus also infects T helper cells; could result in their depletion [45] and causes transient immunosuppression [46] . Unlike HIV , Measles causes acute disease and is rapidly cleared from the body . Notably , Measles has many spikes on its surface [43] . We hypothesize that its rapid mode of propagation resulted in its choice to have high infectivity , producing many virions in the short period during which the patient is infectious . An HIV carrier , on the other hand , is infectious for an extended period . Thus , the virus has to hide for much longer from the immune system and having a low spike density would allow it to do so . Finally , it seems that affinity maturation and antibody response is not the main way by which Measles is cleared . Rather , it induces a T cell response that is dominant in the first two weeks [47] . Only at a much later times ( many weeks ) affinity maturation starts to produce antibodies against Measles’s RNA and proteins [47] . Another virus that infects T helper cells is HTLV-1 . Contrary to HIV , HTLV-1 infection does not appear to result in a sharp decline of the T-cell population but it may impair the function of TfhCs [48] . Our model would suggest that this effect should promote the evolution of a low SD . Another feature of HTLV suggests that a high SD for improving infectivity may be ameliorated; HTLV-1 predominantly infects new cells via cell-to-cell contact [49][50] . It seems that during cell-to-cell transfer , at the intersection between the two cells , the local density of env is high [49] . However , while we could not find precise quantification of their number on the free virions ( which is the key variable during affinity maturation ) , spikes appear sparse on their surface when imaged with cryo-EM [51] . Given this paucity of quantitative data , at this stage , it is not clear how our hypothesis relating spike density and T cell number to the competitive force in the GC should be applied to HTLV-1 . The impact of SD on HIV and SIV propagation has been studied experimentally . A deletion in the tail of gp41 ( part of the Env spike protein ) has been suggested to increase the number of spikes [41] and their mobility [52] . These mutated virions have better infectivity in cell culture [53] . Surprisingly , the deletion is rarely seen in vivo and when macaques are infected with a short tail SIV mutant , the mutant reverts back to the long tail virus [53–55] . These results may suggest an evolutionary driving force , in the face of an immune response , to reduce SD in HIV . Our results are reminiscent of the pioneering discovery by Herman Eisen showing that affinity increase upon AM was smaller for a very high Ag dose , suggesting that too high Ag concentration decreases competition in the GC . There is also experimental evidence that intermediate levels of antigen density lead to the highest Ab titers [56] , related to optimal BCR activation . In this case , Ag molecules can mediate the formation of a BCR cluster when the density is sufficiently high , while for very high density , Ag sequesters the BCR molecules and the number of fully formed clusters is reduced . Our results are relevant to vaccine design . It is possible to design liposomal nanoparticles that display varying density of Ag [11 , 57 , 58] . We have shown here that more is not necessarily better . We suggest that a precise design of the density of Ag can impact B cell selection in the GC , and as a result on the Ab affinity of the memory and plasma cells that are the product of vaccination . Thus , our results may guide the design of vaccination vectors that could optimize immune responses in the lymph node follicle . Our arguments regarding the evolutionary driving forces that have led to HIV being unique in exhibiting an extraordinarily low SD may be difficult to examine experimentally ( as is the case for most problems in evolutionary biology that are not contrived laboratory curiosities ) . However , by depleting TfhCs in mice to different degrees , the veracity of the underlying mechanism could be tested .
To account for the limited capacity of a GC during the competitive phase , we employed a variant of the stochastic logistic growth process [59] , in which the death rate increases with the overall population size , from a basal rate of μ0 , as μ ( n ) = ( μ0+ ( β0−μ0 ) ∑i=1MniN ) . ( 6 ) Here , N is the population capacity taken to be 200; n = ( n1 , n2 , … , nM ) is the vector of cell numbers ni for the M clones such that ∑i=1Mni is the total number of B cells in the GC . The competitive phase lasts about 16 days in mice [31] . The total number of cells in the GC grows gradually until reaching the capacity N , where it remains approximately fixed . We assume here that an arm of a BCR at the tip of a B cell protrusion has a characteristic time τ to find an Ag molecule , after which the protrusion retracts empty . The probability that an arm of a BCR at the tip of a B cell protrusion finds Ag before time τ is Pbinding:=Probability{Onearmbindingbeforetimeτ}= ( 1−e−2qNAgτ ) , ( 7 ) where qNAg=qNAg is the on-rate for the BCR to find an Ag molecule given that there are NAg of them in its scanning radius , and the sequence-dependent on-rate with which a particular BCR binds to Ag is q . This leads to p0=e−2qNAgτ , p1=1−e−2qNAgτ , ( 8 ) where p0 is the probability that no Ag is encountered , while p1 is the probability that one of the arms encounters an Ag molecule in this time period . In order to capture the Ag , the BCR attached to a protrusion of the B cell applies a pulling force that works against the interactions of the BCR with Ag , and that between the Ag and the virus/Fc receptor/FDC membrane ( see Fig 1 ) . We denote the potential interaction energy required to extract the Ag by EAg−mem , and the interaction energy of the BCR/Ab and Ag by EAg−Ab . The characteristic rupture time depends on the force applied by the B cell [60] . The force F does work xbF on the bond , reducing its free energy and increasing the rupture rate to rF=r0eβxbF , where r0 is the characteristic rate for bond disassociation when no force is present , xb is the distance at which the bond ruptures , and β = kBT . The intrinsic rupture rate with no force is estimated by Kramer’s escape from a potential barrier as r0=Ke−βEAg−Ab , ( 9 ) where K is a coefficient that depends on the shape of the interaction potential [61] . We take K = 1 , and note that in writing Eq ( 9 ) we have assumed that the activation barrier to form the bond is much smaller than the energy gained upon binding ( EAg−Ab ) . F is typically of the order of a few pico-Newtons [27] . We assume that each B cell has 100 BCRs but the qualitative behavior of our results does not depend on the number of BCRs . As each BCR can extract 0/1/2 Ag molecules , a B cell can extract between 0 and 200 Ag molecules . We modeled the effect of mutation as a change in the on-rate q or the interaction energy EAg−Ab upon cell division , with one daughter retaining the parent affinity , while for the other daughter: EAg-Ab , daughter=EAg−Ab , parent+N ( 0 , 2D ) , qdaughter=qparent+N ( 0 , 2D ) ( 10 ) Here , N is a normal distribution with zero mean and standard deviation of 2D , with D akin to an effective variability coefficient determining the magnitude of the change in q or EAg−Ab . Within this model , the energy , or q , can increase or decrease with equal probability at every division . We added a reflecting boundary condition at zero such that q is never negative . Since the off-rate scales as r0=e−βEAg−Ab , the affinity of the BCR is calculated as ω = q/r0 . To study the time evolution of the GC reaction , we performed Brownian dynamics simulations . At every time point B cells proliferate or die with a probability which is determined by the time step and the proliferation and death rates ( Table 2 ) . We choose the time step to be smaller than the average time for proliferation . The simulation proceeds according to the following algorithm: | The spike protein on the virus surface mediates its entry to the host cell and a high spike density promotes infection . HIV has a spike density that is almost two orders of magnitude lower than other viruses . This unique feature of HIV has defied explanation since it was first observed . By bringing together theory and computation , rooted in statistical mechanics , with immunology we suggest that the effects of dramatic depletion of T helper cells during HIV infection on antibody production provided an evolutionary driving force for HIV to evolve a low spike density in order to avoid potent immune responses . Additionally , we show that an intermediate spike density induces maximally potent antibody production , a result with implications for vaccine design . | [
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] | 2018 | The low spike density of HIV may have evolved because of the effects of T helper cell depletion on affinity maturation |
Helminth infections can negatively affect the immunologic host control , which may increase the risk of progression from latent Mycobacterium tuberculosis infection to tuberculosis ( TB ) disease and alter the clinical presentation of TB . We assessed the prevalence and determined the clinical relevance of helminth co-infection among TB patients and household contact controls in urban Tanzania . Between November 2013 and October 2015 , we enrolled adult ( ≥18 years ) sputum smear-positive TB patients and household contact controls without TB during an ongoing TB cohort study in Dar es Salaam , Tanzania . We used Baermann , FLOTAC , Kato-Katz , point-of-care circulating cathodic antigen , and urine filtration to diagnose helminth infections . Multivariable logistic regression models with and without random effects for households were used to assess for associations between helminth infection and TB . A total of 597 TB patients and 375 household contact controls were included . The median age was 33 years and 60 . 2% ( 585/972 ) were men . The prevalence of any helminth infection among TB patients was 31 . 8% ( 190/597 ) and 25 . 9% ( 97/375 ) among controls . Strongyloides stercoralis was the predominant helminth species ( 16 . 6% , 161 ) , followed by hookworm ( 9 . 0% , 87 ) and Schistosoma mansoni ( 5 . 7% , 55 ) . An infection with any helminth was not associated with TB ( adjusted odds ratio ( aOR ) 1 . 26 , 95% confidence interval ( CI ) : 0 . 88–1 . 80 , p = 0 . 22 ) , but S . mansoni infection was ( aOR 2 . 15 , 95% CI: 1 . 03–4 . 45 , p = 0 . 040 ) . Moreover , S . mansoni infection was associated with lower sputum bacterial load ( aOR 2 . 63 , 95% CI: 1 . 38–5 . 26 , p = 0 . 004 ) and tended to have fewer lung cavitations ( aOR 0 . 41 , 95% CI: 0 . 12–1 . 16 , p = 0 . 088 ) . S . mansoni infection was an independent risk factor for active TB and altered the clinical presentation in TB patients . These findings suggest a role for schistosomiasis in modulating the pathogenesis of human TB . Treatment of helminths should be considered in clinical management of TB and TB control programs .
Tuberculosis ( TB ) , caused by Mycobacterium tuberculosis remains a challenging disease to control . Indeed , over two billion people are estimated to be infected with M . tuberculosis worldwide [1] . Moreover one billion people are infected with soil-transmitted helminths , schistosomes , filarial worms , and food-borne trematodes [2–4] . In 2014 , an estimated 9 . 6 million new TB patients were notified and 1 . 5 million TB patients died from the disease [1] . TB is a leading cause of deaths from an infectious disease [5] . TB and helminthiases overlap geographically , particularly in areas where poverty persists , for example in countries of sub-Saharan Africa [1 , 6] . Where TB and helminth infections co-occur , they can affect the same individual and thus exacerbate the course of disease [6] . Several conditions such as diabetes mellitus , malnutrition , and malignancies are known to increase the risk of progressing from latent M . tuberculosis infection to active TB [7] . Human immunodeficiency Virus ( HIV ) -induced immunodeficiency is by far the most important risk factor for developing TB [1 , 8] , but parasitic co-infections such as with helminths can also contribute to the development of TB [9–11] . Immune dysregulations caused by helminth infections are known to negatively affect the prognosis of HIV and malaria [6 , 12] . The immune response to helminth infections is characterized by the induction of CD4+ T-helper 2 ( Th2 ) and down-regulation of CD4+ T-helper 1 ( Th1 ) cells [12–15] . This immunological imbalance has been suggested to increase the risk of progression from latent M . tuberculosis infection to active TB and to worsen the clinical outcomes . We aimed to study the interaction between TB and helminth co-infections by comparing the prevalence of helminth infections , using a suite of diagnostic techniques , between TB patients and household contact controls without TB in an ongoing cohort study in Dar es Salaam , Tanzania , and to assess the effects of helminth infection on the clinical presentation and outcomes of TB disease .
The study protocol was approved by the institutional review board of the Ifakara Health Institute ( IHI; reference no . IHI/IRB/No 04–2015 ) and the Medical Research Coordinating Committee of the National Institute of Medical Research ( NIMR; reference no . NIMR/HQ/R . 8c/Vol . I/357 ) in Tanzania , and the ethics committee of north-west and central Switzerland ( EKNZ; reference no . : UBE-15/42 ) . Written informed consent was obtained from all study participants . TB patients were treated according to the National TB and Leprosy Programme ( NTLP ) treatment guideline [8] . Individuals with a Schistosoma spp . infection were treated with praziquantel ( 40 mg/kg ) . Other helminth infections were treated with albendazole ( 400 mg ) immediately after diagnosis , as recommended by the national treatment guidelines [16] . HIV-positive patients were clinically managed according to the Tanzania National HIV and acquired immune deficiency syndrome ( AIDS ) treatment guideline [17] . The study was conducted in the densely populated urban setting of Temeke district in Dar es Salaam , which is the economic capital of Tanzania . The population of Temeke is estimated at 1 . 4 million . In 2014 , about one third of all TB patients from Dar es Salaam were notified in Temeke district ( 4 , 373; 32% ) [18] . The overall HIV prevalence in the general adult population in Dar es Salaam is 5 . 2% [19] . The study area includes two TB sub-districts , Wailes I and Wailes II , whose patients are clinically managed at the Temeke district hospital and the two associated TB diagnostic and treatment centers of Tambukareli and Pasada [20] . The study was conducted within the frame of an ongoing prospective cohort study of TB patients and household contact controls in Dar es Salaam ( TB-DAR ) . We assessed the association of TB and helminth infection in a case-control study design of TB patients ( sputum smear-positives for acid-fast bacilli [AFB] ) and household contact controls ( Xpert MTB/RIF negative ) , who were matched by age ( ±5 years ) and whenever possible by sex . We prospectively followed-up TB patients and assessed the clinical outcomes comparing TB patients with and without helminth infection at 6 and 12 months after recruitment . We consecutively enrolled study participants starting in November 2013 until October 2015 to reach the required sample size . Over this period , we included adult TB patients ( ≥18 years of age and sputum-smear positive ) and household contact controls . Any individual living in the same household as the index TB patients enrolled in the study is referred to as a household contact control . Controls at recruitment were free of symptoms and signs suggestive of TB , healthy on physical examination , and had a negative Xpert MTB/RIF result ( Cepheid; California , United States of America ) . Assuming a helminth prevalence of 45% in TB patients and 26% in controls based on results from previous publications [21] and a power of 80% , the target sample size was 109 study participants ( for each group ) to detect a prevalence difference of 19% between the two groups with a significance level of test 0 . 05 , two-tailed and calculated with Stata version 14 . 0 ( Stata Corp; Texas , United States of America ) . TB patients and household contact controls were interviewed and underwent physical examination during recruitment at the study site ( see under “Data Collection and Definitions” ) . We collected skinfold measurements from four body sites ( biceps , triceps , subscapular , and suprailiac ) using the Harpenden skinfold caliper [22] . The percentage body fat was calculated as previously described [23] . Household contacts with no symptoms or signs of TB submitted a sputum sample for Gene Xpert MTB/RIF to rule-out TB . We collected blood , stool , and urine samples from TB patients and controls for subsequent laboratory investigations . Chest X-rays for TB patients were done at the Temeke district hospital and were interpreted by an experienced board certified radiologist who was blinded to patients’ clinical data . Trained field workers collected geographic coordinates ( global positioning system [GPS] ) from the patients’ homes using Samsung Tab 4 android tablets ( Samsung; Suwon , South Korea ) . We collected socio-demographic indicators including age , sex , ethnicity , education , and household income . Anthropometric data included weight , height , and skinfold measurements . Clinical data collected pertained to presenting symptoms of TB patients , TB treatment category , and treatment outcomes . Laboratory data included ZN sputum smear results and Gene Xpert MTB/RIF results , helminth species infections , HIV status , full blood cell count , and CD4+ cell count . All study participants were asked about their use of anthelmintic treatment in the last 12 months prior to the enrollment into the study . Study data were captured by electronic case report forms using the open-source data collection software ODK on Android PC tablets [30] . Data management was done using the eManagement tool “odk_planner” , as previously described [30] . Data were uploaded to a password protected secure server with regular back-ups . In order to grade the clinical severity of TB , we adopted a previously published clinical TB score [31] , with the following modification: 12 points TB score parameters instead of 13 points as tachycardia was not systematically measured . The following TB score parameters were used: ( i ) coughing; ( ii ) hemoptysis; ( iii ) chest pain; ( iv ) dyspnea; ( v ) night sweating; ( vi ) anemic conjunctivae; ( vii ) positive finding at auscultation; ( viii ) axillary temperature >37 . 0°C; ( ix ) mid upper arm circumference ( MUAC ) <220 mm; ( x ) MUAC <200 mm; ( xi ) body mass index ( BMI ) <18 kg/m2; and ( xii ) BMI <16 kg/m2 . TB score was then categorized into mild ( score of 1–5 ) and severe ( score of ≥6 ) . Low BMI was defined as BMI <18 kg/m2; high sputum bacterial load as AFB sputum smear result ≥2+ ( quantitative scoring ) , which correlates with GeneXpert Ct values [32] . To assess the clinical outcomes among TB patients , we defined poor gain as a change in absolute body weight ( <7 and ≥7 kg ) , BMI ( <2 . 6 and ≥2 . 6 kg/m2 ) and body fat ( <0 and ≥0% ) from recruitment to month 6 of follow-up . “Any helminth infection” was defined as infection with any of the following helminth species: A . lumbricoides , E . vermicularis , hookworm , Hymenolepis diminuta , S . haematobium , S . mansoni , S . stercoralis and T . trichiura . High occupational risk for schistosomiasis was defined as working in rice fields , sand harvesting , washing cars , and fishing in freshwater . The intensity of helminth infection was defined according to WHO classification [33] . The average egg counts from the triplicate Kato-Katz thick smears per stool sample and per individual were multiplied by a factor of 24 to obtain eggs per gram ( EPG ) of stool [25] . We compared the characteristics of TB patients and household contact controls at the time of TB diagnosis or enrolment . The prevalence of helminth infection was calculated from the generalized estimations equation adjusting for clustering at the household level . We used multilevel mixed-effects logistic regression with random intercepts at the level of households to assess risk factors for helminth infection . To assess risk factors for TB , we compared cases and controls using unconditional logistic regression because not all TB cases could be assigned a control . In addition , we also performed conditional logistic regression among matched pairs to confirm the results . Additional analyses assessed the association of TB and with specific helminth species separately . We also examined whether the association between the presence of a helminth infection and a recent history of deworming drugs depended on HIV infection status by including an interaction term in the logistic regression model . Among TB patients , logistic regression models were used to study associations between helminth infection and clinical presentation at the time of TB diagnosis ( such as TB score , high sputum bacterial load , lung infiltration , and cavitation ) , and to study the association between helminth infection and clinical outcomes after 6 months of TB treatment ( change in absolute weight , BMI , and percentage body fat ) . Associations were expressed as crude odds ratios ( ORs ) and adjusted ORs ( aORs ) . All analyses were performed in Stata version 14 . 0 ( Stata Corp; Texas , United States of America ) . We used the geographic coordinates of the TB patients’ homes to analyze the spatial distribution of TB and helminth co-infections . The prevalence of helminths and helminth species was analyzed at the ward level for optimal readability . The average area per ward in the Dar es Salaam region is 15 . 5 km2 [19] . The maps were produced using the software package ArcGIS Desktop version 10 . 2 ( ESRI; California , United States of America ) and the shape files from the National Bureau of Statistics of Tanzania [34] .
A total of 597 TB patients and 375 household contact controls were included . Table 1 summarizes the socio-demographic and clinical characteristics of TB patients and controls . The study participants’ flow diagram is shown in Fig 1 . Among all study participants , the median age was 33 years ( interquartile range [IQR]: 26–41 years ) and 60 . 2% ( 585/972 ) were men . HIV prevalence was 20 . 4% ( 95% confidence interval ( CI ) : 17 . 9–23 . 0% ) . TB patients were more frequently male compared with controls ( 68 . 8% [411/597] vs . 46 . 4% [174/375] ) , HIV-positive ( 27 . 3% [163] vs . 9 . 3% [35] ) , and smokers ( 18 . 1% [108] vs . 8 . 8% [33] ) . TB patients also had a lower median BMI ( 18 . 3 kg/m2 , IQR: 16 . 5–20 . 4 kg/m2 vs . 23 . 9 kg/m2 , IQR: 21 . 6–28 . 1 kg/m2 ) and a lower median hemoglobin level ( 11 . 3 g/dl , IQR: 9 . 9–12 . 7 g/dl vs . 12 . 8 g/dl , IQR: 11 . 5–14 . 1 g/dl ) . The patient characteristics , stratified by HIV status , are shown in S1 Table . Among all participants , the prevalence of any helminth infection was 29 . 5% ( 95% CI: 26 . 7–32 . 6% ) . S . stercoralis ( 16 . 5% , 161 ) was the predominant helminth species , followed by hookworm ( 9 . 0% , 87 ) , S . mansoni ( 5 . 7% , 55 ) and S . haematobium ( 2 . 0% , 19 ) . Overall , TB patients were more frequently co-infected with any helminth species compared with controls ( OR 1 . 34 , 95% CI: 1 . 00–1 . 78 , p = 0 . 048; Table 2 ) . The prevalence of helminth infection was lower in HIV-positive ( 22 . 7% , 45 ) compared with HIV-negative study participants ( 31 . 3% , 242; S1 Table ) . Similarly , helminth infection was lower among TB patients co-infected with HIV ( 22 . 7% , 37 ) compared with HIV-negative TB patients ( 35 . 3% , 153; S2 Table ) . We found that most study participants had light-intensity helminth infection . For example , 96 . 4% ( 54 ) of study participants had light-intensity hookworm infection as determined by the Kato-Katz method ( S3 Table ) . The prevalence and geographic distribution of species-specific helminth infections in the study area is shown in S1 Fig . Study participants with occupational risk for acquiring schistosomiasis , such as working in rice fields , sand harvesting , washing cars , and fishing had higher odds of being infected with any helminth species ( aOR 1 . 42 , 95% CI: 1 . 04–1 . 95 , p = 0 . 029 ) . HIV-positive patients were less likely to be infected with any helminth species ( aOR 0 . 57 , 95% CI: 0 . 37–0 . 87 , p = 0 . 010; Table 3 ) . Study participants who did not take anthelmintic treatment in the past 12 months did not have significant higher odds of being co-infected with any helminth species ( aOR 1 . 35 , 95% CI: 0 . 92–1 . 99 , p = 0 . 12 ) . There was no statistically significant interaction between the effects of HIV infection and deworming status on TB incidence ( P-value from test for interaction: 0 . 5 ) . When analyzing the risk factors for helminth infection separately for TB patients and household controls without TB , we found similar results ( see S5 and S6 Tables ) . Multiple logistic regression models adjusted for patient characteristics and known risk factors for TB showed that any helminth infection was not statistically significantly associated with TB ( aOR 1 . 26 , 95% CI: 0 . 88–1 . 80 , p = 0 . 22 , Table 4 and S7 Table ) . However , when analyzing each helminth species separately , we found that S . mansoni infection was significantly associated with TB ( aOR 2 . 15 , 95% CI: 1 . 03–4 . 45 , p = 0 . 040 ) , but there was no significant association between TB and S . stercoralis or hookworm infection ( S8 Table ) . Other co-factors that were significantly associated with TB included: male sex , HIV co-infection , smoking , living in a household with ≥3 people , and a low BMI ( Table 4 ) . The unadjusted and adjusted ORs for any helminth infection and S . mansoni are shown in S7 Table . Results were more pronounced when using a conditional logistic regression model ( S9 Table ) . TB patients co-infected with any helminth infection were more likely than helminth un-infected TB patients to present with hemoptysis ( 74 [38 . 9%] vs . 123 [30 . 2%] ) , had higher median hemoglobin levels ( 11 . 7 g/dl , IQR: 10 . 1–13 . 0 g/dl vs . 11 . 3 g/dl , IQR: 9 . 8–12 . 5 g/dl ) and higher median eosinophil counts ( 0 . 2 , IQR: 0 . 1–0 . 4 cells/μl vs . 0 . 1 , IQR: 0 . 05–0 . 2 cells/μl; Table 5 ) . TB patients co-infected with S . mansoni were more likely to have lower sputum bacterial load than helminth-uninfected TB patients ( aOR 2 . 63; 95% CI: 1 . 38–5 . 26 , p = 0 . 004 ) . Furthermore , we found that TB patients co-infected with S . mansoni tended to have fewer lung cavities , although this association lacked statistical significance ( aOR 0 . 41 , 95% CI: 0 . 12–1 . 16 , p = 0 . 088; Table 6 ) . There were no statistically significant differences in radiological features between TB patients with and without any helminth infection as shown in S10 Table . Overall , 81 . 7% ( 273 TB patients ) were cured at the end of TB treatment ( at 6 months ) , 17 . 4% ( 58 ) completed treatment ( AFB smear results not available at 6 months , but documented completion of treatment ) , and 0 . 9% ( 3 ) were treatment failures ( positive AFB smear result at 6 months ) . We found no significant associations between helminth infection ( at time of recruitment ) and poor gain in absolute weight ( aOR 0 . 89 , 95% CI: 0 . 55–1 . 45 , p = 0 . 63 ) , BMI ( aOR 0 . 74 , 95% CI: 0 . 46–1 . 21 , p = 0 . 23 ) , and body fat percentage ( aOR 0 . 92 , 95% CI: 0 . 55–1 . 56 , p = 0 . 78 ) after 6 months on TB treatment , as shown in S11 Table .
We present findings on the prevalence and association of TB and helminth co-infection among adult TB patients and household contact controls in a highly-urbanized setting of Dar es Salaam , Tanzania . We found that S . mansoni infection was a risk factor for TB disease . This association remained significant after adjustment for other known risk factors for TB , such as HIV infection , smoking , and underweight [35] . None of the other investigated helminth species or the surrogate measure of “any helminth infection” were significantly associated with TB . Importantly , associations between any helminth co-infection and TB were reported in previous epidemiologic studies [21 , 36 , 37] , as well as in experimental work using animal or macrophage infection models [9 , 13 , 15] . In line with our findings , a recent study with human peripheral mononuclear cells exposed to M . tuberculosis and S . mansoni antigens showed that S . mansoni-induced CD4+ T cells disrupt the control of M . tuberculosis in infected macrophages [9] . Several studies in humans suggested that helminth infections may increase the risk for progression of latent M . tuberculosis infection to active TB [15 , 21 , 37] as well as for exacerbating the disease [15] . However , the results of these studies are conflicting , and no differentiation at the helminth species level was made in these analyses . Indeed , the hypothesis of a helminth species-specific impact on the host response is supported by a recent systematic review , which revealed a trend toward an association between a decrease in HIV viral loads and treatment for S . mansoni , but not for other helminth species [38] . A case-control study from Ethiopia also found an association between TB and helminth infections , and the association was stronger in patients that were infected with multiple helminth species [21] . The small number of study participants with S . mansoni infection ( 31 among TB cases , nine among controls ) may have masked an association between TB and schistosomiasis in that study [21] . In contrast , a cohort study from India showed no difference in TB incidence rates in helminth-infected and helminth-free individuals after 2 . 5 years of follow-up [39] . We also found that S . mansoni , but not other helminth species , was associated with the clinical presentation among TB patients . Patients co-infected with S . mansoni had lower sputum bacterial loads at the time of TB diagnosis than S . mansoni-negative TB patients . Similarly , a study in Ethiopia observed lower sputum bacterial loads at TB diagnosis in TB patients co-infected with any helminth species [40] . Interestingly , our observation in TB patients co-infected with S . mansoni resembles the paucibacillary disease in HIV-positive individuals with severe immunosuppression , who frequently have negative or low bacterial M . tuberculosis loads in the sputum compared with HIV-negative patients [40 , 41] . Hence , the helminth-induced Th1 immunological impairment might have an effect on the sputum bacterial load . Moreover , TB patients with an impaired host immune system rarely present with lung cavitation resulting in fewer M . tuberculosis bacilli being expectorated in the sputum [40 , 41] . This is in line with our findings that TB patients co-infected with S . mansoni tended to present less frequently with lung cavitations compared with S . mansoni-negative TB patients . Any helminth co-infection did not appear to have an effect on clinical outcomes during follow-up . We found no evidence for an effect of helminth co-infection on the gain in the percentage of body fat and BMI after 6 months ( e . g . , at the time of completed TB treatment ) . This might be explained by the fact that the administration of anthelmintic treatment offered to the study participants after diagnosis might have reversed the Th1 immune response [15] , and thus attenuated the effect of helminth infections on clinical outcomes . However , the effect of a reversal of the Th1 immune response could be minimal as the anthelmintic drugs target the worms [42] , which are less immunogenic compared with deposited S . mansoni eggs [9] . We found that TB patients had a higher crude prevalence of helminth infections , as compared with household contact controls . The higher prevalence of helminth infections among TB patients could be the result of the pathogenic role of helminth infection in the progression from M . tuberculosis infection to active TB . The higher prevalence of helminth co-infection in TB patients has also been noted in other studies from different settings [9 , 43] . For example , a study conducted in Ethiopia reported a higher prevalence of helminth infection among TB patients as compared with household contact controls [21] . Overall , the prevalence of helminth infection in our study was 32% and lower compared with the 71% observed in the latter study [21] . It is conceivable that the high proportion of self-reported previous use of anthelmintic drugs in our study ( approximately 80% ) could have reduced the overall prevalence of helminth infection . Hence , we may have underestimated the effects of helminth infection seen in our study . We also found that occupation exposing people to regular water contacts ( for instance rice field workers , sand harvesters , car washers , and fishermen ) were associated with helminth infections . Being exposed to freshwater bodies and being involved in water-related activities have previously been reported to increase the risk of helminth infections [44] . In the current study , HIV-positive individuals were less likely to be co-infected with helminths . A lower prevalence of helminth infections in HIV-positive patients has also been reported in a study conducted in Mwanza in northern Tanzania , which is a highly endemic area for helminthiases [45] . Of note , current clinical practice in Tanzania is to treat any helminth infection in HIV-positive patients at enrolment into HIV care and in case of clinical suspicion of helminth infection during follow-up , as specified in the HIV/AIDS management guideline [17] . The use of anthelmintic drugs is safe and might be beneficial in HIV-positive patients by possibly reducing the HIV-RNA viral load and subsequently improving clinical outcomes [46] . Furthermore , cotrimoxazole preventive therapy ( CPT ) , which is recommended for HIV-positive patients , has also been reported to have limited anthelmintic properties [43 , 47] . This might explain the lower prevalence of helminth infection among HIV-positive individuals in our study [17] . Our research has several strengths and limitations that warrant consideration . An important strength of our study is the large sample size and the recruitment of both TB patients and household contact controls with similar socioeconomic profiles and exposure patterns to both TB and helminth infection . Our findings may well apply to other settings with a similar prevalence of TB , HIV , and helminth infections in sub-Saharan Africa . Furthermore , we used recommended TB diagnostics and a suite of standardized , quality-controlled helminth diagnostics , which have comparable diagnostic performance to resource-intensive molecular test assays [25] . Study limitations include the following . First , this is an observational study which cannot establish a causal relationship between helminth infections and TB disease . Second , we could not fully verify whether or not the household contact controls were latently infected with M . tuberculosis , which is a prerequisite to develop TB . However , because Dar es Salaam is a high-burden setting for TB with considerable risk of transmission , and because living with a TB patient is a strong risk factor for TB [35] , it is reasonable to assume that the controls have previously been exposed and infected with M . tuberculosis . Third , we did not check the helminth infection status for TB patients during and after completion of TB treatment , which could influence the clinical outcomes . However , we do not expect a high helminth re-infection rate after 6 months in our study area [48] . Fourth , we did not use molecular diagnostics such as polymerase chain reaction ( PCR ) which might have identified some more cases , but one of our previous studies revealed that also PCR approaches miss in particular very light intensity infections . Moreover , its performance and sensitivity vary with the helminth species under examination [25] . Hence , also a PCR cannot be considered as the diagnostic gold-standard . In conclusion , co-infection with S . mansoni , but not other helminth species , was found to be an independent risk factor for active TB in our study and was associated with the clinical presentation in TB patients . These findings suggest a role for S . mansoni , or helminth infection in general , in immunomodulation of human TB . Treatment of helminth infections should be considered in the clinical management of TB patients , and helminthiasis control/elimination through preventive chemotherapy might prove to be useful as an additional component of TB control programs . Further research is needed to establish the underlying mechanisms , and compare helminth-induced immune regulation by different helminth species . Prospective cohort studies that evaluate the effect of preventive anthelmintic chemotherapy on the incidence of M . tuberculosis infection and active TB could further help to understand the interaction between these diseases at the population level . Helminthiasis control measures , in combination with traditional TB control strategies , could potentially contribute to the global efforts to reduce TB incidence by 80% until 2030 , as stipulated in WHO’s ambitious End TB Strategy [5] . | Tuberculosis ( TB ) , caused by the bacterium Mycobacterium tuberculosis , and parasitic worm infections are typical diseases of poverty . They often overlap geographically , and can occur in the same individual . Parasitic worm infections contribute to the down-regulation of the essential immune response against TB , and therefore can increase progression from latent M . tuberculosis infection to active TB . We conducted a case-control study in Dar es Salaam , the economic capital of Tanzania , where TB and helminths constitute a considerable burden . We found that infection with the blood fluke Schistosoma mansoni was associated with active TB , while none of the other parasitic worms showed such an association . Interestingly , TB patients infected with S . mansoni had significantly lower sputum bacterial load at diagnosis and tended to have fewer lung cavitations compared with TB patients without any parasitic worm infection . Diagnosis and treatment of parasitic worm infections , particularly schistosomiasis , should be considered during the management of TB patients and in the context of TB control programs . This could help to reduce the TB burden in settings where TB and parasitic worms co-exist . | [
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] | 2017 | Prevalence and clinical relevance of helminth co-infections among tuberculosis patients in urban Tanzania |
Human polymorphonuclear leucocytes , PMN , are highly motile cells with average 12-15 µm diameters and prominent , loboid nuclei . They are produced in the bone marrow , are essential for host defense , and are the most populous of white blood cell types . PMN also participate in acute and chronic inflammatory processes , in the regulation of the immune response , in angiogenesis , and interact with tumors . To accommodate these varied functions , their behavior is adaptive , but still definable in terms of a set of behavioral states . PMN morphodynamics have generally involved a non-equilibrium stationary , spheroid Idling state that transitions to an activated , ellipsoid translocating state in response to chemical signals . These two behavioral shape-states , spheroid and ellipsoid , are generally recognized as making up the vocabulary of a healthy PMN . A third , “random” state has occasionally been reported as associated with disease states . I have observed this third , Treadmilling state , in PMN from healthy subjects , the cells demonstrating metastable dynamical behaviors known to anticipate phase transitions in mathematical , physical , and biological systems . For this study , human PMN were microscopically imaged and analyzed as single living cells . I used a microscope with a novel high aperture , cardioid annular condenser with better than 100 nanometer resolution of simultaneous , mixed dark field and intrinsic fluorescent images to record shape changes in 189 living PMNs . Relative radial roundness , R ( t ) , served as a computable order parameter . Comparison of R ( t ) series of 10 cells in the Idling and 10 in the Treadmilling state reveals the robustness of the “random” appearing Treadmilling state , and the emergence of behaviors observed in the neighborhood of global state transitions , including increased correlation length and variance ( divergence ) , sudden jumps , mixed phases , bimodality , power spectral scaling and temporal slowing . Wavelet transformation of an R ( t ) series of an Idling to Treadmilling state change , demonstrated behaviors concomitant with the observed transition .
We remind ourselves that there are neurobiological limits on the both the resolution afforded by empirically meaningful partitions [16] , and the minimal length of a neurobiologically defensible time series [17] . In these studies I must be concerned with how long the cells being observed maintain their anatomical and functional integrity and within that viability limit , how many observations I can make without the vacuous artifice of over-sampling . The issue more generally is the selection of an appropriate sample size of an intrinsically non-stationary system . Counter-intuitively , it has been shown that under certain conditions of limited information , repeated too-short sample lengths come to be computationally superior globally [18] . In the past I have dealt with this problem by studying repeated time series derived measures yielding populations of not necessarily convergent estimates [19] with , nonetheless , distributional properties of the measures , such that I can estimate each measure's central and higher moments , range of variation and statistical differences between measures in comparisons of varying observed system state [20] . This is the approach to be taken here .
It is difficult to find a global quantitative measure on the dynamics of emergent phenomenon with the nice properties of additivity , continuity and differentiability [21] , [22] . Such a measure has been called an order parameter , named for its use in tracking a system's dynamics through transitions in the system's degree of order . In gas-liquid transitions , the order parameter is density [23] , while in ferromagnet transitions , for instance , it is net magnetization [23] , [24] . Perhaps the best known example of an order parameter is relative phase , the Landau-Ginsberg order parameter [23] , [24] , used in the phenomenological description of thermodynamic and superconducting transitions [25] . I have examined the autonomous , time-dependent shape changes of individual Idling and Treadmilling human white cells as real , spatially extended dynamical systems . I use a global measure on the PMN's relative radial roundness , R ( t ) , as the order parameter . R ( t ) = r1/r2 is computed as the ratio of the radius of the cell assumed to be an ideal circle , r1 = p/2π in which p is the sum of the pixels outlining the cell's perimeter and r2 = ( A/π ) 0 . 5 using the sum of the pixels within the cell's silhouette as the cell's area , A . R ( t ) = r1 ( t ) /r2 ( t ) = {p/2π/ ( A/π ) 0 . 50} ( t ) computed at each time step . If both r1 and r2 were derived from an abstract , idealized circle , R ( t ) = r1/r2 = 1 . 0 , such that log{R ( t ) = r1/r2} = 0 , the characteristic lower limit of a generic order parameter . Deviations from this reference characterize changes in state [21]–[23] , [25] . My use of the global order parameter , R ( t ) , contrasts with a previous use of an averaged local measure , the power spectral transformation of a sequence of angles resulting from the piece wise linear segmentation of the cell's circumference [26] . The use of R ( t ) more closely resembles a differential geometric pattern map [27] . One hundred and eighty-nine PMNs from fifty-three peripheral blood samples were collected from 18 healthy adult volunteers , aged 26 to 72 . The blood samples were allowed to sediment gravitationally for 40 minutes at room temperature . A population of PMNs ( and other white cells and platelets ) were removed from the buffy coat by micropipette and , along with associated plasma , placed within a 12 mm ring painted on a glass slide , forming a ∼20–25 µm deep well , compared to the average 5 . 7 µm vertical space between a plain slide and its cover slip [28] . The 5 . 7 µm gap of standard slides and coverslips is considerably smaller than the average diameter of PMNs , leading to some mechanical compression of the cell contributing to their activation , and allowing the cell to move along the slide substrate and cover slip simultaneously [28] . The slides used in this study do not suffer from these deficits . PMN autonomous motions were observed using an Olympus BX41 microscope fitted with CytoViva dark field and fluorescent optical illumination systems , which includes a unique , high-aperture , cardioid annular condenser ( www . scitech . com . au ) . The CytoViva condenser makes it possible to visualize objects of below 100 nm in diameter in real time , and with the cellular samples in an unfixed , living , active state [29] , [30] . Because PMNs were treated gently , avoiding perturbations of column separation and elution , it became possible to reliably study a PMN continuously for 30 to 60 minutes before the onset of granular clumping , membrane blebbing and other signs of nascent apoptosis [31] . Data collection continued until ten each Idling and Treadmilling cells met the conditions for inclusion in the study . Specifically , only cells that maintained healthy , one state behavior , and did not have contact with any extracellular objects for the entire 30 min recording period were retained for analysis . Idling PMNs are characterized by their near spheroid shape ( quasi-circular in two dimensions ) . In this state , the microscopically visible autonomous motions are limited to standing waves on the cell surface and low amplitude fluctuations of the cell's microvillus border . In contrast , Treadmilling PMNs demonstrate large and irregular changes in cell shape . Multiple transient , often simultaneously appearing , pseudopodia and lamelopodia emerge from the cell surface , oriented apparently randomly and without significant movement of the PMNs center of mass . The cell's movement was less than 1 . 5 times the maximum diameter of the cell over the typical ∼30 minute recording sessions . Figure 1 portrays binary color coded , characteristic silhouettes of the round Idling , Treadmilling and elliptically polarized/translocating shape-states of PMNs . Only videos micrographs of mature , segmented neutrophils that did not have contact with any cells or extracellular entities and remained visibly healthy over the thirty minutes of observation , in addition to manifesting stable state behavior were retained for further analysis . Images were collected every 2 sec for 30 min using an Optronics Microfire 1200×1600 CCD array camera [30] resulting in a 900 point R ( t ) series of high resolution images per PMN . The slowness of the cell shape changing motions led to the finding that more frequent sampling within the time limit of cellular integrity was obviously redundant . In the geometric computations , each primary image was used to produce two binary , 0 , 1 , digital daughter images: an area map of A , and perimeter map of p . The 0 , 1 coding of the pixels of the two daughter images were converted into binary arrays and used compute the R ( t ) time series . In light of the above discussion of biological constraints on sample length and the intrinsic non-stationarity of the PMNs shape motion series , statistical distributions of often individually non-convergent measures made on each of the cells , serve as the basis for comparisons of Idling and Treadmilling states . Statistical evaluations are then made on populations of possibly incomplete measures , not on the raw observations . Rules of thumb concerning sample length requirements for any particular measure [32] though easily attainable in physical and computational systems , often ignore the intrinsic series limits and non-stationarity of real , behaving , biological systems . In addition to the use of the distribution of each particular kind of measure , I study an aggregate of several , often incomplete measures , each reflecting different aspects of the shape-motional dynamics of PMNs . On the R ( t ) of each cell , I study: ( 1 ) The central moments of the R ( t ) distribution , the mean S1 and standard deviation , S2 , as well as the skewness , S3 , indicating the asymmetry of the density distribution of R ( t ) , estimated using the third moment , m3 , divided by the cubed root of the variance squared , S3 = m3/var3/2 . The kurtosis , S4 , of R ( t ) is computed using the relation , S4 = m4/variance2 -3 [33]; ( 2 ) An estimate of the R ( t ) 's orbital divergence , its sensitivity to initial conditions , in a three dimensional embedding space , was computed using a generic algorithm for the leading Lyapounov exponent , Λ1 [34]; ( 3 ) Differences in a hierarchical scaling property of R ( t ) , by computing the scaling exponent α derived from its power ( frequency ) spectrum , as the slope of the middle third of the linear best fit of the log power-log frequency relation [35]; ( 4 ) An example of the time dependence of the scaling of R ( t ) was estimated from a Morlet continuous wavelet transformation using standard algorithms [36]–[38] . To visualize the phase space behavior I used relatively denoised , three dimensional Broomhead-King , B/K , eigenfunction , Ψi embedding of the R ( t ) s . To do this , I computed and plotted Ψ1 , Ψ2 , and Ψ3 with respect to each other [39] , [40] . Each R ( t ) series generated an M-lagged data matrix on which an MxM Hermitean autocovariance , CM , matrix was computed , with M = 8 , a typical correlation decay interval . CM was then decomposed into its eigenvalue-ordered eigenvectors . The eigenvectors associated with the three largest eigenvalues were each composed with the original R ( t ) series to form B/K eigenfunctions Ψ1 , Ψ2 , and Ψ3 . These formed the axes of the B/K eigenspace reconstruction . Because R ( t ) behavior attributable to the lower , excluded , eigenvectors accounts for the trivial , “noise” component of the variance , the resulting eigenfunction space embedding ( each successive point being a triple ) is relatively denoised compared with the more commonly used phase delay space construction [41] . Another graphical representation of the orbital behavior of R ( t ) is its two dimensional , i = τ1 , j = τ2 , Recurrence Plot , RP[R ( t ) ]i , j , introduced by Eckmann [42] . Graphical representations of RP[R ( t ) ]i , j are two dimensional lattices , each point computed as RPi , j = Θ ( ε ) , i , j = 1…N , where in R2 represents the location of the orbit in phase space at time i . is the static distance defining the “closeness” threshold , and Θ is the Heavyside function . The resulting binary series , each point ε- close or not to the previous value , is coded in black and white . Here , a standard time delay three dimensional embedding was used , with delay τ = 1 [43] . If falls within the distance of , is considered to be a recurrence of , otherwise not . Clustering in RPi , j has been used to discriminate among three characteristic patterns of intermittency [44] .
There were highly significant differences between the measures S2 , S3 , Λ1 , and α that were made on the R ( t ) series of the PMNs in the Idling versus the Treadmilling state , see Table 1 . No significant differences were found between the two distributions of S1 or S4 . The qualitative differences in the shape-motional patterns implied by the statistically significant differences in the measures in Table 1 are consistent with behavior that was observed microscopically in the two pre-polarized states: ( 1 ) The small , stochastically wavy border fluctuations in cell shape of the generic Idling PMNs; ( 2 ) A range of large , simultaneously multiscale motions in cell shape variations of R ( t ) in the Treadmilling state . For examples , compared with Idling , the increase in asymmetric amplitude in Treadmilling is reflected in increases in S2 and S3 , and the increase in shape-motional order in Treadmilling is seen in the statistically significant decrease in Λ1 , the leading Lyapounov index of expansive , orbital mixing [45] . The larger , smoother , more correlated shape motions of the Treadmilling state are seen in statistically significant increases in α in the Treadmilling versus Idling states . Without a significant difference in the means of R ( t ) , the variational measures make the discrimination between Idling and Treadmilling states . Consistent with the differences in behavior described by direct observation and the aggregate of measures ( see Table 1 ) , Figure 2 portrays the previously described {Ψ1 , Ψ2 , Ψ3}1…900 B/K eigenfunctions embedding of four representative Idling cells ( left column ) and four Treadmilling cells ( right column ) . The phase portraits of the Idling cells reflect symmetric , small , random fluctuations around a near stationary state . Treadmilling cells manifest larger , more irregular , asymmetric phase space motions which occupy almost an order of magnitude larger volume than that by the Idling state . Another geometric , graphical treatment of the cell's shape motional behavior is displayed in Figure 3 . We see the recurrence plots , RP[R ( t ) ]i , j of the four representative PMNs in the Idling state ( top row ) and four in the Treadmilling state ( bottom row ) , in which ε = 1 for all plots . The RP[R ( t ) ]i , j of the Idling cells demonstrate more homogeneous temporal distributions of returns typical of more random data with shorter correlation lengths/relaxation times . The square patches of only lightly increased density overlaid on the more uniform surround are consistent with both the visualized small amplitude oscillations in R ( t ) in the Idling state and with the statistical results reported in Table 1 . The RP[R ( t ) ]i , j of cells in the Treadmilling state are , as expected , less homogenously distributed , manifesting clustering in the return times across multiple times scales , as well as apparent discontinuous changes in their phase space patterns . For example , short interval “bursting” interleaved with low amplitude , long interval behavior is seen in the Treadmilling cells' RP[R ( t ) ]i , j . Treadmilling PMNs RP[R ( t ) ]i , j portraits are consistent with recurrence patterns of intermittency [44] , [46] . Four of the seven order parameter measures demonstrated statistically significant differences between the Idling and Treadmilling PMNs , Table 1 . While observing and recording the real-time behavior of 189 PMNs , I witnessed many cells transitioning from one state to another among my three defined behavioral regimes . Data series including such transitions were plagued by the same complications as were the single state series ( e . g . , cell-cell interactions , apoptotic behavior ) in addition to too short times in one or more behavioral state to allow any analysis . I was finally able to make sufficient observations portraying a single PMN shape motion transformation in real time . Figure 4 is a Morlet wavelet graph , in continuous time along the x-axis , and scale ( ∼wavelength ) along the Y axis . Figure 4 contributes evidence for a continuous transition in shape motion state , here from Idling to Treadmilling . Table 2 lists measures before and after this single cell transition . Note that the direction and approximate magnitude of change resemble those of the population of statistically significant values in Table 1 .
There are established physiological mechanisms and behavior that are consistent with both our qualitative microscopic observations and quantitative aggregate measure descriptions . PMNs are known to oscillate on multiple time and space scales , from 7 sec , 70 sec , and 260 sec membrane potential fluctuations [47] and 25 sec calcium flux oscillations [48] , to the ∼8 sec bound/unbound actin oscillations [49] , to 21 . 6 sec and 230 sec glycolytic cycles producing NAD ( P ) H oscillations [47] , and 10 sec and 20 sec pericellular proteolysis fluctuations [48] , among many others . The R ( t ) series in this study evidenced scaling , board band power spectra with multiple resonances [50] . It is likely some reported modes contribute to the cell shape fluctuations directly and others contribute to the emergence of other dynamical patterns . The slowest Fourier mode in Sω [R ( t ) ] of the Idling state had an average 8 . 457 minutes oscillation , whereas that of the slowest Sω [R ( t ) ] of the Treadmilling state averaged a 4 . 201 minutes oscillation . It is interesting that these characteristic times correspond roughly to the results of studies of the characteristic remodeling times composed of actin filament diffusion , polymerization and then turnover coordinated with cellular migratory motions [51] , [52] . It appears that the transition from Idling into the intermittent Treadmilling regime occurs as the Idling state loses some of its dynamical structural stability , and its shape motion scenario becomes driven by several quasiperiodic , multi-periodic metabolic and physiological cellular oscillator mechanisms [53] , [54] . As listed in Table 1 and Table 2 , a comparison of Idling with Treadmilling PMNs reveals significantly different R ( t ) order parameter dynamics . Projected to a two dimensional plane ( Figure 1 ) , one sees an associated difference in the underlying planar geometry , with the Idling PMNs manifesting one centroids in their circularity , and the Treadmilling PMNs with two point defined , barycentric ellipses . Many characteristics of the changes in measures in the distinct single state observations and in the computable , real-time transition from Idling to Treadmilling suggest the typical signs of a phase transition [21]–[23] , [25] . These included: ( 1 ) Increasing amplitude of R ( t ) variability seen in the S2 and S3 of the cell shape fluctuations; ( 2 ) Decreased leading Λ1 becoming less positive in the direction of zero , shadowing the leading eigenvalue of the unknown underlying partial differential equation; ( 3 ) An increase in the log-log power spectral scaling index , α , reflecting a “less white” spectral pattern of R ( t ) fluctuations , also consistent with slowing; ( 4 ) The Morlet wavelet transformation of a continuous time R ( t ) , evidenced anticipatory , high amplitude slowing and a mixed phase regimes in the neighborhood of a real-time PMN shape fluctuation transition . The eigenfunction space embedding of the sequence of triples , {Ψ1 , Ψ2 , Ψ3}i demonstrated directly the space-time morphogenic transformation undergone by R ( t ) in the Treadmilling state with reference to that of the Idling cell state . Recurrence plots , RPi , j depicted increased phase space clustering consistent with the more hierarchical , intermittent dynamics of the Treadmilling PMNs in contrast with the more randomly distributed and metrically transitive space of the Idling RPi , j . It should be noted that the action spaces of less uniform intermittency and those of more uniform transitivity reflect common metastable alternatives in the dynamics of some biological sciences [48] . Finally , I have spent hundreds of hours microscopically tracking 189 individual PMN cells in the hopes of answering these questions about state and state transitions . While only one such transition was recorded with sufficient observations in both the Idling and Treadmilling states to allow statistical analyses , many transitions were observed . I have seen Idling cells transition to Treadmilling , and Treadmilling cells ball up and Idle ( although with slightly ragged aprons ) . I have also observed numerous instances of Idling cells polarizing and Translocating until they reach some point at which point they Idle again . The only transitions that were not observed were from the Treadmilling to the polarized , single lamelopod , Translocating state or vice versa . In either case the cells ball-up briefly into an Idling appearance before changing again . See Table 3 . | Human white blood cells , polymorphonuclear leucocytes ( PMN ) , were microscopically imaged and analyzed as single living cells . PMN are generally observed in a spheroid Idling state transitioning to an activated , egg-shaped , translocating state when triggered by the body's signals of infection or inflammation . Occasionally , PMN are observed in a third behavioral state that looks like dancing in place , with protrusions thrown out and retracted , sometimes several simultaneously , in apparently random directions . This behavior previously had been thought to be associated with disease . Here this third state , that I call Treadmilling , is a relatively common way that PMN from healthy people get “stuck” in an intermediate phase . Relative radial roundness , R ( t ) , served as a computable order parameter , and time series of R ( t ) were derived from microscopic image series of each of 189 PMN . Only R ( t ) series from cells that stayed healthy , maintained a single behavioral state and did not have contact with other bodies for the 30 min recording period were analyzed further . Comparison of measures made on the R ( t ) series of cells in the Idling versus Treadmilling states quantitatively distinguish states and suggest behavior in the vicinity of global state transitions . Wavelet transformation of an R ( t ) series of a captured state change supports this finding . | [
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] | 2011 | A Third Measure-Metastable State in the Dynamics of Spontaneous Shape Change in Healthy Human's White Cells |
In muscle , force emerges from myosin binding with actin ( forming a cross-bridge ) . This actomyosin binding depends upon myofilament geometry , kinetics of thin-filament Ca2+ activation , and kinetics of cross-bridge cycling . Binding occurs within a compliant network of protein filaments where there is mechanical coupling between myosins along the thick-filament backbone and between actin monomers along the thin filament . Such mechanical coupling precludes using ordinary differential equation models when examining the effects of lattice geometry , kinetics , or compliance on force production . This study uses two stochastically driven , spatially explicit models to predict levels of cross-bridge binding , force , thin-filament Ca2+ activation , and ATP utilization . One model incorporates the 2-to-1 ratio of thin to thick filaments of vertebrate striated muscle ( multi-filament model ) , while the other comprises only one thick and one thin filament ( two-filament model ) . Simulations comparing these models show that the multi-filament predictions of force , fractional cross-bridge binding , and cross-bridge turnover are more consistent with published experimental values . Furthermore , the values predicted by the multi-filament model are greater than those values predicted by the two-filament model . These increases are larger than the relative increase of potential inter-filament interactions in the multi-filament model versus the two-filament model . This amplification of coordinated cross-bridge binding and cycling indicates a mechanism of cooperativity that depends on sarcomere lattice geometry , specifically the ratio and arrangement of myofilaments .
Muscle contraction is initiated by Ca2+ binding to troponin and the subsequent movement of tropomyosin on the thin filament , enabling myosin to cyclically attach and detach to actin ( cross-bridge cycling ) [1–7] . Underlying this process are myriad factors that contribute to the magnitude and time course of force production . These factors include the geometry of filaments in the sarcomere , the mechanical properties of the filaments and cross-bridges , the kinetics of thin-filament activation by Ca2+ , and the kinetics of cross-bridge cycling . Because contractile proteins interact in a highly structured , compliant lattice , mechanical coupling exists between myosins along the thick-filament backbone , between actin monomers or regulatory proteins ( troponin and tropomyosin ) along the thin filament , and between thick and thin filaments following cross-bridge formation . Thus , kinetic processes responsible for contraction are linked at the molecular level . Considerable evidence shows that Ca2+ and cross-bridge binding at one location in the sarcomere can influence these processes at proximal regions of the sarcomere ( reviewed in [7] ) , implying that coupled kinetics of thin-filament activation and cross-bridge cycling determine the level of force generated in striated muscle . Most models do not explicitly consider that spatial properties of muscle may influence contraction [1 , 3 , 4 , 6 , 8–12] . Of the muscle contraction models containing both spatial and temporal variables , some provide either spatial predictions of steady-state conditions [9 , 13] or temporal predictions of cross-bridge and thin-filament “state” without any spatial detail [12] . In contrast , a few recent spatially explicit models predict both spatial and temporal behavior [14–18] , with some simulations indicating that elasticity of the myofilament lattice contributes to coordination between cross-bridges that enhances cross-bridge binding [15 , 17 , 18] . This cross-bridge–induced cross-bridge recruitment becomes a potential mechanism of cooperativity that results from realignment between compliant myofilaments following myosin binding to actin . Previous spatially explicit models [14–17] lacked a Ca2+ regulatory cycle , spatially coordinated Ca2+ activation along the thin filament , and the physiological ratio of thick to thin filaments . These thin-filament components are particularly important for contraction because regions activated by Ca2+ binding to troponin largely determine the spatial distribution of bound cross-bridges . The current study adopts a spatially explicit model of regulatory proteins along the thin filament , in contrast to prior studies [17 , 18] . These additions enable investigating how force is controlled by two , coupled , spatial , and temporal processes: Ca2+ binding to activate the thin filament and subsequent myosin binding to the proximal , activated region of the thin filament . Spatial and temporal aspects of contraction may be profoundly influenced by the coupled behavior between myosins throughout the compliant myofilament lattice , as nearly 70% of muscle compliance resides in the thick and thin filaments [19–23] . This significant compliance implies that cross-bridges do not operate independently while generating force [15 , 17 , 18] . Moreover , recent measurements [24–26] improve estimates about cross-bridge rate functions , depending on distortion and load , suggesting that the extent of realignment between compliant thick and thin filaments may affect kinetics of cross-bridge cycling ( in addition to number of bound cross-bridges ) . Within this compliant system , however , the consequences of sarcomere lattice structure on cross-bridge dynamics remain unclear . This study compares multiple models that have identical thin-filament and cross-bridge kinetics , but different model geometries , to examine the consequences of sarcomere lattice structure on Ca2+-regulated contraction ( Figure 1 , see Methods section ) . Consistent with previous models [15 , 17 , 18] , motions and forces occur solely along the longitudinal axis of filaments in these current models ( Figure 2 ) . This one-dimensional assumption permits a system of linear equations to describe force-generating interactions between filaments ( Equations 2–4 ) . At the core of each model is a three-state cross-bridge cycle coupled with a three-state thin-filament regulatory model to control actomyosin binding through [Ca2+]-sensitive kinetics ( Figure 3 ) . Initial model comparisons occurred between four different models ( see Text S1 ) . Of these geometric options , only one multi-filament model yields predictions that were consistent with the range of published values for muscle contraction ( see Table S1 ) . Therefore , we focus on comparing this multi-filament model ( Figure 3 ) with a two-filament model ( sensu [15] ) . Throughout this study , we specifically consider contraction in the absence of cooperative , kinetic feedback between thin-filament activation or cross-bridge binding [11 , 12 , 15 , 27–29] . Thus , any differences in simulation predictions between the multi-filament and two-filament models depend solely on differences between model geometry . Simulation results show that additional inter-filament interactions in the multi-filament model lead to greater fractional binding of cross-bridges , force production , and cross-bridge turnover compared with the two-filament model . Importantly , these increases are larger than predicted by normalizing for the additional filaments in the multi-filament model . These results indicate that there is a mechanism of cooperativity dependent upon sarcomere lattice structure ( both the ratio and arrangement of myofilaments ) . Specifically , multi-filament lattice structure further coordinates cross-bridge binding to enhance cross-bridge recruitment and turnover without any requirements for cooperative feedback mechanisms attributed to thin-filament activation . Additional studies investigating other mechanisms of cooperativity acting via kinetic feedback pathways to amplify thin-filament activation or cross-bridge binding are ongoing in our lab and in others [30 , 31] . Findings from the current study , however , imply that certain lattice geometries facilitate greater cross-bridge binding and turnover , which may be an important mechanism of cooperativity contributing to muscle performance . Earlier aspects of this work have been published previously [30 , 32 , 33] .
These models provide both temporal and spatial predictions for force , cross-bridge binding , thin-filament Ca2+ activation ( fraction-available actin nodes ) , and ATP consumption via cross-bridge turnover . Temporal dynamics of these predictions highlight similarities and differences between the simpler two-filament model and the multi-filament model ( Figure 4 ) . Although the multi-filament and two-filament models show similar thin-filament activation dynamics that lead to greater magnitude and rate of force generation with increasing [Ca2+] , the multi-filament geometry produces higher force and signal-to-noise ratio . Maximal , average , steady-state force ( at pCa 4 , where pCa = −log10[Ca2+] ) for the multi-filament model is 958 . 5 ± 32 . 3 pN , compared with 9 . 2 ± 8 . 2 pN for the two-filament model ( mean ±SD ) . This ≈100-fold increase in force occurs even though the potential inter-filament interactions in the multi-filament model increase only 24 times . Relative force ( ratio of predicted force to total myosin ) adjusts for the relative number of potential interactions between models and is about 4-fold greater ( =100/24 ) in the multi-filament model ( Figure 4A ) . The maximal relative force value predicted by the multi-filament model ( ≈2 pN myosin−1 at pCa 4 , Figure 4A ) lies in the range ( ≈1–3 pN myosin−1 ) estimated from experimental studies [25 , 34 , 35] . Isometric force measurements from single fibers set the low end of this value at 1–1 . 7 pN myosin−1 [34 , 35] , depending upon temperature and estimated myosin binding ( fxb ) . Rescaling the value of 1 . 4 pN per head [25] from single-molecule studies sets the upper end of this range at 2 . 8 pN myosin−1 . The predicted relative force values by the two-filament model ( Figure 4A ) are below this range . Furthermore , tensile stress ( force per cross-sectional area ) for the multi-filament model , 171 ± 6 kPa , is also consistent with literature values ( Table S1 , see Methods section for calculated area = 5 , 600 nm2 ) . Collectively , these results suggest that the more physiological lattice geometry in the multi-filament model introduces a geometry-dependent increase in predicted force values , agreeing better with experimental force values than the two-filament model . Comparing relative force traces across multiple [Ca2+] ( Figure 4A ) shows more variation in the two-filament model , even though these traces average 24 times as many simulation runs as multi-filament traces ( see Methods section ) . Although the multi-filament traces show less relative variation in force level , these traces have occasional “spikes” not present in the two-filament traces due to increased two-filament averaging . Greater variation in the two-filament model results from a lower number of cross-bridge interactions between the filaments . Additionally , force-generating events are less frequent in the two-filament model , giving each of these events more influence on the force level . Thin-filament activation does not depend on geometry differences between the two models ( largely because this study does not examine cross-bridge feedback increasing thin-filament activation ) . Thin-filament activation contrasts with force production and cross-bridge binding , which both depend upon the coupled probability of myosin binding a proximal actin node with the probability of that actin node being activated by Ca2+ . The fraction of thin-filament sites available to bind myosin ( fa ) is calculated from the number of actin nodes populating state TF3 divided by the total number of actin nodes . Magnitude and rate of fa increase with [Ca2+] ( Figure 4B ) , and maximal steady-state fa ( pCa 4 ) for the multi-filament and two-filament models is 0 . 90 ± 0 . 01 and 0 . 90 ± 0 . 03 , respectively . Given identical thin-filament activation kinetics in both models , with no influence of cooperative feedback between the kinetics of thin-filament activation and cross-bridge cycling in either model , we expect similar thin-filament activation dynamics . Stochastic variation in fa is slightly less in the multi-filament versus the two-filament model , likely because the increased number of actin nodes decreases the influence of any single actin node . For each model , variation in fa is smaller than variation in the corresponding force trace at a similar [Ca2+] . This decreased variance in fa ( compared with force ) occurs because Ca2+-binding kinetics assume a spatially homogeneous [Ca2+] within the cell ( in contrast to the spatial constraints of cross-bridge binding and force generation ) . Although these differences leading to thin-filament activation and force production may appear subtle , they demonstrate that spatially explicit implementation of thin-filament activation is critical for investigating the molecular mechanisms controlling force production . Lower fractional myosin binding ( fxb ) in the two-filament model is not limited by fa , which implies differences in cross-bridge binding stem purely from geometry differences between the two models ( Figure 4C ) . fxb is calculated from the sum of cross-bridges in states XB2 and XB3 divided by the total number of cross-bridges . Similar to force and fa , the magnitude and rate of fxb increases with increasing [Ca2+] . Maximum fxb ( pCa 4 ) is 0 . 101 ± 0 . 002 and 0 . 015 ± 0 . 014 for the multi-filament and two-filament models , respectively . This predicted multi-filament value is near the low end of estimates [34–36] , and stiffness properties of the lattice or kinetic feedback may augment this value ( discussed below in detail ) . Steady-state predictions ( mean ±SD ) for normalized force , fa , and fxb over a range of [Ca2+] ( Figure 5A–5C ) show little difference in Ca2+ sensitivity between the two models ( Tables 1 and S2; for calculations , see Methods section ) . As mentioned above , the multi-filament model produces ≈100 times the maximal force with ≈6 times the fxb as the two-filament model . While there appears to be more variation in force-pCa plots ( Figure 5A , where force was normalized to the value at pCa 4 for each model ) than in corresponding fxb-pCa plots ( Figure 5C ) , this is a consequence of normalizing force without changing fxb calculations from Figure 4 . The coefficients of variation between the force-pCa and fxb-pCa datasets within a given model are nearly identical . Sensitivity to Ca2+ ( Table 1 ) , is calculated by data fits to a three-parameter Hill equation using pCa as the independent variable ( Equation 22 ) . These values for nH are close to one , as expected in the absence of cooperative feedback between the kinetics of calcium binding to thin filaments and cross-bridge recruitment . The similarities between nH and pCa50 for steady-state force , fa , and fxb result from identical thin-filament kinetics between the two models ( Figure 3 , Table 2 ) . Again this highlights that greater force and fxb in the multi-filament model result from a difference in geometry between the two models . Thus , sarcomere lattice structure introduces a cooperative mechanism that is independent of Ca2+-mediated mechanisms . To determine how model geometry affects the balance of force in each model , we calculated the ratio of steady-state force to number of attached myosin at all simulated [Ca2+] . Average steady-state force per bound myosin ( Figure 5D ) is ≈17 pN for the multi-filament model compared with ≈30 pN for the two-filament model . These predicted values ( at kxb = 5 pN nm−1 ) are greater than experimental estimates of ≈6–8 pN per attached myosin head [35 , 37] , although multi-filament model predictions of force per bound myosin over a range of slightly more compliant kxb values are more consistent with these experimental estimates ( discussed below in detail ) . These values ( Figure 5D ) are consistent with estimates for actomyosin rigor bonds [38–40] , which may set an upper limit on possible force borne per attached myosin . Two-tailed bootstrap analysis [41] of these results ( Figure 5D ) indicates a significant slope ( p < 0 . 05 ) for the multi-filament ( =−0 . 56 pN Bound XB−1 pCa−1 ) and two-filament models ( −8 . 0 × 10−9 pN Bound XB−1 pCa−1 ) . This slope is much larger for the multi-filament model , indicating an increase in force produced by a bound cross-bridge as [Ca2+] increases . This Ca2+-sensitive increase implies a coordination between cross-bridge binding and cycling in the multi-filament model that is not observed in the two-filament model . Steady-state predictions of ATP consumption are greater for the multi-filament model than for the two-filament model for all [Ca2+] ( Figure 6A ) . Maximal ATP consumption ( pCa 4 ) in the multi-filament model is 3 . 9 ± 0 . 2 ATP s−1 myosin−1 compared with 0 . 03 ± 0 . 12 ATP s−1 myosin−1 in the two-filament model , ≈135-fold difference . This multi-filament ATPase value agrees well with measured values from skeletal fibers ( =3 . 5 ATP s−1 myosin−1; [42] ) . Parameter values for Hill-curve fits on these data show a slightly increased Ca2+ sensitivity ( pCa50 , Table 1 ) for ATP consumption compared with the mechanics predictions in Figure 5A–5C . Normalizing ATPase to the number of myosins directly compares the effect of lattice geometry on cross-bridge turnover rate . This indicates that the ≈6-fold increase in cross-bridge binding ( fxb ) in the multi-filament model cooperatively enables ≈135 times the cross-bridge cycling . These results , coupled with the results from Daniel et al . [15] , suggest that increased cross-bridge binding and turnover occurs through enhanced compliant realignment of filaments in the lattice . In contrast , low ATP consumption in the two-filament model suggests that cross-bridges are binding , and producing force , but cross-bridge turnover is less frequent . This implies that the two-filament model remains more static , while the multi-filament model exhibits more active realignment between filaments . Steady-state tension cost , calculated from the quotient of ATP consumption and force , does not significantly differ between the two models at pCa 4 ( multi-filament = 0 . 0041 ± 0 . 0003 versus two-filament = 0 . 0057 ± 0 . 0436 ATP s−1 myosin−1 pN−1 ) . Two-tailed bootstrap analysis [41] of tension cost indicates a small , but significant ( p < 0 . 05 ) slope in the tension cost-pCa relationship ( 0 . 001 ATP s−1 myosin−1 pN−1 ) for the multi-filament model . A similar analysis of tension cost in the two-filament model yields no significant [Ca2+] dependence . This result indicates similar mechanisms of individual cross-bridge cycling in each model . Specifically , if an individual cross-bridge binds in either model , it ultimately undergoes a similar range of distortions throughout the cycle . The two models predict a nonlinear increase in the rate of force generation ( rf ) with increasing [Ca2+] ( calculated as discussed in the Methods section , [17] ) . Maximal rf ( pCa 4 . 0 ) is 48 ± 10 s−1 versus 59 ± 176 s−1 ( mean ±SD ) for the multi-filament and two-filament model , respectively . Importantly , the mean values of rf for a given normalized force are similar for both models . This similarity suggests that individual cross-bridge binding kinetics depend on [Ca2+] and force level , but are independent of model geometry . The nonlinear relationship between predicted rf values versus normalized steady-state force ( Figure 7 ) is similar in shape to measured force redevelopment rates plotted against normalized steady-state force from single , demembranated muscle cell experiments [43–49] . The extraordinarily large SD in the two-filament model predictions follows from an exponential , as opposed to normal , frequency distribution in the set of rf values . Though the shape of the rf -normalized force relationship is similar in both models , the stochastic variation in rf ( error bars in Figure 7 ) is much less for the multi-filament ( A ) than the two-filament ( B ) model . This difference in variation results from greater and more consistent cross-bridge binding at the onset of contraction in the multi-filament model , which follows from a greater number of Ca2+-activated actin nodes . For example , within the first few time steps of a simulation at pCa 4 in both models , roughly 50% of the actin nodes are available to bind with myosin ( fa , Figure 4B ) . This initial fa level creates a finite duration ( ≈50 ms ) when the thin filament is submaximally activated , leading to spatial inhomogeneities of Ca2+-activated regions along the thin filament , even at pCa 4 . Building on results discussed above , the likelihood that these few Ca2+-activated regions align with a proximal myosin is much greater in the multi-filament versus the two-filament model . Hence , variance in the distribution of initial Ca2+-activated actin nodes being spatially available for immediate cross-bridge binding is much less for the multi-filament model . Moreover , any realignment of the filaments following initial cross-bridge binding can increase the probability of additional cross-bridge binding by improving the alignment with these Ca2+-activated regions of the thin filament . Thus , increased compliant realignment in the multi-filament model may also help reduce stochastic variation in rf through increased cross-bridge recruitment . Myofilament stiffness values influence maximal predicted force ( pCa = 4 ) in both models ( Figure 8 ) . To examine the coupling between filament stiffness and predicted force , we used two types of simulations . One set varied thin-filament stiffness ( ka ) and cross-bridge stiffness ( kxb ) , while keeping thick-filament stiffness ( km ) fixed ( Figure 8A and 8B ) . The other set of simulations simultaneously varied both thick- and thin-filament stiffness ( kF ) by a scalar factor ( X ) , while co-varying kxb ( Figure 8C ) . The approach used in the first set of simulations ( varying only ka and kxb , Figure 8A and 8B ) is consistent with prior unregulated models of contraction [15 , 17] , which showed that stiffness of the filament lattice may be “tuned” to maximize predicted force . Generally , varying kxb in both current models produces little force at the most compliant kxb values . Force increases to a maximum ridge near moderate kxb values and then diminishes as kxb further increases . Note , however , the force dependence on ka in the multi-filament model ( Figure 8B ) , which forms an L-shaped ridge of maximum force that is not present in the two-filament model ( Figure 8A ) . Additionally , force decreases at higher ka and kxb values in the multi-filament predictions ( Figure 8B ) . This decrease differs from the results of Chase et al . [17] , where predictions of force continue to rise over increasing values of ka and kxb . Our model produces results similar to those of Chase et al . [17] if we increase values of kxb , but do not correspondingly decrease xb0 ( Equation 13 ) . The contrast between the L-shaped maximum force contour in the multi-filament model and the simple ridge of maximal force in the two-filament model likely follows from increased realignment between compliant filaments in the multi-filament lattice that leads to increased cross-bridge binding at greater kxb values . This increased range of myofilament stiffness values that produce high force levels in the multi-filament model demonstrates the influence of a cooperative mechanism arising solely from geometry differences between the two current models . The second set of simulations varied km , ka , and kxb in the multi-filament model to more fully examine how mechanical properties of the lattice affect force production . This expanded approach ( compared with prior studies [15 , 17] as well as with Figure 8A and 8B ) simultaneously varied both thick- and thin-filament stiffness ( kF ) by a scalar factor ( X ) , while independently varying kxb . These simulation results show a plateau of high force across a range of stiffer kxb values that extends from moderate to high kF values ( Figure 8C ) . This elevated force plateau extends across the stiffest filament and kxb values ( Figure 8C ) , and thus differs from the L-shaped ridge of elevated force when only ka varied ( Figure 8B ) . Similar to Figure 8B , the maximal force contour occurs near parameter values that correspond with experimentally derived filament stiffness values [log10 X = 0] [20–23] . However , there is a slight shift in position of the maximal contour between Figure 8B and 8C . Figure 8C also shows a clearly defined peak of maximal force , in contrast to the ridge of maximal force in simulations tuning ka independently of km ( Figure 8B ) . Multi-filament model predictions of steady-state force as a function of [Ca2+] , cross-bridge stiffness ( kxb ) , and filament stiffness ( kF ) ( uniformly varying both thick- and thin-filament stiffness as in Figure 8C ) show that increasing [Ca2+] increases force ( Figure 9A–9F ) . Because the model contains no feedback between crossbridge binding and thin-filament activation ( fa ) kinetics , fa is not affected by stiffness properties of the myofilament lattice ( unpublished data ) . For all kxb values ( Figure 9A–9F ) , there is similar shape to the surface of force produced over the full range of kF values . Force level is elevated at larger filament stiffness ( kF ) values and diminishes with more compliant filament values . Also , greater kxb values produce a sharper decline in force as filament compliance increases ( lower kF values ) . The maximal contour value of each plateau ( across kxb values ) occurs at kF values that are similar to experimentally measured values for thick- and thin-filament stiffness [log10X = 0] [20–23] . However , the maximal force contour becomes more sharply defined with a peak that shifts slightly toward more compliant kF values at greater kxb values ( white dots in Figure 9A–9F ) . The maximal force value for each panel is moderate at compliant kxb values , increases to a maximum at kxb pN nm−1 ( Figure 9D ) , then slightly diminishes with increasingly stiff kxb . Simulations suggest that the number of bound cross-bridges is not directly correlated with the level of force produced . Altering [Ca2+] , kF , and kxb results in the greatest fractional cross-bridge binding ( fxb ) at lowest kxb values ( Figure 9G–9L ) . This maximal fxb level contrasts with force , which is minimal at lowest kxb values ( Figure 9A–9F ) . There are general similarities between force production and fxb as [Ca2+] and myofilament stiffness varies: 1 ) increased [Ca2+] increases cross-bridge binding , 2 ) the maximal contour of each panel occurs at similar kF values ( corresponding to measured values , [20–23] ) , and 3 ) the maximal contour width narrows with a shift in the peak ( white dots ) as kxb increases . In contrast to force predictions , a plateau of elevated fxb ( over a range of kF values ) exists only at more compliant kxb values ( Figure 9G and 9H ) , and these plateaus narrow into ridges at greater kxb values ( Figure 9I–9L ) . Moreover , fxb consistently declines with increasing kxb values , although force increases and stabilizes at a high magnitude with increasing kxb values . Examining the complex correlation between fxb ( Figure 9G–9L ) and force ( Figure 9A–9F ) indicates that relatively small changes in these values can compound to produce larger shifts in estimates of average force borne per bound myosin . Multi-filament model predictions suggest that cross-bridge turnover decreases with increasing cross-bridge stiffness ( one ATP per cross-bridge cycle , Figure 9M–9R ) . Across all kxb values , with respect to any specific kF value , ATPase increases with increasing [Ca2+] . In all panels ( Figure 9M–9R ) , the maximal contour of cross-bridge turnover occurs at pCa 4 ( white dots ) , and this maximum shifts toward more compliant filament values with decreased kxb . Additionally , the peak of the maximum contour becomes sharper as kxb increases . These results , coupled with force ( Figure 9A–9F ) and fxb ( Figure 9G–9L ) , illustrate how force production results from an interaction between mechanical properties of the lattice and kinetics of Ca2+-regulated cross-bridge binding . In summary , comparing results across the panels of Figure 9 show how muscle contraction depends on Ca2+-regulated cross-bridge binding within a compliant myofilament lattice . The greatest ATP consumption ( Figure 9M ) and cross-bridge binding ( Figure 9G ) occur at kxb = 1pN nm−1 , a kxb value that produces minimal levels of force ( Figure 9A ) . Together , these results suggest that energy consumed at this kxb value is used to stretch out the filaments , increasing both realignment between compliant filaments and cross-bridge cycling , rather than producing force . On the other extreme , where kxb = 15 pN nm−1 , there is a high magnitude of force ( Figure 9F ) , very little ATP consumption ( Figure 9R ) , and minimal cross-bridge binding ( Figure 9L ) , which largely follows from little compliant realignment in the more rigid filament lattice . At intermediate kxb values , there is a transition between compliant realignment in the filament lattice that coordinates force production versus myosin binding . Comparing all panels in Figure 9 indicates that an optimal lattice stiffness leads to a high ratio of force to ATP consumption ( one metric of the energetic consequences of contraction ) at kxb = 3–7 pN nm−1 , near physiological filament stiffness values ( log10X = 0 ) .
Varying the stiffness of cross-bridges and myofilaments alters the relative partitioning of mechanical energy , contributing in part to the behaviors observed in Figure 8 . Several molecular phenomena contribute to the tuning observed in these force surfaces as compliance is varied at maximal [Ca2+] . The key difference between these simulations is the ridge of high force seen in Figure 8B where thin- ( ka ) and thick- ( km ) filament stiffness vary independently . This ridge contrasts with the high force plateau for simulations in which ka and km co-varied ( Figure 8C ) . These simulations suggest that the chemical energy imparted to cross-bridges from ATP hydrolysis is manifest as mechanical energy in the forms of force and deformation within the filament lattice . Thus , for a given amount of energy , some is partitioned as forces transmitted throughout the lattice , and some is partitioned to distortions within the lattice . To illustrate this point , examine the predicted force with respect to the most flexible link in the filament network ( Figure 8 ) . The panels show little force production with very compliant cross-bridges ( lower kxb values ) , which partitions energy primarily into cross-bridge distortion . Comparably , there is little force produced when either ka ( Figure 8A and 8B ) or both ka and km ( Figure 8C ) are very compliant ( for kF , log10X = −2 ) , because energy is partitioned into distortion of compliant filament ( s ) . As cross-bridges' stiffness increases relative to filament stiffness , there is decreased cross-bridge deformation and increased force production . These changes occur through coordinated cross-bridge binding that maintains strain in the filament lattice . Further increasing cross-bridge stiffness forms a ridge of high force as ka and km approach the same order of magnitude ( when log10X = 0 for ka ) by favorably partitioning energy into both force and lattice distortion ( Figure 8A and 8B ) . This ridge falls off as thin filaments become very stiff in comparison with thick filaments ( when log10X > 0 for ka in Figure 8A and 8B ) , because energy is partitioned primarily into thick-filament distortion . Indeed , this also explains development of the high-force plateau when uniformly scaling both ka and km ( Figure 8C ) , in contrast to the high force ridge when changing ka alone ( Figure 8B ) . Two other molecular processes also contribute to the steady-state tuning behaviors; these are recruitment of cross-bridges and their state transitions . As previously reported [15 , 17] , the portion of mechanical energy manifest as lattice distortions alters the position of thin-filament binding sites , thus contributing to an increased probability of cross-bridge attachment . However , high lattice compliance leads to mechanical energy being partitioned almost completely to distortion , and produces little force . Herein lies the crucial tradeoff: distortion allows greater cross-bridge recruitment , but simultaneously decreases the fraction of energy partitioned to force that is distributed throughout the lattice . Energy partitioned to lattice deformation controls another crucial feedback mechanism associated with kinetic state transitions . As shown in Figure 10 and in prior studies [3 , 4 , 15] , the probability of state transitions depends strongly on cross-bridge distortion . Thus , energy imparted to the compliant filament lattice from cross-bridges causes deformation which , in turn , results in cross-bridge distortion . In contrast , cross-bridge binding in an infinitely stiff lattice will have all of the strain-dependent mechanical energy appear as force and none as filament deformation . In this latter situation , all cross-bridges behave independently , with no feedback between cross-bridges to influence additional cross-bridge binding or cycling . Importantly , the mechanism of cross-bridge–induced cross-bridge recruitment requires extensible myofilaments and can only be modeled via spatially explicit methods . Cross-bridge compliance also contributes to cross-bridge recruitment . Chemical energy from ATP hydrolysis is transformed into mechanical energy in the cross-bridge regardless of stiffness ( kxb ) . However , kxb affects the likelihood of a cross-bridge finding an actin site as well as the amount of deformation following binding . This restricts stiffer cross-bridges to bind at nearer sites on the thin filament and may produce higher forces even though distortions will be less . In contrast , a more flexible cross-bridge can bind to more distant regions of the thin filament . These examples illustrate how energy partitioning depends on the stiffness of both filaments and cross-bridges . Thus , smaller deformations associated with stiffer cross-bridges limit additional recruitment of other stiff cross-bridges because they too must be near binding sites , and less compliant realignment of binding sites occurs in a stiffer lattice ( Figure 9G–9L ) . Lattice compliance also contributes to the ATP utilization associated with cross-bridge cycling ( Figure 9M–9R ) . Simulations that varied myofilament compliance result in high force , moderate cross-bridge binding , and moderate ATP consumption near physiological values of ka and km over a kxb range of 3–7 pN nm−1 . Increasing filament or cross-bridge stiffness shows that force remains high with reduced cross-bridge binding and cycling ( Figure 9 ) . On the other hand , if the lattice becomes increasingly compliant , minimal force is produced with high ATP consumption . This suggests that an intermediate level of lattice compliance , near physiological values [20–23] , optimizes coordinated cross-bridge binding and cycling via compliant realignment of the filament lattice while producing a high level of force with a lower ATP cost . Crossbridge–induced crossbridge recruitment results in greater force production and ATPase through realignment of myosin binding sites on compliant thin filaments [15 , 17] . This effect is amplified by the geometry of the multi-filament ( versus the two-filament ) model , which more closely reflects the ratio of thick to thin filaments in muscle . Moreover , the augmented force , cross-bridge recruitment , and cross-bridge turnover is larger than would be predicted simply from the greater number of potential interfilament interactions in the multi-filament model . Thus , a cooperative mechanism of contraction arises solely from differences in sarcomere lattice structure . Even though individual cross-bridges have identical model kinetics , the ensemble average of cross-bridge behavior differs between models ( Figures 5–7 ) . The ratio of ATP utilization to force produced is similar between models , which suggests that any single cross-bridge cycle ( in either model ) preserves the partition of energy from ATP into lattice distortion and force production . Despite this similarity , the force per bound cross-bridge in the multi-filament model is about 40% less than that in the two-filament model ( Figure 5D ) . This likely results from a decrease in the mean distortion of a bound cross-bridge moving through its cycle in the multi-filament model . The decreased mean distortion may result from increased realignment in the multi-filament lattice , which contributes to a decreased force borne by a cross-bridge through the lifetime of a cycle . Alternatively , the increased realignment between filaments in the multi-filament model could enhance coordination between cross-bridges , leading to increased rates of turnover or shifting the temporal distribution of the cycle toward less distorted conformations . Currently , we cannot determine the relative influence from each of these possible mechanisms , as both are intimately coupled given the model kinetics . In any event , the lower force per cross-bridge in the multi-filament model indicates that cross-bridges spend less time in highly distorted configurations and that sarcomere lattice geometry also influences kinetic behavior of cross-bridges . The component of force generation that is solely a consequence of Ca2+ activation of the thin filament is not influenced by sarcomere lattice structure ( Figures 4 and 5 ) . A spatially explicit model of regulatory proteins in a system of compliant filaments is an important component of the spatial–temporal coupling between thin-filament activation and cross-bridge binding . Additionally , coupling these two spatial processes is essential to describe the Ca2+-dependent amplitude and rate of force development ( Figures 4–7 ) . Two important features of the multi-filament model permit investigating contractile dynamics as a function of [Ca2+] ( compared with previous models [15 , 17] ) : 1 ) thin-filament kinetics represent Ca2+ binding with and dissociating from troponin , interactions between troponin subunits , and movement of tropomyosin , and 2 ) these activation kinetics are spatially explicit to represent regulatory characteristics of troponin and tropomyosin along the thin filament . These advances introduce a platform to investigate spatial and kinetic molecular mechanisms of cooperativity that may contribute to contraction [11 , 12 , 15 , 27–29] . The current simulations demonstrate a form of cooperative contraction resulting from sarcomere lattice geometry in a system of compliant filaments , but additional forms of cooperativity may result from feedback by cross-bridges or thin-filament regulatory proteins on Ca2+ activation or tropomyosin mobility [12 , 27 , 29 , 51 , 52] . One example of this kinetic feedback may be coordinated movement between adjacent tropomyosin molecules following Ca2+ binding with troponin , which activates a region of thin filament greater than the 37 nm length of a single tropomyosin molecule [29] . The similarity of Ca2+ sensitivity ( pCa50 ) between thin-filament activation , cross-bridge binding , force production , and ATPase in the current models ( Table 1 ) is likely to diverge with cross-bridge and thin-filament–dependent cooperative feedback mechanisms on Ca2+ activation , as preliminary work suggests [30–32] . Whether or not any form of kinetic cooperativity is considered in future models of muscle contraction , our results show that the structural determinants of cooperative cross-bridge binding will always play a crucial role in force generation .
A central assumption restricts interactions between filaments to prescribed regions along thick and thin filaments that directly face each other . This constraint provides a mathematical accounting that enables multiple filaments to interact and reduces a three-dimensional , nonlinear problem into one-dimensional , linear system . These regions of potential interaction represent myosin molecules along thick filaments or myosin binding sites on actin along thin filaments . Dividing these regions into a set of mathematical structures , called nodes , provides a basis of points along the filaments about which forces balance and motions occur . Two multi-filament model properties permit incorporating hexagonal lattice characteristics of vertebrate skeletal muscle [53 , 54] ( Figure 1A ) . The first is implementing thick and thin filaments with longitudinal and rotational characteristics to produce co-linear facing rows of actin and myosin nodes that align at hexagonal vertices . As discussed above , this property collapses a higher order problem into a linear problem and allows each thick filament to interact with six different thin filaments while each thin filament interacts with three different thick filaments . The second property is a toroidal boundary condition along the longitudinal axis of the half-sarcomere . Employing this boundary condition at the cross-sectional edges of our simulation wraps each edge onto its opposite edge ( Figure 1A ) . This boundary condition removes any inhomogeneities near the edge of our simulation by eliminating any longitudinal simulation boundary and preserves the 2–thin:1–thick filament ratio within a finite simulation volume . The simple lattice structure depicted in Figure 1A represents myosin filaments coaxially spaced at 40 nm [53] . Thus , the interactions simulated in the multi-filament model represent an 80 ( = 2 × 40 ) nm by 70 ( = 2 × 40cos ( π/6 ) ) nm cross-section of infinite lattice space using only four thick filaments and eight thin filaments . Vertebrate thick-filament structure has three-myosins extending from the filament backbone every 14 . 3 nm in relaxed muscle ( myosin layer lines ) [53 , 55] . Our model preserves this physiological spacing between myosin layer lines along the thick filament , producing a similar number of myosins that can potentially bind actin ( =120 multi-filament versus 150 per half-sarcomere length thick filament in vertebrates ) . Modeled thick filaments ( Figures 1 and 2 ) are 858 nm long and consist of 60 myosin nodes and one node at the M-line to permit position control [17] . Myosin nodes represent myosin layer lines , and the resting , unstrained length between adjacent myosin nodes ( m0 ) is 14 . 3 nm . Two myosins extend radially from the filament backbone at each node to form a two-start helix , rotating π / 3 radians every m0 . This thick-filament geometry produces six rows of myosin that project from the center of the thick filament with an overall periodicity of 42 . 9 nm for the filament . Each row of myosin projects toward a different thin filament . Additional geometric comparisons of different lattice structures are provided in Figures S1–S4 . Each thin filament is 1 , 119 nm long , containing a total of 90 actin nodes distributed along two entwined actin strands and one node at the Z-line for position control ( Figures 1 and 2 ) . Each actin strand has a helical pitch identical to vertebrate striated muscle [= π radians every 37 . 3 nm , 53] . Actin nodes along each strand are separated by 24 . 8 nm and rotated by 2π / 3 radians , at rest . The actin nodes represent target binding sites for myosin and provide a spatially explicit accounting for the regulatory proteins to control Ca2+-sensitive activation along the thin filament . Similar to physiological thin-filament structure , these two entwined actin strands oppose each other by π radians . We translate the initial node on one strand by 12 . 4 nm relative to the initial node on the complementary strand , making the nodes rotationally translated by 4π / 3 radians ( = π + π / 3; initial offset plus rotation accompanying the 12 . 4-nm translation ) . This accounting creates a coiled thin filament where the resting length between adjacent actin nodes ( a0 ) is 12 . 4 nm and distributes the 90 actin nodes along three rows ( spaced every 37 . 3 nm along each row ) . Each row of thin-filament nodes directly faces three different thick filaments . Controlling thick- and thin-filament interactions via [Ca2+] with spatial characteristics of regulatory proteins is a fundamental advancement from previous spatially explicit models [14–18] . The spatial and temporal effects of troponin and tropomyosin are explicitly accounted for in the sections describing model geometry and kinetics . As above , the two actin strands provide a basis for modeling Ca2+-activated regions of the thin filament . The spatially explicit model parameter Tmspan represents the influence of tropomyosin by setting the range of adjacent Ca2+-influenced regions along each actin strand , effectively determining the number of adjacent actin nodes ( i . e . , thin-filament length ) available for myosin binding . Tmspan represents the effective distance over which Ca2+ binding with troponin facilitates tropomyosin movement—activating thin-filament regions where myosin can bind to actin . Tmspan was set at 37 nm in this study , making two adjacent actin nodes along an actin strand available to bind myosin . The first region influenced by Tmspan begins with the first actin node on each strand , making the following region along each strand influence the next two actin nodes on that strand . This accounting scheme continues along the entire thin filament . While preliminary studies [32] varied Tmspan to explore how cooperative mechanisms of thin-filament Ca2+ activation may contribute to force generation [29] , this study fixes Tmspan to focus solely on the consequences of different sarcomere lattice geometry between models . Mechanics describing simulated force use a system of linear springs ( Figure 2 ) and balance forces at each node in the filament lattice [15 , 17] . As mentioned above , we model filament sliding and force generation along the longitudinal axis of the half-sarcomere . This assumption collapses the model into a linear system of equations comprising a vector of actin and myosin node positions ( X ) , a matrix of spring constants ( K ) , and a vector of boundary conditions ( V ) . Solving the instantaneous force balance through Gaussian elimination allows us to calculate X given known cross-bridge binding conditions throughout the filament network . Individual entries to K and V result from decomposing Equation 1 into spring constants , rest lengths , and boundary conditions at the filament ends . As with previous models , we also assume that viscous and inertial forces are negligible [15 , 17 , 18] . We assign three spring constants , km , ka , and kxb to the elements between thick-filament nodes , between thin-filament nodes , and between thick and thin filaments following myosin binding to actin ( representing the cross-bridge ) , respectively ( Figure 2 ) . Consistent with the earlier two-filament model [15] , km is ∼1 . 4 times greater than ka ( = 65 pN nm−1 for 1-μm filament length ) [20–23] . Most simulations in this study set km and ka at 6 , 060 and 5 , 229 pN nm−1 to maintain measured filament stiffness values between myosin nodes and actin nodes ( for rest lengths m0 and a0 ) . Also , most simulations in this study use a kxb of 5 pN nm−1 . Although this kxb value is greater than estimates from single molecule measurements , 0 . 69–1 . 3 pN nm−1 [25 , 56] , it is closer to estimates of 3–5 pN nm−1 from muscle fiber measurements [34–37] . Using kxb = 1pN nm−1 in previous models and in this study resulted in relatively low predicted force ( compared with kxb = 5 pN nm−1 [15 , 17] , Figures 8 and 9 ) , suggesting that a parameter value of 5 pN nm−1 better estimates kxb than 1 pN nm−1 . We recognize that kxb is a fundamental myosin property contributing to the chemomechanical energy transduction and force produced in muscle . Therefore , running a large number of simulations characterized the effect of kxb on predicted force , fractional myosin binding ( fxb ) , and cross-bridge turnover ( Figure 9 ) . This approach illustrates how kxb influences simulations across the range of estimated values ( 1–5 pN ) listed above , while recognizing the variability and difficulty associated with exactly specifying this parameter value . The resting distortion of a myosin cross-bridge ( xb0 ) is directly linked to kxb ( Equation 13 ) , and no stiffness parameters ( km , ka , or kxb ) depend on [Ca2+] as suggested by Isambert et al . [19] . The instantaneous sum of forces at each actin or myosin node in the network is zero , independent of any actomyosin binding . As described above , the system of linear equations describing this force balance uses spring constant and position information between all connected nodes . Force development and any corresponding realignment in the filament network may distort the distance between nodes from specified rest lengths . Generally , each term in the equations below ( Equations 2–4 ) contributes to the force balance as a Hookean spring element of stiffness km , ka , or kxb with a distortion from corresponding rest length m0 , a0 , or xb0 . As further described below , xb0 ( Equation 13 ) represents the unbound rest length where myosin S1 heads ( assuming coincident behavior of the two S1 heads per modeled myosin molecule ) position is offset from its corresponding myosin node . The balance of forces about myosin node at position mj oriented to bind with co-linear facing actin node at position ai ( depicted in Figure 2 ) : where mj−1 and mj+1 represent the position of myosin nodes adjacent to mj along the thick filament . Equation 2 is written for a coordinate system defining positive force to the right , such that mj−1 < mj < mj+1 . The first and second terms of Equation 2 balance forces along the thick filament , while the third term accounts for the interaction between thick and thin filaments associated with cross-bridge formation . If there is no cross-bridge binding , the third term disappears from Equation 2 ( kxb = 0 ) . A similar balance of forces occurs about the actin nodes at position ai and ak ( Figure 2 , where ai−1 , ai+1 , ak−1 , and ak+1 are positions of actin nodes adjacent to ai and ak along each respective thin filament: Following any cross-bridge binding in the network , forces balance throughout the lattice causing myofilaments to deform or realign . Any local distortion and node realignment in the system affects the balance of force throughout the entire network . Force per filament is calculated using distortion ( difference from rest length ) in the spring element nearest the Z- or M-line ( Δxi ) . Total force at the Z- or M-line ( FZ-line or FM-line , respectively ) is calculated by summing over the number of filaments in parallel at the ends of our half-sarcomere simulation: and where FZ-line = − FM-line . Model kinetics ( Figure 3 ) use a three-state cross-bridge cycle coupled with a three-state , [Ca2+]-sensitive , thin-filament regulatory cycle ( Figure 3 ) . Cross-bridge kinetics are distortion-dependent , as with previous models [1 , 3 , 4 , 10 , 15 , 17 , 57] . The kinetics of each cross-bridge depends upon the behavior of all other cross-bridges through coupled interactions within the compliant filament lattice [15] . While more complete chemomechanical descriptions of cross-bridge cycling would require an increased number of biochemical states [3 , 4 , 6 , 10] , we continue using a three-state cross-bridge model to directly compare with earlier modeling efforts [15 , 17] . Previous models suggest that a minimum of three mechanical states is required to characterize actomyosin binding and force production [15 , 17]: an unbound or weakly bound , nonforce-bearing state ( XB1 ) , a state where myosin binds to actin in a conformation preceding the mechanical transition , often referred to as the powerstroke ( XB2 ) , and a state where myosin is bound to actin in a conformation following the powerstroke ( XB3 ) ( Figure 3 ) . The pre-powerstroke state ( XB2 ) should contribute less force to the myofilament lattice than the post-powerstroke state ( XB3 ) , similar to the two-attached states outlined by Eisenberg et al . [3] . However , the actual force borne by any cross-bridge ( = kxb ( ai−mj−xb0 ) ( in Equation 2 or Equation 3 , see Figure 2 ) depends on its distortion from rest length . Concomitant with each mechanical state is a biochemical state representing the cyclical hydrolysis of ATP , release of inorganic phosphate ( Pi ) and ADP , and binding of another ATP that leads to dissociation of myosin from actin Figure 3 . These states represent a collapsed version of larger biochemical schemes [6] . The nonforce-bearing state ( XB1 ) corresponds to a biochemical state where myosin binds the ATP hydrolysis products ADP and Pi and is unbound or weakly bound to actin . In the pre-powerstroke state ( XB2 ) , the actomyosin complex is formed with myosin having ADP and Pi bound . While it remains debated whether Pi is released before , concurrent with , or following the powerstroke , the post-powerstroke state ( XB3 ) represents an actomyosin conformation where myosin has released Pi and only ADP is bound [6 , 58–62] . The transition back to the nonforce-bearing state entails myosin releasing ADP , binding ATP , and dissociating from actin . Cross-bridge elasticity [1] imposes position-dependent transition rates throughout the cross-bridge cycle . Elastic sliding between filaments creates either a positive or negative force exerted by the cross-bridge and depends upon cross-bridge distortion [3] . Consistently , the current state transitions intimately couple distortion of a myosin molecule with filament realignment throughout the lattice ( Figure 10 ) . While the thermodynamic and kinetic parameters describing cross-bridge cycling in the model are exactly the same for each myosin , the geometry and mechanical coupling between myosins and filaments does not allow myosin molecules to function independently [15] . The specific functions defining cross-bridge free energies and transition rates are presented below . Biochemical and structural studies demonstrate that both spatial and temporal thin-filament processes regulate actomyosin binding [7] . Thin-filament regulation results from interactions between Ca2+ binding to troponin , and subsequent movement of tropomyosin exposing myosin binding sites on actin to allow cross-bridge cycling [63] . A structural regulatory unit spans 37 . 3 nm along each actin strand of thin filaments , containing one troponin and tropomyosin that covers seven actin monomers . Thus , Ca2+ binding to each troponin will expose only a local region of myosin binding sites along each actin strand in proximity to the troponin complex . The coupled mechanical and structural properties of myofilaments further influence actomyosin binding along the filaments , where up to two myosin can potentially bind per 37 . 3 nm actin strand . Hence , the spatial and kinetic processes of thin-filament activation and cross-bridge cycling are inseparable . Two key events underlying the thin-filament regulatory model are Ca2+ binding to troponin and the ensuing interaction between troponin and tropomyosin ( Figure 3 ) . We simulate these events along each actin strand of the thin filament in conjunction with Tmspam ( introduced above in the geometry section ) . This method directly links spatial and kinetic characteristics of troponin and tropomyosin to regulate actomyosin binding , which is unique to this model . While portions of the thin filament may be activated , whether any binding occurs depends on myosin proximity ( Equation 15 ) . The thin-filament kinetic model ( Figure 3 ) employs three states [51 , 63] , with transition rates defined below ( Table 2 ) . In the first state ( TF1 ) , no Ca2+ is bound to troponin and actin nodes are unavailable to bind cross-bridges . The second state ( TF2 ) has Ca2+ bound to troponin , and actin nodes remain unavailable to bind cross-bridges . In the third state ( TF3 ) , Ca2+ is bound to troponin and actin nodes are available to bind with myosin . The equilibria-associated thin-filament state transitions ( K1 , K2 , or K3 ) adhere to: maintaining thermodynamic stability [64] . Each equilibrium equals the ratio of forward ( rt , ij ) to reverse ( rt , ji ) thin-filament transition rates . The two-filament model in this study is a subset of filament interactions from the multi-filament model . The simulations employ the same thick- and thin-filament geometry of the multi-filament model , but permit interactions between only one thick and one thin filament . Mathematically , this reduces the multi-filament model interaction to a single row of myosin and actin nodes that co-linearly face each other [15] . This results in fewer inter-filament interactions: 20 myosin nodes and 30 actin nodes . In all other regards ( geometry , kinetics , and mechanics ) , the two-filament model is identical to the multi-filament model . All simulations were programmed using Matlab ( version 7 . 0 , The Mathworks , http://www . mathworks . com ) . Monte Carlo simulations use a fixed time step ( Δt ) of 1 ms , and state transitions were accepted by comparing pij ( = rijΔt ) to a random number generated from a uniform distribution [15 , 17] . Simulations evaluate kinetics at all actin and myosin nodes and calculate the resulting force balance at each time step . At each time step , the algorithm scans through each region of thin-filament activation ( set by Tmspan ) using thin-filament kinetics and Monte Carlo methods to determine the thin-filament state associated with each actin node . Following the thin-filament query , the algorithm scans through myosin nodes using Monte Carlo methods to determine cross-bridge state transitions based on cross-bridge kinetics , proximal actin node availability , and filament position . Finally , the program calculates the effects of these kinetic transitions on myofilament realignment to determine the position of all actin and myosin nodes to begin the next time step according to Equation 1 . The total free energy liberated over a complete actomyosin cycle ( ΔG ) depends upon the standard free energy of ATP hydrolysis ( ΔG0 , ATP ) and the concentration of ATP , ADP , and Pi [3 , 4 , 34]: Similar to previous models [3 , 4 , 15 , 17] , Equation 8 defines all free energies in units of RT , where R is the ideal gas constant and T is temperature in Kelvin . ΔG0 , ATP = 13RT at 300K [4] , and we set [ATP] = 5 mM , [ADP] = 30 μM , [Pi] = 3 mM , and T = 288K , which makes ΔG ≈ 24 RT . Free energies for the nonforce bearing ( G1 ) , pre-powerstroke ( G2 ) , and post-powerstroke ( G3 ) states depend on distance ( x ) and cross-bridge stiffness ( kxb , RT ) , in units of RT nm−2 ( which converts to pN nm−1 using an appropriate scale factor ) . x is calculated from the position difference between a myosin node and its nearest available actin node . Using the example depicted in Figure 2 for myosin extending from myosin node at position mj and binding to actin node at position ai: Following binding x determines cross-bridge distortion and consequently its contribution to the force balance ( Equation 2 , kxb ( ai − mj − xb0 ) = kxb ( x – xb0 ) ) . Free energy functions define elastic behavior in each cross-bridge state ( Figure 10A ) : G1 ( x ) arbitrarily sets the reference energy at 0 RT to begin an ATP hydrolysis cycle . Because no force is borne by this state , the energy profile is independent of cross-bridge distortion . The parabolic functions G2 ( x ) and G3 ( x ) depend on kxb , RT , representing elastic behavior of a myosin extending from a node positioned at x = 0 in Figure 10A . The free energy difference between G1 ( x ) and the minimum of each well represents the maximal amount of energy that myosin can convert to work in either force-bearing state . These energies are proportional to ΔG , set by α = 0 . 28 and η = 0 . 68 , representing free energy drops between M-D-Pi and A-M-D-Pi consistent with previous models [3 , 4 , 15] . Cross-bridge distortion following hydrolysis of ATP , is constrained by myosin stiffness and the free-energy of ATP hydrolysis , illustrated by the position offset between the minimum of G2 ( x ) and G3 ( x ) . The energy profile associated with the pre-powerstroke conformation results from thermal fluctuations about xb0 , where mechanical energy is stored in the myosin molecule . Ultimately , this energy stored in the cross-bridge is transferred into the filament lattice to produce force and lattice realignment or filament sliding . The degree of realignment ( as well as the correlated “powerstroke” distance ) becomes a function of local cross-bridge and filament strain , balanced throughout the network . Forward ( rx , ij ) and backward ( rx , ji ) transition rates between cross-bridge states i and j ( Figure 3 ) employ free-energy estimates defined above ( Figure 10A ) to maintain a detailed thermodynamic balance [4 , 15 , 17]: We use Monte Carlo methods to calculate probabilities of state transition ( pij ) : pij = rij Δt , where Δt is the time step of the simulation , and rij is the rate of the transition in question [15 , 17] . The actomyosin binding rate ( rx , 12 ) follows from Daniel et al . [15]: ( Figure 10B ) , where A is assigned the numerical value of 2 , 000 . The dimensions of A and all subsequent scale constants below ( B , C , D , M , N , P ) are set to yield transition rates in s−1 . Because most simulations herein set kxb = 5 pN nm−1 ( five times more than previous simulations [15 , 17] ) , which may have underestimated kxb ) , we doubled A to produce similar likelihoods of myosin binding as these prior studies . Recent measurements suggest that filament strain determines cross-bridge transition rates [24 , 26] . The effects of elastic strain energy ( Estrain = kxbx2 ) on cross-bridge rate functions were originally considered by Daniel et al . [15] for rx , 12 only . Here , we reformulate the rates representing the powerstroke transition ( rx , 23 ) and cross-bridge detachment ( rx , 31 ) to include cross-bridge distortion and stiffness dependencies: where B , C , and D take numerical values of 100 , 1 , and 1 , respectively . Reformulating rx , 23 permits greater transition probability with decreasing cross-bridge stiffness ( Figure 10C ) . Similar to previous models [1 , 4 , 15] , cross-bridge detachment rate is distortion-dependent , with an increased rate for negative distortions . To ensure that the distortion-dependent detachment rate is invariant with cross-bridge strain energy for any kxb value , rx , 31 scales proportionally with : where M , N , and P take numerical values 3600 , 40 , and 20 , respectively , and ( Figure 10D ) . Any reverse transition associated with rx , 13 is unlikely because it requires energy input . The scale constants listed above were selected to preserve the kinetic behaviors outlined in previous models [15 , 17] . Rates of both Ca2+ binding to troponin and subsequent protein interactions leading to tropomyosin movement are not fully defined in striated muscle , but some information is available from solution measurements . Additionally , some estimates of these rates are based on force development and relaxation kinetics in muscle . Thin-filament transition rates for this study are listed in Table 2 . The transition rate representing Ca2+ binding to troponin ( rt , 12 ) becomes a second-order rate transition , dependent upon [Ca2+] . This second-order property activates the thin filament more slowly with lower [Ca2+] , reducing the rate of thin-filament activation from pCa 4 . 0 to 7 . 5 in Figure 4 . The value of rt , 12 lies in the range of literature values derived from solution biochemistry [65–68] and unpublished data from the Regnier lab , but may be slower in the presence of the organized filament lattice . Two equilibria represent quick Ca2+ binding and slower thin-filament activation: K1 represents a fast-equilibrium Ca2+ binding , and K2 is a slower equilibrium representing troponin–tropomyosin interactions [65 , 69 , 70] . Ca2+ dissociation rate ( rt , 31 ) is taken from muscle-relaxation studies and adjusted to represent rates of force relaxation with respect to force generation [71] . Preliminary simulations investigating cooperative force production [30] used only a two-state thin-filament regulatory cycle ( Ca2+ on/off ) . Under this two-state regulatory scheme , it was difficult to control Ca2+ sensitivity in the force–pCa relationship , and we concluded that three states better represent the thin-filament regulatory system . This agrees with experiments suggesting that differences in skeletal and cardiac regulatory proteins are partially responsible for differences in cooperative force production between the two muscle types [28 , 72 , 73] . Setting the maximum simulation time ( tmax ) for each [Ca2+] ensured that force , cross-bridge binding , and thin-filament activation reach their equilibrium values . Although the spatially explicit nature of our model does not permit an analytic solution , tmax is derived from the time-independent solution of a simplified linear differential equation model for thin-filament activation ( see Figure 3 ) : where TF1 ( t ) , TF2 ( t ) , TF3 ( t ) represent the fraction of actin nodes in each state . Averaging the final 10% of each simulation run ( a single [Ca2+] with time range 0 to tmax ) extracts the steady-state , asymptotic value for that run . Previous experience with acceptable standard deviations of steady-state values [15 , 17] helps maximize computational efficiency by calculating the number of runs ( Nruns ) required at each [Ca2+] . Nruns ensures that the set of steady-state averages at each [Ca2+] ( gathered from the last 10% of each run ) is generated using no fewer than 3 , 200 total time steps ( Table 3 ) . Simulation parameters and data reduction methods used for the two-filament simulations are identical to the multi-filament model , with the exception of Nruns . Preliminary tests indicated that two-filament simulations using exactly the same Nruns as the multi-filament model do not provide a reasonable level of certainty for simulation predictions . Increasing Nruns in the two-filament model by a factor of 24 , chosen to represent the increased number of inter-filament interactions between the two models , improves model output through increased numerical averaging and provides a scale factor consistent with geometry differences . During each run , simulations record force and ATP consumption , as well as position and kinetic state for actin and myosin nodes . The reported steady-state means and corresponding standard deviations are calculated from the set of Nruns asymptotic averages at each [Ca2+] ( Table 3 ) . Similarly , the reported rates of force generation ( rf , Figure 7 ) and their corresponding standard deviations are calculated at each [Ca2+] from the set of exponential fits for each run [17] . Averaging the means from each run and calculating the standard deviation at each [Ca2+] focuses on the variation between runs resulting from stochastic kinetics rather than on variations within runs . Data points within a run are not independent because cross-bridge transition probabilities determined in successive time steps depend on the prior history of cross-bridge behavior . Each run is independent of other runs . Calculations of force per cross-bridge ( Figure 5D ) and tension cost subsample the set of asymptotic averages by discarding runs without any cross-bridge binding at the low [Ca2+] . If there is no cross-bridge binding , there is no force production or ATP consumption , so the resulting calculation is singular . Steady-state data are fit to a three-parameter Hill equation: using a nonlinear least squares minimization of X ( pCa ) . Depending on the dataset in question , X ( pCa ) represents: normalized force ( Figure 5A ) , fraction available ( fa , Figure 5B ) , fraction bound ( fxb , Figure 5C ) , or ATP consumption ( Figure 6 ) . Xmax represents the maximal value of the Hill relationship for the dataset in question . pCa50 represents the midpoint of the saturation curve and is a measure of Ca2+ sensitivity in the system . nH is the slope of the Hill curve at pCa50 and represents the cooperativity in the system . For all fits , we minimize residuals using the entire set of asymptotic averages at all pCa levels across all runs . | Striated muscle is highly structured , and the molecular organization of muscle filaments varies within individuals ( by fiber type ) and taxonomically . The consequences of filament arrangement on muscle contraction , however , remain largely unknown . We explore how filament arrangement affects force production in muscle using spatially explicit models of many interacting myofilaments . Our analysis incorporates molecular scale force balance equations with Monte Carlo simulations of both actin–myosin interactions and thin-filament Ca2+ activation . Simulations show that a more physiological representation of vertebrate striated muscle amplifies force production , coordinates dynamic actin–myosin cycling , and may optimize energetics of contraction ( force generated per ATP consumed ) . This coordinated myosin behavior indicates a mechanism of cooperativity in muscle that depends on the ratio and arrangement of filaments . We also demonstrate the importance of mechanical coupling between myosin molecules by varying filament stiffness . Our simulations show a tradeoff between the way myosin molecules partition energy from ATP hydrolysis into force transmitted throughout the filaments versus distortions within the filaments . These findings present a possible consequence of organization in muscle , where the ratio and arrangement of muscle filaments affects contractile performance for the given function across different muscle types . | [
"Abstract",
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] | [
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] | 2007 | Sarcomere Lattice Geometry Influences Cooperative Myosin Binding in Muscle |
Two components of integrin containing attachment complexes , UNC-97/PINCH and UNC-112/MIG-2/Kindlin-2 , were recently identified as negative regulators of muscle protein degradation and as having decreased mRNA levels in response to spaceflight . Integrin complexes transmit force between the inside and outside of muscle cells and signal changes in muscle size in response to force and , perhaps , disuse . We therefore investigated the effects of acute decreases in expression of the genes encoding these multi-protein complexes . We find that in fully developed adult Caenorhabditis elegans muscle , RNAi against genes encoding core , and peripheral , members of these complexes induces protein degradation , myofibrillar and mitochondrial dystrophies , and a movement defect . Genetic disruption of Z-line– or M-line–specific complex members is sufficient to induce these defects . We confirmed that defects occur in temperature-sensitive mutants for two of the genes: unc-52 , which encodes the extra-cellular ligand Perlecan , and unc-112 , which encodes the intracellular component Kindlin-2 . These results demonstrate that integrin containing attachment complexes , as a whole , are required for proper maintenance of adult muscle . These defects , and collapse of arrayed attachment complexes into ball like structures , are blocked when DIM-1 levels are reduced . Degradation is also blocked by RNAi or drugs targeting calpains , implying that disruption of integrin containing complexes results in calpain activation . In wild-type animals , either during development or in adults , RNAi against calpain genes results in integrin muscle attachment disruptions and consequent sub-cellular defects . These results demonstrate that calpains are required for proper assembly and maintenance of integrin attachment complexes . Taken together our data provide in vivo evidence that a calpain-based molecular repair mechanism exists for dealing with attachment complex disruption in adult muscle . Since C . elegans lacks satellite cells , this mechanism is intrinsic to the muscles and raises the question if such a mechanism also exists in higher metazoans .
Muscle is a multifunctional tissue [1]–[4] with a well appreciated role in locomotion . The contractile properties of muscle that allow for coordinated locomotion require a complex protein based machinery [5] and substantial metabolic input [6] . To balance demand with metabolic cost , the quantity of muscle protein is controlled by both use and nutrition . The regulation of muscle protein content is an area of broad interest owing to the fact that locomotion is an essential part of being human , the general acceptance that muscle is important for athletic prowess , and because specific muscle wasting is a clinical problem . These wasting conditions have substantial negative impact on mortality [7] , [8] , morbidity , and public health expenditure [9] , [10] . Conceptually , muscle size is controlled by signals that regulate the balance of muscle protein synthesis and degradation . When bulk protein synthesis exceeds bulk degradation , growth can occur and when bulk protein degradation exceeds bulk synthesis atrophy occurs . While there are a number of ways in which a net shift in balance can lead to atrophy ( e . g . protein synthesis and degradation can each go up or down together or independently and/or to different degrees ) , degradation is required for atrophy to occur . Four main proteolytic systems , the proteasomes [11] , [12] , lysosomes [13] , calpains [14] , and caspases [15] , have been identified as key players in the regulation of muscle size and function . However , despite our knowledge of these proteases we know relatively little of how their activities are regulated by the vast array of extra-muscular signals which appear to control muscle size [16] . Our laboratories have developed the soil nematode Caenorhabditis elegans , a validated muscle and systems biology model , into a model for the discovery of regulatory signals of muscle protein degradation . As with mammalian muscle , protein degradation in C . elegans is observed in response to starvation [17] , denervation [18] , or disruption of endocrine signalling [19] , [20] . Motor neurons release acetylcholine , which acts to inhibit proteasome based degradation in post-synaptic muscle . When animals are starved or “genetically denervated , ” proteasome based degradation occurs unless the animals are supplemented with cholinergic agonist [17] , [18] . Additionally , muscle itself releases Fibroblast Growth Factor [21] which acts to activate autophagic degradation [19] . This constitutive degradation is prevented when Insulin/Insulin-like Growth Factor , from an unknown source , counterbalances the Fibroblast Growth Factor signalling within muscle [20] . Thus , we have begun to gain a picture of the integrated control of muscle protein degradation in C . elegans muscle . Open questions include how calpains and caspases are regulated by extra-muscular signals and how many intra-muscular signalling networks control these four proteolytic systems . Recently it was shown that gene expression in C . elegans muscles responds similarly to mammalian muscle gene expression during spaceflight , with several key genes ( for example , MyoD and myosin heavy chain ) showing similar changes [22] . Two of the roughly 150 muscle genes which were identified as being down regulated in response to spaceflight , unc-97 [23] and unc-112 [24] , produce proteins that are part of integrin containing muscle attachment complexes . Integrin-based attachment complexes are essential for proper muscle development [25] , show changes in protein content in response to loading and unloading [26] , modulate load induced changes in muscle protein synthesis [27] , and serve various other essential cellular functions ( reviewed in [28] ) . A recent genomic screen also uncovered unc-97 and unc-112 as negative regulators of muscle protein degradation [29] . These observations prompted us to investigate if these attachment complexes , as a whole , functioned as negative regulators of muscle protein degradation in fully differentiated muscle . To do this , we used RNAi to knock down the gene products of the core complex components [28] , [30]–[32]: the extracellular ligand , UNC-52/Perlecan; the receptor , PAT-2/Integrin alpha and PAT-3/Integrin beta; and intracellular partners , found at both the Z and M-lines [31] , [32] , PAT-4/Integrin linked kinase , PAT-6/Actopaxin , UNC-112/MIG-2/Kindlin-2 , and UNC-97/PINCH . We also used RNAi to knock down a sub-set of gene products that are peripheral components of the complex . For this we chose: the Z and M-line proteins TLN-1/Talin and ZYX-1/Zyxin [31]–[33]; the Z-line specific proteins ATN-1/alpha actinin and DEB-1/Vinculin [31] , [32]; and the M-line specific proteins UNC-82 and UNC-89/Obscurin [31] , [32] , [34] , [35] . As an alternative hypothesis to the complexes as a whole regulating muscle protein degradation , we also tested a known binding partner of UNC-112 , UIG-1 [36] and the Rho GTPase , CDC-42 , for which UIG-1 is a guanine nucleotide exchange factor [36] . Here we report that integrin attachment complexes are required for proper maintenance of adult muscle and that failure to maintain these complexes results in activation of calpain proteases , general degradation of soluble muscle proteins , myofibrillar and mitochondrial dystrophies , and a severe movement defect . Because the integrin attachment complex member DEB-1/Vinculin is degraded by these activated calpains , we postulate that calpain activation in response to disruption of integrin attachment complexes allows for the reassembly and/or repair of these complexes . In normal adult C . elegans , complex disruption may occur as the result of increased mechanical strain and/or failure of other mechanisms to properly coordinate growth of muscle and adjacent hypodermal cells . Thus , calpains help adult muscle maintain both structural integrity and cross tissue communication .
In fully developed adult worms , acute RNAi treatment against any one of fourteen genes that encode integrin muscle attachment complex components resulted in loss of a transgene-encoded LacZ reporter of muscle protein degradation in the cytosol ( Figure 1A ) . Because this reporter protein is synthesised only until adulthood [17] and remains stable for the next 72 to 96 hours in well-fed wild-type animals [18]–[20] , [37] , [38] , loss of reporter indicates that proteases have been activated and degradation is occurring . As RNAi against the core complex components PAT-2 , PAT-3 , PAT-4 , PAT-6 , UNC-52 , UNC-97 , and UNC-112 all yielded protein degradation , it appears that protease activation occurs in response to disruption of the core integrin complex . Consistent with this , RNAi against peripheral components located at both the Z and M-line ( TLN-1 and ZYX-1 ) , only the Z-line ( ATN-1 and DEB-1 ) , or only the M-line ( UNC-82 ) results in protein degradation . Thus , genetic disruption of either Z-line or M-line specific components is sufficient to result in protease activation . These results suggest that sustained disruption of any integrin containing complex results in activation of a protease in adult C . elegans muscle . RNAi knock down of one of the fifteen genes tested , unc-89 , a known M-line attachment complex component [39] , did not provoke degradation of our transgenic reporter . Below , we report that RNAi against UNC-89 does yield a movement defect and also disruption of the normally arrayed sarcomeres in 100% of animals examined ( see below , Figure 2 and Figure 3 ) . Thus , the lack of degradation is not simply due to lack of an effective RNAi treatment . From these results we tentatively conclude that reduced amounts of UNC-89 are not sufficient to cause sustained protease activation . However , additional studies are required to ( dis ) prove the role of reduced levels of UNC-89 with respect to sustained , or transient , protease activation . It may be that the unique pattern of sub-cellular pathologies seen in response to unc-89 RNAi treatment ( see below ) coupled with the lack of observed degradation of LacZ suggests that UNC-89 acts as a key molecule in the intramuscular maintenance of the normal arraying of attachment complexes . Past studies have shown that when the transgene-encoded LacZ reporter utilised here is degraded , so are other transgene-coded proteins expressed in muscle and so are endogenous cytosolic muscle proteins such as arginine kinase ( the worm equivalent of creatine kinase ) [17] , [19] , [20] , [37] , [38] . Thus , the LacZ protein reports on general rather than protein-specific degradation in the muscle cytosol . To confirm that the degradation observed in response to genetic disruption of the integrin complexes was not specific for LacZ , we treated animals containing cytsosolic Green Fluorescent Protein ( GFP ) or cytosolic DsRed reporters with RNAi against unc-97 or unc-112 ( the other genes in Figure 1A were not tested ) . As expected , GFP and DsRed were also degraded ( not shown ) . We did not observe GFP or DsRed leaking out of the muscle during RNAi treatment ( not shown ) , further confirming that the decrease in reporter protein content is the result of intramuscular degradation rather than loss of cytosol due to impaired membrane maintenance . As RNAi is a relatively new technology , we also confirmed the RNAi results utilizing mutants . We tested temperature sensitive mutants in two members of this attachment complex , UNC-52/Perlecan ( an integrin ligand in the basement membrane ) [40] and UNC-112/Kindlin-2 ( an intramuscular binding partner of the integrin receptor ) [24] . As shown in Figure 1C , acute temperature shift of fully developed adult animals results in protein degradation in both unc-52ts and unc-112ts mutants but not in wild-type animals . Degradation of the pre-existing LacZ reporter was confirmed by western blot analysis ( Figure 1D and 1E ) . Degradation was not observed in unc-112ts animals when unc-112+::GFP was also present ( not shown ) . Whole body protein , as assessed in triplicate 30 worm samples by coomassie staining and quantified in ImageJ , was reduced in unc-112ts mutants , but not wild-type animals , 48 hours post temperature shift ( 17%+/−8% loss vs . 40%+/−9% gain , P<0 . 001 two way repeated measures ANOVA ) . The decline in total protein in unc-112ts mutants further supports the inference that sustained genetic disruption of integrin attachment complexes results in sustained activation of a protease , which results in general degradation of soluble muscle protein . RNAi knockdown reduces the amount of normal gene product whereas the temperature sensitive mutants produce proteins that are structurally and functionally abnormal . Therefore , the similarity of muscle phenotypes ( more are reported below ) suggests that the trigger for these phenotypes is the reduction of attachment complex function , rather than just aberrant assembly during RNAi knockdown . We believe that a severe reduction of function is required , because we found that unc-112ts/+heterozygotes at 25°C did not degrade LacZ reporter and showed no signs of sarcomere disorganisation ( not shown ) . Additionally , degradation was prevented in unc-112ts; unc-112+::GFP animals that had low enough levels of GFP to be undetectable on our epifluorescent microscope ( not shown ) . Thus , the observed degradation of cytosolic protein content may represent the consequence of catastrophic failure of the attachment complexes and/or sustained inability to reassemble partially functional complexes . We next asked whether the reporter degradation was carried out by activation of proteases newly synthesized after disruption of the attachment complexes , or by activation of pre-existing protease ( s ) . We conducted the same temperature shift experiments described above in the presence of the protein synthesis inhibitor cycloheximide ( CHx ) and found that degradation was indeed occurring ( Figure 1C: unc-52ts or unc-112ts+CHx ) . This result suggests that pre-existing proteases are sufficient to account for the protein degradation observed in response to disruption of the integrin complexes . The fact that the gene products ( Figure 1A ) occur together in attachment complexes does not uniquely establish that a common mechanism is responsible for the common catabolic response to knockdown of any of these proteins . We therefore asked if the response to the various knockdowns could be suppressed by mutation in a single gene . In a screen for second-site mutations that suppress the movement defect of unc-112 mutants , only mutations in dim-1 were recovered and characterized [41] . dim-1 encodes a novel immunoglobulin-like repeat protein that localizes around and between , but not within , Z-lines [42] . RNAi knockdown of any attachment complex gene in a dim-1 mutant background showed no obvious LacZ degradation ( Figure 1B ) . Temperature shift experiments on unc-112ts mutants in a dim-1 mutant background ( Figure 1C ) also did not show obvious degradation and western blot analysis confirmed that degradation was suppressed ( Figure 1D and 1F ) . Thus , the mutant studies confirm the RNAi results , suggesting that the protein degradation induced by impaired muscle attachment acts via a common mechanism , regardless of which component of the attachment complex is knocked down . Our data indicate that integrin based attachments are not only important for proper development of muscle , they are also essential to maintenance of cytosolic muscle protein content . To determine whether attachment complex disruption had additional effects on adult muscle cells , we next examined gross movement . Disruption of integrin-based attachment complexes by mutation ( Figure 2A ) or RNAi knockdown of any one of the attachment complex genes tested ( Figure 2B ) also leads to a decline in animal mobility . Although the unc-112ts mutants are not fully normal in movement rate even when grown at the “permissive” 16°C , they show a pronounced loss of mobility within 24 h after a shift to 25°C . This does not occur in unc-112ts/+heterozygotes ( Figure 2A ) or unc-112ts; unc-112+::GFP animals where the GFP is not visible ( not shown ) . This implies that the attachment complexes function at least mostly normally when wild-type and mutant UNC-112 molecules , which presumably mix randomly during attachment complex assembly , are present . This also implies that the phenotypes of unc-112ts mutants likely derive from a reduction in the function of pre-formed attachment complexes upon an increase in temperature , and that reduction of UNC-112 function to 50% of normal ( the presumed situation in a heterozygote ) is not sufficient to disrupt function of the attachment complexes . The functional consequences of RNAi knockdowns in adult animals ( Figure 2B ) must be understood in this light and further suggest a dynamic state of the attachment complexes in adult animals . The largest declines in movement following 72 hour treatment with RNAi were observed for the treatments targeting core integrin complex members ( PAT-2 , PAT-3 , PAT-4 , PAT-6 , UNC-52 , UNC-97 , UNC-112 ) . These declines are significantly different than for all more peripheral complex members other than CDC-42 and TLN-1 . This suggests that genetic disruption of the core complex members , which are found at all attachment sites , has more severe functional consequences than disruption of the more peripheral and/or Z or M-line specific components . However , caution should be applied when analysing quantitative differences between defects seen in response to non-quantitative genetic disruption of multi-protein complexes for which the in vivo stoichiometries and protein to protein binding affinities are not known . We also tested if the movement defect was suppressed in dim-1 mutants . These mutants , without RNAi treatments , move somewhat more slowly than wild-type animals , yet do not show further depression of movement upon RNAi knockdown of many of the genes whose knockdown cause the greatest movement impairments in wild-type ( for example the core complex , compare Figure 2B and 2C ) . This finding is in line with previous reports that impaired basal movement can prevent functional decline in muscular dystrophy gene mutants [43] , [44] . Acute RNAi treatment targeting each of the muscle attachment complex genes tested causes myofibrillar defects ( Figure 3 , Figure S1 ) . However , the myofibrillar defects observed in response to knockdown of each complex member vary considerably in severity and reproducibility ( Figure 3 , Figure S1 ) . To quantify the extent and reproducibility of defects , we took advantage of the fact that defects typically appeared as either aggregates of myosin::GFP that had a ball like appearance ( Example: pat-3 , Figure 3 ) , as tears in myofibrils ( Example: unc-52 , Figure S1 ) , or disruption of the normal arraying of sarcomeres ( Example: unc-89 , Figure 3 ) . Using this classification scheme it appears that all members of the core complex ( PAT-2 , PAT-3 , PAT-4 , PAT-6 , UNC-52 , UNC-97 & UNC-112 ) are required in adult muscle to prevent the collapse of arrayed sarcomeres into ball like structures , and that the Z and M-line peripheral components ( TLN-1 & ZYX-1 ) and M-line specific components ( UNC-82 and UNC-89 ) are required in adult muscle to prevent disorganisation of arrayed sarcomeres . In contrast , the Z-line specific components ( ATN-1 and DEB-1 ) do not appear to be as stringently required to prevent disorganisation or collapse of arrayed sarcomeres , inasmuch as the defects observed are not statistically significant ( P>0 . 05 , two way repeated measures ANOVA ) . Results from RNAi targeting the genes we selected as a potential unc-112 interacting signalling system ( uig-1 & cdc-42 ) suggest that CDC-42 is required in adult muscle to prevent disorganisation of arrayed sarcomeres . The variable severity we observed in disrupted sarcomere structure in adult muscle parallels the variable severity previously observed during development , where mutations in a number of the core complex genes produce both embryonic lethality and collapse of arrayed sarcomeres into ball like structures [45] . The similarity in phenotype in embryonic lethal mutants and fully developed adult muscle further supports the notion that these attachment complexes are dynamic in adult muscle . Our data also confirm past reports of the role of some of these genes in myofibril maintenance [36] , [46] , [47] and support the conjecture , raised in Drosophila , that integrin based attachment complexes , as a whole , are required for myofibrillar maintenance [48] . By using GFP fused to full-length myosin , we have observed torn myofibrils in live adult animals with acutely impaired muscle attachment complexes . These observations are consistent with previous studies of animals with mutations in two of these complex members , UNC-112 and UNC-97 , where impaired resistance to mechanical damage was noted [23] , [41] . Taken together , these observations appear to support the notion that the integrin attachment complexes are important in force transmission and that loss of these complexes can result in mechanical overload , and subsequent collapse , of the arrayed sarcomeres [43] . We utilized the same full length translational fusion of gfp to myo-3 ( myosin heavy chain A ) to confirm that arrayed sarcomeres were disrupted in unc-112ts mutants ( see below ) . We also confirmed that actin filaments are torn in fixed unc-112ts mutant animals stained with RITC-phalloidin at 24 hrs post temperature shift ( not shown ) . In contrast to the general degradation of soluble cytosolic protein seen in unc-112ts mutant animals ( Figure 1C–1F ) , we did not observe degradation of either myosin heavy chain or actin in western blots of unc-112ts mutant animals ( not shown ) . Thus , the dystrophic appearance of sarcomeres in these animals largely represents dystrophy of the sarcomeres and not their degradation . These results suggest that it is degradation of soluble/freely accessible protein that is triggered by attachment complex disruption , and not degradation of insoluble/inaccessible protein . If collapsed arrays of sarcomeres can be repaired or replaced , it may be that other repair or proteolytic processes must be activated ( for example in mammalian muscle , physical disruption of the sarcomere has been postulated to result in activation of alpha-crystallin mediated repair [49] and also proteasome mediated degradation [50] ) . Given the complexity of the sarcomere , it is quite likely that multiple proteolytic and repair processes contribute to sarcomere maintenance . Acute RNAi treatment targeting each of the muscle attachment genes also results in mitochondrial fragmentation ( Figure 4 , Figure S2 ) , suggesting that the capability for energy production in muscle may be impaired [51] . For each of the RNAi treatments , the observed mitochondrial defects varied in severity and reproducibility . To quantify the extent and reproducibility of these defects , we took advantage of the fact that defects typically appeared to have different extents of fragmentation or disorganisation of the mitochondrial network . We classed animals with more than 90% loss of the mitochondrial network as severe fragmentation ( Example: pat-4 , Figure 4 ) , those with more 30–80% loss as moderate ( Example: unc-52 , Figure 4 ) , or as having a disorganised mitochondrial network ( Example: uig-1 , Figure 4 ) . Using this classification scheme it appears that all members of the core complex ( PAT-2 , PAT-3 , PAT-4 , PAT-6 , UNC-52 , UNC-97 & UNC-112 ) are required in adult muscle to prevent fragmentation of the mitochondrial network ( Figure 4 ) . This is the same set of genes that is also required to prevent collapse of arrayed sarcomeres into ball like structures . Data from the other genes tested continues to show a parallel between the genes required in adult muscle to prevent sarcomere disorganisation and mitochondrial fragmentation/disorganisation with the Z and M-line peripheral components ( TLN-1 & ZYX-1 ) and the M-line specific components ( UNC-82 & UNC-89 ) being required in adult muscle to prevent both pathologies ( Compare Figure 3A and Figure 4A ) . Again , RNAi against the genes we selected as a potential unc-112 interacting signalling system ( uig-1 & cdc-42 ) produced significant defects , suggesting CDC-42 is required in adult muscle to prevent mitochondrial disorganisation . In contrast , the Z-line specific components ( ATN-1 and DEB-1 ) , which are not required for maintenance of arrayed sarcomeres , do appear to be required to prevent mitochondrial disorganisation . We also confirmed that severe mitochondrial fragmentation occurs in unc-112ts mutants ( see below ) . Understanding how disruption of muscle attachment results in disorganisation and fragmentation of the mitochondrial network will require further studies . It could be that these defects are caused by disorganisation and/or collapse of the cytoskeleton , to which mitochondria are physically tethered [52] , and/or acidification of the cytosol [53] , as the result of activation of ion channels following loss of attachment to the basement membrane [54] . As shown in Figure 5 , RNAi against the core complex members ( PAT-2 , PAT-3 , PAT-4 , PAT-6 , UNC-52 , UNC-97 , UNC-112 ) results in degradation of the LacZ reporter protein , a marked decline in mobility , collapse of arrayed sarcomeres into ball like structures , and severe mitochondrial fragmentation . The severity of the sarcomere and mitochondrial phenotypes in response to knockdown of the core complex is significantly different than for non-core complex members ( P<0 . 001 , two way repeated measures ANOVA ) . This may suggest that more severe disruption of the attachment complexes occurs in response to these treatments . UNC-95::GFP localizes to the attachment complexes [55] , allowing us to examine attachment structure , in vivo , in response to RNAi treatment against the attachment complex components . We find a statistically significant collapse of the arrayed attachment complexes into ball like structures in response to RNAi against the core complex members ( PAT-2 , PAT-3 , PAT-4 , PAT-6 , UNC-52 , UNC-97 , UNC-112 ) but not the other peripheral components tested ( Figure 5 , Figure S3 , P<0 . 001 , two way repeated measures ANOVA ) ; untreated animals display normal arraying of attachment complexes and these grow in size , but not number , with increasing age ( Figure S4 ) . Thus , it does appear that RNAi against the core complex results in a more severe disruption of the attachment complexes themselves . This disruption likely causes the collapse of the arrayed sarcomeres into similar ball like structures . Of note , RNAi against the M-line component UNC-89 also produces significant disorganisation of the attachment complexes , whereas the RNAi against the other genes tested does not ( Figure 5 , Figure S3 , P<0 . 001 , two way repeated measures ANOVA ) . It may be the case that this disorganisation accounts for the lack of LacZ degradation observed in response to RNAi against unc-89; further studies are clearly needed . Other relationships among the various phenotypes observed in response to attachment complex disruption are discussed below . The general correlation between the phenotypes studied , and the lack of degradation and movement decline in response to acute disruption of attachment complexes in dim-1 mutants , prompted us to further examine if loss of DIM-1 could mitigate the effects of attachment disruption . Chronic growth of unc-112ts mutants on an RNAi feeding vector against dim-1 was sufficient to inhibit collapse of arrayed sarcomeres into ball like structures ( Figure 6A ) and the extent of mitochondrial fragmentation in unc-112ts mutants ( Figure 6B ) . In neither case were the untreated unc-112ts mutants fully normal at 16°C but in both cases the unc-112ts defects were suppressed at 16°C and following temperature upshift to 25°C . Consistent with the past report for unc-112; dim-1 double mutants , we find both the arrayed sarcomeres and the attachment complexes disorganised with an appearance similar/identical to that of dim-1 single mutants [41] . Given that either mutation in or RNAi against dim-1 is capable of suppressing all of the effects of acute genetic disruption of attachment complexes , we next asked if dim-1 is suppressing disruption of the attachment complexes themselves . Given that attachment complexes are disorganised ( e . g . not nicely arrayed Z and M lines ) in dim-1 mutants , we opted to determine if loss of dim-1 could prevent the collapse of attachment complexes into ball like structures ( as observed in response to RNAi against the core complex members ( PAT-2 , PAT-3 , PAT-4 , PAT-6 , UNC-52 , UNC-97 , UNC-112 ( Figure 5 , Figure S3 ) ) . Several lines of evidence suggest that loss of dim-1 does prevent or delay the collapse of the arrayed attachment complexes into ball like structures . First , adults expressing UNC-112+::GFP display collapsed complexes following acute treatment with RNAi targeting pat-2 ( Example: +pat-2 RNAi , Figure 6C ) and the number of animals showing such disruption is significantly reduced in dim-1 mutant animals following identical treatment in parallel ( Figure 6C ) . Second , adults expressing UNC-95::GFP display substantial collapse of complexes following acute treatment with RNAi targeting unc-112 ( Example: +unc-112 RNAi , Figure 6D ) . This is not observed in dim-1 mutants expressing UNC-95::GFP following identical treatment in parallel ( Figure 6D ) . Third , adult progeny of UNC-95::GFP expressing worms grown for two generations on RNAi targeting dim-1 do not display collapsed complexes when later acutely treated with RNAi targeting unc-112 ( Figure 6D ) . Together , these results suggest that the disorganised array of attachment complexes observed in dim-1 mutant animals are resistant to the effects of genetic disruption of the attachment complexes and that this decreased severity of attachment complex disruption in dim-1 mutants accounts for the fact that dim-1 mutants do not display protein degradation , movement decline , collapse of the arrayed sarcomeres , or severe mitochondrial fragmentation in response to RNAi treatments targeting the complexes . The genomic screen that identified unc-97 and unc-112 RNAi treatment as inducing protein degradation in adult C . elegans muscle demonstrated that in both cases , degradation could not be blocked by treatment with the proteasome inhibitor MG132 and occurred in mutants that block pro-autophagy signalling in C . elegans [29] . We confirmed and extended these results . As shown in Figure 7A , degradation in response to loss of UNC-112 was not prevented by treatment with levamisole ( Lev ) , an acetylcholine agonist , nor by MG132 ( ZLLL ) , a proteasome inhibitor , either of which inhibits proteasome based degradation in response to loss of motor neuron input in C . elegans [18] . Similarly , degradation was not prevented by SB201290 , a MAPK inihibitor that blocks pro-autophagy signalling that results from an imbalance in growth factor signalling in C . elegans [19] , [20] , [37] . All compounds were used at concentrations that did block degradation in appropriate control animals and all compounds also failed to block degradation in unc-52ts mutant animals ( not shown ) . Because autophagic degradation in response to loss of IGFR or gain of FGFR signalling also induces a severe movement defect in C . elegans [19] , [20] , [37] , we tested the involvement of this pathway further . We further found that RNAi against unc-112 or unc-97 induces degradation in the genetic background of mek-2 or mpk-1 reduction of function mutations , which block signalling for autophagic degradation [19] , [37] , and found no increase in activated pTpY-MPK-1 MAPK in unc-112 mutants at nonpermissive temperature ( not shown ) . Similarly , mpk-1 or mek-2 RNAi did not suppress protein degradation in unc-112ts or unc-52ts mutants ( these RNAi treatments did suppress degradation in clr-1ts and let-60ts mutants , not shown ) . Finally , N6 , N6-dimethyladenosine , a direct inhibitor of autophagy [56] , failed to block degradation in unc-112ts or unc-52ts mutants ( this compound did suppress degradation in daf-2ts mutants , not shown ) . Thus , our results indicate that degradation observed in response to loss of integrin based attachment does not appear to require either the ubiquitin-proteasome or the autophagic pathways in C . elegans . We therefore tested if caspases or calpains were likely to be involved . The degradation seen in response to acute RNAi treatment against unc-97 or unc-112 was not suppressed in a ced-3 ( caspase 3 ) mutant ( Figure 7B ) . In contrast , acute treatment with calpain inhibitors did suppress protein degradation in unc-112ts and unc-52ts mutants ( Figure 7C and 7D ) and chronic growth on RNAi against calpain genes ( clp-1 , clp-4 , tra-3 , clp-6 or clp-7 ) was also sufficient to block degradation in these mutants ( Figure 7C and 7D ) . Together these results suggest that calpains are activated in response to genetic disruption of integrin containing attachment complexes and that inhibition of this activity can block the general degradation of soluble cytosolic proteins . Only clp-1 [57] and clp-4 [58] have previously been reported to be expressed in C . elegans muscle . We were therefore concerned that RNAi against one clp gene might result in knockdown of several clp gene products . However , a comparison of the nucleotide sequences of all clp gene primary transcripts indicates that there is no region of sequence identity that extends even to 21 nucleotides , making it unlikely that any one clp RNAi treatment affects multiple clp genes . Thus , it may be that several calpains are activated in response to genetic disruption of integrin attachment complexes in C . elegans muscle . Because we knew that RNAi against a combination of protease encoding genes can effectively block protein degradation in muscle [59] , we tested if RNAi against a combination of clp genes could further suppress degradation . We failed to find additional suppression when unc-112ts mutants were treated with RNAi against clp-1 and clp-7 , clp-6 and clp-7 , or clp-1 and clp-6 ( not shown ) . Our results appear to support past in vitro findings that calpain activity is important for the remodelling of integrin containing focal adhesion complexes [60] , and speculation that calpains 1 [60] , 2 [60] and 3 [61]–[64] may serve a similar function in muscle , in vivo . It may be that multiple calpains participate in the maintenance of integrin containing attachment complexes in metazoan muscle . Given that calpains are activated in response to attachment complex disruption , we tested if calpains are important for maintenance of muscle attachment complexes . We acutely treated fully developed , wild-type adult animals with RNAi against the clp genes that suppressed degradation in response to attachment complex disruption and assessed the same sub-cellular structural phenotypes as previously assessed for the core complex members . As shown in Figure 8 and Figure S5 , RNAi against any of the calpain genes tested resulted in defects within adult muscle . However , as was the case for all other genes tested in this work , the results were variable with respect to reproducibility and severity . Using the same classification systems as above , we find the following significant requirements for calpains in adult C . elegans muscle: CLP-1 , TRA-3 , CLP-6 , and CLP-7 appear to be required to maintain arrayed sarcomeres; CLP-1 , CLP-4 , TRA-3 , CLP-6 and CLP-7 appear to be required to prevent mitochondrial fragmentation/disorganisation ( RNAi against clp-4 , tra-3 , and clp-7 results in mitochondrial disorganisation , P<0 . 001 two way repeated measures ANOVA , not shown ) ; and CLP-4 appears to be required to maintain arrayed attachment complexes . These observations further suggest that calpains are important to maintenance of adult muscle . As calpains appear to be needed for proper maintenance of integrin attachments and as integrin complex disruption results in activation of calpain mediated degradation , these results suggest that calpains serve a similar role in maintaining integrin attachment complexes in C . elegans muscle as the one they serve in maintaining focal adhesion complexes in cultured cells [60] . As C . elegans muscle lacks satellite cells , these results suggest a cell intrinsic role for calpains in muscle maintenance . In cultured cells , members of the attachment complex itself are targets of calpain degradation . We therefore tested if this also appeared to be the case following calpain activation in C . elegans muscle . As shown in Figure 8D , DEB-1/Vinculin is degraded in unc-112ts mutants following temperature upshift and this degradation is not observed in unc-112ts mutants treated with calpain inhibitor II nor in unc-112ts; dim-1 double mutants . It is unclear whether DEB-1 degradation is cause or effect of complex disassembly , and whether the degraded DEB-1 was in attachment complexes , in a soluble precursor pool , or both . In any case , this observation further supports the notion that calpains are activated in response to attachment complex disruption in order to facilitate attachment complex repair , and suggests that attachment complex disruption normally occurs in vivo in C . elegans muscle . Our finding that calpains serve to maintain muscle attachment complexes and the fact that mutations in calpain 3 cause Limb Girdle Muscular Dystrophy 2A [63] , prompted us to ask if the calpains also have a role in proper development of muscle . To test this , we used RNAi to knock down the identified calpain genes over two generations and examined such chronically treated adults for the same sub-muscular phenotypes as assessed in acutely treated adults . Worms developed chronically on RNAi against clp-1 , clp-4 , tra-3 , clp-6 or clp-7 displayed disorganised myofibrillar , mitochondrial , and muscle attachment complex structures ( Figure S6 ) . Quantification of these defects ( Figure 9 ) suggests a significant requirement for CLP-1 and CLP-7 for proper sarcomere development , CLP-1 , CLP-4 , TRA-3 and CLP-6 for proper mitochondrial development , and CLP-1 for proper integrin attachment complex formation . These results suggest that each of these calpain genes has a role in normal muscle development . Future study of animals with mutations in each of these genes may allow further dissection of specific and general requirements for calpains in C . elegans muscle development and physiology .
The integrin attachment complexes of the C . elegans body wall muscles serve three overlapping but partially distinct functions . First , they anchor the ends of filaments of the contractile apparatus , actin at the Z-line and myosin at the M-line , to enable proper sarcomere assembly [30] , [31] . Second , they anchor body wall muscles to basement membrane just as mammalian costameres do . As hypodermal cells are also linked to the same basement membrane [30] , this provides a mechanism for muscle-hypodermis communication and enables the contractile force of the muscles to be transmitted to the exoskeleton ( cuticle ) . Third , they anchor body-wall muscles to each other . Since each longitudinal body-wall muscle band is two cells wide , both longitudinal and lateral attachments ( attachment plaques ) are made between muscle cells [65] , [66] . These enable coordination between adjacent muscles and are thought to be akin to mammalian myotendinous junctions [67] . In metazoan muscle , it is often assumed that integrin attachment complexes must be stable in location and rigid in structure [60] , [68] , [69] to facilitate similar intertwined functions . However , it is not completely clear whether we should view the C . elegans muscle-hypodermis or muscle-muscle attachments as inert or perhaps even as stable . A worm increases about fourfold in length ( 250 µm to 1 mm ) in growing from a first-stage larva ( L1 ) to reproductive adulthood [70] , and another 40% ( to ∼1 . 4 mm ) before growth stops . Increase in body width is approximately proportional and only 10 ( of 95 total ) new body-wall muscle cells are added postembryonically [71] , [72] . Thus , for mobility to be maintained over this scale of growth , the muscle and hypodermal tissues must grow in a well-coordinated manner , primarily by hypertrophy rather than by proliferation . Despite the existence of careful studies of sarcomere assembly during embryonic development [30] , [72] , we do not yet know much about whether and how the attachment complexes undergo dynamic changes to accommodate postembryonic growth . Here we have shown that genetic disruption of integrin containing muscle attachment complexes , by temperature-sensitive mutation or acute RNAi knockdown , results in general degradation of proteins in muscle cytosol , disruption of sarcomere organisation , fragmentation of mitochondria , and impairment of mobility . These defects occur when gene products are knocked down in fully developed , adult , muscle , so these results imply that integrin containing attachment complexes are required for maintenance of muscle . Additionally , as C . elegans muscle lacks satellite cells these results demonstrate that it is the attachment complexes within adult muscle cells that are required for maintenance of adult muscle . One of the most striking aspects of our findings is that these phenotypes can be induced in adults by acute RNAi knockdown . In such experiments , normal attachment complexes are present at the start of the RNAi treatment and RNAi can only lower the abundance of functionally normal proteins . We also observed that a set of phenotypes similar to those induced by adult-onset RNAi could be induced when temperature-sensitive mutants ( affected in either the extramuscular ligand UNC-52 or the intramuscular attachment complex protein UNC-112 ) were raised to adulthood at permissive temperature , and then shifted to nonpermissive temperature . These mutants are not fully normal even when grown at nonpermissive temperature , yet the phenotypes become markedly more severe when the temperature is raised . Taken together , these observations imply that the attachment complexes must be in some measure dynamic structures . Consistent with this , a recent study has shown that some proteins associated with C . elegans sarcomeres do display dynamic exchange in vivo [73] . Our examination of animals labelled with an UNC-95::GFP fusion , which does display dynamic exchange [73] , suggests that as the adult animals grow , the number of Z-line attachment complexes remains constant or increases only slightly , while the spacing between adjacent complexes increases to accommodate body growth ( e . g . the Z-line to M-line distance is the same but the Z-line to adjacent Z-line distance is increased ) . This observation is in line with past observations that the number of sarcomeres remains constant after the last larval stage [30] . Both observations are most consistent with a model in which each Z-line attachment complex as an entity is maintained over time , but undergoes continuous dynamic exchange and/or accretion of some constituent proteins to accommodate animal growth . In the case of UNC-95 , the data suggest that it undergoes dynamic exchange [73] and also accretion ( as evidenced by the growing area of UNC-95::GFP labelled Z-line complexes ( Figure S4 ) ) . Thus , strong effects on phenotype might be produced if RNAi depleted the free pool of such an attachment complex protein so as to compromise either exchange or accretion . Additionally , one might predict that the increased distance between adjacent Z-line attachment complexes would alter the tension on each Z-line attachment complex; this has previously been suggested [74] , [75] and is postulated to increase roughly linearly with postembryonic growth [43] . Continuing protein accretion may make the attachment complexes more robust to withstand this additional tension . The mechanism of mechanical coupling between adjacent Z-lines in C . elegans is currently unknown . DIM-1 is localized around the Z-line attachment complexes , so it is possible that DIM-1 is required to allow for mechanical coupling between adjacent Z-lines . The fact that normal arrays of Z and M-lines and of sarcomeres are not typically seen in the absence of DIM-1 suggests decreased mechanical coupling must occur . Loss of dim-1 suppresses the protein degradation , collapse of arrayed sarcomeres and attachment complexes , fragmentation of mitochondria , and movement defects we observed in response to integrin attachment complex disruption . The suppression of these phenotypes by mutation in or RNAi targeting dim-1 suggests , but does not prove , that it is additional mechanical strain placed upon the integrin attachment complexes that causes them to fail in response to acute gene knockdown of protein complex members . Failure of attachment complexes through a combination of decreased gene function and mechanical strain has been observed in animals with embryonic lethal mutations in members of this complex [45] . In these worms , mechanical strain in the form of onset of muscular contraction is known to be the cause of failure of integrin attachment complexes . Though the precise molecular composition and functions of these complexes are different in embryos and adults , the consequences of failure are strikingly similar , inasmuch as the micrographs of myosin in these embryonic mutants look very much like the MYO-3::GFP “balling” phenotype we observed in response to some acute RNAi knockdowns . Clearly , further work on dim-1 is required to fully understand how dim-1 mutants suppress the pathologies observed in response to genetic disruption of integrin attachment complexes in adult muscle . Whereas mutations in or RNAi against dim-1 suppress all of the pathologies observed in response to genetic disruption of the integrin attachment complexes , RNAi against genes encoding calpains , or treatment with calpain inhibitors , suppresses the degradation of cytosolic proteins but not the movement defect . By contrast proteasome inhibitors , autophagy inhibitors , or a caspase mutation fail to block the degradation . These data suggest that calpains are activated in response to integrin attachment complex disruption . The data show that calpains are necessary for the degradation observed in response to genetic disruption of integrin attachment complexes in fully developed muscle , but do not show if calpain activation is sufficient . Calpains are normally ascribed a role in partial rather than complete degradation of proteins [76] . For example , partial degradation of integrin attachment complex member proteins by calpains is believed to have a role in both assembly and disassembly of these complexes [60] . The calpain activation we infer could be solely responsible for the degradation of cytosolic protein as the result of catastrophic failure of the attachment complexes ( e . g . inability to reassemble/repair the complexes ) ; this is the simplest explanation . However , it is also possible that the calpain activation could be required to signal increased degradation via another or a combination of other proteolytic systems . For example , calpains ( CLP-1 and TRA-3 ) and lysosomal proteases ( ASP-3 and ASP-4 ) are required sequentially in C . elegans neurons undergoing excitotoxic cell death [57] . In the present study , RNAi against single calpain genes clp-1 , clp-4 , tra-3 , clp-6 , or clp-7 is sufficient to suppress degradation . Thus , any potential sequential activation of proteases in response to genetic disruption of integrin attachment complexes is at least partially distinct from the sequential activation observed in excitotoxic cell death in C . elegans neurons . Whether or not calpain activation is solely responsible for general degradation of cytosolic protein in response to genetic disruption of integrin attachment complexes , the fact that calpain activation is known to promote remodelling of such attachment complexes [60] suggests that this is the reason for initial activation of the calpains . Consistent with calpains serving to maintain integrin attachment complexes in adult muscle , we found that treating fully developed adult muscle with RNAi against the calpains results in attachment complex disruption , defects in the normal arraying of sarcomeres , and mitochondrial fragmentation . Additionally , we found that calpains do degrade the attachment complex member DEB-1/Vinculin when the complex is disrupted . Thus , in adult C . elegans muscles , integrin attachment complexes and calpains appear to participate in a feedback system whereby integrin attachment complex disruption can activate calpains and calpain activation facilitates repair/remodelling of disrupted complexes . This system allows these complexes to carry out their intertwined functions , including coordinated growth . In support of this suggestion , one of the calpain genes that participates in this system , tra-3 , was recently shown to be mutated in a natural variant strain that fails to alter growth in response to temperature [77] . More work is required to understand the specific roles these calpains serve in vivo and also the interrelationship ( s ) between them . While it is tempting to suggest that the cytosolic degradation , sarcomere disorganisation , or mitochondrial fragmentation is causing the movement decline we observe , each individual defect has previously been shown to be sufficient to cause a movement decline [20] , [78]–[80] . Thus , which particular subcellular damage ( s ) leads to the organismal level mobility defect may be somewhat specific to each RNAi treatment , and each may in fact be the result of quantitatively different effects in multiple subcellular compartments within different individuals receiving the same RNAi treatment . For example , given the extent of intra-muscular pathology observed in response to RNAi against a core complex component , a very severe movement decline is entirely expected if each defect is sufficient to cause a movement decline and each effect is additive . Similarly , it is not particularly surprising that when both the sarcomeres and mitochondria show defects , the movement decline is significantly greater in animals with more severe mitochondrial disruption ( for example tln-1 ) . Conversely , it may be somewhat surprising that RNAi against cdc-42 produces a severe movement decline , as it does not produce similar severities of sub-cellular pathologies ( as the core complex and tln-1 ) and also quite surprising that RNAi against unc-89 produces only a modest decline in movement despite producing highly disorganised arrays of sarcomeres and attachment complexes and substantial fragmentation of mitochondria . Thus , while a clear relationship exists for the core complex as a group , the more peripheral components of the complex appear to have more individual differences , perhaps as the result of more specialized functions of each gene product . Future prospective studies of individual animals where defects in multiple sub-muscular compartments are followed in parallel with movement decline may shed further light on this question , as may further studies of genes for which RNAi treatments produce unique patterns of sub-cellular defects ( for example unc-89 ) . Additionally , as different components of these attachment complexes have been shown to have different kinetics of exchange within these complexes [73] , it could prove quite interesting to conduct studies which attempt to correlate the relative severity of movement defect and/or subcellular pathology with the kinetics of exchange . Regardless of the complexity , it seems clear that loss of integrin attachment complexes in adult muscle has multiple subcellular consequences , which usually result in decline in movement that is related to the extent of intramuscular damage . We started these studies because two members of an integrin attachment complex showed decreased mRNA expression in response to spaceflight [22] , a condition associated with disuse atrophy , and because RNAi targeting these genes provoked general protein degradation within muscle [29] . Here we have shown that acute genetic disruption of the core members of muscle integrin attachment complexes results in activation of calpains , and we have argued that the main physiologic role of this activation is to facilitate repair and/or replacement of damaged complexes . It should prove a matter of much interest to determine if attachment complex disruption occurs in higher animals and if calpains serve a similar muscle intrinsic repair/remodelling role . It may be that the Z-line streaming observed in human muscle subjected to prolonged physical forces [81] is a hint that these complexes can be damaged and repaired in higher animals . If calpains serve such a role in higher animals , then it is almost certainly the case that the general degradation seen in response to integrin complex disruption is transient and thus the main role of this degradation is in facilitating repair/remodelling , not in causing muscle atrophy . However , it is likely that in conditions of prolonged disruption of these attachment complexes , calpain activation and the consequent general degradation would be sustained . It may be the case that the general degradation we observe is relevant to muscle wasting seen in the congenital myopathy in individuals with a mutation in an integrin receptor gene [82] . Additionally , our results add support to the notion that lack of calpain activity accounts for part of the progressive dystrophy noted in rodent calpain mutants [62] and in humans suffering calpain mutations [63] . Here we have reported that genetic disruption of integrin based attachment to the basement membrane induces calpain activation and subsequent general degradation of cytosolic protein content . This is the fourth cell surface receptor on C . elegans muscle identified as a regulator of proteolysis and the third intramuscular proteolytic system shown to be regulated by extramuscular signals [18]–[20] . Thus , we are moving closer to an integrated picture of how C . elegans muscle co-ordinately maintains cytosolic protein content in response to external cues . All signals identified to date have human orthologs [83] , suggesting that some knowledge of integrated control of human muscle protein content can be gleaned from C . elegans .
Nematode strains were maintained and grown using standard C . elegans culturing techniques [84] at 20°C or , in the case of temperature sensitive mutants at 16°C , using the Escherichia coli strain OP50 as food source . Genetic constructions were conducted using standard techniques . Mutant alleles used in this work were as follows: LG I: mek-2 ( ku114 ) ; LG II: unc-52 ( e669su250ts ) ; clr-1 ( e1475ts ) ; LG III: daf-2 ( m41ts ) ; mpk-1 ( n2521 ) ; LG IV: ced-3 ( n717 ) ; cha-1 ( p1182ts ) ; let-60 ( ga89ts ) ; LG V: unc-112 ( r367ts ) ; and LG X: dim-1 ( gk54 ) and ( ra102 ) . The transgenes used in these experiments were as follows: ccIs55 ( sup-7 ( st5 ) ; unc-54::lacZ; integrated on LG V ) for assessing protein degradation with histology as previously described [17]; jIs01 ( rol-6 ( su1006 ) ; myo-3::GFP; unknown site of integration ) for visualising sarcomeres with fluorescent microscopy [38] , ccIs4251 ( pSAK4 ( myo-3 promoter driving mitochondrially targeted GFP ) ; pSAK2 ( myo-3 promoter driving a nuclear-targeted GFP::LacZ fusion ) ; and a dpy-20 subclone; integrated on LG I ) [85] and zcIs14 ( myo-3::GFPmt; unknown site of integration ) [86] for visualising the mitochondria with fluorescent microscopy; ryIs22 ( rol-6 ( su1006 ) ; unc-95::GFP; integrated on LG X ) [55] and raEx16 ( rol-6 ( su1006 ) ; unc-112+::GFP ) [24] for visualising the muscle attachment complexes with fluorescent microscopy , these encode GFP tagged UNC-95 and UNC-112 respectively; and dvIs511 ( pCL197 ( Pmyo-3::Ub-G76V-GFP ) ; pCL148 ( Pmyo-3::DsRed monomer ) ; integrated on LG I ) ( gift from Chris Link , University of Colorado at Boulder ) , and njEX38 ( pGo::GFP , rol-6 ( su1006 ) , punc-54::daf-2+ ) [20] for visualising any loss of cytosolic protein due to membrane disruption , these include DsRed and GFP constructs expressed in the body wall muscle cytosol . raEx16 was also used in the unc-112ts rescue experiments . Culturing was essentially as described [87] . For chronic treatment with RNAi four L4 larvae were transferred to standard RNAi plates with bacterial lawns expressing double-stranded RNA for the relevant genes . Most bacterial lawns expressing double-stranded RNA were grown from bacterial clones from the MRC Ahringer Library [87] . Bioinformatic work was conducted utilizing WormBase [88] . MRC clones used were as follows: atn-1: V-8I08 , cdc-42: II-5P13 , Y71G12B . 11: I-8K21 , pat-2: III-4P15; pat-4: III-1C19; pat-6: IV-1E21; uig-1: V-8D12; unc-52: II-9A20; unc-89: I-1B22; unc-97: X-3I11; unc-112: V-9L03; zyx-1: II-8H13; clp-1: III-4O15; tra-3: IV-7D13; clp-6: IV-1D01; clp-7: IV-1B23; mek-2: I-7L20; mpk-1: III-2I01 . Bacterial lawns expressing double-stranded RNA targeting clp-4 or deb-1 were , respectively , grown from a bacterial clone which was kindly provided by Chris Link ( University of Colorado at Boulder ) or from a clone from the Open Biosystems Vidal Library [89] , clone 10002-B11 . Bacterial lawns expressing double-stranded RNA targeting pat-3 were grown from a bacterial clone produced for this work . Briefly , an L4440 plasmid containing an ∼1 . 8 kB cDNA fragment of pat-3 ( BamHI digestion product from a Yuji Kohara cDNA clone , provided by Hiroshi Qadota ( Emory University ) ) was transfected into HT115 ( ΔlacZ , produced by mutagenesis ) and confirmed as described [87] . Behavioural , developmental , and sub-cellular phenotypes were scored in the F1 and F2 generations as described [29] . Sub-cellular phenotypes were scored as described above at early adulthood and 24 , 48 , and 72 hours later . In experiments where RNAi against a gene was tested for ability to suppress degradation or subcellular pathology ( e . g . mpk-1 , mek-2 , clps , and dim-1 in unc-112ts or unc-52ts mutants or in wt ) , strains were grown on RNAi for at least two generations prior to the onset of acute temperature shift experiments ( e . g . experiments were always conducted in F3 or later generations ) . Animals were roughly age-synchronised as described [17] . Animals were then re-plated to fresh OP50 bacterial lawns and grown at 16°C for 48–60 hours to early adulthood . Subsequently , animals were manually transferred to standard RNAi plates seeded with standard E . coli HT115 RNAi feeding vector ( s ) [87] , as described above . Transgenic animals were analysed for protein degradation , sarcomere defects , mitochondrial defects , or attachment complex abnormalities at 24 , 48 and 72 hours post-adulthood . All images were captured on either a Leitz Orthoplan with Leica DFC300F digital camera and Leica Firecam software ( Pittsburgh ) , a Nikon H600L with a Nikon Digital Sight DS-Fi1 digital camera and proprietary software ( Nottingham ) , or a Zeiss AX10 with an Axiocam MRC digital camera and Axiovision LE software ( Nottingham ) . Confocal images ( Pittsburgh ) were obtained on Leica TCS-SP5 or Olympus FV1000 microscopes and analysed with ImageJ software . Other image analysis and figure preparation was conducted in Photoshop . RITC-phalloidin was used as previously described [37] . Animals were roughly age-synchronised as described [17] . Animals were then re-plated to fresh OP50 bacterial lawns or fresh RNAi clone lawns and grown at 16°C for 48–60 h to early adulthood . Subsequently , animals were transferred to 25°C; in the case of drug experiments animals were placed on either OP50 or drug plates immediately prior to temperature upshift . Transgenic animals were analysed for protein degradation , sarcomere defects , mitochondrial defects , or movement defects at 24 , 48 and 72 h post-adulthood . Western blots in Pittsburgh were conducted as previously described [19] , [20] , [37] , [38] utilizing the following primary antibodies: anti-β-galactosidase JIE7 , anti-myosin heavy-chain A 5–14 , and anti-actin JLA20 [90] , all from Developmental Studies Hybridoma Bank , University of Iowa , USA; and anti-pTpY-ERK 9101S from Cell Signalling Technologies . Peroxidase-conjugated donkey anti-mouse and donkey anti-rabbit secondary antibodies were from Jackson Immunoresearch . For analysis of myosin and actin degradation in unc-112ts mutants , blots were probed for both β-galactosidase and actin or myosin so that a direct comparison of degradation could be made . Western blots in Nottingham were conducted as follows: 30 worms were picked into 13 µl M9 Buffer and 7 µl 3×Laemmli buffer was added prior to boiling for 5 min . Lysates were then frozen at −20°C until analysis . Subsequently , samples were thawed on ice , vortexed thoroughly and loaded onto precast 18-well 12% sodium dodecyl sulfate polyacrylamide electrophoresis gels ( Criterion XT Bis-Tris; Bio-Rad , Hemel Hempstead , UK ) and run at 200 V for 1 h . After equilibration in transfer buffer for 15 min , the gel was transferred on ice at 100 V for 45 min to a methanol pre-wetted 0 . 2 µm Immobilon PVDF membrane ( Millipore ) . Next , the membrane was blocked in 5% ( w/v ) BSA in TBS-T ( Tris Buffered Saline and 0 . 1% Tween-20 ) for 1 h at room temperature and then incubated overnight at 4°C in primary β-galactosidase antibody ( Promega ) or DEB-1 antibody ( [91] , Developmental Studies Hybridoma Bank ) at a 1∶40 , 000 dilution or 1∶1 , 000 ( respectively ) in 5% ( w/v ) BSA in TBS-T . The following morning the membrane was washed ( 3×5 min ) in TBS-T and then incubated in peroxidase-conjugated donkey anti-mouse secondary antibody ( R&D systems ) at a 1∶16 , 000 dilution in 5% BSA/TBS-T for 1 hour at room temperature . After further washes ( 3×5 min ) in TBS-T the membrane was developed using Immunstar ECL reagent ( Bio-Rad ) for 5 min and the protein bands visualised on a Chemidoc XRS system ( Bio-Rad ) . Peak densities of the bands were statistically analysed by two-way repeated measures ANOVA using GraphPad Prism 5 . Cycloheximide ( CHx ) was used as previously described on 5 ml plates [17] . Confirmation that the cycloheximide was active was achieved by confirming the lack of larval development in the F1 generation . Levamisole ( Lev ) and MG132 ( ZLLL ) were used as previously described on 5 ml plates [17] , [18] . Confirmation that levamisole and MG132 were active was achieved by noting block of degradation in cha-1ts animals at 34 hours post temperature upshift . SB201290 was used as previously described on 5 ml plates [20] . Confirmation that SB201290 was active was achieved by noting block of degradation in clr-1ts or let-60ts animals at 48 or 72 hours post temperature upshift , respectively . N6 , N6-dimethyladenosine ( Toronto Research Chemicals ) was used at 0 . 5 mM . Confirmation that N6 , N6-dimethyladenosine was active was achieved by noting block of degradation in daf-2ts mutants at 48–72 h after a shift to 25°C . Calpain inhibitors II and III ( BioChemica ) were prepared as 5 mg/ml stock solutions in DMSO . These stock solutions were added to the bacterial lawn of seeded NGM plates as 1∶1000 dilutions ( typically 5 µl on a 5 ml plates ) and allowed to dry 1–3 days prior to use . Animals were picked directly onto the location of seeding with the drug . DMSO only vehicle control plates were prepared similarly . Movement analysis was conducted essentially as described [19] , [20] . All experiments were conducted by a single individual to prevent individual to individual differences in scores . For Figure 2A ( BF , Pittsburgh ) : Wild-type or unc-112ts animals were temperature shifted as indicated and movement assessed at the indicated times . ( unc-112ts/+heterozygotes were generated by crossing homozygous unc-112ts hermaphrodites with mIs10 ( Pmyo-2::GFP; integrated on LG V ) males and collecting F1 hermaphrodites with pharyngeal GFP ) . Five animals were picked and assessed 10 times for a total of 50 independent measurements for each genotype at each timepoint . For Figure 2B and 2C ( TE , Nottingham ) : Wild-type ( ccIs55 ) or dim-1 ( ra102 ) animals were treated acutely with RNAi and rates of movement were assessed at young adulthood ( t = 0 hour ) and mid-adulthood ( 72 hours post RNAi treatment ) time points . Animals were individually picked into 10 µl M9 buffer . The number of sinusoidal movement patterns completed over 10 seconds was recorded and multiplied to obtain movement rate per minute . This was repeated 10 times for each animal to give a total of 100 measures per experimental treatment . In the case of severe movement defects where there was an absence of sinusoidal movements due to paralysis , the number of times the head of the animal moved from left-to-right and left again was measured . Movement data were analysed for each strain by two-way repeated measures ANOVA in GraphPad Prisim5 . | Muscle is a dynamic tissue that grows in response to use and nutrition and shrinks in response to lack of use , poor nutrition , or disease . Loss of muscle mass is an important public health problem , but we understand little of the genes that regulate muscle shrinkage . We have found that , in adult worm muscle , attachment to the basement membrane is continuously required to prevent catastrophic sub-cellular defects that result in impaired ability of muscle to function . We have also identified a group of proteases that are activated when the attachment fails to be properly maintained . Conversely , when these proteases are lacking in adult muscle , the muscles fail to maintain attachment to the basement membrane . Thus , we have discovered a group of proteases that appear to act to maintain attachment to the basement membrane and therefore to maintain muscle itself . Because these worms lack satellite cells , this maintenance system is intrinsic to muscle , thus raising the question whether a similar or identical system also works in humans . | [
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] | 2012 | Calpains Mediate Integrin Attachment Complex Maintenance of Adult Muscle in Caenorhabditis elegans |
An epidemiological study of Ehrlichia canis infection in dogs in Peninsular Malaysia was carried out using molecular detection techniques . A total of 500 canine blood samples were collected from veterinary clinics and dog shelters . Molecular screening by polymerase chain reaction ( PCR ) was performed using genus-specific primers followed by PCR using E . canis species-specific primers . Ten out of 500 dogs were positive for E . canis . A phylogenetic analysis of the E . canis Malaysia strain showed that it was grouped tightly with other E . canis strains from different geographic regions . The present study revealed for the first time , the presence of genetically confirmed E . canis with a prevalence rate of 2 . 0% in naturally infected dogs in Malaysia .
Ehrlichia canis is a gram-negative obligatory intracellular bacterium with a tropism for monocytes and macrophages in the family Anaplasmataceae and order Rickettsiales [1] , [2] . Canine monocytic ehrlichiosis ( CME ) caused by E . canis is a tick-borne disease of dogs . E . canis is transmitted by the brown dog tick Rhipicephalus sanguineus [3] , [4] . The disease was first described in 1935 in Algeria , as a febrile sickness associated with leukopenia , thrombocytopenia , depression and anemia in several dogs [1] . Some closely related pathogens , including Ehrlichia ewingii , Ehrlichia chaffeensis , Anaplasma phagocytophilum and Neorickettsia risticii , are shown to cause similar clinical and hematological manifestations in dogs as well [2] , [5] . However , E . canis is responsible for the most common and clinically severe form of canine ehrlichiosis , and may also be a cause of human ehrlichiosis [6] , [7] . Because rickettsiales are able to infect a broad range of hosts , and multiple pathogens can co-exist in both vertebrate and invertebrate hosts , the availability of a rapid , highly sensitive , and specific test that can diagnose one or more pathogens , including co-infections , in a test sample will be valuable for timely diagnosis and treatment [8] , [9] . Traditional diagnostic techniques including hematology , cytology , serology and isolation are valuable diagnostic tools for CME , however it is believed that molecular techniques make the most appropriate means of diagnosis of E . canis infection , and would be useful for monitoring and controlling the spread of infection from ticks [10] . Moreover , a multiplex molecular test would be a valuable tool in studies to evaluate the impact of co-infections on the disease outcome , as well as in studies to assess vaccines and therapeutics [9] . Microscopic visualization of morulae in peripheral blood leukocytes may be the simplest test , but it is also the least sensitive technique . Currently serological tests are the most commonly practiced method for diagnosis of E . canis infection . These serological tests reflect the quantity of antibodies present in the serum and therefore indicate exposure but not the severity of disease and the duration of infection [11] . Furthermore , antibodies are usually absent during the first two weeks of onset [11] . Additionally antibodies against several other ehrlichial organisms might cross-react with E . canis and complicate the serological diagnosis [12] . False negative results are another common feature of serological tests and may occur due to the early stage of the disease and lack of antibody which may further impact the final diagnosis [13] . Conversely , polymerase chain reaction ( PCR ) is a sensitive method of detection of acute monocytic ehrlichiosis in dogs; in fact it is designed to aim for the organism itself which makes PCR an invaluable technique capable of detecting traces of pathogen even before the onset of clinical signs [14] . Therefore , the advantages of molecular detection of Ehrlichia include diagnosis before the development of antibodies in early stages of disease and identifying new species and also closely related species of Ehrlichia using species-specific primers and sequencing [15] . To date the presence of ehrlichial agents in dogs in Malaysia has not been investigated using molecular techniques and therefore , this study was undertaken to detect E . canis DNA and to determine the prevalence of the disease caused by this pathogen in dogs in Malaysia .
The research was conducted as per the guidelines of the Animal Care and Use Committee , Faculty of Veterinary Medicine , Universiti Putra Malaysia . This committee follows the Australian code of practice for the care and use of animals for scientific purposes . The committee did not deem it necessary for this research group to obtain formal approval to conduct this study . A total of 500 blood samples were collected from dogs in Peninsular Malaysia , comprising 177 samples from stray dogs at shelters around Selangor state ( 144 ) and Langkawi Island ( 33 ) , and 323 samples from dogs that were presented to private veterinary clinics from Selangor ( 86 ) Johor ( 30 ) , Melaka ( 27 ) , Sabah ( 3 ) , and the veterinary teaching hospital at Universiti Putra Malaysia ( UPM ) , Selangor ( 177 ) for routine health care or specific treatment . Samples were collected in EDTA-anticoagulant tubes once a week randomly from February 2009 to February 2010 , and stored at −20°C until further use . Information ( age , breed , sex , and all laboratory results ) of all the clinic cases was recorded , and the sex of the stray dogs was noted ( Table 1 ) . DNA was extracted from whole blood ( 200 µl ) following the QIAamp animal blood and Tissue Kit procedure ( QIAGEN GmbH , Hilden , Germany ) , adjusted in 200 µl of Tris- EDTA ( TE ) buffer and stored at −20°C until further use . Standard screening conventional PCR was performed on all 500 samples using genus-specific primers; forward EHR16SD ( 5′- GGTACCYACAGAAGAAGTCC-3′ ) and reverse EHR16SR ( 5′-TAGCACTCATCGTTTACAGC-3′ ) [16] . Second PCR was performed on positive samples in the screening PCR using the E . canis species-specific set of primers; forward CANIS ( 5′-CAA-TTA-TTT-ATA-GCC-TCT-GGC-TAT-AGG-A-3′ ) and reverse GA1UR ( 5′-GAG-TTT-GCC-GGG-ACT-TCT-TCT-3 ) ′ that amplifies approximately a 409 bp fragment of the 16S rRNA gene of E . canis [17] , [18] . After detecting positive samples , in order to amplify a longer fragment of the 16SrRNA gene of E . canis including the divergent region , another PCR was performed with oligonucleotide primers: FD1 ( 5′-AGA-GTT-TGA-TCC-TGG-CTC-AG-3′ ) and Rp2 ( 5′-ACG-GCT-ACC-TTG-TTA-CGA-CTT-3′ ) [19] . The PCR amplification was set up within a 25 µl reaction mixture containing 5 µl of DNA template and 20 µl of master mix ( 2 . 5 µl 10× buffer without MgCl2 , 10 µM of dNTP , 5 mM MgCl2 , 0 . 8 µM of each primer , 5 units of Taq polymerase , and sterile distilled water to a final volume of 20 µl ) . The thermal cycling procedure was; 1 cycle of 5 minutes at 95°C , 40 cycles of 30 seconds at 95°C , 30 seconds at 62°C , 60°C or 63°C depending on the primers used , 1 . 30 minutes at 72°C , and final cycle of 5 minutes at 72°C . Sterile distilled water and DNA of an E . canis positive dog were included as a negative and positive control , respectively . The amplification products were visualized on a 1 . 5% agarose gel after electrophoretic migration for 40 minutes at 100 voltages . The gels were stained with ethidium bromide for 10 minutes and visualized by UV illumination . Amplicons were extracted using the QIAPCR purification kit ( QIAGEN ) for direct sequence analysis using ABI prismTM BigdyeTM terminator cycle sequencing Ready reaction kit V . 3 . 1 . All sequences were aligned manually using ClustalW program ( www . ebi . ac . uk/clustalw ) . For comparing and analyzing the nucleotide sequences the BLAST program ( http://www . ncbi . nlm . nih . gov/BLAST ) was used . A similarity tree was inferred using the neighbor- joining method , MEGA software version 5 . The statistical analysis was performed using the chi-square test and the Fisher exact test to determine the relation between the observed variables; prevalence between the stray dogs and clinic cases , sex of the animals ( male and female ) , age , clinical signs ( symptomatic or asymptomatic ) and the dispersion of these frequencies .
Five hundred blood samples ( 323 clinic cases and 177 stray dogs ) were evaluated using PCR in this study out of which ten were identified as E . canis positive , giving an overall prevalence rate of two ( 2 . 0% ) percent . The prevalence of E . canis was calculated as 1 . 2% ( 4 of 323 ) amongst the clinic group , and 3 . 4% ( 6 of 177 ) among the stray dogs . There was no significant difference in the prevalence of E . canis between stray dogs and clinic cases ( X2 = 0 . 400 , P = 0 . 527 ) . The amplification with species-specific primers CANIS/GA1UR produced a clear single band of approximately 409 bp ( Figure 1 ) . The ten positive PCR products were purified and sequenced . BLAST analysis confirmed the isolation of E . canis with 100% identity to other registered E . canis strains in GenBank . The nucleotide sequence was deposited in NCBI GenBank database ( accession number JF429693 . 1 ) . This is the first confirmed detection of E . canis DNA from dogs in Malaysia . Among the clinic cases 27 ( 8 . 3% ) dogs had both thrombocytopenia and anemia , but only one of them was positive for E . canis . However thrombocytopenia appeared to be more consistently associated with the disease as three out of four positive dogs had thrombocytopenia and this proved to be of statistical significance ( P = 0 . 017 ) . The major part of the 16S rRNA sequences ( 1384 bp ) using primers FD1/Rp2 , amplified from the E . canis positive dogs , was 100% identical with the corresponding sequences from E . canis strains in different geographical areas of the world . Nucleotide differences in 16S rRNA sequences among E . canis strains from different geographical areas showed very few differences . An E . canis similarity tree was inferred using the neighbor- joining method , MEGA software version 5 . A similarity tree of E . canis Malaysia based on nucleotide sequences showed that the Malaysia strain was grouped tightly with other E . canis strains from different geographic regions ( accession numbers EU263991 . 1 , EU106856 . 1 , AF373613 . 1 ) .
In the present study E . canis was successfully amplified using molecular techniques and this represents the first molecular survey of this pathogen in Malaysia . The only published investigation based on detection of E . canis via light microscopic examination of peripheral blood films was carried out over 25 years ago revealing a prevalence rate of only 0 . 2% in dogs , and recently the prevalence of E . canis infection was determined to be 15% in Perak state of Malaysia using indirect immunofluorescence assay ( IFA ) [20] , [21] . Due to limitations of light microscopic examination for the detection of E . canis , it was imperative to study the prevalence using more reliable diagnostic methods . Furthermore , due to high prevalence rates of even up to 30% around the world , and because Ehrlichia species are the etiological agents of emerging and life-threatening tick-borne disease in domestic animals , there was a pressing need to determine actual prevalence rates in Malaysia [22]–[24] . In the current study , a relatively large number of blood samples from both stray dogs and clinic cases were subjected to PCR , a rapid , highly sensitive , and specific method for the detection of E . canis . The study revealed that the molecular prevalence of E . canis in the tested samples was 2 . 0% . This low prevalence rate is interesting , as one would expect Malaysia to be a highly endemic region for E . canis due to the suitable climate and the abundance of the tick vector . Thus further studies are needed to detect E . canis DNA from ticks , as there would be no transmission to dogs if the ticks are not infected . Prevalence rates were low in both clinic cases and stray dogs . The reasons for the low detectable rates requires further investigation however it is important to bear in mind that subclinical and chronic ehrlichial infections are not as readily diagnosed as acute infections when canine blood is used for the detection of E . canis . Therefore , ideally , PCR using both blood and splenic aspirates should be considered to overcome this limitation [25] . Furthermore PCR sensitivity varies between laboratories and this fact may have contributed to the low number of positive dogs identified . Analysis of laboratory findings of clinic dogs revealed few associations between hematological findings and E . canis infection status ( Table 2 ) . Furthermore , although 5 ( 1 . 5% ) dogs had laboratory findings typical of CME: thrombocytopenia , anemia , and lymphopenia , none were positive for E . canis . Among the clinic cases 27 ( 8 . 3% ) dogs had both thrombocytopenia and anemia , but only one of them was positive for E . canis . These results however highlighted an important point; over diagnosis or misdiagnosis may result if a diagnosis is made solely on clinical or hematological findings as they are not specific for CME . At the same time it may also have been possible that some of these dogs were subclinically or chronically infected and thus had lab findings consistent with CME but PCR was unable to detect the organism . Therefore a reliable diagnosis of E . canis can only be made based on a combination of clinical signs , laboratory test results , serological tests , and molecular methods of detection . As this was the first molecular study of E . canis it was imperative to carry out genetic characterization of the Malaysian strain for a better understanding of the pathogen in Malaysia . All 1384 base pair ( bp ) amplified sequences of the E . canis 16S rRNA of the Malaysia strain were found to be identical to other deposited strains in NCBI GenBank . Nucleotide differences in 16S rRNA sequences among E . canis strains from different geographical areas showed very few differences which could be explained by the fact that the genetic profile of canine E . canis strains based on the 16S rRNA gene is highly conserved . In conclusion , E . canis DNA was detected for the first time from dogs in Malaysia and the overall prevalence rate of E . canis in naturally infected dogs was 2 . 0% . The detection of E . canis DNA via PCR in this study confirms the presence of the infection in both the pet and stray dog populations in Malaysia . | Canine vector-borne diseases are a worldwide concern particularly in the tropics and sub-tropics that provide favourable climatic conditions for the vectors . Malaysia , a tropical paradise , is thus home to a wide range of vectors as well as the pathogens that they harbor . Ehrlichia canis , a ubiquitous tick-borne pathogen of dogs , is the causative agent of canine monocytic ehrlichiosis , the most common clinically significant tick-borne disease of dogs in Malaysia . The pet explosion coupled with the increasing number of stray dogs , has resulted in a surge in vector-borne diseases in companion animals in Southeast Asia . Despite this , there is very little published information regarding this subject in Malaysia . There are only two published studies on E . canis in Peninsular Malaysia based on traditional light microscopic detection and antibody detection techniques . This disease has been notoriously difficult to diagnose based on the traditional methods . This research investigates this important disease of canids using molecular techniques for the first time in Malaysia providing a more accurate picture of its presence and prevalence in the country . | [
"Abstract",
"Introduction",
"Materials",
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] | [
"veterinary",
"science",
"biology"
] | 2013 | Molecular Detection of Ehrlichia canis in Dogs in Malaysia |
Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system . Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale . In this study , we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort . Correlation-based analysis identified 1 , 109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network , the ‘human blood metabolome-transcriptome interface’ ( BMTI ) . Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites . A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling , transport and metabolic processes , i . e . metabolic reactions mainly belonging to lipid , energy and amino acid metabolism . Moreover , the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level . Using a transcription factor binding site enrichment analysis , this pathway cross-talk was further confirmed at a regulatory level . Finally , we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits . Overall , our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease .
Blood is a connective tissue , which not only ensures nutrient and oxygen supply of all organs of the human body , but also the communication between them . Among the variety of key tasks performed by blood are immunological functions through white blood cells . Due to its diverse functionality , blood is heterogeneous and complex in its composition . Besides cellular constituents , which can be roughly divided into red and white blood cells , blood mainly consists of plasma . Plasma represents the aqueous phase containing proteins , peptides , signaling molecules and steroid hormones , but also other metabolites ( e . g . carbohydrates , amino acids and lipids ) which are consumed and released by the organs . This unique composition of blood , agglomerating both metabolic and transcriptional variation carrying molecular signatures of system-wide processes , together with its minimally invasive accessibility , makes blood a widely used system for integrative biomedical research [1 , 2] . With the development of high-throughput omics technologies for different levels of molecular organization , a systematic analysis of biological mechanisms underlying the functionality ( or dysfunctionality ) of a system became possible . In the case of transcriptomics data , an established framework to systematically investigate the constituents of involved biological processes and their interactions are network-based approaches , where pairwise associations between molecular entities ( nodes ) are modeled as network edges . Such studies commonly identify context-specific functional modules [3] , but also global co-expression networks [4] from different organisms [5] and cell types [6] . When particularly focusing on the blood system , several studies investigated the co-regulation of transcripts either from single white blood cell types or whole blood samples . For example , regulatory networks [7 , 8] or global gene co-expression networks [9–11] were constructed from B- and T-cells to investigate pathways and mechanisms involved in the immune response . Further examples using whole blood data include the identification of disease related gene networks [12 , 13] or molecular signatures of distinct human vaccines captured in blood transcriptional modules [14] . Similarly , for metabolomics data a variety of studies extensively analyzed interactions between metabolites in various tissues , conditions and species [15–17] . Regarding blood measurements , we and others recently systematically characterized molecular interactions in the blood metabolome [18–21] . Utilizing Gaussian graphical models and serum metabolomics data from more than 1000 participants of a population cohort we were able to show that correlations between circulating blood metabolites resemble known metabolic pathways [22] . Furthermore , we have shown that these data-derived metabolic networks can be useful in a variety of applications , e . g . for the functional annotation of unknown metabolites [23] or to identify sex-specific serum metabolome differences [24] . The integration of multiple omics measurements ( e . g . gene expression levels and metabolite concentrations ) is an area of active research with many successful applications investigating the interplay between multiple organizational layers of a biological system [25–28] . However , only few studies with large sample sizes focused on a combined analysis in human blood . One recent example is the work of Inouye et al , who analyzed whole blood transcriptomics data in combination with blood lipid measurements and metabolites from a Finnish population cohort [29] . In their study , the authors associated a module of highly co-expressed genes with 134 blood metabolic markers in the context of heart disorders and identified a link between the immune system and circulating metabolites . The study by Inouye et al was among the first to provide clear evidence for this immune system link in blood , suggesting that gene expression in white blood cells is responsive to changing blood metabolite levels . Thus , it can be concluded that even if not cell-specific , the signals derived from whole blood data still reflect organism-wide processes . This is also in line with previous studies conducted on whole blood transcriptomics or metabolomics data separately [1 , 30 , 31] . The aim of the present study was to make use of the joint power of metabolomic and transcriptomic profiling to comprehensively characterize the complex interplay between serum metabolomics and whole-blood transcriptomics data . While serum metabolomics represent a footprint of whole-body processes , blood transcriptomics data will mainly reflect immune system processes through white blood cells . To this end , we analyzed metabolomics and transcriptomics measurements of 712 individuals from the German population study KORA ( “Kooperative Gesundheitsforschung in der Region Augsburg” ) , comprising 440 metabolites and 16 , 780 genes after filtering . We constructed a global correlation network to elucidate the complex interplay and regulation between these omics layers ( Fig 1A ) . The correlation analysis takes advantage of the naturally occurring variation from individual to individual , which we assume to carry a systematic footprint of the coregulation of metabolites and mRNAs . Such an integrative approach was recently termed “systems genetics” , providing a global view on the information flow between the various biological scales [32] . We deliberately left out an analysis of metabolite-metabolite and transcript-transcript correlations , which were rigorously investigated in the above-mentioned earlier studies . Instead , we specifically sought to assess the interconnection and information flow between the two omics layers . The manuscript is organized as follows: In the first part , we systematically characterize the blood metabolome-transcriptome interface ( BMTI ) using different strategies . First , we manually investigated the strongest associations and provide evidence from literature wherever possible . Moreover , using a Mendelian randomization ( MR ) approach , we examined potential causal relationships between metabolites and transcripts . Second , using the most recent genome-wide human metabolic network Recon 2 [33] , we systematically analyzed correlations between metabolites and transcripts at a pathway level ( Fig 1B ) . Third , we developed a novel network clustering approach based on functional annotations , leading to a pathway interaction network ( PIN ) that allows for fast functional interpretation of the BMTI and furthermore provides insights into the cross-talk among distinct molecular pathways ( Fig 1C ) . In the second part of this manuscript , we demonstrate how the identified networks can be used as a resource to further investigate the link between metabolism and gene regulation by two different applications . First , we investigated whether a common regulatory signature is observable from transcripts connected to the same metabolite or to metabolites that are part of the same metabolic pathways . For this purpose , we analyzed promoter regions of the genes for overrepresented transcription factor binding sites ( Fig 1D ) . Second , we integrated the metabolome-transcriptome and the pathway interaction network with associations to high density lipoprotein cholesterol ( HDL-C ) , low density lipoprotein cholesterol ( LDL-C ) and triglycerides ( TG ) , which are well-known risk factors for cardiovascular disease [34] . To this end , we mapped the results of linear regressions between these clinical lipid parameters with metabolites and mRNAs onto the networks ( Fig 1E ) . Finally , we demonstrate the potential of our systems genetics approach to generate novel hypothesis by combining results from all separate analysis steps and establish an association between the branched-chain amino acid pathway and the levels of plasma TG and HDL-C . All network results are available to the scientific community as interactive versions in graphml and Cytoscape format ( S1 Dataset ) .
For this study , we focused on a subset from the KORA F4 cohort with simultaneously available metabolomics and transcriptomics data . After quality control and filtering , the data set comprised of 712 human blood samples ( 354 males , 358 females ) with gene expression data of 16 , 780 uniquely mapping gene probes and metabolite concentrations of 440 metabolites ( Fig 1A , see Materials and Methods for details ) . 186 of these 440 metabolites were not chemically identified , which is marked by a metabolite name starting with “X-”throughout this manuscript . Both gene expressions and metabolite concentrations were log transformed and adjusted for age and sex effects . Pairwise Spearman’s rank correlations between the measured mRNAs and metabolites were then calculated . We used this correlation method to account for possible non-linear associations and to ensure robustness against outliers . Note that for this particular dataset , Spearman and Pearson correlations produced almost identical results ( S1 Fig ) . S1 Table provides a full list of identified significant associations between blood metabolites and transcripts . Metabolite-mRNA Spearman correlation coefficients were symmetrically distributed around zero ( mean:−4 . 5 × 10−4 ± 0 . 0433 , Fig 2A ) with a maximum absolute correlation value of ρ = 0 . 56 . Moreover , the distribution of inter-omics correlations showed a rather narrow shape , indicating generally lower correlations when compared to the intra-omics correlations ( mRNA-mRNA , metabolite-metabolite ) . The metabolite-metabolite distribution was strongly skewed for positive correlation values , which is in accordance with our previous findings on a different metabolomics panel [22] . In contrast , the mRNA-mRNA distribution displayed a broad and symmetric distribution of correlation values ( Fig 2A ) . We then generated a weighted bipartite network of metabolites and transcripts by constructing an edge between a pair of metabolite and transcript if the respective correlation was significant with a false discovery rate ( FDR ) of 0 . 01 . This corresponded to an absolute correlation cutoff of ~0 . 181 and a p-value threshold at 1 . 07 × 10−6 . Obviously , the number of edges in a correlation network heavily depends on the chosen threshold . It has been shown in previous studies that a biologically reasonable threshold can be found by investigating network density as a function of the correlation cutoff value [35] . According to that study , a cutoff value slightly above the minimal density combined with a decreasing number of nodes and edges leads to biologically meaningful results . As indicated in Fig 2B , a clear decline in the number of included nodes and edges can be observed for increasing correlation threshold levels beginning between correlation values of 0 . 15 and 0 . 25 . Minimal network density was reached for a correlation threshold value between 0 . 13 and 0 . 18 ( S2 Fig ) . Notably , applying the above-mentioned conventional statistical significance threshold to our data set precisely coincides with the network density-based threshold described by [35] . The resulting network , subsequently called the blood metabolome-transcriptome interface ( BMTI ) , consisted of 636 nodes ( 114 metabolites , 522 transcripts ) and 1109 edges , corresponding to a total network connectivity of ~0 . 015% ( Fig 2B and 2D ) . Out of the total number of edges , 63% ( 699 ) were positive correlations and 37% ( 410 ) were negative correlations . The metabolite showing the highest degree was mannose , with significant correlations to 98 different transcripts . In contrast , the mRNA with the highest connectivity was SLC25A20 with 37 metabolites attached ( S1 Table ) . We used data from the DILGOM study , which included NMR metabolomics as well as transcriptomics data for 518 individuals , for independent replication of our correlations . In total , 17 metabolites ( 11 amino acids , 3 lipids , 2 carbohydrates and 1 belonging to the energy metabolism ) overlapped between the KORA F4 dataset and the DILGOM study , which allowed us to investigate the replication of 211 edges ( ~19% of the BMTI ) . 61 out of the 211 edges ( ~29% ) reached a nominal significance ( p-value < 0 . 05 ) in the DILGOM study of which 38 ( ~18% ) remained significant after multiple testing correction ( FDR < 0 . 05 , see S2 Table ) . To investigate the possible origins of the metabolite-transcript correlations , we compared all genes represented in the BMTI with 1 ) two a priori defined blood cell type-specific marker gene lists , and 2 ) a database of more general tissue gene expression signatures ( see Materials and Methods ) . For the first part , we used a list of genes derived from Palmer et al . [36] comprising 907 specifically expressed genes for 5 different blood cell types ( leukocytes only ) and a second list derived from the HaemAtlas as generated by Watkins et al . [37] comprising 1 , 716 genes characterizing 9 different blood cell types . For the second part , we used the HECS database from Shoemaker et al . [38] containing information for more than 6 , 000 genes and 84 tissues . Both comparisons in 1 ) showed that most of the BMTI genes ( 85% and 67% , respectively ) were not specifically attributable to any blood cell type ( see Fig 2C and S3 Table ) . The remaining genes could be assigned to the respective measured cell types , with granulocytes making up the largest blood cell faction in both cases ( 8% and 20% , respectively ) and only minor signals for the other blood cell types . A similar result was observed when comparing the BMTI genes to the HECS database . 52% of the BMTI genes showed no tissue specificity , while 12 out of the 15 strongest tissue signatures where either blood cells or blood related tissues ( S3 Table ) . As a first step to characterizing the BMTI , we performed a manual literature lookup for the strongest absolute correlations in the network ( Fig 1B ) . In the following , we provide a detailed discussion of the 25 strongest edges ( Table 1 ) . Notably , most of the top 25 identified associations reflect biochemically reasonable interactions like transport processes of lipids , but also regulatory signatures between signaling metabolites and transcription factors . The strongest association in the dataset was observed between cortisol and DNA-Damage-Inducible Transcript 4 ( DDIT4 , ρ = 0 . 55 , p-value = 7 . 70 × 10−59 ) , which are known to play a role in stress response [39] . Cortisol is a glucocorticoid whose release is mainly induced by exogenous stress . Via binding to the glucocorticoid nuclear receptor ( GR , official gene symbol NR3C1 ) , it regulates various cellular processes like carbohydrate metabolism and the immune system by direct activation of target genes [40] . Remarkably , DDIT4 was identified as a GR target gene in mouse hepatocytes [41] , rat hippocampus [42] and also in human peripheral blood lymphocytes [43] delivering a potential explanation of an indirect association for the observed correlation . Another GR target gene associated to cortisol is Suppressor Of Cytokine Signaling 1 ( SOCS1 , ρ = 0 . 36 , p-value = 2 . 19 × 10−23 ) , a major constituent of the cytokine signaling pathway and inflammatory response [44] . We observed further top 25 correlations involving cortisol for Kruppel-Like Factor 9 ( KLF9 ) and Dual Specificity Phosphatase 1 ( DUSP1 ) ( ρ = 0 . 34 , p-value = 5 . 20 × 10−21; ρ = 0 . 34 , p-value = 7 . 58 × 10−21 , respectively ) . KLF9 is a ubiquitously expressed transcription factor involved in the regulation of diverse biological processes like cell development and differentiation in adipogenesis [45] . DUSP1 is an enzyme involved in the response to environmental stress [46] . Interestingly , for both transcripts , a cortisol-dependent regulation was already observed in epidermal cells [47] and peripheral blood mononuclear cells [48] . Another metabolite showing several strong associations to blood transcripts was 1-monoolein , which belongs to the class of monoacylgylcerols . This particular class of metabolites are bioactive compounds recently identified to be involved in various signaling processes of the immune system [49 , 50] . The source of 1-monolein in humans is not fully understood . Experiments in rodents suggest that dietary 1 , 3-diacylglycerols are preferentially digested to 1-monoacylglycerols and free fatty acid in the small intestine , making dietary 1 , 3-diacylglycerols containing an oleoyl moiety at position sn-1 or sn-3 a plausible source of 1-monoolein [51] . In our analysis , 1-monoolein showed a strong negative correlation to four transcripts – GATA Binding Protein 2 ( GATA2 ) , Histidine Decarboxylase ( HDC ) , Solute Carrier Family 45 , Member 3 ( SLC45A3 ) and Chromosome 1 Open Reading Frame 186 ( C1ORF186 ) ( ρ between -0 . 41 and -0 . 32 , p-values between 2 . 01 × 10−29 and 4 . 72 × 10−18 ) . HDC is a cytosolic enzyme that catalyzes the conversion of histidine to histamine and thus represents an important immune system trigger molecule [52] . In addition , GATA2 , a key regulator of gene expression in hematopoietic cells [53] , C1ORF186 and SLC45A3 , two membrane-bound proteins , were all identified to play a role in the immune response [13] . Carnitine-acylcarnitine translocase ( SLC25A20 ) occurred in 15 of the 25 top ranked correlations . This gene encodes an enzyme which transports acylcarnitines , i . e . the transport variant of fatty acids , into the mitochondria for subsequent ß-oxidation . Interestingly , the majority of SLC25A20-associated metabolites among our top 25 correlations belonged to the class of long chain fatty acids ( 11 long chain fatty acids , 2 essential fatty acids , 1 medium chain fatty acid , 1 ketone body ) , which is in accordance with its function as a lipid transporter . Of note , among the metabolites associated with SLC25A20 beyond the top 25 correlations were also 5 acylcarnitines , although at lower correlation values . We observed a significant , negative correlation between isoleucine and ATP-Binding Cassette Sub-Family G Member 1 ( ABCG1 , ρ = −0 . 32 , p-value = 3 . 39 × 10−18 ) . It has been shown previously that circulating levels of branched-chain amino acids ( BCAAs ) affect a variety of metabolic processes such as glucose and lipid metabolism [54] . ABCG1 is a major player of lipid metabolism , controlling the transfer of cholesterol from peripheral macrophages to exogenous HDL [55] . Interestingly , an association between circulating BCAA levels and plasma HDL-C levels was also observed in a recent population study [56] and in a previous paper on the same population cohort used in the present study [57] . To assess whether metabolite-transcript links in BMTI contain causal effects , we performed a Mendelian randomization analysis [58] . For each metabolite-mRNA edge , we tested both the causal directions metabolite→mRNA and mRNA→metabolite given that adequate instrumental variables were available . As instruments we used SNP lists from previously published GWAS studies . After filtering for strong instrumental variables , we were left with 15 SNPs identified by a metabolomics GWAS study [23] associated to 16 metabolites in the BMTI . Moreover , for 157 mRNAs in the network , we selected 192 SNPs from [59] . In total , we tested the causal relationship of 440 BMTI edges ( ~40% ) of which 60 could be tested bi-directional . In the BMTI , 42 metabolite-mRNA pairs ( 19 mRNA→metabolite; 23 metabolite→mRNA ) showed a nominally significant ( p-value < 0 . 05 ) causal effect . At an FDR of 0 . 05 , none of the tested pairs remained significant ( S4 Table ) . In order to further reveal the underlying mechanisms determining the observed associations , we systematically analyzed whether correlating pairs of metabolites and transcripts ( i . e . enzymes ) correspond to the structure of the underlying metabolic network . Specifically , we investigated if strong metabolite-transcript edges of the BMTI tend to be in close proximity within biochemical pathways . All pairwise associations between metabolites and transcripts were mapped to their corresponding network nodes in the Human Recon 2 metabolic network reconstruction [33] . As a measure for metabolic network proximity , the length of the shortest path connecting each metabolite-enzyme pair was determined ( Fig 3A ) . This measure is based on the common assumption that the shortest connection between two network entities corresponds to the biologically reasonable one [22 , 60] . To avoid potential biologically meaningless shortcuts , we removed co-factors and currency metabolites prior to the analysis ( see Methods section for details and S5 Table for a list of removed metabolites ) . We could map 121 metabolites and 1 , 467 enzymes out of the 254 metabolites with known identity and 16 , 780 transcripts onto the metabolic network , respectively . While most pairwise correlation coefficients were closely distributed around zero for all investigated network distances , a distinct pattern was observable for statistically significant correlations . The majority of significantly correlating pairs accumulated at short distances and was dominated by positive correlations ( Fig 3B ) . To determine the significance of this observation , one-tailed Fisher’s exact tests were performed by either considering each distance individually or by aggregating all pairs up to a particular distance . The latter aggregation analysis combines all transcript-metabolite pairs which are reachable up to a certain number of steps ( biochemical reactions ) in the metabolic network . For both cases , we observed a substantial overrepresentation of significantly correlating pairs at short distances ( Fig 3C ) . The strongest signals are observed for pairs that take part either directly in the same reaction ( d = 0 ) or for those which are just one reaction apart ( d = 1 ) . For the cumulative distances we also observed significant enrichment up to a distance of d = 2 reactions . Proportions of significant and non-significant pairs per distance are given in S3 Fig and a detailed view on an exemplary path of length 2 is depicted in S4 Fig To further characterize the underlying biochemical pathways , we calculated frequencies of functional annotations from Recon 2 among the significant associations for pathway distances 0 to 2 ( Fig 3D and S5 Fig ) . At a distance of 0 , we identified mainly transport reactions ( 67% ) accompanied by reactions belonging to lipid metabolism ( bile acid synthesis 11% , fatty acid oxidation 11% ) and carbohydrate metabolism ( pyruvate metabolism 11% ) . The transport reactions can be further subdivided into extracellular transport ( 45% ) , or mitochondrial transport ( 11% ) and peroxisomal transport ( 11% ) . Similar signals can be found at distances of 1 and 2 , where we additionally identified reactions belonging to energy metabolism ( oxidative phosphorylation 27% ) and amino acid metabolism ( histidine metabolism 11% , glutamate metabolism 4% ) . Taken together , the BMTI captured a systematic signal of metabolite-enzyme associations to be in close proximity when mapped onto a global metabolic network . Moreover , the strongest signals found for pathway distances of 0 , 1 or 2 reflect distinct metabolic reactions mainly belonging to lipid , energy and amino acid metabolism , and transport mechanisms . Up to this point , our analysis was a reaction-centered approach limited to single edges only , thereby neglecting the global network structure and cross-talk between pathways captured in the BMTI . To derive a comprehensive functional description of the biological modules included in the BMTI , we developed a novel approach based on functional annotations which provides an integrated view on cellular processes . Briefly , the approach consists of three steps: First , we used pathway annotations to define groups of functionally related metabolites and transcripts . For metabolites , we used metabolic pathway annotations provided with the metabolomics dataset , and for transcripts we downloaded the Gene Ontology ( GO ) slim annotations . Second , an aggregated z-score ( aggZ-score ) was calculated for each functional category . Third , we calculated correlations between aggZ-scores of all functional categories . A schematic overview of this multi-step approach is provided in S6 Fig and described in more detail in the Material and Methods . A full list of the resulting categorical correlations can be found in S6 Table . We again constructed a network ( the pathway interaction network , PIN ) by drawing edges between significantly correlated categories . Interestingly , even when applying a stringent Bonferroni-corrected threshold ( α = 0 . 01 , p-value ≤ 2 . 2 × 10−6 ) this resulted in an overly dense connected network of 166 nodes and 1220 edges . To generate a visually interpretable version of this network , an ad-hoc stringent threshold of p-value ≤ 1 . 0 × 10−11 was applied to draw the network . This resulted in a PIN consisting of 113 nodes ( 93 GO terms , 20 metabolic pathways ) connected by 244 edges ( 196 positive correlations , 48 negative; Fig 4A ) . Remarkably , we observed a high conformity between linked metabolic pathways and gene annotations . For example , the metabolic pathway “carnitine metabolism” was connected to the biological processes “lipid metabolic process” and “transmembrane transport” . Moreover , it was linked to the cellular component “mitochondrion” , indicating transport processes of fatty acids into the mitochondrion for subsequent ß-oxidation . Further biologically reasonable pairs were “Valine , Leucine and Isoleucine metabolism” and “Glutamate metabolism” attached to “cellular nitrogen compound metabolic process” . As a last example , “Steroid/Sterol” was connected to “response to stress” and “signal transducer activity” , pointing to an interaction between hormones and regulation of gene expression . In the following , we examine two selected category-category relationships in detail , including the individual metabolites and gene transcripts that gave rise to the association . The BMTI contains a prominent “flower-like” network topology , i . e . many transcripts associated to a single metabolite . We therefore asked whether these coordinated changes around a metabolite and also the network topology can be explained by common transcriptional regulatory processes through transcription factors ( TFs , Fig 1D ) . For the following analysis , we only considered metabolites linked to at least 3 transcripts . We analyzed the promoter regions of all connected genes for an enrichment of known transcription factor binding sites ( TFBS ) derived from the Jaspar database [68] . This resulted in significantly enriched transcription factor binding motifs for 46 single metabolites , 24 subpathways and 7 superpathways . The Methods section provides a detailed explanation of the process . A summary of all enriched TFBS can be found in S7 Table . In total , out of the 205 binding motif matrices used in the analysis , 189 reached a significant enrichment in at least one of the metabolite-derived gene sets , indicating a generally prevalent common regulation . Across all lists of enriched TFBS identified from our network , the motifs that occurred most frequently were Sterol Regulatory Element Binding Transcription Factor 2 ( SREBF2 ) , Peroxisome Proliferator-Activated Receptor Gamma ( PPARG; Jaspar motifs PPARG and PPARG::RXRA ) and Nuclear Factor , Interleukin 3 Regulated ( NFIL3 ) . SREBF2 is a major regulator of cholesterol metabolism [69] while PPARG is known to be activated by fatty acid ligands , thereby regulating fatty acid ß-oxidation and other processes [70] . NFIL3 is a regulator specifically found in activated T cells , natural killer ( NK ) cells , and mast cells , involved in the regulation of the immune response and the circadian rhythm [71] . Branched-chain amino acids were among the metabolites most strongly connected to SREBF2 targets . Specifically , the transcripts correlating with isoleucine and valine show high enrichment of SREBF2 binding sites ( p-value = 5 . 83 × 10−8 and p-value = 2 . 36 × 10−10 , respectively; S7 Table ) . Moreover , considering all 172 genes associated to at least one metabolite from the entire branched-chain amino acid pathway ( “Valine , leucine and isoleucine metabolism” ) yielded significantly enriched binding sites for SREBF1 and SREBF2 ( p-values 6 . 78 × 10−10 and 9 . 11 × 10−10 , respectively; S7 Table ) . Both SREBs are important regulators in lipid homeostasis , including cholesterol and fatty acid biosynthesis , further indicating a regulatory cross-link between HDL-C , TG and BCAA metabolism . The highly interlinked network topologies of both the blood metabolome-transcriptome interface and the pathway interaction network suggest a strong coregulation between the different metabolites , processes , and pathways . As a second step of coregulation analysis , we inferred the number of pairwise shared significant TFBS to determine the extent of coregulation between single metabolites and metabolic pathways ( S8 Table ) . At the single metabolite level , we found a maximum number of 27 shared TFs between histidine and X-03094 ( S7 Fig ) . Moreover , this highly connected unknown metabolite shared 14 TFs with another unknown metabolite ( X-12442 ) and with a peptide ( HWESASXX ) . For the metabolic subpathways , we observed an overlap between “histidine metabolism” and the group of “long chain fatty acids” and between “glycolysis , gluconeogenesis , and pyruvate metabolism” and the group of “fibrinogen cleavage peptides” ( 11 shared TFs each; Fig 5A ) . On the level of superpathways , the highest number of shared TFBS was 4 , identified between “carbohydrate” and “peptide metabolism” ( S8 Fig ) . Overall , we found that TF binding sites are shared to a large extent , indicating a complex coregulation not only within but also between different processes and pathways . To gain further insight into this coregulation , we determined transcription factors which also occur as transcripts in the BMTI . 165 out of the 189 transcription factors with available binding motif were contained in the filtered data set . Only 12 of these transcription factors displayed a significant correlation to any metabolite and are thus included in the BMTI . This observation is not completely unexpected given that TFs are regulated to a large extend at a post-transcriptional level [72] . Interestingly , for two out of these 12 TFs , we also observe enriched binding sites in the promoter region of the other genes connected to the same metabolite , i . e . a “triad” network motif consisting of a metabolite , a transcription factor and its target genes ( Fig 1D , S7 Table ) . The first transcription factor is B-cell CLL/Lymphoma 6 ( BCL6 ) , a transcriptional repressor involved in the STAT-dependent interleukin 4 response of B-cells [73] . BCL6 is negatively correlated with methionine and tyrosine in our network ( Fig 5B ) . The TFBS enrichment analysis using all 15 genes connected to methionine within the BMTI resulted in a significant overrepresentation of the BCL6 binding motif ( p-value = 5 . 71 × 10−09 , 82% of the 15 promoter sequences contained at least one occurrence of the motif ) , while no significant enrichment was observable for the genes connected to tyrosine . The second motif was identified around Nuclear Receptor Subfamily 4 , Group A , Member 2 ( NR4A2 ) , which was associated to 7 metabolites in our network . The 22 neighboring genes of one of those metabolites , kynurenine , showed significantly enriched binding sites for this transcription factor ( p-value = 3 . 79 × 10−09 , 73% of the 22 promoter sequences contained at least one occurrence of the motif; see Fig 5C and S7 Table ) . As a final analysis step , we sought to use the BMTI and the PIN to infer novel insights into the molecular mechanisms and pathways underlying complex traits . To this end , we associated the nodes of both networks with intermediate clinical phenotypes ( Fig 1E , Table 2 ) . As already stated earlier , we chose the levels of HDL-C and LDL-C , as well as concentrations of blood triglycerides ( TG ) , known risk factors for a variety of diseases . We performed multiple linear regression analyses with HDL-C , LDL-C and TG blood parameters as response variables and all 440 metabolites and 16 , 780 transcripts as explanatory variables . All models were corrected for sex and age . Statistical significance was defined by a Bonferroni adjusted p-value cutoff at 2 . 9 × 10−6 ( α = 0 . 05 ) . We then projected the −log10 transformed p-values from this regression as colors onto the corresponding nodes in the two networks . Similarly , the analysis was performed using aggZ-scores of pathways / GO terms as explanatory variables and mapped to the PIN ( Fig 6 and S1 Dataset ) . Note that we presented similar approaches in the past for metabolomics-only networks [24 , 74] . In total , regression analyses yielded 233 ( 54 metabolites , 179 mRNAs ) , 28 ( 28 metabolites , 0 mRNAs ) and 1 , 124 ( 49 metabolites , 1 , 075 mRNAs ) statistically significant associations for HDL-C , LDL-C and TG , respectively . Of those associations , 64% , 28% and 25% , were contained in the BMTI , respectively ( see S9 Table for a complete list of associations ) . We only observed few LDL-C metabolite associations , which can be mainly summarized in the “Glycerolipid metabolism” and “Carnitine metabolism” , while none were observable for the transcripts ( Fig 6E and 6F , S9 Table ) . We will therefore focus on network associations for HDL-C and TG in the following . Examination of the networks for HDL-C and TG revealed localized regions of similar associations , which reflect potentially co-regulated modules ( Fig 6A and 6C ) . Interestingly , when compared to each other , there appeared to be an antagonistic pattern of associations for HDL-C and TG , which is in accordance with an overall negative correlation of the two traits ( ρ = −0 . 53 ) . This opposing pattern also holds for the categorical networks ( Fig 6A–6D and S10 Table ) . To confirm this observation statistically , we utilized an approach to compare the different networks suggested by Floegel et al . [74] . Basically , we calculated the Spearman correlation of the association measures between the different clinical traits . This resulted in a strong negative correlation between the BMTI-HDL-C and the BMTI-TG network ( ρ = −0 . 84 ) which was even more pronounced between the PIN-HDL-C and PIN-TG networks ( ρ = −0 . 94 , S9 and S10 Figs ) . A similar pattern of opposing associations between HDL-C and TG phenotypic traits was already described in previous studies , which suggested a pleiotropic , heritable relation between the two lipid and lipoprotein measures , potentially regulated by a common , complementary mechanism [13 , 75] . In the following , we will discuss exemplary pathway mechanisms identified in the phenotype networks . ABCG1 and ABCA1 , known constituents of the reverse cholesterol transport necessary for the proper formation of plasma HDL-C [55] , were positively correlated with HDL-C ( p-value = 4 . 37 × 10−12 and p-value = 2 . 92 × 10−8 , respectively ) . At the pathway level , processes like “generation of precursor metabolites and energy” or “catabolic process” are negatively associated with HDL-C , while “nucleic acid binding transcription factor activity” and “signal transducer activity” are positively associated ( Fig 6D ) . An inverse pattern can be seen for TG , where positive associations predominate and processes like “generation of precursor metabolites and energy” or “catabolic process” are strongly positively associated ( Fig 6A and 6B ) . Given the known association between HDL/TGs and branched-chain amino acids [57 , 76] , we investigated the phenotypic networks to further examine this metabolic class . First , we examined the edge between isoleucine and ABCG1 within the BMTI-HDL-C network . As already mentioned , ABCG1 was strongly positively associated to HDL-C levels , while we found that isoleucine was significantly negatively associated to the concentration of HDL-C ( β = −4 . 30 , p-value = 5 . 80 × 10−19 ) . Moreover , gamma-glutamyl variants of BCAAs belonging to “gamma-glutamyl metabolism” ( β = −4 . 84 , p-value = 3 . 15 × 10−14 ) and “Valine , leucine and isoleucine metabolism” ( β = −4 . 66 , p-value = 9 . 17 × 10−11 ) displayed profound negative associations to HDL-C ( Fig 6D and S10 Table ) , further validating a connection between HDL-C and BCAA metabolism . For triglycerides , we observed an inverse relationship with BCAAs and BCAA-related pathways ( Fig 6B , S9 and S10 Tables ) .
We constructed a global network model across two levels of biological information by integrating cross-sectional omics data from a large-scale population cohort . The dataset was based on circulating metabolites from plasma and transcriptional variation derived from whole blood . This analysis exploited the naturally-occurring variation caused by genetic variation , environmental and behavioral influences from a natural population over multiple layers of organization . Such an approach was recently referred to as ‘systems genetics’ , enabling the systematic exploration of information flow between the different biological scales [32] . As mentioned in the introduction , blood is a heterogeneous tissue containing a series of distinct cell-types . In this study , we utilized whole blood transcriptomics data from unsorted cells , leading to a complex mixture of transcriptional signals in the transcriptome dataset [36] . Similarly , the levels of circulating metabolites are strongly influenced by metabolically active organs [31] , but also by metabolites from blood cells and those taken up from the environment . The comparison to known cell-type specific markers further suggested that a substantial amount of the signals are derived from specific blood cells . However , the analysis also showed that the majority of the BMTI contained transcripts are not assigned to any cell-type . Thus , we assume that the metabolite-mRNA associations captured in the BMTI mainly reflect cell-type unspecific processes involved in the fundamental maintenance of cellular function , besides some processes specifically related to immune functions . Independent replication of the BMTI edges was investigated using data from the DILGOM study . Out of 211 possible associations , we were able to replicate 29% at a nominal significance and 18% after multiple testing correction ( FDR<0 . 05 ) . This relatively low number of replicated associations might have various reasons . For example , 1 ) The DILGOM study used an NMR-based metabolomics platform in contrast to the mass spectrometry-based methodology used in KORA . 2 ) The smaller sample size of the DILGOM study might limit the power to detect existing associations between metabolites and transcripts . 3 ) Differences in laboratory procedures and protocols or the population structure can affect replication across cohorts . Future high-powered studies with more similar measurement platforms can further address the stability of metabolite-transcript correlations across studies . A comprehensive analysis of the strongest associations between transcripts and metabolites clearly revealed biologically reasonable relationships , such as signaling and transport mechanisms . Many identified associations , e . g . between cortisol and DDIT4 or between SLC25A20 and multiple long chain fatty acids , were in consent with known signaling or metabolic pathways . Others support and extend results from previous studies . As one example , nearly all transcripts included in the lipid-leukocyte ( LL ) module identified by Inouye et al [29] were among the top scoring association pairs . For instance , we were able to confirm associations between CP3A , FCER1A , GATA2 , HDC , MS4A2 , and SLC45A3 , core genes of the LL module , and leucine , isoleucine , and several lipids ( see S1 Table ) . In addition , we found associations which , to the best of our knowledge , have not been described before . These include associations between 1-monoolein and GATA2 , a key regulator of hematopoiesis , or SLC45A3 , a known diagnostic marker for prostate cancer [77] . The identified associations extend the current knowledge about the connection between system-wide metabolism and immunity-related pathways . Causal inference of the metabolite-mRNA associations using Mendelian randomization yielded no statistically significant results . There are various possible reasons for this negative outcome . First , there might be no causal effect in either direction between the investigated transcripts and metabolites . Besides that , the lack of significant findings could also be caused by the limitations of Mendelian randomization . For instance , MR is known to require large numbers of samples to detect true causal relationships , and the power in our study ( n = 712 ) might have been too low [58] . We therefore decided to leave a more detailed discussion and analysis of causal effects to future , high-powered studies . Comparison of the blood metabolome-transcriptome interface with the most recent human genome-scale metabolic reconstruction [33] allowed to assess whether transcript-metabolite correlations also recapitulate known biochemical reactions at a systematic level . We were able to show that strong associations between enzymes ( represented by their respective transcripts ) and metabolites are significantly accumulated at shorter pathway distances ( Fig 3B and 3C ) , which is consistent with previous studies [60 , 78 , 79] . Further functional characterization identified transport , energy , lipid and amino acid subsystems to be predominately present at short pathway distances ( Fig 3D and S5 Fig ) . This observation may reflect metabolic proximity through the uptake of metabolic nutrients by metabolically active blood cells . For instance , in our analysis we found signatures for all three major sources for energy production: lipids , proteins ( in terms of amino acids ) and carbohydrates indicating an active use of fuel molecules for energy generation by the blood cells . Our model-based analysis has several limitations . Obviously , any such analysis is heavily dependent on the quality of the underlying metabolic reconstructions , which are still far from being complete [80] . This incompleteness , together with a prevalent inconsistent nomenclature of metabolites allowed us to map only 121 out of 254 measured metabolites onto the metabolic network model . Another limitation is the incomplete coverage of the metabolome , which is owed to the capabilities of currently available technologies . In this study we used measurements of 440 metabolites , which corresponds to just ~10% of the estimated human serum metabolome [81] . Nevertheless , we believe that despite incomplete pathway mappings , our observations further indicate that combined metabolomics and transcriptomics data from human blood reflect reaction signatures of system-wide biological processes . To further functionally characterize the blood metabolome-transcriptome interface at a global level , we developed a network approach based on functional annotations . To this end , we aggregated z-score transformed measurements of metabolites and transcripts into their corresponding metabolic pathways and gene ontology categories , respectively . This approach allowed us to calculate correlation values between different functional categories , rather than between single metabolites and transcripts only . From these associations , we generated a pathway interaction network ( PIN ) of associated metabolic pathways and Gene Ontology terms , substantially reducing the complexity of the original network and thus facilitating functional interpretations . Detailed inspection of the PIN revealed that correlating nodes resembled not only signatures of well-known biological processes , like the carnitine shuttle , but also suggested novel interactions such as a crosstalk between monoacylglycerols and immune system processes . Taken together , the pathway interaction network enabled us to verify and elevate observations from the single reaction level ( see model-based analysis ) onto a pathway level . Moreover , we are now able to explore associations between biological processes/pathways across different biological scales including those that are not necessarily covered by metabolism , such as signaling or transcriptional processes . Given the high interconnectivity of the BMTI and the PIN , we asked whether these associations contain information about regulatory interactions across the different metabolite classes and pathways . Enrichment analysis of transcription factor binding sites in the promoter regions of the genes contained in our network identified regulatory signatures for transcripts associated to the same metabolite , which are additionally largely shared between metabolites belonging to different metabolic pathways ( Fig 5 , S7 and S8 Figs ) . There is a series of possible explanations for this observation . On the one hand , our findings could indicate that single metabolites/transcripts are fulfilling multiple roles , thus sharing several biochemical pathways . On the other hand , it might reflect regulatory interactions operationally linking different metabolic pathways . In depth investigation of 12 transcription factors included in the BMTI additionally revealed two “triad” network motifs between transcription factors BCL6 and NR4A2 , their target genes and the metabolites methionine and kynurenine , respectively . Remarkably , in a study conducted on mice fed a methionine and choline deficient diet , a significant reduction in the expression of BCL6 was observed [82] . It is widely known that metabolites can act as intermediates in cellular signaling , thereby also regulating gene expression , and together with our findings we suggest that characteristics of metabolic regulation are captured in the BMTI . However , from a correlation network , the detection of an association between a metabolite and a transcript does not necessarily imply a regulatory relationship nor can a conclusion be drawn about the directionality of the relationship . Yet , a combined analysis might offer the opportunity to identify novel molecular mechanisms behind cellular regulation that need to be validated further by experimental evidence . Besides transcriptional regulation mediated by TFs , a substantial fraction of transcripts are expected to be regulated by epigenetic processes [83] . Comparing 1 , 350 reported methylation-metabolite associations from a recent epigenome-wide association study [31] with our results surprisingly revealed only a single overlapping hit: X-03094 and the MAN2A2 transcript correlated in our study and also displayed a comparable methylation-metabolite association in the EWAS study . This sparse overlap could be explained by a phenomenon termed “phenotypic buffering” [32] , where effects in one organizational layer ( e . g . epigenetics ) are not detectable anymore on the next layer ( e . g . transcriptomics ) . A detailed explanation of this observation is beyond the scope of the present paper and needs further investigation . Further following the scheme of a systems genetics approach , we integrated the two identified networks with intermediate clinical trait data . To this end , we investigated the relationships between changing levels of HDL-C , LDL-C and TG and all measured metabolites and transcripts , metabolic pathways and GO terms ( Fig 6 ) . A similar study already described an association between a gene-module derived from whole blood transcriptomics data and circulating lipid parameters including apolipoprotein B ( APOB ) , HDL-C and triglycerides ( TG ) from a Finnish population cohort [29] . Our systematic analysis identified a large number of metabolites , transcripts , metabolic pathways , and functional GO categories that are all associated with the levels of circulating lipids . These findings further strengthen the assumption of a close link between system-wide metabolism , reflected by circulating metabolites and clinical lipid markers , and intracellular gene regulatory processes of blood cells . In addition , an opposite pattern between HDL-C and TG associations ( Fig 6A–6F ) was observed from the phenotype networks which supports a previously suggested antagonistic regulation of both clinical traits [75 , 84] . However , the precise molecular mechanism behind this regulation is not entirely known , and our results might provide a basis for future studies to gain novel insights into the regulatory mechanisms of intermediate physiological phenotypes . Combining results from all analysis steps allows for novel hypothesis generation . For example , for the well-known interactions between HDL-C , TG and BCAAs [57 , 76] , we discovered a potential regulatory pattern on different biological scales . In our first analysis step , we identified a strong negative association between the branched-chain amino acid isoleucine and ABCG1 , a major constituent of lipid homeostasis and cholesterol metabolism [55 , 85] . Second , at a more global level , the phenotype networks revealed an inverse association between HDL-C and TG , and also between HDL-C , TG and BCAAs ( BCAAs are positively associated to TG , negatively to HDL-C , see S9 Table ) . Third , in the TFBS enrichment analysis we were able to identify a clear regulatory signature of SREBPs in the vicinity of BCAAs , which are known to regulate cholesterol metabolism , indicating a potential coregulation between BCAAs and cholesterol metabolism at the transcriptional level . Interestingly , a combined study using cultured hepatocytes in a branched-chain amino acid-rich medium and obese mice showed that BCAAs directly induce the expression of SREBP1C which leads to hypertriglyceridemia , further supporting the suggested regulatory cross-link between HDL-C , TG and BCAAs [76] . This link is of particular interest since all three molecular traits have been associated to various diseases such as coronary artery disease , obesity and diabetes type II [86–88] and our observations might contribute to further decipher their underlying mechanisms . In summary , our study highlights the potential of a systems genetics approach for understanding interactions across multiple biological scales – in this case circulating metabolites and blood cellular gene expression—and how those insights can be used to generate novel hypothesis on mechanisms underlying common diseases .
The Cooperative Health Research in the Region of Augsburg ( KORA ) study is a series of independent population-based epidemiological surveys and follow-up studies of participants living in the region of Augsburg , southern Germany [89 , 90] . In this paper , cross-sectional data from 712 participants of the KORA F4 population cohort was used for whom metabolite concentration , gene expression data and genotyping information were available . This subpopulation contains combined fasting serum metabolomics and whole blood transcriptomics measurements of 354 males and 358 females aged 62–77 years ( mean 68 . 82 ± 4 . 31 ) . All participants are residents of German nationality identified through the registration office and written informed consent was obtained from each participant . The study was approved by the local ethics committee ( Bayerische Landesärztekammer ) . Detailed descriptions of blood sample acquisition and experimental procedures for the metabolomics and transcriptomics data , and clinical trait measurements can be found in [59 , 91–93] . Briefly , metabolic profiling was performed by Metabolon , Inc . using ultrahigh-performance liquid-phase chromatography and gas-chromatography separation , coupled with tandem mass spectrometry . In total , 517 serum metabolites were measured , thereof 293 with known chemical identity and 224 unidentified metabolites ( “unknowns” ) . All identified metabolites were assigned to one out of eight superpathways and one out of 61 subpathways by Metabolon , Inc . , representing two different levels in the metabolic pathway classification hierarchy ( see S5 Table for a full list of annotations ) . Gene expression profiling was performed using total RNA extracts from whole blood samples on Illumina Human HT-12 v3 Expression BeadChips . Genotyping was carried out using the Affymetrix GeneChip array 6 . 0 . A detailed description of the experimental procedures and preprocessing of the genetic data can be found in [92] . Replication of the significant metabolite- mRNA associations identified in the KORA dataset was carried out with the Finish DILGOM cohort dataset which included whole blood NMR metabolomics data as well as transcriptomics data for 518 individuals . A detailed description of the sample acquisition as well as data preparation can be found in [13 , 29] . To ensure data quality , metabolites with more than 50% missing values were excluded , leaving 440 metabolites ( 254 knowns and 186 unknowns ) for further analysis . The remaining metabolite concentrations were log-transformed , since testing for normality indicated that for most cases the log-transformed concentrations were closer to a normal distribution than the untransformed values [23] . For gene expression arrays , quality control and imputation of missing values of the raw intensities was performed as described in [94] . Briefly , the initial preprocessing of the raw intensity data was done with GenomeStudio V2010 . 1 . Raw probe level data was then imported to R and further preprocessed by log transformation and quantile normalization using the ‘lumi’ package [95] from the Bioconductor open source software ( http://www . bioconductor . org ) . To account for technical variation , gene expression intensities data were adjusted for RNA amplification batch , RNA integrity number and sample storage time . Only probes with the annotation flag QC_COMMENT “good” as provided in the updated Illumina Human HT-12 v3 BeadChip annotation file were considered for analysis [94] . In addition , probes mapping to gonosomal chromosomes were removed . Out of 48 , 803 probes on the Illumina Human HT-12 v3 array , 24 , 818 passed these filtering criteria . The metabolite-transcript interface was constructed based on Spearman’s correlation coefficients between the concentrations of all possible metabolite-transcript pairs ( 24 , 818x440 ) across the individuals of the study cohort . Correlation calculation was performed separately for each variable pair , only considering samples without missing values for the metabolites . Statistical significance of correlations was determined at an FDR of 0 . 01 [96] , corresponding to an absolute correlation value of 0 . 1816 and an adjusted significance level of 1 . 07 × 10−06 . To get a unique network node per gene , redundant probes matching the same gene were removed . One representative probe per gene was chosen based on the maximum correlation strength to any metabolite , leaving 16 , 781 unique probes for subsequent analysis . It has to be noted that the applied significance level was still calculated on the whole dataset ( including multiple matching probes per gene ) to properly account for multiple testing . Network density was calculated as described in [35] . More precisely , for a stepwise increasing correlation threshold , the ratio between the total number of observed edges and all possible edges was calculated . Significant correlations between metabolites and transcripts were visualized as a bipartite graph using yEd graph editor ( yWorks GmbH , Tuebingen; http://www . yworks . com ) . BMTI genes were mapped to three published lists of tissue- and cell-specificity based on gene expression microarrays from purified cells or tissues . The first two marker gene lists were taken from Palmer et al . [36] , who defined markers for B-cells , CD4+ T-cells and CD8+ T-cells , lymphocytes and granulocytes , and from the HaemAtlas as generated by Watkins et al . [37] , who reported markers for CD19+ B-cells , CD4+ T-cells and CD8+ T-cells , CD14+ monocytes , CD56+ NK cells , CD66b+ granulocytes , erythroblasts and megakaryocytes . The third marker list was downloaded from the CTen website: http://www . influenza-x . org/~jshoemaker/cten/db_info . php and comprised markers for 84 different human tissues/cell types [38] . The three lists together with the analysis results are provided in S3 Table . Estimation of causal effects within the BMTI was performed using a Mendelian randomization ( MR ) approach [58] . A total of 224 candidate SNPs reported as lead association signals at genome-wide significance in two recent GWAS studies for 16 metabolites and 186 mRNAs ( BMTI contained ) were preselected as instrumental variables[23 , 59] . To ensure the validity of the instrumental variables , only candidate SNPs that showed a significant association with a trait ( metabolite or gene expression level ) at an FDR of 0 . 05 in our data were considered for further analysis ( 32 SNPs were removed ) . Associations between SNPs and traits were assessed using linear regressions with age and sex as covariates . To further avoid potential confounding , all candidate SNPs were checked for pairwise linkage disequilibrium using the SNiPA tool [97] . None of the remaining 192 SNPs were in LD . Based on the metabolite-mRNA edges in the BMTI , 550 SNP-metabolite ( Met ) -mRNA and SNP-mRNA-Met sets were defined , covering 44% of all edges contained in the BMTI . Causal relationships SNP→Met→mRNA and SNP→mRNA→Met were estimated , i . e . whether changes in the metabolite level cause changes in the transcript level and vice versa . Causal effects of both models were calculated using the Wald ratio method [98]: β^Met→mRNA=β^SNP→mRNAβ^SNP→Metandβ^mRNA→Met=β^SNP→Metβ^SNP→mRNA , where β^Met→mRNA and β^mRNA→Met are the causal effects , and β^SNP→mRNA and β^SNP→Met are regression coefficients of the respective mRNA or metabolite levels on SNPs , under a simple linear model with age and sex as adjustment variables . 95% Confidence intervals and p-values of the causal effects were calculated by sample bootstrapping with 10 , 000 repetitions . Q-values were calculated to control the false discovery rate ( FDR ) . Summary information for the utilized SNPs together with detailed results of the MR approach can be found in S4 Table . Metabolic reactions were extracted from the consensus metabolic reconstruction “Recon 2” , v . 02 available at http://humanmetabolism . org as of October 2013 [33] . Compartmental information was removed by merging shared nodes and reactions between different compartments . To avoid potential biologically meaningless shortcuts between network nodes , co-factors and currency metabolites were excluded from the metabolic network prior to the distance calculation ( see S5 Table for a full list of removed metabolites ) . Measured metabolites and transcripts were mapped onto the corresponding network nodes based on KEGG IDs or HMDB identifier for metabolites , and Entrez Gene IDs for transcripts . Distances between all mapped pairs of metabolites and transcripts were defined as the shortest path in the network , i . e . the minimal number of reaction steps between them . For instance , a distance of zero between a transcript and metabolite indicates that the metabolite is a direct reactant of the reaction catalyzed by the particular enzyme encoded by the transcript . A distance of one indicates that the enzyme-encoding transcript catalyzes a directly connected reaction , which takes a product of the particular metabolite as input , and so on . A distance of infinity ( Inf ) was assigned if the respective metabolite and transcript were disconnected in the pathway network . Moreover , a “not mapped” ( NM ) distance was assigned if either the metabolite or the transcript could not be mapped to Recon 2 . Note that the network was treated as undirected , i . e . all reaction directions were ignored . Functional annotations were retrieved from two different sources . For transcripts , the generic GO Slim catalogue was downloaded from Gene Ontology ( GO , http://www . geneontology . org/GO . slims . shtml ) . Generic GO Slim is a broad and non-redundant subset of all Gene Ontology terms consisting of 148 unique terms covering all three GO domains ( cellular component , molecular function and biological process; [99] ) . The three root terms cellular component , molecular function and biological process and terms with no annotations for any of the 16 , 781 transcripts were removed , resulting in 140 terms for further analysis . For metabolites , the subpathway annotations were used ( see above ) . Metabolic pathways ( MP ) with less than two metabolites were excluded from the analysis , leaving 48 metabolic pathways . To aggregate the components belonging to a specific annotation term and to derive a score for each of these functional categories , the average of the associated z-score normalized gene expression profiles or metabolite concentrations was calculated according to aggZCj=1|C|∑i∈CZi , j where C corresponds to a metabolic pathway or GO term , i enumerates all members in this set , and Zi , j is the z-score of the gene/metabolite with index i in sample j . Spearman’s rank cross-correlation between the aggZ-Scores of all possible GO-MP combinations was then calculated ( note that Pearson correlation yielded similar results , see S11 Fig ) . Since it is known that many biological processes include distinct branches often fulfilling complementary tasks controlled by mutual regulation , a consideration of all pathway members simultaneously could obscure the calculation of the aggZ-Score . A similar problem might occur due to the generic property of the GO-terms or metabolite classes used here , often including functionally rather distinct molecules . To account for this , only those members of the two categories were considered for z-score calculations which share at least one mutual edge within the reconstructed network for the respective GO-MP combination ( see S6 Fig for more details ) . Finally , significant associations between the functional annotation pairs were visualized as a bipartite pathway interaction network ( PIN ) . Linear regression analysis was performed with age and sex as covariates: y=β0+β1*x+β2*age+β3*sex+ϵ where y is the concentration of HDL , LDL or TG over all individuals , β0 is the intercept , β1–3 are regression coefficients , x is a vector of expression/concentration values of a particular gene/metabolite and ϵ is a normally distributed error term . In the same way , the association of annotations ( GO and MP ) was tested with all three phenotypic traits using the aggZ-Score for the particular annotation of x . Note that for this analysis , aggZ-Scores were calculated only on those members of a particular annotation that are also contained in the BMTI . Each network node was then color-coded with the −log10 ( p-value ) × sign ( β1 ) , where the p-value and β1 were derived from the linear regression with the respective metabolite , gene or annotation category . To assess statistical significance of the determined associations , a Bonferroni-corrected threshold of 0 . 05/ ( 16 , 780 × 440 ) ≈ 2 . 9 × 10−6 was applied . To investigate regulatory signatures in the BMTI , an enrichment analysis of transcription factor binding sites was performed . Sets of input sequences were created from the neighbors of each metabolite with a degree ≥ 3 ( at least 3 connected genes ) . Analogously , the pathway interaction network was used to construct sequence sets based on the neighborhood of a metabolic pathway node . For each set of input sequences , a separate search for overrepresented TFBS was performed with the sequences of all remaining genes as background model . Promoter regions ( -2 , 000 bp to +200 bp relative to the TSS ) and TSS positions of all genes were extracted from the UCSC database using the R package BSgenome . Hsapiens . UCSC . hg19 version 1 . 3 . 1 . Position-specific weight matrices of the transcription factor binding motifs were taken from the vertebrate collection of the Jaspar database version 5 . 0 alpha [68] . Enrichment analysis was performed with the TFM-Explorer command line tool [100] . The p-value threshold to determine significance of the motifs in all input sets was set to 1 . 0 × 10−7 which lies in the recommended optimal range given the numbers of input sequences we used in this study ( mean number of input sequences: 62 ) [101] . The authors showed that for a fixed false positive rate of 10% , the optimal p-value threshold was 1 . 0 × 10−7 for a dataset of 100 input sequences . | Biological systems operate on multiple , intertwined organizational layers that can nowadays be accesses by high-throughput measurement methods , the so-called ‘omics’ technologies . A major aim in the field of systems biology is to understand the flow of biological information between the different layers at a systems level in both health and disease . To unravel the complex mechanisms underlying those molecular processes and to understand how the different functional levels interact with each other , an integrated analysis of multiple layers , i . e . a ‘multi-omics‘ approach is required . In our present study , we investigate the relationship between circulating metabolites in serum and whole-blood gene expression measured in the blood of individuals from a population-based cohort . To this end , we constructed a correlation network that displays which transcript and metabolite show the same trend of up- and down-regulation . We derived a functional characterization of the network by developing a novel computational analysis . The analysis revealed systematic signatures of signaling , transport and metabolic processes on both a regulatory and a pathway level . Moreover , integrating the network with associations to clinical markers such as HDL-cholesterol , LDL-cholesterol and TG identified coordinately activated pathways or modules which might help to assess the molecular machinery behind such an intermediate phenotype . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | The Human Blood Metabolome-Transcriptome Interface |
Speculative statements communicating experimental findings are frequently found in scientific articles , and their purpose is to provide an impetus for further investigations into the given topic . Automated recognition of speculative statements in scientific text has gained interest in recent years as systematic analysis of such statements could transform speculative thoughts into testable hypotheses . We describe here a pattern matching approach for the detection of speculative statements in scientific text that uses a dictionary of speculative patterns to classify sentences as hypothetical . To demonstrate the practical utility of our approach , we applied it to the domain of Alzheimer's disease and showed that our automated approach captures a wide spectrum of scientific speculations on Alzheimer's disease . Subsequent exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches , and can thus provide added value to ongoing research activities .
Biomedical information published in the scientific articles can be grossly divided into “established knowledge” and “emerging knowledge” . Established knowledge , supported by facts and repeated , independent validation of experimental findings , is widely accepted in the scientific community; this knowledge is extensively used to understand various aspects of a biological phenomenon . In contrast , emerging knowledge refers to new and less “solid” knowledge , which represents novel findings or new thoughts . Although we routinely make extensive use of established knowledge , most of us do not utilize emerging information in a systematic fashion . Systematic analysis of emerging information could , however , help to transform speculative thoughts into testable hypotheses . In the scientific literature , particularly in articles of experimental nature , speculative statements ( hedges ) are frequently found [1] because expressing hypothesis based on experimental results is an important part of this sort of publications [2] . Particularly , speculative statements characterize hypotheses when linked to molecular entities ( genes/proteins ) and backed up by experimental evidence . Such statements are often given in hopes of stimulating further research into a topic . As an example , the following speculative sentence may represent a potential hypothesis: The recognition of such speculative statements in scientific text has gained interest in the recent years [3]–[6] . Several groups have investigated the use of pattern matching [3] , [6] or machine learning ( ML ) approaches [5] , [7] , [8] , [9] to build models for recognition of speculation in text . Both of these approaches had been shown to perform competitively when it comes to detecting speculative statements in scientific text [3]–[9] . However , there are scenarios where pattern matching approaches perform better than ML ( e . g . SVM-based bag of words feature ) approaches [3] , [6] . Additionally , machine learning techniques require comprehensive training data sets and thorough optimization . Taking into consideration the successful strategies applied in previously conducted studies , this work reports the development of a pattern matching approach for the automatic detection of speculative patterns in text ( named “HypothesisFinder” ) that captures most of the existing hypothetical knowledge , independent of the indication area . In order to demonstrate the applicability of our approach , we apply HypothesisFinder to the domain of Alzheimer's disease ( AD ) to demonstrate its usability and to assess its performance . Particularly in case of complex and mostly idiopathic diseases like AD where the etiology of the disease is still unclear , neuroscientists are frequently introducing working hypotheses or speculations in the following form: or A systematic analysis of all speculative statements on a particular topic such as molecular etiology of AD , as shown in these examples , should enable us to capture the diversity of hypotheses that exist within the literature . . Motivated by the same idea , AlzSWAN [10] , one of the most comprehensive community-driven knowledge bases on AD-related information , has produced a special section called ‘SWAN Hypotheses Browser’ where manually curated hypotheses inferred from the scientific literature are made available to the user community . AlzSWAN is one of the most dynamic and most relevant scientific resources representing hypothetical knowledge on AD . However , the rapid growth of publications on AD poses a challenge to curators [11] as manual information extraction is time and resource consuming . Thus , the continued growth of such knowledge bases is confined to the pace of manual curation . The aim of the work presented here was to build an environment with the ability to cross-link speculative statements and clinical/biomedical features . The core of the developed workflow encompasses hand-crafted knowledge-based textual patterns for detecting speculative statements in biomedical free text . Two different methods were taken for the detection of speculative statements: a pattern-based approach and a machine learning ( ML ) -based approach . The performance of both methods was systematically compared against each other . Furthermore , the ability of HypothesisFinder to identify speculative statements in scientific text was evaluated using a dedicated corpus ( Bioscope corpus [12] ) and its ability to detect statements supporting AD-related hypotheses was benchmarked against the human-curated AlzSWAN knowledge base .
In order to assemble a corpus suitable for pattern development , all fields of Medline articles were searched with a text query ‘Alzheimer disease AND hypothesis’ using the PubMed search engine that retrieved 3007 abstracts as on 26-7-2012 . From this initial collection of retrieved abstracts , a preliminary corpus containing 150 randomly chosen abstracts was generated ( referred to as HYPO-DEV-1 ) . All abstracts in HYPO-DEV-1 were annotated with one class named Speculative pattern ( see next section for annotation examples ) . This corpus was manually annotated for the presence of speculative text patterns that characterize scientific hypotheses . In parallel , annotation guidelines for the annotation of speculative statements were developed . First , an annotator ( referred to as the principal annotator ) participated in the annotation and guideline development process . The corpus was iteratively annotated by him along with the standardization of the annotation rules . Textual patterns annotated in the HYPO-DEV-1 were collected to form an initial version of a dictionary containing speculative patterns . In the next step , a secondary corpus containing 200 randomly chosen , but non-overlapping , abstracts were generated ( referred to as HYPO-DEV- 2 ) . HYPO-DEV- 2 was annotated by two different annotators ( referred to as the junior annotators ) . These annotators individually annotated HYPO-DEV-2 corpus based on the previously developed annotation guidelines and their annotations were used to calculate the inter-annotator agreement ( IAA ) . The IAA determines the quality and acceptability of the notion of ‘hypothesis or speculation’ among annotators and provides a rationale for measuring the quality of prior developed annotation guidelines . Novel patterns annotated in the HYPO-DEV-2 were used to enrich the initial version of the dictionary ( i . e . based on patterns collected from the HYPO-DEV- 1 corpus ) . The IAA kappa between the two junior annotators was calculated as high as 0 . 81 , which indicates an acceptable agreement , given the complicated nature of the annotated patterns . Similar to the developmental corpus , a test corpus ( referred to as HYPO-TEST ) was generated by searching PubMed using the keyword ‘Alzheimer’ and then randomly selecting 200 abstracts from the 58922 retrieved abstracts ( as of 26-7-2012 ) . Two new ‘independent annotators’ used the previously developed annotation guidelines for annotating the HYPO-TEST corpus . In case of any possible conflicts occurring between the annotators concerning ‘non-overlapping patterns’ , the principle annotator was involved in resolving them . The following guidelines were applied for resolving the conflicting annotations between the independent annotators . The same workflow as mentioned above was used in annotating the “Remote corpus” mentioned later in the manuscript . HYPO-TEST corpus serves as our gold standard for the performance evaluation of different hypothesis detection approaches ( pattern matching and ML based ) mentioned in the manuscript . To address the concern of data bias that might result from our search and corpus assembly strategy , we also evaluated the performance of our model for speculation detection on Bioscope , an independent and expert-curated corpus . This corpus consists of medical and biological text annotated by experts for speculation and their linguistic scope . The corpus is a good resource for comparison and independent assessment of Natural Language Processing ( NLP ) systems [12]; hence , we used this corpus as a surrogate system to independently assess performance of our model ( mentioned under result section ) . An overview of the methodology described in this section is provided in ( Figure 1 ) . Words and phrases such as ‘may be involved’ , ‘might regulate’ , ‘seems to play’ , and ‘it appears as’ typically render statements as speculative , and are thus called speculation keywords or speculative patterns . These words are strong indicators of speculation in text . Particularly in scientific abstracts , whose purpose is to summarize the scientific knowledge presented in full text articles , these speculative patterns act as a linguistic marker that guides readers in detecting proposed hypotheses within text . In this work , annotations were performed in a way to capture not only speculative patterns ( typically a phrase or a span of text ) but also hypothetical sentences ( sentences that also indicate a scientific hypothesis ) . Based on our experiences obtained from the annotation process , speculative patterns can be further classified as strong , moderate and weak patterns . Further to back up this claim , we parsed a new Alzheimer's related corpus of 58 , 922 abstracts into 707 , 946 sentences . Previously identified speculative patterns were searched for their presence in this ‘sentence corpus’ and a sentence count ( i . e . total number of sentences with specific speculative pattern ) was obtained for each pattern . Additionally , two independent curators manually curated the retrieved sentences ( sentence count up to 50 ) for each specific pattern , hence calculating the ‘percent efficacy’ of each pattern to indicate a sentence as speculative . In most cases when the sentence count exceeded more then 50 , we randomly chose 50 sentences for our analysis . An example of utilizing a strong , moderate , and weak pattern categorization is shown in ( Figure 2 ) . Based on the results , we concluded that phrases containing modal verbs such as ‘may’ , ’might’ , or ‘could’ linguistically represent class of strong speculative identifier . Other words such as ‘potential’ , ‘possibility’ , ‘should ‘ , ‘would’ , and similar ones are not necessarily used in speculative context and may produce false positives . For instance , consider the following sentences: To overcome this problem and to increase the accuracy of our model , we combined weak speculative patterns either with additional speculative words or with additional auxiliary verbs or adjectives before including them in the final dictionary . Revision of annotated patterns after introducing these changes to the dictionary showed improved performance and hence increased the quality of annotations as exemplified by following sentences: In both of these cases , combining ‘potential’ and ‘possibility’ with additional speculative patterns such as ‘could be a’ and ‘raising the … that’ respectively turns these weak indicators to linguistically stronger identifiers of speculation , resulting in less likelihood of producing false annotations . Furthermore , to decrease false annotations and to better define what a speculative sentence is , annotation guidelines were also enriched with examples of patterns and sentences ( Negative controls ) that can conflict with speculative patterns and sentences . The followings are some examples of sentences expressing inferences , results and conclusions , arguments and open questions , which cannot be considered speculative . ‘Inferences’ ‘Results and conclusions’ ‘Argumentative sentences’ ‘Open questions’ Following the recommended annotation guidelines , abstracts in HYPO-DEV- 1 , HYPO-DEV- 2 , and HYPO-TEST datasets were annotated for speculative patterns and hypothetical sentences in text . Speculative patterns appearing in HYPO-DEV- 1 and HYPO-DEV- 2 were extracted to generate a dictionary . Synonymous patterns were manually detected and grouped together to represent constitutive patterns ( see the next section for details ) . Synonymous patterns denote different permutations or combinations of a speculative pattern that can occur within text . For example , the representative speculation pattern ‘appear to’ could have the following synonymous patterns: appeared to be|appears to be|appear that|appears that|appeared that |appearing to|appears to|appears to play|appear related|appeared related |appears related . The dictionary used for recognition of speculative patterns in text comprises 156 representative and 392 synonymous patterns ( Dataset S1 ) . Efforts have been dedicated to cover all possible variations of speculative patterns appearing in developmental corpora . For automated identification of patterns within the HYPO-TEST corpus , ProMiner [13] , a dictionary-based named entity recognition ( NER ) system , was used . In addition to dictionary-based NER , ProMiner also applies pre-defined rules and its search algorithm is geared towards handling the recognition of ambiguous multi-word terms in text . Previous scenarios are present where ProMiner has been used to perform NER by pattern matching or regular expressions in text [14]–[16] . The ProMiner search was performed using case-insensitive , word order-sensitive and the longest string exact match search constraints . To study real-use case scenarios , ProMiner along with the speculative pattern dictionary was applied to the complete MEDLINE abstracts for the identification of speculative patterns . The recognized patterns were indexed and visualized using SCAIView , a scalable indexing and retrieval platform that has exhibited successful information retrieval scenarios from MEDLINE [14] , patents [15] and e-health records [16] . SCAIView supports document retrieval as well as entity extraction . Speculative statements are indexed and made searchable within SCAIView and can be searched in combination with other biomedical terminologies and ontologies . Thus , the hypothetical space related to a particular question of interest can easily be retrieved using SCAIView . An example of such search scenarios using SCAIView is shown in ( Figure 3 ) . Moreover , ‘HypothesisFinder’ has been integrated into SCAIView and is freely available for usage and testing at http://www . scaiview . com/scaiview-academia . html The application of ML-based approaches for sentence classification has demonstrated considerable success in the past [17] . To test whether dictionary-based or ML-based approach performs best for the identification of speculative statements in scientific text , we did a comparative assessment of an established ML-based approach against the pattern-based approach described above . First , the HYPO-DEV-1 and HYPO-DEV-2 datasets were segmented into sentences . As required for ML training , sentences that represented hypotheses , i . e . contained speculative pattern annotations , were labeled as “POSITIVE” whereas those that did not were labeled as “NEGATIVE” . The overall training set contains 483 sentences labeled as POSITIVE and 2049 sentences labeled as NEGATIVE . Sentences present in HYPO-TEST corpus formed an independent test set over which the performance of the trained model was validated . Similar to the training data , POSITIVE and NEGATIVE instances were generated for the test set , resulting in 246 sentences labeled as POSITIVE and 1194 sentences labeled as NEGATIVE . A ML-based sentence classifier developed by Gurulingappa et al [16] was applied to train a sentence classification model ( i . e . over HYPO-DEV-1 and HYPO-DEV-2 sentences ) . The sentence classifier facilitates switching between different classification algorithms that include Naïve Bayes ( NB ) , Nearest Neighour ( NN ) , Decision Trees ( DT ) , Maximum Entropy ( MaxEnt ) , and Support Vector Machines ( SVM ) . The performance of sentence classification system was tested under three conditions using baseline features , speculative features , and lexico-syntactic features . Baseline features use all words appearing in sentences as features for classification . Speculative features were formed by occurrences of hand-crafted patterns ( mentioned in Section Annotation of speculative patterns ) that are potential indicators of hypotheses and Lexico-syntactic features were formed by the following: During training and testing the model with lexico-syntactic features , the above-mentioned textual features were extracted from HYPO-DEV-1 , HYPO-DEV-2 , and HYPO-TEST sentences .
The performance of HypothesisFinder was first evaluated on the HYPO-TEST corpus addressing the following two aspects: Evaluation metrics used for speculative pattern recognition and hypothetical sentence classification were Precision , Recall and F-score . The following formulas were used for the computation of Precision , Recall and F-score values [18] . where true positives are the number of entities/sentences that were annotated by ProMiner and further matches with the human annotated entities/sentences that serves here as our gold standard . False positives are the number of entities/sentences that were recognized by ProMiner , but were not matched to annotations in the gold standard . False negatives are the number of entities/sentences that were not found by ProMiner when compared with the gold standard annotations . The results of the evaluation are listed in ( Table 1 ) . For ‘Pattern recognition’ , HypothesisFinder achieved a precision of 0 . 84 and a recall of 0 . 86 in comparison to the manual annotation in the HYPO-TEST corpus . The major reason accountable for a 14% loss in coverage seems to be deletion of very weak speculative patterns in their original form to avoid chances of false annotations , as shown with the examples in “Annotation of speculative patterns” section . The performance of the maximum-entropy based sentence classification model was evaluated on a corpus composed of HYPO-TEST sentences . Precision , Recall and F-score metrics used for sentence classification over the class ‘POSITIVE’ . Initially , experiments were performed by switching between different classification algorithms provided within the sentence classification system . Among the different ML classifiers tested , the Maximum entropy classifier ( MaxEnt ) provided the best performance whose results are considered here . Comparison of the performance of ProMiner and MaxEnt classifier in their ability to classify sentences in HYPO-TEST as hypothetical is shown in Table 1 . The MaxEnt classifier was applied for sentence classification using baseline features , speculative features , lexicosyntactic features , and their combinations . The MaxEnt classifier achieved the F-score of 0 . 50 and 0 . 62 when using simple words or lexicosyntactic features , respectively . Adding the speculative features boosted the ML classification performance with the F-score of 0 . 88 . This indicates the value of hand-derived speculative patterns in assisting the development of a robust machine-learning model . Classification using speculative features alone was not possible since these features do not appear in all sentences whereas the ProMiner-based classification resulted in the F-score of 0 . 92 . The sentence classifier used here was applied with default input parameters ( as defined by Gurulingappa et al ) . Since the goal of this work was to evaluate the adaptability of pattern-based approach to hypothetical statement detection , no extensive optimization of features for the sentence classifier was performed . Nevertheless , the observable difference in performance of pattern-based approach as compared to ML-based approach drives the interest in applying ‘easy-to-mold’ patterns for identifying hypothetical statements particularly in scientific abstracts where scientific knowledge presented in articles is summarized . Evaluation of our model on Bioscope corpus ( Table 2 ) in comparison to HYPO-TEST corpus show a relatively lower recall ( 0 . 73 ) while a comparably high precision ( 0 . 91 ) is maintained . A possible reason for the recall decrease is the lack of “weak patterns” ( i . e . general terms such as ‘should’ , ‘would’ , ‘potential’ ) in HypothesisFinder's dictionary; these weak patterns are marked as speculative within the Bioscope corpus . Such weak patterns were integrated into the dictionary only in combination with other terms ( e . g . ‘would likely’ , ‘should possibly’ , ‘could be potential’ ) so that their viability for identifying speculative expressions compared to their basic form is improved . Final assessment of our model on Bioscope corpus indicated a good performance with the F-score of 0 . 81 , further confirming the ability of our methodology to detect speculations in text without corpus bias . Performance of HypothesisFinder was also checked on a so-called ‘Remote corpus’ comprising AlzSWAN , Parkinson and Epilepsy corpus . AlzSWAN corpus comprises 143 abstracts presented as primary reference of hypothetical statements quoted in the AlzSWAN knowledge base . Again for calculation of evaluation metrics , each of the sub-corpora was manually annotated for speculative statements based on the previously defined annotation guidelines ( mentioned under section corpus characteristics and annotations ) . The results of this evaluation , listed in Table 2 , show that HypothesisFinder was able to detect speculative statements with 90% accuracy and 97% sensitivity in AlzSWAN corpus . In order to show domain-independent application of HypothesisFinder , we also tested our model for speculation detection on a sub-corpus of randomly derived abstracts from PubMed related to Epilepsy and Parkinson's disease . Again , the high accuracy and sensitivity performance of HypothesisFinder ( Table 2 ) demonstrates that our approach can be extrapolated to any given topic of interest . Since our initial motivation for developing ‘HypothesisFinder’ was to establish an automated approach for capturing the breadth of hypotheses existing in the scientific literature around AD , we tested and benchmarked our automated approach against the expert-curated AD-specific hypothetical knowledge ( inferred from the literature-derived speculations ) in the AlzSWAN database . For this purpose , hypothetical statements were extracted automatically using HypothesisFinder and the results were compared to the AlzSWAN hypothesis browser . We sought to explore the power of HypothesisFinder in a semantically enhanced environment such as SCAIView by including a human gene dictionary and the Alzheimer's disease ontology [19] in the search . Through the combination of these dictionaries , a link between hypothetical statements and molecular ( e . g . proteins ) and clinical ( e . g . disease stage ) features of Alzheimer's disease can be established . Figure 4 ( A ) illustrates the comparison between Alzheimer's stage-specific hypotheses linked to their corresponding biological entity ( i . e . genes or proteins ) extracted by HypothesisFinder as integrated in SCAIView ( See Dataset S2 , S3 , and S4 ) and hypotheses with extended annotations ( i . e . linked to genes and proteins ) present in the AlzSWAN database . It is evident from our analysis that HypothesisFinder collects a large number of speculative statements , significantly more than the number of hypotheses listed in AlzSWAN . Even after curation of the hypotheses identified by HypothesisFinder , the count of normalized genes and proteins linked to these hypotheses ( Figure 4 ( B ) ) is higher than those in the AlzSWAN database . Additionally , we were also able to link extracted hypotheses to clinically established AD stages . In order to analyze the coverage of relevant genes and proteins linked to the hypotheses , we also compared the lists of genes and proteins mentioned in AlzSWAN with the list of entities retrieved by SCAIView . The comparison showed that all genes or proteins represented in AlzSWAN are actually a subset of the list generated by the HypothesisFinder approach , which indicates that HypothesisFinder is extending the hypothetical knowledge space not only at the level of speculative statements but also at the level of molecular entities being linked to disease hypotheses . Nonetheless , it should be noted that our automated workflow is not a replacement for expert-curated knowledge inference such as the one used by AlzSWAN but rather it is complementary to extensive manual curation efforts by domain experts . Therefore , systematic assembly of domain specific speculative knowledge present in Medline as well as full text documents using automated approaches will improve the knowledge aggregation and discovery process , leading to an accelerated growth of knowledge bases such as AlzSWAN . However , for making inferences over the gathered hypotheses , human intervention and expert curation is mandatory , as it is done in AlzSWAN . New scientific findings and discoveries that are expressed in the form of speculative statements represent the category of so-called ‘transient articles , ’ whose citations peak within a short period of time [20] . These articles usually contain emerging trends of a specific knowledge domain , which evolves over time . To show how the intellectual landscape of scientific hypotheses on AD has evolved , we mapped the chronological order of retrieved hypotheses linked to five genes that are frequently speculated in the literature to be involved in the disease . As is shown in ( Figure 5 ) , the AD knowledge domain is currently dominated by speculations around APP ( Amyloid beta ( A4 ) precursor protein ) and MAPT ( Microtubule- associated protein tau ) across all disease stages . Moreover , it is evident that the number of proposed hypotheses across all stages has been increasing since 2005 . In order to show the application value of HypothesisFinder in disease modeling , we used the BioNetBuilder plugin [21] in Cytoscape to build hypothetical protein interaction networks from the set of genes and proteins linked to disease stage-specific hypotheses . ( Figure 6 ) depicts the connected components of three networks representing the three stages in AD . Such stage-specific disease networks visualize the disease progression at the molecular level and can provide a framework for further integrative analyses; e . g . integration of stage-specific gene expression data into network models could help to analyze gene expression perturbations across the three stages of AD .
Hypothesis-encoding statements comprise a good part of a researcher's expectation about the relationship between two or more biological variables [22] . Discovery of new rules and causal relationships linking genotype to phenotype remains one of the main challenges in life sciences [23] . Accordingly , linking hypotheses to the established knowledge or background theory can strengthen the ability of hypothesis-driven data integration across different levels of biological systems . In order to automatically extract hypothetical information from the literature and link the genotypic features ( i . e . genes and proteins ) to the phenotypic features of AD ( i . e . disease stages ) , we started with two independent technological approaches ( pattern and machine learning based ) towards solving the problem of identifying speculative statements in scientific text . A comparative evaluation of both approaches showed that our pattern-based approach outperforms the machine learning-based approach in specific scenario since speculation is represented in text with the help of certain enumerable patterns . As shown in Figure 2 , the pattern matching approach can substantially improve the recognition of speculative statements particularly in scientific abstracts where authors present the core of their research in limited paragraphs . Hence , a speculative pattern , if found , ascertain a presented hypothesis . Nonetheless , authors do believe that there is a room for development of dedicated machine learning modules for hypothesis detection . However , the goal of the presented work was to evaluate the adaptability of pattern-based approach to hypothetical statement detection and not extensive optimization of features . Research into feature selection , instance selection , sequence-based learning , and the integration of pattern and machine-learning approaches will be thoroughly conducted in follow-up studies . The acceptable performance of our pattern-based approach in extracting hypothesis-encoding statements from free text indicates that automated information extraction could possibly reduce human reading and curation efforts for enrichment of knowledge bases , provided that the performance of such technology is comparable to the quality of human expert curation . Another advantage of automated harvesting of scientific hypotheses and speculative statements is that dedicated searches can be aimed at capturing the complete spectrum of speculative statements within a domain . As shown in the case of complex , mostly idiopathic diseases like AD , by formulating a series of reasonable speculations on causes and effects , we gained interesting insights into the disease's staging and progression at the molecular level . Each of the extracted AD hypotheses posits a specific relationship between involved genes/proteins and their corresponding disease phenotype . Such relationships can guide researchers in developing new experiments to test the proposition . Exploration of these hypotheses provides an overview on emerging knowledge niches , which have the potential to add value to ongoing research activities . Speculative patterns linked to molecular entities , when expressed in the abstracts , represent relevant hypothetical knowledge that can be systematically collected and used for modeling purposes as demonstrated above . The patterns used in this work to detect speculative statements in text are of a general nature , which extends the scope of their applicability beyond the AD domain , as shown in the Epilepsy and Parkinson's disease scenarios . We believe that the method developed in the course of this work will prove very useful for biomedical research . HypothesisFinder allows for the systematic collation and analysis of reported speculative findings in a specific context . This can have a tremendous consequence for health-related studies; for instance , it could be used to understand initial speculated mechanisms or modes of action that led to the success or failure of drugs . Systematic collection of hypotheses allows for rationalization of discussions about possible interpretations of data . Since speculations represent the gray zone of scientific knowledge , they can provide incremental support to the main hypothesis underlying the research . Conversely , if the speculations are contradictory then they could shift the direction of the research towards new and rewarding avenues . Captured hypothetical knowledge can be used to model diverse disease scenarios . Our use of this technology on the etiology of AD is driven by our interest in modeling neurodegenerative diseases at the systems level . In the future , we intend to build a service around HypothesisFinder that can systematically identify and rank new emerging hypotheses in different disease areas . We will also conduct additional research into distinguishing novel hypotheses from those already existing , and we will use this capability to set up automated alerts for notification of novel hypotheses . | Published speculations about possible molecular mechanisms underlying normal and diseased biological processes provide valuable input for the generation of new scientific hypotheses . However , a systematic gathering of all scientific speculation that exists in a given context is a non-trivial task and , if done manually , is laborious and time-consuming . The “HypothesisFinder” approach outlined here provides a possible solution for making scientific speculation gathering more tractable . Using a dictionary of speculative patterns , HypothesisFinder detects , collates and analyzes published speculative statements for a specific context . This can be extremely useful , particularly in reference to complex and poorly understood diseases like Alzheimer's disease . For example , by formulating a series of reasonable speculations on causes and effects , we could gain new insights into the directions of Alzheimer's disease etiology and progression . An effective literature search with the help of HypothesisFinder can support the process of knowledge discovery and hypothesis generation , which has the potential to add value to ongoing research activities . | [
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] | 2013 | ‘HypothesisFinder:’ A Strategy for the Detection of Speculative Statements in Scientific Text |
The niche conservatism hypothesis states that related species diverge in niche characteristics at lower rates than expected , given their lineage divergence . Here we analyze whether niche conservatism is a common pattern among vector species ( Hemiptera: Reduviidae: Triatominae ) of Trypanosoma cruzi that inhabit North and Central America , a highly heterogeneous landmass in terms of environmental gradients . Mitochondrial and nuclear loci were used in a multi-locus phylogenetic framework to reconstruct phylogenetic relationships among species and estimate time of divergence of selected clades to draw biogeographic inferences . Then , we estimated similarity between the ecological niche of sister species and tested the niche conservatism hypothesis using our best estimate of phylogeny . Triatoma is not monophyletic . A primary clade with all North and Central American ( NCA ) triatomine species from the genera Triatoma , Dipetalogaster , and Panstrongylus , was consistently recovered . Nearctic species within the NCA clade ( T . p . protracta , T . r . rubida ) diverged during the Pliocene , whereas the Neotropical species ( T . phyllosoma , T . longipennis , T . dimidiata complex ) are estimated to have diverged more recently , during the Pleistocene . The hypothesis of niche conservatism could not be rejected for any of six sister species pairs . Niche similarity between sister species best fits a retention model . While this framework is used here to infer niche evolution , it has a direct impact on spatial vector dynamics driven by human population movements , expansion of transportation networks and climate change scenarios .
Triatomine bugs ( Reduviidae: Triatominae ) are vectors of Trypanosoma cruzi , the etiologic agent of Chagas disease , which is the most common parasitic disease in the Americas [1] . Although T . cruzi transmission can occur without vector involvement ( i . e . T . cruzi contamination through infected bug feces in human dermis or mucosae ) , the rate of other non-vector transmission cases such as congenital and oral transmission ( i . e . involuntary ingestion of infected bugs , or blood or meat of infected mammals ) , blood transfusions , laboratory accidents and organ transplants [2]–[5] are generally lower in vector endemic regions [6] , [7] . Domiciliated vectors are considered the primary exposure hazard for T . cruzi transmission to humans [8]–[10] and triatomine ecology and evolutionary history , and their relationship with the geographic expression of its fundamental niche , is a primary determinant [11]–[13] . The Triatominae , commonly named assassin bugs , is a subfamily that belongs to the large family Reduviidae . The family includes 6600 species classified into 21 subfamilies [14] . It is the least diverse among reduviid subfamilies , having approximately140 species grouped into 11 genera [15]; [14] , [16] . Yet , new species continue to be described in recent years [17] and a new revision of the taxonomy of this group is increasingly necessary [18] . Triatomine bugs are characterized by their obligate haematophagy [19] , which is considered to have evolved once or twice [11] , [16] . The bloodsucking habit and its associated morphological adaptations have been highly influential in taxonomic definition of the subfamily , however , recent morphological and genetic studies are challenging the current classification of Triatominae , as well as clarifying some phylogenetic relationships at different taxonomic levels . For instance , some studies on the Triatoma dimidiata complex [20]–[22] , or the T . brasiliensis [23] subcomplex have highlighted the need for a generic revision within the Triatomini tribe . Triatomine species are mainly distributed in the New World , except for seven species that occur in Asia . In the Americas , they occupy tropical , subtropical and temperate areas approximately between the 40th parallels [24] . Despite wide environmental variation across North America [25] , geographic and environmental ranges of triatomine species tend to be similar [12] . This similarity could be explained by niche conservatism , in which diversification in ecological niche dimensions has been limited [26] . In vector-borne disease systems , vector niche conservatism patterns could influence a particular suite of processes in pathogen dynamics , for instance , to which areas dispersal could occur , and/or with what hosts the vector interacts [27] . Different macro-evolutionary models have been used to explain niche conservatism patterns and divergence rates such as drift , phylogenetic inertia , niche filling/shifting , evolutionary rate , and niche retention [28] . In particular , the niche retention model proposes lower expected divergence , which would be the result of stabilizing selection , or evolutionary constraints such as those imposed by developmental , physiological , or population-level genetic factors [29]–[32] . Ecological niche modeling ( ENM ) is a correlative framework that uses the associations between aspects of climate and environmental characteristics , and known species' occurrences , to define sets of conditions under which species are likely to maintain viable populations . The ENM can be projected onto geographic space , thereby depicting the potential area for each species' distribution [33] . This approach has been widely applied to study several aspects of the geographic distribution of Chagas disease vectors [34] . Warren et al . [35] developed a statistical framework to take advantage of niche model outputs so that the method could be used to test for niche conservatism: testing whether two niches are identical or whether two niches are more similar than expected at random . Niche similarity or divergence between species are not per se proof of niche conservatism , or a lack thereof [35]–[37] . Since niche similarity between species could be a result of niche conservatism or of niche convergence [38] , specific evidence for phylogenetic relationships are crucial for niche comparison interpretation . Additionally , several considerations are essential to correctly interpret niche conservatism in sibling species: ( 1 ) compared taxa should be the most likely sister species , ( 2 ) the sister species should be located in a region of probable historical accessibility of both species [39] , [40] , and ( 3 ) niche predictor variables must be selected based on biological significance and absence of statistical redundancy [41] , [42] . All three design criteria need to be addressed in order to avoid ambiguity as a result of inconsistency in sibling species selection criteria [43] , multi-dimensionality of dataset predictors ( which causes overfitting ) [41] , and failure to integrate geographically explicit potential dispersal areas [36] . In the present study , we test whether niche conservatism is a common pattern for the genus Triatoma of North and Central America ( NCA ) by using combined phylogenetic and biogeographic approaches . Since phylogenetic relationships among triatomines are still a matter of debate [14]–[16] , [44] , [45] , we first reconstructed relationships among New World triatomines , and then analyzed whether phylogenetic patterns were associated with geography , thereby indicating that bioclimatic niches evolve in a continuous and historically-accessible landmass . The hypothesis of niche conservatism between phylogenetically validated Triatoma sister species from NCA was analyzed using niche similarity .
Triatomine species from North ( Mexico and United States ) and Central America ( partial territory of Guatemala and Belize ) located within the North American tectonic plate , and for which at least three of the six gene markers examined were available in GenBank ( see below ) , were included in this study . Sequences from 53 triatomine species from seven genera ( Dipetalogaster , Eratyrus , Paratriatoma , Panstrongylus , Mepraia , Rhodnius , and Triatoma ) , representing 40% of all described species of the subfamily for the entire American continent and the primary Chagas disease vectors , were analyzed . Triatoma species were classified following Lent and Wygodzinsky' taxonomic revision [46] except for T . dimidiata , which was divided into three terminal taxa according to evidence suggesting that this is a complex of cryptic species [20]–[22] , [47] , [48] . We have named the analyzed taxa according to Bargues et al . [20] and Monteiro et al . [22] which names T . dimidiata group 1a for the populations inhabiting the coast of Chiapas as well as Guatemala , Honduras and Nicaragua; T . dimidiata group 2 for those inhabiting Gulf and central Mexico , and T . dimidiata group 3 for those from the Yucatan Peninsula . In this study we analyze 19 of 31 species representing 60% of the Triatoma diversity along the latitudinal and ecological spectrum of the North and Central America region [12] . Two species belonging to the reduviid subfamily Reduviinae ( Zelurus petax and Reduvius personatus ) were used as outgroups to root all trees [11] , [49] . Previously published ( GenBank ) sequences belonging to four mitochondrial ( mt ) and two nuclear gene markers were used to carry out a concatenated phylogenetic analysis . Mt markers included a fragment covering most of cytochrome oxidase I ( COI ) ( 1494 bp ) , 682 bp of cytochrome b ( cyt b ) , 375 bp of 12S ribosomal RNA ( rRNA ) , and 604 bp of 16S rRNA genes . Nuclear markers were ∼1919 bp of the 18S rRNA and ∼620 bp of the 28S rRNA genes . Gene sequences were aligned manually , and ambiguously aligned regions for the rRNA genes were excised using Gillespie's et al . [50] secondary structure model approach . Our concatenated data set included 87% of species for 16S , 63% for 12S , 52% for cyt b , 48% for COI , 37% for 18S and 30% for 28S . A list of the examined species , genes analyzed and GenBank accession numbers is included in Table S1 . The genes included for each of the phylogenetic analyses performed are listed in Table S2 . References for published sequences used in the above phylogenetic analyses are listed in Table S3 . The concatenated data set was analyzed with the partitioned Bayesian method implemented in MrBayes version 3 . 2 . 1 [51] . The analysis consisted of independent runs of four parallel chains and 20 million generations each , using uniform priors , and sampling trees every 1000 generations . Protein-coding genes were divided into three partitions based on codon positions , whereas each rRNA marker was considered as a single partition . JMODELTEST version 2 [52] was used to select the evolutionary model appropriate for each partition following the Akaike Information Criterion ( AIC ) . Stationarity was determined to have occurred before 1 million generations , although we used a conservative approach and deleted the first 5000 sampled trees . The remaining trees were used to reconstruct a tree with posterior probabilities ( PP ) of clades , considering that clades with PP≥0 . 95 were significantly supported [53] . Separate Bayesian and maximum likelihood analyses ( ML ) including the 19 species from the NCA clade ( see below ) were carried out for the cyt b and ITS markers ( Table S2 ) . The Bayesian analyses were conducted using the same parameters described above . The Tamura-Nei model using MEGA version 5 [54] was used for the ML analyses based on a bootstrap consensus tree inferred from 1000 replicates . Branches corresponding to partitions reproduced in <50% of bootstrap replicates were collapsed . Additionally , a discrete Gamma distribution was used to model evolutionary rate differences among sites ( 4 categories +G; cyt b parameter = 0 . 54; ITS2 parameter = 0 . 57 ) . The rate variation model for cyt b allowed for some sites to be invariable ( [+I] , 49 . 9% sites ) . We tested our preferred hypothesis of phylogeny against three alternative topologies using Bayes factor comparisons , which were calculated from estimates of marginal likelihoods using the Stepping Stone ( SS ) sampling approach , which uses importance sampling to estimate each ratio in a series ( the “stepping stones” ) , bridging posterior and prior distributions [55] , implemented in MrBayes version 3 . 2 [51] . The three alternative topologies analyzed were: ( 1 ) a monophyletic Triatoma , ( 2 ) Triatoma species from NCA constrained to be monophyletic , and ( 3 ) the combined species from Triatoma , D . maximus , and P . hirsuta from NCA constrained to be monophyletic . The SS model estimates were obtained by running 10 million generations , followed by 50 steps with 1000 samples within each step , and eliminating the first 25% of samples from each step . Two independent runs were performed for each dataset , one for a constrained and one for a non-constrained topology . The arithmetic difference of the Bayes factors for the two runs in log units is the criterion used to evaluate the null hypothesis ( monophyly ) . A log difference in the range of 3–5 is typically considered strong evidence in favor of a model , whereas a log difference above five is considered very strong evidence [56] . We estimated divergence time of clades using a relaxed molecular clock approach with BEAST version 1 . 7 . 4 [57] . We excluded R . personatus from this analysis in order to have a basal node separating the remaining Triatominae from Z . petax . The G+I+Γ model was used for this analysis , considering each gene marker as a single partition . The analysis was run for 20 million generations and sampling trees , every 1000 generations . The first 10 million generations were discarded , and the remaining trees were used to build a maximum clade credibility tree with TreeAnnotator version 1 . 7 . 4 ( part of BEAST 1 . 7 . 4 ) . The most basal node indicating separation between Reduviinae and Triatominae in our BEAST topology was calibrated to have a normal prior distribution of 52 . 89 my with 4 . 5 my standard deviation . This calibration was defined based on the divergence time estimate recovered by Hwang and Weirauch [11] for the most recent common ancestor ( MRCA ) of the above two subfamilies . The MRCA of the Triatomini clade was calibrated to have a normal prior distribution 30 . 0 my with 1 my standard deviation , based on the age assigned to a Triatoma fossil from Dominican amber [58] . We identified six pairs of sister species belonging to our phylogenetically recovered NCA Triatoma clade for niche conservatism analysis . Only one sister species pair ( T . gerstaeckeri –T . mexicana ) , was defined based on data from the single locus analysis of both cytb and ITS2 , while the rest were supported with the multilocus analysis . Georeferenced occurrence locations reported elsewhere were used to build ENM models [12] , [27] , [59] ( see Ibarra-Cerdena et al . [12] ) for a description of occurrence dataset sources ) . Niche models were constructed for members of each species for pairs , using 188 data points for T . barberi , 477 for T . dimidiata group 2 , 42 for T . dimidiata group 1a , 235 for T . gerstaeckeri , 115 for T . longipennis , 42 for T . mazzottii , 265 for T . mexicana , 22 for T . nitida , 33 for T . phyllosoma , 154 for T . p . protracta , 44 for T . recurva , and 42 for T . rubida ( Table S4 ) . The “M” region [60] or “background area” in the terminology of Warren et al . [35] , was defined for each species by plotting the species' occurrence points on the map of terrestrial eco-regions of the world [61] . We used that map to outline the ecoregions where datapoints of species were located , then we used that contour to create a spatial subset of the bioclimatic ( WorldClim ) and topographic ( Hydro-1k ) rasters which were used for model calibration and randomization tests , as has been recommended elsewhere for “M” delineation [40] , [62] . Ecological niche models ( ENM ) were produced using the Genetic Algorithm for Rule-set Prediction ( GARP; [63] . GARP is an evolutionary-computing software package available in openModeller Desktop version 1 . 1 . 0 ( http://openmodeller . sourceforge . net/ ) . To take advantage of the random-walk nature of the GARP algorithm , we developed 100 replicate models of each species' ecological niche and chose a ‘best subset’ of the 100 models based on optimal combinations of error statistics , which is also implemented in openModeller [64] . To choose best model subsets , we ( 1 ) eliminated all models that had omission error >5% based on independent extrinsic test points , ( 2 ) calculated the median area predicted present among these low-omission points , ( 3 ) identified the 10 models closest to the overall median area predicted , and ( 4 ) summed these ‘best subsets’ models . Binary models were obtained from the 10 best models by using a minimum presence threshold criterion in which a binary projection ( presence-absence ) is a result of the percentage of model agreement that predicts the presence of all data points . The WorldClim “bioclimatic” data [65] include 19 variables that summarize aspects of climate relevant to species distributional ecology . We analyzed correlations among these variables and excluded variables from highly correlated variable pairs ( r>0 . 75 ) . Variables most easily interpretable in terms of species physiological tolerances were retained . Nine variables were retained in model development ( annual mean temperature , maximum temperature of warmest month , minimum temperature of coldest month , annual temperature range , temperature seasonality , annual precipitation , precipitation of wettest and driest months , and precipitation seasonality ) , as well as four topographic variables from the Hydro-1K dataset ( elevation , slope , aspect , compound topographic index; Earth Resources Observations and Science-http://eros . usgs . gov/products/elevation/gtopo30/gtopo30 . html; last accessed Dec , 2011 ) , at the 0 . 01° resolution ( approx . 1 km ) . We applied a randomization test of background similarity to evaluate the niche conservatism hypothesis . To test if sister species pairs had more similar ecological niche than expected by chance , we conducted a background similarity test using ENMTools version 1 . 3 [66] . Briefly , a similarity metric Schoener's D [35] , was calculated from ENM generated in MaxEnt [67] , [68] with the “minimum presence training” threshold tool activated . The modeling parameters were random test percentage of 75% and a maximum number of 500 iterations . A null distribution for these distances was calculated based on comparison of models for each species' occurrence with models generated using random occurrence points within the M of its sister species . The null hypothesis ( species similarity not different than expected ) can be rejected when empirical similarity values are lower ( niche divergence ) than the 100 random similarity replicates ( 95% confidence interval , P<0 . 05 ) , based on a one-tail test .
The Bayesian phylogram derived from the concatenated analysis of the cyt b , COI , 12S , 16S , 18S and 28S gene markers is shown in Figure 1 . The phylogram contains a considerable number of significantly supported clades ( 23 out of 49 clades ) , with three additional clades also having significant PP values ( 0 . 9≤PP≤0 . 94 ) . The tribes Triatomini and Rhodniini each appeared significantly supported as monophyletic ( both with PP = 0 . 96 ) . Species of Triatoma were nested in a significantly supported clade ( PP = 0 . 96 ) together with two species of Panstrongylus ( P . herreri and P . megistus ) , D . maximus , and E . mucronatus . This predominately-Triatoma group contains two clades ( although not significantly supported ) , one exclusively composed of South American species , and the other of NCA species . Sister species from the NCA clade ( Table 1; Figures S1 and S2 ) were supported by PP values≥0 . 90 , except for T . p . protracta-T . barberi ( PP = 0 . 56 ) . This latter sister species pair was , however , significantly supported ( PP = 1; BTP = 99 ) using the ITS2 Bayesian and ML topologies ( Table 1; Figure S2 ) . The separate cyt b and ITS2 ML analyses ( Figures S1 and S2 ) also significantly supported T . mexicana and T . gerstaeckeri as sister species , as did the ITS2 analysis ( PP = 0 . 96 ) . The three Bayes factors comparisons performed with the SS approach strongly supported the relationships derived from the Bayesian concatenated analysis ( Figure 1 ) , indicating that Triatoma , as well as the NCA species of Triatoma , are not monophyletic ( monophyletic vs non-monophyletic Triatoma group = −28556 . 75 and −28475 . 20 , respectively; monophyletic vs non-monophyletic NCA Triatoma group = −28563 . 94 and −28411 . 77 , respectively; monophyletic vs non-monophyletic NCA triatomines = −28399 . 01 and −28653 . 88 , respectively ) . Divergence time estimates between the Triatoma species of NCA and South America are on the order of 14 . 1–22 my . Separation of the Central American clade ( Panstrongylus spp . and the rubrofasciata complex ) and that from North America ( including Mexico ) was estimated to have a similar age to the previous ( 13–20 my ) . Divergence between Nearctic ( T . p . protracta and T . r . rubida ) and Neotropical ( T . phyllosoma and T . dimidiata ) species was dated to have occurred during the Miocene ( 10–16 my ) . Speciation events of the Neotropical species occurred principally during the Pleistocene , in contrast to the Pliocene for the Nearctic species ( Figure 2 ) . The ENM for all NCA Triatoma species covers most of the regional territory of Guatemala , Belize , El Salvador , Nicaragua , Honduras , Mexico and the southern United States ( Figure 3 ) . There was no range overlap between sister species of inter-biogeographical regions ( i . e . the Neotropical T . barberi and the Neartic T . p . protracta ) , in contrast to the extensive range overlap in species pairs within the Neotropical region ( T . mazzottii-T . phyllosoma and T . dimidiata groups 1a and 2; Figure 3E–F ) . The broadest potential distribution range was T . p . protracta in the US and Mexico , almost crossing the complete continental longitudinal gradient , while its sister species , T . barberi , spans both Nearctic and Neotropical regions , covering the highlands of the Transvolcanic Belt ( Figure 3A ) . In general , sister species have allopatric potential distribution along a north/south latitudinal pattern ( Figure 3A , 3B , 3C , 3D ) , although sub-tropical species pairs' potential distributions are partially sympatric ( Figure 3E–F ) . The geographic distribution of all NCA Triatoma sister species niche was more similar between pairs than expected by chance ( P<0 . 01 ) . The observed niche similarity among sister pairs ranged from 0 . 88 to 0 . 99 ( for Schoener's D metric; Figure 4 ) . Niche similarity occurred despite high background divergence ( the range of similarity values calculated from random niche models generated from the background area of each pair shown by the bars in Figure 4; the more the distance between the observed similarity and the mean of random similarities , the higher the contrast between the background areas of the two sister species ) , as demonstrated with the example of the average of 0 . 05 for the random “D” of the T . p . protracta-T . barberi sister pair ( constructed with T . barberi points randomly generated in the background region of T . p . protracta ) . Temperature seasonality ( bio4 in WorldClim nomenclature ) was one of the most important bioclimatic variables shaping the niche of 9 out of the 12 species . This variable was also the most important between three out of six sister pairs ( T . dimidiata group 2 and T . dimidiata group 1a , T . mazzottii and T . phyllosoma , and T . r . rubida and T . nitida ) , having similar patterns of predicted suitability response curves . Other important variables were annual temperature range ( bio7 ) , minimum temperature of coldest month ( bio 7 ) , and precipitation of the wettest month ( bio13 ) . Conversely , topographic variables such as altitude or the topographic index were less conserved particularly between allopatric species ( i . e . T . barberi and T . p . protracta , and T . r . rubida and T . nitida ) .
In our phylogenetic analyses , Triatoma is consistently recovered as not monophyletic with respect to at least three genera , which is supported by the Bayes factor tests . The results strongly support a main clade composed of NCA species of Triatoma , which also includes the monospecific Dipetalogaster , and at least those examined species of Panstrongylus . Eratyrus is deeply nested in the South American clade . Paratriatoma was either recovered within , or as sister , to the NCA clade in the MrBayes and BEAST analyses , respectively , and thus its placement remains uncertain . Our phylogenetic estimates also support previous studies based on single [44] and multi-locus genetic markers [23] , [45] , indicating a sister group relationship between the NCA and the South American Triatoma clades . In the most recent and comprehensive multilocus phylogenetic analysis of Reduviidae , the subfamily was paraphyletic with respect to the Reduviine genus Opisthacidius , although only a small sample of triatomine species was analyzed ( less than 10% of the species described for the subfamily ) [11] . Despite the use of an incomplete matrix of sequences to infer phylogenetic relationships in Triatominae , our results are highly compatible with recent phylogenetic reconstructions based on multilocus analyses [18] , highlighting the ability of our Bayesian approach to extract the phylogenetic information contained in the molecular markers used . A number of recent empirical and simulation studies , have consistently demonstrated that even with a high proportion of missing data ( up to 75% ) , this approach has a high accuracy in the reconstruction of phylogenetic relationships [69]–[73] . The MRCA of the NCA and South American Triatoma clades diverged approximately 14 . 1–22 my ago . Hypsa et al . [44] , had proposed a Central American and/or Great Antilles origin for the Triatominae . A Central American origin for the Triatominae seems more likely , due to the presence in this region of many extant triatomine species from the genera Eratyrus , Cavernicola , Panstrongylus , Triatoma and the presence of Opisthacidius , recovered by Hwang and Weirauch [11] as the a sister group of Triatominae . Our divergence time estimates suggest a dispersal of Triatominae from Central to South America with a subsequent species radiation of the group in the latter region , which could have initiated after the connection of the Isthmus of Panama during the early Oligocene and Miocene [74] . The T . protracta , T . rubida and T . lecticularia complexes and the monotypic genera Dipetalogaster and Paratriatoma , a group of species inhabiting mainly the mid and southern area of the Nearctic region , diverged during the Pliocene , when major climate oscillations occurred [75] , [76] . Moreover , most Neotropical NCA species ( phyllosoma and dimidiata complexes ) diverged during the Pleistocene . Several speciation events for Neotropical triatomines have been proposed before this period for South American triatomines [77] , [78] . Our findings indicate that there were at least two speciation periods for the sister species that we tested for niche conservatism: ( 1 ) late Miocene-Early Pliocene ( >5 my ) for T . p . protracta-T . barberi , and T . nitida-T . rubida , and ( 2 ) late Pliocene-Pleistocene ( <5 my ) for T . phyllosoma-T . mazzottii , T . longipennis-T . recurva , and T . dimidiata group 2-T . dimidiata group 1a . None of the species from the NCA clade are significantly distributed beyond this region , and no species outside of the clade has a significant portion of its range within the NCA region . This study analyzes niche conservatism between sister species by first using a phylogenetic criterion to identify sister species . We have reconstructed phylogenetic relationships and estimated divergence times using a concatenated dataset , which consisted of six gene markers with differing degrees of missing data . Previous studies using both empirical and simulated data have shown that the accuracy of phylogenetic analyses is not influenced by the inclusion of missing data , but rather due to the amount of phylogenetic signal contained in the dataset examined [71] , [72] . None of the niche conservatism hypotheses that were independently tested in six sister species from the NCA region was rejected . This niche conservatism occurs across the different species complexes of Triatoma from NCA . We observed a gradient in range size , distribution region , range overlap , and niche breadth in NCA triatomines . However , despite variations , niche conservatism was observed even for species' pairs with highly divergent background environments and allopatric distributions ( T . p . protracta and T . barberi or T . r . rubida and T . nitida ) . Under the evolutionary species concept , retention of niche characteristics in highly divergent environments could promote speciation , since dispersal between geographic ranges is unlikely [79] . In contrast , the extended niche overlapping between Neotropical species pairs , suggests that sympatry is likely , if broad-scale dispersal occurs ( i . e . driven by human movements ) . Previous studies regarding the dimidiata complex have used biased datasets to simply unite occurrence points and draw polygons in order to project species distributions over geographic space , demonstrating parapatric ranges [20] , [22] , or a minor measure of sympatry [80] . Ecological niche models have proved to be better tools to depict more reasonable range maps with ample application in vector-borne distribution assessments [34] . This robust and validated method demonstrates a large region of range overlapping that more realistically represents the current patterns of dimidiata complex distributions , as validated by more complete sampling and genetic analyses ( Pech-May et al . , unpublished results ) . Here we tested whether niche conservatism is a general pattern for NCA triatomines regardless of the species complex to which they belong ( i . e . protracta , rubida , phyllosoma , dimidiata , etc ) . We did not find evidence for ecological speciation , since niche similarity among the sister species was always higher than that of the background tests . Allopatric species often inhabit different environmental conditions and landscapes . Hence , the fact that their niches are not identical does not prove a lack of niche conservatism [35] , [37] . Conversely , if the niche of two closely related species is more similar than expected despite non-overlapping ranges , this indicates that these retain niche traits , potentially due to a phylogenetic effect over-riding the spatial impact ( i . e . the influence of dispersal and recent evolution in niche similarity ) [81] . Phylogenetic effects ( i . e . the influence of phylogenetic history on niche similarity ) are assumed to limit the evolution of environmental tolerances that shape a species' distribution [28] . Lower tolerances to extreme climatic conditions were the most conserved niche attributes of NCA triatomines . Other studies have found that temperature and seasonality are the most important climatic determinants of triatomine species richness patterns in the American continent [82] . The implications of these findings will require further analysis , in light of deep phylogenetic niche conservatism in NCA triatomines [38] . Niche conservatism in vectors with public health importance has important implications for disease transmission ecology [83] . Niche conservatism of disease vectors could impose constraints on adaptive responses to cope with climate change [84] or natural or anthropogenic dispersal ( i . e . the growth of transportation networks and inter-locality human movement or migration ) [85] , [86] . Since NCA triatomines are both sylvatic and synanthropic [12] , [59] , [87] , [88] , they are prone to suffer involuntary translocations due to human migration . A well-documented case of this dispersal process was the Central American and southern Mexico invasion of Rhodnius prolixus [89] . This species succeeded in sub-tropical forest areas in southern Mexico and CA , although it was never collected in conserved habitats , possibly because niche conservatism prevented the species from establishing outside its ecological requirements . Recent distribution shifts for NCA triatomines have been recorded based on extra-range collections for T . dimidiata and sylvatic T . nitida and T . mazzottii [59] , [88] , [90] . The latter two records are inside the ecological range of each species , as predicted using niche conservatism analysis , but outside reported distribution areas . Niche conservatism analysis contextualized in evolutionary history has provided a robust perspective for the geographic patterns observed in NCA triatomines . Accordingly , knowledge of the evolutionary trends in niche evolution can help to identify areas where vector control can be easily implemented for faster results ( i . e . in sink habitats where low niche suitability prevents long term population establishment ) , while programing more intensive control campaigns in areas where high niche suitability guarantees appropriate climate for population expansion ( source habitats ) . This knowledge can also help to generate dynamic risk maps that integrate environmental changes and the probability for shifts of disease vector species distribution ranges . | Knowledge regarding the evolutionary history of insect vectors of pathogens is essential to design precise and appropriately integrated control strategies , since species' dispersal and invasive capacity are key components to prevent human-vector interaction . Given several well-known invasive or dispersal events of Triatominae , and their adaptive capacity to colonize modified habitats , the question arises whether niche conservatism ( i . e . the limited capacity for niche differentiation within clades ) across sister species could influence distribution patterns . Niche conservatism is herein analyzed across the highly heterogeneous landmass of North and Central America where most species complexes of the Triatominae are vectors of Trypanosoma cruzi . Since phylogenetic relationships among and within triatomine species complexes are still ill-defined , we first used a multi-locus phylogenetic analysis using mitochondrial and nuclear loci to validate relationships between species . Similarity of the ecological niche between six sister species' pairs was used to test the niche conservatism hypothesis . The results provide robust evidence of monophyly for North and Central America triatomine species . The hypothesis of niche conservatism was not rejected for any sister species pair , independently of niche width , distribution range , biogeographic affinity , or environmental heterogeneity; niche similarity correlated inversely with taxa divergence and according to a retention model . | [
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] | 2014 | Phylogeny and Niche Conservatism in North and Central American Triatomine Bugs (Hemiptera: Reduviidae: Triatominae), Vectors of Chagas' Disease |
Traits that are attractive to the opposite sex are often positively correlated when scaled such that scores increase with attractiveness , and this correlation typically has a genetic component . Such traits can be genetically correlated due to genes that affect both traits ( “pleiotropy” ) and/or because assortative mating causes statistical correlations to develop between selected alleles across the traits ( “gametic phase disequilibrium” ) . In this study , we modeled the covariation between monozygotic and dizygotic twins , their siblings , and their parents ( total N = 7 , 905 ) to elucidate the nature of the correlation between two potentially sexually selected traits in humans: height and IQ . Unlike previous designs used to investigate the nature of the height–IQ correlation , the present design accounts for the effects of assortative mating and provides much less biased estimates of additive genetic , non-additive genetic , and shared environmental influences . Both traits were highly heritable , although there was greater evidence for non-additive genetic effects in males . After accounting for assortative mating , the correlation between height and IQ was found to be almost entirely genetic in nature . Model fits indicate that both pleiotropy and assortative mating contribute significantly and about equally to this genetic correlation .
Taller people tend to be smarter . Although the relationship is modest , height and IQ are consistently correlated at ∼ . 10– . 20 [24] , [25] , [26] . Three studies have examined the etiology of this correlation using a bivariate ACE “classical” twin design which uses the covariation of monozygotic ( MZ ) and dizygotic ( DZ ) twins to partition the variation in and covariation between height and IQ due to additive genetic effects ( A ) , environmental factors shared on average between twins or siblings ( C ) , and environmental factors that tend to affect individuals uniquely ( E ) . Sundet et al . [24] found that the correlation between height and IQ in a sample of conscripted , Norwegian twin males was primarily due to shared environmental factors that explained 56% of the association , although overlapping genetic effects influencing both height and IQ were also significant and explained 35% of the association . Similarly , Beauchamp et al . [25] found that the height-IQ correlation was due to both shared environmental factors ( explaining 59% of the association ) and overlapping genetic factors ( explaining 31% of the association ) in a sample of Swedish twins . By contrast , Silventoinen et al . [26] found that overlapping genetic factors accounted for all of the covariation between height and IQ in four cohorts of Dutch twins . Differences between the samples may explain the inconsistency in conclusions: the twins investigated in by Sundet et al . [24] and Beauchamp et al . [25] were born after 1915 and 1886 , respectively , whereas those investigated by Silventoinen et al . [26] were born after 1935 and mostly after 1980 . It is likely that there were greater nutritional differences between families in the early 20th century compared to the late 20th century , which is consistent with substantially higher univariate estimates of shared environmental effects ( ∼20% ) for both height and IQ in the former two studies compared to the latter study ( 0% ) . Thus , the architecture of the IQ-height correlation may itself vary between populations and time points . A central limitation to all three previous studies investigating the height-IQ relationship is that assortative mating was not measured and accounted for . This is important for two reasons . First , if assortative mating occurs but is not modeled , shared environmental effects will be over-estimated and additive genetic effects under-estimated , as described by Eaves [27] , [28] and shown graphically in Keller et al . [29] . As discussed in detail by Beauchamp et al . [25] , this effect is not limited to biases in univariate effects: changing assumptions of cross-trait assortative mating has an equally dramatic influence on estimates of shared environmental and genetic effects on the correlation . If cross-trait assortative mating occurs but is not modeled , then what is actually a genetic correlation between the traits will appear as being due to shared environmental effects ( despite the fact that assortative mating actually increases the true additive genetic variance/covariance ) . Thus , previous estimates showing the importance of shared environmental effects on the height-IQ correlation [24] , [25] may have been biased upwards . The second reason that measuring and modeling assortative mating is important is that it allows researchers to estimate the degree to which any genetic correlation between traits is due to pleiotropy vs . gametic phase disequilibrium . Using a bivariate nuclear twin family design [27] , researchers can determine whether the remaining additive genetic covariance is significant after accounting for the expected increase in additive genetic covariance between traits due to assortative mating . In the present study , we estimated the genetic and environmental influences on height and IQ using a bivariate nuclear twin family design , which models the covariation between MZ and DZ twins , their parents , and their siblings . The model we use here is described in Keller et al . [30] , but owes its origins to models developed by Eaves and Heath [31] , [32] , [33] and Cloninger , Rice , and Reich [34] , [35] in the 1970s and 1980s . As noted above , this model gives much less biased estimates of genetic and shared environmental effects than the classical twin design , estimates and accounts for the effect of assortative mating , and allows tests of the etiology of genetic correlations . Furthermore , the addition of siblings and parents greatly increases the precision of the estimates because adding more individuals within a family exponentially increases the amount of information on which estimates are based . For example , adding two siblings of a twin pair to the model provides five additional covariance estimates whereas adding another twin pair ( also two individuals ) provides only one additional covariance estimate [36] .
We analyzed height and IQ data from 2 , 936 families ( n = 7 , 905 ) from four separate samples ( see Methods ) . Table 1 shows descriptive statistics for IQ and height by sample , and Table 2 shows the phenotypic correlations between IQ and height on the combined data between various relative pair types . Within- and cross-trait correlations between MZ twins were roughly double those for DZ twins , suggesting important influences of additive genetic effects and minor influences of shared environments or genetic dominance on both IQ and height . However , as our results demonstrate below , such an inference can be wrong if genetic dominance and shared environments simultaneously influence variation in traits and if assortative mating is not accounted for [37] , [38] . Correlations between spouses indicate that individuals mate assortatively on both height and IQ , and a cross-trait spousal correlation indicates that smart women partner with tall men ( r = . 18 ) and that smart males partner with tall women ( r = . 11 ) . This pattern of spousal correlations suggests that a genetic correlation between height and IQ could have arisen as a result of cross-trait assortative mating , and not solely by genetic pleiotropy . To formally model the relationship between height and IQ , we used the structural equation modeling framework first introduced by Sewall Wright [39] , which has become the established approach in the behavioral genetics field . In particular , we used a bivariate nuclear twin family ( NTF ) design ( see Methods and Figure 1 ) to model the sex-specific effects of the following influences: A - additive genetic effects shared in common between sexes; B – additive genetic effects specific to males ( see below ) ; D – dominant genetic effects arising from combinations of alleles at the same locus; S – sibling environmental effects arising from environmental factors shared between twins and siblings but not parents ( e . g . , school , peers , cohort , etc . ) ; F - familial environmental effects arising from environmental factors passed from parents to children via “vertical transmission” ( e . g . , SES , education , etc . ) ; T - twin environmental effects arising from environmental factors shared by twins , but not siblings or parents ( e . g . , classes at school , peers , prenatal environments ) ; and E - unique environmental effects arising from factors that are unshared between relatives ( e . g . unique experiences , measurement error , etc . ) . The NTF design assumes that within-family similarity of non-genetic origin is due to either to parent/child vertical transmission ( F ) or to environments shared between siblings/twins but not shared by parents ( S ) ; a model estimating these two parameters simultaneously is not identified . We therefore fit two alternative primary phenotypic assortative mating models: an “ABDSTE” model and an “ABDFTE” model and compared their fits using the Akaike information criterion ( AIC ) . The ABDSTE primary phenotypic assortment model ( AIC = 8247 . 1 ) fit better than the ABDFTE primary phenotypic assortment model ( AIC = 8250 . 0 ) . Primary phenotypic assortment assumes that individuals actively choose similar mates based on their extant phenotype , but other causes of mate similarity are possible . The most commonly discussed alternative in humans is “social homogamy , ” where similarity between mates arises from similar environmental backgrounds [33] . To gauge the degree of evidence for this hypothesis , we also tested an alternative “ABDCTE” NTF social homogamy model of assortment ( see Figure 2 ) , but it fit substantially worse than either of the primary assortative mating models ( AIC = 8261 . 3 ) , indicating that the process of primary phenotypic assortment ( mates choosing similar mates ) is most consistent with our data . We sequentially dropped or fixed parameters in the ABDSTE model , beginning with those explaining the least amount of variance , until dropping additional parameters significantly reduced the fit of the model . Environmental effects unique to twins ( T; χ2 ( 6 ) = 7 . 8 , p = . 253 ) were non-significant , suggesting that environments shared by twins but not other siblings ( e . g . , teachers , peer groups ) have at best minor effects on IQ or height . We also found no evidence for qualitative sex-limited effects ( see Methods ) : ( a ) additive genetic effects unique to males ( B ) had almost no influence on model fit ( χ2 ( 3 ) = . 202 , p = . 91 ) , suggesting that the same genes affect IQ and height across the sexes; and ( b ) cross-sex correlations between both S ( χ2 ( 4 ) = 1 . 46 , p = . 83 ) and D ( χ2 ( 4 ) = 0 , p = 1 ) could be fixed to 1 , indicating that the same shared environmental and dominant genetic effects that influence male IQ and height also influence female IQ and height . No further parameters could be dropped or fixed . Environmental effects shared between siblings ( S; χ2 ( 6 ) = 16 . 99 , p = . 009 ) , non-additive genetic effects ( D; χ2 ( 6 ) = 61 . 8 , p = 1 . 9e-11 ) , and additive genetic effects ( A; χ2 ( 6 ) = 1139 . 5 , p<2e-16 ) were all highly significant factors influencing variation in IQ and/or height . A model dropping E could not be fit for technical reasons , although point estimates for E indicate its importance to model fit . This best-fitting , final model is shown in Figure 3 along with estimated variance components and coefficients for pathways . Results from the final model indicated that the male narrow-sense heritability estimates of IQ ( hn2 = . 57 ) and height ( hn2 = . 65 ) were lower than corresponding narrow-sense heritabilities for females ( hn2 = . 67 and hn2 = . 82 respectively ) . On the other hand , broad-sense heritabilities ( [VA+VD]/VP ) were similar between the sexes ( hb2 = . 75 for male IQ , hb2 = . 85 for male height , hb2 = . 80 for female IQ , and hb2 = . 88 for female height ) . ( It should be noted that these values are standardized by the respective phenotypic variances of males and females , and so are slightly different than the unstandardized values of VA and VA+VD shown in Figure 3 ) . This indicates a greater influence of non-additive genetic effects in males than females for these traits . There were also modest influences of shared environmental effects on IQ for males ( 7% of the variation ) and females ( 10% of the variation ) , but no such effects on height . Finally , most of the covariation between IQ and height was due to shared additive genetic influences: 68% for males and 100% for females . For males , unique environmental influences and dominant genetic influences appeared to play about equal roles in explaining the remaining covariation between IQ and height . We also tested several questions related to assortative mating , which was modeled using a 2×2 full matrix of copaths between mates ( μ ) and a resulting change in additive genetic variance/covariance , modeled as a 2×2 full matrix of genetic variances/covariances , q ( see Methods ) . Although there was stronger evidence that smart females pair with tall males ( μ12 co-path = . 08 ) than that smart males pair with tall females ( μ21 co-path = . 05 ) , these two co-paths were not significantly different from one another ( χ2 ( 1 ) = . 62 , p = . 43 ) and were constrained to be the same in the reduced model ( estimated at μ12 = μ21 = . 06 ) . This co-path was significantly different from 0 ( χ2 ( 1 ) = 4 . 6 , p = . 03 ) . Assortative mating inflates the level of additive genetic variation/covariation in the population , and the q matrix in our model quantified this increase . In particular , the additive genetic variation for IQ and height were 28% and 13% higher , respectively , than they would have been if couples mated at random ( see the diagonals of the q matrix , Figure 3 ) . Furthermore , by comparing the observed height-IQ covariance to the height-IQ covariance implied if q was an identity matrix , which it would be under random mating , we can conclude that the additive genetic covariance between IQ and height was much higher than it would have been under random mating: an estimated 167% higher in males ( a predicted value of . 03 under random mating vs . the observed value of . 08 ) and 88% higher in females ( a predicted value of . 08 under random mating vs . the observed value of . 15 ) . We estimate that the additive genetic correlation between height and IQ is . 08 in males ( ) and . 17 in females ( ) , and these estimates were highly significant ( χ2 ( 3 ) = 47 . 4 , p = 2 . 8e-10 ) . To understand whether pleiotropy ( shared genes ) was a significant cause of these correlations , we compared the final model to a model in which the off-diagonal paths in am and af were constrained to be 0; this model fit substantially worse ( χ2 ( 2 ) = 12 . 9 , p = . 002 ) . Similarly , to understand whether gametic phase disequilibrium ( assortative mating ) was a significant cause of these correlations , we compared the final model to a model in which the off-diagonal elements of q were constrained to be 0; this model also fit substantially worse ( χ2 ( 1 ) = 14 . 4 , p = . 0001 ) . These results give unequivocal support to the hypothesis that both shared genes and assortative mating are simultaneously responsible for the genetic correlation between height and IQ .
A positive correlation exists for many traits related to sexual attractiveness , as predicted by various evolutionary theories , but the true cause of this correlation is typically ambiguous . Here , we demonstrated how a genetically informative design that used twins , siblings , and parents can clarify the etiology of such correlations in humans . In addition , this design can provide estimates of the causes of variation in individual traits that are much more accurate and less biased than estimates from non-twin or twin-only designs . We used this model to demonstrate that the phenotypic correlation between two potentially sexually selected traits in humans , IQ and height , is largely genetic in nature , and that both shared genes and assortative mating contribute importantly to it . We believe that this approach can be used to systematically investigate the nature of correlations that exist between human traits related to attractiveness or to fitness in general . An alternative approach that uses similarity at measured SNPs to estimate genetic relationships among classically ‘unrelated’ individuals has recently been used to estimate genetic correlations between traits [40] , [41] . While the genetic association between height and IQ should be detectable using this method , it suffers from three limitations vis-à-vis the current approach . First , it would require much larger sample sizes than those used in the present study to detect genetic correlations of the magnitude observed here , because genetic relationships from distantly related individuals tend to have much less variance than those among twins and other family members . Second , heritability/genetic correlations estimated from similarity at measured SNPs only capture the effects of common ( MAF> . 01 ) causal variants , and so the covariance between IQ and height due to rarer mutations will not be detectable [42] . Finally , and most importantly , we are not aware of any way to directly estimate the relative contributions of assortative mating vs . pleiotropy on the genetic correlation when estimated from similarity at measured SNPs . The method described in this manuscript can disentangle the effects of pleiotropy from assortative mating because the degree of mate assortment is directly estimated and accounted for in the model . The importance of genetic pleiotropy on the association between IQ and height is notable . On the surface , it might seem that height and IQ involve very different functional systems with different developmental origins . Genetic pleiotropy between IQ and height ( indeed , between any two complex fitness traits ) is consistent with the idea that variation in these traits partly reflects genome-wide mutational loads , and that these traits are components of attractiveness because of this—i . e . , they are honest signals or cues of ‘good genes’ [43] , [44] , [45] . The additional and substantial increase in additive genetic covariance as a function of assortative mating is consistent with both traits being attractive to the opposite sex . Because directional ( including sexual ) selection reduces additive genetic variation more quickly than non-additive genetic variation [46] , [47] , our results showing relatively higher levels of non-additive genetic variation in male height and IQ is consistent with the hypothesis that these traits have been under stronger selection in males than females . However , because the genes that affect these traits appear to be the same between males and females , selection for a trait in one sex would also lead to similar evolution of that trait in the other sex . Given that human mate choice is largely bi-directional , we might also predict that traits that males find particularly attractive in females should show higher levels of non-additive genetic variation in females than in males . Ironically , such depletion of additive genetic variation reduces their usefulness as indicators of ‘good genes , ’ a situation known as the “lek paradox” [48] . A possible resolution to this is if sexually selected traits capture variation in overall condition [49] , which is itself heritable due to , e . g . , recurrent mutations that degrade condition [50] . Our univariate results are broadly consistent with what has been reported about the causes of phenotypic variation in IQ [51] and height [52] from previous studies: the causes of individual differences in these two traits are largely genetic in origin . However , our design does allow for less ( downwardly ) biased estimates of shared environmental influences , and we did detect significant albeit modest shared environmental effects on IQ ( explaining ∼8% of variation ) in both males and females . The effects of the shared environment on the genetic correlation between IQ and height were extremely small and negative , which may indicate a minor role of higher-order non-additive genetic effects on the genetic correlation rather than shared environmental effects per se actually causing dissimilarity between family members . It should be noted that any potential effects of population stratification on height and IQ would appear as positive shared environmental effects on the height-IQ correlation; our results therefore suggest such stratification has little if any effect on the estimated genetic correlation . A limitation of the current study is that the results were based on a sample of different ages , from adolescence to late adulthood , and there is evidence that the genetic architecture of at least IQ changes over time , such that additive genetic influences become more pronounced whereas shared environmental influences decrease as individuals age [53] . It is therefore possible that effects of shared environments on IQ reported here are underestimated for adolescents and overestimated for older adults . Furthermore , as with almost all twin studies , the conclusions of our study rest on the assumption that environmental influences affecting IQ and height do not cause greater similarity in MZ twins than DZ twins . However , the possibility that this assumption is violated for these traits is increasingly unlikely in light of recent findings , also showing very high levels of additive genetic variation in height [42] and IQ [54] , that are based on genomic similarity among unrelated individuals who are unlikely to share environmental factors . A final caveat to our results is worth consideration: it is likely that shared environmental influences play a larger role in height and IQ variation in cultures in which the relevant environmental factors ( e . g . , nutrition ) vary to a greater extent between families . In such cultures , the proportionate effect , but not the absolute effect , of genes should be smaller than in the modern industrialized culture from which our samples were drawn . In summary , this report has introduced an approach that can tease apart the three principal competing explanations ( shared environments , shared genes , and assortative mating ) for the etiology of correlations between sexually selected traits . We use this to conclude that both shared genes and the effects of assortative mating together account for most of the covariation between IQ and height . While other explanations cannot be excluded , our findings are consistent with the hypothesis that height and IQ are attractive because they tap into the same underlying factor of genetic quality—e . g . , mutational loads—and that the resulting genetic correlation is accentuated by assortative mating on overall attractiveness . If so , we expect that many other traits related to attractiveness will also be genetically correlated due both to shared genes and to assortative mating . We hope that the current approach can serve as a template for testing this hypothesis across multiple traits related to human attractiveness .
The study and protocols were approved by Institutional Review Boards at the University of Colorado and the Queensland Institute for Medical Research , and informed consent was obtained from all participants . Data from 2 , 936 families ( n = 7 , 905 ) comes from four separate samples of individuals: the Colorado Longitudinal Twin Sample ( LTS; 552 families; [55] ) , the Colorado Community Twin Sample ( CTS; 1005 families; [55] ) , control subjects from the Colorado Adolescent Substance Abuse Family Study Sample ( FAM; 401 families; [56] ) , and the adolescent twin sample from Queensland Australia ( QIMR; 978 families; [57] ) . Together , these samples provide information from MZ and DZ twins ( including same sex and opposite sex twins ) , parents of twins , and non-twin siblings ( see Table 1 ) . When samples were combined , ages ranged from 12 to 28 for twins , 10 to 35 for non-twin siblings and 29 to 78 for parents . For the three Colorado samples ( LTS , CTS , and FAM ) , IQ was measured using the Wechsler Adult Intelligence Scale ( WAIS-R or WAIS-III; administered to those over the age of 16; [58] ) or the Wechsler Intelligence Scale for Children ( WISC-R or WISC-III , administered to those 16 and under; [59] ) . In the CTS and FAM samples , and for the siblings in the LTS sample , scores on the verbal and performance subscales of the WAIS ( or WISC ) were averaged together to generate a measure of IQ . These two subscales together have been shown to correlate very highly with full-scale IQ [60] . In the LTS sample , full scale IQ was obtained from the WAIS-III or WISC-III . For the QIMR adolescent twin sample , IQ was obtained from a shortened version of the Multidimensional Aptitude Battery [61] , which included three verbal subtests ( Information , Arithmetic , Vocabulary ) and two performance subtests ( Spatial and Object Assembly ) . For all samples , we adjusted IQ for sex , age , and age squared . We also controlled for test version in the LTS and FAM samples ( WAIS-III , WISC-III , WAIS-R , or WISC-R ) because versions differed between individuals within these samples . For all samples , height was self-reported . As with IQ , we adjusted scores of height to account for variance associated with sex , age , and age squared . For both height and IQ , we removed ( set to missing ) any scores that were more than 4 standard deviations above or below the mean because such outlying scores may have rare , non-familial causes ( e . g . , de novo mutations or environmental trauma ) . This affected a total of 10 scores: 8 negative outliers of height and 2 positive outliers of height ( results were nearly identical when outliers were included ) . The final IQ and height scores in all samples were standardized residuals from our regression-based adjustments . Table 1 shows the sample sizes , means , and standard deviations for raw height and IQ scores by sample and relative type after outliers were removed . It should be noted that while overall phenotypic variance for both traits is , by definition , equal to one , variances within sex can be higher or lower than this . We used a bivariate nuclear twin family ( NTF ) design to model the variances of and covariances between MZ twins , DZ twins , their parents , and their non-twin siblings ( Figure 1 ) . For clarity , Figure 1 omits siblings ( which are estimated exactly as DZ twins except that they do not share “twin environments” ) and shows an example where twin 1 is a male and twin 2 a female . Each observed ( squares ) or latent ( circles ) variable in Figure 1 should be considered a 2-by-2 matrix of observed or latent scores of the effect in question , each covariance ( double-headed arrows ) a 2-by-2 full matrix of variance/covariance terms , and each pathway ( single-headed arrows ) a lower triangular matrix , specifying the pathways of a 2-by-2 Cholesky decomposition . The variance of each effect , derived by pre- and post-multiplying the pathway matrices by the variance matrices ( which are identity matrices for all parameters except A , B , and F ) , gives the variance of the effect in question of IQ and height along the diagonals , and the covariance of the effect between IQ and height on the off-diagonals . Primary phenotypic assortative mating , denoted μ in Figure 1 , is modeled as a 2-by-2 matrix of copaths [62] , which has special rules associated with it , as described in Keller et al . [30] . The NTF design assumes that within-family similarity of non-genetic origin is due to either to parent/child vertical transmission ( F ) or to environments shared between siblings/twins but not shared by parents ( S ) ; a model estimating these two parameters simultaneously is not identified . We therefore fit two alternative primary phenotypic assortative mating models: an “ABDSTE” model and an “ABDFTE” model . Environmental factors causing twins to be more similar than non-twin siblings ( T ) could be estimated in all models . This model also assumes no effects of epistasis or gene-environment interactions . Nevertheless , the variance-covariance of D can be interpreted more broadly as reflecting any source of genetic non-additivity , including epistasis and gene-by-age interactions , as these influences tend to be captured by D in the NTF design [29] . We modeled quantitative sex-limited effects ( for example , the same genes having different degrees of additive effects between sexes ) using sex-specific pathways for A , D , S , T , and E . We modeled qualitative sex-limited effects of D , S , and T ( for example , environmental factors causing twins/sibling similarity in females being different than those environmental factors doing so in males ) by directly estimating a correlation between opposite-sex siblings/twins for these variables . Given our modeling approach for additive genetic effects , we had to model additive genetic qualitative sex limited effects ( for example , height in males being affected by a different suite of genes than height in females ) using male-specific additive genetic effects ( B ) , as noted above . Modeling male-specific additive genetic effects was an arbitrary decision; modeling this as female-specific effects would not change the fit of the model or any conclusions . Finally , we estimated parent-offspring specific pathways ( father-son , father-daughter , mother-son , and mother-daughter environmental transmission ) for F . For a full explanation of this model , see Keller et al . [30] . Primary phenotypic assortment assumes that individuals actively choose similar mates based on their extant phenotype , but other causes of mate similarity are possible . The most commonly discussed alternative in humans is “social homogamy , ” where similarity between mates arises from similar environmental backgrounds [33] . To gauge the degree of evidence for this hypothesis , we also tested an alternative “ABDCTE” NTF social homogamy model of assortment . In this model , shared environmental factors ( C ) contributed to covariance between all members within a family , including spouses and parents-offspring; S , F , and μ ( primary phenotypic assortment ) were therefore not estimated . Figure 2 shows this model with parameters found to be non-significant ( see Results , below ) omitted for clarity . We estimated parameters using structural equation modeling on the four combined datasets using the raw data analysis option in OpenMx version 1 . 0 . 7 . This script along with familial correlations that can be used to reproduce these results can be found at: www . matthewckeller . com . We first tested whether variances , covariances , and means of height and IQ could be equated between different types of relative; when they could not , we allowed them to differ in the model . We then ran the social homogamy model as well as two primary assortment models ( ABDFTE and ABDSTE ) and used the AIC to choose between these three non-nested models . We chose the best-fitting ( lowest AIC ) of these three models , and then began dropping non-significant parameters in that model one parameter at a time . Sequentially dropping parameters in a bivariate NTF design can be extremely burdensome due to the large number of parameters that can be tested and because of the dependency of significance on the order of the tests . Here , we adopted a common-sense approach to this , which dropped entire 2-by-2 matrices in an all-or-none manner if the fit of the model changed little after it was dropped ( p> . 10 on a χ2 test comparing -2 log likelihoods of the reduced model against the previous model ) . Given our liberal threshold ( p< . 10 ) for retaining parameters , a parameter ( e . g . , the T matrix , which has three free estimates , t11 , t12 , and t22 ) would only be dropped if there was little evidence for it in both height and IQ; strong evidence for either would result in retaining the parameter . We continued this process until no further parameters could be dropped . We tested whether qualitative sex-limited effects of S , D , and T existed by dropping the cross-sex correlations associated with these latent variables , and tested qualitative sex-limited additive genetic effects by dropping B ( we did not attempt to constrain quantitative sex-limited effects ) . Finally , we investigated effects of assortative mating on model fits , testing whether genetic pleiotropy or assortative mating ( or both ) accounted for any observed genetic associations between height and IQ . | Traits that are attractive to the opposite sex are often positively correlated when scaled such that scores increase with attractiveness , and this correlation typically has a genetic component . Such traits can be genetically correlated due to genes that affect both traits and/or because assortative mating ( people choosing mates who are similar to themselves ) causes statistical correlations to develop between selected alleles across the traits . In this study , we used a large ( total N = 7 , 905 ) , genetically informative dataset to understand why two potentially sexually selected traits in humans—height and IQ—are correlated . We found that both shared genes and assortative mating were about equally important in causing the relationship between these two traits . To our knowledge , this is the first study that has been able to disambiguate the two principal reasons—shared genes versus assortative mating—for why traits can be genetically correlated . | [
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] | 2013 | The Genetic Correlation between Height and IQ: Shared Genes or Assortative Mating? |
Different pathogens share similar medical settings and rely on similar virulence strategies to cause infections . We have previously applied 3-D computational modeling and bioinformatics to discover novel antigens that target more than one human pathogen . Active and passive immunization with the recombinant N-terminus of Candida albicans Hyr1 ( rHyr1p-N ) protect mice against lethal candidemia . Here we determine that Hyr1p shares homology with cell surface proteins of the multidrug resistant Gram negative bacterium , Acinetobacter baumannii including hemagglutinin ( FhaB ) and outer membrane protein A ( OmpA ) . The A . baumannii OmpA binds to C . albicans Hyr1p , leading to a mixed species biofilm . Deletion of HYR1 , or blocking of Hyr1p using polyclonal antibodies , significantly reduce A . baumannii binding to C . albicans hyphae . Furthermore , active vaccination with rHyr1p-N or passive immunization with polyclonal antibodies raised against specific peptide motifs of rHyr1p-N markedly improve survival of diabetic or neutropenic mice infected with A . baumannii bacteremia or pneumonia . Antibody raised against one particular peptide of the rHyr1p-N sequence ( peptide 5 ) confers majority of the protection through blocking A . baumannii invasion of host cells and inducing death of the bacterium by a putative iron starvation mechanism . Anti-Hyr1 peptide 5 antibodies also mitigate A . baumannii /C . albicans mixed biofilm formation in vitro . Consistent with our bioinformatic analysis and structural modeling of Hyr1p , anti-Hyr1p peptide 5 antibodies bound to A . baumannii FhaB , OmpA , and an outer membrane siderophore binding protein . Our studies highlight the concept of cross-kingdom vaccine protection against high priority human pathogens such as A . baumannii and C . albicans that share similar ecological niches in immunocompromised patients .
Acinetobacter baumannii has emerged as a frequent cause of healthcare-associated infections , ranging from bacteremia , pneumonia , urinary tract infections , to skin and wound infections , including those seen in the military theatre [1–10] . Its emergence in the healthcare environment is ascribed to its ubiquitous environmental presence , its ability to survive for prolonged periods of time on abiotic hospital surfaces , and its resistance to many existing antibiotics [11 , 12] . Of great concern is the recent rise in the frequency of extensively drug resistant ( XDR ) A . baumannii infections , from fewer than 4% of all A . baumannii infections in 2000 , to 60–70% in 2010 [2 , 13–15] . Such XDR-A . baumannii infections often require treatment with second-line agents such as tigecycline and colistin , which are associated with clinical failure , development of pan-drug resistance and nephrotoxicity [16–26] . The recalcitrance of Acinetobacter to antibiotics is further exacerbated in the setting of biofilms [27 , 28] , which can promote its adherence to and colonization of indwelling devices such as urinary catheters and endotracheal tubes . [29] A . baumannii is particularly adept at forming polymicrobial biofilms in contexts of healthcare settings [30 , 31] . Notably , A . baumannii often shares an ecological niche with the opportunistic pathogenic yeast , Candida albicans , especially in intensive care units [32–34] . In fact , these organisms represent two of the top five microorganisms associated with failure of endotracheal tube function , often due to biofilm formation [30] . Previous studies have also revealed complex in vitro and in vivo interactions between C . albicans and A . baumannii , in which A . baumannii may exploit C . albicans hyphae for adhesion [35 , 36] . These interactions depend on an interplay between the A . baumannii protein OmpA , with an as yet unidentified receptor on C . albicans . A . baumannii also secretes molecules in growth media that inhibit fungal germination and hyphal formation . Interestingly , C . albicans combats the bacterium by producing quorum sensing molecule of its own—farnesol—and can dominate the shared niche when fungal density exceeds that of the bacterium [37] . This relationship suggests that a competitive dynamic exists between the two species in settings such as biofilms . The outcome of this interaction likely depends on multiple factors , including tissue or device affinity , immune evasion and secondary metabolite production . Interventions that interfere with colonization- or virulence-enabling interactions of the organisms , and/or which abrogate the formation or homeostasis of biofilms , could in concept be harnessed as therapeutic options . We have previously applied computational modeling to discover novel antigen candidates that target more than one human pathogen [38] . This strategy is known as convergent or unnatural immunity , and has been demonstrated repeatedly in the development of viral and bacterial vaccines in which an antigen from a particular organism protects against another pathogen from the same kingdom [38] . Cross-kingdom protective antigens have also been reported , in which an antigen from a source organism protects against organism ( s ) from other biological kingdoms [39–41] . Specifically , we identified unforeseen 3-D structural and functional homology between members of the Agglutinin like sequence ( Als ) family of proteins of C . albicans that induce adhesion to and invasion of mammalian cells , and surface adhesin/invasin molecules ( MSCRAMMs ) of Staphylococcus aureus [42] . Indeed , a recombinant form of the Als3 protein ( currently in clinical trials ) elicits robust T- and B-cell responses and protects mice from both Candida and S . aureus infections [39–41 , 43–46] . Of considerable interest is our current discovery that the C . albicans hyphal cell surface glycosylphosphatidylinositol ( GPI ) -anchored protein Hyr1p is predicted to have 3-D structural and epitope homology to known and putative virulence factors of specific Gram-negative bacteria including A . baumannii . Thus , we pursued studies to examine the potential use of rHyr1p as a vaccine candidate to mediate protection against A . baumannii in mice using the FDA-approved alum as an adjuvant . We further sought to evaluate the potential use of passive immunotherapy against A . baumannii using anti-Hyr1p antibodies , and investigate the functionality of these specific antibodies in abrogating Acinetobacter virulence in vitro and in a mixed species biofilm with C . albicans .
Bioinformatic , homology and energy-based modeling identified a number of conserved physicochemical structural domains within Hyr1p and several Gram-negative antigens . In particular , these algorithms identified multiple β-helical structures composed of parallel β-strands that typically create either two or three faces along within the holoprotein structure . Examples of β-helical templates identified included the phage φAB6 tailspike protein , which specifically binds oligosaccharides on the A . baumannii surface [47] , the Haemophilus influenzae high molecular weight ( HMW1 ) adhesin , and the filamentous hemagglutinin adhesin ( FhaB ) from Bordetella pertussis . Notably , FhaB has also been identified as an outer membrane protein of A . baumannii and is considered a protective immunogen [48] . Once the above domains were identified , they , along with other high scoring templates , were assembled and subjected to energy minimization and hydrogen-bond optimization to ultimately produce 3-D structural models for analysis in relation to Hyr1p ( iTasser model shown; Fig 1A ) . The resulting structure is largely β-helical with limited unstructured and extended domains . Based on this modeling strategy , C . albicans Hyr1p shares striking similarity to A . baumannii FhaB protein ( Fig 1B ) , and contains motifs that appear to be common to other target proteins expressed by this bacterium . To validate the results of our bioinformatics analysis , we raised rabbit polyclonal antibodies against 8 peptides of Hyr1p-N that were predicted to be surface exposed and highly antigenic [49] . These antibodies specifically recognized C . albicans hyphae in vitro and protected mice from hematogenously disseminated candidiasis [50] . The location of the eight peptide sequences on the 3-D structural model for Hyr1p are indicated in Fig 1C . To investigate the specificity of the anti-Hyr1p antibodies to Acinetobacter , we compared their binding capacity to A . baumannii vs . Pseudomonas aeruginosa ( which contains surface proteins predicted to have low homology to Hyr1p ) . Using flow cytometry and immunostaining , the antibodies raised against the Hyr1 8 peptides targeted log phase cells of A . baumannii ( 69–95% ) ( Fig 2 ) . In contrast , the same antibodies did not bind to P . aeruginosa ( Fig 2 ) . We also tested the ability of these antibodies to bind to several clinical isolates of XDR-A . baumannii with clonal variability [51] . The antibodies bound to all tested isolates , indicating that this recognition is not strain specific ( S1 Fig ) . These results demonstrate the specificity of anti-Hyr1p antibodies to A . baumannii and further validate the modeling strategy which revealed these unforeseen homologies and epitopes . We co-cultured Acinetobacter and Candida under planktonic , hyphal growth-permissive conditions . We found that Acinetobacter bound robustly to wild-type or hyr1/hyr1 + HYR1 complemented C . albicans hyphae , prevented elongation , and killed it within 4 h of co-culture as determined by Syto 13 nuclear staining showing fragmented nuclei ( Fig 3A ) . In contrast , a C . albicans hyr1/hyr1 mutant hyphae had minimal A . baumannii attachment and an elongated phenotype harboring intact nuclei ( Fig 3A ) . The role of Hyr1p as a receptor for Acinetobacter was verified by blocking Hyr1p function with anti-Hyr1p-N antibodies . These antibodies blocked bacterial attachment to the fungal hyphal cells ( Fig 3B ) , indicating that Hyr1p is a C . albicans receptor for A . baumannii . Furthermore , we found that at 24 h incubation with the 100 μg/ml antibodies the viability of A . baumannii decreased by 39% ± 4% when compared to bacteria incubated in the same media without the antibodies ( Fig 3C , P <0 . 05 ) . We also found that cell-free filtrates of an overnight culture of A . baumannii prevents C . albicans filamentation and biofilm formation in a static 96 well microtiter plates ( S2A Fig ) , indicating that A . baumannii inhibits C . albicans biofilm formation by secreted substance ( s ) that are yet to be identified . To minimize the effect of quorum sensing molecules secreted by both organisms , we studied the interactions of C . albicans with A . baumannii in a mixed species biofilm model in which there is a continuous flow of fresh medium . Indeed , in the flow system , C . albicans had higher viability when mixed with A . baumannii than in a static model ( S2B Fig ) . Under conditions of flow , and after 24 h of biofilm development , Acinetobacter bound to wild-type or hyr1/hyr1 + HYR1 complemented C . albicans hyphae and developed a visibly thick , white layer of bacterial cells ( Fig 3D ) . Mixed species biofilms formed with the C . albicans hyr1/hyr1 mutant did not display such a robust attachment of the bacteria to the fungi . Aliquots of the two respective biofilms , when stained with Syto13 and observed under the microscope , revealed enhanced attachment of bacterial cells to the wild-type or hyr1/hyr1 + HYR1 complemented fungal filaments , where the bacteria clustered together to fill up spaces between the hyphal cells ( Fig 3E ) . Nuclear staining additionally revealed that the wild-type or hyr1/hyr1 + HYR1 complemented cells were largely non-viable , displaying dark hyphae with degraded nuclei ( depicted by the white arrow in Fig 3E ) . On the other hand , there was a significant reduction in the attachment of A . baumannii to the C . albicans hyr1/hyr1 hyphae , with the cells exhibiting an overall intact nuclear stain and no dark hyphae ( Fig 3E ) . While there was a stark reduction in the numbers of bacteria attached to the hyr1/hyr1 mutant , the hyphae were not completely free of bacterial cells , suggesting receptors other than Hyr1p also contribute to this interaction . Als3p is a key C . albicans hyphal wall adhesin with roles in promoting attachment of other Gram negative bacteria such as P . aeruginosa , to hyphae [52] . Thus , we tested C . albicans als3/als3 mutant in interacting with A . baumannii . A . baumannii adhered robustly to als3/als3 filaments , just like the wild-type filaments ( S3 Fig ) . To identify the complimentary Hyr1p ligand on the bacterium that mediates binding of A . baumannii to C . albicans , we performed affinity purification of biotin-labeled A . baumannii cell membrane proteins using C . albicans hyphae . The following C . albicans strains were used for this interaction: wild-type , hyr1/hyr1 homozygous mutant , hyr1/hyr1 + HYR1 complemented strain , and als3/als3 as an additional control . A . baumannii protein bands that bound to different C . albicans hyphae were visualized on Western blot using anti-biotin antibodies . When incubated with membrane extracts of A . baumannii , C . albicans wild-type , hyr1/hyr1 + HYR1 complemented strain , or als3/als3 mutant hyphae bound a major band at 38 kDa . This band was largely absent when the cell membrane proteins were affinity purified with the hyr1/hry1 mutant ( Fig 4A ) . The mass of the 38 kDa band was equivalent to OmpA , and OmpA was reported to be an A . baumannii receptor for C . albicans hyphae [35] . Therefore , we repeated the affinity purification studies using anti-OmpA antibodies . As expected , this 38 kDa band was strongly bound by OmpA antibodies in the wild-type strain , but little or no binding was observed in the hyr1/hyr1 mutant ( Fig 4B ) . Finally , the identity of the band as OmpA was confirmed by using LC-MS . Thus , A . baumannii binds to C . albicans hyphae via OmpA/Hyr1p interactions . To explore its potential as a cross-kingdom protective antigen , we tested Hyr1p as an active vaccine target against A . baumannii infections in vivo . Mice were subcutaneously vaccinated on day 0 , with rHyr1p-N mixed with alum [49 , 53] , boosted with a similar dose on day 21 , and then infected with a lethal dose of XDR A . baumannii HUMC1 via i . v . injection on day 35 after making them diabetic . Diabetic mice were used because normal mice are resistant to infection , and diabetes is a risk factor for developing A . baumannii infections [51 , 54] . Consistent with our i . v . model [51] , the alum control mice had almost 100% mortality by day 2 post infection , while ~60% of the mice receiving the vaccine survived the infection even after 20 days ( Fig 5A ) . Surviving mice had no bacteria detected in their organs at day 21 and appeared healthy . Corroborating this protective effect , kidneys and lungs harvested as early as 3 days post infection from rHyr1p-N-vaccinated mice had >1 log reduction in their bacterial burden when compared to tissues harvested from mice vaccinated with alum alone ( Fig 5B ) . Further , the bacterial burden of spleen taken from rHyr1p-N-vaccinated mice strongly trended to be a log less than the spleen from alum vaccinated mice ( P = 0 . 05 ) ( Fig 5B ) . Next , we tested the efficacy of pooled purified IgG raised against the eight 14-mer peptides predicted to be surface exposed on Hyr1p-N ( Fig 1C ) [53] . Diabetic mice that prophylactically received the anti-Hyr1p IgG were almost completely protected from Acinetobacter bacteremia when compared to mice receiving isotype matching control antibody ( 85% survival for anti-Hyr1p antibodies treated mice vs . 0% for isotype matching control IgG ) ( Fig 5C ) . Of note , this protection was specific since anti-Hyr1p IgG did not protect mice from P . aeruginosa infection ( Fig 5C , p = 0 . 1 ) , which has no proteins that are predicted to share homology with Hyr1p and bound poorly to Hyr1p antibodies ( Fig 2 ) . We further investigated which peptide-targeting antibodies were responsible for conferring the majority of protection . Diabetic mice were prophylactically treated individually with 8 different antibodies raised against the peptides ( see Fig 1C for the location of the peptide on the modelled Hyr1p ) . Purified IgG from each of the generated polyclonal antibodies was administered 2 h prior to infecting the mice with a lethal dose of A . baumannii via tail vein injection . We found that Anti-Hyr1p IgG raised against peptide #5 ( LKNAVTYDGPVPNN ) [53] protected mice from infection similarly to the combined antiHyr1p IgG pool ( i . e . 85% survival ) ( Fig 5D ) . These results indicate that the protection conferred by the anti-Hyr1p IgG is largely conferred by peptide 5 in cross protection against Acinetobacter infection . Becuase penumonia is a major manifestation of the disease and commonly seen in severly immunosuppressed patients , we tested the ability of anti-peptide 5 antiboides to therapeutically treat A . baumannii penumonia in neutropenic mice . Immunosuppresed mice were infected with A . baumanni cells via inhalation and 24 h later were treated with doses of 100 or 30 μg of anti-peptide 5 purified IgG ( established infection ) . Mice that received a 100 μg of isotype matching control IgG had 100% mortaility by day 14 . In contrast , mice reciving 100 μg or 30 μg doses of anti-peptide 5 antibodies had 70% or 90% long-term survival , respectively ( Fig 5E ) . Surviving mice appeared healthy by day 20 when the expeirment was terminated . Additionally , surviving mice had no residual bacterial burden in their lungs as determined by quantitative culturing . These low doses of curative antibodies confirm their specific protection meachnism and their translational potential as a novel treatment for A . baumannii bacteremia and penumonia in different hosts ( diabetics and neutropenics ) . Because anti-peptide 5 antibodies protected mice from Acinetobacter infections , we tested their ability to recognize A . baumannii cross-reactive cell membrane antigens using high resolution 2-D Western blotting followed by MALDI-TOF-MS/MS analysis . Rabbit anti-peptide-5 antisera intensely recognized four protein spots as compared to pre-immune serum collected from the same rabbit ( Fig 6 ) . These spots were identified as OmpA , a putative ferric siderophore outer membrane binding protein ( TonB-dependent ) , a putative outer membrane protein , and FhaBp . As a complementary approach , we initiated a bioinformatics search to identify potential cross-reactive antigens homologous to peptide 5 in A . baumannii . Several such domains were identified and characterized in FhaBp and OmpA proteins of A . baumannii . Moreover , we identified the Acinetobacter Bacterial Immunoglobulin-like domain ( BIg ) family of proteins as having significant identity with the peptide 5 sequence ( 7/8 [88%] motif identity; 7/14 [50%] identity over the 14 residue span ) . Models of these domains were generated with identical and functionally conserved residues visualized as van der Waals space-filling spheres ( Fig 1D ) . Next , we set out to explain the protective mechanisms elicited by Hyr1p as an immunogen against A . baumannii . Due to the cross reactivity and homology of Hyr1p to FhaBp , and OmpA ( known adhesins/invasins in A . baumannii [55–57] ) we reasoned that the anti-Hyr1p antibodies may interfere with the ability of Acinetobacter to adhere to and/or invade host cells . We chose to investigate the effect of anti-peptide 5 antibodies on the ability of A . baumannii to invade human alveolar epithelial cell line ( A549 ) since pneumonia is a common manifestation of this bacterial infection . The pooled sera raised against the 8 peptides reduced the ability of A . baumannii to invade alveolar epithelial cells by ~70% , while sera raised against peptide 5 almost completely blocked invasion when compared to pre-immune sera ( Fig 7A ) . As mentioned earlier , the antibodies raised against 8 different peptides of Hyr1p , displayed inhibitory effect on A . baumannii viability when incubated with the bacterium for longer periods ( e . g . ≥20 h , Fig 3C ) . To expand on this observation , we tested if the protective anti-peptide 5 serum would display similar or enhanced killing of the bacteria . Indeed , we found that anti-peptide 5 serum , but not anti-peptide 3 serum [which did not protect mice from A . baumannii infection ( Fig 5D ) ] , inhibited the growth of Acinetobacter in vitro when incubated with the bacterium for 20 h . The antiserum did not inhibit the growth of P . aeruginosa ( Fig 7B ) . Further , we determined that this growth inhibition of A . baumannii by anti-peptide 5 serum was bactericidal ( Fig 7C ) . The killing activity of the anti-peptide 5 serum was not abrogated by heating the serum at 60°C for 60 min prior to incubating with the bacteria . Collectively , these results support the specificity of the anti-peptide 5 antibodies in protecting against A . baumannii infection , and demonstrate that the growth inhibition of the anti-peptide 5 antibodies is independent of complement . Our Western blotting analysis using anti-peptide 5 serum ( Fig 6 ) showed that a siderophore outer membrane binding protein ( TonB-dependent ) is a candidate cross-reactive antigen to Hyr1p . Also , our structural homology modeling as well as Western blotting studies implicated OmpA as another antigen that is likely to cross react to Hyr1p antibodies . Both proteins are involved in iron acquisition [58 , 59] . Therefore , we hypothesized that the antibodies might exert their inhibitory effect by iron starvation . Thus , we tested the inhibitory effect of anti-peptide 5 serum in the presence or absence of different concentrations of iron supplementation . Addition of iron in the form of FeSO4 reversed the bactericidal activity of the antibodies . This reversal was concentration dependent with 30 μM FeSO4 showing around 50%-80% reversal of the serum inhibitory effect while 100 μM FeSO4 had almost complete reversal of the bactericidal activity . Consistent with the toxic effect of higher iron concentrations [60] , FeSO4 at 300 μM had less degree of reversal of the bactericidal activity of anti-peptide 5 serum ( Fig 7D ) . These results indicate that the cidal activity of the anti-peptide 5 serum is due , at least in part , to blocking the ability of the bacterium in acquiring iron . Given that anti-peptide 5 antibodies have significant inhibitory activity against A . baumannii growth , we questioned if it could make this XDR organism more susceptible to antibiotics . We chose two drugs for the combination studies: 1 ) imipenem is a carbapenem often used as first line therapy to treat A . baumannii infection; and 2 ) colistin is an antibiotic often used as a last resort for treatment of XDR A . baumannii infections . The HUMC1 study strain ( an XDR clinical isolate ) exhibited a 50% inhibitory concentration ( IC50 ) of imipenem at 32 μg/ml per CLSI method . Colistin exhibited an IC50 of 2 μg/ml against this A . baumannii strain . Likewise , the IC50 of the anti-serum against the test inoculum was a 12 . 5% dilution ( vol/vol ) . When the antibodies were combined with serial dilutions of imipenem , we observed a significant synergistic effect in which the IC50 was reduced from 32 to 4 μl/ml ( Fig 8A ) . A modest but additive effect was observed with antisera combined with colistin , lowering the IC50 to 0 . 5 or 1 . 0 μg/ml when compared to either colistin or the anti-peptide serum alone ( Fig 8B ) . Control pre-vaccinated serum at the identical dilution did not produce this inhibition effect . We further confirmed these results by conducting time kill assays using sub-inhibitory concentrations of colistin at 0 . 5 , 0 . 25 , and 0 . 125 MIC with or without serum ( at 12 . 5% ) . Clear and significant synergistic effect was noticed in killing A . baumannii when the anti-peptide serum was combined with the sub-inhibitory colistin concentrations especially after 24 h of incubation resulting in 50–90% reduction of bacterial count when compared to colistin or anti-peptide serum alone with the highest effect seen with serum combined with the 0 . 5 MIC colistin ( S3 Fig ) . A . baumannii commonly occupies shared niches with C . albicans in patients , and the two organisms can form mixed species biofilms [61 , 62] . Thus , we studied the inhibitory effect of the anti-Hyr1 antibodies in controlling A . baumannii/C . albicans in a mixed biofilm model with or without colistin . The two organisms were grown together for 6 h to initiate development of biofilm . Anti-peptide 5 serum diluted to 12 . 5% ( as above ) was added to the mixed species biofilm with or without 0 . 5 μg/ml colistin , and the biofilms were allowed to evolve for another 12 h . By XTT quantification , in the presence of 12 . 5% anti-peptide 5 antiserum or colistin alone , mixed species biofilms displayed inhibitions of 50% or 30% , respectively . When the biofilms were treated with a combination of colistin and serum , an inhibition of ~70% was observed ( Fig 8C ) . The inhibitory effect of anti-peptide serum combined with colistin is evident in micrographs taken from each individual culture showing decimation of bacterial growth without affecting Candida viability and growth ( Fig 8D ) . These results suggest that immunotherapeutic strategies targeting Hyr1p can act additively with antibiotics to significantly reduce a complex A . baumannii/C . albicans biofilm formation .
Healthcare-associated infections are often caused by commensal organisms . These organisms frequently occupy shared host niches and exploit common vulnerabilities in the compromised host . In the setting of immune deficiency , such commensals exert similar virulence mechanisms ( e . g . adherence to and invasion of host cells , iron acquisition , immune avoidance ) to cause disease . It is logical that such organisms employ common virulence factors structurally and functionally evolved to be convergent in their interaction with the host . In turn , it is highly likely that the host has devised countermeasure defense strategies to recognize and protect against such infections . We hypothesize that such adapted host defense mechanisms can be harnessed for the development of novel immunotherapeutic strategies [38] . We have applied innovative computational molecular modeling and bioinformatic strategies to discover novel vaccine antigen candidates that target more than one high priority human pathogens . This strategy to identify convergent antigens has been validated in cross-kingdom immuno-protection against C . albicans and S . aureus [39 , 40] , in which the C . albicans adhesins/invasins Als family of proteins share structural and functional homology with MSCRAMMs of S . aureus ( e . g . clumping factor A ) [42] . Herein , we used this strategy to identify significant 3-D structural homologies among C . albicans Hyr1p and several candidate antigens of the XDR A . baumannii , two organisms that share similar host niches and previously known to be isolated from the same medical devices [32 , 33] . Indeed , using different mouse models , active or passive immunization with the Hyr1p target , protected mice from A . baumannii infections . Interestingly , as was observed for Als3 and homologues in S . aureus [42] , there are relatively low degrees of sequence identity between Hyr1 and the identified template proteins . Nonetheless , strong protective efficacy in mice was seen in cross-kingdom immunization studies of Als3p vs . S . aureus [39 , 40] and with Hyr1p vs A . baumannii in this study . The observed protection among cross-kingdom antigens are likely due to highly conserved B cell epitopes given the nature of high degree of 3-D structural homology [38] . Homology modeling identified striking similarity in 3-D structures common to Hyr1p and FhaBp . Most notable among these shared structures were peptide 5 from Hyr1 of C . albicans , and its counterpart motif in the FhaBp of A . baumannii . Interestingly , our in vivo passive immunization studies identified antibodies targeting peptide # 5 as the most protective amongst the 8 peptide pool . These anti-Hyr1 antibodies also recognized other A . baumannii antigens including OmpA and outer membrane siderophore binding proteins . Bioinformatic analysis and computational modeling of these proteins further revealed that peptide 5 shares significant sequence identity with FhaB , OmpA and an immunoglobulin protein ( Blg ) that was not detected in our Western blotting analysis . Although the anti-peptide 5 antibodies reacted to FhaBp and OmpA , we do not know which of these antigens might be the targets for the protective activity seen with mice passively or actively vaccinated with Hyr1p . However , it has been previously reported that mice vaccinated with rOmpA are protected from A . baumannii bacteremia [63 , 64] . The lack of detection of Blg in our Western blot studies could be attributed to conducting these analyses under conditions that are not inducing of the expression of the protein or due to technical deficiencies in isolating high molecular weight proteins ( Blg is >260 kDa in mass ) . Our in vitro studies identified two virulence mechanisms that are blocked by the anti-peptide 5 polyclonal antibody . Namely , the ability of A . baumannii to invade alveolar epithelial cells was abrogated in the presence of anti-peptide 5 antibody . In addition , this antibody appeared to have direct killing capacity of the A . baumannii study strain . Further , the bactericidal activity of the antibodies was reversed in the presence of exogenous iron , indicating that the antibody blocked iron uptake of the bacterium as part of its bactericidal mechanism . It is prudent to note that blocking of invasion cannot be attributed to the bactericidal activity of the antibody because the invasion assay is conducted over a 1 h period , while the killing assay is performed after 18–24 hours incubation . Supporting our current findings , OmpA was reported to mediate A . baumannii invasion of epithelial cells [55] . Similarly , an OmpA deficient mutant was shown to be defective in growth under iron-limited conditions as compared to wild-type cells [58] . These results implicate OmpA in both cell adhesion and iron acquisition as virulence functions of A . baumannii . Furthermore , a TonB-dependent outer membrane siderophore that reacted to anti-peptide 5 antibodies in Western blotting assays had been previously shown to be directly involved in A . baumannii iron acquisition [59] . Finally , the hemagglutinin ( FhaBp ) is a known adhesion/invasin of A . baumannii [56 , 57] . Although it is currently unknown if the anti-peptide 5 serum kills the bacteria by specifically binding to the OmpA , siderophore binding protein , and/or FhaBp , our data points to a putative iron starvation mechanism as potential explanation to the protective effect seen with the anti-peptide 5 antibodies in vivo . Studies to determine if the in vivo mechanism ( s ) of protection elicited by Hyr1p vaccination are related to these interesting in vitro observations are currently under investigation . Additionally , the role of OmpA , TonB-dependent outer membrane siderophore binding protein , and/or FhaBp as cross-reactive antigens to Hyr1p , and their definitive role in eliciting murine protection are currently under investigation . Previous studies showed that A . baumannii binds to C . albicans hyphae via OmpA [35] . Our current study demonstrates that Hyr1 serves as a C . albicans receptor for OmpA through multiple lines of evidence . First , under planktonic conditions , A . baumannii was able to bind to and kill C . albicans wild-type and hyr1/hyr1+HYR1 complemented hyphae but not hyr1/hyr1 null mutant hyphae ( Fig 3A ) . Second , anti-Hyr1p antibodies blocked the ability of A . baumannii to bind to C . albicans hyphae ( Fig 3B ) . Third , A . baumannii is able to bind to and develop mixed species biofilm with C . albicans wild-type and hyr1/hyr1+HYR1 complemented cells but not with hyr1/hyr1 deletion mutant ( Fig 3D and 3E ) . Finally , C . albicans wild-type or hyr1/hyr1+HYR1 complemented strains were able to bind OmpA as detected by affinity purification assays , while the hyr1/hyr1 mutant displayed significantly reduced OmpA binding ( Fig 4 ) . This interaction appears to be clinically significant given the recent report that Candida species airway colonization together with A . baumannii , during ventilator-associated pneumonia ( VAP ) are common among ICU patients . In fact , Candida species airway colonization is identified as an independent risk factor for development of A . baumannii VAP [62] , as well as P . aeruginosa VAP [65] . Our findings collectively highlight the potential of using Hyr1 directed antibodies as therapeutic strategies targeting A . baumannii infections including in settings of medical devices where biofilm formation is prominent . Importantly , the antibodies lowered the concentrations of currently-used antibiotics needed to impair growth of the bacterium , including those which are deemed ineffective against XDR A . baumannii ( i . e . imipenem ) . The antibodies were also effective in mitigating mixed species biofilms , resulting in an additive reduction in organism burden in combination with increased susceptibility to antimicrobial agents . Although these findings are yet to be confirmed in an in vivo model of infection , they provide strong rationale for the combined use of active or passive immunotherapy with antibiotics in treating life-threatening A . baumannii infections . A scarcity of novel anti-Acinetobacter agents in the development pipeline , an escalating population of individuals at risk for Acinetobacter infections , and the emergence of XDR , and in some cases strains pan-resistant to all known antibiotics [66 , 67] , increasingly threaten global and personal health . Thus , novel immunotherapeutic approaches to reduce the incidence and severity , or enhance successful treatment of infections caused by such organisms , are highly attractive and likely to yield significant reductions in morbidity and mortality . Moreover , such approaches would be expected to decrease overall use of antibiotics , in turn reducing pressures that select for resistance . Finally , the current studies reinforce the innovative application of convergent immunity [68] to enhance the efficacy of anti-infective vaccines and immunotherapies targeting highest-priority pathogens that are increasingly resistant to conventional antimicrobial agents .
The following bacterial strains were used in the study: A . baumannii HUMC1 , HUMC6 , and HUMC12 –all are XDR clinical strains resistant to all antibiotics except colistin; P . aeruginosa PA01 ( a MDR wound isolate ) [69] . Wild-type C . albicans strains SC5314 and SN250 were used in this study and have been previously described [70 , 71] . The hyr1/hyr1 mutant , and hyr1/hyr1+HYR1 complemented strains were made from SN250 [71] while als3/als3 mutant was originated from SC5314 [72] . C . albicans and A . baumannii were grown overnight in Yeast , peptone , ( 2% ) dextrose ( YPD ) medium , and in Brain heart infusion broth ( BHI ) , respectively . For hyphal development under planktonic conditions , C . albicans overnight culture was washed and inoculated at a concentration of 1x106 cells/ml ( determined by counting using a hemocytometer ) into a pre-warmed 50:50 mixture of YPD:BHI at 37°C , for 2 h . Bacterial counts were determined using McFarland standard using optimal density at 600 nm . 6x His tagged C . albicans rHyr1p-N was produced in E . coli and purified by Ni-agarose affinity column as previously described [49] . Endotoxin was removed from rHyr1p-N using ProteoSpin Endotoxin Removal kit ( Norgen Bioteck Corporation , Ontario , Canada ) , and the endotoxin level was determined with Limulus Amebocyte Lysate endochrome ( Charles River Laboratories ) , per manufacturer’s instruction . Using this procedure , endotoxin was reduced to <0 . 1 EU per dose of the vaccine . C . albicans and A . baumannii were cultivated together ( 2:1 ratio ) under similar growth settings for 2 h . In certain experiments , the organisms were co-cultured or grown individually , in the presence of anti-Hyr1p polyclonal antibodies ( 100 μg/ml ) . Post-incubation , the cells were either visualized by bright field microscopy , or stained with 25 μM concanavalin A ( Con A ) –Alexa 594 and/or 5 μM Syto 13 dyes , then imaged by Confocal Scanning Laser Microscopy ( CSLM ) ( both dyes , Thermo Fisher Sci . Waltham , MA ) . Con A stains the cell wall of fungi red , and Syto 13 stains nuclei green . For biofilm growth , two different models were used—First , a static model: that entailed growth of organisms in 96 well microtiter plate for 24 h under non-shaking conditions , as previously described [73] . C . albicans and A . baumannii were co-cultured at 2:1 ratio ( 1x106 cells/ml C . albicans: 5x105 cells/ml A . baumannii ) in the wells of the microtiter plate ( 100 μl final volume ) for 24 h and visualized under CLSM after staining , as above . C . albicans viability in the presence and absence of A . baumannii was measured by collecting the cells from the wells , and plating dilutions on YPD plates + 5 μg/ml colistin ( a concentration that kills A . baumannii HUMC1 [MIC of 2 μg/ml] ) . In some experiments , C . albicans were allowed to develop biofilms in the presence of A . baumannii spent medium . For this , three-day-old culture of BHI-grown Acinetobacter was centrifuged and filter sterilized , and concentrated by ethyl acetate followed by evaporation . The dried residue was dissolved in RPMI medium and used for C . albicans biofilm development . The extent of biofilm growth was compared to C . albicans grown in RPMI alone , and the overnight-grown biofilm activity measured by the XTT assay , by following a previously published protocol [73] . Biofilms were also developed using a simple flow biofilm model . In this system , cells adhered to silicone strips are allowed to proliferate under continuous flow of fresh medium [74] . For mixed species biofilm cultivation , a suspension of C . albicans cells ( 5x106 cells/ml ) in 50:50 YPD:BHI medium , was layered on top of the strip , and incubated for three hours at 37°C , to promote adhesion . Next , the suspension was decanted and the strip harboring adhered fungal cells were layered with a culture of A . baumannii for another two hours . One set of strips were adhered with C . albicans alone , as a comparative control . The strips were then introduced into the flow chamber , and media made to flow over the strip at the rate of 500 μl/min . After 24 h , the viability of C . albicans in the biofilm was quantified by cutting the strips into equal sizes ( 0 . 5 cm ) , vortexed vigorously , sonicated for 5 s at setting 3 , diluted and plated on YPD containing colistin as above . The cells were also teased out , stained with 5 μM Syto 13 for 10 minutes and visualized under CLSM . Log phase bacterial cells were incubated for 1 h with 3 or 30 μg/ml of pooled serum raised against 8 peptides of Hyr1p that is predicted to be antigenic and surface exposed . The cells were washed and counter stained with 10 μg/ml anti-rabbit IgG conjugated to Alex 488 ( Thermofisher Scientific ) prior to determining the binding capacity of the antibodies by using a FACSCalibur ( Becton Dickinson ) instrument equipped with an argon laser emitting at 488 nm . Fluorescence data were collected with logarithmic amplifiers . The population % fluorescence of 104 events was calculated using the CellQuest software . A . baumannii HUMC1 membrane preparations were produced as described before [75 , 76] . Briefly , the bacterium was grown overnight at 37°C with shaking in BHI . Cells were passaged in fresh medium for 3 h ( log phase ) at 37°C with shaking , washed , and the resultant pellet was resuspended in disintegration buffer ( 7 . 8 g/L NaH2PO4 , 7 . 1 g/L Na2HPO4 , 0 . 247 g/L MgSO4 7 . H2O + protease inhibitor mix ( GE Healthcare ) + nuclease mix ( GE Healthcare ) and sonicated on ice for 3x for 5 min each with the unbroken cells separated by centrifugation at 1 , 500 g . The supernatant was centrifuged for 30 min at 4°C at 4 , 500 rpm and was passed through a 0 . 45 μM filter to remove any additional cell debris . An equal volume of ice-cold 0 . 1 M sodium carbonate ( pH 11 ) was added to the resulting supernatant and the mixture was stirred slowly overnight , on ice . Membrane proteins were collected by ultracentrifugation at 100 , 000 g for 45 min at 4°C , and the membranes were re-suspended in 500 ml water . Finally , the protein extract was processed with a 2-DE Cleanup Kit ( Bio-Rad ) . Two dimensional SDS/10%-PAGE gels of membrane preparations were used to separate proteins by size and isoelectric focusing ( IEF ) , as described [77 , 78] . For isoelectric focusing ( IEF ) , the Bio-Rad-PROTEIN IEF system was used with 4–7 pH gradient strips ( ReadyStrip IPG strips , Bio-Rad ) . Proteins were solubilized in 8 M urea , 2% ( w/v ) CHAPS , 40 mM DTT and 0 . 5% ( v/v ) corresponding rehydrated buffer ( Bio-Rad ) . The strips were rehydrated overnight and underwent electrophoresis at 250 V for 20 min , 4000 V for 2 h , and 4 , 000 V for 10 , 000 V-h , all at room temperature . Prior to the second dimension ( SDS-PAGE ) , the focused IPG strips were equilibrated with buffer I and II for 10 min ( ReadyPrep 2-D Starter Kit , Bio-Rad ) . The proteins were separated on 8–16% Criterion precast Gel ( Bio-Rad ) and transferred to immune-Blot PVDF membranes ( Bio-Rad ) . Membranes were treated with Western Blocking Reagent ( Roche ) overnight and probed with pre-immune or anti-peptide 5 serum . Membranes were washed and incubated with secondary , HRP-conjugated goat anti-rabbit IgG ( Santa Cruz Biotech ) . After incubation with SuperSignal West Dura Extended Duration Substrate ( Pierce ) , signals were detected using a CCD camera . The candidate band from SDS-PAGE was cut and microsequenced using MALDITOF MS/MS ( UCLA Molecular Instrumentation Center ) as previously described [63] . The resulting MS/MS spectra was searched against the A . baumannii strain ATCC 17978 database [63] . Male BALB/c or CD-1 mice were used for all experiments . Diabetes was induced by intraperitoneal injection of 210 mg/kg streptozotocin in 0 . 2 ml citrate buffer 10 days prior to infection . Glycosuria and ketonuria were confirmed in all mice 7 days after streptozotocin treatment , as previously described [51 , 79] . Neutropenia was induced by intraperitoneal injection of cyclophosphamide ( 200 mg/kg ) and subcutaneous administration of cortisone acetate ( 250 mg/kg ) on days -2 and +3 , relative to infection [51] . For the hematogenously disseminated model , mice were infected intravenously with 5 x 107 cells in 0 . 2 ml phosphate buffered saline ( PBS ) of log phase cells of A . baumannii HUMC1 or P . aeruginosa PA01 . For the pneumonia model , mice were infected by aerosolizing bacterial cells in an inhalational chamber through a nebulizer as we previously described [51] . Briefly , mice were introduced to a Plexiglas exposure chamber ( South Bay Plastics ) prior to aerosolizing a 12 mL suspension of bacteria cells ( 1 . 0 x 1011 cells/mL ) via a small-particle nebulizer ( Hudson Micro Mist; Hudson RCI ) driven by compressed air at 100 lb/in2 [80] . A standard exposure time of 1 h was used for all experiments to allow time for complete aerosolization and uniform exposure of the mice . To determine the inhaled inoculum , three mice from each experiment were sacrificed immediately after the procedure and their lungs collected and quantitatively cultured on tryptic soy agar ( TSA ) plates . For survival experiments , mice were followed for at least 20 days , while for tissue bacterial burden , mice were sacrificed at Day +3 , relative to infection . Target tissues were harvested and bacterial burden enumerated by quantitative culturing of colony forming units ( CFU ) . Mice were vaccinated subcutaneously with 30 μg of rHyr1p-N in PBS mixed with 0 . 1% aluminum hydroxide ( alum; Brenntag Biosector ) on Day 0 , boosted with a similar dose on Day +21 , made diabetic on Day +25 prior to infecting them on Day +35 intravenously as described above . Control mice were vaccinated similarly with PBS alone mixed with alum . For passive immunization , diabetic or neutropenic mice were treated with a single dose of pooled anti-Hyr1p antibodies or antibodies raised against the individual peptides either 2 h prior ( prophylactic ) or a day after infecting the mice ( therapy ) . In the therapy experiment , a repeat dose was administered 8 days following the infection . The doses of the antibodies are indicated in the figure legends . Bacterial cells ( 1 x 105 cells ) in Mueller Hinton II ( MHII ) medium were incubated in 96-well plate at 37°C for 20 h with varying concentrations of the immune serum in the presence or absence of 30-300 μM FeSO4 . Killing activity of anti-peptide 5 serum was enumerated by CFU following sonication , and the results expressed as % killing relative to cells incubated without any anti-peptide 5 serum . Susceptibility testing of anti-peptide 5 serum and/or antibiotics , or their combination , were performed in 96-well microtiter plates . A . baumannii ( 1x105 cells/100 μl ) were treated with serum alone ( 12 . 5%/well ) , colistin alone ( concentrations ranging from 2–0 . 125 μg/ml ) or a combination of serum and different concentrations of colistin . Some wells that contained only bacterial cells , free of any treatment were included as controls . The plates were incubated at 37°C for 16 h , and turbidity in each well measured spectrophotometrically at OD 600 . The same protocol was utilized for testing another drug , imipenem ( concentrations ranging from 32–2 μg/ml ) . For a set of experiments , serum , antibiotics and their combination , were used against mixed species biofilms , and their impact was measured at OD 600 . To evaluate the potential enhanced activity of colistin in combination with anti-peptide 5 serum against A . baumannii in vitro ( time kill curves ) , 1x105 bacterial cells [1×106 colony-forming units ( CFU ) /ml] were transferred into wells of a 96-well plate containing MH medium with 0 . 5X , 0 . 25X and 0 . 125X MIC of colistin ( 1 μg/ml , 0 . 5 μg/ml , 0 . 25 μg/ml ) or a combination of the individual concentrations of colistin with 12 . 5% serum and incubated at 37°C . Inoculated MH medium without drug/serum served as controls . Aliquots were obtained at 0 , 2 , 4 , 8 , 12 and 24 h for quantification and data presented as CFU versus time . A . baumannii membranes were prepared as above . The cell membrane protein extracts were biotin labeled by incubation with Ez-Link Sulfo-NHS-LS Biotin ( 0 . 5 mg/ml; Pierce ) for 15 minutes at 37°C . Pre-germinated short hyphae ( 1 x 106 ) of C . albicans SC5314 , C . albicans hyr1/hyr1 , C . albicans hyr1/hyr1+HYR1 complemented , or C . albicans als3/als3 were incubated for 1 h on ice with 250 μg biotin-labeled A . baumannii cell surface proteins in PBS plus 1 . 5% n-octyl-β-d-glucopyranoside and protease inhibitors . Unbound proteins were washed away by three rinses with the same buffer . The Acinetobacter cell proteins that remained bound to the fungal cells were eluted twice with 6M urea ( Fluka ) , and the proteins separated on 8–16% SDS-PAGE and transferred to PVDF-plus membranes ( GE Water & Process Technologies ) . The membrane was treated with Western Blocking Reagent ( Roche ) and probed with a 1:1000 dilution of anti-biotin antibody ( Abcam , Cambridge , MA ) , followed by incubation with SuperSignal West Dura Extended Duration Substrate ( Pierce ) . Some blots were also probed with 1:1000 diluted anti-E . coli OmpA antibodies ( Antibody research Co . , St . Peters , MO ) and signals detected using a CCD camera . Protein bands of interest were excised and identified by MALDI-TOF—MS/MS as above . Complimentary homology and energy-based modeling algorithms were conducted to characterize and compare the overall physicochemical and structural features of C . albicans Hyr1p . Further , these two protocols were used to prioritize potential structural domains that may serve as epitopes for cross reactivity of anti-Hyr1 antibody . While both protocols involve the use of homology-based threading algorithms , initial studies made use of the Phyre2 modeling software package [81] that prioritizes remote template detection , alignment , 3-D modeling and ab initio protocols . Model refinement was carried out using the iTasser server [82 , 83] which utilizes a meta-threading approach to identify PDB templates which are then assembled into continuous domains using replica-exchange Monte Carlo simulations and ab initio modeling . Notably , the iTasser server has consistently ranked as a top homology modeling application and was ranked as the top free modeling protocol in a recent independent modeling study [84] . As a confirmatory measure , additional stochastic modeling was carried out using the Quark server [83] . Select regions of resulting comparative homologues were then subjected to 3-D alignment to identify areas of greatest homology using the Smith-Waterman [85] algorithm as implemented within Chimera [86] . Sequence alignments to identify putative shared epitopes between Hyr1 and other proteins were carried out using CLUSTALW [87] . All procedures involving mice were approved by the IACUC of the Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center ( Protocol number 20295 ) , according to the NIH guidelines for animal housing and care . Moribund mice according to detailed and well-characterized criteria were euthanized by pentobarbital overdose , followed by cervical dislocation . The nonparametric log-rank test was used to determine differences in survival times . The nonparametric Wilcoxon rank sum test was used to analyze differences in tissue bacterial burden , the ability of anti-peptide 5 to affect invasion of host cells , to kill A . baumannii , or to assess the effect of combination treatment of the anti-peptide 5 sera with antibiotics on bacterial survival and biofilm formation . For all comparisons , a P value < 0 . 05 was considered significant . | Different pathogens share similar medical settings and rely on similar virulence strategies to cause infections . We have applied computational modeling and bioinformatics to discover novel antigens that target organisms sharing ecological niches in the intensive care units ( ICUs ) : the fungus Candida albicans , and the multidrug resistant ( MDR ) Gram-negative bacterium , Acinetobacter baumannii . We identified that C . albicans hyphal wall protein Hyr1p shares significant structural homology to A . baumannii cell surface proteins , and is the receptor for A . baumannii binding to the fungus . Active vaccination ( with rHyr1p ) or passive immunization ( anti-Hyr1p antibodies ) protect mice from A . baumannii bacteremia and pneumonia . Anti-Hyr1p antibodies act synergistically with antibiotics in abrogating mixed species biofilms . Our groundbreaking studies reveal novel cross-kingdom immunotherapeutic strategies that target healthcare-associated MDR infections . | [
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] | 2018 | The Hyr1 protein from the fungus Candida albicans is a cross kingdom immunotherapeutic target for Acinetobacter bacterial infection |
Cyclic nucleotides are universally used as secondary messengers to control cellular physiology . Among these signalling molecules , cyclic di-adenosine monophosphate ( c-di-AMP ) is a specific bacterial second messenger recognized by host cells during infections and its synthesis is assumed to be necessary for bacterial growth by controlling a conserved and essential cellular function . In this study , we sought to identify the main c-di-AMP dependent pathway in Streptococcus agalactiae , the etiological agent of neonatal septicaemia and meningitis . By conditionally inactivating dacA , the only diadenyate cyclase gene , we confirm that c-di-AMP synthesis is essential in standard growth conditions . However , c-di-AMP synthesis becomes rapidly dispensable due to the accumulation of compensatory mutations . We identified several mutations restoring the viability of a ΔdacA mutant , in particular a loss-of-function mutation in the osmoprotectant transporter BusAB . Identification of c-di-AMP binding proteins revealed a conserved set of potassium and osmolyte transporters , as well as the BusR transcriptional factor . We showed that BusR negatively regulates busAB transcription by direct binding to the busAB promoter . Loss of BusR repression leads to a toxic busAB expression in absence of c-di-AMP if osmoprotectants , such as glycine betaine , are present in the medium . In contrast , deletion of the gdpP c-di-AMP phosphodiesterase leads to hyperosmotic susceptibility , a phenotype dependent on a functional BusR . Taken together , we demonstrate that c-di-AMP is essential for osmotic homeostasis and that the predominant mechanism is dependent on the c-di-AMP binding transcriptional factor BusR . The regulation of osmotic homeostasis is likely the conserved and essential function of c-di-AMP , but each species has evolved specific c-di-AMP mechanisms of osmoregulation to adapt to its environment .
Cyclic nucleotides are signalling molecules , commonly called second messengers , which regulate cellular processes by binding to targeted effectors [1–3] . Specific cyclic di-nucleotides are synthesized by prokaryotes and eukaryotes , and this specificity is exploited by host cells to monitor bacterial infections [4 , 5] . For example , cyclic-di-AMP ( c-di-AMP ) is synthesized by almost all bacteria , except proteobacteria , and induces a type I interferon response through targeting the STING sensor . STING is also activated by the eukaryotic cyclic di-nucleotide 2’5’cGAMP that is generated in response to the presence of bacterial DNA in the host cytosol [6–9] . Some bacterial pathogens have evolved mechanisms to modulate the immune response to c-di-AMP [9 , 10] , but the understanding of the role of c-di-AMP in bacterial physiology and during infection remains limited . Unlike other second messengers , the synthesis of c-di-AMP was originally assumed to be essential for bacterial growth in standard in vitro conditions [11 , 12] . Yet , genes encoding for essential proteins might be inactivated in specific conditions or their inactivation can be compensated by secondary mutations [13] . This is the case for c-di-AMP synthesis in Listeria monocytogenes , in which spontaneous mutations in genes involved in central metabolism and in adaptation to starvation allow growth without c-di-AMP [14] . Accordingly , c-di-AMP synthesis was shown to be dispensable for growth on minimal media by limiting the downstream effect of the ( p ) ppGpp alarmone on the global regulator CodY [14] . Additionally , spontaneous mutations in pyruvate carboxylase ( PycA ) , an enzyme of the tricarboxylic acid ( TCA ) cycle , also lead to a toxic accumulation of metabolites in the absence of c-di-AMP in several lactic acid bacteria [15–17] . However , the compensatory mechanism appears distinct in other bacteria . In Bacillus subtilis , c-di-AMP synthesis is essential in rich media [18 , 19] , but the absence of c-di-AMP synthesis can be compensated by spontaneous mutations leading to an increased activity of the NhaK cation/proton antiporter allowing to overcome potassium toxicity [20] . In Staphylococcus aureus , c-di-AMP synthesis becomes dispensable when accumulating mutations in amino acid and osmolyte transporters , as well as through mutations in genes encoding for proteins required for respiration , linking c-di-AMP essentiality with osmoregulation and metabolism [21] . Furthermore , it has been shown that c-di-AMP binds to and regulates protein activities or riboswitches [12] . Notably , several RCK_C domain ( regulator of conductance of K+ ) -containing proteins bind c-di-AMP [22] . RCK_C domains are present mainly in Ktr/Trk potassium transporter family proteins and c-di-AMP negatively regulates their transporter activities in different species [22–25] . C-di-AMP also often binds to and regulates the activity of CBS ( cystathionine-ß-synthase ) domains , a widespread nucleotide binding domain [26] present in osmoprotectant transporters , such as in OpuCA homologues [27 , 28] , and in proteins of unknown function [16] . Osmoprotectants , such as glycine betaine or carnitine , are compatible solutes , which are necessary together with potassium , to tolerate hyperosmotic shock [29 , 30] . The KdpDE two-component system of S . aureus [31] and the ydaO riboswitch in B . subtilis [20 , 32] bind c-di-AMP to control the expression of potassium transporters [20 , 32] . Direct regulation of the pyruvate carboxylase activity by c-di-AMP in L . monocytogenes might also be related to intracellular potassium homeostasis through TCA-dependent accumulation of glutamate acting as a counterion of potassium [15 , 16 , 33] . In this study , we have characterized the ‘essential’ c-di-AMP function in Streptococcus agalactiae ( the Group B Streptococcus , GBS ) , the main etiological agent of bacterial invasive infection in neonates [34] . GBS synthesizes and releases c-di-AMP in infected macrophages , but limits its detection by the host immune system by degrading extracellular c-di-AMP with a cell wall-anchored ectonucleotidase [10] . By analysing c-di-AMP synthesis in GBS , we report here that osmotic homeostasis is the critical cellular function regulated by c-di-AMP . The main mechanism involves binding of c-di-AMP to the transcription factor BusR which negatively regulates the expression of the busAB operon encoding for the glycine betaine BusAB transporter . Overall , c-di-AMP-dependent regulation of potassium and compatible solute transporters is conserved , but specific mechanisms of osmoregulation are present in each species and c-di-AMP also regulates these species-specific mechanisms to remain a central osmoregulator .
In the GBS genome , a single gene , thereafter named dacA , encodes a protein containing a DisA_N domain ( PF02457 Pfam domain ) , the only known domain with c-di-AMP synthesis activity [11] . The dacA gene is localized in a highly conserved three-gene operon encoding DacA , a putative DacA activity regulator ( Gbs0903 ) and the essential GlmM enzyme ( Gbs0904 ) involved in synthesis of cell-wall metabolite precursors [9 , 11 , 18 , 19] . All attempts to inactivate dacA using standard protocols were unsuccessful , suggesting that dacA is an essential gene . Therefore , a conditional ΔdacA mutant was constructed in a strain bearing an ectopic copy of dacA cloned on a replicative vector and transcribed from the anhydrotetracycline ( aTc ) -inducible promoter PtetO ( S1 Fig ) . The growth of the ΔdacA / PtetO_dacA mutant is aTc dose-dependent on TH medium incubated in aerobic growth conditions ( Fig 1A ) . The mutant does not grow in the absence of aTc , while its growth was similar to that of the WT strain in presence of 50 ng/ml aTc . By testing Granada medium , a GBS-specific medium developed to detect the orange-red polyenic pigment granadaene under anaerobic conditions [35] , we unexpectedly observed growth of the ΔdacA / PtetO_dacA mutant in the absence of aTc ( Fig 1B ) . This anaerobic growth is independent from the medium components since it was also observed when grown in TH or Columbia Horse Blood ( COH ) agar . In contrast , the growth is aTc-dependent in aerobic condition whatever medium used ( Fig 1B ) . In addition , ΔdacA / PtetO_dacA colonies are not pigmented and not hemolytic in anaerobic conditions , unless the aTc-dependent ectopic dacA copy was expressed ( Fig 1B ) . This indicates that c-di-AMP synthesis is necessary for granadene production , the GBS pigment that is also a ß-hemolysin/cytolysin [36] . The anaerobic growth of the ΔdacA / PtetO_dacA strain was exploited to construct ΔdacA mutants without an ectopic dacA allele . The first ΔdacA-1 mutant was selected after anaerobic growth following the loss of the vector containing the additional dacA copy ( S1 Fig ) . The second ΔdacA-2 mutant was constructed from the parental ΔdacA::dacA integrant by selecting the deletion mutant directly under anaerobic conditions ( S1 Fig ) . Both ΔdacA mutants grow in anaerobiosis , although the ΔdacA-1 mutant growth is slightly altered on TH compared to the WT strain , and they do not grow in aerobiosis ( Fig 1C ) . Re-introduction of the PtetO_dacA vector in the two ΔdacA mutants restored growth , pigmentation , and hemolysis in the presence of aTc ( Fig 1D ) . In contrast , expression of an inactivated DacA* , bearing a R213K substitution in the RHR conserved di-adenylate cyclase motif [37 , 38] , does not complement the ΔdacA phenotypes ( Fig 1D ) . As expected , the purified recombinant DacA protein produces c-di-AMP from two molecules of ATP while the recombinant DacA* is devoid of di-adenylate cyclase activity ( S2 Fig ) . Thus , c-di-AMP synthesis appears essential for growth in aerobiosis and necessary for optimal growth in anaerobiosis . The genomes of the parental WT NEM316 strain , of the two ΔdacA::dacA integrants , and of the two corresponding ΔdacA mutants were sequenced ( S1 Table ) . Compared to the published reference sequence [39] ( RefSeq NCBI NC_004361 ) , fifteen SNPs or INDELs are present in our WT strain and in all of its progeny ( S2 Table ) . The genome sequence of the first ΔdacA::dacA integrant is identical to the parental WT strain , while the second integrant displays a SNP located in the cylD gene of the cyl operon encoding the ß-hemolysin/cytolysin [36 , 40] ( S3 Table ) . Compared to their parental integrants , the two ΔdacA mutants have two additional mutations in the same genes: oppC ( the gbs0146 locus ) and busB ( the gbs1838 locus ) ( Fig 2A and S3 Table ) . The first gene encodes the OppC oligopeptide transporter subunit [41] and the two mutants have independent frameshift mutations ( +A in ΔdacA-1 and—A in ΔdacA-2 ) located at the beginning of the gene ( Fig 2A ) . The second gene encodes a transmembrane protein homologous to the Lactococcus lactis BusB subunit [42] . In this species , BusB and its cytoplasmic partner BusA form an ABC transporter involved in osmolyte import ( Fig 2A ) . In the ΔdacA mutants , busB has either a SNP resulting in a V62D substitution localized in the first transmembrane domain of BusB ( ΔdacA-1 ) or a single nucleotide deletion at position 120 ( ΔdacA-2 ) ( S3 Table ) . In addition to the busB and oppC mutations , the ΔdacA-1 mutant has an additional copy of TnGBS , a 47kb integrative and conjugative element already present three times in the parental strain [39 , 43] , integrated in an intergenic region ( S3 Table ) . To assess the functional significance of the two shared mutated genes , we introduced a replicative vector containing a wild-type copy of oppC or busB under the control of the aTc inducible PtetO promoter in the ΔdacA mutants ( Fig 2B ) . Expression of a WT copy of busB , but not of oppC , inhibited the anaerobic growth of the ΔdacA mutants ( Fig 2B ) . Therefore , a mutation in the osmolyte transporter BusB appears necessary to counteract the effect of dacA inactivation under anaerobic conditions . The independent occurrence of a loss of function mutation in oppC in the two mutants also suggested that this mutation was necessary but not sufficient . Attempts to delete dacA in ΔbusB , ΔoppC , and ΔbusB ΔoppC backgrounds were unsuccessful , suggesting the necessity of additional compensatory mutations . To identify these additional pathways , we selected ΔdacA clones able to grow in aerobiosis . In liquid cultures , the ΔdacA mutants display high growth variability that was recorded by following their aerobic growth in liquid medium ( Fig 3A ) . When isolated colonies ( n = 48 ) grown anaerobically were directly inoculated in liquid media , around 75% were unable to grow under aerobic conditions , the remaining cultures showing weak or intermediate growth defects ( Fig 3B and 3C ) . However , after 4 serial cultures under anaerobic conditions , almost three quarters of these cultures were able to grow as the WT strain under aerobic conditions ( Fig 3C ) . The growth of each culture remains highly variable , suggesting that different populations arose and co-exist during serial cultures . However , this is not due to a higher mutation rate of the ΔdacA mutants since rifampicin resistant colonies were obtained at a similar frequency with WT and ΔdacA mutant strains ( S2 Fig ) . Fourteen independent ΔdacA suppressors ( 5 from ΔdacA-1 and 9 from Δdac-2 ) were isolated on TH in aerobiosis after a single overnight incubation in liquid medium in anaerobiosis . In this condition , the proportion of colonies growing on TH in aerobiosis is highly variable , usually between 0 . 5 and 10−3 ( Fig 3D ) . Each isolated suppressor grew on TH plates as the WT ( Fig 3E ) , and the absence of c-di-AMP in whole bacterial extracts of ΔdacA mutants and of several suppressors was confirmed ( S2 Fig ) , excluding that a cryptic di-adenylate cyclase was activated to compensate for the absence of dacA . The genomes of the 14 ΔdacA suppressor strains were sequenced to identify the compensatory mechanisms , but the number of mutations were variable , with no single mutated gene common to all suppressors ( S3 Table ) . All suppressors carry the two oppC and busB mutations present in the parental ΔdacA mutant and between 1 to 7 additional mutations ( Fig 3F ) . The mutations are mostly SNPs ( n = 24 , including 19 leading to amino-acid substitution ) , followed by small indels ( n = 12 , including 8 in coding sequence ) , three deletions of 36–47 bp , and one 90-bp duplication ( S3 Table ) . Independent mutations in the same gene or functional complex were identified in different suppressors ( Fig 3F ) , including mutations in an operon encoding a second osmolyte ABC transporter homologous to the L . lactis OpuABC glycine betaine transporter [44] , in the glutamine ABC transporter GlnPQ [45 , 46] , and in a putative secreted protein ( Gbs1444 ) of unknown function ( S4 Table ) . Also interesting is the presence of additional loss of function mutations in BusB in suppressor S5 originating from the ΔdacA-1 mutant with the BusB V62D substitution ( S3 Table ) . To identify causative mutations restoring growth in the absence of c-di-AMP , we focused our analysis on nine different suppressor mutants . In each of these suppressors , a WT copy of the mutated genes expressed from the aTc-inducible promoter was introduced . As expected , induction of the WT copy of busB inhibited the growth of six of the nine suppressors in aerobiosis and anaerobiosis ( Fig 3G and 3H , and S3A Fig ) . The expression of a WT busB allele was toxic only under aerobic growth in two suppressor mutants , and had no effect on one suppressor ( Fig 3H ) . These results confirm that busB inactivation is necessary for bacterial growth in the absence of dacA , but reveal that additional mutations can alleviate busB toxicity in the absence of c-di-AMP . Among the eight genes mutated at least once in the nine suppressors , four are toxic upon re-expression of their WT copy in five of the suppressors ( Fig 3H and S3A Fig ) . The expression of Gbs1444 encoding a putative secreted protein is toxic in the three suppressors containing a mutation in this gene . The remaining three genes inhibiting growth upon their re-expression encode for ABC transporters: OpuCA , GlnPQ , and PstB ( a phosphate ABC transporter homolog ) . We excluded a non-specific toxic effect of the tested genes by expressing them in the same condition in a WT background ( S3B Fig ) . Overall , at least one mutated gene in each of the nine suppressors studied was toxic upon conditional expression of a WT copy , suggesting that the corresponding mutation compensates the absence of c-di-AMP . Nevertheless , the pattern of mutations suggests strong epistasis , i . e . the effect of the toxic gene is dependent of the other mutations present in a given suppressor . For instance , in suppressors S30 and S34 , mutation of busB is not sufficient for aerobic growth of the parental ΔdacA mutants , but should be combined with glnPQ or mscS ( Fig 3H ) . In contrast , in suppressors S6 , S35 , and S47 , the toxic effect of a functional busB allele in a ΔdacA background can be attenuated by mutations in gbs1444 or pstB ( Fig 3H ) . The pattern of compensatory mutations and epistatic interactions suggest that c-di-AMP controls a highly regulated and interconnected essential pathway . Compensatory mutations might encode for proteins directly regulated by c-di-AMP . To identify these direct c-di-AMP regulated processes , interaction between c-di-AMP and candidate proteins were assayed by DRaCALA [47] . Fourteen proteins were selected as candidates , including the BusA , OppD , and OppE cytoplasmic ATPases subunits of osmoprotectant and oligopeptide transporters ( Fig 2 ) , the mutated proteins tested for their phenotypes upon re-expression in ΔdacA suppressors ( Fig 3H ) , and three additional proteins containing a RCK_C/TrkA_C domain with a putative c-di-AMP binding motif [22–24] . The corresponding genes were cloned and expressed as a fusion protein in E . coli ( S4 Fig ) , and whole-cell extracts were incubated with radiolabelled c-di-AMP . C-di-AMP binds to four proteins: KtrA ( Gbs1678 ) , TrkH ( Gbs1639 ) , OpuCA ( Gbs0235 ) and Gbs1201 , thereafter named BusR ( Fig 4A ) . The binding of radiolabelled c-di-AMP is specific since it could be displaced by addition of cold c-di-AMP but not of c-di-GMP , cAMP , cGMP , AMP or ATP ( Fig 4B ) . Three of the four c-di-AMP binding proteins are homologs of conserved potassium ( KtrA and TrkH ) and osmolyte ( OpuCA ) transporters . Two of them , KtrA and OpuCA , are mutated in one or three of the nine suppressors , respectively ( S4 Table ) . These two proteins are conserved c-di-AMP binding proteins , where binding is dependent on their RCK_C/TrkA_C [22 , 23] or CBS [27 , 28] domains , respectively . Among the four GBS proteins containing RCK_C/TrkA_C domains tested ( Fig 4C ) , only one , EriC ( Gbs1174 ) , a chloride channel homolog , did not give a positive signal with c-di-AMP in our DRaCALA screen ( Fig 4A ) . However , only the RCK_C domain of EriC was used in this experiment ( S4 Fig ) as we failed to express in E . coli the full-length protein with its eleven transmembrane domains . Therefore , these results do not rule out the possibility that a full length EriC might bind c-di-AMP . It is also interesting to note that BusA and OpuCA are two highly similar subunits of osmolyte transporters ( 55% similarities , e = 6 e-59 ) . The two proteins contain a CBS domain ( Fig 4C ) . However , BusA , the cytoplasmic subunit of the BusAB transporter which is mutated in ΔdacA mutants , does not bind c-di-AMP in contrast to OpuCA ( Fig 4A ) . This confirms that CBS domains may have a similar topology but different physiological ligands [26] . This also implies different mechanisms of regulation for the BusAB and OpuC osmolyte transporters . The fourth c-di-AMP binding protein identified by DRaCALA is a putative transcriptional regulator of the GntR family containing a winged helix-turn-helix DNA binding domain ( Fig 4C ) . BusR is highly similar to the annotated MngR trehalose transcriptional repressor in Chlamydia trachomatis ( e value = 2 e-107 ) and to the L . lactis BusR transcriptional repressor ( 5 e-61 ) . In L . lactis , the busR gene is localized immediately upstream of the busAB operon [48] , whereas in GBS busR and busAB are separated by 655 kb and no transcriptional regulator is located in the vicinity of the busAB operon . The homology with L . lactis suggests a putative conserved function of BusR on busAB transcription in GBS . Therefore , we purified recombinant GBS BusR and tested its binding on the PbusAB promoter of the busAB operon . Gel shift assays show that PbusAB migrates more slowly in the presence of BusR ( Fig 5A ) and footprint experiments show two BusR-protected regions in the PbusAB promoter , one overlapping the -35 and -10 elements and the +1 transcription start site ( Fig 5B ) . Deletion of busR increases expression of the busAB operon compared to the WT or the ΔbusR_c complemented strain ( Fig 5C ) . These results demonstrated that the c-di-AMP binding protein BusR is a transcriptional regulator directly repressing the busAB operon . To test the functional link between c-di-AMP and the BusR-BusAB osmolyte import system , we analysed the phenotypes of the deletion mutants ( ΔbusA , ΔbusB , ΔbusR , ΔgdpP , and ΔbusR ΔgdpP ) in response to osmotic stresses . As observed in several bacteria , deletion of the c-di-AMP phosphodiesterase GdpP increases the intracellular c-di-AMP concentration in GBS ( 20- to 38-fold , S2C Fig ) . Furthermore , the ΔgdpP mutant is more susceptible to hyperosmotic stress compared to the WT strain ( Fig 5D ) . Strikingly , ΔgdpP osmo-susceptibility is dependent on a functional BusR transcriptional regulator . Deletion of BusR has no or a weak effect on bacterial growth upon hyperosmotic challenge , while the two subunits of the BusAB transporter are as important as GdpP to resist the hyperosmotic stress ( Fig 5D ) . The double ΔbusR ΔgdpP deletion abolishes the susceptibility of the ΔgdpP mutant ( Fig 5D ) , showing that elevated c-di-AMP leads to hyperosmotic susceptibility by acting through the transcriptional repressor BusR . The pattern of compensatory mutations and the identification of c-di-AMP binding proteins point towards a coordinated regulation of potassium and osmolyte uptake as the essential function of c-di-AMP in GBS . We therefore tested the growth of the ΔdacA mutant in a chemically defined medium ( CDM ) with variable potassium and osmolyte concentrations ( S5 Table ) . To this end , we used the ΔdacA-2 mutant with an empty vector , a dacA complementing vector , or a busB expressing vector to complement the busB loss-of-function mutation in this mutant . In this CDM , c-di-AMP synthesis is dispensable for bacterial growth regardless of the potassium concentration and incubation condition , except for anaerobic growth of the mutant expressing a WT copy of busB at high potassium concentrations ( 5 mM ) ( Fig 6A ) . Strikingly , addition of glycine betaine to CDM inhibits the growth of the ΔdacA-2 mutant expressing busB regardless the potassium concentration , except in aerobiosis at extremely low concentrations of potassium ( Fig 6A ) . The inhibitory effect of glycine betaine is dependent on busB expression since glycine betaine does not inhibit the growth of the ΔdacA-2 mutant with the empty vector ( Fig 6A ) and has no effect on the ΔdacA-2 / PtetO_busB mutant in the absence of aTc . Similarly , the inhibitory effect of glycine betaine is observed with carnitine , a related osmolyte [49] , while choline , a common precursor of glycine betaine , has no effect ( S5 Fig ) . In the same conditions , busB expression in a WT strain has no effect on growth ( S5 Fig ) , showing that osmolytes such as glycine betaine or carnitine need the expression of busAB and the absence of c-di-AMP to be toxic . Overall , the presence of an osmolyte in the culture medium appears to be the main cause of growth inhibition in the absence of c-di-AMP synthesis . The concentration of potassium is also important under specific conditions ( [K+]high in anaerobiosis and [K+]low in aerobiosis in presence of osmolyte ) , suggesting that growth inhibition results from a combination of dysregulated potassium and osmolyte uptake . To test if the growth condition is sufficient to alleviate the essential function of dacA , we repeated the construction of a ΔdacA mutant except that all steps were performed in CDM without osmolyte and with 0 . 5 mM potassium . In this condition , we readily obtained ΔdacA mutants and their respective WTb controls at high frequency ( S1 Fig ) . On CDM , the growth of the new ΔdacA-A mutant was similar to the WT and WTb controls regardless the potassium concentration and incubation conditions ( Fig 6B ) . Addition of glycine betaine inhibits ΔdacA-A at all tested potassium concentrations in aerobic condition and only at high potassium concentration in anaerobic condition ( Fig 6B ) . Finally , the ΔdacA-A mutant was unable to grow on TH ( Fig 6B ) . Two additional ΔdacA mutants ( -B and–C ) , obtained from independent parental ΔdacA::dacA integrants , displayed the same phenotypes as the ΔdacA-A mutant . These results confirmed that c-di-AMP synthesis is essential in rich medium and dispensable in minimal medium , unless osmolytes are present . The inhibiting effect of osmolytes is dependent on aerobiosis and anaerobiosis and , to a lesser extent , on potassium concentrations , suggesting a link between osmotic regulation and metabolism . The genome of the three new , independent pairs of ΔdacA and WTb strains were sequenced ( S1 Table ) . None of the ΔdacA-A to -C mutants share a mutation with the previously sequenced ΔdacA-1 , ΔdacA-2 , and ΔdacA suppressors ( S3 Table ) . The only exception is the cylD SNP in the ΔdacA-B that is also present in the ΔdacA-2 mutant and their common parental ΔdacA::dacA integrant ( S3 Table ) . Still , the three ΔdacA-A to–C mutants each have one mutation compared to the WT strain . These mutations are localized in gbs0330 , encoding the transcriptional repressor FabT ( S3 Table ) , embedded in the fab operon encoding enzymes of the essential type II fatty acid synthesis pathway [50] . Unexpectedly , the WTb controls and two of the three parental ΔdacA::dacA integrants show the same fabT mutations ( S3 Table ) . The independent fabT mutations imply a strong selective pressure most probably due to the nutritional supply in the medium and not to c-di-AMP depletion . Targeted sequencing of the fabT locus of the WT and ΔdacA::dacA integrants after growth in overnight cultures in TH and CDM 0 . 5 mM K+ confirmed that fabT mutations are selected at a high frequency only on CDM medium independently of c-di-AMP ( S6 Fig ) .
Here we demonstrate that the essential function of c-di-AMP in S . agalactiae is to regulate osmotic homeostasis . The mechanism involves the conserved binding of c-di-AMP to potassium and osmoprotectant transporters ( Ktr , Trk , OpuC ) and the BusR c-di-AMP binding transcriptional regulator controlling the transcription of the busAB operon encoding the BusAB osmoprotectant transporter ( Fig 7 ) . Our study strengthens the recent proposal that c-di-AMP has a conserved and essential role in maintaining osmotic homeostasis in Gram-positive bacteria [51] . Typically , osmoregulation is achieved through three conserved processes: a rapid potassium uptake , the synthesis or import of compatible solutes , and a final ionic exchange to restore the membrane potential [29 , 30] . However , each bacterial species encodes a different set of functionally related transporters and has evolved specific regulatory mechanisms , probably a consequence of the long-term adaptation of the bacteria to their environments [52–54] . Notwithstanding this evolution , c-di-AMP preserves its role in regulating core components of the osmotic response while adapting to control the species-specific transporters and regulators . Direct inhibition of potassium transporters containing a RCK_C domain is a conserved mechanism of regulation exerted by c-di-AMP that is present in many bacteria [20 , 22–24] . For example , such a coordinated regulation of potassium transporters , together with the regulation of the ydaO c-di-AMP riboswitch controlling the kimA gene encoding an additional high affinity potassium transporter , is essential in B . subtilis [20 , 32] . Indeed , in the absence of c-di-AMP , the loss of transporters inhibition leads to a toxic accumulation of potassium , which can be bypassed by depleting potassium in the growth medium or by compensatory mutations increasing potassium efflux [20] . Differently to B . subtilis , we did not observe a strong effect of external potassium concentrations on the growth of S . agalactiae mutants unable to synthesize c-di-AMP , and we did not obtain compensatory mutations increasing potassium efflux , suggesting a different mechanism of regulation . Indeed , we show here that the second step of the osmotic response , the uptake of compatible solutes , is the critical function regulated by c-di-AMP in S . agalactiae . These compatible solutes are necessary to equilibrate the osmotic pressure and to avoid the deleterious consequences of potassium uptake on metabolism . This regulation involves c-di-AMP binding to the OpuC glycine betaine transporter , which is conserved in several species , including S . aureus [27] and L . monocytogenes [28] . As we observed in S . agalactiae , compensatory mutations have been obtained in osmoprotectant transporter encoding genes in S . aureus and L . monocytogenes [15 , 21] . However , these mutations are not localized in the c-di-AMP binding protein OpuC homologues , but inactivated the highly similar S . agalactiae BusAB and L . monocytogenes Gbu [15] ABC transporters , or the S . aureus OpuD transporter belonging to the BCCT family [21] . In S . agalactiae and S . aureus , glycine betaine and related osmoprotectants inhibit the growth of diadenylate cyclase mutants , through the activity of the unrelated BusAB and OpuD transporters , respectively [21] . Therefore , the two species have evolved independent mechanisms allowing the essential regulation of compatible solute uptake by c-di-AMP . In S . agalactiae , the transcriptional repressor BusR represents the link between c-di-AMP and BusAB as it controls the expression of the busAB operon . The BusR regulator belongs to the GntR family of proteins . It is not related to the only c-di-AMP binding transcriptional regulator characterized to date , the TetR-like DarR of Mycobacterium smegmatis [55] . Binding of c-di-AMP on BusR most probably involved its RCK_C regulatory domain which is present in a subset of GntR transcriptional regulators present mainly in streptococci , lactococci , and clostridi [56] . C-di-AMP regulation of transcription factors probably occurs in all these species , including the previously characterized L . lactis BusR whose binding on the promoter of busAB was demonstrated to be dependent on ionic strength [48 , 57] . It is therefore likely that BusR homologues integrate c-di-AMP and intracellular potassium concentration to control gene transcription , but it remains to be determined whether these regulators control only genes involved in osmoregulation . The unregulated import of osmolytes in the absence of c-di-AMP might inhibit growth as a consequence of cell poisoning , loss of membrane potential , or impaired cell division due to an incompatible internal osmotic pressure [52 , 58] . The loss of osmotic homeostasis might be even exacerbated by a c-di-AMP regulation of ionic transporters such as the c-di-AMP binding cation/proton antiporter CpaA of S . aureus [22 , 59] or the RCK_C domain containing chloride channel EriC of S . agalactiae . Indeed , to compensate the global dysregulation of osmotic systems , we observed several compensatory mutations in the S . agalactiae ΔdacA mutants , including one in the mechanosensitive channel protein MscS , a ion channel responding to membrane stress [60] , and the GlnPQ amino acids [45 , 46] and Opp oligopeptide [41 , 61] ABC transporters . Notably , mutations in the oligopeptide transporter OppA-F and in the amino acid transporter AlsT are frequent in L . monocytogenes and S . aureus ΔdacA mutants [14 , 15 , 21] . In these two species , peptide and amino acid uptake is necessary to regulate their internal osmotic pressure , either directly or as precursors of osmoprotectants [15 , 21] . The diversity of compensatory mutations in genes related to osmoregulation suggests that bacteria have different mechanism to restore an osmotic equilibrium to counterbalance potassium and osmoprotectant uptake in the absence of c-di-AMP . It is noteworthy that in S . agalactiae , the growth of our initial ΔdacA mutants is oxygen-dependent . Interestingly , c-di-AMP synthesis is dispensable in Streptococcus mutans [62] , which is routinely cultured under anaerobic conditions , and the link between oxygen and c-di-AMP synthesis was recently reported in S . aureus [21] . In this latter species , the growth inhibition of ΔdacA mutants in aerobiosis is not directly linked to respiration , but the respiratory chain must be inactivated to restore growth [21] . One hypothesis is that respiration is coupled to the TCA cycle , a central metabolic pathway in aerobiosis , which is critical for glutamate metabolism , and hence for osmoregulation [21] . Strikingly , pyruvate carboxylase , one of the key enzymes of the TCA cycle , is directly regulated by c-di-AMP in L . monocytogenes [15 , 16] . In contrast , S . agalactiae , an aerotolerant anaerobe devoid of a functional TCA cycle [63 , 64] , is unable to respire unless an exogenous source of electron acceptors is provided . We observed that the difference between aerobic and anaerobic growth of the ΔdacA mutants in rich media is linked to the BusAB transporter , which suggests a differential regulation upon oxygen availability . Overall , bacteria might have adapted their mechanisms of osmoregulation to their metabolism and , probably , to their environment . In conclusion , our study establishes c-di-AMP as an essential regulator of osmotic homeostasis in S . agalactiae . The main mechanism involves the c-di-AMP binding transcriptional regulator BusR that controls osmoprotectant uptake through the BusAB transporter . It is therefore likely that phylogenetically distant species have developed species-specific mechanisms to maintain their osmotic pressure while keeping c-di-AMP as the major coordinator of this essential cellular function . This functional conservation on a long evolutionary time-scale suggests that osmotic homeostasis is the main essential function regulated by c-di-AMP [33] .
The WT GBS strain used in this study is NEM316 , the originally sequenced ( RefSeq NC_004368 . 1 ) serotype III reference isolate [39] . The usual Todd Hewitt ( TH , Difco Laboratories ) , Columbia supplemented with 10% horse blood ( BioMérieux ) , and Granada medium ( BioMérieux ) were used for propagation and phenotypic tests . A chemically defined medium ( CDM ) containing inorganic salts , vitamins , amino acids , nucleobases , pyruvate and glucose ( S5 Table ) was adapted from reference [65] . Glycine betaine , potassium chloride , and sodium chloride ( Sigma-Aldrich ) are added when stated . Buffering at pH 7 . 3 was done by adding Hepes ( 50 mM ) . Liquid GBS cultures are done in static condition incubated in aerobiosis or anaerobiosis . Anaerobiosis is obtained in hermetic jars with AnaeroGen gas packs ( Oxoid , ThermoFischer ) . Growth curves in aerobiosis were done in 96 wells microplates ( 150 μl ) at 37°C with constant shaking and automatic recording of OD600 every 20 minutes ( BioTek Synergy ) . Erythromycin and kanamycin ( Sigma-Aldrich ) are used for plasmid selection at 10 and 500 μg/ml , respectively . Anhydrotetracycline ( Sigma-Aldrich ) is used for conditional expression from the PtetO inducible promoter at 0–100 ng/ml [66] . Rifampicin ( 50 μg/ml ) was used for the quantification of spontaneous resistant mutations . Bacterial strains and plasmids ( S6 Table ) , oligonucleotides ( S7 Table ) , and detailed vectors construction ( S8 Table ) are provided in the corresponding supplementary tables . The pTCV_PtetO vector was used for anhydrotetracycline inducible expression in GBS [66] , and the shuttle thermosensitive plasmid pG1 was used for chromosomal deletion , as described previously [67 , 68] . Plasmids were constructed by standard restriction and ligation cloning or by Gibson assembly , purified on columns ( Qiaprep , Qiagen ) and all inserts were sequenced . Plasmids were introduced in GBS by electroporation , except for the ΔdacA mutants which were transformed by conjugation with the E . coli HB101/pRK24 donor strain , as described previously [69] , to avoid liquid cultures . For DRaCALA experiments , E . coli Bli5 strain was used with the pET-28a ( N-terminal His-tag ) and pIVEX ( N-terminal His-MBP tag ) vectors . Similar results were obtained with the two vectors , except for TrkH which is detected by Western only with the His-tag , and OpuCA which give a positive signal by DRaCALA only with the His-MBP tag . For OpuCA , the MBP tag might increase the solubility of the tagged protein , as observed previously with the OpuCA homologue in S . aureus [27] . For recombinant rDacA , rDacA* , and rBusR purification , E . coli Bli5 were used with pET28a expression vectors . For E . coli , antibiotics were used at the following concentrations: ticarcillin , 100 μg/ml; chloramphenicol 30 μg/ml; ampicillin 100 μg/ml; erythromycin , 150 μg/ml; and kanamycin 25 μg/ml . GBS mutants were constructed with the corresponding thermosensitive pG1 vectors ( for dacA , gdpP , busA , busB , and busR deletion ) in three steps , involving: i ) selection of transformants at permissive temperature ( 30°C ) with erythromycin; ii ) chromosomal integration of the deletion vector at the targeted loci at restrictive temperature ( 37°C ) ; and iii ) decombination and loss of the deletion vector at permissive temperature ( 30°C ) without selective pressure . The final step can give back to a WT allele ( defined as the WTb controls ) or to deletion of the targeted loci ( unmarked deletion ) . Confirmation of the WTb or deletion genotypes was done by PCR and Sanger sequencing for each mutant . Attempts to delete dacA ( i . e . in-frame deletion of the DacA cytoplasmic domain , codon 106 to 234 of the 283 amino-acids protein ) following the standard protocol were unsuccessful , given only WTb colonies at the final step . Therefore , an additional copy of dacA was cloned into the conditional pTCV_PtetO expression vector [66] and introduced into the ΔdacA::dacA intermediate strain ( called the integrant ) at 30°C with erythromycin and kanamycin ( S1 Fig ) . The final step of losing the integrated vector was repeated in presence of 50 ng/ml aTc and the ΔdacA / PtetO_dacA in-frame deletion mutant was obtained at high frequency . To obtain ΔdacA mutants without the PtetO_dacA expression vector , serial cultures in anaerobic condition were done without the selective pressure to maintain the vector ( S1 Fig ) . The PtetO_dacA vector was lost in a WTb background after two serial cultures but all ΔdacA / PtetO_dacA retain the vector in the same condition , indicating that a leaky expression of the ectopic dacA copy is sufficient to keep a fitness advantage . By testing more than 200 non-pigmented clones after 6 serial cultures on Granada in anaerobic condition , we isolated one ΔdacA mutant ( ΔdacA-1 ) which has lost the PtetO_dacA vector . An independent ΔdacA-2 mutant was obtained from the ΔdacA::dacA integrant by performing all subsequent steps in anaerobiosis on Granada ( S1 Fig ) . The frequency of ΔdacA mutant versus WTb strain was less than 1% , confirming that ΔdacA has a fitness disadvantage compared to the WT strain . Finally , the standard protocol was repeated to construct the ΔdacA-A , -B and -C mutants except that all steps were done in CDM , resulting in high frequencies of ΔdacA mutant . Genomic DNA was purified from 10 ml of overnight cultures in TH or CDM , except for ΔdacA-1 and –2 mutants which were made from colonies on TH plates incubated in anaerobiosis . Bacterial pellets were treated with lysosome ( 20 mg/ml ) and proteinase K before mechanical breaking of the cell by microbeads ( FastPrep , MP Biomedicals ) , and genomic DNA purification ( DNeasy Blood , Qiagen ) and quantification ( Qubit hsDNA , ThermoFisher Scientific ) . Five micrograms of DNA were used for libraries preparations . The first set of DNA ( S1 Table ) was treated and sequenced by the Sequencing Core Facilities of Institut Pasteur ( Paris , France ) with TrueSeq DNA LT kits and single-read sequencing ( 150 bp ) on a MiSeq instrument ( Illumina ) . The second set of DNA ( S1 Table ) was sheared ( Covaris S220 instrument ) , treated with commercial enzymes and purification kits ( Klenow , T4 ligase , T4 polynucleotide kinase , Phusion polymerase from New England Biolabs , and MinElute and QiaQuick columns from Qiagen ) , ligated to multiplex adapters ( NEXTflex , Illumina ) , and purified ( 500 bp mean fragment ) . Paired-end sequencing ( 2 x 76 bp ) was done on a NextSeq 550 apparatus ( Illumina ) . After quality assessments , trimming and de-multiplexing , sequence reads were mapped on the 2 . 2 Mb reference sequence ( RefSeq NC_004368 . 1 ) using Geneious software ( Biomatters Ltd ) , resulting in a mean coverage of 131x and 609x for the MySeq and NextSeq instruments , respectively ( S1–S4 Tables ) . Interaction between c-di-AMP and targeted GBS proteins was tested by DRaCALA [47] on whole E . coli protein extract . Expression of the candidate GBS protein was done in Bli5 containing pET-28a or pIVEX expression vector ( S6 Table ) . Expression of the tagged-GBS protein was induced with IPTG ( 1 mM ) for 6 hours at 30°C . Bacterial pellet from 1 ml culture is suspended in 100 μl binding buffer ( 40 mM Tris pH 7 . 5 , 100 mM NaCl , 10 mM MgCl2 , 0 . 5 mg/ml lysozyme , 20 μg/ml DNase ) , lysed by 3 freeze-thaw cycles , and directly used for DRaCALA and Western blot analysis using anti-His-tag antibodies . For DraCALA , 1 nM 32P-labeled c-di-AMP , synthetized as described in reference [22] , was added to the whole protein extract , incubated at room temperature for 5 min , and 2 . 5 μl was spotted onto nitrocellulose membrane . Membranes are revealed with radiographic films ( Amersham Hyperfilm ECL , GE Healthcare ) and signal intensity quantified with ImageJ ( NIH ) . The c-di-AMP bound fraction was calculated as described [47] . For competition assay 200 μM of cold nucleotides ( c-di-AMP , c-di-GMP , cAMP , cGMP , AMP , and ATP; BioLog Life Science Institute , Germany ) were added to the protein extract altogether with radiolabelled c-di-AMP . Recombinant rDacA ( amino-acids 96 to 243 , deleted from the transmembrane domain ) and the mutated rDacA* ( with a R213K substitution ) were expressed as 6xHis N-terminal tagged forms ( pET28a vector ) in Bli5 E . coli strain . Cultures were done at 37°C in LB until OD600 = 0 . 7 before protein induction with IPTG ( 1 mM ) for 3 hours . After centrifugation and one cycle of freezing ( -20°C ) , pellets are suspended in 20 ml of buffer ( 50 mM Na2HPO4/NaH2PO4 , 300 mM NaCl , pH7 . 0 ) , and broken by one passage through a French press at 14000 p . s . i . Cell debris were eliminated by centrifugation and the recombinant proteins were purified by chromatography ( 5 ml TALON crude column , GE Healthcare ) with a linear gradient from 0 to 150 mM imidazole in 50 mM Na2HPO4/NaH2PO4 , 300 mM NaCl , pH7 . 0 , at 5 ml/min for 20 min . Fractions containing the enzyme were pooled and the buffer was exchanged on PD10 column previously equilibrated with 10 mM Bis-Tris , 100 mM NaCl , pH 7 . 5 . Diadenylate cyclase activities were tested at 37°C with 2 . 5 μM rDacA or rDacA* incubated with 1 mM ATP in 50 mM Tris pH 8 . 5 , 100 mM NaCl and 10 mM MnCl2 . Formation of c-di-AMP was followed each 14 min by RR-HPLC using a reverse-phase column ( Agilent ZORBAX Eclipse XDB-C18 , 2 . 1 x 100 mm , 1 . 8 μm ) . Samples were analyzed by RR-HPLC with a flow rate of 0 . 25 ml/min and a linear gradient of 1–12% acetonitrile ( CH3CN ) in 20 mM triethylammoniumacetate buffer , pH 7 . 5 . The ATP and c-di-AMP peak areas were used to quantify substrate and product formation . C-di-AMP quantification in GBS was done by LC-MS/MS ( BIOLOG Life Science Institute ) , following company instructions . Late-exponential GBS cultures ( OD600 = 0 . 8 ) in TH Hepes 50 mM incubated in aerobiosis or anaerobiosis were centrifuged ( 15 min , 4°C , 2 , 500 g ) , and the pellet washed in PBS . Bacteria were suspended in extraction buffer ( acetonitrile/methanol/water; 2/2/1 ) , incubated 15 min on ice , heat extracted 10 min at 95°C , and incubated for an additional 15 min on ice . A final mechanical cell lysis step was done with 0 . 1 mm microbeads with shaking ( 2 x 30” , FastPrep-24 , MP Biomedicals ) . After centrifugation ( 10 min , 4°C , 20 , 000 g ) , supernatant was transferred into a new tube and the extraction step was repeated twice on cell debris without the heating step . The three supernatants were pooled and store at -20°C overnight to complete protein precipitation . After centrifugation ( 20 min , 4°C , 20 , 800 g ) , the whole extract was evaporated to dryness ( Eppendorf concentrator 5301 ) before quantification by LC-MS/MS . Protein concentration in the bacterial culture was done ( Pierce BCA , Thermo Fischer ) in parallel to the extraction to normalize c-di-AMP concentration to the total protein content . Full length recombinant rBusR ( amino-acids 1 to 213 tagged with a N-terminal 6xHis ) expressed in Bli5 E . coli strain was purified as rDacA , except that IPTG-induction was done at 20°C overnight , and with an additional purification step by gel filtration ( Superdex 10/300 GL , GE Healthcare ) after affinity chromatography in a final buffer containing 20 mM Hepes pH 7 , 150 mM NaCl . Electrophoretic mobility shift assay ( EMSA ) was done with a 245 bp PCR fragment ( primers pLD1 + pLD2 ) corresponding to the promoter region of the busAB operon ( PbusAB ) . This 5’ region includes the transcription start site and the -10 and -35 boxes , as characterized by whole genome TSS mapping [70] . Primer pLD1 is radiolabelled with T4 polynucleotide kinase ( New England Biolabs ) and [γ-32P]-dATP before PCR reaction . Protein-DNA interaction was done with rBusR , radiolabeled PbusAB ( 104 c . p . m ) , 0 . 1 μg/μl of Poly ( dI-dC ) ( Pharmacia ) , and 0 . 02 μg/μl BSA in binding buffer ( 25 mM Na2HPO4/NaH2PO4 pH 8 , 50 mM NaCl , 2 mM MgCl2 , 1 mM DTT , 10% glycerol ) for 20 min at room temperature . Samples were separated onto a 6% polyacrylamide gel for 1 hour at 4°C and analyzed by autoradiography . The same conditions were used for footprinting , with the addition of 62 . 5 ng/ml DNaseI ( Worthington Biochemical ) for 30 seconds at room temperature after incubation in the binding buffer . DNaseI treatments were stopped by the addition of 0 . 4 M sodium acetate , 50 μg ml−1 sonicated calf thymus DNA , and 2 . 5 mM EDTA , before DNA purification by phenol extraction and ethanol precipitation . Purified DNA from each reaction were adjusted to load an equivalent number of radiolabeled product ( 5 × 104 c . p . m . equivalent ) on 6% polyacrylamide/7 M urea sequencing gels . Maxam and Gilbert reactions ( A + G ) on PbusAB was carried out as control and gels were analyzed by autoradiography . Total RNA were extracted from exponentially growing cells ( OD600 = 0 . 4 ) in TH at 37°C ( FastRNA ProBlue , MP Biomedicals ) and residual DNA removed with the TURBO DNase ( Ambion / Thermo Fischer Scientific ) . RNA were quantified ( Nanodrop 2000 , Thermo Fischer ) before reverse transcription ( iScript cDNA synthesis , Bio-Rad ) . Quantitative PCR ( qPCR ) was carried out using specific primer pairs ( S8 Table ) and EvaGreen Universal qPCR Supermix ( Bio-Rad ) in a CFX96 apparatus ( Bio-Rad ) . Relative quantification of specific gene expression was calculated with the ΔΔCq method , with gyrA as the housekeeping reference gene . Results are normalized against the WT strain and each assay was performed in triplicate on three independent cultures . | Nucleotide-based second messengers play central functions in bacterial physiology and host-pathogen interactions . Among these signalling nucleotides , cyclic-di-AMP ( c-di-AMP ) synthesis was originally assumed to be essential for bacterial growth . In this study , we confirmed that the only di-adenylate cyclase enzyme in the opportunistic pathogen Streptococcus agalactiae is essential in standard growth conditions . However , c-di-AMP synthesis becomes rapidly dispensable by accumulating spontaneous mutations in genes involved in osmotic regulation . We identified that c-di-AMP binds directly to four proteins necessary to maintain osmotic homeostasis , including three osmolyte transporters and the BusR transcriptional factor . We demonstrated that BusR negatively controls the expression of the busAB operon and that it is the main component leading to growth inhibition in the absence of c-di-AMP synthesis if osmoprotectants are present in the environment . Overall , c-di-AMP is essential to maintain osmotic homeostasis by coordinating osmolyte uptake and thus bacteria have developed specific mechanisms to keep c-di-AMP as the central regulator of osmotic homeostasis . | [
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] | 2018 | Cyclic di-AMP regulation of osmotic homeostasis is essential in Group B Streptococcus |
Pathogen expulsion from the gut is an important defense strategy against infection , but little is known about how interaction between the intestinal microbiome and host immunity modulates defecation . In Drosophila melanogaster , dual oxidase ( Duox ) kills pathogenic microbes by generating the microbicidal reactive oxygen species ( ROS ) , hypochlorous acid ( HOCl ) in response to bacterially excreted uracil . The physiological function of enzymatically generated HOCl in the gut is , however , unknown aside from its anti-microbial activity . Drosophila TRPA1 is an evolutionarily conserved receptor for reactive chemicals like HOCl , but a role for this molecule in mediating responses to gut microbial content has not been described . Here we identify a molecular mechanism through which bacteria-produced uracil facilitates pathogen-clearing defecation . Ingestion of uracil increases defecation frequency , requiring the Duox pathway and TrpA1 . The TrpA1 ( A ) transcript spliced with exon10b ( TrpA1 ( A ) 10b ) that is present in a subset of midgut enteroendocrine cells ( EECs ) is critical for uracil-dependent defecation . TRPA1 ( A ) 10b heterologously expressed in Xenopus oocytes is an excellent HOCl receptor characterized with elevated sensitivity and fast activation kinetics of macroscopic HOCl-evoked currents compared to those of the alternative TRPA1 ( A ) 10a isoform . Consistent with TrpA1’s role in defecation , uracil-excreting Erwinia carotovora showed higher persistence in TrpA1-deficient guts . Taken together , our results propose that the uracil/Duox pathway promotes bacteria expulsion from the gut through the HOCl-sensitive receptor , TRPA1 ( A ) 10b , thereby minimizing the chances that bacteria adapt to survive host defense systems .
Encountering other organisms in nature offers opportunities of benefits or dangers depending on the relationship of the organisms facing each other . We intimately interact with bacteria in the gut , and the control of such interaction is the key to the health of animals [1–3] . Although pathogen expulsion from the gut is an important defense measure to infection [4] , it is not clearly determined how defecation contributes to the defense against food-borne bacterial pathogens in collaboration with innate immune systems in the intestine . Bacterial homeostasis in the Drosophila gut is under the control of two distinct innate immune mechanisms , the imd and Duox pathways [5–7] . In the latter , bacteria-originated uracil upregulates Duox activity via G-protein signaling pathways [8–10] , which are independent of the imd pathway [5 , 8] . The uracil-stimulated Gαq-PLCβ-Ca2+ pathway increases the enzymatic activity of Duox exploiting the intracellular Ca2+ -binding domain of Duox [8 , 9] , while the uracil-detecting GPCR has yet to be identified . In addition , the sequential activation of MEKK1-MKK3-p38 MAPK , which depends on PLCβ , upregulates transcription of the Duox gene [10] . The upregulation of Duox dramatically increases the concentration of highly microbicidal HOCl [8 , 11] in the gut lumen , as the ROS is enzymatically generated by collaboration of the two cytosolic and extracellular oxidase domains of Duox [12] . The intracellular gp91phox-like oxidase domain of Duox extracts electrons from NADPH , and the electrons are delivered to the extracellular space through two heme structures in the transmembrane domains of Duox , sequentially forming superoxide and hydrogen peroxide ( H2O2 ) . The extracellularly generated H2O2 is then subjected to peroxidation by the peroxidase homology domain ( PHD ) of Duox to generate microbicidal HOCl . Thus , the Duox pathway plays a critical role for the control of the gut microbiome homeostasis by responding to bacterially excreted uracil and producing the reactive chlorine oxidant , HOCl . Gastrointestinal motility is a key determinant of defecation in mammals , and is controlled by enteroendocrine cells as well as enteric and central nervous systems . The majority of endocrine cells in the small and large intestine consists of enterochromaffin cells ( EC cells ) which contain >90% of intestinal serotonin . Serotonin in EC cells is closely associated with gastrointestinal motility [13 , 14] . Recently , the EC cells are reported to express TRPA1 , and the activation of TRPA1 contributes to the gut motility through serotonin release [13] albeit with no known indigenous signal inputs capable of stimulating TRPA1 activity in EC cells . Insect intestinal motility shows similar operational principles , as gut motility of Drosophila larvae was reported to be modulated by diuretic hormone 31 ( DH31 ) [15] which is released from subsets of enteroendocrine cells [16 , 17] . Thus , Drosophila provide the intestinal model system to study functional implication of the enteroendocrine system in various contexts including host/microbe interaction in the intestine . As a reactive chemical , HOCl activates the mammalian ortholog of the evolutionarily conserved reactive chemical receptor TRPA1 [18] . TRPA1s from humans to flies share the mechanism through which the channels are activated by reactive chemicals [19] . While insect TRPA1s are key receptors for both temperature [20 , 21] and reactive chemicals , it was shown that sensory discrimination between these two sensory cues is achieved by expressing a thermally insensitive TRPA1 isoform , TRPA1 ( A ) , in taste neurons [19 , 20 , 22] . In addition to lacking high thermal sensitivity , fruitfly and mosquito TRPA1 ( A ) s have recently been reported to respond to the phytochemical citronellal in an isoform-specific manner [23] , suggesting that TRPA1 ( A ) is a chemosensory-specialized isoform . In addition , a small domain between the N-terminal ankyrin repeats and the transmembrane segment is alternatively encoded by exon10a and exon10b in Drosophila melanogaster [24] , which was proposed to be another determinant of thermal sensitivity [25] . However , the physiological significance of the transcript with exon10b has not been fully established . Recently two independent transcriptome analyses showed that TrpA1 transcripts are present in the Drosophila gut [26 , 27] , but the role of Drosophila TrpA1 in the gut has yet to be examined . In this study , we find that the TRPA1 ( A ) isoform coupled with exon10b ( referred to as TRPA1 ( A ) 10b ) is the key receptor for the detection of the major reactive chlorine HOCl generated from activation of the Duox pathway in the gut and that such functional interaction between TRPA1 and the Duox pathway is critical for expulsion of the opportunistic pathogen Erwinia carotovora from the gut . These results indicate that the Duox innate immune system regulates defecation via TRPA1 , thus maintaining the microbiome homeostasis in the Drosophila gut .
To test if the Duox pathway functionally interacts with TRPA1 and regulates defecation , a behavioral assay was designed to compare defecation spot numbers of two sister fly groups fed with solution containing either sucrose only or sucrose and uracil ( 500 mM sucrose±uracil ) ( Fig 1A , and Materials and Methods for details ) . Defecation spot numbers were normalized with respect to ingestion amounts , yielding data sets of either “output/input” ( spots/mm ) for simple comparison between sucrose only and sucrose+uracil experiments ( S1 Fig ) or “fold change of defecation” for quantitation of increased defecation by uracil . Increasing concentrations of uracil ranging from 20 nM to 100 μM elevated defecation frequency of wild type flies ( wcs ) ( Fig 1B ) , while three independent alleles , TrpA1ins , TrpA1Gal4 and TrpA11 , which are severely impaired for TrpA1-dependent chemical detection [19 , 28 , 29] , lacked uracil-dependent defecation ( Fig 1B and 1C and S1A Fig ) as did TrpA1 RNAi knockdown animals ( S1B Fig ) . Note that such uracil-dependent defecation was not reliably observed with female flies , probably because of physiological complexity of females as observed in previous studies [30 , 31] . For this reason , only male flies were tested for all experiments . Reintroduction of the TrpA1 gene ( genomic rescue [20] ) restored uracil-dependent defecation in TrpA1ins animals ( Fig 1C and S1A Fig ) . To examine if the number of fecal spots represents the approximate amount of defecation , the vial , in which flies were let feed on 0 . 5% brilliant blue FCF-containing 500 mM sucrose solution and defecate for 8 hrs in total , was washed inside with 2 . 5 ml phosphate-buffered saline ( PBS ) ( S2C Fig ) . Spectral absorbance at 628 nm was determined for quantitation of the blue dye , and the results were very similar to those appraised by counting fecal spots . Uracil did not significantly affect gustatory behavior to sucrose ( S3D Fig ) or ingestion of sucrose solutions ( S1F Fig ) . Application of 20 μM extracellular uracil did not activate Drosophila TRPA1 ( A ) 10a or TRPA1 ( A ) 10b expressed in Xenopus oocytes ( S3A–S3C Fig ) , indicating that uracil does not directly stimulate TRPA1 . Thus , ingested uracil increases defecation likely by activating a signaling pathway upstream of TrpA1 . To examine whether the reactivity of ROS resulting from Duox activation is important for TrpA1-dependent defecation , N-methylmaleimide ( NMM ) , a robust sulfhydryl-reactive chemical which activates TRPA1 [19 , 22 , 32] , was fed at 1 mM replacing uracil . The TRPA1 agonist increased defecation frequency in wcs with the extent similar to that of 20 μM uracil with TrpA1 required ( Fig 1D and S1C Fig ) . However , NMM ingestion did not elevate HOCl production when the guts from NMM-ingested animals were examined with the HOCl-specific fluorescent dye , R19S [8 , 33] ( S4B–S4E Fig ) . This result may suggest that Duox-derived chemical reactivity is crucial for TrpA1-dependent defecation . Indeed , the ROS-counteracting dithiothreitol ( DTT , 10 mM ) fed with uracil abolished uracil-dependent defecation increase in wcs without significantly affecting defecation of a TrpA1 mutant ( Fig 1E ) or the gustatory behavior ( S3D Fig ) . Co-ingestion of uracil and DTT is unlikely to change their chemical properties , since two hour incubation of the mixture at room temperature did not alter their spectral absorbances ( S3E–S3G Fig ) . With the use of R19S , it was also confirmed that the HOCl level was readily reduced by co-ingestion of DTT with uracil ( S4B–S4E Fig ) . Consistently , overexpression of the peroxiredoxin gene Jafrac1 [34] under the control of da-Gal4 that drives ubiquitous UAS–dependent expression and was previously used to RNAi-knockdown Duox expression [11] abolished uracil-dependent defecation ( Fig 1F ) . Deterring reactive chemical biosynthesis , RNAi knockdown of Duox significantly reduced uracil-elicited defecation ( Fig 1G ) , which confirms the role of Duox in uracil- and TrpA1-dependent defecation . Consistent with the biochemical Duox activity observed in dissected guts [11] , Duox RNAi knockdown in the nervous system by pan-neuronal appl-Gal4 insignificantly lowered uracil-dependent defecation ( S1D Fig ) , suggesting that HOCl production in the gut is critical for uracil-dependent defecation . In parallel to the reported independent relationship of the other gut innate immune system , the imd pathway , with uracil-evoked HOCl production by Duox [6 , 8] , rele20 , a relish mutant severely defective for the imd immune response [35] showed normal uracil-elicited defecation ( Fig 1H ) . To exclude the possibility that the deficit of defecation caused by genetic manipulation stems from developmental impairment , RNAi knockdown of Duox or TrpA1 and Jafrac1 overexpression were delayed until one day before defecation experiments by means of Gal80ts-dependent transcriptional suppression of Gal4 ( S5 Fig ) , which produced outcomes similar to corresponding experiments described above . Taken together , the requirement of Duox expression and chemical reactivity of its microbicidal ROS product point out that the TRPA1 activity is likely under the direct control of Duox , but not cytosolic [Ca2+] increase or PIP2 depletion by uracil-driven G-protein signaling pathways . Highly reactive HOCl produced by Duox was previously shown to accumulate in the lumen of the gut [8] where TrpA1 transcripts appear to be present [26 , 27] . Indeed , the fly midgut showed TRPA1 immunoreactivity which was absent in TrpA1ins ( Fig 2A , Middle and S6 Fig for higher magnification ) . The immunoreactivity was observed across most of the anterior midgut and in two lateral domains across middle midgut boundaries facing the anterior or posterior midgut ( illustrated in S7A Fig ) . Interestingly , the TRPA1-expressing cells were closely associated with immunoreactivity of the enteroendocrine cell ( EEC ) nuclear marker , Prospero ( Fig 2A , Top ) [36] , but not with those of intestinal stem cell and enteroblast markers ( S8A and S8B Fig ) . Note that the background staining that sometimes appears in the visceral muscle is non-specific or not critical for the defecation , because it was sometimes observed in the TrpA1-deficient guts and TrpA1 RNAi-knockdown by EEC-restricted TrpA1 ( A ) -Gal4 phenocopies TrpA1ins . TrpA1 ( A ) -Gal4 contains the genomic fragment of the TrpA1 ( A ) isoform-specific promoter upstream of the Gal4 coding sequence [25] . GFP expression driven by TrpA1 ( A ) -Gal4 comprised a subset of Prospero-positive cells ( S7B Fig ) as in TRPA1 immunostaining , and was colocalized with the majority of anti-TRPA1 cells ( Fig 2A , Bottom ) throughout the anti-TRPA1-stained regions . In order to functionally demonstrate the role of TrpA1-positive cells in the defecation , TrpA1 RNAi knockdown was conducted with TrpA1 ( A ) -Gal4 , and showed significant reduction of TRPA1 expression in EECs ( S8C Fig ) and uracil-dependent defecation ( Fig 2B and S7C Fig ) . On the other hand , pan-neuronal expression of TrpA1 cDNA by c155-Gal4 was unable to rescue the defecation defect in TrpA1ins mutants , whereas that by EEC-covering TrpA1 ( A ) -Gal4 did ( Fig 3A , Lower ) . These observations together with the Duox-dependent HOCl production in the gut lumen [8 , 11] suggest that the known peripheral and central TrpA1-positive neurons so far identified by immunostaining [19 , 20 , 22 , 37] are not primarily related to uracil-elicited defecation , advocating the role of TrpA1-expressing EECs in uracil-dependent defecation . In sensory systems , it has been reported that alternative use of exons ( Fig 2C ) allows TrpA1 to conform to multiple sensory needs by modulating its temperature sensitivity [22 , 25] . Our reverse transcription analyses of dissected guts revealed that TrpA1 ( A ) , the chemosensory isoform expressed in taste neurons with much reduced thermal sensitivity [22] , is predominant in the gut ( Fig 2D ) and can be alternatively spliced with either exon10a or exon10b ( Fig 2E ) . The exon10 encodes a small region consisting of 37 or 36 amino acids of exon10a or exon10b , respectively , between the N-terminal ankyrin repeat and transmembrane domains ( Fig 2C , Lower ) . Interestingly , TrpA1 ( A ) 10b but not TrpA1 ( A ) 10a cDNA restored the uracil-dependent defecation when expressed in TrpA1 ( A ) -Gal4 cells of TrpA1ins mutants ( Fig 3A ) . The ineptitude of the UAS-TrpA1 ( A ) 10a transgene in restoring the defecation is unlikely due to lack of functional expression based on two lines of evidence . First , functional expression of UAS-TrpA1 ( A ) 10a was comparable to that of UAS-TrpA1 ( A ) 10b in gustatory neurons ( S9H and S9I Fig ) . Second , defecation increase caused by ingestion of the TRPA1 agonist NMM was restored in the TrpA1 mutants by expressing either TrpA1 ( A ) 10a or TrpA1 ( A ) 10b in the TrpA1 ( A ) -Gal4 cells ( Fig 3B ) , as was in the genomic rescue animals ( Fig 1D ) . TRPA1 ( A ) 10a and TRPA1 ( A ) 10b share the key cysteines in the N-terminal ankyrin repeat domain , which are important for detection of electrophilic chemicals [19 , 32 , 38] , and both the isoforms should thus be able to respond to NMM . The defecation increase of TrpA1 ( A ) 10a-expressing TrpA1–deficient animals in response to NMM ingestion demonstrates the functional expression of the UAS-TrpA1 ( A ) 10a transgene in the TrpA1 ( A ) -Gal4 cells where expression of TrpA1 ( A ) 10a was unable to restore uracil-evoked defecation in TrpA1-deficient animals , while the former result ( S9H and S9I Fig ) indicates successful transgenesis of UAS-TrpA1 ( A ) 10a . The isoform dependence in uracil-evoked defecation might result from further functional diversification of TRPA1 ( A ) via alternative exon10s altering response parameters to HOCl of the channel isoforms . To examine for any differential HOCl responsiveness of the two isoforms , TRPA1 isoforms were heterologously expressed in Xenopus laevis oocytes , and HOCl responses of the ion channel isoforms were characterized by the two-electrode voltage clamping approach . Interestingly , TRPA1 ( A ) 10b showed >3 times faster activation kinetics at 1 and 10 ppm of the HOCl-donating NaOCl at the membrane potential of -60 mV ( Fig 3C–3E and 3G ) and >3 times higher responsiveness to 0 . 1 and 1 ppm NaOCl at -60 mV ( Fig 3F ) than other fly TRPA1 variants and human TRPA1 ( S9A–S9F Fig ) . Furthermore , in contrast to comparable responses of the two fly TRPA1 variants to NMM in gustatory neurons , Gr5a-Gal4 chemosensory neurons ectopically expressing TrpA1 ( A ) 10b , but not those expressing TrpA1 ( A ) 10a , showed spiking frequency increases in response to NaOCl 100 ppm ( S9H and S9I Fig ) . These data indicate that TRPA1 ( A ) 10b expressed in EECs is capable of mediating Duox-dependent defecation by rapidly reacting to low amounts of reactive chlorine species that are probably short-lived in the gut mucus due to their high reactivity . The enhanced sensitivity and response kinetics of TRPA1 ( A ) 10b might not originate from its intrinsic receptivity to reactive chemicals , because the transcript spliced with Exon10b loses a cysteine that is known to be important for electrophile sensitivity [19 , 32] . In parallel with this view , the recently characterized non-covalent TRPA1 ( A ) agonist citronellal [23] activates TRPA1 ( A ) 10b with higher sensitivity and response kinetics than TRPA1 ( A ) 10a ( S10 Fig ) as does NaOCl , suggesting that the enhanced NaOCl receptivity of TRPA1 ( A ) 10b is not because it responds better to chemical reactivity than TRPA1 ( A ) 10a . Erwinia carotovora subspecies carotovora 15 ( Ecc15 ) , the well-characterized microbe excreting uracil , is sensitive to the Duox immune response [8 , 11] . The uracil-deficient mutant strain , Ecc15 pyrE , was incapable of eliciting reactive chlorine production in the fly gut [8] . Similar to the nonbacterial uracil experiments above , oral ingestion of uracil-producing Ecc15 WT induced higher defecation frequencies than that of uracil-lacking Ecc15 pyrE , a trait requiring TrpA1 but independent of the imd pathway in flies ( Fig 4A and 4B ) . Ingestion of the bacteria raises overall levels of defecation compared to sucrose conditions . WT flies consuming pyrE or WT ECC15 showed defecation spots per ingested volume ( output/input ) of 0 . 98+/-0 . 08 or 1 . 45+/-0 . 11 , while ingestion of sucrose only or sucrose+uracil was led to output/input of 0 . 41+/-0 . 04 or 0 . 77+/-0 . 05 ( S1 Fig ) , respectively . The defecation increase by bacterial ingestion was similarly observed in the TrpA1-deficient flies . This result is in contrast to the previous observation where ECC15 ingestion did not alter defecation patterns compared to ingestion of LB+sucrose [30] . We suspect that this discrepancy might have resulted from difference in experimental conditions , as our experiments with sucrose ingestion did not include the LB medium offering protein . The difference in defecation between the two bacterial strains is unlikely due to general physiological incompetence of Ecc15 pyrE , as Ecc15 pyrE ingestion supplemented with 1 mM uracil led flies to defecate more than unsupplemented ingestion , which again requires TrpA1 ( Fig 4C ) . Increase of defecation might help flies defend themselves by suppressing growth of gut bacteria . Indeed , the guts from animals lacking TrpA1 exhibited increased colony-forming units ( CFUs ) 5 hrs after the start of the 2-hr Ecc15 ingestion session , and CFUs at the 8-hr time point were similar to those after the 2-hr feeding session ( Fig 5A ) . In contrast , CFUs in the guts of wcs and genomic rescue animals were similar or lowered , respectively , at the 5-hr time point and were further reduced at 8 hrs , compared to 2 hrs , ( Fig 5A ) . Consistent with this result , 5 hrs after the start of ingestion , defecation was significantly increased with uracil-producing Ecc15 in TrpA1-expressing flies ( Fig 5B ) , indicating that uracil-elicited defecation in the experimental time window of 0–5 hrs is responsible for timely pathogen expulsion . These results demonstrate that uracil production in Ecc15 lowers the number of gut Ecc15 by increasing defecation frequencies via TRPA1 . Consistent with our results above indicating the imd pathway to be independent of uracil- and TrpA1-dependent defecation , the gut persistence of ECC15 WT was not elevated rather lowered at the 5- and 8-hr time points in the guts of rele20 ( Fig 5C ) . The rele20 , TrpA1ins double mutants showed the increased CFU of ECC15 WT in the gut , again pointing out the importance of TrpA1 in timely defecation of uracil-producing ECC15 . Ingestion of ECC15 pyrE did not differentiate CFUs from wcs and TrpA1ins , which indicates that the growth of the bacterial stain is similar in the guts of wcs and TrpA1ins ( Fig 5D , left ) . However , inability of uracil excretion was not led to apparent increases of ECC15 pyrE gut persistence . Given the roles of uracil and its derivative in a wide array of enzymatic events as coenzyme and regulator [39–41] as well as in RNA synthesis as a building block , we suspect that the lack of uracil biosynthesis in the bacteria might cause physiological bottlenecks , hindering normal growth in the gut . In parallel with this view , supplementation of ECC15 pyrE with uracil raised CFU in the TrpA1ins not wcs guts albeit at a delayed time point , 8 hrs ( Fig 5D , right ) . Uracil supplementation might aid ECC15 pyrE to grow faster in the gut , but at the same time trigger the Duox pathway to drive the TrpA1-dependent expulsion . Because residing time or growth of ECC15 in the gut can be greatly influenced by the commensal microbiome that can differ in the genotypes of interest , germ-free animals were generated and tested for defecation induction by uracil ( S11 Fig ) and gut persistence ( Fig 5E ) . These experiments produced results very similar to those with non-germ-free animals , suggesting that the TrpA1- and uracil-dependent defecation is little affected by pre-established microbiome . To examine the extent to which TrpA1-dependent defecation is critical for elimination of ECC15 from the animals , ECC15 that stayed in either gut of wcs or TrpA1ins for 5 hrs were assessed for their HOCl resistance by incubation of the bacteria with varying concentrations of HOCl for 30 min . The resistance was similar between the bacteria isolated from wcs and TrpA1ins guts ( S12A Fig ) . However , in three out of eight experiments , ECC15 from TrpA1-deficient guts showed colonies that appeared 2 days after spotting , while ECC15 from wcs guts did not in all eight experiments ( S12B Fig ) . This acquired resistance to HOCl was transient , as the surviving colonies did not grow again with the second incubation in 10 ppm of HOCl . Although the results did not reach statistical significance ( Fisher’s exact test , p = 0 . 2 ) , the opportunistic survival of ECC15 from TrpA1ins guts at the HOCl concentration that prevented the growth of ECC15 from wcs guts suggests that the timely clearance of the pathogenic bacteria might be critical to eliminate the opportunity to gain the ability to survive the Duox defense system in the gut . To examine the impact of uracil-evoked defecation in the mortality rate of flies , flies were subjected to ingestion of ECC15 at OD600 of 10 in 500 mM sucrose , and monitored for survival ( Fig 5F ) . Although TrpA1ins showed resistance to ECC15 similar to wcs , the mortality rate of rele20 was significantly higher than that of wcs . Interestingly , the double mutant flies lacking both rel and TrpA1 ( TrpA1ins , rele20 ) were least resistant to orally ingested ECC15 , revealing that uracil-induced defecation is important for the survival of immune-compromised animals . This enhanced mortality in TrpA1ins , rele20 was not observed when ECC15 pyrE was ingested , suggesting the role of uracil excreted from ECC15 in resisting to the bacteria ( Fig 5G ) . TrpA1ins showed extended life span with the ECC15 pyrE ingestion beyond that of wcs . The extended life span of TrpA1ins ingesting ECC15 pyrE appears to stem from genetic factors other than TrpA1 , as it was not rescued by reintroduction of the TrpA1 gene ( S12C Fig ) . In previous studies , it has been shown that bacterially released uracil stimulates production of microbicidal reactive chlorine by Duox , thus killing bacterial pathogens in the gut . Microbes are , however , often equipped with dedicated chlorine-responsive machineries mounting reactive chlorine resistance [42–44] . Therefore , intestinal bacteria steadily subjected to reactive chlorine might develop resistance to the toxicity , which could be a detrimental outcome for host health . In this regard , expelling pathogenic bacteria from the gut would be a preemptive measure curtailing the possibility . Our results suggest that the TrpA1-mediated response to reactive chlorine helps flies restrain bacterial pathogens from acquiring reactive chlorine resistance by promoting pathogen expulsion . For such processes , TrpA1 expression is required in enteroendocrine cells , where TRPA1 activation might promote release from the EECs of intestinal hormones or transmitters that have yet to be identified . It has been reported in mammals that TRPA1 in enterochromaffin cells is responsible for serotonin release by oxidative stress in the gut [13] and that serotonin biosynthesis is regulated by commensal bacteria thus affecting intestinal motility and hemostasis [14] . It would be interesting to examine in the future if the role of serotonin or equivalent signaling messengers from EECs are conserved for uracil-evoked defecation in Drosophila , and have physiological implications in regards of host/microbiome interaction . Reactive chlorine species are not only highly bactericidal but also harmful to host cells . Although the gut contains a host-protecting antioxidant system [45] , sustained Duox activation by uracil-releasing opportunistic pathobionts causes chronic inflammation through gut cell apoptosis [8] . In order to restrict such hazardous effects of reactive chlorine , Drosophila guts may control the uracil-elicited HOCl production at which level opportunistic pathogens are eliminated but cytotoxicity is insignificant . Our results reveal that low amounts of unstable reactive chlorine can be readily received by TRPA1 ( A ) 10b . Thus , the exceptional activation kinetics and enhanced sensitivity to HOCl of the TRPA1 ( A ) 10b isoform reduce the risk of inflammation which may otherwise result from the need of prolonged exposure to excessive reactive chlorine . Our comparative analysis of human TRPA1 with Drosophila TRPA1 isoforms showed that human TRPA1 is inferior to TRPA1 ( A ) 10b in terms of NaOCl response kinetics and sensitivity ( S4 Fig ) , suggesting that tuning of mammalian TRPA1 for a sufficient HOCl responsiveness may be required for the Duox-dependent defecation in mammalian intestines . Indeed , a newly identified RNA splice variant encodes a TRPA1 isoform that heightens the sensitivity to reactive chemicals by forming heterotetramers with the classical TRPA1 in mice [46] . It would be intriguing to examine that the heterotetramers of mouse TRPA1 show similar functional shifts in response to NaOCl , while the alternative domain occurs in a region spatially distinct that of Drosophila TRPA1 . A recent study proposed that TRPA1 ( A ) 10b is a potential receptor for H2O2 [47] which is the intermediate Duox product , while its H2O2 responsiveness appeared to be similar to that of TRPA1 ( A ) 10a . Considering that TrpA1 ( A ) 10b but not TrpA1 ( A ) 10a is required for Duox-dependent defecation in EECs , HOCl rather than H2O2 is the key player resulting from uracil-induced Duox upregulation . How bacteria affect host defecation in mammals is not entirely understood . Mammals express Duox in mucosal epithelia , where bacteria densely colonize [6] . At the same time , mammalian TRPA1s function in neurons and enterochromaffin cells closely associated with the mucosal tissues [13 , 18] , implying potential crosstalk between Duox and TRPA1 in mammals . Irritable bowel syndrome ( IBS ) is a disease characterized by dysregulated defecation , which can be caused by an imbalance between gut microbiota and host immunity [48] . Moreover , visceral pain and dysregulated defecation are two major symptoms of IBS , which are reminiscent of Drosophila TRPA1 functions in chemical nociception [19 , 22] and defecation ( described here ) . Our discovery of a chemical link between Duox and TRPA1 in flies raises the possibility that oxidative stress from bacterially stimulated mucosal Duox may interact with TRPA1 in mammals to trigger defense responses such as increased bowel movement , the dysregulation of which may lead to pathological states such as IBS .
Site-specific transgenesis [49] was used to introduce the UAS-TrpA1 ( A ) 10b transgene into the genome at the same site as UAS-TrpA1 ( A ) 10a [22] and UAS-TrpA1 ( B ) [19] , attp16 [50] in order to control transgene position effects . Other fly lines were mostly acquired from the Bloomington Drosophila Stock Center , IN , USA . The da-Gal4 line is a kind gift from Won-Jae Lee at Seoul National University , TrpA1 ( A ) -Gal4 [25] from Daniel Tracey at Duke University and TrpA11 and TrpA1Gal4 [29] from Craig Montell at Johns Hopkins University . The UAS-DuoxRNAi and UAS-TrpA1RNAi used in this study were the fly stocks of #33975 and #31384 , respectively , from the Bloomington Drosophila Research Stock Center . These RNAi transgenic lines were generated by the Transgenic RNAi Resource Project ( TRiP ) [51] . Axenic animals were generated for defecation analyses and gut persistence experiments from bleached embryos , which were subsequently transferred to autoclaved media in fly vials . The first and second generations of the germ-free flies were used for experiments in the study . In order to measure defecation frequencies , 12 to 15 male flies were used 4 to 6 days after eclosion . Female defecation is affected by reproduction status [31] , which may mask the effect of uracil on defecation . Siblings were divided into two groups and starved for 16 hrs in humidified conditions at room temperature . One group of flies was fed with 500 mM sucrose solution , and the other was with either uracil ( U1128 , Sigma Aldrich , MO , USA ) or N-methylmaleimide ( NMM; 389412 , Sigma Aldrich , MO , USA ) in addition to 500 mM sucrose . Uracil concentrations used in the dose dependence experiment were 0 . 2 , 20 , 20 , 000 and 100 , 000 nM ( Fig 1B ) . For the rest of uracil-induced defecation experiments , 20 , 000 nM was used . For the convenience of counting defecation spots left dried on the inner wall of regular fly vials ( AS-514 , Fisher Scientific , MA , USA ) , 0 . 01% food colorant Brilliant Blue FCF ( Blue 1 , 861146 , Sigma Aldrich , MO , USA ) was added to all ingested solutions . Flies were fed in a modified capillary feeder ( Café ) configuration [52] , in which sucrose solutions in five-microliter graded capillary tubes ( 2920107 Marienfeld , Lauda-Königshofen , Germany ) were offered for 2 hrs in a fly vial . Subsequently , the flies were removed and further flipped to a new vial every hour for 8 hrs in total including the 2 hr ingestion ( Fig 1A ) . In this way , we could assess the amount of food ingested and normalize the defecation frequency with respect to the level of ingestion . Humidified conditions between 50 and 70% were maintained throughout ingestion and defecation . The ingested amount was expressed in length ( millimeter ) of the change in food height in the capillary tube ( 15 mm/ μl ) . To assess the defecation responses to Ecc15 strains , each strain was precultured for 8 hrs at 30°C in 4 ml of LB broth with the appropriate antibiotics . The preculture was transferred to a larger volume of LB at a ratio of 1:100 for the overnight main culture . The harvested bacteria were washed once in PBS ( 10 mM Na2HPO4 , 1 . 8 mM KH2PO4 , 140 mM NaCl , 2 . 7 mM KCl , pH 7 . 3 ) and resuspended in 500 mM sucrose solution to the optical density ( OD ) of 10 at 600 nm to be offered via the capillaries to flies . To estimate the total amount of defecation , the flies were offered for two hours with conditioned sucrose solutions containing 0 . 5% brilliant blue FCF and allowed to defecate in vials for 6 hours . The inside of each vial was washed with 2 . 5 ml of PBS . The absorbance at 628 nm was determined by spectrometry for comparative quantitation of the dye . The absorbance was then divided by the ingested amount assessed from migration of sucrose solution meniscus in the feeding capillary for normalization . Twenty male flies were transferred every day to a new vial containing a round 3M paper soaked in 500 mM sucrose solution with ECC15 at OD10 . The bacteria culture was prepared freshly every day for consistency of the survival experiments . The inactive flies were counted before the transfer . The data were plotted and analyzed by means of the Kaplan Meier analysis offered in Sigmaplot12 . 0 . To evaluate bacterial persistence in the gut , spectinomycin-resistant Ecc15-GFP cells suspended at OD 10 in 500 mM sucrose solution were orally fed to the flies in the Café configuration . The gut was dissected out at the time points indicated in Fig 4 , and ground in PBS followed by vigorous vortexing for 1 min . The released gut bacteria was serially diluted and plated on spectinomycin LB-agar dishes . Mean CFUs averaged from three serial dilutions were used for data analysis . The previously published gut immunocytochemistry protocol was used [16] with minor modifications . Briefly , whole abdomens were immunostained before the intestines were dissected out and mounted for imaging . Whole abdomens were separated and punctured for reagent infiltration to the intestine in phosphate buffered saline with 0 . 2% Triton X-100 ( PBS-T , pH 7 . 2 ) , and fixed in 4% paraformaldehyde in PBS-T overnight at 4°C . After 3 washes with PBS-T , samples were blocked with 3% goat serum in PBS-T for at least 30 min . Abdomens were incubated with primary antibodies or antisera for 1–2 days at 4°C , washed with PBS-T , and incubated with secondary antibodies overnight at 4°C . The primary antibodies used were rat anti-TRPA1 ( 1:200 ) [19 , 22 , 37] , rabbit anti-GFP ( 1:1000 , Life Technologies , CA , USA ) , anti-Prospero ( 1:10 , Developmental Studies Hybridoma Bank at the University of Iowa ) . The secondary antibodies used were Alexa Fluor Cy3-labeled goat anti-rat ( 1:1000 , Jackson Laboratory , ME , USA ) , Alexa Fluor 488-labeled goat anti-rabbit ( 1:200 , Life Technologies , CA , USA ) , and Alexa Fluor 568-labeled goat anti-mouse IgG ( 1:1000 , Life Technologies , CA , USA ) . A Zeiss LSM 700 laser-scanning confocal microscope was used to acquire images of immunostained samples . TRPA1 currents in Xenopus laevis oocytes evoked by application of chemicals were recorded by two-electrode voltage clamping as previously described [19 , 22] . Briefly , surgically prepared ovaries were subjected to collagenase treatment to free the cells from the tissue . One day after injection of 50 nl of TrpA1 cRNA , oocytes were perfused in the recording solution ( 96 NaCl , 1 KCl , 1 MgCl2 , 5 HEPES , pH 7 . 6 in mM ) . NaOCl ( 425044 , Sigma Aldrich , MO , USA ) was freshly diluted immediately before experiments . TRPA1-expressing oocytes exhibited resting membrane potentials between -10 and -50 mV . Voltage was initially held at -60 mV , and a 300-ms voltage ramp from -60 to +60 mV was applied every second by the GeneClamp 500B amplifier ( Molecular Devices , CA , USA ) during data acquisition ( Digidata 1440A , Molecular Devices , A , USA ) . To determine NaOCl sensitivities of the TRPA1 isoforms , currents were recorded till reaching steady state after evoked by a concentration of NaOCl , rather than fixing the application time across the experiments . Data points from dose dependence experiments were normalized with the respect to the current amplitude achieved by 100 ppm NaOCl , and fitted to the Hill equation by Sigmaplot12 . Since current traces were often unable to be fitted to the single exponential equation , Time to the 70% maximum current at each NaOCl concentration was determined . Five hours after start of ECC15-GFP at OD 10 in 500 mM sucrose , five guts per genotype were dissected out and ground in 0 . 2 ml of PBS . Each aliquot of 0 . 04 ml of ground guts was incubated with either 0 , 0 . 1 , 1 , 5 or 10 ppm of NaOCl for 30 min at room temperature in 1 ml of PBS . Subsequently , the surviving bacteria were spot-titrated on spectinomycin LB agar plates with serial dilution in PBS . Extracellular recordings of gustatory neurons in L-bristles were conducted as previously described in detail [22] . Student’s t-test and ANOVA Tukey multiple comparison and ANOVA repeated measures tests were performed with Sigmaplot12 . Complementary DNA was prepared from dissected fly guts using the RETROscript kit ( AM1710 , Life Technologies , CA , USA ) . Primers used to determine exons encoding the N-terminus were described previously [22] . Regarding exon 10 splicing , the following primers were used for RT-PCR ( Fig 2C ) . E10com-F: 5’-GTGGACAAGGATGGGAAC-3’ 10a-R: 5’-CTCTCCGGTTTTCTCATCA-3’ 10b-R: 5’-GGTAGGGCCAAAACGAA-3’ The response profile of R19S to the concentration range of NaOCl used in the study was determined with Deltascan ( PTI , USA ) . For confocal scanning of the R19S fluorescence signal ( LSM710 , Zeiss , Germany ) , sucrose solutions containing 10 μM R19S with or without 20 nM uracil or with uracil and DTT or with NMM were fed to flies in Café configuration for 1 hr . Subsequently , the intestines of the flies were dissected out and fixed in 2% paraformaldehyde for 15 min . The mean pixel intensity was measured by Zen Pro ( Zeiss , Germany ) in a middle part of the anterior midgut captured with Plan-Apochromat 20x/0 . 8 M27 ( Zeiss , Germany ) . | The amount and pattern of defecation are often determined by the bacterial composition in the gut , and can have a significant impact on human health . It is however unknown how changes in the bacterial community affect defecation . A chemical defense system called the Duox pathway is known to kill ill-causing bacteria in the gut by producing chlorine bleach . Our study reveals that the bleach does not stop there , working further to promote expulsion of the bacteria from the gut through a sensitive bleach detector . Moreover , in the gut deficient for the bleach detector , the bacteria not only stayed longer , but also opportunistically survived the bleach that they encounter afterwards . This is the first identification of a mechanism illustrating how important it is that gut defense systems control defecation , and helps explain why troubles in the gut cause changes in defecation . | [
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] | [] | 2016 | TrpA1 Regulates Defecation of Food-Borne Pathogens under the Control of the Duox Pathway |
Earlier studies had suggested that epigenetic mechanisms play an important role in the control of human cytomegalovirus ( HCMV ) infection . Here we show that productive HCMV infection is indeed under the control of histone H3K27 trimethylation . The histone H3K27 methyltransferase EZH2 , and its regulators JARID2 and NDY1/KDM2B repress GFI1 , a transcriptional repressor of the major immediate-early promoter ( MIEP ) of HCMV . Knocking down EZH2 , NDY1/KDM2B or JARID2 relieves the repression and results in the upregulation of GFI1 . During infection , the incoming HCMV rapidly downregulates the GFI1 mRNA and protein in both wild-type cells and in cells in which EZH2 , NDY1/KDM2B or JARID2 were knocked down . However , since the pre-infection levels of GFI1 in the latter cells are significantly higher , the virus fails to downregulate it to levels permissive for MIEP activation and viral infection . Following the EZH2-NDY1/KDM2B-JARID2-independent downregulation of GFI1 in the early stages of infection , the virus also initiates an EZH2-NDY1/ΚDM2Β-JARID2-dependent program that represses GFI1 throughout the infection cycle . The EZH2 knockdown also delays histone H3K27 trimethylation in the immediate early region of HCMV , which is accompanied by a drop in H3K4 trimethylation that may contribute to the shEZH2-mediated repression of the major immediate early HCMV promoter . These data show that HCMV uses multiple mechanisms to allow the activation of the HCMV MIEP and to prevent cellular mechanisms from blocking the HCMV replication program .
Human cytomegalovirus ( HCMV ) is a double stranded DNA virus that belongs to the beta-herpesvirus subfamily of the herpesvirus family . Other members of this subfamily are the human herpes viruses 6 and 7 ( HHV-6 and HHV-7 ) . HCMV seroprevalence varies widely among populations residing in different geographical regions and among different socioeconomic and age groups [1] . The virus infects many cell types , including fibroblasts , hematopoietic , endothelial , epithelial , smooth muscle and neuronal cells [2] . Most otherwise healthy individuals that are infected with HCMV , experience few if any symptoms . However , some may present symptoms similar to mononucleosis , including fatigue , fever and muscle aches [1] . After the initial infection , the virus enters life-long latency in hematopoietic and endothelial cells , during which the viral genome is maintained as a low-copy number extrachromosomal plasmid . During latency , the productive viral transcription program is almost entirely repressed , with only a subset of latency-associated transcripts being expressed [3] . The Immediate-Early ( IE ) genes whose expression is a prerequisite for the onset and progression of productive infection remain silenced , and as a result , there is no production of infectious virions . Under specific conditions , the viral genomes can undergo sporadic reactivation , re-initiating a full replicative cycle , which results in virus production and dissemination . Latently-infected individuals are typically asymptomatic . Reactivation of the virus is frequently observed in HIV-infected individuals and in patients undergoing treatment with immunosuppressive or chemotherapeutic drugs [1] , [3] , [4] , although it may also occur in immunocompetent hosts [3] . Virus reactivation may be responsible for debilitating or life-threatening illnesses [1] , [3] , [4] . The genome of HCMV consists of unique short ( US ) and unique long ( UL ) segments both of which are flanked by inverted repeats [1] . Viral gene expression , during HCMV infection , occurs in a temporally regulated manner and it is characterized by three sequential and interdependent waves of transcription . The first wave includes the robust transcription of the immediate-early ( IE ) genes IE1-72 KDa and IE2-86 KDa , which antagonize and inactivate the host defenses while in addition they induce the expression of the early viral genes . The early genes , expressed in the course of the second wave of transcription , contribute to viral DNA replication , a prerequisite for the activation of the late genes . The latter encode viral structural proteins and are required for virion assembly and virion release from the infected cells . To initiate the transcription of the immediate-early genes , the virus employs cellular transcriptional activators and inhibits cellular transcriptional repressors targeting the major immediate-early promoter ( MIEP ) [5] . One of the transcriptional repressors targeting this promoter is Growth factor independence 1 ( GFI1 ) , a zinc finger protein with a SNAG repressor domain [6] , [7] . GFI1 was originally identified as a transcription factor that contributes to the transition of IL-2-dependent T cell lymphoma lines to IL-2 Independence [8] . Today , we know that GFI1 is an important regulator of hematopoietic cell differentiation , contributing to multiple steps in hematopoiesis and lymphopoiesis , ( reviewed in [9] , [10] ) . In addition , we know that GFI1 regulates the functional response of macrophages and dendritic cells to Toll like receptor ( TLR ) signals [11] . At the molecular level , it has been shown that GFI1 is part of a large nuclear complex that includes CoREST , lysine-specific demethylase-1 ( LSD1 ) , and HDACs 1 and 2 . CoREST and LSD1 associate with GFI1 by binding the GFI1 SNAG repression domain [12] . Immediate-early gene transcription during HCMV infection , or virus reactivation in latently infected cells , depends on the state of differentiation of the target cells [3] , [13]-[19] . Undifferentiated cells tend to resist productive infection , suggesting that epigenetic mechanisms , including chromatin modifications and DNA methylation may alter the permissiveness to the virus . Earlier studies addressing this hypothesis , confirmed that immediate-early gene transcription can be altered by the acetylation status of histone H3 associated with the major immediate-early promoter [14] , [16] , [20]–[25] . In the present study , we focus our attention on the role of histone methylation in HCMV infection and we show that changes in viral infectivity caused by modulation of the chromatin modification machinery of the cell are due to changes in the transcription of the immediate-early genes . Unlike earlier studies however , the present study focuses on the regulation of cellular transcription factors that control the expression the HCMV immediate-early region . Methylation of histone tails in the promoter region , or the body of a gene , plays a major role in the regulation of gene expression . Histones undergo lysine mono- , di- , or tri-methylation at multiple sites and the functional consequences of histone methylation are site-dependent . Thus , tri-methylation of promoter-associated histone H3 at K4 is a feature of active chromatin , while di-methylation and tri-methylation of histone H3 at K9 , or tri-methylation at K27 are features of inactive chromatin . Moreover , mono- , di- and tri-methylation at other sites , such as K36 in the body of a gene , may affect transcriptional elongation and/or RNA splicing ( reviewed in [26] ) . Methylation of core histones at different sites is catalyzed by a host of site-specific methyltransferases . For example , tri-methylation of histone H3 at K27 is catalyzed by EZH2 , a component of the polycomb repressor complex 2 ( PRC2 ) [27] , [28] , whose activity is regulated by several co-factors , including the jumonji domain-containing proteins JARID2 [29] and NDY1/KDM2B [30] . Histone methylation is reversible , with demethylation being catalyzed by a host of site-specific histone demethylases . The first histone demethylase to be identified ( LSD1 ) removes H3K4me1 and H3K4me2 methyl groups , through an oxidative reaction that uses FAD as a co-factor and produces an unstable imine intermediate [31] . The large family of jumonji domain-containing histone demethylases removes lysine methyl groups from a variety of mono- , di- or tri-methylated sites , through an oxidative reaction that uses Iron ( FeII ) and α-ketoglutarate as co-factors and produces an unstable hydroxymethyl intermediate . Demethylation of histone H3K27me3 is catalyzed by the jumonji domain demethylases , UTX/KDM6A , its homolog UTY , and JMJD3/KDM6B ( Reviewed in [32] ) . The results of the present study showed that immediate-early gene transcription and HCMV infection of human foreskin fibroblasts ( HFFs ) depend on histone H3K27 trimethylation , which is under the control of EZH2 , JARID2 , NDY1/KDM2B and the histone demethylase JMJD3 . The EZH2/NDY1/ /JARID2/JMJD3 axis silences GFI1 , a repressor of the MIEP of HCMV . Inhibition of this axis therefore , upregulates GFI1 and interferes with the activation of the MIEP and HCMV infection . Immediately after virus entry in virus-infected cells , UV-sensitive virus-associated factors facilitate MIEP activation by promoting the rapid downregulation of GFI1 in both wild-type and NDY1/KDM2B , EZH2 or JARID2 knockdown cells . However , since the levels of GFI1 in the latter cells prior to the infection are significantly higher than in wild-type cells , the HCMV-induced GFI1 degradation fails to downregulate GFI1 to levels permissive for MIEP activation and viral infection . To maintain the silencing of GFI1 , the virus also initiates an NDY1/EZH2/JARID2/JMJD3-dependent program , which represses GFI1 throughout the infection cycle . The knockdown of EZH2 may contribute to the repression of the MIEP , also by modulating the accumulation of histone H3K27me3 and H3K4me3 in the immediate early region of HCMV in the first three hours from the start of the viral infection . We conclude that HCMV infection depends on EZH2NDY1/ /JARID2/JMJD3-dependent and independent mechanisms which are activated by the virus and control the expression of GFI1 , a transcriptional repressor of the immediate-early region of HCMV . EZH2-dependent mechanisms also control histone modifications in the immediate-early region of HCMV that may contribute to the activation of the MIEP very early in infection .
To determine whether histone methylation plays a role in the efficiency of viral infection and replication , human foreskin fibroblasts ( HFFs ) were transduced with pLKO . 1-based lentiviral constructs of shRNAs of NDY1/KDM2B , EZH2 , PHF2 and RBP2/JARID1A/KDM5A , or with the empty vector , prior to infection with HCMV . NDY1/KDM2B , PHF2 and JARID1A/RBP2/KDM5A are Jumonji domain-containing histone demethylases that target histone H3K36me2/me1 ( NDY1/KDM2B ) , K9me1 ( PHF2 ) and K4me3/me2/me1 ( RBP2 ) [32] , [33] . EZH2 is a SET domain histone methyltransferase that promotes histone H3K27 tri-methylation [27] , [28] . It is important to note that H3K27 trimethylation is also promoted by the demethylase NDY1/KDM2B , which upregulates the expression of EZH2 and contributes to its functional activation [30] , [34] . Furthermore , NDY1/KDM2B and EZH2 function in concert on a subset of promoters , which cannot be repressed by either of the two acting alone [30] . The results of the experiment revealed that , whereas the knockdown of PHF2 and RBP2 have no effect on the ability of the virus to infect HFFs , the knockdown of NDY1/KDM2B and EZH2 almost completely block the infection ( Fig . 1A ) . Transduction of HFFs with pBabe-puro-based retroviral constructs of the same histone modifying enzymes , or with the empty vector , had no effect on the efficiency of HCMV infection ( Fig . 1B ) . To determine whether the resistance of shNDY1/KDM2B and shEZH2-transduced cells to HCMV infection indicates a block or a mere delay of the infection , we monitored the virus titer in the supernatants of infected cells every other day for up to 9 days post-infection . The results confirmed that the knockdown of NDY1/KDM2B or EZH2 , but not PHF2 or RBP2 , block the infection ( Fig . 1C ) . The preceding experiments revealed that NDY1/KDM2B and EZH2 are both required for HCMV infection and replication , while other histone modifying enzymes are not . Given that NDY1/KDM2B and EZH2 operate in concert to upregulate EZH2 and histone H3K27 trimethylation [30] , [34] , we hypothesized that it is histone H3K27 trimethylation that is required for efficient infection by HCMV . To address this hypothesis , human foreskin fibroblasts were transduced with a pBabe-puro-based retroviral construct of the H3K27me3 demethylase JMJD3 , or with a pLKO . 1-based lentiviral shRNA construct of the same enzyme . Cells transduced with these constructs or with the empty vectors , were infected with HCMV and the efficiency of infection was determined qualitatively , as well as quantitatively , using a plaque assay for virus titration . The results showed that whereas shJMJD3 did not interfere with viral infection and replication , JMJD3 , which demethylates histone H3K27me3 , did ( Fig . 1D ) . EZH2 is known to form a complex with the prototype Jumonji domain protein JARID2 and to bind chromatin in concert with JARID2 [29] . Through this interaction JARID2 regulates the EZH2 methyltransferase activity . We therefore proceeded to address the role of JARID2 in HCMV infection and replication in HFFs . The experiments in Figure 1D showed that whereas tranduction with a pLKO . 1-based lentiviral construct of shJARID2 interferes with HCMV infection and replication , transduction with a pBabe-based construct of JARID2 does not . If histone H3K27 tri-methylation is required for HCMV infection , as suggested by the preceding experiments , replacement of the endogenous EZH2 with its SET domain mutant , which lacks histone methytransferase activity , should fail to rescue the permissiveness of shEZH2-transduced HFFs to HCMV . To address this hypothesis , we knocked down the endogenous EZH2 with an shRNA that targets sequences in the 3′ UTR of the endogenous EZH2 mRNA , and we replaced it with wild-type exogenous EZH2 or with a catalytically-inactive ΔSET mutant of EZH2 . Titration of HCMV in these cells confirmed the hypothesis ( Fig . 1E ) . We conclude that the histone methyltransferase activity of EZH2 is required for infection of human foreskin fibroblasts by HCMV . To explore the mechanism by which histone H3K27 tri-methylation controls viral infection and replication , we first examined whether the knockdown of NDY1/KDM2B , EZH2 or JARID2 , inhibits viral entry . To this end , HFFs transduced with pLKO . 1 or pLKO . 1-based shNDY1/KDM2B , shEZH2 or shJARID2 constructs were infected with the recombinant virus UL32-EGFP-HCMV-TB40 , which expresses the capsid-associated tegument protein pUL32 ( pp150 ) as a fusion with EGFP ( MOI 10 PFU per cell ) , producing fluorescent virions as well as tegument puncta in the cytoplasm and the nucleus of infected cells [35] . One hour after infection , the cells were fixed and the viral entry was visualized by EGFP fluorescence , which monitors the UL32-EGFP-containing viral particles . Quantitative analysis of the UL32-EGFP showed that the efficiency of viral entry in cells transduced with the empty vector and in cells transduced with the shRNA constructs was similar ( Fig . S1A and S1B ) . In a repeat of the experiment with HCMV AD169 , the viral strain used in all other experiments in this study , we examined the efficiency of viral entry by monitoring intracellular pp65 by immunofluorescence . The results confirmed that viral entry is not affected by the knockdown of EZH2 , NDY1/KDM2B or JARID2 ( Fig . S1C and S1D ) . Parallel experiments employing quantitative real time PCR to measure HCMV genomic equivalents in nuclear and cytoplasmic lysates of HFFs transduced with the same shRNA constructs and isolated 6 hours after infection , also revealed no differences ( Fig . S1E and S1F ) . The preceding data combined , suggest that H3K27 tri-methylation does not affect viral entry and thus , the resistance to infection caused by the inhibition of H3K27 trimethylation is due to a barrier at a different step of the infection cycle . To determine whether histone modifying enzymes regulate the activation of the immediate-early gene promoter , cells transduced with shRNAs or expression constructs of these enzymes , or with the corresponding empty vectors , were infected with HCMV and 5 hours later , they were stained for IE1 and counterstained with DAPI . Flow cytometry and fluorescence microscopy of the stained cells illustrated that NDY1/KDM2B and EZH2 are required for HCMV immediate-early gene expression ( Fig . 2A and Fig . S2A ) . The knockdown of PHF2 and RBP2 , which are not required for HCMV infection , also had no effect on IE1 promoter activity ( Fig . 2A ) . The results of these experiments suggested that H3K27 trimethylation is required for HCMV immediate-early gene expression . Parallel experiments provided additional support to this conclusion by showing that both the knockdown of JARID2 and the overexpression of the histone H3K27me3 demethylase JMJD3 also inhibit the activation of the HCMV MIEP ( Fig . 2B and Fig . S2B ) . To determine whether blocking histone H3K27 trimethylation inhibits or simply delays the expression of IE1 , we knocked down EZH2 , or NDY1/KDM2B , in HFFs and we examined the expression of IE1 at multiple time points from the start of the infection . The results demonstrated that these manipulations interfere with the expression of IE1 , at all the time points ( Fig . 2C ) . In agreement with these data , treatment of human foreskin fibroblasts with the EZH2 inhibitor 3-Deazaneplanocin A ( DZNep ) [36]-[38] inhibited both the expression of immediate-early genes and the infection by HCMV ( Fig . 2D–F ) . In parallel experiments , we monitored the abundance of EGFP-positive cells and the intensity of EGFP fluorescence by fluorescence microscopy , or flow cytometry in HeLa cells transduced with shEZH2 or shNDY1/KDM2B lentiviral constructs , and transfected with a reporter construct expressing EGFP from the MIEP of HCMV . The results showed that the knockdown of either NDY1/KDM2B or EZH2 inhibits the activity of the MIE promoter even in cells not infected with HCMV ( Fig . S3 ) . We conclude that H3K27 trimethylation is both necessary and sufficient for the activation of this promoter . Since H3K27me3 normally represses transcription [39] , we hypothesized that it may activate the major immediate-early promoter by repressing a transcriptional repressor . To address this hypothesis , we examined whether the knockdown of NDY1/KDM2B , EZH2 , or JARID2 and the overexpression of JMJD3 in HFFs , alter the expression of known transcriptional regulators of the MIEP ( Fig . 3A ) . Real time RT-PCR revealed that GFI1 is the only MIEP repressor significantly upregulated in these cells ( Fig . 3A ) . Probing western blots of lysates of the same cells with an anti-GFI1 antibody showed that GFI1 is upregulated also at the protein level ( Fig . 3A ) . GFI1 represses the MIE promoter of HCMV by binding to two sites within the promoter [6] ( Fig . 3B , Upper panel ) . The binding of GFI1 to these sites was tested with experiments using foreskin fibroblasts transduced with pLKO . 1 or with the pLKO . 1-based shNDY1/KDM2B , shEZH2 , or shJARID2 constructs , which de-repress GFI1 in these cells ( Fig . 3A ) . These cells were infected with HCMV AD169 . ChIP experiments , using cell lysates harvested 1 hour after infection , confirmed the binding of GFI1 to both GFI1 binding sites in the MIE promoter , but not in the MIE coding region and showed that treatments promoting the upregulation of GFI1 by inhibiting H3K27 trimethylation increase the binding ( Fig 3B Lower panel ) . To determine the functional role of GFI1 binding , we mutated both sites by site-directed mutagenesis ( AATC mutated to AACT and AAGT , respectively ) of an MIEP-EGFP reporter construct . The wild-type and mutant constructs were transfected into HEK 293T cells that had been stably transduced with pLKO1 , shEZH2 , shJARID2 , shNDY1/KDM2B , GFI1 , or shGFI1 . Analyzing the cells by fluorescence microscopy , revealed that whereas the knockdown of EZH2 , JARID2 , or NDY1/KDM2B , and the overexpression of GFI1 inhibit the wild-type promoter , they have no effect on the mutant promoter ( Fig 3C ) . These data combined , confirmed that histone H3K27 tri-methylation represses GFI1 . Inhibiting H3K27 trimethylation de-represses GFI1 , which binds and represses the MIE promoter of HCMV . The preceding data raised the question of the mechanism by which the tri-methylation of histone H3 at K27 regulates the GFI1 promoter . This question was addressed with ChIP assays designed to measure the relative abundance of H3K27me3 at five promoter sites ( from position −1044 bp to position −209 bp ) , in cells transduced either with the empty lentiviral vector , or with shRNA lentivirus constructs targeting EZH2 , NDY1/KDM2B or JARID2 . P16INK4a was used as the positive control . The most 5′ of the five promoter sites ( site #1 , located between −1044 bp and −956 bp ) , maps within a repressive domain [40] . These experiments revealed that the knockdown of any of these chromatin regulators induced a significant decrease in the abundance of H3K27me3 at this site ( Fig . 3D ) . We conclude that EZH2 , NDY1/KDM2B and JARID2 promote histone H3K27 trimethylation within a negative regulatory domain of the GFI1 promoter and may be responsible for the transcriptional repression function previously mapped within this domain [40] . Based on the data in the preceding paragraphs , we conclude that GFI1 is a direct repressor of the HCMV MIEP . However , it is possible that GFI1 may regulate the MIEP by additional indirect mechanisms . One such mechanism is via p21CIP1/WAF1 which is a known target of the GFI1 transcriptional repressor [41] . The repression of GFI1 by NDY1/EZH2/JARID2 may lead to the upregulation of p21CIP/WAF1 , which in turn , may inhibit the progression from the G1 phase to the S phase of the cell cycle . Since the accumulation of cells in G1 favors the expression of the IE genes of HCMV [42] , [43] , GFI1 may regulate the MIEP not only directly , but also indirectly via p21CIP/WAF1 . To address this question , HFFs were transduced with pLKO1-based lentiviral constructs of shEZH2 or shNDY1/KDM2B , or with the empty pLKO1 lentiviral vector . Western blots of lysates of these cells harvested before , and at various time points after infection and probed with an anti-p21CIP/WAF1 antibody , revealed that the expression of p21CIP/WAF1 is not affected by the knockdown of either EZH2 or NDY1 ( Fig . S4 ) . These data suggest that p21CIP/WAF1 is not involved in the regulation of the MIEP of HCMV by NDY1/EZH2/JARID2 . Earlier studies had shown that the resistance to HCMV caused by the repression of the MIEP can be overcome by infection at a high moi [44]–[47] . Based on this observation , we predicted that HCMV infection of shEZH2 , shNDY1 , shJARID2 , or JMJD3-transduced HFFs at an moi of 5 could overcome their resistance to infection . The results confirmed the prediction ( Fig . S5 ) , providing additional support to the hypothesis that the HCMV phenotype induced by the knockdown or overexpression of these molecules is caused by the repression of the MIEP . The preceding data showed that by repressing the MIE promoter , GFI1 can block HCMV infection . Based on these data , we hypothesized that HCMV may down-regulate GFI1 , to increase the permissiveness of the cells to the incoming virus . Experiments addressing this hypothesis showed that GFI1 is indeed down-regulated rapidly in HCMV-infected cells , both at the RNA and protein levels ( Fig . 4A and 4B ) . The rapid downregulation of GFI1 in HCMV-infected cells suggested that the incoming virus may induce the degradation of both the GFI1 mRNA and protein . This hypothesis was addressed with the experiments in Figure 4B . Monitoring the levels of the GFI1 mRNA in Actinomycin D-treated HFFs by real time RT-PCR , confirmed that virus infection accelerates the degradation of the GFI1 mRNA ( Fig . 4C ) . Similarly , monitoring the levels of the GFI1 protein in MG132-treated cells by western blotting showed that MG132 stabilizes the expression of GFI1 in virus-infected cells ( Fig . 4D ) . These data suggested that the incoming virus renders the cells permissive to infection by rapidly degrading GFI1 both at the RNA and the protein levels , and that protein degradation is mediated through the ubiquitin-proteasome pathway . However , since inhibition of the proteasome is also known to stabilize hDaxx [17] , [44] , it is possible that MG132 may block the degradation of GFI1 by inhibiting the degradation of hDaxx and viral infection . The fact that both the GFI1 RNA and the GFI1 protein were degraded rapidly after virus infection , suggests that both processes are initiated by factors entering the cells with the incoming virus . To determine the nature of these factors , we infected the cells with UV-irradiated virus and we examined the expression of GFI1 at 0 , 0 . 5 , 1 and 2 . 5 hours from the start of the exposure to the virus . The results showed that the UV-irradiated virus induced the degradation of hDaxx as expected [44] ( FigS6A ) , but failed to downregulate GFI1 at both the RNA and protein levels ( Fig . S6B and S6C ) . The downregulation of the GFI1 mRNA may be mediated by virion-associated UV-sensitive non-coding RNAs that target GFI1 . The downregulation of the GFI1 protein may be mediated by another UV-sensitive virion-associated molecule , whose nature remains to be determined . However , we have not formally excluded that a de novo expressed protein may be responsible for the phenotype . The regulation of GFI1 by EZH2 , NDY1/KDM2B or JARID2 , prompted us to investigate whether the HCMV-induced GFI1 downregulation is EZH2/NDY1/JARID2-dependent . To address this question , HFFs were transduced with the lentiviral vector pLKO . 1 , or with pLKO . 1-based constructs of shNDY1 , shEZH2 , or shJARID2 , and 48 hours later , they were infected with HCMV . Western blotting of uninfected and HCMV-infected cell lysates , harvested two hours after the infection , revealed that the downregulation of GFI1 was not prevented by the knockdown of any of these chromatin regulators . However , since the starting levels of GFI1 prior to the infection in shEZH2 shNDY1 , or shJARID2-transduced cells were significantly higher than in control cells , GFI1 continued to be expressed , even after the infection ( Fig . 4E ) . Given the inhibitory effects of the NDY1/EZH2/JARID2 knockdown on the activity of the MIEP , we conclude that the levels of GFI1 detected in these cells are sufficient to repress the promoter . Although the rapid downregulation of GFI1 in the initial stages of viral infection may be independent of the NDY1/EZH2/JARID2 axis however , the initial downregulation of GFI1 may be maintained via the activation of this axis throughout the infection cycle . Real time RT-PCR indeed showed that the mRNA levels of NDY1/KDM2B , EZH2 and JARID2 increase gradually , while the RNA levels of JMJD3 decrease in the course of the viral infection ( Fig . 4F ) . UV-irradiated virus , which cannot establish a productive infection , had no effect on the expression of these epigenetic regulators ( Fig . S7 ) . To determine whether HCMV infection depends on the down-regulation of GFI1 , human foreskin fibroblasts were transduced with pBabe-puro-based retroviral constructs of GFI1 or GFI1B , a GFI1-related gene , also encoding a SNAG domain-containing transcriptional repressor , or with the empty vector . The transduced cells were infected with HCMV . Infection was monitored by light microscopy 5 days later ( Fig . S8 ) and the progeny virus was harvested 12 days later and titrated by a plaque assay ( Fig . S8 ) . Alternatively , transduced cells were infected with HCMV and they were stained for IE1 expression 5 hours post-infection . Stained cells were analyzed by flow cytometry ( Fig . S8 ) . The results revealed that GFI1 inhibits the activity of the MIEP and HCMV infection as expected , while GFI1B does not . Since GFI1B also represses p21Cip/WAF1 [48] , these results provide additional support to the conclusion that p21CIP/WAF1 is not involved in the regulation of the MIEP of HCMV by NDY1/EZH2/JARID2/GFI1 ( see above ) . In parallel experiments , we used a TRIPZ-based doxycycline-inducible shRNA construct of EZH2 and a pLKO1-based constitutive shRNA construct of GFI1 to knock down these genes in HFFs , separately or in combination . The knockdown of EZH2 and GFI1 were confirmed by western blotting of lysates harvested from these cells prior to HCMV infection , before and after treatment with doxycycline ( Fig . 5A1 ) . Infection of these cells with HCMV showed that whereas the knockdown of EZH2 inhibits IE1 expression ( Fig . 5A2 lanes 1 and 3 ) and permissiveness to infection ( Fig5A3 , first and third bar ) , the knockdown of GFI1 does not affect either ( Fig . 5A2 , lanes 1 and 2 and5A3 , bars 1 and 2 ) . However , the knockdown of GFI1 , partially restored IE1 expression and permissiveness to viral infection in cells in which EZH2 was also knocked down ( Fig . 5A2 , lanes 3 and 4 and Fig5A3 , bars 3 and 4 ) . We conclude that EZH2 contributes to HCMV infection by inhibiting the expression of GFI1 . The fact that the restoration of IE1 expression and permissiveness to viral infection were only partial , suggests that EZH2 may have additional GFI1-independet effects on IE1 expression . Next we examined the effects of the EZH2 knockdown on histone H3K27 and H3K4 trimethylation in the enhancer , the cis repression sequence ( crs ) and intron 1 in the immediate-early region of HCMV ( Fig . 6 ) . The peak of H3K27 trimethylation in the enhancer and in intron 1 in HFFs transduced with a lentiviral shControl construct was observed at 1 . 5 hours from the start of the exposure to the virus and declined to very low levels at the 3 hour time point . H3K27 trimethylation in the crs increased more rapidly ( 0 . 5 hours ) and remained high throughout the observation period . Knocking down EZH2 delayed H3K27 trimethylation in the crs , and perhaps in intron 1 , with low levels of H3K27me3 at the 0 . 5 hour time point , and in the enhancer , with low levels of H3K27me3 at the 1 . 5 hour time point . More important , in the shEZH2 cells H3K27 trimethylation remained high at the three hour time point in all three sites . These changes in the abundance of H3K27me3 were associated with parallel changes in the abundance of H3K4me3 . In HFFs transduced with the shControl construct , the abundance of H3K4me3 increased in all three sites throughout the three hour observation period . However , in the shEZH2-transduced cells , its abundance in the enhancer region increased more slowly than in control cells . Moreover , in the crs and the intron 1 regions its abundance declined at the three hour time point , with the decline in intron 1 , being dramatic . These data are consistent with the chromatin modification data in the IE region of the murine CMV in the immediate-early stage of the infection [49] . In addition , they are consistent with earlier observations suggesting an initial cell-mediated MIEP repression that precedes viral gene expression in HCMV-infected cells [16] . More important , these data suggest that H3K27 and H3K4 trimethylation in the regulatory elements of the IE region of HCMV are co-ordinatelly regulated . However , the rules of their co-ordinate regulation are not yet known and they will require additional work to be determined . One additional question that remains is how the regulatory elements of the IE region of HCMV undergo delayed H3K27 trimethylation , when EZH2 is knocked down . We hypothesize that this may be happening because of residual EZH2 activity , remaining after the EZH2 knockdown . Alternatively , it may be mediated by EZH1 . This question will be addressed in future studies .
Data presented in this report , showed that NDY1/KDM2B , EZH2 and JARID2 synergize to repress GFI1 , a SNAG domain-containing transcriptional repressor [6]–[8] . In addition , they confirmed that GFI1 represses the MIEP of HCMV by binding to two sites , 159–163 and 105–109 base pairs upstream of the transcription start site , and that the knockdown of NDY1/KDM2B , EZH2 or JARID2 results in the upregulation of GFI1 and in the GFI1-dependent dramatic repression of the MIEP of HCMV . During HCMV infection , the GFI1 protein and mRNA are downregulated rapidly , most likely via degradation , both in control cells and in cells in which NDY1/KDM2B , EZH2 or JARID2 was knocked down . However , the pre-infection levels of GFI1 in the latter cells are significantly higher than in the control cells , and the degradation is not sufficient to extinguish GFI1 expression , which is required for the establishment of HCMV infection . As a result , cells in which NDY1/KDM2B , EZH2 or JARID2 were knocked down , are resistant to HCMV infection . Following the initial degradation of GFI1 , the virus reprograms the epigenetic machinery of the cell , by up-regulating NDY1/KDM2B , EZH2 and JARID2 and by down-regulating the histone H3K27me3 demethylase JMJD3 . This reprogramming is expected to maintain the expression of GFI1 at low levels throughout the infection cycle ( Fig . 7 ) . The combination of NDY1/KDM2B , EZH2 and JARID2 promotes histone H3K27 trimethylation , a chromatin mark associated with transcriptional repression [50] , [51] . EZH2 , the enzyme responsible for histone H3K27 trimethylation , binds JARID2 , a non-canonical jumonji domain protein that regulates the EZH2 methyltransferase activity [29] . NDY1/KDM2B , which can be induced by growth factors such as FGF2 [30] , binds some EZH2 target genes and demethylates histone H3K36 ( me2 ) and H3K36 ( me1 ) [34] . The latter promotes EZH2 binding to the same genes and transcriptional repression [32] . In this report , we presented evidence that GFI1 , a transcriptional repressor of the major immediate-early promoter of HCMV [6] , [52] , is one of the genes targeted by the H3K27 ( me3 ) methyltransferase EZH2 and its regulators JARID2 and NDY1/KDM2B , as well as the histone H3K27 ( me3 ) demethylase JMJD3 . HCMV initiates viral infection by targeting GFI1 via multiple mechanisms . Immediately after exposure to the virus , the GFI1 mRNA and protein are rapidly downregulated , most likely via degradation . The rapid drop in the levels of these molecules immediately after exposure to the virus suggested that they may be degraded by virion-associated factor ( s ) . Their downregulation was UV-sensitive , suggesting that it may be due to degradation by virion-associated nucleic acids [53] , [54] . The GFI1 mRNA may be a direct target of virion-associated non-coding RNAs [55] , [56] . The GFI1 protein may be degraded via the proteasome , which is known to play an important role in the transcription of the HCMV immediate early genes [57] , [58] . However , the mechanism by which virion-associated nucleic acids may regulate the proteasomal degradation of the GFI1 protein remains to be determined . Over the years , the main focus of studies addressing the activation of the proteasome by HCMV is on the tegument protein pp71 [59] . pp71 interacts with hDaxx in PML bodies to inhibit hDaxx-mediated silencing by promoting its degradation [60]-[63] . However , GFI1 cannot be a target of pp71 , because the latter is not UV-sensitive . The rapid downregulation of the GFI1 mRNA and protein , which occurs immediately after exposure to the virus , is followed by epigenetic reprogramming , which is expected to downregulate GFI1 throughout the infection cycle . Thus , in the early stages of infection , HCMV employs mechanisms that have not yet been determined to up-regulate NDY1/KDM2B , EZH2 and JARID2 and to down-regulate JMJD3 . These chromatin modifiers target an inhibitory domain within the GFI1 promoter and enhance the trimethylation of histone H3 at K27 ( Fig . 3D ) . It is not yet known how these chromatin regulators are targeted to the GFI1 promoter . Potentially , they may function as GFI1 co-repressors and they may be targeted to the GFI1 promoter by GFI1 itself . This is suggested by earlier findings showing that GFI1 represses its own promoter [64] and by the observation that within the inhibitory domain of the human GFI1 promoter [40] , there is one GFI1 binding site , containing the characteristic AATC core . The effects of the knockdown of EZH2 , NDY1/KDM2B and JARID2 , along with the effects of the overexpression of JMJD3 , on the activity of the MIEP and viral infection were counterintuitive . One would expect that these genetic manipulations would lead to a decrease in H3K27 trimethylation , both globally and regionally in the MIEP , and that this would result in an increase in MIEP activity . The fact that we see the opposite would suggest that either these genetic manipulations fail to alter the balance of repressive and activating epigenetic marks in the MIEP , or that tipping the balance toward the activating marks is not sufficient to override the effects of the GFI1 repressor . Of course , it is also possible that changes in the pattern of MIEP-associated chromatin modifications , induced by these genetic manipulations , promote the binding of GFI1 , facilitating the MIEP repression . To address these questions , we surveyed the effects of the EZH2 knockdown on the abundance of H3K27me3 ( a repressive mark ) and H3K4me3 ( an activating mark ) in the enhancer , crs and intron 1 in the IE region of HCMV in the first three hours from the start of the infection . The results showed a delay in H3K27 trimethylation . The slow kinetics of this process resulted in an increase in the abundance of H3K27me3 in the IE enhancer and intron 1 at the three hour time point , when the abundance H3K27me3 normally decreases . The rapid increase in the abundance of H3K27me3 , which we observed in HCMV-infected control cells in the very early stages of the infection , is consistent with the results of earlier studies showing that MIEP activation during infection by murine CMV and HCMV is preceded by an increase in the abundance of repressive histone marks . Our data also showed that the increase in the abundance of in H3K27me3 at the three hour time point in shEZH2-transduced cells is paralleled by a significant decrease in the abundance of H3K4me3 , . suggesting that repressive and activating marks are co-ordinately regulated . The rules of this coordinate regulation and the potential involvement of these epigenetic modifications in the recruitment of GFI1 remain to be determined . Overall , the data presented in this report identify a novel pathway of epigenetic regulation of cellular gene expression that regulates the expression of HCMV immediate-early genes and viral infection . Inhibition of the pathway may have preventive or therapeutic applications in viral infection , while selective activation of the pathway may have therapeutic applications in cancer .
Human Foreskin Fibroblasts ( HFFs ) were used for HCMV infection ( kind gift from Dimitrios Iliopoulos , Harvard Medical School ) and HEK 293T cells were transfected to package lentivirus and retrovirus constructs . HEK 293T cells or HELA cells were used for transfection of HCMV MIEP reporter constructs in experiments addressing the regulation of the HCMV major immediate-early promoter by chromatin modifying enzymes . All cell lines were maintained in Dulbecco's modified Eagle's minimal essential medium ( DMEM ) supplemented with 10% fetal bovine serum , penicillin/streptomycin , L-glutamine and non-essential amino acids . The wild-type laboratory strain of HCMV we used was the AD169 strain . The recombinant UL32-EGFP-HCMV-TB40 virus , which expresses the capsid-associated tegument protein pUL32 ( pp150 ) , fused to EGFP [35] was used for some experiments addressing viral entry . To determine the role of the EZH2 enzymatic activity on immediate-early gene transcription and HCMV infection , virus-infected cells were treated with the EZH2 inhibitor 3-deazaneplanocin A ( DZNep ) ( Cayman Chemical Company , MI ) , at the final concentration of 10 µM . DZNep was dissolved in dimethyl sulfoxide ( DMSO ) . To infect HFFs with HCMV , cell monolayers were incubated with the virus at a multiplicity of infection ( MOI ) of 0 . 5 PFU/cell , or at variable multiplicities in virus titration experiments . Unless otherwise specified , the cells were exposed to the virus for 2 hours at 37°C . Subsequently , the virus was removed and replaced with fresh medium . Plaque assays for virus titration were performed on HFFs according to standard protocols [65] . To monitor the growth of HCMV in HFFs transduced with shEZH2 , shNDY1/KDM2B , shPHF2 and shRBP2 lentiviral constructs , cells were infected with HCMV AD169 at an MOI of 0 . 5 PFU/cell . Viral supernatants harvested from these cultures every two days for 9 days were titrated , using plaque assays . To measure the efficiency of viral entry , HFFs transduced with pLKO . 1-based lentiviral constructs of shEZH2 , shNDY1/KDM2B , shJARID2 , or with the empty vector , were infected with the UL32-EGFP-HCMV-TB40 recombinant virus at an MOI of 10 PFU/cell , as previously described [66] . One hour after infection , the cells were fixed and viral entry was visualized by monitoring intracellular EGFP fluorescence via fluorescence microscopy . Fluorescence intensities of UL32-EGFP were calculated with the Zeiss LSM image examiner software . To correct for background fluorescence , we deduced from the fluorescence of infected cells the fluorescence of adjacent non-infected cells . Alternatively , viral entry was monitored by real-time PCR of viral DNA in cell lysates harvested at six hours from the start of the exposure to the wild-type HCMV AD169 virus [45] . The retrovirus and lentivirus constructs we used are listed in Table 1 . Human JARID2 was cloned into the pLenti-CMV-puro-DEST vector ( Addgene , cat no 17452 ) using the LR Clonase II Plus enzyme mix ( Invitrogen , cat no 12538120 ) according to the manufacturer's instructions . Human HA-PHF2 was cloned into the EcoRI site of pBABEpuro , and human FLAG-JMJD3-HA was cloned between the BamHI and XhoI sites of the same vector . The rest of the lentiviral and retroviral constructs were either purchased or kindly provided by others ( Table 1 ) . shEZH2 was induced in TRIPZ-shEZH2-transduced cells with Doxycycline ( 1 µg/ml ) Jarid2 cDNA was PCR-amplified using the pCMV-SPORT6-JARID2 ( Open Biosystems , cat . no . MHS1010-99622028 ) as template , cloned in the pENTR/TOPO vector ( Invitrogen , cat no K2400-20 ) . The sequence was verified and was recombined to the pLentipuro vector ( Addgene , cat no . 17452 ) using the Gateway LR Clonase II Plus kit ( Invitrogen , cat no . 12538-120 ) . To determine the effects of NDY1/KDM2B , EZH2 JARID2 and GFI1 on the activity of the HCMV MIEP in the absence of viral infection , a MIEP-EGFP reporter construct ( pEGFP-C1 ) ( Clontech ) was transfected into HEK 293T cells or their derivatives in which NDY1/KDM2B , EZH2 or JARID2 were knocked down or GFI1 was overexpressed and the expression of EGFP was monitored by fluorescence microscopy or flow cytometry . The same cells were also transfected with a derivative of pEGFP-C1 in which the two GFI1 binding sites in the MIEP [7] were inactivated by point mutation . The mutant construct was generated by site-directed mutagenesis , as previously described [67] , using primers: CMVmut1: 5'-GGGTGGAGACTTGGAAAGTCCCGTGAGTCAAACCG-3′ and CMVmut2: 5'-ATTTTGGAAAGTCCCGTTAGTTTTGGTGCCAAAACAAAC-3′ . All products of PCR mutagenesis were sequenced after cloning , to ensure that no additional mutations were generated . HEK 293T cells were transiently co-transfected with retroviral constructs and the amphotropic packaging construct ( Ampho-pac ) . Alternatively , HEK 293T cells were transiently co-transfected with lentiviral constructs and pCMV/VSV-G ( where VSV-G is vesicular stomatitis virus protein G ) and pCMV-dR8 . 2 dvpr . Transfection was carried out using Fugene 6 ( Roche Applied Science ) . To transduce HFFs with the packaged viruses , early passage cells were incubated with viral supernatants in the presence of 5 µg/ml polybrene ( Sigma-Aldrich , Deisenhofen , Germany ) for 24 hours . Forty-eight hours later , cells were selected with puromycin ( 2 µg/ml ) or hygromycin B ( 200 µg/ml ) . Cells infected with multiple retrovirus or lentivirus constructs , were selected for these constructs sequentially . Cells were washed twice in ice-cold PBS and they were lysed in Triton X-100 lysis buffer [50 mM Tris ( pH 7 . 5 ) , 200 mM NaCl , 1% Triton X-100 , 0 . 1% SDS , 10 mM Na3VO4 , 50 mMNaF , 1 mM β-glycerophosphate , 1 mM sodium pyrophosphate , 1 mM EDTA , 1 mM EGTA , and 1 mM PMSF supplemented with a mixture of protease inhibitors] . The lysates were sonicated in a Misonix 3000 sonicator for 5 seconds at power level 1 . 5 , and they were centrifuged for 20 min at 13 , 000×g . Western blots of the supernatants ( soluble whole-cell lysates ) were probed with the EZH2 rabbit monoclonal antibody ( no . 4905 , Cell Signaling ) , the GFI1 mouse monoclonal antibody ( 2 . 5D17 , Sigma ) , the KDM2B goat polyclonal antibody ( sc-69477 , Santa Cruz ) , the p21WAF1/CIP1 human monoclonal antibody ( no . 05-345 , Cell Signaling ) , the RBP2 ( JARID1B ) rabbit polyclonal antibody ( no . ABE239 , Millipore ) , the PHF2 rabbit polyclonal antibody ( 3497 , Cell Singaling ) , the JMJD3 rabbit polyclonal antibody ( no . 3457 , Cell Singaling ) , the JARID2 rabbit polyclonal antibody ( ab48137 , Abcam ) , the HA-Tag mouse monoclonal antibody ( no . 2367 , Cell Signaling ) , the myc-Tag rabbit monoclonal antibody ( no . 2278 , Cell Signaling ) , the pp71 ( 2H10-9 ) antibody or the IE1 mouse monoclonal antibody ( BS500 ) [68] . Anti-mouse as well as anti-rabbit horseradish peroxidase-conjugated secondary antibodies , obtained from Sigma , were diluted in 5% milk in TBS-T and incubated with the blots for 1 h at room temperature . The bound secondary antibodies were detected with ECL-plus detection reagent ( Amersham Biosciences ) or the ECL SuperSignal ( Pierce ) . Digital images of the proteins were acquired using the LAS-4000 luminescent image analyzer ( Fujifilm Life Science ) . To monitor the activation of the MIE promoter , 0 . 8×105 HFFs were plated on coverslips and they were infected with HCMV [69] . Five hours later , they were fixed and immunostained with an anti-IE1 monoclonal antibody ( BS500 ) as previously described [65] . Anti-mouse as well as anti-rabbit Alexa 488-conjugated secondary antibodies were purchased from Molecular Probes ( Invitrogen ) . The number of IE1-positive cells/coverslip was determined by epifluorescence microscopy . Each experiment was performed in triplicate . HFFs cultured in 12-well plates were infected with HCMV AD169 . HELA or HEK 293T cells , also seeded in 12-well plates , were transfected with pEGFP-C2 ( Clontech ) using Fugene 6 ( Roche Applied Science ) . Infected and transfected cells were harvested , using a cell dissociation buffer ( Molecular Probes ) . Harvested cells were fixed in paraformaldehyde ( 3% vol/vol in PBS ) . The HCMV infected cells were first permeabilized with 0 . 1% saponin in PBS , also supplemented with 2% calf serum , and then washed and resuspended in 100 µl of the same buffer , containing a 1/100 dilution of a mouse anti-IE1 antibody ( BS500 ) [68] Following incubation with the antibody at room temperature for 1 h , the cells were washed twice and then incubated with a fluorescein isothiocyanate ( FITC ) -conjugated sheep anti-mouse secondary antibody ( Sigma ) ( dilution 1∶1000 ) for 1 h . Transfected HELA cells were stored in PBS supplemented with 2% calf serum , after they were fixed . Virus and mock-infected , as well as transfected and non-transfected samples , were analyzed on a CyAn LX High Performance Flow Cytometer . ChIP was performed using a Chromatin Immunoprecipitation assay kit ( Millipore , cat no . 17-295 ) . Chromatin cross-linking was achieved via a 10 minute treatment of nuclear extracts with 1% formaldehyde at 37°C . Cross-linked lysates were sonicated to shear the DNA to an average length of 300 to 1000 base pairs . Following sonication , the lysates were pre-cleared via incubation with a 50% slurry of salmon sperm DNA/Protein A Agarose for 30 minutes . The pre-cleared supernatants were incubated with the primary antibodies anti-H3K27me3 ( no . 9756; Cell Signaling ) , anti-H3K4me3 ( Abcam ab8580 ) and total anti-H3 ( Abcam ab1791 ) ( 1∶50 dilution ) overnight and with salmon sperm DNA/Protein A Agarose beads at 4°C for 1 h . Following multiple washes , the DNA-protein complexes were eluted and the DNA was recovered by reversing the cross-linking with NaCl and proteinase K . The DNA was then extracted using the Qiaquick PCR Purification Kit ( Qiagen , cat . no 28106 ) and it was analyzed by SYBR-Green real-time qPCR , along with the input DNA . The primer sets used to amplify the GFI1 and the p16Ink4a loci as well as the HCMV MIEP are listed in the Table 2 . Total cell RNA was isolated , using Trizol ( Invitrogen ) . cDNA was synthesized from 1 . 0 µg of total RNA , using oligo-dT priming and the Retroscript reverse transcription kit ( Ambion , cat no . AM1710 ) . The genes analyzed and the primers used are listed in Table 3 . Real-time PCR was performed in triplicate using the Universal SYBR Green PCR master mix kit ( Exiqon ) and a 7500 Real-Time System ( Applied Biosystems ) . mRNA levels were normalized to GAPDH , which was used as an internal control . All data are from 3 independent experiments , each performed in triplicate . Nuclear and cytoplasmic fractions were isolated from HFFs cells using the Nuclear/Cytosolic Fractionation kit ( Cat No AKR-171 , Cell Biolabs , Inc . ) according to the manufacturer's instructions . Purified DNA from each fraction was amplified by real-time PCR and HCMV genomes were quantified using the CMV Real-TM Quant kit ( Cat No V7-100/2FRT , Sacacce ) . | Human cytomegalovirus ( HCMV ) is a significant pathogen that belongs to the herpesvirus family . Here we show that the histone H3K27 methyltransferase EZH2 and its regulators JARID2 and NDY1/KDM2B are required for the establishment of productive infection . Mechanistically , the EZH2-NDY1/KDM2B-JARID2 axis downregulates GFI1 , a repressor of the HCMV major-immediate-early promoter ( MIEP ) and inhibition of this axis upregulates GFI1 and interferes with the activation of the MIEP and HCMV infection . GFI1 is rapidly downregulated during infection in both wild-type and EZH2 , NDY1/KDM2B , JARID2 knockdown cells . However , since the starting levels of GFI1 in the latter are significantly higher , they remain high despite the virus-induced GFI1 downregulation , preventing the infection . Following the downregulation of GFI1 immediately after virus entry , HCMV initiates an EZH2-NDY1/KDM2B-JARID2-JMJD3-dependent program to maintain the low expression of GFI1 throughout the infection cycle . The knockdown of EZH2 also modulates the accumulation of histone H3K27me3 and H3K4me3 in the immediate-early region of HCMV , and by doing so , it may contribute directly to the MIEP repression induced by the knockdown of EZH2 . These data show that HCMV uses multiple mechanisms to allow the activation of the HCMV MIEP and to prevent cellular mechanisms from blocking the HCMV replication program . | [
"Abstract",
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] | 2014 | The Downregulation of GFI1 by the EZH2-NDY1/KDM2B-JARID2 Axis and by Human Cytomegalovirus (HCMV) Associated Factors Allows the Activation of the HCMV Major IE Promoter and the Transition to Productive Infection |
Pentavalent antimonials have been the mainstay of antileishmanial therapy for decades , but increasing failure rates under antimonial treatment have challenged further use of these drugs in the Indian subcontinent . Experimental evidence has suggested that parasites which are resistant against antimonials have superior survival skills than sensitive ones even in the absence of antimonial treatment . We use simulation studies based on a mathematical L . donovani transmission model to identify parameters which can explain why treatment failure rates under antimonial treatment increased up to 65% in Bihar between 1980 and 1997 . Model analyses suggest that resistance to treatment alone cannot explain the observed treatment failure rates . We explore two hypotheses referring to an increased fitness of antimony-resistant parasites: the additional fitness is ( i ) disease-related , by causing more clinical cases ( higher pathogenicity ) or more severe disease ( higher virulence ) , or ( ii ) is transmission-related , by increasing the transmissibility from sand flies to humans or vice versa . Both hypotheses can potentially explain the Bihar observations . However , increased transmissibility as an explanation appears more plausible because it can occur in the background of asymptomatically transmitted infection whereas disease-related factors would most probably be observable . Irrespective of the cause of fitness , parasites with a higher fitness will finally replace sensitive parasites , even if antimonials are replaced by another drug .
Visceral leishmaniasis ( VL ) , also known as Kala-azar ( KA ) , causes each year about 200 , 000 to 400 , 000 cases with about 20 , 000 to 40 , 000 deaths worldwide . About 70% of the VL burden occurs in the Indian subcontinent , mostly in the state of Bihar [1] . The disease is caused by the infection with the protozoan flagellate Leishmania donovani . Most infections do not lead to clinical symptoms but progress asymptomatically [2] . In case of a symptomatic course of disease the infection is lethal in the absence of treatment . Pentavalent antimonials were introduced in 1922 as therapeutic drug for leishmaniasis [3] . They remained the first-line treatment for about 70 years and had a treatment success rate of up to 95% [4] . Since the early 1980s , poor treatment responses have increasingly been reported from Bihar [5] , [6] , resulting in WHO's recommendations to increase treatment dosage and duration . Although this initially improved the results , the effects were only temporary [7]–[9] . The worst treatment outcome for pentavalent antimonials was reported from Bihar in 1997 with a treatment success rate of only 35% [10] . We refer to this rapidly increasing treatment failure rate ( TFR ) in Bihar in the following as ‘the Bihar data’ . Increased treatment failure has also been reported from Nepalese districts neighbouring Bihar [11] , [12] . In in vitro tests a reduced sensitivity to pentavalent antimonials could be demonstrated with mice-derived macrophages infected with L . donovani parasites obtained from non-responsive patients [13] . In 2005 , the governments of India , Nepal , and Bangladesh agreed to participate in a regional ‘VL elimination program’ to reduce the annual incidence of VL from about 22 cases per 10 , 000 inhabitants to only one case per 10 , 000 inhabitants by 2015 . This programme was based among others on the replacement of antimonials as first-line treatment by the oral drug miltefosine . So far , the treatment success rate of this drug does not seem to be affected by antimony-resistance . The action mechanism of antimonials is still poorly understood [14] , but they seem to have a dual mode of action . On the one hand , they perturbate the redox-balance of the parasites [15] and on the other hand , they impose extra oxidative and nitrosative stress upon the parasite through interaction with the host cell [16] , [17] . Recent molecular studies showed that ( i ) antimony-resistant parasites emerged from L . donovani populations with different genetic background [18] and ( ii ) that molecular mechanisms of drug resistance may vary with that genetic background [19] . Antimony-resistant L . donovani parasites can inhibit the patient's immune response even more than antimony-sensitive L . donovani [20] and are hereby able to prevent the drug from inducing an effective antileishmanial response through the host cell [21] . These adaptations seem to protect them not only against antimony-induced stress , but also against natural stress such that resistant parasites may have a higher general fitness under drug-free conditions than sensitive ones [22] , [23] . The aim of this study is to investigate the dynamics of the emergence and spread of antimony-resistant L . donovani parasites in Bihar . Our overall work hypothesis is that antimony-resistance alone cannot explain the Bihar data and that increased fitness ( also in absence of the drug ) is required to support the observed data . We use the word ‘fitness’ to describe the potential of the pathogen to promote its survival , its reproduction and its transmission [24] , [25] . Within our model analyses , we assume that the increased fitness of antimony-resistant parasites is a stable condition despite the fact that the exact mechanisms behind this are not fully understood . The first mathematical study of the dynamics of VL used a deterministic model to explain the observed inter-epidemic periods between 1875 and 1950 in Assam , India [26] . This model was extended to canine VL in Malta [27] , [28] to assess the efficacy of various control methods [29] . This model and subsequently developed models have been applied to describe dynamics of canine VL in Brazil , in particular addressing the effects of interventions like culling dogs or using impregnated collars [30]–[32] . Based on data from Morocco , a mathematical model for cutaneous leishmaniasis was developed [33] . The model takes into account a latent period in humans and seasonal vector abundance . To study the dynamics of visceral leishmaniasis in the Sudan , a deterministic model was used to establish threshold conditions for elimination and to investigate the role of cross-immunity [34] , [35] . Related to the data used in the present investigation the problem of under-reporting VL incidence in Bihar , India , has been investigated [36] . Recent modelling approaches addressed the effects of vaccination coverage and human migration on control of VL [37] and disease progression in mice [38] Using our previously published mathematical model for L . donovani transmission in the Indian subcontinent [39] , we investigate which parameter constellations are capable to explain the observed dynamics of the TFR in humans treated with antimonials in Bihar . Following experimental findings , we explore five scenarios on higher fitness in addition to antimony-resistance . On the one hand , resistant parasites survive more likely within macrophages [40] and produce higher parasite burdens in the liver and the spleen of mice [23] . This may indicate that resistant parasites have a higher pathogenicity or virulence in human hosts . A higher capability to survive killing mechanisms of macrophages [40] could lead to more clinical cases , or to more severe disease . We will explore these possibilities as scenarios 1 ( = more clinical cases ) and 2 ( = more severe disease ) which are summarised as ‘disease-related fitness hypothesis’ . On the other hand , it has been shown that resistant parasites have a higher metacyclogenic capacity during in vitro promastigote growth ( which naturally occurs in the vector ) . The infectious metacyclic parasite is non-sensitive to complement lysis in the human host [41] , which might increase the infection probability in humans . It might also increase the parasite density during early human infection or during the entire course which would consequently increase the infectiousness of asymptomatic human carriers or of all humans with resistant parasites . We will explore these possibilities as scenarios 3 ( = increased infectiousness in asymptomatic humans ) , 4 ( = increased infectiousness in all infected humans ) and 5 ( = increased infectiousness in sand flies ) which are summarised as ‘transmission-related fitness hypothesis’ .
Data on the TFR of antimonial treatment originate from a review of clinical trials in Bihar between 1980 and 2004 . Although treatment dosage and duration differed between studies over the study period of two decades the data support the finding of a substantially declining efficacy of antimonial treatment over time [42] . Caused by the alarmingly fast decline of the treatment efficacy , antimonials were abandoned in Muzaffarpur , a district of Bihar , after June 1997 [10] . Thus , we restrict our analyses to studies on antimonial treatment which were performed until 1997 as provided by the mentioned review . In Figure 1 , the observed TFR in the 20 treatment groups of the 12 remaining clinical studies are shown together with their 95% confidence intervals . We extend an existing mathematical model [39] by additional infection states representing infections with parasites resistant to antimonial treatment ( see File S1 , Figure S1 , Tables S1 , S2 , S3 , S4 , S5 , S6 , and S7 ) . In the following , the expression ‘resistant parasite’ is used as a short form for ‘a parasite which is less susceptible to antimonial treatment and thus , increases the TFR’ . We vary parameters controlling the longitudinal evolution of the TFR and select all those parameter combinations for which the simulated TFR curves pass through the confidence intervals in Figure 1; if more than one data set is given for a time point , the simulation result has to pass through at least one of the corresponding confidence intervals . This analysis comprises parameters T , r , fH , fFS , fFA , fFH , fHF which are described as follows . Simulations start in the endemic equilibrium with only sensitive parasites , using a population of 104 millions inhabitants ( population size of Bihar ) . The mathematical model is considered to be in the endemic equilibrium when there is no change in the variables over a period of at least 100 years within an accuracy of five digits after the decimal ( see Figure S1 and Tables S1 and S2 ) . Parameter T is the year when the first resistant parasite emerges in a single patient . Parameter r controls the TFR of patients infected with the resistant strain; it ranges between 0 and 1 . For r = 0 , the TFR is as high for carriers of the resistant strain ( TFRres ) as for carriers of the sensitive one ( TFRsens; i . e . 5% ) , and for r = 1 , treatment fails in all patients infected with the resistant strain . Thus , TFRres is given by TFRsens+r ( 1−TFRsens ) . For purposes of interpretation , parameter r will be reported throughout this manuscript in terms of TFRres . The observed TFR in the population is a weighted average of the TFR of patients infected with the sensitive strain and that of patients infected with the resistant one . We consider five scenarios with modified fitness of resistant parasites: We numerically solve the differential equation model for 700 , 000 sets of parameter values and calculate how the TFR changes over time because of the spread of resistant parasites . Apart from using the standard parameter values given in Tables S3 , S4 , S5 , and S6 , we employ uniformly distributed random parameter values: parameters T ( range 1922–1980 ) and TFRres ( range 5–100% ) are always varied; concomitantly , we pick one of the fitness parameters fH ( range 0–10 ) , fFS ( range 0–10 ) , fFA ( range 0 . 7–1 . 3 ) , fFH ( range 0 . 7–1 . 3 ) or fHF ( range 0 . 7–1 . 3 ) while the others are set to 1 .
Resistant pathogens have a higher fitness because they lead to more clinical cases ( scenario 1: pathogenicity factor fH>1 ) or to more severe disease ( scenario 2: virulence factor fFS>1 ) and are , thus , more likely transmitted . As the results of these scenarios are almost undistinguishable , we show only the results of scenario 2 in Figure 2 . To explain the Bihar data , one of the two disease-related fitness parameters fH or fFS must be larger than 6 . 5 ( see fFS in Figure 2A ) . This means that resistant infections cause at least 6 . 5 times as many symptomatic cases compared to sensitive infections , or that clinical cases infected with resistant parasites infect 6 . 5 times as many sand flies . In the disease-related fitness scenarios , the TFR of patients infected with resistant parasites must be larger than 70% , and it must be assumed that the resistance emerged before 1950 ( Figure 2A ) . Resistant pathogens have a higher fitness because they have a higher transmissibility in asymptomatic carriers ( scenario 3: fFA>1 ) , in all human carriers ( scenario 4: fFH>1 ) , or in sand flies ( scenario 5: fHF>1 ) . As the results of these scenarios are almost undistinguishable , we show in Figure 2 only the results of scenario 3 . To explain the Bihar data , one of the three transmissibility factors fFA , fFH or fHF must be larger than 1 . 05 ( see fFA in Figure 2A ) . This means that the transmissibility in asymptomatic human carriers , in all human carriers , or in sand flies must increase by at least 5% . This minor increase strongly impacts the transmission dynamics because of the large proportion of asymptomatic infections ( see Discussion ) . In the transmission-related fitness scenarios , the TFR of patients infected with resistant parasites must be larger than 60% , while the time point of emergence of resistance can hardly be determined ( emergence between 1922 and 1979 ) ( Figure 2A ) . In summary , the explanation of the Bihar data requires the assumptions that the TFR of patients infected with resistant parasites is high and that resistant parasites have an increased fitness . Both hypotheses on additional fitness offer the potential to explain these data .
Using a mathematical model for L . donovani transmission , we have identified parameter sets which can explain the antimonial treatment failure rates observed in Bihar between 1980 and 1997 . The simulations suggest that antimony-resistance alone cannot explain the observed rapid increase in TFR if L . donovani transmission is largely driven by asymptomatic carriers [39] which are not exposed to a selection pressure originating from antimonial treatment . Even if all patients infected with antimony-resistant strains would be treatment failures ( TFRres = 100% ) , the observed TFR in Bihar cannot be explained . Noteworthy , parameter TFRres is likely to be even lower in reality , close to 60% , as shown in Nepal where antimony-resistant parasites were identified in most cases of treatment failure , but are also found in half the patients who showed definite cure [43] . Two hypotheses on increased fitness can explain the Bihar observations . Hypothesis 1: a disease-related fitness with fH or fFS ranging from 6 . 5 to 10 means that resistant parasites produce 6 . 5 to 10 times more symptomatic cases , or that symptomatic carriers of resistant parasites are 6 . 5 to 10 times more infectious than symptomatic carriers of sensitive parasites . For reasons described below we regard this as the less plausible explanation . Hypothesis 2: a transmission-related fitness with fFA , fFH or fHF ranging from 1 . 05 to 1 . 30 means that the transmissibility by the vector or by the host ( i . e . asymptomatically infected or all infected humans ) increases by 5% to 30% . For reasons described below we regard this as the more plausible explanation . To demonstrate the effects of the increase in disease-related fitness parameters ( Hypothesis 1 in Figure 2 ) , or transmission-related fitness parameters ( Hypothesis 2 in Figure 2 ) , we calculated equilibrium solutions of the model , obtained by those parameter sets which can potentially explain the Bihar observations . From these parameter sets , we choose five representatives ( using the median of each accepted fitness parameter while keeping TFRres = 100% ) and compare the obtained equilibrium solutions with a scenario without any resistance . Higher pathogenicity of antimony-resistant parasites ( fH = 8 . 7 ) would lead to an approximately 15-fold prevalence of KA and an approximately 8-fold prevalence of PKDL . Such an excessive increase of symptomatic cases is likely to be clinically observable and to our knowledge there is no clinical evidence for such an increase . For the other four parameter sets ( fFS = 8 . 9 , fFA = 1 . 12 , fFH = 1 . 12 or fHF = 1 . 12 ) , the KA prevalence would be only approximately doubled . In case of higher virulence , the strongly increased infectiousness of symptomatic cases infected with resistant parasites might be confirmed by xenodiagnosis or by quantitative PCR of blood or skin tissue of the patients . In vivo data in mice infected with antimony-resistant parasites show a 3- to 8-fold higher parasite burden in liver and spleen [23] . This finding would support a disease-related fitness hypothesis if a higher parasite burden implied higher infectiousness and if antimony-resistant parasites had comparable properties in humans and mice . Up to now there is no clinical evidence for more severe disease in patients . However , higher parasite loads in patients may remain clinically undetectable because higher parasite loads do not necessarily lead to more severe symptoms . In case of the three transmission-related fitness scenarios , an increased transmissibility of 5% to 30% can definitely occur in the background of asymptomatic transmission without being recognized and thus may have a higher explanatory potential in this investigation . We investigated disease- and transmission-related fitness parameters separately to quantify the effect of each parameter although combinations of several fitness parameters seem rather realistic as their biological origins are closely inter-linked . Simulations of both hypotheses suggest that sensitive parasites are replaced almost completely by resistant ones already 20 to 40 years after the first noticeable decline in treatment efficacy ( Figure 2C ) . To our knowledge there are no studies available in which random samples of strains were tested for their susceptibility to antimonials . Extrapolations from two studies in India and Nepal indicate a prevalence of antimony-resistant strains of around 65% at the beginning of the century [13] , [43] while the analysis of 19 Indian strains collected in 2009 and 2010 in Bihar showed that 15 of them ( thus 79% ) were antimony-resistant [44] . To our knowledge the only available population-based non-response estimate in humans is from Muzaffarpur district , Bihar , 2008 . Almost 10 years after antimonial treatment has been stopped still 67 of 131 retrospectively investigated VL patients report to have been treated with antimonials whereby treatment failed in 27 patients of them ( 40% , with 95% CI from 28 to 53% ) [45] . From these data we may validate the abovementioned hypothesis by the following simple calculation . If we assume that about 79% [44] of the parasites are antimony-resistant and that treatment failed in about 60% of patients infected with resistant parasites [43] we would expect a TFR of about 47% which would lie within the 95% confidence limits of the above mentioned observed TFR of 40% [45] . Issues on selection pressure in the context of antimony resistance are complex . Poor treatment compliance is suspected to have caused the development of resistance in Bihar [46] . Accordingly , under-dosage of antimonials , which is not lethal to the parasite , may have created conditions under which the development of resistance provides selection benefits . Additional factors might have contributed to antimony resistance , like arsenic contamination of the groundwater [47] . Groundwater as drinking water has become available via tubewells in the 1970s , just before antimonial treatment failures started to increase . The geographic distribution of contaminated tubewells correlates with high rates of antimonial treatment failure . As metalloids , antimony and arsenic share biochemical features . Chronic exposure of a large proportion of the population to arsenic with antileishmanial properties might have contributed to the establishment of a resistant strain . An additional selection pressure might originate from the human host itself due to the nature of antimonial drugs which impose extra oxidative and nitrosative stress upon the parasite through interaction with the host cell [16] , [17] . We currently cannot exclude that antimony resistance emerged on a background of parasites already fitter to their host . This is actually supported by the observation of abundant antimony-resistant L . braziliensis strains in Peru , in a context of zoonotic leishmaniasis where drug pressure is quasi null as most of the parasite bio-mass is in the wild animal reservoir [48] . An emergence of resistant parasites before 1960 seems rather unlikely: VL had almost been eliminated at that time as a consequence of the National Malaria Eradication program [49] . Although results of our analyses suggest that in case of a disease-related fitness factor , emergence must have occurred before 1950 ( Figure 2A ) , this result will not be discussed in detail because it highly correlates with assumptions into multiple emergences of resistant parasites . In case of multiple emergences of resistance [18] and also in case of a contribution of arsenic contaminated groundwater on the spread of resistant parasites [47] , our predictions must be understood as an upper limit for parameter T , the year when the first resistant parasite emerged in a patient . Further data and model related limitations of these modelling analyses are pointed out in the following . To investigate the spread of antimony-resistant parasites we used a previously published mathematical model [39] . Model parameters were chosen from the literature or estimated by fitting the model to data from the KalaNet project , a community intervention trial in India and Nepal . Uncertainties originating from a high-dimensional parameter space and resulting correlations between parameters have been explored in that paper by means of sensitivity analyses The main data related uncertainty originates from the assumption that cellular immunity can be represented by Leishmanin skin test ( LST ) measurements . Under the assumption of a life-long cellular immunity the model showed that the prevalence of LST-positive individuals in the population would be higher than 50% as had been observed ( for further detail see [39] ) . Therefore , loss of LST-positivity had to be assumed , with re-infection occurring in intervals of about two years . In case that LST data do not adequately represent a status of protective immunity and that cellular immunity lasts longer than assumed resistant parasites would spread slower than suggested by this investigation . Model related uncertainties originate from assumptions underlying the deterministic modelling approach as , for instance , homogeneous mixing within and between human and fly populations , an infinitely large population size , age structure of the human population , heterogeneities in living conditions , or seasonal transmission patterns . Such factors can influence short term predictions and would demand a stochastic modelling approach . As , however , this investigation addresses development of resistance over several decades we believe that stochastic influences are of minor relevance and that the deterministic model is adequate to describe a trend over decades . At least partially , antimonial treatment has been replaced in Bihar around the year 2000 by drugs like amphotericin B , miltefosin and paromomycin , which are assumed to be not affected by antimonial-related resistance . This leads to the question whether this will stop or even reverse the process of strain replacement , a question which might be of relevance if a return to antimonials in therapeutic schemes , including combination schemes , would be considered in the future . Thus , in addition to the model predictions in Figure 2C , which are produced under the assumption of unchanged treatment , we investigated scenarios in which antimonials are replaced by the above mentioned drugs around the year 2000 . The predicted curves are visually indistinguishable from those shown in Figure 2C . Even if we assume that antimony-resistant parasites are less fit than sensitive ones in the presence of such a new drug the predicted curves are visually indistinguishable . In general , once a strain with higher fitness has emerged , it overgrows the ‘old’ one [50] . These findings are supported by in vivo data showing that antimony-resistant parasites overgrow antimony-sensitive parasites just few weeks after mice were co-infected with both phenotypes ( PhD thesis MV , University of Antwerp ) .
Our analyses suggest that antimony-resistance alone cannot explain why the TFR observed in Bihar increased up to 65% between 1980 and 1997 . Most infections do not lead to symptomatic disease and thus , only a minority of parasites is exposed to antimonial treatment . This minor proportion of parasites cannot increase the TFR as quickly as it has been observed in Bihar . Following recent experimental findings on increased fitness of resistant parasites , we examined two hypotheses for an additional fitness benefit: disease-related or transmission-related fitness increase . At the current stage of knowledge , we cannot favour one fitness hypothesis over the other; both offer the potential to explain the data . Disease-related fitness , however , requires increasing the proportion of clinical cases among all infections or the infectiousness of symptomatic cases by at least 550% , which most probably would have been observable . Transmission-related fitness , on the other hand , requires increasing the infectiousness in sand flies , in asymptomatically infected humans , or in all infected humans by at least 5% . Such a minor increase can occur in the background of asymptomatic transmission without being apparently recognized under field conditions . After a parasite with higher fitness independent of a treatment-based selection pressure has emerged , it will finally replace the sensitive one , even in complete absence of antimonial treatment . This modelling study suggests that entomological studies are urgently required to gain better data on sand flies abundance , biting rates and infectiousness . Furthermore , research on the fitness of the parasites should also be conducted in the context of the natural vector Phlebotomus argentipes , and last but not least , more research should be done on asymptomatic carriers and the type of parasites they carry , in order to weigh their role in transmission . | The protozoan flagellate Leishmania donovani causes the neglected , life-threatening disease visceral leishmaniasis . Parasites are transmitted from man to man by the bite of the sand fly Phlebotomus argentipes , the vector of the disease . Pentavalent antimonials have been the mainstay of antileishmanial therapy for decades but rapidly increasing failure rates up to 65% observed between 1980 and 1997 in the state of Bihar , India , have challenged further use of these drugs . Comparative in vitro and in vivo experiments indicate that antimony-resistant parasites have a higher fitness than antimony-sensitive ones even in the absence of antimonial treatment . Simulation studies based on a previously published mathematical L . donovani transmission model suggest that resistance to antimonial treatment alone cannot explain the Bihar observations but that resistance together with higher fitness offers the potential to explain the data . After an antimony-resistant parasite with higher fitness has emerged , it will finally replace the antimony-sensitive ones , even in complete absence of antimonial treatment . | [
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] | 2012 | Treatment of Visceral Leishmaniasis: Model-Based Analyses on the Spread of Antimony-Resistant L. donovani in Bihar, India |
The investigation of RNA-based regulation of cellular processes is becoming an increasingly important part of biological or medical research . For the analysis of this type of data , RNA-related prediction tools are integrated into many pipelines and workflows . In order to correctly apply and tune these programs , the user has to have a precise understanding of their limitations and concepts . Within this manuscript , we provide the mathematical foundations and extract the algorithmic ideas that are core to state-of-the-art RNA structure and RNA–RNA interaction prediction algorithms . To allow the reader to change and adapt the algorithms or to play with different inputs , we provide an open-source web interface to JavaScript implementations and visualizations of each algorithm . The conceptual , teaching-focused presentation enables a high-level survey of the approaches , while providing sufficient details for understanding important concepts . This is boosted by the simple generation and study of examples using the web interface available at http://rna . informatik . uni-freiburg . de/Teaching/ . In combination , we provide a valuable resource for teaching , learning , and understanding the discussed prediction tools and thus enable a more informed analysis of RNA-related effects .
Bioinformatics analyses have become indispensable to biological research . While platforms like Galaxy enable the setup of tool pipelines without expert knowledge [1 , 2] , one requires a general understanding of underlying concepts and algorithms to be able to successfully apply and adapt these pipelines to biological data [3 , 4] . Thus , bioinformatics is taught n both computer science and biology studies . It has been established that , when teaching mathematics , a combination of reflective example study and problem solving by hand fosters learning . This learning effect is heightened when done iteratively with increasing difficulty [5] . Thus , diverse examples covering different aspects of the topic have to be provided to guide the learning process . This is even more important in an e-learning or self-study context , in which the study of examples that show different aspects of a problem might compensate for the missing interaction with a teacher [6 , 7] . Here , we focus on RNA-related bioinformatics and especially on approaches for RNA structure and RNA–RNA interaction prediction . Both are essential when investigating the vast amount of regulatory RNA that is common to all kingdoms of life [8 , 9] . The function of many RNA species is guided by their structure that is defined by the formation of intramolecular base pairs . For instance , prokaryotic small RNAs show evolutionary conserved unstructured regions that regulate the expression of their target mRNAs via intermolecular base pairing [10 , 11] . Thus , the prediction of both functional intramolecular structures of RNAs as well as their intermolecular ( RNA–RNA ) interaction potentials are central bioinformatics tasks . Most computational methods for RNA structure or RNA–RNA interaction prediction are based on thermodynamic models and provide an efficient computation , since Richard Bellman's principle of optimality [12] can be applied . This means that optimal solutions of a problem can be composed of optimal solutions of ( independent ) subproblems . This is used by dynamic programming approaches that decompose a problem into smaller problems and tabularize partial solutions . Robert Giegerich and colleagues developed a rigorous framework , namely Algebraic Dynamic Programming ( ADP ) [13 , 14] , to systematically study and develop dynamic programming approaches in a computer science context . In addition , they provided an online platform to study ADP programs for various problems also covering RNA related topics [15] . The central idea of ADP is to separate the strategy of how a problem is decomposed into subproblems from the evaluation strategy , i . e . , the objective of the optimization . We use the counting of structure alternatives for a given RNA to illustrate how dynamic programming can be applied to prediction problems . In particular , we introduce the decomposition strategy for ( nested ) RNA structure models . The teaching of dynamic programming approaches is typically split into a theoretical introduction by the lecturer showing individual examples and a subsequent manual application by students in which the methods are implemented or applied to solve small-scale problems for exercise . This leads often to a very small set of examples discussed due to the high amount of work needed for manual application and the limited gain of knowledge by iterated usage of once-understood solution strategies . To increase the number of examples , e . g . , to focus on different aspects of an individual method or to compare different approaches , either partial solutions have to be provided or implementations made available . Besides single instances like the Nussinov algorithm , most state-of-the-art methods and their underlying algorithmic ideas are not covered by textbooks , e . g . , [16–18] . Resorting to the original literature for teaching these algorithms , however , is complicated , as most approaches are introduced for very sophisticated energy models . While these advanced energy models are required for a successful application of these tools in real-world scenarios , they often mask the basic and transferable algorithmic ideas for the nonexpert reader since they require a high level of background knowledge . We approach the aforementioned problems in two ways . First , we have stripped the model-specific energy details from the state-of-the-art methods for RNA structure prediction and RNA–RNA interaction prediction and present their underlying ( or basic ) algorithmic ideas . For that purpose , we use the most simple energy model available . State-of-the-art energy models take the structural context of base pairs into account . To this end , RNA structures are decomposed into loops ( i . e . , a region that is enclosed by one or more base pairs ) to calculate their overall energy . However , the algorithmic principles are essentially the same when using an energy model that considers base pairs without their structural context as basic units . Since all methods are presented using the same mathematical nomenclature , relationships and differences are easy to understand . Second , we provide a web interface that provides interactive implementations of all algorithms discussed with extensive visualizations . This interface ( i ) helps to understand and follow the algorithms , ( ii ) eases the generation of interesting examples for different aspects to teach , and ( iii ) provides master solutions for comparison with your own calculations or implementations . Each section closes with a list of advanced questions that exemplify what can be studied and answered using the provided web interfaces available at http://rna . informatik . uni-freiburg . de/Teaching/ . RNA structure prediction topics covered within this manuscript are the formalization of RNA secondary structures and simplified energy models , computation of the number of structures with regards to the given model [19 , 20] , identification of the minimum free energy structure [21 , 22] , computation of partition functions [23] , probability calculation for single base pairs and unpaired regions [23 , 24] , and identification of the maximum expected accuracy structure [25 , 26] . RNA–RNA interaction prediction approaches are grouped according to their algorithmic idea , as in [27] , into hybrid-only interaction prediction [28–30] , concatenation-based/cofolding interaction prediction [31 , 32] , and accessibility-based interaction prediction [24 , 33 , 34] .
Ribonucleic acid ( RNA ) is a linear molecule built from nucleotides . The ribose sugars of the nucleotides are bound via interlinking phosphate groups . Furthermore , each sugar is connected to a nitrogenous base , typically one of adenine ( A ) , guanine ( G ) , cytosine ( C ) , or uracil ( U ) . The bases can form hydrogen bonds between two ( nonconsecutive ) nucleotides , which is then called a base pair . Although other forms are possible , the typically considered base pairs are G−C , A−U , and G−U in both orientations . Pairing between nucleotides of the same molecule ( intramolecular ) defines its three-dimensional structure . In order to fulfill a certain regulatory function , typically a stable structure is needed . Thermodynamic analyses have identified base ( pair ) stacking as the major stabilizing force within RNA structures [35] , and according energy estimates have been identified experimentally , e . g . , refer to [36] . The functional structure of an RNA can regulate , e . g . , other RNA molecules by direct ( intermolecular ) base pairing , i . e . , forming base pairs between two RNAs , called RNA–RNA interactions . While the probability of an initial contact is dependent on many factors , such as concentration or location , the subsequent formation of a stable RNA–RNA interaction is assumed to follow the same thermodynamic principles as single structure formation . Thus , most ideas and parameters from RNA structure prediction are transfered to RNA–RNA interaction prediction approaches . It is important to note that thermodynamics-based approaches are again models that do not consider all factors that influence structure/interaction formation , e . g . , already bound molecules , specific solution conditions , or kinetics of structure formation . Nevertheless , they typically allow for accurate predictions for the majority of RNA molecules [37] . In the following , we provide the mathematical framework needed to define and solve RNA-related problems . The primary structure of an RNA molecule can be described by its sequence of bases . That is , an RNA molecule of length n is defined by its sequence S∈{A , C , G , U}n of respective International Union of Pure and Applied Chemistry ( IUPAC ) single-letter codes [38] . The secondary structure P of an RNA S is defined as a set of ( ordered ) base pairs , i . e . , P⊂[1 , n]×[1 , n] with ( i , j ) ∈P→i<j . Typically , it is assumed that each nucleotide can pair with at most one other nucleotide , i . e . , ∀ ( i , j ) ≠ ( p , q ) ∈P:{i , j}∩{p , q} = ∅ , and that only the introduced Watson–Crick or G−U base pairs are allowed , i . e . , ∀ ( i , j ) ∈P:{Si , Sj}∈{{A , U} , {C , G} , {G , U}} extraneous to order . Such base pairs are said to be complementary . Furthermore , to restrict computational complexity of prediction algorithms , structures are constrained to be noncrossing ( nested ) , i . e . , ∄ ( i , j ) , ( p , q ) ∈P:i<p<j<q . Using noncrossing structures generally allow a good estimate of the overall structure stability . However , it is important to note that crossing base pairs do exist , albeit not as abundant as noncrossing base pairs , and contribute to the final stability of the three-dimensional shape . It is typically assumed that first noncrossing structural elements are formed that subsequently are linked via few crossing base pairs [39] . Thus , the majority of the structure can be modeled/predicted via nested base pairing , which strongly reduces the computational complexity . Finally , it is commonly enforced that pairing bases have a minimal sequence distance of l , also called minimal loop length , to incorporate steric constraints of structure formation . In the following , we will denote with P the set of all possible structures ( also referred to as structural ensemble or structure space ) that can be formed by a given sequence S . It has been shown that the size of the structure space P grows exponentially with sequence length n . For a minimal loop length l of 3 , the growth is about 2 . 3n [40] . Nested secondary structures can be visualized as outerplanar graphs in which nucleotides are represented by nodes , and edges represent base pairs or sequential backbone connections . Furthermore , dot-bracket strings can be used that encode for each position i whether it is unpaired “ . ” , it is the smaller index ( opening ) of a base pair “ ( , ” or the larger ( closing ) index “ ) ” . As motivated by Ruth Nussinov and coworkers [21] , we relate the stability of an RNA structure directly with its number of base pairs . Since some algorithms require explicit energy contributions of individual base pairs ( e . g . , McCaskill's algorithm to compute base pair probabilities ) , we set the energy of any base pair Ebp to −1 for simplification purposes . Thus , the energy of a structure is given by E ( P ) = |P|∙Ebp . Note , this is in stark contrast to state-of-the-art RNA structure prediction approaches ( e . g . , using Zuker's algorithm [22] ) , which typically apply a Nearest Neighbor energy model [41 , 42] and experimentally derived energy contributions [36] . Furthermore , all algorithms for RNA–RNA interaction prediction ignore concentration dependence and other factors influencing the duplex formation , which is typically modeled within the Nearest Neighbor model by an “initiation” energy term [24 , 33 , 34] . Nevertheless , the use of the simplified base pair-focused model enables a much clearer presentation of the algorithms , which is better suited ( and sufficient ) to understanding their ideas and mechanisms . The transfer from the simple base pair maximization to the advanced energy models , as done by Michael Zuker and Patrick Stiegler [22] , is generic and can be applied to all problems discussed within this manuscript . References to extended versions and implementations are provided for each approach . A first task that introduces the general structure of dynamic programming approaches used for RNA structure prediction is to compute the number of structures a sequence S can form , i . e . , |P| . Since the structure space P grows exponentially , explicit enumeration is inefficient . In order to apply dynamic programming , we first have to have a strategy of how to decompose such a problem into independent subproblems . Let us consider the subsequence Si . . Sj . We can easily split the problem into two independent problems by introducing a case distinction for its last position Sj; case ( 1 ) Sj is not involved in any base pairing , and case ( 2 ) Sj is paired with some position Sk ( i≤k<j ) . Both cases are depicted in Fig 1 . The first case can be easily reduced to a smaller problem , namely to Si . . Sj−1 , since the unpaired position Sj does not allow any structural alternatives . Thus , the reduced problem directly provides a count for case 1 . On the contrary , each possible base pairing of Sj in the second case decomposes the problem into two smaller independent problems ( one to the left of and one enclosed by the base pair ( k , j ) ) , since no base pair is allowed to cross ( k , j ) ( nestedness condition , see section on RNA secondary structure ) . Since any structural alternative of the left subproblem can be combined with any of the enclosed ones , we have to multiply the numbers from these smaller subproblems to get the overall count for case 2 . Michael S . Waterman and Temple S . Smith applied this idea to solve the counting problem using a table C [19 , 20] . An entry Ci , j provides the number of structures for a subsequence Si . . Sj . Thus , we initialize Ci , i = 1 for all positions i , since any subsequence of length one is confined to the unpaired structure . The recursion for longer subsequences is given by Ci , j=Ci , j−1+∑i≤k< ( j−l ) Sk , Sjcompl . Ci , k−1⋅Ck+1 , j−1 ( 1 ) which combines the two discussed cases to consider all possible “states” of nucleotide Sj in valid structures . The first ( Ci , j−1 ) covers all cases where Sj is unpaired , and the second counts all cases where Sj is paired with an Sk within the subsequence ( second case ) . Note , the base pair ( k , j ) has to respect the minimal loop length l . The overall number of structures is accessed by |P|=C1 , n . Given l and an RNA sequence , our user interface computes and depicts the filled matrix C . Example questions Ruth Nussinov and coworkers introduced in 1978 [21] a first algorithm that efficiently predicts a nested structure with the maximal number of base pairs for a given RNA sequence S , i . e . , argmaxP∈P ( |P| ) . The corresponding recursion Ni , j=max{Ni , j−1Sjunpairedmaxi≤k< ( j−l ) Sk , Sjcompl . ( Ni , k−1+Nk+1 , j−1+1 ) Sk , Sjpair ( 2 ) is strongly related to the counting approach from Eq 1 . Here , an entry Ni , j stores the maximal number of base pairs that can be formed by the subsequence Si . . Sj . Thus , summation in Eq 1 is replaced by maximization and multiplication with summation , while the second case considers the formed base pair with “+1 . ” N is initialized with 0 and can be filled in O ( n3 ) time while using O ( n2 ) memory . A depiction of the recursion is given in Fig 2 . The maximal number of base pairs formed by any structure can be found in N1 , n , and a respective optimal structure P can be identified via traceback starting in N1 , n . Thus , for a given cell Ni , j , the traceback discovers how the value of Ni , j was obtained . To this end , the case distinctions of the ( filling ) forward recursion ( e . g . , from Eq 2 ) are considered . If it holds Ni , j = Ni , j−1 ( first case ) , position j is found to be unpaired , and the traceback proceeds with cell Ni , j−1 . Otherwise , position j has to form a base pair with some position i≤k<j , which is identified in accordance to the second case of Eq 2 . The base pair ( k , j ) is stored as part of the final structure P and the traceback proceeds for both subintervals represented by Ni , k−1 and Nk+1 , j−1 . For the identification of functional structures or the study of structural alternatives , the enumeration of suboptimal structures is of interest . A generic approach was introduced by Stefan Wuchty and coworkers [43] that enables the enumeration of all structures that are in a certain range of the minimal energy . An implementation is also available in our web interface . Our interactive user interface enables the computation of both optimal and suboptimal structures . For a user defined sequence as well as recursion and traceback parameters , the dynamic programming table is provided along with a list of ( sub ) optimal structures . On selection , the according traceback is highlighted within the matrix . This is complemented with a graphical representation of the structure using Forna [44] . Different recursions can be chosen to examine the effects of ambiguous recursions versus the original one . In the following , such an ambiguous variant from [17] is presented . Ni , j=max{Ni+1 , jSiunpairedNi , j−1SjunpairedNi+1 , j−1+1ifSi , Sjcompl . andi+l<jmaxi<k< ( j−1 ) Ni , k+Nk+1 , jdecomposition ( 3 ) While this recursion also computes the same entries of N and thus maximal number of possible base pairs ( N1 , n ) , it is not using a unique decomposition of the structure , i . e . , the same structural variant is considered by different recursion cases . This causes duplicated enumeration of ( sub ) optimal structures when using Wuchty's traceback algorithm , which can be studied in our web server for different recursions . Furthermore , it is not possible to use variants of ambiguous recursions like Eq 3 to count structures ( consider relation of Eqs 2 and 1 ) or to compute the partition function of the structural ensemble ( as discussed next ) , since both requires a unique consideration of each structure . In 1981 , Michael Zuker and Patrick Stiegler introduced a dynamic programming approach that efficiently computes minimum free energy structures using a Nearest Neighbor energy model [22] . Using further restriction , the same time and space complexity compared to Nussinov's algorithm is kept . The approach with according decomposition depictions and how it relates to Nussinov's algorithm is introduced in detail , e . g . , in [45] . Implementations like UNAFold [46] ( formerly mfold [47] ) or RNAfold [31 , 37] are the current state-of-the-art tools for RNA secondary structure prediction . Example questions To estimate the probability of a given structure P within the structural ensemble P , statistical mechanics typically dictate a Boltzmann distribution when using minimal assumptions [48] . Thus , the probability of a structure P is directly related to its energy E ( P ) by Pr ( P ) =exp ( −E ( P ) /kBT ) ∑P′∈Pexp ( −E ( P′ ) /kBT ) ( 4 ) given the Boltzmann factor kB and the system's temperature T . Note , when using an energy model with units “per mole , ” which is typically the case when using a Nearest Neighbor model with measured energy contributions , one has to replace kB with the gas constant R . Note further , the structure with minimal free energy , e . g . , predicted with algorithms discussed above , will always have maximal probability according to Eq 4 . Thus , the most stable structure is automatically the most likely structure . The nominator of Eq 4 is called Boltzmann weight ( of structure P ) . The denominator is called canonical partition function Z , which is the sum of the Boltzmann weights of all structures in P . Since P grows exponentially , its exhaustive enumeration to compute Z is impracticable . Nevertheless , it is possible to compute Z efficiently using a variant of the counting algorithm . This approach was first introduced for the Nearest Neighbor energy model by John S . McCaskill ( 1990 ) [23] , and we rephrase a variant for the simplified base pair model . First , we have to note that the Boltzmann weight of a structure P can be computed based on the energy of its base pairs Ebp , as follows exp ( −E ( P ) /kBT ) =exp ( −∑ ( i , j ) ∈PEbp/kBT ) =∏ ( i , j ) ∈Pexp ( −Ebp/kBT ) . ( 5 ) That is , the structure's weight is computed by the product of individual base pair weights . To simplify notation in the following , qbp = exp ( −Ebp/kBT ) refers to the Boltzmann weight of a single base pair . Given this , we can alter the counting recursion from Eq 1 to Qi , j=Qi , j−1+∑i≤k< ( j−l ) Sk , SjpairQi , k−1⋅Qk+1 , j−1⋅qbp . ( 6 ) This directly provides the partition function Z =Q1 , n in O ( n3 ) time . For some approaches and research questions , probabilities of individual base pairs Prbp ( i , j ) are of interest . This is the probability that a base pair ( i , j ) is formed by some structure , which can be calculated by summing up the probabilities of all structures containing ( i , j ) , i . e . , Prbp ( i , j ) =∑P∈P ( i , j ) ∈Pexp ( −E ( P ) /kBT ) Z . ( 7 ) As for counting , the base pair ( i , j ) decomposes all structures into the enclosed and outer subsequence that are independent concerning base pairing . Thus , the partition functions of the according subsequences can be used to compute Prbp ( i , j ) efficiently . To do so , we need an auxiliary matrix Qbp . Each entry Qi , jbp holds the partition function for the subsequence Si . . Sj , with the side constraint that i and j form the base pair ( i , j ) . If this is not possible due to noncomplementarity or the minimal loop constraint , the entry is 0 . Given this , we can rewrite Eq 6 as follows Qi , j=Qi , j−1+∑i≤k< ( j−l ) Qi , k−1⋅Qk , jbp ( 8 ) Qi , jbp={Qi+1 , j−1⋅qbpifSi , Sjcomplementary0otherwise ( 9 ) and compute the base pair probability using Prbp ( i , j ) =Q1 , i−1⋅Qi , jbp⋅Qj+1 , nQ1 , n+∑p<i , j<qPrbp ( p , q ) ⋅qbp⋅Qp+1 , i−1⋅Qi , jbp⋅Qj+1 , q−1Qp , qbp . ( 10 ) The first term in Eq 10 covers structures where ( i , j ) is an external base pair , i . e . , not enclosed by any other base pair . The second term considers all structures in which ( i , j ) is directly enclosed by a base pair ( p , q ) and corrects the respective base pair probability Prbp ( p , q ) by the probability of the structure subensemble that contains both base pairs and no “in-between spanning” base pair ( k , l ) with p<k<i<j<l<q . The latter probability is defined by the fraction within the second term . Note ( again ) that by using a simple energy model , we omit all the complex case distinctions , which allows one to concentrate on the main cases of algorithmic importance . In the full model , the first case would have been the same , whereas the second one would have been split to consider specifically each structural context a base pair can have . In analogy to base pair probabilities , it is also possible to define and compute the unpaired probability Prss ( i , j ) of a subsequence Si . . Sj ( Eq 11 ) , i . e . , the probability of all structures that show no base pairing in the single-stranded subsequence . Prss ( i , j ) =∑P∈Pi . . jssexp ( −E ( P ) /kBT ) Z ( 11 ) withPi . . jss={P|∄ ( k , l ) ∈P:k∈[i , j]∨l∈[i , j]}⊆P ( 12 ) The unpaired probability is also sometimes termed “accessibility , ” as an unpaired region in an RNA is accessible for pairing to another RNA . For the computation of Prss ( i , j ) , we only have to replace Qi , jbp with 1 in Eq 10 , since only the unpaired structure with energy zero has to be considered for Si . . Sj , which has a Boltzmann weight of 1 . Stephan H . Bernhart and coworkers provide in [49] details for the extension of the introduced recursions to the Nearest Neighbor model , which is also nicely detailed in [45] . Implementations are for instance available in the Vienna RNA package [37] . The authors also show how to reduce the time complexity of the probability computation from O ( n4 ) to O ( n3 ) . To this end , they introduce another auxiliary matrix Q^bp that provides the “outer” partition function , which reflects only base pairs not enclosed by respective subsequences . Our web implementation enables the computation of both base pair probabilities as well as unpaired probabilities . To provide insights into how the temperature and energy model influence structure and base pair probabilities , the user can alter the used temperature as well as Ebp . Besides a visualization of the partition function tables Q and Qbp , the user is provided with a visualization of the base pair and unpaired probabilities using the established dot plot format ( e . g . , used also by UNAfold/mfold [46 , 47] or RNAfold [37 , 50] ) . Within this matrix-like illustration , each base pair probability is represented by a dot of proportional size , i . e . , the higher the probability , the larger the dot and small probabilities are not visible . With a bit of visual practice , dot plots enable an easy identification of highly probable substructures and the study of structural alternatives . Example questions
The fastest class of RNA–RNA interaction prediction approaches focuses only on the identification of the interaction site , i . e . , only on the intermolecular base pairs , without considering the intramolecular structures of the interacting RNAs . To this end , the prefix-based decomposition scheme of global sequence alignment [52] can be adapted . Given two RNA sequences S1 and S2 of lengths n and m , respectively , we denote with S←j2 the reversely indexed S2 to simplify the index notation , since RNA molecules interact in antiparallel orientation . The latter applies to both intra- and intermolecular base pairing . When considering S1 and S←j2 , we can design a dynamic programming approach for the simplified energy model using a two-dimensional matrix H . An entry Hi , j will provide the maximal number of intermolecular base pairs for the prefixes S1 . . i1 and S←1 . . j2 . The decomposition scheme for the recursion of Eq 16 to compute Hi , j is visualized in Fig 3 . As already mentioned , Eq 16 is a variant of the global sequence alignment approach introduced by Saul B . Needleman and Christian D . Wunsch [52] using an adapted scoring scheme ( base pair instead of match/mismatch scoring for Si1 , S←j2 and no gap cost ) . Thus , initializing all Hi , 0/H0 , j with 0 , the entry Hn , m provides the maximal number of intermolecular base pairs that can be formed , and a traceback starting at Hn , m yields the respective interaction details . This approach enables very low runtimes ( O ( nm ) ) , as observed by Brian Tjaden and coworkers , who presented in [30] a variant of Eq 16 . When computing hybridization-only interactions via minimizing a more sophisticated energy model , the strategy has to be altered to follow a scheme similar to local sequence alignment as defined by Temple Smith and Michael S . Waterman [53] , which is detailed in [30] . The web interface of our implementation identifies and reports all optimal interaction sites . For each , an American Standard Code for Information Interchange ( ASCII ) visualization of the intermolecular base pairs is provided . Note , to reduce code redundancy , we do not use an implementation of Eq 16 but use a base pair-maximization variant of Eq 19 , which is discussed in the next section . Adaptations of this approach to the Nearest Neighbor model have been discussed in [28] and , e . g . , implemented in the tools TargetRNA [30] , RNAhybrid [29] , or RNAplex [54] . While such methods have been successfully applied for target site identification of very short RNAs , they often overestimate the length of target sites since intramolecular base pairing is ignored [33 , 54] . These problems are tackled by concatenation- and accessibility-based approaches discussed next . Example questions Among the first approaches to predict the interacting base pairs for two RNA molecules are concatenation-based or cofolding approaches [31 , 32] . Here , two or more RNA sequences are concatenated into a single sequence with special interspacing linker sequences . The resulting hybrid sequence is used within an adaptation of a standard structure prediction that takes special care of the linker sequences . The linked sequences are forbidden to form base pairs , and the structural elements containing linker sequences are treated energetically as external , as discussed by Ivo L . Hofacker and colleagues [31] . The extension of standard structure prediction approaches to RNA–RNA interaction prediction directly yields the possibility to compute according probabilities of interaction sites or intermolecular base pairs [55] . A first implementation of concatenation-based prediction using the Nearest Neighbor energy model was reported for mfold [47] and later implemented in , e . g . , the tools MultiRNAFold [56] and RNAcofold [55] . Our implementation extends the Nussinov recursion from Eq 2 with a special handling for linker sequence characters “X . ” Base pairs ( case 2 ) are not allowed to involve a linker position . No special energy treatment is necessary for the simplified energy model since we treat intra- and intermolecular base pairs equally and without considering their context . The input is restricted to two RNA sequences that are concatenated by a linker of length l+1 ( where l is the minimal loop size ) to ensure the presence of a linker and that the concatenated sequence ends can form a base pair . Our interactive cofolding web interface lists ( sub ) optimal hybridization structures using our generic suboptimal traceback implementation . Within the reported dot-bracket strings , intramolecular base pairs are encoded using parentheses “ ( ) , ” intermolecular base pairs ( spanning the linker ) are represented by brackets “[] , ” and the linker itself is depicted by linker characters “X . ” For each hybridization structure , a traceback is visualized on selection along with a Forna 2D structure graph visualization . Furthermore , an ASCII visualization of only the intermolecular base pairs is provided . Concatentation-based approaches do incorporate the competition of intra- and intermolecular base pairing , which is a central weakness of hybridization-only prediction algorithms . Still , not all important interaction patterns can be predicted using cofolding approaches since the hybrid structure has to be nested . For instance , common kissing stem–loop or kissing–hairpin interactions cannot be predicted because they form a crossing structure in the concatenated model ( see Fig 4 ) . To predict such patterns , accessibility-based approaches , discussed next , can be applied . Example questions The previously introduced concatenation-based approaches directly reflect the competition of intra- and intermolecular base pairing by optimizing both at the same time . Nevertheless , they are neglecting that the intramolecular structure is established before an intermolecular interaction is formed . That is , intramolecular base pairs ( might ) have to be opened/broken such that intermolecular base pairs can form a stable interaction . To be favorable , the interaction energy must outweigh the energy needed to make the subsequences accessible . This two-step process is modeled by accessibility-based interaction prediction approaches . The following formula , depicted in Fig 5 , is used to compute the final interaction energy values Ij , li , k that incorporate both the hybridization/duplex energy D as well as the penalties ΔE1 , ΔE2 for inaccessible sites of the RNAs S1 , S2 , respectively . Note , ΔEj . . l2 is computed for the reversely indexed sequence S←2 to ease the notation . This reversal has to be taken into account for hybridization energy computations , since Nearest Neighbor models have to incorporate the chemical 5′- to 3′-end orientation of RNAs . The entry of I with minimal energy is used to traceback the interaction details of the optimal interaction . Only entries in I with an energy lower than zero mark favorable interactions since here , the duplex energy D outweighs the ΔE penalties to make the respective subsequences accessible . The energy penalties ΔEi . . j resemble the free energy needed to make the interaction site Si . . Sj accessible , i . e . , to unfold the site's intramolecular base pairs [24 , 33] . To reflect the structural flexibility of RNAs , the terms are based on the structure ensembles that can be formed rather than individual structures . The penalties can be computed from the energy difference of the structure ensemble with accessible site that is single stranded , Ei . . jss , versus the whole structure ensemble , Eens . Both energies can be computed from the respective partition functions Zi . . jss ( for Pi . . jss from Eq 12 ) and Z using the inverse Boltzmann weight . In the following , we show the relation of ΔE and the unpaired probability Prss . ΔEi . . j=Ei . . jss−Eens=− ( RT⋅log ( Zi . . jss ) −RT⋅log ( Z ) ) =−RT⋅log ( Zi . . jss/Z ) =−RT⋅log ( Prss ( i , j ) ) . ( 18 ) Note , since Prss ( i , j ) is ≤1 , all ΔEi . . j penalties are ≥0 . To add such site-specific terms to duplex energies , we cannot simply use the prefix-based recursion from Eq 16 , since Hi , j only provides the optimal value for all interaction sites with right ends Si1 and S←j2 and not for individual sites . Thus , for exact results , we have to relate to a subsequence-based computation that explicitly stores values for all subsequence combinations . To further simplify the recursions , we use dedicated calculations ( and matrices ) for the duplex energy ( matrix D , Eq 19 ) and the overall interaction energy including inaccessibility penalties ( matrix I , Eq 17 ) . Both matrices are four-dimensional , in which an entry Dj , li , k provides the duplex energy of the interacting sites Si . . k1 and S←j . . l2 under the assumption that the boundaries form the intermolecular base pairs ( i , j ) and ( k , l ) ; otherwise , the entry is set to ∞ . Dj , li , k=min{EbpSi1 , S←j2compl . , i=k , j=lmini<p≤kj<q≤l ( Ebp+Dq , lp , k ) Si1 , S←j2compl . , i<k , j<l+∞otherwise . ( 19 ) The first case represents the initiation of a new interaction that covers only the intermolecular base pair ( i , j ) with according energy Ebp . The second case extends an already-computed interaction of Sp . . k1 , S←q . . l2 with a new base pair ( i , j ) , while the third case is applied if the base pair ( i , j ) cannot be formed or the indices violate order constraints . Note , the given recursion has an O ( n6 ) time complexity due to arbitrarily large gaps in the second case . Given the typically applied thermodynamic model and statistics from known interactions , the sequential distance between neighbored intermolecular base pairs is normally restricted to a small constant <30 [24] , which reduces the time complexity to O ( n4 ) . The space complexity can be reduced to O ( n2 ) , as shown in [33] , by interactively computing parts of D for a fixed right-boundary base pair ( k , l ) . Our implementation provides the list of all optimal interactions and visualizes the selected interaction details using an ASCII chart . Due to the four-dimensionality of the matrices D and I , only the value Ij , li , k for the current selection as well as the penalty tables ΔE1+ΔE2 used for computation are shown . The interactive web interface enables a straightforward comparison of the effects and restrictions of the three different interaction prediction approaches introduced . For instance , using the simple example sequences S1 = CCC and S2 = CCCGGGGGG , the hybridization-only optimization reports ( as expected ) any interaction patterns of S1 with G nucleotides of S2 . In contrast , intermolecular base pairs predicted by the cofolding approach are restricted to the 3′-end of S2 since the central G nucleotides are blocked by an intramolecular hairpin structure ( similar to Fig 4A ) . Both approaches neglect that RNA S2 will first ( most probably ) fold into a hairpin structure ( with unpaired/accessible nucleotides in the center ) before both interact . Thus , it is most likely this central unpaired region of S2 where interaction formation with S1 will start . The growing interaction would have to break the already-formed intramolecular base pairs for larger interaction patterns , which is not necessarily favorable . This scenario is modeled by accessibility-based approaches , which predict interactions to be restricted to the loop region only . The resulting interaction resembles a kissing stem–loop pattern ( see Fig 4B ) . Note , while accessibility-based approaches are well suited to predict interaction patterns like stem–loop or kissing hairpin interactions , they are still not able to model arbitrary interaction patterns . For instance , double kissing hairpin interactions can not be modeled correctly [57] . The first accessibility-based approach RNAup for the Nearest Neighbor model was introduced by Ulrike Mückstein and colleagues [24] . While it is still among the state-of-the-art prediction tools [27] , its vast runtime requirements of O ( n4 ) render it inapplicable for large-scale data analyses , such as genome wide target screens . This problem was tackled by Anke Busch and coworkers with IntaRNA [33 , 34] , which implements a heuristic version of an accessibility-based approach that extends fast hybridization-only recursions with ΔE penalties . IntaRNA results in a much lower O ( n2 ) time complexity [33] when using precomputed or approximate ΔE terms , as introduced in [58] . A detailed introduction is also given in [45] . A similar heuristic extension was recently reported for TargetRNA2 [59] . Current versions of the initially hybridization-only approach RNAplex [54] and its webserver RNApredator [60] incorporate an approximate , position-specific accessibility model to increase prediction quality [61] . Example questions All discussed algorithms and visualizations have been implemented in JavaScript . This enables client-side computation ( no backend server hardware needed ) as well as local download and application ( from GitHub repository ) for offline usage . Since all algorithms are dynamic-programming approaches , a generic inheritance hierarchy was implemented to reduce code redundancy and to simplify maintenance and extensibility . We use Knockout . js as the controller to bind input/output elements from within the HTML pages with the JavaScript data structures and computations .
The understanding of RNA structure and RNA–RNA interaction prediction approaches is central to ensure correct result interpretation and an awareness of their limitations , both essential to avoid wrong conclusions . Furthermore , it ensures proper embedding in RNA-related analysis pipelines or their extension to new fields of applications . To gain this level of understanding , the original literature is often of limited didactic value , since scientific articles are typically not meant for educational use . Thus , approaches are either represented on a very detailed expert level or sketched briefly , since the manuscript focuses on the biological results rather than algorithmic details . Here , we provide a compact summary of the relevant theoretical background for the most common algorithmic approaches and their state-of-the-art instances currently used . Algorithms are stripped from complicating energy model details to enable an easy understanding of the underlying concepts and the resulting limitations . Furthermore , we provide web-based implementations and visualizations of all presented approaches for their ad hoc use . The latter is of importance , since example-driven ( self- ) study is known to significantly foster learning and understanding . To further support such self-learning efforts based on our manuscript and web service , we provide small exemplary tasks for each algorithm group that can be tackled using our web implementations . The web service [62] is being continually extended with the implementation and visualization of additional methods . Planned implementations cover pseudoknotted ( crossing ) structure prediction approaches as well as comparative approaches for RNA structure and RNA–RNA interaction prediction , e . g . , discussed in [57] . Eventually , we provide both a comprehensive review of current RNA thermodynamic-focused prediction approaches to spark ideas for new approaches and interactive teaching material , which will help ensure that available tools are correctly applied and interpreted . | RNA molecules are central players in many cellular processes . Thus , the analysis of RNA-based regulation has provided valuable insights and is often pivotal to biological and medical research . In order to correctly select appropriate algorithms and apply available RNA structure and RNA–RNA interaction prediction software , it is crucial to have a good understanding of their limitations and concepts . Such an overview is hard to achieve by end users , since most state-of-the-art tools are introduced on expert level and are not discussed in text books . Within this manuscript , we provide the mathematical means and extract the algorithmic concepts that are core to state-of-the-art RNA structure and RNA–RNA interaction prediction algorithms . The conceptual , teaching-focused presentation enables a detailed understanding of the approaches using a simplified model for didactic purposes . We support this process by providing clear examples using the web interface of our algorithm implementation . In summary , we have compiled material and web applications for teaching—and the self-study of—several state-of-the-art algorithms commonly used to investigate the role of RNA in regulatory processes . | [
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] | 2018 | Interactive implementations of thermodynamics-based RNA structure and RNA–RNA interaction prediction approaches for example-driven teaching |
Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as ‘cancer hallmarks’ . The first Pan-Cancer initiative of The Cancer Genome Atlas ( TCGA ) Research Network has aggregated protein expression profiles for 3 , 467 patient samples from 11 tumor types using the antibody based reverse phase protein array ( RPPA ) technology . The resultant proteomic data can be utilized to computationally infer protein-protein interaction ( PPI ) networks and to study the commonalities and differences across tumor types . In this study , we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data . We observe that no single method has the best performance in all tumor types , but a group of six methods , including diverse techniques such as correlation , mutual information , and regression , consistently rank highly among the tested methods . We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody–related technical biases . Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways , innate and adaptive immune signaling , cell cycle , metabolism , and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types . Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer .
The availability of proteomic datasets such as PANCAN11 where protein levels are measured across different conditions provides a unique opportunity to study the functions of proteins . However , the analysis of function requires knowledge of interactions . For instance , in the protein-folding domain , the function of a single residue during folding can be determined only by having knowledge about the residues it is interacting with . Similarly , the function of a protein in the cell can only be understood by determining its interaction partners . Therefore , the units of analysis are not the individual protein expression levels , but the interactions of proteins with other cellular entities . Statistical techniques such as correlation can be used to study the interactions of proteins . However , correlation between two proteins does not imply that they directly interact , because correlation may also be induced by chaining of correlation between a set of intervening , directly interacting proteins . Such indirect correlations are called transitive interactions . It was previously shown that the dominant correlations in a system can be the result of parallel transitive interactions [6] . There are three main network motifs that lead to transitive interactions: fan-in , fan-out and cascade . A fan-in is a case where there are direct interactions from proteins A and B to a third protein C but there is no interaction between A and B . A fan-out is the situation where there is a direct interaction from protein C to both A and B but there is no interaction between A and B . A cascade , on the other hand , is a chain event where there are direct interactions from A to B , and from B to C , but not from A to C . In all these three cases , if the two direct interactions are in the same direction ( both increasing or both decreasing ) , there is a transitive influence observed between the proteins that do not have a direct interaction . Since biological pathways and signaling events contain many fan-in , fan-out and cascade network motifs , transitive effects occur widely across the network and have previously been shown to be a systematic source of false positive errors for many computational network inference methods [7] . Thus , it is crucial to minimize transitive interactions when building network models from high-throughput datasets . A wide suite of computational methods has been proposed in the literature for the identification of direct interactions in networks . The common objective of many of these methods is to call a direct interaction between two entities if they are ‘not conditionally independent’ of each other given a set of other entities . One simple example is the regression-based partial correlation approach . Consider a three-variable system consisting of A , B , and C . When testing the existence of a direct interaction between A and B in this approach , measurements on A and B would first separately be regressed on the measurements on C , the residual vectors would be computed , and then the correlation between the residual vectors would be found . If this ‘partial’ correlation is significantly different from zero , a direct interaction is called between A and B . Despite the similarity in the objective , these methods employ diverse inference procedures such as mutual information [8–11] , regression [12–14] , Gaussian graphical models [15 , 16] , and entropy maximization [17 , 18] . The diversity of algorithms for inferring direct interactions , coupled with the absence of a robust off-the-shelf method , creates challenges for investigators that aim to generate hypotheses and eventually discover novel functional interactions among proteins . We address this challenge by testing different families of network inference methods towards the goal of deriving guidance for the better-performing methods . The RPPA platform , first introduced in Paweletz et al . [19] stands a good chance of becoming a widely used proteomics platform as greater numbers of reliable antibodies are being developed . Here , we present a rigorous comparison of the performance of 13 commonly used network inference algorithms based on PANCAN11 , a pan-cancer RPPA dataset which contains levels of many proteins in a large number of samples , such that reasonably meaningful protein-protein correlations can be computed . The goal of this comparison is two-fold: To investigate 1 ) if the signal-to-noise ratio of the RPPA technology allows the discovery of known and novel protein-protein interactions ( PPIs ) , and 2 ) to what extent algorithms that were originally developed for gene regulatory network inference accomplish the inference of PPIs . Performance evaluation of PPI network inference for different cancers requires a ‘gold standard’ for each cancer type . However , a true gold standard for human PPIs does not exist , let alone a separate one for each tumor type . Most protein interactions in in vivo systems remain unknown or unproven and/or depend on physiological context . Yet public knowledgebases that store collections of curated pathway and/or interaction data contain useful information . For instance , Pathway Commons is a collection of publicly available and curated physical interactions and pathway data including biochemical reactions , complex assembly , transport and catalysis events [20] , aggregated from primary sources such as Reactome , KEGG and HPRD and conveniently represented in the BioPAX pathway knowledge representation framework [21–24] . In this study , we adopted Pathway Commons as a benchmark , and evaluated the performance of 13 network inference methods ( Table 1 ) in their capacity to retrieve ‘true’ PPIs from RPPA datasets of 11 cancer types . We then used a group of high-performing methods to investigate the similarities and differences among the 11 cancer types in our dataset . The workflow of this study ( Fig 1 ) , involves the parallel generation of two PPI network models , one from computational inference and one from the pathway knowledgebase . On the inference side , multiple antibodies are assayed on an RPPA platform ( Step 1 ) and the resulting dataset is normalized to generate a proteomic profile of the cohort such as PANCAN11 . Computational network inference methods are then employed to create a network model with the inferred PPIs ( Step 2 ) . On the knowledgebase side , various wet-lab experiments are performed to generate data , and the resulting information is stored in the scientific literature ( Step 3 ) . Curators sift through the literature to distill multiple-layered information on PPIs ( Step 4 ) , and then this information is catalogued in knowledgebases such as Pathway Commons ( Step 5 ) . A comparison of the PPI network models from the two sides reveals the level of fidelity at which the ‘true’ network is constructed by the computational methods ( Step 6 ) . The mutual information-based methods listed in Table 1 infer unsigned undirected edges , whereas the edges inferred by the ‘correlation’ and ‘partial correlation’ family of methods are undirected but signed ( positive or negative ) . An undirected positive edge between A and B means that the direction of influence is not known , but A and B generally exist at both high or both low levels among all tested experimental conditions . A negative sign , thus , means that one is generally high when the other is low ( tending towards mutual exclusivity ) . The use of positive/negative edges in this study refers to positive/negative signs of the weight . Also , we use the words ‘edge’ and ‘interaction’ interchangeably throughout the manuscript . The workflow of performance evaluation as described above involves certain caveats . These are discussed in detail in the Discussion section and in S1A Text . Here we discuss one of the caveats , the ascertainment bias in pathway knowledgebases ( Step 5 in Fig 1 ) . Wet-lab experiments for PPI plausibly have over-representation of certain proteins due to the perceived interest in the field and ease of study . In a recent paper , a Pearson correlation of 0 . 77 was reported for the correlation between the number of publications in which a protein was mentioned and the number of interactions reported for that protein in literature-curated data [25] . This implies the potential existence of an ascertainment bias in pathway knowledgebases . More documented interactions of a certain protein will exist if that protein is studied more intensively by the community . The ascertainment bias in Pathway Commons precludes our benchmark network from being a perfect gold standard . This and other caveats challenge the comparability of pathway models from a knowledgebase and network models from a computational algorithm . Thus , it is necessary to be mindful of these caveats when interpreting the performance evaluation results in this study .
We obtained network predictions for 11 tumor types listed in Table 2 by using the 13 network inference methods listed in Table 1 . We employed precision–recall curves to first find the optimal parameter values for each method , and then to compare the performance of methods using their optimal values . The precision-recall ( PR ) curves were constructed by first ranking an edge list based on significance , and then plotting precision and recall on the y and x axis respectively for cumulatively increasing numbers of the top ( the most significant ) edges from the list . The trade-off between precision and recall at different cutoffs gives a reliable idea about the performance of a method , and this performance can be quantified with the area under the precision-recall curve ( AUPR ) . The performance comparison for 13 methods was done separately for each tumor type . For a given tumor type , our procedure involved two steps . In the first step , we aimed to put all methods on an equal footing by finding each method’s optimal parameter values . This was achieved by running each method multiple times with different parameter values obtained from a one- or two-dimensional grid , computing the AUPRs for the resulting gene lists , and then finding the parameter or parameter combination with the highest AUPR . The parameters of each method and the design of the grid search are listed in Table A in S1E Text . In the second step , the highest AUPR values from all methods were compared to determine the method with the best performance . This procedure was repeated for each one of the 11 tumor types . Therefore the best-performing method may be different for each one of the tumor types . There is , however , a caveat concerning the computation of AUPRs from the entire span of the PR curves . We observe in PR curves that ( 1 ) there is no significant difference among methods beyond a 10% recall level , and ( 2 ) the precision level of network predictions is very low when recall is 10% or higher , suggesting that network predictions are more likely to be affected by noise . The PR curves for BRCA and GBM are shown in Fig 2A as representative examples of these two phenomena . Therefore , we chose to use AUPR only from the 0–10% recall range ( i . e . limited-recall ) , and not from the entire recall range ( i . e . full-recall ) for the comparison of parameter configurations or the comparison of methods . As the parameter configuration that optimizes AUPR in the limited-recall span can be different from that in the full-recall span , some methods were observed to have different PR curves for the limited-recall case ( Fig 2B ) . The subsequent analysis is carried out with network predictions from the limited-recall case . The optimal parameter values and the number of edges in the limited-recall case for each method and tumor type are shown in S5 and S6 Figs respectively . After identifying the PR curves to compare the methods , we asked whether any particular method is a clear winner by being the best in all of the 11 tumor types . The AUPR values in Fig 3A indicate that there is no single method that performs the best for all investigated tumor types . The tumor types in this figure are ordered from left to right according to increasing coefficient of variation . The differences in the tumor-wise AUPR means and variances indicate that the 11 tumor types are not equally amenable to network inference with RPPA data . These differences could partially be explained by the different statistics of inferred networks such as average-node-degree and network density , which we found to be negatively correlated with AUPR ( Spearman r = –0 . 626 and –0 . 453 respectively ) ( S1B Text , S1 Fig ) . Given the absence of a clear winner among the methods , we next asked what the overall best-performing methods were . To achieve an overall comparison of the methods , we ranked them across all tumor types based on ( 1 ) overall AUPR and ( 2 ) overall AUPR rank . For these two criteria , we computed respectively the sum of a method’s AUPR values in the investigated tumor types ( Fig 3B , left panel ) , and the sum of its AUPR ranks in the same tumor types ( Fig 3B , right panel ) . The different-colored segments in horizontal bars correspond to tumor types as shown in the legend . The numbers next to the horizontal bars indicate the rank of the method for the relevant criterion . Higher AUPR values but lower AUPR ranks indicate better performance . Therefore , the best rank of 1 is given to the highest overall AUPR and the lowest overall AUPR rank . We observe in Fig 3B that the overall AUPR values ( left ) did not show as wide a variability across methods as the overall AUPR ranks ( right ) . This might be due to the overfitting of the methods to the benchmark network , as each method was run with parameters that optimize performance ( AUPR ) against the same benchmark . The small differences in overall AUPR values suggest that these methods may have a general capacity to achieve similar performance in other contexts as long as their respective parameter space is sufficiently explored . However , such similarity in performance does not preclude the possibility that some methods consistently outperform others even if by small margins . To investigate this possibility , we ordered the methods from top to bottom according to increasing overall AUPR rank . This choice in the ordering shows that RIDGENET is the best-performing method overall . Broken down by tumor type , RIDGENET is the best for BRCA , OV , UCEC , BLCA and KIRC; but is not as good as ARACNE variants for HNSC , LUSC , LUAD , GBM , COAD , and READ . On the poor performance side , SIMPLEPARCOR has the worst rank according to both the overall AUPR and the overall AUPR rank ( Fig 3B ) . We next investigated the level of similarity among the network predictions of all 13 methods . One question here is whether the network predictions , as given by the inferred edge weights , would cluster the methods according to shared properties , such as the regularization technique , or the algorithm family listed in Table 1 . To this end , we created one vector for each method by stacking the relevant edge weights from all 11 tumor types . We then computed the Spearman correlation between each pair of methods , and also performed dimensionality reduction on the same vectors using principal component analysis ( PCA ) . Unsupervised clustering on the Spearman correlation matrix ( hierarchical clustering with complete linkage and Euclidean distance ) and PCA on the edge weight matrix reveal concordant results in terms of the grouping of the methods ( Fig 3C and 3D ) . We observe three major groups of methods in Fig 3C: ( 1 ) Mutual information-based methods ARACNE ( variants ) , CLR , MRNET , ( 2 ) correlation-based methods SPEARMANCOR and PEARSONCOR , and ( 3 ) partial correlation-based methods . SIMPLEPARCOR from the third group can be considered an outlier compared with the other partial correlation methods . Therefore , if we remove it as a separate group , the remaining partial correlation methods RIDGENET , LASSONET , ELASTICNET , PLSNET , GLASSO , GENENET can also be categorized as ‘regularized methods’ . In the PCA plots , the 1st principal component ( PC ) primarily separates the correlation-based methods SPEARMANCOR and PEARSONCOR from the others , accounting for 53% of the variance ( Fig 3D ) . Correlation methods are fundamentally different from other investigated methods because they do not attempt to eliminate transitive edges in any way . This defect could predict poor performance for both SPEARMANCOR and PEARSONCOR . However , the superior overall performance of the rank-based SPEARMANCOR compared with the value-based PEARSONCOR and several regularized methods ( Fig 3B ) could be due to the ability of SPEARMANCOR to capture nonlinear relationships and/or its robustness against outliers . The 2nd PC ( 23 . 4% variance ) separates SIMPLEPARCOR , a method that is based on Gaussian graphical models and that employs the sub-optimal pseudo-inverse technique when the covariance matrix is singular . Even when the covariance matrix is non-singular , the inversion of the covariance matrix without any regularization is known to introduce defects into the inference procedure unless the number of samples is at least twice the number of features [16] . As the cohort sizes in this study are less than twice the number of antibodies ( 2*187 = 374 ) for 7 of the 11 tumor types ( Table 2 ) , it is not surprising that SIMPLEPARCOR has poor performance in these tumor types , hence the poorest overall performance by a margin ( Fig 3B ) . Indeed , we can observe that the tumor types where SIMPLEPARCOR achieves relatively better ranks are BRCA , OVCA , KIRC , and UCEC , the four tumor types that have cohort size greater than 374 ( Fig 3A and 3B and Table 2 ) . The 3rd PC ( 8 . 1% variance ) achieves the separation of mutual information methods from regularized methods . Mutual information-based methods have the capability to model nonlinear relationships , but are not able to infer the direction of the relationship . These two fundamental differences may account for the clear separation of these methods from the others . Principal components can achieve a separation of regularization-based methods only at the 5th and 6th PC , which account for as little as 4% and 1 . 4% of the variance respectively ( Fig 3D ) . The modest differences between overall AUPR values in the left panel of Fig 3B , and also the lack of a consistently best-performing method in all tumor types are reasons to refrain from recommending one method as the best off-the-shelf method for PPI inference . Therefore , we propose a set of high performers by taking into consideration both the overall AUPR and the overall AUPR rank criteria . The methods that rank in the top six according to both of these criteria are the same six methods: RIDGENET , ARACNE-M , ARACNE-A , LASSONET , CLR , and SPEARMANCOR ( Fig 3B ) . This set of high performers , referred to as TOP6 from here on , includes representative methods from all algorithm families in Table 1 except for inverse covariance-based partial correlation methods . This may be indicative of inverse covariance being a poor framework to model PPIs in cancer especially if the cohort size is not several times as large as the number of proteins . In contrast , linear measures such as correlation and ( ℓ1- or ℓ2- regularized ) partial correlation , and also nonlinear measures such as mutual information are all represented in the set of high performers . Although ARACNE-M and ARACNE-A differ only in the form of the threshold ( i . e . multiplicative or additive ) used to remove the weakest edge in a triplet , the networks inferred by these methods are a function of the user-specified threshold values ( S1F Text ) , and thus are not necessarily similar . We next asked how the network predictions from the TOP6 methods cluster the 11 tumor types . However , similar to the reduction from 13 methods to the TOP6 methods , it was necessary to apply a significance threshold for edges before performing the clustering . P-values were not a viable option as significance scores because several methods did not return p-values . Even if p-values were obtained from all methods , it would not be possible to combine the p-values in this study in a statistically sound way because all methods used the same data , hence violating the independence requirement . Therefore , we resorted to an alternative method and used edge ranks as a nonparametric proxy for the importance of edges . For a given tumor type , we computed ( 1 ) consensus edge ranks by taking the average of ranks from the TOP6 methods , and ( 2 ) consensus edge weights by taking the average of weights again from the TOP6 methods . The consensus ranks served as a nonparametric proxy for our importance levels , while the consensus weights were used in the clustering steps . Comparing consensus edge ranks obtained from the TOP6 methods with those obtained from all 13 methods ( ALL13 ) showed that the TOP6 methods yielded slightly higher AUPR than ALL13 against the Pathway Commons gold standard ( S3B Fig , S1C Text ) . This finding confirmed the use of TOP6 as a superior choice over ALL13 . The number of edges to use for the unsupervised clustering of tumor types was determined in the following way . For a certain threshold , we extracted all edges from a given tumor type that have a consensus edge rank smaller ( more significant ) than the threshold level . We then formed a matrix of edges by tumor types by combining extracted edges from all 11 tumor types . Next , we computed the PCs constructed as a linear combination of the tumor-type vectors , and inspected the behavior of the percentage of variance explained by the first three PCs as the rank threshold was varied from 25 to 2000 . We observed that the sum of the variance percentages from the first three PCs exhibited an inflection point at rank 425 , and thus determined 425 as the consensus rank threshold that determined significant and non-significant edges in each tumor type ( S4b Fig , S1D Text ) . Using the consensus rank threshold of 425 , we investigated the natural groupings in the set of 11 tumor types when each tumor type was represented with the consensus edge weights obtained from the TOP6 methods . The number of edges in each tumor type that pass the consensus rank threshold is shown in Table B in S1E Text . The union set of these significant edges from the tested tumor types has 1008 edges . We refer to this union set as the discovery set , and use it to perform PCA and hierarchical clustering of tumor types . The edges in the discovery set and the corresponding weights in the 11 tumor types are given in S1A Table . We note that , among the 187 antibodies in our dataset , all but STAT3_pY705 has at least one interaction in the discovery set ( N = 186 ) . We see in the PCA that PC1 and PC2 jointly separate the 11 tumor types into three groups , and also that PC3 further breaks down one group into two to result in a total of four groups: 1 ) COAD , READ; 2 ) LUSC , LUAD , HNSC; 3 ) GBM , KIRC; and 4 ) OV , BRCA , BLCA , and UCEC ( Fig 4A ) . These results are concordant with the clusters from hierarchical clustering ( Fig 4B dendrogram ) and also with the previously defined Pan-Cancer groups in the literature , as we elaborate below . As for the first group , COAD and READ have previously been shown to cluster together in the Pan-Cancer subtypes defined both by RNA expression[27] and by protein expression[4] . These tumors have also been shown to have common DNA-based drivers ( mutations and somatic copy number alterations ) , and hence have been treated as one disease [2 , 3 , 28] . Our finding that COAD and READ have the highest percentage of shared PPIs in this study ( Fig 4B heat map ) is also in line with these observations . Note that the order of tumor types in the heat map is taken from the dendrogram on the left , and that each cell represents the Jaccard index , i . e . the fraction of the intersection set over the union set of edges from two tumor types . The tumors in the second group ( LUSC , LUAD , and HNSC ) have also been previously assigned to a single Pan-Cancer subtype in terms of protein expression[4] . However , RNA expression and somatic copy-number alteration ( SCNA ) data types have divided these tumor types into two groups: ( 1 ) a squamous-like subtype including HNSC and LUSC , and ( 2 ) a separate LUAD-enriched group [3 , 27] . In contrast to this separation where cell histology plays a more important role , both protein expression levels and PPI weights primarily separate these three tumor types based on tissue of origin: ( 1 ) lung-derived tumors LUAD and LUSC , and ( 2 ) a separate HNSC group ( Fig 4B dendrogram and [4] ) . Tumors in the third and fourth groups ( GBM , KIRC , OV , UCEC , BRCA , and BLCA ) can be separated along a continuum in the PC3 dimension ( Fig 4A ) . However , we can consider GBM and KIRC as a separate group as these two tumor types separate from the other four in the unsupervised clustering dendrogram in Fig 4B . GBM and KIRC also cluster most closely among this set of 11 tumor types according to somatic copy-number alterations and protein expression levels [3 , 4] . However , KIRC also shows an outlier behavior for PPI networks in that it exhibits the lowest fraction of shared PPIs with other tumor types ( Fig 4B ) . GBM , on the other hand , has an outlier property by being on one extreme of the separation along the PC3 dimension . This may reflect the fact that GBM samples arise from glial cells in the brain , a histological origin that shows marked differences from epithelial cells . Indeed , GBM was previously shown to have a distinct cluster comprised of only GBM samples in terms of both RNA and protein expression levels [4 , 27] . The fourth group contains OV , UCEC , BRCA , and BLCA; the first three of which are categorized as women’s cancers . The proximity of women’s cancers in clustering results may point to female hormones , such as estrogen and progesterone , causing a similar profile of PPI weights . BLCA is most similar to women’s cancers ( Fig 4B ) , but it also is on one extreme of the separation along the PC3 dimension . This is concordant with the previously discovered Pan-Cancer subtypes because BLCA was shown to have the characteristic property of being one of the most diverse tumor types in the TCGA Pan-Cancer dataset . It had samples in 7 major RNA expression subtypes , and histologies in squamous , adenocarcinoma , and other variants in bladder carcinoma [27] . Next , we performed unsupervised clustering and community detection on the 1008 discovery set interactions to investigate patterns both among the interactions and also in the network formed by the interactions . Unsupervised hierarchical clustering of the 1008 discovery set PPIs shows that these interactions form three main groups ( Fig 5 ) : ( 1 ) a positive dominant group where interactions generally have positive consensus weight and occurrence in multiple tumor types ( mean pan-cancer weight = 0 . 25 , mean pan-cancer recurrence = 4 . 42 , N = 136 , recurrence for an individual edge is computed over the binary values in S1B Table ) , ( 2 ) a negative dominant group where interactions generally have negative consensus weight ( mean pan-cancer weight = –0 . 099 , mean pan-cancer recurrence = 1 . 2 , N = 133 ) , and ( 3 ) a heterogeneous group ( mean pan-cancer weight = 0 . 093 , mean pan-cancer recurrence = 1 . 56 , N = 739 ) which is a mixture of positive and negative , and also recurrent and non-recurrent interactions ( S1A Table ) . In this set of 1008 most significant edges , both the number and the overall weight of negative interactions are smaller with respect to positive interactions . This may indicate either the lower prevalence of mutual exclusivity relationships for in vivo protein-protein interactions , or merely the difficulty of discovering negative PPIs from RPPA data . We next visualized as networks the positive dominant , negative dominant and heterogeneous groups of interactions in order to gain insight on the related biological processes . However , the number of interactions in the heterogeneous group ( N = 739 ) is too large to allow a clear interpretation of the results . Thus , we investigated whether the complete set of 1008 edges could further be broken down into densely connected modules ( with high level of intra-module connectivity , and relatively lower levels of inter-module connectivity ) . To this end , we employed five different community detection algorithms: ( 1 ) fast greedy modularity optimization[29] , ( 2 ) a spin-glass model from statistical mechanics coupled with simulated annealing for optimization[30] , ( 3 ) multi-level modularity optimization[31] , ( 4 ) an information theoretic approach that minimizes the expected description length of a random walker trajectory[32] , and ( 5 ) random walk-based Walktrap community finding algorithm[33] . In the discovery set network , these algorithms detected 6 , 8 , 6 , 11 , and 22 modules respectively with similar and relatively high modularity scores ( range 0 . 41–0 . 44 , modularity due to [29] ) . Even though the number of detected modules was variable across the methods , we defined a consensus measure to identify the agreement/disagreement between the five predictions . For each antibody pair , the number of methods out of five , i . e . the frequency , of being predicted to be in the same module was utilized as a measure to quantify the level of method concordance . The consensus matrix of frequencies formed this way revealed four robust ( Modules 1–4 ) , and two less robust ( Modules 5–6 ) modules among the discovery set interactions ( Fig 6 , S2A Table for consensus matrix , S2B Table for module membership of antibodies , S1A Table for module membership of interactions ) . The four robust modules ( 1–4 ) discovered in Fig 6 are also the densely connected ones with per antibody averages of 7 . 72 , 8 . 24 , 9 . 24 , and 7 . 875 interactions respectively ( Table 3 ) . Mapping the positive dominant , negative dominant , and heterogeneous group memberships onto Module 1 reveals that 88% ( 170/193 ) of the edges in Module 1 are from the heterogeneous group ( Fig 7 ) . The major hubs in this module , N . Cadherin ( 22 edges ) , Mre11 ( 21 edges ) , and Bid ( 15 edges ) , have predominantly heterogeneous-group edges ( Table 3 ) . Interactions in this module may play roles in cell cycle , DNA damage repair , apoptosis , hormone and receptor tyrosine kinase ( RTK ) signaling pathways . Modules 2 and 4 are distinguishable from the other modules by having zero edges from the negative dominant group , and an abundance of edges from both the positive dominant and heterogeneous groups ( Table 3 , Fig 7 ) . Interestingly , 91% ( 30/33 ) of the antibodies in Module 2 are phosphospecific . On the contrary , only 6 . 2% ( 1/16 ) of the antibodies in Module 4 are phosphospecific . These results raise the question whether significant correlations can only be found between antibodies of the same type ( i . e . phosphospecific with each other and non-phosphospecific with each other ) . To address this question , we analyzed the interactions in Module 1 , which is the only module other than Module 2 that contains a subtantial number of phosphospecific antibodies ( N = 10 ) . In this module , phosphospecific antibodies have 64 interactions , but only 14 of these are between two phosphospecific antibodies ( 22% ) ( S3 Table ) . This result shows that it is possible to observe significant correlations between phosphospecific and non-phosphospecific antibodies , and suggests that there may be biological differences between Module 1 and Module 2 antibodies that lead to the differences in correlation patterns . We next compared the biological functions of the phosphospecific antibodies in Module 1 and 2 to investigate potential differences . Processes such as cell cycle , proliferation , RTK signaling , RAS/MAPK signaling were shared between the modules; but Module 2 antibodies were involved in a greater variety of oncogenic pathways such as AKT/mTOR , Wnt , and NFκB signaling . Even though these differences do not rule out technical bias as a reason for the absence of non-phosphospecific antibodies in Module 2 , they provide grounds for a biological reason such as the coordinated regulation of signal transduction pathways . An example of a technical bias that could lead an antibody to correlate highly only with another antibody of the same type would be that non-phosphospecific antibodies are expected to bind to both phosphorylated and non-phosphorylated states of a target protein , whereas phosphospecific antibodies only bind to target phosphosites ( barring off-target activity ) . Module 3 is the densest network among the six , with 9 . 24 interactions per antibody on average . This module almost exclusively contains non-phosphospecific antibodies ( 32 out of 34 ) , but has a good representation of edges from all three of positive dominant , negative dominant and heterogeneous groups ( Table 3 , Fig 7 ) . Akt , Tuberin , and Ku80 antibodies are major hubs ( 20 , 19 , and 18 edges respectively ) with predominantly positive-dominant and heterogeneous-group edges . There is an absence of phosphospecific antibodies , but due to the cross-reactivity of non-phosphospecific antibodies , this module may be related to several signal transduction pathways ( e . g . Akt , mTOR , B . Raf , β-catenin ) and DNA double-strand break repair ( e . g Ku80 , Rad50 ) . Interestingly , this module is the only one that has hubs with predominantly negative-dominant-group edges . Chk1 and PDK1 antibodies have , respectively , 12 negative-dominant-group edges out of 13 ( 92% ) , and 9 negative-dominant-group edges out of 11 ( 82% ) . One speculation for the underlying cause of the negative edges could be the mutual exclusivity relationship between processes that promote cellular proliferation and those that promote cell cycle arrest or apoptosis . Module 5 and 6 are relatively unstable communities with smaller numbers of intra-module interactions , some of which may play roles in cell cycle ( CDK1 , Cyclin_B1 , Cyclin_E1 ) , translation ( eIF4E , 4E . BP1 , 4E . BP1_pT70 ) , and apoptosis ( Bim , Bcl . 2 , Bax ) . The network visualization of the discovery set edges presents an opportunity to ‘discover’ biologically interesting cancer-related interactions . However , a thorough understanding of the interactions in densely connected modules may still be challenging . To facilitate this ‘interpretation’ and potentially identify the biological processes that each interaction may take part in , we mapped the discovery set interactions to Reactome[21] gene lists . This mapping could not be performed with Pathway Commons because the only unit of analysis in Pathway Commons is interactions , i . e . Pathway Commons does not have previously defined gene lists as in Reactome . The mapping of inferred interactions to Reactome gene lists involved multiple steps . We first obtained a complete set of Reactome gene lists ( N = 1705 ) , and filtered these to keep only the human-specific ones ( N = 1669 ) . We then reduced each inferred interaction to the genes corresponding to its interaction partners . If both antibodies corresponded to the same gene , the PPI was left out of the analysis as it would cause a self-interaction at the gene level . We then identified the Reactome gene lists that contained these two interacting genes , and increased their scores by the relevant consensus edge weight . Finally , gene list scores were normalized by the number of matching interactions and also the number of genes in the gene lists to obtain the tumor-type-specific ‘average interaction strengths’ ( S4 Table ) ( Methods ) . 339 out of 1669 gene lists had a match with at least one of the discovery set interactions in one of the tested tumor types . The universe of Reactome gene lists is not a flat structure , but a hierarchy . The top level of the hierarchy for human-specific gene lists consists of 24 biological processes according to Reactome Pathway Browser ( S7 Fig ) . We first performed a parent-child analysis for the 339 gene lists in S4 Table . 16 out of 339 were one of the top-level ( most general ) biological processes , whereas 323 gene lists were child gene lists at different depths of the hierarchy ( S5 Table ) . An analysis of the parents of the 339 gene lists revealed that the top-level ‘events’ signal transduction , cell cycle , and immune system had the greatest number of child gene lists ( 114 , 52 , and 46 respectively ) that matched to at least one interaction in a tested tumor type ( Fig 8A ) . This analysis also showed that more gene lists with positive ‘average interaction strengths’ were shared across tumor types than those specific for one or two tumor types ( Fig 8B ) . In the Fig 8B heat map , we also tracked the number of significant ( consensus rank < 425 ) interactions that match to each gene list broken down by module or group , and averaged over tumor types ( S11 Table ) . Module 2 and 3 have the highest numbers of interactions that match the shown 339 Reactome gene lists , and these interactions are predominantly from the heterogeneous and the positive dominant groups ( Fig 8B ) . The 11 tumor types show strong similarities in terms of signal transduction interaction strengths ( Fig 9 left panel ) . The signal transduction gene lists common to all tested tumor types include signaling by RTKs such as fibroblast growth factor ( FGF ) receptor family , epidermal growth factor ( ErbB ) receptor family , platelet-derived growth factor ( PDGF ) receptor family , vascular endothelial growth factor ( VEGF ) receptor family , insulin receptor family as well as signaling by G-protein-coupled receptors ( GPCR ) and Wnt , AKT/mTOR , and RAS/MAPK pathways . The gene lists not common across tumor types include the Hippo signaling ( specific to LUAD , LUSC , and BRCA ) and phospholipase C-related pathways ( specific to COAD , READ , UCEC , GBM , KIRC , BLCA ) . However , the gene lists not common across tumor types are associated with very few matching interactions as indicated by the ‘group’ membership track on the left of the heatmap . It is possible that one interaction matches with multiple similar Reactome gene lists and populates the heatmap . The disease top-level event recapitulates the above signal transduction-related findings as about twenty interactions from Module 2 positive dominant group , and Module 3 heterogeneous group match with these gene lists ( Fig 9 middle bottom panel , labeled as ‘signaling in disease’ as the majority of the gene lists are associated with signaling ) . All tested tumor types exhibit moderate interaction strengths for cell cycle related gene lists ( Fig 9 middle top panel ) . Most of these interactions are heterogeneous group edges from Module 5 and 6 as indicated by the group and module tracks . Carcinomas of the ovary , uterus , and breast , and adenocarcinoma of the colon have higher interaction strengths compared to other tumor types for several anaphase and metaphase-related gene lists such as those involving anaphase promoting complex ( APC/C ) , its inhibitor Emi1 , NIMA family kinases , and nuclear mitotic apparatus ( NuMA ) . Other biological processes common to all tested tumor types are innate and adaptive immune signaling , metabolism , and DNA repair , as expected with transformed cells and the immune cells infiltrating the tumor microenvironment ( Fig 9 middle and right panels ) . Interestingly , interactions that match to ‘metabolism’ gene lists are predominantly positive dominant group edges from Module 6 . The ‘immune system’ gene lists match mostly with Module 2 edges that are from the heterogeneous or positive dominant groups . ‘DNA repair’ gene lists match with both Module 2 and 3 edges , albeit predominantly from the heterogeneous group . Other Reactome top-level ‘events’ that match to discovery set interactions include apoptosis , extracellular matrix organization , and cell-cell communication ( S8 Fig ) .
Discovering PPIs in cancerous cells is an important but challenging goal . In this study , we computationally inferred proteomic networks in 11 human cancers using 13 different methods , and presented a performance comparison of the methods accepting a simplified reference network from the Pathway Commons information resource , which is based on experiments and publication digests , as the standard of truth . Pathway Commons is a collection of curated interactions from many different normal and disease conditions ( a formal and computable representation of available pathways and interactions ) . We acknowledge that a complete standard of truth for pathways is not currently available and that our methodology is therefore subject to certain caveats , as discussed below . Despite these caveats , computational inference of PPI networks from measurements of protein levels across a set of conditions are attractive in that they can reduce the hypothesis space of interactions and inform researchers on the potentially active pathways in the experimental model . Our comparison of the performance of network inference methods indicates that no single method has the best performance in all tumor types , but a group of six methods , including diverse techniques such as correlation , mutual information , and regression , consistently rank highly among the tested methods . These six methods consist of RIDGENET and LASSONET , ridge and lasso regression-based partial correlation methods employing an ℓ2 and ℓ1 penalty respectively; ARACNE-A , ARACNE-M , and CLR , mutual information methods that differ based on their penalty type or the choice of standardization for mutual information; and SPEARMAN CORRELATION , which assesses the strength of the linear relationship between the ranks of the values in two same-length vectors . From a tumor-type perspective , we find that not all tumor types are equally amenable to network discovery with RPPA data . Five tumor types ( KIRC , OV , COAD , READ , and BLCA ) consistently had lower AUPR predictions by all network inference methods . In a recent multi-method comparison study for gene network inference , regression-based methods were represented mostly by modifications of the ℓ1-penalized lasso algorithm; however methods involving an ℓ2 penalty , such as ridge regression or elastic net , were not included [35] . Moreover , the ℓ1-penalized methods did not achieve the best overall performance in gene network inference . We find in this study that ℓ2-penalized methods such as ridge regression can outperform the lasso in the inference of proteomic networks . Even though the concurrent execution of feature selection and model fitting may appear to be an attractive property for lasso regression , we recommend performing an unbiased test for both ℓ1 and ℓ2-penalized models in the exploratory phase of a study . It is not guaranteed that the variables selected by the ℓ1 penalty will be the most biologically important ones in the system . A network of 1008 most significant interactions inferred by high-performing methods reveals that these interactions can be grouped into three . The group termed ‘positive dominant’ contains mostly positive interactions with generally high weights . Nine interactions that exist in at least 10 of the 11 tumor types with very strong consensus weights are also in this group ( S1A Table ) , and potentially occur due to cross-reactivity of the antibodies . The other two groups are termed the ‘negative dominant’ ( generally negative weights ) , and the ‘heterogeneous’ groups . The network of 1008 edges contains four robust densely connected modules , two of which do not include any edges from the negative dominant group ( Modules 2 and 4 ) . Strikingly , the ratio of phosphospecific antibodies in one of these two modules is 91% ( Module 2 ) . While it is possible that a technical bias may be leading to high correlations between antibodies of the same type , a biological reason such as the coordinated regulation of signal transduction events may also be strongly contributing to the Module 2 interactions between phosphospecific antibodies because phosphospecific and non-phosphosphospecific antibodies may exhibit a high number of interactions as in Module 1 . The positive dominant and heteogeneous groups are scattered , albeit unevenly , to the four robust modules . Mapping these 1008 most significant interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways , innate and adaptive immune signaling , cell cycle , metabolism , and DNA repair; and also suggests several gene lists that may be specific to a subset of tumor types . We observe a paucity of negative dominant group edges that match with Reactome gene lists ( Figs 7 and 8 ) . A possible reason may be that negative correlations in our study imply mutual exclusivity , not inhibitory relationships . Reactome gene lists are not designed to group together mutually exclusive proteins unless there is a flow of influence ( i . e . activation/inhibition ) from one to the other . It is also not implausible that an inhibitory event , such as phosphorylation of protein A by protein B , leads to a positive correlation between A and B as their concentrations may increase or decrease together . The caveats in our workflow , as shown in Fig 1 , concern both the computational inference and the pathway knowledgebase arms of the analysis . In the computational inference arm ( Steps 1 and 2 ) , the caveats include questions regarding ( 1 ) the quality of RPPA experiments and whether the signal-to-noise ratio in RPPA experiments is high enough to allow the inference of direct interactions , and ( 2 ) the reliability of results from computational network inference methods ( S1A Text ) . In the pathway knowledgebase arm ( Steps 3–5 ) , the fidelity of pathway models in knowledgebases is limited due to factors including ( 1 ) the quality of wet-lab experiments for PPIs such as yeast-2-hybrid [34] , ( 2 ) missing or inaccurate information in the database due to poor curation , ( 3 ) the lack of context information for PPIs , such as cell or tissue type or physiological conditions , and ( 4 ) the ascertainment bias in the knowledgebase ( primarily incomplete coverage ) as discussed in the Introduction . More generally , pathways in knowledgebases such as Pathway Commons are only model descriptions of reality typically summarizing a set of experiments and do not represent an absolutely ‘true’ ( and certainly not complete ) set of interactions . In future work , it will be important to assess the predictive power of the inferred PPI networks . For example , it would be useful to evaluate these networks in terms of how much they assist in the understanding of oncogenesis , response to therapy , and design of combination therapies that deal with feedback loops . It is also desirable to incorporate time-dependent readouts from perturbation experiments to be able to build causal models and enhance the predictive power of proteomic networks . An obstacle against building causal models , such as Bayesian networks , with the PANCAN11 RPPA data was the relatively large size of the network ( 187 nodes , i . e . antibodies ) compared with the number of available samples in individual tumor types ( between 127 and 747 ) . Probabilistic models such as Bayesian networks require the number of samples to be at least an order of magnitude larger than the number of nodes for a sound estimation of model parameters [36] . The significance of this work extends beyond cancer . Discovering direct , potentially causal interactions between proteins is an opportunity in all areas of molecular biology where proteins are measured in different conditions and where correlations are informative . The methodology presented here can easily be adopted to study interactions in different molecular biology contexts .
The pan-cancer reverse phase protein array ( RPPA ) dataset was downloaded from The Cancer Proteome Atlas[5] on April 12 , 2013 . This dataset is denoted as PanCan11 and contains protein expression data for 3467 tumor samples and 187 antibodies , 51 of which are phosphospecific and 136 of which are non-phosphospecific . The 11 tumor types represented in this dataset are bladder urothelial carcinoma ( BLCA ) , breast invasive carcinoma ( BRCA ) , colon adenocarcinoma ( COAD ) , glioblastoma multiforme ( GBM ) , head and neck squamous cell carcinoma ( HNSC ) , kidney renal clear cell carcinoma ( KIRC ) , lung adenocarcinoma ( LUAD ) , lung squamous cell carcinoma ( LUSC ) , ovarian serous cystadenocarcinoma ( OVCA ) , rectum adenocarcinoma ( READ ) , and uterine corpus endometrioid carcinoma ( UCEC ) . PanCan11 patient samples were profiled with RPPA in different batches , and normalized with replicate-based normalization ( RBN ) . RBN uses replicate samples that are common between batches to adjust antibody means and standard deviations so that the means and standard deviations of the replicates become the same across batches . Pathway Commons stores pathway information in BioPAX[22] models that contain formal computable representations of diverse events such as biochemical reactions , complex assembly , transport , catalysis , and physical interactions . We queried Pathway Commons with the "prior extraction and reduction algorithm" ( PERA ) [26] for the proteins and phosphoproteins in the PANCAN11 RPPA dataset . PERA is a software tool and a protocol that , given a set of observable ( phospho- and/or whole ) proteins , extracts the direct and indirect relationships between these observables from BioPAX-formatted pathway models[26] . PERA accepts a list of ( phospho ) proteins identified by their HGNC symbols , phosphorylation sites and their molecular status ( one of ‘active’ , ‘inactive’ , or ‘concentration’ ) as input and , based on the pathway information provided by the Pathway Commons information resource[20] , it produces a binary and directed network . The major advantage of PERA over other similar tools , such as STRING[37] or GeneMania[38] , is that it considers not only the name/symbol of a protein but also its phosphorylation sites , enabling finer mapping of entities and pathways . We downloaded the web ontology ( OWL ) file for Pathway Commons version 2 on 10/1/2013 , and implemented PERA v2 . 9 . 1 with the following command: java -Xmx3g -jar bpp291 . jar \ -l 1 –t 3 \ -o output . tsv input . tsv \ pathwaycommons2 . owl The PERA input file ( i . e . input . tsv ) is provided as S6 Table . The–l value of 1 determines that PERA will output an interaction between two entities only if the distance between them is 1 , i . e . there is no intermediary entities . The–t value of 3 determines that the phosphorylation site mismatch tolerance is 3 . For instance , if a PERA input includes phosphorylation site S473 for Akt , PERA will consider all interactions in the residue range ( 470 , 476 ) for this phosphoprotein . The post-processing of the PERA output file included two steps: 1 ) As the network inference methods employed in this study produce only undirected network predictions , we first converted the directed network in the PERA output to an undirected network . 2 ) We then removed any existing duplicate and/or self edges before using this network as a gold standard in performance evaluation . The analysis was performed using the R language[39] . The R functions used to implement the network inference methods are as follows: The cor function in the stats[40] package for PEARSONCOR and SPEARMANCOR; the ggm . estimate . pcor and cor2pcor functions in the GeneNet[41] package for GENENET and SIMPLEPARCOR; the ridge . net , pls . net , and adalasso . net functions in the parcor[42] package for RIDGENET , PLSNET , and LASSONET; the glasso function in the glasso[43] package for GLASSO; the aracne . a , aracne . m , clr , and mrnet functions in the parmigene package[44] for ARACNE-A , ARACNE-M , CLR and MRNET . The ELASTICNET method was implemented as a modification of the adalasso . net function in the parcor package , and is available upon request . Mathematical descriptions of the algorithms used are provided in S1F Text . Community detection algorithms were implemented with the cluster_fast_greedy , cluster_spinglass , cluster_infomap , cluster_louvain , and cluster_walktrap functions in the igraph[45] package version 1 . 0 . 1 . The steps involved in computational network prediction and performance evaluation are discussed in detail in S1E Text . Here we discuss the construction of precision-recall curves . Precision was defined as the fraction of the number of correctly predicted edges ( predicted edges that can be found in the gold standard ) to the number of all predicted edges . Recall was defined as the fraction of correctly predicted edges to the number of all edges in the gold standard . The PR curve for a given parameter configuration was constructed by taking the edge list ranked from the most significant to the least , and then iterating over the edges so that we obtained , at each iteration , a cumulative edge set that included all the edges seen up to and including that iteration . For each iteration , we computed the precision-recall value pair for the edge set and placed this value pair on the PR plot . We plotted a separate PR curve for each parameter configuration for the nine methods that required specification of parameter values ( all methods except PEARSONCOR , SPEARMANCOR , SIMPLEPARCOR , and GENENET ) . The PR curve that had the greatest area under the curve ( AUPR ) between the [0 , 0 . 1] recall range ( i . e . limited-recall ) was identified as the optimal PR curve for that particular method . The optimal parameter values for the limited-recall case are shown in S5 Fig . For methods that did not have user-specified parameters , there was only one PR curve and that was adopted as the optimal PR curve . In the subsequent step , the AUPRs from the optimal PR curves were compared in order to rank the methods and evaluate their performance relative to the gold standard network . We find that the inferred interactions in various tumor types are a relatively small subset of the gold standard network derived from Pathway Commons ( i . e . low recall ) . Low levels of recall are readily acceptable for satisfactory performance because it is expected that interactions inferred from a single disease ( cancer ) and a single cancer type will not retrieve all of the interactions in the gold standard . However , it is desirable that , when an algorithm calls an interaction , there is a high probability that this inference is correct , i . e . high levels of precision are essential for nominating a network inference method as competitive . Three data files were downloaded from the Reactome website http://www . reactome . org/pages/download-data/: 1 ) Reactome Pathways Gene Set ( S7A Table ) on 11/11/2015 , 2 ) Complete list of gene lists ( ReactomePathwayLabels . txt ) on 11/16/2015 , and 3 ) gene list hierarchy relationship ( ReactomePathwaysRelation . txt ) on 11/16/2015 . The first file contained a total of 1705 gene lists . The second file contained gene list labels , unique Reactome identifiers , and species information . The third file contained the unique identifiers of parent gene lists adjacent to those of the child gene lists . The ID in the left column was one level above , in other words a superset of the ID in the right column . Reactome identifiers also include characters to denote the species information . The information in the second and third files was used to filter out non-human gene lists ( S7B and S7C Table respectively ) . After the removal of duplicate entries , the number of human-specific gene lists in file 2 was 1869 . The overlap between these 1869 human-specific gene lists and the 1705 gene lists in file 1 was 1669 , which was used as a gold standard in the subsequent mapping analysis . The PERA input in S6 Table lists the gene ( s ) that correspond to each antibody used in this study . The complete list of these genes including all paralogs is provided in S8 Table , and contains 167 uniqe genes . In mapping the discovery set interactions to Reactome gene lists , each interaction is represented by the gene ( s ) corresponding to the interaction partners . The pseudocode for this mapping is as follows: # Constants N = 11 # Number of tumor types T = 425 # The threshold for consensus edge rank R = 1669 # The number of gene lists in the Reactome # human gold standard Initialize P # 1669 by 11 matrix that stores the # ‘average interaction strength’ of each # Reactome gene list for a given tumor type for i = 1:N # Tumor types q <- Number of edges in tumor type i that pass consensus rank threshold T Initialize M # 1669 by q matrix that maps the most # significant interactions in tumor type # i to Reactome gene lists for j = 1:q # Interactions 1 . Identify the corresponding gene ( s ) for each interaction partner . Let these be set1 and set2 . 2 . If set1 and set2 has a non-empty intersection , this is a self interaction . Skip to next interaction . 3 . Otherwise for k = 1:R # Reactome gene lists 4 . If set1 and set2 both have at least one member in gene list k , this is a match . Assign absolute weight to entry M[k , j] . This weight is the consensus edge weight penalized ( divided ) by the number of genes in gene list k . end end 5 . Average the q interactions ( only the real-valued ones ) for each one of the 1669 gene lists . This is the average interaction strength for a given Reactome gene list in a given tumor type . 6 . Insert the vector created in Step 5 as a column in matrix P end Networks from the TCGA RPPA or tab-delimited user data can be inferred and visualized with the ProtNet web application at http://www . sanderlab . org/protnet ( A tutorial is provided in S1 Protocol ) . R scripts used in the analysis are provided in S2 Protocol . | Pan-cancer proteomic datasets from The Cancer Genome Atlas provide a unique opportunity to study the functions of proteins in human cancers . Such datasets , where proteins are measured in different conditions and where correlations are informative , can enable the discovery of potentially causal protein-protein interactions , which may in turn shed light on the function of proteins . However , it has been shown that the dominant correlations in a system can be the result of parallel transitive ( i . e . indirect ) interactions . A wide suite of computational methods has been proposed in the literature for the discrimination between direct and transitive interactions . These methods have been extensively tested for their performance in gene regulatory network inference due to the prevalence of mRNA data . However , the understanding of the performance and limitations of these methods in retrieving curated pathway interactions is lacking . Here , we utilize a high-throughput proteomic dataset from The Cancer Genome Atlas to systematically test different families of network inference methods . We observe that most methods are able to achieve a similar level of performance provided their parameter space is sufficiently explored; but a group of six methods consistently rank highly among the tested methods . The protein-protein interactions inferred by the high-performing methods reveal the pathways that are shared by or specific to different cancer types . | [
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] | 2016 | A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers |
For many decades , invertebrate immunity was believed to be non-adaptive , poorly specific , relying exclusively on sometimes multiple but germ-line encoded innate receptors and effectors . But recent studies performed in different invertebrate species have shaken this paradigm by providing evidence for various types of somatic adaptations at the level of putative immune receptors leading to an enlarged repertoire of recognition molecules . Fibrinogen Related Proteins ( FREPs ) from the mollusc Biomphalaria glabrata are an example of these putative immune receptors . They are known to be involved in reactions against trematode parasites . Following not yet well understood somatic mechanisms , the FREP repertoire varies considerably from one snail to another , showing a trend towards an individualization of the putative immune repertoire almost comparable to that described from vertebrate adaptive immune system . Nevertheless , their antigenic targets remain unknown . In this study , we show that a specific set of these highly variable FREPs from B . glabrata forms complexes with similarly highly polymorphic and individually variable mucin molecules from its specific trematode parasite S . mansoni ( Schistosoma mansoni Polymorphic Mucins: SmPoMucs ) . This is the first evidence of the interaction between diversified immune receptors and antigenic variant in an invertebrate host/pathogen model . The same order of magnitude in the diversity of the parasite epitopes and the one of the FREP suggests co-evolutionary dynamics between host and parasite regarding this set of determinants that could explain population features like the compatibility polymorphism observed in B . glabrata/S . mansoni interaction . In addition , we identified a third partner associated with the FREPs/SmPoMucs in the immune complex: a Thioester containing Protein ( TEP ) belonging to a molecular category that plays a role in phagocytosis or encapsulation following recognition . The presence of this last partner in this immune complex argues in favor of the involvement of the formed complex in parasite recognition and elimination from the host .
Understanding host-parasite interactions represents a major challenge in evolutionary biology . Parasites cause substantial deleterious effects on their hosts , and therefore represent a major driving force for their evolution [1] . In parallel , parasites have to cope with the evolving host-defence mechanisms , i . e . they must co-evolve with their host to avoid elimination . This antagonistic co-evolution in host-parasite interactions can be illustrated by an arms race in which both host and parasite develop mechanisms to circumvent weapons developed by their opponent . In this context , evolutionary hypotheses like the Red Queen Hypothesis [2] predict that diversity and polymorphism of molecules occurs especially on molecules that play key roles in the host-parasite interplay [3] . In vertebrate host/parasite interactions , adaptive immunity is the ultimate outcome of this molecular arms race . Vertebrates possess an extraordinary system able to generate somatically an exceptional diversity of antigen-specific receptors [4] , [5] , [6] . It consists in a ‘do-it-yourself kit’ , i . e a set of gene segments to be assembled during the ontogeny of lymphocyte that randomly generates receptors . This adaptive immune system can recognize and initiate a protective response against most of the pathogen/antigen encountered . Indeed , gnathostomes as well as agnathes , seem to be able to generate a highly diverse repertoire of lymphocytes , each bearing a different cell surface antigen receptor [7] , [8] . The interaction of the lymphocyte receptor with the epitope present on the antigen leads to a signal transduction and eventually to an effector phase leading to the neutralization or the destruction of the antigen . These diversified immune receptors can be different between vertebrate lineages . They are members of the immunoglobulin superfamily for B or T Cell Receptors in gnathostomes and members of the leucine rich repeat family or Variable Lymphocyte Receptors in agnathans , but in all cases they are generated through recombinatorial processes occurring somatically during lymphocyte differentiation and proliferation . The convergent evolution in all vertebrates of these different genes leading to the acquisition of a vast repertoire of somatically generated receptors proves the high selective value of this mechanism in the living kingdom and suggests that it might be found elsewhere . For the pathogen counterparts , a variety of mechanisms permitting evasion of the host's immune response exist in pathogenic bacteria and viruses [9] . But as expected in an arms race perspective , diversity , polymorphism and variation of antigens from pathogen is a widespread strategy also described ( i ) from numerous pathogens belonging to distant evolutionary lineages [10] and ( ii ) for most of the eukaryotic parasites [11] . In the case of invertebrate hosts and their parasites , the picture was believed to be completely different since the prevailing view was that invertebrates have no acquired adaptive immunity , and their immune system being innate would exhibit less diversity of the receptor repertoire and hence less specificity . The detection of parasites by these organisms was believed to rely exclusively on invariable germline-encoded immune receptors that recognize microbial antigens to limit pathogen invasion [12] . Recent studies have somehow shaken this paradigm . They report the existence of polymorphic and diversified putative immune receptor sequences that are somatically generated , that varies considerably from one individual to the other and that leads to an enlarged repertoire of putative recognition molecules . This was reported in echinoderms ( sea urchin; [13] ) , insects ( Drosophila melanogaster and Anopheles gambiae; [14] , [15] ) , crustaceans [16] and molluscs ( Biomphalaria glabrata; [17] ) . These studies have suggested the existence of a form of specific adaptative immunity in several invertebrates , without providing mechanism , which raised some doubts in the mind of traditional immunologists ( for the polemic see [18] , [19] ) . In addition , the direct proof of a role in immunity of these molecules is not provided . Do these diversified molecules actually interact with antigens ? Are they able to interact with antigenic variants from parasites that are expected , in an arms race perspective , to be diversified and/or polymorphic ? We propose to address this last crucial question in the present study . As a model we choose the interaction between the trematode Schistosoma mansoni and its mollusc host Biomphalaria glabrata , in which several pieces of exciting data were obtained . Firstly , incubations of B . glabrata plasma extracts and soluble antigens from trematodes led to the formation of molecular complexes [20] , [21] . B . glabrata molecules involved in these complexes were characterized , they were called FREPs for Fibrinogen Related Proteins [21] . The FREP genes belong to a multigene family of at least fourteen members [22] , [23] . FREPs consist of one or two amino-terminal IgSF domains and a carboxyl-terminal fibrinogen domain . These molecules undergo apparently somatic variations leading to a remarkable diversification [17] . The superimposition of allelic polymorphism and somatic processes can lead to the expression of 45 isoforms of FREP3 per individual [17] . These genes encode lectin-like hemolymph polypeptides that are able to bind to E . paraensei sporocysts and a variety of microbes [24] . However the ligands themselves are still mysterious . FREP expression increases in response to challenge with the trematode parasites , Echinostoma paraensei and Schistosoma mansoni [21] , [25] . In the parasite S . mansoni , we identified recently polymorphic mucins [26] . They were called SmPoMuc ( for S . mansoni Polymorphic Mucins ) . They display a high level of intra- and inter-strain polymorphism due to a complex hierarchical system that efficiently generates polymorphic variants based on a relatively low number of genes [27] . We hypothesise that these mucins could contain the epitopes that interact with the immune receptors from B . glabrata and make the hypothesis that FREPs are among those receptors . To test this hypothesis we developed two assays . Firstly , we developed a global proteomic approach to the interactome between parasite extracts and plasma extracts from the mollusc host . Co-incubation and precipitation of this total extract led to the identification of SmPoMucs and FREPs in the same fraction . Secondly , the direct interaction of these two partners was confirmed by Co-Immunoprecipitation experiments using antibodies raised specifically against SmPoMuc . Another interesting partner was coimmunoprecipitated in the same molecular complex . It corresponds to a putative opsonin , the ThioEster-containing Protein from B . glabrata .
Nucleotide sequence data reported in this paper are available in the GenBank database under the accession numbers: HM003905 to HM003908 , HM038098 to HM038105 and HM237113 to HM237135 . Fifty µg of sporocyst extracts from C or IC strain and 750µg of plasma extracts were used for each interactome experiment . After thawing , extracts were submitted to a centrifugation step of 7 500g for 30 min at 4°C . The supernatants were recovered , mixed and incubated at 26°C for 2 . 5 hours . After incubation precipitated materials were recovered by two successive centrifugation steps at 7 500g and 15 000g for 30 min and at 4°C . The same procedure was realised with sporocyst and plasma extracts alone to identify proteins precipitating spontaneously . Precipitated proteins were resuspended in 7µl of UTCD ( 8M urea , 40 mM TRIS , 4% CHAPS , 60 mM DTT ) , 3µl of laemmli buffer 3× was added and precipitates were analysed by SDS-PAGE . Gels were silver stained using a staining procedure compatible with mass spectrometry analysis [33] . The procedure used was previously described [26] , [32] , [35] . Bands containing the proteins of interest were excised from gels and digested with trypsin . Eluated peptides were lyophilised and analysed by mass spectrometry ( EDyP Service laboratory , Grenoble , France ) . Peptides were analysed using a nanoscale capillary liquid chromatography Ultimate 3000 coupled to a LTQ-Orbitrap tandem mass spectrometer ( nanoLC–MS/MS ) ( Mann M et al 2001; Ashton PD et al 2001 ) . The resulting MS/MS spectra were processed and converted into peak lists in dta format using the SEQUEST algorithm for interrogation of protein or nucleotide sequence databases . Peptide masses were compared to virtual tryptic digestion of proteins from SwissProt-Trembl ( other metazoan database ) and to translated Expressed Sequences Tags database ( dbEST ) of S . mansoni ( 205 892 Ests ) and B . glabrata ( 54 305 Ests ) using Mascot ( http://www . matrixscience . com/ ) . No missed cleavages were allowed and some variable modifications were taken into account in the search such as Acetylation ( Protein N-term ) , Oxidation and Dioxidation ( M ) , and Trioxidation ( C ) . Searches were performed using an error on experimental peptide mass values of ±15 . 0 ppm and an error for MS/MS fragment ion mass values of 1 . 0 Da . Mascot results were validated using IRMa software ( interpretation of Mascot results ) developed by “EDyP Service” laboratory . IRMa avoids redundant proteins in the analysis and reduced false positive to less than 1% . A protein was considered to be correctly identified if at least two peptides were confidently matched with database sequences with a p-value<0 . 001 for each peptide . In addition , an overall Mascot score was given by the software to the identification , a score greater than 100 was considered significant ( p<0 . 05 , [36] ) . The complete open reading frame ( ORF ) of BgTEP and FREP2 from our laboratory B . glabrata BRA strain were amplified using reverse transcription-polymerase chain reaction ( RT-PCR ) . In order to investigate the variability of FREP2 sequences , total RNA was extracted individually from 5 snails ( whole bodies ) ( 9–13 mm ) . Concerning BgTEP , total RNA was extracted from a pool of five snails . Total RNA extractions from snails were performed using Trizol Reagent according to the manufacturer's instructions ( Invitrogen ) . Total RNA ( 2 µg ) were reverse transcribed with oligo d ( T ) 17 primers and Superscript II reverse transcriptase according to the manufacturer's instructions ( Invitrogen ) . Two µl of the RT reaction was then used for PCR experiments with the following primers corresponding to: - TEP cDNA ( GenBank accession number : FJ480411 ) . 5′ primer: ATG-AGA-ATG-AAG-CTG-AAT-TTG-ATT-TT; 3′ primer: CTA-TGG-GCA-ACA-GTT-GAG-GCA-AAC-ATC . - FREP2 cDNA ( GenBank accession number : AY012700 ) . 5′ primer: ATG-GCG-TCG-CTA-CCA-CTT-CGA-CTT-GTT-C ; 3′ primer: TTA-GTT-TAG-CTC-TAT-TTC-TCT-AAT-TTT-C . The PCR was performed using Advantage 2 PCR Enzyme System ( Clontech ) . The PCR products were amplified , purified and cloned into pCR4-TOPO vector according to the manufacturer's instructions ( Invitrogen ) . Clones were then sequenced using GATC facilities ( GATC Biotech , Germany ) . Thirty four sequences of FREP2 were analysed from the five separated individuals . Five clones were sequenced for BgTEP . All sequence identified from databases or obtained in the present study were imported in the sequencer software ( version 4 . 5 ) . They were aligned and contiged . Primary structure analyses were performed using SignalP 3 . 0 to predict the presence of signal peptide , NetNglyc 1 . 0 and NetOGlyc 3 . 1 ( http://www . cbs . dtu . dk/services/ ) to predict potential glycosylation sites . Putative proteolytic cleavage sites were predicted using PeptideCutter ( http://www . expasy . ch/tools/peptidecutter/ ) program . Protein domain searches were performed using SMART ( http://smart . embl-heidelberg . de/ ) . An unrooted phylogenetic tree was constructed ( based on the multiple alignment performed with ClustalW ) using the neighbour-joining method with MEGA 4 . 0 . 2 . [37] . The reliability of the tree was tested using a bootstrap test ( 1000 replicates ) . Recombinatorial events in BgBRA-FREP2 were investigated using Dna SP 5 . 10 software [38] .
We incubated ( i ) extracts prepared from parasite sporocysts ( intramolluscal stage of S . mansoni ) and ( ii ) extracts from B . glabrata plasma known to contain Pattern Recognition Receptors like FREPs [21] and other lectins [39] , [40] . We use sporocyts from two laboratory strains of S . mansoni ( C and IC for Compatible and InCompatible , respectively ) for these experiments . Both strains were chosen for this differential compatibility in the single host mollusc strain from Brazil . [26] . The C strain infects 100% of the molluscs when 10 miracidia per individual are used for infection . An average number of 3 . 6 sporocysts develop in the mollusc [28] . The IC strain infects only 4% of the molluscs using the same conditions . After incubation of host and parasite extracts , precipitated products were pelleted by centrifugation and analysed by SDS-PAGE . Different centrifugation speeds were used as well as different controls consisting in incubation and centrifugation of plasma or sporocyst extracts alone . The electrophoretic profiles of precipitate materials are shown in figure 1 . Gel analysis revealed that 29 bands were differentially represented between interaction experiments and controls ( Figure 1 ) . These bands were cut . The corresponding proteins were submitted to tryptic digest and analysed by tandem mass spectrometry for identification . Thirty proteins were identified - among them 20 are S . mansoni proteins ( Table 1 ) and 10 are from B . glabrata ( Table 2 ) . During the experimental procedure , extracts were incubated 2 . 5 hours at 26°C . We cannot exclude the fact that proteolysis occurs . This phenomenon could explain why sometimes these multiple bands were obtained for the same proteins . S . mansoni proteins can be classified mainly into 5 groups taking into account their putative function and/or structural features: glycoproteins; calcium binding proteins; chaperone/stress proteins; antioxidant enzymes and proteins involved in immune regulation ( Table 1 ) . As far as B . glabrata proteins are concerned , they correspond mainly to lectins or other proteins listed in Table 2 . The functions of the majority of the proteins identified are speculative because they are inferred from homologies with known molecules from other organisms after BLAST analysis and protein domain searches . Nevertheless , some of them are of particular interest in the present context , especially lectins from the host and glycoproteins from the parasite . Indeed host recognition molecules ( like lectins ) and carbohydrate containing molecular determinants from S . mansoni are excellent candidates for participating in an immune complexe . Several molecules belonging to these functional classes were identified . In B . glabrata , the FREPs [17] , [21] , and another putative lectin , a galactose binding-like , were clearly identified ( Table 2 ) . Different FREP family members were revealed using mass spectrometry . Among the peptides identified , some of them correspond specifically to FREP2 , FREP12 and FREP13 ( see Figure 2 for details ) . In S . mansoni , SmPoMucs were precipitated ( Table 1 ) . As SmPoMuc group 1-specific peptides were identified , the presence of the SmPoMuc from the first group is affirmed ( see Figure 3 for peptides identified ) . Nevertheless , we cannot exclude the presence of SmPoMuc from the two other groups in the precipitated material ( 3 groups of SmPoMucs were previously characterised see [34] . Other glycoproteins like the secretory glycoprotein K5 and the 23 kDa integral membrane protein ( Sm23 ) from S . mansoni were also identified [41] , [42] . Other proteins were identified that could be involved in protection of the parasite or in host immune response . Their putative role will be envisaged in the discussion . We chose to focus then on the putative interaction between FREPs and SmPoMucs . FREPs are highly variable molecules described in B . glabrata , and in at least four other genera of gastropods [21] , [43] and related members , although with a different domain composition , exist in arthropods [44] and in cephalochordates [45] . All the observations on FREPs suggest that these molecules may act as highly diversified recognition and/or effector proteins somehow analogous to antibodies from vertebrate species [46] , [47] . From an evolutionary point of view and in an arms race perspective , these diversified immune receptors are expected to interact with diversified antigens from the pathogen counterpart , but this remains to be demonstrated . SmPoMucs identified in the present study represent possible ligands for these diversified host molecules . Indeed , these proteins correspond to polymorphic mucins that are secreted and preferentially expressed in miracidium or sporocyst stages [34] . SmPoMucs are highly glycosylated and have an extraordinary level of polymorphism facing the diversified FREPs from B . glabrata that could represent a particularly well adapted set of immuno receptors or effectors . To test this hypothesis and to determine which snail proteins may interact or form a complex with SmPoMucs , we carried out CoImmunoPrecipitation ( CoIP ) experiments using antibodies raised against recombinant SmPoMuc ( rSmPoMuc ) . Firstly , rSmPoMuc corresponding to the C-terminal part of SmPoMuc1 ( 234 last residues ) was produced and purified to raise an anti-SmPoMuc1 polyclonal antibody . After purification of IgG by protein A affinity chromatography , the sensivity and specificity of anti-SmPoMuc1 antibody were evaluated by ELISA assay ( data not shown ) and western blot ( Figure 4 ) . In C and IC sporocyst extracts , only the bands corresponding to SmPoMuc were revealed ( Figure 4 , lane 4 & 5 ) . These profiles confirm the SmPoMuc profile obtained in a previous study and show also that anti-SmPoMuc1 polyclonal antibodies recognize all members of the SmPoMuc family [34] . In addition , the absence of cross-reactivity with B . glabrata protein extracts was verified ( Figure 4 , lane 2 & 3 ) . No signal was obtained in ELISA and Western blot assays using Protein A-purified IgG prepared from pre-immune serum ( data not shown ) . For CoIP experiments , controls and coimmunoprecipitated extracts from C and IC combinations were separated by SDS-PAGE ( Figure 5 ) . The ability of antibodies to immunoprecipitate SmPoMucs from C and IC sporocyst extracts was tested . The bands corresponding to SmPoMucs are revealed by silver stain in immunoprecipitated sporocyst extracts ( Figure 5A , lane 1 & 5 ) . The identification of SmPoMucs in coimmunoprecipitated samples was assayed by western blot ( Figure 5B , lane 1 & 3 ) and confirmed by mass spectrometry . Bands corresponding to SmPoMucs in coimmunoprecipitated extracts ( Figure 5A , lane 2 & 4 , position indicated by arrows ) were cut , submitted to tryptic digest and analysed by liquid chromatography-tandem mass spectrometry ( LC-MS/MS ) . These bands correspond to the different groups of SmPoMucs as previously described ( data not shown , [27] ) . By comparison to controls , four specific bands were obtained for the coimmunoprecipitation assay ( Figure 5 A; lane 2 bands n°1 and 2; lane 4 bands n°3 and 4 ) . These bands were excised from the gel and submitted to mass spectrometry analysis . The same procedure was applied to the bands present at the same position in control snail plasma to ascertain protein identification after LC-MS/MS . Mass spectrometry analysis of the four bands of interest led to the identification of three proteins ( Table 3 ) . None of these proteins were identified for the corresponding bands in controls . As expected considering their position in the gel ( ∼70–75 kDa ) , bands 1 and 3 ( from IC and C combinations , respectively ) led to the same identifications: Fibrinogen-related proteins ( FREPs ) and a Thioester-containing protein ( TEP ) , both from B . glabrata . In the case of FREPs , 4 peptides were identified by LC-MS/MS analysis . These are contained in different FREP isoforms available in GenBank database ( Figure 2 ) . The identification of a FREP2-specific peptide ( Figure 2 ) confirms that FREP2 is present in these two bands . However the presence of other FREP family members cannot be excluded . Taking into account the variability previously observed in this gene family , we investigated FREP2 in our own mollusc strain from Brazil ( BRA ) . The cDNA corresponding to FREP2 was amplified by RT-PCR using RNA extracted from seven B . glabrata BRA snails and specific oligonucleotides designed from FREP2 sequence available on databases ( BgMFREP2 , FREP2 from M line B . glabrata , GenBank Accession number: AY012700 ) . The amplicons obtained were cloned . One clone was sequenced . This sequence was called BgBRA-FREP2 and deposited in GenBank ( Accession number: HM003905 ) . The overall sequence identity and similarity between BgMFREP2 and BgBRA-FREP2 ( isoform 1 , HM003905 ) are 99 . 2% and 99 . 7% , respectively . BgBRA-FREP2 shares the structure of BgMFREP2 which has already been described [48] . It contains one IgSF domain upstream the C-terminal fibrinogen domain ( FBG ) ( Figure 2 ) . In addition , we investigate the variability of FREP2 sequences . Using RT-PCR amplification , we amplified FREP2 from five individuals from the BRA strain . Then , we cloned the PCR product obtained for each individual and 12 clones were randomly picked and sequenced . As primers do not discriminate between FREP6 and FREP2 , 26 and 34 sequences of these two FREPs were obtained respectively . BgBRA-FREP2 sequences were further analysed . As expected , these sequences display a high level of similarity ( about 99% ) at the nucleic acid level . Nevertheless , 23 of them are non redundant ( GenBank accession numbers: HM237113 to HM237135 ) , indicating a high degree of diversity ( 88% ) . Interestingly , two individuals express 7 and 8 different isoforms of FREP2 , respectively while a maximum 3 loci per haplotype were estimated in a previous study [22] . No recombinatorial process was observed ( using Dna SP 5 . 10 software ) indicating that at least a part of this FREP2 diversity was generated by somatic nucleotide point mutations with a strong bias for transitions ( A to G and T to C ) . The four peptides identified by LC-MS/MS cover 14 . 28% of BgBRA-FREP2 ( HM003905 ) deduced amino acid sequence ( Figure 2 ) . The theoretical molecular weight of BgBRA-FREP2 deduced amino acid sequence is 43 . 8 kDa . The observed molecular weight of BgBRA-FREP2 ( Figure 5A ) , approximately 70kDa , is not in agreement with the theoretical molecular weight . This phenomenon could be explained by post-translational modifications . Indeed , FREPs are known to be heavily glycosylated proteins [21] and the electrophoretic migration profile of FREPs under reducing conditions were shown to be comprised between 40 and 75 kDa in a previous study [24] . In addition , seven putative glycosylation sites ( 6 N-linked glycosylation and 1 O-linked glycosylation sites ) have been predicted in BgBRA-FREP2 using the NetNglyc 1 . 0 and NetOglyc 3 . 1 servers ( http://www . cbs . dtu . dk/services/ ) . Consequently , we hypothesize that this difference between theoretical and observed molecular weight is due to post-translational glycosylation events . Another protein was identified in the same bands 1 and 3 . It corresponds to a thioester-containing protein ( TEP ) from B . glabrata . This TEP protein superfamily contains three different families of proteins which display distinct functions: ( i ) the vertebrate complement proteins ( C3/C4/C5 ) , ( ii ) the pan-protease inhibitors Alpha2 Macroglobulin ( A2M ) found in both vertebrates and invertebrates and finally , ( iii ) non classical A2M including TEPs subgroup only identified in invertebrate species and cell surface thioester containing protein isoforms ( CD109 subgroup ) . We characterize the ORF of the B . glabrata TEP ( BgTEP ) from RNA of B . glabrata from the BRA strain ( GenBank Accession Number HM003907 ) . The deduced amino acid sequence corresponds to a precursor of 1446 amino acids . The peptides identified by LC-MS/MS cover 6 . 22% of the precursor sequence ( Figure 6 , Accession Number HM003907 ) . The BgTEP sequences contain a putative 21 residue signal peptide as revealed by SignalP 3 . 0 analysis . It displays 14 putative N-glycosylation sites predicted by NetNGlyc 1 . 0 software . SMART program analysis reveals that BgTEPs contain the different domains shared by members of the TEP superfamily [49] . The canonical thioester motif ( GCGEQ ) of the TEP family is located from residue 939 to 943 , and the thioester bond is likely to be formed between C940 and E942 . Proline residues involved in the formation , stability and function of the thioester bond in the human C3 [50] are found around the thioester site . The four residues ( F996 , M1345 , Y1382 , Y1416 ) forming the hydrophobic/aromatic pocket for the protection of the thioester in the human C3 are also found at conserved position . The complement component and the Alpha2 Macroglobulin receptor binding domains are identified at amino acid positions 978–1242 ( Protein domain ID: pfam PF07678 ) and 1343–1427 ( Protein domain ID: pfam PF07677 ) , respectively . BgTEP contains 13 cysteine residues , six of them are located at the C-terminus ( 1334–1445 ) forming a sequence signature shared with Drosophila TEPs , Anopheles gambiae aTEP-1 , and Chlamys farreri TEP [51] , [52] , [53] . This last cysteine array is a specific signature of invertebrate TEPs [51] , [52] , [54] that is not shared by complement and A2M . Finally , BgTEPs possess an aspartate residue ( D1054 ) replacing the catalytic histidine residue usually found in most of the protein of this family including invertebrate TEPs from A . gambiae , A . aegypti , C . elegans , C . farreri and Ephaedusa tau [52] , [53] . This last feature is shared by a TEP from Drosophila melanogaster called TEP2 . TEP2 was shown to be functional and required for the efficient phagocytosis of E . coli [55] . As the catalytic histidine residue determines the binding specificity of the thioester , this difference suggests an alternative binding mechanism already reported in other proteins of the family like alpha2 macroglobulin-related proteins [56] . Another interesting feature concerns the position of the protein in the gel . BgTEPs have a calculated molecular weight around 160 kDa which is not in agreement with the position of the protein in the gel ( 70kDa approximately ) . Interestingly , all the peptides identified by LC-MS/MS are located in the C-terminal part of the protein downstream the thioester site ( Figure 6 ) . These data suggest that we probably identified a cleaved C-terminal portion of the BgTEP . This suggests that BgTEP is processed like other members of the family . Indeed , human C3 , alpha2 macroglobulins and A . gambiae TEP-1 have been shown to be activated by proteolysis [52] . However , no clear cut site has been identified in BgTEP , only a putative cleavage site sensitive to diverse proteases ( trypsin , chymotrypsin , thermolysin , clostripain , LysC and LysN Lysyl endopeptidase , pepsin ) has been predicted using PeptideCutter ( http://www . expasy . ch/tools/peptidecutter/ ) program ( see Figure 6 ) . The phylogenetic position of BgTEP ( Accession number: HM003907 ) was investigated in the present work . Phylogenetic analysis confirms the situation of BgTEP in the group of invertebrates TEPs . An unrooted phylogenetic tree was constructed with the neighbour-joining method using 54 sequences of TEPs ( Figure 7 and Table 4 ) . Three major groups can be distinguished in the TEP family: complement components group , the A2M group and the group formed by invertebrate TEPs and cell surface TEP ( CD109 ) . The topology obtained shows that A2M and invertebrate TEPs are more similar between them than they are with complement components . This phylogenetic distribution is consistent with those previously obtained for this protein family [49] , [52] , [53] , [57] . BgTEP forms a cluster with other mollusc TEPs from C . farreri ( 39 . 5% similarity ) and E . tau ( 55 . 1% similarity ) . This mollusc cluster forms a sister group of the insect TEPs from A . gambiae and D . melanogaster . The third protein ( bands 2 and 4 , Figure 5 ) identified in the coimmunoprecipitated extracts is an alpha-amylase-like protein . The seven peptides obtained by mass spectrometry analysis matched with 2 ESTs ( gi|146763124 , gi|163957465 ) . These contiged sequences display a high similarity to the alpha-amylase from the disk abalone Haliotis discus discus ( E-value 2e-45 ) . As alpha-amylase was known to be located mainly in the digestive tract of molluscs [58] , the presence of this digestive enzyme in this context is surprising . Recovery of alpha-amylase in snail plasma is probably linked to a contamination of hemolymph by digestive mucus [32] . As it was demonstrated that porcine pancreatic alpha-amylase is able to bind N-linked oligosaccharides of glycoproteins [59] , the interaction of alpha-amylase with SmPoMucs or other partners of the complex could be an artefact . There were no differences between C and IC strains in the co-immunoprecipitation experiments .
Two main types of immune receptor systems were described in vertebrates . Firstly , immune receptors participating to innate immune mechanisms that are encoded by germline single or multigene copy genes . And secondly , immune receptors ( immunoglobulins and T cell receptors ) mediating adaptive immunity that are encoded by complex multigene systems submitted to somatic rearrangement and extensive diversification processes . Immunoglobulins and T cell receptors have not been identified either in jawless vertebrates , or in deuterostome or protostome invertebrates [60] and immunity against parasites by these organisms was believed to rely exclusively on invariable germline-encoded receptors and effectors molecules that recognize antigens with low specificity . However these organisms are confronted to an environment filled with complex changing populations of microorganisms and potential pathogens , the selective pressures to which they are submitted are comparable with those of jawed vertebrates [47] . Therefore , it should be expected that they also possess sophisticated recognition systems to deal with these challenges . Recent studies support this view . In jawless vertebrate leucine rich repeat receptors genes were identified [61] . They encode a repertoire of somatically diversified receptors analogous to that of T cell Receptors or Immunoglobulins of gnathostomes and fully able to participate in an immune response [62] . For invertebrates many multigene families have been identified following immunization or examination of the genome of different species . They belong to LRR superfamily [63] , [64] , IgSF ( Immunoglobulin SuperFamily , [17] , [65] ) or yet poorly characterized novel families [13] , [66] . They can be integral membrane proteins , soluble , or intracellular . In invertebrates some cases of somatic adaptation have been reported for the FREPs in Molluscs [17] and for DSCAMs in arthropods [15] . In most case their involvement in immunity is not totally clarified and the interaction of these putative immune receptors with antigenic variants was never demonstrated . We started to investigate this question in the present study . The experimental model we have chosen to answer this question is the interaction between B . glabrata and S . mansoni . As mentioned above somatically diversified immune receptors were discovered in B . glabrata [17] that bind to determinants of the digenetic trematode Echinostoma paraensei . In another trematode , S . mansoni , polymorphic mucins [26] called SmPoMuc ( for S . mansoni Polymorphic Mucins ) displayed a high level of inter-individual polymorphism [34] and we showed that their polymorphism is the result of a complex hierarchical system ( recombination , gene conversion , alternative/aberrant/trans splicing ) that efficiently generates the variants based from a relatively low number of genes [27] . We suggest that these mucins could be the ligand of FREPs from B . glabrata [27] . In order to investigate the putative interaction between these molecules we developed a two step-experimental approach . The first step was aimed at the identification of all the proteins from host plasma extracts that could interact with the parasite . Concerning proteins implicated in recognition and presumably in the immunity , several host lectins and parasite glycoproteins were identified . As expected , FREPs were identified as well as a novel B . glabrata lectin . This latter molecule displays similarities with a secreted galactose binding lectin characterised in another gastropod , Helix pomatia [67] . Considering the parasite molecular determinants that could be recognized by these lectins , several glycosylated proteins have been identified ( Table 1 ) . In addition to SmPoMucs , two other glycoproteins were revealed in our approach: the 23 kDa integral membrane protein ( Sm23 ) ( or tetraspanin ) and the glycoprotein K5 . The tetraspanin was precipitated in both conditions ( C/P and IC/P , Figure 1 and Table 1 ) . The tetraspanin family includes proteins that are involved in physiological processes as diverse as egg-sperm fusion , immunological responses ( antigen presentation ) , tissue differentiation and regulation of protein trafficking [68] , [69] . In Schistosoma mansoni tetraspanin were studied particularly for their potential antigenic properties [41] , [70] , [71] . The glycoprotein K5 was identified solely in IC strain . It was known that glycoprotein K5 was encoded by a single copy gene in S . mansoni [42] . Four N-glycosylation sites and one signal peptide were predicted [42] and it was identified in excretory/secretory products of S . mansoni [72] . All these results taken together suggest that the recognition process between S . mansoni and B . glabrata could be multifactorial involving different immune receptors from the host and different carbohydrate components and/or glycoproteins from the parasite . Host immunity relevant molecules were also revealed by this first interactome approach . Firstly , we identified a putative cytolytic protein related to β pore forming toxin family whose amino acid sequence displays significant similarities to aerolysin sequence of the bacteria Aeromonas hydrophila ( data not shown ) . Aerolysins have cytolytic activity triggered by channel formation in target cell membranes . Secreted as an inactive proenzyme form from bacteria , proaerolysin binds with high affinity to the glycosyl anchor of glycosylphosphatidyl-inositol anchored proteins located on the surface membrane of target eukaryotic cells . Its binding to receptor induces a proteolytic cleavage leading to an active form that oligomerizes , forming a channel that causes lysis of the target cell . For the first time identified in a mollusc , the proteins sharing this specific pore forming sequence motif have been identified mainly in bacteria but also in a few plants and cnidarians [73] , [74] , [75] . In cnidarians , the pore-forming toxin could be either a defensive or offensive allomone that is involved in protecting cnidarians against predators or in killing preys [75] . In our model , aerolysin could be involved in snail innate defense responses after trematode infections . Several other proteins that could be involved in immune processes were also identified . Some of them could be involved in molecular adhesion processes . They correspond to Dec-1-like and Matrilin-like molecules from B . glabrata that are suspected to be involved in extracellular matrix structure or coagulation processes [35] , [76] . A peroxinectin was also identified . This cell adhesion molecule was discovered in other invertebrates species and was involved in cell attachment and spreading , nodule formation , encapsulation , agglutination and phagocytosis [77] . Two other host immune relevant molecules were precipitated: AIF ( Allograft Inflammatory Factor ) which was shown to be crucial in pro-inflammatory activity in innate immunity [78] and a cysteine protease inhibitor ( Cystatin B , [79] ) . The putative functions of these different molecules are very interesting in the context of host-parasite interactions . However their suspected roles are deduced from sequence similarities and further investigations are needed to clarify their function . Finally several other proteins were identified in the interactome approach . Their presence is worth mentioning but their role in the host/parasite interplay context remains unknown . This is the case for several Heat Shock Proteins ( HSP ) as well as for 3 proteins belonging to the EF-hand calcium binding family , all from S . mansoni . It is the case also for six parasite molecules putatively involved in the detoxification of oxidative stress [29] , [80] , or an anti-inflammatory , immunomodulatory protein of S . mansoni , SmSPO-1 [81] . The second approach developed during this study was dedicated to the identification of the suspected interaction between FREPs and SmPoMucs . It consisted in CoIP experiments developed with antibodies raised against SmPoMucs . The FREPs and SmPoMucs were found together in one molecular complex containing in addition at least a third partner , the C-terminal moiety of the ThioEster containing Protein ( TEP ) from B . glabrata . The presence of the C-terminal part of TEP in the complex is exciting as some molecules of this family were recently shown to play key roles in other invertebrate/pathogen interactions , especially in insects . Indeed , TEP1 was shown to play a crucial role in the phagocytosis of bacteria and killing of parasites in the mosquito Anopheles gambiae . TEP1 from the mosquito is secreted by hemocytes and cleaved in hemolymph . The C-terminal part of TEP1 binds to bacteria or ookinetes surfaces through a thioester bound . The involvement of this complement-like molecule in the antiparasitic defense of mosquitoes was recently discussed [82] . In addition , recent work demonstrates that polymorphisms in the gene encoding TEP1 occurs and explains the differences of susceptibility to P . falciparum between A . gambiae individuals [83] , [84] . The identification of these three partners is very interesting in our study context . Two of them ( SmPoMucs and FREPs ) are known to be highly variable and can display individual repertoires ( see [17] , [27] for details ) . Since the work on FREPs cited previously ( Zhang et al . 2004 ) was performed on FREP3 , we investigated the polymorphism of the FREP2 molecules specifically identified in the present study and we confirmed its high level of variability . In principle the molecular diversity of both partners ( FREPs and SmPoMucs ) is perfectly in agreement with their involvement in an immune complex involving several kinds of paratopes and epitopes . Future work will be developed to characterise the FREP binding site and SmPoMuc molecular epitopes involved in this complex . The third partner is the TEP from B . glabrata ( BgTEP ) . Precursor and phylogenetic analysis suggests that BgTEP shares the features of invertebrate TEPs that are known to be involved in antiparasitic defense and microbe phagocytosis [54] , [55] , [85] , [86] . In addition , our LC-MS/MS experiments led to the identification of peptides that are all located in the C-terminal part of BgTEP . This suggests that BgTEP has been submitted to cleavage before its association to the two other partners of the complex . This cleavage was described for numerous members of the TEP family during the activation process , especially for TEP1 from the mosquito [52] . Therefore the BgTEP found in the complex is activated and could play a role in opsonisation processes as described for the members of this family . This hypothesis is clearly supported by the Alpha2 Macroglobulin receptor binding domain ( region 1343–1427 ) found in the C-terminal part of BgTEP precursor . Indeed , this domain is known to be involved in the interaction with macrophage and phagocyte specific receptors [87] . A protein displaying a 18 residues N-terminal sequence identical to our BgTEP was previously characterized from B . glabrata [88] . It displays an α-macroglobulin proteinase inhibitor-like activity . Nevertheless , our phylogenetic analysis and the cystein array identified in the C-terminus part of the Bg TEP [51] , [52] , [54] strongly support that BgTEP belongs to the invertebrate TEP and not to the A2M group . As FREPs display a high level of similarity among themselves , it is difficult to identify without doubt the isoform ( s ) present in the immune complex characterised by mass spectrometry . Nevertheless , we identify a FREP2-specific peptide and consequently , we are sure that FREP2 is present in the immune complex , alone or in combination with other FREPs . This result is interesting because FREP2 is the main gene of the FREP family up-regulated following exposure to S . mansoni [25] , [46] , [89] . Moreover , our analysis of BgBRA-FREP2 diversity in the present study reveals that somatic processes probably occurs and increase their repertoire in individuals . Consequently , FREP2 could represent a sort of induced or selected “antibody” following parasite infection and dedicated to parasite determinant recognition . Finally , the results obtained in this work could help understanding different results obtained during previous population studies . These studies of the interaction between B . glabrata and S . mansoni have revealed a phenomenon called compatibility polymorphism [90] . In natural populations , some snail/schistosome combinations are compatible and others are not , the success or the failure of B . glabrata/S . mansoni infection depending on the matched or mismatched status of the host and parasite phenotypes [90] . The molecular basis of this phenomenon is unknown but molecular determinants like those revealed through this study are promising candidates . Indeed , we can hypothesize that particular combinations of FREPs and SmPoMucs expressed by individuals could interact together or not to define the matched or mismatched status evoked previously . We have recently shown that each S . mansoni individual expresses a particular SmPoMuc profile [27] that could be recognized or not by a particular FREPs profile expressed by the infected mollusc . We are currently testing this hypothesis by analysing the concordance of alleles in compatible combinations in different populations of B . glabrata and S . mansoni in interaction . If this hypothesis is verified , it could illustrate a bet hedging strategy of the parasite based on a diversification/polymorphism process providing an opportunity to certain individuals to infest a host permitting parasite species perpetuation . Bet hedging strategies are well characterized in bacteria [91] and consists in a switching between phenotypes for species confronted to fluctuating and unpredictable environmental variations . The FREP somatic diversification of mollusc individuals is insufficient to allow recognition of all parasite individuals . This somatic diversification could represent a first step towards adaptive immunity in an invertebrate species: individuals are capable of somatic diversification of their immune receptors allowing for an enlargement of their recognition capacity , nevertheless , this repertoire is smaller than the vertebrate immune receptor repertoire and does not allow for the recognition of all putative antigens entering in contact with a given individual . In the future an analysis of the germ line genes of FREP that will allow dn/ds calculations and the modelling of the FREP domains bound to their mucin ligand once crystals are available should shed light on the properties of these variants and on their necessity . | Contrary to the traditional view that immunity in invertebrates is limited to innate mechanisms , recent studies have shown that these several species of protostome invertebrates express putative immune receptors that can be somatically diversified in a way resulting in an analogy with Immunoglobulins or T Cell Receptors of vertebrate species . Other studies have shown the existence of putative antigenic variant counterparts in their specific parasite , as would be expected in an “arms race” between both protagonists . However , the interaction between these immune receptors and antigens was never demonstrated in an interaction involving an invertebrate and its specific pathogen . We demonstrate such an interaction in the present study . We show that a specific set of highly variable immune receptors of the mollusc Biomphalaria glabrata forms immune complexes with highly polymorphic and individually variable mucin determinants from its specific trematode parasite S . mansoni . We demonstrate for the first time in an invertebrate host-parasite interaction that a large repertoire of parasite epitopes matched a large repertoire of host immune receptors . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"immunology/innate",
"immunity",
"infectious",
"diseases/helminth",
"infections"
] | 2010 | A Large Repertoire of Parasite Epitopes Matched by a Large Repertoire of Host Immune Receptors in an Invertebrate Host/Parasite Model |
Paracoccidioidomycosis ( PCM ) is a life-threatening systemic disease and is a neglected public health problem in many endemic regions of Latin America . Though several diagnostic methods are available , almost all of them present with some limitations . A latex immunoassay using sensitized latex particles ( SLPs ) with gp43 antigen , the immunodominant antigen of Paracoccidioides brasiliensis , or the monoclonal antibody mAb17c ( anti-gp43 ) was evaluated for antibody or antigen detection in sera , cerebrospinal fluid ( CSF ) , and bronchoalveolar lavage ( BAL ) from patients with PCM due to P . brasiliensis . The gp43-SLPs performed optimally to detect specific antibodies with high levels of sensitivity ( 98 . 46% , 95% CI 91 . 7–100 . 0 ) , specificity ( 93 . 94% , 95% CI 87 . 3–97 . 7 ) , and positive ( 91 . 4% ) and negative ( 98 . 9% ) predictive values . In addition , we propose the use of mAb17c-SLPs to detect circulating gp43 , which would be particularly important in patients with immune deficiencies who fail to produce normal levels of immunoglobulins , achieving good levels of sensitivity ( 96 . 92% , 95% CI 89 . 3–99 . 6 ) , specificity ( 88 . 89% , 95% CI 81 . 0–94 . 3 ) , and positive ( 85 . 1% ) and negative ( 97 . 8% ) predictive values . Very good agreement between latex tests and double immune diffusion was observed for gp43-SLPs ( k = 0 . 924 ) and mAb17c-SLPs ( k = 0 . 850 ) , which reinforces the usefulness of our tests for the rapid diagnosis of PCM in less than 10 minutes . Minor cross-reactivity occurred with sera from patients with other fungal infections . We successfully detected antigens and antibodies from CSF and BAL samples . In addition , the latex test was useful for monitoring PCM patients receiving therapy . The high diagnostic accuracy , low cost , reduced assay time , and simplicity of this new latex test offer the potential to be commercialized and makes it an attractive diagnostic assay for use not only in clinics and medical mycology laboratories , but mainly in remote locations with limited laboratory infrastructure and/or minimally trained community health workers .
Neglected tropical diseases ( NTDs ) , a group of chronic diseases caused by debilitating parasitic , bacterial , viral , and fungal infections are among the most common causes of illness in the poorest people living in developing countries [1] . Fungal infections represent an important health problem in lower income countries . Paracoccidioidomycosis ( PCM ) is a systemic endemic mycotic disease affecting mainly male rural workers during the most productive years of their life , which poses a significant public health issue and causes important economic losses in Latin America; this epidemiological scenario tends to concentrate around humid forests in subtropical and tropical areas [2] . PCM has an estimated incidence of one to three cases per 100 , 000 inhabitants , the majority occurring in Brazil , Colombia , and Venezuela , where the fungus is endemic [3 , 4] . Multi-locus sequencing studies have clarified species boundaries within etiological agents of Paracoccidioides and support the existence of clinically important cryptic groups beyond Paracoccidioides brasiliensis [5] . The P . brasiliensis complex comprises four genetic groups of clinical interest , including species 1 ( S1 ) , phylogenetic species 2 ( PS2 ) , phylogenetic species 3 ( PS3 ) , and phylogenetic species 4 ( PS4 ) [3 , 5 , 6] . A sister taxa referred to as a new biological species , Paracoccidioides lutzii , is placed at a relatively large distance from the P . brasiliensis complex by phylogenetic analysis [7 , 8] . Epidemiological studies support a broad range for the agents embedded in the P . brasiliensis complex , especially the S1 group , which is predominant in Latin America , whereas the offshoot P . lutzii appears to be prevalent in the Brazilian territory , which has an epicenter in the central-west region [9–11] and few cases reported outside this area [12] , but its real incidence is unknown [13] . Disease acquisition involves inhalation of Paracoccidioides propagules from the environment leading to a primary pulmonary infection with no latency period , or more commonly the reactivation of quiescent foci [14] . Patients present with variable clinical manifestations , ranging from an acute/subacute to chronic form . PCM is classically diagnosed by identifying multiple budding yeast cells in biological fluids or histologically by visualizing yeasts in tissue sections [14–16] . However , the detection of the pathogen in biological fluids is often difficult due to the few pathognomonic structures . In addition , cultures are time consuming and not easily obtained , especially from sputum , the material most commonly sent to the laboratory . In the absence of visualizing fungal structures in biological fluids , serological assays such as double immunodiffusion ( DID ) [17 , 18] , dot-blot [19] , ELISA [20 , 21] , Western blot [22] , and latex agglutination ( LA ) [23] have been extremely useful for confirming diagnosis . These tests are used broadly over classical methods due to low cost , reproducibility , and ease of implementation in the laboratory . Of the recommended serological tests , those that demonstrate the presence of circulating antibodies in the sera are the most frequently employed for diagnosis and patient follow-up [24–26] . The P . brasiliensis immunodominant antigen gp43 , a 43 , 000 Dalton glycoprotein expressed during infection , induces a strong antibody response and has been proposed as an important serological marker because it is recognized by a most PCM sera due to P . brasiliensis [22 , 27] . Despite continuous improvements in immunological tools for the diagnosis of PCM , the techniques used for primary diagnosis , at least in field situations , still rely on direct observation of the fungal structures in biological fluids . Tissue forms of P . brasiliensis are similar to Histoplasma capsulatum and may lead to misdiagnosis; for accurate diagnosis the section often has to be examined carefully to determine the pathognomonic stages of the fungus . Therefore , Paracoccidioides infections need to be diagnosed rapidly , especially among populations living in neglected areas . In this scenario the LA tests are very popular in clinical laboratories for the diagnosis of viral , bacterial , fungal , and parasitic diseases [28] . A rapid and simple latex test to detect and monitor antigens and antibodies in serum samples is overdue in routine field practice , especially for subjects living in neglected areas . Due to the high incidence of PCM caused by P . brasiliensis in Latin America ( S1 , PS2 , and PS3 ) , the present study was designed to standardize a LA test using purified gp43 antigen and anti-gp43 monoclonal antibody coupled to latex particles to evaluate the potential capacity for the detection of specific anti-gp43 antibodies or gp43 antigen in sera , cerebrospinal fluid ( CFS ) , and bronchoalveolar lavage ( BAL ) . Moreover , sera from PCM patients receiving antifungal therapy were followed up based on the antibody titer and antigen detection measured by the LA test in order to verify its usefulness for monitoring the patients .
This study was approved by the Research Ethics Committee of Federal University of São Paulo ( UNIFESP ) . All patients provided informed written consent and the study was approved by the ethical committee under number CEP 1796/10 . Sixty-five serum samples obtained from patients with active PCM ( 61 males and 4 females , age range 3 to 69 years ) were included in this study . Eight patients presented with the acute form of the disease and 57 patients presented with the chronic form . In addition , 14 CSF samples were obtained from neuroPCM patients and 13 samples of BAL fluid from patients with pulmonary PCM . The diagnosis of PCM was confirmed by direct examination of biological fluids and/or serological immunodiffusion tests . Serum samples were obtained from patients with histoplasmosis ( n = 18 ) , aspergillosis ( n = 18 ) , candidiasis ( n = 13 ) , and non-fungal diseases ( n = 12 ) , and sera from healthy individuals ( n = 38 ) were used as controls . In addition , six CSF and six BAL samples from patients with other non-fungal diseases were used as controls . All samples were stored at -20°C until use . The undiluted CSF and BAL samples were inactivated at 56°C for 30 minutes before use . PCM patients ( n = 10 ) undergoing therapy were evaluated by LA for serological follow-up of antigen and antibody detection . The diagnosis was supported by the clinical experience of the physician responsible for the patient presenting with signs and symptoms of the disease at diagnosis . The patients were selected based on the number of samples in the interval between a pickup and another and the type of treatment used . PCM was confirmed in most patients via direct examination of secretions , such as sputum , oral mucosa lesion samples , or biopsies . Serological ID tests using the traditional exoantigen from P . brasiliensis B-339 ( AgPbB339 standard antigen ) confirmed the diagnosis . Five patients presented with the acute form of the disease and five patients presented with the chronic form . Seven patients were treated with itraconazole ( 200 mg ) twice a day and three patients with sulfamethoxazole ( 400 mg ) + trimethoprim ( 80 mg ) twice a day . Patients were evaluated at the moment of diagnosis ( T1 ) and followed up at 3 ( T2 ) and 18–24 months ( T3 ) . Three serum samples from each patient were analyzed , for a total of 30 samples . Patients were aged between 3 and 56 years . P . brasiliensis B-339 ( ATCC 32069; PS3 ) was obtained from Dr . A . Restrepo ( Corporation Investigaciones Biológicas , Medellín , Colombia ) and has been maintained on Sabouraud dextrose agar ( Difco Laboratories , Detroit , Mich . ) in our laboratory since the 1970s . The fungus was converted to the yeast form on modified Sabouraud dextrose agar containing 0 . 01% thiamine ( Difco Laboratories , Detroit , Mich . ) and 0 . 14% asparagine ( Difco Laboratories , Detroit , Mich . ) ( Sab-T-A ) at 35°C . Exoantigen from the B-339 strain was used to purify gp43 as described previously [17] , submitted to SDS-PAGE [29] , and silver stained [30] to verify the purification . Purified protein concentrations were determined by the Bradford method [31] and stored at -20°C until use . The mAb anti-gp-43 ( mAb17c ) was kindly provided by Dr . R . Puccia [32 , 33] . Three millimeters of melted 1% agarose ( Sigma A-6877 ) in PBS was poured onto a glass slide ( 75 × 25 mm ) . The pattern for this micro-ID test consisted of a central well surrounded by six wells , each 3 mm in diameter . The central well was located 6 mm ( edge-to-edge ) from the other wells and filled with the antigen solution . Each slide contained two sets of wells . On each slide , the two central wells were filled with 10 μl of antigen . Surrounded wells were filled with diluted serum ( 1:2 to 1:1024 ) . The slides were incubated in a moist chamber at room temperature ( 20–25°C ) for 48 h , and then washed for 1 h in 5% sodium citrate and 24 h in saline . The slides were dried , stained for 5 min with 0 . 15% Coomassie Brilliant blue ( Sigma ) in ethanol:acetic acid:water ( 4:2:4; v:v ) , and destained in the solvent mixture alone , when necessary . Precipitation bands were recorded by visual observation [17] . Sera from patients with PCM ( n = 65 ) , histoplasmosis ( n = 18 ) , aspergillosis ( n = 18 ) , candidiasis ( n = 13 ) , non-fungal diseases ( n = 12 ) , and healthy individuals ( n = 38 ) were tested individually and the titer of each serum sample determined . Purified gp43 was coupled to carboxylated latex particles ( Carboxyl latex microspheres , Invitrogen ) 0 . 8 μm in diameter according to the manufacturer’s instructions , creating gp43-SLPs ( Fig . 1A ) . Briefly , 250 μl of a 4% ( wt/vol ) suspension of the particles was washed three times in 750 μl of 50 mM borate buffer ( pH 8 . 5 ) . All washes were done at 10 , 000 x g for 10 min at room temperature unless otherwise stated . Following the final wash , the particles were suspended in 1 ml of 50 mM borate buffer ( pH 8 . 5 ) , 400 μg of purified gp43 added to the mixture , and then incubated for 24 h at 4°C with gentle end-to-end rotation . The mixture was centrifuged for 10 min at 10 , 000 x g and the supernatant saved for protein determination [31] . To block non-specific binding sites , the sediment was resuspended in 50 mM phosphate buffered saline ( PBS; pH 7 . 2 ) containing 1% bovine serum albumin ( BSA ) fraction V and incubated for 4 h at room temperature ( 20–25°C ) with gentle mixing . After washing three times in 50 mM PBS ( pH 7 . 4 ) , the sensitized latex particles ( SLPs ) were resuspended in 1 ml of storage buffer ( 50 mM PBS , pH 7 . 4 , containing 1% BSA , 0 . 1% NaN3 , and 5% glycerol ) and stored at 4°C until required . Purified mAb17c was used to coat carboxylated latex particles ( Carboxyl latex microspheres , Invitrogen ) 0 . 8 mm in diameter following the manufacturer’s instructions , creating mAb17c-SLPs ( Fig . 1B ) . Briefly , 250 μl of a 4% ( wt/vol ) suspension of the particles was washed three times with 750 μl of phosphate-citrate buffer ( pH 6 . 8 ) . All washes were done at 10 , 000 x g for 10 min at room temperature unless otherwise stated . Following the final wash , the particles were suspended in 1 ml of phosphate-citrate buffer ( pH 6 . 8 ) , and 200 μg of mAb17c was added to the mixture and incubated for 24 h at 4°C with gentle end-to-end rotation . The mixture was centrifuged for 10 min at 10 , 000 x g and the supernatant saved for protein determination [31] . To block non-specific binding sites , the sediment was resuspended in 50 mM PBS ( pH 7 . 2 ) containing 1% BSA fraction V and incubated for 4 h at room temperature ( 20–25°C ) with gentle mixing . After washing three times in 50 mM PBS ( pH 7 . 4 ) , the SLPs were resuspended in 1 ml of storage buffer ( 50 mM PBS , pH 7 . 4 , containing 1% BSA , 0 . 1% NaN3 , and 5% glycerol ) and stored at 4°C until required . The LA test was performed by mixing 25 μl of gp43-SLPs or mAb17c-SLPs with a total of 25 μl of serum , CSF , or BAL sample on a black-coated glass slide . Just for the test using mAb17c-SLPs ( for the detection of gp43 ) , serum , CSF , and BAL samples were previously treated with Tris-HCl buffer ( 0 . 2 M , pH 8 . 6 ) plus dithiothreitol ( DTT; 0 . 003 M ) and heated at 56°C for 30 min in order to inactivate rheumatoid factor and dissociate immune complexes . After the reagents were mixed , the slide was gently shaken in an orbital shaker for up to 5 min . Negative-control LA consisted of 25 μl of latex solution added to 25 μl of saline solution . Samples were considered positive when agglutination ( clumping ) was observed . The LA tests formed loose aggregates within 5 to 10 min in positive assays or remained as a milky suspension in negative assays . Test results were scored as follows: 4+ indicated that the suspension rapidly formed large clumps in a very clear background , mobilizing 100% of SLPs , with formation of a ring; 3+ indicated moderately sized and large clumps were present against a clear background , involving >75% of SLPs , with minor ring formation; 2+ indicated small to moderately sized clumps were present against a slightly cloudy background , mobilizing ≤50% of SLPs , with no ring formation; 1+ indicated fine particle clumping against a cloudy background , usually involving ≤25% of SLPs; and they were scored as negative when there was no visible agglutination . Final readings were made after 10 min . Results were independently scored by two investigators blinded to the identity of the tested serum and the results of other investigators . Diagnostic values included sensitivity , specificity , positive predictive value ( PPV ) , and negative predictive value ( NPV ) . The receiver operating characteristic ( ROC ) curves were prepared and analyzed to determine the sensitivity and specificity of each LA assay ( gp43-SLPs , mAb17c- SLPs ) and DID test . The area under the ROC curve ( AUC ) was calculated to evaluate the diagnostic value of each LA test . We assumed a test without diagnostic power when the ROC curve was linear with an AUC of 0 . 5 ( i . e . , the ROC curve will coincide with the diagonal ) . On the other hand , a powerful test would provide an AUC of approximately 1 . 0 , indicating the absence of both false-positives and false-negatives ( i . e . , the ROC curve will reach the upper left corner of the plot ) . To measure the degree of concordance of the results of the three different assays ( gp43-SLPs , mAb17c-SLPs , and DID test ) , we calculated the kappa statistic and its 95% confidence interval ( CI ) . Kappa values were interpreted as follows: 0 . 00–0 . 20 , poor agreement; 0 . 21–0 . 40 , fair agreement; 0 . 41–0 . 60 , moderate agreement; 0 . 61–0 . 80 , good agreement; 0 . 81–1 . 00 , very good agreement [34] . A P-value ≤0 . 05 was considered as significant . All statistical calculations were performed with MedCalc Statistical Software version 14 . 8 . 1 ( MedCalc Software bvba , Ostend , Belgium; http://www . medcalc . org; 2014 ) .
To determine if the proposed test is feasible for detecting specific antibodies against P . brasiliensis , we performed an LA test with gp43-SLPs using serum specimens from 65 cases of PCM and controls , including 49 cases with fungal infection ( histoplasmosis , aspergillosis , candidiasis ) and normal healthy subjects ( NHS , n = 38; Table 1 ) . Fig . 2 shows an example of positive and negative LA reactions . Among the 65 PCM sera , 64 ( 98 . 46% ) were positive and 1 ( 1 . 53% ) was negative . Among the heterologous sera: 3 histoplasmosis sera were positive ( 16 . 6% ) and 15 ( 83 . 3% ) negative; 2 aspergillosis sera were positive ( 11 . 11% ) and 16 ( 88 . 8% ) negative; 1 candidiasis sera sample was positive ( 7 . 7% ) and 12 ( 92 . 3% ) negative . All sera from healthy humans were unreactive ( Fig . 3A ) . The sensitivity , specificity , PPV , and NPV values for gp43-SLPs were 98 . 46% ( 95% CI 91 . 7–100 . 0 ) , 93 . 94% ( 95% CI 87 . 3–97 . 7 ) , 91 . 4% , and 98 . 9% , respectively . To rate the performance of the LA test for detecting gp43 antigen , the same set of samples ( PCM and controls ) used for antibody detection were tested using mAb17c-SLPs ( Table 1 and Fig . 3B ) . Among the 65 PCM sera , 63 ( 96 . 92% ) were positive and 2 ( 3 . 08% ) were negative . Among the heterologous sera: 5 histoplasmosis sera were positive ( 27 . 7% ) and 13 ( 72 . 2% ) were negative; 4 aspergillosis sera were positive ( 22 . 2% ) and 14 ( 77 . 8% ) negative; 3 candidiasis sera were positive ( 23% ) and 10 ( 77% ) negative . All sera from healthy people were unreactive , as well as 12 sera samples from patients with no fungal diseases . The sensitivity , specificity , PPV , and NPV values for mAb17c-SLPs were 96 . 92% ( 95% CI 89 . 3–99 . 6 ) , 88 . 89% ( 95% CI 81 . 0–94 . 3 ) , 85 . 1% , and 97 . 8% , respectively . In order to evaluate whether the LA test was feasible for detecting anti-gp43 antibodies in CSF , 14 CSF samples were submitted to the test with gp43-SLPs . Twelve of the 14 CSF samples were positive ( Table 2 and Fig . 4A ) ; two samples had agglutination pattern 4+ ( 100% visible agglutination ) , 1 had pattern 3+ ( 75% visible agglutination ) , 5 had pattern 2+ ( 50% agglutination visible ) , 4 had pattern 1+ ( 25% visible agglutination ) , and two samples exhibited no visible agglutination ( N ) . Previously , we reported that the diagnosis of neuroPCM can be achieved by detection of the gp43 antigen in CSF by ELISA [24] . In this study , when we performed the mAb17c-SLP test to detect the gp43 antigen , we observed that 12 of the 14 samples tested were positive ( Table 2 and Fig . 4B ) . Seven samples had agglutination pattern 4+ ( 100% visible agglutination ) , 3 had pattern 3+ ( 75% of visible agglutination ) , 1 had pattern 2+ ( 50% visible agglutination ) , 1 had pattern 1+ ( 25% visible agglutination ) , and two samples exhibited no visible agglutination ( N ) . None of the six samples tested as negative controls were positive . The LA test for detection of anti-gp43 antibody ( gp43-SLPs ) or antigen ( mAb17c-SLPs ) in CSF had a sensitivity of 85 . 71% ( 95% CI 57 . 2–98 . 2 ) and specificity of 100% ( 95% CI 54 . 1–100 . 0 ) . The PPV and NPV were 100% and 75% , respectively . A total of 13 BAL fluid samples were investigated for anti-gp43 , as it has been successfully used for ELISA [35] . Eight of the tested samples were positive ( Table 3 and Fig . 5A ) . Five samples had agglutination pattern 2+ ( 50% of visible agglutination ) , 3 had pattern 1+ ( 25% of visible agglutination ) , and 5 exhibited no visible agglutination ( N ) . None of the six samples tested as negative controls were positive . The LA test for detection of anti-gp43 in BAL fluid had a sensitivity of 61 . 54% ( 95% CI 31 . 6–86 . 1 ) and specificity of 100% ( 95% CI 54 . 1–100 . 0 ) . The PPV and NPV were 100% and 54 . 5% , respectively . Eleven of 13 BAL fluid samples were positive for gp43 detection ( Table 3 and Fig . 5B ) , indicating that mAb17c-SLPs have a greater detection capability than gp43-SLPs for this type of biological sample . Three samples had agglutination pattern 4+ ( 100% visible agglutination ) , 2 had pattern 3+ ( 75% agglutination visible ) , 5 had pattern 2+ ( 50% visible agglutination ) , 1 had pattern 1+ ( 25% visible agglutination ) , and two samples exhibited no visible agglutination ( N ) . None of the six samples tested as negative controls were positive . The LA test for detection of gp43 antigen in BAL fluid had a sensitivity of 84 . 62% ( 95% CI 54 . 6–98 . 1 ) and specificity of 100% ( 95% CI 54 . 1–100 . 0 ) . The PPV and NPV values were 100% and 75% , respectively . We were able to assess the efficiency of the LA test in treatment follow-up using sera from patients with confirmed PCM who were undergoing therapy . Fig . 6 shows representative curves for the serological follow-up of 10 PCM patients with acute ( n = 5 ) and chronic ( n = 5 ) forms before ( T1 ) , during ( T2 ) , and after treatment ( T3 ) . Fig . 7 shows a ROC curve depicting assay sensitivity and specificity based on testing 65 PCM patient sera and 99 control sera , including patients with histoplasmosis , aspergillosis , non-fungal diseases , and healthy subjects , indicating optimum performance of the LA test . Our results indicate that the LA test using gp43-SLPs provides a better AUC ( 0 . 962±0 . 0143 , 95% CI 0 . 920–0 . 986 , P<0 . 0001 ) than mAb17c-SLPs ( 0 . 929±0 . 0192 , 95% CI 0 . 878–0 . 963 , P<0 . 0001 ) . As previously reported [11] , DID also presented good results ( AUC 0 . 992±0 . 00769 , 95% CI 0 . 964–1 . 000 , P<0 . 0001 ) . In addition , we used the Kappa test to assess the agreement between different serological assays ( gp43-SLPs , mAb17c-SLPs , and DID ) , finding very good agreement between gp43-SLPs and DID ( k = 0 . 92±0 . 030 , 95% CI 0 . 865–0 . 984 ) and between mAb17c-SLPs and DID ( k = 0 . 850±0 . 041 , 95% CI 0 . 770–0 . 931 ) , which reinforces the power of our LA test over DID , especially because we were able to diagnosis PCM in less than 10 min but DID required at least 72 hours . The same agreement was observed for the gp43-SLP LA test vs . mAb17c-SLP LA test ( k = 0 . 926±0 . 030 , 95% CI 0 . 868–0 . 984 ) .
The regions where PCM occurs are primarily vast rural areas where poverty and a poor , sometimes nonexistent , public health system are predominant . Diagnostic centers are scarce and difficult to access for affected individuals . In general , these laboratories have poor infrastructure for serological and mycological diagnosis . Generally , biological samples are collected and sent to research centers that provide specialized diagnosis and treatment options . Therefore , simple serological techniques with the possibility of use in precarious locations should be standardized and implemented in remote rural regions where PCM is endemic . The LA test has been proposed for the diagnosis of several fungal diseases , including histoplasmosis [36–38] , candidiasis [39 , 40] , sporotrichosis [41] , aspergillosis [42] , and coccidioidomycosis [43] , and many other applications are currently emerging [28] . The LA test is a diagnostic method for the detection of antibodies or circulating antigens based on the agglutination of sensitized polystyrene particles . This technical procedure allows rapid diagnosis without prior training or sophisticated equipment . In this study , the gp43-SLP test for detecting specific anti-gp43 antibodies exhibited high performance and was able to detect antibodies in 98 . 4% of serum samples . Minor cross-reactions occurred in sera from patients with histoplasmosis ( 16 . 6% ) , aspergillosis ( 11 . 11% ) , and candidiasis ( 7 . 7% ) , but the sensitivity and specificity of the gp43-SLP test indicated that it was successful . Cross-reactivity is common in serological assays that rely on the detection of antibodies among fungal infections due to antigenic similarity [44] , especially the shared galactose and mannose epitopes of gp43 in P . brasiliensis [45] . Using a crude antigen preparation of P . brasiliensis , Restrepo and Moncada [23] previously showed high cross-reactivity with sera from patients with histoplasmosis , aspergillosis , candidiasis , coccidioidomycosis , and sporotrichosis . Also , more recently , Silveira-Gomes et al . [46] reported 84% sensitivity and 81% specificity using SLPs with a pool of exoantigens of P . brasiliensis . However , cross-reactivity occurred with sera from patients with aspergillosis ( 27% ) , histoplasmosis ( 27% ) , and non-fungal infections ( 22% ) . We demonstrated that the detection of anti-gp43 was very efficient in CSF samples ( 85 . 7% positivity ) , and less efficient in BAL fluid samples ( 61 . 5% positivity ) . In patients with immune deficiencies who fail to produce normal levels of immunoglobulins , the detection of antibodies is difficult and requires more sensitive techniques . However , very sensitive techniques capable of detecting low levels of antibody may lose specificity . Thus , detection of circulating antigens rather than antibodies may constitute an important tool for the diagnosis or monitoring of patients undergoing treatment , particularly for immunocompromised patients in whom early and accurate diagnosis is mandatory for the implementation of an effective treatment [47] . No studies are yet available on antigen detection using the LA test as a diagnostic method for PCM . In our study , the mAb17c-SLP test performed well and was able to detect antigen in 96 . 9% of serum samples . Cross-reactions occurred in sera from patients with histoplasmosis ( 27 . 7% ) , aspergillosis ( 22 . 2% ) , and candidiasis ( 23% ) . Under these conditions , the sensitivity and specificity indicate that the test was successful for diagnostic purposes . Moreover , detection of gp43 in CSF and BAL fluid samples was efficient . Although , other techniques ( e . g . , inhibition-ELISA ) may detect circulating antigens in PCM sera , they are laborious compared to the LA test and require trained personnel for implementation [24 , 47] . A basal antibody level may last for years in patients even after the remission of clinical symptoms [48 , 49] , but antigen and antibody titers may be influenced by therapy [50] and the ability to develop an immune response against the pathogen [51] . Patients followed up using the LA test presented with no known comorbidities , and the therapeutic regimen was based on daily itraconazole or classical treatment with sulfamethoxazole plus trimethoprim . Judging from the positive agglutination using gp43-SLPs or mAb17c-SLPs , distinct titers were observed at the diagnosis of patients with the chronic and acute forms ( Fig . 6 , T1 ) . We observed a decreasing antigen titer during the first period of treatment ( T2 ) , but at the end of the treatment ( T3 ) none of the patients with acute ( Fig . 6B ) or chronic forms ( Fig . 6D ) had a negative test , and they returned to basal antigens titers . From the perspective of antibody titration using gp43-SLPs , most sera had a considerable decrease in the titer of anti-gp43 antibodies . At the end of the treatment , the antibody titers were negative to 1:4 ( Fig . 6A and C ) , which is in agreement with other serological tests [24–26] . With the recent introduction of dissimilar species as etiological agents of PCM [5 , 9] with different antigenic composition [11 , 52 , 53] , the serological tests may develop towards taxonomic advances and offer alternative antigenic preparations for rapid and accurate patient diagnosis . For a long time , gp43 has been highlighted as the immunodominant antigen of PCM due to P . brasiliensis sensu lato [27] . However , experimental evidence from our group shows that patients infected with P . lutzii may not react with P . brasiliensis gp43 [10 , 12 , 52 , 53] , probably due to the lack of identity in antigenic epitopes or the absence or minimal expression of P . lutzii gp43 during host-parasite interplay [12] . Recently , we proposed an antigenic preparation derived from cell-free P . lutzii antigens ( CFA-Pl ) that successfully diagnosed PCM with high sensitivity and specificity due to the offshoot P . lutzii [11] . The utility of this CFA-Pl preparation for LA tests is currently being investigated in our laboratory and may help diagnose possible false-negatives using gp43 as the main antigenic component . In conclusion , due to the high diagnostic accuracy , low cost in terms of production of the antigen , reduced assay time , simplicity , and availability , this LA test may have great potential for the diagnosis of PCM due to P . brasiliensis complex ( S1 , PS2 , and PS3 ) in endemic regions of Latin America . The implementation of simple diagnostic tests in remote areas is preferred to tests that use molecular biology techniques that , although effective , are extremely difficult or even impossible to implement . The LA test is an especially useful screening method and valid in both field research , where there is adequate space and equipment available for diagnosis , and hospital screening . Due to its advantages , the test should be used in clinical laboratories as a new diagnostic method . | Paracoccidioidomycosis is one of the most prevalent systemic mycoses in Latin America , and still poses a significant threat to the health of human hosts , especially those with an impaired immune system . Early and accurate diagnosis is mandatory for the implementation of effective treatment . Currently , most of the diagnostic tests are not simple to implement in areas where laboratory infrastructure or trained personnel are not available . To overcome this problem , we propose a simple and inexpensive assay for use in latex agglutination tests based on the P . brasiliensis-specific antigen gp43 or monoclonal antibody ( mAb17c ) anti-gp43 coupled to latex particles in order to detect both circulating antigens and antibodies in sera , cerebrospinal fluid , and bronchoalveolar lavage . Our results show that PCM can be diagnosed with high sensitivity and specificity in less than 10 min using these tools . In addition , the latex test demonstrated its applicability in the follow-up of PCM patients during antimycotic therapy . The diagnostic accuracy , low cost , and simplicity of this fast test makes it an attractive serological assay that can be implemented in endemic areas with remote access , in laboratories with limited infrastructure , and/or to the community health agents trained to use it in rural areas . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Immunodiagnosis of Paracoccidioidomycosis due to Paracoccidioides brasiliensis Using a Latex Test: Detection of Specific Antibody Anti-gp43 and Specific Antigen gp43 |
The functional role of synchronization has attracted much interest and debate: in particular , synchronization may allow distant sites in the brain to communicate and cooperate with each other , and therefore may play a role in temporal binding , in attention or in sensory-motor integration mechanisms . In this article , we study another role for synchronization: the so-called “collective enhancement of precision” . We argue , in a full nonlinear dynamical context , that synchronization may help protect interconnected neurons from the influence of random perturbations—intrinsic neuronal noise—which affect all neurons in the nervous system . More precisely , our main contribution is a mathematical proof that , under specific , quantified conditions , the impact of noise on individual interconnected systems and on their spatial mean can essentially be cancelled through synchronization . This property then allows reliable computations to be carried out even in the presence of significant noise ( as experimentally found e . g . , in retinal ganglion cells in primates ) . This in turn is key to obtaining meaningful downstream signals , whether in terms of precisely-timed interaction ( temporal coding ) , population coding , or frequency coding . Similar concepts may be applicable to questions of noise and variability in systems biology .
Synchronization phenomena are pervasive in biology . In neuronal networks [1]–[3] , a large number of studies have sought to unveil the mechanisms of synchronization , from both physiological [4] , [5] and computational viewpoints ( see for instance [6] and references therein ) . In addition , the functional role of synchronization has also attracted considerable interest and debates . In particular , synchronization may allow distant sites in the brain to communicate and cooperate with each other [7]–[9] and therefore may play a role in temporal binding [10] , [11] and in attention and sensory-motor integration mechanisms [12]–[14] . In this article , we study another role for synchronization: the so-called collective enhancement of precision ( see e . g . [15]–[17] ) , an intuitive and often quoted phenomenon with comparatively little formal analysis [18] . We explain mathematically why synchronization may help protect interconnected nonlinear dynamic systems from the influence of random perturbations . In the case of neurons , these perturbations would correspond to so-called “intrinsic neuronal noise” [19] , which affect all of the neurons in the nervous system . In the presence of significant noise intensities ( as experimentally found in e . g . retinal ganglion cells in primates [20] ) , this property would be required for meaningful and reliable computations to be carried out . It should be noted that “protection of systems from noise” and “robustness of synchronization to noise” are two different concepts . The latter concept means that the synchronized systems remain so in presence of noise , whereas the former concept means that , thanks to synchronization , the behaviors of the coupled systems are close to the noise-free behaviors . This difference is further addressed in the Discussion . The influence of noise on the behaviors of nonlinear systems is very diverse . In chaotic systems , a small amount of noise can yield dramatic effects . At the other end of the spectrum , the effect of noise on nonlinear contracting systems is bounded by where is the noise intensity – which can be arbitrarily large – and is the contraction rate of the system [21] . Between these two extremes , it has been shown analytically that some limit-cycle oscillators commonly used as simplified neuron models , such as FitzHugh-Nagumo ( FN ) oscillators , are basically unperturbed when they are subject to a small amount of white noise [22] . Yet , a larger amount of noise breaks this “resistance” , both in the state space and in the frequency space [Figures 1 ( A ) – ( D ) ] . This suggests that both temporal coding and frequency coding may be unusable in the context of large neuronal noise . One might argue that it could be possible to recover some information from the noisy FN oscillators by considering the activities of a large number of oscillators simultaneously [19] , [23] . Figure 2 ( A ) shows that the spatial mean of the noisy oscillators still carries very little information when the noise intensities are large , making the population coding hypothesis also unlikely in this context . In other words , if the underlying dynamics are fundamentally nonlinear , as in the case of our FN oscillators , the spatial mean of the signals is “clean , ” but contains very little information: the nonlinear nature of the systems dynamics prevents the familiar “averaging out” of noise through multiple measurements , and getting rid of the noise also gets rid of the signal . By contrast , one can observe that when oscillators are synchronized through mutual couplings , then they become “protected” from noise , whether in temporal [Figure 1 ( E ) ] , frequential [Figure 1 ( F ) ] or “populational” aspects [Figure 2 ( B ) ] . Thus , in some sense , the linear effect of averaging noise while preserving signal [24] can be achieved for these highly nonlinear dynamic components through the process of synchronization . Our aim in this article is to give mathematical elements of explanation for this phenomenon , in a full nonlinear setting . It is also to suggest elements of response to a more general question , namely: what is the precise meaning of ensemble measurements or population codes , and what information do they convey about the underlying dynamics and signals ?
Consider a diffusive network of -dimensional noisy non-linear dynamical systems ( 1 ) where is a function . Note that the noise intensity is intrinsic to the dynamical system ( i . e . independent of the inputs ) , which is consistent with experimental findings [20] . For simplicity , we set to be a constant in this article , although the case of time- and state-dependent noise intensities can be easily adapted from [21] . We consider four mathematical assumptions that will enable us to relate the trajectory of any noisy element of the network to the trajectory of the noise-free system driven by equation ( A1 ) is an assumption on the form of the network . ( A2 ) gives a bound on the nonlinearity of the dynamics . ( A3 ) states that the system trajectories are resistant to small perturbations . Finally , ( A4 ) requires that the dynamical systems in the network are synchronized . We now give conditions to guarantee assumption ( A4 ) for all-to-all networks of FN oscillators with identical couplings . The dynamics of noisy FN oscillators coupled by ( gap-junction-like ) diffusive connections is given by ( 2 ) where . We show in Methods that , after exponential transients of rate , ( 3 ) Thus , ( A4 ) is verified with ( 4 ) For large , we have , which converges to 0 when . Figure 3 ( A ) provides a comparison of this theoretical bound with simulations . Assumption ( A1 ) is also verified because an all-to-all network with identical couplings is symmetric , therefore balanced . Since the are oscillators with stable limit cycles , it can be shown that the trajectories of the are bounded by a common constant . Thus ( A2 ) is verified with . Finally , ( A3 ) may be adapted from [22] . Indeed , we believe that the arguments of [22] can be extended to the case of non-white noise . Making this point precise is the subject of ongoing work . Using now the “general analytical result” , we obtain that , given any ( non necessarily small ) noise intensity , in the limits for and and after exponential transients , the behavior of any oscillator will be arbitrary close to that of a noise-free oscillator ( Figure 1 ) . This statement can be further tested by constructing a model-based nonlinear state estimator ( observer ) [29] . Let be a noisy synchronized oscillator and consider the observer ( 5 ) If has the same trajectory as a noise-free FN oscillator , then it can be shown that tends exponentially to , independently of the observer's initial conditions [29] . Thus the squared distance indicates how close is from a noise-free oscillator [see Figure 3 ( B ) for a comparison this theoretical result with simulations] . We provide in this section simulation results which show that similar observations can be made even for more general network classes that are not yet covered by the theory . We believe that this simulations show the genericity of the concepts presented above .
We have argued that synchronization may represent a fundamental mechanism to protect neuronal assemblies from noise , and have quantified this hypothesis using a simple nonlinear neuron model . This may further strengthen our understanding of synchronization in the brain as playing a key functional role , rather than as being mostly an epiphenomenon . It should be noted that the causal relationship studied here – effect of synchronization on noise – is converse to one usually investigated formally in the literature – effect of noise on synchronization: under certain conditions , adding noise can de-synchronize already synchronized oscillators ( destructive effect ) [32]; under other conditions , adding noise can , on the contrary , synchronize oscillators that were not synchronized ( constructive effect ) [33] , [34]; for a review , see [35] . Also , previous papers have studied a similar phenomenon of improvement in precision by synchronization . Enright [28] shows improvement in a model of coupled relaxation oscillators , all interacting through a common accumulator variable ( possibly being the pineal gland ) . This improvement has been experimentally shown in real heart cells [36] . More recently , [37] shows a way to get better than improvement . However , their studies primarily focused on the case of phase oscillators , which are linear dynamical systems . In contrast , we concentrate here on the more general case of nonlinear oscillators , and quantify in particular the effect of the oscillators' nonlinearities . The assumptions we consider are also different: while most existing approaches ( including [37] ) assume weak couplings and small noise intensities , we consider here strong couplings and arbitrary noise intensities . The mechanisms highlighted in the paper may also underly other types of “redundant” calculations in the presence of noise and variability . In otoliths for instance , ten of thousands of hair cells jointly compute the three components of acceleration [38] , [39] . In muscles , thousands of individual fibers participate in the control of one single degree of freedom . Similar questions may also arise in systems biology , e . g . , in cell mechanisms of quorum sensing where individual cells measure global chemical concentrations in their environment in a fashion functionally similar to all-to-all coupling [25]–[27] , in mechanical coupling of motor proteins [40] , in the context of transcription-regulation networks [41] , [42] , and in differentiation dynamics [43] . Finally , the results point to the general question: what is the precise meaning of ensemble measurements or population codes , what information do they convey about the underlying signals , and is the presence of synchronization mechanisms ( gap-junction mediated or other ) implicit in this interpretation ? As such , they may also shed light on a somewhat “dual” and highly controversial current issue . Ensemble measurements from the brain can correlate to behavior , and they have been suggested e . g . as inputs to brain-machine interfaces . Are these ensemble signals actually available to the brain [44] , perhaps through some process akin to quorum sensing , and therefore functionally similar to ( local ) all-to-all coupling ? Are local field potentials [45] plausible candidates for a role in this picture ?
In the noise-free case ( ) , it can be shown that , for strong enough coupling strengths , the elements of the network synchronize completely , that is , after exponential transients , we have in ( A4 ) [6] . Thus , all the tend to a common trajectory , which is in fact a nominal trajectory of the noise-free system , because all the couplings vanish on the synchronization subspace . In the presence of noise , it is not clear how to relate the trajectory of each to a nominal trajectory of the noise-free system . Nevertheless , we still know that the live “in a small neighborhood” of each other , as quantified by ( A4 ) . Thus , if the center of this small neighborhood follows a trajectory similar to a nominal trajectory of the noise-free system , then one may gain some information on the trajectories of the . To be more precise , let be the center of mass of the , that is ( 6 ) Observe that , after expansion and rearrangement , the sum can be rewritten in terms of the distances of the from Using ( A4 ) then leads to ( 7 ) Summing over the equations followed by the and using assumption ( A1 ) , we have ( 8 ) We now make the dynamics explicit with respect to by letting ( 9 ) so that Equation ( 8 ) can be rewritten as ( 10 ) Using the Taylor formula with integral remainder , we have ( 11 ) where is the gradient of or , equivalently , the vector of the Jacobian matrix of . Summing Equation ( 11 ) over and using assumption ( A2 ) , we get ( 12 ) Summing now inequality ( 12 ) over and using inequality ( 7 ) , we get ( 13 ) which implies that when . Turning now to the noise term in Equation ( 10 ) , we have ( 14 ) since the intrinsic noises of the elements of the network are mutually independent . Thus , for a given ( even large ) noise intensity , the difference between the dynamics followed by and the noise-free dynamics tends to zero when and . Assumption ( A3 ) then implies that . More precisely , the impact of noise on the mean trajectory ( quantified by ) evolves as ( 15 ) Finally , Equation ( 7 ) and the triangle inequality ( 16 ) imply that the trajectory of any synchronized element of the network and that of the noise-free system are also similar [compare Figure 1 ( A ) and Figure 1 ( E ) ] . | Synchronization phenomena are pervasive in biology , creating collective behavior out of local interactions between neurons , cells , or animals . On the other hand , many of these systems function in the presence of large amounts of noise or disturbances , making one wonder how meaningful behavior can arise in these highly perturbed conditions . In this paper we show mathematically , in a general context , that synchronization is actually a means to protect interconnected systems from effects of noise and disturbances . One possible mechanism for synchronization is that the systems jointly create and then share a common signal , such as a mean electrical field or a global chemical concentration , which in turn makes each system directly connected to all others . Conversely , extracting meaningful information from average measurements over populations of cells ( as commonly used for instance in electro-encephalography , or more recently in brain-machine interfaces ) may require the presence of synchronization mechanisms similar to those we describe . | [
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] | 2010 | How Synchronization Protects from Noise |
Metabolomics coupled with heavy-atom isotope-labelled glucose has been used to probe the metabolic pathways active in cultured bloodstream form trypomastigotes of Trypanosoma brucei , a parasite responsible for human African trypanosomiasis . Glucose enters many branches of metabolism beyond glycolysis , which has been widely held to be the sole route of glucose metabolism . Whilst pyruvate is the major end-product of glucose catabolism , its transamination product , alanine , is also produced in significant quantities . The oxidative branch of the pentose phosphate pathway is operative , although the non-oxidative branch is not . Ribose 5-phosphate generated through this pathway distributes widely into nucleotide synthesis and other branches of metabolism . Acetate , derived from glucose , is found associated with a range of acetylated amino acids and , to a lesser extent , fatty acids; while labelled glycerol is found in many glycerophospholipids . Glucose also enters inositol and several sugar nucleotides that serve as precursors to macromolecule biosynthesis . Although a Krebs cycle is not operative , malate , fumarate and succinate , primarily labelled in three carbons , were present , indicating an origin from phosphoenolpyruvate via oxaloacetate . Interestingly , the enzyme responsible for conversion of phosphoenolpyruvate to oxaloacetate , phosphoenolpyruvate carboxykinase , was shown to be essential to the bloodstream form trypanosomes , as demonstrated by the lethal phenotype induced by RNAi-mediated downregulation of its expression . In addition , glucose derivatives enter pyrimidine biosynthesis via oxaloacetate as a precursor to aspartate and orotate .
Trypanosoma brucei is a protozoan parasite , sub-species of which are responsible for human African trypanosomiasis ( HAT ) and animal African trypanosomiasis ( Nagana ) [1] . Current treatments for HAT are inadequate , and new preventative and therapeutic options are urgently required [2] . The parasite is transmitted between mammalian hosts by a tsetse fly vector . The physiological environments of mammalian blood and the tsetse midgut diverge significantly , and bloodstream form and insect form parasites reveal adaptive differences in biochemistry to allow optimised survival in these environments [3 , 4] . T . brucei possess glycosomes , peroxisome-derived organelles that contain enzymes required for glycolysis [5] . Within the tsetse fly , glucose is generally scarce with proline a key energy source [6] . Procyclic form ( PCF ) trypanosomes preferentially use proline as a source of carbon and energy [6 , 7 , 8] , although in glucose-rich culture medium they preferentially utilize glucose through glycolysis [7 , 8] . For many years it has been widely accepted that bloodstream form ( BSF ) trypanosomes exhibit greatly reduced metabolic potential , where glycosomal glucose utilization through glycolysis is the sole energy source [4 , 5 , 9 , 10] . Under aerobic conditions pyruvate was considered the sole end-product , with the redox balance maintained by a mitochondrial alternative oxidase shunt acting to regenerate dihydroxyacetone phosphate ( DHAP ) from glycerol 3-phosphate [11] . Under anaerobiosis , equal quantities of pyruvate and glycerol are produced , where the reverse reaction of glycerol kinase ( GK ) generates glycerol from glycerol 3-phosphate [9] . Early studies on BSF trypanosomes isolated from rodents identified other secreted metabolites , including succinate , aspartate and alanine [12–14] . However , the fact that these products were present in small amounts and that stumpy form trypanosomes , a non-replicative form of the parasite pre-adapted for life in the tsetse fly [15 , 16] , could contaminate the slender BSF preparations , led to these observations being considered of limited relevance in formulating models of a simplified pathway for glucose catabolism in the slender replicative form of T . brucei [9] . Evidence for a more diverse fate of glucose in BSF trypanosome metabolism has , however , emerged . For example , NMR analysis of glucose metabolism [17 , 18] confirmed glycerol and pyruvate as the major end products of glycolysis , while also detecting significant amounts of alanine . The conversion of pyruvate to alanine may even be essential to BSF trypanosomes , since it was not possible to knockout the alanine aminotransferase ( AAT ) gene [19] , although significant reduction of its transcript by RNAi was possible [19] . An oxidative branch of the pentose phosphate pathway ( PPP ) is operative [20] , and 6-phosphogluconate dehydrogenase is essential to these forms [21 , 22] . Glucose-derived ribose was also shown to be incorporated into cellular nucleotides in BSF trypanosomes [23 , 24] , and inositol derived from glucose is incorporated into the glycosylphosphatidyl inositol anchors attached to the abundant variant surface glycoprotein coat of these cells [25 , 26] . Recently it was shown that pyruvate dehydrogenase ( PDH ) becomes essential if slender bloodstream forms are deprived of threonine as a source of acetate [27]; in this situation PDH is needed to generate acetate from glucose metabolism . The systematic application of reverse genetics to PCF trypanosomes combined with NMR analyses of end products of metabolism has demonstrated the importance of succinate fermentation pathways in both the glycosome and mitochondrion , through a route independent of the classical TCA cycle [28–30] . A glycosomal succinate shunt converts phosphoenolpyruvate ( PEP ) produced in the cytosol to oxaloacetate using PEP carboxykinase ( PEPCK ) following glycosomal reuptake . PEP can also be converted to pyruvate , which enters the mitochondrion to feed the PDH complex for production of acetyl-CoA , which is converted by the mitochondrial acetate:succinate CoA-transferase ( ASCT ) and acetyl-CoA thioesterase ( ACH ) to produce acetate [30 , 31] . The advent of metabolomics technology [32–34] allows an unbiased analysis of metabolism across diverse pathways in T . brucei . Stable-isotope labelling permits direct detection of active metabolic pathways within a live cell system [35–39] and offers a direct route to follow the distribution of atoms from a precursor substrate through the metabolic network . We have previously applied the approach to PCF trypanosomes [38] , and here extend the methodology to make a comprehensive assessment of the distribution of glucose-derived carbon through the BSF trypanosome .
BSF T . brucei were fed a 50:50 mixture of U-12C and U-13C-glucose as the major carbon source for one hour and 24 hours . Glycolytic intermediates were rapidly labelled , as expected as a result of high flux through the glycolytic pathway [10] , with complete ( 50% ) labelling of the major end-product , pyruvate . Significant labelling was observed in metabolites of many other pathways , indicating extensive anabolic utilization of glucose . In total , over 150 metabolites were detected with glucose-derived carbon labelling , including sugar phosphates , sugar nucleotides , lipids and secondary metabolites derived from glycolytic intermediates and end-products ( S1 Table ) . Whilst excreted pyruvate accounts for the majority of catabolised glucose [10] , glucose-derived pyruvate also serves as a major source of alanine and acetyl-CoA within the cell . Furthermore , metabolism of glucose through the oxidative branch of the PPP provides ribose 5-phosphate , as evidenced by its being 50% U-13C-labelled , which in turn is utilized for nucleotide biosynthesis . Of significant interest was the labelling of numerous carboxylic acids including malate , fumarate and succinate , and extensive labelling in aspartate and various pyrimidines . In all , these results demonstrate extensive utilization of glucose for anabolic purposes in bloodstream form T . brucei ( Fig 1 ) . The glycolytic pathway is highly active in T . brucei , which explains the 6-carbon labelling in glucose 6-phosphate ( G6P ) , fructose 6-phosphate ( F6P ) and fructose-1 , 6-bisphosphate ( FBP ) , and the 3-carbon labelling in the triose phosphate intermediates of lower glycolysis ( Fig 2 ) . Interestingly , additional 3-carbon labelled isotopologues of hexose phosphates were observed . These isotopologues represent 10% of the intracellular G6P and F6P , and 40% of the FBP . These metabolites were identified by exact mass and retention time using authentic standards , although we cannot absolutely rule out other metabolites of the same mass and retention time ( i . e . unanticipated hexose phosphate isomers ) co-eluting with these metabolites . These findings are inconsistent with unidirectional glucose flow through the Embden-Meyerhof-Parnas glycolytic pathway . Labelling studies with 100% U-13C-glucose produced very little 3-labelled hexose phosphate ( ~2% of the hexose phosphate is 3-labelled even when only ~97% of the glucose is labelled inside the cells ) , indicating that all carbons in the hexose phosphates are originally derived from glucose ( Fig 2d ) . Considering this lack of another source and the absence of other reactions that could produce 3-labelled hexose-phosphates , the 3-labelled hexose-phosphates most likely derive from the 3-labelled fructose bisphosphate ( possibly via the reverse phosphofructokinase reaction although a contribution from fructose bisphosphatase cannot be ruled out ) . Gluconeogenesis from amino acids or other carbon sources is not identified , consistent with previous observations [20] . The high percentage of 3-carbon labelled FBP when using the 50:50 mix of U-13C-glucose and U-12C-glucose seems counter-intuitive , given the very strong net forward flux through glycolysis and the high concentration of FBP . However , our computational analysis shows that it is not impossible . The aldolase reaction is reversible ( the net aldolase flux = forward flux—reverse flux ) . The observed labelling would require that the part of the FBP pool created by the “reverse” aldolase reaction , working in the direction of FBP production , would represent 85% ( in HMI medium ) or 97% ( in CMM medium ) of the total FBP pool ( see S1 Text for detailed explanations ) . These large contributions by the reverse reaction are possible under certain conditions , in spite of the net flux strongly favouring the forward direction . Simulations using a previously published collection of models which are described in detail in [40] show that under the classical assumption that glycosomes are impermeable , reverse aldolase fluxes can vary widely with plausible kinetic parameter values , most frequently with between 0 and 60% of the FBP pool coming from the “reverse” aldolase reaction ( i . e . between 0 and 29% being potentially 3-labelled ) . However , if the glycosome is semi-permeable to metabolites smaller than FBP itself ( model 3 in ref 40 ) due to non-selective pore-forming channels in the glycosomal membrane [41] , high reverse aldolase flux , accounting for 60–99% of the FBP pool is favoured ( S1 Text ) . This would be in excellent agreement with the observed labelling pattern . In addition to pyruvate , its transamination product alanine was produced , probably via AAT , an enzyme previously shown to be essential to BSF trypanosomes [19] . Very low levels of labelled lactate were also detected , with all three carbons labelled . No lactate dehydrogenase has been identified in T . brucei , although L-lactate production has been shown to occur through an unusual methylglyoxal detoxification pathway in trypanosomes [42] , which may correspond to the metabolite we find of the corresponding mass . A number of potential pyruvate-derived conjugated metabolites were observed , although their significance is unknown . For example , an abundant metabolite with either 3C or 6C labelling corresponding to the molecular formula C6H8O6 , was putatively identified as parapyruvate [43] . Pyruvate also appeared to form adducts with basic amino acids to produce metabolites with mass , labelling and predicted retention times consistent with carboxyethyl-L-arginine and carboxyethyl-L-ornithine ( S1 Table ) . Another abundant novel 3-carbon labelled metabolite was detected with a molecular formula of C6H9NO4S , a likely condensation product of pyruvate and cysteine , while C7H11NO5 may correspond to the formation of a pyruvate-threonine adduct . These metabolites were more abundant in cells grown in HMI11 compared to CMM , consistent with the greater abundance of L-cysteine and L-threonine in the richer medium . In the absence of authentic standards , however , further analysis will be required to confirm the structure and physiological relevance of these compounds . The pentose phosphate pathway intermediates ( 6-phosphogluconate and ribose 5-phosphate ) are fully labelled , indicating a linear oxidative PPP derived from glucose 6-phosphate ( Fig 3 ) . The complete ( 5-carbon ) labelling of ribose in purine nucleotides confirms that this pathway is the major source of ribose 5-phosphate for nucleotide synthesis ( Fig 3e ) . This contrasts with the situation in PCF T . brucei , which also possess an active non-oxidative branch of the PPP [20] and show the characteristic 2- , 3- and 5-carbon ribose 5-phosphate labelling as a consequence of the transaldolase and transketolase ( TKT ) reactions that combine fully labelled and fully unlabelled precursors [38] . The lack of these isotopologues thus confirms the lack of TKT in BSFs [20 , 44] . Two high molecular weight sugar phosphates , octulose 8-phosphate ( O8P ) and nonulose 9-phosphate ( N9P ) were detected and putatively identified by accurate mass and predicted retention time ( as authentic standards are not available ) . The labelling patterns ( Fig 3c-d ) indicate production by transaldolase activity from a three carbon unit donated from an aldose donor to either a pentose phosphate ( to produce O8P ) or hexose phosphate ( to produce N9P ) . Interestingly , BSF T . brucei have previously been shown to possess transaldolase activity [20] , in spite of the absence of TKT . The absence of 2C- and 6C- labelled O8P in BSFs , while they are present in PCFs [38] , indicates that a PPP intermediate is the precursor for this metabolite . We expressed and purified the gene encoding T . brucei transaldolase ( Tb927 . 8 . 5600 ) in Escherichia coli . When the enzyme was mixed with ribose 5-phosphate and F6P as substrates , mass spectrometry revealed that O8P was produced , confirming the ability of this enzyme to carry out this reaction ( Fig 3f ) . Moreover , cells were labelled with U-13C-ribose which resulted in extensive labelling of ribose 5-phosphate ( produced by ribokinase ) , and significant 5-carbon labelling in O8P ( Fig 3d ) . The detection of metabolites with formulas consistent with sedoheptulose 7-phosphate ( C7H15O10P ) and erythrose 4-phosphate ( C4H9O7P ) was surprising in a cell without TKT activity . However , the 50% U-13C-glucose labelling reveals only the 3-carbon labelled isotopologue of each , confirming that these are not products of TKT . Further investigation found that the abundance of the C7H15O10P peak was very low in all samples , and the retention time of C4H9O7P was inconsistent with the authentic standard for erythrose 4-phosphate . More work is therefore required to elucidate the identity and source of these metabolites , and to confirm that a non-canonical pathway involved in sugar phosphate remodelling exists in trypanosomes . The lack of uniform labelling in purine bases , nucleosides and nucleotides confirms the requirement for exogenous purine bases in trypanosomatids which lack purine biosynthetic pathways ( Fig 3e ) . However , glucose-derived ribose produces 5-carbon labelled purine nucleotides , as purine salvage pathways are highly active [45] . Transporters for the efficient uptake of purine nucleosides have been characterized , and guanosine and inosine appear to be the major purine sources in in vitro culture , even in the presence of excess hypoxanthine [46] . These labelling data also confirm that nucleoside hydrolases [47] that form purine bases are the primary route for nucleotide salvage from imported nucleosides and that glucose-derived ribose 5-phosphate is added through purine phosphoribosyltransferase [48] . The almost complete labelling of the ribose moiety of adenosine nucleotides indicates minimal purine salvage from exogenous adenosine by adenosine kinase under our in vitro growth conditions , in spite of its high activity in these cells [49] . Intracellular synthesis of various cofactors , including NAD+ and S-adenosyl methionine ( AdoMet ) , was also confirmed by incorporation of labelled ribose into the nucleotide subunits . AdoMet is an important precursor in polyamine synthesis , resulting in production of 5’methylthioadenosine ( MTA ) . The 5-carbon labelling in these two metabolites confirms that the labelling is in the ribose moiety . However , the absence of labelling in methionine precludes activity of a methionine salvage pathway as previously proposed [50] . Methionine uptake from serum [51] apparently fulfils all methionine requirements of BSF parasites . The absence of labelling in α-ketomethylthiobutyrate ( KMTB ) , the last intermediate in the methionine salvage pathway , further confirms the lack of function in this proposed pathway . KMTB probably arises instead from transamination of methionine , and it was previously shown to be excreted from parasites along with other amino acid-derived keto acids [46] . An active TCA cycle is reportedly not operative in BSF T . brucei , and the observation that <1% of detected 2-oxoglutarate contains label from glucose corroborates this ( S1 Table ) . A small amount of acetyl-coA and oxaloacetate were , nevertheless , directed into citrate , as seen/observed by the 2- , 3- and 5-carbon labelling in excess of the natural abundance of 13C by a total of approximately 1% in cells grown in HMI11 , and 5% in CMM-grown cells ( S2 Table ) . The relevance of this low level citrate production is not known . PCF T . brucei do not use the classical citrate—malate shuttle , instead utilizing a unique acetate shuttle to provide acetyl-CoA to the cytosol [31] . Isotope analysis of intracellular metabolites from cells incubated with 50% U-13C-labelled glucose reveals three-carbon labelled isotopologues of succinate and malate , representing 35% ( 70% when corrected for 50% labelled glucose precursor ) of the intracellular malate ( Fig 4 ) and 26% of succinate ( 52% corrected ) . This labelling pattern is consistent with the production of succinate via a succinate fermentation pathway . The first enzyme of this pathway , PEPCK , is found in BSF trypanosomes , albeit at a lower level than PCF [52 , 53] . Although oxaloacetate is not detectable on this analytical platform , the observation of the 3-carbon labelled isotopologue in aspartate ( a transamination product of oxaloacetate ) supports the production of oxaloacetate from PEP . The total labelling in aspartate ( 62% corrected ) is comparable to malate ( 70% corrected ) , demonstrating that intracellular aspartate is primarily synthesized from glucose-derived oxaloacetate , rather than being taken up from the surrounding medium , which contains aspartate at 100 μM [46] . BSF trypanosomes failed to demonstrate appreciable aspartate transport capability [54] . T . brucei encodes three separate isoforms of malate dehydrogenase ( MDH ) , localized to the mitochondrion , glycosomes and the cytosol , respectively [55] . The glycosomal isoform is reportedly absent from the BSF , while the cytosolic isoform is present at higher levels in BSF than procyclics [55] . Additionally a small amount of the mitochondrial isoform is reportedly present [55–57] . The succinate fermentation pathway therefore might involve oxaloacetate produced in the glycosome via PEPCK which might re-enter the cytosol and possibly the mitochondrion for further metabolism . Further work , systematically removing or knocking down expression of different isoforms , followed by metabolic profiling , will be required to deconvolute the sources of these partially reduced products of glucose catabolism in the BSF trypanosome . Oxaloacetate-derived aspartate is a key precursor in pyrimidine synthesis , being converted to orotate via dihydroorotate . Dihydroorotate and orotate were detected with isotope enrichment equivalent to aspartate , ~35% 3-labelled carbons ( 70% corrected ) , confirming de novo synthesis of pyrimidines in this way . Uracil labelling was slightly lower ( 44% corrected ) , suggesting some uptake of uracil from the medium . However , higher levels of 2-labelling in UMP and UDP suggest that de novo synthesized orotate is the major source of pyrimidines under these conditions ( Fig 4 ) . Labelling of uridine and cytidine nucleotides was not significantly different between the two different culture media used in this study , HMI11 and CMM . However , significant differences were observed in thymidine nucleotides . dTTP and dTMP abundance in CMM-grown cells was only half that observed for HMI11 , which contains 20 μM added thymidine . The isotope labelling patterns of dTTP and dTMP in CMM matched those of UMP and the other pyrimidine nucleotides , indicating complete synthesis from de novo synthesized pyrimidines by thymidylate synthetase in thymidine-poor media [58 , 59] . dTMP and dTTP in HMI11-grown cells were less than 10% labelled , confirming a preference for thymidine salvage by the action of thymidine kinase when exogenous thymidine is available ( Fig 4c ) . Aspartate is also an important intermediate in the purine salvage pathway , in which the formation of adenylosuccinate and release of fumarate results in nitrogen transfer from aspartate to IMP , producing AMP . Adenylosuccinate was detected with 3-carbon labelling , which confirms the role of glucose-derived aspartate in the purine salvage pathway ( Fig 3e ) . The discovery that glucose enters amino acid and nucleotide pathways via oxaloacetate indicates that PEPCK might play an unexpectedly important role in BSF trypanosomes . Indeed , a genome-wide RNAi screen indicated the gene could be essential [60] . To test this further , western blot analysis was performed with anti-PEPCK immune serum , showing that PEPCK is expressed in the BSF , albeit at much lower abundance than in the PCF ( Fig 5 ) . Two cell lines ( RNAiPEPCK-B3 and RNAiPEPCK-D6 ) were then generated to investigate the role of PEPCK in BSF T . brucei by RNAi down-regulation of pepck gene expression . Both cell lines showed a growth arrest two days after tetracycline induction . A phenotypic reversion 5 days post-induction correlated with a re-expression of PEPCK ( Fig 5 ) . In order to measure the metabolic role of PEPCK , the production of succinate from glucose in BSF trypanosomes was measured using 1H-NMR , as previously described for PCF T . brucei [61] . The parental BSF 427 strain was incubated for 5 hours in PBS/NaHCO3 medium containing 4 mM D-glucose as the only carbon source and the incubation medium was analyzed by 1H-NMR spectroscopy . In addition to pyruvate , detectable amounts of succinate were quantitatively measured , at levels ~2 . 7% of the excreted pyruvate ( Fig 6a and Table 1 ) . The amount of excreted succinate was decreased 4 . 3 fold in the RNAiPEPCK-B3 mutant after two days of induction compared to the wild-type cells ( 47 ±17 versus 201 ±89 nmol/h/mg of protein ) . Succinate secretion increased five days after induction ( 79 ±18 nmol/h/mg of protein ) ( Fig 6a and Table 1 ) , as a consequence of pepck re-expression ( Fig 5 ) . To confirm reduction of succinate production in the RNAiPEPCK-B3 tetracycline-induced cell line ( at 2 days ) , we determined incorporation of 13C-labelling from U-13C-glucose into intracellular metabolites by LC-MS . A reduction of 13C3 incorporation into malate ( 24 ± 10% ) and succinate ( 29 ± 2% ) was observed in the induced RNAiPEPCK-B3 cell line compared to the non-induced cells ( Fig 6b ) . Labelling in other products of PEPCK-derived oxaloacetate was also significantly reduced , including aspartate ( 15-fold ) , uracil ( 3-fold ) and UTP ( 2-fold ) . In contrast , 13C incorporation into metabolites produced upstream of the PEPCK step ( G6P , 3PG and pyruvate ) , and those in other pathways derived from glycolytic intermediates ( alanine , acetyl-CoA , and ribose 5-phosphate ) were not significantly altered ( Fig 6b ) . In addition to its secretion and conversion to alanine , pyruvate is a major source of acetyl-CoA in BSF trypanosomes . Although the intracellular concentration of acetyl-CoA was below the limit of detection for our analytical method , evidence of its production is provided by 2-hydroxyethyl thiamine pyrophosphate ( Fig 7a ) , the intermediate in pyruvate’s conversion to acetyl-CoA by PDH . The presence of two labelled carbons indicates formation directly from glucose-derived pyruvate . Labelling was minimal after one hour , but complete labelling was observed at 24 hours , suggesting that flux through this pathway is significantly slower than through most other pathways ( Fig 7a ) . It has recently been shown [27] that PDH knockdown is conditionally lethal in BSF T . brucei . Both threonine , via a threonine dehydrogenase ( TDH ) pathway , and glucose , via PDH , can provide acetate and the latter route becomes essential when threonine is absent , or redundant due to loss of TDH [27] . Furthermore , acetylated metabolites including acetylcarnitine , acetyllysine , acetylornithine and acetylglutamine were all detected with 2 labelled carbons from glucose ( S1 Table ) . The unlabelled proportion of these metabolites probably relates to threonine’s key role in acetate provision [27] . De novo fatty acid synthesis was detected in BSF T . brucei by incorporation of two labelled carbon units in free fatty acids and phospholipids ( Fig 7b ) . However , the very low level of labelling in fatty acids suggests that de novo fatty acid synthesis is minor compared to salvage mechanisms , and a high level of lysophospholipid uptake may represent the principal source of fatty acids in these cells [62] with elongation accounting for modelling fatty acids of different chain length [63] . The additional 3-carbon labelling observed in glycerophospholipids demonstrates that lipid salvage utilizes glycolysis-derived glycerol 3-phosphate for synthesis of phospholipid head-groups . Despite the requirement for salvage of host ( or culture medium ) lipids for the provision of choline , and most fatty acids , around half of the glycerol phosphate in lipid head-groups was derived from glycolysis ( ranging from 8 to 100% corrected labelling for detected PC ( phosphatidylcholine ) and PE ( phosphatidylethanolamine ) lipids; Fig 7 and S1 Table , the variability relating to the fact that the relative contributions of the salvage and de novo synthesis pathways are not uniform across lipid species . Several other labelled metabolites were detected ( S1 Table ) indicating that glucose enters amino sugars ( N-acetyl-D-glucosamine and N-acetyl-D-glucosamine 6-P ) , sugar nucleotides ( UDP-Glc , or UDP-Gal GDP-man and UDP-GlcNAC; Fig 7c ) and low-level labelling in other sugars ( tentatively identified as myo-inositol , fructose , glucuronate and arabinonate , although accurate identification of sugars is difficult using accurate mass-based metabolomics , as many isomers are theoretically possible for most carbohydrate metabolites; hence , additional orthogonal approaches are required to confidently identify these structures . Nothwithstanding their abundance is clearly low compared to glucose and fructose the two primary hexoses . Mannose is absent in its free form , its generation occurring only when nucleotide conjugated and in phosphorylated form [24] ) . Targeted studies of sugar nucleotide biosynthesis have already demonstrated de novo biosynthesis of UDP-Glc [64] and GDP-Man [24] from glucose , and analysis of inositol metabolism demonstrated de novo synthesis of inositol for protein glycosylation , in addition to inositol salvage for lipid biosynthesis [25 , 26] . Data from the current study demonstrated less than 10% labelling in inositol , and confirmed that the inositol moiety of the lipid headgroup glycerol-phosphoinositol was unlabelled ( i . e . not formed by de novo synthesis from glucose , consistent with targeted analyses conducted elsewhere to investigate the origin of inositol in T . brucei [25 , 26] ) .
The introduction of metabolomic technologies , particularly in conjunction with stable isotope tracing has , in recent years , transformed our ability to analyse metabolism in biological systems . For example , isotopologue studies into Leishmania [65] , Plasmodium [36 , 37] and Toxoplasma [35] have identified novel pathways and resolved long-standing questions of metabolism . The African trypanosome , Trypanosoma brucei , is the causative agent of human African trypanosomiasis , a neglected tropical disease of sub-Saharan Africa , for which new drugs are urgently needed . The slender bloodstream form of these parasites has generally been considered to have a highly streamlined glucose catabolic pathway [3–5 , 9 , 10] . The localization of the first seven enzymes of the glycolytic pathway to membrane-bounded peroxisome-like organelles known as glycosomes [5] has been proposed to be instrumental to the regulation of flux through glycolysis by providing an environment in which ATP/ADP and NAD/NADH can be balanced [9 , 10] . This arrangement has enabled the production of a well-defined dynamic mathematical model of glycolysis [9 , 10] and the models have been able to predict several important phenomena , such as the contribution of different enzymes to the overall control of the pathway . Recent iterations have included the addition of parameters that explicitly take into account uncertainty related to the system [66] and also the activity of the pentose phosphate pathway [22] and the presence of permeability pores that allow free diffusion of several glycolytic intermediates between the cytosol and glycosome [40] . Here we have taken the systematic route to analyse the full extent of glucose metabolism in long slender BSF trypanosomes . Extensive pyruvate production was observed , consistent with existing models of glycolytic flux for energy production . However , the 3-carbon labelled isotopologues observed in glycolytic hexose phosphates reveals previously unreported complexity in this pathway . The lack of transketolase [20 , 42] indicates that a canonical non-oxidative pentose phosphate cycle is missing from these cells and thus is not the source of 3-labelled hexose phosphates . The role of transaldolase in BSFs is uncertain , and transaldolase-mediated production of 3-labelled fructose 6-phosphate ( from F6P and GA3P ) could be hypothesized , although this appears to be a redundant reaction . Alternatively , aldolase [67] , acting to combine either U-13C or U-12C DHAP with U-13C or U-12C GA3P would produce the relative proportions of 0- , 3- and 6-labelled fructose bisphosphate as identified here . The further production of 3-labelled F6P ( and G6P ) from FBP would require fructose-1 , 6-bisphosphatase activity , and both transcriptomic [68] and proteomic [69] data indicate that this enzyme is present in BSF T . brucei . A family of models , simulating flux through glycolysis whilst taking into account our uncertainties about the system and its distribution between glycosome and cytosol [40] , reveals that if metabolites smaller than FBP are able to exchange between compartments while FBP is not , then a significant proportion of the total FBP in the cell can indeed arise through the condensation of GA3P and DHAP . The data , therefore , supports the proposed role of pores creating a semi-permeable glycosome [41] . Further work is required to fully investigate the intermediate fluxes in glycolytic metabolism and to determine the temporal and spatial aspects of metabolism that give rise to these unexpected observations . At this point we cannot rule out definitively the production of a compound with the mass and retention time of FBP as an artefact of the experimental system . The same might be true of other exotic , unexpected species identified in this study . However , a non-enzymatic aldol condensation of triose phosphates is highly unlikely , given that non-enzymatic condensations and additions generally require extreme pH conditions ( <1 or > 9 ) . To confirm this , DHAP and GA3P were mixed in CMM medium followed by extraction in the conditions used to extract our cellular lysates and storage for seven days at -80°C , and no FBP or other potential condensation or addition products were observed by LC-MS . We therefore consider it likely that many of the metabolites that we describe here are bona fide products of trypanosome metabolism , and the incorporation of carbon-13 label confirms the endogenous production of these metabolites ( compared to the unlabelled metabolites primarily acquired from the serum in the growth media ) , although their physiological function , or indeed relevance remains unknown . Metabolites can be produced either enzymatically or chemically in cells as inevitable by-products of metabolism , methylglyoxal being an example already discussed here [42] . Apart from the identification of numerous unexpected metabolites and pathways operating in BSF trypanosomes , our experiments also rule out the operation of other pathways previously assumed to be functional . For example , a rapid deamination of aromatic amino acids was believed to contribute to a methionine cycle involved in provision of decarboxylated S-adenosylmethionine used in polyamine synthesis [50] . Here , however , we demonstrate that carbons from glucose do not enter methionine , which is provided exclusively through cellular uptake instead [51] . This means that the pathways of methylthioadenosine detoxification remain to be elucidated in the BSF trypanosome . While the majority of glucose flows to secreted pyruvate under aerobic culture conditions , we show that glucose metabolism is pervasive in the BSF parasites . In addition to pyruvate and glycerol , various other metabolites are secreted , including alanine , the transaminated product of pyruvate . This reaction appears to be essential since the alanine transaminase gene could not be knocked out of the T . brucei genome [19] , although knockdown by RNAi was possible . Succinate was also secreted . The succinate produced in BSF trypanosomes is primarily labelled in three carbons , which indicates its derivation from glycolytic phosphoenolpyruvate , rather than from a canonical TCA cycle , which would produce primarily 2-carbon and 4-carbon labelled isotopologues . Analysis of the phosphoproteome of BSFs identified glycosomal NADH-dependent fumarate dehydrogenase [70] . However , it is not possible to distinguish between glycosomal , cytosolic or potentially mitochondrial derivation of these metabolites , and systematic metabolite profiling following gene knockout or knockdown of various enzymes will be necessary to deconvolute these pathways . Although it has been suggested that glucose makes negligible contribution to anabolic processes in BSF trypanosomes [10] , numerous isolated studies have revealed that glucose serves as a precursor in processes including sugar nucleotide and glycoconjugate synthesis [23] , inositol production [25 , 26] and recently acetate production [27] . Glucose also enters pyrimidine biosynthesis via oxaloacetate and lipid metabolism via glycerol 3-phosphate as well as acetate production . RNAi knockdown of PEPCK confirmed this activity as being essential in BSF trypanosomes . The same enzyme was not essential to PCFs , although its contribution is major in this parasitic insect form [71] . We recently demonstrated that loss of bound phosphate from the glycolytic pathway in the glycosome via the PPP requires a mechanism to retain the bound phosphate balance [22] . PEPCK offers an obvious means to restore glycosomal ATP , and this link to the PPP might explain the unexpectedly essential nature of this enzyme to the BSF trypanosome . Although the untargeted mass spectrometry platform we use here is intrinsically unable to quantify the total flux of glucose carbon into the various pathways discussed here , it is clearly the case that the overwhelming majority of glucose is catabolised to pyruvate ( Table 1 ) . The total amount glucose entering these other pathways would be in the order of just a few percent of the total consumed , in agreement with the measurements of Haanstra et al [10] . Although only a small minority of the total glucose consumption , however , glucose is the major carbon source for the production of many of these extra-glycolytic metabolites , and this flux is clearly important given the fact that several enzymes , including PEPCK shown here , turn out to be essential to the metabolism of these parasites . The identification of novel essential enzymes in the BSF trypanosome may have important implications for chemotherapy . Glycolysis has long been considered an attractive target given the absolute dependency on glucose for energy and carbon provision to these cells [72]; our results show that additional pathways around the periphery of glycolysis might be equally promising .
T . brucei brucei bloodstream form ( s427 ) was cultured in vitro in HMI11 [73] and CMM [44] supplemented with 10% fetal calf serum ( FCS ) Gold ( PAA , Piscataway , NJ ) at 37°C , 5% CO2 . Initial 5 ml cultures in 25 ml vented flasks ( Corning ) were grown to a maximum density of 4 × 106 cells/ml and subcultured by 1-in-100 or 1-in-1 , 000 dilution every 2 or 3 days , respectively . A hemocytometer ( Neubauer ) was used for cell counts . Two days prior to the extraction , 2 × 104 cells/ml were seeded in 30 ml of HMI11 and CMM . Growth media was then replaced with media containing 50% of 13C-labelled glucose ( 12 . 5 mM of 12C-glucose + 12 . 5 mM of 13C-glucose for HMI11 , and 5 mM of 12C-glucose and 5 mM of 13C-glucose for CMM culture ) at 24 h and 1 h prior to extraction . All samples were extracted at the same time atequivalent cell densities of approximately 2 ×106 cells/ml . To yield 5 ×107 cells , appropriate volumes of cell culture were harvested and quenched in EtOH/dry ice bath as previously described [74] . Cells were centrifuged at 1250 ×g for 10 min and spent media removed; cells were washed with pre-cooled phosphate buffered saline ( PBS ) at 4°C . After centrifugation at 1250 ×g for 5 min , the supernatant was removed and metabolites extracted by adding 100 μl of ( ice cold ) chloroform/methanol/water ( 1:3:1 ) and mixed vigorously for 1 h at 4°C . The extraction mixtures were centrifuged at 13 , 000 ×g for 10 min and the supernatants collected and stored at -80°C prior to analysis . In order to assess instrument performance , one pooled quality control ( QC ) sample was prepared by mixing an equal volume of all the samples . Independent biological replicates were prepared on different days . Hydrophilic interaction liquid chromatography ( HILIC ) was carried out on a Dionex UltiMate 3000 RSLC system ( Thermo Fisher Scientific , Hemel Hempstead , UK ) using a ZIC-pHILIC column ( 150 mm × 4 . 6 mm , 5 μm column , Merck Sequant ) as previously described [74] . Briefly , the column was maintained at 30°C and samples were eluted with a linear gradient ( 20 mM ammonium carbonate in water , A and acetonitrile , B ) over 46 min at a flow rate of 0 . 3 ml/min as follows: 80% B to 20% B at 30 min , to 5% B at 32 min and held to 39 min for washing , to 80% B at 40 min and held to 46 min for re-equilibration . The injection volume was 10 μl and samples were maintained at 4°C prior to injection . For the MS analysis , a Thermo Orbitrap Exactive ( Thermo Fisher Scientific ) was operated in polarity switching mode and the MS settings were as follows: resolution 50 , 000 , AGC 106 , m/z range 70–1400 , sheath gas 40 , auxiliary gas 5 , sweep gas 1 , probe temperature 150°C , and capillary temperature 275°C . For positive mode ionisation: source voltage +4 . 5 kV , capillary voltage +50 V , tube voltage +70 kV , skimmer voltage +20 V . For negative mode ionisation: source voltage-3 . 5 kV , capillary voltage-50 V , tube voltage-70 V , skimmer voltage-20 V . Mass calibration was performed for each polarity immediately prior to each analysis batch . The calibration mass range was extended to cover small metabolites by inclusion of low-mass contaminants with the standard Thermo calmix masses ( below m/z 1400 ) , C2H6NO2 for positive ion electrospray ionisation ( PIESI ) mode ( m/z 76 . 0393 ) and C3H5O3 for negative ion electospray ionisation ( NIESI ) mode ( m/z 89 . 0244 ) . To enhance calibration stability , lock-mass correction was also applied to each analytical run using these ubiquitous low-mass contaminants . Raw LC-MS data were processed with XCMS for untargeted peak detection [75] , and mzMatch . R [76] was employed for peak matching and annotation of related peaks . Tentative metabolite identification was carried out by IDEOM using the default parameters [77] . Metabolite identification was performed by matching accurate masses and retention times of authentic standards ( MSI confidence level 1 , i . e . an annotated compound matched to a standard with two orthogonal approaches ) , but when standards were not available , predicted retention times were calculated by a previously validated model [78] ( MSI confidence level 2 , i . e . a putatively annotated compound based on its exact mass determination ) . Published literature and pathway/genome databases were considered to improve annotation in cases where isomers could not be differentiated based on accurate mass and retention time . Metadata to support the identification of each metabolite is available in the IDEOM file for each study ( S3 Table ) . Metabolites included in the manuscript were manually annotated using authentic standards where available . However , note that many of the metabolite names given in the Ideom file are generated automatically as the software provides a best match to database entries of the given mass and formula . In the absence of additional information these must be considered as putatively-annotated hits; the confidence score in the column adjacent to that hit serves as a guide to this . Clearly it is beyond the scope of any study to provide authenticated annotations to many hundreds of detected compounds , but the full datasets are included in the spirit of open access data . The mzmatch-ISO software [39] was used to extract all isotopologue abundances from all identified and putative annotated metabolites . Raw data are available in the associated IDEOM files ( S3 Table ) and chromatograms are in separate pdf files ( S1–S2 Figs ) . Labelled metabolites are given in S4 Table and S5 Table . Data analysis and interpretation was based on the 24 hour time-point , at approximate steady state , unless otherwise stated . T . brucei bloodstream form cells ( 2 . 5 x107 ) were collected by centrifugation at 1 , 400 g for 10 min , washed with PBS containing 4 mM glucose and incubated for 5 h at 37°C in 2 . 5 ml of incubation buffer ( PBS supplemented with 5 g/l NaHCO3 , pH 7 . 4 ) containing 4 mM glucose . The integrity of the cells during the incubation was checked by microscopic observation . The supernatant was collected and 50 μl of malate solution in D2O ( 20 mM ) was added as internal reference . 1H-NMR spectra were performed at 125 . 77 MHz on a Bruker DPX500 spectrometer equipped with a 5 mm broadband probe head . Measurements were recorded at 25°C with an ERETIC method . This method provides an electronically synthesized reference signal [79] . Acquisition conditions were as follows: 90° flip angle , 5 , 000 Hz spectral width , 32 K memory size , and 9 . 3 sec total recycle time . Measurements were performed with 256 scans for a total time close to 40 min . Before each experiment , phase of ERETIC peak was precisely adjusted . Resonances of obtained spectra were integrated and results were expressed relative to ERETIC peak integration . Inhibition of pepck gene expression by RNAi in the 427 strain was performed by expression of stem-loop “sense/anti-sense” RNA molecules of the targeted sequences introduced in the pHD1334 expression vector . First , the pLew-PEPCK-SAS plasmid was constructed to target a 481 bp fragment of the pepck gene ( from position 22 bp to 503 bp ) . Briefly , a PCR-amplified 569 bp fragment , containing the antisense pepck sequence with restriction sites added to the primers was inserted into the HindIII and BamHI restriction sites of the pLew100 plasmid . Then a PCR-amplified fragment containing the sense pepck sequence ( 505 bp ) was inserted upstream of the anti-sense sequence , using HindIII and XhoI restriction sites ( XhoI was introduced at the 3’-extremity of the antisense PCR fragment ) . The resulting plasmid ( pLew-PEPCK-SAS ) contains a sense and antisense version of the targeted gene fragment , separated by a 52 bp fragment , under the control of the PARP promoter linked to a prokaryotic tetracycline ( Tet ) operator . Then the PEPCK-SAS HindIII-BamHI cassette extracted from the pLew-PEPCK-SAS plasmid was inserted in HindIII-BamHI digested pHD1334 vector . The RNAiPEPCK cell lines were produced by transfecting the 427 “double marker” ( Hyg-Neo ) monomorphic cell line with the pHD-PEPCK-SAS plasmid . Transfected cells were selected in IMDM medium containing , hygromycin ( 5 μg/ml ) , neomycin ( 2 . 5 μg/ml ) and phleomycin ( 2 . 5 μg/ml ) . Induction of double-stranded RNA expression was performed by addition of 10 μg/ml tetracycline . A gene annotated as transaldolase in the T . brucei genome database Tb427 . 08 . 5600 , was amplified with the following primers 5’-GCCATATGAATCAACTGGAGAGCCT-3’ and 5’-GCCTCGAGTTACAAAAGTGTAGCGTGA-3’ . The PCR product was first cloned into the vector pGEM-T easy and then transferred to the pET28a ( + ) expression vector using the introduced Nde1 and Xho1 restriction sites . The resultant plasmid was sequenced to confirm fidelity and transformed into E . coli strain BL21 ( DE3 ) for expression at 37°C or 4 hours after induction with IPTG . Expressed protein was observed by SDS protein gel electrophoresis and purified using Ni2+ chelate chromatography ( Fig 2f ) . The ability of transaldolase to produce octulose 8-phosphate was determined by mixing ribose 5-phosphate ( 5 mM ) and fructose 6-phosphate ( 5 mM ) in Tris-HCl ( pH 7 . 2 ) and adding transaldolase ( 1 . 2 mg/mL ) then incubating at 20°C for 2 hrs . Alternatively the reaction was set up with no enzyme . The presence of octulose 8-phosphate was determined by direct infusion of post-reaction mix into the Exactive mass spectrometer ( Fig 2f ) . Total protein extracts of wild-type or mutant BSF of T . brucei ( 5 x106 cells ) were size-fractionated by SDS-PAGE ( 10% ) and immunoblotted on Immobilon-P filters ( Millipore ) [80] . Immunodetection was performed as described [81 , 82] using primary antibodies , rat anti-T . brucei PEPCK ( diluted 1:1000; gift from T . Seebeck , Bern , Switzerland ) , rabbit anti- T . brucei enolase ( diluted 1:1 , 000 , gift from P . Michels , Edinburgh , UK ) and mouse anti-HSP60 ( diluted 1:10 000 ) [83] , and as secondary antibodies , anti-rat , anti-rabbit or anti-mouse IgG conjugated to horseradish peroxidase ( BioRad , 1:5 , 000 dilution ) . Revelation was performed using the SuperSignal West Pico Chemiluminescent Substrate as described by the manufacturer ( Thermo Fisher Scientific ) . Alternatively , for quantitative analyses , revelation was performed using the Luminata Crescendo Western HRP Substrate ( Millipore ) . Images were acquired and analyzed with a KODAK Image Station 4000 MM and quantitative analyses were performed with the KODAK MI application . | In this work we have followed the distribution of carbon derived from glucose in bloodstream form trypanosomes , the causative agent of African trypanosomiasis , revealing it to enter a diverse range of metabolites . The work involved using 13C-labelled glucose and following the fate of the labelled carbon with an LC-MS based metabolomics platform . Beyond glycolysis and the oxidative branch of the pentose phosphate pathway the label entered lipid biosynthesis both through glycerol 3-phosphate and also acetate . Glucose derived carbon also entered nucleotide synthesis through ribose and pyrimidine synthesis through oxaloacetate-derived aspartate . Appreciable quantities of the carboxylic acids succinate and malate were identified , although labelling patterns indicate they are not TCA cycle derived . Amino sugars and sugar nucleotides were also labelled as was inositol used in protein modification but not in inositol phospholipid headgroup production . We confirm active and essential oxaloacetate production in bloodstream form trypanosomes and show that phosphoenolpyruvate carboxykinase is essential to these parasites using RNA interference . The amount of glucose entering these metabolites is minor compared to the quantity that enters pyruvate excreted from the cell , but the observation that enzymes contributing to the metabolism of glucose beyond glycolysis can be essential offers potential new targets for chemotherapy against trypanosomiasis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Probing the Metabolic Network in Bloodstream-Form Trypanosoma brucei Using Untargeted Metabolomics with Stable Isotope Labelled Glucose |
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